0:\\n string += 'r' * (col_difference - 1)\\n if row_difference > 1:\\n string += 'druld' * (row_difference - 1)\\n string += 'rulld' * (col_difference - 1)\\n string += 'ruldrdlurdluurddlu'\\n elif col_difference == 0:\\n string += 'rddlu' * (row_difference - 2)\\n if row_difference > 1:\\n string += 'rd'\\n string += 'l'\\n string += 'ruldrdlurdluurddlu'\\n string += 'r' * (self._width - 1)\\n print 'Col 0 Path', string\\n self.update_puzzle(string)\\n assert self.lower_row_invariant(target_row - 1, self._width -1), 'False string'\\n return string\",\n \"def solve_row0_tile(self, target_col):\\n move_str = 'ld'\\n self.update_puzzle(move_str)\\n cur_row, cur_col = self.current_position(0, target_col)\\n if cur_row == 0 and cur_col == target_col:\\n return move_str\\n else:\\n move_str += self.position_tile(1, target_col-1, cur_row, cur_col)\\n move_str += 'urdlurrdluldrruld'\\n self.update_puzzle(move_str[2:])\\n return move_str\",\n \"def solve_row1_tile(self, target_col):\\r\\n # replace with your code\\r\\n whole_move = ''\\r\\n if self._grid[1][target_col] != 0:\\r\\n # print \\\"DEBUG CASE WHEN ZERO IN JOPA solve_row1_tile \\\"\\r\\n \\r\\n # print self\\r\\n # print 'Solwing tile', self._grid[1][target_col]\\r\\n \\r\\n # print 'Searchind indexes of ZERO'\\r\\n for row in self._grid:\\r\\n for col in row:\\r\\n if col == 0:\\r\\n zero_row, zero_col = self._grid.index(row), row.index(col)\\r\\n break\\r\\n # print 'ZERO indexes=', (zero_row, zero_col)\\r\\n #####Moving zero to correct place\\r\\n #path_down = (1 - zero_row) * 'd'\\r\\n # path_left = (zero_col - target_col) * 'l'\\r\\n if target_col - zero_col > 0:\\r\\n #path_right = (target_col - zero_col) * 'r'\\r\\n path_of_zero = (1 - zero_row) * 'd' + (target_col - zero_col) * 'r'\\r\\n else:\\r\\n path_of_zero = (1 - zero_row) * 'd'\\r\\n #zero_col -= len(filter(lambda x: x=='l', path_of_zero))\\r\\n #zero_row -= len(filter(lambda x: x=='u', path_of_zero))\\r\\n zero_col += len(filter(lambda x: x=='r', path_of_zero))\\r\\n zero_row += len(filter(lambda x: x=='d', path_of_zero))\\r\\n self.update_puzzle(path_of_zero)\\r\\n # print 'Grid after moving ZERO to target spot'\\r\\n # print self\\r\\n whole_move += path_of_zero\\r\\n\\r\\n assert self.row1_invariant(target_col), 'Some trouble in row1_invariant' \\r\\n \\r\\n #current_row, current_col = self.current_position(1, target_col)\\r\\n zero_row, zero_col = 1, target_col\\r\\n ######Moving zero tile to the target tile\\r\\n #path_up = (zero_row - current_row) * 'u'\\r\\n #path_side = (zero_col - current_col) * 'l'\\r\\n path_for_zero = (zero_row - self.current_position(1, target_col)[0]) * 'u' + (zero_col - self.current_position(1, target_col)[1]) * 'l'\\r\\n whole_move += path_for_zero\\r\\n zero_col -= len(filter(lambda x: x=='l', path_for_zero))\\r\\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\\r\\n self.update_puzzle(path_for_zero)\\r\\n # print 'grid after move', path_for_zero\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(1, target_col)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (1, target_col)\\r\\n counter = 0\\r\\n while self.current_position(1, target_col) != \\\\\\r\\n (1, target_col) or (zero_row, zero_col) != (0, target_col):\\r\\n # print 'Welcome to while loop!'\\r\\n cyclic_moves = ''\\r\\n #### Case 3 if ZERO located in the left side of the target tile\\r\\n ### like in the owel-test case\\r\\n #current_position = self.current_position(1, target_col)\\r\\n current_col = self.current_position(1, target_col)[1]\\r\\n counter +=1\\r\\n if self.current_position(1, target_col) == \\\\\\r\\n (1, target_col):\\r\\n # print 'ZERO not under TT'\\r\\n cyclic_moves = 'ur'\\r\\n whole_move += cyclic_moves\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif zero_col < current_col and self._grid[zero_row+1][zero_col] < \\\\\\r\\n self._grid[self.current_position(1, target_col)[0]][self.current_position(1, target_col)[1]]:\\r\\n # print 'ZERO tile located in the left side and down move is POSIBLE'\\r\\n if current_col != target_col:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'drrul'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n elif current_col == target_col:\\r\\n # print 'Target tile under target place'\\r\\n cyclic_moves = 'dru'\\r\\n whole_move += cyclic_moves\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif current_col != target_col and self._grid[zero_row+1][zero_col] > \\\\\\r\\n self._grid[self.current_position(1, target_col)[0]][self.current_position(1, target_col)[1]]:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'urrdl'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves)) \\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # elif zero_col < current_col and self._grid[zero_row+1][zero_col] > \\\\\\r\\n # self._grid[current_position[0]][current_position[1]]:\\r\\n # # print 'ZERO tile located in the left side and down move IS NOT POSIBLE'\\r\\n # if current_col != target_col:\\r\\n # # print 'not under the target place'\\r\\n # cyclic_moves = 'urrdl'\\r\\n # whole_move += cyclic_moves\\r\\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n # zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n # zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n # elif current_col == target_col:\\r\\n # # print 'Target tile under target place'\\r\\n # cyclic_moves = 'urd'\\r\\n # whole_move += cyclic_moves\\r\\n # zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n # zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n\\r\\n #cyclic_moves +='ur'\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loop counter =',counter\\r\\n if counter > 10:\\r\\n break\\r\\n return whole_move\",\n \"def solve_row1_tile(self, target_col):\\n movements = self.solve_interior_tile(1, target_col)\\n self.update_puzzle(\\\"ur\\\")\\n return movements + \\\"ur\\\"\",\n \"def solve_row0_tile(self, target_col):\\n # replace with your code\\n string = ''\\n assert self.row0_invariant(target_col), 'False precondition'\\n target = self.current_position(0, target_col)\\n row_difference = target[0]\\n col_difference = target_col - target[1]\\n if row_difference == 0:\\n if col_difference == 1:\\n string += 'ld'\\n elif col_difference > 1:\\n string += 'l' * col_difference\\n string += 'drrul' * (col_difference - 2)\\n string += 'druld'\\n string += 'urdlurrdluldrruld'\\n elif row_difference == 1:\\n if col_difference == 1:\\n string += 'lld'\\n string += 'urdlurrdluldrruld'\\n elif col_difference > 1:\\n string += 'ld'\\n string += 'l' * (col_difference - 1)\\n string += 'urrdl' * (col_difference - 2)\\n string += 'urdlurrdluldrruld'\\n print 'Row 0 Path', string\\n self.update_puzzle(string)\\n assert self.row1_invariant(target_col - 1), 'False string'\\n return string\",\n \"def solve_puzzle(self):\\n # move zero tile to the lower right corner\\n row, col = self.current_position(0, 0)\\n movements = \\\"d\\\" * (self.get_height() - 1 - row) + \\\"r\\\" * (\\n self.get_width() - 1 - col)\\n self.update_puzzle(movements)\\n # solve rowid from 2 by row\\n for row in range(self.get_height() - 1, 1, -1):\\n for col in range(self.get_width() - 1, -1, -1):\\n assert self.lower_row_invariant(row, col)\\n if col == 0:\\n movements += self.solve_col0_tile(row)\\n assert self.lower_row_invariant(row - 1,\\n self.get_width() - 1)\\n else:\\n movements += self.solve_interior_tile(row, col)\\n assert self.lower_row_invariant(row, col - 1)\\n # solve the uppermost two rows by column\\n for col in range(self.get_width() - 1, 1, -1):\\n for row in range(1, -1, -1):\\n if row == 0:\\n assert self.row0_invariant(col)\\n movements += self.solve_row0_tile(col)\\n assert self.row1_invariant(col - 1)\\n else:\\n assert self.row1_invariant(col)\\n movements += self.solve_row1_tile(col)\\n assert self.row0_invariant(col)\\n movements += self.solve_2x2()\\n return movements\",\n \"def solve_row0_tile(self, target_col):\\r\\n # replace with your code\\r\\n assert self.row0_invariant(target_col), 'Some trouble in row0_invariant' \\r\\n whole_move = ''\\r\\n #current_position = self.current_position(0, target_col)\\r\\n current_row, current_col = self.current_position(0, target_col)\\r\\n # print self.get_number(current_row, current_col)\\r\\n zero_row, zero_col = 0, target_col\\r\\n # print 'Target tile position=',self.current_position(0, target_col)\\r\\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\\r\\n # print '0 position=', (0, target_col)\\r\\n # print '------------------------------------------'\\r\\n # print 'Recommended move ld'\\r\\n \\r\\n recomended_move = 'ld'\\r\\n whole_move += recomended_move\\r\\n zero_col -= len(filter(lambda x: x=='l', recomended_move))\\r\\n zero_row += len(filter(lambda x: x=='d', recomended_move))\\r\\n self.update_puzzle(recomended_move)\\r\\n # print 'Grid after move:', recomended_move\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(0, target_col)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (0, target_col)\\r\\n #####Case when we check if recomended move solves the tile\\r\\n if self.current_position(0, target_col) == (0, target_col):\\r\\n # print 'Congrads recomended move made great deal !!'\\r\\n return whole_move\\r\\n #####If not, we position TT to (1, target_col-1),\\r\\n ##### and ZEOR to (1, target_col-2)\\r\\n else:\\r\\n # print '------------------------------'\\r\\n # print 'After base move we are do not finde puzzle'\\r\\n # print 'Lets move zero towards TT'\\r\\n \\r\\n ######Moving zero tile to the target tile\\r\\n path_up = (zero_row - current_row) * 'u'\\r\\n path_side = (zero_col - current_col) * 'l'\\r\\n path_for_zero = path_up + path_side\\r\\n whole_move += path_for_zero\\r\\n zero_col -= len(filter(lambda x: x=='l', path_for_zero))\\r\\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\\r\\n self.update_puzzle(path_for_zero)\\r\\n \\r\\n # print 'grid after move', path_for_zero\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(0, target_col)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (0, target_col)\\r\\n counter = 0\\r\\n # print self.current_position(0, target_col) != (1, target_col-1)\\r\\n # print (zero_row,zero_col) != (1,target_col-2)\\r\\n ####POitioning TT and zero into positions that can be solvable\\r\\n while self.current_position(0, target_col) != (1, target_col-1) or \\\\\\r\\n (zero_row,zero_col) != (1,target_col-2):\\r\\n counter +=1\\r\\n #current_position = self.current_position(0, target_col)\\r\\n current_row, current_col = self.current_position(0, target_col)\\r\\n cyclic_moves = ''\\r\\n # print 'Aloha in the loop'\\r\\n if zero_col < current_col:\\r\\n # print 'ZERO tile located in the left side and down move IS NOT POSIBLE'\\r\\n \\r\\n\\r\\n if current_col != target_col-1 and zero_row == 0:\\r\\n # print 'In the upper row and we can use down cycling'\\r\\n cyclic_moves = 'drrul'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n\\r\\n elif current_col != target_col-1:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'urrdl'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n elif current_col == target_col-1:\\r\\n # print 'Target tile under target place'\\r\\n # print 'DEBUG!!!!'\\r\\n # print self\\r\\n # print zero_col, target_col\\r\\n if zero_col == 0 and current_col == 1:\\r\\n cyclic_moves = 'druld'\\r\\n elif zero_row == 0:\\r\\n cyclic_moves = 'druld'\\r\\n \\r\\n else:\\r\\n cyclic_moves = 'urd'\\r\\n whole_move += cyclic_moves\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n elif zero_row > current_row:\\r\\n # print 'DEBUG'\\r\\n # print 'TT under zero tile'\\r\\n cyclic_moves = 'uld'\\r\\n whole_move += cyclic_moves\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loop counter =',counter\\r\\n \\r\\n if counter > 10:\\r\\n # print 'COUNTER break'\\r\\n break\\r\\n\\r\\n #####Solving using pattern 2 x 3 puzzle\\r\\n # print '--------------------------'\\r\\n # print 'Lets solve 2x3 puzzle formed recently'\\r\\n move2x3 = \\\"urdlurrdluldrruld\\\"\\r\\n whole_move += move2x3\\r\\n zero_col -= len(filter(lambda x: x=='l', move2x3))\\r\\n zero_col += len(filter(lambda x: x=='r', move2x3))\\r\\n zero_row += len(filter(lambda x: x=='d', move2x3))\\r\\n zero_row -= len(filter(lambda x: x=='u', move2x3))\\r\\n self.update_puzzle(move2x3)\\r\\n # print self\\r\\n assert self.row1_invariant(target_col-1), 'Some trouble in row1_invariant' \\r\\n return whole_move\",\n \"def solve_col0_tile(self, target_row):\\r\\n # print '----------------------------------'\\r\\n # print 'SOLVING ZERO COLOUMN'\\r\\n assert self.lower_row_invariant(target_row,0), \\\"Invarian is False\\\"\\r\\n whole_move = ''\\r\\n #current_position = self.current_position(target_row, 0)\\r\\n current_row, current_col = self.current_position(target_row, 0)\\r\\n zero_row, zero_col = target_row, 0\\r\\n # print 'Target tile position=',current_position\\r\\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\\r\\n # print '0 position=', (target_row, 0)\\r\\n # print '------------------------------------------'\\r\\n # print 'Recommended move ur'\\r\\n \\r\\n recomended_move = 'ur'\\r\\n whole_move += recomended_move\\r\\n zero_col += len(filter(lambda x: x=='r', recomended_move))\\r\\n zero_row -= len(filter(lambda x: x=='u', recomended_move))\\r\\n self.update_puzzle(recomended_move)\\r\\n # print 'Grid after move:', recomended_move\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, 0)\\r\\n current_position = self.current_position(target_row, 0)\\r\\n current_row, current_col = current_position\\r\\n if self.current_position(target_row, 0) == \\\\\\r\\n (target_row, 0):\\r\\n # print 'TT stays in correct place after recomended move'\\r\\n zero_to_place_move = 'r' * (self._width-1 - zero_col)\\r\\n self.update_puzzle(zero_to_place_move)\\r\\n whole_move += zero_to_place_move\\r\\n # print self\\r\\n assert self.lower_row_invariant(target_row-1,self._width-1), \\\"Invarian is False\\\"\\r\\n return whole_move\\r\\n #move ZERO tile to the right\\r\\n else:\\r\\n # print '------------------------------'\\r\\n # print 'After base move we are do not finde puzzle'\\r\\n # print 'Lets move zero towards TT'\\r\\n ### reposition TT to (target_row -1, 1)\\r\\n ### reposition ZERO tile to (target_row-1,0)\\r\\n \\r\\n ######Moving zero tile to the target tile\\r\\n #path_up = (zero_row - current_row) * 'u'\\r\\n #path_side = (current_col - zero_col) * 'r'\\r\\n path_for_zero = (zero_row - current_row) * 'u' + (current_col - zero_col) * 'r'\\r\\n whole_move += path_for_zero\\r\\n zero_col += len(filter(lambda x: x=='r', path_for_zero))\\r\\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\\r\\n self.update_puzzle(path_for_zero)\\r\\n # print 'grid after move', path_for_zero\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, 0)\\r\\n counter = 0\\r\\n while self.current_position(target_row, 0) != (target_row -1, 1) or \\\\\\r\\n (zero_row,zero_col) != (target_row-1,0):\\r\\n cyclic_moves = ''\\r\\n current_position = self.current_position(target_row, 0)\\r\\n current_row, current_col = current_position\\r\\n # print 'We are in while loop'\\r\\n counter += 1\\r\\n if zero_row < current_row:\\r\\n # print 'Zero row under current TT '\\r\\n if self.current_position(target_row, 0) == (target_row -1, 1):\\r\\n # print 'TT is in the reccomended posiont (target_row -1, 1) \\\\n and we are movind zero to the left side of TT '\\r\\n cyclic_moves = 'ld'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n else:\\r\\n # print 'TT should be one tile down'\\r\\n cyclic_moves = 'lddru'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n #### Case 1 if ZERO located in the right of\\r\\n #### target tile (after it)\\r\\n if zero_col > current_col:\\r\\n # print ' Look in the up puzzle, zero in the right side'\\r\\n if current_col != 1:\\r\\n # print 'not under target place (target_row -1, 1)'\\r\\n cyclic_moves = 'dllur'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n whole_move += cyclic_moves\\r\\n # elif current_col == 1 and self._grid[zero_row+1][zero_col] < \\\\\\r\\n # self._grid[current_position[0]][current_position[1]]:\\r\\n elif current_col == 1: \\r\\n # print 'Tile tat is under ZERO is',self._grid[zero_row+1][zero_col] \\r\\n # print 'TT under target place'\\r\\n cyclic_moves = 'dlu'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif self._grid[zero_row+1][zero_col] > \\\\\\r\\n self._grid[current_position[0]][current_position[1]]:\\r\\n print 'Tile under zero is illegal to move and we use upper cycle move '\\r\\n \\r\\n cyclic_moves = 'ul'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loop counter =',counter\\r\\n if counter > 10:\\r\\n break\\r\\n ### Solwing 3x2 puzzle\\r\\n # print '--------------------------'\\r\\n # print 'Lets solve 3x2 puzzle formed recently'\\r\\n move3x2 = 'ruldrdlurdluurddlur'\\r\\n whole_move += move3x2\\r\\n zero_col -= len(filter(lambda x: x=='l', move3x2))\\r\\n zero_col += len(filter(lambda x: x=='r', move3x2))\\r\\n zero_row += len(filter(lambda x: x=='d', move3x2))\\r\\n zero_row -= len(filter(lambda x: x=='u', move3x2))\\r\\n self.update_puzzle(move3x2)\\r\\n # print 'Grid afret 3x2 solver move'\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, 0)\\r\\n #####Moving ZERO to the (target_row - 1, n - 1) position where\\r\\n ##### 'n' is a grid height.\\r\\n # print self._width-1 - zero_col\\r\\n zero_to_place_move = 'r' * (self._width-1 - zero_col)\\r\\n whole_move += zero_to_place_move\\r\\n self.update_puzzle(zero_to_place_move)\\r\\n # print self\\r\\n assert self.lower_row_invariant(target_row-1,self._width-1), \\\"Invarian is False\\\"\\r\\n return whole_move\",\n \"def solve_2x2(self):\\n # move zero tile to the left-up corner\\n self.update_puzzle(\\\"lu\\\")\\n movements = \\\"rdlu\\\"\\n for _ in range(3):\\n self.update_puzzle(\\\"rdlu\\\")\\n if self.row0_invariant(0):\\n return \\\"lu\\\" + movements\\n movements += \\\"rdlu\\\"\\n # the final 2x2 cannot be solved\\n return \\\"\\\"\",\n \"def solve_interior_tile(self, target_row, target_col):\\r\\n assert self._grid[target_row][target_col] == 0\\r\\n moves_str = \\\"\\\"\\r\\n target_current_row, target_current_col = self.current_position(target_row, target_col)\\r\\n \\r\\n moves_str += self.position_tile(target_row, target_col, target_current_row, target_current_col) \\r\\n \\r\\n self.update_puzzle(moves_str)\\r\\n print \\\"solve_interior_tile\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve_2x2(self):\\n cur_row, cur_col = self.current_position(0, 0)\\n move_str = 'u' * cur_row + 'l' * cur_col\\n self.update_puzzle(move_str)\\n if self.check_2x2_solved():\\n return move_str\\n else:\\n while not self.check_2x2_solved():\\n move_str += 'rdlu'\\n self.update_puzzle('rdlu')\\n return move_str\",\n \"def solve_row0_tile(self, target_col):\\r\\n # move the zero tile from position (0,j) to (1,j−1) \\r\\n # using the move string \\\"ld\\\" \\r\\n moves_str = \\\"\\\"\\r\\n moves_str += \\\"ld\\\"\\r\\n # check whether target tile is at position (0,j).\\r\\n temp_grid = Puzzle(self._height, self._width, self._grid)\\r\\n temp_grid.update_puzzle(moves_str) \\r\\n current_row, current_col = temp_grid.current_position(0, target_col)\\r\\n zero_row, zero_col = temp_grid.current_position(0, 0)\\r\\n \\r\\n # If target tile is not at position (0,j).\\r\\n # reposition the target tile to position (1,j−1) \\r\\n # with tile zero in position (1,j−2).\\r\\n if current_row != 0 or current_col != target_col:\\r\\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\\r\\n moves_str += \\\"urdlurrdluldrruld\\\"\\r\\n \\r\\n self.update_puzzle(moves_str)\\r\\n print \\\"solve_row0_tile\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve_col0_tile(self, target_row):\\n \\n assert target_row > 1, \\\"target_row cannot be in rows 0 or 1.\\\"\\n assert self.lower_row_invariant(target_row, 0), \\\"tiles to right and below incorrectly ordered\\\"\\n\\n # Move zero tile from target position (target_row, 0) to (target_row - 1, 1).\\n self.update_puzzle(\\\"ur\\\")\\n\\n move_str = \\\"\\\"\\n \\n # correct_tile's position is determined after moving zero tile \\\"ur\\\" \\n # because its position relative to zero tile may have changed as a result.\\n correct_tile = self.current_position(target_row, 0)\\n \\n # Moves to reposition correct_tile to target position.\\n if self.get_number(correct_tile[0], correct_tile[1]) != self.get_number(target_row, 0):\\n move_str += str(self.position_tile(target_row - 1, 1, correct_tile))\\n move_str += str(\\\"ruldrdlurdluurddlur\\\")\\n\\n # Moves to reposition zero tile to end of column of target_row + 1.\\n move_str += str(\\\"r\\\" * (self.get_width() - 2)) \\n \\n self.update_puzzle(move_str)\\n\\n assert self.lower_row_invariant(target_row - 1, self.get_width() - 1) \\n \\n move_str = \\\"ur\\\" + move_str\\n return move_str\",\n \"def solve_interior_tile(self, target_row, target_col):\\n cur_row, cur_col = self.current_position(target_row, target_col)\\n move_str = self.position_tile(target_row, target_col, cur_row, cur_col)\\n self.update_puzzle(move_str)\\n return move_str\",\n \"def solve_interior_tile(self, target_row, target_col):\\n \\n assert target_row > 1, \\\"target_row cannot be in rows 0 or 1.\\\"\\n assert self.lower_row_invariant(target_row, target_col), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n correct_tile = self.current_position(target_row, target_col) \\n move_str = self.position_tile(target_row, target_col, correct_tile) \\n self.update_puzzle(move_str)\\n \\n assert self.lower_row_invariant(target_row, target_col - 1), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n return move_str\",\n \"def solve_puzzle(self):\\r\\n moves_str = \\\"\\\"\\r\\n # move zero to the most botton right\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n downs = self._height - 1 - zero_row\\r\\n rights = self._width - 1 - zero_col\\r\\n for dummy_d in range(downs):\\r\\n moves_str += \\\"d\\\"\\r\\n for dummy_r in range(rights):\\r\\n moves_str += \\\"r\\\"\\r\\n self.update_puzzle(moves_str)\\r\\n # Solve the bottom m−2 rows of the puzzle \\r\\n # in a row by row manner from bottom to top. \\r\\n # Each individual row will be solved in a right to left order.\\r\\n if self._height > 2 and self._width > 2:\\r\\n for row in range(self._height - 1, 1, -1):\\r\\n for col in range(self._width - 1, 0, -1):\\r\\n assert self.lower_row_invariant(row, col)\\r\\n moves_str += self.solve_interior_tile(row, col)\\r\\n assert self.lower_row_invariant(row, col - 1)\\r\\n assert self.lower_row_invariant(row, 0)\\r\\n moves_str += self.solve_col0_tile(row)\\r\\n assert self.lower_row_invariant(row - 1, self._width - 1)\\r\\n # Solve the rightmost n−2 columns of the top two rows\\r\\n # in a right to left order). \\r\\n # Each column consists of two unsolved positions \\r\\n # and will be solved in a bottom to top order.\\r\\n for col in range(self._width - 1, 1, -1):\\r\\n assert self.row1_invariant(col)\\r\\n moves_str += self.solve_row1_tile(col)\\r\\n assert self.row0_invariant(col)\\r\\n moves_str += self.solve_row0_tile(col)\\r\\n assert self.row1_invariant(col - 1)\\r\\n # Solve the upper left 2×2 portion of the puzzle directly.\\r\\n assert self.row1_invariant(1)\\r\\n moves_str += self.solve_2x2()\\r\\n \\r\\n elif self._height <=2 and self._width > 2:\\r\\n for col in range(self._width - 1, 1, -1):\\r\\n assert self.row1_invariant(col)\\r\\n moves_str += self.solve_row1_tile(col)\\r\\n assert self.row0_invariant(col)\\r\\n moves_str += self.solve_row0_tile(col)\\r\\n assert self.row1_invariant(col - 1)\\r\\n # Solve the upper left 2×2 portion of the puzzle directly.\\r\\n assert self.row1_invariant(1)\\r\\n moves_str += self.solve_2x2()\\r\\n elif self._height <= 2 and self._width <= 2:\\r\\n assert self.row1_invariant(1)\\r\\n moves_str += self.solve_2x2()\\r\\n #elif self._height > 2 and self._width <= 2:\\r\\n \\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve_row0_tile(self, target_col):\\r\\n assert self.row0_invariant(target_col)\\r\\n move = \\\"ld\\\"\\r\\n self.update_puzzle(move)\\r\\n \\r\\n row, col = self.current_position(0, target_col)\\r\\n if row == 0 and col == target_col:\\r\\n return move\\r\\n else:\\r\\n move_to_target = self.move_to_target(1, target_col - 1, row, col)\\r\\n # 2x3 puzzle solver\\r\\n move_to_target += \\\"urdlurrdluldrruld\\\"\\r\\n self.update_puzzle(move_to_target)\\r\\n move += move_to_target\\r\\n return move\",\n \"def solve_row0_tile(self, target_col):\\n assert target_col > 1, \\\"target_col must be > 1\\\"\\n assert self.row0_invariant(target_col), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n # Move zero tile from target position (0, target_col) to (1, target_col - 1) \\n self.update_puzzle(\\\"ld\\\")\\n \\n move_str = \\\"\\\"\\n\\n # correct_tile's position is determined after moving zero tile \\\"ld\\\"\\n # because its position relative to zero tile may have changed as a result.\\n correct_tile = self.current_position(0, target_col) \\n \\n # Moves to reposition correct_tile to target position, and\\n # the zero tile to (1, target_col - 1).\\n if self.get_number(correct_tile[0], correct_tile[1]) != self.get_number(0, target_col):\\n move_str += str(self.position_tile(1, target_col - 1, correct_tile))\\n move_str += str(\\\"urdlurrdluldrruld\\\")\\n\\n self.update_puzzle(move_str)\\n\\n assert self.row1_invariant(target_col - 1), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n move_str = \\\"ld\\\" + move_str\\n return move_str\",\n \"def position_tile(self, zero_row, zero_col, correct_tile):\\n \\n ans = \\\"\\\" \\n vert_dist = abs(zero_row - correct_tile[0])\\n horiz_dist = abs(zero_col - correct_tile[1])\\n \\n # Updates ans, the move string, based the correct_tile's\\n # position relative to the target position.\\n \\n # SAME ROW\\n if vert_dist == 0:\\n # Left of target\\n if zero_col > correct_tile[1]:\\n # Moves zero tile left to correct_tile's position.\\n ans += str(\\\"l\\\" * horiz_dist)\\n # Moves correct_tile right to target position,\\n # and moves zero tile to left of target position.\\n if horiz_dist > 1:\\n ans += str(\\\"urrdl\\\" * (horiz_dist - 1))\\n # Right of target\\n else:\\n # Moves zero tile right to correct_tile's position.\\n ans += str(\\\"r\\\" * horiz_dist)\\n # Moves correct_tile left to target position,\\n # and moves zero tile to left of target position.\\n ans += str(\\\"ulldr\\\" * (horiz_dist - 1))\\n ans += str(\\\"ulld\\\")\\n \\n # SAME COL\\n elif horiz_dist == 0:\\n # Moves zero tile up to correct_tile's position.\\n ans += str(\\\"u\\\" * vert_dist)\\n # Moves correct_tile down to target position, \\n # and moves zero tile to left of target position.\\n if vert_dist > 1:\\n ans += str(\\\"lddru\\\" * (vert_dist - 1))\\n ans += str(\\\"ld\\\")\\n \\n # UPPER LEFT\\n elif correct_tile[1] < zero_col:\\n # Moves zero tile up and left to correct_tile's position.\\n ans += str(\\\"u\\\" * vert_dist)\\n ans += str(\\\"l\\\" * horiz_dist)\\n # Moves correct_tile right and down to target position,\\n # and moves zero tile to left of target position.\\n ans += str(\\\"drrul\\\" * (horiz_dist - 1))\\n ans += str(\\\"druld\\\" * vert_dist)\\n\\n # UPPER RIGHT\\n else:\\n # Moves zero tile up and right to correct_tile's position.\\n ans += str(\\\"u\\\" * vert_dist)\\n ans += str(\\\"r\\\" * horiz_dist)\\n # This if-elif-else statement moves correct_tile left and down to target position.\\n # If statement is only used when target position is in row 2.\\n if vert_dist == 1 and correct_tile[0] == 0:\\n ans += str(\\\"dllur\\\" * (horiz_dist - 1))\\n ans += str(\\\"dluld\\\")\\n # Elif statement used when correct_tile is in the row above target position.\\n elif vert_dist == 1: \\n ans += str(\\\"ulldr\\\" * (horiz_dist - 1))\\n ans += str(\\\"ullddruld\\\")\\n # Else statement used when correct_tile is 1+ rows above target position.\\n else:\\n ans += str(\\\"dllur\\\" * (horiz_dist - 1))\\n ans += str(\\\"dlu\\\")\\n ans += str(\\\"lddru\\\" * (vert_dist - 1))\\n ans += str(\\\"ld\\\")\\n \\n return ans\",\n \"def solve_puzzle(self):\\r\\n \\r\\n counter = 0\\r\\n rows = self._height-1\\r\\n cols = self._width-1\\r\\n # print rows, cols\\r\\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \\r\\n solution_move = ''\\r\\n if self.get_number(0,0) == 0 and \\\\\\r\\n self.get_number(0,1) == 1:\\r\\n # print 'Congrads Puxxle is Aolved at start!!!!!'\\r\\n return ''\\r\\n #appropriate_number = (self._height * self._width) - 1\\r\\n appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'First appropriate_number=',appropriate_number\\r\\n # print \\\"Grid first tile that we will solwing has value =\\\", self._grid[rows][cols]\\r\\n \\r\\n while counter < 300:\\r\\n counter +=1\\r\\n # print self\\r\\n #appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'Appropriate number in loop=',appropriate_number\\r\\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \\r\\n ####Case when we use solve_interior_tile\\r\\n if rows > 1 and cols > 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n cols -= 1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solving interior tile', (rows, cols)\\r\\n solution_move += self.solve_interior_tile(rows, cols)\\r\\n # print 'Solution move=', solution_move\\r\\n cols -= 1\\r\\n #### Case when we use solve_col0_tile\\r\\n elif rows > 1 and cols == 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n cols = self._width-1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solwing tile 0 in row', rows\\r\\n # print 'Appropriate number here ='\\r\\n solution_move += self.solve_col0_tile(rows)\\r\\n # print 'Solution move=', solution_move\\r\\n rows -=1\\r\\n cols = self._width-1\\r\\n\\r\\n\\r\\n #### Cases when we use solve_row0_tile\\r\\n elif rows == 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n #cols = self._width-1\\r\\n appropriate_number -= self._width\\r\\n\\r\\n else:\\r\\n # print 'Solving upper 2 rows right side'\\r\\n solution_move += self.solve_row1_tile(cols)\\r\\n rows -=1\\r\\n appropriate_number -= self._width\\r\\n #### Cases when we use solve_row1_tile \\r\\n if rows < 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows += 1\\r\\n cols -= 1\\r\\n appropriate_number +=self._width-1\\r\\n else:\\r\\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\\r\\n # print 'Greed after move solve_row1_tile'\\r\\n # print self\\r\\n solution_move += self.solve_row0_tile(cols)\\r\\n rows +=1\\r\\n cols -=1\\r\\n appropriate_number +=self._width-1\\r\\n\\r\\n\\r\\n #### Case when we use solve_2x2\\r\\n elif rows <= 1 and cols <= 1:\\r\\n # print 'We are solving 2x2 puzzle'\\r\\n solution_move += self.solve_2x2()\\r\\n if self._grid[0][0] == 0 and \\\\\\r\\n self._grid[0][1] == 1:\\r\\n # print 'Congrads Puxxle is SOLVED!!!!!'\\r\\n break\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n if counter > 100:\\r\\n # print 'COUNTER BREAK'\\r\\n break\\r\\n # print solution_move, len(solution_move)\\r\\n return solution_move\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # for row in solution_greed._grid[::-1]:\\r\\n # print solution_greed._grid\\r\\n # print 'Row =',row\\r\\n \\r\\n # if solution_greed._grid.index(row) > 1:\\r\\n # print \\\"Case when we solwing Interior and Tile0 part\\\"\\r\\n \\r\\n\\r\\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\\r\\n # print 'Coloumn value=', col\\r\\n #print row[0]\\r\\n # if col !=row[0]:\\r\\n # print 'Case when we use just Interior tile solution'\\r\\n # print solution_greed._grid.index(row)\\r\\n # print row.index(col)\\r\\n \\r\\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\\r\\n # print 'Solution =', solution\\r\\n # print self \\r\\n # print solution_greed._grid\\r\\n # elif col ==row[0]:\\r\\n # print 'Case when we use just Col0 solution'\\r\\n\\r\\n # else:\\r\\n # print 'Case when we solwing first two rows'\\r\\n\\r\\n #return \\\"\\\"\\r\",\n \"def solve_interior_tile(self, target_row, target_col):\\n # replace with your code\\n string = ''\\n target = self.current_position(target_row, target_col)\\n row_difference = target_row - target[0]\\n #print 'Row difference', row_difference\\n col_difference = target_col - target[1]\\n #print 'Col difference', col_difference\\n string += 'u' * row_difference\\n if col_difference > 0:\\n string += 'l' * col_difference\\n if row_difference == 0 and col_difference > 1:\\n string += 'urrdl' * (col_difference - 1)\\n if row_difference == 1:\\n string += 'urrdl' * (col_difference - 1)\\n string += 'dru'\\n if row_difference > 1:\\n string += 'drrul' * (col_difference - 1)\\n string += 'dru'\\n elif col_difference < 0:\\n col_difference = abs(col_difference)\\n string += 'r' * col_difference\\n if row_difference == 1:\\n string += 'ulldr' * (col_difference - 1)\\n string += 'ullddru'\\n if row_difference > 1:\\n string += 'dllur' * (col_difference - 1)\\n string += 'dlu'\\n string += 'lddru' * (row_difference - 1)\\n if row_difference > 0:\\n string += 'ld'\\n print 'Interior Path', string\\n self.update_puzzle(string)\\n assert self.lower_row_invariant(target_row, target_col - 1), 'False string'\\n return string\",\n \"def solve_2x2(self):\\r\\n # replace with your code\\r\\n assert self.row1_invariant(1), '2x2 Dont pass row1_invariant(1)'\\r\\n whole_move = ''\\r\\n current_position = self.current_position(0, 0)\\r\\n # print 'Zero position =', current_position\\r\\n counter = 0\\r\\n \\r\\n\\r\\n \\r\\n # if current_position == (0,0):\\r\\n # print (0,0)\\r\\n # move_to_00 = 'rdlu' \\r\\n if current_position == (0,1):\\r\\n # print (0,1)\\r\\n move_to_00 = 'l'\\r\\n if current_position == (1,0):\\r\\n # print (1,0)\\r\\n move_to_00 = 'u'\\r\\n if current_position == (1,1):\\r\\n # print (1,1)\\r\\n move_to_00 = 'ul'\\r\\n whole_move += move_to_00\\r\\n self.update_puzzle(move_to_00)\\r\\n # print self\\r\\n # print self.get_number(1,1) < self.get_number(1,0)\\r\\n \\r\\n while self.get_number(0,0) != 0 or self.get_number(0,1) != 1:\\r\\n \\r\\n # print 'Aloha in loop!'\\r\\n counter +=1\\r\\n move = 'rdlu'\\r\\n whole_move += move\\r\\n self.update_puzzle(move)\\r\\n # print self\\r\\n if counter >5:\\r\\n break\\r\\n return whole_move\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\\n for row in self._grid:\\n print row\\n print\",\n \"def solve_2x2(self):\\n \\n assert self.get_number(1,1) == 0, \\\"zero tile should be at row 1, col 1\\\"\\n assert self.row1_invariant(1), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n # Moves the zero tile to (0,0).\\n self.update_puzzle(\\\"lu\\\")\\n\\n # Repositions the upper left 2x2 part up to 3 times, \\n # each time checking whether the puzzle is solved.\\n rotation_num = 0\\n if self.row0_invariant(0) == False:\\n for dummy_rotation in range(3):\\n while self.row0_invariant(0) == False:\\n rotation_num += 1\\n self.update_puzzle(\\\"rdlu\\\")\\n\\n assert self.row0_invariant(0), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n move_str = \\\"lu\\\" + (\\\"rdlu\\\" * rotation_num) \\n return move_str\",\n \"def solve_interior_tile(self, target_row, target_col):\\r\\n whole_move = ''\\r\\n # replace with your code\\r\\n if self._grid[target_row][target_col] != 0:\\r\\n # print \\\"DEBUG CASE WHEN ZERO IN JOPA\\\"\\r\\n \\r\\n # print self\\r\\n # print 'Solwing tile', self._grid[target_row][target_col]\\r\\n \\r\\n # print 'Searchind indexes of ZERO'\\r\\n for row in self._grid:\\r\\n for col in row:\\r\\n if col == 0:\\r\\n zero_row, zero_col = self._grid.index(row), row.index(col)\\r\\n break\\r\\n # print 'ZERO indexes=', (zero_row, zero_col)\\r\\n #####Moving zero to correct place\\r\\n #path_down = (target_row - zero_row) * 'd'\\r\\n #path_left = (zero_col - target_col) * 'l'\\r\\n if target_col - zero_col > 0:\\r\\n #path_right = (target_col - zero_col) * 'r'\\r\\n path_of_zero = (zero_col - target_col) * 'l' + (target_row - zero_row) * 'd' + (target_col - zero_col) * 'r'\\r\\n else:\\r\\n path_of_zero = (zero_col - target_col) * 'l' + (target_row - zero_row) * 'd'\\r\\n zero_col -= len(filter(lambda x: x=='l', path_of_zero))\\r\\n zero_col += len(filter(lambda x: x=='r', path_of_zero))\\r\\n zero_row += len(filter(lambda x: x=='d', path_of_zero))\\r\\n self.update_puzzle(path_of_zero)\\r\\n # print 'Grid after moving ZERO to target spot'\\r\\n # print self\\r\\n whole_move += path_of_zero\\r\\n assert self.lower_row_invariant(target_row, target_col), \\\"Invarian is False\\\"\\r\\n \\r\\n #current_position = self.current_position(target_row, target_col)\\r\\n #current_row, current_col = self.current_position(target_row, target_col)\\r\\n # print 'Target tile position=',current_position\\r\\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\\r\\n # print '0 position=', (target_row, target_col)\\r\\n \\r\\n ######Moving zero tile to the target tile\\r\\n path_up = (target_row - self.current_position(target_row, target_col)[0]) * 'u'\\r\\n zero_row = target_row - len(path_up)\\r\\n if target_col < self.current_position(target_row, target_col)[1]: # Right move\\r\\n path_side = (self.current_position(target_row, target_col)[1] - target_col) * 'r'\\r\\n zero_col = target_col + len(path_side)\\r\\n else: # Left move\\r\\n path_side = (target_col - self.current_position(target_row, target_col)[1]) * 'l'\\r\\n zero_col = target_col - len(path_side)\\r\\n \\r\\n #path_for_zero = path_up + path_side\\r\\n # print '------------------------------------------'\\r\\n # print 'Move to ZERO =', path_for_zero\\r\\n \\r\\n self.update_puzzle(path_up + path_side)\\r\\n \\r\\n # print 'Grid after move:'\\r\\n # print self\\r\\n # current_position = self.current_position(target_row, target_col) \\r\\n # current_row, current_col = current_position\\r\\n # print 'Updated Target tile position=',current_position\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, target_col)\\r\\n # print '-----------------------------------------'\\r\\n \\r\\n\\r\\n ###### New moves after moving ZERO tile into target tile\\r\\n # counter = 0\\r\\n whole_move += path_up + path_side\\r\\n while self.current_position(target_row, target_col) != \\\\\\r\\n (target_row, target_col) or zero_col != target_col - 1:\\r\\n # tt_in_home = self.current_position(target_row, target_col) == \\\\\\r\\n # (target_row, target_col)\\r\\n\\r\\n cyclic_moves = ''\\r\\n # counter += 1\\r\\n #current_position = self.current_position(target_row, target_col) \\r\\n #current_col = self.current_position(target_row, target_col)[1]\\r\\n # print 'Zero coloumn', zero_col, '== Target coloumn', target_col\\r\\n # print zero_col == target_col \\r\\n \\r\\n #### Case 1 if ZERO located in ther right of\\r\\n #### target tile (after it)\\r\\n if zero_col > self.current_position(target_row, target_col)[1]:\\r\\n # print ' Look in the up puzzle, zero on the right side'\\r\\n # if self.current_position(target_row, target_col)[1] != target_col:\\r\\n # # print 'not under target place'\\r\\n # cyclic_moves = 'dllur'\\r\\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n if self.current_position(target_row, target_col)[1] == target_col and self._grid[zero_row+1][zero_col] < \\\\\\r\\n self._grid[self.current_position(target_row, target_col)[0]][self.current_position(target_row, target_col)[1]]:\\r\\n # print 'Tile tat is under ZERO is',self._grid[zero_row+1][zero_col] \\r\\n # print 'TT under target place'\\r\\n cyclic_moves = 'dlu'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n #zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n # elif self._grid[zero_row+1][zero_col] > \\\\\\r\\n # self._grid[self.current_position(target_row, target_col)[0]][self.current_position(target_row, target_col)[1]]:\\r\\n # # print 'Tile under zero is illegal to move and we use upper cycle move '\\r\\n \\r\\n # cyclic_moves = 'ul'\\r\\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n\\r\\n #### Case 2 if ZERO located under the target tile, and both\\r\\n #### of them located under the target position of the target tile\\r\\n elif zero_col == self.current_position(target_row, target_col)[1] and zero_col == target_col:\\r\\n # print 'Both under the target place'\\r\\n # print 'TT in home=', tt_in_home\\r\\n if self.current_position(target_row, target_col) == \\\\\\r\\n (target_row, target_col):\\r\\n cyclic_moves = 'ld'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n #zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n \\r\\n else:\\r\\n cyclic_moves = 'lddru'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n \\r\\n #### Case 3 if ZERO located in the left side of the target tile\\r\\n ### like in the owel-test case\\r\\n elif zero_col < self.current_position(target_row, target_col)[1]:\\r\\n # print 'ZERO tile located in the left side'\\r\\n if self.current_position(target_row, target_col)[1] != target_col:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'drrul'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif self.current_position(target_row, target_col)[1] == target_col:\\r\\n # print 'Target tile under target place'\\r\\n cyclic_moves = 'dru'\\r\\n #zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n\\r\\n\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loot counter =',counter\\r\\n whole_move += cyclic_moves\\r\\n # if counter > 12:\\r\\n # break\\r\\n # print 'Tile is solved with move '+ whole_move\\r\\n assert self.lower_row_invariant(target_row, target_col-1), \\\"Invarian is False\\\"\\r\\n return whole_move\",\n \"def solve(self, board: List[List[str]]) -> None:\\n n = len(board)\\n if n == 0:\\n return\\n m = len(board[0])\\n for i in range(m):\\n self.traverse(board, 0, i, n, m)\\n self.traverse(board, n - 1, i, n, m)\\n for i in range(n):\\n self.traverse(board, i, 0, n, m)\\n self.traverse(board, i, m - 1, n, m)\\n for i in range(n):\\n for j in range(m):\\n board[i][j] = 'X' if board[i][j] == 'O' else board[i][j]\\n for i in range(n):\\n for j in range(m):\\n board[i][j] = 'O' if board[i][j] == '_' else board[i][j]\",\n \"def solve_2x2(self):\\r\\n assert self.row1_invariant(1)\\r\\n pos_1_0 = self.get_number(1, 0)\\r\\n pos_0_0 = self.get_number(0, 0)\\r\\n pos_0_1 = self.get_number(0, 1)\\r\\n # create grid and solve individual cases\\r\\n grid = [pos_1_0, pos_0_0, pos_0_1]\\r\\n if grid == [self.get_width(), 1, self.get_width() + 1]:\\r\\n move = \\\"ul\\\"\\r\\n elif grid == [1, self.get_width() + 1, self.get_width()]:\\r\\n move = \\\"lurdlu\\\"\\r\\n elif grid == [self.get_width() + 1, self.get_width(), 1]:\\r\\n move = \\\"lu\\\"\\r\\n self.update_puzzle(move)\\r\\n return move\",\n \"def update_puzzle(self, move_string):\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n for direction in move_string:\\r\\n if direction == \\\"l\\\":\\r\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\r\\n self._grid[zero_row][zero_col - 1] = 0\\r\\n zero_col -= 1\\r\\n elif direction == \\\"r\\\":\\r\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\r\\n self._grid[zero_row][zero_col + 1] = 0\\r\\n zero_col += 1\\r\\n elif direction == \\\"u\\\":\\r\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\r\\n self._grid[zero_row - 1][zero_col] = 0\\r\\n zero_row -= 1\\r\\n elif direction == \\\"d\\\":\\r\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\r\\n self._grid[zero_row + 1][zero_col] = 0\\r\\n zero_row += 1\\r\\n else:\\r\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n for direction in move_string:\\r\\n if direction == \\\"l\\\":\\r\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\r\\n self._grid[zero_row][zero_col - 1] = 0\\r\\n zero_col -= 1\\r\\n elif direction == \\\"r\\\":\\r\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\r\\n self._grid[zero_row][zero_col + 1] = 0\\r\\n zero_col += 1\\r\\n elif direction == \\\"u\\\":\\r\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\r\\n self._grid[zero_row - 1][zero_col] = 0\\r\\n zero_row -= 1\\r\\n elif direction == \\\"d\\\":\\r\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\r\\n self._grid[zero_row + 1][zero_col] = 0\\r\\n zero_row += 1\\r\\n else:\\r\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n for direction in move_string:\\r\\n if direction == \\\"l\\\":\\r\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\r\\n self._grid[zero_row][zero_col - 1] = 0\\r\\n zero_col -= 1\\r\\n elif direction == \\\"r\\\":\\r\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\r\\n self._grid[zero_row][zero_col + 1] = 0\\r\\n zero_col += 1\\r\\n elif direction == \\\"u\\\":\\r\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\r\\n self._grid[zero_row - 1][zero_col] = 0\\r\\n zero_row -= 1\\r\\n elif direction == \\\"d\\\":\\r\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\r\\n self._grid[zero_row + 1][zero_col] = 0\\r\\n zero_row += 1\\r\\n else:\\r\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col\\n - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col\\n + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][\\n zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][\\n zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def solve(self, board) -> None:\\n if board == [[]] or board == []:\\n return\\n\\n r, c = len(board), len(board[0])\\n\\n from collections import deque\\n queue = deque()\\n for i in range(r):\\n for j in range(c):\\n if (i == 0 or i == r - 1 or j == 0 or j == c - 1) and board[i][j] == 'O':\\n queue.append([i, j])\\n board[i][j] = 'M'\\n\\n while queue:\\n i, j = queue.popleft()\\n for x, y in ((i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)):\\n if 0 <= x <= r - 1 and 0 <= y <= c - 1 and board[x][y] == 'O':\\n board[x][y] = 'M'\\n queue.append([x, y])\\n\\n for i in range(r):\\n for j in range(c):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n elif board[i][j] == 'M':\\n board[i][j] = 'O'\",\n \"def solve_2x2(self):\\r\\n moves_str = \\\"\\\"\\r\\n # move zero to the most upper left\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n ups = zero_row - 0\\r\\n lefts = zero_col - 0\\r\\n for dummy_u in range(ups):\\r\\n moves_str += \\\"u\\\"\\r\\n for dummy_l in range(lefts):\\r\\n moves_str += \\\"l\\\"\\r\\n \\r\\n # get zero, one, two, three positions\\r\\n self.update_puzzle(moves_str)\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n one_row, one_col = self.current_position(0, 1)\\r\\n two_row, two_col = self.current_position(1, 0)\\r\\n three_row, three_col = self.current_position(1, 1)\\r\\n counter = 0\\r\\n while counter <= 3 and \\\\\\r\\n (zero_row != 0 or zero_col != 0 or \\\\\\r\\n one_row!= 0 or one_col != 1 or \\\\\\r\\n two_row != 1 or two_col != 0 or \\\\\\r\\n three_row != 1 or three_col != 1):\\r\\n move = CIRCLES[\\\"RIGHT_CIRCLES\\\"]\\r\\n moves_str += move\\r\\n self.update_puzzle(move)\\r\\n counter += 1\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n one_row, one_col = self.current_position(0, 1)\\r\\n two_row, two_col = self.current_position(1, 0)\\r\\n three_row, three_col = self.current_position(1, 1)\\r\\n \\r\\n print \\\"solve_2x2\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def human_go(self, board):\\r\\n coord_pattern = re.compile(\\\"[0-{}]$\\\".format(board.shape[1]))\\r\\n print(\\\"Enter Column and press enter.\\\")\\r\\n input_str = input(\\\"(from 0-6)\\\\n\\\")\\r\\n if not coord_pattern.match(input_str):\\r\\n print(\\\"That is not in the right format, please try again...\\\")\\r\\n return self.human_go(board)\\r\\n else:\\r\\n col = int(input_str)\\r\\n if board[0][col] != 0:\\r\\n print(\\\"That column is already full, please try again\\\")\\r\\n self.human_go()\\r\\n else:\\r\\n for row in board[::-1]:\\r\\n if row[col] == 0:\\r\\n row[col] = -1\\r\\n return board\",\n \"def make_move(self, column):\\r\\n trans_board = numpy.transpose(self.__board[::1]) # transpose the\\r\\n # board so that columns are now arrays\\r\\n if 0 not in trans_board[column] or self.get_winner() or column >= \\\\\\r\\n self.BOARD_COLUMNS or column < 0:\\r\\n # column is full, illegal or the game is already finished\\r\\n return self.ILLEGAL_MOVE # exception?\\r\\n else:\\r\\n reversed_col = list(reversed(trans_board[column]))\\r\\n for hole in reversed_col:\\r\\n if hole == 0:\\r\\n row_i = self.BOARD_ROWS - 1 - reversed_col.index(hole)\\r\\n self.__board[row_i][column] = self.__cur_player\\r\\n winner = self.get_winner()\\r\\n if winner: # is not none\\r\\n return winner\\r\\n self.__switch_player()\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if board == [] or board == [[]]: # corner case\\n return\\n\\n r, c = len(board), len(board[0])\\n\\n def dfs(i, j): # visited i, j neighbors and change o to M\\n if i < 0 or i > r - 1 or j < 0 or j > c - 1 or board[i][j] == 'X' or board[i][j] == 'M':\\n return\\n\\n board[i][j] = 'M'\\n dfs(i - 1, j)\\n dfs(i + 1, j)\\n dfs(i, j - 1)\\n dfs(i, j + 1)\\n\\n for i in range(r):\\n for j in range(c):\\n if (i == 0 or i == r - 1 or j == 0 or j == c - 1) and board[i][j] == 'O':\\n dfs(i, j)\\n\\n for i in range(r):\\n for j in range(c):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n elif board[i][j] == 'M':\\n board[i][j] = 'O'\",\n \"def solve(self, board: List[List[str]]) -> None:\\n rows = len(board)\\n cols = len(board[0])\\n\\n def explore(i, j):\\n if i < 0 or i >= rows:\\n return\\n if j < 0 or j >= cols:\\n return\\n if board[i][j] != \\\"O\\\":\\n return\\n board[i][j] = \\\"Y\\\"\\n explore(i + 1, j)\\n explore(i - 1, j)\\n explore(i, j + 1)\\n explore(i, j - 1)\\n\\n for i in [0, rows - 1]:\\n for j in range(cols):\\n explore(i, j)\\n\\n for j in [0, cols - 1]:\\n for i in range(rows):\\n explore(i, j)\\n\\n for i in range(rows):\\n for j in range(cols):\\n if board[i][j] == \\\"Y\\\":\\n board[i][j] = \\\"O\\\"\\n elif board[i][j] == \\\"O\\\":\\n board[i][j] = \\\"X\\\"\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if board is None or len(board) == 0:\\n return \\n row, col = len(board), len(board[0])\\n for i in range(row):\\n self.dfs(board, i, 0)\\n self.dfs(board, i, col - 1)\\n for j in range(col):\\n self.dfs(board, 0, j)\\n self.dfs(board, row-1, j)\\n for i in range(row):\\n for j in range(col):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n if board[i][j] == '-':\\n board[i][j] = 'O'\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if not board:\\n return\\n m, n = len(board), len(board[0])\\n\\n def dfs(x, y):\\n if not 0 <= x < m or not 0 <= y < n or board[x][y] != \\\"O\\\":\\n return\\n direction = [[1, 0], [0, 1], [-1, 0], [0, -1]]\\n board[x][y] = \\\"Y\\\"\\n for d in direction:\\n dfs(x+d[0], y+d[1])\\n\\n for i in range(m):\\n dfs(i, 0)\\n dfs(i, n-1)\\n for i in range(1, n-1):\\n dfs(0, i)\\n dfs(m-1, i)\\n\\n for i in range(m):\\n for j in range(n):\\n if board[i][j] == \\\"O\\\":\\n board[i][j] = \\\"X\\\"\\n elif board[i][j] == \\\"Y\\\":\\n board[i][j] = \\\"O\\\"\",\n \"def solve(self, board: List[List[str]]) -> None:\",\n \"def move(self, row: int, col: int, player: int):\\n def addup(dict_name, invalid_set, another_invalid, locx, locy):\\n if locx == locy:\\n diag_name = (1,1)\\n if diag_name not in invalid_set:\\n dict_name[diag_name] += 1\\n if dict_name[diag_name] == self.tar:\\n return player\\n another_invalid.add(diag_name)\\n if locx == self.tar-1-locy:\\n diag_name = (-1, -1)\\n if diag_name not in invalid_set:\\n dict_name[diag_name] += 1\\n if dict_name[diag_name] == self.tar:\\n return player\\n another_invalid.add(diag_name)\\n curcol = (locy, None)\\n currow = (None, locx)\\n if curcol not in invalid_set:\\n dict_name[curcol] += 1\\n if dict_name[curcol] == self.tar:\\n return player\\n another_invalid.add(curcol)\\n if currow not in invalid_set:\\n dict_name[currow] += 1\\n if dict_name[currow] == self.tar:\\n return player\\n another_invalid.add(currow)\\n return 0\\n res = 0\\n if (row, col) not in self.walked:\\n if player == 1:\\n res = addup(self.p1, self.invalid_1, self.invalid_2, row, col)\\n if player == 2:\\n res = addup(self.p2, self.invalid_2, self.invalid_1, row, col)\\n self.walked.add((row, col))\\n return res\",\n \"def solve(self, board: List[List[str]]) -> None:\\r\\n if not board:\\r\\n return\\r\\n m, n = len(board), len(board[0])\\r\\n board_filter = lambda p: 0 <= p[0] < m and 0 <= p[1] < n and board[p[0]][p[1]] == 'O'\\r\\n queue = list(filter(board_filter, [(x, y) for r in range(max(m, n)) \\r\\n for x, y in ((r, 0), (r, n-1), (0, r), (m-1, r))]))\\r\\n while queue:\\r\\n x, y = queue.pop()\\r\\n board[x][y] = 'W'\\r\\n queue.extend(list(filter(board_filter, ((x-1, y), (x+1, y), (x, y-1), (x, y+1)))))\\r\\n for row in board:\\r\\n for i, c in enumerate(row):\\r\\n row[i] = 'XO'[c=='W']\",\n \"def position_tile(self, target_row, target_col, current_row, current_col):\\r\\n moves_str = \\\"\\\"\\r\\n # current target is on the upper of 0\\r\\n if current_col == target_col and current_row < target_row:\\r\\n ups = target_row - current_row\\r\\n for dummy_u in range(ups):\\r\\n moves_str += \\\"u\\\"\\r\\n for dummy_cycle in range(ups - 1):\\r\\n moves_str += CIRCLES[\\\"LEFT_CIRCLE\\\"]\\r\\n moves_str += \\\"ld\\\"\\r\\n # current target is on the left of 0\\r\\n elif current_row == target_row and current_col < target_col:\\r\\n lefts = target_col - current_col\\r\\n for dummy_l in range(lefts):\\r\\n moves_str += \\\"l\\\"\\r\\n for dummy_cycle in range(lefts - 1):\\r\\n moves_str += CIRCLES[\\\"UP_CIRCLE\\\"]\\r\\n # current target is on the upperleft of 0\\r\\n elif current_row < target_row and current_col < target_col:\\r\\n ups = target_row - current_row\\r\\n for dummy_u in range(ups):\\r\\n moves_str += \\\"u\\\"\\r\\n lefts = target_col - current_col\\r\\n for dummy_l in range(lefts):\\r\\n moves_str += \\\"l\\\"\\r\\n for dummy_cycle in range(lefts - 1):\\r\\n if current_row <= 0: # can not go up\\r\\n moves_str += CIRCLES[\\\"DOWN_CIRCLE\\\"]\\r\\n else:\\r\\n moves_str += CIRCLES[\\\"UP_CIRCLE\\\"]\\r\\n moves_str += \\\"dru\\\"\\r\\n for dummy_cycle in range(ups - 1):\\r\\n moves_str += CIRCLES[\\\"LEFT_CIRCLE\\\"]\\r\\n moves_str += \\\"ld\\\"\\r\\n # current target is on the upperright of 0\\r\\n elif current_col > target_col and current_row < target_row:\\r\\n ups = target_row - current_row\\r\\n for dummy_u in range(ups):\\r\\n moves_str += \\\"u\\\"\\r\\n rights = current_col - target_col\\r\\n for dummy_r in range(rights):\\r\\n moves_str += \\\"r\\\"\\r\\n for dummy_cycle in range(rights - 1):\\r\\n if current_row <= 0: # can not go up\\r\\n moves_str += CIRCLES[\\\"DOWN_LEFT_CIRCLE\\\"]\\r\\n else:\\r\\n moves_str += CIRCLES[\\\"UP_LEFT_CIRCLE\\\"] \\r\\n moves_str += \\\"dlu\\\"\\r\\n for dummy_cycle in range(ups - 1):\\r\\n moves_str += CIRCLES[\\\"LEFT_CIRCLE\\\"]\\r\\n moves_str += \\\"ld\\\"\\r\\n # current target is on the right of 0\\r\\n elif current_col > target_col and current_row == target_row:\\r\\n rights = current_col - target_col\\r\\n for dummy_r in range(rights):\\r\\n moves_str += \\\"r\\\"\\r\\n for dummy_cycle in range(rights - 1):\\r\\n if current_row <= 0: # can not go up\\r\\n moves_str += CIRCLES[\\\"DOWN_LEFT_CIRCLE\\\"]\\r\\n else:\\r\\n moves_str += CIRCLES[\\\"UP_LEFT_CIRCLE\\\"] \\r\\n moves_str += \\\"ulld\\\"\\r\\n return moves_str\",\n \"def move_to_target(self, target_row, target_col, row, col):\\r\\n move = \\\"\\\"\\r\\n # typical move to move target tile to target pos.\\r\\n solver_move = \\\"druld\\\"\\r\\n # move up first\\r\\n move = (target_row - row) * \\\"u\\\"\\r\\n # conditional statements for moving the tile:\\r\\n # 1. case curr_pos of tile and target_tile are in same col\\r\\n if (target_col - col) == 0:\\r\\n move += \\\"ld\\\" + ((target_row - row) - 1) * solver_move\\r\\n else:\\r\\n # 2. curr_pos of tile is on the left of target pos\\r\\n if (target_col - col) > 0:\\r\\n move += (target_col - col) * \\\"l\\\"\\r\\n if row == 0:\\r\\n move += (abs(target_col - col) - 1) * \\\"drrul\\\"\\r\\n else:\\r\\n move += (abs(target_col - col) - 1) * \\\"urrdl\\\"\\r\\n # 3. curr_pos of tile is on the right of target pos:\\r\\n elif (target_col - col) < 0:\\r\\n move += (abs(target_col - col) - 1) * \\\"r\\\"\\r\\n if row == 0:\\r\\n move += abs(target_col - col) * \\\"rdllu\\\"\\r\\n else:\\r\\n move += abs(target_col - col) * \\\"rulld\\\"\\r\\n move += (target_row - row) * solver_move\\r\\n return move\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) is 0: return\\n width = len(board[0]) - 1\\n height = len(board) - 1\\n def help(x: int, y: int):\\n if x < 0 or x > height or y < 0 or y > width:\\n return\\n if board[x][y] is \\\"O\\\":\\n board[x][y] = \\\"g\\\"\\n help(x - 1, y)\\n help(x, y - 1)\\n help(x + 1, y)\\n help(x, y + 1)\\n\\n for i in range(width + 1):\\n if board[0][i] is \\\"O\\\":\\n help(0, i)\\n if board[height][i] is \\\"O\\\":\\n help(height, i)\\n for i in range(1, height):\\n if board[i][0] is \\\"O\\\":\\n help(i, 0)\\n if board[i][width] is \\\"O\\\":\\n help(i, width)\\n\\n print(board)\\n for i in range(width + 1):\\n for j in range(height + 1):\\n if board[j][i] is \\\"O\\\":\\n board[j][i] = \\\"X\\\"\\n elif board[j][i] is \\\"g\\\":\\n board[j][i] = \\\"O\\\"\",\n \"def make_move(board, picked_column, player):\\n row = find_first_free_cell(board, picked_column)\\n board[row][picked_column] = player\\n return board, row\",\n \"def move(self, row, col, player):\\r\\n if player == 1:\\r\\n self.mat[row][col] = 1\\r\\n else:\\r\\n self.mat[row][col] = -1\\r\\n if self.checkrow(player,row) or self.checkcol(player,col):\\r\\n return player\\r\\n if row == col or row + col == self.size-1:\\r\\n if self.checkdiag(player):\\r\\n return player\\r\\n return 0\",\n \"def execute(self, row, col, action=None):\\n assert action is not None, \\\"No action selected!\\\"\\n\\n if action == 'north':\\n if (row-1) < 0 or self.board[row-1, col] == '*':\\n return row, col\\n elif action == 'south':\\n if (row+1) >= self.N or self.board[row+1, col] == '*':\\n return row, col\\n elif action == 'east':\\n if (col+1) >= self.M or self.board[row, col+1] == '*':\\n return row, col\\n elif action == 'west':\\n if (col-1) < 0 or self.board[row, col-1] == '*':\\n return row, col\\n\\n return row + self.step_row[action], col + self.step_col[action]\",\n \"def solve(self, board):\\n def dfs(board, r, c):\\n if r < 0 or c < 0 or r > rows - 1 or c > cols - 1 or board[r][c] == 'X' or board[r][c] == '#':\\n return\\n board[r][c] = '#'\\n dfs(board, r - 1, c)\\n dfs(board, r + 1, c)\\n dfs(board, r, c - 1)\\n dfs(board, r, c + 1)\\n\\n if len(board) == 0:\\n return;\\n rows = len(board)\\n cols = len(board[0])\\n for i in range(rows):\\n for j in range(cols):\\n if (i == 0 or j == 0 or i == rows - 1 or j == cols - 1) and board[i][j] == 'O':\\n dfs(board, i, j)\\n for i in range(rows):\\n for j in range(cols):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n elif board[i][j] == '#':\\n board[i][j] = 'O'\",\n \"def find_best_move(board):\\n new_board = board.get_board()\\n\\n # X | X | X <-- Check for win on this row\\n # ---------\\n # 3 | 4 | 5\\n # ---------\\n # 6 | 7 | 9\\n if new_board[0] == new_board[1] and new_board[2] == \\\"2\\\":\\n return 2\\n elif new_board[0] == new_board[2] and new_board[1] == \\\"1\\\":\\n return 1\\n elif new_board[1] == new_board[2] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | 2\\n # ---------\\n # X | X | X <-- Check for win on this row\\n # ---------\\n # 6 | 7 | 9\\n elif new_board[3] == new_board[4] and new_board[5] == \\\"5\\\":\\n return 5\\n elif new_board[3] == new_board[5] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[4] == new_board[5] and new_board[3] == \\\"3\\\":\\n return 3\\n\\n # 0 | 1 | 2\\n # ---------\\n # 3 | 4 | 5\\n # ---------\\n # X | X | X <-- Check for win on this row\\n elif new_board[6] == new_board[7] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[6] == new_board[8] and new_board[7] == \\\"7\\\":\\n return 7\\n elif new_board[7] == new_board[8] and new_board[6] == \\\"6\\\":\\n return 6\\n\\n # X | 1 | 2 Check for win on column one\\n # ---------\\n # X | 4 | 5\\n # ---------\\n # X | 7 | 9\\n elif new_board[0] == new_board[3] and new_board[6] == \\\"6\\\":\\n return 6\\n elif new_board[0] == new_board[6] and new_board[3] == \\\"3\\\":\\n return 3\\n elif new_board[6] == new_board[3] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | X | 2 Checks for win on column two\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # 6 | X | 9\\n elif new_board[1] == new_board[4] and new_board[7] == \\\"7\\\":\\n return 7\\n elif new_board[1] == new_board[7] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[7] == new_board[4] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | X\\n # ---------\\n # 3 | 4 | X\\n # ---------\\n # 6 | 7 | X\\n elif new_board[2] == new_board[5] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[2] == new_board[8] and new_board[5] == \\\"5\\\":\\n return 5\\n elif new_board[8] == new_board[5] and new_board[2] == \\\"2\\\":\\n return 2\\n\\n # X | 1 | 2\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # 6 | 7 | X\\n elif new_board[0] == new_board[4] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[0] == new_board[8] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[8] == new_board[4] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | X\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # X | 7 | 9\\n elif new_board[2] == new_board[4] and new_board[6] == \\\"6\\\":\\n return 6\\n elif new_board[2] == new_board[6] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[6] == new_board[4] and new_board[2] == \\\"2\\\":\\n return 2\\n\\n # If corners are empty, play there\\n elif new_board[0] == \\\"0\\\" or new_board[2] == \\\"2\\\" or new_board[6] == \\\"6\\\" or new_board[8] == \\\"8\\\":\\n try_spot = 0\\n while True:\\n if new_board[try_spot] != \\\"X\\\" and new_board[try_spot] != \\\"O\\\":\\n return try_spot\\n else:\\n try_spot = try_spot + 2\\n\\n # If middle is empty, play there\\n elif new_board[4] == \\\"4\\\":\\n return 4\\n\\n # Finally if edges are empty try there\\n elif new_board[1] == \\\"1\\\" or new_board[3] == \\\"3\\\" or new_board[5] == \\\"5\\\" or new_board[7] == \\\"7\\\":\\n try_spot = 1\\n while True:\\n if new_board[try_spot] != \\\"X\\\" and new_board[try_spot] != \\\"O\\\":\\n return try_spot\\n else:\\n try_spot = try_spot + 2\",\n \"def move(self, row: int, col: int, player: int) -> int:\\n if player == 1:\\n self.newList[row][col] = 1\\n self.colSum[col] += 1\\n self.rowSum[row] += 1\\n if row == col:\\n self.diag += 1\\n if row + col == (self.n - 1):\\n self.revDiag += 1\\n if self.rowSum[row] == self.n or self.colSum[col] == self.n or self.diag == self.n or self.revDiag == self.n:\\n return 1\\n if player == 2:\\n self.newList[row][col] = -1\\n self.colSum[col] -= 1\\n self.rowSum[row] -= 1\\n if row == col:\\n self.diag -= 1\\n if row + col == (self.n - 1):\\n self.revDiag -= 1\\n if self.rowSum[row] == -self.n or self.colSum[col] == -self.n or self.diag == -self.n or self.revDiag == -self.n:\\n return 2\\n \\n return 0\",\n \"def solve(self, board):\\r\\n if not board or not board[0]:\\r\\n return\\r\\n \\r\\n self.m = len(board)\\r\\n self.n = len(board[0])\\r\\n boarder = []\\r\\n \\r\\n # Collecting all the 'O' on the boarder\\r\\n for i in range(self.m):\\r\\n if board[i][0] == 'O':\\r\\n boarder.append([i, 0])\\r\\n if board[i][self.n-1] == 'O':\\r\\n boarder.append([i, self.n-1])\\r\\n for j in range(self.n):\\r\\n if board[0][j] == 'O':\\r\\n boarder.append([0, j])\\r\\n if board[self.m-1][j] == 'O':\\r\\n boarder.append([self.m-1, j])\\r\\n \\r\\n for row, col in boarder:\\r\\n self.BFS(board, row, col)\\r\\n \\r\\n for row in range(self.m):\\r\\n for col in range(self.n):\\r\\n if board[row][col] == 'O':\\r\\n board[row][col] = 'X'\\r\\n elif board[row][col] == 'E':\\r\\n board[row][col] = 'O'\\r\\n print(board)\",\n \"def move(self, row: int, col: int, player: int) -> int:\\n self.board[row][col] = 1 if player == 1 else -1\\n rowsum = sum(self.board[row])\\n colsum = sum([self.board[r][col] for r in range(self.n)])\\n diagsum1 = sum([self.board[i][i] for i in range(self.n)])\\n diagsum2 = sum([self.board[i][-i-1] for i in range(self.n)])\\n if player == 1:\\n if rowsum == self.n or colsum == self.n or diagsum1 == self.n or diagsum2 == self.n:\\n return 1\\n else:\\n if rowsum == -self.n or colsum == -self.n or diagsum1 == -self.n or diagsum2 == -self.n:\\n return 2\\n return 0\",\n \"def make_move(board: Connect4Board) -> \\\"(row, col)\\\":\\r\\n\\r\\n while True:\\r\\n\\r\\n try:\\r\\n\\r\\n print('\\\\nPlease Specify your move. Enter the number column of a cell on the board.')\\r\\n print('-'*85)\\r\\n \\r\\n col = Connect4GameUI.move_col(board)\\r\\n row = Connect4GameUI._get_valid_row(board, col)\\r\\n print(row,col)\\r\\n return row, col\\r\\n\\r\\n break\\r\\n\\r\\n except:\\r\\n print('\\\\nInvalid move!!!')\\r\\n print('Please try it again.')\",\n \"def move(self, row: int, col: int, player: int) -> int:\\n n = self.n\\n if player == 1:\\n self.rows_1[row] += 1\\n self.cols_1[col] += 1\\n if player == 2:\\n self.rows_2[row] += 1\\n self.cols_2[col] += 1\\n if row == col:\\n self.diag1[row] = player\\n if row + col + 1 == n:\\n self.diag2[row] = player\\n f = 0\\n g = 0\\n for i in range(n):\\n if self.rows_1[row] == n or self.cols_1[col] == n:\\n return 1\\n if self.rows_2[row] == n or self.cols_2[col] == n:\\n return 2 \\n if self.diag1[i] != self.diag1[0]:\\n f = 1\\n if self.diag2[i] != self.diag2[0]:\\n g = 1\\n if f == 0:\\n return self.diag1[0]\\n if g == 0:\\n return self.diag2[0]\\n return 0\",\n \"def solve(self, board: List[List[str]]) -> None:\\n def dfs(board, i, j):\\n m = len(board)\\n n = len(board[0])\\n if i < 0 or i >= m or j < 0 or j >= n: return\\n\\n if board[i][j] != 'O': return\\n\\n board[i][j] = '#'\\n [dfs(board, i+di, j+dj) for di, dj in [(0, 1), (1, 0), (0, -1), (-1, 0)]]\\n\\n if len(board) == 0: return\\n m = len(board)\\n n = len(board[0])\\n\\n for i in range(m):\\n dfs(board, i, 0)\\n dfs(board, i, n-1)\\n\\n for j in range(n):\\n dfs(board, 0, j)\\n dfs(board, m-1, j)\\n\\n for i in range(m):\\n for j in range(n):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n\\n for i in range(m):\\n for j in range(n):\\n if board[i][j] == '#':\\n board[i][j] = 'O'\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) <= 2 or len(board[0]) <= 2:\\n return\\n\\n self.nRow, self.nCol = len(board), len(board[0])\\n\\n def helper(kr, kc):\\n board[kr][kc] = '.'\\n kr > 0 and board[kr - 1][kc] == 'O' and helper(kr - 1, kc)\\n kr < self.nRow - 1 and board[kr + 1][kc] == 'O' and helper(kr + 1, kc)\\n kc > 0 and board[kr][kc - 1] == 'O' and helper(kr, kc - 1)\\n kc < self.nCol - 1 and board[kr][kc + 1] == 'O' and helper(kr, kc + 1)\\n\\n for kr in [0, self.nRow - 1]:\\n for kc in range(self.nCol):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n for kc in [0, self.nCol - 1]:\\n for kr in range(self.nRow):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n\\n for kr in range(self.nRow):\\n for kc in range(self.nCol):\\n if board[kr][kc] == 'O':\\n board[kr][kc] = 'X'\\n elif board[kr][kc] == '.':\\n board[kr][kc] = 'O'\\n\\n return\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) == 0:\\n return\\n self.h = len(board)\\n self.w = len(board[0])\\n self.board = board\\n for i in range(self.h):\\n for j in range(self.w):\\n if i == 0 or i == self.h-1 or j == 0 or j == self.w-1:\\n #print (i,j)\\n self.dfs((i,j))\\n for i in range(self.h):\\n for j in range(self.w):\\n if self.board[i][j]==\\\"O\\\":\\n self.board[i][j]=\\\"X\\\"\\n for i in range(self.h):\\n for j in range(self.w):\\n if self.board[i][j]==\\\"#\\\":\\n self.board[i][j]=\\\"O\\\"\",\n \"def move(self, row, col, player):\\n offset = player * 2 - 3 # 1 or -1\\n self.row[row] += offset\\n self.col[col] += offset\\n if row == col:\\n self.diag += offset\\n if row + col == self.n - 1:\\n self.anti_diag += offset\\n if self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\\n return 2\\n if -self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\\n return 1\\n return 0\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if not board:\\n return\\n x=len(board)\\n y=len(board[0])\\n visit=[[False if board[i][j]=='X' else True for j in range(y)] for i in range(x)]\\n for i in range(x):\\n for j in range(y):\\n if visit[i][j] and board[i][j]=='O':\\n queue=[[i,j]]\\n visit[i][j]=False\\n k=0\\n surround=True\\n while len(queue)>k:\\n if queue[k][0]==0 or queue[k][0]==x-1 or queue[k][1]==y-1 or queue[k][1]==0:\\n surround=False\\n if queue[k][1]!=y-1 and visit[queue[k][0]][queue[k][1]+1]:\\n queue.append([queue[k][0],queue[k][1]+1])\\n visit[queue[k][0]][queue[k][1]+1]=False\\n if queue[k][1]!=0 and visit[queue[k][0]][queue[k][1]-1]:\\n queue.append([queue[k][0],queue[k][1]-1])\\n visit[queue[k][0]][queue[k][1]-1]=False\\n if queue[k][0]!=x-1 and visit[queue[k][0]+1][queue[k][1]]:\\n queue.append([queue[k][0]+1,queue[k][1]])\\n visit[queue[k][0]+1][queue[k][1]]=False\\n if queue[k][0]!=0 and visit[queue[k][0]-1][queue[k][1]]:\\n queue.append([queue[k][0]-1,queue[k][1]])\\n visit[queue[k][0]-1][queue[k][1]]=False\\n k+=1\\n if surround:\\n for i1,j1 in queue:\\n board[i1][j1]='X'\\n return\",\n \"def solve_util(self, board, col):\\n try:\\n if col == self.N:\\n self.print_sol(board)\\n return True\\n\\n # Trying to place this queen in all rows one by one\\n res = False\\n for i in range(self.N):\\n if self.is_safe(board, i, col):\\n board[i][col] = 1\\n res = self.solve_util(board, col + 1) or res\\n if type(res) == dict:\\n return res\\n board[i][col] = 0 # Backtracking...\\n\\n # if queen cannot be placed in any row in this col, then alas\\n # we return false..\\n return res\\n except KeyboardInterrupt:\\n print('Keyboard Interrupted!')\\n return self.Outputs\",\n \"def position_tile(self, target_row, target_col, cur_row, cur_col, need_ld=True):\\n move_str = ''\\n if cur_row == target_row:\\n if cur_col < target_col:\\n move_str += 'l' * (target_col - cur_col)\\n if target_col - cur_col > 1:\\n move_str += 'ur'\\n move_str += 'druldru' * (target_col - cur_col - 1)\\n else:\\n move_str += 'ur' if not need_ld else ''\\n need_ld = False\\n else:\\n move_str += 'r' * (cur_col - target_col)\\n if cur_col - target_col > 1:\\n move_str += 'ul'\\n move_str += 'dlurdlu' * (cur_col - target_col - 1)\\n else:\\n need_ld = False\\n else:\\n move_str += 'u' * (target_row - cur_row)\\n if cur_col < target_col:\\n move_str += ('l' * (target_col - cur_col) + 'dru')\\n move_str += 'druldru' * (target_col - cur_col - 1)\\n move_str += 'lddru' * (target_row - cur_row - 1)\\n elif cur_col > target_col:\\n move_str += ('r' * (cur_col - target_col) + 'dlu')\\n move_str += 'dlurdlu' * (cur_col - target_col - 1)\\n move_str += 'lddru' * (target_row - cur_row - 1)\\n else:\\n move_str += 'lddru' * (target_row - cur_row - 1)\\n if need_ld:\\n move_str += 'ld'\\n return move_str\",\n \"def update(self, move):\\n\\n if not 0 <= move < 7:\\n raise InvalidMove\\n\\n placed = False\\n x = None\\n y = None\\n\\n for row in reversed(xrange(self._rows)):\\n if not self._board[row][move]:\\n self._board[row][move] = self.current_player\\n placed = True\\n x = move\\n y = row\\n break\\n\\n if not placed:\\n raise InvalidMove\\n\\n return (x, y)\",\n \"def make_move(self, row, column):\\n\\t\\tif self.board[int(row)][int(column)] == '-':\\n\\t\\t\\tself.board[int(row)][int(column)] = self.marker\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"That spot is occupied, you messed up, you lose your turn for doing bad things\\\")\",\n \"def solve(self, board: 'List[List[str]]') -> 'None':\\n if not board:\\n return\\n m, n = len(board), len(board[0])\\n save = [ij for k in range(m + n) for ij in ((0, k), (m - 1, k), (k, 0), (k, n - 1))]\\n while save:\\n i, j = save.pop()\\n if -1 < i < m and -1 < j < n and board[i][j] == 'O':\\n board[i][j] = 'S'\\n save += (i + 1, j), (i - 1, j), (i, j + 1), (i, j - 1)\\n for row in board:\\n for i, c in enumerate(row):\\n row[i] = 'XO'[c == 'S']\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) <= 2 or len(board[0]) <= 2:\\n return\\n\\n nRow, nCol = len(board), len(board[0])\\n\\n def helper(kr, kc):\\n board[kr][kc] = '.'\\n kr > 0 and board[kr - 1][kc] == 'O' and helper(kr - 1, kc)\\n kr < nRow - 1 and board[kr + 1][kc] == 'O' and helper(kr + 1, kc)\\n kc > 0 and board[kr][kc - 1] == 'O' and helper(kr, kc - 1)\\n kc < nCol - 1 and board[kr][kc + 1] == 'O' and helper(kr, kc + 1)\\n\\n for kr in [0, nRow - 1]:\\n for kc in range(nCol):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n for kc in [0, nCol - 1]:\\n for kr in range(nRow):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n\\n for kr in range(nRow):\\n for kc in range(nCol):\\n if board[kr][kc] == 'O':\\n board[kr][kc] = 'X'\\n elif board[kr][kc] == '.':\\n board[kr][kc] = 'O'\\n\\n return\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) == 0: return\\n\\n m = len(board)\\n n = len(board[0])\\n\\n uf = UnionFind(m * n + 1)\\n dummy = m * n\\n\\n # connect 'O' at first and last col with dummy\\n for i in range(m):\\n if board[i][0] == 'O':\\n uf.union(dummy, i * n)\\n if board[i][-1] == 'O':\\n uf.union(dummy, i * n + n - 1)\\n\\n # connect 'O' at first and last row with dummy\\n for j in range(n):\\n if board[0][j] == 'O':\\n uf.union(dummy, j)\\n if board[-1][j] == 'O':\\n uf.union(dummy, n * (m-1) + j)\\n\\n d = [(1, 0), (0, 1), (0, -1), (-1, 0)]\\n\\n for i in range(1, m-1):\\n for j in range(1, n-1):\\n if board[i][j] == 'O':\\n for di, dj in d:\\n x = i+di\\n y = j+dj\\n if board[x][y] == 'O':\\n uf.union(x*n+y, i*n+j)\\n\\n # change not connected 'O' with 'X'\\n for i in range(1, m-1):\\n for j in range(1, n-1):\\n if not uf.connected(dummy, i * n + j):\\n board[i][j] = 'X'\",\n \"def result(self, row, col, move):\\n start = (row, col)\\n end = self.updateCell(row, col, move)\\n\\n return self.change(start, end)\",\n \"def move_x(self, row, column):\\n\\n #returns false if game has already been won\\n if self._game_state != \\\"UNFINISHED\\\":\\n return False\\n\\n # checks if x tries to move out of bounds\\n if row not in range(8) or column not in range(8):\\n return False\\n\\n # returns false/invalid move if x tries to move more than one row at a time or\\n # non diagonal\\n if (row - self._current_x_row) > 1 or (column - self._current_x_column) > 1 or (\\n self._current_x_row - row) > 1 or (self._current_x_column - column) > 1:\\n return False\\n\\n if self._current_x_column == column:\\n return False\\n\\n if self._current_x_row == row:\\n return False\\n\\n if \\\"o\\\" in self._board[row][column]:\\n return False\\n\\n #places x in the specified row and column if the move is legal\\n else:\\n self._board[self._current_x_row].remove(\\\"x\\\")\\n self._board[self._current_x_row].append(\\\"\\\")\\n self._board[row][column] = \\\"x\\\"\\n self._current_x_row = row\\n self._current_x_column = column\\n self._current_row += 1\\n self._lower_right = (self._current_x_row + 1, self._current_x_column + 1)\\n self._lower_left = (self._current_x_row + 1, self._current_x_column - 1)\\n self._upper_right = (self._current_x_row - 1, self._current_x_column + 1)\\n self._upper_left = (self._current_x_row - 1, self._current_x_column - 1)\\n self._row1 = (\\n self._board[0][0],\\n self._board[1][0],\\n self._board[2][0],\\n self._board[3][0],\\n self._board[4][0],\\n self._board[5][0],\\n self._board[6][0],\\n self._board[7][0])\\n\\n self._row7 = (\\n self._board[0][7],\\n self._board[1][7],\\n self._board[2][7],\\n self._board[3][7],\\n self._board[4][7],\\n self._board[5][7],\\n self._board[6][7],\\n self._board[7][7])\\n\\n\\n # checks if four \\\"o\\\" pieces surrounds x, if so, then x has no more moves and o wins\\n if \\\"x\\\" not in self._board[7]:\\n if \\\"o\\\" in self._board[self._lower_right[0]][self._lower_right[1]] and \\\"o\\\" in \\\\\\n self._board[self._lower_left[0]][\\n self._lower_left[1]] and \\\"o\\\" in self._board[self._upper_right[0]][\\n self._upper_right[1]] and \\\"o\\\" in \\\\\\n self._board[self._upper_left[0]][self._upper_left[1]]:\\n self._game_state = \\\"O_WON\\\"\\n\\n # checks if x is in the last column and o pieces surrounds x, x loses\\n if \\\"x\\\" in self._row7 and \\\"o\\\" in self._board[self._lower_left[0]][self._lower_left[1]] and \\\"o\\\" in \\\\\\n self._board[self._upper_left[0]][self._upper_left[1]]:\\n self._game_state = \\\"O_WON\\\"\\n\\n # checks if x is in the first row and o surrounds x, x loses\\n if \\\"x\\\" in self._board[0] and \\\"o\\\" in self._board[self._lower_right[0]][self._lower_right[1]] and \\\"o\\\" in \\\\\\n self._board[self._lower_left[0]][self._lower_left[1]]:\\n self._game_state = \\\"O_WON\\\"\\n\\n # checks if x is in the first column and o pieces surrounds x, x loses\\n if \\\"x\\\" in self._row1 and \\\"o\\\" in self._board[self._lower_right[0]][self._lower_right[1]] and \\\"o\\\" in \\\\\\n self._board[self._upper_right[0]][self._upper_right[1]]:\\n self._game_state = \\\"O_WON\\\"\\n\\n # winning condition for \\\"x\\\" piece upon reaching last row\\n if \\\"x\\\" in self._board[7]:\\n self._game_state = \\\"X_WON\\\"\\n\\n return True\",\n \"def move(self, row: int, col: int, player: int) -> int:\\n s = -1 if player == 1 else 1\\n\\n self.rows[row] += s\\n if abs(self.rows[row]) == self.n:\\n return player\\n\\n self.cols[col] += s\\n if abs(self.cols[col]) == self.n:\\n return player\\n\\n if row == col:\\n self.diagonals[0] += s\\n if abs(self.diagonals[0]) == self.n:\\n return player\\n\\n if (row + col) == self.n - 1:\\n self.diagonals[1] += s\\n if abs(self.diagonals[1]) == self.n:\\n return player\\n\\n return 0\",\n \"def solve(self, board: List[List[str]]) -> None:\\n\\n def expansion(i,j):\\n for di,dj in {(-1,0),(1,0),(0,-1),(0,1)}:\\n if -1<i+di<m and -1<j+dj<n and state[i+di][j+dj]=='O':\\n return True\\n return False\\n\\n if not board:\\n return board\\n\\n m = len(board)\\n n = len(board[0]) \\n state = [['X']*n for _ in range(m)]\\n\\n for j in range(n):\\n state[0][j] = board[0][j]\\n state[m-1][j] = board[m-1][j]\\n \\n for i in range(m):\\n state[i][0] = board[i][0]\\n state[i][n-1] = board[i][n-1]\\n \\n flag = 1\\n\\n while flag:\\n flag = 0\\n\\n for k in range(1, (1+min(m,n))//2):\\n for j in range(k,n-k):\\n if board[k][j]=='O' and state[k][j] == 'X' and expansion(k,j):\\n state[k][j] = 'O'\\n flag = 1\\n \\n if board[m-1-k][j]=='O' and state[m-1-k][j] == 'X' and expansion(m-1-k,j):\\n state[m-1-k][j] = 'O'\\n flag = 1\\n \\n for i in range(k,m-k):\\n if board[i][k]=='O' and state[i][k] == 'X' and expansion(i,k):\\n state[i][k] = 'O'\\n flag = 1\\n \\n if board[i][n-1-k]=='O' and state[i][n-1-k] == 'X' and expansion(i,n-1-k):\\n state[i][n-1-k] = 'O'\\n flag = 1\\n\\n board[:] = state[:]\",\n \"def move(self, row, column, symbol):\\n game_state = self.determine_game_state()\\n if game_state not in (GameState.GAME_NOT_STARTED, GameState.GAME_IN_PROGRESS):\\n return MoveResults.MOVE_INVALID\\n\\n # check for initial move\\n if self.board == BLANK_BOARD and symbol == O_SYMBOL:\\n return MoveResults.MOVE_INVALID\\n\\n # check for invalid row and column\\n if row < 0 or row > 2 or column < 0 or column > 2:\\n return MoveResults.MOVE_INVALID\\n\\n # make sure the game piece is valid\\n if symbol != X_SYMBOL and symbol != O_SYMBOL:\\n return MoveResults.MOVE_INVALID\\n\\n # make sure the game piece isn't moving out of turn\\n x_moves = self.board.count(X_SYMBOL)\\n o_moves = self.board.count(O_SYMBOL)\\n if symbol == X_SYMBOL and x_moves > o_moves:\\n return MoveResults.MOVE_INVALID\\n elif symbol == O_SYMBOL and o_moves >= x_moves:\\n # note that x always goes first.\\n return MoveResults.MOVE_INVALID \\n\\n # figure out position.\\n position = (3 * row) + column\\n\\n # make sure there's not already a piece there.\\n if self.board[position] != EMPTY_SYMBOL:\\n return MoveResults.MOVE_INVALID\\n\\n self.board = self.board[:position] + symbol + self.board[position+1:] \\n return MoveResults.MOVE_VALID\",\n \"def solve(puzzle):\\n print(\\\"Solving...\\\")\\n array_puzzle = np.asarray(puzzle)\\n array_puzzle.flags.writeable = False # Turn off writable flags to prevent data being ovewritten accidentally.\\n goal_state = __generate_goal(len(array_puzzle[0]), len(array_puzzle))\\n\\n flat_puzzle = list(chain.from_iterable(puzzle)) # Flatten the list\\n\\n # If the puzzle doesn't contain 0, exit.\\n try:\\n flat_puzzle.remove(0) # Remove 0 from the list\\n except:\\n print(\\\"All puzzles must include an open tile (0).\\\")\\n return None\\n\\n inversions = __count_inversions(flat_puzzle) # Count the inversions\\n\\n # width = len(array_puzzle[0]) # Get the width of the puzzle (columns)\\n # length = len(array_puzzle) # Get the length of the puzzle (rows)\\n\\n oddEven = __odd_or_even(len(array_puzzle[0])) # Determine if the width is odd or even.\\n start_position = __find_start(array_puzzle) # Find the start position's row\\n solvable = __is_solvable(oddEven, inversions, len(array_puzzle), start_position) # Cleck if the puzzle is solvable.\\n\\n # If the puzzle is not solvable, return None.\\n if(solvable == \\\"None\\\"):\\n return None\\n\\n # If we cannot calculate a* (for example the given values are not all in sequential order (1-5) 4 is replaced by 6 (1,2,3,5,6))\\n try:\\n return __a_star(array_puzzle, goal_state)\\n except:\\n print(\\\"Please make sure there are no duplicate or skipped inputs.\\\")\\n return None\\n\\n # This code was used in testing to print out the string.\\n # solved = __a_star(array_puzzle, goal_state)\\n # Return the moves needed to complete the puzzle.\\n # return print(str(__build_string(solved)) + \\\" (\\\" + str(len(solved)) + \\\")\\\")\",\n \"def solve(self, board: List[List[str]]) -> None:\\n def DFS(board, i, j):\\n q = []\\n q.append([i, j])\\n \\n while q:\\n x, y = q.pop()\\n board[x][y] = \\\"*\\\"\\n neighbors = ((0, 1), (0, -1), (1, 0), (-1, 0))\\n for dx, dy in neighbors:\\n if 0 <= x + dx <= len(board) - 1 and 0 <= y + dy <= len(board[0]) - 1 and board[x + dx][y + dy] == \\\"O\\\":\\n q.append([x + dx, y + dy])\\n \\n \\n # first row\\n i = 0\\n for j in range(len(board[0])):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n # last row\\n i = len(board) - 1\\n for j in range(len(board[0])):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n # first column\\n j = 0\\n for i in range(len(board)):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n # last column\\n j = len(board[0]) - 1\\n for i in range(len(board)):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n \\n for i in range(len(board)):\\n for j in range(len(board[0])):\\n if board[i][j] == \\\"O\\\":\\n board[i][j] = \\\"X\\\"\\n elif board[i][j] == \\\"*\\\":\\n board[i][j] = \\\"O\\\"\",\n \"def get_move(board, player):\\r\\n row, col = 0, 0\\r\\n return row, col\",\n \"def put(self, column):\\n column -= 1\\n if self.occupied[column] >= 4 or self.won:\\n return 'ERROR'\\n self.history.append(column + 1)\\n row = self.occupied[column]\\n # assign 1 to player 1, and -1 to player 2\\n if len(self.history) % 2 == 1:\\n player = 1\\n self.board[3 - row][column] = 1\\n else:\\n player = -1\\n self.board[3 - row][column] = 2\\n # add player score to column, row and diagonal scores\\n self.columnScore[column] += player\\n self.rowScore[row] += player\\n self.occupied[column] += 1;\\n if column == row:\\n self.diagonal += player\\n if column + row == 3:\\n self.antidiagonal += player\\n # check column, row and diagonal scores\\n # if absolute value of one of them is 4\\n # which means the original value is either 4 or -4\\n # and one of the player has occupied all 4 of them\\n # which means the player has won in that row/column/diagonal\\n # and thus return \\\"WIN\\\"\\n if (abs(self.rowScore[row]) == 4 or abs(self.columnScore[column]) == 4\\n or abs(self.diagonal) == 4 or abs(self.antidiagonal) == 4):\\n self.won = True\\n return 'WIN'\\n # check if there is still non-full columns\\n # in other words check if the board is full\\n for i in range(0, self.size):\\n # if board is not full return \\\"OK\\\"\\n if self.occupied[i] < 4:\\n return 'OK'\\n # if the board is full, return \\\"DRAW\\\"\\n return 'DRAW'\",\n \"def move(self, row, col, player):\\n if self.winning == True:\\n return\\n self.matrix[row][col] = player\\n n = len(self.matrix)\\n indicator = True\\n for i in range(n):\\n if self.matrix[row][i] != player:\\n indicator = False\\n break\\n if indicator == True:\\n self.winning = True\\n return player\\n \\n indicator = True\\n for i in range(n):\\n if self.matrix[i][col] != player:\\n indicator = False\\n break\\n if indicator == True:\\n self.winning = True\\n return player\\n \\n if row == col:\\n indicator = True\\n for i in range(n):\\n if self.matrix[i][i] != player:\\n indicator = False\\n break\\n if indicator == True:\\n self.winning = True\\n return player\\n if row + col == n - 1:\\n indicator = True\\n for i in range(n):\\n if self.matrix[i][n - 1 - i] != player:\\n indicator = False\\n break\\n if indicator == True:\\n self.winning = True\\n return player\\n return 0\",\n \"def solve(self, board: 'List[List[str]]') -> 'None':\\n\\n def dfs(i, j, tmp):\\n nonlocal flag\\n if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]):\\n flag = False\\n return\\n if board[i][j] != 'O' or [i, j] in tmp:\\n return\\n tmp.append([i, j])\\n dfs(i - 1, j, tmp)\\n dfs(i + 1, j, tmp)\\n dfs(i, j + 1, tmp)\\n dfs(i, j - 1, tmp)\\n return tmp\\n\\n change = []\\n for i in range(len(board)):\\n for j in range(len(board[0])):\\n if board[i][j] == 'O' and [i, j] not in change:\\n tmp = []\\n flag = True\\n tmp = dfs(i, j, tmp[:])\\n if flag:\\n for loc in tmp:\\n i, j = loc[0], loc[1]\\n board[i][j] = 'X'\\n\\n for loc in change:\\n i, j = loc[0], loc[1]\\n board[i][j] = 'X'\",\n \"def solve_(self, x, y, board, path):\\n if self.SOLVED:\\n return\\n if self.is_done(board):\\n self.print_path(path)\\n self.SOLVED = True\\n return\\n for new_x, new_y in self.next_click(x, y, board):\\n if new_x is None or new_y is None:\\n return\\n new_board = self.click(new_x, new_y, board)\\n self.solve_(\\n x=0, y=0,\\n board=new_board,\\n path=path + [((new_x, new_y), new_board)]\\n )\",\n \"def move(self, row, col, player):\\n toadd = 1 if player == 1 else -1\\n \\n self.row[row] += toadd\\n self.col[col] += toadd\\n if row == col: self.diagonal += toadd\\n if col == self.n - row -1 : self.antidiag += toadd\\n \\n if abs(self.row[row]) == self.n or abs(self.col[col]) == self.n or abs(self.diagonal) == self.n or abs(self.antidiag) == self.n:\\n return player\\n else:\\n return 0\",\n \"def move(self, row, col, player):\",\n \"def get_move(board, player):\\n row, col = 0, 0\\n return row, col\",\n \"def solve_puzzle(board):\\n # Propagate value effects\\n board = simplify_puzzle(board, [])\\n\\n # Brute force remaining cells\\n board = brute(board)\\n\\n # Verify that the puzzle was successfully solved\\n assert get_length(board)==81\\n assert valid_attempt(board)\\n\\n return board\",\n \"def test_perform_move(self):\\n p = hw.create_tile_puzzle(3, 3)\\n self.assertFalse(p.perform_move(\\\"taco\\\"))\\n self.assertTrue(p.perform_move('up'))\\n self.assertEqual(p.get_board(), [[1,2,3],[4,5,0],[7,8,6]])\\n self.assertFalse(p.perform_move('right'))\\n p = hw.create_tile_puzzle(2, 4)\\n self.assertTrue(p.perform_move('left'))\\n self.assertTrue(p.perform_move('up'))\\n self.assertFalse(p.perform_move('up'))\\n self.assertEqual(p.get_board(), [[1,2,0,4],[5,6,3,7]])\\n p = hw.create_tile_puzzle(1, 4)\\n self.assertTrue(p.perform_move('left'))\\n self.assertTrue(p.perform_move('left'))\\n self.assertTrue(p.perform_move('left'))\\n self.assertFalse(p.perform_move('down'))\\n self.assertFalse(p.perform_move('left'))\\n self.assertEqual(p.get_board(), [[0,1,2,3]])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7611342","0.7530595","0.7489633","0.74769557","0.7346029","0.7312759","0.72872084","0.72763294","0.72613305","0.72458375","0.72417307","0.7236731","0.72223794","0.7210395","0.71957016","0.71925163","0.7142041","0.71206504","0.70840454","0.7064091","0.7038805","0.7027271","0.69623935","0.695526","0.69471264","0.694509","0.69337404","0.69234896","0.6916079","0.6878287","0.68757343","0.68463266","0.6837197","0.67863864","0.6742842","0.6735732","0.67058396","0.66578364","0.66284144","0.6625063","0.6580358","0.65620995","0.65620995","0.65620995","0.65583694","0.6551443","0.6551443","0.645354","0.64497983","0.6408909","0.6379721","0.6374615","0.63398385","0.633639","0.6325701","0.62638223","0.6257799","0.62237","0.62125415","0.61983734","0.6138262","0.60704345","0.6063678","0.6057803","0.6055227","0.6052307","0.6052291","0.60510117","0.60505867","0.60382706","0.6028564","0.60173017","0.6004885","0.6001108","0.59939766","0.5991939","0.59812343","0.5976079","0.59581846","0.5937005","0.5924132","0.59235555","0.59208554","0.591132","0.59080476","0.5894667","0.5890435","0.5885432","0.5883782","0.5882655","0.5874512","0.58741593","0.5873913","0.5868474","0.58598614","0.5855099","0.584924","0.58490455","0.58463395","0.58241105"],"string":"[\n \"0.7611342\",\n \"0.7530595\",\n \"0.7489633\",\n \"0.74769557\",\n \"0.7346029\",\n \"0.7312759\",\n \"0.72872084\",\n \"0.72763294\",\n \"0.72613305\",\n \"0.72458375\",\n \"0.72417307\",\n \"0.7236731\",\n \"0.72223794\",\n \"0.7210395\",\n \"0.71957016\",\n \"0.71925163\",\n \"0.7142041\",\n \"0.71206504\",\n \"0.70840454\",\n \"0.7064091\",\n \"0.7038805\",\n \"0.7027271\",\n \"0.69623935\",\n \"0.695526\",\n \"0.69471264\",\n \"0.694509\",\n \"0.69337404\",\n \"0.69234896\",\n \"0.6916079\",\n \"0.6878287\",\n \"0.68757343\",\n \"0.68463266\",\n \"0.6837197\",\n \"0.67863864\",\n \"0.6742842\",\n \"0.6735732\",\n \"0.67058396\",\n \"0.66578364\",\n \"0.66284144\",\n \"0.6625063\",\n \"0.6580358\",\n \"0.65620995\",\n \"0.65620995\",\n \"0.65620995\",\n \"0.65583694\",\n \"0.6551443\",\n \"0.6551443\",\n \"0.645354\",\n \"0.64497983\",\n \"0.6408909\",\n \"0.6379721\",\n \"0.6374615\",\n \"0.63398385\",\n \"0.633639\",\n \"0.6325701\",\n \"0.62638223\",\n \"0.6257799\",\n \"0.62237\",\n \"0.62125415\",\n \"0.61983734\",\n \"0.6138262\",\n \"0.60704345\",\n \"0.6063678\",\n \"0.6057803\",\n \"0.6055227\",\n \"0.6052307\",\n \"0.6052291\",\n \"0.60510117\",\n \"0.60505867\",\n \"0.60382706\",\n \"0.6028564\",\n \"0.60173017\",\n \"0.6004885\",\n \"0.6001108\",\n \"0.59939766\",\n \"0.5991939\",\n \"0.59812343\",\n \"0.5976079\",\n \"0.59581846\",\n \"0.5937005\",\n \"0.5924132\",\n \"0.59235555\",\n \"0.59208554\",\n \"0.591132\",\n \"0.59080476\",\n \"0.5894667\",\n \"0.5890435\",\n \"0.5885432\",\n \"0.5883782\",\n \"0.5882655\",\n \"0.5874512\",\n \"0.58741593\",\n \"0.5873913\",\n \"0.5868474\",\n \"0.58598614\",\n \"0.5855099\",\n \"0.584924\",\n \"0.58490455\",\n \"0.58463395\",\n \"0.58241105\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74047565"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":79,"cells":{"query":{"kind":"string","value":"Solve the upper left 2x2 part of the puzzle Updates the puzzle and returns a move string"},"document":{"kind":"string","value":"def solve_2x2(self):\n cur_row, cur_col = self.current_position(0, 0)\n move_str = 'u' * cur_row + 'l' * cur_col\n self.update_puzzle(move_str)\n if self.check_2x2_solved():\n return move_str\n else:\n while not self.check_2x2_solved():\n move_str += 'rdlu'\n self.update_puzzle('rdlu')\n return move_str"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def solve_puzzle(self):\n\n move_str = \"\"\n \n # Move zero tile to bottom right corner tile of puzzle.\n zero_pos = self.current_position(0,0) \n vert_dist = (self.get_height() - 1) - zero_pos[0]\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\n move_str += ((\"d\" * vert_dist) + (\"r\" * horiz_dist))\n self.update_puzzle(move_str)\n \n # Solve lower rows\n if self.get_height() > 2:\n for row in range(self.get_height() - 1, 1, -1):\n for col in range(self.get_width() - 1, -1, -1):\n if col != 0:\n move_str += self.solve_interior_tile(row, col)\n else:\n move_str += self.solve_col0_tile(row)\n \n # Solve top 2 rows\n if self.get_width() > 2:\n for col in range(self.get_width() - 1, 1, -1):\n move_str += self.solve_row1_tile(col)\n move_str += self.solve_row0_tile(col)\n \n # Solve 2x2\n move_str += self.solve_2x2()\n\n return move_str","def solve_puzzle(self):\n cur0_row, cur0_col = self.current_position(0, 0)\n move_str = 'd' * (self._height - cur0_row - 1) + 'r' * (self._width - cur0_col - 1)\n self.update_puzzle(move_str)\n for row in range(self._height-1, 1, -1):\n for col in range(self._width-1, -1, -1):\n assert self.lower_row_invariant(row, col)\n if col != 0:\n move_str += self.solve_interior_tile(row, col)\n else:\n move_str += self.solve_col0_tile(row)\n for col in range(self._width-1, 1, -1):\n assert self.row1_invariant(col)\n move_str += self.solve_row1_tile(col)\n assert self.row0_invariant(col)\n move_str += self.solve_row0_tile(col)\n move_str += self.solve_2x2()\n return move_str","def solve_puzzle(self):\r\n # initialize some values and start tile at bottom right corner\r\n col = self.get_width() - 1\r\n row = self.get_height() - 1\r\n move = \"\"\r\n curr_row, curr_col = self.current_position(0, 0)\r\n move_to_target = abs(curr_col - col) * \"r\" + abs(curr_row - row) * \"d\"\r\n self.update_puzzle(move_to_target)\r\n move += move_to_target\r\n\r\n # apply solver methods\r\n for dummy_i in range(row, 1, -1):\r\n for dummy_j in range(col, 0, -1):\r\n move += self.solve_interior_tile(dummy_i, dummy_j)\r\n move += self.solve_col0_tile(dummy_i)\r\n \r\n for dummy_j in range(col, 1, -1):\r\n move += self.solve_row1_tile(dummy_j)\r\n move += self.solve_row0_tile(dummy_j)\r\n \r\n move += self.solve_2x2()\r\n return move","def solve_puzzle(self):\r\n moves_str = \"\"\r\n # move zero to the most botton right\r\n zero_row, zero_col = self.current_position(0, 0)\r\n downs = self._height - 1 - zero_row\r\n rights = self._width - 1 - zero_col\r\n for dummy_d in range(downs):\r\n moves_str += \"d\"\r\n for dummy_r in range(rights):\r\n moves_str += \"r\"\r\n self.update_puzzle(moves_str)\r\n # Solve the bottom m−2 rows of the puzzle \r\n # in a row by row manner from bottom to top. \r\n # Each individual row will be solved in a right to left order.\r\n if self._height > 2 and self._width > 2:\r\n for row in range(self._height - 1, 1, -1):\r\n for col in range(self._width - 1, 0, -1):\r\n assert self.lower_row_invariant(row, col)\r\n moves_str += self.solve_interior_tile(row, col)\r\n assert self.lower_row_invariant(row, col - 1)\r\n assert self.lower_row_invariant(row, 0)\r\n moves_str += self.solve_col0_tile(row)\r\n assert self.lower_row_invariant(row - 1, self._width - 1)\r\n # Solve the rightmost n−2 columns of the top two rows\r\n # in a right to left order). \r\n # Each column consists of two unsolved positions \r\n # and will be solved in a bottom to top order.\r\n for col in range(self._width - 1, 1, -1):\r\n assert self.row1_invariant(col)\r\n moves_str += self.solve_row1_tile(col)\r\n assert self.row0_invariant(col)\r\n moves_str += self.solve_row0_tile(col)\r\n assert self.row1_invariant(col - 1)\r\n # Solve the upper left 2×2 portion of the puzzle directly.\r\n assert self.row1_invariant(1)\r\n moves_str += self.solve_2x2()\r\n \r\n elif self._height <=2 and self._width > 2:\r\n for col in range(self._width - 1, 1, -1):\r\n assert self.row1_invariant(col)\r\n moves_str += self.solve_row1_tile(col)\r\n assert self.row0_invariant(col)\r\n moves_str += self.solve_row0_tile(col)\r\n assert self.row1_invariant(col - 1)\r\n # Solve the upper left 2×2 portion of the puzzle directly.\r\n assert self.row1_invariant(1)\r\n moves_str += self.solve_2x2()\r\n elif self._height <= 2 and self._width <= 2:\r\n assert self.row1_invariant(1)\r\n moves_str += self.solve_2x2()\r\n #elif self._height > 2 and self._width <= 2:\r\n \r\n print moves_str\r\n print self._grid\r\n return moves_str","def solve_puzzle(self):\n # replace with your code\n string = ''\n width = self._width\n height = self._height\n zero = self.current_position(0, 0)\n row_to_zero = height - 1 - zero[0]\n col_to_zero = width - 1 - zero[1]\n string += 'r' * col_to_zero\n string += 'd' * row_to_zero\n self.update_puzzle(string)\n if width == 2 and height == 2:\n string += self.solve_2x2()\n elif width > 2 and height == 2:\n for col in range(width - 1, 1, -1):\n string += self.solve_row1_tile(col)\n string += self.solve_row0_tile(col)\n string += self.solve_2x2()\n elif width == 2 and height > 2:\n for row in range(height - 1, 1, -1):\n for col in range(width - 1, 0, -1):\n string += self.solve_interior_tile(row, col)\n string += self.solve_col0_tile(row)\n string += self.solve_2x2()\n elif width > 2 and height > 2:\n for row in range(height - 1, 1, -1):\n for col in range(width - 1, 0, -1):\n string += self.solve_interior_tile(row, col)\n string += self.solve_col0_tile(row)\n #for row in range(height - 1, -1, -1):\n for col in range(width - 1, 1, -1):\n string += self.solve_row1_tile(col)\n string += self.solve_row0_tile(col)\n string += self.solve_2x2()\n return string","def solve_2x2(self):\n # move zero tile to the left-up corner\n self.update_puzzle(\"lu\")\n movements = \"rdlu\"\n for _ in range(3):\n self.update_puzzle(\"rdlu\")\n if self.row0_invariant(0):\n return \"lu\" + movements\n movements += \"rdlu\"\n # the final 2x2 cannot be solved\n return \"\"","def solve_2x2(self):\r\n moves_str = \"\"\r\n # move zero to the most upper left\r\n zero_row, zero_col = self.current_position(0, 0)\r\n ups = zero_row - 0\r\n lefts = zero_col - 0\r\n for dummy_u in range(ups):\r\n moves_str += \"u\"\r\n for dummy_l in range(lefts):\r\n moves_str += \"l\"\r\n \r\n # get zero, one, two, three positions\r\n self.update_puzzle(moves_str)\r\n zero_row, zero_col = self.current_position(0, 0)\r\n one_row, one_col = self.current_position(0, 1)\r\n two_row, two_col = self.current_position(1, 0)\r\n three_row, three_col = self.current_position(1, 1)\r\n counter = 0\r\n while counter <= 3 and \\\r\n (zero_row != 0 or zero_col != 0 or \\\r\n one_row!= 0 or one_col != 1 or \\\r\n two_row != 1 or two_col != 0 or \\\r\n three_row != 1 or three_col != 1):\r\n move = CIRCLES[\"RIGHT_CIRCLES\"]\r\n moves_str += move\r\n self.update_puzzle(move)\r\n counter += 1\r\n zero_row, zero_col = self.current_position(0, 0)\r\n one_row, one_col = self.current_position(0, 1)\r\n two_row, two_col = self.current_position(1, 0)\r\n three_row, three_col = self.current_position(1, 1)\r\n \r\n print \"solve_2x2\"\r\n print moves_str\r\n print self._grid\r\n return moves_str","def solve_puzzle(self):\n # move zero tile to the lower right corner\n row, col = self.current_position(0, 0)\n movements = \"d\" * (self.get_height() - 1 - row) + \"r\" * (\n self.get_width() - 1 - col)\n self.update_puzzle(movements)\n # solve rowid from 2 by row\n for row in range(self.get_height() - 1, 1, -1):\n for col in range(self.get_width() - 1, -1, -1):\n assert self.lower_row_invariant(row, col)\n if col == 0:\n movements += self.solve_col0_tile(row)\n assert self.lower_row_invariant(row - 1,\n self.get_width() - 1)\n else:\n movements += self.solve_interior_tile(row, col)\n assert self.lower_row_invariant(row, col - 1)\n # solve the uppermost two rows by column\n for col in range(self.get_width() - 1, 1, -1):\n for row in range(1, -1, -1):\n if row == 0:\n assert self.row0_invariant(col)\n movements += self.solve_row0_tile(col)\n assert self.row1_invariant(col - 1)\n else:\n assert self.row1_invariant(col)\n movements += self.solve_row1_tile(col)\n assert self.row0_invariant(col)\n movements += self.solve_2x2()\n return movements","def solve_2x2(self):\r\n # replace with your code\r\n assert self.row1_invariant(1), '2x2 Dont pass row1_invariant(1)'\r\n whole_move = ''\r\n current_position = self.current_position(0, 0)\r\n # print 'Zero position =', current_position\r\n counter = 0\r\n \r\n\r\n \r\n # if current_position == (0,0):\r\n # print (0,0)\r\n # move_to_00 = 'rdlu' \r\n if current_position == (0,1):\r\n # print (0,1)\r\n move_to_00 = 'l'\r\n if current_position == (1,0):\r\n # print (1,0)\r\n move_to_00 = 'u'\r\n if current_position == (1,1):\r\n # print (1,1)\r\n move_to_00 = 'ul'\r\n whole_move += move_to_00\r\n self.update_puzzle(move_to_00)\r\n # print self\r\n # print self.get_number(1,1) < self.get_number(1,0)\r\n \r\n while self.get_number(0,0) != 0 or self.get_number(0,1) != 1:\r\n \r\n # print 'Aloha in loop!'\r\n counter +=1\r\n move = 'rdlu'\r\n whole_move += move\r\n self.update_puzzle(move)\r\n # print self\r\n if counter >5:\r\n break\r\n return whole_move","def solve_2x2(self):\n \n assert self.get_number(1,1) == 0, \"zero tile should be at row 1, col 1\"\n assert self.row1_invariant(1), \"tiles to right and below incorrectly ordered\"\n \n # Moves the zero tile to (0,0).\n self.update_puzzle(\"lu\")\n\n # Repositions the upper left 2x2 part up to 3 times, \n # each time checking whether the puzzle is solved.\n rotation_num = 0\n if self.row0_invariant(0) == False:\n for dummy_rotation in range(3):\n while self.row0_invariant(0) == False:\n rotation_num += 1\n self.update_puzzle(\"rdlu\")\n\n assert self.row0_invariant(0), \"tiles to right and below incorrectly ordered\"\n \n move_str = \"lu\" + (\"rdlu\" * rotation_num) \n return move_str","def solve_2x2(self):\r\n assert self.row1_invariant(1)\r\n pos_1_0 = self.get_number(1, 0)\r\n pos_0_0 = self.get_number(0, 0)\r\n pos_0_1 = self.get_number(0, 1)\r\n # create grid and solve individual cases\r\n grid = [pos_1_0, pos_0_0, pos_0_1]\r\n if grid == [self.get_width(), 1, self.get_width() + 1]:\r\n move = \"ul\"\r\n elif grid == [1, self.get_width() + 1, self.get_width()]:\r\n move = \"lurdlu\"\r\n elif grid == [self.get_width() + 1, self.get_width(), 1]:\r\n move = \"lu\"\r\n self.update_puzzle(move)\r\n return move","def solve_2x2(self):\n # replace with your code\n string = ''\n num1 = self.get_number(0, 0)\n num2 = self.get_number(0, 1)\n num3 = self.get_number(1, 0)\n max_num = max([num1, num2, num3])\n min_num = min([num1, num2, num3])\n if num1 == min_num and num2 == max_num:\n string += 'ul'\n elif num1 == max_num and num3 == min_num:\n string += 'ul'\n string += 'rdlu' * 2\n elif num2 == min_num and num3 == max_num:\n string += 'ul'\n string += 'rdlu'\n print '2x2 Path', string\n self.update_puzzle(string)\n return string","def solve_puzzle(self):\r\n \r\n counter = 0\r\n rows = self._height-1\r\n cols = self._width-1\r\n # print rows, cols\r\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \r\n solution_move = ''\r\n if self.get_number(0,0) == 0 and \\\r\n self.get_number(0,1) == 1:\r\n # print 'Congrads Puxxle is Aolved at start!!!!!'\r\n return ''\r\n #appropriate_number = (self._height * self._width) - 1\r\n appropriate_number = (rows+1) * (cols+1) -1\r\n # print 'First appropriate_number=',appropriate_number\r\n # print \"Grid first tile that we will solwing has value =\", self._grid[rows][cols]\r\n \r\n while counter < 300:\r\n counter +=1\r\n # print self\r\n #appropriate_number = (rows+1) * (cols+1) -1\r\n # print 'Appropriate number in loop=',appropriate_number\r\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \r\n ####Case when we use solve_interior_tile\r\n if rows > 1 and cols > 0:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n cols -= 1\r\n appropriate_number -=1\r\n else:\r\n # print 'We are solving interior tile', (rows, cols)\r\n solution_move += self.solve_interior_tile(rows, cols)\r\n # print 'Solution move=', solution_move\r\n cols -= 1\r\n #### Case when we use solve_col0_tile\r\n elif rows > 1 and cols == 0:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows -= 1\r\n cols = self._width-1\r\n appropriate_number -=1\r\n else:\r\n # print 'We are solwing tile 0 in row', rows\r\n # print 'Appropriate number here ='\r\n solution_move += self.solve_col0_tile(rows)\r\n # print 'Solution move=', solution_move\r\n rows -=1\r\n cols = self._width-1\r\n\r\n\r\n #### Cases when we use solve_row0_tile\r\n elif rows == 1 and cols > 1:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows -= 1\r\n #cols = self._width-1\r\n appropriate_number -= self._width\r\n\r\n else:\r\n # print 'Solving upper 2 rows right side'\r\n solution_move += self.solve_row1_tile(cols)\r\n rows -=1\r\n appropriate_number -= self._width\r\n #### Cases when we use solve_row1_tile \r\n if rows < 1 and cols > 1:\r\n if self._grid[rows][cols] == appropriate_number:\r\n # print 'This tile is already solved!!!'\r\n rows += 1\r\n cols -= 1\r\n appropriate_number +=self._width-1\r\n else:\r\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\r\n # print 'Greed after move solve_row1_tile'\r\n # print self\r\n solution_move += self.solve_row0_tile(cols)\r\n rows +=1\r\n cols -=1\r\n appropriate_number +=self._width-1\r\n\r\n\r\n #### Case when we use solve_2x2\r\n elif rows <= 1 and cols <= 1:\r\n # print 'We are solving 2x2 puzzle'\r\n solution_move += self.solve_2x2()\r\n if self._grid[0][0] == 0 and \\\r\n self._grid[0][1] == 1:\r\n # print 'Congrads Puxxle is SOLVED!!!!!'\r\n break\r\n\r\n\r\n\r\n\r\n if counter > 100:\r\n # print 'COUNTER BREAK'\r\n break\r\n # print solution_move, len(solution_move)\r\n return solution_move\r\n\r\n\r\n\r\n\r\n\r\n\r\n # for row in solution_greed._grid[::-1]:\r\n # print solution_greed._grid\r\n # print 'Row =',row\r\n \r\n # if solution_greed._grid.index(row) > 1:\r\n # print \"Case when we solwing Interior and Tile0 part\"\r\n \r\n\r\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\r\n # print 'Coloumn value=', col\r\n #print row[0]\r\n # if col !=row[0]:\r\n # print 'Case when we use just Interior tile solution'\r\n # print solution_greed._grid.index(row)\r\n # print row.index(col)\r\n \r\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\r\n # print 'Solution =', solution\r\n # print self \r\n # print solution_greed._grid\r\n # elif col ==row[0]:\r\n # print 'Case when we use just Col0 solution'\r\n\r\n # else:\r\n # print 'Case when we solwing first two rows'\r\n\r\n #return \"\"\r","def update_puzzle(self, move_string):\n zero_row, zero_col = self.current_position(0, 0)\n for direction in move_string:\n if direction == \"l\":\n assert zero_col > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\n self._grid[zero_row][zero_col - 1] = 0\n zero_col -= 1\n elif direction == \"r\":\n assert zero_col < self._width - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\n self._grid[zero_row][zero_col + 1] = 0\n zero_col += 1\n elif direction == \"u\":\n assert zero_row > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\n self._grid[zero_row - 1][zero_col] = 0\n zero_row -= 1\n elif direction == \"d\":\n assert zero_row < self._height - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\n self._grid[zero_row + 1][zero_col] = 0\n zero_row += 1\n else:\n assert False, \"invalid direction: \" + direction\n for row in self._grid:\n print row\n print","def update_puzzle(self, move_string):\n zero_row, zero_col = self.current_position(0, 0)\n for direction in move_string:\n if direction == \"l\":\n assert zero_col > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col\n - 1]\n self._grid[zero_row][zero_col - 1] = 0\n zero_col -= 1\n elif direction == \"r\":\n assert zero_col < self._width - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col\n + 1]\n self._grid[zero_row][zero_col + 1] = 0\n zero_col += 1\n elif direction == \"u\":\n assert zero_row > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][\n zero_col]\n self._grid[zero_row - 1][zero_col] = 0\n zero_row -= 1\n elif direction == \"d\":\n assert zero_row < self._height - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][\n zero_col]\n self._grid[zero_row + 1][zero_col] = 0\n zero_row += 1\n else:\n assert False, \"invalid direction: \" + direction","def update_puzzle(self, move_string):\r\n zero_row, zero_col = self.current_position(0, 0)\r\n for direction in move_string:\r\n if direction == \"l\":\r\n assert zero_col > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\r\n self._grid[zero_row][zero_col - 1] = 0\r\n zero_col -= 1\r\n elif direction == \"r\":\r\n assert zero_col < self._width - 1, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\r\n self._grid[zero_row][zero_col + 1] = 0\r\n zero_col += 1\r\n elif direction == \"u\":\r\n assert zero_row > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\r\n self._grid[zero_row - 1][zero_col] = 0\r\n zero_row -= 1\r\n elif direction == \"d\":\r\n assert zero_row < self._height - 1, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\r\n self._grid[zero_row + 1][zero_col] = 0\r\n zero_row += 1\r\n else:\r\n assert False, \"invalid direction: \" + direction","def update_puzzle(self, move_string):\r\n zero_row, zero_col = self.current_position(0, 0)\r\n for direction in move_string:\r\n if direction == \"l\":\r\n assert zero_col > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\r\n self._grid[zero_row][zero_col - 1] = 0\r\n zero_col -= 1\r\n elif direction == \"r\":\r\n assert zero_col < self._width - 1, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\r\n self._grid[zero_row][zero_col + 1] = 0\r\n zero_col += 1\r\n elif direction == \"u\":\r\n assert zero_row > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\r\n self._grid[zero_row - 1][zero_col] = 0\r\n zero_row -= 1\r\n elif direction == \"d\":\r\n assert zero_row < self._height - 1, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\r\n self._grid[zero_row + 1][zero_col] = 0\r\n zero_row += 1\r\n else:\r\n assert False, \"invalid direction: \" + direction","def update_puzzle(self, move_string):\r\n zero_row, zero_col = self.current_position(0, 0)\r\n for direction in move_string:\r\n if direction == \"l\":\r\n assert zero_col > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\r\n self._grid[zero_row][zero_col - 1] = 0\r\n zero_col -= 1\r\n elif direction == \"r\":\r\n assert zero_col < self._width - 1, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\r\n self._grid[zero_row][zero_col + 1] = 0\r\n zero_col += 1\r\n elif direction == \"u\":\r\n assert zero_row > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\r\n self._grid[zero_row - 1][zero_col] = 0\r\n zero_row -= 1\r\n elif direction == \"d\":\r\n assert zero_row < self._height - 1, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\r\n self._grid[zero_row + 1][zero_col] = 0\r\n zero_row += 1\r\n else:\r\n assert False, \"invalid direction: \" + direction","def update_puzzle(self, move_string):\n zero_row, zero_col = self.current_position(0, 0)\n for direction in move_string:\n if direction == \"l\":\n assert zero_col > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\n self._grid[zero_row][zero_col - 1] = 0\n zero_col -= 1\n elif direction == \"r\":\n assert zero_col < self._width - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\n self._grid[zero_row][zero_col + 1] = 0\n zero_col += 1\n elif direction == \"u\":\n assert zero_row > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\n self._grid[zero_row - 1][zero_col] = 0\n zero_row -= 1\n elif direction == \"d\":\n assert zero_row < self._height - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\n self._grid[zero_row + 1][zero_col] = 0\n zero_row += 1\n else:\n assert False, \"invalid direction: \" + direction","def update_puzzle(self, move_string):\n zero_row, zero_col = self.current_position(0, 0)\n for direction in move_string:\n if direction == \"l\":\n assert zero_col > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\n self._grid[zero_row][zero_col - 1] = 0\n zero_col -= 1\n elif direction == \"r\":\n assert zero_col < self._width - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\n self._grid[zero_row][zero_col + 1] = 0\n zero_col += 1\n elif direction == \"u\":\n assert zero_row > 0, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\n self._grid[zero_row - 1][zero_col] = 0\n zero_row -= 1\n elif direction == \"d\":\n assert zero_row < self._height - 1, \"move off grid: \" + direction\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\n self._grid[zero_row + 1][zero_col] = 0\n zero_row += 1\n else:\n assert False, \"invalid direction: \" + direction","def solve_row1_tile(self, target_col):\r\n moves_str = \"\"\r\n current_row, current_col = self.current_position(1, target_col)\r\n zero_row, zero_col = self.current_position(0, 0)\r\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\r\n moves_str += \"ur\"\r\n self.update_puzzle(moves_str)\r\n print \"solve_row1_tile\"\r\n print moves_str\r\n print self._grid\r\n return moves_str","def solve(self, board: List[List[str]]) -> None:\n n = len(board)\n if n == 0:\n return\n m = len(board[0])\n for i in range(m):\n self.traverse(board, 0, i, n, m)\n self.traverse(board, n - 1, i, n, m)\n for i in range(n):\n self.traverse(board, i, 0, n, m)\n self.traverse(board, i, m - 1, n, m)\n for i in range(n):\n for j in range(m):\n board[i][j] = 'X' if board[i][j] == 'O' else board[i][j]\n for i in range(n):\n for j in range(m):\n board[i][j] = 'O' if board[i][j] == '_' else board[i][j]","def solve_interior_tile(self, target_row, target_col):\r\n assert self._grid[target_row][target_col] == 0\r\n moves_str = \"\"\r\n target_current_row, target_current_col = self.current_position(target_row, target_col)\r\n \r\n moves_str += self.position_tile(target_row, target_col, target_current_row, target_current_col) \r\n \r\n self.update_puzzle(moves_str)\r\n print \"solve_interior_tile\"\r\n print moves_str\r\n print self._grid\r\n return moves_str","def solve(puzzle):\n print(\"Solving...\")\n array_puzzle = np.asarray(puzzle)\n array_puzzle.flags.writeable = False # Turn off writable flags to prevent data being ovewritten accidentally.\n goal_state = __generate_goal(len(array_puzzle[0]), len(array_puzzle))\n\n flat_puzzle = list(chain.from_iterable(puzzle)) # Flatten the list\n\n # If the puzzle doesn't contain 0, exit.\n try:\n flat_puzzle.remove(0) # Remove 0 from the list\n except:\n print(\"All puzzles must include an open tile (0).\")\n return None\n\n inversions = __count_inversions(flat_puzzle) # Count the inversions\n\n # width = len(array_puzzle[0]) # Get the width of the puzzle (columns)\n # length = len(array_puzzle) # Get the length of the puzzle (rows)\n\n oddEven = __odd_or_even(len(array_puzzle[0])) # Determine if the width is odd or even.\n start_position = __find_start(array_puzzle) # Find the start position's row\n solvable = __is_solvable(oddEven, inversions, len(array_puzzle), start_position) # Cleck if the puzzle is solvable.\n\n # If the puzzle is not solvable, return None.\n if(solvable == \"None\"):\n return None\n\n # If we cannot calculate a* (for example the given values are not all in sequential order (1-5) 4 is replaced by 6 (1,2,3,5,6))\n try:\n return __a_star(array_puzzle, goal_state)\n except:\n print(\"Please make sure there are no duplicate or skipped inputs.\")\n return None\n\n # This code was used in testing to print out the string.\n # solved = __a_star(array_puzzle, goal_state)\n # Return the moves needed to complete the puzzle.\n # return print(str(__build_string(solved)) + \" (\" + str(len(solved)) + \")\")","def solve(self, board: List[List[str]]) -> None:\n rows = len(board)\n cols = len(board[0])\n\n def explore(i, j):\n if i < 0 or i >= rows:\n return\n if j < 0 or j >= cols:\n return\n if board[i][j] != \"O\":\n return\n board[i][j] = \"Y\"\n explore(i + 1, j)\n explore(i - 1, j)\n explore(i, j + 1)\n explore(i, j - 1)\n\n for i in [0, rows - 1]:\n for j in range(cols):\n explore(i, j)\n\n for j in [0, cols - 1]:\n for i in range(rows):\n explore(i, j)\n\n for i in range(rows):\n for j in range(cols):\n if board[i][j] == \"Y\":\n board[i][j] = \"O\"\n elif board[i][j] == \"O\":\n board[i][j] = \"X\"","def solve(self, board: List[List[str]]) -> None:\n if not board:\n return\n m, n = len(board), len(board[0])\n\n def dfs(x, y):\n if not 0 <= x < m or not 0 <= y < n or board[x][y] != \"O\":\n return\n direction = [[1, 0], [0, 1], [-1, 0], [0, -1]]\n board[x][y] = \"Y\"\n for d in direction:\n dfs(x+d[0], y+d[1])\n\n for i in range(m):\n dfs(i, 0)\n dfs(i, n-1)\n for i in range(1, n-1):\n dfs(0, i)\n dfs(m-1, i)\n\n for i in range(m):\n for j in range(n):\n if board[i][j] == \"O\":\n board[i][j] = \"X\"\n elif board[i][j] == \"Y\":\n board[i][j] = \"O\"","def solve(self, board) -> None:\n if board == [[]] or board == []:\n return\n\n r, c = len(board), len(board[0])\n\n from collections import deque\n queue = deque()\n for i in range(r):\n for j in range(c):\n if (i == 0 or i == r - 1 or j == 0 or j == c - 1) and board[i][j] == 'O':\n queue.append([i, j])\n board[i][j] = 'M'\n\n while queue:\n i, j = queue.popleft()\n for x, y in ((i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)):\n if 0 <= x <= r - 1 and 0 <= y <= c - 1 and board[x][y] == 'O':\n board[x][y] = 'M'\n queue.append([x, y])\n\n for i in range(r):\n for j in range(c):\n if board[i][j] == 'O':\n board[i][j] = 'X'\n elif board[i][j] == 'M':\n board[i][j] = 'O'","def move_tile(self, target_row, target_col, val):\n # a little bit twisted here for the use of both solve_interior_tile and solve_col0_tile\n solved_row, solved_col = self.current_position(0, val)\n movements = \"\"\n if solved_row == target_row and solved_col == target_col:\n return \"\"\n if solved_row == target_row:\n if target_col > solved_col:\n movements = \"l\" * (target_col - solved_col) + \"urrdl\" * (\n target_col - solved_col - 1)\n else:\n movements = \"r\" * (solved_col - target_col) + \"ulldr\" * (\n solved_col - target_col - 1) + \"ulld\"\n elif solved_col == target_col:\n movements = \"u\" * (target_row - solved_row) + \"lddru\" * (\n target_row - solved_row - 1) + \"ld\"\n elif solved_col < target_col:\n if solved_col == 0:\n movements = \"l\" * (target_col - solved_col) + \"u\" * (\n target_row - solved_row) + \"rddlu\" * (\n target_row - solved_row - 1) + \"rdl\" + \"urrdl\" * (\n target_col - solved_col - 1)\n else:\n movements = \"l\" * (target_col - solved_col) + \"u\" * (\n target_row - solved_row) + \"lddru\" * (\n target_row - solved_row - 1) + \"rdl\" + \"urrdl\" * (\n target_col - solved_col - 1)\n elif solved_col > target_col:\n if solved_row == 0:\n movements = \"u\" * (target_row - solved_row) + \"r\" * (\n solved_col - target_col) + \"dllur\" * (\n solved_col - target_col - 1) + \"dlu\" + \"lddru\" * (\n target_row - solved_row - 1) + \"ld\"\n else:\n movements = \"u\" * (target_row - solved_row) + \"r\" * (\n solved_col - target_col) + \"ulldr\" * (\n solved_col - target_col - 1) + \"ullddru\" + \"lddru\" * (\n target_row - solved_row - 1) + \"ld\"\n return movements","def solve_interior_tile(self, target_row, target_col):\n cur_row, cur_col = self.current_position(target_row, target_col)\n move_str = self.position_tile(target_row, target_col, cur_row, cur_col)\n self.update_puzzle(move_str)\n return move_str","def solve(self, board: List[List[str]]) -> None:\n if len(board) is 0: return\n width = len(board[0]) - 1\n height = len(board) - 1\n def help(x: int, y: int):\n if x < 0 or x > height or y < 0 or y > width:\n return\n if board[x][y] is \"O\":\n board[x][y] = \"g\"\n help(x - 1, y)\n help(x, y - 1)\n help(x + 1, y)\n help(x, y + 1)\n\n for i in range(width + 1):\n if board[0][i] is \"O\":\n help(0, i)\n if board[height][i] is \"O\":\n help(height, i)\n for i in range(1, height):\n if board[i][0] is \"O\":\n help(i, 0)\n if board[i][width] is \"O\":\n help(i, width)\n\n print(board)\n for i in range(width + 1):\n for j in range(height + 1):\n if board[j][i] is \"O\":\n board[j][i] = \"X\"\n elif board[j][i] is \"g\":\n board[j][i] = \"O\"","def solve_col0_tile(self, target_row):\n solved_row, solved_col = self.current_position(target_row, 0)\n movements = \"\"\n if solved_row == target_row - 1 and solved_col == 0:\n movements = \"u\"\n else:\n local_board = self.clone()\n local_board.update_puzzle(\"ur\")\n movements = \"ur\" + local_board.move_tile(\n target_row - 1, 1, target_row *\n self.get_width()) + \"ruldrdlurdluurddlu\"\n movements += \"r\" * (self.get_width() - 1)\n self.update_puzzle(movements)\n return movements","def solve_interior_tile(self, target_row, target_col):\n \n assert target_row > 1, \"target_row cannot be in rows 0 or 1.\"\n assert self.lower_row_invariant(target_row, target_col), \"tiles to right and below incorrectly ordered\"\n \n correct_tile = self.current_position(target_row, target_col) \n move_str = self.position_tile(target_row, target_col, correct_tile) \n self.update_puzzle(move_str)\n \n assert self.lower_row_invariant(target_row, target_col - 1), \"tiles to right and below incorrectly ordered\"\n \n return move_str","def solve(self, board: List[List[str]]) -> None:","def solve(self, board: List[List[str]]) -> None:\n if not board:\n return\n x=len(board)\n y=len(board[0])\n visit=[[False if board[i][j]=='X' else True for j in range(y)] for i in range(x)]\n for i in range(x):\n for j in range(y):\n if visit[i][j] and board[i][j]=='O':\n queue=[[i,j]]\n visit[i][j]=False\n k=0\n surround=True\n while len(queue)>k:\n if queue[k][0]==0 or queue[k][0]==x-1 or queue[k][1]==y-1 or queue[k][1]==0:\n surround=False\n if queue[k][1]!=y-1 and visit[queue[k][0]][queue[k][1]+1]:\n queue.append([queue[k][0],queue[k][1]+1])\n visit[queue[k][0]][queue[k][1]+1]=False\n if queue[k][1]!=0 and visit[queue[k][0]][queue[k][1]-1]:\n queue.append([queue[k][0],queue[k][1]-1])\n visit[queue[k][0]][queue[k][1]-1]=False\n if queue[k][0]!=x-1 and visit[queue[k][0]+1][queue[k][1]]:\n queue.append([queue[k][0]+1,queue[k][1]])\n visit[queue[k][0]+1][queue[k][1]]=False\n if queue[k][0]!=0 and visit[queue[k][0]-1][queue[k][1]]:\n queue.append([queue[k][0]-1,queue[k][1]])\n visit[queue[k][0]-1][queue[k][1]]=False\n k+=1\n if surround:\n for i1,j1 in queue:\n board[i1][j1]='X'\n return","def solve(self, board):\r\n if not board or not board[0]:\r\n return\r\n \r\n self.m = len(board)\r\n self.n = len(board[0])\r\n boarder = []\r\n \r\n # Collecting all the 'O' on the boarder\r\n for i in range(self.m):\r\n if board[i][0] == 'O':\r\n boarder.append([i, 0])\r\n if board[i][self.n-1] == 'O':\r\n boarder.append([i, self.n-1])\r\n for j in range(self.n):\r\n if board[0][j] == 'O':\r\n boarder.append([0, j])\r\n if board[self.m-1][j] == 'O':\r\n boarder.append([self.m-1, j])\r\n \r\n for row, col in boarder:\r\n self.BFS(board, row, col)\r\n \r\n for row in range(self.m):\r\n for col in range(self.n):\r\n if board[row][col] == 'O':\r\n board[row][col] = 'X'\r\n elif board[row][col] == 'E':\r\n board[row][col] = 'O'\r\n print(board)","def solve(self, board: List[List[str]]) -> None:\n if board == [] or board == [[]]: # corner case\n return\n\n r, c = len(board), len(board[0])\n\n def dfs(i, j): # visited i, j neighbors and change o to M\n if i < 0 or i > r - 1 or j < 0 or j > c - 1 or board[i][j] == 'X' or board[i][j] == 'M':\n return\n\n board[i][j] = 'M'\n dfs(i - 1, j)\n dfs(i + 1, j)\n dfs(i, j - 1)\n dfs(i, j + 1)\n\n for i in range(r):\n for j in range(c):\n if (i == 0 or i == r - 1 or j == 0 or j == c - 1) and board[i][j] == 'O':\n dfs(i, j)\n\n for i in range(r):\n for j in range(c):\n if board[i][j] == 'O':\n board[i][j] = 'X'\n elif board[i][j] == 'M':\n board[i][j] = 'O'","def solve_row0_tile(self, target_col):\r\n # replace with your code\r\n assert self.row0_invariant(target_col), 'Some trouble in row0_invariant' \r\n whole_move = ''\r\n #current_position = self.current_position(0, target_col)\r\n current_row, current_col = self.current_position(0, target_col)\r\n # print self.get_number(current_row, current_col)\r\n zero_row, zero_col = 0, target_col\r\n # print 'Target tile position=',self.current_position(0, target_col)\r\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\r\n # print '0 position=', (0, target_col)\r\n # print '------------------------------------------'\r\n # print 'Recommended move ld'\r\n \r\n recomended_move = 'ld'\r\n whole_move += recomended_move\r\n zero_col -= len(filter(lambda x: x=='l', recomended_move))\r\n zero_row += len(filter(lambda x: x=='d', recomended_move))\r\n self.update_puzzle(recomended_move)\r\n # print 'Grid after move:', recomended_move\r\n # print self\r\n # print 'Updated Target tile position=',self.current_position(0, target_col)\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (0, target_col)\r\n #####Case when we check if recomended move solves the tile\r\n if self.current_position(0, target_col) == (0, target_col):\r\n # print 'Congrads recomended move made great deal !!'\r\n return whole_move\r\n #####If not, we position TT to (1, target_col-1),\r\n ##### and ZEOR to (1, target_col-2)\r\n else:\r\n # print '------------------------------'\r\n # print 'After base move we are do not finde puzzle'\r\n # print 'Lets move zero towards TT'\r\n \r\n ######Moving zero tile to the target tile\r\n path_up = (zero_row - current_row) * 'u'\r\n path_side = (zero_col - current_col) * 'l'\r\n path_for_zero = path_up + path_side\r\n whole_move += path_for_zero\r\n zero_col -= len(filter(lambda x: x=='l', path_for_zero))\r\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\r\n self.update_puzzle(path_for_zero)\r\n \r\n # print 'grid after move', path_for_zero\r\n # print self\r\n # print 'Updated Target tile position=',self.current_position(0, target_col)\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (0, target_col)\r\n counter = 0\r\n # print self.current_position(0, target_col) != (1, target_col-1)\r\n # print (zero_row,zero_col) != (1,target_col-2)\r\n ####POitioning TT and zero into positions that can be solvable\r\n while self.current_position(0, target_col) != (1, target_col-1) or \\\r\n (zero_row,zero_col) != (1,target_col-2):\r\n counter +=1\r\n #current_position = self.current_position(0, target_col)\r\n current_row, current_col = self.current_position(0, target_col)\r\n cyclic_moves = ''\r\n # print 'Aloha in the loop'\r\n if zero_col < current_col:\r\n # print 'ZERO tile located in the left side and down move IS NOT POSIBLE'\r\n \r\n\r\n if current_col != target_col-1 and zero_row == 0:\r\n # print 'In the upper row and we can use down cycling'\r\n cyclic_moves = 'drrul'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n\r\n elif current_col != target_col-1:\r\n # print 'not under the target place'\r\n cyclic_moves = 'urrdl'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n elif current_col == target_col-1:\r\n # print 'Target tile under target place'\r\n # print 'DEBUG!!!!'\r\n # print self\r\n # print zero_col, target_col\r\n if zero_col == 0 and current_col == 1:\r\n cyclic_moves = 'druld'\r\n elif zero_row == 0:\r\n cyclic_moves = 'druld'\r\n \r\n else:\r\n cyclic_moves = 'urd'\r\n whole_move += cyclic_moves\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n elif zero_row > current_row:\r\n # print 'DEBUG'\r\n # print 'TT under zero tile'\r\n cyclic_moves = 'uld'\r\n whole_move += cyclic_moves\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n # print 'Puzzle after Maded move:', cyclic_moves\r\n self.update_puzzle(cyclic_moves)\r\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\r\n # print self\r\n # print 'Loop counter =',counter\r\n \r\n if counter > 10:\r\n # print 'COUNTER break'\r\n break\r\n\r\n #####Solving using pattern 2 x 3 puzzle\r\n # print '--------------------------'\r\n # print 'Lets solve 2x3 puzzle formed recently'\r\n move2x3 = \"urdlurrdluldrruld\"\r\n whole_move += move2x3\r\n zero_col -= len(filter(lambda x: x=='l', move2x3))\r\n zero_col += len(filter(lambda x: x=='r', move2x3))\r\n zero_row += len(filter(lambda x: x=='d', move2x3))\r\n zero_row -= len(filter(lambda x: x=='u', move2x3))\r\n self.update_puzzle(move2x3)\r\n # print self\r\n assert self.row1_invariant(target_col-1), 'Some trouble in row1_invariant' \r\n return whole_move","def solve_row1_tile(self, target_col):\r\n # replace with your code\r\n whole_move = ''\r\n if self._grid[1][target_col] != 0:\r\n # print \"DEBUG CASE WHEN ZERO IN JOPA solve_row1_tile \"\r\n \r\n # print self\r\n # print 'Solwing tile', self._grid[1][target_col]\r\n \r\n # print 'Searchind indexes of ZERO'\r\n for row in self._grid:\r\n for col in row:\r\n if col == 0:\r\n zero_row, zero_col = self._grid.index(row), row.index(col)\r\n break\r\n # print 'ZERO indexes=', (zero_row, zero_col)\r\n #####Moving zero to correct place\r\n #path_down = (1 - zero_row) * 'd'\r\n # path_left = (zero_col - target_col) * 'l'\r\n if target_col - zero_col > 0:\r\n #path_right = (target_col - zero_col) * 'r'\r\n path_of_zero = (1 - zero_row) * 'd' + (target_col - zero_col) * 'r'\r\n else:\r\n path_of_zero = (1 - zero_row) * 'd'\r\n #zero_col -= len(filter(lambda x: x=='l', path_of_zero))\r\n #zero_row -= len(filter(lambda x: x=='u', path_of_zero))\r\n zero_col += len(filter(lambda x: x=='r', path_of_zero))\r\n zero_row += len(filter(lambda x: x=='d', path_of_zero))\r\n self.update_puzzle(path_of_zero)\r\n # print 'Grid after moving ZERO to target spot'\r\n # print self\r\n whole_move += path_of_zero\r\n\r\n assert self.row1_invariant(target_col), 'Some trouble in row1_invariant' \r\n \r\n #current_row, current_col = self.current_position(1, target_col)\r\n zero_row, zero_col = 1, target_col\r\n ######Moving zero tile to the target tile\r\n #path_up = (zero_row - current_row) * 'u'\r\n #path_side = (zero_col - current_col) * 'l'\r\n path_for_zero = (zero_row - self.current_position(1, target_col)[0]) * 'u' + (zero_col - self.current_position(1, target_col)[1]) * 'l'\r\n whole_move += path_for_zero\r\n zero_col -= len(filter(lambda x: x=='l', path_for_zero))\r\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\r\n self.update_puzzle(path_for_zero)\r\n # print 'grid after move', path_for_zero\r\n # print self\r\n # print 'Updated Target tile position=',self.current_position(1, target_col)\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (1, target_col)\r\n counter = 0\r\n while self.current_position(1, target_col) != \\\r\n (1, target_col) or (zero_row, zero_col) != (0, target_col):\r\n # print 'Welcome to while loop!'\r\n cyclic_moves = ''\r\n #### Case 3 if ZERO located in the left side of the target tile\r\n ### like in the owel-test case\r\n #current_position = self.current_position(1, target_col)\r\n current_col = self.current_position(1, target_col)[1]\r\n counter +=1\r\n if self.current_position(1, target_col) == \\\r\n (1, target_col):\r\n # print 'ZERO not under TT'\r\n cyclic_moves = 'ur'\r\n whole_move += cyclic_moves\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n elif zero_col < current_col and self._grid[zero_row+1][zero_col] < \\\r\n self._grid[self.current_position(1, target_col)[0]][self.current_position(1, target_col)[1]]:\r\n # print 'ZERO tile located in the left side and down move is POSIBLE'\r\n if current_col != target_col:\r\n # print 'not under the target place'\r\n cyclic_moves = 'drrul'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n elif current_col == target_col:\r\n # print 'Target tile under target place'\r\n cyclic_moves = 'dru'\r\n whole_move += cyclic_moves\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n elif current_col != target_col and self._grid[zero_row+1][zero_col] > \\\r\n self._grid[self.current_position(1, target_col)[0]][self.current_position(1, target_col)[1]]:\r\n # print 'not under the target place'\r\n cyclic_moves = 'urrdl'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves)) \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n # elif zero_col < current_col and self._grid[zero_row+1][zero_col] > \\\r\n # self._grid[current_position[0]][current_position[1]]:\r\n # # print 'ZERO tile located in the left side and down move IS NOT POSIBLE'\r\n # if current_col != target_col:\r\n # # print 'not under the target place'\r\n # cyclic_moves = 'urrdl'\r\n # whole_move += cyclic_moves\r\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n # zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n # zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n # elif current_col == target_col:\r\n # # print 'Target tile under target place'\r\n # cyclic_moves = 'urd'\r\n # whole_move += cyclic_moves\r\n # zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n # zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n\r\n #cyclic_moves +='ur'\r\n # print 'Puzzle after Maded move:', cyclic_moves\r\n self.update_puzzle(cyclic_moves)\r\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\r\n # print self\r\n # print 'Loop counter =',counter\r\n if counter > 10:\r\n break\r\n return whole_move","def solve(self, board: List[List[str]]) -> None:\r\n if not board:\r\n return\r\n m, n = len(board), len(board[0])\r\n board_filter = lambda p: 0 <= p[0] < m and 0 <= p[1] < n and board[p[0]][p[1]] == 'O'\r\n queue = list(filter(board_filter, [(x, y) for r in range(max(m, n)) \r\n for x, y in ((r, 0), (r, n-1), (0, r), (m-1, r))]))\r\n while queue:\r\n x, y = queue.pop()\r\n board[x][y] = 'W'\r\n queue.extend(list(filter(board_filter, ((x-1, y), (x+1, y), (x, y-1), (x, y+1)))))\r\n for row in board:\r\n for i, c in enumerate(row):\r\n row[i] = 'XO'[c=='W']","def solve_col0_tile(self, target_row):\r\n moves_str = \"\"\r\n # move the zero tile from (i,0) to (i−1,1) \r\n # using the move string \"ur\"\r\n moves_str += \"ur\"\r\n temp_grid = Puzzle(self._height, self._width, self._grid)\r\n temp_grid.update_puzzle(moves_str)\r\n # If the target tile is now at position (i,0)\r\n # you can simply move tile zero to the end of row i−1\r\n current_row, current_col = temp_grid.current_position(target_row, 0)\r\n zero_row, zero_col = temp_grid.current_position(0, 0)\r\n if current_row == target_row and current_col == 0:\r\n rights = self._width - 1 - zero_col\r\n for dummy_r in range(rights):\r\n moves_str += \"r\" \r\n # However, if the target tile is not positioned at (i,0)\r\n # we suggest the following solution strategy:\r\n else:\r\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\r\n moves_str += \"ruldrdlurdluurddlu\"\r\n for dummy_r in range(self._width - 1):\r\n moves_str += \"r\"\r\n \r\n print \"solve_col0_tile\"\r\n print moves_str\r\n self.update_puzzle(moves_str)\r\n print self._grid\r\n return moves_str","def solve_col0_tile(self, target_row):\r\n # check if curr_pos (i, 0) where i > 1\r\n assert self.lower_row_invariant(target_row, 0)\r\n move = \"ur\"\r\n self.update_puzzle(move)\r\n row, col = self.current_position(target_row, 0)\r\n if row == target_row and col == 0:\r\n move_to_target = (self.get_width() - 2) * \"r\"\r\n self.update_puzzle(move_to_target)\r\n move += move_to_target\r\n else:\r\n move_to_target = self.move_to_target(target_row - 1, 1, row, col)\r\n # add solver move to str\r\n move_to_target += \"ruldrdlurdluurddlu\" + (self.get_width() - 1) * \"r\"\r\n self.update_puzzle(move_to_target)\r\n move += move_to_target\r\n assert self.lower_row_invariant(target_row - 1, self.get_width() - 1)\r\n return move","def solve_col0_tile(self, target_row):\n move_str = 'ur'\n self.update_puzzle(move_str)\n cur_row, cur_col = self.current_position(target_row, 0)\n if cur_row == target_row and cur_col == 0:\n move_str += 'r' * (self._width - 2)\n else:\n move_str += self.position_tile(target_row-1, 1, cur_row, cur_col)\n move_str += 'ruldrdlurdluurddlur'\n move_str += 'r' * (self._width - 2)\n self.update_puzzle(move_str[2:])\n return move_str","def solve(self, board: List[List[str]]) -> None:\n\n def expansion(i,j):\n for di,dj in {(-1,0),(1,0),(0,-1),(0,1)}:\n if -1<i+di<m and -1<j+dj<n and state[i+di][j+dj]=='O':\n return True\n return False\n\n if not board:\n return board\n\n m = len(board)\n n = len(board[0]) \n state = [['X']*n for _ in range(m)]\n\n for j in range(n):\n state[0][j] = board[0][j]\n state[m-1][j] = board[m-1][j]\n \n for i in range(m):\n state[i][0] = board[i][0]\n state[i][n-1] = board[i][n-1]\n \n flag = 1\n\n while flag:\n flag = 0\n\n for k in range(1, (1+min(m,n))//2):\n for j in range(k,n-k):\n if board[k][j]=='O' and state[k][j] == 'X' and expansion(k,j):\n state[k][j] = 'O'\n flag = 1\n \n if board[m-1-k][j]=='O' and state[m-1-k][j] == 'X' and expansion(m-1-k,j):\n state[m-1-k][j] = 'O'\n flag = 1\n \n for i in range(k,m-k):\n if board[i][k]=='O' and state[i][k] == 'X' and expansion(i,k):\n state[i][k] = 'O'\n flag = 1\n \n if board[i][n-1-k]=='O' and state[i][n-1-k] == 'X' and expansion(i,n-1-k):\n state[i][n-1-k] = 'O'\n flag = 1\n\n board[:] = state[:]","def solve_row0_tile(self, target_col):\n solved_row, solved_col = self.current_position(0, target_col)\n movements = \"\"\n if solved_col == target_col - 1 and solved_row == 0:\n movements = \"ld\"\n else:\n local_board = self.clone()\n local_board.update_puzzle(\"ld\")\n movements = \"ld\" + local_board.move_tile(\n 1, target_col - 1, target_col) + \"urdlurrdluldrruld\"\n self.update_puzzle(movements)\n return movements","def solve(self, board: List[List[str]]) -> None:\n if len(board) <= 2 or len(board[0]) <= 2:\n return\n\n self.nRow, self.nCol = len(board), len(board[0])\n\n def helper(kr, kc):\n board[kr][kc] = '.'\n kr > 0 and board[kr - 1][kc] == 'O' and helper(kr - 1, kc)\n kr < self.nRow - 1 and board[kr + 1][kc] == 'O' and helper(kr + 1, kc)\n kc > 0 and board[kr][kc - 1] == 'O' and helper(kr, kc - 1)\n kc < self.nCol - 1 and board[kr][kc + 1] == 'O' and helper(kr, kc + 1)\n\n for kr in [0, self.nRow - 1]:\n for kc in range(self.nCol):\n if board[kr][kc] == 'O':\n helper(kr, kc)\n for kc in [0, self.nCol - 1]:\n for kr in range(self.nRow):\n if board[kr][kc] == 'O':\n helper(kr, kc)\n\n for kr in range(self.nRow):\n for kc in range(self.nCol):\n if board[kr][kc] == 'O':\n board[kr][kc] = 'X'\n elif board[kr][kc] == '.':\n board[kr][kc] = 'O'\n\n return","def solve_col0_tile(self, target_row):\r\n # print '----------------------------------'\r\n # print 'SOLVING ZERO COLOUMN'\r\n assert self.lower_row_invariant(target_row,0), \"Invarian is False\"\r\n whole_move = ''\r\n #current_position = self.current_position(target_row, 0)\r\n current_row, current_col = self.current_position(target_row, 0)\r\n zero_row, zero_col = target_row, 0\r\n # print 'Target tile position=',current_position\r\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\r\n # print '0 position=', (target_row, 0)\r\n # print '------------------------------------------'\r\n # print 'Recommended move ur'\r\n \r\n recomended_move = 'ur'\r\n whole_move += recomended_move\r\n zero_col += len(filter(lambda x: x=='r', recomended_move))\r\n zero_row -= len(filter(lambda x: x=='u', recomended_move))\r\n self.update_puzzle(recomended_move)\r\n # print 'Grid after move:', recomended_move\r\n # print self\r\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (target_row, 0)\r\n current_position = self.current_position(target_row, 0)\r\n current_row, current_col = current_position\r\n if self.current_position(target_row, 0) == \\\r\n (target_row, 0):\r\n # print 'TT stays in correct place after recomended move'\r\n zero_to_place_move = 'r' * (self._width-1 - zero_col)\r\n self.update_puzzle(zero_to_place_move)\r\n whole_move += zero_to_place_move\r\n # print self\r\n assert self.lower_row_invariant(target_row-1,self._width-1), \"Invarian is False\"\r\n return whole_move\r\n #move ZERO tile to the right\r\n else:\r\n # print '------------------------------'\r\n # print 'After base move we are do not finde puzzle'\r\n # print 'Lets move zero towards TT'\r\n ### reposition TT to (target_row -1, 1)\r\n ### reposition ZERO tile to (target_row-1,0)\r\n \r\n ######Moving zero tile to the target tile\r\n #path_up = (zero_row - current_row) * 'u'\r\n #path_side = (current_col - zero_col) * 'r'\r\n path_for_zero = (zero_row - current_row) * 'u' + (current_col - zero_col) * 'r'\r\n whole_move += path_for_zero\r\n zero_col += len(filter(lambda x: x=='r', path_for_zero))\r\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\r\n self.update_puzzle(path_for_zero)\r\n # print 'grid after move', path_for_zero\r\n # print self\r\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (target_row, 0)\r\n counter = 0\r\n while self.current_position(target_row, 0) != (target_row -1, 1) or \\\r\n (zero_row,zero_col) != (target_row-1,0):\r\n cyclic_moves = ''\r\n current_position = self.current_position(target_row, 0)\r\n current_row, current_col = current_position\r\n # print 'We are in while loop'\r\n counter += 1\r\n if zero_row < current_row:\r\n # print 'Zero row under current TT '\r\n if self.current_position(target_row, 0) == (target_row -1, 1):\r\n # print 'TT is in the reccomended posiont (target_row -1, 1) \\n and we are movind zero to the left side of TT '\r\n cyclic_moves = 'ld'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n else:\r\n # print 'TT should be one tile down'\r\n cyclic_moves = 'lddru'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\r\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\r\n #### Case 1 if ZERO located in the right of\r\n #### target tile (after it)\r\n if zero_col > current_col:\r\n # print ' Look in the up puzzle, zero in the right side'\r\n if current_col != 1:\r\n # print 'not under target place (target_row -1, 1)'\r\n cyclic_moves = 'dllur'\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n whole_move += cyclic_moves\r\n # elif current_col == 1 and self._grid[zero_row+1][zero_col] < \\\r\n # self._grid[current_position[0]][current_position[1]]:\r\n elif current_col == 1: \r\n # print 'Tile tat is under ZERO is',self._grid[zero_row+1][zero_col] \r\n # print 'TT under target place'\r\n cyclic_moves = 'dlu'\r\n whole_move += cyclic_moves\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n elif self._grid[zero_row+1][zero_col] > \\\r\n self._grid[current_position[0]][current_position[1]]:\r\n print 'Tile under zero is illegal to move and we use upper cycle move '\r\n \r\n cyclic_moves = 'ul'\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n # print 'Puzzle after Maded move:', cyclic_moves\r\n self.update_puzzle(cyclic_moves)\r\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\r\n # print self\r\n # print 'Loop counter =',counter\r\n if counter > 10:\r\n break\r\n ### Solwing 3x2 puzzle\r\n # print '--------------------------'\r\n # print 'Lets solve 3x2 puzzle formed recently'\r\n move3x2 = 'ruldrdlurdluurddlur'\r\n whole_move += move3x2\r\n zero_col -= len(filter(lambda x: x=='l', move3x2))\r\n zero_col += len(filter(lambda x: x=='r', move3x2))\r\n zero_row += len(filter(lambda x: x=='d', move3x2))\r\n zero_row -= len(filter(lambda x: x=='u', move3x2))\r\n self.update_puzzle(move3x2)\r\n # print 'Grid afret 3x2 solver move'\r\n # print self\r\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (target_row, 0)\r\n #####Moving ZERO to the (target_row - 1, n - 1) position where\r\n ##### 'n' is a grid height.\r\n # print self._width-1 - zero_col\r\n zero_to_place_move = 'r' * (self._width-1 - zero_col)\r\n whole_move += zero_to_place_move\r\n self.update_puzzle(zero_to_place_move)\r\n # print self\r\n assert self.lower_row_invariant(target_row-1,self._width-1), \"Invarian is False\"\r\n return whole_move","def solve(self, board: 'List[List[str]]') -> 'None':\n if not board:\n return\n m, n = len(board), len(board[0])\n save = [ij for k in range(m + n) for ij in ((0, k), (m - 1, k), (k, 0), (k, n - 1))]\n while save:\n i, j = save.pop()\n if -1 < i < m and -1 < j < n and board[i][j] == 'O':\n board[i][j] = 'S'\n save += (i + 1, j), (i - 1, j), (i, j + 1), (i, j - 1)\n for row in board:\n for i, c in enumerate(row):\n row[i] = 'XO'[c == 'S']","def solve_interior_tile(self, target_row, target_col):\n # replace with your code\n string = ''\n target = self.current_position(target_row, target_col)\n row_difference = target_row - target[0]\n #print 'Row difference', row_difference\n col_difference = target_col - target[1]\n #print 'Col difference', col_difference\n string += 'u' * row_difference\n if col_difference > 0:\n string += 'l' * col_difference\n if row_difference == 0 and col_difference > 1:\n string += 'urrdl' * (col_difference - 1)\n if row_difference == 1:\n string += 'urrdl' * (col_difference - 1)\n string += 'dru'\n if row_difference > 1:\n string += 'drrul' * (col_difference - 1)\n string += 'dru'\n elif col_difference < 0:\n col_difference = abs(col_difference)\n string += 'r' * col_difference\n if row_difference == 1:\n string += 'ulldr' * (col_difference - 1)\n string += 'ullddru'\n if row_difference > 1:\n string += 'dllur' * (col_difference - 1)\n string += 'dlu'\n string += 'lddru' * (row_difference - 1)\n if row_difference > 0:\n string += 'ld'\n print 'Interior Path', string\n self.update_puzzle(string)\n assert self.lower_row_invariant(target_row, target_col - 1), 'False string'\n return string","def solve_row1_tile(self, target_col):\n cur_row, cur_col = self.current_position(1, target_col)\n move_str = self.position_tile(1, target_col, cur_row, cur_col, need_ld=False)\n self.update_puzzle(move_str)\n return move_str","def solve_interior_tile(self, target_row, target_col):\r\n whole_move = ''\r\n # replace with your code\r\n if self._grid[target_row][target_col] != 0:\r\n # print \"DEBUG CASE WHEN ZERO IN JOPA\"\r\n \r\n # print self\r\n # print 'Solwing tile', self._grid[target_row][target_col]\r\n \r\n # print 'Searchind indexes of ZERO'\r\n for row in self._grid:\r\n for col in row:\r\n if col == 0:\r\n zero_row, zero_col = self._grid.index(row), row.index(col)\r\n break\r\n # print 'ZERO indexes=', (zero_row, zero_col)\r\n #####Moving zero to correct place\r\n #path_down = (target_row - zero_row) * 'd'\r\n #path_left = (zero_col - target_col) * 'l'\r\n if target_col - zero_col > 0:\r\n #path_right = (target_col - zero_col) * 'r'\r\n path_of_zero = (zero_col - target_col) * 'l' + (target_row - zero_row) * 'd' + (target_col - zero_col) * 'r'\r\n else:\r\n path_of_zero = (zero_col - target_col) * 'l' + (target_row - zero_row) * 'd'\r\n zero_col -= len(filter(lambda x: x=='l', path_of_zero))\r\n zero_col += len(filter(lambda x: x=='r', path_of_zero))\r\n zero_row += len(filter(lambda x: x=='d', path_of_zero))\r\n self.update_puzzle(path_of_zero)\r\n # print 'Grid after moving ZERO to target spot'\r\n # print self\r\n whole_move += path_of_zero\r\n assert self.lower_row_invariant(target_row, target_col), \"Invarian is False\"\r\n \r\n #current_position = self.current_position(target_row, target_col)\r\n #current_row, current_col = self.current_position(target_row, target_col)\r\n # print 'Target tile position=',current_position\r\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\r\n # print '0 position=', (target_row, target_col)\r\n \r\n ######Moving zero tile to the target tile\r\n path_up = (target_row - self.current_position(target_row, target_col)[0]) * 'u'\r\n zero_row = target_row - len(path_up)\r\n if target_col < self.current_position(target_row, target_col)[1]: # Right move\r\n path_side = (self.current_position(target_row, target_col)[1] - target_col) * 'r'\r\n zero_col = target_col + len(path_side)\r\n else: # Left move\r\n path_side = (target_col - self.current_position(target_row, target_col)[1]) * 'l'\r\n zero_col = target_col - len(path_side)\r\n \r\n #path_for_zero = path_up + path_side\r\n # print '------------------------------------------'\r\n # print 'Move to ZERO =', path_for_zero\r\n \r\n self.update_puzzle(path_up + path_side)\r\n \r\n # print 'Grid after move:'\r\n # print self\r\n # current_position = self.current_position(target_row, target_col) \r\n # current_row, current_col = current_position\r\n # print 'Updated Target tile position=',current_position\r\n # print 'Updated 0 position=', (zero_row, zero_col)\r\n # print 'Target position =', (target_row, target_col)\r\n # print '-----------------------------------------'\r\n \r\n\r\n ###### New moves after moving ZERO tile into target tile\r\n # counter = 0\r\n whole_move += path_up + path_side\r\n while self.current_position(target_row, target_col) != \\\r\n (target_row, target_col) or zero_col != target_col - 1:\r\n # tt_in_home = self.current_position(target_row, target_col) == \\\r\n # (target_row, target_col)\r\n\r\n cyclic_moves = ''\r\n # counter += 1\r\n #current_position = self.current_position(target_row, target_col) \r\n #current_col = self.current_position(target_row, target_col)[1]\r\n # print 'Zero coloumn', zero_col, '== Target coloumn', target_col\r\n # print zero_col == target_col \r\n \r\n #### Case 1 if ZERO located in ther right of\r\n #### target tile (after it)\r\n if zero_col > self.current_position(target_row, target_col)[1]:\r\n # print ' Look in the up puzzle, zero on the right side'\r\n # if self.current_position(target_row, target_col)[1] != target_col:\r\n # # print 'not under target place'\r\n # cyclic_moves = 'dllur'\r\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n if self.current_position(target_row, target_col)[1] == target_col and self._grid[zero_row+1][zero_col] < \\\r\n self._grid[self.current_position(target_row, target_col)[0]][self.current_position(target_row, target_col)[1]]:\r\n # print 'Tile tat is under ZERO is',self._grid[zero_row+1][zero_col] \r\n # print 'TT under target place'\r\n cyclic_moves = 'dlu'\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n #zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n # elif self._grid[zero_row+1][zero_col] > \\\r\n # self._grid[self.current_position(target_row, target_col)[0]][self.current_position(target_row, target_col)[1]]:\r\n # # print 'Tile under zero is illegal to move and we use upper cycle move '\r\n \r\n # cyclic_moves = 'ul'\r\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n\r\n #### Case 2 if ZERO located under the target tile, and both\r\n #### of them located under the target position of the target tile\r\n elif zero_col == self.current_position(target_row, target_col)[1] and zero_col == target_col:\r\n # print 'Both under the target place'\r\n # print 'TT in home=', tt_in_home\r\n if self.current_position(target_row, target_col) == \\\r\n (target_row, target_col):\r\n cyclic_moves = 'ld'\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n #zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n \r\n else:\r\n cyclic_moves = 'lddru'\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n \r\n #### Case 3 if ZERO located in the left side of the target tile\r\n ### like in the owel-test case\r\n elif zero_col < self.current_position(target_row, target_col)[1]:\r\n # print 'ZERO tile located in the left side'\r\n if self.current_position(target_row, target_col)[1] != target_col:\r\n # print 'not under the target place'\r\n cyclic_moves = 'drrul'\r\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n elif self.current_position(target_row, target_col)[1] == target_col:\r\n # print 'Target tile under target place'\r\n cyclic_moves = 'dru'\r\n #zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\r\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\r\n\r\n\r\n # print 'Puzzle after Maded move:', cyclic_moves\r\n self.update_puzzle(cyclic_moves)\r\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\r\n # print self\r\n # print 'Loot counter =',counter\r\n whole_move += cyclic_moves\r\n # if counter > 12:\r\n # break\r\n # print 'Tile is solved with move '+ whole_move\r\n assert self.lower_row_invariant(target_row, target_col-1), \"Invarian is False\"\r\n return whole_move","def solve_col0_tile(self, target_row):\n # replace with your code\n string = ''\n target = self.current_position(target_row, 0)\n row_difference = target_row - target[0]\n col_difference = target[1]\n string += 'u' * row_difference\n if col_difference > 0:\n string += 'r' * (col_difference - 1)\n if row_difference > 1:\n string += 'druld' * (row_difference - 1)\n string += 'rulld' * (col_difference - 1)\n string += 'ruldrdlurdluurddlu'\n elif col_difference == 0:\n string += 'rddlu' * (row_difference - 2)\n if row_difference > 1:\n string += 'rd'\n string += 'l'\n string += 'ruldrdlurdluurddlu'\n string += 'r' * (self._width - 1)\n print 'Col 0 Path', string\n self.update_puzzle(string)\n assert self.lower_row_invariant(target_row - 1, self._width -1), 'False string'\n return string","def solve(self, board: List[List[str]]) -> None:\n if len(board) <= 2 or len(board[0]) <= 2:\n return\n\n nRow, nCol = len(board), len(board[0])\n\n def helper(kr, kc):\n board[kr][kc] = '.'\n kr > 0 and board[kr - 1][kc] == 'O' and helper(kr - 1, kc)\n kr < nRow - 1 and board[kr + 1][kc] == 'O' and helper(kr + 1, kc)\n kc > 0 and board[kr][kc - 1] == 'O' and helper(kr, kc - 1)\n kc < nCol - 1 and board[kr][kc + 1] == 'O' and helper(kr, kc + 1)\n\n for kr in [0, nRow - 1]:\n for kc in range(nCol):\n if board[kr][kc] == 'O':\n helper(kr, kc)\n for kc in [0, nCol - 1]:\n for kr in range(nRow):\n if board[kr][kc] == 'O':\n helper(kr, kc)\n\n for kr in range(nRow):\n for kc in range(nCol):\n if board[kr][kc] == 'O':\n board[kr][kc] = 'X'\n elif board[kr][kc] == '.':\n board[kr][kc] = 'O'\n\n return","def solve(self, board) -> None:\n x_length = len(board)\n if x_length == 0: \n return\n\n y_length = len(board[0])\n confirmed = set()\n dfs = []\n for i in range(x_length):\n if board[i][0] == 'O':\n board[i][0] = 'temp'\n dfs.append((i, 0))\n if board[i][y_length - 1] == 'O':\n board[i][y_length - 1] = 'temp'\n dfs.append((i, y_length - 1))\n for j in range(y_length):\n if board[0][j] == 'O':\n board[0][j] = 'temp'\n dfs.append((0, j))\n if board[x_length - 1][j] == 'O':\n board[x_length - 1][j] = 'temp'\n dfs.append((x_length - 1, j))\n while dfs:\n i, j = dfs.pop()\n confirmed.add((i, j))\n if i+1 < x_length and board[i+1][j] == 'O':\n board[i+1][j] = 'temp'\n dfs.append((i + 1, j))\n if i > 0 and board[i-1][j] == 'O':\n board[i-1][j] = 'temp'\n dfs.append((i-1, j))\n if j+1 < y_length and board[i][j+1] == 'O':\n board[i][j+1] = 'temp'\n dfs.append((i, j + 1))\n if j > 0 and board[i][j-1] == 'O':\n board[i][j-1] = 'temp'\n dfs.append((i, j-1))\n for i in range(x_length):\n for j in range(y_length):\n if (i, j) in confirmed:\n board[i][j] = 'O'\n else:\n board[i][j] = 'X'\n return","def solve(board):\r\n \r\n #An O(2mn) time solution; the first O(mn) traversal is to preform a bfs on all tiles attached to edge 'O' tiles (can't convert to 'X's); the second is to convert all remaining 'O's into 'X's\r\n \r\n def bfs(curr,r,c):\r\n if not curr: return\r\n prev = len(curr)\r\n for n in range(0,prev):\r\n i,j = curr[n][0],curr[n][1]\r\n board[i][j] = 'A'\r\n for x,y in [(-1, 0), (0, -1), (1, 0), (0, 1)]:\r\n x_n = i+x\r\n y_n = j+y\r\n if x_n >= 0 and x_n < r and y_n >= 0 and y_n < c and board[x_n][y_n] == \"O\":\r\n curr += [(x_n,y_n)]\r\n bfs(curr[prev:],r,c)\r\n\r\n \r\n q,r,c = [],len(board),len(board[0])\r\n if not r or q: return\r\n\r\n for i in range(r):\r\n for j in range(c):\r\n if (i==0 or j==0 or i==r-1 or j==c-1) and board[i][j] == \"O\":\r\n q += [(i,j)]\r\n \r\n bfs(q,r,c)\r\n\r\n for i in range(r):\r\n for j in range(c): \r\n if board[i][j] == \"O\": \r\n board[i][j] = \"X\"\r\n elif board[i][j] == \"A\":\r\n board[i][j] = \"O\"\r\n \r\n return","def solve(self, board: List[List[str]]) -> None:\n if board is None or len(board) == 0:\n return \n row, col = len(board), len(board[0])\n for i in range(row):\n self.dfs(board, i, 0)\n self.dfs(board, i, col - 1)\n for j in range(col):\n self.dfs(board, 0, j)\n self.dfs(board, row-1, j)\n for i in range(row):\n for j in range(col):\n if board[i][j] == 'O':\n board[i][j] = 'X'\n if board[i][j] == '-':\n board[i][j] = 'O'","def solve_row0_tile(self, target_col):\n move_str = 'ld'\n self.update_puzzle(move_str)\n cur_row, cur_col = self.current_position(0, target_col)\n if cur_row == 0 and cur_col == target_col:\n return move_str\n else:\n move_str += self.position_tile(1, target_col-1, cur_row, cur_col)\n move_str += 'urdlurrdluldrruld'\n self.update_puzzle(move_str[2:])\n return move_str","def solve(self, board: List[List[str]]) -> None:\n if len(board) == 0: return\n\n m = len(board)\n n = len(board[0])\n\n uf = UnionFind(m * n + 1)\n dummy = m * n\n\n # connect 'O' at first and last col with dummy\n for i in range(m):\n if board[i][0] == 'O':\n uf.union(dummy, i * n)\n if board[i][-1] == 'O':\n uf.union(dummy, i * n + n - 1)\n\n # connect 'O' at first and last row with dummy\n for j in range(n):\n if board[0][j] == 'O':\n uf.union(dummy, j)\n if board[-1][j] == 'O':\n uf.union(dummy, n * (m-1) + j)\n\n d = [(1, 0), (0, 1), (0, -1), (-1, 0)]\n\n for i in range(1, m-1):\n for j in range(1, n-1):\n if board[i][j] == 'O':\n for di, dj in d:\n x = i+di\n y = j+dj\n if board[x][y] == 'O':\n uf.union(x*n+y, i*n+j)\n\n # change not connected 'O' with 'X'\n for i in range(1, m-1):\n for j in range(1, n-1):\n if not uf.connected(dummy, i * n + j):\n board[i][j] = 'X'","def position_tile(self, zero_row, zero_col, correct_tile):\n \n ans = \"\" \n vert_dist = abs(zero_row - correct_tile[0])\n horiz_dist = abs(zero_col - correct_tile[1])\n \n # Updates ans, the move string, based the correct_tile's\n # position relative to the target position.\n \n # SAME ROW\n if vert_dist == 0:\n # Left of target\n if zero_col > correct_tile[1]:\n # Moves zero tile left to correct_tile's position.\n ans += str(\"l\" * horiz_dist)\n # Moves correct_tile right to target position,\n # and moves zero tile to left of target position.\n if horiz_dist > 1:\n ans += str(\"urrdl\" * (horiz_dist - 1))\n # Right of target\n else:\n # Moves zero tile right to correct_tile's position.\n ans += str(\"r\" * horiz_dist)\n # Moves correct_tile left to target position,\n # and moves zero tile to left of target position.\n ans += str(\"ulldr\" * (horiz_dist - 1))\n ans += str(\"ulld\")\n \n # SAME COL\n elif horiz_dist == 0:\n # Moves zero tile up to correct_tile's position.\n ans += str(\"u\" * vert_dist)\n # Moves correct_tile down to target position, \n # and moves zero tile to left of target position.\n if vert_dist > 1:\n ans += str(\"lddru\" * (vert_dist - 1))\n ans += str(\"ld\")\n \n # UPPER LEFT\n elif correct_tile[1] < zero_col:\n # Moves zero tile up and left to correct_tile's position.\n ans += str(\"u\" * vert_dist)\n ans += str(\"l\" * horiz_dist)\n # Moves correct_tile right and down to target position,\n # and moves zero tile to left of target position.\n ans += str(\"drrul\" * (horiz_dist - 1))\n ans += str(\"druld\" * vert_dist)\n\n # UPPER RIGHT\n else:\n # Moves zero tile up and right to correct_tile's position.\n ans += str(\"u\" * vert_dist)\n ans += str(\"r\" * horiz_dist)\n # This if-elif-else statement moves correct_tile left and down to target position.\n # If statement is only used when target position is in row 2.\n if vert_dist == 1 and correct_tile[0] == 0:\n ans += str(\"dllur\" * (horiz_dist - 1))\n ans += str(\"dluld\")\n # Elif statement used when correct_tile is in the row above target position.\n elif vert_dist == 1: \n ans += str(\"ulldr\" * (horiz_dist - 1))\n ans += str(\"ullddruld\")\n # Else statement used when correct_tile is 1+ rows above target position.\n else:\n ans += str(\"dllur\" * (horiz_dist - 1))\n ans += str(\"dlu\")\n ans += str(\"lddru\" * (vert_dist - 1))\n ans += str(\"ld\")\n \n return ans","def half_turn_solve(db, position):\n print('- Table Lookup: ', end='')\n solve_sequence = half_turn_lookup.lookup_position(db, position)\n temp_cube = Cube(position)\n for move in solve_sequence:\n dyn_move(temp_cube, move)\n print(move.name, end=' ')\n print()\n return solve_sequence","def solve_puzzle(self):\n moves = self.moves\n peg_pos = self.peg_pos\n move_z = self.move_to_height\n \n print('Solving Tower of Hanoi:')\n for i, move in enumerate(moves):\n des_peg = move[0]\n des_peg_pos = peg_pos[des_peg]\n \n #move to peg\n print(' Moving to peg: '+str(des_peg)+' at: '+str(des_peg_pos))\n self.move_to(des_peg_pos[0], des_peg_pos[1], move_z)\n \n #if index is even, pickup disk, else drop disk\n if i % 2 == 0:\n print(' Picking up disk at height: '+str(move[1]))\n self.pick(move[1])\n else:\n print(' Dropping disk')\n self.drop()\n print('Finished solving puzzle')","def solve(board) -> None:\n rows = len(board)\n if rows==0:\n return board\n cols = len(board[0])\n \n def is_border(rc):\n (rr, cc) =rc\n if rr<rows and rr< cols and rr>=0 and cc>=0 and board[rr][cc]=='O' and (rr==0 or rr==rows-1 or cc==0 or cc==cols-1):\n return True\n return False\n \n transf = []\n for r in range(rows):\n for c in range(cols):\n if board[r][c]=='O' and not is_border((r,c)) and not any(map(is_border, [(r-1, c), (r+1, c), (r, c-1), (r, c+1)])):\n transf.append((r,c))\n if transf:\n for r,c in transf:\n board[r][c]='X'\n return board","def solve(self, board: List[List[str]]) -> None:\n def dfs(board, i, j):\n m = len(board)\n n = len(board[0])\n if i < 0 or i >= m or j < 0 or j >= n: return\n\n if board[i][j] != 'O': return\n\n board[i][j] = '#'\n [dfs(board, i+di, j+dj) for di, dj in [(0, 1), (1, 0), (0, -1), (-1, 0)]]\n\n if len(board) == 0: return\n m = len(board)\n n = len(board[0])\n\n for i in range(m):\n dfs(board, i, 0)\n dfs(board, i, n-1)\n\n for j in range(n):\n dfs(board, 0, j)\n dfs(board, m-1, j)\n\n for i in range(m):\n for j in range(n):\n if board[i][j] == 'O':\n board[i][j] = 'X'\n\n for i in range(m):\n for j in range(n):\n if board[i][j] == '#':\n board[i][j] = 'O'","def solve_tour(self):\n\t\tboard = [[-1 for _ in range(self.N)]for _ in range(self.N)]\n\t\tboard[0][0] = 0\n\n\t\tz = self.find_tour(board, 0, 0, 1)\n\t\tif z:\n\t\t\tfor i in range(self.N):\n\t\t\t\tfor j in range(self.N):\n\t\t\t\t\tself.solution.append(board[i][j])\n\t\t\tprint board\n\t\t\treturn self.solution\n\t\t\t\t\n\t\telse:\n\t\t\tprint(\"No solution\")","def solve(self, board) -> None:\n coords = []\n board_len = len(board)\n row_len = len(board[0]) - 1\n # top\n # coords.append([[0, i] for i, q in enumerate(board[0]) if q == \"O\"])\n # # bottom\n # coords.append(\n # [[board_len, i] for i, q in enumerate(board[board_len]) if q == \"O\"]\n # )\n for i in range(board_len):\n row_coord = [[i,indx] for indx, q in enumerate(board[i]) if q == \"O\"]\n # import pdb; pdb.set_trace()\n for x in row_coord:\n coords.append(x)\n for x in coords:\n if len(x) == 0:\n continue\n if x[0] == 0:\n print(\"top border\")\n elif x[0] == board_len - 1:\n print(\"bottom border\")\n elif x[1] == 0:\n print(\"left border\")\n elif x[1] == row_len:\n prin(\"right border\")","def solve_row1_tile(self, target_col):\n assert target_col > 1, \"target_col must be > 1\"\n assert self.row1_invariant(target_col), \"tiles to right and below incorrectly ordered\"\n\n # Moves correct_tile to the target position (1, target_col),\n # and the zero tile above the target position at (0, target_col). \n correct_tile = self.current_position(1, target_col)\n move_str = self.position_tile(1, target_col, correct_tile) \n move_str += \"ur\"\n self.update_puzzle(move_str)\n\n assert self.row0_invariant(target_col)\n \n return move_str","def solve_puzzle(board):\n # Propagate value effects\n board = simplify_puzzle(board, [])\n\n # Brute force remaining cells\n board = brute(board)\n\n # Verify that the puzzle was successfully solved\n assert get_length(board)==81\n assert valid_attempt(board)\n\n return board","def solve(self, board) -> None:\n for index in range (1, len(board)-1):\n arr = board[index]\n for ch in range(1, len(arr)-1):\n if arr[ch] is 'O':\n safe = True\n if ch-1 == 0 and arr[ch-1] is 'O':\n safe = False\n if ch +1 == len(arr)-1 and arr[ch+1] is 'O':\n safe = False\n if index -1 == 0 and board[index-1][ch] is 'O':\n safe = False\n if index + 1 == len(board)-1 and board[index + 1][ch] is 'O':\n safe = False\n if safe:\n arr[ch] = 'X'","def solve_row1_tile(self, target_col):\r\n row, col = self.current_position(1, target_col)\r\n move = self.move_to_target(1, target_col, row, col)\r\n # for next move\r\n move += \"ur\"\r\n \r\n self.update_puzzle(move)\r\n return move","def solve(self):\n # If board is filled, board is trivially solved\n if self.check_full_board():\n return self.done\n\n # Iterate over every square in the board\n for row in range(self.num_rows):\n for col in range(self.num_columns):\n\n # If square is empty, begin plugging in possible values\n if self.check_empty_space(row, col):\n for val in range(1, 10):\n if not self.check_row(val, row) and \\\n not self.check_column(val, col) and \\\n not self.check_box(val, self.what_box(row, col)):\n self.board[row][col] = val\n \n if self.solve():\n return self.done()\n \n # Didn't work; undo assigment\n self.board[row][col] = ' '\n\n # Bad path; backtrack\n return False","def solve(self, board: 'List[List[str]]') -> 'None':\n\n def dfs(i, j, tmp):\n nonlocal flag\n if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]):\n flag = False\n return\n if board[i][j] != 'O' or [i, j] in tmp:\n return\n tmp.append([i, j])\n dfs(i - 1, j, tmp)\n dfs(i + 1, j, tmp)\n dfs(i, j + 1, tmp)\n dfs(i, j - 1, tmp)\n return tmp\n\n change = []\n for i in range(len(board)):\n for j in range(len(board[0])):\n if board[i][j] == 'O' and [i, j] not in change:\n tmp = []\n flag = True\n tmp = dfs(i, j, tmp[:])\n if flag:\n for loc in tmp:\n i, j = loc[0], loc[1]\n board[i][j] = 'X'\n\n for loc in change:\n i, j = loc[0], loc[1]\n board[i][j] = 'X'","def find_best_move(board):\n new_board = board.get_board()\n\n # X | X | X <-- Check for win on this row\n # ---------\n # 3 | 4 | 5\n # ---------\n # 6 | 7 | 9\n if new_board[0] == new_board[1] and new_board[2] == \"2\":\n return 2\n elif new_board[0] == new_board[2] and new_board[1] == \"1\":\n return 1\n elif new_board[1] == new_board[2] and new_board[0] == \"0\":\n return 0\n\n # 0 | 1 | 2\n # ---------\n # X | X | X <-- Check for win on this row\n # ---------\n # 6 | 7 | 9\n elif new_board[3] == new_board[4] and new_board[5] == \"5\":\n return 5\n elif new_board[3] == new_board[5] and new_board[4] == \"4\":\n return 4\n elif new_board[4] == new_board[5] and new_board[3] == \"3\":\n return 3\n\n # 0 | 1 | 2\n # ---------\n # 3 | 4 | 5\n # ---------\n # X | X | X <-- Check for win on this row\n elif new_board[6] == new_board[7] and new_board[8] == \"8\":\n return 8\n elif new_board[6] == new_board[8] and new_board[7] == \"7\":\n return 7\n elif new_board[7] == new_board[8] and new_board[6] == \"6\":\n return 6\n\n # X | 1 | 2 Check for win on column one\n # ---------\n # X | 4 | 5\n # ---------\n # X | 7 | 9\n elif new_board[0] == new_board[3] and new_board[6] == \"6\":\n return 6\n elif new_board[0] == new_board[6] and new_board[3] == \"3\":\n return 3\n elif new_board[6] == new_board[3] and new_board[0] == \"0\":\n return 0\n\n # 0 | X | 2 Checks for win on column two\n # ---------\n # 3 | X | 5\n # ---------\n # 6 | X | 9\n elif new_board[1] == new_board[4] and new_board[7] == \"7\":\n return 7\n elif new_board[1] == new_board[7] and new_board[4] == \"4\":\n return 4\n elif new_board[7] == new_board[4] and new_board[0] == \"0\":\n return 0\n\n # 0 | 1 | X\n # ---------\n # 3 | 4 | X\n # ---------\n # 6 | 7 | X\n elif new_board[2] == new_board[5] and new_board[8] == \"8\":\n return 8\n elif new_board[2] == new_board[8] and new_board[5] == \"5\":\n return 5\n elif new_board[8] == new_board[5] and new_board[2] == \"2\":\n return 2\n\n # X | 1 | 2\n # ---------\n # 3 | X | 5\n # ---------\n # 6 | 7 | X\n elif new_board[0] == new_board[4] and new_board[8] == \"8\":\n return 8\n elif new_board[0] == new_board[8] and new_board[4] == \"4\":\n return 4\n elif new_board[8] == new_board[4] and new_board[0] == \"0\":\n return 0\n\n # 0 | 1 | X\n # ---------\n # 3 | X | 5\n # ---------\n # X | 7 | 9\n elif new_board[2] == new_board[4] and new_board[6] == \"6\":\n return 6\n elif new_board[2] == new_board[6] and new_board[4] == \"4\":\n return 4\n elif new_board[6] == new_board[4] and new_board[2] == \"2\":\n return 2\n\n # If corners are empty, play there\n elif new_board[0] == \"0\" or new_board[2] == \"2\" or new_board[6] == \"6\" or new_board[8] == \"8\":\n try_spot = 0\n while True:\n if new_board[try_spot] != \"X\" and new_board[try_spot] != \"O\":\n return try_spot\n else:\n try_spot = try_spot + 2\n\n # If middle is empty, play there\n elif new_board[4] == \"4\":\n return 4\n\n # Finally if edges are empty try there\n elif new_board[1] == \"1\" or new_board[3] == \"3\" or new_board[5] == \"5\" or new_board[7] == \"7\":\n try_spot = 1\n while True:\n if new_board[try_spot] != \"X\" and new_board[try_spot] != \"O\":\n return try_spot\n else:\n try_spot = try_spot + 2","def solve(self, board):\n def dfs(board, r, c):\n if r < 0 or c < 0 or r > rows - 1 or c > cols - 1 or board[r][c] == 'X' or board[r][c] == '#':\n return\n board[r][c] = '#'\n dfs(board, r - 1, c)\n dfs(board, r + 1, c)\n dfs(board, r, c - 1)\n dfs(board, r, c + 1)\n\n if len(board) == 0:\n return;\n rows = len(board)\n cols = len(board[0])\n for i in range(rows):\n for j in range(cols):\n if (i == 0 or j == 0 or i == rows - 1 or j == cols - 1) and board[i][j] == 'O':\n dfs(board, i, j)\n for i in range(rows):\n for j in range(cols):\n if board[i][j] == 'O':\n board[i][j] = 'X'\n elif board[i][j] == '#':\n board[i][j] = 'O'","def solve_row0_tile(self, target_col):\n # replace with your code\n string = ''\n assert self.row0_invariant(target_col), 'False precondition'\n target = self.current_position(0, target_col)\n row_difference = target[0]\n col_difference = target_col - target[1]\n if row_difference == 0:\n if col_difference == 1:\n string += 'ld'\n elif col_difference > 1:\n string += 'l' * col_difference\n string += 'drrul' * (col_difference - 2)\n string += 'druld'\n string += 'urdlurrdluldrruld'\n elif row_difference == 1:\n if col_difference == 1:\n string += 'lld'\n string += 'urdlurrdluldrruld'\n elif col_difference > 1:\n string += 'ld'\n string += 'l' * (col_difference - 1)\n string += 'urrdl' * (col_difference - 2)\n string += 'urdlurrdluldrruld'\n print 'Row 0 Path', string\n self.update_puzzle(string)\n assert self.row1_invariant(target_col - 1), 'False string'\n return string","def solve(self, board: List[List[str]]) -> None:\n if len(board) == 0:\n return\n self.h = len(board)\n self.w = len(board[0])\n self.board = board\n for i in range(self.h):\n for j in range(self.w):\n if i == 0 or i == self.h-1 or j == 0 or j == self.w-1:\n #print (i,j)\n self.dfs((i,j))\n for i in range(self.h):\n for j in range(self.w):\n if self.board[i][j]==\"O\":\n self.board[i][j]=\"X\"\n for i in range(self.h):\n for j in range(self.w):\n if self.board[i][j]==\"#\":\n self.board[i][j]=\"O\"","def solve(self, board: List[List[str]]) -> None:\n def _dfs(i, j):\n if i < 0 or j < 0 or i >= len(board) or j >= len(board[0]) or board[i][j] in ['X', '#']: return\n board[i][j] = '#'\n _dfs(i-1, j)\n _dfs(i+1, j)\n _dfs(i, j-1)\n _dfs(i, j+1)\n\n if not board or not board[0]: return\n m, n = len(board), len(board[0])\n for i in range(0, m):\n for j in range(0, n):\n is_edge = i == 0 or j == 0 or i == m-1 or j == n-1\n if is_edge and board[i][j] == 'O':\n _dfs(i, j)\n print(board)\n\n for i in range(0, m):\n for j in range(0, n):\n if board[i][j] == 'O':\n board[i][j] = 'X'\n if board[i][j] == '#':\n board[i][j] = 'O'","def solve(self):\n while self.character.path[-1] != 88:\n n = self.next_move()\n if n is None:\n self.character.path += ['Error: Could not find full path (budget does not suffice or unreachable).']\n break\n self.character.path += [n]\n self.updated_occupied_locations()\n self.currentTurn += 1","def solve(self, board: List[List[str]]) -> None:\n def DFS(board, i, j):\n q = []\n q.append([i, j])\n \n while q:\n x, y = q.pop()\n board[x][y] = \"*\"\n neighbors = ((0, 1), (0, -1), (1, 0), (-1, 0))\n for dx, dy in neighbors:\n if 0 <= x + dx <= len(board) - 1 and 0 <= y + dy <= len(board[0]) - 1 and board[x + dx][y + dy] == \"O\":\n q.append([x + dx, y + dy])\n \n \n # first row\n i = 0\n for j in range(len(board[0])):\n if board[i][j] == \"O\":\n DFS(board, i, j)\n \n # last row\n i = len(board) - 1\n for j in range(len(board[0])):\n if board[i][j] == \"O\":\n DFS(board, i, j)\n \n # first column\n j = 0\n for i in range(len(board)):\n if board[i][j] == \"O\":\n DFS(board, i, j)\n \n # last column\n j = len(board[0]) - 1\n for i in range(len(board)):\n if board[i][j] == \"O\":\n DFS(board, i, j)\n \n \n for i in range(len(board)):\n for j in range(len(board[0])):\n if board[i][j] == \"O\":\n board[i][j] = \"X\"\n elif board[i][j] == \"*\":\n board[i][j] = \"O\"","def solve (M, cpos, move): \n if move == 64:\n print (\"\\n\\nmove: \", move)\n print (\"sum: \", sum(M))\n pprint (M)\n #exit()\n for next in get_moves(cpos, M):\n solve(ulist(M, next, move+1), next, move+1)","def solve_row1_tile(self, target_col):\n # replace with your code\n print target_col\n assert self.row1_invariant(target_col), 'False precondition'\n string = ''\n target = self.current_position(1, target_col)\n row_difference = 1 - target[0]\n col_difference = target_col - target[1]\n string += 'u' * row_difference\n if col_difference > 0:\n string += 'l' * col_difference\n if row_difference == 0:\n string += 'urrdl' * (col_difference - 1)\n string += 'ur'\n elif row_difference == 1:\n string += 'drrul' * (col_difference - 1)\n string += 'dru'\n elif col_difference < 0:\n col_difference = abs(col_difference)\n string += 'r' * col_difference\n string += 'dllur' * (col_difference - 1)\n string += 'dlu'\n print 'Row 1 Path', string\n self.update_puzzle(string)\n assert self.row0_invariant(target_col), 'False string'\n return string","def solve(self, board: List[List[str]]) -> None:\n m = len(board)\n if m == 0:\n return\n n = len(board[0])\n uf = UnionFold(m*n+1)\n dummy = m*n\n # 搜索与边界联通的 “O”\n for i in range(0, m):\n for j in range(0, n):\n # the boundary\n if (i==0 or i == m-1 or j == 0 or j == n-1) and board[i][j] == \"O\":\n uf.union(i*n+j, dummy)\n elif board[i][j] == \"O\":\n for l in [[1,0], [0, 1], [-1, 0], [0, -1]]:\n x, y = l[0]+i, l[1]+j\n if board[x][y] == \"O\":\n uf.union(i*n+j, x*n+y)\n # 所有不和 dummy 连通的 O，都要被替换\n for i in range(1, m-1):\n for j in range(1, n-1):\n if not uf.connected(dummy, i*n+j):\n board[i][j] = \"X\"","def solve_col0_tile(self, target_row):\n \n assert target_row > 1, \"target_row cannot be in rows 0 or 1.\"\n assert self.lower_row_invariant(target_row, 0), \"tiles to right and below incorrectly ordered\"\n\n # Move zero tile from target position (target_row, 0) to (target_row - 1, 1).\n self.update_puzzle(\"ur\")\n\n move_str = \"\"\n \n # correct_tile's position is determined after moving zero tile \"ur\" \n # because its position relative to zero tile may have changed as a result.\n correct_tile = self.current_position(target_row, 0)\n \n # Moves to reposition correct_tile to target position.\n if self.get_number(correct_tile[0], correct_tile[1]) != self.get_number(target_row, 0):\n move_str += str(self.position_tile(target_row - 1, 1, correct_tile))\n move_str += str(\"ruldrdlurdluurddlur\")\n\n # Moves to reposition zero tile to end of column of target_row + 1.\n move_str += str(\"r\" * (self.get_width() - 2)) \n \n self.update_puzzle(move_str)\n\n assert self.lower_row_invariant(target_row - 1, self.get_width() - 1) \n \n move_str = \"ur\" + move_str\n return move_str","def solve_row0_tile(self, target_col):\r\n # move the zero tile from position (0,j) to (1,j−1) \r\n # using the move string \"ld\" \r\n moves_str = \"\"\r\n moves_str += \"ld\"\r\n # check whether target tile is at position (0,j).\r\n temp_grid = Puzzle(self._height, self._width, self._grid)\r\n temp_grid.update_puzzle(moves_str) \r\n current_row, current_col = temp_grid.current_position(0, target_col)\r\n zero_row, zero_col = temp_grid.current_position(0, 0)\r\n \r\n # If target tile is not at position (0,j).\r\n # reposition the target tile to position (1,j−1) \r\n # with tile zero in position (1,j−2).\r\n if current_row != 0 or current_col != target_col:\r\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\r\n moves_str += \"urdlurrdluldrruld\"\r\n \r\n self.update_puzzle(moves_str)\r\n print \"solve_row0_tile\"\r\n print moves_str\r\n print self._grid\r\n return moves_str","def solve(self, board: List[List[str]]) -> None:\n # New Solution: DFS on boarder (140ms: 89.07%)\n if not board or not board[0]: return\n def dfs(i, j):\n if board[i][j]=='O':\n board[i][j] = '*'\n if i-1>=0:\n dfs(i-1, j)\n if i+1<len(board):\n dfs(i+1, j)\n if j-1>=0:\n dfs(i, j-1)\n if j+1<len(board[0]):\n dfs(i, j+1)\n height, width = len(board), len(board[0])\n for i in range(width):\n if board[0][i]=='O':\n dfs(0, i)\n if board[height-1][i]=='O':\n dfs(height-1, i)\n for i in range(height):\n if board[i][0]=='O':\n dfs(i, 0)\n if board[i][width-1]=='O':\n dfs(i, width-1)\n for i in range(height):\n for j in range(width):\n if board[i][j]=='O':\n board[i][j] = 'X'\n elif board[i][j]=='*':\n board[i][j] = 'O'","def solve_row1_tile(self, target_col):\n movements = self.solve_interior_tile(1, target_col)\n self.update_puzzle(\"ur\")\n return movements + \"ur\"","def solve_(self, x, y, board, path):\n if self.SOLVED:\n return\n if self.is_done(board):\n self.print_path(path)\n self.SOLVED = True\n return\n for new_x, new_y in self.next_click(x, y, board):\n if new_x is None or new_y is None:\n return\n new_board = self.click(new_x, new_y, board)\n self.solve_(\n x=0, y=0,\n board=new_board,\n path=path + [((new_x, new_y), new_board)]\n )","def solve(self, board: List[List[str]]) -> None:\n visited = [[False for x in range(len(board[0]))] for y in range(len(board))]\n for i in range(len(board)):\n for j in range(len(board[i])):\n if not visited[i][j] and board[i][j] == 'O':\n res = []\n result = self.gatherO(board, i, j, res, visited)\n if not result:\n for coordinate in res:\n board[coordinate[0]][coordinate[1]] = 'X'","def solveSudoku(self, board):\n\n digits = { str(i) for i in range(1, 10) }\n rows = [ digits.copy() for _ in range(9) ]\n cols = [ digits.copy() for _ in range(9) ]\n boxs = [ [ digits.copy() for _ in range(3) ] for _ in range(3) ]\n unoccupied = set()\n\n def __recursiveSolver():\n if not unoccupied:\n return\n\n choices = digits.copy()\n for row, col in unoccupied:\n possible_moves = rows[row] & cols[col] & boxs[row // 3][col // 3]\n if len(possible_moves) < len(choices):\n action_pos = (row, col)\n choices = possible_moves\n if len(choices) == 1:\n break\n\n for choice in choices:\n (row, col) = action_pos\n\n unoccupied.remove(action_pos)\n board[row][col] = choice\n rows[row].remove(choice)\n cols[col].remove(choice)\n boxs[row // 3][col // 3].remove(choice)\n\n __recursiveSolver()\n if not unoccupied: return\n\n unoccupied.add(action_pos)\n board[row][col] = '.'\n rows[row].add(choice)\n cols[col].add(choice)\n boxs[row // 3][col // 3].add(choice)\n\n for row in range(9):\n for col in range(9):\n ch = board[row][col]\n if ch == '.':\n unoccupied.add((row, col))\n else:\n rows[row].remove(ch)\n cols[col].remove(ch)\n boxs[row // 3][col // 3].remove(ch)\n\n __recursiveSolver()","def part1_2(puzzle_input):\n [initial_state_string, configurations] = puzzle_input.split('\\n\\n')\n initial_state = re.sub('initial state: ', '', initial_state_string)\n rules_arr = configurations.split('\\n')\n rules = [re.split(' => ', line) for line in rules_arr]\n rules = {t[0]: t[1] for t in rules}\n current_state = '..........' + initial_state + '...............................................................................................................................................'\n for i in range(100): # After 100th cycle, the only change is that there is a '#' that shifts right\n next_generation_string = \"\"\n for index, pot in enumerate(current_state):\n if index == 0:\n temp_string = '..' + current_state[:3]\n elif index == 1:\n temp_string = '.' + current_state[:4]\n elif index == len(current_state) - 2:\n temp_string = current_state[-4:] + '.'\n elif index == len(current_state) - 1:\n temp_string = current_state[-3:] + '..'\n else:\n temp_string = current_state[index-2:index+3]\n if temp_string in rules:\n next_generation_string += rules[temp_string]\n else:\n next_generation_string += pot\n current_state = next_generation_string\n\n # For part 1\n part1_sum = 0\n if i == 19:\n for index, pot in enumerate(current_state):\n if pot == '#':\n part1_sum += index - 10\n print(part1_sum)\n\n # Part 2\n part2_sum = 0\n for index, pot in enumerate(current_state):\n if pot == '#':\n part2_sum += index - 10 + 50000000000 - 100\n print(part2_sum)","def solve(self, g: List[List[str]]) -> None:\n n = len(g)\n m = len(g[0])\n for i in range(n):\n for j in range(m):\n if g[i][j] == 'O':\n g[i][j] = ' '\n def dfs(x, y):\n g[x][y]='O'\n for nx, ny in (x+1,y),(x-1,y),(x,y+1),(x,y-1):\n if 0<=nx<n and 0<=ny<m and g[nx][ny]==' ':\n dfs(nx, ny)\n for i in range(n):\n if g[i][0]==' ':\n dfs(i,0)\n if g[i][m-1]==' ':\n dfs(i,m-1)\n for i in range(m):\n if g[0][i]==' ':\n dfs(0,i)\n if g[n-1][i]==' ':\n dfs(n-1,i)\n for i in range(n):\n for j in range(m):\n if g[i][j]==' ':\n g[i][j]='X'\n return g","def solve_puzzle(self):\n\n # for each word in the words list\n # ...for each row in the game board\n # ......for each column in each row\n for word in self.words:\n for y, row in enumerate(self.board):\n for x, col in enumerate(row):\n \n # for each direction\n # try to find a word in said direction\n for dir in self.directions:\n self.scan_word(word, y, x, dir)","def move(self, direction):\r\n direc = list(OFFSETS[direction])\r\n line = []\r\n dummy_board = self.board[:]\r\n if direction == 3:\r\n for i in range(self.height):\r\n self.board[i] = merge(self.board[i])\r\n self.compare(dummy_board)\r\n return self.board\r\n \r\n elif direction == 4:\r\n for i in range(self.height):\r\n line = self.board[i][::-1]\r\n self.board[i] = merge(line)\r\n self.board[i] = self.board[i][::-1]\r\n self.compare(dummy_board)\r\n return self.board\r\n \r\n \r\n elif direction == 1 or 2:\r\n dummy_board = str(self.board[:])\r\n if direction == 1:\r\n tile = [0,0]\r\n elif direction == 2:\r\n tile = [self.height - 1, 0]\r\n for i in range(self.width):\r\n tile2 = tile[:]\r\n while len(line) < self.height:\r\n line.append(self.get_tile(*tile2))\r\n tile2 = [x+y for x,y in zip(direc, tile2)]\r\n line = merge(line)\r\n tile2 = tile[:]\r\n for i in range(self.height):\r\n self.set_tile(*(tile2+[line[0]]))\r\n line.remove(line[0])\r\n tile2 = [x+y for x,y in zip(direc, tile2)]\r\n tile = [x+y for x,y in zip(tile, [0,1])]\r\n if dummy_board != self.__str__():\r\n self.new_tile()\r\n return self.board","def solve(self, board):\n self.memo={}\n def changeVal(board, i, j,memo):\n key =str(i)+','+str(j)\n print(i,j)\n print(memo)\n if key in self.memo.keys():\n return self.memo[key]\n if ((i == 0 or j == 0 or i == len(board) - 1 or j == len(board[0]) - 1) and board[i][j] == 'O'):\n self.memo[key] = False\n return False\n if board[i][j] == 'X':\n self.memo[key] = True\n return True\n if board[i][j] == 'O':\n board[i][j] = 'X'\n ans1 = changeVal(board, i + 1, j,self.memo)\n ans2 = changeVal(board, i - 1, j,self.memo)\n ans3 = changeVal(board, i,j + 1,self.memo)\n ans4 = changeVal(board, i, j - 1,self.memo)\n\n ans = ans1 and ans2 and ans3 and ans4\n self.memo[key] = ans\n if ans == False:\n board[i][j] = 'O'\n return ans\n\n for i in range(0, len(board)):\n for j in range(0, len(board[0])):\n if board[i][j] == 'O':\n changeVal(board, i, j,self.memo)\n\n return board","def make_move(self, state):\r\n # intially set drop phase to true\r\n drop_phase = True\r\n move = [] # list to make moves with to return\r\n succ = self.succ(state) # get the successor of this state\r\n # intial postion of board to set up most advantagous spot if its empty\r\n if sum(x.count(self.my_piece) for x in self.board) == 0 and self.board[2][2] == ' ':\r\n move.insert(0, (2, 2))\r\n return move\r\n \r\n # check the number of 'r' and 'b' on board if theres 4 of each drop phase is false\r\n if sum(x.count('r') for x in self.board) == 4 and sum(x.count('b') for x in self.board) == 4:\r\n drop_phase = False\r\n\r\n # if not during drop phase use minimax to make next move from one postion to next\r\n if not drop_phase:\r\n move = []\r\n d = self.Max_value(state, 0)\r\n val = d['val']\r\n m = d['move']\r\n p = d['pos']\r\n f = d['from']\r\n s = sorted(succ, key=lambda e: e['f'])\r\n moveto = s[-1]\r\n move.insert(1, (moveto['from'][0], moveto['from'][1]))\r\n move.insert(0, (moveto['pos'][0], moveto['pos'][1]))\r\n return move # return the from, to move\r\n\r\n else: #else use minimax and to make move during drop phase selecting spot to place AI piece\r\n d = self.Max_value(state, 0)\r\n val = d['val']\r\n m = d['move']\r\n p = d['pos']\r\n hold = []\r\n move = []\r\n n = None\r\n hold = []\r\n for s in succ:\r\n p = s['pos'][0]\r\n p1 = s['pos'][1]\r\n if s['f'] == val and state[p][p1] == ' ':\r\n hold.append(s)\r\n if len(hold) == 1:\r\n row = hold[0]['pos'][0]\r\n col = hold[0]['pos'][1]\r\n else:\r\n f = sorted(hold, key=lambda e: e['pos'])\r\n row = f[0]['pos'][0]\r\n col = f[0]['pos'][1]\r\n\r\n move.insert(0, (row, col)) # return the move \r\n return move","def action(self):\r\n\r\n\r\n #have we just started?\r\n if self.player_information[\"us\"][\"nTokens\"] == 0:\r\n move = generate_starting_move(self.player_information[\"us\"][\"player_side\"], self.board_array)\r\n return move\r\n\r\n #otherwise do minimax \r\n \r\n #start off with some shallow depth:\r\n if self.turn_no < 5:\r\n depth = 3\r\n else:\r\n depth = 2\r\n \r\n #set a constraint for search depth\r\n if self.total_tokens_on_board < 6:\r\n depth = 3\r\n elif self.total_tokens_on_board < 10:\r\n depth = 2\r\n else:\r\n depth = 1\r\n \r\n #have a time reference\r\n print(f'nthrows: {self.player_information[\"us\"][\"nThrowsRemaining\"]}')\r\n starting_time = int(round(time.time(), 0))\r\n #salvage result from minimax\r\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\r\n None, None, None, depth, True, -math.inf, math.inf,\r\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \r\n starting_time, True, self.turn_no)\r\n\r\n #clean it up a bit \r\n print(self.board_dict)\r\n #tidy it up\r\n result = result[0]\r\n print(f'pre: {result}')\r\n #in case we get a bad move redo but make it very shallow\r\n if len(result) == 1 or result == (-5, -5):\r\n #force it to return a usable move\r\n counter = 0\r\n while (len(result) == 1) or (result == (-5, -5)):\r\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\r\n None, None, None, 1, True, -math.inf, math.inf,\r\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \r\n starting_time, False, self.turn_no)\r\n result = result[0]\r\n counter += 1\r\n \r\n #if its taking too long\r\n if counter > 2: \r\n #generate one random possible move to use \r\n allied_tokens = [token for token in self.player_tokens if self.player_tokens[token] == \"us\"]\r\n move_list = generate_moves(self.board_dict, self.player_tokens, self.co_existance_dict, allied_tokens,\r\n self.player_information, self.board_array, True, \"all\")\r\n \r\n \r\n #if there are no moves\r\n if len(move_list) == 0:\r\n if self.player_information['us']['nThrowsRemaining'] > 0:\r\n throws = generate_possible_throws(self.board_dict, self.player_tokens, self.co_existance_dict, self.player_information, \"us\",\r\n self.player_information[\"us\"][\"player_side\"], self.board_array, \"all\" )\r\n result = random.choice(throws)\r\n \r\n else:\r\n result = random.choice(move_list)\r\n print(f'random: {result}')\r\n break\r\n\r\n print(f' inside: {result}')\r\n\r\n print(result)\r\n #otherwise clean it up\r\n if result[0] == 'throw':\r\n final_result = (result[0].upper(), result[1], result[2])\r\n else:\r\n final_result = (result[0].upper(), result[2], result[3])\r\n # return final result \r\n return final_result","def get_all_possible_moves():\r\n \"\"\"\r\n Creates the labels for the universal chess interface into an array and returns them\r\n \"\"\"\r\n labels_array = []\r\n letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\r\n numbers = ['1', '2', '3', '4', '5', '6', '7', '8']\r\n promoted_to = ['q', 'r', 'b', 'n']\r\n\r\n for l1 in range(8):\r\n for n1 in range(8):\r\n destinations = [(t, n1) for t in range(8)] + \\\r\n [(l1, t) for t in range(8)] + \\\r\n [(l1 + t, n1 + t) for t in range(-7, 8)] + \\\r\n [(l1 + t, n1 - t) for t in range(-7, 8)] + \\\r\n [(l1 + a, n1 + b) for (a, b) in\r\n [(-2, -1), (-1, -2), (-2, 1), (1, -2), (2, -1), (-1, 2), (2, 1), (1, 2)]]\r\n for (l2, n2) in destinations:\r\n if (l1, n1) != (l2, n2) and l2 in range(8) and n2 in range(8):\r\n move = letters[l1] + numbers[n1] + letters[l2] + numbers[n2]\r\n labels_array.append(move)\r\n for l1 in range(8):\r\n l = letters[l1]\r\n for p in promoted_to:\r\n labels_array.append(l + '2' + l + '1' + p)\r\n labels_array.append(l + '7' + l + '8' + p)\r\n if l1 > 0:\r\n l_l = letters[l1 - 1]\r\n labels_array.append(l + '2' + l_l + '1' + p)\r\n labels_array.append(l + '7' + l_l + '8' + p)\r\n if l1 < 7:\r\n l_r = letters[l1 + 1]\r\n labels_array.append(l + '2' + l_r + '1' + p)\r\n labels_array.append(l + '7' + l_r + '8' + p)\r\n return labels_array","def solve(self, board: List[List[str]]) -> None:\n if len(board) == 0: return []\n nr = len(board)\n nc = len(board[0])\n\n # begin dfs from boundaries with letter \"O\"\n for r in range(nr):\n for c in range(nc):\n if r == 0 or r == nr-1 or c == 0 or c == nc-1:\n if board[r][c] == \"O\":\n self.dfs(board, r, c)\n\n # change \"O\" to \"X\" and \"#\" to \"O\"\n for r in range(nr):\n for c in range(nc):\n if board[r][c] == \"O\":\n board[r][c] = \"X\"\n elif board[r][c] == \"#\":\n board[r][c] = \"O\"","def solve(self):\n dim = self.puzzle.dimension\n\n # initial loop\n for value, (row, col) in self.puzzle:\n if value:\n self.clear_row(row, value)\n self.clear_col(col, value)\n self.clear_subgrid(row, col, value)\n self.updates.add((value, (row, col)))\n for ps in self.possibilities:\n ps.discard((row, col))\n\n while self.updates:\n while self.updates:\n # while self.updates:\n value, (row, col) = self.updates.pop()\n for i in range(1, dim + 1):\n self.check_row(i, value)\n self.check_col(i, value)\n for i in range(2, 8, 3):\n self.check_subgrid(row, i, value)\n self.check_subgrid(i, col, value)\n\n for value, (row, col) in self.puzzle:\n if not value:\n self.check_cell(row, col)\n\n # for value in range(1, dim + 1):\n # for row in [2, 5, 8]:\n # for col in [2, 5, 8]:\n # self.check_subgrid(row, col, value)","def solve(self, board: List[List[str]]) -> None:\n if(len(board)==0) : return \n for i in range(0, len(board[0])): \n if(board[0][i]==\"O\"):\n self.DFS(board, 0, i)\n \n if(board[len(board)-1][i]==\"O\"):\n self.DFS(board,len(board)-1,i)\n \n for i in range(0, len(board)):\n if(board[i][0]==\"O\"):\n self.DFS(board, i, 0)\n \n if(board[i][len(board[0])-1]==\"O\"):\n self.DFS(board,i, len(board[0])-1)\n \n \n for i in range(0,len(board)):\n for j in range(0, len(board[0])):\n if(board[i][j]==\"#\"):\n board[i][j]=\"O\"\n \n else:\n board[i][j]=\"X\"","def find_best_move(state: GameState) -> None:","def solve_row0_tile(self, target_col):\n assert target_col > 1, \"target_col must be > 1\"\n assert self.row0_invariant(target_col), \"tiles to right and below incorrectly ordered\"\n \n # Move zero tile from target position (0, target_col) to (1, target_col - 1) \n self.update_puzzle(\"ld\")\n \n move_str = \"\"\n\n # correct_tile's position is determined after moving zero tile \"ld\"\n # because its position relative to zero tile may have changed as a result.\n correct_tile = self.current_position(0, target_col) \n \n # Moves to reposition correct_tile to target position, and\n # the zero tile to (1, target_col - 1).\n if self.get_number(correct_tile[0], correct_tile[1]) != self.get_number(0, target_col):\n move_str += str(self.position_tile(1, target_col - 1, correct_tile))\n move_str += str(\"urdlurrdluldrruld\")\n\n self.update_puzzle(move_str)\n\n assert self.row1_invariant(target_col - 1), \"tiles to right and below incorrectly ordered\"\n \n move_str = \"ld\" + move_str\n return move_str"],"string":"[\n \"def solve_puzzle(self):\\n\\n move_str = \\\"\\\"\\n \\n # Move zero tile to bottom right corner tile of puzzle.\\n zero_pos = self.current_position(0,0) \\n vert_dist = (self.get_height() - 1) - zero_pos[0]\\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\\n move_str += ((\\\"d\\\" * vert_dist) + (\\\"r\\\" * horiz_dist))\\n self.update_puzzle(move_str)\\n \\n # Solve lower rows\\n if self.get_height() > 2:\\n for row in range(self.get_height() - 1, 1, -1):\\n for col in range(self.get_width() - 1, -1, -1):\\n if col != 0:\\n move_str += self.solve_interior_tile(row, col)\\n else:\\n move_str += self.solve_col0_tile(row)\\n \\n # Solve top 2 rows\\n if self.get_width() > 2:\\n for col in range(self.get_width() - 1, 1, -1):\\n move_str += self.solve_row1_tile(col)\\n move_str += self.solve_row0_tile(col)\\n \\n # Solve 2x2\\n move_str += self.solve_2x2()\\n\\n return move_str\",\n \"def solve_puzzle(self):\\n cur0_row, cur0_col = self.current_position(0, 0)\\n move_str = 'd' * (self._height - cur0_row - 1) + 'r' * (self._width - cur0_col - 1)\\n self.update_puzzle(move_str)\\n for row in range(self._height-1, 1, -1):\\n for col in range(self._width-1, -1, -1):\\n assert self.lower_row_invariant(row, col)\\n if col != 0:\\n move_str += self.solve_interior_tile(row, col)\\n else:\\n move_str += self.solve_col0_tile(row)\\n for col in range(self._width-1, 1, -1):\\n assert self.row1_invariant(col)\\n move_str += self.solve_row1_tile(col)\\n assert self.row0_invariant(col)\\n move_str += self.solve_row0_tile(col)\\n move_str += self.solve_2x2()\\n return move_str\",\n \"def solve_puzzle(self):\\r\\n # initialize some values and start tile at bottom right corner\\r\\n col = self.get_width() - 1\\r\\n row = self.get_height() - 1\\r\\n move = \\\"\\\"\\r\\n curr_row, curr_col = self.current_position(0, 0)\\r\\n move_to_target = abs(curr_col - col) * \\\"r\\\" + abs(curr_row - row) * \\\"d\\\"\\r\\n self.update_puzzle(move_to_target)\\r\\n move += move_to_target\\r\\n\\r\\n # apply solver methods\\r\\n for dummy_i in range(row, 1, -1):\\r\\n for dummy_j in range(col, 0, -1):\\r\\n move += self.solve_interior_tile(dummy_i, dummy_j)\\r\\n move += self.solve_col0_tile(dummy_i)\\r\\n \\r\\n for dummy_j in range(col, 1, -1):\\r\\n move += self.solve_row1_tile(dummy_j)\\r\\n move += self.solve_row0_tile(dummy_j)\\r\\n \\r\\n move += self.solve_2x2()\\r\\n return move\",\n \"def solve_puzzle(self):\\r\\n moves_str = \\\"\\\"\\r\\n # move zero to the most botton right\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n downs = self._height - 1 - zero_row\\r\\n rights = self._width - 1 - zero_col\\r\\n for dummy_d in range(downs):\\r\\n moves_str += \\\"d\\\"\\r\\n for dummy_r in range(rights):\\r\\n moves_str += \\\"r\\\"\\r\\n self.update_puzzle(moves_str)\\r\\n # Solve the bottom m−2 rows of the puzzle \\r\\n # in a row by row manner from bottom to top. \\r\\n # Each individual row will be solved in a right to left order.\\r\\n if self._height > 2 and self._width > 2:\\r\\n for row in range(self._height - 1, 1, -1):\\r\\n for col in range(self._width - 1, 0, -1):\\r\\n assert self.lower_row_invariant(row, col)\\r\\n moves_str += self.solve_interior_tile(row, col)\\r\\n assert self.lower_row_invariant(row, col - 1)\\r\\n assert self.lower_row_invariant(row, 0)\\r\\n moves_str += self.solve_col0_tile(row)\\r\\n assert self.lower_row_invariant(row - 1, self._width - 1)\\r\\n # Solve the rightmost n−2 columns of the top two rows\\r\\n # in a right to left order). \\r\\n # Each column consists of two unsolved positions \\r\\n # and will be solved in a bottom to top order.\\r\\n for col in range(self._width - 1, 1, -1):\\r\\n assert self.row1_invariant(col)\\r\\n moves_str += self.solve_row1_tile(col)\\r\\n assert self.row0_invariant(col)\\r\\n moves_str += self.solve_row0_tile(col)\\r\\n assert self.row1_invariant(col - 1)\\r\\n # Solve the upper left 2×2 portion of the puzzle directly.\\r\\n assert self.row1_invariant(1)\\r\\n moves_str += self.solve_2x2()\\r\\n \\r\\n elif self._height <=2 and self._width > 2:\\r\\n for col in range(self._width - 1, 1, -1):\\r\\n assert self.row1_invariant(col)\\r\\n moves_str += self.solve_row1_tile(col)\\r\\n assert self.row0_invariant(col)\\r\\n moves_str += self.solve_row0_tile(col)\\r\\n assert self.row1_invariant(col - 1)\\r\\n # Solve the upper left 2×2 portion of the puzzle directly.\\r\\n assert self.row1_invariant(1)\\r\\n moves_str += self.solve_2x2()\\r\\n elif self._height <= 2 and self._width <= 2:\\r\\n assert self.row1_invariant(1)\\r\\n moves_str += self.solve_2x2()\\r\\n #elif self._height > 2 and self._width <= 2:\\r\\n \\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve_puzzle(self):\\n # replace with your code\\n string = ''\\n width = self._width\\n height = self._height\\n zero = self.current_position(0, 0)\\n row_to_zero = height - 1 - zero[0]\\n col_to_zero = width - 1 - zero[1]\\n string += 'r' * col_to_zero\\n string += 'd' * row_to_zero\\n self.update_puzzle(string)\\n if width == 2 and height == 2:\\n string += self.solve_2x2()\\n elif width > 2 and height == 2:\\n for col in range(width - 1, 1, -1):\\n string += self.solve_row1_tile(col)\\n string += self.solve_row0_tile(col)\\n string += self.solve_2x2()\\n elif width == 2 and height > 2:\\n for row in range(height - 1, 1, -1):\\n for col in range(width - 1, 0, -1):\\n string += self.solve_interior_tile(row, col)\\n string += self.solve_col0_tile(row)\\n string += self.solve_2x2()\\n elif width > 2 and height > 2:\\n for row in range(height - 1, 1, -1):\\n for col in range(width - 1, 0, -1):\\n string += self.solve_interior_tile(row, col)\\n string += self.solve_col0_tile(row)\\n #for row in range(height - 1, -1, -1):\\n for col in range(width - 1, 1, -1):\\n string += self.solve_row1_tile(col)\\n string += self.solve_row0_tile(col)\\n string += self.solve_2x2()\\n return string\",\n \"def solve_2x2(self):\\n # move zero tile to the left-up corner\\n self.update_puzzle(\\\"lu\\\")\\n movements = \\\"rdlu\\\"\\n for _ in range(3):\\n self.update_puzzle(\\\"rdlu\\\")\\n if self.row0_invariant(0):\\n return \\\"lu\\\" + movements\\n movements += \\\"rdlu\\\"\\n # the final 2x2 cannot be solved\\n return \\\"\\\"\",\n \"def solve_2x2(self):\\r\\n moves_str = \\\"\\\"\\r\\n # move zero to the most upper left\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n ups = zero_row - 0\\r\\n lefts = zero_col - 0\\r\\n for dummy_u in range(ups):\\r\\n moves_str += \\\"u\\\"\\r\\n for dummy_l in range(lefts):\\r\\n moves_str += \\\"l\\\"\\r\\n \\r\\n # get zero, one, two, three positions\\r\\n self.update_puzzle(moves_str)\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n one_row, one_col = self.current_position(0, 1)\\r\\n two_row, two_col = self.current_position(1, 0)\\r\\n three_row, three_col = self.current_position(1, 1)\\r\\n counter = 0\\r\\n while counter <= 3 and \\\\\\r\\n (zero_row != 0 or zero_col != 0 or \\\\\\r\\n one_row!= 0 or one_col != 1 or \\\\\\r\\n two_row != 1 or two_col != 0 or \\\\\\r\\n three_row != 1 or three_col != 1):\\r\\n move = CIRCLES[\\\"RIGHT_CIRCLES\\\"]\\r\\n moves_str += move\\r\\n self.update_puzzle(move)\\r\\n counter += 1\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n one_row, one_col = self.current_position(0, 1)\\r\\n two_row, two_col = self.current_position(1, 0)\\r\\n three_row, three_col = self.current_position(1, 1)\\r\\n \\r\\n print \\\"solve_2x2\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve_puzzle(self):\\n # move zero tile to the lower right corner\\n row, col = self.current_position(0, 0)\\n movements = \\\"d\\\" * (self.get_height() - 1 - row) + \\\"r\\\" * (\\n self.get_width() - 1 - col)\\n self.update_puzzle(movements)\\n # solve rowid from 2 by row\\n for row in range(self.get_height() - 1, 1, -1):\\n for col in range(self.get_width() - 1, -1, -1):\\n assert self.lower_row_invariant(row, col)\\n if col == 0:\\n movements += self.solve_col0_tile(row)\\n assert self.lower_row_invariant(row - 1,\\n self.get_width() - 1)\\n else:\\n movements += self.solve_interior_tile(row, col)\\n assert self.lower_row_invariant(row, col - 1)\\n # solve the uppermost two rows by column\\n for col in range(self.get_width() - 1, 1, -1):\\n for row in range(1, -1, -1):\\n if row == 0:\\n assert self.row0_invariant(col)\\n movements += self.solve_row0_tile(col)\\n assert self.row1_invariant(col - 1)\\n else:\\n assert self.row1_invariant(col)\\n movements += self.solve_row1_tile(col)\\n assert self.row0_invariant(col)\\n movements += self.solve_2x2()\\n return movements\",\n \"def solve_2x2(self):\\r\\n # replace with your code\\r\\n assert self.row1_invariant(1), '2x2 Dont pass row1_invariant(1)'\\r\\n whole_move = ''\\r\\n current_position = self.current_position(0, 0)\\r\\n # print 'Zero position =', current_position\\r\\n counter = 0\\r\\n \\r\\n\\r\\n \\r\\n # if current_position == (0,0):\\r\\n # print (0,0)\\r\\n # move_to_00 = 'rdlu' \\r\\n if current_position == (0,1):\\r\\n # print (0,1)\\r\\n move_to_00 = 'l'\\r\\n if current_position == (1,0):\\r\\n # print (1,0)\\r\\n move_to_00 = 'u'\\r\\n if current_position == (1,1):\\r\\n # print (1,1)\\r\\n move_to_00 = 'ul'\\r\\n whole_move += move_to_00\\r\\n self.update_puzzle(move_to_00)\\r\\n # print self\\r\\n # print self.get_number(1,1) < self.get_number(1,0)\\r\\n \\r\\n while self.get_number(0,0) != 0 or self.get_number(0,1) != 1:\\r\\n \\r\\n # print 'Aloha in loop!'\\r\\n counter +=1\\r\\n move = 'rdlu'\\r\\n whole_move += move\\r\\n self.update_puzzle(move)\\r\\n # print self\\r\\n if counter >5:\\r\\n break\\r\\n return whole_move\",\n \"def solve_2x2(self):\\n \\n assert self.get_number(1,1) == 0, \\\"zero tile should be at row 1, col 1\\\"\\n assert self.row1_invariant(1), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n # Moves the zero tile to (0,0).\\n self.update_puzzle(\\\"lu\\\")\\n\\n # Repositions the upper left 2x2 part up to 3 times, \\n # each time checking whether the puzzle is solved.\\n rotation_num = 0\\n if self.row0_invariant(0) == False:\\n for dummy_rotation in range(3):\\n while self.row0_invariant(0) == False:\\n rotation_num += 1\\n self.update_puzzle(\\\"rdlu\\\")\\n\\n assert self.row0_invariant(0), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n move_str = \\\"lu\\\" + (\\\"rdlu\\\" * rotation_num) \\n return move_str\",\n \"def solve_2x2(self):\\r\\n assert self.row1_invariant(1)\\r\\n pos_1_0 = self.get_number(1, 0)\\r\\n pos_0_0 = self.get_number(0, 0)\\r\\n pos_0_1 = self.get_number(0, 1)\\r\\n # create grid and solve individual cases\\r\\n grid = [pos_1_0, pos_0_0, pos_0_1]\\r\\n if grid == [self.get_width(), 1, self.get_width() + 1]:\\r\\n move = \\\"ul\\\"\\r\\n elif grid == [1, self.get_width() + 1, self.get_width()]:\\r\\n move = \\\"lurdlu\\\"\\r\\n elif grid == [self.get_width() + 1, self.get_width(), 1]:\\r\\n move = \\\"lu\\\"\\r\\n self.update_puzzle(move)\\r\\n return move\",\n \"def solve_2x2(self):\\n # replace with your code\\n string = ''\\n num1 = self.get_number(0, 0)\\n num2 = self.get_number(0, 1)\\n num3 = self.get_number(1, 0)\\n max_num = max([num1, num2, num3])\\n min_num = min([num1, num2, num3])\\n if num1 == min_num and num2 == max_num:\\n string += 'ul'\\n elif num1 == max_num and num3 == min_num:\\n string += 'ul'\\n string += 'rdlu' * 2\\n elif num2 == min_num and num3 == max_num:\\n string += 'ul'\\n string += 'rdlu'\\n print '2x2 Path', string\\n self.update_puzzle(string)\\n return string\",\n \"def solve_puzzle(self):\\r\\n \\r\\n counter = 0\\r\\n rows = self._height-1\\r\\n cols = self._width-1\\r\\n # print rows, cols\\r\\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \\r\\n solution_move = ''\\r\\n if self.get_number(0,0) == 0 and \\\\\\r\\n self.get_number(0,1) == 1:\\r\\n # print 'Congrads Puxxle is Aolved at start!!!!!'\\r\\n return ''\\r\\n #appropriate_number = (self._height * self._width) - 1\\r\\n appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'First appropriate_number=',appropriate_number\\r\\n # print \\\"Grid first tile that we will solwing has value =\\\", self._grid[rows][cols]\\r\\n \\r\\n while counter < 300:\\r\\n counter +=1\\r\\n # print self\\r\\n #appropriate_number = (rows+1) * (cols+1) -1\\r\\n # print 'Appropriate number in loop=',appropriate_number\\r\\n # print 'We are solving %s index_row and %s index_col' %(rows, cols) \\r\\n ####Case when we use solve_interior_tile\\r\\n if rows > 1 and cols > 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n cols -= 1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solving interior tile', (rows, cols)\\r\\n solution_move += self.solve_interior_tile(rows, cols)\\r\\n # print 'Solution move=', solution_move\\r\\n cols -= 1\\r\\n #### Case when we use solve_col0_tile\\r\\n elif rows > 1 and cols == 0:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n cols = self._width-1\\r\\n appropriate_number -=1\\r\\n else:\\r\\n # print 'We are solwing tile 0 in row', rows\\r\\n # print 'Appropriate number here ='\\r\\n solution_move += self.solve_col0_tile(rows)\\r\\n # print 'Solution move=', solution_move\\r\\n rows -=1\\r\\n cols = self._width-1\\r\\n\\r\\n\\r\\n #### Cases when we use solve_row0_tile\\r\\n elif rows == 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows -= 1\\r\\n #cols = self._width-1\\r\\n appropriate_number -= self._width\\r\\n\\r\\n else:\\r\\n # print 'Solving upper 2 rows right side'\\r\\n solution_move += self.solve_row1_tile(cols)\\r\\n rows -=1\\r\\n appropriate_number -= self._width\\r\\n #### Cases when we use solve_row1_tile \\r\\n if rows < 1 and cols > 1:\\r\\n if self._grid[rows][cols] == appropriate_number:\\r\\n # print 'This tile is already solved!!!'\\r\\n rows += 1\\r\\n cols -= 1\\r\\n appropriate_number +=self._width-1\\r\\n else:\\r\\n # print '(1,J) tile solved, lets solwe tile (0,j) in tile',(rows,cols)\\r\\n # print 'Greed after move solve_row1_tile'\\r\\n # print self\\r\\n solution_move += self.solve_row0_tile(cols)\\r\\n rows +=1\\r\\n cols -=1\\r\\n appropriate_number +=self._width-1\\r\\n\\r\\n\\r\\n #### Case when we use solve_2x2\\r\\n elif rows <= 1 and cols <= 1:\\r\\n # print 'We are solving 2x2 puzzle'\\r\\n solution_move += self.solve_2x2()\\r\\n if self._grid[0][0] == 0 and \\\\\\r\\n self._grid[0][1] == 1:\\r\\n # print 'Congrads Puxxle is SOLVED!!!!!'\\r\\n break\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n if counter > 100:\\r\\n # print 'COUNTER BREAK'\\r\\n break\\r\\n # print solution_move, len(solution_move)\\r\\n return solution_move\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # for row in solution_greed._grid[::-1]:\\r\\n # print solution_greed._grid\\r\\n # print 'Row =',row\\r\\n \\r\\n # if solution_greed._grid.index(row) > 1:\\r\\n # print \\\"Case when we solwing Interior and Tile0 part\\\"\\r\\n \\r\\n\\r\\n # for col in solution_greed._grid[solution_greed._grid.index(row)][::-1]:\\r\\n # print 'Coloumn value=', col\\r\\n #print row[0]\\r\\n # if col !=row[0]:\\r\\n # print 'Case when we use just Interior tile solution'\\r\\n # print solution_greed._grid.index(row)\\r\\n # print row.index(col)\\r\\n \\r\\n # solution += solution_greed.solve_interior_tile(solution_greed._grid.index(row) , row.index(col))\\r\\n # print 'Solution =', solution\\r\\n # print self \\r\\n # print solution_greed._grid\\r\\n # elif col ==row[0]:\\r\\n # print 'Case when we use just Col0 solution'\\r\\n\\r\\n # else:\\r\\n # print 'Case when we solwing first two rows'\\r\\n\\r\\n #return \\\"\\\"\\r\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\\n for row in self._grid:\\n print row\\n print\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col\\n - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col\\n + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][\\n zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][\\n zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n for direction in move_string:\\r\\n if direction == \\\"l\\\":\\r\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\r\\n self._grid[zero_row][zero_col - 1] = 0\\r\\n zero_col -= 1\\r\\n elif direction == \\\"r\\\":\\r\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\r\\n self._grid[zero_row][zero_col + 1] = 0\\r\\n zero_col += 1\\r\\n elif direction == \\\"u\\\":\\r\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\r\\n self._grid[zero_row - 1][zero_col] = 0\\r\\n zero_row -= 1\\r\\n elif direction == \\\"d\\\":\\r\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\r\\n self._grid[zero_row + 1][zero_col] = 0\\r\\n zero_row += 1\\r\\n else:\\r\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n for direction in move_string:\\r\\n if direction == \\\"l\\\":\\r\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\r\\n self._grid[zero_row][zero_col - 1] = 0\\r\\n zero_col -= 1\\r\\n elif direction == \\\"r\\\":\\r\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\r\\n self._grid[zero_row][zero_col + 1] = 0\\r\\n zero_col += 1\\r\\n elif direction == \\\"u\\\":\\r\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\r\\n self._grid[zero_row - 1][zero_col] = 0\\r\\n zero_row -= 1\\r\\n elif direction == \\\"d\\\":\\r\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\r\\n self._grid[zero_row + 1][zero_col] = 0\\r\\n zero_row += 1\\r\\n else:\\r\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n for direction in move_string:\\r\\n if direction == \\\"l\\\":\\r\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\r\\n self._grid[zero_row][zero_col - 1] = 0\\r\\n zero_col -= 1\\r\\n elif direction == \\\"r\\\":\\r\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\r\\n self._grid[zero_row][zero_col + 1] = 0\\r\\n zero_col += 1\\r\\n elif direction == \\\"u\\\":\\r\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\r\\n self._grid[zero_row - 1][zero_col] = 0\\r\\n zero_row -= 1\\r\\n elif direction == \\\"d\\\":\\r\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\r\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\r\\n self._grid[zero_row + 1][zero_col] = 0\\r\\n zero_row += 1\\r\\n else:\\r\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def update_puzzle(self, move_string):\\n zero_row, zero_col = self.current_position(0, 0)\\n for direction in move_string:\\n if direction == \\\"l\\\":\\n assert zero_col > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]\\n self._grid[zero_row][zero_col - 1] = 0\\n zero_col -= 1\\n elif direction == \\\"r\\\":\\n assert zero_col < self._width - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]\\n self._grid[zero_row][zero_col + 1] = 0\\n zero_col += 1\\n elif direction == \\\"u\\\":\\n assert zero_row > 0, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]\\n self._grid[zero_row - 1][zero_col] = 0\\n zero_row -= 1\\n elif direction == \\\"d\\\":\\n assert zero_row < self._height - 1, \\\"move off grid: \\\" + direction\\n self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]\\n self._grid[zero_row + 1][zero_col] = 0\\n zero_row += 1\\n else:\\n assert False, \\\"invalid direction: \\\" + direction\",\n \"def solve_row1_tile(self, target_col):\\r\\n moves_str = \\\"\\\"\\r\\n current_row, current_col = self.current_position(1, target_col)\\r\\n zero_row, zero_col = self.current_position(0, 0)\\r\\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\\r\\n moves_str += \\\"ur\\\"\\r\\n self.update_puzzle(moves_str)\\r\\n print \\\"solve_row1_tile\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve(self, board: List[List[str]]) -> None:\\n n = len(board)\\n if n == 0:\\n return\\n m = len(board[0])\\n for i in range(m):\\n self.traverse(board, 0, i, n, m)\\n self.traverse(board, n - 1, i, n, m)\\n for i in range(n):\\n self.traverse(board, i, 0, n, m)\\n self.traverse(board, i, m - 1, n, m)\\n for i in range(n):\\n for j in range(m):\\n board[i][j] = 'X' if board[i][j] == 'O' else board[i][j]\\n for i in range(n):\\n for j in range(m):\\n board[i][j] = 'O' if board[i][j] == '_' else board[i][j]\",\n \"def solve_interior_tile(self, target_row, target_col):\\r\\n assert self._grid[target_row][target_col] == 0\\r\\n moves_str = \\\"\\\"\\r\\n target_current_row, target_current_col = self.current_position(target_row, target_col)\\r\\n \\r\\n moves_str += self.position_tile(target_row, target_col, target_current_row, target_current_col) \\r\\n \\r\\n self.update_puzzle(moves_str)\\r\\n print \\\"solve_interior_tile\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve(puzzle):\\n print(\\\"Solving...\\\")\\n array_puzzle = np.asarray(puzzle)\\n array_puzzle.flags.writeable = False # Turn off writable flags to prevent data being ovewritten accidentally.\\n goal_state = __generate_goal(len(array_puzzle[0]), len(array_puzzle))\\n\\n flat_puzzle = list(chain.from_iterable(puzzle)) # Flatten the list\\n\\n # If the puzzle doesn't contain 0, exit.\\n try:\\n flat_puzzle.remove(0) # Remove 0 from the list\\n except:\\n print(\\\"All puzzles must include an open tile (0).\\\")\\n return None\\n\\n inversions = __count_inversions(flat_puzzle) # Count the inversions\\n\\n # width = len(array_puzzle[0]) # Get the width of the puzzle (columns)\\n # length = len(array_puzzle) # Get the length of the puzzle (rows)\\n\\n oddEven = __odd_or_even(len(array_puzzle[0])) # Determine if the width is odd or even.\\n start_position = __find_start(array_puzzle) # Find the start position's row\\n solvable = __is_solvable(oddEven, inversions, len(array_puzzle), start_position) # Cleck if the puzzle is solvable.\\n\\n # If the puzzle is not solvable, return None.\\n if(solvable == \\\"None\\\"):\\n return None\\n\\n # If we cannot calculate a* (for example the given values are not all in sequential order (1-5) 4 is replaced by 6 (1,2,3,5,6))\\n try:\\n return __a_star(array_puzzle, goal_state)\\n except:\\n print(\\\"Please make sure there are no duplicate or skipped inputs.\\\")\\n return None\\n\\n # This code was used in testing to print out the string.\\n # solved = __a_star(array_puzzle, goal_state)\\n # Return the moves needed to complete the puzzle.\\n # return print(str(__build_string(solved)) + \\\" (\\\" + str(len(solved)) + \\\")\\\")\",\n \"def solve(self, board: List[List[str]]) -> None:\\n rows = len(board)\\n cols = len(board[0])\\n\\n def explore(i, j):\\n if i < 0 or i >= rows:\\n return\\n if j < 0 or j >= cols:\\n return\\n if board[i][j] != \\\"O\\\":\\n return\\n board[i][j] = \\\"Y\\\"\\n explore(i + 1, j)\\n explore(i - 1, j)\\n explore(i, j + 1)\\n explore(i, j - 1)\\n\\n for i in [0, rows - 1]:\\n for j in range(cols):\\n explore(i, j)\\n\\n for j in [0, cols - 1]:\\n for i in range(rows):\\n explore(i, j)\\n\\n for i in range(rows):\\n for j in range(cols):\\n if board[i][j] == \\\"Y\\\":\\n board[i][j] = \\\"O\\\"\\n elif board[i][j] == \\\"O\\\":\\n board[i][j] = \\\"X\\\"\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if not board:\\n return\\n m, n = len(board), len(board[0])\\n\\n def dfs(x, y):\\n if not 0 <= x < m or not 0 <= y < n or board[x][y] != \\\"O\\\":\\n return\\n direction = [[1, 0], [0, 1], [-1, 0], [0, -1]]\\n board[x][y] = \\\"Y\\\"\\n for d in direction:\\n dfs(x+d[0], y+d[1])\\n\\n for i in range(m):\\n dfs(i, 0)\\n dfs(i, n-1)\\n for i in range(1, n-1):\\n dfs(0, i)\\n dfs(m-1, i)\\n\\n for i in range(m):\\n for j in range(n):\\n if board[i][j] == \\\"O\\\":\\n board[i][j] = \\\"X\\\"\\n elif board[i][j] == \\\"Y\\\":\\n board[i][j] = \\\"O\\\"\",\n \"def solve(self, board) -> None:\\n if board == [[]] or board == []:\\n return\\n\\n r, c = len(board), len(board[0])\\n\\n from collections import deque\\n queue = deque()\\n for i in range(r):\\n for j in range(c):\\n if (i == 0 or i == r - 1 or j == 0 or j == c - 1) and board[i][j] == 'O':\\n queue.append([i, j])\\n board[i][j] = 'M'\\n\\n while queue:\\n i, j = queue.popleft()\\n for x, y in ((i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)):\\n if 0 <= x <= r - 1 and 0 <= y <= c - 1 and board[x][y] == 'O':\\n board[x][y] = 'M'\\n queue.append([x, y])\\n\\n for i in range(r):\\n for j in range(c):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n elif board[i][j] == 'M':\\n board[i][j] = 'O'\",\n \"def move_tile(self, target_row, target_col, val):\\n # a little bit twisted here for the use of both solve_interior_tile and solve_col0_tile\\n solved_row, solved_col = self.current_position(0, val)\\n movements = \\\"\\\"\\n if solved_row == target_row and solved_col == target_col:\\n return \\\"\\\"\\n if solved_row == target_row:\\n if target_col > solved_col:\\n movements = \\\"l\\\" * (target_col - solved_col) + \\\"urrdl\\\" * (\\n target_col - solved_col - 1)\\n else:\\n movements = \\\"r\\\" * (solved_col - target_col) + \\\"ulldr\\\" * (\\n solved_col - target_col - 1) + \\\"ulld\\\"\\n elif solved_col == target_col:\\n movements = \\\"u\\\" * (target_row - solved_row) + \\\"lddru\\\" * (\\n target_row - solved_row - 1) + \\\"ld\\\"\\n elif solved_col < target_col:\\n if solved_col == 0:\\n movements = \\\"l\\\" * (target_col - solved_col) + \\\"u\\\" * (\\n target_row - solved_row) + \\\"rddlu\\\" * (\\n target_row - solved_row - 1) + \\\"rdl\\\" + \\\"urrdl\\\" * (\\n target_col - solved_col - 1)\\n else:\\n movements = \\\"l\\\" * (target_col - solved_col) + \\\"u\\\" * (\\n target_row - solved_row) + \\\"lddru\\\" * (\\n target_row - solved_row - 1) + \\\"rdl\\\" + \\\"urrdl\\\" * (\\n target_col - solved_col - 1)\\n elif solved_col > target_col:\\n if solved_row == 0:\\n movements = \\\"u\\\" * (target_row - solved_row) + \\\"r\\\" * (\\n solved_col - target_col) + \\\"dllur\\\" * (\\n solved_col - target_col - 1) + \\\"dlu\\\" + \\\"lddru\\\" * (\\n target_row - solved_row - 1) + \\\"ld\\\"\\n else:\\n movements = \\\"u\\\" * (target_row - solved_row) + \\\"r\\\" * (\\n solved_col - target_col) + \\\"ulldr\\\" * (\\n solved_col - target_col - 1) + \\\"ullddru\\\" + \\\"lddru\\\" * (\\n target_row - solved_row - 1) + \\\"ld\\\"\\n return movements\",\n \"def solve_interior_tile(self, target_row, target_col):\\n cur_row, cur_col = self.current_position(target_row, target_col)\\n move_str = self.position_tile(target_row, target_col, cur_row, cur_col)\\n self.update_puzzle(move_str)\\n return move_str\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) is 0: return\\n width = len(board[0]) - 1\\n height = len(board) - 1\\n def help(x: int, y: int):\\n if x < 0 or x > height or y < 0 or y > width:\\n return\\n if board[x][y] is \\\"O\\\":\\n board[x][y] = \\\"g\\\"\\n help(x - 1, y)\\n help(x, y - 1)\\n help(x + 1, y)\\n help(x, y + 1)\\n\\n for i in range(width + 1):\\n if board[0][i] is \\\"O\\\":\\n help(0, i)\\n if board[height][i] is \\\"O\\\":\\n help(height, i)\\n for i in range(1, height):\\n if board[i][0] is \\\"O\\\":\\n help(i, 0)\\n if board[i][width] is \\\"O\\\":\\n help(i, width)\\n\\n print(board)\\n for i in range(width + 1):\\n for j in range(height + 1):\\n if board[j][i] is \\\"O\\\":\\n board[j][i] = \\\"X\\\"\\n elif board[j][i] is \\\"g\\\":\\n board[j][i] = \\\"O\\\"\",\n \"def solve_col0_tile(self, target_row):\\n solved_row, solved_col = self.current_position(target_row, 0)\\n movements = \\\"\\\"\\n if solved_row == target_row - 1 and solved_col == 0:\\n movements = \\\"u\\\"\\n else:\\n local_board = self.clone()\\n local_board.update_puzzle(\\\"ur\\\")\\n movements = \\\"ur\\\" + local_board.move_tile(\\n target_row - 1, 1, target_row *\\n self.get_width()) + \\\"ruldrdlurdluurddlu\\\"\\n movements += \\\"r\\\" * (self.get_width() - 1)\\n self.update_puzzle(movements)\\n return movements\",\n \"def solve_interior_tile(self, target_row, target_col):\\n \\n assert target_row > 1, \\\"target_row cannot be in rows 0 or 1.\\\"\\n assert self.lower_row_invariant(target_row, target_col), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n correct_tile = self.current_position(target_row, target_col) \\n move_str = self.position_tile(target_row, target_col, correct_tile) \\n self.update_puzzle(move_str)\\n \\n assert self.lower_row_invariant(target_row, target_col - 1), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n return move_str\",\n \"def solve(self, board: List[List[str]]) -> None:\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if not board:\\n return\\n x=len(board)\\n y=len(board[0])\\n visit=[[False if board[i][j]=='X' else True for j in range(y)] for i in range(x)]\\n for i in range(x):\\n for j in range(y):\\n if visit[i][j] and board[i][j]=='O':\\n queue=[[i,j]]\\n visit[i][j]=False\\n k=0\\n surround=True\\n while len(queue)>k:\\n if queue[k][0]==0 or queue[k][0]==x-1 or queue[k][1]==y-1 or queue[k][1]==0:\\n surround=False\\n if queue[k][1]!=y-1 and visit[queue[k][0]][queue[k][1]+1]:\\n queue.append([queue[k][0],queue[k][1]+1])\\n visit[queue[k][0]][queue[k][1]+1]=False\\n if queue[k][1]!=0 and visit[queue[k][0]][queue[k][1]-1]:\\n queue.append([queue[k][0],queue[k][1]-1])\\n visit[queue[k][0]][queue[k][1]-1]=False\\n if queue[k][0]!=x-1 and visit[queue[k][0]+1][queue[k][1]]:\\n queue.append([queue[k][0]+1,queue[k][1]])\\n visit[queue[k][0]+1][queue[k][1]]=False\\n if queue[k][0]!=0 and visit[queue[k][0]-1][queue[k][1]]:\\n queue.append([queue[k][0]-1,queue[k][1]])\\n visit[queue[k][0]-1][queue[k][1]]=False\\n k+=1\\n if surround:\\n for i1,j1 in queue:\\n board[i1][j1]='X'\\n return\",\n \"def solve(self, board):\\r\\n if not board or not board[0]:\\r\\n return\\r\\n \\r\\n self.m = len(board)\\r\\n self.n = len(board[0])\\r\\n boarder = []\\r\\n \\r\\n # Collecting all the 'O' on the boarder\\r\\n for i in range(self.m):\\r\\n if board[i][0] == 'O':\\r\\n boarder.append([i, 0])\\r\\n if board[i][self.n-1] == 'O':\\r\\n boarder.append([i, self.n-1])\\r\\n for j in range(self.n):\\r\\n if board[0][j] == 'O':\\r\\n boarder.append([0, j])\\r\\n if board[self.m-1][j] == 'O':\\r\\n boarder.append([self.m-1, j])\\r\\n \\r\\n for row, col in boarder:\\r\\n self.BFS(board, row, col)\\r\\n \\r\\n for row in range(self.m):\\r\\n for col in range(self.n):\\r\\n if board[row][col] == 'O':\\r\\n board[row][col] = 'X'\\r\\n elif board[row][col] == 'E':\\r\\n board[row][col] = 'O'\\r\\n print(board)\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if board == [] or board == [[]]: # corner case\\n return\\n\\n r, c = len(board), len(board[0])\\n\\n def dfs(i, j): # visited i, j neighbors and change o to M\\n if i < 0 or i > r - 1 or j < 0 or j > c - 1 or board[i][j] == 'X' or board[i][j] == 'M':\\n return\\n\\n board[i][j] = 'M'\\n dfs(i - 1, j)\\n dfs(i + 1, j)\\n dfs(i, j - 1)\\n dfs(i, j + 1)\\n\\n for i in range(r):\\n for j in range(c):\\n if (i == 0 or i == r - 1 or j == 0 or j == c - 1) and board[i][j] == 'O':\\n dfs(i, j)\\n\\n for i in range(r):\\n for j in range(c):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n elif board[i][j] == 'M':\\n board[i][j] = 'O'\",\n \"def solve_row0_tile(self, target_col):\\r\\n # replace with your code\\r\\n assert self.row0_invariant(target_col), 'Some trouble in row0_invariant' \\r\\n whole_move = ''\\r\\n #current_position = self.current_position(0, target_col)\\r\\n current_row, current_col = self.current_position(0, target_col)\\r\\n # print self.get_number(current_row, current_col)\\r\\n zero_row, zero_col = 0, target_col\\r\\n # print 'Target tile position=',self.current_position(0, target_col)\\r\\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\\r\\n # print '0 position=', (0, target_col)\\r\\n # print '------------------------------------------'\\r\\n # print 'Recommended move ld'\\r\\n \\r\\n recomended_move = 'ld'\\r\\n whole_move += recomended_move\\r\\n zero_col -= len(filter(lambda x: x=='l', recomended_move))\\r\\n zero_row += len(filter(lambda x: x=='d', recomended_move))\\r\\n self.update_puzzle(recomended_move)\\r\\n # print 'Grid after move:', recomended_move\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(0, target_col)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (0, target_col)\\r\\n #####Case when we check if recomended move solves the tile\\r\\n if self.current_position(0, target_col) == (0, target_col):\\r\\n # print 'Congrads recomended move made great deal !!'\\r\\n return whole_move\\r\\n #####If not, we position TT to (1, target_col-1),\\r\\n ##### and ZEOR to (1, target_col-2)\\r\\n else:\\r\\n # print '------------------------------'\\r\\n # print 'After base move we are do not finde puzzle'\\r\\n # print 'Lets move zero towards TT'\\r\\n \\r\\n ######Moving zero tile to the target tile\\r\\n path_up = (zero_row - current_row) * 'u'\\r\\n path_side = (zero_col - current_col) * 'l'\\r\\n path_for_zero = path_up + path_side\\r\\n whole_move += path_for_zero\\r\\n zero_col -= len(filter(lambda x: x=='l', path_for_zero))\\r\\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\\r\\n self.update_puzzle(path_for_zero)\\r\\n \\r\\n # print 'grid after move', path_for_zero\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(0, target_col)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (0, target_col)\\r\\n counter = 0\\r\\n # print self.current_position(0, target_col) != (1, target_col-1)\\r\\n # print (zero_row,zero_col) != (1,target_col-2)\\r\\n ####POitioning TT and zero into positions that can be solvable\\r\\n while self.current_position(0, target_col) != (1, target_col-1) or \\\\\\r\\n (zero_row,zero_col) != (1,target_col-2):\\r\\n counter +=1\\r\\n #current_position = self.current_position(0, target_col)\\r\\n current_row, current_col = self.current_position(0, target_col)\\r\\n cyclic_moves = ''\\r\\n # print 'Aloha in the loop'\\r\\n if zero_col < current_col:\\r\\n # print 'ZERO tile located in the left side and down move IS NOT POSIBLE'\\r\\n \\r\\n\\r\\n if current_col != target_col-1 and zero_row == 0:\\r\\n # print 'In the upper row and we can use down cycling'\\r\\n cyclic_moves = 'drrul'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n\\r\\n elif current_col != target_col-1:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'urrdl'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n elif current_col == target_col-1:\\r\\n # print 'Target tile under target place'\\r\\n # print 'DEBUG!!!!'\\r\\n # print self\\r\\n # print zero_col, target_col\\r\\n if zero_col == 0 and current_col == 1:\\r\\n cyclic_moves = 'druld'\\r\\n elif zero_row == 0:\\r\\n cyclic_moves = 'druld'\\r\\n \\r\\n else:\\r\\n cyclic_moves = 'urd'\\r\\n whole_move += cyclic_moves\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n elif zero_row > current_row:\\r\\n # print 'DEBUG'\\r\\n # print 'TT under zero tile'\\r\\n cyclic_moves = 'uld'\\r\\n whole_move += cyclic_moves\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loop counter =',counter\\r\\n \\r\\n if counter > 10:\\r\\n # print 'COUNTER break'\\r\\n break\\r\\n\\r\\n #####Solving using pattern 2 x 3 puzzle\\r\\n # print '--------------------------'\\r\\n # print 'Lets solve 2x3 puzzle formed recently'\\r\\n move2x3 = \\\"urdlurrdluldrruld\\\"\\r\\n whole_move += move2x3\\r\\n zero_col -= len(filter(lambda x: x=='l', move2x3))\\r\\n zero_col += len(filter(lambda x: x=='r', move2x3))\\r\\n zero_row += len(filter(lambda x: x=='d', move2x3))\\r\\n zero_row -= len(filter(lambda x: x=='u', move2x3))\\r\\n self.update_puzzle(move2x3)\\r\\n # print self\\r\\n assert self.row1_invariant(target_col-1), 'Some trouble in row1_invariant' \\r\\n return whole_move\",\n \"def solve_row1_tile(self, target_col):\\r\\n # replace with your code\\r\\n whole_move = ''\\r\\n if self._grid[1][target_col] != 0:\\r\\n # print \\\"DEBUG CASE WHEN ZERO IN JOPA solve_row1_tile \\\"\\r\\n \\r\\n # print self\\r\\n # print 'Solwing tile', self._grid[1][target_col]\\r\\n \\r\\n # print 'Searchind indexes of ZERO'\\r\\n for row in self._grid:\\r\\n for col in row:\\r\\n if col == 0:\\r\\n zero_row, zero_col = self._grid.index(row), row.index(col)\\r\\n break\\r\\n # print 'ZERO indexes=', (zero_row, zero_col)\\r\\n #####Moving zero to correct place\\r\\n #path_down = (1 - zero_row) * 'd'\\r\\n # path_left = (zero_col - target_col) * 'l'\\r\\n if target_col - zero_col > 0:\\r\\n #path_right = (target_col - zero_col) * 'r'\\r\\n path_of_zero = (1 - zero_row) * 'd' + (target_col - zero_col) * 'r'\\r\\n else:\\r\\n path_of_zero = (1 - zero_row) * 'd'\\r\\n #zero_col -= len(filter(lambda x: x=='l', path_of_zero))\\r\\n #zero_row -= len(filter(lambda x: x=='u', path_of_zero))\\r\\n zero_col += len(filter(lambda x: x=='r', path_of_zero))\\r\\n zero_row += len(filter(lambda x: x=='d', path_of_zero))\\r\\n self.update_puzzle(path_of_zero)\\r\\n # print 'Grid after moving ZERO to target spot'\\r\\n # print self\\r\\n whole_move += path_of_zero\\r\\n\\r\\n assert self.row1_invariant(target_col), 'Some trouble in row1_invariant' \\r\\n \\r\\n #current_row, current_col = self.current_position(1, target_col)\\r\\n zero_row, zero_col = 1, target_col\\r\\n ######Moving zero tile to the target tile\\r\\n #path_up = (zero_row - current_row) * 'u'\\r\\n #path_side = (zero_col - current_col) * 'l'\\r\\n path_for_zero = (zero_row - self.current_position(1, target_col)[0]) * 'u' + (zero_col - self.current_position(1, target_col)[1]) * 'l'\\r\\n whole_move += path_for_zero\\r\\n zero_col -= len(filter(lambda x: x=='l', path_for_zero))\\r\\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\\r\\n self.update_puzzle(path_for_zero)\\r\\n # print 'grid after move', path_for_zero\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(1, target_col)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (1, target_col)\\r\\n counter = 0\\r\\n while self.current_position(1, target_col) != \\\\\\r\\n (1, target_col) or (zero_row, zero_col) != (0, target_col):\\r\\n # print 'Welcome to while loop!'\\r\\n cyclic_moves = ''\\r\\n #### Case 3 if ZERO located in the left side of the target tile\\r\\n ### like in the owel-test case\\r\\n #current_position = self.current_position(1, target_col)\\r\\n current_col = self.current_position(1, target_col)[1]\\r\\n counter +=1\\r\\n if self.current_position(1, target_col) == \\\\\\r\\n (1, target_col):\\r\\n # print 'ZERO not under TT'\\r\\n cyclic_moves = 'ur'\\r\\n whole_move += cyclic_moves\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif zero_col < current_col and self._grid[zero_row+1][zero_col] < \\\\\\r\\n self._grid[self.current_position(1, target_col)[0]][self.current_position(1, target_col)[1]]:\\r\\n # print 'ZERO tile located in the left side and down move is POSIBLE'\\r\\n if current_col != target_col:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'drrul'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n elif current_col == target_col:\\r\\n # print 'Target tile under target place'\\r\\n cyclic_moves = 'dru'\\r\\n whole_move += cyclic_moves\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif current_col != target_col and self._grid[zero_row+1][zero_col] > \\\\\\r\\n self._grid[self.current_position(1, target_col)[0]][self.current_position(1, target_col)[1]]:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'urrdl'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves)) \\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # elif zero_col < current_col and self._grid[zero_row+1][zero_col] > \\\\\\r\\n # self._grid[current_position[0]][current_position[1]]:\\r\\n # # print 'ZERO tile located in the left side and down move IS NOT POSIBLE'\\r\\n # if current_col != target_col:\\r\\n # # print 'not under the target place'\\r\\n # cyclic_moves = 'urrdl'\\r\\n # whole_move += cyclic_moves\\r\\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n # zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n # zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n # elif current_col == target_col:\\r\\n # # print 'Target tile under target place'\\r\\n # cyclic_moves = 'urd'\\r\\n # whole_move += cyclic_moves\\r\\n # zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n # zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n\\r\\n #cyclic_moves +='ur'\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loop counter =',counter\\r\\n if counter > 10:\\r\\n break\\r\\n return whole_move\",\n \"def solve(self, board: List[List[str]]) -> None:\\r\\n if not board:\\r\\n return\\r\\n m, n = len(board), len(board[0])\\r\\n board_filter = lambda p: 0 <= p[0] < m and 0 <= p[1] < n and board[p[0]][p[1]] == 'O'\\r\\n queue = list(filter(board_filter, [(x, y) for r in range(max(m, n)) \\r\\n for x, y in ((r, 0), (r, n-1), (0, r), (m-1, r))]))\\r\\n while queue:\\r\\n x, y = queue.pop()\\r\\n board[x][y] = 'W'\\r\\n queue.extend(list(filter(board_filter, ((x-1, y), (x+1, y), (x, y-1), (x, y+1)))))\\r\\n for row in board:\\r\\n for i, c in enumerate(row):\\r\\n row[i] = 'XO'[c=='W']\",\n \"def solve_col0_tile(self, target_row):\\r\\n moves_str = \\\"\\\"\\r\\n # move the zero tile from (i,0) to (i−1,1) \\r\\n # using the move string \\\"ur\\\"\\r\\n moves_str += \\\"ur\\\"\\r\\n temp_grid = Puzzle(self._height, self._width, self._grid)\\r\\n temp_grid.update_puzzle(moves_str)\\r\\n # If the target tile is now at position (i,0)\\r\\n # you can simply move tile zero to the end of row i−1\\r\\n current_row, current_col = temp_grid.current_position(target_row, 0)\\r\\n zero_row, zero_col = temp_grid.current_position(0, 0)\\r\\n if current_row == target_row and current_col == 0:\\r\\n rights = self._width - 1 - zero_col\\r\\n for dummy_r in range(rights):\\r\\n moves_str += \\\"r\\\" \\r\\n # However, if the target tile is not positioned at (i,0)\\r\\n # we suggest the following solution strategy:\\r\\n else:\\r\\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\\r\\n moves_str += \\\"ruldrdlurdluurddlu\\\"\\r\\n for dummy_r in range(self._width - 1):\\r\\n moves_str += \\\"r\\\"\\r\\n \\r\\n print \\\"solve_col0_tile\\\"\\r\\n print moves_str\\r\\n self.update_puzzle(moves_str)\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve_col0_tile(self, target_row):\\r\\n # check if curr_pos (i, 0) where i > 1\\r\\n assert self.lower_row_invariant(target_row, 0)\\r\\n move = \\\"ur\\\"\\r\\n self.update_puzzle(move)\\r\\n row, col = self.current_position(target_row, 0)\\r\\n if row == target_row and col == 0:\\r\\n move_to_target = (self.get_width() - 2) * \\\"r\\\"\\r\\n self.update_puzzle(move_to_target)\\r\\n move += move_to_target\\r\\n else:\\r\\n move_to_target = self.move_to_target(target_row - 1, 1, row, col)\\r\\n # add solver move to str\\r\\n move_to_target += \\\"ruldrdlurdluurddlu\\\" + (self.get_width() - 1) * \\\"r\\\"\\r\\n self.update_puzzle(move_to_target)\\r\\n move += move_to_target\\r\\n assert self.lower_row_invariant(target_row - 1, self.get_width() - 1)\\r\\n return move\",\n \"def solve_col0_tile(self, target_row):\\n move_str = 'ur'\\n self.update_puzzle(move_str)\\n cur_row, cur_col = self.current_position(target_row, 0)\\n if cur_row == target_row and cur_col == 0:\\n move_str += 'r' * (self._width - 2)\\n else:\\n move_str += self.position_tile(target_row-1, 1, cur_row, cur_col)\\n move_str += 'ruldrdlurdluurddlur'\\n move_str += 'r' * (self._width - 2)\\n self.update_puzzle(move_str[2:])\\n return move_str\",\n \"def solve(self, board: List[List[str]]) -> None:\\n\\n def expansion(i,j):\\n for di,dj in {(-1,0),(1,0),(0,-1),(0,1)}:\\n if -1<i+di<m and -1<j+dj<n and state[i+di][j+dj]=='O':\\n return True\\n return False\\n\\n if not board:\\n return board\\n\\n m = len(board)\\n n = len(board[0]) \\n state = [['X']*n for _ in range(m)]\\n\\n for j in range(n):\\n state[0][j] = board[0][j]\\n state[m-1][j] = board[m-1][j]\\n \\n for i in range(m):\\n state[i][0] = board[i][0]\\n state[i][n-1] = board[i][n-1]\\n \\n flag = 1\\n\\n while flag:\\n flag = 0\\n\\n for k in range(1, (1+min(m,n))//2):\\n for j in range(k,n-k):\\n if board[k][j]=='O' and state[k][j] == 'X' and expansion(k,j):\\n state[k][j] = 'O'\\n flag = 1\\n \\n if board[m-1-k][j]=='O' and state[m-1-k][j] == 'X' and expansion(m-1-k,j):\\n state[m-1-k][j] = 'O'\\n flag = 1\\n \\n for i in range(k,m-k):\\n if board[i][k]=='O' and state[i][k] == 'X' and expansion(i,k):\\n state[i][k] = 'O'\\n flag = 1\\n \\n if board[i][n-1-k]=='O' and state[i][n-1-k] == 'X' and expansion(i,n-1-k):\\n state[i][n-1-k] = 'O'\\n flag = 1\\n\\n board[:] = state[:]\",\n \"def solve_row0_tile(self, target_col):\\n solved_row, solved_col = self.current_position(0, target_col)\\n movements = \\\"\\\"\\n if solved_col == target_col - 1 and solved_row == 0:\\n movements = \\\"ld\\\"\\n else:\\n local_board = self.clone()\\n local_board.update_puzzle(\\\"ld\\\")\\n movements = \\\"ld\\\" + local_board.move_tile(\\n 1, target_col - 1, target_col) + \\\"urdlurrdluldrruld\\\"\\n self.update_puzzle(movements)\\n return movements\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) <= 2 or len(board[0]) <= 2:\\n return\\n\\n self.nRow, self.nCol = len(board), len(board[0])\\n\\n def helper(kr, kc):\\n board[kr][kc] = '.'\\n kr > 0 and board[kr - 1][kc] == 'O' and helper(kr - 1, kc)\\n kr < self.nRow - 1 and board[kr + 1][kc] == 'O' and helper(kr + 1, kc)\\n kc > 0 and board[kr][kc - 1] == 'O' and helper(kr, kc - 1)\\n kc < self.nCol - 1 and board[kr][kc + 1] == 'O' and helper(kr, kc + 1)\\n\\n for kr in [0, self.nRow - 1]:\\n for kc in range(self.nCol):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n for kc in [0, self.nCol - 1]:\\n for kr in range(self.nRow):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n\\n for kr in range(self.nRow):\\n for kc in range(self.nCol):\\n if board[kr][kc] == 'O':\\n board[kr][kc] = 'X'\\n elif board[kr][kc] == '.':\\n board[kr][kc] = 'O'\\n\\n return\",\n \"def solve_col0_tile(self, target_row):\\r\\n # print '----------------------------------'\\r\\n # print 'SOLVING ZERO COLOUMN'\\r\\n assert self.lower_row_invariant(target_row,0), \\\"Invarian is False\\\"\\r\\n whole_move = ''\\r\\n #current_position = self.current_position(target_row, 0)\\r\\n current_row, current_col = self.current_position(target_row, 0)\\r\\n zero_row, zero_col = target_row, 0\\r\\n # print 'Target tile position=',current_position\\r\\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\\r\\n # print '0 position=', (target_row, 0)\\r\\n # print '------------------------------------------'\\r\\n # print 'Recommended move ur'\\r\\n \\r\\n recomended_move = 'ur'\\r\\n whole_move += recomended_move\\r\\n zero_col += len(filter(lambda x: x=='r', recomended_move))\\r\\n zero_row -= len(filter(lambda x: x=='u', recomended_move))\\r\\n self.update_puzzle(recomended_move)\\r\\n # print 'Grid after move:', recomended_move\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, 0)\\r\\n current_position = self.current_position(target_row, 0)\\r\\n current_row, current_col = current_position\\r\\n if self.current_position(target_row, 0) == \\\\\\r\\n (target_row, 0):\\r\\n # print 'TT stays in correct place after recomended move'\\r\\n zero_to_place_move = 'r' * (self._width-1 - zero_col)\\r\\n self.update_puzzle(zero_to_place_move)\\r\\n whole_move += zero_to_place_move\\r\\n # print self\\r\\n assert self.lower_row_invariant(target_row-1,self._width-1), \\\"Invarian is False\\\"\\r\\n return whole_move\\r\\n #move ZERO tile to the right\\r\\n else:\\r\\n # print '------------------------------'\\r\\n # print 'After base move we are do not finde puzzle'\\r\\n # print 'Lets move zero towards TT'\\r\\n ### reposition TT to (target_row -1, 1)\\r\\n ### reposition ZERO tile to (target_row-1,0)\\r\\n \\r\\n ######Moving zero tile to the target tile\\r\\n #path_up = (zero_row - current_row) * 'u'\\r\\n #path_side = (current_col - zero_col) * 'r'\\r\\n path_for_zero = (zero_row - current_row) * 'u' + (current_col - zero_col) * 'r'\\r\\n whole_move += path_for_zero\\r\\n zero_col += len(filter(lambda x: x=='r', path_for_zero))\\r\\n zero_row -= len(filter(lambda x: x=='u', path_for_zero))\\r\\n self.update_puzzle(path_for_zero)\\r\\n # print 'grid after move', path_for_zero\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, 0)\\r\\n counter = 0\\r\\n while self.current_position(target_row, 0) != (target_row -1, 1) or \\\\\\r\\n (zero_row,zero_col) != (target_row-1,0):\\r\\n cyclic_moves = ''\\r\\n current_position = self.current_position(target_row, 0)\\r\\n current_row, current_col = current_position\\r\\n # print 'We are in while loop'\\r\\n counter += 1\\r\\n if zero_row < current_row:\\r\\n # print 'Zero row under current TT '\\r\\n if self.current_position(target_row, 0) == (target_row -1, 1):\\r\\n # print 'TT is in the reccomended posiont (target_row -1, 1) \\\\n and we are movind zero to the left side of TT '\\r\\n cyclic_moves = 'ld'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n else:\\r\\n # print 'TT should be one tile down'\\r\\n cyclic_moves = 'lddru'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n zero_row += len(filter(lambda x: x=='d', cyclic_moves))\\r\\n zero_row -= len(filter(lambda x: x=='u', cyclic_moves))\\r\\n #### Case 1 if ZERO located in the right of\\r\\n #### target tile (after it)\\r\\n if zero_col > current_col:\\r\\n # print ' Look in the up puzzle, zero in the right side'\\r\\n if current_col != 1:\\r\\n # print 'not under target place (target_row -1, 1)'\\r\\n cyclic_moves = 'dllur'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n whole_move += cyclic_moves\\r\\n # elif current_col == 1 and self._grid[zero_row+1][zero_col] < \\\\\\r\\n # self._grid[current_position[0]][current_position[1]]:\\r\\n elif current_col == 1: \\r\\n # print 'Tile tat is under ZERO is',self._grid[zero_row+1][zero_col] \\r\\n # print 'TT under target place'\\r\\n cyclic_moves = 'dlu'\\r\\n whole_move += cyclic_moves\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif self._grid[zero_row+1][zero_col] > \\\\\\r\\n self._grid[current_position[0]][current_position[1]]:\\r\\n print 'Tile under zero is illegal to move and we use upper cycle move '\\r\\n \\r\\n cyclic_moves = 'ul'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loop counter =',counter\\r\\n if counter > 10:\\r\\n break\\r\\n ### Solwing 3x2 puzzle\\r\\n # print '--------------------------'\\r\\n # print 'Lets solve 3x2 puzzle formed recently'\\r\\n move3x2 = 'ruldrdlurdluurddlur'\\r\\n whole_move += move3x2\\r\\n zero_col -= len(filter(lambda x: x=='l', move3x2))\\r\\n zero_col += len(filter(lambda x: x=='r', move3x2))\\r\\n zero_row += len(filter(lambda x: x=='d', move3x2))\\r\\n zero_row -= len(filter(lambda x: x=='u', move3x2))\\r\\n self.update_puzzle(move3x2)\\r\\n # print 'Grid afret 3x2 solver move'\\r\\n # print self\\r\\n # print 'Updated Target tile position=',self.current_position(target_row, 0)\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, 0)\\r\\n #####Moving ZERO to the (target_row - 1, n - 1) position where\\r\\n ##### 'n' is a grid height.\\r\\n # print self._width-1 - zero_col\\r\\n zero_to_place_move = 'r' * (self._width-1 - zero_col)\\r\\n whole_move += zero_to_place_move\\r\\n self.update_puzzle(zero_to_place_move)\\r\\n # print self\\r\\n assert self.lower_row_invariant(target_row-1,self._width-1), \\\"Invarian is False\\\"\\r\\n return whole_move\",\n \"def solve(self, board: 'List[List[str]]') -> 'None':\\n if not board:\\n return\\n m, n = len(board), len(board[0])\\n save = [ij for k in range(m + n) for ij in ((0, k), (m - 1, k), (k, 0), (k, n - 1))]\\n while save:\\n i, j = save.pop()\\n if -1 < i < m and -1 < j < n and board[i][j] == 'O':\\n board[i][j] = 'S'\\n save += (i + 1, j), (i - 1, j), (i, j + 1), (i, j - 1)\\n for row in board:\\n for i, c in enumerate(row):\\n row[i] = 'XO'[c == 'S']\",\n \"def solve_interior_tile(self, target_row, target_col):\\n # replace with your code\\n string = ''\\n target = self.current_position(target_row, target_col)\\n row_difference = target_row - target[0]\\n #print 'Row difference', row_difference\\n col_difference = target_col - target[1]\\n #print 'Col difference', col_difference\\n string += 'u' * row_difference\\n if col_difference > 0:\\n string += 'l' * col_difference\\n if row_difference == 0 and col_difference > 1:\\n string += 'urrdl' * (col_difference - 1)\\n if row_difference == 1:\\n string += 'urrdl' * (col_difference - 1)\\n string += 'dru'\\n if row_difference > 1:\\n string += 'drrul' * (col_difference - 1)\\n string += 'dru'\\n elif col_difference < 0:\\n col_difference = abs(col_difference)\\n string += 'r' * col_difference\\n if row_difference == 1:\\n string += 'ulldr' * (col_difference - 1)\\n string += 'ullddru'\\n if row_difference > 1:\\n string += 'dllur' * (col_difference - 1)\\n string += 'dlu'\\n string += 'lddru' * (row_difference - 1)\\n if row_difference > 0:\\n string += 'ld'\\n print 'Interior Path', string\\n self.update_puzzle(string)\\n assert self.lower_row_invariant(target_row, target_col - 1), 'False string'\\n return string\",\n \"def solve_row1_tile(self, target_col):\\n cur_row, cur_col = self.current_position(1, target_col)\\n move_str = self.position_tile(1, target_col, cur_row, cur_col, need_ld=False)\\n self.update_puzzle(move_str)\\n return move_str\",\n \"def solve_interior_tile(self, target_row, target_col):\\r\\n whole_move = ''\\r\\n # replace with your code\\r\\n if self._grid[target_row][target_col] != 0:\\r\\n # print \\\"DEBUG CASE WHEN ZERO IN JOPA\\\"\\r\\n \\r\\n # print self\\r\\n # print 'Solwing tile', self._grid[target_row][target_col]\\r\\n \\r\\n # print 'Searchind indexes of ZERO'\\r\\n for row in self._grid:\\r\\n for col in row:\\r\\n if col == 0:\\r\\n zero_row, zero_col = self._grid.index(row), row.index(col)\\r\\n break\\r\\n # print 'ZERO indexes=', (zero_row, zero_col)\\r\\n #####Moving zero to correct place\\r\\n #path_down = (target_row - zero_row) * 'd'\\r\\n #path_left = (zero_col - target_col) * 'l'\\r\\n if target_col - zero_col > 0:\\r\\n #path_right = (target_col - zero_col) * 'r'\\r\\n path_of_zero = (zero_col - target_col) * 'l' + (target_row - zero_row) * 'd' + (target_col - zero_col) * 'r'\\r\\n else:\\r\\n path_of_zero = (zero_col - target_col) * 'l' + (target_row - zero_row) * 'd'\\r\\n zero_col -= len(filter(lambda x: x=='l', path_of_zero))\\r\\n zero_col += len(filter(lambda x: x=='r', path_of_zero))\\r\\n zero_row += len(filter(lambda x: x=='d', path_of_zero))\\r\\n self.update_puzzle(path_of_zero)\\r\\n # print 'Grid after moving ZERO to target spot'\\r\\n # print self\\r\\n whole_move += path_of_zero\\r\\n assert self.lower_row_invariant(target_row, target_col), \\\"Invarian is False\\\"\\r\\n \\r\\n #current_position = self.current_position(target_row, target_col)\\r\\n #current_row, current_col = self.current_position(target_row, target_col)\\r\\n # print 'Target tile position=',current_position\\r\\n # print 'Target tile value=', self._grid[current_position[0]][current_position[1]]\\r\\n # print '0 position=', (target_row, target_col)\\r\\n \\r\\n ######Moving zero tile to the target tile\\r\\n path_up = (target_row - self.current_position(target_row, target_col)[0]) * 'u'\\r\\n zero_row = target_row - len(path_up)\\r\\n if target_col < self.current_position(target_row, target_col)[1]: # Right move\\r\\n path_side = (self.current_position(target_row, target_col)[1] - target_col) * 'r'\\r\\n zero_col = target_col + len(path_side)\\r\\n else: # Left move\\r\\n path_side = (target_col - self.current_position(target_row, target_col)[1]) * 'l'\\r\\n zero_col = target_col - len(path_side)\\r\\n \\r\\n #path_for_zero = path_up + path_side\\r\\n # print '------------------------------------------'\\r\\n # print 'Move to ZERO =', path_for_zero\\r\\n \\r\\n self.update_puzzle(path_up + path_side)\\r\\n \\r\\n # print 'Grid after move:'\\r\\n # print self\\r\\n # current_position = self.current_position(target_row, target_col) \\r\\n # current_row, current_col = current_position\\r\\n # print 'Updated Target tile position=',current_position\\r\\n # print 'Updated 0 position=', (zero_row, zero_col)\\r\\n # print 'Target position =', (target_row, target_col)\\r\\n # print '-----------------------------------------'\\r\\n \\r\\n\\r\\n ###### New moves after moving ZERO tile into target tile\\r\\n # counter = 0\\r\\n whole_move += path_up + path_side\\r\\n while self.current_position(target_row, target_col) != \\\\\\r\\n (target_row, target_col) or zero_col != target_col - 1:\\r\\n # tt_in_home = self.current_position(target_row, target_col) == \\\\\\r\\n # (target_row, target_col)\\r\\n\\r\\n cyclic_moves = ''\\r\\n # counter += 1\\r\\n #current_position = self.current_position(target_row, target_col) \\r\\n #current_col = self.current_position(target_row, target_col)[1]\\r\\n # print 'Zero coloumn', zero_col, '== Target coloumn', target_col\\r\\n # print zero_col == target_col \\r\\n \\r\\n #### Case 1 if ZERO located in ther right of\\r\\n #### target tile (after it)\\r\\n if zero_col > self.current_position(target_row, target_col)[1]:\\r\\n # print ' Look in the up puzzle, zero on the right side'\\r\\n # if self.current_position(target_row, target_col)[1] != target_col:\\r\\n # # print 'not under target place'\\r\\n # cyclic_moves = 'dllur'\\r\\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n if self.current_position(target_row, target_col)[1] == target_col and self._grid[zero_row+1][zero_col] < \\\\\\r\\n self._grid[self.current_position(target_row, target_col)[0]][self.current_position(target_row, target_col)[1]]:\\r\\n # print 'Tile tat is under ZERO is',self._grid[zero_row+1][zero_col] \\r\\n # print 'TT under target place'\\r\\n cyclic_moves = 'dlu'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n #zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n # elif self._grid[zero_row+1][zero_col] > \\\\\\r\\n # self._grid[self.current_position(target_row, target_col)[0]][self.current_position(target_row, target_col)[1]]:\\r\\n # # print 'Tile under zero is illegal to move and we use upper cycle move '\\r\\n \\r\\n # cyclic_moves = 'ul'\\r\\n # zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n # zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n\\r\\n #### Case 2 if ZERO located under the target tile, and both\\r\\n #### of them located under the target position of the target tile\\r\\n elif zero_col == self.current_position(target_row, target_col)[1] and zero_col == target_col:\\r\\n # print 'Both under the target place'\\r\\n # print 'TT in home=', tt_in_home\\r\\n if self.current_position(target_row, target_col) == \\\\\\r\\n (target_row, target_col):\\r\\n cyclic_moves = 'ld'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n #zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n \\r\\n else:\\r\\n cyclic_moves = 'lddru'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n \\r\\n #### Case 3 if ZERO located in the left side of the target tile\\r\\n ### like in the owel-test case\\r\\n elif zero_col < self.current_position(target_row, target_col)[1]:\\r\\n # print 'ZERO tile located in the left side'\\r\\n if self.current_position(target_row, target_col)[1] != target_col:\\r\\n # print 'not under the target place'\\r\\n cyclic_moves = 'drrul'\\r\\n zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n elif self.current_position(target_row, target_col)[1] == target_col:\\r\\n # print 'Target tile under target place'\\r\\n cyclic_moves = 'dru'\\r\\n #zero_col -= len(filter(lambda x: x=='l', cyclic_moves))\\r\\n zero_col += len(filter(lambda x: x=='r', cyclic_moves))\\r\\n\\r\\n\\r\\n # print 'Puzzle after Maded move:', cyclic_moves\\r\\n self.update_puzzle(cyclic_moves)\\r\\n # print 'Zero at home=', 'Zero col', zero_col, '== Target col - 1 is', target_col - 1\\r\\n # print self\\r\\n # print 'Loot counter =',counter\\r\\n whole_move += cyclic_moves\\r\\n # if counter > 12:\\r\\n # break\\r\\n # print 'Tile is solved with move '+ whole_move\\r\\n assert self.lower_row_invariant(target_row, target_col-1), \\\"Invarian is False\\\"\\r\\n return whole_move\",\n \"def solve_col0_tile(self, target_row):\\n # replace with your code\\n string = ''\\n target = self.current_position(target_row, 0)\\n row_difference = target_row - target[0]\\n col_difference = target[1]\\n string += 'u' * row_difference\\n if col_difference > 0:\\n string += 'r' * (col_difference - 1)\\n if row_difference > 1:\\n string += 'druld' * (row_difference - 1)\\n string += 'rulld' * (col_difference - 1)\\n string += 'ruldrdlurdluurddlu'\\n elif col_difference == 0:\\n string += 'rddlu' * (row_difference - 2)\\n if row_difference > 1:\\n string += 'rd'\\n string += 'l'\\n string += 'ruldrdlurdluurddlu'\\n string += 'r' * (self._width - 1)\\n print 'Col 0 Path', string\\n self.update_puzzle(string)\\n assert self.lower_row_invariant(target_row - 1, self._width -1), 'False string'\\n return string\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) <= 2 or len(board[0]) <= 2:\\n return\\n\\n nRow, nCol = len(board), len(board[0])\\n\\n def helper(kr, kc):\\n board[kr][kc] = '.'\\n kr > 0 and board[kr - 1][kc] == 'O' and helper(kr - 1, kc)\\n kr < nRow - 1 and board[kr + 1][kc] == 'O' and helper(kr + 1, kc)\\n kc > 0 and board[kr][kc - 1] == 'O' and helper(kr, kc - 1)\\n kc < nCol - 1 and board[kr][kc + 1] == 'O' and helper(kr, kc + 1)\\n\\n for kr in [0, nRow - 1]:\\n for kc in range(nCol):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n for kc in [0, nCol - 1]:\\n for kr in range(nRow):\\n if board[kr][kc] == 'O':\\n helper(kr, kc)\\n\\n for kr in range(nRow):\\n for kc in range(nCol):\\n if board[kr][kc] == 'O':\\n board[kr][kc] = 'X'\\n elif board[kr][kc] == '.':\\n board[kr][kc] = 'O'\\n\\n return\",\n \"def solve(self, board) -> None:\\n x_length = len(board)\\n if x_length == 0: \\n return\\n\\n y_length = len(board[0])\\n confirmed = set()\\n dfs = []\\n for i in range(x_length):\\n if board[i][0] == 'O':\\n board[i][0] = 'temp'\\n dfs.append((i, 0))\\n if board[i][y_length - 1] == 'O':\\n board[i][y_length - 1] = 'temp'\\n dfs.append((i, y_length - 1))\\n for j in range(y_length):\\n if board[0][j] == 'O':\\n board[0][j] = 'temp'\\n dfs.append((0, j))\\n if board[x_length - 1][j] == 'O':\\n board[x_length - 1][j] = 'temp'\\n dfs.append((x_length - 1, j))\\n while dfs:\\n i, j = dfs.pop()\\n confirmed.add((i, j))\\n if i+1 < x_length and board[i+1][j] == 'O':\\n board[i+1][j] = 'temp'\\n dfs.append((i + 1, j))\\n if i > 0 and board[i-1][j] == 'O':\\n board[i-1][j] = 'temp'\\n dfs.append((i-1, j))\\n if j+1 < y_length and board[i][j+1] == 'O':\\n board[i][j+1] = 'temp'\\n dfs.append((i, j + 1))\\n if j > 0 and board[i][j-1] == 'O':\\n board[i][j-1] = 'temp'\\n dfs.append((i, j-1))\\n for i in range(x_length):\\n for j in range(y_length):\\n if (i, j) in confirmed:\\n board[i][j] = 'O'\\n else:\\n board[i][j] = 'X'\\n return\",\n \"def solve(board):\\r\\n \\r\\n #An O(2mn) time solution; the first O(mn) traversal is to preform a bfs on all tiles attached to edge 'O' tiles (can't convert to 'X's); the second is to convert all remaining 'O's into 'X's\\r\\n \\r\\n def bfs(curr,r,c):\\r\\n if not curr: return\\r\\n prev = len(curr)\\r\\n for n in range(0,prev):\\r\\n i,j = curr[n][0],curr[n][1]\\r\\n board[i][j] = 'A'\\r\\n for x,y in [(-1, 0), (0, -1), (1, 0), (0, 1)]:\\r\\n x_n = i+x\\r\\n y_n = j+y\\r\\n if x_n >= 0 and x_n < r and y_n >= 0 and y_n < c and board[x_n][y_n] == \\\"O\\\":\\r\\n curr += [(x_n,y_n)]\\r\\n bfs(curr[prev:],r,c)\\r\\n\\r\\n \\r\\n q,r,c = [],len(board),len(board[0])\\r\\n if not r or q: return\\r\\n\\r\\n for i in range(r):\\r\\n for j in range(c):\\r\\n if (i==0 or j==0 or i==r-1 or j==c-1) and board[i][j] == \\\"O\\\":\\r\\n q += [(i,j)]\\r\\n \\r\\n bfs(q,r,c)\\r\\n\\r\\n for i in range(r):\\r\\n for j in range(c): \\r\\n if board[i][j] == \\\"O\\\": \\r\\n board[i][j] = \\\"X\\\"\\r\\n elif board[i][j] == \\\"A\\\":\\r\\n board[i][j] = \\\"O\\\"\\r\\n \\r\\n return\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if board is None or len(board) == 0:\\n return \\n row, col = len(board), len(board[0])\\n for i in range(row):\\n self.dfs(board, i, 0)\\n self.dfs(board, i, col - 1)\\n for j in range(col):\\n self.dfs(board, 0, j)\\n self.dfs(board, row-1, j)\\n for i in range(row):\\n for j in range(col):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n if board[i][j] == '-':\\n board[i][j] = 'O'\",\n \"def solve_row0_tile(self, target_col):\\n move_str = 'ld'\\n self.update_puzzle(move_str)\\n cur_row, cur_col = self.current_position(0, target_col)\\n if cur_row == 0 and cur_col == target_col:\\n return move_str\\n else:\\n move_str += self.position_tile(1, target_col-1, cur_row, cur_col)\\n move_str += 'urdlurrdluldrruld'\\n self.update_puzzle(move_str[2:])\\n return move_str\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) == 0: return\\n\\n m = len(board)\\n n = len(board[0])\\n\\n uf = UnionFind(m * n + 1)\\n dummy = m * n\\n\\n # connect 'O' at first and last col with dummy\\n for i in range(m):\\n if board[i][0] == 'O':\\n uf.union(dummy, i * n)\\n if board[i][-1] == 'O':\\n uf.union(dummy, i * n + n - 1)\\n\\n # connect 'O' at first and last row with dummy\\n for j in range(n):\\n if board[0][j] == 'O':\\n uf.union(dummy, j)\\n if board[-1][j] == 'O':\\n uf.union(dummy, n * (m-1) + j)\\n\\n d = [(1, 0), (0, 1), (0, -1), (-1, 0)]\\n\\n for i in range(1, m-1):\\n for j in range(1, n-1):\\n if board[i][j] == 'O':\\n for di, dj in d:\\n x = i+di\\n y = j+dj\\n if board[x][y] == 'O':\\n uf.union(x*n+y, i*n+j)\\n\\n # change not connected 'O' with 'X'\\n for i in range(1, m-1):\\n for j in range(1, n-1):\\n if not uf.connected(dummy, i * n + j):\\n board[i][j] = 'X'\",\n \"def position_tile(self, zero_row, zero_col, correct_tile):\\n \\n ans = \\\"\\\" \\n vert_dist = abs(zero_row - correct_tile[0])\\n horiz_dist = abs(zero_col - correct_tile[1])\\n \\n # Updates ans, the move string, based the correct_tile's\\n # position relative to the target position.\\n \\n # SAME ROW\\n if vert_dist == 0:\\n # Left of target\\n if zero_col > correct_tile[1]:\\n # Moves zero tile left to correct_tile's position.\\n ans += str(\\\"l\\\" * horiz_dist)\\n # Moves correct_tile right to target position,\\n # and moves zero tile to left of target position.\\n if horiz_dist > 1:\\n ans += str(\\\"urrdl\\\" * (horiz_dist - 1))\\n # Right of target\\n else:\\n # Moves zero tile right to correct_tile's position.\\n ans += str(\\\"r\\\" * horiz_dist)\\n # Moves correct_tile left to target position,\\n # and moves zero tile to left of target position.\\n ans += str(\\\"ulldr\\\" * (horiz_dist - 1))\\n ans += str(\\\"ulld\\\")\\n \\n # SAME COL\\n elif horiz_dist == 0:\\n # Moves zero tile up to correct_tile's position.\\n ans += str(\\\"u\\\" * vert_dist)\\n # Moves correct_tile down to target position, \\n # and moves zero tile to left of target position.\\n if vert_dist > 1:\\n ans += str(\\\"lddru\\\" * (vert_dist - 1))\\n ans += str(\\\"ld\\\")\\n \\n # UPPER LEFT\\n elif correct_tile[1] < zero_col:\\n # Moves zero tile up and left to correct_tile's position.\\n ans += str(\\\"u\\\" * vert_dist)\\n ans += str(\\\"l\\\" * horiz_dist)\\n # Moves correct_tile right and down to target position,\\n # and moves zero tile to left of target position.\\n ans += str(\\\"drrul\\\" * (horiz_dist - 1))\\n ans += str(\\\"druld\\\" * vert_dist)\\n\\n # UPPER RIGHT\\n else:\\n # Moves zero tile up and right to correct_tile's position.\\n ans += str(\\\"u\\\" * vert_dist)\\n ans += str(\\\"r\\\" * horiz_dist)\\n # This if-elif-else statement moves correct_tile left and down to target position.\\n # If statement is only used when target position is in row 2.\\n if vert_dist == 1 and correct_tile[0] == 0:\\n ans += str(\\\"dllur\\\" * (horiz_dist - 1))\\n ans += str(\\\"dluld\\\")\\n # Elif statement used when correct_tile is in the row above target position.\\n elif vert_dist == 1: \\n ans += str(\\\"ulldr\\\" * (horiz_dist - 1))\\n ans += str(\\\"ullddruld\\\")\\n # Else statement used when correct_tile is 1+ rows above target position.\\n else:\\n ans += str(\\\"dllur\\\" * (horiz_dist - 1))\\n ans += str(\\\"dlu\\\")\\n ans += str(\\\"lddru\\\" * (vert_dist - 1))\\n ans += str(\\\"ld\\\")\\n \\n return ans\",\n \"def half_turn_solve(db, position):\\n print('- Table Lookup: ', end='')\\n solve_sequence = half_turn_lookup.lookup_position(db, position)\\n temp_cube = Cube(position)\\n for move in solve_sequence:\\n dyn_move(temp_cube, move)\\n print(move.name, end=' ')\\n print()\\n return solve_sequence\",\n \"def solve_puzzle(self):\\n moves = self.moves\\n peg_pos = self.peg_pos\\n move_z = self.move_to_height\\n \\n print('Solving Tower of Hanoi:')\\n for i, move in enumerate(moves):\\n des_peg = move[0]\\n des_peg_pos = peg_pos[des_peg]\\n \\n #move to peg\\n print(' Moving to peg: '+str(des_peg)+' at: '+str(des_peg_pos))\\n self.move_to(des_peg_pos[0], des_peg_pos[1], move_z)\\n \\n #if index is even, pickup disk, else drop disk\\n if i % 2 == 0:\\n print(' Picking up disk at height: '+str(move[1]))\\n self.pick(move[1])\\n else:\\n print(' Dropping disk')\\n self.drop()\\n print('Finished solving puzzle')\",\n \"def solve(board) -> None:\\n rows = len(board)\\n if rows==0:\\n return board\\n cols = len(board[0])\\n \\n def is_border(rc):\\n (rr, cc) =rc\\n if rr<rows and rr< cols and rr>=0 and cc>=0 and board[rr][cc]=='O' and (rr==0 or rr==rows-1 or cc==0 or cc==cols-1):\\n return True\\n return False\\n \\n transf = []\\n for r in range(rows):\\n for c in range(cols):\\n if board[r][c]=='O' and not is_border((r,c)) and not any(map(is_border, [(r-1, c), (r+1, c), (r, c-1), (r, c+1)])):\\n transf.append((r,c))\\n if transf:\\n for r,c in transf:\\n board[r][c]='X'\\n return board\",\n \"def solve(self, board: List[List[str]]) -> None:\\n def dfs(board, i, j):\\n m = len(board)\\n n = len(board[0])\\n if i < 0 or i >= m or j < 0 or j >= n: return\\n\\n if board[i][j] != 'O': return\\n\\n board[i][j] = '#'\\n [dfs(board, i+di, j+dj) for di, dj in [(0, 1), (1, 0), (0, -1), (-1, 0)]]\\n\\n if len(board) == 0: return\\n m = len(board)\\n n = len(board[0])\\n\\n for i in range(m):\\n dfs(board, i, 0)\\n dfs(board, i, n-1)\\n\\n for j in range(n):\\n dfs(board, 0, j)\\n dfs(board, m-1, j)\\n\\n for i in range(m):\\n for j in range(n):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n\\n for i in range(m):\\n for j in range(n):\\n if board[i][j] == '#':\\n board[i][j] = 'O'\",\n \"def solve_tour(self):\\n\\t\\tboard = [[-1 for _ in range(self.N)]for _ in range(self.N)]\\n\\t\\tboard[0][0] = 0\\n\\n\\t\\tz = self.find_tour(board, 0, 0, 1)\\n\\t\\tif z:\\n\\t\\t\\tfor i in range(self.N):\\n\\t\\t\\t\\tfor j in range(self.N):\\n\\t\\t\\t\\t\\tself.solution.append(board[i][j])\\n\\t\\t\\tprint board\\n\\t\\t\\treturn self.solution\\n\\t\\t\\t\\t\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"No solution\\\")\",\n \"def solve(self, board) -> None:\\n coords = []\\n board_len = len(board)\\n row_len = len(board[0]) - 1\\n # top\\n # coords.append([[0, i] for i, q in enumerate(board[0]) if q == \\\"O\\\"])\\n # # bottom\\n # coords.append(\\n # [[board_len, i] for i, q in enumerate(board[board_len]) if q == \\\"O\\\"]\\n # )\\n for i in range(board_len):\\n row_coord = [[i,indx] for indx, q in enumerate(board[i]) if q == \\\"O\\\"]\\n # import pdb; pdb.set_trace()\\n for x in row_coord:\\n coords.append(x)\\n for x in coords:\\n if len(x) == 0:\\n continue\\n if x[0] == 0:\\n print(\\\"top border\\\")\\n elif x[0] == board_len - 1:\\n print(\\\"bottom border\\\")\\n elif x[1] == 0:\\n print(\\\"left border\\\")\\n elif x[1] == row_len:\\n prin(\\\"right border\\\")\",\n \"def solve_row1_tile(self, target_col):\\n assert target_col > 1, \\\"target_col must be > 1\\\"\\n assert self.row1_invariant(target_col), \\\"tiles to right and below incorrectly ordered\\\"\\n\\n # Moves correct_tile to the target position (1, target_col),\\n # and the zero tile above the target position at (0, target_col). \\n correct_tile = self.current_position(1, target_col)\\n move_str = self.position_tile(1, target_col, correct_tile) \\n move_str += \\\"ur\\\"\\n self.update_puzzle(move_str)\\n\\n assert self.row0_invariant(target_col)\\n \\n return move_str\",\n \"def solve_puzzle(board):\\n # Propagate value effects\\n board = simplify_puzzle(board, [])\\n\\n # Brute force remaining cells\\n board = brute(board)\\n\\n # Verify that the puzzle was successfully solved\\n assert get_length(board)==81\\n assert valid_attempt(board)\\n\\n return board\",\n \"def solve(self, board) -> None:\\n for index in range (1, len(board)-1):\\n arr = board[index]\\n for ch in range(1, len(arr)-1):\\n if arr[ch] is 'O':\\n safe = True\\n if ch-1 == 0 and arr[ch-1] is 'O':\\n safe = False\\n if ch +1 == len(arr)-1 and arr[ch+1] is 'O':\\n safe = False\\n if index -1 == 0 and board[index-1][ch] is 'O':\\n safe = False\\n if index + 1 == len(board)-1 and board[index + 1][ch] is 'O':\\n safe = False\\n if safe:\\n arr[ch] = 'X'\",\n \"def solve_row1_tile(self, target_col):\\r\\n row, col = self.current_position(1, target_col)\\r\\n move = self.move_to_target(1, target_col, row, col)\\r\\n # for next move\\r\\n move += \\\"ur\\\"\\r\\n \\r\\n self.update_puzzle(move)\\r\\n return move\",\n \"def solve(self):\\n # If board is filled, board is trivially solved\\n if self.check_full_board():\\n return self.done\\n\\n # Iterate over every square in the board\\n for row in range(self.num_rows):\\n for col in range(self.num_columns):\\n\\n # If square is empty, begin plugging in possible values\\n if self.check_empty_space(row, col):\\n for val in range(1, 10):\\n if not self.check_row(val, row) and \\\\\\n not self.check_column(val, col) and \\\\\\n not self.check_box(val, self.what_box(row, col)):\\n self.board[row][col] = val\\n \\n if self.solve():\\n return self.done()\\n \\n # Didn't work; undo assigment\\n self.board[row][col] = ' '\\n\\n # Bad path; backtrack\\n return False\",\n \"def solve(self, board: 'List[List[str]]') -> 'None':\\n\\n def dfs(i, j, tmp):\\n nonlocal flag\\n if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]):\\n flag = False\\n return\\n if board[i][j] != 'O' or [i, j] in tmp:\\n return\\n tmp.append([i, j])\\n dfs(i - 1, j, tmp)\\n dfs(i + 1, j, tmp)\\n dfs(i, j + 1, tmp)\\n dfs(i, j - 1, tmp)\\n return tmp\\n\\n change = []\\n for i in range(len(board)):\\n for j in range(len(board[0])):\\n if board[i][j] == 'O' and [i, j] not in change:\\n tmp = []\\n flag = True\\n tmp = dfs(i, j, tmp[:])\\n if flag:\\n for loc in tmp:\\n i, j = loc[0], loc[1]\\n board[i][j] = 'X'\\n\\n for loc in change:\\n i, j = loc[0], loc[1]\\n board[i][j] = 'X'\",\n \"def find_best_move(board):\\n new_board = board.get_board()\\n\\n # X | X | X <-- Check for win on this row\\n # ---------\\n # 3 | 4 | 5\\n # ---------\\n # 6 | 7 | 9\\n if new_board[0] == new_board[1] and new_board[2] == \\\"2\\\":\\n return 2\\n elif new_board[0] == new_board[2] and new_board[1] == \\\"1\\\":\\n return 1\\n elif new_board[1] == new_board[2] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | 2\\n # ---------\\n # X | X | X <-- Check for win on this row\\n # ---------\\n # 6 | 7 | 9\\n elif new_board[3] == new_board[4] and new_board[5] == \\\"5\\\":\\n return 5\\n elif new_board[3] == new_board[5] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[4] == new_board[5] and new_board[3] == \\\"3\\\":\\n return 3\\n\\n # 0 | 1 | 2\\n # ---------\\n # 3 | 4 | 5\\n # ---------\\n # X | X | X <-- Check for win on this row\\n elif new_board[6] == new_board[7] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[6] == new_board[8] and new_board[7] == \\\"7\\\":\\n return 7\\n elif new_board[7] == new_board[8] and new_board[6] == \\\"6\\\":\\n return 6\\n\\n # X | 1 | 2 Check for win on column one\\n # ---------\\n # X | 4 | 5\\n # ---------\\n # X | 7 | 9\\n elif new_board[0] == new_board[3] and new_board[6] == \\\"6\\\":\\n return 6\\n elif new_board[0] == new_board[6] and new_board[3] == \\\"3\\\":\\n return 3\\n elif new_board[6] == new_board[3] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | X | 2 Checks for win on column two\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # 6 | X | 9\\n elif new_board[1] == new_board[4] and new_board[7] == \\\"7\\\":\\n return 7\\n elif new_board[1] == new_board[7] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[7] == new_board[4] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | X\\n # ---------\\n # 3 | 4 | X\\n # ---------\\n # 6 | 7 | X\\n elif new_board[2] == new_board[5] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[2] == new_board[8] and new_board[5] == \\\"5\\\":\\n return 5\\n elif new_board[8] == new_board[5] and new_board[2] == \\\"2\\\":\\n return 2\\n\\n # X | 1 | 2\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # 6 | 7 | X\\n elif new_board[0] == new_board[4] and new_board[8] == \\\"8\\\":\\n return 8\\n elif new_board[0] == new_board[8] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[8] == new_board[4] and new_board[0] == \\\"0\\\":\\n return 0\\n\\n # 0 | 1 | X\\n # ---------\\n # 3 | X | 5\\n # ---------\\n # X | 7 | 9\\n elif new_board[2] == new_board[4] and new_board[6] == \\\"6\\\":\\n return 6\\n elif new_board[2] == new_board[6] and new_board[4] == \\\"4\\\":\\n return 4\\n elif new_board[6] == new_board[4] and new_board[2] == \\\"2\\\":\\n return 2\\n\\n # If corners are empty, play there\\n elif new_board[0] == \\\"0\\\" or new_board[2] == \\\"2\\\" or new_board[6] == \\\"6\\\" or new_board[8] == \\\"8\\\":\\n try_spot = 0\\n while True:\\n if new_board[try_spot] != \\\"X\\\" and new_board[try_spot] != \\\"O\\\":\\n return try_spot\\n else:\\n try_spot = try_spot + 2\\n\\n # If middle is empty, play there\\n elif new_board[4] == \\\"4\\\":\\n return 4\\n\\n # Finally if edges are empty try there\\n elif new_board[1] == \\\"1\\\" or new_board[3] == \\\"3\\\" or new_board[5] == \\\"5\\\" or new_board[7] == \\\"7\\\":\\n try_spot = 1\\n while True:\\n if new_board[try_spot] != \\\"X\\\" and new_board[try_spot] != \\\"O\\\":\\n return try_spot\\n else:\\n try_spot = try_spot + 2\",\n \"def solve(self, board):\\n def dfs(board, r, c):\\n if r < 0 or c < 0 or r > rows - 1 or c > cols - 1 or board[r][c] == 'X' or board[r][c] == '#':\\n return\\n board[r][c] = '#'\\n dfs(board, r - 1, c)\\n dfs(board, r + 1, c)\\n dfs(board, r, c - 1)\\n dfs(board, r, c + 1)\\n\\n if len(board) == 0:\\n return;\\n rows = len(board)\\n cols = len(board[0])\\n for i in range(rows):\\n for j in range(cols):\\n if (i == 0 or j == 0 or i == rows - 1 or j == cols - 1) and board[i][j] == 'O':\\n dfs(board, i, j)\\n for i in range(rows):\\n for j in range(cols):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n elif board[i][j] == '#':\\n board[i][j] = 'O'\",\n \"def solve_row0_tile(self, target_col):\\n # replace with your code\\n string = ''\\n assert self.row0_invariant(target_col), 'False precondition'\\n target = self.current_position(0, target_col)\\n row_difference = target[0]\\n col_difference = target_col - target[1]\\n if row_difference == 0:\\n if col_difference == 1:\\n string += 'ld'\\n elif col_difference > 1:\\n string += 'l' * col_difference\\n string += 'drrul' * (col_difference - 2)\\n string += 'druld'\\n string += 'urdlurrdluldrruld'\\n elif row_difference == 1:\\n if col_difference == 1:\\n string += 'lld'\\n string += 'urdlurrdluldrruld'\\n elif col_difference > 1:\\n string += 'ld'\\n string += 'l' * (col_difference - 1)\\n string += 'urrdl' * (col_difference - 2)\\n string += 'urdlurrdluldrruld'\\n print 'Row 0 Path', string\\n self.update_puzzle(string)\\n assert self.row1_invariant(target_col - 1), 'False string'\\n return string\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) == 0:\\n return\\n self.h = len(board)\\n self.w = len(board[0])\\n self.board = board\\n for i in range(self.h):\\n for j in range(self.w):\\n if i == 0 or i == self.h-1 or j == 0 or j == self.w-1:\\n #print (i,j)\\n self.dfs((i,j))\\n for i in range(self.h):\\n for j in range(self.w):\\n if self.board[i][j]==\\\"O\\\":\\n self.board[i][j]=\\\"X\\\"\\n for i in range(self.h):\\n for j in range(self.w):\\n if self.board[i][j]==\\\"#\\\":\\n self.board[i][j]=\\\"O\\\"\",\n \"def solve(self, board: List[List[str]]) -> None:\\n def _dfs(i, j):\\n if i < 0 or j < 0 or i >= len(board) or j >= len(board[0]) or board[i][j] in ['X', '#']: return\\n board[i][j] = '#'\\n _dfs(i-1, j)\\n _dfs(i+1, j)\\n _dfs(i, j-1)\\n _dfs(i, j+1)\\n\\n if not board or not board[0]: return\\n m, n = len(board), len(board[0])\\n for i in range(0, m):\\n for j in range(0, n):\\n is_edge = i == 0 or j == 0 or i == m-1 or j == n-1\\n if is_edge and board[i][j] == 'O':\\n _dfs(i, j)\\n print(board)\\n\\n for i in range(0, m):\\n for j in range(0, n):\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n if board[i][j] == '#':\\n board[i][j] = 'O'\",\n \"def solve(self):\\n while self.character.path[-1] != 88:\\n n = self.next_move()\\n if n is None:\\n self.character.path += ['Error: Could not find full path (budget does not suffice or unreachable).']\\n break\\n self.character.path += [n]\\n self.updated_occupied_locations()\\n self.currentTurn += 1\",\n \"def solve(self, board: List[List[str]]) -> None:\\n def DFS(board, i, j):\\n q = []\\n q.append([i, j])\\n \\n while q:\\n x, y = q.pop()\\n board[x][y] = \\\"*\\\"\\n neighbors = ((0, 1), (0, -1), (1, 0), (-1, 0))\\n for dx, dy in neighbors:\\n if 0 <= x + dx <= len(board) - 1 and 0 <= y + dy <= len(board[0]) - 1 and board[x + dx][y + dy] == \\\"O\\\":\\n q.append([x + dx, y + dy])\\n \\n \\n # first row\\n i = 0\\n for j in range(len(board[0])):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n # last row\\n i = len(board) - 1\\n for j in range(len(board[0])):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n # first column\\n j = 0\\n for i in range(len(board)):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n # last column\\n j = len(board[0]) - 1\\n for i in range(len(board)):\\n if board[i][j] == \\\"O\\\":\\n DFS(board, i, j)\\n \\n \\n for i in range(len(board)):\\n for j in range(len(board[0])):\\n if board[i][j] == \\\"O\\\":\\n board[i][j] = \\\"X\\\"\\n elif board[i][j] == \\\"*\\\":\\n board[i][j] = \\\"O\\\"\",\n \"def solve (M, cpos, move): \\n if move == 64:\\n print (\\\"\\\\n\\\\nmove: \\\", move)\\n print (\\\"sum: \\\", sum(M))\\n pprint (M)\\n #exit()\\n for next in get_moves(cpos, M):\\n solve(ulist(M, next, move+1), next, move+1)\",\n \"def solve_row1_tile(self, target_col):\\n # replace with your code\\n print target_col\\n assert self.row1_invariant(target_col), 'False precondition'\\n string = ''\\n target = self.current_position(1, target_col)\\n row_difference = 1 - target[0]\\n col_difference = target_col - target[1]\\n string += 'u' * row_difference\\n if col_difference > 0:\\n string += 'l' * col_difference\\n if row_difference == 0:\\n string += 'urrdl' * (col_difference - 1)\\n string += 'ur'\\n elif row_difference == 1:\\n string += 'drrul' * (col_difference - 1)\\n string += 'dru'\\n elif col_difference < 0:\\n col_difference = abs(col_difference)\\n string += 'r' * col_difference\\n string += 'dllur' * (col_difference - 1)\\n string += 'dlu'\\n print 'Row 1 Path', string\\n self.update_puzzle(string)\\n assert self.row0_invariant(target_col), 'False string'\\n return string\",\n \"def solve(self, board: List[List[str]]) -> None:\\n m = len(board)\\n if m == 0:\\n return\\n n = len(board[0])\\n uf = UnionFold(m*n+1)\\n dummy = m*n\\n # 搜索与边界联通的 “O”\\n for i in range(0, m):\\n for j in range(0, n):\\n # the boundary\\n if (i==0 or i == m-1 or j == 0 or j == n-1) and board[i][j] == \\\"O\\\":\\n uf.union(i*n+j, dummy)\\n elif board[i][j] == \\\"O\\\":\\n for l in [[1,0], [0, 1], [-1, 0], [0, -1]]:\\n x, y = l[0]+i, l[1]+j\\n if board[x][y] == \\\"O\\\":\\n uf.union(i*n+j, x*n+y)\\n # 所有不和 dummy 连通的 O，都要被替换\\n for i in range(1, m-1):\\n for j in range(1, n-1):\\n if not uf.connected(dummy, i*n+j):\\n board[i][j] = \\\"X\\\"\",\n \"def solve_col0_tile(self, target_row):\\n \\n assert target_row > 1, \\\"target_row cannot be in rows 0 or 1.\\\"\\n assert self.lower_row_invariant(target_row, 0), \\\"tiles to right and below incorrectly ordered\\\"\\n\\n # Move zero tile from target position (target_row, 0) to (target_row - 1, 1).\\n self.update_puzzle(\\\"ur\\\")\\n\\n move_str = \\\"\\\"\\n \\n # correct_tile's position is determined after moving zero tile \\\"ur\\\" \\n # because its position relative to zero tile may have changed as a result.\\n correct_tile = self.current_position(target_row, 0)\\n \\n # Moves to reposition correct_tile to target position.\\n if self.get_number(correct_tile[0], correct_tile[1]) != self.get_number(target_row, 0):\\n move_str += str(self.position_tile(target_row - 1, 1, correct_tile))\\n move_str += str(\\\"ruldrdlurdluurddlur\\\")\\n\\n # Moves to reposition zero tile to end of column of target_row + 1.\\n move_str += str(\\\"r\\\" * (self.get_width() - 2)) \\n \\n self.update_puzzle(move_str)\\n\\n assert self.lower_row_invariant(target_row - 1, self.get_width() - 1) \\n \\n move_str = \\\"ur\\\" + move_str\\n return move_str\",\n \"def solve_row0_tile(self, target_col):\\r\\n # move the zero tile from position (0,j) to (1,j−1) \\r\\n # using the move string \\\"ld\\\" \\r\\n moves_str = \\\"\\\"\\r\\n moves_str += \\\"ld\\\"\\r\\n # check whether target tile is at position (0,j).\\r\\n temp_grid = Puzzle(self._height, self._width, self._grid)\\r\\n temp_grid.update_puzzle(moves_str) \\r\\n current_row, current_col = temp_grid.current_position(0, target_col)\\r\\n zero_row, zero_col = temp_grid.current_position(0, 0)\\r\\n \\r\\n # If target tile is not at position (0,j).\\r\\n # reposition the target tile to position (1,j−1) \\r\\n # with tile zero in position (1,j−2).\\r\\n if current_row != 0 or current_col != target_col:\\r\\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\\r\\n moves_str += \\\"urdlurrdluldrruld\\\"\\r\\n \\r\\n self.update_puzzle(moves_str)\\r\\n print \\\"solve_row0_tile\\\"\\r\\n print moves_str\\r\\n print self._grid\\r\\n return moves_str\",\n \"def solve(self, board: List[List[str]]) -> None:\\n # New Solution: DFS on boarder (140ms: 89.07%)\\n if not board or not board[0]: return\\n def dfs(i, j):\\n if board[i][j]=='O':\\n board[i][j] = '*'\\n if i-1>=0:\\n dfs(i-1, j)\\n if i+1<len(board):\\n dfs(i+1, j)\\n if j-1>=0:\\n dfs(i, j-1)\\n if j+1<len(board[0]):\\n dfs(i, j+1)\\n height, width = len(board), len(board[0])\\n for i in range(width):\\n if board[0][i]=='O':\\n dfs(0, i)\\n if board[height-1][i]=='O':\\n dfs(height-1, i)\\n for i in range(height):\\n if board[i][0]=='O':\\n dfs(i, 0)\\n if board[i][width-1]=='O':\\n dfs(i, width-1)\\n for i in range(height):\\n for j in range(width):\\n if board[i][j]=='O':\\n board[i][j] = 'X'\\n elif board[i][j]=='*':\\n board[i][j] = 'O'\",\n \"def solve_row1_tile(self, target_col):\\n movements = self.solve_interior_tile(1, target_col)\\n self.update_puzzle(\\\"ur\\\")\\n return movements + \\\"ur\\\"\",\n \"def solve_(self, x, y, board, path):\\n if self.SOLVED:\\n return\\n if self.is_done(board):\\n self.print_path(path)\\n self.SOLVED = True\\n return\\n for new_x, new_y in self.next_click(x, y, board):\\n if new_x is None or new_y is None:\\n return\\n new_board = self.click(new_x, new_y, board)\\n self.solve_(\\n x=0, y=0,\\n board=new_board,\\n path=path + [((new_x, new_y), new_board)]\\n )\",\n \"def solve(self, board: List[List[str]]) -> None:\\n visited = [[False for x in range(len(board[0]))] for y in range(len(board))]\\n for i in range(len(board)):\\n for j in range(len(board[i])):\\n if not visited[i][j] and board[i][j] == 'O':\\n res = []\\n result = self.gatherO(board, i, j, res, visited)\\n if not result:\\n for coordinate in res:\\n board[coordinate[0]][coordinate[1]] = 'X'\",\n \"def solveSudoku(self, board):\\n\\n digits = { str(i) for i in range(1, 10) }\\n rows = [ digits.copy() for _ in range(9) ]\\n cols = [ digits.copy() for _ in range(9) ]\\n boxs = [ [ digits.copy() for _ in range(3) ] for _ in range(3) ]\\n unoccupied = set()\\n\\n def __recursiveSolver():\\n if not unoccupied:\\n return\\n\\n choices = digits.copy()\\n for row, col in unoccupied:\\n possible_moves = rows[row] & cols[col] & boxs[row // 3][col // 3]\\n if len(possible_moves) < len(choices):\\n action_pos = (row, col)\\n choices = possible_moves\\n if len(choices) == 1:\\n break\\n\\n for choice in choices:\\n (row, col) = action_pos\\n\\n unoccupied.remove(action_pos)\\n board[row][col] = choice\\n rows[row].remove(choice)\\n cols[col].remove(choice)\\n boxs[row // 3][col // 3].remove(choice)\\n\\n __recursiveSolver()\\n if not unoccupied: return\\n\\n unoccupied.add(action_pos)\\n board[row][col] = '.'\\n rows[row].add(choice)\\n cols[col].add(choice)\\n boxs[row // 3][col // 3].add(choice)\\n\\n for row in range(9):\\n for col in range(9):\\n ch = board[row][col]\\n if ch == '.':\\n unoccupied.add((row, col))\\n else:\\n rows[row].remove(ch)\\n cols[col].remove(ch)\\n boxs[row // 3][col // 3].remove(ch)\\n\\n __recursiveSolver()\",\n \"def part1_2(puzzle_input):\\n [initial_state_string, configurations] = puzzle_input.split('\\\\n\\\\n')\\n initial_state = re.sub('initial state: ', '', initial_state_string)\\n rules_arr = configurations.split('\\\\n')\\n rules = [re.split(' => ', line) for line in rules_arr]\\n rules = {t[0]: t[1] for t in rules}\\n current_state = '..........' + initial_state + '...............................................................................................................................................'\\n for i in range(100): # After 100th cycle, the only change is that there is a '#' that shifts right\\n next_generation_string = \\\"\\\"\\n for index, pot in enumerate(current_state):\\n if index == 0:\\n temp_string = '..' + current_state[:3]\\n elif index == 1:\\n temp_string = '.' + current_state[:4]\\n elif index == len(current_state) - 2:\\n temp_string = current_state[-4:] + '.'\\n elif index == len(current_state) - 1:\\n temp_string = current_state[-3:] + '..'\\n else:\\n temp_string = current_state[index-2:index+3]\\n if temp_string in rules:\\n next_generation_string += rules[temp_string]\\n else:\\n next_generation_string += pot\\n current_state = next_generation_string\\n\\n # For part 1\\n part1_sum = 0\\n if i == 19:\\n for index, pot in enumerate(current_state):\\n if pot == '#':\\n part1_sum += index - 10\\n print(part1_sum)\\n\\n # Part 2\\n part2_sum = 0\\n for index, pot in enumerate(current_state):\\n if pot == '#':\\n part2_sum += index - 10 + 50000000000 - 100\\n print(part2_sum)\",\n \"def solve(self, g: List[List[str]]) -> None:\\n n = len(g)\\n m = len(g[0])\\n for i in range(n):\\n for j in range(m):\\n if g[i][j] == 'O':\\n g[i][j] = ' '\\n def dfs(x, y):\\n g[x][y]='O'\\n for nx, ny in (x+1,y),(x-1,y),(x,y+1),(x,y-1):\\n if 0<=nx<n and 0<=ny<m and g[nx][ny]==' ':\\n dfs(nx, ny)\\n for i in range(n):\\n if g[i][0]==' ':\\n dfs(i,0)\\n if g[i][m-1]==' ':\\n dfs(i,m-1)\\n for i in range(m):\\n if g[0][i]==' ':\\n dfs(0,i)\\n if g[n-1][i]==' ':\\n dfs(n-1,i)\\n for i in range(n):\\n for j in range(m):\\n if g[i][j]==' ':\\n g[i][j]='X'\\n return g\",\n \"def solve_puzzle(self):\\n\\n # for each word in the words list\\n # ...for each row in the game board\\n # ......for each column in each row\\n for word in self.words:\\n for y, row in enumerate(self.board):\\n for x, col in enumerate(row):\\n \\n # for each direction\\n # try to find a word in said direction\\n for dir in self.directions:\\n self.scan_word(word, y, x, dir)\",\n \"def move(self, direction):\\r\\n direc = list(OFFSETS[direction])\\r\\n line = []\\r\\n dummy_board = self.board[:]\\r\\n if direction == 3:\\r\\n for i in range(self.height):\\r\\n self.board[i] = merge(self.board[i])\\r\\n self.compare(dummy_board)\\r\\n return self.board\\r\\n \\r\\n elif direction == 4:\\r\\n for i in range(self.height):\\r\\n line = self.board[i][::-1]\\r\\n self.board[i] = merge(line)\\r\\n self.board[i] = self.board[i][::-1]\\r\\n self.compare(dummy_board)\\r\\n return self.board\\r\\n \\r\\n \\r\\n elif direction == 1 or 2:\\r\\n dummy_board = str(self.board[:])\\r\\n if direction == 1:\\r\\n tile = [0,0]\\r\\n elif direction == 2:\\r\\n tile = [self.height - 1, 0]\\r\\n for i in range(self.width):\\r\\n tile2 = tile[:]\\r\\n while len(line) < self.height:\\r\\n line.append(self.get_tile(*tile2))\\r\\n tile2 = [x+y for x,y in zip(direc, tile2)]\\r\\n line = merge(line)\\r\\n tile2 = tile[:]\\r\\n for i in range(self.height):\\r\\n self.set_tile(*(tile2+[line[0]]))\\r\\n line.remove(line[0])\\r\\n tile2 = [x+y for x,y in zip(direc, tile2)]\\r\\n tile = [x+y for x,y in zip(tile, [0,1])]\\r\\n if dummy_board != self.__str__():\\r\\n self.new_tile()\\r\\n return self.board\",\n \"def solve(self, board):\\n self.memo={}\\n def changeVal(board, i, j,memo):\\n key =str(i)+','+str(j)\\n print(i,j)\\n print(memo)\\n if key in self.memo.keys():\\n return self.memo[key]\\n if ((i == 0 or j == 0 or i == len(board) - 1 or j == len(board[0]) - 1) and board[i][j] == 'O'):\\n self.memo[key] = False\\n return False\\n if board[i][j] == 'X':\\n self.memo[key] = True\\n return True\\n if board[i][j] == 'O':\\n board[i][j] = 'X'\\n ans1 = changeVal(board, i + 1, j,self.memo)\\n ans2 = changeVal(board, i - 1, j,self.memo)\\n ans3 = changeVal(board, i,j + 1,self.memo)\\n ans4 = changeVal(board, i, j - 1,self.memo)\\n\\n ans = ans1 and ans2 and ans3 and ans4\\n self.memo[key] = ans\\n if ans == False:\\n board[i][j] = 'O'\\n return ans\\n\\n for i in range(0, len(board)):\\n for j in range(0, len(board[0])):\\n if board[i][j] == 'O':\\n changeVal(board, i, j,self.memo)\\n\\n return board\",\n \"def make_move(self, state):\\r\\n # intially set drop phase to true\\r\\n drop_phase = True\\r\\n move = [] # list to make moves with to return\\r\\n succ = self.succ(state) # get the successor of this state\\r\\n # intial postion of board to set up most advantagous spot if its empty\\r\\n if sum(x.count(self.my_piece) for x in self.board) == 0 and self.board[2][2] == ' ':\\r\\n move.insert(0, (2, 2))\\r\\n return move\\r\\n \\r\\n # check the number of 'r' and 'b' on board if theres 4 of each drop phase is false\\r\\n if sum(x.count('r') for x in self.board) == 4 and sum(x.count('b') for x in self.board) == 4:\\r\\n drop_phase = False\\r\\n\\r\\n # if not during drop phase use minimax to make next move from one postion to next\\r\\n if not drop_phase:\\r\\n move = []\\r\\n d = self.Max_value(state, 0)\\r\\n val = d['val']\\r\\n m = d['move']\\r\\n p = d['pos']\\r\\n f = d['from']\\r\\n s = sorted(succ, key=lambda e: e['f'])\\r\\n moveto = s[-1]\\r\\n move.insert(1, (moveto['from'][0], moveto['from'][1]))\\r\\n move.insert(0, (moveto['pos'][0], moveto['pos'][1]))\\r\\n return move # return the from, to move\\r\\n\\r\\n else: #else use minimax and to make move during drop phase selecting spot to place AI piece\\r\\n d = self.Max_value(state, 0)\\r\\n val = d['val']\\r\\n m = d['move']\\r\\n p = d['pos']\\r\\n hold = []\\r\\n move = []\\r\\n n = None\\r\\n hold = []\\r\\n for s in succ:\\r\\n p = s['pos'][0]\\r\\n p1 = s['pos'][1]\\r\\n if s['f'] == val and state[p][p1] == ' ':\\r\\n hold.append(s)\\r\\n if len(hold) == 1:\\r\\n row = hold[0]['pos'][0]\\r\\n col = hold[0]['pos'][1]\\r\\n else:\\r\\n f = sorted(hold, key=lambda e: e['pos'])\\r\\n row = f[0]['pos'][0]\\r\\n col = f[0]['pos'][1]\\r\\n\\r\\n move.insert(0, (row, col)) # return the move \\r\\n return move\",\n \"def action(self):\\r\\n\\r\\n\\r\\n #have we just started?\\r\\n if self.player_information[\\\"us\\\"][\\\"nTokens\\\"] == 0:\\r\\n move = generate_starting_move(self.player_information[\\\"us\\\"][\\\"player_side\\\"], self.board_array)\\r\\n return move\\r\\n\\r\\n #otherwise do minimax \\r\\n \\r\\n #start off with some shallow depth:\\r\\n if self.turn_no < 5:\\r\\n depth = 3\\r\\n else:\\r\\n depth = 2\\r\\n \\r\\n #set a constraint for search depth\\r\\n if self.total_tokens_on_board < 6:\\r\\n depth = 3\\r\\n elif self.total_tokens_on_board < 10:\\r\\n depth = 2\\r\\n else:\\r\\n depth = 1\\r\\n \\r\\n #have a time reference\\r\\n print(f'nthrows: {self.player_information[\\\"us\\\"][\\\"nThrowsRemaining\\\"]}')\\r\\n starting_time = int(round(time.time(), 0))\\r\\n #salvage result from minimax\\r\\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\\r\\n None, None, None, depth, True, -math.inf, math.inf,\\r\\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \\r\\n starting_time, True, self.turn_no)\\r\\n\\r\\n #clean it up a bit \\r\\n print(self.board_dict)\\r\\n #tidy it up\\r\\n result = result[0]\\r\\n print(f'pre: {result}')\\r\\n #in case we get a bad move redo but make it very shallow\\r\\n if len(result) == 1 or result == (-5, -5):\\r\\n #force it to return a usable move\\r\\n counter = 0\\r\\n while (len(result) == 1) or (result == (-5, -5)):\\r\\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\\r\\n None, None, None, 1, True, -math.inf, math.inf,\\r\\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \\r\\n starting_time, False, self.turn_no)\\r\\n result = result[0]\\r\\n counter += 1\\r\\n \\r\\n #if its taking too long\\r\\n if counter > 2: \\r\\n #generate one random possible move to use \\r\\n allied_tokens = [token for token in self.player_tokens if self.player_tokens[token] == \\\"us\\\"]\\r\\n move_list = generate_moves(self.board_dict, self.player_tokens, self.co_existance_dict, allied_tokens,\\r\\n self.player_information, self.board_array, True, \\\"all\\\")\\r\\n \\r\\n \\r\\n #if there are no moves\\r\\n if len(move_list) == 0:\\r\\n if self.player_information['us']['nThrowsRemaining'] > 0:\\r\\n throws = generate_possible_throws(self.board_dict, self.player_tokens, self.co_existance_dict, self.player_information, \\\"us\\\",\\r\\n self.player_information[\\\"us\\\"][\\\"player_side\\\"], self.board_array, \\\"all\\\" )\\r\\n result = random.choice(throws)\\r\\n \\r\\n else:\\r\\n result = random.choice(move_list)\\r\\n print(f'random: {result}')\\r\\n break\\r\\n\\r\\n print(f' inside: {result}')\\r\\n\\r\\n print(result)\\r\\n #otherwise clean it up\\r\\n if result[0] == 'throw':\\r\\n final_result = (result[0].upper(), result[1], result[2])\\r\\n else:\\r\\n final_result = (result[0].upper(), result[2], result[3])\\r\\n # return final result \\r\\n return final_result\",\n \"def get_all_possible_moves():\\r\\n \\\"\\\"\\\"\\r\\n Creates the labels for the universal chess interface into an array and returns them\\r\\n \\\"\\\"\\\"\\r\\n labels_array = []\\r\\n letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\\r\\n numbers = ['1', '2', '3', '4', '5', '6', '7', '8']\\r\\n promoted_to = ['q', 'r', 'b', 'n']\\r\\n\\r\\n for l1 in range(8):\\r\\n for n1 in range(8):\\r\\n destinations = [(t, n1) for t in range(8)] + \\\\\\r\\n [(l1, t) for t in range(8)] + \\\\\\r\\n [(l1 + t, n1 + t) for t in range(-7, 8)] + \\\\\\r\\n [(l1 + t, n1 - t) for t in range(-7, 8)] + \\\\\\r\\n [(l1 + a, n1 + b) for (a, b) in\\r\\n [(-2, -1), (-1, -2), (-2, 1), (1, -2), (2, -1), (-1, 2), (2, 1), (1, 2)]]\\r\\n for (l2, n2) in destinations:\\r\\n if (l1, n1) != (l2, n2) and l2 in range(8) and n2 in range(8):\\r\\n move = letters[l1] + numbers[n1] + letters[l2] + numbers[n2]\\r\\n labels_array.append(move)\\r\\n for l1 in range(8):\\r\\n l = letters[l1]\\r\\n for p in promoted_to:\\r\\n labels_array.append(l + '2' + l + '1' + p)\\r\\n labels_array.append(l + '7' + l + '8' + p)\\r\\n if l1 > 0:\\r\\n l_l = letters[l1 - 1]\\r\\n labels_array.append(l + '2' + l_l + '1' + p)\\r\\n labels_array.append(l + '7' + l_l + '8' + p)\\r\\n if l1 < 7:\\r\\n l_r = letters[l1 + 1]\\r\\n labels_array.append(l + '2' + l_r + '1' + p)\\r\\n labels_array.append(l + '7' + l_r + '8' + p)\\r\\n return labels_array\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if len(board) == 0: return []\\n nr = len(board)\\n nc = len(board[0])\\n\\n # begin dfs from boundaries with letter \\\"O\\\"\\n for r in range(nr):\\n for c in range(nc):\\n if r == 0 or r == nr-1 or c == 0 or c == nc-1:\\n if board[r][c] == \\\"O\\\":\\n self.dfs(board, r, c)\\n\\n # change \\\"O\\\" to \\\"X\\\" and \\\"#\\\" to \\\"O\\\"\\n for r in range(nr):\\n for c in range(nc):\\n if board[r][c] == \\\"O\\\":\\n board[r][c] = \\\"X\\\"\\n elif board[r][c] == \\\"#\\\":\\n board[r][c] = \\\"O\\\"\",\n \"def solve(self):\\n dim = self.puzzle.dimension\\n\\n # initial loop\\n for value, (row, col) in self.puzzle:\\n if value:\\n self.clear_row(row, value)\\n self.clear_col(col, value)\\n self.clear_subgrid(row, col, value)\\n self.updates.add((value, (row, col)))\\n for ps in self.possibilities:\\n ps.discard((row, col))\\n\\n while self.updates:\\n while self.updates:\\n # while self.updates:\\n value, (row, col) = self.updates.pop()\\n for i in range(1, dim + 1):\\n self.check_row(i, value)\\n self.check_col(i, value)\\n for i in range(2, 8, 3):\\n self.check_subgrid(row, i, value)\\n self.check_subgrid(i, col, value)\\n\\n for value, (row, col) in self.puzzle:\\n if not value:\\n self.check_cell(row, col)\\n\\n # for value in range(1, dim + 1):\\n # for row in [2, 5, 8]:\\n # for col in [2, 5, 8]:\\n # self.check_subgrid(row, col, value)\",\n \"def solve(self, board: List[List[str]]) -> None:\\n if(len(board)==0) : return \\n for i in range(0, len(board[0])): \\n if(board[0][i]==\\\"O\\\"):\\n self.DFS(board, 0, i)\\n \\n if(board[len(board)-1][i]==\\\"O\\\"):\\n self.DFS(board,len(board)-1,i)\\n \\n for i in range(0, len(board)):\\n if(board[i][0]==\\\"O\\\"):\\n self.DFS(board, i, 0)\\n \\n if(board[i][len(board[0])-1]==\\\"O\\\"):\\n self.DFS(board,i, len(board[0])-1)\\n \\n \\n for i in range(0,len(board)):\\n for j in range(0, len(board[0])):\\n if(board[i][j]==\\\"#\\\"):\\n board[i][j]=\\\"O\\\"\\n \\n else:\\n board[i][j]=\\\"X\\\"\",\n \"def find_best_move(state: GameState) -> None:\",\n \"def solve_row0_tile(self, target_col):\\n assert target_col > 1, \\\"target_col must be > 1\\\"\\n assert self.row0_invariant(target_col), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n # Move zero tile from target position (0, target_col) to (1, target_col - 1) \\n self.update_puzzle(\\\"ld\\\")\\n \\n move_str = \\\"\\\"\\n\\n # correct_tile's position is determined after moving zero tile \\\"ld\\\"\\n # because its position relative to zero tile may have changed as a result.\\n correct_tile = self.current_position(0, target_col) \\n \\n # Moves to reposition correct_tile to target position, and\\n # the zero tile to (1, target_col - 1).\\n if self.get_number(correct_tile[0], correct_tile[1]) != self.get_number(0, target_col):\\n move_str += str(self.position_tile(1, target_col - 1, correct_tile))\\n move_str += str(\\\"urdlurrdluldrruld\\\")\\n\\n self.update_puzzle(move_str)\\n\\n assert self.row1_invariant(target_col - 1), \\\"tiles to right and below incorrectly ordered\\\"\\n \\n move_str = \\\"ld\\\" + move_str\\n return move_str\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.86358064","0.84391785","0.820359","0.81158245","0.8079695","0.7876614","0.7855013","0.77959996","0.7629816","0.75706357","0.7446708","0.71919894","0.70876247","0.6987711","0.6876523","0.686447","0.686447","0.686447","0.6857152","0.6857152","0.6825898","0.682045","0.680644","0.6798767","0.67694706","0.6757338","0.67503834","0.6743881","0.6727839","0.66785896","0.66642034","0.665565","0.6631162","0.66305166","0.6597714","0.6597469","0.65839773","0.6572454","0.6566848","0.65513307","0.6532281","0.65215236","0.65060896","0.64793384","0.6475916","0.64727217","0.64694023","0.6466018","0.6461253","0.6459988","0.64553595","0.64140576","0.63782644","0.63752735","0.63594425","0.6354533","0.63507825","0.6346083","0.6341721","0.6335916","0.6334586","0.6311966","0.6311564","0.6310484","0.6304807","0.63016754","0.6295452","0.62954044","0.6293516","0.62665886","0.6245019","0.6223726","0.6215188","0.6211001","0.62100565","0.6206542","0.61962146","0.6182959","0.6178695","0.6177864","0.61600417","0.61391354","0.61234325","0.61214095","0.60961217","0.6078878","0.60724854","0.60497886","0.6030426","0.60152173","0.60047674","0.6000283","0.59923935","0.5980893","0.59638155","0.59557575","0.5953203","0.5951496","0.5951158","0.5948482"],"string":"[\n \"0.86358064\",\n \"0.84391785\",\n \"0.820359\",\n \"0.81158245\",\n \"0.8079695\",\n \"0.7876614\",\n \"0.7855013\",\n \"0.77959996\",\n \"0.7629816\",\n \"0.75706357\",\n \"0.7446708\",\n \"0.71919894\",\n \"0.70876247\",\n \"0.6987711\",\n \"0.6876523\",\n \"0.686447\",\n \"0.686447\",\n \"0.686447\",\n \"0.6857152\",\n \"0.6857152\",\n \"0.6825898\",\n \"0.682045\",\n \"0.680644\",\n \"0.6798767\",\n \"0.67694706\",\n \"0.6757338\",\n \"0.67503834\",\n \"0.6743881\",\n \"0.6727839\",\n \"0.66785896\",\n \"0.66642034\",\n \"0.665565\",\n \"0.6631162\",\n \"0.66305166\",\n \"0.6597714\",\n \"0.6597469\",\n \"0.65839773\",\n \"0.6572454\",\n \"0.6566848\",\n \"0.65513307\",\n \"0.6532281\",\n \"0.65215236\",\n \"0.65060896\",\n \"0.64793384\",\n \"0.6475916\",\n \"0.64727217\",\n \"0.64694023\",\n \"0.6466018\",\n \"0.6461253\",\n \"0.6459988\",\n \"0.64553595\",\n \"0.64140576\",\n \"0.63782644\",\n \"0.63752735\",\n \"0.63594425\",\n \"0.6354533\",\n \"0.63507825\",\n \"0.6346083\",\n \"0.6341721\",\n \"0.6335916\",\n \"0.6334586\",\n \"0.6311966\",\n \"0.6311564\",\n \"0.6310484\",\n \"0.6304807\",\n \"0.63016754\",\n \"0.6295452\",\n \"0.62954044\",\n \"0.6293516\",\n \"0.62665886\",\n \"0.6245019\",\n \"0.6223726\",\n \"0.6215188\",\n \"0.6211001\",\n \"0.62100565\",\n \"0.6206542\",\n \"0.61962146\",\n \"0.6182959\",\n \"0.6178695\",\n \"0.6177864\",\n \"0.61600417\",\n \"0.61391354\",\n \"0.61234325\",\n \"0.61214095\",\n \"0.60961217\",\n \"0.6078878\",\n \"0.60724854\",\n \"0.60497886\",\n \"0.6030426\",\n \"0.60152173\",\n \"0.60047674\",\n \"0.6000283\",\n \"0.59923935\",\n \"0.5980893\",\n \"0.59638155\",\n \"0.59557575\",\n \"0.5953203\",\n \"0.5951496\",\n \"0.5951158\",\n \"0.5948482\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8153259"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":80,"cells":{"query":{"kind":"string","value":"This function will return data about the number of triangles on each vertex in a file inputs"},"document":{"kind":"string","value":"def get_triangles_per_vertex(my_core, native_ranges):\n\n t_p_v_data = []\n tri_dimension = 2\n for vertex in native_ranges[types.MBVERTEX]:\n t_p_v_data.append(my_core.get_adjacencies(vertex, tri_dimension).size())\n return np.array(t_p_v_data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def count_triangles(self, file):\n self.nibble(80)\n return struct.unpack(\"@i\", self.nibble(4))[0]","def get_num_vertices(triangles):\n return numpy.amax(numpy.reshape(triangles, -1)) + 1","def count_cells(fpath):\n cells = []\n for i in range(40):\n fname = f\"{fpath}/Mesh2d_{i}.vtu\"\n if not os.path.exists(fname):\n print(f\"File {fname} does not exist.\")\n break\n mesh = meshio.read(fname)\n for cell_block in mesh.cells:\n if cell_block.type in (\"triangle\"):\n num_cells = len(cell_block)\n print(f\"{i:2d}: {num_cells:6d} elements, {len(mesh.points):6d} vertices\")\n cells.append(num_cells)\n continue\n return cells","def test_triangle_count_05(self):\n body = {\"direction\": \"IN\", \"degree\": -1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 2}\n else:\n assert 0","def test_triangle_count_08(self):\n body = {\"direction\": \"IN\", \"degree\": 1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 0}\n else:\n assert 0","def count_aux(self, infile):\n n_aux = 0\n n_tokens = 0\n dgs_in = self._file_handler.file_to_dg_list(infile)\n for dg in dgs_in:\n n_tokens += len(dg)\n transform = VGtransformer(dg, dep_style=self._dep_style)\n transform.transform()\n n_aux += transform.tot_aux\n return n_aux, n_tokens, len(dgs_in)","def test_triangle_count_06(self):\n body = {\"direction\": \"OUT\", \"degree\": -1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\n else:\n assert 0","def test_triangle_count_04(self):\n body = {\"direction\": \"OUT\"}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\n else:\n assert 0","def num_data_lines(filepath):\n\tif not file_exists(filepath):\n\t\treturn -1\n\tcount = 0\n\twith open(filepath, 'r') as f:\n\t\twhile read_floats(f):\n\t\t\tcount += 1\n\tf.close()\n\treturn count","def get_vertices_count(self) -> int:\n # TODO: verify the following claim:\n raise NotImplementedError","def test_triangle_count_07(self):\n body = {\"direction\": \"OUT\", \"degree\": 1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 0}\n else:\n assert 0","def obtener_cantidad_vertices(self):\n return len(self.vertices.keys())","def countSamples(filename):\n with open(filename, \"r\") as f:\n line = f.readline().split(\"\\t\")\n return len(line) - 2","def num_vertices(self):\n return len(self)","def ss_triangle_count(graph: ScipyGraph) -> int:\n props = ScipyGraph.Type.compute_abstract_properties(graph, {\"edge_type\"})\n if props[\"edge_type\"] == \"map\":\n # Drop weights before performing triangle count\n m = graph.value.copy()\n m.data = np.ones_like(m.data)\n elif props[\"edge_type\"] == \"set\":\n m = graph.value\n L = ss.tril(m, k=-1).tocsr()\n U = ss.triu(m, k=1).tocsc()\n return int((L @ U.T).multiply(L).sum())","def findTriangles(p):\n triangleCount = 0\n for a in range(3, p//3 + 1):\n for b in range(a+1, p//2):\n c = p - (a+b)\n if (a**2 + b**2) == c**2:\n triangleCount += 1\n return triangleCount","def num_vertices(self):\n return self.n * (1 + int(self.variant.is_bipartite()))","def gather_counts(directory):\n counts_un = defaultdict(int)\n counts_bi = defaultdict(int)\n counts_tri = defaultdict(int)\n prev_prev = \"<s>\"\n prev = \"<s>\"\n for filename in os.listdir(f\"./{directory}\"):\n if \".DS_Store\" in filename:\n continue\n with open(f\"./{directory}/{filename}\", \"r\") as f:\n for line in f:\n line = line.strip()\n if len(line) == 0:\n continue\n counts_un[line+\"\\n\"] += 1\n counts_bi[prev+\"\\n\"+line+\"\\n\"] += 1\n counts_tri[prev_prev+\"\\n\"+prev+\"\\n\"+line+\"\\n\"] += 1\n prev_prev = prev\n prev = line\n counts_un[\"</s>\\n\"] += 2\n counts_bi[\"</s>\\n</s>\\n\"] += 1\n counts_bi[prev+\"\\n\"+\"</s>\\n\"] += 1\n counts_tri[prev_prev+\"\\n\"+prev+\"\\n\" + \"</s>\\n\"] += 1\n counts_tri[prev+\"\\n</s>\\n</s>\\n\"] += 1\n return counts_un, counts_bi, counts_tri","def load_data():\n data = []\n with euler.Resource('triangle.txt') as datafile:\n for line in datafile.readlines():\n data.insert(0, map(int, line.strip().split()))\n return data","def num_vertices(self):\n return len(self.vertices)","def num_vertices(self):\n return len(self.vertices)","def return_num_vertices(self):\n return self.__size","def n_vertices(self):\n try: \n return self._n_vertices\n except AttributeError:\n self._n_vertices = 0\n for v in self.vertex_generator(): self._n_vertices += 1\n return self._n_vertices","def get_edge_lengths(points: np.ndarray, triangles: np.ndarray) -> np.ndarray:\n edges, _ = get_edges(triangles)\n return np.linalg.norm(np.diff(points[edges], axis=1), axis=2).squeeze()","def countDataSize(self,filename):\n \n try:\n d = h5py.File(filename,'r')\n except:\n print(filename)\n return \n\n N = 0\n scan_edges = d['level2/Statistics/scan_edges'][:]\n for (start,end) in scan_edges:\n N += (end-start)//self.offsetLen * self.offsetLen\n d.close()\n\n N = N*self.Nfeeds\n\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\n self.datasizes += [int(N/self.Nfeeds)]\n self.Nsamples += int(N)","def _get_las_npoints(fpath):\n with laspy.file.File(fpath) as f:\n return f.header.count","def std_triangles_count(graph):\n if nx.is_directed(graph):\n raise Exception(\"Graph is not undirected\")\n\n return sum(nx.triangles(graph).values()) // 3","def total_number():\r\n total_number = 0\r\n file_read = read_file()\r\n for key in file_read:\r\n total_number = total_number + len(file_read[key])\r\n return total_number","def vertexCount(self):\n return self._nVertices","def numverts(self):\n return self._numvertstotal","def getNumVertices(self):\n return len(self.V)","def num_vertices(self, p):\n ret_val = self._num_vertices(p)\n return ret_val","def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):\r\n global count\r\n with open(inputfilename, 'r') as swmmput:\r\n contents = swmmput.readlines()\r\n count = len(contents)\r\n return(count)","def get_dataset_length(file_path, had_header=True):\n with open(file_path, 'r') as f:\n length = 0\n for _ in f:\n length += 1\n length = length - had_header\n return length","def get_triangle_count(remote, objectid):\n cmd1 = mmapi.StoredCommands()\n key1 = cmd1.AppendSceneCommand_GetTriangleCount(objectid)\n remote.runCommand(cmd1)\n result_val = mmapi.any_result()\n cmd1.GetSceneCommandResult_GetTriangleCount(key1, result_val)\n return result_val.i","def get_array_size():\n tg_file = 'NA_CAS_gauges.txt'\n lines = open(tg_file).readlines()\n tg_nbr = len(lines)\n return tg_nbr","def test_number_of_vertex_elements_in_MESH_chunk(self):\n for O in self.mod.objts.itervalues():\n for M in O.meshes.itervalues():\n self.assertEqual(M.vsize, len(M.vert))","def num_vertices(self):\r\n return len(self.__graph_dict.keys())","def countgenes():\n directory = openfile('db_directory.txt')\n no_genes_file = directory+'GENES_IN_HPO.txt'\n GENES_IN_HPO = openfile(no_genes_file)\n #GENES_IN_HPO = openfile(numbergenes_file)\n return int(GENES_IN_HPO)","def num_vertices(self):\n return self._top_exp.number_of_vertices()","def n_vertices(self):\n return len(self.minimized_generators())","def read_off_size(path):\n try:\n with open(path, 'r') as file:\n lines = file.readlines()\n if lines[0] != 'OFF\\n':\n print(path, 'Error: is not an .off file')\n num_vertices, num_faces = tuple(lines[1].split()[:2])\n return int(num_vertices), int(num_faces)\n except IOError:\n print('Error: Failed reading file:', path)","def count_lines(filename):\n with open(filename, 'r', encoding='utf-8') as file:\n lines_count = int()\n for line in file:\n lines_count += 1\n info_tuple = (filename, lines_count)\n return info_tuple","def get_vertex_data(\n mesh: object,\n g: BinaryReader,\n v1: int,\n v2: int,\n v3: int,\n v4: int,\n n: int,\n verbose=False,\n):\n for i in range(v1):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v1 v_offset\": v_offset,\n \"v1 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n mesh.skinWeightList.append([0, 0, 0, 1])\n\n for i in range(v2):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v2 v_offset\": v_offset,\n \"v2 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n w1 = g.f(1)[0]\n w2 = 1.0 - w1\n mesh.skinWeightList.append([0, 0, w2, w1])\n\n for i in range(v3):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v3 v_offset\": v_offset,\n \"v3 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n w1 = g.f(1)[0]\n w2 = g.f(1)[0]\n w3 = 1.0 - w1 - w2\n mesh.skinWeightList.append([0, w3, w2, w1])\n\n for i in range(v4):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v4 v_offset\": v_offset,\n \"v4 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n w1 = g.f(1)[0]\n w2 = g.f(1)[0]\n w3 = g.f(1)[0]\n w4 = 1.0 - w1 - w2 - w3\n mesh.skinWeightList.append([w4, w3, w2, w1])","def vertex_count(self) -> int:\n return len(self._vertex_map)","def countDataSize(self,filename):\n \n d = h5py.File(filename,'r')\n features = d['spectrometer/features'][:]\n select = self.selectData(features.astype(float), self.ifeature, d)\n N = len(features[select])\n d.close()\n\n N = (N//self.offsetLen) * self.offsetLen\n\n N = N*self.Nfeeds\n\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\n self.datasizes += [int(N/self.Nfeeds)]\n self.Nsamples += int(N)","def read_triangle(f, layers=1):\n # Read nodes\n with open(f + '.node') as h:\n num_nodes = int(h.readline().split(' ')[0])\n node_values = np.zeros((num_nodes, 2), dtype=np.float64)\n for line in h:\n if line[0] == '#':\n continue\n node, x, y = line.split()[:3]\n node_values[int(node) - 1, :] = [float(x), float(y)]\n\n nodes = op2.Set(num_nodes, \"nodes\")\n coords = op2.Dat(nodes ** 2, node_values, name=\"coords\")\n\n # Read elements\n with open(f + '.ele') as h:\n num_tri, nodes_per_tri, num_attrs = [int(x) for x in h.readline().split()]\n map_values = np.zeros((num_tri, nodes_per_tri), dtype=np.int32)\n for line in h:\n if line[0] == '#':\n continue\n vals = [int(x) - 1 for x in line.split()]\n map_values[vals[0], :] = vals[1:nodes_per_tri + 1]\n\n elements = op2.Set(num_tri, \"elements\", layers=layers)\n elem_node = op2.Map(elements, nodes, nodes_per_tri, map_values, \"elem_node\")\n\n return nodes, coords, elements, elem_node","def bpCount(file):\n amount_bp = len(file)\n return amount_bp","def size(self):\n num_vert = 0\n num_edg = 0\n for vertex in self.vertices():\n num_vert += 1\n num_edg += len(self.neighbors(vertex))\n return (num_vert, num_edg)","def part2(fname: dict) -> int:\n return sum(len(set.intersection(*[set(pax) for pax in group])) for group in get_data(fname))","def number_of_trips(filename): \r\n \r\n with open(filename, 'r') as f_in:\r\n # set up csv reader object\r\n trip_reader = csv.DictReader(f_in)\r\n \r\n # initialize count variables\r\n n_subscribers = 0\r\n n_customers = 0\r\n \r\n # tally up ride types\r\n for row in trip_reader:\r\n if row['user_type'] == 'Subscriber':\r\n n_subscribers += 1\r\n else:\r\n n_customers += 1\r\n \r\n # compute total number of rides\r\n n_total = n_subscribers + n_customers\r\n \r\n # return tallies as a tuple\r\n return(n_subscribers, n_customers, n_total)","def file_len(fname):\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n Nrows = i + 1\n return Nrows","def n_n(output_path):\n lef = {}\n rig = {}\n rellef = {}\n relrig = {}\n\n triple = open(Path(output_path, \"train2id.txt\"), \"r\")\n valid = open(Path(output_path, \"valid2id.txt\"), \"r\")\n test = open(Path(output_path, \"test2id.txt\"), \"r\")\n\n ls = triple.readlines()\n tot = len(ls) - 1\n\n # (int)(triple.readline())\n for i in range(tot):\n content = ls[i + 1]\n # content = triple.readline()\n h, t, r = content.strip().split()\n if not (h, r) in lef:\n lef[(h, r)] = []\n if not (r, t) in rig:\n rig[(r, t)] = []\n lef[(h, r)].append(t)\n rig[(r, t)].append(h)\n if not r in rellef:\n rellef[r] = {}\n if not r in relrig:\n relrig[r] = {}\n rellef[r][h] = 1\n relrig[r][t] = 1\n\n ls = valid.readlines()\n tot = len(ls) - 1\n # (int)(valid.readline())\n for i in range(tot):\n content = ls[i + 1]\n # content = valid.readline()\n h, t, r = content.strip().split()\n if not (h, r) in lef:\n lef[(h, r)] = []\n if not (r, t) in rig:\n rig[(r, t)] = []\n lef[(h, r)].append(t)\n rig[(r, t)].append(h)\n if not r in rellef:\n rellef[r] = {}\n if not r in relrig:\n relrig[r] = {}\n rellef[r][h] = 1\n relrig[r][t] = 1\n\n ls = test.readlines()\n tot = len(ls) - 1\n # (int)(test.readline())\n for i in range(tot):\n content = ls[i + 1]\n # content = test.readline()\n h, t, r = content.strip().split()\n if not (h, r) in lef:\n lef[(h, r)] = []\n if not (r, t) in rig:\n rig[(r, t)] = []\n lef[(h, r)].append(t)\n rig[(r, t)].append(h)\n if not r in rellef:\n rellef[r] = {}\n if not r in relrig:\n relrig[r] = {}\n rellef[r][h] = 1\n relrig[r][t] = 1\n\n test.close()\n valid.close()\n triple.close()\n\n f = open(Path(output_path, \"type_constrain.txt\"), \"w\")\n f.write(\"%d\\n\" % (len(rellef)))\n for i in rellef:\n f.write(\"%s\\t%d\" % (i, len(rellef[i])))\n for j in rellef[i]:\n f.write(\"\\t%s\" % (j))\n f.write(\"\\n\")\n f.write(\"%s\\t%d\" % (i, len(relrig[i])))\n for j in relrig[i]:\n f.write(\"\\t%s\" % (j))\n f.write(\"\\n\")\n f.close()\n\n rellef = {}\n totlef = {}\n relrig = {}\n totrig = {}\n\n for i in lef:\n if not i[1] in rellef:\n rellef[i[1]] = 0\n totlef[i[1]] = 0\n rellef[i[1]] += len(lef[i])\n totlef[i[1]] += 1.0\n\n for i in rig:\n if not i[0] in relrig:\n relrig[i[0]] = 0\n totrig[i[0]] = 0\n relrig[i[0]] += len(rig[i])\n totrig[i[0]] += 1.0\n\n s11 = 0\n s1n = 0\n sn1 = 0\n snn = 0\n f = open(Path(output_path, \"test2id.txt\"), \"r\")\n ls = f.readlines()\n tot = len(ls) - 1\n # tot = (int)(f.readline())\n for i in range(tot):\n content = ls[i + 1]\n # content = f.readline()\n h, t, r = content.strip().split()\n rign = rellef[r] / totlef[r]\n lefn = relrig[r] / totrig[r]\n if rign <= 1.5 and lefn <= 1.5:\n s11 += 1\n if rign > 1.5 and lefn <= 1.5:\n s1n += 1\n if rign <= 1.5 and lefn > 1.5:\n sn1 += 1\n if rign > 1.5 and lefn > 1.5:\n snn += 1\n f.close()\n\n f = open(Path(output_path, \"test2id.txt\"), \"r\")\n f11 = open(Path(output_path, \"1-1.txt\"), \"w\")\n f1n = open(Path(output_path, \"1-n.txt\"), \"w\")\n fn1 = open(Path(output_path, \"n-1.txt\"), \"w\")\n fnn = open(Path(output_path, \"n-n.txt\"), \"w\")\n fall = open(Path(output_path,\"test2id_all.txt\"), \"w\")\n fall = open(Path(output_path,\"test2id_all.txt\"), \"w\")\n\n ls = f.readlines()\n tot = len(ls) - 1\n\n # tot = (int)(f.readline())\n fall.write(\"%d\\n\" % (tot))\n f11.write(\"%d\\n\" % (s11))\n f1n.write(\"%d\\n\" % (s1n))\n fn1.write(\"%d\\n\" % (sn1))\n fnn.write(\"%d\\n\" % (snn))\n for i in range(tot):\n content = ls[i + 1]\n # content = f.readline()\n h, t, r = content.strip().split()\n rign = rellef[r] / totlef[r]\n lefn = relrig[r] / totrig[r]\n if rign <= 1.5 and lefn <= 1.5:\n f11.write(content)\n fall.write(\"0\" + \"\\t\" + content)\n if rign > 1.5 and lefn <= 1.5:\n f1n.write(content)\n fall.write(\"1\" + \"\\t\" + content)\n if rign <= 1.5 and lefn > 1.5:\n fn1.write(content)\n fall.write(\"2\" + \"\\t\" + content)\n if rign > 1.5 and lefn > 1.5:\n fnn.write(content)\n fall.write(\"3\" + \"\\t\" + content)\n fall.close()\n f.close()\n f11.close()\n f1n.close()\n fn1.close()\n fnn.close()","def count_positions(fname):\r\n with open(fname) as f:\r\n for i, l in enumerate(f):\r\n pass\r\n return i + 1","def data_info(data):\n filename = data[\"filename\"]\n X_var = data[\"X_var\"]\n Y_var = data[\"Y_var\"]\n X,Y = read_file(filename,X_var,Y_var)\n input_dim = len(X_var)\n output_dim = len(Y_var)\n return X,Y,input_dim,output_dim","def read_input(filename):\n with open(filename, 'r') as f:\n (N) = map(int, next(f).split())\n def parse_line(line):\n l = line.split()\n h = 0 if l[0] == 'H' else 1\n n = int(l[1])\n return [h, l[2:]]\n\n photos = transform_strings([parse_line(line) for line in f])\n return (len(photos), photos)","def getNumVertexes(self):\n return _osgAnimation.RigTransformHardware_getNumVertexes(self)","def __len__(self):\n return len(self._vertices)","def getFileCount(self) -> int:\n ...","def parse_triangle_files(self):\n nodes = {}\n boundary_nodes = []\n\n # parse node file into nodes\n with open(self.files['node']) as node_file:\n header = True\n for line in node_file:\n if header:\n header = False\n continue\n content = list(filter(lambda a: bool(a), line.split(' '))) # pylint: disable=W0108\n if not '#' in content[0]:\n is_boundary = content[3] == '1\\n'\n nodes[int(content[0])] = {\n 'id': int(content[0]),\n 'coords': [int(content[1]), int(content[2])],\n 'distance': 0 if is_boundary else None,\n 'relations': [],\n 'level_cycles': [], # ids of any level cycles this node is a part of\n 'level_paths': [], # ids of any level paths this node is a part of\n 'is_root_element': False,\n 'betweener_paths': []\n }\n if is_boundary:\n boundary_nodes.append(int(content[0]))\n node_file.close()\n\n # parse edge files into node relations\n with open(self.files['edge']) as edge_file:\n header = True\n for line in edge_file:\n if header:\n header = False\n continue\n content = list(filter(bool, line.split(' ')))\n if not '#' in content[0]:\n nodes[int(content[1])]['relations'].append(int(content[2]))\n nodes[int(content[2])]['relations'].append(int(content[1]))\n edge_file.close()\n\n # with open(self.files['ele']) as ele_file:\n # header = True\n # for line in edge_file:\n # if header:\n # header = False\n # continue\n # content = list(filter(bool, line.split(' ')))\n # if not '#' in content[0]:\n # nodes[int(content[1])]['relations'].append(int(content[2]))\n # nodes[int(content[2])]['relations'].append(int(content[1]))\n # edge_file.close()\n\n # sorts relations clockwise\n for node_id, node in nodes.items():\n nodes[node_id]['relations'] = sorted(node['relations'], key=(\n lambda related_node_id: (\n self.calculate_clockwise_angle_and_distance(node, nodes.get(related_node_id)) # pylint: disable=W0640\n )\n ))\n\n levels = self.get_levels(nodes, boundary_nodes)\n\n for level in levels:\n for node_id in level['node_ids']:\n self.identify_special_nodes(nodes, node_id)\n\n return nodes, boundary_nodes, levels","def line_count(file):\n with open(file, \"r\") as f:\n return sum(1 for line in f)","def get_num_examples(path_in):\n i = 0\n with open(path_in, 'r', encoding='utf8') as f:\n for _ in f:\n i += 1\n return i","def read_test_tuples():\n lines = read_input(25, True)\n point_sets = list(parse_points(lines))\n expected_counts = [4, 3, 8]\n\n return zip(point_sets, expected_counts)","def fileCount(self):\n pass","def loadVTKPolydataFile(infile):\n\n lines = None\n\n with open(infile, 'rt') as f:\n lines = f.readlines()\n\n lines = [l.strip() for l in lines]\n\n if lines[3] != 'DATASET POLYDATA':\n raise ValueError('Only the POLYDATA data type is supported')\n\n nVertices = int(lines[4].split()[1])\n nPolygons = int(lines[5 + nVertices].split()[1])\n nIndices = int(lines[5 + nVertices].split()[2]) - nPolygons\n\n vertices = np.zeros((nVertices, 3), dtype=np.float32)\n polygonLengths = np.zeros( nPolygons, dtype=np.uint32)\n indices = np.zeros( nIndices, dtype=np.uint32)\n\n for i in range(nVertices):\n vertLine = lines[i + 5]\n vertices[i, :] = [float(w) for w in vertLine.split()]\n\n indexOffset = 0\n for i in range(nPolygons):\n\n polyLine = lines[6 + nVertices + i].split()\n polygonLengths[i] = int(polyLine[0])\n\n start = indexOffset\n end = indexOffset + polygonLengths[i]\n indices[start:end] = [int(w) for w in polyLine[1:]]\n\n indexOffset += polygonLengths[i]\n\n return vertices, polygonLengths, indices","def readGR3File(inputFilename):\n print 'Reading ' + inputFilename + ' ...'\n infile = open(inputFilename, 'r')\n description = infile.readline().strip() # remove leading/trailing whitespace\n tmpStr = infile.readline()\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\n\n # nodes\n nodeArray = readNodeBlock(infile, nNodes)\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\n nodexyz = np.zeros((nNodes, 3))\n nodexyz[:, :2] = nodeArray[:, 1:3]\n nodalValues = nodeArray[:, 3]\n\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\n\n # triangular elements\n triArray = readElemBlock(infile, nTriangles)\n\n trinum = triArray[:, 0].flatten()\n tritype = triArray[0, 1]\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\n #triangles = meshElements(trinodes,trinum,tritype)\n\n x = nodexyz[:, 0]\n y = nodexyz[:, 1]\n\n tmpStr = infile.readline()\n boundaries = []\n if len(tmpStr) > 0:\n # boundary information, if not end of file\n nOpenBndSegments = int(tmpStr.split()[0])\n nOpenBndNodesTot = int(infile.readline().split()[0])\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\n for iBnd in range(nOpenBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n tag = bndHeader[-1]\n if tag.isdigit():\n tag = 'open' + tag\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\n nLandBndSegments = int(infile.readline().split()[0])\n nLandBndNodesTot = int(infile.readline().split()[0])\n landBndTags = range(\n nOpenBndSegments + 1,\n nOpenBndSegments + nLandBndSegments + 1)\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\n for iBnd in range(nLandBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n try:\n landType = int(bndHeader[1])\n except:\n print \"\"\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\n 1002 = Total number of closed boundary nodes\n 501 0 = Number of nodes in closed boundary 1\"\"\"\n raise Exception(\n 'Could not parse land boundary type (0/1 - land/island)\\n')\n landType = 'island' if landType == 1 else 'land'\n tag = landType + bndHeader[-1]\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\n\n infile.close()\n\n # for better interpolation, round coordinates to 1e-4\n nDig = 4\n x = np.round(x, nDig)\n y = np.round(y, nDig)\n\n return x, y, nodalValues, trinodes, boundaries, description","def count_data_items(fileids, train=True):\n sizes = 28000 if train else 22500\n return len(fileids) * sizes","def linesCountingAux(file_name, nProcesses):\r\n\r\n linesPerProcessesList = []\r\n\r\n with open(file_name, \"r\") as file:\r\n lineCounting = 0\r\n\r\n for line in file:\r\n lineCounting += 1 #discover the lines in the text file\r\n\r\n linesPerProcesses = lineCounting // nProcesses\r\n\r\n for number in range(nProcesses):\r\n linesPerProcessesList.append(linesPerProcesses)\r\n if sum(linesPerProcessesList) < lineCounting:\r\n for number in range (lineCounting - sum(linesPerProcessesList)):\r\n linesPerProcessesList[number] += 1\r\n\r\n return linesPerProcessesList","def data_edge_count(self) -> int:\n return int(self.graph_tuple_stats.data_edge_count or 0)","def create_1d_coil_geometry(division, filename, directory):\n os.chdir(directory)\n npoints = division + 1\n length_array = np.zeros((npoints, 2))\n current_length = 0\n array = np.loadtxt(filename)\n for i in range(1, npoints):\n current_length += ((array[i, 1] - array[i - 1, 1]) ** 2 + (array[i, 2] - array[i - 1, 2]) ** 2 +\n (array[i, 3] - array[i - 1, 3]) ** 2) ** 0.5\n length_array[i - 1, 0] = i\n length_array[i, 1] = current_length\n length_array[npoints - 1, 0] = npoints\n return length_array","def get_number_lines(running_reward_file, running_loss_file, action_count_file):\n if Path(running_reward_file).exists():\n data = np.loadtxt(running_reward_file).reshape(-1,2)\n return data.shape[0]\n if Path(running_loss_file).exists():\n data = np.loadtxt(running_loss_file).reshape(-1,2)\n return data.shape[0]\n if Path(action_count_file).exists():\n data = np.loadtxt(action_count_file).reshape(-1,2)\n return data.shape[0]\n raise NameError(\"No files to count lines\")","def calc_side_lengths(triangles):\n first_vec = [2, 0, 1]\n second_vec = [1, 2, 0]\n sides = triangles[:, first_vec] - triangles[:, second_vec]\n lengths = np.sqrt(np.sum(sides**2, axis=2))\n return lengths","def countLength():\n counter = 0\n\n with open('bc.processed3.csv', 'r') as openfile:\n for line in openfile:\n counter += 1\n if counter == 1:\n print line\n\n print('Length: ', counter)","def pre_process_file(filename):\n\n num_lines = 0\n vm_ids = set()\n with open(filename) as trace:\n for item in csv.reader(trace, delimiter=','):\n num_lines += 1\n disk_id = int(item[2])\n vm_ids.add(disk_id) # can make it more efficient\n no_of_vms = len(vm_ids)\n return (num_lines, no_of_vms, vm_ids)","def load_triangles(triangle_bytes, header):\n triangles = list()\n for i in range(header.num_tris):\n triangle = triangle_t(list(struct.unpack(\"<hhh\", triangle_bytes[12*i:12*i+6])), list(struct.unpack(\"<hhh\", triangle_bytes[12*i+6:12*i+12])))\n # print(triangle)\n triangles.append(triangle)\n return triangles","def xFileInfo(filename):\n delim = getDelimiter(filename)\n f = open(filename, 'r')\n reader = csv.reader(f, delimiter=delim)\n num_rows = 0\n for (row_i, row) in enumerate(reader):\n if row_i == 0: #ignore empty strings (e.g. at end of row)\n num_cols = len([val for val in row if val])\n num_rows += 1\n f.close()\n return (num_rows, num_cols)","def load_counts(filename, lengths=None, base=None):\n n = None\n if lengths is not None:\n n = lengths.sum()\n shape = (n, n)\n else:\n shape = None\n # This is the interaction count files\n dataframe = pd.read_csv(filename, sep=\"\\t\", comment=\"#\", header=None)\n row, col, data = dataframe.as_matrix().T\n\n # If there are NAs remove them\n mask = np.isnan(data)\n if np.any(mask):\n warnings.warn(\n \"NAs detected in %s. \"\n \"Removing NAs and replacing with 0.\" % filename)\n row = row[np.invert(mask)]\n col = col[np.invert(mask)]\n data = data[np.invert(mask)]\n\n # XXX We need to deal with the fact that we should not duplicate entries\n # for the diagonal.\n # XXX what if n doesn't exist?\n if base is not None:\n if base not in [0, 1]:\n raise ValueError(\"indices should start either at 0 or 1\")\n col -= base\n row -= base\n else:\n warnings.warn(\n \"Attempting to guess whether counts are 0 or 1 based\")\n\n if (col.min() >= 1 and row.min() >= 1) and \\\n ((n is None) or (col.max() == n)):\n # This is a hack to deal with the fact that sometimes, the files\n # are indexed at 1 and not 0\n\n col -= 1\n row -= 1\n\n if shape is None:\n n = max(col.max(), row.max()) + 1\n shape = (n, n)\n\n data = data.astype(float)\n counts = sparse.coo_matrix((data, (row, col)), shape=shape)\n return counts","def get_filesize(inputfile) -> int:\n with open(inputfile, \"rb\") as f:\n lines = 0\n buf_size = 1024 * 1024\n read_f = f.raw.read\n\n buf = read_f(buf_size)\n while buf:\n lines += buf.count(b\"\\n\")\n buf = read_f(buf_size)\n\n return lines","def get_dimensions ( file_in, separator ) :\n try :\n logger.info ( \"Extract dimensions from xyz file \" + str(file_in) ) \n d = {}\n first_row = True\n d[NOPS] = 0\n file = open(file_in, 'r')\n for line in file :\n d[NOPS] = d[NOPS] + 1\n l = line.rstrip().split(separator)\n x = float(l[0])\n y = float(l[1])\n z = float(l[2])\n if first_row :\n d[MINX] = x\n d[MAXX] = x\n d[MINY] = y\n d[MAXY] = y\n d[MINZ] = z\n d[MAXZ] = z\n first_row = False\n else :\n if x < d[MINX] :\n d[MINX] = x\n if x > d[MAXX] :\n d[MAXX] = x \n if y < d[MINY] :\n d[MINY] = y\n if y > d[MAXY] :\n d[MAXY] = y \n if z < d[MINZ] :\n d[MINZ] = z\n if z > d[MAXZ] :\n d[MAXZ] = z \n file.close() \n logger.info ('Now return')\n return d\n except Exception, err:\n logger.critical(\"Extract dimensions from xyz file failed: ERROR: %s\\n\" % str(err))\n raise","def count_cop(self, infile):\n n_cop = 0\n dgs_in = self._file_handler.file_to_dg_list(infile)\n for dg in dgs_in:\n if dg.has_cop_deprel():\n n_cop += 1\n return n_cop, len(dgs_in)","def listRows(file):\n\twith open(file) as f:\n\t\tcount = 0\n\t\tfor line in f.readlines():\n\t\t\tcount += 1\n\t\treturn count -1","def getVertexNumbers(self):\n return self.vertexIndex.keys()","def reader(filename,only_length=False):\n print(\"Counting lines in file %s\"%filename)\n total_lines=0\n for n,line in enumerate(open(filename,\"r\")):\n total_lines+=1\n \n if only_length:\n return total_lines\n \n X,Y,Z,W,J=[np.zeros(total_lines) for _ in range(5)]\n \n for n, line in enumerate(open(filename, 'r')):\n if n%1000000==0:\n print(\"Reading line %d of %d from file %s\" %(n,total_lines,filename))\n split_line=np.array(line.split(\" \"), dtype=float) \n X[n]=split_line[0];\n Y[n]=split_line[1];\n Z[n]=split_line[2];\n W[n]=split_line[3];\n J[n]=int(split_line[4]);\n return X,Y,Z,W,J","def pfd_read (r) :\n s = r.readline()\n if s == \"\" :\n return False\n l = s.split()\n numVert = int(l[0])\n numRule = int(l[1])\n assert numVert > 0 and numVert <= 100\n assert numRule > 0 and numRule <= 100\n v = [[0,[]]] #build the Vertex array\n for i in range(1, numVert):\n temp = [0,[]]\n v.append(temp)\n s = r.readline()\n for i in range(0, numRule):\n if s == \"\":\n return False\n l = s.split()\n v[int(l[0])-1][0] = int(l[1]) #verts[l[0]].numPred = l[1]\n #verts[l[0]].preds = [0] * (len(l)-2) #I don't know whether this line is necessary\n lenl = len(l)\n for j in range(2,lenl):\n v[int(l[j])-1][1].append(int(l[0]))\n #verts[l[j]-1].succ.append(l[0]) \n s = r.readline()\n return v","def part1(filename: str) -> int:\n data = first_line(filename)\n data = json.loads(data)\n return total_nums(data)","def get_num_vertices(self):\n\n return self._graph_state.get_num_vertices()","def get_dataset_size(file_path):\n size = 1\n file_list = tf.io.gfile.glob(file_path)\n for file in file_list:\n for record in tf.compat.v1.io.tf_record_iterator(file, options=tf.io.TFRecordOptions(\n compression_type='GZIP')):\n size += 1\n return size","def from_polyfile(name):\n\n from anuga.utilities.numerical_tools import anglediff\n from math import pi\n import os.path\n root, ext = os.path.splitext(name)\n\n if ext == 'poly':\n filename = name\n else:\n filename = name + '.poly'\n\n\n fid = open(filename)\n\n points = [] #x, y\n values = [] #z\n ##vertex_values = [] #Repeated z\n triangles = [] #v0, v1, v2\n\n lines = fid.readlines()\n\n keyword = lines[0].strip()\n msg = 'First line in .poly file must contain the keyword: POINTS'\n assert keyword == 'POINTS', msg\n\n offending = 0\n i = 1\n while keyword == 'POINTS':\n line = lines[i].strip()\n i += 1\n\n if line == 'POLYS':\n keyword = line\n break\n\n fields = line.split(':')\n assert int(fields[0]) == i-1, 'Point indices not consecutive'\n\n #Split the three floats\n xyz = fields[1].split()\n\n x = float(xyz[0])\n y = float(xyz[1])\n z = float(xyz[2])\n\n points.append([x, y])\n values.append(z)\n\n\n k = i\n while keyword == 'POLYS':\n line = lines[i].strip()\n i += 1\n\n if line == 'END':\n keyword = line\n break\n\n\n fields = line.split(':')\n assert int(fields[0]) == i-k, 'Poly indices not consecutive'\n\n #Split the three indices\n vvv = fields[1].split()\n\n i0 = int(vvv[0])-1\n i1 = int(vvv[1])-1\n i2 = int(vvv[2])-1\n\n #Check for and exclude degenerate areas\n x0 = points[i0][0]\n y0 = points[i0][1]\n x1 = points[i1][0]\n y1 = points[i1][1]\n x2 = points[i2][0]\n y2 = points[i2][1]\n\n area = abs((x1*y0-x0*y1)+(x2*y1-x1*y2)+(x0*y2-x2*y0))/2\n if area > 0:\n\n #Ensure that points are arranged in counter clock-wise order\n v0 = [x1-x0, y1-y0]\n v1 = [x2-x1, y2-y1]\n v2 = [x0-x2, y0-y2]\n\n a0 = anglediff(v1, v0)\n a1 = anglediff(v2, v1)\n a2 = anglediff(v0, v2)\n\n\n if a0 < pi and a1 < pi and a2 < pi:\n #all is well\n j0 = i0\n j1 = i1\n j2 = i2\n else:\n #Swap two vertices\n j0 = i1\n j1 = i0\n j2 = i2\n\n triangles.append([j0, j1, j2])\n ##vertex_values.append([values[j0], values[j1], values[j2]])\n else:\n offending +=1\n\n log.critical('Removed %d offending triangles out of %d'\n % (offending, len(lines)))\n return points, triangles, values","def __len__(self) -> int:\n import h5py\n\n with h5py.File(\n os.path.join(self.root, self.data_dir, self.img_file_name), \"r\"\n ) as f:\n num_datapoints: int = f[self.split][\"pv_log\"].shape[0]\n\n return num_datapoints","def get_triangles( self, N ):\n\n # store N as an instance variable\n self.N = N\n\n # initialize array to store locations of points for all triangles in the\n # tessellation sequence\n self.triangles = np.zeros( ( self.N, 3, 2 ) )\n\n # define points of the first triangle in the tessellation sequence\n point_c = np.array( [ 0, 0 ] )\n point_b = self.a * np.array( [ np.cos( self.C ), np.sin( self.C ) ] )\n point_a = np.array( [ self.b, 0 ] )\n\n # stack the points into a single array of shape (3, 2 )\n triangle = np.vstack( [ point_c, point_b, point_a ] )\n\n # loop over the number of triangles in the sequence\n for i in range( self.N ):\n\n # store the points of the i-th triangle in the array\n self.triangles[ i ] = triangle\n\n # compute the next triangle in the tessellation sequence\n triangle = self.next_triangle( triangle = triangle )\n\n # shift the next triangle in the tessellation sequence such that its\n # point C is in the same location as point B of the previous triangle\n triangle += ( self.triangles[ i - 1, 1 ] - self.triangles[ 0, 0 ] )","def so_data_statistics(data_file):\n with open(data_file, \"r\") as f:\n data = json.load(f)\n\n answer_to_num_questions = collections.Counter()\n comment_to_num_questions = collections.Counter()\n num_comments = 0\n num_answers = 0\n num_questions = len(data)\n\n for q in data:\n q = json.loads(q)\n q_comments = 0\n q_comments += len(q[\"comments\"])\n q_answers = len(q[\"answers\"])\n for a in q[\"answers\"]:\n q_comments += len(a[\"comments\"])\n\n answer_to_num_questions[q_answers] += 1\n comment_to_num_questions[q_comments] += 1\n\n num_comments += q_comments\n num_answers += q_answers\n\n print \"Num comments: {0}, Num answers: {1}, Num_questions: {2}\".format(\n num_comments, num_answers, num_questions)\n print \"-\" * 10\n print \"Answers map: \", answer_to_num_questions\n print \"Comments map: \", comment_to_num_questions\n\n return num_comments, num_answers, num_questions, answer_to_num_questions, \\\n comment_to_num_questions","def numberFiles(self):\n with open(self.inputfile) as fin:\n for n, _ in enumerate(fin, start=1): pass\n self.n = n\n return self.n","def get_vertices_num(self):\n return self.coords.shape[0]","def strang_mesh(filename):\n\n from math import pi\n from anuga.utilities.numerical_tools import anglediff\n\n\n fid = open(filename)\n points = [] # List of x, y coordinates\n triangles = [] # List of vertex ids as listed in the file\n\n for line in fid.readlines():\n fields = line.split()\n if len(fields) == 2:\n # we are reading vertex coordinates\n points.append([float(fields[0]), float(fields[1])])\n elif len(fields) == 3:\n # we are reading triangle point id's (format ae+b)\n triangles.append([int(float(fields[0]))-1,\n int(float(fields[1]))-1,\n int(float(fields[2]))-1])\n else:\n raise Excetion('wrong format in %s' % filename)\n\n elements = [] #Final list of elements\n\n for t in triangles:\n #Get vertex coordinates\n v0 = t[0]\n v1 = t[1]\n v2 = t[2]\n\n x0 = points[v0][0]\n y0 = points[v0][1]\n x1 = points[v1][0]\n y1 = points[v1][1]\n x2 = points[v2][0]\n y2 = points[v2][1]\n\n #Check that points are arranged in counter clock-wise order\n vec0 = [x1-x0, y1-y0]\n vec1 = [x2-x1, y2-y1]\n vec2 = [x0-x2, y0-y2]\n\n a0 = anglediff(vec1, vec0)\n a1 = anglediff(vec2, vec1)\n a2 = anglediff(vec0, vec2)\n\n if a0 < pi and a1 < pi and a2 < pi:\n elements.append([v0, v1, v2])\n else:\n elements.append([v0, v2, v1])\n\n return points, elements","def vertex_count(self):\n return len(self._outgoing)","def n_file_elements(cls):\n \n return randint(1, (3 * Root.size))","def contig_count(contig):\n return sum([1 for line in open(contig, 'rU').readlines() if line.startswith('>')])","def load_lengths(filename, return_base=False):\n data = pd.read_csv(filename, sep=\"\\t\", comment=\"#\", header=None)\n data = data.as_matrix()\n _, idx, lengths = np.unique(data[:, 0], return_counts=True,\n return_index=True)\n if return_base:\n return lengths[idx.argsort()], data[0, 3]\n else:\n return lengths[idx.argsort()]","def __init__(self, file_path):\n\n # Comments\n # mtllib mtl_name\n # o object_name\n # v x y z\n # vt u v\n # vn x y z\n # f v0/t0/n0 v1/t1/n1 v2/t2/n2\n\n print('loading mesh \"%s\"' % file_path)\n mesh_file = open(file_path, 'r')\n\n verts = []\n texs = []\n normals = []\n faces = []\n\n # For each line of the input file\n for line in mesh_file:\n line = line.rstrip(' \\r\\n')\n\n # Skip comments\n if line.startswith('#') or line == '':\n continue\n\n tokens = line.split(' ')\n tokens = map(lambda t: t.strip(' '), tokens)\n tokens = list(filter(lambda t: t != '', tokens))\n\n prefix = tokens[0]\n tokens = tokens[1:]\n\n if prefix == 'v':\n vert = list(map(lambda v: float(v), tokens))\n verts.append(vert)\n\n if prefix == 'vt':\n tc = list(map(lambda v: float(v), tokens))\n texs.append(tc)\n\n if prefix == 'vn':\n normal = list(map(lambda v: float(v), tokens))\n normals.append(normal)\n\n if prefix == 'f':\n assert len(tokens) == 3, \"only triangle faces are supported\"\n\n face = []\n for token in tokens:\n indices = list(map(lambda idx: int(idx), token.split('/')))\n face.append(indices)\n\n faces.append(face)\n\n mesh_file.close()\n\n self.num_faces = len(faces)\n\n print('num verts=%d' % len(verts))\n print('num_faces=%d' % self.num_faces)\n\n # Create numpy arrays to store the vertex data\n list_verts = np.zeros(shape=(3 * self.num_faces, 3), dtype=np.float32)\n list_texcs = np.zeros(shape=3 * 2 * self.num_faces, dtype=np.float32)\n list_norms = np.zeros(shape=3 * 3 * self.num_faces, dtype=np.float32)\n\n cur_vert_idx = 0\n\n # For each triangle\n for face in faces:\n # For each triplet of indices\n for triplet in face:\n v_idx, t_idx, n_idx = triplet\n\n # Note: OBJ uses 1-based indexing\n vert = verts[v_idx-1]\n texc = texs[t_idx-1]\n normal = normals[n_idx-1]\n\n list_verts[cur_vert_idx, :] = vert\n list_texcs[2*cur_vert_idx:2*(cur_vert_idx+1)] = texc\n list_norms[3*cur_vert_idx:3*cur_vert_idx+3] = normal\n\n cur_vert_idx += 1\n\n # Re-center the object so that y=0 is at the base,\n # and the object is centered in x and z\n x_coords = list_verts[:, 0]\n z_coords = list_verts[:, 2]\n min_y = list_verts[:, 1].min()\n mean_x = (x_coords.min() + x_coords.max()) / 2\n mean_z = (z_coords.min() + z_coords.max()) / 2\n list_verts[:, 1] -= min_y\n list_verts[:, 0] -= mean_x\n list_verts[:, 2] -= mean_z\n\n # Compute the object extents after centering\n x_coords = list_verts[:, 0]\n y_coords = list_verts[:, 1]\n z_coords = list_verts[:, 2]\n self.y_max = y_coords.max()\n\n # Create a vertex list to be used for rendering\n self.vlist = pyglet.graphics.vertex_list(\n 3 * self.num_faces,\n ('v3f', list_verts.reshape(-1)),\n ('t2f', list_texcs),\n ('n3f', list_norms)\n )\n\n # Load the texture associated with this mesh\n file_name = os.path.split(file_path)[-1]\n tex_name = file_name.split('.')[0]\n tex_path = get_file_path('textures', tex_name, 'png')\n self.texture = load_texture(tex_path)","def count_elements(path):\n count = 0\n with open(path, 'r') as f:\n groups = f.read().split('\\n\\n')\n for idx in range(len(groups)):\n word = groups[idx].split('\\n')\n no_of_ele = len(word)\n for i in range(no_of_ele-1):\n word[0] = word[0]+word[i+1]\n count += len(''.join(set(word[0])))\n return count"],"string":"[\n \"def count_triangles(self, file):\\n self.nibble(80)\\n return struct.unpack(\\\"@i\\\", self.nibble(4))[0]\",\n \"def get_num_vertices(triangles):\\n return numpy.amax(numpy.reshape(triangles, -1)) + 1\",\n \"def count_cells(fpath):\\n cells = []\\n for i in range(40):\\n fname = f\\\"{fpath}/Mesh2d_{i}.vtu\\\"\\n if not os.path.exists(fname):\\n print(f\\\"File {fname} does not exist.\\\")\\n break\\n mesh = meshio.read(fname)\\n for cell_block in mesh.cells:\\n if cell_block.type in (\\\"triangle\\\"):\\n num_cells = len(cell_block)\\n print(f\\\"{i:2d}: {num_cells:6d} elements, {len(mesh.points):6d} vertices\\\")\\n cells.append(num_cells)\\n continue\\n return cells\",\n \"def test_triangle_count_05(self):\\n body = {\\\"direction\\\": \\\"IN\\\", \\\"degree\\\": -1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 2}\\n else:\\n assert 0\",\n \"def test_triangle_count_08(self):\\n body = {\\\"direction\\\": \\\"IN\\\", \\\"degree\\\": 1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 0}\\n else:\\n assert 0\",\n \"def count_aux(self, infile):\\n n_aux = 0\\n n_tokens = 0\\n dgs_in = self._file_handler.file_to_dg_list(infile)\\n for dg in dgs_in:\\n n_tokens += len(dg)\\n transform = VGtransformer(dg, dep_style=self._dep_style)\\n transform.transform()\\n n_aux += transform.tot_aux\\n return n_aux, n_tokens, len(dgs_in)\",\n \"def test_triangle_count_06(self):\\n body = {\\\"direction\\\": \\\"OUT\\\", \\\"degree\\\": -1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\\n else:\\n assert 0\",\n \"def test_triangle_count_04(self):\\n body = {\\\"direction\\\": \\\"OUT\\\"}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\\n else:\\n assert 0\",\n \"def num_data_lines(filepath):\\n\\tif not file_exists(filepath):\\n\\t\\treturn -1\\n\\tcount = 0\\n\\twith open(filepath, 'r') as f:\\n\\t\\twhile read_floats(f):\\n\\t\\t\\tcount += 1\\n\\tf.close()\\n\\treturn count\",\n \"def get_vertices_count(self) -> int:\\n # TODO: verify the following claim:\\n raise NotImplementedError\",\n \"def test_triangle_count_07(self):\\n body = {\\\"direction\\\": \\\"OUT\\\", \\\"degree\\\": 1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 0}\\n else:\\n assert 0\",\n \"def obtener_cantidad_vertices(self):\\n return len(self.vertices.keys())\",\n \"def countSamples(filename):\\n with open(filename, \\\"r\\\") as f:\\n line = f.readline().split(\\\"\\\\t\\\")\\n return len(line) - 2\",\n \"def num_vertices(self):\\n return len(self)\",\n \"def ss_triangle_count(graph: ScipyGraph) -> int:\\n props = ScipyGraph.Type.compute_abstract_properties(graph, {\\\"edge_type\\\"})\\n if props[\\\"edge_type\\\"] == \\\"map\\\":\\n # Drop weights before performing triangle count\\n m = graph.value.copy()\\n m.data = np.ones_like(m.data)\\n elif props[\\\"edge_type\\\"] == \\\"set\\\":\\n m = graph.value\\n L = ss.tril(m, k=-1).tocsr()\\n U = ss.triu(m, k=1).tocsc()\\n return int((L @ U.T).multiply(L).sum())\",\n \"def findTriangles(p):\\n triangleCount = 0\\n for a in range(3, p//3 + 1):\\n for b in range(a+1, p//2):\\n c = p - (a+b)\\n if (a**2 + b**2) == c**2:\\n triangleCount += 1\\n return triangleCount\",\n \"def num_vertices(self):\\n return self.n * (1 + int(self.variant.is_bipartite()))\",\n \"def gather_counts(directory):\\n counts_un = defaultdict(int)\\n counts_bi = defaultdict(int)\\n counts_tri = defaultdict(int)\\n prev_prev = \\\"<s>\\\"\\n prev = \\\"<s>\\\"\\n for filename in os.listdir(f\\\"./{directory}\\\"):\\n if \\\".DS_Store\\\" in filename:\\n continue\\n with open(f\\\"./{directory}/{filename}\\\", \\\"r\\\") as f:\\n for line in f:\\n line = line.strip()\\n if len(line) == 0:\\n continue\\n counts_un[line+\\\"\\\\n\\\"] += 1\\n counts_bi[prev+\\\"\\\\n\\\"+line+\\\"\\\\n\\\"] += 1\\n counts_tri[prev_prev+\\\"\\\\n\\\"+prev+\\\"\\\\n\\\"+line+\\\"\\\\n\\\"] += 1\\n prev_prev = prev\\n prev = line\\n counts_un[\\\"</s>\\\\n\\\"] += 2\\n counts_bi[\\\"</s>\\\\n</s>\\\\n\\\"] += 1\\n counts_bi[prev+\\\"\\\\n\\\"+\\\"</s>\\\\n\\\"] += 1\\n counts_tri[prev_prev+\\\"\\\\n\\\"+prev+\\\"\\\\n\\\" + \\\"</s>\\\\n\\\"] += 1\\n counts_tri[prev+\\\"\\\\n</s>\\\\n</s>\\\\n\\\"] += 1\\n return counts_un, counts_bi, counts_tri\",\n \"def load_data():\\n data = []\\n with euler.Resource('triangle.txt') as datafile:\\n for line in datafile.readlines():\\n data.insert(0, map(int, line.strip().split()))\\n return data\",\n \"def num_vertices(self):\\n return len(self.vertices)\",\n \"def num_vertices(self):\\n return len(self.vertices)\",\n \"def return_num_vertices(self):\\n return self.__size\",\n \"def n_vertices(self):\\n try: \\n return self._n_vertices\\n except AttributeError:\\n self._n_vertices = 0\\n for v in self.vertex_generator(): self._n_vertices += 1\\n return self._n_vertices\",\n \"def get_edge_lengths(points: np.ndarray, triangles: np.ndarray) -> np.ndarray:\\n edges, _ = get_edges(triangles)\\n return np.linalg.norm(np.diff(points[edges], axis=1), axis=2).squeeze()\",\n \"def countDataSize(self,filename):\\n \\n try:\\n d = h5py.File(filename,'r')\\n except:\\n print(filename)\\n return \\n\\n N = 0\\n scan_edges = d['level2/Statistics/scan_edges'][:]\\n for (start,end) in scan_edges:\\n N += (end-start)//self.offsetLen * self.offsetLen\\n d.close()\\n\\n N = N*self.Nfeeds\\n\\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\\n self.datasizes += [int(N/self.Nfeeds)]\\n self.Nsamples += int(N)\",\n \"def _get_las_npoints(fpath):\\n with laspy.file.File(fpath) as f:\\n return f.header.count\",\n \"def std_triangles_count(graph):\\n if nx.is_directed(graph):\\n raise Exception(\\\"Graph is not undirected\\\")\\n\\n return sum(nx.triangles(graph).values()) // 3\",\n \"def total_number():\\r\\n total_number = 0\\r\\n file_read = read_file()\\r\\n for key in file_read:\\r\\n total_number = total_number + len(file_read[key])\\r\\n return total_number\",\n \"def vertexCount(self):\\n return self._nVertices\",\n \"def numverts(self):\\n return self._numvertstotal\",\n \"def getNumVertices(self):\\n return len(self.V)\",\n \"def num_vertices(self, p):\\n ret_val = self._num_vertices(p)\\n return ret_val\",\n \"def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):\\r\\n global count\\r\\n with open(inputfilename, 'r') as swmmput:\\r\\n contents = swmmput.readlines()\\r\\n count = len(contents)\\r\\n return(count)\",\n \"def get_dataset_length(file_path, had_header=True):\\n with open(file_path, 'r') as f:\\n length = 0\\n for _ in f:\\n length += 1\\n length = length - had_header\\n return length\",\n \"def get_triangle_count(remote, objectid):\\n cmd1 = mmapi.StoredCommands()\\n key1 = cmd1.AppendSceneCommand_GetTriangleCount(objectid)\\n remote.runCommand(cmd1)\\n result_val = mmapi.any_result()\\n cmd1.GetSceneCommandResult_GetTriangleCount(key1, result_val)\\n return result_val.i\",\n \"def get_array_size():\\n tg_file = 'NA_CAS_gauges.txt'\\n lines = open(tg_file).readlines()\\n tg_nbr = len(lines)\\n return tg_nbr\",\n \"def test_number_of_vertex_elements_in_MESH_chunk(self):\\n for O in self.mod.objts.itervalues():\\n for M in O.meshes.itervalues():\\n self.assertEqual(M.vsize, len(M.vert))\",\n \"def num_vertices(self):\\r\\n return len(self.__graph_dict.keys())\",\n \"def countgenes():\\n directory = openfile('db_directory.txt')\\n no_genes_file = directory+'GENES_IN_HPO.txt'\\n GENES_IN_HPO = openfile(no_genes_file)\\n #GENES_IN_HPO = openfile(numbergenes_file)\\n return int(GENES_IN_HPO)\",\n \"def num_vertices(self):\\n return self._top_exp.number_of_vertices()\",\n \"def n_vertices(self):\\n return len(self.minimized_generators())\",\n \"def read_off_size(path):\\n try:\\n with open(path, 'r') as file:\\n lines = file.readlines()\\n if lines[0] != 'OFF\\\\n':\\n print(path, 'Error: is not an .off file')\\n num_vertices, num_faces = tuple(lines[1].split()[:2])\\n return int(num_vertices), int(num_faces)\\n except IOError:\\n print('Error: Failed reading file:', path)\",\n \"def count_lines(filename):\\n with open(filename, 'r', encoding='utf-8') as file:\\n lines_count = int()\\n for line in file:\\n lines_count += 1\\n info_tuple = (filename, lines_count)\\n return info_tuple\",\n \"def get_vertex_data(\\n mesh: object,\\n g: BinaryReader,\\n v1: int,\\n v2: int,\\n v3: int,\\n v4: int,\\n n: int,\\n verbose=False,\\n):\\n for i in range(v1):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v1 v_offset\\\": v_offset,\\n \\\"v1 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n mesh.skinWeightList.append([0, 0, 0, 1])\\n\\n for i in range(v2):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v2 v_offset\\\": v_offset,\\n \\\"v2 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n w1 = g.f(1)[0]\\n w2 = 1.0 - w1\\n mesh.skinWeightList.append([0, 0, w2, w1])\\n\\n for i in range(v3):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v3 v_offset\\\": v_offset,\\n \\\"v3 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n w1 = g.f(1)[0]\\n w2 = g.f(1)[0]\\n w3 = 1.0 - w1 - w2\\n mesh.skinWeightList.append([0, w3, w2, w1])\\n\\n for i in range(v4):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v4 v_offset\\\": v_offset,\\n \\\"v4 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n w1 = g.f(1)[0]\\n w2 = g.f(1)[0]\\n w3 = g.f(1)[0]\\n w4 = 1.0 - w1 - w2 - w3\\n mesh.skinWeightList.append([w4, w3, w2, w1])\",\n \"def vertex_count(self) -> int:\\n return len(self._vertex_map)\",\n \"def countDataSize(self,filename):\\n \\n d = h5py.File(filename,'r')\\n features = d['spectrometer/features'][:]\\n select = self.selectData(features.astype(float), self.ifeature, d)\\n N = len(features[select])\\n d.close()\\n\\n N = (N//self.offsetLen) * self.offsetLen\\n\\n N = N*self.Nfeeds\\n\\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\\n self.datasizes += [int(N/self.Nfeeds)]\\n self.Nsamples += int(N)\",\n \"def read_triangle(f, layers=1):\\n # Read nodes\\n with open(f + '.node') as h:\\n num_nodes = int(h.readline().split(' ')[0])\\n node_values = np.zeros((num_nodes, 2), dtype=np.float64)\\n for line in h:\\n if line[0] == '#':\\n continue\\n node, x, y = line.split()[:3]\\n node_values[int(node) - 1, :] = [float(x), float(y)]\\n\\n nodes = op2.Set(num_nodes, \\\"nodes\\\")\\n coords = op2.Dat(nodes ** 2, node_values, name=\\\"coords\\\")\\n\\n # Read elements\\n with open(f + '.ele') as h:\\n num_tri, nodes_per_tri, num_attrs = [int(x) for x in h.readline().split()]\\n map_values = np.zeros((num_tri, nodes_per_tri), dtype=np.int32)\\n for line in h:\\n if line[0] == '#':\\n continue\\n vals = [int(x) - 1 for x in line.split()]\\n map_values[vals[0], :] = vals[1:nodes_per_tri + 1]\\n\\n elements = op2.Set(num_tri, \\\"elements\\\", layers=layers)\\n elem_node = op2.Map(elements, nodes, nodes_per_tri, map_values, \\\"elem_node\\\")\\n\\n return nodes, coords, elements, elem_node\",\n \"def bpCount(file):\\n amount_bp = len(file)\\n return amount_bp\",\n \"def size(self):\\n num_vert = 0\\n num_edg = 0\\n for vertex in self.vertices():\\n num_vert += 1\\n num_edg += len(self.neighbors(vertex))\\n return (num_vert, num_edg)\",\n \"def part2(fname: dict) -> int:\\n return sum(len(set.intersection(*[set(pax) for pax in group])) for group in get_data(fname))\",\n \"def number_of_trips(filename): \\r\\n \\r\\n with open(filename, 'r') as f_in:\\r\\n # set up csv reader object\\r\\n trip_reader = csv.DictReader(f_in)\\r\\n \\r\\n # initialize count variables\\r\\n n_subscribers = 0\\r\\n n_customers = 0\\r\\n \\r\\n # tally up ride types\\r\\n for row in trip_reader:\\r\\n if row['user_type'] == 'Subscriber':\\r\\n n_subscribers += 1\\r\\n else:\\r\\n n_customers += 1\\r\\n \\r\\n # compute total number of rides\\r\\n n_total = n_subscribers + n_customers\\r\\n \\r\\n # return tallies as a tuple\\r\\n return(n_subscribers, n_customers, n_total)\",\n \"def file_len(fname):\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n Nrows = i + 1\\n return Nrows\",\n \"def n_n(output_path):\\n lef = {}\\n rig = {}\\n rellef = {}\\n relrig = {}\\n\\n triple = open(Path(output_path, \\\"train2id.txt\\\"), \\\"r\\\")\\n valid = open(Path(output_path, \\\"valid2id.txt\\\"), \\\"r\\\")\\n test = open(Path(output_path, \\\"test2id.txt\\\"), \\\"r\\\")\\n\\n ls = triple.readlines()\\n tot = len(ls) - 1\\n\\n # (int)(triple.readline())\\n for i in range(tot):\\n content = ls[i + 1]\\n # content = triple.readline()\\n h, t, r = content.strip().split()\\n if not (h, r) in lef:\\n lef[(h, r)] = []\\n if not (r, t) in rig:\\n rig[(r, t)] = []\\n lef[(h, r)].append(t)\\n rig[(r, t)].append(h)\\n if not r in rellef:\\n rellef[r] = {}\\n if not r in relrig:\\n relrig[r] = {}\\n rellef[r][h] = 1\\n relrig[r][t] = 1\\n\\n ls = valid.readlines()\\n tot = len(ls) - 1\\n # (int)(valid.readline())\\n for i in range(tot):\\n content = ls[i + 1]\\n # content = valid.readline()\\n h, t, r = content.strip().split()\\n if not (h, r) in lef:\\n lef[(h, r)] = []\\n if not (r, t) in rig:\\n rig[(r, t)] = []\\n lef[(h, r)].append(t)\\n rig[(r, t)].append(h)\\n if not r in rellef:\\n rellef[r] = {}\\n if not r in relrig:\\n relrig[r] = {}\\n rellef[r][h] = 1\\n relrig[r][t] = 1\\n\\n ls = test.readlines()\\n tot = len(ls) - 1\\n # (int)(test.readline())\\n for i in range(tot):\\n content = ls[i + 1]\\n # content = test.readline()\\n h, t, r = content.strip().split()\\n if not (h, r) in lef:\\n lef[(h, r)] = []\\n if not (r, t) in rig:\\n rig[(r, t)] = []\\n lef[(h, r)].append(t)\\n rig[(r, t)].append(h)\\n if not r in rellef:\\n rellef[r] = {}\\n if not r in relrig:\\n relrig[r] = {}\\n rellef[r][h] = 1\\n relrig[r][t] = 1\\n\\n test.close()\\n valid.close()\\n triple.close()\\n\\n f = open(Path(output_path, \\\"type_constrain.txt\\\"), \\\"w\\\")\\n f.write(\\\"%d\\\\n\\\" % (len(rellef)))\\n for i in rellef:\\n f.write(\\\"%s\\\\t%d\\\" % (i, len(rellef[i])))\\n for j in rellef[i]:\\n f.write(\\\"\\\\t%s\\\" % (j))\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"%s\\\\t%d\\\" % (i, len(relrig[i])))\\n for j in relrig[i]:\\n f.write(\\\"\\\\t%s\\\" % (j))\\n f.write(\\\"\\\\n\\\")\\n f.close()\\n\\n rellef = {}\\n totlef = {}\\n relrig = {}\\n totrig = {}\\n\\n for i in lef:\\n if not i[1] in rellef:\\n rellef[i[1]] = 0\\n totlef[i[1]] = 0\\n rellef[i[1]] += len(lef[i])\\n totlef[i[1]] += 1.0\\n\\n for i in rig:\\n if not i[0] in relrig:\\n relrig[i[0]] = 0\\n totrig[i[0]] = 0\\n relrig[i[0]] += len(rig[i])\\n totrig[i[0]] += 1.0\\n\\n s11 = 0\\n s1n = 0\\n sn1 = 0\\n snn = 0\\n f = open(Path(output_path, \\\"test2id.txt\\\"), \\\"r\\\")\\n ls = f.readlines()\\n tot = len(ls) - 1\\n # tot = (int)(f.readline())\\n for i in range(tot):\\n content = ls[i + 1]\\n # content = f.readline()\\n h, t, r = content.strip().split()\\n rign = rellef[r] / totlef[r]\\n lefn = relrig[r] / totrig[r]\\n if rign <= 1.5 and lefn <= 1.5:\\n s11 += 1\\n if rign > 1.5 and lefn <= 1.5:\\n s1n += 1\\n if rign <= 1.5 and lefn > 1.5:\\n sn1 += 1\\n if rign > 1.5 and lefn > 1.5:\\n snn += 1\\n f.close()\\n\\n f = open(Path(output_path, \\\"test2id.txt\\\"), \\\"r\\\")\\n f11 = open(Path(output_path, \\\"1-1.txt\\\"), \\\"w\\\")\\n f1n = open(Path(output_path, \\\"1-n.txt\\\"), \\\"w\\\")\\n fn1 = open(Path(output_path, \\\"n-1.txt\\\"), \\\"w\\\")\\n fnn = open(Path(output_path, \\\"n-n.txt\\\"), \\\"w\\\")\\n fall = open(Path(output_path,\\\"test2id_all.txt\\\"), \\\"w\\\")\\n fall = open(Path(output_path,\\\"test2id_all.txt\\\"), \\\"w\\\")\\n\\n ls = f.readlines()\\n tot = len(ls) - 1\\n\\n # tot = (int)(f.readline())\\n fall.write(\\\"%d\\\\n\\\" % (tot))\\n f11.write(\\\"%d\\\\n\\\" % (s11))\\n f1n.write(\\\"%d\\\\n\\\" % (s1n))\\n fn1.write(\\\"%d\\\\n\\\" % (sn1))\\n fnn.write(\\\"%d\\\\n\\\" % (snn))\\n for i in range(tot):\\n content = ls[i + 1]\\n # content = f.readline()\\n h, t, r = content.strip().split()\\n rign = rellef[r] / totlef[r]\\n lefn = relrig[r] / totrig[r]\\n if rign <= 1.5 and lefn <= 1.5:\\n f11.write(content)\\n fall.write(\\\"0\\\" + \\\"\\\\t\\\" + content)\\n if rign > 1.5 and lefn <= 1.5:\\n f1n.write(content)\\n fall.write(\\\"1\\\" + \\\"\\\\t\\\" + content)\\n if rign <= 1.5 and lefn > 1.5:\\n fn1.write(content)\\n fall.write(\\\"2\\\" + \\\"\\\\t\\\" + content)\\n if rign > 1.5 and lefn > 1.5:\\n fnn.write(content)\\n fall.write(\\\"3\\\" + \\\"\\\\t\\\" + content)\\n fall.close()\\n f.close()\\n f11.close()\\n f1n.close()\\n fn1.close()\\n fnn.close()\",\n \"def count_positions(fname):\\r\\n with open(fname) as f:\\r\\n for i, l in enumerate(f):\\r\\n pass\\r\\n return i + 1\",\n \"def data_info(data):\\n filename = data[\\\"filename\\\"]\\n X_var = data[\\\"X_var\\\"]\\n Y_var = data[\\\"Y_var\\\"]\\n X,Y = read_file(filename,X_var,Y_var)\\n input_dim = len(X_var)\\n output_dim = len(Y_var)\\n return X,Y,input_dim,output_dim\",\n \"def read_input(filename):\\n with open(filename, 'r') as f:\\n (N) = map(int, next(f).split())\\n def parse_line(line):\\n l = line.split()\\n h = 0 if l[0] == 'H' else 1\\n n = int(l[1])\\n return [h, l[2:]]\\n\\n photos = transform_strings([parse_line(line) for line in f])\\n return (len(photos), photos)\",\n \"def getNumVertexes(self):\\n return _osgAnimation.RigTransformHardware_getNumVertexes(self)\",\n \"def __len__(self):\\n return len(self._vertices)\",\n \"def getFileCount(self) -> int:\\n ...\",\n \"def parse_triangle_files(self):\\n nodes = {}\\n boundary_nodes = []\\n\\n # parse node file into nodes\\n with open(self.files['node']) as node_file:\\n header = True\\n for line in node_file:\\n if header:\\n header = False\\n continue\\n content = list(filter(lambda a: bool(a), line.split(' '))) # pylint: disable=W0108\\n if not '#' in content[0]:\\n is_boundary = content[3] == '1\\\\n'\\n nodes[int(content[0])] = {\\n 'id': int(content[0]),\\n 'coords': [int(content[1]), int(content[2])],\\n 'distance': 0 if is_boundary else None,\\n 'relations': [],\\n 'level_cycles': [], # ids of any level cycles this node is a part of\\n 'level_paths': [], # ids of any level paths this node is a part of\\n 'is_root_element': False,\\n 'betweener_paths': []\\n }\\n if is_boundary:\\n boundary_nodes.append(int(content[0]))\\n node_file.close()\\n\\n # parse edge files into node relations\\n with open(self.files['edge']) as edge_file:\\n header = True\\n for line in edge_file:\\n if header:\\n header = False\\n continue\\n content = list(filter(bool, line.split(' ')))\\n if not '#' in content[0]:\\n nodes[int(content[1])]['relations'].append(int(content[2]))\\n nodes[int(content[2])]['relations'].append(int(content[1]))\\n edge_file.close()\\n\\n # with open(self.files['ele']) as ele_file:\\n # header = True\\n # for line in edge_file:\\n # if header:\\n # header = False\\n # continue\\n # content = list(filter(bool, line.split(' ')))\\n # if not '#' in content[0]:\\n # nodes[int(content[1])]['relations'].append(int(content[2]))\\n # nodes[int(content[2])]['relations'].append(int(content[1]))\\n # edge_file.close()\\n\\n # sorts relations clockwise\\n for node_id, node in nodes.items():\\n nodes[node_id]['relations'] = sorted(node['relations'], key=(\\n lambda related_node_id: (\\n self.calculate_clockwise_angle_and_distance(node, nodes.get(related_node_id)) # pylint: disable=W0640\\n )\\n ))\\n\\n levels = self.get_levels(nodes, boundary_nodes)\\n\\n for level in levels:\\n for node_id in level['node_ids']:\\n self.identify_special_nodes(nodes, node_id)\\n\\n return nodes, boundary_nodes, levels\",\n \"def line_count(file):\\n with open(file, \\\"r\\\") as f:\\n return sum(1 for line in f)\",\n \"def get_num_examples(path_in):\\n i = 0\\n with open(path_in, 'r', encoding='utf8') as f:\\n for _ in f:\\n i += 1\\n return i\",\n \"def read_test_tuples():\\n lines = read_input(25, True)\\n point_sets = list(parse_points(lines))\\n expected_counts = [4, 3, 8]\\n\\n return zip(point_sets, expected_counts)\",\n \"def fileCount(self):\\n pass\",\n \"def loadVTKPolydataFile(infile):\\n\\n lines = None\\n\\n with open(infile, 'rt') as f:\\n lines = f.readlines()\\n\\n lines = [l.strip() for l in lines]\\n\\n if lines[3] != 'DATASET POLYDATA':\\n raise ValueError('Only the POLYDATA data type is supported')\\n\\n nVertices = int(lines[4].split()[1])\\n nPolygons = int(lines[5 + nVertices].split()[1])\\n nIndices = int(lines[5 + nVertices].split()[2]) - nPolygons\\n\\n vertices = np.zeros((nVertices, 3), dtype=np.float32)\\n polygonLengths = np.zeros( nPolygons, dtype=np.uint32)\\n indices = np.zeros( nIndices, dtype=np.uint32)\\n\\n for i in range(nVertices):\\n vertLine = lines[i + 5]\\n vertices[i, :] = [float(w) for w in vertLine.split()]\\n\\n indexOffset = 0\\n for i in range(nPolygons):\\n\\n polyLine = lines[6 + nVertices + i].split()\\n polygonLengths[i] = int(polyLine[0])\\n\\n start = indexOffset\\n end = indexOffset + polygonLengths[i]\\n indices[start:end] = [int(w) for w in polyLine[1:]]\\n\\n indexOffset += polygonLengths[i]\\n\\n return vertices, polygonLengths, indices\",\n \"def readGR3File(inputFilename):\\n print 'Reading ' + inputFilename + ' ...'\\n infile = open(inputFilename, 'r')\\n description = infile.readline().strip() # remove leading/trailing whitespace\\n tmpStr = infile.readline()\\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\\n\\n # nodes\\n nodeArray = readNodeBlock(infile, nNodes)\\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\\n nodexyz = np.zeros((nNodes, 3))\\n nodexyz[:, :2] = nodeArray[:, 1:3]\\n nodalValues = nodeArray[:, 3]\\n\\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\\n\\n # triangular elements\\n triArray = readElemBlock(infile, nTriangles)\\n\\n trinum = triArray[:, 0].flatten()\\n tritype = triArray[0, 1]\\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\\n #triangles = meshElements(trinodes,trinum,tritype)\\n\\n x = nodexyz[:, 0]\\n y = nodexyz[:, 1]\\n\\n tmpStr = infile.readline()\\n boundaries = []\\n if len(tmpStr) > 0:\\n # boundary information, if not end of file\\n nOpenBndSegments = int(tmpStr.split()[0])\\n nOpenBndNodesTot = int(infile.readline().split()[0])\\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\\n for iBnd in range(nOpenBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n tag = bndHeader[-1]\\n if tag.isdigit():\\n tag = 'open' + tag\\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\\n nLandBndSegments = int(infile.readline().split()[0])\\n nLandBndNodesTot = int(infile.readline().split()[0])\\n landBndTags = range(\\n nOpenBndSegments + 1,\\n nOpenBndSegments + nLandBndSegments + 1)\\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\\n for iBnd in range(nLandBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n try:\\n landType = int(bndHeader[1])\\n except:\\n print \\\"\\\"\\\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\\n 1002 = Total number of closed boundary nodes\\n 501 0 = Number of nodes in closed boundary 1\\\"\\\"\\\"\\n raise Exception(\\n 'Could not parse land boundary type (0/1 - land/island)\\\\n')\\n landType = 'island' if landType == 1 else 'land'\\n tag = landType + bndHeader[-1]\\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\\n\\n infile.close()\\n\\n # for better interpolation, round coordinates to 1e-4\\n nDig = 4\\n x = np.round(x, nDig)\\n y = np.round(y, nDig)\\n\\n return x, y, nodalValues, trinodes, boundaries, description\",\n \"def count_data_items(fileids, train=True):\\n sizes = 28000 if train else 22500\\n return len(fileids) * sizes\",\n \"def linesCountingAux(file_name, nProcesses):\\r\\n\\r\\n linesPerProcessesList = []\\r\\n\\r\\n with open(file_name, \\\"r\\\") as file:\\r\\n lineCounting = 0\\r\\n\\r\\n for line in file:\\r\\n lineCounting += 1 #discover the lines in the text file\\r\\n\\r\\n linesPerProcesses = lineCounting // nProcesses\\r\\n\\r\\n for number in range(nProcesses):\\r\\n linesPerProcessesList.append(linesPerProcesses)\\r\\n if sum(linesPerProcessesList) < lineCounting:\\r\\n for number in range (lineCounting - sum(linesPerProcessesList)):\\r\\n linesPerProcessesList[number] += 1\\r\\n\\r\\n return linesPerProcessesList\",\n \"def data_edge_count(self) -> int:\\n return int(self.graph_tuple_stats.data_edge_count or 0)\",\n \"def create_1d_coil_geometry(division, filename, directory):\\n os.chdir(directory)\\n npoints = division + 1\\n length_array = np.zeros((npoints, 2))\\n current_length = 0\\n array = np.loadtxt(filename)\\n for i in range(1, npoints):\\n current_length += ((array[i, 1] - array[i - 1, 1]) ** 2 + (array[i, 2] - array[i - 1, 2]) ** 2 +\\n (array[i, 3] - array[i - 1, 3]) ** 2) ** 0.5\\n length_array[i - 1, 0] = i\\n length_array[i, 1] = current_length\\n length_array[npoints - 1, 0] = npoints\\n return length_array\",\n \"def get_number_lines(running_reward_file, running_loss_file, action_count_file):\\n if Path(running_reward_file).exists():\\n data = np.loadtxt(running_reward_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(running_loss_file).exists():\\n data = np.loadtxt(running_loss_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(action_count_file).exists():\\n data = np.loadtxt(action_count_file).reshape(-1,2)\\n return data.shape[0]\\n raise NameError(\\\"No files to count lines\\\")\",\n \"def calc_side_lengths(triangles):\\n first_vec = [2, 0, 1]\\n second_vec = [1, 2, 0]\\n sides = triangles[:, first_vec] - triangles[:, second_vec]\\n lengths = np.sqrt(np.sum(sides**2, axis=2))\\n return lengths\",\n \"def countLength():\\n counter = 0\\n\\n with open('bc.processed3.csv', 'r') as openfile:\\n for line in openfile:\\n counter += 1\\n if counter == 1:\\n print line\\n\\n print('Length: ', counter)\",\n \"def pre_process_file(filename):\\n\\n num_lines = 0\\n vm_ids = set()\\n with open(filename) as trace:\\n for item in csv.reader(trace, delimiter=','):\\n num_lines += 1\\n disk_id = int(item[2])\\n vm_ids.add(disk_id) # can make it more efficient\\n no_of_vms = len(vm_ids)\\n return (num_lines, no_of_vms, vm_ids)\",\n \"def load_triangles(triangle_bytes, header):\\n triangles = list()\\n for i in range(header.num_tris):\\n triangle = triangle_t(list(struct.unpack(\\\"<hhh\\\", triangle_bytes[12*i:12*i+6])), list(struct.unpack(\\\"<hhh\\\", triangle_bytes[12*i+6:12*i+12])))\\n # print(triangle)\\n triangles.append(triangle)\\n return triangles\",\n \"def xFileInfo(filename):\\n delim = getDelimiter(filename)\\n f = open(filename, 'r')\\n reader = csv.reader(f, delimiter=delim)\\n num_rows = 0\\n for (row_i, row) in enumerate(reader):\\n if row_i == 0: #ignore empty strings (e.g. at end of row)\\n num_cols = len([val for val in row if val])\\n num_rows += 1\\n f.close()\\n return (num_rows, num_cols)\",\n \"def load_counts(filename, lengths=None, base=None):\\n n = None\\n if lengths is not None:\\n n = lengths.sum()\\n shape = (n, n)\\n else:\\n shape = None\\n # This is the interaction count files\\n dataframe = pd.read_csv(filename, sep=\\\"\\\\t\\\", comment=\\\"#\\\", header=None)\\n row, col, data = dataframe.as_matrix().T\\n\\n # If there are NAs remove them\\n mask = np.isnan(data)\\n if np.any(mask):\\n warnings.warn(\\n \\\"NAs detected in %s. \\\"\\n \\\"Removing NAs and replacing with 0.\\\" % filename)\\n row = row[np.invert(mask)]\\n col = col[np.invert(mask)]\\n data = data[np.invert(mask)]\\n\\n # XXX We need to deal with the fact that we should not duplicate entries\\n # for the diagonal.\\n # XXX what if n doesn't exist?\\n if base is not None:\\n if base not in [0, 1]:\\n raise ValueError(\\\"indices should start either at 0 or 1\\\")\\n col -= base\\n row -= base\\n else:\\n warnings.warn(\\n \\\"Attempting to guess whether counts are 0 or 1 based\\\")\\n\\n if (col.min() >= 1 and row.min() >= 1) and \\\\\\n ((n is None) or (col.max() == n)):\\n # This is a hack to deal with the fact that sometimes, the files\\n # are indexed at 1 and not 0\\n\\n col -= 1\\n row -= 1\\n\\n if shape is None:\\n n = max(col.max(), row.max()) + 1\\n shape = (n, n)\\n\\n data = data.astype(float)\\n counts = sparse.coo_matrix((data, (row, col)), shape=shape)\\n return counts\",\n \"def get_filesize(inputfile) -> int:\\n with open(inputfile, \\\"rb\\\") as f:\\n lines = 0\\n buf_size = 1024 * 1024\\n read_f = f.raw.read\\n\\n buf = read_f(buf_size)\\n while buf:\\n lines += buf.count(b\\\"\\\\n\\\")\\n buf = read_f(buf_size)\\n\\n return lines\",\n \"def get_dimensions ( file_in, separator ) :\\n try :\\n logger.info ( \\\"Extract dimensions from xyz file \\\" + str(file_in) ) \\n d = {}\\n first_row = True\\n d[NOPS] = 0\\n file = open(file_in, 'r')\\n for line in file :\\n d[NOPS] = d[NOPS] + 1\\n l = line.rstrip().split(separator)\\n x = float(l[0])\\n y = float(l[1])\\n z = float(l[2])\\n if first_row :\\n d[MINX] = x\\n d[MAXX] = x\\n d[MINY] = y\\n d[MAXY] = y\\n d[MINZ] = z\\n d[MAXZ] = z\\n first_row = False\\n else :\\n if x < d[MINX] :\\n d[MINX] = x\\n if x > d[MAXX] :\\n d[MAXX] = x \\n if y < d[MINY] :\\n d[MINY] = y\\n if y > d[MAXY] :\\n d[MAXY] = y \\n if z < d[MINZ] :\\n d[MINZ] = z\\n if z > d[MAXZ] :\\n d[MAXZ] = z \\n file.close() \\n logger.info ('Now return')\\n return d\\n except Exception, err:\\n logger.critical(\\\"Extract dimensions from xyz file failed: ERROR: %s\\\\n\\\" % str(err))\\n raise\",\n \"def count_cop(self, infile):\\n n_cop = 0\\n dgs_in = self._file_handler.file_to_dg_list(infile)\\n for dg in dgs_in:\\n if dg.has_cop_deprel():\\n n_cop += 1\\n return n_cop, len(dgs_in)\",\n \"def listRows(file):\\n\\twith open(file) as f:\\n\\t\\tcount = 0\\n\\t\\tfor line in f.readlines():\\n\\t\\t\\tcount += 1\\n\\t\\treturn count -1\",\n \"def getVertexNumbers(self):\\n return self.vertexIndex.keys()\",\n \"def reader(filename,only_length=False):\\n print(\\\"Counting lines in file %s\\\"%filename)\\n total_lines=0\\n for n,line in enumerate(open(filename,\\\"r\\\")):\\n total_lines+=1\\n \\n if only_length:\\n return total_lines\\n \\n X,Y,Z,W,J=[np.zeros(total_lines) for _ in range(5)]\\n \\n for n, line in enumerate(open(filename, 'r')):\\n if n%1000000==0:\\n print(\\\"Reading line %d of %d from file %s\\\" %(n,total_lines,filename))\\n split_line=np.array(line.split(\\\" \\\"), dtype=float) \\n X[n]=split_line[0];\\n Y[n]=split_line[1];\\n Z[n]=split_line[2];\\n W[n]=split_line[3];\\n J[n]=int(split_line[4]);\\n return X,Y,Z,W,J\",\n \"def pfd_read (r) :\\n s = r.readline()\\n if s == \\\"\\\" :\\n return False\\n l = s.split()\\n numVert = int(l[0])\\n numRule = int(l[1])\\n assert numVert > 0 and numVert <= 100\\n assert numRule > 0 and numRule <= 100\\n v = [[0,[]]] #build the Vertex array\\n for i in range(1, numVert):\\n temp = [0,[]]\\n v.append(temp)\\n s = r.readline()\\n for i in range(0, numRule):\\n if s == \\\"\\\":\\n return False\\n l = s.split()\\n v[int(l[0])-1][0] = int(l[1]) #verts[l[0]].numPred = l[1]\\n #verts[l[0]].preds = [0] * (len(l)-2) #I don't know whether this line is necessary\\n lenl = len(l)\\n for j in range(2,lenl):\\n v[int(l[j])-1][1].append(int(l[0]))\\n #verts[l[j]-1].succ.append(l[0]) \\n s = r.readline()\\n return v\",\n \"def part1(filename: str) -> int:\\n data = first_line(filename)\\n data = json.loads(data)\\n return total_nums(data)\",\n \"def get_num_vertices(self):\\n\\n return self._graph_state.get_num_vertices()\",\n \"def get_dataset_size(file_path):\\n size = 1\\n file_list = tf.io.gfile.glob(file_path)\\n for file in file_list:\\n for record in tf.compat.v1.io.tf_record_iterator(file, options=tf.io.TFRecordOptions(\\n compression_type='GZIP')):\\n size += 1\\n return size\",\n \"def from_polyfile(name):\\n\\n from anuga.utilities.numerical_tools import anglediff\\n from math import pi\\n import os.path\\n root, ext = os.path.splitext(name)\\n\\n if ext == 'poly':\\n filename = name\\n else:\\n filename = name + '.poly'\\n\\n\\n fid = open(filename)\\n\\n points = [] #x, y\\n values = [] #z\\n ##vertex_values = [] #Repeated z\\n triangles = [] #v0, v1, v2\\n\\n lines = fid.readlines()\\n\\n keyword = lines[0].strip()\\n msg = 'First line in .poly file must contain the keyword: POINTS'\\n assert keyword == 'POINTS', msg\\n\\n offending = 0\\n i = 1\\n while keyword == 'POINTS':\\n line = lines[i].strip()\\n i += 1\\n\\n if line == 'POLYS':\\n keyword = line\\n break\\n\\n fields = line.split(':')\\n assert int(fields[0]) == i-1, 'Point indices not consecutive'\\n\\n #Split the three floats\\n xyz = fields[1].split()\\n\\n x = float(xyz[0])\\n y = float(xyz[1])\\n z = float(xyz[2])\\n\\n points.append([x, y])\\n values.append(z)\\n\\n\\n k = i\\n while keyword == 'POLYS':\\n line = lines[i].strip()\\n i += 1\\n\\n if line == 'END':\\n keyword = line\\n break\\n\\n\\n fields = line.split(':')\\n assert int(fields[0]) == i-k, 'Poly indices not consecutive'\\n\\n #Split the three indices\\n vvv = fields[1].split()\\n\\n i0 = int(vvv[0])-1\\n i1 = int(vvv[1])-1\\n i2 = int(vvv[2])-1\\n\\n #Check for and exclude degenerate areas\\n x0 = points[i0][0]\\n y0 = points[i0][1]\\n x1 = points[i1][0]\\n y1 = points[i1][1]\\n x2 = points[i2][0]\\n y2 = points[i2][1]\\n\\n area = abs((x1*y0-x0*y1)+(x2*y1-x1*y2)+(x0*y2-x2*y0))/2\\n if area > 0:\\n\\n #Ensure that points are arranged in counter clock-wise order\\n v0 = [x1-x0, y1-y0]\\n v1 = [x2-x1, y2-y1]\\n v2 = [x0-x2, y0-y2]\\n\\n a0 = anglediff(v1, v0)\\n a1 = anglediff(v2, v1)\\n a2 = anglediff(v0, v2)\\n\\n\\n if a0 < pi and a1 < pi and a2 < pi:\\n #all is well\\n j0 = i0\\n j1 = i1\\n j2 = i2\\n else:\\n #Swap two vertices\\n j0 = i1\\n j1 = i0\\n j2 = i2\\n\\n triangles.append([j0, j1, j2])\\n ##vertex_values.append([values[j0], values[j1], values[j2]])\\n else:\\n offending +=1\\n\\n log.critical('Removed %d offending triangles out of %d'\\n % (offending, len(lines)))\\n return points, triangles, values\",\n \"def __len__(self) -> int:\\n import h5py\\n\\n with h5py.File(\\n os.path.join(self.root, self.data_dir, self.img_file_name), \\\"r\\\"\\n ) as f:\\n num_datapoints: int = f[self.split][\\\"pv_log\\\"].shape[0]\\n\\n return num_datapoints\",\n \"def get_triangles( self, N ):\\n\\n # store N as an instance variable\\n self.N = N\\n\\n # initialize array to store locations of points for all triangles in the\\n # tessellation sequence\\n self.triangles = np.zeros( ( self.N, 3, 2 ) )\\n\\n # define points of the first triangle in the tessellation sequence\\n point_c = np.array( [ 0, 0 ] )\\n point_b = self.a * np.array( [ np.cos( self.C ), np.sin( self.C ) ] )\\n point_a = np.array( [ self.b, 0 ] )\\n\\n # stack the points into a single array of shape (3, 2 )\\n triangle = np.vstack( [ point_c, point_b, point_a ] )\\n\\n # loop over the number of triangles in the sequence\\n for i in range( self.N ):\\n\\n # store the points of the i-th triangle in the array\\n self.triangles[ i ] = triangle\\n\\n # compute the next triangle in the tessellation sequence\\n triangle = self.next_triangle( triangle = triangle )\\n\\n # shift the next triangle in the tessellation sequence such that its\\n # point C is in the same location as point B of the previous triangle\\n triangle += ( self.triangles[ i - 1, 1 ] - self.triangles[ 0, 0 ] )\",\n \"def so_data_statistics(data_file):\\n with open(data_file, \\\"r\\\") as f:\\n data = json.load(f)\\n\\n answer_to_num_questions = collections.Counter()\\n comment_to_num_questions = collections.Counter()\\n num_comments = 0\\n num_answers = 0\\n num_questions = len(data)\\n\\n for q in data:\\n q = json.loads(q)\\n q_comments = 0\\n q_comments += len(q[\\\"comments\\\"])\\n q_answers = len(q[\\\"answers\\\"])\\n for a in q[\\\"answers\\\"]:\\n q_comments += len(a[\\\"comments\\\"])\\n\\n answer_to_num_questions[q_answers] += 1\\n comment_to_num_questions[q_comments] += 1\\n\\n num_comments += q_comments\\n num_answers += q_answers\\n\\n print \\\"Num comments: {0}, Num answers: {1}, Num_questions: {2}\\\".format(\\n num_comments, num_answers, num_questions)\\n print \\\"-\\\" * 10\\n print \\\"Answers map: \\\", answer_to_num_questions\\n print \\\"Comments map: \\\", comment_to_num_questions\\n\\n return num_comments, num_answers, num_questions, answer_to_num_questions, \\\\\\n comment_to_num_questions\",\n \"def numberFiles(self):\\n with open(self.inputfile) as fin:\\n for n, _ in enumerate(fin, start=1): pass\\n self.n = n\\n return self.n\",\n \"def get_vertices_num(self):\\n return self.coords.shape[0]\",\n \"def strang_mesh(filename):\\n\\n from math import pi\\n from anuga.utilities.numerical_tools import anglediff\\n\\n\\n fid = open(filename)\\n points = [] # List of x, y coordinates\\n triangles = [] # List of vertex ids as listed in the file\\n\\n for line in fid.readlines():\\n fields = line.split()\\n if len(fields) == 2:\\n # we are reading vertex coordinates\\n points.append([float(fields[0]), float(fields[1])])\\n elif len(fields) == 3:\\n # we are reading triangle point id's (format ae+b)\\n triangles.append([int(float(fields[0]))-1,\\n int(float(fields[1]))-1,\\n int(float(fields[2]))-1])\\n else:\\n raise Excetion('wrong format in %s' % filename)\\n\\n elements = [] #Final list of elements\\n\\n for t in triangles:\\n #Get vertex coordinates\\n v0 = t[0]\\n v1 = t[1]\\n v2 = t[2]\\n\\n x0 = points[v0][0]\\n y0 = points[v0][1]\\n x1 = points[v1][0]\\n y1 = points[v1][1]\\n x2 = points[v2][0]\\n y2 = points[v2][1]\\n\\n #Check that points are arranged in counter clock-wise order\\n vec0 = [x1-x0, y1-y0]\\n vec1 = [x2-x1, y2-y1]\\n vec2 = [x0-x2, y0-y2]\\n\\n a0 = anglediff(vec1, vec0)\\n a1 = anglediff(vec2, vec1)\\n a2 = anglediff(vec0, vec2)\\n\\n if a0 < pi and a1 < pi and a2 < pi:\\n elements.append([v0, v1, v2])\\n else:\\n elements.append([v0, v2, v1])\\n\\n return points, elements\",\n \"def vertex_count(self):\\n return len(self._outgoing)\",\n \"def n_file_elements(cls):\\n \\n return randint(1, (3 * Root.size))\",\n \"def contig_count(contig):\\n return sum([1 for line in open(contig, 'rU').readlines() if line.startswith('>')])\",\n \"def load_lengths(filename, return_base=False):\\n data = pd.read_csv(filename, sep=\\\"\\\\t\\\", comment=\\\"#\\\", header=None)\\n data = data.as_matrix()\\n _, idx, lengths = np.unique(data[:, 0], return_counts=True,\\n return_index=True)\\n if return_base:\\n return lengths[idx.argsort()], data[0, 3]\\n else:\\n return lengths[idx.argsort()]\",\n \"def __init__(self, file_path):\\n\\n # Comments\\n # mtllib mtl_name\\n # o object_name\\n # v x y z\\n # vt u v\\n # vn x y z\\n # f v0/t0/n0 v1/t1/n1 v2/t2/n2\\n\\n print('loading mesh \\\"%s\\\"' % file_path)\\n mesh_file = open(file_path, 'r')\\n\\n verts = []\\n texs = []\\n normals = []\\n faces = []\\n\\n # For each line of the input file\\n for line in mesh_file:\\n line = line.rstrip(' \\\\r\\\\n')\\n\\n # Skip comments\\n if line.startswith('#') or line == '':\\n continue\\n\\n tokens = line.split(' ')\\n tokens = map(lambda t: t.strip(' '), tokens)\\n tokens = list(filter(lambda t: t != '', tokens))\\n\\n prefix = tokens[0]\\n tokens = tokens[1:]\\n\\n if prefix == 'v':\\n vert = list(map(lambda v: float(v), tokens))\\n verts.append(vert)\\n\\n if prefix == 'vt':\\n tc = list(map(lambda v: float(v), tokens))\\n texs.append(tc)\\n\\n if prefix == 'vn':\\n normal = list(map(lambda v: float(v), tokens))\\n normals.append(normal)\\n\\n if prefix == 'f':\\n assert len(tokens) == 3, \\\"only triangle faces are supported\\\"\\n\\n face = []\\n for token in tokens:\\n indices = list(map(lambda idx: int(idx), token.split('/')))\\n face.append(indices)\\n\\n faces.append(face)\\n\\n mesh_file.close()\\n\\n self.num_faces = len(faces)\\n\\n print('num verts=%d' % len(verts))\\n print('num_faces=%d' % self.num_faces)\\n\\n # Create numpy arrays to store the vertex data\\n list_verts = np.zeros(shape=(3 * self.num_faces, 3), dtype=np.float32)\\n list_texcs = np.zeros(shape=3 * 2 * self.num_faces, dtype=np.float32)\\n list_norms = np.zeros(shape=3 * 3 * self.num_faces, dtype=np.float32)\\n\\n cur_vert_idx = 0\\n\\n # For each triangle\\n for face in faces:\\n # For each triplet of indices\\n for triplet in face:\\n v_idx, t_idx, n_idx = triplet\\n\\n # Note: OBJ uses 1-based indexing\\n vert = verts[v_idx-1]\\n texc = texs[t_idx-1]\\n normal = normals[n_idx-1]\\n\\n list_verts[cur_vert_idx, :] = vert\\n list_texcs[2*cur_vert_idx:2*(cur_vert_idx+1)] = texc\\n list_norms[3*cur_vert_idx:3*cur_vert_idx+3] = normal\\n\\n cur_vert_idx += 1\\n\\n # Re-center the object so that y=0 is at the base,\\n # and the object is centered in x and z\\n x_coords = list_verts[:, 0]\\n z_coords = list_verts[:, 2]\\n min_y = list_verts[:, 1].min()\\n mean_x = (x_coords.min() + x_coords.max()) / 2\\n mean_z = (z_coords.min() + z_coords.max()) / 2\\n list_verts[:, 1] -= min_y\\n list_verts[:, 0] -= mean_x\\n list_verts[:, 2] -= mean_z\\n\\n # Compute the object extents after centering\\n x_coords = list_verts[:, 0]\\n y_coords = list_verts[:, 1]\\n z_coords = list_verts[:, 2]\\n self.y_max = y_coords.max()\\n\\n # Create a vertex list to be used for rendering\\n self.vlist = pyglet.graphics.vertex_list(\\n 3 * self.num_faces,\\n ('v3f', list_verts.reshape(-1)),\\n ('t2f', list_texcs),\\n ('n3f', list_norms)\\n )\\n\\n # Load the texture associated with this mesh\\n file_name = os.path.split(file_path)[-1]\\n tex_name = file_name.split('.')[0]\\n tex_path = get_file_path('textures', tex_name, 'png')\\n self.texture = load_texture(tex_path)\",\n \"def count_elements(path):\\n count = 0\\n with open(path, 'r') as f:\\n groups = f.read().split('\\\\n\\\\n')\\n for idx in range(len(groups)):\\n word = groups[idx].split('\\\\n')\\n no_of_ele = len(word)\\n for i in range(no_of_ele-1):\\n word[0] = word[0]+word[i+1]\\n count += len(''.join(set(word[0])))\\n return count\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7772032","0.68836695","0.6398507","0.6369426","0.6362666","0.6353974","0.6323438","0.6275137","0.6170703","0.6165812","0.6144632","0.613628","0.61066425","0.6011404","0.59843576","0.59520715","0.5947087","0.594126","0.5935394","0.59270984","0.59270984","0.5926359","0.58514494","0.58492893","0.5836909","0.5831675","0.5831274","0.5829534","0.5826601","0.5811575","0.5778336","0.57675093","0.57538956","0.5736303","0.57076925","0.56999564","0.5698479","0.5696968","0.5686956","0.56687146","0.56588656","0.5653763","0.56502515","0.56390417","0.56024665","0.55870193","0.55844635","0.5567423","0.55649316","0.55533016","0.55512464","0.5543442","0.5538092","0.5535526","0.5528358","0.55240124","0.55178404","0.5515604","0.5512616","0.55055326","0.5478094","0.5473247","0.5468789","0.54656523","0.5454213","0.5453736","0.5441365","0.5434383","0.54289347","0.5426822","0.54233533","0.5419709","0.541386","0.5409464","0.54024774","0.54002786","0.53972715","0.5392659","0.5391981","0.53919524","0.53839743","0.53838426","0.538381","0.5370671","0.5369772","0.5365828","0.5359394","0.53585804","0.5356658","0.5348002","0.5342111","0.53373665","0.53328633","0.5328268","0.53233385","0.5323326","0.53231263","0.5319525","0.5303401","0.5298637"],"string":"[\n \"0.7772032\",\n \"0.68836695\",\n \"0.6398507\",\n \"0.6369426\",\n \"0.6362666\",\n \"0.6353974\",\n \"0.6323438\",\n \"0.6275137\",\n \"0.6170703\",\n \"0.6165812\",\n \"0.6144632\",\n \"0.613628\",\n \"0.61066425\",\n \"0.6011404\",\n \"0.59843576\",\n \"0.59520715\",\n \"0.5947087\",\n \"0.594126\",\n \"0.5935394\",\n \"0.59270984\",\n \"0.59270984\",\n \"0.5926359\",\n \"0.58514494\",\n \"0.58492893\",\n \"0.5836909\",\n \"0.5831675\",\n \"0.5831274\",\n \"0.5829534\",\n \"0.5826601\",\n \"0.5811575\",\n \"0.5778336\",\n \"0.57675093\",\n \"0.57538956\",\n \"0.5736303\",\n \"0.57076925\",\n \"0.56999564\",\n \"0.5698479\",\n \"0.5696968\",\n \"0.5686956\",\n \"0.56687146\",\n \"0.56588656\",\n \"0.5653763\",\n \"0.56502515\",\n \"0.56390417\",\n \"0.56024665\",\n \"0.55870193\",\n \"0.55844635\",\n \"0.5567423\",\n \"0.55649316\",\n \"0.55533016\",\n \"0.55512464\",\n \"0.5543442\",\n \"0.5538092\",\n \"0.5535526\",\n \"0.5528358\",\n \"0.55240124\",\n \"0.55178404\",\n \"0.5515604\",\n \"0.5512616\",\n \"0.55055326\",\n \"0.5478094\",\n \"0.5473247\",\n \"0.5468789\",\n \"0.54656523\",\n \"0.5454213\",\n \"0.5453736\",\n \"0.5441365\",\n \"0.5434383\",\n \"0.54289347\",\n \"0.5426822\",\n \"0.54233533\",\n \"0.5419709\",\n \"0.541386\",\n \"0.5409464\",\n \"0.54024774\",\n \"0.54002786\",\n \"0.53972715\",\n \"0.5392659\",\n \"0.5391981\",\n \"0.53919524\",\n \"0.53839743\",\n \"0.53838426\",\n \"0.538381\",\n \"0.5370671\",\n \"0.5369772\",\n \"0.5365828\",\n \"0.5359394\",\n \"0.53585804\",\n \"0.5356658\",\n \"0.5348002\",\n \"0.5342111\",\n \"0.53373665\",\n \"0.53328633\",\n \"0.5328268\",\n \"0.53233385\",\n \"0.5323326\",\n \"0.53231263\",\n \"0.5319525\",\n \"0.5303401\",\n \"0.5298637\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64086694"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":81,"cells":{"query":{"kind":"string","value":"This function will return data about the number of triangles on each surface in a file inputs"},"document":{"kind":"string","value":"def get_triangles_per_surface(my_core, entity_ranges):\n\n t_p_s = {}\n for surface in entity_ranges['Surfaces']:\n t_p_s[surface] = my_core.get_entities_by_type(\n surface, types.MBTRI).size()\n return t_p_s"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def count_triangles(self, file):\n self.nibble(80)\n return struct.unpack(\"@i\", self.nibble(4))[0]","def get_num_vertices(triangles):\n return numpy.amax(numpy.reshape(triangles, -1)) + 1","def count_cells(fpath):\n cells = []\n for i in range(40):\n fname = f\"{fpath}/Mesh2d_{i}.vtu\"\n if not os.path.exists(fname):\n print(f\"File {fname} does not exist.\")\n break\n mesh = meshio.read(fname)\n for cell_block in mesh.cells:\n if cell_block.type in (\"triangle\"):\n num_cells = len(cell_block)\n print(f\"{i:2d}: {num_cells:6d} elements, {len(mesh.points):6d} vertices\")\n cells.append(num_cells)\n continue\n return cells","def num_data_lines(filepath):\n\tif not file_exists(filepath):\n\t\treturn -1\n\tcount = 0\n\twith open(filepath, 'r') as f:\n\t\twhile read_floats(f):\n\t\t\tcount += 1\n\tf.close()\n\treturn count","def countSamples(filename):\n with open(filename, \"r\") as f:\n line = f.readline().split(\"\\t\")\n return len(line) - 2","def _get_las_npoints(fpath):\n with laspy.file.File(fpath) as f:\n return f.header.count","def get_triangles_per_vertex(my_core, native_ranges):\n\n t_p_v_data = []\n tri_dimension = 2\n for vertex in native_ranges[types.MBVERTEX]:\n t_p_v_data.append(my_core.get_adjacencies(vertex, tri_dimension).size())\n return np.array(t_p_v_data)","def test_triangle_count_05(self):\n body = {\"direction\": \"IN\", \"degree\": -1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 2}\n else:\n assert 0","def test_triangle_count_08(self):\n body = {\"direction\": \"IN\", \"degree\": 1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 0}\n else:\n assert 0","def test_triangle_count_04(self):\n body = {\"direction\": \"OUT\"}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\n else:\n assert 0","def test_triangle_count_06(self):\n body = {\"direction\": \"OUT\", \"degree\": -1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\n else:\n assert 0","def countDataSize(self,filename):\n \n try:\n d = h5py.File(filename,'r')\n except:\n print(filename)\n return \n\n N = 0\n scan_edges = d['level2/Statistics/scan_edges'][:]\n for (start,end) in scan_edges:\n N += (end-start)//self.offsetLen * self.offsetLen\n d.close()\n\n N = N*self.Nfeeds\n\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\n self.datasizes += [int(N/self.Nfeeds)]\n self.Nsamples += int(N)","def countDataSize(self,filename):\n \n d = h5py.File(filename,'r')\n features = d['spectrometer/features'][:]\n select = self.selectData(features.astype(float), self.ifeature, d)\n N = len(features[select])\n d.close()\n\n N = (N//self.offsetLen) * self.offsetLen\n\n N = N*self.Nfeeds\n\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\n self.datasizes += [int(N/self.Nfeeds)]\n self.Nsamples += int(N)","def data_info(data):\n filename = data[\"filename\"]\n X_var = data[\"X_var\"]\n Y_var = data[\"Y_var\"]\n X,Y = read_file(filename,X_var,Y_var)\n input_dim = len(X_var)\n output_dim = len(Y_var)\n return X,Y,input_dim,output_dim","def count_aux(self, infile):\n n_aux = 0\n n_tokens = 0\n dgs_in = self._file_handler.file_to_dg_list(infile)\n for dg in dgs_in:\n n_tokens += len(dg)\n transform = VGtransformer(dg, dep_style=self._dep_style)\n transform.transform()\n n_aux += transform.tot_aux\n return n_aux, n_tokens, len(dgs_in)","def read_datafile(shower_name):\n\twith open(datafile_dir+'datafile_'+shower_name) as file:\n\t\tamplitudes = [float(line) for line in file]\n\tcount = [x+1 for x in range(len(amplitudes))[::-1]]\n\treturn amplitudes, count","def get_array_size():\n tg_file = 'NA_CAS_gauges.txt'\n lines = open(tg_file).readlines()\n tg_nbr = len(lines)\n return tg_nbr","def __len__(self):\n return self._num_samples_per_file * len(self._files) // self._world_size","def create_1d_coil_geometry(division, filename, directory):\n os.chdir(directory)\n npoints = division + 1\n length_array = np.zeros((npoints, 2))\n current_length = 0\n array = np.loadtxt(filename)\n for i in range(1, npoints):\n current_length += ((array[i, 1] - array[i - 1, 1]) ** 2 + (array[i, 2] - array[i - 1, 2]) ** 2 +\n (array[i, 3] - array[i - 1, 3]) ** 2) ** 0.5\n length_array[i - 1, 0] = i\n length_array[i, 1] = current_length\n length_array[npoints - 1, 0] = npoints\n return length_array","def load_data():\n data = []\n with euler.Resource('triangle.txt') as datafile:\n for line in datafile.readlines():\n data.insert(0, map(int, line.strip().split()))\n return data","def test_triangle_count_07(self):\n body = {\"direction\": \"OUT\", \"degree\": 1}\n code, res = Algorithm().post_triangle_count(body, auth=auth)\n id = res[\"task_id\"]\n if id > 0:\n result = get_task_res(id, 120, auth=auth)\n print(result)\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 0}\n else:\n assert 0","def read_input(filename):\n with open(filename, 'r') as f:\n (N) = map(int, next(f).split())\n def parse_line(line):\n l = line.split()\n h = 0 if l[0] == 'H' else 1\n n = int(l[1])\n return [h, l[2:]]\n\n photos = transform_strings([parse_line(line) for line in f])\n return (len(photos), photos)","def get_edge_lengths(points: np.ndarray, triangles: np.ndarray) -> np.ndarray:\n edges, _ = get_edges(triangles)\n return np.linalg.norm(np.diff(points[edges], axis=1), axis=2).squeeze()","def dimension(self):\r\n a = 0\r\n for x in self.faces():\r\n if (len(x) > a):\r\n a = len(x) \r\n return a-1","def count_timepoints(sc, session, files):\n tuples = zip(range(len(files)), files)\n files_sc = sc.parallelize(tuples)\n\n def count_planes(kv):\n index, path2 = kv\n try:\n from ScanImageTiffReader import ScanImageTiffReader\n img = ScanImageTiffReader(path2).data()\n except Exception:\n import tifffile\n img = tifffile.imread(path2)\n return img.shape[0]\n\n data2 = files_sc.map(count_planes).collect()\n frame_numbers = np.array(data2)\n vol_numbers = frame_numbers / len(session.fieldMask)\n return vol_numbers.astype(int)","def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):\r\n global count\r\n with open(inputfilename, 'r') as swmmput:\r\n contents = swmmput.readlines()\r\n count = len(contents)\r\n return(count)","def read_off_size(path):\n try:\n with open(path, 'r') as file:\n lines = file.readlines()\n if lines[0] != 'OFF\\n':\n print(path, 'Error: is not an .off file')\n num_vertices, num_faces = tuple(lines[1].split()[:2])\n return int(num_vertices), int(num_faces)\n except IOError:\n print('Error: Failed reading file:', path)","def num_quadrature_points(self) -> int:","def findTriangles(p):\n triangleCount = 0\n for a in range(3, p//3 + 1):\n for b in range(a+1, p//2):\n c = p - (a+b)\n if (a**2 + b**2) == c**2:\n triangleCount += 1\n return triangleCount","def get_dimensions ( file_in, separator ) :\n try :\n logger.info ( \"Extract dimensions from xyz file \" + str(file_in) ) \n d = {}\n first_row = True\n d[NOPS] = 0\n file = open(file_in, 'r')\n for line in file :\n d[NOPS] = d[NOPS] + 1\n l = line.rstrip().split(separator)\n x = float(l[0])\n y = float(l[1])\n z = float(l[2])\n if first_row :\n d[MINX] = x\n d[MAXX] = x\n d[MINY] = y\n d[MAXY] = y\n d[MINZ] = z\n d[MAXZ] = z\n first_row = False\n else :\n if x < d[MINX] :\n d[MINX] = x\n if x > d[MAXX] :\n d[MAXX] = x \n if y < d[MINY] :\n d[MINY] = y\n if y > d[MAXY] :\n d[MAXY] = y \n if z < d[MINZ] :\n d[MINZ] = z\n if z > d[MAXZ] :\n d[MAXZ] = z \n file.close() \n logger.info ('Now return')\n return d\n except Exception, err:\n logger.critical(\"Extract dimensions from xyz file failed: ERROR: %s\\n\" % str(err))\n raise","def reader(filename,only_length=False):\n print(\"Counting lines in file %s\"%filename)\n total_lines=0\n for n,line in enumerate(open(filename,\"r\")):\n total_lines+=1\n \n if only_length:\n return total_lines\n \n X,Y,Z,W,J=[np.zeros(total_lines) for _ in range(5)]\n \n for n, line in enumerate(open(filename, 'r')):\n if n%1000000==0:\n print(\"Reading line %d of %d from file %s\" %(n,total_lines,filename))\n split_line=np.array(line.split(\" \"), dtype=float) \n X[n]=split_line[0];\n Y[n]=split_line[1];\n Z[n]=split_line[2];\n W[n]=split_line[3];\n J[n]=int(split_line[4]);\n return X,Y,Z,W,J","def total_number():\r\n total_number = 0\r\n file_read = read_file()\r\n for key in file_read:\r\n total_number = total_number + len(file_read[key])\r\n return total_number","def calc_side_lengths(triangles):\n first_vec = [2, 0, 1]\n second_vec = [1, 2, 0]\n sides = triangles[:, first_vec] - triangles[:, second_vec]\n lengths = np.sqrt(np.sum(sides**2, axis=2))\n return lengths","def numverts(self):\n return self._numvertstotal","def readGR3File(inputFilename):\n print 'Reading ' + inputFilename + ' ...'\n infile = open(inputFilename, 'r')\n description = infile.readline().strip() # remove leading/trailing whitespace\n tmpStr = infile.readline()\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\n\n # nodes\n nodeArray = readNodeBlock(infile, nNodes)\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\n nodexyz = np.zeros((nNodes, 3))\n nodexyz[:, :2] = nodeArray[:, 1:3]\n nodalValues = nodeArray[:, 3]\n\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\n\n # triangular elements\n triArray = readElemBlock(infile, nTriangles)\n\n trinum = triArray[:, 0].flatten()\n tritype = triArray[0, 1]\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\n #triangles = meshElements(trinodes,trinum,tritype)\n\n x = nodexyz[:, 0]\n y = nodexyz[:, 1]\n\n tmpStr = infile.readline()\n boundaries = []\n if len(tmpStr) > 0:\n # boundary information, if not end of file\n nOpenBndSegments = int(tmpStr.split()[0])\n nOpenBndNodesTot = int(infile.readline().split()[0])\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\n for iBnd in range(nOpenBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n tag = bndHeader[-1]\n if tag.isdigit():\n tag = 'open' + tag\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\n nLandBndSegments = int(infile.readline().split()[0])\n nLandBndNodesTot = int(infile.readline().split()[0])\n landBndTags = range(\n nOpenBndSegments + 1,\n nOpenBndSegments + nLandBndSegments + 1)\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\n for iBnd in range(nLandBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n try:\n landType = int(bndHeader[1])\n except:\n print \"\"\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\n 1002 = Total number of closed boundary nodes\n 501 0 = Number of nodes in closed boundary 1\"\"\"\n raise Exception(\n 'Could not parse land boundary type (0/1 - land/island)\\n')\n landType = 'island' if landType == 1 else 'land'\n tag = landType + bndHeader[-1]\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\n\n infile.close()\n\n # for better interpolation, round coordinates to 1e-4\n nDig = 4\n x = np.round(x, nDig)\n y = np.round(y, nDig)\n\n return x, y, nodalValues, trinodes, boundaries, description","def file_read(file_name):\n \n #open specified file in read mode\n in_file = open(file_name, \"r\")\n \n #create data lists\n sp_length_v3 = []\n sp_period_v3 = [] \n\n #save header to string and split into list\n header_string = in_file.readline()\n header_v3 = header_string.split()\n \n #save revelent data to respective lists\n for line in in_file:\n values = line.split()\n sp_length_v3.append(float(values[1]))\n sp_period_v3.append(float(values[2]))\n \n #close the file\n in_file.close()\n \n #return 3D lists of lists containing data\n ans = [sp_length_v3, sp_period_v3, header_v3]\n \n return ans","def from_polyfile(name):\n\n from anuga.utilities.numerical_tools import anglediff\n from math import pi\n import os.path\n root, ext = os.path.splitext(name)\n\n if ext == 'poly':\n filename = name\n else:\n filename = name + '.poly'\n\n\n fid = open(filename)\n\n points = [] #x, y\n values = [] #z\n ##vertex_values = [] #Repeated z\n triangles = [] #v0, v1, v2\n\n lines = fid.readlines()\n\n keyword = lines[0].strip()\n msg = 'First line in .poly file must contain the keyword: POINTS'\n assert keyword == 'POINTS', msg\n\n offending = 0\n i = 1\n while keyword == 'POINTS':\n line = lines[i].strip()\n i += 1\n\n if line == 'POLYS':\n keyword = line\n break\n\n fields = line.split(':')\n assert int(fields[0]) == i-1, 'Point indices not consecutive'\n\n #Split the three floats\n xyz = fields[1].split()\n\n x = float(xyz[0])\n y = float(xyz[1])\n z = float(xyz[2])\n\n points.append([x, y])\n values.append(z)\n\n\n k = i\n while keyword == 'POLYS':\n line = lines[i].strip()\n i += 1\n\n if line == 'END':\n keyword = line\n break\n\n\n fields = line.split(':')\n assert int(fields[0]) == i-k, 'Poly indices not consecutive'\n\n #Split the three indices\n vvv = fields[1].split()\n\n i0 = int(vvv[0])-1\n i1 = int(vvv[1])-1\n i2 = int(vvv[2])-1\n\n #Check for and exclude degenerate areas\n x0 = points[i0][0]\n y0 = points[i0][1]\n x1 = points[i1][0]\n y1 = points[i1][1]\n x2 = points[i2][0]\n y2 = points[i2][1]\n\n area = abs((x1*y0-x0*y1)+(x2*y1-x1*y2)+(x0*y2-x2*y0))/2\n if area > 0:\n\n #Ensure that points are arranged in counter clock-wise order\n v0 = [x1-x0, y1-y0]\n v1 = [x2-x1, y2-y1]\n v2 = [x0-x2, y0-y2]\n\n a0 = anglediff(v1, v0)\n a1 = anglediff(v2, v1)\n a2 = anglediff(v0, v2)\n\n\n if a0 < pi and a1 < pi and a2 < pi:\n #all is well\n j0 = i0\n j1 = i1\n j2 = i2\n else:\n #Swap two vertices\n j0 = i1\n j1 = i0\n j2 = i2\n\n triangles.append([j0, j1, j2])\n ##vertex_values.append([values[j0], values[j1], values[j2]])\n else:\n offending +=1\n\n log.critical('Removed %d offending triangles out of %d'\n % (offending, len(lines)))\n return points, triangles, values","def n_file_elements(cls):\n \n return randint(1, (3 * Root.size))","def gather_counts(directory):\n counts_un = defaultdict(int)\n counts_bi = defaultdict(int)\n counts_tri = defaultdict(int)\n prev_prev = \"<s>\"\n prev = \"<s>\"\n for filename in os.listdir(f\"./{directory}\"):\n if \".DS_Store\" in filename:\n continue\n with open(f\"./{directory}/{filename}\", \"r\") as f:\n for line in f:\n line = line.strip()\n if len(line) == 0:\n continue\n counts_un[line+\"\\n\"] += 1\n counts_bi[prev+\"\\n\"+line+\"\\n\"] += 1\n counts_tri[prev_prev+\"\\n\"+prev+\"\\n\"+line+\"\\n\"] += 1\n prev_prev = prev\n prev = line\n counts_un[\"</s>\\n\"] += 2\n counts_bi[\"</s>\\n</s>\\n\"] += 1\n counts_bi[prev+\"\\n\"+\"</s>\\n\"] += 1\n counts_tri[prev_prev+\"\\n\"+prev+\"\\n\" + \"</s>\\n\"] += 1\n counts_tri[prev+\"\\n</s>\\n</s>\\n\"] += 1\n return counts_un, counts_bi, counts_tri","def get_triangle_count(remote, objectid):\n cmd1 = mmapi.StoredCommands()\n key1 = cmd1.AppendSceneCommand_GetTriangleCount(objectid)\n remote.runCommand(cmd1)\n result_val = mmapi.any_result()\n cmd1.GetSceneCommandResult_GetTriangleCount(key1, result_val)\n return result_val.i","def get_dimensions(self):\r\n x = []\r\n y = []\r\n z = []\r\n for i in self.verts:\r\n x.append(i[0])\r\n y.append(i[1])\r\n z.append(i[2])\r\n\r\n x.append(abs(min(x)))\r\n y.append(abs(min(y)))\r\n z.append(abs(min(z)))\r\n\r\n return max(x), max(y), max(z)","def get_triangles( self, N ):\n\n # store N as an instance variable\n self.N = N\n\n # initialize array to store locations of points for all triangles in the\n # tessellation sequence\n self.triangles = np.zeros( ( self.N, 3, 2 ) )\n\n # define points of the first triangle in the tessellation sequence\n point_c = np.array( [ 0, 0 ] )\n point_b = self.a * np.array( [ np.cos( self.C ), np.sin( self.C ) ] )\n point_a = np.array( [ self.b, 0 ] )\n\n # stack the points into a single array of shape (3, 2 )\n triangle = np.vstack( [ point_c, point_b, point_a ] )\n\n # loop over the number of triangles in the sequence\n for i in range( self.N ):\n\n # store the points of the i-th triangle in the array\n self.triangles[ i ] = triangle\n\n # compute the next triangle in the tessellation sequence\n triangle = self.next_triangle( triangle = triangle )\n\n # shift the next triangle in the tessellation sequence such that its\n # point C is in the same location as point B of the previous triangle\n triangle += ( self.triangles[ i - 1, 1 ] - self.triangles[ 0, 0 ] )","def std_triangles_count(graph):\n if nx.is_directed(graph):\n raise Exception(\"Graph is not undirected\")\n\n return sum(nx.triangles(graph).values()) // 3","def getNbins(self,axis,includeTotalBin = True):\n\n\t\tif axis == \"f\":\n\t\t\tnCells = 1 if self.nCells == 0 else self.nCells\n\t\t\treturn nCells\n\n\t\tif axis == \"i\":\n\t\t\treturn self.meshInfo[1]\n\n\t\tif axis == \"j\":\n\t\t\treturn self.meshInfo[2]\n\n\t\tif axis == \"k\":\n\t\t\treturn self.meshInfo[3]\n\n\t\tif axis == \"d\":\n\t\t\tnDir = 1 if self.nDir == 0 else self.nDir\n\t\t\treturn nDir\n\n\t\tif axis == \"u\":\n\t\t\tnUsr = 1 if self.nUsr == 0 else self.nUsr\n\t\t\tnUsr = nUsr - 1 if self.usrTC == \"t\" and not includeTotalBin else nUsr\n\t\t\treturn nUsr\n\n\t\tif axis == \"s\":\n\t\t\tnSeg = 1 if self.nSeg == 0 else self.nSeg\n\t\t\tnSeg = nSeg - 1 if self.segTC == \"t\" and not includeTotalBin else nSeg\n\t\t\treturn nSeg\n\n\t\tif axis == \"m\":\n\t\t\tnMul = 1 if self.nMul == 0 else self.nMul\n\t\t\tnMul = nMul - 1 if self.mulTC == \"t\" and not includeTotalBin else nMul\n\t\t\treturn nMul\n\n\t\tif axis == \"c\":\n\t\t\tnCos = 1 if self.nCos == 0 else self.nCos\n\t\t\tnCos = nCos - 1 if self.cosTC == \"t\" and not includeTotalBin else nCos\n\t\t\treturn nCos\n\n\t\tif axis == \"e\":\n\t\t\tnErg = 1 if self.nErg == 0 else self.nErg\n\t\t\tnErg = nErg - 1 if self.ergTC == \"t\" and not includeTotalBin else nErg\n\t\t\treturn nErg\n\n\t\tif axis == \"t\":\n\t\t\tnTim = 1 if self.nTim == 0 else self.nTim\n\t\t\tnTim = nTim - 1 if self.timTC == \"t\" and not includeTotalBin else nTim\n\t\t\treturn nTim","def read_txt(if_name):\n n = 0\n paper_shape = []\n present_shape = []\n input_file = open(if_name,'r')\n i = 0\n\n for line in input_file:\n\n if i > 1:\n\n i += 1\n line = line.strip().split(' ')\n if len(line) < 2:\n break\n present_shape.append([int(e) for e in line])\n\n if i == 1:\n i += 1\n line = line.strip()\n n = int(line)\n\n if i == 0:\n i += 1\n line = line.strip().split(' ')\n paper_shape = [int(e) for e in line]\n\n input_file.close()\n return n, paper_shape, present_shape","def bpCount(file):\n amount_bp = len(file)\n return amount_bp","def get_all_object_triangles( filename, scale , translation=(0,0,0)):\n import warnings\n warnings.warn(\"@PendingDeprecationWarning\", PendingDeprecationWarning)\n vertexObjs = read_vertices_objects( filename )\n faceObjs = read_faces_objects( filename )\n\n r = []\n j = 0\n \n # Validation:\n vertices, faces = [],[]\n for obj in range(len(vertexObjs)):\n vertices += vertexObjs[obj]\n faces += faceObjs[obj]\n max_vertex_index = max([max(x) for x in faces])\n if len(vertices) != max_vertex_index:\n logging.warning( \"ParseWarning: A face's vertex index number is does not match the quantity of read vertices.\" )\n logging.warning( \"Qty of Vertices: \"+str(len(vertices))+\", Largest Face Index: \"+str(max_vertex_index) )\n\n # Parse as Tris:\n for obj in range(len(vertexObjs)):\n vertices = vertexObjs[obj]\n faces = faceObjs[obj]\n r.append([])\n c = 0\n for f in faces: # for every face\n for i in f: # for each index point in face\n c+=1\n try:\n # Get the face[i] vertex\n v = vertices[i-1]\n except IndexError as indErr:\n logging.warning(\"IndexError: Attempted to access index: \"+str(i-1)+\" in list of length: \"+str(len(vertices)))\n raise IndexError\n # Scale the face[i] vertex\n scV = [v[0]*scale, v[1]*scale, v[2]*scale]\n # Translate the scaled face vertex:\n t = translation\n tmpv = [scV[0]+t[0],scV[1]+t[1], scV[2]+t[2]]\n # Retain this vertex\n r[j].append(tmpv)\n # ---------------------\n if c % 3 == 0:\n j+=1\n r.append([])\n r = r[:len(r)-1] # remove the final empty list.\n\n checkShapeValidity( r )\n return r","def load_triangles(triangle_bytes, header):\n triangles = list()\n for i in range(header.num_tris):\n triangle = triangle_t(list(struct.unpack(\"<hhh\", triangle_bytes[12*i:12*i+6])), list(struct.unpack(\"<hhh\", triangle_bytes[12*i+6:12*i+12])))\n # print(triangle)\n triangles.append(triangle)\n return triangles","def test_number_of_surface_objects(self):\n for O in self.mod.objts.itervalues():\n no_of_surfaces = 0\n for C in O.conts.itervalues():\n if C.surf != 0:\n no_of_surfaces += 1\n self.assertEqual(O.surfsize, no_of_surfaces)","def dimensions():","def test_number_of_vertex_elements_in_MESH_chunk(self):\n for O in self.mod.objts.itervalues():\n for M in O.meshes.itervalues():\n self.assertEqual(M.vsize, len(M.vert))","def read_triangle(f, layers=1):\n # Read nodes\n with open(f + '.node') as h:\n num_nodes = int(h.readline().split(' ')[0])\n node_values = np.zeros((num_nodes, 2), dtype=np.float64)\n for line in h:\n if line[0] == '#':\n continue\n node, x, y = line.split()[:3]\n node_values[int(node) - 1, :] = [float(x), float(y)]\n\n nodes = op2.Set(num_nodes, \"nodes\")\n coords = op2.Dat(nodes ** 2, node_values, name=\"coords\")\n\n # Read elements\n with open(f + '.ele') as h:\n num_tri, nodes_per_tri, num_attrs = [int(x) for x in h.readline().split()]\n map_values = np.zeros((num_tri, nodes_per_tri), dtype=np.int32)\n for line in h:\n if line[0] == '#':\n continue\n vals = [int(x) - 1 for x in line.split()]\n map_values[vals[0], :] = vals[1:nodes_per_tri + 1]\n\n elements = op2.Set(num_tri, \"elements\", layers=layers)\n elem_node = op2.Map(elements, nodes, nodes_per_tri, map_values, \"elem_node\")\n\n return nodes, coords, elements, elem_node","def get_number_lines(running_reward_file, running_loss_file, action_count_file):\n if Path(running_reward_file).exists():\n data = np.loadtxt(running_reward_file).reshape(-1,2)\n return data.shape[0]\n if Path(running_loss_file).exists():\n data = np.loadtxt(running_loss_file).reshape(-1,2)\n return data.shape[0]\n if Path(action_count_file).exists():\n data = np.loadtxt(action_count_file).reshape(-1,2)\n return data.shape[0]\n raise NameError(\"No files to count lines\")","def get_dataset_length(file_path, had_header=True):\n with open(file_path, 'r') as f:\n length = 0\n for _ in f:\n length += 1\n length = length - had_header\n return length","def __len__(self) -> int:\n import h5py\n\n with h5py.File(\n os.path.join(self.root, self.data_dir, self.img_file_name), \"r\"\n ) as f:\n num_datapoints: int = f[self.split][\"pv_log\"].shape[0]\n\n return num_datapoints","def get_library_sizes(args):\n with open(args.counts, \"r\") as counts:\n sizes = []\n head = True\n for line in counts:\n line = line.strip()\n if head:\n head = False\n samples = line.split(\"\\t\")[3:]\n total_counts = [0] * len(samples)\n else:\n counts = line.split(\"\\t\")\n if counts[1] == \"NA\":\n break\n else:\n counts = counts[3:]\n for i in range(len(counts)):\n total_counts[i] += int(counts[i])\n\n for i in range(len(samples)):\n sizes.append([samples[i], total_counts[i]])\n\n return sizes","def data_len(self):\n Nrows_data = 0\n with self._compression_safe_file_opener(self.input_fname, \"r\") as f:\n for i, l in enumerate(f):\n if (l[0 : len(self.header_char)] != self.header_char) and (l != \"\\n\"):\n Nrows_data += 1\n return Nrows_data","def getFileCount(self) -> int:\n ...","def test_number_of_MESH_chunks(self):\n for O in self.mod.objts.itervalues():\n self.assertEqual(O.meshsize, len(O.meshes))","def _tvb_surface_to_tri(self, surface_file_name):\n surface_file_path = os.path.join(OM_STORAGE_DIR, surface_file_name)\n\n #TODO: check file doesn't already exist\n LOG.info(\"Writing TVB surface to .tri file: %s\" % surface_file_path)\n file_handle = file(surface_file_path, \"a\")\n\n file_handle.write(\"- %d \\n\" % self.sources.number_of_vertices)\n verts_norms = numpy.hstack((self.sources.vertices, \n self.sources.vertex_normals))\n numpy.savetxt(file_handle, verts_norms)\n\n tri_str = \"- \" + (3 * (str(self.sources.number_of_triangles) + \" \")) + \"\\n\"\n file_handle.write(tri_str)\n numpy.savetxt(file_handle, self.sources.triangles, fmt=\"%d\")\n\n file_handle.close()\n LOG.info(\"%s written successfully.\" % surface_file_name)\n\n return surface_file_path","def dataDimensions(data):\n logging.info('Number of rows of data: %s' % len(data))\n logging.info('Number of columns of data: %s' % len(data[1]))","def _extract_track_lengths(track_key,conn):\n print track_key\n \n (fname,iden_key,track_key) = conn.execute(\"select fout,iden_key,comp_key from tracking where comp_key = ?\",\n track_key).fetchone()\n \n F = h5py.File(fname,'r')\n len_vec = F[fd('tracking',track_key)]['length'][:]\n \n temp = 0\n dtime = 0\n fr_count = 0\n for g in F.keys():\n if g[0:5] == 'frame':\n temp += F[g].attrs['temperature']\n dtime += F[g].attrs['dtime']\n fr_count += 1\n\n \n F.close()\n del F\n return len_vec, temp/fr_count, dtime/fr_count","def ss_triangle_count(graph: ScipyGraph) -> int:\n props = ScipyGraph.Type.compute_abstract_properties(graph, {\"edge_type\"})\n if props[\"edge_type\"] == \"map\":\n # Drop weights before performing triangle count\n m = graph.value.copy()\n m.data = np.ones_like(m.data)\n elif props[\"edge_type\"] == \"set\":\n m = graph.value\n L = ss.tril(m, k=-1).tocsr()\n U = ss.triu(m, k=1).tocsc()\n return int((L @ U.T).multiply(L).sum())","def analyze_pressure_dump(filename, Lx=200., Ly=200, Lz=900., N=10, bin_divide_flag=False, Natoms=113579):\n myfile = open(filename+'.txt')\n trajectory = []\n traj_pd = []\n frames = []\n\n for _ in range(3):\n next(myfile)\n count = 0\n while EOF(myfile):\n count += 1\n s = next(myfile) # info with the time step\n\n x = np.zeros(N, dtype=[('Chunk',np.float32), ('Coord1',np.float32), ('Ncount',np.float32), ('density',np.float32), ('temp',np.float32), ('vx',np.float32), ('fx',np.float32),('c_pciKE[1]',np.float32), ('c_pciKE[2]',np.float32), ('c_pciKE[3]',np.float32), ('c_pciVIR[1]',np.float32), ('c_pciVIR[2]',np.float32), ('c_pciVIR[3]',np.float32), ('c_pgelELAS[1]',np.float32), ('c_pgelELAS[2]',np.float32), ('c_pgelELAS[3]',np.float32), ('c_pgelVIR[1]', np.float32), ('c_pgelVIR[2]', np.float32), ('c_pgelVIR[3]', np.float32), ('c_pgelPAIR[1]', np.float32), ('c_pgelPAIR[2]', np.float32), ('c_pgelPAIR[3]', np.float32)])\n\n# Chunk Coord1 Ncount density/number temp vx fx c_pciKE[1] c_pciKE[2] c_pciKE[3] c_pciVIR[1] c_pciVIR[2] c_pciVIR[3] c_pgelELAS[1] c_pgelELAS[2] c_pgelELAS[3] c_pgelVIR[1] c_pgelVIR[2] c_pgelVIR[3] c_pgelPAIR[1] c_pgelPAIR[2] c_pgelPAIR[3]\n\n list_line = re.findall(\"[-+]?\\d+[\\.]?\\d*[eE]?[-+]?\\d*\", s)\n frame, _, _ = list_line\n frames.append(int(frame))\n # print( \"reading lines\")\n\n for i in xrange(N):\n count += 1\n s = next(myfile)\n list_line = re.findall(\"[-+]?\\d+[\\.]?\\d*[eE]?[-+]?\\d*\", s)\n # print( \"reading line\", i, list_line)\n for il, l in enumerate(list_line):\n x[i][il] = float(l)\n\n trajectory.append(x)\n\n # names = x.dtype.fields.keys()\n # data = x.dtype.fields.values()\n\n df = pd.DataFrame.from_records(x)\n traj_pd.append(df)\n\n myfile.close()\n\n\n\n # # volume = 218.*44.*44.\n volume = Lx*Ly*Lz\n # N_atoms = 113579\n # if bin_divide_flag:\n # bin_volume = volume / float(N)\n # else:\n # bin_volume = 1.\n\n bin_volume = volume / float(N)\n # bin_volume = volume\n # bin_volume /= float(Natoms)\n\n Combine_PD = pd.concat(traj_pd)\n FINAL_PD = pd.DataFrame()\n\n FINAL_PD['Coord1'] = Combine_PD['Coord1']\n FINAL_PD['p_ciKE'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciKE[1]'] + Combine_PD['c_pciKE[2]'] + Combine_PD['c_pciKE[3]'])/(3.*bin_volume)\n FINAL_PD['p_ciVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciVIR[1]'] + Combine_PD['c_pciVIR[2]'] + Combine_PD['c_pciVIR[3]'])/(3.*bin_volume)\n FINAL_PD['p_gelELAS'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelELAS[1]'] + Combine_PD['c_pgelELAS[2]'] + Combine_PD['c_pgelELAS[3]'])/(3.*bin_volume)\n\n FINAL_PD['p_gelVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelVIR[1]'] + Combine_PD['c_pgelVIR[2]'] + Combine_PD['c_pgelVIR[3]'])/(3.*bin_volume)\n FINAL_PD['p_gelPAIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelPAIR[1]'] + Combine_PD['c_pgelPAIR[2]'] + Combine_PD['c_pgelPAIR[3]'])/(3.*bin_volume)\n\n # So now I have to\n # P_bin = (sigma_per_atom_xx + ... + sigma_per_atom_zz)/(bin_volume*3)\n # *N_atoms_per_bin\n # N_atoms_per_bin = number_density*N_atoms\n\n\n df_concat = FINAL_PD\n\n by_row_index = df_concat.groupby(df_concat.index)\n df_means = by_row_index.mean()\n by_row_index_2 = df_concat.groupby(df_concat.index)\n df_stds = by_row_index_2.std()\n\n # print( df_means.head())\n # print( df_stds.head())\n return df_means, df_stds","def fileCount(self):\n pass","def count_lines(filename):\n with open(filename, 'r', encoding='utf-8') as file:\n lines_count = int()\n for line in file:\n lines_count += 1\n info_tuple = (filename, lines_count)\n return info_tuple","def _ReadExtent(self):\n # Read the mesh file as line strings, remove lines with comment = !\n v = np.array(np.__version__.split('.')[0:2], dtype=int)\n FileName = self.GetMeshFileName()\n try:\n if v[0] >= 1 and v[1] >= 10:\n # max_rows in numpy versions >= 1.10\n msh = np.genfromtxt(FileName, delimiter='\\n', dtype=np.str,comments='!', max_rows=1)\n else:\n # This reads whole file :(\n msh = np.genfromtxt(FileName, delimiter='\\n', dtype=np.str, comments='!')[0]\n except (IOError, OSError) as fe:\n raise _helpers.PVGeoError(str(fe))\n # Fist line is the size of the model\n self.__sizeM = np.array(msh.ravel()[0].split(), dtype=int)\n # Check if the mesh is a UBC 2D mesh\n if self.__sizeM.shape[0] == 1:\n # Read in data from file\n xpts, xdisc, zpts, zdisc = ubcMeshReaderBase._ubcMesh2D_part(FileName)\n nx = np.sum(np.array(xdisc,dtype=int))+1\n nz = np.sum(np.array(zdisc,dtype=int))+1\n return (0,nx, 0,1, 0,nz)\n # Check if the mesh is a UBC 3D mesh or OcTree\n elif self.__sizeM.shape[0] >= 3:\n # Get mesh dimensions\n dim = self.__sizeM[0:3]\n ne,nn,nz = dim[0], dim[1], dim[2]\n return (0,ne, 0,nn, 0,nz)\n else:\n raise _helpers.PVGeoError('File format not recognized')","def get_num_chunks(self) -> int:","def part2(fname: dict) -> int:\n return sum(len(set.intersection(*[set(pax) for pax in group])) for group in get_data(fname))","def voxel_count(self):\n return self.cols * self.rows * self.sections","def calc_face_dimensions(face):\n vertical = filter_vertical_edges(face.edges, face.normal).pop()\n horizontal = filter_horizontal_edges(face.edges, face.normal).pop()\n return horizontal.calc_length(), vertical.calc_length()","def _count_data(path):\n matcher = re.compile(r'[0-9]+\\.dec')\n match = lambda name: bool(matcher.match(name))\n names = os.listdir(path)\n n_data = len(list(filter(match, names)))\n return n_data","def file_len(fname):\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n Nrows = i + 1\n return Nrows","def ReadFenics(self, filename, element_type):\n\n if element_type == \"tet\":\n etype = \"tetrahedron\"\n elif element_type == \"hex\":\n etype = \"hexahedron\"\n elif element_type == \"tri\":\n etype = \"triangle\"\n elif element_type == \"quad\":\n etype = \"quadrilateral\"\n\n import xml.etree.cElementTree as ET\n root = ET.parse(filename).getroot()\n X = []\n T = []\n for child in root:\n if child.attrib['celltype'] != etype:\n raise ValueError(\"xml file does not contain {} elements\".format(element_type))\n\n for child in root:\n for cchild in child:\n if cchild.tag == \"vertices\":\n if element_type == \"tet\" or element_type == \"hex\":\n for child3 in cchild:\n x = float(child3.attrib['x'])\n y = float(child3.attrib['y'])\n z = float(child3.attrib['z'])\n X.append([x,y,z])\n elif element_type == \"tri\" or element_type == \"quad\":\n for child3 in cchild:\n x = float(child3.attrib['x'])\n y = float(child3.attrib['y'])\n X.append([x,y])\n\n elif cchild.tag == \"cells\":\n if element_type == \"tet\":\n for child3 in cchild:\n v0 = int(child3.attrib['v0'])\n v1 = int(child3.attrib['v1'])\n v2 = int(child3.attrib['v2'])\n v3 = int(child3.attrib['v3'])\n T.append([v0,v1,v2,v3])\n elif element_type == \"tri\":\n for child3 in cchild:\n v0 = int(child3.attrib['v0'])\n v1 = int(child3.attrib['v1'])\n v2 = int(child3.attrib['v2'])\n T.append([v0,v1,v2])\n\n\n X = np.array(X)\n T = np.array(T,dtype=np.int64)\n\n self.elements = T\n self.points = X\n self.element_type = element_type\n self.nelem = self.elements.shape[0]\n self.nnode = self.points.shape[0]\n\n if self.points.shape[1] == 3:\n if np.allclose(self.points[:,2],0.):\n self.points = np.ascontiguousarray(self.points[:,:2])\n\n ndim = self.InferSpatialDimension()\n if self.element_type == \"tri\" or self.element_type == \"quad\":\n self.GetEdges()\n self.GetBoundaryEdges()\n elif self.element_type == \"tet\" or self.element_type == \"hex\":\n self.GetFaces()\n self.GetBoundaryFaces()\n self.GetBoundaryEdges()","def read_geometry(filepath, read_metadata=False, read_stamp=False):\n volume_info = OrderedDict()\n\n TRIANGLE_MAGIC = 16777214\n QUAD_MAGIC = 16777215\n NEW_QUAD_MAGIC = 16777213\n with open(filepath, \"rb\") as fobj:\n magic = _fread3(fobj)\n if magic in (QUAD_MAGIC, NEW_QUAD_MAGIC): # Quad file\n nvert = _fread3(fobj)\n nquad = _fread3(fobj)\n (fmt, div) = (\">i2\", 100.) if magic == QUAD_MAGIC else (\">f4\", 1.)\n coords = np.fromfile(fobj, fmt, nvert * 3).astype(np.float) / div\n coords = coords.reshape(-1, 3)\n quads = _fread3_many(fobj, nquad * 4)\n quads = quads.reshape(nquad, 4)\n #\n # Face splitting follows\n #\n faces = np.zeros((2 * nquad, 3), dtype=np.int)\n nface = 0\n for quad in quads:\n if (quad[0] % 2) == 0:\n faces[nface] = quad[0], quad[1], quad[3]\n nface += 1\n faces[nface] = quad[2], quad[3], quad[1]\n nface += 1\n else:\n faces[nface] = quad[0], quad[1], quad[2]\n nface += 1\n faces[nface] = quad[0], quad[2], quad[3]\n nface += 1\n\n elif magic == TRIANGLE_MAGIC: # Triangle file\n create_stamp = fobj.readline().rstrip(b'\\n').decode('utf-8')\n fobj.readline()\n vnum = np.fromfile(fobj, \">i4\", 1)[0]\n fnum = np.fromfile(fobj, \">i4\", 1)[0]\n coords = np.fromfile(fobj, \">f4\", vnum * 3).reshape(vnum, 3)\n faces = np.fromfile(fobj, \">i4\", fnum * 3).reshape(fnum, 3)\n\n if read_metadata:\n volume_info = _read_volume_info(fobj)\n else:\n raise ValueError(\"File does not appear to be a Freesurfer surface\")\n\n coords = coords.astype(np.float) # XXX: due to mayavi bug on mac 32bits\n\n ret = (coords, faces)\n if read_metadata:\n if len(volume_info) == 0:\n warnings.warn('No volume information contained in the file')\n ret += (volume_info,)\n if read_stamp:\n ret += (create_stamp,)\n\n return ret","def readCubeFile(self, filename):\n\n inputfile = open(filename, \"r\")\n header = \"\".join([inputfile.readline(), inputfile.readline()])\n\n temp = inputfile.readline().strip().split()\n self.numAtoms = int(temp[0])\n self.origin = list(map(float, temp[1:]))\n\n self.numPoints = [0] * 3\n self.spacing = [0] * 3\n for i in range(3):\n line = inputfile.readline().strip().split()\n self.numPoints[i] = int(line[0])\n temp = list(map(float, line[1:]))\n self.spacing[i] = temp[i]\n assert sum(temp[:i] + temp[i + 1:]) == 0\n\n # Read in the lines with atom data\n for i in range(self.numAtoms):\n line = inputfile.readline()\n\n self.data = np.zeros((self.numPoints[1], self.numPoints[0], self.numPoints[2]), \"float\")\n i = j = k = 0\n while i < self.numPoints[1]:\n line = next(inputfile)\n temp = list(map(float, line.strip().split()))\n for x in range(0, len(temp)):\n self.data[j, i, x + k] = temp[x]\n\n k += len(temp)\n if k == self.numPoints[2]:\n j += 1\n k = 0\n if j == self.numPoints[1]:\n i += 1\n j = 0\n\n inputfile.close()","def parseInputFile(fn):\n\twith open(fn) as f:\n\t\tpoly = [float(x) for x in f.readline().strip().split()]\n\t\titers = int(f.readline().strip())\n\t\treturn (poly, iters)","def load_faces(file_data, headers, indices):\n\n\n def swap_winding(indices):\n return (indices[0], indices[2], indices[1])\n \n\n def indices_from_face(face_data):\n base_vertex = face_data[3]\n base_index = face_data[5]\n index_count = face_data[6]\n\n faces_indices = [base_vertex + indices[base_index + current_index] \n for current_index in range(index_count)]\n\n #Split into lists of 3 - ie triangles\n faces = []\n for current_face_idx in range(0, len(faces_indices), 3):\n faces.append(faces_indices[current_face_idx:current_face_idx+3])\n\n return faces\n\n\n def face_from_pack(face_data):\n \"\"\" \n Extract just the data we want from the full chunk\n \"\"\"\n triangle_list = indices_from_face(face_data)\n return [(face_data[0], triangles,) for triangles in triangle_list]\n\n face_offset, face_length = headers[13]\n face_chunk = Struct(\"iiiiiiii2i2i3f3f3f3f2i\") \n face_size = face_chunk.size\n face_count = int(face_length / face_size)\n\n faces = []\n\n for current_face_idx in range(face_count):\n face_file_position = face_offset + current_face_idx * face_size\n current_face = face_chunk.unpack(file_data[face_file_position : face_file_position+face_size])\n\n #Check we are a valid face (Could use a filter later)\n if current_face[2] != 1: continue #Only support meshes at the moment\n\n new_faces = face_from_pack(current_face)\n faces.extend(new_faces)\n\n return faces","def __len__(self):\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios","def __len__(self):\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios","def __len__(self):\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios","def __len__(self):\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios","def get_filesize(inputfile) -> int:\n with open(inputfile, \"rb\") as f:\n lines = 0\n buf_size = 1024 * 1024\n read_f = f.raw.read\n\n buf = read_f(buf_size)\n while buf:\n lines += buf.count(b\"\\n\")\n buf = read_f(buf_size)\n\n return lines","def extract_triangles(mesh, materials_list):\n tri_list = []\n do_uv = bool(mesh.tessface_uv_textures)\n\n for mat in materials_list:\n for i, face in enumerate(mesh.tessfaces):\n f_v = face.vertices\n if mesh.materials[face.material_index].name != mat: continue\n\n uf = mesh.tessface_uv_textures.active.data[i] if do_uv else None\n\n fmt = 0\n if(do_uv): fmt = face.material_index\n\n if do_uv:\n f_uv = uf.uv\n\n if len(f_v) == 3:\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\n if (do_uv):\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\n else: new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\n tri_list.append(new_tri)\n\n else: # it's a quad\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\n new_tri_2 = tri_wrapper((f_v[0], f_v[2], f_v[3]), fmt)\n\n if (do_uv):\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\n new_tri_2.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[2]), uv_key(f_uv[3])\n else:\n new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\n new_tri_2.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\n\n tri_list.append(new_tri)\n tri_list.append(new_tri_2)\n\n return tri_list","def N_POINTS(self) -> int:\n try:\n with self.fs.open(\n self.get_url().replace(\".\" + self.erddap.response, \".ncHeader\")\n ) as of:\n ncHeader = of.read().decode(\"utf-8\")\n lines = [line for line in ncHeader.splitlines() if \"row = \" in line][0]\n return int(lines.split(\"=\")[1].split(\";\")[0])\n except Exception:\n pass","def get_n(self):\n return np.append([self.n_init],[s.n for s in self.surfaces])","def inithr(_filename):\n # Open file provided\n _file = open(_filename)\n # Create empty array to hold data\n _data = np.zeros((1, 3), dtype=float)\n\n # Iterate through the file line by line\n for _line in _file:\n # Split each line into constituent values\n _x = _line.split()\n # Append data array with each value, converted to float, convert parallax angle to distance\n _data = np.append(_data, np.array([float(_x[1]), float(_x[2]), (1 / float(_x[3]))], ndmin=2), axis=0)\n\n # Iterate through data array\n for _row in _data:\n np.seterr(divide='ignore')\n # Convert magnitude to luminosity\n _row[0] = _row[0] - 5 * (np.log10(_row[2]) - 1)\n # Convert B-V colour to temperature\n _row[1] = 4600 * ((1 / (0.92 * _row[1] + 1.7)) + 1 / (0.92 * _row[1] + 0.62))\n\n # Delete first empty row\n _data = np.delete(_data, 0, axis=0)\n\n # Return parsed data\n return _data","def read_file(file):\n if opts.input_type == 'fits':\n data = fileio.read_fits(file)\n else:\n data = fileio.read_ascii(file)\n c_id = data[0,:]\n g_num = np.array(range(len(c_id)), dtype = 'int')\n g_id = data[3,:]\n g_ra = np.array(data[4,:], dtype = 'float')\n g_dec = np.array(data[5,:], dtype = 'float')\n g_z = np.array(data[6,:], dtype = 'float')\n return c_id, g_num, g_id, g_ra, g_dec, g_z","def bufcount(filename):\n\timport gzip\n\tif filename.split('.')[-1] in ['gz','gzip']: f = gzip.open(filename)\n\telse: f = open(filename)\n\tlines = 0\n\tbuf_size = 1024 * 1024\n\tread_f = f.read # loop optimization\n\t\n\tbuf = read_f(buf_size)\n\twhile buf:\n\t\tlines += buf.count('\\n')\n\t\tbuf = read_f(buf_size)\n\t\tf.close\n\treturn lines","def part2(filename: str) -> int:\n data = first_line(filename)\n data = json.loads(data)\n return total_nums_no_red(data)","def compute_surface_area(self):\n return np.sum(self._find_triangle_areas())","def part_1() -> int:\n initial_input = _load_input()\n rows = len(initial_input)\n cols = len(initial_input[0])\n\n input = initial_input.copy()\n total_glow_count = 0\n\n for _ in range(100):\n flashed = list()\n for row in range(rows):\n for col in range(cols):\n coords = [[col, row]]\n new_input, glow_count = _get_glow_counts(coords, input, flashed)\n input = new_input\n total_glow_count += glow_count\n\n return total_glow_count","def get_data(data_file_path):\n data_file = open(data_file_path, 'r').readlines()\n data = []\n n = -1\n dim = -1\n for i in range(len(data_file)):\n line_elems = [float(x) for x in data_file[i].split()]\n if i == 0:\n n = int(line_elems[0])\n dim = int(line_elems[1])\n else:\n data.append(np.array(line_elems))\n return data, n, dim","def get_faces_nr(self):\r\n\r\n logger.debug('Getting number of faces in each frame')\r\n\r\n if len(self.tracked_faces) == 0:\r\n\r\n # Try to load YAML file\r\n\r\n if os.path.exists(self.track_path):\r\n\r\n print 'Loading YAML file with tracking results'\r\n logger.debug('Loading YAML file with tracking results')\r\n\r\n with open(self.track_path) as f:\r\n\r\n self.tracked_faces = yaml.load(f)\r\n\r\n print 'YAML file with tracking results loaded'\r\n logger.debug('YAML file with tracking results loaded')\r\n\r\n else:\r\n\r\n print 'Warning! No tracking results found!'\r\n logger.warning('No tracking results found!')\r\n\r\n return\r\n\r\n self.faces_nr = {}\r\n\r\n for segment_dict in self.tracked_faces:\r\n\r\n frame_list = segment_dict[c.FRAMES_KEY]\r\n\r\n for frame_dict in frame_list:\r\n\r\n frame_name = frame_dict[c.SAVED_FRAME_NAME_KEY]\r\n\r\n if frame_name in self.faces_nr:\r\n\r\n self.faces_nr[frame_name] += 1\r\n\r\n else:\r\n\r\n self.faces_nr[frame_name] = 1\r\n\r\n # Save YAML file\r\n\r\n utils.save_YAML_file(self.faces_nr_path, self.faces_nr)","def readprimitive(f): \n \n ## read in lines from input file and ignore blank lines and comment lines\n lines = [line.rstrip() for line in f if line.rstrip() if line[0] != '#']\n\n # a1,a2,a3\n A = np.array([[float(lines[0].split()[0]),float(lines[0].split()[1]),float(lines[0].split()[2])],\n [float(lines[1].split()[0]),float(lines[1].split()[1]),float(lines[1].split()[2])],\n [float(lines[2].split()[0]),float(lines[2].split()[1]),float(lines[2].split()[2])]]).T\n \n # number of basis atoms\n num_basis = int(lines[3].split()[0]) \n\n # basis atom positions in unit cell\n unitcell_pos = []\n for i in range(num_basis): \n unitcell_pos.append([float(lines[4+i].split()[0]),float(lines[4+i].split()[1]),float(lines[4+i].split()[2])]) \n \n return (A,unitcell_pos)","def part1(filename: str) -> int:\n data = first_line(filename)\n data = json.loads(data)\n return total_nums(data)","def obtener_cantidad_vertices(self):\n return len(self.vertices.keys())","def get_counts(filename, alphabet, kmin, kmax):\n # get the list of kmers to count with length between kmin and kmax\n kmers_list = get_all_possible_kmers(alphabet, kmin, kmax)\n # initialyze the counter with all possible kmer with length\n # between kmin and kmax with zero counts\n counter = Counter(dict([(km, 0) for km in kmers_list]))\n # open and read in the kmers/string in the file\n with gzip.open(filename, 'rt') as fh:\n # iterates through the strings\n for line in fh:\n # make the adjustments int the strings\n kmer = line.replace('\\n', '')\n # check if kmer/string is in the counter\n if kmer in counter:\n # if kmer is in add 1 other wise keep the zero count\n counter[kmer] += 1\n return counter","def load_verts(file_data, headers, scale_factor):\n\n\n def vert_from_pack(vert_data):\n return (\n (vert_data[0] * scale_factor, vert_data[1] * scale_factor, vert_data[2] * scale_factor,), #XYZ\n (vert_data[3], vert_data[4],), #UV1\n (vert_data[5], vert_data[6],), #UV2\n (vert_data[7], vert_data[8], vert_data[9],), #Normal\n (vert_data[10], vert_data[11], vert_data[12], vert_data[13],), #RGBA\n )\n\n vert_offset, vert_length = headers[10]\n vert_chunk = Struct(\"3f2f2f3f4B\") \n vert_size = vert_chunk.size\n vert_count = int(vert_length / vert_size)\n\n print (\"Found {} vertices\".format(vert_count))\n\n vertices = []\n\n for current_vert_idx in range(vert_count):\n vert_file_position = vert_offset + current_vert_idx * vert_size\n current_vert = vert_chunk.unpack(file_data[vert_file_position : vert_file_position+vert_size])\n vertices.append(vert_from_pack(current_vert))\n\n return vertices","def get_number_of_letters(self):\n filename = f'{self.path}/{self.filename}'\n file = open(filename, 'r', encoding='utf-8')\n \"\"\"Count number of lettes without digits, non letter characters, without xml tags\"\"\"\n data = file.read()\n data = re.sub('<.*?binary.*?>*<.*?binary.*?>',' ', data)\n data = re.sub('\\\\s\\\\s*', '', (re.sub('\\\\W|\\\\d', ' ', re.sub('<.*?>', '', data))))\n let_count = len(data)\n sqlite_for_ht.CreateTable.update_table(f_1, self.filename, 'number_of_letters', let_count)\n print(datetime.now(), '-', 'number_of_letters for', self.filename, 'calculated =', let_count)\n return None"],"string":"[\n \"def count_triangles(self, file):\\n self.nibble(80)\\n return struct.unpack(\\\"@i\\\", self.nibble(4))[0]\",\n \"def get_num_vertices(triangles):\\n return numpy.amax(numpy.reshape(triangles, -1)) + 1\",\n \"def count_cells(fpath):\\n cells = []\\n for i in range(40):\\n fname = f\\\"{fpath}/Mesh2d_{i}.vtu\\\"\\n if not os.path.exists(fname):\\n print(f\\\"File {fname} does not exist.\\\")\\n break\\n mesh = meshio.read(fname)\\n for cell_block in mesh.cells:\\n if cell_block.type in (\\\"triangle\\\"):\\n num_cells = len(cell_block)\\n print(f\\\"{i:2d}: {num_cells:6d} elements, {len(mesh.points):6d} vertices\\\")\\n cells.append(num_cells)\\n continue\\n return cells\",\n \"def num_data_lines(filepath):\\n\\tif not file_exists(filepath):\\n\\t\\treturn -1\\n\\tcount = 0\\n\\twith open(filepath, 'r') as f:\\n\\t\\twhile read_floats(f):\\n\\t\\t\\tcount += 1\\n\\tf.close()\\n\\treturn count\",\n \"def countSamples(filename):\\n with open(filename, \\\"r\\\") as f:\\n line = f.readline().split(\\\"\\\\t\\\")\\n return len(line) - 2\",\n \"def _get_las_npoints(fpath):\\n with laspy.file.File(fpath) as f:\\n return f.header.count\",\n \"def get_triangles_per_vertex(my_core, native_ranges):\\n\\n t_p_v_data = []\\n tri_dimension = 2\\n for vertex in native_ranges[types.MBVERTEX]:\\n t_p_v_data.append(my_core.get_adjacencies(vertex, tri_dimension).size())\\n return np.array(t_p_v_data)\",\n \"def test_triangle_count_05(self):\\n body = {\\\"direction\\\": \\\"IN\\\", \\\"degree\\\": -1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 2}\\n else:\\n assert 0\",\n \"def test_triangle_count_08(self):\\n body = {\\\"direction\\\": \\\"IN\\\", \\\"degree\\\": 1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_in': 13, 'vertices_in': 9, 'triangles': 0}\\n else:\\n assert 0\",\n \"def test_triangle_count_04(self):\\n body = {\\\"direction\\\": \\\"OUT\\\"}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\\n else:\\n assert 0\",\n \"def test_triangle_count_06(self):\\n body = {\\\"direction\\\": \\\"OUT\\\", \\\"degree\\\": -1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 2}\\n else:\\n assert 0\",\n \"def countDataSize(self,filename):\\n \\n try:\\n d = h5py.File(filename,'r')\\n except:\\n print(filename)\\n return \\n\\n N = 0\\n scan_edges = d['level2/Statistics/scan_edges'][:]\\n for (start,end) in scan_edges:\\n N += (end-start)//self.offsetLen * self.offsetLen\\n d.close()\\n\\n N = N*self.Nfeeds\\n\\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\\n self.datasizes += [int(N/self.Nfeeds)]\\n self.Nsamples += int(N)\",\n \"def countDataSize(self,filename):\\n \\n d = h5py.File(filename,'r')\\n features = d['spectrometer/features'][:]\\n select = self.selectData(features.astype(float), self.ifeature, d)\\n N = len(features[select])\\n d.close()\\n\\n N = (N//self.offsetLen) * self.offsetLen\\n\\n N = N*self.Nfeeds\\n\\n self.chunks += [[int(self.Nsamples), int(self.Nsamples+N)]]\\n self.datasizes += [int(N/self.Nfeeds)]\\n self.Nsamples += int(N)\",\n \"def data_info(data):\\n filename = data[\\\"filename\\\"]\\n X_var = data[\\\"X_var\\\"]\\n Y_var = data[\\\"Y_var\\\"]\\n X,Y = read_file(filename,X_var,Y_var)\\n input_dim = len(X_var)\\n output_dim = len(Y_var)\\n return X,Y,input_dim,output_dim\",\n \"def count_aux(self, infile):\\n n_aux = 0\\n n_tokens = 0\\n dgs_in = self._file_handler.file_to_dg_list(infile)\\n for dg in dgs_in:\\n n_tokens += len(dg)\\n transform = VGtransformer(dg, dep_style=self._dep_style)\\n transform.transform()\\n n_aux += transform.tot_aux\\n return n_aux, n_tokens, len(dgs_in)\",\n \"def read_datafile(shower_name):\\n\\twith open(datafile_dir+'datafile_'+shower_name) as file:\\n\\t\\tamplitudes = [float(line) for line in file]\\n\\tcount = [x+1 for x in range(len(amplitudes))[::-1]]\\n\\treturn amplitudes, count\",\n \"def get_array_size():\\n tg_file = 'NA_CAS_gauges.txt'\\n lines = open(tg_file).readlines()\\n tg_nbr = len(lines)\\n return tg_nbr\",\n \"def __len__(self):\\n return self._num_samples_per_file * len(self._files) // self._world_size\",\n \"def create_1d_coil_geometry(division, filename, directory):\\n os.chdir(directory)\\n npoints = division + 1\\n length_array = np.zeros((npoints, 2))\\n current_length = 0\\n array = np.loadtxt(filename)\\n for i in range(1, npoints):\\n current_length += ((array[i, 1] - array[i - 1, 1]) ** 2 + (array[i, 2] - array[i - 1, 2]) ** 2 +\\n (array[i, 3] - array[i - 1, 3]) ** 2) ** 0.5\\n length_array[i - 1, 0] = i\\n length_array[i, 1] = current_length\\n length_array[npoints - 1, 0] = npoints\\n return length_array\",\n \"def load_data():\\n data = []\\n with euler.Resource('triangle.txt') as datafile:\\n for line in datafile.readlines():\\n data.insert(0, map(int, line.strip().split()))\\n return data\",\n \"def test_triangle_count_07(self):\\n body = {\\\"direction\\\": \\\"OUT\\\", \\\"degree\\\": 1}\\n code, res = Algorithm().post_triangle_count(body, auth=auth)\\n id = res[\\\"task_id\\\"]\\n if id > 0:\\n result = get_task_res(id, 120, auth=auth)\\n print(result)\\n assert result == {'edges_out': 13, 'vertices_out': 7, 'triangles': 0}\\n else:\\n assert 0\",\n \"def read_input(filename):\\n with open(filename, 'r') as f:\\n (N) = map(int, next(f).split())\\n def parse_line(line):\\n l = line.split()\\n h = 0 if l[0] == 'H' else 1\\n n = int(l[1])\\n return [h, l[2:]]\\n\\n photos = transform_strings([parse_line(line) for line in f])\\n return (len(photos), photos)\",\n \"def get_edge_lengths(points: np.ndarray, triangles: np.ndarray) -> np.ndarray:\\n edges, _ = get_edges(triangles)\\n return np.linalg.norm(np.diff(points[edges], axis=1), axis=2).squeeze()\",\n \"def dimension(self):\\r\\n a = 0\\r\\n for x in self.faces():\\r\\n if (len(x) > a):\\r\\n a = len(x) \\r\\n return a-1\",\n \"def count_timepoints(sc, session, files):\\n tuples = zip(range(len(files)), files)\\n files_sc = sc.parallelize(tuples)\\n\\n def count_planes(kv):\\n index, path2 = kv\\n try:\\n from ScanImageTiffReader import ScanImageTiffReader\\n img = ScanImageTiffReader(path2).data()\\n except Exception:\\n import tifffile\\n img = tifffile.imread(path2)\\n return img.shape[0]\\n\\n data2 = files_sc.map(count_planes).collect()\\n frame_numbers = np.array(data2)\\n vol_numbers = frame_numbers / len(session.fieldMask)\\n return vol_numbers.astype(int)\",\n \"def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):\\r\\n global count\\r\\n with open(inputfilename, 'r') as swmmput:\\r\\n contents = swmmput.readlines()\\r\\n count = len(contents)\\r\\n return(count)\",\n \"def read_off_size(path):\\n try:\\n with open(path, 'r') as file:\\n lines = file.readlines()\\n if lines[0] != 'OFF\\\\n':\\n print(path, 'Error: is not an .off file')\\n num_vertices, num_faces = tuple(lines[1].split()[:2])\\n return int(num_vertices), int(num_faces)\\n except IOError:\\n print('Error: Failed reading file:', path)\",\n \"def num_quadrature_points(self) -> int:\",\n \"def findTriangles(p):\\n triangleCount = 0\\n for a in range(3, p//3 + 1):\\n for b in range(a+1, p//2):\\n c = p - (a+b)\\n if (a**2 + b**2) == c**2:\\n triangleCount += 1\\n return triangleCount\",\n \"def get_dimensions ( file_in, separator ) :\\n try :\\n logger.info ( \\\"Extract dimensions from xyz file \\\" + str(file_in) ) \\n d = {}\\n first_row = True\\n d[NOPS] = 0\\n file = open(file_in, 'r')\\n for line in file :\\n d[NOPS] = d[NOPS] + 1\\n l = line.rstrip().split(separator)\\n x = float(l[0])\\n y = float(l[1])\\n z = float(l[2])\\n if first_row :\\n d[MINX] = x\\n d[MAXX] = x\\n d[MINY] = y\\n d[MAXY] = y\\n d[MINZ] = z\\n d[MAXZ] = z\\n first_row = False\\n else :\\n if x < d[MINX] :\\n d[MINX] = x\\n if x > d[MAXX] :\\n d[MAXX] = x \\n if y < d[MINY] :\\n d[MINY] = y\\n if y > d[MAXY] :\\n d[MAXY] = y \\n if z < d[MINZ] :\\n d[MINZ] = z\\n if z > d[MAXZ] :\\n d[MAXZ] = z \\n file.close() \\n logger.info ('Now return')\\n return d\\n except Exception, err:\\n logger.critical(\\\"Extract dimensions from xyz file failed: ERROR: %s\\\\n\\\" % str(err))\\n raise\",\n \"def reader(filename,only_length=False):\\n print(\\\"Counting lines in file %s\\\"%filename)\\n total_lines=0\\n for n,line in enumerate(open(filename,\\\"r\\\")):\\n total_lines+=1\\n \\n if only_length:\\n return total_lines\\n \\n X,Y,Z,W,J=[np.zeros(total_lines) for _ in range(5)]\\n \\n for n, line in enumerate(open(filename, 'r')):\\n if n%1000000==0:\\n print(\\\"Reading line %d of %d from file %s\\\" %(n,total_lines,filename))\\n split_line=np.array(line.split(\\\" \\\"), dtype=float) \\n X[n]=split_line[0];\\n Y[n]=split_line[1];\\n Z[n]=split_line[2];\\n W[n]=split_line[3];\\n J[n]=int(split_line[4]);\\n return X,Y,Z,W,J\",\n \"def total_number():\\r\\n total_number = 0\\r\\n file_read = read_file()\\r\\n for key in file_read:\\r\\n total_number = total_number + len(file_read[key])\\r\\n return total_number\",\n \"def calc_side_lengths(triangles):\\n first_vec = [2, 0, 1]\\n second_vec = [1, 2, 0]\\n sides = triangles[:, first_vec] - triangles[:, second_vec]\\n lengths = np.sqrt(np.sum(sides**2, axis=2))\\n return lengths\",\n \"def numverts(self):\\n return self._numvertstotal\",\n \"def readGR3File(inputFilename):\\n print 'Reading ' + inputFilename + ' ...'\\n infile = open(inputFilename, 'r')\\n description = infile.readline().strip() # remove leading/trailing whitespace\\n tmpStr = infile.readline()\\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\\n\\n # nodes\\n nodeArray = readNodeBlock(infile, nNodes)\\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\\n nodexyz = np.zeros((nNodes, 3))\\n nodexyz[:, :2] = nodeArray[:, 1:3]\\n nodalValues = nodeArray[:, 3]\\n\\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\\n\\n # triangular elements\\n triArray = readElemBlock(infile, nTriangles)\\n\\n trinum = triArray[:, 0].flatten()\\n tritype = triArray[0, 1]\\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\\n #triangles = meshElements(trinodes,trinum,tritype)\\n\\n x = nodexyz[:, 0]\\n y = nodexyz[:, 1]\\n\\n tmpStr = infile.readline()\\n boundaries = []\\n if len(tmpStr) > 0:\\n # boundary information, if not end of file\\n nOpenBndSegments = int(tmpStr.split()[0])\\n nOpenBndNodesTot = int(infile.readline().split()[0])\\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\\n for iBnd in range(nOpenBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n tag = bndHeader[-1]\\n if tag.isdigit():\\n tag = 'open' + tag\\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\\n nLandBndSegments = int(infile.readline().split()[0])\\n nLandBndNodesTot = int(infile.readline().split()[0])\\n landBndTags = range(\\n nOpenBndSegments + 1,\\n nOpenBndSegments + nLandBndSegments + 1)\\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\\n for iBnd in range(nLandBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n try:\\n landType = int(bndHeader[1])\\n except:\\n print \\\"\\\"\\\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\\n 1002 = Total number of closed boundary nodes\\n 501 0 = Number of nodes in closed boundary 1\\\"\\\"\\\"\\n raise Exception(\\n 'Could not parse land boundary type (0/1 - land/island)\\\\n')\\n landType = 'island' if landType == 1 else 'land'\\n tag = landType + bndHeader[-1]\\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\\n\\n infile.close()\\n\\n # for better interpolation, round coordinates to 1e-4\\n nDig = 4\\n x = np.round(x, nDig)\\n y = np.round(y, nDig)\\n\\n return x, y, nodalValues, trinodes, boundaries, description\",\n \"def file_read(file_name):\\n \\n #open specified file in read mode\\n in_file = open(file_name, \\\"r\\\")\\n \\n #create data lists\\n sp_length_v3 = []\\n sp_period_v3 = [] \\n\\n #save header to string and split into list\\n header_string = in_file.readline()\\n header_v3 = header_string.split()\\n \\n #save revelent data to respective lists\\n for line in in_file:\\n values = line.split()\\n sp_length_v3.append(float(values[1]))\\n sp_period_v3.append(float(values[2]))\\n \\n #close the file\\n in_file.close()\\n \\n #return 3D lists of lists containing data\\n ans = [sp_length_v3, sp_period_v3, header_v3]\\n \\n return ans\",\n \"def from_polyfile(name):\\n\\n from anuga.utilities.numerical_tools import anglediff\\n from math import pi\\n import os.path\\n root, ext = os.path.splitext(name)\\n\\n if ext == 'poly':\\n filename = name\\n else:\\n filename = name + '.poly'\\n\\n\\n fid = open(filename)\\n\\n points = [] #x, y\\n values = [] #z\\n ##vertex_values = [] #Repeated z\\n triangles = [] #v0, v1, v2\\n\\n lines = fid.readlines()\\n\\n keyword = lines[0].strip()\\n msg = 'First line in .poly file must contain the keyword: POINTS'\\n assert keyword == 'POINTS', msg\\n\\n offending = 0\\n i = 1\\n while keyword == 'POINTS':\\n line = lines[i].strip()\\n i += 1\\n\\n if line == 'POLYS':\\n keyword = line\\n break\\n\\n fields = line.split(':')\\n assert int(fields[0]) == i-1, 'Point indices not consecutive'\\n\\n #Split the three floats\\n xyz = fields[1].split()\\n\\n x = float(xyz[0])\\n y = float(xyz[1])\\n z = float(xyz[2])\\n\\n points.append([x, y])\\n values.append(z)\\n\\n\\n k = i\\n while keyword == 'POLYS':\\n line = lines[i].strip()\\n i += 1\\n\\n if line == 'END':\\n keyword = line\\n break\\n\\n\\n fields = line.split(':')\\n assert int(fields[0]) == i-k, 'Poly indices not consecutive'\\n\\n #Split the three indices\\n vvv = fields[1].split()\\n\\n i0 = int(vvv[0])-1\\n i1 = int(vvv[1])-1\\n i2 = int(vvv[2])-1\\n\\n #Check for and exclude degenerate areas\\n x0 = points[i0][0]\\n y0 = points[i0][1]\\n x1 = points[i1][0]\\n y1 = points[i1][1]\\n x2 = points[i2][0]\\n y2 = points[i2][1]\\n\\n area = abs((x1*y0-x0*y1)+(x2*y1-x1*y2)+(x0*y2-x2*y0))/2\\n if area > 0:\\n\\n #Ensure that points are arranged in counter clock-wise order\\n v0 = [x1-x0, y1-y0]\\n v1 = [x2-x1, y2-y1]\\n v2 = [x0-x2, y0-y2]\\n\\n a0 = anglediff(v1, v0)\\n a1 = anglediff(v2, v1)\\n a2 = anglediff(v0, v2)\\n\\n\\n if a0 < pi and a1 < pi and a2 < pi:\\n #all is well\\n j0 = i0\\n j1 = i1\\n j2 = i2\\n else:\\n #Swap two vertices\\n j0 = i1\\n j1 = i0\\n j2 = i2\\n\\n triangles.append([j0, j1, j2])\\n ##vertex_values.append([values[j0], values[j1], values[j2]])\\n else:\\n offending +=1\\n\\n log.critical('Removed %d offending triangles out of %d'\\n % (offending, len(lines)))\\n return points, triangles, values\",\n \"def n_file_elements(cls):\\n \\n return randint(1, (3 * Root.size))\",\n \"def gather_counts(directory):\\n counts_un = defaultdict(int)\\n counts_bi = defaultdict(int)\\n counts_tri = defaultdict(int)\\n prev_prev = \\\"<s>\\\"\\n prev = \\\"<s>\\\"\\n for filename in os.listdir(f\\\"./{directory}\\\"):\\n if \\\".DS_Store\\\" in filename:\\n continue\\n with open(f\\\"./{directory}/{filename}\\\", \\\"r\\\") as f:\\n for line in f:\\n line = line.strip()\\n if len(line) == 0:\\n continue\\n counts_un[line+\\\"\\\\n\\\"] += 1\\n counts_bi[prev+\\\"\\\\n\\\"+line+\\\"\\\\n\\\"] += 1\\n counts_tri[prev_prev+\\\"\\\\n\\\"+prev+\\\"\\\\n\\\"+line+\\\"\\\\n\\\"] += 1\\n prev_prev = prev\\n prev = line\\n counts_un[\\\"</s>\\\\n\\\"] += 2\\n counts_bi[\\\"</s>\\\\n</s>\\\\n\\\"] += 1\\n counts_bi[prev+\\\"\\\\n\\\"+\\\"</s>\\\\n\\\"] += 1\\n counts_tri[prev_prev+\\\"\\\\n\\\"+prev+\\\"\\\\n\\\" + \\\"</s>\\\\n\\\"] += 1\\n counts_tri[prev+\\\"\\\\n</s>\\\\n</s>\\\\n\\\"] += 1\\n return counts_un, counts_bi, counts_tri\",\n \"def get_triangle_count(remote, objectid):\\n cmd1 = mmapi.StoredCommands()\\n key1 = cmd1.AppendSceneCommand_GetTriangleCount(objectid)\\n remote.runCommand(cmd1)\\n result_val = mmapi.any_result()\\n cmd1.GetSceneCommandResult_GetTriangleCount(key1, result_val)\\n return result_val.i\",\n \"def get_dimensions(self):\\r\\n x = []\\r\\n y = []\\r\\n z = []\\r\\n for i in self.verts:\\r\\n x.append(i[0])\\r\\n y.append(i[1])\\r\\n z.append(i[2])\\r\\n\\r\\n x.append(abs(min(x)))\\r\\n y.append(abs(min(y)))\\r\\n z.append(abs(min(z)))\\r\\n\\r\\n return max(x), max(y), max(z)\",\n \"def get_triangles( self, N ):\\n\\n # store N as an instance variable\\n self.N = N\\n\\n # initialize array to store locations of points for all triangles in the\\n # tessellation sequence\\n self.triangles = np.zeros( ( self.N, 3, 2 ) )\\n\\n # define points of the first triangle in the tessellation sequence\\n point_c = np.array( [ 0, 0 ] )\\n point_b = self.a * np.array( [ np.cos( self.C ), np.sin( self.C ) ] )\\n point_a = np.array( [ self.b, 0 ] )\\n\\n # stack the points into a single array of shape (3, 2 )\\n triangle = np.vstack( [ point_c, point_b, point_a ] )\\n\\n # loop over the number of triangles in the sequence\\n for i in range( self.N ):\\n\\n # store the points of the i-th triangle in the array\\n self.triangles[ i ] = triangle\\n\\n # compute the next triangle in the tessellation sequence\\n triangle = self.next_triangle( triangle = triangle )\\n\\n # shift the next triangle in the tessellation sequence such that its\\n # point C is in the same location as point B of the previous triangle\\n triangle += ( self.triangles[ i - 1, 1 ] - self.triangles[ 0, 0 ] )\",\n \"def std_triangles_count(graph):\\n if nx.is_directed(graph):\\n raise Exception(\\\"Graph is not undirected\\\")\\n\\n return sum(nx.triangles(graph).values()) // 3\",\n \"def getNbins(self,axis,includeTotalBin = True):\\n\\n\\t\\tif axis == \\\"f\\\":\\n\\t\\t\\tnCells = 1 if self.nCells == 0 else self.nCells\\n\\t\\t\\treturn nCells\\n\\n\\t\\tif axis == \\\"i\\\":\\n\\t\\t\\treturn self.meshInfo[1]\\n\\n\\t\\tif axis == \\\"j\\\":\\n\\t\\t\\treturn self.meshInfo[2]\\n\\n\\t\\tif axis == \\\"k\\\":\\n\\t\\t\\treturn self.meshInfo[3]\\n\\n\\t\\tif axis == \\\"d\\\":\\n\\t\\t\\tnDir = 1 if self.nDir == 0 else self.nDir\\n\\t\\t\\treturn nDir\\n\\n\\t\\tif axis == \\\"u\\\":\\n\\t\\t\\tnUsr = 1 if self.nUsr == 0 else self.nUsr\\n\\t\\t\\tnUsr = nUsr - 1 if self.usrTC == \\\"t\\\" and not includeTotalBin else nUsr\\n\\t\\t\\treturn nUsr\\n\\n\\t\\tif axis == \\\"s\\\":\\n\\t\\t\\tnSeg = 1 if self.nSeg == 0 else self.nSeg\\n\\t\\t\\tnSeg = nSeg - 1 if self.segTC == \\\"t\\\" and not includeTotalBin else nSeg\\n\\t\\t\\treturn nSeg\\n\\n\\t\\tif axis == \\\"m\\\":\\n\\t\\t\\tnMul = 1 if self.nMul == 0 else self.nMul\\n\\t\\t\\tnMul = nMul - 1 if self.mulTC == \\\"t\\\" and not includeTotalBin else nMul\\n\\t\\t\\treturn nMul\\n\\n\\t\\tif axis == \\\"c\\\":\\n\\t\\t\\tnCos = 1 if self.nCos == 0 else self.nCos\\n\\t\\t\\tnCos = nCos - 1 if self.cosTC == \\\"t\\\" and not includeTotalBin else nCos\\n\\t\\t\\treturn nCos\\n\\n\\t\\tif axis == \\\"e\\\":\\n\\t\\t\\tnErg = 1 if self.nErg == 0 else self.nErg\\n\\t\\t\\tnErg = nErg - 1 if self.ergTC == \\\"t\\\" and not includeTotalBin else nErg\\n\\t\\t\\treturn nErg\\n\\n\\t\\tif axis == \\\"t\\\":\\n\\t\\t\\tnTim = 1 if self.nTim == 0 else self.nTim\\n\\t\\t\\tnTim = nTim - 1 if self.timTC == \\\"t\\\" and not includeTotalBin else nTim\\n\\t\\t\\treturn nTim\",\n \"def read_txt(if_name):\\n n = 0\\n paper_shape = []\\n present_shape = []\\n input_file = open(if_name,'r')\\n i = 0\\n\\n for line in input_file:\\n\\n if i > 1:\\n\\n i += 1\\n line = line.strip().split(' ')\\n if len(line) < 2:\\n break\\n present_shape.append([int(e) for e in line])\\n\\n if i == 1:\\n i += 1\\n line = line.strip()\\n n = int(line)\\n\\n if i == 0:\\n i += 1\\n line = line.strip().split(' ')\\n paper_shape = [int(e) for e in line]\\n\\n input_file.close()\\n return n, paper_shape, present_shape\",\n \"def bpCount(file):\\n amount_bp = len(file)\\n return amount_bp\",\n \"def get_all_object_triangles( filename, scale , translation=(0,0,0)):\\n import warnings\\n warnings.warn(\\\"@PendingDeprecationWarning\\\", PendingDeprecationWarning)\\n vertexObjs = read_vertices_objects( filename )\\n faceObjs = read_faces_objects( filename )\\n\\n r = []\\n j = 0\\n \\n # Validation:\\n vertices, faces = [],[]\\n for obj in range(len(vertexObjs)):\\n vertices += vertexObjs[obj]\\n faces += faceObjs[obj]\\n max_vertex_index = max([max(x) for x in faces])\\n if len(vertices) != max_vertex_index:\\n logging.warning( \\\"ParseWarning: A face's vertex index number is does not match the quantity of read vertices.\\\" )\\n logging.warning( \\\"Qty of Vertices: \\\"+str(len(vertices))+\\\", Largest Face Index: \\\"+str(max_vertex_index) )\\n\\n # Parse as Tris:\\n for obj in range(len(vertexObjs)):\\n vertices = vertexObjs[obj]\\n faces = faceObjs[obj]\\n r.append([])\\n c = 0\\n for f in faces: # for every face\\n for i in f: # for each index point in face\\n c+=1\\n try:\\n # Get the face[i] vertex\\n v = vertices[i-1]\\n except IndexError as indErr:\\n logging.warning(\\\"IndexError: Attempted to access index: \\\"+str(i-1)+\\\" in list of length: \\\"+str(len(vertices)))\\n raise IndexError\\n # Scale the face[i] vertex\\n scV = [v[0]*scale, v[1]*scale, v[2]*scale]\\n # Translate the scaled face vertex:\\n t = translation\\n tmpv = [scV[0]+t[0],scV[1]+t[1], scV[2]+t[2]]\\n # Retain this vertex\\n r[j].append(tmpv)\\n # ---------------------\\n if c % 3 == 0:\\n j+=1\\n r.append([])\\n r = r[:len(r)-1] # remove the final empty list.\\n\\n checkShapeValidity( r )\\n return r\",\n \"def load_triangles(triangle_bytes, header):\\n triangles = list()\\n for i in range(header.num_tris):\\n triangle = triangle_t(list(struct.unpack(\\\"<hhh\\\", triangle_bytes[12*i:12*i+6])), list(struct.unpack(\\\"<hhh\\\", triangle_bytes[12*i+6:12*i+12])))\\n # print(triangle)\\n triangles.append(triangle)\\n return triangles\",\n \"def test_number_of_surface_objects(self):\\n for O in self.mod.objts.itervalues():\\n no_of_surfaces = 0\\n for C in O.conts.itervalues():\\n if C.surf != 0:\\n no_of_surfaces += 1\\n self.assertEqual(O.surfsize, no_of_surfaces)\",\n \"def dimensions():\",\n \"def test_number_of_vertex_elements_in_MESH_chunk(self):\\n for O in self.mod.objts.itervalues():\\n for M in O.meshes.itervalues():\\n self.assertEqual(M.vsize, len(M.vert))\",\n \"def read_triangle(f, layers=1):\\n # Read nodes\\n with open(f + '.node') as h:\\n num_nodes = int(h.readline().split(' ')[0])\\n node_values = np.zeros((num_nodes, 2), dtype=np.float64)\\n for line in h:\\n if line[0] == '#':\\n continue\\n node, x, y = line.split()[:3]\\n node_values[int(node) - 1, :] = [float(x), float(y)]\\n\\n nodes = op2.Set(num_nodes, \\\"nodes\\\")\\n coords = op2.Dat(nodes ** 2, node_values, name=\\\"coords\\\")\\n\\n # Read elements\\n with open(f + '.ele') as h:\\n num_tri, nodes_per_tri, num_attrs = [int(x) for x in h.readline().split()]\\n map_values = np.zeros((num_tri, nodes_per_tri), dtype=np.int32)\\n for line in h:\\n if line[0] == '#':\\n continue\\n vals = [int(x) - 1 for x in line.split()]\\n map_values[vals[0], :] = vals[1:nodes_per_tri + 1]\\n\\n elements = op2.Set(num_tri, \\\"elements\\\", layers=layers)\\n elem_node = op2.Map(elements, nodes, nodes_per_tri, map_values, \\\"elem_node\\\")\\n\\n return nodes, coords, elements, elem_node\",\n \"def get_number_lines(running_reward_file, running_loss_file, action_count_file):\\n if Path(running_reward_file).exists():\\n data = np.loadtxt(running_reward_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(running_loss_file).exists():\\n data = np.loadtxt(running_loss_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(action_count_file).exists():\\n data = np.loadtxt(action_count_file).reshape(-1,2)\\n return data.shape[0]\\n raise NameError(\\\"No files to count lines\\\")\",\n \"def get_dataset_length(file_path, had_header=True):\\n with open(file_path, 'r') as f:\\n length = 0\\n for _ in f:\\n length += 1\\n length = length - had_header\\n return length\",\n \"def __len__(self) -> int:\\n import h5py\\n\\n with h5py.File(\\n os.path.join(self.root, self.data_dir, self.img_file_name), \\\"r\\\"\\n ) as f:\\n num_datapoints: int = f[self.split][\\\"pv_log\\\"].shape[0]\\n\\n return num_datapoints\",\n \"def get_library_sizes(args):\\n with open(args.counts, \\\"r\\\") as counts:\\n sizes = []\\n head = True\\n for line in counts:\\n line = line.strip()\\n if head:\\n head = False\\n samples = line.split(\\\"\\\\t\\\")[3:]\\n total_counts = [0] * len(samples)\\n else:\\n counts = line.split(\\\"\\\\t\\\")\\n if counts[1] == \\\"NA\\\":\\n break\\n else:\\n counts = counts[3:]\\n for i in range(len(counts)):\\n total_counts[i] += int(counts[i])\\n\\n for i in range(len(samples)):\\n sizes.append([samples[i], total_counts[i]])\\n\\n return sizes\",\n \"def data_len(self):\\n Nrows_data = 0\\n with self._compression_safe_file_opener(self.input_fname, \\\"r\\\") as f:\\n for i, l in enumerate(f):\\n if (l[0 : len(self.header_char)] != self.header_char) and (l != \\\"\\\\n\\\"):\\n Nrows_data += 1\\n return Nrows_data\",\n \"def getFileCount(self) -> int:\\n ...\",\n \"def test_number_of_MESH_chunks(self):\\n for O in self.mod.objts.itervalues():\\n self.assertEqual(O.meshsize, len(O.meshes))\",\n \"def _tvb_surface_to_tri(self, surface_file_name):\\n surface_file_path = os.path.join(OM_STORAGE_DIR, surface_file_name)\\n\\n #TODO: check file doesn't already exist\\n LOG.info(\\\"Writing TVB surface to .tri file: %s\\\" % surface_file_path)\\n file_handle = file(surface_file_path, \\\"a\\\")\\n\\n file_handle.write(\\\"- %d \\\\n\\\" % self.sources.number_of_vertices)\\n verts_norms = numpy.hstack((self.sources.vertices, \\n self.sources.vertex_normals))\\n numpy.savetxt(file_handle, verts_norms)\\n\\n tri_str = \\\"- \\\" + (3 * (str(self.sources.number_of_triangles) + \\\" \\\")) + \\\"\\\\n\\\"\\n file_handle.write(tri_str)\\n numpy.savetxt(file_handle, self.sources.triangles, fmt=\\\"%d\\\")\\n\\n file_handle.close()\\n LOG.info(\\\"%s written successfully.\\\" % surface_file_name)\\n\\n return surface_file_path\",\n \"def dataDimensions(data):\\n logging.info('Number of rows of data: %s' % len(data))\\n logging.info('Number of columns of data: %s' % len(data[1]))\",\n \"def _extract_track_lengths(track_key,conn):\\n print track_key\\n \\n (fname,iden_key,track_key) = conn.execute(\\\"select fout,iden_key,comp_key from tracking where comp_key = ?\\\",\\n track_key).fetchone()\\n \\n F = h5py.File(fname,'r')\\n len_vec = F[fd('tracking',track_key)]['length'][:]\\n \\n temp = 0\\n dtime = 0\\n fr_count = 0\\n for g in F.keys():\\n if g[0:5] == 'frame':\\n temp += F[g].attrs['temperature']\\n dtime += F[g].attrs['dtime']\\n fr_count += 1\\n\\n \\n F.close()\\n del F\\n return len_vec, temp/fr_count, dtime/fr_count\",\n \"def ss_triangle_count(graph: ScipyGraph) -> int:\\n props = ScipyGraph.Type.compute_abstract_properties(graph, {\\\"edge_type\\\"})\\n if props[\\\"edge_type\\\"] == \\\"map\\\":\\n # Drop weights before performing triangle count\\n m = graph.value.copy()\\n m.data = np.ones_like(m.data)\\n elif props[\\\"edge_type\\\"] == \\\"set\\\":\\n m = graph.value\\n L = ss.tril(m, k=-1).tocsr()\\n U = ss.triu(m, k=1).tocsc()\\n return int((L @ U.T).multiply(L).sum())\",\n \"def analyze_pressure_dump(filename, Lx=200., Ly=200, Lz=900., N=10, bin_divide_flag=False, Natoms=113579):\\n myfile = open(filename+'.txt')\\n trajectory = []\\n traj_pd = []\\n frames = []\\n\\n for _ in range(3):\\n next(myfile)\\n count = 0\\n while EOF(myfile):\\n count += 1\\n s = next(myfile) # info with the time step\\n\\n x = np.zeros(N, dtype=[('Chunk',np.float32), ('Coord1',np.float32), ('Ncount',np.float32), ('density',np.float32), ('temp',np.float32), ('vx',np.float32), ('fx',np.float32),('c_pciKE[1]',np.float32), ('c_pciKE[2]',np.float32), ('c_pciKE[3]',np.float32), ('c_pciVIR[1]',np.float32), ('c_pciVIR[2]',np.float32), ('c_pciVIR[3]',np.float32), ('c_pgelELAS[1]',np.float32), ('c_pgelELAS[2]',np.float32), ('c_pgelELAS[3]',np.float32), ('c_pgelVIR[1]', np.float32), ('c_pgelVIR[2]', np.float32), ('c_pgelVIR[3]', np.float32), ('c_pgelPAIR[1]', np.float32), ('c_pgelPAIR[2]', np.float32), ('c_pgelPAIR[3]', np.float32)])\\n\\n# Chunk Coord1 Ncount density/number temp vx fx c_pciKE[1] c_pciKE[2] c_pciKE[3] c_pciVIR[1] c_pciVIR[2] c_pciVIR[3] c_pgelELAS[1] c_pgelELAS[2] c_pgelELAS[3] c_pgelVIR[1] c_pgelVIR[2] c_pgelVIR[3] c_pgelPAIR[1] c_pgelPAIR[2] c_pgelPAIR[3]\\n\\n list_line = re.findall(\\\"[-+]?\\\\d+[\\\\.]?\\\\d*[eE]?[-+]?\\\\d*\\\", s)\\n frame, _, _ = list_line\\n frames.append(int(frame))\\n # print( \\\"reading lines\\\")\\n\\n for i in xrange(N):\\n count += 1\\n s = next(myfile)\\n list_line = re.findall(\\\"[-+]?\\\\d+[\\\\.]?\\\\d*[eE]?[-+]?\\\\d*\\\", s)\\n # print( \\\"reading line\\\", i, list_line)\\n for il, l in enumerate(list_line):\\n x[i][il] = float(l)\\n\\n trajectory.append(x)\\n\\n # names = x.dtype.fields.keys()\\n # data = x.dtype.fields.values()\\n\\n df = pd.DataFrame.from_records(x)\\n traj_pd.append(df)\\n\\n myfile.close()\\n\\n\\n\\n # # volume = 218.*44.*44.\\n volume = Lx*Ly*Lz\\n # N_atoms = 113579\\n # if bin_divide_flag:\\n # bin_volume = volume / float(N)\\n # else:\\n # bin_volume = 1.\\n\\n bin_volume = volume / float(N)\\n # bin_volume = volume\\n # bin_volume /= float(Natoms)\\n\\n Combine_PD = pd.concat(traj_pd)\\n FINAL_PD = pd.DataFrame()\\n\\n FINAL_PD['Coord1'] = Combine_PD['Coord1']\\n FINAL_PD['p_ciKE'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciKE[1]'] + Combine_PD['c_pciKE[2]'] + Combine_PD['c_pciKE[3]'])/(3.*bin_volume)\\n FINAL_PD['p_ciVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pciVIR[1]'] + Combine_PD['c_pciVIR[2]'] + Combine_PD['c_pciVIR[3]'])/(3.*bin_volume)\\n FINAL_PD['p_gelELAS'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelELAS[1]'] + Combine_PD['c_pgelELAS[2]'] + Combine_PD['c_pgelELAS[3]'])/(3.*bin_volume)\\n\\n FINAL_PD['p_gelVIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelVIR[1]'] + Combine_PD['c_pgelVIR[2]'] + Combine_PD['c_pgelVIR[3]'])/(3.*bin_volume)\\n FINAL_PD['p_gelPAIR'] = -1 * Combine_PD['Ncount'] * (Combine_PD['c_pgelPAIR[1]'] + Combine_PD['c_pgelPAIR[2]'] + Combine_PD['c_pgelPAIR[3]'])/(3.*bin_volume)\\n\\n # So now I have to\\n # P_bin = (sigma_per_atom_xx + ... + sigma_per_atom_zz)/(bin_volume*3)\\n # *N_atoms_per_bin\\n # N_atoms_per_bin = number_density*N_atoms\\n\\n\\n df_concat = FINAL_PD\\n\\n by_row_index = df_concat.groupby(df_concat.index)\\n df_means = by_row_index.mean()\\n by_row_index_2 = df_concat.groupby(df_concat.index)\\n df_stds = by_row_index_2.std()\\n\\n # print( df_means.head())\\n # print( df_stds.head())\\n return df_means, df_stds\",\n \"def fileCount(self):\\n pass\",\n \"def count_lines(filename):\\n with open(filename, 'r', encoding='utf-8') as file:\\n lines_count = int()\\n for line in file:\\n lines_count += 1\\n info_tuple = (filename, lines_count)\\n return info_tuple\",\n \"def _ReadExtent(self):\\n # Read the mesh file as line strings, remove lines with comment = !\\n v = np.array(np.__version__.split('.')[0:2], dtype=int)\\n FileName = self.GetMeshFileName()\\n try:\\n if v[0] >= 1 and v[1] >= 10:\\n # max_rows in numpy versions >= 1.10\\n msh = np.genfromtxt(FileName, delimiter='\\\\n', dtype=np.str,comments='!', max_rows=1)\\n else:\\n # This reads whole file :(\\n msh = np.genfromtxt(FileName, delimiter='\\\\n', dtype=np.str, comments='!')[0]\\n except (IOError, OSError) as fe:\\n raise _helpers.PVGeoError(str(fe))\\n # Fist line is the size of the model\\n self.__sizeM = np.array(msh.ravel()[0].split(), dtype=int)\\n # Check if the mesh is a UBC 2D mesh\\n if self.__sizeM.shape[0] == 1:\\n # Read in data from file\\n xpts, xdisc, zpts, zdisc = ubcMeshReaderBase._ubcMesh2D_part(FileName)\\n nx = np.sum(np.array(xdisc,dtype=int))+1\\n nz = np.sum(np.array(zdisc,dtype=int))+1\\n return (0,nx, 0,1, 0,nz)\\n # Check if the mesh is a UBC 3D mesh or OcTree\\n elif self.__sizeM.shape[0] >= 3:\\n # Get mesh dimensions\\n dim = self.__sizeM[0:3]\\n ne,nn,nz = dim[0], dim[1], dim[2]\\n return (0,ne, 0,nn, 0,nz)\\n else:\\n raise _helpers.PVGeoError('File format not recognized')\",\n \"def get_num_chunks(self) -> int:\",\n \"def part2(fname: dict) -> int:\\n return sum(len(set.intersection(*[set(pax) for pax in group])) for group in get_data(fname))\",\n \"def voxel_count(self):\\n return self.cols * self.rows * self.sections\",\n \"def calc_face_dimensions(face):\\n vertical = filter_vertical_edges(face.edges, face.normal).pop()\\n horizontal = filter_horizontal_edges(face.edges, face.normal).pop()\\n return horizontal.calc_length(), vertical.calc_length()\",\n \"def _count_data(path):\\n matcher = re.compile(r'[0-9]+\\\\.dec')\\n match = lambda name: bool(matcher.match(name))\\n names = os.listdir(path)\\n n_data = len(list(filter(match, names)))\\n return n_data\",\n \"def file_len(fname):\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n Nrows = i + 1\\n return Nrows\",\n \"def ReadFenics(self, filename, element_type):\\n\\n if element_type == \\\"tet\\\":\\n etype = \\\"tetrahedron\\\"\\n elif element_type == \\\"hex\\\":\\n etype = \\\"hexahedron\\\"\\n elif element_type == \\\"tri\\\":\\n etype = \\\"triangle\\\"\\n elif element_type == \\\"quad\\\":\\n etype = \\\"quadrilateral\\\"\\n\\n import xml.etree.cElementTree as ET\\n root = ET.parse(filename).getroot()\\n X = []\\n T = []\\n for child in root:\\n if child.attrib['celltype'] != etype:\\n raise ValueError(\\\"xml file does not contain {} elements\\\".format(element_type))\\n\\n for child in root:\\n for cchild in child:\\n if cchild.tag == \\\"vertices\\\":\\n if element_type == \\\"tet\\\" or element_type == \\\"hex\\\":\\n for child3 in cchild:\\n x = float(child3.attrib['x'])\\n y = float(child3.attrib['y'])\\n z = float(child3.attrib['z'])\\n X.append([x,y,z])\\n elif element_type == \\\"tri\\\" or element_type == \\\"quad\\\":\\n for child3 in cchild:\\n x = float(child3.attrib['x'])\\n y = float(child3.attrib['y'])\\n X.append([x,y])\\n\\n elif cchild.tag == \\\"cells\\\":\\n if element_type == \\\"tet\\\":\\n for child3 in cchild:\\n v0 = int(child3.attrib['v0'])\\n v1 = int(child3.attrib['v1'])\\n v2 = int(child3.attrib['v2'])\\n v3 = int(child3.attrib['v3'])\\n T.append([v0,v1,v2,v3])\\n elif element_type == \\\"tri\\\":\\n for child3 in cchild:\\n v0 = int(child3.attrib['v0'])\\n v1 = int(child3.attrib['v1'])\\n v2 = int(child3.attrib['v2'])\\n T.append([v0,v1,v2])\\n\\n\\n X = np.array(X)\\n T = np.array(T,dtype=np.int64)\\n\\n self.elements = T\\n self.points = X\\n self.element_type = element_type\\n self.nelem = self.elements.shape[0]\\n self.nnode = self.points.shape[0]\\n\\n if self.points.shape[1] == 3:\\n if np.allclose(self.points[:,2],0.):\\n self.points = np.ascontiguousarray(self.points[:,:2])\\n\\n ndim = self.InferSpatialDimension()\\n if self.element_type == \\\"tri\\\" or self.element_type == \\\"quad\\\":\\n self.GetEdges()\\n self.GetBoundaryEdges()\\n elif self.element_type == \\\"tet\\\" or self.element_type == \\\"hex\\\":\\n self.GetFaces()\\n self.GetBoundaryFaces()\\n self.GetBoundaryEdges()\",\n \"def read_geometry(filepath, read_metadata=False, read_stamp=False):\\n volume_info = OrderedDict()\\n\\n TRIANGLE_MAGIC = 16777214\\n QUAD_MAGIC = 16777215\\n NEW_QUAD_MAGIC = 16777213\\n with open(filepath, \\\"rb\\\") as fobj:\\n magic = _fread3(fobj)\\n if magic in (QUAD_MAGIC, NEW_QUAD_MAGIC): # Quad file\\n nvert = _fread3(fobj)\\n nquad = _fread3(fobj)\\n (fmt, div) = (\\\">i2\\\", 100.) if magic == QUAD_MAGIC else (\\\">f4\\\", 1.)\\n coords = np.fromfile(fobj, fmt, nvert * 3).astype(np.float) / div\\n coords = coords.reshape(-1, 3)\\n quads = _fread3_many(fobj, nquad * 4)\\n quads = quads.reshape(nquad, 4)\\n #\\n # Face splitting follows\\n #\\n faces = np.zeros((2 * nquad, 3), dtype=np.int)\\n nface = 0\\n for quad in quads:\\n if (quad[0] % 2) == 0:\\n faces[nface] = quad[0], quad[1], quad[3]\\n nface += 1\\n faces[nface] = quad[2], quad[3], quad[1]\\n nface += 1\\n else:\\n faces[nface] = quad[0], quad[1], quad[2]\\n nface += 1\\n faces[nface] = quad[0], quad[2], quad[3]\\n nface += 1\\n\\n elif magic == TRIANGLE_MAGIC: # Triangle file\\n create_stamp = fobj.readline().rstrip(b'\\\\n').decode('utf-8')\\n fobj.readline()\\n vnum = np.fromfile(fobj, \\\">i4\\\", 1)[0]\\n fnum = np.fromfile(fobj, \\\">i4\\\", 1)[0]\\n coords = np.fromfile(fobj, \\\">f4\\\", vnum * 3).reshape(vnum, 3)\\n faces = np.fromfile(fobj, \\\">i4\\\", fnum * 3).reshape(fnum, 3)\\n\\n if read_metadata:\\n volume_info = _read_volume_info(fobj)\\n else:\\n raise ValueError(\\\"File does not appear to be a Freesurfer surface\\\")\\n\\n coords = coords.astype(np.float) # XXX: due to mayavi bug on mac 32bits\\n\\n ret = (coords, faces)\\n if read_metadata:\\n if len(volume_info) == 0:\\n warnings.warn('No volume information contained in the file')\\n ret += (volume_info,)\\n if read_stamp:\\n ret += (create_stamp,)\\n\\n return ret\",\n \"def readCubeFile(self, filename):\\n\\n inputfile = open(filename, \\\"r\\\")\\n header = \\\"\\\".join([inputfile.readline(), inputfile.readline()])\\n\\n temp = inputfile.readline().strip().split()\\n self.numAtoms = int(temp[0])\\n self.origin = list(map(float, temp[1:]))\\n\\n self.numPoints = [0] * 3\\n self.spacing = [0] * 3\\n for i in range(3):\\n line = inputfile.readline().strip().split()\\n self.numPoints[i] = int(line[0])\\n temp = list(map(float, line[1:]))\\n self.spacing[i] = temp[i]\\n assert sum(temp[:i] + temp[i + 1:]) == 0\\n\\n # Read in the lines with atom data\\n for i in range(self.numAtoms):\\n line = inputfile.readline()\\n\\n self.data = np.zeros((self.numPoints[1], self.numPoints[0], self.numPoints[2]), \\\"float\\\")\\n i = j = k = 0\\n while i < self.numPoints[1]:\\n line = next(inputfile)\\n temp = list(map(float, line.strip().split()))\\n for x in range(0, len(temp)):\\n self.data[j, i, x + k] = temp[x]\\n\\n k += len(temp)\\n if k == self.numPoints[2]:\\n j += 1\\n k = 0\\n if j == self.numPoints[1]:\\n i += 1\\n j = 0\\n\\n inputfile.close()\",\n \"def parseInputFile(fn):\\n\\twith open(fn) as f:\\n\\t\\tpoly = [float(x) for x in f.readline().strip().split()]\\n\\t\\titers = int(f.readline().strip())\\n\\t\\treturn (poly, iters)\",\n \"def load_faces(file_data, headers, indices):\\n\\n\\n def swap_winding(indices):\\n return (indices[0], indices[2], indices[1])\\n \\n\\n def indices_from_face(face_data):\\n base_vertex = face_data[3]\\n base_index = face_data[5]\\n index_count = face_data[6]\\n\\n faces_indices = [base_vertex + indices[base_index + current_index] \\n for current_index in range(index_count)]\\n\\n #Split into lists of 3 - ie triangles\\n faces = []\\n for current_face_idx in range(0, len(faces_indices), 3):\\n faces.append(faces_indices[current_face_idx:current_face_idx+3])\\n\\n return faces\\n\\n\\n def face_from_pack(face_data):\\n \\\"\\\"\\\" \\n Extract just the data we want from the full chunk\\n \\\"\\\"\\\"\\n triangle_list = indices_from_face(face_data)\\n return [(face_data[0], triangles,) for triangles in triangle_list]\\n\\n face_offset, face_length = headers[13]\\n face_chunk = Struct(\\\"iiiiiiii2i2i3f3f3f3f2i\\\") \\n face_size = face_chunk.size\\n face_count = int(face_length / face_size)\\n\\n faces = []\\n\\n for current_face_idx in range(face_count):\\n face_file_position = face_offset + current_face_idx * face_size\\n current_face = face_chunk.unpack(file_data[face_file_position : face_file_position+face_size])\\n\\n #Check we are a valid face (Could use a filter later)\\n if current_face[2] != 1: continue #Only support meshes at the moment\\n\\n new_faces = face_from_pack(current_face)\\n faces.extend(new_faces)\\n\\n return faces\",\n \"def __len__(self):\\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios\",\n \"def __len__(self):\\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios\",\n \"def __len__(self):\\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios\",\n \"def __len__(self):\\n return self.params['nbins_sfh']+2 # z, mass, met, + logsfr_ratios\",\n \"def get_filesize(inputfile) -> int:\\n with open(inputfile, \\\"rb\\\") as f:\\n lines = 0\\n buf_size = 1024 * 1024\\n read_f = f.raw.read\\n\\n buf = read_f(buf_size)\\n while buf:\\n lines += buf.count(b\\\"\\\\n\\\")\\n buf = read_f(buf_size)\\n\\n return lines\",\n \"def extract_triangles(mesh, materials_list):\\n tri_list = []\\n do_uv = bool(mesh.tessface_uv_textures)\\n\\n for mat in materials_list:\\n for i, face in enumerate(mesh.tessfaces):\\n f_v = face.vertices\\n if mesh.materials[face.material_index].name != mat: continue\\n\\n uf = mesh.tessface_uv_textures.active.data[i] if do_uv else None\\n\\n fmt = 0\\n if(do_uv): fmt = face.material_index\\n\\n if do_uv:\\n f_uv = uf.uv\\n\\n if len(f_v) == 3:\\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\\n if (do_uv):\\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\\n else: new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\\n tri_list.append(new_tri)\\n\\n else: # it's a quad\\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\\n new_tri_2 = tri_wrapper((f_v[0], f_v[2], f_v[3]), fmt)\\n\\n if (do_uv):\\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\\n new_tri_2.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[2]), uv_key(f_uv[3])\\n else:\\n new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\\n new_tri_2.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\\n\\n tri_list.append(new_tri)\\n tri_list.append(new_tri_2)\\n\\n return tri_list\",\n \"def N_POINTS(self) -> int:\\n try:\\n with self.fs.open(\\n self.get_url().replace(\\\".\\\" + self.erddap.response, \\\".ncHeader\\\")\\n ) as of:\\n ncHeader = of.read().decode(\\\"utf-8\\\")\\n lines = [line for line in ncHeader.splitlines() if \\\"row = \\\" in line][0]\\n return int(lines.split(\\\"=\\\")[1].split(\\\";\\\")[0])\\n except Exception:\\n pass\",\n \"def get_n(self):\\n return np.append([self.n_init],[s.n for s in self.surfaces])\",\n \"def inithr(_filename):\\n # Open file provided\\n _file = open(_filename)\\n # Create empty array to hold data\\n _data = np.zeros((1, 3), dtype=float)\\n\\n # Iterate through the file line by line\\n for _line in _file:\\n # Split each line into constituent values\\n _x = _line.split()\\n # Append data array with each value, converted to float, convert parallax angle to distance\\n _data = np.append(_data, np.array([float(_x[1]), float(_x[2]), (1 / float(_x[3]))], ndmin=2), axis=0)\\n\\n # Iterate through data array\\n for _row in _data:\\n np.seterr(divide='ignore')\\n # Convert magnitude to luminosity\\n _row[0] = _row[0] - 5 * (np.log10(_row[2]) - 1)\\n # Convert B-V colour to temperature\\n _row[1] = 4600 * ((1 / (0.92 * _row[1] + 1.7)) + 1 / (0.92 * _row[1] + 0.62))\\n\\n # Delete first empty row\\n _data = np.delete(_data, 0, axis=0)\\n\\n # Return parsed data\\n return _data\",\n \"def read_file(file):\\n if opts.input_type == 'fits':\\n data = fileio.read_fits(file)\\n else:\\n data = fileio.read_ascii(file)\\n c_id = data[0,:]\\n g_num = np.array(range(len(c_id)), dtype = 'int')\\n g_id = data[3,:]\\n g_ra = np.array(data[4,:], dtype = 'float')\\n g_dec = np.array(data[5,:], dtype = 'float')\\n g_z = np.array(data[6,:], dtype = 'float')\\n return c_id, g_num, g_id, g_ra, g_dec, g_z\",\n \"def bufcount(filename):\\n\\timport gzip\\n\\tif filename.split('.')[-1] in ['gz','gzip']: f = gzip.open(filename)\\n\\telse: f = open(filename)\\n\\tlines = 0\\n\\tbuf_size = 1024 * 1024\\n\\tread_f = f.read # loop optimization\\n\\t\\n\\tbuf = read_f(buf_size)\\n\\twhile buf:\\n\\t\\tlines += buf.count('\\\\n')\\n\\t\\tbuf = read_f(buf_size)\\n\\t\\tf.close\\n\\treturn lines\",\n \"def part2(filename: str) -> int:\\n data = first_line(filename)\\n data = json.loads(data)\\n return total_nums_no_red(data)\",\n \"def compute_surface_area(self):\\n return np.sum(self._find_triangle_areas())\",\n \"def part_1() -> int:\\n initial_input = _load_input()\\n rows = len(initial_input)\\n cols = len(initial_input[0])\\n\\n input = initial_input.copy()\\n total_glow_count = 0\\n\\n for _ in range(100):\\n flashed = list()\\n for row in range(rows):\\n for col in range(cols):\\n coords = [[col, row]]\\n new_input, glow_count = _get_glow_counts(coords, input, flashed)\\n input = new_input\\n total_glow_count += glow_count\\n\\n return total_glow_count\",\n \"def get_data(data_file_path):\\n data_file = open(data_file_path, 'r').readlines()\\n data = []\\n n = -1\\n dim = -1\\n for i in range(len(data_file)):\\n line_elems = [float(x) for x in data_file[i].split()]\\n if i == 0:\\n n = int(line_elems[0])\\n dim = int(line_elems[1])\\n else:\\n data.append(np.array(line_elems))\\n return data, n, dim\",\n \"def get_faces_nr(self):\\r\\n\\r\\n logger.debug('Getting number of faces in each frame')\\r\\n\\r\\n if len(self.tracked_faces) == 0:\\r\\n\\r\\n # Try to load YAML file\\r\\n\\r\\n if os.path.exists(self.track_path):\\r\\n\\r\\n print 'Loading YAML file with tracking results'\\r\\n logger.debug('Loading YAML file with tracking results')\\r\\n\\r\\n with open(self.track_path) as f:\\r\\n\\r\\n self.tracked_faces = yaml.load(f)\\r\\n\\r\\n print 'YAML file with tracking results loaded'\\r\\n logger.debug('YAML file with tracking results loaded')\\r\\n\\r\\n else:\\r\\n\\r\\n print 'Warning! No tracking results found!'\\r\\n logger.warning('No tracking results found!')\\r\\n\\r\\n return\\r\\n\\r\\n self.faces_nr = {}\\r\\n\\r\\n for segment_dict in self.tracked_faces:\\r\\n\\r\\n frame_list = segment_dict[c.FRAMES_KEY]\\r\\n\\r\\n for frame_dict in frame_list:\\r\\n\\r\\n frame_name = frame_dict[c.SAVED_FRAME_NAME_KEY]\\r\\n\\r\\n if frame_name in self.faces_nr:\\r\\n\\r\\n self.faces_nr[frame_name] += 1\\r\\n\\r\\n else:\\r\\n\\r\\n self.faces_nr[frame_name] = 1\\r\\n\\r\\n # Save YAML file\\r\\n\\r\\n utils.save_YAML_file(self.faces_nr_path, self.faces_nr)\",\n \"def readprimitive(f): \\n \\n ## read in lines from input file and ignore blank lines and comment lines\\n lines = [line.rstrip() for line in f if line.rstrip() if line[0] != '#']\\n\\n # a1,a2,a3\\n A = np.array([[float(lines[0].split()[0]),float(lines[0].split()[1]),float(lines[0].split()[2])],\\n [float(lines[1].split()[0]),float(lines[1].split()[1]),float(lines[1].split()[2])],\\n [float(lines[2].split()[0]),float(lines[2].split()[1]),float(lines[2].split()[2])]]).T\\n \\n # number of basis atoms\\n num_basis = int(lines[3].split()[0]) \\n\\n # basis atom positions in unit cell\\n unitcell_pos = []\\n for i in range(num_basis): \\n unitcell_pos.append([float(lines[4+i].split()[0]),float(lines[4+i].split()[1]),float(lines[4+i].split()[2])]) \\n \\n return (A,unitcell_pos)\",\n \"def part1(filename: str) -> int:\\n data = first_line(filename)\\n data = json.loads(data)\\n return total_nums(data)\",\n \"def obtener_cantidad_vertices(self):\\n return len(self.vertices.keys())\",\n \"def get_counts(filename, alphabet, kmin, kmax):\\n # get the list of kmers to count with length between kmin and kmax\\n kmers_list = get_all_possible_kmers(alphabet, kmin, kmax)\\n # initialyze the counter with all possible kmer with length\\n # between kmin and kmax with zero counts\\n counter = Counter(dict([(km, 0) for km in kmers_list]))\\n # open and read in the kmers/string in the file\\n with gzip.open(filename, 'rt') as fh:\\n # iterates through the strings\\n for line in fh:\\n # make the adjustments int the strings\\n kmer = line.replace('\\\\n', '')\\n # check if kmer/string is in the counter\\n if kmer in counter:\\n # if kmer is in add 1 other wise keep the zero count\\n counter[kmer] += 1\\n return counter\",\n \"def load_verts(file_data, headers, scale_factor):\\n\\n\\n def vert_from_pack(vert_data):\\n return (\\n (vert_data[0] * scale_factor, vert_data[1] * scale_factor, vert_data[2] * scale_factor,), #XYZ\\n (vert_data[3], vert_data[4],), #UV1\\n (vert_data[5], vert_data[6],), #UV2\\n (vert_data[7], vert_data[8], vert_data[9],), #Normal\\n (vert_data[10], vert_data[11], vert_data[12], vert_data[13],), #RGBA\\n )\\n\\n vert_offset, vert_length = headers[10]\\n vert_chunk = Struct(\\\"3f2f2f3f4B\\\") \\n vert_size = vert_chunk.size\\n vert_count = int(vert_length / vert_size)\\n\\n print (\\\"Found {} vertices\\\".format(vert_count))\\n\\n vertices = []\\n\\n for current_vert_idx in range(vert_count):\\n vert_file_position = vert_offset + current_vert_idx * vert_size\\n current_vert = vert_chunk.unpack(file_data[vert_file_position : vert_file_position+vert_size])\\n vertices.append(vert_from_pack(current_vert))\\n\\n return vertices\",\n \"def get_number_of_letters(self):\\n filename = f'{self.path}/{self.filename}'\\n file = open(filename, 'r', encoding='utf-8')\\n \\\"\\\"\\\"Count number of lettes without digits, non letter characters, without xml tags\\\"\\\"\\\"\\n data = file.read()\\n data = re.sub('<.*?binary.*?>*<.*?binary.*?>',' ', data)\\n data = re.sub('\\\\\\\\s\\\\\\\\s*', '', (re.sub('\\\\\\\\W|\\\\\\\\d', ' ', re.sub('<.*?>', '', data))))\\n let_count = len(data)\\n sqlite_for_ht.CreateTable.update_table(f_1, self.filename, 'number_of_letters', let_count)\\n print(datetime.now(), '-', 'number_of_letters for', self.filename, 'calculated =', let_count)\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78216827","0.65307426","0.64875203","0.6224915","0.6100354","0.60187995","0.59512895","0.5924314","0.59203315","0.5906817","0.59059787","0.5882943","0.5867339","0.585497","0.58549577","0.5812365","0.57997227","0.57795846","0.57746047","0.57551175","0.5748937","0.5748146","0.5725021","0.5702728","0.5698386","0.5691206","0.56838804","0.56726533","0.5660846","0.5656267","0.5645008","0.56413954","0.56186074","0.56131804","0.5612977","0.5591385","0.55882674","0.5583263","0.55814636","0.55635816","0.55606204","0.55409354","0.5535321","0.55073744","0.55027145","0.5488749","0.5487895","0.54793304","0.54753","0.5460062","0.54573315","0.54482156","0.54470176","0.54455364","0.5443752","0.5437656","0.5428198","0.5427783","0.541538","0.54097253","0.5398149","0.53920686","0.5391353","0.5389164","0.5385579","0.53842545","0.5364776","0.5356398","0.5355718","0.53523314","0.5346526","0.53442633","0.53398955","0.5333428","0.5324331","0.5324294","0.53221965","0.5320378","0.5317432","0.5317432","0.5317432","0.5317432","0.5309065","0.5297589","0.529438","0.52812845","0.5279945","0.5278592","0.5276384","0.52760696","0.527073","0.52688897","0.526617","0.5262776","0.52577895","0.52572966","0.52495706","0.5245097","0.52390534","0.5237486"],"string":"[\n \"0.78216827\",\n \"0.65307426\",\n \"0.64875203\",\n \"0.6224915\",\n \"0.6100354\",\n \"0.60187995\",\n \"0.59512895\",\n \"0.5924314\",\n \"0.59203315\",\n \"0.5906817\",\n \"0.59059787\",\n \"0.5882943\",\n \"0.5867339\",\n \"0.585497\",\n \"0.58549577\",\n \"0.5812365\",\n \"0.57997227\",\n \"0.57795846\",\n \"0.57746047\",\n \"0.57551175\",\n \"0.5748937\",\n \"0.5748146\",\n \"0.5725021\",\n \"0.5702728\",\n \"0.5698386\",\n \"0.5691206\",\n \"0.56838804\",\n \"0.56726533\",\n \"0.5660846\",\n \"0.5656267\",\n \"0.5645008\",\n \"0.56413954\",\n \"0.56186074\",\n \"0.56131804\",\n \"0.5612977\",\n \"0.5591385\",\n \"0.55882674\",\n \"0.5583263\",\n \"0.55814636\",\n \"0.55635816\",\n \"0.55606204\",\n \"0.55409354\",\n \"0.5535321\",\n \"0.55073744\",\n \"0.55027145\",\n \"0.5488749\",\n \"0.5487895\",\n \"0.54793304\",\n \"0.54753\",\n \"0.5460062\",\n \"0.54573315\",\n \"0.54482156\",\n \"0.54470176\",\n \"0.54455364\",\n \"0.5443752\",\n \"0.5437656\",\n \"0.5428198\",\n \"0.5427783\",\n \"0.541538\",\n \"0.54097253\",\n \"0.5398149\",\n \"0.53920686\",\n \"0.5391353\",\n \"0.5389164\",\n \"0.5385579\",\n \"0.53842545\",\n \"0.5364776\",\n \"0.5356398\",\n \"0.5355718\",\n \"0.53523314\",\n \"0.5346526\",\n \"0.53442633\",\n \"0.53398955\",\n \"0.5333428\",\n \"0.5324331\",\n \"0.5324294\",\n \"0.53221965\",\n \"0.5320378\",\n \"0.5317432\",\n \"0.5317432\",\n \"0.5317432\",\n \"0.5317432\",\n \"0.5309065\",\n \"0.5297589\",\n \"0.529438\",\n \"0.52812845\",\n \"0.5279945\",\n \"0.5278592\",\n \"0.5276384\",\n \"0.52760696\",\n \"0.527073\",\n \"0.52688897\",\n \"0.526617\",\n \"0.5262776\",\n \"0.52577895\",\n \"0.52572966\",\n \"0.52495706\",\n \"0.5245097\",\n \"0.52390534\",\n \"0.5237486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6084134"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":82,"cells":{"query":{"kind":"string","value":"Open a PCAP, seek to a packet offset, then get all packets belonging to the same connection"},"document":{"kind":"string","value":"def packets_for_stream(fobj, offset):\n pcap = dpkt.pcap.Reader(fobj)\n pcapiter = iter(pcap)\n ts, raw = pcapiter.next()\n\n fobj.seek(offset)\n for p in next_connection_packets(pcapiter, linktype=pcap.datalink()):\n yield p"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def print_packets(pcap):\n\n # For each packet in the pcap process the contents\n for timestamp, buf, hdr_len in pcap:\n \n # Unpack the Ethernet frame (mac src/dst, ethertype)\n eth = dpkt.ethernet.Ethernet(buf)\n # print('Ethernet Frame: ', mac_addr(eth.src), mac_addr(eth.dst), eth.type)\n\n # Make sure the Ethernet data contains an IP packet\n if not isinstance(eth.data, dpkt.ip.IP):\n # print('Non IP Packet type not supported %s\\n' % eth.data.__class__.__name__)\n continue\n\n # Now unpack the data within the Ethernet frame (the IP packet)\n # Pulling out src, dst, length, fragment info, TTL, and Protocol\n ip = eth.data\n\n # Pull out fragment information (flags and offset all packed into off field, so use bitmasks)\n # do_not_fragment = bool(ip.off & dpkt.ip.IP_DF)\n # more_fragments = bool(ip.off & dpkt.ip.IP_MF)\n # fragment_offset = ip.off & dpkt.ip.IP_OFFMASK\n\n # Print out the info\n # print('IP: %s -> %s (len=%d ttl=%d DF=%d MF=%d offset=%d)\\n' % \\\n # (inet_to_str(ip.src), inet_to_str(ip.dst), ip.len, ip.ttl, do_not_fragment, more_fragments, fragment_offset)) \n\n pkt = Packet(timestamp, buf, hdr_len)\n\n if ip.p == dpkt.ip.IP_PROTO_TCP or ip.p == dpkt.ip.IP_PROTO_UDP: \n # all flow\n flow = Flow(ip.src, ip.dst, ip.data.sport, ip.data.dport, ip.p)\n if flow not in all_flows:\n all_flows[flow] = [pkt]\n else:\n x = len(all_flows[flow]) - 1\n if x < 0:\n all_flows[flow].append(pkt)\n else:\n if time_diff(all_flows[flow][x].timestamp, timestamp) <= 5400: #90mins\n all_flows[flow].append(pkt)\n\n if ip.p == dpkt.ip.IP_PROTO_TCP: \n # TCP flow\n flow = Flow(ip.src, ip.dst, ip.data.sport, ip.data.dport, ip.p)\n if flow not in tcp_flows:\n tcp_flows[flow] = [pkt]\n else:\n x = len(tcp_flows[flow]) - 1\n if x < 0:\n tcp_flows[flow].append(pkt)\n else:\n if time_diff(tcp_flows[flow][x].timestamp, timestamp) <= 5400:\n tcp_flows[flow].append(pkt)\n all_host_pairs(pkt, ip)\n elif ip.p == dpkt.ip.IP_PROTO_UDP:\n # UDP flow\n flow = Flow(ip.src, ip.dst, ip.data.sport, ip.data.dport, ip.p)\n if flow not in udp_flows:\n udp_flows[flow] = [pkt]\n else:\n x = len(udp_flows[flow]) - 1\n if x < 0:\n udp_flows[flow].append(pkt)\n else:\n if time_diff(udp_flows[flow][x].timestamp, timestamp) <= 5400:\n udp_flows[flow].append(pkt)\n else:\n continue\n\n print(\"Number of All flows: %d | Number of TCP flows: %d | Number of UDP flows: %d\" % (len(all_flows), len(tcp_flows), len(udp_flows)))\n\n # -- Flow Duration\n for f in all_flows:\n size = len(all_flows[f])\n if size >= 2:\n all_flow_dur.append(time_diff(all_flows[f][0].timestamp, all_flows[f][size-1].timestamp))\n \n for f in tcp_flows:\n size = len(tcp_flows[f])\n if size >= 2:\n tcp_flow_dur.append(time_diff(tcp_flows[f][0].timestamp, tcp_flows[f][size-1].timestamp))\n \n for f in udp_flows:\n size = len(udp_flows[f])\n if size >= 2:\n udp_flow_dur.append(time_diff(udp_flows[f][0].timestamp, udp_flows[f][size-1].timestamp))\n\n print \"lens: \", len(all_flow_dur), len(tcp_flow_dur), len(udp_flow_dur)\n\n # -- Flow Size\n for f in all_flows:\n f_bytes = 0\n size = len(all_flows[f])\n all_flow_size_pkt.append(size)\n for p in all_flows[f]:\n f_bytes += p.length\n all_flow_size_byte.append(f_bytes)\n \n for f in tcp_flows:\n f_bytes = 0\n f_overhead = 0\n size = len(tcp_flows[f])\n tcp_flow_size_pkt.append(size)\n for p in tcp_flows[f]:\n f_bytes += p.length\n f_overhead += 18 + 20 #+ tcp_hdr\n tcp_flow_size_byte.append(f_bytes)\n if f_bytes == 0:\n f_bytes = 9999\n tcp_flow_size_overhead.append(f_overhead/float(f_bytes))\n \n for f in udp_flows:\n f_bytes = 0\n size = len(udp_flows[f])\n udp_flow_size_pkt.append(size)\n for p in udp_flows[f]:\n f_bytes += p.length\n udp_flow_size_byte.append(f_bytes)\n\n # -- Inter-packet Arrival time\n for f in all_flows:\n for i in range(len(all_flows[f])-1):\n all_flow_time.append(time_diff(all_flows[f][i].timestamp, all_flows[f][i+1].timestamp))\n\n for f in tcp_flows:\n for i in range(len(tcp_flows[f])-1):\n tcp_flow_time.append(time_diff(tcp_flows[f][i].timestamp, tcp_flows[f][i+1].timestamp))\n\n for f in udp_flows:\n for i in range(len(udp_flows[f])-1):\n udp_flow_time.append(time_diff(udp_flows[f][i].timestamp, udp_flows[f][i+1].timestamp))\n\n # -- TCP State\n for f in tcp_flows:\n size = len(tcp_flows[f])\n last_pkt = tcp_flows[f][size-1]\n tcp = dpkt.ethernet.Ethernet(last_pkt.buf).data.data\n \n if (tcp.flags & dpkt.tcp.TH_SYN) != 0:\n f.state = 'Request'\n elif (tcp.flags & dpkt.tcp.TH_RST) != 0:\n f.state = 'Reset'\n elif (tcp.flags & dpkt.tcp.TH_FIN) != 0 and (tcp.flags & dpkt.tcp.TH_ACK) != 0:\n f.state = 'Finished'\n elif time_diff(tcp_flows[f][0].timestamp, tcp_flows[f][size-1].timestamp) <= 300:\n f.state = 'Ongoing'\n elif time_diff(tcp_flows[f][0].timestamp, tcp_flows[f][size-1].timestamp) > 300 \\\n and (tcp.flags & dpkt.tcp.TH_RST) == 0 and (tcp.flags & dpkt.tcp.TH_FIN) == 0:\n f.state = 'Failed'\n\n show_cdf_graphs()","def pcap(self, fname):\n\t\tcap = pcapy.open_offline(fname)\n\n\t\tself.map = []\n\t\tself.p = PacketDecoder()\n\t\tcap.loop(0, self.process)\n\n\t\treturn self.map","def _parse(self):\n \n # HUA determine the host ip address\n # read 20 packages and set the most frequent one\n ips_dict = {}\n count = 0\n for raw_packet in self.raw_packets:\n if count > 100: break\n ethernet = Ethernet(raw_packet[0:14])\n if(ethernet.type != 'IP'):\n continue\n ip = Ip(raw_packet[14:])\n if(ip.protocol != 'TCP') :\n continue\n if(ip.src not in ips_dict):\n ips_dict[ip.src] = 0\n ips_dict[ip.src] += 1\n if(ip.dst not in ips_dict):\n ips_dict[ip.dst] = 0\n ips_dict[ip.dst] += 1\n # get the most frequent one\n max_appear = 0\n ip = None\n for key, value in ips_dict.items():\n if value > max_appear:\n ip = key\n max_appear = value\n\n global _device_ip\n if not self.enableFilter or not _device_ip:\n _device_ip = ip\n\n global _tcp_buf\n _tcp_buf = {}\n number = 0\n self.begin_ts = self.packet_headers[-1]['ts']\n rcount = 0\n for raw_packet in self.raw_packets:\n pcap_packet = Pcap_packet()\n pcap_packet.pcap_num = rcount#number # add one to be consistent with wireshark\n pcap_packet.top_layer = 1\n pcap_packet.ethernet = Ethernet(raw_packet[0:14])\n \n #skip the packet that is not ip packet\n \n rcount += 1\n\n if (pcap_packet.ethernet.type != 'IP'):\n continue\n\n pcap_packet.top_layer = 2\n pcap_packet.ip = Ip(raw_packet[14:])\n\n\n\n\n # just collect the packets between \n \n if self.enableFilter and not (pcap_packet.ip.src == _device_ip and pcap_packet.ip.dst == SERVER_IP) \\\n and not (pcap_packet.ip.dst == _device_ip and pcap_packet.ip.src == SERVER_IP):\n #print \"Ignore ip not ok\"\n continue\n '''\n if rcount < 10 or rcount > 2600:\n print 'rcount %d, time %d ---: %f' % (rcount, number, self.packet_headers[rcount - 1]['ts'] - self._ts_base)\n '''\n \n self.pcap_packets.append(pcap_packet)\n \n\n #skip the packet that is not tcp message\n if (pcap_packet.ip.protocol != 'TCP'):\n continue\n \n\n\n pcap_packet.top_layer = 3\n pcap_packet.tcp = Tcp(pcap_packet.ip, number)\n\n if pcap_packet.ip.src == _device_ip:\n pcap_packet.tcp.direction = \"out\"\n else:\n pcap_packet.tcp.direction = \"in\"\n\n\n #dispatch the tcp into tcp streams\n self._add_pkt_into_tcp_stream(pcap_packet, number)\n \n #reassemble tcp packet\n self._tcp_reassemble(pcap_packet.pcap_num, pcap_packet.ip.src, pcap_packet.ip.dst, pcap_packet.tcp)\n number += 1\n #endof for\n #flush the tcp_buf, other wise it will lose some http response\n for sockets in _tcp_buf.keys():\n self._tcp_flush(sockets)\n del _tcp_buf[sockets]","def test():\n with open('univ1_pt8.pcap', 'rb') as f: #univ1_trace/univ1_pt8\n pcap = Reader(f)\n print_packets(pcap)\n # top_flows()\n host_pairs()","def find_dac():\n\n\ts = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\ts.bind((\"0.0.0.0\", 7654))\n\n\twhile True:\n\t\tdata, addr = s.recvfrom(1024)\n\t\tbp = BroadcastPacket(data)\n\t\t\n\t\tprint \"Packet from %s: \" % (addr, )\n\t\tbp.dump()","def parse_packets(pcap):\n # For each packet in the pcap process the contents\n flow_Info = []\n times = 0\n for timestamp, buf in pcap:\n times += 1\n tmp_flow_Info = {}\n\n # Unpack the Ethernet frame (mac src/dst, ethertype)\n eth = dpkt.ethernet.Ethernet(buf)\n # Unpack the data whthin the Ethernet frame (the IP packet)\n ip = eth.data\n\n # if protocol(ip.p) is not UDP(17) ,skip this packet\n if ip.p != 17:\n continue\n\n udp = ip.data\n # Temp_data = parse_data(eth.data.udp.data)\n # Filter CoAP by port\n if(udp.sport != 5683 or udp.dport != 5683):\n continue\n\n str_udp_data = parse_data(eth.data.udp.data)\n # skip packets of Non_confirmable\n if str_udp_data[0] == '5': \n continue\n\n cycle = 0\n index = 0\n Udp_data = []\n \n len_str_udp_data = len(str_udp_data)\n while cycle < (len_str_udp_data//3+1):\n # Udp_data.append(int('0x'+Str_Udp_data[index:index + 2], 16))\n Udp_data.append(int('0x' + str_udp_data[index:index + 2], 16))\n cycle += 1\n index += 3\n tmp_flow_Info['udp_data'] = (Udp_data)\n\n # confirmable or ack\n tmp_flow_Info['Coap_type'] = str_udp_data[0]\n #print(str_udp_data) \n \n # skip space and get \"Message ID\" \n HexMide = str_udp_data[6:8] + str_udp_data[9:11]\n tmp_flow_Info['Mid'] = int('0x'+HexMide, 16)\n\n tmp_flow_Info['Timestamp'] = str(datetime.datetime.fromtimestamp(timestamp))\n # print('Ethernet Frame: ', mac_addr(eth.src), mac_addr(eth.dst), eth.type)\n tmp_flow_Info['src'] = inet_to_str(ip.src)\n tmp_flow_Info['dst'] = inet_to_str(ip.dst)\n\n tmp_flow_Info['sport'] = udp.sport\n tmp_flow_Info['dport'] = udp.dport\n flow_Info.append(tmp_flow_Info)\n\n return flow_Info","def pull(self):\n\n # For each packet in the pcap process the contents\n for item in self.input_stream:\n\n # Print out the timestamp in UTC\n print('%s -' % item['timestamp'], end='')\n\n # Transport info\n if item['transport']:\n print(item['transport']['type'], end='')\n\n # Print out the Packet info\n packet_type = item['packet']['type']\n print(packet_type, end='')\n packet = item['packet']\n if packet_type in ['IP', 'IP6']:\n # Is there domain info?\n if 'src_domain' in packet:\n print('%s(%s) --> %s(%s)' % (net_utils.inet_to_str(packet['src']), packet['src_domain'],\n net_utils.inet_to_str(packet['dst']), packet['dst_domain']), end='')\n else:\n print('%s --> %s' % (net_utils.inet_to_str(packet['src']), net_utils.inet_to_str(packet['dst'])), end='')\n else:\n print(str(packet))\n\n # Only include application if we have it\n if item['application']:\n print('Application: %s' % item['application']['type'], end='')\n print(str(item['application']), end='')\n\n # Just for newline\n print()","def _parse(self):\n \n global _tcp_buf\n _tcp_buf = {}\n number = 1\n for raw_packet in self.raw_packets:\n pcap_packet = Pcap_packet()\n self.pcap_packets.append(pcap_packet)\n pcap_packet.pcap_num = number\n number += 1\n pcap_packet.top_layer = 1\n pcap_packet.ethernet = Ethernet(raw_packet[0:14])\n \n #skip the packet that is not ip packet\n if (pcap_packet.ethernet.type != 'IP'):\n continue\n \n pcap_packet.top_layer = 2\n pcap_packet.ip = Ip(raw_packet[14:])\n \n #skip the packet that is not tcp message\n if (pcap_packet.ip.protocol != 'TCP'):\n continue\n \n pcap_packet.top_layer = 3\n pcap_packet.tcp = Tcp(pcap_packet.ip.packet[pcap_packet.ip.header_len: ])\n \n #skip the packets that is not http packet\n if (pcap_packet.tcp.src_port != 80 and pcap_packet.tcp.dst_port != 80):\n continue\n \n #dispatch the tcp into tcp streams\n self._add_pkt_into_tcp_stream(pcap_packet, pcap_packet.pcap_num)\n \n #reassemble tcp packet\n self._tcp_reassemble(pcap_packet.pcap_num, pcap_packet.ip.src, pcap_packet.ip.dst, pcap_packet.tcp)\n #endof for\n #flush the tcp_buf, other wise it will lose some http response\n for sockets in _tcp_buf.keys():\n self._tcp_flush(sockets)\n del _tcp_buf[sockets]","def recv_raw(self) -> Dict[str, Any]:\n while True:\n try:\n packet = self.__recv_frame()\n except UnknownPacketException:\n continue\n\n # Hack for sniffing on localhost\n if packet['address']['interface'] == 'lo' and packet['address']['type'] != 4:\n continue\n\n if self.address and self.port:\n if (\n packet['ip_header']['source_address'] == self.address and\n packet['tcp_header']['source_port'] == self.port\n ):\n return packet\n if (\n packet['ip_header']['destination_address'] == self.address and\n packet['tcp_header']['destination_port'] == self.port\n ):\n return packet\n elif self.address:\n if (\n packet['ip_header']['source_address'] == self.address or\n packet['ip_header']['destination_address'] == self.address\n ):\n return packet\n elif self.port:\n if (\n packet['tcp_header']['source_port'] == self.port or\n packet['tcp_header']['destination_port'] == self.port\n ):\n return packet\n else:\n return packet","def extract_tstat_data(pcap_filepath):\n connections = {}\n conn_id = 0\n print('We are here')\n with co.cd(os.path.basename(pcap_filepath[:-5])):\n with co.cd(os.listdir('.')[0]):\n print(connections)\n # Complete TCP connections\n connections, conn_id = extract_tstat_data_tcp_complete('log_tcp_complete', connections, conn_id)\n # Non complete TCP connections (less info, but still interesting data)\n connections, conn_id = extract_tstat_data_tcp_nocomplete('log_tcp_nocomplete', connections, conn_id)\n\n return connections","def next_packet(filename, memorymap=True):\n with open(filename, 'rb') as f:\n \n #memory map the file if necessary (prob requires 64 bit systems)\n _file = f\n if memorymap:\n _file = mmap.mmap(f.fileno(), 0, prot=mmap.PROT_READ)\n \n while True:\n packet = _file.read(TS.PACKET_SIZE)\n if packet:\n # first byte SHOULD be the sync byte\n # but if it isn't find one.\n if packet[0] != TS.SYNC_BYTE:\n start_byte = 0\n print packet[0]\n for i in range(start_byte, TS.PACKET_SIZE):\n if packet[i] == TS.SYNC_BYTE:\n start_byte = i\n break\n # didn't find a new start? FAIL\n if start_byte == 0:\n raise Exception(\"failure to find sync byte in ts packet size.\")\n continue\n remainder = _file.read(TS.PACKET_SIZE - start_byte)\n packet = packet[start_byte:] + remainder\n yield packet\n else:\n break","def _packet_in(self, ev):\n\n dp = ev.msg.datapath\n ofp = dp.ofproto\n parser = dp.ofproto_parser\n match = ev.msg.match\n\n ##SNDCP packet with multiple fragments recieved - print warning, send ICMP fragmentation needed\n ##TODO: Not WOrking correctly\n ## File \"/usr/local/lib/python2.7/dist-packages/ryu/ofproto/ofproto_v1_3_parser.py\", line 746, in __getitem__\n ## return dict(self._fields2)[key]\n ## KeyError: 'udp_dst'\n\n # if (match['eth_type'] == 0x0800 and match['ip_proto'] == inet.IPPROTO_UDP\n # and match['udp_dst'] == VGSN_PORT and match['sndcp_first_segment'] == 1\n # and match['sndcp_more_segments'] == 1):\n # _icmp_send(dp,match['in_port'],match['ipv4_dst'],match['ipv4_src'],match['eth_dst'],match['eth_src'],icmp_type=3,icmp_code=4)\n # LOG.warning('WARNING: Device with IP: '+match['ipv4_src']+' sent fragmented sndcp packet')\n # return\n\n ##ARP request recieved - send 'I'm here' response\n if match['eth_type'] == 0x0806 and match['arp_op'] == 1:\n LOG.debug(\"ARP request accepted\")\n _arp_send(dp=dp, port_out=match['in_port'], arp_code=2, eth_dst=match['eth_src'], eth_target=match['arp_sha'],\n ip_target=match['arp_spa'], ip_sender=match['arp_tpa'])\n LOG.debug('Reply to '+match['arp_spa'] +': Host '+match['arp_tpa']+' is at forwarder '+str(dp.id) + \" with ethX source MAC address\")\n return\n\n ##ARP response with target_ip==DISCOVERY_ARP_IP recieved - we found APN\n #\n # FIXED: All ARP responses are replied, regardless of the target IP\n #\n # TODO : At this point only ARPs belonging to the APNs networks subnet should\n # be answered\n if match['eth_type'] == 0x0806 and match['arp_op'] == 2:\n LOG.debug('TUNNEL MNGR: ARP response with target APN discovery IP recieved at controller, processing for APN extraction')\n pkt = packet.Packet(array.array('B', ev.msg.data))\n arp_pkt=pkt.get_protocol(arp.arp)\n apn_ip = arp_pkt.src_ip\n apn_mac= arp_pkt.src_mac\n port = match['in_port']\n\n ##Search for apn in APN_POOL to add mac addr. and update topology\n for sApn in APN_POOL:\n if sApn.ip_addr == apn_ip:\n LOG.debug('Recieved ARP response was from ' + sApn.name + ' APN')\n sApn.eth_addr = apn_mac\n sApn.port = port\n sApn.dpid = dp.id\n # Links towards APNs will not be measured\n topo.add_link(dp.id,str(sApn.name),port)\n topo.add_link(str(sApn.name),dp.id,0)\n topo.reload_topology()\n LOG.debug('TUNNEL MNGR: APN '+str(sApn.name)+' found at forwarder: '+str(dp.id)+', port: '+str(port) + ' by ARP search')\n\n ##Add special rules to edge forwarder\n self.on_edge_inet_dp_join(dp, port, sApn)\n\n # FIX: We do not handle bss as a special APN\n # For greater extensibility, BSS/UTRAN/LAN APNs (exit/enter) points\n # will be handled in a generic manner\n #\n ##Create MAC-tunnels between APN and all BSSs\n #for bss in BSS_POOL:\n # self.add_tunnel(bss,apn)\n #break\n\n ### WMNC: In this case, we are not making tunnels between\n # two types of ingress/egress point, but actually same type\n\n for dApn in APN_POOL:\n # we are cycling through all possible APNs, looking for different APN tupples\n # with filled HW addresses (already found by APN search)\n if sApn != dApn and dApn.eth_addr != None:\n LOG.debug('TUNNEL MNGR: Different APNs with filled HW address found, lets find out if there is tunnel between them')\n\n paths = False\n try:\n paths = nx.all_simple_paths(topo.DynamicGraph, source=sApn.name, target=dApn.name)\n except:\n LOG.debug('TUNNEL MNGR: No path between: ' + sApn.name + ' and ' + dApn.name + '. Retry when next APN discovered.')\n\n LOG.debug('TUNNEL MNGR: These are the paths between them (possible tunnels):')\n if paths:\n for path in paths:\n LOG.debug('TUNNEL MNGR: Calling add_plainMacTunnel for ' + sApn.name + ' and ' + dApn.name + ' with path: ' + str(path))\n self.add_plainMacTunnel(sApn, dApn, path)\n else:\n LOG.debug('TUNNEL MNGR: PATHS == 0 ????????????????')\n\n\n return\n\n ##ICMP echo with dst_ip==DISCOVERY_IP_DST recieved - new link between forwarders is up\n if match['eth_type'] == 0x0800 and match['ipv4_dst'] == DISCOVERY_IP_DST and match['ip_proto'] == 1:\n #LOG.debug('TOPO MNGR: ICMP echo recieved at controller, processing for link extraction or latency measurement')\n\n pkt = packet.Packet(array.array('B', ev.msg.data))\n\n ##Discovery pings carry information about sending datapath in payload of icmp packet\n ##these information are in Dictionary format, we parse the out with _icmp_parse_payload() method\n body = _icmp_parse_payload(pkt)\n neighbourDPID=body['dpid']\n neighbourPort=body['port_out']\n\n ## measurement\n ## currentClock moved way up to improve precision\n receivedClock=float(body['clock'])\n currentClock = time.clock()\n latency = currentClock - receivedClock\n\n currentDate = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n ##Update latency or add new edges to topology.\n if topo.DynamicGraph.has_edge(dp.id, neighbourDPID) and topo.DynamicGraph.has_edge(neighbourDPID, dp.id):\n topo.StaticGraph[neighbourDPID][dp.id]['pdv'] = topo.StaticGraph[neighbourDPID][dp.id]['lat'] - latency\n topo.StaticGraph[neighbourDPID][dp.id]['lat'] = latency\n topo.StaticGraph[neighbourDPID][dp.id]['upt'] = currentDate\n #topo.StaticGraph[neighbourDPID][dp.id]['upt'] = currentDate\n loss = self.loss_update(neighbourDPID, dp.id, currentDate)\n #LOG.debug('TOPO MNGR: Updating latency ' + str(latency) + ' and date ' + str(currentDate) + ' LOSS: ' + str(loss))\n topo.reload_topology()\n else:\n ## latency not correct for both directions when adding links\n ## update occurs on receive of next measurement packet from oposite direction\n topo.add_link(dp.id, neighbourDPID, ev.msg.match['in_port'], latency, currentDate)\n topo.add_link(neighbourDPID, dp.id, neighbourPort , latency, currentDate)\n LOG.debug('TOPO MNGR: Topology changed: New link between forwarder ID '+str(dp.id)+ ' via port ' + str(ev.msg.match['in_port'])\n +' and forwarder ID '+str(neighbourDPID)+ ' via port ' + str(neighbourPort) + ' was discovered.')\n\n topo.reload_topology()\n ## retry to create tunnels\n ## find better paths between APNs\n for sApn in APN_POOL:\n for dApn in APN_POOL:\n if sApn != dApn:\n LOG.debug('TOPO MNGR: Topology changed: trying to re-build inactive tunnel between:' + sApn.name + ' and ' + dApn.name)\n paths = False\n try:\n paths = nx.all_simple_paths(topo.DynamicGraph, source=sApn.name, target=dApn.name)\n except:\n LOG.debug('No path between: ' + sApn.name + ' and ' + dApn.name + '. Retry when next fwd connects.')\n\n LOG.debug('TUNNEL MNGR: These are the paths between them (possible tunnels):')\n if paths:\n for path in paths:\n LOG.debug('TUNNEL MNGR: Calling add_plainMacTunnel for ' + sApn.name + ' and ' + dApn.name + ' with path: ' + str(path))\n self.add_plainMacTunnel(sApn, dApn, path)\n else:\n LOG.debug('TUNNEL MNGR: PATHS == 0 ????????????????')\n return\n\n # flow of last resort (process for routing)\n if match['eth_type'] == 0x0800:\n # LOG.debug('*****************Flow of last resort matched(plain IP), process for routing********'\n # + ' match[ipv4_dst]: ' + str(match['ipv4_dst'] + ' match[ipv4_src]: ' + str(match['ipv4_src']) + ' DSCP: ' + str(match['ip_dscp'])))\n ## Not very proud of myself, but it will do the trick\n ## Turbo lumberjack routing logic\n ## TODO: Implement a longest prefix match routing\n\n candidates = []\n\n for source, destination, ip_dscp in routesList:\n if ((source == match['ipv4_dst'] and destination == match['ipv4_src']) or (source == match['ipv4_src'] and destination == match['ipv4_dst'])) and ip_dscp == match['ip_dscp']:\n # LOG.debug('ROUTING: route source: ' + str(source) + 'destination: ' + str(destination)\n # + ' match[ipv4_dst]: ' + str(match['ipv4_dst'])\n # + ' match[ipv4_src]: ' + str(match['ipv4_src']) + ' DSCP: ' + str(ip_dscp)\n # + ' already exists, aborting addition of new route')\n return\n\n for tunnel in TUNNELS:\n if (tunnel.sApn.ip_addr == match['ipv4_dst'] and tunnel.dApn.ip_addr == match['ipv4_src']) or (tunnel.sApn.ip_addr == match['ipv4_src'] and tunnel.dApn.ip_addr == match['ipv4_dst']):\n LOG.debug('ROUTING: Tunnel candidate found in list of tunnels. Adding tunnel path: ' + str(tunnel.po_edges) + ' to candidates.')\n candidates.append(tunnel)\n\n trafficClass = self.TC_selection(match['ip_dscp'])\n\n if len(candidates) == 0:\n LOG.debug('ROUTING: match[ipv4_dst]: ' + str(match['ipv4_dst'])\n + ' match[ipv4_src]: ' + str(match['ipv4_src']) + ' DSCP: ' + str(match['ip_dscp']))\n LOG.debug('ROUTING: ERROR, NO feasible tunnels for such route.')\n return\n\n LOG.debug('Looking for tunnels: DST_IP: ' + match['ipv4_dst'] + ' SRC_IP: ' + match['ipv4_src'] + ' DSCP: ' + str(match['ip_dscp']) + '(traffic class: ' + str(trafficClass) + ')' + ' Incoming from FWD: ' + str(dp.id))\n tunnel = self.tunnel_selection(trafficClass, candidates)\n LOG.debug('TE MNGR: Selected tunnel Path out: ' + str(tunnel.path_out_str) + ' meter_id: ' + str(tunnel.meter_id))\n\n dscp = match['ip_dscp']\n\n ## meter_id\n ## 2,4,6,8,10 = 500kbps, 1,3,5,7,9 = 1000kbps ...\n ## 0 = 100Gbps\n meter_id = tunnel.meter_id\n\n #\n # FIXME: incomplete set of rules installed on LAN Access forwarders\n # TODO : Philosophy of table IDs should be clarified, as now it total mess!!!\n # TODO : this should be done only once, from that moment, all user plane packets\n # should travelse only forwarder and should not be sent to controller\n\n\n\n #WAY OUT\n dp = dpset.get(tunnel.sApn.dpid)\n parser = dp.ofproto_parser\n ofp = dp.ofproto\n match = parser.OFPMatch (eth_type=0x0800, ipv4_dst=tunnel.dApn.ip_addr, ip_dscp=dscp)\n actions = [parser.OFPActionSetField(eth_src=tunnel.tid_in), parser.OFPActionSetField(eth_dst=tunnel.tid_out)]\n inst = [parser.OFPInstructionGotoTable(MAC_TUNNEL_TABLE), parser.OFPInstructionMeter(meter_id), parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\n req = parser.OFPFlowMod(datapath=dp, priority=100, match=match, instructions=inst, table_id=INGRESS_TABLE)\n dp.send_msg(req)\n\n LOG.debug('ROUTING: Installing flow ON WAY OUT to forwarderID: ' + str(dp.id) + ',Table: ' + str(INGRESS_TABLE) + ' DP ID: ' + str(tunnel.dApn.dpid) + ' Tunel dApn IP addr: ' + str(tunnel.dApn.ip_addr) + ' Tunnel ID: ' + str(tunnel.tid_out))\n\n dp = dpset.get(tunnel.dApn.dpid)\n parser = dp.ofproto_parser\n ofp = dp.ofproto\n match = parser.OFPMatch (eth_dst=tunnel.tid_out)\n actions = [parser.OFPActionSetField(eth_dst=tunnel.dApn.eth_addr), parser.OFPActionOutput(tunnel.path_out[-1].port_out)]\n inst = [parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\n req = parser.OFPFlowMod(datapath=dp, priority=300, match=match, instructions=inst, table_id=ACCESS_ADAPTATION_TABLE_OUT)\n dp.send_msg(req)\n LOG.debug('ROUTING: Installing flow ON WAY OUT to forwarderID: ' + str(dp.id) + ',Table: ' + str(ACCESS_ADAPTATION_TABLE_OUT) + ' DP ID: ' + str(tunnel.dApn.dpid)+ ' Tunel ID: ' + str(tunnel.tid_out)+ ' dApn ETH addr: ' + str(tunnel.dApn.eth_addr))\n\n #WAY IN\n dp = dpset.get(tunnel.dApn.dpid)\n parser = dp.ofproto_parser\n ofp = dp.ofproto\n match = parser.OFPMatch (eth_type=0x0800, ipv4_dst=tunnel.sApn.ip_addr, ip_dscp=dscp)\n actions = [parser.OFPActionSetField(eth_dst=tunnel.tid_in), parser.OFPActionSetField(eth_src=tunnel.tid_out)]\n inst = [parser.OFPInstructionGotoTable(MAC_TUNNEL_TABLE), parser.OFPInstructionMeter(meter_id), parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\n req = parser.OFPFlowMod(datapath=dp, priority=100, match=match, instructions=inst, table_id = INGRESS_TABLE)\n dp.send_msg(req)\n LOG.debug('ROUTING: Installing flow ON WAY IN to forwarderID: ' + str(dp.id) + ',Table: ' + str(INGRESS_TABLE) + ' DP ID: ' + str(tunnel.sApn.dpid) + ' Tunel dApn IP addr: ' + str(tunnel.sApn.ip_addr) + ' Tunnel ID: ' + str(tunnel.tid_in))\n\n\n dp = dpset.get(tunnel.sApn.dpid)\n parser = dp.ofproto_parser\n ofp = dp.ofproto\n match = parser.OFPMatch (eth_dst=tunnel.tid_in)\n actions = [parser.OFPActionSetField(eth_dst=tunnel.sApn.eth_addr), parser.OFPActionOutput(tunnel.path_in[-1].port_out)]\n inst = [parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\n req = parser.OFPFlowMod(datapath=dp, priority=300, match=match, instructions=inst, table_id=ACCESS_ADAPTATION_TABLE_OUT)\n dp.send_msg(req)\n LOG.debug('ROUTING: Installing flow ON WAY IN to forwarderID: ' + str(dp.id) + ',Table: ' + str(ACCESS_ADAPTATION_TABLE_OUT) + ' DP ID: ' + str(tunnel.sApn.dpid)+ ' Tunel ID: ' + str(tunnel.tid_in)+ ' sApn ETH addr: ' + str(tunnel.sApn.eth_addr))\n\n\n LOG.debug('ROUTING: Rules on access edge forwarders installed')\n LOG.debug('ROUTING: Adding route: DST_IP: ' + tunnel.dApn.ip_addr + ' SRC_IP: ' + tunnel.sApn.ip_addr + ' dscp: ' + str(dscp) + ' path out str: ' + tunnel.path_out_str )\n routesList.append( ( tunnel.sApn.ip_addr, tunnel.dApn.ip_addr, dscp) )\n\n parser = dp.ofproto_parser\n\n for dpid in LAN_TYPE_FORWARDERS:\n ## DUNNO why this rule with low priority still hits traffic which is also matched by rules with IP address matches\n ## Here I delete the rule, it is added on FWD when it connects to controoller\n LOG.debug('TOPO MNGR: Forwarder: ' + str(dpid) + ' is a LAN edge forwarder, deleting rules')\n dp = dpset.get(dpid)\n priority = 2\n match = parser.OFPMatch(eth_type=0x0800)\n actions = [parser.OFPActionOutput(ofp.OFPP_CONTROLLER)]\n self.mod_flow(dp, command=dp.ofproto.OFPFC_DELETE_STRICT,\n table_id=0, actions=actions,\n match=match, priority=priority)\n\n LOG.debug('TOPO MNGR: Forwarder: ' + str(dp.id) + ' is a LAN edge forwarder, installing rules again :)')\n match = parser.OFPMatch(eth_type=0x0800)\n actions = [parser.OFPActionOutput(ofp.OFPP_CONTROLLER)]\n self.add_flow(dp, 2, match, actions)","def process(self):\n fp = open(self.pcap, \"rb\")\n pkts = dpkt.pcap.Reader(fp)\n if self.sip and self.dip and self.sp and self.dp:\n self.process_pkts(pkts)","def filter(self):\n # outfile = open(self.newpcap, 'wb')\n # writer = dpkt.pcap.Writer(outfile)\n f = open(self.pcapfile, 'rb')\n packets = dpkt.pcap.Reader(f)\n\n for timestamp, buf in packets:\n eth = dpkt.ethernet.Ethernet(buf)\n if not isinstance(eth.data, dpkt.ip.IP): # 确保以太网数据包含一个IP数据包, Non IP Packet type not supported\n continue # 过滤空IP包\n ip = eth.data # 获取以太网帧（IP数据包）\n if not isinstance(ip.data, dpkt.tcp.TCP): # 在传输层中检查TCP\n continue\n tcp = ip.data # 获取tcp数据\n # print('-->TCP Data: ', repr(tcp))\n\n \"\"\" 过滤三次握手后的首包\"\"\"\n seq = self.seq_pattern.findall(repr(tcp))\n ack = self.ack_pattern.findall(repr(tcp))\n if not (seq or ack): # seq、ack必须有一个, 一真即真\n continue\n if ack:\n ack = ack[0]\n if seq:\n seq = seq[0]\n\n if not ack and seq: # 一次握手请求\n self.hash_table[seq] = {}\n self.stream_table[seq] = [buf]\n if ack and seq: # 二次、三次、交流包\n if str(int(ack) - 1) in self.hash_table.keys(): # 有一次握手记录\n number = str(int(ack) - 1)\n if 'second' not in self.hash_table[number].keys(): # 新增二次握手\n self.hash_table[number]['second'] = {'seq': seq, 'ack': ack}\n self.stream_table[number].append(buf) # 将二次握手添加到buf\n self.resp_relation[seq] = ack # 新增关系表\n\n # 存在二次握手记录, 看hash表有无第三次握手记录, 有就保存stream流\n # 基本就是traffic响应包了\n elif 'three' in self.hash_table[number].keys():\n if number not in self.stream_table.keys():\n self.stream_table[number] = []\n self.stream_table[number].append(buf)\n else:\n self.stream_table[number].append(buf)\n\n # ack-1没有对应的hash表, 可能是三次握手或traffic请求包\n elif str(int(seq) - 1) in self.hash_table.keys():\n number = str(int(seq) - 1)\n if 'second' not in self.hash_table[number]:\n pass\n elif 'three' not in self.hash_table[number]: # 三次包\n self.hash_table[number]['three'] = {'seq': seq, 'ack': ack}\n self.stream_table[number].append(buf)\n # 否则就是traffic包了\n else:\n if number not in self.stream_table.keys():\n self.stream_table[number] = []\n self.stream_table[number].append(buf)\n else:\n self.stream_table[number].append(buf)\n # traffic响应包\n elif str(int(seq) - 1) in self.resp_relation.keys():\n number = str(int(seq) - 1)\n second_ack = self.resp_relation[number]\n number = str(int(second_ack) - 1)\n if number not in self.stream_table.keys():\n self.stream_table[number] = []\n self.stream_table[number].append(buf)\n else:\n self.stream_table[number].append(buf)\n else:\n continue # seq不存在\n\n # outfile.close()\n f.close()","def get_connections(capture):\n ip_dict = dict()\n for pkt in capture:\n\n if not hasattr(pkt, \"ip\") and not hasattr(pkt, \"ipv6\"):\n continue\n\n protocol = pkt.highest_layer\n\n tcp_dst_port = None\n tcp_src_port = None\n if hasattr(pkt, \"tcp\"):\n tcp_src_port = pkt.tcp.srcport\n tcp_dst_port = pkt.tcp.dstport\n\n if hasattr(pkt, \"ip\"):\n if pkt.ip.src.startswith(\"192.168.178\"):\n ip, dst = pkt.ip.src, pkt.ip.dst\n else:\n ip, dst = pkt.ip.dst, pkt.ip.src\n tcp_dst_port = tcp_src_port\n else:\n # TODO: how to discern src and dst in IPv6?\n ip, dst = pkt.ipv6.src, pkt.ipv6.dst\n\n ip = \"%s\" % ip\n dkey = (\n \"%s\" % protocol,\n int(tcp_dst_port) if tcp_dst_port else None,\n \"%s\" % dst\n )\n if ip not in ip_dict:\n ip_dict[ip] = {dkey: 1}\n else:\n ip_dict[ip][dkey] = ip_dict[ip].get(dkey, 0) + 1\n return ip_dict","def process_pcap(self):\n fp = open(self.pcap, \"rb\")\n pkts = dpkt.pcap.Reader(fp)\n self.process_pkts(pkts)","def handle_tcp(pkt, packets, i, start_point):\r\n src_port = int(pkt[start_point:start_point+4], 16)\r\n start_point += 4\r\n dest_port = int(pkt[start_point:start_point+4], 16)\r\n start_point += 4\r\n sequence_num = int(pkt[start_point:start_point+8], 16)\r\n start_point += 8\r\n acknowledgment = int(pkt[start_point:start_point+8], 16)\r\n start_point += 8\r\n data_offset = int(pkt[start_point], 16) * 4\r\n start_point += 2\r\n flags = pkt[start_point:start_point+2]\r\n flags_str = \"\"\r\n for f in flags:\r\n flags_str += str(format(int(f), '04b'))\r\n start_point += 2\r\n window_size = int(pkt[start_point:start_point+4], 16)\r\n start_point += 4\r\n checksum_value = pkt[start_point:start_point+4]\r\n start_point += 4\r\n urgent_pointer = int(pkt[start_point:start_point+4], 16)\r\n start_point += 4\r\n options = int((2 * packets[i][0][0] - start_point)/2)\r\n\r\n packets[i][2].append(src_port)\r\n packets[i][2].append(dest_port)\r\n packets[i][2].append(sequence_num)\r\n packets[i][2].append(acknowledgment)\r\n packets[i][2].append(data_offset)\r\n packets[i][2].append(flags_str)\r\n packets[i][2].append(window_size)\r\n packets[i][2].append(checksum_value)\r\n packets[i][2].append(urgent_pointer)\r\n packets[i][2].append(options)\r\n return packets","def reader(self):\n while self.alive:\n try:\n data = self.serial.read_until(b'~')[:-1]\n packet = ethernet.Ethernet(data)\n if packet[icmp.ICMP]:\n packet[ethernet.Ethernet].dst_s = \"dc:a6:32:00:a7:8b\"\n packet[ip.IP].dst_s = \"192.168.1.35\"\n packet[icmp.ICMP].sum = b'0x1783'\n print(\"\\n\\n__________________RESPONSE FROM VISIBLE PI__________________\")\n print(packet)\n if data:\n self.write(packet.bin())\n except socket.error as msg:\n break\n self.alive = False","def udp_iterator(pc):\n\tfor time,pkt in pc:\n\t\teth = dpkt.ethernet.Ethernet(pkt)\n\t\tif eth.type == dpkt.ethernet.ETH_TYPE_IP:\n\t\t ip = eth.data\n\t\t # if the IP protocol is UDP, process it further\n\t\t if ip.p == dpkt.ip.IP_PROTO_UDP :\n\t\t\tudp = ip.data\n\t\t\tyield( ip.src, udp.sport, ip.dst, udp.dport, udp.data )","def read_and_store_pcap(file_name):\r\n file = open(file_name, \"rb\")\r\n global_header = file.read(24).hex()\r\n byte = file.read(16)\r\n packets = []\r\n bytes = []\r\n sizes = []\r\n while byte:\r\n packet_header = byte.hex()\r\n # parse the size for each packet\r\n size = struct.unpack(\"<L\", codecs.decode(str(packet_header[16:24]), \"hex\"))[0]\r\n sizes.append(size)\r\n # read the whole packet by its size from the bytes\r\n byte = file.read(size).hex()\r\n bytes.append(byte)\r\n byte = file.read(16)\r\n for size in sizes:\r\n packets.append(([size], [], []))\r\n i = 0\r\n\r\n for pkt in bytes:\r\n packets = handle_pkt_header(pkt, packets, i)\r\n packets, start_point = handle_ip_header(pkt, packets, i)\r\n protocol = packets[i][1][7]\r\n if protocol == 1:\r\n packets = handle_icmp(pkt, packets, i, start_point)\r\n elif protocol == 6:\r\n packets = handle_tcp(pkt, packets, i, start_point)\r\n elif protocol == 17:\r\n packets = handle_udp(pkt, packets, i, start_point)\r\n i += 1\r\n # print(packets)\r\n return packets","def testParse(self):\n parser = pcap.PcapParser()\n storage_writer = self._ParseFile(['test.pcap'], parser)\n\n # PCAP information:\n # Number of streams: 96 (TCP: 47, UDP: 39, ICMP: 0, Other: 10)\n #\n # For each stream 2 events are generated one for the start\n # and one for the end time.\n\n self.assertEqual(storage_writer.number_of_events, 192)\n\n events = list(storage_writer.GetEvents())\n\n # Test stream 3 (event 6).\n # Protocol: TCP\n # Source IP: 192.168.195.130\n # Dest IP: 63.245.217.43\n # Source Port: 1038\n # Dest Port: 443\n # Stream Type: SSL\n # Starting Packet: 4\n # Ending Packet: 6\n\n event = events[6]\n self.assertEqual(event.packet_count, 3)\n self.assertEqual(event.protocol, 'TCP')\n self.assertEqual(event.source_ip, '192.168.195.130')\n self.assertEqual(event.dest_ip, '63.245.217.43')\n self.assertEqual(event.dest_port, 443)\n self.assertEqual(event.source_port, 1038)\n self.assertEqual(event.stream_type, 'SSL')\n self.assertEqual(event.first_packet_id, 4)\n self.assertEqual(event.last_packet_id, 6)\n\n # Test stream 6 (event 12).\n # Protocol: UDP\n # Source IP: 192.168.195.130\n # Dest IP: 192.168.195.2\n # Source Port: 55679\n # Dest Port: 53\n # Stream Type: DNS\n # Starting Packet: 4\n # Ending Packet: 6\n # Protocol Data: DNS Query for wpad.localdomain\n\n event = events[12]\n self.assertEqual(event.packet_count, 5)\n self.assertEqual(event.protocol, 'UDP')\n self.assertEqual(event.source_ip, '192.168.195.130')\n self.assertEqual(event.dest_ip, '192.168.195.2')\n self.assertEqual(event.dest_port, 53)\n self.assertEqual(event.source_port, 55679)\n self.assertEqual(event.stream_type, 'DNS')\n self.assertEqual(event.first_packet_id, 11)\n self.assertEqual(event.last_packet_id, 1307)\n self.assertEqual(\n event.protocol_data, 'DNS Query for wpad.localdomain')\n\n expected_message = (\n 'Source IP: 192.168.195.130 '\n 'Destination IP: 192.168.195.2 '\n 'Source Port: 55679 '\n 'Destination Port: 53 '\n 'Protocol: UDP '\n 'Type: DNS '\n 'Size: 380 '\n 'Protocol Data: DNS Query for wpad.localdomain '\n 'Stream Data: \\'\\\\xb8\\\\x9c\\\\x01\\\\x00\\\\x00\\\\x01\\\\x00\\\\x00\\\\x00\\\\x00'\n '\\\\x00\\\\x00\\\\x04wpad\\\\x0blocaldomain\\\\x00\\\\x00\\\\x01\\\\x00\\\\x01\\\\xb8'\n '\\\\x9c\\\\x01\\\\x00\\\\x00\\\\x01\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x04wpa\\' '\n 'First Packet ID: 11 '\n 'Last Packet ID: 1307 '\n 'Packet Count: 5')\n expected_short_message = (\n 'Type: DNS '\n 'First Packet ID: 11')\n\n self._TestGetMessageStrings(event, expected_message, expected_short_message)","def convert_pcap_to_dataframe(input_file):\r\n if not os.path.exists(input_file):\r\n raise IOError(\"File \" + input_file + \" does not exist\")\r\n\r\n tshark_fields = \"-e frame.time_epoch \" \\\r\n \"-e _ws.col.Source \" \\\r\n \"-e _ws.col.Destination \" \\\r\n \"-e _ws.col.Protocol \" \\\r\n \"-e frame.len \" \\\r\n \"-e ip.ttl \" \\\r\n \"-e ip.flags.mf \" \\\r\n \"-e ip.frag_offset \" \\\r\n \"-e icmp.type \" \\\r\n \"-e tcp.srcport \" \\\r\n \"-e tcp.dstport \" \\\r\n \"-e udp.srcport \" \\\r\n \"-e udp.dstport \" \\\r\n \"-e dns.qry.name \" \\\r\n \"-e dns.qry.type \" \\\r\n \"-e http.request \" \\\r\n \"-e http.response \" \\\r\n \"-e http.user_agent \" \\\r\n \"-e tcp.flags.str \" \\\r\n \"-e ntp.priv.reqcode \"\r\n\r\n temporary_file = tempfile.TemporaryFile(\"r+b\")\r\n\r\n # print(shutil.which(command))\r\n\r\n p = subprocess.Popen([settings.TSHARK + \" -n -r \\\"\" + input_file + \"\\\" -E separator='\\x03' -E header=y -T fields \" + tshark_fields],\r\n shell=True, stdout=temporary_file) #\\x03 is ETX\r\n p.communicate()\r\n p.wait()\r\n\r\n # Reset file pointer to start of file\r\n temporary_file.seek(0)\r\n\r\n df = pd.read_csv(temporary_file, sep=\"\\x03\", low_memory=False, error_bad_lines=False)\r\n\r\n temporary_file.close()\r\n\r\n if ('tcp.srcport' in df.columns) and ('udp.srcport' in df.columns) and ('tcp.dstport' in df.columns) and \\\r\n ('udp.dstport' in df.columns):\r\n # Combine source and destination ports from tcp and udp\r\n df['srcport'] = df['tcp.srcport'].fillna(df['udp.srcport'])\r\n df['dstport'] = df['tcp.dstport'].fillna(df['udp.dstport'])\r\n\r\n df['srcport'] = df['srcport'].apply(lambda x: int(x) if str(x).replace('.', '', 1).isdigit() else 0)\r\n df['dstport'] = df['dstport'].apply(lambda x: int(x) if str(x).replace('.', '', 1).isdigit() else 0)\r\n\r\n # Remove columns: 'tcp.srcport', 'udp.srcport','tcp.dstport', 'udp.dstport'\r\n df.drop(['tcp.srcport', 'udp.srcport', 'tcp.dstport', 'udp.dstport'], axis=1, inplace=True)\r\n\r\n # Drop all empty columns (for making the analysis more efficient! less memory.)\r\n df.dropna(axis=1, how='all', inplace=True)\r\n df = df.fillna(0)\r\n\r\n if 'icmp.type' in df.columns:\r\n df['icmp.type'] = df['icmp.type'].astype(str)\r\n\r\n if 'ip.frag_offset' in df.columns:\r\n df['ip.frag_offset'] = df['ip.frag_offset'].astype(str)\r\n\r\n if 'ip.flags.mf' in df.columns:\r\n df['ip.flags.mf'] = df['ip.flags.mf'].astype(str)\r\n\r\n if ('ip.flags.mf' in df.columns) and ('ip.frag_offset' in df.columns):\r\n # Analyse fragmented packets\r\n df['fragmentation'] = (df['ip.flags.mf'] == '1') | (df['ip.frag_offset'] != '0')\r\n df.drop(['ip.flags.mf', 'ip.frag_offset'], axis=1, inplace=True)\r\n\r\n if 'tcp.flags.str' in df.columns:\r\n df['tcp.flags.str'] = df['tcp.flags.str'].str.encode(\"utf-8\") \r\n\r\n return df","def print_info(self):\n \n i = 1\n for pcap_packet in self.pcap_packets:\n print '----------------frame: %d------------' % i\n i += 1\n pcap_packet.ethernet.print_info()\n \n #skip the packet that is not ip packet\n if (pcap_packet.ethernet.type != 'IP'):\n continue\n \n print '################# packet in the frame ################'\n pcap_packet.ip.print_info()\n \n #skp the packet that is not tcp message\n if (pcap_packet.ip.protocol != 'TCP'):\n continue\n \n print '@@@@@@@@@@@@@@@@@@@ tcp fields @@@@@@@@@@@@@@@@@@@@'\n pcap_packet.tcp.print_info()\n \n print\n #endof for","def print_info(self):\n \n i = 1\n for pcap_packet in self.pcap_packets:\n print '----------------frame: %d------------' % i\n i += 1\n pcap_packet.ethernet.print_info()\n \n #skip the packet that is not ip packet\n if (pcap_packet.ethernet.type != 'IP'):\n continue\n \n print '################# packet in the frame ################'\n pcap_packet.ip.print_info()\n \n #skp the packet that is not tcp message\n if (pcap_packet.ip.protocol != 'TCP'):\n continue\n \n print '@@@@@@@@@@@@@@@@@@@ tcp fields @@@@@@@@@@@@@@@@@@@@'\n pcap_packet.tcp.print_info()\n \n print\n #endof for","def _read_packets(self, reader: Par2FileReader):\n start_count = len(self)\n pointers = reader.get_pointers()\n # Create RecoverySets if needed\n for set_id, pointer_set in packets.by_set_id(pointers).items():\n print(set_id.hex(), pointer_set)\n if set_id not in self.recovery_sets.keys():\n # Create a RecoverySet if needed\n self.recovery_sets[set_id] = RecoverySet(set_id)\n for pointer in pointer_set:\n self.recovery_sets[set_id].packets.add(pointer)\n logger.info(\"Added {} new packets\".format(len(self) - start_count))","def locator(pcap_obj,kml_file):\r\n ip_list = []\r\n for ts, buf in pcap_obj:\r\n eth = dpkt.ethernet.Ethernet(buf)\r\n ip = eth.data\r\n try: # extract all unique IPs\r\n src_ip = str(socket.inet_ntoa(ip.src))\r\n dst_ip = str(socket.inet_ntoa(ip.dst))\r\n if src_ip in ip_list:\r\n pass\r\n else:\r\n ip_list.append(src_ip)\r\n if dst_ip in ip_list:\r\n pass\r\n else:\r\n ip_list.append(dst_ip)\r\n except AttributeError:\r\n pass\r\n\r\n try:\r\n reader = geoip2.database.Reader('GeoLite2-City_20190129.mmdb') # reading from db(can be redacted)\r\n except FileNotFoundError:\r\n print(f'[!]DB file not in current directory or with a different file name')\r\n sys.exit(1)\r\n area = []\r\n longitude = []\r\n latitude = []\r\n ips = []\r\n for ip_addr in ip_list:\r\n try:\r\n rec = reader.city(ip_addr) # reading IP\r\n country = rec.country.iso_code # assigning country and city\r\n city = rec.city.name\r\n if city is None and country is None:\r\n area.append('Unknown')\r\n elif city is None:\r\n area.append(f'Unknown city:{country}') # looking for unknown country\r\n elif country is None:\r\n area.append(f'Unknown country:{city}') # looking for unknown city\r\n else:\r\n area.append(f'{city} {country}')\r\n\r\n longitude.append(rec.location.longitude)\r\n latitude.append(rec.location.latitude)\r\n ips.append(ip_addr)\r\n except geoip2.errors.AddressNotFoundError:\r\n pass\r\n\r\n try:\r\n kml = simplekml.Kml()\r\n final_path = str(os.getcwd() + os.sep + kml_file) # defining full canonical path\r\n for i in range(0, len(ips)):\r\n kml.newpoint(name=(area[i]),\r\n coords=[(longitude[i], latitude[i])],\r\n description=f'[+] Location = {area[i]}\\n IP: {ips[i]}')\r\n kml.save(final_path)\r\n print(f\"[+] Writing IP locations to {kml_file}\") # writing data to a KML file\r\n print(f\"[+] Opening Google Earth with:{kml_file}\\n\") # reading file with google earth\r\n try:\r\n os.startfile(final_path)\r\n except OSError:\r\n print(f'[!] Warning: Google Earth must be installed to open the kml')\r\n except FileNotFoundError:\r\n pass","def capture_packets(self, interface, count=1, timeout=None):\n if interface not in self.packet_captures:\n raise ObjectNotFoundException(\n 'No packet capture is running or was run on host/interface' +\n self.name + '/' + interface)\n tcpd = self.packet_captures[interface]\n return tcpd.wait_for_packets(count, timeout)","def process_pkts(self, pkts: list):\n pkt_count = 0\n for ts, buf in pkts:\n eth = dpkt.ethernet.Ethernet(buf)\n if not isinstance(eth.data, dpkt.ip.IP):\n continue\n ip = eth.data\n if ((inet_to_str(ip.src) == self.sip and inet_to_str(ip.dst) == self.dip) or\n (inet_to_str(ip.src) == self.dip and inet_to_str(ip.dst) == self.sip)):\n if isinstance(ip.data, dpkt.tcp.TCP):\n tcp = ip.data\n if ((tcp.sport == self.sp and tcp.dport == self.dp) or\n (tcp.dport == self.sp and tcp.sport == self.dp)):\n pkt_count += 1\n self._process(buf, ts, pkt_count)\n if self._c_state == self._s_state and self._c_state == TCPState.CLOSED:\n logger.info(\"Session finished.\")\n logger.info(\"Number of packets in the session id: {} is {}\".format(\n self.session_count, len(self.sessions[self.session_count])))\n self.__reset_state__()","def extract_from_pcap(device=None, pcap=None, flags=\"-v -r\", path_to_chaosreader=\"/tmp/\"):\n if device is None or pcap is None:\n raise Exception(\"device and pcap are mandatory arguments\")\n device.shell(command=\"cd /tmp\")\n\n cmd = path_to_chaosreader + \"chaosreader0.94 \" + flags + \" \" + pcap\n output = device.shell(command=cmd)\n\n if not re.match(\".*Creating files.*\", output.response(), re.DOTALL):\n device.log(level=\"ERROR\", message=\"Chaosreader ran into an error\")\n raise Exception(\"Chaosreader ran into an error\")\n\n return True","def handle_icmp(pkt, packets, i, start_point):\r\n icmp_type = int(pkt[start_point:start_point+2], 16)\r\n start_point = start_point + 2\r\n icmp_code = int(pkt[start_point:start_point+2], 16)\r\n start_point = start_point + 2\r\n icmp_checksum = pkt[start_point:start_point+4]\r\n packets[i][2].append(icmp_type)\r\n packets[i][2].append(icmp_code)\r\n packets[i][2].append(icmp_checksum)\r\n return packets","def get_total_and_retrans_frames(pcap_filepath, connections):\n # First init values to avoid strange errors if connection is empty\n for conn_id, conn in connections.iteritems():\n for direction in co.DIRECTIONS:\n connections[conn_id].flow.attr[direction][co.FRAMES_TOTAL] = 0\n connections[conn_id].flow.attr[direction][co.BYTES_FRAMES_TOTAL] = 0\n connections[conn_id].flow.attr[direction][co.FRAMES_RETRANS] = 0\n connections[conn_id].flow.attr[direction][co.BYTES_FRAMES_RETRANS] = 0\n\n stats_filename = os.path.basename(pcap_filepath)[:-5] + \"_tshark_total\"\n stats_file = open(stats_filename, 'w')\n co.tshark_stats(None, pcap_filepath, print_out=stats_file)\n stats_file.close()\n\n stats_file = open(stats_filename)\n data = stats_file.readlines()\n stats_file.close()\n for line in data:\n split_line = \" \".join(line.split()).split(\" \")\n if len(split_line) == 11:\n # Manage case with ipv6\n ip_src, port_src = get_ip_port_tshark(split_line[0])\n ip_dst, port_dst = get_ip_port_tshark(split_line[2])\n for conn_id, conn in connections.iteritems():\n if conn.flow.attr[co.SADDR] == ip_src and conn.flow.attr[co.SPORT] == port_src and \\\n conn.flow.attr[co.DADDR] == ip_dst and conn.flow.attr[co.DPORT]:\n connections[conn_id].flow.attr[co.S2C][co.FRAMES_TOTAL] = int(split_line[3])\n connections[conn_id].flow.attr[co.S2C][co.BYTES_FRAMES_TOTAL] = int(split_line[4])\n connections[conn_id].flow.attr[co.C2S][co.FRAMES_TOTAL] = int(split_line[5])\n connections[conn_id].flow.attr[co.C2S][co.BYTES_FRAMES_TOTAL] = int(split_line[6])\n break\n\n stats_file.close()\n os.remove(stats_filename)\n\n stats_filename = os.path.basename(pcap_filepath)[:-5] + \"_tshark_retrans\"\n stats_file = open(stats_filename, 'w')\n co.tshark_stats('tcp.analysis.retransmission', pcap_filepath, print_out=stats_file)\n stats_file.close()\n\n stats_file = open(stats_filename)\n data = stats_file.readlines()\n stats_file.close()\n for line in data:\n split_line = \" \".join(line.split()).split(\" \")\n if len(split_line) == 11:\n ip_src, port_src = get_ip_port_tshark(split_line[0])\n ip_dst, port_dst = get_ip_port_tshark(split_line[2])\n for conn_id, conn in connections.iteritems():\n if conn.flow.attr[co.SADDR] == ip_src and conn.flow.attr[co.SPORT] == port_src and \\\n conn.flow.attr[co.DADDR] == ip_dst and conn.flow.attr[co.DPORT]:\n connections[conn_id].flow.attr[co.S2C][co.FRAMES_RETRANS] = int(split_line[3])\n connections[conn_id].flow.attr[co.S2C][co.BYTES_FRAMES_RETRANS] = int(split_line[4])\n connections[conn_id].flow.attr[co.C2S][co.FRAMES_RETRANS] = int(split_line[5])\n connections[conn_id].flow.attr[co.C2S][co.BYTES_FRAMES_RETRANS] = int(split_line[6])\n break\n\n stats_file.close()\n os.remove(stats_filename)","def next_connection_packets(piter, linktype=1):\n first_ft = None\n\n for ts, raw in piter:\n ft = flowtuple_from_raw(raw, linktype)\n if not first_ft: first_ft = ft\n\n sip, dip, sport, dport, proto = ft\n if not (first_ft == ft or first_ft == (dip, sip, dport, sport, proto)):\n break\n\n yield {\n \"src\": sip, \"dst\": dip, \"sport\": sport, \"dport\": dport, \"proto\": proto,\n \"raw\": payload_from_raw(raw, linktype).encode(\"base64\"), \"direction\": first_ft == ft,\n }","def get_pcap_traffic_series(self):\n parsed_pcap_data = {}\n\n if (self.mac_address_binary is not None):\n parsed_pcap_data[self.mac_address_binary] = []\n\n with open(self.pcap_file_path, 'rb') as pcap_file:\n try:\n pcap = dpkt.pcap.Reader(pcap_file)\n for ts, buf in pcap:\n # Skip non ethernet frames\n try:\n eth = dpkt.ethernet.Ethernet(buf)\n except:\n continue\n\n # Skip non-IP packets\n if eth.type != 2048:\n continue\n \n # Apply eth filter\n if (self.mac_address_binary is not None):\n self.append_data(parsed_pcap_data, self.mac_address_binary, eth, ts)\n else:\n if (eth.src not in parsed_pcap_data):\n parsed_pcap_data[eth.src] = []\n if (eth.dst not in parsed_pcap_data):\n parsed_pcap_data[eth.dst] = []\n\n self.append_data(parsed_pcap_data, eth.src, eth, ts)\n self.append_data(parsed_pcap_data, eth.dst, eth, ts)\n except:\n print \"Error parsing file: %s\" % pcap_file\n \n # Remove mac addresses that didn't send data\n receivers_only = []\n for mac_addr in parsed_pcap_data:\n data_sent = False\n for data in parsed_pcap_data[mac_addr]:\n if (data[1] > 0):\n data_sent = True\n break\n if (not data_sent):\n receivers_only.append(mac_addr)\n\n for mac_addr in receivers_only:\n parsed_pcap_data.pop(mac_addr, None)\n\n # Sort the data \n for mac_addr in parsed_pcap_data:\n series = sorted(parsed_pcap_data[mac_addr], key=operator.itemgetter(0))\n parsed_pcap_data[mac_addr] = series\n\n return parsed_pcap_data","def process_pcap(pcap):\n\n print \"Processing\", pcap\n pcap_path, _ = os.path.splitext(pcap)\n # strip_payload_from_pcap(pcap)\n os.system(\"tshark -nn -T fields -E separator=/t -e frame.time_epoch\"\n \" -e ip.src -e ip.dst -e tcp.srcport -e tcp.dstport\"\n \" -e ip.proto -e ip.len -e ip.hdr_len -e tcp.hdr_len -e data.len\"\n \" -e tcp.flags -e tcp.options.timestamp.tsval\"\n \" -e tcp.options.timestamp.tsecr -e tcp.seq -e tcp.ack\"\n \" -e tcp.window_size_value -e expert.message \"\n \" -r %s > %s.tshark\" % (pcap, pcap_path))\n # tcpdump command from Panchenko's raw-to-tcp script\n os.system(\"\"\"tcpdump -r {0} -n -l -tt -q -v | sed -e 's/^[ ]*//' |\n awk '/length ([0-9][0-9]*)/{{printf \"%s \",$0;next}}{{print}}' > {1}\"\"\".\\\n format(pcap, pcap_path + '.tcpdump'))","def packetSniff():\n\n packets = psutil.net_io_counters(pernic=True)\n interfaces = {}\n x = 0\n for p in packets.items():\n values = {}\n values['name'] = p[0]\n values['bytes_sent'] = p[1][0]\n values['bytes_recv'] = p[1][1]\n values['pckt_sent'] = p[1][2]\n values['pckt_recv'] = p[1][3]\n values['errin'] = p[1][4]\n values['errout'] = p[1][5]\n values['dropin'] = p[1][6]\n values['dropout'] = p[1][7]\n\n if ((values['bytes_sent'] or values['bytes_recv'] or\n values['pckt_sent'] or values['pckt_recv']) != 0):\n\n interfaces[x] = values\n x += 1\n else:\n pass\n\n return interfaces","def start_tcpdump(self):\n\t\tlog_file = os.path.join(self.cfg.file_log_dir,self.info[\"hash_md5\"]+\".pcap\")\n\t\tself.info[\"tcpdump_log_path\"] = log_file\n\t\tcmd = [\"/usr/sbin/tcpdump\", \"-iany\", \"-w\"+self.info[\"tcpdump_log_path\"], \"-c%d\"%(self.cfg.tcpdump_limit)]\n\t\tself.p_tcpdump = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\tself.log.info(\"tcpdump starts, logfile:%s\",self.info[\"tcpdump_log_path\"] )","def tcpdump(timeout, q, interface):\t\n\tlogging.debug('tcpdump -s 1024 -lqnAt tcp port 80 -i eth0')\n\t# tcpdump -s 1024 -lqnAt tcp port 80\n\t\t\n\tcommand = Command(['/usr/sbin/tcpdump', '-s 1024', '-lnAq', '-i', interface], timeout)\n\tcommand.run()\n\n\t# when it's executing here, the results have been available\n\t# print command.out\n\n\tif command.out is not None:\n\t\t# pattern = \"time=([0-9]+\\.[0-9]+) ms\"\n\t\tip_pattern = \"IP ([0-9]+.[0-9]+.[0-9]+.[0-9]+).[0-9]+ > [0-9]+.[0-9]+.[0-9]+.[0-9]+.[0-9]\"\n\t\tgoogle_pattern = \"domain=.google.com\"\n\t\tlines = command.out.split('\\n')\n\t\tlast_ip = None\n\n\t\t# first time scan for google's return ip\n\t\tfor line in lines:\n\t\t\tip_src = re.search(ip_pattern, line)\n\t\t\tif ip_src is not None:\n\t\t\t\tlast_ip = ip_src.group(1)\n\t\t\tif re.search(google_pattern, line):\n\t\t\t\tprint last_ip\n\t\t\t\tbreak\n\n\t\tgEntries = []\n\t\tif last_ip is not None:\n\t\t\t\n\t\t\t# second time scan parse tcpdump for query entries\n\t\t\tfor line in lines:\n\t\t\t\tlast_ip_pos = re.search(last_ip, line)\n\t\t\t\tif last_ip_pos is None:\n\t\t\t\t\tcontinue\n\t\t\t\n\t\t\t\tif line.index('>') > last_ip_pos.start():\n\t\t\t\t\t# from remote to this place\n\t\t\t\t\ttraffic_type = 1\t\t\t\n\t\t\t\telse:\n\t\t\t\t\t# out to remote\n\t\t\t\t\ttraffic_type = 0\n\t\t\t\n\t\t\t\ttime_pattern = \"([0-9]+:[0-9]+:[0-9]+.[0-9]+) IP\"\n\t\t\t\ttimestamp = re.search(time_pattern, line)\n\t\t\t\tif timestamp is not None:\n\t\t\t\t\ttime_str = timestamp.group(1)\n\t\t\t\t\th, m, s, ms = map(int, re.split(r'[.:]+', time_str))\n\t\t\t\t\ttimestamp_delta = timedelta(hours=h, minutes=m, seconds=s, microseconds=ms)\n\t\t\t\t\tgEntries.append( (timestamp_delta, traffic_type) )\n\t\t\t\telse:\n\t\t\t\t\tgEntries.append( (None, -1))\n\n\t\tq.put((command.returncode, last_ip, gEntries))\n\t\treturn","def download_pcap(self, args=None):\r\n result = {\"Task\": \"DownloadPCAP\", \"Error\": \"NoError\", \"Status\": \"FileNotFound\", \"FileName\": args['filename'],\r\n \"FileSize\": 0, \"FileType\": \"UnKnown\", \"FileURL\": 'UnKnown', \"FileUser\": 'UnKnown'}\r\n with EndaceWebSession(app_url=self.applianceurl, username=self.username, password=self.password,\r\n cert_verify=self.cert_verify) as sess:\r\n api = EndaceVisionAPIAdapter(sess)\r\n path = \"files\"\r\n rf = api.get(path)\r\n if rf.status_code == 200:\r\n try:\r\n response = rf.json()\r\n except json.decoder.JSONDecodeError:\r\n raise Exception(f\"JsonDecodeError - path {path}\")\r\n else:\r\n meta = response.get(\"meta\", {})\r\n payload = response.get(\"payload\")\r\n if meta:\r\n meta_error = meta[\"error\"]\r\n if meta_error is not None:\r\n if meta_error is not False:\r\n result['Status'] = \"FileNotFound\"\r\n result['Error'] = str(meta_error)\r\n else:\r\n # Download PCAP File\r\n for file in payload:\r\n if result['FileName'] == file['name'] and len(file[\"id\"]):\r\n file_numerical_part = float(re.findall(r'[\\d\\.]+', file['usage'])[0])\r\n\r\n if 'KB' in file['usage']:\r\n filesize = file_numerical_part * 0.001\r\n elif 'GB' in file['usage']:\r\n filesize = file_numerical_part * 1000\r\n elif 'TB' in file['usage']:\r\n filesize = file_numerical_part * 1000000\r\n else:\r\n filesize = file_numerical_part * 1\r\n\r\n if filesize <= int(args['filesizelimit']):\r\n result['FileName'] = file['name'] + \".pcap\"\r\n if not file['status']['inUse']:\r\n # File available to download\r\n pcapfile_url_path = (\"files/%s/stream?format=pcap\" % file[\"id\"])\r\n d = api.get(pcapfile_url_path)\r\n if d.status_code == 200:\r\n demisto.results(fileResult(f'{result[\"FileName\"]}', d.content,\r\n file_type=entryTypes['entryInfoFile']))\r\n\r\n result['FileURL'] = f'[Endace PCAP URL]'\\\r\n f'({self.applianceurl}/vision2/data/'\\\r\n f'{pcapfile_url_path})'\r\n\r\n result['FileSize'] = file['usage']\r\n result['Status'] = \"DownloadFinished\"\r\n result['FileType'] = file['type']\r\n result['FileUser'] = file['user']\r\n else:\r\n result['Status'] = \"FileNotFound\"\r\n result['Error'] = f\"ServerError - HTTP {rf.status_code} to /{path}\"\r\n else:\r\n result['Status'] = \"FileInUse\"\r\n else:\r\n result['Status'] = \"FileExceedsSizeLimit\"\r\n else:\r\n result['Status'] = \"Failed\"\r\n result['Error'] = f\"ServerError - empty meta data from {path}\"\r\n else:\r\n result['Status'] = \"Failed\"\r\n result['Error'] = f\"ServerError - HTTP {rf.status_code} to /{path}\"\r\n\r\n if result['Status'] == 'Failed':\r\n self.handle_error_notifications(result['Error'])\r\n return result","def retrieve(self, trace_filter={}, limit=0):\n\n if isinstance(limit, int) and limit > 0:\n max_r = limit\n else:\n max_r = 0\n\n try:\n packets = self.__db.find_packets(self._collection, trace_filter, max_r)\n except MemoryError:\n print(\"Warning: cannot allocate sufficient memory for packets, perhaps you are using Windows?\")\n return []\n except:\n return []\n\n # Attempt to decode base64 payloads.\n for packet in packets:\n if packet[\"tcp_info\"] is not None:\n if isinstance(packet[\"tcp_info\"][\"payload\"], bytes):\n try:\n packet[\"tcp_info\"][\"payload\"] = b64decode(packet[\"tcp_info\"][\"payload\"])\n except:\n continue\n\n if packet[\"tls_info\"] is not None:\n for i, data in enumerate(packet[\"tls_info\"][\"data\"]):\n if isinstance(data, bytes):\n try:\n packet[\"tls_info\"][\"data\"][i] = b64decode(data)\n except:\n continue\n\n return packets","def loadpts(skip=40, filt=None, ref_frame=None):\n pts = []\n for i in range(42):\n print('loading file: ', i)\n if filt is not None:\n traj = md.load(DCD_PROT(i), top=PDB_PROT, atom_indices=filt, stride=skip)\n else:\n traj = md.load(DCD_PROT(i), top=PDB_PROT, stride=skip)\n traj.center_coordinates()\n if ref_frame is not None:\n traj.superpose(ref_frame)\n for i in traj.xyz:\n pts.append(i)\n return np.array(pts)","def create_stream(cls, packet_count=test_packet_count):\n for i in range(0, packet_count):\n info = cls.create_packet_info(cls.src_dst_if, cls.src_dst_if)\n payload = cls.info_to_payload(info)\n p = (\n Ether(dst=cls.src_dst_if.local_mac, src=cls.src_dst_if.remote_mac)\n / IP(\n id=info.index,\n src=cls.src_dst_if.remote_ip4,\n dst=cls.src_dst_if.local_ip4,\n )\n / ICMP(type=\"echo-request\", id=1234)\n / Raw(payload)\n )\n cls.extend_packet(p, 1518, cls.padding)\n info.data = p","def accumulate_packets():\n l = []\n packets = sniff(count=NUMBER_OF_SNIFFING_ROUNDS, lfilter=fltr, prn=printing)\n print(\"Processing packets!\")\n for packet in packets:\n l.append({\"ip\": get_ip(packet),\n \"country\": get_country(packet),\n \"entering\": is_entering(packet),\n \"port\": get_partner_port(packet),\n \"size\": packet[IP].len, #the len of the ip layer is the len of the entire packet\n \"program\": get_program(packet)})\n return l","def sniff_online(args):\n print('viewer: listening on ' + args.interface)\n\n try:\n sniffer = pcapy.open_live(args.interface, 65536, 1, 1)\n sniffer.setfilter('icmp')\n except Exception as e:\n print(e)\n sys.exit(-1)\n\n if not args.count:\n count = True\n else:\n count = args.count\n\n while count:\n (header, packet) = sniffer.next()\n if header:\n tts = header.getts()\n ret = parse_ICMP_Echo(tts[0] + tts[1] / 1000000, packet)\n\n if ret and args.count:\n count -= 1","def tcp_traceflow(packet, *, count=NotImplemented):\n if 'TCP' in packet:\n ip = packet['IP'] if 'IP' in packet else packet['IPv6']\n tcp = packet['TCP']\n data = dict(\n protocol=LINKTYPE.get(packet.name.upper()), # data link type from global header\n index=count, # frame number\n frame=packet2dict(packet), # extracted packet\n syn=bool(tcp.flags.S), # TCP synchronise (SYN) flag\n fin=bool(tcp.flags.F), # TCP finish (FIN) flag\n src=ipaddress.ip_address(ip.src), # source IP\n dst=ipaddress.ip_address(ip.dst), # destination IP\n srcport=tcp.sport, # TCP source port\n dstport=tcp.dport, # TCP destination port\n timestamp=time.time(), # timestamp\n )\n return True, data\n return False, None","def process_pcap(self):\n # Create Core Controller\n controller = Controller(self.args.input, self.args.extraTests, self.args.non_verbose, self.args.output,\n self.args.debug)\n\n if not self.args.skip:\n # Load PCAP statistics\n recalculate_intervals = None\n if self.args.recalculate_delete:\n recalculate_intervals = True\n elif self.args.recalculate_yes:\n recalculate_intervals = True\n self.args.recalculate = True\n elif self.args.recalculate_no:\n recalculate_intervals = False\n self.args.recalculate = True\n controller.load_pcap_statistics(self.args.export, self.args.recalculate, self.args.statistics,\n self.args.statistics_interval, self.args.recalculate_delete,\n recalculate_intervals)\n\n if self.args.list_intervals:\n controller.list_interval_statistics()\n\n # Create statistics plots\n if self.args.plot is not None:\n do_entropy = False\n if self.args.extraTests:\n do_entropy = True\n controller.create_statistics_plot(self.args.plot, do_entropy)\n\n # Check rng seed\n if not isinstance(self.args.rngSeed, list):\n self.args.rngSeed = [self.args.rngSeed]\n\n # Process attack(s) with given attack params\n if self.args.attack is not None:\n # If attack is present, load attack with params\n controller.process_attacks(self.args.attack, self.args.rngSeed, self.args.time, self.args.inject_empty)\n\n # Parameter -q without arguments was given -> go into query loop\n if self.args.query == [None]:\n controller.enter_query_mode()\n # Parameter -q with arguments was given -> process query\n elif self.args.query is not None:\n controller.process_db_queries(self.args.query, True)","def next_batch(self):\n\n while self.cap.isOpened():\n flag, frame = self.cap.read()\n yield frame","def main():\n connection = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.htons(0x03))\n\n # Start the main loop.\n while True:\n # 65536 is the biggest buffer size that can be used.\n raw_data, addr = connection.recvfrom(65536)\n dest_mac, src_mac, eth_proto, data = ethernet_frame(raw_data)\n print('\\nEthernet Frame:')\n print('Destination: {}, Source: {}, Protocol: {}'.format(dest_mac, src_mac, eth_proto))","def read_packets(serial_input):\n while 1:\n header = scan_to_headerword(serial_input)\n yield header.read_packet(serial_input)","def sniffing():\n sniff(store=False, prn=lambda p: threading.Thread(target=next, args=(p,)).start(), iface=IFACE)","def read_pkt_seq(self):\n pkt = self.read_pkt_line()\n while pkt:\n yield pkt\n pkt = self.read_pkt_line()","def sniff_continuously(self, packet_count=None):\n \n self.lcapture_tshark = (self.lcapture_tshark or \n self.eventloop.run_until_complete(self._get_tshark_process()))\n\n self._running_processes.add(self.lcapture_tshark)\n\n # Retained for backwards compatibility and to add documentation.\n return self._packets_from_tshark_sync(packet_count=packet_count, \n tshark_process=self.lcapture_tshark)","def preprocess_capture(data, ip_version=4, transp_layer=\"TCP\"):\n #SEE: https://www.winpcap.org/ntar/draft/PCAP-DumpFileFormat.html\n\n #TODO Implement ipv6, udp and ICMP\n if ip_version == 4:\n pass\n else:\n raise ValueError('IP version must be \"4\"')\n\n if transp_layer == \"TCP\":\n pass\n else:\n raise ValueError('transport layer must be TCP')\n\n try:\n capt = pyshark.FileCapture(data, keep_packets=False, display_filter='tcp')\n except:\n exit(\"Could not open pcap file\")\n\n ip_fields = ['src', 'dst', 'flags_df', 'flags_mf', 'hdr_len', 'len', 'ttl']\n tcp_fields = ['srcport', 'dstport', 'flags_ack', 'flags_fin', 'flags_push',\n 'flags_reset', 'flags_syn', 'flags_urg', 'hdr_len', 'len']\n\n #Temporary list to feed the final DataFrame (Performance)\n tmp = []\n counter = 0\n logging.info(\"Starting packet processing\")\n for pkt in capt:\n filtered = {}\n #First field is a empty string (ignoring)\n if hasattr(pkt, 'ip'):\n for field in ip_fields:\n #Changing field names for disambiguation in columns\n filtered[\"ip_\"+field] = pkt[\"ip\"].get_field(field)\n else:\n continue\n if hasattr(pkt, 'tcp'):\n for field in tcp_fields:\n #Changing field names for disambiguation in columns\n filtered[\"tcp_\"+field] = pkt[\"tcp\"].get_field(field)\n else:\n continue\n tmp.append(filtered)\n counter += 1\n if counter % 1000 == 0:\n logging.info(\"Processed %d packets\", counter)\n logging.info(\"Ended packet processing\")\n logging.info(\"Converting list to DataFrame\")\n X = pd.DataFrame(tmp)\n logging.info(\"Ended list conversion\")\n return X","def process_trace(pcap_filepath, graph_dir_exp, stat_dir_exp, failed_conns_dir_exp, acksize_tcp_dir_exp, tcpcsm, mptcp_connections=None, print_out=sys.stdout, light=False, return_dict=False):\n cmd = ['tstat', '-s', os.path.basename(pcap_filepath[:-5]), pcap_filepath]\n\n keep_tstat_log = False if return_dict else True\n\n try:\n connections = process_tstat_cmd(cmd, pcap_filepath, keep_log=keep_tstat_log, graph_dir_exp=graph_dir_exp)\n except TstatError as e:\n print(str(e) + \": skip process\", file=sys.stderr)\n return\n\n # Directory containing all TCPConnections that tried to be MPTCP subflows, but failed to\n failed_conns = {}\n\n if tcpcsm:\n retransmissions_tcpcsm(pcap_filepath, connections)\n\n acksize_all = {co.C2S: {}, co.S2C: {}}\n\n if not light:\n inverse_conns = create_inverse_tcp_dictionary(connections)\n\n acksize_all = compute_tcp_acks_retrans(pcap_filepath, connections, inverse_conns)\n\n acksize_all_mptcp = {co.C2S: {}, co.S2C: {}}\n\n if mptcp_connections:\n fast_conns = get_preprocessed_connections(mptcp_connections)\n for flow_id in connections:\n # Copy info to mptcp connections\n copy_info_to_mptcp_connections(connections, mptcp_connections, failed_conns, acksize_all, acksize_all_mptcp, flow_id,\n fast_conns=fast_conns)\n\n if not light:\n for conn_id, conn in mptcp_connections.iteritems():\n for direction in co.DIRECTIONS:\n max_ack = timedelta(0)\n max_payload = timedelta(0)\n for flow_id, flow in conn.flows.iteritems():\n if co.TIME_LAST_ACK_TCP in flow.attr[direction] and (flow.attr[direction][co.TIME_LAST_ACK_TCP] - max_ack).total_seconds() > 0.0:\n max_ack = flow.attr[direction][co.TIME_LAST_ACK_TCP]\n\n if co.TIME_LAST_PAYLD_TCP in flow.attr[direction] and (flow.attr[direction][co.TIME_LAST_PAYLD_TCP] - max_payload).total_seconds() > 0.0:\n max_payload = flow.attr[direction][co.TIME_LAST_PAYLD_TCP]\n\n mptcp_connections[conn_id].attr[direction][co.TIME_LAST_ACK_TCP] = max_ack\n mptcp_connections[conn_id].attr[direction][co.TIME_LAST_PAYLD_TCP] = max_payload\n\n try:\n compute_mptcp_dss_retransmissions(pcap_filepath, mptcp_connections, fast_conns)\n except dpkt.NeedData as e:\n print(e, \": trying to continue...\", file=sys.stderr)\n\n if return_dict:\n if mptcp_connections:\n return connections, acksize_all_mptcp\n else:\n return connections, acksize_all\n else:\n # Save connections info\n if mptcp_connections:\n co.save_data(pcap_filepath, acksize_tcp_dir_exp, acksize_all_mptcp)\n # Also save TCP connections that failed to be MPTCP subflows\n co.save_data(pcap_filepath, failed_conns_dir_exp, failed_conns)\n else:\n co.save_data(pcap_filepath, acksize_tcp_dir_exp, acksize_all)\n co.save_data(pcap_filepath, stat_dir_exp, connections)","def ingest_packet(self, pkt, pkt_receive_timestamp):\n #*** Packet length on the wire:\n self.packet_length = len(pkt)\n #*** Read into dpkt:\n eth = dpkt.ethernet.Ethernet(pkt)\n eth_src = _mac_addr(eth.src)\n eth_dst = _mac_addr(eth.dst)\n eth_type = eth.type\n #*** We only support IPv4 (TBD: add IPv6 support):\n if eth_type != 2048:\n self.logger.error(\"Non IPv4 packet, eth_type is %s\", eth_type)\n return 0\n ip = eth.data\n self.ip_src = socket.inet_ntop(socket.AF_INET, ip.src)\n self.ip_dst = socket.inet_ntop(socket.AF_INET, ip.dst)\n #*** We only support TCP:\n if ip.p != 6:\n self.logger.error(\"Non TCP packet, ip_proto=%s\",\n ip.p)\n return 0\n proto = 'tcp'\n tcp = ip.data\n self.tcp_src = tcp.sport\n self.tcp_dst = tcp.dport\n self.tcp_seq = tcp.seq\n self.tcp_acq = tcp.ack\n self.tcp_flags = tcp.flags\n self.payload = tcp.data\n #*** Generate a hash unique to flow for packets in either direction\n self.fcip_hash = _hash_5tuple(self.ip_src, self.ip_dst, self.tcp_src,\n self.tcp_dst, proto)\n #*** Check to see if we already know this identity:\n db_data = {'hash': self.fcip_hash}\n self.fcip_doc = self.fcip.find_one(db_data)\n if not self.fcip_doc:\n #*** Get flow direction (which way is TCP initiated). Client is\n #*** the end that sends the initial TCP SYN:\n if _is_tcp_syn(tcp.flags):\n self.logger.debug(\"Matched TCP SYN first pkt, src_ip=%s\",\n self.ip_src)\n self.client = self.ip_src\n self.server = self.ip_dst\n self.packet_direction = 'c2s'\n self.verified_direction = 'verified-SYN'\n elif _is_tcp_synack(tcp.flags):\n self.logger.debug(\"Matched TCP SYN+ACK first pkt, src_ip=%s\",\n self.ip_src)\n self.client = self.ip_dst\n self.server = self.ip_src\n self.packet_direction = 's2c'\n self.verified_direction = 'verified-SYNACK'\n else:\n self.logger.debug(\"Unmatch state first pkt, tcp_flags=%s\",\n tcp.flags)\n self.client = self.ip_src\n self.server = self.ip_dst\n self.packet_direction = 'c2s'\n self.verified_direction = 0\n #*** Neither direction found, so add to FCIP database:\n self.fcip_doc = {'hash': self.fcip_hash,\n 'ip_A': self.ip_src,\n 'ip_B': self.ip_dst,\n 'port_A': self.tcp_src,\n 'port_B': self.tcp_dst,\n 'proto': proto,\n 'finalised': 0,\n 'packet_count': 1,\n 'latest_timestamp' : pkt_receive_timestamp,\n 'packet_timestamps': [pkt_receive_timestamp,],\n 'tcp_flags': [tcp.flags,],\n 'packet_lengths': [self.packet_length,],\n 'client': self.client,\n 'server': self.server,\n 'packet_directions': [self.packet_direction,],\n 'verified_direction': self.verified_direction,\n 'suppressed': 0}\n self.logger.debug(\"FCIP: Adding record for %s to DB\",\n self.fcip_doc)\n db_result = self.fcip.insert_one(self.fcip_doc)\n self.packet_count = 1\n\n elif self.fcip_doc['finalised']:\n #*** The flow is already finalised just increment packet count:\n self.fcip_doc['packet_count'] += 1\n #*** Write updated FCIP data back to database:\n db_result = self.fcip.update_one({'hash': self.fcip_hash},\n {'$set': {'packet_count': self.fcip_doc['packet_count']},})\n self.packet_count = self.fcip_doc['packet_count']\n\n else:\n #*** We've found the flow in the FCIP database, now update it:\n self.logger.debug(\"FCIP: found existing record %s\", self.fcip_doc)\n #*** Rate this packet as c2s or s2c direction:\n if self.client == self.ip_src:\n self.packet_direction = 'c2s'\n elif self.client == self.ip_dst:\n self.packet_direction = 's2c'\n else:\n self.packet_direction = 'unknown'\n #*** Increment packet count. Is it at max?:\n self.fcip_doc['packet_count'] += 1\n self.packet_count = self.fcip_doc['packet_count']\n if self.fcip_doc['packet_count'] >= self.max_packet_count:\n #*** TBD:\n self.fcip_doc['finalised'] = 1\n self.logger.debug(\"Finalising...\")\n #*** Read suppressed status to variable:\n self.suppressed = self.fcip_doc['suppressed']\n #*** Read verified_direction status to variable:\n self.verified_direction = self.fcip_doc['verified_direction']\n #*** Add packet timestamps, tcp flags etc:\n self.fcip_doc['latest_timestamp'] = pkt_receive_timestamp\n self.fcip_doc['packet_timestamps'].append(pkt_receive_timestamp)\n self.fcip_doc['tcp_flags'].append(tcp.flags)\n self.fcip_doc['packet_lengths'].append(self.packet_length)\n self.fcip_doc['packet_directions'].append(self.packet_direction)\n #*** Write updated FCIP data back to database:\n db_result = self.fcip.update_one({'hash': self.fcip_hash},\n {'$set': {'packet_count': self.fcip_doc['packet_count'],\n 'finalised': self.fcip_doc['finalised'],\n 'packet_timestamps': self.fcip_doc['packet_timestamps'],\n 'tcp_flags': self.fcip_doc['tcp_flags'],\n 'packet_lengths': self.fcip_doc['packet_lengths'],\n 'packet_directions': self.fcip_doc['packet_directions']\n },})\n #*** Tests:\n self.logger.debug(\"max_packet_size is %s\", self.max_packet_size())\n self.logger.debug(\"max_interpacket_interval is %s\",\n self.max_interpacket_interval())\n self.logger.debug(\"min_interpacket_interval is %s\",\n self.min_interpacket_interval())","def get_available_portoffset(target=\"localhost\"):\n target_ip = socket.gethostbyname(target)\n for portoffset in range(10000, 61000, 1000):\n i = portoffset + 873\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n result = sock.connect_ex((target_ip, i))\n sock.close()\n if result != 0:\n logger.debug(\"port open {0}\".format(portoffset))\n return portoffset\n return None","def iplayer_from_raw(raw, linktype=1):\n if linktype == 1: # ethernet\n pkt = dpkt.ethernet.Ethernet(raw)\n ip = pkt.data\n elif linktype == 101: # raw\n ip = dpkt.ip.IP(raw)\n else:\n raise Exception(\"unknown PCAP linktype\")\n return ip","def mptcp_connections(self, pkts):\n\t\tcount = 0\n\t\t#MPTCP_Capable = 0x0\n\t\t#MPTCP_CapableACK ---> successful handshake\n\t\tprint \"======================================================================\"\n\t\tprint \"Successful Handshake --- Look for Ack packets with MPTCP option Header\"\n\t\tprint \"\"\"Token = connectionID = SHA1(key)[0-32] of Other party's key. (Capture from\n\t\t either step 2 or 3 in the first handshake)\"\"\"\n\t\tprint \"Total packets: %s\" % len(pkts)\n\t\tprint \"======================================================================\"\n\t\tprint \"Identifying MPTCP Connections....\"\n\t\tfor i in range(len(pkts)):\n\t\t\tif(MPTCP_CapableACK in pkts[i] and pkts[i][TCPOption_MP].mptcp.subtype == 0):\n\t\t\t\tcount +=1 #Count the number of distinct MPTCP connections\n\t\t\t\t\n\t\t\t\t#Compute the receiver's token\n\t\t\t\tself.key_rcv = pkts[i][TCPOption_MP].mptcp.rcv_key\n\t\t\t\tself.rcv_token, self.rcv_dsn = self.key2tokenAndDSN(self.key_rcv)\n\n\t\t\t\t#Compute the sender's token\n\t\t\t\tself.key_snd = pkts[i][TCPOption_MP].mptcp.snd_key\n\t\t\t\tself.snd_token, self.snd_dsn = self.key2tokenAndDSN(self.key_snd)\n\n\t\t\t\tprint (\"%i. New MPTCP Connection (Successful Handshake) src: %s; dest: %s; Sender's key: %s; Receiver's key: %s; Receivers Token (connectionID): %s; Sender's Token: %s\" % (count, pkts[i][IP].src, pkts[i][IP].dst, pkts[i][TCPOption_MP].mptcp.snd_key, pkts[i][TCPOption_MP].mptcp.rcv_key, self.rcv_token, self.snd_token))\n\t\tprint \"Total MPTCP Connections: %i\" % count","def sniff_ip(time_to_sniff):\r\n ip_dict = dict()\r\n port_dict = dict()\r\n packets = sniff(timeout=time_to_sniff, filter=\"ip\")\r\n\r\n for i in packets:\r\n sport = 0\r\n src = i['IP'].src\r\n\r\n if \"TCP\" in i:\r\n sport = i['TCP'].sport\r\n\r\n elif \"UDP\" in i:\r\n sport = i['UDP'].sport\r\n\r\n if not src in ip_dict.keys():\r\n ip_dict[src] = 1\r\n\r\n else:\r\n ip_dict[src] += 1\r\n\r\n if sport:\r\n if not sport in port_dict.keys():\r\n port_dict[sport] = 1\r\n\r\n else:\r\n port_dict[sport] += 1\r\n\r\n return ip_dict, port_dict","def handle_udp(pkt, packets, i, start_point):\r\n src_port = int(pkt[start_point:start_point + 4], 16)\r\n start_point += 4\r\n dest_port = int(pkt[start_point:start_point + 4], 16)\r\n start_point += 4\r\n length = int(pkt[start_point:start_point+4], 16)\r\n start_point += 4\r\n checksum_value = pkt[start_point:start_point+4]\r\n packets[i][2].append(src_port)\r\n packets[i][2].append(dest_port)\r\n packets[i][2].append(length)\r\n packets[i][2].append(checksum_value)\r\n\r\n return packets","def MatchIpAddressInArpPackets(self):\n return self._get_attribute('matchIpAddressInArpPackets')","def group_packets(self, packets):\n sessions = packets.sessions() # groups connections from X to Y as a Scapy PacketList in a dict\n # example: dict['TCP 172.217.17.102:443 > 10.7.2.60:38386'] = PacketList\n\n session_keys = list(sessions.keys()) # force copy so we can alter the dictionary at runtime\n for key in session_keys:\n reversed_key = self.reverse_dict_key(key)\n if(reversed_key != key and sessions.__contains__(reversed_key)):\n sessions[key] += sessions.pop(reversed_key)\n session_keys.remove(reversed_key)\n\n return self.sort_grouped_packets(list(sessions.values()))","def get_connections(self, kind='inet'):\r\n # Note: in case of UNIX sockets we're only able to determine the\r\n # local bound path while the remote endpoint is not retrievable:\r\n # http://goo.gl/R3GHM\r\n inodes = {}\r\n # os.listdir() is gonna raise a lot of access denied\r\n # exceptions in case of unprivileged user; that's fine:\r\n # lsof does the same so it's unlikely that we can to better.\r\n for fd in os.listdir(\"/proc/%s/fd\" % self.pid):\r\n try:\r\n inode = os.readlink(\"/proc/%s/fd/%s\" % (self.pid, fd))\r\n except OSError:\r\n continue\r\n if inode.startswith('socket:['):\r\n # the process is using a socket\r\n inode = inode[8:][:-1]\r\n inodes[inode] = fd\r\n\r\n if not inodes:\r\n # no connections for this process\r\n return []\r\n\r\n def process(fin, family, type_):\r\n retlist = []\r\n try:\r\n f = open(fin, 'r')\r\n except IOError:\r\n # IPv6 not supported on this platform\r\n err = sys.exc_info()[1]\r\n if err.errno == errno.ENOENT and fin.endswith('6'):\r\n return []\r\n else:\r\n raise\r\n try:\r\n f.readline() # skip the first line\r\n for line in f:\r\n # IPv4 / IPv6\r\n if family in (socket.AF_INET, socket.AF_INET6):\r\n _, laddr, raddr, status, _, _, _, _, _, inode = \\\r\n line.split()[:10]\r\n if inode in inodes:\r\n laddr = self._decode_address(laddr, family)\r\n raddr = self._decode_address(raddr, family)\r\n if type_ == socket.SOCK_STREAM:\r\n status = _TCP_STATES_TABLE[status]\r\n else:\r\n status = \"\"\r\n fd = int(inodes[inode])\r\n conn = nt_connection(fd, family, type_, laddr,\r\n raddr, status)\r\n retlist.append(conn)\r\n elif family == socket.AF_UNIX:\r\n tokens = line.split()\r\n _, _, _, _, type_, _, inode = tokens[0:7]\r\n if inode in inodes:\r\n\r\n if len(tokens) == 8:\r\n path = tokens[-1]\r\n else:\r\n path = \"\"\r\n fd = int(inodes[inode])\r\n type_ = int(type_)\r\n conn = nt_connection(fd, family, type_, path,\r\n None, \"\")\r\n retlist.append(conn)\r\n else:\r\n raise ValueError(family)\r\n return retlist\r\n finally:\r\n f.close()\r\n\r\n tcp4 = (\"tcp\" , socket.AF_INET , socket.SOCK_STREAM)\r\n tcp6 = (\"tcp6\", socket.AF_INET6, socket.SOCK_STREAM)\r\n udp4 = (\"udp\" , socket.AF_INET , socket.SOCK_DGRAM)\r\n udp6 = (\"udp6\", socket.AF_INET6, socket.SOCK_DGRAM)\r\n unix = (\"unix\", socket.AF_UNIX, None)\r\n\r\n tmap = {\r\n \"all\" : (tcp4, tcp6, udp4, udp6, unix),\r\n \"tcp\" : (tcp4, tcp6),\r\n \"tcp4\" : (tcp4,),\r\n \"tcp6\" : (tcp6,),\r\n \"udp\" : (udp4, udp6),\r\n \"udp4\" : (udp4,),\r\n \"udp6\" : (udp6,),\r\n \"unix\" : (unix,),\r\n \"inet\" : (tcp4, tcp6, udp4, udp6),\r\n \"inet4\": (tcp4, udp4),\r\n \"inet6\": (tcp6, udp6),\r\n }\r\n if kind not in tmap:\r\n raise ValueError(\"invalid %r kind argument; choose between %s\"\r\n % (kind, ', '.join([repr(x) for x in tmap])))\r\n ret = []\r\n for f, family, type_ in tmap[kind]:\r\n ret += process(\"/proc/net/%s\" % f, family, type_)\r\n # raise NSP if the process disappeared on us\r\n os.stat('/proc/%s' % self.pid)\r\n return ret","def __iter__(self) -> Iterator[packets.Packet]:\n for packet in self._packets:\n yield packet\n for pointer in self._packet_pointers:\n yield pointer.get()","def sniff_packets(iface=None):\n if iface: # (http)\n sniff(filter=\"port 80\", prn=process_packet, iface=iface, store=False)\n # 'process_packet' is the callback\n else:\n sniff(filter=\"port 80\", prn=process_packet, store=False)\n # default interface","def start_pcap(host, pcap_file_name, interface, pcap_args='',\n func_ip=None, tool_path=None):\n with LydianClient(_get_host_ip(host, func_ip)) as client:\n client.pcap.start_pcap(pcap_file_name, interface, pcap_args, tool_path)","def sniff_traffic(hs, count, timeout, traffic_type, pkt_type, exp_dst, step):\n iface = hs.ports['eth1']\n step('Scapy capture started')\n if (traffic_type == \"encap\"):\n packet = hs.libs.scapy.sniff2(\"iface='{}', count={}, timeout={}, \"\n \" filter='port 4789 and (!icmp or !ip6)', \"\n \" prn=lambda x:x.show()\".format(\n iface, count, timeout), True)\n parse_packet(packet, traffic_type, pkt_type, exp_dst, step=step)\n elif (traffic_type == \"decap\"):\n packet = hs.libs.scapy.sniff2(\"iface='{}', count={}, \"\n \" timeout={}, filter='!icmp or !ip6', \"\n \" prn=lambda x:x.show()\".format(\n iface, count, timeout), True)\n parse_packet(packet, traffic_type, pkt_type, exp_dst, step=step)","def offline_pcap_input_to_pipe(pcap=None, p=None, quick=False):\n\n tdm_cnt = 0\n\n print(\"Offline mode: Reading TDMs from PCAP/PCAPNG file and writing to pipe.\")\n pkt_list = rdpcap(pcap) # read the PCAP/PCAPNG file and return a list of packets\n\n # Parse the Packet List for TmNS Data Messages (TDMs), and write them to the binary TDM file\n print(\"Named Pipe '{0}' has been opened for writing. Waiting for Pipe Reader to connect.\".format(p))\n pipeout = open(p, 'wb')\n print(\"Connected to Named Pipe '{0}'. Writing binary TDMs into pipe.\".format(p))\n\n delta_from_current_time = time.time() - pkt_list[0].time\n\n try:\n for pkt in pkt_list:\n if pkt[UDP].dport == TDM_PORT:\n if quick is False:\n while (pkt.time + delta_from_current_time) > time.time():\n sleep(0.0001)\n pipeout.write(bytes(pkt[UDP].payload))\n tdm_cnt += 1\n print(\"\\rTDM Count: {0}\".format(tdm_cnt), end=\" \")\n pipeout.close()\n except IOError as e:\n if e.errno == errno.EPIPE:\n print(\"\\nBroken Pipe: EPIPE\")\n print(\"\")\n\n if tdm_cnt == 0:\n print(\"ZERO TmNS Data Messages found in {0}. No data written to {1}.\".format(pcap, p))\n else:\n print(\"\\nThere were {0} TmNS Data Messages written to {1}.\".format(tdm_cnt, p))","def iterate(cls, disc, track_number):\n\n assert track_number >= 0 and track_number < len(disc.tracks)\n\n track = disc.tracks[track_number]\n\n packet_frame_size = (\n disc.audio_format.rate / cls.PACKETS_PER_SECOND)\n\n # Mock up a packet that ends at the start of index 1, so the\n # first packet generated starts at that position\n p = cls(disc, track, track_number, track.pregap_offset, 0)\n\n while True:\n # Calculate offsets of next packet\n abs_pos = p.abs_pos + p.length\n\n if abs_pos < track.pregap_offset:\n length = min(track.pregap_offset - abs_pos, packet_frame_size)\n else:\n length = min(track.length - abs_pos, packet_frame_size)\n\n assert length >= 0\n\n if length == 0:\n # Reached end of track, switch to next. Simplify this\n # code by generating a dummy packet for the next\n # iteration to work on (but don't yield it!)\n\n track_number += 1\n\n try:\n track = disc.tracks[track_number]\n except IndexError:\n # That was the last track, no more packets\n return\n\n p = cls(disc, track, track_number, 0, 0)\n\n else:\n # Generate next packet\n flags = 0\n if (track.pause_after\n and abs_pos + length == track.length\n and track_number + 1 < len(disc.tracks)):\n flags |= p.PAUSE_AFTER\n\n p = cls(disc, track, track_number, abs_pos, length, flags)\n yield p","def IperfTCP(target_src, target_dst, dst, length, window=None):\n iperf = IperfSet(target_src, target_dst, dst)\n iperf.Start(length, None, window)\n return iperf.Results()","def _process(self, buf, ts=None, pkt_num=None):\n\n if not buf:\n return\n self.pkt_num = pkt_num\n eth = dpkt.ethernet.Ethernet(buf)\n ip = eth.data\n tcp = ip.data\n sip = inet_to_str(ip.src)\n dip = inet_to_str(ip.dst)\n fin_flag = tcp.flags & 0x001\n ack_flag = tcp.flags & 0x010\n syn_flag = tcp.flags & 0x002\n rst_flag = tcp.flags & 0x004\n syn_unacceptable_states = [TCPState.ESTABLISHED, TCPState.FIN_WAIT_1, TCPState.FIN_WAIT_2,\n TCPState.CLOSING, TCPState.LAST_ACK]\n data_acceptable_states = [TCPState.ESTABLISHED, TCPState.CLOSE_WAIT]\n tcp_opts = dpkt.tcp.parse_opts(tcp.opts) if tcp.opts else None\n tcp_opts = tcp_opts_tuple_list_to_dict(tcp_opts) if tcp_opts else None\n num_pkt_session_pkt = len(self.sessions[self.session_count]) if self.session_count else 0\n\n # Only Window size can change in ACKs (in other words - after SYNs), nothing else like - window-scaling, or\n # MSS, or Selective-SYN can't be changed. If present in options after SYN, should be ignored in my opinion\n # https://superuser.com/questions/966212/does-the-sequence-number-of-tcp-packet-headers-wrap-around\n # TODO: seq number in coming packet is ahead of the expected one, then it should be held for processing\n\n def slide_window():\n\n if len(self.sessions[self.session_count]):\n if sip == self.sip:\n if self._s_mss != -1 and get_tcp_packet_payload_len_with_options(eth) > self._s_mss:\n return\n prev_ip = dpkt.ethernet.Ethernet(self.get_last_c_pkt()).data\n rcv_nxt = self._s_rcv_next\n win_left_end = self._s_win_left_edge\n early_pkts = self._s_early_pkts\n other_end_win_size = self._s_win_size\n current_state = self._c_state\n else:\n if self._c_mss != -1 and get_tcp_packet_payload_len_with_options(ip) > self._c_mss:\n return\n prev_ip = dpkt.ethernet.Ethernet(self.get_last_s_pkt()).data\n rcv_nxt = self._c_rcv_next\n win_left_end = self._c_win_left_edge\n early_pkts = self._c_early_pkts\n other_end_win_size = self._c_win_size\n current_state = self._s_state\n if self._print_debug_info:\n logger.debug(self.client_server_next_rcv(), tcp_pkt_debug_info(ip))\n prev_tcp = prev_ip.data\n prev_tcp_data_offset = prev_tcp.off * 4\n prev_ip_header_len = prev_ip.hl * 4\n prev_tcp_payload_len = prev_ip.len - (prev_tcp_data_offset + prev_ip_header_len)\n tcp_payload_len = get_tcp_packet_payload_len(ip)\n if (tcp_seq_number_in_window(win_left_end, tcp.seq, other_end_win_size) or\n tcp_seq_number_in_window(win_left_end,\n inc_tcp_seq_number(tcp.seq, tcp_payload_len), other_end_win_size)):\n if inc_tcp_seq_number(tcp.seq, tcp_payload_len) == rcv_nxt:\n \"\"\"\n \n Since there is no new payload sent, just store the tcp packet with empty payload.\n This is going to increase the packet count but not going to add duplicated data\n in session data, by session data here it means actual data sent (after discarding\n the retransmission) to application layer. To do that - we will empty out the payload,\n if packets has some, then add the packet to the session, else add the empty packet as it is\n to the session. This logic will easily handle the TCP connections supporting\n TCP Timestamp options describe in https://tools.ietf.org/html/rfc1323\n \n \"\"\"\n # one case is when seq number is < rcv_nxt but sender want to ack more data\n # which means it is sending the same data again but its acking more received content\n \"\"\"\n 1. packet has Data\n a. prev_packet has data\n A. header change (change cur packet and change previous packet) add to list\n B. no header change retransmission ( sum check)\n b. prev_packete has no data\n A. header change (change cur packet only) add to list\n B. no header change retransmission (change cur packet only)\n 2. packet has no data\n a. prev_packet has data\n A. header change (change previous packet only) add to list\n B. no header change (change previous packet only)\n b. prev_packet has no data\n A. header change (sum check) add to list\n B. no header change retransmission (sum check)\n \"\"\"\n if prev_tcp.sum == tcp.sum:\n cur_sum = tcp_shasum_calc(ip.src, ip.dst, ip.p, ip.data.pack())\n prev_sum = tcp_shasum_calc(prev_ip.src, prev_ip.dst, prev_ip.p, prev_ip.data.pack())\n if cur_sum == prev_sum:\n # covers 1.a.B and 2.b.B\n return\n\n empty_prev_ip = copy.deepcopy(prev_ip)\n empty_prev_tcp = empty_prev_ip.data\n empty_prev_tcp.seq = rcv_nxt\n empty_prev_ip.len -= prev_tcp_payload_len\n empty_prev_tcp.data = b\"\"\n empty_prev_ip = tcp_fix_checksum(empty_prev_ip)\n new_part_ip = copy.deepcopy(ip)\n new_part_tcp = new_part_ip.data\n new_part_tcp.data = b\"\"\n new_part_tcp.seq = rcv_nxt\n new_part_ip.len -= tcp_payload_len\n new_part_ip.sum = 0\n new_part_tcp.sum = 0\n new_part_ip = tcp_fix_checksum(new_part_ip)\n eth.data = new_part_ip\n cur_pkt = eth.pack()\n new_pkt = dpkt.ethernet.Ethernet(cur_pkt)\n new_part_ip = new_pkt.data\n new_part_tcp = new_part_ip.data\n\n \"\"\"\n Checksum comparision logic is kept to discard the straight duplicates packets\n without Timestamp Options. These kind of packet will not serve any purposes.\n If removal of these checksum comparison code blocks felt necessary, it could\n be removed -- that will add few extra retransmitted packets -- but that would\n also requrie to update the testcases built around this code blocks.\n \"\"\"\n if new_part_tcp.sum == empty_prev_tcp.sum:\n # covers 1.b.B\n # covers case 2.a.B\n if tcp_shasum_calc(ip.src, ip.dst, ip.p, ip.data.pack()) == tcp_shasum_calc(\n prev_ip.src, prev_ip.dst, prev_ip.p, empty_prev_ip.data.pack()):\n return\n \"\"\"\n needs to added to list under cases 2.a.A, 2.b.A, 1.a.A and 1.b.A\n cur_pkt is updated earlier\n \"\"\"\n if sip == self.sip:\n if inc_tcp_seq_number(self._c_rcv_next, 1) <= new_part_tcp.ack:\n self._c_rcv_next = new_part_tcp.ack\n else:\n if inc_tcp_seq_number(self._s_rcv_next, 1) <= new_part_tcp.ack:\n self._s_rcv_next = new_part_tcp.ack\n elif (current_state in data_acceptable_states and\n tcp_seq_number_in_window(tcp.seq, rcv_nxt, tcp_payload_len)):\n stale_data_len = seq_numbers_diff(tcp.seq, rcv_nxt)\n win_right_end = inc_tcp_seq_number(win_left_end, other_end_win_size)\n if tcp_seq_number_in_window(rcv_nxt, inc_tcp_seq_number(tcp.seq, tcp_payload_len),\n seq_numbers_diff(rcv_nxt, win_right_end)):\n tcp.data = tcp.data[stale_data_len:]\n else:\n allowed_payload_size = seq_numbers_diff(rcv_nxt, win_right_end)\n remaining_eth = dpkt.ethernet.Ethernet(eth.pack())\n #remaining_ip = eth.data\n #remaining_tcp = remaining_ip.data\n remaining_eth.data.data.seq = inc_tcp_seq_number(tcp.seq, stale_data_len + allowed_payload_size)\n remaining_eth.data.data.data = tcp.data[stale_data_len + allowed_payload_size:]\n remaining_eth.data.len -= stale_data_len + allowed_payload_size\n remaining_eth.data = tcp_fix_checksum(remaining_eth.data)\n #remaining_eth.data = remaining_ip\n tcp.data = tcp.data[stale_data_len: stale_data_len + allowed_payload_size]\n if self.sip == sip:\n self._s_early_pkts.append(((ts, self.pkt_num), remaining_eth.pack()))\n else:\n self._c_early_pkts.append(((ts, self.pkt_num), remaining_eth.pack()))\n tcp.sum = 0\n # ip.len -= stale_data_len\n tcp.seq = rcv_nxt\n ip.data = tcp\n ip.sum = 0\n eth.data = ip\n cur_pkt = eth.pack()\n if sip == self.sip:\n self._s_rcv_next = inc_tcp_seq_number(self._s_rcv_next,\n (ip.len - (ip.hl * 4 + tcp.off * 4)))\n else:\n self._c_rcv_next = inc_tcp_seq_number(self._c_rcv_next,\n (ip.len - (ip.hl * 4 + tcp.off * 4)))\n elif (current_state in data_acceptable_states and\n tcp_seq_number_in_window(rcv_nxt, tcp.seq, other_end_win_size)):\n # hold it for further processing\n if self.sip == sip:\n self._s_early_pkts.append(((ts, self.pkt_num), buf))\n else:\n self._c_early_pkts.append(((ts, self.pkt_num), buf))\n return\n else:\n return\n self.sessions[self.session_count].append(((ts, self.pkt_num), cur_pkt))\n # as this packet is accepted, might need to update the rwnd size and left end of rwnd\n if sip == self.sip:\n self._c_payload_size += len(eth.data.data.data)\n logger.debug(\"Client send data size: {}. Accepted data size is: {}.\"\n \" Total data sent from client is: {}\".format(\n len(tcp.data), len(eth.data.data.data), self._c_payload_size))\n self._c_prev_pkt_ind = len(self.sessions[self.session_count]) - 1\n rcv_nxt = self._s_rcv_next\n if (not tcp.ack == self._c_win_left_edge and\n tcp_seq_number_in_window(inc_tcp_seq_number(self._c_win_left_edge, 1),\n tcp.ack, self._c_win_size)):\n self._c_win_left_edge = tcp.ack\n self._c_win_size = tcp.win << self._c_win_scaling_factor\n else:\n self._s_payload_size += len(eth.data.data.data)\n logger.debug(\"Server send data of size: {}. Accepted data size is: {}.\"\n \" Total data sent from server is: {}\".format(\n len(tcp.data), len(eth.data.data.data), self._s_payload_size))\n self._s_prev_pkt_ind = len(self.sessions[self.session_count]) - 1\n rcv_nxt = self._c_rcv_next\n # left edge is incremented by one becuase in_window function checks for inclusive seq number\n # starting at left edge but ACK tells what's the next expected seq number, which could be 1 next\n # to the end of window\n if (not tcp.ack == self._s_win_left_edge and\n tcp_seq_number_in_window(inc_tcp_seq_number(self._s_win_left_edge, 1),\n tcp.ack, self._s_win_size)):\n self._s_win_left_edge = tcp.ack\n self._s_win_size = tcp.win << self._s_win_scaling_factor\n # check if packet at the head of queue is ready to be processed\n while True:\n if len(early_pkts) == 0:\n break\n (_ts, _pkt_num), _buf = early_pkts.popleft()\n early_eth = dpkt.ethernet.Ethernet(_buf)\n early_ip = early_eth.data\n early_tcp = early_ip.data\n if tcp_seq_number_in_window(early_tcp.seq, rcv_nxt, get_tcp_packet_payload_len(early_ip)):\n # if early_tcp.seq <= rcv_nxt:\n self._process(early_eth.pack(), _ts, _pkt_num)\n else:\n early_pkts.appendleft(((_ts, _pkt_num), early_eth.pack()))\n break\n\n \"\"\"\n TCP flags:0x000 (12 bits)\n [11 10 9 8 7 6 5 4 3 2 1 0]\n - Bit 11 10 9: reserved\n - Bit 8: nonce\n - Bit 7: CWR (Congestion window reduced)\n - Bit 6: ECN-Echo (Explicit Congestion Notification)\n - Bit 5: Urgent\n - Bit 4: ACK\n - Bit 3: Push\n - Bit 2: Reset\n - Bit 1: SYN\n - Bit 0: FIN\n \"\"\"\n\n \"\"\"TCP flags for SYN [000000010111]\"\"\"\n\n prev_c_pkt = dpkt.ethernet.Ethernet(self.get_last_c_pkt()) if self.get_last_c_pkt() else None\n prev_c_tcp = prev_c_pkt.data.data if prev_c_pkt else None\n prev_s_pkt = dpkt.ethernet.Ethernet(self.get_last_s_pkt()) if self.get_last_s_pkt() else None\n prev_s_tcp = prev_s_pkt.data.data if prev_s_pkt else None\n logger.debug(tcp_pkt_debug_info(ip))\n logger.debug(tcp_pkt_options_debug_info(tcp))\n logger.debug(\"Processing packet number: {} in the current session\".format(self.pkt_num))\n if rst_flag:\n logger.info(\"Received a RESET flag, packet info: {}\".format(tcp_pkt_debug_info(ip)))\n logger.info(\"TCP state before processing of packet: {}\".format(self.get_printable_state()))\n if self._c_state == TCPState.CLOSED and self._s_state == TCPState.LISTENING:\n self.session_count += 1\n self.sessions[self.session_count] = [((ts, self.pkt_num), buf)]\n self._c_state = self._s_state = TCPState.CLOSED\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n return\n self._c_state = self._s_state = TCPState.CLOSED\n if self.sip == sip:\n self._c_prev_pkt_ind = len(self.sessions[self.session_count])\n else:\n self._s_prev_pkt_ind = len(self.sessions[self.session_count])\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif syn_flag and (self._c_state in syn_unacceptable_states or self._s_state in syn_unacceptable_states):\n logger.info(\"Received a unacceptable SYN flag, packet info: {}\".format(tcp_pkt_debug_info(ip)))\n logger.info(\"TCP state before processing of packet: {}\".format(self.get_printable_state()))\n self._s_state = self._c_state = TCPState.CLOSED\n self.sessions[self.session_count].append(((ts,self.pkt_num), buf))\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif (self._c_state == TCPState.CLOSED and self._s_state == TCPState.LISTENING and\n self.sip == sip):\n if tcp.flags & 0x017 == 0x002:\n self.session_count += 1\n logger.info(\"number of sessions so far: {}\".format(self.session_count - 1))\n logger.info(\"starting a new session, pkt info: {}\".format(tcp_pkt_debug_info(ip)))\n logger.info(\"TCP state before processing of packet: {}\".format(self.get_printable_state()))\n self.sessions[self.session_count] = []\n self._c_prev_pkt_ind = len(self.sessions[self.session_count])\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\n self._c_state = TCPState.SYN_SENT\n self._s_state = TCPState.SYN_RECEIVED\n self._c_seq = tcp.seq\n if tcp_opts:\n if dpkt.tcp.TCP_OPT_WSCALE in tcp_opts:\n self._c_win_scaling_factor = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_WSCALE], \"big\")\n if dpkt.tcp.TCP_OPT_MSS in tcp_opts:\n self._c_mss = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_MSS], \"big\")\n else:\n self._c_win_scaling_factor = 0\n self._c_mss = -1\n self._c_win_size = tcp.win << self._c_win_scaling_factor\n logger.info(\"SYN flag from: {}:{}. Full TCP Flag is: {}\".format(self.sip, self.sp, hex(tcp.flags)))\n logger.info(\"TCP options in the packet: {}\".format(tcp_pkt_options_debug_info(tcp)))\n\n elif self._c_state == TCPState.SYN_SENT and self._s_state == TCPState.SYN_RECEIVED:\n logger.info(\"TCP packet info: {}\".format(tcp_pkt_debug_info(ip)))\n logger.info(\"TCP state before processing of packet: {}\".format(self.get_printable_state()))\n if self.sip == dip:\n exp_ack = inc_tcp_seq_number(prev_c_tcp.seq, 1)\n if not (tcp.flags & 0x017 == 0x012):\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\n self._s_state = self._c_state = TCPState.CLOSED\n logger.info(\"SYN-ACK flag is not set in the TCP flags: {} from: {}:{}\".format(hex(tcp.flags),\n self.dip, self.dp))\n return\n if tcp.ack == exp_ack:\n self._s_prev_pkt_ind = len(self.sessions[self.session_count])\n self._s_rcv_next = exp_ack\n self._s_win_left_edge = exp_ack\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\n if tcp_opts:\n if dpkt.tcp.TCP_OPT_WSCALE in tcp_opts:\n self._s_win_scaling_factor = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_WSCALE], \"big\")\n if dpkt.tcp.TCP_OPT_MSS in tcp_opts:\n self._s_mss = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_MSS], \"big\")\n else:\n self._s_win_scaling_factor = 0\n self._s_mss = -1\n self._s_win_size = tcp.win << self._s_win_scaling_factor\n logger.info(\"SYN-ACK flag from: {}:{}. Full TCP flag is: {}\".format(\n self.dip, self.dp, hex(tcp.flags)))\n logger.info(\"TCP options in the packet: {}\".format(tcp_pkt_options_debug_info(tcp)))\n elif prev_s_tcp:\n exp_ack = inc_tcp_seq_number(prev_s_tcp.seq, 1)\n if tcp.flags & 0x017 == 0x010:\n if tcp.ack == exp_ack and tcp.seq == prev_s_tcp.ack:\n self._s_state = self._c_state = TCPState.ESTABLISHED\n self._c_seq = tcp.seq\n self._c_prev_pkt_ind = len(self.sessions[self.session_count])\n self._c_rcv_next = exp_ack\n self._c_win_left_edge = exp_ack\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\n self._c_win_size = tcp.win << self._c_win_scaling_factor\n logger.info(\"TCP handshake complete.\")\n else:\n self._s_state = self._c_state = TCPState.CLOSED\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\n logger.info(\"TCP handshake was not completed.\")\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif self._c_state == TCPState.ESTABLISHED and self._s_state == TCPState.ESTABLISHED:\n if ack_flag:\n \"\"\" if ACK flag is off drop the segment as per:\n https://tools.ietf.org/html/rfc793#page-37\n \"\"\"\n logger.debug(tcp_pkt_debug_info(ip))\n logger.debug(tcp_pkt_options_debug_info(tcp))\n num_pkt_session_pkt = len(self.sessions[self.session_count])\n slide_window()\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and fin_flag:\n logger.info(\"Received a FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n if self.sip == sip:\n self._c_state = TCPState.FIN_WAIT_1\n else:\n self._s_state = TCPState.FIN_WAIT_1\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif self._c_state == TCPState.FIN_WAIT_1 and self._s_state == TCPState.ESTABLISHED:\n if ack_flag:\n slide_window()\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and sip == self.dip:\n if inc_tcp_seq_number(prev_c_tcp.seq, max(get_tcp_packet_payload_len(prev_c_pkt), 1)) == tcp.ack:\n logger.info(\"Received a ACK for FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._c_state = TCPState.FIN_WAIT_2\n self._s_state = TCPState.CLOSE_WAIT\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n if fin_flag:\n logger.info(\"Received FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n if self._c_state == TCPState.FIN_WAIT_1:\n self._s_state = self._c_state = TCPState.CLOSING\n else:\n self._s_state = TCPState.LAST_ACK\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif self._s_state == TCPState.FIN_WAIT_1 and self._c_state == TCPState.ESTABLISHED:\n if ack_flag:\n slide_window()\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and sip == self.sip:\n if inc_tcp_seq_number(prev_s_tcp.seq, max(get_tcp_packet_payload_len(prev_s_pkt), 1)) == tcp.ack:\n logger.info(\"Received a ACK for FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._s_state = TCPState.FIN_WAIT_2\n self._c_state = TCPState.CLOSE_WAIT\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n if fin_flag:\n logger.info(\"Received FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n if self._s_state == TCPState.FIN_WAIT_1:\n self._s_state = self._c_state = TCPState.CLOSING\n else:\n self._c_state = TCPState.LAST_ACK\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif self._c_state == TCPState.FIN_WAIT_2:\n if sip == self.sip:\n if ack_flag:\n slide_window()\n if self._s_state == TCPState.LAST_ACK:\n if (num_pkt_session_pkt < len(self.sessions[self.session_count]) and\n inc_tcp_seq_number(prev_s_tcp.seq,\n max(get_tcp_packet_payload_len(prev_s_pkt), 1)) == tcp.ack):\n logger.info(\"ACKed FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._c_state = self._s_state = TCPState.CLOSED\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n else:\n if self._s_state == TCPState.CLOSE_WAIT and ack_flag:\n slide_window()\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and fin_flag:\n logger.info(\"Received FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._s_state = TCPState.LAST_ACK\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif self._s_state == TCPState.FIN_WAIT_2:\n if sip == self.dip:\n if ack_flag:\n slide_window()\n if (self._c_state == TCPState.LAST_ACK and\n num_pkt_session_pkt < len(self.sessions[self.session_count]) and\n inc_tcp_seq_number(prev_c_tcp.seq,\n max(get_tcp_packet_payload_len(prev_c_pkt), 1)) == tcp.ack):\n logger.info(\"ACKed FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._s_state = self._c_state = TCPState.CLOSED\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n else:\n if self._c_state == TCPState.CLOSE_WAIT and ack_flag:\n slide_window()\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and fin_flag:\n logger.info(\"Received FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._c_state = TCPState.LAST_ACK\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n elif self._c_state == TCPState.CLOSING or self._s_state == TCPState.CLOSING:\n if ack_flag:\n slide_window()\n if sip == self.sip and num_pkt_session_pkt < len(self.sessions[self.session_count]):\n if inc_tcp_seq_number(ack_flag and prev_s_tcp.seq, 1) == tcp.ack:\n logger.info(\"ACKed FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._s_state = TCPState.CLOSED\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n else:\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and \\\n inc_tcp_seq_number(ack_flag and prev_c_tcp.seq, 1) == tcp.ack:\n logger.info(\"ACKed FIN flag: {}\".format(tcp_pkt_debug_info(ip)))\n self._c_state = TCPState.CLOSED\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n else:\n logger.info(\"Packet didn't match any valid state: {}\".format(tcp_pkt_debug_info(ip)))\n #self._s_state = self._c_state = TCPState.CLOSED\n logger.info(\"TCP state after processing of packet: {}\".format(self.get_printable_state()))\n logger.debug(self.get_printable_state())","def start_capture(self, interface, count=0, ptype='', pfilter=None,\n callback=None, callback_args=None, save_dump_file=False,\n save_dump_filename=None):\n tcpd = (self.packet_captures[interface]\n if interface in self.packet_captures\n else TCPDump())\n \"\"\" :type: TCPDump \"\"\"\n\n self.LOG.debug('Starting tcpdump on host: ' + self.name)\n\n old_log = self.cli.log_cmd\n if self.debug:\n self.cli.log_cmd = True\n\n tcpd.start_capture(cli=self.cli, interface=interface, count=count,\n packet_type=ptype, pcap_filter=pfilter,\n callback=callback, callback_args=callback_args,\n save_dump_file=save_dump_file,\n save_dump_filename=save_dump_filename,\n blocking=False)\n\n self.cli.log_cmd = old_log\n\n self.packet_captures[interface] = tcpd","def tcp_pkt_debug_info(pkt: dpkt.ip.IP) -> str:\n if isinstance(pkt, dpkt.ip.IP):\n paylod_len = pkt.len - (4 * pkt.hl) - (4 * pkt.data.off)\n return \"{}:{}-> {}:{}, seq: {}, ack:{}, flag:{}, payload len: {}, payload: {}, sum: {}\".format(\n inet_to_str(pkt.src), pkt.data.sport, inet_to_str(pkt.dst), pkt.data.dport, hex(pkt.data.seq),\n hex(pkt.data.ack), hex(pkt.data.flags), hex(paylod_len), pkt.data.data, hex(pkt.data.sum))","def process_pkt_from_client(ts_delta, acks, nb_acks, connections, tcp, ip, saddr, daddr, sport, dport, fin_flag):\n if acks[saddr, sport, daddr, dport][co.S2C] >= 0:\n conn_id = acks[saddr, sport, daddr, dport][co.CONN_ID]\n connections[conn_id].flow.attr[co.S2C][co.TIME_LAST_ACK_TCP] = ts_delta\n if fin_flag:\n connections[conn_id].flow.attr[co.S2C][co.TIME_FIN_ACK_TCP] = ts_delta\n\n bytes_acked = (tcp.ack - acks[saddr, sport, daddr, dport][co.S2C]) % 4294967296\n if bytes_acked >= 2000000000:\n # Ack of 2GB or more is just not possible here\n return\n\n increment_value_dict(nb_acks[co.S2C][conn_id], bytes_acked)\n size_payload = ip.len - ip.hl * 4 - tcp.off * 4\n\n # If SOCKS command\n if size_payload == 7 and connections[conn_id].attr.get(co.SOCKS_PORT, None) is None:\n crypted_socks_cmd = tcp.data\n # This is possible because of packet stripping\n if len(crypted_socks_cmd) == 7:\n decrypted_socks_cmd = socks_parser.decode(crypted_socks_cmd)\n if decrypted_socks_cmd[0] == b'\\x01': # Connect\n connections[conn_id].attr[co.SOCKS_DADDR] = socks_parser.get_ip_address(decrypted_socks_cmd)\n connections[conn_id].attr[co.SOCKS_PORT] = socks_parser.get_port_number(decrypted_socks_cmd)\n\n if size_payload > 0 and tcp.seq in acks[saddr, sport, daddr, dport][SEQ_C2S]:\n # This is a retransmission! (take into account the seq overflow)\n connections[conn_id].flow.attr[co.C2S][co.TIME_LAST_PAYLD_WITH_RETRANS_TCP] = ts_delta\n connections[conn_id].flow.attr[co.C2S][co.TIMESTAMP_RETRANS].append((ts_delta,\n ts_delta - acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq][0],\n ts_delta - acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq][1],\n ts_delta - acks[saddr, sport, daddr, dport][co.TIMESTAMP][CLIENT]))\n acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq][1] = ts_delta\n elif size_payload > 0:\n acks[saddr, sport, daddr, dport][SEQ_C2S].add(tcp.seq)\n connections[conn_id].flow.attr[co.C2S][co.TIME_LAST_PAYLD_WITH_RETRANS_TCP] = ts_delta\n connections[conn_id].flow.attr[co.C2S][co.TIME_LAST_PAYLD_TCP] = ts_delta\n acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq] = [ts_delta, ts_delta]\n # Don't think will face this issue\n# if len(acks[saddr, sport, daddr, dport][SEQ][co.C2S]) >= 3000000:\n# for x in range(50000):\n# acks[saddr, sport, daddr, dport][SEQ][co.C2S].popleft()\n\n acks[saddr, sport, daddr, dport][co.S2C] = tcp.ack\n acks[saddr, sport, daddr, dport][co.TIMESTAMP][CLIENT] = ts_delta","def query_sniff(pkt):\n if IP in pkt:\n ip_src = pkt[IP].src\n ip_dst = pkt[IP].dst\n\n if pkt.haslayer(DNS) and pkt.getlayer(DNS).qr == 0:\n domain = pkt.getlayer(DNS).qd.qname.decode(\"utf-8\")\n now = datetime.now()\n stored_dns_requests.update({datetime.timestamp(now): domain})\n print(\"SRC: {} - DST: {} : {}\".format(ip_src, ip_dst, domain))","def _get_next_packet(self):\n raise NotImplementedError(\"Do not instantiate csvAbstractReader directly.\")","def receive_captured_list(self):\n reply = self.socket.recv(4096)\n print(\"Pokemon capturados\")\n print(reply[1:].decode())","def scan(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast/arp_request\n answered_list = scapy.srp(arp_request_broadcast , timeout = 1, verbose=False)[0]\n target_list=[]\n for element in answered_list:\n target_dict = {\"ip\":element[1].psrc, \"mac\":element[1].hwsrc}\n target_list.append(target_dict)\n return target_list","def processpacket(p):\n\n\tglobal SynSentToTCPService\n\tglobal SynAckSentToTCPClient\n\tglobal LiveTCPService\n\tglobal LiveTCPClient\n\tglobal LiveUDPService\n\tglobal LiveUDPClient\n\tglobal NmapServerDescription\n\tglobal ManualServerDescription\n\tglobal ClientDescription\n\tglobal MacAddr\n\tglobal OSDescription\n\tglobal ServiceFPs\n\tglobal SipPhoneMatch\n\tglobal Devel\n\tglobal IsRouter\n\tglobal DNSRecord\n\tglobal HostIPs\n\n\tif (type(p) == Dot3) and (type(p['LLC']) == LLC):\n\t\tUnhandledPacket(p)\n\t\t#Spanning Tree Protocol\n\t\t#Debug(\"802.3\")\n\t\t#p.show()\n\t\t#print type(p['LLC'])\n\telif (p['Ethernet'] == None):\n\t\tDebug(\"non-ethernet packet\")\t\t#Need more details on how to handle.\n\t\tUnhandledPacket(p)\n\t\t#p.show()\n\t\t#print type(p)\n\t\t#quit()\n\telif p['Ethernet'].type == 0x0806:\t\t#ARP\n\t\t#pull arp data from here instead of tcp/udp packets, as these are all local\n\t\tif (p['ARP'].op == 1):\t\t\t#1 is request (\"who-has\")\n\t\t\tpass\n\t\tif (p['ARP'].op == 2):\t\t\t#2 is reply (\"is-at\")\n\t\t\tif (p['ARP.psrc'] != None) and (p['ARP.hwsrc'] != None):\n\t\t\t\tIPAddr=p['ARP.psrc']\n\t\t\t\tMyMac=p['ARP.hwsrc'].upper()\n\t\t\t\tif (not MacAddr.has_key(IPAddr)) or (MacAddr[IPAddr] != MyMac):\n\t\t\t\t\tReportId(\"MA\", IPAddr, 'Ethernet', MyMac, '')\n\t\t\telse:\n\t\t\t\tUnhandledPacket(p)\n\t\telse:\n\t\t\tUnhandledPacket(p)\n\telif p['Ethernet'].type == 0x0800:\t\t#IP\n\t\tsIP=str(p['IP'].src)\n\t\tdIP=str(p['IP'].dst)\n\t\t#Best to get these from arps instead; if we get them from here, we get router macs for foreign addresses.\n\t\t#if not MacAddr.has_key(sIP):\n\t\t#\tReportId(\"MA\", sIP, \"Ethernet\", p['Ethernet'].src, '')\n\t\t#if not MacAddr.has_key(dIP):\n\t\t#\tReportId(\"MA\", dIP, \"Ethernet\", p['Ethernet'].dst, '')\n\n\t\tif p['IP'].proto == 1:\t\t\t#ICMP\n\t\t\tType = p['ICMP'].type\n\t\t\tCode = p['ICMP'].code\n\n\t\t\tif (Type == 0):\t\t\t\t\t\t#Echo reply\n\t\t\t\tif (not(OSDescription.has_key(sIP))):\n\t\t\t\t\tReportId(\"IP\", sIP, \"IP\", \"live\", 'icmp echo reply')\n\t\t\telif (Type == 3) and (type(p[IPerror]) == IPerror):\t#Unreachable, check that we have an actual embedded packet\n\t\t\t\t#if (type(p[IPerror]) != IPerror):\n\t\t\t\t#\tp.show()\n\t\t\t\t#\tprint type(p[IPerror])\n\t\t\t\t#\tquit()\n\t\t\t\tOrigdIP = p[IPerror].dst\n\t\t\t\tif (Code == 0):\t\t\t\t\t#Net unreachable\n\t\t\t\t\tif (not(OSDescription.has_key(OrigdIP))):\n\t\t\t\t\t\tReportId(\"IP\", OrigdIP, \"IP\", \"dead\", 'net unreachable')\n\t\t\t\t\tif (not(IsRouter.has_key(sIP))):\n\t\t\t\t\t\tReportId(\"RO\", sIP, \"NetUn\", \"router\", \"\")\n\t\t\t\telif (Code == 1):\t\t\t\t#Host unreachable\n\t\t\t\t\tif (not(OSDescription.has_key(OrigdIP))):\n\t\t\t\t\t\tReportId(\"IP\", OrigdIP, \"IP\", \"dead\", 'host unreachable')\n\t\t\t\t\tif (not(IsRouter.has_key(sIP))):\n\t\t\t\t\t\tReportId(\"RO\", sIP, \"HostUn\", \"router\", \"\")\n\t\t\t\telif (Code == 3) and (p[IPerror].proto == 17):\t#Port unreachable and embedded protocol = 17, UDP, as it should be\n\t\t\t\t\tDNSServerLoc = p[IPerror].src + \",UDP_53\"\n\t\t\t\t\tif (p[UDPerror].sport == 53) and (ManualServerDescription.has_key(DNSServerLoc)) and (ManualServerDescription[DNSServerLoc] == \"dns/server\"):\n\t\t\t\t\t\t#If orig packet coming from 53 and coming from a dns server, don't do anything (closed port on client is a common effect)\n\t\t\t\t\t\t#Don't waste time on port unreachables going back to a dns server; too common, and ephemeral anyways.\n\t\t\t\t\t\tpass\n\t\t\t\t\telse:\n\t\t\t\t\t\t#If orig packet coming from something other than 53, or coming from 53 and NOT coming from a dns server, log as closed\n\t\t\t\t\t\tOrigDPort = str(p[UDPerror].dport)\n\t\t\t\t\t\tOrigDstService = OrigdIP + \",UDP_\" + OrigDPort\n\t\t\t\t\t\tif ((not LiveUDPService.has_key(OrigDstService)) or (LiveUDPService[OrigDstService] == True)):\n\t\t\t\t\t\t\tLiveUDPService[OrigDstService] = False\n\t\t\t\t\t\t\tReportId(\"US\", OrigdIP, \"UDP_\" + OrigDPort, \"closed\", \"port unreachable\")\n\t\t\t\telif (Code == 3) and (p[IPerror].proto == 6) and (p[TCPerror].dport == 113):\t#Port unreachable and embedded protocol = 6, TCP, which it shouldn't. May be the same firewall providing the TCP FR's\n\t\t\t\t\tpass\n\t\t\t\telif (Code == 6):\t\t\t\t#Net unknown\n\t\t\t\t\tif (not(OSDescription.has_key(OrigdIP))):\n\t\t\t\t\t\tReportId(\"IP\", OrigdIP, \"IP\", \"dead\", 'net unknown')\n\t\t\t\telif (Code == 7):\t\t\t\t#Host unknown\n\t\t\t\t\tif (not(OSDescription.has_key(OrigdIP))):\n\t\t\t\t\t\tReportId(\"IP\", OrigdIP, \"IP\", \"dead\", 'host unknown')\n\t\t\t\telif (Code == 9):\t\t\t\t#Network Administratively Prohibited\n\t\t\t\t\tpass\t\t\t\t\t#Can't tell much from this type of traffic. Possibly list as firewall?\n\t\t\t\telif (Code == 10):\t\t\t\t#Host Administratively Prohibited\n\t\t\t\t\tpass\n\t\t\t\telif (Code == 11):\t\t\t\t#Network unreachable for TOS\n\t\t\t\t\tpass\n\t\t\t\telif (Code == 12):\t\t\t\t#Host unreachable for TOS\n\t\t\t\t\tpass\n\t\t\t\telif (Code == 13):\t\t\t\t#Communication Administratively prohibited\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (Type == 8):\t\t\t\t\t#ping\n\t\t\t\t#FIXME - check payload for ping sender type, perhaps\n\t\t\t\tpass\n\t\t\telif (Type == 11):\t\t\t\t\t#Time exceeded\n\t\t\t\tif (Code == 0):\t\t\t\t\t#TTL exceeded\n\t\t\t\t\tif (not(IsRouter.has_key(sIP))):\n\t\t\t\t\t\t#FIXME - put original target IP as column 5?\n\t\t\t\t\t\tReportId(\"RO\", sIP, \"TTLEx\", \"router\", \"\")\n\t\t\t\telse:\n\t\t\t\t\tUnhandledPacket(p)\n\t\t\telse:\n\t\t\t\tUnhandledPacket(p)\n\t\telif p['IP'].proto == 2:\t\t#IGMP\n\t\t\tUnhandledPacket(p)\n\t\telif p['IP'].proto == 6:\t\t#TCP\n\t\t\tsport=str(p['TCP'].sport)\n\t\t\tdport=str(p['TCP'].dport)\n\t\t\t#print p['IP'].src + \":\" + sport + \" -> \", p['IP'].dst + \":\" + dport,\n\t\t\tif (p['TCP'].flags & 0x17) == 0x12:\t#SYN/ACK (RST and FIN off)\n\t\t\t\tCliService = dIP + \",TCP_\" + sport\n\t\t\t\tif not SynAckSentToTCPClient.has_key(CliService):\n\t\t\t\t\tSynAckSentToTCPClient[CliService] = True\n\n\t\t\t\t#If we've seen a syn sent to this port and have either not seen any SA/R, or we've seen a R in the past:\n\t\t\t\t#The last test is for a service that was previously closed and is now open; report each transition once.\n\t\t\t\tService = sIP + \",TCP_\" + sport\n\t\t\t\tif ( (SynSentToTCPService.has_key(Service)) and ((not LiveTCPService.has_key(Service)) or (LiveTCPService[Service] == False)) ):\n\t\t\t\t\tLiveTCPService[Service] = True\n\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", '')\n\t\t\telif (p['TCP'].flags & 0x17) == 0x02:\t#SYN (ACK, RST, and FIN off)\n\t\t\t\tService = dIP + \",TCP_\" + dport\n\t\t\t\tif not SynSentToTCPService.has_key(Service):\n\t\t\t\t\tSynSentToTCPService[Service] = True\n\t\t\t\t#Debug(\"trying to fingerprint \" + sIP)\n\t\t\t\ttry:\n\t\t\t\t\tp0fdata = p0f(p)\n\t\t\t\t\t#FIXME - reasonably common occurence, don't whine, just fix it.\n\t\t\t\t\t#if (len(p0fdata) >1):\n\t\t\t\t\t#\tDebug(\"More than one OS fingerprint for \" + sIP + \", using the first.\")\n\t\t\t\t\tif (len(p0fdata) >=1):\n\t\t\t\t\t\tPDescription = p0fdata[0][0] + \" \" + p0fdata[0][1] + \" (\" + str(int(p0fdata[0][2]) + 1)\t#FIXME - Grabbing just the first candidate, may need to compare correlation values; provided?\n\t\t\t\t\t\tif (p0fdata[0][2] == 0):\n\t\t\t\t\t\t\tPDescription = PDescription + \" hop away)\"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tPDescription = PDescription + \" hops away)\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t#[N][2] param appears to be distance away in hops (but add 1 to this to get real hop count?)\n\t\t\t\t\t\tPDescription = PDescription.replace(',', ';')\t\t#Commas are delimiters in output\n\t\t\t\t\t\tif (not(OSDescription.has_key(sIP))) or (OSDescription[sIP] != PDescription):\n\t\t\t\t\t\t\tOSDescription[sIP] = PDescription\n\t\t\t\t\t\t\tReportId(\"IP\", sIP, \"IP\", \"live\", PDescription)\n\t\t\t\texcept:\n\t\t\t\t\tPDescription = 'p0f failure'\n\t\t\t\t\tif (not(OSDescription.has_key(sIP))) or (OSDescription[sIP] != PDescription):\n\t\t\t\t\t\tDebug(\"P0f failure in \" + sIP + \":\" + sport + \" -> \" + dIP + \":\" + dport)\n\t\t\t\t\t\tOSDescription[sIP] = PDescription\n\t\t\t\t\t\tReportId(\"IP\", sIP, \"IP\", \"live\", PDescription)\n\t\t\telif (p['TCP'].flags & 0x07) == 0x01:\t#FIN (SYN/RST off)\n\t\t\t\tCliService = sIP + \",TCP_\" + dport\n\t\t\t\tif ( (SynAckSentToTCPClient.has_key(CliService)) and ((not LiveTCPClient.has_key(CliService)) or (LiveTCPClient[CliService] == False)) ):\n\t\t\t\t\tLiveTCPClient[CliService] = True\n\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", '')\n\t\t\telif (p['TCP'].flags & 0x07) == 0x04:\t#RST (SYN and FIN off)\n\t\t\t\t#FIXME - handle rst going in the other direction?\n\t\t\t\tService = sIP + \",TCP_\" + sport\n\t\t\t\tif ( (SynSentToTCPService.has_key(Service)) and ((not LiveTCPService.has_key(Service)) or (LiveTCPService[Service] == True)) ):\n\t\t\t\t\tLiveTCPService[Service] = False\n\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"closed\", '')\n\t\t\telif ((p['TCP'].flags & 0x3F) == 0x15) and (sport == \"113\"):\t#FIN, RST, ACK (SYN, PSH, URG off)\n\t\t\t\t#This may be a firewall or some other device stepping in for 113 with a FIN/RST.\n\t\t\t\tpass\n\t\t\telif (p['TCP'].flags & 0x17) == 0x10:\t#ACK (RST, SYN, and FIN off)\n\t\t\t\t#FIXME - check for UnhandledPacket placement in ACK\n\t\t\t\tFromPort = sIP + \",TCP_\" + sport\n\t\t\t\tToPort = dIP + \",TCP_\" + dport\n\t\t\t\tPayload = str(p['Raw.load'])\t\t\t#For some reason this doesn't handle p['Raw'].load\n\t\t\t\tif ( (LiveTCPService.has_key(FromPort)) and (LiveTCPService[FromPort] == True) and (LiveTCPService.has_key(ToPort)) and (LiveTCPService[ToPort] == True)):\n\t\t\t\t\tprint \"Logic failure: both \" + FromPort + \" and \" + ToPort + \" are listed as live services.\"\n\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\telif ((LiveTCPService.has_key(FromPort)) and (LiveTCPService[FromPort] == True)):\t#If the \"From\" side is a known TCP server:\n\t\t\t\t\tif (not NmapServerDescription.has_key(FromPort) ):\t\t#Check nmap fingerprint strings for this server port\n\t\t\t\t\t\tif (ServiceFPs.has_key(int(sport))):\n\t\t\t\t\t\t\tfor OneTuple in ServiceFPs[int(sport)]:\n\t\t\t\t\t\t\t\tMatchObj = OneTuple[0].search(Payload)\n\t\t\t\t\t\t\t\tif (MatchObj != None):\n\t\t\t\t\t\t\t\t\t#Debugging:\n\t\t\t\t\t\t\t\t\t#FIXME - removeme once understood:\n\t\t\t\t\t\t\t\t\t#File \"/home/wstearns/med/programming/python/passer/passer.py\", line 504, in processpacket\n\t\t\t\t\t\t\t\t\t#OutputDescription = OutputDescription.replace('$' + str(Index), MatchObj.group(Index))\n\t\t\t\t\t\t\t\t\t#TypeError: expected a character buffer object\n\t\t\t\t\t\t\t\t\tif (OneTuple[1] == None):\n\t\t\t\t\t\t\t\t\t\tDebug(\"Null description for \" + OneTuple[0])\n\t\t\t\t\t\t\t\t\t\t#quit()\n\t\t\t\t\t\t\t\t\tOutputDescription = OneTuple[1]\n\t\t\t\t\t\t\t\t\tif len(MatchObj.groups()) >= 1:\n\t\t\t\t\t\t\t\t\t\t#We have subexpressions matched, these need to be inserted into the description string\n\t\t\t\t\t\t\t\t\t\tfor Index in range(1,len(MatchObj.groups())+1):\n\t\t\t\t\t\t\t\t\t\t\t#Example: Replace \"$1\" with MatchObj.group(1)\n\t\t\t\t\t\t\t\t\t\t\tOutputDescription = OutputDescription.replace('$' + str(Index), str(MatchObj.group(Index)))\n\t\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", OutputDescription)\n\t\t\t\t\t\t\t\t\tNmapServerDescription[sIP + \",TCP_\" + sport] = OutputDescription\n\t\t\t\t\t\t\t\t\tbreak\t\t\t\t\t#Exit for loop, no need to check any more fingerprints now that we've found a match\n\n\t\t\t\t\tif (not NmapServerDescription.has_key(FromPort)):\t\t#If the above loop didn't find a server description\n\t\t\t\t\t\tif (ServiceFPs.has_key('all')):\t\t\t\t#Now recheck against regexes not associated with a specific port (port 'all').\n\t\t\t\t\t\t\tfor OneTuple in ServiceFPs['all']:\n\t\t\t\t\t\t\t\tMatchObj = OneTuple[0].search(Payload)\n\t\t\t\t\t\t\t\tif (MatchObj != None):\n\t\t\t\t\t\t\t\t\tOutputDescription = OneTuple[1]\n\t\t\t\t\t\t\t\t\tif len(MatchObj.groups()) >= 1:\n\t\t\t\t\t\t\t\t\t\t#We have subexpressions matched, these need to be inserted into the description string\n\t\t\t\t\t\t\t\t\t\tfor Index in range(1,len(MatchObj.groups())+1):\n\t\t\t\t\t\t\t\t\t\t\tOutputDescription = OutputDescription.replace('$' + str(Index), MatchObj.group(Index))\n\t\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", OutputDescription)\n\t\t\t\t\t\t\t\t\tNmapServerDescription[sIP + \",TCP_\" + sport] = OutputDescription\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif (not ManualServerDescription.has_key(FromPort) ):\n\t\t\t\t\t\tif (sport == \"22\") and (Payload != None) and (Payload.find('SSH-') > -1):\n\t\t\t\t\t\t\tif ( (Payload.find('SSH-1.99-OpenSSH_') > -1) or (Payload.find('SSH-2.0-OpenSSH_') > -1) ):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"ssh/openssh\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"ssh/openssh\"\n\t\t\t\t\t\t\telif (Payload.find('SSH-1.5-') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"ssh/generic\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"ssh/generic\"\n\t\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\t\t\telif (sport == \"25\") and (Payload != None) and (Payload.find(' ESMTP Sendmail ') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"smtp/sendmail\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"smtp/sendmail\"\n\t\t\t\t\t\telif (sport == \"25\") and (Payload != None) and (Payload.find(' - Welcome to our SMTP server ESMTP') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"smtp/generic\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"smtp/generic\"\n\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\t\t\t#Check for port 80 and search for \"Server: \" once\n\t\t\t\t\t\telif (sport == \"80\") and (Payload != None) and (Payload.find('Server: ') > -1):\n\t\t\t\t\t\t\tif (Payload.find('Server: Apache') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/apache\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/apache\"\n\t\t\t\t\t\t\telif (Payload.find('Server: Embedded HTTP Server') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/embedded\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/embedded\"\n\t\t\t\t\t\t\telif (Payload.find('Server: gws') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/gws\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/gws\"\n\t\t\t\t\t\t\telif (Payload.find('Server: KFWebServer') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/kfwebserver\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/kfwebserver\"\n\t\t\t\t\t\t\telif (Payload.find('Server: micro_httpd') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/micro-httpd\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/micro-httpd\"\n\t\t\t\t\t\t\telif (Payload.find('Server: Microsoft-IIS') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/iis\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/iis\"\n\t\t\t\t\t\t\telif (Payload.find('Server: lighttpd') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/lighttpd\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/lighttpd\"\n\t\t\t\t\t\t\telif (Payload.find('Server: MIIxpc') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/mirrorimage\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/mirrorimage\"\n\t\t\t\t\t\t\telif (Payload.find('Server: mini_httpd') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/mini-httpd\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/mini-httpd\"\n\t\t\t\t\t\t\telif (Payload.find('Server: nc -l -p 80') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/nc\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/nc\"\n\t\t\t\t\t\t\telif (Payload.find('Server: nginx/') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/nginx\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/nginx\"\n\t\t\t\t\t\t\telif (Payload.find('Server: Nucleus') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/nucleus\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/nucleus\"\n\t\t\t\t\t\t\telif (Payload.find('Server: RomPager') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/rompager\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/rompager\"\n\t\t\t\t\t\t\telif (Payload.find('Server: Server') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/server\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/server\"\n\t\t\t\t\t\t\telif (Payload.find('Server: Sun-ONE-Web-Server/') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/sun-one\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/sun-one\"\n\t\t\t\t\t\t\telif (Payload.find('Server: TrustRank Frontend') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/trustrank\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/trustrank\"\n\t\t\t\t\t\t\telif (Payload.find('Server: YTS/') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/yahoo\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/yahoo\"\n\t\t\t\t\t\t\telif (Payload.find('HTTP/1.0 404 Not Found') > -1) or (Payload.find('HTTP/1.1 200 OK') > -1):\n\t\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"http/generic\")\n\t\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"http/generic\"\n\t\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\t\t\telif (sport == \"110\") and (Payload != None) and (Payload.find('POP3 Server Ready') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"pop3/generic\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"pop3/generic\"\n\t\t\t\t\t\telif (sport == \"143\") and (Payload != None) and (Payload.find('* OK dovecot ready') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"imap/dovecot\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"imap/dovecot\"\n\t\t\t\t\t\telif (sport == \"143\") and (Payload != None) and (Payload.find(' IMAP4rev1 ') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"imap/generic\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"imap/generic\"\n\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\t\t\telif (sport == \"783\") and (Payload != None) and (Payload.find('SPAMD/1.1 ') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"spamd/spamd\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"spamd/spamd\"\n\t\t\t\t\t\telif ( (sport == \"3128\") or (sport == \"80\") ) and (Payload != None) and (Payload.find('Via: ') > -1) and (Payload.find(' (squid/') > -1):\n\t\t\t\t\t\t\tReportId(\"TS\", sIP, \"TCP_\" + sport, \"listening\", \"proxy/squid\")\n\t\t\t\t\t\t\tManualServerDescription[sIP + \",TCP_\" + sport] = \"proxy/squid\"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\n\t\t\t\telif ((LiveTCPService.has_key(ToPort)) and (LiveTCPService[ToPort] == True)):\t\t#If the \"To\" side is a known TCP server:\n\t\t\t\t\tClientKey = sIP + \",TCP_\" + dport\t#Note: CLIENT ip and SERVER port\n\t\t\t\t\tif (not ClientDescription.has_key(ClientKey)):\n\t\t\t\t\t\tif (dport == \"22\") and (Payload != None) and ( (Payload.find('SSH-2.0-OpenSSH_') > -1) or (Payload.find('SSH-1.5-OpenSSH_') > -1) ):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"ssh/openssh\")\n\t\t\t\t\t\t#As cute as it is to catch this, it miscatches any relay that's carrying a pine-generated mail.\n\t\t\t\t\t\t#elif (dport == \"25\") and (Payload != None) and (Payload.find('Message-ID: <Pine.') > -1):\n\t\t\t\t\t\t#\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"smtp/pine\")\n\t\t\t\t\t\telif ( (dport == \"80\") or (dport == \"3128\") ) and (Payload != None) and (Payload.find('User-Agent: libwww-perl/') > -1):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"http/libwww-perl\")\n\t\t\t\t\t\telif ( (dport == \"80\") or (dport == \"3128\") ) and (Payload != None) and (Payload.find('User-Agent: Lynx') > -1):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"http/lynx\")\n\t\t\t\t\t\telif ( (dport == \"80\") or (dport == \"3128\") ) and (Payload != None) and (Payload.find('User-Agent: Mozilla') > -1) and (Payload.find(' Firefox/') > -1):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"http/firefox\")\n\t\t\t\t\t\telif ( (dport == \"80\") or (dport == \"3128\") ) and (Payload != None) and (Payload.find('User-Agent: Wget/') > -1):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"http/wget\")\n\t\t\t\t\t\telif (dport == \"143\") and (Payload != None) and (Payload.find('A0001 CAPABILITY') > -1):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"imap/generic\")\n\t\t\t\t\t\t\t#LogNewPayload(ClientPayloadDir, ClientKey, Payload)\n\t\t\t\t\t\telif (dport == \"783\") and (Payload != None) and (Payload.find('PROCESS SPAMC') > -1):\n\t\t\t\t\t\t\tReportId(\"TC\", sIP, \"TCP_\" + dport, \"open\", \"spamd/spamc\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t\t\t#LogNewPayload(ClientPayloadDir, ClientKey, Payload)\n\t\t\t\telse:\t#Neither port pair is known as a server\n\t\t\t\t\tUnhandledPacket(p)\n\t\t\t\t\t#Following is debugging at best; it should only show up early on as the sniffer listens to conversations for which it didn't hear the SYN/ACK\n\t\t\t\t\t#print \"note: neither \" + FromPort + \" nor \" + ToPort + \" is listed as a live service.\"\n\t\t\telse:\t#Other TCP flag combinations here\n\t\t\t\tUnhandledPacket(p)\n\t\telif p['IP'].proto == 17 and (type(p['UDP']) == UDP):\t\t#UDP. We have to check the object type as well as we do get (corrupted? truncated?) packets with type 17 that aren't udp: AttributeError: 'NoneType' object has no attribute 'sport'\n\t\t\t#FIXME - possibly run udp packets through ServiceFPs as well?\n\t\t\tsport=str(p['UDP'].sport)\n\t\t\tdport=str(p['UDP'].dport)\n\t\t\tSrcService = sIP + \",UDP_\" + sport\n\t\t\tDstService = dIP + \",UDP_\" + dport\n\t\t\tSrcClient = sIP + \",UDP_\" + dport\n\t\t\tPayload = p['Raw.load']\n\n\t\t\t#Multicast DNS: http://files.multicastdns.org/draft-cheshire-dnsext-multicastdns.txt\n\t\t\t#- usually sent to 224.0.0.251 (or FF02::FB) (link-local multicast).\n\t\t\t#\t- if \".local.\" in query, these MUST be the target IPs\n\t\t\t#\t- non-local queries may be sent to these or normal dns servers\n\t\t\t#\t- rdns queries for \"254.169.in-addr.arpa.\" MUST be sent to 224.0.0.251\n\t\t\t#\t- rdns queries for \"8.e.f.ip6.arpa.\", \"9.e.f.ip6.arpa.\",\"a.e.f.ip6.arpa.\", and \"b.e.f.ip6.arpa.\" MUST be sent to the IPv6 mDNS link-local multicast address FF02::FB.\n\t\t\t#- sent to udp port 5353\n\t\t\t#- generic clients may use \"single-dns-object.local.\", such as \"sparrow.local.\"\n\t\t\t#- responses have IP TTL = 255 to check that packet originated on-lan\n\n\t\t\t#Multicast DNS, placed next to normal dns, out of numerical order\n\t\t\tif (dport == \"5353\") and ( (p['IP'].ttl == 1) or (p['IP'].ttl == 255) ):\n\t\t\t\tif ((not LiveUDPService.has_key(SrcClient)) or (LiveUDPService[SrcClient] == False)):\n\t\t\t\t\tLiveUDPService[SrcClient] = True\n\t\t\t\t\tif (dIP == \"224.0.0.251\"):\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"mdns/broadcastclient\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"mdns/client\")\n\n\t\t\t\t\t#Extract dns answers like with 53; change elif to if and add 5353 to ports on next if?\n\t\t\t\t\t#At the moment, no; scapy does not appear to parse 5353 as dns.\n\t\t\t\t\t#else:\n\t\t\t\t\t#\tUnhandledPacket(p)\n\t\t\t#FIXME - add check for \"if isinstance(p['DNS'], whatevertype):\there and at all p[] accesses.\n\t\t\telif (sport == \"53\") and (isinstance(p['DNS'], DNS)) and (p['DNS'].qr == 1):\t\t#qr == 1 is a response\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\t#FIXME - Also report the TLD from one of the query answers to show what it's willing to answer for?\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"dns/server\")\n\t\t\t\t#Now we extract dns answers. First, check that there's no dns error:\n\t\t\t\tif (p['DNS'].rcode == 0):\t\t\t#No error\n\t\t\t\t\tDNSBlocks = [ ]\n\t\t\t\t\tCNAMERecs = [ ]\t\t\t\t#We hold onto all cnames until we've processed all PTR's and A's here\n\t\t\t\t\tif (p['DNS'].ancount > 0):\t\t#If we have at least one answer from the answer block, process it\n\t\t\t\t\t\tDNSBlocks.append(p[DNS].an)\n\t\t\t\t\tif (p['DNS'].arcount > 0):\t\t#Likewise for the \"additional\" block\n\t\t\t\t\t\tDNSBlocks.append(p[DNS].ar)\n\t\t\t\t\tfor OneAn in DNSBlocks:\n\t\t\t\t\t\t#Thanks to Philippe Biondi for showing me how to extract additional records.\n\t\t\t\t\t\t#Debug(\"Start dns extract\" + str(p['DNS'].ancount))\n\t\t\t\t\t\t#OneAn = p[DNS].an\n\t\t\t\t\t\t#while OneAn is not NoPayload:\t\t#This doesn't seem to stop at the end of the list; incorrect syntax.\n\t\t\t\t\t\twhile isinstance(OneAn,DNSRR):\t\t#Somewhat equivalent:\twhile not isinstance(an, NoPayload):\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t#print \"Type: \" + str(type(OneAn))\t\t#All of type scapy.DNSRR\n\t\t\t\t\t\t\tif (OneAn.rclass == 1) and (OneAn.type == 1):\t\t#\"IN\" class and \"A\" type answer\n\t\t\t\t\t\t\t\tDNSIPAddr = OneAn.rdata\n\t\t\t\t\t\t\t\tDNSHostname = OneAn.rrname.lower()\n\t\t\t\t\t\t\t\t#Check new hostname to see if it's in the list.\n\t\t\t\t\t\t\t\tif (not DNSRecord.has_key(DNSIPAddr + \",A\")) or (not(DNSHostname in DNSRecord[DNSIPAddr + \",A\"])):\n\t\t\t\t\t\t\t\t\tReportId(\"DN\", DNSIPAddr, \"A\", DNSHostname, \"\")\n\t\t\t\t\t\t\telif (OneAn.rclass == 1) and (OneAn.type == 2):\t\t\t#\"IN\" class and \"NS\" answer\n\t\t\t\t\t\t\t\tpass\t\t\t\t\t\t\t#Perhaps later\n\t\t\t\t\t\t\t\t#Like cnames, this is object -> nameserver hostname, so these would need to be queued like cnames until we're done with A's and PTR's.\n\t\t\t\t\t\t\telif (OneAn.rclass == 1) and (OneAn.type == 5):\t\t\t#\"IN\" class and \"CNAME\" answer\n\t\t\t\t\t\t\t\tCNAMERecs.append(OneAn)\t\t\t\t\t#Remember the record; we'll process these after the PTR's and A's\n\t\t\t\t\t\t\telif (OneAn.rclass == 1) and (OneAn.type == 6):\t\t\t#\"IN\" class and \"SOA\" answer\n\t\t\t\t\t\t\t\tpass\t\t\t\t\t\t\t#Not immediately useful, perhaps later\n\t\t\t\t\t\t\telif (OneAn.rclass == 1) and (OneAn.type == 12):\t\t#\"IN\" class and \"PTR\" type answer\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#For input of '182.111.59.66.in-addr.arpa.' :\n\t\t\t\t\t\t\t\tDNSIPAddr = OneAn.rrname.replace(\".in-addr.arpa.\", \"\")\t\t# '182.111.59.66'\n\t\t\t\t\t\t\t\tDNSIPAddr = DNSIPAddr.split('.')\t\t\t\t# ['182', '111', '59', '66']\n\t\t\t\t\t\t\t\tDNSIPAddr.reverse()\t\t\t\t\t\t# ['66', '59', '111', '182']\n\t\t\t\t\t\t\t\tDNSIPAddr = string.join(DNSIPAddr, '.')\t\t\t\t# '66.59.111.182'\n\t\t\t\t\t\t\t\t#Check that we end up with a legal IPv4 address before continuing; we're getting garbage.\n\t\t\t\t\t\t\t\tif (re.search('^[1-9][0-9\\.]*[0-9]$', DNSIPAddr) == None):\n\t\t\t\t\t\t\t\t\tDebug(\"Odd PTR rrname: \" + OneAn.rrname)\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tDNSHostname = OneAn.rdata.lower()\n\t\t\t\t\t\t\t\t\tif (not DNSRecord.has_key(DNSIPAddr + \",PTR\")) or (not(DNSHostname in DNSRecord[DNSIPAddr + \",PTR\"])):\n\t\t\t\t\t\t\t\t\t\tReportId(\"DN\", DNSIPAddr, \"PTR\", DNSHostname, \"\")\n\t\t\t\t\t\t\telif (OneAn.rclass == 1) and (OneAn.type == 15):\t\t#\"IN\" class and \"MX\" answer\n\t\t\t\t\t\t\t\tpass\t\t\t\t\t\t\t#Possibly later\n\t\t\t\t\t\t\telif (OneAn.rclass == 1) and (OneAn.type == 28):\t\t#\"IN\" class and \"AAAA\" answer\n\t\t\t\t\t\t\t\tDNSIPAddr = OneAn.rdata.upper()\n\t\t\t\t\t\t\t\tDNSHostname = OneAn.rrname.lower()\n\t\t\t\t\t\t\t\tif (not DNSRecord.has_key(DNSIPAddr + \",AAAA\")) or (not(DNSHostname in DNSRecord[DNSIPAddr + \",AAAA\"])):\n\t\t\t\t\t\t\t\t\tReportId(\"DN\", DNSIPAddr, \"AAAA\", DNSHostname, \"\")\n\n\t\t\t\t\t\t\t#Move to the next DNS object in the \"an\" block\n\t\t\t\t\t\t\tOneAn = OneAn.payload\n\t\t\t\t\tfor OneCNAME in CNAMERecs:\t\t#Now that we have all A/PTR's, go back and turn cname records into pseudo-A's\n\t\t\t\t\t\tif isinstance(OneCNAME,DNSRR):\n\t\t\t\t\t\t\tAlias = OneCNAME.rrname.lower()\n\t\t\t\t\t\t\tExisting = OneCNAME.rdata.lower()\n\t\t\t\t\t\t\tif isFQDN(Alias) and isFQDN(Existing):\n\t\t\t\t\t\t\t\tif HostIPs.has_key(Existing):\n\t\t\t\t\t\t\t\t\tfor OneIP in HostIPs[Existing]:\t\t\t\t#Loop through each of the IPs for the canonical name, and\n\t\t\t\t\t\t\t\t\t\tif (not DNSRecord.has_key(OneIP + \",CNAME\")) or (not(Alias in DNSRecord[OneIP + \",CNAME\"])):\n\t\t\t\t\t\t\t\t\t\t\tReportId(\"DN\", OneIP, \"CNAME\", Alias, \"\")\t#report them as kind-of A records for the Alias.\n\t\t\t\t\t\t\t\t#If we don't have a A/PTR record for \"Existing\", just ignore it. Hopefully we'll get the Existing A/PTR in the next few answers, and will re-ask for the CNAME later, at which point we'll get a full cname record.\n\t\t\t\t\t\t\t\t#else:\n\t\t\t\t\t\t\t\t#\tDebug(\"CNAME \" + Alias + \" -> \" + Existing + \" requested, but no IP's for the latter, skipping.\")\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tDebug(\"One of \" + Alias + \" and \" + Existing + \" isn't an FQDN, skipping cname processing.\")\n\t\t\t\telif (p['DNS'].rcode == 1):\t\t\t#FormErr: server responding to an improperly formatted request\n\t\t\t\t\tpass\n\t\t\t\telif (p['DNS'].rcode == 2):\t\t\t#ServFail: domain exists, root nameservers list authoritative name servers, but authNS's won't answer queries\n\t\t\t\t\tpass\n\t\t\t\telif (p['DNS'].rcode == 3):\t\t\t#NXDOMAIN: root nameservers don't have any listing (domain doesn't exist or is on hold)\n\t\t\t\t\tpass\n\t\t\t\telif (p['DNS'].rcode == 5):\t\t\t#Query refused\n\t\t\t\t\tpass\n\t\t\t\telse:\t#rcode indicates an error\n\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (dport == \"53\") and (type(p['DNS']) == DNS) and (p['DNS'].qr == 0):\t#dns query\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"dns/client\")\n\t\t\telif (sport == \"67\") and (dport == \"68\"):\t\t#Bootp/dhcp server talking to client\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"bootpordhcp/server\")\n\t\t\telif (sport == \"68\") and (dport == \"67\"):\t\t#Bootp/dhcp client talking to server\n\t\t\t\tif (sIP != \"0.0.0.0\"):\t\t\t\t#If the client is simply renewing an IP, remember it.\n\t\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"bootpordhcp/client\")\n\t\t\t\t#else:\t\t\t\t\t\t#If you want to record which macs are asking for addresses, do it here.\n\t\t\t\t#\tpass\n\t\t\telif (sport == \"123\") and (dport == \"123\") and (p['NTP'].stratum != ''):\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"ntp/generic\")\n\t\t\telif (dport == \"123\") and ( (dIP == \"216.115.23.75\") or (dIP == \"216.115.23.76\") or (dIP == \"69.59.240.75\") ):\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"ntp/vonageclient\")\n\t\t\telif (sport == \"123\") and ( (sIP == \"216.115.23.75\") or (sIP == \"216.115.23.76\") or (sIP == \"69.59.240.75\") ):\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"ntp/vonageserver\")\n\t\t\telif (dport == \"137\"):\t\t\t#netbios-ns\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (p['Ethernet'].dst.upper() == \"FF:FF:FF:FF:FF:FF\"):\t\t\t#broadcast\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"netbios-ns/broadcastclient\")\n\t\t\t\t\telif (Payload != None) and (Payload.find('CKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') > -1):\t#wildcard\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"netbios-ns/wildcardclient\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"netbios-ns/unicastclient\")\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"500\") and (dport == \"500\") and (p['ISAKMP'].init_cookie != ''):\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"isakmp/generic\")\n\t\t\telif (dport == \"512\"):\t\t\t#BIFF\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('@') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"biff/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif ( (dport == \"1026\") or (dport == \"1027\") or (dport == \"1028\") ):\t#winpopup spam client\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and ( (Payload.find('Download Registry Update from:') > -1) or (Payload.find('CRITICAL ERROR MESSAGE! - REGISTRY DAMAGED AND CORRUPTED.') > -1) or (Payload.find('Your system registry is corrupted and needs to be cleaned immediately.') > -1) or (Payload.find('CRITICAL SYSTEM ERRORS') > -1) ):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"winpopup/spamclient\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (dport == \"1434\"):\t\t#Probable mssql attack\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('Qh.dll') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"mssql/clientattack\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"1900\") and (dport == \"1900\") and (dIP == \"239.255.255.250\"):\t\t#SSDP\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('NOTIFY') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"ssdp/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (dport == \"3865\") and (dIP == \"255.255.255.255\"):\t\t#XPL, http://wiki.xplproject.org.uk/index.php/Main_Page\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"xpl/client\")\n\t\t\telif (sport == \"5061\") and (dport == \"5061\") and ( (dIP == \"216.115.30.28\") or (dIP == \"69.59.227.77\") or (dIP == \"69.59.232.33\") or (dIP == \"69.59.240.84\") ):\t\t#Vonage SIP client\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('.vonage.net:5061 SIP/2.0') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tSipMatch = SipPhoneMatch.search(Payload)\n\t\t\t\t\t\tif (SipMatch != None) and (len(SipMatch.groups()) >= 1):\n\t\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"sip/vonage_client, phone number: \" + SipMatch.group(1))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"sip/vonage_client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"5061\") and (dport == \"5061\") and ( (sIP == \"216.115.30.28\") or (sIP == \"69.59.227.77\") or (sIP == \"69.59.232.33\") or (sIP == \"69.59.240.84\") ):\t#Vonage SIP server\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('.vonage.net:5061>') > -1):\n\t\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"sip/vonage_server\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"6515\") and (dport == \"6514\") and (dIP == \"255.255.255.255\"):\t\t#mcafee ASaP broadcast, looking for a proxy out. http://www.myasap.de/intl/EN/content/virusscan_asap/faq_new.asp\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('<rumor version=') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"asap/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif ( (sport == \"9052\") or (sport == \"9053\") or (sport == \"9054\") ) and ( (sIP == \"205.188.146.72\") or (sIP == \"205.188.157.241\") or (sIP == \"205.188.157.242\") or (sIP == \"205.188.157.243\") or (sIP == \"205.188.157.244\") or (sIP == \"64.12.51.145\") or (sIP == \"64.12.51.148\") or (sIP == \"149.174.54.131\") ):\t#Possibly AOL dns response\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('dns-01') > -1):\n\t\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"aoldns/server\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"27005\") and ( (dport == \"27016\") or (dport == \"27017\") ):\t\t\t\t#Halflife client live game\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"halflife/client\")\n\t\t\telif (dport == \"27013\") and (dIP == \"207.173.177.12\"):\t\t\t\t#variable payload, so can't (Payload != None) and (Payload.find('Steam.exe') > -1)\t\t\t\t#Halflife client\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"halflife/client\")\n\t\t\telif (sport == \"27013\") and (sIP == \"207.173.177.12\"):\t\t\t\t\t\t\t#halflife server\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"halflife/server\")\n\t\t\telif ( (sport == \"27016\") or (sport == \"27017\") ) and (dport == \"27005\"):\t\t\t\t#halflife server live game\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"halflife/server\")\n\t\t\telif ( (dport == \"27015\") or (dport == \"27016\") or (dport == \"27025\") or (dport == \"27026\") ):\t\t#Variable payload, so can't: (Payload != None) and (Payload.find('basic') > -1)\t#Halflife client\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"halflife/client\")\n\t\t\telif (dport == \"27017\") and ( (dIP == \"69.28.148.250\") or (dIP == \"69.28.156.250\") or (dIP == \"72.165.61.161\") or (dIP == \"72.165.61.185\") or (dIP == \"72.165.61.186\") or (dIP == \"72.165.61.188\") or (dIP == \"68.142.64.164\") or (dIP == \"68.142.64.165\") or (dIP == \"68.142.64.166\") ):\t#Steamfriends client\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('VS01') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"steamfriends/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"27017\") and ( (sIP == \"69.28.148.250\") or (sIP == \"69.28.156.250\") or (sIP == \"72.165.61.161\") or (sIP == \"72.165.61.185\") or (sIP == \"72.165.61.186\") or (sIP == \"72.165.61.188\") or (sIP == \"68.142.64.164\") or (sIP == \"68.142.64.165\") or (sIP == \"68.142.64.166\") ):\t#Steamfriends server\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('VS01') > -1):\n\t\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"steamfriends/server\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif ( (sport == \"21020\") or (sport == \"21250\") or (sport == \"27016\") or (sport == \"27017\") or (sport == \"27018\") or (sport == \"27030\") or (sport == \"27035\") or (sport == \"27040\") or (sport == \"28015\") ):\t\t\t\t\t\t\t#halflife server\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('Team Fortress') > -1):\n\t\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"halflife/server\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sport == \"27019\"):\t\t\t\t\t\t\t#halflife server\n\t\t\t\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\n\t\t\t\t\tLiveUDPService[SrcService] = True\n\t\t\t\t\tReportId(\"US\", sIP, \"UDP_\" + sport, \"open\", \"halflife/server\")\n\t\t\telif ( (dport == \"1265\") or (dport == \"20100\") or (dport == \"21550\") or (dport == \"27000\") or (dport == \"27017\") or (dport == \"27018\") or (dport == \"27019\") or (dport == \"27022\") or (dport == \"27030\") or (dport == \"27035\") or (dport == \"27050\") or (dport == \"27078\") or (dport == \"27080\") or (dport == \"28015\") or (dport == \"28100\") or (dport == \"45081\") ):\t\t#Halflife client\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('Source Engine Query') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"halflife/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (dport == \"24441\"):\t\t\t#Pyzor\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('User:') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"pyzor/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\t#FIXME - interesting issue; the ttl<5 test will catch traceroutes coming into us, but not ones we're creating to go out. Hmmm.\n\t\t\telif ( (dport >= \"33434\") and (dport <= \"33524\") ) and (p['IP'].ttl <= 5):\t#udptraceroute client\n\t\t\t\tif ((not LiveUDPClient.has_key(sIP + \"UDP_33434\")) or (LiveUDPClient[sIP + \"UDP_33434\"] == False)):\n\t\t\t\t\tLiveUDPClient[sIP + \"UDP_33434\"] = True\n\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_33434\", \"open\", \"udptraceroute/client\")\n\t\t\telif (dport == \"40348\"):\n\t\t\t\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\n\t\t\t\t\tif (Payload != None) and (Payload.find('HLS') > -1):\n\t\t\t\t\t\tLiveUDPClient[SrcClient] = True\n\t\t\t\t\t\tReportId(\"UC\", sIP, \"UDP_\" + dport, \"open\", \"halflife/client\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tUnhandledPacket(p)\n\t\t\telif (p['IP'].frag > 0):\n\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sIP == \"207.46.51.74\") or (sIP == \"65.55.251.10\"):\t\t\t\t#Bigfish.com - dns?\n\t\t\t\tUnhandledPacket(p)\n\t\t\telif (sIP == \"61.215.106.146\"):\t\t\t\t#junk\n\t\t\t\tUnhandledPacket(p)\n\t\t\telse:\n\t\t\t\tUnhandledPacket(p)\n\t\telse:\n\t\t\tDebug(\"Other IP protocol (\" + str(p['IP'].src) + \"->\" + str(p['IP'].dst) + \"): \" + str(p['IP'].proto))\n\t\t\tUnhandledPacket(p)\n\telif p['Ethernet'].type == 0x86DD:\t\t#IPv6\n\t\tUnhandledPacket(p)\n\telse:\n\t\tprint \"Unregistered ethernet type:\", p['Ethernet'].type\n\t\tUnhandledPacket(p)","def _add_pkt_into_tcp_stream(self, pcap_packet, num):\n \n # the src is server, remote(dst) is client\n if (pcap_packet.ip.dst == _device_ip): # HUA use ip (not 80 port) as direction judgement\n server_addr = pcap_packet.ip.src\n server_port = pcap_packet.tcp.src_port\n client_addr = pcap_packet.ip.dst\n client_port = pcap_packet.tcp.dst_port\n else:\n server_addr = pcap_packet.ip.dst\n server_port = pcap_packet.tcp.dst_port\n client_addr = pcap_packet.ip.src\n client_port = pcap_packet.tcp.src_port\n socket_tuple = (client_addr, client_port, server_addr, server_port)\n if (socket_tuple not in self.tcp_stream_container):\n self.tcp_stream_container[socket_tuple] = Tcp_stream()\n pcap_packet.tcp.stream_index = self.tcp_stream_container[socket_tuple].stream_index\n self.tcp_stream_container[socket_tuple].pcap_num_list.append(num)","def get_route(self, srcip, daddr): #destinations add of this packet\n # TODO fill in peer?\n peer = None\n\n pos_routes = self.lookup_routes(daddr)\n\n #prefix stuff\n\n pos_routes = self.prefix_stuff(daddr, pos_routes) #look through possible\n #and find prefix matching destination ip\n\n # Rules go here\n #if pos_routes:\n # 1. Highest Preference\n #pos_routes = self.get_highest_preference(pos_routes)\n # 2. Self Origin\n # pos_routes = self.get_self_origin(pos_routes)\n # 3. Shortest ASPath\n # pos_routes = self.get_shortest_as_path(pos_routes)\n # 4. EGP > IGP > UNK\n # pos_routes = self.get_origin_routes(pos_routes)\n # 5. Lowest IP Address\n\n #daddrbit = self.ip_to_bits(daddr)\n\n # Final check: enforce peering relationships\n\n #route = self.filter_relationships(srcip, pos_routes)\n #lowestip = 0;\n\n\n peer = pos_routes[0]\n\n return self.sockets[peer] if peer else None","def routePacket(self, packet):\n\n for rule in self._rules:\n # check rule mask vs packet ip\n ip = IPHelper.ipToLong(packet._ip)\n\n if rule._raw_ip == '*' or (rule._ip_mask_val & ip == rule._good_ip):\n if rule._direction == packet._direction:\n for p in rule._ports:\n if p == packet._port or p == '*':\n if rule._flag is None:\n #packet is non-established connection\n return rule\n elif rule._flag == 'established' and packet._flag == '1':\n #packet is established connection and flag has been set for established connection\n return rule\n else:\n pass\n else:\n pass\n else:\n pass\n else:\n pass\n\n return None","def test_overlap1(self):\n\n fragments = []\n for _, frags_400, frags_300 in self.pkt_infos:\n if len(frags_300) == 1:\n fragments.extend(frags_400)\n else:\n for i, j in zip(frags_300, frags_400):\n fragments.extend(i)\n fragments.extend(j)\n\n dropped_packet_indexes = set(\n index for (index, _, frags) in self.pkt_infos if len(frags) > 1\n )\n\n self.pg_enable_capture()\n self.src_if.add_stream(fragments)\n self.pg_start()\n\n packets = self.dst_if.get_capture(\n len(self.pkt_infos) - len(dropped_packet_indexes)\n )\n self.verify_capture(packets, dropped_packet_indexes)\n self.src_if.assert_nothing_captured()","def main(self):\n self.pid = os.getpid()\n self.fdp.close() # Close fdp on child side\n if self.datasock is None:\n # Create session's data socket and load file\n self.datasock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n self.load_file()\n logging.info(\"Child process finished loading file\")\n port = UDP_START + self.meta.sessionid # Port used by the session\n poller = select.poll() # poll fdc and datasock\n poller.register(self.fdc.fileno(), select.POLLIN)\n poller.register(self.datasock.fileno(), select.POLLOUT)\n pkt_p = snc.snc_alloc_empty_packet(snc.snc_get_parameters(self.sc))\n while True:\n for fd, event in poller.poll():\n if fd == self.fdc.fileno() and event is select.POLLIN:\n pkt, ip = self.fdc.recv()\n logging.info(\"Session [%d] received msg <%s> from %s.\" %\n (self.meta.sessionid, iMSG[pkt.header.mtype], ip))\n if pkt.header.mtype == MSG['REQ_SEG']:\n self.add_client(HostInfo(ip, self.meta.sessionid))\n self.fdc.send(CCPacket(CCHeader(MSG['OK'])))\n elif pkt.header.mtype == MSG['HEARTBEAT']:\n self.client_heartbeat(ip)\n self.fdc.send(CCPacket(CCHeader(MSG['OK'])))\n elif pkt.header.mtype == MSG['REQ_STOP'] or pkt.header.mtype == MSG['EXIT']:\n self.remove_client(ip)\n self.fdc.send(CCPacket(CCHeader(MSG['OK'])))\n\n if fd == self.datasock.fileno() and event is select.POLLOUT:\n # writable datasock, send data packets to clients\n for cli in self.clients:\n snc.snc_generate_packet_im(self.sc, pkt_p)\n pktstr = pkt_p.contents.serialize(self.meta.sp.size_g,\n self.meta.sp.size_p,\n self.meta.sp.bnc)\n try:\n # Construct data packet with serialized snc_packet\n self.datasock.sendto(CCPacket(CCHeader(MSG['DATA']), pktstr).packed(), (cli.ip, port))\n except:\n logging.warning(\"Caught exception in session %s.\"\n % (self.meta.sessionid,))\n self.lastIdle = datetime.now() # Refresh idle time\n self.housekeeping()","def read_packet(self):\n\n\t\t#self.debug(\"READ BUFFER SIZE: %d\" % len(self.buff))\n\t\tbackup = self.buff[:]\n\t\tpacket = Packet()\n\t\ttry:\n\t\t\tpacket.direction = self.node\n\t\t\tpacket.ident = self.unpack('ubyte')\n\t\t\t\n\t\t\t#Defined structs from huge dict\n\t\t\tfor datatype, name in self.get_struct(packet):\n\t\t\t\t# this populates packet.data with {name: value}\n\t\t\t\tpacket.data[name] = self.unpack(datatype)\n\n\t\t\t# I believe the following are packet-type specific fixes for variable-length packets.\n\n\t\t\t#0x17\n\t\t\tif packet.ident == 0x17:\n\t\t\t\tif packet.data['unknown'] > 0:\n\t\t\t\t\tpacket.data['x2'] = self.unpack('short')\n\t\t\t\t\tpacket.data['y2'] = self.unpack('short')\n\t\t\t\t\tpacket.data['z2'] = self.unpack('short')\n\t\t\n\t\t\t#0x33\n\t\t\tif packet.ident in (0x33, 0x34):\n\t\t\t\tpacket.data['data'] = self.unpack_array_fast('byte', packet.data['data_size'])\n\t\t\t\tdel packet.data[\"data_size\"]\n\t\t\n#\t\t\t#0x34\n#\t\t\tif packet.ident == 0x34:\n#\t\t\t\tcoords = self.unpack_array_fast('short', packet.data['data_size'])\n#\t\t\t\tbtype = self.unpack_array_fast('byte', packet.data['data_size'])\n#\t\t\t\tmetadata = self.unpack_array_fast('byte', packet.data['data_size'])\n#\t\t\t\tpacket.data[\"blocks\"] = []\n#\t\t\t\tfor i in zip(coords, btype, metadata):\n#\t\t\t\t\tblock = {}\n#\t\t\t\t\tblock[\"x\"] =\t\ti[0] >> 12\n#\t\t\t\t\tblock[\"z\"] = 0x0F & i[0] >> 8\n#\t\t\t\t\tblock[\"y\"] = 0xFF & i[0]\n#\t\t\t\t\tblock[\"type\"] = i[1]\n#\t\t\t\t\tblock[\"metadata\"] = i[2]\n#\t\t\t\t\tpacket.data[\"blocks\"].append(block)\n#\t\t\t\tdel packet.data[\"data_size\"]\n\t\t\n\t\t\t#0x3C\n\t\t\tif packet.ident == 0x3C:\n\t\t\t\trecords = self.unpack_array_fast('byte', packet.data['data_size']*3)\n\t\t\t\ti = 0\n\t\t\t\tpacket.data[\"blocks\"] = []\n\t\t\t\twhile i < packet.data['data_size']*3:\n\t\t\t\t\tpacket.data[\"blocks\"].append(dict(zip(('x','y','z'), records[i:i+3])))\n\t\t\t\t\ti+=3\n\t\t\t\tdel packet.data[\"data_size\"]\n\t\t\n\t\t\t#0x68\n\t\t\tif packet.ident == 0x68:\n\t\t\t\tpacket.data[\"slots_data\"] = self.unpack_array('slot', packet.data[\"data_size\"])\n\t\t\t\tdel packet.data[\"data_size\"]\n\t\t\t#0x82:\n\t\t\tif packet.ident == 0x82:\n\t\t\t\tpacket.data[\"text\"] = []\n\t\t\t\tfor i in range(4):\n\t\t\t\t\tpacket.data[\"text\"].append(packet.data[\"line_%s\" % (i+1)])\n\t\t\t\t\t\n\t\t\t#0x83\n\t\t\tif packet.ident == 0x83:\n\t\t\t\tpacket.data[\"data\"] = self.unpack_array_fast('byte', packet.data['data_size'])\n\t\t\t\tdel packet.data[\"data_size\"]\n\n\t\t\t# Sets packet.original to the byte string that the packet was decoded from.\n\t\t\tpacket.original = backup[:len(backup) - len(self.buff)]\n\n\t\t\treturn packet\n\n\t\texcept IncompleteData:\n\t\t\tself.buff = backup\n\t\t\treturn None\n\t\texcept Exception, ex:\n\t\t\tself.buff = backup\n\t\t\tex.args += (self.buff[20:],)\n\t\t\traise","def _packets_from_tshark_sync(self, tshark_process, packet_count=None, timeout:float=3.0,\n max_data_length:int=10000):\n # NOTE: This has code duplication with the async version, think about how to solve this\n\n psml_structure, data = self.eventloop.run_until_complete(self._get_psml_struct(tshark_process.stdout))\n packets_captured = 0\n\n data = b\"\"\n try:\n while self.is_open.value:\n try:\n packet, data = self.eventloop.run_until_complete(\n self._get_packet_from_stream(tshark_process.stdout, \n data,\n psml_structure=psml_structure,\n got_first_packet=packets_captured > 0, \n timeout=timeout))\n except EOFError:\n echo(\"Caught EOF\", file=Interceptor.stdout)\n self._log.debug(\"EOF reached (sync)\")\n break\n\n if(packet is False): continue\n\n if packet:\n packets_captured += 1\n yield packet\n if packet_count and packets_captured >= packet_count:\n break\n if len(data) > max_data_length:\n data = b''\n finally:\n if tshark_process in self._running_processes:\n self.eventloop.run_until_complete(self._cleanup_subprocess(tshark_process))","def agent_player(env_name, ip):\n\n # Create the main generator\n receiver_gen = atari_frames_generator(env_name, ip)\n\n # Loop\n while True:\n\n # Receive\n frame, termination = next(receiver_gen)\n\n # Skip if repeated\n assert termination in (\"continue\", \"last\", \"repeated_last\")\n if termination == \"repeated_last\":\n continue\n\n # Return\n yield frame","def read_pkt_line(self):\n if self._readahead is None:\n read = self.read\n else:\n read = self._readahead.read\n self._readahead = None\n\n try:\n sizestr = read(4)\n if not sizestr:\n raise HangupException()\n size = int(sizestr, 16)\n if size == 0:\n if self.report_activity:\n self.report_activity(4, \"read\")\n return None\n if self.report_activity:\n self.report_activity(size, \"read\")\n pkt_contents = read(size - 4)\n except socket.error as e:\n raise GitProtocolError(e)\n else:\n if len(pkt_contents) + 4 != size:\n raise GitProtocolError(\n \"Length of pkt read %04x does not match length prefix %04x\"\n % (len(pkt_contents) + 4, size)\n )\n return pkt_contents","def getPacket(self, index):\n\t\treturn self.packets[index.row()]","def print_packet(self, pkt):\n ip_layer = pkt.getlayer(IP)\n print(\"[!] New Packet: {src} -> {dst}\".format(src=ip_layer.src, dst=ip_layer.dst))","def sniffer():\n try:\n sniff(iface=INTERFACE, prn=print_frame, filter='udp and (port bootps or bootps)', store=0)\n except Exception as _e:\n print(\"ERROR - sniffer(): {} {}\".format(_e.args, _e.message))","def from_physical_layer(conn, FRAME_LENGTH, FORMAT):\r\n frame = conn.recv(FRAME_LENGTH).decode(FORMAT)\r\n print(f\"[from_physical_layer] frame:{frame}\")\r\n return frame","def recieve_can(offset):\n panda = Panda()\n while True:\n data = panda.can_recv()\n if data != []: \n for x in data:\n if x[0] == offset: \n if x[3] == 0: \n mes = f'{x[0]}, 0x{x[2].hex()}, {x[3]}'\n print(mes)","def nat_openconn(destmac, destport, localip=None, localport=None, timeout = 5, forwarderIP=None,forwarderPort=None,usetimeoutsock=False): \r\n # cast the destmac to a string, internal methods may fail otherwise\r\n destmac = str(destmac)\r\n\r\n # use the forwarderIP and port provided\r\n if forwarderIP != None and forwarderPort != None:\r\n return _nat_try_connection_list([(forwarderIP,forwarderPort)],\r\n localip,localport,timeout,destmac,destport,usetimeoutsock) \r\n \r\n \r\n # lookup the destmac if forwarderIP and port are not provided\r\n else: \r\n # check the cache\r\n if destmac in NAT_SRV_CACHE:\r\n forwarders = NAT_SRV_CACHE[destmac]\r\n \r\n try:\r\n return _nat_try_connection_list(forwarders,localip,localport,timeout,destmac,destport,usetimeoutsock)\r\n \r\n except: # remove this entry from the cache\r\n del NAT_SRV_CACHE[destmac] \r\n \r\n # the cache failed, so do a fresh lookup\r\n forwarders = nat_server_list_lookup(destmac)\r\n socket = _nat_try_connection_list(forwarders,localip,localport,timeout,destmac,destport,usetimeoutsock)\r\n \r\n #this list succeded so add it to the cache\r\n NAT_SRV_CACHE[destmac] = forwarders\r\n \r\n return socket","def payload_data(self, pkts):\n\n\t\t#Get all the payload bytes exchanged over MPTCP connections\n\t\tpayload_bytes = 0\n\t\tprint \"Determining the number of payload bytes excluding headers....\"\n\t\t#DSS = 0x2\n\t\tfor i in range(len(pkts)):\n\t\t\tif(TCPOption_MP in pkts[i] and pkts[i][TCPOption_MP].mptcp.subtype == 2 and Raw in pkts[i]):\n\t\t\t\tpayload_bytes += len(pkts[i][Raw].load)\n\t\t\t\t#print(\"DSN: %s; subflow_seqnum: %s; Data(bytes): %s\" % (pkts[i][TCPOption_MP].mptcp.dsn, pkts[i][TCPOption_MP].mptcp.subflow_seqnum, len(pkts[i][Raw].load)))\n\n\t\tprint \"Total Number of payload bytes in the file (entire MPTCP connections) excluding headers): %s\" % (payload_bytes)\n\t\t#MPTCP WITH SUBFLOW CONNECTIONS\n\t\t#MPTCP_JOINs = 0x1\n\t\tprint \"============================================================\"\n\t\tprint \"SUBFLOW Connections with their respective MPTCP connection (identified by connectionID)\"\n\t\tfor i in range(len(pkts)):\n\n\t\t\t#Initial Join Message\n\t\t\t#rcv_token Identifies the connection to which the subflow belongs: connectionID\n\t\t\tif(MPTCP_JoinSYN in pkts[i] and pkts[i][TCPOption_MP].mptcp.subtype == 1):\n\t\t\t\tprint(\"New subflow: connectionID: %s; src: %s; dest: %s; snd_nonce: %s\" % (pkts[i][TCPOption_MP].mptcp.rcv_token, pkts[i][IP].src, pkts[i][IP].dst, pkts[i][TCPOption_MP].mptcp.snd_nonce))\n\n\t\t#TODO: Now Need to track per-connection and per-subflow state","def test_overlap1(self):\n\n fragments = []\n for _, _, frags_300, frags_200 in self.pkt_infos:\n if len(frags_300) == 1:\n fragments.extend(frags_300)\n else:\n for i, j in zip(frags_200, frags_300):\n fragments.extend(i)\n fragments.extend(j)\n\n self.pg_enable_capture()\n self.src_if.add_stream(fragments)\n self.pg_start()\n\n packets = self.dst_if.get_capture(len(self.pkt_infos))\n self.verify_capture(packets)\n self.src_if.assert_nothing_captured()\n\n # run it all to verify correctness\n self.pg_enable_capture()\n self.src_if.add_stream(fragments)\n self.pg_start()\n\n packets = self.dst_if.get_capture(len(self.pkt_infos))\n self.verify_capture(packets)\n self.src_if.assert_nothing_captured()","def sniff_offline(args):\n print('viewer: reading from ' + args.read)\n\n try:\n with open(args.read, 'rb') as f:\n reader = dpkt.pcap.Reader(f)\n\n if not args.count:\n count = True\n else:\n count = args.count\n\n while count:\n ts, pkt = next(iter(reader))\n ret = parse_ICMP_Echo(ts, pkt)\n\n if ret and args.count:\n count -= 1\n except FileNotFoundError as e:\n print('File \\'{}\\' not found.'.format(args.read))\n sys.exit(1)\n except StopIteration:\n sys.exit(0)","def packet_to_kml(packet, reader):\n\n try:\n src_ip = packet[IP].src\n src_kml = ip_to_kml(src_ip, reader)\n except:\n src_kml = None\n try:\n dest_ip = packet[IP].dest\n dest_kml = ip_to_kml(dest_ip, reader)\n except:\n dest_kml = None\n\n if src_kml is not None and dest_kml is not None:\n connect_kml = ips_to_line_kml(src_ip, dest_ip, reader)\n print(\"Added connection\")\n else:\n connect_kml = None\n\n return src_kml, dest_kml, connect_kml","def create_stream(cls, packet_sizes, packet_count=test_packet_count):\n for i in range(0, packet_count):\n info = cls.create_packet_info(cls.src_if, cls.src_if)\n payload = cls.info_to_payload(info)\n p = (\n IP(id=info.index, src=cls.src_if.remote_ip4, dst=cls.dst_if.remote_ip4)\n / UDP(sport=1234, dport=5678)\n / Raw(payload)\n )\n size = packet_sizes[(i // 2) % len(packet_sizes)]\n cls.extend_packet(p, size, cls.padding)\n info.data = p","def ReceiveMessageFromPacketInfo(self) -> IPPacketInformation:","def give_packets(self, packets, verbose=False, cache=False, tunnel=None, source_sock_addr=None):\n assert isinstance(packets, list)\n assert all(isinstance(packet, str) for packet in packets)\n assert isinstance(verbose, bool)\n assert isinstance(cache, bool)\n assert tunnel is None, \"TUNNEL property is set using init_socket(...)\"\n assert source_sock_addr is None or isinstance(source_sock_addr, tuple), type(source_sock_addr)\n if verbose:\n logger.debug(\"giving %d bytes\", sum(len(packet) for packet in packets))\n if source_sock_addr is None:\n source_sock_addr = self.lan_address\n candidate = Candidate(source_sock_addr, self._tunnel)\n self._dispersy.on_incoming_packets([(candidate, packet) for packet in packets], cache=cache, timestamp=time())\n return packets"],"string":"[\n \"def print_packets(pcap):\\n\\n # For each packet in the pcap process the contents\\n for timestamp, buf, hdr_len in pcap:\\n \\n # Unpack the Ethernet frame (mac src/dst, ethertype)\\n eth = dpkt.ethernet.Ethernet(buf)\\n # print('Ethernet Frame: ', mac_addr(eth.src), mac_addr(eth.dst), eth.type)\\n\\n # Make sure the Ethernet data contains an IP packet\\n if not isinstance(eth.data, dpkt.ip.IP):\\n # print('Non IP Packet type not supported %s\\\\n' % eth.data.__class__.__name__)\\n continue\\n\\n # Now unpack the data within the Ethernet frame (the IP packet)\\n # Pulling out src, dst, length, fragment info, TTL, and Protocol\\n ip = eth.data\\n\\n # Pull out fragment information (flags and offset all packed into off field, so use bitmasks)\\n # do_not_fragment = bool(ip.off & dpkt.ip.IP_DF)\\n # more_fragments = bool(ip.off & dpkt.ip.IP_MF)\\n # fragment_offset = ip.off & dpkt.ip.IP_OFFMASK\\n\\n # Print out the info\\n # print('IP: %s -> %s (len=%d ttl=%d DF=%d MF=%d offset=%d)\\\\n' % \\\\\\n # (inet_to_str(ip.src), inet_to_str(ip.dst), ip.len, ip.ttl, do_not_fragment, more_fragments, fragment_offset)) \\n\\n pkt = Packet(timestamp, buf, hdr_len)\\n\\n if ip.p == dpkt.ip.IP_PROTO_TCP or ip.p == dpkt.ip.IP_PROTO_UDP: \\n # all flow\\n flow = Flow(ip.src, ip.dst, ip.data.sport, ip.data.dport, ip.p)\\n if flow not in all_flows:\\n all_flows[flow] = [pkt]\\n else:\\n x = len(all_flows[flow]) - 1\\n if x < 0:\\n all_flows[flow].append(pkt)\\n else:\\n if time_diff(all_flows[flow][x].timestamp, timestamp) <= 5400: #90mins\\n all_flows[flow].append(pkt)\\n\\n if ip.p == dpkt.ip.IP_PROTO_TCP: \\n # TCP flow\\n flow = Flow(ip.src, ip.dst, ip.data.sport, ip.data.dport, ip.p)\\n if flow not in tcp_flows:\\n tcp_flows[flow] = [pkt]\\n else:\\n x = len(tcp_flows[flow]) - 1\\n if x < 0:\\n tcp_flows[flow].append(pkt)\\n else:\\n if time_diff(tcp_flows[flow][x].timestamp, timestamp) <= 5400:\\n tcp_flows[flow].append(pkt)\\n all_host_pairs(pkt, ip)\\n elif ip.p == dpkt.ip.IP_PROTO_UDP:\\n # UDP flow\\n flow = Flow(ip.src, ip.dst, ip.data.sport, ip.data.dport, ip.p)\\n if flow not in udp_flows:\\n udp_flows[flow] = [pkt]\\n else:\\n x = len(udp_flows[flow]) - 1\\n if x < 0:\\n udp_flows[flow].append(pkt)\\n else:\\n if time_diff(udp_flows[flow][x].timestamp, timestamp) <= 5400:\\n udp_flows[flow].append(pkt)\\n else:\\n continue\\n\\n print(\\\"Number of All flows: %d | Number of TCP flows: %d | Number of UDP flows: %d\\\" % (len(all_flows), len(tcp_flows), len(udp_flows)))\\n\\n # -- Flow Duration\\n for f in all_flows:\\n size = len(all_flows[f])\\n if size >= 2:\\n all_flow_dur.append(time_diff(all_flows[f][0].timestamp, all_flows[f][size-1].timestamp))\\n \\n for f in tcp_flows:\\n size = len(tcp_flows[f])\\n if size >= 2:\\n tcp_flow_dur.append(time_diff(tcp_flows[f][0].timestamp, tcp_flows[f][size-1].timestamp))\\n \\n for f in udp_flows:\\n size = len(udp_flows[f])\\n if size >= 2:\\n udp_flow_dur.append(time_diff(udp_flows[f][0].timestamp, udp_flows[f][size-1].timestamp))\\n\\n print \\\"lens: \\\", len(all_flow_dur), len(tcp_flow_dur), len(udp_flow_dur)\\n\\n # -- Flow Size\\n for f in all_flows:\\n f_bytes = 0\\n size = len(all_flows[f])\\n all_flow_size_pkt.append(size)\\n for p in all_flows[f]:\\n f_bytes += p.length\\n all_flow_size_byte.append(f_bytes)\\n \\n for f in tcp_flows:\\n f_bytes = 0\\n f_overhead = 0\\n size = len(tcp_flows[f])\\n tcp_flow_size_pkt.append(size)\\n for p in tcp_flows[f]:\\n f_bytes += p.length\\n f_overhead += 18 + 20 #+ tcp_hdr\\n tcp_flow_size_byte.append(f_bytes)\\n if f_bytes == 0:\\n f_bytes = 9999\\n tcp_flow_size_overhead.append(f_overhead/float(f_bytes))\\n \\n for f in udp_flows:\\n f_bytes = 0\\n size = len(udp_flows[f])\\n udp_flow_size_pkt.append(size)\\n for p in udp_flows[f]:\\n f_bytes += p.length\\n udp_flow_size_byte.append(f_bytes)\\n\\n # -- Inter-packet Arrival time\\n for f in all_flows:\\n for i in range(len(all_flows[f])-1):\\n all_flow_time.append(time_diff(all_flows[f][i].timestamp, all_flows[f][i+1].timestamp))\\n\\n for f in tcp_flows:\\n for i in range(len(tcp_flows[f])-1):\\n tcp_flow_time.append(time_diff(tcp_flows[f][i].timestamp, tcp_flows[f][i+1].timestamp))\\n\\n for f in udp_flows:\\n for i in range(len(udp_flows[f])-1):\\n udp_flow_time.append(time_diff(udp_flows[f][i].timestamp, udp_flows[f][i+1].timestamp))\\n\\n # -- TCP State\\n for f in tcp_flows:\\n size = len(tcp_flows[f])\\n last_pkt = tcp_flows[f][size-1]\\n tcp = dpkt.ethernet.Ethernet(last_pkt.buf).data.data\\n \\n if (tcp.flags & dpkt.tcp.TH_SYN) != 0:\\n f.state = 'Request'\\n elif (tcp.flags & dpkt.tcp.TH_RST) != 0:\\n f.state = 'Reset'\\n elif (tcp.flags & dpkt.tcp.TH_FIN) != 0 and (tcp.flags & dpkt.tcp.TH_ACK) != 0:\\n f.state = 'Finished'\\n elif time_diff(tcp_flows[f][0].timestamp, tcp_flows[f][size-1].timestamp) <= 300:\\n f.state = 'Ongoing'\\n elif time_diff(tcp_flows[f][0].timestamp, tcp_flows[f][size-1].timestamp) > 300 \\\\\\n and (tcp.flags & dpkt.tcp.TH_RST) == 0 and (tcp.flags & dpkt.tcp.TH_FIN) == 0:\\n f.state = 'Failed'\\n\\n show_cdf_graphs()\",\n \"def pcap(self, fname):\\n\\t\\tcap = pcapy.open_offline(fname)\\n\\n\\t\\tself.map = []\\n\\t\\tself.p = PacketDecoder()\\n\\t\\tcap.loop(0, self.process)\\n\\n\\t\\treturn self.map\",\n \"def _parse(self):\\n \\n # HUA determine the host ip address\\n # read 20 packages and set the most frequent one\\n ips_dict = {}\\n count = 0\\n for raw_packet in self.raw_packets:\\n if count > 100: break\\n ethernet = Ethernet(raw_packet[0:14])\\n if(ethernet.type != 'IP'):\\n continue\\n ip = Ip(raw_packet[14:])\\n if(ip.protocol != 'TCP') :\\n continue\\n if(ip.src not in ips_dict):\\n ips_dict[ip.src] = 0\\n ips_dict[ip.src] += 1\\n if(ip.dst not in ips_dict):\\n ips_dict[ip.dst] = 0\\n ips_dict[ip.dst] += 1\\n # get the most frequent one\\n max_appear = 0\\n ip = None\\n for key, value in ips_dict.items():\\n if value > max_appear:\\n ip = key\\n max_appear = value\\n\\n global _device_ip\\n if not self.enableFilter or not _device_ip:\\n _device_ip = ip\\n\\n global _tcp_buf\\n _tcp_buf = {}\\n number = 0\\n self.begin_ts = self.packet_headers[-1]['ts']\\n rcount = 0\\n for raw_packet in self.raw_packets:\\n pcap_packet = Pcap_packet()\\n pcap_packet.pcap_num = rcount#number # add one to be consistent with wireshark\\n pcap_packet.top_layer = 1\\n pcap_packet.ethernet = Ethernet(raw_packet[0:14])\\n \\n #skip the packet that is not ip packet\\n \\n rcount += 1\\n\\n if (pcap_packet.ethernet.type != 'IP'):\\n continue\\n\\n pcap_packet.top_layer = 2\\n pcap_packet.ip = Ip(raw_packet[14:])\\n\\n\\n\\n\\n # just collect the packets between \\n \\n if self.enableFilter and not (pcap_packet.ip.src == _device_ip and pcap_packet.ip.dst == SERVER_IP) \\\\\\n and not (pcap_packet.ip.dst == _device_ip and pcap_packet.ip.src == SERVER_IP):\\n #print \\\"Ignore ip not ok\\\"\\n continue\\n '''\\n if rcount < 10 or rcount > 2600:\\n print 'rcount %d, time %d ---: %f' % (rcount, number, self.packet_headers[rcount - 1]['ts'] - self._ts_base)\\n '''\\n \\n self.pcap_packets.append(pcap_packet)\\n \\n\\n #skip the packet that is not tcp message\\n if (pcap_packet.ip.protocol != 'TCP'):\\n continue\\n \\n\\n\\n pcap_packet.top_layer = 3\\n pcap_packet.tcp = Tcp(pcap_packet.ip, number)\\n\\n if pcap_packet.ip.src == _device_ip:\\n pcap_packet.tcp.direction = \\\"out\\\"\\n else:\\n pcap_packet.tcp.direction = \\\"in\\\"\\n\\n\\n #dispatch the tcp into tcp streams\\n self._add_pkt_into_tcp_stream(pcap_packet, number)\\n \\n #reassemble tcp packet\\n self._tcp_reassemble(pcap_packet.pcap_num, pcap_packet.ip.src, pcap_packet.ip.dst, pcap_packet.tcp)\\n number += 1\\n #endof for\\n #flush the tcp_buf, other wise it will lose some http response\\n for sockets in _tcp_buf.keys():\\n self._tcp_flush(sockets)\\n del _tcp_buf[sockets]\",\n \"def test():\\n with open('univ1_pt8.pcap', 'rb') as f: #univ1_trace/univ1_pt8\\n pcap = Reader(f)\\n print_packets(pcap)\\n # top_flows()\\n host_pairs()\",\n \"def find_dac():\\n\\n\\ts = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n\\ts.bind((\\\"0.0.0.0\\\", 7654))\\n\\n\\twhile True:\\n\\t\\tdata, addr = s.recvfrom(1024)\\n\\t\\tbp = BroadcastPacket(data)\\n\\t\\t\\n\\t\\tprint \\\"Packet from %s: \\\" % (addr, )\\n\\t\\tbp.dump()\",\n \"def parse_packets(pcap):\\n # For each packet in the pcap process the contents\\n flow_Info = []\\n times = 0\\n for timestamp, buf in pcap:\\n times += 1\\n tmp_flow_Info = {}\\n\\n # Unpack the Ethernet frame (mac src/dst, ethertype)\\n eth = dpkt.ethernet.Ethernet(buf)\\n # Unpack the data whthin the Ethernet frame (the IP packet)\\n ip = eth.data\\n\\n # if protocol(ip.p) is not UDP(17) ,skip this packet\\n if ip.p != 17:\\n continue\\n\\n udp = ip.data\\n # Temp_data = parse_data(eth.data.udp.data)\\n # Filter CoAP by port\\n if(udp.sport != 5683 or udp.dport != 5683):\\n continue\\n\\n str_udp_data = parse_data(eth.data.udp.data)\\n # skip packets of Non_confirmable\\n if str_udp_data[0] == '5': \\n continue\\n\\n cycle = 0\\n index = 0\\n Udp_data = []\\n \\n len_str_udp_data = len(str_udp_data)\\n while cycle < (len_str_udp_data//3+1):\\n # Udp_data.append(int('0x'+Str_Udp_data[index:index + 2], 16))\\n Udp_data.append(int('0x' + str_udp_data[index:index + 2], 16))\\n cycle += 1\\n index += 3\\n tmp_flow_Info['udp_data'] = (Udp_data)\\n\\n # confirmable or ack\\n tmp_flow_Info['Coap_type'] = str_udp_data[0]\\n #print(str_udp_data) \\n \\n # skip space and get \\\"Message ID\\\" \\n HexMide = str_udp_data[6:8] + str_udp_data[9:11]\\n tmp_flow_Info['Mid'] = int('0x'+HexMide, 16)\\n\\n tmp_flow_Info['Timestamp'] = str(datetime.datetime.fromtimestamp(timestamp))\\n # print('Ethernet Frame: ', mac_addr(eth.src), mac_addr(eth.dst), eth.type)\\n tmp_flow_Info['src'] = inet_to_str(ip.src)\\n tmp_flow_Info['dst'] = inet_to_str(ip.dst)\\n\\n tmp_flow_Info['sport'] = udp.sport\\n tmp_flow_Info['dport'] = udp.dport\\n flow_Info.append(tmp_flow_Info)\\n\\n return flow_Info\",\n \"def pull(self):\\n\\n # For each packet in the pcap process the contents\\n for item in self.input_stream:\\n\\n # Print out the timestamp in UTC\\n print('%s -' % item['timestamp'], end='')\\n\\n # Transport info\\n if item['transport']:\\n print(item['transport']['type'], end='')\\n\\n # Print out the Packet info\\n packet_type = item['packet']['type']\\n print(packet_type, end='')\\n packet = item['packet']\\n if packet_type in ['IP', 'IP6']:\\n # Is there domain info?\\n if 'src_domain' in packet:\\n print('%s(%s) --> %s(%s)' % (net_utils.inet_to_str(packet['src']), packet['src_domain'],\\n net_utils.inet_to_str(packet['dst']), packet['dst_domain']), end='')\\n else:\\n print('%s --> %s' % (net_utils.inet_to_str(packet['src']), net_utils.inet_to_str(packet['dst'])), end='')\\n else:\\n print(str(packet))\\n\\n # Only include application if we have it\\n if item['application']:\\n print('Application: %s' % item['application']['type'], end='')\\n print(str(item['application']), end='')\\n\\n # Just for newline\\n print()\",\n \"def _parse(self):\\n \\n global _tcp_buf\\n _tcp_buf = {}\\n number = 1\\n for raw_packet in self.raw_packets:\\n pcap_packet = Pcap_packet()\\n self.pcap_packets.append(pcap_packet)\\n pcap_packet.pcap_num = number\\n number += 1\\n pcap_packet.top_layer = 1\\n pcap_packet.ethernet = Ethernet(raw_packet[0:14])\\n \\n #skip the packet that is not ip packet\\n if (pcap_packet.ethernet.type != 'IP'):\\n continue\\n \\n pcap_packet.top_layer = 2\\n pcap_packet.ip = Ip(raw_packet[14:])\\n \\n #skip the packet that is not tcp message\\n if (pcap_packet.ip.protocol != 'TCP'):\\n continue\\n \\n pcap_packet.top_layer = 3\\n pcap_packet.tcp = Tcp(pcap_packet.ip.packet[pcap_packet.ip.header_len: ])\\n \\n #skip the packets that is not http packet\\n if (pcap_packet.tcp.src_port != 80 and pcap_packet.tcp.dst_port != 80):\\n continue\\n \\n #dispatch the tcp into tcp streams\\n self._add_pkt_into_tcp_stream(pcap_packet, pcap_packet.pcap_num)\\n \\n #reassemble tcp packet\\n self._tcp_reassemble(pcap_packet.pcap_num, pcap_packet.ip.src, pcap_packet.ip.dst, pcap_packet.tcp)\\n #endof for\\n #flush the tcp_buf, other wise it will lose some http response\\n for sockets in _tcp_buf.keys():\\n self._tcp_flush(sockets)\\n del _tcp_buf[sockets]\",\n \"def recv_raw(self) -> Dict[str, Any]:\\n while True:\\n try:\\n packet = self.__recv_frame()\\n except UnknownPacketException:\\n continue\\n\\n # Hack for sniffing on localhost\\n if packet['address']['interface'] == 'lo' and packet['address']['type'] != 4:\\n continue\\n\\n if self.address and self.port:\\n if (\\n packet['ip_header']['source_address'] == self.address and\\n packet['tcp_header']['source_port'] == self.port\\n ):\\n return packet\\n if (\\n packet['ip_header']['destination_address'] == self.address and\\n packet['tcp_header']['destination_port'] == self.port\\n ):\\n return packet\\n elif self.address:\\n if (\\n packet['ip_header']['source_address'] == self.address or\\n packet['ip_header']['destination_address'] == self.address\\n ):\\n return packet\\n elif self.port:\\n if (\\n packet['tcp_header']['source_port'] == self.port or\\n packet['tcp_header']['destination_port'] == self.port\\n ):\\n return packet\\n else:\\n return packet\",\n \"def extract_tstat_data(pcap_filepath):\\n connections = {}\\n conn_id = 0\\n print('We are here')\\n with co.cd(os.path.basename(pcap_filepath[:-5])):\\n with co.cd(os.listdir('.')[0]):\\n print(connections)\\n # Complete TCP connections\\n connections, conn_id = extract_tstat_data_tcp_complete('log_tcp_complete', connections, conn_id)\\n # Non complete TCP connections (less info, but still interesting data)\\n connections, conn_id = extract_tstat_data_tcp_nocomplete('log_tcp_nocomplete', connections, conn_id)\\n\\n return connections\",\n \"def next_packet(filename, memorymap=True):\\n with open(filename, 'rb') as f:\\n \\n #memory map the file if necessary (prob requires 64 bit systems)\\n _file = f\\n if memorymap:\\n _file = mmap.mmap(f.fileno(), 0, prot=mmap.PROT_READ)\\n \\n while True:\\n packet = _file.read(TS.PACKET_SIZE)\\n if packet:\\n # first byte SHOULD be the sync byte\\n # but if it isn't find one.\\n if packet[0] != TS.SYNC_BYTE:\\n start_byte = 0\\n print packet[0]\\n for i in range(start_byte, TS.PACKET_SIZE):\\n if packet[i] == TS.SYNC_BYTE:\\n start_byte = i\\n break\\n # didn't find a new start? FAIL\\n if start_byte == 0:\\n raise Exception(\\\"failure to find sync byte in ts packet size.\\\")\\n continue\\n remainder = _file.read(TS.PACKET_SIZE - start_byte)\\n packet = packet[start_byte:] + remainder\\n yield packet\\n else:\\n break\",\n \"def _packet_in(self, ev):\\n\\n dp = ev.msg.datapath\\n ofp = dp.ofproto\\n parser = dp.ofproto_parser\\n match = ev.msg.match\\n\\n ##SNDCP packet with multiple fragments recieved - print warning, send ICMP fragmentation needed\\n ##TODO: Not WOrking correctly\\n ## File \\\"/usr/local/lib/python2.7/dist-packages/ryu/ofproto/ofproto_v1_3_parser.py\\\", line 746, in __getitem__\\n ## return dict(self._fields2)[key]\\n ## KeyError: 'udp_dst'\\n\\n # if (match['eth_type'] == 0x0800 and match['ip_proto'] == inet.IPPROTO_UDP\\n # and match['udp_dst'] == VGSN_PORT and match['sndcp_first_segment'] == 1\\n # and match['sndcp_more_segments'] == 1):\\n # _icmp_send(dp,match['in_port'],match['ipv4_dst'],match['ipv4_src'],match['eth_dst'],match['eth_src'],icmp_type=3,icmp_code=4)\\n # LOG.warning('WARNING: Device with IP: '+match['ipv4_src']+' sent fragmented sndcp packet')\\n # return\\n\\n ##ARP request recieved - send 'I'm here' response\\n if match['eth_type'] == 0x0806 and match['arp_op'] == 1:\\n LOG.debug(\\\"ARP request accepted\\\")\\n _arp_send(dp=dp, port_out=match['in_port'], arp_code=2, eth_dst=match['eth_src'], eth_target=match['arp_sha'],\\n ip_target=match['arp_spa'], ip_sender=match['arp_tpa'])\\n LOG.debug('Reply to '+match['arp_spa'] +': Host '+match['arp_tpa']+' is at forwarder '+str(dp.id) + \\\" with ethX source MAC address\\\")\\n return\\n\\n ##ARP response with target_ip==DISCOVERY_ARP_IP recieved - we found APN\\n #\\n # FIXED: All ARP responses are replied, regardless of the target IP\\n #\\n # TODO : At this point only ARPs belonging to the APNs networks subnet should\\n # be answered\\n if match['eth_type'] == 0x0806 and match['arp_op'] == 2:\\n LOG.debug('TUNNEL MNGR: ARP response with target APN discovery IP recieved at controller, processing for APN extraction')\\n pkt = packet.Packet(array.array('B', ev.msg.data))\\n arp_pkt=pkt.get_protocol(arp.arp)\\n apn_ip = arp_pkt.src_ip\\n apn_mac= arp_pkt.src_mac\\n port = match['in_port']\\n\\n ##Search for apn in APN_POOL to add mac addr. and update topology\\n for sApn in APN_POOL:\\n if sApn.ip_addr == apn_ip:\\n LOG.debug('Recieved ARP response was from ' + sApn.name + ' APN')\\n sApn.eth_addr = apn_mac\\n sApn.port = port\\n sApn.dpid = dp.id\\n # Links towards APNs will not be measured\\n topo.add_link(dp.id,str(sApn.name),port)\\n topo.add_link(str(sApn.name),dp.id,0)\\n topo.reload_topology()\\n LOG.debug('TUNNEL MNGR: APN '+str(sApn.name)+' found at forwarder: '+str(dp.id)+', port: '+str(port) + ' by ARP search')\\n\\n ##Add special rules to edge forwarder\\n self.on_edge_inet_dp_join(dp, port, sApn)\\n\\n # FIX: We do not handle bss as a special APN\\n # For greater extensibility, BSS/UTRAN/LAN APNs (exit/enter) points\\n # will be handled in a generic manner\\n #\\n ##Create MAC-tunnels between APN and all BSSs\\n #for bss in BSS_POOL:\\n # self.add_tunnel(bss,apn)\\n #break\\n\\n ### WMNC: In this case, we are not making tunnels between\\n # two types of ingress/egress point, but actually same type\\n\\n for dApn in APN_POOL:\\n # we are cycling through all possible APNs, looking for different APN tupples\\n # with filled HW addresses (already found by APN search)\\n if sApn != dApn and dApn.eth_addr != None:\\n LOG.debug('TUNNEL MNGR: Different APNs with filled HW address found, lets find out if there is tunnel between them')\\n\\n paths = False\\n try:\\n paths = nx.all_simple_paths(topo.DynamicGraph, source=sApn.name, target=dApn.name)\\n except:\\n LOG.debug('TUNNEL MNGR: No path between: ' + sApn.name + ' and ' + dApn.name + '. Retry when next APN discovered.')\\n\\n LOG.debug('TUNNEL MNGR: These are the paths between them (possible tunnels):')\\n if paths:\\n for path in paths:\\n LOG.debug('TUNNEL MNGR: Calling add_plainMacTunnel for ' + sApn.name + ' and ' + dApn.name + ' with path: ' + str(path))\\n self.add_plainMacTunnel(sApn, dApn, path)\\n else:\\n LOG.debug('TUNNEL MNGR: PATHS == 0 ????????????????')\\n\\n\\n return\\n\\n ##ICMP echo with dst_ip==DISCOVERY_IP_DST recieved - new link between forwarders is up\\n if match['eth_type'] == 0x0800 and match['ipv4_dst'] == DISCOVERY_IP_DST and match['ip_proto'] == 1:\\n #LOG.debug('TOPO MNGR: ICMP echo recieved at controller, processing for link extraction or latency measurement')\\n\\n pkt = packet.Packet(array.array('B', ev.msg.data))\\n\\n ##Discovery pings carry information about sending datapath in payload of icmp packet\\n ##these information are in Dictionary format, we parse the out with _icmp_parse_payload() method\\n body = _icmp_parse_payload(pkt)\\n neighbourDPID=body['dpid']\\n neighbourPort=body['port_out']\\n\\n ## measurement\\n ## currentClock moved way up to improve precision\\n receivedClock=float(body['clock'])\\n currentClock = time.clock()\\n latency = currentClock - receivedClock\\n\\n currentDate = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n ##Update latency or add new edges to topology.\\n if topo.DynamicGraph.has_edge(dp.id, neighbourDPID) and topo.DynamicGraph.has_edge(neighbourDPID, dp.id):\\n topo.StaticGraph[neighbourDPID][dp.id]['pdv'] = topo.StaticGraph[neighbourDPID][dp.id]['lat'] - latency\\n topo.StaticGraph[neighbourDPID][dp.id]['lat'] = latency\\n topo.StaticGraph[neighbourDPID][dp.id]['upt'] = currentDate\\n #topo.StaticGraph[neighbourDPID][dp.id]['upt'] = currentDate\\n loss = self.loss_update(neighbourDPID, dp.id, currentDate)\\n #LOG.debug('TOPO MNGR: Updating latency ' + str(latency) + ' and date ' + str(currentDate) + ' LOSS: ' + str(loss))\\n topo.reload_topology()\\n else:\\n ## latency not correct for both directions when adding links\\n ## update occurs on receive of next measurement packet from oposite direction\\n topo.add_link(dp.id, neighbourDPID, ev.msg.match['in_port'], latency, currentDate)\\n topo.add_link(neighbourDPID, dp.id, neighbourPort , latency, currentDate)\\n LOG.debug('TOPO MNGR: Topology changed: New link between forwarder ID '+str(dp.id)+ ' via port ' + str(ev.msg.match['in_port'])\\n +' and forwarder ID '+str(neighbourDPID)+ ' via port ' + str(neighbourPort) + ' was discovered.')\\n\\n topo.reload_topology()\\n ## retry to create tunnels\\n ## find better paths between APNs\\n for sApn in APN_POOL:\\n for dApn in APN_POOL:\\n if sApn != dApn:\\n LOG.debug('TOPO MNGR: Topology changed: trying to re-build inactive tunnel between:' + sApn.name + ' and ' + dApn.name)\\n paths = False\\n try:\\n paths = nx.all_simple_paths(topo.DynamicGraph, source=sApn.name, target=dApn.name)\\n except:\\n LOG.debug('No path between: ' + sApn.name + ' and ' + dApn.name + '. Retry when next fwd connects.')\\n\\n LOG.debug('TUNNEL MNGR: These are the paths between them (possible tunnels):')\\n if paths:\\n for path in paths:\\n LOG.debug('TUNNEL MNGR: Calling add_plainMacTunnel for ' + sApn.name + ' and ' + dApn.name + ' with path: ' + str(path))\\n self.add_plainMacTunnel(sApn, dApn, path)\\n else:\\n LOG.debug('TUNNEL MNGR: PATHS == 0 ????????????????')\\n return\\n\\n # flow of last resort (process for routing)\\n if match['eth_type'] == 0x0800:\\n # LOG.debug('*****************Flow of last resort matched(plain IP), process for routing********'\\n # + ' match[ipv4_dst]: ' + str(match['ipv4_dst'] + ' match[ipv4_src]: ' + str(match['ipv4_src']) + ' DSCP: ' + str(match['ip_dscp'])))\\n ## Not very proud of myself, but it will do the trick\\n ## Turbo lumberjack routing logic\\n ## TODO: Implement a longest prefix match routing\\n\\n candidates = []\\n\\n for source, destination, ip_dscp in routesList:\\n if ((source == match['ipv4_dst'] and destination == match['ipv4_src']) or (source == match['ipv4_src'] and destination == match['ipv4_dst'])) and ip_dscp == match['ip_dscp']:\\n # LOG.debug('ROUTING: route source: ' + str(source) + 'destination: ' + str(destination)\\n # + ' match[ipv4_dst]: ' + str(match['ipv4_dst'])\\n # + ' match[ipv4_src]: ' + str(match['ipv4_src']) + ' DSCP: ' + str(ip_dscp)\\n # + ' already exists, aborting addition of new route')\\n return\\n\\n for tunnel in TUNNELS:\\n if (tunnel.sApn.ip_addr == match['ipv4_dst'] and tunnel.dApn.ip_addr == match['ipv4_src']) or (tunnel.sApn.ip_addr == match['ipv4_src'] and tunnel.dApn.ip_addr == match['ipv4_dst']):\\n LOG.debug('ROUTING: Tunnel candidate found in list of tunnels. Adding tunnel path: ' + str(tunnel.po_edges) + ' to candidates.')\\n candidates.append(tunnel)\\n\\n trafficClass = self.TC_selection(match['ip_dscp'])\\n\\n if len(candidates) == 0:\\n LOG.debug('ROUTING: match[ipv4_dst]: ' + str(match['ipv4_dst'])\\n + ' match[ipv4_src]: ' + str(match['ipv4_src']) + ' DSCP: ' + str(match['ip_dscp']))\\n LOG.debug('ROUTING: ERROR, NO feasible tunnels for such route.')\\n return\\n\\n LOG.debug('Looking for tunnels: DST_IP: ' + match['ipv4_dst'] + ' SRC_IP: ' + match['ipv4_src'] + ' DSCP: ' + str(match['ip_dscp']) + '(traffic class: ' + str(trafficClass) + ')' + ' Incoming from FWD: ' + str(dp.id))\\n tunnel = self.tunnel_selection(trafficClass, candidates)\\n LOG.debug('TE MNGR: Selected tunnel Path out: ' + str(tunnel.path_out_str) + ' meter_id: ' + str(tunnel.meter_id))\\n\\n dscp = match['ip_dscp']\\n\\n ## meter_id\\n ## 2,4,6,8,10 = 500kbps, 1,3,5,7,9 = 1000kbps ...\\n ## 0 = 100Gbps\\n meter_id = tunnel.meter_id\\n\\n #\\n # FIXME: incomplete set of rules installed on LAN Access forwarders\\n # TODO : Philosophy of table IDs should be clarified, as now it total mess!!!\\n # TODO : this should be done only once, from that moment, all user plane packets\\n # should travelse only forwarder and should not be sent to controller\\n\\n\\n\\n #WAY OUT\\n dp = dpset.get(tunnel.sApn.dpid)\\n parser = dp.ofproto_parser\\n ofp = dp.ofproto\\n match = parser.OFPMatch (eth_type=0x0800, ipv4_dst=tunnel.dApn.ip_addr, ip_dscp=dscp)\\n actions = [parser.OFPActionSetField(eth_src=tunnel.tid_in), parser.OFPActionSetField(eth_dst=tunnel.tid_out)]\\n inst = [parser.OFPInstructionGotoTable(MAC_TUNNEL_TABLE), parser.OFPInstructionMeter(meter_id), parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\\n req = parser.OFPFlowMod(datapath=dp, priority=100, match=match, instructions=inst, table_id=INGRESS_TABLE)\\n dp.send_msg(req)\\n\\n LOG.debug('ROUTING: Installing flow ON WAY OUT to forwarderID: ' + str(dp.id) + ',Table: ' + str(INGRESS_TABLE) + ' DP ID: ' + str(tunnel.dApn.dpid) + ' Tunel dApn IP addr: ' + str(tunnel.dApn.ip_addr) + ' Tunnel ID: ' + str(tunnel.tid_out))\\n\\n dp = dpset.get(tunnel.dApn.dpid)\\n parser = dp.ofproto_parser\\n ofp = dp.ofproto\\n match = parser.OFPMatch (eth_dst=tunnel.tid_out)\\n actions = [parser.OFPActionSetField(eth_dst=tunnel.dApn.eth_addr), parser.OFPActionOutput(tunnel.path_out[-1].port_out)]\\n inst = [parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\\n req = parser.OFPFlowMod(datapath=dp, priority=300, match=match, instructions=inst, table_id=ACCESS_ADAPTATION_TABLE_OUT)\\n dp.send_msg(req)\\n LOG.debug('ROUTING: Installing flow ON WAY OUT to forwarderID: ' + str(dp.id) + ',Table: ' + str(ACCESS_ADAPTATION_TABLE_OUT) + ' DP ID: ' + str(tunnel.dApn.dpid)+ ' Tunel ID: ' + str(tunnel.tid_out)+ ' dApn ETH addr: ' + str(tunnel.dApn.eth_addr))\\n\\n #WAY IN\\n dp = dpset.get(tunnel.dApn.dpid)\\n parser = dp.ofproto_parser\\n ofp = dp.ofproto\\n match = parser.OFPMatch (eth_type=0x0800, ipv4_dst=tunnel.sApn.ip_addr, ip_dscp=dscp)\\n actions = [parser.OFPActionSetField(eth_dst=tunnel.tid_in), parser.OFPActionSetField(eth_src=tunnel.tid_out)]\\n inst = [parser.OFPInstructionGotoTable(MAC_TUNNEL_TABLE), parser.OFPInstructionMeter(meter_id), parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\\n req = parser.OFPFlowMod(datapath=dp, priority=100, match=match, instructions=inst, table_id = INGRESS_TABLE)\\n dp.send_msg(req)\\n LOG.debug('ROUTING: Installing flow ON WAY IN to forwarderID: ' + str(dp.id) + ',Table: ' + str(INGRESS_TABLE) + ' DP ID: ' + str(tunnel.sApn.dpid) + ' Tunel dApn IP addr: ' + str(tunnel.sApn.ip_addr) + ' Tunnel ID: ' + str(tunnel.tid_in))\\n\\n\\n dp = dpset.get(tunnel.sApn.dpid)\\n parser = dp.ofproto_parser\\n ofp = dp.ofproto\\n match = parser.OFPMatch (eth_dst=tunnel.tid_in)\\n actions = [parser.OFPActionSetField(eth_dst=tunnel.sApn.eth_addr), parser.OFPActionOutput(tunnel.path_in[-1].port_out)]\\n inst = [parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)]\\n req = parser.OFPFlowMod(datapath=dp, priority=300, match=match, instructions=inst, table_id=ACCESS_ADAPTATION_TABLE_OUT)\\n dp.send_msg(req)\\n LOG.debug('ROUTING: Installing flow ON WAY IN to forwarderID: ' + str(dp.id) + ',Table: ' + str(ACCESS_ADAPTATION_TABLE_OUT) + ' DP ID: ' + str(tunnel.sApn.dpid)+ ' Tunel ID: ' + str(tunnel.tid_in)+ ' sApn ETH addr: ' + str(tunnel.sApn.eth_addr))\\n\\n\\n LOG.debug('ROUTING: Rules on access edge forwarders installed')\\n LOG.debug('ROUTING: Adding route: DST_IP: ' + tunnel.dApn.ip_addr + ' SRC_IP: ' + tunnel.sApn.ip_addr + ' dscp: ' + str(dscp) + ' path out str: ' + tunnel.path_out_str )\\n routesList.append( ( tunnel.sApn.ip_addr, tunnel.dApn.ip_addr, dscp) )\\n\\n parser = dp.ofproto_parser\\n\\n for dpid in LAN_TYPE_FORWARDERS:\\n ## DUNNO why this rule with low priority still hits traffic which is also matched by rules with IP address matches\\n ## Here I delete the rule, it is added on FWD when it connects to controoller\\n LOG.debug('TOPO MNGR: Forwarder: ' + str(dpid) + ' is a LAN edge forwarder, deleting rules')\\n dp = dpset.get(dpid)\\n priority = 2\\n match = parser.OFPMatch(eth_type=0x0800)\\n actions = [parser.OFPActionOutput(ofp.OFPP_CONTROLLER)]\\n self.mod_flow(dp, command=dp.ofproto.OFPFC_DELETE_STRICT,\\n table_id=0, actions=actions,\\n match=match, priority=priority)\\n\\n LOG.debug('TOPO MNGR: Forwarder: ' + str(dp.id) + ' is a LAN edge forwarder, installing rules again :)')\\n match = parser.OFPMatch(eth_type=0x0800)\\n actions = [parser.OFPActionOutput(ofp.OFPP_CONTROLLER)]\\n self.add_flow(dp, 2, match, actions)\",\n \"def process(self):\\n fp = open(self.pcap, \\\"rb\\\")\\n pkts = dpkt.pcap.Reader(fp)\\n if self.sip and self.dip and self.sp and self.dp:\\n self.process_pkts(pkts)\",\n \"def filter(self):\\n # outfile = open(self.newpcap, 'wb')\\n # writer = dpkt.pcap.Writer(outfile)\\n f = open(self.pcapfile, 'rb')\\n packets = dpkt.pcap.Reader(f)\\n\\n for timestamp, buf in packets:\\n eth = dpkt.ethernet.Ethernet(buf)\\n if not isinstance(eth.data, dpkt.ip.IP): # 确保以太网数据包含一个IP数据包, Non IP Packet type not supported\\n continue # 过滤空IP包\\n ip = eth.data # 获取以太网帧（IP数据包）\\n if not isinstance(ip.data, dpkt.tcp.TCP): # 在传输层中检查TCP\\n continue\\n tcp = ip.data # 获取tcp数据\\n # print('-->TCP Data: ', repr(tcp))\\n\\n \\\"\\\"\\\" 过滤三次握手后的首包\\\"\\\"\\\"\\n seq = self.seq_pattern.findall(repr(tcp))\\n ack = self.ack_pattern.findall(repr(tcp))\\n if not (seq or ack): # seq、ack必须有一个, 一真即真\\n continue\\n if ack:\\n ack = ack[0]\\n if seq:\\n seq = seq[0]\\n\\n if not ack and seq: # 一次握手请求\\n self.hash_table[seq] = {}\\n self.stream_table[seq] = [buf]\\n if ack and seq: # 二次、三次、交流包\\n if str(int(ack) - 1) in self.hash_table.keys(): # 有一次握手记录\\n number = str(int(ack) - 1)\\n if 'second' not in self.hash_table[number].keys(): # 新增二次握手\\n self.hash_table[number]['second'] = {'seq': seq, 'ack': ack}\\n self.stream_table[number].append(buf) # 将二次握手添加到buf\\n self.resp_relation[seq] = ack # 新增关系表\\n\\n # 存在二次握手记录, 看hash表有无第三次握手记录, 有就保存stream流\\n # 基本就是traffic响应包了\\n elif 'three' in self.hash_table[number].keys():\\n if number not in self.stream_table.keys():\\n self.stream_table[number] = []\\n self.stream_table[number].append(buf)\\n else:\\n self.stream_table[number].append(buf)\\n\\n # ack-1没有对应的hash表, 可能是三次握手或traffic请求包\\n elif str(int(seq) - 1) in self.hash_table.keys():\\n number = str(int(seq) - 1)\\n if 'second' not in self.hash_table[number]:\\n pass\\n elif 'three' not in self.hash_table[number]: # 三次包\\n self.hash_table[number]['three'] = {'seq': seq, 'ack': ack}\\n self.stream_table[number].append(buf)\\n # 否则就是traffic包了\\n else:\\n if number not in self.stream_table.keys():\\n self.stream_table[number] = []\\n self.stream_table[number].append(buf)\\n else:\\n self.stream_table[number].append(buf)\\n # traffic响应包\\n elif str(int(seq) - 1) in self.resp_relation.keys():\\n number = str(int(seq) - 1)\\n second_ack = self.resp_relation[number]\\n number = str(int(second_ack) - 1)\\n if number not in self.stream_table.keys():\\n self.stream_table[number] = []\\n self.stream_table[number].append(buf)\\n else:\\n self.stream_table[number].append(buf)\\n else:\\n continue # seq不存在\\n\\n # outfile.close()\\n f.close()\",\n \"def get_connections(capture):\\n ip_dict = dict()\\n for pkt in capture:\\n\\n if not hasattr(pkt, \\\"ip\\\") and not hasattr(pkt, \\\"ipv6\\\"):\\n continue\\n\\n protocol = pkt.highest_layer\\n\\n tcp_dst_port = None\\n tcp_src_port = None\\n if hasattr(pkt, \\\"tcp\\\"):\\n tcp_src_port = pkt.tcp.srcport\\n tcp_dst_port = pkt.tcp.dstport\\n\\n if hasattr(pkt, \\\"ip\\\"):\\n if pkt.ip.src.startswith(\\\"192.168.178\\\"):\\n ip, dst = pkt.ip.src, pkt.ip.dst\\n else:\\n ip, dst = pkt.ip.dst, pkt.ip.src\\n tcp_dst_port = tcp_src_port\\n else:\\n # TODO: how to discern src and dst in IPv6?\\n ip, dst = pkt.ipv6.src, pkt.ipv6.dst\\n\\n ip = \\\"%s\\\" % ip\\n dkey = (\\n \\\"%s\\\" % protocol,\\n int(tcp_dst_port) if tcp_dst_port else None,\\n \\\"%s\\\" % dst\\n )\\n if ip not in ip_dict:\\n ip_dict[ip] = {dkey: 1}\\n else:\\n ip_dict[ip][dkey] = ip_dict[ip].get(dkey, 0) + 1\\n return ip_dict\",\n \"def process_pcap(self):\\n fp = open(self.pcap, \\\"rb\\\")\\n pkts = dpkt.pcap.Reader(fp)\\n self.process_pkts(pkts)\",\n \"def handle_tcp(pkt, packets, i, start_point):\\r\\n src_port = int(pkt[start_point:start_point+4], 16)\\r\\n start_point += 4\\r\\n dest_port = int(pkt[start_point:start_point+4], 16)\\r\\n start_point += 4\\r\\n sequence_num = int(pkt[start_point:start_point+8], 16)\\r\\n start_point += 8\\r\\n acknowledgment = int(pkt[start_point:start_point+8], 16)\\r\\n start_point += 8\\r\\n data_offset = int(pkt[start_point], 16) * 4\\r\\n start_point += 2\\r\\n flags = pkt[start_point:start_point+2]\\r\\n flags_str = \\\"\\\"\\r\\n for f in flags:\\r\\n flags_str += str(format(int(f), '04b'))\\r\\n start_point += 2\\r\\n window_size = int(pkt[start_point:start_point+4], 16)\\r\\n start_point += 4\\r\\n checksum_value = pkt[start_point:start_point+4]\\r\\n start_point += 4\\r\\n urgent_pointer = int(pkt[start_point:start_point+4], 16)\\r\\n start_point += 4\\r\\n options = int((2 * packets[i][0][0] - start_point)/2)\\r\\n\\r\\n packets[i][2].append(src_port)\\r\\n packets[i][2].append(dest_port)\\r\\n packets[i][2].append(sequence_num)\\r\\n packets[i][2].append(acknowledgment)\\r\\n packets[i][2].append(data_offset)\\r\\n packets[i][2].append(flags_str)\\r\\n packets[i][2].append(window_size)\\r\\n packets[i][2].append(checksum_value)\\r\\n packets[i][2].append(urgent_pointer)\\r\\n packets[i][2].append(options)\\r\\n return packets\",\n \"def reader(self):\\n while self.alive:\\n try:\\n data = self.serial.read_until(b'~')[:-1]\\n packet = ethernet.Ethernet(data)\\n if packet[icmp.ICMP]:\\n packet[ethernet.Ethernet].dst_s = \\\"dc:a6:32:00:a7:8b\\\"\\n packet[ip.IP].dst_s = \\\"192.168.1.35\\\"\\n packet[icmp.ICMP].sum = b'0x1783'\\n print(\\\"\\\\n\\\\n__________________RESPONSE FROM VISIBLE PI__________________\\\")\\n print(packet)\\n if data:\\n self.write(packet.bin())\\n except socket.error as msg:\\n break\\n self.alive = False\",\n \"def udp_iterator(pc):\\n\\tfor time,pkt in pc:\\n\\t\\teth = dpkt.ethernet.Ethernet(pkt)\\n\\t\\tif eth.type == dpkt.ethernet.ETH_TYPE_IP:\\n\\t\\t ip = eth.data\\n\\t\\t # if the IP protocol is UDP, process it further\\n\\t\\t if ip.p == dpkt.ip.IP_PROTO_UDP :\\n\\t\\t\\tudp = ip.data\\n\\t\\t\\tyield( ip.src, udp.sport, ip.dst, udp.dport, udp.data )\",\n \"def read_and_store_pcap(file_name):\\r\\n file = open(file_name, \\\"rb\\\")\\r\\n global_header = file.read(24).hex()\\r\\n byte = file.read(16)\\r\\n packets = []\\r\\n bytes = []\\r\\n sizes = []\\r\\n while byte:\\r\\n packet_header = byte.hex()\\r\\n # parse the size for each packet\\r\\n size = struct.unpack(\\\"<L\\\", codecs.decode(str(packet_header[16:24]), \\\"hex\\\"))[0]\\r\\n sizes.append(size)\\r\\n # read the whole packet by its size from the bytes\\r\\n byte = file.read(size).hex()\\r\\n bytes.append(byte)\\r\\n byte = file.read(16)\\r\\n for size in sizes:\\r\\n packets.append(([size], [], []))\\r\\n i = 0\\r\\n\\r\\n for pkt in bytes:\\r\\n packets = handle_pkt_header(pkt, packets, i)\\r\\n packets, start_point = handle_ip_header(pkt, packets, i)\\r\\n protocol = packets[i][1][7]\\r\\n if protocol == 1:\\r\\n packets = handle_icmp(pkt, packets, i, start_point)\\r\\n elif protocol == 6:\\r\\n packets = handle_tcp(pkt, packets, i, start_point)\\r\\n elif protocol == 17:\\r\\n packets = handle_udp(pkt, packets, i, start_point)\\r\\n i += 1\\r\\n # print(packets)\\r\\n return packets\",\n \"def testParse(self):\\n parser = pcap.PcapParser()\\n storage_writer = self._ParseFile(['test.pcap'], parser)\\n\\n # PCAP information:\\n # Number of streams: 96 (TCP: 47, UDP: 39, ICMP: 0, Other: 10)\\n #\\n # For each stream 2 events are generated one for the start\\n # and one for the end time.\\n\\n self.assertEqual(storage_writer.number_of_events, 192)\\n\\n events = list(storage_writer.GetEvents())\\n\\n # Test stream 3 (event 6).\\n # Protocol: TCP\\n # Source IP: 192.168.195.130\\n # Dest IP: 63.245.217.43\\n # Source Port: 1038\\n # Dest Port: 443\\n # Stream Type: SSL\\n # Starting Packet: 4\\n # Ending Packet: 6\\n\\n event = events[6]\\n self.assertEqual(event.packet_count, 3)\\n self.assertEqual(event.protocol, 'TCP')\\n self.assertEqual(event.source_ip, '192.168.195.130')\\n self.assertEqual(event.dest_ip, '63.245.217.43')\\n self.assertEqual(event.dest_port, 443)\\n self.assertEqual(event.source_port, 1038)\\n self.assertEqual(event.stream_type, 'SSL')\\n self.assertEqual(event.first_packet_id, 4)\\n self.assertEqual(event.last_packet_id, 6)\\n\\n # Test stream 6 (event 12).\\n # Protocol: UDP\\n # Source IP: 192.168.195.130\\n # Dest IP: 192.168.195.2\\n # Source Port: 55679\\n # Dest Port: 53\\n # Stream Type: DNS\\n # Starting Packet: 4\\n # Ending Packet: 6\\n # Protocol Data: DNS Query for wpad.localdomain\\n\\n event = events[12]\\n self.assertEqual(event.packet_count, 5)\\n self.assertEqual(event.protocol, 'UDP')\\n self.assertEqual(event.source_ip, '192.168.195.130')\\n self.assertEqual(event.dest_ip, '192.168.195.2')\\n self.assertEqual(event.dest_port, 53)\\n self.assertEqual(event.source_port, 55679)\\n self.assertEqual(event.stream_type, 'DNS')\\n self.assertEqual(event.first_packet_id, 11)\\n self.assertEqual(event.last_packet_id, 1307)\\n self.assertEqual(\\n event.protocol_data, 'DNS Query for wpad.localdomain')\\n\\n expected_message = (\\n 'Source IP: 192.168.195.130 '\\n 'Destination IP: 192.168.195.2 '\\n 'Source Port: 55679 '\\n 'Destination Port: 53 '\\n 'Protocol: UDP '\\n 'Type: DNS '\\n 'Size: 380 '\\n 'Protocol Data: DNS Query for wpad.localdomain '\\n 'Stream Data: \\\\'\\\\\\\\xb8\\\\\\\\x9c\\\\\\\\x01\\\\\\\\x00\\\\\\\\x00\\\\\\\\x01\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00'\\n '\\\\\\\\x00\\\\\\\\x00\\\\\\\\x04wpad\\\\\\\\x0blocaldomain\\\\\\\\x00\\\\\\\\x00\\\\\\\\x01\\\\\\\\x00\\\\\\\\x01\\\\\\\\xb8'\\n '\\\\\\\\x9c\\\\\\\\x01\\\\\\\\x00\\\\\\\\x00\\\\\\\\x01\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00\\\\\\\\x04wpa\\\\' '\\n 'First Packet ID: 11 '\\n 'Last Packet ID: 1307 '\\n 'Packet Count: 5')\\n expected_short_message = (\\n 'Type: DNS '\\n 'First Packet ID: 11')\\n\\n self._TestGetMessageStrings(event, expected_message, expected_short_message)\",\n \"def convert_pcap_to_dataframe(input_file):\\r\\n if not os.path.exists(input_file):\\r\\n raise IOError(\\\"File \\\" + input_file + \\\" does not exist\\\")\\r\\n\\r\\n tshark_fields = \\\"-e frame.time_epoch \\\" \\\\\\r\\n \\\"-e _ws.col.Source \\\" \\\\\\r\\n \\\"-e _ws.col.Destination \\\" \\\\\\r\\n \\\"-e _ws.col.Protocol \\\" \\\\\\r\\n \\\"-e frame.len \\\" \\\\\\r\\n \\\"-e ip.ttl \\\" \\\\\\r\\n \\\"-e ip.flags.mf \\\" \\\\\\r\\n \\\"-e ip.frag_offset \\\" \\\\\\r\\n \\\"-e icmp.type \\\" \\\\\\r\\n \\\"-e tcp.srcport \\\" \\\\\\r\\n \\\"-e tcp.dstport \\\" \\\\\\r\\n \\\"-e udp.srcport \\\" \\\\\\r\\n \\\"-e udp.dstport \\\" \\\\\\r\\n \\\"-e dns.qry.name \\\" \\\\\\r\\n \\\"-e dns.qry.type \\\" \\\\\\r\\n \\\"-e http.request \\\" \\\\\\r\\n \\\"-e http.response \\\" \\\\\\r\\n \\\"-e http.user_agent \\\" \\\\\\r\\n \\\"-e tcp.flags.str \\\" \\\\\\r\\n \\\"-e ntp.priv.reqcode \\\"\\r\\n\\r\\n temporary_file = tempfile.TemporaryFile(\\\"r+b\\\")\\r\\n\\r\\n # print(shutil.which(command))\\r\\n\\r\\n p = subprocess.Popen([settings.TSHARK + \\\" -n -r \\\\\\\"\\\" + input_file + \\\"\\\\\\\" -E separator='\\\\x03' -E header=y -T fields \\\" + tshark_fields],\\r\\n shell=True, stdout=temporary_file) #\\\\x03 is ETX\\r\\n p.communicate()\\r\\n p.wait()\\r\\n\\r\\n # Reset file pointer to start of file\\r\\n temporary_file.seek(0)\\r\\n\\r\\n df = pd.read_csv(temporary_file, sep=\\\"\\\\x03\\\", low_memory=False, error_bad_lines=False)\\r\\n\\r\\n temporary_file.close()\\r\\n\\r\\n if ('tcp.srcport' in df.columns) and ('udp.srcport' in df.columns) and ('tcp.dstport' in df.columns) and \\\\\\r\\n ('udp.dstport' in df.columns):\\r\\n # Combine source and destination ports from tcp and udp\\r\\n df['srcport'] = df['tcp.srcport'].fillna(df['udp.srcport'])\\r\\n df['dstport'] = df['tcp.dstport'].fillna(df['udp.dstport'])\\r\\n\\r\\n df['srcport'] = df['srcport'].apply(lambda x: int(x) if str(x).replace('.', '', 1).isdigit() else 0)\\r\\n df['dstport'] = df['dstport'].apply(lambda x: int(x) if str(x).replace('.', '', 1).isdigit() else 0)\\r\\n\\r\\n # Remove columns: 'tcp.srcport', 'udp.srcport','tcp.dstport', 'udp.dstport'\\r\\n df.drop(['tcp.srcport', 'udp.srcport', 'tcp.dstport', 'udp.dstport'], axis=1, inplace=True)\\r\\n\\r\\n # Drop all empty columns (for making the analysis more efficient! less memory.)\\r\\n df.dropna(axis=1, how='all', inplace=True)\\r\\n df = df.fillna(0)\\r\\n\\r\\n if 'icmp.type' in df.columns:\\r\\n df['icmp.type'] = df['icmp.type'].astype(str)\\r\\n\\r\\n if 'ip.frag_offset' in df.columns:\\r\\n df['ip.frag_offset'] = df['ip.frag_offset'].astype(str)\\r\\n\\r\\n if 'ip.flags.mf' in df.columns:\\r\\n df['ip.flags.mf'] = df['ip.flags.mf'].astype(str)\\r\\n\\r\\n if ('ip.flags.mf' in df.columns) and ('ip.frag_offset' in df.columns):\\r\\n # Analyse fragmented packets\\r\\n df['fragmentation'] = (df['ip.flags.mf'] == '1') | (df['ip.frag_offset'] != '0')\\r\\n df.drop(['ip.flags.mf', 'ip.frag_offset'], axis=1, inplace=True)\\r\\n\\r\\n if 'tcp.flags.str' in df.columns:\\r\\n df['tcp.flags.str'] = df['tcp.flags.str'].str.encode(\\\"utf-8\\\") \\r\\n\\r\\n return df\",\n \"def print_info(self):\\n \\n i = 1\\n for pcap_packet in self.pcap_packets:\\n print '----------------frame: %d------------' % i\\n i += 1\\n pcap_packet.ethernet.print_info()\\n \\n #skip the packet that is not ip packet\\n if (pcap_packet.ethernet.type != 'IP'):\\n continue\\n \\n print '################# packet in the frame ################'\\n pcap_packet.ip.print_info()\\n \\n #skp the packet that is not tcp message\\n if (pcap_packet.ip.protocol != 'TCP'):\\n continue\\n \\n print '@@@@@@@@@@@@@@@@@@@ tcp fields @@@@@@@@@@@@@@@@@@@@'\\n pcap_packet.tcp.print_info()\\n \\n print\\n #endof for\",\n \"def print_info(self):\\n \\n i = 1\\n for pcap_packet in self.pcap_packets:\\n print '----------------frame: %d------------' % i\\n i += 1\\n pcap_packet.ethernet.print_info()\\n \\n #skip the packet that is not ip packet\\n if (pcap_packet.ethernet.type != 'IP'):\\n continue\\n \\n print '################# packet in the frame ################'\\n pcap_packet.ip.print_info()\\n \\n #skp the packet that is not tcp message\\n if (pcap_packet.ip.protocol != 'TCP'):\\n continue\\n \\n print '@@@@@@@@@@@@@@@@@@@ tcp fields @@@@@@@@@@@@@@@@@@@@'\\n pcap_packet.tcp.print_info()\\n \\n print\\n #endof for\",\n \"def _read_packets(self, reader: Par2FileReader):\\n start_count = len(self)\\n pointers = reader.get_pointers()\\n # Create RecoverySets if needed\\n for set_id, pointer_set in packets.by_set_id(pointers).items():\\n print(set_id.hex(), pointer_set)\\n if set_id not in self.recovery_sets.keys():\\n # Create a RecoverySet if needed\\n self.recovery_sets[set_id] = RecoverySet(set_id)\\n for pointer in pointer_set:\\n self.recovery_sets[set_id].packets.add(pointer)\\n logger.info(\\\"Added {} new packets\\\".format(len(self) - start_count))\",\n \"def locator(pcap_obj,kml_file):\\r\\n ip_list = []\\r\\n for ts, buf in pcap_obj:\\r\\n eth = dpkt.ethernet.Ethernet(buf)\\r\\n ip = eth.data\\r\\n try: # extract all unique IPs\\r\\n src_ip = str(socket.inet_ntoa(ip.src))\\r\\n dst_ip = str(socket.inet_ntoa(ip.dst))\\r\\n if src_ip in ip_list:\\r\\n pass\\r\\n else:\\r\\n ip_list.append(src_ip)\\r\\n if dst_ip in ip_list:\\r\\n pass\\r\\n else:\\r\\n ip_list.append(dst_ip)\\r\\n except AttributeError:\\r\\n pass\\r\\n\\r\\n try:\\r\\n reader = geoip2.database.Reader('GeoLite2-City_20190129.mmdb') # reading from db(can be redacted)\\r\\n except FileNotFoundError:\\r\\n print(f'[!]DB file not in current directory or with a different file name')\\r\\n sys.exit(1)\\r\\n area = []\\r\\n longitude = []\\r\\n latitude = []\\r\\n ips = []\\r\\n for ip_addr in ip_list:\\r\\n try:\\r\\n rec = reader.city(ip_addr) # reading IP\\r\\n country = rec.country.iso_code # assigning country and city\\r\\n city = rec.city.name\\r\\n if city is None and country is None:\\r\\n area.append('Unknown')\\r\\n elif city is None:\\r\\n area.append(f'Unknown city:{country}') # looking for unknown country\\r\\n elif country is None:\\r\\n area.append(f'Unknown country:{city}') # looking for unknown city\\r\\n else:\\r\\n area.append(f'{city} {country}')\\r\\n\\r\\n longitude.append(rec.location.longitude)\\r\\n latitude.append(rec.location.latitude)\\r\\n ips.append(ip_addr)\\r\\n except geoip2.errors.AddressNotFoundError:\\r\\n pass\\r\\n\\r\\n try:\\r\\n kml = simplekml.Kml()\\r\\n final_path = str(os.getcwd() + os.sep + kml_file) # defining full canonical path\\r\\n for i in range(0, len(ips)):\\r\\n kml.newpoint(name=(area[i]),\\r\\n coords=[(longitude[i], latitude[i])],\\r\\n description=f'[+] Location = {area[i]}\\\\n IP: {ips[i]}')\\r\\n kml.save(final_path)\\r\\n print(f\\\"[+] Writing IP locations to {kml_file}\\\") # writing data to a KML file\\r\\n print(f\\\"[+] Opening Google Earth with:{kml_file}\\\\n\\\") # reading file with google earth\\r\\n try:\\r\\n os.startfile(final_path)\\r\\n except OSError:\\r\\n print(f'[!] Warning: Google Earth must be installed to open the kml')\\r\\n except FileNotFoundError:\\r\\n pass\",\n \"def capture_packets(self, interface, count=1, timeout=None):\\n if interface not in self.packet_captures:\\n raise ObjectNotFoundException(\\n 'No packet capture is running or was run on host/interface' +\\n self.name + '/' + interface)\\n tcpd = self.packet_captures[interface]\\n return tcpd.wait_for_packets(count, timeout)\",\n \"def process_pkts(self, pkts: list):\\n pkt_count = 0\\n for ts, buf in pkts:\\n eth = dpkt.ethernet.Ethernet(buf)\\n if not isinstance(eth.data, dpkt.ip.IP):\\n continue\\n ip = eth.data\\n if ((inet_to_str(ip.src) == self.sip and inet_to_str(ip.dst) == self.dip) or\\n (inet_to_str(ip.src) == self.dip and inet_to_str(ip.dst) == self.sip)):\\n if isinstance(ip.data, dpkt.tcp.TCP):\\n tcp = ip.data\\n if ((tcp.sport == self.sp and tcp.dport == self.dp) or\\n (tcp.dport == self.sp and tcp.sport == self.dp)):\\n pkt_count += 1\\n self._process(buf, ts, pkt_count)\\n if self._c_state == self._s_state and self._c_state == TCPState.CLOSED:\\n logger.info(\\\"Session finished.\\\")\\n logger.info(\\\"Number of packets in the session id: {} is {}\\\".format(\\n self.session_count, len(self.sessions[self.session_count])))\\n self.__reset_state__()\",\n \"def extract_from_pcap(device=None, pcap=None, flags=\\\"-v -r\\\", path_to_chaosreader=\\\"/tmp/\\\"):\\n if device is None or pcap is None:\\n raise Exception(\\\"device and pcap are mandatory arguments\\\")\\n device.shell(command=\\\"cd /tmp\\\")\\n\\n cmd = path_to_chaosreader + \\\"chaosreader0.94 \\\" + flags + \\\" \\\" + pcap\\n output = device.shell(command=cmd)\\n\\n if not re.match(\\\".*Creating files.*\\\", output.response(), re.DOTALL):\\n device.log(level=\\\"ERROR\\\", message=\\\"Chaosreader ran into an error\\\")\\n raise Exception(\\\"Chaosreader ran into an error\\\")\\n\\n return True\",\n \"def handle_icmp(pkt, packets, i, start_point):\\r\\n icmp_type = int(pkt[start_point:start_point+2], 16)\\r\\n start_point = start_point + 2\\r\\n icmp_code = int(pkt[start_point:start_point+2], 16)\\r\\n start_point = start_point + 2\\r\\n icmp_checksum = pkt[start_point:start_point+4]\\r\\n packets[i][2].append(icmp_type)\\r\\n packets[i][2].append(icmp_code)\\r\\n packets[i][2].append(icmp_checksum)\\r\\n return packets\",\n \"def get_total_and_retrans_frames(pcap_filepath, connections):\\n # First init values to avoid strange errors if connection is empty\\n for conn_id, conn in connections.iteritems():\\n for direction in co.DIRECTIONS:\\n connections[conn_id].flow.attr[direction][co.FRAMES_TOTAL] = 0\\n connections[conn_id].flow.attr[direction][co.BYTES_FRAMES_TOTAL] = 0\\n connections[conn_id].flow.attr[direction][co.FRAMES_RETRANS] = 0\\n connections[conn_id].flow.attr[direction][co.BYTES_FRAMES_RETRANS] = 0\\n\\n stats_filename = os.path.basename(pcap_filepath)[:-5] + \\\"_tshark_total\\\"\\n stats_file = open(stats_filename, 'w')\\n co.tshark_stats(None, pcap_filepath, print_out=stats_file)\\n stats_file.close()\\n\\n stats_file = open(stats_filename)\\n data = stats_file.readlines()\\n stats_file.close()\\n for line in data:\\n split_line = \\\" \\\".join(line.split()).split(\\\" \\\")\\n if len(split_line) == 11:\\n # Manage case with ipv6\\n ip_src, port_src = get_ip_port_tshark(split_line[0])\\n ip_dst, port_dst = get_ip_port_tshark(split_line[2])\\n for conn_id, conn in connections.iteritems():\\n if conn.flow.attr[co.SADDR] == ip_src and conn.flow.attr[co.SPORT] == port_src and \\\\\\n conn.flow.attr[co.DADDR] == ip_dst and conn.flow.attr[co.DPORT]:\\n connections[conn_id].flow.attr[co.S2C][co.FRAMES_TOTAL] = int(split_line[3])\\n connections[conn_id].flow.attr[co.S2C][co.BYTES_FRAMES_TOTAL] = int(split_line[4])\\n connections[conn_id].flow.attr[co.C2S][co.FRAMES_TOTAL] = int(split_line[5])\\n connections[conn_id].flow.attr[co.C2S][co.BYTES_FRAMES_TOTAL] = int(split_line[6])\\n break\\n\\n stats_file.close()\\n os.remove(stats_filename)\\n\\n stats_filename = os.path.basename(pcap_filepath)[:-5] + \\\"_tshark_retrans\\\"\\n stats_file = open(stats_filename, 'w')\\n co.tshark_stats('tcp.analysis.retransmission', pcap_filepath, print_out=stats_file)\\n stats_file.close()\\n\\n stats_file = open(stats_filename)\\n data = stats_file.readlines()\\n stats_file.close()\\n for line in data:\\n split_line = \\\" \\\".join(line.split()).split(\\\" \\\")\\n if len(split_line) == 11:\\n ip_src, port_src = get_ip_port_tshark(split_line[0])\\n ip_dst, port_dst = get_ip_port_tshark(split_line[2])\\n for conn_id, conn in connections.iteritems():\\n if conn.flow.attr[co.SADDR] == ip_src and conn.flow.attr[co.SPORT] == port_src and \\\\\\n conn.flow.attr[co.DADDR] == ip_dst and conn.flow.attr[co.DPORT]:\\n connections[conn_id].flow.attr[co.S2C][co.FRAMES_RETRANS] = int(split_line[3])\\n connections[conn_id].flow.attr[co.S2C][co.BYTES_FRAMES_RETRANS] = int(split_line[4])\\n connections[conn_id].flow.attr[co.C2S][co.FRAMES_RETRANS] = int(split_line[5])\\n connections[conn_id].flow.attr[co.C2S][co.BYTES_FRAMES_RETRANS] = int(split_line[6])\\n break\\n\\n stats_file.close()\\n os.remove(stats_filename)\",\n \"def next_connection_packets(piter, linktype=1):\\n first_ft = None\\n\\n for ts, raw in piter:\\n ft = flowtuple_from_raw(raw, linktype)\\n if not first_ft: first_ft = ft\\n\\n sip, dip, sport, dport, proto = ft\\n if not (first_ft == ft or first_ft == (dip, sip, dport, sport, proto)):\\n break\\n\\n yield {\\n \\\"src\\\": sip, \\\"dst\\\": dip, \\\"sport\\\": sport, \\\"dport\\\": dport, \\\"proto\\\": proto,\\n \\\"raw\\\": payload_from_raw(raw, linktype).encode(\\\"base64\\\"), \\\"direction\\\": first_ft == ft,\\n }\",\n \"def get_pcap_traffic_series(self):\\n parsed_pcap_data = {}\\n\\n if (self.mac_address_binary is not None):\\n parsed_pcap_data[self.mac_address_binary] = []\\n\\n with open(self.pcap_file_path, 'rb') as pcap_file:\\n try:\\n pcap = dpkt.pcap.Reader(pcap_file)\\n for ts, buf in pcap:\\n # Skip non ethernet frames\\n try:\\n eth = dpkt.ethernet.Ethernet(buf)\\n except:\\n continue\\n\\n # Skip non-IP packets\\n if eth.type != 2048:\\n continue\\n \\n # Apply eth filter\\n if (self.mac_address_binary is not None):\\n self.append_data(parsed_pcap_data, self.mac_address_binary, eth, ts)\\n else:\\n if (eth.src not in parsed_pcap_data):\\n parsed_pcap_data[eth.src] = []\\n if (eth.dst not in parsed_pcap_data):\\n parsed_pcap_data[eth.dst] = []\\n\\n self.append_data(parsed_pcap_data, eth.src, eth, ts)\\n self.append_data(parsed_pcap_data, eth.dst, eth, ts)\\n except:\\n print \\\"Error parsing file: %s\\\" % pcap_file\\n \\n # Remove mac addresses that didn't send data\\n receivers_only = []\\n for mac_addr in parsed_pcap_data:\\n data_sent = False\\n for data in parsed_pcap_data[mac_addr]:\\n if (data[1] > 0):\\n data_sent = True\\n break\\n if (not data_sent):\\n receivers_only.append(mac_addr)\\n\\n for mac_addr in receivers_only:\\n parsed_pcap_data.pop(mac_addr, None)\\n\\n # Sort the data \\n for mac_addr in parsed_pcap_data:\\n series = sorted(parsed_pcap_data[mac_addr], key=operator.itemgetter(0))\\n parsed_pcap_data[mac_addr] = series\\n\\n return parsed_pcap_data\",\n \"def process_pcap(pcap):\\n\\n print \\\"Processing\\\", pcap\\n pcap_path, _ = os.path.splitext(pcap)\\n # strip_payload_from_pcap(pcap)\\n os.system(\\\"tshark -nn -T fields -E separator=/t -e frame.time_epoch\\\"\\n \\\" -e ip.src -e ip.dst -e tcp.srcport -e tcp.dstport\\\"\\n \\\" -e ip.proto -e ip.len -e ip.hdr_len -e tcp.hdr_len -e data.len\\\"\\n \\\" -e tcp.flags -e tcp.options.timestamp.tsval\\\"\\n \\\" -e tcp.options.timestamp.tsecr -e tcp.seq -e tcp.ack\\\"\\n \\\" -e tcp.window_size_value -e expert.message \\\"\\n \\\" -r %s > %s.tshark\\\" % (pcap, pcap_path))\\n # tcpdump command from Panchenko's raw-to-tcp script\\n os.system(\\\"\\\"\\\"tcpdump -r {0} -n -l -tt -q -v | sed -e 's/^[ ]*//' |\\n awk '/length ([0-9][0-9]*)/{{printf \\\"%s \\\",$0;next}}{{print}}' > {1}\\\"\\\"\\\".\\\\\\n format(pcap, pcap_path + '.tcpdump'))\",\n \"def packetSniff():\\n\\n packets = psutil.net_io_counters(pernic=True)\\n interfaces = {}\\n x = 0\\n for p in packets.items():\\n values = {}\\n values['name'] = p[0]\\n values['bytes_sent'] = p[1][0]\\n values['bytes_recv'] = p[1][1]\\n values['pckt_sent'] = p[1][2]\\n values['pckt_recv'] = p[1][3]\\n values['errin'] = p[1][4]\\n values['errout'] = p[1][5]\\n values['dropin'] = p[1][6]\\n values['dropout'] = p[1][7]\\n\\n if ((values['bytes_sent'] or values['bytes_recv'] or\\n values['pckt_sent'] or values['pckt_recv']) != 0):\\n\\n interfaces[x] = values\\n x += 1\\n else:\\n pass\\n\\n return interfaces\",\n \"def start_tcpdump(self):\\n\\t\\tlog_file = os.path.join(self.cfg.file_log_dir,self.info[\\\"hash_md5\\\"]+\\\".pcap\\\")\\n\\t\\tself.info[\\\"tcpdump_log_path\\\"] = log_file\\n\\t\\tcmd = [\\\"/usr/sbin/tcpdump\\\", \\\"-iany\\\", \\\"-w\\\"+self.info[\\\"tcpdump_log_path\\\"], \\\"-c%d\\\"%(self.cfg.tcpdump_limit)]\\n\\t\\tself.p_tcpdump = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t\\tself.log.info(\\\"tcpdump starts, logfile:%s\\\",self.info[\\\"tcpdump_log_path\\\"] )\",\n \"def tcpdump(timeout, q, interface):\\t\\n\\tlogging.debug('tcpdump -s 1024 -lqnAt tcp port 80 -i eth0')\\n\\t# tcpdump -s 1024 -lqnAt tcp port 80\\n\\t\\t\\n\\tcommand = Command(['/usr/sbin/tcpdump', '-s 1024', '-lnAq', '-i', interface], timeout)\\n\\tcommand.run()\\n\\n\\t# when it's executing here, the results have been available\\n\\t# print command.out\\n\\n\\tif command.out is not None:\\n\\t\\t# pattern = \\\"time=([0-9]+\\\\.[0-9]+) ms\\\"\\n\\t\\tip_pattern = \\\"IP ([0-9]+.[0-9]+.[0-9]+.[0-9]+).[0-9]+ > [0-9]+.[0-9]+.[0-9]+.[0-9]+.[0-9]\\\"\\n\\t\\tgoogle_pattern = \\\"domain=.google.com\\\"\\n\\t\\tlines = command.out.split('\\\\n')\\n\\t\\tlast_ip = None\\n\\n\\t\\t# first time scan for google's return ip\\n\\t\\tfor line in lines:\\n\\t\\t\\tip_src = re.search(ip_pattern, line)\\n\\t\\t\\tif ip_src is not None:\\n\\t\\t\\t\\tlast_ip = ip_src.group(1)\\n\\t\\t\\tif re.search(google_pattern, line):\\n\\t\\t\\t\\tprint last_ip\\n\\t\\t\\t\\tbreak\\n\\n\\t\\tgEntries = []\\n\\t\\tif last_ip is not None:\\n\\t\\t\\t\\n\\t\\t\\t# second time scan parse tcpdump for query entries\\n\\t\\t\\tfor line in lines:\\n\\t\\t\\t\\tlast_ip_pos = re.search(last_ip, line)\\n\\t\\t\\t\\tif last_ip_pos is None:\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\n\\t\\t\\t\\tif line.index('>') > last_ip_pos.start():\\n\\t\\t\\t\\t\\t# from remote to this place\\n\\t\\t\\t\\t\\ttraffic_type = 1\\t\\t\\t\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# out to remote\\n\\t\\t\\t\\t\\ttraffic_type = 0\\n\\t\\t\\t\\n\\t\\t\\t\\ttime_pattern = \\\"([0-9]+:[0-9]+:[0-9]+.[0-9]+) IP\\\"\\n\\t\\t\\t\\ttimestamp = re.search(time_pattern, line)\\n\\t\\t\\t\\tif timestamp is not None:\\n\\t\\t\\t\\t\\ttime_str = timestamp.group(1)\\n\\t\\t\\t\\t\\th, m, s, ms = map(int, re.split(r'[.:]+', time_str))\\n\\t\\t\\t\\t\\ttimestamp_delta = timedelta(hours=h, minutes=m, seconds=s, microseconds=ms)\\n\\t\\t\\t\\t\\tgEntries.append( (timestamp_delta, traffic_type) )\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tgEntries.append( (None, -1))\\n\\n\\t\\tq.put((command.returncode, last_ip, gEntries))\\n\\t\\treturn\",\n \"def download_pcap(self, args=None):\\r\\n result = {\\\"Task\\\": \\\"DownloadPCAP\\\", \\\"Error\\\": \\\"NoError\\\", \\\"Status\\\": \\\"FileNotFound\\\", \\\"FileName\\\": args['filename'],\\r\\n \\\"FileSize\\\": 0, \\\"FileType\\\": \\\"UnKnown\\\", \\\"FileURL\\\": 'UnKnown', \\\"FileUser\\\": 'UnKnown'}\\r\\n with EndaceWebSession(app_url=self.applianceurl, username=self.username, password=self.password,\\r\\n cert_verify=self.cert_verify) as sess:\\r\\n api = EndaceVisionAPIAdapter(sess)\\r\\n path = \\\"files\\\"\\r\\n rf = api.get(path)\\r\\n if rf.status_code == 200:\\r\\n try:\\r\\n response = rf.json()\\r\\n except json.decoder.JSONDecodeError:\\r\\n raise Exception(f\\\"JsonDecodeError - path {path}\\\")\\r\\n else:\\r\\n meta = response.get(\\\"meta\\\", {})\\r\\n payload = response.get(\\\"payload\\\")\\r\\n if meta:\\r\\n meta_error = meta[\\\"error\\\"]\\r\\n if meta_error is not None:\\r\\n if meta_error is not False:\\r\\n result['Status'] = \\\"FileNotFound\\\"\\r\\n result['Error'] = str(meta_error)\\r\\n else:\\r\\n # Download PCAP File\\r\\n for file in payload:\\r\\n if result['FileName'] == file['name'] and len(file[\\\"id\\\"]):\\r\\n file_numerical_part = float(re.findall(r'[\\\\d\\\\.]+', file['usage'])[0])\\r\\n\\r\\n if 'KB' in file['usage']:\\r\\n filesize = file_numerical_part * 0.001\\r\\n elif 'GB' in file['usage']:\\r\\n filesize = file_numerical_part * 1000\\r\\n elif 'TB' in file['usage']:\\r\\n filesize = file_numerical_part * 1000000\\r\\n else:\\r\\n filesize = file_numerical_part * 1\\r\\n\\r\\n if filesize <= int(args['filesizelimit']):\\r\\n result['FileName'] = file['name'] + \\\".pcap\\\"\\r\\n if not file['status']['inUse']:\\r\\n # File available to download\\r\\n pcapfile_url_path = (\\\"files/%s/stream?format=pcap\\\" % file[\\\"id\\\"])\\r\\n d = api.get(pcapfile_url_path)\\r\\n if d.status_code == 200:\\r\\n demisto.results(fileResult(f'{result[\\\"FileName\\\"]}', d.content,\\r\\n file_type=entryTypes['entryInfoFile']))\\r\\n\\r\\n result['FileURL'] = f'[Endace PCAP URL]'\\\\\\r\\n f'({self.applianceurl}/vision2/data/'\\\\\\r\\n f'{pcapfile_url_path})'\\r\\n\\r\\n result['FileSize'] = file['usage']\\r\\n result['Status'] = \\\"DownloadFinished\\\"\\r\\n result['FileType'] = file['type']\\r\\n result['FileUser'] = file['user']\\r\\n else:\\r\\n result['Status'] = \\\"FileNotFound\\\"\\r\\n result['Error'] = f\\\"ServerError - HTTP {rf.status_code} to /{path}\\\"\\r\\n else:\\r\\n result['Status'] = \\\"FileInUse\\\"\\r\\n else:\\r\\n result['Status'] = \\\"FileExceedsSizeLimit\\\"\\r\\n else:\\r\\n result['Status'] = \\\"Failed\\\"\\r\\n result['Error'] = f\\\"ServerError - empty meta data from {path}\\\"\\r\\n else:\\r\\n result['Status'] = \\\"Failed\\\"\\r\\n result['Error'] = f\\\"ServerError - HTTP {rf.status_code} to /{path}\\\"\\r\\n\\r\\n if result['Status'] == 'Failed':\\r\\n self.handle_error_notifications(result['Error'])\\r\\n return result\",\n \"def retrieve(self, trace_filter={}, limit=0):\\n\\n if isinstance(limit, int) and limit > 0:\\n max_r = limit\\n else:\\n max_r = 0\\n\\n try:\\n packets = self.__db.find_packets(self._collection, trace_filter, max_r)\\n except MemoryError:\\n print(\\\"Warning: cannot allocate sufficient memory for packets, perhaps you are using Windows?\\\")\\n return []\\n except:\\n return []\\n\\n # Attempt to decode base64 payloads.\\n for packet in packets:\\n if packet[\\\"tcp_info\\\"] is not None:\\n if isinstance(packet[\\\"tcp_info\\\"][\\\"payload\\\"], bytes):\\n try:\\n packet[\\\"tcp_info\\\"][\\\"payload\\\"] = b64decode(packet[\\\"tcp_info\\\"][\\\"payload\\\"])\\n except:\\n continue\\n\\n if packet[\\\"tls_info\\\"] is not None:\\n for i, data in enumerate(packet[\\\"tls_info\\\"][\\\"data\\\"]):\\n if isinstance(data, bytes):\\n try:\\n packet[\\\"tls_info\\\"][\\\"data\\\"][i] = b64decode(data)\\n except:\\n continue\\n\\n return packets\",\n \"def loadpts(skip=40, filt=None, ref_frame=None):\\n pts = []\\n for i in range(42):\\n print('loading file: ', i)\\n if filt is not None:\\n traj = md.load(DCD_PROT(i), top=PDB_PROT, atom_indices=filt, stride=skip)\\n else:\\n traj = md.load(DCD_PROT(i), top=PDB_PROT, stride=skip)\\n traj.center_coordinates()\\n if ref_frame is not None:\\n traj.superpose(ref_frame)\\n for i in traj.xyz:\\n pts.append(i)\\n return np.array(pts)\",\n \"def create_stream(cls, packet_count=test_packet_count):\\n for i in range(0, packet_count):\\n info = cls.create_packet_info(cls.src_dst_if, cls.src_dst_if)\\n payload = cls.info_to_payload(info)\\n p = (\\n Ether(dst=cls.src_dst_if.local_mac, src=cls.src_dst_if.remote_mac)\\n / IP(\\n id=info.index,\\n src=cls.src_dst_if.remote_ip4,\\n dst=cls.src_dst_if.local_ip4,\\n )\\n / ICMP(type=\\\"echo-request\\\", id=1234)\\n / Raw(payload)\\n )\\n cls.extend_packet(p, 1518, cls.padding)\\n info.data = p\",\n \"def accumulate_packets():\\n l = []\\n packets = sniff(count=NUMBER_OF_SNIFFING_ROUNDS, lfilter=fltr, prn=printing)\\n print(\\\"Processing packets!\\\")\\n for packet in packets:\\n l.append({\\\"ip\\\": get_ip(packet),\\n \\\"country\\\": get_country(packet),\\n \\\"entering\\\": is_entering(packet),\\n \\\"port\\\": get_partner_port(packet),\\n \\\"size\\\": packet[IP].len, #the len of the ip layer is the len of the entire packet\\n \\\"program\\\": get_program(packet)})\\n return l\",\n \"def sniff_online(args):\\n print('viewer: listening on ' + args.interface)\\n\\n try:\\n sniffer = pcapy.open_live(args.interface, 65536, 1, 1)\\n sniffer.setfilter('icmp')\\n except Exception as e:\\n print(e)\\n sys.exit(-1)\\n\\n if not args.count:\\n count = True\\n else:\\n count = args.count\\n\\n while count:\\n (header, packet) = sniffer.next()\\n if header:\\n tts = header.getts()\\n ret = parse_ICMP_Echo(tts[0] + tts[1] / 1000000, packet)\\n\\n if ret and args.count:\\n count -= 1\",\n \"def tcp_traceflow(packet, *, count=NotImplemented):\\n if 'TCP' in packet:\\n ip = packet['IP'] if 'IP' in packet else packet['IPv6']\\n tcp = packet['TCP']\\n data = dict(\\n protocol=LINKTYPE.get(packet.name.upper()), # data link type from global header\\n index=count, # frame number\\n frame=packet2dict(packet), # extracted packet\\n syn=bool(tcp.flags.S), # TCP synchronise (SYN) flag\\n fin=bool(tcp.flags.F), # TCP finish (FIN) flag\\n src=ipaddress.ip_address(ip.src), # source IP\\n dst=ipaddress.ip_address(ip.dst), # destination IP\\n srcport=tcp.sport, # TCP source port\\n dstport=tcp.dport, # TCP destination port\\n timestamp=time.time(), # timestamp\\n )\\n return True, data\\n return False, None\",\n \"def process_pcap(self):\\n # Create Core Controller\\n controller = Controller(self.args.input, self.args.extraTests, self.args.non_verbose, self.args.output,\\n self.args.debug)\\n\\n if not self.args.skip:\\n # Load PCAP statistics\\n recalculate_intervals = None\\n if self.args.recalculate_delete:\\n recalculate_intervals = True\\n elif self.args.recalculate_yes:\\n recalculate_intervals = True\\n self.args.recalculate = True\\n elif self.args.recalculate_no:\\n recalculate_intervals = False\\n self.args.recalculate = True\\n controller.load_pcap_statistics(self.args.export, self.args.recalculate, self.args.statistics,\\n self.args.statistics_interval, self.args.recalculate_delete,\\n recalculate_intervals)\\n\\n if self.args.list_intervals:\\n controller.list_interval_statistics()\\n\\n # Create statistics plots\\n if self.args.plot is not None:\\n do_entropy = False\\n if self.args.extraTests:\\n do_entropy = True\\n controller.create_statistics_plot(self.args.plot, do_entropy)\\n\\n # Check rng seed\\n if not isinstance(self.args.rngSeed, list):\\n self.args.rngSeed = [self.args.rngSeed]\\n\\n # Process attack(s) with given attack params\\n if self.args.attack is not None:\\n # If attack is present, load attack with params\\n controller.process_attacks(self.args.attack, self.args.rngSeed, self.args.time, self.args.inject_empty)\\n\\n # Parameter -q without arguments was given -> go into query loop\\n if self.args.query == [None]:\\n controller.enter_query_mode()\\n # Parameter -q with arguments was given -> process query\\n elif self.args.query is not None:\\n controller.process_db_queries(self.args.query, True)\",\n \"def next_batch(self):\\n\\n while self.cap.isOpened():\\n flag, frame = self.cap.read()\\n yield frame\",\n \"def main():\\n connection = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.htons(0x03))\\n\\n # Start the main loop.\\n while True:\\n # 65536 is the biggest buffer size that can be used.\\n raw_data, addr = connection.recvfrom(65536)\\n dest_mac, src_mac, eth_proto, data = ethernet_frame(raw_data)\\n print('\\\\nEthernet Frame:')\\n print('Destination: {}, Source: {}, Protocol: {}'.format(dest_mac, src_mac, eth_proto))\",\n \"def read_packets(serial_input):\\n while 1:\\n header = scan_to_headerword(serial_input)\\n yield header.read_packet(serial_input)\",\n \"def sniffing():\\n sniff(store=False, prn=lambda p: threading.Thread(target=next, args=(p,)).start(), iface=IFACE)\",\n \"def read_pkt_seq(self):\\n pkt = self.read_pkt_line()\\n while pkt:\\n yield pkt\\n pkt = self.read_pkt_line()\",\n \"def sniff_continuously(self, packet_count=None):\\n \\n self.lcapture_tshark = (self.lcapture_tshark or \\n self.eventloop.run_until_complete(self._get_tshark_process()))\\n\\n self._running_processes.add(self.lcapture_tshark)\\n\\n # Retained for backwards compatibility and to add documentation.\\n return self._packets_from_tshark_sync(packet_count=packet_count, \\n tshark_process=self.lcapture_tshark)\",\n \"def preprocess_capture(data, ip_version=4, transp_layer=\\\"TCP\\\"):\\n #SEE: https://www.winpcap.org/ntar/draft/PCAP-DumpFileFormat.html\\n\\n #TODO Implement ipv6, udp and ICMP\\n if ip_version == 4:\\n pass\\n else:\\n raise ValueError('IP version must be \\\"4\\\"')\\n\\n if transp_layer == \\\"TCP\\\":\\n pass\\n else:\\n raise ValueError('transport layer must be TCP')\\n\\n try:\\n capt = pyshark.FileCapture(data, keep_packets=False, display_filter='tcp')\\n except:\\n exit(\\\"Could not open pcap file\\\")\\n\\n ip_fields = ['src', 'dst', 'flags_df', 'flags_mf', 'hdr_len', 'len', 'ttl']\\n tcp_fields = ['srcport', 'dstport', 'flags_ack', 'flags_fin', 'flags_push',\\n 'flags_reset', 'flags_syn', 'flags_urg', 'hdr_len', 'len']\\n\\n #Temporary list to feed the final DataFrame (Performance)\\n tmp = []\\n counter = 0\\n logging.info(\\\"Starting packet processing\\\")\\n for pkt in capt:\\n filtered = {}\\n #First field is a empty string (ignoring)\\n if hasattr(pkt, 'ip'):\\n for field in ip_fields:\\n #Changing field names for disambiguation in columns\\n filtered[\\\"ip_\\\"+field] = pkt[\\\"ip\\\"].get_field(field)\\n else:\\n continue\\n if hasattr(pkt, 'tcp'):\\n for field in tcp_fields:\\n #Changing field names for disambiguation in columns\\n filtered[\\\"tcp_\\\"+field] = pkt[\\\"tcp\\\"].get_field(field)\\n else:\\n continue\\n tmp.append(filtered)\\n counter += 1\\n if counter % 1000 == 0:\\n logging.info(\\\"Processed %d packets\\\", counter)\\n logging.info(\\\"Ended packet processing\\\")\\n logging.info(\\\"Converting list to DataFrame\\\")\\n X = pd.DataFrame(tmp)\\n logging.info(\\\"Ended list conversion\\\")\\n return X\",\n \"def process_trace(pcap_filepath, graph_dir_exp, stat_dir_exp, failed_conns_dir_exp, acksize_tcp_dir_exp, tcpcsm, mptcp_connections=None, print_out=sys.stdout, light=False, return_dict=False):\\n cmd = ['tstat', '-s', os.path.basename(pcap_filepath[:-5]), pcap_filepath]\\n\\n keep_tstat_log = False if return_dict else True\\n\\n try:\\n connections = process_tstat_cmd(cmd, pcap_filepath, keep_log=keep_tstat_log, graph_dir_exp=graph_dir_exp)\\n except TstatError as e:\\n print(str(e) + \\\": skip process\\\", file=sys.stderr)\\n return\\n\\n # Directory containing all TCPConnections that tried to be MPTCP subflows, but failed to\\n failed_conns = {}\\n\\n if tcpcsm:\\n retransmissions_tcpcsm(pcap_filepath, connections)\\n\\n acksize_all = {co.C2S: {}, co.S2C: {}}\\n\\n if not light:\\n inverse_conns = create_inverse_tcp_dictionary(connections)\\n\\n acksize_all = compute_tcp_acks_retrans(pcap_filepath, connections, inverse_conns)\\n\\n acksize_all_mptcp = {co.C2S: {}, co.S2C: {}}\\n\\n if mptcp_connections:\\n fast_conns = get_preprocessed_connections(mptcp_connections)\\n for flow_id in connections:\\n # Copy info to mptcp connections\\n copy_info_to_mptcp_connections(connections, mptcp_connections, failed_conns, acksize_all, acksize_all_mptcp, flow_id,\\n fast_conns=fast_conns)\\n\\n if not light:\\n for conn_id, conn in mptcp_connections.iteritems():\\n for direction in co.DIRECTIONS:\\n max_ack = timedelta(0)\\n max_payload = timedelta(0)\\n for flow_id, flow in conn.flows.iteritems():\\n if co.TIME_LAST_ACK_TCP in flow.attr[direction] and (flow.attr[direction][co.TIME_LAST_ACK_TCP] - max_ack).total_seconds() > 0.0:\\n max_ack = flow.attr[direction][co.TIME_LAST_ACK_TCP]\\n\\n if co.TIME_LAST_PAYLD_TCP in flow.attr[direction] and (flow.attr[direction][co.TIME_LAST_PAYLD_TCP] - max_payload).total_seconds() > 0.0:\\n max_payload = flow.attr[direction][co.TIME_LAST_PAYLD_TCP]\\n\\n mptcp_connections[conn_id].attr[direction][co.TIME_LAST_ACK_TCP] = max_ack\\n mptcp_connections[conn_id].attr[direction][co.TIME_LAST_PAYLD_TCP] = max_payload\\n\\n try:\\n compute_mptcp_dss_retransmissions(pcap_filepath, mptcp_connections, fast_conns)\\n except dpkt.NeedData as e:\\n print(e, \\\": trying to continue...\\\", file=sys.stderr)\\n\\n if return_dict:\\n if mptcp_connections:\\n return connections, acksize_all_mptcp\\n else:\\n return connections, acksize_all\\n else:\\n # Save connections info\\n if mptcp_connections:\\n co.save_data(pcap_filepath, acksize_tcp_dir_exp, acksize_all_mptcp)\\n # Also save TCP connections that failed to be MPTCP subflows\\n co.save_data(pcap_filepath, failed_conns_dir_exp, failed_conns)\\n else:\\n co.save_data(pcap_filepath, acksize_tcp_dir_exp, acksize_all)\\n co.save_data(pcap_filepath, stat_dir_exp, connections)\",\n \"def ingest_packet(self, pkt, pkt_receive_timestamp):\\n #*** Packet length on the wire:\\n self.packet_length = len(pkt)\\n #*** Read into dpkt:\\n eth = dpkt.ethernet.Ethernet(pkt)\\n eth_src = _mac_addr(eth.src)\\n eth_dst = _mac_addr(eth.dst)\\n eth_type = eth.type\\n #*** We only support IPv4 (TBD: add IPv6 support):\\n if eth_type != 2048:\\n self.logger.error(\\\"Non IPv4 packet, eth_type is %s\\\", eth_type)\\n return 0\\n ip = eth.data\\n self.ip_src = socket.inet_ntop(socket.AF_INET, ip.src)\\n self.ip_dst = socket.inet_ntop(socket.AF_INET, ip.dst)\\n #*** We only support TCP:\\n if ip.p != 6:\\n self.logger.error(\\\"Non TCP packet, ip_proto=%s\\\",\\n ip.p)\\n return 0\\n proto = 'tcp'\\n tcp = ip.data\\n self.tcp_src = tcp.sport\\n self.tcp_dst = tcp.dport\\n self.tcp_seq = tcp.seq\\n self.tcp_acq = tcp.ack\\n self.tcp_flags = tcp.flags\\n self.payload = tcp.data\\n #*** Generate a hash unique to flow for packets in either direction\\n self.fcip_hash = _hash_5tuple(self.ip_src, self.ip_dst, self.tcp_src,\\n self.tcp_dst, proto)\\n #*** Check to see if we already know this identity:\\n db_data = {'hash': self.fcip_hash}\\n self.fcip_doc = self.fcip.find_one(db_data)\\n if not self.fcip_doc:\\n #*** Get flow direction (which way is TCP initiated). Client is\\n #*** the end that sends the initial TCP SYN:\\n if _is_tcp_syn(tcp.flags):\\n self.logger.debug(\\\"Matched TCP SYN first pkt, src_ip=%s\\\",\\n self.ip_src)\\n self.client = self.ip_src\\n self.server = self.ip_dst\\n self.packet_direction = 'c2s'\\n self.verified_direction = 'verified-SYN'\\n elif _is_tcp_synack(tcp.flags):\\n self.logger.debug(\\\"Matched TCP SYN+ACK first pkt, src_ip=%s\\\",\\n self.ip_src)\\n self.client = self.ip_dst\\n self.server = self.ip_src\\n self.packet_direction = 's2c'\\n self.verified_direction = 'verified-SYNACK'\\n else:\\n self.logger.debug(\\\"Unmatch state first pkt, tcp_flags=%s\\\",\\n tcp.flags)\\n self.client = self.ip_src\\n self.server = self.ip_dst\\n self.packet_direction = 'c2s'\\n self.verified_direction = 0\\n #*** Neither direction found, so add to FCIP database:\\n self.fcip_doc = {'hash': self.fcip_hash,\\n 'ip_A': self.ip_src,\\n 'ip_B': self.ip_dst,\\n 'port_A': self.tcp_src,\\n 'port_B': self.tcp_dst,\\n 'proto': proto,\\n 'finalised': 0,\\n 'packet_count': 1,\\n 'latest_timestamp' : pkt_receive_timestamp,\\n 'packet_timestamps': [pkt_receive_timestamp,],\\n 'tcp_flags': [tcp.flags,],\\n 'packet_lengths': [self.packet_length,],\\n 'client': self.client,\\n 'server': self.server,\\n 'packet_directions': [self.packet_direction,],\\n 'verified_direction': self.verified_direction,\\n 'suppressed': 0}\\n self.logger.debug(\\\"FCIP: Adding record for %s to DB\\\",\\n self.fcip_doc)\\n db_result = self.fcip.insert_one(self.fcip_doc)\\n self.packet_count = 1\\n\\n elif self.fcip_doc['finalised']:\\n #*** The flow is already finalised just increment packet count:\\n self.fcip_doc['packet_count'] += 1\\n #*** Write updated FCIP data back to database:\\n db_result = self.fcip.update_one({'hash': self.fcip_hash},\\n {'$set': {'packet_count': self.fcip_doc['packet_count']},})\\n self.packet_count = self.fcip_doc['packet_count']\\n\\n else:\\n #*** We've found the flow in the FCIP database, now update it:\\n self.logger.debug(\\\"FCIP: found existing record %s\\\", self.fcip_doc)\\n #*** Rate this packet as c2s or s2c direction:\\n if self.client == self.ip_src:\\n self.packet_direction = 'c2s'\\n elif self.client == self.ip_dst:\\n self.packet_direction = 's2c'\\n else:\\n self.packet_direction = 'unknown'\\n #*** Increment packet count. Is it at max?:\\n self.fcip_doc['packet_count'] += 1\\n self.packet_count = self.fcip_doc['packet_count']\\n if self.fcip_doc['packet_count'] >= self.max_packet_count:\\n #*** TBD:\\n self.fcip_doc['finalised'] = 1\\n self.logger.debug(\\\"Finalising...\\\")\\n #*** Read suppressed status to variable:\\n self.suppressed = self.fcip_doc['suppressed']\\n #*** Read verified_direction status to variable:\\n self.verified_direction = self.fcip_doc['verified_direction']\\n #*** Add packet timestamps, tcp flags etc:\\n self.fcip_doc['latest_timestamp'] = pkt_receive_timestamp\\n self.fcip_doc['packet_timestamps'].append(pkt_receive_timestamp)\\n self.fcip_doc['tcp_flags'].append(tcp.flags)\\n self.fcip_doc['packet_lengths'].append(self.packet_length)\\n self.fcip_doc['packet_directions'].append(self.packet_direction)\\n #*** Write updated FCIP data back to database:\\n db_result = self.fcip.update_one({'hash': self.fcip_hash},\\n {'$set': {'packet_count': self.fcip_doc['packet_count'],\\n 'finalised': self.fcip_doc['finalised'],\\n 'packet_timestamps': self.fcip_doc['packet_timestamps'],\\n 'tcp_flags': self.fcip_doc['tcp_flags'],\\n 'packet_lengths': self.fcip_doc['packet_lengths'],\\n 'packet_directions': self.fcip_doc['packet_directions']\\n },})\\n #*** Tests:\\n self.logger.debug(\\\"max_packet_size is %s\\\", self.max_packet_size())\\n self.logger.debug(\\\"max_interpacket_interval is %s\\\",\\n self.max_interpacket_interval())\\n self.logger.debug(\\\"min_interpacket_interval is %s\\\",\\n self.min_interpacket_interval())\",\n \"def get_available_portoffset(target=\\\"localhost\\\"):\\n target_ip = socket.gethostbyname(target)\\n for portoffset in range(10000, 61000, 1000):\\n i = portoffset + 873\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n result = sock.connect_ex((target_ip, i))\\n sock.close()\\n if result != 0:\\n logger.debug(\\\"port open {0}\\\".format(portoffset))\\n return portoffset\\n return None\",\n \"def iplayer_from_raw(raw, linktype=1):\\n if linktype == 1: # ethernet\\n pkt = dpkt.ethernet.Ethernet(raw)\\n ip = pkt.data\\n elif linktype == 101: # raw\\n ip = dpkt.ip.IP(raw)\\n else:\\n raise Exception(\\\"unknown PCAP linktype\\\")\\n return ip\",\n \"def mptcp_connections(self, pkts):\\n\\t\\tcount = 0\\n\\t\\t#MPTCP_Capable = 0x0\\n\\t\\t#MPTCP_CapableACK ---> successful handshake\\n\\t\\tprint \\\"======================================================================\\\"\\n\\t\\tprint \\\"Successful Handshake --- Look for Ack packets with MPTCP option Header\\\"\\n\\t\\tprint \\\"\\\"\\\"Token = connectionID = SHA1(key)[0-32] of Other party's key. (Capture from\\n\\t\\t either step 2 or 3 in the first handshake)\\\"\\\"\\\"\\n\\t\\tprint \\\"Total packets: %s\\\" % len(pkts)\\n\\t\\tprint \\\"======================================================================\\\"\\n\\t\\tprint \\\"Identifying MPTCP Connections....\\\"\\n\\t\\tfor i in range(len(pkts)):\\n\\t\\t\\tif(MPTCP_CapableACK in pkts[i] and pkts[i][TCPOption_MP].mptcp.subtype == 0):\\n\\t\\t\\t\\tcount +=1 #Count the number of distinct MPTCP connections\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t#Compute the receiver's token\\n\\t\\t\\t\\tself.key_rcv = pkts[i][TCPOption_MP].mptcp.rcv_key\\n\\t\\t\\t\\tself.rcv_token, self.rcv_dsn = self.key2tokenAndDSN(self.key_rcv)\\n\\n\\t\\t\\t\\t#Compute the sender's token\\n\\t\\t\\t\\tself.key_snd = pkts[i][TCPOption_MP].mptcp.snd_key\\n\\t\\t\\t\\tself.snd_token, self.snd_dsn = self.key2tokenAndDSN(self.key_snd)\\n\\n\\t\\t\\t\\tprint (\\\"%i. New MPTCP Connection (Successful Handshake) src: %s; dest: %s; Sender's key: %s; Receiver's key: %s; Receivers Token (connectionID): %s; Sender's Token: %s\\\" % (count, pkts[i][IP].src, pkts[i][IP].dst, pkts[i][TCPOption_MP].mptcp.snd_key, pkts[i][TCPOption_MP].mptcp.rcv_key, self.rcv_token, self.snd_token))\\n\\t\\tprint \\\"Total MPTCP Connections: %i\\\" % count\",\n \"def sniff_ip(time_to_sniff):\\r\\n ip_dict = dict()\\r\\n port_dict = dict()\\r\\n packets = sniff(timeout=time_to_sniff, filter=\\\"ip\\\")\\r\\n\\r\\n for i in packets:\\r\\n sport = 0\\r\\n src = i['IP'].src\\r\\n\\r\\n if \\\"TCP\\\" in i:\\r\\n sport = i['TCP'].sport\\r\\n\\r\\n elif \\\"UDP\\\" in i:\\r\\n sport = i['UDP'].sport\\r\\n\\r\\n if not src in ip_dict.keys():\\r\\n ip_dict[src] = 1\\r\\n\\r\\n else:\\r\\n ip_dict[src] += 1\\r\\n\\r\\n if sport:\\r\\n if not sport in port_dict.keys():\\r\\n port_dict[sport] = 1\\r\\n\\r\\n else:\\r\\n port_dict[sport] += 1\\r\\n\\r\\n return ip_dict, port_dict\",\n \"def handle_udp(pkt, packets, i, start_point):\\r\\n src_port = int(pkt[start_point:start_point + 4], 16)\\r\\n start_point += 4\\r\\n dest_port = int(pkt[start_point:start_point + 4], 16)\\r\\n start_point += 4\\r\\n length = int(pkt[start_point:start_point+4], 16)\\r\\n start_point += 4\\r\\n checksum_value = pkt[start_point:start_point+4]\\r\\n packets[i][2].append(src_port)\\r\\n packets[i][2].append(dest_port)\\r\\n packets[i][2].append(length)\\r\\n packets[i][2].append(checksum_value)\\r\\n\\r\\n return packets\",\n \"def MatchIpAddressInArpPackets(self):\\n return self._get_attribute('matchIpAddressInArpPackets')\",\n \"def group_packets(self, packets):\\n sessions = packets.sessions() # groups connections from X to Y as a Scapy PacketList in a dict\\n # example: dict['TCP 172.217.17.102:443 > 10.7.2.60:38386'] = PacketList\\n\\n session_keys = list(sessions.keys()) # force copy so we can alter the dictionary at runtime\\n for key in session_keys:\\n reversed_key = self.reverse_dict_key(key)\\n if(reversed_key != key and sessions.__contains__(reversed_key)):\\n sessions[key] += sessions.pop(reversed_key)\\n session_keys.remove(reversed_key)\\n\\n return self.sort_grouped_packets(list(sessions.values()))\",\n \"def get_connections(self, kind='inet'):\\r\\n # Note: in case of UNIX sockets we're only able to determine the\\r\\n # local bound path while the remote endpoint is not retrievable:\\r\\n # http://goo.gl/R3GHM\\r\\n inodes = {}\\r\\n # os.listdir() is gonna raise a lot of access denied\\r\\n # exceptions in case of unprivileged user; that's fine:\\r\\n # lsof does the same so it's unlikely that we can to better.\\r\\n for fd in os.listdir(\\\"/proc/%s/fd\\\" % self.pid):\\r\\n try:\\r\\n inode = os.readlink(\\\"/proc/%s/fd/%s\\\" % (self.pid, fd))\\r\\n except OSError:\\r\\n continue\\r\\n if inode.startswith('socket:['):\\r\\n # the process is using a socket\\r\\n inode = inode[8:][:-1]\\r\\n inodes[inode] = fd\\r\\n\\r\\n if not inodes:\\r\\n # no connections for this process\\r\\n return []\\r\\n\\r\\n def process(fin, family, type_):\\r\\n retlist = []\\r\\n try:\\r\\n f = open(fin, 'r')\\r\\n except IOError:\\r\\n # IPv6 not supported on this platform\\r\\n err = sys.exc_info()[1]\\r\\n if err.errno == errno.ENOENT and fin.endswith('6'):\\r\\n return []\\r\\n else:\\r\\n raise\\r\\n try:\\r\\n f.readline() # skip the first line\\r\\n for line in f:\\r\\n # IPv4 / IPv6\\r\\n if family in (socket.AF_INET, socket.AF_INET6):\\r\\n _, laddr, raddr, status, _, _, _, _, _, inode = \\\\\\r\\n line.split()[:10]\\r\\n if inode in inodes:\\r\\n laddr = self._decode_address(laddr, family)\\r\\n raddr = self._decode_address(raddr, family)\\r\\n if type_ == socket.SOCK_STREAM:\\r\\n status = _TCP_STATES_TABLE[status]\\r\\n else:\\r\\n status = \\\"\\\"\\r\\n fd = int(inodes[inode])\\r\\n conn = nt_connection(fd, family, type_, laddr,\\r\\n raddr, status)\\r\\n retlist.append(conn)\\r\\n elif family == socket.AF_UNIX:\\r\\n tokens = line.split()\\r\\n _, _, _, _, type_, _, inode = tokens[0:7]\\r\\n if inode in inodes:\\r\\n\\r\\n if len(tokens) == 8:\\r\\n path = tokens[-1]\\r\\n else:\\r\\n path = \\\"\\\"\\r\\n fd = int(inodes[inode])\\r\\n type_ = int(type_)\\r\\n conn = nt_connection(fd, family, type_, path,\\r\\n None, \\\"\\\")\\r\\n retlist.append(conn)\\r\\n else:\\r\\n raise ValueError(family)\\r\\n return retlist\\r\\n finally:\\r\\n f.close()\\r\\n\\r\\n tcp4 = (\\\"tcp\\\" , socket.AF_INET , socket.SOCK_STREAM)\\r\\n tcp6 = (\\\"tcp6\\\", socket.AF_INET6, socket.SOCK_STREAM)\\r\\n udp4 = (\\\"udp\\\" , socket.AF_INET , socket.SOCK_DGRAM)\\r\\n udp6 = (\\\"udp6\\\", socket.AF_INET6, socket.SOCK_DGRAM)\\r\\n unix = (\\\"unix\\\", socket.AF_UNIX, None)\\r\\n\\r\\n tmap = {\\r\\n \\\"all\\\" : (tcp4, tcp6, udp4, udp6, unix),\\r\\n \\\"tcp\\\" : (tcp4, tcp6),\\r\\n \\\"tcp4\\\" : (tcp4,),\\r\\n \\\"tcp6\\\" : (tcp6,),\\r\\n \\\"udp\\\" : (udp4, udp6),\\r\\n \\\"udp4\\\" : (udp4,),\\r\\n \\\"udp6\\\" : (udp6,),\\r\\n \\\"unix\\\" : (unix,),\\r\\n \\\"inet\\\" : (tcp4, tcp6, udp4, udp6),\\r\\n \\\"inet4\\\": (tcp4, udp4),\\r\\n \\\"inet6\\\": (tcp6, udp6),\\r\\n }\\r\\n if kind not in tmap:\\r\\n raise ValueError(\\\"invalid %r kind argument; choose between %s\\\"\\r\\n % (kind, ', '.join([repr(x) for x in tmap])))\\r\\n ret = []\\r\\n for f, family, type_ in tmap[kind]:\\r\\n ret += process(\\\"/proc/net/%s\\\" % f, family, type_)\\r\\n # raise NSP if the process disappeared on us\\r\\n os.stat('/proc/%s' % self.pid)\\r\\n return ret\",\n \"def __iter__(self) -> Iterator[packets.Packet]:\\n for packet in self._packets:\\n yield packet\\n for pointer in self._packet_pointers:\\n yield pointer.get()\",\n \"def sniff_packets(iface=None):\\n if iface: # (http)\\n sniff(filter=\\\"port 80\\\", prn=process_packet, iface=iface, store=False)\\n # 'process_packet' is the callback\\n else:\\n sniff(filter=\\\"port 80\\\", prn=process_packet, store=False)\\n # default interface\",\n \"def start_pcap(host, pcap_file_name, interface, pcap_args='',\\n func_ip=None, tool_path=None):\\n with LydianClient(_get_host_ip(host, func_ip)) as client:\\n client.pcap.start_pcap(pcap_file_name, interface, pcap_args, tool_path)\",\n \"def sniff_traffic(hs, count, timeout, traffic_type, pkt_type, exp_dst, step):\\n iface = hs.ports['eth1']\\n step('Scapy capture started')\\n if (traffic_type == \\\"encap\\\"):\\n packet = hs.libs.scapy.sniff2(\\\"iface='{}', count={}, timeout={}, \\\"\\n \\\" filter='port 4789 and (!icmp or !ip6)', \\\"\\n \\\" prn=lambda x:x.show()\\\".format(\\n iface, count, timeout), True)\\n parse_packet(packet, traffic_type, pkt_type, exp_dst, step=step)\\n elif (traffic_type == \\\"decap\\\"):\\n packet = hs.libs.scapy.sniff2(\\\"iface='{}', count={}, \\\"\\n \\\" timeout={}, filter='!icmp or !ip6', \\\"\\n \\\" prn=lambda x:x.show()\\\".format(\\n iface, count, timeout), True)\\n parse_packet(packet, traffic_type, pkt_type, exp_dst, step=step)\",\n \"def offline_pcap_input_to_pipe(pcap=None, p=None, quick=False):\\n\\n tdm_cnt = 0\\n\\n print(\\\"Offline mode: Reading TDMs from PCAP/PCAPNG file and writing to pipe.\\\")\\n pkt_list = rdpcap(pcap) # read the PCAP/PCAPNG file and return a list of packets\\n\\n # Parse the Packet List for TmNS Data Messages (TDMs), and write them to the binary TDM file\\n print(\\\"Named Pipe '{0}' has been opened for writing. Waiting for Pipe Reader to connect.\\\".format(p))\\n pipeout = open(p, 'wb')\\n print(\\\"Connected to Named Pipe '{0}'. Writing binary TDMs into pipe.\\\".format(p))\\n\\n delta_from_current_time = time.time() - pkt_list[0].time\\n\\n try:\\n for pkt in pkt_list:\\n if pkt[UDP].dport == TDM_PORT:\\n if quick is False:\\n while (pkt.time + delta_from_current_time) > time.time():\\n sleep(0.0001)\\n pipeout.write(bytes(pkt[UDP].payload))\\n tdm_cnt += 1\\n print(\\\"\\\\rTDM Count: {0}\\\".format(tdm_cnt), end=\\\" \\\")\\n pipeout.close()\\n except IOError as e:\\n if e.errno == errno.EPIPE:\\n print(\\\"\\\\nBroken Pipe: EPIPE\\\")\\n print(\\\"\\\")\\n\\n if tdm_cnt == 0:\\n print(\\\"ZERO TmNS Data Messages found in {0}. No data written to {1}.\\\".format(pcap, p))\\n else:\\n print(\\\"\\\\nThere were {0} TmNS Data Messages written to {1}.\\\".format(tdm_cnt, p))\",\n \"def iterate(cls, disc, track_number):\\n\\n assert track_number >= 0 and track_number < len(disc.tracks)\\n\\n track = disc.tracks[track_number]\\n\\n packet_frame_size = (\\n disc.audio_format.rate / cls.PACKETS_PER_SECOND)\\n\\n # Mock up a packet that ends at the start of index 1, so the\\n # first packet generated starts at that position\\n p = cls(disc, track, track_number, track.pregap_offset, 0)\\n\\n while True:\\n # Calculate offsets of next packet\\n abs_pos = p.abs_pos + p.length\\n\\n if abs_pos < track.pregap_offset:\\n length = min(track.pregap_offset - abs_pos, packet_frame_size)\\n else:\\n length = min(track.length - abs_pos, packet_frame_size)\\n\\n assert length >= 0\\n\\n if length == 0:\\n # Reached end of track, switch to next. Simplify this\\n # code by generating a dummy packet for the next\\n # iteration to work on (but don't yield it!)\\n\\n track_number += 1\\n\\n try:\\n track = disc.tracks[track_number]\\n except IndexError:\\n # That was the last track, no more packets\\n return\\n\\n p = cls(disc, track, track_number, 0, 0)\\n\\n else:\\n # Generate next packet\\n flags = 0\\n if (track.pause_after\\n and abs_pos + length == track.length\\n and track_number + 1 < len(disc.tracks)):\\n flags |= p.PAUSE_AFTER\\n\\n p = cls(disc, track, track_number, abs_pos, length, flags)\\n yield p\",\n \"def IperfTCP(target_src, target_dst, dst, length, window=None):\\n iperf = IperfSet(target_src, target_dst, dst)\\n iperf.Start(length, None, window)\\n return iperf.Results()\",\n \"def _process(self, buf, ts=None, pkt_num=None):\\n\\n if not buf:\\n return\\n self.pkt_num = pkt_num\\n eth = dpkt.ethernet.Ethernet(buf)\\n ip = eth.data\\n tcp = ip.data\\n sip = inet_to_str(ip.src)\\n dip = inet_to_str(ip.dst)\\n fin_flag = tcp.flags & 0x001\\n ack_flag = tcp.flags & 0x010\\n syn_flag = tcp.flags & 0x002\\n rst_flag = tcp.flags & 0x004\\n syn_unacceptable_states = [TCPState.ESTABLISHED, TCPState.FIN_WAIT_1, TCPState.FIN_WAIT_2,\\n TCPState.CLOSING, TCPState.LAST_ACK]\\n data_acceptable_states = [TCPState.ESTABLISHED, TCPState.CLOSE_WAIT]\\n tcp_opts = dpkt.tcp.parse_opts(tcp.opts) if tcp.opts else None\\n tcp_opts = tcp_opts_tuple_list_to_dict(tcp_opts) if tcp_opts else None\\n num_pkt_session_pkt = len(self.sessions[self.session_count]) if self.session_count else 0\\n\\n # Only Window size can change in ACKs (in other words - after SYNs), nothing else like - window-scaling, or\\n # MSS, or Selective-SYN can't be changed. If present in options after SYN, should be ignored in my opinion\\n # https://superuser.com/questions/966212/does-the-sequence-number-of-tcp-packet-headers-wrap-around\\n # TODO: seq number in coming packet is ahead of the expected one, then it should be held for processing\\n\\n def slide_window():\\n\\n if len(self.sessions[self.session_count]):\\n if sip == self.sip:\\n if self._s_mss != -1 and get_tcp_packet_payload_len_with_options(eth) > self._s_mss:\\n return\\n prev_ip = dpkt.ethernet.Ethernet(self.get_last_c_pkt()).data\\n rcv_nxt = self._s_rcv_next\\n win_left_end = self._s_win_left_edge\\n early_pkts = self._s_early_pkts\\n other_end_win_size = self._s_win_size\\n current_state = self._c_state\\n else:\\n if self._c_mss != -1 and get_tcp_packet_payload_len_with_options(ip) > self._c_mss:\\n return\\n prev_ip = dpkt.ethernet.Ethernet(self.get_last_s_pkt()).data\\n rcv_nxt = self._c_rcv_next\\n win_left_end = self._c_win_left_edge\\n early_pkts = self._c_early_pkts\\n other_end_win_size = self._c_win_size\\n current_state = self._s_state\\n if self._print_debug_info:\\n logger.debug(self.client_server_next_rcv(), tcp_pkt_debug_info(ip))\\n prev_tcp = prev_ip.data\\n prev_tcp_data_offset = prev_tcp.off * 4\\n prev_ip_header_len = prev_ip.hl * 4\\n prev_tcp_payload_len = prev_ip.len - (prev_tcp_data_offset + prev_ip_header_len)\\n tcp_payload_len = get_tcp_packet_payload_len(ip)\\n if (tcp_seq_number_in_window(win_left_end, tcp.seq, other_end_win_size) or\\n tcp_seq_number_in_window(win_left_end,\\n inc_tcp_seq_number(tcp.seq, tcp_payload_len), other_end_win_size)):\\n if inc_tcp_seq_number(tcp.seq, tcp_payload_len) == rcv_nxt:\\n \\\"\\\"\\\"\\n \\n Since there is no new payload sent, just store the tcp packet with empty payload.\\n This is going to increase the packet count but not going to add duplicated data\\n in session data, by session data here it means actual data sent (after discarding\\n the retransmission) to application layer. To do that - we will empty out the payload,\\n if packets has some, then add the packet to the session, else add the empty packet as it is\\n to the session. This logic will easily handle the TCP connections supporting\\n TCP Timestamp options describe in https://tools.ietf.org/html/rfc1323\\n \\n \\\"\\\"\\\"\\n # one case is when seq number is < rcv_nxt but sender want to ack more data\\n # which means it is sending the same data again but its acking more received content\\n \\\"\\\"\\\"\\n 1. packet has Data\\n a. prev_packet has data\\n A. header change (change cur packet and change previous packet) add to list\\n B. no header change retransmission ( sum check)\\n b. prev_packete has no data\\n A. header change (change cur packet only) add to list\\n B. no header change retransmission (change cur packet only)\\n 2. packet has no data\\n a. prev_packet has data\\n A. header change (change previous packet only) add to list\\n B. no header change (change previous packet only)\\n b. prev_packet has no data\\n A. header change (sum check) add to list\\n B. no header change retransmission (sum check)\\n \\\"\\\"\\\"\\n if prev_tcp.sum == tcp.sum:\\n cur_sum = tcp_shasum_calc(ip.src, ip.dst, ip.p, ip.data.pack())\\n prev_sum = tcp_shasum_calc(prev_ip.src, prev_ip.dst, prev_ip.p, prev_ip.data.pack())\\n if cur_sum == prev_sum:\\n # covers 1.a.B and 2.b.B\\n return\\n\\n empty_prev_ip = copy.deepcopy(prev_ip)\\n empty_prev_tcp = empty_prev_ip.data\\n empty_prev_tcp.seq = rcv_nxt\\n empty_prev_ip.len -= prev_tcp_payload_len\\n empty_prev_tcp.data = b\\\"\\\"\\n empty_prev_ip = tcp_fix_checksum(empty_prev_ip)\\n new_part_ip = copy.deepcopy(ip)\\n new_part_tcp = new_part_ip.data\\n new_part_tcp.data = b\\\"\\\"\\n new_part_tcp.seq = rcv_nxt\\n new_part_ip.len -= tcp_payload_len\\n new_part_ip.sum = 0\\n new_part_tcp.sum = 0\\n new_part_ip = tcp_fix_checksum(new_part_ip)\\n eth.data = new_part_ip\\n cur_pkt = eth.pack()\\n new_pkt = dpkt.ethernet.Ethernet(cur_pkt)\\n new_part_ip = new_pkt.data\\n new_part_tcp = new_part_ip.data\\n\\n \\\"\\\"\\\"\\n Checksum comparision logic is kept to discard the straight duplicates packets\\n without Timestamp Options. These kind of packet will not serve any purposes.\\n If removal of these checksum comparison code blocks felt necessary, it could\\n be removed -- that will add few extra retransmitted packets -- but that would\\n also requrie to update the testcases built around this code blocks.\\n \\\"\\\"\\\"\\n if new_part_tcp.sum == empty_prev_tcp.sum:\\n # covers 1.b.B\\n # covers case 2.a.B\\n if tcp_shasum_calc(ip.src, ip.dst, ip.p, ip.data.pack()) == tcp_shasum_calc(\\n prev_ip.src, prev_ip.dst, prev_ip.p, empty_prev_ip.data.pack()):\\n return\\n \\\"\\\"\\\"\\n needs to added to list under cases 2.a.A, 2.b.A, 1.a.A and 1.b.A\\n cur_pkt is updated earlier\\n \\\"\\\"\\\"\\n if sip == self.sip:\\n if inc_tcp_seq_number(self._c_rcv_next, 1) <= new_part_tcp.ack:\\n self._c_rcv_next = new_part_tcp.ack\\n else:\\n if inc_tcp_seq_number(self._s_rcv_next, 1) <= new_part_tcp.ack:\\n self._s_rcv_next = new_part_tcp.ack\\n elif (current_state in data_acceptable_states and\\n tcp_seq_number_in_window(tcp.seq, rcv_nxt, tcp_payload_len)):\\n stale_data_len = seq_numbers_diff(tcp.seq, rcv_nxt)\\n win_right_end = inc_tcp_seq_number(win_left_end, other_end_win_size)\\n if tcp_seq_number_in_window(rcv_nxt, inc_tcp_seq_number(tcp.seq, tcp_payload_len),\\n seq_numbers_diff(rcv_nxt, win_right_end)):\\n tcp.data = tcp.data[stale_data_len:]\\n else:\\n allowed_payload_size = seq_numbers_diff(rcv_nxt, win_right_end)\\n remaining_eth = dpkt.ethernet.Ethernet(eth.pack())\\n #remaining_ip = eth.data\\n #remaining_tcp = remaining_ip.data\\n remaining_eth.data.data.seq = inc_tcp_seq_number(tcp.seq, stale_data_len + allowed_payload_size)\\n remaining_eth.data.data.data = tcp.data[stale_data_len + allowed_payload_size:]\\n remaining_eth.data.len -= stale_data_len + allowed_payload_size\\n remaining_eth.data = tcp_fix_checksum(remaining_eth.data)\\n #remaining_eth.data = remaining_ip\\n tcp.data = tcp.data[stale_data_len: stale_data_len + allowed_payload_size]\\n if self.sip == sip:\\n self._s_early_pkts.append(((ts, self.pkt_num), remaining_eth.pack()))\\n else:\\n self._c_early_pkts.append(((ts, self.pkt_num), remaining_eth.pack()))\\n tcp.sum = 0\\n # ip.len -= stale_data_len\\n tcp.seq = rcv_nxt\\n ip.data = tcp\\n ip.sum = 0\\n eth.data = ip\\n cur_pkt = eth.pack()\\n if sip == self.sip:\\n self._s_rcv_next = inc_tcp_seq_number(self._s_rcv_next,\\n (ip.len - (ip.hl * 4 + tcp.off * 4)))\\n else:\\n self._c_rcv_next = inc_tcp_seq_number(self._c_rcv_next,\\n (ip.len - (ip.hl * 4 + tcp.off * 4)))\\n elif (current_state in data_acceptable_states and\\n tcp_seq_number_in_window(rcv_nxt, tcp.seq, other_end_win_size)):\\n # hold it for further processing\\n if self.sip == sip:\\n self._s_early_pkts.append(((ts, self.pkt_num), buf))\\n else:\\n self._c_early_pkts.append(((ts, self.pkt_num), buf))\\n return\\n else:\\n return\\n self.sessions[self.session_count].append(((ts, self.pkt_num), cur_pkt))\\n # as this packet is accepted, might need to update the rwnd size and left end of rwnd\\n if sip == self.sip:\\n self._c_payload_size += len(eth.data.data.data)\\n logger.debug(\\\"Client send data size: {}. Accepted data size is: {}.\\\"\\n \\\" Total data sent from client is: {}\\\".format(\\n len(tcp.data), len(eth.data.data.data), self._c_payload_size))\\n self._c_prev_pkt_ind = len(self.sessions[self.session_count]) - 1\\n rcv_nxt = self._s_rcv_next\\n if (not tcp.ack == self._c_win_left_edge and\\n tcp_seq_number_in_window(inc_tcp_seq_number(self._c_win_left_edge, 1),\\n tcp.ack, self._c_win_size)):\\n self._c_win_left_edge = tcp.ack\\n self._c_win_size = tcp.win << self._c_win_scaling_factor\\n else:\\n self._s_payload_size += len(eth.data.data.data)\\n logger.debug(\\\"Server send data of size: {}. Accepted data size is: {}.\\\"\\n \\\" Total data sent from server is: {}\\\".format(\\n len(tcp.data), len(eth.data.data.data), self._s_payload_size))\\n self._s_prev_pkt_ind = len(self.sessions[self.session_count]) - 1\\n rcv_nxt = self._c_rcv_next\\n # left edge is incremented by one becuase in_window function checks for inclusive seq number\\n # starting at left edge but ACK tells what's the next expected seq number, which could be 1 next\\n # to the end of window\\n if (not tcp.ack == self._s_win_left_edge and\\n tcp_seq_number_in_window(inc_tcp_seq_number(self._s_win_left_edge, 1),\\n tcp.ack, self._s_win_size)):\\n self._s_win_left_edge = tcp.ack\\n self._s_win_size = tcp.win << self._s_win_scaling_factor\\n # check if packet at the head of queue is ready to be processed\\n while True:\\n if len(early_pkts) == 0:\\n break\\n (_ts, _pkt_num), _buf = early_pkts.popleft()\\n early_eth = dpkt.ethernet.Ethernet(_buf)\\n early_ip = early_eth.data\\n early_tcp = early_ip.data\\n if tcp_seq_number_in_window(early_tcp.seq, rcv_nxt, get_tcp_packet_payload_len(early_ip)):\\n # if early_tcp.seq <= rcv_nxt:\\n self._process(early_eth.pack(), _ts, _pkt_num)\\n else:\\n early_pkts.appendleft(((_ts, _pkt_num), early_eth.pack()))\\n break\\n\\n \\\"\\\"\\\"\\n TCP flags:0x000 (12 bits)\\n [11 10 9 8 7 6 5 4 3 2 1 0]\\n - Bit 11 10 9: reserved\\n - Bit 8: nonce\\n - Bit 7: CWR (Congestion window reduced)\\n - Bit 6: ECN-Echo (Explicit Congestion Notification)\\n - Bit 5: Urgent\\n - Bit 4: ACK\\n - Bit 3: Push\\n - Bit 2: Reset\\n - Bit 1: SYN\\n - Bit 0: FIN\\n \\\"\\\"\\\"\\n\\n \\\"\\\"\\\"TCP flags for SYN [000000010111]\\\"\\\"\\\"\\n\\n prev_c_pkt = dpkt.ethernet.Ethernet(self.get_last_c_pkt()) if self.get_last_c_pkt() else None\\n prev_c_tcp = prev_c_pkt.data.data if prev_c_pkt else None\\n prev_s_pkt = dpkt.ethernet.Ethernet(self.get_last_s_pkt()) if self.get_last_s_pkt() else None\\n prev_s_tcp = prev_s_pkt.data.data if prev_s_pkt else None\\n logger.debug(tcp_pkt_debug_info(ip))\\n logger.debug(tcp_pkt_options_debug_info(tcp))\\n logger.debug(\\\"Processing packet number: {} in the current session\\\".format(self.pkt_num))\\n if rst_flag:\\n logger.info(\\\"Received a RESET flag, packet info: {}\\\".format(tcp_pkt_debug_info(ip)))\\n logger.info(\\\"TCP state before processing of packet: {}\\\".format(self.get_printable_state()))\\n if self._c_state == TCPState.CLOSED and self._s_state == TCPState.LISTENING:\\n self.session_count += 1\\n self.sessions[self.session_count] = [((ts, self.pkt_num), buf)]\\n self._c_state = self._s_state = TCPState.CLOSED\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n return\\n self._c_state = self._s_state = TCPState.CLOSED\\n if self.sip == sip:\\n self._c_prev_pkt_ind = len(self.sessions[self.session_count])\\n else:\\n self._s_prev_pkt_ind = len(self.sessions[self.session_count])\\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif syn_flag and (self._c_state in syn_unacceptable_states or self._s_state in syn_unacceptable_states):\\n logger.info(\\\"Received a unacceptable SYN flag, packet info: {}\\\".format(tcp_pkt_debug_info(ip)))\\n logger.info(\\\"TCP state before processing of packet: {}\\\".format(self.get_printable_state()))\\n self._s_state = self._c_state = TCPState.CLOSED\\n self.sessions[self.session_count].append(((ts,self.pkt_num), buf))\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif (self._c_state == TCPState.CLOSED and self._s_state == TCPState.LISTENING and\\n self.sip == sip):\\n if tcp.flags & 0x017 == 0x002:\\n self.session_count += 1\\n logger.info(\\\"number of sessions so far: {}\\\".format(self.session_count - 1))\\n logger.info(\\\"starting a new session, pkt info: {}\\\".format(tcp_pkt_debug_info(ip)))\\n logger.info(\\\"TCP state before processing of packet: {}\\\".format(self.get_printable_state()))\\n self.sessions[self.session_count] = []\\n self._c_prev_pkt_ind = len(self.sessions[self.session_count])\\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\\n self._c_state = TCPState.SYN_SENT\\n self._s_state = TCPState.SYN_RECEIVED\\n self._c_seq = tcp.seq\\n if tcp_opts:\\n if dpkt.tcp.TCP_OPT_WSCALE in tcp_opts:\\n self._c_win_scaling_factor = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_WSCALE], \\\"big\\\")\\n if dpkt.tcp.TCP_OPT_MSS in tcp_opts:\\n self._c_mss = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_MSS], \\\"big\\\")\\n else:\\n self._c_win_scaling_factor = 0\\n self._c_mss = -1\\n self._c_win_size = tcp.win << self._c_win_scaling_factor\\n logger.info(\\\"SYN flag from: {}:{}. Full TCP Flag is: {}\\\".format(self.sip, self.sp, hex(tcp.flags)))\\n logger.info(\\\"TCP options in the packet: {}\\\".format(tcp_pkt_options_debug_info(tcp)))\\n\\n elif self._c_state == TCPState.SYN_SENT and self._s_state == TCPState.SYN_RECEIVED:\\n logger.info(\\\"TCP packet info: {}\\\".format(tcp_pkt_debug_info(ip)))\\n logger.info(\\\"TCP state before processing of packet: {}\\\".format(self.get_printable_state()))\\n if self.sip == dip:\\n exp_ack = inc_tcp_seq_number(prev_c_tcp.seq, 1)\\n if not (tcp.flags & 0x017 == 0x012):\\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\\n self._s_state = self._c_state = TCPState.CLOSED\\n logger.info(\\\"SYN-ACK flag is not set in the TCP flags: {} from: {}:{}\\\".format(hex(tcp.flags),\\n self.dip, self.dp))\\n return\\n if tcp.ack == exp_ack:\\n self._s_prev_pkt_ind = len(self.sessions[self.session_count])\\n self._s_rcv_next = exp_ack\\n self._s_win_left_edge = exp_ack\\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\\n if tcp_opts:\\n if dpkt.tcp.TCP_OPT_WSCALE in tcp_opts:\\n self._s_win_scaling_factor = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_WSCALE], \\\"big\\\")\\n if dpkt.tcp.TCP_OPT_MSS in tcp_opts:\\n self._s_mss = int.from_bytes(tcp_opts[dpkt.tcp.TCP_OPT_MSS], \\\"big\\\")\\n else:\\n self._s_win_scaling_factor = 0\\n self._s_mss = -1\\n self._s_win_size = tcp.win << self._s_win_scaling_factor\\n logger.info(\\\"SYN-ACK flag from: {}:{}. Full TCP flag is: {}\\\".format(\\n self.dip, self.dp, hex(tcp.flags)))\\n logger.info(\\\"TCP options in the packet: {}\\\".format(tcp_pkt_options_debug_info(tcp)))\\n elif prev_s_tcp:\\n exp_ack = inc_tcp_seq_number(prev_s_tcp.seq, 1)\\n if tcp.flags & 0x017 == 0x010:\\n if tcp.ack == exp_ack and tcp.seq == prev_s_tcp.ack:\\n self._s_state = self._c_state = TCPState.ESTABLISHED\\n self._c_seq = tcp.seq\\n self._c_prev_pkt_ind = len(self.sessions[self.session_count])\\n self._c_rcv_next = exp_ack\\n self._c_win_left_edge = exp_ack\\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\\n self._c_win_size = tcp.win << self._c_win_scaling_factor\\n logger.info(\\\"TCP handshake complete.\\\")\\n else:\\n self._s_state = self._c_state = TCPState.CLOSED\\n self.sessions[self.session_count].append(((ts, self.pkt_num), buf))\\n logger.info(\\\"TCP handshake was not completed.\\\")\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif self._c_state == TCPState.ESTABLISHED and self._s_state == TCPState.ESTABLISHED:\\n if ack_flag:\\n \\\"\\\"\\\" if ACK flag is off drop the segment as per:\\n https://tools.ietf.org/html/rfc793#page-37\\n \\\"\\\"\\\"\\n logger.debug(tcp_pkt_debug_info(ip))\\n logger.debug(tcp_pkt_options_debug_info(tcp))\\n num_pkt_session_pkt = len(self.sessions[self.session_count])\\n slide_window()\\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and fin_flag:\\n logger.info(\\\"Received a FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n if self.sip == sip:\\n self._c_state = TCPState.FIN_WAIT_1\\n else:\\n self._s_state = TCPState.FIN_WAIT_1\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif self._c_state == TCPState.FIN_WAIT_1 and self._s_state == TCPState.ESTABLISHED:\\n if ack_flag:\\n slide_window()\\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and sip == self.dip:\\n if inc_tcp_seq_number(prev_c_tcp.seq, max(get_tcp_packet_payload_len(prev_c_pkt), 1)) == tcp.ack:\\n logger.info(\\\"Received a ACK for FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._c_state = TCPState.FIN_WAIT_2\\n self._s_state = TCPState.CLOSE_WAIT\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n if fin_flag:\\n logger.info(\\\"Received FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n if self._c_state == TCPState.FIN_WAIT_1:\\n self._s_state = self._c_state = TCPState.CLOSING\\n else:\\n self._s_state = TCPState.LAST_ACK\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif self._s_state == TCPState.FIN_WAIT_1 and self._c_state == TCPState.ESTABLISHED:\\n if ack_flag:\\n slide_window()\\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and sip == self.sip:\\n if inc_tcp_seq_number(prev_s_tcp.seq, max(get_tcp_packet_payload_len(prev_s_pkt), 1)) == tcp.ack:\\n logger.info(\\\"Received a ACK for FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._s_state = TCPState.FIN_WAIT_2\\n self._c_state = TCPState.CLOSE_WAIT\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n if fin_flag:\\n logger.info(\\\"Received FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n if self._s_state == TCPState.FIN_WAIT_1:\\n self._s_state = self._c_state = TCPState.CLOSING\\n else:\\n self._c_state = TCPState.LAST_ACK\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif self._c_state == TCPState.FIN_WAIT_2:\\n if sip == self.sip:\\n if ack_flag:\\n slide_window()\\n if self._s_state == TCPState.LAST_ACK:\\n if (num_pkt_session_pkt < len(self.sessions[self.session_count]) and\\n inc_tcp_seq_number(prev_s_tcp.seq,\\n max(get_tcp_packet_payload_len(prev_s_pkt), 1)) == tcp.ack):\\n logger.info(\\\"ACKed FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._c_state = self._s_state = TCPState.CLOSED\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n else:\\n if self._s_state == TCPState.CLOSE_WAIT and ack_flag:\\n slide_window()\\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and fin_flag:\\n logger.info(\\\"Received FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._s_state = TCPState.LAST_ACK\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif self._s_state == TCPState.FIN_WAIT_2:\\n if sip == self.dip:\\n if ack_flag:\\n slide_window()\\n if (self._c_state == TCPState.LAST_ACK and\\n num_pkt_session_pkt < len(self.sessions[self.session_count]) and\\n inc_tcp_seq_number(prev_c_tcp.seq,\\n max(get_tcp_packet_payload_len(prev_c_pkt), 1)) == tcp.ack):\\n logger.info(\\\"ACKed FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._s_state = self._c_state = TCPState.CLOSED\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n else:\\n if self._c_state == TCPState.CLOSE_WAIT and ack_flag:\\n slide_window()\\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and fin_flag:\\n logger.info(\\\"Received FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._c_state = TCPState.LAST_ACK\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n elif self._c_state == TCPState.CLOSING or self._s_state == TCPState.CLOSING:\\n if ack_flag:\\n slide_window()\\n if sip == self.sip and num_pkt_session_pkt < len(self.sessions[self.session_count]):\\n if inc_tcp_seq_number(ack_flag and prev_s_tcp.seq, 1) == tcp.ack:\\n logger.info(\\\"ACKed FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._s_state = TCPState.CLOSED\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n else:\\n if num_pkt_session_pkt < len(self.sessions[self.session_count]) and \\\\\\n inc_tcp_seq_number(ack_flag and prev_c_tcp.seq, 1) == tcp.ack:\\n logger.info(\\\"ACKed FIN flag: {}\\\".format(tcp_pkt_debug_info(ip)))\\n self._c_state = TCPState.CLOSED\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n else:\\n logger.info(\\\"Packet didn't match any valid state: {}\\\".format(tcp_pkt_debug_info(ip)))\\n #self._s_state = self._c_state = TCPState.CLOSED\\n logger.info(\\\"TCP state after processing of packet: {}\\\".format(self.get_printable_state()))\\n logger.debug(self.get_printable_state())\",\n \"def start_capture(self, interface, count=0, ptype='', pfilter=None,\\n callback=None, callback_args=None, save_dump_file=False,\\n save_dump_filename=None):\\n tcpd = (self.packet_captures[interface]\\n if interface in self.packet_captures\\n else TCPDump())\\n \\\"\\\"\\\" :type: TCPDump \\\"\\\"\\\"\\n\\n self.LOG.debug('Starting tcpdump on host: ' + self.name)\\n\\n old_log = self.cli.log_cmd\\n if self.debug:\\n self.cli.log_cmd = True\\n\\n tcpd.start_capture(cli=self.cli, interface=interface, count=count,\\n packet_type=ptype, pcap_filter=pfilter,\\n callback=callback, callback_args=callback_args,\\n save_dump_file=save_dump_file,\\n save_dump_filename=save_dump_filename,\\n blocking=False)\\n\\n self.cli.log_cmd = old_log\\n\\n self.packet_captures[interface] = tcpd\",\n \"def tcp_pkt_debug_info(pkt: dpkt.ip.IP) -> str:\\n if isinstance(pkt, dpkt.ip.IP):\\n paylod_len = pkt.len - (4 * pkt.hl) - (4 * pkt.data.off)\\n return \\\"{}:{}-> {}:{}, seq: {}, ack:{}, flag:{}, payload len: {}, payload: {}, sum: {}\\\".format(\\n inet_to_str(pkt.src), pkt.data.sport, inet_to_str(pkt.dst), pkt.data.dport, hex(pkt.data.seq),\\n hex(pkt.data.ack), hex(pkt.data.flags), hex(paylod_len), pkt.data.data, hex(pkt.data.sum))\",\n \"def process_pkt_from_client(ts_delta, acks, nb_acks, connections, tcp, ip, saddr, daddr, sport, dport, fin_flag):\\n if acks[saddr, sport, daddr, dport][co.S2C] >= 0:\\n conn_id = acks[saddr, sport, daddr, dport][co.CONN_ID]\\n connections[conn_id].flow.attr[co.S2C][co.TIME_LAST_ACK_TCP] = ts_delta\\n if fin_flag:\\n connections[conn_id].flow.attr[co.S2C][co.TIME_FIN_ACK_TCP] = ts_delta\\n\\n bytes_acked = (tcp.ack - acks[saddr, sport, daddr, dport][co.S2C]) % 4294967296\\n if bytes_acked >= 2000000000:\\n # Ack of 2GB or more is just not possible here\\n return\\n\\n increment_value_dict(nb_acks[co.S2C][conn_id], bytes_acked)\\n size_payload = ip.len - ip.hl * 4 - tcp.off * 4\\n\\n # If SOCKS command\\n if size_payload == 7 and connections[conn_id].attr.get(co.SOCKS_PORT, None) is None:\\n crypted_socks_cmd = tcp.data\\n # This is possible because of packet stripping\\n if len(crypted_socks_cmd) == 7:\\n decrypted_socks_cmd = socks_parser.decode(crypted_socks_cmd)\\n if decrypted_socks_cmd[0] == b'\\\\x01': # Connect\\n connections[conn_id].attr[co.SOCKS_DADDR] = socks_parser.get_ip_address(decrypted_socks_cmd)\\n connections[conn_id].attr[co.SOCKS_PORT] = socks_parser.get_port_number(decrypted_socks_cmd)\\n\\n if size_payload > 0 and tcp.seq in acks[saddr, sport, daddr, dport][SEQ_C2S]:\\n # This is a retransmission! (take into account the seq overflow)\\n connections[conn_id].flow.attr[co.C2S][co.TIME_LAST_PAYLD_WITH_RETRANS_TCP] = ts_delta\\n connections[conn_id].flow.attr[co.C2S][co.TIMESTAMP_RETRANS].append((ts_delta,\\n ts_delta - acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq][0],\\n ts_delta - acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq][1],\\n ts_delta - acks[saddr, sport, daddr, dport][co.TIMESTAMP][CLIENT]))\\n acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq][1] = ts_delta\\n elif size_payload > 0:\\n acks[saddr, sport, daddr, dport][SEQ_C2S].add(tcp.seq)\\n connections[conn_id].flow.attr[co.C2S][co.TIME_LAST_PAYLD_WITH_RETRANS_TCP] = ts_delta\\n connections[conn_id].flow.attr[co.C2S][co.TIME_LAST_PAYLD_TCP] = ts_delta\\n acks[saddr, sport, daddr, dport][HSEQ_C2S][tcp.seq] = [ts_delta, ts_delta]\\n # Don't think will face this issue\\n# if len(acks[saddr, sport, daddr, dport][SEQ][co.C2S]) >= 3000000:\\n# for x in range(50000):\\n# acks[saddr, sport, daddr, dport][SEQ][co.C2S].popleft()\\n\\n acks[saddr, sport, daddr, dport][co.S2C] = tcp.ack\\n acks[saddr, sport, daddr, dport][co.TIMESTAMP][CLIENT] = ts_delta\",\n \"def query_sniff(pkt):\\n if IP in pkt:\\n ip_src = pkt[IP].src\\n ip_dst = pkt[IP].dst\\n\\n if pkt.haslayer(DNS) and pkt.getlayer(DNS).qr == 0:\\n domain = pkt.getlayer(DNS).qd.qname.decode(\\\"utf-8\\\")\\n now = datetime.now()\\n stored_dns_requests.update({datetime.timestamp(now): domain})\\n print(\\\"SRC: {} - DST: {} : {}\\\".format(ip_src, ip_dst, domain))\",\n \"def _get_next_packet(self):\\n raise NotImplementedError(\\\"Do not instantiate csvAbstractReader directly.\\\")\",\n \"def receive_captured_list(self):\\n reply = self.socket.recv(4096)\\n print(\\\"Pokemon capturados\\\")\\n print(reply[1:].decode())\",\n \"def scan(ip):\\n arp_request = scapy.ARP(pdst=ip)\\n broadcast = scapy.Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")\\n arp_request_broadcast = broadcast/arp_request\\n answered_list = scapy.srp(arp_request_broadcast , timeout = 1, verbose=False)[0]\\n target_list=[]\\n for element in answered_list:\\n target_dict = {\\\"ip\\\":element[1].psrc, \\\"mac\\\":element[1].hwsrc}\\n target_list.append(target_dict)\\n return target_list\",\n \"def processpacket(p):\\n\\n\\tglobal SynSentToTCPService\\n\\tglobal SynAckSentToTCPClient\\n\\tglobal LiveTCPService\\n\\tglobal LiveTCPClient\\n\\tglobal LiveUDPService\\n\\tglobal LiveUDPClient\\n\\tglobal NmapServerDescription\\n\\tglobal ManualServerDescription\\n\\tglobal ClientDescription\\n\\tglobal MacAddr\\n\\tglobal OSDescription\\n\\tglobal ServiceFPs\\n\\tglobal SipPhoneMatch\\n\\tglobal Devel\\n\\tglobal IsRouter\\n\\tglobal DNSRecord\\n\\tglobal HostIPs\\n\\n\\tif (type(p) == Dot3) and (type(p['LLC']) == LLC):\\n\\t\\tUnhandledPacket(p)\\n\\t\\t#Spanning Tree Protocol\\n\\t\\t#Debug(\\\"802.3\\\")\\n\\t\\t#p.show()\\n\\t\\t#print type(p['LLC'])\\n\\telif (p['Ethernet'] == None):\\n\\t\\tDebug(\\\"non-ethernet packet\\\")\\t\\t#Need more details on how to handle.\\n\\t\\tUnhandledPacket(p)\\n\\t\\t#p.show()\\n\\t\\t#print type(p)\\n\\t\\t#quit()\\n\\telif p['Ethernet'].type == 0x0806:\\t\\t#ARP\\n\\t\\t#pull arp data from here instead of tcp/udp packets, as these are all local\\n\\t\\tif (p['ARP'].op == 1):\\t\\t\\t#1 is request (\\\"who-has\\\")\\n\\t\\t\\tpass\\n\\t\\tif (p['ARP'].op == 2):\\t\\t\\t#2 is reply (\\\"is-at\\\")\\n\\t\\t\\tif (p['ARP.psrc'] != None) and (p['ARP.hwsrc'] != None):\\n\\t\\t\\t\\tIPAddr=p['ARP.psrc']\\n\\t\\t\\t\\tMyMac=p['ARP.hwsrc'].upper()\\n\\t\\t\\t\\tif (not MacAddr.has_key(IPAddr)) or (MacAddr[IPAddr] != MyMac):\\n\\t\\t\\t\\t\\tReportId(\\\"MA\\\", IPAddr, 'Ethernet', MyMac, '')\\n\\t\\t\\telse:\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\telse:\\n\\t\\t\\tUnhandledPacket(p)\\n\\telif p['Ethernet'].type == 0x0800:\\t\\t#IP\\n\\t\\tsIP=str(p['IP'].src)\\n\\t\\tdIP=str(p['IP'].dst)\\n\\t\\t#Best to get these from arps instead; if we get them from here, we get router macs for foreign addresses.\\n\\t\\t#if not MacAddr.has_key(sIP):\\n\\t\\t#\\tReportId(\\\"MA\\\", sIP, \\\"Ethernet\\\", p['Ethernet'].src, '')\\n\\t\\t#if not MacAddr.has_key(dIP):\\n\\t\\t#\\tReportId(\\\"MA\\\", dIP, \\\"Ethernet\\\", p['Ethernet'].dst, '')\\n\\n\\t\\tif p['IP'].proto == 1:\\t\\t\\t#ICMP\\n\\t\\t\\tType = p['ICMP'].type\\n\\t\\t\\tCode = p['ICMP'].code\\n\\n\\t\\t\\tif (Type == 0):\\t\\t\\t\\t\\t\\t#Echo reply\\n\\t\\t\\t\\tif (not(OSDescription.has_key(sIP))):\\n\\t\\t\\t\\t\\tReportId(\\\"IP\\\", sIP, \\\"IP\\\", \\\"live\\\", 'icmp echo reply')\\n\\t\\t\\telif (Type == 3) and (type(p[IPerror]) == IPerror):\\t#Unreachable, check that we have an actual embedded packet\\n\\t\\t\\t\\t#if (type(p[IPerror]) != IPerror):\\n\\t\\t\\t\\t#\\tp.show()\\n\\t\\t\\t\\t#\\tprint type(p[IPerror])\\n\\t\\t\\t\\t#\\tquit()\\n\\t\\t\\t\\tOrigdIP = p[IPerror].dst\\n\\t\\t\\t\\tif (Code == 0):\\t\\t\\t\\t\\t#Net unreachable\\n\\t\\t\\t\\t\\tif (not(OSDescription.has_key(OrigdIP))):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"IP\\\", OrigdIP, \\\"IP\\\", \\\"dead\\\", 'net unreachable')\\n\\t\\t\\t\\t\\tif (not(IsRouter.has_key(sIP))):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"RO\\\", sIP, \\\"NetUn\\\", \\\"router\\\", \\\"\\\")\\n\\t\\t\\t\\telif (Code == 1):\\t\\t\\t\\t#Host unreachable\\n\\t\\t\\t\\t\\tif (not(OSDescription.has_key(OrigdIP))):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"IP\\\", OrigdIP, \\\"IP\\\", \\\"dead\\\", 'host unreachable')\\n\\t\\t\\t\\t\\tif (not(IsRouter.has_key(sIP))):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"RO\\\", sIP, \\\"HostUn\\\", \\\"router\\\", \\\"\\\")\\n\\t\\t\\t\\telif (Code == 3) and (p[IPerror].proto == 17):\\t#Port unreachable and embedded protocol = 17, UDP, as it should be\\n\\t\\t\\t\\t\\tDNSServerLoc = p[IPerror].src + \\\",UDP_53\\\"\\n\\t\\t\\t\\t\\tif (p[UDPerror].sport == 53) and (ManualServerDescription.has_key(DNSServerLoc)) and (ManualServerDescription[DNSServerLoc] == \\\"dns/server\\\"):\\n\\t\\t\\t\\t\\t\\t#If orig packet coming from 53 and coming from a dns server, don't do anything (closed port on client is a common effect)\\n\\t\\t\\t\\t\\t\\t#Don't waste time on port unreachables going back to a dns server; too common, and ephemeral anyways.\\n\\t\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t#If orig packet coming from something other than 53, or coming from 53 and NOT coming from a dns server, log as closed\\n\\t\\t\\t\\t\\t\\tOrigDPort = str(p[UDPerror].dport)\\n\\t\\t\\t\\t\\t\\tOrigDstService = OrigdIP + \\\",UDP_\\\" + OrigDPort\\n\\t\\t\\t\\t\\t\\tif ((not LiveUDPService.has_key(OrigDstService)) or (LiveUDPService[OrigDstService] == True)):\\n\\t\\t\\t\\t\\t\\t\\tLiveUDPService[OrigDstService] = False\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"US\\\", OrigdIP, \\\"UDP_\\\" + OrigDPort, \\\"closed\\\", \\\"port unreachable\\\")\\n\\t\\t\\t\\telif (Code == 3) and (p[IPerror].proto == 6) and (p[TCPerror].dport == 113):\\t#Port unreachable and embedded protocol = 6, TCP, which it shouldn't. May be the same firewall providing the TCP FR's\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (Code == 6):\\t\\t\\t\\t#Net unknown\\n\\t\\t\\t\\t\\tif (not(OSDescription.has_key(OrigdIP))):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"IP\\\", OrigdIP, \\\"IP\\\", \\\"dead\\\", 'net unknown')\\n\\t\\t\\t\\telif (Code == 7):\\t\\t\\t\\t#Host unknown\\n\\t\\t\\t\\t\\tif (not(OSDescription.has_key(OrigdIP))):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"IP\\\", OrigdIP, \\\"IP\\\", \\\"dead\\\", 'host unknown')\\n\\t\\t\\t\\telif (Code == 9):\\t\\t\\t\\t#Network Administratively Prohibited\\n\\t\\t\\t\\t\\tpass\\t\\t\\t\\t\\t#Can't tell much from this type of traffic. Possibly list as firewall?\\n\\t\\t\\t\\telif (Code == 10):\\t\\t\\t\\t#Host Administratively Prohibited\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (Code == 11):\\t\\t\\t\\t#Network unreachable for TOS\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (Code == 12):\\t\\t\\t\\t#Host unreachable for TOS\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (Code == 13):\\t\\t\\t\\t#Communication Administratively prohibited\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (Type == 8):\\t\\t\\t\\t\\t#ping\\n\\t\\t\\t\\t#FIXME - check payload for ping sender type, perhaps\\n\\t\\t\\t\\tpass\\n\\t\\t\\telif (Type == 11):\\t\\t\\t\\t\\t#Time exceeded\\n\\t\\t\\t\\tif (Code == 0):\\t\\t\\t\\t\\t#TTL exceeded\\n\\t\\t\\t\\t\\tif (not(IsRouter.has_key(sIP))):\\n\\t\\t\\t\\t\\t\\t#FIXME - put original target IP as column 5?\\n\\t\\t\\t\\t\\t\\tReportId(\\\"RO\\\", sIP, \\\"TTLEx\\\", \\\"router\\\", \\\"\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\telif p['IP'].proto == 2:\\t\\t#IGMP\\n\\t\\t\\tUnhandledPacket(p)\\n\\t\\telif p['IP'].proto == 6:\\t\\t#TCP\\n\\t\\t\\tsport=str(p['TCP'].sport)\\n\\t\\t\\tdport=str(p['TCP'].dport)\\n\\t\\t\\t#print p['IP'].src + \\\":\\\" + sport + \\\" -> \\\", p['IP'].dst + \\\":\\\" + dport,\\n\\t\\t\\tif (p['TCP'].flags & 0x17) == 0x12:\\t#SYN/ACK (RST and FIN off)\\n\\t\\t\\t\\tCliService = dIP + \\\",TCP_\\\" + sport\\n\\t\\t\\t\\tif not SynAckSentToTCPClient.has_key(CliService):\\n\\t\\t\\t\\t\\tSynAckSentToTCPClient[CliService] = True\\n\\n\\t\\t\\t\\t#If we've seen a syn sent to this port and have either not seen any SA/R, or we've seen a R in the past:\\n\\t\\t\\t\\t#The last test is for a service that was previously closed and is now open; report each transition once.\\n\\t\\t\\t\\tService = sIP + \\\",TCP_\\\" + sport\\n\\t\\t\\t\\tif ( (SynSentToTCPService.has_key(Service)) and ((not LiveTCPService.has_key(Service)) or (LiveTCPService[Service] == False)) ):\\n\\t\\t\\t\\t\\tLiveTCPService[Service] = True\\n\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", '')\\n\\t\\t\\telif (p['TCP'].flags & 0x17) == 0x02:\\t#SYN (ACK, RST, and FIN off)\\n\\t\\t\\t\\tService = dIP + \\\",TCP_\\\" + dport\\n\\t\\t\\t\\tif not SynSentToTCPService.has_key(Service):\\n\\t\\t\\t\\t\\tSynSentToTCPService[Service] = True\\n\\t\\t\\t\\t#Debug(\\\"trying to fingerprint \\\" + sIP)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tp0fdata = p0f(p)\\n\\t\\t\\t\\t\\t#FIXME - reasonably common occurence, don't whine, just fix it.\\n\\t\\t\\t\\t\\t#if (len(p0fdata) >1):\\n\\t\\t\\t\\t\\t#\\tDebug(\\\"More than one OS fingerprint for \\\" + sIP + \\\", using the first.\\\")\\n\\t\\t\\t\\t\\tif (len(p0fdata) >=1):\\n\\t\\t\\t\\t\\t\\tPDescription = p0fdata[0][0] + \\\" \\\" + p0fdata[0][1] + \\\" (\\\" + str(int(p0fdata[0][2]) + 1)\\t#FIXME - Grabbing just the first candidate, may need to compare correlation values; provided?\\n\\t\\t\\t\\t\\t\\tif (p0fdata[0][2] == 0):\\n\\t\\t\\t\\t\\t\\t\\tPDescription = PDescription + \\\" hop away)\\\"\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tPDescription = PDescription + \\\" hops away)\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#[N][2] param appears to be distance away in hops (but add 1 to this to get real hop count?)\\n\\t\\t\\t\\t\\t\\tPDescription = PDescription.replace(',', ';')\\t\\t#Commas are delimiters in output\\n\\t\\t\\t\\t\\t\\tif (not(OSDescription.has_key(sIP))) or (OSDescription[sIP] != PDescription):\\n\\t\\t\\t\\t\\t\\t\\tOSDescription[sIP] = PDescription\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"IP\\\", sIP, \\\"IP\\\", \\\"live\\\", PDescription)\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tPDescription = 'p0f failure'\\n\\t\\t\\t\\t\\tif (not(OSDescription.has_key(sIP))) or (OSDescription[sIP] != PDescription):\\n\\t\\t\\t\\t\\t\\tDebug(\\\"P0f failure in \\\" + sIP + \\\":\\\" + sport + \\\" -> \\\" + dIP + \\\":\\\" + dport)\\n\\t\\t\\t\\t\\t\\tOSDescription[sIP] = PDescription\\n\\t\\t\\t\\t\\t\\tReportId(\\\"IP\\\", sIP, \\\"IP\\\", \\\"live\\\", PDescription)\\n\\t\\t\\telif (p['TCP'].flags & 0x07) == 0x01:\\t#FIN (SYN/RST off)\\n\\t\\t\\t\\tCliService = sIP + \\\",TCP_\\\" + dport\\n\\t\\t\\t\\tif ( (SynAckSentToTCPClient.has_key(CliService)) and ((not LiveTCPClient.has_key(CliService)) or (LiveTCPClient[CliService] == False)) ):\\n\\t\\t\\t\\t\\tLiveTCPClient[CliService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", '')\\n\\t\\t\\telif (p['TCP'].flags & 0x07) == 0x04:\\t#RST (SYN and FIN off)\\n\\t\\t\\t\\t#FIXME - handle rst going in the other direction?\\n\\t\\t\\t\\tService = sIP + \\\",TCP_\\\" + sport\\n\\t\\t\\t\\tif ( (SynSentToTCPService.has_key(Service)) and ((not LiveTCPService.has_key(Service)) or (LiveTCPService[Service] == True)) ):\\n\\t\\t\\t\\t\\tLiveTCPService[Service] = False\\n\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"closed\\\", '')\\n\\t\\t\\telif ((p['TCP'].flags & 0x3F) == 0x15) and (sport == \\\"113\\\"):\\t#FIN, RST, ACK (SYN, PSH, URG off)\\n\\t\\t\\t\\t#This may be a firewall or some other device stepping in for 113 with a FIN/RST.\\n\\t\\t\\t\\tpass\\n\\t\\t\\telif (p['TCP'].flags & 0x17) == 0x10:\\t#ACK (RST, SYN, and FIN off)\\n\\t\\t\\t\\t#FIXME - check for UnhandledPacket placement in ACK\\n\\t\\t\\t\\tFromPort = sIP + \\\",TCP_\\\" + sport\\n\\t\\t\\t\\tToPort = dIP + \\\",TCP_\\\" + dport\\n\\t\\t\\t\\tPayload = str(p['Raw.load'])\\t\\t\\t#For some reason this doesn't handle p['Raw'].load\\n\\t\\t\\t\\tif ( (LiveTCPService.has_key(FromPort)) and (LiveTCPService[FromPort] == True) and (LiveTCPService.has_key(ToPort)) and (LiveTCPService[ToPort] == True)):\\n\\t\\t\\t\\t\\tprint \\\"Logic failure: both \\\" + FromPort + \\\" and \\\" + ToPort + \\\" are listed as live services.\\\"\\n\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\telif ((LiveTCPService.has_key(FromPort)) and (LiveTCPService[FromPort] == True)):\\t#If the \\\"From\\\" side is a known TCP server:\\n\\t\\t\\t\\t\\tif (not NmapServerDescription.has_key(FromPort) ):\\t\\t#Check nmap fingerprint strings for this server port\\n\\t\\t\\t\\t\\t\\tif (ServiceFPs.has_key(int(sport))):\\n\\t\\t\\t\\t\\t\\t\\tfor OneTuple in ServiceFPs[int(sport)]:\\n\\t\\t\\t\\t\\t\\t\\t\\tMatchObj = OneTuple[0].search(Payload)\\n\\t\\t\\t\\t\\t\\t\\t\\tif (MatchObj != None):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#Debugging:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#FIXME - removeme once understood:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#File \\\"/home/wstearns/med/programming/python/passer/passer.py\\\", line 504, in processpacket\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#OutputDescription = OutputDescription.replace('$' + str(Index), MatchObj.group(Index))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#TypeError: expected a character buffer object\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif (OneTuple[1] == None):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tDebug(\\\"Null description for \\\" + OneTuple[0])\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#quit()\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tOutputDescription = OneTuple[1]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif len(MatchObj.groups()) >= 1:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#We have subexpressions matched, these need to be inserted into the description string\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor Index in range(1,len(MatchObj.groups())+1):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Example: Replace \\\"$1\\\" with MatchObj.group(1)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tOutputDescription = OutputDescription.replace('$' + str(Index), str(MatchObj.group(Index)))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", OutputDescription)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tNmapServerDescription[sIP + \\\",TCP_\\\" + sport] = OutputDescription\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tbreak\\t\\t\\t\\t\\t#Exit for loop, no need to check any more fingerprints now that we've found a match\\n\\n\\t\\t\\t\\t\\tif (not NmapServerDescription.has_key(FromPort)):\\t\\t#If the above loop didn't find a server description\\n\\t\\t\\t\\t\\t\\tif (ServiceFPs.has_key('all')):\\t\\t\\t\\t#Now recheck against regexes not associated with a specific port (port 'all').\\n\\t\\t\\t\\t\\t\\t\\tfor OneTuple in ServiceFPs['all']:\\n\\t\\t\\t\\t\\t\\t\\t\\tMatchObj = OneTuple[0].search(Payload)\\n\\t\\t\\t\\t\\t\\t\\t\\tif (MatchObj != None):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tOutputDescription = OneTuple[1]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif len(MatchObj.groups()) >= 1:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#We have subexpressions matched, these need to be inserted into the description string\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor Index in range(1,len(MatchObj.groups())+1):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tOutputDescription = OutputDescription.replace('$' + str(Index), MatchObj.group(Index))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", OutputDescription)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tNmapServerDescription[sIP + \\\",TCP_\\\" + sport] = OutputDescription\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\t\\tif (not ManualServerDescription.has_key(FromPort) ):\\n\\t\\t\\t\\t\\t\\tif (sport == \\\"22\\\") and (Payload != None) and (Payload.find('SSH-') > -1):\\n\\t\\t\\t\\t\\t\\t\\tif ( (Payload.find('SSH-1.99-OpenSSH_') > -1) or (Payload.find('SSH-2.0-OpenSSH_') > -1) ):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"ssh/openssh\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"ssh/openssh\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('SSH-1.5-') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"ssh/generic\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"ssh/generic\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"25\\\") and (Payload != None) and (Payload.find(' ESMTP Sendmail ') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"smtp/sendmail\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"smtp/sendmail\\\"\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"25\\\") and (Payload != None) and (Payload.find(' - Welcome to our SMTP server ESMTP') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"smtp/generic\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"smtp/generic\\\"\\n\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\t\\t\\t#Check for port 80 and search for \\\"Server: \\\" once\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"80\\\") and (Payload != None) and (Payload.find('Server: ') > -1):\\n\\t\\t\\t\\t\\t\\t\\tif (Payload.find('Server: Apache') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/apache\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/apache\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: Embedded HTTP Server') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/embedded\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/embedded\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: gws') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/gws\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/gws\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: KFWebServer') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/kfwebserver\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/kfwebserver\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: micro_httpd') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/micro-httpd\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/micro-httpd\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: Microsoft-IIS') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/iis\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/iis\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: lighttpd') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/lighttpd\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/lighttpd\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: MIIxpc') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/mirrorimage\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/mirrorimage\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: mini_httpd') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/mini-httpd\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/mini-httpd\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: nc -l -p 80') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/nc\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/nc\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: nginx/') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/nginx\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/nginx\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: Nucleus') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/nucleus\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/nucleus\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: RomPager') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/rompager\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/rompager\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: Server') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/server\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/server\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: Sun-ONE-Web-Server/') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/sun-one\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/sun-one\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: TrustRank Frontend') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/trustrank\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/trustrank\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('Server: YTS/') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/yahoo\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/yahoo\\\"\\n\\t\\t\\t\\t\\t\\t\\telif (Payload.find('HTTP/1.0 404 Not Found') > -1) or (Payload.find('HTTP/1.1 200 OK') > -1):\\n\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"http/generic\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"http/generic\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"110\\\") and (Payload != None) and (Payload.find('POP3 Server Ready') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"pop3/generic\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"pop3/generic\\\"\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"143\\\") and (Payload != None) and (Payload.find('* OK dovecot ready') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"imap/dovecot\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"imap/dovecot\\\"\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"143\\\") and (Payload != None) and (Payload.find(' IMAP4rev1 ') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"imap/generic\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"imap/generic\\\"\\n\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\t\\t\\telif (sport == \\\"783\\\") and (Payload != None) and (Payload.find('SPAMD/1.1 ') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"spamd/spamd\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"spamd/spamd\\\"\\n\\t\\t\\t\\t\\t\\telif ( (sport == \\\"3128\\\") or (sport == \\\"80\\\") ) and (Payload != None) and (Payload.find('Via: ') > -1) and (Payload.find(' (squid/') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TS\\\", sIP, \\\"TCP_\\\" + sport, \\\"listening\\\", \\\"proxy/squid\\\")\\n\\t\\t\\t\\t\\t\\t\\tManualServerDescription[sIP + \\\",TCP_\\\" + sport] = \\\"proxy/squid\\\"\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ServerPayloadDir, FromPort, Payload)\\n\\t\\t\\t\\telif ((LiveTCPService.has_key(ToPort)) and (LiveTCPService[ToPort] == True)):\\t\\t#If the \\\"To\\\" side is a known TCP server:\\n\\t\\t\\t\\t\\tClientKey = sIP + \\\",TCP_\\\" + dport\\t#Note: CLIENT ip and SERVER port\\n\\t\\t\\t\\t\\tif (not ClientDescription.has_key(ClientKey)):\\n\\t\\t\\t\\t\\t\\tif (dport == \\\"22\\\") and (Payload != None) and ( (Payload.find('SSH-2.0-OpenSSH_') > -1) or (Payload.find('SSH-1.5-OpenSSH_') > -1) ):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"ssh/openssh\\\")\\n\\t\\t\\t\\t\\t\\t#As cute as it is to catch this, it miscatches any relay that's carrying a pine-generated mail.\\n\\t\\t\\t\\t\\t\\t#elif (dport == \\\"25\\\") and (Payload != None) and (Payload.find('Message-ID: <Pine.') > -1):\\n\\t\\t\\t\\t\\t\\t#\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"smtp/pine\\\")\\n\\t\\t\\t\\t\\t\\telif ( (dport == \\\"80\\\") or (dport == \\\"3128\\\") ) and (Payload != None) and (Payload.find('User-Agent: libwww-perl/') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"http/libwww-perl\\\")\\n\\t\\t\\t\\t\\t\\telif ( (dport == \\\"80\\\") or (dport == \\\"3128\\\") ) and (Payload != None) and (Payload.find('User-Agent: Lynx') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"http/lynx\\\")\\n\\t\\t\\t\\t\\t\\telif ( (dport == \\\"80\\\") or (dport == \\\"3128\\\") ) and (Payload != None) and (Payload.find('User-Agent: Mozilla') > -1) and (Payload.find(' Firefox/') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"http/firefox\\\")\\n\\t\\t\\t\\t\\t\\telif ( (dport == \\\"80\\\") or (dport == \\\"3128\\\") ) and (Payload != None) and (Payload.find('User-Agent: Wget/') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"http/wget\\\")\\n\\t\\t\\t\\t\\t\\telif (dport == \\\"143\\\") and (Payload != None) and (Payload.find('A0001 CAPABILITY') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"imap/generic\\\")\\n\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ClientPayloadDir, ClientKey, Payload)\\n\\t\\t\\t\\t\\t\\telif (dport == \\\"783\\\") and (Payload != None) and (Payload.find('PROCESS SPAMC') > -1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"TC\\\", sIP, \\\"TCP_\\\" + dport, \\\"open\\\", \\\"spamd/spamc\\\")\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t\\t\\t#LogNewPayload(ClientPayloadDir, ClientKey, Payload)\\n\\t\\t\\t\\telse:\\t#Neither port pair is known as a server\\n\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t\\t\\t#Following is debugging at best; it should only show up early on as the sniffer listens to conversations for which it didn't hear the SYN/ACK\\n\\t\\t\\t\\t\\t#print \\\"note: neither \\\" + FromPort + \\\" nor \\\" + ToPort + \\\" is listed as a live service.\\\"\\n\\t\\t\\telse:\\t#Other TCP flag combinations here\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\telif p['IP'].proto == 17 and (type(p['UDP']) == UDP):\\t\\t#UDP. We have to check the object type as well as we do get (corrupted? truncated?) packets with type 17 that aren't udp: AttributeError: 'NoneType' object has no attribute 'sport'\\n\\t\\t\\t#FIXME - possibly run udp packets through ServiceFPs as well?\\n\\t\\t\\tsport=str(p['UDP'].sport)\\n\\t\\t\\tdport=str(p['UDP'].dport)\\n\\t\\t\\tSrcService = sIP + \\\",UDP_\\\" + sport\\n\\t\\t\\tDstService = dIP + \\\",UDP_\\\" + dport\\n\\t\\t\\tSrcClient = sIP + \\\",UDP_\\\" + dport\\n\\t\\t\\tPayload = p['Raw.load']\\n\\n\\t\\t\\t#Multicast DNS: http://files.multicastdns.org/draft-cheshire-dnsext-multicastdns.txt\\n\\t\\t\\t#- usually sent to 224.0.0.251 (or FF02::FB) (link-local multicast).\\n\\t\\t\\t#\\t- if \\\".local.\\\" in query, these MUST be the target IPs\\n\\t\\t\\t#\\t- non-local queries may be sent to these or normal dns servers\\n\\t\\t\\t#\\t- rdns queries for \\\"254.169.in-addr.arpa.\\\" MUST be sent to 224.0.0.251\\n\\t\\t\\t#\\t- rdns queries for \\\"8.e.f.ip6.arpa.\\\", \\\"9.e.f.ip6.arpa.\\\",\\\"a.e.f.ip6.arpa.\\\", and \\\"b.e.f.ip6.arpa.\\\" MUST be sent to the IPv6 mDNS link-local multicast address FF02::FB.\\n\\t\\t\\t#- sent to udp port 5353\\n\\t\\t\\t#- generic clients may use \\\"single-dns-object.local.\\\", such as \\\"sparrow.local.\\\"\\n\\t\\t\\t#- responses have IP TTL = 255 to check that packet originated on-lan\\n\\n\\t\\t\\t#Multicast DNS, placed next to normal dns, out of numerical order\\n\\t\\t\\tif (dport == \\\"5353\\\") and ( (p['IP'].ttl == 1) or (p['IP'].ttl == 255) ):\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcClient)) or (LiveUDPService[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcClient] = True\\n\\t\\t\\t\\t\\tif (dIP == \\\"224.0.0.251\\\"):\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"mdns/broadcastclient\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"mdns/client\\\")\\n\\n\\t\\t\\t\\t\\t#Extract dns answers like with 53; change elif to if and add 5353 to ports on next if?\\n\\t\\t\\t\\t\\t#At the moment, no; scapy does not appear to parse 5353 as dns.\\n\\t\\t\\t\\t\\t#else:\\n\\t\\t\\t\\t\\t#\\tUnhandledPacket(p)\\n\\t\\t\\t#FIXME - add check for \\\"if isinstance(p['DNS'], whatevertype):\\there and at all p[] accesses.\\n\\t\\t\\telif (sport == \\\"53\\\") and (isinstance(p['DNS'], DNS)) and (p['DNS'].qr == 1):\\t\\t#qr == 1 is a response\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\t#FIXME - Also report the TLD from one of the query answers to show what it's willing to answer for?\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"dns/server\\\")\\n\\t\\t\\t\\t#Now we extract dns answers. First, check that there's no dns error:\\n\\t\\t\\t\\tif (p['DNS'].rcode == 0):\\t\\t\\t#No error\\n\\t\\t\\t\\t\\tDNSBlocks = [ ]\\n\\t\\t\\t\\t\\tCNAMERecs = [ ]\\t\\t\\t\\t#We hold onto all cnames until we've processed all PTR's and A's here\\n\\t\\t\\t\\t\\tif (p['DNS'].ancount > 0):\\t\\t#If we have at least one answer from the answer block, process it\\n\\t\\t\\t\\t\\t\\tDNSBlocks.append(p[DNS].an)\\n\\t\\t\\t\\t\\tif (p['DNS'].arcount > 0):\\t\\t#Likewise for the \\\"additional\\\" block\\n\\t\\t\\t\\t\\t\\tDNSBlocks.append(p[DNS].ar)\\n\\t\\t\\t\\t\\tfor OneAn in DNSBlocks:\\n\\t\\t\\t\\t\\t\\t#Thanks to Philippe Biondi for showing me how to extract additional records.\\n\\t\\t\\t\\t\\t\\t#Debug(\\\"Start dns extract\\\" + str(p['DNS'].ancount))\\n\\t\\t\\t\\t\\t\\t#OneAn = p[DNS].an\\n\\t\\t\\t\\t\\t\\t#while OneAn is not NoPayload:\\t\\t#This doesn't seem to stop at the end of the list; incorrect syntax.\\n\\t\\t\\t\\t\\t\\twhile isinstance(OneAn,DNSRR):\\t\\t#Somewhat equivalent:\\twhile not isinstance(an, NoPayload):\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t#print \\\"Type: \\\" + str(type(OneAn))\\t\\t#All of type scapy.DNSRR\\n\\t\\t\\t\\t\\t\\t\\tif (OneAn.rclass == 1) and (OneAn.type == 1):\\t\\t#\\\"IN\\\" class and \\\"A\\\" type answer\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSIPAddr = OneAn.rdata\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSHostname = OneAn.rrname.lower()\\n\\t\\t\\t\\t\\t\\t\\t\\t#Check new hostname to see if it's in the list.\\n\\t\\t\\t\\t\\t\\t\\t\\tif (not DNSRecord.has_key(DNSIPAddr + \\\",A\\\")) or (not(DNSHostname in DNSRecord[DNSIPAddr + \\\",A\\\"])):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"DN\\\", DNSIPAddr, \\\"A\\\", DNSHostname, \\\"\\\")\\n\\t\\t\\t\\t\\t\\t\\telif (OneAn.rclass == 1) and (OneAn.type == 2):\\t\\t\\t#\\\"IN\\\" class and \\\"NS\\\" answer\\n\\t\\t\\t\\t\\t\\t\\t\\tpass\\t\\t\\t\\t\\t\\t\\t#Perhaps later\\n\\t\\t\\t\\t\\t\\t\\t\\t#Like cnames, this is object -> nameserver hostname, so these would need to be queued like cnames until we're done with A's and PTR's.\\n\\t\\t\\t\\t\\t\\t\\telif (OneAn.rclass == 1) and (OneAn.type == 5):\\t\\t\\t#\\\"IN\\\" class and \\\"CNAME\\\" answer\\n\\t\\t\\t\\t\\t\\t\\t\\tCNAMERecs.append(OneAn)\\t\\t\\t\\t\\t#Remember the record; we'll process these after the PTR's and A's\\n\\t\\t\\t\\t\\t\\t\\telif (OneAn.rclass == 1) and (OneAn.type == 6):\\t\\t\\t#\\\"IN\\\" class and \\\"SOA\\\" answer\\n\\t\\t\\t\\t\\t\\t\\t\\tpass\\t\\t\\t\\t\\t\\t\\t#Not immediately useful, perhaps later\\n\\t\\t\\t\\t\\t\\t\\telif (OneAn.rclass == 1) and (OneAn.type == 12):\\t\\t#\\\"IN\\\" class and \\\"PTR\\\" type answer\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#For input of '182.111.59.66.in-addr.arpa.' :\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSIPAddr = OneAn.rrname.replace(\\\".in-addr.arpa.\\\", \\\"\\\")\\t\\t# '182.111.59.66'\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSIPAddr = DNSIPAddr.split('.')\\t\\t\\t\\t# ['182', '111', '59', '66']\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSIPAddr.reverse()\\t\\t\\t\\t\\t\\t# ['66', '59', '111', '182']\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSIPAddr = string.join(DNSIPAddr, '.')\\t\\t\\t\\t# '66.59.111.182'\\n\\t\\t\\t\\t\\t\\t\\t\\t#Check that we end up with a legal IPv4 address before continuing; we're getting garbage.\\n\\t\\t\\t\\t\\t\\t\\t\\tif (re.search('^[1-9][0-9\\\\.]*[0-9]$', DNSIPAddr) == None):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tDebug(\\\"Odd PTR rrname: \\\" + OneAn.rrname)\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tDNSHostname = OneAn.rdata.lower()\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif (not DNSRecord.has_key(DNSIPAddr + \\\",PTR\\\")) or (not(DNSHostname in DNSRecord[DNSIPAddr + \\\",PTR\\\"])):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"DN\\\", DNSIPAddr, \\\"PTR\\\", DNSHostname, \\\"\\\")\\n\\t\\t\\t\\t\\t\\t\\telif (OneAn.rclass == 1) and (OneAn.type == 15):\\t\\t#\\\"IN\\\" class and \\\"MX\\\" answer\\n\\t\\t\\t\\t\\t\\t\\t\\tpass\\t\\t\\t\\t\\t\\t\\t#Possibly later\\n\\t\\t\\t\\t\\t\\t\\telif (OneAn.rclass == 1) and (OneAn.type == 28):\\t\\t#\\\"IN\\\" class and \\\"AAAA\\\" answer\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSIPAddr = OneAn.rdata.upper()\\n\\t\\t\\t\\t\\t\\t\\t\\tDNSHostname = OneAn.rrname.lower()\\n\\t\\t\\t\\t\\t\\t\\t\\tif (not DNSRecord.has_key(DNSIPAddr + \\\",AAAA\\\")) or (not(DNSHostname in DNSRecord[DNSIPAddr + \\\",AAAA\\\"])):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"DN\\\", DNSIPAddr, \\\"AAAA\\\", DNSHostname, \\\"\\\")\\n\\n\\t\\t\\t\\t\\t\\t\\t#Move to the next DNS object in the \\\"an\\\" block\\n\\t\\t\\t\\t\\t\\t\\tOneAn = OneAn.payload\\n\\t\\t\\t\\t\\tfor OneCNAME in CNAMERecs:\\t\\t#Now that we have all A/PTR's, go back and turn cname records into pseudo-A's\\n\\t\\t\\t\\t\\t\\tif isinstance(OneCNAME,DNSRR):\\n\\t\\t\\t\\t\\t\\t\\tAlias = OneCNAME.rrname.lower()\\n\\t\\t\\t\\t\\t\\t\\tExisting = OneCNAME.rdata.lower()\\n\\t\\t\\t\\t\\t\\t\\tif isFQDN(Alias) and isFQDN(Existing):\\n\\t\\t\\t\\t\\t\\t\\t\\tif HostIPs.has_key(Existing):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfor OneIP in HostIPs[Existing]:\\t\\t\\t\\t#Loop through each of the IPs for the canonical name, and\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tif (not DNSRecord.has_key(OneIP + \\\",CNAME\\\")) or (not(Alias in DNSRecord[OneIP + \\\",CNAME\\\"])):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tReportId(\\\"DN\\\", OneIP, \\\"CNAME\\\", Alias, \\\"\\\")\\t#report them as kind-of A records for the Alias.\\n\\t\\t\\t\\t\\t\\t\\t\\t#If we don't have a A/PTR record for \\\"Existing\\\", just ignore it. Hopefully we'll get the Existing A/PTR in the next few answers, and will re-ask for the CNAME later, at which point we'll get a full cname record.\\n\\t\\t\\t\\t\\t\\t\\t\\t#else:\\n\\t\\t\\t\\t\\t\\t\\t\\t#\\tDebug(\\\"CNAME \\\" + Alias + \\\" -> \\\" + Existing + \\\" requested, but no IP's for the latter, skipping.\\\")\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tDebug(\\\"One of \\\" + Alias + \\\" and \\\" + Existing + \\\" isn't an FQDN, skipping cname processing.\\\")\\n\\t\\t\\t\\telif (p['DNS'].rcode == 1):\\t\\t\\t#FormErr: server responding to an improperly formatted request\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (p['DNS'].rcode == 2):\\t\\t\\t#ServFail: domain exists, root nameservers list authoritative name servers, but authNS's won't answer queries\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (p['DNS'].rcode == 3):\\t\\t\\t#NXDOMAIN: root nameservers don't have any listing (domain doesn't exist or is on hold)\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telif (p['DNS'].rcode == 5):\\t\\t\\t#Query refused\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\t\\telse:\\t#rcode indicates an error\\n\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (dport == \\\"53\\\") and (type(p['DNS']) == DNS) and (p['DNS'].qr == 0):\\t#dns query\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"dns/client\\\")\\n\\t\\t\\telif (sport == \\\"67\\\") and (dport == \\\"68\\\"):\\t\\t#Bootp/dhcp server talking to client\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"bootpordhcp/server\\\")\\n\\t\\t\\telif (sport == \\\"68\\\") and (dport == \\\"67\\\"):\\t\\t#Bootp/dhcp client talking to server\\n\\t\\t\\t\\tif (sIP != \\\"0.0.0.0\\\"):\\t\\t\\t\\t#If the client is simply renewing an IP, remember it.\\n\\t\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"bootpordhcp/client\\\")\\n\\t\\t\\t\\t#else:\\t\\t\\t\\t\\t\\t#If you want to record which macs are asking for addresses, do it here.\\n\\t\\t\\t\\t#\\tpass\\n\\t\\t\\telif (sport == \\\"123\\\") and (dport == \\\"123\\\") and (p['NTP'].stratum != ''):\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"ntp/generic\\\")\\n\\t\\t\\telif (dport == \\\"123\\\") and ( (dIP == \\\"216.115.23.75\\\") or (dIP == \\\"216.115.23.76\\\") or (dIP == \\\"69.59.240.75\\\") ):\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"ntp/vonageclient\\\")\\n\\t\\t\\telif (sport == \\\"123\\\") and ( (sIP == \\\"216.115.23.75\\\") or (sIP == \\\"216.115.23.76\\\") or (sIP == \\\"69.59.240.75\\\") ):\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"ntp/vonageserver\\\")\\n\\t\\t\\telif (dport == \\\"137\\\"):\\t\\t\\t#netbios-ns\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (p['Ethernet'].dst.upper() == \\\"FF:FF:FF:FF:FF:FF\\\"):\\t\\t\\t#broadcast\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"netbios-ns/broadcastclient\\\")\\n\\t\\t\\t\\t\\telif (Payload != None) and (Payload.find('CKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') > -1):\\t#wildcard\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"netbios-ns/wildcardclient\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"netbios-ns/unicastclient\\\")\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"500\\\") and (dport == \\\"500\\\") and (p['ISAKMP'].init_cookie != ''):\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"isakmp/generic\\\")\\n\\t\\t\\telif (dport == \\\"512\\\"):\\t\\t\\t#BIFF\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('@') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"biff/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif ( (dport == \\\"1026\\\") or (dport == \\\"1027\\\") or (dport == \\\"1028\\\") ):\\t#winpopup spam client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and ( (Payload.find('Download Registry Update from:') > -1) or (Payload.find('CRITICAL ERROR MESSAGE! - REGISTRY DAMAGED AND CORRUPTED.') > -1) or (Payload.find('Your system registry is corrupted and needs to be cleaned immediately.') > -1) or (Payload.find('CRITICAL SYSTEM ERRORS') > -1) ):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"winpopup/spamclient\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (dport == \\\"1434\\\"):\\t\\t#Probable mssql attack\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('Qh.dll') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"mssql/clientattack\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"1900\\\") and (dport == \\\"1900\\\") and (dIP == \\\"239.255.255.250\\\"):\\t\\t#SSDP\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('NOTIFY') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"ssdp/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (dport == \\\"3865\\\") and (dIP == \\\"255.255.255.255\\\"):\\t\\t#XPL, http://wiki.xplproject.org.uk/index.php/Main_Page\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"xpl/client\\\")\\n\\t\\t\\telif (sport == \\\"5061\\\") and (dport == \\\"5061\\\") and ( (dIP == \\\"216.115.30.28\\\") or (dIP == \\\"69.59.227.77\\\") or (dIP == \\\"69.59.232.33\\\") or (dIP == \\\"69.59.240.84\\\") ):\\t\\t#Vonage SIP client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('.vonage.net:5061 SIP/2.0') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tSipMatch = SipPhoneMatch.search(Payload)\\n\\t\\t\\t\\t\\t\\tif (SipMatch != None) and (len(SipMatch.groups()) >= 1):\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"sip/vonage_client, phone number: \\\" + SipMatch.group(1))\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"sip/vonage_client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"5061\\\") and (dport == \\\"5061\\\") and ( (sIP == \\\"216.115.30.28\\\") or (sIP == \\\"69.59.227.77\\\") or (sIP == \\\"69.59.232.33\\\") or (sIP == \\\"69.59.240.84\\\") ):\\t#Vonage SIP server\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('.vonage.net:5061>') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"sip/vonage_server\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"6515\\\") and (dport == \\\"6514\\\") and (dIP == \\\"255.255.255.255\\\"):\\t\\t#mcafee ASaP broadcast, looking for a proxy out. http://www.myasap.de/intl/EN/content/virusscan_asap/faq_new.asp\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('<rumor version=') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"asap/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif ( (sport == \\\"9052\\\") or (sport == \\\"9053\\\") or (sport == \\\"9054\\\") ) and ( (sIP == \\\"205.188.146.72\\\") or (sIP == \\\"205.188.157.241\\\") or (sIP == \\\"205.188.157.242\\\") or (sIP == \\\"205.188.157.243\\\") or (sIP == \\\"205.188.157.244\\\") or (sIP == \\\"64.12.51.145\\\") or (sIP == \\\"64.12.51.148\\\") or (sIP == \\\"149.174.54.131\\\") ):\\t#Possibly AOL dns response\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('dns-01') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"aoldns/server\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"27005\\\") and ( (dport == \\\"27016\\\") or (dport == \\\"27017\\\") ):\\t\\t\\t\\t#Halflife client live game\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"halflife/client\\\")\\n\\t\\t\\telif (dport == \\\"27013\\\") and (dIP == \\\"207.173.177.12\\\"):\\t\\t\\t\\t#variable payload, so can't (Payload != None) and (Payload.find('Steam.exe') > -1)\\t\\t\\t\\t#Halflife client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"halflife/client\\\")\\n\\t\\t\\telif (sport == \\\"27013\\\") and (sIP == \\\"207.173.177.12\\\"):\\t\\t\\t\\t\\t\\t\\t#halflife server\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"halflife/server\\\")\\n\\t\\t\\telif ( (sport == \\\"27016\\\") or (sport == \\\"27017\\\") ) and (dport == \\\"27005\\\"):\\t\\t\\t\\t#halflife server live game\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"halflife/server\\\")\\n\\t\\t\\telif ( (dport == \\\"27015\\\") or (dport == \\\"27016\\\") or (dport == \\\"27025\\\") or (dport == \\\"27026\\\") ):\\t\\t#Variable payload, so can't: (Payload != None) and (Payload.find('basic') > -1)\\t#Halflife client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"halflife/client\\\")\\n\\t\\t\\telif (dport == \\\"27017\\\") and ( (dIP == \\\"69.28.148.250\\\") or (dIP == \\\"69.28.156.250\\\") or (dIP == \\\"72.165.61.161\\\") or (dIP == \\\"72.165.61.185\\\") or (dIP == \\\"72.165.61.186\\\") or (dIP == \\\"72.165.61.188\\\") or (dIP == \\\"68.142.64.164\\\") or (dIP == \\\"68.142.64.165\\\") or (dIP == \\\"68.142.64.166\\\") ):\\t#Steamfriends client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('VS01') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"steamfriends/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"27017\\\") and ( (sIP == \\\"69.28.148.250\\\") or (sIP == \\\"69.28.156.250\\\") or (sIP == \\\"72.165.61.161\\\") or (sIP == \\\"72.165.61.185\\\") or (sIP == \\\"72.165.61.186\\\") or (sIP == \\\"72.165.61.188\\\") or (sIP == \\\"68.142.64.164\\\") or (sIP == \\\"68.142.64.165\\\") or (sIP == \\\"68.142.64.166\\\") ):\\t#Steamfriends server\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('VS01') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"steamfriends/server\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif ( (sport == \\\"21020\\\") or (sport == \\\"21250\\\") or (sport == \\\"27016\\\") or (sport == \\\"27017\\\") or (sport == \\\"27018\\\") or (sport == \\\"27030\\\") or (sport == \\\"27035\\\") or (sport == \\\"27040\\\") or (sport == \\\"28015\\\") ):\\t\\t\\t\\t\\t\\t\\t#halflife server\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('Team Fortress') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"halflife/server\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sport == \\\"27019\\\"):\\t\\t\\t\\t\\t\\t\\t#halflife server\\n\\t\\t\\t\\tif ((not LiveUDPService.has_key(SrcService)) or (LiveUDPService[SrcService] == False)):\\n\\t\\t\\t\\t\\tLiveUDPService[SrcService] = True\\n\\t\\t\\t\\t\\tReportId(\\\"US\\\", sIP, \\\"UDP_\\\" + sport, \\\"open\\\", \\\"halflife/server\\\")\\n\\t\\t\\telif ( (dport == \\\"1265\\\") or (dport == \\\"20100\\\") or (dport == \\\"21550\\\") or (dport == \\\"27000\\\") or (dport == \\\"27017\\\") or (dport == \\\"27018\\\") or (dport == \\\"27019\\\") or (dport == \\\"27022\\\") or (dport == \\\"27030\\\") or (dport == \\\"27035\\\") or (dport == \\\"27050\\\") or (dport == \\\"27078\\\") or (dport == \\\"27080\\\") or (dport == \\\"28015\\\") or (dport == \\\"28100\\\") or (dport == \\\"45081\\\") ):\\t\\t#Halflife client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('Source Engine Query') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"halflife/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (dport == \\\"24441\\\"):\\t\\t\\t#Pyzor\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('User:') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"pyzor/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\t#FIXME - interesting issue; the ttl<5 test will catch traceroutes coming into us, but not ones we're creating to go out. Hmmm.\\n\\t\\t\\telif ( (dport >= \\\"33434\\\") and (dport <= \\\"33524\\\") ) and (p['IP'].ttl <= 5):\\t#udptraceroute client\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(sIP + \\\"UDP_33434\\\")) or (LiveUDPClient[sIP + \\\"UDP_33434\\\"] == False)):\\n\\t\\t\\t\\t\\tLiveUDPClient[sIP + \\\"UDP_33434\\\"] = True\\n\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_33434\\\", \\\"open\\\", \\\"udptraceroute/client\\\")\\n\\t\\t\\telif (dport == \\\"40348\\\"):\\n\\t\\t\\t\\tif ((not LiveUDPClient.has_key(SrcClient)) or (LiveUDPClient[SrcClient] == False)):\\n\\t\\t\\t\\t\\tif (Payload != None) and (Payload.find('HLS') > -1):\\n\\t\\t\\t\\t\\t\\tLiveUDPClient[SrcClient] = True\\n\\t\\t\\t\\t\\t\\tReportId(\\\"UC\\\", sIP, \\\"UDP_\\\" + dport, \\\"open\\\", \\\"halflife/client\\\")\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (p['IP'].frag > 0):\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sIP == \\\"207.46.51.74\\\") or (sIP == \\\"65.55.251.10\\\"):\\t\\t\\t\\t#Bigfish.com - dns?\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telif (sIP == \\\"61.215.106.146\\\"):\\t\\t\\t\\t#junk\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tUnhandledPacket(p)\\n\\t\\telse:\\n\\t\\t\\tDebug(\\\"Other IP protocol (\\\" + str(p['IP'].src) + \\\"->\\\" + str(p['IP'].dst) + \\\"): \\\" + str(p['IP'].proto))\\n\\t\\t\\tUnhandledPacket(p)\\n\\telif p['Ethernet'].type == 0x86DD:\\t\\t#IPv6\\n\\t\\tUnhandledPacket(p)\\n\\telse:\\n\\t\\tprint \\\"Unregistered ethernet type:\\\", p['Ethernet'].type\\n\\t\\tUnhandledPacket(p)\",\n \"def _add_pkt_into_tcp_stream(self, pcap_packet, num):\\n \\n # the src is server, remote(dst) is client\\n if (pcap_packet.ip.dst == _device_ip): # HUA use ip (not 80 port) as direction judgement\\n server_addr = pcap_packet.ip.src\\n server_port = pcap_packet.tcp.src_port\\n client_addr = pcap_packet.ip.dst\\n client_port = pcap_packet.tcp.dst_port\\n else:\\n server_addr = pcap_packet.ip.dst\\n server_port = pcap_packet.tcp.dst_port\\n client_addr = pcap_packet.ip.src\\n client_port = pcap_packet.tcp.src_port\\n socket_tuple = (client_addr, client_port, server_addr, server_port)\\n if (socket_tuple not in self.tcp_stream_container):\\n self.tcp_stream_container[socket_tuple] = Tcp_stream()\\n pcap_packet.tcp.stream_index = self.tcp_stream_container[socket_tuple].stream_index\\n self.tcp_stream_container[socket_tuple].pcap_num_list.append(num)\",\n \"def get_route(self, srcip, daddr): #destinations add of this packet\\n # TODO fill in peer?\\n peer = None\\n\\n pos_routes = self.lookup_routes(daddr)\\n\\n #prefix stuff\\n\\n pos_routes = self.prefix_stuff(daddr, pos_routes) #look through possible\\n #and find prefix matching destination ip\\n\\n # Rules go here\\n #if pos_routes:\\n # 1. Highest Preference\\n #pos_routes = self.get_highest_preference(pos_routes)\\n # 2. Self Origin\\n # pos_routes = self.get_self_origin(pos_routes)\\n # 3. Shortest ASPath\\n # pos_routes = self.get_shortest_as_path(pos_routes)\\n # 4. EGP > IGP > UNK\\n # pos_routes = self.get_origin_routes(pos_routes)\\n # 5. Lowest IP Address\\n\\n #daddrbit = self.ip_to_bits(daddr)\\n\\n # Final check: enforce peering relationships\\n\\n #route = self.filter_relationships(srcip, pos_routes)\\n #lowestip = 0;\\n\\n\\n peer = pos_routes[0]\\n\\n return self.sockets[peer] if peer else None\",\n \"def routePacket(self, packet):\\n\\n for rule in self._rules:\\n # check rule mask vs packet ip\\n ip = IPHelper.ipToLong(packet._ip)\\n\\n if rule._raw_ip == '*' or (rule._ip_mask_val & ip == rule._good_ip):\\n if rule._direction == packet._direction:\\n for p in rule._ports:\\n if p == packet._port or p == '*':\\n if rule._flag is None:\\n #packet is non-established connection\\n return rule\\n elif rule._flag == 'established' and packet._flag == '1':\\n #packet is established connection and flag has been set for established connection\\n return rule\\n else:\\n pass\\n else:\\n pass\\n else:\\n pass\\n else:\\n pass\\n\\n return None\",\n \"def test_overlap1(self):\\n\\n fragments = []\\n for _, frags_400, frags_300 in self.pkt_infos:\\n if len(frags_300) == 1:\\n fragments.extend(frags_400)\\n else:\\n for i, j in zip(frags_300, frags_400):\\n fragments.extend(i)\\n fragments.extend(j)\\n\\n dropped_packet_indexes = set(\\n index for (index, _, frags) in self.pkt_infos if len(frags) > 1\\n )\\n\\n self.pg_enable_capture()\\n self.src_if.add_stream(fragments)\\n self.pg_start()\\n\\n packets = self.dst_if.get_capture(\\n len(self.pkt_infos) - len(dropped_packet_indexes)\\n )\\n self.verify_capture(packets, dropped_packet_indexes)\\n self.src_if.assert_nothing_captured()\",\n \"def main(self):\\n self.pid = os.getpid()\\n self.fdp.close() # Close fdp on child side\\n if self.datasock is None:\\n # Create session's data socket and load file\\n self.datasock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n self.load_file()\\n logging.info(\\\"Child process finished loading file\\\")\\n port = UDP_START + self.meta.sessionid # Port used by the session\\n poller = select.poll() # poll fdc and datasock\\n poller.register(self.fdc.fileno(), select.POLLIN)\\n poller.register(self.datasock.fileno(), select.POLLOUT)\\n pkt_p = snc.snc_alloc_empty_packet(snc.snc_get_parameters(self.sc))\\n while True:\\n for fd, event in poller.poll():\\n if fd == self.fdc.fileno() and event is select.POLLIN:\\n pkt, ip = self.fdc.recv()\\n logging.info(\\\"Session [%d] received msg <%s> from %s.\\\" %\\n (self.meta.sessionid, iMSG[pkt.header.mtype], ip))\\n if pkt.header.mtype == MSG['REQ_SEG']:\\n self.add_client(HostInfo(ip, self.meta.sessionid))\\n self.fdc.send(CCPacket(CCHeader(MSG['OK'])))\\n elif pkt.header.mtype == MSG['HEARTBEAT']:\\n self.client_heartbeat(ip)\\n self.fdc.send(CCPacket(CCHeader(MSG['OK'])))\\n elif pkt.header.mtype == MSG['REQ_STOP'] or pkt.header.mtype == MSG['EXIT']:\\n self.remove_client(ip)\\n self.fdc.send(CCPacket(CCHeader(MSG['OK'])))\\n\\n if fd == self.datasock.fileno() and event is select.POLLOUT:\\n # writable datasock, send data packets to clients\\n for cli in self.clients:\\n snc.snc_generate_packet_im(self.sc, pkt_p)\\n pktstr = pkt_p.contents.serialize(self.meta.sp.size_g,\\n self.meta.sp.size_p,\\n self.meta.sp.bnc)\\n try:\\n # Construct data packet with serialized snc_packet\\n self.datasock.sendto(CCPacket(CCHeader(MSG['DATA']), pktstr).packed(), (cli.ip, port))\\n except:\\n logging.warning(\\\"Caught exception in session %s.\\\"\\n % (self.meta.sessionid,))\\n self.lastIdle = datetime.now() # Refresh idle time\\n self.housekeeping()\",\n \"def read_packet(self):\\n\\n\\t\\t#self.debug(\\\"READ BUFFER SIZE: %d\\\" % len(self.buff))\\n\\t\\tbackup = self.buff[:]\\n\\t\\tpacket = Packet()\\n\\t\\ttry:\\n\\t\\t\\tpacket.direction = self.node\\n\\t\\t\\tpacket.ident = self.unpack('ubyte')\\n\\t\\t\\t\\n\\t\\t\\t#Defined structs from huge dict\\n\\t\\t\\tfor datatype, name in self.get_struct(packet):\\n\\t\\t\\t\\t# this populates packet.data with {name: value}\\n\\t\\t\\t\\tpacket.data[name] = self.unpack(datatype)\\n\\n\\t\\t\\t# I believe the following are packet-type specific fixes for variable-length packets.\\n\\n\\t\\t\\t#0x17\\n\\t\\t\\tif packet.ident == 0x17:\\n\\t\\t\\t\\tif packet.data['unknown'] > 0:\\n\\t\\t\\t\\t\\tpacket.data['x2'] = self.unpack('short')\\n\\t\\t\\t\\t\\tpacket.data['y2'] = self.unpack('short')\\n\\t\\t\\t\\t\\tpacket.data['z2'] = self.unpack('short')\\n\\t\\t\\n\\t\\t\\t#0x33\\n\\t\\t\\tif packet.ident in (0x33, 0x34):\\n\\t\\t\\t\\tpacket.data['data'] = self.unpack_array_fast('byte', packet.data['data_size'])\\n\\t\\t\\t\\tdel packet.data[\\\"data_size\\\"]\\n\\t\\t\\n#\\t\\t\\t#0x34\\n#\\t\\t\\tif packet.ident == 0x34:\\n#\\t\\t\\t\\tcoords = self.unpack_array_fast('short', packet.data['data_size'])\\n#\\t\\t\\t\\tbtype = self.unpack_array_fast('byte', packet.data['data_size'])\\n#\\t\\t\\t\\tmetadata = self.unpack_array_fast('byte', packet.data['data_size'])\\n#\\t\\t\\t\\tpacket.data[\\\"blocks\\\"] = []\\n#\\t\\t\\t\\tfor i in zip(coords, btype, metadata):\\n#\\t\\t\\t\\t\\tblock = {}\\n#\\t\\t\\t\\t\\tblock[\\\"x\\\"] =\\t\\ti[0] >> 12\\n#\\t\\t\\t\\t\\tblock[\\\"z\\\"] = 0x0F & i[0] >> 8\\n#\\t\\t\\t\\t\\tblock[\\\"y\\\"] = 0xFF & i[0]\\n#\\t\\t\\t\\t\\tblock[\\\"type\\\"] = i[1]\\n#\\t\\t\\t\\t\\tblock[\\\"metadata\\\"] = i[2]\\n#\\t\\t\\t\\t\\tpacket.data[\\\"blocks\\\"].append(block)\\n#\\t\\t\\t\\tdel packet.data[\\\"data_size\\\"]\\n\\t\\t\\n\\t\\t\\t#0x3C\\n\\t\\t\\tif packet.ident == 0x3C:\\n\\t\\t\\t\\trecords = self.unpack_array_fast('byte', packet.data['data_size']*3)\\n\\t\\t\\t\\ti = 0\\n\\t\\t\\t\\tpacket.data[\\\"blocks\\\"] = []\\n\\t\\t\\t\\twhile i < packet.data['data_size']*3:\\n\\t\\t\\t\\t\\tpacket.data[\\\"blocks\\\"].append(dict(zip(('x','y','z'), records[i:i+3])))\\n\\t\\t\\t\\t\\ti+=3\\n\\t\\t\\t\\tdel packet.data[\\\"data_size\\\"]\\n\\t\\t\\n\\t\\t\\t#0x68\\n\\t\\t\\tif packet.ident == 0x68:\\n\\t\\t\\t\\tpacket.data[\\\"slots_data\\\"] = self.unpack_array('slot', packet.data[\\\"data_size\\\"])\\n\\t\\t\\t\\tdel packet.data[\\\"data_size\\\"]\\n\\t\\t\\t#0x82:\\n\\t\\t\\tif packet.ident == 0x82:\\n\\t\\t\\t\\tpacket.data[\\\"text\\\"] = []\\n\\t\\t\\t\\tfor i in range(4):\\n\\t\\t\\t\\t\\tpacket.data[\\\"text\\\"].append(packet.data[\\\"line_%s\\\" % (i+1)])\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t#0x83\\n\\t\\t\\tif packet.ident == 0x83:\\n\\t\\t\\t\\tpacket.data[\\\"data\\\"] = self.unpack_array_fast('byte', packet.data['data_size'])\\n\\t\\t\\t\\tdel packet.data[\\\"data_size\\\"]\\n\\n\\t\\t\\t# Sets packet.original to the byte string that the packet was decoded from.\\n\\t\\t\\tpacket.original = backup[:len(backup) - len(self.buff)]\\n\\n\\t\\t\\treturn packet\\n\\n\\t\\texcept IncompleteData:\\n\\t\\t\\tself.buff = backup\\n\\t\\t\\treturn None\\n\\t\\texcept Exception, ex:\\n\\t\\t\\tself.buff = backup\\n\\t\\t\\tex.args += (self.buff[20:],)\\n\\t\\t\\traise\",\n \"def _packets_from_tshark_sync(self, tshark_process, packet_count=None, timeout:float=3.0,\\n max_data_length:int=10000):\\n # NOTE: This has code duplication with the async version, think about how to solve this\\n\\n psml_structure, data = self.eventloop.run_until_complete(self._get_psml_struct(tshark_process.stdout))\\n packets_captured = 0\\n\\n data = b\\\"\\\"\\n try:\\n while self.is_open.value:\\n try:\\n packet, data = self.eventloop.run_until_complete(\\n self._get_packet_from_stream(tshark_process.stdout, \\n data,\\n psml_structure=psml_structure,\\n got_first_packet=packets_captured > 0, \\n timeout=timeout))\\n except EOFError:\\n echo(\\\"Caught EOF\\\", file=Interceptor.stdout)\\n self._log.debug(\\\"EOF reached (sync)\\\")\\n break\\n\\n if(packet is False): continue\\n\\n if packet:\\n packets_captured += 1\\n yield packet\\n if packet_count and packets_captured >= packet_count:\\n break\\n if len(data) > max_data_length:\\n data = b''\\n finally:\\n if tshark_process in self._running_processes:\\n self.eventloop.run_until_complete(self._cleanup_subprocess(tshark_process))\",\n \"def agent_player(env_name, ip):\\n\\n # Create the main generator\\n receiver_gen = atari_frames_generator(env_name, ip)\\n\\n # Loop\\n while True:\\n\\n # Receive\\n frame, termination = next(receiver_gen)\\n\\n # Skip if repeated\\n assert termination in (\\\"continue\\\", \\\"last\\\", \\\"repeated_last\\\")\\n if termination == \\\"repeated_last\\\":\\n continue\\n\\n # Return\\n yield frame\",\n \"def read_pkt_line(self):\\n if self._readahead is None:\\n read = self.read\\n else:\\n read = self._readahead.read\\n self._readahead = None\\n\\n try:\\n sizestr = read(4)\\n if not sizestr:\\n raise HangupException()\\n size = int(sizestr, 16)\\n if size == 0:\\n if self.report_activity:\\n self.report_activity(4, \\\"read\\\")\\n return None\\n if self.report_activity:\\n self.report_activity(size, \\\"read\\\")\\n pkt_contents = read(size - 4)\\n except socket.error as e:\\n raise GitProtocolError(e)\\n else:\\n if len(pkt_contents) + 4 != size:\\n raise GitProtocolError(\\n \\\"Length of pkt read %04x does not match length prefix %04x\\\"\\n % (len(pkt_contents) + 4, size)\\n )\\n return pkt_contents\",\n \"def getPacket(self, index):\\n\\t\\treturn self.packets[index.row()]\",\n \"def print_packet(self, pkt):\\n ip_layer = pkt.getlayer(IP)\\n print(\\\"[!] New Packet: {src} -> {dst}\\\".format(src=ip_layer.src, dst=ip_layer.dst))\",\n \"def sniffer():\\n try:\\n sniff(iface=INTERFACE, prn=print_frame, filter='udp and (port bootps or bootps)', store=0)\\n except Exception as _e:\\n print(\\\"ERROR - sniffer(): {} {}\\\".format(_e.args, _e.message))\",\n \"def from_physical_layer(conn, FRAME_LENGTH, FORMAT):\\r\\n frame = conn.recv(FRAME_LENGTH).decode(FORMAT)\\r\\n print(f\\\"[from_physical_layer] frame:{frame}\\\")\\r\\n return frame\",\n \"def recieve_can(offset):\\n panda = Panda()\\n while True:\\n data = panda.can_recv()\\n if data != []: \\n for x in data:\\n if x[0] == offset: \\n if x[3] == 0: \\n mes = f'{x[0]}, 0x{x[2].hex()}, {x[3]}'\\n print(mes)\",\n \"def nat_openconn(destmac, destport, localip=None, localport=None, timeout = 5, forwarderIP=None,forwarderPort=None,usetimeoutsock=False): \\r\\n # cast the destmac to a string, internal methods may fail otherwise\\r\\n destmac = str(destmac)\\r\\n\\r\\n # use the forwarderIP and port provided\\r\\n if forwarderIP != None and forwarderPort != None:\\r\\n return _nat_try_connection_list([(forwarderIP,forwarderPort)],\\r\\n localip,localport,timeout,destmac,destport,usetimeoutsock) \\r\\n \\r\\n \\r\\n # lookup the destmac if forwarderIP and port are not provided\\r\\n else: \\r\\n # check the cache\\r\\n if destmac in NAT_SRV_CACHE:\\r\\n forwarders = NAT_SRV_CACHE[destmac]\\r\\n \\r\\n try:\\r\\n return _nat_try_connection_list(forwarders,localip,localport,timeout,destmac,destport,usetimeoutsock)\\r\\n \\r\\n except: # remove this entry from the cache\\r\\n del NAT_SRV_CACHE[destmac] \\r\\n \\r\\n # the cache failed, so do a fresh lookup\\r\\n forwarders = nat_server_list_lookup(destmac)\\r\\n socket = _nat_try_connection_list(forwarders,localip,localport,timeout,destmac,destport,usetimeoutsock)\\r\\n \\r\\n #this list succeded so add it to the cache\\r\\n NAT_SRV_CACHE[destmac] = forwarders\\r\\n \\r\\n return socket\",\n \"def payload_data(self, pkts):\\n\\n\\t\\t#Get all the payload bytes exchanged over MPTCP connections\\n\\t\\tpayload_bytes = 0\\n\\t\\tprint \\\"Determining the number of payload bytes excluding headers....\\\"\\n\\t\\t#DSS = 0x2\\n\\t\\tfor i in range(len(pkts)):\\n\\t\\t\\tif(TCPOption_MP in pkts[i] and pkts[i][TCPOption_MP].mptcp.subtype == 2 and Raw in pkts[i]):\\n\\t\\t\\t\\tpayload_bytes += len(pkts[i][Raw].load)\\n\\t\\t\\t\\t#print(\\\"DSN: %s; subflow_seqnum: %s; Data(bytes): %s\\\" % (pkts[i][TCPOption_MP].mptcp.dsn, pkts[i][TCPOption_MP].mptcp.subflow_seqnum, len(pkts[i][Raw].load)))\\n\\n\\t\\tprint \\\"Total Number of payload bytes in the file (entire MPTCP connections) excluding headers): %s\\\" % (payload_bytes)\\n\\t\\t#MPTCP WITH SUBFLOW CONNECTIONS\\n\\t\\t#MPTCP_JOINs = 0x1\\n\\t\\tprint \\\"============================================================\\\"\\n\\t\\tprint \\\"SUBFLOW Connections with their respective MPTCP connection (identified by connectionID)\\\"\\n\\t\\tfor i in range(len(pkts)):\\n\\n\\t\\t\\t#Initial Join Message\\n\\t\\t\\t#rcv_token Identifies the connection to which the subflow belongs: connectionID\\n\\t\\t\\tif(MPTCP_JoinSYN in pkts[i] and pkts[i][TCPOption_MP].mptcp.subtype == 1):\\n\\t\\t\\t\\tprint(\\\"New subflow: connectionID: %s; src: %s; dest: %s; snd_nonce: %s\\\" % (pkts[i][TCPOption_MP].mptcp.rcv_token, pkts[i][IP].src, pkts[i][IP].dst, pkts[i][TCPOption_MP].mptcp.snd_nonce))\\n\\n\\t\\t#TODO: Now Need to track per-connection and per-subflow state\",\n \"def test_overlap1(self):\\n\\n fragments = []\\n for _, _, frags_300, frags_200 in self.pkt_infos:\\n if len(frags_300) == 1:\\n fragments.extend(frags_300)\\n else:\\n for i, j in zip(frags_200, frags_300):\\n fragments.extend(i)\\n fragments.extend(j)\\n\\n self.pg_enable_capture()\\n self.src_if.add_stream(fragments)\\n self.pg_start()\\n\\n packets = self.dst_if.get_capture(len(self.pkt_infos))\\n self.verify_capture(packets)\\n self.src_if.assert_nothing_captured()\\n\\n # run it all to verify correctness\\n self.pg_enable_capture()\\n self.src_if.add_stream(fragments)\\n self.pg_start()\\n\\n packets = self.dst_if.get_capture(len(self.pkt_infos))\\n self.verify_capture(packets)\\n self.src_if.assert_nothing_captured()\",\n \"def sniff_offline(args):\\n print('viewer: reading from ' + args.read)\\n\\n try:\\n with open(args.read, 'rb') as f:\\n reader = dpkt.pcap.Reader(f)\\n\\n if not args.count:\\n count = True\\n else:\\n count = args.count\\n\\n while count:\\n ts, pkt = next(iter(reader))\\n ret = parse_ICMP_Echo(ts, pkt)\\n\\n if ret and args.count:\\n count -= 1\\n except FileNotFoundError as e:\\n print('File \\\\'{}\\\\' not found.'.format(args.read))\\n sys.exit(1)\\n except StopIteration:\\n sys.exit(0)\",\n \"def packet_to_kml(packet, reader):\\n\\n try:\\n src_ip = packet[IP].src\\n src_kml = ip_to_kml(src_ip, reader)\\n except:\\n src_kml = None\\n try:\\n dest_ip = packet[IP].dest\\n dest_kml = ip_to_kml(dest_ip, reader)\\n except:\\n dest_kml = None\\n\\n if src_kml is not None and dest_kml is not None:\\n connect_kml = ips_to_line_kml(src_ip, dest_ip, reader)\\n print(\\\"Added connection\\\")\\n else:\\n connect_kml = None\\n\\n return src_kml, dest_kml, connect_kml\",\n \"def create_stream(cls, packet_sizes, packet_count=test_packet_count):\\n for i in range(0, packet_count):\\n info = cls.create_packet_info(cls.src_if, cls.src_if)\\n payload = cls.info_to_payload(info)\\n p = (\\n IP(id=info.index, src=cls.src_if.remote_ip4, dst=cls.dst_if.remote_ip4)\\n / UDP(sport=1234, dport=5678)\\n / Raw(payload)\\n )\\n size = packet_sizes[(i // 2) % len(packet_sizes)]\\n cls.extend_packet(p, size, cls.padding)\\n info.data = p\",\n \"def ReceiveMessageFromPacketInfo(self) -> IPPacketInformation:\",\n \"def give_packets(self, packets, verbose=False, cache=False, tunnel=None, source_sock_addr=None):\\n assert isinstance(packets, list)\\n assert all(isinstance(packet, str) for packet in packets)\\n assert isinstance(verbose, bool)\\n assert isinstance(cache, bool)\\n assert tunnel is None, \\\"TUNNEL property is set using init_socket(...)\\\"\\n assert source_sock_addr is None or isinstance(source_sock_addr, tuple), type(source_sock_addr)\\n if verbose:\\n logger.debug(\\\"giving %d bytes\\\", sum(len(packet) for packet in packets))\\n if source_sock_addr is None:\\n source_sock_addr = self.lan_address\\n candidate = Candidate(source_sock_addr, self._tunnel)\\n self._dispersy.on_incoming_packets([(candidate, packet) for packet in packets], cache=cache, timestamp=time())\\n return packets\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5710354","0.5529603","0.5436237","0.5389503","0.5370659","0.5341635","0.5319951","0.5312948","0.5296589","0.52821374","0.5232271","0.5231817","0.5208476","0.52023923","0.51755023","0.5116855","0.5112408","0.5110333","0.5092085","0.5089987","0.5086152","0.5080938","0.5051528","0.5051528","0.5011665","0.50084186","0.5007459","0.49958622","0.49733213","0.49542275","0.495417","0.49498022","0.49460396","0.49303383","0.48881605","0.48586914","0.48477185","0.4837219","0.48050243","0.4789931","0.47893512","0.4788178","0.47869468","0.47613704","0.4752663","0.47500047","0.47426605","0.47332984","0.47233394","0.4714982","0.47131625","0.4697844","0.4697158","0.46857035","0.46792635","0.46711436","0.4664068","0.4660284","0.46519208","0.46505415","0.46443844","0.46024713","0.45999685","0.45896453","0.4589577","0.45727187","0.45695475","0.4561564","0.45561558","0.45406654","0.45351273","0.45255303","0.4524231","0.45210078","0.4517039","0.4505856","0.44961685","0.4495365","0.448878","0.44851944","0.44837207","0.44794542","0.44755214","0.44672287","0.4463968","0.44579118","0.44571388","0.44558287","0.44551775","0.44505128","0.4447302","0.4434669","0.4434428","0.4431039","0.44281664","0.44205582","0.44168368","0.4408865","0.44071707","0.4406922"],"string":"[\n \"0.5710354\",\n \"0.5529603\",\n \"0.5436237\",\n \"0.5389503\",\n \"0.5370659\",\n \"0.5341635\",\n \"0.5319951\",\n \"0.5312948\",\n \"0.5296589\",\n \"0.52821374\",\n \"0.5232271\",\n \"0.5231817\",\n \"0.5208476\",\n \"0.52023923\",\n \"0.51755023\",\n \"0.5116855\",\n \"0.5112408\",\n \"0.5110333\",\n \"0.5092085\",\n \"0.5089987\",\n \"0.5086152\",\n \"0.5080938\",\n \"0.5051528\",\n \"0.5051528\",\n \"0.5011665\",\n \"0.50084186\",\n \"0.5007459\",\n \"0.49958622\",\n \"0.49733213\",\n \"0.49542275\",\n \"0.495417\",\n \"0.49498022\",\n \"0.49460396\",\n \"0.49303383\",\n \"0.48881605\",\n \"0.48586914\",\n \"0.48477185\",\n \"0.4837219\",\n \"0.48050243\",\n \"0.4789931\",\n \"0.47893512\",\n \"0.4788178\",\n \"0.47869468\",\n \"0.47613704\",\n \"0.4752663\",\n \"0.47500047\",\n \"0.47426605\",\n \"0.47332984\",\n \"0.47233394\",\n \"0.4714982\",\n \"0.47131625\",\n \"0.4697844\",\n \"0.4697158\",\n \"0.46857035\",\n \"0.46792635\",\n \"0.46711436\",\n \"0.4664068\",\n \"0.4660284\",\n \"0.46519208\",\n \"0.46505415\",\n \"0.46443844\",\n \"0.46024713\",\n \"0.45999685\",\n \"0.45896453\",\n \"0.4589577\",\n \"0.45727187\",\n \"0.45695475\",\n \"0.4561564\",\n \"0.45561558\",\n \"0.45406654\",\n \"0.45351273\",\n \"0.45255303\",\n \"0.4524231\",\n \"0.45210078\",\n \"0.4517039\",\n \"0.4505856\",\n \"0.44961685\",\n \"0.4495365\",\n \"0.448878\",\n \"0.44851944\",\n \"0.44837207\",\n \"0.44794542\",\n \"0.44755214\",\n \"0.44672287\",\n \"0.4463968\",\n \"0.44579118\",\n \"0.44571388\",\n \"0.44558287\",\n \"0.44551775\",\n \"0.44505128\",\n \"0.4447302\",\n \"0.4434669\",\n \"0.4434428\",\n \"0.4431039\",\n \"0.44281664\",\n \"0.44205582\",\n \"0.44168368\",\n \"0.4408865\",\n \"0.44071707\",\n \"0.4406922\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69073343"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":83,"cells":{"query":{"kind":"string","value":"gathers selected cards in order to take action on selected cards (either discarding them or preparing them)"},"document":{"kind":"string","value":"def gatherSelected(self):\n self.selected_list = []\n for element in self.hand_info:\n if element.status == 1:\n self.selected_list.append(element)\n return self.selected_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def pick(self, pack, cards_owned, draft_info):\n pass","def card_sel(\n self, num=1, **kwargs\n ): # pylint: disable=too-many-locals, too-many-branches\n selectfrom = self.card_selSource(**kwargs)\n force = kwargs[\"force\"] if \"force\" in kwargs else False\n showdesc = kwargs[\"showdesc\"] if \"showdesc\" in kwargs else True\n verbs = kwargs.get(\"verbs\", (\"Select\", \"Unselect\"))\n\n if \"prompt\" in kwargs:\n self.output(kwargs[\"prompt\"])\n\n if \"anynum\" in kwargs and kwargs[\"anynum\"]:\n anynum = True\n num = 0\n else:\n anynum = False\n\n selected = []\n types = kwargs[\"types\"] if \"types\" in kwargs else {}\n types = self._type_selector(types)\n while True:\n options = []\n if (\n anynum\n or (force and num == len(selected))\n or (not force and num >= len(selected))\n ):\n o = Option(selector=\"0\", verb=\"Finish Selecting\", card=None)\n options.append(o)\n index = 1\n for c in sorted(selectfrom):\n if \"exclude\" in kwargs and c.name in kwargs[\"exclude\"]:\n continue\n if not self.select_by_type(c, types):\n continue\n sel = \"%d\" % index\n index += 1\n if c not in selected:\n verb = verbs[0]\n else:\n verb = verbs[1]\n o = Option(selector=sel, verb=verb, card=c, name=c.name)\n if showdesc:\n o[\"desc\"] = c.description(self)\n if kwargs.get(\"printcost\"):\n o[\"details\"] = str(self.card_cost(c))\n if kwargs.get(\"printtypes\"):\n o[\"details\"] = c.get_cardtype_repr()\n options.append(o)\n ui = self.user_input(options, \"Select which card?\")\n if not ui[\"card\"]:\n break\n if ui[\"card\"] in selected:\n selected.remove(ui[\"card\"])\n else:\n selected.append(ui[\"card\"])\n if num == 1 and len(selected) == 1:\n break\n return selected","def card(bot, update):\n query = update.callback_query\n user = query.from_user\n chat_id = query.message.chat_id\n selected_card = query.data\n\n if (chats[chat_id].player1.card_played == []) and (chats[chat_id].player2.card_played == []):\n bot.send_message(text=Strings.CARD_SELECTED.format(user.first_name),\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n parse_mode=ParseMode.MARKDOWN,\n isgroup=True)\n if chats[chat_id].player1.user == user:\n chats[chat_id].player1.card_played = chats[chat_id].player1.hand[int(selected_card)]\n chats[chat_id].player1.hand.remove(chats[chat_id].player1.hand[int(selected_card)])\n\n elif chats[chat_id].player2.user == user:\n chats[chat_id].player2.card_played = chats[chat_id].player2.hand[int(selected_card)]\n chats[chat_id].player2.hand.remove(chats[chat_id].player2.hand[int(selected_card)])\n return CARD\n\n else:\n if chats[chat_id].player1.user == user and chats[chat_id].player1.card_played != []:\n bot.send_message(text=Strings.CARD_SELECTED2.format(user.first_name),\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\n return CARD\n elif chats[chat_id].player2.user == user and chats[chat_id].player2.card_played != []:\n bot.send_message(text=Strings.CARD_SELECTED2.format(user.first_name),\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\n return CARD\n else:\n if chats[chat_id].player1.user == user:\n chats[chat_id].player1.card_played = chats[chat_id].player1.hand[int(selected_card)]\n chats[chat_id].player1.hand.remove(chats[chat_id].player1.hand[int(selected_card)])\n\n elif chats[chat_id].player2.user == user:\n chats[chat_id].player2.card_played = chats[chat_id].player2.hand[int(selected_card)]\n chats[chat_id].player2.hand.remove(chats[chat_id].player2.hand[int(selected_card)])\n\n bot.edit_message_text(text=Strings.CARD_SELECTED.format(user.first_name),\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n parse_mode=ParseMode.MARKDOWN)\n bot.send_message(chat_id,\n Strings.SELECTION_COMPLETED,\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\n\n reply_markup = ReplyKeyboardMarkup(c_b_keyboard, selective=False)\n bot.send_message(chat_id,\n Strings.QUESTION,\n reply_markup=reply_markup,\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\n return BET_CHECK","def choose_card(playable_cards):\r\n\r\n playing = playable_cards[0]\r\n print('\\n choosing \\n', playing)\r\n\r\n return playing # for now\r","def next_card_selection(deck):\n # First grab the colors and format\n # Then generate the query, execute, and trim\n # Then get the selections, and return them!\n colors = deck['colors']\n deck_format = deck['format']\n seed = deck.get('seed')\n\n query_type = pick_next_card_query(deck_format, seed)\n trimmed_query_results = compile_execute_and_trim_query(colors, query_type, deck_format)\n selections = generate_pickable_selections(trimmed_query_results, deck_format, deck)\n print('QUERY: ', query_type, ' NUM RESULTS:', len(trimmed_query_results))\n\n return selections","def choose_card_to_discard(self):\n random.choice(self.hand.card_list).use()","def select_card(self, cards):\n idx = -1 # should not be inital value\n while True:\n print(\"Please select a card by index:\")\n inpt = self.input(list(enumerate(cards)))\n try:\n idx = int(inpt)\n except ValueError:\n print(f\"'{inpt}' is not a valid index.\")\n if idx < 0 or idx >= len(cards):\n print(f\"The index {idx} is not available.\")\n else:\n break\n assert idx != -1 # make sure it's not initial value\n return cards.pop(idx)","def cards(self, cards):\n\n self._cards = cards","def cards(self, cards):\n\n self._cards = cards","def choose_card(self, state=None):\n # if self.at_last_stich():\n # allowed = yield self.cards[0]\n # else:\n self.observation_received.acquire()\n self.observation = self.build_observation(state, self.cards)\n logger.debug(f\"choose_card received observation: {self.observation}\")\n self.observation_received.notify_all() # notify all threads to be sure\n self.observation_received.release()\n\n self.action_received.acquire()\n received = self.action_received.wait()\n if not received:\n logger.debug(\"Timeout occurred. action_received condition has not been notified.\")\n logger.debug(f\"choose_card received action: {self.action}\")\n allowed_cards = self.allowed_cards(state=state)\n chosen_card = allowed_cards[0] # set chosen_card to the first allowed card in case anything goes south\n chosen_card = self.set_chosen_card(allowed_cards, chosen_card)\n self.action_received.release()\n\n allowed = yield chosen_card\n\n if allowed:\n yield None","def set_chosen_card(self, allowed_cards, chosen_card):\n if self.action is not None:\n if self.action in allowed_cards:\n logger.info(f\"Successfully chose the card: {self.action}\")\n chosen_card = self.action\n else:\n logger.error(f\"{self.action} is not a valid card! Choosing the first allowed card now.\")\n else:\n logger.debug(\"chosen card is None\")\n return chosen_card","def play_selected_card(_screen, player):\n card = Card(player.selected_card.card_id, 400, 350)\n card.image_of_card(_screen)","def play_all(self):\n for _ in range(self._hand.size()):\n card = self._hand.pop()\n self._active.push(card)\n self._money = self._money + card.money\n self._attack = self._attack + card.attack\n print '\\nPlayed all cards!'","def deal_cards():\n for _ in range(2):\n user_cards.append(random.choice(deck))\n dealer_cards.append(random.choice(deck))","def cards(self) -> None:\n self._cards = []","def Deal():\r\n cardsout = []\r\n cardoptions = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]\r\n topcardoptions = [0,2,3,4,5,6]\r\n topcard = topcardoptions[random.randint(0,5)]\r\n cardoptions.pop(cardoptions.index(topcard))\r\n cardsout.append(topcard)\r\n\r\n if SHOWHAPPENINGS == True:\r\n disp = card_dict[topcard]\r\n print(\"Topcard is: {}\".format(disp)) \r\n\r\n for i in range(4):\r\n numcards = 0\r\n while numcards < 5:\r\n possiblerange = len(cardoptions) - 1\r\n cardindex = random.randint(0,possiblerange)\r\n card = cardoptions[cardindex]\r\n cardsout.append(card)\r\n cardoptions.pop(cardoptions.index(card))\r\n PlayerHands[i].append(card)\r\n numcards += 1\r\n PlayerHands[i] = sorted(PlayerHands[i]) #putting into ascending order\r\n if i == 0 or i == 2:\r\n PlayerHands[i].append(\"RedTeam\")\r\n else: \r\n PlayerHands[i].append(\"BlackTeam\")\r\n \r\n PlayerHands[0].append(PLAYER1)\r\n PlayerHands[1].append(PLAYER2)\r\n PlayerHands[2].append(PLAYER3)\r\n PlayerHands[3].append(PLAYER4)\r\n #PlayerHand format = [card1,card2,card3,card4,card5,Team,Name]\r\n\r\n return topcard","def going_out(self, cards):\n for card in cards:\n self.out_of_use.append(int(card))\n # print(self.out_of_use)","def followUpAttack(self, validCards):\n print(\"Select card from... \")\n cardManager.printHand(validCards)\n card = int(input(\"to your attack: \"))\n while card not in validCards: # error checking\n print(card)\n print(\"Please select a valid card from...\")\n cardManager.printHand(validCards)\n card = int(input(\"to your attack: \"))\n self.currentHand.remove(card)\n card = self.checkDoubles(card)\n return card","def clearCards(self):\r\n self.cards = []","def __init__(self):\r\n self.cards = []","def open_cards(self) -> None:\r\n self.dealer.deal_cards_to(self.card_stack, PokerRules.CARDS_PER_ROUND[self.round_num])","def get_card_list(self):\n return self.cards","def __init__(self):\n self._cards = []","def upgrade_all_cards(self):\n self.game.go_to_comic_cards()\n logger.info(\"Comic Cards: upgrading all available cards.\")\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=3, ui_element=self.ui['CARDS_UPGRADE_ALL']):\n self.emulator.click_button(self.ui['CARDS_UPGRADE_ALL'].button)\n for card_index in range(1, 6):\n card_select_ui = self.ui[f'CARDS_SELECT_GRADE_{card_index}']\n self.emulator.click_button(card_select_ui.button)\n logger.debug(f\"Comic Cards: starting to upgrade UI Element {card_select_ui.name}\")\n if not wait_until(self.emulator.is_image_on_screen, timeout=3, ui_element=card_select_ui):\n logger.warning(\"Comic Cards: can't select card's grade.\")\n continue\n logger.debug(f\"Comic Cards: successfully selected UI Element {card_select_ui.name}\")\n self.emulator.click_button(self.ui['CARDS_SELECT_GRADE'].button)\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=3,\n ui_element=self.ui['CARDS_UPGRADE_CONFIRM']):\n self.emulator.click_button(self.ui['CARDS_UPGRADE_CONFIRM'].button)\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=10,\n ui_element=self.ui['CARDS_UPGRADE_RESULTS_OK']):\n logger.debug(f\"Comic Cards: successfully upgraded UI Element {card_select_ui.name}\")\n self.emulator.click_button(self.ui['CARDS_UPGRADE_RESULTS_OK'].button)\n wait_until(self.emulator.is_image_on_screen, timeout=3, ui_element=card_select_ui)\n continue\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=3, ui_element=self.ui['CARDS_UPGRADE_ALL_CANCEL']):\n self.emulator.click_button(self.ui['CARDS_UPGRADE_ALL_CANCEL'].button)\n self.close_after_mission_notifications()\n self.game.go_to_main_menu()","def __init__(self, cards):\n self.cards = cards","def user_turn(self):\r\n\r\n self.display_state() # display the current state\r\n print(\r\n '\\nTURN: You -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')\r\n # Get the row and col number of the card you want to select\r\n x1, y1 = self.input_validation('Enter the location of the first card you pick (row, col) -> ')\r\n self.selected = [x1, y1] # a temporary holder for the first choice\r\n\r\n # Get the corresponding card ID which is also the key for the dictionary with all the cards\r\n choice1_key = self.state[x1, y1]\r\n print('The card you selected: {0}'.format(self.deck[choice1_key]))\r\n\r\n # Repeat this for your second choice\r\n x2, y2 = self.input_validation('Enter the location of the second card you pick (row, col) -> ')\r\n self.selected = [-1, -1] # reset the temporary hold\r\n\r\n choice2_key = self.state[x2, y2]\r\n print('The card you selected: {0}'.format(self.deck[choice2_key]))\r\n\r\n # Check if the two cards are a match or not\r\n if self.check_card(self.deck[choice1_key], self.deck[choice2_key]):\r\n print('MATCH')\r\n # Replace the corresponding cards in the remaining inventory and state with -1\r\n self.remaining[choice1_key] = -1\r\n self.remaining[choice2_key] = -1\r\n self.state[x1, y1] = -1\r\n self.state[x2, y2] = -1\r\n self.player_cards += 2 # the player gets 2 cards\r\n self.bin.append([x1, y1]) # move the location of the card to the already-taken bin\r\n self.bin.append([x2, y2])\r\n self.forget_memory(choice1_key) # remove from computer's memory\r\n self.forget_memory(choice2_key)\r\n self.match = 1 # player will continue to choose cards\r\n else:\r\n print('NOT a match')\r\n # Add these cards to the computer's memory\r\n self.computer_memory[choice1_key] = [x1, y1]\r\n self.computer_memory[choice2_key] = [x2, y2]\r\n self.match = 0 # computer's turn\r","def test_consumed_cards(self):\n game = TestGames.replay(9, [3, 1, 0, 0])\n consumed_cards = game.consumed_cards()\n self.assertEqual(len(consumed_cards), 8)\n\n self.assertListEqual(list(consumed_cards),\n [2 / 5, # guards\n 0 / 2, # priest\n 1 / 2, # baron\n 0 / 2, # handmaid\n 1 / 2, # prince\n 0 / 1, # king\n 0 / 1, # countess\n 0 / 1]) # princess","def deliver_card(data, access=None):\n\n schema = get_card_schema(data)\n if not schema:\n schema = card_schema\n data = deepcopy(data)\n\n if access is 'learn' and data['kind'] is 'choice':\n if data['order'] == 'random':\n shuffle(data['options'])\n\n if data['max_options_to_show']:\n data['options'] = data['options'][:data['max_options_to_show']]\n\n return deliver_fields(schema, data, access)","def all_cards_selected(self, game_key):\n participants = models.Participant.query(\n models.Participant.playing == True,\n models.Participant.selected_card == None,\n ancestor=game_key).fetch()\n logging.debug(\"participants who have not selected a card: %s\", participants)\n if participants:\n return False\n else:\n return True","def draw_a_card(cards):\n import random\n card_drawn = random.choices(card_deck)\n cards.append(card_drawn[0])\n return","def deal_card():\r\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\r\n return (random.choice(cards))","def attack(self): # need to check defenders handcount\n \"\"\"Always returns a list of values\"\"\"\n if self.AI:\n # return rand.randint(0,len(self.currentHand))\n Error(\"AI not yet implemented for Attacking\")\n else:\n print(\"Select card from... \")\n cardManager.printHand(self.currentHand)\n card = int(input(\"to your attack: \"))\n while card not in self.currentHand: # error checking\n print(\"Please select a valid card from...\", end = \" \")\n cardManager.printHand(self.currentHand)\n card = int(input())\n self.currentHand.remove(card)\n card = self.checkDoubles(card)\n return card","def test_cards_get(self):\n pass","def hit(self, deck):\n self.cards.append(deck.draw_card())","def dealer_card_choice(update, context):\n query = update.callback_query\n if query.message.reply_to_message:\n user = query.message.reply_to_message.from_user\n else:\n user = query.message.chat\n bot = context.bot\n CURRENT_USER = USERS[user.username]\n CURRENT_CONTEXT = process_card_value(query.data, CURRENT_USER)\n message = f'Round: {CURRENT_CONTEXT[\"round\"]} ({CURRENT_CONTEXT[\"username\"]}) \\nDealers Card: {CURRENT_CONTEXT[\"dealer_card\"]}\\nYour Cards: {CURRENT_CONTEXT[\"player_cards\"]} \\nYour total: {CURRENT_CONTEXT[\"player_total\"]} \\n\\nChoose Dealers Card: '\n bot.edit_message_text(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=message,\n reply_markup=card_markup\n )\n\n # Tell ConversationHandler that we're in state `STRATEGY` now\n return STRATEGY","def get_game_cards(gameId):\n pass","def indicate_discard_card(whose_turn,players):\n cards_to_choose_from = players[whose_turn].hand.cards\n players[whose_turn].hand.print_cards()\n chosen_to_discard = int(input('Select a card to discard. Type a number. '))\n return chosen_to_discard","def get_card_sets(self, name: str) -> List:","def get_card(self, card):\n\n\t\tself.add_card_to_grps(card)\n\n\t\tself.grps = sorted(self.grps, key = lambda x: -len(x))\n\n\n\t\t# check if # of cards forming sets is more than 5; if yes, then break the set to allow computer to form runs\n\t\tnum_set_cards = 0\n\t\tpos = -1\n\t\tfor i in range(len(self.grps)):\n\t\t\tif len(self.grps[i]) > 1 and self.grps[i][0] == self.grps[i][1]:\n\t\t\t\tnum_set_cards += len(self.grps[i])\n\t\t\t\tpos = i\n\n\t\tif num_set_cards > 5:\n\t\t\tcard = self.grps[pos][-1]\n\t\t\tself.grps[pos].remove(card)\n\t\t\tlogger.info(f\"In computer.py/get_card: computer returned {card} to break too many set, computer = {self}\")\n\t\t\treturn card\n\n\n\t\t# if # of sets is fine, then remove a card from the group with least size\n\t\tcard = self.grps[-1][-1]\n\n\t\t\n\t\tif len(self.grps[-1]) == 1:\n\t\t\tself.grps.remove(self.grps[-1])\n\t\telse:\n\t\t\tself.grps[-1].remove(self.grps[-1][-1])\n\n\t\tlogger.info(f\"In computer.py/get_card: computer returned {card}, computer = {self}\")\n\n\t\treturn card","def cards(\n self,\n cards: Union[List[Tuple[int, str, str]], List[Any]]\n ) -> None:\n self._cards: List[List[Tuple[int, str, str]]] = [cards]","def deal_card():\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n card = random.choice(cards)\n return card","def get_cards(self):\n card = self._starting_card\n return card","def __init__(self, cards = []):\n self.cards=cards","def receive_card(self, card: Card) -> None:\n\t\tself.deck.append(card)\n\t\t\n\t\t# Sorts the Deck by type and colour for aesthetic purposes\n\t\t\"\"\"self.deck.sort(key=lambda x: repr(x.type))\n\t\tself.deck.sort(key=lambda x: repr(x.colour))\"\"\"","def choice():\n list_cards = [0, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9,\n 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n list_cards= list_cards*4\n play_1 = random.choice(list_cards)\n list_cards_1 = list_cards\n list_cards_1.remove(play_1)\n dealer_1 = random.choice(list_cards_1)\n list_cards_1.remove(dealer_1)\n play_2 = random.choice(list_cards_1)\n list_cards_1.remove(play_2)\n dealer_2 = random.choice(list_cards_1)\n list_cards_1.remove(dealer_2)\n list1=[play_1,play_2]\n list=[dealer_1,dealer_2]\n return (list1,list,list_cards_1)","def card_selSource(self, **kwargs):\n if \"cardsrc\" in kwargs:\n if kwargs[\"cardsrc\"] == \"hand\":\n selectfrom = self.piles[Piles.HAND]\n elif kwargs[\"cardsrc\"] == \"played\":\n selectfrom = self.piles[Piles.PLAYED]\n elif kwargs[\"cardsrc\"] == \"discard\":\n selectfrom = self.piles[Piles.DISCARD]\n else:\n selectfrom = kwargs[\"cardsrc\"]\n else:\n selectfrom = self.piles[Piles.HAND]\n return selectfrom","def selectAmbassadorInfluence(self, choices, influenceRemaining):\n # todo: raise notImplemented. should be overriden by the input class\n \n selected = []\n for i in range(influenceRemaining):\n card = random.choice(choices)\n selected.append(card)\n choices.remove(card)\n \n return selected","def get_cards(query_param):\n return _query_scryfall(query_param)","def add_card(self, added_cards):\n\n self.hand[:0] = added_cards","def deal_cards(self):\n aux = random.randint(0, len(self.deck))\n card = self.deck[aux]\n self.deck.pop(aux)\n print(f\"Received: {card}\")\n return card","def cards(self):\n return self._cards","def test_cards_get_list(self):\n pass","def step(self, action):\n assert self.completed_rounds < self.num_rounds\n\n player = self.players[self.current_player_id]\n card = action\n\n if card not in player.hand:\n raise ValueError(\"Action not allowed because the card is not in the player's hand\")\n\n player.hand.remove(card)\n player.played.add(card)\n # print(f\"Player {self.current_player_id} with hand {[c.id for c in player.hand]} played the card {card.id}\")\n best_combination_on_the_table = self._get_best_combination(card)\n if best_combination_on_the_table:\n self.last_player_capturing_id = self.current_player_id\n player.captured.add(card)\n for c in best_combination_on_the_table:\n self.table.remove(c)\n player.captured.add(c)\n if not self.table and not (self._is_last_round and self._is_round_over()):\n player.scope += 1\n else:\n self.table.add(card)\n # print(f\"Cards on the table after play: {[c.id for c in self.table]}\")\n\n if self._is_round_over():\n self.completed_rounds += 1\n # print(f\"=========== Round {self.current_round} completed ============\")\n self.current_player_id = (self.current_player_id + 1) % self.num_players\n\n if self.is_over():\n last_player_capturing = self.players[self.last_player_capturing_id]\n # print(f\"Giving the remaining cards to player {last_player_capturing.player_id}\")\n for card in self.table:\n last_player_capturing.captured.add(card)\n self.table = set()\n assert all([len(p.played) == 10 for p in self.players])\n assert all([len(p.hand) == 0 for p in self.players])\n return self.get_state(), self.current_player_id","def action_hit(self) -> None:\n print(self.deal_card(self.user))","def check_selected_card(_player1, _player2):\n if _player1.selected_card and _player2.selected_card:\n color = _player1.selected_card.suit\n if _player2.selected_card.suit != color and check_color_card(_player2, color):\n _player2.selected_card = None","def collect_cards():\n \n cards_list = []\n while (cards_input := input(\"Enter card: \")) != '#':\n i = cards_input.upper()\n if not is_valid(i):\n print(f\"Please enter a valid card.\")\n continue\n cards_list.append(i)\n cards_decoded = [Board.translate(card) for card in cards_list]\n return cards_decoded","def Send_newCards(self, cards): \n serialized = [c.serialize() for c in cards]\n self.Send({\"action\": \"newCards\", \"cards\": serialized})","def __init__(self):\n self.cards = [Card(face=card[0], value=card[1], suit=suit)\n for card in CARD_VALUES().items() for suit in CARD_SUITS()]","def check_card_action(self, card):\n if card.value == \"7\":\n self.seven_punishment()\n elif card.value == \"8\":\n self.eight_punishment()\n elif card.value == \"9\":\n self.nine_punishment()\n elif card.value == \"B\":\n self.jack_wish()","def hook_buy_this_card(self, game, player):\n totrash = [c for c in player.piles[Piles.PLAYED] if c.isTreasure()]\n for c in totrash:\n player.output(f\"Mint trashing {c.name}\")\n player.trash_card(c)","def get_card_info_list(self):\n self._get_card_info_list = pa_card_info_cb_t(self._card_info_cb)\n pa_context_get_card_info_list(self._context,\n self._get_card_info_list,\n None)","def hit(self, deck):\n self.showOneCard = False\n while self.getPoints() < 17:\n self.cards.append(deck.deal())","def __refresh_search_results(self):\n\n # Define the query\n try:\n query = CardQuery()\n query.max_count = int(self.__box_count.get_text())\n query.max_proficiency = self.__combo_proficiency.get_index()\n query.max_score = float(self.__box_score.get_text())\n if self.__combo_card_type.get_index() > 0:\n query.card_type = getattr(WordType, self.__combo_card_type.get_text())\n except:\n cards = []\n traceback.print_exc()\n else:\n # Query the cards\n cards = []\n for card in self.__cards_source.get_cards():\n study_data = self.__study_database.get_card_study_data(card)\n if query.matches(card, study_data):\n cards.append((card, study_data))\n\n # Sort the list\n sort_method = self.__combo_sort.get_text()\n if sort_method == SortMethod.RANDOM:\n random.shuffle(cards)\n all_cards = list(cards)\n cards = []\n while all_cards and len(cards) < query.max_count:\n index = random.randrange(len(all_cards))\n elem = all_cards[index]\n del all_cards[index] \n cards.append(elem)\n elif sort_method == SortMethod.LOWEST_SCORE:\n cards.sort(key=lambda x: x[1].get_history_score())\n elif sort_method == SortMethod.OLDEST:\n cards.sort(key=lambda x: x[1].get_last_encounter_time() or x[1].get_history_score())\n elif sort_method == SortMethod.NEWEST:\n cards.sort(key=lambda x: x[1].get_last_encounter_time() or x[1].get_history_score(), reverse=True)\n\n cards = cards[:query.max_count]\n\n # Define the study params\n params = StudyParams()\n params.random_side = self.__combo_side.get_index() == 0\n params.random_form = self.__checkbox_random_forms.is_checked()\n params.shown_side = (CardSide.English if self.__combo_side.get_index() == 1\n else CardSide.Russian)\n self.__study_params = params\n\n # Define the scheduler params\n self.__scheduler_params = SchedulerParams(\n max_repetitions=1 if self.__checkbox_only_once.is_checked() else 0)\n \n # Popluate the table\n self.__table_cards.clear()\n for card, study_data in cards:\n color = Config.proficiency_level_colors[study_data.get_proficiency_level()]\n row = self.__table_cards.add(card, color=color)\n cards = [card for card, _ in cards]\n\n self.__cards = cards\n self.__button_begin.set_enabled(len(cards) > 0 and self.__study_params)\n self.__label_result_count.set_text(\"{} Results\".format(len(self.__cards)))","def hit(\n self,\n card: List[Tuple[int, str, str]],\n card_index: int = 0\n ) -> None:\n self._cards[card_index].extend(card)","def refresh(self):\n self.deck = []\n\n for _suit in Suit:\n for _face in Face:\n self.insert(Card(_suit, _face, self))","def _cards_getter(self):\n pass","def add_cards(self, cards):\n self.get_cards().extend(cards)","async def get_available_cards(self, game_id): # pass in a list of card ids\n all_cards = await self.get_all_cards()\n available_cards = []\n game = await self.get_game(game_id)\n player1_cards = await self.get_current_cards(game[1])\n player2_cards = await self.get_current_cards(game[2])\n for card in all_cards:\n if card not in player1_cards and card not in player2_cards:\n available_cards.append(card)\n return available_cards","def draw_card(self, card):\n self.current_hand.append(card)","def get_selected_card(self, pos, double_clicking=False):\n if self.selectable:\n double_select = False\n relative_pos_x = pos[0] - self.x\n relative_pos_y = pos[1] - self.y\n mouse_pos = (relative_pos_x, relative_pos_y)\n self.selected_card = -1\n if not self.draw_from_last:\n for i, card in enumerate(reversed(self.cards)):\n if card.rect.collidepoint(mouse_pos):\n self.selected_card = len(self.cards) - 1 - i\n break\n else:\n for i, card in enumerate(self.cards):\n if card.rect.collidepoint(mouse_pos):\n self.selected_card = i\n break\n\n if self.prev_selected[-1] == self.selected_card:\n if not double_clicking:\n self.selected_card = -1\n\n self.record_selected_history()\n self.update_deck_display()\n\n selected_history = [sel for sel in self.prev_selected if sel >= 0]\n\n return (len(selected_history) == 2 and self.prev_selected.count(self.selected_card) == 2\n and self.selected_card >= 0) and double_clicking\n return False","def deal_card():\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n random_card = random.choice(cards)\n return random_card","def select_card(player, _mouse_x=None, _mouse_y=None):\n if _mouse_x:\n for card in player.cards:\n lower_x = card.positionx\n lower_y = card.positiony\n if lower_x < _mouse_x < lower_x + 100 and lower_y < _mouse_y < lower_y + 100:\n player.selected_card = card","def receive_chance_card(self, card):\r\n self.chance_cards.append(card)","def build_deck_screen_my_deck_card_display(screen,buttons, screen_status, button_status, card_database_filter, user):\n # Draw the character card\n if user.character_card == '':\n pass\n else:\n user.character_card.rect.x = 65\n user.character_card.rect.y = 600\n screen.blit(user.character_card.image, user.character_card.rect)\n #Clear duplicate amount each frame and render the refined list\n for card_new in user.deck_list:\n card_new.duplicate = 1\n local_store_list = build_deck_screen_my_deck_card_list_refine(user)\n #use refined list to draw\n rect_position_x = 245 #local variables for rect position for the first card in the user deck\n rect_position_y = 600\n row_number = 1\n #Display cards in local_store_list:\n\n if screen_status.build_deck_screen_my_deck_page_id <= 0:\n screen_status.build_deck_screen_my_deck_page_id = 1\n # Edge cases when len() = 6,12,18....\n if len(local_store_list) % 6 == 0 and len(local_store_list) != 0:\n if screen_status.build_deck_screen_my_deck_page_id >= (len(local_store_list))//6 + 1:\n screen_status.build_deck_screen_my_deck_page_id = (len(local_store_list))//6 + 0\n\n else:\n if screen_status.build_deck_screen_my_deck_page_id >= (len(local_store_list))//6 + 2:\n screen_status.build_deck_screen_my_deck_page_id = (len(local_store_list))//6 + 1\n # Algorithm to draw all cards in local_store_list, 6 card per page.\n for card in local_store_list[6*(screen_status.build_deck_screen_my_deck_page_id - 1):6 * screen_status.build_deck_screen_my_deck_page_id]:\n if row_number <= 6:\n card.rect.x = rect_position_x\n card.rect.y = rect_position_y\n screen.blit(card.image, card.rect)\n rect_position_x += 145\n row_number += 1\n build_deck_screen_my_deck_duplicate_number_display(card, screen)\n if row_number >= 7:\n row_number = 1","def deal_cards(self):\n self.card = random.randint(1, 13)\n return self.card","def deal_opening_cards(self) -> None:\r\n for i in range(self.num_of_players):\r\n self.dealer.deal_cards_to(self.players[i].cards_stack, PokerRules.CARDS_PER_PLAYER)","def cards_to_deal(cls, context={}):\n\t\traise NotImplementedError()","def cards():\n if user_loggined():\n user = models.User.query.get(session['user_id'])\n u_cards = user.cards.all()\n prep_cards = []\n for card in u_cards:\n prep_cards.append(card.type + ' **** '+card.cnb[-9:])\n else:\n return redirect(url_for('index'))\n return redirect(url_for('index'))","def take(self, table):\n # take card\n self.hand.add(table.card)\n table.card = table.cards.pop()\n # take chips\n self.chips += table.chips\n table.chips = 0","def receive_card(self, card, new_suite=None, is_using_chameleon=False):\n assert isinstance(card, tuple)\n assert len(card) == 2\n\n if card.value == 14 or card.value == self.chameleon and is_using_chameleon:\n assert new_suite != None, print('You must specify a new suite.')\n assert new_suite in ['hearts', 'spades', 'diamonds', 'clubs']\n\n self.current_suite = new_suite\n self.current_value = None\n\n else:\n self.cards.append(card)\n self.current_suite = card.suite\n self.current_value = card.value","def card_output():\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n return random.choice(cards)","def initial_draw(self):\n self.player.take_card(self.deck)\n self.dealer.take_card(self.deck)\n self.player.take_card(self.deck)\n self.dealer.put_face_down(self.deck)","def get_options(cls, player, context={}):\n\t\toptions = []\n\t\tfor card in player.hand:\n\t\t\tif cls.can_be_played(card, context):\n\t\t\t\toptions.extend(card.actions)\n\t\toptions.append(Action(None, \"DRAW\", [DrawCard]))\n\t\treturn options","def check_cards_eligibility(self):\n for c in self.hand:\n c.check_actions(self)\n for c in self.phand:\n c.check_actions(self)\n for c in self.discard:\n c.check_actions(self)\n for c in self.active_player.phand:\n c.check_actions(self)\n for c in self.active_player.hand:\n c.check_actions(self)\n for c in self.active_player.discard:\n c.check_actions(self)\n for c in self.played_user_cards:\n c.check_actions(self)\n if ACTION_KEEP in self.actions:\n for p in self.players:\n for c in p.phand:\n c.check_actions(self)\n for c in p.hand:\n c.check_actions(self)\n for c in p.discard:\n c.check_actions(self)","def get_hand(self):\n return self.cards","def get_cards(self):\n return deepcopy(self._cards)","def lobby_screen_pick_deck_display(ai_settings, screen, buttons,screen_status, button_status, card_database_filter, user, action, player2):\n # Pick deck text\n button_text_1 = Button('Pick an exist deck or create a new one: ','', (250,250,250),400, 100, 400, 35, font_color = (0,0,0), alpha = 150)\n button_text_1.update()\n button_text_1.draw(screen)\n\n # Deck list buttons\n with open('user_deck_list_string.txt','r') as f:\n f.seek(0)\n if len(f.readlines()) >= 12:\n pass\n else:\n button_new_deck = Button('+ New Deck','', (250,250,250),1020, 110, 120, 35, font_color = (0,0,0), alpha = 150)\n button_new_deck.update()\n button_new_deck.draw(screen)\n\n\n f.seek(0)\n x = len(f.readlines())\n y = 0\n deck_list_index = 0\n\n for i in range(1,7):\n f.seek(0)\n for line in f:\n if 'DECK_LIST_' + str(i) not in line:\n y += 1\n if y < x: # DECK_LIST_i exist\n f.seek(0)\n for line in f:\n if 'DECK_LIST_' + str(i) in line:\n deck_length = len(make_card_list_from_string(line.replace('DECK_LIST_' + str(i) + ' = ', ''), ai_settings, screen, buttons,screen_status, button_status, card_database_filter, user, player2))\n # deck_length = int((len(line.replace('DECK_LIST_' + str(i) + ' = ', '')) -1)/14)\n if 'CHARACTER_' + str(i) in line:\n character_length = 1\n character_card = eval('card_' + line.replace('CHARACTER_' + str(i) + ' = ', '')[7:12])\n\n if user.deck_list_index == str(i):\n\n button_top = Button(character_card.name + ': ','', (100,30,130),85 + 180* (i-1), 165, 130, 60)\n button_top.update()\n button_top.draw(screen)\n\n if deck_length < 40:\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (100,30,130),85 + 180* (i-1), 225, 130, 50, font_color = (250,0,0))\n button_bottom.update()\n button_bottom.draw(screen)\n else:\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (100,30,130),85 + 180* (i-1), 225, 130, 50)\n button_bottom.update()\n button_bottom.draw(screen)\n\n else:\n\n button_top = Button(character_card.name + ': ','', (160,160,160),85 + 180* (i-1), 165, 130, 60, alpha = 240)\n button_top.update()\n button_top.draw(screen)\n\n if deck_length < 40:\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (160,160,160),85 + 180* (i-1), 225, 130, 50, font_color = (200,0,0), alpha = 240)\n button_bottom.update()\n button_bottom.draw(screen)\n else:\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (160,160,160),85 + 180* (i-1), 225, 130, 50, alpha = 240)\n button_bottom.update()\n button_bottom.draw(screen)\n\n y = 0\n\n else: # DECK_LIST_i not exist\n\n button = Button('Empty','', (200,200,200),85 + 180* (i-1), 165, 130, 110, alpha = 80)\n button.update()\n button.draw(screen)\n\n y = 0\n\n\n for i in range(1,7):\n if user.deck_list_index == str(i):\n button_edit = Button('Edit','', (50,50,170),85 + 180* (i-1), 282, 60, 30)\n button_edit.update()\n button_edit.draw(screen)\n\n button_delete = Button('Delete','', (160,30,30), 155 + 180* (i-1), 282, 60, 30)\n button_delete.update()\n button_delete.draw(screen)","def hit(hand=bj.player1.hand):\r\n hand.append(bj.deck.remove_card())","def test_play_card(self):\n self.plr.piles[Piles.DECK].set(\"Silver\", \"Province\", \"Moat\", \"Gold\")\n self.vic.piles[Piles.DECK].set(\"Duchy\")\n self.plr.test_input = [\"discard\", \"discard\", \"putback\"]\n self.plr.play_card(self.card)\n self.g.print_state()\n self.assertEqual(self.plr.actions.get(), 1)\n self.assertIn(\"Duchy\", self.vic.piles[Piles.DISCARD])\n self.assertIn(\"Gold\", self.plr.piles[Piles.DISCARD])\n self.assertIn(\"Province\", self.plr.piles[Piles.HAND])\n self.assertIn(\"Moat\", self.plr.piles[Piles.HAND])\n self.assertIn(\"Silver\", self.plr.piles[Piles.DECK])","def hit(self, deck):\n try:\n self.hand.append(deck.pop(0))\n except IndexError:\n print('There are no more cards in the deck!')","def get_cards(self):\n return [card.view_model() for card in self._deck.loc]","def deal(self, cards_num):\n\n cards = []\n while cards_num > 0:\n\n x = random.randint(0, 53)\n if self.in_use[x] == 0:\n self.in_use[x] += 1\n cards.append(x)\n cards_num -= 1\n\n return cards","def player_card_one_choice(update, context):\n query = update.callback_query\n if query.message.reply_to_message:\n user = query.message.reply_to_message.from_user\n else:\n user = query.message.chat\n bot = context.bot\n CURRENT_CONTEXT = USERS[user.username]\n message = f'Round: {CURRENT_CONTEXT[\"round\"]} ({CURRENT_CONTEXT[\"username\"]}) \\nDealers Card: {CURRENT_CONTEXT[\"dealer_card\"]}\\nYour Cards: {CURRENT_CONTEXT[\"player_cards\"]} \\nYour total: {CURRENT_CONTEXT[\"player_total\"]} \\n\\nChoose Your 1st Card: '\n bot.edit_message_text(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=message,\n reply_markup=card_markup\n )\n\n return PLAYER_CARD_TWO","def main():\n\n # call to OS for positioning window\n os.environ['SDL_VIDEO_WINDOW_POS'] = \"%d,%d\" % (0, 25)\n\n # Initialization block\n pygame.init() # Initialize pygame module\n screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) # initialize screen\n\n # Testing\n # model_card = m_card.Card(m_card.CardType.TEMPURA)\n # view_card = v_card.CardView(screen, model_card)\n\n deck = Deck()\n player = Player()\n b_pack = deck.generate_booster(10)\n player.booster_pack = b_pack\n\n hand_view = HandView(screen, (0, SCREEN_HEIGHT - SCREEN_HEIGHT / 5), (SCREEN_WIDTH, SCREEN_HEIGHT / 5), player)\n pick_crds = PickedCardsView(screen, (0, 0), (SCREEN_WIDTH, SCREEN_HEIGHT / 5), player, 0)\n pick_crds2 = PickedCardsView(screen, (0, 0), (SCREEN_WIDTH, SCREEN_HEIGHT / 5), player, 180)\n # Game loop\n while True:\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n\n elif event.type == pygame.MOUSEBUTTONUP:\n is_clicked([hand_view, pick_crds, pick_crds2], pygame.mouse.get_pos())\n screen.fill((0, 0, 0))\n hand_view.draw()\n pick_crds.draw()\n pick_crds2.draw()\n pygame.display.flip()","def battle_screen_my_hand_card_display(screen,buttons, screen_status, button_status, card_database_filter, user):\n rect_position_x = 100\n rect_position_y = 610\n row_number = 1\n if screen_status.battle_screen_action_indicator == 'stage-0':\n pass\n else :\n\n if screen_status.battle_screen_my_hand_page_id <= 0:\n screen_status.battle_screen_my_hand_page_id = 1\n # Edge cases when len() = 6,12,18....\n if len(user.hand_list) % 7 == 0 and len(user.hand_list) != 0:\n if screen_status.battle_screen_my_hand_page_id >= (len(user.hand_list))//7 + 1:\n screen_status.battle_screen_my_hand_page_id = (len(user.hand_list))//7 + 0\n\n else:\n if screen_status.battle_screen_my_hand_page_id >= (len(user.hand_list))//7 + 2:\n screen_status.battle_screen_my_hand_page_id = (len(user.hand_list))//7 + 1\n # Algorithm to draw all cards in local_store_list, 6 card per page.\n for card in user.hand_list[7*(screen_status.battle_screen_my_hand_page_id - 1):7 * screen_status.battle_screen_my_hand_page_id]:\n if row_number <= 7:\n card.rect.x = rect_position_x\n card.rect.y = rect_position_y\n screen.blit(card.image, card.rect)\n rect_position_x += 145\n row_number += 1\n if row_number >= 8:\n row_number = 1","def dealCards(deck, player, numCards):\n print \"dealing %s cards to %s...\" % (numCards, player.name)\n for card in range(numCards):\n card = deck[0]\n deck.pop(0)\n player.cards.append(card)\n print \"added %s card for %s\" % (card, player.name)\n print player.cards","def setup_cards(self, server):\r\n\t\tversions_list = self.ice.getVersionsList()\r\n\t\talarm_list = self.ice.getAlarmStatus()\r\n\t\tstatus_list = self.ice.getStatus()\r\n\t\twarning_list = self.ice.getWarnings()\r\n\r\n\t\tdateTimeObj = datetime.now()\r\n\t\ttimestampStr = dateTimeObj.strftime(\"%d-%b-%Y (%H:%M:%S)\")\r\n\t\tcards = self.ice.getCardsAlive()\r\n\t\tfor i in range(len(versions_list)):\r\n\t\t\tjson_body = versions_list[i]\r\n\t\t\tjson_body.update({'alarm':alarm_list[i],'status':status_list[i], 'card':cards[i], 'warning':warning_list[i],'update':timestampStr, 'hostname':self.ip})\r\n\t\t\tserver.index(index='icepap_info', id=self.ip + '_' + str(cards[i]), body=json_body)","def getAllCards(self):\n return self._cards","def build_deck_screen_card_gallery_card_display(screen, buttons, screen_status, button_status, card_database_filter):\n rect_position_x = 100 #local variables for rect position for the first card in the card gallery\n rect_position_y = 130\n row_number = 1 # local variable to help keep track of position of card\n # Check the page number to make sure if will not go negative or randomly large\n if screen_status.build_deck_screen_card_gallery_page_id <= 0:\n screen_status.build_deck_screen_card_gallery_page_id = 1\n # Edge cases when len() = 14, 28, 42...\n if len(cdf.request_card_list(card_database_filter)) % 14 == 0 and len(cdf.request_card_list(card_database_filter)) != 0:\n if screen_status.build_deck_screen_card_gallery_page_id >= (len(cdf.request_card_list(card_database_filter)))//14 + 1:\n screen_status.build_deck_screen_card_gallery_page_id = (len(cdf.request_card_list(card_database_filter)))//14 + 0\n\n else:\n if screen_status.build_deck_screen_card_gallery_page_id >= (len(cdf.request_card_list(card_database_filter)))//14 + 2:\n screen_status.build_deck_screen_card_gallery_page_id = (len(cdf.request_card_list(card_database_filter)))//14 + 1\n # Algorithm to draw all cards in request_card_list, 14 card per page.\n for card in cdf.request_card_list(card_database_filter)[14*(screen_status.build_deck_screen_card_gallery_page_id - 1):14 * screen_status.build_deck_screen_card_gallery_page_id]:\n if row_number <= 7:\n card.rect.x = rect_position_x\n card.rect.y = rect_position_y\n screen.blit(card.image, card.rect)\n rect_position_x += 145\n row_number += 1\n elif row_number <= 14:\n card.rect.x = rect_position_x - 1015\n card.rect.y = rect_position_y + 200\n screen.blit(card.image, card.rect)\n rect_position_x += 145\n row_number += 1\n if row_number >= 15:\n row_number = 1","def setup_newgame(self):\n global chips\n self.bet = 100\n if chips < self.bet: \n self.game_over = True\n chips -= self.bet\n \n\n self.cards_list = arcade.SpriteList()\n\n #resets on newgame\n self.top_card_int = 0 ## this had to be moved here to make it so that you are not drawing over the 52 card limit\n self.player_hand = []\n self.dealer_hand = []\n self.player_value = 0\n self.dealer_value = 0\n self.player_ace_count = 0\n self.dealer_ace_count = 0\n self.player_almost_bust = 0\n self.dealer_almost_bust = 0\n self.blackjack = False\n self.victory = False\n self.defeat = False\n \n #creates deck\n for card_suit in CARD_SUITS:\n for card_value in CARD_VALUES:\n card = Card(card_suit, card_value, CARD_SCALE)\n self.cards_list.append(card)\n #shuffles deck\n for pos1 in range(len(self.cards_list)):\n pos2 = random.randrange(len(self.cards_list))\n self.cards_list.swap(pos1, pos2)\n \n #Current way to add cards to player and dealer hands since using .pop() on self.cards_list deletes the card itself even in the other hands\n \n #self.dealer_hand.append(self.top_card_int)\n self.hit(\"dealer\")\n self.dealer_hand[0].face_down()\n #first_card = self.dealer_hand[0]\n #first_card.face_down()\n #self.dealer_hand[0].face_down()\n self.hit(\"player\")\n self.player_hand[0].face_down()\n self.hit(\"dealer\")\n self.dealer_hand[1].face_down()\n self.hit(\"player\")\n self.player_hand[1].face_down()\n self.update_card_positions()"],"string":"[\n \"def pick(self, pack, cards_owned, draft_info):\\n pass\",\n \"def card_sel(\\n self, num=1, **kwargs\\n ): # pylint: disable=too-many-locals, too-many-branches\\n selectfrom = self.card_selSource(**kwargs)\\n force = kwargs[\\\"force\\\"] if \\\"force\\\" in kwargs else False\\n showdesc = kwargs[\\\"showdesc\\\"] if \\\"showdesc\\\" in kwargs else True\\n verbs = kwargs.get(\\\"verbs\\\", (\\\"Select\\\", \\\"Unselect\\\"))\\n\\n if \\\"prompt\\\" in kwargs:\\n self.output(kwargs[\\\"prompt\\\"])\\n\\n if \\\"anynum\\\" in kwargs and kwargs[\\\"anynum\\\"]:\\n anynum = True\\n num = 0\\n else:\\n anynum = False\\n\\n selected = []\\n types = kwargs[\\\"types\\\"] if \\\"types\\\" in kwargs else {}\\n types = self._type_selector(types)\\n while True:\\n options = []\\n if (\\n anynum\\n or (force and num == len(selected))\\n or (not force and num >= len(selected))\\n ):\\n o = Option(selector=\\\"0\\\", verb=\\\"Finish Selecting\\\", card=None)\\n options.append(o)\\n index = 1\\n for c in sorted(selectfrom):\\n if \\\"exclude\\\" in kwargs and c.name in kwargs[\\\"exclude\\\"]:\\n continue\\n if not self.select_by_type(c, types):\\n continue\\n sel = \\\"%d\\\" % index\\n index += 1\\n if c not in selected:\\n verb = verbs[0]\\n else:\\n verb = verbs[1]\\n o = Option(selector=sel, verb=verb, card=c, name=c.name)\\n if showdesc:\\n o[\\\"desc\\\"] = c.description(self)\\n if kwargs.get(\\\"printcost\\\"):\\n o[\\\"details\\\"] = str(self.card_cost(c))\\n if kwargs.get(\\\"printtypes\\\"):\\n o[\\\"details\\\"] = c.get_cardtype_repr()\\n options.append(o)\\n ui = self.user_input(options, \\\"Select which card?\\\")\\n if not ui[\\\"card\\\"]:\\n break\\n if ui[\\\"card\\\"] in selected:\\n selected.remove(ui[\\\"card\\\"])\\n else:\\n selected.append(ui[\\\"card\\\"])\\n if num == 1 and len(selected) == 1:\\n break\\n return selected\",\n \"def card(bot, update):\\n query = update.callback_query\\n user = query.from_user\\n chat_id = query.message.chat_id\\n selected_card = query.data\\n\\n if (chats[chat_id].player1.card_played == []) and (chats[chat_id].player2.card_played == []):\\n bot.send_message(text=Strings.CARD_SELECTED.format(user.first_name),\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n parse_mode=ParseMode.MARKDOWN,\\n isgroup=True)\\n if chats[chat_id].player1.user == user:\\n chats[chat_id].player1.card_played = chats[chat_id].player1.hand[int(selected_card)]\\n chats[chat_id].player1.hand.remove(chats[chat_id].player1.hand[int(selected_card)])\\n\\n elif chats[chat_id].player2.user == user:\\n chats[chat_id].player2.card_played = chats[chat_id].player2.hand[int(selected_card)]\\n chats[chat_id].player2.hand.remove(chats[chat_id].player2.hand[int(selected_card)])\\n return CARD\\n\\n else:\\n if chats[chat_id].player1.user == user and chats[chat_id].player1.card_played != []:\\n bot.send_message(text=Strings.CARD_SELECTED2.format(user.first_name),\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\\n return CARD\\n elif chats[chat_id].player2.user == user and chats[chat_id].player2.card_played != []:\\n bot.send_message(text=Strings.CARD_SELECTED2.format(user.first_name),\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\\n return CARD\\n else:\\n if chats[chat_id].player1.user == user:\\n chats[chat_id].player1.card_played = chats[chat_id].player1.hand[int(selected_card)]\\n chats[chat_id].player1.hand.remove(chats[chat_id].player1.hand[int(selected_card)])\\n\\n elif chats[chat_id].player2.user == user:\\n chats[chat_id].player2.card_played = chats[chat_id].player2.hand[int(selected_card)]\\n chats[chat_id].player2.hand.remove(chats[chat_id].player2.hand[int(selected_card)])\\n\\n bot.edit_message_text(text=Strings.CARD_SELECTED.format(user.first_name),\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n parse_mode=ParseMode.MARKDOWN)\\n bot.send_message(chat_id,\\n Strings.SELECTION_COMPLETED,\\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\\n\\n reply_markup = ReplyKeyboardMarkup(c_b_keyboard, selective=False)\\n bot.send_message(chat_id,\\n Strings.QUESTION,\\n reply_markup=reply_markup,\\n parse_mode=ParseMode.MARKDOWN, isgroup=True)\\n return BET_CHECK\",\n \"def choose_card(playable_cards):\\r\\n\\r\\n playing = playable_cards[0]\\r\\n print('\\\\n choosing \\\\n', playing)\\r\\n\\r\\n return playing # for now\\r\",\n \"def next_card_selection(deck):\\n # First grab the colors and format\\n # Then generate the query, execute, and trim\\n # Then get the selections, and return them!\\n colors = deck['colors']\\n deck_format = deck['format']\\n seed = deck.get('seed')\\n\\n query_type = pick_next_card_query(deck_format, seed)\\n trimmed_query_results = compile_execute_and_trim_query(colors, query_type, deck_format)\\n selections = generate_pickable_selections(trimmed_query_results, deck_format, deck)\\n print('QUERY: ', query_type, ' NUM RESULTS:', len(trimmed_query_results))\\n\\n return selections\",\n \"def choose_card_to_discard(self):\\n random.choice(self.hand.card_list).use()\",\n \"def select_card(self, cards):\\n idx = -1 # should not be inital value\\n while True:\\n print(\\\"Please select a card by index:\\\")\\n inpt = self.input(list(enumerate(cards)))\\n try:\\n idx = int(inpt)\\n except ValueError:\\n print(f\\\"'{inpt}' is not a valid index.\\\")\\n if idx < 0 or idx >= len(cards):\\n print(f\\\"The index {idx} is not available.\\\")\\n else:\\n break\\n assert idx != -1 # make sure it's not initial value\\n return cards.pop(idx)\",\n \"def cards(self, cards):\\n\\n self._cards = cards\",\n \"def cards(self, cards):\\n\\n self._cards = cards\",\n \"def choose_card(self, state=None):\\n # if self.at_last_stich():\\n # allowed = yield self.cards[0]\\n # else:\\n self.observation_received.acquire()\\n self.observation = self.build_observation(state, self.cards)\\n logger.debug(f\\\"choose_card received observation: {self.observation}\\\")\\n self.observation_received.notify_all() # notify all threads to be sure\\n self.observation_received.release()\\n\\n self.action_received.acquire()\\n received = self.action_received.wait()\\n if not received:\\n logger.debug(\\\"Timeout occurred. action_received condition has not been notified.\\\")\\n logger.debug(f\\\"choose_card received action: {self.action}\\\")\\n allowed_cards = self.allowed_cards(state=state)\\n chosen_card = allowed_cards[0] # set chosen_card to the first allowed card in case anything goes south\\n chosen_card = self.set_chosen_card(allowed_cards, chosen_card)\\n self.action_received.release()\\n\\n allowed = yield chosen_card\\n\\n if allowed:\\n yield None\",\n \"def set_chosen_card(self, allowed_cards, chosen_card):\\n if self.action is not None:\\n if self.action in allowed_cards:\\n logger.info(f\\\"Successfully chose the card: {self.action}\\\")\\n chosen_card = self.action\\n else:\\n logger.error(f\\\"{self.action} is not a valid card! Choosing the first allowed card now.\\\")\\n else:\\n logger.debug(\\\"chosen card is None\\\")\\n return chosen_card\",\n \"def play_selected_card(_screen, player):\\n card = Card(player.selected_card.card_id, 400, 350)\\n card.image_of_card(_screen)\",\n \"def play_all(self):\\n for _ in range(self._hand.size()):\\n card = self._hand.pop()\\n self._active.push(card)\\n self._money = self._money + card.money\\n self._attack = self._attack + card.attack\\n print '\\\\nPlayed all cards!'\",\n \"def deal_cards():\\n for _ in range(2):\\n user_cards.append(random.choice(deck))\\n dealer_cards.append(random.choice(deck))\",\n \"def cards(self) -> None:\\n self._cards = []\",\n \"def Deal():\\r\\n cardsout = []\\r\\n cardoptions = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]\\r\\n topcardoptions = [0,2,3,4,5,6]\\r\\n topcard = topcardoptions[random.randint(0,5)]\\r\\n cardoptions.pop(cardoptions.index(topcard))\\r\\n cardsout.append(topcard)\\r\\n\\r\\n if SHOWHAPPENINGS == True:\\r\\n disp = card_dict[topcard]\\r\\n print(\\\"Topcard is: {}\\\".format(disp)) \\r\\n\\r\\n for i in range(4):\\r\\n numcards = 0\\r\\n while numcards < 5:\\r\\n possiblerange = len(cardoptions) - 1\\r\\n cardindex = random.randint(0,possiblerange)\\r\\n card = cardoptions[cardindex]\\r\\n cardsout.append(card)\\r\\n cardoptions.pop(cardoptions.index(card))\\r\\n PlayerHands[i].append(card)\\r\\n numcards += 1\\r\\n PlayerHands[i] = sorted(PlayerHands[i]) #putting into ascending order\\r\\n if i == 0 or i == 2:\\r\\n PlayerHands[i].append(\\\"RedTeam\\\")\\r\\n else: \\r\\n PlayerHands[i].append(\\\"BlackTeam\\\")\\r\\n \\r\\n PlayerHands[0].append(PLAYER1)\\r\\n PlayerHands[1].append(PLAYER2)\\r\\n PlayerHands[2].append(PLAYER3)\\r\\n PlayerHands[3].append(PLAYER4)\\r\\n #PlayerHand format = [card1,card2,card3,card4,card5,Team,Name]\\r\\n\\r\\n return topcard\",\n \"def going_out(self, cards):\\n for card in cards:\\n self.out_of_use.append(int(card))\\n # print(self.out_of_use)\",\n \"def followUpAttack(self, validCards):\\n print(\\\"Select card from... \\\")\\n cardManager.printHand(validCards)\\n card = int(input(\\\"to your attack: \\\"))\\n while card not in validCards: # error checking\\n print(card)\\n print(\\\"Please select a valid card from...\\\")\\n cardManager.printHand(validCards)\\n card = int(input(\\\"to your attack: \\\"))\\n self.currentHand.remove(card)\\n card = self.checkDoubles(card)\\n return card\",\n \"def clearCards(self):\\r\\n self.cards = []\",\n \"def __init__(self):\\r\\n self.cards = []\",\n \"def open_cards(self) -> None:\\r\\n self.dealer.deal_cards_to(self.card_stack, PokerRules.CARDS_PER_ROUND[self.round_num])\",\n \"def get_card_list(self):\\n return self.cards\",\n \"def __init__(self):\\n self._cards = []\",\n \"def upgrade_all_cards(self):\\n self.game.go_to_comic_cards()\\n logger.info(\\\"Comic Cards: upgrading all available cards.\\\")\\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=3, ui_element=self.ui['CARDS_UPGRADE_ALL']):\\n self.emulator.click_button(self.ui['CARDS_UPGRADE_ALL'].button)\\n for card_index in range(1, 6):\\n card_select_ui = self.ui[f'CARDS_SELECT_GRADE_{card_index}']\\n self.emulator.click_button(card_select_ui.button)\\n logger.debug(f\\\"Comic Cards: starting to upgrade UI Element {card_select_ui.name}\\\")\\n if not wait_until(self.emulator.is_image_on_screen, timeout=3, ui_element=card_select_ui):\\n logger.warning(\\\"Comic Cards: can't select card's grade.\\\")\\n continue\\n logger.debug(f\\\"Comic Cards: successfully selected UI Element {card_select_ui.name}\\\")\\n self.emulator.click_button(self.ui['CARDS_SELECT_GRADE'].button)\\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=3,\\n ui_element=self.ui['CARDS_UPGRADE_CONFIRM']):\\n self.emulator.click_button(self.ui['CARDS_UPGRADE_CONFIRM'].button)\\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=10,\\n ui_element=self.ui['CARDS_UPGRADE_RESULTS_OK']):\\n logger.debug(f\\\"Comic Cards: successfully upgraded UI Element {card_select_ui.name}\\\")\\n self.emulator.click_button(self.ui['CARDS_UPGRADE_RESULTS_OK'].button)\\n wait_until(self.emulator.is_image_on_screen, timeout=3, ui_element=card_select_ui)\\n continue\\n if wait_until(self.emulator.is_ui_element_on_screen, timeout=3, ui_element=self.ui['CARDS_UPGRADE_ALL_CANCEL']):\\n self.emulator.click_button(self.ui['CARDS_UPGRADE_ALL_CANCEL'].button)\\n self.close_after_mission_notifications()\\n self.game.go_to_main_menu()\",\n \"def __init__(self, cards):\\n self.cards = cards\",\n \"def user_turn(self):\\r\\n\\r\\n self.display_state() # display the current state\\r\\n print(\\r\\n '\\\\nTURN: You -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')\\r\\n # Get the row and col number of the card you want to select\\r\\n x1, y1 = self.input_validation('Enter the location of the first card you pick (row, col) -> ')\\r\\n self.selected = [x1, y1] # a temporary holder for the first choice\\r\\n\\r\\n # Get the corresponding card ID which is also the key for the dictionary with all the cards\\r\\n choice1_key = self.state[x1, y1]\\r\\n print('The card you selected: {0}'.format(self.deck[choice1_key]))\\r\\n\\r\\n # Repeat this for your second choice\\r\\n x2, y2 = self.input_validation('Enter the location of the second card you pick (row, col) -> ')\\r\\n self.selected = [-1, -1] # reset the temporary hold\\r\\n\\r\\n choice2_key = self.state[x2, y2]\\r\\n print('The card you selected: {0}'.format(self.deck[choice2_key]))\\r\\n\\r\\n # Check if the two cards are a match or not\\r\\n if self.check_card(self.deck[choice1_key], self.deck[choice2_key]):\\r\\n print('MATCH')\\r\\n # Replace the corresponding cards in the remaining inventory and state with -1\\r\\n self.remaining[choice1_key] = -1\\r\\n self.remaining[choice2_key] = -1\\r\\n self.state[x1, y1] = -1\\r\\n self.state[x2, y2] = -1\\r\\n self.player_cards += 2 # the player gets 2 cards\\r\\n self.bin.append([x1, y1]) # move the location of the card to the already-taken bin\\r\\n self.bin.append([x2, y2])\\r\\n self.forget_memory(choice1_key) # remove from computer's memory\\r\\n self.forget_memory(choice2_key)\\r\\n self.match = 1 # player will continue to choose cards\\r\\n else:\\r\\n print('NOT a match')\\r\\n # Add these cards to the computer's memory\\r\\n self.computer_memory[choice1_key] = [x1, y1]\\r\\n self.computer_memory[choice2_key] = [x2, y2]\\r\\n self.match = 0 # computer's turn\\r\",\n \"def test_consumed_cards(self):\\n game = TestGames.replay(9, [3, 1, 0, 0])\\n consumed_cards = game.consumed_cards()\\n self.assertEqual(len(consumed_cards), 8)\\n\\n self.assertListEqual(list(consumed_cards),\\n [2 / 5, # guards\\n 0 / 2, # priest\\n 1 / 2, # baron\\n 0 / 2, # handmaid\\n 1 / 2, # prince\\n 0 / 1, # king\\n 0 / 1, # countess\\n 0 / 1]) # princess\",\n \"def deliver_card(data, access=None):\\n\\n schema = get_card_schema(data)\\n if not schema:\\n schema = card_schema\\n data = deepcopy(data)\\n\\n if access is 'learn' and data['kind'] is 'choice':\\n if data['order'] == 'random':\\n shuffle(data['options'])\\n\\n if data['max_options_to_show']:\\n data['options'] = data['options'][:data['max_options_to_show']]\\n\\n return deliver_fields(schema, data, access)\",\n \"def all_cards_selected(self, game_key):\\n participants = models.Participant.query(\\n models.Participant.playing == True,\\n models.Participant.selected_card == None,\\n ancestor=game_key).fetch()\\n logging.debug(\\\"participants who have not selected a card: %s\\\", participants)\\n if participants:\\n return False\\n else:\\n return True\",\n \"def draw_a_card(cards):\\n import random\\n card_drawn = random.choices(card_deck)\\n cards.append(card_drawn[0])\\n return\",\n \"def deal_card():\\r\\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\\r\\n return (random.choice(cards))\",\n \"def attack(self): # need to check defenders handcount\\n \\\"\\\"\\\"Always returns a list of values\\\"\\\"\\\"\\n if self.AI:\\n # return rand.randint(0,len(self.currentHand))\\n Error(\\\"AI not yet implemented for Attacking\\\")\\n else:\\n print(\\\"Select card from... \\\")\\n cardManager.printHand(self.currentHand)\\n card = int(input(\\\"to your attack: \\\"))\\n while card not in self.currentHand: # error checking\\n print(\\\"Please select a valid card from...\\\", end = \\\" \\\")\\n cardManager.printHand(self.currentHand)\\n card = int(input())\\n self.currentHand.remove(card)\\n card = self.checkDoubles(card)\\n return card\",\n \"def test_cards_get(self):\\n pass\",\n \"def hit(self, deck):\\n self.cards.append(deck.draw_card())\",\n \"def dealer_card_choice(update, context):\\n query = update.callback_query\\n if query.message.reply_to_message:\\n user = query.message.reply_to_message.from_user\\n else:\\n user = query.message.chat\\n bot = context.bot\\n CURRENT_USER = USERS[user.username]\\n CURRENT_CONTEXT = process_card_value(query.data, CURRENT_USER)\\n message = f'Round: {CURRENT_CONTEXT[\\\"round\\\"]} ({CURRENT_CONTEXT[\\\"username\\\"]}) \\\\nDealers Card: {CURRENT_CONTEXT[\\\"dealer_card\\\"]}\\\\nYour Cards: {CURRENT_CONTEXT[\\\"player_cards\\\"]} \\\\nYour total: {CURRENT_CONTEXT[\\\"player_total\\\"]} \\\\n\\\\nChoose Dealers Card: '\\n bot.edit_message_text(\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n text=message,\\n reply_markup=card_markup\\n )\\n\\n # Tell ConversationHandler that we're in state `STRATEGY` now\\n return STRATEGY\",\n \"def get_game_cards(gameId):\\n pass\",\n \"def indicate_discard_card(whose_turn,players):\\n cards_to_choose_from = players[whose_turn].hand.cards\\n players[whose_turn].hand.print_cards()\\n chosen_to_discard = int(input('Select a card to discard. Type a number. '))\\n return chosen_to_discard\",\n \"def get_card_sets(self, name: str) -> List:\",\n \"def get_card(self, card):\\n\\n\\t\\tself.add_card_to_grps(card)\\n\\n\\t\\tself.grps = sorted(self.grps, key = lambda x: -len(x))\\n\\n\\n\\t\\t# check if # of cards forming sets is more than 5; if yes, then break the set to allow computer to form runs\\n\\t\\tnum_set_cards = 0\\n\\t\\tpos = -1\\n\\t\\tfor i in range(len(self.grps)):\\n\\t\\t\\tif len(self.grps[i]) > 1 and self.grps[i][0] == self.grps[i][1]:\\n\\t\\t\\t\\tnum_set_cards += len(self.grps[i])\\n\\t\\t\\t\\tpos = i\\n\\n\\t\\tif num_set_cards > 5:\\n\\t\\t\\tcard = self.grps[pos][-1]\\n\\t\\t\\tself.grps[pos].remove(card)\\n\\t\\t\\tlogger.info(f\\\"In computer.py/get_card: computer returned {card} to break too many set, computer = {self}\\\")\\n\\t\\t\\treturn card\\n\\n\\n\\t\\t# if # of sets is fine, then remove a card from the group with least size\\n\\t\\tcard = self.grps[-1][-1]\\n\\n\\t\\t\\n\\t\\tif len(self.grps[-1]) == 1:\\n\\t\\t\\tself.grps.remove(self.grps[-1])\\n\\t\\telse:\\n\\t\\t\\tself.grps[-1].remove(self.grps[-1][-1])\\n\\n\\t\\tlogger.info(f\\\"In computer.py/get_card: computer returned {card}, computer = {self}\\\")\\n\\n\\t\\treturn card\",\n \"def cards(\\n self,\\n cards: Union[List[Tuple[int, str, str]], List[Any]]\\n ) -> None:\\n self._cards: List[List[Tuple[int, str, str]]] = [cards]\",\n \"def deal_card():\\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\\n card = random.choice(cards)\\n return card\",\n \"def get_cards(self):\\n card = self._starting_card\\n return card\",\n \"def __init__(self, cards = []):\\n self.cards=cards\",\n \"def receive_card(self, card: Card) -> None:\\n\\t\\tself.deck.append(card)\\n\\t\\t\\n\\t\\t# Sorts the Deck by type and colour for aesthetic purposes\\n\\t\\t\\\"\\\"\\\"self.deck.sort(key=lambda x: repr(x.type))\\n\\t\\tself.deck.sort(key=lambda x: repr(x.colour))\\\"\\\"\\\"\",\n \"def choice():\\n list_cards = [0, 0, 0, 0, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9,\\n 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\\n list_cards= list_cards*4\\n play_1 = random.choice(list_cards)\\n list_cards_1 = list_cards\\n list_cards_1.remove(play_1)\\n dealer_1 = random.choice(list_cards_1)\\n list_cards_1.remove(dealer_1)\\n play_2 = random.choice(list_cards_1)\\n list_cards_1.remove(play_2)\\n dealer_2 = random.choice(list_cards_1)\\n list_cards_1.remove(dealer_2)\\n list1=[play_1,play_2]\\n list=[dealer_1,dealer_2]\\n return (list1,list,list_cards_1)\",\n \"def card_selSource(self, **kwargs):\\n if \\\"cardsrc\\\" in kwargs:\\n if kwargs[\\\"cardsrc\\\"] == \\\"hand\\\":\\n selectfrom = self.piles[Piles.HAND]\\n elif kwargs[\\\"cardsrc\\\"] == \\\"played\\\":\\n selectfrom = self.piles[Piles.PLAYED]\\n elif kwargs[\\\"cardsrc\\\"] == \\\"discard\\\":\\n selectfrom = self.piles[Piles.DISCARD]\\n else:\\n selectfrom = kwargs[\\\"cardsrc\\\"]\\n else:\\n selectfrom = self.piles[Piles.HAND]\\n return selectfrom\",\n \"def selectAmbassadorInfluence(self, choices, influenceRemaining):\\n # todo: raise notImplemented. should be overriden by the input class\\n \\n selected = []\\n for i in range(influenceRemaining):\\n card = random.choice(choices)\\n selected.append(card)\\n choices.remove(card)\\n \\n return selected\",\n \"def get_cards(query_param):\\n return _query_scryfall(query_param)\",\n \"def add_card(self, added_cards):\\n\\n self.hand[:0] = added_cards\",\n \"def deal_cards(self):\\n aux = random.randint(0, len(self.deck))\\n card = self.deck[aux]\\n self.deck.pop(aux)\\n print(f\\\"Received: {card}\\\")\\n return card\",\n \"def cards(self):\\n return self._cards\",\n \"def test_cards_get_list(self):\\n pass\",\n \"def step(self, action):\\n assert self.completed_rounds < self.num_rounds\\n\\n player = self.players[self.current_player_id]\\n card = action\\n\\n if card not in player.hand:\\n raise ValueError(\\\"Action not allowed because the card is not in the player's hand\\\")\\n\\n player.hand.remove(card)\\n player.played.add(card)\\n # print(f\\\"Player {self.current_player_id} with hand {[c.id for c in player.hand]} played the card {card.id}\\\")\\n best_combination_on_the_table = self._get_best_combination(card)\\n if best_combination_on_the_table:\\n self.last_player_capturing_id = self.current_player_id\\n player.captured.add(card)\\n for c in best_combination_on_the_table:\\n self.table.remove(c)\\n player.captured.add(c)\\n if not self.table and not (self._is_last_round and self._is_round_over()):\\n player.scope += 1\\n else:\\n self.table.add(card)\\n # print(f\\\"Cards on the table after play: {[c.id for c in self.table]}\\\")\\n\\n if self._is_round_over():\\n self.completed_rounds += 1\\n # print(f\\\"=========== Round {self.current_round} completed ============\\\")\\n self.current_player_id = (self.current_player_id + 1) % self.num_players\\n\\n if self.is_over():\\n last_player_capturing = self.players[self.last_player_capturing_id]\\n # print(f\\\"Giving the remaining cards to player {last_player_capturing.player_id}\\\")\\n for card in self.table:\\n last_player_capturing.captured.add(card)\\n self.table = set()\\n assert all([len(p.played) == 10 for p in self.players])\\n assert all([len(p.hand) == 0 for p in self.players])\\n return self.get_state(), self.current_player_id\",\n \"def action_hit(self) -> None:\\n print(self.deal_card(self.user))\",\n \"def check_selected_card(_player1, _player2):\\n if _player1.selected_card and _player2.selected_card:\\n color = _player1.selected_card.suit\\n if _player2.selected_card.suit != color and check_color_card(_player2, color):\\n _player2.selected_card = None\",\n \"def collect_cards():\\n \\n cards_list = []\\n while (cards_input := input(\\\"Enter card: \\\")) != '#':\\n i = cards_input.upper()\\n if not is_valid(i):\\n print(f\\\"Please enter a valid card.\\\")\\n continue\\n cards_list.append(i)\\n cards_decoded = [Board.translate(card) for card in cards_list]\\n return cards_decoded\",\n \"def Send_newCards(self, cards): \\n serialized = [c.serialize() for c in cards]\\n self.Send({\\\"action\\\": \\\"newCards\\\", \\\"cards\\\": serialized})\",\n \"def __init__(self):\\n self.cards = [Card(face=card[0], value=card[1], suit=suit)\\n for card in CARD_VALUES().items() for suit in CARD_SUITS()]\",\n \"def check_card_action(self, card):\\n if card.value == \\\"7\\\":\\n self.seven_punishment()\\n elif card.value == \\\"8\\\":\\n self.eight_punishment()\\n elif card.value == \\\"9\\\":\\n self.nine_punishment()\\n elif card.value == \\\"B\\\":\\n self.jack_wish()\",\n \"def hook_buy_this_card(self, game, player):\\n totrash = [c for c in player.piles[Piles.PLAYED] if c.isTreasure()]\\n for c in totrash:\\n player.output(f\\\"Mint trashing {c.name}\\\")\\n player.trash_card(c)\",\n \"def get_card_info_list(self):\\n self._get_card_info_list = pa_card_info_cb_t(self._card_info_cb)\\n pa_context_get_card_info_list(self._context,\\n self._get_card_info_list,\\n None)\",\n \"def hit(self, deck):\\n self.showOneCard = False\\n while self.getPoints() < 17:\\n self.cards.append(deck.deal())\",\n \"def __refresh_search_results(self):\\n\\n # Define the query\\n try:\\n query = CardQuery()\\n query.max_count = int(self.__box_count.get_text())\\n query.max_proficiency = self.__combo_proficiency.get_index()\\n query.max_score = float(self.__box_score.get_text())\\n if self.__combo_card_type.get_index() > 0:\\n query.card_type = getattr(WordType, self.__combo_card_type.get_text())\\n except:\\n cards = []\\n traceback.print_exc()\\n else:\\n # Query the cards\\n cards = []\\n for card in self.__cards_source.get_cards():\\n study_data = self.__study_database.get_card_study_data(card)\\n if query.matches(card, study_data):\\n cards.append((card, study_data))\\n\\n # Sort the list\\n sort_method = self.__combo_sort.get_text()\\n if sort_method == SortMethod.RANDOM:\\n random.shuffle(cards)\\n all_cards = list(cards)\\n cards = []\\n while all_cards and len(cards) < query.max_count:\\n index = random.randrange(len(all_cards))\\n elem = all_cards[index]\\n del all_cards[index] \\n cards.append(elem)\\n elif sort_method == SortMethod.LOWEST_SCORE:\\n cards.sort(key=lambda x: x[1].get_history_score())\\n elif sort_method == SortMethod.OLDEST:\\n cards.sort(key=lambda x: x[1].get_last_encounter_time() or x[1].get_history_score())\\n elif sort_method == SortMethod.NEWEST:\\n cards.sort(key=lambda x: x[1].get_last_encounter_time() or x[1].get_history_score(), reverse=True)\\n\\n cards = cards[:query.max_count]\\n\\n # Define the study params\\n params = StudyParams()\\n params.random_side = self.__combo_side.get_index() == 0\\n params.random_form = self.__checkbox_random_forms.is_checked()\\n params.shown_side = (CardSide.English if self.__combo_side.get_index() == 1\\n else CardSide.Russian)\\n self.__study_params = params\\n\\n # Define the scheduler params\\n self.__scheduler_params = SchedulerParams(\\n max_repetitions=1 if self.__checkbox_only_once.is_checked() else 0)\\n \\n # Popluate the table\\n self.__table_cards.clear()\\n for card, study_data in cards:\\n color = Config.proficiency_level_colors[study_data.get_proficiency_level()]\\n row = self.__table_cards.add(card, color=color)\\n cards = [card for card, _ in cards]\\n\\n self.__cards = cards\\n self.__button_begin.set_enabled(len(cards) > 0 and self.__study_params)\\n self.__label_result_count.set_text(\\\"{} Results\\\".format(len(self.__cards)))\",\n \"def hit(\\n self,\\n card: List[Tuple[int, str, str]],\\n card_index: int = 0\\n ) -> None:\\n self._cards[card_index].extend(card)\",\n \"def refresh(self):\\n self.deck = []\\n\\n for _suit in Suit:\\n for _face in Face:\\n self.insert(Card(_suit, _face, self))\",\n \"def _cards_getter(self):\\n pass\",\n \"def add_cards(self, cards):\\n self.get_cards().extend(cards)\",\n \"async def get_available_cards(self, game_id): # pass in a list of card ids\\n all_cards = await self.get_all_cards()\\n available_cards = []\\n game = await self.get_game(game_id)\\n player1_cards = await self.get_current_cards(game[1])\\n player2_cards = await self.get_current_cards(game[2])\\n for card in all_cards:\\n if card not in player1_cards and card not in player2_cards:\\n available_cards.append(card)\\n return available_cards\",\n \"def draw_card(self, card):\\n self.current_hand.append(card)\",\n \"def get_selected_card(self, pos, double_clicking=False):\\n if self.selectable:\\n double_select = False\\n relative_pos_x = pos[0] - self.x\\n relative_pos_y = pos[1] - self.y\\n mouse_pos = (relative_pos_x, relative_pos_y)\\n self.selected_card = -1\\n if not self.draw_from_last:\\n for i, card in enumerate(reversed(self.cards)):\\n if card.rect.collidepoint(mouse_pos):\\n self.selected_card = len(self.cards) - 1 - i\\n break\\n else:\\n for i, card in enumerate(self.cards):\\n if card.rect.collidepoint(mouse_pos):\\n self.selected_card = i\\n break\\n\\n if self.prev_selected[-1] == self.selected_card:\\n if not double_clicking:\\n self.selected_card = -1\\n\\n self.record_selected_history()\\n self.update_deck_display()\\n\\n selected_history = [sel for sel in self.prev_selected if sel >= 0]\\n\\n return (len(selected_history) == 2 and self.prev_selected.count(self.selected_card) == 2\\n and self.selected_card >= 0) and double_clicking\\n return False\",\n \"def deal_card():\\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\\n random_card = random.choice(cards)\\n return random_card\",\n \"def select_card(player, _mouse_x=None, _mouse_y=None):\\n if _mouse_x:\\n for card in player.cards:\\n lower_x = card.positionx\\n lower_y = card.positiony\\n if lower_x < _mouse_x < lower_x + 100 and lower_y < _mouse_y < lower_y + 100:\\n player.selected_card = card\",\n \"def receive_chance_card(self, card):\\r\\n self.chance_cards.append(card)\",\n \"def build_deck_screen_my_deck_card_display(screen,buttons, screen_status, button_status, card_database_filter, user):\\n # Draw the character card\\n if user.character_card == '':\\n pass\\n else:\\n user.character_card.rect.x = 65\\n user.character_card.rect.y = 600\\n screen.blit(user.character_card.image, user.character_card.rect)\\n #Clear duplicate amount each frame and render the refined list\\n for card_new in user.deck_list:\\n card_new.duplicate = 1\\n local_store_list = build_deck_screen_my_deck_card_list_refine(user)\\n #use refined list to draw\\n rect_position_x = 245 #local variables for rect position for the first card in the user deck\\n rect_position_y = 600\\n row_number = 1\\n #Display cards in local_store_list:\\n\\n if screen_status.build_deck_screen_my_deck_page_id <= 0:\\n screen_status.build_deck_screen_my_deck_page_id = 1\\n # Edge cases when len() = 6,12,18....\\n if len(local_store_list) % 6 == 0 and len(local_store_list) != 0:\\n if screen_status.build_deck_screen_my_deck_page_id >= (len(local_store_list))//6 + 1:\\n screen_status.build_deck_screen_my_deck_page_id = (len(local_store_list))//6 + 0\\n\\n else:\\n if screen_status.build_deck_screen_my_deck_page_id >= (len(local_store_list))//6 + 2:\\n screen_status.build_deck_screen_my_deck_page_id = (len(local_store_list))//6 + 1\\n # Algorithm to draw all cards in local_store_list, 6 card per page.\\n for card in local_store_list[6*(screen_status.build_deck_screen_my_deck_page_id - 1):6 * screen_status.build_deck_screen_my_deck_page_id]:\\n if row_number <= 6:\\n card.rect.x = rect_position_x\\n card.rect.y = rect_position_y\\n screen.blit(card.image, card.rect)\\n rect_position_x += 145\\n row_number += 1\\n build_deck_screen_my_deck_duplicate_number_display(card, screen)\\n if row_number >= 7:\\n row_number = 1\",\n \"def deal_cards(self):\\n self.card = random.randint(1, 13)\\n return self.card\",\n \"def deal_opening_cards(self) -> None:\\r\\n for i in range(self.num_of_players):\\r\\n self.dealer.deal_cards_to(self.players[i].cards_stack, PokerRules.CARDS_PER_PLAYER)\",\n \"def cards_to_deal(cls, context={}):\\n\\t\\traise NotImplementedError()\",\n \"def cards():\\n if user_loggined():\\n user = models.User.query.get(session['user_id'])\\n u_cards = user.cards.all()\\n prep_cards = []\\n for card in u_cards:\\n prep_cards.append(card.type + ' **** '+card.cnb[-9:])\\n else:\\n return redirect(url_for('index'))\\n return redirect(url_for('index'))\",\n \"def take(self, table):\\n # take card\\n self.hand.add(table.card)\\n table.card = table.cards.pop()\\n # take chips\\n self.chips += table.chips\\n table.chips = 0\",\n \"def receive_card(self, card, new_suite=None, is_using_chameleon=False):\\n assert isinstance(card, tuple)\\n assert len(card) == 2\\n\\n if card.value == 14 or card.value == self.chameleon and is_using_chameleon:\\n assert new_suite != None, print('You must specify a new suite.')\\n assert new_suite in ['hearts', 'spades', 'diamonds', 'clubs']\\n\\n self.current_suite = new_suite\\n self.current_value = None\\n\\n else:\\n self.cards.append(card)\\n self.current_suite = card.suite\\n self.current_value = card.value\",\n \"def card_output():\\n cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\\n return random.choice(cards)\",\n \"def initial_draw(self):\\n self.player.take_card(self.deck)\\n self.dealer.take_card(self.deck)\\n self.player.take_card(self.deck)\\n self.dealer.put_face_down(self.deck)\",\n \"def get_options(cls, player, context={}):\\n\\t\\toptions = []\\n\\t\\tfor card in player.hand:\\n\\t\\t\\tif cls.can_be_played(card, context):\\n\\t\\t\\t\\toptions.extend(card.actions)\\n\\t\\toptions.append(Action(None, \\\"DRAW\\\", [DrawCard]))\\n\\t\\treturn options\",\n \"def check_cards_eligibility(self):\\n for c in self.hand:\\n c.check_actions(self)\\n for c in self.phand:\\n c.check_actions(self)\\n for c in self.discard:\\n c.check_actions(self)\\n for c in self.active_player.phand:\\n c.check_actions(self)\\n for c in self.active_player.hand:\\n c.check_actions(self)\\n for c in self.active_player.discard:\\n c.check_actions(self)\\n for c in self.played_user_cards:\\n c.check_actions(self)\\n if ACTION_KEEP in self.actions:\\n for p in self.players:\\n for c in p.phand:\\n c.check_actions(self)\\n for c in p.hand:\\n c.check_actions(self)\\n for c in p.discard:\\n c.check_actions(self)\",\n \"def get_hand(self):\\n return self.cards\",\n \"def get_cards(self):\\n return deepcopy(self._cards)\",\n \"def lobby_screen_pick_deck_display(ai_settings, screen, buttons,screen_status, button_status, card_database_filter, user, action, player2):\\n # Pick deck text\\n button_text_1 = Button('Pick an exist deck or create a new one: ','', (250,250,250),400, 100, 400, 35, font_color = (0,0,0), alpha = 150)\\n button_text_1.update()\\n button_text_1.draw(screen)\\n\\n # Deck list buttons\\n with open('user_deck_list_string.txt','r') as f:\\n f.seek(0)\\n if len(f.readlines()) >= 12:\\n pass\\n else:\\n button_new_deck = Button('+ New Deck','', (250,250,250),1020, 110, 120, 35, font_color = (0,0,0), alpha = 150)\\n button_new_deck.update()\\n button_new_deck.draw(screen)\\n\\n\\n f.seek(0)\\n x = len(f.readlines())\\n y = 0\\n deck_list_index = 0\\n\\n for i in range(1,7):\\n f.seek(0)\\n for line in f:\\n if 'DECK_LIST_' + str(i) not in line:\\n y += 1\\n if y < x: # DECK_LIST_i exist\\n f.seek(0)\\n for line in f:\\n if 'DECK_LIST_' + str(i) in line:\\n deck_length = len(make_card_list_from_string(line.replace('DECK_LIST_' + str(i) + ' = ', ''), ai_settings, screen, buttons,screen_status, button_status, card_database_filter, user, player2))\\n # deck_length = int((len(line.replace('DECK_LIST_' + str(i) + ' = ', '')) -1)/14)\\n if 'CHARACTER_' + str(i) in line:\\n character_length = 1\\n character_card = eval('card_' + line.replace('CHARACTER_' + str(i) + ' = ', '')[7:12])\\n\\n if user.deck_list_index == str(i):\\n\\n button_top = Button(character_card.name + ': ','', (100,30,130),85 + 180* (i-1), 165, 130, 60)\\n button_top.update()\\n button_top.draw(screen)\\n\\n if deck_length < 40:\\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (100,30,130),85 + 180* (i-1), 225, 130, 50, font_color = (250,0,0))\\n button_bottom.update()\\n button_bottom.draw(screen)\\n else:\\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (100,30,130),85 + 180* (i-1), 225, 130, 50)\\n button_bottom.update()\\n button_bottom.draw(screen)\\n\\n else:\\n\\n button_top = Button(character_card.name + ': ','', (160,160,160),85 + 180* (i-1), 165, 130, 60, alpha = 240)\\n button_top.update()\\n button_top.draw(screen)\\n\\n if deck_length < 40:\\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (160,160,160),85 + 180* (i-1), 225, 130, 50, font_color = (200,0,0), alpha = 240)\\n button_bottom.update()\\n button_bottom.draw(screen)\\n else:\\n button_bottom = Button(str(character_length) + '/1 | ' + str(deck_length) +'/40','', (160,160,160),85 + 180* (i-1), 225, 130, 50, alpha = 240)\\n button_bottom.update()\\n button_bottom.draw(screen)\\n\\n y = 0\\n\\n else: # DECK_LIST_i not exist\\n\\n button = Button('Empty','', (200,200,200),85 + 180* (i-1), 165, 130, 110, alpha = 80)\\n button.update()\\n button.draw(screen)\\n\\n y = 0\\n\\n\\n for i in range(1,7):\\n if user.deck_list_index == str(i):\\n button_edit = Button('Edit','', (50,50,170),85 + 180* (i-1), 282, 60, 30)\\n button_edit.update()\\n button_edit.draw(screen)\\n\\n button_delete = Button('Delete','', (160,30,30), 155 + 180* (i-1), 282, 60, 30)\\n button_delete.update()\\n button_delete.draw(screen)\",\n \"def hit(hand=bj.player1.hand):\\r\\n hand.append(bj.deck.remove_card())\",\n \"def test_play_card(self):\\n self.plr.piles[Piles.DECK].set(\\\"Silver\\\", \\\"Province\\\", \\\"Moat\\\", \\\"Gold\\\")\\n self.vic.piles[Piles.DECK].set(\\\"Duchy\\\")\\n self.plr.test_input = [\\\"discard\\\", \\\"discard\\\", \\\"putback\\\"]\\n self.plr.play_card(self.card)\\n self.g.print_state()\\n self.assertEqual(self.plr.actions.get(), 1)\\n self.assertIn(\\\"Duchy\\\", self.vic.piles[Piles.DISCARD])\\n self.assertIn(\\\"Gold\\\", self.plr.piles[Piles.DISCARD])\\n self.assertIn(\\\"Province\\\", self.plr.piles[Piles.HAND])\\n self.assertIn(\\\"Moat\\\", self.plr.piles[Piles.HAND])\\n self.assertIn(\\\"Silver\\\", self.plr.piles[Piles.DECK])\",\n \"def hit(self, deck):\\n try:\\n self.hand.append(deck.pop(0))\\n except IndexError:\\n print('There are no more cards in the deck!')\",\n \"def get_cards(self):\\n return [card.view_model() for card in self._deck.loc]\",\n \"def deal(self, cards_num):\\n\\n cards = []\\n while cards_num > 0:\\n\\n x = random.randint(0, 53)\\n if self.in_use[x] == 0:\\n self.in_use[x] += 1\\n cards.append(x)\\n cards_num -= 1\\n\\n return cards\",\n \"def player_card_one_choice(update, context):\\n query = update.callback_query\\n if query.message.reply_to_message:\\n user = query.message.reply_to_message.from_user\\n else:\\n user = query.message.chat\\n bot = context.bot\\n CURRENT_CONTEXT = USERS[user.username]\\n message = f'Round: {CURRENT_CONTEXT[\\\"round\\\"]} ({CURRENT_CONTEXT[\\\"username\\\"]}) \\\\nDealers Card: {CURRENT_CONTEXT[\\\"dealer_card\\\"]}\\\\nYour Cards: {CURRENT_CONTEXT[\\\"player_cards\\\"]} \\\\nYour total: {CURRENT_CONTEXT[\\\"player_total\\\"]} \\\\n\\\\nChoose Your 1st Card: '\\n bot.edit_message_text(\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n text=message,\\n reply_markup=card_markup\\n )\\n\\n return PLAYER_CARD_TWO\",\n \"def main():\\n\\n # call to OS for positioning window\\n os.environ['SDL_VIDEO_WINDOW_POS'] = \\\"%d,%d\\\" % (0, 25)\\n\\n # Initialization block\\n pygame.init() # Initialize pygame module\\n screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) # initialize screen\\n\\n # Testing\\n # model_card = m_card.Card(m_card.CardType.TEMPURA)\\n # view_card = v_card.CardView(screen, model_card)\\n\\n deck = Deck()\\n player = Player()\\n b_pack = deck.generate_booster(10)\\n player.booster_pack = b_pack\\n\\n hand_view = HandView(screen, (0, SCREEN_HEIGHT - SCREEN_HEIGHT / 5), (SCREEN_WIDTH, SCREEN_HEIGHT / 5), player)\\n pick_crds = PickedCardsView(screen, (0, 0), (SCREEN_WIDTH, SCREEN_HEIGHT / 5), player, 0)\\n pick_crds2 = PickedCardsView(screen, (0, 0), (SCREEN_WIDTH, SCREEN_HEIGHT / 5), player, 180)\\n # Game loop\\n while True:\\n\\n for event in pygame.event.get():\\n if event.type == pygame.QUIT:\\n sys.exit()\\n\\n elif event.type == pygame.MOUSEBUTTONUP:\\n is_clicked([hand_view, pick_crds, pick_crds2], pygame.mouse.get_pos())\\n screen.fill((0, 0, 0))\\n hand_view.draw()\\n pick_crds.draw()\\n pick_crds2.draw()\\n pygame.display.flip()\",\n \"def battle_screen_my_hand_card_display(screen,buttons, screen_status, button_status, card_database_filter, user):\\n rect_position_x = 100\\n rect_position_y = 610\\n row_number = 1\\n if screen_status.battle_screen_action_indicator == 'stage-0':\\n pass\\n else :\\n\\n if screen_status.battle_screen_my_hand_page_id <= 0:\\n screen_status.battle_screen_my_hand_page_id = 1\\n # Edge cases when len() = 6,12,18....\\n if len(user.hand_list) % 7 == 0 and len(user.hand_list) != 0:\\n if screen_status.battle_screen_my_hand_page_id >= (len(user.hand_list))//7 + 1:\\n screen_status.battle_screen_my_hand_page_id = (len(user.hand_list))//7 + 0\\n\\n else:\\n if screen_status.battle_screen_my_hand_page_id >= (len(user.hand_list))//7 + 2:\\n screen_status.battle_screen_my_hand_page_id = (len(user.hand_list))//7 + 1\\n # Algorithm to draw all cards in local_store_list, 6 card per page.\\n for card in user.hand_list[7*(screen_status.battle_screen_my_hand_page_id - 1):7 * screen_status.battle_screen_my_hand_page_id]:\\n if row_number <= 7:\\n card.rect.x = rect_position_x\\n card.rect.y = rect_position_y\\n screen.blit(card.image, card.rect)\\n rect_position_x += 145\\n row_number += 1\\n if row_number >= 8:\\n row_number = 1\",\n \"def dealCards(deck, player, numCards):\\n print \\\"dealing %s cards to %s...\\\" % (numCards, player.name)\\n for card in range(numCards):\\n card = deck[0]\\n deck.pop(0)\\n player.cards.append(card)\\n print \\\"added %s card for %s\\\" % (card, player.name)\\n print player.cards\",\n \"def setup_cards(self, server):\\r\\n\\t\\tversions_list = self.ice.getVersionsList()\\r\\n\\t\\talarm_list = self.ice.getAlarmStatus()\\r\\n\\t\\tstatus_list = self.ice.getStatus()\\r\\n\\t\\twarning_list = self.ice.getWarnings()\\r\\n\\r\\n\\t\\tdateTimeObj = datetime.now()\\r\\n\\t\\ttimestampStr = dateTimeObj.strftime(\\\"%d-%b-%Y (%H:%M:%S)\\\")\\r\\n\\t\\tcards = self.ice.getCardsAlive()\\r\\n\\t\\tfor i in range(len(versions_list)):\\r\\n\\t\\t\\tjson_body = versions_list[i]\\r\\n\\t\\t\\tjson_body.update({'alarm':alarm_list[i],'status':status_list[i], 'card':cards[i], 'warning':warning_list[i],'update':timestampStr, 'hostname':self.ip})\\r\\n\\t\\t\\tserver.index(index='icepap_info', id=self.ip + '_' + str(cards[i]), body=json_body)\",\n \"def getAllCards(self):\\n return self._cards\",\n \"def build_deck_screen_card_gallery_card_display(screen, buttons, screen_status, button_status, card_database_filter):\\n rect_position_x = 100 #local variables for rect position for the first card in the card gallery\\n rect_position_y = 130\\n row_number = 1 # local variable to help keep track of position of card\\n # Check the page number to make sure if will not go negative or randomly large\\n if screen_status.build_deck_screen_card_gallery_page_id <= 0:\\n screen_status.build_deck_screen_card_gallery_page_id = 1\\n # Edge cases when len() = 14, 28, 42...\\n if len(cdf.request_card_list(card_database_filter)) % 14 == 0 and len(cdf.request_card_list(card_database_filter)) != 0:\\n if screen_status.build_deck_screen_card_gallery_page_id >= (len(cdf.request_card_list(card_database_filter)))//14 + 1:\\n screen_status.build_deck_screen_card_gallery_page_id = (len(cdf.request_card_list(card_database_filter)))//14 + 0\\n\\n else:\\n if screen_status.build_deck_screen_card_gallery_page_id >= (len(cdf.request_card_list(card_database_filter)))//14 + 2:\\n screen_status.build_deck_screen_card_gallery_page_id = (len(cdf.request_card_list(card_database_filter)))//14 + 1\\n # Algorithm to draw all cards in request_card_list, 14 card per page.\\n for card in cdf.request_card_list(card_database_filter)[14*(screen_status.build_deck_screen_card_gallery_page_id - 1):14 * screen_status.build_deck_screen_card_gallery_page_id]:\\n if row_number <= 7:\\n card.rect.x = rect_position_x\\n card.rect.y = rect_position_y\\n screen.blit(card.image, card.rect)\\n rect_position_x += 145\\n row_number += 1\\n elif row_number <= 14:\\n card.rect.x = rect_position_x - 1015\\n card.rect.y = rect_position_y + 200\\n screen.blit(card.image, card.rect)\\n rect_position_x += 145\\n row_number += 1\\n if row_number >= 15:\\n row_number = 1\",\n \"def setup_newgame(self):\\n global chips\\n self.bet = 100\\n if chips < self.bet: \\n self.game_over = True\\n chips -= self.bet\\n \\n\\n self.cards_list = arcade.SpriteList()\\n\\n #resets on newgame\\n self.top_card_int = 0 ## this had to be moved here to make it so that you are not drawing over the 52 card limit\\n self.player_hand = []\\n self.dealer_hand = []\\n self.player_value = 0\\n self.dealer_value = 0\\n self.player_ace_count = 0\\n self.dealer_ace_count = 0\\n self.player_almost_bust = 0\\n self.dealer_almost_bust = 0\\n self.blackjack = False\\n self.victory = False\\n self.defeat = False\\n \\n #creates deck\\n for card_suit in CARD_SUITS:\\n for card_value in CARD_VALUES:\\n card = Card(card_suit, card_value, CARD_SCALE)\\n self.cards_list.append(card)\\n #shuffles deck\\n for pos1 in range(len(self.cards_list)):\\n pos2 = random.randrange(len(self.cards_list))\\n self.cards_list.swap(pos1, pos2)\\n \\n #Current way to add cards to player and dealer hands since using .pop() on self.cards_list deletes the card itself even in the other hands\\n \\n #self.dealer_hand.append(self.top_card_int)\\n self.hit(\\\"dealer\\\")\\n self.dealer_hand[0].face_down()\\n #first_card = self.dealer_hand[0]\\n #first_card.face_down()\\n #self.dealer_hand[0].face_down()\\n self.hit(\\\"player\\\")\\n self.player_hand[0].face_down()\\n self.hit(\\\"dealer\\\")\\n self.dealer_hand[1].face_down()\\n self.hit(\\\"player\\\")\\n self.player_hand[1].face_down()\\n self.update_card_positions()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6779038","0.64504594","0.6393454","0.6248788","0.6065166","0.60555077","0.60298246","0.6014514","0.6014514","0.5978343","0.5904059","0.5888115","0.5883593","0.5855397","0.58428776","0.584109","0.5830821","0.582095","0.5810005","0.5807255","0.5788884","0.57709986","0.574667","0.5738221","0.5737967","0.57058537","0.5680407","0.56735003","0.5642896","0.56291485","0.562021","0.5614011","0.5595327","0.5593024","0.556809","0.55641556","0.55591893","0.5558305","0.5548686","0.55382204","0.5533083","0.55326396","0.5528881","0.5528702","0.55202246","0.5513971","0.55041254","0.5503086","0.55018395","0.5488135","0.54830146","0.54814434","0.54784435","0.5475379","0.5463058","0.544991","0.54472756","0.544597","0.54333675","0.5411004","0.54085934","0.5400689","0.54006267","0.53977484","0.5396665","0.539553","0.5387312","0.53800654","0.5378856","0.537282","0.53693086","0.53602904","0.53573686","0.5354459","0.5354429","0.5347059","0.53427076","0.5342567","0.53405184","0.53245234","0.53223515","0.5321809","0.5314615","0.5301498","0.52926624","0.52908504","0.5289689","0.5285544","0.528017","0.52695525","0.526711","0.5264777","0.52642614","0.52633953","0.52546424","0.52470315","0.5243854","0.52418876","0.52303237","0.52292776"],"string":"[\n \"0.6779038\",\n \"0.64504594\",\n \"0.6393454\",\n \"0.6248788\",\n \"0.6065166\",\n \"0.60555077\",\n \"0.60298246\",\n \"0.6014514\",\n \"0.6014514\",\n \"0.5978343\",\n \"0.5904059\",\n \"0.5888115\",\n \"0.5883593\",\n \"0.5855397\",\n \"0.58428776\",\n \"0.584109\",\n \"0.5830821\",\n \"0.582095\",\n \"0.5810005\",\n \"0.5807255\",\n \"0.5788884\",\n \"0.57709986\",\n \"0.574667\",\n \"0.5738221\",\n \"0.5737967\",\n \"0.57058537\",\n \"0.5680407\",\n \"0.56735003\",\n \"0.5642896\",\n \"0.56291485\",\n \"0.562021\",\n \"0.5614011\",\n \"0.5595327\",\n \"0.5593024\",\n \"0.556809\",\n \"0.55641556\",\n \"0.55591893\",\n \"0.5558305\",\n \"0.5548686\",\n \"0.55382204\",\n \"0.5533083\",\n \"0.55326396\",\n \"0.5528881\",\n \"0.5528702\",\n \"0.55202246\",\n \"0.5513971\",\n \"0.55041254\",\n \"0.5503086\",\n \"0.55018395\",\n \"0.5488135\",\n \"0.54830146\",\n \"0.54814434\",\n \"0.54784435\",\n \"0.5475379\",\n \"0.5463058\",\n \"0.544991\",\n \"0.54472756\",\n \"0.544597\",\n \"0.54333675\",\n \"0.5411004\",\n \"0.54085934\",\n \"0.5400689\",\n \"0.54006267\",\n \"0.53977484\",\n \"0.5396665\",\n \"0.539553\",\n \"0.5387312\",\n \"0.53800654\",\n \"0.5378856\",\n \"0.537282\",\n \"0.53693086\",\n \"0.53602904\",\n \"0.53573686\",\n \"0.5354459\",\n \"0.5354429\",\n \"0.5347059\",\n \"0.53427076\",\n \"0.5342567\",\n \"0.53405184\",\n \"0.53245234\",\n \"0.53223515\",\n \"0.5321809\",\n \"0.5314615\",\n \"0.5301498\",\n \"0.52926624\",\n \"0.52908504\",\n \"0.5289689\",\n \"0.5285544\",\n \"0.528017\",\n \"0.52695525\",\n \"0.526711\",\n \"0.5264777\",\n \"0.52642614\",\n \"0.52633953\",\n \"0.52546424\",\n \"0.52470315\",\n \"0.5243854\",\n \"0.52418876\",\n \"0.52303237\",\n \"0.52292776\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5896568"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":84,"cells":{"query":{"kind":"string","value":"Flattened array of ints, specifying the index of this object. This has to account for shaped parameters!"},"document":{"kind":"string","value":"def _raveled_index(self):\n return np.r_[:self.size]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _idxs_postformat_array(self):\n self.idxs = np.array(self.idxs)","def flatten_idx(idx, axis=-1):\n idx = numpy.asanyarray(idx)\n if not idx.dtype.kind in ('i', 'u'):\n idx = idx.astype(int)\n preshape = idx.shape[:axis]\n postshape = idx.shape[axis:]\n stride = int(numpy.product(postshape[1:])) #1 if applied to empty\n #The index on this axis moves stride elements in flat\n outidx = idx.flatten() * stride #makes a copy\n #First add the offsets to get us to [..., idx @ axis = 0, 0...)\n outidx += numpy.repeat(\n numpy.arange(0, len(outidx), int(numpy.product(postshape)),\n dtype=idx.dtype),\n numpy.product(postshape))\n #Now offsets for non-zero on the trailing axes [0, 0, ... 0@axis, ...]\n outidx += numpy.tile(numpy.arange(0, stride, dtype=idx.dtype),\n int(numpy.product(preshape)) * idx.shape[axis])\n return outidx","def flatten(self):\n xv, yv = np.meshgrid(self.columns, self.index, indexing='xy')\n return np.array([xv.ravel(), yv.ravel(), self.values.ravel()])","def _to_flat_index(self, idx_in):\n idx_in = tuple([np.array(z, ndmin=1, copy=False) for z in idx_in])\n msk = np.all(np.stack([t < n for t, n in zip(idx_in, self.shape)]), axis=0)\n idx = np.ravel_multi_index(\n tuple([t[msk] for t in idx_in]), self.shape, mode=\"wrap\"\n )\n\n return idx, msk","def getIntArray2D(self) -> typing.List[typing.List[int]]:\n ...","def indices(self):\n return tuple([slice(*r) for r in self.location])","def flatten(self):\n\n if self.ndim == 1:\n return self.copy()\n\n return ArrayCoordinates1d(self.coordinates.flatten(), **self.properties)","def getShortArray2D(self) -> typing.List[typing.List[int]]:\n ...","def master_ndindex(self): # itermaster_indices(self):\n return itertools_product(\n *[range(*r) for r in self.location]\n ) # TODO check","def atom_idxs(self):\n\n return np.array([atom.atom_idxs for atom in self])","def matrix_to_flat(self, idx_rows):\n idx = []\n for i in range(self.nts):\n idx.append(self._matrix_to_flat_by_ts(idx_rows, i))\n return idx","def flatten(x):\n return x.view(x.size(0), -1)","def flatten(self):\n return [e for es in self.array for e in es]","def batch_flatten(this,x):\n shape = x.get_shape().as_list()[1:]\n if None not in shape:\n return tf.reshape(x, [-1, int(np.prod(shape))])\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))","def batch_flatten(this,x):\n shape = x.get_shape().as_list()[1:]\n if None not in shape:\n return tf.reshape(x, [-1, int(np.prod(shape))])\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))","def FlattenModelData(y, i):\n outs = np.array([y[j][i][0] for j in range(len(y))])\n return outs","def to_flat_index(self) -> Index: # type: ignore[override]\n return Index(self._values, tupleize_cols=False)","def _flatten(self):\n n = self.B\n idx = self.nodect - 1\n self.seq = []\n while n is not None:\n n['idx'] = idx\n self.seq.insert(0, n)\n idx -= 1\n n = n['pred']","def flatten(self, arr):\n shape = arr.shape\n return arr.reshape(shape[0] * shape[1], *shape[2:])","def GetPointToIncidentEdgesArray(self, p_int):\n ...","def reconstruct_input(self, ix):","def flatten(self) -> np.ndarray:\n\n return self.data.copy()","def flatten(a, start=0, count=2):\n s = a.shape\n return np.reshape(a, s[:start] + (-1,) + s[start+count:])","def flatten_npar(np_array):\n \n itr = len(np_array)\n start = np_array[0]\n \n for i in range(1,itr):\n start = np.hstack((start,np_array[i]))\n \n return(np.array(start))","def array_form(self):\n return tuple(self)","def flatten(self):\n pass","def do_flatten(obj):\n if type(obj) == list:\n return np.array(obj).flatten()\n return obj.flatten()","def flatten(x):\n return reshape(x, (x.shape[0], -1))","def flatten_stimulus(stimulus):\n n, h, w = stimulus.shape\n return stimulus.reshape((n, h * w))","def get(self, idx_in):\n shape_out = idx_in[0].shape\n idx_flat_in, msk_in = self._to_flat_index(idx_in)\n idx, msk = find_in_array(idx_flat_in, self.idx)\n val_out = np.full(shape_out, self._fill_value)\n val_out.flat[np.flatnonzero(msk_in)[msk]] = self._data[idx[msk]]\n return np.squeeze(val_out)","def unstacked_index(size, index):\n return index % size, index // size","def ravel_indices(shape, *args):\n new_positions = []\n for arg in args:\n new_positions.append(np.ravel_multi_index(arg, shape))\n return new_positions","def row(self):\n return self.reshape((1, self.size))","def GetPointToIncidentFacesArray(self, p_int):\n ...","def flatten():","def GetPointToOneRingPointsArray(self, p_int):\n ...","def batch_indices(self):\n b = self.batch_size\n return [np.arange(i*b, i*b+b) for i in range(self.num_batches)]","def _flatten(self, inputT, size):\n return tf.reshape(inputT, (-1, size))","def _indarray(np_array):\n return skil_client.INDArray(\n ordering='c',\n shape=list(np_array.shape),\n data=np_array.reshape(-1).tolist()\n )","def flatten_array(X_input):\r\n X_input_flat = np.array([x.flatten() for x in X_input])\r\n return X_input_flat","def index_object(idxs=None):","def serialize_flatten(name, value, array_indices=True):\r\n # call the recursive function that returns a tuple of tuples\r\n return tuple(serialize_flatten_rec(name, value, array_indices))","def row(self, index: int) -> List[int]:\n return self.matrix[index - 1]","def _multi_index(indexes, shape):\n indexes = indexes if isinstance(indexes, typing.Sequence) else (indexes,)\n if any(isinstance(i, type(Ellipsis)) for i in indexes):\n raise IndexError('Ellipsis index currently is not supported.')\n # Fill the right-most elements.\n indexes = indexes + (slice(0, None, None),) * (len(shape) - len(indexes))\n # Convert to positive index.\n positive_indexes = []\n for i, index in enumerate(indexes):\n if isinstance(index, slice):\n index = slice(\n index.start or 0, index.stop or shape[i], index.step or 1\n )\n positive_indexes.append(\n slice(\n index.start + shape[i] if index.start < 0 else index.start,\n index.stop + shape[i] if index.stop < 0 else index.stop,\n # Negative step means index backward, no need to convert to\n # positive interger.\n index.step,\n )\n )\n elif isinstance(index, int):\n positive_indexes.append(index + shape[i] if index < 0 else index)\n else:\n raise TypeError(f'Not supported index type {index}.')\n return tuple(positive_indexes)","def indices(self):\n slice_list = []\n for axis in range(self.ndim):\n if axis in self.displayed:\n slice_list.append(slice(None))\n else:\n if self.clip:\n p = np.clip(\n self.point[axis],\n np.round(self.range[axis][0]),\n np.round(self.range[axis][1]) - 1,\n )\n else:\n p = self.point[axis]\n p = np.round(p / self.range[axis][2]).astype(int)\n slice_list.append(p)\n return tuple(slice_list)","def GetEdgeArray(self, p_int):\n ...","def wrap(self, flatten_x):\n batch_size = flatten_x.size(0)\n x = torch.reshape(flatten_x, (batch_size, self.num_frames, self.num_ticks_per_frame, -1))\n return x","def one_dim_index(self, i, j):\n return int(i + j * self.nx)","def flatten(self):\n return DataArray([s for s in self.unstructured()])","def unflatten(self, flat, unused_shaped_like):\n return next(flat)","def getLongArray2D(self) -> typing.List[typing.List[int]]:\n ...","def get(self):\n return _n.reshape(self.next_x, self.original_shape)","def indXtoJ(indX):\n return np.unravel_index(indX % xx.size, xx.shape)","def _shaped_arange(*shape):\n return np.random.randn(np.prod(shape)).astype(np.float32).reshape(\n *shape\n ) * np.prod(shape)","def _flatten_parameters(self):\n [m.flatten_parameters() for m in self._to_flatten]","def flat(self):\n return Op('flat', self)","def getBinIndices(self, linear_index):\n return linear_index / self.magic_array % self.nbins_across_dims","def flatten(X):\n N = X.shape[-1]\n flat = np.zeros((N, 3072))\n for idx, i in enumerate(range(N)):\n # if not idx:\n # print(X[:,:,:,i].reshape(3072))\n flat[i] = X[:,:,:,i].reshape(3072)\n return flat","def _get_args(idx, *args):\n new_args = []\n for arg in list(args[0]):\n if isinstance(arg, Iterable):\n new_args.append(arg[idx])\n else:\n new_args.append(arg)\n\n return new_args","def BatchCreator(self, j, n_batch):\n j_start = (j-1)*n_batch + 1\n j_end = j*n_batch + 1\n ind = np.arange(start= j_start, stop=j_end, step=1)\n return ind","def batched_index_select(input, dim, index):\n views = [input.shape[0]] + [1 if i != dim else -1 for i in range(1, len(input.shape))]\n expanse = list(input.shape)\n expanse[0] = -1\n expanse[dim] = -1\n index = index.view(views).expand(expanse)\n return torch.gather(input, dim, index)","def __getitem__(self, index):\n if index == Ellipsis:\n index = tuple(self.dim*[slice(None)])\n\n if len(index) < self.dim:\n # --- Add extra dims to index if needed\n index = list(index)\n for i in range(len(index), self.dim):\n index.append(slice(None))\n index = tuple(index)\n\n if self.dim == 2:\n return self._getitem2d(index)\n elif self.dim == 3:\n return self._getitem3d(index)","def batch_flatten(x):\n shape = x.get_shape().as_list()[1:]\n if None not in shape:\n return tf.reshape(x, [-1, int(np.prod(shape))])\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))","def batch_flatten(x):\n shape = x.get_shape().as_list()[1:]\n if None not in shape:\n return tf.reshape(x, [-1, int(np.prod(shape))])\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))","def transform(self, x):\n res = [x[i] for i in range(len(x))\n if i not in self.index_value_pairs]\n return res if isinstance(x, list) else np.asarray(res)","def _extend_index_dim(input_index, new_index, new_index_max):\n # Construct an iterator from new_index\n if isinstance(new_index, (int, np.integer)):\n it = [new_index]\n else:\n if isinstance(new_index, slice):\n # slices don't work very well with multi-dimensional circular mappings.\n it = _conv_slice_to_list(slice_obj=new_index, stop_def=new_index_max)\n else:\n it = new_index\n # Index extension\n if input_index is None:\n output = []\n for i in it:\n output.append(tuple([i]))\n return output\n else:\n output = []\n for _i in input_index:\n output_row = []\n for i in it:\n output_row.append(tuple(list(_i) + [i]))\n output.append(output_row)\n return output","def _flatten(params):\n params, _ = tree_flatten(params)\n return jnp.concatenate([jnp.reshape(param, [-1]) for param in params])","def flatten(self):\n return tuple(map(lambda i: 1 if i > 0 else 0, self.freq()))","def make_indices(dimensions):\n\n level = len(dimensions)\n\n if level == 1:\n return range(dimensions[0])\n\n indices = [[]]\n\n while level:\n\n _indices = []\n\n for j in range(dimensions[level - 1]):\n\n _indices += [[j] + i for i in indices]\n\n indices = _indices\n\n level -= 1\n\n try:\n return [tuple(i) for i in indices]\n except TypeError:\n return indices","def GetFaceToAdjacentFacesArray(self, p_int):\n ...","def sub2ind(self, ix, iy):\n idx = np.ravel_multi_index((ix, iy), self.shape)\n return idx","def flatten_numpy(ndarray):\n return np.reshape(ndarray, (-1,), 'F')","def loc_data_idx(loc_idx):\n retval = []\n for i in as_tuple(loc_idx):\n if isinstance(i, slice) and i.step is not None and i.step == -1:\n if i.stop is None:\n retval.append(slice(0, i.start+1, -i.step))\n else:\n retval.append(slice(i.stop+1, i.start+1, -i.step))\n elif isinstance(i, slice) and i.step is not None and i.step < -1:\n if i.stop is None:\n lmin = i.start\n while lmin >= 0:\n lmin += i.step\n retval.append(slice(lmin-i.step, i.start+1, -i.step))\n else:\n retval.append(slice(i.stop+1, i.start+1, -i.step))\n elif is_integer(i):\n retval.append(slice(i, i+1, 1))\n else:\n retval.append(i)\n return as_tuple(retval)","def __getitem__(self, index: list) -> (np.array, np.array):\n # Generate indexes of the batch\n indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\n\n # Find list of IDs\n list_IDs_temp = [self.list_IDs[k] for k in indexes]\n\n # Generate data\n X, M = self.__data_generation(list_IDs_temp)\n\n return X, M","def get_x(self):\n return self.x[:self.nump, :]","def a(a,N): \n a=np.ravel(a, order='F') # Same order\n return a","def a(a,N): \n a=np.ravel(a, order='F') # Same order\n return a","def flatten_array(self):\n numel = self.xyz_array[:, :, 0].size # Number of elements in dataset\n self.flat_array = np.zeros([self._len_z, numel]) # Create array to hold flattened array\n\n # Loop through each dimension (dataset) and flatten it into new array\n for dim in range(self._len_z):\n self.flat_array[dim, :] = np.ravel(self.xyz_array[:, :, dim])","def flatten_parameters(self):","def get_scatter_indices_for_bboxes(flatted_indices, batch_size, total_bboxes):\n indices_size = len(flatted_indices)\n scatter_indices = tf.concat(flatted_indices, 1)\n return tf.reshape(scatter_indices, (batch_size, total_bboxes, indices_size))","def encode_to_flat_array_index(row, column, matrix):\n return row * matrix.cols + column","def mainIndices(self):\n return self.i1, self.i2","def flatten(self, x):\n return np.concatenate([c.flatten(xi) for c, xi in zip(self.spaces, x)])","def sliceshape(slicetuple, totshape):\n res = []\n for i,s in enumerate(slicetuple):\n if isinstance(s,int):\n #n = 1\n pass\n else:\n i0,i1,istep = s.indices(totshape[i])\n n = (i1-i0)//istep\n res.append(n)\n return res","def gather(x, idx, method=2):\n\n if method == 0:\n return x[idx]\n elif method == 1:\n x = x.unsqueeze(1)\n x = x.expand((-1, idx.shape[-1], -1))\n idx = idx.unsqueeze(2)\n idx = idx.expand((-1, -1, x.shape[-1]))\n return x.gather(0, idx)\n elif method == 2:\n for i, ni in enumerate(idx.size()[1:]):\n x = x.unsqueeze(i+1)\n new_s = list(x.size())\n new_s[i+1] = ni\n x = x.expand(new_s)\n n = len(idx.size())\n for i, di in enumerate(x.size()[n:]):\n idx = idx.unsqueeze(i+n)\n new_s = list(idx.size())\n new_s[i+n] = di\n idx = idx.expand(new_s)\n return x.gather(0, idx)\n else:\n raise ValueError('Unkown method')","def _asarray1d(arr, copy=False):\n if copy:\n return asarray(arr).flatten()\n else:\n return asarray(arr).ravel()","def _normalize_index(self, index: int):\n if index < 0:\n return len(self) + index\n else:\n return index","def _simplify_index(indices, shape):\n # First clean up and check indices, unpacking ellipsis and boolean arrays\n indices = da.slicing.normalize_index(indices, shape)\n out = []\n axis = 0\n for index in indices:\n if index is not np.newaxis:\n length = shape[axis]\n axis += 1\n # If there is 1-D fancy index on this axis, try to convert to slice\n if isinstance(index, np.ndarray) and index.ndim == 1:\n try:\n index = _range_to_slice(index)\n except ValueError:\n pass\n else:\n index = da.slicing.normalize_slice(index, length)\n out.append(index)\n return tuple(out)","def advanced_indexing_op(input, index):\n batch_size = tf.shape(input)[0]\n max_length = int(input.get_shape()[1])\n dim_size = int(input.get_shape()[2])\n index = tf.range(0, batch_size) * max_length + (index - 1)\n flat = tf.reshape(input, [-1, dim_size])\n relevant = tf.gather(flat, index)\n return relevant","def recursive_index_decode(int_array, max=32767, min=-32768):\n out_arr = []\n decoded_val = 0\n for item in int_array.tolist():\n if item==max or item==min:\n decoded_val += item\n else:\n decoded_val += item\n out_arr.append(decoded_val)\n decoded_val = 0\n return numpy.asarray(out_arr,dtype=numpy.int32)","def ordered_indices(self):\r\n return np.arange(len(self), dtype=np.int64)","def _create_flatten(cls, onnx_node, inputs, opset_version):\n factor = onnx_node.getattr('axis', 1)\n if factor < 0:\n # in order to support the negative axis\n factor = len(inputs[0].shape) + factor\n\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\n opset_version)\n return None, forward(axis=factor)","def flattened_indices_from_row_col_indices(row_indices, col_indices, num_cols):\n return (row_indices * num_cols) + col_indices","def state_from_id(index, dims_state_grid):\n\n entries = [index] * len(dims_state_grid)\n for i in range(1, len(dims_state_grid)):\n value = 1\n for j in range(i, len(dims_state_grid)):\n value *= dims_state_grid[j]\n for k in range(i - 1, len(dims_state_grid)):\n if k == i - 1:\n entries[k] //= value\n else:\n entries[k] %= value\n\n out = np.array(object=entries)\n\n return out","def index2d(src, idx):\n broadcast_to = P.BroadcastTo(idx.shape)\n offs = broadcast_to(P.range(Tensor(0, mindspore.int32),\n Tensor(idx.shape[0], mindspore.int32),\n Tensor(1, mindspore.int32))[:, None])\n idx = idx + (offs()) * idx.shape[1]\n\n return src.view(-1)[idx.view(-1)].view(idx.shpe)","def duplicate_flatten(size: int) -> List[List[int]]:\n duplicate = []\n for _ in range(size):\n temp = [-1] * size\n duplicate.append(temp)\n return duplicate","def unflatten(self, x):\n dims = [c.flat_dim for c in self.spaces]\n flat_x = np.split(x, np.cumsum(dims)[:-1])\n return tuple(c.unflatten(xi) for c, xi in zip(self.spaces, flat_x))","def _asarray(self, vec):\n shape = self.domain[0][0].shape + self.pshape\n arr = np.empty(shape, dtype=self.domain.dtype)\n for i, xi in enumerate(vec):\n for j, xij in enumerate(xi):\n arr[..., i, j] = xij.asarray()\n\n return arr","def flattenImage(input_array):\r\n shp = np.size(input_array)\r\n return np.reshape(input_array, (shp,))"],"string":"[\n \"def _idxs_postformat_array(self):\\n self.idxs = np.array(self.idxs)\",\n \"def flatten_idx(idx, axis=-1):\\n idx = numpy.asanyarray(idx)\\n if not idx.dtype.kind in ('i', 'u'):\\n idx = idx.astype(int)\\n preshape = idx.shape[:axis]\\n postshape = idx.shape[axis:]\\n stride = int(numpy.product(postshape[1:])) #1 if applied to empty\\n #The index on this axis moves stride elements in flat\\n outidx = idx.flatten() * stride #makes a copy\\n #First add the offsets to get us to [..., idx @ axis = 0, 0...)\\n outidx += numpy.repeat(\\n numpy.arange(0, len(outidx), int(numpy.product(postshape)),\\n dtype=idx.dtype),\\n numpy.product(postshape))\\n #Now offsets for non-zero on the trailing axes [0, 0, ... 0@axis, ...]\\n outidx += numpy.tile(numpy.arange(0, stride, dtype=idx.dtype),\\n int(numpy.product(preshape)) * idx.shape[axis])\\n return outidx\",\n \"def flatten(self):\\n xv, yv = np.meshgrid(self.columns, self.index, indexing='xy')\\n return np.array([xv.ravel(), yv.ravel(), self.values.ravel()])\",\n \"def _to_flat_index(self, idx_in):\\n idx_in = tuple([np.array(z, ndmin=1, copy=False) for z in idx_in])\\n msk = np.all(np.stack([t < n for t, n in zip(idx_in, self.shape)]), axis=0)\\n idx = np.ravel_multi_index(\\n tuple([t[msk] for t in idx_in]), self.shape, mode=\\\"wrap\\\"\\n )\\n\\n return idx, msk\",\n \"def getIntArray2D(self) -> typing.List[typing.List[int]]:\\n ...\",\n \"def indices(self):\\n return tuple([slice(*r) for r in self.location])\",\n \"def flatten(self):\\n\\n if self.ndim == 1:\\n return self.copy()\\n\\n return ArrayCoordinates1d(self.coordinates.flatten(), **self.properties)\",\n \"def getShortArray2D(self) -> typing.List[typing.List[int]]:\\n ...\",\n \"def master_ndindex(self): # itermaster_indices(self):\\n return itertools_product(\\n *[range(*r) for r in self.location]\\n ) # TODO check\",\n \"def atom_idxs(self):\\n\\n return np.array([atom.atom_idxs for atom in self])\",\n \"def matrix_to_flat(self, idx_rows):\\n idx = []\\n for i in range(self.nts):\\n idx.append(self._matrix_to_flat_by_ts(idx_rows, i))\\n return idx\",\n \"def flatten(x):\\n return x.view(x.size(0), -1)\",\n \"def flatten(self):\\n return [e for es in self.array for e in es]\",\n \"def batch_flatten(this,x):\\n shape = x.get_shape().as_list()[1:]\\n if None not in shape:\\n return tf.reshape(x, [-1, int(np.prod(shape))])\\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))\",\n \"def batch_flatten(this,x):\\n shape = x.get_shape().as_list()[1:]\\n if None not in shape:\\n return tf.reshape(x, [-1, int(np.prod(shape))])\\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))\",\n \"def FlattenModelData(y, i):\\n outs = np.array([y[j][i][0] for j in range(len(y))])\\n return outs\",\n \"def to_flat_index(self) -> Index: # type: ignore[override]\\n return Index(self._values, tupleize_cols=False)\",\n \"def _flatten(self):\\n n = self.B\\n idx = self.nodect - 1\\n self.seq = []\\n while n is not None:\\n n['idx'] = idx\\n self.seq.insert(0, n)\\n idx -= 1\\n n = n['pred']\",\n \"def flatten(self, arr):\\n shape = arr.shape\\n return arr.reshape(shape[0] * shape[1], *shape[2:])\",\n \"def GetPointToIncidentEdgesArray(self, p_int):\\n ...\",\n \"def reconstruct_input(self, ix):\",\n \"def flatten(self) -> np.ndarray:\\n\\n return self.data.copy()\",\n \"def flatten(a, start=0, count=2):\\n s = a.shape\\n return np.reshape(a, s[:start] + (-1,) + s[start+count:])\",\n \"def flatten_npar(np_array):\\n \\n itr = len(np_array)\\n start = np_array[0]\\n \\n for i in range(1,itr):\\n start = np.hstack((start,np_array[i]))\\n \\n return(np.array(start))\",\n \"def array_form(self):\\n return tuple(self)\",\n \"def flatten(self):\\n pass\",\n \"def do_flatten(obj):\\n if type(obj) == list:\\n return np.array(obj).flatten()\\n return obj.flatten()\",\n \"def flatten(x):\\n return reshape(x, (x.shape[0], -1))\",\n \"def flatten_stimulus(stimulus):\\n n, h, w = stimulus.shape\\n return stimulus.reshape((n, h * w))\",\n \"def get(self, idx_in):\\n shape_out = idx_in[0].shape\\n idx_flat_in, msk_in = self._to_flat_index(idx_in)\\n idx, msk = find_in_array(idx_flat_in, self.idx)\\n val_out = np.full(shape_out, self._fill_value)\\n val_out.flat[np.flatnonzero(msk_in)[msk]] = self._data[idx[msk]]\\n return np.squeeze(val_out)\",\n \"def unstacked_index(size, index):\\n return index % size, index // size\",\n \"def ravel_indices(shape, *args):\\n new_positions = []\\n for arg in args:\\n new_positions.append(np.ravel_multi_index(arg, shape))\\n return new_positions\",\n \"def row(self):\\n return self.reshape((1, self.size))\",\n \"def GetPointToIncidentFacesArray(self, p_int):\\n ...\",\n \"def flatten():\",\n \"def GetPointToOneRingPointsArray(self, p_int):\\n ...\",\n \"def batch_indices(self):\\n b = self.batch_size\\n return [np.arange(i*b, i*b+b) for i in range(self.num_batches)]\",\n \"def _flatten(self, inputT, size):\\n return tf.reshape(inputT, (-1, size))\",\n \"def _indarray(np_array):\\n return skil_client.INDArray(\\n ordering='c',\\n shape=list(np_array.shape),\\n data=np_array.reshape(-1).tolist()\\n )\",\n \"def flatten_array(X_input):\\r\\n X_input_flat = np.array([x.flatten() for x in X_input])\\r\\n return X_input_flat\",\n \"def index_object(idxs=None):\",\n \"def serialize_flatten(name, value, array_indices=True):\\r\\n # call the recursive function that returns a tuple of tuples\\r\\n return tuple(serialize_flatten_rec(name, value, array_indices))\",\n \"def row(self, index: int) -> List[int]:\\n return self.matrix[index - 1]\",\n \"def _multi_index(indexes, shape):\\n indexes = indexes if isinstance(indexes, typing.Sequence) else (indexes,)\\n if any(isinstance(i, type(Ellipsis)) for i in indexes):\\n raise IndexError('Ellipsis index currently is not supported.')\\n # Fill the right-most elements.\\n indexes = indexes + (slice(0, None, None),) * (len(shape) - len(indexes))\\n # Convert to positive index.\\n positive_indexes = []\\n for i, index in enumerate(indexes):\\n if isinstance(index, slice):\\n index = slice(\\n index.start or 0, index.stop or shape[i], index.step or 1\\n )\\n positive_indexes.append(\\n slice(\\n index.start + shape[i] if index.start < 0 else index.start,\\n index.stop + shape[i] if index.stop < 0 else index.stop,\\n # Negative step means index backward, no need to convert to\\n # positive interger.\\n index.step,\\n )\\n )\\n elif isinstance(index, int):\\n positive_indexes.append(index + shape[i] if index < 0 else index)\\n else:\\n raise TypeError(f'Not supported index type {index}.')\\n return tuple(positive_indexes)\",\n \"def indices(self):\\n slice_list = []\\n for axis in range(self.ndim):\\n if axis in self.displayed:\\n slice_list.append(slice(None))\\n else:\\n if self.clip:\\n p = np.clip(\\n self.point[axis],\\n np.round(self.range[axis][0]),\\n np.round(self.range[axis][1]) - 1,\\n )\\n else:\\n p = self.point[axis]\\n p = np.round(p / self.range[axis][2]).astype(int)\\n slice_list.append(p)\\n return tuple(slice_list)\",\n \"def GetEdgeArray(self, p_int):\\n ...\",\n \"def wrap(self, flatten_x):\\n batch_size = flatten_x.size(0)\\n x = torch.reshape(flatten_x, (batch_size, self.num_frames, self.num_ticks_per_frame, -1))\\n return x\",\n \"def one_dim_index(self, i, j):\\n return int(i + j * self.nx)\",\n \"def flatten(self):\\n return DataArray([s for s in self.unstructured()])\",\n \"def unflatten(self, flat, unused_shaped_like):\\n return next(flat)\",\n \"def getLongArray2D(self) -> typing.List[typing.List[int]]:\\n ...\",\n \"def get(self):\\n return _n.reshape(self.next_x, self.original_shape)\",\n \"def indXtoJ(indX):\\n return np.unravel_index(indX % xx.size, xx.shape)\",\n \"def _shaped_arange(*shape):\\n return np.random.randn(np.prod(shape)).astype(np.float32).reshape(\\n *shape\\n ) * np.prod(shape)\",\n \"def _flatten_parameters(self):\\n [m.flatten_parameters() for m in self._to_flatten]\",\n \"def flat(self):\\n return Op('flat', self)\",\n \"def getBinIndices(self, linear_index):\\n return linear_index / self.magic_array % self.nbins_across_dims\",\n \"def flatten(X):\\n N = X.shape[-1]\\n flat = np.zeros((N, 3072))\\n for idx, i in enumerate(range(N)):\\n # if not idx:\\n # print(X[:,:,:,i].reshape(3072))\\n flat[i] = X[:,:,:,i].reshape(3072)\\n return flat\",\n \"def _get_args(idx, *args):\\n new_args = []\\n for arg in list(args[0]):\\n if isinstance(arg, Iterable):\\n new_args.append(arg[idx])\\n else:\\n new_args.append(arg)\\n\\n return new_args\",\n \"def BatchCreator(self, j, n_batch):\\n j_start = (j-1)*n_batch + 1\\n j_end = j*n_batch + 1\\n ind = np.arange(start= j_start, stop=j_end, step=1)\\n return ind\",\n \"def batched_index_select(input, dim, index):\\n views = [input.shape[0]] + [1 if i != dim else -1 for i in range(1, len(input.shape))]\\n expanse = list(input.shape)\\n expanse[0] = -1\\n expanse[dim] = -1\\n index = index.view(views).expand(expanse)\\n return torch.gather(input, dim, index)\",\n \"def __getitem__(self, index):\\n if index == Ellipsis:\\n index = tuple(self.dim*[slice(None)])\\n\\n if len(index) < self.dim:\\n # --- Add extra dims to index if needed\\n index = list(index)\\n for i in range(len(index), self.dim):\\n index.append(slice(None))\\n index = tuple(index)\\n\\n if self.dim == 2:\\n return self._getitem2d(index)\\n elif self.dim == 3:\\n return self._getitem3d(index)\",\n \"def batch_flatten(x):\\n shape = x.get_shape().as_list()[1:]\\n if None not in shape:\\n return tf.reshape(x, [-1, int(np.prod(shape))])\\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))\",\n \"def batch_flatten(x):\\n shape = x.get_shape().as_list()[1:]\\n if None not in shape:\\n return tf.reshape(x, [-1, int(np.prod(shape))])\\n return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))\",\n \"def transform(self, x):\\n res = [x[i] for i in range(len(x))\\n if i not in self.index_value_pairs]\\n return res if isinstance(x, list) else np.asarray(res)\",\n \"def _extend_index_dim(input_index, new_index, new_index_max):\\n # Construct an iterator from new_index\\n if isinstance(new_index, (int, np.integer)):\\n it = [new_index]\\n else:\\n if isinstance(new_index, slice):\\n # slices don't work very well with multi-dimensional circular mappings.\\n it = _conv_slice_to_list(slice_obj=new_index, stop_def=new_index_max)\\n else:\\n it = new_index\\n # Index extension\\n if input_index is None:\\n output = []\\n for i in it:\\n output.append(tuple([i]))\\n return output\\n else:\\n output = []\\n for _i in input_index:\\n output_row = []\\n for i in it:\\n output_row.append(tuple(list(_i) + [i]))\\n output.append(output_row)\\n return output\",\n \"def _flatten(params):\\n params, _ = tree_flatten(params)\\n return jnp.concatenate([jnp.reshape(param, [-1]) for param in params])\",\n \"def flatten(self):\\n return tuple(map(lambda i: 1 if i > 0 else 0, self.freq()))\",\n \"def make_indices(dimensions):\\n\\n level = len(dimensions)\\n\\n if level == 1:\\n return range(dimensions[0])\\n\\n indices = [[]]\\n\\n while level:\\n\\n _indices = []\\n\\n for j in range(dimensions[level - 1]):\\n\\n _indices += [[j] + i for i in indices]\\n\\n indices = _indices\\n\\n level -= 1\\n\\n try:\\n return [tuple(i) for i in indices]\\n except TypeError:\\n return indices\",\n \"def GetFaceToAdjacentFacesArray(self, p_int):\\n ...\",\n \"def sub2ind(self, ix, iy):\\n idx = np.ravel_multi_index((ix, iy), self.shape)\\n return idx\",\n \"def flatten_numpy(ndarray):\\n return np.reshape(ndarray, (-1,), 'F')\",\n \"def loc_data_idx(loc_idx):\\n retval = []\\n for i in as_tuple(loc_idx):\\n if isinstance(i, slice) and i.step is not None and i.step == -1:\\n if i.stop is None:\\n retval.append(slice(0, i.start+1, -i.step))\\n else:\\n retval.append(slice(i.stop+1, i.start+1, -i.step))\\n elif isinstance(i, slice) and i.step is not None and i.step < -1:\\n if i.stop is None:\\n lmin = i.start\\n while lmin >= 0:\\n lmin += i.step\\n retval.append(slice(lmin-i.step, i.start+1, -i.step))\\n else:\\n retval.append(slice(i.stop+1, i.start+1, -i.step))\\n elif is_integer(i):\\n retval.append(slice(i, i+1, 1))\\n else:\\n retval.append(i)\\n return as_tuple(retval)\",\n \"def __getitem__(self, index: list) -> (np.array, np.array):\\n # Generate indexes of the batch\\n indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]\\n\\n # Find list of IDs\\n list_IDs_temp = [self.list_IDs[k] for k in indexes]\\n\\n # Generate data\\n X, M = self.__data_generation(list_IDs_temp)\\n\\n return X, M\",\n \"def get_x(self):\\n return self.x[:self.nump, :]\",\n \"def a(a,N): \\n a=np.ravel(a, order='F') # Same order\\n return a\",\n \"def a(a,N): \\n a=np.ravel(a, order='F') # Same order\\n return a\",\n \"def flatten_array(self):\\n numel = self.xyz_array[:, :, 0].size # Number of elements in dataset\\n self.flat_array = np.zeros([self._len_z, numel]) # Create array to hold flattened array\\n\\n # Loop through each dimension (dataset) and flatten it into new array\\n for dim in range(self._len_z):\\n self.flat_array[dim, :] = np.ravel(self.xyz_array[:, :, dim])\",\n \"def flatten_parameters(self):\",\n \"def get_scatter_indices_for_bboxes(flatted_indices, batch_size, total_bboxes):\\n indices_size = len(flatted_indices)\\n scatter_indices = tf.concat(flatted_indices, 1)\\n return tf.reshape(scatter_indices, (batch_size, total_bboxes, indices_size))\",\n \"def encode_to_flat_array_index(row, column, matrix):\\n return row * matrix.cols + column\",\n \"def mainIndices(self):\\n return self.i1, self.i2\",\n \"def flatten(self, x):\\n return np.concatenate([c.flatten(xi) for c, xi in zip(self.spaces, x)])\",\n \"def sliceshape(slicetuple, totshape):\\n res = []\\n for i,s in enumerate(slicetuple):\\n if isinstance(s,int):\\n #n = 1\\n pass\\n else:\\n i0,i1,istep = s.indices(totshape[i])\\n n = (i1-i0)//istep\\n res.append(n)\\n return res\",\n \"def gather(x, idx, method=2):\\n\\n if method == 0:\\n return x[idx]\\n elif method == 1:\\n x = x.unsqueeze(1)\\n x = x.expand((-1, idx.shape[-1], -1))\\n idx = idx.unsqueeze(2)\\n idx = idx.expand((-1, -1, x.shape[-1]))\\n return x.gather(0, idx)\\n elif method == 2:\\n for i, ni in enumerate(idx.size()[1:]):\\n x = x.unsqueeze(i+1)\\n new_s = list(x.size())\\n new_s[i+1] = ni\\n x = x.expand(new_s)\\n n = len(idx.size())\\n for i, di in enumerate(x.size()[n:]):\\n idx = idx.unsqueeze(i+n)\\n new_s = list(idx.size())\\n new_s[i+n] = di\\n idx = idx.expand(new_s)\\n return x.gather(0, idx)\\n else:\\n raise ValueError('Unkown method')\",\n \"def _asarray1d(arr, copy=False):\\n if copy:\\n return asarray(arr).flatten()\\n else:\\n return asarray(arr).ravel()\",\n \"def _normalize_index(self, index: int):\\n if index < 0:\\n return len(self) + index\\n else:\\n return index\",\n \"def _simplify_index(indices, shape):\\n # First clean up and check indices, unpacking ellipsis and boolean arrays\\n indices = da.slicing.normalize_index(indices, shape)\\n out = []\\n axis = 0\\n for index in indices:\\n if index is not np.newaxis:\\n length = shape[axis]\\n axis += 1\\n # If there is 1-D fancy index on this axis, try to convert to slice\\n if isinstance(index, np.ndarray) and index.ndim == 1:\\n try:\\n index = _range_to_slice(index)\\n except ValueError:\\n pass\\n else:\\n index = da.slicing.normalize_slice(index, length)\\n out.append(index)\\n return tuple(out)\",\n \"def advanced_indexing_op(input, index):\\n batch_size = tf.shape(input)[0]\\n max_length = int(input.get_shape()[1])\\n dim_size = int(input.get_shape()[2])\\n index = tf.range(0, batch_size) * max_length + (index - 1)\\n flat = tf.reshape(input, [-1, dim_size])\\n relevant = tf.gather(flat, index)\\n return relevant\",\n \"def recursive_index_decode(int_array, max=32767, min=-32768):\\n out_arr = []\\n decoded_val = 0\\n for item in int_array.tolist():\\n if item==max or item==min:\\n decoded_val += item\\n else:\\n decoded_val += item\\n out_arr.append(decoded_val)\\n decoded_val = 0\\n return numpy.asarray(out_arr,dtype=numpy.int32)\",\n \"def ordered_indices(self):\\r\\n return np.arange(len(self), dtype=np.int64)\",\n \"def _create_flatten(cls, onnx_node, inputs, opset_version):\\n factor = onnx_node.getattr('axis', 1)\\n if factor < 0:\\n # in order to support the negative axis\\n factor = len(inputs[0].shape) + factor\\n\\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\\n opset_version)\\n return None, forward(axis=factor)\",\n \"def flattened_indices_from_row_col_indices(row_indices, col_indices, num_cols):\\n return (row_indices * num_cols) + col_indices\",\n \"def state_from_id(index, dims_state_grid):\\n\\n entries = [index] * len(dims_state_grid)\\n for i in range(1, len(dims_state_grid)):\\n value = 1\\n for j in range(i, len(dims_state_grid)):\\n value *= dims_state_grid[j]\\n for k in range(i - 1, len(dims_state_grid)):\\n if k == i - 1:\\n entries[k] //= value\\n else:\\n entries[k] %= value\\n\\n out = np.array(object=entries)\\n\\n return out\",\n \"def index2d(src, idx):\\n broadcast_to = P.BroadcastTo(idx.shape)\\n offs = broadcast_to(P.range(Tensor(0, mindspore.int32),\\n Tensor(idx.shape[0], mindspore.int32),\\n Tensor(1, mindspore.int32))[:, None])\\n idx = idx + (offs()) * idx.shape[1]\\n\\n return src.view(-1)[idx.view(-1)].view(idx.shpe)\",\n \"def duplicate_flatten(size: int) -> List[List[int]]:\\n duplicate = []\\n for _ in range(size):\\n temp = [-1] * size\\n duplicate.append(temp)\\n return duplicate\",\n \"def unflatten(self, x):\\n dims = [c.flat_dim for c in self.spaces]\\n flat_x = np.split(x, np.cumsum(dims)[:-1])\\n return tuple(c.unflatten(xi) for c, xi in zip(self.spaces, flat_x))\",\n \"def _asarray(self, vec):\\n shape = self.domain[0][0].shape + self.pshape\\n arr = np.empty(shape, dtype=self.domain.dtype)\\n for i, xi in enumerate(vec):\\n for j, xij in enumerate(xi):\\n arr[..., i, j] = xij.asarray()\\n\\n return arr\",\n \"def flattenImage(input_array):\\r\\n shp = np.size(input_array)\\r\\n return np.reshape(input_array, (shp,))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65175533","0.62058824","0.5974461","0.59025675","0.58936995","0.58421","0.58367753","0.57612103","0.5670626","0.5655063","0.5648909","0.56180966","0.5589195","0.55857176","0.55857176","0.5580362","0.55702096","0.5560747","0.544082","0.5435741","0.54188216","0.5409668","0.53965384","0.5367334","0.53597945","0.5351686","0.5341007","0.52925646","0.5280118","0.52661866","0.52611285","0.5256359","0.52527046","0.52487624","0.5247055","0.524415","0.5242159","0.5231501","0.5228461","0.5195386","0.5180177","0.5172795","0.51692456","0.5161007","0.5160354","0.5155424","0.515318","0.5135404","0.5124941","0.5120456","0.5118928","0.51058185","0.51023453","0.5096304","0.50954634","0.5094139","0.50928885","0.50887007","0.50881743","0.5080389","0.5077059","0.5072143","0.50647575","0.50647575","0.50589573","0.5048208","0.5047156","0.5045411","0.504235","0.5034239","0.50310206","0.5030486","0.5030197","0.50243735","0.5024333","0.50241196","0.50241196","0.50136197","0.5009852","0.4994477","0.4993231","0.49893743","0.49876082","0.49783012","0.49714625","0.49661586","0.49639437","0.4962034","0.49616212","0.4958802","0.4941388","0.49397555","0.49366266","0.49348867","0.49333957","0.49313772","0.49312207","0.49266088","0.49158248"],"string":"[\n \"0.65175533\",\n \"0.62058824\",\n \"0.5974461\",\n \"0.59025675\",\n \"0.58936995\",\n \"0.58421\",\n \"0.58367753\",\n \"0.57612103\",\n \"0.5670626\",\n \"0.5655063\",\n \"0.5648909\",\n \"0.56180966\",\n \"0.5589195\",\n \"0.55857176\",\n \"0.55857176\",\n \"0.5580362\",\n \"0.55702096\",\n \"0.5560747\",\n \"0.544082\",\n \"0.5435741\",\n \"0.54188216\",\n \"0.5409668\",\n \"0.53965384\",\n \"0.5367334\",\n \"0.53597945\",\n \"0.5351686\",\n \"0.5341007\",\n \"0.52925646\",\n \"0.5280118\",\n \"0.52661866\",\n \"0.52611285\",\n \"0.5256359\",\n \"0.52527046\",\n \"0.52487624\",\n \"0.5247055\",\n \"0.524415\",\n \"0.5242159\",\n \"0.5231501\",\n \"0.5228461\",\n \"0.5195386\",\n \"0.5180177\",\n \"0.5172795\",\n \"0.51692456\",\n \"0.5161007\",\n \"0.5160354\",\n \"0.5155424\",\n \"0.515318\",\n \"0.5135404\",\n \"0.5124941\",\n \"0.5120456\",\n \"0.5118928\",\n \"0.51058185\",\n \"0.51023453\",\n \"0.5096304\",\n \"0.50954634\",\n \"0.5094139\",\n \"0.50928885\",\n \"0.50887007\",\n \"0.50881743\",\n \"0.5080389\",\n \"0.5077059\",\n \"0.5072143\",\n \"0.50647575\",\n \"0.50647575\",\n \"0.50589573\",\n \"0.5048208\",\n \"0.5047156\",\n \"0.5045411\",\n \"0.504235\",\n \"0.5034239\",\n \"0.50310206\",\n \"0.5030486\",\n \"0.5030197\",\n \"0.50243735\",\n \"0.5024333\",\n \"0.50241196\",\n \"0.50241196\",\n \"0.50136197\",\n \"0.5009852\",\n \"0.4994477\",\n \"0.4993231\",\n \"0.49893743\",\n \"0.49876082\",\n \"0.49783012\",\n \"0.49714625\",\n \"0.49661586\",\n \"0.49639437\",\n \"0.4962034\",\n \"0.49616212\",\n \"0.4958802\",\n \"0.4941388\",\n \"0.49397555\",\n \"0.49366266\",\n \"0.49348867\",\n \"0.49333957\",\n \"0.49313772\",\n \"0.49312207\",\n \"0.49266088\",\n \"0.49158248\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53458685"},"document_rank":{"kind":"string","value":"27"}}},{"rowIdx":85,"cells":{"query":{"kind":"string","value":"Validate the response that came back from the API, return True if it's good, False if bad"},"document":{"kind":"string","value":"def _validate_response(self, response):\n # Check for unexpected response - all should be JSON dicts that have\n # already been deserialised\n if not isinstance(response, types.DictionaryType):\n self.message(\n \"\\t\\t[!] ERROR - Unexpected value returned from the API: '%s'\" %\n (response))\n return False\n\n # Check for valid errors\n if \"error\" in response and \"msg\" in response:\n self.message(\n \"\\t\\t[!] ERROR - %s (%s)\" %\n (response[\"msg\"], response[\"timestamp\"]))\n return False\n\n # Is this a valid response message\n if \"msg\" in response:\n return True\n\n # Catch all...dictionary returned but does not contain expected keys?\n # Who know's what's going on here?!\n else:\n self.message(\n \"\\t\\t[!] ERROR - Unexpected dictionary response returned from the API: '%s'\" %\n (response))\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def validate(self, response):\n return response[\"status_code\"] == 1","def is_valid_response(self, response):\r\n if response.status_code in VALID_CODES:\r\n return True\r\n return False","def validate_response(self, response):\n pass","def validate_response(response: json):\n if \"error\" in response:\n print(\"ERROR: Request returned error\")\n print_request_response(response)\n exit(1)","def response_check(response):\n print(response)\n print(response.text)\n return response.status_code == 201","def check_response_errors(self, resp):\n return True","def validate_response(response):\n assert response.ok\n rpcdict = response.json()\n assert rpcdict['jsonrpc'] == '2.0'\n assert rpcdict['id']\n assert 'error' in rpcdict.keys() or 'result' in rpcdict.keys()","def user_should_get_an_ok_response():\n assert web_app.validate_reponse()","def check_response_invalid_fields(response: HTTPResponse) -> bool:\n return response.status_code == 422","def is_okay(self) -> bool:\n if getattr(self.api_response, \"status_code\", 200) != 200:\n return False\n\n return self.result_code in [0, 810, 820]","def response_check(response):\n print(response)\n print(response.text)\n return response.status_code == 200","def check_http_response(self, response, url):\n try:\n response.raise_for_status()\n success = True\n except (requests.HTTPError, ValueError):\n success = False\n excpt_msg = \"Invalid API response.\"\n try:\n excpt_msg = response.headers[\"cause-message\"]\n except:\n try:\n excpt_msg = response.json()[\"error\"][\"message\"][\"value\"]\n except:\n excpt_msg = \"Unknown error ('{0}'), check url in a web browser: '{1}'\".format(response.reason, url)\n api_error = EODataDownResponseException(excpt_msg, response)\n api_error.__cause__ = None\n raise api_error\n return success","def checkResponseOK(response):\n assert response['result'] == 'OK'","def check_no_data_response(response: HTTPResponse) -> bool:\n return response.status_code == 422","def check_response_no_data(response: HTTPResponse) -> bool:\n return response.status_code == 422","def check_response(response):\n status = response.get('status')\n ret = status and status == 'OK'\n if not ret:\n logging.error('Received unexpected failure response from polyswarmd: %s', response)\n return ret","def check_response_valid_update(response: HTTPResponse) -> bool:\n return response.status_code == 200","def verif_response(response):\n if response.status_code >= 200 and response.status_code <= 299:\n logging.debug(\"response server OK::{}\".format(response.text))\n return True\n\n logging.error(\"response server KO::{}\".format(response.text))\n return False","def validate_response(self, response: requests.Response) -> None:\n if 400 <= response.status_code < 500:\n msg = (\n f\"{response.status_code} Client Error: \"\n f\"{response.reason} for path: {self.path}. \"\n f\"Request payload: {response.request.body}\"\n )\n raise FatalAPIError(msg)\n\n elif 500 <= response.status_code < 600:\n msg = (\n f\"{response.status_code} Server Error: \"\n f\"{response.reason} for path: {self.path}\"\n )\n raise RetriableAPIError(msg)","def is_success(self):\r\n if self.status_code < 400:\r\n return True\r\n return False","def is_success(self):\r\n if self.status_code < 400:\r\n return True\r\n return False","def _check_response(self, response):\n if response.status_code == requests.codes.ok:\n # Since the ZenHub REST API does not send back 204 when there is\n # no content, we have to check the Content-Length for 0 :(\n if int(response.headers['Content-Length']):\n return response.json()\n elif response.status_code == requests.codes.not_found:\n return None\n else:\n return response.raise_for_status()","def is_response_valid(response):\n valid_keys = ('action', 'time', 'data', 'code', 'address')\n if all(key in response for key in valid_keys):\n return True\n raise ValueError","def the_response_should_be_result(result):\n assert web_app.check_response(result)","def _is_successful(response) -> bool:\n return response.status_code == 200","def is_response_ok(cls, data):\n if data is None:\n cls.notify_widget.show_message(\"一个不好的消息\", \"网络出现一点问题\")\n return False\n\n if not isinstance(data, dict):\n return True\n\n if data['code'] == 200:\n return True\n\n cls.notify_widget.show_message(\"一个不好的消息\", \"网络出现一点问题\")\n return False","def check_response(self, resp):\n\n # For successful API call, response code will be 200 (OK)\n if resp.ok:\n json = resp.json()\n self.response = ResponseHolder()\n self.response.response = json\n\n # Check the code\n if 'status' not in json:\n raise InvalidResponse('No status field')\n\n self.response.status = self.field_to_long(json['status'])\n if self.response.status != EBConsts.STATUS_OK:\n txt_status = self.get_text_status(json)\n raise InvalidStatus('Status is %s (%04X)'\n % (txt_status if txt_status is not None else \"\", self.response.status))\n\n if self.response_checker is not None:\n self.response_checker(self.response)\n\n return self.response\n\n else:\n # If response code is not ok (200), print the resulting http error code with description\n resp.raise_for_status()\n pass","def is_raw_response(self, response: object) -> bool:","def fusion_api_validate_response(self, respDict, valDict):\n success = True\n returnDict = {}\n keys = []\n for key in valDict:\n if not valDict[key]:\n continue\n # logger._log_to_console_and_log_file('key: %s' % (key))\n keyDict = {'key': key, 'expected': valDict[\n key], 'actual': respDict[key], 'success': True}\n if key in respDict:\n pattern = re.compile(str(valDict[key]))\n # if not re.search(str(valDict[key]), str(respDict[key])):\n # t = re.compile('(?i)Warning|Unknown|Terminated|Killed|Error|Completed')\n\n if not re.search(pattern, str(respDict[key])):\n\n success = False\n keyDict['success'] = False\n else:\n success = False\n keyDict['success'] = False\n keys.append(keyDict)\n\n returnDict['success'] = success\n returnDict['keys'] = keys\n return returnDict","def check(self):\n invalid = []\n\n if not self.route:\n invalid.append(('route', 'missing'))\n elif not self.route[1] in ['GET', 'POST', 'PUT']:\n invalid.append(('route', 'invalid method: %s' % self.route[1]))\n\n has_2xx = False\n for rcode in self.return_codes:\n code = rcode[0]\n if code >= 200 and code < 300:\n has_2xx = True\n break\n if not has_2xx:\n invalid.append(('return_codes', 'Missing succes return code doc'))\n\n if self.client_auth is None:\n invalid.append(\n ('client_auth', 'Please provide client auth requirement'))\n\n if self.user_auth is None:\n invalid.append(\n ('user_auth', 'Please provide user auth requirement'))\n\n if invalid:\n msgs = []\n for error in invalid:\n msgs.append(\"%s: %s\" % error)\n raise ValueError(\n \"APIFunc for %s is invalid: %s\"\n % (self.viewfunc.__name__,\n ', '.join(msgs)))","def validate_response(response):\n\n r = response\n try:\n r.raise_for_status()\n except HTTPError as e:\n message = dict(status_code=r.status_code, exception=e)\n\n try:\n response = r.json()\n message['response'] = response\n except JSONDecodeError as e:\n message['response'] = r.content\n\n raise HTTPError(message)","def _check_response(self, res: requests.Response, token: str) -> None:\n return","def validate_get_response(self, response):\n\n self.validate_response(response)\n if response.status_code not in self.model._meta['valid_get_status']:\n raise InvalidStatusError(\n self.model._meta['valid_get_status'], response\n )","def check_status(response):\n if response.status_code == 200:\n return True","def is_success_response(resp: Response) -> bool:\n return \\\n resp and \\\n is_dict(resp) and \\\n resp.get(\"success\", False) is True","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200 \n and content_type is not None)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None)","def has_error(self, response):\n return response.find(' Matched') == -1 and response.find(' Failed') == -1","def error_invalid_response(self):\r\n return self.type() == 0x00","def _is_error_call(self, response):\n status = response.get('ResponseMetadata', {}).get('HTTPStatusCode')\n return status != 200","def is_error(response: str) -> bool:\n return \"ERROR\" in response","def validation_check():\n is_valid = blockchain.is_chain_valid(blockchain.chain)\n if is_valid:\n response = {'message': 'Blockchain is valid',\n 'chain': blockchain.chain,\n 'length': len(blockchain.chain)}\n else:\n response = {'error': 'There are errors in the Blockchain',\n 'chain': blockchain.chain,\n 'length': len(blockchain.chain)}\n return jsonify(response), 200","def is_good_response(res):\n content_type = res.headers['Content-Type'].lower()\n return (res.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def _check_200(self, response):\n if response.code != 200:\n raise YubiKeyVerificationError(\n \"Received {0} response.\".format(response.code))\n return response","def is_good_response(resp):\n #Headers is a method in requests library\n content_type = resp.headers['Content-Type'].lower()\n \n return (resp.status_code == 200 # Returns true if the website is online and available (statuscode=200)\n #Returns true if content_type exists\n and content_type is not None\n #Returns true if it is an html document or a json document.\n and (content_type.find('json') > -1 or content_type.find('html')))","def verify(self, response):","def is_good_response(resp) -> bool:\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","async def test_is_error_response() -> None:\n assert not is_error_response(\"True\")\n assert not is_error_response(True)\n assert not is_error_response(\"\")\n assert is_error_response(\n json.loads(\n '{\"errorCode\": \"INVALID_NUMBER_PARAMETER_VALUE\", \"minValue\": 0.0, \"maxValue\": 1.01}'\n )\n )\n assert not is_error_response(json.loads('{\"errorCode\": \"\"}'))","def is_error(self):\r\n if self.status not in [STATUS_CODES['200'], ]:\r\n return True\r\n else:\r\n return False","def _process_unsuccessful_response(\n self,\n response: Response,\n case: Literal['validate_api_key', 'balances', 'trades', 'asset_movements'],\n ) -> Union[\n list,\n tuple[bool, str],\n ExchangeQueryBalances,\n ]:\n try:\n response_list = jsonloads_list(response.text)\n except JSONDecodeError as e:\n msg = f'{self.name} {case} returned an invalid JSON response: {response.text}.'\n log.error(msg)\n\n if case in ('validate_api_key', 'balances'):\n return False, msg\n if case in ('trades', 'asset_movements'):\n self.msg_aggregator.add_error(\n f'Got remote error while querying {self.name} {case}: {msg}',\n )\n return []\n\n raise AssertionError(f'Unexpected {self.name} response_case: {case}') from e\n\n error_data = self._get_error_response_data(response_list)\n if error_data.error_code == API_ERR_AUTH_NONCE_CODE:\n message = API_ERR_AUTH_NONCE_MESSAGE\n # Errors related with the API key return a human readable message\n elif case == 'validate_api_key' and error_data.error_code == API_KEY_ERROR_CODE:\n message = API_KEY_ERROR_MESSAGE\n else:\n # Below any other error not related with the system clock or the API key\n reason = error_data.reason or response.text\n message = (\n f'{self.name} query responded with error status code: {response.status_code} '\n f'and text: {reason}.'\n )\n log.error(message)\n\n if case in ('validate_api_key', 'balances'):\n return False, message\n if case in ('trades', 'asset_movements'):\n self.msg_aggregator.add_error(\n f'Got remote error while querying {self.name} {case}: {message}',\n )\n return []\n\n raise AssertionError(f'Unexpected {self.name} response_case: {case}')","def assert_valid_responses(response) -> None:\n assert valid_resp_name in response.text\n assert valid_resp_addr in response.text\n assert response.status_code == 200","def valid_in_response(self):\n return self._repeatable[1] is not None","def _check_response(response: requests.Response) -> None:\n logger.debug('Received response:\\n%s', response.content)\n try:\n response.raise_for_status()\n if not response.json()['status']:\n _report_failure('your e-mail address appears to be invalid')\n except requests.exceptions.HTTPError:\n _report_failure()\n except (ValueError, KeyError):\n _report_failure('there was a problem with the server response')","def check_status():\n try:\n return HTTPClient().fetch(\"https://api.random.org/\").code == 200\n except Exception: # pylint: disable=broad-except\n return False","def is_good_response(self, resp):\r\n\t\tcontent_type = resp.headers['Content-Type'].lower()\r\n\t\treturn (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)","def check_response(rv):\n if rv != 'OK':\n print \"No message found\"\n return False\n return True","def IsOk(self):\r\n \r\n return True","def is_good_response(self, resp):\r\n content_type = resp.headers['Content-Type'].lower()\r\n return (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200 and content_type is not None\n and content_type.find('html') > -1)","def check(self, response, payload):\n # check response body\n if not response.text:\n return False\n\n # check for output\n # uid=user gid=group groups=groups\n regex = r\"(uid=\\d+[\\(\\)\\w\\-]+)(\\s+gid=\\d+[\\(\\)\\w\\-]+)(\\s+groups=\\d+[\\(\\)\\w\\-,]+)?\"\n if re.search(regex, response.text):\n return True\n else:\n return False","def isGoodResponse(resp):\r\n content_type = resp.headers['Content-Type'].lower()\r\n return (resp.status_code == 200\r\n and content_type is not None\r\n and content_type.find('html') > -1)","def test_generating(resp):\n errors = []\n if not check_int(resp[\"tightness\"]):\n errors.append(\"Invalid type for Itinerary response's 'tightness' field.\")\n\n if not isinstance(resp, bool):\n errors.append(\"Invalid type for Itinerary response's 'start_from_airport' field.\")","def check_event_registration_response_valid(\n response: HTTPResponse, user_id: user_models.UserId) -> bool:\n try:\n assert response.status_code == 201\n assert response.json()\n event_id = response.json().get(\"event_id\")\n assert check_event_id_added_to_user(event_id, user_id)\n assert check_event_status_set_properly(event_id)\n return True\n except AssertionError as assert_error:\n debug_msg = f\"failed at: {assert_error}. resp json: {response.json()}\"\n logging.debug(debug_msg)\n return False","def is_good_response(resp):\r\n content_type = resp.headers['Content-Type'].lower()\r\n return (resp.status_code == 200 \r\n and content_type is not None \r\n and content_type.find('html') > -1)","def test_response_ok():\n\t\n\t# Send GET request to API given endpoint and store the response.\n\tresponse = get_items()\n\n\t# Confirm that the request-response cycle completed successfully.\n\t#assert_true(response.ok)\n\tif ('None' in response): print(\"Failed calling REST API: {}\".format(response))\n\telse: print(\"TC Passed, Response OK: {}\".format(response))","def __CheckResponse(self, response):\n\n status = response.status\n if (status == httplib.OK or status == httplib.CREATED\n or status == httplib.NO_CONTENT):\n return\n elif (status == httplib.UNAUTHORIZED):\n raise BadCredentialsException\n elif (status == httplib.SERVICE_UNAVAILABLE):\n raise ServerBusyException\n elif (status == httplib.BAD_REQUEST\n or status == httplib.UNPROCESSABLE_ENTITY):\n raise BadArgumentsException\n elif (status == httplib.NOT_FOUND):\n raise NotFoundException\n else:\n raise BadOperationException","def validate_token(self):\n r = requests.get(urljoin(self._url, Client._token_resource),\n params={\"tokenid\": self._token_id})\n\n if r.status_code == requests.status_codes.codes.unauthorized:\n raise ClientUnauthorized()\n elif r.status_code != requests.status_codes.codes.ok:\n error_messages = self._parse_invalid_request(r.text)\n raise ClientException(r.status_code, error_messages)\n\n try:\n type_, value = r.text.split(\"=\")\n value = value.strip(\" \\r\\n\")\n except Exception, e:\n raise ClientException(r.status_code,\n \"Some error has ocurred getting the result value from %s\"\n % r.text)\n\n return value == \"true\"","def is_good_response(resp):\n\tcontent_type = resp.headers['Content-Type'].lower()\n\treturn (resp.status_code == 200 \n\t\tand content_type is not None \n\t\tand content_type.find('html') > -1)","def validate_response(self, response):\n crypted = response[-0x100:]\n # check that not all values are the same\n if all(v == crypted[0] for v in crypted):\n return False\n # return if chunks of 0x10 repeat\n return (len([True for i in range(0x10, len(crypted), 0x10)\n if crypted[:0x10] == crypted[i:i+0x10]])) == 0xf","def _validate_response(request, response, schema_data, schema_resolver):\n try:\n validate_outgoing_response(\n request,\n response,\n schema_data,\n schema_resolver\n )\n except jsonschema.exceptions.ValidationError as exc:\n # This will alter our stack trace slightly, but Pyramid knows how\n # to render it. And the real value is in the message anyway.\n raise HTTPInternalServerError(str(exc))","def is_good_response(resp):\n\tcontent_type = resp.headers['Content-Type'].lower()\n\treturn (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)","def check_no_header_response(response: HTTPResponse) -> bool:\n return response.status_code == 422","def is_good_response(resp):\r\n content_type = resp.headers['Content-Type'].lower()\r\n return (resp.status_code == 200\r\n and content_type is not None\r\n and content_type.find('html') > -1)","def check_string():\n\n # Forcing check for valid json and headers with Content-Type:application/json\n content = request.get_json(silent=False, force=True)\n\n payload = content.get('data', None)\n \n if not payload:\n return response_handler(\n {\"error\": \"'data' key missing from JSON payload.\"},\n 400\n )\n if not isinstance(payload, basestring):\n return response_handler(\n {\"error\": \"Value of 'data' key is not of type 'string'.\"},\n 400\n )\n \n pangram = analyze_string(payload)\n if not pangram:\n return response_handler(\n {\"error\": False},\n 400\n )\n\n return response_handler(\n {\"success\": True},\n 200\n )","def assertValidJSONResponse(self, resp):\r\n self.assertHttpOK(resp)\r\n self.assertTrue(resp['Content-Type'].startswith('application/json'))\r\n self.assertValidJSON(resp.content)","def verify_object(self, data):\n rv = self.get(data[self.id_field])\n result = not is_404(rv)\n if result:\n for key, value in data:\n if not in_response(rv, value):\n return False\n return result","def is_ok(r) -> bool:\n\tif r.status_code == 200:\n\t\treturn True","def mora_assert(response):\n assert response.status_code in (200, 201, 400, 404), response.status_code\n if response.status_code == 400:\n # Check actual response\n assert (\n response.text.find(\"not give raise to a new registration\") > 0\n ), response.text\n logger.debug(\"Request had no effect\")\n return None","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200 \n and content_type is not None \n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200 \n and content_type is not None \n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200 \n and content_type is not None \n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200 \n and content_type is not None \n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)","def _is_valid_result(result):\n return result.get(\"version\", \"\") != \"\"","def _assert_bad_request(self, response, field, zendesk_mock_class, datadog_mock):\r\n self.assertEqual(response.status_code, 400)\r\n resp_json = json.loads(response.content)\r\n self.assertTrue(\"field\" in resp_json)\r\n self.assertEqual(resp_json[\"field\"], field)\r\n self.assertTrue(\"error\" in resp_json)\r\n # There should be absolutely no interaction with Zendesk\r\n self.assertFalse(zendesk_mock_class.return_value.mock_calls)\r\n self.assertFalse(datadog_mock.mock_calls)","def is_good_response(resp):\n content_type = resp.headers[\"Content-Type\"].lower()\n return (\n resp.status_code == 200\n and content_type is not None\n and content_type.find(\"html\") > -1\n )","def CheckSuccess(self, response):\n if 'error' in response:\n raise ComputeEngineApiError('An operation completed with errors:\\n%s'\n % response)","async def get_invalid(self, **kwargs: Any) -> bool:\n error_map = {\n 401: ClientAuthenticationError,\n 404: ResourceNotFoundError,\n 409: ResourceExistsError,\n 304: ResourceNotModifiedError,\n }\n error_map.update(kwargs.pop(\"error_map\", {}) or {})\n\n _headers = kwargs.pop(\"headers\", {}) or {}\n _params = kwargs.pop(\"params\", {}) or {}\n\n cls: ClsType[bool] = kwargs.pop(\"cls\", None)\n\n request = build_bool_get_invalid_request(\n headers=_headers,\n params=_params,\n )\n request.url = self._client.format_url(request.url)\n\n _stream = False\n pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access\n request, stream=_stream, **kwargs\n )\n\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n if _stream:\n await response.read() # Load the body in memory and close the socket\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n raise HttpResponseError(response=response)\n\n if response.content:\n deserialized = response.json()\n else:\n deserialized = None\n\n if cls:\n return cls(pipeline_response, cast(bool, deserialized), {})\n\n return cast(bool, deserialized)","def assert_response_correct(self, response, expected_status, expected_content):\n assert response.status_code == expected_status\n parsed_content = json.loads(response.content.decode('utf-8'))\n assert parsed_content == expected_content","def assert_json_failure_response_is_missing_social_auth(self, response):\r\n self.assertEqual(200, response.status_code) # Yes, it's a 200 even though it's a failure.\r\n payload = json.loads(response.content)\r\n self.assertFalse(payload.get('success'))\r\n self.assertIn('associated with your %s account' % self.PROVIDER_CLASS.NAME, payload.get('value'))","def successful(self) -> bool:\n return self._unparsed_response is not None","def verify_response_dict(api_key, response):\n LOGGER.debug('Verifying WSAPI response signature')\n\n # Remove signature from the response\n r = dict(response)\n del r['h']\n\n # Convert to HTML query as that is used by Yubico to sign the response\n query = sorted_urlencode(list(r.iteritems()))\n\n # We unquote it because it's not the HTTP quoted version\n query = urllib.unquote_plus(query)\n\n status = sign(api_key, query) == response['h']\n LOGGER.debug('Signature result ' + str(status))\n return status"],"string":"[\n \"def validate(self, response):\\n return response[\\\"status_code\\\"] == 1\",\n \"def is_valid_response(self, response):\\r\\n if response.status_code in VALID_CODES:\\r\\n return True\\r\\n return False\",\n \"def validate_response(self, response):\\n pass\",\n \"def validate_response(response: json):\\n if \\\"error\\\" in response:\\n print(\\\"ERROR: Request returned error\\\")\\n print_request_response(response)\\n exit(1)\",\n \"def response_check(response):\\n print(response)\\n print(response.text)\\n return response.status_code == 201\",\n \"def check_response_errors(self, resp):\\n return True\",\n \"def validate_response(response):\\n assert response.ok\\n rpcdict = response.json()\\n assert rpcdict['jsonrpc'] == '2.0'\\n assert rpcdict['id']\\n assert 'error' in rpcdict.keys() or 'result' in rpcdict.keys()\",\n \"def user_should_get_an_ok_response():\\n assert web_app.validate_reponse()\",\n \"def check_response_invalid_fields(response: HTTPResponse) -> bool:\\n return response.status_code == 422\",\n \"def is_okay(self) -> bool:\\n if getattr(self.api_response, \\\"status_code\\\", 200) != 200:\\n return False\\n\\n return self.result_code in [0, 810, 820]\",\n \"def response_check(response):\\n print(response)\\n print(response.text)\\n return response.status_code == 200\",\n \"def check_http_response(self, response, url):\\n try:\\n response.raise_for_status()\\n success = True\\n except (requests.HTTPError, ValueError):\\n success = False\\n excpt_msg = \\\"Invalid API response.\\\"\\n try:\\n excpt_msg = response.headers[\\\"cause-message\\\"]\\n except:\\n try:\\n excpt_msg = response.json()[\\\"error\\\"][\\\"message\\\"][\\\"value\\\"]\\n except:\\n excpt_msg = \\\"Unknown error ('{0}'), check url in a web browser: '{1}'\\\".format(response.reason, url)\\n api_error = EODataDownResponseException(excpt_msg, response)\\n api_error.__cause__ = None\\n raise api_error\\n return success\",\n \"def checkResponseOK(response):\\n assert response['result'] == 'OK'\",\n \"def check_no_data_response(response: HTTPResponse) -> bool:\\n return response.status_code == 422\",\n \"def check_response_no_data(response: HTTPResponse) -> bool:\\n return response.status_code == 422\",\n \"def check_response(response):\\n status = response.get('status')\\n ret = status and status == 'OK'\\n if not ret:\\n logging.error('Received unexpected failure response from polyswarmd: %s', response)\\n return ret\",\n \"def check_response_valid_update(response: HTTPResponse) -> bool:\\n return response.status_code == 200\",\n \"def verif_response(response):\\n if response.status_code >= 200 and response.status_code <= 299:\\n logging.debug(\\\"response server OK::{}\\\".format(response.text))\\n return True\\n\\n logging.error(\\\"response server KO::{}\\\".format(response.text))\\n return False\",\n \"def validate_response(self, response: requests.Response) -> None:\\n if 400 <= response.status_code < 500:\\n msg = (\\n f\\\"{response.status_code} Client Error: \\\"\\n f\\\"{response.reason} for path: {self.path}. \\\"\\n f\\\"Request payload: {response.request.body}\\\"\\n )\\n raise FatalAPIError(msg)\\n\\n elif 500 <= response.status_code < 600:\\n msg = (\\n f\\\"{response.status_code} Server Error: \\\"\\n f\\\"{response.reason} for path: {self.path}\\\"\\n )\\n raise RetriableAPIError(msg)\",\n \"def is_success(self):\\r\\n if self.status_code < 400:\\r\\n return True\\r\\n return False\",\n \"def is_success(self):\\r\\n if self.status_code < 400:\\r\\n return True\\r\\n return False\",\n \"def _check_response(self, response):\\n if response.status_code == requests.codes.ok:\\n # Since the ZenHub REST API does not send back 204 when there is\\n # no content, we have to check the Content-Length for 0 :(\\n if int(response.headers['Content-Length']):\\n return response.json()\\n elif response.status_code == requests.codes.not_found:\\n return None\\n else:\\n return response.raise_for_status()\",\n \"def is_response_valid(response):\\n valid_keys = ('action', 'time', 'data', 'code', 'address')\\n if all(key in response for key in valid_keys):\\n return True\\n raise ValueError\",\n \"def the_response_should_be_result(result):\\n assert web_app.check_response(result)\",\n \"def _is_successful(response) -> bool:\\n return response.status_code == 200\",\n \"def is_response_ok(cls, data):\\n if data is None:\\n cls.notify_widget.show_message(\\\"一个不好的消息\\\", \\\"网络出现一点问题\\\")\\n return False\\n\\n if not isinstance(data, dict):\\n return True\\n\\n if data['code'] == 200:\\n return True\\n\\n cls.notify_widget.show_message(\\\"一个不好的消息\\\", \\\"网络出现一点问题\\\")\\n return False\",\n \"def check_response(self, resp):\\n\\n # For successful API call, response code will be 200 (OK)\\n if resp.ok:\\n json = resp.json()\\n self.response = ResponseHolder()\\n self.response.response = json\\n\\n # Check the code\\n if 'status' not in json:\\n raise InvalidResponse('No status field')\\n\\n self.response.status = self.field_to_long(json['status'])\\n if self.response.status != EBConsts.STATUS_OK:\\n txt_status = self.get_text_status(json)\\n raise InvalidStatus('Status is %s (%04X)'\\n % (txt_status if txt_status is not None else \\\"\\\", self.response.status))\\n\\n if self.response_checker is not None:\\n self.response_checker(self.response)\\n\\n return self.response\\n\\n else:\\n # If response code is not ok (200), print the resulting http error code with description\\n resp.raise_for_status()\\n pass\",\n \"def is_raw_response(self, response: object) -> bool:\",\n \"def fusion_api_validate_response(self, respDict, valDict):\\n success = True\\n returnDict = {}\\n keys = []\\n for key in valDict:\\n if not valDict[key]:\\n continue\\n # logger._log_to_console_and_log_file('key: %s' % (key))\\n keyDict = {'key': key, 'expected': valDict[\\n key], 'actual': respDict[key], 'success': True}\\n if key in respDict:\\n pattern = re.compile(str(valDict[key]))\\n # if not re.search(str(valDict[key]), str(respDict[key])):\\n # t = re.compile('(?i)Warning|Unknown|Terminated|Killed|Error|Completed')\\n\\n if not re.search(pattern, str(respDict[key])):\\n\\n success = False\\n keyDict['success'] = False\\n else:\\n success = False\\n keyDict['success'] = False\\n keys.append(keyDict)\\n\\n returnDict['success'] = success\\n returnDict['keys'] = keys\\n return returnDict\",\n \"def check(self):\\n invalid = []\\n\\n if not self.route:\\n invalid.append(('route', 'missing'))\\n elif not self.route[1] in ['GET', 'POST', 'PUT']:\\n invalid.append(('route', 'invalid method: %s' % self.route[1]))\\n\\n has_2xx = False\\n for rcode in self.return_codes:\\n code = rcode[0]\\n if code >= 200 and code < 300:\\n has_2xx = True\\n break\\n if not has_2xx:\\n invalid.append(('return_codes', 'Missing succes return code doc'))\\n\\n if self.client_auth is None:\\n invalid.append(\\n ('client_auth', 'Please provide client auth requirement'))\\n\\n if self.user_auth is None:\\n invalid.append(\\n ('user_auth', 'Please provide user auth requirement'))\\n\\n if invalid:\\n msgs = []\\n for error in invalid:\\n msgs.append(\\\"%s: %s\\\" % error)\\n raise ValueError(\\n \\\"APIFunc for %s is invalid: %s\\\"\\n % (self.viewfunc.__name__,\\n ', '.join(msgs)))\",\n \"def validate_response(response):\\n\\n r = response\\n try:\\n r.raise_for_status()\\n except HTTPError as e:\\n message = dict(status_code=r.status_code, exception=e)\\n\\n try:\\n response = r.json()\\n message['response'] = response\\n except JSONDecodeError as e:\\n message['response'] = r.content\\n\\n raise HTTPError(message)\",\n \"def _check_response(self, res: requests.Response, token: str) -> None:\\n return\",\n \"def validate_get_response(self, response):\\n\\n self.validate_response(response)\\n if response.status_code not in self.model._meta['valid_get_status']:\\n raise InvalidStatusError(\\n self.model._meta['valid_get_status'], response\\n )\",\n \"def check_status(response):\\n if response.status_code == 200:\\n return True\",\n \"def is_success_response(resp: Response) -> bool:\\n return \\\\\\n resp and \\\\\\n is_dict(resp) and \\\\\\n resp.get(\\\"success\\\", False) is True\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200 \\n and content_type is not None)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None)\",\n \"def has_error(self, response):\\n return response.find(' Matched') == -1 and response.find(' Failed') == -1\",\n \"def error_invalid_response(self):\\r\\n return self.type() == 0x00\",\n \"def _is_error_call(self, response):\\n status = response.get('ResponseMetadata', {}).get('HTTPStatusCode')\\n return status != 200\",\n \"def is_error(response: str) -> bool:\\n return \\\"ERROR\\\" in response\",\n \"def validation_check():\\n is_valid = blockchain.is_chain_valid(blockchain.chain)\\n if is_valid:\\n response = {'message': 'Blockchain is valid',\\n 'chain': blockchain.chain,\\n 'length': len(blockchain.chain)}\\n else:\\n response = {'error': 'There are errors in the Blockchain',\\n 'chain': blockchain.chain,\\n 'length': len(blockchain.chain)}\\n return jsonify(response), 200\",\n \"def is_good_response(res):\\n content_type = res.headers['Content-Type'].lower()\\n return (res.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def _check_200(self, response):\\n if response.code != 200:\\n raise YubiKeyVerificationError(\\n \\\"Received {0} response.\\\".format(response.code))\\n return response\",\n \"def is_good_response(resp):\\n #Headers is a method in requests library\\n content_type = resp.headers['Content-Type'].lower()\\n \\n return (resp.status_code == 200 # Returns true if the website is online and available (statuscode=200)\\n #Returns true if content_type exists\\n and content_type is not None\\n #Returns true if it is an html document or a json document.\\n and (content_type.find('json') > -1 or content_type.find('html')))\",\n \"def verify(self, response):\",\n \"def is_good_response(resp) -> bool:\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"async def test_is_error_response() -> None:\\n assert not is_error_response(\\\"True\\\")\\n assert not is_error_response(True)\\n assert not is_error_response(\\\"\\\")\\n assert is_error_response(\\n json.loads(\\n '{\\\"errorCode\\\": \\\"INVALID_NUMBER_PARAMETER_VALUE\\\", \\\"minValue\\\": 0.0, \\\"maxValue\\\": 1.01}'\\n )\\n )\\n assert not is_error_response(json.loads('{\\\"errorCode\\\": \\\"\\\"}'))\",\n \"def is_error(self):\\r\\n if self.status not in [STATUS_CODES['200'], ]:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def _process_unsuccessful_response(\\n self,\\n response: Response,\\n case: Literal['validate_api_key', 'balances', 'trades', 'asset_movements'],\\n ) -> Union[\\n list,\\n tuple[bool, str],\\n ExchangeQueryBalances,\\n ]:\\n try:\\n response_list = jsonloads_list(response.text)\\n except JSONDecodeError as e:\\n msg = f'{self.name} {case} returned an invalid JSON response: {response.text}.'\\n log.error(msg)\\n\\n if case in ('validate_api_key', 'balances'):\\n return False, msg\\n if case in ('trades', 'asset_movements'):\\n self.msg_aggregator.add_error(\\n f'Got remote error while querying {self.name} {case}: {msg}',\\n )\\n return []\\n\\n raise AssertionError(f'Unexpected {self.name} response_case: {case}') from e\\n\\n error_data = self._get_error_response_data(response_list)\\n if error_data.error_code == API_ERR_AUTH_NONCE_CODE:\\n message = API_ERR_AUTH_NONCE_MESSAGE\\n # Errors related with the API key return a human readable message\\n elif case == 'validate_api_key' and error_data.error_code == API_KEY_ERROR_CODE:\\n message = API_KEY_ERROR_MESSAGE\\n else:\\n # Below any other error not related with the system clock or the API key\\n reason = error_data.reason or response.text\\n message = (\\n f'{self.name} query responded with error status code: {response.status_code} '\\n f'and text: {reason}.'\\n )\\n log.error(message)\\n\\n if case in ('validate_api_key', 'balances'):\\n return False, message\\n if case in ('trades', 'asset_movements'):\\n self.msg_aggregator.add_error(\\n f'Got remote error while querying {self.name} {case}: {message}',\\n )\\n return []\\n\\n raise AssertionError(f'Unexpected {self.name} response_case: {case}')\",\n \"def assert_valid_responses(response) -> None:\\n assert valid_resp_name in response.text\\n assert valid_resp_addr in response.text\\n assert response.status_code == 200\",\n \"def valid_in_response(self):\\n return self._repeatable[1] is not None\",\n \"def _check_response(response: requests.Response) -> None:\\n logger.debug('Received response:\\\\n%s', response.content)\\n try:\\n response.raise_for_status()\\n if not response.json()['status']:\\n _report_failure('your e-mail address appears to be invalid')\\n except requests.exceptions.HTTPError:\\n _report_failure()\\n except (ValueError, KeyError):\\n _report_failure('there was a problem with the server response')\",\n \"def check_status():\\n try:\\n return HTTPClient().fetch(\\\"https://api.random.org/\\\").code == 200\\n except Exception: # pylint: disable=broad-except\\n return False\",\n \"def is_good_response(self, resp):\\r\\n\\t\\tcontent_type = resp.headers['Content-Type'].lower()\\r\\n\\t\\treturn (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)\",\n \"def check_response(rv):\\n if rv != 'OK':\\n print \\\"No message found\\\"\\n return False\\n return True\",\n \"def IsOk(self):\\r\\n \\r\\n return True\",\n \"def is_good_response(self, resp):\\r\\n content_type = resp.headers['Content-Type'].lower()\\r\\n return (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200 and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def check(self, response, payload):\\n # check response body\\n if not response.text:\\n return False\\n\\n # check for output\\n # uid=user gid=group groups=groups\\n regex = r\\\"(uid=\\\\d+[\\\\(\\\\)\\\\w\\\\-]+)(\\\\s+gid=\\\\d+[\\\\(\\\\)\\\\w\\\\-]+)(\\\\s+groups=\\\\d+[\\\\(\\\\)\\\\w\\\\-,]+)?\\\"\\n if re.search(regex, response.text):\\n return True\\n else:\\n return False\",\n \"def isGoodResponse(resp):\\r\\n content_type = resp.headers['Content-Type'].lower()\\r\\n return (resp.status_code == 200\\r\\n and content_type is not None\\r\\n and content_type.find('html') > -1)\",\n \"def test_generating(resp):\\n errors = []\\n if not check_int(resp[\\\"tightness\\\"]):\\n errors.append(\\\"Invalid type for Itinerary response's 'tightness' field.\\\")\\n\\n if not isinstance(resp, bool):\\n errors.append(\\\"Invalid type for Itinerary response's 'start_from_airport' field.\\\")\",\n \"def check_event_registration_response_valid(\\n response: HTTPResponse, user_id: user_models.UserId) -> bool:\\n try:\\n assert response.status_code == 201\\n assert response.json()\\n event_id = response.json().get(\\\"event_id\\\")\\n assert check_event_id_added_to_user(event_id, user_id)\\n assert check_event_status_set_properly(event_id)\\n return True\\n except AssertionError as assert_error:\\n debug_msg = f\\\"failed at: {assert_error}. resp json: {response.json()}\\\"\\n logging.debug(debug_msg)\\n return False\",\n \"def is_good_response(resp):\\r\\n content_type = resp.headers['Content-Type'].lower()\\r\\n return (resp.status_code == 200 \\r\\n and content_type is not None \\r\\n and content_type.find('html') > -1)\",\n \"def test_response_ok():\\n\\t\\n\\t# Send GET request to API given endpoint and store the response.\\n\\tresponse = get_items()\\n\\n\\t# Confirm that the request-response cycle completed successfully.\\n\\t#assert_true(response.ok)\\n\\tif ('None' in response): print(\\\"Failed calling REST API: {}\\\".format(response))\\n\\telse: print(\\\"TC Passed, Response OK: {}\\\".format(response))\",\n \"def __CheckResponse(self, response):\\n\\n status = response.status\\n if (status == httplib.OK or status == httplib.CREATED\\n or status == httplib.NO_CONTENT):\\n return\\n elif (status == httplib.UNAUTHORIZED):\\n raise BadCredentialsException\\n elif (status == httplib.SERVICE_UNAVAILABLE):\\n raise ServerBusyException\\n elif (status == httplib.BAD_REQUEST\\n or status == httplib.UNPROCESSABLE_ENTITY):\\n raise BadArgumentsException\\n elif (status == httplib.NOT_FOUND):\\n raise NotFoundException\\n else:\\n raise BadOperationException\",\n \"def validate_token(self):\\n r = requests.get(urljoin(self._url, Client._token_resource),\\n params={\\\"tokenid\\\": self._token_id})\\n\\n if r.status_code == requests.status_codes.codes.unauthorized:\\n raise ClientUnauthorized()\\n elif r.status_code != requests.status_codes.codes.ok:\\n error_messages = self._parse_invalid_request(r.text)\\n raise ClientException(r.status_code, error_messages)\\n\\n try:\\n type_, value = r.text.split(\\\"=\\\")\\n value = value.strip(\\\" \\\\r\\\\n\\\")\\n except Exception, e:\\n raise ClientException(r.status_code,\\n \\\"Some error has ocurred getting the result value from %s\\\"\\n % r.text)\\n\\n return value == \\\"true\\\"\",\n \"def is_good_response(resp):\\n\\tcontent_type = resp.headers['Content-Type'].lower()\\n\\treturn (resp.status_code == 200 \\n\\t\\tand content_type is not None \\n\\t\\tand content_type.find('html') > -1)\",\n \"def validate_response(self, response):\\n crypted = response[-0x100:]\\n # check that not all values are the same\\n if all(v == crypted[0] for v in crypted):\\n return False\\n # return if chunks of 0x10 repeat\\n return (len([True for i in range(0x10, len(crypted), 0x10)\\n if crypted[:0x10] == crypted[i:i+0x10]])) == 0xf\",\n \"def _validate_response(request, response, schema_data, schema_resolver):\\n try:\\n validate_outgoing_response(\\n request,\\n response,\\n schema_data,\\n schema_resolver\\n )\\n except jsonschema.exceptions.ValidationError as exc:\\n # This will alter our stack trace slightly, but Pyramid knows how\\n # to render it. And the real value is in the message anyway.\\n raise HTTPInternalServerError(str(exc))\",\n \"def is_good_response(resp):\\n\\tcontent_type = resp.headers['Content-Type'].lower()\\n\\treturn (resp.status_code == 200 and content_type is not None and content_type.find('html') > -1)\",\n \"def check_no_header_response(response: HTTPResponse) -> bool:\\n return response.status_code == 422\",\n \"def is_good_response(resp):\\r\\n content_type = resp.headers['Content-Type'].lower()\\r\\n return (resp.status_code == 200\\r\\n and content_type is not None\\r\\n and content_type.find('html') > -1)\",\n \"def check_string():\\n\\n # Forcing check for valid json and headers with Content-Type:application/json\\n content = request.get_json(silent=False, force=True)\\n\\n payload = content.get('data', None)\\n \\n if not payload:\\n return response_handler(\\n {\\\"error\\\": \\\"'data' key missing from JSON payload.\\\"},\\n 400\\n )\\n if not isinstance(payload, basestring):\\n return response_handler(\\n {\\\"error\\\": \\\"Value of 'data' key is not of type 'string'.\\\"},\\n 400\\n )\\n \\n pangram = analyze_string(payload)\\n if not pangram:\\n return response_handler(\\n {\\\"error\\\": False},\\n 400\\n )\\n\\n return response_handler(\\n {\\\"success\\\": True},\\n 200\\n )\",\n \"def assertValidJSONResponse(self, resp):\\r\\n self.assertHttpOK(resp)\\r\\n self.assertTrue(resp['Content-Type'].startswith('application/json'))\\r\\n self.assertValidJSON(resp.content)\",\n \"def verify_object(self, data):\\n rv = self.get(data[self.id_field])\\n result = not is_404(rv)\\n if result:\\n for key, value in data:\\n if not in_response(rv, value):\\n return False\\n return result\",\n \"def is_ok(r) -> bool:\\n\\tif r.status_code == 200:\\n\\t\\treturn True\",\n \"def mora_assert(response):\\n assert response.status_code in (200, 201, 400, 404), response.status_code\\n if response.status_code == 400:\\n # Check actual response\\n assert (\\n response.text.find(\\\"not give raise to a new registration\\\") > 0\\n ), response.text\\n logger.debug(\\\"Request had no effect\\\")\\n return None\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200 \\n and content_type is not None \\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200 \\n and content_type is not None \\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200 \\n and content_type is not None \\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200 \\n and content_type is not None \\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def is_good_response(resp):\\n content_type = resp.headers['Content-Type'].lower()\\n return (resp.status_code == 200\\n and content_type is not None\\n and content_type.find('html') > -1)\",\n \"def _is_valid_result(result):\\n return result.get(\\\"version\\\", \\\"\\\") != \\\"\\\"\",\n \"def _assert_bad_request(self, response, field, zendesk_mock_class, datadog_mock):\\r\\n self.assertEqual(response.status_code, 400)\\r\\n resp_json = json.loads(response.content)\\r\\n self.assertTrue(\\\"field\\\" in resp_json)\\r\\n self.assertEqual(resp_json[\\\"field\\\"], field)\\r\\n self.assertTrue(\\\"error\\\" in resp_json)\\r\\n # There should be absolutely no interaction with Zendesk\\r\\n self.assertFalse(zendesk_mock_class.return_value.mock_calls)\\r\\n self.assertFalse(datadog_mock.mock_calls)\",\n \"def is_good_response(resp):\\n content_type = resp.headers[\\\"Content-Type\\\"].lower()\\n return (\\n resp.status_code == 200\\n and content_type is not None\\n and content_type.find(\\\"html\\\") > -1\\n )\",\n \"def CheckSuccess(self, response):\\n if 'error' in response:\\n raise ComputeEngineApiError('An operation completed with errors:\\\\n%s'\\n % response)\",\n \"async def get_invalid(self, **kwargs: Any) -> bool:\\n error_map = {\\n 401: ClientAuthenticationError,\\n 404: ResourceNotFoundError,\\n 409: ResourceExistsError,\\n 304: ResourceNotModifiedError,\\n }\\n error_map.update(kwargs.pop(\\\"error_map\\\", {}) or {})\\n\\n _headers = kwargs.pop(\\\"headers\\\", {}) or {}\\n _params = kwargs.pop(\\\"params\\\", {}) or {}\\n\\n cls: ClsType[bool] = kwargs.pop(\\\"cls\\\", None)\\n\\n request = build_bool_get_invalid_request(\\n headers=_headers,\\n params=_params,\\n )\\n request.url = self._client.format_url(request.url)\\n\\n _stream = False\\n pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access\\n request, stream=_stream, **kwargs\\n )\\n\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n if _stream:\\n await response.read() # Load the body in memory and close the socket\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n raise HttpResponseError(response=response)\\n\\n if response.content:\\n deserialized = response.json()\\n else:\\n deserialized = None\\n\\n if cls:\\n return cls(pipeline_response, cast(bool, deserialized), {})\\n\\n return cast(bool, deserialized)\",\n \"def assert_response_correct(self, response, expected_status, expected_content):\\n assert response.status_code == expected_status\\n parsed_content = json.loads(response.content.decode('utf-8'))\\n assert parsed_content == expected_content\",\n \"def assert_json_failure_response_is_missing_social_auth(self, response):\\r\\n self.assertEqual(200, response.status_code) # Yes, it's a 200 even though it's a failure.\\r\\n payload = json.loads(response.content)\\r\\n self.assertFalse(payload.get('success'))\\r\\n self.assertIn('associated with your %s account' % self.PROVIDER_CLASS.NAME, payload.get('value'))\",\n \"def successful(self) -> bool:\\n return self._unparsed_response is not None\",\n \"def verify_response_dict(api_key, response):\\n LOGGER.debug('Verifying WSAPI response signature')\\n\\n # Remove signature from the response\\n r = dict(response)\\n del r['h']\\n\\n # Convert to HTML query as that is used by Yubico to sign the response\\n query = sorted_urlencode(list(r.iteritems()))\\n\\n # We unquote it because it's not the HTTP quoted version\\n query = urllib.unquote_plus(query)\\n\\n status = sign(api_key, query) == response['h']\\n LOGGER.debug('Signature result ' + str(status))\\n return status\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8024064","0.78625363","0.77986264","0.7515456","0.74777824","0.74465626","0.73384","0.7265737","0.7262182","0.72441804","0.7198832","0.712405","0.7028976","0.701251","0.69981617","0.69959635","0.6970569","0.6948191","0.6897628","0.68025744","0.68025744","0.6800186","0.67974865","0.67858255","0.6780485","0.676281","0.67326874","0.6712715","0.67036015","0.66709816","0.66679287","0.66633785","0.66413915","0.6637445","0.6618039","0.6610584","0.66000175","0.6586345","0.65496165","0.6531632","0.65289193","0.6482799","0.646065","0.6457958","0.6457733","0.6433299","0.6422639","0.64125013","0.6410823","0.6407977","0.6402173","0.6393354","0.6390981","0.6384058","0.6378157","0.6358686","0.6338603","0.63369715","0.633359","0.6324581","0.6315461","0.6308886","0.63048613","0.6301798","0.62915355","0.6285658","0.6281826","0.62785673","0.62781996","0.62777996","0.6270371","0.6267701","0.6242745","0.6234679","0.62320304","0.62277687","0.6223658","0.6216222","0.62107384","0.62107384","0.62107384","0.62107384","0.62092763","0.62092763","0.62092763","0.62092763","0.62092763","0.62092763","0.62092763","0.62092763","0.62092763","0.6208142","0.6202829","0.619694","0.61769086","0.61766976","0.6169507","0.61694765","0.6152311","0.61425745"],"string":"[\n \"0.8024064\",\n \"0.78625363\",\n \"0.77986264\",\n \"0.7515456\",\n \"0.74777824\",\n \"0.74465626\",\n \"0.73384\",\n \"0.7265737\",\n \"0.7262182\",\n \"0.72441804\",\n \"0.7198832\",\n \"0.712405\",\n \"0.7028976\",\n \"0.701251\",\n \"0.69981617\",\n \"0.69959635\",\n \"0.6970569\",\n \"0.6948191\",\n \"0.6897628\",\n \"0.68025744\",\n \"0.68025744\",\n \"0.6800186\",\n \"0.67974865\",\n \"0.67858255\",\n \"0.6780485\",\n \"0.676281\",\n \"0.67326874\",\n \"0.6712715\",\n \"0.67036015\",\n \"0.66709816\",\n \"0.66679287\",\n \"0.66633785\",\n \"0.66413915\",\n \"0.6637445\",\n \"0.6618039\",\n \"0.6610584\",\n \"0.66000175\",\n \"0.6586345\",\n \"0.65496165\",\n \"0.6531632\",\n \"0.65289193\",\n \"0.6482799\",\n \"0.646065\",\n \"0.6457958\",\n \"0.6457733\",\n \"0.6433299\",\n \"0.6422639\",\n \"0.64125013\",\n \"0.6410823\",\n \"0.6407977\",\n \"0.6402173\",\n \"0.6393354\",\n \"0.6390981\",\n \"0.6384058\",\n \"0.6378157\",\n \"0.6358686\",\n \"0.6338603\",\n \"0.63369715\",\n \"0.633359\",\n \"0.6324581\",\n \"0.6315461\",\n \"0.6308886\",\n \"0.63048613\",\n \"0.6301798\",\n \"0.62915355\",\n \"0.6285658\",\n \"0.6281826\",\n \"0.62785673\",\n \"0.62781996\",\n \"0.62777996\",\n \"0.6270371\",\n \"0.6267701\",\n \"0.6242745\",\n \"0.6234679\",\n \"0.62320304\",\n \"0.62277687\",\n \"0.6223658\",\n \"0.6216222\",\n \"0.62107384\",\n \"0.62107384\",\n \"0.62107384\",\n \"0.62107384\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.62092763\",\n \"0.6208142\",\n \"0.6202829\",\n \"0.619694\",\n \"0.61769086\",\n \"0.61766976\",\n \"0.6169507\",\n \"0.61694765\",\n \"0.6152311\",\n \"0.61425745\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76538676"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":86,"cells":{"query":{"kind":"string","value":"estimate an MxF user factor matrix and an FxN item factor matrix from the MxN rating matrix"},"document":{"kind":"string","value":"def factor_mat(all_dat, f_num, iterations, regularization):\n\n\t# get # of users and # of items\n\t[u_num, i_num] = all_dat.shape\n\n\t# init user factors and item factors with random values\n\tu_fac = np.matrix(np.random.rand(u_num, f_num))\t# MxF\n\ti_fac = np.matrix(np.random.rand(i_num, f_num))\t# NxF\n\n\t# calculate the preference matrix\n\tpreference = cal_preference(all_dat)\n\n\t# calculate the confidence matrix\n\tconfidence = cal_confidence(all_dat)\n\t\n\t# recalculate the user factors and item factors using the alternating least square method\n\tfor itr in range(iterations):\n\t\tu_fac = alternate_ls(u_num, i_fac, preference, confidence, regularization)\n\t\t#print itr, \"u_fac\"\n\t\ti_fac = alternate_ls(i_num, u_fac, preference.T, confidence.T, regularization)\n\t\t#print itr, \"i_fac\"\n\t\n\t# save the output\n\tdf = pd.DataFrame(u_fac)\n\tdf.to_csv(\"tmp/u_fac.tmp\", index=False, header=False, sep='\\t', encoding='utf-8')\n\tdf = pd.DataFrame(i_fac.T)\n\tdf.to_csv(\"tmp/i_fac.tmp\", index=False, header=False, sep='\\t', encoding='utf-8')\n\n\t# an MxF user factor matrix and an FxN item factor matrix\n\treturn [u_fac, i_fac.T]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_matrix(self):\n\n self.matrix = np.zeros((len(self.users), len(self.items)))\n\n for user in self.train_set['users']:\n for item in self.train_set['feedback'][user]:\n self.matrix[self.user_to_user_id[user]][self.item_to_item_id[item]] = \\\n self.train_set['feedback'][user][item]","def get_user_item_matrix(datafile, user_index, product_index):\n num_users = len(user_index)\n num_items = len(product_index)\n result = np.zeros((num_users, num_items))\n num_reviews = len(datafile)\n result_dense = np.zeros((num_reviews, 3))\n for line in datafile.iterrows():\n i = line[0]\n user_id = datafile['user_id'][i]\n product_id = datafile['business_id'][i]\n user = user_index[user_id]\n product = product_index[product_id]\n rating = datafile['stars'][i]\n result[user, product] = rating\n result_dense[i, 0] = user\n result_dense[i, 1] = product\n result_dense[i, 2] = rating\n return result, result_dense","def create_user_item_matrix(data,type='unary'): \n if type == 'unary':\n\n # for unary rating drop duplicates\n data = data.drop_duplicates()\n\n # create sparse matrix\n matrix = csr_matrix((data['rating'], (data['user_id'],data['product_id'])))\n\n # rows and cols with empty values will be dropped (doesnt make any difference in size for sparse matrix, but if later converted to dense, it saves space)\n # get all non empty rows and cols\n rows, cols = matrix.nonzero()\n unique_rows = np.unique(rows)\n unique_cols = np.unique(cols)\n\n # select only rows and cols with values\n matrix = matrix[unique_rows]\n matrix = matrix[:,unique_cols]\n\n return matrix\n\n if type == 'count':\n\n # create sparse matrix with counted ratings\n matrix = csr_matrix((data['rating'], (data['user_id'],data['product_id'])))\n\n # rows and cols with empty values will be dropped (doesnt make any difference in size for sparse matrix, but if later converted to dense, it saves space)\n # get all non empty rows and cols\n rows, cols = matrix.nonzero()\n unique_rows = np.unique(rows)\n unique_cols = np.unique(cols)\n\n # select only rows and cols with values\n matrix = matrix[unique_rows]\n matrix = matrix[:,unique_cols]\n\n '''\n Im Gegensatz zum Memory Based approach, muss beim Model Based Approach noch das Rating angepasst werden. \n Das heisst, dass Produkte die viel eingekauft wurden ein höhers Rating erhalten und solche die weniger \n eingekauft wudren ein tieferes. Gleichzeitig muss das Maximum limitiert werden. Dies wird mittels \n dem max_count integer in der Funktion bewerkstelligt\n '''\n\n # create diagonal Matrix with 1 divided by maximum values per row. This needs to be done because there is no divide function for csr matrices implemented\n # source: https://stackoverflow.com/questions/42225269/scipy-sparse-matrix-division\n diag = diags(1/matrix.max(axis=1).A.ravel())\n\n # multipy the matrix with the maximum values to get range from 0-1\n matrix = diag.dot(matrix)\n\n # sort indices; not really needed, just cosmetics\n matrix.sort_indices()\n\n return matrix","def example():\n num_user, num_item, ratings = build_ticket()\n \n # suffle_data\n np.random.shuffle(ratings)\n\n # split data to training & validation\n train_pct = 0.9\n train_size = int(train_pct * len(ratings))\n train = ratings[:train_size]\n validation = ratings[train_size:]\n\n # params\n num_feature = 5\n bmf_model = BayesianMatrixFactorization(\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\n\n start_time = time.clock()\n bmf_model.estimate(10)\n end_time = time.clock()\n \n mat = np.matrix(bmf_model.item_features)\n with open('../data/ticket/item_feature', 'w') as f:\n for line in mat:\n np.savetxt(f, line, fmt='%.5f')\n\n print \"time spend = %.3f\" % (end_time - start_time)\n\n return bmf_model","def get_user_feature_matrix_p(user_dict, user_index, aspect_index, N, popularity, A_dense, Polarity):\n result = np.zeros((len(user_index), len(aspect_index)))\n for key in user_dict.keys():\n index_user = user_index[key]\n user_reviews = user_dict[key]\n count_dict = {}\n max = 0\n min = 1000\n for review in user_reviews:\n feature = review[0]\n if feature not in aspect_index:\n continue\n aspect = aspect_index[feature]\n if aspect not in count_dict:\n count_dict[aspect] = 0;\n if Polarity == False:\n count_dict[aspect] += 1\n else:\n count_dict[aspect] += review[1]\n for aspect in count_dict.keys():\n count = count_dict[aspect]\n if count > max:\n max = count\n if count < min:\n min = count\n for aspect in count_dict.keys():\n count = count_dict[aspect]\n result[index_user, aspect] = (((count - min)/(max - min))*5)\n\n if len(popularity) > 0:\n col = np.zeros((len(result), 1))\n result = np.append(result, col, axis=1)\n for i in range(len(result)):\n items = A_dense[A_dense[:, 0] == i][:, 1]\n items = items.astype(int)\n result[i, len(result[1]) - 1] = np.mean(popularity[items, 1])\n return result","def fit(self, users, items, ratings, test_users=[], test_items=[], test_ratings=[], **kargs):\n global_mean_bias_init = np.float32(np.mean(ratings))\n global_mean_bias_init = 0.01\n self.model = DeepFM_(**self.dfm_params, global_mean_bias_init=global_mean_bias_init, first_half_fit_only_fm=self.first_half_fit_only_fm)\n \n # もし、CTR予測の場合は、y=0のデータをランダム生成する。\n if self.ctr_prediction:\n users = list(users) + list(np.random.choice(list(set(users)), size=len(users)))\n items = list(items) + list(np.random.choice(list(set(items)), size=len(items)))\n ratings = list((np.array(ratings)>0).astype(int)) + [0]*len(ratings)\n test_ratings = list((np.array(test_ratings)>0).astype(int))\n \n Xi, Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(users, items)\n \n if len(test_users)>0:\n test_Xi, test_Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(test_users, test_items)\n self.model.fit(Xi, Xv, ratings, test_Xi, test_Xv, test_ratings, early_stopping=True)\n else:\n self.model.fit(Xi, Xv, ratings, early_stopping=True, **kargs)\n \n # load data\n self.trained_users = list(set(users))\n self.trained_items = list(set(items))\n self.global_mean = self.model.predict(Xi, Xv).mean()","def calculate_recommendations(self, vote_list, itemMatch, itemIgnored):\n #print \"--------------------------------------------------\"\n #print \"calculate_recommendations\"\n #print \"--------------------------------------------------\"\n\n # http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/\n\n # U = np.array('users')\n # D = np.array('video_games')\n\n # R = |U| cross |D|\n\n # We want to discover K latent features\n\n # Find\n # P(a | |U| corss K matrix)\n # Q(a | |D| cross K matrix)\n # Such that their product approximates R\n # R approx= P cross transpose(Q) = hat(R)\n #\n\n # r[i][j] = transpose(p)[i] * q[j]\n # = sum( 1..k, p[i][k] * q[k][j] )\n\n # e[i][j]**2 = (r[i][j] - hat(r)[i][j])**2\n # = (r[i][j] - sum( 1..K, p[i][k] * q[k][j]))**2\n # squared error, estimated rating can be either higher or lower than the real thing\n\n # find the gradient\n # diff(e[i][j]**2, p[i][k]) = -2*(r[i][j] - hat(r)[i][j]) * (q[k][j]) = -2*e[i][j] * q[k][j]\n # diff(e[i][j]**2, q[k][j]) = -2*(r[i][j] - hat(r)[i][j]) * (p[i][k]) = -2*e[i][j] * p[i][k]\n\n # update rules\n # alpha = settings.alpha # learning_rate\n # alpha = 0.0002 # learning_rate\n # p[i][k]' = p[i][k] + alpha * diff(e[i][j]**2, p[i][k])\n # = p[i][k] + 2 * alpha * e[i][j] * q[k][j]\n # q[k][j]' = q[k][j] + alpha * diff(e[i][j]**2, q[k][j])\n # = q[k][j] + 2 * alpha * e[i][j] * p[i][k]\n\n # training data\n # T = (u[i], d[j], r[i][j])\n # np.array()\n\n # iterate until convergance\n # E = sum((u[i], d[j], r[i][j]) in T, e[i][j])\n # = sum((u[i], d[j], r[i][j]) in T, r[i][j]\n # - sum(1..k, p[i][k]*q[k][j]))**2\n\n # regularization\n # beta = 0.02\n # e[i][j]**2 = (r[i][j] - sum(1..K, p[i][j]*q[k][j]))**2\n # + ((beta/2) * sum(1..K, norm(P)**2 + norm(Q)**2))\n #\n # p[i][k]' = p[i][k] + alpha * (2 * e[i][j] * q[k][j] - beta * p[i][k])\n # q[k][j]' = q[k][j] + alpha * (2 * e[i][j] * p[i][k] - beta * q[k][j])\n\n data = np.array(vote_list)\n\n encoder = OneHotEncoder()\n\n users = data[:,0]\n unique_users = list(set(users))\n for i in range(len(users)):\n users[i] = unique_users.index(users[i])\n\n video_games = data[:,1]\n unique_games = list(set(video_games))\n for i in range(len(video_games)):\n video_games[i] = unique_games.index(video_games[i])\n\n ratings = data[:,2]\n M = len(set(video_games))\n N = len(set(users))\n R = np.zeros((N,M))\n for i in range(len(users)):\n user = users[i]\n game = video_games[i]\n rating = ratings[i]\n R[user][game] = rating\n\n K = 2\n\n P = np.random.rand(N,K)\n Q = np.random.rand(M,K)\n\n nP, nQ = self.matrix_factorization(R, P, Q, K)\n nR = np.dot(nP, nQ.T)\n\n itemMatch = {}\n for i in range(N):\n user = unique_users[i]\n itemMatch[user] = []\n for j in range(M):\n if R[i][j] == 0:\n video_game = unique_games[j]\n recommendation = (video_game, nR[i][j])\n itemMatch[user].append(recommendation)\n itemMatch[None] = []\n print 'pmf recommendations', itemMatch.items()\n print '\\n'\n recommendations = itemMatch.items()\n\n # returns\n # [\n # (<user1>, [\n # (\"<object_identifier1>\", <score>),\n # (\"<object_identifier2>\", <score>),\n # ]),\n # (<user2>, [\n # (\"<object_identifier1>\", <score>),\n # (\"<object_identifier2>\", <score>),\n # ]),\n # ]\n\n return recommendations","def init_matrix(self, train, num_features):\n user_matrix = np.random.rand(self.num_users, num_features)\n item_matrix = np.random.rand(num_features, self.num_items)\n item_nnz = train.getnnz(axis=0)\n item_sum = train.sum(axis=0)\n item_matrix[0, :] = item_sum / item_nnz\n return user_matrix, item_matrix","def create_adjust_matrix(self, is_sub: bool):\n matrix = None\n if not is_sub:\n ratings = np.ones_like(self._user, dtype=np.float32)\n matrix = sp.csr_matrix(\n (ratings, (self._user, self._item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n else:\n if self.type == \"ND\":\n drop_user = self.rand_sample(\n self.n_users,\n size=int(self.n_users * self.drop_ratio),\n replace=False,\n )\n drop_item = self.rand_sample(\n self.n_items,\n size=int(self.n_items * self.drop_ratio),\n replace=False,\n )\n R_user = np.ones(self.n_users, dtype=np.float32)\n R_user[drop_user] = 0.0\n R_item = np.ones(self.n_items, dtype=np.float32)\n R_item[drop_item] = 0.0\n R_user = sp.diags(R_user)\n R_item = sp.diags(R_item)\n R_G = sp.csr_matrix(\n (\n np.ones_like(self._user, dtype=np.float32),\n (self._user, self._item),\n ),\n shape=(self.n_users, self.n_items),\n )\n res = R_user.dot(R_G)\n res = res.dot(R_item)\n\n user, item = res.nonzero()\n ratings = res.data\n matrix = sp.csr_matrix(\n (ratings, (user, item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n\n elif self.type == \"ED\" or self.type == \"RW\":\n keep_item = self.rand_sample(\n len(self._user),\n size=int(len(self._user) * (1 - self.drop_ratio)),\n replace=False,\n )\n user = self._user[keep_item]\n item = self._item[keep_item]\n\n matrix = sp.csr_matrix(\n (np.ones_like(user), (user, item + self.n_users)),\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\n )\n\n matrix = matrix + matrix.T\n D = np.array(matrix.sum(axis=1)) + 1e-7\n D = np.power(D, -0.5).flatten()\n D = sp.diags(D)\n return D.dot(matrix).dot(D)","def forward(self, user, item):\n item_emb = self.product_factors(item.view(-1)) + self.product_bias(\n item.view(-1)\n )\n user_emb = self.user_factors(user.view(-1)) + self.user_bias(user.view(-1))\n mat_mult = (item_emb * user_emb).sum(1)\n\n return mat_mult","def affinity_matrix(test_specs):\n\n np.random.seed(test_specs[\"seed\"])\n\n # uniform probability for the 5 ratings\n s = [(1 - test_specs[\"spars\"]) / test_specs[\"ratings\"]] * test_specs[\"ratings\"]\n s.append(test_specs[\"spars\"])\n P = s[::-1]\n\n # generates the user/item affinity matrix. Ratings are from 1 to 5, with 0s denoting unrated items\n X = np.random.choice(\n test_specs[\"ratings\"] + 1, (test_specs[\"users\"], test_specs[\"items\"]), p=P\n )\n\n Xtr, Xtst = numpy_stratified_split(\n X, ratio=test_specs[\"ratio\"], seed=test_specs[\"seed\"]\n )\n\n return Xtr, Xtst","def update_model(self):\n for itemidx, itemid in self._allitems.iterrows():\n self.__itemid2idx[str(itemid['itemid'])] = itemidx\n self.__itemidx2id[itemidx] = str(itemid['itemid'])\n for useridx, userid in enumerate(self._uservisits['userid'].unique()):\n self.__userid2idx[str(userid)] = useridx\n self.__useridx2id[useridx] = str(userid)\n userid = self._uservisits['userid'].values\n itemid = self._uservisits['itemid'].values\n rating = self._uservisits['rating'].values\n useridx = [self.__userid2idx[str(int(uid))] for uid in userid]\n itemidx = [self.__itemid2idx[str(int(iid))] for iid in itemid]\n rating = list(map(numpy.double, rating))\n self.__itemuser = csr_matrix((rating, (useridx, itemidx)), shape=(len(set(useridx)), len(set(itemidx))))\n self.__recommender = implicit.als.AlternatingLeastSquares(factors=self.__numtopics)\n self.__recommender.fit(self.__itemuser)","def recommend_NMF(new_user,movies_num,movies_ratings):\n list_id_movies = movies_ratings['movieId'].unique()\n nmf,Q = load_NMF_model()\n new_user_vector = pd.DataFrame(new_user, index=list_id_movies).T\n new_user_vector_filled = new_user_vector.fillna(3)\n #calculate Matrix P (Genres)\n P = nmf.transform(new_user_vector_filled)\n #make predictions\n predictions = np.dot(P,Q)\n recommendations = pd.DataFrame(predictions.reshape(-1), index=list_id_movies).T\n #Remove already watched movies:\n not_watched_movies_mask = np.isnan(new_user_vector)\n not_watched = recommendations[not_watched_movies_mask]\n\n top_movies_ids = not_watched.T.sort_values(by=[0], ascending=False).index[:movies_num]\n\n Top_recommended = movieId_to_title(top_movies_ids,movies_ratings) \n return Top_recommended","def fit(self, df):\n\n # generate continuous indices if this hasn't been done\n if self.index2item is None:\n self.set_index(df)\n\n logger.info(\"Collecting user affinity matrix\")\n if not np.issubdtype(df[self.col_rating].dtype, np.number):\n raise TypeError(\"Rating column data type must be numeric\")\n\n # copy the DataFrame to avoid modification of the input\n temp_df = df[[self.col_user, self.col_item, self.col_rating]].copy()\n\n if self.time_decay_flag:\n logger.info(\"Calculating time-decayed affinities\")\n # if time_now is None use the latest time\n if not self.time_now:\n self.time_now = df[self.col_timestamp].max()\n\n # apply time decay to each rating\n temp_df[self.col_rating] *= exponential_decay(\n value=df[self.col_timestamp],\n max_val=self.time_now,\n half_life=self.time_decay_half_life,\n )\n\n # group time decayed ratings by user-item and take the sum as the user-item affinity\n temp_df = (\n temp_df.groupby([self.col_user, self.col_item]).sum().reset_index()\n )\n else:\n # without time decay use the latest user-item rating in the dataset as the affinity score\n logger.info(\"De-duplicating the user-item counts\")\n temp_df = temp_df.drop_duplicates(\n [self.col_user, self.col_item], keep=\"last\"\n )\n\n logger.info(\"Creating index columns\")\n # map users and items according to the two dicts. Add the two new columns to temp_df.\n temp_df.loc[:, self.col_item_id] = temp_df[self.col_item].map(self.item2index)\n temp_df.loc[:, self.col_user_id] = temp_df[self.col_user].map(self.user2index)\n\n # retain seen items for removal at prediction time\n self.seen_items = temp_df[[self.col_user_id, self.col_item_id]].values\n\n # affinity matrix\n logger.info(\"Building user affinity sparse matrix\")\n self.user_affinity = self.compute_affinity_matrix(\n temp_df, self.n_users, self.n_items\n )\n\n # calculate item co-occurrence\n logger.info(\"Calculating item co-occurrence\")\n item_cooccurrence = self.compute_coocurrence_matrix(\n temp_df, self.n_users, self.n_items\n )\n\n # free up some space\n del temp_df\n\n self.item_frequencies = item_cooccurrence.diagonal()\n\n logger.info(\"Calculating item similarity\")\n if self.similarity_type == sar.SIM_COOCCUR:\n self.item_similarity = item_cooccurrence\n elif self.similarity_type == sar.SIM_JACCARD:\n logger.info(\"Calculating jaccard\")\n self.item_similarity = jaccard(item_cooccurrence).astype(\n df[self.col_rating].dtype\n )\n elif self.similarity_type == sar.SIM_LIFT:\n logger.info(\"Calculating lift\")\n self.item_similarity = lift(item_cooccurrence).astype(\n df[self.col_rating].dtype\n )\n else:\n raise ValueError(\n \"Unknown similarity type: {0}\".format(self.similarity_type)\n )\n\n # free up some space\n del item_cooccurrence\n\n logger.info(\"Done training\")","def fit_data(self, matrix, user_features=None, item_features=None):\r\n matrix.sort_index(inplace=True)\r\n matrix.sort_index(inplace=True, axis=1)\r\n dataset = Dataset()\r\n dataset.fit((x for x in matrix.index),\r\n (x for x in matrix.columns))\r\n interactions = pd.melt(matrix.replace(0, np.nan).reset_index(),\r\n id_vars='index',\r\n value_vars=list(matrix.columns[1:]),\r\n var_name='plu_id',\r\n value_name='rating').dropna().sort_values('index')\r\n interactions.columns = ['crd_no', 'plu_id', 'rating']\r\n self.interactions, self.weights = dataset.build_interactions([tuple(x) for x in interactions.values])\r\n\r\n if user_features is not None:\r\n user_features.sort_index(inplace=True)\r\n dataset.fit_partial(users=user_features.index,\r\n user_features=user_features)\r\n self.user_features = dataset.build_user_features(\r\n ((index, dict(row)) for index, row in user_features.iterrows()))\r\n else:\r\n self.user_features = None\r\n if item_features is not None:\r\n item_features.sort_index(inplace=True)\r\n dataset.fit_partial(items=item_features.index,\r\n item_features=item_features)\r\n self.item_features = dataset.build_item_features(\r\n ((index, dict(row)) for index, row in item_features.iterrows()))\r\n else:\r\n self.item_features = None","def __init__(self, ratings, rank, user_reg_loss, item_reg_loss):\n self._ratings = ratings\n self._users_num, self._items_num = ratings.shape\n self._rank = rank\n self._user_reg_loss = user_reg_loss\n self._item_reg_loss = item_reg_loss\n \n self._train_function = self._als_partial_step_explicit\n \n # DONE init latent factors for user and item matrix\n # losowo ustalamy inicjalne wartości X i Y\n self._user_factors = np.random.random((self._users_num, rank))\n self._item_factors = np.random.random((self._items_num, rank))","def predict_rating(user_id,item_id):\n user_preference = latent_user_preferences[user_id]\n item_preference = latent_item_features[item_id]\n return user_preference.dot(item_preference)","def compute_affinity_matrix(self, df, n_users, n_items):\n\n return sparse.coo_matrix(\n (df[self.col_rating], (df[self.col_user_id], df[self.col_item_id])),\n shape=(n_users, n_items),\n ).tocsr()","def init_MF(train, num_features):\n num_user = train.shape[1]\n num_item = train.shape[0]\n user_features = np.random.rand(num_features,num_user) # user_features shape (20,943)\n item_features = np.random.rand(num_item, num_features) # item_features shape (1152,20)\n return user_features, item_features","def feature_matrix(df, user_id=None, item_id=None):\n print(\"get feature matrix\")\n df1 = df.drop_duplicates(subset=['user_id'], keep='first', inplace=False)\n user_x = None\n if user_id is not None:\n user_x = int(np.argwhere(df1['user_id'].values == user_id))\n user_features = df1[['average_stars']].values\n csr_user_features = sparse.csr_matrix(user_features)\n\n df2 = df.drop_duplicates(\n subset=['business_id'],\n keep='first',\n inplace=False)\n item_x = None\n if item_id is not None:\n item_x = int(np.argwhere(df2['business_id'].values == item_id))\n item_features = df2.iloc[:, 10:].values\n\n csr_item_features = sparse.csr_matrix(item_features)\n return csr_user_features, csr_item_features, user_x, item_x","def simMatrix(self, d = 1/5):\n \n self.fit_baseline(d)\n self.evalBaseline()\n \n \n df_mat = np.array(self.df[[\"user ind\", \"item ind\", \"rating\"]].merge(self.r_b, on = [\"user ind\", \"item ind\"]))\n df_ind = df_mat[:,:2].astype(int)\n df_rat = df_mat[:,2] - df_mat[:,3]\n \n \n self.M = np.zeros((self.n_us, self.n_it))\n \n \n widgets = ['Test: ', Percentage(), ' ', Bar(\"#\"), ' ', ETA()]\n pbar = ProgressBar(widgets = widgets, maxval = self.n_us)\n pbar.start()\n \n for us in self.user_ind:\n it = df_ind[np.where(df_ind[:,0] == us)[0], 1]\n rat1 = df_rat[np.where(df_ind[:,0] == us)[0]]\n self.M[us,it] = rat1\n \n pbar.update(us)\n \n pbar.finish()\n \n #self.M = self.UI.toarray()\n pbar = ProgressBar(widgets = widgets, maxval = self.n_it * (self.n_it - 1) / 2)\n pbar.start()\n \n self.S = np.empty((self.n_it, self.n_it)) * np.nan\n \n for i1 in range(self.n_it):\n # self.S[i1,i1] = 1\n x1 = self.M[:,i1]\n for i2 in range(i1+1,self.n_it):\n x2 = self.M[:,i2]\n I = np.logical_and(x1, x2)\n if (len(I) > 1):\n self.S[i1,i2] = self.S[i2,i1] = Sim.cos2(x1.T[I], self.M[:,i2].T[I])\n \n pbar.update((self.n_it)*(i1+1) - (i1+2)*(i1+1)/2)\n \n pbar.finish()\n \n return self.S","def score_items(X, U, mu,\n scoremethod='lowhigh',\n missingmethod='none',\n feature_weights=[]):\n\n # Use U to model and then reconstruct the data in X.\n # 1. Project all data in X into space defined by U,\n # then reconstruct it.\n if missingmethod.lower() != 'ignore':\n # All missing values should have been replaced with 0,\n # or non-existent.\n # 1a. Subtract the mean and project onto U\n proj = np.dot(U.T, (X - mu))\n # 1b. Reconstruct by projecting back up and adding mean\n reproj = np.dot(U, proj) + mu\n # 1c. Compute the residual\n #print('X:', X.T)\n #print('reproj:', reproj.T)\n err = X - reproj\n #print('err:', err.T)\n #input()\n \n else:\n # Missing method must be 'ignore' (Brand 2002)\n (err, reproj) = compute_error_with_missing(X, U, mu)\n\n # 2. Compute reconstruction error\n if scoremethod == 'low': # Blank out all errors > 0\n err[err>0] = 0\n elif scoremethod == 'high': # Blank out all errors < 0\n err[err<0] = 0\n else: # default, count everything\n pass\n \n # Weight features if requested\n if len(feature_weights) > 0:\n for i in range(len(feature_weights)):\n err[i,:] = err[i,:] * feature_weights[i]\n\n if missingmethod.lower() == 'ignore':\n # Only tally error for observed features.\n # This means that items with missing values are not penalized\n # for those features, which is probably the best we can do.\n scores = np.nansum(np.array(np.power(err, 2)), axis=0)\n else:\n scores = np.sum(np.array(np.power(err, 2)), axis=0)\n\n #print('scores:', scores)\n #print('reproj:', reproj)\n #input()\n return (scores, reproj)","def ratings_to_matrix(ratings_df, user_col, item_col, rating_col, forced_shape=None):\n users_num = ratings_df.user_id.max() + 1\n items_num = ratings_df.item_id.max() + 1\n \n if forced_shape:\n users_num = max(users_num, forced_shape[0])\n items_num = max(items_num, forced_shape[1])\n \n ratings_mat = np.zeros([users_num, items_num])\n for rating in ratings_df.itertuples():\n ratings_mat[rating[user_col], rating[item_col]] = rating[rating_col]\n \n return ratings_mat","def __init__(self, user_factors, item_factors):\n self._user_factors = np.copy(user_factors)\n self._item_factors = np.copy(item_factors)\n \n self._users_num = user_factors.shape[0]\n self._items_num = item_factors.shape[0]\n\n assert user_factors.shape[1] == item_factors.shape[1]","def fit(self, ratings):\n # Training proceeds in 2 steps:\n # 1. Normalize item vectors to be mean-centered and unit-normalized\n # 2. Compute similarities with pairwise dot products\n self._timer = util.Stopwatch()\n\n init_rmat, users, items = matrix.sparse_ratings(ratings)\n n_items = len(items)\n _logger.info('[%s] made sparse matrix for %d items (%d ratings from %d users)',\n self._timer, len(items), init_rmat.nnz, len(users))\n\n rmat, item_means = self._mean_center(ratings, init_rmat, items)\n\n rmat = self._normalize(rmat)\n\n _logger.info('[%s] computing similarity matrix', self._timer)\n smat = self._compute_similarities(rmat)\n\n _logger.info('[%s] got neighborhoods for %d of %d items',\n self._timer, np.sum(np.diff(smat.rowptrs) > 0), n_items)\n\n _logger.info('[%s] computed %d neighbor pairs', self._timer, smat.nnz)\n\n self.item_index_ = items\n self.item_means_ = item_means\n self.item_counts_ = np.diff(smat.rowptrs)\n self.sim_matrix_ = smat\n self.user_index_ = users\n self.rating_matrix_ = init_rmat\n\n return self","def important_factors_based_on_ratings(data: pd.DataFrame) -> np.ndarray:\n # Turn labels into binary classification for equal class distribution\n data = utils.add_ratings_binary(data)\n # Get feature and label data for classifcation from original dataset\n X, y = utils.get_rating_features_labels(data)\n\n # Grab features from feature matrix\n features = X.columns\n\n # split data into train and test set\n X_train, X_test, y_train, y_test = model_selection.train_test_split(X.values, y, test_size=0.2) \n\n # Instantiate and train xgboost model for rating classfication\n xgb_model = xgboost.XGBClassifier()\n xgb_model.fit(X_train, y_train)\n\n # Grab feature importance scores from trained model\n feature_importance = xgb_model.feature_importances_\n # Find indices of top 2 important features\n top_important_features_ind = np.argpartition(feature_importance, -2)[-2:]\n\n print(f\"The top 2 important features are {features[top_important_features_ind]}\")\n\n return feature_importance","def get_user_feature_matrix(user_dict, user_index, aspect_index, N):\n result = np.zeros((len(user_index), len(aspect_index)))\n for key in user_dict.keys():\n index_user = user_index[key]\n user_reviews = user_dict[key]\n count_dict = {}\n for review in user_reviews:\n feature = review[0]\n if feature not in aspect_index:\n continue\n aspect = aspect_index[feature]\n if aspect not in count_dict:\n count_dict[aspect] = 0;\n count_dict[aspect] += 1\n for aspect in count_dict.keys():\n count = count_dict[aspect]\n result[index_user, aspect] = 1 + (N - 1) * (2 / (1 + exp(-count)) - 1)\n return result","def personalization(prediction, n):\n # prediction\n # n top n recommendation\n\n top_n = get_top_n(prediction, n)\n\n rec_dict = {}\n for uid, user_ratings in top_n.items():\n rec_dict[uid] = [iid for (iid, _) in user_ratings]\n\n rec_user_ls = [pred[0] for pred in prediction]\n rec_item_ls = [pred[1] for pred in prediction]\n\n unique_rec_user_ls = np.unique(rec_user_ls)\n unique_rec_item_ls = np.unique(rec_item_ls)\n\n # assign each item with index number\n unique_rec_item_dict = {item: ind for ind,\n item in enumerate(unique_rec_item_ls)}\n\n n_unique_rec_user = len(unique_rec_user_ls)\n n_unique_rec_item = len(unique_rec_item_ls)\n\n # recommended user item matrix\n rec_matrix = np.zeros(shape=(n_unique_rec_user, n_unique_rec_item))\n\n # represent recommended item for each user as binary 0/1\n for user in range(n_unique_rec_user):\n # get userid\n user_id = unique_rec_user_ls[user]\n # get rec item list\n item_ls = rec_dict[user_id]\n\n for item_id in item_ls:\n # get item index\n item = unique_rec_item_dict[item_id]\n rec_matrix[user, item] = 1\n\n # calculate cosine similarity matrix across all user recommendations\n similarity = cosine_similarity(X=rec_matrix, dense_output=False)\n # calculate average of upper triangle of cosine matrix\n upper_right = np.triu_indices(similarity.shape[0], k=1)\n # personalization is 1-average cosine similarity\n score = 1 - np.mean(similarity[upper_right])\n return score","def update_item_feature(\n train, item_features, user_features, lambda_item,\n nz_item_userindices, I):\n for n, item_n in enumerate(nz_item_userindices):\n nnz_users_per_item = len(item_n[1]) # Number of users who rated item n\n if (nnz_users_per_item == 0): nnz_users_per_item = 1\n # Least squares solution\n A_n = np.dot(user_features[:,item_n[1]], user_features[:,item_n[1]].T) + lambda_item * nnz_users_per_item * I\n V_n = np.dot(user_features[:,item_n[1]], train.T[item_n[1],item_n[0]].todense())\n #if (n%3 == 0): print(\"item_n: {}\".format(item_n[0]), np.linalg.det(A_n))\n if (np.linalg.det(A_n) != 0): item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)\n else: \n A_n[0,0] += 1; A_n[1,1] += 1; A_n[2,2] += 1; A_n[3,3] += 1; A_n[4,4] += 1; A_n[5,5] += 1 # if matrix A_n is singular, slightly modify several values\n item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)","def item_user_matrix(X):\n X['user_id'] = X['user_id'].astype(\"category\")\n X['song_id'] = X['song_id'].astype(\"category\")\n\n row = X['song_id'].cat.codes.copy()\n col = X['user_id'].cat.codes.copy()\n\n nrow = len(X['song_id'].cat.categories)\n ncol = len(X['user_id'].cat.categories)\n\n item_user = csr_matrix((X['score'], (row, col)), shape=(nrow, ncol))\n\n user = dict(enumerate(X['user_id'].cat.categories))\n user_index = {u: i for i, u in user.items()}\n\n item = dict(enumerate(X['song_id'].cat.categories))\n item_index = {s: i for i, s in item.items()}\n\n return item_user, item_index, user_index","def fit(self, ratings_mat):\n self.logger.debug(\"starting fit\")\n # self.n = ratings.max()['user']+1\n # self.p = ratings.max()['movie']+1\n self.ratings_mat = ratings_mat\n self.k = ratings_mat.shape[0]//20\n\n #ratings_array = ratings[ratings.columns[:-1].values].values\n\n #self.ratings_mat = np.zeros((self.n, self.p))\n\n #for i, rating in ratings.iterrows():\n # self.ratings_mat[( rating['user'], rating['movie'] )] = rating['rating']\n\n self.cosine_dists = squareform(pdist(ratings_mat, 'cosine'))\n\n #if a user has no ratings data, cosine dist will return a nan. In this case, we assume they are as different as possible, since we cannot predict using those users anyways\n self.cosine_dists = 1 - np.nan_to_num(1 - self.cosine_dists)\n\n self.similarity_ranks = self.cosine_dists.argsort(axis = 1)\n\n # ...\n\n self.logger.debug(\"finishing fit\")\n return(self)","def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, loss_type='softmax', temperature=0.05,\n sampler_config=None, seed=1024,\n ):\n\n embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,\n seed=seed,\n seq_mask_zero=True)\n\n user_features = build_input_features(user_feature_columns)\n user_inputs_list = list(user_features.values())\n user_sparse_embedding_list, _ = input_from_feature_columns(user_features,\n user_feature_columns,\n l2_reg_embedding, seed=seed,\n support_dense=False,\n embedding_matrix_dict=embedding_matrix_dict)\n\n item_features = build_input_features(item_feature_columns)\n item_inputs_list = list(item_features.values())\n item_sparse_embedding_list, _ = input_from_feature_columns(item_features,\n item_feature_columns,\n l2_reg_embedding, seed=seed,\n support_dense=False,\n embedding_matrix_dict=embedding_matrix_dict)\n\n user_dnn_input = concat_func(user_sparse_embedding_list, axis=1)\n user_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(user_dnn_input)\n user_vector_sum = l2_normalize(user_vector_sum)\n\n item_dnn_input = concat_func(item_sparse_embedding_list, axis=1)\n item_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(item_dnn_input)\n item_vector_sum = l2_normalize(item_vector_sum)\n\n if loss_type == \"logistic\":\n score = inner_product(user_vector_sum, item_vector_sum, temperature)\n output = PredictionLayer(\"binary\", False)(score)\n\n elif loss_type == \"softmax\":\n output = InBatchSoftmaxLayer(sampler_config._asdict(), temperature)(\n [user_vector_sum, item_vector_sum, item_features[sampler_config.item_name]])\n else:\n raise ValueError(' `loss_type` must be `logistic` or `softmax` ')\n\n model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output)\n\n model.__setattr__(\"user_input\", user_inputs_list)\n model.__setattr__(\"user_embedding\", user_vector_sum)\n\n model.__setattr__(\"item_input\", item_inputs_list)\n model.__setattr__(\"item_embedding\", item_vector_sum)\n\n return model","def getUserItemMatrix(self):\n\t\tdf = self.getrating()\n\n\t\trows_index = df.user_id.unique()\n\t\tcolumn_index = df.venue_id.unique() \n\n\t\trow_len = len(rows_index)\n\t\tcol_len = len(column_index)\n\n\t\tX = lil_matrix((row_len, col_len))\n\t\trow_map = dict(zip(rows_index, range(row_len)))\n\t\tcol_map = dict(zip(column_index, range(col_len)))\n\n\t\t# Get mapping table for rows and columns\n\t\td = {}\n\t\td[\"row\"] = row_map\n\t\td[\"col\"] = col_map\n\n\t\tfor index, row in df.iterrows():\n\t\t\tX[d[\"row\"][row[\"user_id\"]], d[\"col\"][row[\"venue_id\"]]] = row[\"Rating\"]\n\n\t\tX = X.tocsr() # Allow efficient row slicing\n\n\t\treturn [d,X]","def find_predictions(actives, train_rdd_gbitem_dict, train_rdd_gbuser_dict, num_items):\n active_user = actives[0][0]\n active_item = actives[0][1]\n\n # -----------------------------------\n # train_rdd_gbitem_dict = (item, ([(user,r),(user,r)...],avg_of_item))\n # train_rdd_gbuser_dict = (user, [(item,r),(item,r)...]\n\n if active_user not in train_rdd_gbuser_dict and active_item not in train_rdd_gbitem_dict:\n return (active_user, active_item), 2.5\n\n # all user, ratings that have rated active_item\n if active_item in train_rdd_gbitem_dict:\n active_item_avg = train_rdd_gbitem_dict[active_item][1]\n active_item_dict = dict(train_rdd_gbitem_dict[active_item][0]) # {user: rating, user: rating, ...}\n else:\n # item not found in training set\n # new item problem.\n average_of_user_list = train_rdd_gbuser_dict[active_user]\n average_of_user = sum([x[1] for x in average_of_user_list]) / len(average_of_user_list)\n return (active_user, active_item), average_of_user\n\n # user rated items - all (item, ratings) that the user has rated\n if active_user in train_rdd_gbuser_dict:\n active_user_rated_items = train_rdd_gbuser_dict[active_user] # [(item, rating), (item, rating), ...]\n else:\n # user not found in training set\n # new user problem.\n return (active_user, active_item), train_rdd_gbitem_dict[active_item][1]\n\n similarity_list = []\n for item, rating in active_user_rated_items:\n item_dict = dict(train_rdd_gbitem_dict[item][0])\n item_avg = train_rdd_gbitem_dict[item][1]\n similarity = find_similarity(dict(active_item_dict), active_item_avg, dict(item_dict), item_avg)\n similarity_list.append((rating, similarity))\n\n # Have obtained similarity list for active item and item from the above code.\n # Filter according to a top 'N' items and then take avg rating.\n # similarity_list.sort(key=lambda x: x[1], reverse=True)\n # similarity_list = similarity_list[:len(similarity_list) // 4]\n # similarity_list = [(x[0], x[1]*abs(x[1])**1.5) for x in similarity_list]\n # print(similarity_list)\n pred_rating = find_weighted_average(similarity_list, num_items)\n\n # for i in similarity_list:\n # print(i)\n # print(\"Pred-rating: \", pred_rating)\n\n return (active_user, active_item), pred_rating","def convert_fidelity_matrix(fidelities, fidelity, sx, factor):\n\n matrix = np.zeros((sx, fidelities - 1))\n for idx in range(0, fidelity):\n matrix[:, idx] = np.ones((sx)) * factor[fidelity]\n\n return matrix","def train_nmf(movies_ratings):\n \n #pivot the dataframe\n movies_ratings = movies_ratings.pivot_table(index='userId', columns='movieId', values='rating')\n #Fill Nan with 3.0 rating:\n movies_ratings.fillna(3.0, inplace=True)\n\n nmf_model = NMF(\n n_components=20,\n init='random',\n random_state=10,\n max_iter=10000\n )\n nmf_model.fit(movies_ratings)\n\n #save nmf model\n pickle.dump(nmf_model,open(\"models/nmf_model.sav\", 'wb'))","def metric_test(self):\n k = 10\n latent_factor = 10\n n_users = 10\n n_items = 12\n\n interactions, user_features, item_features = util.generate_dummy_data_with_indicator (num_users=n_users, num_items=n_items, interaction_density=.5)\n print (\"interactiosn shape={}\".format( np.shape(interactions) ))\n print (\"user features shape={}\".format( np.shape(user_features.toarray()) ))\n print (\"item features shape={}\".format( np.shape(item_features.toarray()) ))\n\n model = TensorRec(n_components=latent_factor)\n\n model.fit(interactions, user_features, item_features, epochs=19)\n\n ranks = model.predict_rank(user_features=user_features, item_features=item_features)\n\n print (\"Ranks shape={}\".format(np.shape(ranks)))\n\n self.assertTrue(np.shape(interactions) == np.shape(ranks))\n\n tr_recall_result = eval.recall_at_k(predicted_ranks=ranks, test_interactions=interactions, k=k, preserve_rows=False)\n # print (tr_recall_result.mean())\n\n tr_precision_result = eval.precision_at_k(predicted_ranks=ranks, test_interactions=interactions, k=k, preserve_rows=False)\n # print(tr_precision_result.mean())\n\n # we need csr for interactions data\n interactions_ = interactions.tocsr()\n recall_result = metrics.recall_at_k(ranks, interactions_, k=k, preserve_rows=False)\n # print(recall_result.mean())\n\n precision_result = metrics.precision_at_k(ranks, interactions_, k=k, preserve_rows=False)\n # print (precision_result.mean())\n\n self.assertTrue (tr_recall_result.mean() == recall_result.mean())\n self.assertTrue (tr_precision_result.mean() == precision_result.mean())","def update_user_feature(\n train, user_features, item_features, lambda_user,\n nz_user_itemindices, I):\n for d, user_d in enumerate(nz_user_itemindices): # iterate over non zero users\n nnz_items_per_user = len(user_d[1]) # Number of items user d has rated\n if (nnz_items_per_user == 0): nnz_items_per_user = 1\n \n # Least squares solution\n A_d = np.dot(item_features[user_d[1]].T, item_features[user_d[1]]) + lambda_user * nnz_items_per_user * I\n V_d = np.dot(item_features[user_d[1]].T, train[user_d[1],user_d[0]].todense())\n user_features[:,user_d[0]] = np.linalg.solve(A_d,V_d)","def _estimate_ratings(self, U, V):\n Rhat = np.matmul(U, V.transpose()) # estimate R with UV^T\n Rhat = np.where(self.R_missing, 0, Rhat) # fill in missing values of R with 0s\n return Rhat","def estimate(self, u, j):\n if not (self.trainset.knows_user(u) and self.trainset.knows_item(j)):\n raise PredictionImpossible('User and/or item is unknown.')\n\n u_ratings = self.trainset.ur[u]\n\n if self.weighting == 'linear':\n weight = sum(self.freqs[i, j, self.to_index(r)] for i, r in u_ratings)\n score = sum(self.sums[i, j, self.to_index(r)] for i, r in u_ratings)\n return score / weight\n\n # self.weighting == 'log' or None\n weights = [self.freqs[i, j, self.to_index(r)] for i, r in u_ratings]\n reciprocals = [1 / w if w else 0 for w in weights]\n scores = [self.sums[i, j, self.to_index(r)] for i, r in u_ratings]\n scores = [s * w for s, w in zip(scores, reciprocals)]\n\n if self.weighting is None:\n return np.mean(scores)\n # self.weighting == 'log'\n logs = [np.log(w + 1) if w >= 1 else 0 for w in weights]\n return np.dot(scores, logs) / np.sum(logs)","def compute_user_user_sim_base_on_common_items(self):\n self.sim_matrix = {}\n for item in self.items.values():\n # convert to list of tuples for indexing\n users = list(item.covered_users.items())\n item_popularity = len(users)\n # iter through all user pairs\n for i in range(len(users)-1):\n for j in range(i+1, len(users)):\n user_A_info, user_B_info = users[i], users[j]\n # remember to update pair wise!\n self.update_user_user_sim(user_A_info, user_B_info,\n item_popularity)\n self.update_user_user_sim(user_B_info, user_A_info,\n item_popularity)","def compute_pmf(X_inp, rank, c1, c2):\n\n X_arr = []\n u_arr = []\n v_arr = []\n d_arr = []\n\n X = deepcopy(X_inp)\n\n v_init = np.ones((np.shape(X)[1],1))\n\n for i in range(rank):\n X_arr.append(X)\n (d,u,v) = compute_factor(X, v_init, c1, c2)\n\n\n assert abs(npla.norm(u) - 1 ) < 1e-3\n assert abs(npla.norm(v) - 1 ) < 1e-3\n\n d_arr.append(d)\n u_arr.append(u)\n v_arr.append(v)\n\n toSub = np.outer(u,v)\n assert np.shape(toSub) == np.shape(X)\n X -= d * toSub\n\n return (X_arr, u_arr, v_arr, d_arr)","def item_based_CF(G):\n\n\tcode, pmid = nx.bipartite.sets(G)\n\tX = nx.bipartite.biadjacency_matrix(G,pmid,column_order=code)\n\tmean_X = np.mean(X,axis=0)\n\tadjusted_X = X - mean_X\n\tsimilarity = cosine_similarity(X.T)\n\trating = mean_X + np.dot(adjusted_X,similarity)/np.sum(np.abs(similarity),axis=1)\n\tdf = pd.DataFrame(data=rating,index=pmid,columns=code)\n\n\treturn df","def predict_rating(self, user_id, item_id):\n user_preference = self.latent_user_preferences[user_id]\n item_feature = self.latent_item_features[item_id]\n return user_preference.dot(item_feature)","def create_user_item_array(self):\n user_em = self.user_factors.weight.detach()\n item_em = self.product_factors.weight.detach()\n user_b = self.user_bias.weight.detach()\n item_b = self.product_bias.weight.detach()\n\n user_item_array = (item_em + item_b) @ (user_em + user_b).transpose(0, 1)\n preds = self._prob_to_class(user_item_array).numpy()\n\n return preds","def als(user_ids : numpy.ndarray, item_ids : numpy.ndarray,\n ratings : numpy.ndarray, num_item_factors : int,\n num_users: int, num_items : int, min_r_decrease=0.01,\n max_iterations=200, algorithm=1):\n # allocate \"user_factors\" and \"item_factors\"\n num_user_factors = num_item_factors + 1\n user_factors = numpy.random.uniform(-1, 1, num_users * num_user_factors)\n item_factors = numpy.random.uniform(-1, 1, num_items * num_item_factors)\n\n # argument construction\n user_ids_ptr = user_ids.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n item_ids_ptr = item_ids.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n\n ratings_length = len(ratings)\n ratings_ptr = ratings.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n\n user_factors_length = len(user_factors)\n user_factors_ptr = user_factors.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n\n item_factors_length = len(item_factors)\n item_factors_ptr = item_factors.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n\n iterations = _dll.als_from_python(\n user_ids_ptr, item_ids_ptr, ratings_length, ratings_ptr,\n num_item_factors, user_factors_length, user_factors_ptr,\n item_factors_length, item_factors_ptr, ctypes.c_double(min_r_decrease),\n max_iterations, algorithm)\n\n return user_factors, item_factors, iterations","def NMF(model, maxIter=100, beliefs=None, verbose=False):\n if beliefs is None: beliefs = [Factor([Xi],1.0/Xi.states) for Xi in model.X]\n \n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\n for f in model.factors:\n m = f.log()\n for v in f.vars: m *= beliefs[v]\n lnZ += m.sum()\n if verbose: print(\"Iter 0: \"+str(lnZ))\n\n for t in xrange(1,maxIter+1): # for each iteration:\n # Update all the beliefs via coordinate ascent:\n for Xi in model.X: # for each variable, \n bNew = 0.0 # compute E[ log f ] as a function of Xi:\n for f in model.factorsWith(Xi,copy=False): # for each factor f_a, compute:\n m = f.log() # E[log f_a] = \\sum \\log f_a \\prod b_v\n for v in f.vars - [Xi]: m *= beliefs[v]\n bNew += m.marginal([Xi]) # sum them up to get E[log f]\n bNew -= bNew.max() # (numerical issues)\n bNew = bNew.exp()\n bNew /= bNew.sum() # set b(Xi) = exp( E[log f] ) / Z\n beliefs[Xi] = bNew\n #\n # Compute the lower bound on the partition function:\n # E_b [ log f ] + H(b) = \\sum_a E[log f_a] + \\sum_i H(b_i) for independent beliefs\n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\n for f in model.factors:\n m = f.log()\n for v in f.vars: m *= beliefs[v]\n lnZ += m.sum()\n if verbose: print(\"Iter \"+str(t)+\": \"+str(lnZ))\n return lnZ,beliefs","def final_recommendation_score_matrix_helper_for_different_knn_types(self, type_of_method, index_matrix, value_matrix):\n if type_of_method == \"user\":\n for i in range(index_matrix.shape[1]):\n temp = index_matrix[:,i].astype(int)\n k_similar_users_profiles = self.utility_matrix[temp]\n k_similar_users_similarity_values = value_matrix[:,i]\n self.final_score_matrix[i,:] = np.dot(k_similar_users_similarity_values,k_similar_users_profiles)\n elif type_of_method == \"item\":\n for i in range(index_matrix.shape[1]):\n temp = index_matrix[:,i].astype(int)\n k_similar_item_profiles = self.utility_matrix.T[temp]\n k_similar_item_similarity_values = value_matrix[:,i]\n self.final_score_matrix[:,i] = np.dot(k_similar_item_similarity_values,k_similar_item_profiles)","def get_users_features_matrix(games_features_matrix: csr_matrix, users_games_matrix: csr_matrix) -> csr_matrix:\n logging.getLogger(__name__).debug('Users features matrix calculating...')\n users_features_matrix = users_games_matrix * games_features_matrix\n logging.getLogger(__name__).debug('users_features_matrix.shape: ' + str(users_features_matrix.shape))\n return users_features_matrix","def NMF(X,r):\n nmf_mdl = nmf.NMF(X,num_bases=r)\n nmf_mdl.initialization()\n nmf_mdl.factorize()\n return nmf_mdl.W,nmf_mdl.H,np.dot(nmf_mdl.W,nmf_mdl.H)","def fit(self, data, num_features, lambda_user, lambda_item, gamma):\n user_matrix, item_matrix = self.init_matrix(data, num_features)\n nnz_users, nnz_items = data.nonzero()\n nnz_data = list(zip(nnz_users, nnz_items))\n for it in tqdm(range(self.num_epochs)):\n gamma /= 1.1\n np.random.shuffle(nnz_data)\n for u, i in nnz_data:\n user = user_matrix[u, :]\n item = item_matrix[:, i]\n err = data[u, i] - user @ item\n user_matrix[u, :] += gamma * (err * item - lambda_user * user)\n item_matrix[:, i] += gamma * (err * user - lambda_item * item)\n\n self.user_matrix = user_matrix\n self.item_matrix = item_matrix","def evaluate_hmdb51_fusion():\n vlen = 0\n ob_suffix = '-max.feat.npy.gz'\n fv_suffix = '_fv.npy.gz'\n ob_root = '/home/syq/research_final/data/features/ob_hmdb51_pooled_python/'\n fv_root = '/home/syq/research_final/data/dense-traj/fv_hmdb51_python/'\n hmdb_splits = 'testTrainMulti_7030_splits/'\n categories = os.listdir(fv_root)\n weight = 1.0\n weights = [i / 20.0 for i in range(21)]\n acc_to_weights = {}\n\n for weight in weights:\n print \"Weight: %.2f\" % weight\n accs = np.zeros(3)\n for splitnum in range(1,4):\n ts = time.time()\n trainfiles, testfiles = hmdb51_splits.loadsplit(categories,\n hmdb_splits,\n splitnum)\n print 'Have %d train files' % len(trainfiles)\n print 'Have %d test files' % len(testfiles)\n\n if not vlen:\n fp = gzip.open(os.path.join(ob_root,'%s%s'%(trainfiles[0][0][:-4],\n ob_suffix)),\"rb\")\n vlen_ob = len(np.load(fp))\n fp.close()\n print \"OB vector length is %d\" % vlen_ob\n fp = gzip.open(os.path.join(fv_root,'%s%s'%(trainfiles[0][0][:-4],\n fv_suffix)),\"rb\")\n vlen_fv = len(np.load(fp))\n fp.close()\n print \"IDTFV vector length is %d\" % vlen_fv\n\n Dtrain_ob = np.zeros( (len(trainfiles),vlen_ob), np.float32 )\n Dtrain_fv = np.zeros( (len(trainfiles),vlen_fv), np.float32 )\n\n Ytrain = np.ones ( (len(trainfiles) )) * -1000\n\n for fi,f in enumerate(trainfiles):\n fp = gzip.open(os.path.join(ob_root,'%s%s'%(f[0][:-4],\n ob_suffix)),\"rb\")\n Dtrain_ob[fi][:] = np.load(fp)\n fp.close()\n Ytrain[fi] = f[1]\n\n fp = gzip.open(os.path.join(fv_root,'%s%s'%(f[0][:-4],\n fv_suffix)),\"rb\")\n Dtrain_fv[fi][:] = np.load(fp)\n fp.close()\n\n Dtest_ob = np.zeros( (len(testfiles),vlen_ob), np.float32 )\n Dtest_fv = np.zeros( (len(testfiles),vlen_fv), np.float32 )\n\n Ytest = np.ones ( (len(testfiles) )) * -1000\n\n for fi,f in enumerate(testfiles):\n fp = gzip.open(os.path.join(ob_root,'%s%s'%(f[0][:-4],\n ob_suffix)),\"rb\")\n Dtest_ob[fi][:] = np.load(fp)\n fp.close()\n Ytest[fi] = f[1]\n\n fp = gzip.open(os.path.join(fv_root,'%s%s'%(f[0][:-4],\n fv_suffix)),\"rb\")\n Dtest_fv[fi][:] = np.load(fp)\n fp.close()\n\n \"\"\"\n Early fusion\n Dtrain = np.hstack((Dtrain_ob, Dtrain_fv))\n Dtest = np.hstack((Dtest_ob, Dtest_fv))\n\n clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\n acc = clf.fit(Dtrain, Ytrain).score(Dtest, Ytest)\n \"\"\"\n fv_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\n\n ob_clf = OneVsRestClassifier(estimator=SVC(C=10,\n cache_size=1000,\n kernel='linear',\n probability=True),\n n_jobs=-1)\n\n # Get probabilities for late fusion\n Dtrain_fv = fv_clf.fit(Dtrain_fv, Ytrain).decision_function(Dtrain_fv)\n Dtrain_ob = ob_clf.fit(Dtrain_ob, Ytrain).decision_function(Dtrain_ob)\n Dtest_fv = fv_clf.decision_function(Dtest_fv)\n Dtest_ob = ob_clf.decision_function(Dtest_ob)\n\n # Scale decision values b/w 0 and 1\n Dtrain_fv = preprocessing.normalize(Dtrain_fv)\n Dtrain_ob = preprocessing.normalize(Dtrain_ob)\n Dtest_fv = preprocessing.normalize(Dtest_fv)\n Dtest_ob = preprocessing.normalize(Dtest_ob)\n\n # Late fusion\n scores_train = (Dtrain_fv * weight) + (Dtrain_ob * (1 - weight))\n latefusion_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=-1)\n latefusion_clf.fit(scores_train, Ytrain)\n\n scores_test = (Dtest_fv * weight) + (Dtest_ob * (1 - weight))\n acc = latefusion_clf.score(scores_test, Ytest)\n print 'Fold', splitnum, 'late fusion acc', acc\n print \"Train & testing time %.3f\" % (time.time() - ts)\n accs[splitnum-1] = acc\n acc_to_weights[weight] = accs\n\n print \"Mean accuracy: %.3f\" % accs.mean()\n with open(\"hmdb51_weight_gridsearch.txt\", \"w\") as f:\n for weight, accs in acc_to_weights.items():\n f.write(str(weight) + str(accs) + '\\n')\n return acc_to_weights\n\n \"\"\"\n with open('fv_hmdb51_accs.txt', 'w') as f:\n f.write(\"%s\\nMean:%.3f\" % (str(accs), np.mean(accs)))\n \"\"\"","def mrr(self):\n _test = self.drop_bad_ratings()\n merged = pd.merge(left=_test, right=self.predict, on=['user', 'item'], how='right')[\n ['user', 'item', 'rating_x', 'rating_y']]\n nott = np.vectorize(lambda x: not x)\n mrrs = []\n for user in merged.user.unique():\n frame = merged[merged.user == user].sort_values(by='rating_y', ascending=False)\n true_ratings = frame.rating_x.values\n positions = np.where(nott(np.isnan(true_ratings)))[0]\n if len(positions) > 0:\n mrrs.append(1 / (positions[0] + 1))\n else:\n mrrs.append(0)\n\n return sum(mrrs) / len(mrrs)","def feature_processing(array2d):\n new_array2d = np.zeros([array2d.shape[0], 29])\n # items/ orders\n new_array2d[:, 0] = array2d[:, 4] / array2d[:, 3]\n # cancels / orders\n new_array2d[:, 1] = array2d[:, 5] / array2d[:, 3]\n # returns / items\n new_array2d[:, 2] = array2d[:, 6] / array2d[:, 4]\n # voucher / orders\n new_array2d[:, 3] = array2d[:, 10] / array2d[:, 3]\n # female_items / female_items + male_items\n new_array2d[:, 4] = array2d[:, 15] / ([1 if x == 0 else x for x in (array2d[:, 15] + array2d[:, 16])])\n # male_items / female_items + male_items\n new_array2d[:, 5] = array2d[:, 16] / ([1 if x == 0 else x for x in (array2d[:, 15] + array2d[:, 16])])\n # unisex_items / items\n new_array2d[:, 6] = array2d[:, 17] / array2d[:, 4]\n # wapp_items / items\n new_array2d[:, 7] = array2d[:, 18] / array2d[:, 4]\n # wftw_items / items\n new_array2d[:, 8] = array2d[:, 19] / array2d[:, 4]\n # mapp_items / items\n new_array2d[:, 9] = array2d[:, 20] / array2d[:, 4]\n # wacc_items / items\n new_array2d[:, 10] = array2d[:, 21] / array2d[:, 4]\n # macc_items / items\n new_array2d[:, 11] = array2d[:, 22] / array2d[:, 4]\n # mftw_items / items\n new_array2d[:, 12] = array2d[:, 23] / array2d[:, 4]\n # wspt_items / items\n new_array2d[:, 13] = array2d[:, 24] / array2d[:, 4]\n # mspt_items / items\n new_array2d[:, 14] = array2d[:, 25] / array2d[:, 4]\n # curvy_items / items\n # Curvy item has a strong correlation with gender, however they are very right-skewed use np.power(1/6) to smooth it\n new_array2d[:, 15] = np.power(array2d[:, 26] / array2d[:, 4], 1 / 6)\n # sacc_items / items\n new_array2d[:, 16] = array2d[:, 27] / array2d[:, 4]\n # msite_orders / orders\n new_array2d[:, 17] = array2d[:, 28] / array2d[:, 3]\n # desktop_orders / orders\n new_array2d[:, 18] = array2d[:, 29] / array2d[:, 3]\n # android_orders / orders\n new_array2d[:, 19] = array2d[:, 30] / array2d[:, 3]\n # ios_orders / orders\n new_array2d[:, 20] = array2d[:, 31] / array2d[:, 3]\n # other_device_orders / orders\n new_array2d[:, 21] = array2d[:, 32] / array2d[:, 3]\n # work_orders / orders\n new_array2d[:, 22] = array2d[:, 33] / array2d[:, 3]\n # home_orders / orders\n new_array2d[:, 23] = array2d[:, 34] / array2d[:, 3]\n # parcelpoint_orders / orders\n new_array2d[:, 24] = array2d[:, 35] / array2d[:, 3]\n # other_collection_orders / orders\n new_array2d[:, 25] = array2d[:, 36] / array2d[:, 3]\n # average_discount_onoffer\n new_array2d[:, 26] = array2d[:, 39]\n # average_discount_used\n new_array2d[:, 27] = array2d[:, 40]\n # revenue / order\n new_array2d[:, 28] = array2d[:, 41] / array2d[:, 3]\n\n # normalize by each feature\n new_array2d = normalize(new_array2d, axis=0, norm='max')\n return new_array2d","def __init__(\n self,\n n_users,\n n_products,\n n_factors=20,\n optimizer=torch.optim.SGD,\n lr=0.001,\n l2=0,\n momentum=0,\n loss_fn=nn.BCEWithLogitsLoss,\n activation=nn.Sigmoid,\n ):\n super(NNMatrixFactorization, self).__init__()\n\n self.l2 = l2\n self.lr = lr\n self.momentum = momentum\n self.user_factors = ScaledEmbedding(n_users, n_factors)\n self.product_factors = ScaledEmbedding(n_products, n_factors)\n self.user_bias = ZeroEmbedding(n_users, 1)\n self.product_bias = ZeroEmbedding(n_products, 1)\n\n self.activation = activation()\n self.loss_fn = loss_fn()\n self.optimizer = optimizer(\n self.parameters(), lr=self.lr, weight_decay=self.l2, momentum=self.momentum\n )","def get_scores(self):\n hist = self.confusion_matrix\n # hist = [TN,FP;FN,TP]\n acc = np.diag(hist).sum() / hist.sum()\n acc_cls = np.diag(hist) / hist.sum(axis=1)\n acc_cls = np.nanmean(acc_cls)\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\n # iou = iu.sum() / self.n_classes\n mean_iou = np.nanmean(iu) # if classes = 2: iou = miou\n freq = hist.sum(axis=1) / hist.sum()\n fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()\n cls_iou = dict(zip(range(self.n_classes), iu))\n\n ##############################################\n tn = hist[0, 0]\n tp = np.diag(hist).sum() - tn\n fp = np.triu(hist, 1).sum()\n fn = np.tril(hist, -1).sum()\n precision = tp / (tp + fp)\n recall = tp / (tp + fn)\n f1 = 2 * precision * recall / (precision + recall)\n\n # for medical img, img_seg \\in [0,1]\n dice = 2 * tp / (tp + tp + fn + fp)\n # dice = f1-score\n dsc = 2 * tp / (tp + fn + fp)\n # dsc = jaccard\n # voe = 2 * abs(fp + fn) / (tp + tp + fn + fp)\n # voe = 1 - dsc\n\n k2 = {\n # \"Overall Acc: \\t\": acc,\n 'Mean Acc': float(judge_nan(acc_cls)),\n # \"FreqW Acc : \\t\": fwavacc,\n 'Mean IoU': float(judge_nan(mean_iou)),\n 'F1-score': float(judge_nan(f1)),\n 'DSC': float(judge_nan(dsc)),\n 'Precision': float(judge_nan(precision)),\n 'Recall': float(judge_nan(recall)),\n }\n\n return k2","def predict(user_id, item_id, ratings):\n\n # convert long to wide\n ratings_wide = ratings.pivot(index='user', columns='movie', values='rating')\n\n # compute user similarities\n similarities = compute_similarities(user_id, ratings_wide)\n \n prediction = predict_rating(item_id, ratings_wide, similarities, N=N_NEIGHBORS)\n \n return prediction","def __factor_matrix(self, R, K, alpha, steps, beta, error_limit):\n # Transform regular array to numpy array\n R = numpy.array(R)\n\n # Generate P - N x K\n # Use random values to start. Best performance\n N = len(R)\n M = len(R[0])\n P = numpy.random.rand(N, K)\n\n # Generate Q - M x K\n # Use random values to start. Best performance\n Q = numpy.random.rand(M, K)\n Q = Q.T\n\n error = 0\n\n # iterate through max # of steps\n for step in xrange(steps):\n\n # iterate each cell in r\n for i in xrange(len(R)):\n for j in xrange(len(R[i])):\n if R[i][j] > 0:\n\n # get the eij (error) side of the equation\n eij = R[i][j] - numpy.dot(P[i, :], Q[:, j])\n\n for k in xrange(K):\n # (*update_rule) update pik_hat\n P[i][k] = P[i][k] + alpha * (2 * eij * Q[k][j] - beta * P[i][k])\n\n # (*update_rule) update qkj_hat\n Q[k][j] = Q[k][j] + alpha * ( 2 * eij * P[i][k] - beta * Q[k][j] )\n\n # Measure error\n error = self.__error(R, P, Q, K, beta)\n\n # Terminate when we converge\n if error < error_limit:\n break\n\n # track Q, P (learned params)\n # Q = Products x feature strength\n # P = Users x feature strength\n self.Q = Q.T\n self.P = P\n\n self.__print_fit_stats(error, N, M)","def recommend(self, user_id, N=10):\n scores = self.user_factors[user_id] @ self.product_factors.T\n best = np.argpartition(scores, -N)[-N:]\n return sorted(zip(best, scores[best]), key=lambda x: -x[1])","def weighted_majority_vote(c_pred,m_pred,f_pred,acc_c,acc_m,acc_f, dataset):\n c,m,f = np.argmax(c_pred),np.argmax(m_pred),np.argmax(f_pred)\n coarse = np.zeros(2)\n middle = np.zeros(4)\n fine = np.zeros(10)\n\n if dataset == 'cifar10':\n middle = np.zeros(5)\n coarse[c] = 1\n middle[m] = 1\n fine[f] = 1\n res = np.zeros(10)\n w1 = np.log(acc_c/(1.-acc_c))\n w2 = np.log(acc_m/(1.-acc_m))\n w3 = np.log(acc_f/(1.-acc_f))\n if dataset == 'cifar10':\n for i in range(10):\n if i <2:\n res[i] = w1*coarse[0] + w2*middle[0] + w3*fine[i]\n elif 2<=i <4:\n res[i] = w1*coarse[0] + w2*middle[1] + w3*fine[i]\n elif 4 <=i<6:\n res[i] = w1*coarse[1] + w2*middle[2] + w3*fine[i]\n elif 6<=i<8:\n res[i] = w1*coarse[1] + w2*middle[3] + w3*fine[i]\n else:\n res[i] = w1*coarse[1] + w2*middle[4] + w3*fine[i]\n else :\n for i in range(10):\n if i <3:\n res[i] = w1*coarse[0] + w2*middle[0] + w3*fine[i]\n elif 3<=i <5:\n res[i] = w1*coarse[0] + w2*middle[1] + w3*fine[i]\n elif 5 <=i<8:\n res[i] = w1*coarse[1] + w2*middle[2] + w3*fine[i]\n else:\n res[i] = w1*coarse[1] + w2*middle[3] + w3*fine[i]\n index = np.argmax(res)\n return(index)","def _compute_mu_factor2(*input_mols):\n mu_factor = 1\n for mol in input_mols:\n mu_factor *= np.prod(fact(mol))\n return mu_factor","def update_model(self):\n temp_uservisits = self._uservisits\n unique_users = temp_uservisits['userid'].unique()\n for itemidx, itemid in self._allitems.iterrows():\n self.__itemid2idx[str(itemid['itemid'])] = itemidx\n self.__itemidx2id[itemidx] = str(itemid['itemid'])\n for useridx, userid in enumerate(unique_users):\n self.__userid2idx[str(userid)] = useridx\n self.__useridx2id[useridx] = str(userid)\n useritem = set(temp_uservisits[temp_uservisits['userid'] == userid]['itemid'].astype('str').values)\n allitem = set(self.__itemid2idx.keys())\n itemsnotinuser = allitem - useritem\n self.__useridx2nvitems[useridx] = list(itemsnotinuser)\n temp = pandas.DataFrame([{\"userid\": userid, \"itemid\": t, \"rating\": 0, \"timestamp\": \"NA\"} for t in itemsnotinuser])\n temp_uservisits = pandas.concat([temp_uservisits, temp])\n userid = temp_uservisits['userid'].values\n itemid = temp_uservisits['itemid'].values\n rating = temp_uservisits['rating'].values\n useridx = [self.__userid2idx[str(int(uid))] for uid in userid]\n itemidx = [self.__itemid2idx[str(int(iid))] for iid in itemid]\n model = self.__get_model(num_users=len(temp_uservisits['userid'].unique()), num_items=len(self._allitems),\n mf_dim=self.__numtopics)\n if self.__learner.lower() == \"adagrad\":\n model.compile(optimizer=Adagrad(lr=self.__learningrate), loss='binary_crossentropy')\n elif self.__learner.lower() == \"rmsprop\":\n model.compile(optimizer=RMSprop(lr=self.__learningrate), loss='binary_crossentropy')\n elif self.__learner.lower() == \"adam\":\n model.compile(optimizer=Adam(lr=self.__learningrate), loss='binary_crossentropy')\n else:\n model.compile(optimizer=SGD(lr=self.__learningrate), loss='binary_crossentropy')\n\n for epoch in range(self.__numiterations):\n t1 = time.time()\n hist = model.fit([numpy.array(useridx), numpy.array(itemidx)], numpy.array(rating),\n batch_size=self.__batchsize, nb_epoch=1, verbose=0, shuffle=True)\n t2 = time.time()\n self.__recommender1 = model\n rating = list(map(numpy.double, rating))\n self.__itemuser = csr_matrix((rating, (useridx, itemidx)), shape=(len(set(useridx)), len(set(itemidx))))\n self.__recommender2 = implicit.als.AlternatingLeastSquares(factors=self.__numtopics)\n self.__recommender2.fit(self.__itemuser)","def create_matrix(ratings_df, jokes_df):\r\n \"\"\" note: empty entries are populated with zeros \"\"\"\r\n\r\n matrix_handler = matrix_object()\r\n\r\n num_joke_features = 5\r\n\r\n ''' add all joke features '''\r\n for row_idx in range(0, jokes_df.shape[0]):\r\n joke_idx = int(jokes_df.iloc[row_idx][\"Idx\"])\r\n isAggressive = jokes_df.iloc[row_idx][\"isAggressive\"]\r\n isIncongruence = jokes_df.iloc[row_idx][\"isIncongruence\"]\r\n generation = jokes_df.iloc[row_idx][\"Generation\"]\r\n isMillenial = (generation == \"Millenial\")\r\n isGenX = (generation == \"Gen X\")\r\n isGenZ = (generation == \"Gen Z\")\r\n\r\n if(int(isMillenial) == 1.0 and int(isGenX) == 1.0):\r\n raise Valueerror()\r\n\r\n matrix_handler.add_value(joke_idx - 1, 0, int(isAggressive))\r\n matrix_handler.add_value(joke_idx - 1, 1, int(isIncongruence))\r\n matrix_handler.add_value(joke_idx - 1, 2, int(isMillenial))\r\n matrix_handler.add_value(joke_idx - 1, 3, int(isGenX))\r\n matrix_handler.add_value(joke_idx - 1, 4, int(isGenZ))\r\n\r\n ''' add all ratings '''\r\n for row_idx in range(0, ratings_df.shape[0]):\r\n for joke_idx in range(1, 122):\r\n col_name = \"joke\" + str(joke_idx)\r\n matrix_handler.add_value(joke_idx - 1, row_idx + num_joke_features, ratings_df.iloc[row_idx][col_name])\r\n\r\n matrix = matrix_handler.compile_matrix()\r\n new_df = matrix_handler.to_df(matrix)\r\n\r\n return matrix, new_df","def feature_selection_classifier_3(array2d):\n newArray2d = np.zeros([array2d.shape[0], 18])\n # female_items / female_items + male_items\n newArray2d[:, 0] = array2d[:, 4]\n # male_items / female_items + male_items\n newArray2d[:, 1] = array2d[:, 5]\n # wapp_items / items\n newArray2d[:, 2] = array2d[:, 7]\n # wftw_items / items\n newArray2d[:, 3] = array2d[:, 8]\n # mapp_items / items\n newArray2d[:, 4] = array2d[:, 9]\n # wacc_items / items\n newArray2d[:, 5] = array2d[:, 10]\n # macc_items / items\n newArray2d[:, 6] = array2d[:, 11]\n # mftw_items / items\n newArray2d[:, 7] = array2d[:, 12]\n # curvy_items / items\n newArray2d[:, 8] = array2d[:, 15]\n # msite_orders / orders\n newArray2d[:, 9] = array2d[:, 17]\n # desktop_orders / orders\n newArray2d[:, 10] = array2d[:, 18]\n # android_orders / orders\n newArray2d[:, 11] = array2d[:, 19]\n # ios_orders / orders\n newArray2d[:, 12] = array2d[:, 20]\n # other_device_orders / orders\n newArray2d[:, 13] = array2d[:, 21]\n # home_orders / orders\n newArray2d[:, 14] = array2d[:, 23]\n # other_collection_orders / orders\n newArray2d[:, 15] = array2d[:, 25]\n # average_discount_onoffer\n newArray2d[:, 16] = array2d[:, 26]\n # average_discount_used\n newArray2d[:, 17] = array2d[:, 27]\n return newArray2d","def fit(self):\n\n rmse_old = .0\n for epoch in range(self.epochs):\n error_final = .0\n\n for user, item, feedback in self.feedback_triples:\n pu = self.p[user] + self.y_sum_rows(user)\n\n # Calculate error\n eui = feedback - self._predict_svd_plus_plus_score(user, item, pu, False)\n error_final += (eui ** 2.0)\n\n # update bu and bi\n self.bu[user] += self.bias_learn_rate * (eui - self.delta_bias * self.bu[user])\n self.bi[item] += self.bias_learn_rate * (eui - self.delta_bias * self.bi[item])\n\n # Adjust the factors\n norm_eui = eui / self.n_u[user]\n\n i_f = self.q[item]\n\n # Compute factor updates\n delta_u = np.subtract(np.multiply(eui, i_f), np.multiply(self.delta, self.p[user]))\n self.p[user] += np.multiply(self.learn_rate, delta_u)\n\n delta_i = np.subtract(np.multiply(eui, pu), np.multiply(self.delta, i_f))\n self.q[item] += np.multiply(self.learn_rate, delta_i)\n\n # update y (implicit factor)\n common_update = norm_eui * i_f\n\n for j in self.items_id_seen_by_user[user]:\n delta_y = np.subtract(common_update, self.delta * self.y[j])\n self.y[j] += self.learn_rate * delta_y\n\n rmse_new = np.sqrt(error_final / self.train_set[\"number_interactions\"])\n\n if np.fabs(rmse_new - rmse_old) <= self.stop_criteria:\n break\n else:\n rmse_old = rmse_new","def ndpm(self):\n\n merged = pd.merge(left=self.test, right=self.predict, on=['user', 'item'], how='inner')[\n ['user', 'rating_x', 'rating_y']]\n ndpms = []\n for user in merged.user.unique():\n frame = merged[merged.user == user]\n if frame.shape[0] <= 1:\n continue\n C_plus = self.num_of_ordered_positive(frame, 'rating_x', 'rating_y')\n C_minus = self.num_of_ordered_negative(frame, 'rating_x', 'rating_y')\n C_u = self.num_of_ordered(frame, 'rating_x')\n if C_u == 0:\n continue\n C_s = self.num_of_ordered(frame, 'rating_y')\n C_u0 = C_u - (C_plus + C_minus)\n ndpms.append(1 - (C_minus + 0.5 * C_u0) / C_u)\n\n return sum(ndpms) / len(ndpms)","def gen_W(users, items, ratings):\n\n # initiate graph\n user = users.tolist()\n item = items.tolist()\n rating = ratings.tolist()\n B = nx.Graph()\n B.add_nodes_from(user, bipartite=0)\n B.add_nodes_from(item, bipartite=1)\n\n # create edges\n for i in range(len(user)):\n B.add_edges_from([(user[i], item[i])])\n B[user[i]][item[i]]['weight'] = rating[i]\n\n users_unique = users.unique() \n items_unique = items.unique()\n\n # BiAdjacency matrix - for bipartite network\n W = biadjacency_matrix(B, users_unique,items_unique).toarray()\n\n # sparce form of Biadjacency matrix\n W = spa.csr_matrix(W)\n print('Shape of W: '+str(W.shape))\n\n return W, users_unique, items_unique","def alternate_ls (u_num, Y, P, C, reg):\n\n\t# get # of items/users and # of latent factors\n\t[i_num, f_num] = Y.shape\n\n\t# output buffer\n\tX = np.zeros((u_num, f_num))\n\n\t# precalculate YtY to improve the performance\n\tYtY = Y.T * Y\n\n\t# iterate over each user/item\n\tfor u in range(u_num):\n\n\t\t# store the diagonal elements of the matrix Cu discussed in the paper in a vector\n\t\tCu = C[u,:]\n\n\t\t# store the coresponding row/column of the preference matrix\n\t\tPu = P[u,:]\n\n\t\t# compute Cu-I\n\t\tCu_I = Cu - 1\n\n\t\t# calculate Yt(Cu-I)Y\n\t\tYtCu_IY = np.zeros((f_num, f_num))\n\t\tCuIY = np.multiply(Y, Cu_I.T) # weight each row of Y with Cu-I\n\t\tfor row in range(f_num):\n\t\t\tfor col in range(f_num):\n\t\t\t\tYtCu_IY[row,col] = Y[:,row].T * CuIY[:,col]\n\t\t\n\t\t# left term : ((YtCuY + regI)^(-1)) = (YtY + Yt(Cu-I)Y + regI)^(-1)\n\t\tleft_inv = YtY + YtCu_IY + reg*np.eye(f_num)\n\t\tleft = np.linalg.inv(left_inv)\n\n\t\t# right term : YtCuPu\n\t\tright = Y.T * np.multiply(Cu.T, Pu.T)\n\n\t\t# compute the latent factor of the user/item\n\t\tx = left * right\n\t\t\n\t\t# store it in a matrix\n\t\tX[u,:] = x.T\n\n\t# return an MxF or NxF matrix\n\treturn np.matrix(X)","def calc_score(self, user_id, item_id): \n p = np.dot(self.U[user_id], self.V[item_id])\n if self.trunc_score_rule==None:pass\n else: p=self.trunc_score_rule(p)\n \n return p","def evaluate_ucf50_fusion():\n accs = np.zeros(3)\n ob_suffix = '-max.feat.npy.gz'\n fv_suffix = '_fv.npy.gz'\n ob_root = '/home/syq/research_final/data/features/ob_ucf50_pooled_python/'\n fv_root = '/home/syq/research_final/data/features/fv_ucf50_python/'\n fv_groups, full, sets = utility.split_data(fv_root,\n suffix=fv_suffix,\n useLooCV=False)\n\n ob_groups, _, _ = utility.split_data(ob_root,\n suffix=ob_suffix,\n useLooCV=False)\n weights = [i / 20.0 for i in range(8, 13)]\n acc_to_weights = {}\n\n for weight in weights:\n print \"Weight: %.2f\" % weight\n accs = np.zeros(2)\n for i in xrange(2):\n ts = time.time()\n Dtrain_fv, Dtest_fv, Ytrain, Ytest = utility.load_groups(\n fv_groups, np.setdiff1d(full, sets[i]),\n sets[i], scale=False, verbose=False)\n\n Dtrain_ob, Dtest_ob, Ytrain, Ytest = utility.load_groups(\n ob_groups, np.setdiff1d(full, sets[i]),\n sets[i], scale=False, verbose=False)\n\n fv_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\n\n ob_clf = OneVsRestClassifier(estimator=SVC(C=10,\n cache_size=1000,\n kernel='linear',\n probability=True),\n n_jobs=-1)\n\n # Get probabilities for late fusion\n Dtrain_fv = fv_clf.fit(Dtrain_fv, Ytrain).decision_function(Dtrain_fv)\n Dtrain_ob = ob_clf.fit(Dtrain_ob, Ytrain).decision_function(Dtrain_ob)\n Dtest_fv = fv_clf.decision_function(Dtest_fv)\n Dtest_ob = ob_clf.decision_function(Dtest_ob)\n\n # Scale decision values b/w 0 and 1\n Dtrain_fv = preprocessing.normalize(Dtrain_fv)\n Dtrain_ob = preprocessing.normalize(Dtrain_ob)\n Dtest_fv = preprocessing.normalize(Dtest_fv)\n Dtest_ob = preprocessing.normalize(Dtest_ob)\n\n # weighted averaging\n scores_train = (Dtrain_fv * weight) + (Dtrain_ob * (1 - weight))\n latefusion_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=-1)\n latefusion_clf.fit(scores_train, Ytrain)\n\n scores_test = (Dtest_fv * weight) + (Dtest_ob * (1 - weight))\n latefusion_acc = latefusion_clf.score(scores_test, Ytest)\n print 'Fold', i, 'late fusion acc', latefusion_acc\n print \"Train & testing time %.3f\" % (time.time() - ts)\n accs[i] = latefusion_acc\n\n print \"Mean accuracy: %.3f\" % accs.mean()\n with open(\"fv_ucf50_accs_5fold_gridsearch.txt\", \"w\") as f:\n for weight, accs in acc_to_weights.items():\n f.write(str(weight) + str(accs) + '\\n')\n return acc_to_weights","def _predict_user_item(self, user, item):\n if not isinstance(user, int):\n user = self._user_to_ndx[user]\n if not isinstance(item, int):\n item = self._item_to_ndx[item]\n\n try:\n rating_mean = self._averages[user]\n except AttributeError:\n raise RuntimeError('Must fit before predicting')\n\n other_users = [other for other in self._users if other != user and\n np.isfinite(self._votes[other][item])]\n weights = np.array([self._weight(user, other)\n for other in other_users])\n deviations = np.array([self._votes[other][item] - self._averages[other]\n for other in other_users])\n\n weight_sum = np.sum(np.absolute(weights))\n if weight_sum < _EPSILON:\n return rating_mean # No similar users, so guess their avg rating\n\n norm_const = 1 / weight_sum\n\n weighted_avg = np.sum(weights * deviations)\n return rating_mean + norm_const * weighted_avg","def construct_classifier_for_user(user_indexed_reviews, restaurant_indexed_reviews):\t\n # compute all_reviews\n all_reviews = []\n for restaurant in restaurant_indexed_reviews:\n reviews_content = ''\n for user in restaurant_indexed_reviews[restaurant]:\n reviews = restaurant_indexed_reviews[restaurant][user]\n for review in reviews:\n reviews_content += review['text'][0:len(review['text'])-1]\n all_reviews.append(reviews_content)\n\n print 'extract feature...'\n # construct classifier\n X_total, y_total, restaurant_feature = extracttfidf_user(user_indexed_reviews, all_reviews, restaurant_indexed_reviews)\n\n print 'construct classifier...'\n i = 0\n for user in user_indexed_reviews:\n print i\n i += 1\n classifier = MultinomialNB(alpha=.01)\n X_train = X_total[user]\n y_train = y_total[user]\n if X_train != None:\n try:\n classifier.fit(X_train, y_train)\n update_rating(user, restaurant_feature, classifier, user_indexed_reviews, restaurant_indexed_reviews)\n except:\n continue","def model_frequency(ref, alt, bases_all_reads, f_vector, error_rates):\n # model Mx: NO alt allele\n if ref == alt:\n return np.array([error_rates[ref][eb] for eb in \"ACGT-\"])\n # model Mxy: two alleles\n mat = np.zeros((K, 5))\n for b, base in enumerate(\"ACGT-\"):\n for i, observed_base in enumerate(bases_all_reads):\n mat[i][b] = error_rates[base][observed_base]\n\n m2 = (mat * f_vector)[:, [bases_index[alt], bases_index[ref]]]\n alt_ref_f_vector = (m2 / m2.sum(axis=1)[:, np.newaxis]).sum(axis=0) / K\n\n l = [alt_ref_f_vector[0], ]*5\n l[bases_index[ref]] = alt_ref_f_vector[1]\n updated_f_vector = np.array(l)\n\n return updated_f_vector","def __init__(self, customer_vendor_full, valid_rating_mean):\r\n super(User_CF, self).__init__()\r\n self.customer_vendor_full = customer_vendor_full\r\n self.customer_vendor_ratings = self.select_features()\r\n self.customer_vendor_matrix = self.customer_vendor_ratings.pivot(\r\n index='customer_id', columns='vendor_id', values='mean_rating') # (26779, 100)\r\n self.rating_matrix = self.customer_vendor_matrix.fillna(0).values.astype(np.float32)\r\n self.valid_rating_mean = valid_rating_mean\r\n self.vendor2rating = self.get_vendors_mean()\r\n self.customer_similarity, = self.get_similarity()","def __init__(self, dim=20, nIter=5, lamb=0.05, alph=40,\n user_features=[\"user\"], item_features=[\"item\"]):\n self.setParams(dim,nIter, lamb, alph)\n self.user_features = {}\n self.item_features = {}\n self.factors = {}\n\n self.user_column_names = user_features\n self.item_column_names = item_features","def evaluate_model(test_user_item_matrix, user_mat, portfolio_mat):\r\n n = np.count_nonzero(~np.isnan(test_user_item_matrix))\r\n\r\n # keep track of the sum of squares\r\n sse = 0\r\n\r\n for user_id in test_user_item_matrix.index:\r\n for offer_id in test_user_item_matrix.columns.values:\r\n if ~np.isnan(test_user_item_matrix.loc[user_id, offer_id]):\r\n pred = predict(test_user_item_matrix, user_mat, portfolio_mat, user_id, offer_id)\r\n if pred:\r\n diff = test_user_item_matrix.loc[user_id, offer_id] - pred\r\n sse += diff ** 2\r\n return sse / n","def make_predictions(movies, ratings_train, ratings_test):\n ###TODO\n \n user_result = [] \n \n for index,row in ratings_test.iterrows():\n userid_test = row['userId']\n #print(\"userid_test::\",userid_test) \n movieid_test = row['movieId'] \n #print(\"movieid_test::\",movieid_test) \n x = list(movies[movies.movieId==movieid_test]['features'])[0]\n #print(\"CSR_GOTT+X::\",x)\n #print(\"TYPE of CSR_GOTT_X::\",type(x))\n subset_train = ratings_train[ratings_train.userId == userid_test]\n #print(\"SUB MOVIE SET::\",subset_train)\n #print(\"TYPE of SUB MOVIE SET::\",type(x))\n total_if_zero=0\n rating_if_zero=0\n sum_main_result=0\n sum_cosine=0 \n for index1,row1 in subset_train.iterrows():\n userid_train = row1['userId']\n #print(\"userid_train::\",userid_train) \n if(userid_test == userid_train ):\n #print(\"HII IN IFFF:::\")\n movieid_train = row1['movieId']\n #print(\"movieid_train::\",movieid_train)\n rating_train = row1['rating']\n #print(\"rating_train::\",rating_train)\n total_if_zero = total_if_zero + 1 \n rating_if_zero = rating_if_zero + rating_train\n y = list(movies[movies.movieId==movieid_train]['features'])[0]\n #print(\"CSR_GOTT_Y::\",y)\n #print(\"TYPE of CSR_GOTT_Y::\",type(y))\n result_cos = cosine_sim(x,y)\n sum_main_result += result_cos * rating_train\n sum_cosine += result_cos \n \n if(sum_main_result != 0):\n user_result.append(sum_main_result/sum_cosine)\n #print(\"user_result::\",user_result) \n else:\n user_result.append(rating_if_zero / total_if_zero)\n #print(\"user_result::\",user_result) \n \n return_result_arr = np.array(user_result) \n \n return return_result_arr\n \n pass","def predict(self, user_id, item_id):\n # DONEreturn prediction for given pair\n return self._user_factors[user_id, : ].dot(self._item_factors[item_id, :])","def get_user_vector(self, user_id):\r\n if user_id in self.df_ratmat.index:\r\n # user from base dataset\r\n return np.array([self.df_ratmat.loc[user_id]])\r\n # construct a vector out of app user rating data\r\n movies_rated = self.get_valid_user_ratings(user_id)\r\n movie_size = self.df_ratmat.shape[1]\r\n cols = [str(i) for i in range(1, movie_size + 1)]\r\n df = pd.DataFrame(columns=cols)\r\n new_row = {}\r\n for i, r in movies_rated[['item_id', 'rating']].iterrows():\r\n new_row[str(int(r['item_id']))] = int(r['rating'])\r\n df = df.append(new_row, ignore_index=True)\r\n # mark 0 (=not rated) if not rated by the user\r\n return df.fillna(0)","def _factorsX(self, inputs):\n return tensor.dot(inputs[0], self.wxf)","def print_maxes(mat):\r\n u_to_likes = load_or_create(\"/Matrix/UserIdToLikes.matrix\", create_matrix_user_likes)\r\n dict_names = load_or_create('/DICT/MovieIdToName.dict', create_dict_names)\r\n dict_ecc = load_or_create('/DICT/MovieIdToItemEccentricity.dict', create_dict_ecc)\r\n user_to_ecc = load_or_create('/DICT/UserIdToUserEccentricity.dict',createDictUserIdToUserEccentricity)\r\n dict_userid_to_moviesliked = load_or_create('/DICT/UserIdToLikedMovies.dict', create_dict_user_id_to_liked_items)\r\n\r\n dict_userid_to_recommends = dict()\r\n print(\"STARTING ECC CALC\")\r\n recommends = []\r\n for i in range(int(mat.shape[0]*0.5)):\r\n row = mat.getrow(i)\r\n if len(row.nonzero()[0]) != 0:\r\n # print(u_to_likes.getrow(i).nonzero()[1])\r\n if len(u_to_likes.getrow(i).nonzero()[1])<10 and user_to_ecc[i+1]>0:\r\n # print(\"Amount of recommends:\",len(row.nonzero()[0]))\r\n row = row.toarray()[0].tolist()\r\n max_val = max(val for val in row if str(row.index(val) + 1) not in dict_userid_to_moviesliked[i+1])\r\n print('SUM is:',sum(val for val in row if str(row.index(val) + 1) not in dict_userid_to_moviesliked[i+1]))\r\n print('SUM with all is:',sum(val for val in row))\r\n\r\n index_max=row.index(max_val) + 1\r\n\r\n recommends.append(\r\n [max_val, row.index(max_val) + 1, i + 1, [i + 1 for i in u_to_likes.getrow(i).nonzero()[1]],\r\n [row.index(max_val) + 1],user_to_ecc[i+1]])\r\n\r\n recommends = sorted(recommends, key=itemgetter(0))\r\n\r\n for i in recommends[-100:]:\r\n print(\"MAX id:\", i[1])\r\n print(\"MAX val:\", i[0])\r\n print(\"Users ECC:\",i[5])\r\n print(\"for user:\", i[2])\r\n print(\"MOVIES HE ALREADY LIKED\", 50 * \"=\")\r\n item_names_print(i[3], dict_names, dict_ecc)\r\n print(\"Movie Well recommend:\" + 50 * '*')\r\n item_names_print(i[4], dict_names, dict_ecc)\r\n print(50 * \"#\")","def test_matrix(self, tol):\n\n res_static = qml.QFT.compute_matrix(2)\n res_dynamic = qml.QFT(wires=[0, 1]).matrix()\n res_reordered = qml.QFT(wires=[0, 1]).matrix([1, 0])\n\n expected = np.array(\n [\n [0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j],\n [0.5 + 0.0j, 0.0 + 0.5j, -0.5 + 0.0j, -0.0 - 0.5j],\n [0.5 + 0.0j, -0.5 + 0.0j, 0.5 - 0.0j, -0.5 + 0.0j],\n [0.5 + 0.0j, -0.0 - 0.5j, -0.5 + 0.0j, 0.0 + 0.5j],\n ]\n )\n\n assert np.allclose(res_static, expected, atol=tol, rtol=0)\n assert np.allclose(res_dynamic, expected, atol=tol, rtol=0)\n\n expected_permuted = [\n [0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j],\n [0.5 + 0.0j, 0.5 - 0.0j, -0.5 + 0.0j, -0.5 + 0.0j],\n [0.5 + 0.0j, -0.5 + 0.0j, 0.0 + 0.5j, -0.0 - 0.5j],\n [0.5 + 0.0j, -0.5 + 0.0j, -0.0 - 0.5j, 0.0 + 0.5j],\n ]\n assert np.allclose(res_reordered, expected_permuted, atol=tol, rtol=0)","def calculate_total(self, calcu_user_n=20, user_n=20, item_n=10, seed=1):\n self._split_data(seed=seed)\n self._set_top(user_n=user_n, item_n=item_n)\n test_user_list = list(set(self.test['userId'].unique()))\n user_list = [test_user_list[random.randint(0, len(test_user_list)) - 1]\n for i in range(calcu_user_n)]\n hit = 0 # Hit score\n all_recom = 0 # num of all recommendations, calculate the accuracy rate\n like_item = 0 # num of the item the user likes in the test set, calculate the recall rate\n all_recom_set = set()\n all_item = set(self.train['movieId'].unique())\n item_popular = Counter(self.train['movieId'].values)\n ret = 0\n n = 0\n print('\\nCalculate all evaluation indicators...')\n for user in tqdm(user_list):\n recom_data = self._get_recommend(user, )\n recom_item = set([data[0] for data in recom_data])\n user_item = set(\n self.test[self.test['userId'] == user]['movieId'].values)\n overlap = recom_item & user_item\n hit += len(overlap)\n like_item += len(user_item)\n all_recom += len(recom_item)\n all_recom_set.update(recom_item)\n for rec in set([data[0] for data in recom_data]):\n ret += math.log(1 + item_popular.get(rec))\n n += 1\n print('\\nCalculate over')\n print('Precision is: ', hit / (all_recom * 1.0))\n print('Recall is: ', hit / (like_item * 1.0))\n print('Coverage is: ', len(all_recom_set) / (len(all_item) * 1.0))\n print('Popularity is:', (ret / n * 1.0))","def prediction(prediction_file_name, clusters_list, svd_use_flag):\n \n coords = misc_functions.getWindowCoords()\n \n test_users = range(coords[0], coords[2] + 1) \n test_items = range(coords[1], coords[3] + 1)\n \n #print \"len(test_users) = \", len(test_users)\n #print \"len(test_items) = \", len(test_items)\n #print \"test_items = \", test_items\n \n # this matrix to be written as result finally\n #misc_functions.step()\n prediction_matrix = zeros((len(test_users), len(test_items)), dtype = float)\n \n training_matrix = scipy.io.mmio.mmread(\"history.mtx\").tocsr()\n \n item_X_meta_matrix = scipy.io.mmio.mmread(\"../../../well_done/items-metas_global.mtx\").toarray()\n \n # getting meta matrices for corresponding using metas\n meta_ctr = 0\n meta_matrices = []\n for meta in METAS_TO_USE:\n if svd_use_flag:\n meta_matrice_file_name = \"users-\" + METAS_TO_USE[meta] + \".svd.mtx\"\n else:\n meta_matrice_file_name = \"users-\" + METAS_TO_USE[meta] + \".mtx\"\n exec(\"meta_matrices.append(scipy.io.mmio.mmread(\\\"\" + meta_matrice_file_name + \"\\\").toarray())\")\n\n #user_counter = 0\n #for user in test_users:\n for cur_cluster in clusters_list:\n \n #print \"cur_cluster[0] = \", cur_cluster[0]\n user = int (cur_cluster[0].split(\"\\t\")[1])\n #print \"user #\", user\n \n #user_metas = {} - changed to list because of problem with dimension\n user_metas = []\n \n values = zeros((len(METAS_TO_USE), len(test_items)), dtype = float)\n meta_ctr = 0\n for meta in METAS_TO_USE:\n \n #print \" meta_matrices = \", meta_matrices\n #print \" meta_matrices[meta_ctr] = \", meta_matrices[meta_ctr]\n user_vector = meta_matrices[meta_ctr][user]\n #print \" user_vector = \", user_vector\n #print \" len(user_metas) = \", len(user_metas)\n #print \" meta_ctr = \", meta_ctr\n #print \"meta = \", meta\n #misc_functions.step()\n \n # normalizing counts of visited metas to use them as weights later\n if max(user_vector) != 0:\n user_metas.append(1.0 * user_vector / max(user_vector))\n else:\n user_metas.append(zeros((len(user_vector), ), dtype = float))\n #print \" user_metas[meta_ctr] = \", user_metas[meta_ctr]\n #print \" user_metas[meta_ctr].shape = \", user_metas[meta_ctr].shape\n \n #for item in test_items:\n for cluster in cur_cluster[1 : ]:\n start_cluster_item = int(cluster.split(\"\\t\")[0])\n stop_cluster_item = int(cluster.split(\"\\t\")[2])\n \n cluster_items = range(start_cluster_item, stop_cluster_item + 1)\n \n for item in cluster_items:\n meta_value = item_X_meta_matrix[item, meta]\n \n # PRICE\n if meta == 8:\n meta_value = priceToPriceCat(meta_value)\n \n # CITY HEURISTIC\n if meta == 11:\n if user_metas[meta_ctr][meta_value - 1] < CITY_TRESHOLD:\n values[:, item - coords[1]] *= CITY_COEF\n \"\"\"\n # DAYTIME\n if meta == 17:\n meta_value = dayTime(meta_value)\n \"\"\"\n \n #print \" meta_value = \", meta_value\n #print \" item = \", item\n #step()\n values[meta_ctr][item - coords[1]] = (user_metas[meta_ctr])[meta_value - 1]\n \n \"\"\"HEURISTICS \"\"\"\n \n \n \n \n \n \"\"\"\\\\ HEURISTICS \"\"\"\n\n meta_ctr += 1\n #print \"values[:, 0:10] = \", values[:, 0:10]\n prediction_vector = numpy.sum(META_WEIGHTS * values, axis = 0)\n #print \"prediction_vector[0:10] = \", prediction_vector[0:10]\n #print \"sum(prediction_vector) = \", sum(prediction_vector)\n prediction_matrix[user - coords[0]] = prediction_vector\n \n #step()\n \n# ===== END OF MAIN CYCLE ===== \n\n result_matrix = scipy.sparse.csr_matrix(prediction_matrix)\n scipy.io.mmio.mmwrite(prediction_file_name, result_matrix, field = 'real', precision = 5)","def test_init_ratings():\n env = FixedRating(num_users=50,\n num_items=50,\n rating_frequency=1.0,\n num_init_ratings=100)\n env.seed(0)\n _, _, ratings = env.reset()\n assert len(ratings) == 100\n for (user_id, item_id), (rating, context) in ratings.items():\n assert context.shape == (0,)\n assert user_id < 50\n assert item_id < 50\n if rating == 5.0:\n assert item_id >= 25\n else:\n assert item_id < 25","def proba_fm(m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:2]))\n else:\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[2:4]))\n else:\n if i <6:\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[4:6]))\n elif i <8:\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[6:8]))\n else:\n p[i] = (m_pred[4])*(f_pred[i]/np.sum(f_pred[8:]))\n else :\n for i in range(10):\n if i <5:\n if i <3:\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:3]))\n else:\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[3:5]))\n else:\n if i <8:\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[5:8]))\n else:\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[8:]))\n return(p)","def _create_sparse_train_and_test(ratings, n_users, n_items):\n \n # pick a random set of data as testing data, sorted ascending\n test_set_size = len(ratings) / TEST_SET_RATIO\n test_set_idx = np.random.choice(xrange(len(ratings)), size=test_set_size, replace=False)\n test_set_idx = sorted(test_set_idx)\n \n # use the remaining data to create a training set\n ts_ratings = ratings[test_set_idx]\n tr_ratings = np.delete(ratings, test_set_idx, axis=0)\n \n # create training and test matrices as coo_matrix\n u_tr, i_tr, r_tr = zip(*tr_ratings)\n tr_sparse = coo_matrix((r_tr, (u_tr, i_tr)), shape=(n_users, n_items))\n u_ts, i_ts, r_ts = zip(*ts_ratings)\n test_sparse = coo_matrix((r_ts, (u_ts, i_ts)), shape=(n_users, n_items))\n \n return tr_sparse, test_sparse","def prediction(uid, pair, rating_bd, sim_bd, item_bd):\n iid, real_rating = pair[0], pair[1]\n if iid not in sim_bd.value.keys():\n return ()\n iid_neighbors = [\n (i[0], i[1], rating_bd.value[i[0]]) for i in sim_bd.value[iid]]\n average_iid_rating = item_bd.value[iid][0]\n sim_rating = []\n for info in iid_neighbors:\n niid, nsim, ratings = info\n sim_rating += [\n (iid, nsim, rating[1] - item_bd.value[niid][0], rating[2])\n for rating in ratings if uid in rating[0]]\n if len(sim_rating) != 0:\n sim_ratings = [\n (line[1] * line[2], abs(line[1]), line[3])\n for line in sim_rating]\n predicted_rating_no_decay = average_iid_rating + sum(\n map(lambda line: line[0], sim_ratings)) / sum(\n map(lambda line: line[1], sim_ratings))\n predicted_rating_decay = \\\n average_iid_rating + add_decay(sim_ratings)\n else:\n predicted_rating_no_decay = average_iid_rating\n predicted_rating_decay = average_iid_rating\n return iid, real_rating, \\\n self.bound_rating(predicted_rating_no_decay), \\\n self.bound_rating(predicted_rating_decay)","def select_next(X, U, mu,\n scoremethod='lowhigh',\n missingmethod='none',\n feature_weights=[],\n oldscores=[], oldreproj=[]):\n\n print(\"------------ SELECTING --------------\")\n if len(U) == 0:\n printt(\"Empty DEMUD model: selecting item number %d from data set\" % \\\n (opts['iitem']))\n return opts['iitem'], [], []\n\n if X.shape[1] < 1 or len(U) == 0 or len(mu) == 0:\n printt(\"Error: No data in X and/or U and/or mu.\")\n return None, [], []\n\n if X.shape[0] != U.shape[0] or X.shape[0] != mu.shape[0]:\n printt(\"Mismatch in dimensions; must have X mxn, U mxk, mu mx1.\")\n return None, [], []\n\n # If oldscores is empty, compute the score for each item\n if len(oldscores) == 0:\n (scores, reproj) = score_items(X, U, mu, scoremethod, missingmethod)\n elif len(oldreproj) == 0:\n printt(\"Error: oldscores provided, but not oldreproj.\")\n return None, [], []\n else: # both are valid, so use them here\n (scores, reproj) = (oldscores, oldreproj)\n\n # Select and return index of item with max reconstruction error,\n # plus the updated scores and reproj\n m = scores.argmax()\n #print('mu:',mu)\n #print('selected:', X[:,m])\n #print('selected-mu:', (X-mu)[:,m])\n #print('reproj:', reproj[:,m])\n #print('reproj-mu:', (reproj-mu)[:,m])\n #input()\n\n return m, scores, reproj","def test_conversion():\r\n f1 = factor([0,1],[2,2],scipy.rand(4))\r\n f2 = factor([1,2],[2,2],scipy.rand(4))\r\n f3 = factor([3],[2],scipy.rand(2))\r\n\r\n F = FactorList([f1,f2,f3])\r\n theta = factors2ExpFam(F)\r\n F2 = expfam2Factors(theta)\r\n ratio = F2.JointDistn().val/ (F.JointDistn().val)\r\n ratio = ratio/ratio[0]\r\n print scipy.allclose(ratio,1)","def get5x5matrix(self): #modified from nxvasc get3x3matrix()\n try:\n i = na.identity(3)\n \n self.d124 = i.copy()\n self.ds124 = na.zeros(124,na.float64)\n \n for k in range(1,124):\n self.d124 = na.concatenate((self.d124,i))\n# print len(self.d124)\n count = 0\n a = []\n for k in range(-2,3):\n for j in range(-2,3):\n for i in range(-2,3):\n if( i != 0 or j != 0 or k != 0 ):\n self.ds124[count] = math.sqrt(i**2+j**2+k**2)\n count += 1\n a.append(i)\n a.append(j)\n a.append(k)\n# print len(a)\n a = na.reshape(na.array(a),(372,1))\n# print len(self.d124)\n self.d124 = na.concatenate((self.d124,a),axis=1)\n except Exception as error:\n print(\"failed in get5x5matrix(): \", error)","def fit(self, user_x_product, latent_features_guess=2, learning_rate=0.0002, steps=5000, regularization_penalty=0.02, convergeance_threshold=0.001):\n print 'training model...'\n return self.__factor_matrix(user_x_product, latent_features_guess, learning_rate, steps, regularization_penalty, convergeance_threshold)","def update_factors(self,u,i,j,update_u=True,update_i=True):\n #print(\"session run\")\n loss_v = self.sess.run(self.train_step , feed_dict={\n self.u: u,\n self.i: i,\n self.j: j})\n\n returnText = \"\"\n\n if self.alg_type == \"TFL\" or self.alg_type == \"TFLWM\":\n sum_lambda = 0\n for k in self.sim_matrix_names:\n sum_lambda += abs(self.sim_lambda[k].eval())\n #print(sum_lambda,self.sim_lambda)\n for k in self.sim_matrix_names:\n if math.isnan(sum_lambda):\n print(\"sim_lambda overflow\")\n tf.assign(self.sim_lambda[k], [self.sim_lambda_zero], validate_shape=False).eval()\n returnText = \"sim_lambda overflow\"\n else:\n tf.assign(self.sim_lambda[k], self.sim_lambda[k].eval()/sum_lambda).eval()\n else:\n for k in self.sim_matrix_names:\n val = self.sim_lambda[k].eval()\n if math.isnan(val[0]):\n print(\"sim_lambda overflow\")\n tf.assign(self.sim_lambda[k], [self.sim_lambda_zero], validate_shape=False).eval()\n returnText = \"sim_lambda overflow\"\n if val[0] <= 0.0:\n tf.assign(self.sim_lambda[k], [self.delta], validate_shape=False).eval()\n elif val[0] >= 1.0:\n tf.assign(self.sim_lambda[k], [self.one - self.delta], validate_shape=False).eval()\n\n return returnText","def calc_adv_U(self):\n num_U = 0\n adv_U = numpy.zeros((3,3), float)\n\n for atm in self:\n ## use the atom's U matrix if it exists, otherwise use the\n ## temperature factor\n\n if atm.U is not None:\n adv_U += atm.U\n num_U += 1\n\n return adv_U / num_U","def calculate_user_similarity_profile(self, ratings_vector):\r\n num_users, num_movies = self.ratings_matrix.get_shape()\r\n\r\n user_similarities = sp.dok_matrix((1, num_users))\r\n for i in range(num_users):\r\n\r\n user_similarities[0, i] = self.calculate_pairwise_user_similarity(self.ratings_matrix.getrow(i), ratings_vector)\r\n\r\n return user_similarities.tocsr()","def calc_metrics(metric_scores_list):\n\n N_split, N_miss, N_add, Q_P, Q_R, Q_F, N_gt, N_pred = [], [], [], [], [], [], [], []\n Q_rand, Q_jaccard, Q_aggregated_jaccard, Q_ctc, Q_piou = [], [], [], [], []\n tp, fp, fn = [], [], []\n\n for score in metric_scores_list:\n N_split.append(score['N_split']), N_miss.append(score['N_miss']), N_add.append(score['N_add'])\n Q_P.append(score['Q_P']), Q_R.append(score['Q_R']), Q_F.append(score['Q_F'])\n Q_rand.append(score['Q_rand']), Q_jaccard.append(score['Q_jaccard'])\n Q_aggregated_jaccard.append(score['Q_aggregated_jaccard'])\n if \"Q_ctc\" in score:\n Q_ctc.append(score['Q_ctc']), \n Q_piou.append(score['Q_piou'])\n N_gt.append(score['N_gt']), N_pred.append(score['N_pred'])\n tp.append(score['tp']), fp.append(score['fp']), fn.append(score['fn'])\n\n N_split, N_miss, N_add = np.array(N_split), np.array(N_miss), np.array(N_add)\n N_gt, N_pred = np.array(N_gt), np.array(N_pred)\n tp, fp, fn = np.array(tp), np.array(fp), np.array(fn)\n Q_P_macro, Q_R_macro, Q_F_macro = np.mean(np.array(Q_P)), np.mean(np.array(Q_R)), np.mean(np.array(Q_F))\n Q_P_micro = np.sum(tp) / (np.sum(tp) + np.sum(fp)) if (np.sum(tp) + np.sum(fp)) > 0 else 0\n Q_R_micro = np.sum(tp) / (np.sum(tp) + np.sum(fn)) if (np.sum(tp) + np.sum(fn)) > 0 else 0\n Q_rand_macro, Q_jaccard_macro = np.mean(np.array(Q_rand)), np.mean(np.array(Q_jaccard))\n Q_aggregated_jaccard_macro = np.mean(np.array(Q_aggregated_jaccard))\n Q_ctc_macro, Q_piou_macro = np.mean(np.array(Q_ctc)), np.mean(np.array(Q_piou))\n\n metrics = {\n 'Q_split_micro': float(np.sum(N_split) / np.sum(N_gt)),\n 'Q_split_macro': float(np.mean(N_split / N_gt)),\n 'Q_miss_micro': float(np.sum(N_miss) / np.sum(N_gt)),\n 'Q_miss_macro': float(np.mean(N_miss / N_gt)),\n 'Q_add_micro': float(np.sum(N_add) / np.sum(N_gt)),\n 'Q_add_macro': float(np.mean(N_add / N_gt)),\n 'N_gt': int(np.sum(N_gt)),\n 'N_pred': int(np.sum(N_pred)),\n 'Q_rand_macro': float(Q_rand_macro),\n 'Q_jaccard_macro': float(Q_jaccard_macro),\n 'Q_aggregated_jaccard_macro': float(Q_aggregated_jaccard_macro),\n 'Q_ctc_macro': float(Q_ctc_macro),\n 'Q_piou_macro': float(Q_piou_macro),\n 'Q_P_micro': float(Q_P_micro),\n 'Q_P_macro': float(Q_P_macro),\n 'Q_R_micro': float(Q_R_micro),\n 'Q_R_macro': float(Q_R_macro),\n 'Q_F_macro': float(Q_F_macro),\n 'Q_F_micro': float(2 * Q_P_micro * Q_R_micro / (Q_P_micro + Q_R_micro)) if (Q_P_micro + Q_R_micro) > 0 else 0\n }\n return metrics","def multiply_fisher_factor(self, vector: jnp.ndarray) -> jnp.ndarray:\n return utils.scalar_mul(\n self.multiply_fisher_factor_unweighted(vector), jnp.sqrt(self.weight))","def predict(self, u, i, P=None, Q=None, F=None, w=None, user_bias=None, item_bias=None):\n if P is None:\n P = self.P\n if Q is None:\n Q = self.Q\n if F is None:\n F = self.F\n if w is None:\n w = self.w\n if user_bias is None:\n user_bias = self.user_bias\n if item_bias is None:\n item_bias = self.item_bias\n\n known_user = self._known('user', u)\n known_item = self._known('item', i)\n rui_hat = self.mean\n if known_user:\n rui_hat += user_bias[u]\n if known_item:\n rui_hat += item_bias[i]\n if known_user and known_item:\n F_sum = np.sum(F[self.Fr[u], :], axis=0)\n #F_num = np.sqrt(self.Fr[u].shape[0]) # Try without sqrt\n F_num = self.Fr[u].shape[0]\n if F_num > 0:\n F_sum /= F_num\n rui_hat += (F_sum + w * P[u, :]).dot(Q[:, i])\n # Apply potential non-linearity (activation) g\n rui_hat = self.g(rui_hat)\n return rui_hat","def build_user_user_similarity_matrix(self, event_data):\n self.compute_user_user_sim_base_on_common_items()\n self.standardize_sim_values()","def test_lu_factor():\n\t#[A, b] = lu_read('test1.txt')\n\t# it is poor form to read an external file into a test function, as above\n\tA = np.array([\n\t\t[ 2., 3., -4., 2.],\n\t\t[-4., -5., 6., -3.],\n\t\t[ 2., 2., 1., 0.],\n\t\t[-6., -7., 14., -4.]])\t\n\tLU,p = lu_factor(A, pivot=False)\n\tLU_soln = np.array([\n\t\t[ 2, 3,-4, 2],\n\t\t[-2, 1,-2, 1],\n\t\t[ 1,-1, 3,-1],\n\t\t[-3, 2, 2, 2]])\t\n\tassert norm(LU - LU_soln) < 1.e-10\t\n\n\n\t# test 2\n\t[A2, b2] = lu_read('test2.txt')\t\t\t\t\t\t# read a matrix and RHS vector\n\tLU2,p2 = lu_factor(A2) \t\t\t\t\t\t\t\t# change display to False when LU_FACTOR working\n\tLU_soln2 = np.array([\n\t\t [0.01, 0., 0., 0., 0., 0., 0., 0., 0., 0., 1],\n\t\t [-100., 0.01, 0., 0., 0., 0., 0., 0., 0., 0., 100],\n\t\t [0., -100., 0.01, 0., 0., 0., 0., 0., 0., 0., 10000],\n\t\t [0., 0., -100., 0.01, 0., 0., 0., 0., 0., 0., 1000000],\n\t\t [0., 0., 0., -100., 0.01, 0., 0., 0., 0., 0., 100000000],\n\t\t [0., 0., 0., 0., -100., 0.01, 0., 0., 0., 0., 10000000000],\n\t\t [0., 0., 0., 0., 0., -100., 0.01, 0., 0., 0., 1000000000000],\n\t\t [0., 0., 0., 0., 0., 0., -100., 0.01, 0., 0., 100000000000000],\n\t\t [0., 0., 0., 0., 0., 0., 0., -100., 0.01, 0., 10000000000000000],\n\t\t [0., 0., 0., 0., 0., 0., 0., 0., -100, 0.01, 1000000000000000000],\n\t\t [0., 0., 0., 0., 0., 0., 0., 0., 0., -100., 100000000000000000000]])\n\tassert norm(LU2 - LU_soln2) < 1.e-10"],"string":"[\n \"def create_matrix(self):\\n\\n self.matrix = np.zeros((len(self.users), len(self.items)))\\n\\n for user in self.train_set['users']:\\n for item in self.train_set['feedback'][user]:\\n self.matrix[self.user_to_user_id[user]][self.item_to_item_id[item]] = \\\\\\n self.train_set['feedback'][user][item]\",\n \"def get_user_item_matrix(datafile, user_index, product_index):\\n num_users = len(user_index)\\n num_items = len(product_index)\\n result = np.zeros((num_users, num_items))\\n num_reviews = len(datafile)\\n result_dense = np.zeros((num_reviews, 3))\\n for line in datafile.iterrows():\\n i = line[0]\\n user_id = datafile['user_id'][i]\\n product_id = datafile['business_id'][i]\\n user = user_index[user_id]\\n product = product_index[product_id]\\n rating = datafile['stars'][i]\\n result[user, product] = rating\\n result_dense[i, 0] = user\\n result_dense[i, 1] = product\\n result_dense[i, 2] = rating\\n return result, result_dense\",\n \"def create_user_item_matrix(data,type='unary'): \\n if type == 'unary':\\n\\n # for unary rating drop duplicates\\n data = data.drop_duplicates()\\n\\n # create sparse matrix\\n matrix = csr_matrix((data['rating'], (data['user_id'],data['product_id'])))\\n\\n # rows and cols with empty values will be dropped (doesnt make any difference in size for sparse matrix, but if later converted to dense, it saves space)\\n # get all non empty rows and cols\\n rows, cols = matrix.nonzero()\\n unique_rows = np.unique(rows)\\n unique_cols = np.unique(cols)\\n\\n # select only rows and cols with values\\n matrix = matrix[unique_rows]\\n matrix = matrix[:,unique_cols]\\n\\n return matrix\\n\\n if type == 'count':\\n\\n # create sparse matrix with counted ratings\\n matrix = csr_matrix((data['rating'], (data['user_id'],data['product_id'])))\\n\\n # rows and cols with empty values will be dropped (doesnt make any difference in size for sparse matrix, but if later converted to dense, it saves space)\\n # get all non empty rows and cols\\n rows, cols = matrix.nonzero()\\n unique_rows = np.unique(rows)\\n unique_cols = np.unique(cols)\\n\\n # select only rows and cols with values\\n matrix = matrix[unique_rows]\\n matrix = matrix[:,unique_cols]\\n\\n '''\\n Im Gegensatz zum Memory Based approach, muss beim Model Based Approach noch das Rating angepasst werden. \\n Das heisst, dass Produkte die viel eingekauft wurden ein höhers Rating erhalten und solche die weniger \\n eingekauft wudren ein tieferes. Gleichzeitig muss das Maximum limitiert werden. Dies wird mittels \\n dem max_count integer in der Funktion bewerkstelligt\\n '''\\n\\n # create diagonal Matrix with 1 divided by maximum values per row. This needs to be done because there is no divide function for csr matrices implemented\\n # source: https://stackoverflow.com/questions/42225269/scipy-sparse-matrix-division\\n diag = diags(1/matrix.max(axis=1).A.ravel())\\n\\n # multipy the matrix with the maximum values to get range from 0-1\\n matrix = diag.dot(matrix)\\n\\n # sort indices; not really needed, just cosmetics\\n matrix.sort_indices()\\n\\n return matrix\",\n \"def example():\\n num_user, num_item, ratings = build_ticket()\\n \\n # suffle_data\\n np.random.shuffle(ratings)\\n\\n # split data to training & validation\\n train_pct = 0.9\\n train_size = int(train_pct * len(ratings))\\n train = ratings[:train_size]\\n validation = ratings[train_size:]\\n\\n # params\\n num_feature = 5\\n bmf_model = BayesianMatrixFactorization(\\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\\n\\n start_time = time.clock()\\n bmf_model.estimate(10)\\n end_time = time.clock()\\n \\n mat = np.matrix(bmf_model.item_features)\\n with open('../data/ticket/item_feature', 'w') as f:\\n for line in mat:\\n np.savetxt(f, line, fmt='%.5f')\\n\\n print \\\"time spend = %.3f\\\" % (end_time - start_time)\\n\\n return bmf_model\",\n \"def get_user_feature_matrix_p(user_dict, user_index, aspect_index, N, popularity, A_dense, Polarity):\\n result = np.zeros((len(user_index), len(aspect_index)))\\n for key in user_dict.keys():\\n index_user = user_index[key]\\n user_reviews = user_dict[key]\\n count_dict = {}\\n max = 0\\n min = 1000\\n for review in user_reviews:\\n feature = review[0]\\n if feature not in aspect_index:\\n continue\\n aspect = aspect_index[feature]\\n if aspect not in count_dict:\\n count_dict[aspect] = 0;\\n if Polarity == False:\\n count_dict[aspect] += 1\\n else:\\n count_dict[aspect] += review[1]\\n for aspect in count_dict.keys():\\n count = count_dict[aspect]\\n if count > max:\\n max = count\\n if count < min:\\n min = count\\n for aspect in count_dict.keys():\\n count = count_dict[aspect]\\n result[index_user, aspect] = (((count - min)/(max - min))*5)\\n\\n if len(popularity) > 0:\\n col = np.zeros((len(result), 1))\\n result = np.append(result, col, axis=1)\\n for i in range(len(result)):\\n items = A_dense[A_dense[:, 0] == i][:, 1]\\n items = items.astype(int)\\n result[i, len(result[1]) - 1] = np.mean(popularity[items, 1])\\n return result\",\n \"def fit(self, users, items, ratings, test_users=[], test_items=[], test_ratings=[], **kargs):\\n global_mean_bias_init = np.float32(np.mean(ratings))\\n global_mean_bias_init = 0.01\\n self.model = DeepFM_(**self.dfm_params, global_mean_bias_init=global_mean_bias_init, first_half_fit_only_fm=self.first_half_fit_only_fm)\\n \\n # もし、CTR予測の場合は、y=0のデータをランダム生成する。\\n if self.ctr_prediction:\\n users = list(users) + list(np.random.choice(list(set(users)), size=len(users)))\\n items = list(items) + list(np.random.choice(list(set(items)), size=len(items)))\\n ratings = list((np.array(ratings)>0).astype(int)) + [0]*len(ratings)\\n test_ratings = list((np.array(test_ratings)>0).astype(int))\\n \\n Xi, Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(users, items)\\n \\n if len(test_users)>0:\\n test_Xi, test_Xv = self.data_manager.transform_users_and_items_to_Xi_Xv(test_users, test_items)\\n self.model.fit(Xi, Xv, ratings, test_Xi, test_Xv, test_ratings, early_stopping=True)\\n else:\\n self.model.fit(Xi, Xv, ratings, early_stopping=True, **kargs)\\n \\n # load data\\n self.trained_users = list(set(users))\\n self.trained_items = list(set(items))\\n self.global_mean = self.model.predict(Xi, Xv).mean()\",\n \"def calculate_recommendations(self, vote_list, itemMatch, itemIgnored):\\n #print \\\"--------------------------------------------------\\\"\\n #print \\\"calculate_recommendations\\\"\\n #print \\\"--------------------------------------------------\\\"\\n\\n # http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/\\n\\n # U = np.array('users')\\n # D = np.array('video_games')\\n\\n # R = |U| cross |D|\\n\\n # We want to discover K latent features\\n\\n # Find\\n # P(a | |U| corss K matrix)\\n # Q(a | |D| cross K matrix)\\n # Such that their product approximates R\\n # R approx= P cross transpose(Q) = hat(R)\\n #\\n\\n # r[i][j] = transpose(p)[i] * q[j]\\n # = sum( 1..k, p[i][k] * q[k][j] )\\n\\n # e[i][j]**2 = (r[i][j] - hat(r)[i][j])**2\\n # = (r[i][j] - sum( 1..K, p[i][k] * q[k][j]))**2\\n # squared error, estimated rating can be either higher or lower than the real thing\\n\\n # find the gradient\\n # diff(e[i][j]**2, p[i][k]) = -2*(r[i][j] - hat(r)[i][j]) * (q[k][j]) = -2*e[i][j] * q[k][j]\\n # diff(e[i][j]**2, q[k][j]) = -2*(r[i][j] - hat(r)[i][j]) * (p[i][k]) = -2*e[i][j] * p[i][k]\\n\\n # update rules\\n # alpha = settings.alpha # learning_rate\\n # alpha = 0.0002 # learning_rate\\n # p[i][k]' = p[i][k] + alpha * diff(e[i][j]**2, p[i][k])\\n # = p[i][k] + 2 * alpha * e[i][j] * q[k][j]\\n # q[k][j]' = q[k][j] + alpha * diff(e[i][j]**2, q[k][j])\\n # = q[k][j] + 2 * alpha * e[i][j] * p[i][k]\\n\\n # training data\\n # T = (u[i], d[j], r[i][j])\\n # np.array()\\n\\n # iterate until convergance\\n # E = sum((u[i], d[j], r[i][j]) in T, e[i][j])\\n # = sum((u[i], d[j], r[i][j]) in T, r[i][j]\\n # - sum(1..k, p[i][k]*q[k][j]))**2\\n\\n # regularization\\n # beta = 0.02\\n # e[i][j]**2 = (r[i][j] - sum(1..K, p[i][j]*q[k][j]))**2\\n # + ((beta/2) * sum(1..K, norm(P)**2 + norm(Q)**2))\\n #\\n # p[i][k]' = p[i][k] + alpha * (2 * e[i][j] * q[k][j] - beta * p[i][k])\\n # q[k][j]' = q[k][j] + alpha * (2 * e[i][j] * p[i][k] - beta * q[k][j])\\n\\n data = np.array(vote_list)\\n\\n encoder = OneHotEncoder()\\n\\n users = data[:,0]\\n unique_users = list(set(users))\\n for i in range(len(users)):\\n users[i] = unique_users.index(users[i])\\n\\n video_games = data[:,1]\\n unique_games = list(set(video_games))\\n for i in range(len(video_games)):\\n video_games[i] = unique_games.index(video_games[i])\\n\\n ratings = data[:,2]\\n M = len(set(video_games))\\n N = len(set(users))\\n R = np.zeros((N,M))\\n for i in range(len(users)):\\n user = users[i]\\n game = video_games[i]\\n rating = ratings[i]\\n R[user][game] = rating\\n\\n K = 2\\n\\n P = np.random.rand(N,K)\\n Q = np.random.rand(M,K)\\n\\n nP, nQ = self.matrix_factorization(R, P, Q, K)\\n nR = np.dot(nP, nQ.T)\\n\\n itemMatch = {}\\n for i in range(N):\\n user = unique_users[i]\\n itemMatch[user] = []\\n for j in range(M):\\n if R[i][j] == 0:\\n video_game = unique_games[j]\\n recommendation = (video_game, nR[i][j])\\n itemMatch[user].append(recommendation)\\n itemMatch[None] = []\\n print 'pmf recommendations', itemMatch.items()\\n print '\\\\n'\\n recommendations = itemMatch.items()\\n\\n # returns\\n # [\\n # (<user1>, [\\n # (\\\"<object_identifier1>\\\", <score>),\\n # (\\\"<object_identifier2>\\\", <score>),\\n # ]),\\n # (<user2>, [\\n # (\\\"<object_identifier1>\\\", <score>),\\n # (\\\"<object_identifier2>\\\", <score>),\\n # ]),\\n # ]\\n\\n return recommendations\",\n \"def init_matrix(self, train, num_features):\\n user_matrix = np.random.rand(self.num_users, num_features)\\n item_matrix = np.random.rand(num_features, self.num_items)\\n item_nnz = train.getnnz(axis=0)\\n item_sum = train.sum(axis=0)\\n item_matrix[0, :] = item_sum / item_nnz\\n return user_matrix, item_matrix\",\n \"def create_adjust_matrix(self, is_sub: bool):\\n matrix = None\\n if not is_sub:\\n ratings = np.ones_like(self._user, dtype=np.float32)\\n matrix = sp.csr_matrix(\\n (ratings, (self._user, self._item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n else:\\n if self.type == \\\"ND\\\":\\n drop_user = self.rand_sample(\\n self.n_users,\\n size=int(self.n_users * self.drop_ratio),\\n replace=False,\\n )\\n drop_item = self.rand_sample(\\n self.n_items,\\n size=int(self.n_items * self.drop_ratio),\\n replace=False,\\n )\\n R_user = np.ones(self.n_users, dtype=np.float32)\\n R_user[drop_user] = 0.0\\n R_item = np.ones(self.n_items, dtype=np.float32)\\n R_item[drop_item] = 0.0\\n R_user = sp.diags(R_user)\\n R_item = sp.diags(R_item)\\n R_G = sp.csr_matrix(\\n (\\n np.ones_like(self._user, dtype=np.float32),\\n (self._user, self._item),\\n ),\\n shape=(self.n_users, self.n_items),\\n )\\n res = R_user.dot(R_G)\\n res = res.dot(R_item)\\n\\n user, item = res.nonzero()\\n ratings = res.data\\n matrix = sp.csr_matrix(\\n (ratings, (user, item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n\\n elif self.type == \\\"ED\\\" or self.type == \\\"RW\\\":\\n keep_item = self.rand_sample(\\n len(self._user),\\n size=int(len(self._user) * (1 - self.drop_ratio)),\\n replace=False,\\n )\\n user = self._user[keep_item]\\n item = self._item[keep_item]\\n\\n matrix = sp.csr_matrix(\\n (np.ones_like(user), (user, item + self.n_users)),\\n shape=(self.n_users + self.n_items, self.n_users + self.n_items),\\n )\\n\\n matrix = matrix + matrix.T\\n D = np.array(matrix.sum(axis=1)) + 1e-7\\n D = np.power(D, -0.5).flatten()\\n D = sp.diags(D)\\n return D.dot(matrix).dot(D)\",\n \"def forward(self, user, item):\\n item_emb = self.product_factors(item.view(-1)) + self.product_bias(\\n item.view(-1)\\n )\\n user_emb = self.user_factors(user.view(-1)) + self.user_bias(user.view(-1))\\n mat_mult = (item_emb * user_emb).sum(1)\\n\\n return mat_mult\",\n \"def affinity_matrix(test_specs):\\n\\n np.random.seed(test_specs[\\\"seed\\\"])\\n\\n # uniform probability for the 5 ratings\\n s = [(1 - test_specs[\\\"spars\\\"]) / test_specs[\\\"ratings\\\"]] * test_specs[\\\"ratings\\\"]\\n s.append(test_specs[\\\"spars\\\"])\\n P = s[::-1]\\n\\n # generates the user/item affinity matrix. Ratings are from 1 to 5, with 0s denoting unrated items\\n X = np.random.choice(\\n test_specs[\\\"ratings\\\"] + 1, (test_specs[\\\"users\\\"], test_specs[\\\"items\\\"]), p=P\\n )\\n\\n Xtr, Xtst = numpy_stratified_split(\\n X, ratio=test_specs[\\\"ratio\\\"], seed=test_specs[\\\"seed\\\"]\\n )\\n\\n return Xtr, Xtst\",\n \"def update_model(self):\\n for itemidx, itemid in self._allitems.iterrows():\\n self.__itemid2idx[str(itemid['itemid'])] = itemidx\\n self.__itemidx2id[itemidx] = str(itemid['itemid'])\\n for useridx, userid in enumerate(self._uservisits['userid'].unique()):\\n self.__userid2idx[str(userid)] = useridx\\n self.__useridx2id[useridx] = str(userid)\\n userid = self._uservisits['userid'].values\\n itemid = self._uservisits['itemid'].values\\n rating = self._uservisits['rating'].values\\n useridx = [self.__userid2idx[str(int(uid))] for uid in userid]\\n itemidx = [self.__itemid2idx[str(int(iid))] for iid in itemid]\\n rating = list(map(numpy.double, rating))\\n self.__itemuser = csr_matrix((rating, (useridx, itemidx)), shape=(len(set(useridx)), len(set(itemidx))))\\n self.__recommender = implicit.als.AlternatingLeastSquares(factors=self.__numtopics)\\n self.__recommender.fit(self.__itemuser)\",\n \"def recommend_NMF(new_user,movies_num,movies_ratings):\\n list_id_movies = movies_ratings['movieId'].unique()\\n nmf,Q = load_NMF_model()\\n new_user_vector = pd.DataFrame(new_user, index=list_id_movies).T\\n new_user_vector_filled = new_user_vector.fillna(3)\\n #calculate Matrix P (Genres)\\n P = nmf.transform(new_user_vector_filled)\\n #make predictions\\n predictions = np.dot(P,Q)\\n recommendations = pd.DataFrame(predictions.reshape(-1), index=list_id_movies).T\\n #Remove already watched movies:\\n not_watched_movies_mask = np.isnan(new_user_vector)\\n not_watched = recommendations[not_watched_movies_mask]\\n\\n top_movies_ids = not_watched.T.sort_values(by=[0], ascending=False).index[:movies_num]\\n\\n Top_recommended = movieId_to_title(top_movies_ids,movies_ratings) \\n return Top_recommended\",\n \"def fit(self, df):\\n\\n # generate continuous indices if this hasn't been done\\n if self.index2item is None:\\n self.set_index(df)\\n\\n logger.info(\\\"Collecting user affinity matrix\\\")\\n if not np.issubdtype(df[self.col_rating].dtype, np.number):\\n raise TypeError(\\\"Rating column data type must be numeric\\\")\\n\\n # copy the DataFrame to avoid modification of the input\\n temp_df = df[[self.col_user, self.col_item, self.col_rating]].copy()\\n\\n if self.time_decay_flag:\\n logger.info(\\\"Calculating time-decayed affinities\\\")\\n # if time_now is None use the latest time\\n if not self.time_now:\\n self.time_now = df[self.col_timestamp].max()\\n\\n # apply time decay to each rating\\n temp_df[self.col_rating] *= exponential_decay(\\n value=df[self.col_timestamp],\\n max_val=self.time_now,\\n half_life=self.time_decay_half_life,\\n )\\n\\n # group time decayed ratings by user-item and take the sum as the user-item affinity\\n temp_df = (\\n temp_df.groupby([self.col_user, self.col_item]).sum().reset_index()\\n )\\n else:\\n # without time decay use the latest user-item rating in the dataset as the affinity score\\n logger.info(\\\"De-duplicating the user-item counts\\\")\\n temp_df = temp_df.drop_duplicates(\\n [self.col_user, self.col_item], keep=\\\"last\\\"\\n )\\n\\n logger.info(\\\"Creating index columns\\\")\\n # map users and items according to the two dicts. Add the two new columns to temp_df.\\n temp_df.loc[:, self.col_item_id] = temp_df[self.col_item].map(self.item2index)\\n temp_df.loc[:, self.col_user_id] = temp_df[self.col_user].map(self.user2index)\\n\\n # retain seen items for removal at prediction time\\n self.seen_items = temp_df[[self.col_user_id, self.col_item_id]].values\\n\\n # affinity matrix\\n logger.info(\\\"Building user affinity sparse matrix\\\")\\n self.user_affinity = self.compute_affinity_matrix(\\n temp_df, self.n_users, self.n_items\\n )\\n\\n # calculate item co-occurrence\\n logger.info(\\\"Calculating item co-occurrence\\\")\\n item_cooccurrence = self.compute_coocurrence_matrix(\\n temp_df, self.n_users, self.n_items\\n )\\n\\n # free up some space\\n del temp_df\\n\\n self.item_frequencies = item_cooccurrence.diagonal()\\n\\n logger.info(\\\"Calculating item similarity\\\")\\n if self.similarity_type == sar.SIM_COOCCUR:\\n self.item_similarity = item_cooccurrence\\n elif self.similarity_type == sar.SIM_JACCARD:\\n logger.info(\\\"Calculating jaccard\\\")\\n self.item_similarity = jaccard(item_cooccurrence).astype(\\n df[self.col_rating].dtype\\n )\\n elif self.similarity_type == sar.SIM_LIFT:\\n logger.info(\\\"Calculating lift\\\")\\n self.item_similarity = lift(item_cooccurrence).astype(\\n df[self.col_rating].dtype\\n )\\n else:\\n raise ValueError(\\n \\\"Unknown similarity type: {0}\\\".format(self.similarity_type)\\n )\\n\\n # free up some space\\n del item_cooccurrence\\n\\n logger.info(\\\"Done training\\\")\",\n \"def fit_data(self, matrix, user_features=None, item_features=None):\\r\\n matrix.sort_index(inplace=True)\\r\\n matrix.sort_index(inplace=True, axis=1)\\r\\n dataset = Dataset()\\r\\n dataset.fit((x for x in matrix.index),\\r\\n (x for x in matrix.columns))\\r\\n interactions = pd.melt(matrix.replace(0, np.nan).reset_index(),\\r\\n id_vars='index',\\r\\n value_vars=list(matrix.columns[1:]),\\r\\n var_name='plu_id',\\r\\n value_name='rating').dropna().sort_values('index')\\r\\n interactions.columns = ['crd_no', 'plu_id', 'rating']\\r\\n self.interactions, self.weights = dataset.build_interactions([tuple(x) for x in interactions.values])\\r\\n\\r\\n if user_features is not None:\\r\\n user_features.sort_index(inplace=True)\\r\\n dataset.fit_partial(users=user_features.index,\\r\\n user_features=user_features)\\r\\n self.user_features = dataset.build_user_features(\\r\\n ((index, dict(row)) for index, row in user_features.iterrows()))\\r\\n else:\\r\\n self.user_features = None\\r\\n if item_features is not None:\\r\\n item_features.sort_index(inplace=True)\\r\\n dataset.fit_partial(items=item_features.index,\\r\\n item_features=item_features)\\r\\n self.item_features = dataset.build_item_features(\\r\\n ((index, dict(row)) for index, row in item_features.iterrows()))\\r\\n else:\\r\\n self.item_features = None\",\n \"def __init__(self, ratings, rank, user_reg_loss, item_reg_loss):\\n self._ratings = ratings\\n self._users_num, self._items_num = ratings.shape\\n self._rank = rank\\n self._user_reg_loss = user_reg_loss\\n self._item_reg_loss = item_reg_loss\\n \\n self._train_function = self._als_partial_step_explicit\\n \\n # DONE init latent factors for user and item matrix\\n # losowo ustalamy inicjalne wartości X i Y\\n self._user_factors = np.random.random((self._users_num, rank))\\n self._item_factors = np.random.random((self._items_num, rank))\",\n \"def predict_rating(user_id,item_id):\\n user_preference = latent_user_preferences[user_id]\\n item_preference = latent_item_features[item_id]\\n return user_preference.dot(item_preference)\",\n \"def compute_affinity_matrix(self, df, n_users, n_items):\\n\\n return sparse.coo_matrix(\\n (df[self.col_rating], (df[self.col_user_id], df[self.col_item_id])),\\n shape=(n_users, n_items),\\n ).tocsr()\",\n \"def init_MF(train, num_features):\\n num_user = train.shape[1]\\n num_item = train.shape[0]\\n user_features = np.random.rand(num_features,num_user) # user_features shape (20,943)\\n item_features = np.random.rand(num_item, num_features) # item_features shape (1152,20)\\n return user_features, item_features\",\n \"def feature_matrix(df, user_id=None, item_id=None):\\n print(\\\"get feature matrix\\\")\\n df1 = df.drop_duplicates(subset=['user_id'], keep='first', inplace=False)\\n user_x = None\\n if user_id is not None:\\n user_x = int(np.argwhere(df1['user_id'].values == user_id))\\n user_features = df1[['average_stars']].values\\n csr_user_features = sparse.csr_matrix(user_features)\\n\\n df2 = df.drop_duplicates(\\n subset=['business_id'],\\n keep='first',\\n inplace=False)\\n item_x = None\\n if item_id is not None:\\n item_x = int(np.argwhere(df2['business_id'].values == item_id))\\n item_features = df2.iloc[:, 10:].values\\n\\n csr_item_features = sparse.csr_matrix(item_features)\\n return csr_user_features, csr_item_features, user_x, item_x\",\n \"def simMatrix(self, d = 1/5):\\n \\n self.fit_baseline(d)\\n self.evalBaseline()\\n \\n \\n df_mat = np.array(self.df[[\\\"user ind\\\", \\\"item ind\\\", \\\"rating\\\"]].merge(self.r_b, on = [\\\"user ind\\\", \\\"item ind\\\"]))\\n df_ind = df_mat[:,:2].astype(int)\\n df_rat = df_mat[:,2] - df_mat[:,3]\\n \\n \\n self.M = np.zeros((self.n_us, self.n_it))\\n \\n \\n widgets = ['Test: ', Percentage(), ' ', Bar(\\\"#\\\"), ' ', ETA()]\\n pbar = ProgressBar(widgets = widgets, maxval = self.n_us)\\n pbar.start()\\n \\n for us in self.user_ind:\\n it = df_ind[np.where(df_ind[:,0] == us)[0], 1]\\n rat1 = df_rat[np.where(df_ind[:,0] == us)[0]]\\n self.M[us,it] = rat1\\n \\n pbar.update(us)\\n \\n pbar.finish()\\n \\n #self.M = self.UI.toarray()\\n pbar = ProgressBar(widgets = widgets, maxval = self.n_it * (self.n_it - 1) / 2)\\n pbar.start()\\n \\n self.S = np.empty((self.n_it, self.n_it)) * np.nan\\n \\n for i1 in range(self.n_it):\\n # self.S[i1,i1] = 1\\n x1 = self.M[:,i1]\\n for i2 in range(i1+1,self.n_it):\\n x2 = self.M[:,i2]\\n I = np.logical_and(x1, x2)\\n if (len(I) > 1):\\n self.S[i1,i2] = self.S[i2,i1] = Sim.cos2(x1.T[I], self.M[:,i2].T[I])\\n \\n pbar.update((self.n_it)*(i1+1) - (i1+2)*(i1+1)/2)\\n \\n pbar.finish()\\n \\n return self.S\",\n \"def score_items(X, U, mu,\\n scoremethod='lowhigh',\\n missingmethod='none',\\n feature_weights=[]):\\n\\n # Use U to model and then reconstruct the data in X.\\n # 1. Project all data in X into space defined by U,\\n # then reconstruct it.\\n if missingmethod.lower() != 'ignore':\\n # All missing values should have been replaced with 0,\\n # or non-existent.\\n # 1a. Subtract the mean and project onto U\\n proj = np.dot(U.T, (X - mu))\\n # 1b. Reconstruct by projecting back up and adding mean\\n reproj = np.dot(U, proj) + mu\\n # 1c. Compute the residual\\n #print('X:', X.T)\\n #print('reproj:', reproj.T)\\n err = X - reproj\\n #print('err:', err.T)\\n #input()\\n \\n else:\\n # Missing method must be 'ignore' (Brand 2002)\\n (err, reproj) = compute_error_with_missing(X, U, mu)\\n\\n # 2. Compute reconstruction error\\n if scoremethod == 'low': # Blank out all errors > 0\\n err[err>0] = 0\\n elif scoremethod == 'high': # Blank out all errors < 0\\n err[err<0] = 0\\n else: # default, count everything\\n pass\\n \\n # Weight features if requested\\n if len(feature_weights) > 0:\\n for i in range(len(feature_weights)):\\n err[i,:] = err[i,:] * feature_weights[i]\\n\\n if missingmethod.lower() == 'ignore':\\n # Only tally error for observed features.\\n # This means that items with missing values are not penalized\\n # for those features, which is probably the best we can do.\\n scores = np.nansum(np.array(np.power(err, 2)), axis=0)\\n else:\\n scores = np.sum(np.array(np.power(err, 2)), axis=0)\\n\\n #print('scores:', scores)\\n #print('reproj:', reproj)\\n #input()\\n return (scores, reproj)\",\n \"def ratings_to_matrix(ratings_df, user_col, item_col, rating_col, forced_shape=None):\\n users_num = ratings_df.user_id.max() + 1\\n items_num = ratings_df.item_id.max() + 1\\n \\n if forced_shape:\\n users_num = max(users_num, forced_shape[0])\\n items_num = max(items_num, forced_shape[1])\\n \\n ratings_mat = np.zeros([users_num, items_num])\\n for rating in ratings_df.itertuples():\\n ratings_mat[rating[user_col], rating[item_col]] = rating[rating_col]\\n \\n return ratings_mat\",\n \"def __init__(self, user_factors, item_factors):\\n self._user_factors = np.copy(user_factors)\\n self._item_factors = np.copy(item_factors)\\n \\n self._users_num = user_factors.shape[0]\\n self._items_num = item_factors.shape[0]\\n\\n assert user_factors.shape[1] == item_factors.shape[1]\",\n \"def fit(self, ratings):\\n # Training proceeds in 2 steps:\\n # 1. Normalize item vectors to be mean-centered and unit-normalized\\n # 2. Compute similarities with pairwise dot products\\n self._timer = util.Stopwatch()\\n\\n init_rmat, users, items = matrix.sparse_ratings(ratings)\\n n_items = len(items)\\n _logger.info('[%s] made sparse matrix for %d items (%d ratings from %d users)',\\n self._timer, len(items), init_rmat.nnz, len(users))\\n\\n rmat, item_means = self._mean_center(ratings, init_rmat, items)\\n\\n rmat = self._normalize(rmat)\\n\\n _logger.info('[%s] computing similarity matrix', self._timer)\\n smat = self._compute_similarities(rmat)\\n\\n _logger.info('[%s] got neighborhoods for %d of %d items',\\n self._timer, np.sum(np.diff(smat.rowptrs) > 0), n_items)\\n\\n _logger.info('[%s] computed %d neighbor pairs', self._timer, smat.nnz)\\n\\n self.item_index_ = items\\n self.item_means_ = item_means\\n self.item_counts_ = np.diff(smat.rowptrs)\\n self.sim_matrix_ = smat\\n self.user_index_ = users\\n self.rating_matrix_ = init_rmat\\n\\n return self\",\n \"def important_factors_based_on_ratings(data: pd.DataFrame) -> np.ndarray:\\n # Turn labels into binary classification for equal class distribution\\n data = utils.add_ratings_binary(data)\\n # Get feature and label data for classifcation from original dataset\\n X, y = utils.get_rating_features_labels(data)\\n\\n # Grab features from feature matrix\\n features = X.columns\\n\\n # split data into train and test set\\n X_train, X_test, y_train, y_test = model_selection.train_test_split(X.values, y, test_size=0.2) \\n\\n # Instantiate and train xgboost model for rating classfication\\n xgb_model = xgboost.XGBClassifier()\\n xgb_model.fit(X_train, y_train)\\n\\n # Grab feature importance scores from trained model\\n feature_importance = xgb_model.feature_importances_\\n # Find indices of top 2 important features\\n top_important_features_ind = np.argpartition(feature_importance, -2)[-2:]\\n\\n print(f\\\"The top 2 important features are {features[top_important_features_ind]}\\\")\\n\\n return feature_importance\",\n \"def get_user_feature_matrix(user_dict, user_index, aspect_index, N):\\n result = np.zeros((len(user_index), len(aspect_index)))\\n for key in user_dict.keys():\\n index_user = user_index[key]\\n user_reviews = user_dict[key]\\n count_dict = {}\\n for review in user_reviews:\\n feature = review[0]\\n if feature not in aspect_index:\\n continue\\n aspect = aspect_index[feature]\\n if aspect not in count_dict:\\n count_dict[aspect] = 0;\\n count_dict[aspect] += 1\\n for aspect in count_dict.keys():\\n count = count_dict[aspect]\\n result[index_user, aspect] = 1 + (N - 1) * (2 / (1 + exp(-count)) - 1)\\n return result\",\n \"def personalization(prediction, n):\\n # prediction\\n # n top n recommendation\\n\\n top_n = get_top_n(prediction, n)\\n\\n rec_dict = {}\\n for uid, user_ratings in top_n.items():\\n rec_dict[uid] = [iid for (iid, _) in user_ratings]\\n\\n rec_user_ls = [pred[0] for pred in prediction]\\n rec_item_ls = [pred[1] for pred in prediction]\\n\\n unique_rec_user_ls = np.unique(rec_user_ls)\\n unique_rec_item_ls = np.unique(rec_item_ls)\\n\\n # assign each item with index number\\n unique_rec_item_dict = {item: ind for ind,\\n item in enumerate(unique_rec_item_ls)}\\n\\n n_unique_rec_user = len(unique_rec_user_ls)\\n n_unique_rec_item = len(unique_rec_item_ls)\\n\\n # recommended user item matrix\\n rec_matrix = np.zeros(shape=(n_unique_rec_user, n_unique_rec_item))\\n\\n # represent recommended item for each user as binary 0/1\\n for user in range(n_unique_rec_user):\\n # get userid\\n user_id = unique_rec_user_ls[user]\\n # get rec item list\\n item_ls = rec_dict[user_id]\\n\\n for item_id in item_ls:\\n # get item index\\n item = unique_rec_item_dict[item_id]\\n rec_matrix[user, item] = 1\\n\\n # calculate cosine similarity matrix across all user recommendations\\n similarity = cosine_similarity(X=rec_matrix, dense_output=False)\\n # calculate average of upper triangle of cosine matrix\\n upper_right = np.triu_indices(similarity.shape[0], k=1)\\n # personalization is 1-average cosine similarity\\n score = 1 - np.mean(similarity[upper_right])\\n return score\",\n \"def update_item_feature(\\n train, item_features, user_features, lambda_item,\\n nz_item_userindices, I):\\n for n, item_n in enumerate(nz_item_userindices):\\n nnz_users_per_item = len(item_n[1]) # Number of users who rated item n\\n if (nnz_users_per_item == 0): nnz_users_per_item = 1\\n # Least squares solution\\n A_n = np.dot(user_features[:,item_n[1]], user_features[:,item_n[1]].T) + lambda_item * nnz_users_per_item * I\\n V_n = np.dot(user_features[:,item_n[1]], train.T[item_n[1],item_n[0]].todense())\\n #if (n%3 == 0): print(\\\"item_n: {}\\\".format(item_n[0]), np.linalg.det(A_n))\\n if (np.linalg.det(A_n) != 0): item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)\\n else: \\n A_n[0,0] += 1; A_n[1,1] += 1; A_n[2,2] += 1; A_n[3,3] += 1; A_n[4,4] += 1; A_n[5,5] += 1 # if matrix A_n is singular, slightly modify several values\\n item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)\",\n \"def item_user_matrix(X):\\n X['user_id'] = X['user_id'].astype(\\\"category\\\")\\n X['song_id'] = X['song_id'].astype(\\\"category\\\")\\n\\n row = X['song_id'].cat.codes.copy()\\n col = X['user_id'].cat.codes.copy()\\n\\n nrow = len(X['song_id'].cat.categories)\\n ncol = len(X['user_id'].cat.categories)\\n\\n item_user = csr_matrix((X['score'], (row, col)), shape=(nrow, ncol))\\n\\n user = dict(enumerate(X['user_id'].cat.categories))\\n user_index = {u: i for i, u in user.items()}\\n\\n item = dict(enumerate(X['song_id'].cat.categories))\\n item_index = {s: i for i, s in item.items()}\\n\\n return item_user, item_index, user_index\",\n \"def fit(self, ratings_mat):\\n self.logger.debug(\\\"starting fit\\\")\\n # self.n = ratings.max()['user']+1\\n # self.p = ratings.max()['movie']+1\\n self.ratings_mat = ratings_mat\\n self.k = ratings_mat.shape[0]//20\\n\\n #ratings_array = ratings[ratings.columns[:-1].values].values\\n\\n #self.ratings_mat = np.zeros((self.n, self.p))\\n\\n #for i, rating in ratings.iterrows():\\n # self.ratings_mat[( rating['user'], rating['movie'] )] = rating['rating']\\n\\n self.cosine_dists = squareform(pdist(ratings_mat, 'cosine'))\\n\\n #if a user has no ratings data, cosine dist will return a nan. In this case, we assume they are as different as possible, since we cannot predict using those users anyways\\n self.cosine_dists = 1 - np.nan_to_num(1 - self.cosine_dists)\\n\\n self.similarity_ranks = self.cosine_dists.argsort(axis = 1)\\n\\n # ...\\n\\n self.logger.debug(\\\"finishing fit\\\")\\n return(self)\",\n \"def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, loss_type='softmax', temperature=0.05,\\n sampler_config=None, seed=1024,\\n ):\\n\\n embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,\\n seed=seed,\\n seq_mask_zero=True)\\n\\n user_features = build_input_features(user_feature_columns)\\n user_inputs_list = list(user_features.values())\\n user_sparse_embedding_list, _ = input_from_feature_columns(user_features,\\n user_feature_columns,\\n l2_reg_embedding, seed=seed,\\n support_dense=False,\\n embedding_matrix_dict=embedding_matrix_dict)\\n\\n item_features = build_input_features(item_feature_columns)\\n item_inputs_list = list(item_features.values())\\n item_sparse_embedding_list, _ = input_from_feature_columns(item_features,\\n item_feature_columns,\\n l2_reg_embedding, seed=seed,\\n support_dense=False,\\n embedding_matrix_dict=embedding_matrix_dict)\\n\\n user_dnn_input = concat_func(user_sparse_embedding_list, axis=1)\\n user_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(user_dnn_input)\\n user_vector_sum = l2_normalize(user_vector_sum)\\n\\n item_dnn_input = concat_func(item_sparse_embedding_list, axis=1)\\n item_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(item_dnn_input)\\n item_vector_sum = l2_normalize(item_vector_sum)\\n\\n if loss_type == \\\"logistic\\\":\\n score = inner_product(user_vector_sum, item_vector_sum, temperature)\\n output = PredictionLayer(\\\"binary\\\", False)(score)\\n\\n elif loss_type == \\\"softmax\\\":\\n output = InBatchSoftmaxLayer(sampler_config._asdict(), temperature)(\\n [user_vector_sum, item_vector_sum, item_features[sampler_config.item_name]])\\n else:\\n raise ValueError(' `loss_type` must be `logistic` or `softmax` ')\\n\\n model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output)\\n\\n model.__setattr__(\\\"user_input\\\", user_inputs_list)\\n model.__setattr__(\\\"user_embedding\\\", user_vector_sum)\\n\\n model.__setattr__(\\\"item_input\\\", item_inputs_list)\\n model.__setattr__(\\\"item_embedding\\\", item_vector_sum)\\n\\n return model\",\n \"def getUserItemMatrix(self):\\n\\t\\tdf = self.getrating()\\n\\n\\t\\trows_index = df.user_id.unique()\\n\\t\\tcolumn_index = df.venue_id.unique() \\n\\n\\t\\trow_len = len(rows_index)\\n\\t\\tcol_len = len(column_index)\\n\\n\\t\\tX = lil_matrix((row_len, col_len))\\n\\t\\trow_map = dict(zip(rows_index, range(row_len)))\\n\\t\\tcol_map = dict(zip(column_index, range(col_len)))\\n\\n\\t\\t# Get mapping table for rows and columns\\n\\t\\td = {}\\n\\t\\td[\\\"row\\\"] = row_map\\n\\t\\td[\\\"col\\\"] = col_map\\n\\n\\t\\tfor index, row in df.iterrows():\\n\\t\\t\\tX[d[\\\"row\\\"][row[\\\"user_id\\\"]], d[\\\"col\\\"][row[\\\"venue_id\\\"]]] = row[\\\"Rating\\\"]\\n\\n\\t\\tX = X.tocsr() # Allow efficient row slicing\\n\\n\\t\\treturn [d,X]\",\n \"def find_predictions(actives, train_rdd_gbitem_dict, train_rdd_gbuser_dict, num_items):\\n active_user = actives[0][0]\\n active_item = actives[0][1]\\n\\n # -----------------------------------\\n # train_rdd_gbitem_dict = (item, ([(user,r),(user,r)...],avg_of_item))\\n # train_rdd_gbuser_dict = (user, [(item,r),(item,r)...]\\n\\n if active_user not in train_rdd_gbuser_dict and active_item not in train_rdd_gbitem_dict:\\n return (active_user, active_item), 2.5\\n\\n # all user, ratings that have rated active_item\\n if active_item in train_rdd_gbitem_dict:\\n active_item_avg = train_rdd_gbitem_dict[active_item][1]\\n active_item_dict = dict(train_rdd_gbitem_dict[active_item][0]) # {user: rating, user: rating, ...}\\n else:\\n # item not found in training set\\n # new item problem.\\n average_of_user_list = train_rdd_gbuser_dict[active_user]\\n average_of_user = sum([x[1] for x in average_of_user_list]) / len(average_of_user_list)\\n return (active_user, active_item), average_of_user\\n\\n # user rated items - all (item, ratings) that the user has rated\\n if active_user in train_rdd_gbuser_dict:\\n active_user_rated_items = train_rdd_gbuser_dict[active_user] # [(item, rating), (item, rating), ...]\\n else:\\n # user not found in training set\\n # new user problem.\\n return (active_user, active_item), train_rdd_gbitem_dict[active_item][1]\\n\\n similarity_list = []\\n for item, rating in active_user_rated_items:\\n item_dict = dict(train_rdd_gbitem_dict[item][0])\\n item_avg = train_rdd_gbitem_dict[item][1]\\n similarity = find_similarity(dict(active_item_dict), active_item_avg, dict(item_dict), item_avg)\\n similarity_list.append((rating, similarity))\\n\\n # Have obtained similarity list for active item and item from the above code.\\n # Filter according to a top 'N' items and then take avg rating.\\n # similarity_list.sort(key=lambda x: x[1], reverse=True)\\n # similarity_list = similarity_list[:len(similarity_list) // 4]\\n # similarity_list = [(x[0], x[1]*abs(x[1])**1.5) for x in similarity_list]\\n # print(similarity_list)\\n pred_rating = find_weighted_average(similarity_list, num_items)\\n\\n # for i in similarity_list:\\n # print(i)\\n # print(\\\"Pred-rating: \\\", pred_rating)\\n\\n return (active_user, active_item), pred_rating\",\n \"def convert_fidelity_matrix(fidelities, fidelity, sx, factor):\\n\\n matrix = np.zeros((sx, fidelities - 1))\\n for idx in range(0, fidelity):\\n matrix[:, idx] = np.ones((sx)) * factor[fidelity]\\n\\n return matrix\",\n \"def train_nmf(movies_ratings):\\n \\n #pivot the dataframe\\n movies_ratings = movies_ratings.pivot_table(index='userId', columns='movieId', values='rating')\\n #Fill Nan with 3.0 rating:\\n movies_ratings.fillna(3.0, inplace=True)\\n\\n nmf_model = NMF(\\n n_components=20,\\n init='random',\\n random_state=10,\\n max_iter=10000\\n )\\n nmf_model.fit(movies_ratings)\\n\\n #save nmf model\\n pickle.dump(nmf_model,open(\\\"models/nmf_model.sav\\\", 'wb'))\",\n \"def metric_test(self):\\n k = 10\\n latent_factor = 10\\n n_users = 10\\n n_items = 12\\n\\n interactions, user_features, item_features = util.generate_dummy_data_with_indicator (num_users=n_users, num_items=n_items, interaction_density=.5)\\n print (\\\"interactiosn shape={}\\\".format( np.shape(interactions) ))\\n print (\\\"user features shape={}\\\".format( np.shape(user_features.toarray()) ))\\n print (\\\"item features shape={}\\\".format( np.shape(item_features.toarray()) ))\\n\\n model = TensorRec(n_components=latent_factor)\\n\\n model.fit(interactions, user_features, item_features, epochs=19)\\n\\n ranks = model.predict_rank(user_features=user_features, item_features=item_features)\\n\\n print (\\\"Ranks shape={}\\\".format(np.shape(ranks)))\\n\\n self.assertTrue(np.shape(interactions) == np.shape(ranks))\\n\\n tr_recall_result = eval.recall_at_k(predicted_ranks=ranks, test_interactions=interactions, k=k, preserve_rows=False)\\n # print (tr_recall_result.mean())\\n\\n tr_precision_result = eval.precision_at_k(predicted_ranks=ranks, test_interactions=interactions, k=k, preserve_rows=False)\\n # print(tr_precision_result.mean())\\n\\n # we need csr for interactions data\\n interactions_ = interactions.tocsr()\\n recall_result = metrics.recall_at_k(ranks, interactions_, k=k, preserve_rows=False)\\n # print(recall_result.mean())\\n\\n precision_result = metrics.precision_at_k(ranks, interactions_, k=k, preserve_rows=False)\\n # print (precision_result.mean())\\n\\n self.assertTrue (tr_recall_result.mean() == recall_result.mean())\\n self.assertTrue (tr_precision_result.mean() == precision_result.mean())\",\n \"def update_user_feature(\\n train, user_features, item_features, lambda_user,\\n nz_user_itemindices, I):\\n for d, user_d in enumerate(nz_user_itemindices): # iterate over non zero users\\n nnz_items_per_user = len(user_d[1]) # Number of items user d has rated\\n if (nnz_items_per_user == 0): nnz_items_per_user = 1\\n \\n # Least squares solution\\n A_d = np.dot(item_features[user_d[1]].T, item_features[user_d[1]]) + lambda_user * nnz_items_per_user * I\\n V_d = np.dot(item_features[user_d[1]].T, train[user_d[1],user_d[0]].todense())\\n user_features[:,user_d[0]] = np.linalg.solve(A_d,V_d)\",\n \"def _estimate_ratings(self, U, V):\\n Rhat = np.matmul(U, V.transpose()) # estimate R with UV^T\\n Rhat = np.where(self.R_missing, 0, Rhat) # fill in missing values of R with 0s\\n return Rhat\",\n \"def estimate(self, u, j):\\n if not (self.trainset.knows_user(u) and self.trainset.knows_item(j)):\\n raise PredictionImpossible('User and/or item is unknown.')\\n\\n u_ratings = self.trainset.ur[u]\\n\\n if self.weighting == 'linear':\\n weight = sum(self.freqs[i, j, self.to_index(r)] for i, r in u_ratings)\\n score = sum(self.sums[i, j, self.to_index(r)] for i, r in u_ratings)\\n return score / weight\\n\\n # self.weighting == 'log' or None\\n weights = [self.freqs[i, j, self.to_index(r)] for i, r in u_ratings]\\n reciprocals = [1 / w if w else 0 for w in weights]\\n scores = [self.sums[i, j, self.to_index(r)] for i, r in u_ratings]\\n scores = [s * w for s, w in zip(scores, reciprocals)]\\n\\n if self.weighting is None:\\n return np.mean(scores)\\n # self.weighting == 'log'\\n logs = [np.log(w + 1) if w >= 1 else 0 for w in weights]\\n return np.dot(scores, logs) / np.sum(logs)\",\n \"def compute_user_user_sim_base_on_common_items(self):\\n self.sim_matrix = {}\\n for item in self.items.values():\\n # convert to list of tuples for indexing\\n users = list(item.covered_users.items())\\n item_popularity = len(users)\\n # iter through all user pairs\\n for i in range(len(users)-1):\\n for j in range(i+1, len(users)):\\n user_A_info, user_B_info = users[i], users[j]\\n # remember to update pair wise!\\n self.update_user_user_sim(user_A_info, user_B_info,\\n item_popularity)\\n self.update_user_user_sim(user_B_info, user_A_info,\\n item_popularity)\",\n \"def compute_pmf(X_inp, rank, c1, c2):\\n\\n X_arr = []\\n u_arr = []\\n v_arr = []\\n d_arr = []\\n\\n X = deepcopy(X_inp)\\n\\n v_init = np.ones((np.shape(X)[1],1))\\n\\n for i in range(rank):\\n X_arr.append(X)\\n (d,u,v) = compute_factor(X, v_init, c1, c2)\\n\\n\\n assert abs(npla.norm(u) - 1 ) < 1e-3\\n assert abs(npla.norm(v) - 1 ) < 1e-3\\n\\n d_arr.append(d)\\n u_arr.append(u)\\n v_arr.append(v)\\n\\n toSub = np.outer(u,v)\\n assert np.shape(toSub) == np.shape(X)\\n X -= d * toSub\\n\\n return (X_arr, u_arr, v_arr, d_arr)\",\n \"def item_based_CF(G):\\n\\n\\tcode, pmid = nx.bipartite.sets(G)\\n\\tX = nx.bipartite.biadjacency_matrix(G,pmid,column_order=code)\\n\\tmean_X = np.mean(X,axis=0)\\n\\tadjusted_X = X - mean_X\\n\\tsimilarity = cosine_similarity(X.T)\\n\\trating = mean_X + np.dot(adjusted_X,similarity)/np.sum(np.abs(similarity),axis=1)\\n\\tdf = pd.DataFrame(data=rating,index=pmid,columns=code)\\n\\n\\treturn df\",\n \"def predict_rating(self, user_id, item_id):\\n user_preference = self.latent_user_preferences[user_id]\\n item_feature = self.latent_item_features[item_id]\\n return user_preference.dot(item_feature)\",\n \"def create_user_item_array(self):\\n user_em = self.user_factors.weight.detach()\\n item_em = self.product_factors.weight.detach()\\n user_b = self.user_bias.weight.detach()\\n item_b = self.product_bias.weight.detach()\\n\\n user_item_array = (item_em + item_b) @ (user_em + user_b).transpose(0, 1)\\n preds = self._prob_to_class(user_item_array).numpy()\\n\\n return preds\",\n \"def als(user_ids : numpy.ndarray, item_ids : numpy.ndarray,\\n ratings : numpy.ndarray, num_item_factors : int,\\n num_users: int, num_items : int, min_r_decrease=0.01,\\n max_iterations=200, algorithm=1):\\n # allocate \\\"user_factors\\\" and \\\"item_factors\\\"\\n num_user_factors = num_item_factors + 1\\n user_factors = numpy.random.uniform(-1, 1, num_users * num_user_factors)\\n item_factors = numpy.random.uniform(-1, 1, num_items * num_item_factors)\\n\\n # argument construction\\n user_ids_ptr = user_ids.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\\n item_ids_ptr = item_ids.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\\n\\n ratings_length = len(ratings)\\n ratings_ptr = ratings.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\\n\\n user_factors_length = len(user_factors)\\n user_factors_ptr = user_factors.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\\n\\n item_factors_length = len(item_factors)\\n item_factors_ptr = item_factors.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\\n\\n iterations = _dll.als_from_python(\\n user_ids_ptr, item_ids_ptr, ratings_length, ratings_ptr,\\n num_item_factors, user_factors_length, user_factors_ptr,\\n item_factors_length, item_factors_ptr, ctypes.c_double(min_r_decrease),\\n max_iterations, algorithm)\\n\\n return user_factors, item_factors, iterations\",\n \"def NMF(model, maxIter=100, beliefs=None, verbose=False):\\n if beliefs is None: beliefs = [Factor([Xi],1.0/Xi.states) for Xi in model.X]\\n \\n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\\n for f in model.factors:\\n m = f.log()\\n for v in f.vars: m *= beliefs[v]\\n lnZ += m.sum()\\n if verbose: print(\\\"Iter 0: \\\"+str(lnZ))\\n\\n for t in xrange(1,maxIter+1): # for each iteration:\\n # Update all the beliefs via coordinate ascent:\\n for Xi in model.X: # for each variable, \\n bNew = 0.0 # compute E[ log f ] as a function of Xi:\\n for f in model.factorsWith(Xi,copy=False): # for each factor f_a, compute:\\n m = f.log() # E[log f_a] = \\\\sum \\\\log f_a \\\\prod b_v\\n for v in f.vars - [Xi]: m *= beliefs[v]\\n bNew += m.marginal([Xi]) # sum them up to get E[log f]\\n bNew -= bNew.max() # (numerical issues)\\n bNew = bNew.exp()\\n bNew /= bNew.sum() # set b(Xi) = exp( E[log f] ) / Z\\n beliefs[Xi] = bNew\\n #\\n # Compute the lower bound on the partition function:\\n # E_b [ log f ] + H(b) = \\\\sum_a E[log f_a] + \\\\sum_i H(b_i) for independent beliefs\\n lnZ = sum([beliefs[Xi].entropy() for Xi in model.X])\\n for f in model.factors:\\n m = f.log()\\n for v in f.vars: m *= beliefs[v]\\n lnZ += m.sum()\\n if verbose: print(\\\"Iter \\\"+str(t)+\\\": \\\"+str(lnZ))\\n return lnZ,beliefs\",\n \"def final_recommendation_score_matrix_helper_for_different_knn_types(self, type_of_method, index_matrix, value_matrix):\\n if type_of_method == \\\"user\\\":\\n for i in range(index_matrix.shape[1]):\\n temp = index_matrix[:,i].astype(int)\\n k_similar_users_profiles = self.utility_matrix[temp]\\n k_similar_users_similarity_values = value_matrix[:,i]\\n self.final_score_matrix[i,:] = np.dot(k_similar_users_similarity_values,k_similar_users_profiles)\\n elif type_of_method == \\\"item\\\":\\n for i in range(index_matrix.shape[1]):\\n temp = index_matrix[:,i].astype(int)\\n k_similar_item_profiles = self.utility_matrix.T[temp]\\n k_similar_item_similarity_values = value_matrix[:,i]\\n self.final_score_matrix[:,i] = np.dot(k_similar_item_similarity_values,k_similar_item_profiles)\",\n \"def get_users_features_matrix(games_features_matrix: csr_matrix, users_games_matrix: csr_matrix) -> csr_matrix:\\n logging.getLogger(__name__).debug('Users features matrix calculating...')\\n users_features_matrix = users_games_matrix * games_features_matrix\\n logging.getLogger(__name__).debug('users_features_matrix.shape: ' + str(users_features_matrix.shape))\\n return users_features_matrix\",\n \"def NMF(X,r):\\n nmf_mdl = nmf.NMF(X,num_bases=r)\\n nmf_mdl.initialization()\\n nmf_mdl.factorize()\\n return nmf_mdl.W,nmf_mdl.H,np.dot(nmf_mdl.W,nmf_mdl.H)\",\n \"def fit(self, data, num_features, lambda_user, lambda_item, gamma):\\n user_matrix, item_matrix = self.init_matrix(data, num_features)\\n nnz_users, nnz_items = data.nonzero()\\n nnz_data = list(zip(nnz_users, nnz_items))\\n for it in tqdm(range(self.num_epochs)):\\n gamma /= 1.1\\n np.random.shuffle(nnz_data)\\n for u, i in nnz_data:\\n user = user_matrix[u, :]\\n item = item_matrix[:, i]\\n err = data[u, i] - user @ item\\n user_matrix[u, :] += gamma * (err * item - lambda_user * user)\\n item_matrix[:, i] += gamma * (err * user - lambda_item * item)\\n\\n self.user_matrix = user_matrix\\n self.item_matrix = item_matrix\",\n \"def evaluate_hmdb51_fusion():\\n vlen = 0\\n ob_suffix = '-max.feat.npy.gz'\\n fv_suffix = '_fv.npy.gz'\\n ob_root = '/home/syq/research_final/data/features/ob_hmdb51_pooled_python/'\\n fv_root = '/home/syq/research_final/data/dense-traj/fv_hmdb51_python/'\\n hmdb_splits = 'testTrainMulti_7030_splits/'\\n categories = os.listdir(fv_root)\\n weight = 1.0\\n weights = [i / 20.0 for i in range(21)]\\n acc_to_weights = {}\\n\\n for weight in weights:\\n print \\\"Weight: %.2f\\\" % weight\\n accs = np.zeros(3)\\n for splitnum in range(1,4):\\n ts = time.time()\\n trainfiles, testfiles = hmdb51_splits.loadsplit(categories,\\n hmdb_splits,\\n splitnum)\\n print 'Have %d train files' % len(trainfiles)\\n print 'Have %d test files' % len(testfiles)\\n\\n if not vlen:\\n fp = gzip.open(os.path.join(ob_root,'%s%s'%(trainfiles[0][0][:-4],\\n ob_suffix)),\\\"rb\\\")\\n vlen_ob = len(np.load(fp))\\n fp.close()\\n print \\\"OB vector length is %d\\\" % vlen_ob\\n fp = gzip.open(os.path.join(fv_root,'%s%s'%(trainfiles[0][0][:-4],\\n fv_suffix)),\\\"rb\\\")\\n vlen_fv = len(np.load(fp))\\n fp.close()\\n print \\\"IDTFV vector length is %d\\\" % vlen_fv\\n\\n Dtrain_ob = np.zeros( (len(trainfiles),vlen_ob), np.float32 )\\n Dtrain_fv = np.zeros( (len(trainfiles),vlen_fv), np.float32 )\\n\\n Ytrain = np.ones ( (len(trainfiles) )) * -1000\\n\\n for fi,f in enumerate(trainfiles):\\n fp = gzip.open(os.path.join(ob_root,'%s%s'%(f[0][:-4],\\n ob_suffix)),\\\"rb\\\")\\n Dtrain_ob[fi][:] = np.load(fp)\\n fp.close()\\n Ytrain[fi] = f[1]\\n\\n fp = gzip.open(os.path.join(fv_root,'%s%s'%(f[0][:-4],\\n fv_suffix)),\\\"rb\\\")\\n Dtrain_fv[fi][:] = np.load(fp)\\n fp.close()\\n\\n Dtest_ob = np.zeros( (len(testfiles),vlen_ob), np.float32 )\\n Dtest_fv = np.zeros( (len(testfiles),vlen_fv), np.float32 )\\n\\n Ytest = np.ones ( (len(testfiles) )) * -1000\\n\\n for fi,f in enumerate(testfiles):\\n fp = gzip.open(os.path.join(ob_root,'%s%s'%(f[0][:-4],\\n ob_suffix)),\\\"rb\\\")\\n Dtest_ob[fi][:] = np.load(fp)\\n fp.close()\\n Ytest[fi] = f[1]\\n\\n fp = gzip.open(os.path.join(fv_root,'%s%s'%(f[0][:-4],\\n fv_suffix)),\\\"rb\\\")\\n Dtest_fv[fi][:] = np.load(fp)\\n fp.close()\\n\\n \\\"\\\"\\\"\\n Early fusion\\n Dtrain = np.hstack((Dtrain_ob, Dtrain_fv))\\n Dtest = np.hstack((Dtest_ob, Dtest_fv))\\n\\n clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\\n acc = clf.fit(Dtrain, Ytrain).score(Dtest, Ytest)\\n \\\"\\\"\\\"\\n fv_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\\n\\n ob_clf = OneVsRestClassifier(estimator=SVC(C=10,\\n cache_size=1000,\\n kernel='linear',\\n probability=True),\\n n_jobs=-1)\\n\\n # Get probabilities for late fusion\\n Dtrain_fv = fv_clf.fit(Dtrain_fv, Ytrain).decision_function(Dtrain_fv)\\n Dtrain_ob = ob_clf.fit(Dtrain_ob, Ytrain).decision_function(Dtrain_ob)\\n Dtest_fv = fv_clf.decision_function(Dtest_fv)\\n Dtest_ob = ob_clf.decision_function(Dtest_ob)\\n\\n # Scale decision values b/w 0 and 1\\n Dtrain_fv = preprocessing.normalize(Dtrain_fv)\\n Dtrain_ob = preprocessing.normalize(Dtrain_ob)\\n Dtest_fv = preprocessing.normalize(Dtest_fv)\\n Dtest_ob = preprocessing.normalize(Dtest_ob)\\n\\n # Late fusion\\n scores_train = (Dtrain_fv * weight) + (Dtrain_ob * (1 - weight))\\n latefusion_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=-1)\\n latefusion_clf.fit(scores_train, Ytrain)\\n\\n scores_test = (Dtest_fv * weight) + (Dtest_ob * (1 - weight))\\n acc = latefusion_clf.score(scores_test, Ytest)\\n print 'Fold', splitnum, 'late fusion acc', acc\\n print \\\"Train & testing time %.3f\\\" % (time.time() - ts)\\n accs[splitnum-1] = acc\\n acc_to_weights[weight] = accs\\n\\n print \\\"Mean accuracy: %.3f\\\" % accs.mean()\\n with open(\\\"hmdb51_weight_gridsearch.txt\\\", \\\"w\\\") as f:\\n for weight, accs in acc_to_weights.items():\\n f.write(str(weight) + str(accs) + '\\\\n')\\n return acc_to_weights\\n\\n \\\"\\\"\\\"\\n with open('fv_hmdb51_accs.txt', 'w') as f:\\n f.write(\\\"%s\\\\nMean:%.3f\\\" % (str(accs), np.mean(accs)))\\n \\\"\\\"\\\"\",\n \"def mrr(self):\\n _test = self.drop_bad_ratings()\\n merged = pd.merge(left=_test, right=self.predict, on=['user', 'item'], how='right')[\\n ['user', 'item', 'rating_x', 'rating_y']]\\n nott = np.vectorize(lambda x: not x)\\n mrrs = []\\n for user in merged.user.unique():\\n frame = merged[merged.user == user].sort_values(by='rating_y', ascending=False)\\n true_ratings = frame.rating_x.values\\n positions = np.where(nott(np.isnan(true_ratings)))[0]\\n if len(positions) > 0:\\n mrrs.append(1 / (positions[0] + 1))\\n else:\\n mrrs.append(0)\\n\\n return sum(mrrs) / len(mrrs)\",\n \"def feature_processing(array2d):\\n new_array2d = np.zeros([array2d.shape[0], 29])\\n # items/ orders\\n new_array2d[:, 0] = array2d[:, 4] / array2d[:, 3]\\n # cancels / orders\\n new_array2d[:, 1] = array2d[:, 5] / array2d[:, 3]\\n # returns / items\\n new_array2d[:, 2] = array2d[:, 6] / array2d[:, 4]\\n # voucher / orders\\n new_array2d[:, 3] = array2d[:, 10] / array2d[:, 3]\\n # female_items / female_items + male_items\\n new_array2d[:, 4] = array2d[:, 15] / ([1 if x == 0 else x for x in (array2d[:, 15] + array2d[:, 16])])\\n # male_items / female_items + male_items\\n new_array2d[:, 5] = array2d[:, 16] / ([1 if x == 0 else x for x in (array2d[:, 15] + array2d[:, 16])])\\n # unisex_items / items\\n new_array2d[:, 6] = array2d[:, 17] / array2d[:, 4]\\n # wapp_items / items\\n new_array2d[:, 7] = array2d[:, 18] / array2d[:, 4]\\n # wftw_items / items\\n new_array2d[:, 8] = array2d[:, 19] / array2d[:, 4]\\n # mapp_items / items\\n new_array2d[:, 9] = array2d[:, 20] / array2d[:, 4]\\n # wacc_items / items\\n new_array2d[:, 10] = array2d[:, 21] / array2d[:, 4]\\n # macc_items / items\\n new_array2d[:, 11] = array2d[:, 22] / array2d[:, 4]\\n # mftw_items / items\\n new_array2d[:, 12] = array2d[:, 23] / array2d[:, 4]\\n # wspt_items / items\\n new_array2d[:, 13] = array2d[:, 24] / array2d[:, 4]\\n # mspt_items / items\\n new_array2d[:, 14] = array2d[:, 25] / array2d[:, 4]\\n # curvy_items / items\\n # Curvy item has a strong correlation with gender, however they are very right-skewed use np.power(1/6) to smooth it\\n new_array2d[:, 15] = np.power(array2d[:, 26] / array2d[:, 4], 1 / 6)\\n # sacc_items / items\\n new_array2d[:, 16] = array2d[:, 27] / array2d[:, 4]\\n # msite_orders / orders\\n new_array2d[:, 17] = array2d[:, 28] / array2d[:, 3]\\n # desktop_orders / orders\\n new_array2d[:, 18] = array2d[:, 29] / array2d[:, 3]\\n # android_orders / orders\\n new_array2d[:, 19] = array2d[:, 30] / array2d[:, 3]\\n # ios_orders / orders\\n new_array2d[:, 20] = array2d[:, 31] / array2d[:, 3]\\n # other_device_orders / orders\\n new_array2d[:, 21] = array2d[:, 32] / array2d[:, 3]\\n # work_orders / orders\\n new_array2d[:, 22] = array2d[:, 33] / array2d[:, 3]\\n # home_orders / orders\\n new_array2d[:, 23] = array2d[:, 34] / array2d[:, 3]\\n # parcelpoint_orders / orders\\n new_array2d[:, 24] = array2d[:, 35] / array2d[:, 3]\\n # other_collection_orders / orders\\n new_array2d[:, 25] = array2d[:, 36] / array2d[:, 3]\\n # average_discount_onoffer\\n new_array2d[:, 26] = array2d[:, 39]\\n # average_discount_used\\n new_array2d[:, 27] = array2d[:, 40]\\n # revenue / order\\n new_array2d[:, 28] = array2d[:, 41] / array2d[:, 3]\\n\\n # normalize by each feature\\n new_array2d = normalize(new_array2d, axis=0, norm='max')\\n return new_array2d\",\n \"def __init__(\\n self,\\n n_users,\\n n_products,\\n n_factors=20,\\n optimizer=torch.optim.SGD,\\n lr=0.001,\\n l2=0,\\n momentum=0,\\n loss_fn=nn.BCEWithLogitsLoss,\\n activation=nn.Sigmoid,\\n ):\\n super(NNMatrixFactorization, self).__init__()\\n\\n self.l2 = l2\\n self.lr = lr\\n self.momentum = momentum\\n self.user_factors = ScaledEmbedding(n_users, n_factors)\\n self.product_factors = ScaledEmbedding(n_products, n_factors)\\n self.user_bias = ZeroEmbedding(n_users, 1)\\n self.product_bias = ZeroEmbedding(n_products, 1)\\n\\n self.activation = activation()\\n self.loss_fn = loss_fn()\\n self.optimizer = optimizer(\\n self.parameters(), lr=self.lr, weight_decay=self.l2, momentum=self.momentum\\n )\",\n \"def get_scores(self):\\n hist = self.confusion_matrix\\n # hist = [TN,FP;FN,TP]\\n acc = np.diag(hist).sum() / hist.sum()\\n acc_cls = np.diag(hist) / hist.sum(axis=1)\\n acc_cls = np.nanmean(acc_cls)\\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\\n # iou = iu.sum() / self.n_classes\\n mean_iou = np.nanmean(iu) # if classes = 2: iou = miou\\n freq = hist.sum(axis=1) / hist.sum()\\n fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()\\n cls_iou = dict(zip(range(self.n_classes), iu))\\n\\n ##############################################\\n tn = hist[0, 0]\\n tp = np.diag(hist).sum() - tn\\n fp = np.triu(hist, 1).sum()\\n fn = np.tril(hist, -1).sum()\\n precision = tp / (tp + fp)\\n recall = tp / (tp + fn)\\n f1 = 2 * precision * recall / (precision + recall)\\n\\n # for medical img, img_seg \\\\in [0,1]\\n dice = 2 * tp / (tp + tp + fn + fp)\\n # dice = f1-score\\n dsc = 2 * tp / (tp + fn + fp)\\n # dsc = jaccard\\n # voe = 2 * abs(fp + fn) / (tp + tp + fn + fp)\\n # voe = 1 - dsc\\n\\n k2 = {\\n # \\\"Overall Acc: \\\\t\\\": acc,\\n 'Mean Acc': float(judge_nan(acc_cls)),\\n # \\\"FreqW Acc : \\\\t\\\": fwavacc,\\n 'Mean IoU': float(judge_nan(mean_iou)),\\n 'F1-score': float(judge_nan(f1)),\\n 'DSC': float(judge_nan(dsc)),\\n 'Precision': float(judge_nan(precision)),\\n 'Recall': float(judge_nan(recall)),\\n }\\n\\n return k2\",\n \"def predict(user_id, item_id, ratings):\\n\\n # convert long to wide\\n ratings_wide = ratings.pivot(index='user', columns='movie', values='rating')\\n\\n # compute user similarities\\n similarities = compute_similarities(user_id, ratings_wide)\\n \\n prediction = predict_rating(item_id, ratings_wide, similarities, N=N_NEIGHBORS)\\n \\n return prediction\",\n \"def __factor_matrix(self, R, K, alpha, steps, beta, error_limit):\\n # Transform regular array to numpy array\\n R = numpy.array(R)\\n\\n # Generate P - N x K\\n # Use random values to start. Best performance\\n N = len(R)\\n M = len(R[0])\\n P = numpy.random.rand(N, K)\\n\\n # Generate Q - M x K\\n # Use random values to start. Best performance\\n Q = numpy.random.rand(M, K)\\n Q = Q.T\\n\\n error = 0\\n\\n # iterate through max # of steps\\n for step in xrange(steps):\\n\\n # iterate each cell in r\\n for i in xrange(len(R)):\\n for j in xrange(len(R[i])):\\n if R[i][j] > 0:\\n\\n # get the eij (error) side of the equation\\n eij = R[i][j] - numpy.dot(P[i, :], Q[:, j])\\n\\n for k in xrange(K):\\n # (*update_rule) update pik_hat\\n P[i][k] = P[i][k] + alpha * (2 * eij * Q[k][j] - beta * P[i][k])\\n\\n # (*update_rule) update qkj_hat\\n Q[k][j] = Q[k][j] + alpha * ( 2 * eij * P[i][k] - beta * Q[k][j] )\\n\\n # Measure error\\n error = self.__error(R, P, Q, K, beta)\\n\\n # Terminate when we converge\\n if error < error_limit:\\n break\\n\\n # track Q, P (learned params)\\n # Q = Products x feature strength\\n # P = Users x feature strength\\n self.Q = Q.T\\n self.P = P\\n\\n self.__print_fit_stats(error, N, M)\",\n \"def recommend(self, user_id, N=10):\\n scores = self.user_factors[user_id] @ self.product_factors.T\\n best = np.argpartition(scores, -N)[-N:]\\n return sorted(zip(best, scores[best]), key=lambda x: -x[1])\",\n \"def weighted_majority_vote(c_pred,m_pred,f_pred,acc_c,acc_m,acc_f, dataset):\\n c,m,f = np.argmax(c_pred),np.argmax(m_pred),np.argmax(f_pred)\\n coarse = np.zeros(2)\\n middle = np.zeros(4)\\n fine = np.zeros(10)\\n\\n if dataset == 'cifar10':\\n middle = np.zeros(5)\\n coarse[c] = 1\\n middle[m] = 1\\n fine[f] = 1\\n res = np.zeros(10)\\n w1 = np.log(acc_c/(1.-acc_c))\\n w2 = np.log(acc_m/(1.-acc_m))\\n w3 = np.log(acc_f/(1.-acc_f))\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <2:\\n res[i] = w1*coarse[0] + w2*middle[0] + w3*fine[i]\\n elif 2<=i <4:\\n res[i] = w1*coarse[0] + w2*middle[1] + w3*fine[i]\\n elif 4 <=i<6:\\n res[i] = w1*coarse[1] + w2*middle[2] + w3*fine[i]\\n elif 6<=i<8:\\n res[i] = w1*coarse[1] + w2*middle[3] + w3*fine[i]\\n else:\\n res[i] = w1*coarse[1] + w2*middle[4] + w3*fine[i]\\n else :\\n for i in range(10):\\n if i <3:\\n res[i] = w1*coarse[0] + w2*middle[0] + w3*fine[i]\\n elif 3<=i <5:\\n res[i] = w1*coarse[0] + w2*middle[1] + w3*fine[i]\\n elif 5 <=i<8:\\n res[i] = w1*coarse[1] + w2*middle[2] + w3*fine[i]\\n else:\\n res[i] = w1*coarse[1] + w2*middle[3] + w3*fine[i]\\n index = np.argmax(res)\\n return(index)\",\n \"def _compute_mu_factor2(*input_mols):\\n mu_factor = 1\\n for mol in input_mols:\\n mu_factor *= np.prod(fact(mol))\\n return mu_factor\",\n \"def update_model(self):\\n temp_uservisits = self._uservisits\\n unique_users = temp_uservisits['userid'].unique()\\n for itemidx, itemid in self._allitems.iterrows():\\n self.__itemid2idx[str(itemid['itemid'])] = itemidx\\n self.__itemidx2id[itemidx] = str(itemid['itemid'])\\n for useridx, userid in enumerate(unique_users):\\n self.__userid2idx[str(userid)] = useridx\\n self.__useridx2id[useridx] = str(userid)\\n useritem = set(temp_uservisits[temp_uservisits['userid'] == userid]['itemid'].astype('str').values)\\n allitem = set(self.__itemid2idx.keys())\\n itemsnotinuser = allitem - useritem\\n self.__useridx2nvitems[useridx] = list(itemsnotinuser)\\n temp = pandas.DataFrame([{\\\"userid\\\": userid, \\\"itemid\\\": t, \\\"rating\\\": 0, \\\"timestamp\\\": \\\"NA\\\"} for t in itemsnotinuser])\\n temp_uservisits = pandas.concat([temp_uservisits, temp])\\n userid = temp_uservisits['userid'].values\\n itemid = temp_uservisits['itemid'].values\\n rating = temp_uservisits['rating'].values\\n useridx = [self.__userid2idx[str(int(uid))] for uid in userid]\\n itemidx = [self.__itemid2idx[str(int(iid))] for iid in itemid]\\n model = self.__get_model(num_users=len(temp_uservisits['userid'].unique()), num_items=len(self._allitems),\\n mf_dim=self.__numtopics)\\n if self.__learner.lower() == \\\"adagrad\\\":\\n model.compile(optimizer=Adagrad(lr=self.__learningrate), loss='binary_crossentropy')\\n elif self.__learner.lower() == \\\"rmsprop\\\":\\n model.compile(optimizer=RMSprop(lr=self.__learningrate), loss='binary_crossentropy')\\n elif self.__learner.lower() == \\\"adam\\\":\\n model.compile(optimizer=Adam(lr=self.__learningrate), loss='binary_crossentropy')\\n else:\\n model.compile(optimizer=SGD(lr=self.__learningrate), loss='binary_crossentropy')\\n\\n for epoch in range(self.__numiterations):\\n t1 = time.time()\\n hist = model.fit([numpy.array(useridx), numpy.array(itemidx)], numpy.array(rating),\\n batch_size=self.__batchsize, nb_epoch=1, verbose=0, shuffle=True)\\n t2 = time.time()\\n self.__recommender1 = model\\n rating = list(map(numpy.double, rating))\\n self.__itemuser = csr_matrix((rating, (useridx, itemidx)), shape=(len(set(useridx)), len(set(itemidx))))\\n self.__recommender2 = implicit.als.AlternatingLeastSquares(factors=self.__numtopics)\\n self.__recommender2.fit(self.__itemuser)\",\n \"def create_matrix(ratings_df, jokes_df):\\r\\n \\\"\\\"\\\" note: empty entries are populated with zeros \\\"\\\"\\\"\\r\\n\\r\\n matrix_handler = matrix_object()\\r\\n\\r\\n num_joke_features = 5\\r\\n\\r\\n ''' add all joke features '''\\r\\n for row_idx in range(0, jokes_df.shape[0]):\\r\\n joke_idx = int(jokes_df.iloc[row_idx][\\\"Idx\\\"])\\r\\n isAggressive = jokes_df.iloc[row_idx][\\\"isAggressive\\\"]\\r\\n isIncongruence = jokes_df.iloc[row_idx][\\\"isIncongruence\\\"]\\r\\n generation = jokes_df.iloc[row_idx][\\\"Generation\\\"]\\r\\n isMillenial = (generation == \\\"Millenial\\\")\\r\\n isGenX = (generation == \\\"Gen X\\\")\\r\\n isGenZ = (generation == \\\"Gen Z\\\")\\r\\n\\r\\n if(int(isMillenial) == 1.0 and int(isGenX) == 1.0):\\r\\n raise Valueerror()\\r\\n\\r\\n matrix_handler.add_value(joke_idx - 1, 0, int(isAggressive))\\r\\n matrix_handler.add_value(joke_idx - 1, 1, int(isIncongruence))\\r\\n matrix_handler.add_value(joke_idx - 1, 2, int(isMillenial))\\r\\n matrix_handler.add_value(joke_idx - 1, 3, int(isGenX))\\r\\n matrix_handler.add_value(joke_idx - 1, 4, int(isGenZ))\\r\\n\\r\\n ''' add all ratings '''\\r\\n for row_idx in range(0, ratings_df.shape[0]):\\r\\n for joke_idx in range(1, 122):\\r\\n col_name = \\\"joke\\\" + str(joke_idx)\\r\\n matrix_handler.add_value(joke_idx - 1, row_idx + num_joke_features, ratings_df.iloc[row_idx][col_name])\\r\\n\\r\\n matrix = matrix_handler.compile_matrix()\\r\\n new_df = matrix_handler.to_df(matrix)\\r\\n\\r\\n return matrix, new_df\",\n \"def feature_selection_classifier_3(array2d):\\n newArray2d = np.zeros([array2d.shape[0], 18])\\n # female_items / female_items + male_items\\n newArray2d[:, 0] = array2d[:, 4]\\n # male_items / female_items + male_items\\n newArray2d[:, 1] = array2d[:, 5]\\n # wapp_items / items\\n newArray2d[:, 2] = array2d[:, 7]\\n # wftw_items / items\\n newArray2d[:, 3] = array2d[:, 8]\\n # mapp_items / items\\n newArray2d[:, 4] = array2d[:, 9]\\n # wacc_items / items\\n newArray2d[:, 5] = array2d[:, 10]\\n # macc_items / items\\n newArray2d[:, 6] = array2d[:, 11]\\n # mftw_items / items\\n newArray2d[:, 7] = array2d[:, 12]\\n # curvy_items / items\\n newArray2d[:, 8] = array2d[:, 15]\\n # msite_orders / orders\\n newArray2d[:, 9] = array2d[:, 17]\\n # desktop_orders / orders\\n newArray2d[:, 10] = array2d[:, 18]\\n # android_orders / orders\\n newArray2d[:, 11] = array2d[:, 19]\\n # ios_orders / orders\\n newArray2d[:, 12] = array2d[:, 20]\\n # other_device_orders / orders\\n newArray2d[:, 13] = array2d[:, 21]\\n # home_orders / orders\\n newArray2d[:, 14] = array2d[:, 23]\\n # other_collection_orders / orders\\n newArray2d[:, 15] = array2d[:, 25]\\n # average_discount_onoffer\\n newArray2d[:, 16] = array2d[:, 26]\\n # average_discount_used\\n newArray2d[:, 17] = array2d[:, 27]\\n return newArray2d\",\n \"def fit(self):\\n\\n rmse_old = .0\\n for epoch in range(self.epochs):\\n error_final = .0\\n\\n for user, item, feedback in self.feedback_triples:\\n pu = self.p[user] + self.y_sum_rows(user)\\n\\n # Calculate error\\n eui = feedback - self._predict_svd_plus_plus_score(user, item, pu, False)\\n error_final += (eui ** 2.0)\\n\\n # update bu and bi\\n self.bu[user] += self.bias_learn_rate * (eui - self.delta_bias * self.bu[user])\\n self.bi[item] += self.bias_learn_rate * (eui - self.delta_bias * self.bi[item])\\n\\n # Adjust the factors\\n norm_eui = eui / self.n_u[user]\\n\\n i_f = self.q[item]\\n\\n # Compute factor updates\\n delta_u = np.subtract(np.multiply(eui, i_f), np.multiply(self.delta, self.p[user]))\\n self.p[user] += np.multiply(self.learn_rate, delta_u)\\n\\n delta_i = np.subtract(np.multiply(eui, pu), np.multiply(self.delta, i_f))\\n self.q[item] += np.multiply(self.learn_rate, delta_i)\\n\\n # update y (implicit factor)\\n common_update = norm_eui * i_f\\n\\n for j in self.items_id_seen_by_user[user]:\\n delta_y = np.subtract(common_update, self.delta * self.y[j])\\n self.y[j] += self.learn_rate * delta_y\\n\\n rmse_new = np.sqrt(error_final / self.train_set[\\\"number_interactions\\\"])\\n\\n if np.fabs(rmse_new - rmse_old) <= self.stop_criteria:\\n break\\n else:\\n rmse_old = rmse_new\",\n \"def ndpm(self):\\n\\n merged = pd.merge(left=self.test, right=self.predict, on=['user', 'item'], how='inner')[\\n ['user', 'rating_x', 'rating_y']]\\n ndpms = []\\n for user in merged.user.unique():\\n frame = merged[merged.user == user]\\n if frame.shape[0] <= 1:\\n continue\\n C_plus = self.num_of_ordered_positive(frame, 'rating_x', 'rating_y')\\n C_minus = self.num_of_ordered_negative(frame, 'rating_x', 'rating_y')\\n C_u = self.num_of_ordered(frame, 'rating_x')\\n if C_u == 0:\\n continue\\n C_s = self.num_of_ordered(frame, 'rating_y')\\n C_u0 = C_u - (C_plus + C_minus)\\n ndpms.append(1 - (C_minus + 0.5 * C_u0) / C_u)\\n\\n return sum(ndpms) / len(ndpms)\",\n \"def gen_W(users, items, ratings):\\n\\n # initiate graph\\n user = users.tolist()\\n item = items.tolist()\\n rating = ratings.tolist()\\n B = nx.Graph()\\n B.add_nodes_from(user, bipartite=0)\\n B.add_nodes_from(item, bipartite=1)\\n\\n # create edges\\n for i in range(len(user)):\\n B.add_edges_from([(user[i], item[i])])\\n B[user[i]][item[i]]['weight'] = rating[i]\\n\\n users_unique = users.unique() \\n items_unique = items.unique()\\n\\n # BiAdjacency matrix - for bipartite network\\n W = biadjacency_matrix(B, users_unique,items_unique).toarray()\\n\\n # sparce form of Biadjacency matrix\\n W = spa.csr_matrix(W)\\n print('Shape of W: '+str(W.shape))\\n\\n return W, users_unique, items_unique\",\n \"def alternate_ls (u_num, Y, P, C, reg):\\n\\n\\t# get # of items/users and # of latent factors\\n\\t[i_num, f_num] = Y.shape\\n\\n\\t# output buffer\\n\\tX = np.zeros((u_num, f_num))\\n\\n\\t# precalculate YtY to improve the performance\\n\\tYtY = Y.T * Y\\n\\n\\t# iterate over each user/item\\n\\tfor u in range(u_num):\\n\\n\\t\\t# store the diagonal elements of the matrix Cu discussed in the paper in a vector\\n\\t\\tCu = C[u,:]\\n\\n\\t\\t# store the coresponding row/column of the preference matrix\\n\\t\\tPu = P[u,:]\\n\\n\\t\\t# compute Cu-I\\n\\t\\tCu_I = Cu - 1\\n\\n\\t\\t# calculate Yt(Cu-I)Y\\n\\t\\tYtCu_IY = np.zeros((f_num, f_num))\\n\\t\\tCuIY = np.multiply(Y, Cu_I.T) # weight each row of Y with Cu-I\\n\\t\\tfor row in range(f_num):\\n\\t\\t\\tfor col in range(f_num):\\n\\t\\t\\t\\tYtCu_IY[row,col] = Y[:,row].T * CuIY[:,col]\\n\\t\\t\\n\\t\\t# left term : ((YtCuY + regI)^(-1)) = (YtY + Yt(Cu-I)Y + regI)^(-1)\\n\\t\\tleft_inv = YtY + YtCu_IY + reg*np.eye(f_num)\\n\\t\\tleft = np.linalg.inv(left_inv)\\n\\n\\t\\t# right term : YtCuPu\\n\\t\\tright = Y.T * np.multiply(Cu.T, Pu.T)\\n\\n\\t\\t# compute the latent factor of the user/item\\n\\t\\tx = left * right\\n\\t\\t\\n\\t\\t# store it in a matrix\\n\\t\\tX[u,:] = x.T\\n\\n\\t# return an MxF or NxF matrix\\n\\treturn np.matrix(X)\",\n \"def calc_score(self, user_id, item_id): \\n p = np.dot(self.U[user_id], self.V[item_id])\\n if self.trunc_score_rule==None:pass\\n else: p=self.trunc_score_rule(p)\\n \\n return p\",\n \"def evaluate_ucf50_fusion():\\n accs = np.zeros(3)\\n ob_suffix = '-max.feat.npy.gz'\\n fv_suffix = '_fv.npy.gz'\\n ob_root = '/home/syq/research_final/data/features/ob_ucf50_pooled_python/'\\n fv_root = '/home/syq/research_final/data/features/fv_ucf50_python/'\\n fv_groups, full, sets = utility.split_data(fv_root,\\n suffix=fv_suffix,\\n useLooCV=False)\\n\\n ob_groups, _, _ = utility.split_data(ob_root,\\n suffix=ob_suffix,\\n useLooCV=False)\\n weights = [i / 20.0 for i in range(8, 13)]\\n acc_to_weights = {}\\n\\n for weight in weights:\\n print \\\"Weight: %.2f\\\" % weight\\n accs = np.zeros(2)\\n for i in xrange(2):\\n ts = time.time()\\n Dtrain_fv, Dtest_fv, Ytrain, Ytest = utility.load_groups(\\n fv_groups, np.setdiff1d(full, sets[i]),\\n sets[i], scale=False, verbose=False)\\n\\n Dtrain_ob, Dtest_ob, Ytrain, Ytest = utility.load_groups(\\n ob_groups, np.setdiff1d(full, sets[i]),\\n sets[i], scale=False, verbose=False)\\n\\n fv_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\\n\\n ob_clf = OneVsRestClassifier(estimator=SVC(C=10,\\n cache_size=1000,\\n kernel='linear',\\n probability=True),\\n n_jobs=-1)\\n\\n # Get probabilities for late fusion\\n Dtrain_fv = fv_clf.fit(Dtrain_fv, Ytrain).decision_function(Dtrain_fv)\\n Dtrain_ob = ob_clf.fit(Dtrain_ob, Ytrain).decision_function(Dtrain_ob)\\n Dtest_fv = fv_clf.decision_function(Dtest_fv)\\n Dtest_ob = ob_clf.decision_function(Dtest_ob)\\n\\n # Scale decision values b/w 0 and 1\\n Dtrain_fv = preprocessing.normalize(Dtrain_fv)\\n Dtrain_ob = preprocessing.normalize(Dtrain_ob)\\n Dtest_fv = preprocessing.normalize(Dtest_fv)\\n Dtest_ob = preprocessing.normalize(Dtest_ob)\\n\\n # weighted averaging\\n scores_train = (Dtrain_fv * weight) + (Dtrain_ob * (1 - weight))\\n latefusion_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=-1)\\n latefusion_clf.fit(scores_train, Ytrain)\\n\\n scores_test = (Dtest_fv * weight) + (Dtest_ob * (1 - weight))\\n latefusion_acc = latefusion_clf.score(scores_test, Ytest)\\n print 'Fold', i, 'late fusion acc', latefusion_acc\\n print \\\"Train & testing time %.3f\\\" % (time.time() - ts)\\n accs[i] = latefusion_acc\\n\\n print \\\"Mean accuracy: %.3f\\\" % accs.mean()\\n with open(\\\"fv_ucf50_accs_5fold_gridsearch.txt\\\", \\\"w\\\") as f:\\n for weight, accs in acc_to_weights.items():\\n f.write(str(weight) + str(accs) + '\\\\n')\\n return acc_to_weights\",\n \"def _predict_user_item(self, user, item):\\n if not isinstance(user, int):\\n user = self._user_to_ndx[user]\\n if not isinstance(item, int):\\n item = self._item_to_ndx[item]\\n\\n try:\\n rating_mean = self._averages[user]\\n except AttributeError:\\n raise RuntimeError('Must fit before predicting')\\n\\n other_users = [other for other in self._users if other != user and\\n np.isfinite(self._votes[other][item])]\\n weights = np.array([self._weight(user, other)\\n for other in other_users])\\n deviations = np.array([self._votes[other][item] - self._averages[other]\\n for other in other_users])\\n\\n weight_sum = np.sum(np.absolute(weights))\\n if weight_sum < _EPSILON:\\n return rating_mean # No similar users, so guess their avg rating\\n\\n norm_const = 1 / weight_sum\\n\\n weighted_avg = np.sum(weights * deviations)\\n return rating_mean + norm_const * weighted_avg\",\n \"def construct_classifier_for_user(user_indexed_reviews, restaurant_indexed_reviews):\\t\\n # compute all_reviews\\n all_reviews = []\\n for restaurant in restaurant_indexed_reviews:\\n reviews_content = ''\\n for user in restaurant_indexed_reviews[restaurant]:\\n reviews = restaurant_indexed_reviews[restaurant][user]\\n for review in reviews:\\n reviews_content += review['text'][0:len(review['text'])-1]\\n all_reviews.append(reviews_content)\\n\\n print 'extract feature...'\\n # construct classifier\\n X_total, y_total, restaurant_feature = extracttfidf_user(user_indexed_reviews, all_reviews, restaurant_indexed_reviews)\\n\\n print 'construct classifier...'\\n i = 0\\n for user in user_indexed_reviews:\\n print i\\n i += 1\\n classifier = MultinomialNB(alpha=.01)\\n X_train = X_total[user]\\n y_train = y_total[user]\\n if X_train != None:\\n try:\\n classifier.fit(X_train, y_train)\\n update_rating(user, restaurant_feature, classifier, user_indexed_reviews, restaurant_indexed_reviews)\\n except:\\n continue\",\n \"def model_frequency(ref, alt, bases_all_reads, f_vector, error_rates):\\n # model Mx: NO alt allele\\n if ref == alt:\\n return np.array([error_rates[ref][eb] for eb in \\\"ACGT-\\\"])\\n # model Mxy: two alleles\\n mat = np.zeros((K, 5))\\n for b, base in enumerate(\\\"ACGT-\\\"):\\n for i, observed_base in enumerate(bases_all_reads):\\n mat[i][b] = error_rates[base][observed_base]\\n\\n m2 = (mat * f_vector)[:, [bases_index[alt], bases_index[ref]]]\\n alt_ref_f_vector = (m2 / m2.sum(axis=1)[:, np.newaxis]).sum(axis=0) / K\\n\\n l = [alt_ref_f_vector[0], ]*5\\n l[bases_index[ref]] = alt_ref_f_vector[1]\\n updated_f_vector = np.array(l)\\n\\n return updated_f_vector\",\n \"def __init__(self, customer_vendor_full, valid_rating_mean):\\r\\n super(User_CF, self).__init__()\\r\\n self.customer_vendor_full = customer_vendor_full\\r\\n self.customer_vendor_ratings = self.select_features()\\r\\n self.customer_vendor_matrix = self.customer_vendor_ratings.pivot(\\r\\n index='customer_id', columns='vendor_id', values='mean_rating') # (26779, 100)\\r\\n self.rating_matrix = self.customer_vendor_matrix.fillna(0).values.astype(np.float32)\\r\\n self.valid_rating_mean = valid_rating_mean\\r\\n self.vendor2rating = self.get_vendors_mean()\\r\\n self.customer_similarity, = self.get_similarity()\",\n \"def __init__(self, dim=20, nIter=5, lamb=0.05, alph=40,\\n user_features=[\\\"user\\\"], item_features=[\\\"item\\\"]):\\n self.setParams(dim,nIter, lamb, alph)\\n self.user_features = {}\\n self.item_features = {}\\n self.factors = {}\\n\\n self.user_column_names = user_features\\n self.item_column_names = item_features\",\n \"def evaluate_model(test_user_item_matrix, user_mat, portfolio_mat):\\r\\n n = np.count_nonzero(~np.isnan(test_user_item_matrix))\\r\\n\\r\\n # keep track of the sum of squares\\r\\n sse = 0\\r\\n\\r\\n for user_id in test_user_item_matrix.index:\\r\\n for offer_id in test_user_item_matrix.columns.values:\\r\\n if ~np.isnan(test_user_item_matrix.loc[user_id, offer_id]):\\r\\n pred = predict(test_user_item_matrix, user_mat, portfolio_mat, user_id, offer_id)\\r\\n if pred:\\r\\n diff = test_user_item_matrix.loc[user_id, offer_id] - pred\\r\\n sse += diff ** 2\\r\\n return sse / n\",\n \"def make_predictions(movies, ratings_train, ratings_test):\\n ###TODO\\n \\n user_result = [] \\n \\n for index,row in ratings_test.iterrows():\\n userid_test = row['userId']\\n #print(\\\"userid_test::\\\",userid_test) \\n movieid_test = row['movieId'] \\n #print(\\\"movieid_test::\\\",movieid_test) \\n x = list(movies[movies.movieId==movieid_test]['features'])[0]\\n #print(\\\"CSR_GOTT+X::\\\",x)\\n #print(\\\"TYPE of CSR_GOTT_X::\\\",type(x))\\n subset_train = ratings_train[ratings_train.userId == userid_test]\\n #print(\\\"SUB MOVIE SET::\\\",subset_train)\\n #print(\\\"TYPE of SUB MOVIE SET::\\\",type(x))\\n total_if_zero=0\\n rating_if_zero=0\\n sum_main_result=0\\n sum_cosine=0 \\n for index1,row1 in subset_train.iterrows():\\n userid_train = row1['userId']\\n #print(\\\"userid_train::\\\",userid_train) \\n if(userid_test == userid_train ):\\n #print(\\\"HII IN IFFF:::\\\")\\n movieid_train = row1['movieId']\\n #print(\\\"movieid_train::\\\",movieid_train)\\n rating_train = row1['rating']\\n #print(\\\"rating_train::\\\",rating_train)\\n total_if_zero = total_if_zero + 1 \\n rating_if_zero = rating_if_zero + rating_train\\n y = list(movies[movies.movieId==movieid_train]['features'])[0]\\n #print(\\\"CSR_GOTT_Y::\\\",y)\\n #print(\\\"TYPE of CSR_GOTT_Y::\\\",type(y))\\n result_cos = cosine_sim(x,y)\\n sum_main_result += result_cos * rating_train\\n sum_cosine += result_cos \\n \\n if(sum_main_result != 0):\\n user_result.append(sum_main_result/sum_cosine)\\n #print(\\\"user_result::\\\",user_result) \\n else:\\n user_result.append(rating_if_zero / total_if_zero)\\n #print(\\\"user_result::\\\",user_result) \\n \\n return_result_arr = np.array(user_result) \\n \\n return return_result_arr\\n \\n pass\",\n \"def predict(self, user_id, item_id):\\n # DONEreturn prediction for given pair\\n return self._user_factors[user_id, : ].dot(self._item_factors[item_id, :])\",\n \"def get_user_vector(self, user_id):\\r\\n if user_id in self.df_ratmat.index:\\r\\n # user from base dataset\\r\\n return np.array([self.df_ratmat.loc[user_id]])\\r\\n # construct a vector out of app user rating data\\r\\n movies_rated = self.get_valid_user_ratings(user_id)\\r\\n movie_size = self.df_ratmat.shape[1]\\r\\n cols = [str(i) for i in range(1, movie_size + 1)]\\r\\n df = pd.DataFrame(columns=cols)\\r\\n new_row = {}\\r\\n for i, r in movies_rated[['item_id', 'rating']].iterrows():\\r\\n new_row[str(int(r['item_id']))] = int(r['rating'])\\r\\n df = df.append(new_row, ignore_index=True)\\r\\n # mark 0 (=not rated) if not rated by the user\\r\\n return df.fillna(0)\",\n \"def _factorsX(self, inputs):\\n return tensor.dot(inputs[0], self.wxf)\",\n \"def print_maxes(mat):\\r\\n u_to_likes = load_or_create(\\\"/Matrix/UserIdToLikes.matrix\\\", create_matrix_user_likes)\\r\\n dict_names = load_or_create('/DICT/MovieIdToName.dict', create_dict_names)\\r\\n dict_ecc = load_or_create('/DICT/MovieIdToItemEccentricity.dict', create_dict_ecc)\\r\\n user_to_ecc = load_or_create('/DICT/UserIdToUserEccentricity.dict',createDictUserIdToUserEccentricity)\\r\\n dict_userid_to_moviesliked = load_or_create('/DICT/UserIdToLikedMovies.dict', create_dict_user_id_to_liked_items)\\r\\n\\r\\n dict_userid_to_recommends = dict()\\r\\n print(\\\"STARTING ECC CALC\\\")\\r\\n recommends = []\\r\\n for i in range(int(mat.shape[0]*0.5)):\\r\\n row = mat.getrow(i)\\r\\n if len(row.nonzero()[0]) != 0:\\r\\n # print(u_to_likes.getrow(i).nonzero()[1])\\r\\n if len(u_to_likes.getrow(i).nonzero()[1])<10 and user_to_ecc[i+1]>0:\\r\\n # print(\\\"Amount of recommends:\\\",len(row.nonzero()[0]))\\r\\n row = row.toarray()[0].tolist()\\r\\n max_val = max(val for val in row if str(row.index(val) + 1) not in dict_userid_to_moviesliked[i+1])\\r\\n print('SUM is:',sum(val for val in row if str(row.index(val) + 1) not in dict_userid_to_moviesliked[i+1]))\\r\\n print('SUM with all is:',sum(val for val in row))\\r\\n\\r\\n index_max=row.index(max_val) + 1\\r\\n\\r\\n recommends.append(\\r\\n [max_val, row.index(max_val) + 1, i + 1, [i + 1 for i in u_to_likes.getrow(i).nonzero()[1]],\\r\\n [row.index(max_val) + 1],user_to_ecc[i+1]])\\r\\n\\r\\n recommends = sorted(recommends, key=itemgetter(0))\\r\\n\\r\\n for i in recommends[-100:]:\\r\\n print(\\\"MAX id:\\\", i[1])\\r\\n print(\\\"MAX val:\\\", i[0])\\r\\n print(\\\"Users ECC:\\\",i[5])\\r\\n print(\\\"for user:\\\", i[2])\\r\\n print(\\\"MOVIES HE ALREADY LIKED\\\", 50 * \\\"=\\\")\\r\\n item_names_print(i[3], dict_names, dict_ecc)\\r\\n print(\\\"Movie Well recommend:\\\" + 50 * '*')\\r\\n item_names_print(i[4], dict_names, dict_ecc)\\r\\n print(50 * \\\"#\\\")\",\n \"def test_matrix(self, tol):\\n\\n res_static = qml.QFT.compute_matrix(2)\\n res_dynamic = qml.QFT(wires=[0, 1]).matrix()\\n res_reordered = qml.QFT(wires=[0, 1]).matrix([1, 0])\\n\\n expected = np.array(\\n [\\n [0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j],\\n [0.5 + 0.0j, 0.0 + 0.5j, -0.5 + 0.0j, -0.0 - 0.5j],\\n [0.5 + 0.0j, -0.5 + 0.0j, 0.5 - 0.0j, -0.5 + 0.0j],\\n [0.5 + 0.0j, -0.0 - 0.5j, -0.5 + 0.0j, 0.0 + 0.5j],\\n ]\\n )\\n\\n assert np.allclose(res_static, expected, atol=tol, rtol=0)\\n assert np.allclose(res_dynamic, expected, atol=tol, rtol=0)\\n\\n expected_permuted = [\\n [0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j, 0.5 + 0.0j],\\n [0.5 + 0.0j, 0.5 - 0.0j, -0.5 + 0.0j, -0.5 + 0.0j],\\n [0.5 + 0.0j, -0.5 + 0.0j, 0.0 + 0.5j, -0.0 - 0.5j],\\n [0.5 + 0.0j, -0.5 + 0.0j, -0.0 - 0.5j, 0.0 + 0.5j],\\n ]\\n assert np.allclose(res_reordered, expected_permuted, atol=tol, rtol=0)\",\n \"def calculate_total(self, calcu_user_n=20, user_n=20, item_n=10, seed=1):\\n self._split_data(seed=seed)\\n self._set_top(user_n=user_n, item_n=item_n)\\n test_user_list = list(set(self.test['userId'].unique()))\\n user_list = [test_user_list[random.randint(0, len(test_user_list)) - 1]\\n for i in range(calcu_user_n)]\\n hit = 0 # Hit score\\n all_recom = 0 # num of all recommendations, calculate the accuracy rate\\n like_item = 0 # num of the item the user likes in the test set, calculate the recall rate\\n all_recom_set = set()\\n all_item = set(self.train['movieId'].unique())\\n item_popular = Counter(self.train['movieId'].values)\\n ret = 0\\n n = 0\\n print('\\\\nCalculate all evaluation indicators...')\\n for user in tqdm(user_list):\\n recom_data = self._get_recommend(user, )\\n recom_item = set([data[0] for data in recom_data])\\n user_item = set(\\n self.test[self.test['userId'] == user]['movieId'].values)\\n overlap = recom_item & user_item\\n hit += len(overlap)\\n like_item += len(user_item)\\n all_recom += len(recom_item)\\n all_recom_set.update(recom_item)\\n for rec in set([data[0] for data in recom_data]):\\n ret += math.log(1 + item_popular.get(rec))\\n n += 1\\n print('\\\\nCalculate over')\\n print('Precision is: ', hit / (all_recom * 1.0))\\n print('Recall is: ', hit / (like_item * 1.0))\\n print('Coverage is: ', len(all_recom_set) / (len(all_item) * 1.0))\\n print('Popularity is:', (ret / n * 1.0))\",\n \"def prediction(prediction_file_name, clusters_list, svd_use_flag):\\n \\n coords = misc_functions.getWindowCoords()\\n \\n test_users = range(coords[0], coords[2] + 1) \\n test_items = range(coords[1], coords[3] + 1)\\n \\n #print \\\"len(test_users) = \\\", len(test_users)\\n #print \\\"len(test_items) = \\\", len(test_items)\\n #print \\\"test_items = \\\", test_items\\n \\n # this matrix to be written as result finally\\n #misc_functions.step()\\n prediction_matrix = zeros((len(test_users), len(test_items)), dtype = float)\\n \\n training_matrix = scipy.io.mmio.mmread(\\\"history.mtx\\\").tocsr()\\n \\n item_X_meta_matrix = scipy.io.mmio.mmread(\\\"../../../well_done/items-metas_global.mtx\\\").toarray()\\n \\n # getting meta matrices for corresponding using metas\\n meta_ctr = 0\\n meta_matrices = []\\n for meta in METAS_TO_USE:\\n if svd_use_flag:\\n meta_matrice_file_name = \\\"users-\\\" + METAS_TO_USE[meta] + \\\".svd.mtx\\\"\\n else:\\n meta_matrice_file_name = \\\"users-\\\" + METAS_TO_USE[meta] + \\\".mtx\\\"\\n exec(\\\"meta_matrices.append(scipy.io.mmio.mmread(\\\\\\\"\\\" + meta_matrice_file_name + \\\"\\\\\\\").toarray())\\\")\\n\\n #user_counter = 0\\n #for user in test_users:\\n for cur_cluster in clusters_list:\\n \\n #print \\\"cur_cluster[0] = \\\", cur_cluster[0]\\n user = int (cur_cluster[0].split(\\\"\\\\t\\\")[1])\\n #print \\\"user #\\\", user\\n \\n #user_metas = {} - changed to list because of problem with dimension\\n user_metas = []\\n \\n values = zeros((len(METAS_TO_USE), len(test_items)), dtype = float)\\n meta_ctr = 0\\n for meta in METAS_TO_USE:\\n \\n #print \\\" meta_matrices = \\\", meta_matrices\\n #print \\\" meta_matrices[meta_ctr] = \\\", meta_matrices[meta_ctr]\\n user_vector = meta_matrices[meta_ctr][user]\\n #print \\\" user_vector = \\\", user_vector\\n #print \\\" len(user_metas) = \\\", len(user_metas)\\n #print \\\" meta_ctr = \\\", meta_ctr\\n #print \\\"meta = \\\", meta\\n #misc_functions.step()\\n \\n # normalizing counts of visited metas to use them as weights later\\n if max(user_vector) != 0:\\n user_metas.append(1.0 * user_vector / max(user_vector))\\n else:\\n user_metas.append(zeros((len(user_vector), ), dtype = float))\\n #print \\\" user_metas[meta_ctr] = \\\", user_metas[meta_ctr]\\n #print \\\" user_metas[meta_ctr].shape = \\\", user_metas[meta_ctr].shape\\n \\n #for item in test_items:\\n for cluster in cur_cluster[1 : ]:\\n start_cluster_item = int(cluster.split(\\\"\\\\t\\\")[0])\\n stop_cluster_item = int(cluster.split(\\\"\\\\t\\\")[2])\\n \\n cluster_items = range(start_cluster_item, stop_cluster_item + 1)\\n \\n for item in cluster_items:\\n meta_value = item_X_meta_matrix[item, meta]\\n \\n # PRICE\\n if meta == 8:\\n meta_value = priceToPriceCat(meta_value)\\n \\n # CITY HEURISTIC\\n if meta == 11:\\n if user_metas[meta_ctr][meta_value - 1] < CITY_TRESHOLD:\\n values[:, item - coords[1]] *= CITY_COEF\\n \\\"\\\"\\\"\\n # DAYTIME\\n if meta == 17:\\n meta_value = dayTime(meta_value)\\n \\\"\\\"\\\"\\n \\n #print \\\" meta_value = \\\", meta_value\\n #print \\\" item = \\\", item\\n #step()\\n values[meta_ctr][item - coords[1]] = (user_metas[meta_ctr])[meta_value - 1]\\n \\n \\\"\\\"\\\"HEURISTICS \\\"\\\"\\\"\\n \\n \\n \\n \\n \\n \\\"\\\"\\\"\\\\\\\\ HEURISTICS \\\"\\\"\\\"\\n\\n meta_ctr += 1\\n #print \\\"values[:, 0:10] = \\\", values[:, 0:10]\\n prediction_vector = numpy.sum(META_WEIGHTS * values, axis = 0)\\n #print \\\"prediction_vector[0:10] = \\\", prediction_vector[0:10]\\n #print \\\"sum(prediction_vector) = \\\", sum(prediction_vector)\\n prediction_matrix[user - coords[0]] = prediction_vector\\n \\n #step()\\n \\n# ===== END OF MAIN CYCLE ===== \\n\\n result_matrix = scipy.sparse.csr_matrix(prediction_matrix)\\n scipy.io.mmio.mmwrite(prediction_file_name, result_matrix, field = 'real', precision = 5)\",\n \"def test_init_ratings():\\n env = FixedRating(num_users=50,\\n num_items=50,\\n rating_frequency=1.0,\\n num_init_ratings=100)\\n env.seed(0)\\n _, _, ratings = env.reset()\\n assert len(ratings) == 100\\n for (user_id, item_id), (rating, context) in ratings.items():\\n assert context.shape == (0,)\\n assert user_id < 50\\n assert item_id < 50\\n if rating == 5.0:\\n assert item_id >= 25\\n else:\\n assert item_id < 25\",\n \"def proba_fm(m_pred,f_pred, dataset):\\n p = np.zeros(10)\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <4:\\n if i <2:\\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:2]))\\n else:\\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[2:4]))\\n else:\\n if i <6:\\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[4:6]))\\n elif i <8:\\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[6:8]))\\n else:\\n p[i] = (m_pred[4])*(f_pred[i]/np.sum(f_pred[8:]))\\n else :\\n for i in range(10):\\n if i <5:\\n if i <3:\\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:3]))\\n else:\\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[3:5]))\\n else:\\n if i <8:\\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[5:8]))\\n else:\\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[8:]))\\n return(p)\",\n \"def _create_sparse_train_and_test(ratings, n_users, n_items):\\n \\n # pick a random set of data as testing data, sorted ascending\\n test_set_size = len(ratings) / TEST_SET_RATIO\\n test_set_idx = np.random.choice(xrange(len(ratings)), size=test_set_size, replace=False)\\n test_set_idx = sorted(test_set_idx)\\n \\n # use the remaining data to create a training set\\n ts_ratings = ratings[test_set_idx]\\n tr_ratings = np.delete(ratings, test_set_idx, axis=0)\\n \\n # create training and test matrices as coo_matrix\\n u_tr, i_tr, r_tr = zip(*tr_ratings)\\n tr_sparse = coo_matrix((r_tr, (u_tr, i_tr)), shape=(n_users, n_items))\\n u_ts, i_ts, r_ts = zip(*ts_ratings)\\n test_sparse = coo_matrix((r_ts, (u_ts, i_ts)), shape=(n_users, n_items))\\n \\n return tr_sparse, test_sparse\",\n \"def prediction(uid, pair, rating_bd, sim_bd, item_bd):\\n iid, real_rating = pair[0], pair[1]\\n if iid not in sim_bd.value.keys():\\n return ()\\n iid_neighbors = [\\n (i[0], i[1], rating_bd.value[i[0]]) for i in sim_bd.value[iid]]\\n average_iid_rating = item_bd.value[iid][0]\\n sim_rating = []\\n for info in iid_neighbors:\\n niid, nsim, ratings = info\\n sim_rating += [\\n (iid, nsim, rating[1] - item_bd.value[niid][0], rating[2])\\n for rating in ratings if uid in rating[0]]\\n if len(sim_rating) != 0:\\n sim_ratings = [\\n (line[1] * line[2], abs(line[1]), line[3])\\n for line in sim_rating]\\n predicted_rating_no_decay = average_iid_rating + sum(\\n map(lambda line: line[0], sim_ratings)) / sum(\\n map(lambda line: line[1], sim_ratings))\\n predicted_rating_decay = \\\\\\n average_iid_rating + add_decay(sim_ratings)\\n else:\\n predicted_rating_no_decay = average_iid_rating\\n predicted_rating_decay = average_iid_rating\\n return iid, real_rating, \\\\\\n self.bound_rating(predicted_rating_no_decay), \\\\\\n self.bound_rating(predicted_rating_decay)\",\n \"def select_next(X, U, mu,\\n scoremethod='lowhigh',\\n missingmethod='none',\\n feature_weights=[],\\n oldscores=[], oldreproj=[]):\\n\\n print(\\\"------------ SELECTING --------------\\\")\\n if len(U) == 0:\\n printt(\\\"Empty DEMUD model: selecting item number %d from data set\\\" % \\\\\\n (opts['iitem']))\\n return opts['iitem'], [], []\\n\\n if X.shape[1] < 1 or len(U) == 0 or len(mu) == 0:\\n printt(\\\"Error: No data in X and/or U and/or mu.\\\")\\n return None, [], []\\n\\n if X.shape[0] != U.shape[0] or X.shape[0] != mu.shape[0]:\\n printt(\\\"Mismatch in dimensions; must have X mxn, U mxk, mu mx1.\\\")\\n return None, [], []\\n\\n # If oldscores is empty, compute the score for each item\\n if len(oldscores) == 0:\\n (scores, reproj) = score_items(X, U, mu, scoremethod, missingmethod)\\n elif len(oldreproj) == 0:\\n printt(\\\"Error: oldscores provided, but not oldreproj.\\\")\\n return None, [], []\\n else: # both are valid, so use them here\\n (scores, reproj) = (oldscores, oldreproj)\\n\\n # Select and return index of item with max reconstruction error,\\n # plus the updated scores and reproj\\n m = scores.argmax()\\n #print('mu:',mu)\\n #print('selected:', X[:,m])\\n #print('selected-mu:', (X-mu)[:,m])\\n #print('reproj:', reproj[:,m])\\n #print('reproj-mu:', (reproj-mu)[:,m])\\n #input()\\n\\n return m, scores, reproj\",\n \"def test_conversion():\\r\\n f1 = factor([0,1],[2,2],scipy.rand(4))\\r\\n f2 = factor([1,2],[2,2],scipy.rand(4))\\r\\n f3 = factor([3],[2],scipy.rand(2))\\r\\n\\r\\n F = FactorList([f1,f2,f3])\\r\\n theta = factors2ExpFam(F)\\r\\n F2 = expfam2Factors(theta)\\r\\n ratio = F2.JointDistn().val/ (F.JointDistn().val)\\r\\n ratio = ratio/ratio[0]\\r\\n print scipy.allclose(ratio,1)\",\n \"def get5x5matrix(self): #modified from nxvasc get3x3matrix()\\n try:\\n i = na.identity(3)\\n \\n self.d124 = i.copy()\\n self.ds124 = na.zeros(124,na.float64)\\n \\n for k in range(1,124):\\n self.d124 = na.concatenate((self.d124,i))\\n# print len(self.d124)\\n count = 0\\n a = []\\n for k in range(-2,3):\\n for j in range(-2,3):\\n for i in range(-2,3):\\n if( i != 0 or j != 0 or k != 0 ):\\n self.ds124[count] = math.sqrt(i**2+j**2+k**2)\\n count += 1\\n a.append(i)\\n a.append(j)\\n a.append(k)\\n# print len(a)\\n a = na.reshape(na.array(a),(372,1))\\n# print len(self.d124)\\n self.d124 = na.concatenate((self.d124,a),axis=1)\\n except Exception as error:\\n print(\\\"failed in get5x5matrix(): \\\", error)\",\n \"def fit(self, user_x_product, latent_features_guess=2, learning_rate=0.0002, steps=5000, regularization_penalty=0.02, convergeance_threshold=0.001):\\n print 'training model...'\\n return self.__factor_matrix(user_x_product, latent_features_guess, learning_rate, steps, regularization_penalty, convergeance_threshold)\",\n \"def update_factors(self,u,i,j,update_u=True,update_i=True):\\n #print(\\\"session run\\\")\\n loss_v = self.sess.run(self.train_step , feed_dict={\\n self.u: u,\\n self.i: i,\\n self.j: j})\\n\\n returnText = \\\"\\\"\\n\\n if self.alg_type == \\\"TFL\\\" or self.alg_type == \\\"TFLWM\\\":\\n sum_lambda = 0\\n for k in self.sim_matrix_names:\\n sum_lambda += abs(self.sim_lambda[k].eval())\\n #print(sum_lambda,self.sim_lambda)\\n for k in self.sim_matrix_names:\\n if math.isnan(sum_lambda):\\n print(\\\"sim_lambda overflow\\\")\\n tf.assign(self.sim_lambda[k], [self.sim_lambda_zero], validate_shape=False).eval()\\n returnText = \\\"sim_lambda overflow\\\"\\n else:\\n tf.assign(self.sim_lambda[k], self.sim_lambda[k].eval()/sum_lambda).eval()\\n else:\\n for k in self.sim_matrix_names:\\n val = self.sim_lambda[k].eval()\\n if math.isnan(val[0]):\\n print(\\\"sim_lambda overflow\\\")\\n tf.assign(self.sim_lambda[k], [self.sim_lambda_zero], validate_shape=False).eval()\\n returnText = \\\"sim_lambda overflow\\\"\\n if val[0] <= 0.0:\\n tf.assign(self.sim_lambda[k], [self.delta], validate_shape=False).eval()\\n elif val[0] >= 1.0:\\n tf.assign(self.sim_lambda[k], [self.one - self.delta], validate_shape=False).eval()\\n\\n return returnText\",\n \"def calc_adv_U(self):\\n num_U = 0\\n adv_U = numpy.zeros((3,3), float)\\n\\n for atm in self:\\n ## use the atom's U matrix if it exists, otherwise use the\\n ## temperature factor\\n\\n if atm.U is not None:\\n adv_U += atm.U\\n num_U += 1\\n\\n return adv_U / num_U\",\n \"def calculate_user_similarity_profile(self, ratings_vector):\\r\\n num_users, num_movies = self.ratings_matrix.get_shape()\\r\\n\\r\\n user_similarities = sp.dok_matrix((1, num_users))\\r\\n for i in range(num_users):\\r\\n\\r\\n user_similarities[0, i] = self.calculate_pairwise_user_similarity(self.ratings_matrix.getrow(i), ratings_vector)\\r\\n\\r\\n return user_similarities.tocsr()\",\n \"def calc_metrics(metric_scores_list):\\n\\n N_split, N_miss, N_add, Q_P, Q_R, Q_F, N_gt, N_pred = [], [], [], [], [], [], [], []\\n Q_rand, Q_jaccard, Q_aggregated_jaccard, Q_ctc, Q_piou = [], [], [], [], []\\n tp, fp, fn = [], [], []\\n\\n for score in metric_scores_list:\\n N_split.append(score['N_split']), N_miss.append(score['N_miss']), N_add.append(score['N_add'])\\n Q_P.append(score['Q_P']), Q_R.append(score['Q_R']), Q_F.append(score['Q_F'])\\n Q_rand.append(score['Q_rand']), Q_jaccard.append(score['Q_jaccard'])\\n Q_aggregated_jaccard.append(score['Q_aggregated_jaccard'])\\n if \\\"Q_ctc\\\" in score:\\n Q_ctc.append(score['Q_ctc']), \\n Q_piou.append(score['Q_piou'])\\n N_gt.append(score['N_gt']), N_pred.append(score['N_pred'])\\n tp.append(score['tp']), fp.append(score['fp']), fn.append(score['fn'])\\n\\n N_split, N_miss, N_add = np.array(N_split), np.array(N_miss), np.array(N_add)\\n N_gt, N_pred = np.array(N_gt), np.array(N_pred)\\n tp, fp, fn = np.array(tp), np.array(fp), np.array(fn)\\n Q_P_macro, Q_R_macro, Q_F_macro = np.mean(np.array(Q_P)), np.mean(np.array(Q_R)), np.mean(np.array(Q_F))\\n Q_P_micro = np.sum(tp) / (np.sum(tp) + np.sum(fp)) if (np.sum(tp) + np.sum(fp)) > 0 else 0\\n Q_R_micro = np.sum(tp) / (np.sum(tp) + np.sum(fn)) if (np.sum(tp) + np.sum(fn)) > 0 else 0\\n Q_rand_macro, Q_jaccard_macro = np.mean(np.array(Q_rand)), np.mean(np.array(Q_jaccard))\\n Q_aggregated_jaccard_macro = np.mean(np.array(Q_aggregated_jaccard))\\n Q_ctc_macro, Q_piou_macro = np.mean(np.array(Q_ctc)), np.mean(np.array(Q_piou))\\n\\n metrics = {\\n 'Q_split_micro': float(np.sum(N_split) / np.sum(N_gt)),\\n 'Q_split_macro': float(np.mean(N_split / N_gt)),\\n 'Q_miss_micro': float(np.sum(N_miss) / np.sum(N_gt)),\\n 'Q_miss_macro': float(np.mean(N_miss / N_gt)),\\n 'Q_add_micro': float(np.sum(N_add) / np.sum(N_gt)),\\n 'Q_add_macro': float(np.mean(N_add / N_gt)),\\n 'N_gt': int(np.sum(N_gt)),\\n 'N_pred': int(np.sum(N_pred)),\\n 'Q_rand_macro': float(Q_rand_macro),\\n 'Q_jaccard_macro': float(Q_jaccard_macro),\\n 'Q_aggregated_jaccard_macro': float(Q_aggregated_jaccard_macro),\\n 'Q_ctc_macro': float(Q_ctc_macro),\\n 'Q_piou_macro': float(Q_piou_macro),\\n 'Q_P_micro': float(Q_P_micro),\\n 'Q_P_macro': float(Q_P_macro),\\n 'Q_R_micro': float(Q_R_micro),\\n 'Q_R_macro': float(Q_R_macro),\\n 'Q_F_macro': float(Q_F_macro),\\n 'Q_F_micro': float(2 * Q_P_micro * Q_R_micro / (Q_P_micro + Q_R_micro)) if (Q_P_micro + Q_R_micro) > 0 else 0\\n }\\n return metrics\",\n \"def multiply_fisher_factor(self, vector: jnp.ndarray) -> jnp.ndarray:\\n return utils.scalar_mul(\\n self.multiply_fisher_factor_unweighted(vector), jnp.sqrt(self.weight))\",\n \"def predict(self, u, i, P=None, Q=None, F=None, w=None, user_bias=None, item_bias=None):\\n if P is None:\\n P = self.P\\n if Q is None:\\n Q = self.Q\\n if F is None:\\n F = self.F\\n if w is None:\\n w = self.w\\n if user_bias is None:\\n user_bias = self.user_bias\\n if item_bias is None:\\n item_bias = self.item_bias\\n\\n known_user = self._known('user', u)\\n known_item = self._known('item', i)\\n rui_hat = self.mean\\n if known_user:\\n rui_hat += user_bias[u]\\n if known_item:\\n rui_hat += item_bias[i]\\n if known_user and known_item:\\n F_sum = np.sum(F[self.Fr[u], :], axis=0)\\n #F_num = np.sqrt(self.Fr[u].shape[0]) # Try without sqrt\\n F_num = self.Fr[u].shape[0]\\n if F_num > 0:\\n F_sum /= F_num\\n rui_hat += (F_sum + w * P[u, :]).dot(Q[:, i])\\n # Apply potential non-linearity (activation) g\\n rui_hat = self.g(rui_hat)\\n return rui_hat\",\n \"def build_user_user_similarity_matrix(self, event_data):\\n self.compute_user_user_sim_base_on_common_items()\\n self.standardize_sim_values()\",\n \"def test_lu_factor():\\n\\t#[A, b] = lu_read('test1.txt')\\n\\t# it is poor form to read an external file into a test function, as above\\n\\tA = np.array([\\n\\t\\t[ 2., 3., -4., 2.],\\n\\t\\t[-4., -5., 6., -3.],\\n\\t\\t[ 2., 2., 1., 0.],\\n\\t\\t[-6., -7., 14., -4.]])\\t\\n\\tLU,p = lu_factor(A, pivot=False)\\n\\tLU_soln = np.array([\\n\\t\\t[ 2, 3,-4, 2],\\n\\t\\t[-2, 1,-2, 1],\\n\\t\\t[ 1,-1, 3,-1],\\n\\t\\t[-3, 2, 2, 2]])\\t\\n\\tassert norm(LU - LU_soln) < 1.e-10\\t\\n\\n\\n\\t# test 2\\n\\t[A2, b2] = lu_read('test2.txt')\\t\\t\\t\\t\\t\\t# read a matrix and RHS vector\\n\\tLU2,p2 = lu_factor(A2) \\t\\t\\t\\t\\t\\t\\t\\t# change display to False when LU_FACTOR working\\n\\tLU_soln2 = np.array([\\n\\t\\t [0.01, 0., 0., 0., 0., 0., 0., 0., 0., 0., 1],\\n\\t\\t [-100., 0.01, 0., 0., 0., 0., 0., 0., 0., 0., 100],\\n\\t\\t [0., -100., 0.01, 0., 0., 0., 0., 0., 0., 0., 10000],\\n\\t\\t [0., 0., -100., 0.01, 0., 0., 0., 0., 0., 0., 1000000],\\n\\t\\t [0., 0., 0., -100., 0.01, 0., 0., 0., 0., 0., 100000000],\\n\\t\\t [0., 0., 0., 0., -100., 0.01, 0., 0., 0., 0., 10000000000],\\n\\t\\t [0., 0., 0., 0., 0., -100., 0.01, 0., 0., 0., 1000000000000],\\n\\t\\t [0., 0., 0., 0., 0., 0., -100., 0.01, 0., 0., 100000000000000],\\n\\t\\t [0., 0., 0., 0., 0., 0., 0., -100., 0.01, 0., 10000000000000000],\\n\\t\\t [0., 0., 0., 0., 0., 0., 0., 0., -100, 0.01, 1000000000000000000],\\n\\t\\t [0., 0., 0., 0., 0., 0., 0., 0., 0., -100., 100000000000000000000]])\\n\\tassert norm(LU2 - LU_soln2) < 1.e-10\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6282623","0.62621325","0.60587424","0.6045789","0.6040702","0.6002245","0.5951851","0.59179777","0.59059614","0.58943605","0.589419","0.587945","0.5858747","0.58264637","0.5798391","0.57919794","0.574544","0.5737683","0.5693354","0.5668427","0.56560904","0.565145","0.5631024","0.5595122","0.55878645","0.5560108","0.55080867","0.5492173","0.5471623","0.54714626","0.5467762","0.54663336","0.5445127","0.5433413","0.5393641","0.53686476","0.53676146","0.53589565","0.53349036","0.53346205","0.53162575","0.52985704","0.52616876","0.5258938","0.5252249","0.52483475","0.52311176","0.52225137","0.5220475","0.52171636","0.5198918","0.51938695","0.5181593","0.51795137","0.517728","0.51635116","0.5155167","0.5149963","0.51439816","0.51246136","0.5115969","0.5105796","0.5086189","0.50842553","0.5078923","0.5075725","0.5066789","0.5055791","0.505126","0.5040315","0.50370866","0.5033983","0.5028434","0.5021411","0.5020064","0.5016048","0.4992419","0.49900427","0.49797973","0.49795952","0.49742973","0.49736246","0.49720296","0.49668035","0.49609035","0.49598455","0.49560553","0.49484748","0.4937664","0.49338803","0.4931834","0.4930841","0.49298579","0.49219868","0.4919247","0.49165457","0.49121004","0.49035972","0.49034655","0.49016014"],"string":"[\n \"0.6282623\",\n \"0.62621325\",\n \"0.60587424\",\n \"0.6045789\",\n \"0.6040702\",\n \"0.6002245\",\n \"0.5951851\",\n \"0.59179777\",\n \"0.59059614\",\n \"0.58943605\",\n \"0.589419\",\n \"0.587945\",\n \"0.5858747\",\n \"0.58264637\",\n \"0.5798391\",\n \"0.57919794\",\n \"0.574544\",\n \"0.5737683\",\n \"0.5693354\",\n \"0.5668427\",\n \"0.56560904\",\n \"0.565145\",\n \"0.5631024\",\n \"0.5595122\",\n \"0.55878645\",\n \"0.5560108\",\n \"0.55080867\",\n \"0.5492173\",\n \"0.5471623\",\n \"0.54714626\",\n \"0.5467762\",\n \"0.54663336\",\n \"0.5445127\",\n \"0.5433413\",\n \"0.5393641\",\n \"0.53686476\",\n \"0.53676146\",\n \"0.53589565\",\n \"0.53349036\",\n \"0.53346205\",\n \"0.53162575\",\n \"0.52985704\",\n \"0.52616876\",\n \"0.5258938\",\n \"0.5252249\",\n \"0.52483475\",\n \"0.52311176\",\n \"0.52225137\",\n \"0.5220475\",\n \"0.52171636\",\n \"0.5198918\",\n \"0.51938695\",\n \"0.5181593\",\n \"0.51795137\",\n \"0.517728\",\n \"0.51635116\",\n \"0.5155167\",\n \"0.5149963\",\n \"0.51439816\",\n \"0.51246136\",\n \"0.5115969\",\n \"0.5105796\",\n \"0.5086189\",\n \"0.50842553\",\n \"0.5078923\",\n \"0.5075725\",\n \"0.5066789\",\n \"0.5055791\",\n \"0.505126\",\n \"0.5040315\",\n \"0.50370866\",\n \"0.5033983\",\n \"0.5028434\",\n \"0.5021411\",\n \"0.5020064\",\n \"0.5016048\",\n \"0.4992419\",\n \"0.49900427\",\n \"0.49797973\",\n \"0.49795952\",\n \"0.49742973\",\n \"0.49736246\",\n \"0.49720296\",\n \"0.49668035\",\n \"0.49609035\",\n \"0.49598455\",\n \"0.49560553\",\n \"0.49484748\",\n \"0.4937664\",\n \"0.49338803\",\n \"0.4931834\",\n \"0.4930841\",\n \"0.49298579\",\n \"0.49219868\",\n \"0.4919247\",\n \"0.49165457\",\n \"0.49121004\",\n \"0.49035972\",\n \"0.49034655\",\n \"0.49016014\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72323316"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":87,"cells":{"query":{"kind":"string","value":"Return list of choices for combo box, each a tuple of edit text and any annotation text"},"document":{"kind":"string","value":"def getEditChoices(self, currentText=''):\n return [(text, '') for text in self.formatList]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_choices(self):\n raise NotImplementedError()","def get_choices(self):\n raise NotImplementedError()","def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n return [(int(code), name) for name, code in cls.__members__.items()]","def getEditChoices(self, currentText=''):\n currentChoices, valid = self.sortedChoices(currentText)\n nonChoices = [text for text in self.formatList\n if text not in currentChoices]\n results = []\n for choice in nonChoices: # menu entries to add a choice\n allChoices = currentChoices + [choice]\n allChoices = [text for text in self.formatList\n if text in allChoices]\n results.append((self.editSep.join(allChoices),\n '(%s %s)' % (_('add'), choice)))\n if currentChoices:\n results.append((None, None)) # separator\n for choice in currentChoices: # menu entries to remove a choice\n allChoices = currentChoices[:]\n allChoices.remove(choice)\n allChoices = [text for text in self.formatList\n if text in allChoices]\n results.append((self.editSep.join(allChoices),\n '(%s %s)' % (_('remove'), choice)))\n return results","def choices(self, typename, value_field='description'):\n rows = self.type(typename).values('id', value_field)\n return [(r['id'], r[value_field]) for r in rows]","def get_choices(self, instance):\n if instance.type == BaseParameter.CHOICE_TYPE:\n return [\n x.value\n for x in instance.get_typed_parameter().get_available_choices()\n ]\n else:\n return None","def get_choices_for_model_field(cls):\n return [c[0:2] for c in cls.attr_choices]","def initDefaultChoices(self):\n return [entry[0] for entry in self.getEditChoices()]","def select_combo_text(cb, text, index=0):\n i = 0\n for n in cb.get_model():\n if n[index] == text:\n break\n i += 1\n cb.set_active(i)","def application_command_autocomplete_choice_builder(name, value):\n return {\n 'name': name,\n 'value': value,\n }","def choices(self) -> list:\n return [self.mapper(i) for i in self.keys()]","def choices(self):\n return tuple(self._choices)","def iter_choices(self):\n\n for pk, obj in self._get_object_list():\n if hasattr(obj, self.id):\n selected = getattr(obj, self.id)\n else:\n selected = obj in self.data\n\n yield (pk, self.get_label(obj), selected)","def _get_choices_str(self):\n return ', '.join(\n '\"%s\"' % choice\n for choice in self.choices\n )","def getEditChoices(self, currentText=''):\n format = globalref.options.strData('EditDateFormat', True)\n today = GenDate().dateStr(format)\n yesterday = (GenDate() - 1).dateStr(format)\n tomorrow = (GenDate() + 1).dateStr(format)\n return [(today, '(%s)' % _('today')),\n (yesterday, '(%s)' % _('yesterday')),\n (tomorrow, '(%s)' % _('tomorrow'))]","def fill_combobox_attributes(self):\n\n list_char = [\"\"]\n list_num = [\"\"]\n if self.ui.radioButton_file.isChecked():\n for a in self.attributes:\n if a['caseOrFile'] == \"file\" and a['valuetype'] == \"character\":\n list_char.append(a['name'])\n if a['caseOrFile'] == \"file\" and a['valuetype'] == \"numeric\":\n list_num.append(a['name'])\n else:\n for a in self.attributes:\n if a['caseOrFile'] == \"case\" and a['valuetype'] == \"character\":\n list_char.append(a['name'])\n if a['caseOrFile'] == \"case\" and a['valuetype'] == \"numeric\":\n list_num.append(a['name'])\n self.ui.comboBox_num_attributes.blockSignals(True)\n self.ui.comboBox_char_attributes.blockSignals(True)\n self.ui.comboBox_num_attributes.clear()\n self.ui.comboBox_char_attributes.clear()\n self.ui.comboBox_char_attributes.addItems(list_char)\n self.ui.comboBox_num_attributes.addItems(list_num)\n self.ui.comboBox_num_attributes.blockSignals(False)\n self.ui.comboBox_char_attributes.blockSignals(False)","def getComboTerms(tuples):\t\t\t\n\t\t\t#return \"[{0}]\".format('; '.join([\"({0})\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\n\t\t\treturn \"{0}\".format('; '.join((\"{0}\".format(text[indices[0]:indices[1]]) for indices in tuples)))","def getEditChoices(self, currentText=''):\n format = globalref.options.strData('EditTimeFormat', True)\n now = GenTime().timeStr(format)\n choices = [(now, '(%s)' % _('now'))]\n for hr in (6, 9, 12, 15, 18, 21, 0):\n time = GenTime((hr, 0)).timeStr(format)\n choices.append((time, ''))\n return choices","def get_choices(cls):\n return cls.values.items()","def _find_options(self, inputfield):\r\n elements = inputfield.findall('./options/option')\r\n return [{\r\n 'id': index,\r\n 'description': option.text,\r\n 'choice': option.get('choice')\r\n } for (index, option) in enumerate(elements)]","def create_combo(self) -> typing.Iterable[Combo]:\n raise NotImplementedError()","def choices(cls):\n return tuple(item.as_tuple for item in list(cls))","def choices(self) -> list:\n return [self.mapper(item) for item in self]","def choices(cls):\n _choices = []\n for attr in _user_attributes(cls):\n val = getattr(cls, attr)\n setattr(cls, attr[1:], val[0])\n _choices.append((val[0], val[1]))\n setattr(cls, 'CHOICES', tuple(_choices))\n return cls","def extract_choices(element):\r\n\r\n choices = []\r\n\r\n for choice in element:\r\n if choice.tag != 'choice':\r\n raise Exception(\r\n \"[capa.inputtypes.extract_choices] Expected a <choice>\" +\r\n \"tag; got {0} instead\".format(choice.tag)\r\n )\r\n\r\n components = []\r\n choice_text = ''\r\n if choice.text is not None:\r\n choice_text += choice.text\r\n # Initialize our dict for the next content\r\n adder = {\r\n 'type': 'text',\r\n 'contents': choice_text,\r\n 'tail_text': '',\r\n 'value': ''\r\n }\r\n components.append(adder)\r\n\r\n for elt in choice:\r\n # for elements in the choice e.g. <text> <numtolerance_input>\r\n adder = {\r\n 'type': 'text',\r\n 'contents': '',\r\n 'tail_text': '',\r\n 'value': ''\r\n }\r\n tag_type = elt.tag\r\n # If the current `elt` is a <numtolerance_input> set the\r\n # `adder`type to 'numtolerance_input', and 'contents' to\r\n # the `elt`'s name.\r\n # Treat decoy_inputs and numtolerance_inputs the same in order\r\n # to prevent students from reading the Html and figuring out\r\n # which inputs are valid\r\n if tag_type in ('numtolerance_input', 'decoy_input'):\r\n # We set this to textinput, so that we get a textinput html\r\n # element.\r\n adder['type'] = 'textinput'\r\n adder['contents'] = elt.get('name')\r\n else:\r\n adder['contents'] = elt.text\r\n\r\n # Add any tail text(\"is the mean\" in the example)\r\n adder['tail_text'] = elt.tail if elt.tail else ''\r\n components.append(adder)\r\n\r\n # Add the tuple for the current choice to the list of choices\r\n choices.append((choice.get(\"name\"), components))\r\n return choices","def initDefaultChoices(self):\n choices = [entry[0] for entry in self.getEditChoices()]\n choices.insert(0, DateFormat.dateStampStrings[1])\n return choices","def initDefaultChoices(self):\n choices = [entry[0] for entry in self.getEditChoices()]\n choices.insert(0, TimeFormat.timeStampStrings[1])\n return choices","def combo_callback(self, eventobj):\n print(self.combo_input.get()) # print name\n print(self.combo_input.current()) # print index","def choices(self):\n # Needs to be implmented by subclass\n raise Exception(NotImplemented)","def initDefaultChoices(self):\n return [text for text in self.formatList]","def __init__(self,choices,caption='ListSelection',default=[],single=False,check=False,sort=False,*args,**kargs):\n InputDialog.__init__(self,caption=caption,items = [\n dict(name='input',value=default,itemtype='list',choices=choices,\n text='',single=single,check=check,sort=sort,*args,**kargs),\n ],)","def _build_dropdown(options):\n return [(x, x) for x in options]","def set_choices(self, index, choices):\n if len(choices) == 1:\n self._label(index)\n self._widgets[index][\"text\"] = str(choices[0])\n else:\n self._combo(index)\n self._widgets[index][\"values\"] = [str(t) for t in choices]\n width = max(len(str(t)) for t in choices)\n width = max(5, width)\n self._widgets[index][\"width\"] = width","def choices() -> List[str]:\n return [t.name.upper() for t in ConfigurationVariable]","def extract_choices(element):\r\n\r\n choices = []\r\n\r\n for choice in element:\r\n if choice.tag != 'choice':\r\n raise Exception(\r\n \"[capa.inputtypes.extract_choices] Expected a <choice> tag; got %s instead\"\r\n % choice.tag)\r\n choices.append((choice.get(\"name\"), stringify_children(choice)))\r\n return choices","def __init__(self, \n num_fld=1, \n lab_txt=[\"1\"], \n txt_fld=[\"1\"], \n title_txt=\"test\", \n comb_txt=[],\n comb_lab_txt=[], \n comb_num=0, \n root_x=50, \n root_y=50):\n super().__init__()\n self.geometry(f'+{root_x}+{root_y}') #head=y+20px\n self.str_in=[]\n self.title(title_txt)\n if comb_txt:\n self.name=[0]*num_fld\n ent=[0]*num_fld\n self.comb=[]\n self.act=[]\n lab=[0]*num_fld\n lab_comb=[0]*comb_num\n else:\n self.name=[0]*num_fld\n ent=[0]*num_fld\n lab=[0]*num_fld\n self.comb=[]\n self.act=[]\n for i in range(num_fld):\n self.name[i]=tk.StringVar()\n ent[i]=tk.Entry(self,textvariable=self.name[i])\n ent[i].insert(0, txt_fld[i])\n lab[i] = tk.Label(self,width=15, text=lab_txt[i])\n lab[i].pack()\n ent[i].pack()\n for i in range(comb_num):\n lab_comb[i]=tk.Label(self,width=35, text=comb_lab_txt[i])\n self.comb.append(ttk.Combobox(self, values=comb_txt))\n lab_comb[i].pack()\n self.comb[i].pack()\n self.comb[i].current(1)\n\n but_ac=tk.Button(self, text=\"Accept\", command=self.ins)\n but_ac.pack()\n self.mainloop","def input_choices_from_list(choices, text):\n no_courses_text = \"\"\"\n init will only list the courses you are enrolled in\n and there seem to be none.\n Either enrol in a course or add the course id as command line argument.\n \"\"\"\n if choices is None or len(choices) == 0:\n print(no_courses_text)\n raise SystemExit(1)\n\n digits = str(math.ceil(math.log10(len(choices))))\n format_str = '{:' + digits + 'd} {}'\n for n, c in enumerate(choices):\n print(format_str.format(n, c))\n try:\n return [int(c) for c in input(text).split()]\n except EOFError:\n return []","def valid_options(self):\n choices = self.choices()\n\n if not choices:\n return None\n\n return [opt[0] for opt in choices]","def combobox(self):\n return self._combo","def widgets_from_abbreviations(self, seq):\n result = []\n for name, abbrev, default in seq:\n widget = self.widget_from_abbrev(abbrev, default)\n if not (isinstance(widget, ValueWidget) or isinstance(widget, fixed)):\n if widget is None:\n raise ValueError(\"{!r} cannot be transformed to a widget\".format(abbrev))\n else:\n raise TypeError(\"{!r} is not a ValueWidget\".format(widget))\n if not widget.description:\n widget.description = name\n widget._kwarg = name\n result.append(widget)\n return result","def multiple_choice():\n\n return [\"MAR\", \"MAR\", \"NI\", \"NI\", \"MCAR\"]","def get_chosen_options(user):\n user_profile = user.get_profile()\n user_application = user_profile.application\n np = user_application.np\n ocl = eval(user_application.options_selected)\n chosen_options = []\n for oc in ocl:\n chosen_options.append(Option.objects.get(opt_code=int(oc))) \n return chosen_options","def build_comboboxes(activities, events):\n global comboboxes\n # For each activity set up a selector for an event\n\n for activity in activities:\n\n # Setup frame for better display in gui\n frame = Frame(main_window)\n frame.configure(background=\"gray30\")\n\n # Label the left column as activity in a model + \"beautify gui\"\n text = \"Activity name (model):\"\n Label(frame, text=text, bg=\"gray30\", fg=\"white\", padx=5).grid(column=0, row=0)\n Label(frame, text=activity, bg=\"gray30\", fg=\"white\").grid(column=0, row=1)\n\n # Set up the combobox for an event\n combo = Combobox(frame)\n combo['values'] = events\n\n # If activity is in events preselect the current one\n if activity in events:\n combo.current(events.index(activity))\n\n # Label the combobox and place label and box in frame\n Label(frame, text=\"Event name (log):\", bg=\"gray30\", fg=\"white\", padx=5).grid(column=1, row=0)\n combo.grid(column=1, row=1)\n\n # If the last activity in the graph is handled then do not write a separator\n if activity != activities[-1]:\n Separator(frame, orient=\"horizontal\").grid(row=2, columnspan=2, sticky=\"ew\", pady=10)\n\n comboboxes[activity] = combo\n # place the frame in the main_window\n frame.grid(column=0)","def get_generic_choices(model, key, allow_null=False):\n CHOICES = [('', '--------')] if allow_null else []\n for i in model.objects.all().values_list(key, flat=True).distinct():\n CHOICES.append((str(i), str(i)))\n CHOICES.sort()\n\n return CHOICES","def _combobox_choice(self, _=None):\n combobox_string = self.value_combobox.var.get()\n if combobox_string.startswith(\"Unknown: \"):\n value = int(combobox_string[len(\"Unknown: \"):])\n else:\n value = int(self.value_combobox.var.get().split(\" \")[0])\n self.master.change_field_value(self.field_name, value)","def widgets(parameter: Parameter):\n widgets = []\n for key in parameter.keys():\n textEdit = QTextEdit()\n textEdit.setText(key)\n widgets.append(textEdit)\n if isinstance(parameter[key], Enum):\n comboBox = MyQtEnumComboBox()\n comboBox.fillValues(type(parameter[key]))\n widgets.append(comboBox)\n elif isinstance(parameter[key], bool):\n comboBox = QComboBox()\n comboBox.addItems((\"False\", \"True\"))\n widgets.append(comboBox)\n else:\n textEdit = QTextEdit()\n textEdit.setText(str(parameter[key]))\n widgets.append(textEdit)\n for widget in widgets:\n widget.setFixedHeight(30)\n return widgets","def _getBrailleRegionsForComboBox(self, obj):\n\n self._debugGenerator(\"_getBrailleRegionsForComboBox\", obj)\n\n regions = []\n\n focusedRegionIndex = 0\n label = self._script.getDisplayedLabel(obj)\n if label and (len(label) > 0):\n regions.append(braille.Region(label + \" \"))\n focusedRegionIndex = 1\n\n # Check to see if the text is editable. If so, then we want\n # to show the text attributes (such as selection -- see bug\n # 496846 for more details).\n #\n textObj = None\n for child in obj:\n if child and child.getRole() == pyatspi.ROLE_TEXT:\n textObj = child\n if textObj and textObj.getState().contains(pyatspi.STATE_EDITABLE):\n textRegion = braille.Text(textObj)\n regions.append(textRegion)\n else:\n displayedText = self._script.getDisplayedText(obj)\n if displayedText:\n regions.append(braille.Region(displayedText))\n\n regions.append(braille.Region(\n \" \" + rolenames.getBrailleForRoleName(obj)))\n\n # Things may not have gone as expected above, so we'll do some\n # defensive programming to make sure we don't get an index out\n # of bounds.\n #\n if focusedRegionIndex >= len(regions):\n focusedRegionIndex = 0\n if len(regions) == 0:\n focusedRegion = None\n else:\n focusedRegion = regions[focusedRegionIndex]\n\n # [[[TODO: WDW - perhaps if a text area was created, we should\n # give focus to it.]]]\n #\n return [regions, focusedRegion]","def form_SelectChoiceCallableOptions(request):\n schema = schemaish.Structure()\n schema.add('mySelect', schemaish.Integer())\n def _():\n options = [(1,'a'),(2,'b'),(3,'c')]\n for option in options:\n yield option\n\n form = formish.Form(schema, 'form')\n form['mySelect'].widget = formish.SelectChoice(_)\n return form","def __init__(self, *args, **kwargs):\n super(TaggedContentItemForm, self).__init__(*args, **kwargs)\n wtf = Tag.objects.filter(group__system=False)\n wlist = [w for t, w in self.fields.items() if t.endswith(\"tags\")]\n choices = []\n for choice in wtf:\n choices.append((choice.id, str(choice)))\n [setattr(w, 'choices', choices) for w in wlist]","def _comboSlot(self, select):\n select = self.sender().itemText(select)\n if qt4:\n qs = str(self.sender().property(\"dom address\").toPyObject())\n else:\n qs = str(self.sender().property(\"dom address\"))\n item = QtXml.QDomElement()\n\n ind = qs.rfind('/')\n ids = qs[ind:]\n\n item = self.qhash[qs].elem.firstChildElement(\"Item\")\n while(item.isNull() is False):\n itemName = item.firstChildElement(\"Name\")\n if(str(itemName.text()).strip() != select):\n activ = item.firstChildElement(\"Activate\")\n while(activ.isNull() is False):\n s = str(activ.text()).strip() + ids\n h = self.qhash[s]\n widget_enabled = h.elem.attribute(\"Enabled\", \"True\")\n widget_visible = h.elem.attribute(\"Visible\", \"Unknown\")\n h.widget.setEnabled(False)\n if(widget_visible != \"Unknown\"):\n h.label.hide()\n h.widget.hide()\n activ = activ.nextSiblingElement(\"Activate\")\n item = item.nextSiblingElement(\"Item\")\n\n item = self.qhash[qs].elem.firstChildElement(\"Item\")\n while(item.isNull() is False):\n itemName = item.firstChildElement(\"Name\")\n if(str(itemName.text()).strip() == select):\n activ = item.firstChildElement(\"Activate\")\n while(activ.isNull() is False):\n s = str(activ.text()).strip() + ids\n h = self.qhash[s]\n h.widget.setEnabled(True)\n h.label.show()\n h.widget.show()\n activ = activ.nextSiblingElement(\"Activate\")\n item = item.nextSiblingElement(\"Item\")","def get_choices(self, cutoff=None):\n queryset = self.get_queryset()\n if queryset is None:\n # Ensure that field.choices returns something sensible\n # even when accessed with a read-only field.\n return {}\n\n if cutoff is not None:\n queryset = queryset[:cutoff]\n\n return OrderedDict(\n [\n (\n # This line below is modifed.\n item.pk,\n self.display_value(item),\n )\n for item in queryset\n ]\n )","def test_rendering_combobox(qtbot):\n layer = Image(np.random.rand(8, 8))\n qtctrl = QtImageControls(layer)\n qtbot.addWidget(qtctrl)\n combo = qtctrl.renderComboBox\n opts = {combo.itemText(i) for i in range(combo.count())}\n rendering_options = {\n 'translucent',\n 'additive',\n 'iso',\n 'mip',\n 'minip',\n 'attenuated_mip',\n 'average',\n }\n assert opts == rendering_options\n # programmatically updating rendering mode updates the combobox\n layer.rendering = 'iso'\n assert combo.findText('iso') == combo.currentIndex()","def get_choicesdata(self):\n selected_value = self.get_cleaned_value()\n choicesdata = []\n for value, label in self.get_choices_cached():\n is_selected = value == selected_value\n url = self.build_set_values_url(values=[value])\n choicesdata.append({\n 'url': url,\n 'label': label,\n 'is_selected': is_selected,\n 'dom_id': '{}_{}'.format(self.get_inputfield_dom_id(), value)\n })\n return choicesdata","def choices(self):\n return self.__class__.get_setting_choices(self.key, **self.get_kwargs())","def get_poll_choices(self, games: [Game]) -> [dict]:\n answer_texts = []\n for g in games:\n answer_texts.append(g.name + \" - \" + g.genre)\n answer_texts = sorted(answer_texts, key=str.lower)\n poll_choices = []\n for at in answer_texts:\n poll_choices.append({\"text\": at})\n return poll_choices","def __str__(self):\n return \"choice_text: \" + self.choice_text","def _parse_choices(self, text):\n choices = dict()\n\n matches = re.findall(self.choice_regex, text)\n for match in matches:\n # remove the brackets\n match = match.replace('[[', '')\n match = match.replace(']]', '')\n\n if '|' in match:\n # format is {text}|{node_id}, the text and node id are different\n text, node_id = match.split('|')\n choices[node_id] = text\n else:\n choices[match] = match\n\n return choices","def get_choices_new_protected():\n ret = []\n ret.append( (1, _(u'Nur Community-Mitglieder dürfen neue Beiträge leisten')) )\n ret.append( (-1, _(u'Offener Zugang')) )\n return ret","def comboBox(args: list, slot) -> QComboBox:\n comboBox = QComboBox()\n comboBox.addItems(args[0])\n comboBox.currentTextChanged.connect(slot)\n return comboBox","def choices(self):\n\n if self._choices == None:\n self._choices = [ExperimentChoice(self, choice_name) for choice_name in self.choice_names]\n\n return self._choices","def make_ddls( self, parent ):\n # ---- 0\n a_widget = ttk.Combobox( parent,\n width = self.arg_width,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n a_widget.bind( \"<<ComboboxSelected>>\", self.sync_ddl_0 )\n\n # AppGlobal.gui_style.style_combobox( a_widget )\n self.ddl_0_widget = a_widget\n\n # ---- 1\n a_widget = ttk.Combobox( parent,\n width = self.arg_width,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n self.ddl_1_widget = a_widget\n\n # ---- 2\n a_widget = ttk.Combobox( parent,\n width = self.arg_width + 5,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n # AppGlobal.gui_style.style_combobox( a_widget )\n self.ddl_2_widget = a_widget\n\n # self.ddl_widgets = [ self.ddl_0_widget, self.ddl_1_widget, self.ddl_2_widget, ]\n # print( self.ddl_widgets[ 0 ] == self.ddl_widgets[ 1 ])\n\n #self.load_ddl_0( )","def __str__(self):\n return self.choice_text","def as_choices():\n return (\n # Live is currently disabled as a choice\n # pending implementation\n (\"live\", \"Use working directory\"),\n (\"latest\", \"Use latest snapshot\"),\n (\"pinned\", \"Pinned to snapshot\"),\n )","def comboBoxes(self):\r\n # Cities Combo Button\r\n self.comboCities = QComboBox()\r\n self.comboCities.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: 15px;\r\n background-color : #A8DBC5;\r\n border: 1px solid white;\r\n\r\n \"\"\")\r\n self.comboCities.addItems(\r\n ['Girón', 'Piedecuesta', 'Floridablanca', 'Bucaramanga'])\r\n self.grid.addWidget(self.comboCities, 6, 1, 1, 2)\r\n self.comboCities.setCurrentText(\"Bucaramanga\")\r\n # Payment Combo Button\r\n self.comboPayment = QComboBox()\r\n self.comboPayment.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: 15px;\r\n background-color : #A8DBC5;\r\n border: 1px solid white;\r\n\r\n \"\"\")\r\n self.comboPayment.addItems(['Efectivo', 'Nequi'])\r\n self.grid.addWidget(self.comboPayment, 7, 1, 1, 2)","def get_choicesdata(self):\n # selected_value = self.get_cleaned_value()\n # choicesdata = []\n # found_selected_value = False\n # for value, label in self.get_choices():\n # is_selected = value == selected_value\n # if is_selected:\n # found_selected_value = True\n # url = self.build_set_values_url(values=[value])\n # choicesdata.append({\n # 'url': url,\n # 'label': label,\n # 'is_selected': is_selected\n # })\n choicesdata, found_selected_value = self.__make_choicesdata_list(\n choices=self.get_choices(),\n selected_value=self.get_cleaned_value())\n if not found_selected_value and len(choicesdata) > 0:\n selected_index = self.get_default_is_selected_index(choicesdata=choicesdata)\n choicesdata[selected_index]['is_selected'] = True\n return choicesdata","def _find_options(self):\r\n elements = self.xml.findall('./options/option')\r\n return [{\r\n 'id': index,\r\n 'description': option.text,\r\n 'choice': option.get('choice')\r\n } for (index, option) in enumerate(elements)]","def __str__(self):\n return gettext('One of %s') % self._get_choices_str()","def initDefaultChoices(self):\n return []","def list_selector_widget(members=None,\n preselect=None,\n entry=False,\n callback=None):\n store, i=generate_list_model(members,\n active_element=preselect)\n\n if entry:\n combobox=gtk.ComboBoxEntry(store, column=0)\n else:\n combobox=gtk.ComboBox(store)\n cell = gtk.CellRendererText()\n combobox.pack_start(cell, expand=True)\n combobox.add_attribute(cell, 'text', 0)\n combobox.add_attribute(cell, 'background', 2)\n\n combobox.set_active(-1)\n if i is None:\n i = store.get_iter_first()\n if i is not None:\n combobox.set_active_iter(i)\n\n if entry:\n def get_current_element(combo):\n try:\n return combo.get_model().get_value(combo.get_active_iter(), 1)\n except (TypeError, AttributeError):\n return unicode(combo.child.get_text())\n def set_current_element(combo, t):\n combo.child.set_text(t)\n else:\n def get_current_element(combo):\n if combo.get_active_iter() is not None:\n return combo.get_model().get_value(combo.get_active_iter(), 1)\n else:\n return None\n def set_current_element(combo, el):\n # Find the index of the element\n l=[ t[0] for t in enumerate(combo.get_model()) if t[1][1] == el ]\n if l:\n # The element is present.\n combo.set_active(l[0])\n else:\n combo.set_active_iter(combo.get_model().append( (unicode(el), el, None) ))\n\n # Bind the method to the combobox object\n combobox.get_current_element = get_current_element.__get__(combobox)\n combobox.set_current_element = set_current_element.__get__(combobox)\n\n if callback is not None:\n combobox.connect('changed', callback)\n\n return combobox","def occurrence_choices():\n OCCURRENCE_CHOICES = [('one_off', 'One Off'), ('weekly', 'Weekly'), ('fortnightly', 'Fortnightly')]\n occurrence = forms.ChoiceField(choices=OCCURRENCE_CHOICES, widget=forms.RadioSelect())\n return occurrence","def get_action_choices():\n from hardware.management.commands.gpio_buttons import Command\n import re\n pattern = re.compile(r'^on_(?P<name>\\w+)_press$')\n choices = []\n for member in dir(Command):\n match = pattern.match(member)\n if match:\n action = match.groupdict()['name']\n name = action.replace('_', ' ').title()\n choices.append((action, name))\n return choices","def DrawComboBox(*args, **kwargs):\n return _gdi_.RendererNative_DrawComboBox(*args, **kwargs)","def fill_combobox(self):\n sqlstring = \"SELECT first_name, last_name from employees WHERE enabled = 1 ORDER BY last_name ASC\"\n self.CB_employee.addItem(\"\")\n for employee in self.ms.c.execute(sqlstring):\n self.CB_employee.addItem(\" \".join((employee[0], employee[1])))\n sqlstring = \"SELECT first_name, last_name from employees WHERE enabled = 0 ORDER BY last_name ASC\"\n for employee in self.ms.c.execute(sqlstring):\n self.CB_employee.addItem(\" \".join((employee[0], employee[1])))","def _get_choices ( self, context, path = '' ):\n choices = []\n gdc = context.get_data_context\n for name in context.data_contexts:\n next_path = path_for( path, name )\n choices.append( TemplateChoice( choice_value = next_path ) )\n choices.extend( self._get_choices( gdc( name ), next_path ) )\n \n return choices","def get_context(self, name, value, attrs=None, choices=()):\n context = super(TriStateCheckboxSelectMultiple, self).get_context(\n name, value, attrs\n )\n\n choices = dict(it.chain(self.choices, choices))\n if value is None:\n value = dict.fromkeys(choices, False)\n else:\n value = dict(dict.fromkeys(choices, False).items() +\n value.items())\n\n context['values'] = [\n (choice, label, value[choice])\n for choice, label in choices.iteritems()\n ]\n\n return context","def get_text_type():\n opt = ['text', 'email', 'password']\n inp = option_menu(opt, 'Select text type:')\n\n # mark text type with option\n OPTIONS['text-type'] = opt[inp]\n\n # add option to collected list\n add_to_collected('text type', opt[inp])\n\n return","def set_dropdown_b_options(value):\n options_c = []\n if value=='C':\n options_c = [{'label': '1', 'value': '1'},\n {'label': '2', 'value': '2'}]\n if value == 'D':\n options_c = [{'label': '3', 'value': '3'},\n {'label': '4', 'value': '4'}]\n if value=='E':\n options_c = [{'label': '5', 'value': '5'},\n {'label': '6', 'value': '6'}]\n if value == 'F':\n options_c = [{'label': '7', 'value': '7'},\n {'label': '8', 'value': '8'}]\n return options_c","def objects_to_choices(queryset):\n res = []\n for elm in queryset:\n res.append((elm.pk, unicode(elm)))\n return res","def choice(text, choices, **kwargs):\n return click.prompt(click.style('> {}'.format(text), fg='blue', bold=True),\n type=click.Choice(choices),\n **kwargs)","def on_comboBox_enceinte_activated(self, index):\n nom_enceinte = self.comboBox_enceinte.currentText()\n marque = [x[2] for x in self.enceintes if x[1] == nom_enceinte][0]\n n_serie = [x[4] for x in self.enceintes if x[1] == nom_enceinte][0]\n model =[x[3] for x in self.enceintes if x[1] == nom_enceinte][0]\n \n \n self.lineEdit_marque.setText(marque)\n self.lineEdit_n_serie.setText(n_serie)\n self.lineEdit_model.setText(model)","def display_choose(self, text, choices):\n cur_index = 0\n key = None\n while key != 'KEY_NEWLINE':\n if key == 'KEY_UP':\n cur_index = max(cur_index - 1, 0)\n elif key == 'KEY_DOWN':\n cur_index = min(cur_index + 1, len(choices) - 1)\n self.stdscr.erase()\n for line in text:\n self.stdscr.addstr(f'{PADCHAR}{line}\\n')\n for index, value in enumerate(choices):\n self.stdscr.addstr('\\n')\n self.stdscr.addstr(PADCHAR)\n self.stdscr.addstr(value, color_pair(7 if index == cur_index else 1))\n self.stdscr.addstr(f'\\n\\n{PADCHAR}') \n key = self.get_key() \n return cur_index","def list_selector(title=None,\n text=None,\n members=None,\n controller=None,\n preselect=None,\n entry=False):\n combobox = list_selector_widget(members=members,\n preselect=preselect,\n entry=entry)\n\n d = gtk.Dialog(title=title,\n parent=None,\n flags=gtk.DIALOG_DESTROY_WITH_PARENT,\n buttons=( gtk.STOCK_OK, gtk.RESPONSE_OK,\n gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL ))\n\n if text is not None:\n l=gtk.Label(text)\n l.show()\n d.vbox.add(l)\n\n d.vbox.add(combobox)\n combobox.show_all()\n\n d.connect('key-press-event', dialog_keypressed_cb)\n\n d.show()\n center_on_mouse(d)\n res=d.run()\n retval=None\n if res == gtk.RESPONSE_OK:\n retval=combobox.get_current_element()\n d.destroy()\n return retval","def get_classes(self, code):\n \n select = v.Combobox(\n _metadata={'name':code}, \n items=self.items, \n v_model=None, \n dense=True,\n hide_details=True\n )\n \n select.observe(partial(self.store, code), 'v_model')\n \n return select","def getOptionsNames(self) -> List[unicode]:\n ...","def __init__(self, *args, **kwargs):\n super(ChoiceFieldType, self).__init__(*args, **kwargs)\n\n self.choices = self.get_field_info_key('choices')","def the_option_named(text: str) -> \"SelectByText\":\n return SelectByText(text)","def prepareAutoComplete(editor, text, charPos, lineStartCharPos,\r\n wikiDocument, settings):\r\n return []","def autocomplete_possibilities():\n try:\n # get data sent by client\n typed_input = request.args.get('q')\n print(' ')\n print('\\n------ getting autocomplete_possibilities ------')\n print(f\"recived: input:{typed_input}\")\n\n # call the google API\n results = gmaps.places_autocomplete(typed_input)\n data = [\n {'value': r['place_id'], 'text': r['description']}\n for r in results\n ]\n\n # Pass data to the front end\n print(f'returning: {data}')\n return jsonify(data)\n\n except Exception as e:\n print(\"AJAX excepted \" + str(e))\n return str(e)","def _validate_selects(text, response):\n answer_options = re.split(settings.MULTISELECT_DELIMITER_RE, str(text))\n choices = map(lambda choice: choice.lower(), response.event.choices)\n logger.debug('Question (%s) answer choices are: %s, given answers: %s' % (datatype, choices, answer_options))\n new_answers = copy(answer_options)\n for idx, opt in enumerate(answer_options):\n logger.debug('Trying to format (m)select answer: \"%s\"' % opt)\n try: \n #in the case that we accept numbers to indicate option selection\n opt_int = int(opt)\n if not (1 <= opt_int <= len(choices)): \n return text, 'Answer %s must be between 1 and %s' % (opt_int, len(choices))\n else:\n new_answers[idx] = str(opt_int)\n\n except ValueError: \n # in the case where we accept the actual text of the question\n logger.debug('Caught value error, trying to parse answer string choice of: %s' % choices)\n if opt.lower() not in choices:\n return text, 'Answer must be one of the choices'\n else:\n new_answers[idx] = str(choices.index(opt.lower()) + 1)\n return ' '.join(new_answers), None","def choice(choices=[], message=\"Pick something.\", title=None):\n return dialog(\n \"choice\",\n choices=choices,\n message=message,\n title=title,\n )","def complete_opt_format(self, text, *_):\n return [t + \" \" for t in FORMATTERS if t.startswith(text)]","def choices(self, cl):\n # TODO: Determine if non-static choices would be cleaner here.\n # Honestly, I tried a more generic version and it was even harder to\n # follow than this version.\n yield {\n 'selected': not (self.lookup_val_gte or self.lookup_val_lt),\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg_gte,\n self.lookup_kwarg_lt]),\n 'display': 'All'\n }\n\n goal = settings.FACEBOOK_CLICK_GOAL\n yield {\n 'selected': self.lookup_val_gte and not self.lookup_val_lt,\n 'query_string': cl.get_query_string({self.lookup_kwarg_gte: goal},\n [self.lookup_kwarg_lt]),\n 'display': 'Yes'\n }\n yield {\n 'selected': self.lookup_val_lt and not self.lookup_val_gte,\n 'query_string': cl.get_query_string({self.lookup_kwarg_lt: goal},\n [self.lookup_kwarg_gte]),\n 'display': 'No'\n }","def build_options(slot, snacks):\n \n if slot == 'Fast':\n return [\n {'text': 'Pizza', 'value': 'Pizza'},\n {'text': 'Fries', 'value': 'Fries'},\n {'text': 'Franky', 'value': 'Franky'},\n {'text': 'Burger', 'value': 'Burger'},\n {'text': 'Sandwich', 'value': 'Sandwich'}\n \n \n ]\n elif slot == 'drink':\n return [\n {'text': 'Coca-Cola', 'value': 'Coca-cola'},\n {'text': 'Appy', 'value': 'Appy'},\n \n {'text': 'Beer', 'value': 'Beer'},\n {'text': 'Frooti', 'value': 'Frooti'},\n {'text': 'Pepsi', 'value': 'Pepsi'}\n \n ]","def sortedChoices(self, inText):\n choices = self.splitText(inText)\n sortedChoices = [text for text in self.formatList if text in choices]\n if len(choices) == len(sortedChoices):\n return (sortedChoices, True)\n else:\n return (sortedChoices, False)","def _getChoices(self, acronym):\n # get matches from acronymDB\n matches = []\n if(acronym in self.acronymDB):\n matches += self.acronymDB[acronym]\n if(acronym[-1] == \"s\" and acronym[:-1] in self.acronymDB):\n matches += self.acronymDB[acronym]\n\n # create training data\n X_train, y_train = [], []\n for definition, articleID, ignored_var in matches:\n text = self.articleDB[articleID]\n X_train.append(\n ExpansionChoice(article_id=articleID, article_text=text))\n y_train.append(definition)\n\n # create y labels to group similar acronyms\n y_labels, labelToExpansion = self._processChoices(y_train)\n\n return X_train, y_labels, labelToExpansion","def multiple_choices(self, choices, response):\n for elem in self.method_order:\n if elem in choices:\n return [elem]\n raise NoData","def set_dropdown_b_options(value):\n options_b = []\n if value=='A':\n options_b = [{'label': 'C', 'value': 'C'},\n {'label': 'D', 'value': 'D'}]\n if value == 'B':\n options_b = [{'label': 'E', 'value': 'E'},\n {'label': 'F', 'value': 'F'}]\n return options_b","def choices(self, choices: Iterable[Tuple[str, str]]):\n try:\n iter(choices)\n except TypeError:\n raise TypeError(\"'choices' isn't a valid iterable\")\n\n apply_choices = []\n for i, (choice_id, choice_label) in enumerate(choices):\n apply_choices.append((str(choice_id), str(choice_label)))\n\n if len(apply_choices) < 2:\n raise ValueError(\"you need to specify at least two choices\")\n\n self._choices = apply_choices\n self.specific.refresh()\n self._selected = 0","def before_choose_candidate_listener(self, session, task):\n choices = [PromptChoice('d', 'eDit', self.importer_edit)]\n if task.candidates:\n choices.append(PromptChoice('c', 'edit Candidates',\n self.importer_edit_candidate))\n\n return choices","def _get_completion(self, original_text, remaining_text, options):\n if self.should_hide_completions(original_text=original_text,\n remaining_text=remaining_text,\n allowed_suffixes=(\" \", \".\")):\n return []\n\n return [(option, len(remaining_text)) for option in options\n if option.startswith(remaining_text) and not option.startswith(\"_\")]"],"string":"[\n \"def get_choices(self):\\n raise NotImplementedError()\",\n \"def get_choices(self):\\n raise NotImplementedError()\",\n \"def choices(cls):\\n # return list(map(tuple, cls.__members__.items()))\\n return [(int(code), name) for name, code in cls.__members__.items()]\",\n \"def getEditChoices(self, currentText=''):\\n currentChoices, valid = self.sortedChoices(currentText)\\n nonChoices = [text for text in self.formatList\\n if text not in currentChoices]\\n results = []\\n for choice in nonChoices: # menu entries to add a choice\\n allChoices = currentChoices + [choice]\\n allChoices = [text for text in self.formatList\\n if text in allChoices]\\n results.append((self.editSep.join(allChoices),\\n '(%s %s)' % (_('add'), choice)))\\n if currentChoices:\\n results.append((None, None)) # separator\\n for choice in currentChoices: # menu entries to remove a choice\\n allChoices = currentChoices[:]\\n allChoices.remove(choice)\\n allChoices = [text for text in self.formatList\\n if text in allChoices]\\n results.append((self.editSep.join(allChoices),\\n '(%s %s)' % (_('remove'), choice)))\\n return results\",\n \"def choices(self, typename, value_field='description'):\\n rows = self.type(typename).values('id', value_field)\\n return [(r['id'], r[value_field]) for r in rows]\",\n \"def get_choices(self, instance):\\n if instance.type == BaseParameter.CHOICE_TYPE:\\n return [\\n x.value\\n for x in instance.get_typed_parameter().get_available_choices()\\n ]\\n else:\\n return None\",\n \"def get_choices_for_model_field(cls):\\n return [c[0:2] for c in cls.attr_choices]\",\n \"def initDefaultChoices(self):\\n return [entry[0] for entry in self.getEditChoices()]\",\n \"def select_combo_text(cb, text, index=0):\\n i = 0\\n for n in cb.get_model():\\n if n[index] == text:\\n break\\n i += 1\\n cb.set_active(i)\",\n \"def application_command_autocomplete_choice_builder(name, value):\\n return {\\n 'name': name,\\n 'value': value,\\n }\",\n \"def choices(self) -> list:\\n return [self.mapper(i) for i in self.keys()]\",\n \"def choices(self):\\n return tuple(self._choices)\",\n \"def iter_choices(self):\\n\\n for pk, obj in self._get_object_list():\\n if hasattr(obj, self.id):\\n selected = getattr(obj, self.id)\\n else:\\n selected = obj in self.data\\n\\n yield (pk, self.get_label(obj), selected)\",\n \"def _get_choices_str(self):\\n return ', '.join(\\n '\\\"%s\\\"' % choice\\n for choice in self.choices\\n )\",\n \"def getEditChoices(self, currentText=''):\\n format = globalref.options.strData('EditDateFormat', True)\\n today = GenDate().dateStr(format)\\n yesterday = (GenDate() - 1).dateStr(format)\\n tomorrow = (GenDate() + 1).dateStr(format)\\n return [(today, '(%s)' % _('today')),\\n (yesterday, '(%s)' % _('yesterday')),\\n (tomorrow, '(%s)' % _('tomorrow'))]\",\n \"def fill_combobox_attributes(self):\\n\\n list_char = [\\\"\\\"]\\n list_num = [\\\"\\\"]\\n if self.ui.radioButton_file.isChecked():\\n for a in self.attributes:\\n if a['caseOrFile'] == \\\"file\\\" and a['valuetype'] == \\\"character\\\":\\n list_char.append(a['name'])\\n if a['caseOrFile'] == \\\"file\\\" and a['valuetype'] == \\\"numeric\\\":\\n list_num.append(a['name'])\\n else:\\n for a in self.attributes:\\n if a['caseOrFile'] == \\\"case\\\" and a['valuetype'] == \\\"character\\\":\\n list_char.append(a['name'])\\n if a['caseOrFile'] == \\\"case\\\" and a['valuetype'] == \\\"numeric\\\":\\n list_num.append(a['name'])\\n self.ui.comboBox_num_attributes.blockSignals(True)\\n self.ui.comboBox_char_attributes.blockSignals(True)\\n self.ui.comboBox_num_attributes.clear()\\n self.ui.comboBox_char_attributes.clear()\\n self.ui.comboBox_char_attributes.addItems(list_char)\\n self.ui.comboBox_num_attributes.addItems(list_num)\\n self.ui.comboBox_num_attributes.blockSignals(False)\\n self.ui.comboBox_char_attributes.blockSignals(False)\",\n \"def getComboTerms(tuples):\\t\\t\\t\\n\\t\\t\\t#return \\\"[{0}]\\\".format('; '.join([\\\"({0})\\\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\\n\\t\\t\\treturn \\\"{0}\\\".format('; '.join((\\\"{0}\\\".format(text[indices[0]:indices[1]]) for indices in tuples)))\",\n \"def getEditChoices(self, currentText=''):\\n format = globalref.options.strData('EditTimeFormat', True)\\n now = GenTime().timeStr(format)\\n choices = [(now, '(%s)' % _('now'))]\\n for hr in (6, 9, 12, 15, 18, 21, 0):\\n time = GenTime((hr, 0)).timeStr(format)\\n choices.append((time, ''))\\n return choices\",\n \"def get_choices(cls):\\n return cls.values.items()\",\n \"def _find_options(self, inputfield):\\r\\n elements = inputfield.findall('./options/option')\\r\\n return [{\\r\\n 'id': index,\\r\\n 'description': option.text,\\r\\n 'choice': option.get('choice')\\r\\n } for (index, option) in enumerate(elements)]\",\n \"def create_combo(self) -> typing.Iterable[Combo]:\\n raise NotImplementedError()\",\n \"def choices(cls):\\n return tuple(item.as_tuple for item in list(cls))\",\n \"def choices(self) -> list:\\n return [self.mapper(item) for item in self]\",\n \"def choices(cls):\\n _choices = []\\n for attr in _user_attributes(cls):\\n val = getattr(cls, attr)\\n setattr(cls, attr[1:], val[0])\\n _choices.append((val[0], val[1]))\\n setattr(cls, 'CHOICES', tuple(_choices))\\n return cls\",\n \"def extract_choices(element):\\r\\n\\r\\n choices = []\\r\\n\\r\\n for choice in element:\\r\\n if choice.tag != 'choice':\\r\\n raise Exception(\\r\\n \\\"[capa.inputtypes.extract_choices] Expected a <choice>\\\" +\\r\\n \\\"tag; got {0} instead\\\".format(choice.tag)\\r\\n )\\r\\n\\r\\n components = []\\r\\n choice_text = ''\\r\\n if choice.text is not None:\\r\\n choice_text += choice.text\\r\\n # Initialize our dict for the next content\\r\\n adder = {\\r\\n 'type': 'text',\\r\\n 'contents': choice_text,\\r\\n 'tail_text': '',\\r\\n 'value': ''\\r\\n }\\r\\n components.append(adder)\\r\\n\\r\\n for elt in choice:\\r\\n # for elements in the choice e.g. <text> <numtolerance_input>\\r\\n adder = {\\r\\n 'type': 'text',\\r\\n 'contents': '',\\r\\n 'tail_text': '',\\r\\n 'value': ''\\r\\n }\\r\\n tag_type = elt.tag\\r\\n # If the current `elt` is a <numtolerance_input> set the\\r\\n # `adder`type to 'numtolerance_input', and 'contents' to\\r\\n # the `elt`'s name.\\r\\n # Treat decoy_inputs and numtolerance_inputs the same in order\\r\\n # to prevent students from reading the Html and figuring out\\r\\n # which inputs are valid\\r\\n if tag_type in ('numtolerance_input', 'decoy_input'):\\r\\n # We set this to textinput, so that we get a textinput html\\r\\n # element.\\r\\n adder['type'] = 'textinput'\\r\\n adder['contents'] = elt.get('name')\\r\\n else:\\r\\n adder['contents'] = elt.text\\r\\n\\r\\n # Add any tail text(\\\"is the mean\\\" in the example)\\r\\n adder['tail_text'] = elt.tail if elt.tail else ''\\r\\n components.append(adder)\\r\\n\\r\\n # Add the tuple for the current choice to the list of choices\\r\\n choices.append((choice.get(\\\"name\\\"), components))\\r\\n return choices\",\n \"def initDefaultChoices(self):\\n choices = [entry[0] for entry in self.getEditChoices()]\\n choices.insert(0, DateFormat.dateStampStrings[1])\\n return choices\",\n \"def initDefaultChoices(self):\\n choices = [entry[0] for entry in self.getEditChoices()]\\n choices.insert(0, TimeFormat.timeStampStrings[1])\\n return choices\",\n \"def combo_callback(self, eventobj):\\n print(self.combo_input.get()) # print name\\n print(self.combo_input.current()) # print index\",\n \"def choices(self):\\n # Needs to be implmented by subclass\\n raise Exception(NotImplemented)\",\n \"def initDefaultChoices(self):\\n return [text for text in self.formatList]\",\n \"def __init__(self,choices,caption='ListSelection',default=[],single=False,check=False,sort=False,*args,**kargs):\\n InputDialog.__init__(self,caption=caption,items = [\\n dict(name='input',value=default,itemtype='list',choices=choices,\\n text='',single=single,check=check,sort=sort,*args,**kargs),\\n ],)\",\n \"def _build_dropdown(options):\\n return [(x, x) for x in options]\",\n \"def set_choices(self, index, choices):\\n if len(choices) == 1:\\n self._label(index)\\n self._widgets[index][\\\"text\\\"] = str(choices[0])\\n else:\\n self._combo(index)\\n self._widgets[index][\\\"values\\\"] = [str(t) for t in choices]\\n width = max(len(str(t)) for t in choices)\\n width = max(5, width)\\n self._widgets[index][\\\"width\\\"] = width\",\n \"def choices() -> List[str]:\\n return [t.name.upper() for t in ConfigurationVariable]\",\n \"def extract_choices(element):\\r\\n\\r\\n choices = []\\r\\n\\r\\n for choice in element:\\r\\n if choice.tag != 'choice':\\r\\n raise Exception(\\r\\n \\\"[capa.inputtypes.extract_choices] Expected a <choice> tag; got %s instead\\\"\\r\\n % choice.tag)\\r\\n choices.append((choice.get(\\\"name\\\"), stringify_children(choice)))\\r\\n return choices\",\n \"def __init__(self, \\n num_fld=1, \\n lab_txt=[\\\"1\\\"], \\n txt_fld=[\\\"1\\\"], \\n title_txt=\\\"test\\\", \\n comb_txt=[],\\n comb_lab_txt=[], \\n comb_num=0, \\n root_x=50, \\n root_y=50):\\n super().__init__()\\n self.geometry(f'+{root_x}+{root_y}') #head=y+20px\\n self.str_in=[]\\n self.title(title_txt)\\n if comb_txt:\\n self.name=[0]*num_fld\\n ent=[0]*num_fld\\n self.comb=[]\\n self.act=[]\\n lab=[0]*num_fld\\n lab_comb=[0]*comb_num\\n else:\\n self.name=[0]*num_fld\\n ent=[0]*num_fld\\n lab=[0]*num_fld\\n self.comb=[]\\n self.act=[]\\n for i in range(num_fld):\\n self.name[i]=tk.StringVar()\\n ent[i]=tk.Entry(self,textvariable=self.name[i])\\n ent[i].insert(0, txt_fld[i])\\n lab[i] = tk.Label(self,width=15, text=lab_txt[i])\\n lab[i].pack()\\n ent[i].pack()\\n for i in range(comb_num):\\n lab_comb[i]=tk.Label(self,width=35, text=comb_lab_txt[i])\\n self.comb.append(ttk.Combobox(self, values=comb_txt))\\n lab_comb[i].pack()\\n self.comb[i].pack()\\n self.comb[i].current(1)\\n\\n but_ac=tk.Button(self, text=\\\"Accept\\\", command=self.ins)\\n but_ac.pack()\\n self.mainloop\",\n \"def input_choices_from_list(choices, text):\\n no_courses_text = \\\"\\\"\\\"\\n init will only list the courses you are enrolled in\\n and there seem to be none.\\n Either enrol in a course or add the course id as command line argument.\\n \\\"\\\"\\\"\\n if choices is None or len(choices) == 0:\\n print(no_courses_text)\\n raise SystemExit(1)\\n\\n digits = str(math.ceil(math.log10(len(choices))))\\n format_str = '{:' + digits + 'd} {}'\\n for n, c in enumerate(choices):\\n print(format_str.format(n, c))\\n try:\\n return [int(c) for c in input(text).split()]\\n except EOFError:\\n return []\",\n \"def valid_options(self):\\n choices = self.choices()\\n\\n if not choices:\\n return None\\n\\n return [opt[0] for opt in choices]\",\n \"def combobox(self):\\n return self._combo\",\n \"def widgets_from_abbreviations(self, seq):\\n result = []\\n for name, abbrev, default in seq:\\n widget = self.widget_from_abbrev(abbrev, default)\\n if not (isinstance(widget, ValueWidget) or isinstance(widget, fixed)):\\n if widget is None:\\n raise ValueError(\\\"{!r} cannot be transformed to a widget\\\".format(abbrev))\\n else:\\n raise TypeError(\\\"{!r} is not a ValueWidget\\\".format(widget))\\n if not widget.description:\\n widget.description = name\\n widget._kwarg = name\\n result.append(widget)\\n return result\",\n \"def multiple_choice():\\n\\n return [\\\"MAR\\\", \\\"MAR\\\", \\\"NI\\\", \\\"NI\\\", \\\"MCAR\\\"]\",\n \"def get_chosen_options(user):\\n user_profile = user.get_profile()\\n user_application = user_profile.application\\n np = user_application.np\\n ocl = eval(user_application.options_selected)\\n chosen_options = []\\n for oc in ocl:\\n chosen_options.append(Option.objects.get(opt_code=int(oc))) \\n return chosen_options\",\n \"def build_comboboxes(activities, events):\\n global comboboxes\\n # For each activity set up a selector for an event\\n\\n for activity in activities:\\n\\n # Setup frame for better display in gui\\n frame = Frame(main_window)\\n frame.configure(background=\\\"gray30\\\")\\n\\n # Label the left column as activity in a model + \\\"beautify gui\\\"\\n text = \\\"Activity name (model):\\\"\\n Label(frame, text=text, bg=\\\"gray30\\\", fg=\\\"white\\\", padx=5).grid(column=0, row=0)\\n Label(frame, text=activity, bg=\\\"gray30\\\", fg=\\\"white\\\").grid(column=0, row=1)\\n\\n # Set up the combobox for an event\\n combo = Combobox(frame)\\n combo['values'] = events\\n\\n # If activity is in events preselect the current one\\n if activity in events:\\n combo.current(events.index(activity))\\n\\n # Label the combobox and place label and box in frame\\n Label(frame, text=\\\"Event name (log):\\\", bg=\\\"gray30\\\", fg=\\\"white\\\", padx=5).grid(column=1, row=0)\\n combo.grid(column=1, row=1)\\n\\n # If the last activity in the graph is handled then do not write a separator\\n if activity != activities[-1]:\\n Separator(frame, orient=\\\"horizontal\\\").grid(row=2, columnspan=2, sticky=\\\"ew\\\", pady=10)\\n\\n comboboxes[activity] = combo\\n # place the frame in the main_window\\n frame.grid(column=0)\",\n \"def get_generic_choices(model, key, allow_null=False):\\n CHOICES = [('', '--------')] if allow_null else []\\n for i in model.objects.all().values_list(key, flat=True).distinct():\\n CHOICES.append((str(i), str(i)))\\n CHOICES.sort()\\n\\n return CHOICES\",\n \"def _combobox_choice(self, _=None):\\n combobox_string = self.value_combobox.var.get()\\n if combobox_string.startswith(\\\"Unknown: \\\"):\\n value = int(combobox_string[len(\\\"Unknown: \\\"):])\\n else:\\n value = int(self.value_combobox.var.get().split(\\\" \\\")[0])\\n self.master.change_field_value(self.field_name, value)\",\n \"def widgets(parameter: Parameter):\\n widgets = []\\n for key in parameter.keys():\\n textEdit = QTextEdit()\\n textEdit.setText(key)\\n widgets.append(textEdit)\\n if isinstance(parameter[key], Enum):\\n comboBox = MyQtEnumComboBox()\\n comboBox.fillValues(type(parameter[key]))\\n widgets.append(comboBox)\\n elif isinstance(parameter[key], bool):\\n comboBox = QComboBox()\\n comboBox.addItems((\\\"False\\\", \\\"True\\\"))\\n widgets.append(comboBox)\\n else:\\n textEdit = QTextEdit()\\n textEdit.setText(str(parameter[key]))\\n widgets.append(textEdit)\\n for widget in widgets:\\n widget.setFixedHeight(30)\\n return widgets\",\n \"def _getBrailleRegionsForComboBox(self, obj):\\n\\n self._debugGenerator(\\\"_getBrailleRegionsForComboBox\\\", obj)\\n\\n regions = []\\n\\n focusedRegionIndex = 0\\n label = self._script.getDisplayedLabel(obj)\\n if label and (len(label) > 0):\\n regions.append(braille.Region(label + \\\" \\\"))\\n focusedRegionIndex = 1\\n\\n # Check to see if the text is editable. If so, then we want\\n # to show the text attributes (such as selection -- see bug\\n # 496846 for more details).\\n #\\n textObj = None\\n for child in obj:\\n if child and child.getRole() == pyatspi.ROLE_TEXT:\\n textObj = child\\n if textObj and textObj.getState().contains(pyatspi.STATE_EDITABLE):\\n textRegion = braille.Text(textObj)\\n regions.append(textRegion)\\n else:\\n displayedText = self._script.getDisplayedText(obj)\\n if displayedText:\\n regions.append(braille.Region(displayedText))\\n\\n regions.append(braille.Region(\\n \\\" \\\" + rolenames.getBrailleForRoleName(obj)))\\n\\n # Things may not have gone as expected above, so we'll do some\\n # defensive programming to make sure we don't get an index out\\n # of bounds.\\n #\\n if focusedRegionIndex >= len(regions):\\n focusedRegionIndex = 0\\n if len(regions) == 0:\\n focusedRegion = None\\n else:\\n focusedRegion = regions[focusedRegionIndex]\\n\\n # [[[TODO: WDW - perhaps if a text area was created, we should\\n # give focus to it.]]]\\n #\\n return [regions, focusedRegion]\",\n \"def form_SelectChoiceCallableOptions(request):\\n schema = schemaish.Structure()\\n schema.add('mySelect', schemaish.Integer())\\n def _():\\n options = [(1,'a'),(2,'b'),(3,'c')]\\n for option in options:\\n yield option\\n\\n form = formish.Form(schema, 'form')\\n form['mySelect'].widget = formish.SelectChoice(_)\\n return form\",\n \"def __init__(self, *args, **kwargs):\\n super(TaggedContentItemForm, self).__init__(*args, **kwargs)\\n wtf = Tag.objects.filter(group__system=False)\\n wlist = [w for t, w in self.fields.items() if t.endswith(\\\"tags\\\")]\\n choices = []\\n for choice in wtf:\\n choices.append((choice.id, str(choice)))\\n [setattr(w, 'choices', choices) for w in wlist]\",\n \"def _comboSlot(self, select):\\n select = self.sender().itemText(select)\\n if qt4:\\n qs = str(self.sender().property(\\\"dom address\\\").toPyObject())\\n else:\\n qs = str(self.sender().property(\\\"dom address\\\"))\\n item = QtXml.QDomElement()\\n\\n ind = qs.rfind('/')\\n ids = qs[ind:]\\n\\n item = self.qhash[qs].elem.firstChildElement(\\\"Item\\\")\\n while(item.isNull() is False):\\n itemName = item.firstChildElement(\\\"Name\\\")\\n if(str(itemName.text()).strip() != select):\\n activ = item.firstChildElement(\\\"Activate\\\")\\n while(activ.isNull() is False):\\n s = str(activ.text()).strip() + ids\\n h = self.qhash[s]\\n widget_enabled = h.elem.attribute(\\\"Enabled\\\", \\\"True\\\")\\n widget_visible = h.elem.attribute(\\\"Visible\\\", \\\"Unknown\\\")\\n h.widget.setEnabled(False)\\n if(widget_visible != \\\"Unknown\\\"):\\n h.label.hide()\\n h.widget.hide()\\n activ = activ.nextSiblingElement(\\\"Activate\\\")\\n item = item.nextSiblingElement(\\\"Item\\\")\\n\\n item = self.qhash[qs].elem.firstChildElement(\\\"Item\\\")\\n while(item.isNull() is False):\\n itemName = item.firstChildElement(\\\"Name\\\")\\n if(str(itemName.text()).strip() == select):\\n activ = item.firstChildElement(\\\"Activate\\\")\\n while(activ.isNull() is False):\\n s = str(activ.text()).strip() + ids\\n h = self.qhash[s]\\n h.widget.setEnabled(True)\\n h.label.show()\\n h.widget.show()\\n activ = activ.nextSiblingElement(\\\"Activate\\\")\\n item = item.nextSiblingElement(\\\"Item\\\")\",\n \"def get_choices(self, cutoff=None):\\n queryset = self.get_queryset()\\n if queryset is None:\\n # Ensure that field.choices returns something sensible\\n # even when accessed with a read-only field.\\n return {}\\n\\n if cutoff is not None:\\n queryset = queryset[:cutoff]\\n\\n return OrderedDict(\\n [\\n (\\n # This line below is modifed.\\n item.pk,\\n self.display_value(item),\\n )\\n for item in queryset\\n ]\\n )\",\n \"def test_rendering_combobox(qtbot):\\n layer = Image(np.random.rand(8, 8))\\n qtctrl = QtImageControls(layer)\\n qtbot.addWidget(qtctrl)\\n combo = qtctrl.renderComboBox\\n opts = {combo.itemText(i) for i in range(combo.count())}\\n rendering_options = {\\n 'translucent',\\n 'additive',\\n 'iso',\\n 'mip',\\n 'minip',\\n 'attenuated_mip',\\n 'average',\\n }\\n assert opts == rendering_options\\n # programmatically updating rendering mode updates the combobox\\n layer.rendering = 'iso'\\n assert combo.findText('iso') == combo.currentIndex()\",\n \"def get_choicesdata(self):\\n selected_value = self.get_cleaned_value()\\n choicesdata = []\\n for value, label in self.get_choices_cached():\\n is_selected = value == selected_value\\n url = self.build_set_values_url(values=[value])\\n choicesdata.append({\\n 'url': url,\\n 'label': label,\\n 'is_selected': is_selected,\\n 'dom_id': '{}_{}'.format(self.get_inputfield_dom_id(), value)\\n })\\n return choicesdata\",\n \"def choices(self):\\n return self.__class__.get_setting_choices(self.key, **self.get_kwargs())\",\n \"def get_poll_choices(self, games: [Game]) -> [dict]:\\n answer_texts = []\\n for g in games:\\n answer_texts.append(g.name + \\\" - \\\" + g.genre)\\n answer_texts = sorted(answer_texts, key=str.lower)\\n poll_choices = []\\n for at in answer_texts:\\n poll_choices.append({\\\"text\\\": at})\\n return poll_choices\",\n \"def __str__(self):\\n return \\\"choice_text: \\\" + self.choice_text\",\n \"def _parse_choices(self, text):\\n choices = dict()\\n\\n matches = re.findall(self.choice_regex, text)\\n for match in matches:\\n # remove the brackets\\n match = match.replace('[[', '')\\n match = match.replace(']]', '')\\n\\n if '|' in match:\\n # format is {text}|{node_id}, the text and node id are different\\n text, node_id = match.split('|')\\n choices[node_id] = text\\n else:\\n choices[match] = match\\n\\n return choices\",\n \"def get_choices_new_protected():\\n ret = []\\n ret.append( (1, _(u'Nur Community-Mitglieder dürfen neue Beiträge leisten')) )\\n ret.append( (-1, _(u'Offener Zugang')) )\\n return ret\",\n \"def comboBox(args: list, slot) -> QComboBox:\\n comboBox = QComboBox()\\n comboBox.addItems(args[0])\\n comboBox.currentTextChanged.connect(slot)\\n return comboBox\",\n \"def choices(self):\\n\\n if self._choices == None:\\n self._choices = [ExperimentChoice(self, choice_name) for choice_name in self.choice_names]\\n\\n return self._choices\",\n \"def make_ddls( self, parent ):\\n # ---- 0\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n a_widget.bind( \\\"<<ComboboxSelected>>\\\", self.sync_ddl_0 )\\n\\n # AppGlobal.gui_style.style_combobox( a_widget )\\n self.ddl_0_widget = a_widget\\n\\n # ---- 1\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n self.ddl_1_widget = a_widget\\n\\n # ---- 2\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width + 5,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n # AppGlobal.gui_style.style_combobox( a_widget )\\n self.ddl_2_widget = a_widget\\n\\n # self.ddl_widgets = [ self.ddl_0_widget, self.ddl_1_widget, self.ddl_2_widget, ]\\n # print( self.ddl_widgets[ 0 ] == self.ddl_widgets[ 1 ])\\n\\n #self.load_ddl_0( )\",\n \"def __str__(self):\\n return self.choice_text\",\n \"def as_choices():\\n return (\\n # Live is currently disabled as a choice\\n # pending implementation\\n (\\\"live\\\", \\\"Use working directory\\\"),\\n (\\\"latest\\\", \\\"Use latest snapshot\\\"),\\n (\\\"pinned\\\", \\\"Pinned to snapshot\\\"),\\n )\",\n \"def comboBoxes(self):\\r\\n # Cities Combo Button\\r\\n self.comboCities = QComboBox()\\r\\n self.comboCities.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: 15px;\\r\\n background-color : #A8DBC5;\\r\\n border: 1px solid white;\\r\\n\\r\\n \\\"\\\"\\\")\\r\\n self.comboCities.addItems(\\r\\n ['Girón', 'Piedecuesta', 'Floridablanca', 'Bucaramanga'])\\r\\n self.grid.addWidget(self.comboCities, 6, 1, 1, 2)\\r\\n self.comboCities.setCurrentText(\\\"Bucaramanga\\\")\\r\\n # Payment Combo Button\\r\\n self.comboPayment = QComboBox()\\r\\n self.comboPayment.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: 15px;\\r\\n background-color : #A8DBC5;\\r\\n border: 1px solid white;\\r\\n\\r\\n \\\"\\\"\\\")\\r\\n self.comboPayment.addItems(['Efectivo', 'Nequi'])\\r\\n self.grid.addWidget(self.comboPayment, 7, 1, 1, 2)\",\n \"def get_choicesdata(self):\\n # selected_value = self.get_cleaned_value()\\n # choicesdata = []\\n # found_selected_value = False\\n # for value, label in self.get_choices():\\n # is_selected = value == selected_value\\n # if is_selected:\\n # found_selected_value = True\\n # url = self.build_set_values_url(values=[value])\\n # choicesdata.append({\\n # 'url': url,\\n # 'label': label,\\n # 'is_selected': is_selected\\n # })\\n choicesdata, found_selected_value = self.__make_choicesdata_list(\\n choices=self.get_choices(),\\n selected_value=self.get_cleaned_value())\\n if not found_selected_value and len(choicesdata) > 0:\\n selected_index = self.get_default_is_selected_index(choicesdata=choicesdata)\\n choicesdata[selected_index]['is_selected'] = True\\n return choicesdata\",\n \"def _find_options(self):\\r\\n elements = self.xml.findall('./options/option')\\r\\n return [{\\r\\n 'id': index,\\r\\n 'description': option.text,\\r\\n 'choice': option.get('choice')\\r\\n } for (index, option) in enumerate(elements)]\",\n \"def __str__(self):\\n return gettext('One of %s') % self._get_choices_str()\",\n \"def initDefaultChoices(self):\\n return []\",\n \"def list_selector_widget(members=None,\\n preselect=None,\\n entry=False,\\n callback=None):\\n store, i=generate_list_model(members,\\n active_element=preselect)\\n\\n if entry:\\n combobox=gtk.ComboBoxEntry(store, column=0)\\n else:\\n combobox=gtk.ComboBox(store)\\n cell = gtk.CellRendererText()\\n combobox.pack_start(cell, expand=True)\\n combobox.add_attribute(cell, 'text', 0)\\n combobox.add_attribute(cell, 'background', 2)\\n\\n combobox.set_active(-1)\\n if i is None:\\n i = store.get_iter_first()\\n if i is not None:\\n combobox.set_active_iter(i)\\n\\n if entry:\\n def get_current_element(combo):\\n try:\\n return combo.get_model().get_value(combo.get_active_iter(), 1)\\n except (TypeError, AttributeError):\\n return unicode(combo.child.get_text())\\n def set_current_element(combo, t):\\n combo.child.set_text(t)\\n else:\\n def get_current_element(combo):\\n if combo.get_active_iter() is not None:\\n return combo.get_model().get_value(combo.get_active_iter(), 1)\\n else:\\n return None\\n def set_current_element(combo, el):\\n # Find the index of the element\\n l=[ t[0] for t in enumerate(combo.get_model()) if t[1][1] == el ]\\n if l:\\n # The element is present.\\n combo.set_active(l[0])\\n else:\\n combo.set_active_iter(combo.get_model().append( (unicode(el), el, None) ))\\n\\n # Bind the method to the combobox object\\n combobox.get_current_element = get_current_element.__get__(combobox)\\n combobox.set_current_element = set_current_element.__get__(combobox)\\n\\n if callback is not None:\\n combobox.connect('changed', callback)\\n\\n return combobox\",\n \"def occurrence_choices():\\n OCCURRENCE_CHOICES = [('one_off', 'One Off'), ('weekly', 'Weekly'), ('fortnightly', 'Fortnightly')]\\n occurrence = forms.ChoiceField(choices=OCCURRENCE_CHOICES, widget=forms.RadioSelect())\\n return occurrence\",\n \"def get_action_choices():\\n from hardware.management.commands.gpio_buttons import Command\\n import re\\n pattern = re.compile(r'^on_(?P<name>\\\\w+)_press$')\\n choices = []\\n for member in dir(Command):\\n match = pattern.match(member)\\n if match:\\n action = match.groupdict()['name']\\n name = action.replace('_', ' ').title()\\n choices.append((action, name))\\n return choices\",\n \"def DrawComboBox(*args, **kwargs):\\n return _gdi_.RendererNative_DrawComboBox(*args, **kwargs)\",\n \"def fill_combobox(self):\\n sqlstring = \\\"SELECT first_name, last_name from employees WHERE enabled = 1 ORDER BY last_name ASC\\\"\\n self.CB_employee.addItem(\\\"\\\")\\n for employee in self.ms.c.execute(sqlstring):\\n self.CB_employee.addItem(\\\" \\\".join((employee[0], employee[1])))\\n sqlstring = \\\"SELECT first_name, last_name from employees WHERE enabled = 0 ORDER BY last_name ASC\\\"\\n for employee in self.ms.c.execute(sqlstring):\\n self.CB_employee.addItem(\\\" \\\".join((employee[0], employee[1])))\",\n \"def _get_choices ( self, context, path = '' ):\\n choices = []\\n gdc = context.get_data_context\\n for name in context.data_contexts:\\n next_path = path_for( path, name )\\n choices.append( TemplateChoice( choice_value = next_path ) )\\n choices.extend( self._get_choices( gdc( name ), next_path ) )\\n \\n return choices\",\n \"def get_context(self, name, value, attrs=None, choices=()):\\n context = super(TriStateCheckboxSelectMultiple, self).get_context(\\n name, value, attrs\\n )\\n\\n choices = dict(it.chain(self.choices, choices))\\n if value is None:\\n value = dict.fromkeys(choices, False)\\n else:\\n value = dict(dict.fromkeys(choices, False).items() +\\n value.items())\\n\\n context['values'] = [\\n (choice, label, value[choice])\\n for choice, label in choices.iteritems()\\n ]\\n\\n return context\",\n \"def get_text_type():\\n opt = ['text', 'email', 'password']\\n inp = option_menu(opt, 'Select text type:')\\n\\n # mark text type with option\\n OPTIONS['text-type'] = opt[inp]\\n\\n # add option to collected list\\n add_to_collected('text type', opt[inp])\\n\\n return\",\n \"def set_dropdown_b_options(value):\\n options_c = []\\n if value=='C':\\n options_c = [{'label': '1', 'value': '1'},\\n {'label': '2', 'value': '2'}]\\n if value == 'D':\\n options_c = [{'label': '3', 'value': '3'},\\n {'label': '4', 'value': '4'}]\\n if value=='E':\\n options_c = [{'label': '5', 'value': '5'},\\n {'label': '6', 'value': '6'}]\\n if value == 'F':\\n options_c = [{'label': '7', 'value': '7'},\\n {'label': '8', 'value': '8'}]\\n return options_c\",\n \"def objects_to_choices(queryset):\\n res = []\\n for elm in queryset:\\n res.append((elm.pk, unicode(elm)))\\n return res\",\n \"def choice(text, choices, **kwargs):\\n return click.prompt(click.style('> {}'.format(text), fg='blue', bold=True),\\n type=click.Choice(choices),\\n **kwargs)\",\n \"def on_comboBox_enceinte_activated(self, index):\\n nom_enceinte = self.comboBox_enceinte.currentText()\\n marque = [x[2] for x in self.enceintes if x[1] == nom_enceinte][0]\\n n_serie = [x[4] for x in self.enceintes if x[1] == nom_enceinte][0]\\n model =[x[3] for x in self.enceintes if x[1] == nom_enceinte][0]\\n \\n \\n self.lineEdit_marque.setText(marque)\\n self.lineEdit_n_serie.setText(n_serie)\\n self.lineEdit_model.setText(model)\",\n \"def display_choose(self, text, choices):\\n cur_index = 0\\n key = None\\n while key != 'KEY_NEWLINE':\\n if key == 'KEY_UP':\\n cur_index = max(cur_index - 1, 0)\\n elif key == 'KEY_DOWN':\\n cur_index = min(cur_index + 1, len(choices) - 1)\\n self.stdscr.erase()\\n for line in text:\\n self.stdscr.addstr(f'{PADCHAR}{line}\\\\n')\\n for index, value in enumerate(choices):\\n self.stdscr.addstr('\\\\n')\\n self.stdscr.addstr(PADCHAR)\\n self.stdscr.addstr(value, color_pair(7 if index == cur_index else 1))\\n self.stdscr.addstr(f'\\\\n\\\\n{PADCHAR}') \\n key = self.get_key() \\n return cur_index\",\n \"def list_selector(title=None,\\n text=None,\\n members=None,\\n controller=None,\\n preselect=None,\\n entry=False):\\n combobox = list_selector_widget(members=members,\\n preselect=preselect,\\n entry=entry)\\n\\n d = gtk.Dialog(title=title,\\n parent=None,\\n flags=gtk.DIALOG_DESTROY_WITH_PARENT,\\n buttons=( gtk.STOCK_OK, gtk.RESPONSE_OK,\\n gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL ))\\n\\n if text is not None:\\n l=gtk.Label(text)\\n l.show()\\n d.vbox.add(l)\\n\\n d.vbox.add(combobox)\\n combobox.show_all()\\n\\n d.connect('key-press-event', dialog_keypressed_cb)\\n\\n d.show()\\n center_on_mouse(d)\\n res=d.run()\\n retval=None\\n if res == gtk.RESPONSE_OK:\\n retval=combobox.get_current_element()\\n d.destroy()\\n return retval\",\n \"def get_classes(self, code):\\n \\n select = v.Combobox(\\n _metadata={'name':code}, \\n items=self.items, \\n v_model=None, \\n dense=True,\\n hide_details=True\\n )\\n \\n select.observe(partial(self.store, code), 'v_model')\\n \\n return select\",\n \"def getOptionsNames(self) -> List[unicode]:\\n ...\",\n \"def __init__(self, *args, **kwargs):\\n super(ChoiceFieldType, self).__init__(*args, **kwargs)\\n\\n self.choices = self.get_field_info_key('choices')\",\n \"def the_option_named(text: str) -> \\\"SelectByText\\\":\\n return SelectByText(text)\",\n \"def prepareAutoComplete(editor, text, charPos, lineStartCharPos,\\r\\n wikiDocument, settings):\\r\\n return []\",\n \"def autocomplete_possibilities():\\n try:\\n # get data sent by client\\n typed_input = request.args.get('q')\\n print(' ')\\n print('\\\\n------ getting autocomplete_possibilities ------')\\n print(f\\\"recived: input:{typed_input}\\\")\\n\\n # call the google API\\n results = gmaps.places_autocomplete(typed_input)\\n data = [\\n {'value': r['place_id'], 'text': r['description']}\\n for r in results\\n ]\\n\\n # Pass data to the front end\\n print(f'returning: {data}')\\n return jsonify(data)\\n\\n except Exception as e:\\n print(\\\"AJAX excepted \\\" + str(e))\\n return str(e)\",\n \"def _validate_selects(text, response):\\n answer_options = re.split(settings.MULTISELECT_DELIMITER_RE, str(text))\\n choices = map(lambda choice: choice.lower(), response.event.choices)\\n logger.debug('Question (%s) answer choices are: %s, given answers: %s' % (datatype, choices, answer_options))\\n new_answers = copy(answer_options)\\n for idx, opt in enumerate(answer_options):\\n logger.debug('Trying to format (m)select answer: \\\"%s\\\"' % opt)\\n try: \\n #in the case that we accept numbers to indicate option selection\\n opt_int = int(opt)\\n if not (1 <= opt_int <= len(choices)): \\n return text, 'Answer %s must be between 1 and %s' % (opt_int, len(choices))\\n else:\\n new_answers[idx] = str(opt_int)\\n\\n except ValueError: \\n # in the case where we accept the actual text of the question\\n logger.debug('Caught value error, trying to parse answer string choice of: %s' % choices)\\n if opt.lower() not in choices:\\n return text, 'Answer must be one of the choices'\\n else:\\n new_answers[idx] = str(choices.index(opt.lower()) + 1)\\n return ' '.join(new_answers), None\",\n \"def choice(choices=[], message=\\\"Pick something.\\\", title=None):\\n return dialog(\\n \\\"choice\\\",\\n choices=choices,\\n message=message,\\n title=title,\\n )\",\n \"def complete_opt_format(self, text, *_):\\n return [t + \\\" \\\" for t in FORMATTERS if t.startswith(text)]\",\n \"def choices(self, cl):\\n # TODO: Determine if non-static choices would be cleaner here.\\n # Honestly, I tried a more generic version and it was even harder to\\n # follow than this version.\\n yield {\\n 'selected': not (self.lookup_val_gte or self.lookup_val_lt),\\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg_gte,\\n self.lookup_kwarg_lt]),\\n 'display': 'All'\\n }\\n\\n goal = settings.FACEBOOK_CLICK_GOAL\\n yield {\\n 'selected': self.lookup_val_gte and not self.lookup_val_lt,\\n 'query_string': cl.get_query_string({self.lookup_kwarg_gte: goal},\\n [self.lookup_kwarg_lt]),\\n 'display': 'Yes'\\n }\\n yield {\\n 'selected': self.lookup_val_lt and not self.lookup_val_gte,\\n 'query_string': cl.get_query_string({self.lookup_kwarg_lt: goal},\\n [self.lookup_kwarg_gte]),\\n 'display': 'No'\\n }\",\n \"def build_options(slot, snacks):\\n \\n if slot == 'Fast':\\n return [\\n {'text': 'Pizza', 'value': 'Pizza'},\\n {'text': 'Fries', 'value': 'Fries'},\\n {'text': 'Franky', 'value': 'Franky'},\\n {'text': 'Burger', 'value': 'Burger'},\\n {'text': 'Sandwich', 'value': 'Sandwich'}\\n \\n \\n ]\\n elif slot == 'drink':\\n return [\\n {'text': 'Coca-Cola', 'value': 'Coca-cola'},\\n {'text': 'Appy', 'value': 'Appy'},\\n \\n {'text': 'Beer', 'value': 'Beer'},\\n {'text': 'Frooti', 'value': 'Frooti'},\\n {'text': 'Pepsi', 'value': 'Pepsi'}\\n \\n ]\",\n \"def sortedChoices(self, inText):\\n choices = self.splitText(inText)\\n sortedChoices = [text for text in self.formatList if text in choices]\\n if len(choices) == len(sortedChoices):\\n return (sortedChoices, True)\\n else:\\n return (sortedChoices, False)\",\n \"def _getChoices(self, acronym):\\n # get matches from acronymDB\\n matches = []\\n if(acronym in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n if(acronym[-1] == \\\"s\\\" and acronym[:-1] in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n\\n # create training data\\n X_train, y_train = [], []\\n for definition, articleID, ignored_var in matches:\\n text = self.articleDB[articleID]\\n X_train.append(\\n ExpansionChoice(article_id=articleID, article_text=text))\\n y_train.append(definition)\\n\\n # create y labels to group similar acronyms\\n y_labels, labelToExpansion = self._processChoices(y_train)\\n\\n return X_train, y_labels, labelToExpansion\",\n \"def multiple_choices(self, choices, response):\\n for elem in self.method_order:\\n if elem in choices:\\n return [elem]\\n raise NoData\",\n \"def set_dropdown_b_options(value):\\n options_b = []\\n if value=='A':\\n options_b = [{'label': 'C', 'value': 'C'},\\n {'label': 'D', 'value': 'D'}]\\n if value == 'B':\\n options_b = [{'label': 'E', 'value': 'E'},\\n {'label': 'F', 'value': 'F'}]\\n return options_b\",\n \"def choices(self, choices: Iterable[Tuple[str, str]]):\\n try:\\n iter(choices)\\n except TypeError:\\n raise TypeError(\\\"'choices' isn't a valid iterable\\\")\\n\\n apply_choices = []\\n for i, (choice_id, choice_label) in enumerate(choices):\\n apply_choices.append((str(choice_id), str(choice_label)))\\n\\n if len(apply_choices) < 2:\\n raise ValueError(\\\"you need to specify at least two choices\\\")\\n\\n self._choices = apply_choices\\n self.specific.refresh()\\n self._selected = 0\",\n \"def before_choose_candidate_listener(self, session, task):\\n choices = [PromptChoice('d', 'eDit', self.importer_edit)]\\n if task.candidates:\\n choices.append(PromptChoice('c', 'edit Candidates',\\n self.importer_edit_candidate))\\n\\n return choices\",\n \"def _get_completion(self, original_text, remaining_text, options):\\n if self.should_hide_completions(original_text=original_text,\\n remaining_text=remaining_text,\\n allowed_suffixes=(\\\" \\\", \\\".\\\")):\\n return []\\n\\n return [(option, len(remaining_text)) for option in options\\n if option.startswith(remaining_text) and not option.startswith(\\\"_\\\")]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64435107","0.64435107","0.6406375","0.6369448","0.6282898","0.61602914","0.61550856","0.6096663","0.6079173","0.6074226","0.599768","0.5979722","0.5946701","0.59085536","0.58665997","0.5852769","0.5851758","0.5840527","0.58387506","0.5816007","0.5803215","0.5797734","0.57951343","0.57936877","0.57807535","0.5749749","0.57457596","0.5732043","0.57093215","0.57050306","0.5695638","0.5680274","0.5638338","0.5617769","0.56013155","0.5580123","0.5573768","0.55674773","0.55656695","0.5561425","0.5539777","0.5525614","0.55243665","0.55211055","0.5516054","0.55135965","0.5486497","0.54864573","0.5484098","0.5467698","0.5450487","0.5444694","0.5435837","0.5432833","0.542561","0.54099566","0.5406829","0.5394251","0.53907686","0.5388395","0.53733003","0.5353227","0.5352402","0.53441244","0.5335833","0.5330664","0.5320152","0.5317789","0.53159815","0.5291184","0.52660507","0.5261751","0.52587485","0.5247112","0.52468276","0.5246636","0.52386904","0.523807","0.52264065","0.52225775","0.521434","0.52137464","0.5197637","0.5192666","0.51882684","0.5188186","0.5170487","0.516354","0.5163382","0.5161854","0.5159188","0.5158065","0.51575136","0.51539713","0.5146208","0.5145707","0.5143474","0.5142414","0.51338685","0.51279086"],"string":"[\n \"0.64435107\",\n \"0.64435107\",\n \"0.6406375\",\n \"0.6369448\",\n \"0.6282898\",\n \"0.61602914\",\n \"0.61550856\",\n \"0.6096663\",\n \"0.6079173\",\n \"0.6074226\",\n \"0.599768\",\n \"0.5979722\",\n \"0.5946701\",\n \"0.59085536\",\n \"0.58665997\",\n \"0.5852769\",\n \"0.5851758\",\n \"0.5840527\",\n \"0.58387506\",\n \"0.5816007\",\n \"0.5803215\",\n \"0.5797734\",\n \"0.57951343\",\n \"0.57936877\",\n \"0.57807535\",\n \"0.5749749\",\n \"0.57457596\",\n \"0.5732043\",\n \"0.57093215\",\n \"0.57050306\",\n \"0.5695638\",\n \"0.5680274\",\n \"0.5638338\",\n \"0.5617769\",\n \"0.56013155\",\n \"0.5580123\",\n \"0.5573768\",\n \"0.55674773\",\n \"0.55656695\",\n \"0.5561425\",\n \"0.5539777\",\n \"0.5525614\",\n \"0.55243665\",\n \"0.55211055\",\n \"0.5516054\",\n \"0.55135965\",\n \"0.5486497\",\n \"0.54864573\",\n \"0.5484098\",\n \"0.5467698\",\n \"0.5450487\",\n \"0.5444694\",\n \"0.5435837\",\n \"0.5432833\",\n \"0.542561\",\n \"0.54099566\",\n \"0.5406829\",\n \"0.5394251\",\n \"0.53907686\",\n \"0.5388395\",\n \"0.53733003\",\n \"0.5353227\",\n \"0.5352402\",\n \"0.53441244\",\n \"0.5335833\",\n \"0.5330664\",\n \"0.5320152\",\n \"0.5317789\",\n \"0.53159815\",\n \"0.5291184\",\n \"0.52660507\",\n \"0.5261751\",\n \"0.52587485\",\n \"0.5247112\",\n \"0.52468276\",\n \"0.5246636\",\n \"0.52386904\",\n \"0.523807\",\n \"0.52264065\",\n \"0.52225775\",\n \"0.521434\",\n \"0.52137464\",\n \"0.5197637\",\n \"0.5192666\",\n \"0.51882684\",\n \"0.5188186\",\n \"0.5170487\",\n \"0.516354\",\n \"0.5163382\",\n \"0.5161854\",\n \"0.5159188\",\n \"0.5158065\",\n \"0.51575136\",\n \"0.51539713\",\n \"0.5146208\",\n \"0.5145707\",\n \"0.5143474\",\n \"0.5142414\",\n \"0.51338685\",\n \"0.51279086\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6981332"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":88,"cells":{"query":{"kind":"string","value":"Return list of choices for combo box, each a tuple of edit text and any annotation text"},"document":{"kind":"string","value":"def getEditChoices(self, currentText=''):\n currentChoices, valid = self.sortedChoices(currentText)\n nonChoices = [text for text in self.formatList\n if text not in currentChoices]\n results = []\n for choice in nonChoices: # menu entries to add a choice\n allChoices = currentChoices + [choice]\n allChoices = [text for text in self.formatList\n if text in allChoices]\n results.append((self.editSep.join(allChoices),\n '(%s %s)' % (_('add'), choice)))\n if currentChoices:\n results.append((None, None)) # separator\n for choice in currentChoices: # menu entries to remove a choice\n allChoices = currentChoices[:]\n allChoices.remove(choice)\n allChoices = [text for text in self.formatList\n if text in allChoices]\n results.append((self.editSep.join(allChoices),\n '(%s %s)' % (_('remove'), choice)))\n return results"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getEditChoices(self, currentText=''):\n return [(text, '') for text in self.formatList]","def get_choices(self):\n raise NotImplementedError()","def get_choices(self):\n raise NotImplementedError()","def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n return [(int(code), name) for name, code in cls.__members__.items()]","def choices(self, typename, value_field='description'):\n rows = self.type(typename).values('id', value_field)\n return [(r['id'], r[value_field]) for r in rows]","def get_choices(self, instance):\n if instance.type == BaseParameter.CHOICE_TYPE:\n return [\n x.value\n for x in instance.get_typed_parameter().get_available_choices()\n ]\n else:\n return None","def get_choices_for_model_field(cls):\n return [c[0:2] for c in cls.attr_choices]","def initDefaultChoices(self):\n return [entry[0] for entry in self.getEditChoices()]","def select_combo_text(cb, text, index=0):\n i = 0\n for n in cb.get_model():\n if n[index] == text:\n break\n i += 1\n cb.set_active(i)","def application_command_autocomplete_choice_builder(name, value):\n return {\n 'name': name,\n 'value': value,\n }","def choices(self) -> list:\n return [self.mapper(i) for i in self.keys()]","def choices(self):\n return tuple(self._choices)","def iter_choices(self):\n\n for pk, obj in self._get_object_list():\n if hasattr(obj, self.id):\n selected = getattr(obj, self.id)\n else:\n selected = obj in self.data\n\n yield (pk, self.get_label(obj), selected)","def _get_choices_str(self):\n return ', '.join(\n '\"%s\"' % choice\n for choice in self.choices\n )","def getEditChoices(self, currentText=''):\n format = globalref.options.strData('EditDateFormat', True)\n today = GenDate().dateStr(format)\n yesterday = (GenDate() - 1).dateStr(format)\n tomorrow = (GenDate() + 1).dateStr(format)\n return [(today, '(%s)' % _('today')),\n (yesterday, '(%s)' % _('yesterday')),\n (tomorrow, '(%s)' % _('tomorrow'))]","def fill_combobox_attributes(self):\n\n list_char = [\"\"]\n list_num = [\"\"]\n if self.ui.radioButton_file.isChecked():\n for a in self.attributes:\n if a['caseOrFile'] == \"file\" and a['valuetype'] == \"character\":\n list_char.append(a['name'])\n if a['caseOrFile'] == \"file\" and a['valuetype'] == \"numeric\":\n list_num.append(a['name'])\n else:\n for a in self.attributes:\n if a['caseOrFile'] == \"case\" and a['valuetype'] == \"character\":\n list_char.append(a['name'])\n if a['caseOrFile'] == \"case\" and a['valuetype'] == \"numeric\":\n list_num.append(a['name'])\n self.ui.comboBox_num_attributes.blockSignals(True)\n self.ui.comboBox_char_attributes.blockSignals(True)\n self.ui.comboBox_num_attributes.clear()\n self.ui.comboBox_char_attributes.clear()\n self.ui.comboBox_char_attributes.addItems(list_char)\n self.ui.comboBox_num_attributes.addItems(list_num)\n self.ui.comboBox_num_attributes.blockSignals(False)\n self.ui.comboBox_char_attributes.blockSignals(False)","def getComboTerms(tuples):\t\t\t\n\t\t\t#return \"[{0}]\".format('; '.join([\"({0})\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\n\t\t\treturn \"{0}\".format('; '.join((\"{0}\".format(text[indices[0]:indices[1]]) for indices in tuples)))","def getEditChoices(self, currentText=''):\n format = globalref.options.strData('EditTimeFormat', True)\n now = GenTime().timeStr(format)\n choices = [(now, '(%s)' % _('now'))]\n for hr in (6, 9, 12, 15, 18, 21, 0):\n time = GenTime((hr, 0)).timeStr(format)\n choices.append((time, ''))\n return choices","def get_choices(cls):\n return cls.values.items()","def _find_options(self, inputfield):\r\n elements = inputfield.findall('./options/option')\r\n return [{\r\n 'id': index,\r\n 'description': option.text,\r\n 'choice': option.get('choice')\r\n } for (index, option) in enumerate(elements)]","def create_combo(self) -> typing.Iterable[Combo]:\n raise NotImplementedError()","def choices(cls):\n return tuple(item.as_tuple for item in list(cls))","def choices(self) -> list:\n return [self.mapper(item) for item in self]","def choices(cls):\n _choices = []\n for attr in _user_attributes(cls):\n val = getattr(cls, attr)\n setattr(cls, attr[1:], val[0])\n _choices.append((val[0], val[1]))\n setattr(cls, 'CHOICES', tuple(_choices))\n return cls","def extract_choices(element):\r\n\r\n choices = []\r\n\r\n for choice in element:\r\n if choice.tag != 'choice':\r\n raise Exception(\r\n \"[capa.inputtypes.extract_choices] Expected a <choice>\" +\r\n \"tag; got {0} instead\".format(choice.tag)\r\n )\r\n\r\n components = []\r\n choice_text = ''\r\n if choice.text is not None:\r\n choice_text += choice.text\r\n # Initialize our dict for the next content\r\n adder = {\r\n 'type': 'text',\r\n 'contents': choice_text,\r\n 'tail_text': '',\r\n 'value': ''\r\n }\r\n components.append(adder)\r\n\r\n for elt in choice:\r\n # for elements in the choice e.g. <text> <numtolerance_input>\r\n adder = {\r\n 'type': 'text',\r\n 'contents': '',\r\n 'tail_text': '',\r\n 'value': ''\r\n }\r\n tag_type = elt.tag\r\n # If the current `elt` is a <numtolerance_input> set the\r\n # `adder`type to 'numtolerance_input', and 'contents' to\r\n # the `elt`'s name.\r\n # Treat decoy_inputs and numtolerance_inputs the same in order\r\n # to prevent students from reading the Html and figuring out\r\n # which inputs are valid\r\n if tag_type in ('numtolerance_input', 'decoy_input'):\r\n # We set this to textinput, so that we get a textinput html\r\n # element.\r\n adder['type'] = 'textinput'\r\n adder['contents'] = elt.get('name')\r\n else:\r\n adder['contents'] = elt.text\r\n\r\n # Add any tail text(\"is the mean\" in the example)\r\n adder['tail_text'] = elt.tail if elt.tail else ''\r\n components.append(adder)\r\n\r\n # Add the tuple for the current choice to the list of choices\r\n choices.append((choice.get(\"name\"), components))\r\n return choices","def initDefaultChoices(self):\n choices = [entry[0] for entry in self.getEditChoices()]\n choices.insert(0, DateFormat.dateStampStrings[1])\n return choices","def initDefaultChoices(self):\n choices = [entry[0] for entry in self.getEditChoices()]\n choices.insert(0, TimeFormat.timeStampStrings[1])\n return choices","def combo_callback(self, eventobj):\n print(self.combo_input.get()) # print name\n print(self.combo_input.current()) # print index","def choices(self):\n # Needs to be implmented by subclass\n raise Exception(NotImplemented)","def initDefaultChoices(self):\n return [text for text in self.formatList]","def __init__(self,choices,caption='ListSelection',default=[],single=False,check=False,sort=False,*args,**kargs):\n InputDialog.__init__(self,caption=caption,items = [\n dict(name='input',value=default,itemtype='list',choices=choices,\n text='',single=single,check=check,sort=sort,*args,**kargs),\n ],)","def _build_dropdown(options):\n return [(x, x) for x in options]","def set_choices(self, index, choices):\n if len(choices) == 1:\n self._label(index)\n self._widgets[index][\"text\"] = str(choices[0])\n else:\n self._combo(index)\n self._widgets[index][\"values\"] = [str(t) for t in choices]\n width = max(len(str(t)) for t in choices)\n width = max(5, width)\n self._widgets[index][\"width\"] = width","def choices() -> List[str]:\n return [t.name.upper() for t in ConfigurationVariable]","def extract_choices(element):\r\n\r\n choices = []\r\n\r\n for choice in element:\r\n if choice.tag != 'choice':\r\n raise Exception(\r\n \"[capa.inputtypes.extract_choices] Expected a <choice> tag; got %s instead\"\r\n % choice.tag)\r\n choices.append((choice.get(\"name\"), stringify_children(choice)))\r\n return choices","def __init__(self, \n num_fld=1, \n lab_txt=[\"1\"], \n txt_fld=[\"1\"], \n title_txt=\"test\", \n comb_txt=[],\n comb_lab_txt=[], \n comb_num=0, \n root_x=50, \n root_y=50):\n super().__init__()\n self.geometry(f'+{root_x}+{root_y}') #head=y+20px\n self.str_in=[]\n self.title(title_txt)\n if comb_txt:\n self.name=[0]*num_fld\n ent=[0]*num_fld\n self.comb=[]\n self.act=[]\n lab=[0]*num_fld\n lab_comb=[0]*comb_num\n else:\n self.name=[0]*num_fld\n ent=[0]*num_fld\n lab=[0]*num_fld\n self.comb=[]\n self.act=[]\n for i in range(num_fld):\n self.name[i]=tk.StringVar()\n ent[i]=tk.Entry(self,textvariable=self.name[i])\n ent[i].insert(0, txt_fld[i])\n lab[i] = tk.Label(self,width=15, text=lab_txt[i])\n lab[i].pack()\n ent[i].pack()\n for i in range(comb_num):\n lab_comb[i]=tk.Label(self,width=35, text=comb_lab_txt[i])\n self.comb.append(ttk.Combobox(self, values=comb_txt))\n lab_comb[i].pack()\n self.comb[i].pack()\n self.comb[i].current(1)\n\n but_ac=tk.Button(self, text=\"Accept\", command=self.ins)\n but_ac.pack()\n self.mainloop","def input_choices_from_list(choices, text):\n no_courses_text = \"\"\"\n init will only list the courses you are enrolled in\n and there seem to be none.\n Either enrol in a course or add the course id as command line argument.\n \"\"\"\n if choices is None or len(choices) == 0:\n print(no_courses_text)\n raise SystemExit(1)\n\n digits = str(math.ceil(math.log10(len(choices))))\n format_str = '{:' + digits + 'd} {}'\n for n, c in enumerate(choices):\n print(format_str.format(n, c))\n try:\n return [int(c) for c in input(text).split()]\n except EOFError:\n return []","def valid_options(self):\n choices = self.choices()\n\n if not choices:\n return None\n\n return [opt[0] for opt in choices]","def combobox(self):\n return self._combo","def widgets_from_abbreviations(self, seq):\n result = []\n for name, abbrev, default in seq:\n widget = self.widget_from_abbrev(abbrev, default)\n if not (isinstance(widget, ValueWidget) or isinstance(widget, fixed)):\n if widget is None:\n raise ValueError(\"{!r} cannot be transformed to a widget\".format(abbrev))\n else:\n raise TypeError(\"{!r} is not a ValueWidget\".format(widget))\n if not widget.description:\n widget.description = name\n widget._kwarg = name\n result.append(widget)\n return result","def multiple_choice():\n\n return [\"MAR\", \"MAR\", \"NI\", \"NI\", \"MCAR\"]","def get_chosen_options(user):\n user_profile = user.get_profile()\n user_application = user_profile.application\n np = user_application.np\n ocl = eval(user_application.options_selected)\n chosen_options = []\n for oc in ocl:\n chosen_options.append(Option.objects.get(opt_code=int(oc))) \n return chosen_options","def build_comboboxes(activities, events):\n global comboboxes\n # For each activity set up a selector for an event\n\n for activity in activities:\n\n # Setup frame for better display in gui\n frame = Frame(main_window)\n frame.configure(background=\"gray30\")\n\n # Label the left column as activity in a model + \"beautify gui\"\n text = \"Activity name (model):\"\n Label(frame, text=text, bg=\"gray30\", fg=\"white\", padx=5).grid(column=0, row=0)\n Label(frame, text=activity, bg=\"gray30\", fg=\"white\").grid(column=0, row=1)\n\n # Set up the combobox for an event\n combo = Combobox(frame)\n combo['values'] = events\n\n # If activity is in events preselect the current one\n if activity in events:\n combo.current(events.index(activity))\n\n # Label the combobox and place label and box in frame\n Label(frame, text=\"Event name (log):\", bg=\"gray30\", fg=\"white\", padx=5).grid(column=1, row=0)\n combo.grid(column=1, row=1)\n\n # If the last activity in the graph is handled then do not write a separator\n if activity != activities[-1]:\n Separator(frame, orient=\"horizontal\").grid(row=2, columnspan=2, sticky=\"ew\", pady=10)\n\n comboboxes[activity] = combo\n # place the frame in the main_window\n frame.grid(column=0)","def get_generic_choices(model, key, allow_null=False):\n CHOICES = [('', '--------')] if allow_null else []\n for i in model.objects.all().values_list(key, flat=True).distinct():\n CHOICES.append((str(i), str(i)))\n CHOICES.sort()\n\n return CHOICES","def _combobox_choice(self, _=None):\n combobox_string = self.value_combobox.var.get()\n if combobox_string.startswith(\"Unknown: \"):\n value = int(combobox_string[len(\"Unknown: \"):])\n else:\n value = int(self.value_combobox.var.get().split(\" \")[0])\n self.master.change_field_value(self.field_name, value)","def widgets(parameter: Parameter):\n widgets = []\n for key in parameter.keys():\n textEdit = QTextEdit()\n textEdit.setText(key)\n widgets.append(textEdit)\n if isinstance(parameter[key], Enum):\n comboBox = MyQtEnumComboBox()\n comboBox.fillValues(type(parameter[key]))\n widgets.append(comboBox)\n elif isinstance(parameter[key], bool):\n comboBox = QComboBox()\n comboBox.addItems((\"False\", \"True\"))\n widgets.append(comboBox)\n else:\n textEdit = QTextEdit()\n textEdit.setText(str(parameter[key]))\n widgets.append(textEdit)\n for widget in widgets:\n widget.setFixedHeight(30)\n return widgets","def _getBrailleRegionsForComboBox(self, obj):\n\n self._debugGenerator(\"_getBrailleRegionsForComboBox\", obj)\n\n regions = []\n\n focusedRegionIndex = 0\n label = self._script.getDisplayedLabel(obj)\n if label and (len(label) > 0):\n regions.append(braille.Region(label + \" \"))\n focusedRegionIndex = 1\n\n # Check to see if the text is editable. If so, then we want\n # to show the text attributes (such as selection -- see bug\n # 496846 for more details).\n #\n textObj = None\n for child in obj:\n if child and child.getRole() == pyatspi.ROLE_TEXT:\n textObj = child\n if textObj and textObj.getState().contains(pyatspi.STATE_EDITABLE):\n textRegion = braille.Text(textObj)\n regions.append(textRegion)\n else:\n displayedText = self._script.getDisplayedText(obj)\n if displayedText:\n regions.append(braille.Region(displayedText))\n\n regions.append(braille.Region(\n \" \" + rolenames.getBrailleForRoleName(obj)))\n\n # Things may not have gone as expected above, so we'll do some\n # defensive programming to make sure we don't get an index out\n # of bounds.\n #\n if focusedRegionIndex >= len(regions):\n focusedRegionIndex = 0\n if len(regions) == 0:\n focusedRegion = None\n else:\n focusedRegion = regions[focusedRegionIndex]\n\n # [[[TODO: WDW - perhaps if a text area was created, we should\n # give focus to it.]]]\n #\n return [regions, focusedRegion]","def form_SelectChoiceCallableOptions(request):\n schema = schemaish.Structure()\n schema.add('mySelect', schemaish.Integer())\n def _():\n options = [(1,'a'),(2,'b'),(3,'c')]\n for option in options:\n yield option\n\n form = formish.Form(schema, 'form')\n form['mySelect'].widget = formish.SelectChoice(_)\n return form","def __init__(self, *args, **kwargs):\n super(TaggedContentItemForm, self).__init__(*args, **kwargs)\n wtf = Tag.objects.filter(group__system=False)\n wlist = [w for t, w in self.fields.items() if t.endswith(\"tags\")]\n choices = []\n for choice in wtf:\n choices.append((choice.id, str(choice)))\n [setattr(w, 'choices', choices) for w in wlist]","def _comboSlot(self, select):\n select = self.sender().itemText(select)\n if qt4:\n qs = str(self.sender().property(\"dom address\").toPyObject())\n else:\n qs = str(self.sender().property(\"dom address\"))\n item = QtXml.QDomElement()\n\n ind = qs.rfind('/')\n ids = qs[ind:]\n\n item = self.qhash[qs].elem.firstChildElement(\"Item\")\n while(item.isNull() is False):\n itemName = item.firstChildElement(\"Name\")\n if(str(itemName.text()).strip() != select):\n activ = item.firstChildElement(\"Activate\")\n while(activ.isNull() is False):\n s = str(activ.text()).strip() + ids\n h = self.qhash[s]\n widget_enabled = h.elem.attribute(\"Enabled\", \"True\")\n widget_visible = h.elem.attribute(\"Visible\", \"Unknown\")\n h.widget.setEnabled(False)\n if(widget_visible != \"Unknown\"):\n h.label.hide()\n h.widget.hide()\n activ = activ.nextSiblingElement(\"Activate\")\n item = item.nextSiblingElement(\"Item\")\n\n item = self.qhash[qs].elem.firstChildElement(\"Item\")\n while(item.isNull() is False):\n itemName = item.firstChildElement(\"Name\")\n if(str(itemName.text()).strip() == select):\n activ = item.firstChildElement(\"Activate\")\n while(activ.isNull() is False):\n s = str(activ.text()).strip() + ids\n h = self.qhash[s]\n h.widget.setEnabled(True)\n h.label.show()\n h.widget.show()\n activ = activ.nextSiblingElement(\"Activate\")\n item = item.nextSiblingElement(\"Item\")","def get_choices(self, cutoff=None):\n queryset = self.get_queryset()\n if queryset is None:\n # Ensure that field.choices returns something sensible\n # even when accessed with a read-only field.\n return {}\n\n if cutoff is not None:\n queryset = queryset[:cutoff]\n\n return OrderedDict(\n [\n (\n # This line below is modifed.\n item.pk,\n self.display_value(item),\n )\n for item in queryset\n ]\n )","def test_rendering_combobox(qtbot):\n layer = Image(np.random.rand(8, 8))\n qtctrl = QtImageControls(layer)\n qtbot.addWidget(qtctrl)\n combo = qtctrl.renderComboBox\n opts = {combo.itemText(i) for i in range(combo.count())}\n rendering_options = {\n 'translucent',\n 'additive',\n 'iso',\n 'mip',\n 'minip',\n 'attenuated_mip',\n 'average',\n }\n assert opts == rendering_options\n # programmatically updating rendering mode updates the combobox\n layer.rendering = 'iso'\n assert combo.findText('iso') == combo.currentIndex()","def get_choicesdata(self):\n selected_value = self.get_cleaned_value()\n choicesdata = []\n for value, label in self.get_choices_cached():\n is_selected = value == selected_value\n url = self.build_set_values_url(values=[value])\n choicesdata.append({\n 'url': url,\n 'label': label,\n 'is_selected': is_selected,\n 'dom_id': '{}_{}'.format(self.get_inputfield_dom_id(), value)\n })\n return choicesdata","def choices(self):\n return self.__class__.get_setting_choices(self.key, **self.get_kwargs())","def get_poll_choices(self, games: [Game]) -> [dict]:\n answer_texts = []\n for g in games:\n answer_texts.append(g.name + \" - \" + g.genre)\n answer_texts = sorted(answer_texts, key=str.lower)\n poll_choices = []\n for at in answer_texts:\n poll_choices.append({\"text\": at})\n return poll_choices","def __str__(self):\n return \"choice_text: \" + self.choice_text","def _parse_choices(self, text):\n choices = dict()\n\n matches = re.findall(self.choice_regex, text)\n for match in matches:\n # remove the brackets\n match = match.replace('[[', '')\n match = match.replace(']]', '')\n\n if '|' in match:\n # format is {text}|{node_id}, the text and node id are different\n text, node_id = match.split('|')\n choices[node_id] = text\n else:\n choices[match] = match\n\n return choices","def get_choices_new_protected():\n ret = []\n ret.append( (1, _(u'Nur Community-Mitglieder dürfen neue Beiträge leisten')) )\n ret.append( (-1, _(u'Offener Zugang')) )\n return ret","def comboBox(args: list, slot) -> QComboBox:\n comboBox = QComboBox()\n comboBox.addItems(args[0])\n comboBox.currentTextChanged.connect(slot)\n return comboBox","def choices(self):\n\n if self._choices == None:\n self._choices = [ExperimentChoice(self, choice_name) for choice_name in self.choice_names]\n\n return self._choices","def make_ddls( self, parent ):\n # ---- 0\n a_widget = ttk.Combobox( parent,\n width = self.arg_width,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n a_widget.bind( \"<<ComboboxSelected>>\", self.sync_ddl_0 )\n\n # AppGlobal.gui_style.style_combobox( a_widget )\n self.ddl_0_widget = a_widget\n\n # ---- 1\n a_widget = ttk.Combobox( parent,\n width = self.arg_width,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n self.ddl_1_widget = a_widget\n\n # ---- 2\n a_widget = ttk.Combobox( parent,\n width = self.arg_width + 5,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n # AppGlobal.gui_style.style_combobox( a_widget )\n self.ddl_2_widget = a_widget\n\n # self.ddl_widgets = [ self.ddl_0_widget, self.ddl_1_widget, self.ddl_2_widget, ]\n # print( self.ddl_widgets[ 0 ] == self.ddl_widgets[ 1 ])\n\n #self.load_ddl_0( )","def __str__(self):\n return self.choice_text","def as_choices():\n return (\n # Live is currently disabled as a choice\n # pending implementation\n (\"live\", \"Use working directory\"),\n (\"latest\", \"Use latest snapshot\"),\n (\"pinned\", \"Pinned to snapshot\"),\n )","def comboBoxes(self):\r\n # Cities Combo Button\r\n self.comboCities = QComboBox()\r\n self.comboCities.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: 15px;\r\n background-color : #A8DBC5;\r\n border: 1px solid white;\r\n\r\n \"\"\")\r\n self.comboCities.addItems(\r\n ['Girón', 'Piedecuesta', 'Floridablanca', 'Bucaramanga'])\r\n self.grid.addWidget(self.comboCities, 6, 1, 1, 2)\r\n self.comboCities.setCurrentText(\"Bucaramanga\")\r\n # Payment Combo Button\r\n self.comboPayment = QComboBox()\r\n self.comboPayment.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: 15px;\r\n background-color : #A8DBC5;\r\n border: 1px solid white;\r\n\r\n \"\"\")\r\n self.comboPayment.addItems(['Efectivo', 'Nequi'])\r\n self.grid.addWidget(self.comboPayment, 7, 1, 1, 2)","def get_choicesdata(self):\n # selected_value = self.get_cleaned_value()\n # choicesdata = []\n # found_selected_value = False\n # for value, label in self.get_choices():\n # is_selected = value == selected_value\n # if is_selected:\n # found_selected_value = True\n # url = self.build_set_values_url(values=[value])\n # choicesdata.append({\n # 'url': url,\n # 'label': label,\n # 'is_selected': is_selected\n # })\n choicesdata, found_selected_value = self.__make_choicesdata_list(\n choices=self.get_choices(),\n selected_value=self.get_cleaned_value())\n if not found_selected_value and len(choicesdata) > 0:\n selected_index = self.get_default_is_selected_index(choicesdata=choicesdata)\n choicesdata[selected_index]['is_selected'] = True\n return choicesdata","def _find_options(self):\r\n elements = self.xml.findall('./options/option')\r\n return [{\r\n 'id': index,\r\n 'description': option.text,\r\n 'choice': option.get('choice')\r\n } for (index, option) in enumerate(elements)]","def __str__(self):\n return gettext('One of %s') % self._get_choices_str()","def initDefaultChoices(self):\n return []","def list_selector_widget(members=None,\n preselect=None,\n entry=False,\n callback=None):\n store, i=generate_list_model(members,\n active_element=preselect)\n\n if entry:\n combobox=gtk.ComboBoxEntry(store, column=0)\n else:\n combobox=gtk.ComboBox(store)\n cell = gtk.CellRendererText()\n combobox.pack_start(cell, expand=True)\n combobox.add_attribute(cell, 'text', 0)\n combobox.add_attribute(cell, 'background', 2)\n\n combobox.set_active(-1)\n if i is None:\n i = store.get_iter_first()\n if i is not None:\n combobox.set_active_iter(i)\n\n if entry:\n def get_current_element(combo):\n try:\n return combo.get_model().get_value(combo.get_active_iter(), 1)\n except (TypeError, AttributeError):\n return unicode(combo.child.get_text())\n def set_current_element(combo, t):\n combo.child.set_text(t)\n else:\n def get_current_element(combo):\n if combo.get_active_iter() is not None:\n return combo.get_model().get_value(combo.get_active_iter(), 1)\n else:\n return None\n def set_current_element(combo, el):\n # Find the index of the element\n l=[ t[0] for t in enumerate(combo.get_model()) if t[1][1] == el ]\n if l:\n # The element is present.\n combo.set_active(l[0])\n else:\n combo.set_active_iter(combo.get_model().append( (unicode(el), el, None) ))\n\n # Bind the method to the combobox object\n combobox.get_current_element = get_current_element.__get__(combobox)\n combobox.set_current_element = set_current_element.__get__(combobox)\n\n if callback is not None:\n combobox.connect('changed', callback)\n\n return combobox","def occurrence_choices():\n OCCURRENCE_CHOICES = [('one_off', 'One Off'), ('weekly', 'Weekly'), ('fortnightly', 'Fortnightly')]\n occurrence = forms.ChoiceField(choices=OCCURRENCE_CHOICES, widget=forms.RadioSelect())\n return occurrence","def get_action_choices():\n from hardware.management.commands.gpio_buttons import Command\n import re\n pattern = re.compile(r'^on_(?P<name>\\w+)_press$')\n choices = []\n for member in dir(Command):\n match = pattern.match(member)\n if match:\n action = match.groupdict()['name']\n name = action.replace('_', ' ').title()\n choices.append((action, name))\n return choices","def DrawComboBox(*args, **kwargs):\n return _gdi_.RendererNative_DrawComboBox(*args, **kwargs)","def fill_combobox(self):\n sqlstring = \"SELECT first_name, last_name from employees WHERE enabled = 1 ORDER BY last_name ASC\"\n self.CB_employee.addItem(\"\")\n for employee in self.ms.c.execute(sqlstring):\n self.CB_employee.addItem(\" \".join((employee[0], employee[1])))\n sqlstring = \"SELECT first_name, last_name from employees WHERE enabled = 0 ORDER BY last_name ASC\"\n for employee in self.ms.c.execute(sqlstring):\n self.CB_employee.addItem(\" \".join((employee[0], employee[1])))","def _get_choices ( self, context, path = '' ):\n choices = []\n gdc = context.get_data_context\n for name in context.data_contexts:\n next_path = path_for( path, name )\n choices.append( TemplateChoice( choice_value = next_path ) )\n choices.extend( self._get_choices( gdc( name ), next_path ) )\n \n return choices","def get_context(self, name, value, attrs=None, choices=()):\n context = super(TriStateCheckboxSelectMultiple, self).get_context(\n name, value, attrs\n )\n\n choices = dict(it.chain(self.choices, choices))\n if value is None:\n value = dict.fromkeys(choices, False)\n else:\n value = dict(dict.fromkeys(choices, False).items() +\n value.items())\n\n context['values'] = [\n (choice, label, value[choice])\n for choice, label in choices.iteritems()\n ]\n\n return context","def get_text_type():\n opt = ['text', 'email', 'password']\n inp = option_menu(opt, 'Select text type:')\n\n # mark text type with option\n OPTIONS['text-type'] = opt[inp]\n\n # add option to collected list\n add_to_collected('text type', opt[inp])\n\n return","def set_dropdown_b_options(value):\n options_c = []\n if value=='C':\n options_c = [{'label': '1', 'value': '1'},\n {'label': '2', 'value': '2'}]\n if value == 'D':\n options_c = [{'label': '3', 'value': '3'},\n {'label': '4', 'value': '4'}]\n if value=='E':\n options_c = [{'label': '5', 'value': '5'},\n {'label': '6', 'value': '6'}]\n if value == 'F':\n options_c = [{'label': '7', 'value': '7'},\n {'label': '8', 'value': '8'}]\n return options_c","def objects_to_choices(queryset):\n res = []\n for elm in queryset:\n res.append((elm.pk, unicode(elm)))\n return res","def choice(text, choices, **kwargs):\n return click.prompt(click.style('> {}'.format(text), fg='blue', bold=True),\n type=click.Choice(choices),\n **kwargs)","def on_comboBox_enceinte_activated(self, index):\n nom_enceinte = self.comboBox_enceinte.currentText()\n marque = [x[2] for x in self.enceintes if x[1] == nom_enceinte][0]\n n_serie = [x[4] for x in self.enceintes if x[1] == nom_enceinte][0]\n model =[x[3] for x in self.enceintes if x[1] == nom_enceinte][0]\n \n \n self.lineEdit_marque.setText(marque)\n self.lineEdit_n_serie.setText(n_serie)\n self.lineEdit_model.setText(model)","def display_choose(self, text, choices):\n cur_index = 0\n key = None\n while key != 'KEY_NEWLINE':\n if key == 'KEY_UP':\n cur_index = max(cur_index - 1, 0)\n elif key == 'KEY_DOWN':\n cur_index = min(cur_index + 1, len(choices) - 1)\n self.stdscr.erase()\n for line in text:\n self.stdscr.addstr(f'{PADCHAR}{line}\\n')\n for index, value in enumerate(choices):\n self.stdscr.addstr('\\n')\n self.stdscr.addstr(PADCHAR)\n self.stdscr.addstr(value, color_pair(7 if index == cur_index else 1))\n self.stdscr.addstr(f'\\n\\n{PADCHAR}') \n key = self.get_key() \n return cur_index","def list_selector(title=None,\n text=None,\n members=None,\n controller=None,\n preselect=None,\n entry=False):\n combobox = list_selector_widget(members=members,\n preselect=preselect,\n entry=entry)\n\n d = gtk.Dialog(title=title,\n parent=None,\n flags=gtk.DIALOG_DESTROY_WITH_PARENT,\n buttons=( gtk.STOCK_OK, gtk.RESPONSE_OK,\n gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL ))\n\n if text is not None:\n l=gtk.Label(text)\n l.show()\n d.vbox.add(l)\n\n d.vbox.add(combobox)\n combobox.show_all()\n\n d.connect('key-press-event', dialog_keypressed_cb)\n\n d.show()\n center_on_mouse(d)\n res=d.run()\n retval=None\n if res == gtk.RESPONSE_OK:\n retval=combobox.get_current_element()\n d.destroy()\n return retval","def get_classes(self, code):\n \n select = v.Combobox(\n _metadata={'name':code}, \n items=self.items, \n v_model=None, \n dense=True,\n hide_details=True\n )\n \n select.observe(partial(self.store, code), 'v_model')\n \n return select","def getOptionsNames(self) -> List[unicode]:\n ...","def __init__(self, *args, **kwargs):\n super(ChoiceFieldType, self).__init__(*args, **kwargs)\n\n self.choices = self.get_field_info_key('choices')","def the_option_named(text: str) -> \"SelectByText\":\n return SelectByText(text)","def prepareAutoComplete(editor, text, charPos, lineStartCharPos,\r\n wikiDocument, settings):\r\n return []","def autocomplete_possibilities():\n try:\n # get data sent by client\n typed_input = request.args.get('q')\n print(' ')\n print('\\n------ getting autocomplete_possibilities ------')\n print(f\"recived: input:{typed_input}\")\n\n # call the google API\n results = gmaps.places_autocomplete(typed_input)\n data = [\n {'value': r['place_id'], 'text': r['description']}\n for r in results\n ]\n\n # Pass data to the front end\n print(f'returning: {data}')\n return jsonify(data)\n\n except Exception as e:\n print(\"AJAX excepted \" + str(e))\n return str(e)","def _validate_selects(text, response):\n answer_options = re.split(settings.MULTISELECT_DELIMITER_RE, str(text))\n choices = map(lambda choice: choice.lower(), response.event.choices)\n logger.debug('Question (%s) answer choices are: %s, given answers: %s' % (datatype, choices, answer_options))\n new_answers = copy(answer_options)\n for idx, opt in enumerate(answer_options):\n logger.debug('Trying to format (m)select answer: \"%s\"' % opt)\n try: \n #in the case that we accept numbers to indicate option selection\n opt_int = int(opt)\n if not (1 <= opt_int <= len(choices)): \n return text, 'Answer %s must be between 1 and %s' % (opt_int, len(choices))\n else:\n new_answers[idx] = str(opt_int)\n\n except ValueError: \n # in the case where we accept the actual text of the question\n logger.debug('Caught value error, trying to parse answer string choice of: %s' % choices)\n if opt.lower() not in choices:\n return text, 'Answer must be one of the choices'\n else:\n new_answers[idx] = str(choices.index(opt.lower()) + 1)\n return ' '.join(new_answers), None","def choice(choices=[], message=\"Pick something.\", title=None):\n return dialog(\n \"choice\",\n choices=choices,\n message=message,\n title=title,\n )","def complete_opt_format(self, text, *_):\n return [t + \" \" for t in FORMATTERS if t.startswith(text)]","def choices(self, cl):\n # TODO: Determine if non-static choices would be cleaner here.\n # Honestly, I tried a more generic version and it was even harder to\n # follow than this version.\n yield {\n 'selected': not (self.lookup_val_gte or self.lookup_val_lt),\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg_gte,\n self.lookup_kwarg_lt]),\n 'display': 'All'\n }\n\n goal = settings.FACEBOOK_CLICK_GOAL\n yield {\n 'selected': self.lookup_val_gte and not self.lookup_val_lt,\n 'query_string': cl.get_query_string({self.lookup_kwarg_gte: goal},\n [self.lookup_kwarg_lt]),\n 'display': 'Yes'\n }\n yield {\n 'selected': self.lookup_val_lt and not self.lookup_val_gte,\n 'query_string': cl.get_query_string({self.lookup_kwarg_lt: goal},\n [self.lookup_kwarg_gte]),\n 'display': 'No'\n }","def build_options(slot, snacks):\n \n if slot == 'Fast':\n return [\n {'text': 'Pizza', 'value': 'Pizza'},\n {'text': 'Fries', 'value': 'Fries'},\n {'text': 'Franky', 'value': 'Franky'},\n {'text': 'Burger', 'value': 'Burger'},\n {'text': 'Sandwich', 'value': 'Sandwich'}\n \n \n ]\n elif slot == 'drink':\n return [\n {'text': 'Coca-Cola', 'value': 'Coca-cola'},\n {'text': 'Appy', 'value': 'Appy'},\n \n {'text': 'Beer', 'value': 'Beer'},\n {'text': 'Frooti', 'value': 'Frooti'},\n {'text': 'Pepsi', 'value': 'Pepsi'}\n \n ]","def sortedChoices(self, inText):\n choices = self.splitText(inText)\n sortedChoices = [text for text in self.formatList if text in choices]\n if len(choices) == len(sortedChoices):\n return (sortedChoices, True)\n else:\n return (sortedChoices, False)","def _getChoices(self, acronym):\n # get matches from acronymDB\n matches = []\n if(acronym in self.acronymDB):\n matches += self.acronymDB[acronym]\n if(acronym[-1] == \"s\" and acronym[:-1] in self.acronymDB):\n matches += self.acronymDB[acronym]\n\n # create training data\n X_train, y_train = [], []\n for definition, articleID, ignored_var in matches:\n text = self.articleDB[articleID]\n X_train.append(\n ExpansionChoice(article_id=articleID, article_text=text))\n y_train.append(definition)\n\n # create y labels to group similar acronyms\n y_labels, labelToExpansion = self._processChoices(y_train)\n\n return X_train, y_labels, labelToExpansion","def multiple_choices(self, choices, response):\n for elem in self.method_order:\n if elem in choices:\n return [elem]\n raise NoData","def set_dropdown_b_options(value):\n options_b = []\n if value=='A':\n options_b = [{'label': 'C', 'value': 'C'},\n {'label': 'D', 'value': 'D'}]\n if value == 'B':\n options_b = [{'label': 'E', 'value': 'E'},\n {'label': 'F', 'value': 'F'}]\n return options_b","def choices(self, choices: Iterable[Tuple[str, str]]):\n try:\n iter(choices)\n except TypeError:\n raise TypeError(\"'choices' isn't a valid iterable\")\n\n apply_choices = []\n for i, (choice_id, choice_label) in enumerate(choices):\n apply_choices.append((str(choice_id), str(choice_label)))\n\n if len(apply_choices) < 2:\n raise ValueError(\"you need to specify at least two choices\")\n\n self._choices = apply_choices\n self.specific.refresh()\n self._selected = 0","def before_choose_candidate_listener(self, session, task):\n choices = [PromptChoice('d', 'eDit', self.importer_edit)]\n if task.candidates:\n choices.append(PromptChoice('c', 'edit Candidates',\n self.importer_edit_candidate))\n\n return choices","def _get_completion(self, original_text, remaining_text, options):\n if self.should_hide_completions(original_text=original_text,\n remaining_text=remaining_text,\n allowed_suffixes=(\" \", \".\")):\n return []\n\n return [(option, len(remaining_text)) for option in options\n if option.startswith(remaining_text) and not option.startswith(\"_\")]"],"string":"[\n \"def getEditChoices(self, currentText=''):\\n return [(text, '') for text in self.formatList]\",\n \"def get_choices(self):\\n raise NotImplementedError()\",\n \"def get_choices(self):\\n raise NotImplementedError()\",\n \"def choices(cls):\\n # return list(map(tuple, cls.__members__.items()))\\n return [(int(code), name) for name, code in cls.__members__.items()]\",\n \"def choices(self, typename, value_field='description'):\\n rows = self.type(typename).values('id', value_field)\\n return [(r['id'], r[value_field]) for r in rows]\",\n \"def get_choices(self, instance):\\n if instance.type == BaseParameter.CHOICE_TYPE:\\n return [\\n x.value\\n for x in instance.get_typed_parameter().get_available_choices()\\n ]\\n else:\\n return None\",\n \"def get_choices_for_model_field(cls):\\n return [c[0:2] for c in cls.attr_choices]\",\n \"def initDefaultChoices(self):\\n return [entry[0] for entry in self.getEditChoices()]\",\n \"def select_combo_text(cb, text, index=0):\\n i = 0\\n for n in cb.get_model():\\n if n[index] == text:\\n break\\n i += 1\\n cb.set_active(i)\",\n \"def application_command_autocomplete_choice_builder(name, value):\\n return {\\n 'name': name,\\n 'value': value,\\n }\",\n \"def choices(self) -> list:\\n return [self.mapper(i) for i in self.keys()]\",\n \"def choices(self):\\n return tuple(self._choices)\",\n \"def iter_choices(self):\\n\\n for pk, obj in self._get_object_list():\\n if hasattr(obj, self.id):\\n selected = getattr(obj, self.id)\\n else:\\n selected = obj in self.data\\n\\n yield (pk, self.get_label(obj), selected)\",\n \"def _get_choices_str(self):\\n return ', '.join(\\n '\\\"%s\\\"' % choice\\n for choice in self.choices\\n )\",\n \"def getEditChoices(self, currentText=''):\\n format = globalref.options.strData('EditDateFormat', True)\\n today = GenDate().dateStr(format)\\n yesterday = (GenDate() - 1).dateStr(format)\\n tomorrow = (GenDate() + 1).dateStr(format)\\n return [(today, '(%s)' % _('today')),\\n (yesterday, '(%s)' % _('yesterday')),\\n (tomorrow, '(%s)' % _('tomorrow'))]\",\n \"def fill_combobox_attributes(self):\\n\\n list_char = [\\\"\\\"]\\n list_num = [\\\"\\\"]\\n if self.ui.radioButton_file.isChecked():\\n for a in self.attributes:\\n if a['caseOrFile'] == \\\"file\\\" and a['valuetype'] == \\\"character\\\":\\n list_char.append(a['name'])\\n if a['caseOrFile'] == \\\"file\\\" and a['valuetype'] == \\\"numeric\\\":\\n list_num.append(a['name'])\\n else:\\n for a in self.attributes:\\n if a['caseOrFile'] == \\\"case\\\" and a['valuetype'] == \\\"character\\\":\\n list_char.append(a['name'])\\n if a['caseOrFile'] == \\\"case\\\" and a['valuetype'] == \\\"numeric\\\":\\n list_num.append(a['name'])\\n self.ui.comboBox_num_attributes.blockSignals(True)\\n self.ui.comboBox_char_attributes.blockSignals(True)\\n self.ui.comboBox_num_attributes.clear()\\n self.ui.comboBox_char_attributes.clear()\\n self.ui.comboBox_char_attributes.addItems(list_char)\\n self.ui.comboBox_num_attributes.addItems(list_num)\\n self.ui.comboBox_num_attributes.blockSignals(False)\\n self.ui.comboBox_char_attributes.blockSignals(False)\",\n \"def getComboTerms(tuples):\\t\\t\\t\\n\\t\\t\\t#return \\\"[{0}]\\\".format('; '.join([\\\"({0})\\\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\\n\\t\\t\\treturn \\\"{0}\\\".format('; '.join((\\\"{0}\\\".format(text[indices[0]:indices[1]]) for indices in tuples)))\",\n \"def getEditChoices(self, currentText=''):\\n format = globalref.options.strData('EditTimeFormat', True)\\n now = GenTime().timeStr(format)\\n choices = [(now, '(%s)' % _('now'))]\\n for hr in (6, 9, 12, 15, 18, 21, 0):\\n time = GenTime((hr, 0)).timeStr(format)\\n choices.append((time, ''))\\n return choices\",\n \"def get_choices(cls):\\n return cls.values.items()\",\n \"def _find_options(self, inputfield):\\r\\n elements = inputfield.findall('./options/option')\\r\\n return [{\\r\\n 'id': index,\\r\\n 'description': option.text,\\r\\n 'choice': option.get('choice')\\r\\n } for (index, option) in enumerate(elements)]\",\n \"def create_combo(self) -> typing.Iterable[Combo]:\\n raise NotImplementedError()\",\n \"def choices(cls):\\n return tuple(item.as_tuple for item in list(cls))\",\n \"def choices(self) -> list:\\n return [self.mapper(item) for item in self]\",\n \"def choices(cls):\\n _choices = []\\n for attr in _user_attributes(cls):\\n val = getattr(cls, attr)\\n setattr(cls, attr[1:], val[0])\\n _choices.append((val[0], val[1]))\\n setattr(cls, 'CHOICES', tuple(_choices))\\n return cls\",\n \"def extract_choices(element):\\r\\n\\r\\n choices = []\\r\\n\\r\\n for choice in element:\\r\\n if choice.tag != 'choice':\\r\\n raise Exception(\\r\\n \\\"[capa.inputtypes.extract_choices] Expected a <choice>\\\" +\\r\\n \\\"tag; got {0} instead\\\".format(choice.tag)\\r\\n )\\r\\n\\r\\n components = []\\r\\n choice_text = ''\\r\\n if choice.text is not None:\\r\\n choice_text += choice.text\\r\\n # Initialize our dict for the next content\\r\\n adder = {\\r\\n 'type': 'text',\\r\\n 'contents': choice_text,\\r\\n 'tail_text': '',\\r\\n 'value': ''\\r\\n }\\r\\n components.append(adder)\\r\\n\\r\\n for elt in choice:\\r\\n # for elements in the choice e.g. <text> <numtolerance_input>\\r\\n adder = {\\r\\n 'type': 'text',\\r\\n 'contents': '',\\r\\n 'tail_text': '',\\r\\n 'value': ''\\r\\n }\\r\\n tag_type = elt.tag\\r\\n # If the current `elt` is a <numtolerance_input> set the\\r\\n # `adder`type to 'numtolerance_input', and 'contents' to\\r\\n # the `elt`'s name.\\r\\n # Treat decoy_inputs and numtolerance_inputs the same in order\\r\\n # to prevent students from reading the Html and figuring out\\r\\n # which inputs are valid\\r\\n if tag_type in ('numtolerance_input', 'decoy_input'):\\r\\n # We set this to textinput, so that we get a textinput html\\r\\n # element.\\r\\n adder['type'] = 'textinput'\\r\\n adder['contents'] = elt.get('name')\\r\\n else:\\r\\n adder['contents'] = elt.text\\r\\n\\r\\n # Add any tail text(\\\"is the mean\\\" in the example)\\r\\n adder['tail_text'] = elt.tail if elt.tail else ''\\r\\n components.append(adder)\\r\\n\\r\\n # Add the tuple for the current choice to the list of choices\\r\\n choices.append((choice.get(\\\"name\\\"), components))\\r\\n return choices\",\n \"def initDefaultChoices(self):\\n choices = [entry[0] for entry in self.getEditChoices()]\\n choices.insert(0, DateFormat.dateStampStrings[1])\\n return choices\",\n \"def initDefaultChoices(self):\\n choices = [entry[0] for entry in self.getEditChoices()]\\n choices.insert(0, TimeFormat.timeStampStrings[1])\\n return choices\",\n \"def combo_callback(self, eventobj):\\n print(self.combo_input.get()) # print name\\n print(self.combo_input.current()) # print index\",\n \"def choices(self):\\n # Needs to be implmented by subclass\\n raise Exception(NotImplemented)\",\n \"def initDefaultChoices(self):\\n return [text for text in self.formatList]\",\n \"def __init__(self,choices,caption='ListSelection',default=[],single=False,check=False,sort=False,*args,**kargs):\\n InputDialog.__init__(self,caption=caption,items = [\\n dict(name='input',value=default,itemtype='list',choices=choices,\\n text='',single=single,check=check,sort=sort,*args,**kargs),\\n ],)\",\n \"def _build_dropdown(options):\\n return [(x, x) for x in options]\",\n \"def set_choices(self, index, choices):\\n if len(choices) == 1:\\n self._label(index)\\n self._widgets[index][\\\"text\\\"] = str(choices[0])\\n else:\\n self._combo(index)\\n self._widgets[index][\\\"values\\\"] = [str(t) for t in choices]\\n width = max(len(str(t)) for t in choices)\\n width = max(5, width)\\n self._widgets[index][\\\"width\\\"] = width\",\n \"def choices() -> List[str]:\\n return [t.name.upper() for t in ConfigurationVariable]\",\n \"def extract_choices(element):\\r\\n\\r\\n choices = []\\r\\n\\r\\n for choice in element:\\r\\n if choice.tag != 'choice':\\r\\n raise Exception(\\r\\n \\\"[capa.inputtypes.extract_choices] Expected a <choice> tag; got %s instead\\\"\\r\\n % choice.tag)\\r\\n choices.append((choice.get(\\\"name\\\"), stringify_children(choice)))\\r\\n return choices\",\n \"def __init__(self, \\n num_fld=1, \\n lab_txt=[\\\"1\\\"], \\n txt_fld=[\\\"1\\\"], \\n title_txt=\\\"test\\\", \\n comb_txt=[],\\n comb_lab_txt=[], \\n comb_num=0, \\n root_x=50, \\n root_y=50):\\n super().__init__()\\n self.geometry(f'+{root_x}+{root_y}') #head=y+20px\\n self.str_in=[]\\n self.title(title_txt)\\n if comb_txt:\\n self.name=[0]*num_fld\\n ent=[0]*num_fld\\n self.comb=[]\\n self.act=[]\\n lab=[0]*num_fld\\n lab_comb=[0]*comb_num\\n else:\\n self.name=[0]*num_fld\\n ent=[0]*num_fld\\n lab=[0]*num_fld\\n self.comb=[]\\n self.act=[]\\n for i in range(num_fld):\\n self.name[i]=tk.StringVar()\\n ent[i]=tk.Entry(self,textvariable=self.name[i])\\n ent[i].insert(0, txt_fld[i])\\n lab[i] = tk.Label(self,width=15, text=lab_txt[i])\\n lab[i].pack()\\n ent[i].pack()\\n for i in range(comb_num):\\n lab_comb[i]=tk.Label(self,width=35, text=comb_lab_txt[i])\\n self.comb.append(ttk.Combobox(self, values=comb_txt))\\n lab_comb[i].pack()\\n self.comb[i].pack()\\n self.comb[i].current(1)\\n\\n but_ac=tk.Button(self, text=\\\"Accept\\\", command=self.ins)\\n but_ac.pack()\\n self.mainloop\",\n \"def input_choices_from_list(choices, text):\\n no_courses_text = \\\"\\\"\\\"\\n init will only list the courses you are enrolled in\\n and there seem to be none.\\n Either enrol in a course or add the course id as command line argument.\\n \\\"\\\"\\\"\\n if choices is None or len(choices) == 0:\\n print(no_courses_text)\\n raise SystemExit(1)\\n\\n digits = str(math.ceil(math.log10(len(choices))))\\n format_str = '{:' + digits + 'd} {}'\\n for n, c in enumerate(choices):\\n print(format_str.format(n, c))\\n try:\\n return [int(c) for c in input(text).split()]\\n except EOFError:\\n return []\",\n \"def valid_options(self):\\n choices = self.choices()\\n\\n if not choices:\\n return None\\n\\n return [opt[0] for opt in choices]\",\n \"def combobox(self):\\n return self._combo\",\n \"def widgets_from_abbreviations(self, seq):\\n result = []\\n for name, abbrev, default in seq:\\n widget = self.widget_from_abbrev(abbrev, default)\\n if not (isinstance(widget, ValueWidget) or isinstance(widget, fixed)):\\n if widget is None:\\n raise ValueError(\\\"{!r} cannot be transformed to a widget\\\".format(abbrev))\\n else:\\n raise TypeError(\\\"{!r} is not a ValueWidget\\\".format(widget))\\n if not widget.description:\\n widget.description = name\\n widget._kwarg = name\\n result.append(widget)\\n return result\",\n \"def multiple_choice():\\n\\n return [\\\"MAR\\\", \\\"MAR\\\", \\\"NI\\\", \\\"NI\\\", \\\"MCAR\\\"]\",\n \"def get_chosen_options(user):\\n user_profile = user.get_profile()\\n user_application = user_profile.application\\n np = user_application.np\\n ocl = eval(user_application.options_selected)\\n chosen_options = []\\n for oc in ocl:\\n chosen_options.append(Option.objects.get(opt_code=int(oc))) \\n return chosen_options\",\n \"def build_comboboxes(activities, events):\\n global comboboxes\\n # For each activity set up a selector for an event\\n\\n for activity in activities:\\n\\n # Setup frame for better display in gui\\n frame = Frame(main_window)\\n frame.configure(background=\\\"gray30\\\")\\n\\n # Label the left column as activity in a model + \\\"beautify gui\\\"\\n text = \\\"Activity name (model):\\\"\\n Label(frame, text=text, bg=\\\"gray30\\\", fg=\\\"white\\\", padx=5).grid(column=0, row=0)\\n Label(frame, text=activity, bg=\\\"gray30\\\", fg=\\\"white\\\").grid(column=0, row=1)\\n\\n # Set up the combobox for an event\\n combo = Combobox(frame)\\n combo['values'] = events\\n\\n # If activity is in events preselect the current one\\n if activity in events:\\n combo.current(events.index(activity))\\n\\n # Label the combobox and place label and box in frame\\n Label(frame, text=\\\"Event name (log):\\\", bg=\\\"gray30\\\", fg=\\\"white\\\", padx=5).grid(column=1, row=0)\\n combo.grid(column=1, row=1)\\n\\n # If the last activity in the graph is handled then do not write a separator\\n if activity != activities[-1]:\\n Separator(frame, orient=\\\"horizontal\\\").grid(row=2, columnspan=2, sticky=\\\"ew\\\", pady=10)\\n\\n comboboxes[activity] = combo\\n # place the frame in the main_window\\n frame.grid(column=0)\",\n \"def get_generic_choices(model, key, allow_null=False):\\n CHOICES = [('', '--------')] if allow_null else []\\n for i in model.objects.all().values_list(key, flat=True).distinct():\\n CHOICES.append((str(i), str(i)))\\n CHOICES.sort()\\n\\n return CHOICES\",\n \"def _combobox_choice(self, _=None):\\n combobox_string = self.value_combobox.var.get()\\n if combobox_string.startswith(\\\"Unknown: \\\"):\\n value = int(combobox_string[len(\\\"Unknown: \\\"):])\\n else:\\n value = int(self.value_combobox.var.get().split(\\\" \\\")[0])\\n self.master.change_field_value(self.field_name, value)\",\n \"def widgets(parameter: Parameter):\\n widgets = []\\n for key in parameter.keys():\\n textEdit = QTextEdit()\\n textEdit.setText(key)\\n widgets.append(textEdit)\\n if isinstance(parameter[key], Enum):\\n comboBox = MyQtEnumComboBox()\\n comboBox.fillValues(type(parameter[key]))\\n widgets.append(comboBox)\\n elif isinstance(parameter[key], bool):\\n comboBox = QComboBox()\\n comboBox.addItems((\\\"False\\\", \\\"True\\\"))\\n widgets.append(comboBox)\\n else:\\n textEdit = QTextEdit()\\n textEdit.setText(str(parameter[key]))\\n widgets.append(textEdit)\\n for widget in widgets:\\n widget.setFixedHeight(30)\\n return widgets\",\n \"def _getBrailleRegionsForComboBox(self, obj):\\n\\n self._debugGenerator(\\\"_getBrailleRegionsForComboBox\\\", obj)\\n\\n regions = []\\n\\n focusedRegionIndex = 0\\n label = self._script.getDisplayedLabel(obj)\\n if label and (len(label) > 0):\\n regions.append(braille.Region(label + \\\" \\\"))\\n focusedRegionIndex = 1\\n\\n # Check to see if the text is editable. If so, then we want\\n # to show the text attributes (such as selection -- see bug\\n # 496846 for more details).\\n #\\n textObj = None\\n for child in obj:\\n if child and child.getRole() == pyatspi.ROLE_TEXT:\\n textObj = child\\n if textObj and textObj.getState().contains(pyatspi.STATE_EDITABLE):\\n textRegion = braille.Text(textObj)\\n regions.append(textRegion)\\n else:\\n displayedText = self._script.getDisplayedText(obj)\\n if displayedText:\\n regions.append(braille.Region(displayedText))\\n\\n regions.append(braille.Region(\\n \\\" \\\" + rolenames.getBrailleForRoleName(obj)))\\n\\n # Things may not have gone as expected above, so we'll do some\\n # defensive programming to make sure we don't get an index out\\n # of bounds.\\n #\\n if focusedRegionIndex >= len(regions):\\n focusedRegionIndex = 0\\n if len(regions) == 0:\\n focusedRegion = None\\n else:\\n focusedRegion = regions[focusedRegionIndex]\\n\\n # [[[TODO: WDW - perhaps if a text area was created, we should\\n # give focus to it.]]]\\n #\\n return [regions, focusedRegion]\",\n \"def form_SelectChoiceCallableOptions(request):\\n schema = schemaish.Structure()\\n schema.add('mySelect', schemaish.Integer())\\n def _():\\n options = [(1,'a'),(2,'b'),(3,'c')]\\n for option in options:\\n yield option\\n\\n form = formish.Form(schema, 'form')\\n form['mySelect'].widget = formish.SelectChoice(_)\\n return form\",\n \"def __init__(self, *args, **kwargs):\\n super(TaggedContentItemForm, self).__init__(*args, **kwargs)\\n wtf = Tag.objects.filter(group__system=False)\\n wlist = [w for t, w in self.fields.items() if t.endswith(\\\"tags\\\")]\\n choices = []\\n for choice in wtf:\\n choices.append((choice.id, str(choice)))\\n [setattr(w, 'choices', choices) for w in wlist]\",\n \"def _comboSlot(self, select):\\n select = self.sender().itemText(select)\\n if qt4:\\n qs = str(self.sender().property(\\\"dom address\\\").toPyObject())\\n else:\\n qs = str(self.sender().property(\\\"dom address\\\"))\\n item = QtXml.QDomElement()\\n\\n ind = qs.rfind('/')\\n ids = qs[ind:]\\n\\n item = self.qhash[qs].elem.firstChildElement(\\\"Item\\\")\\n while(item.isNull() is False):\\n itemName = item.firstChildElement(\\\"Name\\\")\\n if(str(itemName.text()).strip() != select):\\n activ = item.firstChildElement(\\\"Activate\\\")\\n while(activ.isNull() is False):\\n s = str(activ.text()).strip() + ids\\n h = self.qhash[s]\\n widget_enabled = h.elem.attribute(\\\"Enabled\\\", \\\"True\\\")\\n widget_visible = h.elem.attribute(\\\"Visible\\\", \\\"Unknown\\\")\\n h.widget.setEnabled(False)\\n if(widget_visible != \\\"Unknown\\\"):\\n h.label.hide()\\n h.widget.hide()\\n activ = activ.nextSiblingElement(\\\"Activate\\\")\\n item = item.nextSiblingElement(\\\"Item\\\")\\n\\n item = self.qhash[qs].elem.firstChildElement(\\\"Item\\\")\\n while(item.isNull() is False):\\n itemName = item.firstChildElement(\\\"Name\\\")\\n if(str(itemName.text()).strip() == select):\\n activ = item.firstChildElement(\\\"Activate\\\")\\n while(activ.isNull() is False):\\n s = str(activ.text()).strip() + ids\\n h = self.qhash[s]\\n h.widget.setEnabled(True)\\n h.label.show()\\n h.widget.show()\\n activ = activ.nextSiblingElement(\\\"Activate\\\")\\n item = item.nextSiblingElement(\\\"Item\\\")\",\n \"def get_choices(self, cutoff=None):\\n queryset = self.get_queryset()\\n if queryset is None:\\n # Ensure that field.choices returns something sensible\\n # even when accessed with a read-only field.\\n return {}\\n\\n if cutoff is not None:\\n queryset = queryset[:cutoff]\\n\\n return OrderedDict(\\n [\\n (\\n # This line below is modifed.\\n item.pk,\\n self.display_value(item),\\n )\\n for item in queryset\\n ]\\n )\",\n \"def test_rendering_combobox(qtbot):\\n layer = Image(np.random.rand(8, 8))\\n qtctrl = QtImageControls(layer)\\n qtbot.addWidget(qtctrl)\\n combo = qtctrl.renderComboBox\\n opts = {combo.itemText(i) for i in range(combo.count())}\\n rendering_options = {\\n 'translucent',\\n 'additive',\\n 'iso',\\n 'mip',\\n 'minip',\\n 'attenuated_mip',\\n 'average',\\n }\\n assert opts == rendering_options\\n # programmatically updating rendering mode updates the combobox\\n layer.rendering = 'iso'\\n assert combo.findText('iso') == combo.currentIndex()\",\n \"def get_choicesdata(self):\\n selected_value = self.get_cleaned_value()\\n choicesdata = []\\n for value, label in self.get_choices_cached():\\n is_selected = value == selected_value\\n url = self.build_set_values_url(values=[value])\\n choicesdata.append({\\n 'url': url,\\n 'label': label,\\n 'is_selected': is_selected,\\n 'dom_id': '{}_{}'.format(self.get_inputfield_dom_id(), value)\\n })\\n return choicesdata\",\n \"def choices(self):\\n return self.__class__.get_setting_choices(self.key, **self.get_kwargs())\",\n \"def get_poll_choices(self, games: [Game]) -> [dict]:\\n answer_texts = []\\n for g in games:\\n answer_texts.append(g.name + \\\" - \\\" + g.genre)\\n answer_texts = sorted(answer_texts, key=str.lower)\\n poll_choices = []\\n for at in answer_texts:\\n poll_choices.append({\\\"text\\\": at})\\n return poll_choices\",\n \"def __str__(self):\\n return \\\"choice_text: \\\" + self.choice_text\",\n \"def _parse_choices(self, text):\\n choices = dict()\\n\\n matches = re.findall(self.choice_regex, text)\\n for match in matches:\\n # remove the brackets\\n match = match.replace('[[', '')\\n match = match.replace(']]', '')\\n\\n if '|' in match:\\n # format is {text}|{node_id}, the text and node id are different\\n text, node_id = match.split('|')\\n choices[node_id] = text\\n else:\\n choices[match] = match\\n\\n return choices\",\n \"def get_choices_new_protected():\\n ret = []\\n ret.append( (1, _(u'Nur Community-Mitglieder dürfen neue Beiträge leisten')) )\\n ret.append( (-1, _(u'Offener Zugang')) )\\n return ret\",\n \"def comboBox(args: list, slot) -> QComboBox:\\n comboBox = QComboBox()\\n comboBox.addItems(args[0])\\n comboBox.currentTextChanged.connect(slot)\\n return comboBox\",\n \"def choices(self):\\n\\n if self._choices == None:\\n self._choices = [ExperimentChoice(self, choice_name) for choice_name in self.choice_names]\\n\\n return self._choices\",\n \"def make_ddls( self, parent ):\\n # ---- 0\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n a_widget.bind( \\\"<<ComboboxSelected>>\\\", self.sync_ddl_0 )\\n\\n # AppGlobal.gui_style.style_combobox( a_widget )\\n self.ddl_0_widget = a_widget\\n\\n # ---- 1\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n self.ddl_1_widget = a_widget\\n\\n # ---- 2\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width + 5,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n # AppGlobal.gui_style.style_combobox( a_widget )\\n self.ddl_2_widget = a_widget\\n\\n # self.ddl_widgets = [ self.ddl_0_widget, self.ddl_1_widget, self.ddl_2_widget, ]\\n # print( self.ddl_widgets[ 0 ] == self.ddl_widgets[ 1 ])\\n\\n #self.load_ddl_0( )\",\n \"def __str__(self):\\n return self.choice_text\",\n \"def as_choices():\\n return (\\n # Live is currently disabled as a choice\\n # pending implementation\\n (\\\"live\\\", \\\"Use working directory\\\"),\\n (\\\"latest\\\", \\\"Use latest snapshot\\\"),\\n (\\\"pinned\\\", \\\"Pinned to snapshot\\\"),\\n )\",\n \"def comboBoxes(self):\\r\\n # Cities Combo Button\\r\\n self.comboCities = QComboBox()\\r\\n self.comboCities.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: 15px;\\r\\n background-color : #A8DBC5;\\r\\n border: 1px solid white;\\r\\n\\r\\n \\\"\\\"\\\")\\r\\n self.comboCities.addItems(\\r\\n ['Girón', 'Piedecuesta', 'Floridablanca', 'Bucaramanga'])\\r\\n self.grid.addWidget(self.comboCities, 6, 1, 1, 2)\\r\\n self.comboCities.setCurrentText(\\\"Bucaramanga\\\")\\r\\n # Payment Combo Button\\r\\n self.comboPayment = QComboBox()\\r\\n self.comboPayment.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: 15px;\\r\\n background-color : #A8DBC5;\\r\\n border: 1px solid white;\\r\\n\\r\\n \\\"\\\"\\\")\\r\\n self.comboPayment.addItems(['Efectivo', 'Nequi'])\\r\\n self.grid.addWidget(self.comboPayment, 7, 1, 1, 2)\",\n \"def get_choicesdata(self):\\n # selected_value = self.get_cleaned_value()\\n # choicesdata = []\\n # found_selected_value = False\\n # for value, label in self.get_choices():\\n # is_selected = value == selected_value\\n # if is_selected:\\n # found_selected_value = True\\n # url = self.build_set_values_url(values=[value])\\n # choicesdata.append({\\n # 'url': url,\\n # 'label': label,\\n # 'is_selected': is_selected\\n # })\\n choicesdata, found_selected_value = self.__make_choicesdata_list(\\n choices=self.get_choices(),\\n selected_value=self.get_cleaned_value())\\n if not found_selected_value and len(choicesdata) > 0:\\n selected_index = self.get_default_is_selected_index(choicesdata=choicesdata)\\n choicesdata[selected_index]['is_selected'] = True\\n return choicesdata\",\n \"def _find_options(self):\\r\\n elements = self.xml.findall('./options/option')\\r\\n return [{\\r\\n 'id': index,\\r\\n 'description': option.text,\\r\\n 'choice': option.get('choice')\\r\\n } for (index, option) in enumerate(elements)]\",\n \"def __str__(self):\\n return gettext('One of %s') % self._get_choices_str()\",\n \"def initDefaultChoices(self):\\n return []\",\n \"def list_selector_widget(members=None,\\n preselect=None,\\n entry=False,\\n callback=None):\\n store, i=generate_list_model(members,\\n active_element=preselect)\\n\\n if entry:\\n combobox=gtk.ComboBoxEntry(store, column=0)\\n else:\\n combobox=gtk.ComboBox(store)\\n cell = gtk.CellRendererText()\\n combobox.pack_start(cell, expand=True)\\n combobox.add_attribute(cell, 'text', 0)\\n combobox.add_attribute(cell, 'background', 2)\\n\\n combobox.set_active(-1)\\n if i is None:\\n i = store.get_iter_first()\\n if i is not None:\\n combobox.set_active_iter(i)\\n\\n if entry:\\n def get_current_element(combo):\\n try:\\n return combo.get_model().get_value(combo.get_active_iter(), 1)\\n except (TypeError, AttributeError):\\n return unicode(combo.child.get_text())\\n def set_current_element(combo, t):\\n combo.child.set_text(t)\\n else:\\n def get_current_element(combo):\\n if combo.get_active_iter() is not None:\\n return combo.get_model().get_value(combo.get_active_iter(), 1)\\n else:\\n return None\\n def set_current_element(combo, el):\\n # Find the index of the element\\n l=[ t[0] for t in enumerate(combo.get_model()) if t[1][1] == el ]\\n if l:\\n # The element is present.\\n combo.set_active(l[0])\\n else:\\n combo.set_active_iter(combo.get_model().append( (unicode(el), el, None) ))\\n\\n # Bind the method to the combobox object\\n combobox.get_current_element = get_current_element.__get__(combobox)\\n combobox.set_current_element = set_current_element.__get__(combobox)\\n\\n if callback is not None:\\n combobox.connect('changed', callback)\\n\\n return combobox\",\n \"def occurrence_choices():\\n OCCURRENCE_CHOICES = [('one_off', 'One Off'), ('weekly', 'Weekly'), ('fortnightly', 'Fortnightly')]\\n occurrence = forms.ChoiceField(choices=OCCURRENCE_CHOICES, widget=forms.RadioSelect())\\n return occurrence\",\n \"def get_action_choices():\\n from hardware.management.commands.gpio_buttons import Command\\n import re\\n pattern = re.compile(r'^on_(?P<name>\\\\w+)_press$')\\n choices = []\\n for member in dir(Command):\\n match = pattern.match(member)\\n if match:\\n action = match.groupdict()['name']\\n name = action.replace('_', ' ').title()\\n choices.append((action, name))\\n return choices\",\n \"def DrawComboBox(*args, **kwargs):\\n return _gdi_.RendererNative_DrawComboBox(*args, **kwargs)\",\n \"def fill_combobox(self):\\n sqlstring = \\\"SELECT first_name, last_name from employees WHERE enabled = 1 ORDER BY last_name ASC\\\"\\n self.CB_employee.addItem(\\\"\\\")\\n for employee in self.ms.c.execute(sqlstring):\\n self.CB_employee.addItem(\\\" \\\".join((employee[0], employee[1])))\\n sqlstring = \\\"SELECT first_name, last_name from employees WHERE enabled = 0 ORDER BY last_name ASC\\\"\\n for employee in self.ms.c.execute(sqlstring):\\n self.CB_employee.addItem(\\\" \\\".join((employee[0], employee[1])))\",\n \"def _get_choices ( self, context, path = '' ):\\n choices = []\\n gdc = context.get_data_context\\n for name in context.data_contexts:\\n next_path = path_for( path, name )\\n choices.append( TemplateChoice( choice_value = next_path ) )\\n choices.extend( self._get_choices( gdc( name ), next_path ) )\\n \\n return choices\",\n \"def get_context(self, name, value, attrs=None, choices=()):\\n context = super(TriStateCheckboxSelectMultiple, self).get_context(\\n name, value, attrs\\n )\\n\\n choices = dict(it.chain(self.choices, choices))\\n if value is None:\\n value = dict.fromkeys(choices, False)\\n else:\\n value = dict(dict.fromkeys(choices, False).items() +\\n value.items())\\n\\n context['values'] = [\\n (choice, label, value[choice])\\n for choice, label in choices.iteritems()\\n ]\\n\\n return context\",\n \"def get_text_type():\\n opt = ['text', 'email', 'password']\\n inp = option_menu(opt, 'Select text type:')\\n\\n # mark text type with option\\n OPTIONS['text-type'] = opt[inp]\\n\\n # add option to collected list\\n add_to_collected('text type', opt[inp])\\n\\n return\",\n \"def set_dropdown_b_options(value):\\n options_c = []\\n if value=='C':\\n options_c = [{'label': '1', 'value': '1'},\\n {'label': '2', 'value': '2'}]\\n if value == 'D':\\n options_c = [{'label': '3', 'value': '3'},\\n {'label': '4', 'value': '4'}]\\n if value=='E':\\n options_c = [{'label': '5', 'value': '5'},\\n {'label': '6', 'value': '6'}]\\n if value == 'F':\\n options_c = [{'label': '7', 'value': '7'},\\n {'label': '8', 'value': '8'}]\\n return options_c\",\n \"def objects_to_choices(queryset):\\n res = []\\n for elm in queryset:\\n res.append((elm.pk, unicode(elm)))\\n return res\",\n \"def choice(text, choices, **kwargs):\\n return click.prompt(click.style('> {}'.format(text), fg='blue', bold=True),\\n type=click.Choice(choices),\\n **kwargs)\",\n \"def on_comboBox_enceinte_activated(self, index):\\n nom_enceinte = self.comboBox_enceinte.currentText()\\n marque = [x[2] for x in self.enceintes if x[1] == nom_enceinte][0]\\n n_serie = [x[4] for x in self.enceintes if x[1] == nom_enceinte][0]\\n model =[x[3] for x in self.enceintes if x[1] == nom_enceinte][0]\\n \\n \\n self.lineEdit_marque.setText(marque)\\n self.lineEdit_n_serie.setText(n_serie)\\n self.lineEdit_model.setText(model)\",\n \"def display_choose(self, text, choices):\\n cur_index = 0\\n key = None\\n while key != 'KEY_NEWLINE':\\n if key == 'KEY_UP':\\n cur_index = max(cur_index - 1, 0)\\n elif key == 'KEY_DOWN':\\n cur_index = min(cur_index + 1, len(choices) - 1)\\n self.stdscr.erase()\\n for line in text:\\n self.stdscr.addstr(f'{PADCHAR}{line}\\\\n')\\n for index, value in enumerate(choices):\\n self.stdscr.addstr('\\\\n')\\n self.stdscr.addstr(PADCHAR)\\n self.stdscr.addstr(value, color_pair(7 if index == cur_index else 1))\\n self.stdscr.addstr(f'\\\\n\\\\n{PADCHAR}') \\n key = self.get_key() \\n return cur_index\",\n \"def list_selector(title=None,\\n text=None,\\n members=None,\\n controller=None,\\n preselect=None,\\n entry=False):\\n combobox = list_selector_widget(members=members,\\n preselect=preselect,\\n entry=entry)\\n\\n d = gtk.Dialog(title=title,\\n parent=None,\\n flags=gtk.DIALOG_DESTROY_WITH_PARENT,\\n buttons=( gtk.STOCK_OK, gtk.RESPONSE_OK,\\n gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL ))\\n\\n if text is not None:\\n l=gtk.Label(text)\\n l.show()\\n d.vbox.add(l)\\n\\n d.vbox.add(combobox)\\n combobox.show_all()\\n\\n d.connect('key-press-event', dialog_keypressed_cb)\\n\\n d.show()\\n center_on_mouse(d)\\n res=d.run()\\n retval=None\\n if res == gtk.RESPONSE_OK:\\n retval=combobox.get_current_element()\\n d.destroy()\\n return retval\",\n \"def get_classes(self, code):\\n \\n select = v.Combobox(\\n _metadata={'name':code}, \\n items=self.items, \\n v_model=None, \\n dense=True,\\n hide_details=True\\n )\\n \\n select.observe(partial(self.store, code), 'v_model')\\n \\n return select\",\n \"def getOptionsNames(self) -> List[unicode]:\\n ...\",\n \"def __init__(self, *args, **kwargs):\\n super(ChoiceFieldType, self).__init__(*args, **kwargs)\\n\\n self.choices = self.get_field_info_key('choices')\",\n \"def the_option_named(text: str) -> \\\"SelectByText\\\":\\n return SelectByText(text)\",\n \"def prepareAutoComplete(editor, text, charPos, lineStartCharPos,\\r\\n wikiDocument, settings):\\r\\n return []\",\n \"def autocomplete_possibilities():\\n try:\\n # get data sent by client\\n typed_input = request.args.get('q')\\n print(' ')\\n print('\\\\n------ getting autocomplete_possibilities ------')\\n print(f\\\"recived: input:{typed_input}\\\")\\n\\n # call the google API\\n results = gmaps.places_autocomplete(typed_input)\\n data = [\\n {'value': r['place_id'], 'text': r['description']}\\n for r in results\\n ]\\n\\n # Pass data to the front end\\n print(f'returning: {data}')\\n return jsonify(data)\\n\\n except Exception as e:\\n print(\\\"AJAX excepted \\\" + str(e))\\n return str(e)\",\n \"def _validate_selects(text, response):\\n answer_options = re.split(settings.MULTISELECT_DELIMITER_RE, str(text))\\n choices = map(lambda choice: choice.lower(), response.event.choices)\\n logger.debug('Question (%s) answer choices are: %s, given answers: %s' % (datatype, choices, answer_options))\\n new_answers = copy(answer_options)\\n for idx, opt in enumerate(answer_options):\\n logger.debug('Trying to format (m)select answer: \\\"%s\\\"' % opt)\\n try: \\n #in the case that we accept numbers to indicate option selection\\n opt_int = int(opt)\\n if not (1 <= opt_int <= len(choices)): \\n return text, 'Answer %s must be between 1 and %s' % (opt_int, len(choices))\\n else:\\n new_answers[idx] = str(opt_int)\\n\\n except ValueError: \\n # in the case where we accept the actual text of the question\\n logger.debug('Caught value error, trying to parse answer string choice of: %s' % choices)\\n if opt.lower() not in choices:\\n return text, 'Answer must be one of the choices'\\n else:\\n new_answers[idx] = str(choices.index(opt.lower()) + 1)\\n return ' '.join(new_answers), None\",\n \"def choice(choices=[], message=\\\"Pick something.\\\", title=None):\\n return dialog(\\n \\\"choice\\\",\\n choices=choices,\\n message=message,\\n title=title,\\n )\",\n \"def complete_opt_format(self, text, *_):\\n return [t + \\\" \\\" for t in FORMATTERS if t.startswith(text)]\",\n \"def choices(self, cl):\\n # TODO: Determine if non-static choices would be cleaner here.\\n # Honestly, I tried a more generic version and it was even harder to\\n # follow than this version.\\n yield {\\n 'selected': not (self.lookup_val_gte or self.lookup_val_lt),\\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg_gte,\\n self.lookup_kwarg_lt]),\\n 'display': 'All'\\n }\\n\\n goal = settings.FACEBOOK_CLICK_GOAL\\n yield {\\n 'selected': self.lookup_val_gte and not self.lookup_val_lt,\\n 'query_string': cl.get_query_string({self.lookup_kwarg_gte: goal},\\n [self.lookup_kwarg_lt]),\\n 'display': 'Yes'\\n }\\n yield {\\n 'selected': self.lookup_val_lt and not self.lookup_val_gte,\\n 'query_string': cl.get_query_string({self.lookup_kwarg_lt: goal},\\n [self.lookup_kwarg_gte]),\\n 'display': 'No'\\n }\",\n \"def build_options(slot, snacks):\\n \\n if slot == 'Fast':\\n return [\\n {'text': 'Pizza', 'value': 'Pizza'},\\n {'text': 'Fries', 'value': 'Fries'},\\n {'text': 'Franky', 'value': 'Franky'},\\n {'text': 'Burger', 'value': 'Burger'},\\n {'text': 'Sandwich', 'value': 'Sandwich'}\\n \\n \\n ]\\n elif slot == 'drink':\\n return [\\n {'text': 'Coca-Cola', 'value': 'Coca-cola'},\\n {'text': 'Appy', 'value': 'Appy'},\\n \\n {'text': 'Beer', 'value': 'Beer'},\\n {'text': 'Frooti', 'value': 'Frooti'},\\n {'text': 'Pepsi', 'value': 'Pepsi'}\\n \\n ]\",\n \"def sortedChoices(self, inText):\\n choices = self.splitText(inText)\\n sortedChoices = [text for text in self.formatList if text in choices]\\n if len(choices) == len(sortedChoices):\\n return (sortedChoices, True)\\n else:\\n return (sortedChoices, False)\",\n \"def _getChoices(self, acronym):\\n # get matches from acronymDB\\n matches = []\\n if(acronym in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n if(acronym[-1] == \\\"s\\\" and acronym[:-1] in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n\\n # create training data\\n X_train, y_train = [], []\\n for definition, articleID, ignored_var in matches:\\n text = self.articleDB[articleID]\\n X_train.append(\\n ExpansionChoice(article_id=articleID, article_text=text))\\n y_train.append(definition)\\n\\n # create y labels to group similar acronyms\\n y_labels, labelToExpansion = self._processChoices(y_train)\\n\\n return X_train, y_labels, labelToExpansion\",\n \"def multiple_choices(self, choices, response):\\n for elem in self.method_order:\\n if elem in choices:\\n return [elem]\\n raise NoData\",\n \"def set_dropdown_b_options(value):\\n options_b = []\\n if value=='A':\\n options_b = [{'label': 'C', 'value': 'C'},\\n {'label': 'D', 'value': 'D'}]\\n if value == 'B':\\n options_b = [{'label': 'E', 'value': 'E'},\\n {'label': 'F', 'value': 'F'}]\\n return options_b\",\n \"def choices(self, choices: Iterable[Tuple[str, str]]):\\n try:\\n iter(choices)\\n except TypeError:\\n raise TypeError(\\\"'choices' isn't a valid iterable\\\")\\n\\n apply_choices = []\\n for i, (choice_id, choice_label) in enumerate(choices):\\n apply_choices.append((str(choice_id), str(choice_label)))\\n\\n if len(apply_choices) < 2:\\n raise ValueError(\\\"you need to specify at least two choices\\\")\\n\\n self._choices = apply_choices\\n self.specific.refresh()\\n self._selected = 0\",\n \"def before_choose_candidate_listener(self, session, task):\\n choices = [PromptChoice('d', 'eDit', self.importer_edit)]\\n if task.candidates:\\n choices.append(PromptChoice('c', 'edit Candidates',\\n self.importer_edit_candidate))\\n\\n return choices\",\n \"def _get_completion(self, original_text, remaining_text, options):\\n if self.should_hide_completions(original_text=original_text,\\n remaining_text=remaining_text,\\n allowed_suffixes=(\\\" \\\", \\\".\\\")):\\n return []\\n\\n return [(option, len(remaining_text)) for option in options\\n if option.startswith(remaining_text) and not option.startswith(\\\"_\\\")]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6981332","0.64435107","0.64435107","0.6406375","0.6282898","0.61602914","0.61550856","0.6096663","0.6079173","0.6074226","0.599768","0.5979722","0.5946701","0.59085536","0.58665997","0.5852769","0.5851758","0.5840527","0.58387506","0.5816007","0.5803215","0.5797734","0.57951343","0.57936877","0.57807535","0.5749749","0.57457596","0.5732043","0.57093215","0.57050306","0.5695638","0.5680274","0.5638338","0.5617769","0.56013155","0.5580123","0.5573768","0.55674773","0.55656695","0.5561425","0.5539777","0.5525614","0.55243665","0.55211055","0.5516054","0.55135965","0.5486497","0.54864573","0.5484098","0.5467698","0.5450487","0.5444694","0.5435837","0.5432833","0.542561","0.54099566","0.5406829","0.5394251","0.53907686","0.5388395","0.53733003","0.5353227","0.5352402","0.53441244","0.5335833","0.5330664","0.5320152","0.5317789","0.53159815","0.5291184","0.52660507","0.5261751","0.52587485","0.5247112","0.52468276","0.5246636","0.52386904","0.523807","0.52264065","0.52225775","0.521434","0.52137464","0.5197637","0.5192666","0.51882684","0.5188186","0.5170487","0.516354","0.5163382","0.5161854","0.5159188","0.5158065","0.51575136","0.51539713","0.5146208","0.5145707","0.5143474","0.5142414","0.51338685","0.51279086"],"string":"[\n \"0.6981332\",\n \"0.64435107\",\n \"0.64435107\",\n \"0.6406375\",\n \"0.6282898\",\n \"0.61602914\",\n \"0.61550856\",\n \"0.6096663\",\n \"0.6079173\",\n \"0.6074226\",\n \"0.599768\",\n \"0.5979722\",\n \"0.5946701\",\n \"0.59085536\",\n \"0.58665997\",\n \"0.5852769\",\n \"0.5851758\",\n \"0.5840527\",\n \"0.58387506\",\n \"0.5816007\",\n \"0.5803215\",\n \"0.5797734\",\n \"0.57951343\",\n \"0.57936877\",\n \"0.57807535\",\n \"0.5749749\",\n \"0.57457596\",\n \"0.5732043\",\n \"0.57093215\",\n \"0.57050306\",\n \"0.5695638\",\n \"0.5680274\",\n \"0.5638338\",\n \"0.5617769\",\n \"0.56013155\",\n \"0.5580123\",\n \"0.5573768\",\n \"0.55674773\",\n \"0.55656695\",\n \"0.5561425\",\n \"0.5539777\",\n \"0.5525614\",\n \"0.55243665\",\n \"0.55211055\",\n \"0.5516054\",\n \"0.55135965\",\n \"0.5486497\",\n \"0.54864573\",\n \"0.5484098\",\n \"0.5467698\",\n \"0.5450487\",\n \"0.5444694\",\n \"0.5435837\",\n \"0.5432833\",\n \"0.542561\",\n \"0.54099566\",\n \"0.5406829\",\n \"0.5394251\",\n \"0.53907686\",\n \"0.5388395\",\n \"0.53733003\",\n \"0.5353227\",\n \"0.5352402\",\n \"0.53441244\",\n \"0.5335833\",\n \"0.5330664\",\n \"0.5320152\",\n \"0.5317789\",\n \"0.53159815\",\n \"0.5291184\",\n \"0.52660507\",\n \"0.5261751\",\n \"0.52587485\",\n \"0.5247112\",\n \"0.52468276\",\n \"0.5246636\",\n \"0.52386904\",\n \"0.523807\",\n \"0.52264065\",\n \"0.52225775\",\n \"0.521434\",\n \"0.52137464\",\n \"0.5197637\",\n \"0.5192666\",\n \"0.51882684\",\n \"0.5188186\",\n \"0.5170487\",\n \"0.516354\",\n \"0.5163382\",\n \"0.5161854\",\n \"0.5159188\",\n \"0.5158065\",\n \"0.51575136\",\n \"0.51539713\",\n \"0.5146208\",\n \"0.5145707\",\n \"0.5143474\",\n \"0.5142414\",\n \"0.51338685\",\n \"0.51279086\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6369448"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":89,"cells":{"query":{"kind":"string","value":"Return list of choices for combo box, each a tuple of edit text and any annotation text"},"document":{"kind":"string","value":"def getEditChoices(self, currentText=''):\n format = globalref.options.strData('EditDateFormat', True)\n today = GenDate().dateStr(format)\n yesterday = (GenDate() - 1).dateStr(format)\n tomorrow = (GenDate() + 1).dateStr(format)\n return [(today, '(%s)' % _('today')),\n (yesterday, '(%s)' % _('yesterday')),\n (tomorrow, '(%s)' % _('tomorrow'))]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getEditChoices(self, currentText=''):\n return [(text, '') for text in self.formatList]","def get_choices(self):\n raise NotImplementedError()","def get_choices(self):\n raise NotImplementedError()","def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n return [(int(code), name) for name, code in cls.__members__.items()]","def getEditChoices(self, currentText=''):\n currentChoices, valid = self.sortedChoices(currentText)\n nonChoices = [text for text in self.formatList\n if text not in currentChoices]\n results = []\n for choice in nonChoices: # menu entries to add a choice\n allChoices = currentChoices + [choice]\n allChoices = [text for text in self.formatList\n if text in allChoices]\n results.append((self.editSep.join(allChoices),\n '(%s %s)' % (_('add'), choice)))\n if currentChoices:\n results.append((None, None)) # separator\n for choice in currentChoices: # menu entries to remove a choice\n allChoices = currentChoices[:]\n allChoices.remove(choice)\n allChoices = [text for text in self.formatList\n if text in allChoices]\n results.append((self.editSep.join(allChoices),\n '(%s %s)' % (_('remove'), choice)))\n return results","def choices(self, typename, value_field='description'):\n rows = self.type(typename).values('id', value_field)\n return [(r['id'], r[value_field]) for r in rows]","def get_choices(self, instance):\n if instance.type == BaseParameter.CHOICE_TYPE:\n return [\n x.value\n for x in instance.get_typed_parameter().get_available_choices()\n ]\n else:\n return None","def get_choices_for_model_field(cls):\n return [c[0:2] for c in cls.attr_choices]","def initDefaultChoices(self):\n return [entry[0] for entry in self.getEditChoices()]","def select_combo_text(cb, text, index=0):\n i = 0\n for n in cb.get_model():\n if n[index] == text:\n break\n i += 1\n cb.set_active(i)","def application_command_autocomplete_choice_builder(name, value):\n return {\n 'name': name,\n 'value': value,\n }","def choices(self) -> list:\n return [self.mapper(i) for i in self.keys()]","def choices(self):\n return tuple(self._choices)","def iter_choices(self):\n\n for pk, obj in self._get_object_list():\n if hasattr(obj, self.id):\n selected = getattr(obj, self.id)\n else:\n selected = obj in self.data\n\n yield (pk, self.get_label(obj), selected)","def _get_choices_str(self):\n return ', '.join(\n '\"%s\"' % choice\n for choice in self.choices\n )","def fill_combobox_attributes(self):\n\n list_char = [\"\"]\n list_num = [\"\"]\n if self.ui.radioButton_file.isChecked():\n for a in self.attributes:\n if a['caseOrFile'] == \"file\" and a['valuetype'] == \"character\":\n list_char.append(a['name'])\n if a['caseOrFile'] == \"file\" and a['valuetype'] == \"numeric\":\n list_num.append(a['name'])\n else:\n for a in self.attributes:\n if a['caseOrFile'] == \"case\" and a['valuetype'] == \"character\":\n list_char.append(a['name'])\n if a['caseOrFile'] == \"case\" and a['valuetype'] == \"numeric\":\n list_num.append(a['name'])\n self.ui.comboBox_num_attributes.blockSignals(True)\n self.ui.comboBox_char_attributes.blockSignals(True)\n self.ui.comboBox_num_attributes.clear()\n self.ui.comboBox_char_attributes.clear()\n self.ui.comboBox_char_attributes.addItems(list_char)\n self.ui.comboBox_num_attributes.addItems(list_num)\n self.ui.comboBox_num_attributes.blockSignals(False)\n self.ui.comboBox_char_attributes.blockSignals(False)","def getComboTerms(tuples):\t\t\t\n\t\t\t#return \"[{0}]\".format('; '.join([\"({0})\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\n\t\t\treturn \"{0}\".format('; '.join((\"{0}\".format(text[indices[0]:indices[1]]) for indices in tuples)))","def getEditChoices(self, currentText=''):\n format = globalref.options.strData('EditTimeFormat', True)\n now = GenTime().timeStr(format)\n choices = [(now, '(%s)' % _('now'))]\n for hr in (6, 9, 12, 15, 18, 21, 0):\n time = GenTime((hr, 0)).timeStr(format)\n choices.append((time, ''))\n return choices","def get_choices(cls):\n return cls.values.items()","def _find_options(self, inputfield):\r\n elements = inputfield.findall('./options/option')\r\n return [{\r\n 'id': index,\r\n 'description': option.text,\r\n 'choice': option.get('choice')\r\n } for (index, option) in enumerate(elements)]","def create_combo(self) -> typing.Iterable[Combo]:\n raise NotImplementedError()","def choices(cls):\n return tuple(item.as_tuple for item in list(cls))","def choices(self) -> list:\n return [self.mapper(item) for item in self]","def choices(cls):\n _choices = []\n for attr in _user_attributes(cls):\n val = getattr(cls, attr)\n setattr(cls, attr[1:], val[0])\n _choices.append((val[0], val[1]))\n setattr(cls, 'CHOICES', tuple(_choices))\n return cls","def extract_choices(element):\r\n\r\n choices = []\r\n\r\n for choice in element:\r\n if choice.tag != 'choice':\r\n raise Exception(\r\n \"[capa.inputtypes.extract_choices] Expected a <choice>\" +\r\n \"tag; got {0} instead\".format(choice.tag)\r\n )\r\n\r\n components = []\r\n choice_text = ''\r\n if choice.text is not None:\r\n choice_text += choice.text\r\n # Initialize our dict for the next content\r\n adder = {\r\n 'type': 'text',\r\n 'contents': choice_text,\r\n 'tail_text': '',\r\n 'value': ''\r\n }\r\n components.append(adder)\r\n\r\n for elt in choice:\r\n # for elements in the choice e.g. <text> <numtolerance_input>\r\n adder = {\r\n 'type': 'text',\r\n 'contents': '',\r\n 'tail_text': '',\r\n 'value': ''\r\n }\r\n tag_type = elt.tag\r\n # If the current `elt` is a <numtolerance_input> set the\r\n # `adder`type to 'numtolerance_input', and 'contents' to\r\n # the `elt`'s name.\r\n # Treat decoy_inputs and numtolerance_inputs the same in order\r\n # to prevent students from reading the Html and figuring out\r\n # which inputs are valid\r\n if tag_type in ('numtolerance_input', 'decoy_input'):\r\n # We set this to textinput, so that we get a textinput html\r\n # element.\r\n adder['type'] = 'textinput'\r\n adder['contents'] = elt.get('name')\r\n else:\r\n adder['contents'] = elt.text\r\n\r\n # Add any tail text(\"is the mean\" in the example)\r\n adder['tail_text'] = elt.tail if elt.tail else ''\r\n components.append(adder)\r\n\r\n # Add the tuple for the current choice to the list of choices\r\n choices.append((choice.get(\"name\"), components))\r\n return choices","def initDefaultChoices(self):\n choices = [entry[0] for entry in self.getEditChoices()]\n choices.insert(0, DateFormat.dateStampStrings[1])\n return choices","def initDefaultChoices(self):\n choices = [entry[0] for entry in self.getEditChoices()]\n choices.insert(0, TimeFormat.timeStampStrings[1])\n return choices","def combo_callback(self, eventobj):\n print(self.combo_input.get()) # print name\n print(self.combo_input.current()) # print index","def choices(self):\n # Needs to be implmented by subclass\n raise Exception(NotImplemented)","def initDefaultChoices(self):\n return [text for text in self.formatList]","def __init__(self,choices,caption='ListSelection',default=[],single=False,check=False,sort=False,*args,**kargs):\n InputDialog.__init__(self,caption=caption,items = [\n dict(name='input',value=default,itemtype='list',choices=choices,\n text='',single=single,check=check,sort=sort,*args,**kargs),\n ],)","def _build_dropdown(options):\n return [(x, x) for x in options]","def set_choices(self, index, choices):\n if len(choices) == 1:\n self._label(index)\n self._widgets[index][\"text\"] = str(choices[0])\n else:\n self._combo(index)\n self._widgets[index][\"values\"] = [str(t) for t in choices]\n width = max(len(str(t)) for t in choices)\n width = max(5, width)\n self._widgets[index][\"width\"] = width","def choices() -> List[str]:\n return [t.name.upper() for t in ConfigurationVariable]","def extract_choices(element):\r\n\r\n choices = []\r\n\r\n for choice in element:\r\n if choice.tag != 'choice':\r\n raise Exception(\r\n \"[capa.inputtypes.extract_choices] Expected a <choice> tag; got %s instead\"\r\n % choice.tag)\r\n choices.append((choice.get(\"name\"), stringify_children(choice)))\r\n return choices","def __init__(self, \n num_fld=1, \n lab_txt=[\"1\"], \n txt_fld=[\"1\"], \n title_txt=\"test\", \n comb_txt=[],\n comb_lab_txt=[], \n comb_num=0, \n root_x=50, \n root_y=50):\n super().__init__()\n self.geometry(f'+{root_x}+{root_y}') #head=y+20px\n self.str_in=[]\n self.title(title_txt)\n if comb_txt:\n self.name=[0]*num_fld\n ent=[0]*num_fld\n self.comb=[]\n self.act=[]\n lab=[0]*num_fld\n lab_comb=[0]*comb_num\n else:\n self.name=[0]*num_fld\n ent=[0]*num_fld\n lab=[0]*num_fld\n self.comb=[]\n self.act=[]\n for i in range(num_fld):\n self.name[i]=tk.StringVar()\n ent[i]=tk.Entry(self,textvariable=self.name[i])\n ent[i].insert(0, txt_fld[i])\n lab[i] = tk.Label(self,width=15, text=lab_txt[i])\n lab[i].pack()\n ent[i].pack()\n for i in range(comb_num):\n lab_comb[i]=tk.Label(self,width=35, text=comb_lab_txt[i])\n self.comb.append(ttk.Combobox(self, values=comb_txt))\n lab_comb[i].pack()\n self.comb[i].pack()\n self.comb[i].current(1)\n\n but_ac=tk.Button(self, text=\"Accept\", command=self.ins)\n but_ac.pack()\n self.mainloop","def input_choices_from_list(choices, text):\n no_courses_text = \"\"\"\n init will only list the courses you are enrolled in\n and there seem to be none.\n Either enrol in a course or add the course id as command line argument.\n \"\"\"\n if choices is None or len(choices) == 0:\n print(no_courses_text)\n raise SystemExit(1)\n\n digits = str(math.ceil(math.log10(len(choices))))\n format_str = '{:' + digits + 'd} {}'\n for n, c in enumerate(choices):\n print(format_str.format(n, c))\n try:\n return [int(c) for c in input(text).split()]\n except EOFError:\n return []","def combobox(self):\n return self._combo","def valid_options(self):\n choices = self.choices()\n\n if not choices:\n return None\n\n return [opt[0] for opt in choices]","def widgets_from_abbreviations(self, seq):\n result = []\n for name, abbrev, default in seq:\n widget = self.widget_from_abbrev(abbrev, default)\n if not (isinstance(widget, ValueWidget) or isinstance(widget, fixed)):\n if widget is None:\n raise ValueError(\"{!r} cannot be transformed to a widget\".format(abbrev))\n else:\n raise TypeError(\"{!r} is not a ValueWidget\".format(widget))\n if not widget.description:\n widget.description = name\n widget._kwarg = name\n result.append(widget)\n return result","def multiple_choice():\n\n return [\"MAR\", \"MAR\", \"NI\", \"NI\", \"MCAR\"]","def build_comboboxes(activities, events):\n global comboboxes\n # For each activity set up a selector for an event\n\n for activity in activities:\n\n # Setup frame for better display in gui\n frame = Frame(main_window)\n frame.configure(background=\"gray30\")\n\n # Label the left column as activity in a model + \"beautify gui\"\n text = \"Activity name (model):\"\n Label(frame, text=text, bg=\"gray30\", fg=\"white\", padx=5).grid(column=0, row=0)\n Label(frame, text=activity, bg=\"gray30\", fg=\"white\").grid(column=0, row=1)\n\n # Set up the combobox for an event\n combo = Combobox(frame)\n combo['values'] = events\n\n # If activity is in events preselect the current one\n if activity in events:\n combo.current(events.index(activity))\n\n # Label the combobox and place label and box in frame\n Label(frame, text=\"Event name (log):\", bg=\"gray30\", fg=\"white\", padx=5).grid(column=1, row=0)\n combo.grid(column=1, row=1)\n\n # If the last activity in the graph is handled then do not write a separator\n if activity != activities[-1]:\n Separator(frame, orient=\"horizontal\").grid(row=2, columnspan=2, sticky=\"ew\", pady=10)\n\n comboboxes[activity] = combo\n # place the frame in the main_window\n frame.grid(column=0)","def get_chosen_options(user):\n user_profile = user.get_profile()\n user_application = user_profile.application\n np = user_application.np\n ocl = eval(user_application.options_selected)\n chosen_options = []\n for oc in ocl:\n chosen_options.append(Option.objects.get(opt_code=int(oc))) \n return chosen_options","def get_generic_choices(model, key, allow_null=False):\n CHOICES = [('', '--------')] if allow_null else []\n for i in model.objects.all().values_list(key, flat=True).distinct():\n CHOICES.append((str(i), str(i)))\n CHOICES.sort()\n\n return CHOICES","def _combobox_choice(self, _=None):\n combobox_string = self.value_combobox.var.get()\n if combobox_string.startswith(\"Unknown: \"):\n value = int(combobox_string[len(\"Unknown: \"):])\n else:\n value = int(self.value_combobox.var.get().split(\" \")[0])\n self.master.change_field_value(self.field_name, value)","def widgets(parameter: Parameter):\n widgets = []\n for key in parameter.keys():\n textEdit = QTextEdit()\n textEdit.setText(key)\n widgets.append(textEdit)\n if isinstance(parameter[key], Enum):\n comboBox = MyQtEnumComboBox()\n comboBox.fillValues(type(parameter[key]))\n widgets.append(comboBox)\n elif isinstance(parameter[key], bool):\n comboBox = QComboBox()\n comboBox.addItems((\"False\", \"True\"))\n widgets.append(comboBox)\n else:\n textEdit = QTextEdit()\n textEdit.setText(str(parameter[key]))\n widgets.append(textEdit)\n for widget in widgets:\n widget.setFixedHeight(30)\n return widgets","def _getBrailleRegionsForComboBox(self, obj):\n\n self._debugGenerator(\"_getBrailleRegionsForComboBox\", obj)\n\n regions = []\n\n focusedRegionIndex = 0\n label = self._script.getDisplayedLabel(obj)\n if label and (len(label) > 0):\n regions.append(braille.Region(label + \" \"))\n focusedRegionIndex = 1\n\n # Check to see if the text is editable. If so, then we want\n # to show the text attributes (such as selection -- see bug\n # 496846 for more details).\n #\n textObj = None\n for child in obj:\n if child and child.getRole() == pyatspi.ROLE_TEXT:\n textObj = child\n if textObj and textObj.getState().contains(pyatspi.STATE_EDITABLE):\n textRegion = braille.Text(textObj)\n regions.append(textRegion)\n else:\n displayedText = self._script.getDisplayedText(obj)\n if displayedText:\n regions.append(braille.Region(displayedText))\n\n regions.append(braille.Region(\n \" \" + rolenames.getBrailleForRoleName(obj)))\n\n # Things may not have gone as expected above, so we'll do some\n # defensive programming to make sure we don't get an index out\n # of bounds.\n #\n if focusedRegionIndex >= len(regions):\n focusedRegionIndex = 0\n if len(regions) == 0:\n focusedRegion = None\n else:\n focusedRegion = regions[focusedRegionIndex]\n\n # [[[TODO: WDW - perhaps if a text area was created, we should\n # give focus to it.]]]\n #\n return [regions, focusedRegion]","def form_SelectChoiceCallableOptions(request):\n schema = schemaish.Structure()\n schema.add('mySelect', schemaish.Integer())\n def _():\n options = [(1,'a'),(2,'b'),(3,'c')]\n for option in options:\n yield option\n\n form = formish.Form(schema, 'form')\n form['mySelect'].widget = formish.SelectChoice(_)\n return form","def __init__(self, *args, **kwargs):\n super(TaggedContentItemForm, self).__init__(*args, **kwargs)\n wtf = Tag.objects.filter(group__system=False)\n wlist = [w for t, w in self.fields.items() if t.endswith(\"tags\")]\n choices = []\n for choice in wtf:\n choices.append((choice.id, str(choice)))\n [setattr(w, 'choices', choices) for w in wlist]","def _comboSlot(self, select):\n select = self.sender().itemText(select)\n if qt4:\n qs = str(self.sender().property(\"dom address\").toPyObject())\n else:\n qs = str(self.sender().property(\"dom address\"))\n item = QtXml.QDomElement()\n\n ind = qs.rfind('/')\n ids = qs[ind:]\n\n item = self.qhash[qs].elem.firstChildElement(\"Item\")\n while(item.isNull() is False):\n itemName = item.firstChildElement(\"Name\")\n if(str(itemName.text()).strip() != select):\n activ = item.firstChildElement(\"Activate\")\n while(activ.isNull() is False):\n s = str(activ.text()).strip() + ids\n h = self.qhash[s]\n widget_enabled = h.elem.attribute(\"Enabled\", \"True\")\n widget_visible = h.elem.attribute(\"Visible\", \"Unknown\")\n h.widget.setEnabled(False)\n if(widget_visible != \"Unknown\"):\n h.label.hide()\n h.widget.hide()\n activ = activ.nextSiblingElement(\"Activate\")\n item = item.nextSiblingElement(\"Item\")\n\n item = self.qhash[qs].elem.firstChildElement(\"Item\")\n while(item.isNull() is False):\n itemName = item.firstChildElement(\"Name\")\n if(str(itemName.text()).strip() == select):\n activ = item.firstChildElement(\"Activate\")\n while(activ.isNull() is False):\n s = str(activ.text()).strip() + ids\n h = self.qhash[s]\n h.widget.setEnabled(True)\n h.label.show()\n h.widget.show()\n activ = activ.nextSiblingElement(\"Activate\")\n item = item.nextSiblingElement(\"Item\")","def get_choices(self, cutoff=None):\n queryset = self.get_queryset()\n if queryset is None:\n # Ensure that field.choices returns something sensible\n # even when accessed with a read-only field.\n return {}\n\n if cutoff is not None:\n queryset = queryset[:cutoff]\n\n return OrderedDict(\n [\n (\n # This line below is modifed.\n item.pk,\n self.display_value(item),\n )\n for item in queryset\n ]\n )","def test_rendering_combobox(qtbot):\n layer = Image(np.random.rand(8, 8))\n qtctrl = QtImageControls(layer)\n qtbot.addWidget(qtctrl)\n combo = qtctrl.renderComboBox\n opts = {combo.itemText(i) for i in range(combo.count())}\n rendering_options = {\n 'translucent',\n 'additive',\n 'iso',\n 'mip',\n 'minip',\n 'attenuated_mip',\n 'average',\n }\n assert opts == rendering_options\n # programmatically updating rendering mode updates the combobox\n layer.rendering = 'iso'\n assert combo.findText('iso') == combo.currentIndex()","def get_choicesdata(self):\n selected_value = self.get_cleaned_value()\n choicesdata = []\n for value, label in self.get_choices_cached():\n is_selected = value == selected_value\n url = self.build_set_values_url(values=[value])\n choicesdata.append({\n 'url': url,\n 'label': label,\n 'is_selected': is_selected,\n 'dom_id': '{}_{}'.format(self.get_inputfield_dom_id(), value)\n })\n return choicesdata","def choices(self):\n return self.__class__.get_setting_choices(self.key, **self.get_kwargs())","def get_poll_choices(self, games: [Game]) -> [dict]:\n answer_texts = []\n for g in games:\n answer_texts.append(g.name + \" - \" + g.genre)\n answer_texts = sorted(answer_texts, key=str.lower)\n poll_choices = []\n for at in answer_texts:\n poll_choices.append({\"text\": at})\n return poll_choices","def __str__(self):\n return \"choice_text: \" + self.choice_text","def _parse_choices(self, text):\n choices = dict()\n\n matches = re.findall(self.choice_regex, text)\n for match in matches:\n # remove the brackets\n match = match.replace('[[', '')\n match = match.replace(']]', '')\n\n if '|' in match:\n # format is {text}|{node_id}, the text and node id are different\n text, node_id = match.split('|')\n choices[node_id] = text\n else:\n choices[match] = match\n\n return choices","def get_choices_new_protected():\n ret = []\n ret.append( (1, _(u'Nur Community-Mitglieder dürfen neue Beiträge leisten')) )\n ret.append( (-1, _(u'Offener Zugang')) )\n return ret","def comboBox(args: list, slot) -> QComboBox:\n comboBox = QComboBox()\n comboBox.addItems(args[0])\n comboBox.currentTextChanged.connect(slot)\n return comboBox","def choices(self):\n\n if self._choices == None:\n self._choices = [ExperimentChoice(self, choice_name) for choice_name in self.choice_names]\n\n return self._choices","def make_ddls( self, parent ):\n # ---- 0\n a_widget = ttk.Combobox( parent,\n width = self.arg_width,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n a_widget.bind( \"<<ComboboxSelected>>\", self.sync_ddl_0 )\n\n # AppGlobal.gui_style.style_combobox( a_widget )\n self.ddl_0_widget = a_widget\n\n # ---- 1\n a_widget = ttk.Combobox( parent,\n width = self.arg_width,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n self.ddl_1_widget = a_widget\n\n # ---- 2\n a_widget = ttk.Combobox( parent,\n width = self.arg_width + 5,\n state = \"normal\",\n textvariable = \"self.arg_2_var\" )\n\n # AppGlobal.gui_style.style_combobox( a_widget )\n self.ddl_2_widget = a_widget\n\n # self.ddl_widgets = [ self.ddl_0_widget, self.ddl_1_widget, self.ddl_2_widget, ]\n # print( self.ddl_widgets[ 0 ] == self.ddl_widgets[ 1 ])\n\n #self.load_ddl_0( )","def __str__(self):\n return self.choice_text","def as_choices():\n return (\n # Live is currently disabled as a choice\n # pending implementation\n (\"live\", \"Use working directory\"),\n (\"latest\", \"Use latest snapshot\"),\n (\"pinned\", \"Pinned to snapshot\"),\n )","def comboBoxes(self):\r\n # Cities Combo Button\r\n self.comboCities = QComboBox()\r\n self.comboCities.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: 15px;\r\n background-color : #A8DBC5;\r\n border: 1px solid white;\r\n\r\n \"\"\")\r\n self.comboCities.addItems(\r\n ['Girón', 'Piedecuesta', 'Floridablanca', 'Bucaramanga'])\r\n self.grid.addWidget(self.comboCities, 6, 1, 1, 2)\r\n self.comboCities.setCurrentText(\"Bucaramanga\")\r\n # Payment Combo Button\r\n self.comboPayment = QComboBox()\r\n self.comboPayment.setStyleSheet(\"\"\"\r\n font-family: times;\r\n font-size: 15px;\r\n background-color : #A8DBC5;\r\n border: 1px solid white;\r\n\r\n \"\"\")\r\n self.comboPayment.addItems(['Efectivo', 'Nequi'])\r\n self.grid.addWidget(self.comboPayment, 7, 1, 1, 2)","def get_choicesdata(self):\n # selected_value = self.get_cleaned_value()\n # choicesdata = []\n # found_selected_value = False\n # for value, label in self.get_choices():\n # is_selected = value == selected_value\n # if is_selected:\n # found_selected_value = True\n # url = self.build_set_values_url(values=[value])\n # choicesdata.append({\n # 'url': url,\n # 'label': label,\n # 'is_selected': is_selected\n # })\n choicesdata, found_selected_value = self.__make_choicesdata_list(\n choices=self.get_choices(),\n selected_value=self.get_cleaned_value())\n if not found_selected_value and len(choicesdata) > 0:\n selected_index = self.get_default_is_selected_index(choicesdata=choicesdata)\n choicesdata[selected_index]['is_selected'] = True\n return choicesdata","def _find_options(self):\r\n elements = self.xml.findall('./options/option')\r\n return [{\r\n 'id': index,\r\n 'description': option.text,\r\n 'choice': option.get('choice')\r\n } for (index, option) in enumerate(elements)]","def __str__(self):\n return gettext('One of %s') % self._get_choices_str()","def list_selector_widget(members=None,\n preselect=None,\n entry=False,\n callback=None):\n store, i=generate_list_model(members,\n active_element=preselect)\n\n if entry:\n combobox=gtk.ComboBoxEntry(store, column=0)\n else:\n combobox=gtk.ComboBox(store)\n cell = gtk.CellRendererText()\n combobox.pack_start(cell, expand=True)\n combobox.add_attribute(cell, 'text', 0)\n combobox.add_attribute(cell, 'background', 2)\n\n combobox.set_active(-1)\n if i is None:\n i = store.get_iter_first()\n if i is not None:\n combobox.set_active_iter(i)\n\n if entry:\n def get_current_element(combo):\n try:\n return combo.get_model().get_value(combo.get_active_iter(), 1)\n except (TypeError, AttributeError):\n return unicode(combo.child.get_text())\n def set_current_element(combo, t):\n combo.child.set_text(t)\n else:\n def get_current_element(combo):\n if combo.get_active_iter() is not None:\n return combo.get_model().get_value(combo.get_active_iter(), 1)\n else:\n return None\n def set_current_element(combo, el):\n # Find the index of the element\n l=[ t[0] for t in enumerate(combo.get_model()) if t[1][1] == el ]\n if l:\n # The element is present.\n combo.set_active(l[0])\n else:\n combo.set_active_iter(combo.get_model().append( (unicode(el), el, None) ))\n\n # Bind the method to the combobox object\n combobox.get_current_element = get_current_element.__get__(combobox)\n combobox.set_current_element = set_current_element.__get__(combobox)\n\n if callback is not None:\n combobox.connect('changed', callback)\n\n return combobox","def initDefaultChoices(self):\n return []","def occurrence_choices():\n OCCURRENCE_CHOICES = [('one_off', 'One Off'), ('weekly', 'Weekly'), ('fortnightly', 'Fortnightly')]\n occurrence = forms.ChoiceField(choices=OCCURRENCE_CHOICES, widget=forms.RadioSelect())\n return occurrence","def get_action_choices():\n from hardware.management.commands.gpio_buttons import Command\n import re\n pattern = re.compile(r'^on_(?P<name>\\w+)_press$')\n choices = []\n for member in dir(Command):\n match = pattern.match(member)\n if match:\n action = match.groupdict()['name']\n name = action.replace('_', ' ').title()\n choices.append((action, name))\n return choices","def DrawComboBox(*args, **kwargs):\n return _gdi_.RendererNative_DrawComboBox(*args, **kwargs)","def fill_combobox(self):\n sqlstring = \"SELECT first_name, last_name from employees WHERE enabled = 1 ORDER BY last_name ASC\"\n self.CB_employee.addItem(\"\")\n for employee in self.ms.c.execute(sqlstring):\n self.CB_employee.addItem(\" \".join((employee[0], employee[1])))\n sqlstring = \"SELECT first_name, last_name from employees WHERE enabled = 0 ORDER BY last_name ASC\"\n for employee in self.ms.c.execute(sqlstring):\n self.CB_employee.addItem(\" \".join((employee[0], employee[1])))","def get_text_type():\n opt = ['text', 'email', 'password']\n inp = option_menu(opt, 'Select text type:')\n\n # mark text type with option\n OPTIONS['text-type'] = opt[inp]\n\n # add option to collected list\n add_to_collected('text type', opt[inp])\n\n return","def get_context(self, name, value, attrs=None, choices=()):\n context = super(TriStateCheckboxSelectMultiple, self).get_context(\n name, value, attrs\n )\n\n choices = dict(it.chain(self.choices, choices))\n if value is None:\n value = dict.fromkeys(choices, False)\n else:\n value = dict(dict.fromkeys(choices, False).items() +\n value.items())\n\n context['values'] = [\n (choice, label, value[choice])\n for choice, label in choices.iteritems()\n ]\n\n return context","def _get_choices ( self, context, path = '' ):\n choices = []\n gdc = context.get_data_context\n for name in context.data_contexts:\n next_path = path_for( path, name )\n choices.append( TemplateChoice( choice_value = next_path ) )\n choices.extend( self._get_choices( gdc( name ), next_path ) )\n \n return choices","def set_dropdown_b_options(value):\n options_c = []\n if value=='C':\n options_c = [{'label': '1', 'value': '1'},\n {'label': '2', 'value': '2'}]\n if value == 'D':\n options_c = [{'label': '3', 'value': '3'},\n {'label': '4', 'value': '4'}]\n if value=='E':\n options_c = [{'label': '5', 'value': '5'},\n {'label': '6', 'value': '6'}]\n if value == 'F':\n options_c = [{'label': '7', 'value': '7'},\n {'label': '8', 'value': '8'}]\n return options_c","def objects_to_choices(queryset):\n res = []\n for elm in queryset:\n res.append((elm.pk, unicode(elm)))\n return res","def choice(text, choices, **kwargs):\n return click.prompt(click.style('> {}'.format(text), fg='blue', bold=True),\n type=click.Choice(choices),\n **kwargs)","def on_comboBox_enceinte_activated(self, index):\n nom_enceinte = self.comboBox_enceinte.currentText()\n marque = [x[2] for x in self.enceintes if x[1] == nom_enceinte][0]\n n_serie = [x[4] for x in self.enceintes if x[1] == nom_enceinte][0]\n model =[x[3] for x in self.enceintes if x[1] == nom_enceinte][0]\n \n \n self.lineEdit_marque.setText(marque)\n self.lineEdit_n_serie.setText(n_serie)\n self.lineEdit_model.setText(model)","def list_selector(title=None,\n text=None,\n members=None,\n controller=None,\n preselect=None,\n entry=False):\n combobox = list_selector_widget(members=members,\n preselect=preselect,\n entry=entry)\n\n d = gtk.Dialog(title=title,\n parent=None,\n flags=gtk.DIALOG_DESTROY_WITH_PARENT,\n buttons=( gtk.STOCK_OK, gtk.RESPONSE_OK,\n gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL ))\n\n if text is not None:\n l=gtk.Label(text)\n l.show()\n d.vbox.add(l)\n\n d.vbox.add(combobox)\n combobox.show_all()\n\n d.connect('key-press-event', dialog_keypressed_cb)\n\n d.show()\n center_on_mouse(d)\n res=d.run()\n retval=None\n if res == gtk.RESPONSE_OK:\n retval=combobox.get_current_element()\n d.destroy()\n return retval","def display_choose(self, text, choices):\n cur_index = 0\n key = None\n while key != 'KEY_NEWLINE':\n if key == 'KEY_UP':\n cur_index = max(cur_index - 1, 0)\n elif key == 'KEY_DOWN':\n cur_index = min(cur_index + 1, len(choices) - 1)\n self.stdscr.erase()\n for line in text:\n self.stdscr.addstr(f'{PADCHAR}{line}\\n')\n for index, value in enumerate(choices):\n self.stdscr.addstr('\\n')\n self.stdscr.addstr(PADCHAR)\n self.stdscr.addstr(value, color_pair(7 if index == cur_index else 1))\n self.stdscr.addstr(f'\\n\\n{PADCHAR}') \n key = self.get_key() \n return cur_index","def get_classes(self, code):\n \n select = v.Combobox(\n _metadata={'name':code}, \n items=self.items, \n v_model=None, \n dense=True,\n hide_details=True\n )\n \n select.observe(partial(self.store, code), 'v_model')\n \n return select","def getOptionsNames(self) -> List[unicode]:\n ...","def the_option_named(text: str) -> \"SelectByText\":\n return SelectByText(text)","def __init__(self, *args, **kwargs):\n super(ChoiceFieldType, self).__init__(*args, **kwargs)\n\n self.choices = self.get_field_info_key('choices')","def prepareAutoComplete(editor, text, charPos, lineStartCharPos,\r\n wikiDocument, settings):\r\n return []","def _validate_selects(text, response):\n answer_options = re.split(settings.MULTISELECT_DELIMITER_RE, str(text))\n choices = map(lambda choice: choice.lower(), response.event.choices)\n logger.debug('Question (%s) answer choices are: %s, given answers: %s' % (datatype, choices, answer_options))\n new_answers = copy(answer_options)\n for idx, opt in enumerate(answer_options):\n logger.debug('Trying to format (m)select answer: \"%s\"' % opt)\n try: \n #in the case that we accept numbers to indicate option selection\n opt_int = int(opt)\n if not (1 <= opt_int <= len(choices)): \n return text, 'Answer %s must be between 1 and %s' % (opt_int, len(choices))\n else:\n new_answers[idx] = str(opt_int)\n\n except ValueError: \n # in the case where we accept the actual text of the question\n logger.debug('Caught value error, trying to parse answer string choice of: %s' % choices)\n if opt.lower() not in choices:\n return text, 'Answer must be one of the choices'\n else:\n new_answers[idx] = str(choices.index(opt.lower()) + 1)\n return ' '.join(new_answers), None","def choice(choices=[], message=\"Pick something.\", title=None):\n return dialog(\n \"choice\",\n choices=choices,\n message=message,\n title=title,\n )","def autocomplete_possibilities():\n try:\n # get data sent by client\n typed_input = request.args.get('q')\n print(' ')\n print('\\n------ getting autocomplete_possibilities ------')\n print(f\"recived: input:{typed_input}\")\n\n # call the google API\n results = gmaps.places_autocomplete(typed_input)\n data = [\n {'value': r['place_id'], 'text': r['description']}\n for r in results\n ]\n\n # Pass data to the front end\n print(f'returning: {data}')\n return jsonify(data)\n\n except Exception as e:\n print(\"AJAX excepted \" + str(e))\n return str(e)","def complete_opt_format(self, text, *_):\n return [t + \" \" for t in FORMATTERS if t.startswith(text)]","def choices(self, cl):\n # TODO: Determine if non-static choices would be cleaner here.\n # Honestly, I tried a more generic version and it was even harder to\n # follow than this version.\n yield {\n 'selected': not (self.lookup_val_gte or self.lookup_val_lt),\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg_gte,\n self.lookup_kwarg_lt]),\n 'display': 'All'\n }\n\n goal = settings.FACEBOOK_CLICK_GOAL\n yield {\n 'selected': self.lookup_val_gte and not self.lookup_val_lt,\n 'query_string': cl.get_query_string({self.lookup_kwarg_gte: goal},\n [self.lookup_kwarg_lt]),\n 'display': 'Yes'\n }\n yield {\n 'selected': self.lookup_val_lt and not self.lookup_val_gte,\n 'query_string': cl.get_query_string({self.lookup_kwarg_lt: goal},\n [self.lookup_kwarg_gte]),\n 'display': 'No'\n }","def build_options(slot, snacks):\n \n if slot == 'Fast':\n return [\n {'text': 'Pizza', 'value': 'Pizza'},\n {'text': 'Fries', 'value': 'Fries'},\n {'text': 'Franky', 'value': 'Franky'},\n {'text': 'Burger', 'value': 'Burger'},\n {'text': 'Sandwich', 'value': 'Sandwich'}\n \n \n ]\n elif slot == 'drink':\n return [\n {'text': 'Coca-Cola', 'value': 'Coca-cola'},\n {'text': 'Appy', 'value': 'Appy'},\n \n {'text': 'Beer', 'value': 'Beer'},\n {'text': 'Frooti', 'value': 'Frooti'},\n {'text': 'Pepsi', 'value': 'Pepsi'}\n \n ]","def sortedChoices(self, inText):\n choices = self.splitText(inText)\n sortedChoices = [text for text in self.formatList if text in choices]\n if len(choices) == len(sortedChoices):\n return (sortedChoices, True)\n else:\n return (sortedChoices, False)","def _getChoices(self, acronym):\n # get matches from acronymDB\n matches = []\n if(acronym in self.acronymDB):\n matches += self.acronymDB[acronym]\n if(acronym[-1] == \"s\" and acronym[:-1] in self.acronymDB):\n matches += self.acronymDB[acronym]\n\n # create training data\n X_train, y_train = [], []\n for definition, articleID, ignored_var in matches:\n text = self.articleDB[articleID]\n X_train.append(\n ExpansionChoice(article_id=articleID, article_text=text))\n y_train.append(definition)\n\n # create y labels to group similar acronyms\n y_labels, labelToExpansion = self._processChoices(y_train)\n\n return X_train, y_labels, labelToExpansion","def multiple_choices(self, choices, response):\n for elem in self.method_order:\n if elem in choices:\n return [elem]\n raise NoData","def set_dropdown_b_options(value):\n options_b = []\n if value=='A':\n options_b = [{'label': 'C', 'value': 'C'},\n {'label': 'D', 'value': 'D'}]\n if value == 'B':\n options_b = [{'label': 'E', 'value': 'E'},\n {'label': 'F', 'value': 'F'}]\n return options_b","def choices(self, choices: Iterable[Tuple[str, str]]):\n try:\n iter(choices)\n except TypeError:\n raise TypeError(\"'choices' isn't a valid iterable\")\n\n apply_choices = []\n for i, (choice_id, choice_label) in enumerate(choices):\n apply_choices.append((str(choice_id), str(choice_label)))\n\n if len(apply_choices) < 2:\n raise ValueError(\"you need to specify at least two choices\")\n\n self._choices = apply_choices\n self.specific.refresh()\n self._selected = 0","def before_choose_candidate_listener(self, session, task):\n choices = [PromptChoice('d', 'eDit', self.importer_edit)]\n if task.candidates:\n choices.append(PromptChoice('c', 'edit Candidates',\n self.importer_edit_candidate))\n\n return choices","def _get_completion(self, original_text, remaining_text, options):\n if self.should_hide_completions(original_text=original_text,\n remaining_text=remaining_text,\n allowed_suffixes=(\" \", \".\")):\n return []\n\n return [(option, len(remaining_text)) for option in options\n if option.startswith(remaining_text) and not option.startswith(\"_\")]"],"string":"[\n \"def getEditChoices(self, currentText=''):\\n return [(text, '') for text in self.formatList]\",\n \"def get_choices(self):\\n raise NotImplementedError()\",\n \"def get_choices(self):\\n raise NotImplementedError()\",\n \"def choices(cls):\\n # return list(map(tuple, cls.__members__.items()))\\n return [(int(code), name) for name, code in cls.__members__.items()]\",\n \"def getEditChoices(self, currentText=''):\\n currentChoices, valid = self.sortedChoices(currentText)\\n nonChoices = [text for text in self.formatList\\n if text not in currentChoices]\\n results = []\\n for choice in nonChoices: # menu entries to add a choice\\n allChoices = currentChoices + [choice]\\n allChoices = [text for text in self.formatList\\n if text in allChoices]\\n results.append((self.editSep.join(allChoices),\\n '(%s %s)' % (_('add'), choice)))\\n if currentChoices:\\n results.append((None, None)) # separator\\n for choice in currentChoices: # menu entries to remove a choice\\n allChoices = currentChoices[:]\\n allChoices.remove(choice)\\n allChoices = [text for text in self.formatList\\n if text in allChoices]\\n results.append((self.editSep.join(allChoices),\\n '(%s %s)' % (_('remove'), choice)))\\n return results\",\n \"def choices(self, typename, value_field='description'):\\n rows = self.type(typename).values('id', value_field)\\n return [(r['id'], r[value_field]) for r in rows]\",\n \"def get_choices(self, instance):\\n if instance.type == BaseParameter.CHOICE_TYPE:\\n return [\\n x.value\\n for x in instance.get_typed_parameter().get_available_choices()\\n ]\\n else:\\n return None\",\n \"def get_choices_for_model_field(cls):\\n return [c[0:2] for c in cls.attr_choices]\",\n \"def initDefaultChoices(self):\\n return [entry[0] for entry in self.getEditChoices()]\",\n \"def select_combo_text(cb, text, index=0):\\n i = 0\\n for n in cb.get_model():\\n if n[index] == text:\\n break\\n i += 1\\n cb.set_active(i)\",\n \"def application_command_autocomplete_choice_builder(name, value):\\n return {\\n 'name': name,\\n 'value': value,\\n }\",\n \"def choices(self) -> list:\\n return [self.mapper(i) for i in self.keys()]\",\n \"def choices(self):\\n return tuple(self._choices)\",\n \"def iter_choices(self):\\n\\n for pk, obj in self._get_object_list():\\n if hasattr(obj, self.id):\\n selected = getattr(obj, self.id)\\n else:\\n selected = obj in self.data\\n\\n yield (pk, self.get_label(obj), selected)\",\n \"def _get_choices_str(self):\\n return ', '.join(\\n '\\\"%s\\\"' % choice\\n for choice in self.choices\\n )\",\n \"def fill_combobox_attributes(self):\\n\\n list_char = [\\\"\\\"]\\n list_num = [\\\"\\\"]\\n if self.ui.radioButton_file.isChecked():\\n for a in self.attributes:\\n if a['caseOrFile'] == \\\"file\\\" and a['valuetype'] == \\\"character\\\":\\n list_char.append(a['name'])\\n if a['caseOrFile'] == \\\"file\\\" and a['valuetype'] == \\\"numeric\\\":\\n list_num.append(a['name'])\\n else:\\n for a in self.attributes:\\n if a['caseOrFile'] == \\\"case\\\" and a['valuetype'] == \\\"character\\\":\\n list_char.append(a['name'])\\n if a['caseOrFile'] == \\\"case\\\" and a['valuetype'] == \\\"numeric\\\":\\n list_num.append(a['name'])\\n self.ui.comboBox_num_attributes.blockSignals(True)\\n self.ui.comboBox_char_attributes.blockSignals(True)\\n self.ui.comboBox_num_attributes.clear()\\n self.ui.comboBox_char_attributes.clear()\\n self.ui.comboBox_char_attributes.addItems(list_char)\\n self.ui.comboBox_num_attributes.addItems(list_num)\\n self.ui.comboBox_num_attributes.blockSignals(False)\\n self.ui.comboBox_char_attributes.blockSignals(False)\",\n \"def getComboTerms(tuples):\\t\\t\\t\\n\\t\\t\\t#return \\\"[{0}]\\\".format('; '.join([\\\"({0})\\\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\\n\\t\\t\\treturn \\\"{0}\\\".format('; '.join((\\\"{0}\\\".format(text[indices[0]:indices[1]]) for indices in tuples)))\",\n \"def getEditChoices(self, currentText=''):\\n format = globalref.options.strData('EditTimeFormat', True)\\n now = GenTime().timeStr(format)\\n choices = [(now, '(%s)' % _('now'))]\\n for hr in (6, 9, 12, 15, 18, 21, 0):\\n time = GenTime((hr, 0)).timeStr(format)\\n choices.append((time, ''))\\n return choices\",\n \"def get_choices(cls):\\n return cls.values.items()\",\n \"def _find_options(self, inputfield):\\r\\n elements = inputfield.findall('./options/option')\\r\\n return [{\\r\\n 'id': index,\\r\\n 'description': option.text,\\r\\n 'choice': option.get('choice')\\r\\n } for (index, option) in enumerate(elements)]\",\n \"def create_combo(self) -> typing.Iterable[Combo]:\\n raise NotImplementedError()\",\n \"def choices(cls):\\n return tuple(item.as_tuple for item in list(cls))\",\n \"def choices(self) -> list:\\n return [self.mapper(item) for item in self]\",\n \"def choices(cls):\\n _choices = []\\n for attr in _user_attributes(cls):\\n val = getattr(cls, attr)\\n setattr(cls, attr[1:], val[0])\\n _choices.append((val[0], val[1]))\\n setattr(cls, 'CHOICES', tuple(_choices))\\n return cls\",\n \"def extract_choices(element):\\r\\n\\r\\n choices = []\\r\\n\\r\\n for choice in element:\\r\\n if choice.tag != 'choice':\\r\\n raise Exception(\\r\\n \\\"[capa.inputtypes.extract_choices] Expected a <choice>\\\" +\\r\\n \\\"tag; got {0} instead\\\".format(choice.tag)\\r\\n )\\r\\n\\r\\n components = []\\r\\n choice_text = ''\\r\\n if choice.text is not None:\\r\\n choice_text += choice.text\\r\\n # Initialize our dict for the next content\\r\\n adder = {\\r\\n 'type': 'text',\\r\\n 'contents': choice_text,\\r\\n 'tail_text': '',\\r\\n 'value': ''\\r\\n }\\r\\n components.append(adder)\\r\\n\\r\\n for elt in choice:\\r\\n # for elements in the choice e.g. <text> <numtolerance_input>\\r\\n adder = {\\r\\n 'type': 'text',\\r\\n 'contents': '',\\r\\n 'tail_text': '',\\r\\n 'value': ''\\r\\n }\\r\\n tag_type = elt.tag\\r\\n # If the current `elt` is a <numtolerance_input> set the\\r\\n # `adder`type to 'numtolerance_input', and 'contents' to\\r\\n # the `elt`'s name.\\r\\n # Treat decoy_inputs and numtolerance_inputs the same in order\\r\\n # to prevent students from reading the Html and figuring out\\r\\n # which inputs are valid\\r\\n if tag_type in ('numtolerance_input', 'decoy_input'):\\r\\n # We set this to textinput, so that we get a textinput html\\r\\n # element.\\r\\n adder['type'] = 'textinput'\\r\\n adder['contents'] = elt.get('name')\\r\\n else:\\r\\n adder['contents'] = elt.text\\r\\n\\r\\n # Add any tail text(\\\"is the mean\\\" in the example)\\r\\n adder['tail_text'] = elt.tail if elt.tail else ''\\r\\n components.append(adder)\\r\\n\\r\\n # Add the tuple for the current choice to the list of choices\\r\\n choices.append((choice.get(\\\"name\\\"), components))\\r\\n return choices\",\n \"def initDefaultChoices(self):\\n choices = [entry[0] for entry in self.getEditChoices()]\\n choices.insert(0, DateFormat.dateStampStrings[1])\\n return choices\",\n \"def initDefaultChoices(self):\\n choices = [entry[0] for entry in self.getEditChoices()]\\n choices.insert(0, TimeFormat.timeStampStrings[1])\\n return choices\",\n \"def combo_callback(self, eventobj):\\n print(self.combo_input.get()) # print name\\n print(self.combo_input.current()) # print index\",\n \"def choices(self):\\n # Needs to be implmented by subclass\\n raise Exception(NotImplemented)\",\n \"def initDefaultChoices(self):\\n return [text for text in self.formatList]\",\n \"def __init__(self,choices,caption='ListSelection',default=[],single=False,check=False,sort=False,*args,**kargs):\\n InputDialog.__init__(self,caption=caption,items = [\\n dict(name='input',value=default,itemtype='list',choices=choices,\\n text='',single=single,check=check,sort=sort,*args,**kargs),\\n ],)\",\n \"def _build_dropdown(options):\\n return [(x, x) for x in options]\",\n \"def set_choices(self, index, choices):\\n if len(choices) == 1:\\n self._label(index)\\n self._widgets[index][\\\"text\\\"] = str(choices[0])\\n else:\\n self._combo(index)\\n self._widgets[index][\\\"values\\\"] = [str(t) for t in choices]\\n width = max(len(str(t)) for t in choices)\\n width = max(5, width)\\n self._widgets[index][\\\"width\\\"] = width\",\n \"def choices() -> List[str]:\\n return [t.name.upper() for t in ConfigurationVariable]\",\n \"def extract_choices(element):\\r\\n\\r\\n choices = []\\r\\n\\r\\n for choice in element:\\r\\n if choice.tag != 'choice':\\r\\n raise Exception(\\r\\n \\\"[capa.inputtypes.extract_choices] Expected a <choice> tag; got %s instead\\\"\\r\\n % choice.tag)\\r\\n choices.append((choice.get(\\\"name\\\"), stringify_children(choice)))\\r\\n return choices\",\n \"def __init__(self, \\n num_fld=1, \\n lab_txt=[\\\"1\\\"], \\n txt_fld=[\\\"1\\\"], \\n title_txt=\\\"test\\\", \\n comb_txt=[],\\n comb_lab_txt=[], \\n comb_num=0, \\n root_x=50, \\n root_y=50):\\n super().__init__()\\n self.geometry(f'+{root_x}+{root_y}') #head=y+20px\\n self.str_in=[]\\n self.title(title_txt)\\n if comb_txt:\\n self.name=[0]*num_fld\\n ent=[0]*num_fld\\n self.comb=[]\\n self.act=[]\\n lab=[0]*num_fld\\n lab_comb=[0]*comb_num\\n else:\\n self.name=[0]*num_fld\\n ent=[0]*num_fld\\n lab=[0]*num_fld\\n self.comb=[]\\n self.act=[]\\n for i in range(num_fld):\\n self.name[i]=tk.StringVar()\\n ent[i]=tk.Entry(self,textvariable=self.name[i])\\n ent[i].insert(0, txt_fld[i])\\n lab[i] = tk.Label(self,width=15, text=lab_txt[i])\\n lab[i].pack()\\n ent[i].pack()\\n for i in range(comb_num):\\n lab_comb[i]=tk.Label(self,width=35, text=comb_lab_txt[i])\\n self.comb.append(ttk.Combobox(self, values=comb_txt))\\n lab_comb[i].pack()\\n self.comb[i].pack()\\n self.comb[i].current(1)\\n\\n but_ac=tk.Button(self, text=\\\"Accept\\\", command=self.ins)\\n but_ac.pack()\\n self.mainloop\",\n \"def input_choices_from_list(choices, text):\\n no_courses_text = \\\"\\\"\\\"\\n init will only list the courses you are enrolled in\\n and there seem to be none.\\n Either enrol in a course or add the course id as command line argument.\\n \\\"\\\"\\\"\\n if choices is None or len(choices) == 0:\\n print(no_courses_text)\\n raise SystemExit(1)\\n\\n digits = str(math.ceil(math.log10(len(choices))))\\n format_str = '{:' + digits + 'd} {}'\\n for n, c in enumerate(choices):\\n print(format_str.format(n, c))\\n try:\\n return [int(c) for c in input(text).split()]\\n except EOFError:\\n return []\",\n \"def combobox(self):\\n return self._combo\",\n \"def valid_options(self):\\n choices = self.choices()\\n\\n if not choices:\\n return None\\n\\n return [opt[0] for opt in choices]\",\n \"def widgets_from_abbreviations(self, seq):\\n result = []\\n for name, abbrev, default in seq:\\n widget = self.widget_from_abbrev(abbrev, default)\\n if not (isinstance(widget, ValueWidget) or isinstance(widget, fixed)):\\n if widget is None:\\n raise ValueError(\\\"{!r} cannot be transformed to a widget\\\".format(abbrev))\\n else:\\n raise TypeError(\\\"{!r} is not a ValueWidget\\\".format(widget))\\n if not widget.description:\\n widget.description = name\\n widget._kwarg = name\\n result.append(widget)\\n return result\",\n \"def multiple_choice():\\n\\n return [\\\"MAR\\\", \\\"MAR\\\", \\\"NI\\\", \\\"NI\\\", \\\"MCAR\\\"]\",\n \"def build_comboboxes(activities, events):\\n global comboboxes\\n # For each activity set up a selector for an event\\n\\n for activity in activities:\\n\\n # Setup frame for better display in gui\\n frame = Frame(main_window)\\n frame.configure(background=\\\"gray30\\\")\\n\\n # Label the left column as activity in a model + \\\"beautify gui\\\"\\n text = \\\"Activity name (model):\\\"\\n Label(frame, text=text, bg=\\\"gray30\\\", fg=\\\"white\\\", padx=5).grid(column=0, row=0)\\n Label(frame, text=activity, bg=\\\"gray30\\\", fg=\\\"white\\\").grid(column=0, row=1)\\n\\n # Set up the combobox for an event\\n combo = Combobox(frame)\\n combo['values'] = events\\n\\n # If activity is in events preselect the current one\\n if activity in events:\\n combo.current(events.index(activity))\\n\\n # Label the combobox and place label and box in frame\\n Label(frame, text=\\\"Event name (log):\\\", bg=\\\"gray30\\\", fg=\\\"white\\\", padx=5).grid(column=1, row=0)\\n combo.grid(column=1, row=1)\\n\\n # If the last activity in the graph is handled then do not write a separator\\n if activity != activities[-1]:\\n Separator(frame, orient=\\\"horizontal\\\").grid(row=2, columnspan=2, sticky=\\\"ew\\\", pady=10)\\n\\n comboboxes[activity] = combo\\n # place the frame in the main_window\\n frame.grid(column=0)\",\n \"def get_chosen_options(user):\\n user_profile = user.get_profile()\\n user_application = user_profile.application\\n np = user_application.np\\n ocl = eval(user_application.options_selected)\\n chosen_options = []\\n for oc in ocl:\\n chosen_options.append(Option.objects.get(opt_code=int(oc))) \\n return chosen_options\",\n \"def get_generic_choices(model, key, allow_null=False):\\n CHOICES = [('', '--------')] if allow_null else []\\n for i in model.objects.all().values_list(key, flat=True).distinct():\\n CHOICES.append((str(i), str(i)))\\n CHOICES.sort()\\n\\n return CHOICES\",\n \"def _combobox_choice(self, _=None):\\n combobox_string = self.value_combobox.var.get()\\n if combobox_string.startswith(\\\"Unknown: \\\"):\\n value = int(combobox_string[len(\\\"Unknown: \\\"):])\\n else:\\n value = int(self.value_combobox.var.get().split(\\\" \\\")[0])\\n self.master.change_field_value(self.field_name, value)\",\n \"def widgets(parameter: Parameter):\\n widgets = []\\n for key in parameter.keys():\\n textEdit = QTextEdit()\\n textEdit.setText(key)\\n widgets.append(textEdit)\\n if isinstance(parameter[key], Enum):\\n comboBox = MyQtEnumComboBox()\\n comboBox.fillValues(type(parameter[key]))\\n widgets.append(comboBox)\\n elif isinstance(parameter[key], bool):\\n comboBox = QComboBox()\\n comboBox.addItems((\\\"False\\\", \\\"True\\\"))\\n widgets.append(comboBox)\\n else:\\n textEdit = QTextEdit()\\n textEdit.setText(str(parameter[key]))\\n widgets.append(textEdit)\\n for widget in widgets:\\n widget.setFixedHeight(30)\\n return widgets\",\n \"def _getBrailleRegionsForComboBox(self, obj):\\n\\n self._debugGenerator(\\\"_getBrailleRegionsForComboBox\\\", obj)\\n\\n regions = []\\n\\n focusedRegionIndex = 0\\n label = self._script.getDisplayedLabel(obj)\\n if label and (len(label) > 0):\\n regions.append(braille.Region(label + \\\" \\\"))\\n focusedRegionIndex = 1\\n\\n # Check to see if the text is editable. If so, then we want\\n # to show the text attributes (such as selection -- see bug\\n # 496846 for more details).\\n #\\n textObj = None\\n for child in obj:\\n if child and child.getRole() == pyatspi.ROLE_TEXT:\\n textObj = child\\n if textObj and textObj.getState().contains(pyatspi.STATE_EDITABLE):\\n textRegion = braille.Text(textObj)\\n regions.append(textRegion)\\n else:\\n displayedText = self._script.getDisplayedText(obj)\\n if displayedText:\\n regions.append(braille.Region(displayedText))\\n\\n regions.append(braille.Region(\\n \\\" \\\" + rolenames.getBrailleForRoleName(obj)))\\n\\n # Things may not have gone as expected above, so we'll do some\\n # defensive programming to make sure we don't get an index out\\n # of bounds.\\n #\\n if focusedRegionIndex >= len(regions):\\n focusedRegionIndex = 0\\n if len(regions) == 0:\\n focusedRegion = None\\n else:\\n focusedRegion = regions[focusedRegionIndex]\\n\\n # [[[TODO: WDW - perhaps if a text area was created, we should\\n # give focus to it.]]]\\n #\\n return [regions, focusedRegion]\",\n \"def form_SelectChoiceCallableOptions(request):\\n schema = schemaish.Structure()\\n schema.add('mySelect', schemaish.Integer())\\n def _():\\n options = [(1,'a'),(2,'b'),(3,'c')]\\n for option in options:\\n yield option\\n\\n form = formish.Form(schema, 'form')\\n form['mySelect'].widget = formish.SelectChoice(_)\\n return form\",\n \"def __init__(self, *args, **kwargs):\\n super(TaggedContentItemForm, self).__init__(*args, **kwargs)\\n wtf = Tag.objects.filter(group__system=False)\\n wlist = [w for t, w in self.fields.items() if t.endswith(\\\"tags\\\")]\\n choices = []\\n for choice in wtf:\\n choices.append((choice.id, str(choice)))\\n [setattr(w, 'choices', choices) for w in wlist]\",\n \"def _comboSlot(self, select):\\n select = self.sender().itemText(select)\\n if qt4:\\n qs = str(self.sender().property(\\\"dom address\\\").toPyObject())\\n else:\\n qs = str(self.sender().property(\\\"dom address\\\"))\\n item = QtXml.QDomElement()\\n\\n ind = qs.rfind('/')\\n ids = qs[ind:]\\n\\n item = self.qhash[qs].elem.firstChildElement(\\\"Item\\\")\\n while(item.isNull() is False):\\n itemName = item.firstChildElement(\\\"Name\\\")\\n if(str(itemName.text()).strip() != select):\\n activ = item.firstChildElement(\\\"Activate\\\")\\n while(activ.isNull() is False):\\n s = str(activ.text()).strip() + ids\\n h = self.qhash[s]\\n widget_enabled = h.elem.attribute(\\\"Enabled\\\", \\\"True\\\")\\n widget_visible = h.elem.attribute(\\\"Visible\\\", \\\"Unknown\\\")\\n h.widget.setEnabled(False)\\n if(widget_visible != \\\"Unknown\\\"):\\n h.label.hide()\\n h.widget.hide()\\n activ = activ.nextSiblingElement(\\\"Activate\\\")\\n item = item.nextSiblingElement(\\\"Item\\\")\\n\\n item = self.qhash[qs].elem.firstChildElement(\\\"Item\\\")\\n while(item.isNull() is False):\\n itemName = item.firstChildElement(\\\"Name\\\")\\n if(str(itemName.text()).strip() == select):\\n activ = item.firstChildElement(\\\"Activate\\\")\\n while(activ.isNull() is False):\\n s = str(activ.text()).strip() + ids\\n h = self.qhash[s]\\n h.widget.setEnabled(True)\\n h.label.show()\\n h.widget.show()\\n activ = activ.nextSiblingElement(\\\"Activate\\\")\\n item = item.nextSiblingElement(\\\"Item\\\")\",\n \"def get_choices(self, cutoff=None):\\n queryset = self.get_queryset()\\n if queryset is None:\\n # Ensure that field.choices returns something sensible\\n # even when accessed with a read-only field.\\n return {}\\n\\n if cutoff is not None:\\n queryset = queryset[:cutoff]\\n\\n return OrderedDict(\\n [\\n (\\n # This line below is modifed.\\n item.pk,\\n self.display_value(item),\\n )\\n for item in queryset\\n ]\\n )\",\n \"def test_rendering_combobox(qtbot):\\n layer = Image(np.random.rand(8, 8))\\n qtctrl = QtImageControls(layer)\\n qtbot.addWidget(qtctrl)\\n combo = qtctrl.renderComboBox\\n opts = {combo.itemText(i) for i in range(combo.count())}\\n rendering_options = {\\n 'translucent',\\n 'additive',\\n 'iso',\\n 'mip',\\n 'minip',\\n 'attenuated_mip',\\n 'average',\\n }\\n assert opts == rendering_options\\n # programmatically updating rendering mode updates the combobox\\n layer.rendering = 'iso'\\n assert combo.findText('iso') == combo.currentIndex()\",\n \"def get_choicesdata(self):\\n selected_value = self.get_cleaned_value()\\n choicesdata = []\\n for value, label in self.get_choices_cached():\\n is_selected = value == selected_value\\n url = self.build_set_values_url(values=[value])\\n choicesdata.append({\\n 'url': url,\\n 'label': label,\\n 'is_selected': is_selected,\\n 'dom_id': '{}_{}'.format(self.get_inputfield_dom_id(), value)\\n })\\n return choicesdata\",\n \"def choices(self):\\n return self.__class__.get_setting_choices(self.key, **self.get_kwargs())\",\n \"def get_poll_choices(self, games: [Game]) -> [dict]:\\n answer_texts = []\\n for g in games:\\n answer_texts.append(g.name + \\\" - \\\" + g.genre)\\n answer_texts = sorted(answer_texts, key=str.lower)\\n poll_choices = []\\n for at in answer_texts:\\n poll_choices.append({\\\"text\\\": at})\\n return poll_choices\",\n \"def __str__(self):\\n return \\\"choice_text: \\\" + self.choice_text\",\n \"def _parse_choices(self, text):\\n choices = dict()\\n\\n matches = re.findall(self.choice_regex, text)\\n for match in matches:\\n # remove the brackets\\n match = match.replace('[[', '')\\n match = match.replace(']]', '')\\n\\n if '|' in match:\\n # format is {text}|{node_id}, the text and node id are different\\n text, node_id = match.split('|')\\n choices[node_id] = text\\n else:\\n choices[match] = match\\n\\n return choices\",\n \"def get_choices_new_protected():\\n ret = []\\n ret.append( (1, _(u'Nur Community-Mitglieder dürfen neue Beiträge leisten')) )\\n ret.append( (-1, _(u'Offener Zugang')) )\\n return ret\",\n \"def comboBox(args: list, slot) -> QComboBox:\\n comboBox = QComboBox()\\n comboBox.addItems(args[0])\\n comboBox.currentTextChanged.connect(slot)\\n return comboBox\",\n \"def choices(self):\\n\\n if self._choices == None:\\n self._choices = [ExperimentChoice(self, choice_name) for choice_name in self.choice_names]\\n\\n return self._choices\",\n \"def make_ddls( self, parent ):\\n # ---- 0\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n a_widget.bind( \\\"<<ComboboxSelected>>\\\", self.sync_ddl_0 )\\n\\n # AppGlobal.gui_style.style_combobox( a_widget )\\n self.ddl_0_widget = a_widget\\n\\n # ---- 1\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n self.ddl_1_widget = a_widget\\n\\n # ---- 2\\n a_widget = ttk.Combobox( parent,\\n width = self.arg_width + 5,\\n state = \\\"normal\\\",\\n textvariable = \\\"self.arg_2_var\\\" )\\n\\n # AppGlobal.gui_style.style_combobox( a_widget )\\n self.ddl_2_widget = a_widget\\n\\n # self.ddl_widgets = [ self.ddl_0_widget, self.ddl_1_widget, self.ddl_2_widget, ]\\n # print( self.ddl_widgets[ 0 ] == self.ddl_widgets[ 1 ])\\n\\n #self.load_ddl_0( )\",\n \"def __str__(self):\\n return self.choice_text\",\n \"def as_choices():\\n return (\\n # Live is currently disabled as a choice\\n # pending implementation\\n (\\\"live\\\", \\\"Use working directory\\\"),\\n (\\\"latest\\\", \\\"Use latest snapshot\\\"),\\n (\\\"pinned\\\", \\\"Pinned to snapshot\\\"),\\n )\",\n \"def comboBoxes(self):\\r\\n # Cities Combo Button\\r\\n self.comboCities = QComboBox()\\r\\n self.comboCities.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: 15px;\\r\\n background-color : #A8DBC5;\\r\\n border: 1px solid white;\\r\\n\\r\\n \\\"\\\"\\\")\\r\\n self.comboCities.addItems(\\r\\n ['Girón', 'Piedecuesta', 'Floridablanca', 'Bucaramanga'])\\r\\n self.grid.addWidget(self.comboCities, 6, 1, 1, 2)\\r\\n self.comboCities.setCurrentText(\\\"Bucaramanga\\\")\\r\\n # Payment Combo Button\\r\\n self.comboPayment = QComboBox()\\r\\n self.comboPayment.setStyleSheet(\\\"\\\"\\\"\\r\\n font-family: times;\\r\\n font-size: 15px;\\r\\n background-color : #A8DBC5;\\r\\n border: 1px solid white;\\r\\n\\r\\n \\\"\\\"\\\")\\r\\n self.comboPayment.addItems(['Efectivo', 'Nequi'])\\r\\n self.grid.addWidget(self.comboPayment, 7, 1, 1, 2)\",\n \"def get_choicesdata(self):\\n # selected_value = self.get_cleaned_value()\\n # choicesdata = []\\n # found_selected_value = False\\n # for value, label in self.get_choices():\\n # is_selected = value == selected_value\\n # if is_selected:\\n # found_selected_value = True\\n # url = self.build_set_values_url(values=[value])\\n # choicesdata.append({\\n # 'url': url,\\n # 'label': label,\\n # 'is_selected': is_selected\\n # })\\n choicesdata, found_selected_value = self.__make_choicesdata_list(\\n choices=self.get_choices(),\\n selected_value=self.get_cleaned_value())\\n if not found_selected_value and len(choicesdata) > 0:\\n selected_index = self.get_default_is_selected_index(choicesdata=choicesdata)\\n choicesdata[selected_index]['is_selected'] = True\\n return choicesdata\",\n \"def _find_options(self):\\r\\n elements = self.xml.findall('./options/option')\\r\\n return [{\\r\\n 'id': index,\\r\\n 'description': option.text,\\r\\n 'choice': option.get('choice')\\r\\n } for (index, option) in enumerate(elements)]\",\n \"def __str__(self):\\n return gettext('One of %s') % self._get_choices_str()\",\n \"def list_selector_widget(members=None,\\n preselect=None,\\n entry=False,\\n callback=None):\\n store, i=generate_list_model(members,\\n active_element=preselect)\\n\\n if entry:\\n combobox=gtk.ComboBoxEntry(store, column=0)\\n else:\\n combobox=gtk.ComboBox(store)\\n cell = gtk.CellRendererText()\\n combobox.pack_start(cell, expand=True)\\n combobox.add_attribute(cell, 'text', 0)\\n combobox.add_attribute(cell, 'background', 2)\\n\\n combobox.set_active(-1)\\n if i is None:\\n i = store.get_iter_first()\\n if i is not None:\\n combobox.set_active_iter(i)\\n\\n if entry:\\n def get_current_element(combo):\\n try:\\n return combo.get_model().get_value(combo.get_active_iter(), 1)\\n except (TypeError, AttributeError):\\n return unicode(combo.child.get_text())\\n def set_current_element(combo, t):\\n combo.child.set_text(t)\\n else:\\n def get_current_element(combo):\\n if combo.get_active_iter() is not None:\\n return combo.get_model().get_value(combo.get_active_iter(), 1)\\n else:\\n return None\\n def set_current_element(combo, el):\\n # Find the index of the element\\n l=[ t[0] for t in enumerate(combo.get_model()) if t[1][1] == el ]\\n if l:\\n # The element is present.\\n combo.set_active(l[0])\\n else:\\n combo.set_active_iter(combo.get_model().append( (unicode(el), el, None) ))\\n\\n # Bind the method to the combobox object\\n combobox.get_current_element = get_current_element.__get__(combobox)\\n combobox.set_current_element = set_current_element.__get__(combobox)\\n\\n if callback is not None:\\n combobox.connect('changed', callback)\\n\\n return combobox\",\n \"def initDefaultChoices(self):\\n return []\",\n \"def occurrence_choices():\\n OCCURRENCE_CHOICES = [('one_off', 'One Off'), ('weekly', 'Weekly'), ('fortnightly', 'Fortnightly')]\\n occurrence = forms.ChoiceField(choices=OCCURRENCE_CHOICES, widget=forms.RadioSelect())\\n return occurrence\",\n \"def get_action_choices():\\n from hardware.management.commands.gpio_buttons import Command\\n import re\\n pattern = re.compile(r'^on_(?P<name>\\\\w+)_press$')\\n choices = []\\n for member in dir(Command):\\n match = pattern.match(member)\\n if match:\\n action = match.groupdict()['name']\\n name = action.replace('_', ' ').title()\\n choices.append((action, name))\\n return choices\",\n \"def DrawComboBox(*args, **kwargs):\\n return _gdi_.RendererNative_DrawComboBox(*args, **kwargs)\",\n \"def fill_combobox(self):\\n sqlstring = \\\"SELECT first_name, last_name from employees WHERE enabled = 1 ORDER BY last_name ASC\\\"\\n self.CB_employee.addItem(\\\"\\\")\\n for employee in self.ms.c.execute(sqlstring):\\n self.CB_employee.addItem(\\\" \\\".join((employee[0], employee[1])))\\n sqlstring = \\\"SELECT first_name, last_name from employees WHERE enabled = 0 ORDER BY last_name ASC\\\"\\n for employee in self.ms.c.execute(sqlstring):\\n self.CB_employee.addItem(\\\" \\\".join((employee[0], employee[1])))\",\n \"def get_text_type():\\n opt = ['text', 'email', 'password']\\n inp = option_menu(opt, 'Select text type:')\\n\\n # mark text type with option\\n OPTIONS['text-type'] = opt[inp]\\n\\n # add option to collected list\\n add_to_collected('text type', opt[inp])\\n\\n return\",\n \"def get_context(self, name, value, attrs=None, choices=()):\\n context = super(TriStateCheckboxSelectMultiple, self).get_context(\\n name, value, attrs\\n )\\n\\n choices = dict(it.chain(self.choices, choices))\\n if value is None:\\n value = dict.fromkeys(choices, False)\\n else:\\n value = dict(dict.fromkeys(choices, False).items() +\\n value.items())\\n\\n context['values'] = [\\n (choice, label, value[choice])\\n for choice, label in choices.iteritems()\\n ]\\n\\n return context\",\n \"def _get_choices ( self, context, path = '' ):\\n choices = []\\n gdc = context.get_data_context\\n for name in context.data_contexts:\\n next_path = path_for( path, name )\\n choices.append( TemplateChoice( choice_value = next_path ) )\\n choices.extend( self._get_choices( gdc( name ), next_path ) )\\n \\n return choices\",\n \"def set_dropdown_b_options(value):\\n options_c = []\\n if value=='C':\\n options_c = [{'label': '1', 'value': '1'},\\n {'label': '2', 'value': '2'}]\\n if value == 'D':\\n options_c = [{'label': '3', 'value': '3'},\\n {'label': '4', 'value': '4'}]\\n if value=='E':\\n options_c = [{'label': '5', 'value': '5'},\\n {'label': '6', 'value': '6'}]\\n if value == 'F':\\n options_c = [{'label': '7', 'value': '7'},\\n {'label': '8', 'value': '8'}]\\n return options_c\",\n \"def objects_to_choices(queryset):\\n res = []\\n for elm in queryset:\\n res.append((elm.pk, unicode(elm)))\\n return res\",\n \"def choice(text, choices, **kwargs):\\n return click.prompt(click.style('> {}'.format(text), fg='blue', bold=True),\\n type=click.Choice(choices),\\n **kwargs)\",\n \"def on_comboBox_enceinte_activated(self, index):\\n nom_enceinte = self.comboBox_enceinte.currentText()\\n marque = [x[2] for x in self.enceintes if x[1] == nom_enceinte][0]\\n n_serie = [x[4] for x in self.enceintes if x[1] == nom_enceinte][0]\\n model =[x[3] for x in self.enceintes if x[1] == nom_enceinte][0]\\n \\n \\n self.lineEdit_marque.setText(marque)\\n self.lineEdit_n_serie.setText(n_serie)\\n self.lineEdit_model.setText(model)\",\n \"def list_selector(title=None,\\n text=None,\\n members=None,\\n controller=None,\\n preselect=None,\\n entry=False):\\n combobox = list_selector_widget(members=members,\\n preselect=preselect,\\n entry=entry)\\n\\n d = gtk.Dialog(title=title,\\n parent=None,\\n flags=gtk.DIALOG_DESTROY_WITH_PARENT,\\n buttons=( gtk.STOCK_OK, gtk.RESPONSE_OK,\\n gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL ))\\n\\n if text is not None:\\n l=gtk.Label(text)\\n l.show()\\n d.vbox.add(l)\\n\\n d.vbox.add(combobox)\\n combobox.show_all()\\n\\n d.connect('key-press-event', dialog_keypressed_cb)\\n\\n d.show()\\n center_on_mouse(d)\\n res=d.run()\\n retval=None\\n if res == gtk.RESPONSE_OK:\\n retval=combobox.get_current_element()\\n d.destroy()\\n return retval\",\n \"def display_choose(self, text, choices):\\n cur_index = 0\\n key = None\\n while key != 'KEY_NEWLINE':\\n if key == 'KEY_UP':\\n cur_index = max(cur_index - 1, 0)\\n elif key == 'KEY_DOWN':\\n cur_index = min(cur_index + 1, len(choices) - 1)\\n self.stdscr.erase()\\n for line in text:\\n self.stdscr.addstr(f'{PADCHAR}{line}\\\\n')\\n for index, value in enumerate(choices):\\n self.stdscr.addstr('\\\\n')\\n self.stdscr.addstr(PADCHAR)\\n self.stdscr.addstr(value, color_pair(7 if index == cur_index else 1))\\n self.stdscr.addstr(f'\\\\n\\\\n{PADCHAR}') \\n key = self.get_key() \\n return cur_index\",\n \"def get_classes(self, code):\\n \\n select = v.Combobox(\\n _metadata={'name':code}, \\n items=self.items, \\n v_model=None, \\n dense=True,\\n hide_details=True\\n )\\n \\n select.observe(partial(self.store, code), 'v_model')\\n \\n return select\",\n \"def getOptionsNames(self) -> List[unicode]:\\n ...\",\n \"def the_option_named(text: str) -> \\\"SelectByText\\\":\\n return SelectByText(text)\",\n \"def __init__(self, *args, **kwargs):\\n super(ChoiceFieldType, self).__init__(*args, **kwargs)\\n\\n self.choices = self.get_field_info_key('choices')\",\n \"def prepareAutoComplete(editor, text, charPos, lineStartCharPos,\\r\\n wikiDocument, settings):\\r\\n return []\",\n \"def _validate_selects(text, response):\\n answer_options = re.split(settings.MULTISELECT_DELIMITER_RE, str(text))\\n choices = map(lambda choice: choice.lower(), response.event.choices)\\n logger.debug('Question (%s) answer choices are: %s, given answers: %s' % (datatype, choices, answer_options))\\n new_answers = copy(answer_options)\\n for idx, opt in enumerate(answer_options):\\n logger.debug('Trying to format (m)select answer: \\\"%s\\\"' % opt)\\n try: \\n #in the case that we accept numbers to indicate option selection\\n opt_int = int(opt)\\n if not (1 <= opt_int <= len(choices)): \\n return text, 'Answer %s must be between 1 and %s' % (opt_int, len(choices))\\n else:\\n new_answers[idx] = str(opt_int)\\n\\n except ValueError: \\n # in the case where we accept the actual text of the question\\n logger.debug('Caught value error, trying to parse answer string choice of: %s' % choices)\\n if opt.lower() not in choices:\\n return text, 'Answer must be one of the choices'\\n else:\\n new_answers[idx] = str(choices.index(opt.lower()) + 1)\\n return ' '.join(new_answers), None\",\n \"def choice(choices=[], message=\\\"Pick something.\\\", title=None):\\n return dialog(\\n \\\"choice\\\",\\n choices=choices,\\n message=message,\\n title=title,\\n )\",\n \"def autocomplete_possibilities():\\n try:\\n # get data sent by client\\n typed_input = request.args.get('q')\\n print(' ')\\n print('\\\\n------ getting autocomplete_possibilities ------')\\n print(f\\\"recived: input:{typed_input}\\\")\\n\\n # call the google API\\n results = gmaps.places_autocomplete(typed_input)\\n data = [\\n {'value': r['place_id'], 'text': r['description']}\\n for r in results\\n ]\\n\\n # Pass data to the front end\\n print(f'returning: {data}')\\n return jsonify(data)\\n\\n except Exception as e:\\n print(\\\"AJAX excepted \\\" + str(e))\\n return str(e)\",\n \"def complete_opt_format(self, text, *_):\\n return [t + \\\" \\\" for t in FORMATTERS if t.startswith(text)]\",\n \"def choices(self, cl):\\n # TODO: Determine if non-static choices would be cleaner here.\\n # Honestly, I tried a more generic version and it was even harder to\\n # follow than this version.\\n yield {\\n 'selected': not (self.lookup_val_gte or self.lookup_val_lt),\\n 'query_string': cl.get_query_string({}, [self.lookup_kwarg_gte,\\n self.lookup_kwarg_lt]),\\n 'display': 'All'\\n }\\n\\n goal = settings.FACEBOOK_CLICK_GOAL\\n yield {\\n 'selected': self.lookup_val_gte and not self.lookup_val_lt,\\n 'query_string': cl.get_query_string({self.lookup_kwarg_gte: goal},\\n [self.lookup_kwarg_lt]),\\n 'display': 'Yes'\\n }\\n yield {\\n 'selected': self.lookup_val_lt and not self.lookup_val_gte,\\n 'query_string': cl.get_query_string({self.lookup_kwarg_lt: goal},\\n [self.lookup_kwarg_gte]),\\n 'display': 'No'\\n }\",\n \"def build_options(slot, snacks):\\n \\n if slot == 'Fast':\\n return [\\n {'text': 'Pizza', 'value': 'Pizza'},\\n {'text': 'Fries', 'value': 'Fries'},\\n {'text': 'Franky', 'value': 'Franky'},\\n {'text': 'Burger', 'value': 'Burger'},\\n {'text': 'Sandwich', 'value': 'Sandwich'}\\n \\n \\n ]\\n elif slot == 'drink':\\n return [\\n {'text': 'Coca-Cola', 'value': 'Coca-cola'},\\n {'text': 'Appy', 'value': 'Appy'},\\n \\n {'text': 'Beer', 'value': 'Beer'},\\n {'text': 'Frooti', 'value': 'Frooti'},\\n {'text': 'Pepsi', 'value': 'Pepsi'}\\n \\n ]\",\n \"def sortedChoices(self, inText):\\n choices = self.splitText(inText)\\n sortedChoices = [text for text in self.formatList if text in choices]\\n if len(choices) == len(sortedChoices):\\n return (sortedChoices, True)\\n else:\\n return (sortedChoices, False)\",\n \"def _getChoices(self, acronym):\\n # get matches from acronymDB\\n matches = []\\n if(acronym in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n if(acronym[-1] == \\\"s\\\" and acronym[:-1] in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n\\n # create training data\\n X_train, y_train = [], []\\n for definition, articleID, ignored_var in matches:\\n text = self.articleDB[articleID]\\n X_train.append(\\n ExpansionChoice(article_id=articleID, article_text=text))\\n y_train.append(definition)\\n\\n # create y labels to group similar acronyms\\n y_labels, labelToExpansion = self._processChoices(y_train)\\n\\n return X_train, y_labels, labelToExpansion\",\n \"def multiple_choices(self, choices, response):\\n for elem in self.method_order:\\n if elem in choices:\\n return [elem]\\n raise NoData\",\n \"def set_dropdown_b_options(value):\\n options_b = []\\n if value=='A':\\n options_b = [{'label': 'C', 'value': 'C'},\\n {'label': 'D', 'value': 'D'}]\\n if value == 'B':\\n options_b = [{'label': 'E', 'value': 'E'},\\n {'label': 'F', 'value': 'F'}]\\n return options_b\",\n \"def choices(self, choices: Iterable[Tuple[str, str]]):\\n try:\\n iter(choices)\\n except TypeError:\\n raise TypeError(\\\"'choices' isn't a valid iterable\\\")\\n\\n apply_choices = []\\n for i, (choice_id, choice_label) in enumerate(choices):\\n apply_choices.append((str(choice_id), str(choice_label)))\\n\\n if len(apply_choices) < 2:\\n raise ValueError(\\\"you need to specify at least two choices\\\")\\n\\n self._choices = apply_choices\\n self.specific.refresh()\\n self._selected = 0\",\n \"def before_choose_candidate_listener(self, session, task):\\n choices = [PromptChoice('d', 'eDit', self.importer_edit)]\\n if task.candidates:\\n choices.append(PromptChoice('c', 'edit Candidates',\\n self.importer_edit_candidate))\\n\\n return choices\",\n \"def _get_completion(self, original_text, remaining_text, options):\\n if self.should_hide_completions(original_text=original_text,\\n remaining_text=remaining_text,\\n allowed_suffixes=(\\\" \\\", \\\".\\\")):\\n return []\\n\\n return [(option, len(remaining_text)) for option in options\\n if option.startswith(remaining_text) and not option.startswith(\\\"_\\\")]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6981832","0.64423245","0.64423245","0.6404376","0.63695693","0.62808853","0.615837","0.61527145","0.60969144","0.60806596","0.6074747","0.59950155","0.59769976","0.59458613","0.59082","0.5853147","0.5850785","0.58412063","0.58367765","0.58151263","0.58029526","0.57948583","0.5792428","0.5791611","0.57795537","0.575053","0.57465565","0.5732782","0.57096493","0.57060355","0.56966597","0.5679937","0.563918","0.5615509","0.5599425","0.55810773","0.557214","0.55665624","0.5565737","0.55615264","0.5538559","0.55243224","0.552361","0.5519529","0.5518102","0.55137795","0.5486652","0.5486258","0.5484074","0.5468984","0.54495764","0.5447661","0.5433353","0.54303634","0.5424338","0.5411812","0.5406592","0.5394407","0.5391574","0.53862125","0.53735393","0.53548867","0.5352198","0.5345724","0.5333625","0.53295","0.53213644","0.5318512","0.53181034","0.5290123","0.526435","0.5263117","0.5259166","0.5246472","0.5245967","0.5245039","0.5238816","0.52362823","0.52285147","0.5223594","0.5216064","0.5216059","0.5197635","0.51916355","0.5189832","0.51882505","0.5170863","0.5163809","0.5163533","0.51623505","0.51605934","0.5157795","0.5157584","0.5154029","0.5144694","0.5143628","0.5143539","0.5141602","0.5133782","0.5127406"],"string":"[\n \"0.6981832\",\n \"0.64423245\",\n \"0.64423245\",\n \"0.6404376\",\n \"0.63695693\",\n \"0.62808853\",\n \"0.615837\",\n \"0.61527145\",\n \"0.60969144\",\n \"0.60806596\",\n \"0.6074747\",\n \"0.59950155\",\n \"0.59769976\",\n \"0.59458613\",\n \"0.59082\",\n \"0.5853147\",\n \"0.5850785\",\n \"0.58412063\",\n \"0.58367765\",\n \"0.58151263\",\n \"0.58029526\",\n \"0.57948583\",\n \"0.5792428\",\n \"0.5791611\",\n \"0.57795537\",\n \"0.575053\",\n \"0.57465565\",\n \"0.5732782\",\n \"0.57096493\",\n \"0.57060355\",\n \"0.56966597\",\n \"0.5679937\",\n \"0.563918\",\n \"0.5615509\",\n \"0.5599425\",\n \"0.55810773\",\n \"0.557214\",\n \"0.55665624\",\n \"0.5565737\",\n \"0.55615264\",\n \"0.5538559\",\n \"0.55243224\",\n \"0.552361\",\n \"0.5519529\",\n \"0.5518102\",\n \"0.55137795\",\n \"0.5486652\",\n \"0.5486258\",\n \"0.5484074\",\n \"0.5468984\",\n \"0.54495764\",\n \"0.5447661\",\n \"0.5433353\",\n \"0.54303634\",\n \"0.5424338\",\n \"0.5411812\",\n \"0.5406592\",\n \"0.5394407\",\n \"0.5391574\",\n \"0.53862125\",\n \"0.53735393\",\n \"0.53548867\",\n \"0.5352198\",\n \"0.5345724\",\n \"0.5333625\",\n \"0.53295\",\n \"0.53213644\",\n \"0.5318512\",\n \"0.53181034\",\n \"0.5290123\",\n \"0.526435\",\n \"0.5263117\",\n \"0.5259166\",\n \"0.5246472\",\n \"0.5245967\",\n \"0.5245039\",\n \"0.5238816\",\n \"0.52362823\",\n \"0.52285147\",\n \"0.5223594\",\n \"0.5216064\",\n \"0.5216059\",\n \"0.5197635\",\n \"0.51916355\",\n \"0.5189832\",\n \"0.51882505\",\n \"0.5170863\",\n \"0.5163809\",\n \"0.5163533\",\n \"0.51623505\",\n \"0.51605934\",\n \"0.5157795\",\n \"0.5157584\",\n \"0.5154029\",\n \"0.5144694\",\n \"0.5143628\",\n \"0.5143539\",\n \"0.5141602\",\n \"0.5133782\",\n \"0.5127406\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5867416"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":90,"cells":{"query":{"kind":"string","value":"Note this initialization command will start spawning traffic and select the specified human demonstrators for imitation learning"},"document":{"kind":"string","value":"def initialize_element(self):\n init_command = {\n \"StartLearning\": True,\n \"AgentID\": 1854\n }\n msg = json.dumps(init_command).encode('unicode_escape')\n self.socket_control.send(msg)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def starting_tests(self):\n# disable menus during testing, because their message loop seems to interfere\n# with the natlink message loop which waits for recognitionMimic to\n# finish\n self.testing = 1","def initMana():\n run(\"chariot-me -i\")","def starting_tests(self):\n# disable menus during testing, because their message loop seems to interfere\n# with the natlink message loop which waits for recognitionMimic to\n# finish\n self.enable_menus(0)\n self.testing = 1\n self.parent.starting_tests()","def main(argv):\n\n IP_ADDRESS = \"10.0.1.16\"\n\n robot = MistyRobot(IP_ADDRESS)\n\n print \"HELP: %s\\r\\n\" % pprint(robot.GetHelp())\n print \"DEVICE INFORMATION: %s\\r\\n\" % pprint(robot.GetDeviceInformation())\n print \"BATTERY LEVEL: %s\\r\\n\" % pprint(robot.GetBatteryLevel())\n print \"AUDIO CLIPS: %s\\r\\n\" % pprint(robot.GetListOfAudioClips())\n print \"AUDIO FILES: %s\\r\\n\" % pprint(robot.GetListOfAudioFiles())\n print \"VIDEO CLIPS: %s\\r\\n\" % pprint(robot.GetListOfVideoClips())\n\n print \"SENSORS: %s\\r\\n\" % pprint(robot.GetStringSensorValues())\n\n robot.LocomotionTrack(leftTrackSpeed=3, rightTrackSpeed=3)\n robot.Stop(delay=4)\n\n # This API call doesn't seem to work properly or consistently,\n # only moves head down, regardless of values\n #robot.MoveHead(pitch=-5, roll=0, yaw=0, velocity=4)\n #robot.MoveHead(pitch=5, roll=0, yaw=0, velocity=4, delay=3)\n\n # This API call doesn't seem to work\n robot.DriveTime(linearVelocity=3, angularVelocity=5, timeMS=5000, degrees=0)\n\n # This API call doesn't seem to work\n robot.Drive(linearVelocity=3, angularVelocity=5)\n robot.Stop(delay=4)\n\n robot.StartFaceTraining(faceId=\"person1\")\n robot.CancelFaceTraining(delay=5)\n\n print \"LEARNED FACES: %s\\r\\n\" % pprint(robot.GetLearnedFaces())\n\n robot.ClearLearnedFaces()\n\n print \"LEARNED FACES AFTER CLEAR: %s\\r\\n\" % pprint(robot.GetLearnedFaces())\n\n robot.SetMood(\"sad\")\n robot.SetMood(\"angry\", delay=3)\n robot.SetMood(\"groggy\", delay=3)\n robot.SetMood(\"confused\", delay=3)\n robot.SetMood(\"content\", delay=3)\n robot.SetMood(\"concerned\", delay=3)\n robot.SetMood(\"unamused\", delay=3)\n robot.SetMood(\"happy\", delay=3)\n robot.SetMood(\"love\", delay=3)","def setUp(self):\n super().setUp()\n self.devices = _DEVICE_STRATEGY()\n command_line = [\"pool\", \"create\", self._POOLNAME] + self.devices\n RUNNER(command_line)","def init(self, sevabot):\n self.sevabot = sevabot\n self.standard_xml = \"sevabot/alice/std-startup.xml\"\n\n self.commands = {\n \"!alice start\": self.start,\n \"!alice stop\" : self.stop\n }","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setup_start_agents = False","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline= True ) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","def discovery():\n launch_training_on_all_splits(experiment='discovery', splits=DISCOVERY_SPLIT, base_model='pretrained', dropout=0.7987, learning_rate=0.00009659)","def setup_interactive(shared):\n parser = setup_args()\n parser.add_argument('--port', type=int, default=PORT, help='Port to listen on.')\n SHARED['opt'] = parser.parse_args(print_args=False)\n\n SHARED['opt']['task'] = 'parlai.agents.local_human.local_human:LocalHumanAgent'\n\n # Create model and assign it to the specified task\n agent = create_agent(SHARED.get('opt'), requireModelExists=True)\n SHARED['agent'] = agent\n # SHARED['world'] = create_task(SHARED.get('opt'), SHARED['agent'])\n\n # show args after loading model\n parser.opt = agent.opt\n parser.print_args()\n return agent.opt","def setup(self):\n self.machine = Machine(['a', 'b', 'c', '_'])","def train_main(cls):\n launcher = cls()\n launcher.launch()","def __init__(self):\n super().__init__(sys.argv)\n self.s1 = serverControl()\n self.c1 = clientControl(\"Markus\")\n self.c2 = clientControl(\"Hannes\")","def init_mission(agent_host, port=0, agent_type='Unknown', mission_type='Unknown', mission_seed=0,\n movement_type='Continuous'):\n\n # -- Set up the mission via XML definition --#\n mission_xml, msize, reward_goal, reward_intermediate, n_intermediate_rewards, reward_timeout, reward_sendcommand, timeout = GetMissionInstance(\n mission_type, mission_seed, agent_type)\n my_mission = MalmoPython.MissionSpec(mission_xml, True)\n my_mission.forceWorldReset()\n\n # -- Enforce the specific restriction for the assessed exercise --#\n # -- If you want a super agent, define one for you self --#\n my_mission.setModeToCreative()\n if agent_type.lower() == 'random':\n n = msize\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid')\n my_mission.requestVideoWithDepth(320, 240)\n elif agent_type.lower() == 'simple':\n n = msize\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid');\n my_mission.requestVideo(320, 240)\n elif agent_type.lower() == 'realistic':\n n = 1 # n=1 means local info only !\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid');\n my_mission.requestVideoWithDepth(320, 240)\n my_mission.observeFullInventory()\n elif agent_type.lower() == 'helper':\n n = 100\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid');\n my_mission.requestVideoWithDepth(320, 240)\n else:\n # -- Define a custom agent and add the sensors you need --#\n n = 100\n my_mission.observeGrid(-n, -1, -n, n, 1, n, 'grid');\n my_mission.requestVideoWithDepth(320, 240)\n\n # -- Add support for the specific movement type requested (and given the constraints of the assignment) --#\n # -- See e.g. http://microsoft.github.io/malmo/0.17.0/Schemas/MissionHandlers.html --#\n if movement_type.lower() == 'absolute':\n my_mission.allowAllAbsoluteMovementCommands()\n elif movement_type.lower() == 'continuous':\n my_mission.allowContinuousMovementCommand('move')\n my_mission.allowContinuousMovementCommand('strafe')\n my_mission.allowContinuousMovementCommand('pitch')\n my_mission.allowContinuousMovementCommand('turn')\n my_mission.allowContinuousMovementCommand('crouch')\n elif movement_type.lower() == 'discrete':\n my_mission.allowDiscreteMovementCommand('turn')\n my_mission.allowDiscreteMovementCommand('move')\n my_mission.allowDiscreteMovementCommand('movenorth')\n my_mission.allowDiscreteMovementCommand('moveeast')\n my_mission.allowDiscreteMovementCommand('movesouth')\n my_mission.allowDiscreteMovementCommand('movewest')\n my_mission.allowDiscreteMovementCommand('look')\n\n # -- Get the resulting xml (and return in order to check that conditions match the report) --#\n final_xml = my_mission.getAsXML(True)\n\n # Set up a recording for later inspection\n my_mission_record = MalmoPython.MissionRecordSpec('tmp' + \".tgz\")\n my_mission_record.recordRewards()\n my_mission_record.recordMP4(24, 400000)\n\n # -- Attempt to start a mission --#\n max_retries = 5\n for retry in range(max_retries):\n try:\n agent_host.startMission(my_mission, my_mission_record)\n break\n except RuntimeError as e:\n if retry == max_retries - 1:\n print(\"Error starting mission:\", e)\n exit(1)\n else:\n time.sleep(2)\n\n # -- Loop until mission starts: --#\n print(\"Waiting for the mission to start \")\n state_t = agent_host.getWorldState()\n while not state_t.has_mission_begun:\n sys.stdout.write(\".\")\n time.sleep(0.1)\n state_t = agent_host.getWorldState()\n for error in state_t.errors:\n print(\"Error:\", error.text)\n\n print\n print(\"Mission started (xml returned)... \")\n return final_xml, reward_goal, reward_intermediate, n_intermediate_rewards, reward_timeout, reward_sendcommand, timeout","def __init__(self, agent_id=\"default\", experiment_id=\"default\"):\n self.runtime = runtime()\n self.agent_id = agent_id\n self.experiment_id = experiment_id","def initialize_robot():\n\n proxy_motion = naoqi.ALProxy(\"ALMotion\", IP_ROBOT, PORT_ROBOT)\n proxy_motion.wakeUp()\n\n proxy_autonomous_life = naoqi.ALProxy(\"ALAutonomousLife\", IP_ROBOT, PORT_ROBOT)\n proxy_autonomous_life.setState(\"disabled\")\n\n proxy_motion = naoqi.ALProxy(\"ALMotion\", IP_ROBOT, PORT_ROBOT)\n proxy_motion.wakeUp()","def __init__(self, machine):\n super().__init__(machine)\n self.features['has_steppers'] = True","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.00000001, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.001, display=True) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","def __init__(self, numOfGames, muteOutput, randomAI, AIforHuman):\n self.numOfGames = numOfGames\n self.muteOutput = muteOutput\n self.maxTimeOut = 10000\n\n self.AIforHuman = AIforHuman\n self.gameRules = GameRules()\n self.AIPlayer = TicTacToeAgent()\n\n if randomAI:\n self.AIPlayer = randomAgent()\n else:\n self.AIPlayer = TicTacToeAgent()\n if AIforHuman:\n self.HumanAgent = randomAgent()\n else:\n self.HumanAgent = TicTacToeAgent1()","def main():\n print(\"runner\")\n runner = Runner()\n stop_on_idle = True\n probes = []\n for url in urls:\n probe_cls = random.choice((HttpProbe, ThreadProbe, ShellProbe))\n runner.probes.append(probe_cls(url))\n\n runner.run()","def setUp(self):\n lang = self._sim_lang\n self._simulator = self._find_resource(\n f\"drake/examples/hardware_sim/hardware_sim_{lang}\")\n self._example_scenarios = self._find_resource(\n \"drake/examples/hardware_sim/example_scenarios.yaml\")\n self._test_scenarios = self._find_resource(\n \"drake/examples/hardware_sim/test/test_scenarios.yaml\")\n self._default_extra = {\n # For our smoke test, exit fairly quickly.\n \"simulation_duration\": 0.0625,\n }","def __init__(self, numOfGames, muteOutput, randomAI, AIforHuman):\n self.numOfGames = numOfGames\n self.muteOutput = muteOutput\n self.maxTimeOut = 30 \n\n self.AIforHuman = AIforHuman\n self.gameRules = GameRules()\n self.AIPlayer = TicTacToeAgent()\n\n if randomAI:\n self.AIPlayer = randomAgent()\n else:\n self.AIPlayer = TicTacToeAgent()\n if AIforHuman:\n self.HumanAgent = randomAgent()\n else:\n self.HumanAgent = keyboardAgent()","def run(num_trials):\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.1, display=True) \n # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=num_trials) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n\n a.performace_report(num_trials)","def multi_agent_example():\n env = holodeck.make(\"CyberPunkCity-FollowSight\")\n\n cmd0 = np.array([0, 0, -2, 10])\n cmd1 = np.array([0, 0, 0])\n for i in range(10):\n env.reset()\n env.tick()\n env.act(\"uav0\", cmd0)\n env.act(\"nav0\", cmd1)\n for _ in range(1000):\n states = env.tick()\n pixels = states[\"uav0\"][\"RGBCamera\"]","def experiment_init(self):\n pass","def __init__(self, network: Network):\n if LOG[\"ExperimentAI\"]:\n print(\"[ExperimentAI] Initializing AI\")\n self.network = network","def init():\n\n @click.group(cls=cli.make_commands(__name__))\n def run():\n \"\"\"Cross-cell supervision tools.\"\"\"\n cli.init_logger('daemon.conf')\n\n return run","def main():\n\n\t# Run setup\n\ts = Setup()\n\tcontrolRoom, studio, newscaster = s.beginSetup()\n\n\t# Start cameras\n\tcontrolRoom.startCameras()\n\tprint 'Cameras started'\n\tcontrolRoom.setCameraSize()\n\n\tif len(controlRoom.studio.cameras) > 1:\n\t\tprint 'Everything up and running...'\n\n\t\t# Fetch a camera that best matches the headpose angle\n\t\tcamera = controlRoom.getClosestCamera()\n\t\twhile True:\n\t\t\t# If advance camera selection algo indicates true, fetch camera closest to headpose\n\t\t\tif controlRoom.cameraSelectionADV():\n\t\t\t\tcamera = controlRoom.getClosestCamera()\n\t\t\tprint 'Active camera: ' + str(camera.cameraID)\n\t\t\t\n\t\t\t# Capture frame or in simulation mode, light up led\n\t\t\tcamera.capture()\n\n\telif len(controlRoom.studio.cameras) == 1:\n\t\twhile True:\n\t\t\tcontrolRoom.studio.cameras[0].capture()\n\t\t\ttime.sleep(2)\n\telse:\n\t\tprint 'No cameras found! Something seems to be wrong...'\n\n\t# Shutdown all cameras and kill all windows\n\tcontrolRoom.shutdownCameras()","def parley(self):\n wait_times = constants.TUTORIAL_WAIT_TIMES\n self.introduce_chat_interface()\n self.wait_for_response(\n message='Please type a greeting message to continue.',\n delay_time=wait_times['chat-interface'],\n )\n self.introduce_persona()\n self.wait_for_response(\n message=constants.APPRENTICE_PERSONA_ROLE_INSTRUCTION,\n delay_time=wait_times['persona'],\n )\n self.introduce_partner_entity()\n self.wait_for_response(\n message=constants.APPRENTICE_CHITCHAT_INSTRUCTION,\n delay_time=wait_times['persona'],\n )\n self.introduce_partner_knowledge()\n self.wait_for_response(\n message=constants.APPRENTICE_PERSONA_MSG_INSTRUCTION,\n delay_time=wait_times['knowledge'],\n )\n self.go_for_start()\n self.episodeDone = True","def agent_init(self):\n pass","def main(_):\n description = xm.ExperimentDescription(\n 'HIS - trial=%d' % FLAGS.trial, tags=['his'])\n experiment = build_experiment()\n xm.launch_experiment(description, experiment)","def initialize_home_hub(argv):\n parse_cmd_line_opts(argv)\n init_logging()\n init_error_reporting()\n \n # Verify we have a valid home id\n if HOME_ID is None:\n print('Home ID is invalid or missing. Please provide an integer following the -i flag')\n exit()\n\n # Begin Home Hub Specific Setup\n logger.info('Starting the Home Hub main program for Home: %s', HOME_ID)\n\n # Get the email and password for this HH's user from the env vars\n powernet_user_email = os.getenv('POWERNET_USER_EMAIL', None)\n powernet_user_password = os.getenv('POWERNET_USER_PASSWORD', None)\n \n if powernet_user_email is None:\n logger.info('Missing the required login email address')\n logger.info('Please set the POWERNET_USER_EMAIL environment variable and try again')\n exit()\n \n if powernet_user_password is None:\n logger.info('Missing the required login password')\n logger.info('Please set the POWERNET_USER_PASSWORD environment variable and try again')\n exit()\n \n # attempt to authenticate against our API\n form_payload = {'email': powernet_user_email, 'password': powernet_user_password}\n response = requests.post('https://pwrnet-158117.appspot.com/api/v1/powernet_user/auth/', data=form_payload)\n auth_token = response.json()['token']\n\n # Initializing variables for queue and threads\n rpi = HardwareInterface(house_id=HOME_ID, gpio_map=None, auth_token=auth_token)\n buffer_size = 8\n q_ai = Queue(buffer_size)\n\n # Initialize threads\n producer_ai_thread = Thread(name='Producer', target=rpi.producer_ai, args=(q_ai,))\n producer_ai_thread.start()\n\n consumer_ai_thread = Thread(name='Consumer', target=rpi.consumer_ai, args=(q_ai,))\n consumer_ai_thread.start()\n\n devices_thread = Thread(name='Device', target=rpi.devices_th)\n devices_thread.start()\n\n load_control_thread = Thread(name=\"LoadControl\", target=rpi.local_controller_th)\n load_control_thread.start()","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.start_agents_once = False\n self.start_servers_once = False\n self.setup_start_agents = False\n self.setup_start_servers = False","def __init__(self, a):\n # sanity check\n assert(isinstance(a, agent.Agent))\n \n # save the agent reference\n self.a = a\n \n self.a.tc_h1(\"CONNECT TESTS\")\n \n # create the host instance\n self.host = host.Host(a, STA)\n # create the access point instances\n self.ap1 = accesspoint.AccessPoint(a, AP1, channel=5, period=100, ssid=\"louis\")\n self.ap2 = accesspoint.AccessPoint(a, AP2, channel=11, period=100, ssid=\"louis\")\n\n # reset the host (resetting the MIBs)\n self.host.reset(True)\n\n # set the host MAC address\n self.host.dbg_macaddr()","def start(self):\n self.__init__()\n self.set_n_players()\n self.init_players()\n self.init_territory_selection_phase()\n self.init_troop_deployment_phase()\n # self.game_phase()","def main():\n utils.vip_main(actuator_agent, identity='platform.d.actuator')","def onSpawn(self):\n self.spawned = True\n self._interactor.initialiseDevices()","def _starting_up():\n global bus, skill_manager, event_scheduler\n\n bus.on('intent_failure', FallbackSkill.make_intent_failure_handler(bus))\n\n # Create the Intent manager, which converts utterances to intents\n # This is the heart of the voice invoked skill system\n service = IntentService(bus)\n try:\n PadatiousService(bus, service)\n except Exception as e:\n LOG.exception('Failed to create padatious handlers '\n '({})'.format(repr(e)))\n event_scheduler = EventScheduler(bus)\n\n # Create a thread that monitors the loaded skills, looking for updates\n try:\n skill_manager = SkillManager(bus)\n except MsmException:\n # skill manager couldn't be created, wait for network connection and\n # retry\n LOG.info('Msm is uninitialized and requires network connection',\n 'to fetch skill information\\n'\n 'Waiting for network connection...')\n while not connected():\n time.sleep(30)\n skill_manager = SkillManager(bus)\n\n skill_manager.daemon = True\n # Wait until priority skills have been loaded before checking\n # network connection\n skill_manager.load_priority()\n skill_manager.start()\n check_connection()","def main():\n\n run_manual_session()\n # run_automated_session()","def __init__(self, scenario):\n client.__init__(self, scenario)\n # TODO: Your initialization, if any (not likely). Oh, and remove the next line.\n raise Exception( \"DO NOT instantiate the skeleton implementation\" )","def setup(client):\n client.add_cog(ProcessDisplay(client))","def main():\n driver = Driver()\n driver.start()","def launch(**kwargs):\n\n logger, loghost, logport, clients, guis, params = unpack_launcher(**kwargs)\n config = load_config(kwargs['config'], logger=logger)\n\n\n ao_client = find_client(logger, clients, 'nidaqmx')\n ai_client = find_client(logger, clients, 'nidaqmx_ai')\n\n # Instantiate Monitor script\n laser_stabilizer = LaserStabilizer(\n config=kwargs['config'],\n ao_client=ao_client,\n ai_client=ai_client\n )\n\n update_service = Service()\n update_service.assign_module(module=laser_stabilizer)\n update_service.assign_logger(logger=logger)\n update_server, update_port = create_server(update_service, logger, host=get_ip())\n logger.update_data(data={'port': update_port})\n laser_stabilizer.gui.set_network_info(port=update_port)\n update_server.start()\n\n # Run continuously\n # Note that the actual operation inside run() can be paused using the update server\n while True:\n\n laser_stabilizer.run()","def setUp(self):\n _, instance_path, shared_inputs = sys.argv\n app = lnt.server.ui.app.App.create_standalone(instance_path)\n app.testing = True\n self.client = app.test_client()\n self.shared_inputs = shared_inputs","def main():\n tester = Tester()\n # parse args, load configuration and create all required objects.\n tester.setup_experiment()\n # GO!\n tester.run_experiment()","def minimal_interactive_cli_bootstrap(client):\n # Fetch available TAN mechanisms by the bank, if we don't know it already. If the client was created with cached data,\n # the function is already set.\n if not client.get_current_tan_mechanism():\n client.fetch_tan_mechanisms()\n mechanisms = list(client.get_tan_mechanisms().items())\n if len(mechanisms) > 1:\n print(\"Multiple tan mechanisms available. Which one do you prefer?\")\n for i, m in enumerate(mechanisms):\n print(i, \"Function {p.security_function}: {p.name}\".format(p=m[1]))\n choice = input(\"Choice: \").strip()\n client.set_tan_mechanism(mechanisms[int(choice)][0])\n\n if client.is_tan_media_required() and not client.selected_tan_medium:\n print(\"We need the name of the TAN medium, let's fetch them from the bank\")\n m = client.get_tan_media()\n if len(m[1]) == 1:\n client.set_tan_medium(m[1][0])\n else:\n print(\"Multiple tan media available. Which one do you prefer?\")\n for i, mm in enumerate(m[1]):\n print(i,\n \"Medium {p.tan_medium_name}: Phone no. {p.mobile_number_masked}, Last used {p.last_use}\".format(\n p=mm))\n choice = input(\"Choice: \").strip()\n client.set_tan_medium(m[1][int(choice)])","async def start(self, roles, dialogs):\n self.__init__(self.active_role, (self.active_help, self.external_help))","def init():\r\n\t# add grabber tools based on proxy tools\r\n\tfor proxyWrapper in vizconnect.getToolsWithMode('Proxy'):\r\n\t\tgrabberTool = tools.grabber.HandGrabber(usingPhysics=False,\r\n\t\t\t\t\t\t\t\t\t\t\t\tusingSprings=False,\r\n\t\t\t\t\t\t\t\t\t\t\t\tplacementMode=tools.placer.MODE_DROP_DOWN)\r\n\t\t\r\n\t\tname = 'grabber_tool_based_on_'+proxyWrapper.getName()\r\n\t\tgrabberWrapper = vizconnect.addTool(raw=grabberTool,\r\n\t\t\t\t\t\t\t\t\t\t\tname=name,\r\n\t\t\t\t\t\t\t\t\t\t\tmake='Virtual',\r\n\t\t\t\t\t\t\t\t\t\t\tmodel='Grabber')\r\n\t\t# parent the grabber wrapper to the proxy's parent\r\n\t\tgrabberWrapper.setParent(proxyWrapper)\r\n\t\t\r\n\t\tgrabberTool.setItems(grabbableItems)\r\n\t\r\n\tviz.callback(viz.getEventID('RESET_THE_LOFT_LAYOUT'), lambda e: resetMovedObjects())","def main():\n args = _parse_arguments()\n\n util.log_init(\"sitl_A%s_%s.txt\" % (args.id, util.get_latest_log(\"latest_sitl.txt\")), util.log_level[args.level])\n\n shared.AGENT_ID = 'A%s' % args.id\n shared.AGENT_COUNT = args.n\n shared.CURRENT_ALGORITHM = args.algorithm\n shared.AGENT_CHARACTER = args.character\n shared.des_alt = args.alt\n \n util.log_info(\"AGENT_ID = %s\" % shared.AGENT_ID)\n util.log_info(\"Algorithm: %s\" % shared.CURRENT_ALGORITHM)\n util.log_info(\"Agent type: %s\" % shared.AGENT_CHARACTER)\n\n print \"Start simulator (SITL)\"\n sitl = SITL(args.pix) # initialize SITL with firmware path\n\n if shared.AGENT_ID in start_loc:\n sitl_args = ['--home=%s' % start_loc[shared.AGENT_ID]]\n else:\n sitl_args = ['--home=%s' % start_loc['FFF']]\n \n # Pre-recorded coordinates.\n #sitl_args = ['-I0', '--model', 'quad', '--home=31.301201,121.498192,9,353']\t\n sitl.launch(sitl_args, await_ready=True, restart=True)\n\n # Connect to the vehicle. (Spawn an instance of Vehicle named \"vehicle\")\n # connection port is coded in the file name of the firmware like \"ac3.4.5_port5760\"\n # use regular expression to search the string and extract port number\n port = re.search(r'port\\d{4}', args.pix)\n port = re.search(r'\\d{4}', port.group()).group()\n\n print \"Connecting to copter on: TCP: 127.0.0.1:%s\" % port\n copter = nav.connect('tcp:127.0.0.1:%s' % port, wait_ready=True, rate=20)\n util.log_info(\"Copter connected. Firmware: %s\" % copter.version)\n \n if not args.xbee: # simulate XBee using ZeroMQ\n [pub, sub] = comm.zmq_init(comm_port_list[shared.AGENT_ID], comm_port_list)\n subscriber_thread = comm.Subscriber(shared.AGENT_ID, sub)\n subscriber_thread.start()\n xbee = pub # make xbee the publisher\n util.log_info(\"ZeroMQ initialzied.\") \n \n else: # use actual xbee ports\n ser = serial.Serial(args.xbee, 57600)\n xbee = comm.xbee_init(ser)\n util.log_info(\"Xbee initialzed.\")\n\n info = \"IFO,%s connected with firmware %s\" % (shared.AGENT_ID, copter.version)\n comm.xbee_broadcast(xbee, info)\n\n _add_listeners(copter)\n\n takeoff_thread = nav.Takeoff(copter, xbee, shared.des_alt, 3)\n purge_thread = comm.Purge(shared.neighbors)\n broadcast_thread = comm.Broadcast(shared.AGENT_ID, copter, xbee)\n flocking_thread = _choose_algorithm(copter, xbee, shared.neighbors)\n\n takeoff_thread.start()\n takeoff_thread.join() # wait until takeoff procedure completed\n\n if shared.status['airborne']: # only execute the threads when airborne\n util.log_info(\"Copter is airborne, starting threads.\")\n broadcast_thread.start()\n purge_thread.start()\n flocking_thread.start()\n\n # main loop\n while True:\n try: time.sleep(.2)\n except KeyboardInterrupt: break\n \n if shared.status['airborne']:\n # echo exiting status\n if shared.status['exiting']:\n info = \"IFO,%s %s-ing.\" % (shared.AGENT_ID,shared.status['command'])\n comm.xbee_broadcast(xbee, info)\n util.log_info(info)\n\n # if an rtl or land command is received, kill flocking and set the `exiting` flag\n elif shared.status['command'] == 'RTL' or shared.status['command'] == 'LAND':\n shared.status['thread_flag'] |= shared.FLOCKING_FLAG\n nav.set_mode(copter, shared.status['command'])\n shared.status['exiting'] = True\n\n if not flocking_thread.is_alive(): # break the loop if finished\n break\n\n nav.wait_for_disarm(copter) # wait for disarm\n comm.xbee_broadcast(xbee, 'IFO,%s terminated.' % shared.AGENT_ID)\n\n # clean up\n purge_thread.stop()\n while purge_thread.is_alive(): \n util.log_info('Waiting for purge to shutdown') \n purge_thread.join(3)\n util.log_info('Purge killed.')\n\n broadcast_thread.stop()\n while broadcast_thread.is_alive(): \n util.log_info('Waiting for broadcast to shutdown') \n broadcast_thread.join(3)\n util.log_info('Broadcast killed.')\n\n copter.close()\n util.log_info(\"Copter shutdown.\")\n\n if args.xbee:\n xbee.halt()\n ser.close()\n util.log_info(\"Xbee and serial closed.\")\n else:\n subscriber_thread.stop()\n while subscriber_thread.is_alive(): \n util.log_info('Waiting for Subscriber to shutdown') \n subscriber_thread.join(3)\n util.log_info('Subscriber killed.')\n\n sitl.stop()\n util.log_info(\"SITL shutdown.\")","def initialize(self):\n if self.real:\n self.agent.connect(self)\n else:\n self.connect() # Connect python client to VREP\n self.agent.connect(self)","def setup(cls):\n cls.runner = CliRunner()\n cls.agent_name = \"myagent\"\n cls.cwd = os.getcwd()\n cls.t = tempfile.mkdtemp()\n # copy the 'packages' directory in the parent of the agent folder.\n shutil.copytree(Path(CUR_PATH, \"..\", \"packages\"), Path(cls.t, \"packages\"))\n\n os.chdir(cls.t)\n result = cls.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"init\", \"--author\", AUTHOR],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n result = cls.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"create\", \"--local\", cls.agent_name],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n os.chdir(cls.agent_name)\n # add connection first time","def setup(bot):\n bot.add_cog(MyAnimeList())","def launch_devices(self):\n self.data[0], temp = alghoritm.temperature(self.data[0], self.set_thermostat, 0) # get value\n HC35_3S.launch(self.data_path, self.samples_size, temp) # set it via device\n\n self.data[1], humidi = alghoritm.humidity(self.data[1], self.set_humidifier, 0)\n humidifier.launch(self.data_path, self.samples_size, humidi)\n\n self.data[2], moistu = alghoritm.moisture(self.data[2], self.set_sprinklers, 0)\n HUNTER.launch(self.data_path, self.samples_size, moistu)\n\n self.data[3], o2 = alghoritm.o2(self.data[3], self.set_ventilation, 0)\n ventilation.launch_o2(self.data_path, self.samples_size, o2)\n\n self.data[4], co2 = alghoritm.co2(self.data[4], self.set_ventilation, 0)\n ventilation.launch_co2(self.data_path, self.samples_size, co2)","def parley(self):\n wait_times = constants.TUTORIAL_WAIT_TIMES\n self.introduce_chat_interface()\n self.wait_for_response(\n message='Please type a greeting message to continue.',\n delay_time=wait_times['chat-interface'],\n )\n self.introduce_knowledgeable_entity()\n self.wait_for_response(\n message=constants.ONBOARDING_ACKNOWLEDGE_UNDERSTOOD,\n delay_time=wait_times['chat-interface'],\n )\n self.introduce_search()\n self.wait_for_response(\n message=constants.ONBOARDING_ACKNOWLEDGE_UNDERSTOOD,\n delay_time=wait_times['knowledge'],\n )\n self.try_search()\n self.wait_for_response_with_search()\n self.introduce_persona()\n self.wait_for_response_with_search()\n self.go_for_start()\n self.episodeDone = True","def start_run(self, context: RobotRunnerContext) -> None:\n rospy.init_node(\"robot_runner\", disable_signals=True)\n self.ina219_profiler = INA219ProfilerClient()\n self.cpu_mem_profiler = ResourceProfilerClient()","def setup(bot: Bot) -> None:\n bot.add_cog(Latency(bot))","def setUp(self):\n # Direct connection used to match the property values\n self.sockobj = socket(AF_INET, SOCK_STREAM)\n self.sockobj.settimeout(socket_timeout)\n # Connect to the selected server\n self.sockobj.connect(server) \n self.pyclient = PySimpleClient()\n self.cmd_num = 0\n for servo_type in app_nr.values():\n self.__dict__[servo_type] = self.pyclient.getComponent(\"MINORSERVO/\" + servo_type)","def load_scenario(self):\n logger.info(\"Loading scenario...\")\n for manager in self.config[\"fleets\"]:\n name = manager[\"name\"]\n password = manager[\"password\"] if \"password\" in manager else faker_factory.password()\n fleet_type = manager[\"fleet_type\"]\n strategy = manager.get(\"strategy\")\n icon = manager.get(\"icon\")\n agent = self.create_fleetmanager_agent(name, password, fleet_type=fleet_type, strategy=strategy)\n\n self.set_icon(agent, icon, default=fleet_type)\n\n while len(self.manager_agents) < self.config.num_managers:\n time.sleep(0.1)\n\n for transport in self.config[\"transports\"]:\n name = transport[\"name\"]\n password = transport[\"password\"] if \"password\" in transport else faker_factory.password()\n position = transport[\"position\"]\n fleetmanager = transport[\"fleet\"]\n fleet_type = transport[\"fleet_type\"]\n speed = transport.get(\"speed\")\n trust = transport.get(\"trust\")\n rates = transport.get(\"rates\")\n velocity_factor = transport.get(\"velocity_factor\")\n fuel = transport.get(\"fuel\")\n autonomy = transport.get(\"autonomy\")\n current_autonomy = transport.get(\"current_autonomy\")\n strategy = transport.get(\"strategy\")\n icon = transport.get(\"icon\")\n agent = self.create_transport_agent(name, password, position=position, speed=speed, fleet_type=fleet_type,\n fleetmanager=fleetmanager, strategy=strategy, autonomy=autonomy,\n current_autonomy=current_autonomy, trust=trust, rates=rates, velocity_factor=velocity_factor)\n\n if icon:\n self.set_icon(agent, icon, default=fleet_type)\n\n for customer in self.config[\"customers\"]:\n name = customer[\"name\"]\n password = customer[\"password\"] if \"password\" in customer else faker_factory.password()\n fleet_type = customer[\"fleet_type\"]\n position = customer[\"position\"]\n target = customer[\"destination\"]\n strategy = customer.get(\"strategy\")\n icon = customer.get(\"icon\")\n agent = self.create_customer_agent(name, password, fleet_type, position=position, target=target,\n strategy=strategy)\n\n self.set_icon(agent, icon, default=\"customer\")\n\n for station in self.config[\"stations\"]:\n password = station[\"password\"] if \"password\" in station else faker_factory.password()\n strategy = station.get(\"strategy\")\n icon = station.get(\"icon\")\n agent = self.create_station_agent(station[\"name\"], password, position=station[\"position\"],\n power=station[\"power\"], places=station[\"places\"], strategy=strategy)\n self.set_icon(agent, icon, default=\"electric_station\")","def initialize(self):\n\n # --------- BEGIN YOUR CODE ----------\n\n # This is exactly the same as Human.initialize, just copy the code over\n\n # --------- END YOUR CODE ----------\n pass","def __init__(self, configs, simulator, wait_time=3):\n self.configs = configs\n self.sim = simulator.sim\n self.gripper = VREP_Gripper()\n self.open()","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Always start the servers for each test variant\n self.start_agents_once = False\n self.start_servers_once = False\n\n # Whether to skip tearDown\n self.skip_teardown = False","def run(): \n learning_rate = 0.42\n discount_rate = 0.15\n initial_q_hat = 4\n \n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n print \"Failed trials: \"\n print a.get_failed_trials()\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","def setUp(self):\n bed_patient = PatientsGenerator(0, 1, 0, 'a')\n self.admitgen = AdmissionsGenerator(bed_patient, [-1])","def initialize_ai(self):\n\n self.gid, self.genome = constants.genomes_to_run[self.identifier]\n self.genome.fitness = -1\n self.net = neat.nn.FeedForwardNetwork.create(self.genome, constants.conf)\n # self.net = neat.nn.RecurrentNetwork\n # .create(self.genome, constants.conf)","def test_init_experiment(self):\n optimizer = \"RandomSearch\"\n name = \"test_init_experiment\"\n param_defs = {\n \"x\": MinMaxNumericParamDef(0, 1),\n \"name\": NominalParamDef([\"A\", \"B\", \"C\"])\n }\n minimization = True\n\n LAss = PrettyLabAssistant()\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)\n\n exp_ass = LAss.exp_assistants[name]\n\n assert_equal(exp_ass.optimizer, optimizer)\n assert_is_none(exp_ass.optimizer_arguments, None)\n assert_equal(exp_ass.experiment.minimization_problem, minimization)\n with assert_raises(ValueError):\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=False) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.0000001, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\n\n print 'alpha, gamma:', a.alpha, a.gamma\n print 'penalties:', a.total_penalties\n print 'total rewards:', a.total_rewards","def test_create_tang_1(self):\n command_line = (\n self._MENU\n + [self._POOLNAME]\n + self._DEVICES\n + [\"--clevis=tang\", \"--trust-url\", \"--tang-url=http\"]\n )\n TEST_RUNNER(command_line)","def _init_agents(self):\n self.agents = [Agent(e=0.1, a=0.1, row=self.row, col=self.col) for i in range(2)]","def __init__(self,\n environment_spec: specs.EnvironmentSpec,\n ):\n # Create the actor\n actor = delta_hedge_actor.DeltaHedgeActor(environment_spec.actions)\n learner = fake_learner.FakeLeaner()\n\n super().__init__(\n actor=actor,\n learner=learner,\n min_observations=100,\n observations_per_step=1e9)","def initialize(self):\n self.ha_url = self.args.get(\"ha_url\", None)\n self.use_current_brightness = self.args.get(\"use_current_brightness\", False)\n self.condition = self.args.get(\"condition\")\n self.lights = self.args[\"lights\"]\n self.listen_state(self.change_lights_color, self.args[\"media_player\"], attribute = self.args.get(\"photo_attribute\", \"entity_picture\"))","def main():\n import optparse\n import sys\n parser = optparse.OptionParser()\n parser.add_option('-c', '--controller', default='NewController',\n help='Controller class to instantiate.')\n parser.add_option('-o', '--outputs', type='int', default=5,\n help='Number of outputs to use on the hardware.')\n options, _arguments = parser.parse_args()\n try:\n Demo(options.controller, options.outputs)\n except controller.ConnectionError:\n sys.exit('ABORT: Could not find a suitable device.')","def __init__(self, machine):\n super().__init__(machine)\n\n # Set default platform features. Each platform interface can change\n # these to notify the framework of the specific features it supports.\n self.features['has_drivers'] = True\n self.features['max_pulse'] = 255","def __init__(self, machine):\n super().__init__(machine)\n\n # Set default platform features. Each platform interface can change\n # these to notify the framework of the specific features it supports.\n self.features['has_drivers'] = True\n self.features['max_pulse'] = 255","def editor_multi_agent_example():\n agent_definitions = [\n AgentDefinition(\"uav0\", agents.UavAgent, [sensors.RGBCamera, sensors.LocationSensor]),\n AgentDefinition(\"uav1\", agents.UavAgent, [sensors.LocationSensor, sensors.VelocitySensor])\n ]\n env = HolodeckEnvironment(agent_definitions, start_world=False)\n\n cmd0 = np.array([0, 0, -2, 10])\n cmd1 = np.array([0, 0, 5, 10])\n\n for i in range(10):\n env.reset()\n env.act(\"uav0\", cmd0)\n env.act(\"uav1\", cmd1)\n for _ in range(1000):\n states = env.tick()","def setUp(self):\n # Start the servers and agents\n super().setUp()\n\n # Get the parameters\n self.ior_flags = self.params.get(\n \"ior_flags\", \"/run/ior/*\")\n self.test_file = self.ior_cmd.test_file.value","def test_defaults():\n model = torch.nn.Module()\n dataset = torch.utils.data.Dataset()\n dataloader = torch.utils.data.DataLoader(dataset)\n loaders = OrderedDict()\n loaders[\"train\"] = dataloader\n\n test_callbacks = OrderedDict(\n [\n (\"_timer\", TimerCallback),\n (\"_metrics\", MetricManagerCallback),\n (\"_validation\", ValidationManagerCallback),\n (\"_saver\", CheckpointCallback),\n (\"_console\", ConsoleLogger),\n (\"_tensorboard\", TensorboardLogger),\n (\"_exception\", ExceptionCallback),\n ]\n )\n\n exp = SupervisedExperiment(model=model, loaders=loaders)\n _test_callbacks(test_callbacks, exp)","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create learning agent\n # a = e.create_agent(RandomAgent) # create random agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.01)\n # reduce update_delay to speed up simulation\n sys.stdout = open(\"./output.txt\", \"w\")\n tic = time()\n sim.run(n_trials=100) # press Esc or close pygame window to quit\n toc = time()\n sys.stdout = sys.__stdout__\n\n print \"Totoal time used: {}.\".format(toc - tic)\n parse(\"./output.txt\")","def test_mcts_agent(self):\n logging.info(\"Starting test_mcts_agent\")\n dirname = os.path.dirname(__file__)\n filename = os.path.join(dirname, \"../configs/factory_floor_simple.yaml\")\n parameters = getParameters(filename)\n env = FactoryFloor(parameters)\n obs = env.reset()\n\n mctsAgents = []\n\n randomagent = 'aiagents.single.RandomAgent.RandomAgent'\n for robotId in env.action_space.spaces.keys():\n mctsparams = {'treeAgent':{'class': randomagent, 'id':robotId, 'parameters':{} },\n 'rolloutAgent':{'class': randomagent, 'id':robotId, 'parameters':{} }} \n mctsparams['simulator'] = dict(parameters)\n mctsparams['simulator']['fullname'] = \"aienvs.FactoryFloor.FactoryFloor.FactoryFloor\"\n \n mctsAgents.append(MctsAgent(robotId, env.action_space, env.observation_space , mctsparams))\n\n complexAgent = BasicComplexAgent(mctsAgents, env.action_space, env.observation_space)\n\n episode = Episode(complexAgent, env, obs, render=True)\n episode.run()","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--identifier\", required=True,\n help=\"A short name/identifier for your experiment, e.g. 'ex42b'.\")\n args = parser.parse_args()\n\n train(args)","def _initialize_runners_startup(self):\n if self.command_group.is_cmd0_runner():\n self._initialize_runner(self.command_group.cmd0)\n if self.command_group.is_cmd1_runner():\n self._initialize_runner(self.command_group.cmd1)\n if self.command_group.is_cmd2_runner():\n self._initialize_runner(self.command_group.cmd2)","def setUp(self):\n self.anime_link = \"https://animepertutti.com/sword-art-online-alicization-war-of-underworld-sub-ita-streaming-download-z\" #noqa","def start_with_console():\n print_welcome()\n option = input(\"Choose a number [1/2/3]: \")\n cexc.check_start_exceptions(option)\n if option == \"1\":\n picture = create_white_picture_with_inputs()\n elif option == \"2\":\n picture = load_picture_with_inputs()\n elif option == \"3\":\n picture = create_probability_picture_with_inputs()\n steps = get_steps(input(\"Give a number of steps to do (max=30000): \"))\n print_big_number_announcement(steps)\n Simulator(steps, picture).simulate()","def setup_class(cls):\n super(TestCliTransferFetchAINetwork, cls).setup_class()\n cls.agent_name2 = \"agent_\" + \"\".join(\n random.choices(string.ascii_lowercase, k=5) # nosec\n )\n cls.create_agents(cls.agent_name2)\n\n cls.gen_key(cls.agent_name)\n cls.gen_key(cls.agent_name2)","def setUp(self):\n self.s = Simulation()\n self.s['Retina']=GeneratorSheet(nominal_density=4.0)\n self.s['V1']= CFSheet(nominal_density=4.0)\n self.s['V2'] = CFSheet(nominal_density=4.0)\n\n self.s.connect('Retina','V1',delay=0.5,connection_type=CFProjection,\n name='RtoV1',learning_fn=CFPLF_Hebbian())\n\n self.s.connect('Retina','V2',delay=0.5,connection_type=CFProjection,\n name='RtoV2',learning_fn=CFPLF_Hebbian())","def setUp(self):\n self.delegate = AlwaysHitDelegate(\"\")\n self.environment = BattleEnvironment()","def setup(bot: Bot) -> None:\n bot.add_cog(VoiceGate(bot))","def pre_launch(mission):\n started_since = mission.ut() - mission.current_step[\"start_ut\"]\n if started_since > 5:\n mission.next()\n elif mission.current_step[\"first_call\"]:\n vessel = mission.conn.space_center.active_vessel\n ap = vessel.auto_pilot\n\n ap.engage()\n ap.target_pitch_and_heading(90, 90)\n vessel.control.throttle = 1\n vessel.control.sas = False\n vessel.control.rcs = mission.parameters.get('use_rcs', False)","async def setup(bot):\n await bot.add_cog(People(bot))","def start(self):\n # iPhone\n #driver = webdriver.Remote(browser_name=\"iphone\", command_executor='http://172.24.101.36:3001/hub')\n # Android\n #driver = webdriver.Remote(browser_name=\"android\", command_executor='http://127.0.0.1:8080/hub')\n # Google Chrome \n #driver = webdriver.Chrome()\n # Firefox \n #FirefoxProfile fp = new FirefoxProfile();\n #fp.setPreference(\"webdriver.load.strategy\", \"unstable\");\n #WebDriver driver = new FirefoxDriver(fp);\n \n #driver = webdriver.Firefox(firefox_profile=self.disableImages())\n driver = webdriver.Firefox()\n \n self.driver = driver","def start(self):\n # iPhone\n #driver = webdriver.Remote(browser_name=\"iphone\", command_executor='http://172.24.101.36:3001/hub')\n # Android\n #driver = webdriver.Remote(browser_name=\"android\", command_executor='http://127.0.0.1:8080/hub')\n # Google Chrome \n #driver = webdriver.Chrome()\n # Firefox \n #FirefoxProfile fp = new FirefoxProfile();\n #fp.setPreference(\"webdriver.load.strategy\", \"unstable\");\n #WebDriver driver = new FirefoxDriver(fp);\n \n #driver = webdriver.Firefox(firefox_profile=self.disableImages())\n driver = webdriver.Firefox()\n \n self.driver = driver","def setUp(self):\n os.environ[\"PADDLE_TRAINERS_NUM\"] = \"2\"\n os.environ[\n \"PADDLE_PSERVERS_IP_PORT_LIST\"\n ] = \"127.0.0.1:36001,127.0.0.2:36001\"","def initialise_sim(self):\n pass","def generate(simulator, p, starts, goals, environment, r):\n num_gen_humans = min(len(starts), len(goals))\n print(\"Generating auto humans:\", num_gen_humans)\n from agents.humans.human_configs import HumanConfigs\n for i in range(num_gen_humans):\n start_config = generate_config_from_pos_3(starts[i])\n goal_config = generate_config_from_pos_3(goals[i])\n start_goal_configs = HumanConfigs(start_config, goal_config)\n human_i_name = \"auto_%04d\" % i\n # Generates a random human from the environment\n new_human_i = Human.generate_human_with_configs(\n start_goal_configs,\n generate_appearance=p.render_3D,\n name=human_i_name\n )\n # update renderer and get human traversible if it exists\n if p.render_3D:\n r.add_human(new_human_i)\n environment[\"human_traversible\"] = \\\n np.array(r.get_human_traversible())\n\n # Input human fields into simulator\n simulator.add_agent(new_human_i)","def init_process(mech):\n gases[mech] = ct.Solution(mech)\n gases[mech].transport_model = 'Multi'","def main():\n if input(\"Mode 0=raw / 1=wrapper: ? [0]\") == \"1\":\n # Wrapped mode\n try:\n number = os.environ[\"ROBOT_ID\"]\n except KeyError:\n number = int(input(\"Enter robot number (1-5):\"))\n robot = PiBot.PiBot(robot_nr=number, directory=\"../\")\n suite = get_suite(robot, number)\n gripper = input(\"Include gripper tests (0=no, 1=yes)? [1]\")\n if gripper != \"0\":\n suite.add(\"Clear gripper space... testing gripper up-down\",\n \"gripper up-down\", robot.set_grabber_height,\n [60, 10], [5, 5], [], [], 1)\n suite.add(\"Clear gripper space... testing gripper open-close\",\n \"gripper open-close\", robot.close_grabber,\n [80, 5], [5, 5], [], [], 1)\n else:\n # Raw mode\n robot = commRaspMain.PiBot()\n while not all(map(lambda fn: fn(), [robot._motors_enable,\n robot._encoders_enable,\n robot._servo_enable])):\n time.sleep(0.05)\n robot._tof_init()\n robot._gyro_start()\n robot._adc_conf(3)\n number = int(input(\"Enter robot number (1-5):\"))\n suite = get_suite(robot, number)\n\n robot._motorL_set(0)\n robot._motorR_set(0)\n\n suite.execute()","def swait_setup_random_number(swait, **kw):\n swait.reset()\n swait.scan.put(\"Passive\")\n swait.calc.put(\"RNDM\")\n swait.scan.put(\".1 second\")\n swait.desc.put(\"uniform random numbers\")","def maya_start_up():\n import maya.utils as mu\n mu.executeDeferred(\"import mliber;reload(mliber);mliber.show_in_maya()\")","def test_Demo(self):\n self._run(self._example_scenarios, \"Demo\")"],"string":"[\n \"def starting_tests(self):\\n# disable menus during testing, because their message loop seems to interfere\\n# with the natlink message loop which waits for recognitionMimic to\\n# finish\\n self.testing = 1\",\n \"def initMana():\\n run(\\\"chariot-me -i\\\")\",\n \"def starting_tests(self):\\n# disable menus during testing, because their message loop seems to interfere\\n# with the natlink message loop which waits for recognitionMimic to\\n# finish\\n self.enable_menus(0)\\n self.testing = 1\\n self.parent.starting_tests()\",\n \"def main(argv):\\n\\n IP_ADDRESS = \\\"10.0.1.16\\\"\\n\\n robot = MistyRobot(IP_ADDRESS)\\n\\n print \\\"HELP: %s\\\\r\\\\n\\\" % pprint(robot.GetHelp())\\n print \\\"DEVICE INFORMATION: %s\\\\r\\\\n\\\" % pprint(robot.GetDeviceInformation())\\n print \\\"BATTERY LEVEL: %s\\\\r\\\\n\\\" % pprint(robot.GetBatteryLevel())\\n print \\\"AUDIO CLIPS: %s\\\\r\\\\n\\\" % pprint(robot.GetListOfAudioClips())\\n print \\\"AUDIO FILES: %s\\\\r\\\\n\\\" % pprint(robot.GetListOfAudioFiles())\\n print \\\"VIDEO CLIPS: %s\\\\r\\\\n\\\" % pprint(robot.GetListOfVideoClips())\\n\\n print \\\"SENSORS: %s\\\\r\\\\n\\\" % pprint(robot.GetStringSensorValues())\\n\\n robot.LocomotionTrack(leftTrackSpeed=3, rightTrackSpeed=3)\\n robot.Stop(delay=4)\\n\\n # This API call doesn't seem to work properly or consistently,\\n # only moves head down, regardless of values\\n #robot.MoveHead(pitch=-5, roll=0, yaw=0, velocity=4)\\n #robot.MoveHead(pitch=5, roll=0, yaw=0, velocity=4, delay=3)\\n\\n # This API call doesn't seem to work\\n robot.DriveTime(linearVelocity=3, angularVelocity=5, timeMS=5000, degrees=0)\\n\\n # This API call doesn't seem to work\\n robot.Drive(linearVelocity=3, angularVelocity=5)\\n robot.Stop(delay=4)\\n\\n robot.StartFaceTraining(faceId=\\\"person1\\\")\\n robot.CancelFaceTraining(delay=5)\\n\\n print \\\"LEARNED FACES: %s\\\\r\\\\n\\\" % pprint(robot.GetLearnedFaces())\\n\\n robot.ClearLearnedFaces()\\n\\n print \\\"LEARNED FACES AFTER CLEAR: %s\\\\r\\\\n\\\" % pprint(robot.GetLearnedFaces())\\n\\n robot.SetMood(\\\"sad\\\")\\n robot.SetMood(\\\"angry\\\", delay=3)\\n robot.SetMood(\\\"groggy\\\", delay=3)\\n robot.SetMood(\\\"confused\\\", delay=3)\\n robot.SetMood(\\\"content\\\", delay=3)\\n robot.SetMood(\\\"concerned\\\", delay=3)\\n robot.SetMood(\\\"unamused\\\", delay=3)\\n robot.SetMood(\\\"happy\\\", delay=3)\\n robot.SetMood(\\\"love\\\", delay=3)\",\n \"def setUp(self):\\n super().setUp()\\n self.devices = _DEVICE_STRATEGY()\\n command_line = [\\\"pool\\\", \\\"create\\\", self._POOLNAME] + self.devices\\n RUNNER(command_line)\",\n \"def init(self, sevabot):\\n self.sevabot = sevabot\\n self.standard_xml = \\\"sevabot/alice/std-startup.xml\\\"\\n\\n self.commands = {\\n \\\"!alice start\\\": self.start,\\n \\\"!alice stop\\\" : self.stop\\n }\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.setup_start_agents = False\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline= True ) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"def discovery():\\n launch_training_on_all_splits(experiment='discovery', splits=DISCOVERY_SPLIT, base_model='pretrained', dropout=0.7987, learning_rate=0.00009659)\",\n \"def setup_interactive(shared):\\n parser = setup_args()\\n parser.add_argument('--port', type=int, default=PORT, help='Port to listen on.')\\n SHARED['opt'] = parser.parse_args(print_args=False)\\n\\n SHARED['opt']['task'] = 'parlai.agents.local_human.local_human:LocalHumanAgent'\\n\\n # Create model and assign it to the specified task\\n agent = create_agent(SHARED.get('opt'), requireModelExists=True)\\n SHARED['agent'] = agent\\n # SHARED['world'] = create_task(SHARED.get('opt'), SHARED['agent'])\\n\\n # show args after loading model\\n parser.opt = agent.opt\\n parser.print_args()\\n return agent.opt\",\n \"def setup(self):\\n self.machine = Machine(['a', 'b', 'c', '_'])\",\n \"def train_main(cls):\\n launcher = cls()\\n launcher.launch()\",\n \"def __init__(self):\\n super().__init__(sys.argv)\\n self.s1 = serverControl()\\n self.c1 = clientControl(\\\"Markus\\\")\\n self.c2 = clientControl(\\\"Hannes\\\")\",\n \"def init_mission(agent_host, port=0, agent_type='Unknown', mission_type='Unknown', mission_seed=0,\\n movement_type='Continuous'):\\n\\n # -- Set up the mission via XML definition --#\\n mission_xml, msize, reward_goal, reward_intermediate, n_intermediate_rewards, reward_timeout, reward_sendcommand, timeout = GetMissionInstance(\\n mission_type, mission_seed, agent_type)\\n my_mission = MalmoPython.MissionSpec(mission_xml, True)\\n my_mission.forceWorldReset()\\n\\n # -- Enforce the specific restriction for the assessed exercise --#\\n # -- If you want a super agent, define one for you self --#\\n my_mission.setModeToCreative()\\n if agent_type.lower() == 'random':\\n n = msize\\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid')\\n my_mission.requestVideoWithDepth(320, 240)\\n elif agent_type.lower() == 'simple':\\n n = msize\\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid');\\n my_mission.requestVideo(320, 240)\\n elif agent_type.lower() == 'realistic':\\n n = 1 # n=1 means local info only !\\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid');\\n my_mission.requestVideoWithDepth(320, 240)\\n my_mission.observeFullInventory()\\n elif agent_type.lower() == 'helper':\\n n = 100\\n my_mission.observeGrid(-n, -1, -n, n, -1, n, 'grid');\\n my_mission.requestVideoWithDepth(320, 240)\\n else:\\n # -- Define a custom agent and add the sensors you need --#\\n n = 100\\n my_mission.observeGrid(-n, -1, -n, n, 1, n, 'grid');\\n my_mission.requestVideoWithDepth(320, 240)\\n\\n # -- Add support for the specific movement type requested (and given the constraints of the assignment) --#\\n # -- See e.g. http://microsoft.github.io/malmo/0.17.0/Schemas/MissionHandlers.html --#\\n if movement_type.lower() == 'absolute':\\n my_mission.allowAllAbsoluteMovementCommands()\\n elif movement_type.lower() == 'continuous':\\n my_mission.allowContinuousMovementCommand('move')\\n my_mission.allowContinuousMovementCommand('strafe')\\n my_mission.allowContinuousMovementCommand('pitch')\\n my_mission.allowContinuousMovementCommand('turn')\\n my_mission.allowContinuousMovementCommand('crouch')\\n elif movement_type.lower() == 'discrete':\\n my_mission.allowDiscreteMovementCommand('turn')\\n my_mission.allowDiscreteMovementCommand('move')\\n my_mission.allowDiscreteMovementCommand('movenorth')\\n my_mission.allowDiscreteMovementCommand('moveeast')\\n my_mission.allowDiscreteMovementCommand('movesouth')\\n my_mission.allowDiscreteMovementCommand('movewest')\\n my_mission.allowDiscreteMovementCommand('look')\\n\\n # -- Get the resulting xml (and return in order to check that conditions match the report) --#\\n final_xml = my_mission.getAsXML(True)\\n\\n # Set up a recording for later inspection\\n my_mission_record = MalmoPython.MissionRecordSpec('tmp' + \\\".tgz\\\")\\n my_mission_record.recordRewards()\\n my_mission_record.recordMP4(24, 400000)\\n\\n # -- Attempt to start a mission --#\\n max_retries = 5\\n for retry in range(max_retries):\\n try:\\n agent_host.startMission(my_mission, my_mission_record)\\n break\\n except RuntimeError as e:\\n if retry == max_retries - 1:\\n print(\\\"Error starting mission:\\\", e)\\n exit(1)\\n else:\\n time.sleep(2)\\n\\n # -- Loop until mission starts: --#\\n print(\\\"Waiting for the mission to start \\\")\\n state_t = agent_host.getWorldState()\\n while not state_t.has_mission_begun:\\n sys.stdout.write(\\\".\\\")\\n time.sleep(0.1)\\n state_t = agent_host.getWorldState()\\n for error in state_t.errors:\\n print(\\\"Error:\\\", error.text)\\n\\n print\\n print(\\\"Mission started (xml returned)... \\\")\\n return final_xml, reward_goal, reward_intermediate, n_intermediate_rewards, reward_timeout, reward_sendcommand, timeout\",\n \"def __init__(self, agent_id=\\\"default\\\", experiment_id=\\\"default\\\"):\\n self.runtime = runtime()\\n self.agent_id = agent_id\\n self.experiment_id = experiment_id\",\n \"def initialize_robot():\\n\\n proxy_motion = naoqi.ALProxy(\\\"ALMotion\\\", IP_ROBOT, PORT_ROBOT)\\n proxy_motion.wakeUp()\\n\\n proxy_autonomous_life = naoqi.ALProxy(\\\"ALAutonomousLife\\\", IP_ROBOT, PORT_ROBOT)\\n proxy_autonomous_life.setState(\\\"disabled\\\")\\n\\n proxy_motion = naoqi.ALProxy(\\\"ALMotion\\\", IP_ROBOT, PORT_ROBOT)\\n proxy_motion.wakeUp()\",\n \"def __init__(self, machine):\\n super().__init__(machine)\\n self.features['has_steppers'] = True\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.00000001, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.001, display=True) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"def __init__(self, numOfGames, muteOutput, randomAI, AIforHuman):\\n self.numOfGames = numOfGames\\n self.muteOutput = muteOutput\\n self.maxTimeOut = 10000\\n\\n self.AIforHuman = AIforHuman\\n self.gameRules = GameRules()\\n self.AIPlayer = TicTacToeAgent()\\n\\n if randomAI:\\n self.AIPlayer = randomAgent()\\n else:\\n self.AIPlayer = TicTacToeAgent()\\n if AIforHuman:\\n self.HumanAgent = randomAgent()\\n else:\\n self.HumanAgent = TicTacToeAgent1()\",\n \"def main():\\n print(\\\"runner\\\")\\n runner = Runner()\\n stop_on_idle = True\\n probes = []\\n for url in urls:\\n probe_cls = random.choice((HttpProbe, ThreadProbe, ShellProbe))\\n runner.probes.append(probe_cls(url))\\n\\n runner.run()\",\n \"def setUp(self):\\n lang = self._sim_lang\\n self._simulator = self._find_resource(\\n f\\\"drake/examples/hardware_sim/hardware_sim_{lang}\\\")\\n self._example_scenarios = self._find_resource(\\n \\\"drake/examples/hardware_sim/example_scenarios.yaml\\\")\\n self._test_scenarios = self._find_resource(\\n \\\"drake/examples/hardware_sim/test/test_scenarios.yaml\\\")\\n self._default_extra = {\\n # For our smoke test, exit fairly quickly.\\n \\\"simulation_duration\\\": 0.0625,\\n }\",\n \"def __init__(self, numOfGames, muteOutput, randomAI, AIforHuman):\\n self.numOfGames = numOfGames\\n self.muteOutput = muteOutput\\n self.maxTimeOut = 30 \\n\\n self.AIforHuman = AIforHuman\\n self.gameRules = GameRules()\\n self.AIPlayer = TicTacToeAgent()\\n\\n if randomAI:\\n self.AIPlayer = randomAgent()\\n else:\\n self.AIPlayer = TicTacToeAgent()\\n if AIforHuman:\\n self.HumanAgent = randomAgent()\\n else:\\n self.HumanAgent = keyboardAgent()\",\n \"def run(num_trials):\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.1, display=True) \\n # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=num_trials) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n\\n a.performace_report(num_trials)\",\n \"def multi_agent_example():\\n env = holodeck.make(\\\"CyberPunkCity-FollowSight\\\")\\n\\n cmd0 = np.array([0, 0, -2, 10])\\n cmd1 = np.array([0, 0, 0])\\n for i in range(10):\\n env.reset()\\n env.tick()\\n env.act(\\\"uav0\\\", cmd0)\\n env.act(\\\"nav0\\\", cmd1)\\n for _ in range(1000):\\n states = env.tick()\\n pixels = states[\\\"uav0\\\"][\\\"RGBCamera\\\"]\",\n \"def experiment_init(self):\\n pass\",\n \"def __init__(self, network: Network):\\n if LOG[\\\"ExperimentAI\\\"]:\\n print(\\\"[ExperimentAI] Initializing AI\\\")\\n self.network = network\",\n \"def init():\\n\\n @click.group(cls=cli.make_commands(__name__))\\n def run():\\n \\\"\\\"\\\"Cross-cell supervision tools.\\\"\\\"\\\"\\n cli.init_logger('daemon.conf')\\n\\n return run\",\n \"def main():\\n\\n\\t# Run setup\\n\\ts = Setup()\\n\\tcontrolRoom, studio, newscaster = s.beginSetup()\\n\\n\\t# Start cameras\\n\\tcontrolRoom.startCameras()\\n\\tprint 'Cameras started'\\n\\tcontrolRoom.setCameraSize()\\n\\n\\tif len(controlRoom.studio.cameras) > 1:\\n\\t\\tprint 'Everything up and running...'\\n\\n\\t\\t# Fetch a camera that best matches the headpose angle\\n\\t\\tcamera = controlRoom.getClosestCamera()\\n\\t\\twhile True:\\n\\t\\t\\t# If advance camera selection algo indicates true, fetch camera closest to headpose\\n\\t\\t\\tif controlRoom.cameraSelectionADV():\\n\\t\\t\\t\\tcamera = controlRoom.getClosestCamera()\\n\\t\\t\\tprint 'Active camera: ' + str(camera.cameraID)\\n\\t\\t\\t\\n\\t\\t\\t# Capture frame or in simulation mode, light up led\\n\\t\\t\\tcamera.capture()\\n\\n\\telif len(controlRoom.studio.cameras) == 1:\\n\\t\\twhile True:\\n\\t\\t\\tcontrolRoom.studio.cameras[0].capture()\\n\\t\\t\\ttime.sleep(2)\\n\\telse:\\n\\t\\tprint 'No cameras found! Something seems to be wrong...'\\n\\n\\t# Shutdown all cameras and kill all windows\\n\\tcontrolRoom.shutdownCameras()\",\n \"def parley(self):\\n wait_times = constants.TUTORIAL_WAIT_TIMES\\n self.introduce_chat_interface()\\n self.wait_for_response(\\n message='Please type a greeting message to continue.',\\n delay_time=wait_times['chat-interface'],\\n )\\n self.introduce_persona()\\n self.wait_for_response(\\n message=constants.APPRENTICE_PERSONA_ROLE_INSTRUCTION,\\n delay_time=wait_times['persona'],\\n )\\n self.introduce_partner_entity()\\n self.wait_for_response(\\n message=constants.APPRENTICE_CHITCHAT_INSTRUCTION,\\n delay_time=wait_times['persona'],\\n )\\n self.introduce_partner_knowledge()\\n self.wait_for_response(\\n message=constants.APPRENTICE_PERSONA_MSG_INSTRUCTION,\\n delay_time=wait_times['knowledge'],\\n )\\n self.go_for_start()\\n self.episodeDone = True\",\n \"def agent_init(self):\\n pass\",\n \"def main(_):\\n description = xm.ExperimentDescription(\\n 'HIS - trial=%d' % FLAGS.trial, tags=['his'])\\n experiment = build_experiment()\\n xm.launch_experiment(description, experiment)\",\n \"def initialize_home_hub(argv):\\n parse_cmd_line_opts(argv)\\n init_logging()\\n init_error_reporting()\\n \\n # Verify we have a valid home id\\n if HOME_ID is None:\\n print('Home ID is invalid or missing. Please provide an integer following the -i flag')\\n exit()\\n\\n # Begin Home Hub Specific Setup\\n logger.info('Starting the Home Hub main program for Home: %s', HOME_ID)\\n\\n # Get the email and password for this HH's user from the env vars\\n powernet_user_email = os.getenv('POWERNET_USER_EMAIL', None)\\n powernet_user_password = os.getenv('POWERNET_USER_PASSWORD', None)\\n \\n if powernet_user_email is None:\\n logger.info('Missing the required login email address')\\n logger.info('Please set the POWERNET_USER_EMAIL environment variable and try again')\\n exit()\\n \\n if powernet_user_password is None:\\n logger.info('Missing the required login password')\\n logger.info('Please set the POWERNET_USER_PASSWORD environment variable and try again')\\n exit()\\n \\n # attempt to authenticate against our API\\n form_payload = {'email': powernet_user_email, 'password': powernet_user_password}\\n response = requests.post('https://pwrnet-158117.appspot.com/api/v1/powernet_user/auth/', data=form_payload)\\n auth_token = response.json()['token']\\n\\n # Initializing variables for queue and threads\\n rpi = HardwareInterface(house_id=HOME_ID, gpio_map=None, auth_token=auth_token)\\n buffer_size = 8\\n q_ai = Queue(buffer_size)\\n\\n # Initialize threads\\n producer_ai_thread = Thread(name='Producer', target=rpi.producer_ai, args=(q_ai,))\\n producer_ai_thread.start()\\n\\n consumer_ai_thread = Thread(name='Consumer', target=rpi.consumer_ai, args=(q_ai,))\\n consumer_ai_thread.start()\\n\\n devices_thread = Thread(name='Device', target=rpi.devices_th)\\n devices_thread.start()\\n\\n load_control_thread = Thread(name=\\\"LoadControl\\\", target=rpi.local_controller_th)\\n load_control_thread.start()\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.start_agents_once = False\\n self.start_servers_once = False\\n self.setup_start_agents = False\\n self.setup_start_servers = False\",\n \"def __init__(self, a):\\n # sanity check\\n assert(isinstance(a, agent.Agent))\\n \\n # save the agent reference\\n self.a = a\\n \\n self.a.tc_h1(\\\"CONNECT TESTS\\\")\\n \\n # create the host instance\\n self.host = host.Host(a, STA)\\n # create the access point instances\\n self.ap1 = accesspoint.AccessPoint(a, AP1, channel=5, period=100, ssid=\\\"louis\\\")\\n self.ap2 = accesspoint.AccessPoint(a, AP2, channel=11, period=100, ssid=\\\"louis\\\")\\n\\n # reset the host (resetting the MIBs)\\n self.host.reset(True)\\n\\n # set the host MAC address\\n self.host.dbg_macaddr()\",\n \"def start(self):\\n self.__init__()\\n self.set_n_players()\\n self.init_players()\\n self.init_territory_selection_phase()\\n self.init_troop_deployment_phase()\\n # self.game_phase()\",\n \"def main():\\n utils.vip_main(actuator_agent, identity='platform.d.actuator')\",\n \"def onSpawn(self):\\n self.spawned = True\\n self._interactor.initialiseDevices()\",\n \"def _starting_up():\\n global bus, skill_manager, event_scheduler\\n\\n bus.on('intent_failure', FallbackSkill.make_intent_failure_handler(bus))\\n\\n # Create the Intent manager, which converts utterances to intents\\n # This is the heart of the voice invoked skill system\\n service = IntentService(bus)\\n try:\\n PadatiousService(bus, service)\\n except Exception as e:\\n LOG.exception('Failed to create padatious handlers '\\n '({})'.format(repr(e)))\\n event_scheduler = EventScheduler(bus)\\n\\n # Create a thread that monitors the loaded skills, looking for updates\\n try:\\n skill_manager = SkillManager(bus)\\n except MsmException:\\n # skill manager couldn't be created, wait for network connection and\\n # retry\\n LOG.info('Msm is uninitialized and requires network connection',\\n 'to fetch skill information\\\\n'\\n 'Waiting for network connection...')\\n while not connected():\\n time.sleep(30)\\n skill_manager = SkillManager(bus)\\n\\n skill_manager.daemon = True\\n # Wait until priority skills have been loaded before checking\\n # network connection\\n skill_manager.load_priority()\\n skill_manager.start()\\n check_connection()\",\n \"def main():\\n\\n run_manual_session()\\n # run_automated_session()\",\n \"def __init__(self, scenario):\\n client.__init__(self, scenario)\\n # TODO: Your initialization, if any (not likely). Oh, and remove the next line.\\n raise Exception( \\\"DO NOT instantiate the skeleton implementation\\\" )\",\n \"def setup(client):\\n client.add_cog(ProcessDisplay(client))\",\n \"def main():\\n driver = Driver()\\n driver.start()\",\n \"def launch(**kwargs):\\n\\n logger, loghost, logport, clients, guis, params = unpack_launcher(**kwargs)\\n config = load_config(kwargs['config'], logger=logger)\\n\\n\\n ao_client = find_client(logger, clients, 'nidaqmx')\\n ai_client = find_client(logger, clients, 'nidaqmx_ai')\\n\\n # Instantiate Monitor script\\n laser_stabilizer = LaserStabilizer(\\n config=kwargs['config'],\\n ao_client=ao_client,\\n ai_client=ai_client\\n )\\n\\n update_service = Service()\\n update_service.assign_module(module=laser_stabilizer)\\n update_service.assign_logger(logger=logger)\\n update_server, update_port = create_server(update_service, logger, host=get_ip())\\n logger.update_data(data={'port': update_port})\\n laser_stabilizer.gui.set_network_info(port=update_port)\\n update_server.start()\\n\\n # Run continuously\\n # Note that the actual operation inside run() can be paused using the update server\\n while True:\\n\\n laser_stabilizer.run()\",\n \"def setUp(self):\\n _, instance_path, shared_inputs = sys.argv\\n app = lnt.server.ui.app.App.create_standalone(instance_path)\\n app.testing = True\\n self.client = app.test_client()\\n self.shared_inputs = shared_inputs\",\n \"def main():\\n tester = Tester()\\n # parse args, load configuration and create all required objects.\\n tester.setup_experiment()\\n # GO!\\n tester.run_experiment()\",\n \"def minimal_interactive_cli_bootstrap(client):\\n # Fetch available TAN mechanisms by the bank, if we don't know it already. If the client was created with cached data,\\n # the function is already set.\\n if not client.get_current_tan_mechanism():\\n client.fetch_tan_mechanisms()\\n mechanisms = list(client.get_tan_mechanisms().items())\\n if len(mechanisms) > 1:\\n print(\\\"Multiple tan mechanisms available. Which one do you prefer?\\\")\\n for i, m in enumerate(mechanisms):\\n print(i, \\\"Function {p.security_function}: {p.name}\\\".format(p=m[1]))\\n choice = input(\\\"Choice: \\\").strip()\\n client.set_tan_mechanism(mechanisms[int(choice)][0])\\n\\n if client.is_tan_media_required() and not client.selected_tan_medium:\\n print(\\\"We need the name of the TAN medium, let's fetch them from the bank\\\")\\n m = client.get_tan_media()\\n if len(m[1]) == 1:\\n client.set_tan_medium(m[1][0])\\n else:\\n print(\\\"Multiple tan media available. Which one do you prefer?\\\")\\n for i, mm in enumerate(m[1]):\\n print(i,\\n \\\"Medium {p.tan_medium_name}: Phone no. {p.mobile_number_masked}, Last used {p.last_use}\\\".format(\\n p=mm))\\n choice = input(\\\"Choice: \\\").strip()\\n client.set_tan_medium(m[1][int(choice)])\",\n \"async def start(self, roles, dialogs):\\n self.__init__(self.active_role, (self.active_help, self.external_help))\",\n \"def init():\\r\\n\\t# add grabber tools based on proxy tools\\r\\n\\tfor proxyWrapper in vizconnect.getToolsWithMode('Proxy'):\\r\\n\\t\\tgrabberTool = tools.grabber.HandGrabber(usingPhysics=False,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tusingSprings=False,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tplacementMode=tools.placer.MODE_DROP_DOWN)\\r\\n\\t\\t\\r\\n\\t\\tname = 'grabber_tool_based_on_'+proxyWrapper.getName()\\r\\n\\t\\tgrabberWrapper = vizconnect.addTool(raw=grabberTool,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tname=name,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmake='Virtual',\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmodel='Grabber')\\r\\n\\t\\t# parent the grabber wrapper to the proxy's parent\\r\\n\\t\\tgrabberWrapper.setParent(proxyWrapper)\\r\\n\\t\\t\\r\\n\\t\\tgrabberTool.setItems(grabbableItems)\\r\\n\\t\\r\\n\\tviz.callback(viz.getEventID('RESET_THE_LOFT_LAYOUT'), lambda e: resetMovedObjects())\",\n \"def main():\\n args = _parse_arguments()\\n\\n util.log_init(\\\"sitl_A%s_%s.txt\\\" % (args.id, util.get_latest_log(\\\"latest_sitl.txt\\\")), util.log_level[args.level])\\n\\n shared.AGENT_ID = 'A%s' % args.id\\n shared.AGENT_COUNT = args.n\\n shared.CURRENT_ALGORITHM = args.algorithm\\n shared.AGENT_CHARACTER = args.character\\n shared.des_alt = args.alt\\n \\n util.log_info(\\\"AGENT_ID = %s\\\" % shared.AGENT_ID)\\n util.log_info(\\\"Algorithm: %s\\\" % shared.CURRENT_ALGORITHM)\\n util.log_info(\\\"Agent type: %s\\\" % shared.AGENT_CHARACTER)\\n\\n print \\\"Start simulator (SITL)\\\"\\n sitl = SITL(args.pix) # initialize SITL with firmware path\\n\\n if shared.AGENT_ID in start_loc:\\n sitl_args = ['--home=%s' % start_loc[shared.AGENT_ID]]\\n else:\\n sitl_args = ['--home=%s' % start_loc['FFF']]\\n \\n # Pre-recorded coordinates.\\n #sitl_args = ['-I0', '--model', 'quad', '--home=31.301201,121.498192,9,353']\\t\\n sitl.launch(sitl_args, await_ready=True, restart=True)\\n\\n # Connect to the vehicle. (Spawn an instance of Vehicle named \\\"vehicle\\\")\\n # connection port is coded in the file name of the firmware like \\\"ac3.4.5_port5760\\\"\\n # use regular expression to search the string and extract port number\\n port = re.search(r'port\\\\d{4}', args.pix)\\n port = re.search(r'\\\\d{4}', port.group()).group()\\n\\n print \\\"Connecting to copter on: TCP: 127.0.0.1:%s\\\" % port\\n copter = nav.connect('tcp:127.0.0.1:%s' % port, wait_ready=True, rate=20)\\n util.log_info(\\\"Copter connected. Firmware: %s\\\" % copter.version)\\n \\n if not args.xbee: # simulate XBee using ZeroMQ\\n [pub, sub] = comm.zmq_init(comm_port_list[shared.AGENT_ID], comm_port_list)\\n subscriber_thread = comm.Subscriber(shared.AGENT_ID, sub)\\n subscriber_thread.start()\\n xbee = pub # make xbee the publisher\\n util.log_info(\\\"ZeroMQ initialzied.\\\") \\n \\n else: # use actual xbee ports\\n ser = serial.Serial(args.xbee, 57600)\\n xbee = comm.xbee_init(ser)\\n util.log_info(\\\"Xbee initialzed.\\\")\\n\\n info = \\\"IFO,%s connected with firmware %s\\\" % (shared.AGENT_ID, copter.version)\\n comm.xbee_broadcast(xbee, info)\\n\\n _add_listeners(copter)\\n\\n takeoff_thread = nav.Takeoff(copter, xbee, shared.des_alt, 3)\\n purge_thread = comm.Purge(shared.neighbors)\\n broadcast_thread = comm.Broadcast(shared.AGENT_ID, copter, xbee)\\n flocking_thread = _choose_algorithm(copter, xbee, shared.neighbors)\\n\\n takeoff_thread.start()\\n takeoff_thread.join() # wait until takeoff procedure completed\\n\\n if shared.status['airborne']: # only execute the threads when airborne\\n util.log_info(\\\"Copter is airborne, starting threads.\\\")\\n broadcast_thread.start()\\n purge_thread.start()\\n flocking_thread.start()\\n\\n # main loop\\n while True:\\n try: time.sleep(.2)\\n except KeyboardInterrupt: break\\n \\n if shared.status['airborne']:\\n # echo exiting status\\n if shared.status['exiting']:\\n info = \\\"IFO,%s %s-ing.\\\" % (shared.AGENT_ID,shared.status['command'])\\n comm.xbee_broadcast(xbee, info)\\n util.log_info(info)\\n\\n # if an rtl or land command is received, kill flocking and set the `exiting` flag\\n elif shared.status['command'] == 'RTL' or shared.status['command'] == 'LAND':\\n shared.status['thread_flag'] |= shared.FLOCKING_FLAG\\n nav.set_mode(copter, shared.status['command'])\\n shared.status['exiting'] = True\\n\\n if not flocking_thread.is_alive(): # break the loop if finished\\n break\\n\\n nav.wait_for_disarm(copter) # wait for disarm\\n comm.xbee_broadcast(xbee, 'IFO,%s terminated.' % shared.AGENT_ID)\\n\\n # clean up\\n purge_thread.stop()\\n while purge_thread.is_alive(): \\n util.log_info('Waiting for purge to shutdown') \\n purge_thread.join(3)\\n util.log_info('Purge killed.')\\n\\n broadcast_thread.stop()\\n while broadcast_thread.is_alive(): \\n util.log_info('Waiting for broadcast to shutdown') \\n broadcast_thread.join(3)\\n util.log_info('Broadcast killed.')\\n\\n copter.close()\\n util.log_info(\\\"Copter shutdown.\\\")\\n\\n if args.xbee:\\n xbee.halt()\\n ser.close()\\n util.log_info(\\\"Xbee and serial closed.\\\")\\n else:\\n subscriber_thread.stop()\\n while subscriber_thread.is_alive(): \\n util.log_info('Waiting for Subscriber to shutdown') \\n subscriber_thread.join(3)\\n util.log_info('Subscriber killed.')\\n\\n sitl.stop()\\n util.log_info(\\\"SITL shutdown.\\\")\",\n \"def initialize(self):\\n if self.real:\\n self.agent.connect(self)\\n else:\\n self.connect() # Connect python client to VREP\\n self.agent.connect(self)\",\n \"def setup(cls):\\n cls.runner = CliRunner()\\n cls.agent_name = \\\"myagent\\\"\\n cls.cwd = os.getcwd()\\n cls.t = tempfile.mkdtemp()\\n # copy the 'packages' directory in the parent of the agent folder.\\n shutil.copytree(Path(CUR_PATH, \\\"..\\\", \\\"packages\\\"), Path(cls.t, \\\"packages\\\"))\\n\\n os.chdir(cls.t)\\n result = cls.runner.invoke(\\n cli,\\n [*CLI_LOG_OPTION, \\\"init\\\", \\\"--author\\\", AUTHOR],\\n standalone_mode=False,\\n )\\n assert result.exit_code == 0\\n result = cls.runner.invoke(\\n cli,\\n [*CLI_LOG_OPTION, \\\"create\\\", \\\"--local\\\", cls.agent_name],\\n standalone_mode=False,\\n )\\n assert result.exit_code == 0\\n os.chdir(cls.agent_name)\\n # add connection first time\",\n \"def setup(bot):\\n bot.add_cog(MyAnimeList())\",\n \"def launch_devices(self):\\n self.data[0], temp = alghoritm.temperature(self.data[0], self.set_thermostat, 0) # get value\\n HC35_3S.launch(self.data_path, self.samples_size, temp) # set it via device\\n\\n self.data[1], humidi = alghoritm.humidity(self.data[1], self.set_humidifier, 0)\\n humidifier.launch(self.data_path, self.samples_size, humidi)\\n\\n self.data[2], moistu = alghoritm.moisture(self.data[2], self.set_sprinklers, 0)\\n HUNTER.launch(self.data_path, self.samples_size, moistu)\\n\\n self.data[3], o2 = alghoritm.o2(self.data[3], self.set_ventilation, 0)\\n ventilation.launch_o2(self.data_path, self.samples_size, o2)\\n\\n self.data[4], co2 = alghoritm.co2(self.data[4], self.set_ventilation, 0)\\n ventilation.launch_co2(self.data_path, self.samples_size, co2)\",\n \"def parley(self):\\n wait_times = constants.TUTORIAL_WAIT_TIMES\\n self.introduce_chat_interface()\\n self.wait_for_response(\\n message='Please type a greeting message to continue.',\\n delay_time=wait_times['chat-interface'],\\n )\\n self.introduce_knowledgeable_entity()\\n self.wait_for_response(\\n message=constants.ONBOARDING_ACKNOWLEDGE_UNDERSTOOD,\\n delay_time=wait_times['chat-interface'],\\n )\\n self.introduce_search()\\n self.wait_for_response(\\n message=constants.ONBOARDING_ACKNOWLEDGE_UNDERSTOOD,\\n delay_time=wait_times['knowledge'],\\n )\\n self.try_search()\\n self.wait_for_response_with_search()\\n self.introduce_persona()\\n self.wait_for_response_with_search()\\n self.go_for_start()\\n self.episodeDone = True\",\n \"def start_run(self, context: RobotRunnerContext) -> None:\\n rospy.init_node(\\\"robot_runner\\\", disable_signals=True)\\n self.ina219_profiler = INA219ProfilerClient()\\n self.cpu_mem_profiler = ResourceProfilerClient()\",\n \"def setup(bot: Bot) -> None:\\n bot.add_cog(Latency(bot))\",\n \"def setUp(self):\\n # Direct connection used to match the property values\\n self.sockobj = socket(AF_INET, SOCK_STREAM)\\n self.sockobj.settimeout(socket_timeout)\\n # Connect to the selected server\\n self.sockobj.connect(server) \\n self.pyclient = PySimpleClient()\\n self.cmd_num = 0\\n for servo_type in app_nr.values():\\n self.__dict__[servo_type] = self.pyclient.getComponent(\\\"MINORSERVO/\\\" + servo_type)\",\n \"def load_scenario(self):\\n logger.info(\\\"Loading scenario...\\\")\\n for manager in self.config[\\\"fleets\\\"]:\\n name = manager[\\\"name\\\"]\\n password = manager[\\\"password\\\"] if \\\"password\\\" in manager else faker_factory.password()\\n fleet_type = manager[\\\"fleet_type\\\"]\\n strategy = manager.get(\\\"strategy\\\")\\n icon = manager.get(\\\"icon\\\")\\n agent = self.create_fleetmanager_agent(name, password, fleet_type=fleet_type, strategy=strategy)\\n\\n self.set_icon(agent, icon, default=fleet_type)\\n\\n while len(self.manager_agents) < self.config.num_managers:\\n time.sleep(0.1)\\n\\n for transport in self.config[\\\"transports\\\"]:\\n name = transport[\\\"name\\\"]\\n password = transport[\\\"password\\\"] if \\\"password\\\" in transport else faker_factory.password()\\n position = transport[\\\"position\\\"]\\n fleetmanager = transport[\\\"fleet\\\"]\\n fleet_type = transport[\\\"fleet_type\\\"]\\n speed = transport.get(\\\"speed\\\")\\n trust = transport.get(\\\"trust\\\")\\n rates = transport.get(\\\"rates\\\")\\n velocity_factor = transport.get(\\\"velocity_factor\\\")\\n fuel = transport.get(\\\"fuel\\\")\\n autonomy = transport.get(\\\"autonomy\\\")\\n current_autonomy = transport.get(\\\"current_autonomy\\\")\\n strategy = transport.get(\\\"strategy\\\")\\n icon = transport.get(\\\"icon\\\")\\n agent = self.create_transport_agent(name, password, position=position, speed=speed, fleet_type=fleet_type,\\n fleetmanager=fleetmanager, strategy=strategy, autonomy=autonomy,\\n current_autonomy=current_autonomy, trust=trust, rates=rates, velocity_factor=velocity_factor)\\n\\n if icon:\\n self.set_icon(agent, icon, default=fleet_type)\\n\\n for customer in self.config[\\\"customers\\\"]:\\n name = customer[\\\"name\\\"]\\n password = customer[\\\"password\\\"] if \\\"password\\\" in customer else faker_factory.password()\\n fleet_type = customer[\\\"fleet_type\\\"]\\n position = customer[\\\"position\\\"]\\n target = customer[\\\"destination\\\"]\\n strategy = customer.get(\\\"strategy\\\")\\n icon = customer.get(\\\"icon\\\")\\n agent = self.create_customer_agent(name, password, fleet_type, position=position, target=target,\\n strategy=strategy)\\n\\n self.set_icon(agent, icon, default=\\\"customer\\\")\\n\\n for station in self.config[\\\"stations\\\"]:\\n password = station[\\\"password\\\"] if \\\"password\\\" in station else faker_factory.password()\\n strategy = station.get(\\\"strategy\\\")\\n icon = station.get(\\\"icon\\\")\\n agent = self.create_station_agent(station[\\\"name\\\"], password, position=station[\\\"position\\\"],\\n power=station[\\\"power\\\"], places=station[\\\"places\\\"], strategy=strategy)\\n self.set_icon(agent, icon, default=\\\"electric_station\\\")\",\n \"def initialize(self):\\n\\n # --------- BEGIN YOUR CODE ----------\\n\\n # This is exactly the same as Human.initialize, just copy the code over\\n\\n # --------- END YOUR CODE ----------\\n pass\",\n \"def __init__(self, configs, simulator, wait_time=3):\\n self.configs = configs\\n self.sim = simulator.sim\\n self.gripper = VREP_Gripper()\\n self.open()\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n # Always start the servers for each test variant\\n self.start_agents_once = False\\n self.start_servers_once = False\\n\\n # Whether to skip tearDown\\n self.skip_teardown = False\",\n \"def run(): \\n learning_rate = 0.42\\n discount_rate = 0.15\\n initial_q_hat = 4\\n \\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n print \\\"Failed trials: \\\"\\n print a.get_failed_trials()\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"def setUp(self):\\n bed_patient = PatientsGenerator(0, 1, 0, 'a')\\n self.admitgen = AdmissionsGenerator(bed_patient, [-1])\",\n \"def initialize_ai(self):\\n\\n self.gid, self.genome = constants.genomes_to_run[self.identifier]\\n self.genome.fitness = -1\\n self.net = neat.nn.FeedForwardNetwork.create(self.genome, constants.conf)\\n # self.net = neat.nn.RecurrentNetwork\\n # .create(self.genome, constants.conf)\",\n \"def test_init_experiment(self):\\n optimizer = \\\"RandomSearch\\\"\\n name = \\\"test_init_experiment\\\"\\n param_defs = {\\n \\\"x\\\": MinMaxNumericParamDef(0, 1),\\n \\\"name\\\": NominalParamDef([\\\"A\\\", \\\"B\\\", \\\"C\\\"])\\n }\\n minimization = True\\n\\n LAss = PrettyLabAssistant()\\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)\\n\\n exp_ass = LAss.exp_assistants[name]\\n\\n assert_equal(exp_ass.optimizer, optimizer)\\n assert_is_none(exp_ass.optimizer_arguments, None)\\n assert_equal(exp_ass.experiment.minimization_problem, minimization)\\n with assert_raises(ValueError):\\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=False) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.0000001, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\\n\\n print 'alpha, gamma:', a.alpha, a.gamma\\n print 'penalties:', a.total_penalties\\n print 'total rewards:', a.total_rewards\",\n \"def test_create_tang_1(self):\\n command_line = (\\n self._MENU\\n + [self._POOLNAME]\\n + self._DEVICES\\n + [\\\"--clevis=tang\\\", \\\"--trust-url\\\", \\\"--tang-url=http\\\"]\\n )\\n TEST_RUNNER(command_line)\",\n \"def _init_agents(self):\\n self.agents = [Agent(e=0.1, a=0.1, row=self.row, col=self.col) for i in range(2)]\",\n \"def __init__(self,\\n environment_spec: specs.EnvironmentSpec,\\n ):\\n # Create the actor\\n actor = delta_hedge_actor.DeltaHedgeActor(environment_spec.actions)\\n learner = fake_learner.FakeLeaner()\\n\\n super().__init__(\\n actor=actor,\\n learner=learner,\\n min_observations=100,\\n observations_per_step=1e9)\",\n \"def initialize(self):\\n self.ha_url = self.args.get(\\\"ha_url\\\", None)\\n self.use_current_brightness = self.args.get(\\\"use_current_brightness\\\", False)\\n self.condition = self.args.get(\\\"condition\\\")\\n self.lights = self.args[\\\"lights\\\"]\\n self.listen_state(self.change_lights_color, self.args[\\\"media_player\\\"], attribute = self.args.get(\\\"photo_attribute\\\", \\\"entity_picture\\\"))\",\n \"def main():\\n import optparse\\n import sys\\n parser = optparse.OptionParser()\\n parser.add_option('-c', '--controller', default='NewController',\\n help='Controller class to instantiate.')\\n parser.add_option('-o', '--outputs', type='int', default=5,\\n help='Number of outputs to use on the hardware.')\\n options, _arguments = parser.parse_args()\\n try:\\n Demo(options.controller, options.outputs)\\n except controller.ConnectionError:\\n sys.exit('ABORT: Could not find a suitable device.')\",\n \"def __init__(self, machine):\\n super().__init__(machine)\\n\\n # Set default platform features. Each platform interface can change\\n # these to notify the framework of the specific features it supports.\\n self.features['has_drivers'] = True\\n self.features['max_pulse'] = 255\",\n \"def __init__(self, machine):\\n super().__init__(machine)\\n\\n # Set default platform features. Each platform interface can change\\n # these to notify the framework of the specific features it supports.\\n self.features['has_drivers'] = True\\n self.features['max_pulse'] = 255\",\n \"def editor_multi_agent_example():\\n agent_definitions = [\\n AgentDefinition(\\\"uav0\\\", agents.UavAgent, [sensors.RGBCamera, sensors.LocationSensor]),\\n AgentDefinition(\\\"uav1\\\", agents.UavAgent, [sensors.LocationSensor, sensors.VelocitySensor])\\n ]\\n env = HolodeckEnvironment(agent_definitions, start_world=False)\\n\\n cmd0 = np.array([0, 0, -2, 10])\\n cmd1 = np.array([0, 0, 5, 10])\\n\\n for i in range(10):\\n env.reset()\\n env.act(\\\"uav0\\\", cmd0)\\n env.act(\\\"uav1\\\", cmd1)\\n for _ in range(1000):\\n states = env.tick()\",\n \"def setUp(self):\\n # Start the servers and agents\\n super().setUp()\\n\\n # Get the parameters\\n self.ior_flags = self.params.get(\\n \\\"ior_flags\\\", \\\"/run/ior/*\\\")\\n self.test_file = self.ior_cmd.test_file.value\",\n \"def test_defaults():\\n model = torch.nn.Module()\\n dataset = torch.utils.data.Dataset()\\n dataloader = torch.utils.data.DataLoader(dataset)\\n loaders = OrderedDict()\\n loaders[\\\"train\\\"] = dataloader\\n\\n test_callbacks = OrderedDict(\\n [\\n (\\\"_timer\\\", TimerCallback),\\n (\\\"_metrics\\\", MetricManagerCallback),\\n (\\\"_validation\\\", ValidationManagerCallback),\\n (\\\"_saver\\\", CheckpointCallback),\\n (\\\"_console\\\", ConsoleLogger),\\n (\\\"_tensorboard\\\", TensorboardLogger),\\n (\\\"_exception\\\", ExceptionCallback),\\n ]\\n )\\n\\n exp = SupervisedExperiment(model=model, loaders=loaders)\\n _test_callbacks(test_callbacks, exp)\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create learning agent\\n # a = e.create_agent(RandomAgent) # create random agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.01)\\n # reduce update_delay to speed up simulation\\n sys.stdout = open(\\\"./output.txt\\\", \\\"w\\\")\\n tic = time()\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\\n toc = time()\\n sys.stdout = sys.__stdout__\\n\\n print \\\"Totoal time used: {}.\\\".format(toc - tic)\\n parse(\\\"./output.txt\\\")\",\n \"def test_mcts_agent(self):\\n logging.info(\\\"Starting test_mcts_agent\\\")\\n dirname = os.path.dirname(__file__)\\n filename = os.path.join(dirname, \\\"../configs/factory_floor_simple.yaml\\\")\\n parameters = getParameters(filename)\\n env = FactoryFloor(parameters)\\n obs = env.reset()\\n\\n mctsAgents = []\\n\\n randomagent = 'aiagents.single.RandomAgent.RandomAgent'\\n for robotId in env.action_space.spaces.keys():\\n mctsparams = {'treeAgent':{'class': randomagent, 'id':robotId, 'parameters':{} },\\n 'rolloutAgent':{'class': randomagent, 'id':robotId, 'parameters':{} }} \\n mctsparams['simulator'] = dict(parameters)\\n mctsparams['simulator']['fullname'] = \\\"aienvs.FactoryFloor.FactoryFloor.FactoryFloor\\\"\\n \\n mctsAgents.append(MctsAgent(robotId, env.action_space, env.observation_space , mctsparams))\\n\\n complexAgent = BasicComplexAgent(mctsAgents, env.action_space, env.observation_space)\\n\\n episode = Episode(complexAgent, env, obs, render=True)\\n episode.run()\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"--identifier\\\", required=True,\\n help=\\\"A short name/identifier for your experiment, e.g. 'ex42b'.\\\")\\n args = parser.parse_args()\\n\\n train(args)\",\n \"def _initialize_runners_startup(self):\\n if self.command_group.is_cmd0_runner():\\n self._initialize_runner(self.command_group.cmd0)\\n if self.command_group.is_cmd1_runner():\\n self._initialize_runner(self.command_group.cmd1)\\n if self.command_group.is_cmd2_runner():\\n self._initialize_runner(self.command_group.cmd2)\",\n \"def setUp(self):\\n self.anime_link = \\\"https://animepertutti.com/sword-art-online-alicization-war-of-underworld-sub-ita-streaming-download-z\\\" #noqa\",\n \"def start_with_console():\\n print_welcome()\\n option = input(\\\"Choose a number [1/2/3]: \\\")\\n cexc.check_start_exceptions(option)\\n if option == \\\"1\\\":\\n picture = create_white_picture_with_inputs()\\n elif option == \\\"2\\\":\\n picture = load_picture_with_inputs()\\n elif option == \\\"3\\\":\\n picture = create_probability_picture_with_inputs()\\n steps = get_steps(input(\\\"Give a number of steps to do (max=30000): \\\"))\\n print_big_number_announcement(steps)\\n Simulator(steps, picture).simulate()\",\n \"def setup_class(cls):\\n super(TestCliTransferFetchAINetwork, cls).setup_class()\\n cls.agent_name2 = \\\"agent_\\\" + \\\"\\\".join(\\n random.choices(string.ascii_lowercase, k=5) # nosec\\n )\\n cls.create_agents(cls.agent_name2)\\n\\n cls.gen_key(cls.agent_name)\\n cls.gen_key(cls.agent_name2)\",\n \"def setUp(self):\\n self.s = Simulation()\\n self.s['Retina']=GeneratorSheet(nominal_density=4.0)\\n self.s['V1']= CFSheet(nominal_density=4.0)\\n self.s['V2'] = CFSheet(nominal_density=4.0)\\n\\n self.s.connect('Retina','V1',delay=0.5,connection_type=CFProjection,\\n name='RtoV1',learning_fn=CFPLF_Hebbian())\\n\\n self.s.connect('Retina','V2',delay=0.5,connection_type=CFProjection,\\n name='RtoV2',learning_fn=CFPLF_Hebbian())\",\n \"def setUp(self):\\n self.delegate = AlwaysHitDelegate(\\\"\\\")\\n self.environment = BattleEnvironment()\",\n \"def setup(bot: Bot) -> None:\\n bot.add_cog(VoiceGate(bot))\",\n \"def pre_launch(mission):\\n started_since = mission.ut() - mission.current_step[\\\"start_ut\\\"]\\n if started_since > 5:\\n mission.next()\\n elif mission.current_step[\\\"first_call\\\"]:\\n vessel = mission.conn.space_center.active_vessel\\n ap = vessel.auto_pilot\\n\\n ap.engage()\\n ap.target_pitch_and_heading(90, 90)\\n vessel.control.throttle = 1\\n vessel.control.sas = False\\n vessel.control.rcs = mission.parameters.get('use_rcs', False)\",\n \"async def setup(bot):\\n await bot.add_cog(People(bot))\",\n \"def start(self):\\n # iPhone\\n #driver = webdriver.Remote(browser_name=\\\"iphone\\\", command_executor='http://172.24.101.36:3001/hub')\\n # Android\\n #driver = webdriver.Remote(browser_name=\\\"android\\\", command_executor='http://127.0.0.1:8080/hub')\\n # Google Chrome \\n #driver = webdriver.Chrome()\\n # Firefox \\n #FirefoxProfile fp = new FirefoxProfile();\\n #fp.setPreference(\\\"webdriver.load.strategy\\\", \\\"unstable\\\");\\n #WebDriver driver = new FirefoxDriver(fp);\\n \\n #driver = webdriver.Firefox(firefox_profile=self.disableImages())\\n driver = webdriver.Firefox()\\n \\n self.driver = driver\",\n \"def start(self):\\n # iPhone\\n #driver = webdriver.Remote(browser_name=\\\"iphone\\\", command_executor='http://172.24.101.36:3001/hub')\\n # Android\\n #driver = webdriver.Remote(browser_name=\\\"android\\\", command_executor='http://127.0.0.1:8080/hub')\\n # Google Chrome \\n #driver = webdriver.Chrome()\\n # Firefox \\n #FirefoxProfile fp = new FirefoxProfile();\\n #fp.setPreference(\\\"webdriver.load.strategy\\\", \\\"unstable\\\");\\n #WebDriver driver = new FirefoxDriver(fp);\\n \\n #driver = webdriver.Firefox(firefox_profile=self.disableImages())\\n driver = webdriver.Firefox()\\n \\n self.driver = driver\",\n \"def setUp(self):\\n os.environ[\\\"PADDLE_TRAINERS_NUM\\\"] = \\\"2\\\"\\n os.environ[\\n \\\"PADDLE_PSERVERS_IP_PORT_LIST\\\"\\n ] = \\\"127.0.0.1:36001,127.0.0.2:36001\\\"\",\n \"def initialise_sim(self):\\n pass\",\n \"def generate(simulator, p, starts, goals, environment, r):\\n num_gen_humans = min(len(starts), len(goals))\\n print(\\\"Generating auto humans:\\\", num_gen_humans)\\n from agents.humans.human_configs import HumanConfigs\\n for i in range(num_gen_humans):\\n start_config = generate_config_from_pos_3(starts[i])\\n goal_config = generate_config_from_pos_3(goals[i])\\n start_goal_configs = HumanConfigs(start_config, goal_config)\\n human_i_name = \\\"auto_%04d\\\" % i\\n # Generates a random human from the environment\\n new_human_i = Human.generate_human_with_configs(\\n start_goal_configs,\\n generate_appearance=p.render_3D,\\n name=human_i_name\\n )\\n # update renderer and get human traversible if it exists\\n if p.render_3D:\\n r.add_human(new_human_i)\\n environment[\\\"human_traversible\\\"] = \\\\\\n np.array(r.get_human_traversible())\\n\\n # Input human fields into simulator\\n simulator.add_agent(new_human_i)\",\n \"def init_process(mech):\\n gases[mech] = ct.Solution(mech)\\n gases[mech].transport_model = 'Multi'\",\n \"def main():\\n if input(\\\"Mode 0=raw / 1=wrapper: ? [0]\\\") == \\\"1\\\":\\n # Wrapped mode\\n try:\\n number = os.environ[\\\"ROBOT_ID\\\"]\\n except KeyError:\\n number = int(input(\\\"Enter robot number (1-5):\\\"))\\n robot = PiBot.PiBot(robot_nr=number, directory=\\\"../\\\")\\n suite = get_suite(robot, number)\\n gripper = input(\\\"Include gripper tests (0=no, 1=yes)? [1]\\\")\\n if gripper != \\\"0\\\":\\n suite.add(\\\"Clear gripper space... testing gripper up-down\\\",\\n \\\"gripper up-down\\\", robot.set_grabber_height,\\n [60, 10], [5, 5], [], [], 1)\\n suite.add(\\\"Clear gripper space... testing gripper open-close\\\",\\n \\\"gripper open-close\\\", robot.close_grabber,\\n [80, 5], [5, 5], [], [], 1)\\n else:\\n # Raw mode\\n robot = commRaspMain.PiBot()\\n while not all(map(lambda fn: fn(), [robot._motors_enable,\\n robot._encoders_enable,\\n robot._servo_enable])):\\n time.sleep(0.05)\\n robot._tof_init()\\n robot._gyro_start()\\n robot._adc_conf(3)\\n number = int(input(\\\"Enter robot number (1-5):\\\"))\\n suite = get_suite(robot, number)\\n\\n robot._motorL_set(0)\\n robot._motorR_set(0)\\n\\n suite.execute()\",\n \"def swait_setup_random_number(swait, **kw):\\n swait.reset()\\n swait.scan.put(\\\"Passive\\\")\\n swait.calc.put(\\\"RNDM\\\")\\n swait.scan.put(\\\".1 second\\\")\\n swait.desc.put(\\\"uniform random numbers\\\")\",\n \"def maya_start_up():\\n import maya.utils as mu\\n mu.executeDeferred(\\\"import mliber;reload(mliber);mliber.show_in_maya()\\\")\",\n \"def test_Demo(self):\\n self._run(self._example_scenarios, \\\"Demo\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63875884","0.61016667","0.60508204","0.60147184","0.5999624","0.5965473","0.59024185","0.587887","0.58662325","0.5809647","0.58030933","0.5800339","0.5754827","0.574139","0.5726314","0.5693041","0.56924486","0.5661824","0.5648796","0.5642869","0.5640304","0.56317896","0.56188244","0.5617767","0.561329","0.55837846","0.55784434","0.5563329","0.5561879","0.55580723","0.5550675","0.5546213","0.55422115","0.5540608","0.5537232","0.5533412","0.5530924","0.5514835","0.55099905","0.5509989","0.5509385","0.5506967","0.5500754","0.55005676","0.5496803","0.5491379","0.5471824","0.5468343","0.5450747","0.544664","0.5439867","0.54340297","0.54304373","0.54292995","0.54171884","0.5415282","0.54124653","0.53878367","0.53864515","0.5381608","0.5380916","0.5371356","0.5370087","0.53618115","0.53581744","0.5353703","0.534307","0.53407735","0.53384405","0.5335234","0.5334325","0.5330372","0.5323556","0.5322977","0.5322977","0.53112054","0.5307562","0.5300312","0.5290742","0.5287836","0.5286958","0.52771133","0.52755904","0.5273739","0.52717316","0.5269291","0.5255589","0.52404785","0.5233554","0.5225836","0.5224202","0.5224202","0.5221116","0.52200943","0.5220085","0.5217254","0.52085954","0.52057254","0.5204776","0.5200377"],"string":"[\n \"0.63875884\",\n \"0.61016667\",\n \"0.60508204\",\n \"0.60147184\",\n \"0.5999624\",\n \"0.5965473\",\n \"0.59024185\",\n \"0.587887\",\n \"0.58662325\",\n \"0.5809647\",\n \"0.58030933\",\n \"0.5800339\",\n \"0.5754827\",\n \"0.574139\",\n \"0.5726314\",\n \"0.5693041\",\n \"0.56924486\",\n \"0.5661824\",\n \"0.5648796\",\n \"0.5642869\",\n \"0.5640304\",\n \"0.56317896\",\n \"0.56188244\",\n \"0.5617767\",\n \"0.561329\",\n \"0.55837846\",\n \"0.55784434\",\n \"0.5563329\",\n \"0.5561879\",\n \"0.55580723\",\n \"0.5550675\",\n \"0.5546213\",\n \"0.55422115\",\n \"0.5540608\",\n \"0.5537232\",\n \"0.5533412\",\n \"0.5530924\",\n \"0.5514835\",\n \"0.55099905\",\n \"0.5509989\",\n \"0.5509385\",\n \"0.5506967\",\n \"0.5500754\",\n \"0.55005676\",\n \"0.5496803\",\n \"0.5491379\",\n \"0.5471824\",\n \"0.5468343\",\n \"0.5450747\",\n \"0.544664\",\n \"0.5439867\",\n \"0.54340297\",\n \"0.54304373\",\n \"0.54292995\",\n \"0.54171884\",\n \"0.5415282\",\n \"0.54124653\",\n \"0.53878367\",\n \"0.53864515\",\n \"0.5381608\",\n \"0.5380916\",\n \"0.5371356\",\n \"0.5370087\",\n \"0.53618115\",\n \"0.53581744\",\n \"0.5353703\",\n \"0.534307\",\n \"0.53407735\",\n \"0.53384405\",\n \"0.5335234\",\n \"0.5334325\",\n \"0.5330372\",\n \"0.5323556\",\n \"0.5322977\",\n \"0.5322977\",\n \"0.53112054\",\n \"0.5307562\",\n \"0.5300312\",\n \"0.5290742\",\n \"0.5287836\",\n \"0.5286958\",\n \"0.52771133\",\n \"0.52755904\",\n \"0.5273739\",\n \"0.52717316\",\n \"0.5269291\",\n \"0.5255589\",\n \"0.52404785\",\n \"0.5233554\",\n \"0.5225836\",\n \"0.5224202\",\n \"0.5224202\",\n \"0.5221116\",\n \"0.52200943\",\n \"0.5220085\",\n \"0.5217254\",\n \"0.52085954\",\n \"0.52057254\",\n \"0.5204776\",\n \"0.5200377\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5699686"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":91,"cells":{"query":{"kind":"string","value":"Ensure that a folder exists and create it if it doesn't, including any parent folders, as necessary."},"document":{"kind":"string","value":"def create_folder(target_folder):\n try:\n os.makedirs(target_folder)\n except OSError as e:\n pass\n return os.path.exists(target_folder)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ensure_folder(*arg):\n if len(arg) == 0:\n raise Exception(\"No input to ensure_folder\")\n path = get_dir(Path(*arg))\n path.mkdir(parents=True, exist_ok=True)","def create_folder_if_needed(path):\n if os.path.exists(path):\n print(\"{} dir exists\".format(path))\n else:\n print(\"{} dir does not exist. Creating dir.\".format(path))\n os.mkdir(path)","def ensure_folder_exists(folder_path: str) -> None:\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)","def create_folder(path):\n folder_missing = not os.path.exists(path)\n\n if folder_missing:\n # Using makedirs since the path hierarchy might not fully exist.\n try:\n os.makedirs(path)\n except OSError as e:\n if (e.errno, e.strerror) == (17, 'File exists'):\n print(e)\n else:\n raise\n\n print('Created folder {0}'.format(path))\n\n return folder_missing","def create_folder(path):\n if not exists(path):\n os.makedirs(path)","def _create_folder(file_path):\r\n file_base = os.path.dirname(file_path)\r\n if not os.path.exists(file_base):\r\n try:\r\n os.makedirs(file_base)\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise","def _create_folder_if_not_exist(filename):\n os.makedirs(os.path.dirname(filename), exist_ok=True)","def create_folder(folder):\n import errno\n try:\n os.makedirs(folder)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise e","def CreateFolderIfNotExisting(folder_path, communicator):\n if not os.path.isdir(folder_path) and communicator.MyPID() == 0:\n os.makedirs(folder_path)\n communicator.Barrier()","def check_make(folder_check):\n if not os.path.isdir(folder_check):\n os.mkdir(folder_check)","def create_folder(path: str):\n try:\n Path(path).mkdir(parents=True, exist_ok=True)\n return True\n except:\n print(\"An error occured.\")","def create_folder(path):\n if not os.path.exists(path):\n os.makedirs(path)","def create_folder(folder):\n if not os.path.exists(folder):\n os.makedirs(folder)","def create_folder(folder):\n if not os.path.exists(folder):\n os.makedirs(folder)\n logging.debug(\"Folder %s Created!\" % folder)\n else:\n logging.debug(\"Folder %s Exists!\" % folder)","def folder_guard(folder_path):\n if not os.path.isdir(folder_path):\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\n os.mkdir(folder_path)","def create_folder(folder):\n flag = True\n if not os.path.exists(folder):\n try:\n os.makedirs(folder)\n initlog('Folder path:%s created by me; ' % folder) \n except Exception, e:\n initlog('failed to create Folder path; %s' % str(e))\n flag = False\n return flag","def create_folder(folder_name):\n\n try:\n os.makedirs(folder_name)\n except FileExistsError:\n pass","def create_dir_if_necessary(path):\n try:\n os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise","def ensure_dirs(cls, folder_path):\n try:\n cls.mkdirs(folder_path)\n except exceptions.PlotlyRequestError as e:\n if \"already exists\" in e.message:\n pass\n else:\n raise e","def create_test_folder_if_does_not_exist(path):\n print('')\n if os.path.exists(path):\n print(' Skip creation of existing folder: {}'.format(path))\n else:\n print(' Create non-existing test folder: {}'.format(path))\n os.makedirs(path, mode=0o775)","def ensure_dir(path):\n parent = os.path.dirname(path)\n if not os.path.exists(parent):\n os.makedirs(parent)","def _check_directory(my_folder):\n if not os.path.exists(my_folder):\n os.makedirs(my_folder)","def createFolder(folder):\n folder_ = os.path.join(os.getcwd(),folder)\n if not(os.path.isdir(folder_)):\n os.mkdir(folder_)","def folder_guard(folder_path):\n \n if not os.path.isdir(folder_path):\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\n os.mkdir(folder_path)","def createFolder(self):\n self.destination = self.getPath() #Find the destination to create the folder\n try:\n os.makedirs(self.destination) #Try and make a folder\n except FileExistsError:\n pass #Otherwise continue if an error is encountered because the file exists already","def ensure_dir_exists(path):\n if not os.path.exists(path):\n os.makedirs(path)","def ensure_folders_if(path, condition=True):\n if not os.path.exists(path) and condition:\n os.makedirs(path)","def create_folder(folder_path: str) -> None:\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)","def create_folder(path):\n try:\n os.listdir(path)\n except:\n os.makedirs(path)\n else:\n shutil.rmtree(path)\n os.makedirs(path)\n return path","def create_folder(name_folder: str):\n try:\n # Create a new direcctory\n os.mkdir(name_folder)\n except FileExistsError:\n # If the direcctory already exits print.\n print(f\"The directory {name_folder} already exists.\")","def check_folder(filepath):\n if not os.path.exists(filepath):\n os.mkdir(filepath)\n return filepath","def create_folder(path: str):\n if not os.path.exists(path):\n os.makedirs(path)","def mkdir(folder_name: str) -> None:\n if exist(folder_name):\n print(\"The folder is already exist\")\n return \n\n os.mkdir(folder_name)","def prepare_folder(path):\n if not os.path.isdir(path):\n os.makedirs(path)","def _folderCheck(self, folder):\n logger.debug(\"Func: _folderCheck\")\n\n if not os.path.isdir(os.path.normpath(folder)):\n os.makedirs(os.path.normpath(folder))","def create_folder(location: str):\n try:\n os.mkdir(location)\n except FileExistsError:\n pass","def EnsureDirExists(path):\n try:\n os.makedirs(os.path.dirname(path))\n except OSError:\n pass","def mkdir_if_not_exists(path):\n if not os.path.exists(path):\n os.makedirs(path)","def maybe_makedirs(path_to_create):\n try: \n os.makedirs(path_to_create)\n except OSError:\n if not os.path.isdir(path_to_create):\n raise","def check_folder(directory):\n global path_checked\n if not os.path.exists(directory):\n os.makedirs(directory)\n else:\n path_checked = True","def ensure_path(full_path):\n full_path = Path(full_path)\n if not full_path.exists():\n full_path.mkdir(parents=True, exist_ok=True)","def create_folder_path(folder_path):\n try:\n if os.path.exists(folder_path):\n shutil.rmtree(folder_path)\n os.makedirs(folder_path)\n except Exception:\n raise Error('Create {folder_path} exception'.format(folder_path))","def create_folder(folder_name):\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)","def create_folder(folder_name):\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)","def ensure_dir(dir_path):\n try:\n os.mkdir(dir_path)\n except FileExistsError:\n pass","def _ensure_dir(dir_name):\n if not os.path.exists(dir_name):\n os.makedirs(dir_name)","def create_directory(parent_path, new_folder):\n newdir = os.path.join(parent_path, new_folder)\n if os.path.isdir(newdir):\n return False\n else:\n os.mkdir(newdir)\n return True","def ensure_dir(path):\n\n \n try:\n os.makedirs(path)\n except (EnvironmentError) as e:\n if not(e.errno == errno.EEXIST and \n e.filename == path):\n raise\n return","def ensure_directory_exists(path):\n directory = os.path.dirname(path)\n if not os.path.exists(directory):\n os.makedirs(directory, exist_ok=True)\n return","def _mkdir_if_not_exist(path):\n if not(os.path.isdir(path)):\n os.mkdir(path)\n else:\n _logger.info('Skipping existing directory %s' % path)","def create_folder(folder):\n\n try:\n os.mkdir(folder, 0740)\n except OSError:\n return False\n else:\n return True","def ensure_directory(path):\n\tdir_path = os.path.dirname(path)\n\tif os.path.exists(dir_path):\n\t\treturn\n\tensure_directory(dir_path)\n\ttry:\n\t\tos.mkdir(dir_path)\n\texcept OSError as e:\n\t\t# Ignore if EEXISTS. This is needed to avoid a race if two getters run at once.\n\t\tif e.errno != errno.EEXIST:\n\t\t\traise","def ensure_dir_exists(path: Union[str,Path]) -> None:\n# path = str(path)\n assert not os.path.isfile(path)\n os.makedirs(path, exist_ok=True)\n assert os.path.isdir(path)","def _ensure_dir(directory):\r\n try:\r\n os.makedirs(directory)\r\n except OSError as exc:\r\n if exc.errno == errno.EEXIST:\r\n pass\r\n else:\r\n raise","def ensure_dir_exists(dir_path):\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)","def ensure_dirpath_exists(path: Path) -> Path:\n assert path\n out_path: Path = path\n\n if not out_path.exists():\n out_path.mkdir(parents=True, exist_ok=True)\n\n return out_path","def ensure_directory(explorer, parent_id, dirname):\n cache_key = (parent_id, dirname)\n if cache_key in DIR_CACHE:\n return DIR_CACHE[cache_key]\n\n for folder in explorer.list_folder(parent_id):\n if folder['name'] == dirname:\n folder_id = folder['id']\n break\n else:\n print(\"Creating folder {!r} in parent {}\".format(dirname, parent_id))\n folder_id = explorer.create_folder(dirname, parent_id)\n DIR_CACHE[cache_key] = folder_id\n return folder_id","def create_folder(self, unformatted_path):\n os.makedirs(self.format_path(unformatted_path), exist_ok=True)","def maybe_makedirs(path_to_create):\n try:\n os.makedirs(path_to_create)\n except OSError:\n if not os.path.isdir(path_to_create):\n raise","def make_dir_if_needed(path):\n\n if not os.path.exists(path):\n os.makedirs(path)\n return path","def create_folder(directory):\n try:\n if not os.path.exists(directory):\n os.makedirs(directory)\n except OSError:\n print('Error: The folder wasn\\'t able to be created: ' + directory)","def _ensure_directory(self, dirname):\n if not os.path.exists(dirname):\n os.makedirs(dirname)","def create_folder(name):\n try:\n dirname = os.path.dirname(__file__)\n filename = os.path.join(dirname, name)\n os.makedirs(filename)\n return 0\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n return -1","def _check_path(path):\n os.system(\"if [ ! -d \" + path + \" ]; then mkdir -p \" + path + \"; fi\")","def create_folder(folders_to_create=[]):\n for f in folders_to_create:\n if not os.path.exists(f):\n os.makedirs(f)","def check_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def create_directory(folder_name):\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)","def create_dir():\n if check_dir_exist():\n return False\n else:\n os.makedirs(path_structure)\n return True","def make_sure_path_exists(path):\n try:\n os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise","def make_sure_path_exists(path):\n try: os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST: raise","def _ensure_dir_exists(self, directory):\n directory = directory.strip()\n if not Path(directory).exists():\n os.mkdir(directory)","def createFolder(directory) -> None:\n try:\n if not os.path.exists(directory):\n os.makedirs(directory)\n except Exception as error:\n print(f\"Error: createFolder({directory}) -> {error}\")","def ensure_dir(d):\n\n if not os.path.exists(d):\n os.makedirs(d, exist_ok=True)\n\n return","def _mkdir_if_not_exist(path):\n if not os.path.exists(path):\n try:\n os.makedirs(path)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(path):\n logger.warning(\n 'be happy if some process has already created {}'.format(\n path))\n else:\n raise OSError('Failed to mkdir {}'.format(path))","def create_dir_if_doesnt_exist(dir_path):\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n return","def make_dir_if_needed(dir) :\n\tif not exists(dir) :\n\t\tos.makedirs(dir)","def exist_ok_mkdir (path, mode=0777):\n try:\n os.mkdir (path, mode)\n except OSError:\n if not os.path.isdir (path):\n raise","def ensure_dir(f):\n\td=os.path.dirname(f)\n\tif not os.path.exists(d):\n\t\tos.makedirs(d)","def ensure_dir(root, path):\n full_path = root\n for seg in path.split(os.sep):\n full_path += os.sep + seg\n if os.path.exists(full_path):\n if not os.path.isdir(full_path):\n raise ValueError(\"'{}' is not a directory\".format(full_path))\n else:\n os.makedirs(full_path)","def mkdir(path):\n\tif not Path(path).exists():\n\t\tPath(path).mkdir(parents=True, exist_ok=True)","def assure_path_exists(self, path):\n\n dir = os.path.dirname(path)\n if not os.path.exists(dir):\n os.makedirs(dir)","def ensuredir(path):\n # Copied from sphinx.util.osutil.ensuredir(): BSD licensed code, so it's OK\n # to add to this project.\n EEXIST = getattr(errno, 'EEXIST', 0)\n try:\n os.makedirs(path)\n except OSError as err:\n # 0 for Jython/Win32\n if err.errno not in [0, EEXIST]:\n raise","def _check_or_create_dir(directory):\n if not tf.gfile.Exists(directory):\n tf.gfile.MakeDirs(directory)","def maybe_make_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def ensure_dir( dirName ):\r\n if not os.path.exists( dirName ):\r\n os.makedirs( dirName )","def ensure_dir(dir_):\n try:\n os.mkdir(dir_)\n except OSError:\n assert os.path.isdir(dir_)","def ensure_dirs_exist(path):\n os.makedirs(path, exist_ok=True)","def mkdir_if_notexists(path):\n try:\n os.mkdir(path)\n except FileExistsError:\n pass","def create_folders(folder_name):\n\n if os.path.exists(downloads_path + '\\\\' + folder_name):\n pass\n else:\n os.makedirs(folder_name)\n print(f'Folder: {folder_name} has been created in {downloads_path}')","def checking_path():\n path = Path(\"phonebook\")\n try:\n path.mkdir(parents=True, exist_ok=False)\n except FileExistsError:\n pass\n else:\n pass","def mkdir_if_missing(d):\n if not os.path.exists(d):\n os.makedirs(d)","def create_directory_if_not_exists(directory_path):\n os.makedirs(directory_path, exist_ok=True)","def ifnotexistmkdir(directory):\n if not os.path.exists(directory):\n os.mkdir(directory)\n return Path(directory)","def check_path(dir_path):\n if not os.path.exists(dir_path):\n os.mkdir(dir_path, 0755)","def mkdir(folder_path, exist_ok=True):\n\n # Generate dir\n os.makedirs(folder_path, exist_ok=exist_ok)\n\n return True","def ensure_path(directory):\n if not path.exists(directory):\n os.makedirs(directory)\n # end if\n return directory","def createFolder(folderFullPath):\n os.makedirs(folderFullPath, exist_ok=True)","def exist_ok_makedirs (path, mode=0777):\n if not os.path.isdir (path):\n head, tail = os.path.split (path)\n if not tail:\n head, tail = os.path.split (head)\n if head and tail:\n exist_ok_makedirs (head, mode)\n exist_ok_mkdir (path, mode)","def ensure_dirs_exists(self):\n os.makedirs(os.path.join(self.location, \"batches\"), exist_ok=True)\n os.makedirs(os.path.join(self.location, \"results\"), exist_ok=True)"],"string":"[\n \"def ensure_folder(*arg):\\n if len(arg) == 0:\\n raise Exception(\\\"No input to ensure_folder\\\")\\n path = get_dir(Path(*arg))\\n path.mkdir(parents=True, exist_ok=True)\",\n \"def create_folder_if_needed(path):\\n if os.path.exists(path):\\n print(\\\"{} dir exists\\\".format(path))\\n else:\\n print(\\\"{} dir does not exist. Creating dir.\\\".format(path))\\n os.mkdir(path)\",\n \"def ensure_folder_exists(folder_path: str) -> None:\\n if not os.path.exists(folder_path):\\n os.makedirs(folder_path)\",\n \"def create_folder(path):\\n folder_missing = not os.path.exists(path)\\n\\n if folder_missing:\\n # Using makedirs since the path hierarchy might not fully exist.\\n try:\\n os.makedirs(path)\\n except OSError as e:\\n if (e.errno, e.strerror) == (17, 'File exists'):\\n print(e)\\n else:\\n raise\\n\\n print('Created folder {0}'.format(path))\\n\\n return folder_missing\",\n \"def create_folder(path):\\n if not exists(path):\\n os.makedirs(path)\",\n \"def _create_folder(file_path):\\r\\n file_base = os.path.dirname(file_path)\\r\\n if not os.path.exists(file_base):\\r\\n try:\\r\\n os.makedirs(file_base)\\r\\n except OSError as e:\\r\\n if e.errno != errno.EEXIST:\\r\\n raise\",\n \"def _create_folder_if_not_exist(filename):\\n os.makedirs(os.path.dirname(filename), exist_ok=True)\",\n \"def create_folder(folder):\\n import errno\\n try:\\n os.makedirs(folder)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise e\",\n \"def CreateFolderIfNotExisting(folder_path, communicator):\\n if not os.path.isdir(folder_path) and communicator.MyPID() == 0:\\n os.makedirs(folder_path)\\n communicator.Barrier()\",\n \"def check_make(folder_check):\\n if not os.path.isdir(folder_check):\\n os.mkdir(folder_check)\",\n \"def create_folder(path: str):\\n try:\\n Path(path).mkdir(parents=True, exist_ok=True)\\n return True\\n except:\\n print(\\\"An error occured.\\\")\",\n \"def create_folder(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def create_folder(folder):\\n if not os.path.exists(folder):\\n os.makedirs(folder)\",\n \"def create_folder(folder):\\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n logging.debug(\\\"Folder %s Created!\\\" % folder)\\n else:\\n logging.debug(\\\"Folder %s Exists!\\\" % folder)\",\n \"def folder_guard(folder_path):\\n if not os.path.isdir(folder_path):\\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\\n os.mkdir(folder_path)\",\n \"def create_folder(folder):\\n flag = True\\n if not os.path.exists(folder):\\n try:\\n os.makedirs(folder)\\n initlog('Folder path:%s created by me; ' % folder) \\n except Exception, e:\\n initlog('failed to create Folder path; %s' % str(e))\\n flag = False\\n return flag\",\n \"def create_folder(folder_name):\\n\\n try:\\n os.makedirs(folder_name)\\n except FileExistsError:\\n pass\",\n \"def create_dir_if_necessary(path):\\n try:\\n os.makedirs(path)\\n except OSError as exception:\\n if exception.errno != errno.EEXIST:\\n raise\",\n \"def ensure_dirs(cls, folder_path):\\n try:\\n cls.mkdirs(folder_path)\\n except exceptions.PlotlyRequestError as e:\\n if \\\"already exists\\\" in e.message:\\n pass\\n else:\\n raise e\",\n \"def create_test_folder_if_does_not_exist(path):\\n print('')\\n if os.path.exists(path):\\n print(' Skip creation of existing folder: {}'.format(path))\\n else:\\n print(' Create non-existing test folder: {}'.format(path))\\n os.makedirs(path, mode=0o775)\",\n \"def ensure_dir(path):\\n parent = os.path.dirname(path)\\n if not os.path.exists(parent):\\n os.makedirs(parent)\",\n \"def _check_directory(my_folder):\\n if not os.path.exists(my_folder):\\n os.makedirs(my_folder)\",\n \"def createFolder(folder):\\n folder_ = os.path.join(os.getcwd(),folder)\\n if not(os.path.isdir(folder_)):\\n os.mkdir(folder_)\",\n \"def folder_guard(folder_path):\\n \\n if not os.path.isdir(folder_path):\\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\\n os.mkdir(folder_path)\",\n \"def createFolder(self):\\n self.destination = self.getPath() #Find the destination to create the folder\\n try:\\n os.makedirs(self.destination) #Try and make a folder\\n except FileExistsError:\\n pass #Otherwise continue if an error is encountered because the file exists already\",\n \"def ensure_dir_exists(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def ensure_folders_if(path, condition=True):\\n if not os.path.exists(path) and condition:\\n os.makedirs(path)\",\n \"def create_folder(folder_path: str) -> None:\\n if not os.path.exists(folder_path):\\n os.makedirs(folder_path)\",\n \"def create_folder(path):\\n try:\\n os.listdir(path)\\n except:\\n os.makedirs(path)\\n else:\\n shutil.rmtree(path)\\n os.makedirs(path)\\n return path\",\n \"def create_folder(name_folder: str):\\n try:\\n # Create a new direcctory\\n os.mkdir(name_folder)\\n except FileExistsError:\\n # If the direcctory already exits print.\\n print(f\\\"The directory {name_folder} already exists.\\\")\",\n \"def check_folder(filepath):\\n if not os.path.exists(filepath):\\n os.mkdir(filepath)\\n return filepath\",\n \"def create_folder(path: str):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def mkdir(folder_name: str) -> None:\\n if exist(folder_name):\\n print(\\\"The folder is already exist\\\")\\n return \\n\\n os.mkdir(folder_name)\",\n \"def prepare_folder(path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\",\n \"def _folderCheck(self, folder):\\n logger.debug(\\\"Func: _folderCheck\\\")\\n\\n if not os.path.isdir(os.path.normpath(folder)):\\n os.makedirs(os.path.normpath(folder))\",\n \"def create_folder(location: str):\\n try:\\n os.mkdir(location)\\n except FileExistsError:\\n pass\",\n \"def EnsureDirExists(path):\\n try:\\n os.makedirs(os.path.dirname(path))\\n except OSError:\\n pass\",\n \"def mkdir_if_not_exists(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def maybe_makedirs(path_to_create):\\n try: \\n os.makedirs(path_to_create)\\n except OSError:\\n if not os.path.isdir(path_to_create):\\n raise\",\n \"def check_folder(directory):\\n global path_checked\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n else:\\n path_checked = True\",\n \"def ensure_path(full_path):\\n full_path = Path(full_path)\\n if not full_path.exists():\\n full_path.mkdir(parents=True, exist_ok=True)\",\n \"def create_folder_path(folder_path):\\n try:\\n if os.path.exists(folder_path):\\n shutil.rmtree(folder_path)\\n os.makedirs(folder_path)\\n except Exception:\\n raise Error('Create {folder_path} exception'.format(folder_path))\",\n \"def create_folder(folder_name):\\n if not os.path.exists(folder_name):\\n os.makedirs(folder_name)\",\n \"def create_folder(folder_name):\\n if not os.path.exists(folder_name):\\n os.makedirs(folder_name)\",\n \"def ensure_dir(dir_path):\\n try:\\n os.mkdir(dir_path)\\n except FileExistsError:\\n pass\",\n \"def _ensure_dir(dir_name):\\n if not os.path.exists(dir_name):\\n os.makedirs(dir_name)\",\n \"def create_directory(parent_path, new_folder):\\n newdir = os.path.join(parent_path, new_folder)\\n if os.path.isdir(newdir):\\n return False\\n else:\\n os.mkdir(newdir)\\n return True\",\n \"def ensure_dir(path):\\n\\n \\n try:\\n os.makedirs(path)\\n except (EnvironmentError) as e:\\n if not(e.errno == errno.EEXIST and \\n e.filename == path):\\n raise\\n return\",\n \"def ensure_directory_exists(path):\\n directory = os.path.dirname(path)\\n if not os.path.exists(directory):\\n os.makedirs(directory, exist_ok=True)\\n return\",\n \"def _mkdir_if_not_exist(path):\\n if not(os.path.isdir(path)):\\n os.mkdir(path)\\n else:\\n _logger.info('Skipping existing directory %s' % path)\",\n \"def create_folder(folder):\\n\\n try:\\n os.mkdir(folder, 0740)\\n except OSError:\\n return False\\n else:\\n return True\",\n \"def ensure_directory(path):\\n\\tdir_path = os.path.dirname(path)\\n\\tif os.path.exists(dir_path):\\n\\t\\treturn\\n\\tensure_directory(dir_path)\\n\\ttry:\\n\\t\\tos.mkdir(dir_path)\\n\\texcept OSError as e:\\n\\t\\t# Ignore if EEXISTS. This is needed to avoid a race if two getters run at once.\\n\\t\\tif e.errno != errno.EEXIST:\\n\\t\\t\\traise\",\n \"def ensure_dir_exists(path: Union[str,Path]) -> None:\\n# path = str(path)\\n assert not os.path.isfile(path)\\n os.makedirs(path, exist_ok=True)\\n assert os.path.isdir(path)\",\n \"def _ensure_dir(directory):\\r\\n try:\\r\\n os.makedirs(directory)\\r\\n except OSError as exc:\\r\\n if exc.errno == errno.EEXIST:\\r\\n pass\\r\\n else:\\r\\n raise\",\n \"def ensure_dir_exists(dir_path):\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\",\n \"def ensure_dirpath_exists(path: Path) -> Path:\\n assert path\\n out_path: Path = path\\n\\n if not out_path.exists():\\n out_path.mkdir(parents=True, exist_ok=True)\\n\\n return out_path\",\n \"def ensure_directory(explorer, parent_id, dirname):\\n cache_key = (parent_id, dirname)\\n if cache_key in DIR_CACHE:\\n return DIR_CACHE[cache_key]\\n\\n for folder in explorer.list_folder(parent_id):\\n if folder['name'] == dirname:\\n folder_id = folder['id']\\n break\\n else:\\n print(\\\"Creating folder {!r} in parent {}\\\".format(dirname, parent_id))\\n folder_id = explorer.create_folder(dirname, parent_id)\\n DIR_CACHE[cache_key] = folder_id\\n return folder_id\",\n \"def create_folder(self, unformatted_path):\\n os.makedirs(self.format_path(unformatted_path), exist_ok=True)\",\n \"def maybe_makedirs(path_to_create):\\n try:\\n os.makedirs(path_to_create)\\n except OSError:\\n if not os.path.isdir(path_to_create):\\n raise\",\n \"def make_dir_if_needed(path):\\n\\n if not os.path.exists(path):\\n os.makedirs(path)\\n return path\",\n \"def create_folder(directory):\\n try:\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n except OSError:\\n print('Error: The folder wasn\\\\'t able to be created: ' + directory)\",\n \"def _ensure_directory(self, dirname):\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\",\n \"def create_folder(name):\\n try:\\n dirname = os.path.dirname(__file__)\\n filename = os.path.join(dirname, name)\\n os.makedirs(filename)\\n return 0\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n return -1\",\n \"def _check_path(path):\\n os.system(\\\"if [ ! -d \\\" + path + \\\" ]; then mkdir -p \\\" + path + \\\"; fi\\\")\",\n \"def create_folder(folders_to_create=[]):\\n for f in folders_to_create:\\n if not os.path.exists(f):\\n os.makedirs(f)\",\n \"def check_dir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def create_directory(folder_name):\\n if not os.path.exists(folder_name):\\n os.makedirs(folder_name)\",\n \"def create_dir():\\n if check_dir_exist():\\n return False\\n else:\\n os.makedirs(path_structure)\\n return True\",\n \"def make_sure_path_exists(path):\\n try:\\n os.makedirs(path)\\n except OSError as exception:\\n if exception.errno != errno.EEXIST:\\n raise\",\n \"def make_sure_path_exists(path):\\n try: os.makedirs(path)\\n except OSError as exception:\\n if exception.errno != errno.EEXIST: raise\",\n \"def _ensure_dir_exists(self, directory):\\n directory = directory.strip()\\n if not Path(directory).exists():\\n os.mkdir(directory)\",\n \"def createFolder(directory) -> None:\\n try:\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n except Exception as error:\\n print(f\\\"Error: createFolder({directory}) -> {error}\\\")\",\n \"def ensure_dir(d):\\n\\n if not os.path.exists(d):\\n os.makedirs(d, exist_ok=True)\\n\\n return\",\n \"def _mkdir_if_not_exist(path):\\n if not os.path.exists(path):\\n try:\\n os.makedirs(path)\\n except OSError as e:\\n if e.errno == errno.EEXIST and os.path.isdir(path):\\n logger.warning(\\n 'be happy if some process has already created {}'.format(\\n path))\\n else:\\n raise OSError('Failed to mkdir {}'.format(path))\",\n \"def create_dir_if_doesnt_exist(dir_path):\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\\n return\",\n \"def make_dir_if_needed(dir) :\\n\\tif not exists(dir) :\\n\\t\\tos.makedirs(dir)\",\n \"def exist_ok_mkdir (path, mode=0777):\\n try:\\n os.mkdir (path, mode)\\n except OSError:\\n if not os.path.isdir (path):\\n raise\",\n \"def ensure_dir(f):\\n\\td=os.path.dirname(f)\\n\\tif not os.path.exists(d):\\n\\t\\tos.makedirs(d)\",\n \"def ensure_dir(root, path):\\n full_path = root\\n for seg in path.split(os.sep):\\n full_path += os.sep + seg\\n if os.path.exists(full_path):\\n if not os.path.isdir(full_path):\\n raise ValueError(\\\"'{}' is not a directory\\\".format(full_path))\\n else:\\n os.makedirs(full_path)\",\n \"def mkdir(path):\\n\\tif not Path(path).exists():\\n\\t\\tPath(path).mkdir(parents=True, exist_ok=True)\",\n \"def assure_path_exists(self, path):\\n\\n dir = os.path.dirname(path)\\n if not os.path.exists(dir):\\n os.makedirs(dir)\",\n \"def ensuredir(path):\\n # Copied from sphinx.util.osutil.ensuredir(): BSD licensed code, so it's OK\\n # to add to this project.\\n EEXIST = getattr(errno, 'EEXIST', 0)\\n try:\\n os.makedirs(path)\\n except OSError as err:\\n # 0 for Jython/Win32\\n if err.errno not in [0, EEXIST]:\\n raise\",\n \"def _check_or_create_dir(directory):\\n if not tf.gfile.Exists(directory):\\n tf.gfile.MakeDirs(directory)\",\n \"def maybe_make_dir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def ensure_dir( dirName ):\\r\\n if not os.path.exists( dirName ):\\r\\n os.makedirs( dirName )\",\n \"def ensure_dir(dir_):\\n try:\\n os.mkdir(dir_)\\n except OSError:\\n assert os.path.isdir(dir_)\",\n \"def ensure_dirs_exist(path):\\n os.makedirs(path, exist_ok=True)\",\n \"def mkdir_if_notexists(path):\\n try:\\n os.mkdir(path)\\n except FileExistsError:\\n pass\",\n \"def create_folders(folder_name):\\n\\n if os.path.exists(downloads_path + '\\\\\\\\' + folder_name):\\n pass\\n else:\\n os.makedirs(folder_name)\\n print(f'Folder: {folder_name} has been created in {downloads_path}')\",\n \"def checking_path():\\n path = Path(\\\"phonebook\\\")\\n try:\\n path.mkdir(parents=True, exist_ok=False)\\n except FileExistsError:\\n pass\\n else:\\n pass\",\n \"def mkdir_if_missing(d):\\n if not os.path.exists(d):\\n os.makedirs(d)\",\n \"def create_directory_if_not_exists(directory_path):\\n os.makedirs(directory_path, exist_ok=True)\",\n \"def ifnotexistmkdir(directory):\\n if not os.path.exists(directory):\\n os.mkdir(directory)\\n return Path(directory)\",\n \"def check_path(dir_path):\\n if not os.path.exists(dir_path):\\n os.mkdir(dir_path, 0755)\",\n \"def mkdir(folder_path, exist_ok=True):\\n\\n # Generate dir\\n os.makedirs(folder_path, exist_ok=exist_ok)\\n\\n return True\",\n \"def ensure_path(directory):\\n if not path.exists(directory):\\n os.makedirs(directory)\\n # end if\\n return directory\",\n \"def createFolder(folderFullPath):\\n os.makedirs(folderFullPath, exist_ok=True)\",\n \"def exist_ok_makedirs (path, mode=0777):\\n if not os.path.isdir (path):\\n head, tail = os.path.split (path)\\n if not tail:\\n head, tail = os.path.split (head)\\n if head and tail:\\n exist_ok_makedirs (head, mode)\\n exist_ok_mkdir (path, mode)\",\n \"def ensure_dirs_exists(self):\\n os.makedirs(os.path.join(self.location, \\\"batches\\\"), exist_ok=True)\\n os.makedirs(os.path.join(self.location, \\\"results\\\"), exist_ok=True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82459253","0.8094383","0.8059282","0.788112","0.7779735","0.7752992","0.77248335","0.77168036","0.77027094","0.76686084","0.7639529","0.76316696","0.76276046","0.7627525","0.7594649","0.7567482","0.7552197","0.75503033","0.7529557","0.7526078","0.75254","0.7512916","0.7490926","0.7458066","0.7444984","0.7444063","0.7442587","0.7422026","0.741305","0.7402993","0.73841494","0.73776454","0.7368256","0.7350468","0.7343404","0.73284996","0.73280597","0.73096055","0.7301028","0.7300524","0.7294661","0.7283377","0.7275137","0.7275137","0.72726554","0.7258377","0.72576815","0.7249299","0.7245505","0.72423774","0.72401345","0.7233169","0.7230621","0.7229388","0.72276115","0.721832","0.7214569","0.7208516","0.7191891","0.7187161","0.7185111","0.71689636","0.7161454","0.715934","0.71543145","0.7151872","0.7141743","0.7138145","0.7137444","0.7129204","0.71253014","0.7108062","0.7104926","0.70908177","0.7080884","0.70764124","0.70741844","0.7073596","0.70734847","0.7070537","0.7059517","0.70580304","0.7049929","0.70492524","0.70486563","0.7041085","0.7037446","0.70371246","0.7037098","0.7030301","0.7027226","0.7018647","0.7014373","0.70121783","0.70119876","0.6997651","0.69945335","0.6990592","0.69841814"],"string":"[\n \"0.82459253\",\n \"0.8094383\",\n \"0.8059282\",\n \"0.788112\",\n \"0.7779735\",\n \"0.7752992\",\n \"0.77248335\",\n \"0.77168036\",\n \"0.77027094\",\n \"0.76686084\",\n \"0.7639529\",\n \"0.76316696\",\n \"0.76276046\",\n \"0.7627525\",\n \"0.7594649\",\n \"0.7567482\",\n \"0.7552197\",\n \"0.75503033\",\n \"0.7529557\",\n \"0.7526078\",\n \"0.75254\",\n \"0.7512916\",\n \"0.7490926\",\n \"0.7458066\",\n \"0.7444984\",\n \"0.7444063\",\n \"0.7442587\",\n \"0.7422026\",\n \"0.741305\",\n \"0.7402993\",\n \"0.73841494\",\n \"0.73776454\",\n \"0.7368256\",\n \"0.7350468\",\n \"0.7343404\",\n \"0.73284996\",\n \"0.73280597\",\n \"0.73096055\",\n \"0.7301028\",\n \"0.7300524\",\n \"0.7294661\",\n \"0.7283377\",\n \"0.7275137\",\n \"0.7275137\",\n \"0.72726554\",\n \"0.7258377\",\n \"0.72576815\",\n \"0.7249299\",\n \"0.7245505\",\n \"0.72423774\",\n \"0.72401345\",\n \"0.7233169\",\n \"0.7230621\",\n \"0.7229388\",\n \"0.72276115\",\n \"0.721832\",\n \"0.7214569\",\n \"0.7208516\",\n \"0.7191891\",\n \"0.7187161\",\n \"0.7185111\",\n \"0.71689636\",\n \"0.7161454\",\n \"0.715934\",\n \"0.71543145\",\n \"0.7151872\",\n \"0.7141743\",\n \"0.7138145\",\n \"0.7137444\",\n \"0.7129204\",\n \"0.71253014\",\n \"0.7108062\",\n \"0.7104926\",\n \"0.70908177\",\n \"0.7080884\",\n \"0.70764124\",\n \"0.70741844\",\n \"0.7073596\",\n \"0.70734847\",\n \"0.7070537\",\n \"0.7059517\",\n \"0.70580304\",\n \"0.7049929\",\n \"0.70492524\",\n \"0.70486563\",\n \"0.7041085\",\n \"0.7037446\",\n \"0.70371246\",\n \"0.7037098\",\n \"0.7030301\",\n \"0.7027226\",\n \"0.7018647\",\n \"0.7014373\",\n \"0.70121783\",\n \"0.70119876\",\n \"0.6997651\",\n \"0.69945335\",\n \"0.6990592\",\n \"0.69841814\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7339701"},"document_rank":{"kind":"string","value":"36"}}},{"rowIdx":92,"cells":{"query":{"kind":"string","value":"Sends a POST request every second to the monitoring server indicating that the process is still running."},"document":{"kind":"string","value":"def post_heartbeat(host, name, auth=None):\n data = {'name': name, 'status': 'ok'}\n try:\n response = post('{host}/monitoring/heartbeat'.format(host=host), data, auth)\n except urllib2.URLError:\n print(\"Failed to send heartbeat.\", file=sys.stderr)\n else:\n if response.strip() != 'ok':\n print('POST got response {response}'.format(response=response), file=sys.stderr)\n\n timer = threading.Timer(1.0, post_heartbeat, args=(host, name, auth))\n # set the thread as a daemon to exit the program cleanly\n # when the main thread finishes\n timer.daemon = True\n timer.start()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def periodic_timer(self):\n while self.running:\n self.sendStatusQuery()\n time.sleep(REPORT_INTERVAL)","def keep_alive():\r\n app = Flask(\"\")\r\n @app.route(\"/\")\r\n def home():\r\n return \"Your bot is now alive!\"\r\n\r\n def run():\r\n app.run(host=\"0.0.0.0\", port=8080)\r\n\r\n\r\n server = Thread(target=run)\r\n server.start()","def background_catch_up():\n while True:\n time.sleep(interval)\n s = 'http://{0}:{1}'.format(args.host, port)\n req = urllib2.Request(s)\n try:\n response = urllib2.urlopen(req)\n response.read()\n except Exception as e:\n pass","def your_process(seconds):\r\n global STATUS\r\n sleep(seconds)\r\n STATUS = True","def process_request(t):\n time.sleep(t)","def process_request(t):\n time.sleep(t)","def process_request(t):\n time.sleep(t)","def keepalive(self) -> None:","def perform_action(self):\n logger.info(\"Now sending a keepalive to the primary\")\n self.connection_handler.send_message(\"I am still alive, client: {num}\".format(num=self.uuid))\n time.sleep(5)","def console_server(request):\n return run_server(interval='1')","def ping_moonrat():\n threading.Timer(3600, ping_moonrat).start()\n text = \"Moonrat is still active\\n\"\n slack_client.api_call(\n \"chat.postMessage\",\n channel='G9P7X8Q0H',\n text=text,\n )","def inform_alive(self):\n\n response = requests.get(\n \"https://brokenprotocol.xyz/Device/Heartbeat\",\n headers={\"auth\": self.token}\n )\n response.raise_for_status()","def restart():\n info = request.get_json() or {}\n delay_secs = int(info.get('delay', 0))\n\n t = threading.Timer(delay_secs, update_trigger_file)\n t.start()\n\n return jsonify('Success')","def _keep_alive(self):\n self.send_keep_alive(self.server.startup_time_delta)\n return TaskSignal.AGAIN","def keepAliveReceived(self):","def run( self ):\n while True:\n try:\n time.sleep( 5 )\n self._monitorProcess()\n except Exception, e:\n self.logger.exception( \"Error starting monitor process\" )","def post(host):\n redis.setex('dispatcher',host,60)\n timer = threading.Timer(20.0, post, args=[host])\n timer.daemon = True\n timer.start()","def startHeartbeat(self):\n self.post.__sendHeartbeat()","def background_thread():\n count = 0\n while True:\n socketio.sleep(10)\n count += 1\n socketio.emit('my_response',\n {'data': 'Server generated event', 'count': count},\n namespace='/test')","async def check_status(self):\n while True:\n async with self._loop_lock:\n new_monitor_processes = {}\n for class_name in self.monitor_processes:\n monitor = self.monitor_processes[class_name][\"process\"]\n if monitor.poll() is not None:\n log = f\"Monitor {class_name} has stopped with code: {monitor.returncode}\"\n if monitor.returncode:\n self.general_logger.warning(log)\n if self.config[\"WebhookConfig\"][\"crash_webhook\"]:\n embed = get_mm_crash_embed(\n \"Monitor \" + class_name,\n monitor.returncode,\n monitor.pid,\n )\n ts = datetime.now().strftime(\n self.config[\"WebhookConfig\"][\"timestamp_format\"]\n )\n\n embed.set_footer(\n text=f\"{self.config['WebhookConfig']['provider']} | {ts}\",\n icon_url=self.config[\"WebhookConfig\"][\n \"provider_icon\"\n ],\n )\n data = json.dumps(\n {\n \"embeds\": [embed.to_dict()],\n \"username\": \"MonitorManager process watcher\",\n \"avatar_url\": self.config[\"WebhookConfig\"][\n \"provider_icon\"\n ],\n }\n )\n r = await self.client.fetch(\n self.config[\"WebhookConfig\"][\"crash_webhook\"],\n method=\"POST\",\n body=data,\n headers={\"content-type\": \"application/json\"},\n raise_error=False,\n )\n else:\n self.general_logger.info(log)\n else:\n new_monitor_processes[class_name] = self.monitor_processes[\n class_name\n ]\n self.monitor_processes = new_monitor_processes\n\n new_scraper_processes = {}\n for class_name in self.scraper_processes:\n scraper = self.scraper_processes[class_name][\"process\"]\n if scraper.poll() is not None:\n log = f\"Scraper {class_name} has stopped with code: {scraper.returncode}\"\n if scraper.returncode:\n self.general_logger.warning(log)\n if self.config[\"WebhookConfig\"][\"crash_webhook\"]:\n embed = get_mm_crash_embed(\n \"Scraper \" + class_name,\n scraper.returncode,\n scraper.pid,\n )\n ts = datetime.now().strftime(\n self.config[\"WebhookConfig\"][\"timestamp_format\"]\n )\n\n embed.set_footer(\n text=f\"{self.config['WebhookConfig']['provider']} | {ts}\",\n icon_url=self.config[\"WebhookConfig\"][\n \"provider_icon\"\n ],\n )\n data = json.dumps(\n {\n \"embeds\": [embed.to_dict()],\n \"username\": \"MonitorManager process watcher\",\n \"avatar_url\": self.config[\"WebhookConfig\"][\n \"provider_icon\"\n ],\n }\n )\n r = await self.client.fetch(\n self.config[\"WebhookConfig\"][\"crash_webhook\"],\n method=\"POST\",\n body=data,\n headers={\"content-type\": \"application/json\"},\n raise_error=False,\n )\n else:\n self.general_logger.info(log)\n else:\n new_scraper_processes[class_name] = self.scraper_processes[\n class_name\n ]\n self.scraper_processes = new_scraper_processes\n await asyncio.sleep(1)","def _keep_alive(self):\n while self._is_running:\n secs_since_last_cmd = time.time() - self._last_cmd_time\n if not self._is_action_active and secs_since_last_cmd > self._keep_alive_secs:\n self._transport.send(activity_cmd(\" \"))\n self._last_cmd_time = time.time()\n self._log.debug(\"Keeping alive\")\n self._keep_alive_event.wait(timeout=self._keep_alive_secs)","async def monitor(self, session: ClientSession, params: MonitoringData) -> None:\n while True:\n result = await self.check_website(session, params)\n self._producer.send(result)\n await asyncio.sleep(params.interval)","def background_thread():\n count = 0\n while True:\n time.sleep(10)\n count += 1\n socketio.emit('my response',\n {'data': 'Server generated event', 'count': count},\n namespace='/test')","def watch(self, url):\n self.__url = url\n self.downtime_info = None\n self.__timer.start()","def run(self):\n while True:\n print(\"I'm running in the background\")\n time.sleep(self.interval)","def background_thread():\n count = 0\n while True:\n socketio.sleep(1)\n count += 1\n t = time.strftime('%M:%S', time.localtime())\n cpus = [1,2,3,4] #\n print('sending')\n socketio.emit('server_response',\n {'data': [t, cpus[0],cpus[1],cpus[2],cpus[3]], 'count': count})","def _keep_alive(self):\n self.send_keep_alive()\n return TaskSignal.AGAIN","def slot_keepalive_timer(self, _sender, _data):\r\n if self.connected:\r\n #self.debug(\"### sending keepalive\")\r\n self._try_send_raw(\"2::\")","def thread_function(client):\n threading.Timer(30.0, thread_function).start()\n client.publish(\"serverCommand/keepalive\", \"0\")\n print(\"Message Sent. (keepalive)\")","def continue_server():\n update_server_status({'ready': True})","def run(self):\n while self.running:\n self.handle_request()","def monitorOne(self,website):\n checkInterval = website.checkInterval\n time.sleep(checkInterval)\n while self\\\n .queueTermination.empty():\n startSubProcess = time.time()\n # todo define timeout for requests\n try :\n req = requests.get(website.url, timeout=checkInterval)\n reqCode = req.status_code\n reqTime = req.elapsed\n # Generic to handle all kind of http exceptions\n # Possible enhancement\n except Exception:\n continue\n # unix epoch time good for comparison\n currentTime = time.time()\n website.log[currentTime] = {'code': reqCode, 'responseTime': reqTime}\n # 2 mins\n twoMinsDic = self.getTimeframedData(website, 120, currentTime=currentTime)\n self.queueTwoMin.put(twoMinsDic)\n # 10 mins\n tenMinsDic = self.getTimeframedData(website, 600, currentTime=currentTime)\n self.queueTenMin.put(tenMinsDic)\n # 1 hour\n hourDic = self.getTimeframedData(website, 3600, currentTime=currentTime)\n self.queueHour.put(hourDic)\n\n endSubProcess = time.time()\n # Wait for the next check\n try:\n time.sleep(checkInterval-(endSubProcess-startSubProcess))\n except ValueError:\n pass\n\n # Terminate all processes\n self._terminateAll()\n return","async def handle_live(self, websocket):\n while True:\n payload = self.generate_payload()\n await websocket.send(json.dumps(payload))\n await asyncio.sleep(self.update_frequency_milliseconds / 1000)","def monitor(self):\n while not self.terminated:\n try:\n if (time.time() - self.updated_time) < 5:\n messages = self.messages.copy()\n # procs = np.min([ len(messages), 9 ]) + 1\n # pool = ThreadPool(procs)\n # pool.map(self.process, messages)\n # pool.close()\n # pool.join()\n for message in messages:\n self.process(message)\n elif self.ws:\n self.updated_time += 10\n self.ws.close()\n except Exception as e:\n self.on_error(None, \"Monitoring Error: {}\".format(e))\n continue\n finally:\n time.sleep(0.1)","def admin_server(request):\n return run_server(interval='10000')","def periodicCall(self):\n self.gui.processIncoming()\n if not self.running:\n import sys\n sys.exit(1)\n self.master.after(UPDATE_DELAY, self.periodicCall)","def Listen(self):\n while True:\n time.sleep(1)","def poll_http(interval, port_no):\n import requests\n\n url_string = 'http://0.0.0.0:{}/refresh'.format(port_no)\n\n print('DBG: poll_http({}): got to thread @ {}'.format(\n interval, time.strftime(\"%I:%M:%S\")))\n print('url_string = {}'.format(repr(url_string)))\n\n while True:\n time.sleep(interval)\n print('DBG: thread woke up @ {}'.format(time.strftime(\"%I:%M:%S\")))\n r = requests.get(url_string)\n print('DBG: Requests.text = {}'.format(repr(r.text)))","def daemon_job(interval):\n time.sleep(3) # Wait for api server to start first\n while True:\n try:\n crawl()\n process_notification()\n except Exception:\n traceback.print_exc()\n time.sleep(interval)","def service( self ):\n\n self.alive = time.time()","def watch_process(self):\n psutil.wait_procs([psutil.Process(self._proc.pid)],\n callback=self.start)","async def monitor():\n global counter\n while True:\n time.sleep(1)\n print(counter, 'reqs/sec')\n counter = 0","def on_keep_alive(self):\n log.debug(\"Received keep-alive signal\")","def status_task():\n props = [\n (STAT_TIME, current_time),\n (STAT_CONDITION, weather_condition)\n ]\n\n # Send the status request with the current time and condition.\n send_status_request(props)\n\n # Create and start a timer to repeat this task periodically.\n t = Timer(report_interval, status_task)\n t.setDaemon(True)\n t.start()","def status(self):\n # process running ?\n pid = self.get_pidfile()\n \n running = True\n \n # process is not running\n if pid is None:\n running = False\n else:\n if not self.send_signal(pid,0):\n running = False\n # abnormal state, delete the file\n self.delete_pidfile()\n \n if running:\n message = \"server is running\\n\"\n else:\n message = \"server is not running\\n\"\n sys.stdout.write(message)\n \n return running","def monitor(self):\r\n while True:\r\n for worker, start_time in self.workers.items():\r\n if (not worker.isAlive() or\r\n self.timeout\r\n and datetime.now() - start_time > self.timeout): \r\n\r\n self.work_count.get_nowait()\r\n self.jobs.task_done()\r\n del self.workers[worker]\r\n\r\n time.sleep(1)","def serve_forever(self, unused_parameter=0.5):\r\n self.stop = False\r\n while not self.stop:\r\n self.handle_request()","def run_while_true(server_class=BaseHTTPServer.HTTPServer,\n handler_class=BaseHTTPServer.BaseHTTPRequestHandler):\n server_address = ('localhost', 8080)\n httpd = server_class(server_address, handler_class)\n while keep_running():\n httpd.handle_request()","def periodically_request_websites_metrics() -> int:\n # Making a get request\n configuration_path = os.path.abspath('configuration/service_configuration.json')\n list_of_websites_to_check = read_service_configuration(\n configuration_file=configuration_path, section='list_of_websites_to_check')\n try:\n\n remote_service_url = os.environ.get('websites_checker_service_url','http://192.168.1.101:8080/api/v1/websites_metrics')\n response = requests.post(url=remote_service_url, json=list_of_websites_to_check)\n if response:\n logger.info(f\"The request has been sent to {remote_service_url} with payload: {list_of_websites_to_check}\")\n\n else:\n logger.error(f\"Error contacting the service {remote_service_url}\")\n except Exception as error:\n logger.error(f\"The Exception {error} occurred\")\n return 1","def KeepAlive(self) -> bool:","def is_alive():\n return jsonify({'message': 'Service is alive'}), 200","def pingEsp(self):\n\t\twhile True:\n\t\t\tprint (\"[{}] Keeping alive the ESP8266 connection\".format(\n\t\t\t\tint(time.time()),\n\t\t\t))\n\t\t\tmqttCli.publish(\"ping\", mqttJsonDump('void'))\n\t\t\ttime.sleep(30)","def do_monitor(self):\n while not self.expired:\n self.expired = True\n time.sleep(self.interval)\n self.dead_fn()","def background_thread():\n count = 0\n with open(\"logs.txt\", \"r\") as logfile:\n while True:\n socketio.sleep(1)\n count += 1\n\n line = logfile.readline()\n if line:\n socketio.emit('my_response',\n {'data': line, 'count': count},\n namespace='/test')","def check_loop(\n url, period=5, timeout=10, body_check_re='',\n producer=None, oneshot=False):\n while True:\n worker = threading.Thread(target=check, kwargs={\n 'url': url,\n 'timeout': timeout,\n 'body_check_re': body_check_re,\n 'producer': producer,\n })\n logger.info('check url=%s' % url)\n worker.start()\n time.sleep(period)\n if oneshot:\n return","def run(self):\n while True :\n try:\n appinfo = self.db.hgetall(self.appname)\n appinfo_str = json.dumps(appinfo)\n data = {'applicationname':self.appname,'appinfo':appinfo_str}\n response = requests.post(REGISTRAR_URL, data=data)\n time.sleep(2)\n except :\n pass","def monitor(self, target):\n while self.RUNNING:\n check_time = datetime.now()\n next_check = check_time + timedelta(seconds=target[\"frequency\"])\n\n try:\n self.produce(\n get(target[\"url\"], timeout=target[\"frequency\"] - 0.5),\n target.get(\"regex\"),\n check_time,\n )\n except Timeout:\n self.logger.warning(\"Check for %s timed out\", target[\"url\"])\n except RequestException as e:\n self.logger.error(e)\n except re.error as e:\n self.logger.error(e)\n break\n\n # Busy loop until next check_time\n while datetime.now() < next_check:\n sleep(1)","def keep_alive(self):\n req = BFGlobalFactory.create(\"ns1:KeepAliveReq\")\n rsp = self._soapcall(BFGlobalService.keepAlive, req)\n if rsp.header.errorCode != APIErrorEnum.OK:\n logger.error(\"{keepAlive} failed with error {%s}\",\n rsp.header.errorCode)","def cron_health(request):\n if checkIfProcessRunning('crond'):\n print('Yes a crond process was running')\n return HttpResponse(status=200)\n else:\n print('crond process is Not running')\n\n return HttpResponse(status=500)","def stream():\n while True:\n try:\n r = requests.post(\"http://streamer_0:5000/stream\", json={})\n break\n except requests.exceptions.ConnectionError:\n logging.error(\"Could not connect to server streamer_0, retrying\")\n time.sleep(2)\n continue\n logging.info(\"'http://streamer_0:5000/stream', response = {}\".format(r.status_code))\n if r.status_code != 200:\n time.sleep(2)\n stream()","def schedule_alive_job(self):\n if 'alive-notification' not in settings:\n return\n\n self.scheduler.add_job(\n lambda: self.event_notifier.send_message('Surveillance is alive'),\n **settings['alive-notification'])","async def keep_alive(self):\n self._keepalive = True\n while True:\n await gen.sleep(self.KEEPALIVE_INTERVAL)\n if not self._keepalive:\n return\n try:\n # lines that start with : are comments\n # and should be ignored by event consumers\n self.write(\":keepalive\\n\\n\")\n await self.flush()\n except StreamClosedError:\n return","def serve_forever(self, poll_interval=0.5):\n logging.info('RPC server starting')\n self._idle_thread.start()\n SimpleJSONRPCServer.SimpleJSONRPCServer.serve_forever(self, poll_interval)","def run(self):\r\n self.rpc_server.serve_forever(0.5)","def __call__(self):\n hub.sleep(random.randint(1, self.interval))\n while True:\n self.send_req()\n self.reply_pending = True\n hub.sleep(self.interval)\n if self.reply_pending:\n self.no_response()","def monitor_and_terminate(self):\n import time\n import datetime\n\n keep_running = True\n\n while keep_running:\n\n print()\n print(datetime.datetime.now().replace(microsecond=0))\n print(self.get_monitor_string())\n\n time.sleep(30)\n\n _, status = self.reporter.get_job_status(self.info)\n if status[\"active\"]+status[\"running\"] == 0:\n keep_running = False\n\n print(\"All tasks done.\")","def serve(self):\n\t\tself.keep_running=1\n\t\tif self.debug:\n\t\t\tprint \"server started\"\n\t\ttry:\n\t\t\twhile self.keep_running:\n\t\t\t\tself.handle_request()\n\t\tfinally:\n\t\t\tif self.debug:\n\t\t\t\tprint \"server finished\"\n\t\t\tself.keep_running=0\n\t\t\tself.close()","def __sendHeartbeat(self):\n \n while not rospy.is_shutdown():\n rospy.sleep(5)\n self.setOutput(self.write_start+1,0)","def updated():\n ws = request.environ.get('wsgi.websocket', None)\n print(\"web socket retrieved\")\n app.number_of_connexion += 1\n if ws:\n while True:\n delay = random.randint(MIN_DELAY, MAX_DELAY)\n gevent.sleep(delay)\n try:\n ws.send(str(app.number_of_connexion))\n except WebSocketError:\n print(\"socket died\")\n app.number_of_connexion -= 1\n return \"disconnected\"\n else:\n raise RuntimeError(\"Environment lacks WSGI WebSocket support\")","def heartbeat(jsonify=None):\n url = URLS.heartbeat()\n payload = {\n \"request\": URLS.get_endpoint(url)\n }\n generate_signature(payload)\n data, err = request_post(url, payload, jsonify)\n return data, err","def do_POST(self):\n bugsnag = self.server.bugsnag\n body = self.rfile.read(int(self.headers['Content-Length']))\n bugsnag.enqueue(body)\n\n response = (\"OK %s:%s -> %s (%s/%s)\\n\" % (bugsnag.listen, bugsnag.port, bugsnag.endpoint, bugsnag.queue.qsize(), bugsnag.queue.maxsize))\n\n try:\n self.send_response(200)\n self.send_header('Content-Length', len(response))\n self.end_headers()\n self.wfile.write(response)\n except:\n print \"Client disconnected before waiting for response\"\n print_exception(*sys.exc_info())\n print \"continuing...\"","def _heartbeat_loop(self):\n # set last time so that \"if t_now - t_last >= HEARTBEAT_LOG_INTERVAL\"\n # below evalutes to True on the first run\n t_last = time.time() - HEARTBEAT_LOG_INTERVAL - 1\n while True:\n alive = 0\n # count alive processes \n for p in PROCESSES:\n if p.is_alive():\n alive += 1\n\n # no processes are alive - exit heartbeat loop\n if alive == 0:\n return\n\n t_now = time.time()\n if t_now - t_last >= HEARTBEAT_LOG_INTERVAL:\n # log heartbeat\n obj = { \n 'timestamp': time.time(),\n 'child_procs_total': self._procs_total,\n 'child_procs_alive': alive,\n 'probe_req_queue_len': self._probe_request_queue.qsize(),\n 'probe_resp_queue_len': \\\n self._probe_response_queue.qsize(), \n }\n \n # push to shared mem\n self._sm.set(config.BASE['SHARED_MEM_HEARTBEAT_KEY'],\n json.dumps(obj), HEARTBEAT_TTL)\n LOG.debug('pushed a heartbeat to the shared memory')\n\n t_last = t_now\n\n time.sleep(HEARTBEAT_LOOP_INTERVAL)","def is_monitor_process_live(pid_file):\n live = False\n\n try:\n check_process_status(pid_file)\n live = True\n except ComponentIsNotRunning:\n pass\n\n return live","def serve(self):\n super(BlacknetSensor, self).serve(BlacknetSensorThread, BLACKNET_PING_INTERVAL, self.do_ping)","def REP_watcher():\n global REQ_sent_time, REP_recd_time, pid, patience_seconds\n while True:\n time.sleep(patience_seconds) # how often to check\n try:\n recent_REQ_sent_time = REQ_sent_time.popleft()\n # if we got here; we have a recent_REQ_sent_time\n time.sleep(patience_seconds) # allow time for receipt of the REP\n try:\n recent_REP_recd_time = REP_recd_time.popleft()\n # if we got here; we have a recent_REP_recd_time\n interval = recent_REP_recd_time - recent_REQ_sent_time\n if interval.total_seconds() <= 0.0:\n # recent_REP_recd_time is not later than recent_REQ_sent_time\n print('After image send in REP_watcher test,')\n print('No REP received within', patience_seconds, 'seconds.')\n print('Ending sending program.')\n os.kill(pid, signal.SIGTERM)\n pass\n continue # Got REP after REQ so continue to next REQ\n except IndexError: # there was a REQ, but no timely REP\n print('After image send in REP_watcher test,')\n print('No REP received within', patience_seconds, 'seconds.')\n print('Ending sending program.')\n os.kill(pid, signal.SIGTERM)\n pass\n except IndexError: # there wasn't a time in REQ_sent_time\n # so there is no REP expected,\n # ... continue to loop until there is a time in REQ_sent_time\n pass","def post(self):\n\n headers = ''\n for key, value in self.request.headers.iteritems():\n headers += '%s: %s' % (key, value)\n headers += '\\r\\n'\n InboundRequest.add_record(datetime.utcnow(), self.request.host_url,\n self.request.path, headers, self.request.query_string, self.request.body)\n\n taskqueue.add(url='/check_wipe_task')","def run(self):\n while True:\n req = self._requests.get()[1]\n req.start()\n logging.info('Running request %s', req)","def register_background_task_reporter():\n from tornado import ioloop\n\n cb = ioloop.PeriodicCallback(log_background_task_status, 60 * 3 * 1000)\n cb.start()\n return cb","def Serve(self):\n t = threading.Thread(target=self.serve_forever)\n t.setDaemon(True)\n t.start()\n self._Announce()","def monitor_ws(self, endpoint='https://www.bitmex.com/api/v1'): #TODO: implement\n started = False\n restart_count = 0\n while True:\n if not started or self.ws.exited or self.ws is None:\n self.ws = BitMEXWebsocket(endpoint=endpoint, symbol=self.symbol,\n api_key=self.api_key, api_secret=self.api_secret)\n time.sleep(1)\n if self.ws.started:\n self.logger.info('Websocket is running.')\n started = True\n self.ws_restarting = False\n else:\n if self.ws.lagging:\n self.logger.error('Ws is lagging ,forcing a restart...')\n self.ws.exit()\n started = False\n self.ws_restarting = True\n restart_count += 1\n self.logger.info(f'Ws starts this session: {restart_count}')\n\n else:\n time.sleep(1)","def loop_forever(self):\n while self.running:\n time.sleep(0.01)","def worker_status(request):\n try:\n status = app.control.ping() or []\n except:\n # TODO: What celery exceptions are we catching here?\n status = []\n return HttpResponse(\n json.dumps(status),\n content_type=\"application/json\"\n )","def background_stuff():\n print \"BACKGROUND\"\n\n\n\n # # global socketio\n\n # # print(wsClient.url, wsClient.products)\n # while (wsClient.MessageCount < 30):\n # print(\"\\nMessageCount =\", \"%i \\n\" % wsClient.MessageCount)\n # # time.sleep(1)\n # # socketio.emit('my response', {'data': [\"TEST\"]}, namespace=\"/test\", broadcast=True)\n # #\n # wsClient.close()\n #\n # while True:\n # time.sleep(1)\n # t = str(time.clock())\n # print t\n\n def minute_passed(oldepoch):\n return time.time() - oldepoch >= .1\n\n global wsClient\n\n # t = time.time()\n # for i in range(3000):\n # # while True:\n # # print time.clock(), t\n # # if time.clock() > ( t + .1 ):\n # # show = True #minute_passed(t)\n # # if show:\n # # print show, time.time(), t, time.time() - t\n # t = time.time()\n # cur_time = str(t)\n # socketio.emit('message', {'data': cur_time, \"msg\": wsClient.message['price'] }, namespace=\"/test\", broadcast=True)\n\n # global thread\n # thread = None","def on_post(cls, req, resp):\n try:\n message = req.body['message']\n runnable = req.body['runnable']\n\n except KeyError as ke:\n raise falcon.HTTPMissingParam(ke.args[0]) from None\n\n session = req.headers.get('API-SESSION')\n if not session:\n raise falcon.HTTPMissingParam('API-SESSION')\n\n req.context['session'] = ObjectId(session)\n req.context['internal'] = True\n\n cls.logger.debug('Running runnable %s', runnable, extra={'session': session})\n\n runnable_class = cls.api.runnables[runnable]\n runnable_class(req).run_local(message)\n\n resp.body = {\n 'runnable': runnable,\n 'message': message,\n }\n cls.logger.debug('runnable %s status: OK', runnable, extra={'session': session})","def get_status(self) -> bool:\n try:\n self.__driver.service.assert_process_still_running()\n return True\n except AttributeError:\n return False","def run(self):\n while True:\n # Do something\n print('Doing something imporant in the background')\n\n self.loadData()\n time.sleep(self.interval)","async def update_stats(self):\r\n\r\n\t\twhile True:\r\n\t\t\tlogging.info('Attempting to post server count')\r\n\t\t\ttry:\r\n\t\t\t\tawait self.dblpy.post_server_count()\r\n\t\t\t\tlogging.info(f'Posted server count ({len(self.bot.guilds)})')\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tlogging.exception(f'Failed to post server count\\n{type(e).__name__}: {e}')\r\n\t\t\tawait asyncio.sleep(1800)","def start_watcher():\n while True:\n request_date = datetime.datetime.utcnow().strftime(\"%Y%m%d\")\n pull_request_from_remote(remote_files=\"*%s*\" % request_date)\n new_requests = check_for_new_request(request_date=request_date)\n if not new_requests:\n time.sleep(5)\n continue\n\n # noinspection PyTypeChecker\n for r in new_requests:\n print(\"Processing %s\" % r)\n try:\n ret = process_new_request(r, request_date=request_date,\n add2db=True)\n print(ret)\n except:\n os.system('cp -r %s /home/sedm/growth_marshal/archived/failed/'\n % r)\n os.system('cp -r %s /home/sedm/growth_marshal/archived/%s/' %\n (r, request_date))\n\n print(\"Waiting %ss before checking for new request\" % 5)\n time.sleep(5)","def heartbeat_process(client_id):\n asyncio.run(Client.heartbeat(client_id))","def start(self):\n threading.Thread(target=self.serve_forever).start()","def post(self):\n try:\n msg = json.loads(self.request.body)\n command = msg[\"command\"]\n # start/stop data_worker\n if command == \"start\":\n message = self.start_data_worker()\n self.write({\"success\": True, \"message\": message})\n elif command == \"stop\":\n message = self.stop_data_worker()\n self.write({\"success\": True, \"message\": message})\n else:\n self.write({\"success\": False, \"message\": \"unknown command\"})\n except Exception:\n log.exception(\"Exception\")\n self.write({\"success\": False, \"message\": \"error during control\"})","def monitor_ws(self):\n started = False\n restart_count = 0\n while True:\n if not started or self.ws is None:\n self.ws = BitMEXWebsocket(endpoint=\"https://www.bitmex.com/api/v1\", symbol=self.strategy.symbol,\n api_key=self.api_key, api_secret=self.api_secret)\n sleep(1)\n #try:\n if self.ws.started:\n self.logger.info('Websocket is running.')\n started = True\n self.ws_restarting = False\n #except Exception as fuck:\n #self.logger.error(f'Error in monitor_ws: {fuck}')\n else:\n if self.ws.lagging:\n self.logger.error('Ws is lagging ,forcing a restart...')\n self.ws.exit()\n started = False\n self.ws_restarting = True\n restart_count += 1\n self.logger.info(f'Ws starts this session: {restart_count}')\n\n else:\n sleep(1)","async def keep_alive(self, period=1, margin=.3):\n self.KeepAlive.interval = period\n self.KeepAlive.margin = margin","async def handle_ping(request):\n return web.Response(text=f\"OK {datetime.now().isoformat()}\\n\", headers={'Content-Type': 'text/event-stream'})","def ping_daemon(self):\n s = self.ping_interval\n while True:\n p = domintell.messages.Ping()\n self.send(p)\n time.sleep(s)","def _is_alive(self) -> bool:\n\n if self._on:\n return True\n\n try:\n os.kill(self.proc.pid, 0)\n except (OSError, ProcessLookupError):\n return False\n\n return True","def running(self) -> bool:\n return self._state == AsyncPostRequest._RUNNING","def timerAction():\n timer = threading.Timer(30.0, timerAction)\n timer.daemon = True\n timer.start()\n save()","def post(self):\n sleep(pow((self.unit * self.timeout), self.count))","def is_alive(self):\n if self.status == 1:\n return True\n else:\n return False"],"string":"[\n \"def periodic_timer(self):\\n while self.running:\\n self.sendStatusQuery()\\n time.sleep(REPORT_INTERVAL)\",\n \"def keep_alive():\\r\\n app = Flask(\\\"\\\")\\r\\n @app.route(\\\"/\\\")\\r\\n def home():\\r\\n return \\\"Your bot is now alive!\\\"\\r\\n\\r\\n def run():\\r\\n app.run(host=\\\"0.0.0.0\\\", port=8080)\\r\\n\\r\\n\\r\\n server = Thread(target=run)\\r\\n server.start()\",\n \"def background_catch_up():\\n while True:\\n time.sleep(interval)\\n s = 'http://{0}:{1}'.format(args.host, port)\\n req = urllib2.Request(s)\\n try:\\n response = urllib2.urlopen(req)\\n response.read()\\n except Exception as e:\\n pass\",\n \"def your_process(seconds):\\r\\n global STATUS\\r\\n sleep(seconds)\\r\\n STATUS = True\",\n \"def process_request(t):\\n time.sleep(t)\",\n \"def process_request(t):\\n time.sleep(t)\",\n \"def process_request(t):\\n time.sleep(t)\",\n \"def keepalive(self) -> None:\",\n \"def perform_action(self):\\n logger.info(\\\"Now sending a keepalive to the primary\\\")\\n self.connection_handler.send_message(\\\"I am still alive, client: {num}\\\".format(num=self.uuid))\\n time.sleep(5)\",\n \"def console_server(request):\\n return run_server(interval='1')\",\n \"def ping_moonrat():\\n threading.Timer(3600, ping_moonrat).start()\\n text = \\\"Moonrat is still active\\\\n\\\"\\n slack_client.api_call(\\n \\\"chat.postMessage\\\",\\n channel='G9P7X8Q0H',\\n text=text,\\n )\",\n \"def inform_alive(self):\\n\\n response = requests.get(\\n \\\"https://brokenprotocol.xyz/Device/Heartbeat\\\",\\n headers={\\\"auth\\\": self.token}\\n )\\n response.raise_for_status()\",\n \"def restart():\\n info = request.get_json() or {}\\n delay_secs = int(info.get('delay', 0))\\n\\n t = threading.Timer(delay_secs, update_trigger_file)\\n t.start()\\n\\n return jsonify('Success')\",\n \"def _keep_alive(self):\\n self.send_keep_alive(self.server.startup_time_delta)\\n return TaskSignal.AGAIN\",\n \"def keepAliveReceived(self):\",\n \"def run( self ):\\n while True:\\n try:\\n time.sleep( 5 )\\n self._monitorProcess()\\n except Exception, e:\\n self.logger.exception( \\\"Error starting monitor process\\\" )\",\n \"def post(host):\\n redis.setex('dispatcher',host,60)\\n timer = threading.Timer(20.0, post, args=[host])\\n timer.daemon = True\\n timer.start()\",\n \"def startHeartbeat(self):\\n self.post.__sendHeartbeat()\",\n \"def background_thread():\\n count = 0\\n while True:\\n socketio.sleep(10)\\n count += 1\\n socketio.emit('my_response',\\n {'data': 'Server generated event', 'count': count},\\n namespace='/test')\",\n \"async def check_status(self):\\n while True:\\n async with self._loop_lock:\\n new_monitor_processes = {}\\n for class_name in self.monitor_processes:\\n monitor = self.monitor_processes[class_name][\\\"process\\\"]\\n if monitor.poll() is not None:\\n log = f\\\"Monitor {class_name} has stopped with code: {monitor.returncode}\\\"\\n if monitor.returncode:\\n self.general_logger.warning(log)\\n if self.config[\\\"WebhookConfig\\\"][\\\"crash_webhook\\\"]:\\n embed = get_mm_crash_embed(\\n \\\"Monitor \\\" + class_name,\\n monitor.returncode,\\n monitor.pid,\\n )\\n ts = datetime.now().strftime(\\n self.config[\\\"WebhookConfig\\\"][\\\"timestamp_format\\\"]\\n )\\n\\n embed.set_footer(\\n text=f\\\"{self.config['WebhookConfig']['provider']} | {ts}\\\",\\n icon_url=self.config[\\\"WebhookConfig\\\"][\\n \\\"provider_icon\\\"\\n ],\\n )\\n data = json.dumps(\\n {\\n \\\"embeds\\\": [embed.to_dict()],\\n \\\"username\\\": \\\"MonitorManager process watcher\\\",\\n \\\"avatar_url\\\": self.config[\\\"WebhookConfig\\\"][\\n \\\"provider_icon\\\"\\n ],\\n }\\n )\\n r = await self.client.fetch(\\n self.config[\\\"WebhookConfig\\\"][\\\"crash_webhook\\\"],\\n method=\\\"POST\\\",\\n body=data,\\n headers={\\\"content-type\\\": \\\"application/json\\\"},\\n raise_error=False,\\n )\\n else:\\n self.general_logger.info(log)\\n else:\\n new_monitor_processes[class_name] = self.monitor_processes[\\n class_name\\n ]\\n self.monitor_processes = new_monitor_processes\\n\\n new_scraper_processes = {}\\n for class_name in self.scraper_processes:\\n scraper = self.scraper_processes[class_name][\\\"process\\\"]\\n if scraper.poll() is not None:\\n log = f\\\"Scraper {class_name} has stopped with code: {scraper.returncode}\\\"\\n if scraper.returncode:\\n self.general_logger.warning(log)\\n if self.config[\\\"WebhookConfig\\\"][\\\"crash_webhook\\\"]:\\n embed = get_mm_crash_embed(\\n \\\"Scraper \\\" + class_name,\\n scraper.returncode,\\n scraper.pid,\\n )\\n ts = datetime.now().strftime(\\n self.config[\\\"WebhookConfig\\\"][\\\"timestamp_format\\\"]\\n )\\n\\n embed.set_footer(\\n text=f\\\"{self.config['WebhookConfig']['provider']} | {ts}\\\",\\n icon_url=self.config[\\\"WebhookConfig\\\"][\\n \\\"provider_icon\\\"\\n ],\\n )\\n data = json.dumps(\\n {\\n \\\"embeds\\\": [embed.to_dict()],\\n \\\"username\\\": \\\"MonitorManager process watcher\\\",\\n \\\"avatar_url\\\": self.config[\\\"WebhookConfig\\\"][\\n \\\"provider_icon\\\"\\n ],\\n }\\n )\\n r = await self.client.fetch(\\n self.config[\\\"WebhookConfig\\\"][\\\"crash_webhook\\\"],\\n method=\\\"POST\\\",\\n body=data,\\n headers={\\\"content-type\\\": \\\"application/json\\\"},\\n raise_error=False,\\n )\\n else:\\n self.general_logger.info(log)\\n else:\\n new_scraper_processes[class_name] = self.scraper_processes[\\n class_name\\n ]\\n self.scraper_processes = new_scraper_processes\\n await asyncio.sleep(1)\",\n \"def _keep_alive(self):\\n while self._is_running:\\n secs_since_last_cmd = time.time() - self._last_cmd_time\\n if not self._is_action_active and secs_since_last_cmd > self._keep_alive_secs:\\n self._transport.send(activity_cmd(\\\" \\\"))\\n self._last_cmd_time = time.time()\\n self._log.debug(\\\"Keeping alive\\\")\\n self._keep_alive_event.wait(timeout=self._keep_alive_secs)\",\n \"async def monitor(self, session: ClientSession, params: MonitoringData) -> None:\\n while True:\\n result = await self.check_website(session, params)\\n self._producer.send(result)\\n await asyncio.sleep(params.interval)\",\n \"def background_thread():\\n count = 0\\n while True:\\n time.sleep(10)\\n count += 1\\n socketio.emit('my response',\\n {'data': 'Server generated event', 'count': count},\\n namespace='/test')\",\n \"def watch(self, url):\\n self.__url = url\\n self.downtime_info = None\\n self.__timer.start()\",\n \"def run(self):\\n while True:\\n print(\\\"I'm running in the background\\\")\\n time.sleep(self.interval)\",\n \"def background_thread():\\n count = 0\\n while True:\\n socketio.sleep(1)\\n count += 1\\n t = time.strftime('%M:%S', time.localtime())\\n cpus = [1,2,3,4] #\\n print('sending')\\n socketio.emit('server_response',\\n {'data': [t, cpus[0],cpus[1],cpus[2],cpus[3]], 'count': count})\",\n \"def _keep_alive(self):\\n self.send_keep_alive()\\n return TaskSignal.AGAIN\",\n \"def slot_keepalive_timer(self, _sender, _data):\\r\\n if self.connected:\\r\\n #self.debug(\\\"### sending keepalive\\\")\\r\\n self._try_send_raw(\\\"2::\\\")\",\n \"def thread_function(client):\\n threading.Timer(30.0, thread_function).start()\\n client.publish(\\\"serverCommand/keepalive\\\", \\\"0\\\")\\n print(\\\"Message Sent. (keepalive)\\\")\",\n \"def continue_server():\\n update_server_status({'ready': True})\",\n \"def run(self):\\n while self.running:\\n self.handle_request()\",\n \"def monitorOne(self,website):\\n checkInterval = website.checkInterval\\n time.sleep(checkInterval)\\n while self\\\\\\n .queueTermination.empty():\\n startSubProcess = time.time()\\n # todo define timeout for requests\\n try :\\n req = requests.get(website.url, timeout=checkInterval)\\n reqCode = req.status_code\\n reqTime = req.elapsed\\n # Generic to handle all kind of http exceptions\\n # Possible enhancement\\n except Exception:\\n continue\\n # unix epoch time good for comparison\\n currentTime = time.time()\\n website.log[currentTime] = {'code': reqCode, 'responseTime': reqTime}\\n # 2 mins\\n twoMinsDic = self.getTimeframedData(website, 120, currentTime=currentTime)\\n self.queueTwoMin.put(twoMinsDic)\\n # 10 mins\\n tenMinsDic = self.getTimeframedData(website, 600, currentTime=currentTime)\\n self.queueTenMin.put(tenMinsDic)\\n # 1 hour\\n hourDic = self.getTimeframedData(website, 3600, currentTime=currentTime)\\n self.queueHour.put(hourDic)\\n\\n endSubProcess = time.time()\\n # Wait for the next check\\n try:\\n time.sleep(checkInterval-(endSubProcess-startSubProcess))\\n except ValueError:\\n pass\\n\\n # Terminate all processes\\n self._terminateAll()\\n return\",\n \"async def handle_live(self, websocket):\\n while True:\\n payload = self.generate_payload()\\n await websocket.send(json.dumps(payload))\\n await asyncio.sleep(self.update_frequency_milliseconds / 1000)\",\n \"def monitor(self):\\n while not self.terminated:\\n try:\\n if (time.time() - self.updated_time) < 5:\\n messages = self.messages.copy()\\n # procs = np.min([ len(messages), 9 ]) + 1\\n # pool = ThreadPool(procs)\\n # pool.map(self.process, messages)\\n # pool.close()\\n # pool.join()\\n for message in messages:\\n self.process(message)\\n elif self.ws:\\n self.updated_time += 10\\n self.ws.close()\\n except Exception as e:\\n self.on_error(None, \\\"Monitoring Error: {}\\\".format(e))\\n continue\\n finally:\\n time.sleep(0.1)\",\n \"def admin_server(request):\\n return run_server(interval='10000')\",\n \"def periodicCall(self):\\n self.gui.processIncoming()\\n if not self.running:\\n import sys\\n sys.exit(1)\\n self.master.after(UPDATE_DELAY, self.periodicCall)\",\n \"def Listen(self):\\n while True:\\n time.sleep(1)\",\n \"def poll_http(interval, port_no):\\n import requests\\n\\n url_string = 'http://0.0.0.0:{}/refresh'.format(port_no)\\n\\n print('DBG: poll_http({}): got to thread @ {}'.format(\\n interval, time.strftime(\\\"%I:%M:%S\\\")))\\n print('url_string = {}'.format(repr(url_string)))\\n\\n while True:\\n time.sleep(interval)\\n print('DBG: thread woke up @ {}'.format(time.strftime(\\\"%I:%M:%S\\\")))\\n r = requests.get(url_string)\\n print('DBG: Requests.text = {}'.format(repr(r.text)))\",\n \"def daemon_job(interval):\\n time.sleep(3) # Wait for api server to start first\\n while True:\\n try:\\n crawl()\\n process_notification()\\n except Exception:\\n traceback.print_exc()\\n time.sleep(interval)\",\n \"def service( self ):\\n\\n self.alive = time.time()\",\n \"def watch_process(self):\\n psutil.wait_procs([psutil.Process(self._proc.pid)],\\n callback=self.start)\",\n \"async def monitor():\\n global counter\\n while True:\\n time.sleep(1)\\n print(counter, 'reqs/sec')\\n counter = 0\",\n \"def on_keep_alive(self):\\n log.debug(\\\"Received keep-alive signal\\\")\",\n \"def status_task():\\n props = [\\n (STAT_TIME, current_time),\\n (STAT_CONDITION, weather_condition)\\n ]\\n\\n # Send the status request with the current time and condition.\\n send_status_request(props)\\n\\n # Create and start a timer to repeat this task periodically.\\n t = Timer(report_interval, status_task)\\n t.setDaemon(True)\\n t.start()\",\n \"def status(self):\\n # process running ?\\n pid = self.get_pidfile()\\n \\n running = True\\n \\n # process is not running\\n if pid is None:\\n running = False\\n else:\\n if not self.send_signal(pid,0):\\n running = False\\n # abnormal state, delete the file\\n self.delete_pidfile()\\n \\n if running:\\n message = \\\"server is running\\\\n\\\"\\n else:\\n message = \\\"server is not running\\\\n\\\"\\n sys.stdout.write(message)\\n \\n return running\",\n \"def monitor(self):\\r\\n while True:\\r\\n for worker, start_time in self.workers.items():\\r\\n if (not worker.isAlive() or\\r\\n self.timeout\\r\\n and datetime.now() - start_time > self.timeout): \\r\\n\\r\\n self.work_count.get_nowait()\\r\\n self.jobs.task_done()\\r\\n del self.workers[worker]\\r\\n\\r\\n time.sleep(1)\",\n \"def serve_forever(self, unused_parameter=0.5):\\r\\n self.stop = False\\r\\n while not self.stop:\\r\\n self.handle_request()\",\n \"def run_while_true(server_class=BaseHTTPServer.HTTPServer,\\n handler_class=BaseHTTPServer.BaseHTTPRequestHandler):\\n server_address = ('localhost', 8080)\\n httpd = server_class(server_address, handler_class)\\n while keep_running():\\n httpd.handle_request()\",\n \"def periodically_request_websites_metrics() -> int:\\n # Making a get request\\n configuration_path = os.path.abspath('configuration/service_configuration.json')\\n list_of_websites_to_check = read_service_configuration(\\n configuration_file=configuration_path, section='list_of_websites_to_check')\\n try:\\n\\n remote_service_url = os.environ.get('websites_checker_service_url','http://192.168.1.101:8080/api/v1/websites_metrics')\\n response = requests.post(url=remote_service_url, json=list_of_websites_to_check)\\n if response:\\n logger.info(f\\\"The request has been sent to {remote_service_url} with payload: {list_of_websites_to_check}\\\")\\n\\n else:\\n logger.error(f\\\"Error contacting the service {remote_service_url}\\\")\\n except Exception as error:\\n logger.error(f\\\"The Exception {error} occurred\\\")\\n return 1\",\n \"def KeepAlive(self) -> bool:\",\n \"def is_alive():\\n return jsonify({'message': 'Service is alive'}), 200\",\n \"def pingEsp(self):\\n\\t\\twhile True:\\n\\t\\t\\tprint (\\\"[{}] Keeping alive the ESP8266 connection\\\".format(\\n\\t\\t\\t\\tint(time.time()),\\n\\t\\t\\t))\\n\\t\\t\\tmqttCli.publish(\\\"ping\\\", mqttJsonDump('void'))\\n\\t\\t\\ttime.sleep(30)\",\n \"def do_monitor(self):\\n while not self.expired:\\n self.expired = True\\n time.sleep(self.interval)\\n self.dead_fn()\",\n \"def background_thread():\\n count = 0\\n with open(\\\"logs.txt\\\", \\\"r\\\") as logfile:\\n while True:\\n socketio.sleep(1)\\n count += 1\\n\\n line = logfile.readline()\\n if line:\\n socketio.emit('my_response',\\n {'data': line, 'count': count},\\n namespace='/test')\",\n \"def check_loop(\\n url, period=5, timeout=10, body_check_re='',\\n producer=None, oneshot=False):\\n while True:\\n worker = threading.Thread(target=check, kwargs={\\n 'url': url,\\n 'timeout': timeout,\\n 'body_check_re': body_check_re,\\n 'producer': producer,\\n })\\n logger.info('check url=%s' % url)\\n worker.start()\\n time.sleep(period)\\n if oneshot:\\n return\",\n \"def run(self):\\n while True :\\n try:\\n appinfo = self.db.hgetall(self.appname)\\n appinfo_str = json.dumps(appinfo)\\n data = {'applicationname':self.appname,'appinfo':appinfo_str}\\n response = requests.post(REGISTRAR_URL, data=data)\\n time.sleep(2)\\n except :\\n pass\",\n \"def monitor(self, target):\\n while self.RUNNING:\\n check_time = datetime.now()\\n next_check = check_time + timedelta(seconds=target[\\\"frequency\\\"])\\n\\n try:\\n self.produce(\\n get(target[\\\"url\\\"], timeout=target[\\\"frequency\\\"] - 0.5),\\n target.get(\\\"regex\\\"),\\n check_time,\\n )\\n except Timeout:\\n self.logger.warning(\\\"Check for %s timed out\\\", target[\\\"url\\\"])\\n except RequestException as e:\\n self.logger.error(e)\\n except re.error as e:\\n self.logger.error(e)\\n break\\n\\n # Busy loop until next check_time\\n while datetime.now() < next_check:\\n sleep(1)\",\n \"def keep_alive(self):\\n req = BFGlobalFactory.create(\\\"ns1:KeepAliveReq\\\")\\n rsp = self._soapcall(BFGlobalService.keepAlive, req)\\n if rsp.header.errorCode != APIErrorEnum.OK:\\n logger.error(\\\"{keepAlive} failed with error {%s}\\\",\\n rsp.header.errorCode)\",\n \"def cron_health(request):\\n if checkIfProcessRunning('crond'):\\n print('Yes a crond process was running')\\n return HttpResponse(status=200)\\n else:\\n print('crond process is Not running')\\n\\n return HttpResponse(status=500)\",\n \"def stream():\\n while True:\\n try:\\n r = requests.post(\\\"http://streamer_0:5000/stream\\\", json={})\\n break\\n except requests.exceptions.ConnectionError:\\n logging.error(\\\"Could not connect to server streamer_0, retrying\\\")\\n time.sleep(2)\\n continue\\n logging.info(\\\"'http://streamer_0:5000/stream', response = {}\\\".format(r.status_code))\\n if r.status_code != 200:\\n time.sleep(2)\\n stream()\",\n \"def schedule_alive_job(self):\\n if 'alive-notification' not in settings:\\n return\\n\\n self.scheduler.add_job(\\n lambda: self.event_notifier.send_message('Surveillance is alive'),\\n **settings['alive-notification'])\",\n \"async def keep_alive(self):\\n self._keepalive = True\\n while True:\\n await gen.sleep(self.KEEPALIVE_INTERVAL)\\n if not self._keepalive:\\n return\\n try:\\n # lines that start with : are comments\\n # and should be ignored by event consumers\\n self.write(\\\":keepalive\\\\n\\\\n\\\")\\n await self.flush()\\n except StreamClosedError:\\n return\",\n \"def serve_forever(self, poll_interval=0.5):\\n logging.info('RPC server starting')\\n self._idle_thread.start()\\n SimpleJSONRPCServer.SimpleJSONRPCServer.serve_forever(self, poll_interval)\",\n \"def run(self):\\r\\n self.rpc_server.serve_forever(0.5)\",\n \"def __call__(self):\\n hub.sleep(random.randint(1, self.interval))\\n while True:\\n self.send_req()\\n self.reply_pending = True\\n hub.sleep(self.interval)\\n if self.reply_pending:\\n self.no_response()\",\n \"def monitor_and_terminate(self):\\n import time\\n import datetime\\n\\n keep_running = True\\n\\n while keep_running:\\n\\n print()\\n print(datetime.datetime.now().replace(microsecond=0))\\n print(self.get_monitor_string())\\n\\n time.sleep(30)\\n\\n _, status = self.reporter.get_job_status(self.info)\\n if status[\\\"active\\\"]+status[\\\"running\\\"] == 0:\\n keep_running = False\\n\\n print(\\\"All tasks done.\\\")\",\n \"def serve(self):\\n\\t\\tself.keep_running=1\\n\\t\\tif self.debug:\\n\\t\\t\\tprint \\\"server started\\\"\\n\\t\\ttry:\\n\\t\\t\\twhile self.keep_running:\\n\\t\\t\\t\\tself.handle_request()\\n\\t\\tfinally:\\n\\t\\t\\tif self.debug:\\n\\t\\t\\t\\tprint \\\"server finished\\\"\\n\\t\\t\\tself.keep_running=0\\n\\t\\t\\tself.close()\",\n \"def __sendHeartbeat(self):\\n \\n while not rospy.is_shutdown():\\n rospy.sleep(5)\\n self.setOutput(self.write_start+1,0)\",\n \"def updated():\\n ws = request.environ.get('wsgi.websocket', None)\\n print(\\\"web socket retrieved\\\")\\n app.number_of_connexion += 1\\n if ws:\\n while True:\\n delay = random.randint(MIN_DELAY, MAX_DELAY)\\n gevent.sleep(delay)\\n try:\\n ws.send(str(app.number_of_connexion))\\n except WebSocketError:\\n print(\\\"socket died\\\")\\n app.number_of_connexion -= 1\\n return \\\"disconnected\\\"\\n else:\\n raise RuntimeError(\\\"Environment lacks WSGI WebSocket support\\\")\",\n \"def heartbeat(jsonify=None):\\n url = URLS.heartbeat()\\n payload = {\\n \\\"request\\\": URLS.get_endpoint(url)\\n }\\n generate_signature(payload)\\n data, err = request_post(url, payload, jsonify)\\n return data, err\",\n \"def do_POST(self):\\n bugsnag = self.server.bugsnag\\n body = self.rfile.read(int(self.headers['Content-Length']))\\n bugsnag.enqueue(body)\\n\\n response = (\\\"OK %s:%s -> %s (%s/%s)\\\\n\\\" % (bugsnag.listen, bugsnag.port, bugsnag.endpoint, bugsnag.queue.qsize(), bugsnag.queue.maxsize))\\n\\n try:\\n self.send_response(200)\\n self.send_header('Content-Length', len(response))\\n self.end_headers()\\n self.wfile.write(response)\\n except:\\n print \\\"Client disconnected before waiting for response\\\"\\n print_exception(*sys.exc_info())\\n print \\\"continuing...\\\"\",\n \"def _heartbeat_loop(self):\\n # set last time so that \\\"if t_now - t_last >= HEARTBEAT_LOG_INTERVAL\\\"\\n # below evalutes to True on the first run\\n t_last = time.time() - HEARTBEAT_LOG_INTERVAL - 1\\n while True:\\n alive = 0\\n # count alive processes \\n for p in PROCESSES:\\n if p.is_alive():\\n alive += 1\\n\\n # no processes are alive - exit heartbeat loop\\n if alive == 0:\\n return\\n\\n t_now = time.time()\\n if t_now - t_last >= HEARTBEAT_LOG_INTERVAL:\\n # log heartbeat\\n obj = { \\n 'timestamp': time.time(),\\n 'child_procs_total': self._procs_total,\\n 'child_procs_alive': alive,\\n 'probe_req_queue_len': self._probe_request_queue.qsize(),\\n 'probe_resp_queue_len': \\\\\\n self._probe_response_queue.qsize(), \\n }\\n \\n # push to shared mem\\n self._sm.set(config.BASE['SHARED_MEM_HEARTBEAT_KEY'],\\n json.dumps(obj), HEARTBEAT_TTL)\\n LOG.debug('pushed a heartbeat to the shared memory')\\n\\n t_last = t_now\\n\\n time.sleep(HEARTBEAT_LOOP_INTERVAL)\",\n \"def is_monitor_process_live(pid_file):\\n live = False\\n\\n try:\\n check_process_status(pid_file)\\n live = True\\n except ComponentIsNotRunning:\\n pass\\n\\n return live\",\n \"def serve(self):\\n super(BlacknetSensor, self).serve(BlacknetSensorThread, BLACKNET_PING_INTERVAL, self.do_ping)\",\n \"def REP_watcher():\\n global REQ_sent_time, REP_recd_time, pid, patience_seconds\\n while True:\\n time.sleep(patience_seconds) # how often to check\\n try:\\n recent_REQ_sent_time = REQ_sent_time.popleft()\\n # if we got here; we have a recent_REQ_sent_time\\n time.sleep(patience_seconds) # allow time for receipt of the REP\\n try:\\n recent_REP_recd_time = REP_recd_time.popleft()\\n # if we got here; we have a recent_REP_recd_time\\n interval = recent_REP_recd_time - recent_REQ_sent_time\\n if interval.total_seconds() <= 0.0:\\n # recent_REP_recd_time is not later than recent_REQ_sent_time\\n print('After image send in REP_watcher test,')\\n print('No REP received within', patience_seconds, 'seconds.')\\n print('Ending sending program.')\\n os.kill(pid, signal.SIGTERM)\\n pass\\n continue # Got REP after REQ so continue to next REQ\\n except IndexError: # there was a REQ, but no timely REP\\n print('After image send in REP_watcher test,')\\n print('No REP received within', patience_seconds, 'seconds.')\\n print('Ending sending program.')\\n os.kill(pid, signal.SIGTERM)\\n pass\\n except IndexError: # there wasn't a time in REQ_sent_time\\n # so there is no REP expected,\\n # ... continue to loop until there is a time in REQ_sent_time\\n pass\",\n \"def post(self):\\n\\n headers = ''\\n for key, value in self.request.headers.iteritems():\\n headers += '%s: %s' % (key, value)\\n headers += '\\\\r\\\\n'\\n InboundRequest.add_record(datetime.utcnow(), self.request.host_url,\\n self.request.path, headers, self.request.query_string, self.request.body)\\n\\n taskqueue.add(url='/check_wipe_task')\",\n \"def run(self):\\n while True:\\n req = self._requests.get()[1]\\n req.start()\\n logging.info('Running request %s', req)\",\n \"def register_background_task_reporter():\\n from tornado import ioloop\\n\\n cb = ioloop.PeriodicCallback(log_background_task_status, 60 * 3 * 1000)\\n cb.start()\\n return cb\",\n \"def Serve(self):\\n t = threading.Thread(target=self.serve_forever)\\n t.setDaemon(True)\\n t.start()\\n self._Announce()\",\n \"def monitor_ws(self, endpoint='https://www.bitmex.com/api/v1'): #TODO: implement\\n started = False\\n restart_count = 0\\n while True:\\n if not started or self.ws.exited or self.ws is None:\\n self.ws = BitMEXWebsocket(endpoint=endpoint, symbol=self.symbol,\\n api_key=self.api_key, api_secret=self.api_secret)\\n time.sleep(1)\\n if self.ws.started:\\n self.logger.info('Websocket is running.')\\n started = True\\n self.ws_restarting = False\\n else:\\n if self.ws.lagging:\\n self.logger.error('Ws is lagging ,forcing a restart...')\\n self.ws.exit()\\n started = False\\n self.ws_restarting = True\\n restart_count += 1\\n self.logger.info(f'Ws starts this session: {restart_count}')\\n\\n else:\\n time.sleep(1)\",\n \"def loop_forever(self):\\n while self.running:\\n time.sleep(0.01)\",\n \"def worker_status(request):\\n try:\\n status = app.control.ping() or []\\n except:\\n # TODO: What celery exceptions are we catching here?\\n status = []\\n return HttpResponse(\\n json.dumps(status),\\n content_type=\\\"application/json\\\"\\n )\",\n \"def background_stuff():\\n print \\\"BACKGROUND\\\"\\n\\n\\n\\n # # global socketio\\n\\n # # print(wsClient.url, wsClient.products)\\n # while (wsClient.MessageCount < 30):\\n # print(\\\"\\\\nMessageCount =\\\", \\\"%i \\\\n\\\" % wsClient.MessageCount)\\n # # time.sleep(1)\\n # # socketio.emit('my response', {'data': [\\\"TEST\\\"]}, namespace=\\\"/test\\\", broadcast=True)\\n # #\\n # wsClient.close()\\n #\\n # while True:\\n # time.sleep(1)\\n # t = str(time.clock())\\n # print t\\n\\n def minute_passed(oldepoch):\\n return time.time() - oldepoch >= .1\\n\\n global wsClient\\n\\n # t = time.time()\\n # for i in range(3000):\\n # # while True:\\n # # print time.clock(), t\\n # # if time.clock() > ( t + .1 ):\\n # # show = True #minute_passed(t)\\n # # if show:\\n # # print show, time.time(), t, time.time() - t\\n # t = time.time()\\n # cur_time = str(t)\\n # socketio.emit('message', {'data': cur_time, \\\"msg\\\": wsClient.message['price'] }, namespace=\\\"/test\\\", broadcast=True)\\n\\n # global thread\\n # thread = None\",\n \"def on_post(cls, req, resp):\\n try:\\n message = req.body['message']\\n runnable = req.body['runnable']\\n\\n except KeyError as ke:\\n raise falcon.HTTPMissingParam(ke.args[0]) from None\\n\\n session = req.headers.get('API-SESSION')\\n if not session:\\n raise falcon.HTTPMissingParam('API-SESSION')\\n\\n req.context['session'] = ObjectId(session)\\n req.context['internal'] = True\\n\\n cls.logger.debug('Running runnable %s', runnable, extra={'session': session})\\n\\n runnable_class = cls.api.runnables[runnable]\\n runnable_class(req).run_local(message)\\n\\n resp.body = {\\n 'runnable': runnable,\\n 'message': message,\\n }\\n cls.logger.debug('runnable %s status: OK', runnable, extra={'session': session})\",\n \"def get_status(self) -> bool:\\n try:\\n self.__driver.service.assert_process_still_running()\\n return True\\n except AttributeError:\\n return False\",\n \"def run(self):\\n while True:\\n # Do something\\n print('Doing something imporant in the background')\\n\\n self.loadData()\\n time.sleep(self.interval)\",\n \"async def update_stats(self):\\r\\n\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\tlogging.info('Attempting to post server count')\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tawait self.dblpy.post_server_count()\\r\\n\\t\\t\\t\\tlogging.info(f'Posted server count ({len(self.bot.guilds)})')\\r\\n\\t\\t\\texcept Exception as e:\\r\\n\\t\\t\\t\\tlogging.exception(f'Failed to post server count\\\\n{type(e).__name__}: {e}')\\r\\n\\t\\t\\tawait asyncio.sleep(1800)\",\n \"def start_watcher():\\n while True:\\n request_date = datetime.datetime.utcnow().strftime(\\\"%Y%m%d\\\")\\n pull_request_from_remote(remote_files=\\\"*%s*\\\" % request_date)\\n new_requests = check_for_new_request(request_date=request_date)\\n if not new_requests:\\n time.sleep(5)\\n continue\\n\\n # noinspection PyTypeChecker\\n for r in new_requests:\\n print(\\\"Processing %s\\\" % r)\\n try:\\n ret = process_new_request(r, request_date=request_date,\\n add2db=True)\\n print(ret)\\n except:\\n os.system('cp -r %s /home/sedm/growth_marshal/archived/failed/'\\n % r)\\n os.system('cp -r %s /home/sedm/growth_marshal/archived/%s/' %\\n (r, request_date))\\n\\n print(\\\"Waiting %ss before checking for new request\\\" % 5)\\n time.sleep(5)\",\n \"def heartbeat_process(client_id):\\n asyncio.run(Client.heartbeat(client_id))\",\n \"def start(self):\\n threading.Thread(target=self.serve_forever).start()\",\n \"def post(self):\\n try:\\n msg = json.loads(self.request.body)\\n command = msg[\\\"command\\\"]\\n # start/stop data_worker\\n if command == \\\"start\\\":\\n message = self.start_data_worker()\\n self.write({\\\"success\\\": True, \\\"message\\\": message})\\n elif command == \\\"stop\\\":\\n message = self.stop_data_worker()\\n self.write({\\\"success\\\": True, \\\"message\\\": message})\\n else:\\n self.write({\\\"success\\\": False, \\\"message\\\": \\\"unknown command\\\"})\\n except Exception:\\n log.exception(\\\"Exception\\\")\\n self.write({\\\"success\\\": False, \\\"message\\\": \\\"error during control\\\"})\",\n \"def monitor_ws(self):\\n started = False\\n restart_count = 0\\n while True:\\n if not started or self.ws is None:\\n self.ws = BitMEXWebsocket(endpoint=\\\"https://www.bitmex.com/api/v1\\\", symbol=self.strategy.symbol,\\n api_key=self.api_key, api_secret=self.api_secret)\\n sleep(1)\\n #try:\\n if self.ws.started:\\n self.logger.info('Websocket is running.')\\n started = True\\n self.ws_restarting = False\\n #except Exception as fuck:\\n #self.logger.error(f'Error in monitor_ws: {fuck}')\\n else:\\n if self.ws.lagging:\\n self.logger.error('Ws is lagging ,forcing a restart...')\\n self.ws.exit()\\n started = False\\n self.ws_restarting = True\\n restart_count += 1\\n self.logger.info(f'Ws starts this session: {restart_count}')\\n\\n else:\\n sleep(1)\",\n \"async def keep_alive(self, period=1, margin=.3):\\n self.KeepAlive.interval = period\\n self.KeepAlive.margin = margin\",\n \"async def handle_ping(request):\\n return web.Response(text=f\\\"OK {datetime.now().isoformat()}\\\\n\\\", headers={'Content-Type': 'text/event-stream'})\",\n \"def ping_daemon(self):\\n s = self.ping_interval\\n while True:\\n p = domintell.messages.Ping()\\n self.send(p)\\n time.sleep(s)\",\n \"def _is_alive(self) -> bool:\\n\\n if self._on:\\n return True\\n\\n try:\\n os.kill(self.proc.pid, 0)\\n except (OSError, ProcessLookupError):\\n return False\\n\\n return True\",\n \"def running(self) -> bool:\\n return self._state == AsyncPostRequest._RUNNING\",\n \"def timerAction():\\n timer = threading.Timer(30.0, timerAction)\\n timer.daemon = True\\n timer.start()\\n save()\",\n \"def post(self):\\n sleep(pow((self.unit * self.timeout), self.count))\",\n \"def is_alive(self):\\n if self.status == 1:\\n return True\\n else:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6335293","0.6263304","0.6176471","0.61680967","0.6098956","0.6098956","0.6098956","0.6034498","0.5934776","0.5786157","0.5784672","0.5767236","0.57396203","0.57379746","0.5700311","0.5679326","0.5662139","0.56477326","0.5647195","0.56367123","0.5632368","0.56243","0.5592091","0.5568196","0.55598336","0.5550228","0.5538802","0.5535337","0.5510907","0.5478055","0.5476954","0.54576606","0.5450369","0.5446191","0.5445085","0.54326385","0.541886","0.54011893","0.539024","0.5388052","0.5384775","0.53689957","0.53668517","0.53545487","0.53452295","0.531786","0.5303038","0.53027874","0.5291096","0.5290996","0.52885085","0.52801585","0.52793306","0.5247848","0.52417064","0.52389586","0.523801","0.5237575","0.523165","0.522742","0.52157426","0.5210976","0.52099115","0.520755","0.5205918","0.5200901","0.51947063","0.5186975","0.5178605","0.51681745","0.51674783","0.5157048","0.51501524","0.51451546","0.51331717","0.5128908","0.51261455","0.51228744","0.5103215","0.5090531","0.5089325","0.50857455","0.5082183","0.507677","0.50720435","0.50597346","0.5036731","0.50349003","0.50279707","0.50272673","0.50147766","0.50093746","0.5002977","0.50025254","0.49996516","0.49982074","0.49855337","0.4984125","0.49797288","0.49721736"],"string":"[\n \"0.6335293\",\n \"0.6263304\",\n \"0.6176471\",\n \"0.61680967\",\n \"0.6098956\",\n \"0.6098956\",\n \"0.6098956\",\n \"0.6034498\",\n \"0.5934776\",\n \"0.5786157\",\n \"0.5784672\",\n \"0.5767236\",\n \"0.57396203\",\n \"0.57379746\",\n \"0.5700311\",\n \"0.5679326\",\n \"0.5662139\",\n \"0.56477326\",\n \"0.5647195\",\n \"0.56367123\",\n \"0.5632368\",\n \"0.56243\",\n \"0.5592091\",\n \"0.5568196\",\n \"0.55598336\",\n \"0.5550228\",\n \"0.5538802\",\n \"0.5535337\",\n \"0.5510907\",\n \"0.5478055\",\n \"0.5476954\",\n \"0.54576606\",\n \"0.5450369\",\n \"0.5446191\",\n \"0.5445085\",\n \"0.54326385\",\n \"0.541886\",\n \"0.54011893\",\n \"0.539024\",\n \"0.5388052\",\n \"0.5384775\",\n \"0.53689957\",\n \"0.53668517\",\n \"0.53545487\",\n \"0.53452295\",\n \"0.531786\",\n \"0.5303038\",\n \"0.53027874\",\n \"0.5291096\",\n \"0.5290996\",\n \"0.52885085\",\n \"0.52801585\",\n \"0.52793306\",\n \"0.5247848\",\n \"0.52417064\",\n \"0.52389586\",\n \"0.523801\",\n \"0.5237575\",\n \"0.523165\",\n \"0.522742\",\n \"0.52157426\",\n \"0.5210976\",\n \"0.52099115\",\n \"0.520755\",\n \"0.5205918\",\n \"0.5200901\",\n \"0.51947063\",\n \"0.5186975\",\n \"0.5178605\",\n \"0.51681745\",\n \"0.51674783\",\n \"0.5157048\",\n \"0.51501524\",\n \"0.51451546\",\n \"0.51331717\",\n \"0.5128908\",\n \"0.51261455\",\n \"0.51228744\",\n \"0.5103215\",\n \"0.5090531\",\n \"0.5089325\",\n \"0.50857455\",\n \"0.5082183\",\n \"0.507677\",\n \"0.50720435\",\n \"0.50597346\",\n \"0.5036731\",\n \"0.50349003\",\n \"0.50279707\",\n \"0.50272673\",\n \"0.50147766\",\n \"0.50093746\",\n \"0.5002977\",\n \"0.50025254\",\n \"0.49996516\",\n \"0.49982074\",\n \"0.49855337\",\n \"0.4984125\",\n \"0.49797288\",\n \"0.49721736\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57290554"},"document_rank":{"kind":"string","value":"14"}}},{"rowIdx":93,"cells":{"query":{"kind":"string","value":"Gets the contact_list of this MessagingCampaign. The contact list that this messaging campaign will send messages for."},"document":{"kind":"string","value":"def contact_list(self):\n return self._contact_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def list_contacts(self):\n return self.contacts","def get_contacts(self):\n\n\t\treturn self.__contacts","def GetContactList(self):\n\t\tfeeds = []\n\t\tfeed = self.client.GetContacts()\n\t\tfeeds.append(feed)\n\t\tnext = feed.GetNextLink()\n\t\twhile next:\n\t\t\tfeed = self.client.GetContacts(uri=next.href)\n\t\t\tfeeds.append(feed)\n\t\t\tnext = feed.GetNextLink()\n\t\t\n\t\tcontacts = []\n\t\tfor feed in feeds:\n\t\t\tif not feed.entry:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tfor i, entry in enumerate(feed.entry):\n\t\t\t\t\tcontacts.append(entry)\n\t\treturn contacts","def get_contacts_list(self):\n return [(id + 1, contact) for id, contact in enumerate(self.contact_list)]","def contact_lists(self):\n from hubspot3.contact_lists import ContactListsClient\n\n return ContactListsClient(**self.auth, **self.options)","def get_contacts(self):\n contacts = Membership.objects.filter(entity = self, key_contact = True).order_by('importance_to_entity')\n return contacts","def contacts(self):\r\n return contacts.Contacts(self)","def ListAllContacts(self):\n feed = self.gd_client.GetContacts()\n self.contacts = self.CleanPhoneNumbers(self.GetContactsInfo(feed))\n return self.contacts","def contacts(self):\n return ContactCollection(self.request)","def _get_receivers_list(self):\n\n # TODO: document what this plugin expects to be in Dockerfile/where it gets info from\n global_component = self._get_component_label()\n # this relies on bump_release plugin configuring source.git_commit to actually be\n # branch name, not a commit\n if not isinstance(self.workflow.source, GitSource):\n raise PluginFailedException('Source is not of type \"GitSource\", panic!')\n git_branch = self.workflow.source.git_commit\n try:\n r = requests.get(urljoin(self.pdc_url, 'rest_api/v1/release-component-contacts/'),\n headers={'Authorization': 'Token %s' % self._get_pdc_token()},\n params={'global_component': global_component,\n 'dist_git_branch': git_branch,\n 'role': self.pdc_contact_role},\n verify=self.pdc_verify_cert)\n except requests.RequestException as e:\n self.log.error('failed to connect to PDC: %s', str(e))\n raise RuntimeError(e)\n\n if r.status_code != 200:\n self.log.error('PDC returned status code %s, full response: %s',\n r.status_code, r.text)\n raise RuntimeError('PDC returned non-200 status code (%s), see referenced build log' %\n r.status_code)\n\n contacts = r.json()\n\n if contacts['count'] == 0:\n self.log.error('no %s role for the component', self.pdc_contact_role)\n raise RuntimeError('no %s role for the component' % self.pdc_contact_role)\n\n send_to = []\n for contact in contacts['results']:\n send_to.append(contact['contact']['email'])\n\n return send_to","def get_recipients(self) -> List[Client]:\n\n index_list = [i for i in range(len(self.int_var_list)) if self.int_var_list[i].get() == 1]\n return [self.client_list[i] for i in index_list]","def get_contacts_list(self):\n contacts = self.driver.find_elements_by_class_name(\"_1wjpf\")\n s= [contact.text for contact in contacts] #extracts chats and last messsages\n print (\"get contacts: \"+str(s)) #print only chat names\n return s[::2] #returns only chat names","def get_active_contact(self):\n list_contact = Contact.objects.filter(phonebook__campaign=self.id,\n status=CONTACT_STATUS.ACTIVE).all()\n if not list_contact:\n return False\n return list_contact","def contact(self):\n return self._contact","def contact(self):\n return self._contact","def get_contacts(self, count=-1, excluded_guid=None):\n current_len = len(self._contacts)\n if current_len == 0 or count == 0:\n return []\n\n if count < 0:\n count = current_len\n else:\n count = min(count, current_len)\n\n if excluded_guid is None:\n # Get the last `count` contacts.\n contact_list = self._contacts[-count:]\n else:\n contact_list = []\n for contact in reversed(self._contacts):\n if contact.guid == excluded_guid:\n continue\n contact_list.append(contact)\n if len(contact_list) >= count:\n break\n return contact_list","async def get_contacts(self, **kwargs) -> List[CertificateContact]:\n contacts = await self._client.get_certificate_contacts(\n vault_base_url=self._vault_url, **kwargs\n )\n return [CertificateContact._from_certificate_contacts_item(contact_item=item) for item in contacts.contact_list]","def get_cached_contacts(self):\n return list(self._replacement_cache)","def get_all_contacts(self):\n self.init_db(self._testing)\n\n query = \"SELECT {} FROM {} ORDER BY id;\".format(\", \".join(Contact.columns_with_uid), Contact.table_name)\n\n data = self.db.conn.execute(query)\n\n return [Contact(*item) for item in data]","def get_queryset(self):\n contact_data = Contact.objects.filter(contact_groups__in=Member.objects.filter(\n user=self.request.user).values('group_id').distinct())\n\n return contact_data","def Contact(self):\n return self.__contact","def support_contacts(self):\n return self._support_contacts","def contacts(self):\n from hubspot3.contacts import ContactsClient\n\n return ContactsClient(**self.auth, **self.options)","def get_queryset(self):\n return self.request.user.contacts.all()","def receiveContactList(self, contactList):","def update_contacts(self):\n self.contacts = self.db.list_contacts()\n return self.list_contacts()","def list_contact(self, key, value):\n self.db.list_contact(\n key,\n value,\n )","def get_contact(self, username, password):\n\t\tdn, username = self.auth(username, password)\n\t\tif self.is_blacklisted(username):\n\t\t\traise ServiceForbidden()\n\n\t\tuser = self.get_udm_user(username=username)\n\t\tif not self.send_plugins:\n\t\t\traise ServiceForbidden()\n\n\t\treturn [{\n\t\t\t\"id\": p.send_method(),\n\t\t\t\"label\": p.send_method_label(),\n\t\t\t\"value\": user[p.udm_property]\n\t\t} for p in self.send_plugins.values() if p.udm_property in user]","def contacts(request):\n User = get_user_model()\n ids = set(request.user.chatmessage_set.all().values_list(\"recipients\", flat=True))\n context = {\n 'contacts': User.objects.filter(pk__in=ids)\n }\n return render(request, \"chat/contacts.html\", context)","def contact_info(self):\n return self._contact_info","def contacts(self):\n service_root = self._get_webservice_url(\"contacts\")\n return ContactsService(service_root, self.session, self.params)","def cc_email_address(self):\n return self._cc_recipients","def getcontacts():\n contacts = {}\n\n try:\n #get list of contact ids\n contactids = r.smembers(\"contacts\")\n\n #for each contact id get data\n for contactid in contactids:\n contacts.update(_getcontact(str(contactid)))\n return contacts\n except:\n print \"Unexpected error:\", sys.exc_info()[0]\n raise","def get_messages(self):\n return self.addresses","def fetch_contact_messages(self, org, contact, created_after, created_before):\n pass","def get_message_list(self):\n \n result = requests.get(\n url = root_url + '/{}'.format(\"message\"),\n headers = { 'Authorization': api_key },\n )\n\n message_list = result.json()\n\n self.message_list = message_list","def contact_list(self, contact_list):\n \n self._contact_list = contact_list","def update_contacts(self, contact_list):\n updated_contacts = 0\n request_list = list()\n\n # stale_contacts contains all old contacts at first, all current\n # contacts get then removed so that the remaining can get deleted\n stale_contacts = set(self.contacts)\n\n for contact in contact_list:\n c = Persona.query.get(contact[\"id\"])\n\n if c is None:\n c = Persona(id=contact[\"id\"], _stub=True)\n\n if c._stub is True:\n request_list.append(contact[\"id\"])\n\n try:\n # Old and new contact; remove from stale list\n stale_contacts.remove(c)\n except KeyError:\n # New contact\n self.contacts.append(c)\n updated_contacts += 1\n\n # Remove old contacts that are not new contacts\n for contact in stale_contacts:\n self.contacts.remove(contact)\n\n app.logger.info(\"Updated {}'s contacts: {} added, {} removed, {} requested\".format(\n self.username, updated_contacts, len(stale_contacts), len(request_list)))\n\n return request_list","def get_drip_campaigns(self):\n return list(DripCampaign.objects(user_id=self.user_id))","def contacts(self):\n if \"contacts\" in self._prop_dict:\n return ContactsCollectionPage(self._prop_dict[\"contacts\"])\n else:\n return None","def get_list_of_campaigns(self, limit=0, offset=0):\n logger.info(\"Function call: get_list_of_campaigns\")\n return self.__handle_result(self.__send_request('campaigns', 'GET', {'limit': limit or 0, 'offset': offset or 0}))","def get_campaign_name_list(self):\n campaigns = self.find('campaigns', {})\n campaign_names = []\n for campaign in campaigns:\n if 'name' in campaign:\n campaign_names.append(campaign['name'])\n return campaign_names","def billing_contact(self):\n return self._billing_contact","def get_a_contact(self, uid):\n self.init_db(self._testing)\n\n query = \"SELECT {} FROM {} WHERE (id=?) ORDER BY id;\".format(\n \", \".join(Contact.columns_with_uid), Contact.table_name)\n\n data = self.db.conn.execute(query, (uid,))\n\n return [Contact(*item) for item in data]","def recipients(self) -> ty.List[str]:","def search_contact_list(self):\n\n search_db = Database()\n result = search_db.contact_search(self.name)\n if not result:\n print Fore.YELLOW + ' No such contact'\n return None\n if result > 1:\n print ' Which contact ??'\n for items in result:\n if items[2] > 1:\n print Fore.BLUE + ' %s %s %s' % ([items[0]], items[1], items[2])\n else:\n print str(items[1]), items[2]\n\n return result","def contact_info(self):\n return [\n {\n 'contact_info': c.get('contactInfo'),\n 'type': c.get('type'),\n 'primary': c.get('primary'),\n 'verified': c.get('verified'),\n }\n for c in self.entity_payload.get('contactInfo')]","def resulting_contact(self):\n return self._resulting_contact","def getallcontacts(self):\n feed_url = self.contacts_client.GetFeedUri(projection='full')\n total_read = 0\n while True:\n print('Retrieving contacts... (%d retrieved so far)' % total_read)\n feed = self.contacts_client.get_feed(uri=feed_url,\n auth_token=None,\n desired_class=gdata.contacts.data.ContactsFeed)\n total_read += len(feed.entry)\n for entry in feed.entry:\n yield entry\n next_link = feed.GetNextLink()\n if next_link is None:\n print('All contacts retrieved: %d total' % total_read)\n break\n feed_url = next_link.href","def source_contact(self):\n return self._source_contact","def contact_information(self) -> ContactInformation:\n return self._contact_information","def get_contacts(self, uuid=None, urn=None, group=None, deleted=None, before=None, after=None):\n params = self._build_params(uuid=uuid, urn=urn, group=group, deleted=deleted, before=before, after=after)\n return self._get_query('contacts', params, Contact)","def get_contacts():\n return jsonify(g.driver.get_contacts())","def contact_points(self) -> object:\n return self._contact_points","def get_contacts_by_company(self, company_id):\n\n contacts = self._request('getContactsByCompany', {'company_id': company_id})\n for contact in contacts:\n yield contact","def contacts_list_update(self):\n\t\tself.database.contacts_clear()\n\t\tclient_log.debug(f'Запрос контакт листа для пользователся {self.name}')\n\t\treq = {\n\t\t\tACTION: GET_CONTACTS,\n\t\t\tTIME: time.time(),\n\t\t\tUSER: self.username\n\t\t}\n\t\tclient_log.debug(f'Сформирован запрос {req}')\n\t\twith socket_lock:\n\t\t\tsend_message(self.transport, req)\n\t\t\tans = get_message(self.transport)\n\t\tclient_log.debug(f'Получен ответ {ans}')\n\t\tif RESPONSE in ans and ans[RESPONSE] == 202:\n\t\t\tfor contact in ans[LIST_INFO]:\n\t\t\t\tself.database.add_contact(contact)\n\t\telse:\n\t\t\tclient_log.error('Не удалось обновить список контактов.')","def cc_emails(self):\n return self._cc_emails","def get_customer_list(self):\n return self._customer_repo.get_customer_list()","def get_company_to_contacts(self, company_id: str):\n return self.get(object_id=company_id, definition=Definitions.COMPANY_TO_CONTACT)","def get_phone_numbers_to_send_to(self):\n # Get the phone numbers we want to send to, excluding those that have\n # already done the thing we want to remind them of\n phone_numbers = self.PhoneModel.objects.exclude(phone_number__in=self.to_exclude())\\\n .values_list('phone_number', flat=True)\n\n message_text = self.get_message_text()\n # Set from_number to REPORTS_SHORT_CODE so that recipient can\n # simply just respond to this message with their report.\n from_shortcode = settings.REPORTS_SHORT_CODE\n for phone_number in phone_numbers:\n yield phone_number, message_text, from_shortcode","def get_all(self):\n total_contacts = []\n get_count = {\n 'query': {\n 'object': 'CONTACT',\n 'select': {\n 'field': 'RECORDNO'\n },\n 'pagesize': '1'\n }\n }\n\n response = self.format_and_send_request(get_count)\n count = int(response['data']['@totalcount'])\n pagesize = 2000\n offset = 0\n for i in range(0, count, pagesize):\n data = {\n 'query': {\n 'object': 'CONTACT',\n 'select': {\n 'field': [\n 'RECORDNO',\n 'CONTACTNAME',\n 'COMPANYNAME',\n 'FIRSTNAME',\n 'LASTNAME',\n 'INITIAL',\n 'PRINTAS',\n 'TAXABLE',\n 'MAILADDRESS.ADDRESS1'\n ]\n },\n 'pagesize': pagesize,\n 'offset': offset\n }\n }\n contacts = self.format_and_send_request(data)['data']['CONTACT']\n total_contacts = total_contacts + contacts\n offset = offset + pagesize\n return total_contacts","def contact(self, contactid):\r\n return contacts.Contact(self, contactid)","def _messages_list(self, queue):\n\n return queue.messages()","async def delete_contacts(self, **kwargs) -> List[CertificateContact]:\n contacts = await self._client.delete_certificate_contacts(\n vault_base_url=self.vault_url, **kwargs\n )\n return [CertificateContact._from_certificate_contacts_item(contact_item=item) for item in contacts.contact_list]","async def set_contacts(self, contacts: List[CertificateContact], **kwargs) -> List[CertificateContact]:\n new_contacts = await self._client.set_certificate_contacts(\n vault_base_url=self.vault_url,\n contacts=self._models.Contacts(contact_list=[c._to_certificate_contacts_item() for c in contacts]),\n **kwargs\n )\n return [\n CertificateContact._from_certificate_contacts_item(contact_item=item) for item in new_contacts.contact_list\n ]","def get_conversations(self):\n\t\treturn self.conversations","def get_mailing_list():\n\t\tresult = {}\n\t\tconnection = DbHelper.connect()\n\n\t\twith connection.cursor() as cursor:\n\t\t\tsql = \"SELECT email FROM mail_list \\\n\t\t\t\t WHERE is_activated=1;\"\n\t\t\tcursor.execute(sql)\n\t\t\tresult = cursor.fetchall()\n\n\t\treturn [email_data['email'] for email_data in result]","def get_sent_messages(self):\n return self.sent_messages","def getList(self):\n return self.list_","def get(self) -> List[Conversation]:\n return get_all_conversations(), 200","def get_dmarc_messages(self):\n messages = []\n try:\n if self.opt_use_ssl:\n self.server = poplib.POP3_SSL(self.opt_pop3_server)\n self.server.user(self.opt_global_account[\"username\"])\n self.server.pass_(self.opt_global_account[\"password\"])\n else:\n self.server = poplib.POP3(self.opt_pop3_server)\n self.server.user(self.opt_global_account[\"username\"])\n self.server.pass_(self.opt_global_account[\"password\"])\n except Exception as e:\n raise Exception(\n \"Error connecting to %s with exception %s\" %\n (self.opt_pop3_server, str(e)))\n else:\n self.helper.log_debug(\n 'get_dmarc_messages: successfully connected to %s' %\n self.opt_pop3_server)\n messages = self.byte2str(self.server.uidl()[1])\n self.helper.log_info(\n 'get_dmarc_messages: %d messages' %\n len(messages))\n return messages","async def get_contacts_for_contact_group(dbcon: DBConnection, contact_group_id: int) -> Iterable[object_models.Contact]:\n q = \"\"\"select\n contacts.id, contacts.name, contacts.email, contacts.phone, contacts.active\n from contact_group_contacts, contacts\n where contact_group_contacts.contact_group_id = %s\n and contact_group_contacts.contact_id = contacts.id\"\"\"\n return [object_models.Contact(*row) for row in await dbcon.fetch_all(q, (contact_group_id,))]","def get(self):\n args = GET_PARSER.parse_args()\n print(f'args={args}')\n\n return Contacts().get_all(\n args[\"phonetypeOne\"],\n args[\"phonetypeTwo\"],\n args[\"phonetypeThree\"],\n args[\"firstName\"],\n args[\"lastName\"],)","def get_queryset(self):\n user = self.request.user\n return Contact.objects.filter(owner=user)","def getList(self):\n return self.list","def get_recipients(self):\n recipients = set([user.email for user in self.users.all()])\n for group in self.groups.all():\n recipients.update([user.email for user in group.user_set.all()])\n return list(recipients)","async def get_all_contacts_for_active_monitor(dbcon: DBConnection, monitor_id: int) -> Iterable[object_models.Contact]:\n contacts = set()\n contacts.update(await _active_monitor_contacts(dbcon, monitor_id))\n contacts.update(await _active_monitor_contact_groups(dbcon, monitor_id))\n contacts.update(await _active_monitor_monitor_group_contacts(dbcon, monitor_id))\n contacts.update(await _active_monitor_monitor_group_contact_groups(dbcon, monitor_id))\n return list(contacts)","def get_campaigns(self, uuid=None):\n params = self._build_params(uuid=uuid)\n return self._get_query('campaigns', params, Campaign)","def first_contact(self) -> List[str]:\n error_list = []\n return error_list","def get_recipients(self):\n return [\n participant.user for participant in self.obj.activity.accepted_participants\n ]","def display_contact(self):\n contacts = \"\".join(str(contact) for contact in self.contact_list)\n print(contacts)","def get_candidate_list(self):\n return self.candidate_list","def contact(self, id_or_email):\n return ContactCollection(self.request).find(id_or_email)","def contact_details(self) -> 'outputs.ContactDetailsResponse':\n return pulumi.get(self, \"contact_details\")","def contact_details(self) -> 'outputs.ContactDetailsResponse':\n return pulumi.get(self, \"contact_details\")","def contact_details(self) -> 'outputs.ContactDetailsResponse':\n return pulumi.get(self, \"contact_details\")","def get_citation_child_list(self):\n return self.address_list","def get_messages(self):\r\n return self.messages","def pull_one_contact(self, name):\n contact = []\n for x in self.contacts:\n if x[0] == name:\n contact_name = x[0]\n number = x[1]\n email = x[2]\n zipcode = x[3]\n contact = [contact_name, number, email, zipcode]\n print(contact)\n return contact, self.contacts.index(x)","def get_group_list(self):\n return [(item[0], item[1][0]) for item in self.contacts_by_group_list]","def get_message_list(self):\n count = 0\n for msg in self.mbox:\n if msg['From'].find(self.config['tgt_email']) > -1:\n dtime = arrow.get(msg['Date'], 'ddd, D MMM YYYY HH:mm:ss ZZ')\n message = dict({'from': msg['From'],\n 'date': dtime,\n 'subject': msg['Subject']})\n # boundary = msg.get_boundary()\n # if boundary is not None:\n # bounds = [m.start() for m\n # in re.finditer(boundary, str(msg))]\n # else:\n # bounds = list()\n # if len(bounds) > 2:\n # message['text'] = str(msg)[bounds[1]:bounds[2]]\n # else:\n # message['text'] = None\n pl = None\n if msg['Subject'].find(\":\") == -1:\n finished = False\n pl = msg.get_payload()\n while finished is False:\n if isinstance(pl, str):\n finished = True\n elif isinstance(pl, list):\n pl = pl[0].get_payload()\n else:\n raise ValueError(\"Non-list, non-str payload?\")\n break\n message['text'] = self.clean_text(str(pl))\n\n if message['text'] is not None:\n self.messages.append(message)\n count += 1\n # print count\n self.messages.sort(key=lambda item: item['date'])","def get_contacts(self):\n feet = [\"REAR_RIGHT_FOOT\", \"REAR_LEFT_FOOT\",\n \"FRONT_RIGHT_FOOT\", \"FRONT_LEFT_FOOT\"]\n contacts = np.zeros(4, dtype=np.float32)\n for i, foot in enumerate(feet):\n if self.supervisor.getFromDef(foot).getNumberOfContactPoints() > 0:\n contacts[i] = 1.0\n return contacts","def recipients(self, content_object):\n if not hasattr(content_object, self.recipient_function):\n # the content object does not provide this function, test to see if its a lambda\n if not self.recipient_function.lower().startswith(\"lambda\"):\n raise NotImplementedError(\"%s does not exist for %s\" % (self.recipient_function,\n content_object))\n recipients = eval(self.recipient_function)(content_object)\n else:\n func_or_attr = getattr(content_object, self.recipient_function)\n recipients = func_or_attr() if inspect.ismethod(func_or_attr) else func_or_attr\n if not hasattr(recipients, \"__iter__\"):\n if recipients is None:\n return None\n recipients = [recipients]\n return [(r.email, r) if hasattr(r, \"email\") else (r, None) for r in recipients]","def get_contacts(self, uuids=None, urns=None, groups=None, before=None, after=None, deleted=None, pager=None):\n params = self._build_params(uuid=uuids, urns=urns, group_uuids=groups, before=before, after=after,\n deleted=deleted)\n return Contact.deserialize_list(self._get_multiple('contacts', params, pager))","def get_messages(self):\n res = self.conn.cursor().execute(\"SELECT * FROM messages\")\n return res.fetchall()","def recipients(self):\n return self._recips","def list(self):\n try:\n response = self.service.users().messages().list(userId=self.user_id,\n q=self.query).execute()\n messages = []\n if 'messages' in response:\n messages.extend(response['messages'])\n\n while 'nextPageToken' in response:\n page_token = response['nextPageToken']\n response = self.service.users().messages().list(userId=self.user_id, q=self.query,\n pageToken=page_token).execute()\n messages.extend(response['messages'])\n\n return messages\n except errors.HttpError as error:\n print('An error occurred: %s' % error)","def get(self, set=''):\n params = {}\n if set: params['set'] = set\n\n request = self._connection.get('contacts.json', params=params)\n if request.status_code != 200:\n raise Exception('status code {0}: cannot get contacts'.format(request.status_code))\n return [User.parse(self._connection, each) for each in request.json()]","def target_contact(self):\n return self._target_contact","def get_recipients(msg_parsed):\n recipients = []\n addr_fields = ['From', 'To', 'Cc', 'Bcc']\n\n for f in addr_fields:\n rfield = msg_parsed.get(f, \"\") # Empty string if field not present\n rlist = re.findall(ADDR_PATTERN, rfield)\n recipients.extend(rlist)\n\n return recipients"],"string":"[\n \"def list_contacts(self):\\n return self.contacts\",\n \"def get_contacts(self):\\n\\n\\t\\treturn self.__contacts\",\n \"def GetContactList(self):\\n\\t\\tfeeds = []\\n\\t\\tfeed = self.client.GetContacts()\\n\\t\\tfeeds.append(feed)\\n\\t\\tnext = feed.GetNextLink()\\n\\t\\twhile next:\\n\\t\\t\\tfeed = self.client.GetContacts(uri=next.href)\\n\\t\\t\\tfeeds.append(feed)\\n\\t\\t\\tnext = feed.GetNextLink()\\n\\t\\t\\n\\t\\tcontacts = []\\n\\t\\tfor feed in feeds:\\n\\t\\t\\tif not feed.entry:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor i, entry in enumerate(feed.entry):\\n\\t\\t\\t\\t\\tcontacts.append(entry)\\n\\t\\treturn contacts\",\n \"def get_contacts_list(self):\\n return [(id + 1, contact) for id, contact in enumerate(self.contact_list)]\",\n \"def contact_lists(self):\\n from hubspot3.contact_lists import ContactListsClient\\n\\n return ContactListsClient(**self.auth, **self.options)\",\n \"def get_contacts(self):\\n contacts = Membership.objects.filter(entity = self, key_contact = True).order_by('importance_to_entity')\\n return contacts\",\n \"def contacts(self):\\r\\n return contacts.Contacts(self)\",\n \"def ListAllContacts(self):\\n feed = self.gd_client.GetContacts()\\n self.contacts = self.CleanPhoneNumbers(self.GetContactsInfo(feed))\\n return self.contacts\",\n \"def contacts(self):\\n return ContactCollection(self.request)\",\n \"def _get_receivers_list(self):\\n\\n # TODO: document what this plugin expects to be in Dockerfile/where it gets info from\\n global_component = self._get_component_label()\\n # this relies on bump_release plugin configuring source.git_commit to actually be\\n # branch name, not a commit\\n if not isinstance(self.workflow.source, GitSource):\\n raise PluginFailedException('Source is not of type \\\"GitSource\\\", panic!')\\n git_branch = self.workflow.source.git_commit\\n try:\\n r = requests.get(urljoin(self.pdc_url, 'rest_api/v1/release-component-contacts/'),\\n headers={'Authorization': 'Token %s' % self._get_pdc_token()},\\n params={'global_component': global_component,\\n 'dist_git_branch': git_branch,\\n 'role': self.pdc_contact_role},\\n verify=self.pdc_verify_cert)\\n except requests.RequestException as e:\\n self.log.error('failed to connect to PDC: %s', str(e))\\n raise RuntimeError(e)\\n\\n if r.status_code != 200:\\n self.log.error('PDC returned status code %s, full response: %s',\\n r.status_code, r.text)\\n raise RuntimeError('PDC returned non-200 status code (%s), see referenced build log' %\\n r.status_code)\\n\\n contacts = r.json()\\n\\n if contacts['count'] == 0:\\n self.log.error('no %s role for the component', self.pdc_contact_role)\\n raise RuntimeError('no %s role for the component' % self.pdc_contact_role)\\n\\n send_to = []\\n for contact in contacts['results']:\\n send_to.append(contact['contact']['email'])\\n\\n return send_to\",\n \"def get_recipients(self) -> List[Client]:\\n\\n index_list = [i for i in range(len(self.int_var_list)) if self.int_var_list[i].get() == 1]\\n return [self.client_list[i] for i in index_list]\",\n \"def get_contacts_list(self):\\n contacts = self.driver.find_elements_by_class_name(\\\"_1wjpf\\\")\\n s= [contact.text for contact in contacts] #extracts chats and last messsages\\n print (\\\"get contacts: \\\"+str(s)) #print only chat names\\n return s[::2] #returns only chat names\",\n \"def get_active_contact(self):\\n list_contact = Contact.objects.filter(phonebook__campaign=self.id,\\n status=CONTACT_STATUS.ACTIVE).all()\\n if not list_contact:\\n return False\\n return list_contact\",\n \"def contact(self):\\n return self._contact\",\n \"def contact(self):\\n return self._contact\",\n \"def get_contacts(self, count=-1, excluded_guid=None):\\n current_len = len(self._contacts)\\n if current_len == 0 or count == 0:\\n return []\\n\\n if count < 0:\\n count = current_len\\n else:\\n count = min(count, current_len)\\n\\n if excluded_guid is None:\\n # Get the last `count` contacts.\\n contact_list = self._contacts[-count:]\\n else:\\n contact_list = []\\n for contact in reversed(self._contacts):\\n if contact.guid == excluded_guid:\\n continue\\n contact_list.append(contact)\\n if len(contact_list) >= count:\\n break\\n return contact_list\",\n \"async def get_contacts(self, **kwargs) -> List[CertificateContact]:\\n contacts = await self._client.get_certificate_contacts(\\n vault_base_url=self._vault_url, **kwargs\\n )\\n return [CertificateContact._from_certificate_contacts_item(contact_item=item) for item in contacts.contact_list]\",\n \"def get_cached_contacts(self):\\n return list(self._replacement_cache)\",\n \"def get_all_contacts(self):\\n self.init_db(self._testing)\\n\\n query = \\\"SELECT {} FROM {} ORDER BY id;\\\".format(\\\", \\\".join(Contact.columns_with_uid), Contact.table_name)\\n\\n data = self.db.conn.execute(query)\\n\\n return [Contact(*item) for item in data]\",\n \"def get_queryset(self):\\n contact_data = Contact.objects.filter(contact_groups__in=Member.objects.filter(\\n user=self.request.user).values('group_id').distinct())\\n\\n return contact_data\",\n \"def Contact(self):\\n return self.__contact\",\n \"def support_contacts(self):\\n return self._support_contacts\",\n \"def contacts(self):\\n from hubspot3.contacts import ContactsClient\\n\\n return ContactsClient(**self.auth, **self.options)\",\n \"def get_queryset(self):\\n return self.request.user.contacts.all()\",\n \"def receiveContactList(self, contactList):\",\n \"def update_contacts(self):\\n self.contacts = self.db.list_contacts()\\n return self.list_contacts()\",\n \"def list_contact(self, key, value):\\n self.db.list_contact(\\n key,\\n value,\\n )\",\n \"def get_contact(self, username, password):\\n\\t\\tdn, username = self.auth(username, password)\\n\\t\\tif self.is_blacklisted(username):\\n\\t\\t\\traise ServiceForbidden()\\n\\n\\t\\tuser = self.get_udm_user(username=username)\\n\\t\\tif not self.send_plugins:\\n\\t\\t\\traise ServiceForbidden()\\n\\n\\t\\treturn [{\\n\\t\\t\\t\\\"id\\\": p.send_method(),\\n\\t\\t\\t\\\"label\\\": p.send_method_label(),\\n\\t\\t\\t\\\"value\\\": user[p.udm_property]\\n\\t\\t} for p in self.send_plugins.values() if p.udm_property in user]\",\n \"def contacts(request):\\n User = get_user_model()\\n ids = set(request.user.chatmessage_set.all().values_list(\\\"recipients\\\", flat=True))\\n context = {\\n 'contacts': User.objects.filter(pk__in=ids)\\n }\\n return render(request, \\\"chat/contacts.html\\\", context)\",\n \"def contact_info(self):\\n return self._contact_info\",\n \"def contacts(self):\\n service_root = self._get_webservice_url(\\\"contacts\\\")\\n return ContactsService(service_root, self.session, self.params)\",\n \"def cc_email_address(self):\\n return self._cc_recipients\",\n \"def getcontacts():\\n contacts = {}\\n\\n try:\\n #get list of contact ids\\n contactids = r.smembers(\\\"contacts\\\")\\n\\n #for each contact id get data\\n for contactid in contactids:\\n contacts.update(_getcontact(str(contactid)))\\n return contacts\\n except:\\n print \\\"Unexpected error:\\\", sys.exc_info()[0]\\n raise\",\n \"def get_messages(self):\\n return self.addresses\",\n \"def fetch_contact_messages(self, org, contact, created_after, created_before):\\n pass\",\n \"def get_message_list(self):\\n \\n result = requests.get(\\n url = root_url + '/{}'.format(\\\"message\\\"),\\n headers = { 'Authorization': api_key },\\n )\\n\\n message_list = result.json()\\n\\n self.message_list = message_list\",\n \"def contact_list(self, contact_list):\\n \\n self._contact_list = contact_list\",\n \"def update_contacts(self, contact_list):\\n updated_contacts = 0\\n request_list = list()\\n\\n # stale_contacts contains all old contacts at first, all current\\n # contacts get then removed so that the remaining can get deleted\\n stale_contacts = set(self.contacts)\\n\\n for contact in contact_list:\\n c = Persona.query.get(contact[\\\"id\\\"])\\n\\n if c is None:\\n c = Persona(id=contact[\\\"id\\\"], _stub=True)\\n\\n if c._stub is True:\\n request_list.append(contact[\\\"id\\\"])\\n\\n try:\\n # Old and new contact; remove from stale list\\n stale_contacts.remove(c)\\n except KeyError:\\n # New contact\\n self.contacts.append(c)\\n updated_contacts += 1\\n\\n # Remove old contacts that are not new contacts\\n for contact in stale_contacts:\\n self.contacts.remove(contact)\\n\\n app.logger.info(\\\"Updated {}'s contacts: {} added, {} removed, {} requested\\\".format(\\n self.username, updated_contacts, len(stale_contacts), len(request_list)))\\n\\n return request_list\",\n \"def get_drip_campaigns(self):\\n return list(DripCampaign.objects(user_id=self.user_id))\",\n \"def contacts(self):\\n if \\\"contacts\\\" in self._prop_dict:\\n return ContactsCollectionPage(self._prop_dict[\\\"contacts\\\"])\\n else:\\n return None\",\n \"def get_list_of_campaigns(self, limit=0, offset=0):\\n logger.info(\\\"Function call: get_list_of_campaigns\\\")\\n return self.__handle_result(self.__send_request('campaigns', 'GET', {'limit': limit or 0, 'offset': offset or 0}))\",\n \"def get_campaign_name_list(self):\\n campaigns = self.find('campaigns', {})\\n campaign_names = []\\n for campaign in campaigns:\\n if 'name' in campaign:\\n campaign_names.append(campaign['name'])\\n return campaign_names\",\n \"def billing_contact(self):\\n return self._billing_contact\",\n \"def get_a_contact(self, uid):\\n self.init_db(self._testing)\\n\\n query = \\\"SELECT {} FROM {} WHERE (id=?) ORDER BY id;\\\".format(\\n \\\", \\\".join(Contact.columns_with_uid), Contact.table_name)\\n\\n data = self.db.conn.execute(query, (uid,))\\n\\n return [Contact(*item) for item in data]\",\n \"def recipients(self) -> ty.List[str]:\",\n \"def search_contact_list(self):\\n\\n search_db = Database()\\n result = search_db.contact_search(self.name)\\n if not result:\\n print Fore.YELLOW + ' No such contact'\\n return None\\n if result > 1:\\n print ' Which contact ??'\\n for items in result:\\n if items[2] > 1:\\n print Fore.BLUE + ' %s %s %s' % ([items[0]], items[1], items[2])\\n else:\\n print str(items[1]), items[2]\\n\\n return result\",\n \"def contact_info(self):\\n return [\\n {\\n 'contact_info': c.get('contactInfo'),\\n 'type': c.get('type'),\\n 'primary': c.get('primary'),\\n 'verified': c.get('verified'),\\n }\\n for c in self.entity_payload.get('contactInfo')]\",\n \"def resulting_contact(self):\\n return self._resulting_contact\",\n \"def getallcontacts(self):\\n feed_url = self.contacts_client.GetFeedUri(projection='full')\\n total_read = 0\\n while True:\\n print('Retrieving contacts... (%d retrieved so far)' % total_read)\\n feed = self.contacts_client.get_feed(uri=feed_url,\\n auth_token=None,\\n desired_class=gdata.contacts.data.ContactsFeed)\\n total_read += len(feed.entry)\\n for entry in feed.entry:\\n yield entry\\n next_link = feed.GetNextLink()\\n if next_link is None:\\n print('All contacts retrieved: %d total' % total_read)\\n break\\n feed_url = next_link.href\",\n \"def source_contact(self):\\n return self._source_contact\",\n \"def contact_information(self) -> ContactInformation:\\n return self._contact_information\",\n \"def get_contacts(self, uuid=None, urn=None, group=None, deleted=None, before=None, after=None):\\n params = self._build_params(uuid=uuid, urn=urn, group=group, deleted=deleted, before=before, after=after)\\n return self._get_query('contacts', params, Contact)\",\n \"def get_contacts():\\n return jsonify(g.driver.get_contacts())\",\n \"def contact_points(self) -> object:\\n return self._contact_points\",\n \"def get_contacts_by_company(self, company_id):\\n\\n contacts = self._request('getContactsByCompany', {'company_id': company_id})\\n for contact in contacts:\\n yield contact\",\n \"def contacts_list_update(self):\\n\\t\\tself.database.contacts_clear()\\n\\t\\tclient_log.debug(f'Запрос контакт листа для пользователся {self.name}')\\n\\t\\treq = {\\n\\t\\t\\tACTION: GET_CONTACTS,\\n\\t\\t\\tTIME: time.time(),\\n\\t\\t\\tUSER: self.username\\n\\t\\t}\\n\\t\\tclient_log.debug(f'Сформирован запрос {req}')\\n\\t\\twith socket_lock:\\n\\t\\t\\tsend_message(self.transport, req)\\n\\t\\t\\tans = get_message(self.transport)\\n\\t\\tclient_log.debug(f'Получен ответ {ans}')\\n\\t\\tif RESPONSE in ans and ans[RESPONSE] == 202:\\n\\t\\t\\tfor contact in ans[LIST_INFO]:\\n\\t\\t\\t\\tself.database.add_contact(contact)\\n\\t\\telse:\\n\\t\\t\\tclient_log.error('Не удалось обновить список контактов.')\",\n \"def cc_emails(self):\\n return self._cc_emails\",\n \"def get_customer_list(self):\\n return self._customer_repo.get_customer_list()\",\n \"def get_company_to_contacts(self, company_id: str):\\n return self.get(object_id=company_id, definition=Definitions.COMPANY_TO_CONTACT)\",\n \"def get_phone_numbers_to_send_to(self):\\n # Get the phone numbers we want to send to, excluding those that have\\n # already done the thing we want to remind them of\\n phone_numbers = self.PhoneModel.objects.exclude(phone_number__in=self.to_exclude())\\\\\\n .values_list('phone_number', flat=True)\\n\\n message_text = self.get_message_text()\\n # Set from_number to REPORTS_SHORT_CODE so that recipient can\\n # simply just respond to this message with their report.\\n from_shortcode = settings.REPORTS_SHORT_CODE\\n for phone_number in phone_numbers:\\n yield phone_number, message_text, from_shortcode\",\n \"def get_all(self):\\n total_contacts = []\\n get_count = {\\n 'query': {\\n 'object': 'CONTACT',\\n 'select': {\\n 'field': 'RECORDNO'\\n },\\n 'pagesize': '1'\\n }\\n }\\n\\n response = self.format_and_send_request(get_count)\\n count = int(response['data']['@totalcount'])\\n pagesize = 2000\\n offset = 0\\n for i in range(0, count, pagesize):\\n data = {\\n 'query': {\\n 'object': 'CONTACT',\\n 'select': {\\n 'field': [\\n 'RECORDNO',\\n 'CONTACTNAME',\\n 'COMPANYNAME',\\n 'FIRSTNAME',\\n 'LASTNAME',\\n 'INITIAL',\\n 'PRINTAS',\\n 'TAXABLE',\\n 'MAILADDRESS.ADDRESS1'\\n ]\\n },\\n 'pagesize': pagesize,\\n 'offset': offset\\n }\\n }\\n contacts = self.format_and_send_request(data)['data']['CONTACT']\\n total_contacts = total_contacts + contacts\\n offset = offset + pagesize\\n return total_contacts\",\n \"def contact(self, contactid):\\r\\n return contacts.Contact(self, contactid)\",\n \"def _messages_list(self, queue):\\n\\n return queue.messages()\",\n \"async def delete_contacts(self, **kwargs) -> List[CertificateContact]:\\n contacts = await self._client.delete_certificate_contacts(\\n vault_base_url=self.vault_url, **kwargs\\n )\\n return [CertificateContact._from_certificate_contacts_item(contact_item=item) for item in contacts.contact_list]\",\n \"async def set_contacts(self, contacts: List[CertificateContact], **kwargs) -> List[CertificateContact]:\\n new_contacts = await self._client.set_certificate_contacts(\\n vault_base_url=self.vault_url,\\n contacts=self._models.Contacts(contact_list=[c._to_certificate_contacts_item() for c in contacts]),\\n **kwargs\\n )\\n return [\\n CertificateContact._from_certificate_contacts_item(contact_item=item) for item in new_contacts.contact_list\\n ]\",\n \"def get_conversations(self):\\n\\t\\treturn self.conversations\",\n \"def get_mailing_list():\\n\\t\\tresult = {}\\n\\t\\tconnection = DbHelper.connect()\\n\\n\\t\\twith connection.cursor() as cursor:\\n\\t\\t\\tsql = \\\"SELECT email FROM mail_list \\\\\\n\\t\\t\\t\\t WHERE is_activated=1;\\\"\\n\\t\\t\\tcursor.execute(sql)\\n\\t\\t\\tresult = cursor.fetchall()\\n\\n\\t\\treturn [email_data['email'] for email_data in result]\",\n \"def get_sent_messages(self):\\n return self.sent_messages\",\n \"def getList(self):\\n return self.list_\",\n \"def get(self) -> List[Conversation]:\\n return get_all_conversations(), 200\",\n \"def get_dmarc_messages(self):\\n messages = []\\n try:\\n if self.opt_use_ssl:\\n self.server = poplib.POP3_SSL(self.opt_pop3_server)\\n self.server.user(self.opt_global_account[\\\"username\\\"])\\n self.server.pass_(self.opt_global_account[\\\"password\\\"])\\n else:\\n self.server = poplib.POP3(self.opt_pop3_server)\\n self.server.user(self.opt_global_account[\\\"username\\\"])\\n self.server.pass_(self.opt_global_account[\\\"password\\\"])\\n except Exception as e:\\n raise Exception(\\n \\\"Error connecting to %s with exception %s\\\" %\\n (self.opt_pop3_server, str(e)))\\n else:\\n self.helper.log_debug(\\n 'get_dmarc_messages: successfully connected to %s' %\\n self.opt_pop3_server)\\n messages = self.byte2str(self.server.uidl()[1])\\n self.helper.log_info(\\n 'get_dmarc_messages: %d messages' %\\n len(messages))\\n return messages\",\n \"async def get_contacts_for_contact_group(dbcon: DBConnection, contact_group_id: int) -> Iterable[object_models.Contact]:\\n q = \\\"\\\"\\\"select\\n contacts.id, contacts.name, contacts.email, contacts.phone, contacts.active\\n from contact_group_contacts, contacts\\n where contact_group_contacts.contact_group_id = %s\\n and contact_group_contacts.contact_id = contacts.id\\\"\\\"\\\"\\n return [object_models.Contact(*row) for row in await dbcon.fetch_all(q, (contact_group_id,))]\",\n \"def get(self):\\n args = GET_PARSER.parse_args()\\n print(f'args={args}')\\n\\n return Contacts().get_all(\\n args[\\\"phonetypeOne\\\"],\\n args[\\\"phonetypeTwo\\\"],\\n args[\\\"phonetypeThree\\\"],\\n args[\\\"firstName\\\"],\\n args[\\\"lastName\\\"],)\",\n \"def get_queryset(self):\\n user = self.request.user\\n return Contact.objects.filter(owner=user)\",\n \"def getList(self):\\n return self.list\",\n \"def get_recipients(self):\\n recipients = set([user.email for user in self.users.all()])\\n for group in self.groups.all():\\n recipients.update([user.email for user in group.user_set.all()])\\n return list(recipients)\",\n \"async def get_all_contacts_for_active_monitor(dbcon: DBConnection, monitor_id: int) -> Iterable[object_models.Contact]:\\n contacts = set()\\n contacts.update(await _active_monitor_contacts(dbcon, monitor_id))\\n contacts.update(await _active_monitor_contact_groups(dbcon, monitor_id))\\n contacts.update(await _active_monitor_monitor_group_contacts(dbcon, monitor_id))\\n contacts.update(await _active_monitor_monitor_group_contact_groups(dbcon, monitor_id))\\n return list(contacts)\",\n \"def get_campaigns(self, uuid=None):\\n params = self._build_params(uuid=uuid)\\n return self._get_query('campaigns', params, Campaign)\",\n \"def first_contact(self) -> List[str]:\\n error_list = []\\n return error_list\",\n \"def get_recipients(self):\\n return [\\n participant.user for participant in self.obj.activity.accepted_participants\\n ]\",\n \"def display_contact(self):\\n contacts = \\\"\\\".join(str(contact) for contact in self.contact_list)\\n print(contacts)\",\n \"def get_candidate_list(self):\\n return self.candidate_list\",\n \"def contact(self, id_or_email):\\n return ContactCollection(self.request).find(id_or_email)\",\n \"def contact_details(self) -> 'outputs.ContactDetailsResponse':\\n return pulumi.get(self, \\\"contact_details\\\")\",\n \"def contact_details(self) -> 'outputs.ContactDetailsResponse':\\n return pulumi.get(self, \\\"contact_details\\\")\",\n \"def contact_details(self) -> 'outputs.ContactDetailsResponse':\\n return pulumi.get(self, \\\"contact_details\\\")\",\n \"def get_citation_child_list(self):\\n return self.address_list\",\n \"def get_messages(self):\\r\\n return self.messages\",\n \"def pull_one_contact(self, name):\\n contact = []\\n for x in self.contacts:\\n if x[0] == name:\\n contact_name = x[0]\\n number = x[1]\\n email = x[2]\\n zipcode = x[3]\\n contact = [contact_name, number, email, zipcode]\\n print(contact)\\n return contact, self.contacts.index(x)\",\n \"def get_group_list(self):\\n return [(item[0], item[1][0]) for item in self.contacts_by_group_list]\",\n \"def get_message_list(self):\\n count = 0\\n for msg in self.mbox:\\n if msg['From'].find(self.config['tgt_email']) > -1:\\n dtime = arrow.get(msg['Date'], 'ddd, D MMM YYYY HH:mm:ss ZZ')\\n message = dict({'from': msg['From'],\\n 'date': dtime,\\n 'subject': msg['Subject']})\\n # boundary = msg.get_boundary()\\n # if boundary is not None:\\n # bounds = [m.start() for m\\n # in re.finditer(boundary, str(msg))]\\n # else:\\n # bounds = list()\\n # if len(bounds) > 2:\\n # message['text'] = str(msg)[bounds[1]:bounds[2]]\\n # else:\\n # message['text'] = None\\n pl = None\\n if msg['Subject'].find(\\\":\\\") == -1:\\n finished = False\\n pl = msg.get_payload()\\n while finished is False:\\n if isinstance(pl, str):\\n finished = True\\n elif isinstance(pl, list):\\n pl = pl[0].get_payload()\\n else:\\n raise ValueError(\\\"Non-list, non-str payload?\\\")\\n break\\n message['text'] = self.clean_text(str(pl))\\n\\n if message['text'] is not None:\\n self.messages.append(message)\\n count += 1\\n # print count\\n self.messages.sort(key=lambda item: item['date'])\",\n \"def get_contacts(self):\\n feet = [\\\"REAR_RIGHT_FOOT\\\", \\\"REAR_LEFT_FOOT\\\",\\n \\\"FRONT_RIGHT_FOOT\\\", \\\"FRONT_LEFT_FOOT\\\"]\\n contacts = np.zeros(4, dtype=np.float32)\\n for i, foot in enumerate(feet):\\n if self.supervisor.getFromDef(foot).getNumberOfContactPoints() > 0:\\n contacts[i] = 1.0\\n return contacts\",\n \"def recipients(self, content_object):\\n if not hasattr(content_object, self.recipient_function):\\n # the content object does not provide this function, test to see if its a lambda\\n if not self.recipient_function.lower().startswith(\\\"lambda\\\"):\\n raise NotImplementedError(\\\"%s does not exist for %s\\\" % (self.recipient_function,\\n content_object))\\n recipients = eval(self.recipient_function)(content_object)\\n else:\\n func_or_attr = getattr(content_object, self.recipient_function)\\n recipients = func_or_attr() if inspect.ismethod(func_or_attr) else func_or_attr\\n if not hasattr(recipients, \\\"__iter__\\\"):\\n if recipients is None:\\n return None\\n recipients = [recipients]\\n return [(r.email, r) if hasattr(r, \\\"email\\\") else (r, None) for r in recipients]\",\n \"def get_contacts(self, uuids=None, urns=None, groups=None, before=None, after=None, deleted=None, pager=None):\\n params = self._build_params(uuid=uuids, urns=urns, group_uuids=groups, before=before, after=after,\\n deleted=deleted)\\n return Contact.deserialize_list(self._get_multiple('contacts', params, pager))\",\n \"def get_messages(self):\\n res = self.conn.cursor().execute(\\\"SELECT * FROM messages\\\")\\n return res.fetchall()\",\n \"def recipients(self):\\n return self._recips\",\n \"def list(self):\\n try:\\n response = self.service.users().messages().list(userId=self.user_id,\\n q=self.query).execute()\\n messages = []\\n if 'messages' in response:\\n messages.extend(response['messages'])\\n\\n while 'nextPageToken' in response:\\n page_token = response['nextPageToken']\\n response = self.service.users().messages().list(userId=self.user_id, q=self.query,\\n pageToken=page_token).execute()\\n messages.extend(response['messages'])\\n\\n return messages\\n except errors.HttpError as error:\\n print('An error occurred: %s' % error)\",\n \"def get(self, set=''):\\n params = {}\\n if set: params['set'] = set\\n\\n request = self._connection.get('contacts.json', params=params)\\n if request.status_code != 200:\\n raise Exception('status code {0}: cannot get contacts'.format(request.status_code))\\n return [User.parse(self._connection, each) for each in request.json()]\",\n \"def target_contact(self):\\n return self._target_contact\",\n \"def get_recipients(msg_parsed):\\n recipients = []\\n addr_fields = ['From', 'To', 'Cc', 'Bcc']\\n\\n for f in addr_fields:\\n rfield = msg_parsed.get(f, \\\"\\\") # Empty string if field not present\\n rlist = re.findall(ADDR_PATTERN, rfield)\\n recipients.extend(rlist)\\n\\n return recipients\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72631705","0.71663344","0.7121245","0.6652589","0.65349084","0.64175344","0.63972867","0.6386485","0.63770306","0.63157517","0.61904186","0.61765337","0.6132532","0.6121634","0.6121634","0.602559","0.6003591","0.59747976","0.5964907","0.5926964","0.5926448","0.5915741","0.59078896","0.5905416","0.589102","0.5790044","0.5783242","0.57187986","0.5704222","0.5675407","0.567043","0.56384206","0.56341976","0.5627944","0.56250626","0.559637","0.552889","0.5514794","0.5482435","0.54757184","0.545075","0.54494613","0.544242","0.5432188","0.5395183","0.53910005","0.534315","0.53385097","0.53358686","0.53316194","0.53243816","0.53139645","0.52997684","0.52973974","0.5296391","0.529599","0.52929693","0.52762717","0.5262194","0.5249087","0.5246305","0.5237774","0.52237517","0.52132976","0.5200423","0.51861614","0.518084","0.51734054","0.5170489","0.5152326","0.51436543","0.5137565","0.51349556","0.51309234","0.50918293","0.507489","0.50743437","0.5072385","0.50628895","0.5050648","0.50476044","0.5032133","0.5026486","0.5022638","0.5022638","0.5022638","0.5017613","0.50095385","0.50027764","0.4992955","0.49862403","0.49730676","0.49716076","0.49650335","0.4963129","0.49521157","0.49408746","0.4938071","0.49301484","0.49292713"],"string":"[\n \"0.72631705\",\n \"0.71663344\",\n \"0.7121245\",\n \"0.6652589\",\n \"0.65349084\",\n \"0.64175344\",\n \"0.63972867\",\n \"0.6386485\",\n \"0.63770306\",\n \"0.63157517\",\n \"0.61904186\",\n \"0.61765337\",\n \"0.6132532\",\n \"0.6121634\",\n \"0.6121634\",\n \"0.602559\",\n \"0.6003591\",\n \"0.59747976\",\n \"0.5964907\",\n \"0.5926964\",\n \"0.5926448\",\n \"0.5915741\",\n \"0.59078896\",\n \"0.5905416\",\n \"0.589102\",\n \"0.5790044\",\n \"0.5783242\",\n \"0.57187986\",\n \"0.5704222\",\n \"0.5675407\",\n \"0.567043\",\n \"0.56384206\",\n \"0.56341976\",\n \"0.5627944\",\n \"0.56250626\",\n \"0.559637\",\n \"0.552889\",\n \"0.5514794\",\n \"0.5482435\",\n \"0.54757184\",\n \"0.545075\",\n \"0.54494613\",\n \"0.544242\",\n \"0.5432188\",\n \"0.5395183\",\n \"0.53910005\",\n \"0.534315\",\n \"0.53385097\",\n \"0.53358686\",\n \"0.53316194\",\n \"0.53243816\",\n \"0.53139645\",\n \"0.52997684\",\n \"0.52973974\",\n \"0.5296391\",\n \"0.529599\",\n \"0.52929693\",\n \"0.52762717\",\n \"0.5262194\",\n \"0.5249087\",\n \"0.5246305\",\n \"0.5237774\",\n \"0.52237517\",\n \"0.52132976\",\n \"0.5200423\",\n \"0.51861614\",\n \"0.518084\",\n \"0.51734054\",\n \"0.5170489\",\n \"0.5152326\",\n \"0.51436543\",\n \"0.5137565\",\n \"0.51349556\",\n \"0.51309234\",\n \"0.50918293\",\n \"0.507489\",\n \"0.50743437\",\n \"0.5072385\",\n \"0.50628895\",\n \"0.5050648\",\n \"0.50476044\",\n \"0.5032133\",\n \"0.5026486\",\n \"0.5022638\",\n \"0.5022638\",\n \"0.5022638\",\n \"0.5017613\",\n \"0.50095385\",\n \"0.50027764\",\n \"0.4992955\",\n \"0.49862403\",\n \"0.49730676\",\n \"0.49716076\",\n \"0.49650335\",\n \"0.4963129\",\n \"0.49521157\",\n \"0.49408746\",\n \"0.4938071\",\n \"0.49301484\",\n \"0.49292713\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8081953"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94,"cells":{"query":{"kind":"string","value":"Sets the contact_list of this MessagingCampaign. The contact list that this messaging campaign will send messages for."},"document":{"kind":"string","value":"def contact_list(self, contact_list):\n \n self._contact_list = contact_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def receiveContactList(self, contactList):","def set_contacts(self, contacts):\n\n\t\tif contacts is not None and not isinstance(contacts, list):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: contacts EXPECTED TYPE: list', None, None)\n\t\t\n\t\tself.__contacts = contacts\n\t\tself.__key_modified['Contacts'] = 1","def contacts(self, contacts):\n\n self._contacts = contacts","def contacts(self, contacts):\n\n self._contacts = contacts","def contact_list(self):\n return self._contact_list","def update_contacts(self, contact_list):\n updated_contacts = 0\n request_list = list()\n\n # stale_contacts contains all old contacts at first, all current\n # contacts get then removed so that the remaining can get deleted\n stale_contacts = set(self.contacts)\n\n for contact in contact_list:\n c = Persona.query.get(contact[\"id\"])\n\n if c is None:\n c = Persona(id=contact[\"id\"], _stub=True)\n\n if c._stub is True:\n request_list.append(contact[\"id\"])\n\n try:\n # Old and new contact; remove from stale list\n stale_contacts.remove(c)\n except KeyError:\n # New contact\n self.contacts.append(c)\n updated_contacts += 1\n\n # Remove old contacts that are not new contacts\n for contact in stale_contacts:\n self.contacts.remove(contact)\n\n app.logger.info(\"Updated {}'s contacts: {} added, {} removed, {} requested\".format(\n self.username, updated_contacts, len(stale_contacts), len(request_list)))\n\n return request_list","def contact(self, contact):\n\n self.logger.debug(\"In 'contact' setter.\")\n\n self._contact = contact","async def set_contacts(self, contacts: List[CertificateContact], **kwargs) -> List[CertificateContact]:\n new_contacts = await self._client.set_certificate_contacts(\n vault_base_url=self.vault_url,\n contacts=self._models.Contacts(contact_list=[c._to_certificate_contacts_item() for c in contacts]),\n **kwargs\n )\n return [\n CertificateContact._from_certificate_contacts_item(contact_item=item) for item in new_contacts.contact_list\n ]","def contact_points(self, contact_points: object):\n\n self._contact_points = contact_points","def contact(self, contact):\n\n self._contact = contact","def contact(self, contact):\n\n self._contact = contact","def support_contacts(self, support_contacts):\n self._support_contacts = support_contacts","def contact_lists(self):\n from hubspot3.contact_lists import ContactListsClient\n\n return ContactListsClient(**self.auth, **self.options)","def contacts_list_update(self):\n\t\tself.database.contacts_clear()\n\t\tclient_log.debug(f'Запрос контакт листа для пользователся {self.name}')\n\t\treq = {\n\t\t\tACTION: GET_CONTACTS,\n\t\t\tTIME: time.time(),\n\t\t\tUSER: self.username\n\t\t}\n\t\tclient_log.debug(f'Сформирован запрос {req}')\n\t\twith socket_lock:\n\t\t\tsend_message(self.transport, req)\n\t\t\tans = get_message(self.transport)\n\t\tclient_log.debug(f'Получен ответ {ans}')\n\t\tif RESPONSE in ans and ans[RESPONSE] == 202:\n\t\t\tfor contact in ans[LIST_INFO]:\n\t\t\t\tself.database.add_contact(contact)\n\t\telse:\n\t\t\tclient_log.error('Не удалось обновить список контактов.')","def send_mass_messages(self, recipient_list, sender, message=\"\", subject=\"\"):\n try:\n for s in recipient_list:\n self.send_message(to=s, sender=sender, message=message, subject=subject)\n except TypeError:\n return -1\n return 1","def list(self, list):\n if list is None:\n raise ValueError(\"Invalid value for `list`, must not be `None`\") # noqa: E501\n\n self._list = list","def do_send_list( self, a_list ):\r\n # --- this needs to be moved to task some set up here then on there\r\n self.logger.info( \"turn on sendList\" )\r\n self.send_list_ix = 0\r\n\r\n #self.send_list = [ 180, 920, 160, 1740, 160, 780, 160, 2840, 160, 1320, 160, 1340, 160, ] # 1180, 160, 2700, 160, 12780, 200, 920, \\\r\n #160, 2680, 160, 780, 160, 800, 160, 780, 160, 920, 160, 800, 140, 800, \\\r\n # 160 ]\r\n self.send_list = a_list\r\n self.com_driver.send( \"z\\n\" )\r\n self.list_send = True # if we were mult-threaded this would have to be here\r\n\r\n return","def contact_point(self, contact_point: object):\n\n self._contact_point = contact_point","def contact_reference(self, contact_reference):\n\n self._contact_reference = contact_reference","def list_contact(self, key, value):\n self.db.list_contact(\n key,\n value,\n )","def target_contact(self, target_contact):\n \n self._target_contact = target_contact","def contactListClicked(self):\n \n contacts = self.userList.getSelectedItems()\n self.mergeButton.setEnabled(contacts != None and len(contacts) > 1)\n \n if contacts != None and len(contacts) == 1:\n self.messageList.filterByContact(contacts[0])\n else:\n self.messageList.removeFilter()","def _create_mailing_list(cls):\n cls.mailing_list_1 = cls.env['mailing.list'].with_context(cls._test_context).create({\n 'name': 'List1',\n 'contact_ids': [\n (0, 0, {'name': 'Déboulonneur', 'email': 'fleurus@example.com'}),\n (0, 0, {'name': 'Gorramts', 'email': 'gorramts@example.com'}),\n (0, 0, {'name': 'Ybrant', 'email': 'ybrant@example.com'}),\n ]\n })\n cls.mailing_list_2 = cls.env['mailing.list'].with_context(cls._test_context).create({\n 'name': 'List2',\n 'contact_ids': [\n (0, 0, {'name': 'Gilberte', 'email': 'gilberte@example.com'}),\n (0, 0, {'name': 'Gilberte En Mieux', 'email': 'gilberte@example.com'}),\n (0, 0, {'name': 'Norbert', 'email': 'norbert@example.com'}),\n (0, 0, {'name': 'Ybrant', 'email': 'ybrant@example.com'}),\n ]\n })","def member_list(self, member_list):\n\n self._member_list = member_list","def contact_info(self, contact_info):\n\n self._contact_info = contact_info","def SetDomainsList(self, domainsList) :\n\t\t...","def source_contact(self, source_contact):\n \n self._source_contact = source_contact","def remove_from_contact_list(self, contacts_to_remove_list):\n if self.contact_list is None:\n return\n for id in contacts_to_remove_list:\n if id in range(0, len(self.contact_list) + 1):\n self.contact_list[id - 1] = None\n self.contact_list = [contact for contact in self.contact_list if contact is not None]","def add_contact(self, name, number, email, zipcode):\n \n new_contact = f\"{name}, {number}, {email}, {zipcode}\"\n contact_list = [name,number,email,zipcode]\n self.contacts.append(contact_list)\n self.save()\n print(f\"Thank you {new_contact} has been added to your contact book.\")","def contact_email(self, contact_email):\n\n self._contact_email = contact_email","def contact_email(self, contact_email):\n\n self._contact_email = contact_email","def update_contacts(self, contacts):\n\n if contacts.time.size != 1:\n raise IndexError(\"Contacts should be from one frame only\")\n if contacts.channel.size != self.contacts.channel.size:\n self.new_contact_set(contacts)\n return # Prevent calling update_contacts recursively\n self.contacts = contacts\n contacts = np.array(contacts)\n\n for i, actor in enumerate(self.contacts_actors):\n # mapper = actors.GetNextActor().GetMapper()\n mapper = actor.GetMapper()\n self.contacts_actors[i].GetProperty().SetColor(self.contacts_color)\n self.contacts_actors[i].GetProperty().SetOpacity(self.contacts_opacity)\n source = vtkSphereSource()\n source.SetCenter(contacts[0:3, i])\n source.SetRadius(self.contacts_size)\n mapper.SetInputConnection(source.GetOutputPort())","def contact_information(self, contact_information: ContactInformation):\n\n self._contact_information = contact_information","def new_contact_set(self, contacts):\n if contacts.time.size != 1:\n raise IndexError(\"Contacts should be from one frame only\")\n self.contacts = contacts\n\n # Remove previous actors from the scene\n for actor in self.contacts_actors:\n self.parent_window.ren.RemoveActor(actor)\n self.contacts_actors = list()\n\n # Create the geometry of a point (the coordinate) points = vtk.vtkPoints()\n for i in range(contacts.channel.size):\n # Create a mapper\n mapper = vtkPolyDataMapper()\n\n # Create an actor\n self.contacts_actors.append(vtkActor())\n self.contacts_actors[i].SetMapper(mapper)\n\n self.parent_window.ren.AddActor(self.contacts_actors[i])\n\n # Update marker position\n self.update_contacts(self.contacts)","def test_create_contact_list(self):\n c1 = ContactFactory(company_id=self.company.id)\n data = {\n 'title': 'ContactList1',\n 'contact_ids': [c1.id],\n }\n\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\n \n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n self.authenticate_user()\n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n content = json.loads(response.content)\n self.assertEqual(content['title'], 'ContactList1')\n self.assertEqual(content['contacts'], [c1.id])\n self.assertNotEqual(content['company_id'], None)\n self.assertNotEqual(content['owner'], None)\n\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\n content = json.loads(response.content)\n self.assertEqual(self.contact_lists_count+1, len(content))","def billing_contact(self, billing_contact):\n\n self._billing_contact = billing_contact","def contact_number(self, contact_number):\n if contact_number is None:\n raise ValueError(\"Invalid value for `contact_number`, must not be `None`\") # noqa: E501\n\n self._contact_number = contact_number","def update_contacts(self):\n self.contacts = self.db.list_contacts()\n return self.list_contacts()","def contact_person(self, contact_person):\n\n self._contact_person = contact_person","def contact_person(self, contact_person):\n\n self._contact_person = contact_person","def list_contacts(self):\n return self.contacts","def set_contact_mechanisms(cls, records, name, value=None):\n Party = Pool().get('party.party')\n\n for record in records:\n Party.write([record.party], {'contact_mechanisms': value})","def cc_email_address(self, val: list):\n self._cc_recipients = []\n if val is not None:\n for item in val:\n if isinstance(item, EmailAddress):\n self._cc_recipients.append(item)","def contact_name(self, contact_name):\n\n self._contact_name = contact_name","def contact_name(self, contact_name):\n\n self._contact_name = contact_name","def tearDown(self):\n Contact.contact_list = []","def set_customer(self, customer_list):\n self.multiple_items_selection_from_kendo_dropdown(self.customer_dropdown_locator, customer_list)\n self.wait_for_ajax_spinner_load()","def __init__(self, first_name=\" \", last_name=\" \", phone_number=0, phone_number_type=\" \", contact_list=[]):\n self.first_name = first_name\n self.last_name = last_name\n self.phone_number = phone_number\n self.phone_number_type = phone_number_type\n self.valid_phone_number_types = [\"home\", \"office\", \"cell\"]\n self.contact_list = contact_list","def mailman_add(contact, listname=None, send_welcome_msg=None, admin_notify=None):\n mm, listname = _get_maillist(listname)\n print('mailman adding %s to %s' % (contact.email, listname), file=sys.stderr)\n if send_welcome_msg is None:\n send_welcome_msg = mm.send_welcome_msg\n\n userdesc = UserDesc()\n userdesc.fullname = contact.full_name\n userdesc.address = contact.email\n userdesc.digest = False\n\n if mm.isMember(contact.email):\n print(_('Already Subscribed: %s' % contact.email), file=sys.stderr)\n else:\n try:\n try:\n mm.Lock()\n mm.ApprovedAddMember(userdesc, send_welcome_msg, admin_notify)\n mm.Save()\n print(_('Subscribed: %(email)s') % { 'email' : contact.email }, file=sys.stderr)\n except Errors.MMAlreadyAMember:\n print(_('Already a member: %(email)s') % { 'email' : contact.email }, file=sys.stderr)\n except Errors.MMBadEmailError:\n if userdesc.address == '':\n print(_('Bad/Invalid email address: blank line'), file=sys.stderr)\n else:\n print(_('Bad/Invalid email address: %(email)s') % { 'email' : contact.email }, file=sys.stderr)\n except Errors.MMHostileAddress:\n print(_('Hostile address (illegal characters): %(email)s') % { 'email' : contact.email }, file=sys.stderr)\n finally:\n mm.Unlock()","def contact_id(self, contact_id):\n if self.local_vars_configuration.client_side_validation and contact_id is None: # noqa: E501\n raise ValueError(\"Invalid value for `contact_id`, must not be `None`\") # noqa: E501\n\n self._contact_id = contact_id","def set_contacts_size(self, contacts_size):\n self.contacts_size = contacts_size\n self.update_contacts(self.contacts)","def block(self, report=False):\n prefix = \"28\" if isinstance(self, SkypeBotUser) else \"8\"\n self.skype.conn(\"PUT\", \"{0}/users/{1}/contacts/blocklist/{2}:{3}\"\n .format(SkypeConnection.API_CONTACTS, self.skype.userId, prefix, self.id),\n auth=SkypeConnection.Auth.SkypeToken, json={\"report_abuse\": report, \"ui_version\": \"skype.com\"})\n self.blocked = True","def test_save_multiple_contact(self):\n self.new_contact.save_contact()\n # new contact\n test_contact = Contact(\"Test\", \"user\", \"0798765432\", \"test@user.com\")\n test_contact.save_contact()\n self.assertEqual(len(Contact.contact_list), 2)","def send_email(subject, message, recipient_list, from_email=None,\n fail_silently=False, connection=None):\n if not from_email:\n from_email = _s('SERVER_EMAIL') or _s('DEFAULT_FROM_EMAIL')\n try:\n subj = unicode(subject)\n except UnicodeDecodeError:\n subj = subject.decode('utf8')\n datatuple = [(subj, message, from_email, [recipient],) \\\n for recipient in recipient_list]\n send_mass_mail(datatuple)","def setListDoc(self, list):\n if list is None: list__o = None\n else: list__o = list._o\n libxml2mod.xmlSetListDoc(list__o, self._o)","def set_raw_contact(self, value: Atoms):\n self._raw_contact = value","async def send_contact(self, chat_id: typing.Union[base.Integer, base.String],\n phone_number: base.String,\n first_name: base.String, last_name: typing.Union[base.String, None] = None,\n disable_notification: typing.Union[base.Boolean, None] = None,\n reply_to_message_id: typing.Union[base.Integer, None] = None,\n reply_markup: typing.Union[types.InlineKeyboardMarkup,\n types.ReplyKeyboardMarkup,\n types.ReplyKeyboardRemove,\n types.ForceReply, None] = None) -> types.Message:\n reply_markup = prepare_arg(reply_markup)\n payload = generate_payload(**locals())\n result = await self.request(api.Methods.SEND_CONTACT, payload)\n\n return types.Message(**result)","def set_contacts_color(self, contacts_color):\n self.contacts_color = contacts_color\n self.update_contacts(self.contacts)","def contact_method(self, contact_method):\n\n self._contact_method = contact_method","def add_contact(self):\n contact_list = {}\n contact_list[self.my_number] = self.name\n connect_db = Database()\n connect_db.add_contact(self.name, self.my_number)","def sequencing_contact(self, sequencing_contact):\n self.logger.debug(\"In 'sequencing_contact' setter.\")\n\n self._sequencing_contact = sequencing_contact","def cc_emails(self, cc_emails):\n\n self._cc_emails = cc_emails","def email_ml_set_can_send_on_behalf(self, maillist=None, maillist_uid=None, subscriber=None, subscriber_uid=None, can_send_on_behalf=None):\n if not maillist and not maillist_uid:\n raise ValueError('Maillist or uid required')\n if not subscriber and not subscriber_uid:\n raise ValueError('Subscriber or uid required')\n return self._request('email/ml/set_can_send_on_behalf', inspect_args_func(inspect.currentframe()))","def ip_addresses_list(self, ip_addresses_list):\n\n self._ip_addresses_list = ip_addresses_list","def ListAllContacts(self):\n feed = self.gd_client.GetContacts()\n self.contacts = self.CleanPhoneNumbers(self.GetContactsInfo(feed))\n return self.contacts","def test_save_multiple_contacts(self):\n self.new_contact.save_contact() # saving the new contact\n test_contact = Contact(\"Test\", \"User\", 254712345678, \"test@user.com\") # new user\n test_contact.save_contact() # saving the new contact\n self.assertEqual(len(Contact.contact_list), 2)","def email_process(recipient_list: List[Client]) -> None:\n\n if recipient_list:\n send_email(recipient_list)\n update_only_emailed_clients(recipient_list)\n remove_fully_contacted_clients()\n else:\n print(\"No emails were sent.\")","def block_contacts(self, contacts):\n self._post('contact_actions', None, self._build_params(contacts=contacts, action='block'))","def GetContactList(self):\n\t\tfeeds = []\n\t\tfeed = self.client.GetContacts()\n\t\tfeeds.append(feed)\n\t\tnext = feed.GetNextLink()\n\t\twhile next:\n\t\t\tfeed = self.client.GetContacts(uri=next.href)\n\t\t\tfeeds.append(feed)\n\t\t\tnext = feed.GetNextLink()\n\t\t\n\t\tcontacts = []\n\t\tfor feed in feeds:\n\t\t\tif not feed.entry:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tfor i, entry in enumerate(feed.entry):\n\t\t\t\t\tcontacts.append(entry)\n\t\treturn contacts","def resulting_contact(self, resulting_contact):\n \n self._resulting_contact = resulting_contact","def fetch_contact_messages(self, org, contact, created_after, created_before):\n pass","def setListPunctCmplx(self, list):\n\t\tself.list_punct_cmplx = list","def configure_tls_to_endpoint_in_sipserver(\n self,\n phone_list: List[Union[fxo_template.FXOTemplate, SIPPhoneTemplate]],\n ) -> None:","def add_contact_to_google_account(self, i):\n\n self.add_contact_to_phone(i)","def activities_list(self, new_activities_list):\n self._activities_list = new_activities_list\n self.__save_activities_from_memory_to_file()","def display_contact(self):\n contacts = \"\".join(str(contact) for contact in self.contact_list)\n print(contacts)","def update_device_list(self, device_list):\n self.device_list = device_list\n\n self.device_combo.clear()\n\n if not device_list:\n return\n\n self.device_combo.addItem(\"\")\n\n active_entry = None\n\n for dev in device_list:\n\n action_string = \"{model:<18} - {contype:<7} - {serial}\".format(model=dev.model,\n contype=dev.device_type,\n serial=dev.serial)\n if dev.serial == self.serial:\n active_entry = action_string\n self.device_combo.addItem(action_string)\n\n if active_entry is not None:\n self.device_combo.setCurrentText(active_entry)","def AddContact(self, contact):\n\t\tcontact.group_membership_info = [gdata.contacts.data.GroupMembershipInfo(href=self.GetFirstGroupId())]\n\t\ttry:\n\t\t\tself.client.CreateContact(contact)\n\t\texcept gdata.client.RequestError:\n\t\t\tpass","def email_ml_subscribers(self, maillist=None, maillist_uid=None):\n if not maillist and not maillist_uid:\n raise ValueError('Maillist or uid required')\n return self._request('email/ml/subscribers', inspect_args_func(inspect.currentframe()), method='get')","def contact_started(self, node, contact):\n for subscriber in self.subscribers:\n subscriber.contact_started(node, contact)","def bcc_email_address(self, val: list):\n self._bcc_recipients = []\n if val is not None:\n for item in val:\n if isinstance(item, EmailAddress):\n self._bcc_recipients.append(item)","def contacts(self):\n from hubspot3.contacts import ContactsClient\n\n return ContactsClient(**self.auth, **self.options)","async def set_contact_group_contacts(dbcon: DBConnection,\n contact_group_id: int, contact_ids: Iterable[int]) -> None:\n\n async def _run(cur: Cursor) -> None:\n q = \"\"\"delete from contact_group_contacts where contact_group_id=%s\"\"\"\n await cur.execute(q, (contact_group_id,))\n for contact_id in contact_ids:\n q = \"\"\"insert into contact_group_contacts (contact_group_id, contact_id) values (%s, %s)\"\"\"\n q_args = (contact_group_id, contact_id)\n await cur.execute(q, q_args)\n\n if not await contact_group_exists(dbcon, contact_group_id):\n raise errors.InvalidArguments('contact group does not exist')\n await dbcon.transact(_run)","def push_all(self, contacts):\n for ell in contacts:\n self.push(ell)","def new_soft_contacts_set(self, soft_contacts):\n if soft_contacts.time.size != 1:\n raise IndexError(\"soft_contacts should be from one frame only\")\n self.soft_contacts = soft_contacts\n\n # Remove previous actors from the scene\n for actor in self.soft_contacts_actors:\n self.parent_window.ren.RemoveActor(actor)\n self.soft_contacts_actors = list()\n\n # Create the geometry of a point (the coordinate) points = vtk.vtkPoints()\n for i in range(soft_contacts.channel.size):\n # Create a mapper\n mapper = vtkPolyDataMapper()\n\n # Create an actor\n self.soft_contacts_actors.append(vtkActor())\n self.soft_contacts_actors[i].SetMapper(mapper)\n\n self.parent_window.ren.AddActor(self.soft_contacts_actors[i])\n # Update marker position\n self.update_soft_contacts(self.soft_contacts)","def contact_list(request):\n if request.method == 'GET':\n contact = Contact.objects.all()\n serializer = ContactSerializer(contact, many=True)\n return Response(serializer.data)\n elif request.method == 'POST':\n serializer = ContactSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data,\n status=status.HTTP_201_CREATED)\n else:\n return Response(serializer.errors,\n status=status.HTTP_400_BAD_REQUEST)","def set_response_list(self, r_list):\n self.response_list = r_list","def set_coupled_pair_list(self, coupled_pair_list):\n self.coupled_pair_list = coupled_pair_list\n self.reg_coupled_pair = True","def get_contacts_list(self):\n return [(id + 1, contact) for id, contact in enumerate(self.contact_list)]","def test_delete_contact_list(self):\n contact_list = ContactList.objects.first()\n\n url, parsed = self.prepare_urls('v1:contact_list-detail', subdomain=self.company.subdomain, kwargs={'pk':contact_list.id})\n \n response = self.client.delete(url, HTTP_HOST=parsed.netloc)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n self.authenticate_user()\n response = self.client.delete(url, HTTP_HOST=parsed.netloc)\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n \n response = self.client.get(url, HTTP_HOST=parsed.netloc)\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\n content = json.loads(response.content)\n self.assertEqual(self.contact_lists_count-1, len(content))","def __init__(self, contacts_client):\n self.contacts_client = contacts_client","def contacts(self):\n return ContactCollection(self.request)","def send(self, from_email, to_list, cc_list, bcc_list, subject, text):\n\n to_address_list = []\n\n if len(to_list) > 0:\n for to_address in to_list:\n to_address_list.append(\n {\n \"email\": to_address,\n \"type\": \"to\"\n }\n )\n\n if len(cc_list) > 0:\n for cc_address in cc_list:\n to_address_list.append(\n {\n \"email\": cc_address,\n \"type\": \"cc\"\n }\n )\n\n if len(bcc_list) > 0:\n for bcc_address in bcc_list:\n to_address_list.append(\n {\n \"email\": bcc_address,\n \"type\": \"bcc\"\n }\n )\n\n sendgrid_data = {\n \"key\": sendgrid_key,\n \"message\": {\n \"text\": text,\n \"subject\": subject,\n \"from_email\": from_email,\n \"to\": to_address_list\n },\n \"async\": False,\n }\n\n response = requests.post(\n sendgrid_url,\n data=json.dumps(sendgrid_data)\n )\n\n if response.ok:\n status = 0\n else:\n status = 1\n\n message = str(response.content)\n\n return status, message","def appendedEntries(self):\n self.contact_list.append({\"name\": self.first_name.title() + \" \" + self.last_name.title(), \"phone number\": self.phone_number, \"phone number type\": self.phone_number_type})","def archive_contact_messages(self, org, contact):\n pass","def fields_in_list(self, fields_in_list):\n\n self._fields_in_list = fields_in_list","def fields_in_list(self, fields_in_list):\n\n self._fields_in_list = fields_in_list","def add_contacts(self, contacts, group=None, group_uuid=None):\n payload = self._build_params(contacts=contacts, action='add', group=group, group_uuid=group_uuid)\n self._post('contact_actions', None, payload)","def __init__(self, contact_loader):\n self.contacts_by_group_list = contact_loader.contacts_by_group_list\n self.contact_list = None","def update_only_emailed_clients(recipient_list, file=\"db.json\") -> None:\n\n with TinyDB(file) as db:\n for client in recipient_list:\n query = Query()\n db.update(add(\"times contacted\", 1), (query[\"first name\"].matches(client.get_first_name())\n & (query[\"last name\"].matches(client.get_last_name()))))\n db.update(set_val(\"rem date\", datetime_to_string(default_rem_date)),\n (query[\"first name\"].matches(client.get_first_name())\n & (query[\"last name\"].matches(client.get_last_name())\n )))"],"string":"[\n \"def receiveContactList(self, contactList):\",\n \"def set_contacts(self, contacts):\\n\\n\\t\\tif contacts is not None and not isinstance(contacts, list):\\n\\t\\t\\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: contacts EXPECTED TYPE: list', None, None)\\n\\t\\t\\n\\t\\tself.__contacts = contacts\\n\\t\\tself.__key_modified['Contacts'] = 1\",\n \"def contacts(self, contacts):\\n\\n self._contacts = contacts\",\n \"def contacts(self, contacts):\\n\\n self._contacts = contacts\",\n \"def contact_list(self):\\n return self._contact_list\",\n \"def update_contacts(self, contact_list):\\n updated_contacts = 0\\n request_list = list()\\n\\n # stale_contacts contains all old contacts at first, all current\\n # contacts get then removed so that the remaining can get deleted\\n stale_contacts = set(self.contacts)\\n\\n for contact in contact_list:\\n c = Persona.query.get(contact[\\\"id\\\"])\\n\\n if c is None:\\n c = Persona(id=contact[\\\"id\\\"], _stub=True)\\n\\n if c._stub is True:\\n request_list.append(contact[\\\"id\\\"])\\n\\n try:\\n # Old and new contact; remove from stale list\\n stale_contacts.remove(c)\\n except KeyError:\\n # New contact\\n self.contacts.append(c)\\n updated_contacts += 1\\n\\n # Remove old contacts that are not new contacts\\n for contact in stale_contacts:\\n self.contacts.remove(contact)\\n\\n app.logger.info(\\\"Updated {}'s contacts: {} added, {} removed, {} requested\\\".format(\\n self.username, updated_contacts, len(stale_contacts), len(request_list)))\\n\\n return request_list\",\n \"def contact(self, contact):\\n\\n self.logger.debug(\\\"In 'contact' setter.\\\")\\n\\n self._contact = contact\",\n \"async def set_contacts(self, contacts: List[CertificateContact], **kwargs) -> List[CertificateContact]:\\n new_contacts = await self._client.set_certificate_contacts(\\n vault_base_url=self.vault_url,\\n contacts=self._models.Contacts(contact_list=[c._to_certificate_contacts_item() for c in contacts]),\\n **kwargs\\n )\\n return [\\n CertificateContact._from_certificate_contacts_item(contact_item=item) for item in new_contacts.contact_list\\n ]\",\n \"def contact_points(self, contact_points: object):\\n\\n self._contact_points = contact_points\",\n \"def contact(self, contact):\\n\\n self._contact = contact\",\n \"def contact(self, contact):\\n\\n self._contact = contact\",\n \"def support_contacts(self, support_contacts):\\n self._support_contacts = support_contacts\",\n \"def contact_lists(self):\\n from hubspot3.contact_lists import ContactListsClient\\n\\n return ContactListsClient(**self.auth, **self.options)\",\n \"def contacts_list_update(self):\\n\\t\\tself.database.contacts_clear()\\n\\t\\tclient_log.debug(f'Запрос контакт листа для пользователся {self.name}')\\n\\t\\treq = {\\n\\t\\t\\tACTION: GET_CONTACTS,\\n\\t\\t\\tTIME: time.time(),\\n\\t\\t\\tUSER: self.username\\n\\t\\t}\\n\\t\\tclient_log.debug(f'Сформирован запрос {req}')\\n\\t\\twith socket_lock:\\n\\t\\t\\tsend_message(self.transport, req)\\n\\t\\t\\tans = get_message(self.transport)\\n\\t\\tclient_log.debug(f'Получен ответ {ans}')\\n\\t\\tif RESPONSE in ans and ans[RESPONSE] == 202:\\n\\t\\t\\tfor contact in ans[LIST_INFO]:\\n\\t\\t\\t\\tself.database.add_contact(contact)\\n\\t\\telse:\\n\\t\\t\\tclient_log.error('Не удалось обновить список контактов.')\",\n \"def send_mass_messages(self, recipient_list, sender, message=\\\"\\\", subject=\\\"\\\"):\\n try:\\n for s in recipient_list:\\n self.send_message(to=s, sender=sender, message=message, subject=subject)\\n except TypeError:\\n return -1\\n return 1\",\n \"def list(self, list):\\n if list is None:\\n raise ValueError(\\\"Invalid value for `list`, must not be `None`\\\") # noqa: E501\\n\\n self._list = list\",\n \"def do_send_list( self, a_list ):\\r\\n # --- this needs to be moved to task some set up here then on there\\r\\n self.logger.info( \\\"turn on sendList\\\" )\\r\\n self.send_list_ix = 0\\r\\n\\r\\n #self.send_list = [ 180, 920, 160, 1740, 160, 780, 160, 2840, 160, 1320, 160, 1340, 160, ] # 1180, 160, 2700, 160, 12780, 200, 920, \\\\\\r\\n #160, 2680, 160, 780, 160, 800, 160, 780, 160, 920, 160, 800, 140, 800, \\\\\\r\\n # 160 ]\\r\\n self.send_list = a_list\\r\\n self.com_driver.send( \\\"z\\\\n\\\" )\\r\\n self.list_send = True # if we were mult-threaded this would have to be here\\r\\n\\r\\n return\",\n \"def contact_point(self, contact_point: object):\\n\\n self._contact_point = contact_point\",\n \"def contact_reference(self, contact_reference):\\n\\n self._contact_reference = contact_reference\",\n \"def list_contact(self, key, value):\\n self.db.list_contact(\\n key,\\n value,\\n )\",\n \"def target_contact(self, target_contact):\\n \\n self._target_contact = target_contact\",\n \"def contactListClicked(self):\\n \\n contacts = self.userList.getSelectedItems()\\n self.mergeButton.setEnabled(contacts != None and len(contacts) > 1)\\n \\n if contacts != None and len(contacts) == 1:\\n self.messageList.filterByContact(contacts[0])\\n else:\\n self.messageList.removeFilter()\",\n \"def _create_mailing_list(cls):\\n cls.mailing_list_1 = cls.env['mailing.list'].with_context(cls._test_context).create({\\n 'name': 'List1',\\n 'contact_ids': [\\n (0, 0, {'name': 'Déboulonneur', 'email': 'fleurus@example.com'}),\\n (0, 0, {'name': 'Gorramts', 'email': 'gorramts@example.com'}),\\n (0, 0, {'name': 'Ybrant', 'email': 'ybrant@example.com'}),\\n ]\\n })\\n cls.mailing_list_2 = cls.env['mailing.list'].with_context(cls._test_context).create({\\n 'name': 'List2',\\n 'contact_ids': [\\n (0, 0, {'name': 'Gilberte', 'email': 'gilberte@example.com'}),\\n (0, 0, {'name': 'Gilberte En Mieux', 'email': 'gilberte@example.com'}),\\n (0, 0, {'name': 'Norbert', 'email': 'norbert@example.com'}),\\n (0, 0, {'name': 'Ybrant', 'email': 'ybrant@example.com'}),\\n ]\\n })\",\n \"def member_list(self, member_list):\\n\\n self._member_list = member_list\",\n \"def contact_info(self, contact_info):\\n\\n self._contact_info = contact_info\",\n \"def SetDomainsList(self, domainsList) :\\n\\t\\t...\",\n \"def source_contact(self, source_contact):\\n \\n self._source_contact = source_contact\",\n \"def remove_from_contact_list(self, contacts_to_remove_list):\\n if self.contact_list is None:\\n return\\n for id in contacts_to_remove_list:\\n if id in range(0, len(self.contact_list) + 1):\\n self.contact_list[id - 1] = None\\n self.contact_list = [contact for contact in self.contact_list if contact is not None]\",\n \"def add_contact(self, name, number, email, zipcode):\\n \\n new_contact = f\\\"{name}, {number}, {email}, {zipcode}\\\"\\n contact_list = [name,number,email,zipcode]\\n self.contacts.append(contact_list)\\n self.save()\\n print(f\\\"Thank you {new_contact} has been added to your contact book.\\\")\",\n \"def contact_email(self, contact_email):\\n\\n self._contact_email = contact_email\",\n \"def contact_email(self, contact_email):\\n\\n self._contact_email = contact_email\",\n \"def update_contacts(self, contacts):\\n\\n if contacts.time.size != 1:\\n raise IndexError(\\\"Contacts should be from one frame only\\\")\\n if contacts.channel.size != self.contacts.channel.size:\\n self.new_contact_set(contacts)\\n return # Prevent calling update_contacts recursively\\n self.contacts = contacts\\n contacts = np.array(contacts)\\n\\n for i, actor in enumerate(self.contacts_actors):\\n # mapper = actors.GetNextActor().GetMapper()\\n mapper = actor.GetMapper()\\n self.contacts_actors[i].GetProperty().SetColor(self.contacts_color)\\n self.contacts_actors[i].GetProperty().SetOpacity(self.contacts_opacity)\\n source = vtkSphereSource()\\n source.SetCenter(contacts[0:3, i])\\n source.SetRadius(self.contacts_size)\\n mapper.SetInputConnection(source.GetOutputPort())\",\n \"def contact_information(self, contact_information: ContactInformation):\\n\\n self._contact_information = contact_information\",\n \"def new_contact_set(self, contacts):\\n if contacts.time.size != 1:\\n raise IndexError(\\\"Contacts should be from one frame only\\\")\\n self.contacts = contacts\\n\\n # Remove previous actors from the scene\\n for actor in self.contacts_actors:\\n self.parent_window.ren.RemoveActor(actor)\\n self.contacts_actors = list()\\n\\n # Create the geometry of a point (the coordinate) points = vtk.vtkPoints()\\n for i in range(contacts.channel.size):\\n # Create a mapper\\n mapper = vtkPolyDataMapper()\\n\\n # Create an actor\\n self.contacts_actors.append(vtkActor())\\n self.contacts_actors[i].SetMapper(mapper)\\n\\n self.parent_window.ren.AddActor(self.contacts_actors[i])\\n\\n # Update marker position\\n self.update_contacts(self.contacts)\",\n \"def test_create_contact_list(self):\\n c1 = ContactFactory(company_id=self.company.id)\\n data = {\\n 'title': 'ContactList1',\\n 'contact_ids': [c1.id],\\n }\\n\\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\\n \\n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n self.authenticate_user()\\n response = self.client.post(url, data, HTTP_HOST=parsed.netloc, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n content = json.loads(response.content)\\n self.assertEqual(content['title'], 'ContactList1')\\n self.assertEqual(content['contacts'], [c1.id])\\n self.assertNotEqual(content['company_id'], None)\\n self.assertNotEqual(content['owner'], None)\\n\\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\\n content = json.loads(response.content)\\n self.assertEqual(self.contact_lists_count+1, len(content))\",\n \"def billing_contact(self, billing_contact):\\n\\n self._billing_contact = billing_contact\",\n \"def contact_number(self, contact_number):\\n if contact_number is None:\\n raise ValueError(\\\"Invalid value for `contact_number`, must not be `None`\\\") # noqa: E501\\n\\n self._contact_number = contact_number\",\n \"def update_contacts(self):\\n self.contacts = self.db.list_contacts()\\n return self.list_contacts()\",\n \"def contact_person(self, contact_person):\\n\\n self._contact_person = contact_person\",\n \"def contact_person(self, contact_person):\\n\\n self._contact_person = contact_person\",\n \"def list_contacts(self):\\n return self.contacts\",\n \"def set_contact_mechanisms(cls, records, name, value=None):\\n Party = Pool().get('party.party')\\n\\n for record in records:\\n Party.write([record.party], {'contact_mechanisms': value})\",\n \"def cc_email_address(self, val: list):\\n self._cc_recipients = []\\n if val is not None:\\n for item in val:\\n if isinstance(item, EmailAddress):\\n self._cc_recipients.append(item)\",\n \"def contact_name(self, contact_name):\\n\\n self._contact_name = contact_name\",\n \"def contact_name(self, contact_name):\\n\\n self._contact_name = contact_name\",\n \"def tearDown(self):\\n Contact.contact_list = []\",\n \"def set_customer(self, customer_list):\\n self.multiple_items_selection_from_kendo_dropdown(self.customer_dropdown_locator, customer_list)\\n self.wait_for_ajax_spinner_load()\",\n \"def __init__(self, first_name=\\\" \\\", last_name=\\\" \\\", phone_number=0, phone_number_type=\\\" \\\", contact_list=[]):\\n self.first_name = first_name\\n self.last_name = last_name\\n self.phone_number = phone_number\\n self.phone_number_type = phone_number_type\\n self.valid_phone_number_types = [\\\"home\\\", \\\"office\\\", \\\"cell\\\"]\\n self.contact_list = contact_list\",\n \"def mailman_add(contact, listname=None, send_welcome_msg=None, admin_notify=None):\\n mm, listname = _get_maillist(listname)\\n print('mailman adding %s to %s' % (contact.email, listname), file=sys.stderr)\\n if send_welcome_msg is None:\\n send_welcome_msg = mm.send_welcome_msg\\n\\n userdesc = UserDesc()\\n userdesc.fullname = contact.full_name\\n userdesc.address = contact.email\\n userdesc.digest = False\\n\\n if mm.isMember(contact.email):\\n print(_('Already Subscribed: %s' % contact.email), file=sys.stderr)\\n else:\\n try:\\n try:\\n mm.Lock()\\n mm.ApprovedAddMember(userdesc, send_welcome_msg, admin_notify)\\n mm.Save()\\n print(_('Subscribed: %(email)s') % { 'email' : contact.email }, file=sys.stderr)\\n except Errors.MMAlreadyAMember:\\n print(_('Already a member: %(email)s') % { 'email' : contact.email }, file=sys.stderr)\\n except Errors.MMBadEmailError:\\n if userdesc.address == '':\\n print(_('Bad/Invalid email address: blank line'), file=sys.stderr)\\n else:\\n print(_('Bad/Invalid email address: %(email)s') % { 'email' : contact.email }, file=sys.stderr)\\n except Errors.MMHostileAddress:\\n print(_('Hostile address (illegal characters): %(email)s') % { 'email' : contact.email }, file=sys.stderr)\\n finally:\\n mm.Unlock()\",\n \"def contact_id(self, contact_id):\\n if self.local_vars_configuration.client_side_validation and contact_id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `contact_id`, must not be `None`\\\") # noqa: E501\\n\\n self._contact_id = contact_id\",\n \"def set_contacts_size(self, contacts_size):\\n self.contacts_size = contacts_size\\n self.update_contacts(self.contacts)\",\n \"def block(self, report=False):\\n prefix = \\\"28\\\" if isinstance(self, SkypeBotUser) else \\\"8\\\"\\n self.skype.conn(\\\"PUT\\\", \\\"{0}/users/{1}/contacts/blocklist/{2}:{3}\\\"\\n .format(SkypeConnection.API_CONTACTS, self.skype.userId, prefix, self.id),\\n auth=SkypeConnection.Auth.SkypeToken, json={\\\"report_abuse\\\": report, \\\"ui_version\\\": \\\"skype.com\\\"})\\n self.blocked = True\",\n \"def test_save_multiple_contact(self):\\n self.new_contact.save_contact()\\n # new contact\\n test_contact = Contact(\\\"Test\\\", \\\"user\\\", \\\"0798765432\\\", \\\"test@user.com\\\")\\n test_contact.save_contact()\\n self.assertEqual(len(Contact.contact_list), 2)\",\n \"def send_email(subject, message, recipient_list, from_email=None,\\n fail_silently=False, connection=None):\\n if not from_email:\\n from_email = _s('SERVER_EMAIL') or _s('DEFAULT_FROM_EMAIL')\\n try:\\n subj = unicode(subject)\\n except UnicodeDecodeError:\\n subj = subject.decode('utf8')\\n datatuple = [(subj, message, from_email, [recipient],) \\\\\\n for recipient in recipient_list]\\n send_mass_mail(datatuple)\",\n \"def setListDoc(self, list):\\n if list is None: list__o = None\\n else: list__o = list._o\\n libxml2mod.xmlSetListDoc(list__o, self._o)\",\n \"def set_raw_contact(self, value: Atoms):\\n self._raw_contact = value\",\n \"async def send_contact(self, chat_id: typing.Union[base.Integer, base.String],\\n phone_number: base.String,\\n first_name: base.String, last_name: typing.Union[base.String, None] = None,\\n disable_notification: typing.Union[base.Boolean, None] = None,\\n reply_to_message_id: typing.Union[base.Integer, None] = None,\\n reply_markup: typing.Union[types.InlineKeyboardMarkup,\\n types.ReplyKeyboardMarkup,\\n types.ReplyKeyboardRemove,\\n types.ForceReply, None] = None) -> types.Message:\\n reply_markup = prepare_arg(reply_markup)\\n payload = generate_payload(**locals())\\n result = await self.request(api.Methods.SEND_CONTACT, payload)\\n\\n return types.Message(**result)\",\n \"def set_contacts_color(self, contacts_color):\\n self.contacts_color = contacts_color\\n self.update_contacts(self.contacts)\",\n \"def contact_method(self, contact_method):\\n\\n self._contact_method = contact_method\",\n \"def add_contact(self):\\n contact_list = {}\\n contact_list[self.my_number] = self.name\\n connect_db = Database()\\n connect_db.add_contact(self.name, self.my_number)\",\n \"def sequencing_contact(self, sequencing_contact):\\n self.logger.debug(\\\"In 'sequencing_contact' setter.\\\")\\n\\n self._sequencing_contact = sequencing_contact\",\n \"def cc_emails(self, cc_emails):\\n\\n self._cc_emails = cc_emails\",\n \"def email_ml_set_can_send_on_behalf(self, maillist=None, maillist_uid=None, subscriber=None, subscriber_uid=None, can_send_on_behalf=None):\\n if not maillist and not maillist_uid:\\n raise ValueError('Maillist or uid required')\\n if not subscriber and not subscriber_uid:\\n raise ValueError('Subscriber or uid required')\\n return self._request('email/ml/set_can_send_on_behalf', inspect_args_func(inspect.currentframe()))\",\n \"def ip_addresses_list(self, ip_addresses_list):\\n\\n self._ip_addresses_list = ip_addresses_list\",\n \"def ListAllContacts(self):\\n feed = self.gd_client.GetContacts()\\n self.contacts = self.CleanPhoneNumbers(self.GetContactsInfo(feed))\\n return self.contacts\",\n \"def test_save_multiple_contacts(self):\\n self.new_contact.save_contact() # saving the new contact\\n test_contact = Contact(\\\"Test\\\", \\\"User\\\", 254712345678, \\\"test@user.com\\\") # new user\\n test_contact.save_contact() # saving the new contact\\n self.assertEqual(len(Contact.contact_list), 2)\",\n \"def email_process(recipient_list: List[Client]) -> None:\\n\\n if recipient_list:\\n send_email(recipient_list)\\n update_only_emailed_clients(recipient_list)\\n remove_fully_contacted_clients()\\n else:\\n print(\\\"No emails were sent.\\\")\",\n \"def block_contacts(self, contacts):\\n self._post('contact_actions', None, self._build_params(contacts=contacts, action='block'))\",\n \"def GetContactList(self):\\n\\t\\tfeeds = []\\n\\t\\tfeed = self.client.GetContacts()\\n\\t\\tfeeds.append(feed)\\n\\t\\tnext = feed.GetNextLink()\\n\\t\\twhile next:\\n\\t\\t\\tfeed = self.client.GetContacts(uri=next.href)\\n\\t\\t\\tfeeds.append(feed)\\n\\t\\t\\tnext = feed.GetNextLink()\\n\\t\\t\\n\\t\\tcontacts = []\\n\\t\\tfor feed in feeds:\\n\\t\\t\\tif not feed.entry:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor i, entry in enumerate(feed.entry):\\n\\t\\t\\t\\t\\tcontacts.append(entry)\\n\\t\\treturn contacts\",\n \"def resulting_contact(self, resulting_contact):\\n \\n self._resulting_contact = resulting_contact\",\n \"def fetch_contact_messages(self, org, contact, created_after, created_before):\\n pass\",\n \"def setListPunctCmplx(self, list):\\n\\t\\tself.list_punct_cmplx = list\",\n \"def configure_tls_to_endpoint_in_sipserver(\\n self,\\n phone_list: List[Union[fxo_template.FXOTemplate, SIPPhoneTemplate]],\\n ) -> None:\",\n \"def add_contact_to_google_account(self, i):\\n\\n self.add_contact_to_phone(i)\",\n \"def activities_list(self, new_activities_list):\\n self._activities_list = new_activities_list\\n self.__save_activities_from_memory_to_file()\",\n \"def display_contact(self):\\n contacts = \\\"\\\".join(str(contact) for contact in self.contact_list)\\n print(contacts)\",\n \"def update_device_list(self, device_list):\\n self.device_list = device_list\\n\\n self.device_combo.clear()\\n\\n if not device_list:\\n return\\n\\n self.device_combo.addItem(\\\"\\\")\\n\\n active_entry = None\\n\\n for dev in device_list:\\n\\n action_string = \\\"{model:<18} - {contype:<7} - {serial}\\\".format(model=dev.model,\\n contype=dev.device_type,\\n serial=dev.serial)\\n if dev.serial == self.serial:\\n active_entry = action_string\\n self.device_combo.addItem(action_string)\\n\\n if active_entry is not None:\\n self.device_combo.setCurrentText(active_entry)\",\n \"def AddContact(self, contact):\\n\\t\\tcontact.group_membership_info = [gdata.contacts.data.GroupMembershipInfo(href=self.GetFirstGroupId())]\\n\\t\\ttry:\\n\\t\\t\\tself.client.CreateContact(contact)\\n\\t\\texcept gdata.client.RequestError:\\n\\t\\t\\tpass\",\n \"def email_ml_subscribers(self, maillist=None, maillist_uid=None):\\n if not maillist and not maillist_uid:\\n raise ValueError('Maillist or uid required')\\n return self._request('email/ml/subscribers', inspect_args_func(inspect.currentframe()), method='get')\",\n \"def contact_started(self, node, contact):\\n for subscriber in self.subscribers:\\n subscriber.contact_started(node, contact)\",\n \"def bcc_email_address(self, val: list):\\n self._bcc_recipients = []\\n if val is not None:\\n for item in val:\\n if isinstance(item, EmailAddress):\\n self._bcc_recipients.append(item)\",\n \"def contacts(self):\\n from hubspot3.contacts import ContactsClient\\n\\n return ContactsClient(**self.auth, **self.options)\",\n \"async def set_contact_group_contacts(dbcon: DBConnection,\\n contact_group_id: int, contact_ids: Iterable[int]) -> None:\\n\\n async def _run(cur: Cursor) -> None:\\n q = \\\"\\\"\\\"delete from contact_group_contacts where contact_group_id=%s\\\"\\\"\\\"\\n await cur.execute(q, (contact_group_id,))\\n for contact_id in contact_ids:\\n q = \\\"\\\"\\\"insert into contact_group_contacts (contact_group_id, contact_id) values (%s, %s)\\\"\\\"\\\"\\n q_args = (contact_group_id, contact_id)\\n await cur.execute(q, q_args)\\n\\n if not await contact_group_exists(dbcon, contact_group_id):\\n raise errors.InvalidArguments('contact group does not exist')\\n await dbcon.transact(_run)\",\n \"def push_all(self, contacts):\\n for ell in contacts:\\n self.push(ell)\",\n \"def new_soft_contacts_set(self, soft_contacts):\\n if soft_contacts.time.size != 1:\\n raise IndexError(\\\"soft_contacts should be from one frame only\\\")\\n self.soft_contacts = soft_contacts\\n\\n # Remove previous actors from the scene\\n for actor in self.soft_contacts_actors:\\n self.parent_window.ren.RemoveActor(actor)\\n self.soft_contacts_actors = list()\\n\\n # Create the geometry of a point (the coordinate) points = vtk.vtkPoints()\\n for i in range(soft_contacts.channel.size):\\n # Create a mapper\\n mapper = vtkPolyDataMapper()\\n\\n # Create an actor\\n self.soft_contacts_actors.append(vtkActor())\\n self.soft_contacts_actors[i].SetMapper(mapper)\\n\\n self.parent_window.ren.AddActor(self.soft_contacts_actors[i])\\n # Update marker position\\n self.update_soft_contacts(self.soft_contacts)\",\n \"def contact_list(request):\\n if request.method == 'GET':\\n contact = Contact.objects.all()\\n serializer = ContactSerializer(contact, many=True)\\n return Response(serializer.data)\\n elif request.method == 'POST':\\n serializer = ContactSerializer(data=request.data)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data,\\n status=status.HTTP_201_CREATED)\\n else:\\n return Response(serializer.errors,\\n status=status.HTTP_400_BAD_REQUEST)\",\n \"def set_response_list(self, r_list):\\n self.response_list = r_list\",\n \"def set_coupled_pair_list(self, coupled_pair_list):\\n self.coupled_pair_list = coupled_pair_list\\n self.reg_coupled_pair = True\",\n \"def get_contacts_list(self):\\n return [(id + 1, contact) for id, contact in enumerate(self.contact_list)]\",\n \"def test_delete_contact_list(self):\\n contact_list = ContactList.objects.first()\\n\\n url, parsed = self.prepare_urls('v1:contact_list-detail', subdomain=self.company.subdomain, kwargs={'pk':contact_list.id})\\n \\n response = self.client.delete(url, HTTP_HOST=parsed.netloc)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n self.authenticate_user()\\n response = self.client.delete(url, HTTP_HOST=parsed.netloc)\\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\\n \\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\\n\\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\\n content = json.loads(response.content)\\n self.assertEqual(self.contact_lists_count-1, len(content))\",\n \"def __init__(self, contacts_client):\\n self.contacts_client = contacts_client\",\n \"def contacts(self):\\n return ContactCollection(self.request)\",\n \"def send(self, from_email, to_list, cc_list, bcc_list, subject, text):\\n\\n to_address_list = []\\n\\n if len(to_list) > 0:\\n for to_address in to_list:\\n to_address_list.append(\\n {\\n \\\"email\\\": to_address,\\n \\\"type\\\": \\\"to\\\"\\n }\\n )\\n\\n if len(cc_list) > 0:\\n for cc_address in cc_list:\\n to_address_list.append(\\n {\\n \\\"email\\\": cc_address,\\n \\\"type\\\": \\\"cc\\\"\\n }\\n )\\n\\n if len(bcc_list) > 0:\\n for bcc_address in bcc_list:\\n to_address_list.append(\\n {\\n \\\"email\\\": bcc_address,\\n \\\"type\\\": \\\"bcc\\\"\\n }\\n )\\n\\n sendgrid_data = {\\n \\\"key\\\": sendgrid_key,\\n \\\"message\\\": {\\n \\\"text\\\": text,\\n \\\"subject\\\": subject,\\n \\\"from_email\\\": from_email,\\n \\\"to\\\": to_address_list\\n },\\n \\\"async\\\": False,\\n }\\n\\n response = requests.post(\\n sendgrid_url,\\n data=json.dumps(sendgrid_data)\\n )\\n\\n if response.ok:\\n status = 0\\n else:\\n status = 1\\n\\n message = str(response.content)\\n\\n return status, message\",\n \"def appendedEntries(self):\\n self.contact_list.append({\\\"name\\\": self.first_name.title() + \\\" \\\" + self.last_name.title(), \\\"phone number\\\": self.phone_number, \\\"phone number type\\\": self.phone_number_type})\",\n \"def archive_contact_messages(self, org, contact):\\n pass\",\n \"def fields_in_list(self, fields_in_list):\\n\\n self._fields_in_list = fields_in_list\",\n \"def fields_in_list(self, fields_in_list):\\n\\n self._fields_in_list = fields_in_list\",\n \"def add_contacts(self, contacts, group=None, group_uuid=None):\\n payload = self._build_params(contacts=contacts, action='add', group=group, group_uuid=group_uuid)\\n self._post('contact_actions', None, payload)\",\n \"def __init__(self, contact_loader):\\n self.contacts_by_group_list = contact_loader.contacts_by_group_list\\n self.contact_list = None\",\n \"def update_only_emailed_clients(recipient_list, file=\\\"db.json\\\") -> None:\\n\\n with TinyDB(file) as db:\\n for client in recipient_list:\\n query = Query()\\n db.update(add(\\\"times contacted\\\", 1), (query[\\\"first name\\\"].matches(client.get_first_name())\\n & (query[\\\"last name\\\"].matches(client.get_last_name()))))\\n db.update(set_val(\\\"rem date\\\", datetime_to_string(default_rem_date)),\\n (query[\\\"first name\\\"].matches(client.get_first_name())\\n & (query[\\\"last name\\\"].matches(client.get_last_name())\\n )))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6209503","0.6202445","0.588594","0.588594","0.5883504","0.5845821","0.5795876","0.5775629","0.55796677","0.5562263","0.5562263","0.5559013","0.5479643","0.5460591","0.53779536","0.5320598","0.53073066","0.5305575","0.52854943","0.5274175","0.5192591","0.5121968","0.5113486","0.50693697","0.5029035","0.5010891","0.50096","0.5007492","0.49756786","0.49653065","0.49653065","0.4957423","0.49395514","0.4926072","0.49036422","0.48886657","0.48883614","0.4827787","0.48177895","0.48177895","0.47843698","0.47793344","0.47782195","0.4772784","0.4772784","0.47690243","0.47679976","0.47509006","0.47490525","0.4745818","0.47420013","0.4738187","0.47350198","0.4732864","0.47066173","0.4680452","0.46777913","0.46690828","0.46402803","0.46320206","0.46003407","0.45983198","0.45887333","0.45852426","0.4579518","0.45610544","0.45582846","0.455033","0.452969","0.45121688","0.45078608","0.45051056","0.45047635","0.44964176","0.4493843","0.44880277","0.4487089","0.44746184","0.4472952","0.44660527","0.4462495","0.44624025","0.44611588","0.4439524","0.44338965","0.44322392","0.44172618","0.44003206","0.43955433","0.43901408","0.4382922","0.4382315","0.4381088","0.43688384","0.43652815","0.43610385","0.43610385","0.43607998","0.43484876","0.4348318"],"string":"[\n \"0.6209503\",\n \"0.6202445\",\n \"0.588594\",\n \"0.588594\",\n \"0.5883504\",\n \"0.5845821\",\n \"0.5795876\",\n \"0.5775629\",\n \"0.55796677\",\n \"0.5562263\",\n \"0.5562263\",\n \"0.5559013\",\n \"0.5479643\",\n \"0.5460591\",\n \"0.53779536\",\n \"0.5320598\",\n \"0.53073066\",\n \"0.5305575\",\n \"0.52854943\",\n \"0.5274175\",\n \"0.5192591\",\n \"0.5121968\",\n \"0.5113486\",\n \"0.50693697\",\n \"0.5029035\",\n \"0.5010891\",\n \"0.50096\",\n \"0.5007492\",\n \"0.49756786\",\n \"0.49653065\",\n \"0.49653065\",\n \"0.4957423\",\n \"0.49395514\",\n \"0.4926072\",\n \"0.49036422\",\n \"0.48886657\",\n \"0.48883614\",\n \"0.4827787\",\n \"0.48177895\",\n \"0.48177895\",\n \"0.47843698\",\n \"0.47793344\",\n \"0.47782195\",\n \"0.4772784\",\n \"0.4772784\",\n \"0.47690243\",\n \"0.47679976\",\n \"0.47509006\",\n \"0.47490525\",\n \"0.4745818\",\n \"0.47420013\",\n \"0.4738187\",\n \"0.47350198\",\n \"0.4732864\",\n \"0.47066173\",\n \"0.4680452\",\n \"0.46777913\",\n \"0.46690828\",\n \"0.46402803\",\n \"0.46320206\",\n \"0.46003407\",\n \"0.45983198\",\n \"0.45887333\",\n \"0.45852426\",\n \"0.4579518\",\n \"0.45610544\",\n \"0.45582846\",\n \"0.455033\",\n \"0.452969\",\n \"0.45121688\",\n \"0.45078608\",\n \"0.45051056\",\n \"0.45047635\",\n \"0.44964176\",\n \"0.4493843\",\n \"0.44880277\",\n \"0.4487089\",\n \"0.44746184\",\n \"0.4472952\",\n \"0.44660527\",\n \"0.4462495\",\n \"0.44624025\",\n \"0.44611588\",\n \"0.4439524\",\n \"0.44338965\",\n \"0.44322392\",\n \"0.44172618\",\n \"0.44003206\",\n \"0.43955433\",\n \"0.43901408\",\n \"0.4382922\",\n \"0.4382315\",\n \"0.4381088\",\n \"0.43688384\",\n \"0.43652815\",\n \"0.43610385\",\n \"0.43610385\",\n \"0.43607998\",\n \"0.43484876\",\n \"0.4348318\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83265656"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95,"cells":{"query":{"kind":"string","value":"Gets the errors of this MessagingCampaign. A list of current error conditions associated with this messaging campaign."},"document":{"kind":"string","value":"def errors(self):\n return self._errors"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_errors(self):\n return [result for result in self.values() if result.outcome == Result.ERROR]","def Errors(self):\n return self._get_attribute('errors')","def getErrorsList(self):\n return self.__errors","def errors (self):\n return self._errors","def errors (self):\n return self._errors","def getErrors(self):\n return self.errors","def errors(self):\n return self.__errors","def errors(self) -> List[Error]:\n return self._errors_files + list(self._errors.values())","def errors(self):\r\n if not hasattr(self, '_errors_cache'):\r\n self._errors_cache = self.form.get_field_errors(self)\r\n return self._errors_cache","def get_errors(self, request):\n\n value = request._get_parameter_value(self)\n return value.errors","def Errors(self):\r\n\t\treturn self._get_attribute('errors')","def errors(self):\n return self._properties.get(\"errors\")","def errors(self) -> Tuple[MqexsErrorInfo, ...]:\n return self.__errors","def retrieve_error_messages(self):\n return self.errors_seen[:]","def validation_errors(self):\n return self._validation_errors","def errors(self) -> pulumi.Output[Sequence['outputs.BatchAIErrorResponse']]:\n return pulumi.get(self, \"errors\")","def get_validation_errors(self):\n return [err.to_dict() for err in self._schema.validator.validation_errors]","def errors(self):\n _errors = {}\n # pylint: disable=no-member\n for name, field in self._fields.items():\n if field.errors:\n _errors[name] = field.errors.pop()\n\n return _errors","def all_errors(self) -> List[XMLSchemaParseError]:\n errors = []\n for comp in self.iter_components():\n if comp.errors:\n errors.extend(comp.errors)\n return errors","def get_errors(self):\n df = self.get_events()\n return df[df.error.notnull()]","def errors(self) -> List[Error]:\n # May have inherited errors with a different path.\n for error in self._errors.values():\n error.path = self.path\n if self.is_removed: # Mark all of our errors as non-persistant.\n error.is_persistant = False\n return list(self._errors.values())","def get_error_messages(self):\n\n if len(self._sensor_results_list) == 0:\n return\n\n error_msgs = []\n\n for reading in self._sensor_results_list:\n if reading.is_error():\n error_msgs.append(reading.get_error_msg())\n\n if len(error_msgs) > 0:\n return error_msgs\n else:\n return \"No Error Readings\"","def analysis_errors(self) -> str:\n errors = []\n\n # Get any callback errors\n for cid, callback in self._analysis_callbacks.items():\n if callback.status == AnalysisStatus.ERROR:\n errors.append(f\"\\n[Analysis Callback ID: {cid}]: {callback.error_msg}\")\n\n return \"\".join(errors)","def job_errors(self) -> str:\n errors = []\n\n # Get any job errors\n for job in self._jobs.values():\n if job and job.status() == JobStatus.ERROR:\n if hasattr(job, \"error_message\"):\n error_msg = job.error_message()\n else:\n error_msg = \"\"\n errors.append(f\"\\n[Job ID: {job.job_id()}]: {error_msg}\")\n\n # Get any job futures errors:\n for jid, fut in self._job_futures.items():\n if fut and fut.done() and fut.exception():\n ex = fut.exception()\n errors.append(\n f\"[Job ID: {jid}]\"\n \"\\n\".join(traceback.format_exception(type(ex), ex, ex.__traceback__))\n )\n return \"\".join(errors)","def error_data(self):\n\n if not self.__settings:\n return []\n\n return self.__transaction_errors","def _get_errors(exc):\n if hasattr(exc, 'message'):\n errors = exc.messages\n else:\n errors = [str(exc)]\n return errors","def errors(self) -> List[Error]:","def getBuildErrors(self):\n return [x for x in self.xeps if x.buildErrors]","def errors(self) -> str:\n return self.job_errors() + self.analysis_errors()","def getErrors(self) -> java.util.Collection:\n ...","def error(self) -> list:\n return self.__err","def errors():\n return THE_LOGGER.errors","def report_transaction_error_messages(self):\n response = self.__get_transaction_response()\n\n # get response data from response object\n response_data = response.json()\n\n # get error messages\n response_error = response_data['Error']\n response_error_messages = response_error['messages']\n\n # add all error messages to the report\n error_messages_to_report = []\n for response_error_message in response_error_messages:\n error_description = response_error_message['description']\n error_messages_to_report.append(error_description)\n\n return error_messages_to_report","def error_messages(self) -> List[str]:\n spatial_msgs = []\n temporal_msgs = []\n if self.spatial:\n spatial_msgs = [m for v, m in self.spatial_validations if not v(self.spatial)]\n if self.temporal:\n temporal_msgs = [m for v, m in self.temporal_validations if not v(self.temporal)]\n\n return spatial_msgs + temporal_msgs","def getErrorLog(self):\n return _libsbml.SBMLValidator_getErrorLog(self)","def get_errors(self):\n errors = []\n\n if not self.title:\n msg = 'Title not found: {0}'.format(self.number)\n print(msg)\n errors.append(msg)\n\n if not self.ref:\n msg = 'Ref not found: {0}'.format(self.number)\n print(msg)\n errors.append(msg)\n\n chapter_index = int(self.number) - 1\n\n # get the expected number of frames for this chapter\n expected_frame_count = chapters_and_frames.frame_counts[chapter_index]\n\n for x in range(1, expected_frame_count + 1):\n\n # frame id is formatted like '01-01'\n frame_id = '{0}-{1}'.format(self.number.zfill(2), str(x).zfill(2))\n\n # get the next frame\n frame = next((f for f in self.frames if f['id'] == frame_id), None) # type: dict\n if not frame:\n msg = 'Frame not found: {0}'.format(frame_id)\n print(msg)\n errors.append(msg)\n else:\n # check the frame img and values\n if 'img' not in frame or not frame['img']:\n msg = 'Attribute \"img\" is missing for frame {0}'.format(frame_id)\n print(msg)\n errors.append(msg)\n\n if 'text' not in frame or not frame['text']:\n msg = 'Attribute \"text\" is missing for frame {0}'.format(frame_id)\n print(msg)\n errors.append(msg)\n\n return errors","def GetAll(self):\n return self._errors.copy()","def getErrors(self):\n errorList = []\n\n # E0\n try:\n if not self.e0.isValid():\n errorList.append(\"Invalid first error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No first error axis in ErrorEllipse Class.\")\n\n # E1\n try:\n if not self.e1.isValid():\n errorList.append(\"Invalid second error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No second error axis in ErrorEllipse Class.\")\n\n # E2\n try:\n if not self.e2.isValid():\n errorList.append(\"Invalid third error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No third error axis in ErrorEllipse Class.\")\n\n # maximumHorizontalProjection\n try:\n self.maximumHorizontalProjection\n except (NameError, AttributeError):\n errorList.append(\"No MaximumHorizontalProjection in ErrorEllipse Class.\")\n\n # maximumVerticalProjection\n try:\n self.maximumVerticalProjection\n except (NameError, AttributeError):\n errorList.append(\"No MaximumVerticalProjection in ErrorEllipse Class\")\n\n # equivalentHorizontalRadius\n try:\n self.equivalentHorizontalRadius\n except (NameError, AttributeError):\n errorList.append(\"No EquivalentHorizontalRadius in ErrorEllipse class\")\n\n return errorList","def get_form_errors(form):\n all_errors = []\n for field in form.errors:\n all_errors += form.errors[field]\n return all_errors","def get_error(self) -> List[str]:\n return []","def get_error(self) -> List[str]:\n return []","def getNumErrors(self):\n return _libsbml.XMLErrorLog_getNumErrors(self)","def security_errors(self):\n errors = ErrorDict()\n for f in [\"honeypot\", \"timestamp\", \"security_hash\"]:\n if f in self.errors:\n errors[f] = self.errors[f]\n return errors","def get_render_errors(self, revision_id):\n url = DeckhandClient.get_path(\n DeckhandPaths.RENDERED_REVISION_DOCS\n ).format(revision_id)\n\n errors = []\n\n LOG.debug(\"Retrieving rendered docs checking for validation messages\")\n response = self._get_request(url)\n if response.status_code >= 400:\n err_resp = yaml.safe_load(response.text)\n errors = err_resp.get('details', {}).get('messageList', [])\n if not errors:\n # default message if none were specified.\n errors.append({\n \"error\": True,\n \"message\": (\"Deckhand has reported an error but did not \"\n \"specify messages. Response: {}\".format(\n response.text))})\n return errors","def get_errors(response):\n errors = response.get(\"error\")\n if errors:\n return [e.get(\"message\") for e in errors]\n return None","def errors(self):\n return self.args[1]","def errors(self):\n raise NotImplementedError","def getParseErrors(self):\n return [x for x in self.xeps if x.parseErrors]","def get_field_errors(self, field):\r\n identifier = format_html('{0}.{1}', self.form_name, field.name)\r\n return self.error_class([SafeTuple((identifier, '$pristine', '$pristine', 'invalid', e))\r\n for e in self.errors.get(field.name, [])])","def get_all_failures(self):\n return self._get_filtered_results(success=False)","def errors(self):\n\n dict = {\"Stellar Mass Error\":[self.st_masserr1,self.st_masserr2],\n \"Stellar Radius Error\":[self.st_raderr1,self.st_raderr2]}\n\n return dict","def get_errors(self, path: str,\n is_ancillary: bool = False,\n is_system: bool = False,\n is_removed: bool = False) -> List[str]:\n u_file = self.__api.files.get(path, is_ancillary=is_ancillary,\n is_system=is_system,\n is_removed=is_removed)\n return [e.message for e in u_file.errors]","def get_encoding_errors(self):\n return self._encoding_errors","def get_errors(self):\n return {'loss': self.loss.data[0]}","def date_errors(self):\r\n try:\r\n _date_errors = self._validate_date(self.sourceDateCol)\r\n return _date_errors\r\n except:\r\n return None","def formatErrors(self):\n errorlist = []\n xepsWithErrors = sorted(\n set(self.getParseErrors() + self.getBuildErrors()),\n key=lambda x: str(x))\n if self.getErrors() or xepsWithErrors:\n if self.getErrors():\n errorlist.append(\"********** Read errors **********\")\n for error in self.getErrors():\n errorlist.append(error)\n for xep in xepsWithErrors:\n errorlist.append(\n \"********** Error report for {} **********\".format(str(xep)))\n if xep.parseErrors:\n errorlist.append(\"********** Parsing Errors **********\")\n errors = list(set(xep.parseErrors))\n for error in errors:\n errorlist.append(error)\n if xep.buildErrors:\n errorlist.append(\"********** Build Errors **********\")\n for error in xep.buildErrors:\n if len(error.splitlines()) > 4:\n error = ''.join(error.splitlines()[:4])\n errorlist.append(error)\n return '\\n'.join(errorlist)\n else:\n return None","def error_details(self):\n return self._error_details","def errors_fatal(self) -> List[Error]:\n return self._errors_fatal_files + self._errors_fatal","def error_map(self):\n return self._error_map","def getErrorLog(self):\n return _libsbml.XMLInputStream_getErrorLog(self)","def getErrorMessage(self):\n return self._errorMessage","def failed_messages(self, namespace, queue):\n failed = []\n for m in self.messages(namespace, queue):\n if m.error:\n failed.append(m)\n return failed","def get_aggregated_exceptions(self) -> Payload:\n return Payload(aggregated_errors=list(self._aggregated_exceptions.values()))","def _get_resp_body_errors(self):\n\n if self._resp_body_errors and len(self._resp_body_errors) > 0:\n return self._resp_body_errors\n\n errors = []\n warnings = []\n resp_codes = []\n\n if self.verb is None:\n return errors\n\n dom = self.response.dom()\n if dom is None:\n return errors\n\n for e in dom.findall('Errors'):\n eSeverity = None\n eClass = None\n eShortMsg = None\n eLongMsg = None\n eCode = None\n\n try:\n eSeverity = e.findall('SeverityCode')[0].text\n except IndexError:\n pass\n\n try:\n eClass = e.findall('ErrorClassification')[0].text\n except IndexError:\n pass\n\n try:\n eCode = e.findall('ErrorCode')[0].text\n except IndexError:\n pass\n\n try:\n eShortMsg = smart_encode(e.findall('ShortMessage')[0].text)\n except IndexError:\n pass\n\n try:\n eLongMsg = smart_encode(e.findall('LongMessage')[0].text)\n except IndexError:\n pass\n\n try:\n eCode = e.findall('ErrorCode')[0].text\n if int(eCode) not in resp_codes:\n resp_codes.append(int(eCode))\n except IndexError:\n pass\n\n msg = str(\"Class: {eClass}, Severity: {severity}, Code: {code}, {shortMsg} {longMsg}\") \\\n .format(eClass=eClass, severity=eSeverity, code=eCode, shortMsg=eShortMsg,\n longMsg=eLongMsg)\n\n # from IPython import embed; embed()\n\n if eSeverity == 'Warning':\n warnings.append(msg)\n else:\n errors.append(msg)\n\n self._resp_body_warnings = warnings\n self._resp_body_errors = errors\n self._resp_codes = resp_codes\n\n if self.config.get('warnings') and len(warnings) > 0:\n log.warn(\"{verb}: {message}\\n\\n\".format(\n verb=self.verb, message=\"\\n\".join(warnings)))\n\n if self.response.reply.Ack == 'Failure':\n if self.config.get('errors'):\n log.error(\"{verb}: {message}\\n\\n\".format(\n verb=self.verb, message=\"\\n\".join(errors)))\n\n return errors\n\n return []","def get_errors(self, response: response_domain_model.Response, question_code: str) -> Sequence['ValidationError']:\n ...","def _find_errors(self, new_response: response_domain_model.Response):\n self._reset_child_state()\n for response in self._responses_to_replay:\n for question_code, conditional in self.requirements.items():\n uncaught_errors = conditional.get_errors(response, question_code=question_code)\n if uncaught_errors:\n # Should not be able to get errors on previously passed responses\n # (invalid answers should be ignored when checking conditionals)\n raise Exception('Invalid answers found in previously checked responses')\n\n new_errors = []\n for question_code, conditional in self.requirements.items():\n new_errors.extend(conditional.get_errors(new_response, question_code=question_code))\n\n if new_errors:\n # Recurse again until there are no new errors found\n new_errors.extend(self._find_errors(new_response))\n\n return new_errors","def get_error_count(self):\n return sum(1 for outcome in (r.outcome for r in self.values()) if outcome == Result.ERROR)","def invalid_values(self):\n\n return self._get_value(self.invalid_values_provider)","def getErrorReport(self):\n return self.sError;","def getErrorReport(self):\n return self.sError;","def filter_draft_errors(result):\n error_messages = []\n for field, msgs in result.get('messages', {}).items():\n if msgs.get('state', None) == 'error':\n for m in msgs['messages']:\n error_messages.append(dict(\n field=field,\n message=m,\n code=error_codes['validation_error'],\n ))\n return error_messages","def getError(self):\n \n return self.resp[\"error\"]","def error_count(self):\n return len(self.errors)","def _get_errors(sheet, row, col):\n field = _FIELDS['primary data']\n val = sheet.cell(row + field['row'], col + field['column']).value\n if not val:\n return []\n final_row = row + field['row']\n error = sheet.cell(final_row, col + field['column']).value\n while error:\n final_row += 1\n error = sheet.cell(final_row, col + field['column']).value\n return [sheet.cell(i, col + field['column']).value\n for i in range(row + field['row'], final_row)]","def tasks_with_errors(self):\n errs = []\n while True:\n try:\n errs.append(self._errq.get_nowait())\n except Queue.Empty:\n break\n return errs","def getErrorMessage(self):\n return self._message","def CalculateErrors(self, X, D):\n Y = self.Forward(X)\n self.errors = len([Y[i] for i in range(len(Y)) if Y[i] != D[i]])\n return self.errors","def grammarErrors(self, text, language):\n if not self.__isValidInput(text):\n return []\n\n textUnicode = unicode_str(text)\n errorList = []\n offset = 0\n for paragraph in textUnicode.split(\"\\n\"):\n errorList = errorList + self.__grammarParagraph(paragraph, offset, language)\n offset = offset + len(paragraph) + 1\n return errorList","def sex_errors(self):\n result = {}\n disable_sex_check = app_settings.get_app_setting(\n \"variants\", \"disable_pedigree_sex_check\", project=self\n )\n if disable_sex_check:\n return result\n for case in self.case_set.all():\n result.update(case.sex_errors(disable_sex_check))\n return result","def exception(self) -> exceptions.ErrorMessageException:\n\n return ErrorMessage.ERROR_CODES_TO_EXCEPTIONS.get(\n self.error_code,\n exceptions.GenericException\n )","def get_error(self, idx=0):\n return self.portal.error_log.getLogEntries()[idx]","def refined_errors(self):\r\n errs = []\r\n for err in self.errors:\r\n if err['typo'].lower() not in self.terms:\r\n errs.append(err)\r\n return errs","def error_codes(self):\n self._sort_measurements()\n return self._error_codes","def _pydantic_errors_to_validation_results(\n errors: list[dict | Exception] | ValidationError,\n file_path: Path,\n scope: Scope,\n) -> list[ValidationResult]:\n out = []\n for e in (\n errors.errors() if isinstance(errors, ValidationError) else cast(list, errors)\n ):\n if isinstance(e, Exception):\n message = getattr(e, \"message\", str(e))\n id = \"exception\"\n scope = Scope.FILE\n else:\n id = \".\".join(\n filter(\n bool,\n (\n \"dandischema\",\n e.get(\"type\", \"UNKNOWN\"),\n \"+\".join(e.get(\"loc\", [])),\n ),\n )\n )\n message = e.get(\"message\", e.get(\"msg\", None))\n out.append(\n ValidationResult(\n origin=ValidationOrigin(\n name=\"dandischema\",\n version=dandischema.__version__,\n ),\n severity=Severity.ERROR,\n id=id,\n scope=scope,\n path=file_path,\n message=message,\n # TODO? dataset_path=dataset_path,\n # TODO? dandiset_path=dandiset_path,\n )\n )\n return out","def error_message(self):\n\n return self._error_message","def get_errors_list(jobs_object, job_id):\n\n # Get the error keys of the concrete job ii\n jenkins_errors = jobs_object[\"jobsConfig\"][\"errorMsg\"]\n\n error_keys = jobs_object[\"jobsConfig\"][\"jenkinsJobs\"][job_id][\"errorType\"]\n\n # Get the error messages of the error keys\n error_list = []\n # We append the action to perform to the error message\n for ii in error_keys:\n if jenkins_errors[ii][\"action\"] == \"retryBuild\":\n for error in jenkins_errors[ii][\"errorStr\"]:\n error_list.append(error + \" - retryBuild\")\n elif jenkins_errors[ii][\"action\"] == \"nodeOff\":\n for error in jenkins_errors[ii][\"errorStr\"]:\n error_list.append(error + \" - nodeOff\")\n elif jenkins_errors[ii][\"action\"] == \"nodeReconnect\":\n for error in jenkins_errors[ii][\"errorStr\"]:\n error_list.append(error + \" - nodeReconnect\")\n else:\n print(\n \"Action not defined. Please define a valid action in \"\n + jobs_config_path\n )\n return error_list","def get_errors(self, path: str,\n is_ancillary: bool = False,\n is_system: bool = False,\n is_removed: bool = False) -> List[str]:","def field_errors(bound_field):\n seen = []\n errors = {}\n if hasattr(bound_field.field, \"fields\"):\n for idx, subfield in enumerate(bound_field.field.fields):\n key = \"%s_%d\" % (bound_field.auto_id, idx)\n subfield_errors = getattr(subfield.widget, \"errors\", [])\n errors[key] = subfield_errors\n seen.extend(subfield_errors)\n for error in bound_field.errors:\n if error not in seen:\n errors.setdefault(bound_field.auto_id, [])\n errors[bound_field.auto_id].append(error)\n return errors.items()","def getExceptions(self):\n return self.getOrDefault(\"exceptions\")","def GetFailures(self):\n return self._compute_client.all_failures","def errorbars (self):\n return self._errorbars","def warnings(self) -> List[Error]:\n return self._get_warnings()","def check_errors(self):\n\n errors = []\n while True:\n err = self.values(\"SYST:ERR?\")\n if int(err[0]) != 0:\n errmsg = \"Agilent 5313xA: {0}: {1}\".format(err[0], err[1])\n log.error(errmsg + '\\n')\n errors.append(errmsg)\n else:\n break\n\n return errors","def ErrorMessage(self):\n return self._errormessage","def errorcaps (self):\n return self._errorcaps","def get_errored_courses(self):\r\n return dict((k, self.errored_courses[k].errors) for k in self.errored_courses)","def messages(self):\n return self._messages","def messages(self):\n return self._messages","def messages(self):\n return self._messages","def query_error(self):\n return self.details[KEY_QUERY_ERROR]"],"string":"[\n \"def get_errors(self):\\n return [result for result in self.values() if result.outcome == Result.ERROR]\",\n \"def Errors(self):\\n return self._get_attribute('errors')\",\n \"def getErrorsList(self):\\n return self.__errors\",\n \"def errors (self):\\n return self._errors\",\n \"def errors (self):\\n return self._errors\",\n \"def getErrors(self):\\n return self.errors\",\n \"def errors(self):\\n return self.__errors\",\n \"def errors(self) -> List[Error]:\\n return self._errors_files + list(self._errors.values())\",\n \"def errors(self):\\r\\n if not hasattr(self, '_errors_cache'):\\r\\n self._errors_cache = self.form.get_field_errors(self)\\r\\n return self._errors_cache\",\n \"def get_errors(self, request):\\n\\n value = request._get_parameter_value(self)\\n return value.errors\",\n \"def Errors(self):\\r\\n\\t\\treturn self._get_attribute('errors')\",\n \"def errors(self):\\n return self._properties.get(\\\"errors\\\")\",\n \"def errors(self) -> Tuple[MqexsErrorInfo, ...]:\\n return self.__errors\",\n \"def retrieve_error_messages(self):\\n return self.errors_seen[:]\",\n \"def validation_errors(self):\\n return self._validation_errors\",\n \"def errors(self) -> pulumi.Output[Sequence['outputs.BatchAIErrorResponse']]:\\n return pulumi.get(self, \\\"errors\\\")\",\n \"def get_validation_errors(self):\\n return [err.to_dict() for err in self._schema.validator.validation_errors]\",\n \"def errors(self):\\n _errors = {}\\n # pylint: disable=no-member\\n for name, field in self._fields.items():\\n if field.errors:\\n _errors[name] = field.errors.pop()\\n\\n return _errors\",\n \"def all_errors(self) -> List[XMLSchemaParseError]:\\n errors = []\\n for comp in self.iter_components():\\n if comp.errors:\\n errors.extend(comp.errors)\\n return errors\",\n \"def get_errors(self):\\n df = self.get_events()\\n return df[df.error.notnull()]\",\n \"def errors(self) -> List[Error]:\\n # May have inherited errors with a different path.\\n for error in self._errors.values():\\n error.path = self.path\\n if self.is_removed: # Mark all of our errors as non-persistant.\\n error.is_persistant = False\\n return list(self._errors.values())\",\n \"def get_error_messages(self):\\n\\n if len(self._sensor_results_list) == 0:\\n return\\n\\n error_msgs = []\\n\\n for reading in self._sensor_results_list:\\n if reading.is_error():\\n error_msgs.append(reading.get_error_msg())\\n\\n if len(error_msgs) > 0:\\n return error_msgs\\n else:\\n return \\\"No Error Readings\\\"\",\n \"def analysis_errors(self) -> str:\\n errors = []\\n\\n # Get any callback errors\\n for cid, callback in self._analysis_callbacks.items():\\n if callback.status == AnalysisStatus.ERROR:\\n errors.append(f\\\"\\\\n[Analysis Callback ID: {cid}]: {callback.error_msg}\\\")\\n\\n return \\\"\\\".join(errors)\",\n \"def job_errors(self) -> str:\\n errors = []\\n\\n # Get any job errors\\n for job in self._jobs.values():\\n if job and job.status() == JobStatus.ERROR:\\n if hasattr(job, \\\"error_message\\\"):\\n error_msg = job.error_message()\\n else:\\n error_msg = \\\"\\\"\\n errors.append(f\\\"\\\\n[Job ID: {job.job_id()}]: {error_msg}\\\")\\n\\n # Get any job futures errors:\\n for jid, fut in self._job_futures.items():\\n if fut and fut.done() and fut.exception():\\n ex = fut.exception()\\n errors.append(\\n f\\\"[Job ID: {jid}]\\\"\\n \\\"\\\\n\\\".join(traceback.format_exception(type(ex), ex, ex.__traceback__))\\n )\\n return \\\"\\\".join(errors)\",\n \"def error_data(self):\\n\\n if not self.__settings:\\n return []\\n\\n return self.__transaction_errors\",\n \"def _get_errors(exc):\\n if hasattr(exc, 'message'):\\n errors = exc.messages\\n else:\\n errors = [str(exc)]\\n return errors\",\n \"def errors(self) -> List[Error]:\",\n \"def getBuildErrors(self):\\n return [x for x in self.xeps if x.buildErrors]\",\n \"def errors(self) -> str:\\n return self.job_errors() + self.analysis_errors()\",\n \"def getErrors(self) -> java.util.Collection:\\n ...\",\n \"def error(self) -> list:\\n return self.__err\",\n \"def errors():\\n return THE_LOGGER.errors\",\n \"def report_transaction_error_messages(self):\\n response = self.__get_transaction_response()\\n\\n # get response data from response object\\n response_data = response.json()\\n\\n # get error messages\\n response_error = response_data['Error']\\n response_error_messages = response_error['messages']\\n\\n # add all error messages to the report\\n error_messages_to_report = []\\n for response_error_message in response_error_messages:\\n error_description = response_error_message['description']\\n error_messages_to_report.append(error_description)\\n\\n return error_messages_to_report\",\n \"def error_messages(self) -> List[str]:\\n spatial_msgs = []\\n temporal_msgs = []\\n if self.spatial:\\n spatial_msgs = [m for v, m in self.spatial_validations if not v(self.spatial)]\\n if self.temporal:\\n temporal_msgs = [m for v, m in self.temporal_validations if not v(self.temporal)]\\n\\n return spatial_msgs + temporal_msgs\",\n \"def getErrorLog(self):\\n return _libsbml.SBMLValidator_getErrorLog(self)\",\n \"def get_errors(self):\\n errors = []\\n\\n if not self.title:\\n msg = 'Title not found: {0}'.format(self.number)\\n print(msg)\\n errors.append(msg)\\n\\n if not self.ref:\\n msg = 'Ref not found: {0}'.format(self.number)\\n print(msg)\\n errors.append(msg)\\n\\n chapter_index = int(self.number) - 1\\n\\n # get the expected number of frames for this chapter\\n expected_frame_count = chapters_and_frames.frame_counts[chapter_index]\\n\\n for x in range(1, expected_frame_count + 1):\\n\\n # frame id is formatted like '01-01'\\n frame_id = '{0}-{1}'.format(self.number.zfill(2), str(x).zfill(2))\\n\\n # get the next frame\\n frame = next((f for f in self.frames if f['id'] == frame_id), None) # type: dict\\n if not frame:\\n msg = 'Frame not found: {0}'.format(frame_id)\\n print(msg)\\n errors.append(msg)\\n else:\\n # check the frame img and values\\n if 'img' not in frame or not frame['img']:\\n msg = 'Attribute \\\"img\\\" is missing for frame {0}'.format(frame_id)\\n print(msg)\\n errors.append(msg)\\n\\n if 'text' not in frame or not frame['text']:\\n msg = 'Attribute \\\"text\\\" is missing for frame {0}'.format(frame_id)\\n print(msg)\\n errors.append(msg)\\n\\n return errors\",\n \"def GetAll(self):\\n return self._errors.copy()\",\n \"def getErrors(self):\\n errorList = []\\n\\n # E0\\n try:\\n if not self.e0.isValid():\\n errorList.append(\\\"Invalid first error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No first error axis in ErrorEllipse Class.\\\")\\n\\n # E1\\n try:\\n if not self.e1.isValid():\\n errorList.append(\\\"Invalid second error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No second error axis in ErrorEllipse Class.\\\")\\n\\n # E2\\n try:\\n if not self.e2.isValid():\\n errorList.append(\\\"Invalid third error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No third error axis in ErrorEllipse Class.\\\")\\n\\n # maximumHorizontalProjection\\n try:\\n self.maximumHorizontalProjection\\n except (NameError, AttributeError):\\n errorList.append(\\\"No MaximumHorizontalProjection in ErrorEllipse Class.\\\")\\n\\n # maximumVerticalProjection\\n try:\\n self.maximumVerticalProjection\\n except (NameError, AttributeError):\\n errorList.append(\\\"No MaximumVerticalProjection in ErrorEllipse Class\\\")\\n\\n # equivalentHorizontalRadius\\n try:\\n self.equivalentHorizontalRadius\\n except (NameError, AttributeError):\\n errorList.append(\\\"No EquivalentHorizontalRadius in ErrorEllipse class\\\")\\n\\n return errorList\",\n \"def get_form_errors(form):\\n all_errors = []\\n for field in form.errors:\\n all_errors += form.errors[field]\\n return all_errors\",\n \"def get_error(self) -> List[str]:\\n return []\",\n \"def get_error(self) -> List[str]:\\n return []\",\n \"def getNumErrors(self):\\n return _libsbml.XMLErrorLog_getNumErrors(self)\",\n \"def security_errors(self):\\n errors = ErrorDict()\\n for f in [\\\"honeypot\\\", \\\"timestamp\\\", \\\"security_hash\\\"]:\\n if f in self.errors:\\n errors[f] = self.errors[f]\\n return errors\",\n \"def get_render_errors(self, revision_id):\\n url = DeckhandClient.get_path(\\n DeckhandPaths.RENDERED_REVISION_DOCS\\n ).format(revision_id)\\n\\n errors = []\\n\\n LOG.debug(\\\"Retrieving rendered docs checking for validation messages\\\")\\n response = self._get_request(url)\\n if response.status_code >= 400:\\n err_resp = yaml.safe_load(response.text)\\n errors = err_resp.get('details', {}).get('messageList', [])\\n if not errors:\\n # default message if none were specified.\\n errors.append({\\n \\\"error\\\": True,\\n \\\"message\\\": (\\\"Deckhand has reported an error but did not \\\"\\n \\\"specify messages. Response: {}\\\".format(\\n response.text))})\\n return errors\",\n \"def get_errors(response):\\n errors = response.get(\\\"error\\\")\\n if errors:\\n return [e.get(\\\"message\\\") for e in errors]\\n return None\",\n \"def errors(self):\\n return self.args[1]\",\n \"def errors(self):\\n raise NotImplementedError\",\n \"def getParseErrors(self):\\n return [x for x in self.xeps if x.parseErrors]\",\n \"def get_field_errors(self, field):\\r\\n identifier = format_html('{0}.{1}', self.form_name, field.name)\\r\\n return self.error_class([SafeTuple((identifier, '$pristine', '$pristine', 'invalid', e))\\r\\n for e in self.errors.get(field.name, [])])\",\n \"def get_all_failures(self):\\n return self._get_filtered_results(success=False)\",\n \"def errors(self):\\n\\n dict = {\\\"Stellar Mass Error\\\":[self.st_masserr1,self.st_masserr2],\\n \\\"Stellar Radius Error\\\":[self.st_raderr1,self.st_raderr2]}\\n\\n return dict\",\n \"def get_errors(self, path: str,\\n is_ancillary: bool = False,\\n is_system: bool = False,\\n is_removed: bool = False) -> List[str]:\\n u_file = self.__api.files.get(path, is_ancillary=is_ancillary,\\n is_system=is_system,\\n is_removed=is_removed)\\n return [e.message for e in u_file.errors]\",\n \"def get_encoding_errors(self):\\n return self._encoding_errors\",\n \"def get_errors(self):\\n return {'loss': self.loss.data[0]}\",\n \"def date_errors(self):\\r\\n try:\\r\\n _date_errors = self._validate_date(self.sourceDateCol)\\r\\n return _date_errors\\r\\n except:\\r\\n return None\",\n \"def formatErrors(self):\\n errorlist = []\\n xepsWithErrors = sorted(\\n set(self.getParseErrors() + self.getBuildErrors()),\\n key=lambda x: str(x))\\n if self.getErrors() or xepsWithErrors:\\n if self.getErrors():\\n errorlist.append(\\\"********** Read errors **********\\\")\\n for error in self.getErrors():\\n errorlist.append(error)\\n for xep in xepsWithErrors:\\n errorlist.append(\\n \\\"********** Error report for {} **********\\\".format(str(xep)))\\n if xep.parseErrors:\\n errorlist.append(\\\"********** Parsing Errors **********\\\")\\n errors = list(set(xep.parseErrors))\\n for error in errors:\\n errorlist.append(error)\\n if xep.buildErrors:\\n errorlist.append(\\\"********** Build Errors **********\\\")\\n for error in xep.buildErrors:\\n if len(error.splitlines()) > 4:\\n error = ''.join(error.splitlines()[:4])\\n errorlist.append(error)\\n return '\\\\n'.join(errorlist)\\n else:\\n return None\",\n \"def error_details(self):\\n return self._error_details\",\n \"def errors_fatal(self) -> List[Error]:\\n return self._errors_fatal_files + self._errors_fatal\",\n \"def error_map(self):\\n return self._error_map\",\n \"def getErrorLog(self):\\n return _libsbml.XMLInputStream_getErrorLog(self)\",\n \"def getErrorMessage(self):\\n return self._errorMessage\",\n \"def failed_messages(self, namespace, queue):\\n failed = []\\n for m in self.messages(namespace, queue):\\n if m.error:\\n failed.append(m)\\n return failed\",\n \"def get_aggregated_exceptions(self) -> Payload:\\n return Payload(aggregated_errors=list(self._aggregated_exceptions.values()))\",\n \"def _get_resp_body_errors(self):\\n\\n if self._resp_body_errors and len(self._resp_body_errors) > 0:\\n return self._resp_body_errors\\n\\n errors = []\\n warnings = []\\n resp_codes = []\\n\\n if self.verb is None:\\n return errors\\n\\n dom = self.response.dom()\\n if dom is None:\\n return errors\\n\\n for e in dom.findall('Errors'):\\n eSeverity = None\\n eClass = None\\n eShortMsg = None\\n eLongMsg = None\\n eCode = None\\n\\n try:\\n eSeverity = e.findall('SeverityCode')[0].text\\n except IndexError:\\n pass\\n\\n try:\\n eClass = e.findall('ErrorClassification')[0].text\\n except IndexError:\\n pass\\n\\n try:\\n eCode = e.findall('ErrorCode')[0].text\\n except IndexError:\\n pass\\n\\n try:\\n eShortMsg = smart_encode(e.findall('ShortMessage')[0].text)\\n except IndexError:\\n pass\\n\\n try:\\n eLongMsg = smart_encode(e.findall('LongMessage')[0].text)\\n except IndexError:\\n pass\\n\\n try:\\n eCode = e.findall('ErrorCode')[0].text\\n if int(eCode) not in resp_codes:\\n resp_codes.append(int(eCode))\\n except IndexError:\\n pass\\n\\n msg = str(\\\"Class: {eClass}, Severity: {severity}, Code: {code}, {shortMsg} {longMsg}\\\") \\\\\\n .format(eClass=eClass, severity=eSeverity, code=eCode, shortMsg=eShortMsg,\\n longMsg=eLongMsg)\\n\\n # from IPython import embed; embed()\\n\\n if eSeverity == 'Warning':\\n warnings.append(msg)\\n else:\\n errors.append(msg)\\n\\n self._resp_body_warnings = warnings\\n self._resp_body_errors = errors\\n self._resp_codes = resp_codes\\n\\n if self.config.get('warnings') and len(warnings) > 0:\\n log.warn(\\\"{verb}: {message}\\\\n\\\\n\\\".format(\\n verb=self.verb, message=\\\"\\\\n\\\".join(warnings)))\\n\\n if self.response.reply.Ack == 'Failure':\\n if self.config.get('errors'):\\n log.error(\\\"{verb}: {message}\\\\n\\\\n\\\".format(\\n verb=self.verb, message=\\\"\\\\n\\\".join(errors)))\\n\\n return errors\\n\\n return []\",\n \"def get_errors(self, response: response_domain_model.Response, question_code: str) -> Sequence['ValidationError']:\\n ...\",\n \"def _find_errors(self, new_response: response_domain_model.Response):\\n self._reset_child_state()\\n for response in self._responses_to_replay:\\n for question_code, conditional in self.requirements.items():\\n uncaught_errors = conditional.get_errors(response, question_code=question_code)\\n if uncaught_errors:\\n # Should not be able to get errors on previously passed responses\\n # (invalid answers should be ignored when checking conditionals)\\n raise Exception('Invalid answers found in previously checked responses')\\n\\n new_errors = []\\n for question_code, conditional in self.requirements.items():\\n new_errors.extend(conditional.get_errors(new_response, question_code=question_code))\\n\\n if new_errors:\\n # Recurse again until there are no new errors found\\n new_errors.extend(self._find_errors(new_response))\\n\\n return new_errors\",\n \"def get_error_count(self):\\n return sum(1 for outcome in (r.outcome for r in self.values()) if outcome == Result.ERROR)\",\n \"def invalid_values(self):\\n\\n return self._get_value(self.invalid_values_provider)\",\n \"def getErrorReport(self):\\n return self.sError;\",\n \"def getErrorReport(self):\\n return self.sError;\",\n \"def filter_draft_errors(result):\\n error_messages = []\\n for field, msgs in result.get('messages', {}).items():\\n if msgs.get('state', None) == 'error':\\n for m in msgs['messages']:\\n error_messages.append(dict(\\n field=field,\\n message=m,\\n code=error_codes['validation_error'],\\n ))\\n return error_messages\",\n \"def getError(self):\\n \\n return self.resp[\\\"error\\\"]\",\n \"def error_count(self):\\n return len(self.errors)\",\n \"def _get_errors(sheet, row, col):\\n field = _FIELDS['primary data']\\n val = sheet.cell(row + field['row'], col + field['column']).value\\n if not val:\\n return []\\n final_row = row + field['row']\\n error = sheet.cell(final_row, col + field['column']).value\\n while error:\\n final_row += 1\\n error = sheet.cell(final_row, col + field['column']).value\\n return [sheet.cell(i, col + field['column']).value\\n for i in range(row + field['row'], final_row)]\",\n \"def tasks_with_errors(self):\\n errs = []\\n while True:\\n try:\\n errs.append(self._errq.get_nowait())\\n except Queue.Empty:\\n break\\n return errs\",\n \"def getErrorMessage(self):\\n return self._message\",\n \"def CalculateErrors(self, X, D):\\n Y = self.Forward(X)\\n self.errors = len([Y[i] for i in range(len(Y)) if Y[i] != D[i]])\\n return self.errors\",\n \"def grammarErrors(self, text, language):\\n if not self.__isValidInput(text):\\n return []\\n\\n textUnicode = unicode_str(text)\\n errorList = []\\n offset = 0\\n for paragraph in textUnicode.split(\\\"\\\\n\\\"):\\n errorList = errorList + self.__grammarParagraph(paragraph, offset, language)\\n offset = offset + len(paragraph) + 1\\n return errorList\",\n \"def sex_errors(self):\\n result = {}\\n disable_sex_check = app_settings.get_app_setting(\\n \\\"variants\\\", \\\"disable_pedigree_sex_check\\\", project=self\\n )\\n if disable_sex_check:\\n return result\\n for case in self.case_set.all():\\n result.update(case.sex_errors(disable_sex_check))\\n return result\",\n \"def exception(self) -> exceptions.ErrorMessageException:\\n\\n return ErrorMessage.ERROR_CODES_TO_EXCEPTIONS.get(\\n self.error_code,\\n exceptions.GenericException\\n )\",\n \"def get_error(self, idx=0):\\n return self.portal.error_log.getLogEntries()[idx]\",\n \"def refined_errors(self):\\r\\n errs = []\\r\\n for err in self.errors:\\r\\n if err['typo'].lower() not in self.terms:\\r\\n errs.append(err)\\r\\n return errs\",\n \"def error_codes(self):\\n self._sort_measurements()\\n return self._error_codes\",\n \"def _pydantic_errors_to_validation_results(\\n errors: list[dict | Exception] | ValidationError,\\n file_path: Path,\\n scope: Scope,\\n) -> list[ValidationResult]:\\n out = []\\n for e in (\\n errors.errors() if isinstance(errors, ValidationError) else cast(list, errors)\\n ):\\n if isinstance(e, Exception):\\n message = getattr(e, \\\"message\\\", str(e))\\n id = \\\"exception\\\"\\n scope = Scope.FILE\\n else:\\n id = \\\".\\\".join(\\n filter(\\n bool,\\n (\\n \\\"dandischema\\\",\\n e.get(\\\"type\\\", \\\"UNKNOWN\\\"),\\n \\\"+\\\".join(e.get(\\\"loc\\\", [])),\\n ),\\n )\\n )\\n message = e.get(\\\"message\\\", e.get(\\\"msg\\\", None))\\n out.append(\\n ValidationResult(\\n origin=ValidationOrigin(\\n name=\\\"dandischema\\\",\\n version=dandischema.__version__,\\n ),\\n severity=Severity.ERROR,\\n id=id,\\n scope=scope,\\n path=file_path,\\n message=message,\\n # TODO? dataset_path=dataset_path,\\n # TODO? dandiset_path=dandiset_path,\\n )\\n )\\n return out\",\n \"def error_message(self):\\n\\n return self._error_message\",\n \"def get_errors_list(jobs_object, job_id):\\n\\n # Get the error keys of the concrete job ii\\n jenkins_errors = jobs_object[\\\"jobsConfig\\\"][\\\"errorMsg\\\"]\\n\\n error_keys = jobs_object[\\\"jobsConfig\\\"][\\\"jenkinsJobs\\\"][job_id][\\\"errorType\\\"]\\n\\n # Get the error messages of the error keys\\n error_list = []\\n # We append the action to perform to the error message\\n for ii in error_keys:\\n if jenkins_errors[ii][\\\"action\\\"] == \\\"retryBuild\\\":\\n for error in jenkins_errors[ii][\\\"errorStr\\\"]:\\n error_list.append(error + \\\" - retryBuild\\\")\\n elif jenkins_errors[ii][\\\"action\\\"] == \\\"nodeOff\\\":\\n for error in jenkins_errors[ii][\\\"errorStr\\\"]:\\n error_list.append(error + \\\" - nodeOff\\\")\\n elif jenkins_errors[ii][\\\"action\\\"] == \\\"nodeReconnect\\\":\\n for error in jenkins_errors[ii][\\\"errorStr\\\"]:\\n error_list.append(error + \\\" - nodeReconnect\\\")\\n else:\\n print(\\n \\\"Action not defined. Please define a valid action in \\\"\\n + jobs_config_path\\n )\\n return error_list\",\n \"def get_errors(self, path: str,\\n is_ancillary: bool = False,\\n is_system: bool = False,\\n is_removed: bool = False) -> List[str]:\",\n \"def field_errors(bound_field):\\n seen = []\\n errors = {}\\n if hasattr(bound_field.field, \\\"fields\\\"):\\n for idx, subfield in enumerate(bound_field.field.fields):\\n key = \\\"%s_%d\\\" % (bound_field.auto_id, idx)\\n subfield_errors = getattr(subfield.widget, \\\"errors\\\", [])\\n errors[key] = subfield_errors\\n seen.extend(subfield_errors)\\n for error in bound_field.errors:\\n if error not in seen:\\n errors.setdefault(bound_field.auto_id, [])\\n errors[bound_field.auto_id].append(error)\\n return errors.items()\",\n \"def getExceptions(self):\\n return self.getOrDefault(\\\"exceptions\\\")\",\n \"def GetFailures(self):\\n return self._compute_client.all_failures\",\n \"def errorbars (self):\\n return self._errorbars\",\n \"def warnings(self) -> List[Error]:\\n return self._get_warnings()\",\n \"def check_errors(self):\\n\\n errors = []\\n while True:\\n err = self.values(\\\"SYST:ERR?\\\")\\n if int(err[0]) != 0:\\n errmsg = \\\"Agilent 5313xA: {0}: {1}\\\".format(err[0], err[1])\\n log.error(errmsg + '\\\\n')\\n errors.append(errmsg)\\n else:\\n break\\n\\n return errors\",\n \"def ErrorMessage(self):\\n return self._errormessage\",\n \"def errorcaps (self):\\n return self._errorcaps\",\n \"def get_errored_courses(self):\\r\\n return dict((k, self.errored_courses[k].errors) for k in self.errored_courses)\",\n \"def messages(self):\\n return self._messages\",\n \"def messages(self):\\n return self._messages\",\n \"def messages(self):\\n return self._messages\",\n \"def query_error(self):\\n return self.details[KEY_QUERY_ERROR]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7164323","0.70850545","0.7084039","0.7073318","0.7073318","0.7032286","0.69743216","0.6840167","0.6840085","0.6816689","0.68060446","0.67511207","0.66765666","0.6626607","0.65734386","0.65553755","0.65435886","0.6522403","0.6402078","0.63631475","0.6350149","0.63170594","0.6312729","0.63122153","0.6284837","0.62512636","0.61444694","0.6065811","0.60224736","0.6016319","0.6014446","0.5982093","0.5935994","0.5854258","0.583771","0.5777689","0.5770109","0.5731137","0.5721682","0.570453","0.570453","0.566765","0.5632113","0.5595717","0.5565627","0.5503202","0.54932886","0.5477905","0.53716326","0.53676915","0.5365297","0.53583497","0.53526014","0.5341042","0.5331032","0.53226525","0.5310991","0.5303637","0.5286214","0.5285876","0.5285202","0.52813214","0.52592516","0.52442986","0.52141994","0.52115345","0.52092403","0.5201769","0.5199416","0.5199416","0.517958","0.51787317","0.5171295","0.51709306","0.51611435","0.5154321","0.5152182","0.5130895","0.50871825","0.5076235","0.5074987","0.50728893","0.5070535","0.50689095","0.50684375","0.5067607","0.5066021","0.50616026","0.50598854","0.5041734","0.50276005","0.5024531","0.5020144","0.5019323","0.50067776","0.5000856","0.4998048","0.4998048","0.4998048","0.4997809"],"string":"[\n \"0.7164323\",\n \"0.70850545\",\n \"0.7084039\",\n \"0.7073318\",\n \"0.7073318\",\n \"0.7032286\",\n \"0.69743216\",\n \"0.6840167\",\n \"0.6840085\",\n \"0.6816689\",\n \"0.68060446\",\n \"0.67511207\",\n \"0.66765666\",\n \"0.6626607\",\n \"0.65734386\",\n \"0.65553755\",\n \"0.65435886\",\n \"0.6522403\",\n \"0.6402078\",\n \"0.63631475\",\n \"0.6350149\",\n \"0.63170594\",\n \"0.6312729\",\n \"0.63122153\",\n \"0.6284837\",\n \"0.62512636\",\n \"0.61444694\",\n \"0.6065811\",\n \"0.60224736\",\n \"0.6016319\",\n \"0.6014446\",\n \"0.5982093\",\n \"0.5935994\",\n \"0.5854258\",\n \"0.583771\",\n \"0.5777689\",\n \"0.5770109\",\n \"0.5731137\",\n \"0.5721682\",\n \"0.570453\",\n \"0.570453\",\n \"0.566765\",\n \"0.5632113\",\n \"0.5595717\",\n \"0.5565627\",\n \"0.5503202\",\n \"0.54932886\",\n \"0.5477905\",\n \"0.53716326\",\n \"0.53676915\",\n \"0.5365297\",\n \"0.53583497\",\n \"0.53526014\",\n \"0.5341042\",\n \"0.5331032\",\n \"0.53226525\",\n \"0.5310991\",\n \"0.5303637\",\n \"0.5286214\",\n \"0.5285876\",\n \"0.5285202\",\n \"0.52813214\",\n \"0.52592516\",\n \"0.52442986\",\n \"0.52141994\",\n \"0.52115345\",\n \"0.52092403\",\n \"0.5201769\",\n \"0.5199416\",\n \"0.5199416\",\n \"0.517958\",\n \"0.51787317\",\n \"0.5171295\",\n \"0.51709306\",\n \"0.51611435\",\n \"0.5154321\",\n \"0.5152182\",\n \"0.5130895\",\n \"0.50871825\",\n \"0.5076235\",\n \"0.5074987\",\n \"0.50728893\",\n \"0.5070535\",\n \"0.50689095\",\n \"0.50684375\",\n \"0.5067607\",\n \"0.5066021\",\n \"0.50616026\",\n \"0.50598854\",\n \"0.5041734\",\n \"0.50276005\",\n \"0.5024531\",\n \"0.5020144\",\n \"0.5019323\",\n \"0.50067776\",\n \"0.5000856\",\n \"0.4998048\",\n \"0.4998048\",\n \"0.4998048\",\n \"0.4997809\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7075878"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":96,"cells":{"query":{"kind":"string","value":"Sets the errors of this MessagingCampaign. A list of current error conditions associated with this messaging campaign."},"document":{"kind":"string","value":"def errors(self, errors):\n \n self._errors = errors"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def errors(self, errors):\n\n self._errors = errors","def validation_errors(self, validation_errors):\n self._validation_errors = validation_errors","def add_errors(self, errors):\n self.errors = merge_errors(self.errors, errors)","def errors (self):\n return self._errors","def errors (self):\n return self._errors","def errors(self):\n return self._errors","def errors(self):\n return self.__errors","def Errors(self):\n return self._get_attribute('errors')","def errors(self):\r\n if not hasattr(self, '_errors_cache'):\r\n self._errors_cache = self.form.get_field_errors(self)\r\n return self._errors_cache","def errors(self):\n return self._properties.get(\"errors\")","def errors(self) -> Tuple[MqexsErrorInfo, ...]:\n return self.__errors","def errors(self) -> pulumi.Output[Sequence['outputs.BatchAIErrorResponse']]:\n return pulumi.get(self, \"errors\")","def getErrors(self):\n return self.errors","def getErrorsList(self):\n return self.__errors","def Errors(self):\r\n\t\treturn self._get_attribute('errors')","def validation_errors(self):\n return self._validation_errors","def errors(self):\n _errors = {}\n # pylint: disable=no-member\n for name, field in self._fields.items():\n if field.errors:\n _errors[name] = field.errors.pop()\n\n return _errors","def error(self, msg, transfers):\n self.validation_exceptions.extend(self._create_exceptions(msg, transfers, ValidationType.ERROR))","def errors(self) -> List[Error]:\n return self._errors_files + list(self._errors.values())","def errors(self):\n raise NotImplementedError","def set_limit(self, errors):\n self.limit = errors","def errors(self) -> str:\n return self.job_errors() + self.analysis_errors()","def on_errors(self, errors):\n log.error(\"Received errors: %s\", errors)","def get_errors(self):\n return [result for result in self.values() if result.outcome == Result.ERROR]","def get_errors(self, request):\n\n value = request._get_parameter_value(self)\n return value.errors","def error_message(self, error_message):\n if (self.local_vars_configuration.client_side_validation and\n error_message is not None and len(error_message) > 500):\n raise ValueError(\"Invalid value for `error_message`, length must be less than or equal to `500`\") # noqa: E501\n\n self._error_message = error_message","def retrieve_error_messages(self):\n return self.errors_seen[:]","def report_transaction_error_messages(self):\n response = self.__get_transaction_response()\n\n # get response data from response object\n response_data = response.json()\n\n # get error messages\n response_error = response_data['Error']\n response_error_messages = response_error['messages']\n\n # add all error messages to the report\n error_messages_to_report = []\n for response_error_message in response_error_messages:\n error_description = response_error_message['description']\n error_messages_to_report.append(error_description)\n\n return error_messages_to_report","def errors(self) -> List[Error]:","def analysis_errors(self) -> str:\n errors = []\n\n # Get any callback errors\n for cid, callback in self._analysis_callbacks.items():\n if callback.status == AnalysisStatus.ERROR:\n errors.append(f\"\\n[Analysis Callback ID: {cid}]: {callback.error_msg}\")\n\n return \"\".join(errors)","def check_set_errors(self):\n raise NotImplementedError(\"Implement it in a subclass.\")","def get_validation_errors(self):\n return [err.to_dict() for err in self._schema.validator.validation_errors]","def error_data(self):\n\n if not self.__settings:\n return []\n\n return self.__transaction_errors","def errors(self) -> List[Error]:\n # May have inherited errors with a different path.\n for error in self._errors.values():\n error.path = self.path\n if self.is_removed: # Mark all of our errors as non-persistant.\n error.is_persistant = False\n return list(self._errors.values())","def job_errors(self) -> str:\n errors = []\n\n # Get any job errors\n for job in self._jobs.values():\n if job and job.status() == JobStatus.ERROR:\n if hasattr(job, \"error_message\"):\n error_msg = job.error_message()\n else:\n error_msg = \"\"\n errors.append(f\"\\n[Job ID: {job.job_id()}]: {error_msg}\")\n\n # Get any job futures errors:\n for jid, fut in self._job_futures.items():\n if fut and fut.done() and fut.exception():\n ex = fut.exception()\n errors.append(\n f\"[Job ID: {jid}]\"\n \"\\n\".join(traceback.format_exception(type(ex), ex, ex.__traceback__))\n )\n return \"\".join(errors)","def set_error(self, name, value):\n self.errors[name] = value","def error_count(self, error_count):\n\n self._error_count = error_count","async def flush_errors(self, errors):\n logger.error(errors)\n data = {\"output\": {\"errors\": errors}, \"state\": \"completed\", \"status\": \"error\"}\n await self.c_task.update(data)","def errors(self):\n\n dict = {\"Stellar Mass Error\":[self.st_masserr1,self.st_masserr2],\n \"Stellar Radius Error\":[self.st_raderr1,self.st_raderr2]}\n\n return dict","def setErrorMessage(self, errorMessage):\n self._errorMessage = errorMessage","def mark_error(self):\r\n self.status = ERROR","def error_message(self, error_message):\n\n self._error_message = error_message","def _initialize_error_dictionaries(self):\n for task_id in self.task_ids.keys():\n self.training_errors[task_id] = []\n self.validation_errors[task_id] = []","def errors_and_warnings(self, errors_and_warnings):\n\n self._errors_and_warnings = errors_and_warnings","def error(self) -> list:\n return self.__err","def errors_summary(self, errors_summary):\n\n self._errors_summary = errors_summary","def all_errors(self) -> List[XMLSchemaParseError]:\n errors = []\n for comp in self.iter_components():\n if comp.errors:\n errors.extend(comp.errors)\n return errors","def getNumErrors(self):\n return _libsbml.XMLErrorLog_getNumErrors(self)","def setError(self,err):\n self.error = err","def getErrors(self) -> java.util.Collection:\n ...","def set_error(self, error):\n self._set_sub_text('error', text=str(error))\n return self","def error_messages(self) -> List[str]:\n spatial_msgs = []\n temporal_msgs = []\n if self.spatial:\n spatial_msgs = [m for v, m in self.spatial_validations if not v(self.spatial)]\n if self.temporal:\n temporal_msgs = [m for v, m in self.temporal_validations if not v(self.temporal)]\n\n return spatial_msgs + temporal_msgs","def error_message(self, error_message: str):\n\n self._error_message = error_message","def _find_errors(self, new_response: response_domain_model.Response):\n self._reset_child_state()\n for response in self._responses_to_replay:\n for question_code, conditional in self.requirements.items():\n uncaught_errors = conditional.get_errors(response, question_code=question_code)\n if uncaught_errors:\n # Should not be able to get errors on previously passed responses\n # (invalid answers should be ignored when checking conditionals)\n raise Exception('Invalid answers found in previously checked responses')\n\n new_errors = []\n for question_code, conditional in self.requirements.items():\n new_errors.extend(conditional.get_errors(new_response, question_code=question_code))\n\n if new_errors:\n # Recurse again until there are no new errors found\n new_errors.extend(self._find_errors(new_response))\n\n return new_errors","def email_error_report(self, digest_content, jats_content, error_messages):\n datetime_string = time.strftime(utils.DATE_TIME_FORMAT, time.gmtime())\n doi = None\n if digest_content:\n doi = digest_content.doi\n body_content = requests_provider.error_email_body_content(\n doi, jats_content, error_messages\n )\n body = email_provider.simple_email_body(datetime_string, body_content)\n author = None\n if digest_content:\n author = digest_content.author\n subject = requests_provider.error_email_subject_msid_author(\n \"digest\", digest_provider.get_digest_msid(digest_content), author\n )\n sender_email = self.settings.digest_sender_email\n\n recipient_email_list = email_provider.list_email_recipients(\n self.settings.digest_jats_error_recipient_email\n )\n\n messages = email_provider.simple_messages(\n sender_email, recipient_email_list, subject, body, logger=self.logger\n )\n self.logger.info(\n \"Formatted %d error email messages in %s\" % (len(messages), self.name)\n )\n\n details = email_provider.smtp_send_messages(\n self.settings, messages, self.logger\n )\n self.logger.info(\"Email sending details: %s\" % str(details))\n\n return True","def add_error_tables(self, error_tables):\n self.error_distribution = error_tables","def get_error_messages(self):\n\n if len(self._sensor_results_list) == 0:\n return\n\n error_msgs = []\n\n for reading in self._sensor_results_list:\n if reading.is_error():\n error_msgs.append(reading.get_error_msg())\n\n if len(error_msgs) > 0:\n return error_msgs\n else:\n return \"No Error Readings\"","def error_count(self):\n return len(self.errors)","def __set_errors_json(self, error_count_by_operation, errors_by_operation):\n message = \"{0} error/s reported.\".format(error_count_by_operation)\n log_file_path = self.logger.file_logger.log_file_path\n message += \" The latest {0} error/s are shared in detail. To view all errors, review this log file on the machine: {1}\".format(len(errors_by_operation), log_file_path) if error_count_by_operation > 0 else \"\"\n return {\n \"code\": Constants.PatchOperationTopLevelErrorCode.SUCCESS if error_count_by_operation == 0 else Constants.PatchOperationTopLevelErrorCode.ERROR,\n \"details\": errors_by_operation,\n \"message\": message\n }","def security_errors(self):\n errors = ErrorDict()\n for f in [\"honeypot\", \"timestamp\", \"security_hash\"]:\n if f in self.errors:\n errors[f] = self.errors[f]\n return errors","def GetAll(self):\n return self._errors.copy()","def errors():\n return THE_LOGGER.errors","def add_error(self, field, message):\n add_list_value(self.errors, field, message)","def error_recovery_settings(self, error_recovery_settings):\n\n self._error_recovery_settings = error_recovery_settings","def setErrorThreshold(self, threshold):\n return self._set(errorThreshold=threshold)","def setErrorThreshold(self, threshold):\n return self._set(errorThreshold=threshold)","def _get_resp_body_errors(self):\n\n if self._resp_body_errors and len(self._resp_body_errors) > 0:\n return self._resp_body_errors\n\n errors = []\n warnings = []\n resp_codes = []\n\n if self.verb is None:\n return errors\n\n dom = self.response.dom()\n if dom is None:\n return errors\n\n for e in dom.findall('Errors'):\n eSeverity = None\n eClass = None\n eShortMsg = None\n eLongMsg = None\n eCode = None\n\n try:\n eSeverity = e.findall('SeverityCode')[0].text\n except IndexError:\n pass\n\n try:\n eClass = e.findall('ErrorClassification')[0].text\n except IndexError:\n pass\n\n try:\n eCode = e.findall('ErrorCode')[0].text\n except IndexError:\n pass\n\n try:\n eShortMsg = smart_encode(e.findall('ShortMessage')[0].text)\n except IndexError:\n pass\n\n try:\n eLongMsg = smart_encode(e.findall('LongMessage')[0].text)\n except IndexError:\n pass\n\n try:\n eCode = e.findall('ErrorCode')[0].text\n if int(eCode) not in resp_codes:\n resp_codes.append(int(eCode))\n except IndexError:\n pass\n\n msg = str(\"Class: {eClass}, Severity: {severity}, Code: {code}, {shortMsg} {longMsg}\") \\\n .format(eClass=eClass, severity=eSeverity, code=eCode, shortMsg=eShortMsg,\n longMsg=eLongMsg)\n\n # from IPython import embed; embed()\n\n if eSeverity == 'Warning':\n warnings.append(msg)\n else:\n errors.append(msg)\n\n self._resp_body_warnings = warnings\n self._resp_body_errors = errors\n self._resp_codes = resp_codes\n\n if self.config.get('warnings') and len(warnings) > 0:\n log.warn(\"{verb}: {message}\\n\\n\".format(\n verb=self.verb, message=\"\\n\".join(warnings)))\n\n if self.response.reply.Ack == 'Failure':\n if self.config.get('errors'):\n log.error(\"{verb}: {message}\\n\\n\".format(\n verb=self.verb, message=\"\\n\".join(errors)))\n\n return errors\n\n return []","def add_error(self, message):\n self.errors.append(message)","def setErrorMax(self, error_max):\n\t\tself.error_max = error_max","def getBuildErrors(self):\n return [x for x in self.xeps if x.buildErrors]","def sex_errors(self):\n result = {}\n disable_sex_check = app_settings.get_app_setting(\n \"variants\", \"disable_pedigree_sex_check\", project=self\n )\n if disable_sex_check:\n return result\n for case in self.case_set.all():\n result.update(case.sex_errors(disable_sex_check))\n return result","def set_max_errors(self,newMaxErrors):\n previousErrors = self.errors[max(0, self._errorCursor - newMaxErrors) : self._errorCursor]\n #Too complicated fuck me\n wrappingErrors = self.errors[self._errorCursor + max(0, self.maxErrors - newMaxErrors) : self.maxErrors]\n del self.errors\n freeSpace = [0] * (newMaxErrors - len(previousErrors) - len(wrappingErrors))\n self._errorCursor = (len(wrappingErrors) + len(previousErrors)) % newMaxErrors\n self.errors = wrappingErrors + previousErrors + freeSpace\n self.maxErrors = newMaxErrors\n self._errorSum = sum(self.errors)\n del previousErrors\n del wrappingErrors\n del freeSpace\n gc.collect()","def processErrors(self):\n e = self.formatErrors()\n if not self.config.nologtostdout:\n if e:\n print e\n else:\n print \"No errors\"\n if self.config.logtomail:\n if e:\n m = xeputils.mail.LogMail(self.config, e)\n m.send()\n if self.config.logtofile:\n f = open(self.config.logtofile, 'a')\n f.write(\"\\n===================\\n\")\n f.write(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n f.write(\"\\n===================\\n\")\n if e:\n f.write(e)\n else:\n f.write(\"No errors\")\n f.close()","def failed_replication_jobs(self, failed_replication_jobs):\n if self._configuration.client_side_validation and failed_replication_jobs is None:\n raise ValueError(\"Invalid value for `failed_replication_jobs`, must not be `None`\") # noqa: E501\n if (self._configuration.client_side_validation and\n failed_replication_jobs is not None and failed_replication_jobs < 0): # noqa: E501\n raise ValueError(\"Invalid value for `failed_replication_jobs`, must be a value greater than or equal to `0`\") # noqa: E501\n\n self._failed_replication_jobs = failed_replication_jobs","def init_matches_errors(self) -> None:\n\n self.matches = set()\n self.ignored = set()\n self.errors = set()","def form_invalid_add_global_errormessages(self, form):\n if self.get_selected_items_form_attribute() in form.errors:\n errormessages = form.errors[self.get_selected_items_form_attribute()]\n for errormessage in errormessages:\n messages.error(self.request, errormessage)","def record_error(self, message, keys=None, type=None, **kwargs):\n keys = list(keys) if keys is not None else []\n self.errors.append(\n dict(\n message=message,\n keys=keys,\n type=type or EntityErrors.UNCATEGORIZED,\n **kwargs\n )\n )","def _get_errors(exc):\n if hasattr(exc, 'message'):\n errors = exc.messages\n else:\n errors = [str(exc)]\n return errors","def report_errors(errors):\n if len(errors) > 0:\n for error in errors:\n logger.debug(error)\n sys.exit(0)","def WriteFlowErrors(self, errors):\n # Errors are similar to results, as they represent a somewhat related\n # concept. Error is a kind of a negative result. Given the structural\n # similarity, we can share large chunks of implementation between\n # errors and results DB code.\n self._WriteFlowResultsOrErrors(self.flow_errors, errors)","def setError(self, index, error):\n\t\tself.membersWithErrors[index][1] = error","def add_error(self, msg):\n self._add_message(msg, self._errors)","def error(self, error):\n\n self._error = error","def error(self, error):\n\n self._error = error","def error(self, error):\n\n self._error = error","def get_errors(self):\n df = self.get_events()\n return df[df.error.notnull()]","def error(self, message, code='UnknownError', error_code=None, http_status=400):\n\n # Some backwards compatibility\n if error_code is not None and code == 'UnknownError':\n code = error_code\n\n self._add_message( message, self.ERROR, code=code )\n self.n_errors += 1\n self.status = 'ERROR'\n self.http_status = http_status\n self.error_code = code\n self.message = message","def diagnostic_trouble_codes(self, diagnostic_trouble_codes):\n\n self._diagnostic_trouble_codes = diagnostic_trouble_codes","def CalculateErrors(self, X, D):\n Y = self.Forward(X)\n self.errors = len([Y[i] for i in range(len(Y)) if Y[i] != D[i]])\n return self.errors","def error(self, exception=None):\n self._error = exception","def pin_errors(self):\n for m in range(self.stage_width_list[-1]):\n error, _ = rqrmilib.calculate_submodel_error(self._get_native_object(), self.probe, len(self)-1, m)\n if error < 0: error = 0\n self.error_list[m] = int(error)\n self.rqrmi_state_changed = True\n return self.error_list","def display_errors(self):\r\n\r\n def format_name(field_name):\r\n \"\"\"Formats field names for error display\"\"\"\r\n if field_name == \"celebration_tier\":\r\n return \"{wLargesse{n\"\r\n return \"{w%s{n\" % field_name.capitalize()\r\n\r\n msg = \"Please correct the following errors:\\n\"\r\n msg += \"\\n\".join(\r\n \"%s: {r%s{n\" % (format_name(field), \", \".join(errs))\r\n for field, errs in self.errors.items()\r\n )\r\n return msg","def setErrorLog(self, *args):\n return _libsbml.XMLInputStream_setErrorLog(self, *args)","def error_entity(self, error_entity):\n \n self._error_entity = error_entity","def error_map(self):\n return self._error_map","def _update_errors_report(self, model_name, error_msg):\n errors_filename = os.path.join(self._get_results_path(), \"errors.md\")\n with open(errors_filename, \"a\") as fout:\n self.verbose_print(f\"There was an error during {model_name} training.\")\n self.verbose_print(f\"Please check {errors_filename} for details.\")\n fout.write(f\"## Error for {model_name}\\n\\n\")\n fout.write(error_msg)\n link = \"https://github.com/mljar/mljar-supervised/issues/new\"\n fout.write(\n f\"\\n\\nPlease set a GitHub issue with above error message at: {link}\"\n )\n fout.write(\"\\n\\n\")","def set_error(cls, ekindataset, dp, col):\n if ekindataset[col][dp]['var'] == '':\n return\n if not ekindataset[col][dp].has_key('error'):\n ekindataset[col][dp]['error'] = 0\n return","def error_reason(self, error_reason):\n\n self._error_reason = error_reason","def _setErrorNodes(self, errorNodes):\n self._errorNodes = errorNodes","def add_error(self, resource_type, seq, attr_name, error):\n resource_errors = self.errors.setdefault(resource_type, {})\n seq_errors = resource_errors.setdefault(seq, {})\n attr_errors = seq_errors.setdefault(attr_name, [])\n attr_errors.append(error)"],"string":"[\n \"def errors(self, errors):\\n\\n self._errors = errors\",\n \"def validation_errors(self, validation_errors):\\n self._validation_errors = validation_errors\",\n \"def add_errors(self, errors):\\n self.errors = merge_errors(self.errors, errors)\",\n \"def errors (self):\\n return self._errors\",\n \"def errors (self):\\n return self._errors\",\n \"def errors(self):\\n return self._errors\",\n \"def errors(self):\\n return self.__errors\",\n \"def Errors(self):\\n return self._get_attribute('errors')\",\n \"def errors(self):\\r\\n if not hasattr(self, '_errors_cache'):\\r\\n self._errors_cache = self.form.get_field_errors(self)\\r\\n return self._errors_cache\",\n \"def errors(self):\\n return self._properties.get(\\\"errors\\\")\",\n \"def errors(self) -> Tuple[MqexsErrorInfo, ...]:\\n return self.__errors\",\n \"def errors(self) -> pulumi.Output[Sequence['outputs.BatchAIErrorResponse']]:\\n return pulumi.get(self, \\\"errors\\\")\",\n \"def getErrors(self):\\n return self.errors\",\n \"def getErrorsList(self):\\n return self.__errors\",\n \"def Errors(self):\\r\\n\\t\\treturn self._get_attribute('errors')\",\n \"def validation_errors(self):\\n return self._validation_errors\",\n \"def errors(self):\\n _errors = {}\\n # pylint: disable=no-member\\n for name, field in self._fields.items():\\n if field.errors:\\n _errors[name] = field.errors.pop()\\n\\n return _errors\",\n \"def error(self, msg, transfers):\\n self.validation_exceptions.extend(self._create_exceptions(msg, transfers, ValidationType.ERROR))\",\n \"def errors(self) -> List[Error]:\\n return self._errors_files + list(self._errors.values())\",\n \"def errors(self):\\n raise NotImplementedError\",\n \"def set_limit(self, errors):\\n self.limit = errors\",\n \"def errors(self) -> str:\\n return self.job_errors() + self.analysis_errors()\",\n \"def on_errors(self, errors):\\n log.error(\\\"Received errors: %s\\\", errors)\",\n \"def get_errors(self):\\n return [result for result in self.values() if result.outcome == Result.ERROR]\",\n \"def get_errors(self, request):\\n\\n value = request._get_parameter_value(self)\\n return value.errors\",\n \"def error_message(self, error_message):\\n if (self.local_vars_configuration.client_side_validation and\\n error_message is not None and len(error_message) > 500):\\n raise ValueError(\\\"Invalid value for `error_message`, length must be less than or equal to `500`\\\") # noqa: E501\\n\\n self._error_message = error_message\",\n \"def retrieve_error_messages(self):\\n return self.errors_seen[:]\",\n \"def report_transaction_error_messages(self):\\n response = self.__get_transaction_response()\\n\\n # get response data from response object\\n response_data = response.json()\\n\\n # get error messages\\n response_error = response_data['Error']\\n response_error_messages = response_error['messages']\\n\\n # add all error messages to the report\\n error_messages_to_report = []\\n for response_error_message in response_error_messages:\\n error_description = response_error_message['description']\\n error_messages_to_report.append(error_description)\\n\\n return error_messages_to_report\",\n \"def errors(self) -> List[Error]:\",\n \"def analysis_errors(self) -> str:\\n errors = []\\n\\n # Get any callback errors\\n for cid, callback in self._analysis_callbacks.items():\\n if callback.status == AnalysisStatus.ERROR:\\n errors.append(f\\\"\\\\n[Analysis Callback ID: {cid}]: {callback.error_msg}\\\")\\n\\n return \\\"\\\".join(errors)\",\n \"def check_set_errors(self):\\n raise NotImplementedError(\\\"Implement it in a subclass.\\\")\",\n \"def get_validation_errors(self):\\n return [err.to_dict() for err in self._schema.validator.validation_errors]\",\n \"def error_data(self):\\n\\n if not self.__settings:\\n return []\\n\\n return self.__transaction_errors\",\n \"def errors(self) -> List[Error]:\\n # May have inherited errors with a different path.\\n for error in self._errors.values():\\n error.path = self.path\\n if self.is_removed: # Mark all of our errors as non-persistant.\\n error.is_persistant = False\\n return list(self._errors.values())\",\n \"def job_errors(self) -> str:\\n errors = []\\n\\n # Get any job errors\\n for job in self._jobs.values():\\n if job and job.status() == JobStatus.ERROR:\\n if hasattr(job, \\\"error_message\\\"):\\n error_msg = job.error_message()\\n else:\\n error_msg = \\\"\\\"\\n errors.append(f\\\"\\\\n[Job ID: {job.job_id()}]: {error_msg}\\\")\\n\\n # Get any job futures errors:\\n for jid, fut in self._job_futures.items():\\n if fut and fut.done() and fut.exception():\\n ex = fut.exception()\\n errors.append(\\n f\\\"[Job ID: {jid}]\\\"\\n \\\"\\\\n\\\".join(traceback.format_exception(type(ex), ex, ex.__traceback__))\\n )\\n return \\\"\\\".join(errors)\",\n \"def set_error(self, name, value):\\n self.errors[name] = value\",\n \"def error_count(self, error_count):\\n\\n self._error_count = error_count\",\n \"async def flush_errors(self, errors):\\n logger.error(errors)\\n data = {\\\"output\\\": {\\\"errors\\\": errors}, \\\"state\\\": \\\"completed\\\", \\\"status\\\": \\\"error\\\"}\\n await self.c_task.update(data)\",\n \"def errors(self):\\n\\n dict = {\\\"Stellar Mass Error\\\":[self.st_masserr1,self.st_masserr2],\\n \\\"Stellar Radius Error\\\":[self.st_raderr1,self.st_raderr2]}\\n\\n return dict\",\n \"def setErrorMessage(self, errorMessage):\\n self._errorMessage = errorMessage\",\n \"def mark_error(self):\\r\\n self.status = ERROR\",\n \"def error_message(self, error_message):\\n\\n self._error_message = error_message\",\n \"def _initialize_error_dictionaries(self):\\n for task_id in self.task_ids.keys():\\n self.training_errors[task_id] = []\\n self.validation_errors[task_id] = []\",\n \"def errors_and_warnings(self, errors_and_warnings):\\n\\n self._errors_and_warnings = errors_and_warnings\",\n \"def error(self) -> list:\\n return self.__err\",\n \"def errors_summary(self, errors_summary):\\n\\n self._errors_summary = errors_summary\",\n \"def all_errors(self) -> List[XMLSchemaParseError]:\\n errors = []\\n for comp in self.iter_components():\\n if comp.errors:\\n errors.extend(comp.errors)\\n return errors\",\n \"def getNumErrors(self):\\n return _libsbml.XMLErrorLog_getNumErrors(self)\",\n \"def setError(self,err):\\n self.error = err\",\n \"def getErrors(self) -> java.util.Collection:\\n ...\",\n \"def set_error(self, error):\\n self._set_sub_text('error', text=str(error))\\n return self\",\n \"def error_messages(self) -> List[str]:\\n spatial_msgs = []\\n temporal_msgs = []\\n if self.spatial:\\n spatial_msgs = [m for v, m in self.spatial_validations if not v(self.spatial)]\\n if self.temporal:\\n temporal_msgs = [m for v, m in self.temporal_validations if not v(self.temporal)]\\n\\n return spatial_msgs + temporal_msgs\",\n \"def error_message(self, error_message: str):\\n\\n self._error_message = error_message\",\n \"def _find_errors(self, new_response: response_domain_model.Response):\\n self._reset_child_state()\\n for response in self._responses_to_replay:\\n for question_code, conditional in self.requirements.items():\\n uncaught_errors = conditional.get_errors(response, question_code=question_code)\\n if uncaught_errors:\\n # Should not be able to get errors on previously passed responses\\n # (invalid answers should be ignored when checking conditionals)\\n raise Exception('Invalid answers found in previously checked responses')\\n\\n new_errors = []\\n for question_code, conditional in self.requirements.items():\\n new_errors.extend(conditional.get_errors(new_response, question_code=question_code))\\n\\n if new_errors:\\n # Recurse again until there are no new errors found\\n new_errors.extend(self._find_errors(new_response))\\n\\n return new_errors\",\n \"def email_error_report(self, digest_content, jats_content, error_messages):\\n datetime_string = time.strftime(utils.DATE_TIME_FORMAT, time.gmtime())\\n doi = None\\n if digest_content:\\n doi = digest_content.doi\\n body_content = requests_provider.error_email_body_content(\\n doi, jats_content, error_messages\\n )\\n body = email_provider.simple_email_body(datetime_string, body_content)\\n author = None\\n if digest_content:\\n author = digest_content.author\\n subject = requests_provider.error_email_subject_msid_author(\\n \\\"digest\\\", digest_provider.get_digest_msid(digest_content), author\\n )\\n sender_email = self.settings.digest_sender_email\\n\\n recipient_email_list = email_provider.list_email_recipients(\\n self.settings.digest_jats_error_recipient_email\\n )\\n\\n messages = email_provider.simple_messages(\\n sender_email, recipient_email_list, subject, body, logger=self.logger\\n )\\n self.logger.info(\\n \\\"Formatted %d error email messages in %s\\\" % (len(messages), self.name)\\n )\\n\\n details = email_provider.smtp_send_messages(\\n self.settings, messages, self.logger\\n )\\n self.logger.info(\\\"Email sending details: %s\\\" % str(details))\\n\\n return True\",\n \"def add_error_tables(self, error_tables):\\n self.error_distribution = error_tables\",\n \"def get_error_messages(self):\\n\\n if len(self._sensor_results_list) == 0:\\n return\\n\\n error_msgs = []\\n\\n for reading in self._sensor_results_list:\\n if reading.is_error():\\n error_msgs.append(reading.get_error_msg())\\n\\n if len(error_msgs) > 0:\\n return error_msgs\\n else:\\n return \\\"No Error Readings\\\"\",\n \"def error_count(self):\\n return len(self.errors)\",\n \"def __set_errors_json(self, error_count_by_operation, errors_by_operation):\\n message = \\\"{0} error/s reported.\\\".format(error_count_by_operation)\\n log_file_path = self.logger.file_logger.log_file_path\\n message += \\\" The latest {0} error/s are shared in detail. To view all errors, review this log file on the machine: {1}\\\".format(len(errors_by_operation), log_file_path) if error_count_by_operation > 0 else \\\"\\\"\\n return {\\n \\\"code\\\": Constants.PatchOperationTopLevelErrorCode.SUCCESS if error_count_by_operation == 0 else Constants.PatchOperationTopLevelErrorCode.ERROR,\\n \\\"details\\\": errors_by_operation,\\n \\\"message\\\": message\\n }\",\n \"def security_errors(self):\\n errors = ErrorDict()\\n for f in [\\\"honeypot\\\", \\\"timestamp\\\", \\\"security_hash\\\"]:\\n if f in self.errors:\\n errors[f] = self.errors[f]\\n return errors\",\n \"def GetAll(self):\\n return self._errors.copy()\",\n \"def errors():\\n return THE_LOGGER.errors\",\n \"def add_error(self, field, message):\\n add_list_value(self.errors, field, message)\",\n \"def error_recovery_settings(self, error_recovery_settings):\\n\\n self._error_recovery_settings = error_recovery_settings\",\n \"def setErrorThreshold(self, threshold):\\n return self._set(errorThreshold=threshold)\",\n \"def setErrorThreshold(self, threshold):\\n return self._set(errorThreshold=threshold)\",\n \"def _get_resp_body_errors(self):\\n\\n if self._resp_body_errors and len(self._resp_body_errors) > 0:\\n return self._resp_body_errors\\n\\n errors = []\\n warnings = []\\n resp_codes = []\\n\\n if self.verb is None:\\n return errors\\n\\n dom = self.response.dom()\\n if dom is None:\\n return errors\\n\\n for e in dom.findall('Errors'):\\n eSeverity = None\\n eClass = None\\n eShortMsg = None\\n eLongMsg = None\\n eCode = None\\n\\n try:\\n eSeverity = e.findall('SeverityCode')[0].text\\n except IndexError:\\n pass\\n\\n try:\\n eClass = e.findall('ErrorClassification')[0].text\\n except IndexError:\\n pass\\n\\n try:\\n eCode = e.findall('ErrorCode')[0].text\\n except IndexError:\\n pass\\n\\n try:\\n eShortMsg = smart_encode(e.findall('ShortMessage')[0].text)\\n except IndexError:\\n pass\\n\\n try:\\n eLongMsg = smart_encode(e.findall('LongMessage')[0].text)\\n except IndexError:\\n pass\\n\\n try:\\n eCode = e.findall('ErrorCode')[0].text\\n if int(eCode) not in resp_codes:\\n resp_codes.append(int(eCode))\\n except IndexError:\\n pass\\n\\n msg = str(\\\"Class: {eClass}, Severity: {severity}, Code: {code}, {shortMsg} {longMsg}\\\") \\\\\\n .format(eClass=eClass, severity=eSeverity, code=eCode, shortMsg=eShortMsg,\\n longMsg=eLongMsg)\\n\\n # from IPython import embed; embed()\\n\\n if eSeverity == 'Warning':\\n warnings.append(msg)\\n else:\\n errors.append(msg)\\n\\n self._resp_body_warnings = warnings\\n self._resp_body_errors = errors\\n self._resp_codes = resp_codes\\n\\n if self.config.get('warnings') and len(warnings) > 0:\\n log.warn(\\\"{verb}: {message}\\\\n\\\\n\\\".format(\\n verb=self.verb, message=\\\"\\\\n\\\".join(warnings)))\\n\\n if self.response.reply.Ack == 'Failure':\\n if self.config.get('errors'):\\n log.error(\\\"{verb}: {message}\\\\n\\\\n\\\".format(\\n verb=self.verb, message=\\\"\\\\n\\\".join(errors)))\\n\\n return errors\\n\\n return []\",\n \"def add_error(self, message):\\n self.errors.append(message)\",\n \"def setErrorMax(self, error_max):\\n\\t\\tself.error_max = error_max\",\n \"def getBuildErrors(self):\\n return [x for x in self.xeps if x.buildErrors]\",\n \"def sex_errors(self):\\n result = {}\\n disable_sex_check = app_settings.get_app_setting(\\n \\\"variants\\\", \\\"disable_pedigree_sex_check\\\", project=self\\n )\\n if disable_sex_check:\\n return result\\n for case in self.case_set.all():\\n result.update(case.sex_errors(disable_sex_check))\\n return result\",\n \"def set_max_errors(self,newMaxErrors):\\n previousErrors = self.errors[max(0, self._errorCursor - newMaxErrors) : self._errorCursor]\\n #Too complicated fuck me\\n wrappingErrors = self.errors[self._errorCursor + max(0, self.maxErrors - newMaxErrors) : self.maxErrors]\\n del self.errors\\n freeSpace = [0] * (newMaxErrors - len(previousErrors) - len(wrappingErrors))\\n self._errorCursor = (len(wrappingErrors) + len(previousErrors)) % newMaxErrors\\n self.errors = wrappingErrors + previousErrors + freeSpace\\n self.maxErrors = newMaxErrors\\n self._errorSum = sum(self.errors)\\n del previousErrors\\n del wrappingErrors\\n del freeSpace\\n gc.collect()\",\n \"def processErrors(self):\\n e = self.formatErrors()\\n if not self.config.nologtostdout:\\n if e:\\n print e\\n else:\\n print \\\"No errors\\\"\\n if self.config.logtomail:\\n if e:\\n m = xeputils.mail.LogMail(self.config, e)\\n m.send()\\n if self.config.logtofile:\\n f = open(self.config.logtofile, 'a')\\n f.write(\\\"\\\\n===================\\\\n\\\")\\n f.write(datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\"))\\n f.write(\\\"\\\\n===================\\\\n\\\")\\n if e:\\n f.write(e)\\n else:\\n f.write(\\\"No errors\\\")\\n f.close()\",\n \"def failed_replication_jobs(self, failed_replication_jobs):\\n if self._configuration.client_side_validation and failed_replication_jobs is None:\\n raise ValueError(\\\"Invalid value for `failed_replication_jobs`, must not be `None`\\\") # noqa: E501\\n if (self._configuration.client_side_validation and\\n failed_replication_jobs is not None and failed_replication_jobs < 0): # noqa: E501\\n raise ValueError(\\\"Invalid value for `failed_replication_jobs`, must be a value greater than or equal to `0`\\\") # noqa: E501\\n\\n self._failed_replication_jobs = failed_replication_jobs\",\n \"def init_matches_errors(self) -> None:\\n\\n self.matches = set()\\n self.ignored = set()\\n self.errors = set()\",\n \"def form_invalid_add_global_errormessages(self, form):\\n if self.get_selected_items_form_attribute() in form.errors:\\n errormessages = form.errors[self.get_selected_items_form_attribute()]\\n for errormessage in errormessages:\\n messages.error(self.request, errormessage)\",\n \"def record_error(self, message, keys=None, type=None, **kwargs):\\n keys = list(keys) if keys is not None else []\\n self.errors.append(\\n dict(\\n message=message,\\n keys=keys,\\n type=type or EntityErrors.UNCATEGORIZED,\\n **kwargs\\n )\\n )\",\n \"def _get_errors(exc):\\n if hasattr(exc, 'message'):\\n errors = exc.messages\\n else:\\n errors = [str(exc)]\\n return errors\",\n \"def report_errors(errors):\\n if len(errors) > 0:\\n for error in errors:\\n logger.debug(error)\\n sys.exit(0)\",\n \"def WriteFlowErrors(self, errors):\\n # Errors are similar to results, as they represent a somewhat related\\n # concept. Error is a kind of a negative result. Given the structural\\n # similarity, we can share large chunks of implementation between\\n # errors and results DB code.\\n self._WriteFlowResultsOrErrors(self.flow_errors, errors)\",\n \"def setError(self, index, error):\\n\\t\\tself.membersWithErrors[index][1] = error\",\n \"def add_error(self, msg):\\n self._add_message(msg, self._errors)\",\n \"def error(self, error):\\n\\n self._error = error\",\n \"def error(self, error):\\n\\n self._error = error\",\n \"def error(self, error):\\n\\n self._error = error\",\n \"def get_errors(self):\\n df = self.get_events()\\n return df[df.error.notnull()]\",\n \"def error(self, message, code='UnknownError', error_code=None, http_status=400):\\n\\n # Some backwards compatibility\\n if error_code is not None and code == 'UnknownError':\\n code = error_code\\n\\n self._add_message( message, self.ERROR, code=code )\\n self.n_errors += 1\\n self.status = 'ERROR'\\n self.http_status = http_status\\n self.error_code = code\\n self.message = message\",\n \"def diagnostic_trouble_codes(self, diagnostic_trouble_codes):\\n\\n self._diagnostic_trouble_codes = diagnostic_trouble_codes\",\n \"def CalculateErrors(self, X, D):\\n Y = self.Forward(X)\\n self.errors = len([Y[i] for i in range(len(Y)) if Y[i] != D[i]])\\n return self.errors\",\n \"def error(self, exception=None):\\n self._error = exception\",\n \"def pin_errors(self):\\n for m in range(self.stage_width_list[-1]):\\n error, _ = rqrmilib.calculate_submodel_error(self._get_native_object(), self.probe, len(self)-1, m)\\n if error < 0: error = 0\\n self.error_list[m] = int(error)\\n self.rqrmi_state_changed = True\\n return self.error_list\",\n \"def display_errors(self):\\r\\n\\r\\n def format_name(field_name):\\r\\n \\\"\\\"\\\"Formats field names for error display\\\"\\\"\\\"\\r\\n if field_name == \\\"celebration_tier\\\":\\r\\n return \\\"{wLargesse{n\\\"\\r\\n return \\\"{w%s{n\\\" % field_name.capitalize()\\r\\n\\r\\n msg = \\\"Please correct the following errors:\\\\n\\\"\\r\\n msg += \\\"\\\\n\\\".join(\\r\\n \\\"%s: {r%s{n\\\" % (format_name(field), \\\", \\\".join(errs))\\r\\n for field, errs in self.errors.items()\\r\\n )\\r\\n return msg\",\n \"def setErrorLog(self, *args):\\n return _libsbml.XMLInputStream_setErrorLog(self, *args)\",\n \"def error_entity(self, error_entity):\\n \\n self._error_entity = error_entity\",\n \"def error_map(self):\\n return self._error_map\",\n \"def _update_errors_report(self, model_name, error_msg):\\n errors_filename = os.path.join(self._get_results_path(), \\\"errors.md\\\")\\n with open(errors_filename, \\\"a\\\") as fout:\\n self.verbose_print(f\\\"There was an error during {model_name} training.\\\")\\n self.verbose_print(f\\\"Please check {errors_filename} for details.\\\")\\n fout.write(f\\\"## Error for {model_name}\\\\n\\\\n\\\")\\n fout.write(error_msg)\\n link = \\\"https://github.com/mljar/mljar-supervised/issues/new\\\"\\n fout.write(\\n f\\\"\\\\n\\\\nPlease set a GitHub issue with above error message at: {link}\\\"\\n )\\n fout.write(\\\"\\\\n\\\\n\\\")\",\n \"def set_error(cls, ekindataset, dp, col):\\n if ekindataset[col][dp]['var'] == '':\\n return\\n if not ekindataset[col][dp].has_key('error'):\\n ekindataset[col][dp]['error'] = 0\\n return\",\n \"def error_reason(self, error_reason):\\n\\n self._error_reason = error_reason\",\n \"def _setErrorNodes(self, errorNodes):\\n self._errorNodes = errorNodes\",\n \"def add_error(self, resource_type, seq, attr_name, error):\\n resource_errors = self.errors.setdefault(resource_type, {})\\n seq_errors = resource_errors.setdefault(seq, {})\\n attr_errors = seq_errors.setdefault(attr_name, [])\\n attr_errors.append(error)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7063868","0.6316049","0.62133807","0.5995449","0.5995449","0.5993789","0.5955976","0.58611673","0.5738866","0.57359105","0.56898946","0.56828547","0.56606567","0.56536406","0.5637317","0.5577304","0.5540206","0.5453656","0.54534554","0.54445463","0.5427907","0.5297211","0.5279484","0.525997","0.5244819","0.5221872","0.5194199","0.51878995","0.5151027","0.51397586","0.5121268","0.5107964","0.50943774","0.5080314","0.50717455","0.506621","0.50495","0.50426507","0.5004578","0.5003011","0.49701798","0.4967973","0.49484527","0.49450395","0.49142504","0.49068043","0.48854777","0.48778656","0.4864789","0.48622867","0.48615256","0.4830137","0.4813637","0.48103064","0.4775602","0.47728068","0.47710347","0.47584313","0.47584012","0.47515887","0.47194967","0.47092918","0.47020674","0.4701304","0.4700421","0.4700421","0.4686703","0.46864355","0.46795753","0.46674326","0.46588477","0.4658053","0.4642491","0.46408594","0.4626274","0.46254867","0.4603615","0.45933828","0.45904434","0.45894766","0.4588426","0.45809337","0.45782277","0.45782277","0.45782277","0.45703274","0.45577374","0.4540596","0.45389873","0.45238033","0.4520139","0.45176798","0.4509601","0.45089304","0.4501302","0.44994268","0.44923404","0.44890392","0.4487223","0.4486627"],"string":"[\n \"0.7063868\",\n \"0.6316049\",\n \"0.62133807\",\n \"0.5995449\",\n \"0.5995449\",\n \"0.5993789\",\n \"0.5955976\",\n \"0.58611673\",\n \"0.5738866\",\n \"0.57359105\",\n \"0.56898946\",\n \"0.56828547\",\n \"0.56606567\",\n \"0.56536406\",\n \"0.5637317\",\n \"0.5577304\",\n \"0.5540206\",\n \"0.5453656\",\n \"0.54534554\",\n \"0.54445463\",\n \"0.5427907\",\n \"0.5297211\",\n \"0.5279484\",\n \"0.525997\",\n \"0.5244819\",\n \"0.5221872\",\n \"0.5194199\",\n \"0.51878995\",\n \"0.5151027\",\n \"0.51397586\",\n \"0.5121268\",\n \"0.5107964\",\n \"0.50943774\",\n \"0.5080314\",\n \"0.50717455\",\n \"0.506621\",\n \"0.50495\",\n \"0.50426507\",\n \"0.5004578\",\n \"0.5003011\",\n \"0.49701798\",\n \"0.4967973\",\n \"0.49484527\",\n \"0.49450395\",\n \"0.49142504\",\n \"0.49068043\",\n \"0.48854777\",\n \"0.48778656\",\n \"0.4864789\",\n \"0.48622867\",\n \"0.48615256\",\n \"0.4830137\",\n \"0.4813637\",\n \"0.48103064\",\n \"0.4775602\",\n \"0.47728068\",\n \"0.47710347\",\n \"0.47584313\",\n \"0.47584012\",\n \"0.47515887\",\n \"0.47194967\",\n \"0.47092918\",\n \"0.47020674\",\n \"0.4701304\",\n \"0.4700421\",\n \"0.4700421\",\n \"0.4686703\",\n \"0.46864355\",\n \"0.46795753\",\n \"0.46674326\",\n \"0.46588477\",\n \"0.4658053\",\n \"0.4642491\",\n \"0.46408594\",\n \"0.4626274\",\n \"0.46254867\",\n \"0.4603615\",\n \"0.45933828\",\n \"0.45904434\",\n \"0.45894766\",\n \"0.4588426\",\n \"0.45809337\",\n \"0.45782277\",\n \"0.45782277\",\n \"0.45782277\",\n \"0.45703274\",\n \"0.45577374\",\n \"0.4540596\",\n \"0.45389873\",\n \"0.45238033\",\n \"0.4520139\",\n \"0.45176798\",\n \"0.4509601\",\n \"0.45089304\",\n \"0.4501302\",\n \"0.44994268\",\n \"0.44923404\",\n \"0.44890392\",\n \"0.4487223\",\n \"0.4486627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7172415"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":97,"cells":{"query":{"kind":"string","value":"Calculate matrix of number of edits to convert every subset of y to every subset of x"},"document":{"kind":"string","value":"def distance_matrix(self, x, y, keyboard_weight=None):\r\n # create distance matrix\r\n size_x = len(x) + 1\r\n size_y = len(y) + 1\r\n dist_matrix = np.zeros((size_x, size_y))\r\n for i in range(size_x):\r\n dist_matrix[i, 0] = i\r\n for j in range(size_y):\r\n dist_matrix[0, j] = j\r\n\r\n ## fill distance matrix\r\n # no keyboard weight\r\n if not keyboard_weight:\r\n for i in range(1, size_x):\r\n for j in range(1, size_y):\r\n # if letters are same\r\n if x[i-1] == y[j-1]:\r\n dist_matrix[i, j] = dist_matrix[i-1, j-1]\r\n # if letters are different\r\n else:\r\n subs = dist_matrix[i-1, j-1] + 1\r\n delete = dist_matrix[i-1, j] + 1\r\n insert = dist_matrix[i, j-1] + 1 \r\n dist_matrix[i, j] = min(subs, delete, insert)\r\n # manhattan keyboard weight\r\n elif keyboard_weight == \"manhattan\":\r\n for i in range(1, size_x):\r\n for j in range(1, size_y):\r\n # if letters are same\r\n if x[i-1] == y[j-1]:\r\n dist_matrix[i, j] = dist_matrix[i-1, j-1]\r\n # if letters are different\r\n else:\r\n dist = self.key_distance(x[i-1], y[j-1], keyboard_weight)\r\n subs_weight = dist * self.manhattan_coef\r\n subs = dist_matrix[i-1, j-1] + subs_weight\r\n delete = dist_matrix[i-1, j] + 1\r\n insert = dist_matrix[i, j-1] + 1 \r\n dist_matrix[i, j] = min(subs, delete, insert)\r\n # euclidean keyboard weight\r\n elif keyboard_weight == \"euclidean\":\r\n for i in range(1, size_x):\r\n for j in range(1, size_y):\r\n # if letters are same\r\n if x[i-1] == y[j-1]:\r\n dist_matrix[i, j] = dist_matrix[i-1, j-1]\r\n # if letters are different\r\n else:\r\n dist = self.key_distance(x[i-1], y[j-1], keyboard_weight)\r\n subs_weight = dist * self.euclidean_coef\r\n subs = dist_matrix[i-1, j-1] + subs_weight\r\n delete = dist_matrix[i-1, j] + 1\r\n insert = dist_matrix[i, j-1] + 1 \r\n dist_matrix[i, j] = min(subs, delete, insert)\r\n \r\n return dist_matrix"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def topsolutions(self):\n answers = []\n for y in xrange(0, self.y):\n answer = self.retrieve(y,self.y)\n i = 0\n for x in xrange(0,y):\n answer -= self.retrieve(y,x)*answers[i]\n i += 1\n answers.append(answer)\n return answers","def build_set(x, y):\n # E_w[yy^T]\n y_y_t = la.inv(np.dot(y, y.transpose()))\n h_matrix = np.dot(np.dot(x, y), y_y_t)\n return h_matrix","def transform(self, y):\n\n y = self.ensure_output_format(\n y, sparse_format='lil', enforce_sparse=True)\n\n self.clean()\n self.label_count = y.shape[1]\n\n last_id = 0\n train_vector = []\n for labels_applied in y.rows:\n label_string = \",\".join(map(str, labels_applied))\n\n if label_string not in self.unique_combinations:\n self.unique_combinations[label_string] = last_id\n self.reverse_combinations.append(labels_applied)\n last_id += 1\n\n train_vector.append(self.unique_combinations[label_string])\n\n return np.array(train_vector)","def _get_edit_distance_matrix(x: str, y: str) -> list:\n matrix = [[-1 for _ in range(len(y) + 1)] for _ in range(len(x) + 1)]\n\n for j in range(len(matrix[0])):\n matrix[0][j] = j\n\n for i, _ in enumerate(matrix):\n matrix[i][0] = i\n\n return matrix","def editDist(X, Y, m, n, normalize_len=False):\n if normalize_len:\n if m < n:\n quot = n // m\n new_X = []\n for i in range(quot):\n new_X += X\n X = new_X\n # print(\"Normalized length for X: {}\".format(len(X)))\n else:\n quot = m // n\n new_Y = []\n for i in range(quot):\n new_Y += Y\n Y = new_Y\n # print(\"Normalized length for Y: {}\".format(len(Y)))\n\n\n dp = [[0 for x in range(n+1)] for x in range(m+1)]\n for i in range(m+1):\n for j in range(n+1):\n\n if i == 0:\n dp[i][j] = j\n elif j == 0:\n dp[i][j] = i\n elif X[i-1] == Y[j-1]:\n dp[i][j] = dp[i - 1][j - 1]\n else:\n dp[i][j] = 1 + min(dp[i][j - 1], dp[i-1][j], dp[i-1][j-1])\n\n return dp[m][n]","def p_y_x_knn(y, k):\n result = np.zeros((len(y), 4))\n for i in range(len(y)):\n for j in range(k):\n result[i, y[i, j]] = result[i, y[i, j]] + 1\n return 1 / k * result","def transform( self, X, y = None ):\n matrix = np.zeros((len(X),len(self.feature_names)))\n for i,bag in enumerate(X):\n for test in bag:\n try:\n matrix[i,self.feature_names.index(test)] = 1\n except ValueError:\n pass\n return matrix","def toCartesian(self, y):\r\n return Size - y","def add_matrices(x, y):\n return [[x[i][j] + y[i][j] for j in range(len(x[0]))] for i in range(len(x))]","def associate_comp(x, y):\n return torch.cat([x[:1] * y[:1] - x[1:] * y[1:], x[:1] * y[1:] + x[1:] * y[:1]])","def UpdateCostMatrix( self, extraXs ):\n for x in extraXs:\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\n self.C.append(newRow)","def copnorm_cat_1d(x, y):\n assert isinstance(x, np.ndarray) and (x.ndim == 1)\n assert isinstance(y, np.ndarray) and (x.ndim == 1)\n assert y.dtype in CONFIG['INT_DTYPE']\n x_cop = np.zeros_like(x)\n y_u = np.unique(y)\n for yi in y_u:\n _idx = y == yi\n x_cop[_idx] = copnorm_1d(x[_idx])\n return x_cop","def cost_matrix(x, y, p=2):\n xc = tf.expand_dims(x, 1)\n yr = tf.expand_dims(y, 0)\n d = tf.math.pow(tf.abs(xc - yr), p)\n return tf.reduce_sum(d, axis=-1)","def fillCostMatrix(xs0,ys0,xs1,ys1):\n M = int ( max(len(xs0),len(xs1)) ) #Number of centroids.\n costMatrix = np.ones((M,M))*-1\n x_rows = np.zeros(M)\n x_rows[0:len(xs0)] = xs0\n y_rows = np.zeros(M)\n y_rows[0:len(xs0)] = ys0\n \n x_cols = np.zeros(M)\n x_cols[0:len(xs1)] = xs1\n y_cols = np.zeros(M)\n y_cols[0:len(xs1)] = ys1\n\n for i in range(len(xs0)):\n for j in range(len(xs1)):\n costMatrix[i,j]=(y_rows[i]-y_cols[j])**2\n costMatrix[i,j] += (x_rows[i]-x_cols[j])**2\n return costMatrix","def reconstruct_input(self, ix):","def inverse_transform(self, y):\n n_samples = len(y)\n result = sparse.lil_matrix((n_samples, self.label_count), dtype='i8')\n for row in range(n_samples):\n assignment = y[row]\n result[row, self.reverse_combinations[assignment]] = 1\n\n return result","def countNeighbors(oldgen, x, y):\n temp = 1\n\n count = 0\n for i in range(-1, 2):\n for j in range(-1, 2):\n\n # TODO: this needs rewritin to be more understandable\n if not (i == 0 and j == 0):\n count += int(oldgen[(x + i + WID) % WID][(y + j + HGT) % HGT])\n\n for i in range(-1, 2):\n for j in range(-1, 2):\n temp += 1\n\n count -= int(oldgen[x][y])\n\n return count","def init_output_mat(self, y_list):","def associate(x, y):\n x = torch.cat([x[1:], x])\n xx, yy = x.reshape(1,1,-1), y.flip(0).reshape(1,1,-1)\n zz = torch.nn.functional.conv1d(xx, yy)\n z = zz.reshape(-1)\n return normalize(z)","def fillCostMatrix(xs0,ys0,xs1,ys1):\n M = int ( max(len(xs0),len(xs1)) ) #Number of centroids.\n costMatrix = np.zeros((M,M))\n x_rows = np.zeros(M)\n x_rows[0:len(xs0)] = xs0\n y_rows = np.zeros(M)\n y_rows[0:len(xs0)] = ys0\n \n x_cols = np.zeros(M)\n x_cols[0:len(xs1)] = xs1\n y_cols = np.zeros(M)\n y_cols[0:len(xs1)] = ys1\n\n for i in range(len(xs0)):\n for j in range(len(xs1)):\n costMatrix[i,j]=(y_rows[i]-y_cols[j])**2\n costMatrix[i,j] += (x_rows[i]-x_cols[j])**2\n return costMatrix","def alloc2d(x,y,iv=0):\n return [[iv for j in range(int(x))] for i in range(int(y))]","def dim_reduction(data_set, components):\n transformed = []\n index = -1\n transformed = data_set @ components\n return transformed","def inverse_transform(self, y: Array2D) -> Array2D:","def _lcs(x, y):\n n, m = len(x), len(y)\n table = dict()\n for i in range(n + 1):\n for j in range(m + 1):\n if i == 0 or j == 0:\n table[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n table[i, j] = table[i - 1, j - 1] + 1\n else:\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\n return table","def _lcs(x, y):\n n, m = len(x), len(y)\n table = dict()\n for i in range(n + 1):\n for j in range(m + 1):\n if i == 0 or j == 0:\n table[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n table[i, j] = table[i - 1, j - 1] + 1\n else:\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\n return table","def transform(self, x: Array2D) -> Array2D:","def transform(self,X):\n # conver X to a list\n X = X.tolist()\n result = []\n\n # iterate over the length of X\n for b in range(len(X)):\n\n # change dataset accoring to bias\n if self.include_bias:\n X[b].insert(0, 1)\n \n # initialize an array to store dynamically all array of indices\n init_arr = []\n for j in range(len(X[b])):\n init_arr.append([j])\n\n # array of indices\n arr = [j for j in range(len(X[b]))]\n separate_arr = init_arr.copy()\n\n # iterate for the degree given\n for k in range(0,self.degree-1):\n # for len of the array containing indices\n for i in range(len(arr)):\n temp = i\n # this loop will have different length since length increases\n for j in range((k)*len(arr),len(separate_arr)):\n element = init_arr[j].copy()\n element.append(temp)\n init_arr.append(element) \n separate_arr = init_arr.copy()\n # sort the array obtained to remove repeated elements\n array = []\n for m in range(len(init_arr)):\n init_arr[m].sort()\n if(init_arr[m] not in array):\n array.append(init_arr[m])\n\n # calculate the final values by multiplying the numbers or columns at the place of indices\n final = []\n for i in array:\n lst = []\n # only if lenth satisfies the given degree\n if len(i)==self.degree:\n for j in i: \n lst.append(X[b][j]) \n final.append(np.product(lst))\n result.append(final)\n return result","def solutions(self):\n answers = []\n for y in reversed(xrange(0, self.y)):\n answer = self.retrieve(y,self.y)\n i = 0\n for x in reversed(xrange(y+1, self.y)):\n answer -= self.retrieve(y,x)*answers[i]\n i += 1\n answers.append(answer)\n answers.reverse()\n return answers","def transform(self, y):\n return self.cidx_by_size_[self.le_.transform(y)]","def get_imin(x1, x2, y, k=1, normalize=None, norm=np.inf):\n\n if normalize:\n y = normalize(y)\n\n y_tree = cKDTree(y)\n\n n = len(y)\n i_spec = np.zeros((2, n))\n\n for jj, x in enumerate([x1, x2]):\n\n if normalize:\n x = normalize(x)\n\n # construct state array for the joint processes:\n xy = np.c_[x,y]\n\n # store data pts in kd-trees for efficient nearest neighbour computations\n # TODO: choose a better leaf size\n x_tree = cKDTree(x)\n xy_tree = cKDTree(xy)\n\n # kth nearest neighbour distances for every state\n # query with k=k+1 to return the nearest neighbour, not counting the data point itself\n # dist, idx = xy_tree.query(xy, k=k+1, p=norm)\n dist, idx = xy_tree.query(xy, k=k+1, p=np.inf)\n epsilon = dist[:, -1]\n\n # for each point, count the number of neighbours\n # whose distance in the x-subspace is strictly < epsilon\n # repeat for the y subspace\n nx = np.empty(n, dtype=np.int)\n ny = np.empty(n, dtype=np.int)\n for ii in xrange(N):\n # nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=norm)) - 1\n # ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=norm)) - 1\n nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\n ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\n\n i_spec[jj] = digamma(k) - digamma(nx+1) + digamma(ny+1) + digamma(n) # version (1)\n\n i_min = np.mean(np.min(i_spec, 0))\n\n return i_min","def _lcs(x, y):\n n, m = len(x), len(y)\n table = dict()\n for i in range(n + 1):\n for j in range(m + 1):\n if i == 0 or j == 0:\n table[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n table[i, j] = table[i - 1, j - 1] + 1\n else:\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\n return table","def create(matrix):\n limit_y = len(matrix)\n limit_x = len(matrix[0])\n\n for y in range(1, limit_y):\n bit.create(matrix[y])\n\n for x in range(1, limit_x):\n for y in range(1, limit_y):\n k = y + (y & -y)\n if k < limit_y:\n matrix[k][x] += matrix[y][x]","def spaceeff_dyna(x, y, c):\n n = len(y)\n m = len(x)\n for i in range(1, m + 1):\n c[0][1] = 0\n for j in range(1, n + 1):\n if x[i - 1] == y[j - 1]:\n c[j][1] = c[j - 1][0] + 1\n else:\n ctop = c[j][0]\n cleft = c[j - 1][1]\n ctopleft = c[j - 1][0]\n\n c[j][1], d = max3(ctopleft, ctop, cleft)\n for k in range(len(c)):\n c[k][0] = c[k][1]","def matrixReduction(setHor, setVer, arrayToReduce):\r\n listTemp = []\r\n for i in range(len(setVer)):\r\n listTemp.append(arrayToReduce[setVer[i].index, :])\r\n arrayTemp = numpy.array(listTemp)\r\n listTemp = []\r\n for i in range(len(setHor)):\r\n listTemp.append(arrayTemp[:, setHor[i].index])\r\n result = numpy.transpose(numpy.array(listTemp))\r\n\r\n return result","def ipset_num_x_y_different():\n return IPSet(x=np.linspace(0, 10, 11), y=np.linspace(-1, 1, 3), x_new=np.linspace(2, 5, 4))","def make_likelihood_table(x, y):\n\n Y = np.unique(y)\n X = np.unique(x)\n\n likelihood = [[0 for i in range(len(Y))] for j in range(len(X))]\n\n freq = make_frequency_table(x, y, X, Y)\n\n for j in range(len(Y)):\n Sum = (y == Y[j]).sum()\n for i in range(len(X)):\n likelihood[i][j] = freq[X[i]][j] / Sum\n\n return likelihood","def fit_transform(self, x: Array2D) -> Array2D:","def _permute_observations(x, y, num_perms):\r\n vals = hstack([array(x), array(y)])\r\n lenx = len(x)\r\n # sorting step is unnecessary for this code, but it ensure that test code\r\n # which relies on seeding the prng works (if we dont do this then different\r\n # observation orders in x and y for eg. the mc_t_two_sample test will fail\r\n # to produce the same results)\r\n vals.sort()\r\n inds = arange(vals.size)\r\n xs, ys = [], []\r\n for i in range(num_perms):\r\n shuffle(inds)\r\n xs.append(vals[inds[:lenx]])\r\n ys.append(vals[inds[lenx:]])\r\n return xs, ys","def _lev(x, y):\n mat = initialize_matrix(x, y)\n\n for i in range(1, len(y)+1):\n for j in range(1, len(x)+1):\n mat[i].append(_generate_new_node(mat, i, j, x, y))\n\n return mat","def _lev(x, y):\n mat = initialize_matrix(x, y)\n\n for i in range(1, len(y)+1):\n for j in range(1, len(x)+1):\n mat[i].append(_generate_new_node(mat, i, j, x, y))\n\n return mat","def brute_multiply(x, y):\n \n n = x.shape[0]\n res = np.zeros(x.shape)\n \n for i in range(n):\n for j in range(n):\n for k in range(n):\n res[i, j] += x[i, k] * y[k, j]\n \n return res","def filt1(X, yvals, xvals, ny, nx):\n \n ylen = X.shape[0]\n xlen = X.shape[1]\n\n yflen = (ylen-1)//ny\n xflen = (xlen-1)//nx\n\n Y = np.zeros((X.shape))\n\n #Y = Y[0:yflen,0:xflen,]\n\n ymax = ny*yflen+1\n xmax = nx*xflen+1\n\n Y = Y[0:ymax,0:xmax,]\n Xnew = X[0:ymax,0:xmax,]\n yvals = yvals[0:ymax,0:xmax,]\n xvals = xvals[0:ymax,0:xmax,] \n\n counter = np.zeros((Y.shape))\n \n for i in range(xflen):\n xmin = nx*i\n xmax = nx*(i+1)+1\n for j in range(yflen):\n ymin = ny*j\n ymax = ny*(j + 1)+1\n #print((xmin,xmax), (ymin,ymax))\n Y[ymin:ymax,xmin:xmax,] = Y[ymin:ymax,xmin:xmax,] + np.mean(X[ymin:ymax,xmin:xmax,], axis=(0,1))\n counter[ymin:ymax,xmin:xmax,] = counter[ymin:ymax,xmin:xmax,] + 1\n\n Y = Y/counter #We take the average of the points that appear more than once\n\n return Xnew, Y, yvals, xvals","def fit(self, x, y):\n tempdf = pd.DataFrame({'x':x, 'y':y})\n self.d = tempdf.groupby('x').apply(lambda g: g.y.sum()/len(g)).to_dict()","def cemap_cal(y_pred,y_true):\r\n nTest = y_true.shape[0]\r\n nLabel = y_true.shape[1]\r\n ap = np.zeros(nTest)\r\n for i in range(0,nTest):\r\n for j in range(0,nLabel):\r\n R = np.sum(y_true[i,:])\r\n if y_true[i,j]==1:\r\n r = np.sum(y_pred[i,:]>=y_pred[i,j])\r\n rb = np.sum(y_pred[i,np.nonzero(y_true[i,:])] >= y_pred[i,j])\r\n ap[i] = ap[i] + rb/(r*1.0)\r\n ap[i] = ap[i]/R\r\n imap = np.nanmean(ap)\r\n\r\n ap = np.zeros(nLabel)\r\n for i in range(0,nLabel):\r\n for j in range(0,nTest):\r\n R = np.sum(y_true[:,i])\r\n if y_true[j,i]==1:\r\n r = np.sum(y_pred[:,i] >= y_pred[j,i])\r\n rb = np.sum(y_pred[np.nonzero(y_true[:,i]),i] >= y_pred[j,i])\r\n ap[i] = ap[i] + rb/(r*1.0)\r\n ap[i] = ap[i]/R\r\n lmap = np.nanmean(ap)\r\n\r\n return lmap,imap","def fit(self, X, y):\n # TODO: Implement\n self.cols = X.columns\n self.nCols = len(self.cols)\n X = np.array(X)\n y = np.array(y)\n \n for i in range(X.shape[1]): \n uniq = np.unique(X[:, i])\n self.possible.append(list(uniq)) # add possible values\n self.valN.append(len(uniq)) # and how many\n index = np.argmax(self.valN)\n print(index)\n self.tree = self.buildTree(X, y)","def ipset_y_2d():\n return IPSet(x=np.linspace(0, 10, 11), y=np.random.randn(11, 4), x_new=np.linspace(1, 4, 3))","def sum_outers(x, y):\n # In PyTorch 4.0, `einsum` modifies variables inplace. This will not work\n # unless you have PyTorch 4.1:\n #\n # https://github.com/pytorch/pytorch/issues/7763\n #\n return torch.einsum('ab,cb->ac', [x, y])","def UpdateS(s, Difference, WorkingSet):\n for i in range(len(x_train)):\n Sum = 0.0\n for j in range(q):\n Sum = Sum + (Difference[j])*y_train[int(WorkingSet[j,0])]*Kernel(i, int(WorkingSet[j,0]))\n s[i] = s[i] + Sum\n return s","def _diffmat_objective(a,X):\n \n (n,p) = X.shape\n return(X - np.tile(a,(n,1)))","def multiple(x, y):\n curRow = [1] * x\n for _ in range(1,y):\n for N in range(1,x):\n curRow[N] = curRow[N-1] + curRow[N]\n return curRow[x-1]","def interpolV(y, x, newX):\r\n \r\n num = len(x)\r\n #if (num != len(y)):\r\n #//System.out.println(\"Toolbox.interpolV(): Old x and y must be same length\"); \r\n \r\n newNum = len(newX)\r\n #//System.out.println(\"interpolV: newNum \" + newNum + \" num \" + num); \r\n #newY = [0.0 for i in range(newNum)]\r\n\r\n#//Renormalize ordinates:\r\n \r\n iMinAndMax = minMax(y)\r\n norm = y[iMinAndMax[1]]\r\n #//System.out.println(\"norm \" + norm);\r\n #yNorm = [0.0 for i in range(num)]\r\n newYNorm = [0.0 for i in range(newNum)] \r\n #for i in range(num):\r\n # yNorm[i] = y[i] / norm \r\n yNorm = [ x / norm for x in y ]\r\n\r\n#// Set any newX elements that are *less than* the first x element to th first \r\n#// x element - \"0th order extrapolation\"\r\n#//\r\n start = 0\r\n for i in range(newNum):\r\n if (newX[i] <= x[1]):\r\n newYNorm[i] = yNorm[0]\r\n start += 1\r\n \r\n if (newX[i] > x[1]):\r\n break\r\n \r\n \r\n#//System.out.println(\"start \" + start);\r\n#//System.out.println(\"x[0] \" + x[0] + \" x[1] \" + x[1] + \" newX[start] \" + newX[start]);\r\n#double jWght, jm1Wght, denom;\r\n\r\n\r\n if (start < newNum-1):\r\n\r\n j = 1 #//initialize old abscissae index\r\n #//outer loop over new abscissae\r\n for i in range(start, newNum):\r\n\r\n #//System.out.println(\"i \" + i + \" j \" + j);\r\n\r\n#// break out if current element newX is *greater* that last x element\r\n if ( (newX[i] > x[num-1]) or (j > (num-1)) ):\r\n break \r\n \r\n\r\n while (x[j] < newX[i]): \r\n j += 1\r\n \r\n #//System.out.println(\"i \" + i + \" newX[i] \" + newX[i] + \" j \" + j + \" x[j-1] \" + x[j-1] + \" x[j] \" + x[j]);\r\n #//1st order Lagrange method:\r\n jWght = newX[i] * (1.0 - (x[j-1]/newX[i])) #//(newX[i]-x[j-1])\r\n jm1Wght = x[j] * (1.0 - (newX[i]/x[j])) #//(x[j]-newX[i])\r\n denom = x[j] * (1.0 - (x[j-1]/x[j])) #//(x[j]-x[j-1])\r\n jWght = jWght / denom\r\n jm1Wght = jm1Wght / denom\r\n #//newYNorm[i] = (yNorm[j]*(newX[i]-x[j-1])) + (yNorm[j-1]*(x[j]-newX[i]));\r\n newYNorm[i] = (yNorm[j]*jWght) + (yNorm[j-1]*jm1Wght)\r\n #//System.out.println(\"i \" + i + \" newYNorm[i] \" + newYNorm[i] + \" j \" + j + \" yNorm[j-1] \" + yNorm[j-1] + \" yNorm[j] \" + yNorm[j]);\r\n \r\n\r\n#// Set any newX elements that are *greater than* the first x element to the last \r\n#// x element - \"0th order extrapolation\"\r\n#//\r\n for i in range(newNum):\r\n if (newX[i] >= x[num-1]):\r\n newYNorm[i] = yNorm[num-1]\r\n \r\n \r\n\r\n #//Restore orinate scale\r\n #for i in range(newNum):\r\n # newY[i] = newYNorm[i] * norm \r\n newY = [ x * norm for x in newYNorm ]\r\n\r\n\r\n return newY","def back_spaceeff_dyna(x, y, c):\n n = len(y)\n m = len(x)\n for i in range(m - 1, -1, -1):\n c[n][1] = 0\n for j in range(n - 1, -1, -1):\n if x[i] == y[j]:\n c[j][1] = c[j + 1][0] + 1\n else:\n cbottom = c[j][0]\n cright = c[j + 1][1]\n cbottomright = c[j + 1][0]\n\n c[j][1], d = max3(cbottomright, cbottom, cright)\n for k in range(len(c)):\n c[k][0] = c[k][1]","def expand_features_and_labels(x_feat, y_labels):\n x_expanded = []\n y_expanded = []\n for x, y in zip(x_feat, y_labels):\n for segment in x:\n x_expanded.append(segment)\n y_expanded.append(y)\n return x_expanded, y_expanded","def transform(self, X, y=None):\n rows = []\n cols = []\n data = []\n #Loop through each reviews\n for row, word_count in enumerate(X):\n #Analyze each review with the total vocab of this dataset\n for word, count in word_count.items():\n rows.append(row)\n cols.append(self.vocabulary_.get(word, 0)) #If non, replace it with 0\n data.append(count)\n \n return csr_matrix((data, (rows, cols)), shape=(len(X), self.vocabulary_size + 1))","def update_grid(self, x):\r\n\r\n # Append boundary rows and columns to matrix\r\n x = self.append_boundary(x) # the boundary is recomputed at each step\r\n y = np.copy(x)\r\n\r\n # For each cell within boundary, compute state according to rules.\r\n chg_0_1 = 0 # the number of cells that changed from state 0 to state 1\r\n chg_1_0 = 0 # the number of cells that changes from state 1 to state 0\r\n chg_none = 0 # the number of cells that did not change\r\n index = np.arange(1, x.shape[0] - 1)\r\n for i in index:\r\n for j in index:\r\n neighborhood = x[i - 1:i + 2:1, j - 1:j + 2:1] # 3x3 sub matrix centered at i, j\r\n y[i, j] = self.update_cell(neighborhood)\r\n change = int(y[i, j] - x[i, j])\r\n if change == -1:\r\n chg_1_0 += 1\r\n if change == 0:\r\n chg_none += 1\r\n if change == 1:\r\n chg_0_1 += 1\r\n\r\n # Compute statistics excluding boundary\r\n total = np.power(x[1:-1:1, 1:-1:1].shape[0] - 1, 2)\r\n start_1 = np.sum(x[1:-1:1, 1:-1:1])\r\n end_1 = np.sum(y[1:-1:1, 1:-1:1])\r\n stats = [total, start_1, end_1, chg_1_0, chg_none, chg_0_1]\r\n\r\n return y[1:-1:1, 1:-1:1], stats # remove the boundary\r","def reconstruct(self, X, y):\n return self.sess.run(self.x_reconstr_mean,\n feed_dict={self.x: X, self.y: y.reshape([-1, 1])})","def transform(i, j, k):\n return i * N * N + j * N + k + 1","def poly_matrix(x, y, order=2):\r\n ncols = (order + 1)**2\r\n G = np.zeros((x.size, ncols))\r\n ij = itertools.product(range(order+1), range(order+1))\r\n for k, (i, j) in enumerate(ij):\r\n G[:, k] = x**i * y**j\r\n return G","def y_to_z_mapping(self, Y):\n if len(Y[0])!=self.label_width**2:\n print('input labels have different dimension')\n Z = []\n for label in Y:\n z_label = np.array(label)\n for i in range(self.label_width**2):\n z_label = np.concatenate((z_label, (label[i+1:]==label[i]).astype(int)))\n Z.append(z_label)\n return Z","def count_subs(x,y):\n\t# Encases diagonals in square grid of size 'square'\n\tsquare = x + y - 2\n\tsubs = 0\n\t# For every point counts the number of rectagles with (a,b) as upper left corner\n\tfor a in range(square):\n\t\tfor b in range(square):\n\t\t\tif valid(a,b,x,y):\n\t\t\t\tthis_subs = subs_at_point(a,b,x,y)\n\t\t\t\tprint \"%3d \" %(this_subs),\n\t\t\tprint \"\"\n\treturn subs","def expand_ism(ism, Y1_labels):\n \n import random\n import pandas as pd\n import numpy as np\n import time\n print('debug expand ism1') \n voxel_num=len(Y1_labels)\n voxel_ism = np.zeros((voxel_num,voxel_num))\n transform_mat=np.zeros((len(ism),voxel_num))\n \n matrixtime = time.time()\n print('debug expand ism2') \n #import pdb; pdb.set_trace()\n\n for i in range(0,voxel_num):\n transform_mat[Y1_labels[i],i]=1\n\n print('debug expand ism3') \n\n temp=np.dot(ism,transform_mat)\n print('debug expand ism4') \n\n target_mat=np.dot(temp.T,transform_mat)\n \n \n XM_time= time.time() - matrixtime\n #print('Matrix expansion took', (time.time() - matrixtime), ' seconds')\n voxel_ism=target_mat\n \n return voxel_ism","def get_pool_data(x, y):\n\n y_classes = np.max(y)+1\n\n xs = [] # xs to put in the training set\n ys = [] # ys to put in the training set\n idxs = [] # indexes of data put in the training set\n for y_class in range(y_classes):\n idx = np.array( np.where(y == y_class) ).T\n idx = idx[0:2, 0]\n xs.append(x[idx])\n ys.append(y[idx])\n idxs.extend(idx)\n\n x_train = np.concatenate(xs, axis=0)\n y_train = np.concatenate(ys, axis=0)\n\n x_pool = np.delete(x, idxs, axis=0)\n y_pool = np.delete(y, idxs, axis=0)\n \n return (x_train, y_train), (x_pool, y_pool)","def pull(self,x,y):\n\t\tself.x_sum -= np.sum(x,axis=0)[:,np.newaxis]\n\t\tself.y_sum -= np.sum(y,axis=0)[:,np.newaxis]\n\t\tself.xy_sum -= np.matmul(np.transpose(x),y)\n\t\tself.xx_sum -= np.matmul(np.transpose(x),x)\n\t\tself.yy_sum -= np.matmul(np.transpose(y),y)\n\t\tself.n -= np.shape(x)[0]","def get_X_Y_vectorized_int(dataset: dict):\n X = []\n Y = []\n\n d_list = list(dataset)\n\n for k in dataset:\n X += dataset[k]\n\n temp = [0] * len(d_list)\n\n index_in_d_list = d_list.index(k)\n\n temp[index_in_d_list] = 1\n\n for i in range(len(dataset[k])):\n Y += [temp]\n\n assert len(X) == len(Y)\n return X, Y","def make_frequency_table(x, y, X, Y):\n freq = dict()\n\n for i in range(len(X)):\n freq[X[i]] = [0, 0]\n\n # merging the two to get a matrix\n\n M = np.array([[x[i], y[i]] for i in range(len(x))])\n\n for i in range(len(M)):\n if M[i][1] == Y[0]:\n freq[M[i][0]][0] += 1\n else:\n freq[M[i][0]][1] += 1\n\n return freq","def _get_matrix(self, source_points, destination_points):\n return [\n [self.measure_between_two_points(point_a, point_b) for point_b in destination_points]\n for point_a in source_points\n ]","def zenith_nadir(x, y):\n if y == 'm':\n bb = []\n cc = []\n for i in range(x.shape[1]):\n bb.append(amax(x[:, i:i + 1]))\n b = array(bb)\n cc.append(amin(x[:, i:i + 1]))\n c = array(cc)\n return (b, c)\n else:\n b = ones(x.shape[1])\n c = zeros(x.shape[1])\n return (b, c)","def fit(self, X, y):\r\n from collections import defaultdict\r\n y_set = set(y)\r\n ver_cls = dict()\r\n for i in y_set:\r\n ver_cls[i] = y.count(i) / len(y)\r\n add_data_units_cls = defaultdict(dict)\r\n add_data_all_cls = defaultdict(int)\r\n ver_words = defaultdict(dict)\r\n for sentence in range(len(X)):\r\n for word in X[sentence].split():\r\n add_data_all_cls[word] += 1\r\n if y[sentence] in add_data_units_cls[word].keys():\r\n add_data_units_cls[word][y[sentence]] += 1\r\n else:\r\n add_data_units_cls[word][y[sentence]] = 1\r\n znam = defaultdict(int)\r\n for key in add_data_units_cls.keys():\r\n for i in y_set:\r\n if i in add_data_units_cls[key].keys():\r\n znam[i] += add_data_units_cls[key][i]\r\n print(znam)\r\n for key in add_data_all_cls.keys():\r\n for i in y_set:\r\n if i not in add_data_units_cls[key].keys():\r\n add_data_units_cls[key][i] = 0\r\n ver_words[key][i] = (add_data_units_cls[key][i] + self.alpha) / (\r\n znam[i] + self.alpha * len(add_data_all_cls.keys()))\r\n self.ver_dict = ver_words\r\n self.ver_cls = ver_cls\r\n pass","def gen_ys(self, y_func):\n y_matrix = np.zeros((self.sample_size, self.n_act, self.ydim))\n for trt in range(1, self.n_act + 1):\n y_matrix[:, trt - 1] = y_func(self.x,\n np.ones(self.sample_size).reshape(-1, 1) * trt,\n self.ydim, self.generator)\n self.ys = y_matrix","def update_cnt_map(self,s):\r\n cnts = []\r\n num_grid = self.cnt_map.shape[0]*self.cnt_map.shape[1]\r\n old_coverage =num_grid- self.cnt_map.flatten().tolist().count(0)\r\n for sj in s:\r\n grid_s = self.get_gridState(sj)\r\n self.cnt_map[grid_s[0], grid_s[1]] += 1\r\n cnts.append(self.cnt_map[grid_s[0], grid_s[1]])\r\n\r\n self.map_coverage = num_grid - self.cnt_map.flatten().tolist().count(0)\r\n print(\"Coverage:\",self.map_coverage)\r\n print(\"Change of coverage:\",self.map_coverage-old_coverage)\r\n\r\n return cnts","def conv_1x1(x, y):\n (S, B, C, H, W) = x.shape\n assert x.shape[-1] == y.shape[-1]\n assert x.shape[-2] == y.shape[-2]\n assert x.shape[-4] == y.shape[-4]\n\n #[..., B, C, H, W] -> [..., B, C, HW]\n x = x.view(*x.shape[:-2], H*W)\n y = y.view(*y.shape[:-2], H*W)\n\n #[..., B, C, C']\n XTX = (x @ x.transpose(-1, -2)).sum(-3)\n XTY = (x @ y.transpose(-1, -2)).sum(-3)\n\n return XTX, XTY","def __init__(self, xint, yint):\n self.xint = xint\n self.yint = yint\n self.n = len(xint)\n w = np.ones(self.n)\n self.C = (np.max(xint) - np.min(xint)) / 4\n shuffle = np.random.permutation(self.n - 1)\n for j in range(self.n):\n temp = (xint[j] - np.delete(xint,j)) / self.C\n temp = temp[shuffle]\n w[j] /= np.product(temp)\n self.weights = w","def transform(self, Xs, y=None):\n pass # pragma: no cover","def resizeXY(X, Y, occurrency, dx, dz):\n\tsumY = sum(Y) \n\tsumX = sum(X)\n\tvisitedY = [False]*len(Y)\n\tfor y_index in range(len(Y)):\n\t\tupdate = True\n\t\tfor x_index in range(len(X)):\n\t\t\tif(occurrency[x_index][y_index] == False):\n\t\t\t\tupdate = False \n\t\tif(update):\n\t\t\tsumY = sumY - Y[y_index]\n\t\t\tsumX = sumX - X[y_index]\n\t\t\tdx = dx - X[y_index]\n\t\t\tdz = dz - Y[y_index]\n\n\tfor x_index in range(len(X)):\n\t\tmodifyX = False\n\t\tfor y_index in range(len(Y)):\n\t\t\tif(occurrency[x_index][y_index] == False and visitedY[y_index] == False):\n\t\t\t\tY[y_index] = (dz * Y[y_index])/sumY\n\t\t\t\tvisitedY[y_index] = True\n\t\t\t\tmodifyX = True\n\t\t\tif(occurrency[x_index][y_index] == False and visitedY[y_index] == True and not modifyX):\n\t\t\t\tmodifyX = True\n\t\tif(modifyX):\n\t\t\tX[x_index] = (dx * X[x_index])/sumX","def p_y_x_knn(y, k):\n number_of_classes = 4\n resized = np.delete(y, range(k, y.shape[1]), axis=1)\n summed_with_zero = np.vstack(np.apply_along_axis(np.bincount, axis=1, arr=resized, minlength=number_of_classes + 1))\n summed = np.delete(summed_with_zero, 0, axis=1)\n return summed / k","def create_subsets(x, y):\n # initiate empty list for return variables.\n sets_x = []\n sets_y = []\n indices = []\n\n # iterate through value of PRI_JET_NUM (ranged inclusively from 0 until 3)\n for pri_jet_num_val in np.unique(x[:,22]):\n \n # Find subset which DER_MASS_MMC is not equal to -999\n mask = (x[:,22] == pri_jet_num_val) & (x[:,0] != -999)\n x_tmp = x[mask,:]\n y_tmp = y[mask]\n\n # store the subset into list\n sets_x.append(x_tmp)\n sets_y.append(y_tmp)\n indices.append(mask)\n\n # Find subset which DER_MASS_MMC is equal to -999\n mask = (x[:,22] == pri_jet_num_val) & (x[:,0] == -999)\n x_tmp = x[mask,:]\n y_tmp = y[mask]\n\n # store the subset into list\n sets_x.append(x_tmp)\n sets_y.append(y_tmp)\n indices.append(mask) \n \n # return subsets of x, y, and corresponding indices\n return sets_x, sets_y, indices","def confusion_matrix(y_true, y_pred, labels):\r\n matrix = []\r\n\r\n for i, yt in enumerate(labels):\r\n matrix.append([])\r\n for _, yp in enumerate(labels):\r\n matrix[i].append(0)\r\n\r\n for t, p in zip(y_true, y_pred):\r\n t_num = labels.index(t)\r\n p_num = labels.index(p)\r\n matrix[t_num][p_num] += 1\r\n\r\n return matrix","def get2DBins(x, y, binSizeX, binSizeY):\n\n result = []\n xlength = len(x)\n ylength = len(y)\n\n i = 0\n xcount = 0\n for i1 in range(0, xlength, binSizeX):\n i2 = i1 + binSizeX\n if i2 >= xlength:\n i2 = xlength - 1\n xcount += 1\n ycount = 0\n for j1 in range(0, ylength, binSizeY):\n j2 = j1 + binSizeY\n if j2 >= ylength:\n j2 = ylength - 1\n result.append((i1, i2, j1, j2))\n ycount += 1\n return result, xcount, ycount","def needleman_wunsch(x, y, lodict={}, gop=-2.5, gep=-1.75, local=False, indel=''):\n n, m = len(x), len(y)\n dp = np.zeros((n + 1, m + 1))\n pointers = np.zeros((n + 1, m + 1), np.int32)\n if not local:\n for i1, c1 in enumerate(x):\n if gop is None:\n dp[i1 + 1, 0] = lodict.get((c1, indel), gep)\n else:\n dp[i1 + 1, 0] = dp[i1, 0]+(gep if i1 + 1 > 1 else gop)\n pointers[i1 + 1, 0] = 1\n for i2, c2 in enumerate(y):\n if gop is None:\n dp[0, i2 + 1] = lodict.get((indel, c2), gep)\n else:\n dp[0, i2 + 1] = dp[0, i2]+(gep if i2 + 1 > 1 else gop)\n pointers[0, i2 + 1] = 2\n for i1, c1 in enumerate(x):\n for i2, c2 in enumerate(y):\n match = dp[i1, i2] + lodict.get(\n (c1, c2),\n 1 if c1 == c2 else -1)\n insert = dp[i1, i2 + 1] + (\n lodict.get((c1, indel), gep) if gop is None else\n gep if pointers[i1, i2 + 1] == 1 else gop)\n delet = dp[i1 + 1, i2] + (\n lodict.get((indel, c2), gep) if gop is None else\n gep if pointers[i1 + 1, i2] == 2 else gop)\n pointers[i1 + 1, i2 + 1] = p = np.argmax([match, insert, delet])\n max_score = [match, insert, delet][p]\n if local and max_score < 0:\n max_score = 0\n dp[i1 + 1, i2 + 1] = max_score\n alg = []\n if local:\n i, j = np.unravel_index(dp.argmax(), dp.shape)\n else:\n i, j = n, m\n score = dp[i, j]\n while (i > 0 or j > 0):\n pt = pointers[i, j]\n if pt == 0:\n i -= 1\n j -= 1\n alg = [(x[i], y[j])] + alg\n if pt == 1:\n i -= 1\n alg = [(x[i], indel)] + alg\n if pt == 2:\n j -= 1\n alg = [(indel, y[j])] + alg\n if local and dp[i, j] == 0:\n break\n return score, alg","def reformat(x, y):\r\n # img_size, num_ch, num_class = int(np.sqrt(x.shape[1])), 1, len(np.unique(np.argmax(y, 1)))\r\n img_size, num_ch, num_class = 14, 1, 16\r\n dataset = x.reshape((-1, img_size, img_size, num_ch)).astype(np.float32)\r\n labels = (np.arange(num_class) == y[:, None]).astype(np.float32) # =[1 2 3 ... 10]??\r\n return dataset, labels","def reduce_y(y, mask):\n return y","def possible_splits(self,feature,y):\n\n yi = y[:-1]\n yi1= y[1:]\n idx=np.argwhere((yi1-yi)!=0)\n return idx.flatten()","def matrix_add(x,y) -> [[]]:\n X = x\n\n Y = y\n\n if len(X) == len(Y) and len(X[0]) == len(Y[0]):\n return [[X[a][b] + Y[a][b] for b in range(len(X[0]))]\n for a in range(len(X))]","def __get_masks(x_shape, y, n_train=None):\n # type: (Tuple[int], np.ndarray, int) -> (np.ndarray, np.ndarray)\n n_train = n_train if n_train is not None else const.n_train\n\n if n_train <= 0 or n_train > x_shape[0]:\n return np.full(shape=x_shape, fill_value=True, dtype=bool), np.full(shape=y.shape, fill_value=True, dtype=bool)\n\n all_indexes = defaultdict(list) # type: Dict[int, List[int]]\n for i in range(len(y)):\n curr = int(y[i])\n all_indexes[curr].append(i)\n\n ratios = defaultdict() # type: Dict[int, float]\n\n for i, j in all_indexes.items():\n ratios[i] = (len(j) * 1. / len(all_indexes[0]))\n\n # Ratios split the whole dataset to ratios given class and first class.\n # Part scales these ratios up, so that, 'part' corresponds to size of first class.\n part = n_train * 1. / sum(ratios.values())\n if part == 0: # n_train is 0.\n part = len(y) * 1. / sum(ratios.values())\n\n # Masks of what to keep.\n indexes_x = np.full(shape=x_shape, fill_value=False, dtype=bool)\n indexes_y = np.full(shape=y.shape, fill_value=False, dtype=bool)\n\n for i in all_indexes.keys():\n chosen_idxs = random.sample(all_indexes[i], int(part * ratios[i]))\n indexes_y[chosen_idxs] = True\n indexes_x[chosen_idxs, ...] = True\n\n return indexes_x, indexes_y","def effmat(tp: np.ndarray, t: np.ndarray) -> np.ndarray:\n n = len(t) # batch size\n max_true_n_sources = max(t)\n max_pred_n_sources = max(tp)\n eff_mat = np.zeros((max_pred_n_sources + 1, max_true_n_sources + 1))\n for ii in range(n):\n eff_mat[tp[ii], t[ii]] += 1\n return eff_mat","def trans_o(self):\n temp_array = []\n for j in range(self.O.shape[1]):\n for i in range(self.V.shape[1]):\n if self.V[0, i] == self.O[0, j]:\n temp_array.append(i)\n self.O = mat(temp_array)","def make_oneq_cliffords():\n ixyz_list = [g().to_matrix() for g in (IGate, XGate, YGate, ZGate)]\n ih_list = [g().to_matrix() for g in (IGate, HGate)]\n irs_list = [\n IGate().to_matrix(),\n SdgGate().to_matrix() @ HGate().to_matrix(),\n HGate().to_matrix() @ SGate().to_matrix(),\n ]\n oneq_cliffords = [\n Operator(ixyz @ ih @ irs) for ixyz in ixyz_list for ih in ih_list for irs in irs_list\n ]\n return oneq_cliffords","def get_score_matrix(self) -> int:","def fit(self, X, y):\n\t\tself._initialize_weights(X.shape[1])\n\t\tself.cost_ = []\n\n\t\tfor i in range(self.n_iter):\n\t\t\tif self.shuffle:\n\t\t\t\tX, y = self._shuffle(X,y)\n\t\t\tcost = []\n\t\t\t#calculate for each sample\n\t\t\tfor xi, target in zip(X, y):\n\t\t\t\tcost.append(self._update_weights(xi, target))\n\t\t\tave_cost = sum(cost)/len(y)\n\t\t\tself.cost_.append(ave_cost)\n\t\treturn self","def monomio(x,datos_x,datos_y):\n matriz=np.zeros([datos_x.shape[0],datos_x.shape[0]])\n for j in range(datos_x.shape[0]): #Se contruye la matriz de vandermonde\n matriz[:,j]= datos_x**(j)\n matriz,datos_y=pivoteo_parcial(matriz,datos_y)\n x1= descompo_LU(matriz,datos_y)# se resulve el sistema de ecuaciones por metodo directo\n\n puntos=[] #se almacenan los valores de y para cada punto de x que se quiera calcular \n\n for p in x: #va a ir tomando los valores de x uno por uno \n prod=np.zeros(x1.shape[0])\n for i in range(x1.shape[0]):\n if i==0:\n prod[i]=1\n else:\n prod[i]=prod[i-1]*p #Se hace el calculo de los polimonios con todos los valores de x \n solucion=x1@prod\n puntos.append(solucion) # se agregan los valores de y a la lista final \n puntos=np.array(puntos)# se convierte la lista en array para mejor manejo\n\n return puntos","def calcAllIntensities(self, xc, yc):\n\n tp = 0.0\n ix = 0\n iy = 0\n h = 0\n ints = np.zeros([5, 5])\n ints_inner = np.zeros([5, 5])\n # ints = [[0.0] * 5] * 5\n # ints_inner = [[0.0] * 5] * 5\n x = 0.0\n y = 0.0\n xc1 = 0.0\n yc1 = 0.0\n xc1 = xc\n yc1 = yc\n \n for h in np.arange(1,5,1):\n for k in np.arange(1,5,1):\n ints[h][k] = 0.0\n ints_inner[h][k] = 0.0\n\n for k in np.arange(0, 2, 1):\n for h in np.arange(0, 2, 1):\n for ix in np.arange(0, self.stepp + 1, 1):\n for iy in np.arange(0, self.stepp + 1, 1):\n #print(k, h, ix, iy)\n if self.qc_format == 0 :\n x = -(1 + self.G) + h * (1 + 2 * self.G) + (ix * (1.0 / self.stepp))\n y = -(1 + self.G) + k * (1 + 2 * self.G) + (iy * (1.0 / self.stepp))\n if self.spot_radius == 0 or math.sqrt(math.pow((x - xc1),2) + math.pow((y - yc1),2)) == 0 :\n tp = 0.0\n else :\n tp = (math.sin((1 / self.spot_radius) * math.sqrt(math.pow((x - xc1),2) + math.pow((y - yc1),2)))) / ((1 / self.spot_radius) * math.sqrt(math.pow((x - xc1),2) + math.pow((y - yc1),2)))\n tp = math.pow(tp,2)\n #print(tp)\n elif self.qc_format == 1 :\n x = -1 + h + (ix * (1 / self.stepp))\n y = -1 + k + (iy * (1 / self.stepp))\n ints[h + 1][k + 1] += math.pow(math.exp((math.pow((x - xc1),2) + math.pow((y - yc1),2) ) / math.pow(self.spot_radius,2)), -1)\n if (self.spot_radius * self.spot_radius) == 0 or ((x - xc1) * (y - yc1) * np.pi * np.pi) == 0 :\n tp = 0.0\n else :\n tp = (math.sin((x - xc1) * np.pi / self.spot_radius) * math.sin((y - yc1) * np.pi / self.spot_radius)) / (((x - xc1) * (y - yc1) * np.pi * np.pi) / (self.spot_radius * self.spot_radius))\n\n if (math.pow(x,2) + math.pow(y,2)) <= math.pow(self.radius_inner,2):\n ints_inner[h + 1][k + 1] += tp\n else :\n if self.qc_format == 1 :\n if (math.pow(x,2) + math.pow(y,2)) <= math.pow(self.cell_qc, 2):\n ints[h + 1][k + 1] += tp\n if (math.pow(x,2) + math.pow(y,2)) <= 1 :\n #print(math.pow(x,2) + math.pow(y,2))\n ints[h + 1][k + 1] += tp\n # print(ints[h + 1][k + 1])\t\t\t\t\t\t\n tp = 0.0\n\n # print(ints)\n\n Aq = 0.0\n Bq = 0.0\n Cq = 0.0\n Dq = 0.0\n Ac_inner = 0.0\n Bc_inner = 0.0\n Cc_inner = 0.0\n Dc_inner = 0.0\n Ac = 0.0\n Bc = 0.0\n Cc = 0.0\n Dc = 0.0\n Ac = ints[1][2]\n Bc = ints[2][2]\n Cc = ints[2][1]\n Dc = ints[1][1]\n\n Ac_inner = ints_inner[1][2]\n Bc_inner = ints_inner[2][2]\n Cc_inner = ints_inner[2][1]\n Dc_inner = ints_inner[1][1]\n Ac *= self.QE\n Bc *= self.QE\n Cc *= self.QE\n Dc *= self.QE\n\n Ac_inner *= self.QE_inner\n Bc_inner *= self.QE_inner\n Cc_inner *= self.QE_inner\n Dc_inner *= self.QE_inner\n Ac += Ac_inner\n Bc += Bc_inner\n Cc += Cc_inner\n Dc += Dc_inner\n\n Aq = Ac\n Bq = Bc\n Cq = Cc\n Dq = Dc\n\n #tp/TP = cotribution percentage of the spot with respect to max (spot center)\n if self.smooth == 0 :\n if (Config.hplk_c0_e * self.TP) == 0 :\n cnst = 0\n else :\n cnst = ((Parameters.TPS / (self.n_ml * self.n_ml)) * self.lamb) / (Config.hplk_c0_e * self.TP) #Número de fótons efeticos\n if Config.flag_spice == 1 :\n Ac *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP) #W\n Bc *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP)\n Cc *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP)\n Dc *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP)\n Ac *= 1 / (math.pow(self.cell_qc * 1e-6,2)) #W/(m^2)\n Bc *= 1 / (math.pow(self.cell_qc * 1e-6,2))\n Cc *= 1 / (math.pow(self.cell_qc * 1e-6,2))\n Dc *= 1 / (math.pow(self.cell_qc * 1e-6,2))\n #Ac *= 1 / (self.lamb * 1e6); #Adequação da irradiância para a unidade W/m2micm conforme necessário no SPICE\n #Bc *= 1 / (self.lamb * 1e6);\n #Cc *= 1 / (self.lamb * 1e6);\n #Dc *= 1 / (self.lamb * 1e6);\n \n ############################## DOUBLE CHECK ##############################\n # self.grava_arquivos = 1\n # self.flag_V_QC = 0\n # grava_le_arquivos(0) # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n # self.flag_V_QC = 1\n # self.grava_arquivos = 0\n ############################## DOUBLE CHECK ##############################\n Aq *= cnst * 1e9\n Bq *= cnst * 1e9\n Cq *= cnst * 1e9\n Dq *= cnst * 1e9\n else :\n Aq *= cnst * 1e9\n Bq *= cnst * 1e9\n Cq *= cnst * 1e9\n Dq *= cnst * 1e9\n\n # 'returns' all the intensities\n self.A_intensity = Aq\n self.B_intensity = Bq\n self.C_intensity = Cq\n self.D_intensity = Dq","def correspondences(labels1,labels2,return_counts=True):\n q = 100000\n assert amin(labels1)>=0 and amin(labels2)>=0\n assert amax(labels2)<q\n combo = labels1*q+labels2\n result = unique(combo, return_counts=return_counts)\n if return_counts:\n result, counts = result\n result = array([result//q,result%q,counts])\n else:\n result = array([result//q,result%q])\n return result","def clifford_set(u):\n i, x, y, z = u.v\n result = []\n result.append(u.clone()) # I\n result.append(Uop(-x, i, -z, y, u.hierarchy, u.construction + [\"X\"], gateset=u.gateset)) # iX, but treat it as X due to only phase difference\n result.append(Uop((i-x)/SQRT2, (x+i)/SQRT2, (y-z)/SQRT2, (z+y)/SQRT2, u.hierarchy, u.construction + [\"(I+iX)\"], gateset=u.gateset))\n result.append(Uop((i+x)/SQRT2, (x-i)/SQRT2, (y+z)/SQRT2, (z-y)/SQRT2, u.hierarchy, u.construction + [\"(I-iX)\"], gateset=u.gateset))\n result.append(Uop((i-y)/SQRT2, (x+z)/SQRT2, (y+i)/SQRT2, (z-x)/SQRT2, u.hierarchy, u.construction + [\"(I+iY)\"], gateset=u.gateset))\n result.append(Uop((i+y)/SQRT2, (x-z)/SQRT2, (y-i)/SQRT2, (z+x)/SQRT2, u.hierarchy, u.construction + [\"(I-iY)\"], gateset=u.gateset))\n for idx in range(6):\n i, x, y, z = result[idx].v\n c = result[idx].construction[-1:] if idx != 0 else []\n result.append(Uop(-z, -y, x, i, u.hierarchy, u.construction + c + [\"Z\"], gateset=u.gateset)) # iZ\n result.append(Uop((i-z)/SQRT2, (x-y)/SQRT2, (y+x)/SQRT2, (z+i)/SQRT2, u.hierarchy, u.construction + c + [\"(I+iZ)\"], gateset=u.gateset))\n result.append(Uop((i+z)/SQRT2, (x+y)/SQRT2, (y-x)/SQRT2, (z-i)/SQRT2, u.hierarchy, u.construction + c + [\"(I-iZ)\"], gateset=u.gateset))\n\n return result","def _split_indices(X, y, n_folds=5):\n # TODO: check if indices are permuted\n n = X.shape[0]\n print('n:', n)\n #original_indices = np.arange(n)\n shuffle = np.random.permutation(n)\n subset_proportion = 1./float(n_folds)\n fold_size = int(subset_proportion*n)\n folds = [shuffle[i*fold_size:(i+1)*fold_size] for i in range(n_folds)]\n return folds","def mi_bin_conn_time(x, y, bins_x, bins_y):\n n_times, n_trials = x.shape\n mi = np.zeros((n_times), dtype=np.float32)\n for t in range(n_times):\n mi[t] = mi_bin(x[t, :], y[t, :], bins_x, bins_y)\n return mi","def cumulative_capacity_rule(_m, g, y):\r\n\r\n return sum(m.x_c[g, j] for j in m.Y if j <= y)","def reconstructXY(self, inputs):\n return (self.reconstructX(inputs),\n self.reconstructY(inputs))","def update_output(self, latent_mat, weight_mat, y_list):","def _recon_lcs(x, y):\n i, j = len(x), len(y)\n table = _lcs(x, y)\n\n def _recon(i, j):\n \"\"\"private recon calculation\"\"\"\n if i == 0 or j == 0:\n return []\n elif x[i - 1] == y[j - 1]:\n return _recon(i - 1, j - 1) + [(x[i - 1], i)]\n elif table[i - 1, j] > table[i, j - 1]:\n return _recon(i - 1, j)\n else:\n return _recon(i, j - 1)\n\n recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))\n return recon_tuple","def add_dims_to_match(x, y):\n x_shape = tf.shape(x)\n new_dims = tf.rank(y)-tf.rank(x)\n if new_dims > 0:\n new_shape = tf.concat((x_shape, tf.ones((new_dims,), dtype=tf.int32)), axis=0)\n return tf.reshape(x, new_shape)\n else:\n return x","def inplace(block_size=20000):\n y = np.empty(len(x))\n for k in range(len(x) // block_size + 1):\n b, e = k * block_size, (k+1) * block_size\n y[b:e] = x[b:e]\n y[b:e] *= .25\n y[b:e] += .75\n y[b:e] *= x[b:e]\n y[b:e] -= 1.5\n y[b:e] *= x[b:e]\n y[b:e] -= 2\n\n return y"],"string":"[\n \"def topsolutions(self):\\n answers = []\\n for y in xrange(0, self.y):\\n answer = self.retrieve(y,self.y)\\n i = 0\\n for x in xrange(0,y):\\n answer -= self.retrieve(y,x)*answers[i]\\n i += 1\\n answers.append(answer)\\n return answers\",\n \"def build_set(x, y):\\n # E_w[yy^T]\\n y_y_t = la.inv(np.dot(y, y.transpose()))\\n h_matrix = np.dot(np.dot(x, y), y_y_t)\\n return h_matrix\",\n \"def transform(self, y):\\n\\n y = self.ensure_output_format(\\n y, sparse_format='lil', enforce_sparse=True)\\n\\n self.clean()\\n self.label_count = y.shape[1]\\n\\n last_id = 0\\n train_vector = []\\n for labels_applied in y.rows:\\n label_string = \\\",\\\".join(map(str, labels_applied))\\n\\n if label_string not in self.unique_combinations:\\n self.unique_combinations[label_string] = last_id\\n self.reverse_combinations.append(labels_applied)\\n last_id += 1\\n\\n train_vector.append(self.unique_combinations[label_string])\\n\\n return np.array(train_vector)\",\n \"def _get_edit_distance_matrix(x: str, y: str) -> list:\\n matrix = [[-1 for _ in range(len(y) + 1)] for _ in range(len(x) + 1)]\\n\\n for j in range(len(matrix[0])):\\n matrix[0][j] = j\\n\\n for i, _ in enumerate(matrix):\\n matrix[i][0] = i\\n\\n return matrix\",\n \"def editDist(X, Y, m, n, normalize_len=False):\\n if normalize_len:\\n if m < n:\\n quot = n // m\\n new_X = []\\n for i in range(quot):\\n new_X += X\\n X = new_X\\n # print(\\\"Normalized length for X: {}\\\".format(len(X)))\\n else:\\n quot = m // n\\n new_Y = []\\n for i in range(quot):\\n new_Y += Y\\n Y = new_Y\\n # print(\\\"Normalized length for Y: {}\\\".format(len(Y)))\\n\\n\\n dp = [[0 for x in range(n+1)] for x in range(m+1)]\\n for i in range(m+1):\\n for j in range(n+1):\\n\\n if i == 0:\\n dp[i][j] = j\\n elif j == 0:\\n dp[i][j] = i\\n elif X[i-1] == Y[j-1]:\\n dp[i][j] = dp[i - 1][j - 1]\\n else:\\n dp[i][j] = 1 + min(dp[i][j - 1], dp[i-1][j], dp[i-1][j-1])\\n\\n return dp[m][n]\",\n \"def p_y_x_knn(y, k):\\n result = np.zeros((len(y), 4))\\n for i in range(len(y)):\\n for j in range(k):\\n result[i, y[i, j]] = result[i, y[i, j]] + 1\\n return 1 / k * result\",\n \"def transform( self, X, y = None ):\\n matrix = np.zeros((len(X),len(self.feature_names)))\\n for i,bag in enumerate(X):\\n for test in bag:\\n try:\\n matrix[i,self.feature_names.index(test)] = 1\\n except ValueError:\\n pass\\n return matrix\",\n \"def toCartesian(self, y):\\r\\n return Size - y\",\n \"def add_matrices(x, y):\\n return [[x[i][j] + y[i][j] for j in range(len(x[0]))] for i in range(len(x))]\",\n \"def associate_comp(x, y):\\n return torch.cat([x[:1] * y[:1] - x[1:] * y[1:], x[:1] * y[1:] + x[1:] * y[:1]])\",\n \"def UpdateCostMatrix( self, extraXs ):\\n for x in extraXs:\\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\\n self.C.append(newRow)\",\n \"def copnorm_cat_1d(x, y):\\n assert isinstance(x, np.ndarray) and (x.ndim == 1)\\n assert isinstance(y, np.ndarray) and (x.ndim == 1)\\n assert y.dtype in CONFIG['INT_DTYPE']\\n x_cop = np.zeros_like(x)\\n y_u = np.unique(y)\\n for yi in y_u:\\n _idx = y == yi\\n x_cop[_idx] = copnorm_1d(x[_idx])\\n return x_cop\",\n \"def cost_matrix(x, y, p=2):\\n xc = tf.expand_dims(x, 1)\\n yr = tf.expand_dims(y, 0)\\n d = tf.math.pow(tf.abs(xc - yr), p)\\n return tf.reduce_sum(d, axis=-1)\",\n \"def fillCostMatrix(xs0,ys0,xs1,ys1):\\n M = int ( max(len(xs0),len(xs1)) ) #Number of centroids.\\n costMatrix = np.ones((M,M))*-1\\n x_rows = np.zeros(M)\\n x_rows[0:len(xs0)] = xs0\\n y_rows = np.zeros(M)\\n y_rows[0:len(xs0)] = ys0\\n \\n x_cols = np.zeros(M)\\n x_cols[0:len(xs1)] = xs1\\n y_cols = np.zeros(M)\\n y_cols[0:len(xs1)] = ys1\\n\\n for i in range(len(xs0)):\\n for j in range(len(xs1)):\\n costMatrix[i,j]=(y_rows[i]-y_cols[j])**2\\n costMatrix[i,j] += (x_rows[i]-x_cols[j])**2\\n return costMatrix\",\n \"def reconstruct_input(self, ix):\",\n \"def inverse_transform(self, y):\\n n_samples = len(y)\\n result = sparse.lil_matrix((n_samples, self.label_count), dtype='i8')\\n for row in range(n_samples):\\n assignment = y[row]\\n result[row, self.reverse_combinations[assignment]] = 1\\n\\n return result\",\n \"def countNeighbors(oldgen, x, y):\\n temp = 1\\n\\n count = 0\\n for i in range(-1, 2):\\n for j in range(-1, 2):\\n\\n # TODO: this needs rewritin to be more understandable\\n if not (i == 0 and j == 0):\\n count += int(oldgen[(x + i + WID) % WID][(y + j + HGT) % HGT])\\n\\n for i in range(-1, 2):\\n for j in range(-1, 2):\\n temp += 1\\n\\n count -= int(oldgen[x][y])\\n\\n return count\",\n \"def init_output_mat(self, y_list):\",\n \"def associate(x, y):\\n x = torch.cat([x[1:], x])\\n xx, yy = x.reshape(1,1,-1), y.flip(0).reshape(1,1,-1)\\n zz = torch.nn.functional.conv1d(xx, yy)\\n z = zz.reshape(-1)\\n return normalize(z)\",\n \"def fillCostMatrix(xs0,ys0,xs1,ys1):\\n M = int ( max(len(xs0),len(xs1)) ) #Number of centroids.\\n costMatrix = np.zeros((M,M))\\n x_rows = np.zeros(M)\\n x_rows[0:len(xs0)] = xs0\\n y_rows = np.zeros(M)\\n y_rows[0:len(xs0)] = ys0\\n \\n x_cols = np.zeros(M)\\n x_cols[0:len(xs1)] = xs1\\n y_cols = np.zeros(M)\\n y_cols[0:len(xs1)] = ys1\\n\\n for i in range(len(xs0)):\\n for j in range(len(xs1)):\\n costMatrix[i,j]=(y_rows[i]-y_cols[j])**2\\n costMatrix[i,j] += (x_rows[i]-x_cols[j])**2\\n return costMatrix\",\n \"def alloc2d(x,y,iv=0):\\n return [[iv for j in range(int(x))] for i in range(int(y))]\",\n \"def dim_reduction(data_set, components):\\n transformed = []\\n index = -1\\n transformed = data_set @ components\\n return transformed\",\n \"def inverse_transform(self, y: Array2D) -> Array2D:\",\n \"def _lcs(x, y):\\n n, m = len(x), len(y)\\n table = dict()\\n for i in range(n + 1):\\n for j in range(m + 1):\\n if i == 0 or j == 0:\\n table[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n table[i, j] = table[i - 1, j - 1] + 1\\n else:\\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\\n return table\",\n \"def _lcs(x, y):\\n n, m = len(x), len(y)\\n table = dict()\\n for i in range(n + 1):\\n for j in range(m + 1):\\n if i == 0 or j == 0:\\n table[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n table[i, j] = table[i - 1, j - 1] + 1\\n else:\\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\\n return table\",\n \"def transform(self, x: Array2D) -> Array2D:\",\n \"def transform(self,X):\\n # conver X to a list\\n X = X.tolist()\\n result = []\\n\\n # iterate over the length of X\\n for b in range(len(X)):\\n\\n # change dataset accoring to bias\\n if self.include_bias:\\n X[b].insert(0, 1)\\n \\n # initialize an array to store dynamically all array of indices\\n init_arr = []\\n for j in range(len(X[b])):\\n init_arr.append([j])\\n\\n # array of indices\\n arr = [j for j in range(len(X[b]))]\\n separate_arr = init_arr.copy()\\n\\n # iterate for the degree given\\n for k in range(0,self.degree-1):\\n # for len of the array containing indices\\n for i in range(len(arr)):\\n temp = i\\n # this loop will have different length since length increases\\n for j in range((k)*len(arr),len(separate_arr)):\\n element = init_arr[j].copy()\\n element.append(temp)\\n init_arr.append(element) \\n separate_arr = init_arr.copy()\\n # sort the array obtained to remove repeated elements\\n array = []\\n for m in range(len(init_arr)):\\n init_arr[m].sort()\\n if(init_arr[m] not in array):\\n array.append(init_arr[m])\\n\\n # calculate the final values by multiplying the numbers or columns at the place of indices\\n final = []\\n for i in array:\\n lst = []\\n # only if lenth satisfies the given degree\\n if len(i)==self.degree:\\n for j in i: \\n lst.append(X[b][j]) \\n final.append(np.product(lst))\\n result.append(final)\\n return result\",\n \"def solutions(self):\\n answers = []\\n for y in reversed(xrange(0, self.y)):\\n answer = self.retrieve(y,self.y)\\n i = 0\\n for x in reversed(xrange(y+1, self.y)):\\n answer -= self.retrieve(y,x)*answers[i]\\n i += 1\\n answers.append(answer)\\n answers.reverse()\\n return answers\",\n \"def transform(self, y):\\n return self.cidx_by_size_[self.le_.transform(y)]\",\n \"def get_imin(x1, x2, y, k=1, normalize=None, norm=np.inf):\\n\\n if normalize:\\n y = normalize(y)\\n\\n y_tree = cKDTree(y)\\n\\n n = len(y)\\n i_spec = np.zeros((2, n))\\n\\n for jj, x in enumerate([x1, x2]):\\n\\n if normalize:\\n x = normalize(x)\\n\\n # construct state array for the joint processes:\\n xy = np.c_[x,y]\\n\\n # store data pts in kd-trees for efficient nearest neighbour computations\\n # TODO: choose a better leaf size\\n x_tree = cKDTree(x)\\n xy_tree = cKDTree(xy)\\n\\n # kth nearest neighbour distances for every state\\n # query with k=k+1 to return the nearest neighbour, not counting the data point itself\\n # dist, idx = xy_tree.query(xy, k=k+1, p=norm)\\n dist, idx = xy_tree.query(xy, k=k+1, p=np.inf)\\n epsilon = dist[:, -1]\\n\\n # for each point, count the number of neighbours\\n # whose distance in the x-subspace is strictly < epsilon\\n # repeat for the y subspace\\n nx = np.empty(n, dtype=np.int)\\n ny = np.empty(n, dtype=np.int)\\n for ii in xrange(N):\\n # nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=norm)) - 1\\n # ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=norm)) - 1\\n nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\\n ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\\n\\n i_spec[jj] = digamma(k) - digamma(nx+1) + digamma(ny+1) + digamma(n) # version (1)\\n\\n i_min = np.mean(np.min(i_spec, 0))\\n\\n return i_min\",\n \"def _lcs(x, y):\\n n, m = len(x), len(y)\\n table = dict()\\n for i in range(n + 1):\\n for j in range(m + 1):\\n if i == 0 or j == 0:\\n table[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n table[i, j] = table[i - 1, j - 1] + 1\\n else:\\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\\n return table\",\n \"def create(matrix):\\n limit_y = len(matrix)\\n limit_x = len(matrix[0])\\n\\n for y in range(1, limit_y):\\n bit.create(matrix[y])\\n\\n for x in range(1, limit_x):\\n for y in range(1, limit_y):\\n k = y + (y & -y)\\n if k < limit_y:\\n matrix[k][x] += matrix[y][x]\",\n \"def spaceeff_dyna(x, y, c):\\n n = len(y)\\n m = len(x)\\n for i in range(1, m + 1):\\n c[0][1] = 0\\n for j in range(1, n + 1):\\n if x[i - 1] == y[j - 1]:\\n c[j][1] = c[j - 1][0] + 1\\n else:\\n ctop = c[j][0]\\n cleft = c[j - 1][1]\\n ctopleft = c[j - 1][0]\\n\\n c[j][1], d = max3(ctopleft, ctop, cleft)\\n for k in range(len(c)):\\n c[k][0] = c[k][1]\",\n \"def matrixReduction(setHor, setVer, arrayToReduce):\\r\\n listTemp = []\\r\\n for i in range(len(setVer)):\\r\\n listTemp.append(arrayToReduce[setVer[i].index, :])\\r\\n arrayTemp = numpy.array(listTemp)\\r\\n listTemp = []\\r\\n for i in range(len(setHor)):\\r\\n listTemp.append(arrayTemp[:, setHor[i].index])\\r\\n result = numpy.transpose(numpy.array(listTemp))\\r\\n\\r\\n return result\",\n \"def ipset_num_x_y_different():\\n return IPSet(x=np.linspace(0, 10, 11), y=np.linspace(-1, 1, 3), x_new=np.linspace(2, 5, 4))\",\n \"def make_likelihood_table(x, y):\\n\\n Y = np.unique(y)\\n X = np.unique(x)\\n\\n likelihood = [[0 for i in range(len(Y))] for j in range(len(X))]\\n\\n freq = make_frequency_table(x, y, X, Y)\\n\\n for j in range(len(Y)):\\n Sum = (y == Y[j]).sum()\\n for i in range(len(X)):\\n likelihood[i][j] = freq[X[i]][j] / Sum\\n\\n return likelihood\",\n \"def fit_transform(self, x: Array2D) -> Array2D:\",\n \"def _permute_observations(x, y, num_perms):\\r\\n vals = hstack([array(x), array(y)])\\r\\n lenx = len(x)\\r\\n # sorting step is unnecessary for this code, but it ensure that test code\\r\\n # which relies on seeding the prng works (if we dont do this then different\\r\\n # observation orders in x and y for eg. the mc_t_two_sample test will fail\\r\\n # to produce the same results)\\r\\n vals.sort()\\r\\n inds = arange(vals.size)\\r\\n xs, ys = [], []\\r\\n for i in range(num_perms):\\r\\n shuffle(inds)\\r\\n xs.append(vals[inds[:lenx]])\\r\\n ys.append(vals[inds[lenx:]])\\r\\n return xs, ys\",\n \"def _lev(x, y):\\n mat = initialize_matrix(x, y)\\n\\n for i in range(1, len(y)+1):\\n for j in range(1, len(x)+1):\\n mat[i].append(_generate_new_node(mat, i, j, x, y))\\n\\n return mat\",\n \"def _lev(x, y):\\n mat = initialize_matrix(x, y)\\n\\n for i in range(1, len(y)+1):\\n for j in range(1, len(x)+1):\\n mat[i].append(_generate_new_node(mat, i, j, x, y))\\n\\n return mat\",\n \"def brute_multiply(x, y):\\n \\n n = x.shape[0]\\n res = np.zeros(x.shape)\\n \\n for i in range(n):\\n for j in range(n):\\n for k in range(n):\\n res[i, j] += x[i, k] * y[k, j]\\n \\n return res\",\n \"def filt1(X, yvals, xvals, ny, nx):\\n \\n ylen = X.shape[0]\\n xlen = X.shape[1]\\n\\n yflen = (ylen-1)//ny\\n xflen = (xlen-1)//nx\\n\\n Y = np.zeros((X.shape))\\n\\n #Y = Y[0:yflen,0:xflen,]\\n\\n ymax = ny*yflen+1\\n xmax = nx*xflen+1\\n\\n Y = Y[0:ymax,0:xmax,]\\n Xnew = X[0:ymax,0:xmax,]\\n yvals = yvals[0:ymax,0:xmax,]\\n xvals = xvals[0:ymax,0:xmax,] \\n\\n counter = np.zeros((Y.shape))\\n \\n for i in range(xflen):\\n xmin = nx*i\\n xmax = nx*(i+1)+1\\n for j in range(yflen):\\n ymin = ny*j\\n ymax = ny*(j + 1)+1\\n #print((xmin,xmax), (ymin,ymax))\\n Y[ymin:ymax,xmin:xmax,] = Y[ymin:ymax,xmin:xmax,] + np.mean(X[ymin:ymax,xmin:xmax,], axis=(0,1))\\n counter[ymin:ymax,xmin:xmax,] = counter[ymin:ymax,xmin:xmax,] + 1\\n\\n Y = Y/counter #We take the average of the points that appear more than once\\n\\n return Xnew, Y, yvals, xvals\",\n \"def fit(self, x, y):\\n tempdf = pd.DataFrame({'x':x, 'y':y})\\n self.d = tempdf.groupby('x').apply(lambda g: g.y.sum()/len(g)).to_dict()\",\n \"def cemap_cal(y_pred,y_true):\\r\\n nTest = y_true.shape[0]\\r\\n nLabel = y_true.shape[1]\\r\\n ap = np.zeros(nTest)\\r\\n for i in range(0,nTest):\\r\\n for j in range(0,nLabel):\\r\\n R = np.sum(y_true[i,:])\\r\\n if y_true[i,j]==1:\\r\\n r = np.sum(y_pred[i,:]>=y_pred[i,j])\\r\\n rb = np.sum(y_pred[i,np.nonzero(y_true[i,:])] >= y_pred[i,j])\\r\\n ap[i] = ap[i] + rb/(r*1.0)\\r\\n ap[i] = ap[i]/R\\r\\n imap = np.nanmean(ap)\\r\\n\\r\\n ap = np.zeros(nLabel)\\r\\n for i in range(0,nLabel):\\r\\n for j in range(0,nTest):\\r\\n R = np.sum(y_true[:,i])\\r\\n if y_true[j,i]==1:\\r\\n r = np.sum(y_pred[:,i] >= y_pred[j,i])\\r\\n rb = np.sum(y_pred[np.nonzero(y_true[:,i]),i] >= y_pred[j,i])\\r\\n ap[i] = ap[i] + rb/(r*1.0)\\r\\n ap[i] = ap[i]/R\\r\\n lmap = np.nanmean(ap)\\r\\n\\r\\n return lmap,imap\",\n \"def fit(self, X, y):\\n # TODO: Implement\\n self.cols = X.columns\\n self.nCols = len(self.cols)\\n X = np.array(X)\\n y = np.array(y)\\n \\n for i in range(X.shape[1]): \\n uniq = np.unique(X[:, i])\\n self.possible.append(list(uniq)) # add possible values\\n self.valN.append(len(uniq)) # and how many\\n index = np.argmax(self.valN)\\n print(index)\\n self.tree = self.buildTree(X, y)\",\n \"def ipset_y_2d():\\n return IPSet(x=np.linspace(0, 10, 11), y=np.random.randn(11, 4), x_new=np.linspace(1, 4, 3))\",\n \"def sum_outers(x, y):\\n # In PyTorch 4.0, `einsum` modifies variables inplace. This will not work\\n # unless you have PyTorch 4.1:\\n #\\n # https://github.com/pytorch/pytorch/issues/7763\\n #\\n return torch.einsum('ab,cb->ac', [x, y])\",\n \"def UpdateS(s, Difference, WorkingSet):\\n for i in range(len(x_train)):\\n Sum = 0.0\\n for j in range(q):\\n Sum = Sum + (Difference[j])*y_train[int(WorkingSet[j,0])]*Kernel(i, int(WorkingSet[j,0]))\\n s[i] = s[i] + Sum\\n return s\",\n \"def _diffmat_objective(a,X):\\n \\n (n,p) = X.shape\\n return(X - np.tile(a,(n,1)))\",\n \"def multiple(x, y):\\n curRow = [1] * x\\n for _ in range(1,y):\\n for N in range(1,x):\\n curRow[N] = curRow[N-1] + curRow[N]\\n return curRow[x-1]\",\n \"def interpolV(y, x, newX):\\r\\n \\r\\n num = len(x)\\r\\n #if (num != len(y)):\\r\\n #//System.out.println(\\\"Toolbox.interpolV(): Old x and y must be same length\\\"); \\r\\n \\r\\n newNum = len(newX)\\r\\n #//System.out.println(\\\"interpolV: newNum \\\" + newNum + \\\" num \\\" + num); \\r\\n #newY = [0.0 for i in range(newNum)]\\r\\n\\r\\n#//Renormalize ordinates:\\r\\n \\r\\n iMinAndMax = minMax(y)\\r\\n norm = y[iMinAndMax[1]]\\r\\n #//System.out.println(\\\"norm \\\" + norm);\\r\\n #yNorm = [0.0 for i in range(num)]\\r\\n newYNorm = [0.0 for i in range(newNum)] \\r\\n #for i in range(num):\\r\\n # yNorm[i] = y[i] / norm \\r\\n yNorm = [ x / norm for x in y ]\\r\\n\\r\\n#// Set any newX elements that are *less than* the first x element to th first \\r\\n#// x element - \\\"0th order extrapolation\\\"\\r\\n#//\\r\\n start = 0\\r\\n for i in range(newNum):\\r\\n if (newX[i] <= x[1]):\\r\\n newYNorm[i] = yNorm[0]\\r\\n start += 1\\r\\n \\r\\n if (newX[i] > x[1]):\\r\\n break\\r\\n \\r\\n \\r\\n#//System.out.println(\\\"start \\\" + start);\\r\\n#//System.out.println(\\\"x[0] \\\" + x[0] + \\\" x[1] \\\" + x[1] + \\\" newX[start] \\\" + newX[start]);\\r\\n#double jWght, jm1Wght, denom;\\r\\n\\r\\n\\r\\n if (start < newNum-1):\\r\\n\\r\\n j = 1 #//initialize old abscissae index\\r\\n #//outer loop over new abscissae\\r\\n for i in range(start, newNum):\\r\\n\\r\\n #//System.out.println(\\\"i \\\" + i + \\\" j \\\" + j);\\r\\n\\r\\n#// break out if current element newX is *greater* that last x element\\r\\n if ( (newX[i] > x[num-1]) or (j > (num-1)) ):\\r\\n break \\r\\n \\r\\n\\r\\n while (x[j] < newX[i]): \\r\\n j += 1\\r\\n \\r\\n #//System.out.println(\\\"i \\\" + i + \\\" newX[i] \\\" + newX[i] + \\\" j \\\" + j + \\\" x[j-1] \\\" + x[j-1] + \\\" x[j] \\\" + x[j]);\\r\\n #//1st order Lagrange method:\\r\\n jWght = newX[i] * (1.0 - (x[j-1]/newX[i])) #//(newX[i]-x[j-1])\\r\\n jm1Wght = x[j] * (1.0 - (newX[i]/x[j])) #//(x[j]-newX[i])\\r\\n denom = x[j] * (1.0 - (x[j-1]/x[j])) #//(x[j]-x[j-1])\\r\\n jWght = jWght / denom\\r\\n jm1Wght = jm1Wght / denom\\r\\n #//newYNorm[i] = (yNorm[j]*(newX[i]-x[j-1])) + (yNorm[j-1]*(x[j]-newX[i]));\\r\\n newYNorm[i] = (yNorm[j]*jWght) + (yNorm[j-1]*jm1Wght)\\r\\n #//System.out.println(\\\"i \\\" + i + \\\" newYNorm[i] \\\" + newYNorm[i] + \\\" j \\\" + j + \\\" yNorm[j-1] \\\" + yNorm[j-1] + \\\" yNorm[j] \\\" + yNorm[j]);\\r\\n \\r\\n\\r\\n#// Set any newX elements that are *greater than* the first x element to the last \\r\\n#// x element - \\\"0th order extrapolation\\\"\\r\\n#//\\r\\n for i in range(newNum):\\r\\n if (newX[i] >= x[num-1]):\\r\\n newYNorm[i] = yNorm[num-1]\\r\\n \\r\\n \\r\\n\\r\\n #//Restore orinate scale\\r\\n #for i in range(newNum):\\r\\n # newY[i] = newYNorm[i] * norm \\r\\n newY = [ x * norm for x in newYNorm ]\\r\\n\\r\\n\\r\\n return newY\",\n \"def back_spaceeff_dyna(x, y, c):\\n n = len(y)\\n m = len(x)\\n for i in range(m - 1, -1, -1):\\n c[n][1] = 0\\n for j in range(n - 1, -1, -1):\\n if x[i] == y[j]:\\n c[j][1] = c[j + 1][0] + 1\\n else:\\n cbottom = c[j][0]\\n cright = c[j + 1][1]\\n cbottomright = c[j + 1][0]\\n\\n c[j][1], d = max3(cbottomright, cbottom, cright)\\n for k in range(len(c)):\\n c[k][0] = c[k][1]\",\n \"def expand_features_and_labels(x_feat, y_labels):\\n x_expanded = []\\n y_expanded = []\\n for x, y in zip(x_feat, y_labels):\\n for segment in x:\\n x_expanded.append(segment)\\n y_expanded.append(y)\\n return x_expanded, y_expanded\",\n \"def transform(self, X, y=None):\\n rows = []\\n cols = []\\n data = []\\n #Loop through each reviews\\n for row, word_count in enumerate(X):\\n #Analyze each review with the total vocab of this dataset\\n for word, count in word_count.items():\\n rows.append(row)\\n cols.append(self.vocabulary_.get(word, 0)) #If non, replace it with 0\\n data.append(count)\\n \\n return csr_matrix((data, (rows, cols)), shape=(len(X), self.vocabulary_size + 1))\",\n \"def update_grid(self, x):\\r\\n\\r\\n # Append boundary rows and columns to matrix\\r\\n x = self.append_boundary(x) # the boundary is recomputed at each step\\r\\n y = np.copy(x)\\r\\n\\r\\n # For each cell within boundary, compute state according to rules.\\r\\n chg_0_1 = 0 # the number of cells that changed from state 0 to state 1\\r\\n chg_1_0 = 0 # the number of cells that changes from state 1 to state 0\\r\\n chg_none = 0 # the number of cells that did not change\\r\\n index = np.arange(1, x.shape[0] - 1)\\r\\n for i in index:\\r\\n for j in index:\\r\\n neighborhood = x[i - 1:i + 2:1, j - 1:j + 2:1] # 3x3 sub matrix centered at i, j\\r\\n y[i, j] = self.update_cell(neighborhood)\\r\\n change = int(y[i, j] - x[i, j])\\r\\n if change == -1:\\r\\n chg_1_0 += 1\\r\\n if change == 0:\\r\\n chg_none += 1\\r\\n if change == 1:\\r\\n chg_0_1 += 1\\r\\n\\r\\n # Compute statistics excluding boundary\\r\\n total = np.power(x[1:-1:1, 1:-1:1].shape[0] - 1, 2)\\r\\n start_1 = np.sum(x[1:-1:1, 1:-1:1])\\r\\n end_1 = np.sum(y[1:-1:1, 1:-1:1])\\r\\n stats = [total, start_1, end_1, chg_1_0, chg_none, chg_0_1]\\r\\n\\r\\n return y[1:-1:1, 1:-1:1], stats # remove the boundary\\r\",\n \"def reconstruct(self, X, y):\\n return self.sess.run(self.x_reconstr_mean,\\n feed_dict={self.x: X, self.y: y.reshape([-1, 1])})\",\n \"def transform(i, j, k):\\n return i * N * N + j * N + k + 1\",\n \"def poly_matrix(x, y, order=2):\\r\\n ncols = (order + 1)**2\\r\\n G = np.zeros((x.size, ncols))\\r\\n ij = itertools.product(range(order+1), range(order+1))\\r\\n for k, (i, j) in enumerate(ij):\\r\\n G[:, k] = x**i * y**j\\r\\n return G\",\n \"def y_to_z_mapping(self, Y):\\n if len(Y[0])!=self.label_width**2:\\n print('input labels have different dimension')\\n Z = []\\n for label in Y:\\n z_label = np.array(label)\\n for i in range(self.label_width**2):\\n z_label = np.concatenate((z_label, (label[i+1:]==label[i]).astype(int)))\\n Z.append(z_label)\\n return Z\",\n \"def count_subs(x,y):\\n\\t# Encases diagonals in square grid of size 'square'\\n\\tsquare = x + y - 2\\n\\tsubs = 0\\n\\t# For every point counts the number of rectagles with (a,b) as upper left corner\\n\\tfor a in range(square):\\n\\t\\tfor b in range(square):\\n\\t\\t\\tif valid(a,b,x,y):\\n\\t\\t\\t\\tthis_subs = subs_at_point(a,b,x,y)\\n\\t\\t\\t\\tprint \\\"%3d \\\" %(this_subs),\\n\\t\\t\\tprint \\\"\\\"\\n\\treturn subs\",\n \"def expand_ism(ism, Y1_labels):\\n \\n import random\\n import pandas as pd\\n import numpy as np\\n import time\\n print('debug expand ism1') \\n voxel_num=len(Y1_labels)\\n voxel_ism = np.zeros((voxel_num,voxel_num))\\n transform_mat=np.zeros((len(ism),voxel_num))\\n \\n matrixtime = time.time()\\n print('debug expand ism2') \\n #import pdb; pdb.set_trace()\\n\\n for i in range(0,voxel_num):\\n transform_mat[Y1_labels[i],i]=1\\n\\n print('debug expand ism3') \\n\\n temp=np.dot(ism,transform_mat)\\n print('debug expand ism4') \\n\\n target_mat=np.dot(temp.T,transform_mat)\\n \\n \\n XM_time= time.time() - matrixtime\\n #print('Matrix expansion took', (time.time() - matrixtime), ' seconds')\\n voxel_ism=target_mat\\n \\n return voxel_ism\",\n \"def get_pool_data(x, y):\\n\\n y_classes = np.max(y)+1\\n\\n xs = [] # xs to put in the training set\\n ys = [] # ys to put in the training set\\n idxs = [] # indexes of data put in the training set\\n for y_class in range(y_classes):\\n idx = np.array( np.where(y == y_class) ).T\\n idx = idx[0:2, 0]\\n xs.append(x[idx])\\n ys.append(y[idx])\\n idxs.extend(idx)\\n\\n x_train = np.concatenate(xs, axis=0)\\n y_train = np.concatenate(ys, axis=0)\\n\\n x_pool = np.delete(x, idxs, axis=0)\\n y_pool = np.delete(y, idxs, axis=0)\\n \\n return (x_train, y_train), (x_pool, y_pool)\",\n \"def pull(self,x,y):\\n\\t\\tself.x_sum -= np.sum(x,axis=0)[:,np.newaxis]\\n\\t\\tself.y_sum -= np.sum(y,axis=0)[:,np.newaxis]\\n\\t\\tself.xy_sum -= np.matmul(np.transpose(x),y)\\n\\t\\tself.xx_sum -= np.matmul(np.transpose(x),x)\\n\\t\\tself.yy_sum -= np.matmul(np.transpose(y),y)\\n\\t\\tself.n -= np.shape(x)[0]\",\n \"def get_X_Y_vectorized_int(dataset: dict):\\n X = []\\n Y = []\\n\\n d_list = list(dataset)\\n\\n for k in dataset:\\n X += dataset[k]\\n\\n temp = [0] * len(d_list)\\n\\n index_in_d_list = d_list.index(k)\\n\\n temp[index_in_d_list] = 1\\n\\n for i in range(len(dataset[k])):\\n Y += [temp]\\n\\n assert len(X) == len(Y)\\n return X, Y\",\n \"def make_frequency_table(x, y, X, Y):\\n freq = dict()\\n\\n for i in range(len(X)):\\n freq[X[i]] = [0, 0]\\n\\n # merging the two to get a matrix\\n\\n M = np.array([[x[i], y[i]] for i in range(len(x))])\\n\\n for i in range(len(M)):\\n if M[i][1] == Y[0]:\\n freq[M[i][0]][0] += 1\\n else:\\n freq[M[i][0]][1] += 1\\n\\n return freq\",\n \"def _get_matrix(self, source_points, destination_points):\\n return [\\n [self.measure_between_two_points(point_a, point_b) for point_b in destination_points]\\n for point_a in source_points\\n ]\",\n \"def zenith_nadir(x, y):\\n if y == 'm':\\n bb = []\\n cc = []\\n for i in range(x.shape[1]):\\n bb.append(amax(x[:, i:i + 1]))\\n b = array(bb)\\n cc.append(amin(x[:, i:i + 1]))\\n c = array(cc)\\n return (b, c)\\n else:\\n b = ones(x.shape[1])\\n c = zeros(x.shape[1])\\n return (b, c)\",\n \"def fit(self, X, y):\\r\\n from collections import defaultdict\\r\\n y_set = set(y)\\r\\n ver_cls = dict()\\r\\n for i in y_set:\\r\\n ver_cls[i] = y.count(i) / len(y)\\r\\n add_data_units_cls = defaultdict(dict)\\r\\n add_data_all_cls = defaultdict(int)\\r\\n ver_words = defaultdict(dict)\\r\\n for sentence in range(len(X)):\\r\\n for word in X[sentence].split():\\r\\n add_data_all_cls[word] += 1\\r\\n if y[sentence] in add_data_units_cls[word].keys():\\r\\n add_data_units_cls[word][y[sentence]] += 1\\r\\n else:\\r\\n add_data_units_cls[word][y[sentence]] = 1\\r\\n znam = defaultdict(int)\\r\\n for key in add_data_units_cls.keys():\\r\\n for i in y_set:\\r\\n if i in add_data_units_cls[key].keys():\\r\\n znam[i] += add_data_units_cls[key][i]\\r\\n print(znam)\\r\\n for key in add_data_all_cls.keys():\\r\\n for i in y_set:\\r\\n if i not in add_data_units_cls[key].keys():\\r\\n add_data_units_cls[key][i] = 0\\r\\n ver_words[key][i] = (add_data_units_cls[key][i] + self.alpha) / (\\r\\n znam[i] + self.alpha * len(add_data_all_cls.keys()))\\r\\n self.ver_dict = ver_words\\r\\n self.ver_cls = ver_cls\\r\\n pass\",\n \"def gen_ys(self, y_func):\\n y_matrix = np.zeros((self.sample_size, self.n_act, self.ydim))\\n for trt in range(1, self.n_act + 1):\\n y_matrix[:, trt - 1] = y_func(self.x,\\n np.ones(self.sample_size).reshape(-1, 1) * trt,\\n self.ydim, self.generator)\\n self.ys = y_matrix\",\n \"def update_cnt_map(self,s):\\r\\n cnts = []\\r\\n num_grid = self.cnt_map.shape[0]*self.cnt_map.shape[1]\\r\\n old_coverage =num_grid- self.cnt_map.flatten().tolist().count(0)\\r\\n for sj in s:\\r\\n grid_s = self.get_gridState(sj)\\r\\n self.cnt_map[grid_s[0], grid_s[1]] += 1\\r\\n cnts.append(self.cnt_map[grid_s[0], grid_s[1]])\\r\\n\\r\\n self.map_coverage = num_grid - self.cnt_map.flatten().tolist().count(0)\\r\\n print(\\\"Coverage:\\\",self.map_coverage)\\r\\n print(\\\"Change of coverage:\\\",self.map_coverage-old_coverage)\\r\\n\\r\\n return cnts\",\n \"def conv_1x1(x, y):\\n (S, B, C, H, W) = x.shape\\n assert x.shape[-1] == y.shape[-1]\\n assert x.shape[-2] == y.shape[-2]\\n assert x.shape[-4] == y.shape[-4]\\n\\n #[..., B, C, H, W] -> [..., B, C, HW]\\n x = x.view(*x.shape[:-2], H*W)\\n y = y.view(*y.shape[:-2], H*W)\\n\\n #[..., B, C, C']\\n XTX = (x @ x.transpose(-1, -2)).sum(-3)\\n XTY = (x @ y.transpose(-1, -2)).sum(-3)\\n\\n return XTX, XTY\",\n \"def __init__(self, xint, yint):\\n self.xint = xint\\n self.yint = yint\\n self.n = len(xint)\\n w = np.ones(self.n)\\n self.C = (np.max(xint) - np.min(xint)) / 4\\n shuffle = np.random.permutation(self.n - 1)\\n for j in range(self.n):\\n temp = (xint[j] - np.delete(xint,j)) / self.C\\n temp = temp[shuffle]\\n w[j] /= np.product(temp)\\n self.weights = w\",\n \"def transform(self, Xs, y=None):\\n pass # pragma: no cover\",\n \"def resizeXY(X, Y, occurrency, dx, dz):\\n\\tsumY = sum(Y) \\n\\tsumX = sum(X)\\n\\tvisitedY = [False]*len(Y)\\n\\tfor y_index in range(len(Y)):\\n\\t\\tupdate = True\\n\\t\\tfor x_index in range(len(X)):\\n\\t\\t\\tif(occurrency[x_index][y_index] == False):\\n\\t\\t\\t\\tupdate = False \\n\\t\\tif(update):\\n\\t\\t\\tsumY = sumY - Y[y_index]\\n\\t\\t\\tsumX = sumX - X[y_index]\\n\\t\\t\\tdx = dx - X[y_index]\\n\\t\\t\\tdz = dz - Y[y_index]\\n\\n\\tfor x_index in range(len(X)):\\n\\t\\tmodifyX = False\\n\\t\\tfor y_index in range(len(Y)):\\n\\t\\t\\tif(occurrency[x_index][y_index] == False and visitedY[y_index] == False):\\n\\t\\t\\t\\tY[y_index] = (dz * Y[y_index])/sumY\\n\\t\\t\\t\\tvisitedY[y_index] = True\\n\\t\\t\\t\\tmodifyX = True\\n\\t\\t\\tif(occurrency[x_index][y_index] == False and visitedY[y_index] == True and not modifyX):\\n\\t\\t\\t\\tmodifyX = True\\n\\t\\tif(modifyX):\\n\\t\\t\\tX[x_index] = (dx * X[x_index])/sumX\",\n \"def p_y_x_knn(y, k):\\n number_of_classes = 4\\n resized = np.delete(y, range(k, y.shape[1]), axis=1)\\n summed_with_zero = np.vstack(np.apply_along_axis(np.bincount, axis=1, arr=resized, minlength=number_of_classes + 1))\\n summed = np.delete(summed_with_zero, 0, axis=1)\\n return summed / k\",\n \"def create_subsets(x, y):\\n # initiate empty list for return variables.\\n sets_x = []\\n sets_y = []\\n indices = []\\n\\n # iterate through value of PRI_JET_NUM (ranged inclusively from 0 until 3)\\n for pri_jet_num_val in np.unique(x[:,22]):\\n \\n # Find subset which DER_MASS_MMC is not equal to -999\\n mask = (x[:,22] == pri_jet_num_val) & (x[:,0] != -999)\\n x_tmp = x[mask,:]\\n y_tmp = y[mask]\\n\\n # store the subset into list\\n sets_x.append(x_tmp)\\n sets_y.append(y_tmp)\\n indices.append(mask)\\n\\n # Find subset which DER_MASS_MMC is equal to -999\\n mask = (x[:,22] == pri_jet_num_val) & (x[:,0] == -999)\\n x_tmp = x[mask,:]\\n y_tmp = y[mask]\\n\\n # store the subset into list\\n sets_x.append(x_tmp)\\n sets_y.append(y_tmp)\\n indices.append(mask) \\n \\n # return subsets of x, y, and corresponding indices\\n return sets_x, sets_y, indices\",\n \"def confusion_matrix(y_true, y_pred, labels):\\r\\n matrix = []\\r\\n\\r\\n for i, yt in enumerate(labels):\\r\\n matrix.append([])\\r\\n for _, yp in enumerate(labels):\\r\\n matrix[i].append(0)\\r\\n\\r\\n for t, p in zip(y_true, y_pred):\\r\\n t_num = labels.index(t)\\r\\n p_num = labels.index(p)\\r\\n matrix[t_num][p_num] += 1\\r\\n\\r\\n return matrix\",\n \"def get2DBins(x, y, binSizeX, binSizeY):\\n\\n result = []\\n xlength = len(x)\\n ylength = len(y)\\n\\n i = 0\\n xcount = 0\\n for i1 in range(0, xlength, binSizeX):\\n i2 = i1 + binSizeX\\n if i2 >= xlength:\\n i2 = xlength - 1\\n xcount += 1\\n ycount = 0\\n for j1 in range(0, ylength, binSizeY):\\n j2 = j1 + binSizeY\\n if j2 >= ylength:\\n j2 = ylength - 1\\n result.append((i1, i2, j1, j2))\\n ycount += 1\\n return result, xcount, ycount\",\n \"def needleman_wunsch(x, y, lodict={}, gop=-2.5, gep=-1.75, local=False, indel=''):\\n n, m = len(x), len(y)\\n dp = np.zeros((n + 1, m + 1))\\n pointers = np.zeros((n + 1, m + 1), np.int32)\\n if not local:\\n for i1, c1 in enumerate(x):\\n if gop is None:\\n dp[i1 + 1, 0] = lodict.get((c1, indel), gep)\\n else:\\n dp[i1 + 1, 0] = dp[i1, 0]+(gep if i1 + 1 > 1 else gop)\\n pointers[i1 + 1, 0] = 1\\n for i2, c2 in enumerate(y):\\n if gop is None:\\n dp[0, i2 + 1] = lodict.get((indel, c2), gep)\\n else:\\n dp[0, i2 + 1] = dp[0, i2]+(gep if i2 + 1 > 1 else gop)\\n pointers[0, i2 + 1] = 2\\n for i1, c1 in enumerate(x):\\n for i2, c2 in enumerate(y):\\n match = dp[i1, i2] + lodict.get(\\n (c1, c2),\\n 1 if c1 == c2 else -1)\\n insert = dp[i1, i2 + 1] + (\\n lodict.get((c1, indel), gep) if gop is None else\\n gep if pointers[i1, i2 + 1] == 1 else gop)\\n delet = dp[i1 + 1, i2] + (\\n lodict.get((indel, c2), gep) if gop is None else\\n gep if pointers[i1 + 1, i2] == 2 else gop)\\n pointers[i1 + 1, i2 + 1] = p = np.argmax([match, insert, delet])\\n max_score = [match, insert, delet][p]\\n if local and max_score < 0:\\n max_score = 0\\n dp[i1 + 1, i2 + 1] = max_score\\n alg = []\\n if local:\\n i, j = np.unravel_index(dp.argmax(), dp.shape)\\n else:\\n i, j = n, m\\n score = dp[i, j]\\n while (i > 0 or j > 0):\\n pt = pointers[i, j]\\n if pt == 0:\\n i -= 1\\n j -= 1\\n alg = [(x[i], y[j])] + alg\\n if pt == 1:\\n i -= 1\\n alg = [(x[i], indel)] + alg\\n if pt == 2:\\n j -= 1\\n alg = [(indel, y[j])] + alg\\n if local and dp[i, j] == 0:\\n break\\n return score, alg\",\n \"def reformat(x, y):\\r\\n # img_size, num_ch, num_class = int(np.sqrt(x.shape[1])), 1, len(np.unique(np.argmax(y, 1)))\\r\\n img_size, num_ch, num_class = 14, 1, 16\\r\\n dataset = x.reshape((-1, img_size, img_size, num_ch)).astype(np.float32)\\r\\n labels = (np.arange(num_class) == y[:, None]).astype(np.float32) # =[1 2 3 ... 10]??\\r\\n return dataset, labels\",\n \"def reduce_y(y, mask):\\n return y\",\n \"def possible_splits(self,feature,y):\\n\\n yi = y[:-1]\\n yi1= y[1:]\\n idx=np.argwhere((yi1-yi)!=0)\\n return idx.flatten()\",\n \"def matrix_add(x,y) -> [[]]:\\n X = x\\n\\n Y = y\\n\\n if len(X) == len(Y) and len(X[0]) == len(Y[0]):\\n return [[X[a][b] + Y[a][b] for b in range(len(X[0]))]\\n for a in range(len(X))]\",\n \"def __get_masks(x_shape, y, n_train=None):\\n # type: (Tuple[int], np.ndarray, int) -> (np.ndarray, np.ndarray)\\n n_train = n_train if n_train is not None else const.n_train\\n\\n if n_train <= 0 or n_train > x_shape[0]:\\n return np.full(shape=x_shape, fill_value=True, dtype=bool), np.full(shape=y.shape, fill_value=True, dtype=bool)\\n\\n all_indexes = defaultdict(list) # type: Dict[int, List[int]]\\n for i in range(len(y)):\\n curr = int(y[i])\\n all_indexes[curr].append(i)\\n\\n ratios = defaultdict() # type: Dict[int, float]\\n\\n for i, j in all_indexes.items():\\n ratios[i] = (len(j) * 1. / len(all_indexes[0]))\\n\\n # Ratios split the whole dataset to ratios given class and first class.\\n # Part scales these ratios up, so that, 'part' corresponds to size of first class.\\n part = n_train * 1. / sum(ratios.values())\\n if part == 0: # n_train is 0.\\n part = len(y) * 1. / sum(ratios.values())\\n\\n # Masks of what to keep.\\n indexes_x = np.full(shape=x_shape, fill_value=False, dtype=bool)\\n indexes_y = np.full(shape=y.shape, fill_value=False, dtype=bool)\\n\\n for i in all_indexes.keys():\\n chosen_idxs = random.sample(all_indexes[i], int(part * ratios[i]))\\n indexes_y[chosen_idxs] = True\\n indexes_x[chosen_idxs, ...] = True\\n\\n return indexes_x, indexes_y\",\n \"def effmat(tp: np.ndarray, t: np.ndarray) -> np.ndarray:\\n n = len(t) # batch size\\n max_true_n_sources = max(t)\\n max_pred_n_sources = max(tp)\\n eff_mat = np.zeros((max_pred_n_sources + 1, max_true_n_sources + 1))\\n for ii in range(n):\\n eff_mat[tp[ii], t[ii]] += 1\\n return eff_mat\",\n \"def trans_o(self):\\n temp_array = []\\n for j in range(self.O.shape[1]):\\n for i in range(self.V.shape[1]):\\n if self.V[0, i] == self.O[0, j]:\\n temp_array.append(i)\\n self.O = mat(temp_array)\",\n \"def make_oneq_cliffords():\\n ixyz_list = [g().to_matrix() for g in (IGate, XGate, YGate, ZGate)]\\n ih_list = [g().to_matrix() for g in (IGate, HGate)]\\n irs_list = [\\n IGate().to_matrix(),\\n SdgGate().to_matrix() @ HGate().to_matrix(),\\n HGate().to_matrix() @ SGate().to_matrix(),\\n ]\\n oneq_cliffords = [\\n Operator(ixyz @ ih @ irs) for ixyz in ixyz_list for ih in ih_list for irs in irs_list\\n ]\\n return oneq_cliffords\",\n \"def get_score_matrix(self) -> int:\",\n \"def fit(self, X, y):\\n\\t\\tself._initialize_weights(X.shape[1])\\n\\t\\tself.cost_ = []\\n\\n\\t\\tfor i in range(self.n_iter):\\n\\t\\t\\tif self.shuffle:\\n\\t\\t\\t\\tX, y = self._shuffle(X,y)\\n\\t\\t\\tcost = []\\n\\t\\t\\t#calculate for each sample\\n\\t\\t\\tfor xi, target in zip(X, y):\\n\\t\\t\\t\\tcost.append(self._update_weights(xi, target))\\n\\t\\t\\tave_cost = sum(cost)/len(y)\\n\\t\\t\\tself.cost_.append(ave_cost)\\n\\t\\treturn self\",\n \"def monomio(x,datos_x,datos_y):\\n matriz=np.zeros([datos_x.shape[0],datos_x.shape[0]])\\n for j in range(datos_x.shape[0]): #Se contruye la matriz de vandermonde\\n matriz[:,j]= datos_x**(j)\\n matriz,datos_y=pivoteo_parcial(matriz,datos_y)\\n x1= descompo_LU(matriz,datos_y)# se resulve el sistema de ecuaciones por metodo directo\\n\\n puntos=[] #se almacenan los valores de y para cada punto de x que se quiera calcular \\n\\n for p in x: #va a ir tomando los valores de x uno por uno \\n prod=np.zeros(x1.shape[0])\\n for i in range(x1.shape[0]):\\n if i==0:\\n prod[i]=1\\n else:\\n prod[i]=prod[i-1]*p #Se hace el calculo de los polimonios con todos los valores de x \\n solucion=x1@prod\\n puntos.append(solucion) # se agregan los valores de y a la lista final \\n puntos=np.array(puntos)# se convierte la lista en array para mejor manejo\\n\\n return puntos\",\n \"def calcAllIntensities(self, xc, yc):\\n\\n tp = 0.0\\n ix = 0\\n iy = 0\\n h = 0\\n ints = np.zeros([5, 5])\\n ints_inner = np.zeros([5, 5])\\n # ints = [[0.0] * 5] * 5\\n # ints_inner = [[0.0] * 5] * 5\\n x = 0.0\\n y = 0.0\\n xc1 = 0.0\\n yc1 = 0.0\\n xc1 = xc\\n yc1 = yc\\n \\n for h in np.arange(1,5,1):\\n for k in np.arange(1,5,1):\\n ints[h][k] = 0.0\\n ints_inner[h][k] = 0.0\\n\\n for k in np.arange(0, 2, 1):\\n for h in np.arange(0, 2, 1):\\n for ix in np.arange(0, self.stepp + 1, 1):\\n for iy in np.arange(0, self.stepp + 1, 1):\\n #print(k, h, ix, iy)\\n if self.qc_format == 0 :\\n x = -(1 + self.G) + h * (1 + 2 * self.G) + (ix * (1.0 / self.stepp))\\n y = -(1 + self.G) + k * (1 + 2 * self.G) + (iy * (1.0 / self.stepp))\\n if self.spot_radius == 0 or math.sqrt(math.pow((x - xc1),2) + math.pow((y - yc1),2)) == 0 :\\n tp = 0.0\\n else :\\n tp = (math.sin((1 / self.spot_radius) * math.sqrt(math.pow((x - xc1),2) + math.pow((y - yc1),2)))) / ((1 / self.spot_radius) * math.sqrt(math.pow((x - xc1),2) + math.pow((y - yc1),2)))\\n tp = math.pow(tp,2)\\n #print(tp)\\n elif self.qc_format == 1 :\\n x = -1 + h + (ix * (1 / self.stepp))\\n y = -1 + k + (iy * (1 / self.stepp))\\n ints[h + 1][k + 1] += math.pow(math.exp((math.pow((x - xc1),2) + math.pow((y - yc1),2) ) / math.pow(self.spot_radius,2)), -1)\\n if (self.spot_radius * self.spot_radius) == 0 or ((x - xc1) * (y - yc1) * np.pi * np.pi) == 0 :\\n tp = 0.0\\n else :\\n tp = (math.sin((x - xc1) * np.pi / self.spot_radius) * math.sin((y - yc1) * np.pi / self.spot_radius)) / (((x - xc1) * (y - yc1) * np.pi * np.pi) / (self.spot_radius * self.spot_radius))\\n\\n if (math.pow(x,2) + math.pow(y,2)) <= math.pow(self.radius_inner,2):\\n ints_inner[h + 1][k + 1] += tp\\n else :\\n if self.qc_format == 1 :\\n if (math.pow(x,2) + math.pow(y,2)) <= math.pow(self.cell_qc, 2):\\n ints[h + 1][k + 1] += tp\\n if (math.pow(x,2) + math.pow(y,2)) <= 1 :\\n #print(math.pow(x,2) + math.pow(y,2))\\n ints[h + 1][k + 1] += tp\\n # print(ints[h + 1][k + 1])\\t\\t\\t\\t\\t\\t\\n tp = 0.0\\n\\n # print(ints)\\n\\n Aq = 0.0\\n Bq = 0.0\\n Cq = 0.0\\n Dq = 0.0\\n Ac_inner = 0.0\\n Bc_inner = 0.0\\n Cc_inner = 0.0\\n Dc_inner = 0.0\\n Ac = 0.0\\n Bc = 0.0\\n Cc = 0.0\\n Dc = 0.0\\n Ac = ints[1][2]\\n Bc = ints[2][2]\\n Cc = ints[2][1]\\n Dc = ints[1][1]\\n\\n Ac_inner = ints_inner[1][2]\\n Bc_inner = ints_inner[2][2]\\n Cc_inner = ints_inner[2][1]\\n Dc_inner = ints_inner[1][1]\\n Ac *= self.QE\\n Bc *= self.QE\\n Cc *= self.QE\\n Dc *= self.QE\\n\\n Ac_inner *= self.QE_inner\\n Bc_inner *= self.QE_inner\\n Cc_inner *= self.QE_inner\\n Dc_inner *= self.QE_inner\\n Ac += Ac_inner\\n Bc += Bc_inner\\n Cc += Cc_inner\\n Dc += Dc_inner\\n\\n Aq = Ac\\n Bq = Bc\\n Cq = Cc\\n Dq = Dc\\n\\n #tp/TP = cotribution percentage of the spot with respect to max (spot center)\\n if self.smooth == 0 :\\n if (Config.hplk_c0_e * self.TP) == 0 :\\n cnst = 0\\n else :\\n cnst = ((Parameters.TPS / (self.n_ml * self.n_ml)) * self.lamb) / (Config.hplk_c0_e * self.TP) #Número de fótons efeticos\\n if Config.flag_spice == 1 :\\n Ac *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP) #W\\n Bc *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP)\\n Cc *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP)\\n Dc *= Parameters.TPS / (self.n_ml * self.n_ml * self.TP)\\n Ac *= 1 / (math.pow(self.cell_qc * 1e-6,2)) #W/(m^2)\\n Bc *= 1 / (math.pow(self.cell_qc * 1e-6,2))\\n Cc *= 1 / (math.pow(self.cell_qc * 1e-6,2))\\n Dc *= 1 / (math.pow(self.cell_qc * 1e-6,2))\\n #Ac *= 1 / (self.lamb * 1e6); #Adequação da irradiância para a unidade W/m2micm conforme necessário no SPICE\\n #Bc *= 1 / (self.lamb * 1e6);\\n #Cc *= 1 / (self.lamb * 1e6);\\n #Dc *= 1 / (self.lamb * 1e6);\\n \\n ############################## DOUBLE CHECK ##############################\\n # self.grava_arquivos = 1\\n # self.flag_V_QC = 0\\n # grava_le_arquivos(0) # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\\n # self.flag_V_QC = 1\\n # self.grava_arquivos = 0\\n ############################## DOUBLE CHECK ##############################\\n Aq *= cnst * 1e9\\n Bq *= cnst * 1e9\\n Cq *= cnst * 1e9\\n Dq *= cnst * 1e9\\n else :\\n Aq *= cnst * 1e9\\n Bq *= cnst * 1e9\\n Cq *= cnst * 1e9\\n Dq *= cnst * 1e9\\n\\n # 'returns' all the intensities\\n self.A_intensity = Aq\\n self.B_intensity = Bq\\n self.C_intensity = Cq\\n self.D_intensity = Dq\",\n \"def correspondences(labels1,labels2,return_counts=True):\\n q = 100000\\n assert amin(labels1)>=0 and amin(labels2)>=0\\n assert amax(labels2)<q\\n combo = labels1*q+labels2\\n result = unique(combo, return_counts=return_counts)\\n if return_counts:\\n result, counts = result\\n result = array([result//q,result%q,counts])\\n else:\\n result = array([result//q,result%q])\\n return result\",\n \"def clifford_set(u):\\n i, x, y, z = u.v\\n result = []\\n result.append(u.clone()) # I\\n result.append(Uop(-x, i, -z, y, u.hierarchy, u.construction + [\\\"X\\\"], gateset=u.gateset)) # iX, but treat it as X due to only phase difference\\n result.append(Uop((i-x)/SQRT2, (x+i)/SQRT2, (y-z)/SQRT2, (z+y)/SQRT2, u.hierarchy, u.construction + [\\\"(I+iX)\\\"], gateset=u.gateset))\\n result.append(Uop((i+x)/SQRT2, (x-i)/SQRT2, (y+z)/SQRT2, (z-y)/SQRT2, u.hierarchy, u.construction + [\\\"(I-iX)\\\"], gateset=u.gateset))\\n result.append(Uop((i-y)/SQRT2, (x+z)/SQRT2, (y+i)/SQRT2, (z-x)/SQRT2, u.hierarchy, u.construction + [\\\"(I+iY)\\\"], gateset=u.gateset))\\n result.append(Uop((i+y)/SQRT2, (x-z)/SQRT2, (y-i)/SQRT2, (z+x)/SQRT2, u.hierarchy, u.construction + [\\\"(I-iY)\\\"], gateset=u.gateset))\\n for idx in range(6):\\n i, x, y, z = result[idx].v\\n c = result[idx].construction[-1:] if idx != 0 else []\\n result.append(Uop(-z, -y, x, i, u.hierarchy, u.construction + c + [\\\"Z\\\"], gateset=u.gateset)) # iZ\\n result.append(Uop((i-z)/SQRT2, (x-y)/SQRT2, (y+x)/SQRT2, (z+i)/SQRT2, u.hierarchy, u.construction + c + [\\\"(I+iZ)\\\"], gateset=u.gateset))\\n result.append(Uop((i+z)/SQRT2, (x+y)/SQRT2, (y-x)/SQRT2, (z-i)/SQRT2, u.hierarchy, u.construction + c + [\\\"(I-iZ)\\\"], gateset=u.gateset))\\n\\n return result\",\n \"def _split_indices(X, y, n_folds=5):\\n # TODO: check if indices are permuted\\n n = X.shape[0]\\n print('n:', n)\\n #original_indices = np.arange(n)\\n shuffle = np.random.permutation(n)\\n subset_proportion = 1./float(n_folds)\\n fold_size = int(subset_proportion*n)\\n folds = [shuffle[i*fold_size:(i+1)*fold_size] for i in range(n_folds)]\\n return folds\",\n \"def mi_bin_conn_time(x, y, bins_x, bins_y):\\n n_times, n_trials = x.shape\\n mi = np.zeros((n_times), dtype=np.float32)\\n for t in range(n_times):\\n mi[t] = mi_bin(x[t, :], y[t, :], bins_x, bins_y)\\n return mi\",\n \"def cumulative_capacity_rule(_m, g, y):\\r\\n\\r\\n return sum(m.x_c[g, j] for j in m.Y if j <= y)\",\n \"def reconstructXY(self, inputs):\\n return (self.reconstructX(inputs),\\n self.reconstructY(inputs))\",\n \"def update_output(self, latent_mat, weight_mat, y_list):\",\n \"def _recon_lcs(x, y):\\n i, j = len(x), len(y)\\n table = _lcs(x, y)\\n\\n def _recon(i, j):\\n \\\"\\\"\\\"private recon calculation\\\"\\\"\\\"\\n if i == 0 or j == 0:\\n return []\\n elif x[i - 1] == y[j - 1]:\\n return _recon(i - 1, j - 1) + [(x[i - 1], i)]\\n elif table[i - 1, j] > table[i, j - 1]:\\n return _recon(i - 1, j)\\n else:\\n return _recon(i, j - 1)\\n\\n recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))\\n return recon_tuple\",\n \"def add_dims_to_match(x, y):\\n x_shape = tf.shape(x)\\n new_dims = tf.rank(y)-tf.rank(x)\\n if new_dims > 0:\\n new_shape = tf.concat((x_shape, tf.ones((new_dims,), dtype=tf.int32)), axis=0)\\n return tf.reshape(x, new_shape)\\n else:\\n return x\",\n \"def inplace(block_size=20000):\\n y = np.empty(len(x))\\n for k in range(len(x) // block_size + 1):\\n b, e = k * block_size, (k+1) * block_size\\n y[b:e] = x[b:e]\\n y[b:e] *= .25\\n y[b:e] += .75\\n y[b:e] *= x[b:e]\\n y[b:e] -= 1.5\\n y[b:e] *= x[b:e]\\n y[b:e] -= 2\\n\\n return y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5772924","0.573736","0.57085884","0.5653021","0.56054556","0.54707366","0.5380854","0.53584445","0.5358038","0.53385925","0.5314144","0.5285138","0.52835494","0.52652776","0.52577674","0.5253505","0.5253083","0.524562","0.5237841","0.52344394","0.5211222","0.51828897","0.51802444","0.5178981","0.5178981","0.51725096","0.51699585","0.5166547","0.5157785","0.515223","0.51469713","0.51321095","0.5115334","0.5087791","0.50812095","0.5076138","0.5074466","0.5057698","0.50500864","0.50500864","0.5035716","0.50207555","0.49975905","0.4982586","0.49759686","0.49691507","0.49595478","0.4948456","0.49445125","0.49383506","0.49262735","0.4912366","0.4910826","0.4906799","0.49047226","0.48978412","0.4896438","0.48920682","0.4891731","0.48900634","0.48893338","0.48886907","0.4885065","0.48842517","0.4881399","0.4880126","0.48797235","0.48771152","0.4866483","0.48636025","0.48633644","0.485049","0.48362094","0.4832051","0.48197523","0.48140198","0.48039287","0.4798375","0.47875795","0.47807667","0.477661","0.47749743","0.47744423","0.47732797","0.47727358","0.4771551","0.47644138","0.4764349","0.47624502","0.47509244","0.47450113","0.4740621","0.47403514","0.47384232","0.47349578","0.47333637","0.47283027","0.47274932","0.47198072","0.47189203","0.4718727"],"string":"[\n \"0.5772924\",\n \"0.573736\",\n \"0.57085884\",\n \"0.5653021\",\n \"0.56054556\",\n \"0.54707366\",\n \"0.5380854\",\n \"0.53584445\",\n \"0.5358038\",\n \"0.53385925\",\n \"0.5314144\",\n \"0.5285138\",\n \"0.52835494\",\n \"0.52652776\",\n \"0.52577674\",\n \"0.5253505\",\n \"0.5253083\",\n \"0.524562\",\n \"0.5237841\",\n \"0.52344394\",\n \"0.5211222\",\n \"0.51828897\",\n \"0.51802444\",\n \"0.5178981\",\n \"0.5178981\",\n \"0.51725096\",\n \"0.51699585\",\n \"0.5166547\",\n \"0.5157785\",\n \"0.515223\",\n \"0.51469713\",\n \"0.51321095\",\n \"0.5115334\",\n \"0.5087791\",\n \"0.50812095\",\n \"0.5076138\",\n \"0.5074466\",\n \"0.5057698\",\n \"0.50500864\",\n \"0.50500864\",\n \"0.5035716\",\n \"0.50207555\",\n \"0.49975905\",\n \"0.4982586\",\n \"0.49759686\",\n \"0.49691507\",\n \"0.49595478\",\n \"0.4948456\",\n \"0.49445125\",\n \"0.49383506\",\n \"0.49262735\",\n \"0.4912366\",\n \"0.4910826\",\n \"0.4906799\",\n \"0.49047226\",\n \"0.48978412\",\n \"0.4896438\",\n \"0.48920682\",\n \"0.4891731\",\n \"0.48900634\",\n \"0.48893338\",\n \"0.48886907\",\n \"0.4885065\",\n \"0.48842517\",\n \"0.4881399\",\n \"0.4880126\",\n \"0.48797235\",\n \"0.48771152\",\n \"0.4866483\",\n \"0.48636025\",\n \"0.48633644\",\n \"0.485049\",\n \"0.48362094\",\n \"0.4832051\",\n \"0.48197523\",\n \"0.48140198\",\n \"0.48039287\",\n \"0.4798375\",\n \"0.47875795\",\n \"0.47807667\",\n \"0.477661\",\n \"0.47749743\",\n \"0.47744423\",\n \"0.47732797\",\n \"0.47727358\",\n \"0.4771551\",\n \"0.47644138\",\n \"0.4764349\",\n \"0.47624502\",\n \"0.47509244\",\n \"0.47450113\",\n \"0.4740621\",\n \"0.47403514\",\n \"0.47384232\",\n \"0.47349578\",\n \"0.47333637\",\n \"0.47283027\",\n \"0.47274932\",\n \"0.47198072\",\n \"0.47189203\",\n \"0.4718727\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":98,"cells":{"query":{"kind":"string","value":"A function for generating reaction likelihoods for a given genome according to the Probabilistic Annotation algorithm as"},"document":{"kind":"string","value":"def generate_reaction_probabilities(fasta_file, template_model_file, genome_id=None):\n if genome_id is None:\n # Use fasta_file name minus extension. worker uses only for file names and logging\n genome_id = '.'.join(fasta_file.split('.')[0:-1])\n # Create a worker for running the algorithm.\n worker = ProbAnnotationWorker(genome_id)\n try:\n template_model = _load_template_file(template_model_file)\n\n # Run blast using the fasta file.\n blast_result_file = worker.runBlast(fasta_file)\n\n # Calculate roleset probabilities.\n rolestring_tuples = worker.rolesetProbabilitiesMarble(blast_result_file)\n\n # Calculate per-gene role probabilities.\n role_probs = worker.rolesetProbabilitiesToRoleProbabilities(rolestring_tuples)\n\n # Calculate whole cell role probabilities.\n total_role_probs = worker.totalRoleProbabilities(role_probs)\n\n # Calculate complex probabilities.\n complex_probs = worker.complexProbabilities(total_role_probs, complexesToRequiredRoles=_complex_to_roles_dict(template_model))\n\n # Calculate reaction probabilities.\n rxn_probs = worker.reactionProbabilities(complex_probs, rxnsToComplexes=_reactions_to_complexes_dict(template_model))\n\n # Store in dictionary for better serialization\n return ReactionProbabilities([{'reaction': r[0], 'probability': r[1], 'type': r[2], 'complexes': _deserialize_cplx(r[3], worker.config['separator']), 'gpr': r[4]} for r in rxn_probs])\n finally:\n worker.cleanup() # worker creates lots of temporary and intermediate files. Allow it to clean up"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \n \"*** YOUR CODE HERE ***\"\n \n # -- OUR CODE HERE\n \n \n import math\n for label in self.legalLabels:\n sumThing = 0.0\n for pixel in self.conditionalProb[label]:\n if datum[pixel] is 1:\n #assert self.conditionalProb[label][pixel] < 1.0 # -- sanity check that the probability is valid\n sumThing += math.log((self.conditionalProb[label][pixel]*1.0))\n else:\n sumThing+=math.log(1-self.conditionalProb[label][pixel]*1.0)\n logJoint[label] = math.log(self.prior[label]*1.0) + sumThing*1.0\n \n\n \n \n import time\n #print \"logJoint is :: \", logJoint\n #time.sleep(2)\n \n \n # -- uses the conditional probability tables computed in the current iteration\n # -- in train and tune\n \n return logJoint","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \"*** YOUR CODE HERE ***\"\n\t#Adds log(P(y)) to calculate P(y|f1,f2...)\n for label in self.legalLabels:\n\t\tlogJoint[label] += math.log(self.prior[label])\n\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\n for key in datum:\n\t\t#if key == (7, 3):\n\t\t\t#print self.condprobs[key, 0]\n\t\tfor label in self.legalLabels:\n\t\t\t#print str(key) + str(datum[key])\n\t\t\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\n return logJoint","def log_prob(self):","def evaluate(genome):\n # base fitness\n fit = 1.0\n # promote 1001 starting motif\n matches = 0\n if genome.sequence_A[0] == 1:\n matches += 1\n if genome.sequence_A[1] == 0:\n matches += 1\n if genome.sequence_A[2] == 0:\n matches += 1\n if genome.sequence_A[3] == 1:\n matches += 1\n fit += matches * 0.1\n # finish\n return fit","def likelihood(seq):\n global qmap\n if qmap is None:\n qmap = {'!': 1.0, '\"': 0.7943282347242815, '#': 0.6309573444801932, '$': 0.5011872336272722, '%': 0.3981071705534972, '&': 0.31622776601683794, \"'\": 0.251188643150958, '(': 0.19952623149688797, ')': 0.15848931924611134, '*': 0.12589254117941673, '+': 0.1, ',': 0.07943282347242814, '-': 0.06309573444801933, '.': 0.05011872336272722, '/': 0.039810717055349734, '0': 0.03162277660168379, '1': 0.025118864315095794, '2': 0.0199526231496888, '3': 0.015848931924611134, '4': 0.012589254117941675, '5': 0.01, '6': 0.007943282347242814, '7': 0.00630957344480193, '8': 0.005011872336272725, '9': 0.003981071705534973, ':': 0.0031622776601683794, ';': 0.0025118864315095794, '<': 0.001995262314968879, '=': 0.001584893192461114, '>': 0.0012589254117941675, '?': 0.001, '@': 0.0007943282347242813, 'A': 0.000630957344480193, 'B': 0.0005011872336272725, 'C': 0.00039810717055349735, 'D': 0.00031622776601683794, 'E': 0.00025118864315095795, 'F': 0.00019952623149688788, 'G': 0.00015848931924611142, 'H': 0.00012589254117941674, 'I': 0.0001, 'J': 7.943282347242822e-05, 'K': 6.309573444801929e-05, 'L': 5.011872336272725e-05, 'M': 3.9810717055349695e-05, 'N': 3.1622776601683795e-05, 'O': 2.5118864315095822e-05, 'P': 1.9952623149688786e-05, 'Q': 1.584893192461114e-05, 'R': 1.2589254117941661e-05, 'S': 1e-05, 'T': 7.943282347242822e-06, 'U': 6.30957344480193e-06, 'V': 5.011872336272725e-06, 'W': 3.981071705534969e-06, 'X': 3.162277660168379e-06, 'Y': 2.5118864315095823e-06, 'Z': 1.9952623149688787e-06, '[': 1.584893192461114e-06, '\\\\': 1.2589254117941661e-06, ']': 1e-06, '^': 7.943282347242822e-07, '_': 6.30957344480193e-07, '`': 5.011872336272725e-07, 'a': 3.981071705534969e-07, 'b': 3.162277660168379e-07, 'c': 2.5118864315095823e-07, 'd': 1.9952623149688787e-07, 'e': 1.584893192461114e-07, 'f': 1.2589254117941662e-07, 'g': 1e-07, 'h': 7.943282347242822e-08, 'i': 6.30957344480193e-08, 'j': 5.011872336272725e-08, 'k': 3.981071705534969e-08, 'l': 3.162277660168379e-08, 'm': 2.511886431509582e-08, 'n': 1.9952623149688786e-08, 'o': 1.5848931924611143e-08, 'p': 1.2589254117941661e-08, 'q': 1e-08, 'r': 7.943282347242822e-09, 's': 6.309573444801943e-09, 't': 5.011872336272715e-09, 'u': 3.981071705534969e-09, 'v': 3.1622776601683795e-09, 'w': 2.511886431509582e-09, 'x': 1.9952623149688828e-09, 'y': 1.584893192461111e-09, 'z': 1.2589254117941663e-09, '{': 1e-09, '|': 7.943282347242822e-10, '}': 6.309573444801942e-10, '~': 5.011872336272714e-10, '\\x7f': 3.9810717055349694e-10, '\\x80': 3.1622776601683795e-10, '\\x81': 2.511886431509582e-10, '\\x82': 1.9952623149688828e-10, '\\x83': 1.584893192461111e-10, '\\x84': 1.2589254117941662e-10, '\\x85': 1e-10}\n return [qmap[i] for i in seq]","def likelihood_genotype(genotype, bases_all_reads, error_rates):\n likelihood = 1\n for observed_base in bases_all_reads:\n p = 0\n for base in \"ACGT-\":\n l = prob_t_N(genotype, base) * error_rates[base][observed_base]\n p += l\n likelihood *= p\n\n return likelihood","def calculateLogJointProbabilities(self, datum):\n\tlogJoint = util.Counter()\n\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\n\t#where y is a label\n\tfor label in self.legalLabels:\n\t\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\n\t\tfor feature, value in datum.items():\n\t\t\tcp = self.conditional_prob[label][feature][value]\n\t\t\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\n\t\t\t\tlogJoint[label] += math.log(cp) #summing up\n\t\t\t\t\n\treturn logJoint","def likelihood(self):\n \n raise NotImplementedError()","def annotate_effect(cds_dict, genome, snp):\n # List to save the coding effect\n coding_effect = []\n \n # Change the SNP position from 1-indexed to 0-indexed\n snp = (snp[0]-1, snp[1])\n \n # Determine which genes the SNP is located in\n genes = []\n for k,v in cds_dict.items():\n if snp[0] in range(v.location.start, v.location.end): \n genes.append(k)\n # Check that SNP is in a gene\n if genes: \n # Some SNPs will be in more than one gene, SARS has overlaping ORFs\n for gene in genes: \n gene_tuple = list(zip(list(cds_dict[gene].location), cds_dict[gene].location.extract(genome)))\n # Get the indicies relative to the gene, add 1 to get 1-indexed values\n indicies = [x + 1 for x, y in enumerate(gene_tuple) if y[0] == snp[0]]\n # Determine codon position from gene index\n for i in indicies:\n # First position in codon\n if i % 3 == 1:\n codonpos = 1\n wtcodon = [gene_tuple[i-1], gene_tuple[i], gene_tuple[i+1]]\n # Second position in codon\n elif i % 3 == 2:\n codonpos = 2\n wtcodon = [gene_tuple[i-2], gene_tuple[i-1], gene_tuple[i]]\n # Third position in codon \n elif i % 3 == 0:\n codonpos = 3\n wtcodon = [gene_tuple[i-3], gene_tuple[i-2], gene_tuple[i-1]]\n \n # From the wt codon sequence, determine the alterative codon, coding change, and effect\n altcodon = [snp if i == (codonpos-1) else b for i, b in enumerate(wtcodon)]\n wtaa = translate(\"\".join(y for x,y in wtcodon))\n altaa = translate(\"\".join(y for x,y in altcodon))\n if wtaa == altaa:\n effect = \"synonymous\"\n elif wtaa != altaa and altaa == '*':\n effect = \"nonsense\"\n elif wtaa != altaa and altaa != '*':\n effect = \"missense\"\n # Save the codon effects and information\n coding_effect.append((codonpos, f\"{wtaa}{-(i // -3)}{altaa}\", effect, gene))\n # If the SNP isn't in a gene, it's intergeneic and has no coding effect\n else:\n coding_effect.append((\"NA\", \"NA\", \"NA\", \"intergeneic\"))\n \n \n # Deal with SNPs in multiple genes with multiple effects \n if len(coding_effect) == 1:\n return list(coding_effect[0])\n else: \n if len(set([(a,b,c) for a,b,c,d in coding_effect])) == 1: \n return list(list(set(coding_effect))[0])\n # TODO: Deal with ambiguous sequences\n else:\n return [\"NA\", \"NA\", \"NA\", \"ambiguous\"]","def target_log_prob_fn(self, *args, **kwargs): # pylint: disable=unused-argument\n\n def log_joint_fn(*args, **kwargs): # pylint: disable=unused-argument\n states = dict(zip(self.unobserved.keys(), args))\n states.update(self.observed)\n interceptor = interceptors.CollectLogProb(states)\n with ed.interception(interceptor):\n self._f(self._cfg)\n\n log_prob = sum(interceptor.log_probs)\n return log_prob\n return log_joint_fn","def score_sequence(seq, ngramlogprobs):\n return","def get_log_likelihood(response_probability, response):\n pass","def probability(structure,seq, react=None):\n return energy_to_proba(get_ens_energy(seq,react),get_stru_energy(structure,seq,react))","def Log_OB(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n n = nX[1]\n t = nX[2]\n\n G = np.zeros((m,n,t))\n\n for r in range(t):\n\n # Correct for permuations\n\n Xout,PiA= CorrectPerm(xref,x[:,:,r])\n\n G[:,:,r] = Xout - np.dot(xref,np.dot(PiA,Xout))\n\n return G","def calc_prob(number_of_strings, GC_content, DNA):\r\n\r\n AT = 0\r\n GC = 0\r\n\r\n for nt in DNA:\r\n if nt == \"A\" or nt == \"T\":\r\n AT += 1\r\n elif nt == \"G\" or nt == \"C\":\r\n GC += 1\r\n\r\n #P(at least 1 match of s) = 1 − P(no matches out of N strings) = 1 − [1 - P_no_match]^N\r\n\r\n P_no_match = (((1 - GC_content)/2) **AT) * ((GC_content/2) **GC)\r\n prob = 1 - (1-P_no_match) **number_of_strings\r\n\r\n print(\"%0.3f\" %prob)","def log_likelihood(self, data, reward_model, bias_params):","def biopythonMM(pwmFileName,genomeDict,mpbsDict,scoringMethod,tempLocation,pseudocounts=0.1,bitscore=12.0,fpr=0.01,precision=10**4,highCutoff=0.7,functionalDepth=0.9):\n \n # Reading PWM\n pwm = readPwmFile(pwmFileName,tempLocation,pseudocounts)\n pwmName = pwmFileName.split(\"/\")[-1].split(\".\")[0]\n pwmLen = len(pwm)\n\n # Evaluating threshold\n pwmThreshold = 0.0\n if(scoringMethod == \"bitscore\"):\n pwmThreshold = bitscore\n elif(scoringMethod == \"fpr\"):\n sd = Motif.ScoreDistribution(pwm,precision=precision)\n pwmThreshold = sd.threshold_fpr(fpr)\n elif(scoringMethod == \"boyle\"):\n maxScore = pwm.max_score()\n minScore = 0.0 # TODO Boyle's rule is not suited for negative values.\n pwmThreshold = min(highCutoff*maxScore,functionalDepth*(maxScore-minScore))\n else:\n sys.stderr.write(\"Choose a valid scoring method.\\n\")\n sys.exit(0)\n\n # Creating aditional parameters\n chrList = constants.getChromList(reference=[mpbsDict])\n tempMpbsDict = dict([(e,[]) for e in chrList])\n maxValue = -99.0\n\n # Iterating on chromosomes\n for chrName in chrList:\n\n # Reading genome\n sequence = genomeDict[chrName]\n\n # Performing biopython's motif matching\n for pos, score in pwm.search_pwm(sequence,threshold=pwmThreshold):\n if(score > maxValue): maxValue = score\n if(pos >= 0): tempMpbsDict[chrName].append([pos,pos+pwmLen,pwmName,score,\"+\"])\n else: tempMpbsDict[chrName].append([-pos,-pos+pwmLen,pwmName,score,\"-\"])\n\n # Update scores - new scores are within [0,1000]\n for chrName in chrList:\n for e in tempMpbsDict[chrName]:\n mpbsDict[chrName].append([e[0],e[1],e[2],int(1000*(e[3]-pwmThreshold)/(maxValue-pwmThreshold)),e[4]])\n \n return 0","def gomeroccupancyscore(pwm_dictionary, seq):\n if \"N\" in seq:\n return 0\n else:\n # pwm_length = len(pwm_dictionary)\n pwm_length = len(pwm_dictionary[\"A\"])\n gomer_occupancy = 1\n area_pwm_rc = rc_pwm(pwm_dictionary, pwm_length)\n for i in range(pwm_length - 1, 1, -1):\n prod_gomer = 1\n prod_gomer_rc = 1\n for j in range(pwm_length):\n if j <= i:\n prod_gomer *= 0.25\n prod_gomer_rc *= 0.25\n elif (j + i) > len(seq) - 1:\n prod_gomer *= 0.25\n prod_gomer_rc *= 0.25\n else:\n # print \"got to else\"\n s = seq[j + i]\n prod_gomer *= pwm_dictionary[s][j]\n prod_gomer_rc *= area_pwm_rc[s][j]\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\n for i in range(len(seq) - 1):\n prod_gomer = 1\n prod_gomer_rc = 1\n for j in range(pwm_length - 1):\n if (j + i) >= len(seq) - 1:\n prod_gomer *= 0.25\n prod_gomer_rc *= 0.25\n else:\n prod_gomer *= pwm_dictionary[seq[j + i]][j]\n prod_gomer_rc *= area_pwm_rc[seq[j + i]][j]\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\n gomer_occupancy = 1 - gomer_occupancy\n\n return gomer_occupancy","def likelihood_prediction():\n # Get info\n selected_word = prompt_tech_selection()\n article_json = get_json_from_file()\n\n # Calculate results\n total_word_counter, selected_word_counter = count_occurrences(article_json, selected_word)\n probability = selected_word_counter / total_word_counter\n total_time = article_json[-1]['time'] - article_json[0]['time'] # unix subtraction = seconds\n months_in_train_set = total_time / SECONDS_IN_MONTH\n expected_posts_per_month = int(total_word_counter / months_in_train_set)\n\n # Show results\n print_text_results(expected_posts_per_month, probability, selected_word)\n plot_likelihood(expected_posts_per_month, probability)","def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n for cls in self.classes:\n class_probability = self.prior_prob[cls]\n for key, value in datum.items():\n relative_feature_values = self.likelihoods[cls][key]\n class_probability += math.log(relative_feature_values.get(datum[key], 0.01))\n\n logJoint[cls] = class_probability\n\n return logJoint","def viterbi(prob_matrix):\n TINY = 1e-6 # to avoid NaNs in logs\n\n # if prob_matrix is 1D, make it 2D\n if len(np.shape(prob_matrix)) == 1:\n prob_matrix = [prob_matrix]\n \n length = len(prob_matrix)\n\n probs = np.zeros_like(prob_matrix)\n backpt = np.ones_like(prob_matrix, dtype=np.int32) * -1\n \n for i in [0,1,2,3,4]:\n probs[0][i] = np.log(prob_matrix[0][i]+TINY)\n \n # {B, M, E, S} <=== 0:begin, 1:middle, 2:end, 3:single\n for t in range(1, length):\n # E, S -> B | B, M -> M | B, M -> E | E, S -> S\n previous_of = [[0,0], [3,4], [1,2], [1,2], [3,4]]\n for i in range(5):\n prevs = previous_of[i]\n max_id = prevs[np.argmax([probs[t-1][prevs[0]], probs[t-1][prevs[1]]])]\n backpt[t][i] = max_id\n probs[t][i] = np.log(prob_matrix[t][i]+TINY) + probs[t-1][max_id]\n\n seq = np.ones(length, 'int32') * -1\n #print(probs[length-1])\n seq[length-1] = np.argmax(probs[length-1])\n #print(seq[length-1])\n max_prob = probs[length-1][seq[length-1]]\n for t in range(1, length):\n seq[length-1-t] = backpt[length-t][seq[length-t]]\n \n return seq","def complex(self, sentence):\r\n repetition = 6000\r\n warmup = 2500\r\n pos_mcmc_dict = {\"pos_\" + str(i): {} for i in range(len(sentence))}\r\n sequence = [\"noun\"] * len(sentence)\r\n for i in range(len(sentence)):\r\n if i == 0:\r\n prob_first = self.posterior_first(sentence[i])\r\n sample_first = list(\r\n np.random.choice(\r\n [keys for keys in prob_first.keys()],\r\n repetition,\r\n p=[\r\n float(prob_first[keys]) / sum(prob_first.values())\r\n for keys in prob_first.keys()\r\n ],\r\n )\r\n )\r\n sample_first = sample_first[warmup:]\r\n pos_mcmc_dict[\"pos_\" + str(i)] = {\r\n pos: (float(sample_first.count(pos)) / len(sample_first))\r\n for pos in self.position_list\r\n }\r\n sequence[i] = max(\r\n pos_mcmc_dict[\"pos_\" + str(i)],\r\n key=pos_mcmc_dict[\"pos_\" + str(i)].get,\r\n )\r\n elif i == 1:\r\n prob_second = self.post_second(sentence[i], sequence[i - 1])\r\n sample_second = list(\r\n np.random.choice(\r\n [keys for keys in prob_second.keys()],\r\n repetition,\r\n p=[\r\n float(prob_second[keys]) / sum(prob_second.values())\r\n for keys in prob_second.keys()\r\n ],\r\n )\r\n )\r\n sample_second = sample_second[warmup:]\r\n pos_mcmc_dict[\"pos_\" + str(i)] = {\r\n pos: (float(sample_second.count(pos)) / len(sample_second))\r\n for pos in self.position_list\r\n }\r\n sequence[i] = max(\r\n pos_mcmc_dict[\"pos_\" + str(i)],\r\n key=pos_mcmc_dict[\"pos_\" + str(i)].get,\r\n )\r\n else:\r\n prob_other = self.posterior_else(\r\n sentence[i], sequence[i - 1], sequence[i - 2]\r\n )\r\n sample_other = list(\r\n np.random.choice(\r\n [keys for keys in prob_other.keys()],\r\n repetition,\r\n p=[\r\n float(prob_other[keys]) / sum(prob_other.values())\r\n for keys in prob_other.keys()\r\n ],\r\n )\r\n )\r\n sample_other = sample_other[warmup:]\r\n pos_mcmc_dict[\"pos_\" + str(i)] = {\r\n pos: (float(sample_other.count(pos)) / len(sample_other))\r\n for pos in self.position_list\r\n }\r\n sequence[i] = max(\r\n pos_mcmc_dict[\"pos_\" + str(i)],\r\n key=pos_mcmc_dict[\"pos_\" + str(i)].get,\r\n )\r\n return sequence","def make_fitness_function(cantus_firmus):\n def fitness_function(genome, debug=False):\n \"\"\"\n Given a candidate solution will return its fitness score assuming\n the cantus_firmus in this closure. Caches the fitness score in the\n genome.\n \"\"\"\n cp = genome.chromosome\n\n # calculate some information for easier scoring\n ScoringInfo = namedtuple(\"ScoringInfo\", \"pitch duration measure beat vi hi_next hi_prev voice_dir\")\n melody_info = list()\n beat = 4\n measure = 0\n for i in range(1, len(cp)):\n hi_next = abs(cp[i][0] - cp[i + 1][0]) if i != len(cp) - 1 else -1 # next horizontal interval\n hi_prev = abs(cp[i][0] - cp[i - 1][0]) if i != 1 else -1 # previous horizontal interval\n vi = abs(cp[i][0] - cantus_firmus[measure]) # vertical interval\n # voice movement direction\n voice_dir = 0 if i == len(cp) - 1 or cp[i + 1][0] == cp[i][0] else copysign(1, cp[i + 1][0] - cp[i][0])\n melody_info.append(ScoringInfo(cp[i][0], cp[i][1], measure, beat, vi, hi_next, hi_prev, voice_dir))\n beat += cp[i][1]\n measure += beat / 8\n beat %= 8\n\n if debug:\n print \"MELODY INFO: \", melody_info\n\n hscores = list()\n vscores = list()\n # hscore 1: 8th notes must move in step\n amount_of_8th = 0\n amount_of_missteps = 0\n for note in melody_info:\n if note.duration == 1:\n amount_of_8th += 1\n if note.hi_next > 1:\n amount_of_missteps += 1\n if note.hi_prev > 1:\n amount_of_missteps += 1\n hscores.append(float(amount_of_missteps) / (amount_of_8th * 2))\n if debug:\n print \"HSCORE 1: 8TH - \", amount_of_8th, \", MISSTEPS - \", amount_of_missteps\n\n # hscore 2: one climax, that can be repeated only after neighboring tone\n # hscore 3: Climax should be on the strong beat\n highest_note = max([note.pitch for note in melody_info])\n climax_count = 0\n climax_on_weak_beat_count = 0\n for i, note in enumerate(melody_info):\n if note.pitch == highest_note:\n climax_count += 1\n if note.beat not in [0, 4]:\n climax_on_weak_beat_count += 1\n if i < len(melody_info) - 2 and note.pitch == melody_info[i + 2].pitch: # If next note is\n if note.hi_next == 1 and melody_info[i + 2].hi_prev == 1: # neighboring tone\n if note.vi in CONSONANCES and melody_info[i + 2].vi in CONSONANCES: # And surrounding notes are consonant\n climax_count -= 1 # we can allow 2nd climax\n if melody_info[i + 2].beat not in [0, 4]:\n climax_on_weak_beat_count -= 1 # And 2nd climax may be on weak beat\n\n hscores.append(float(climax_count - 1) / len(melody_info))\n hscores.append(float(climax_on_weak_beat_count) / climax_count)\n\n if debug:\n print \"HSCORE 2+3: CLIMAX CNT - \", climax_count, \", WEAK CLIMAX CNT - \", climax_on_weak_beat_count\n\n # hscore 4: Horizontal intervals are consonant\n unconsonant_amount = len(filter(lambda x: x.hi_next not in CONSONANCES + [1], melody_info[:-1]))\n hscores.append(float(unconsonant_amount) / (len(melody_info) - 1))\n\n if debug:\n print \"HSCORE 4: UNCONSANANT AMOUNT - \", unconsonant_amount\n\n # hscore 5: Stepwise movement should predominate\n leaps_count = len(filter(lambda x: x.hi_next != 1, melody_info[:-1]))\n sections = round(float(len(cantus_firmus)) / 16)\n if leaps_count < (2 * sections):\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\n elif leaps_count > (4 * sections):\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\n else:\n hscores.append(0.0)\n\n if debug:\n print \"HSCORE 5: LEAPS - \", leaps_count, \"SECTIONS - \", sections\n\n # hscore 6: After large leap - stepwise motion\n large_leaps_count = 0\n large_leaps_not_followed_count = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.hi_next >= 3:\n large_leaps_count += 1\n if melody_info[i + 1].hi_next != 1:\n large_leaps_not_followed_count += 1\n hscores.append(float(large_leaps_not_followed_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\n\n if debug:\n print \"HSCORE 6: LL CNT - \", large_leaps_count, \"LL NOT FOLLOWED CNT - \", large_leaps_not_followed_count\n\n # hscore 7: change direction after each large leap\n large_leaps_not_changedir_count = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.hi_next >= 3 and note.voice_dir != -melody_info[i + 1].voice_dir:\n large_leaps_not_changedir_count += 1\n hscores.append(float(large_leaps_not_changedir_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\n\n if debug:\n print \"HSCORE 7: LL NOT CHNGDIR CNT - \", large_leaps_not_changedir_count\n\n # hscore 8: climax should be melodically consonant with tonic\n hscores.append(1.0 if highest_note - 4 in CONSONANCES else 0.0)\n\n # hscore 9: no more than 2 consecutive leaps\n conseq_leaps = 0\n punish_score = 0\n for note in melody_info:\n conseq_leaps += 1\n if note.hi_next in [0, 1]:\n conseq_leaps = 0\n if conseq_leaps > 3:\n punish_score += 1\n hscores.append(float(punish_score) / (len(melody_info) - 3))\n\n if debug:\n print \"HSCORE 9: CONSEQ LEAPS PUNISH SCORE - \", punish_score\n\n # hscore 10: no more than 2 large leaps per section\n if large_leaps_count > 2 * sections:\n hscores.append(float(large_leaps_count - 2 * sections) / (len(melody_info) - 1 - 2 * sections))\n else:\n hscores.append(0.0)\n\n # hscore 11: not too long stepwise in same direction\n longest_stepwise_seq = 0\n current_stepwise_seq = 0\n prev_dir = 0\n num_changes = 0\n motion_vector = list()\n for note in melody_info:\n if note.hi_next <= 1:\n if note.voice_dir in [prev_dir, 0]:\n current_stepwise_seq += 1\n longest_stepwise_seq = max(longest_stepwise_seq, current_stepwise_seq)\n else:\n prev_dir = note.voice_dir\n current_stepwise_seq = 0\n num_changes += 1\n motion_vector.append(note.pitch)\n else:\n if note.voice_dir != prev_dir and note.voice_dir != 0:\n prev_dir = note.voice_dir\n num_changes += 1\n motion_vector.append(note.pitch)\n current_stepwise_seq = 0\n motion_vector.append(cp[-1][0])\n if longest_stepwise_seq < 5:\n longest_stepwise_seq = 0\n hscores.append(float(longest_stepwise_seq) / len(cp))\n\n if debug:\n print \"HSCORE 11: LONGEST STEPWISE SEQUENCE - \", longest_stepwise_seq\n\n # hscore 12: direction needs to change several times\n if num_changes < 3 * sections:\n hscores.append(1 - float(num_changes) / (3 * sections))\n else:\n hscores.append(0.0)\n\n # hscore 13: ending note is tonic\n hscores.append(0)\n\n # hscore 14: penultimate note is leading tone\n hscores.append(0)\n\n # hscore 15: the start of a motion is consonant with the end of a motion\n unconsotant_count = 0\n big_leaps_count = 0\n for i in range(1, len(motion_vector) - 1):\n if abs(motion_vector[i] - motion_vector[i + 1]) not in CONSONANCES:\n unconsotant_count += 1\n if abs(motion_vector[i] - motion_vector[i + 1]) > 6:\n big_leaps_count += 1\n hscores.append(float(unconsotant_count) / len(motion_vector))\n\n if debug:\n print \"HSCORE 15: UNCONSONANT MOTIONS - \", unconsotant_count\n\n # hscore 16: Large motion intervals (>6 tones) should be avoided\n hscores.append(float(big_leaps_count) / len(motion_vector))\n\n if debug:\n print \"HSCORE 16: LARGE MOTIONS - \", big_leaps_count\n\n # hscore 17: No frequent repetition of the same note\n rep_count = 0\n for note in melody_info:\n if note.hi_next == 0:\n rep_count += 1\n if rep_count > 2 * sections:\n rep_count -= 2 * sections\n else:\n rep_count = 0\n hscores.append(float(rep_count) / (len(cp) - 2 * sections))\n\n if debug:\n print \"HSCORE 17: REPETITIONS COUNT - \", rep_count\n\n # hscore 18: no repetition of sequence within a 4 measure interval\n repeated = set()\n for i in range(len(melody_info) - 2):\n j = i + 1\n while melody_info[j].measure < melody_info[i].measure + 4 and j < len(melody_info) - 1:\n if melody_info[i].pitch == melody_info[j].pitch:\n k = 1\n while j + k < len(melody_info) and melody_info[j + k].pitch == melody_info[i + k].pitch:\n if k == 1:\n repeated.add(j)\n repeated.add(j + k)\n k += 1\n j += 1\n\n hscores.append(float(len(repeated)) / len(cp))\n\n if debug:\n print \"HSCORE 18: REPEATED POSITIONS - \", repeated\n\n # hscore 19: largest allowed interval is octave\n more_than_ocatave_amount = len(filter(lambda x: x.hi_next > 7, melody_info[:-1]))\n hscores.append(float(more_than_ocatave_amount) / len(cp))\n\n if debug:\n print \"HSCORE 19: MORE THAN OCTAVES - \", more_than_ocatave_amount\n\n # vscore 1: whole notes should be consonant (ensured by generation and hscore 13)\n vscores.append(0.0)\n\n # vscores 2 and 3: halves and quarters should be consonant on first beat.\n # or can be dissonant on other beats beat, if passing tone\n amount_of_notes = 0\n amount_of_wrong_notes = 0\n for i, note in enumerate(melody_info):\n if note.duration >= 2:\n amount_of_notes += 1\n if note.beat == 0 and note.vi not in CONSONANCES:\n amount_of_wrong_notes += 1\n if note.beat != 0 and note.vi not in CONSONANCES:\n amount_of_wrong_notes += 1\n if note.hi_prev == 1 and note.hi_next == 1 and note.voice_dir == melody_info[i - 1].voice_dir:\n if melody_info[i - 1].vi in CONSONANCES and melody_info[i + 1].vi in CONSONANCES:\n amount_of_wrong_notes -= 1\n\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\n\n if debug:\n print \"VSCORE 2+3: NOTES > THAN 8TH - \", amount_of_notes, \",DISSONANT ONES - \", amount_of_wrong_notes\n\n # vscore 4: one of eight notes from pair should be consonant\n amount_of_wrong_notes = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.duration == 1 and melody_info[i + 1].duration == 1:\n if note.vi not in CONSONANCES and melody_info[i + 1].vi not in CONSONANCES:\n amount_of_wrong_notes += 1\n beat += cp[i][1]\n vscores.append(float(amount_of_wrong_notes) / amount_of_8th)\n\n if debug:\n print \"VSCORE 4: 8TH NOTES - \", amount_of_8th, \",DISSONANT ONES - \", amount_of_wrong_notes\n\n # vscore 5: unisons ok if on 1st beat through suspension or if tied over (ensured by storing format)\n # else: if followed by step\n\n wrong_unsiones = len(filter(lambda x: x.vi == 0 and x.hi_next != 1, melody_info))\n vscores.append(float(wrong_unsiones) / len(cp))\n\n if debug:\n print \"VSCORE 5: WRONG UNISONES - \", wrong_unsiones\n\n # vscore 6: max allowed interval between voices is 10th, except for climax\n big_vert_intervals = len(filter(lambda x: x.vi > 9 and x.pitch != highest_note, melody_info))\n vscores.append(float(big_vert_intervals) / len(melody_info))\n\n if debug:\n print \"VSCORE 6: VERT INTERVALS > 10TH - \", big_vert_intervals\n\n\n # vscore 7: There should be no crossing (ensured by generation)\n vscores.append(0.0)\n\n # vscore 8: avoid the overlapping of parts (ensured by generation)\n vscores.append(0.0)\n\n # vscore 9: no leaps from unison to octave and vice versa\n uni_to_oct_count = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.vi == 7 and melody_info[i + 1].vi == 0:\n uni_to_oct_count += 1\n if note.vi == 0 and melody_info[i + 1].vi == 7:\n uni_to_oct_count += 1\n\n vscores.append(float(uni_to_oct_count) / len(melody_info))\n\n if debug:\n print \"VSCORE 9: UNISON-OCTAVE LEAPS - \", uni_to_oct_count\n\n # vscore 10: The ending is unison or octave (ensured by generation)\n vscores.append(0.0)\n\n # vscore 11: all perfect intervals (also perfect fourth) should be approached by contrary or oblique motion\n bad_perfect_intervals_count = 0\n battuda = 0\n for i in range(1, len(melody_info)):\n if melody_info[i].beat == 0 and melody_info[i].vi in PERFECT_INTERVALS:\n bad_perfect_intervals_count += 1\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\n prev_cp = melody_info[i - 1].pitch\n cur_cf = cantus_firmus[melody_info[i].measure]\n cur_cp = melody_info[i].pitch\n if prev_cp == cur_cp and prev_cf != cur_cf: # oblique\n bad_perfect_intervals_count -= 1\n if prev_cp > cur_cp and prev_cf <= cur_cf: # contrary\n bad_perfect_intervals_count -= 1\n if melody_info[i].vi in [4, 7]:\n battuda += 1\n if prev_cp < cur_cp and prev_cf >= cur_cf: # contrary\n bad_perfect_intervals_count -= 1\n if melody_info[i].vi in [4, 7]:\n battuda += 1\n\n vscores.append(float(bad_perfect_intervals_count) / len(cantus_firmus))\n\n if debug:\n print \"VSCORE 11: PERF INTERVALS APPROACHED BADLY - \", bad_perfect_intervals_count\n\n # vscore 12: avoid simultaneous leaps in cf and cp, especially large leaps in same direction\n leaps_count = 0\n large_leaps_count = 0\n for i in range(len(melody_info) - 1):\n if melody_info[i + 1].beat == 0:\n leap_cp = melody_info[i + 1].pitch - melody_info[i].pitch\n leap_cf = cantus_firmus[melody_info[i + 1].measure] - cantus_firmus[melody_info[i].measure]\n if abs(leap_cf) > 1 and abs(leap_cp) > 1:\n leaps_count += 1\n if leap_cf > 6 and leap_cp > 6:\n large_leaps_count += 1\n if leap_cf < 6 and leap_cp < 6:\n large_leaps_count += 1\n vscores.append(float(leaps_count + large_leaps_count) / (len(cantus_firmus) * 2))\n\n if debug:\n print \"VSCORE 12: SIM LEAPS - \", leaps_count, \", LARGE SIM LEAPS - \", large_leaps_count\n\n # vscore 13: use all types of motion\n similar = 0\n contrary = 0\n oblique = 0\n parallel = 0\n for i in range(1, len(melody_info)):\n if melody_info[i].beat == 0:\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\n prev_cp = melody_info[i - 1].pitch\n cur_cf = cantus_firmus[melody_info[i].measure]\n cur_cp = melody_info[i].pitch\n if prev_cp == cur_cp:\n if prev_cf != cur_cf:\n oblique = 1\n else:\n similar = 1\n if prev_cp > cur_cp:\n if prev_cf <= cur_cf:\n contrary = 1\n else:\n parallel = 1\n if prev_cp < cur_cp:\n if prev_cf >= cur_cf:\n contrary = 1\n else:\n parallel = 1\n types_of_motion = similar + oblique + contrary + parallel\n vscores.append(1 - float(types_of_motion) / 4)\n if debug:\n print \"VSCORE 13: MOTION TYPES (SOCP) - \", similar, oblique, contrary, parallel\n\n # vscore 14: climax of the CF and CP should not coincide\n cf_highest_note = max(cantus_firmus)\n coincide = 0\n for note in melody_info:\n if note.pitch == highest_note and cantus_firmus[note.measure] == cf_highest_note:\n coincide = 1\n\n vscores.append(coincide)\n if debug:\n print \"VSCORE 14: COINCIDE - \", coincide\n\n # vscore 15: Successive unisons, octaves and fifths on first beats are only valid\n # when separated by three quarter notes.\n bad_intervals_count = 0\n\n for i in range(len(melody_info) - 1):\n if melody_info[i].beat == 0 and melody_info[i].measure != len(cantus_firmus) - 1:\n if melody_info[i].vi in [0, 4, 7]:\n separated = True\n j = 1\n while melody_info[i + j].measure == melody_info[i].measure:\n if melody_info[i + j].duration > 2:\n separated = False\n j += 1\n if melody_info[i + j].vi in [0, 4, 7] and not separated:\n bad_intervals_count += 1\n vscores.append(float(bad_intervals_count) / (len(cantus_firmus) - 1))\n if debug:\n print \"VSCORE 15: BAD INTERVALS - \", bad_intervals_count\n\n # vscore 16: successive unisons, octaves and fifths not on first beats:\n # valid when separated by at least 2 notes, otherwise not.\n # Unless it is a consonant suspension of quarter note: ok for afterbeat fifths and octaves\n # separated only by a single quearter.\n # ***what a complex rule, we don't care about consonant suspension due to storing format >_<***\n bad_intervals_count = 0\n\n for i in range(len(melody_info) - 2):\n separated = True\n if melody_info[i].beat != 0:\n if melody_info[i].vi in [0, 4, 7]:\n if melody_info[i + 1].vi in [0, 4, 7]:\n separated = False\n if separated:\n if melody_info[i + 2].vi in [0, 4, 7]:\n separated = False\n if not separated:\n bad_intervals_count += 1\n\n vscores.append(float(bad_intervals_count) / (len(melody_info) - len(cantus_firmus)))\n\n if debug:\n print \"VSCORE 16: BAD INTERVALS - \", bad_intervals_count\n\n # vscore 17: no ottava or quinta battuda, whatever it means\n vscores.append(float(battuda) / len(cantus_firmus))\n\n if debug:\n print \"VSCORE 17: BATTUDAS - \", battuda\n\n # vscore 18: best ending: dissonant suspension into the leading tone (ensured by generation)\n vscores.append(0)\n\n # vscore 19: Thirds, sixths and tenths should predominate.\n good_interval_count = len(filter(lambda x: x.vi in [2, 5, 9], melody_info))\n if good_interval_count * 2 > len(melody_info):\n vscores.append(0.0)\n else:\n vscores.append(1.0 - float(2 * good_interval_count) / len(melody_info))\n\n if debug:\n print \"VSCORE 19: 3RDS 6THS 10THS - \", good_interval_count\n\n genome.fitness = sum([x * y for x, y in zip(hscores, HSCORE_WEIGHTS)]) + \\\n sum([x * y for x, y in zip(vscores, VSCORE_WEIGHTS)])\n if debug:\n print \"HSCORES: \", hscores\n print \"VSCORES: \", vscores\n print \"FINAL SCORE: \", genome.fitness\n print \"FINAL SCORE UNSCALED: \", sum(hscores) + sum(vscores)\n return genome.fitness\n\n return fitness_function","def get_likelihood(\n self,\n qb,\n inv_fish,\n map_tag=None,\n null_first_cmb=False,\n lmin=33,\n lmax=250,\n mcmc=True,\n alpha_tags=[\"95\", \"150\"],\n beam_tags=[\"95\", \"150\"],\n r_prior=[0, np.inf],\n alpha_prior=[0, np.inf],\n res_prior=None,\n beam_prior=[0, 1],\n betad_prior=[0, 1],\n dust_amp_prior=[0, np.inf],\n dust_ellind_prior=[0, 1],\n num_walkers=50,\n num_steps=20000,\n converge_criteria=0.01,\n reset_backend=None,\n file_tag=None,\n ):\n\n for x in [\n r_prior,\n alpha_prior,\n res_prior,\n beam_prior,\n betad_prior,\n dust_amp_prior,\n dust_ellind_prior,\n ]:\n if x is not None:\n x[:] = [float(x[0]), float(x[1])]\n\n save_name = \"like_mcmc\"\n if not mcmc:\n alpha_prior = None\n res_prior = None\n beam_prior = None\n betad_prior = None\n dust_amp_prior = None\n dust_ellind_prior = None\n\n # no template cleaning if there aren't any templates specified\n if not getattr(self, \"template_cleaned\", False):\n alpha_prior = None\n\n # null out unused priors\n self.template_alpha = getattr(self, \"template_alpha\", None)\n if self.template_alpha is None or all(\n [x is None for x in self.template_alpha.values()]\n ):\n alpha_prior = None\n\n # count alpha parameters to fit\n alpha_tags = [x for x in alpha_tags if x in self.map_tags_orig]\n if not len(alpha_tags):\n alpha_prior = None\n\n num_alpha = 0\n if alpha_prior is not None:\n num_alpha = len(alpha_tags)\n\n # count beam parameters to fit\n beam_tags = [x for x in beam_tags if x in self.map_tags_orig]\n if not len(beam_tags):\n beam_prior = None\n\n num_beam = 0\n if beam_prior is not None:\n num_beam = len(beam_tags)\n\n if not any([k.startswith(\"res_\") for k in qb]):\n res_prior = None\n\n if np.any(\n [\n betad_prior is not None,\n dust_amp_prior is not None,\n dust_ellind_prior is not None,\n ]\n ):\n dust_ell_fit = True\n else:\n dust_ell_fit = False\n\n # bookkeeping: ordered priors\n priors = {\n \"r_prior\": r_prior,\n \"alpha_prior\": alpha_prior,\n \"res_prior\": res_prior,\n \"beam_prior\": beam_prior,\n \"betad_prior\": betad_prior,\n \"dust_amp_prior\": dust_amp_prior,\n \"dust_ellind_prior\": dust_ellind_prior,\n }\n # priors on quantities that affect Dmat_obs or gmat (precalculated)\n obs_priors = [alpha_prior]\n\n # check parameter space\n if all([x is None for x in priors.values()]):\n raise RuntimeError(\"Empty parameter space\")\n\n out = dict(\n r_prior=r_prior,\n alpha_prior=alpha_prior,\n res_prior=res_prior,\n beam_prior=beam_prior,\n betad_prior=betad_prior,\n dust_amp_prior=dust_amp_prior,\n dust_ellind_prior=dust_ellind_prior,\n alpha_tags=alpha_tags,\n num_walkers=num_walkers,\n null_first_cmb=null_first_cmb,\n apply_gcorr=self.apply_gcorr,\n weighted_bins=self.weighted_bins,\n lmin=lmin,\n lmax=lmax,\n )\n\n if mcmc and reset_backend is None:\n ret = self.load_data(\n save_name,\n \"likelihood\",\n bp_opts=True,\n to_attrs=False,\n map_tag=map_tag,\n value_ref=out,\n extra_tag=file_tag,\n )\n if ret is not None and ret.get(\"converged\", False):\n if converge_criteria >= ret.get(\"converge_criteria\", 0.01):\n return ret\n if ret is not None:\n for pname, pval in priors.items():\n if np.all(pval != ret.get(pname, None)):\n ret = None\n # clear chain cache if rerunning, otherwise append to chain by default\n reset_backend = ret is None\n\n out.update(converge_criteria=converge_criteria)\n\n # save state\n if mcmc and reset_backend:\n self.save_data(\n save_name, map_tag=map_tag, extra_tag=file_tag, bp_opts=True, **out\n )\n\n # clear pre-computed quantities\n self.clear_precalc()\n use_precalc = all([x is None for x in obs_priors])\n\n cls_input, cls_noise, cls_debias = self.get_data_spectra()\n\n # extract residual bins, ignoring bins outside of lmin/lmax\n if res_prior is not None:\n bin_def_orig = copy.deepcopy(self.bin_def)\n nbins_res_orig = self.nbins_res\n qb_res = OrderedDict()\n num_res = 0\n for k in list(qb):\n if k.startswith(\"res_\"):\n bd = self.bin_def[k]\n good = np.where((bd[:, 1] > lmin) & (bd[:, 0] < lmax))[0]\n # use all qb res in range lmin, lmax\n self.bin_def[k] = bd[good]\n v = qb.pop(k)[good]\n num_res += len(v)\n\n # use average qb res in good range per map\n # self.bin_def[k] = np.array([[lmin, lmax + 1]])\n # v = np.array([(qb.pop(k)[good]).mean()])\n # num_res += 1\n qb_res[k] = v\n self.nbins_res = num_res\n\n # set CMB model bandpowers to unity, since we are computing\n # the likelihood of this model given the data\n if r_prior is None:\n self.log(\"Computing model spectrum\", \"debug\")\n self.warn(\"Beam variation not implemented for case of no r fit\")\n cbl = self.bin_cl_template(map_tag=map_tag)\n cls_model = self.get_model_spectra(qb, cbl, delta=True, cls_noise=cls_noise)\n else:\n qb = copy.deepcopy(qb)\n for spec in self.specs:\n stags = [\"cmb_{}\".format(spec), \"fg_{}\".format(spec)]\n for stag in stags:\n if stag not in qb:\n continue\n qb[stag] = np.ones_like(qb[stag])\n\n self.log(\"Computing r model spectrum\", \"debug\")\n cls_shape_scalar = self.get_signal_shape(\n r=1.0, save=False, component=\"scalar\"\n )\n\n cls_shape_tensor = self.get_signal_shape(\n r=1.0, save=False, component=\"tensor\"\n )\n\n # load tensor and scalar terms separately\n cbl_scalar = self.bin_cl_template(cls_shape_scalar, map_tag)\n cls_model_scalar = self.get_model_spectra(\n qb, cbl_scalar, delta=True, cls_noise=cls_noise\n )\n cbl_tensor = self.bin_cl_template(cls_shape_tensor, map_tag)\n cls_model_tensor = self.get_model_spectra(\n qb, cbl_tensor, delta=False, res=False\n )\n if beam_prior is not None:\n # load beam error term for tensor and scalar\n cbl_scalar_beam = self.bin_cl_template(\n cls_shape_scalar, map_tag, beam_error=True\n )\n cls_mod_scal_beam = self.get_model_spectra(\n qb, cbl_scalar_beam, delta=True, res=False\n )\n cbl_tensor_beam = self.bin_cl_template(\n cls_shape_tensor, map_tag, beam_error=True\n )\n cls_mod_tens_beam = self.get_model_spectra(\n qb, cbl_tensor_beam, delta=False, res=False\n )\n\n # load foreground shape\n if dust_ell_fit:\n cls_shape_dust = self.get_signal_shape(save=False, component=\"fg\")\n # if dust_ellind_prior is None:\n # # can preload shape since not varying ell index\n cbl_fg = self.bin_cl_template(cls_shape_dust, map_tag=map_tag)\n if beam_prior is not None:\n cbl_fg_beam = self.bin_cl_template(\n cls_shape_dust, map_tag, beam_error=True\n )\n\n cbl = copy.deepcopy(cbl_scalar)\n cls_model = copy.deepcopy(cls_model_scalar)\n\n # XXX TODO\n # how to marginalize over the garbage bin?\n\n def parse_params(theta):\n \"\"\"\n Parse array of parameters into a dict\n \"\"\"\n params = {}\n if r_prior is not None:\n params[\"r\"] = theta[0]\n theta = theta[1:]\n if alpha_prior is not None:\n params[\"alpha\"] = theta[:num_alpha]\n theta = theta[num_alpha:]\n if res_prior is not None:\n params[\"res\"] = theta[:num_res]\n theta = theta[num_res:]\n if beam_prior is not None:\n params[\"beam\"] = theta[:num_beam]\n theta = theta[num_beam:]\n if betad_prior is not None:\n params[\"betad\"] = theta[0]\n theta = theta[1:]\n if dust_amp_prior is not None:\n # param for ee and bb\n params[\"dust_amp\"] = theta[:2]\n theta = theta[2:]\n if dust_ellind_prior is not None:\n params[\"dust_ellind\"] = theta[0]\n theta = theta[1:]\n if len(theta):\n raise ValueError(\"Too many parameters to parse\")\n return params\n\n def log_prior(\n r=None,\n alpha=None,\n res=None,\n beam=None,\n betad=None,\n dust_amp=None,\n dust_ellind=None,\n ):\n \"\"\"\n Log prior function constructed from input options\n \"\"\"\n values = {\n \"r_prior\": r,\n \"alpha_prior\": alpha,\n \"res_prior\": res,\n \"dust_amp_prior\": dust_amp,\n }\n for v, pval in values.items():\n prior = priors[v]\n if pval is not None and prior is not None:\n if np.any(pval < prior[0]) or np.any(pval > prior[1]):\n return -np.inf\n\n values_gauss = {\n \"beam_prior\": beam,\n \"betad_prior\": betad,\n \"dust_ellind_prior\": dust_ellind,\n }\n # for beam and betad, use gaussian prior\n log_prob = 0.0\n for v, pval in values_gauss.items():\n prior = priors[v]\n if pval is not None and prior is not None:\n pval = np.atleast_1d(pval)\n norm = np.log(1.0 / (prior[1] * np.sqrt(2 * np.pi)))\n chi = (pval - prior[0]) / prior[1]\n log_prob += np.sum(norm - chi ** 2 / 2.0)\n\n return log_prob\n\n def log_like(\n r=None,\n alpha=None,\n res=None,\n beam=None,\n betad=None,\n dust_amp=None,\n dust_ellind=None,\n ):\n \"\"\"\n Log likelihood function constructed from input options\n \"\"\"\n cls_model0 = copy.deepcopy(cls_model)\n\n # compute new template subtracted data spectra\n if alpha is None:\n clsi = cls_input\n else:\n self.get_masked_data(template_alpha=OrderedDict(zip(alpha_tags, alpha)))\n clsi = self.get_data_spectra(do_noise=False)\n\n if beam is not None:\n beam = dict(zip(beam_tags, beam))\n beam_coeffs = dict()\n for xname, (m0, m1) in self.map_pairs_orig.items():\n d = {}\n b0, b1 = [beam.get(m, None) for m in (m0, m1)]\n if b0 is not None:\n d[\"b1\"] = b0\n if b1 is not None:\n d[\"b2\"] = b1\n if b0 is not None:\n d[\"b3\"] = b0 * b1\n beam_coeffs[xname] = d\n\n # compute new signal shape by scaling tensor component by r\n if r is not None:\n for stag, d in cls_model0.items():\n comp, spec = stag.split(\"_\", 1)\n if spec not in [\"ee\", \"bb\"] or comp not in [\"cmb\", \"total\"]:\n continue\n ctag = \"cmb_{}\".format(spec)\n for xname, dd in d.items():\n dd[:] = (\n cls_model_scalar[stag][xname]\n + r * cls_model_tensor[ctag][xname]\n )\n\n if beam is None:\n continue\n beam_term = 0\n for bn, bc in beam_coeffs[xname].items():\n beam_term += bc * (\n cls_mod_scal_beam[ctag][xname][bn]\n + r * cls_mod_tens_beam[ctag][xname][bn]\n )\n dd[:] += beam_term\n\n elif beam is not None:\n for stag, d in cls_model0.items():\n comp, spec = stag.split(\"_\", 1)\n if spec not in [\"ee\", \"bb\"] or comp not in [\"cmb\", \"total\"]:\n continue\n ctag = \"cmb_{}\".format(spec)\n for xname, dd in d.items():\n beam_term = 0\n for bn, bc in beam_coeffs[xname].items():\n beam_term += bc * cls_mod_scal_beam[ctag][xname][bn]\n dd[:] = cls_model_scalar[stag][xname] + beam_term\n\n # fg term, including beam modifications. Because mix terms are\n # dependent on dust amp, get model specs here.\n if dust_ell_fit:\n if dust_amp is None:\n qb[\"fg_ee\"][:] = 1\n qb[\"fg_bb\"][:] = 1\n else:\n qb[\"fg_ee\"][:] = dust_amp[0]\n qb[\"fg_bb\"][:] = dust_amp[1]\n if betad is None:\n qb[\"delta_beta\"][:] = 0\n else:\n qb[\"delta_beta\"][:] = betad\n if dust_ellind is not None:\n cbl_fg0 = self.bin_cl_template(\n cls_shape_dust, map_tag=map_tag, fg_ell_ind=dust_ellind\n )\n if beam is not None:\n cbl_fg_beam0 = self.bin_cl_template(\n cls_shape_dust,\n map_tag,\n fg_ell_ind=dust_ellind,\n beam_error=True,\n )\n else:\n cbl_fg0 = cbl_fg\n if beam is not None:\n cbl_fg_beam0 = cbl_fg_beam\n\n cls_model_fg = self.get_model_spectra(\n qb, cbl_fg0, delta=True, res=False\n )\n if beam is not None:\n cls_mod_fg_beam = self.get_model_spectra(\n qb, cbl_fg_beam0, delta=True, res=False\n )\n # add fg field to model, and add fg to total model\n for stag, d in cls_model_fg.items():\n comp, spec = stag.split(\"_\", 1)\n if spec not in [\"ee\", \"bb\"] or comp not in [\"fg\", \"total\"]:\n continue\n ftag = \"fg_{}\".format(spec)\n if stag not in cls_model0:\n cls_model0[stag] = OrderedDict()\n for xname, dd in d.items():\n if xname not in cls_model0[stag]:\n cls_model0[stag][xname] = cls_model_fg[ftag][xname]\n else:\n cls_model0[stag][xname] += cls_model_fg[ftag][xname]\n\n # add beam terms to fg and total fields\n if beam is not None:\n beam_term = 0\n for bn, bc in beam_coeffs[xname].items():\n beam_term += bc * cls_mod_fg_beam[ftag][xname][bn]\n cls_model0[stag][xname] += beam_term\n\n # compute noise model terms\n if res is None:\n clsm = cls_model0\n else:\n res = pt.arr_to_dict(res, qb_res)\n clsm = copy.deepcopy(cls_model0)\n cls_res = self.get_model_spectra(res, cbl)\n for stag, d in cls_res.items():\n if stag not in clsm:\n clsm[stag] = OrderedDict()\n for xname, dd in d.items():\n if xname not in clsm[stag]:\n clsm[stag][xname] = dd\n else:\n clsm[stag][xname] += dd\n\n # compute likelihood\n like = self.fisher_calc(\n qb,\n cbl,\n clsi,\n cls_noise=cls_noise,\n cls_debias=cls_debias,\n cls_model=clsm,\n null_first_cmb=null_first_cmb,\n likelihood=True,\n use_precalc=use_precalc,\n like_lmin=lmin,\n like_lmax=lmax,\n )\n return like\n\n def log_prob(theta):\n \"\"\"\n Log posterior probability from prior and likelihood\n\n Returns log_prior with each step\n \"\"\"\n params = parse_params(theta)\n prior = log_prior(**params)\n if not np.isfinite(prior):\n return -np.inf, -np.inf\n like = log_like(**params)\n if not np.isfinite(like):\n return -np.inf, prior\n return prior + like, prior\n\n # initial values\n x0 = []\n brute_force = True if not mcmc else False # only vary r\n if r_prior is not None:\n x0 += [0.01]\n if alpha_prior is not None:\n alphas = [self.template_alpha[tag] for tag in alpha_tags]\n x0 += [0.01 if a == 0 else a for a in alphas]\n brute_force = False\n if res_prior is not None:\n x0 += list(pt.dict_to_arr(qb_res, flatten=True))\n brute_force = False\n if beam_prior is not None:\n # add a beam term for each frequency\n x0 += [0.01] * len(beam_tags)\n brute_force = False\n if betad_prior is not None:\n x0 += [0.01]\n brute_force = False\n if dust_amp_prior is not None:\n x0 += [1, 1]\n brute_force = False\n if dust_ellind_prior is not None:\n x0 += [0.01]\n brute_force = False\n\n ndim = len(x0)\n if ndim * 2 > num_walkers:\n num_walkers = int(np.round(ndim / float(num_walkers)) * num_walkers * 2)\n self.warn(\n \"Found {} parameters, increasing number of MCMC walkers to {}\".format(\n ndim, num_walkers\n )\n )\n x0 = np.array(x0)[None, :] * (1 + 1e-4 * np.random.randn(num_walkers, len(x0)))\n\n if brute_force or (r_prior is not None and ndim == 1):\n self.log(\"Computing brute-force r profile likelihood\", \"info\")\n likefile = self.get_filename(\n save_name, ext=\".txt\", map_tag=map_tag, extra_tag=file_tag, bp_opts=True\n )\n rs = np.linspace(0, 3, 500)\n likes = np.zeros_like(rs)\n for idx, r in enumerate(rs):\n like = log_like(r=r)\n if idx % 20 == 0:\n self.log(\"r = {:.3f}, loglike = {:.2f}\".format(r, like), \"debug\")\n likes[idx] = like\n header = \"{} r likelihood\\nColumns: r, loglike\".format(\n \"Multi-map\" if map_tag is None else \"Map {}\".format(map_tag)\n )\n np.savetxt(likefile, np.column_stack((rs, likes)), header=header)\n\n if not mcmc:\n return [rs, likes]\n\n # run chains!\n import emcee\n\n # setup sampler output file\n filename = self.get_filename(\n save_name, ext=\".h5\", map_tag=map_tag, extra_tag=file_tag, bp_opts=True\n )\n backend_exists = os.path.exists(filename)\n backend = emcee.backends.HDFBackend(filename)\n if backend_exists and backend.shape != (num_walkers, ndim):\n self.warn(\n \"Expected backend of shape ({}, {}), found {}. Resetting\".format(\n num_walkers, ndim, backend.shape\n )\n )\n reset_backend = True\n if reset_backend:\n backend.reset(num_walkers, ndim)\n\n # initialize sampler\n self.log(\"Initializing sampler\", \"info\")\n sampler = emcee.EnsembleSampler(num_walkers, ndim, log_prob, backend=backend)\n if not reset_backend and backend_exists:\n # grab the last sample if appending to an existing run\n x0 = sampler.run_mcmc(None, 1)\n\n # track autocorrelation time\n old_tau = np.inf\n converged = False\n\n self.log(\n \"Starting {} iterations with {} parameters\".format(num_steps, ndim), \"info\"\n )\n for sample in sampler.sample(x0, iterations=num_steps):\n if not sampler.iteration % 10:\n self.log(\"MCMC iteration {}\".format(sampler.iteration), \"debug\")\n # check convergence every 100 steps\n if sampler.iteration % 100:\n continue\n\n # compute autocorrelation time\n tau = sampler.get_autocorr_time(tol=0)\n\n # check convergence\n converged = np.all(tau / converge_criteria < sampler.iteration)\n converged &= np.all(np.abs(old_tau - tau) / tau < converge_criteria)\n self.log(\n \"MCMC iteration {} autocorr time: mean {:.1f} min {:.1f} max {:.1f}\".format(\n sampler.iteration, np.mean(tau), np.min(tau), np.max(tau)\n ),\n \"info\",\n )\n if converged:\n break\n old_tau = tau\n\n out.update(converged=converged, num_steps=sampler.iteration)\n\n # converged posterior distribution\n if converged:\n self.log(\n \"MCMC converged in {} iterations\".format(sampler.iteration), \"info\"\n )\n tau = sampler.get_autocorr_time()\n burnin = int(2 * np.max(tau))\n thin = int(0.5 * np.min(tau))\n samples = sampler.get_chain(discard=burnin, thin=thin, flat=True)\n out.update(tau=tau, burnin=burnin, thin=thin, samples=samples)\n else:\n self.warn(\"MCMC not converged in {} iterations\".format(num_steps))\n\n if res_prior is not None:\n self.bin_def = bin_def_orig\n self.nbins_res = nbins_res_orig\n\n # save and return\n return self.save_data(\n save_name, map_tag=map_tag, extra_tag=file_tag, bp_opts=True, **out\n )","def __logprob__(self, cv, vsense):\n return 1.0 / (1.0 + np.exp(-np.dot(cv, vsense)))","def generate_log(nexp, clpeaks, timepeaks, remap, gap=0):\n # Select the index of the experiment\n peakini = 0\n i = 0\n while i < nexp:\n exp = timepeaks[i]\n peakini += exp.shape[0]\n i += 1\n\n exp = timepeaks[nexp]\n peakend = peakini + exp.shape[0]\n\n # Build the sequence string\n peakstr = []\n peakset = []\n\n for i in range(peakini, peakend):\n peakset.append(voc[remap[clpeaks[i][0] - 1]])\n if i < peakend - 1 and gap != 0:\n if (timepeaks[nexp][i - peakini + 1] - timepeaks[nexp][i - peakini]) > gap:\n peakstr.append(peakset)\n peakset = []\n\n return peakstr","def log_likelihood(self, state, obs, act):\n indices = np.array([self.Gittins[state['successes'][i], state['failures'][i]] for i in range(self.env.n_arms)])\n greedy_arms = np.where(np.isclose(indices,indices.max()))[0]\n return np.log(1/len(greedy_arms)) if act in greedy_arms else -1e8","def fitness_function(genome, debug=False):\n cp = genome.chromosome\n\n # calculate some information for easier scoring\n ScoringInfo = namedtuple(\"ScoringInfo\", \"pitch duration measure beat vi hi_next hi_prev voice_dir\")\n melody_info = list()\n beat = 4\n measure = 0\n for i in range(1, len(cp)):\n hi_next = abs(cp[i][0] - cp[i + 1][0]) if i != len(cp) - 1 else -1 # next horizontal interval\n hi_prev = abs(cp[i][0] - cp[i - 1][0]) if i != 1 else -1 # previous horizontal interval\n vi = abs(cp[i][0] - cantus_firmus[measure]) # vertical interval\n # voice movement direction\n voice_dir = 0 if i == len(cp) - 1 or cp[i + 1][0] == cp[i][0] else copysign(1, cp[i + 1][0] - cp[i][0])\n melody_info.append(ScoringInfo(cp[i][0], cp[i][1], measure, beat, vi, hi_next, hi_prev, voice_dir))\n beat += cp[i][1]\n measure += beat / 8\n beat %= 8\n\n if debug:\n print \"MELODY INFO: \", melody_info\n\n hscores = list()\n vscores = list()\n # hscore 1: 8th notes must move in step\n amount_of_8th = 0\n amount_of_missteps = 0\n for note in melody_info:\n if note.duration == 1:\n amount_of_8th += 1\n if note.hi_next > 1:\n amount_of_missteps += 1\n if note.hi_prev > 1:\n amount_of_missteps += 1\n hscores.append(float(amount_of_missteps) / (amount_of_8th * 2))\n if debug:\n print \"HSCORE 1: 8TH - \", amount_of_8th, \", MISSTEPS - \", amount_of_missteps\n\n # hscore 2: one climax, that can be repeated only after neighboring tone\n # hscore 3: Climax should be on the strong beat\n highest_note = max([note.pitch for note in melody_info])\n climax_count = 0\n climax_on_weak_beat_count = 0\n for i, note in enumerate(melody_info):\n if note.pitch == highest_note:\n climax_count += 1\n if note.beat not in [0, 4]:\n climax_on_weak_beat_count += 1\n if i < len(melody_info) - 2 and note.pitch == melody_info[i + 2].pitch: # If next note is\n if note.hi_next == 1 and melody_info[i + 2].hi_prev == 1: # neighboring tone\n if note.vi in CONSONANCES and melody_info[i + 2].vi in CONSONANCES: # And surrounding notes are consonant\n climax_count -= 1 # we can allow 2nd climax\n if melody_info[i + 2].beat not in [0, 4]:\n climax_on_weak_beat_count -= 1 # And 2nd climax may be on weak beat\n\n hscores.append(float(climax_count - 1) / len(melody_info))\n hscores.append(float(climax_on_weak_beat_count) / climax_count)\n\n if debug:\n print \"HSCORE 2+3: CLIMAX CNT - \", climax_count, \", WEAK CLIMAX CNT - \", climax_on_weak_beat_count\n\n # hscore 4: Horizontal intervals are consonant\n unconsonant_amount = len(filter(lambda x: x.hi_next not in CONSONANCES + [1], melody_info[:-1]))\n hscores.append(float(unconsonant_amount) / (len(melody_info) - 1))\n\n if debug:\n print \"HSCORE 4: UNCONSANANT AMOUNT - \", unconsonant_amount\n\n # hscore 5: Stepwise movement should predominate\n leaps_count = len(filter(lambda x: x.hi_next != 1, melody_info[:-1]))\n sections = round(float(len(cantus_firmus)) / 16)\n if leaps_count < (2 * sections):\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\n elif leaps_count > (4 * sections):\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\n else:\n hscores.append(0.0)\n\n if debug:\n print \"HSCORE 5: LEAPS - \", leaps_count, \"SECTIONS - \", sections\n\n # hscore 6: After large leap - stepwise motion\n large_leaps_count = 0\n large_leaps_not_followed_count = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.hi_next >= 3:\n large_leaps_count += 1\n if melody_info[i + 1].hi_next != 1:\n large_leaps_not_followed_count += 1\n hscores.append(float(large_leaps_not_followed_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\n\n if debug:\n print \"HSCORE 6: LL CNT - \", large_leaps_count, \"LL NOT FOLLOWED CNT - \", large_leaps_not_followed_count\n\n # hscore 7: change direction after each large leap\n large_leaps_not_changedir_count = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.hi_next >= 3 and note.voice_dir != -melody_info[i + 1].voice_dir:\n large_leaps_not_changedir_count += 1\n hscores.append(float(large_leaps_not_changedir_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\n\n if debug:\n print \"HSCORE 7: LL NOT CHNGDIR CNT - \", large_leaps_not_changedir_count\n\n # hscore 8: climax should be melodically consonant with tonic\n hscores.append(1.0 if highest_note - 4 in CONSONANCES else 0.0)\n\n # hscore 9: no more than 2 consecutive leaps\n conseq_leaps = 0\n punish_score = 0\n for note in melody_info:\n conseq_leaps += 1\n if note.hi_next in [0, 1]:\n conseq_leaps = 0\n if conseq_leaps > 3:\n punish_score += 1\n hscores.append(float(punish_score) / (len(melody_info) - 3))\n\n if debug:\n print \"HSCORE 9: CONSEQ LEAPS PUNISH SCORE - \", punish_score\n\n # hscore 10: no more than 2 large leaps per section\n if large_leaps_count > 2 * sections:\n hscores.append(float(large_leaps_count - 2 * sections) / (len(melody_info) - 1 - 2 * sections))\n else:\n hscores.append(0.0)\n\n # hscore 11: not too long stepwise in same direction\n longest_stepwise_seq = 0\n current_stepwise_seq = 0\n prev_dir = 0\n num_changes = 0\n motion_vector = list()\n for note in melody_info:\n if note.hi_next <= 1:\n if note.voice_dir in [prev_dir, 0]:\n current_stepwise_seq += 1\n longest_stepwise_seq = max(longest_stepwise_seq, current_stepwise_seq)\n else:\n prev_dir = note.voice_dir\n current_stepwise_seq = 0\n num_changes += 1\n motion_vector.append(note.pitch)\n else:\n if note.voice_dir != prev_dir and note.voice_dir != 0:\n prev_dir = note.voice_dir\n num_changes += 1\n motion_vector.append(note.pitch)\n current_stepwise_seq = 0\n motion_vector.append(cp[-1][0])\n if longest_stepwise_seq < 5:\n longest_stepwise_seq = 0\n hscores.append(float(longest_stepwise_seq) / len(cp))\n\n if debug:\n print \"HSCORE 11: LONGEST STEPWISE SEQUENCE - \", longest_stepwise_seq\n\n # hscore 12: direction needs to change several times\n if num_changes < 3 * sections:\n hscores.append(1 - float(num_changes) / (3 * sections))\n else:\n hscores.append(0.0)\n\n # hscore 13: ending note is tonic\n hscores.append(0)\n\n # hscore 14: penultimate note is leading tone\n hscores.append(0)\n\n # hscore 15: the start of a motion is consonant with the end of a motion\n unconsotant_count = 0\n big_leaps_count = 0\n for i in range(1, len(motion_vector) - 1):\n if abs(motion_vector[i] - motion_vector[i + 1]) not in CONSONANCES:\n unconsotant_count += 1\n if abs(motion_vector[i] - motion_vector[i + 1]) > 6:\n big_leaps_count += 1\n hscores.append(float(unconsotant_count) / len(motion_vector))\n\n if debug:\n print \"HSCORE 15: UNCONSONANT MOTIONS - \", unconsotant_count\n\n # hscore 16: Large motion intervals (>6 tones) should be avoided\n hscores.append(float(big_leaps_count) / len(motion_vector))\n\n if debug:\n print \"HSCORE 16: LARGE MOTIONS - \", big_leaps_count\n\n # hscore 17: No frequent repetition of the same note\n rep_count = 0\n for note in melody_info:\n if note.hi_next == 0:\n rep_count += 1\n if rep_count > 2 * sections:\n rep_count -= 2 * sections\n else:\n rep_count = 0\n hscores.append(float(rep_count) / (len(cp) - 2 * sections))\n\n if debug:\n print \"HSCORE 17: REPETITIONS COUNT - \", rep_count\n\n # hscore 18: no repetition of sequence within a 4 measure interval\n repeated = set()\n for i in range(len(melody_info) - 2):\n j = i + 1\n while melody_info[j].measure < melody_info[i].measure + 4 and j < len(melody_info) - 1:\n if melody_info[i].pitch == melody_info[j].pitch:\n k = 1\n while j + k < len(melody_info) and melody_info[j + k].pitch == melody_info[i + k].pitch:\n if k == 1:\n repeated.add(j)\n repeated.add(j + k)\n k += 1\n j += 1\n\n hscores.append(float(len(repeated)) / len(cp))\n\n if debug:\n print \"HSCORE 18: REPEATED POSITIONS - \", repeated\n\n # hscore 19: largest allowed interval is octave\n more_than_ocatave_amount = len(filter(lambda x: x.hi_next > 7, melody_info[:-1]))\n hscores.append(float(more_than_ocatave_amount) / len(cp))\n\n if debug:\n print \"HSCORE 19: MORE THAN OCTAVES - \", more_than_ocatave_amount\n\n # vscore 1: whole notes should be consonant (ensured by generation and hscore 13)\n vscores.append(0.0)\n\n # vscores 2 and 3: halves and quarters should be consonant on first beat.\n # or can be dissonant on other beats beat, if passing tone\n amount_of_notes = 0\n amount_of_wrong_notes = 0\n for i, note in enumerate(melody_info):\n if note.duration >= 2:\n amount_of_notes += 1\n if note.beat == 0 and note.vi not in CONSONANCES:\n amount_of_wrong_notes += 1\n if note.beat != 0 and note.vi not in CONSONANCES:\n amount_of_wrong_notes += 1\n if note.hi_prev == 1 and note.hi_next == 1 and note.voice_dir == melody_info[i - 1].voice_dir:\n if melody_info[i - 1].vi in CONSONANCES and melody_info[i + 1].vi in CONSONANCES:\n amount_of_wrong_notes -= 1\n\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\n\n if debug:\n print \"VSCORE 2+3: NOTES > THAN 8TH - \", amount_of_notes, \",DISSONANT ONES - \", amount_of_wrong_notes\n\n # vscore 4: one of eight notes from pair should be consonant\n amount_of_wrong_notes = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.duration == 1 and melody_info[i + 1].duration == 1:\n if note.vi not in CONSONANCES and melody_info[i + 1].vi not in CONSONANCES:\n amount_of_wrong_notes += 1\n beat += cp[i][1]\n vscores.append(float(amount_of_wrong_notes) / amount_of_8th)\n\n if debug:\n print \"VSCORE 4: 8TH NOTES - \", amount_of_8th, \",DISSONANT ONES - \", amount_of_wrong_notes\n\n # vscore 5: unisons ok if on 1st beat through suspension or if tied over (ensured by storing format)\n # else: if followed by step\n\n wrong_unsiones = len(filter(lambda x: x.vi == 0 and x.hi_next != 1, melody_info))\n vscores.append(float(wrong_unsiones) / len(cp))\n\n if debug:\n print \"VSCORE 5: WRONG UNISONES - \", wrong_unsiones\n\n # vscore 6: max allowed interval between voices is 10th, except for climax\n big_vert_intervals = len(filter(lambda x: x.vi > 9 and x.pitch != highest_note, melody_info))\n vscores.append(float(big_vert_intervals) / len(melody_info))\n\n if debug:\n print \"VSCORE 6: VERT INTERVALS > 10TH - \", big_vert_intervals\n\n\n # vscore 7: There should be no crossing (ensured by generation)\n vscores.append(0.0)\n\n # vscore 8: avoid the overlapping of parts (ensured by generation)\n vscores.append(0.0)\n\n # vscore 9: no leaps from unison to octave and vice versa\n uni_to_oct_count = 0\n for i, note in enumerate(melody_info[:-1]):\n if note.vi == 7 and melody_info[i + 1].vi == 0:\n uni_to_oct_count += 1\n if note.vi == 0 and melody_info[i + 1].vi == 7:\n uni_to_oct_count += 1\n\n vscores.append(float(uni_to_oct_count) / len(melody_info))\n\n if debug:\n print \"VSCORE 9: UNISON-OCTAVE LEAPS - \", uni_to_oct_count\n\n # vscore 10: The ending is unison or octave (ensured by generation)\n vscores.append(0.0)\n\n # vscore 11: all perfect intervals (also perfect fourth) should be approached by contrary or oblique motion\n bad_perfect_intervals_count = 0\n battuda = 0\n for i in range(1, len(melody_info)):\n if melody_info[i].beat == 0 and melody_info[i].vi in PERFECT_INTERVALS:\n bad_perfect_intervals_count += 1\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\n prev_cp = melody_info[i - 1].pitch\n cur_cf = cantus_firmus[melody_info[i].measure]\n cur_cp = melody_info[i].pitch\n if prev_cp == cur_cp and prev_cf != cur_cf: # oblique\n bad_perfect_intervals_count -= 1\n if prev_cp > cur_cp and prev_cf <= cur_cf: # contrary\n bad_perfect_intervals_count -= 1\n if melody_info[i].vi in [4, 7]:\n battuda += 1\n if prev_cp < cur_cp and prev_cf >= cur_cf: # contrary\n bad_perfect_intervals_count -= 1\n if melody_info[i].vi in [4, 7]:\n battuda += 1\n\n vscores.append(float(bad_perfect_intervals_count) / len(cantus_firmus))\n\n if debug:\n print \"VSCORE 11: PERF INTERVALS APPROACHED BADLY - \", bad_perfect_intervals_count\n\n # vscore 12: avoid simultaneous leaps in cf and cp, especially large leaps in same direction\n leaps_count = 0\n large_leaps_count = 0\n for i in range(len(melody_info) - 1):\n if melody_info[i + 1].beat == 0:\n leap_cp = melody_info[i + 1].pitch - melody_info[i].pitch\n leap_cf = cantus_firmus[melody_info[i + 1].measure] - cantus_firmus[melody_info[i].measure]\n if abs(leap_cf) > 1 and abs(leap_cp) > 1:\n leaps_count += 1\n if leap_cf > 6 and leap_cp > 6:\n large_leaps_count += 1\n if leap_cf < 6 and leap_cp < 6:\n large_leaps_count += 1\n vscores.append(float(leaps_count + large_leaps_count) / (len(cantus_firmus) * 2))\n\n if debug:\n print \"VSCORE 12: SIM LEAPS - \", leaps_count, \", LARGE SIM LEAPS - \", large_leaps_count\n\n # vscore 13: use all types of motion\n similar = 0\n contrary = 0\n oblique = 0\n parallel = 0\n for i in range(1, len(melody_info)):\n if melody_info[i].beat == 0:\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\n prev_cp = melody_info[i - 1].pitch\n cur_cf = cantus_firmus[melody_info[i].measure]\n cur_cp = melody_info[i].pitch\n if prev_cp == cur_cp:\n if prev_cf != cur_cf:\n oblique = 1\n else:\n similar = 1\n if prev_cp > cur_cp:\n if prev_cf <= cur_cf:\n contrary = 1\n else:\n parallel = 1\n if prev_cp < cur_cp:\n if prev_cf >= cur_cf:\n contrary = 1\n else:\n parallel = 1\n types_of_motion = similar + oblique + contrary + parallel\n vscores.append(1 - float(types_of_motion) / 4)\n if debug:\n print \"VSCORE 13: MOTION TYPES (SOCP) - \", similar, oblique, contrary, parallel\n\n # vscore 14: climax of the CF and CP should not coincide\n cf_highest_note = max(cantus_firmus)\n coincide = 0\n for note in melody_info:\n if note.pitch == highest_note and cantus_firmus[note.measure] == cf_highest_note:\n coincide = 1\n\n vscores.append(coincide)\n if debug:\n print \"VSCORE 14: COINCIDE - \", coincide\n\n # vscore 15: Successive unisons, octaves and fifths on first beats are only valid\n # when separated by three quarter notes.\n bad_intervals_count = 0\n\n for i in range(len(melody_info) - 1):\n if melody_info[i].beat == 0 and melody_info[i].measure != len(cantus_firmus) - 1:\n if melody_info[i].vi in [0, 4, 7]:\n separated = True\n j = 1\n while melody_info[i + j].measure == melody_info[i].measure:\n if melody_info[i + j].duration > 2:\n separated = False\n j += 1\n if melody_info[i + j].vi in [0, 4, 7] and not separated:\n bad_intervals_count += 1\n vscores.append(float(bad_intervals_count) / (len(cantus_firmus) - 1))\n if debug:\n print \"VSCORE 15: BAD INTERVALS - \", bad_intervals_count\n\n # vscore 16: successive unisons, octaves and fifths not on first beats:\n # valid when separated by at least 2 notes, otherwise not.\n # Unless it is a consonant suspension of quarter note: ok for afterbeat fifths and octaves\n # separated only by a single quearter.\n # ***what a complex rule, we don't care about consonant suspension due to storing format >_<***\n bad_intervals_count = 0\n\n for i in range(len(melody_info) - 2):\n separated = True\n if melody_info[i].beat != 0:\n if melody_info[i].vi in [0, 4, 7]:\n if melody_info[i + 1].vi in [0, 4, 7]:\n separated = False\n if separated:\n if melody_info[i + 2].vi in [0, 4, 7]:\n separated = False\n if not separated:\n bad_intervals_count += 1\n\n vscores.append(float(bad_intervals_count) / (len(melody_info) - len(cantus_firmus)))\n\n if debug:\n print \"VSCORE 16: BAD INTERVALS - \", bad_intervals_count\n\n # vscore 17: no ottava or quinta battuda, whatever it means\n vscores.append(float(battuda) / len(cantus_firmus))\n\n if debug:\n print \"VSCORE 17: BATTUDAS - \", battuda\n\n # vscore 18: best ending: dissonant suspension into the leading tone (ensured by generation)\n vscores.append(0)\n\n # vscore 19: Thirds, sixths and tenths should predominate.\n good_interval_count = len(filter(lambda x: x.vi in [2, 5, 9], melody_info))\n if good_interval_count * 2 > len(melody_info):\n vscores.append(0.0)\n else:\n vscores.append(1.0 - float(2 * good_interval_count) / len(melody_info))\n\n if debug:\n print \"VSCORE 19: 3RDS 6THS 10THS - \", good_interval_count\n\n genome.fitness = sum([x * y for x, y in zip(hscores, HSCORE_WEIGHTS)]) + \\\n sum([x * y for x, y in zip(vscores, VSCORE_WEIGHTS)])\n if debug:\n print \"HSCORES: \", hscores\n print \"VSCORES: \", vscores\n print \"FINAL SCORE: \", genome.fitness\n print \"FINAL SCORE UNSCALED: \", sum(hscores) + sum(vscores)\n return genome.fitness","def _compute_likelihood(self, mus, pmfs):\n expected_counts = pmfs.copy()\n for mu, _p_bin_source in zip(mus, expected_counts):\n _p_bin_source *= mu # Works because of numpy view magic...\n expected_total = np.sum(expected_counts, axis=0)\n\n observed_counts = self.data_events_per_bin.histogram\n\n ret = observed_counts * np.log(expected_total) - expected_total - gammaln(observed_counts + 1.).real\n return np.sum(ret)","def compute_movie_rating_likelihood(M):\n\n # define the size to begin with\n likelihood = np.zeros((M, M))\n\n # -------------------------------------------------------------------------\n # YOUR CODE GOES HERE FOR PART (c)\n #\n # Remember to normalize the likelihood, so that each column is a\n # probability distribution.\n \n for i in range(M):\n for j in range(M):\n if i == j:\n likelihood[i][j] = 2\n else:\n likelihood[i][j] = 1/abs(j-i)\n \n likelihood = likelihood / likelihood.sum(axis = 1)\n \n #\n # END OF YOUR CODE FOR PART (c)\n # -------------------------------------------------------------------------\n\n return likelihood","def translate_sequence(sequence, genetic_code = {'GUC': 'V', 'ACC': 'T', 'GUA': 'V', 'GUG': 'V', 'ACU': 'T', 'AAC': 'N', 'CCU': 'P', 'UGG': 'W', 'AGC': 'S', 'AUC': 'I', 'CAU': 'H', 'AAU': 'N', 'AGU': 'S', 'GUU': 'V', 'CAC': 'H', 'ACG': 'T', 'CCG': 'P', 'CCA': 'P', 'ACA': 'T', 'CCC': 'P', 'UGU': 'C', 'GGU': 'G', 'UCU': 'S', 'GCG': 'A', 'UGC': 'C', 'CAG': 'Q', 'GAU': 'D', 'UAU': 'Y', 'CGG': 'R', 'UCG': 'S', 'AGG': 'R', 'GGG': 'G', 'UCC': 'S', 'UCA': 'S', 'UAA': '*', 'GGA': 'G', 'UAC': 'Y', 'GAC': 'D', 'UAG': '*', 'AUA': 'I', 'GCA': 'A', 'CUU': 'L', 'GGC': 'G', 'AUG': 'M', 'CUG': 'L', 'GAG': 'E', 'CUC': 'L', 'AGA': 'R', 'CUA': 'L', 'GCC': 'A', 'AAA': 'K', 'AAG': 'K', 'CAA': 'Q', 'UUU': 'F', 'CGU': 'R', 'CGC': 'R', 'CGA': 'R', 'GCU': 'A', 'GAA': 'E', 'AUU': 'I', 'UUG': 'L', 'UUA': 'L', 'UGA': '*', 'UUC': 'F'}, start_pos = 0):\n #find first orf\n #first_orf_seq = find_first_orf(sequence)\n\n # ensure sequence is uppercase\n seq = sequence.upper()\n\n #translate the sequence\n protein = \"\"\n for i in range(0, len(seq) - (len(seq) % 3), 3):\n codon = seq[i:i + 3]\n if genetic_code[codon] == \"*\":\n break\n protein += genetic_code[codon]\n return protein","def outputGenerationProbability(self):\n self.b = zeros((self.noOfEmmittingStates, self.T))\n for row in range(self.noOfEmmittingStates):\n for col in range(self.T):\n self.b[row, col] = self.gaussianDist(self.observationSequence[0, col],\n self.outputProbabilities[row, 0],\n self.outputProbabilities[row, 1])","def boost_probability_for(fixation):\n probabilities = np.zeros(Number_of_locs) #MOD Number_of_locs deleted\n for possible_target_location in xrange(Number_of_locs): #MOD Number_of_locs deleted\n Lib_c.set_target(possible_target_location)\n probabilities[possible_target_location] = integrate.quad(\n Lib_c.function,\n -np.inf, np.inf,\n epsabs=0,\n limit=50,\n full_output=1\n )[0]\n return np.sum(Post_probs * probabilities) #MOD Post_probs deleted","def _compute_log_likelihood(self, parameters):\n raise NotImplementedError('')","def get_ngramlogprobs_fromcorpus(tokenizedseqs, n):\n return","def design_guide_and_donor_for_genes(input_design_table_filename, input_annot_gff_filename, input_genome_fasta_filename,\n PAM_seq = 'GA', excluded_seqs = ['TTTT'], BOWTIE_exe = '',\n off_targets_min_mismatch = 10,\n min_azimuth_score = 0.5,\n sort_by = '5to3', \n min_dist_cut_to_donor_edge = 30,\n filter_out_donors_containing_excluded_seqs = False,\n max_guide_pos_frac_in_gene = 1.1,\n output_guides_df_filename = '', output_donors_df_filename = ''):\n \n ##################\n # processing input\n ##################\n\n # fixing the mapping command to be the default bowtie\n if not BOWTIE_exe or len(BOWTIE_exe) < 3:\n BOWTIE_exe = '/Users/eilon/software/bowtie2-2.2.8/bowtie2'\n \n # defining the bowtie mapping cmd\n bowtie_cmd = shlex.split(BOWTIE_exe + ' -x ' + os.path.splitext(input_genome_fasta_filename)[0] + ' -U - -f -D 20 -R 3 -N 1 -L 10 -i S,1,0.50 --gbar 3 --end-to-end -k 30 --no-head -t --rdg 10,6 --rfg 10,6')\n mapping_cmd = bowtie_cmd\n\n \n # loading design matrix \n # fields: gene_id\tguide_num\tdonor_mut_type\tnum_donor_variants\tmut_pos_in_guide\tdonor_length\tdonor_seq_offsets\n\n design_df = pd.read_table(input_design_table_filename, sep='\\t', na_values = \"\")\n design_df['donor_seq_offsets'] = design_df['donor_seq_offsets'].apply(ast.literal_eval)\n design_df['donor_seq_offsets'] = design_df['donor_seq_offsets'].apply(np.array,dtype=np.float)\n \n design_df['gene_id'] = design_df['gene_id'].str.strip()\n design_df['donor_mut_type'] = design_df['donor_mut_type'].str.strip()\n \n \n # loading gene gff matrix\n print \"loading genes gff file: \" + input_annot_gff_filename\n genes_gff_df = sgd_gff2dataframe(input_annot_gff_filename, ['CDS'])\n \n # loading genome fasta file\n print \"loading genome fasta file: \" + input_genome_fasta_filename\n genome_seq = SeqIO.to_dict(SeqIO.parse(open(input_genome_fasta_filename),'fasta', alphabet=generic_dna))\n \n\n \n # init output dataframes\n out_guide_df = pd.DataFrame(data=None)\n out_guide_donor_df = pd.DataFrame(data=None)\n \n ####################\n # running gene by gene such that the donor sequences will use the same guides\n # if differnet number of guides are specified for each donor desing the guides will be selected from best to worst\n ####################\n \n # grouping by gene\n design_grouped = design_df.groupby('gene_id')\n \n\n # iterating over the genes\n for cur_gene_id, cur_gene_df in design_grouped:\n\n print \"Designing guides for gene:\" + cur_gene_id\n \n # the current gene object\n cur_gene = CRISPR_Gene(cur_gene_id, genes_gff_df, genome_seq)\n \n # if differnet number of guides are specified for each donor desing the guides will be selected from best to worst\n cur_gene_max_guide_num = max(cur_gene_df['guide_num'])\n \n # if even one row requires the guide to be entirely in the CDS - find only guides that are entirely in the CDS\n #cur_require_entire_guide_to_be_in_CDS = np.any(~ np.isnan(cur_gene_df['mut_pos_in_guide'].values))\n # np.any(cur_gene_df['require_entire_guide_in_CDS'])\n #\n cur_min_mut_pos_in_guide = min(int(-cur_gene.CRISPR_CUT_INDEX - 1*(cur_gene.is_neg_strand())),np.nanmin(cur_gene_df['mut_pos_in_guide'].values))\n cur_max_mut_pos_in_guide = max(2,np.nanmax(cur_gene_df['mut_pos_in_guide'].values))\n \n \n # max half region length to look for excluded sequences (should be at least ~20 to test the guide)\n \n\n cur_max_donor_seq_offset = np.nanmax(np.abs(cur_gene_df['donor_seq_offsets'].apply(np.nanmax).values))\n if np.isnan(cur_max_donor_seq_offset):\n cur_max_donor_seq_offset = 0\n \n \n \n cur_max_seq_len_around_cut = int( np.ceil(max(cur_gene_df['donor_length']) / 2) + \\\n cur_max_donor_seq_offset)\n \n #print \"Extracting all guides\"\n \n # get all guides (computing filter guides that contain excluded sequences in the sequences around them)\n cur_all_gene_guides_df = cur_gene.get_all_guides_that_cut_in_cds(pam = PAM_seq, seq_len_around_cut = cur_max_seq_len_around_cut, \n min_mut_pos_in_guide = cur_min_mut_pos_in_guide,\n max_mut_pos_in_guide = cur_max_mut_pos_in_guide,\n excluded_seqs = excluded_seqs, mapping_cmd = mapping_cmd,\n sort_by = sort_by)\n \n #DEBUG\n #print \"before get_K_best_guide_ids\" #DEBUG\n #print sum(cur_all_gene_guides_df['guide_id']== 'YAL001C_pS_33') #DEBUG\n \n #print cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id']== 'YAL001C_pS_33' ]\n #print cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id']== 'YAL001C_pS_20' ]\n #print cur_gene_max_guide_num\n #print off_targets_min_mismatch\n #print min_azimuth_score\n #print max_guide_pos_frac_in_gene\n\n # select top 'best' guides\n selected_guide_ids = cur_gene.get_K_best_guide_ids(guides_df = cur_all_gene_guides_df, K = cur_gene_max_guide_num, \n off_targets_min_mismatch = off_targets_min_mismatch,\n min_azimuth_score = min_azimuth_score,\n max_guide_pos_frac_in_gene = max_guide_pos_frac_in_gene,\n sort_by = sort_by)\n #print \"after get_K_best_guide_ids\" #DEBUG\n #for gi in selected_guide_ids:\n # print gi\n #print ('YAL001C_pS_33' in list(selected_guide_ids) ) \n #print 'XXX-------------------' #DEBUG\n \n # selected guides (each guide should have at least one donor designed)\n cur_selected_guide_df = cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id'].isin( list(selected_guide_ids) ) ]\n \n print \"--- Designing donor sequences for gene:\" + cur_gene_id + \", for \" + str(len(selected_guide_ids)) + \" guides (out of \" + str(cur_gene_max_guide_num) + \" requested)\"\n #print cur_selected_guide_df[ cur_selected_guide_df['guide_id'] == 'YAL001C_pS_33' ]\n \n # concating with the output dataframe\n out_guide_df = pd.concat([out_guide_df,cur_selected_guide_df],ignore_index=True)\n\n \n # desinging the donor sequences\n for idx,row in cur_gene_df.iterrows():\n \n if len(selected_guide_ids) < int(row['guide_num']):\n warnings.warn(\"There are NOT enough guides in --- %s --- for the design. There are %d guides and the design if for %d\" % (str(row[\"gene_id\"]),len(selected_guide_ids), row['guide_num'] ))\n \n if len(selected_guide_ids) <= 0:\n continue\n \n cur_selected_guide_ids = selected_guide_ids.iloc[range( min(int(row['guide_num']),len(selected_guide_ids)) )]\n \n \n # do reverse complement for donor sequences\n if 'do_revcomp_donor' in cur_gene_df.columns:\n do_revcomp_donor = bool(row['do_revcomp_donor']==True)\n else:\n do_revcomp_donor = False\n \n \n \n \n # do reverse complement for donor sequences\n if 'do_scramble_guide_and_donor' in cur_gene_df.columns:\n scramble_guide_and_donor = bool(row['do_scramble_guide_and_donor']==True)\n else:\n scramble_guide_and_donor = False\n \n #print \"do_revcomp_donor %d\" % (do_revcomp_donor)\n #print \"scramble_guide_and_donor %d\" % (scramble_guide_and_donor)\n \n cur_all_gene_guides_df_fordonors = cur_all_gene_guides_df\n \n # permuting the guides and adding it to the guide df\n if scramble_guide_and_donor:\n print 'Scramble donor guides...'\n cur_all_gene_guides_df_scramble = cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id'].isin(cur_selected_guide_ids) ].copy()\n # scrambling the guides and updating their ids\n cur_all_gene_guides_df_scramble['guide_id'] = cur_all_gene_guides_df_scramble['guide_id'] + '_scramble'\n scramble_func = lambda x: ''.join(random.sample(str(x),len(str(x))))\n cur_all_gene_guides_df_scramble['guide'] = cur_all_gene_guides_df_scramble['guide'].apply(scramble_func)\n cur_all_gene_guides_df_scramble['guide_noPAM'] = cur_all_gene_guides_df_scramble['guide_noPAM'].apply(scramble_func)\n cur_all_gene_guides_df_scramble['guide_PAM_p7'] = cur_all_gene_guides_df_scramble['guide_PAM_p7'].apply(scramble_func)\n cur_all_gene_guides_df_scramble['guide_PAM_m4p3'] = cur_all_gene_guides_df_scramble['guide_PAM_m4p3'].apply(scramble_func)\n # adding the scrambeles guides to the guides table\n out_guide_df = pd.concat([out_guide_df,cur_all_gene_guides_df_scramble],ignore_index=True)\n\n \n # for donors design\n cur_all_gene_guides_df_fordonors = cur_all_gene_guides_df_scramble\n cur_selected_guide_ids = cur_selected_guide_ids + '_scramble'\n \n \n cur_gene_donor_df = cur_gene.get_donor_mut_for_guides(cur_all_gene_guides_df_fordonors, cur_selected_guide_ids, \n donor_mut_type=str(row['donor_mut_type']), \n num_donor_variants = int(row['num_donor_variants']), \n mut_pos_in_guide = row['mut_pos_in_guide'], \n donor_length=int(row['donor_length']), \n donor_seq_offsets = row['donor_seq_offsets'],\n set_name = row['set_name'],\n min_dist_cut_to_donor_edge = min_dist_cut_to_donor_edge,\n excluded_seqs = excluded_seqs,\n do_revcomp_donor = do_revcomp_donor,\n scramble_guide_and_donor = scramble_guide_and_donor)\n \n out_guide_donor_df = pd.concat([out_guide_donor_df,cur_gene_donor_df],ignore_index=True)\n\n #print \"---------------------------- Finished designing guide for the above gene\"\n\n\n # adding for each guide tis location in the gene (now added in )\n #out_guide_df[\"guide_cut_gene_pos_frac\"] = out_guide_df[\"guide_cut_gene_nt_pos\"] / out_guide_df[\"CDS_len_nts\"]\n\n # filtering out donor sequences with excluded sequences\n if filter_out_donors_containing_excluded_seqs:\n if out_guide_donor_df.shape[1] > 1: # if not null df\n out_guide_donor_df = out_guide_donor_df[ (~ out_guide_donor_df[\"contain_excluded_sequences\"]).values ]\n \n \n if len(output_guides_df_filename) > 3:\n print \"saving guides to: \" + output_guides_df_filename\n out_guide_df.to_csv(output_guides_df_filename, sep='\\t', index = False)\n \n \n \n if len(output_donors_df_filename) > 3:\n print \"saving donor sequences to: \" + output_donors_df_filename\n out_guide_donor_df.to_csv(output_donors_df_filename, sep='\\t', index = False)\n \n \n return( (out_guide_df, out_guide_donor_df) )","def generate_rbpdb_experimental_to_pwm(letter_strength, n_repeat_req):\n rbpdb_experiment_file_path = (\n \"./website/data/RBPDB_v1.3.1_experiments_human_2012-11-21.tdt\"\n )\n rbpdb_pfm_file_directory = \"./website/data/rbpdb-human-pfm-matrices/\"\n experimental_to_pwm_dict = {}\n with open(rbpdb_experiment_file_path) as handle:\n line = handle.readline()\n while line:\n columns = line.split(\"\\t\")\n # Here we expect the columns to be:\n # experimental_id, PUBMED_ID, exp_type, notes, seq_motif,\n # selex_file, aligned_selex_file,\n # aligned_motif_file, PWM_file, PFM_file, logo_file,\n # secondary_structure, in_vivo_notes, in_vivo_file, flag\n if columns[14] == \"1\":\n # The flag means this data is unreliable, according to the RBPDB\n # Readme files\n line = handle.readline()\n continue\n\n experimental_id = columns[0]\n\n assert len(experimental_id) > 0\n pfm_file = columns[9]\n seq_motifs = columns[4]\n pwms = []\n if pfm_file != \"\\\\N\":\n pfm_file_path = rbpdb_pfm_file_directory + pfm_file\n with open(pfm_file_path) as pfm_file_handle:\n raw_pwm_str = pfm_file_handle.read()\n pwm = str_to_pwm(raw_pwm_str, is_transpose=True)\n pwms += [pwm]\n elif seq_motifs not in (\"\\\\N\", \"\"):\n # This experiment still generated some useful data\n seq_motifs = seq_motifs.split(\";\")\n i = 0\n while i != len(seq_motifs):\n seq_motif = seq_motifs[i]\n while \")(\" in seq_motif:\n repeat_end = seq_motif.find(\")(\")\n assert seq_motif[repeat_end] == \")\"\n repeat_start = repeat_end\n while seq_motif[repeat_start] != \"(\":\n repeat_start -= 1\n\n number_start = repeat_end + 2\n assert (\n seq_motif[number_start].isdigit()\n or seq_motif[number_start] == \"n\"\n )\n number_end = number_start\n while seq_motif[number_end] != \")\":\n number_end += 1\n\n # deal with cases where the number of repeats is\n # \"15-30\". Take minimum to be conservative.\n # Note that most cases would be a single number like\n # \"15\".\n num_of_repeats = (\n min(\n [\n int(s) if s != \"n\" else\n math.ceil(\n n_repeat_req\n / (repeat_end - repeat_start - 1)\n )\n for s in\n seq_motif[number_start: number_end]\n .split(\"-\")\n ]\n )\n )\n\n seq_motif = (\n seq_motif.replace(\n seq_motif[repeat_start: number_end + 1],\n seq_motif[repeat_start + 1: repeat_end]\n * num_of_repeats\n )\n )\n\n\n maketrans = str.maketrans\n all_letters = 'wruysn'\n upper_map = maketrans(all_letters, all_letters.upper())\n seq_motif = seq_motif.translate(upper_map)\n if \"/\" in seq_motif:\n bracket_start = bracket_end = middle = (\n seq_motif.find(\"/\")\n )\n while seq_motif[bracket_start] != \"(\":\n bracket_start -= 1\n while seq_motif[bracket_end] != \")\":\n bracket_end += 1\n seq_motif_1 = (\n seq_motif.replace(\n seq_motif[bracket_start: bracket_end + 1],\n seq_motif[bracket_start + 1: middle]\n )\n )\n seq_motif_2 = (\n seq_motif.replace(\n seq_motif[bracket_start: bracket_end + 1],\n seq_motif[middle + 1: bracket_end]\n )\n )\n seq_motifs += [seq_motif_1, seq_motif_2]\n else:\n pwm = motif_to_pwm(\n seq_motif, letter_strength=letter_strength\n )\n pwms += [pwm]\n i += 1\n\n # Now we have the raw text, we convert it to pwm and add to\n # dictionary\n experimental_to_pwm_dict[experimental_id] = pwms\n line = handle.readline()\n\n return experimental_to_pwm_dict","def get_ngramlogprobs(freqdict):\n return","def model_likelihood(\n self, obs: Tensor, actions: Tensor, next_obs: Tensor\n ) -> Tensor:\n return self.model.log_prob(obs, actions, next_obs)","def _logp(self, trace, **inputs):\n def calc_log(step):\n exp_pred = np.dot(inputs['gwas_gen'],\n step['beta_med'].T).ravel()\n phen_pred = step['alpha'] * exp_pred\n phen_prob = norm.logpdf(x=inputs['gwas_phen'],\n loc=phen_pred,\n scale=step['phenotype_sigma'])\n return phen_prob\n\n phen_probs = [calc_log(trace[idx])\n for idx in np.random.randint(0, len(self.trace), 500)]\n phen_probs = np.asmatrix(phen_probs)\n mc_logp = phen_probs.sum(axis=1).mean()\n return mc_logp","def calc_prob_prior(iterations, lam):\n return list(map(lambda x: math.exp(-lam * x), range(iterations)))","def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\n perplexity = 0\n\n ### YOUR CODE HERE\n def calc_prob(sentense, i, word, trigram_counts, bigram_counts, unigram_counts, train_token_count, model):\n prob = 0.0\n prev_word = sentense[i - 1]\n prev_to_prev_word = sentense[i - 2]\n\n if model == \"unigram\":\n if word in unigram_counts:\n prob = (unigram_counts[word] + 0.0) / train_token_count\n else:\n prob = (unigram_counts[word_to_num['UUUNKKK']] + 0.0) / \\\n train_token_count\n\n if model == \"bigram\":\n if (prev_word, word) in bigram_counts:\n prob = (bigram_counts[(prev_word, word)] + 0.0) / \\\n unigram_counts[prev_word]\n # print(num_to_word[prev_word] ,num_to_word[word])\n # print(bigram_counts[(prev_word, word)])\n # print(unigram_counts[prev_word])\n # print(\"---------------------------\")\n else:\n prob = 0.0\n\n if model == \"trigram\":\n if (prev_to_prev_word, prev_word, word) in trigram_counts:\n prob = (trigram_counts[(prev_to_prev_word, prev_word, word)] + 0.0) \\\n / bigram_counts[(prev_to_prev_word, prev_word)]\n # / bigram_counts[(prev_word, word)] #this according to lecture notes slide 27\n else:\n prob = 0.0\n\n return prob\n\n l = 0\n num_of_words = 0\n\n ##########3\n better_than_chance = 0\n ###########\n\n for sentense in eval_dataset:\n for i, word in enumerate(sentense[2:]):\n num_of_words += 1\n prob = lambda1 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\n train_token_count, \"trigram\") + \\\n lambda2 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\n train_token_count, \"bigram\") + \\\n (1 - lambda1 - lambda2) * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts,\n unigram_counts, train_token_count, \"unigram\")\n ######################################\n if prob > (1.0 / vocabsize):\n better_than_chance += 1\n #########################\n l += np.log2(prob)\n l /= num_of_words\n perplexity = 2 ** -l\n\n print(\"better_than_chance:\", (better_than_chance + 0.0) / num_of_words)\n\n ### END YOUR CODE\n return perplexity","def compute_AttentionLRP(self, cell_state, relevance_a, output_states):\r\n\r\n\r\n\t\t#Reconstructing the concatenated encoder states\r\n\t\tmax_encoding_len = len(output_states)\r\n\t\tu = np.zeros(output_states[0].shape)\r\n\t\tfor i in range(max_encoding_len):\r\n\t\t\tu += cell_state[\"alpha\"][i, 0] * output_states[i]\r\n\t\ta = np.matmul(self.attentonLayer, np.concatenate([cell_state[\"lstm\"]['last_h'], u]))\r\n\r\n\t\t# LRP for the attention layer\r\n\t\tinp_a_rel = layerLRP(np.concatenate([cell_state[\"lstm\"]['last_h'], u]), self.attentonLayer, np.zeros((self.attentionSize, 1)), a, relevance_a)\r\n\r\n\t\th_relevance= inp_a_rel[:self.cellSize]\r\n\t\tu_relevance = inp_a_rel[self.cellSize:]\r\n\r\n\t\tforward_encoder_relevance = []\r\n\t\tbackward_decoder_relevance = []\r\n\r\n\r\n\t\tinput_lrp_vector = np.concatenate([cell_state[\"alpha\"][i, 0] * output_states[i] for i in range(max_encoding_len)])\r\n\t\tinput_lrp_matrix = np.concatenate([np.identity(2*self.cellSize) for i in range(max_encoding_len)], 1)\r\n\t\t#for i in range(max_encoding_len):\r\n\t\t\t#inp_c_rel = layerLRP(cell_state[\"alpha\"][i, 0] * output_states[i], np.identity(2*self.cellSize), np.zeros((2*self.cellSize, 1)), u, u_relevance, epsilon = 0.001, delta=1.0)\r\n\t\t\t#forward_encoder_relevance.append(inp_c_rel[:self.cellSize])\r\n\t\t\t#backward_decoder_relevance.append(inp_c_rel[self.cellSize:])\r\n\t\tinp_c_rel = layerLRP(input_lrp_vector, input_lrp_matrix, np.zeros((2*self.cellSize, 1)), u, u_relevance)\r\n\t\tfor i in range(max_encoding_len):\r\n\t\t\tforward_encoder_relevance.append(inp_c_rel[2*i*self.cellSize:(2*i+1)*self.cellSize])\r\n\t\t\tbackward_decoder_relevance.append(inp_c_rel[(2*i+1)*self.cellSize:(2*(i+1))*self.cellSize])\r\n\r\n\t\treturn h_relevance, forward_encoder_relevance, backward_decoder_relevance","def detectionProb(efficiency, trigger=1):\n if trigger > 1.:\n raise ValueError('Trigger > 1 not implemented yet\\n')\n q = 1.0 - np.asarray(efficiency)\n\n # probability of 0 detections\n logq = np.log(q)\n logpiq = logq.sum()\n piq = np.exp(logpiq)\n\n return 1.0 - piq","def log_likelihood(data, probs):\n # Assume data is given as counts\n return _np.sum([nlogp(n, p) for n, p in zip(data, probs) if n > 0])","def updateResidueProbAnnotation(residueProb):\n\n for resonance in residueProb.resonanceGroup.resonances:\n updateResonanceAnnotation(resonance)","def figure_of_merit_from(self, parameter_list):\r\n return self.log_likelihood_from(parameter_list=parameter_list)","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def prob_to_llre(meth_prob):\n return math.log((meth_prob + EPSLONG) / (1 - meth_prob + EPSLONG))","def compute_log_probability_of_text(text, char_to_ix, frequency_statistics, transition_matrix):\n t = text\n cix = char_to_ix\n fr = frequency_statistics\n tm = transition_matrix\n \n i0 = cix[t[0]]\n p = np.log(fr[i0])\n i = 0\n while i < len(t)-1:\n i1 = cix[t[i+1]]\n p += np.log(tm[i0, i1])\n i0 = i1\n i += 1\n \n return p","def main(rand,mu,lamb,cxpb,mutpb,ngen,param):\n \n random.seed(rand)\n NGEN = ngen\n MU = mu\n LAMBDA = lamb\n CXPB = cxpb\n MUTPB = mutpb\n \n # Used for printing the results. It is the parameter that is changed one run from another\n if param==\"rand\" or param==\"optimal\":\n list_results=[rand]\n elif param==\"mu\":\n list_results=[mu]\n elif param==\"lamb\":\n list_results=[lamb]\n elif param==\"cross\":\n list_results=[cxpb]\n elif param==\"mutate\":\n list_results=[mutpb]\n elif param==\"ngen\":\n list_results=[ngen]\n elif param==\"original\":\n list_results=[0]\n \n # Initialization of the objects for the GA\n pop = toolbox.population(n=MU)\n hof = tools.ParetoFront()\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", np.mean, axis=0)\n stats.register(\"std\", np.std, axis=0)\n stats.register(\"min\", np.min, axis=0)\n stats.register(\"max\", np.max, axis=0)\n\n # Run of the GA\n p,logbook=algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n halloffame=hof,verbose=0)\n with open(results_path+param+'_logbook.csv', 'a',newline='') as f:\n w = csv.DictWriter(f, logbook[0].keys())\n w.writeheader()\n for el in logbook:\n w.writerow(el)\n w.writerow({})\n \n # Takes the max fitness of the population from all of the runs\n max_fit=0\n max_gen=0\n for elt in logbook:\n if elt['max'][0]>max_fit:\n max_fit=elt['max'][0]\n max_gen=elt['gen']\n list_results.append(max_fit)\n list_results.append(max_gen)\n \n #TODO\n# for ind in hof:\n# dist = numpy.linalg.norm(a-b)\n\n print (\"{0} {1} {2} {3}\".format(round(list_results[1],3),round(list_results[2],3),round(list_results[0],3),hof[0]))\n current_out_writer.writerow([list_results[0],list_results[1],list_results[2],hof[0]])\n \n return pop, stats, hof","def probability(N_dr, L_opmin, L_opmax, L_min, L_max, L_d):\n opening_nomullignas = []\n opening_withmullignas = []\n sum_nomulligans = 0\n sum_withmulligans = 0\n mulligan_coeff = 0\n\n for i in range(L_opmin, min(L_opmax + 1, 8)): # first make a list of tuples of the form:\n # (number_of_lands_in_opening_hand, probability_of_drawing_such_a_hand)\n a = hypergeom(i, 7, 60, L_d)\n opening_nomullignas.append((i, a))\n mulligan_coeff = mulligan_coeff + a # this will be used later for calculating the probability of\n # taking the mulligan and is used as a coefficient before the mulligan sum\n for (x, y) in opening_nomullignas: # use the list of tuples to calculate the first part of equation 5\n partial_nomulligans = 0\n for j in range(L_min - x, L_max - x + 1):\n partial_nomulligans = partial_nomulligans + hypergeom(j, N_dr, 53, L_d - x)\n sum_nomulligans = sum_nomulligans + partial_nomulligans * y\n\n mulligan_coeff = 1 - mulligan_coeff # probability of mulliganing\n for i in range(L_opmin, min(L_opmax + 1, 7)): # doing the same thing as before, but drawing 6 instead of 7 cards\n a = hypergeom(i, 6, 60, L_d)\n opening_withmullignas.append((i, a))\n\n for (x, y) in opening_withmullignas:\n partial_withmulligans = 0\n for j in range(L_min - x, L_max - x + 1):\n partial_withmulligans = partial_withmulligans + hypergeom(j, N_dr, 54, L_d - x)\n sum_withmulligans = sum_withmulligans + partial_withmulligans * y\n total_withmulligans = mulligan_coeff * sum_withmulligans\n\n return total_withmulligans + sum_nomulligans","def prob4():\n #get Omega, F, and initialize\n Omega = np.array([[-3/2,3/4],[0,1],[0,1/2],[0,1]])\n f = lambda x: np.exp(np.sum(-x**2/2,axis=0))/(2*np.pi)**(2)\n means, cov = np.zeros(4), np.eye(4)\n truth = scipy.stats.mvn.mvnun(list(Omega[:,0]),list(Omega[:,1]), means, cov)[0]\n domain = np.logspace(1,5,20)\n approxs = []\n error = []\n for N in domain:\n #calculate approx for various sizes of samples\n approx = mc_integrate(f,Omega[:,0],Omega[:,1],N)\n approxs.append(approx)\n #calculate relative err.\n error.append(np.abs((truth-approx)/truth))\n #PLOT it all\n plt.title(\"Error vs Sample Size\")\n plt.plot(domain,1/np.sqrt(domain),label = \"1/sqrt(N)\")\n plt.plot(domain,error,label = \"Error\")\n plt.loglog()\n plt.xlabel(\"N\")\n plt.ylabel(\"Relative Error\")\n plt.legend()\n plt.show()","def gene_heritability(\n input_snp_filename: \"Data Input, use the SNPs file from dataParse\",\n output_genes_filename: 'output file for gene-level results, use .csv',\n output_summary_filename: 'output file for the genomewide results summary, use .csv',\n logger_filename: 'file for the logger, use a txt',\n sweeps: \"number of samples for each chain\" = 1000,\n burnin: \"number of burnin samples\" = 1000,\n n_chains: \"number of chains of the sampler\" = 4,\n n_cores: \"number of parallel cores to use\" = 4,\n N_1kG: \"number of SNPs onwhich the LD-score is calculated\" = 1290028,\n chromosome: \"chromosome on which the analysis is run\" = \"all\",\n snp_thr: \"threshold for the minimum number of SNPs in a gene\" = 10,\n sep: \"separator for the input files, use t for tab separated (not \\t)\" = \",\",\n model: 'specify the model for the regression, one betwenn normal/gamma' = 'normal',\n fix_intercept = False,\n ):\n\n # Initialisation of the logger\n output_logger = log.setup_logger(\"output_logger\", logger_filename)\n log.initialise_log(output_logger,\n 'gene level regression, model: %s' %model,\n [input_snp_filename],\n [output_genes_filename,output_summary_filename],\n sweeps,\n burnin,\n chromosome = str(chromosome),\n other_params_diz = {'chains': n_chains, 'cores': n_cores, 'SNP threshold': snp_thr})\n\n # Initialisation function, it reads the summary stats file, filters the SNPs,\n # creates the output files\n\n logging.info(\"Start Analysis\")\n\n snps = s.Snps()\n # read table\n logging.info(\"Reading SNP file: %s,\\n\\t with %s delimiter\"%(input_snp_filename, sep))\n snps.read_table(input_snp_filename, separator=sep)\n # generate chi squared stats\n snps.generate_stats()\n # update the summary stats\n snps.update_summary()\n output_logger.info(\" Sample size \" + str(snps.n_patients) + \"\\n\")\n\n\n\n snps.apply_filter_table(s.baghera_filter)\n snps.update_summary()\n output_logger.info(\"After baghera init filter.\\n\\t Number of SNPs: %s\\n\\t Number of genes: %s\\n\" \\\n %(str(snps.n_snps), str(snps.n_genes)) )\n\n # Non coding SNPs are assigned to a dummy gene, such that the regression is done on the entire SNPs' set\n snps.rename_non_annotated(name='NonCoding')\n\n if chromosome != \"all\":\n snps.apply_filter_table(snps.cut_single_chrom, **{'chromosome': chromosome})\n output_logger.info(\n \"Analysis restricted to chr %s\" %str(chromosome) )\n\n snps.update_summary()\n output_logger.info(\"Analysis. Number of SNPs: %s\\n, Number of genes: %s\\n\" \\\n %(str(snps.n_snps), str(snps.n_genes)) )\n\n # Creates the genes table with the number of SNPs for each gene and the basic stats values\n genes=g.Genes()\n genes.initialise_genes(snps.table.copy(), snps_thr=snp_thr)\n\n output_logger.info(\"Output gene table initialised:\\nNumber of genes: %s\\n\" \\\n %(str(genes.n_genes)) )\n\n snps.set_non_annotated(genes.cut_genes, 'NonCoding')\n\n if model == 'gamma':\n result = gr.analyse_gamma(snps, output_summary_filename, output_logger,\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept,\n )\n else:\n result = gr.analyse_normal(snps, output_summary_filename, output_logger,\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept,\n )\n\n logging.info(\"Saving genes table\")\n genes.table = genes.table.merge(\n result, left_index=False, left_on=\"name\", right_on=\"name\")\n\n k = genes.table.n_snps / float(N_1kG)\n genes.table[\"h2g\"] = genes.table.bg_mean.astype(\"float\") * k\n\n genes.table = genes.table.sort_values(by=[\"P\", \"bg_median\"])\n\n genes.save_table(output_genes_filename)\n\n non_coding = genes.table[genes.table.name == \"NonCoding\"]\n h2g_tot = np.sum(genes.table[\"h2g\"].values) - non_coding[\"h2g\"].values\n\n output_logger.info(\" Non coding heritability : \" +\n str(non_coding[\"h2g\"].values) + \"\\n\")\n output_logger.info(\" Coding heritability : \" + str(h2g_tot) + \"\\n\")","def plot_likelihood(expected_posts_per_month, probability):\n bar_amount = max(10, int(5 * expected_posts_per_month * probability)) # at least 10 bars, not too long of a tail\n print(\"Generating likelihood plot\")\n distribution = [binom.pmf(option, expected_posts_per_month, probability) for option in range(bar_amount)]\n plt.bar(range(bar_amount), distribution)\n plt.xlabel(\"occurrences\")\n plt.ylabel(\"likelihood\")\n plt.title(\"Likelihood of word occurences next month\")\n plt.show()","def annotate_ISM(data_df, REFERENCE, position_list, reference_genbank_name=\"data/covid-19-genbank.gb\"):\n seq_list = data_df['sequence'].values.tolist()\n \n seq_index = []\n index = 0\n for base in REFERENCE[1]:\n if base == '-':\n seq_index.append(index)\n else:\n index += 1\n seq_index.append(index)\n reference_local_index_map = np.array(seq_index)\n mapped_reference_index = []\n for index, entropy in position_list:\n mapped_reference_index.append((index, reference_local_index_map[index], entropy))\n REFERENCE_ISM = ''.join([REFERENCE[1][item[0]] for item in position_list])\n logging.info('Reference ISM: {}.'.format(REFERENCE_ISM))\n \n gene_dict = load_gene_dict(reference_genbank_name)\n reference_raw = REFERENCE[1].replace('-', '')\n res = OrderedDict()\n res['Ref position'] = []\n res['Entropy'] = []\n res['Gene'] = []\n res['Is silent'] = []\n res['AA position'] = []\n for align_index, ref_index, entropy in mapped_reference_index:\n codon, codon_idx, name, codon_pos = find_SNP(ref_index, gene_dict, reference_raw)\n base_freq = Counter([item[align_index] for item in seq_list]).most_common()\n for alt_base, count in base_freq:\n if alt_base != reference_raw[ref_index-1]:\n break\n if codon is None:\n if_silence = True\n else:\n alt_codon = list(codon)\n alt_codon[codon_idx] = alt_base\n alt_codon = ''.join(alt_codon)\n ref_aa = translate(codon)\n ism_aa = translate(alt_codon)\n if ref_aa == ism_aa:\n if_silence = True\n else:\n if_silence = False\n res['Ref position'].append(ref_index)\n res['Entropy'].append(entropy)\n if name is None:\n name = 'Non-coding'\n res['Gene'].append(name)\n res['Is silent'].append(if_silence)\n if codon_pos is None:\n res['AA position'].append('NaN')\n else:\n res['AA position'].append('{}{}{}'.format(ref_aa, codon_pos, ism_aa))\n annotation_df = pd.DataFrame.from_dict(res)\n return annotation_df","def gw_heritability(\n input_snp_filename: \"Data Input, use the SNPs file from dataParse\",\n output_summary_filename: 'output file for the genomewide results summary, use .csv',\n logger_filename: 'file for the logger, use a txt',\n sweeps: \"number of samples for each chain\" = 1000,\n burnin: \"number of burnin samples\" = 1000,\n n_chains: \"number of chains of the sampler\" = 4,\n n_cores: \"number of parallel cores to use\" = 4,\n N_1kG: \"number of SNPs onwhich the LD-score is calculates\" = 1290028,\n chromosome: \"chromosome on which the analysis is run\" = \"all\",\n sep: \"separator for the input files, use t for tab separated (not \\t)\" = \",\",\n model: 'regression model'='normal',\n fix_intercept = False,\n ):\n\n # Initialisation of the logger\n output_logger = log.setup_logger(\"output_logger\", logger_filename)\n log.initialise_log(output_logger,\n 'genome-wide regression, model: %s' %model,\n [input_snp_filename],\n [output_summary_filename],\n sweeps,\n burnin,\n chromosome = str(chromosome),\n other_params_diz = {'chains': n_chains, 'cores': n_cores})\n\n # Initialisation function, it reads the summary stats file, filters the SNPs,\n # creates the output files\n\n logging.info(\"Start Analysis\")\n\n snps = s.Snps()\n # read table\n snps.read_table(input_snp_filename, separator=sep)\n # generate chi squared stats\n snps.generate_stats()\n # update the summary stats\n snps.update_summary()\n output_logger.info(\" Sample size \" + str(snps.n_patients) + \"\\n\")\n\n\n snps.apply_filter_table(s.baghera_filter)\n snps.update_summary()\n output_logger.info(\"After baghera init filter.\\nNumber of SNPs: %s\\nNumber of genes: %s\\n\" \\\n %(str(snps.n_snps), str(snps.n_genes)) )\n\n # Non coding SNPs are assigned to a dummy gene, such that the regression is done on the entire SNPs' set\n snps.rename_non_annotated(name='NonCoding')\n\n if chromosome != \"all\":\n snps.apply_filter_table(snps.cut_single_chrom, **{'chromosome': chromosome})\n output_logger.info(\n \"Analysis restricted to chr %s\" %str(chromosome) )\n\n snps.update_summary()\n output_logger.info(\"Analysis. Number of SNPs: %s\\n, Number of genes: %s\\n\" \\\n %(str(snps.n_snps), str(snps.n_genes)) )\n\n\n if model =='normal':\n [intercept, slope] = heritability.gw_normal(snps, output_summary_filename, output_logger,\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept)\n elif model=='gamma':\n [intercept, slope] = heritability.gw_normal(snps, output_summary_filename, output_logger,\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept)\n else:\n logging.info('Normal model by default')\n [intercept, slope] = heritability.gw_normal(snps, output_summary_filename, output_logger,\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept)\n logging.info(\"Analysis complete\")","def log_likelihood_grad_rew(self, data, reward_model, bias_params):","def gene_finder(dna, threshold):\n\n # YOUR IMPLEMENTATION HERE","def process_observations(message, agent):\n if not message:\n print(\"Message is empty\");\n # return None\n else:\n # # Check if joint values are in the expected order and size.\n if message.joint_names != agent['joint_order']:\n # Check that the message is of same size as the expected message.\n if len(message.joint_names) != len(agent['joint_order']):\n raise MSG_INVALID_JOINT_NAMES_DIFFER\n\n # Check that all the expected joint values are present in a message.\n if not all(map(lambda x,y: x in y, message.joint_names,\n raise MSG_INVALID_JOINT_NAMES_DIFFER\n print(\"Joints differ\")\n return np.array(message.actual.positions) # + message.actual.velocities\n\ndef get_jacobians(state, scara_chain, jac_solver):\n \"\"\"\n Produce a Jacobian from the urdf that maps from joint angles to x, y, z.\n This makes a 6x6 matrix from 6 joint angles to x, y, z and 3 angles.\n The angles are roll, pitch, and yaw (not Euler angles) and are not needed.\n Returns a repackaged Jacobian that is 3x6.\n \"\"\"\n # Initialize a Jacobian for scara_chain.getNrOfJoints() joint angles by 3 cartesian coords and 3 orientation angles\n jacobian = Jacobian(scara_chain.getNrOfJoints())\n # Initialize a joint array for the present self.scara_chain.getNrOfJoints() joint angles.\n angles = JntArray(scara_chain.getNrOfJoints())\n # Construct the joint array from the most recent joint angles.\n for i in range(scara_chain.getNrOfJoints()):\n angles[i] = state[i]\n # Update the jacobian by solving for the given angles.observation_callback\n jac_solver.JntToJac(angles, jacobian)\n # Initialize a numpy array to store the Jacobian.\n J = np.array([[jacobian[i, j] for j in range(jacobian.columns())] for i in range(jacobian.rows())])\n # Only want the cartesian position, not Roll, Pitch, Yaw (RPY) Angles\n ee_jacobians = J\n return ee_jacobians","def compute_probabilities():\n global total_spam_words, total_ham_words\n total_words = total_spam_words+total_ham_words\n unique_words = len(all_dict)\n print(\"Training Set Description: \")\n len_ham = len(ham_file_list)\n len_spam = len(spam_file_list)\n print(\"SPAM EMAILS: \",len_spam)\n print(\"HAM EMAILS: \",len_ham)\n print(\"Total words: \",total_words)\n print(\"Training...\")\n \n spam_probability = math.log((len_spam)/(len_spam+len_ham))\n ham_probability = math.log((len_ham)/(len_spam+len_ham))\n \n \n \n output_file = open(\"nbmodel.txt\", \"w+\", encoding=\"latin-1\")\n output_file.write(\"model_params \"+str(spam_probability)+\" \"+str(ham_probability)+\"\\n\")\n \n nbmodel = {}\n nbmodel[\"model_params\"] = (spam_probability,ham_probability)\n for word in all_dict.keys():\n spam_count = 1\n if word in spam_dict:\n spam_count+= spam_dict[word]\n \n word_spam_probability = math.log(spam_count / (total_spam_words+unique_words))\n \n ham_count = 1\n if word in ham_dict:\n ham_count+= ham_dict[word]\n \n word_ham_probability = math.log(ham_count / (total_ham_words+unique_words))\n \n output_file.write(word+\" \"+str(word_spam_probability)+\" \"+str(word_ham_probability)+\"\\n\")\n nbmodel[word] = (word_spam_probability, word_ham_probability) \n \n print(\"nbmodel.txt generated successfully...\")\n print(\"SPAM Probability: \",spam_probability)\n print(\"HAM Probability: \",ham_probability)\n output_file.close()","def compute_probability_for(fixation):\n probabilities = np.zeros(Number_of_locs) #MOD Number_of_locs deleted\n for possible_target_location in xrange(Number_of_locs): #MOD Number_of_locs deleted\n probabilities[possible_target_location] = integrate.quad(\n integral_function,\n -np.inf, np.inf,\n args=(possible_target_location,Dprime_map[fixation]),\n epsabs=0,\n limit=100,\n full_output=1\n )[0] #MOD Dprime_map deleted\n return np.sum(Post_probs * probabilities) #MOD Post_probs deleted","def prob(self, sequence):\n prob = 1\n for event, context in self.extract_ngrams(sequence):\n prob *= self.cond_prob(event, context)\n return prob","def loglikelihood(self):\n raise NotImplementedError(\"To be implemented\")","def giveMotevoParamFile(genome, wmlen, inter_dir, tag, aligned, ufemodel_path, ATfreq, GCfreq, emprior, bgorder, bgprior):\n\n ##UFE_models from genome_dict are not used anymore\n #UFEmodel_hg19 is UFE model for mammal species\n genome_dict = {}\n genome_dict['hg19'] = ['((((hg19:0.032973,rheMac2:0.057695):0.09821,mm9:0.352605):0.020666,(bosTau6:0.186713,(equCab2:0.107726,canFam2:0.150374):0.010431):0.032764):0.156024,monDom5:0.425899);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFEmodel_hg19']\n genome_dict['hg18'] = ['((((hg18:0.032973,rheMac2:0.057695):0.09821,mm9:0.352605):0.020666,(bosTau3:0.186713,(equCab1:0.107726,canFam2:0.150374):0.010431):0.032764):0.156024,monDom4:0.425899);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFE_mammals']\n #genome_dict['dm3'] = ['((((((dm3:0.059,droSim1:0.075):0.041,(droYak2:0.104,droEre2:0.107):0.054):0.120,droAna3:0.377):0.072,dp4:0.397):0.061,droWil1:0.536):0.020,((droVir3:0.196,droMoj3:0.255):0.073,droGri2:0.291):0.337);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFEmodel_dm3']\n genome_dict['dm3'] = ['((((((dm3:0.059,droSim1:0.075):0.041,(droYak2:0.104,droEre2:0.107):0.054):0.120,droAna3:0.377):0.072,dp4:0.397):0.061,droWil1:0.536):0.020,((droVir3:0.196,droMoj3:0.255):0.073,droGri2:0.291):0.337);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/dm3UFEparallel/UFEmodel_dm3']\n genome_dict['mm9'] = ['((((hg19:0.032973,rheMac2:0.057695):0.09821,mm9:0.352605):0.020666,(bosTau7:0.186713,(equCab2:0.107726,canFam2:0.150374):0.010431):0.032764):0.156024,monDom5:0.425899);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFEmodel_mm9']\n\n\n sitefilepath = os.path.join(inter_dir, 'sites_' + tag)\n priorfilepath = os.path.join(inter_dir, 'priors_' + tag)\n loglikfile = os.path.join(inter_dir, 'loglik_' + tag)\n\n\n print '\\nCreate motevo parameter file %s' %tag\n print 'aligned', aligned\n if aligned:\n motevo_params = '\\n'.join(['refspecies %s' %genome,\n 'TREE %s' %genome_dict[genome][0],\n 'Mode TFBS',\n 'EMprior %s' %emprior,\n 'priordiff %s' %0.05,\n 'UFEwmprior %s' %200,\n 'UFEwmfile %s' %ufemodel_path,\n 'UFEwmlen %s' %wmlen,\n 'UFEprint %s' %0,\n 'markovorderBG %s' %bgorder,\n 'bgprior %s' %bgprior,\n 'bg A %s' %ATfreq,\n 'bg T %s' %ATfreq,\n 'bg G %s' %GCfreq,\n 'bg C %s' %GCfreq,\n 'restrictparses %s' %0,\n 'sitefile %s' %sitefilepath,\n 'priorfile %s' %priorfilepath,\n 'printsiteals %s' %0,\n 'minposterior %f' %0.0,\n 'loglikfile %s' %loglikfile])\n else:\n motevo_params = '\\n'.join(['refspecies %s' %genome,\n 'TREE (%s: 1)' %genome,\n 'Mode TFBS',\n 'EMprior %s' %emprior,\n 'priordiff %s' %0.05,\n 'markovorderBG %s' %bgorder,\n 'bgprior %s' %bgprior,\n 'bg A %s' %ATfreq,\n 'bg T %s' %ATfreq,\n 'bg G %s' %GCfreq,\n 'bg C %s' %GCfreq,\n 'restrictparses %s' %0,\n 'sitefile %s' %sitefilepath,\n 'priorfile %s' %priorfilepath,\n 'printsiteals %s' %0,\n 'minposterior %f' %0.0,\n 'loglikfile %s' %loglikfile]) \n\n params_path = os.path.join(inter_dir, 'motevo_TFBS_params_' + tag)\n pf = open(params_path, 'w')\n pf.write(motevo_params)\n return (params_path, sitefilepath, priorfilepath, loglikfile)","def log_prob_of_file(filepath, model):\n vocab = set(counts_un.keys())\n tot = 0\n count = 0\n prev_prev = \"<s>\\n\"\n prev = \"<s>\\n\"\n with open(filepath) as f:\n for line in f:\n count += 2\n line = line.strip()+\"\\n\"\n tri_prob = model.get_trigram_prob(prev_prev, prev, line)\n tot += math.log(tri_prob)\n prev_prev = prev\n prev = line \n for line in [\"</s>\\n\", \"</s>\\n\"]:\n tri_prob = model.get_trigram_prob(prev_prev, prev, line)\n tot += math.log(tri_prob)\n prev_prev = prev\n prev = line \n return tot, count","def __compute_log_likelihood(self, outputs, data, boolean):\n end_loc, pi_loc, mu_loc, sigma_loc, rho_loc = outputs\n\n mu1_loc, mu2_loc = mu_loc[:, :, :20], mu_loc[:, :, 20:]\n sig1_loc, sig2_loc = (\n sigma_loc[:, :, :20] + 10e-10,\n sigma_loc[:, :, 20:] + 10e-10,\n )\n\n x1_loc = data[:, 1:, 1].unsqueeze(2).to(self.__device)\n x2_loc = data[:, 1:, 2].unsqueeze(2).to(self.__device)\n x3_loc = data[:, 1:, 0].to(self.__device)\n\n end_loc = end_loc[:, :-1, -1].to(self.__device)\n pi_loc = pi_loc[:, :-1, :].to(self.__device)\n mu1_loc = mu1_loc[:, :-1, :].to(self.__device)\n mu2_loc = mu2_loc[:, :-1, :].to(self.__device)\n sig1_loc = sig1_loc[:, :-1, :].to(self.__device)\n sig2_loc = sig2_loc[:, :-1, :].to(self.__device)\n rho_loc = rho_loc[:, :-1, :].to(self.__device)\n\n boolean = boolean[:, :-1].to(self.__device)\n\n gauss = pi_loc * self.__bivariate_gaussian(\n sig1_loc, sig2_loc, mu1_loc, mu2_loc, x1_loc, x2_loc, rho_loc\n )\n gauss = torch.sum(gauss, dim=2).to(self.__device)\n\n log_lik = torch.sum(\n -boolean * torch.log(gauss + 10e-10)\n - boolean * torch.log(end_loc + 10e-10) * (x3_loc)\n - boolean * torch.log(1 - end_loc + 10e-10) * (1 - x3_loc)\n )\n\n return log_lik","def __compute_log_likelihood(self, outputs, data, boolean):\n end_loc, pi_loc, mu_loc, sigma_loc, rho_loc = outputs\n\n mu1_loc, mu2_loc = mu_loc[:, :, :20], mu_loc[:, :, 20:]\n sig1_loc, sig2_loc = (\n sigma_loc[:, :, :20] + 10e-10,\n sigma_loc[:, :, 20:] + 10e-10,\n )\n\n x1_loc = data[:, 1:, 1].unsqueeze(2).to(self.__device)\n x2_loc = data[:, 1:, 2].unsqueeze(2).to(self.__device)\n x3_loc = data[:, 1:, 0].to(self.__device)\n\n end_loc = end_loc[:, :-1, -1].to(self.__device)\n pi_loc = pi_loc[:, :-1, :].to(self.__device)\n mu1_loc = mu1_loc[:, :-1, :].to(self.__device)\n mu2_loc = mu2_loc[:, :-1, :].to(self.__device)\n sig1_loc = sig1_loc[:, :-1, :].to(self.__device)\n sig2_loc = sig2_loc[:, :-1, :].to(self.__device)\n rho_loc = rho_loc[:, :-1, :].to(self.__device)\n\n boolean = boolean[:, :-1].to(self.__device)\n\n gauss = pi_loc * self.__bivariate_gaussian(\n sig1_loc, sig2_loc, mu1_loc, mu2_loc, x1_loc, x2_loc, rho_loc\n )\n gauss = torch.sum(gauss, dim=2).to(self.__device)\n\n log_lik = torch.sum(\n -boolean * torch.log(gauss + 10e-10)\n - boolean * torch.log(end_loc + 10e-10) * (x3_loc)\n - boolean * torch.log(1 - end_loc + 10e-10) * (1 - x3_loc)\n )\n\n return log_lik","def backward(log_emlik, log_startprob, log_transmat):\n N, M = log_emlik.shape\n backward_prob = np.zeros((N,M))\n\n backward_prob[N-1, :] = 0.0\n\n for i in range(N-2,-1,-1):\n for k in range(M):\n # probability of transitioning from k to state l * probability of emitting symbol at state l at ts i+1 * recursive backward probability\n backward_prob[i,k] = logsumexp(log_transmat[k,:] + log_emlik[i+1,:] + backward_prob[i+1,:])\n\n return backward_prob","def sentence_logprob(self, sentence):\n grams = get_ngrams(sentence, 3)\n p = 1\n\n for gram in grams:\n p *= np.longfloat(self.smoothed_trigram_probability(gram))\n\n return np.log2(p)","def dna_probability(dna:str, gc:float, return_log=False) -> float:\n at = (1 - gc) / 2.0\n gc /= 2.0\n\n p = 1\n for l in dna:\n if l in \"AT\":\n p *= at\n elif l in \"CG\":\n p *= gc\n else:\n raise ValueError(\"You should use dna string.\")\n if return_log:\n return math.log(p, 10)\n else:\n return p","def likelihood(params,data):\n spec, isnflux, igalflux = data\n chi2=0\n modflux = (params[0]*isnflux + params[1]*igalflux)\n chi2 += sum((spec.flux - modflux)**2)/((0.05*sum(spec.var)**2)/2.0)\n return np.exp(-chi2/2.0)","def test_GA():\n\tgenerationSize = 150\n\tmutationProb = 0.01\n\tgenerations = 500\n\tX = []\n\tT = []\n\tY = [] \n\tfitnesses = [0]*generationSize\n\tfor i in range(DATA_POINTS_NUM):\n\t\tX.append((i - DATA_POINTS_NUM/2)*0.1)\n\t\tT.append(polynomi_3N(REFERENCE, X[-1]))\n\t\tY.append(0)\n\t\n\tga = GA.GA(generationSize, 4, mutationProb)\n\tgenomes = ga.seedGenomes()\n\t#plot initial genomes\n\tplt.figure(1)\n\tplt.title('Initial genomes')\n\tfor i in range(len(genomes)):\n\t\tGenome = prescale(genomes[i])\n\t\tprint Genome\n\t\tfor j in range(DATA_POINTS_NUM):\n\t\t\tY[j] = (polynomi_3N(Genome, X[j]))\n\t\tfitnesses[i] = calculate_fitness(T, Y)\n\t\tplt.plot(X,Y, 'b-')\n\tplt.plot(X,T, 'r-')\n\t\t\n\t\n\t#live and learn\n\tfor k in range(generations):\n\t\tprint \".\",\n\t\tfor i in range(len(genomes)):\n\t\t\tGenome = prescale(genomes[i])\n\t\t\tfor j in range(DATA_POINTS_NUM):\n\t\t\t\tY[j] = (polynomi_3N(Genome,X[j]))\n\t\t\tfitnesses[i] = calculate_fitness(T, Y)\n\t\tga.fitnessUpdate(fitnesses)\n\t\tgenomes = ga.createNextGeneration()\n\t\t\n\t#plot final genomes\n\tplt.figure(2)\n\tplt.title('Final genomes')\n\tprint \"\\nfinal Genomes\"\n\tfor i in range(len(genomes)):\n\t\tGenome = prescale(genomes[i])\n\t\tfor j in range(DATA_POINTS_NUM):\n\t\t\tY[j] = (polynomi_3N(Genome,X[j]))\n\t\tprint \"fit:%5.1f [%7.4f, %7.4f, %7.4f, %7.4f]\"%\\\n\t\t (calculate_fitness(T, Y), Genome[0],\n\t\t Genome[1], Genome[2], Genome[3])\n\t\tplt.plot(X,Y, 'b-')\n\tplt.plot(X,T, 'r-')\n\t\t\n\t#plot progress\n\tP = []\n\thistory = ga.generations[:]\n\tfor f in history:\n\t\t#f[1].sort()\n\t\tP.append(max(f[1]))\n\tplt.figure(3)\n\tplt.title('progress')\n\tplt.plot(P)\n\tplt.show()\n\t\n\t#print the result:\t\n\tbestGene = fitnesses.index(max(fitnesses))\n\tG = prescale(genomes[bestGene])\n print \"\"\n\tprint \"And the result is:\"\n\tprint \"%.4f => %.4f (%.4f)\"%(A, G[0], abs(A - G[0]))\n\tprint \"%.4f => %.4f (%.4f)\"%(B, G[1], abs(B - G[1]))\n\tprint \"%.4f => %.4f (%.4f)\"%(C, G[2], abs(C - G[2]))\n\tprint \"%.4f => %.4f (%.4f)\"%(D, G[3], abs(D - G[3]))","def return_likelihood(self, params):\n\n self._y_pred = self._int_spec(self._x, **params) * self._atten\n\n # Add the cascade\n if self._casc is not None:\n params_casc = deepcopy(params)\n\n # apply the weights\n if self._cr_spec is not None:\n # add units to the parameters where neccessary\n params_casc['Prefactor_CR'] *= u.Unit(\"TeV-1 cm-2 s-1\")\n params_casc['Scale_CR'] *= u.Unit(\"eV\").to(\"eV\") * u.eV\n params_casc['Emin_CR'] *= u.Unit(\"eV\").to(\"eV\") * u.eV\n params_casc['Emax_CR'] *= u.Unit(\"eV\").to(\"eV\") * u.eV\n self._casc.apply_spectral_weights(lambda x: self._cr_spec(x, **params_casc),\n smooth=True)\n\n else:\n # add units to the parameters where neccessary\n params_casc['Prefactor'] *= u.Unit(\"TeV-1 cm-2 s-1\")\n params_casc['Scale'] *= u.Unit(\"TeV\").to(\"eV\") * u.eV\n params_casc['Cutoff'] *= u.Unit(\"TeV\").to(\"eV\") * u.eV\n self._casc.apply_spectral_weights(lambda x: self._int_spec(x, **params_casc),\n smooth=True)\n\n # and get the flux in the ON region\n spec_halo = self._casc.get_obs_spectrum(\n region=self._on_region\n )\n # convert the units back\n flux_unit_conversion = (spec_halo.quantity.unit).to(\"TeV-1 cm-2 s-1\")\n\n # either add directly if energy bins are the same or use 1D interpolation\n if self._interp_casc:\n m = spec_halo.data[:, 0, 0] > 0.\n if not np.sum(m):\n raise ValueError(\"Predicted cascade flux is zero!\")\n interp = interp1d(np.log(spec_halo.geom.get_axis_by_name('energy').center.to(\"TeV\").value[m]),\n np.log(spec_halo.data[:, 0, 0][m] * flux_unit_conversion),\n fill_value='extrapolate', kind='cubic'\n )\n self._y_pred += np.exp(interp(np.log(self._x)))\n\n else:\n self._y_pred += spec_halo.data[:, 0, 0] * flux_unit_conversion\n\n if self._cov_inv is None:\n self._llh = -1. * ((self._y - self._y_pred) ** 2. / self._dy ** 2.).sum()\n else:\n self._llh = -1. * np.dot(self._y - self._y_pred, np.dot(self._cov_inv, self._y - self._y_pred))\n\n # add contribution from profile likelihood\n if self._llh_fermi_interp is not None:\n # change parameters to the values over which grid was interpolated\n params_llh = deepcopy(params)\n params_llh['Prefactor'] *= u.Unit(\"TeV-1 cm-2 s-1\").to(\"MeV-1 cm-2 s-1\")\n params_llh['Index'] *= -1.\n self._llh_fermi = 2. * self._llh_fermi_interp([params_llh['Cutoff'],\n -1. * params_llh['Index'],\n np.log10(params_llh['Prefactor'])])[0]\n else:\n self._llh_fermi = 0\n\n return -1. * (self._llh + self._llh_fermi)","def get_log_likelihood(response_probability, observed_response):\n \n return np.log(response_probability[observed_response])","def test_relevance_with_itself():\n state = gen_state_cgpm(get_data_separated)\n assert np.allclose(state.relevance_probability(2, [2], 1), 1.0)","def p(self) -> Probability:\n ...","def _trajectory_prob(self, trajectory):\n\n # Accumulate probabilities in log space for numerical stability\n logprob = np.log(self._starting_prob(trajectory[0][0]))\n logprob += np.sum([\n np.log(self._transition_prob(s1, a, s2))\n for (s1, a), (s2, _) in zip(trajectory[:-1], trajectory[1:])\n ])\n\n return np.exp(logprob)","def log_probability(self, sequence):\n sequence = self._transform(sequence)\n\n T = len(sequence)\n\n if T > 0 and sequence[0][_TAG]:\n last_state = sequence[0][_TAG]\n p = self._priors.logprob(last_state) + self._output_logprob(\n last_state, sequence[0][_TEXT]\n )\n for t in range(1, T):\n state = sequence[t][_TAG]\n p += self._transitions[last_state].logprob(\n state\n ) + self._output_logprob(state, sequence[t][_TEXT])\n last_state = state\n return p\n else:\n alpha = self._forward_probability(sequence)\n p = logsumexp2(alpha[T - 1])\n return p","def _create_log_likelihood(self, individual):\n # Get individuals data\n times = []\n observations = []\n mask = self._data[self._id_key] == individual\n data = self._data[mask][\n [self._time_key, self._obs_key, self._value_key]]\n for output in self._mechanistic_model.outputs():\n # Mask data for observable\n observable = self._output_observable_dict[output]\n mask = data[self._obs_key] == observable\n temp_df = data[mask]\n\n # Filter times and observations for non-NaN entries\n mask = temp_df[self._value_key].notnull()\n temp_df = temp_df[[self._time_key, self._value_key]][mask]\n mask = temp_df[self._time_key].notnull()\n temp_df = temp_df[mask]\n\n # Collect data for output\n times.append(temp_df[self._time_key].to_numpy())\n observations.append(temp_df[self._value_key].to_numpy())\n\n # # Count outputs that were measured\n # # TODO: copy mechanistic model and update model outputs.\n # # (Useful for e.g. control group and dose group training)\n # n_measured_outputs = 0\n # for output_measurements in observations:\n # if len(output_measurements) > 0:\n # n_measured_outputs += 1\n\n # Create log-likelihood and set ID to individual\n log_likelihood = chi.LogLikelihood(\n self._mechanistic_model, self._error_models, observations, times)\n log_likelihood.set_id(individual)\n\n return log_likelihood","def probabilities_of_structures(sequence, structure_list, react=None):\n ensemble_energy = get_ens_energy(sequence, react = react)\n energies = [get_stru_energy(x, sequence, react = react) for x in structure_list]\n probabilities = [energy_to_proba(ensemble_energy, x) for x in energies]\n #probabilities = normalize(probabilities, norm='l1').tolist()[0]\n return [(stru,proba) for stru,proba in zip(structure_list,probabilities)]","def sentence_logprob(self, sentence):\n line = get_ngrams(sentence,3)\n log_por = 0.0\n for item in line:\n raw_por = self.smoothed_trigram_probability(item)\n log_por = log_por+math.log2(raw_por)\n\n return float(log_por)","def _logprob(self, sample):\n return 0, 0","def log_probability(self, samples):\n pass","def calc_likelihood(par_num, par_rng):\n\n likelihoods = np.zeros(np.size(par_rng))\n\n trivial_prior = trivial_prior_class()\n\n pipe = pipeline(observables_generator=hammu12,\n likelihood=likelihood,\n prior=trivial_prior,\n optimizer_class=Hamiltonian_Monte_Carlo)\n\n parameters = [0]*hammu12.get_parameter_dimension()\n for par_val in par_rng:\n parameters[par_num] = par_val\n likelihoods[par_val-par_rng[0]] = pipe._calc_posterior(parameters)\n\n np.save('data%s_RM' % (par_num), likelihoods)","def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\n\n log_prob = 0\n\n for word in token_list:\n\n # do not allow zero probabilites\n assert word in token_probabilities\n\n if use_log_prob:\n log_prob += token_probabilities[word]\n else:\n log_prob += log10(token_probabilities[word])\n\n if use_log_prob:\n return log_prob\n\n return 10**log_prob","def MAP(cpts, obs, terms):\r\n\r\n # a list to store the computed probabilities\r\n all_sums = []\r\n # initialize all terms to false\r\n for value in range(len(terms)):\r\n terms[value] = [terms[value], '0']\r\n search_array = terms + obs\r\n # if all terms are being watched, just call MPE\r\n if len(search_array) == len(cpts):\r\n return MPE(cpts, obs)\r\n # we need to know what terms we aren't interested in so we start with \r\n # or terms and observations and note the variables that appear in CPT but\r\n # not in those\r\n dont_count = []\r\n for var in cpts:\r\n if [var[0], '0'] not in search_array and [var[0], '1'] not in search_array:\r\n dont_count.append(var[0])\r\n terms.append([var[0],'1'])\r\n # sort the terms to ensure correct ordering\r\n terms.sort()\r\n # creates a list of all possible bit strings\r\n # just an easy way to create all possible truth assignments\r\n seq = [\"\".join(seq) for seq in itertools.product(\"01\", repeat=len(terms))]\r\n # loop through all possible truth assignments\r\n for j in range(len(seq)):\r\n # we initialize at probability = 100%\r\n chance = 1\r\n # assign the truth values\r\n for k in range(len(seq[j])):\r\n terms[k][1] = seq[j][k]\r\n # this computes the probability using the chaining rule\r\n for i in range(len(terms)):\r\n new_terms = terms[:-i-1] + obs\r\n new_terms.sort()\r\n chance *= probability(cpts,terms[-i-1], new_terms)\r\n # add the probabilities to our list\r\n all_sums.append(chance)\r\n combine = []\r\n # note all variables which weren't in obs or Vs\r\n for i in dont_count:\r\n combine.append(terms.index([i,'1']))\r\n # this will store the final probabilities\r\n final_array = [0] * len(seq)\r\n # another complicated looking loop, it just serves to combine probabilities\r\n # for example, if we have a CPT with x_1, x_2, x_3, x_4 and we observe \r\n # x_1 to be true and have Vs = [x_3, x_4] then we need to combine the \r\n # probabilities that are the same except for x_2 = true vs false\r\n for loc in combine:\r\n for sequence in range(len(seq)):\r\n for alt_sequence in range(sequence+1,len(seq)):\r\n if (seq[sequence][:loc] + seq[sequence][loc+1:]) == (seq[alt_sequence][:loc] + seq[alt_sequence][loc+1:]):\r\n final_array[sequence] = all_sums[sequence] + all_sums[alt_sequence]\r\n\r\n # get the truth assignment for the highest probability\r\n location = seq[final_array.index(max(final_array))]\r\n truth_assignment = []\r\n # place the truth assignment in a more readable fashion\r\n for value in range(len(terms)):\r\n if terms[value] in search_array:\r\n if location[value] == '0':\r\n truth_assignment.append(terms[value][0]+ ' = False')\r\n else:\r\n truth_assignment.append(terms[value][0]+ ' = True')\r\n return (truth_assignment)","def random_strings(sequence, GC_array):\r\n\r\n AT = 0\r\n GC = 0\r\n\r\n for nt in sequence:\r\n if nt == \"A\" or nt == \"T\":\r\n AT += 1\r\n elif nt == \"G\" or nt == \"C\":\r\n GC += 1\r\n\r\n probabilities = []\r\n\r\n #Calculate probability of G = probability of C = %GC / 2\r\n #Calculate probability of A = probability of T = (1 - %GC) / 2\r\n\r\n #For each consecutive base in provided sequence:\r\n #1. Convert total probability to logarithm using math.log(probability, base=10)\r\n #2. Total probability to be multiplied by probability of specifically that base\r\n\r\n for i in range(len(GC_array)):\r\n prob = (AT * math.log10((1 - GC_array[i])/2)) + (GC * math.log10(GC_array[i]/2))\r\n\r\n probabilities.append('%0.3f' % prob)\r\n\r\n print(*probabilities, sep= \" \")","def log_marg_likelihood(self):\n self.A = np.linalg.inv(self.Sn)\n term1 = self.t - self.design_matrix@self.mn\n self.Evidence_mN = (self.beta/2)*np.linalg.norm(term1)+ (self.alpha/2)*self.mn.T@self.mn\n A_abs = np.linalg.eigvals(self.A)\n A_abs = np.prod(A_abs)\n\n self.marg_lik = ((self.p)/2)*np.log(self.alpha) + (self.n/2)*np.log(self.beta) - self.Evidence_mN - (1/2)*np.log(A_abs) - (self.n/2)*np.log(2*np.pi)\n\n return self.marg_lik","def Log_OB_S1(xref,x):\n\n nX = np.shape(x)\n\n m = nX[0]\n n = nX[1]\n t = nX[2]\n\n G = np.zeros((n,t))\n Gv = np.zeros((m,n,t))\n\n for r in range(t):\n\n # Correct for permuations\n\n #Xout,PiA= CorrectPerm(xref,x[:,:,r])\n Xout = dp(x[:,:,r])\n\n for q in range(n):\n\n a = np.sum(Xout[:,q]*xref[:,q])/np.sqrt(np.sum(xref[:,q]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\n if a > 1:\n a = 1\n if a < -1:\n a = -1\n G[q,r] = np.arccos(a) # Computing the angles\n v = Xout[:,q] - a*xref[:,q]\n Gv[:,q,r] = v / (1e-12 + np.linalg.norm(v)) # Unit vector in the tangent subspace\n\n return G,Gv","def _log_likelihood_poisson(self, df, dfo, n_bins=10):\n cond = df[\"selected_jig\"].values == 1\n range = parameter_ranges['uae'], parameter_ranges['rec']\n\n uae_obs = dfo[\"mueff_av\"].values\n rec_obs = dfo[\"rec_arcsec\"].values\n obs, xedges, yedges = np.histogram2d(uae_obs, rec_obs, range=range, bins=n_bins)\n\n uae_mod = df[\"uae_obs_jig\"].values[cond]\n rec_mod = df[\"rec_obs_jig\"].values[cond]\n model, _, _ = np.histogram2d(uae_mod, rec_mod, range=range, bins=n_bins, density=True)\n\n # Rescale model by number of observations\n model = model.astype(\"float\") * dfo.shape[0]\n\n # Calculate Poisson probability for each bin\n obs = obs.reshape(-1).astype(\"float\")\n model = model.reshape(-1)\n probs = stats.poisson(mu=model).pmf(obs)\n\n # Return overall log likelihood\n return np.log(probs).sum()","def GoAnnot(prots, gos, onlyProts=False):\r\n with resources.open_text(\"autoprot.data\",\"Homo_sapiens.gene_info\") as d:\r\n geneInfo = pd.read_csv(d, sep='\\t')\r\n with resources.open_text(\"autoprot.data\",\"gene2go_alt\") as d:\r\n gene2go = pd.read_csv(d, sep='\\t')\r\n prots = pd.DataFrame(pd.Series([str(i).upper().split(';')[0] for i in prots]), columns=[\"Gene names\"])\r\n prots = prots.merge(geneInfo[[\"Symbol\", \"GeneID\"]], left_on=\"Gene names\", right_on=\"Symbol\", how='inner')\r\n \r\n prots = prots.merge(gene2go[[\"GeneID\", \"GO_ID\", \"GO_term\"]], on=\"GeneID\", how='inner')\r\n if onlyProts == True:\r\n for idx, go in enumerate(gos):\r\n if idx == 0:\r\n redProts = prots[\"Symbol\"][prots[\"GO_term\"].str.contains(go)]\r\n else:\r\n redProts = redProts.append(prots[\"Symbol\"][prots[\"GO_term\"].str.contains(go)])\r\n return redProts.drop_duplicates()\r\n else: \r\n for idx, go in enumerate(gos):\r\n if idx == 0:\r\n redProts = prots[prots[\"GO_term\"]==go]\r\n else:\r\n redProts = redProts.append(prots[prots[\"GO_term\"]==go])\r\n return redProts.drop_duplicates()","def calculate_likelihood_probability(measurement, predicted_measurement, covariance):\n \n return None","def compute_correspondence_likelihoods(self, measurement,\r\n number_of_landmarks,\r\n Qt_measurement_covariance):\r\n likelihoods = []\r\n for i in range(number_of_landmarks):\r\n likelihoods.append(\r\n self.compute_weight(measurement, i, Qt_measurement_covariance))\r\n \r\n return likelihoods","def label_propagate_probabilistic(embeddings, seeds_map, normalize=True, **kwargs):\n words = embeddings.iw\n M = transition_matrix(embeddings, **kwargs)\n teleport_set_map={}\n for seed_key, seed_list in seeds_map.items():\n teleport_set_map[seed_key]=teleport_set(words, seed_list)\n def update_seeds(r):\n idm= np.eye(len(seeds_map))\n for seed_key, w_indices in teleport_set_map.items():\n r[w_indices] = idm[seed_key]\n r /= np.sum(r, axis=1)[:, np.newaxis]\n r = run_iterative(M, np.random.random((M.shape[0], len(seeds_map))), update_seeds, **kwargs)\n polarities={}\n for i, w in enumerate(words):\n polarities[w]=Counter()\n for seed_key in seeds_map:\n polarities[w][seed_key]=r[i][seed_key]\n if normalize:\n polarities[w]=normalize_counter(polarities[w])\n return polarities","def build_mm_df(sralist):\n\n def convert_to_codon(nts_array):\n \"\"\"\n pysam output is in nucleotides resolution, but scikit_curated_df uses codon resolution.\n This function converts nucleotide arrays to codon length (nts to codon resolution):\n \"\"\"\n \n nts_array = np.array(nts_array)\n codon_array = np.sum( np.reshape(A, (int(np.floor(nts_array[1]/3)),3) ), 1)/3.\n\n return codon_array\n\n\n def compute_mm(mmdata):\n \"\"\"\n get per gene average multi-mapping score\n \"\"\"\n\n mm_df = pd.DataFrame(columns=['ORF', 'MM'])\n counter = 0\n\n for gene in mmdata.keys():\n current_matrix = mmdata[gene]\n current_avrg = np.mean( np.sum(current_matrix, 1) / current_matrix.shape[1] )\n mm_df.loc[counter] = [gene, current_avrg]\n counter += 1\n\n return mm_df\n\n\n mm_mat = {}\n mm_pct = {}\n\n N = len(sralist)\n\n for ix, dataset in enumerate(sralist):\n samfile = pysam.AlignmentFile(TMP_DIR+'/ambiguous_reads/'+dataset+'_STAR_transcriptome_multi_mapped_sorted.bam', 'rb')\n genes_list = list(samfile.references)\n print(ix, dataset)\n\n for geneID in genes_list:\n # count the coverage of genomic positions by reads in region.\n # Returns: four array.arrays of the same length in order A C G T\n # The coverage is computed per-base [ACGT]\n cov = samfile.count_coverage(geneID, read_callback='nofilter')\n # Summ all 4 arrays\n cov_sum = np.sum(cov, axis=0)\n #print(geneID, cov_sum)\n codon_cov = convert_to_codon(cov_sum)\n codon_bool = np.asarray([1 if i > 0 else 0 for i in codon_cov])\n \n M = len(codon_bool)\n\n if ix == 0:\n \tmm_mat[geneID] = np.zeros((N,M)) * np.nan\n \n current_matrix = mm_mat[geneID]\n current_matrix[ix,:] = np.copy(codon_bool)\n mm_mat[geneID] = current_matrix\n\n\n mm_avrg = compute_mm(mm_mat)\n #mm_avrg.to_json('yeast_mm.json')\n #mm_avrg.to_csv('yeast_mm.txt', header=True, index=False, sep='\\t')\n\n \n mm_profile = {}\n theta_mm = 5\n for orf in mm_mat.keys():\n current_mat = mm_mat[orf]\n current_bool = np.sum(current_mat, 0) <= theta_mm\n mm_profile[orf] = current_bool\n\n with open('../data/processed/mm_consensus.pkl', 'wb') as f_mm:\n pickle.dump(mm_profile, f_mm)\n\n\n return mm_mat, mm_avrg, mm_profile","def ICA_log_likelihood(X, model):\n\n # TODO: YOUR CODE HERE","def ref_lamanno(\n fasta_path,\n gtf_path,\n cdna_path,\n intron_path,\n index_path,\n t2g_path,\n cdna_t2c_path,\n intron_t2c_path,\n temp_dir='tmp',\n overwrite=False,\n):\n results = {}\n if not os.path.exists(index_path) or overwrite:\n fasta_path = decompress_file(fasta_path, temp_dir=temp_dir)\n sorted_fasta_path, fasta_chromosomes = sort_fasta(\n fasta_path, os.path.join(temp_dir, SORTED_FASTA_FILENAME)\n )\n gtf_path = decompress_file(gtf_path, temp_dir=temp_dir)\n sorted_gtf_path, gtf_chromosomes = sort_gtf(\n gtf_path, os.path.join(temp_dir, SORTED_GTF_FILENAME)\n )\n logger.info('Splitting genome into cDNA at {}'.format(cdna_path))\n chromosomes = check_chromosomes(fasta_chromosomes, gtf_chromosomes)\n cdna_fasta_path = generate_cdna_fasta(\n sorted_fasta_path,\n sorted_gtf_path,\n cdna_path,\n chromosomes=chromosomes\n )\n results.update({'cdna_fasta': cdna_fasta_path})\n logger.info(\n 'Creating cDNA transcripts-to-capture at {}'.format(cdna_t2c_path)\n )\n cdna_t2c_result = create_t2c(cdna_fasta_path, cdna_t2c_path)\n results.update({'cdna_t2c': cdna_t2c_result['t2c']})\n logger.info('Splitting genome into introns at {}'.format(intron_path))\n intron_fasta_path = generate_intron_fasta(\n sorted_fasta_path,\n sorted_gtf_path,\n intron_path,\n chromosomes=chromosomes\n )\n results.update({'intron_fasta': intron_fasta_path})\n logger.info(\n 'Creating intron transcripts-to-capture at {}'.\n format(cdna_t2c_path)\n )\n intron_t2c_result = create_t2c(intron_fasta_path, intron_t2c_path)\n results.update({'intron_t2c': intron_t2c_result['t2c']})\n logger.info('Concatenating cDNA and intron FASTAs')\n combined_path = concatenate_files(\n cdna_fasta_path,\n intron_fasta_path,\n out_path=os.path.join(temp_dir, COMBINED_FILENAME),\n temp_dir=temp_dir\n )\n t2g_result = create_t2g_from_fasta(combined_path, t2g_path)\n results.update(t2g_result)\n index_result = kallisto_index(combined_path, index_path)\n results.update(index_result)\n else:\n logger.info(\n 'Skipping kallisto index because {} already exists. Use the --overwrite flag to overwrite.'\n .format(index_path)\n )\n\n return results","def calories_protein(og, fg):\n\n return 0.994 * fg * real_extract(og, fg)","def bayesian_info_criterion(log_likelihood, n_params, n_samples):\n return n_params * np.log(n_samples) - 2.0 * log_likelihood"],"string":"[\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\n \\\"*** YOUR CODE HERE ***\\\"\\n \\n # -- OUR CODE HERE\\n \\n \\n import math\\n for label in self.legalLabels:\\n sumThing = 0.0\\n for pixel in self.conditionalProb[label]:\\n if datum[pixel] is 1:\\n #assert self.conditionalProb[label][pixel] < 1.0 # -- sanity check that the probability is valid\\n sumThing += math.log((self.conditionalProb[label][pixel]*1.0))\\n else:\\n sumThing+=math.log(1-self.conditionalProb[label][pixel]*1.0)\\n logJoint[label] = math.log(self.prior[label]*1.0) + sumThing*1.0\\n \\n\\n \\n \\n import time\\n #print \\\"logJoint is :: \\\", logJoint\\n #time.sleep(2)\\n \\n \\n # -- uses the conditional probability tables computed in the current iteration\\n # -- in train and tune\\n \\n return logJoint\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\t#Adds log(P(y)) to calculate P(y|f1,f2...)\\n for label in self.legalLabels:\\n\\t\\tlogJoint[label] += math.log(self.prior[label])\\n\\t#Adds log(P(f1|y)), log(P(f2|y))... to calculate P(y|f1, f2...)\\n for key in datum:\\n\\t\\t#if key == (7, 3):\\n\\t\\t\\t#print self.condprobs[key, 0]\\n\\t\\tfor label in self.legalLabels:\\n\\t\\t\\t#print str(key) + str(datum[key])\\n\\t\\t\\tlogJoint[label] += math.log(self.condprobs[key, label][datum[key]])\\n return logJoint\",\n \"def log_prob(self):\",\n \"def evaluate(genome):\\n # base fitness\\n fit = 1.0\\n # promote 1001 starting motif\\n matches = 0\\n if genome.sequence_A[0] == 1:\\n matches += 1\\n if genome.sequence_A[1] == 0:\\n matches += 1\\n if genome.sequence_A[2] == 0:\\n matches += 1\\n if genome.sequence_A[3] == 1:\\n matches += 1\\n fit += matches * 0.1\\n # finish\\n return fit\",\n \"def likelihood(seq):\\n global qmap\\n if qmap is None:\\n qmap = {'!': 1.0, '\\\"': 0.7943282347242815, '#': 0.6309573444801932, '$': 0.5011872336272722, '%': 0.3981071705534972, '&': 0.31622776601683794, \\\"'\\\": 0.251188643150958, '(': 0.19952623149688797, ')': 0.15848931924611134, '*': 0.12589254117941673, '+': 0.1, ',': 0.07943282347242814, '-': 0.06309573444801933, '.': 0.05011872336272722, '/': 0.039810717055349734, '0': 0.03162277660168379, '1': 0.025118864315095794, '2': 0.0199526231496888, '3': 0.015848931924611134, '4': 0.012589254117941675, '5': 0.01, '6': 0.007943282347242814, '7': 0.00630957344480193, '8': 0.005011872336272725, '9': 0.003981071705534973, ':': 0.0031622776601683794, ';': 0.0025118864315095794, '<': 0.001995262314968879, '=': 0.001584893192461114, '>': 0.0012589254117941675, '?': 0.001, '@': 0.0007943282347242813, 'A': 0.000630957344480193, 'B': 0.0005011872336272725, 'C': 0.00039810717055349735, 'D': 0.00031622776601683794, 'E': 0.00025118864315095795, 'F': 0.00019952623149688788, 'G': 0.00015848931924611142, 'H': 0.00012589254117941674, 'I': 0.0001, 'J': 7.943282347242822e-05, 'K': 6.309573444801929e-05, 'L': 5.011872336272725e-05, 'M': 3.9810717055349695e-05, 'N': 3.1622776601683795e-05, 'O': 2.5118864315095822e-05, 'P': 1.9952623149688786e-05, 'Q': 1.584893192461114e-05, 'R': 1.2589254117941661e-05, 'S': 1e-05, 'T': 7.943282347242822e-06, 'U': 6.30957344480193e-06, 'V': 5.011872336272725e-06, 'W': 3.981071705534969e-06, 'X': 3.162277660168379e-06, 'Y': 2.5118864315095823e-06, 'Z': 1.9952623149688787e-06, '[': 1.584893192461114e-06, '\\\\\\\\': 1.2589254117941661e-06, ']': 1e-06, '^': 7.943282347242822e-07, '_': 6.30957344480193e-07, '`': 5.011872336272725e-07, 'a': 3.981071705534969e-07, 'b': 3.162277660168379e-07, 'c': 2.5118864315095823e-07, 'd': 1.9952623149688787e-07, 'e': 1.584893192461114e-07, 'f': 1.2589254117941662e-07, 'g': 1e-07, 'h': 7.943282347242822e-08, 'i': 6.30957344480193e-08, 'j': 5.011872336272725e-08, 'k': 3.981071705534969e-08, 'l': 3.162277660168379e-08, 'm': 2.511886431509582e-08, 'n': 1.9952623149688786e-08, 'o': 1.5848931924611143e-08, 'p': 1.2589254117941661e-08, 'q': 1e-08, 'r': 7.943282347242822e-09, 's': 6.309573444801943e-09, 't': 5.011872336272715e-09, 'u': 3.981071705534969e-09, 'v': 3.1622776601683795e-09, 'w': 2.511886431509582e-09, 'x': 1.9952623149688828e-09, 'y': 1.584893192461111e-09, 'z': 1.2589254117941663e-09, '{': 1e-09, '|': 7.943282347242822e-10, '}': 6.309573444801942e-10, '~': 5.011872336272714e-10, '\\\\x7f': 3.9810717055349694e-10, '\\\\x80': 3.1622776601683795e-10, '\\\\x81': 2.511886431509582e-10, '\\\\x82': 1.9952623149688828e-10, '\\\\x83': 1.584893192461111e-10, '\\\\x84': 1.2589254117941662e-10, '\\\\x85': 1e-10}\\n return [qmap[i] for i in seq]\",\n \"def likelihood_genotype(genotype, bases_all_reads, error_rates):\\n likelihood = 1\\n for observed_base in bases_all_reads:\\n p = 0\\n for base in \\\"ACGT-\\\":\\n l = prob_t_N(genotype, base) * error_rates[base][observed_base]\\n p += l\\n likelihood *= p\\n\\n return likelihood\",\n \"def calculateLogJointProbabilities(self, datum):\\n\\tlogJoint = util.Counter()\\n\\t#want to calculate log(P(y)) + log(sum(P(fi|y)))\\n\\t#where y is a label\\n\\tfor label in self.legalLabels:\\n\\t\\tlogJoint[label] = math.log(self.prior_distribution_prob[label])\\n\\t\\tfor feature, value in datum.items():\\n\\t\\t\\tcp = self.conditional_prob[label][feature][value]\\n\\t\\t\\tif cp > 0: #condition check for values < 0 because log(0) is undefined and math domain error occurs\\n\\t\\t\\t\\tlogJoint[label] += math.log(cp) #summing up\\n\\t\\t\\t\\t\\n\\treturn logJoint\",\n \"def likelihood(self):\\n \\n raise NotImplementedError()\",\n \"def annotate_effect(cds_dict, genome, snp):\\n # List to save the coding effect\\n coding_effect = []\\n \\n # Change the SNP position from 1-indexed to 0-indexed\\n snp = (snp[0]-1, snp[1])\\n \\n # Determine which genes the SNP is located in\\n genes = []\\n for k,v in cds_dict.items():\\n if snp[0] in range(v.location.start, v.location.end): \\n genes.append(k)\\n # Check that SNP is in a gene\\n if genes: \\n # Some SNPs will be in more than one gene, SARS has overlaping ORFs\\n for gene in genes: \\n gene_tuple = list(zip(list(cds_dict[gene].location), cds_dict[gene].location.extract(genome)))\\n # Get the indicies relative to the gene, add 1 to get 1-indexed values\\n indicies = [x + 1 for x, y in enumerate(gene_tuple) if y[0] == snp[0]]\\n # Determine codon position from gene index\\n for i in indicies:\\n # First position in codon\\n if i % 3 == 1:\\n codonpos = 1\\n wtcodon = [gene_tuple[i-1], gene_tuple[i], gene_tuple[i+1]]\\n # Second position in codon\\n elif i % 3 == 2:\\n codonpos = 2\\n wtcodon = [gene_tuple[i-2], gene_tuple[i-1], gene_tuple[i]]\\n # Third position in codon \\n elif i % 3 == 0:\\n codonpos = 3\\n wtcodon = [gene_tuple[i-3], gene_tuple[i-2], gene_tuple[i-1]]\\n \\n # From the wt codon sequence, determine the alterative codon, coding change, and effect\\n altcodon = [snp if i == (codonpos-1) else b for i, b in enumerate(wtcodon)]\\n wtaa = translate(\\\"\\\".join(y for x,y in wtcodon))\\n altaa = translate(\\\"\\\".join(y for x,y in altcodon))\\n if wtaa == altaa:\\n effect = \\\"synonymous\\\"\\n elif wtaa != altaa and altaa == '*':\\n effect = \\\"nonsense\\\"\\n elif wtaa != altaa and altaa != '*':\\n effect = \\\"missense\\\"\\n # Save the codon effects and information\\n coding_effect.append((codonpos, f\\\"{wtaa}{-(i // -3)}{altaa}\\\", effect, gene))\\n # If the SNP isn't in a gene, it's intergeneic and has no coding effect\\n else:\\n coding_effect.append((\\\"NA\\\", \\\"NA\\\", \\\"NA\\\", \\\"intergeneic\\\"))\\n \\n \\n # Deal with SNPs in multiple genes with multiple effects \\n if len(coding_effect) == 1:\\n return list(coding_effect[0])\\n else: \\n if len(set([(a,b,c) for a,b,c,d in coding_effect])) == 1: \\n return list(list(set(coding_effect))[0])\\n # TODO: Deal with ambiguous sequences\\n else:\\n return [\\\"NA\\\", \\\"NA\\\", \\\"NA\\\", \\\"ambiguous\\\"]\",\n \"def target_log_prob_fn(self, *args, **kwargs): # pylint: disable=unused-argument\\n\\n def log_joint_fn(*args, **kwargs): # pylint: disable=unused-argument\\n states = dict(zip(self.unobserved.keys(), args))\\n states.update(self.observed)\\n interceptor = interceptors.CollectLogProb(states)\\n with ed.interception(interceptor):\\n self._f(self._cfg)\\n\\n log_prob = sum(interceptor.log_probs)\\n return log_prob\\n return log_joint_fn\",\n \"def score_sequence(seq, ngramlogprobs):\\n return\",\n \"def get_log_likelihood(response_probability, response):\\n pass\",\n \"def probability(structure,seq, react=None):\\n return energy_to_proba(get_ens_energy(seq,react),get_stru_energy(structure,seq,react))\",\n \"def Log_OB(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n n = nX[1]\\n t = nX[2]\\n\\n G = np.zeros((m,n,t))\\n\\n for r in range(t):\\n\\n # Correct for permuations\\n\\n Xout,PiA= CorrectPerm(xref,x[:,:,r])\\n\\n G[:,:,r] = Xout - np.dot(xref,np.dot(PiA,Xout))\\n\\n return G\",\n \"def calc_prob(number_of_strings, GC_content, DNA):\\r\\n\\r\\n AT = 0\\r\\n GC = 0\\r\\n\\r\\n for nt in DNA:\\r\\n if nt == \\\"A\\\" or nt == \\\"T\\\":\\r\\n AT += 1\\r\\n elif nt == \\\"G\\\" or nt == \\\"C\\\":\\r\\n GC += 1\\r\\n\\r\\n #P(at least 1 match of s) = 1 − P(no matches out of N strings) = 1 − [1 - P_no_match]^N\\r\\n\\r\\n P_no_match = (((1 - GC_content)/2) **AT) * ((GC_content/2) **GC)\\r\\n prob = 1 - (1-P_no_match) **number_of_strings\\r\\n\\r\\n print(\\\"%0.3f\\\" %prob)\",\n \"def log_likelihood(self, data, reward_model, bias_params):\",\n \"def biopythonMM(pwmFileName,genomeDict,mpbsDict,scoringMethod,tempLocation,pseudocounts=0.1,bitscore=12.0,fpr=0.01,precision=10**4,highCutoff=0.7,functionalDepth=0.9):\\n \\n # Reading PWM\\n pwm = readPwmFile(pwmFileName,tempLocation,pseudocounts)\\n pwmName = pwmFileName.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\n pwmLen = len(pwm)\\n\\n # Evaluating threshold\\n pwmThreshold = 0.0\\n if(scoringMethod == \\\"bitscore\\\"):\\n pwmThreshold = bitscore\\n elif(scoringMethod == \\\"fpr\\\"):\\n sd = Motif.ScoreDistribution(pwm,precision=precision)\\n pwmThreshold = sd.threshold_fpr(fpr)\\n elif(scoringMethod == \\\"boyle\\\"):\\n maxScore = pwm.max_score()\\n minScore = 0.0 # TODO Boyle's rule is not suited for negative values.\\n pwmThreshold = min(highCutoff*maxScore,functionalDepth*(maxScore-minScore))\\n else:\\n sys.stderr.write(\\\"Choose a valid scoring method.\\\\n\\\")\\n sys.exit(0)\\n\\n # Creating aditional parameters\\n chrList = constants.getChromList(reference=[mpbsDict])\\n tempMpbsDict = dict([(e,[]) for e in chrList])\\n maxValue = -99.0\\n\\n # Iterating on chromosomes\\n for chrName in chrList:\\n\\n # Reading genome\\n sequence = genomeDict[chrName]\\n\\n # Performing biopython's motif matching\\n for pos, score in pwm.search_pwm(sequence,threshold=pwmThreshold):\\n if(score > maxValue): maxValue = score\\n if(pos >= 0): tempMpbsDict[chrName].append([pos,pos+pwmLen,pwmName,score,\\\"+\\\"])\\n else: tempMpbsDict[chrName].append([-pos,-pos+pwmLen,pwmName,score,\\\"-\\\"])\\n\\n # Update scores - new scores are within [0,1000]\\n for chrName in chrList:\\n for e in tempMpbsDict[chrName]:\\n mpbsDict[chrName].append([e[0],e[1],e[2],int(1000*(e[3]-pwmThreshold)/(maxValue-pwmThreshold)),e[4]])\\n \\n return 0\",\n \"def gomeroccupancyscore(pwm_dictionary, seq):\\n if \\\"N\\\" in seq:\\n return 0\\n else:\\n # pwm_length = len(pwm_dictionary)\\n pwm_length = len(pwm_dictionary[\\\"A\\\"])\\n gomer_occupancy = 1\\n area_pwm_rc = rc_pwm(pwm_dictionary, pwm_length)\\n for i in range(pwm_length - 1, 1, -1):\\n prod_gomer = 1\\n prod_gomer_rc = 1\\n for j in range(pwm_length):\\n if j <= i:\\n prod_gomer *= 0.25\\n prod_gomer_rc *= 0.25\\n elif (j + i) > len(seq) - 1:\\n prod_gomer *= 0.25\\n prod_gomer_rc *= 0.25\\n else:\\n # print \\\"got to else\\\"\\n s = seq[j + i]\\n prod_gomer *= pwm_dictionary[s][j]\\n prod_gomer_rc *= area_pwm_rc[s][j]\\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\\n for i in range(len(seq) - 1):\\n prod_gomer = 1\\n prod_gomer_rc = 1\\n for j in range(pwm_length - 1):\\n if (j + i) >= len(seq) - 1:\\n prod_gomer *= 0.25\\n prod_gomer_rc *= 0.25\\n else:\\n prod_gomer *= pwm_dictionary[seq[j + i]][j]\\n prod_gomer_rc *= area_pwm_rc[seq[j + i]][j]\\n gomer_occupancy *= (1 - prod_gomer) * (1 - prod_gomer_rc)\\n gomer_occupancy = 1 - gomer_occupancy\\n\\n return gomer_occupancy\",\n \"def likelihood_prediction():\\n # Get info\\n selected_word = prompt_tech_selection()\\n article_json = get_json_from_file()\\n\\n # Calculate results\\n total_word_counter, selected_word_counter = count_occurrences(article_json, selected_word)\\n probability = selected_word_counter / total_word_counter\\n total_time = article_json[-1]['time'] - article_json[0]['time'] # unix subtraction = seconds\\n months_in_train_set = total_time / SECONDS_IN_MONTH\\n expected_posts_per_month = int(total_word_counter / months_in_train_set)\\n\\n # Show results\\n print_text_results(expected_posts_per_month, probability, selected_word)\\n plot_likelihood(expected_posts_per_month, probability)\",\n \"def calculateLogJointProbabilities(self, datum):\\n logJoint = util.Counter()\\n for cls in self.classes:\\n class_probability = self.prior_prob[cls]\\n for key, value in datum.items():\\n relative_feature_values = self.likelihoods[cls][key]\\n class_probability += math.log(relative_feature_values.get(datum[key], 0.01))\\n\\n logJoint[cls] = class_probability\\n\\n return logJoint\",\n \"def viterbi(prob_matrix):\\n TINY = 1e-6 # to avoid NaNs in logs\\n\\n # if prob_matrix is 1D, make it 2D\\n if len(np.shape(prob_matrix)) == 1:\\n prob_matrix = [prob_matrix]\\n \\n length = len(prob_matrix)\\n\\n probs = np.zeros_like(prob_matrix)\\n backpt = np.ones_like(prob_matrix, dtype=np.int32) * -1\\n \\n for i in [0,1,2,3,4]:\\n probs[0][i] = np.log(prob_matrix[0][i]+TINY)\\n \\n # {B, M, E, S} <=== 0:begin, 1:middle, 2:end, 3:single\\n for t in range(1, length):\\n # E, S -> B | B, M -> M | B, M -> E | E, S -> S\\n previous_of = [[0,0], [3,4], [1,2], [1,2], [3,4]]\\n for i in range(5):\\n prevs = previous_of[i]\\n max_id = prevs[np.argmax([probs[t-1][prevs[0]], probs[t-1][prevs[1]]])]\\n backpt[t][i] = max_id\\n probs[t][i] = np.log(prob_matrix[t][i]+TINY) + probs[t-1][max_id]\\n\\n seq = np.ones(length, 'int32') * -1\\n #print(probs[length-1])\\n seq[length-1] = np.argmax(probs[length-1])\\n #print(seq[length-1])\\n max_prob = probs[length-1][seq[length-1]]\\n for t in range(1, length):\\n seq[length-1-t] = backpt[length-t][seq[length-t]]\\n \\n return seq\",\n \"def complex(self, sentence):\\r\\n repetition = 6000\\r\\n warmup = 2500\\r\\n pos_mcmc_dict = {\\\"pos_\\\" + str(i): {} for i in range(len(sentence))}\\r\\n sequence = [\\\"noun\\\"] * len(sentence)\\r\\n for i in range(len(sentence)):\\r\\n if i == 0:\\r\\n prob_first = self.posterior_first(sentence[i])\\r\\n sample_first = list(\\r\\n np.random.choice(\\r\\n [keys for keys in prob_first.keys()],\\r\\n repetition,\\r\\n p=[\\r\\n float(prob_first[keys]) / sum(prob_first.values())\\r\\n for keys in prob_first.keys()\\r\\n ],\\r\\n )\\r\\n )\\r\\n sample_first = sample_first[warmup:]\\r\\n pos_mcmc_dict[\\\"pos_\\\" + str(i)] = {\\r\\n pos: (float(sample_first.count(pos)) / len(sample_first))\\r\\n for pos in self.position_list\\r\\n }\\r\\n sequence[i] = max(\\r\\n pos_mcmc_dict[\\\"pos_\\\" + str(i)],\\r\\n key=pos_mcmc_dict[\\\"pos_\\\" + str(i)].get,\\r\\n )\\r\\n elif i == 1:\\r\\n prob_second = self.post_second(sentence[i], sequence[i - 1])\\r\\n sample_second = list(\\r\\n np.random.choice(\\r\\n [keys for keys in prob_second.keys()],\\r\\n repetition,\\r\\n p=[\\r\\n float(prob_second[keys]) / sum(prob_second.values())\\r\\n for keys in prob_second.keys()\\r\\n ],\\r\\n )\\r\\n )\\r\\n sample_second = sample_second[warmup:]\\r\\n pos_mcmc_dict[\\\"pos_\\\" + str(i)] = {\\r\\n pos: (float(sample_second.count(pos)) / len(sample_second))\\r\\n for pos in self.position_list\\r\\n }\\r\\n sequence[i] = max(\\r\\n pos_mcmc_dict[\\\"pos_\\\" + str(i)],\\r\\n key=pos_mcmc_dict[\\\"pos_\\\" + str(i)].get,\\r\\n )\\r\\n else:\\r\\n prob_other = self.posterior_else(\\r\\n sentence[i], sequence[i - 1], sequence[i - 2]\\r\\n )\\r\\n sample_other = list(\\r\\n np.random.choice(\\r\\n [keys for keys in prob_other.keys()],\\r\\n repetition,\\r\\n p=[\\r\\n float(prob_other[keys]) / sum(prob_other.values())\\r\\n for keys in prob_other.keys()\\r\\n ],\\r\\n )\\r\\n )\\r\\n sample_other = sample_other[warmup:]\\r\\n pos_mcmc_dict[\\\"pos_\\\" + str(i)] = {\\r\\n pos: (float(sample_other.count(pos)) / len(sample_other))\\r\\n for pos in self.position_list\\r\\n }\\r\\n sequence[i] = max(\\r\\n pos_mcmc_dict[\\\"pos_\\\" + str(i)],\\r\\n key=pos_mcmc_dict[\\\"pos_\\\" + str(i)].get,\\r\\n )\\r\\n return sequence\",\n \"def make_fitness_function(cantus_firmus):\\n def fitness_function(genome, debug=False):\\n \\\"\\\"\\\"\\n Given a candidate solution will return its fitness score assuming\\n the cantus_firmus in this closure. Caches the fitness score in the\\n genome.\\n \\\"\\\"\\\"\\n cp = genome.chromosome\\n\\n # calculate some information for easier scoring\\n ScoringInfo = namedtuple(\\\"ScoringInfo\\\", \\\"pitch duration measure beat vi hi_next hi_prev voice_dir\\\")\\n melody_info = list()\\n beat = 4\\n measure = 0\\n for i in range(1, len(cp)):\\n hi_next = abs(cp[i][0] - cp[i + 1][0]) if i != len(cp) - 1 else -1 # next horizontal interval\\n hi_prev = abs(cp[i][0] - cp[i - 1][0]) if i != 1 else -1 # previous horizontal interval\\n vi = abs(cp[i][0] - cantus_firmus[measure]) # vertical interval\\n # voice movement direction\\n voice_dir = 0 if i == len(cp) - 1 or cp[i + 1][0] == cp[i][0] else copysign(1, cp[i + 1][0] - cp[i][0])\\n melody_info.append(ScoringInfo(cp[i][0], cp[i][1], measure, beat, vi, hi_next, hi_prev, voice_dir))\\n beat += cp[i][1]\\n measure += beat / 8\\n beat %= 8\\n\\n if debug:\\n print \\\"MELODY INFO: \\\", melody_info\\n\\n hscores = list()\\n vscores = list()\\n # hscore 1: 8th notes must move in step\\n amount_of_8th = 0\\n amount_of_missteps = 0\\n for note in melody_info:\\n if note.duration == 1:\\n amount_of_8th += 1\\n if note.hi_next > 1:\\n amount_of_missteps += 1\\n if note.hi_prev > 1:\\n amount_of_missteps += 1\\n hscores.append(float(amount_of_missteps) / (amount_of_8th * 2))\\n if debug:\\n print \\\"HSCORE 1: 8TH - \\\", amount_of_8th, \\\", MISSTEPS - \\\", amount_of_missteps\\n\\n # hscore 2: one climax, that can be repeated only after neighboring tone\\n # hscore 3: Climax should be on the strong beat\\n highest_note = max([note.pitch for note in melody_info])\\n climax_count = 0\\n climax_on_weak_beat_count = 0\\n for i, note in enumerate(melody_info):\\n if note.pitch == highest_note:\\n climax_count += 1\\n if note.beat not in [0, 4]:\\n climax_on_weak_beat_count += 1\\n if i < len(melody_info) - 2 and note.pitch == melody_info[i + 2].pitch: # If next note is\\n if note.hi_next == 1 and melody_info[i + 2].hi_prev == 1: # neighboring tone\\n if note.vi in CONSONANCES and melody_info[i + 2].vi in CONSONANCES: # And surrounding notes are consonant\\n climax_count -= 1 # we can allow 2nd climax\\n if melody_info[i + 2].beat not in [0, 4]:\\n climax_on_weak_beat_count -= 1 # And 2nd climax may be on weak beat\\n\\n hscores.append(float(climax_count - 1) / len(melody_info))\\n hscores.append(float(climax_on_weak_beat_count) / climax_count)\\n\\n if debug:\\n print \\\"HSCORE 2+3: CLIMAX CNT - \\\", climax_count, \\\", WEAK CLIMAX CNT - \\\", climax_on_weak_beat_count\\n\\n # hscore 4: Horizontal intervals are consonant\\n unconsonant_amount = len(filter(lambda x: x.hi_next not in CONSONANCES + [1], melody_info[:-1]))\\n hscores.append(float(unconsonant_amount) / (len(melody_info) - 1))\\n\\n if debug:\\n print \\\"HSCORE 4: UNCONSANANT AMOUNT - \\\", unconsonant_amount\\n\\n # hscore 5: Stepwise movement should predominate\\n leaps_count = len(filter(lambda x: x.hi_next != 1, melody_info[:-1]))\\n sections = round(float(len(cantus_firmus)) / 16)\\n if leaps_count < (2 * sections):\\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\\n elif leaps_count > (4 * sections):\\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\\n else:\\n hscores.append(0.0)\\n\\n if debug:\\n print \\\"HSCORE 5: LEAPS - \\\", leaps_count, \\\"SECTIONS - \\\", sections\\n\\n # hscore 6: After large leap - stepwise motion\\n large_leaps_count = 0\\n large_leaps_not_followed_count = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.hi_next >= 3:\\n large_leaps_count += 1\\n if melody_info[i + 1].hi_next != 1:\\n large_leaps_not_followed_count += 1\\n hscores.append(float(large_leaps_not_followed_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\\n\\n if debug:\\n print \\\"HSCORE 6: LL CNT - \\\", large_leaps_count, \\\"LL NOT FOLLOWED CNT - \\\", large_leaps_not_followed_count\\n\\n # hscore 7: change direction after each large leap\\n large_leaps_not_changedir_count = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.hi_next >= 3 and note.voice_dir != -melody_info[i + 1].voice_dir:\\n large_leaps_not_changedir_count += 1\\n hscores.append(float(large_leaps_not_changedir_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\\n\\n if debug:\\n print \\\"HSCORE 7: LL NOT CHNGDIR CNT - \\\", large_leaps_not_changedir_count\\n\\n # hscore 8: climax should be melodically consonant with tonic\\n hscores.append(1.0 if highest_note - 4 in CONSONANCES else 0.0)\\n\\n # hscore 9: no more than 2 consecutive leaps\\n conseq_leaps = 0\\n punish_score = 0\\n for note in melody_info:\\n conseq_leaps += 1\\n if note.hi_next in [0, 1]:\\n conseq_leaps = 0\\n if conseq_leaps > 3:\\n punish_score += 1\\n hscores.append(float(punish_score) / (len(melody_info) - 3))\\n\\n if debug:\\n print \\\"HSCORE 9: CONSEQ LEAPS PUNISH SCORE - \\\", punish_score\\n\\n # hscore 10: no more than 2 large leaps per section\\n if large_leaps_count > 2 * sections:\\n hscores.append(float(large_leaps_count - 2 * sections) / (len(melody_info) - 1 - 2 * sections))\\n else:\\n hscores.append(0.0)\\n\\n # hscore 11: not too long stepwise in same direction\\n longest_stepwise_seq = 0\\n current_stepwise_seq = 0\\n prev_dir = 0\\n num_changes = 0\\n motion_vector = list()\\n for note in melody_info:\\n if note.hi_next <= 1:\\n if note.voice_dir in [prev_dir, 0]:\\n current_stepwise_seq += 1\\n longest_stepwise_seq = max(longest_stepwise_seq, current_stepwise_seq)\\n else:\\n prev_dir = note.voice_dir\\n current_stepwise_seq = 0\\n num_changes += 1\\n motion_vector.append(note.pitch)\\n else:\\n if note.voice_dir != prev_dir and note.voice_dir != 0:\\n prev_dir = note.voice_dir\\n num_changes += 1\\n motion_vector.append(note.pitch)\\n current_stepwise_seq = 0\\n motion_vector.append(cp[-1][0])\\n if longest_stepwise_seq < 5:\\n longest_stepwise_seq = 0\\n hscores.append(float(longest_stepwise_seq) / len(cp))\\n\\n if debug:\\n print \\\"HSCORE 11: LONGEST STEPWISE SEQUENCE - \\\", longest_stepwise_seq\\n\\n # hscore 12: direction needs to change several times\\n if num_changes < 3 * sections:\\n hscores.append(1 - float(num_changes) / (3 * sections))\\n else:\\n hscores.append(0.0)\\n\\n # hscore 13: ending note is tonic\\n hscores.append(0)\\n\\n # hscore 14: penultimate note is leading tone\\n hscores.append(0)\\n\\n # hscore 15: the start of a motion is consonant with the end of a motion\\n unconsotant_count = 0\\n big_leaps_count = 0\\n for i in range(1, len(motion_vector) - 1):\\n if abs(motion_vector[i] - motion_vector[i + 1]) not in CONSONANCES:\\n unconsotant_count += 1\\n if abs(motion_vector[i] - motion_vector[i + 1]) > 6:\\n big_leaps_count += 1\\n hscores.append(float(unconsotant_count) / len(motion_vector))\\n\\n if debug:\\n print \\\"HSCORE 15: UNCONSONANT MOTIONS - \\\", unconsotant_count\\n\\n # hscore 16: Large motion intervals (>6 tones) should be avoided\\n hscores.append(float(big_leaps_count) / len(motion_vector))\\n\\n if debug:\\n print \\\"HSCORE 16: LARGE MOTIONS - \\\", big_leaps_count\\n\\n # hscore 17: No frequent repetition of the same note\\n rep_count = 0\\n for note in melody_info:\\n if note.hi_next == 0:\\n rep_count += 1\\n if rep_count > 2 * sections:\\n rep_count -= 2 * sections\\n else:\\n rep_count = 0\\n hscores.append(float(rep_count) / (len(cp) - 2 * sections))\\n\\n if debug:\\n print \\\"HSCORE 17: REPETITIONS COUNT - \\\", rep_count\\n\\n # hscore 18: no repetition of sequence within a 4 measure interval\\n repeated = set()\\n for i in range(len(melody_info) - 2):\\n j = i + 1\\n while melody_info[j].measure < melody_info[i].measure + 4 and j < len(melody_info) - 1:\\n if melody_info[i].pitch == melody_info[j].pitch:\\n k = 1\\n while j + k < len(melody_info) and melody_info[j + k].pitch == melody_info[i + k].pitch:\\n if k == 1:\\n repeated.add(j)\\n repeated.add(j + k)\\n k += 1\\n j += 1\\n\\n hscores.append(float(len(repeated)) / len(cp))\\n\\n if debug:\\n print \\\"HSCORE 18: REPEATED POSITIONS - \\\", repeated\\n\\n # hscore 19: largest allowed interval is octave\\n more_than_ocatave_amount = len(filter(lambda x: x.hi_next > 7, melody_info[:-1]))\\n hscores.append(float(more_than_ocatave_amount) / len(cp))\\n\\n if debug:\\n print \\\"HSCORE 19: MORE THAN OCTAVES - \\\", more_than_ocatave_amount\\n\\n # vscore 1: whole notes should be consonant (ensured by generation and hscore 13)\\n vscores.append(0.0)\\n\\n # vscores 2 and 3: halves and quarters should be consonant on first beat.\\n # or can be dissonant on other beats beat, if passing tone\\n amount_of_notes = 0\\n amount_of_wrong_notes = 0\\n for i, note in enumerate(melody_info):\\n if note.duration >= 2:\\n amount_of_notes += 1\\n if note.beat == 0 and note.vi not in CONSONANCES:\\n amount_of_wrong_notes += 1\\n if note.beat != 0 and note.vi not in CONSONANCES:\\n amount_of_wrong_notes += 1\\n if note.hi_prev == 1 and note.hi_next == 1 and note.voice_dir == melody_info[i - 1].voice_dir:\\n if melody_info[i - 1].vi in CONSONANCES and melody_info[i + 1].vi in CONSONANCES:\\n amount_of_wrong_notes -= 1\\n\\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\\n\\n if debug:\\n print \\\"VSCORE 2+3: NOTES > THAN 8TH - \\\", amount_of_notes, \\\",DISSONANT ONES - \\\", amount_of_wrong_notes\\n\\n # vscore 4: one of eight notes from pair should be consonant\\n amount_of_wrong_notes = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.duration == 1 and melody_info[i + 1].duration == 1:\\n if note.vi not in CONSONANCES and melody_info[i + 1].vi not in CONSONANCES:\\n amount_of_wrong_notes += 1\\n beat += cp[i][1]\\n vscores.append(float(amount_of_wrong_notes) / amount_of_8th)\\n\\n if debug:\\n print \\\"VSCORE 4: 8TH NOTES - \\\", amount_of_8th, \\\",DISSONANT ONES - \\\", amount_of_wrong_notes\\n\\n # vscore 5: unisons ok if on 1st beat through suspension or if tied over (ensured by storing format)\\n # else: if followed by step\\n\\n wrong_unsiones = len(filter(lambda x: x.vi == 0 and x.hi_next != 1, melody_info))\\n vscores.append(float(wrong_unsiones) / len(cp))\\n\\n if debug:\\n print \\\"VSCORE 5: WRONG UNISONES - \\\", wrong_unsiones\\n\\n # vscore 6: max allowed interval between voices is 10th, except for climax\\n big_vert_intervals = len(filter(lambda x: x.vi > 9 and x.pitch != highest_note, melody_info))\\n vscores.append(float(big_vert_intervals) / len(melody_info))\\n\\n if debug:\\n print \\\"VSCORE 6: VERT INTERVALS > 10TH - \\\", big_vert_intervals\\n\\n\\n # vscore 7: There should be no crossing (ensured by generation)\\n vscores.append(0.0)\\n\\n # vscore 8: avoid the overlapping of parts (ensured by generation)\\n vscores.append(0.0)\\n\\n # vscore 9: no leaps from unison to octave and vice versa\\n uni_to_oct_count = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.vi == 7 and melody_info[i + 1].vi == 0:\\n uni_to_oct_count += 1\\n if note.vi == 0 and melody_info[i + 1].vi == 7:\\n uni_to_oct_count += 1\\n\\n vscores.append(float(uni_to_oct_count) / len(melody_info))\\n\\n if debug:\\n print \\\"VSCORE 9: UNISON-OCTAVE LEAPS - \\\", uni_to_oct_count\\n\\n # vscore 10: The ending is unison or octave (ensured by generation)\\n vscores.append(0.0)\\n\\n # vscore 11: all perfect intervals (also perfect fourth) should be approached by contrary or oblique motion\\n bad_perfect_intervals_count = 0\\n battuda = 0\\n for i in range(1, len(melody_info)):\\n if melody_info[i].beat == 0 and melody_info[i].vi in PERFECT_INTERVALS:\\n bad_perfect_intervals_count += 1\\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\\n prev_cp = melody_info[i - 1].pitch\\n cur_cf = cantus_firmus[melody_info[i].measure]\\n cur_cp = melody_info[i].pitch\\n if prev_cp == cur_cp and prev_cf != cur_cf: # oblique\\n bad_perfect_intervals_count -= 1\\n if prev_cp > cur_cp and prev_cf <= cur_cf: # contrary\\n bad_perfect_intervals_count -= 1\\n if melody_info[i].vi in [4, 7]:\\n battuda += 1\\n if prev_cp < cur_cp and prev_cf >= cur_cf: # contrary\\n bad_perfect_intervals_count -= 1\\n if melody_info[i].vi in [4, 7]:\\n battuda += 1\\n\\n vscores.append(float(bad_perfect_intervals_count) / len(cantus_firmus))\\n\\n if debug:\\n print \\\"VSCORE 11: PERF INTERVALS APPROACHED BADLY - \\\", bad_perfect_intervals_count\\n\\n # vscore 12: avoid simultaneous leaps in cf and cp, especially large leaps in same direction\\n leaps_count = 0\\n large_leaps_count = 0\\n for i in range(len(melody_info) - 1):\\n if melody_info[i + 1].beat == 0:\\n leap_cp = melody_info[i + 1].pitch - melody_info[i].pitch\\n leap_cf = cantus_firmus[melody_info[i + 1].measure] - cantus_firmus[melody_info[i].measure]\\n if abs(leap_cf) > 1 and abs(leap_cp) > 1:\\n leaps_count += 1\\n if leap_cf > 6 and leap_cp > 6:\\n large_leaps_count += 1\\n if leap_cf < 6 and leap_cp < 6:\\n large_leaps_count += 1\\n vscores.append(float(leaps_count + large_leaps_count) / (len(cantus_firmus) * 2))\\n\\n if debug:\\n print \\\"VSCORE 12: SIM LEAPS - \\\", leaps_count, \\\", LARGE SIM LEAPS - \\\", large_leaps_count\\n\\n # vscore 13: use all types of motion\\n similar = 0\\n contrary = 0\\n oblique = 0\\n parallel = 0\\n for i in range(1, len(melody_info)):\\n if melody_info[i].beat == 0:\\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\\n prev_cp = melody_info[i - 1].pitch\\n cur_cf = cantus_firmus[melody_info[i].measure]\\n cur_cp = melody_info[i].pitch\\n if prev_cp == cur_cp:\\n if prev_cf != cur_cf:\\n oblique = 1\\n else:\\n similar = 1\\n if prev_cp > cur_cp:\\n if prev_cf <= cur_cf:\\n contrary = 1\\n else:\\n parallel = 1\\n if prev_cp < cur_cp:\\n if prev_cf >= cur_cf:\\n contrary = 1\\n else:\\n parallel = 1\\n types_of_motion = similar + oblique + contrary + parallel\\n vscores.append(1 - float(types_of_motion) / 4)\\n if debug:\\n print \\\"VSCORE 13: MOTION TYPES (SOCP) - \\\", similar, oblique, contrary, parallel\\n\\n # vscore 14: climax of the CF and CP should not coincide\\n cf_highest_note = max(cantus_firmus)\\n coincide = 0\\n for note in melody_info:\\n if note.pitch == highest_note and cantus_firmus[note.measure] == cf_highest_note:\\n coincide = 1\\n\\n vscores.append(coincide)\\n if debug:\\n print \\\"VSCORE 14: COINCIDE - \\\", coincide\\n\\n # vscore 15: Successive unisons, octaves and fifths on first beats are only valid\\n # when separated by three quarter notes.\\n bad_intervals_count = 0\\n\\n for i in range(len(melody_info) - 1):\\n if melody_info[i].beat == 0 and melody_info[i].measure != len(cantus_firmus) - 1:\\n if melody_info[i].vi in [0, 4, 7]:\\n separated = True\\n j = 1\\n while melody_info[i + j].measure == melody_info[i].measure:\\n if melody_info[i + j].duration > 2:\\n separated = False\\n j += 1\\n if melody_info[i + j].vi in [0, 4, 7] and not separated:\\n bad_intervals_count += 1\\n vscores.append(float(bad_intervals_count) / (len(cantus_firmus) - 1))\\n if debug:\\n print \\\"VSCORE 15: BAD INTERVALS - \\\", bad_intervals_count\\n\\n # vscore 16: successive unisons, octaves and fifths not on first beats:\\n # valid when separated by at least 2 notes, otherwise not.\\n # Unless it is a consonant suspension of quarter note: ok for afterbeat fifths and octaves\\n # separated only by a single quearter.\\n # ***what a complex rule, we don't care about consonant suspension due to storing format >_<***\\n bad_intervals_count = 0\\n\\n for i in range(len(melody_info) - 2):\\n separated = True\\n if melody_info[i].beat != 0:\\n if melody_info[i].vi in [0, 4, 7]:\\n if melody_info[i + 1].vi in [0, 4, 7]:\\n separated = False\\n if separated:\\n if melody_info[i + 2].vi in [0, 4, 7]:\\n separated = False\\n if not separated:\\n bad_intervals_count += 1\\n\\n vscores.append(float(bad_intervals_count) / (len(melody_info) - len(cantus_firmus)))\\n\\n if debug:\\n print \\\"VSCORE 16: BAD INTERVALS - \\\", bad_intervals_count\\n\\n # vscore 17: no ottava or quinta battuda, whatever it means\\n vscores.append(float(battuda) / len(cantus_firmus))\\n\\n if debug:\\n print \\\"VSCORE 17: BATTUDAS - \\\", battuda\\n\\n # vscore 18: best ending: dissonant suspension into the leading tone (ensured by generation)\\n vscores.append(0)\\n\\n # vscore 19: Thirds, sixths and tenths should predominate.\\n good_interval_count = len(filter(lambda x: x.vi in [2, 5, 9], melody_info))\\n if good_interval_count * 2 > len(melody_info):\\n vscores.append(0.0)\\n else:\\n vscores.append(1.0 - float(2 * good_interval_count) / len(melody_info))\\n\\n if debug:\\n print \\\"VSCORE 19: 3RDS 6THS 10THS - \\\", good_interval_count\\n\\n genome.fitness = sum([x * y for x, y in zip(hscores, HSCORE_WEIGHTS)]) + \\\\\\n sum([x * y for x, y in zip(vscores, VSCORE_WEIGHTS)])\\n if debug:\\n print \\\"HSCORES: \\\", hscores\\n print \\\"VSCORES: \\\", vscores\\n print \\\"FINAL SCORE: \\\", genome.fitness\\n print \\\"FINAL SCORE UNSCALED: \\\", sum(hscores) + sum(vscores)\\n return genome.fitness\\n\\n return fitness_function\",\n \"def get_likelihood(\\n self,\\n qb,\\n inv_fish,\\n map_tag=None,\\n null_first_cmb=False,\\n lmin=33,\\n lmax=250,\\n mcmc=True,\\n alpha_tags=[\\\"95\\\", \\\"150\\\"],\\n beam_tags=[\\\"95\\\", \\\"150\\\"],\\n r_prior=[0, np.inf],\\n alpha_prior=[0, np.inf],\\n res_prior=None,\\n beam_prior=[0, 1],\\n betad_prior=[0, 1],\\n dust_amp_prior=[0, np.inf],\\n dust_ellind_prior=[0, 1],\\n num_walkers=50,\\n num_steps=20000,\\n converge_criteria=0.01,\\n reset_backend=None,\\n file_tag=None,\\n ):\\n\\n for x in [\\n r_prior,\\n alpha_prior,\\n res_prior,\\n beam_prior,\\n betad_prior,\\n dust_amp_prior,\\n dust_ellind_prior,\\n ]:\\n if x is not None:\\n x[:] = [float(x[0]), float(x[1])]\\n\\n save_name = \\\"like_mcmc\\\"\\n if not mcmc:\\n alpha_prior = None\\n res_prior = None\\n beam_prior = None\\n betad_prior = None\\n dust_amp_prior = None\\n dust_ellind_prior = None\\n\\n # no template cleaning if there aren't any templates specified\\n if not getattr(self, \\\"template_cleaned\\\", False):\\n alpha_prior = None\\n\\n # null out unused priors\\n self.template_alpha = getattr(self, \\\"template_alpha\\\", None)\\n if self.template_alpha is None or all(\\n [x is None for x in self.template_alpha.values()]\\n ):\\n alpha_prior = None\\n\\n # count alpha parameters to fit\\n alpha_tags = [x for x in alpha_tags if x in self.map_tags_orig]\\n if not len(alpha_tags):\\n alpha_prior = None\\n\\n num_alpha = 0\\n if alpha_prior is not None:\\n num_alpha = len(alpha_tags)\\n\\n # count beam parameters to fit\\n beam_tags = [x for x in beam_tags if x in self.map_tags_orig]\\n if not len(beam_tags):\\n beam_prior = None\\n\\n num_beam = 0\\n if beam_prior is not None:\\n num_beam = len(beam_tags)\\n\\n if not any([k.startswith(\\\"res_\\\") for k in qb]):\\n res_prior = None\\n\\n if np.any(\\n [\\n betad_prior is not None,\\n dust_amp_prior is not None,\\n dust_ellind_prior is not None,\\n ]\\n ):\\n dust_ell_fit = True\\n else:\\n dust_ell_fit = False\\n\\n # bookkeeping: ordered priors\\n priors = {\\n \\\"r_prior\\\": r_prior,\\n \\\"alpha_prior\\\": alpha_prior,\\n \\\"res_prior\\\": res_prior,\\n \\\"beam_prior\\\": beam_prior,\\n \\\"betad_prior\\\": betad_prior,\\n \\\"dust_amp_prior\\\": dust_amp_prior,\\n \\\"dust_ellind_prior\\\": dust_ellind_prior,\\n }\\n # priors on quantities that affect Dmat_obs or gmat (precalculated)\\n obs_priors = [alpha_prior]\\n\\n # check parameter space\\n if all([x is None for x in priors.values()]):\\n raise RuntimeError(\\\"Empty parameter space\\\")\\n\\n out = dict(\\n r_prior=r_prior,\\n alpha_prior=alpha_prior,\\n res_prior=res_prior,\\n beam_prior=beam_prior,\\n betad_prior=betad_prior,\\n dust_amp_prior=dust_amp_prior,\\n dust_ellind_prior=dust_ellind_prior,\\n alpha_tags=alpha_tags,\\n num_walkers=num_walkers,\\n null_first_cmb=null_first_cmb,\\n apply_gcorr=self.apply_gcorr,\\n weighted_bins=self.weighted_bins,\\n lmin=lmin,\\n lmax=lmax,\\n )\\n\\n if mcmc and reset_backend is None:\\n ret = self.load_data(\\n save_name,\\n \\\"likelihood\\\",\\n bp_opts=True,\\n to_attrs=False,\\n map_tag=map_tag,\\n value_ref=out,\\n extra_tag=file_tag,\\n )\\n if ret is not None and ret.get(\\\"converged\\\", False):\\n if converge_criteria >= ret.get(\\\"converge_criteria\\\", 0.01):\\n return ret\\n if ret is not None:\\n for pname, pval in priors.items():\\n if np.all(pval != ret.get(pname, None)):\\n ret = None\\n # clear chain cache if rerunning, otherwise append to chain by default\\n reset_backend = ret is None\\n\\n out.update(converge_criteria=converge_criteria)\\n\\n # save state\\n if mcmc and reset_backend:\\n self.save_data(\\n save_name, map_tag=map_tag, extra_tag=file_tag, bp_opts=True, **out\\n )\\n\\n # clear pre-computed quantities\\n self.clear_precalc()\\n use_precalc = all([x is None for x in obs_priors])\\n\\n cls_input, cls_noise, cls_debias = self.get_data_spectra()\\n\\n # extract residual bins, ignoring bins outside of lmin/lmax\\n if res_prior is not None:\\n bin_def_orig = copy.deepcopy(self.bin_def)\\n nbins_res_orig = self.nbins_res\\n qb_res = OrderedDict()\\n num_res = 0\\n for k in list(qb):\\n if k.startswith(\\\"res_\\\"):\\n bd = self.bin_def[k]\\n good = np.where((bd[:, 1] > lmin) & (bd[:, 0] < lmax))[0]\\n # use all qb res in range lmin, lmax\\n self.bin_def[k] = bd[good]\\n v = qb.pop(k)[good]\\n num_res += len(v)\\n\\n # use average qb res in good range per map\\n # self.bin_def[k] = np.array([[lmin, lmax + 1]])\\n # v = np.array([(qb.pop(k)[good]).mean()])\\n # num_res += 1\\n qb_res[k] = v\\n self.nbins_res = num_res\\n\\n # set CMB model bandpowers to unity, since we are computing\\n # the likelihood of this model given the data\\n if r_prior is None:\\n self.log(\\\"Computing model spectrum\\\", \\\"debug\\\")\\n self.warn(\\\"Beam variation not implemented for case of no r fit\\\")\\n cbl = self.bin_cl_template(map_tag=map_tag)\\n cls_model = self.get_model_spectra(qb, cbl, delta=True, cls_noise=cls_noise)\\n else:\\n qb = copy.deepcopy(qb)\\n for spec in self.specs:\\n stags = [\\\"cmb_{}\\\".format(spec), \\\"fg_{}\\\".format(spec)]\\n for stag in stags:\\n if stag not in qb:\\n continue\\n qb[stag] = np.ones_like(qb[stag])\\n\\n self.log(\\\"Computing r model spectrum\\\", \\\"debug\\\")\\n cls_shape_scalar = self.get_signal_shape(\\n r=1.0, save=False, component=\\\"scalar\\\"\\n )\\n\\n cls_shape_tensor = self.get_signal_shape(\\n r=1.0, save=False, component=\\\"tensor\\\"\\n )\\n\\n # load tensor and scalar terms separately\\n cbl_scalar = self.bin_cl_template(cls_shape_scalar, map_tag)\\n cls_model_scalar = self.get_model_spectra(\\n qb, cbl_scalar, delta=True, cls_noise=cls_noise\\n )\\n cbl_tensor = self.bin_cl_template(cls_shape_tensor, map_tag)\\n cls_model_tensor = self.get_model_spectra(\\n qb, cbl_tensor, delta=False, res=False\\n )\\n if beam_prior is not None:\\n # load beam error term for tensor and scalar\\n cbl_scalar_beam = self.bin_cl_template(\\n cls_shape_scalar, map_tag, beam_error=True\\n )\\n cls_mod_scal_beam = self.get_model_spectra(\\n qb, cbl_scalar_beam, delta=True, res=False\\n )\\n cbl_tensor_beam = self.bin_cl_template(\\n cls_shape_tensor, map_tag, beam_error=True\\n )\\n cls_mod_tens_beam = self.get_model_spectra(\\n qb, cbl_tensor_beam, delta=False, res=False\\n )\\n\\n # load foreground shape\\n if dust_ell_fit:\\n cls_shape_dust = self.get_signal_shape(save=False, component=\\\"fg\\\")\\n # if dust_ellind_prior is None:\\n # # can preload shape since not varying ell index\\n cbl_fg = self.bin_cl_template(cls_shape_dust, map_tag=map_tag)\\n if beam_prior is not None:\\n cbl_fg_beam = self.bin_cl_template(\\n cls_shape_dust, map_tag, beam_error=True\\n )\\n\\n cbl = copy.deepcopy(cbl_scalar)\\n cls_model = copy.deepcopy(cls_model_scalar)\\n\\n # XXX TODO\\n # how to marginalize over the garbage bin?\\n\\n def parse_params(theta):\\n \\\"\\\"\\\"\\n Parse array of parameters into a dict\\n \\\"\\\"\\\"\\n params = {}\\n if r_prior is not None:\\n params[\\\"r\\\"] = theta[0]\\n theta = theta[1:]\\n if alpha_prior is not None:\\n params[\\\"alpha\\\"] = theta[:num_alpha]\\n theta = theta[num_alpha:]\\n if res_prior is not None:\\n params[\\\"res\\\"] = theta[:num_res]\\n theta = theta[num_res:]\\n if beam_prior is not None:\\n params[\\\"beam\\\"] = theta[:num_beam]\\n theta = theta[num_beam:]\\n if betad_prior is not None:\\n params[\\\"betad\\\"] = theta[0]\\n theta = theta[1:]\\n if dust_amp_prior is not None:\\n # param for ee and bb\\n params[\\\"dust_amp\\\"] = theta[:2]\\n theta = theta[2:]\\n if dust_ellind_prior is not None:\\n params[\\\"dust_ellind\\\"] = theta[0]\\n theta = theta[1:]\\n if len(theta):\\n raise ValueError(\\\"Too many parameters to parse\\\")\\n return params\\n\\n def log_prior(\\n r=None,\\n alpha=None,\\n res=None,\\n beam=None,\\n betad=None,\\n dust_amp=None,\\n dust_ellind=None,\\n ):\\n \\\"\\\"\\\"\\n Log prior function constructed from input options\\n \\\"\\\"\\\"\\n values = {\\n \\\"r_prior\\\": r,\\n \\\"alpha_prior\\\": alpha,\\n \\\"res_prior\\\": res,\\n \\\"dust_amp_prior\\\": dust_amp,\\n }\\n for v, pval in values.items():\\n prior = priors[v]\\n if pval is not None and prior is not None:\\n if np.any(pval < prior[0]) or np.any(pval > prior[1]):\\n return -np.inf\\n\\n values_gauss = {\\n \\\"beam_prior\\\": beam,\\n \\\"betad_prior\\\": betad,\\n \\\"dust_ellind_prior\\\": dust_ellind,\\n }\\n # for beam and betad, use gaussian prior\\n log_prob = 0.0\\n for v, pval in values_gauss.items():\\n prior = priors[v]\\n if pval is not None and prior is not None:\\n pval = np.atleast_1d(pval)\\n norm = np.log(1.0 / (prior[1] * np.sqrt(2 * np.pi)))\\n chi = (pval - prior[0]) / prior[1]\\n log_prob += np.sum(norm - chi ** 2 / 2.0)\\n\\n return log_prob\\n\\n def log_like(\\n r=None,\\n alpha=None,\\n res=None,\\n beam=None,\\n betad=None,\\n dust_amp=None,\\n dust_ellind=None,\\n ):\\n \\\"\\\"\\\"\\n Log likelihood function constructed from input options\\n \\\"\\\"\\\"\\n cls_model0 = copy.deepcopy(cls_model)\\n\\n # compute new template subtracted data spectra\\n if alpha is None:\\n clsi = cls_input\\n else:\\n self.get_masked_data(template_alpha=OrderedDict(zip(alpha_tags, alpha)))\\n clsi = self.get_data_spectra(do_noise=False)\\n\\n if beam is not None:\\n beam = dict(zip(beam_tags, beam))\\n beam_coeffs = dict()\\n for xname, (m0, m1) in self.map_pairs_orig.items():\\n d = {}\\n b0, b1 = [beam.get(m, None) for m in (m0, m1)]\\n if b0 is not None:\\n d[\\\"b1\\\"] = b0\\n if b1 is not None:\\n d[\\\"b2\\\"] = b1\\n if b0 is not None:\\n d[\\\"b3\\\"] = b0 * b1\\n beam_coeffs[xname] = d\\n\\n # compute new signal shape by scaling tensor component by r\\n if r is not None:\\n for stag, d in cls_model0.items():\\n comp, spec = stag.split(\\\"_\\\", 1)\\n if spec not in [\\\"ee\\\", \\\"bb\\\"] or comp not in [\\\"cmb\\\", \\\"total\\\"]:\\n continue\\n ctag = \\\"cmb_{}\\\".format(spec)\\n for xname, dd in d.items():\\n dd[:] = (\\n cls_model_scalar[stag][xname]\\n + r * cls_model_tensor[ctag][xname]\\n )\\n\\n if beam is None:\\n continue\\n beam_term = 0\\n for bn, bc in beam_coeffs[xname].items():\\n beam_term += bc * (\\n cls_mod_scal_beam[ctag][xname][bn]\\n + r * cls_mod_tens_beam[ctag][xname][bn]\\n )\\n dd[:] += beam_term\\n\\n elif beam is not None:\\n for stag, d in cls_model0.items():\\n comp, spec = stag.split(\\\"_\\\", 1)\\n if spec not in [\\\"ee\\\", \\\"bb\\\"] or comp not in [\\\"cmb\\\", \\\"total\\\"]:\\n continue\\n ctag = \\\"cmb_{}\\\".format(spec)\\n for xname, dd in d.items():\\n beam_term = 0\\n for bn, bc in beam_coeffs[xname].items():\\n beam_term += bc * cls_mod_scal_beam[ctag][xname][bn]\\n dd[:] = cls_model_scalar[stag][xname] + beam_term\\n\\n # fg term, including beam modifications. Because mix terms are\\n # dependent on dust amp, get model specs here.\\n if dust_ell_fit:\\n if dust_amp is None:\\n qb[\\\"fg_ee\\\"][:] = 1\\n qb[\\\"fg_bb\\\"][:] = 1\\n else:\\n qb[\\\"fg_ee\\\"][:] = dust_amp[0]\\n qb[\\\"fg_bb\\\"][:] = dust_amp[1]\\n if betad is None:\\n qb[\\\"delta_beta\\\"][:] = 0\\n else:\\n qb[\\\"delta_beta\\\"][:] = betad\\n if dust_ellind is not None:\\n cbl_fg0 = self.bin_cl_template(\\n cls_shape_dust, map_tag=map_tag, fg_ell_ind=dust_ellind\\n )\\n if beam is not None:\\n cbl_fg_beam0 = self.bin_cl_template(\\n cls_shape_dust,\\n map_tag,\\n fg_ell_ind=dust_ellind,\\n beam_error=True,\\n )\\n else:\\n cbl_fg0 = cbl_fg\\n if beam is not None:\\n cbl_fg_beam0 = cbl_fg_beam\\n\\n cls_model_fg = self.get_model_spectra(\\n qb, cbl_fg0, delta=True, res=False\\n )\\n if beam is not None:\\n cls_mod_fg_beam = self.get_model_spectra(\\n qb, cbl_fg_beam0, delta=True, res=False\\n )\\n # add fg field to model, and add fg to total model\\n for stag, d in cls_model_fg.items():\\n comp, spec = stag.split(\\\"_\\\", 1)\\n if spec not in [\\\"ee\\\", \\\"bb\\\"] or comp not in [\\\"fg\\\", \\\"total\\\"]:\\n continue\\n ftag = \\\"fg_{}\\\".format(spec)\\n if stag not in cls_model0:\\n cls_model0[stag] = OrderedDict()\\n for xname, dd in d.items():\\n if xname not in cls_model0[stag]:\\n cls_model0[stag][xname] = cls_model_fg[ftag][xname]\\n else:\\n cls_model0[stag][xname] += cls_model_fg[ftag][xname]\\n\\n # add beam terms to fg and total fields\\n if beam is not None:\\n beam_term = 0\\n for bn, bc in beam_coeffs[xname].items():\\n beam_term += bc * cls_mod_fg_beam[ftag][xname][bn]\\n cls_model0[stag][xname] += beam_term\\n\\n # compute noise model terms\\n if res is None:\\n clsm = cls_model0\\n else:\\n res = pt.arr_to_dict(res, qb_res)\\n clsm = copy.deepcopy(cls_model0)\\n cls_res = self.get_model_spectra(res, cbl)\\n for stag, d in cls_res.items():\\n if stag not in clsm:\\n clsm[stag] = OrderedDict()\\n for xname, dd in d.items():\\n if xname not in clsm[stag]:\\n clsm[stag][xname] = dd\\n else:\\n clsm[stag][xname] += dd\\n\\n # compute likelihood\\n like = self.fisher_calc(\\n qb,\\n cbl,\\n clsi,\\n cls_noise=cls_noise,\\n cls_debias=cls_debias,\\n cls_model=clsm,\\n null_first_cmb=null_first_cmb,\\n likelihood=True,\\n use_precalc=use_precalc,\\n like_lmin=lmin,\\n like_lmax=lmax,\\n )\\n return like\\n\\n def log_prob(theta):\\n \\\"\\\"\\\"\\n Log posterior probability from prior and likelihood\\n\\n Returns log_prior with each step\\n \\\"\\\"\\\"\\n params = parse_params(theta)\\n prior = log_prior(**params)\\n if not np.isfinite(prior):\\n return -np.inf, -np.inf\\n like = log_like(**params)\\n if not np.isfinite(like):\\n return -np.inf, prior\\n return prior + like, prior\\n\\n # initial values\\n x0 = []\\n brute_force = True if not mcmc else False # only vary r\\n if r_prior is not None:\\n x0 += [0.01]\\n if alpha_prior is not None:\\n alphas = [self.template_alpha[tag] for tag in alpha_tags]\\n x0 += [0.01 if a == 0 else a for a in alphas]\\n brute_force = False\\n if res_prior is not None:\\n x0 += list(pt.dict_to_arr(qb_res, flatten=True))\\n brute_force = False\\n if beam_prior is not None:\\n # add a beam term for each frequency\\n x0 += [0.01] * len(beam_tags)\\n brute_force = False\\n if betad_prior is not None:\\n x0 += [0.01]\\n brute_force = False\\n if dust_amp_prior is not None:\\n x0 += [1, 1]\\n brute_force = False\\n if dust_ellind_prior is not None:\\n x0 += [0.01]\\n brute_force = False\\n\\n ndim = len(x0)\\n if ndim * 2 > num_walkers:\\n num_walkers = int(np.round(ndim / float(num_walkers)) * num_walkers * 2)\\n self.warn(\\n \\\"Found {} parameters, increasing number of MCMC walkers to {}\\\".format(\\n ndim, num_walkers\\n )\\n )\\n x0 = np.array(x0)[None, :] * (1 + 1e-4 * np.random.randn(num_walkers, len(x0)))\\n\\n if brute_force or (r_prior is not None and ndim == 1):\\n self.log(\\\"Computing brute-force r profile likelihood\\\", \\\"info\\\")\\n likefile = self.get_filename(\\n save_name, ext=\\\".txt\\\", map_tag=map_tag, extra_tag=file_tag, bp_opts=True\\n )\\n rs = np.linspace(0, 3, 500)\\n likes = np.zeros_like(rs)\\n for idx, r in enumerate(rs):\\n like = log_like(r=r)\\n if idx % 20 == 0:\\n self.log(\\\"r = {:.3f}, loglike = {:.2f}\\\".format(r, like), \\\"debug\\\")\\n likes[idx] = like\\n header = \\\"{} r likelihood\\\\nColumns: r, loglike\\\".format(\\n \\\"Multi-map\\\" if map_tag is None else \\\"Map {}\\\".format(map_tag)\\n )\\n np.savetxt(likefile, np.column_stack((rs, likes)), header=header)\\n\\n if not mcmc:\\n return [rs, likes]\\n\\n # run chains!\\n import emcee\\n\\n # setup sampler output file\\n filename = self.get_filename(\\n save_name, ext=\\\".h5\\\", map_tag=map_tag, extra_tag=file_tag, bp_opts=True\\n )\\n backend_exists = os.path.exists(filename)\\n backend = emcee.backends.HDFBackend(filename)\\n if backend_exists and backend.shape != (num_walkers, ndim):\\n self.warn(\\n \\\"Expected backend of shape ({}, {}), found {}. Resetting\\\".format(\\n num_walkers, ndim, backend.shape\\n )\\n )\\n reset_backend = True\\n if reset_backend:\\n backend.reset(num_walkers, ndim)\\n\\n # initialize sampler\\n self.log(\\\"Initializing sampler\\\", \\\"info\\\")\\n sampler = emcee.EnsembleSampler(num_walkers, ndim, log_prob, backend=backend)\\n if not reset_backend and backend_exists:\\n # grab the last sample if appending to an existing run\\n x0 = sampler.run_mcmc(None, 1)\\n\\n # track autocorrelation time\\n old_tau = np.inf\\n converged = False\\n\\n self.log(\\n \\\"Starting {} iterations with {} parameters\\\".format(num_steps, ndim), \\\"info\\\"\\n )\\n for sample in sampler.sample(x0, iterations=num_steps):\\n if not sampler.iteration % 10:\\n self.log(\\\"MCMC iteration {}\\\".format(sampler.iteration), \\\"debug\\\")\\n # check convergence every 100 steps\\n if sampler.iteration % 100:\\n continue\\n\\n # compute autocorrelation time\\n tau = sampler.get_autocorr_time(tol=0)\\n\\n # check convergence\\n converged = np.all(tau / converge_criteria < sampler.iteration)\\n converged &= np.all(np.abs(old_tau - tau) / tau < converge_criteria)\\n self.log(\\n \\\"MCMC iteration {} autocorr time: mean {:.1f} min {:.1f} max {:.1f}\\\".format(\\n sampler.iteration, np.mean(tau), np.min(tau), np.max(tau)\\n ),\\n \\\"info\\\",\\n )\\n if converged:\\n break\\n old_tau = tau\\n\\n out.update(converged=converged, num_steps=sampler.iteration)\\n\\n # converged posterior distribution\\n if converged:\\n self.log(\\n \\\"MCMC converged in {} iterations\\\".format(sampler.iteration), \\\"info\\\"\\n )\\n tau = sampler.get_autocorr_time()\\n burnin = int(2 * np.max(tau))\\n thin = int(0.5 * np.min(tau))\\n samples = sampler.get_chain(discard=burnin, thin=thin, flat=True)\\n out.update(tau=tau, burnin=burnin, thin=thin, samples=samples)\\n else:\\n self.warn(\\\"MCMC not converged in {} iterations\\\".format(num_steps))\\n\\n if res_prior is not None:\\n self.bin_def = bin_def_orig\\n self.nbins_res = nbins_res_orig\\n\\n # save and return\\n return self.save_data(\\n save_name, map_tag=map_tag, extra_tag=file_tag, bp_opts=True, **out\\n )\",\n \"def __logprob__(self, cv, vsense):\\n return 1.0 / (1.0 + np.exp(-np.dot(cv, vsense)))\",\n \"def generate_log(nexp, clpeaks, timepeaks, remap, gap=0):\\n # Select the index of the experiment\\n peakini = 0\\n i = 0\\n while i < nexp:\\n exp = timepeaks[i]\\n peakini += exp.shape[0]\\n i += 1\\n\\n exp = timepeaks[nexp]\\n peakend = peakini + exp.shape[0]\\n\\n # Build the sequence string\\n peakstr = []\\n peakset = []\\n\\n for i in range(peakini, peakend):\\n peakset.append(voc[remap[clpeaks[i][0] - 1]])\\n if i < peakend - 1 and gap != 0:\\n if (timepeaks[nexp][i - peakini + 1] - timepeaks[nexp][i - peakini]) > gap:\\n peakstr.append(peakset)\\n peakset = []\\n\\n return peakstr\",\n \"def log_likelihood(self, state, obs, act):\\n indices = np.array([self.Gittins[state['successes'][i], state['failures'][i]] for i in range(self.env.n_arms)])\\n greedy_arms = np.where(np.isclose(indices,indices.max()))[0]\\n return np.log(1/len(greedy_arms)) if act in greedy_arms else -1e8\",\n \"def fitness_function(genome, debug=False):\\n cp = genome.chromosome\\n\\n # calculate some information for easier scoring\\n ScoringInfo = namedtuple(\\\"ScoringInfo\\\", \\\"pitch duration measure beat vi hi_next hi_prev voice_dir\\\")\\n melody_info = list()\\n beat = 4\\n measure = 0\\n for i in range(1, len(cp)):\\n hi_next = abs(cp[i][0] - cp[i + 1][0]) if i != len(cp) - 1 else -1 # next horizontal interval\\n hi_prev = abs(cp[i][0] - cp[i - 1][0]) if i != 1 else -1 # previous horizontal interval\\n vi = abs(cp[i][0] - cantus_firmus[measure]) # vertical interval\\n # voice movement direction\\n voice_dir = 0 if i == len(cp) - 1 or cp[i + 1][0] == cp[i][0] else copysign(1, cp[i + 1][0] - cp[i][0])\\n melody_info.append(ScoringInfo(cp[i][0], cp[i][1], measure, beat, vi, hi_next, hi_prev, voice_dir))\\n beat += cp[i][1]\\n measure += beat / 8\\n beat %= 8\\n\\n if debug:\\n print \\\"MELODY INFO: \\\", melody_info\\n\\n hscores = list()\\n vscores = list()\\n # hscore 1: 8th notes must move in step\\n amount_of_8th = 0\\n amount_of_missteps = 0\\n for note in melody_info:\\n if note.duration == 1:\\n amount_of_8th += 1\\n if note.hi_next > 1:\\n amount_of_missteps += 1\\n if note.hi_prev > 1:\\n amount_of_missteps += 1\\n hscores.append(float(amount_of_missteps) / (amount_of_8th * 2))\\n if debug:\\n print \\\"HSCORE 1: 8TH - \\\", amount_of_8th, \\\", MISSTEPS - \\\", amount_of_missteps\\n\\n # hscore 2: one climax, that can be repeated only after neighboring tone\\n # hscore 3: Climax should be on the strong beat\\n highest_note = max([note.pitch for note in melody_info])\\n climax_count = 0\\n climax_on_weak_beat_count = 0\\n for i, note in enumerate(melody_info):\\n if note.pitch == highest_note:\\n climax_count += 1\\n if note.beat not in [0, 4]:\\n climax_on_weak_beat_count += 1\\n if i < len(melody_info) - 2 and note.pitch == melody_info[i + 2].pitch: # If next note is\\n if note.hi_next == 1 and melody_info[i + 2].hi_prev == 1: # neighboring tone\\n if note.vi in CONSONANCES and melody_info[i + 2].vi in CONSONANCES: # And surrounding notes are consonant\\n climax_count -= 1 # we can allow 2nd climax\\n if melody_info[i + 2].beat not in [0, 4]:\\n climax_on_weak_beat_count -= 1 # And 2nd climax may be on weak beat\\n\\n hscores.append(float(climax_count - 1) / len(melody_info))\\n hscores.append(float(climax_on_weak_beat_count) / climax_count)\\n\\n if debug:\\n print \\\"HSCORE 2+3: CLIMAX CNT - \\\", climax_count, \\\", WEAK CLIMAX CNT - \\\", climax_on_weak_beat_count\\n\\n # hscore 4: Horizontal intervals are consonant\\n unconsonant_amount = len(filter(lambda x: x.hi_next not in CONSONANCES + [1], melody_info[:-1]))\\n hscores.append(float(unconsonant_amount) / (len(melody_info) - 1))\\n\\n if debug:\\n print \\\"HSCORE 4: UNCONSANANT AMOUNT - \\\", unconsonant_amount\\n\\n # hscore 5: Stepwise movement should predominate\\n leaps_count = len(filter(lambda x: x.hi_next != 1, melody_info[:-1]))\\n sections = round(float(len(cantus_firmus)) / 16)\\n if leaps_count < (2 * sections):\\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\\n elif leaps_count > (4 * sections):\\n hscores.append(float(leaps_count) / (len(melody_info) - 1 - 4 * sections))\\n else:\\n hscores.append(0.0)\\n\\n if debug:\\n print \\\"HSCORE 5: LEAPS - \\\", leaps_count, \\\"SECTIONS - \\\", sections\\n\\n # hscore 6: After large leap - stepwise motion\\n large_leaps_count = 0\\n large_leaps_not_followed_count = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.hi_next >= 3:\\n large_leaps_count += 1\\n if melody_info[i + 1].hi_next != 1:\\n large_leaps_not_followed_count += 1\\n hscores.append(float(large_leaps_not_followed_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\\n\\n if debug:\\n print \\\"HSCORE 6: LL CNT - \\\", large_leaps_count, \\\"LL NOT FOLLOWED CNT - \\\", large_leaps_not_followed_count\\n\\n # hscore 7: change direction after each large leap\\n large_leaps_not_changedir_count = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.hi_next >= 3 and note.voice_dir != -melody_info[i + 1].voice_dir:\\n large_leaps_not_changedir_count += 1\\n hscores.append(float(large_leaps_not_changedir_count) / large_leaps_count if large_leaps_count != 0 else 0.0)\\n\\n if debug:\\n print \\\"HSCORE 7: LL NOT CHNGDIR CNT - \\\", large_leaps_not_changedir_count\\n\\n # hscore 8: climax should be melodically consonant with tonic\\n hscores.append(1.0 if highest_note - 4 in CONSONANCES else 0.0)\\n\\n # hscore 9: no more than 2 consecutive leaps\\n conseq_leaps = 0\\n punish_score = 0\\n for note in melody_info:\\n conseq_leaps += 1\\n if note.hi_next in [0, 1]:\\n conseq_leaps = 0\\n if conseq_leaps > 3:\\n punish_score += 1\\n hscores.append(float(punish_score) / (len(melody_info) - 3))\\n\\n if debug:\\n print \\\"HSCORE 9: CONSEQ LEAPS PUNISH SCORE - \\\", punish_score\\n\\n # hscore 10: no more than 2 large leaps per section\\n if large_leaps_count > 2 * sections:\\n hscores.append(float(large_leaps_count - 2 * sections) / (len(melody_info) - 1 - 2 * sections))\\n else:\\n hscores.append(0.0)\\n\\n # hscore 11: not too long stepwise in same direction\\n longest_stepwise_seq = 0\\n current_stepwise_seq = 0\\n prev_dir = 0\\n num_changes = 0\\n motion_vector = list()\\n for note in melody_info:\\n if note.hi_next <= 1:\\n if note.voice_dir in [prev_dir, 0]:\\n current_stepwise_seq += 1\\n longest_stepwise_seq = max(longest_stepwise_seq, current_stepwise_seq)\\n else:\\n prev_dir = note.voice_dir\\n current_stepwise_seq = 0\\n num_changes += 1\\n motion_vector.append(note.pitch)\\n else:\\n if note.voice_dir != prev_dir and note.voice_dir != 0:\\n prev_dir = note.voice_dir\\n num_changes += 1\\n motion_vector.append(note.pitch)\\n current_stepwise_seq = 0\\n motion_vector.append(cp[-1][0])\\n if longest_stepwise_seq < 5:\\n longest_stepwise_seq = 0\\n hscores.append(float(longest_stepwise_seq) / len(cp))\\n\\n if debug:\\n print \\\"HSCORE 11: LONGEST STEPWISE SEQUENCE - \\\", longest_stepwise_seq\\n\\n # hscore 12: direction needs to change several times\\n if num_changes < 3 * sections:\\n hscores.append(1 - float(num_changes) / (3 * sections))\\n else:\\n hscores.append(0.0)\\n\\n # hscore 13: ending note is tonic\\n hscores.append(0)\\n\\n # hscore 14: penultimate note is leading tone\\n hscores.append(0)\\n\\n # hscore 15: the start of a motion is consonant with the end of a motion\\n unconsotant_count = 0\\n big_leaps_count = 0\\n for i in range(1, len(motion_vector) - 1):\\n if abs(motion_vector[i] - motion_vector[i + 1]) not in CONSONANCES:\\n unconsotant_count += 1\\n if abs(motion_vector[i] - motion_vector[i + 1]) > 6:\\n big_leaps_count += 1\\n hscores.append(float(unconsotant_count) / len(motion_vector))\\n\\n if debug:\\n print \\\"HSCORE 15: UNCONSONANT MOTIONS - \\\", unconsotant_count\\n\\n # hscore 16: Large motion intervals (>6 tones) should be avoided\\n hscores.append(float(big_leaps_count) / len(motion_vector))\\n\\n if debug:\\n print \\\"HSCORE 16: LARGE MOTIONS - \\\", big_leaps_count\\n\\n # hscore 17: No frequent repetition of the same note\\n rep_count = 0\\n for note in melody_info:\\n if note.hi_next == 0:\\n rep_count += 1\\n if rep_count > 2 * sections:\\n rep_count -= 2 * sections\\n else:\\n rep_count = 0\\n hscores.append(float(rep_count) / (len(cp) - 2 * sections))\\n\\n if debug:\\n print \\\"HSCORE 17: REPETITIONS COUNT - \\\", rep_count\\n\\n # hscore 18: no repetition of sequence within a 4 measure interval\\n repeated = set()\\n for i in range(len(melody_info) - 2):\\n j = i + 1\\n while melody_info[j].measure < melody_info[i].measure + 4 and j < len(melody_info) - 1:\\n if melody_info[i].pitch == melody_info[j].pitch:\\n k = 1\\n while j + k < len(melody_info) and melody_info[j + k].pitch == melody_info[i + k].pitch:\\n if k == 1:\\n repeated.add(j)\\n repeated.add(j + k)\\n k += 1\\n j += 1\\n\\n hscores.append(float(len(repeated)) / len(cp))\\n\\n if debug:\\n print \\\"HSCORE 18: REPEATED POSITIONS - \\\", repeated\\n\\n # hscore 19: largest allowed interval is octave\\n more_than_ocatave_amount = len(filter(lambda x: x.hi_next > 7, melody_info[:-1]))\\n hscores.append(float(more_than_ocatave_amount) / len(cp))\\n\\n if debug:\\n print \\\"HSCORE 19: MORE THAN OCTAVES - \\\", more_than_ocatave_amount\\n\\n # vscore 1: whole notes should be consonant (ensured by generation and hscore 13)\\n vscores.append(0.0)\\n\\n # vscores 2 and 3: halves and quarters should be consonant on first beat.\\n # or can be dissonant on other beats beat, if passing tone\\n amount_of_notes = 0\\n amount_of_wrong_notes = 0\\n for i, note in enumerate(melody_info):\\n if note.duration >= 2:\\n amount_of_notes += 1\\n if note.beat == 0 and note.vi not in CONSONANCES:\\n amount_of_wrong_notes += 1\\n if note.beat != 0 and note.vi not in CONSONANCES:\\n amount_of_wrong_notes += 1\\n if note.hi_prev == 1 and note.hi_next == 1 and note.voice_dir == melody_info[i - 1].voice_dir:\\n if melody_info[i - 1].vi in CONSONANCES and melody_info[i + 1].vi in CONSONANCES:\\n amount_of_wrong_notes -= 1\\n\\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\\n vscores.append(float(amount_of_wrong_notes) / amount_of_notes)\\n\\n if debug:\\n print \\\"VSCORE 2+3: NOTES > THAN 8TH - \\\", amount_of_notes, \\\",DISSONANT ONES - \\\", amount_of_wrong_notes\\n\\n # vscore 4: one of eight notes from pair should be consonant\\n amount_of_wrong_notes = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.duration == 1 and melody_info[i + 1].duration == 1:\\n if note.vi not in CONSONANCES and melody_info[i + 1].vi not in CONSONANCES:\\n amount_of_wrong_notes += 1\\n beat += cp[i][1]\\n vscores.append(float(amount_of_wrong_notes) / amount_of_8th)\\n\\n if debug:\\n print \\\"VSCORE 4: 8TH NOTES - \\\", amount_of_8th, \\\",DISSONANT ONES - \\\", amount_of_wrong_notes\\n\\n # vscore 5: unisons ok if on 1st beat through suspension or if tied over (ensured by storing format)\\n # else: if followed by step\\n\\n wrong_unsiones = len(filter(lambda x: x.vi == 0 and x.hi_next != 1, melody_info))\\n vscores.append(float(wrong_unsiones) / len(cp))\\n\\n if debug:\\n print \\\"VSCORE 5: WRONG UNISONES - \\\", wrong_unsiones\\n\\n # vscore 6: max allowed interval between voices is 10th, except for climax\\n big_vert_intervals = len(filter(lambda x: x.vi > 9 and x.pitch != highest_note, melody_info))\\n vscores.append(float(big_vert_intervals) / len(melody_info))\\n\\n if debug:\\n print \\\"VSCORE 6: VERT INTERVALS > 10TH - \\\", big_vert_intervals\\n\\n\\n # vscore 7: There should be no crossing (ensured by generation)\\n vscores.append(0.0)\\n\\n # vscore 8: avoid the overlapping of parts (ensured by generation)\\n vscores.append(0.0)\\n\\n # vscore 9: no leaps from unison to octave and vice versa\\n uni_to_oct_count = 0\\n for i, note in enumerate(melody_info[:-1]):\\n if note.vi == 7 and melody_info[i + 1].vi == 0:\\n uni_to_oct_count += 1\\n if note.vi == 0 and melody_info[i + 1].vi == 7:\\n uni_to_oct_count += 1\\n\\n vscores.append(float(uni_to_oct_count) / len(melody_info))\\n\\n if debug:\\n print \\\"VSCORE 9: UNISON-OCTAVE LEAPS - \\\", uni_to_oct_count\\n\\n # vscore 10: The ending is unison or octave (ensured by generation)\\n vscores.append(0.0)\\n\\n # vscore 11: all perfect intervals (also perfect fourth) should be approached by contrary or oblique motion\\n bad_perfect_intervals_count = 0\\n battuda = 0\\n for i in range(1, len(melody_info)):\\n if melody_info[i].beat == 0 and melody_info[i].vi in PERFECT_INTERVALS:\\n bad_perfect_intervals_count += 1\\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\\n prev_cp = melody_info[i - 1].pitch\\n cur_cf = cantus_firmus[melody_info[i].measure]\\n cur_cp = melody_info[i].pitch\\n if prev_cp == cur_cp and prev_cf != cur_cf: # oblique\\n bad_perfect_intervals_count -= 1\\n if prev_cp > cur_cp and prev_cf <= cur_cf: # contrary\\n bad_perfect_intervals_count -= 1\\n if melody_info[i].vi in [4, 7]:\\n battuda += 1\\n if prev_cp < cur_cp and prev_cf >= cur_cf: # contrary\\n bad_perfect_intervals_count -= 1\\n if melody_info[i].vi in [4, 7]:\\n battuda += 1\\n\\n vscores.append(float(bad_perfect_intervals_count) / len(cantus_firmus))\\n\\n if debug:\\n print \\\"VSCORE 11: PERF INTERVALS APPROACHED BADLY - \\\", bad_perfect_intervals_count\\n\\n # vscore 12: avoid simultaneous leaps in cf and cp, especially large leaps in same direction\\n leaps_count = 0\\n large_leaps_count = 0\\n for i in range(len(melody_info) - 1):\\n if melody_info[i + 1].beat == 0:\\n leap_cp = melody_info[i + 1].pitch - melody_info[i].pitch\\n leap_cf = cantus_firmus[melody_info[i + 1].measure] - cantus_firmus[melody_info[i].measure]\\n if abs(leap_cf) > 1 and abs(leap_cp) > 1:\\n leaps_count += 1\\n if leap_cf > 6 and leap_cp > 6:\\n large_leaps_count += 1\\n if leap_cf < 6 and leap_cp < 6:\\n large_leaps_count += 1\\n vscores.append(float(leaps_count + large_leaps_count) / (len(cantus_firmus) * 2))\\n\\n if debug:\\n print \\\"VSCORE 12: SIM LEAPS - \\\", leaps_count, \\\", LARGE SIM LEAPS - \\\", large_leaps_count\\n\\n # vscore 13: use all types of motion\\n similar = 0\\n contrary = 0\\n oblique = 0\\n parallel = 0\\n for i in range(1, len(melody_info)):\\n if melody_info[i].beat == 0:\\n prev_cf = cantus_firmus[melody_info[i - 1].measure]\\n prev_cp = melody_info[i - 1].pitch\\n cur_cf = cantus_firmus[melody_info[i].measure]\\n cur_cp = melody_info[i].pitch\\n if prev_cp == cur_cp:\\n if prev_cf != cur_cf:\\n oblique = 1\\n else:\\n similar = 1\\n if prev_cp > cur_cp:\\n if prev_cf <= cur_cf:\\n contrary = 1\\n else:\\n parallel = 1\\n if prev_cp < cur_cp:\\n if prev_cf >= cur_cf:\\n contrary = 1\\n else:\\n parallel = 1\\n types_of_motion = similar + oblique + contrary + parallel\\n vscores.append(1 - float(types_of_motion) / 4)\\n if debug:\\n print \\\"VSCORE 13: MOTION TYPES (SOCP) - \\\", similar, oblique, contrary, parallel\\n\\n # vscore 14: climax of the CF and CP should not coincide\\n cf_highest_note = max(cantus_firmus)\\n coincide = 0\\n for note in melody_info:\\n if note.pitch == highest_note and cantus_firmus[note.measure] == cf_highest_note:\\n coincide = 1\\n\\n vscores.append(coincide)\\n if debug:\\n print \\\"VSCORE 14: COINCIDE - \\\", coincide\\n\\n # vscore 15: Successive unisons, octaves and fifths on first beats are only valid\\n # when separated by three quarter notes.\\n bad_intervals_count = 0\\n\\n for i in range(len(melody_info) - 1):\\n if melody_info[i].beat == 0 and melody_info[i].measure != len(cantus_firmus) - 1:\\n if melody_info[i].vi in [0, 4, 7]:\\n separated = True\\n j = 1\\n while melody_info[i + j].measure == melody_info[i].measure:\\n if melody_info[i + j].duration > 2:\\n separated = False\\n j += 1\\n if melody_info[i + j].vi in [0, 4, 7] and not separated:\\n bad_intervals_count += 1\\n vscores.append(float(bad_intervals_count) / (len(cantus_firmus) - 1))\\n if debug:\\n print \\\"VSCORE 15: BAD INTERVALS - \\\", bad_intervals_count\\n\\n # vscore 16: successive unisons, octaves and fifths not on first beats:\\n # valid when separated by at least 2 notes, otherwise not.\\n # Unless it is a consonant suspension of quarter note: ok for afterbeat fifths and octaves\\n # separated only by a single quearter.\\n # ***what a complex rule, we don't care about consonant suspension due to storing format >_<***\\n bad_intervals_count = 0\\n\\n for i in range(len(melody_info) - 2):\\n separated = True\\n if melody_info[i].beat != 0:\\n if melody_info[i].vi in [0, 4, 7]:\\n if melody_info[i + 1].vi in [0, 4, 7]:\\n separated = False\\n if separated:\\n if melody_info[i + 2].vi in [0, 4, 7]:\\n separated = False\\n if not separated:\\n bad_intervals_count += 1\\n\\n vscores.append(float(bad_intervals_count) / (len(melody_info) - len(cantus_firmus)))\\n\\n if debug:\\n print \\\"VSCORE 16: BAD INTERVALS - \\\", bad_intervals_count\\n\\n # vscore 17: no ottava or quinta battuda, whatever it means\\n vscores.append(float(battuda) / len(cantus_firmus))\\n\\n if debug:\\n print \\\"VSCORE 17: BATTUDAS - \\\", battuda\\n\\n # vscore 18: best ending: dissonant suspension into the leading tone (ensured by generation)\\n vscores.append(0)\\n\\n # vscore 19: Thirds, sixths and tenths should predominate.\\n good_interval_count = len(filter(lambda x: x.vi in [2, 5, 9], melody_info))\\n if good_interval_count * 2 > len(melody_info):\\n vscores.append(0.0)\\n else:\\n vscores.append(1.0 - float(2 * good_interval_count) / len(melody_info))\\n\\n if debug:\\n print \\\"VSCORE 19: 3RDS 6THS 10THS - \\\", good_interval_count\\n\\n genome.fitness = sum([x * y for x, y in zip(hscores, HSCORE_WEIGHTS)]) + \\\\\\n sum([x * y for x, y in zip(vscores, VSCORE_WEIGHTS)])\\n if debug:\\n print \\\"HSCORES: \\\", hscores\\n print \\\"VSCORES: \\\", vscores\\n print \\\"FINAL SCORE: \\\", genome.fitness\\n print \\\"FINAL SCORE UNSCALED: \\\", sum(hscores) + sum(vscores)\\n return genome.fitness\",\n \"def _compute_likelihood(self, mus, pmfs):\\n expected_counts = pmfs.copy()\\n for mu, _p_bin_source in zip(mus, expected_counts):\\n _p_bin_source *= mu # Works because of numpy view magic...\\n expected_total = np.sum(expected_counts, axis=0)\\n\\n observed_counts = self.data_events_per_bin.histogram\\n\\n ret = observed_counts * np.log(expected_total) - expected_total - gammaln(observed_counts + 1.).real\\n return np.sum(ret)\",\n \"def compute_movie_rating_likelihood(M):\\n\\n # define the size to begin with\\n likelihood = np.zeros((M, M))\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE GOES HERE FOR PART (c)\\n #\\n # Remember to normalize the likelihood, so that each column is a\\n # probability distribution.\\n \\n for i in range(M):\\n for j in range(M):\\n if i == j:\\n likelihood[i][j] = 2\\n else:\\n likelihood[i][j] = 1/abs(j-i)\\n \\n likelihood = likelihood / likelihood.sum(axis = 1)\\n \\n #\\n # END OF YOUR CODE FOR PART (c)\\n # -------------------------------------------------------------------------\\n\\n return likelihood\",\n \"def translate_sequence(sequence, genetic_code = {'GUC': 'V', 'ACC': 'T', 'GUA': 'V', 'GUG': 'V', 'ACU': 'T', 'AAC': 'N', 'CCU': 'P', 'UGG': 'W', 'AGC': 'S', 'AUC': 'I', 'CAU': 'H', 'AAU': 'N', 'AGU': 'S', 'GUU': 'V', 'CAC': 'H', 'ACG': 'T', 'CCG': 'P', 'CCA': 'P', 'ACA': 'T', 'CCC': 'P', 'UGU': 'C', 'GGU': 'G', 'UCU': 'S', 'GCG': 'A', 'UGC': 'C', 'CAG': 'Q', 'GAU': 'D', 'UAU': 'Y', 'CGG': 'R', 'UCG': 'S', 'AGG': 'R', 'GGG': 'G', 'UCC': 'S', 'UCA': 'S', 'UAA': '*', 'GGA': 'G', 'UAC': 'Y', 'GAC': 'D', 'UAG': '*', 'AUA': 'I', 'GCA': 'A', 'CUU': 'L', 'GGC': 'G', 'AUG': 'M', 'CUG': 'L', 'GAG': 'E', 'CUC': 'L', 'AGA': 'R', 'CUA': 'L', 'GCC': 'A', 'AAA': 'K', 'AAG': 'K', 'CAA': 'Q', 'UUU': 'F', 'CGU': 'R', 'CGC': 'R', 'CGA': 'R', 'GCU': 'A', 'GAA': 'E', 'AUU': 'I', 'UUG': 'L', 'UUA': 'L', 'UGA': '*', 'UUC': 'F'}, start_pos = 0):\\n #find first orf\\n #first_orf_seq = find_first_orf(sequence)\\n\\n # ensure sequence is uppercase\\n seq = sequence.upper()\\n\\n #translate the sequence\\n protein = \\\"\\\"\\n for i in range(0, len(seq) - (len(seq) % 3), 3):\\n codon = seq[i:i + 3]\\n if genetic_code[codon] == \\\"*\\\":\\n break\\n protein += genetic_code[codon]\\n return protein\",\n \"def outputGenerationProbability(self):\\n self.b = zeros((self.noOfEmmittingStates, self.T))\\n for row in range(self.noOfEmmittingStates):\\n for col in range(self.T):\\n self.b[row, col] = self.gaussianDist(self.observationSequence[0, col],\\n self.outputProbabilities[row, 0],\\n self.outputProbabilities[row, 1])\",\n \"def boost_probability_for(fixation):\\n probabilities = np.zeros(Number_of_locs) #MOD Number_of_locs deleted\\n for possible_target_location in xrange(Number_of_locs): #MOD Number_of_locs deleted\\n Lib_c.set_target(possible_target_location)\\n probabilities[possible_target_location] = integrate.quad(\\n Lib_c.function,\\n -np.inf, np.inf,\\n epsabs=0,\\n limit=50,\\n full_output=1\\n )[0]\\n return np.sum(Post_probs * probabilities) #MOD Post_probs deleted\",\n \"def _compute_log_likelihood(self, parameters):\\n raise NotImplementedError('')\",\n \"def get_ngramlogprobs_fromcorpus(tokenizedseqs, n):\\n return\",\n \"def design_guide_and_donor_for_genes(input_design_table_filename, input_annot_gff_filename, input_genome_fasta_filename,\\n PAM_seq = 'GA', excluded_seqs = ['TTTT'], BOWTIE_exe = '',\\n off_targets_min_mismatch = 10,\\n min_azimuth_score = 0.5,\\n sort_by = '5to3', \\n min_dist_cut_to_donor_edge = 30,\\n filter_out_donors_containing_excluded_seqs = False,\\n max_guide_pos_frac_in_gene = 1.1,\\n output_guides_df_filename = '', output_donors_df_filename = ''):\\n \\n ##################\\n # processing input\\n ##################\\n\\n # fixing the mapping command to be the default bowtie\\n if not BOWTIE_exe or len(BOWTIE_exe) < 3:\\n BOWTIE_exe = '/Users/eilon/software/bowtie2-2.2.8/bowtie2'\\n \\n # defining the bowtie mapping cmd\\n bowtie_cmd = shlex.split(BOWTIE_exe + ' -x ' + os.path.splitext(input_genome_fasta_filename)[0] + ' -U - -f -D 20 -R 3 -N 1 -L 10 -i S,1,0.50 --gbar 3 --end-to-end -k 30 --no-head -t --rdg 10,6 --rfg 10,6')\\n mapping_cmd = bowtie_cmd\\n\\n \\n # loading design matrix \\n # fields: gene_id\\tguide_num\\tdonor_mut_type\\tnum_donor_variants\\tmut_pos_in_guide\\tdonor_length\\tdonor_seq_offsets\\n\\n design_df = pd.read_table(input_design_table_filename, sep='\\\\t', na_values = \\\"\\\")\\n design_df['donor_seq_offsets'] = design_df['donor_seq_offsets'].apply(ast.literal_eval)\\n design_df['donor_seq_offsets'] = design_df['donor_seq_offsets'].apply(np.array,dtype=np.float)\\n \\n design_df['gene_id'] = design_df['gene_id'].str.strip()\\n design_df['donor_mut_type'] = design_df['donor_mut_type'].str.strip()\\n \\n \\n # loading gene gff matrix\\n print \\\"loading genes gff file: \\\" + input_annot_gff_filename\\n genes_gff_df = sgd_gff2dataframe(input_annot_gff_filename, ['CDS'])\\n \\n # loading genome fasta file\\n print \\\"loading genome fasta file: \\\" + input_genome_fasta_filename\\n genome_seq = SeqIO.to_dict(SeqIO.parse(open(input_genome_fasta_filename),'fasta', alphabet=generic_dna))\\n \\n\\n \\n # init output dataframes\\n out_guide_df = pd.DataFrame(data=None)\\n out_guide_donor_df = pd.DataFrame(data=None)\\n \\n ####################\\n # running gene by gene such that the donor sequences will use the same guides\\n # if differnet number of guides are specified for each donor desing the guides will be selected from best to worst\\n ####################\\n \\n # grouping by gene\\n design_grouped = design_df.groupby('gene_id')\\n \\n\\n # iterating over the genes\\n for cur_gene_id, cur_gene_df in design_grouped:\\n\\n print \\\"Designing guides for gene:\\\" + cur_gene_id\\n \\n # the current gene object\\n cur_gene = CRISPR_Gene(cur_gene_id, genes_gff_df, genome_seq)\\n \\n # if differnet number of guides are specified for each donor desing the guides will be selected from best to worst\\n cur_gene_max_guide_num = max(cur_gene_df['guide_num'])\\n \\n # if even one row requires the guide to be entirely in the CDS - find only guides that are entirely in the CDS\\n #cur_require_entire_guide_to_be_in_CDS = np.any(~ np.isnan(cur_gene_df['mut_pos_in_guide'].values))\\n # np.any(cur_gene_df['require_entire_guide_in_CDS'])\\n #\\n cur_min_mut_pos_in_guide = min(int(-cur_gene.CRISPR_CUT_INDEX - 1*(cur_gene.is_neg_strand())),np.nanmin(cur_gene_df['mut_pos_in_guide'].values))\\n cur_max_mut_pos_in_guide = max(2,np.nanmax(cur_gene_df['mut_pos_in_guide'].values))\\n \\n \\n # max half region length to look for excluded sequences (should be at least ~20 to test the guide)\\n \\n\\n cur_max_donor_seq_offset = np.nanmax(np.abs(cur_gene_df['donor_seq_offsets'].apply(np.nanmax).values))\\n if np.isnan(cur_max_donor_seq_offset):\\n cur_max_donor_seq_offset = 0\\n \\n \\n \\n cur_max_seq_len_around_cut = int( np.ceil(max(cur_gene_df['donor_length']) / 2) + \\\\\\n cur_max_donor_seq_offset)\\n \\n #print \\\"Extracting all guides\\\"\\n \\n # get all guides (computing filter guides that contain excluded sequences in the sequences around them)\\n cur_all_gene_guides_df = cur_gene.get_all_guides_that_cut_in_cds(pam = PAM_seq, seq_len_around_cut = cur_max_seq_len_around_cut, \\n min_mut_pos_in_guide = cur_min_mut_pos_in_guide,\\n max_mut_pos_in_guide = cur_max_mut_pos_in_guide,\\n excluded_seqs = excluded_seqs, mapping_cmd = mapping_cmd,\\n sort_by = sort_by)\\n \\n #DEBUG\\n #print \\\"before get_K_best_guide_ids\\\" #DEBUG\\n #print sum(cur_all_gene_guides_df['guide_id']== 'YAL001C_pS_33') #DEBUG\\n \\n #print cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id']== 'YAL001C_pS_33' ]\\n #print cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id']== 'YAL001C_pS_20' ]\\n #print cur_gene_max_guide_num\\n #print off_targets_min_mismatch\\n #print min_azimuth_score\\n #print max_guide_pos_frac_in_gene\\n\\n # select top 'best' guides\\n selected_guide_ids = cur_gene.get_K_best_guide_ids(guides_df = cur_all_gene_guides_df, K = cur_gene_max_guide_num, \\n off_targets_min_mismatch = off_targets_min_mismatch,\\n min_azimuth_score = min_azimuth_score,\\n max_guide_pos_frac_in_gene = max_guide_pos_frac_in_gene,\\n sort_by = sort_by)\\n #print \\\"after get_K_best_guide_ids\\\" #DEBUG\\n #for gi in selected_guide_ids:\\n # print gi\\n #print ('YAL001C_pS_33' in list(selected_guide_ids) ) \\n #print 'XXX-------------------' #DEBUG\\n \\n # selected guides (each guide should have at least one donor designed)\\n cur_selected_guide_df = cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id'].isin( list(selected_guide_ids) ) ]\\n \\n print \\\"--- Designing donor sequences for gene:\\\" + cur_gene_id + \\\", for \\\" + str(len(selected_guide_ids)) + \\\" guides (out of \\\" + str(cur_gene_max_guide_num) + \\\" requested)\\\"\\n #print cur_selected_guide_df[ cur_selected_guide_df['guide_id'] == 'YAL001C_pS_33' ]\\n \\n # concating with the output dataframe\\n out_guide_df = pd.concat([out_guide_df,cur_selected_guide_df],ignore_index=True)\\n\\n \\n # desinging the donor sequences\\n for idx,row in cur_gene_df.iterrows():\\n \\n if len(selected_guide_ids) < int(row['guide_num']):\\n warnings.warn(\\\"There are NOT enough guides in --- %s --- for the design. There are %d guides and the design if for %d\\\" % (str(row[\\\"gene_id\\\"]),len(selected_guide_ids), row['guide_num'] ))\\n \\n if len(selected_guide_ids) <= 0:\\n continue\\n \\n cur_selected_guide_ids = selected_guide_ids.iloc[range( min(int(row['guide_num']),len(selected_guide_ids)) )]\\n \\n \\n # do reverse complement for donor sequences\\n if 'do_revcomp_donor' in cur_gene_df.columns:\\n do_revcomp_donor = bool(row['do_revcomp_donor']==True)\\n else:\\n do_revcomp_donor = False\\n \\n \\n \\n \\n # do reverse complement for donor sequences\\n if 'do_scramble_guide_and_donor' in cur_gene_df.columns:\\n scramble_guide_and_donor = bool(row['do_scramble_guide_and_donor']==True)\\n else:\\n scramble_guide_and_donor = False\\n \\n #print \\\"do_revcomp_donor %d\\\" % (do_revcomp_donor)\\n #print \\\"scramble_guide_and_donor %d\\\" % (scramble_guide_and_donor)\\n \\n cur_all_gene_guides_df_fordonors = cur_all_gene_guides_df\\n \\n # permuting the guides and adding it to the guide df\\n if scramble_guide_and_donor:\\n print 'Scramble donor guides...'\\n cur_all_gene_guides_df_scramble = cur_all_gene_guides_df[ cur_all_gene_guides_df['guide_id'].isin(cur_selected_guide_ids) ].copy()\\n # scrambling the guides and updating their ids\\n cur_all_gene_guides_df_scramble['guide_id'] = cur_all_gene_guides_df_scramble['guide_id'] + '_scramble'\\n scramble_func = lambda x: ''.join(random.sample(str(x),len(str(x))))\\n cur_all_gene_guides_df_scramble['guide'] = cur_all_gene_guides_df_scramble['guide'].apply(scramble_func)\\n cur_all_gene_guides_df_scramble['guide_noPAM'] = cur_all_gene_guides_df_scramble['guide_noPAM'].apply(scramble_func)\\n cur_all_gene_guides_df_scramble['guide_PAM_p7'] = cur_all_gene_guides_df_scramble['guide_PAM_p7'].apply(scramble_func)\\n cur_all_gene_guides_df_scramble['guide_PAM_m4p3'] = cur_all_gene_guides_df_scramble['guide_PAM_m4p3'].apply(scramble_func)\\n # adding the scrambeles guides to the guides table\\n out_guide_df = pd.concat([out_guide_df,cur_all_gene_guides_df_scramble],ignore_index=True)\\n\\n \\n # for donors design\\n cur_all_gene_guides_df_fordonors = cur_all_gene_guides_df_scramble\\n cur_selected_guide_ids = cur_selected_guide_ids + '_scramble'\\n \\n \\n cur_gene_donor_df = cur_gene.get_donor_mut_for_guides(cur_all_gene_guides_df_fordonors, cur_selected_guide_ids, \\n donor_mut_type=str(row['donor_mut_type']), \\n num_donor_variants = int(row['num_donor_variants']), \\n mut_pos_in_guide = row['mut_pos_in_guide'], \\n donor_length=int(row['donor_length']), \\n donor_seq_offsets = row['donor_seq_offsets'],\\n set_name = row['set_name'],\\n min_dist_cut_to_donor_edge = min_dist_cut_to_donor_edge,\\n excluded_seqs = excluded_seqs,\\n do_revcomp_donor = do_revcomp_donor,\\n scramble_guide_and_donor = scramble_guide_and_donor)\\n \\n out_guide_donor_df = pd.concat([out_guide_donor_df,cur_gene_donor_df],ignore_index=True)\\n\\n #print \\\"---------------------------- Finished designing guide for the above gene\\\"\\n\\n\\n # adding for each guide tis location in the gene (now added in )\\n #out_guide_df[\\\"guide_cut_gene_pos_frac\\\"] = out_guide_df[\\\"guide_cut_gene_nt_pos\\\"] / out_guide_df[\\\"CDS_len_nts\\\"]\\n\\n # filtering out donor sequences with excluded sequences\\n if filter_out_donors_containing_excluded_seqs:\\n if out_guide_donor_df.shape[1] > 1: # if not null df\\n out_guide_donor_df = out_guide_donor_df[ (~ out_guide_donor_df[\\\"contain_excluded_sequences\\\"]).values ]\\n \\n \\n if len(output_guides_df_filename) > 3:\\n print \\\"saving guides to: \\\" + output_guides_df_filename\\n out_guide_df.to_csv(output_guides_df_filename, sep='\\\\t', index = False)\\n \\n \\n \\n if len(output_donors_df_filename) > 3:\\n print \\\"saving donor sequences to: \\\" + output_donors_df_filename\\n out_guide_donor_df.to_csv(output_donors_df_filename, sep='\\\\t', index = False)\\n \\n \\n return( (out_guide_df, out_guide_donor_df) )\",\n \"def generate_rbpdb_experimental_to_pwm(letter_strength, n_repeat_req):\\n rbpdb_experiment_file_path = (\\n \\\"./website/data/RBPDB_v1.3.1_experiments_human_2012-11-21.tdt\\\"\\n )\\n rbpdb_pfm_file_directory = \\\"./website/data/rbpdb-human-pfm-matrices/\\\"\\n experimental_to_pwm_dict = {}\\n with open(rbpdb_experiment_file_path) as handle:\\n line = handle.readline()\\n while line:\\n columns = line.split(\\\"\\\\t\\\")\\n # Here we expect the columns to be:\\n # experimental_id, PUBMED_ID, exp_type, notes, seq_motif,\\n # selex_file, aligned_selex_file,\\n # aligned_motif_file, PWM_file, PFM_file, logo_file,\\n # secondary_structure, in_vivo_notes, in_vivo_file, flag\\n if columns[14] == \\\"1\\\":\\n # The flag means this data is unreliable, according to the RBPDB\\n # Readme files\\n line = handle.readline()\\n continue\\n\\n experimental_id = columns[0]\\n\\n assert len(experimental_id) > 0\\n pfm_file = columns[9]\\n seq_motifs = columns[4]\\n pwms = []\\n if pfm_file != \\\"\\\\\\\\N\\\":\\n pfm_file_path = rbpdb_pfm_file_directory + pfm_file\\n with open(pfm_file_path) as pfm_file_handle:\\n raw_pwm_str = pfm_file_handle.read()\\n pwm = str_to_pwm(raw_pwm_str, is_transpose=True)\\n pwms += [pwm]\\n elif seq_motifs not in (\\\"\\\\\\\\N\\\", \\\"\\\"):\\n # This experiment still generated some useful data\\n seq_motifs = seq_motifs.split(\\\";\\\")\\n i = 0\\n while i != len(seq_motifs):\\n seq_motif = seq_motifs[i]\\n while \\\")(\\\" in seq_motif:\\n repeat_end = seq_motif.find(\\\")(\\\")\\n assert seq_motif[repeat_end] == \\\")\\\"\\n repeat_start = repeat_end\\n while seq_motif[repeat_start] != \\\"(\\\":\\n repeat_start -= 1\\n\\n number_start = repeat_end + 2\\n assert (\\n seq_motif[number_start].isdigit()\\n or seq_motif[number_start] == \\\"n\\\"\\n )\\n number_end = number_start\\n while seq_motif[number_end] != \\\")\\\":\\n number_end += 1\\n\\n # deal with cases where the number of repeats is\\n # \\\"15-30\\\". Take minimum to be conservative.\\n # Note that most cases would be a single number like\\n # \\\"15\\\".\\n num_of_repeats = (\\n min(\\n [\\n int(s) if s != \\\"n\\\" else\\n math.ceil(\\n n_repeat_req\\n / (repeat_end - repeat_start - 1)\\n )\\n for s in\\n seq_motif[number_start: number_end]\\n .split(\\\"-\\\")\\n ]\\n )\\n )\\n\\n seq_motif = (\\n seq_motif.replace(\\n seq_motif[repeat_start: number_end + 1],\\n seq_motif[repeat_start + 1: repeat_end]\\n * num_of_repeats\\n )\\n )\\n\\n\\n maketrans = str.maketrans\\n all_letters = 'wruysn'\\n upper_map = maketrans(all_letters, all_letters.upper())\\n seq_motif = seq_motif.translate(upper_map)\\n if \\\"/\\\" in seq_motif:\\n bracket_start = bracket_end = middle = (\\n seq_motif.find(\\\"/\\\")\\n )\\n while seq_motif[bracket_start] != \\\"(\\\":\\n bracket_start -= 1\\n while seq_motif[bracket_end] != \\\")\\\":\\n bracket_end += 1\\n seq_motif_1 = (\\n seq_motif.replace(\\n seq_motif[bracket_start: bracket_end + 1],\\n seq_motif[bracket_start + 1: middle]\\n )\\n )\\n seq_motif_2 = (\\n seq_motif.replace(\\n seq_motif[bracket_start: bracket_end + 1],\\n seq_motif[middle + 1: bracket_end]\\n )\\n )\\n seq_motifs += [seq_motif_1, seq_motif_2]\\n else:\\n pwm = motif_to_pwm(\\n seq_motif, letter_strength=letter_strength\\n )\\n pwms += [pwm]\\n i += 1\\n\\n # Now we have the raw text, we convert it to pwm and add to\\n # dictionary\\n experimental_to_pwm_dict[experimental_id] = pwms\\n line = handle.readline()\\n\\n return experimental_to_pwm_dict\",\n \"def get_ngramlogprobs(freqdict):\\n return\",\n \"def model_likelihood(\\n self, obs: Tensor, actions: Tensor, next_obs: Tensor\\n ) -> Tensor:\\n return self.model.log_prob(obs, actions, next_obs)\",\n \"def _logp(self, trace, **inputs):\\n def calc_log(step):\\n exp_pred = np.dot(inputs['gwas_gen'],\\n step['beta_med'].T).ravel()\\n phen_pred = step['alpha'] * exp_pred\\n phen_prob = norm.logpdf(x=inputs['gwas_phen'],\\n loc=phen_pred,\\n scale=step['phenotype_sigma'])\\n return phen_prob\\n\\n phen_probs = [calc_log(trace[idx])\\n for idx in np.random.randint(0, len(self.trace), 500)]\\n phen_probs = np.asmatrix(phen_probs)\\n mc_logp = phen_probs.sum(axis=1).mean()\\n return mc_logp\",\n \"def calc_prob_prior(iterations, lam):\\n return list(map(lambda x: math.exp(-lam * x), range(iterations)))\",\n \"def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\\n perplexity = 0\\n\\n ### YOUR CODE HERE\\n def calc_prob(sentense, i, word, trigram_counts, bigram_counts, unigram_counts, train_token_count, model):\\n prob = 0.0\\n prev_word = sentense[i - 1]\\n prev_to_prev_word = sentense[i - 2]\\n\\n if model == \\\"unigram\\\":\\n if word in unigram_counts:\\n prob = (unigram_counts[word] + 0.0) / train_token_count\\n else:\\n prob = (unigram_counts[word_to_num['UUUNKKK']] + 0.0) / \\\\\\n train_token_count\\n\\n if model == \\\"bigram\\\":\\n if (prev_word, word) in bigram_counts:\\n prob = (bigram_counts[(prev_word, word)] + 0.0) / \\\\\\n unigram_counts[prev_word]\\n # print(num_to_word[prev_word] ,num_to_word[word])\\n # print(bigram_counts[(prev_word, word)])\\n # print(unigram_counts[prev_word])\\n # print(\\\"---------------------------\\\")\\n else:\\n prob = 0.0\\n\\n if model == \\\"trigram\\\":\\n if (prev_to_prev_word, prev_word, word) in trigram_counts:\\n prob = (trigram_counts[(prev_to_prev_word, prev_word, word)] + 0.0) \\\\\\n / bigram_counts[(prev_to_prev_word, prev_word)]\\n # / bigram_counts[(prev_word, word)] #this according to lecture notes slide 27\\n else:\\n prob = 0.0\\n\\n return prob\\n\\n l = 0\\n num_of_words = 0\\n\\n ##########3\\n better_than_chance = 0\\n ###########\\n\\n for sentense in eval_dataset:\\n for i, word in enumerate(sentense[2:]):\\n num_of_words += 1\\n prob = lambda1 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\\n train_token_count, \\\"trigram\\\") + \\\\\\n lambda2 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\\n train_token_count, \\\"bigram\\\") + \\\\\\n (1 - lambda1 - lambda2) * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts,\\n unigram_counts, train_token_count, \\\"unigram\\\")\\n ######################################\\n if prob > (1.0 / vocabsize):\\n better_than_chance += 1\\n #########################\\n l += np.log2(prob)\\n l /= num_of_words\\n perplexity = 2 ** -l\\n\\n print(\\\"better_than_chance:\\\", (better_than_chance + 0.0) / num_of_words)\\n\\n ### END YOUR CODE\\n return perplexity\",\n \"def compute_AttentionLRP(self, cell_state, relevance_a, output_states):\\r\\n\\r\\n\\r\\n\\t\\t#Reconstructing the concatenated encoder states\\r\\n\\t\\tmax_encoding_len = len(output_states)\\r\\n\\t\\tu = np.zeros(output_states[0].shape)\\r\\n\\t\\tfor i in range(max_encoding_len):\\r\\n\\t\\t\\tu += cell_state[\\\"alpha\\\"][i, 0] * output_states[i]\\r\\n\\t\\ta = np.matmul(self.attentonLayer, np.concatenate([cell_state[\\\"lstm\\\"]['last_h'], u]))\\r\\n\\r\\n\\t\\t# LRP for the attention layer\\r\\n\\t\\tinp_a_rel = layerLRP(np.concatenate([cell_state[\\\"lstm\\\"]['last_h'], u]), self.attentonLayer, np.zeros((self.attentionSize, 1)), a, relevance_a)\\r\\n\\r\\n\\t\\th_relevance= inp_a_rel[:self.cellSize]\\r\\n\\t\\tu_relevance = inp_a_rel[self.cellSize:]\\r\\n\\r\\n\\t\\tforward_encoder_relevance = []\\r\\n\\t\\tbackward_decoder_relevance = []\\r\\n\\r\\n\\r\\n\\t\\tinput_lrp_vector = np.concatenate([cell_state[\\\"alpha\\\"][i, 0] * output_states[i] for i in range(max_encoding_len)])\\r\\n\\t\\tinput_lrp_matrix = np.concatenate([np.identity(2*self.cellSize) for i in range(max_encoding_len)], 1)\\r\\n\\t\\t#for i in range(max_encoding_len):\\r\\n\\t\\t\\t#inp_c_rel = layerLRP(cell_state[\\\"alpha\\\"][i, 0] * output_states[i], np.identity(2*self.cellSize), np.zeros((2*self.cellSize, 1)), u, u_relevance, epsilon = 0.001, delta=1.0)\\r\\n\\t\\t\\t#forward_encoder_relevance.append(inp_c_rel[:self.cellSize])\\r\\n\\t\\t\\t#backward_decoder_relevance.append(inp_c_rel[self.cellSize:])\\r\\n\\t\\tinp_c_rel = layerLRP(input_lrp_vector, input_lrp_matrix, np.zeros((2*self.cellSize, 1)), u, u_relevance)\\r\\n\\t\\tfor i in range(max_encoding_len):\\r\\n\\t\\t\\tforward_encoder_relevance.append(inp_c_rel[2*i*self.cellSize:(2*i+1)*self.cellSize])\\r\\n\\t\\t\\tbackward_decoder_relevance.append(inp_c_rel[(2*i+1)*self.cellSize:(2*(i+1))*self.cellSize])\\r\\n\\r\\n\\t\\treturn h_relevance, forward_encoder_relevance, backward_decoder_relevance\",\n \"def detectionProb(efficiency, trigger=1):\\n if trigger > 1.:\\n raise ValueError('Trigger > 1 not implemented yet\\\\n')\\n q = 1.0 - np.asarray(efficiency)\\n\\n # probability of 0 detections\\n logq = np.log(q)\\n logpiq = logq.sum()\\n piq = np.exp(logpiq)\\n\\n return 1.0 - piq\",\n \"def log_likelihood(data, probs):\\n # Assume data is given as counts\\n return _np.sum([nlogp(n, p) for n, p in zip(data, probs) if n > 0])\",\n \"def updateResidueProbAnnotation(residueProb):\\n\\n for resonance in residueProb.resonanceGroup.resonances:\\n updateResonanceAnnotation(resonance)\",\n \"def figure_of_merit_from(self, parameter_list):\\r\\n return self.log_likelihood_from(parameter_list=parameter_list)\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def prob_to_llre(meth_prob):\\n return math.log((meth_prob + EPSLONG) / (1 - meth_prob + EPSLONG))\",\n \"def compute_log_probability_of_text(text, char_to_ix, frequency_statistics, transition_matrix):\\n t = text\\n cix = char_to_ix\\n fr = frequency_statistics\\n tm = transition_matrix\\n \\n i0 = cix[t[0]]\\n p = np.log(fr[i0])\\n i = 0\\n while i < len(t)-1:\\n i1 = cix[t[i+1]]\\n p += np.log(tm[i0, i1])\\n i0 = i1\\n i += 1\\n \\n return p\",\n \"def main(rand,mu,lamb,cxpb,mutpb,ngen,param):\\n \\n random.seed(rand)\\n NGEN = ngen\\n MU = mu\\n LAMBDA = lamb\\n CXPB = cxpb\\n MUTPB = mutpb\\n \\n # Used for printing the results. It is the parameter that is changed one run from another\\n if param==\\\"rand\\\" or param==\\\"optimal\\\":\\n list_results=[rand]\\n elif param==\\\"mu\\\":\\n list_results=[mu]\\n elif param==\\\"lamb\\\":\\n list_results=[lamb]\\n elif param==\\\"cross\\\":\\n list_results=[cxpb]\\n elif param==\\\"mutate\\\":\\n list_results=[mutpb]\\n elif param==\\\"ngen\\\":\\n list_results=[ngen]\\n elif param==\\\"original\\\":\\n list_results=[0]\\n \\n # Initialization of the objects for the GA\\n pop = toolbox.population(n=MU)\\n hof = tools.ParetoFront()\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", np.mean, axis=0)\\n stats.register(\\\"std\\\", np.std, axis=0)\\n stats.register(\\\"min\\\", np.min, axis=0)\\n stats.register(\\\"max\\\", np.max, axis=0)\\n\\n # Run of the GA\\n p,logbook=algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\\n halloffame=hof,verbose=0)\\n with open(results_path+param+'_logbook.csv', 'a',newline='') as f:\\n w = csv.DictWriter(f, logbook[0].keys())\\n w.writeheader()\\n for el in logbook:\\n w.writerow(el)\\n w.writerow({})\\n \\n # Takes the max fitness of the population from all of the runs\\n max_fit=0\\n max_gen=0\\n for elt in logbook:\\n if elt['max'][0]>max_fit:\\n max_fit=elt['max'][0]\\n max_gen=elt['gen']\\n list_results.append(max_fit)\\n list_results.append(max_gen)\\n \\n #TODO\\n# for ind in hof:\\n# dist = numpy.linalg.norm(a-b)\\n\\n print (\\\"{0} {1} {2} {3}\\\".format(round(list_results[1],3),round(list_results[2],3),round(list_results[0],3),hof[0]))\\n current_out_writer.writerow([list_results[0],list_results[1],list_results[2],hof[0]])\\n \\n return pop, stats, hof\",\n \"def probability(N_dr, L_opmin, L_opmax, L_min, L_max, L_d):\\n opening_nomullignas = []\\n opening_withmullignas = []\\n sum_nomulligans = 0\\n sum_withmulligans = 0\\n mulligan_coeff = 0\\n\\n for i in range(L_opmin, min(L_opmax + 1, 8)): # first make a list of tuples of the form:\\n # (number_of_lands_in_opening_hand, probability_of_drawing_such_a_hand)\\n a = hypergeom(i, 7, 60, L_d)\\n opening_nomullignas.append((i, a))\\n mulligan_coeff = mulligan_coeff + a # this will be used later for calculating the probability of\\n # taking the mulligan and is used as a coefficient before the mulligan sum\\n for (x, y) in opening_nomullignas: # use the list of tuples to calculate the first part of equation 5\\n partial_nomulligans = 0\\n for j in range(L_min - x, L_max - x + 1):\\n partial_nomulligans = partial_nomulligans + hypergeom(j, N_dr, 53, L_d - x)\\n sum_nomulligans = sum_nomulligans + partial_nomulligans * y\\n\\n mulligan_coeff = 1 - mulligan_coeff # probability of mulliganing\\n for i in range(L_opmin, min(L_opmax + 1, 7)): # doing the same thing as before, but drawing 6 instead of 7 cards\\n a = hypergeom(i, 6, 60, L_d)\\n opening_withmullignas.append((i, a))\\n\\n for (x, y) in opening_withmullignas:\\n partial_withmulligans = 0\\n for j in range(L_min - x, L_max - x + 1):\\n partial_withmulligans = partial_withmulligans + hypergeom(j, N_dr, 54, L_d - x)\\n sum_withmulligans = sum_withmulligans + partial_withmulligans * y\\n total_withmulligans = mulligan_coeff * sum_withmulligans\\n\\n return total_withmulligans + sum_nomulligans\",\n \"def prob4():\\n #get Omega, F, and initialize\\n Omega = np.array([[-3/2,3/4],[0,1],[0,1/2],[0,1]])\\n f = lambda x: np.exp(np.sum(-x**2/2,axis=0))/(2*np.pi)**(2)\\n means, cov = np.zeros(4), np.eye(4)\\n truth = scipy.stats.mvn.mvnun(list(Omega[:,0]),list(Omega[:,1]), means, cov)[0]\\n domain = np.logspace(1,5,20)\\n approxs = []\\n error = []\\n for N in domain:\\n #calculate approx for various sizes of samples\\n approx = mc_integrate(f,Omega[:,0],Omega[:,1],N)\\n approxs.append(approx)\\n #calculate relative err.\\n error.append(np.abs((truth-approx)/truth))\\n #PLOT it all\\n plt.title(\\\"Error vs Sample Size\\\")\\n plt.plot(domain,1/np.sqrt(domain),label = \\\"1/sqrt(N)\\\")\\n plt.plot(domain,error,label = \\\"Error\\\")\\n plt.loglog()\\n plt.xlabel(\\\"N\\\")\\n plt.ylabel(\\\"Relative Error\\\")\\n plt.legend()\\n plt.show()\",\n \"def gene_heritability(\\n input_snp_filename: \\\"Data Input, use the SNPs file from dataParse\\\",\\n output_genes_filename: 'output file for gene-level results, use .csv',\\n output_summary_filename: 'output file for the genomewide results summary, use .csv',\\n logger_filename: 'file for the logger, use a txt',\\n sweeps: \\\"number of samples for each chain\\\" = 1000,\\n burnin: \\\"number of burnin samples\\\" = 1000,\\n n_chains: \\\"number of chains of the sampler\\\" = 4,\\n n_cores: \\\"number of parallel cores to use\\\" = 4,\\n N_1kG: \\\"number of SNPs onwhich the LD-score is calculated\\\" = 1290028,\\n chromosome: \\\"chromosome on which the analysis is run\\\" = \\\"all\\\",\\n snp_thr: \\\"threshold for the minimum number of SNPs in a gene\\\" = 10,\\n sep: \\\"separator for the input files, use t for tab separated (not \\\\t)\\\" = \\\",\\\",\\n model: 'specify the model for the regression, one betwenn normal/gamma' = 'normal',\\n fix_intercept = False,\\n ):\\n\\n # Initialisation of the logger\\n output_logger = log.setup_logger(\\\"output_logger\\\", logger_filename)\\n log.initialise_log(output_logger,\\n 'gene level regression, model: %s' %model,\\n [input_snp_filename],\\n [output_genes_filename,output_summary_filename],\\n sweeps,\\n burnin,\\n chromosome = str(chromosome),\\n other_params_diz = {'chains': n_chains, 'cores': n_cores, 'SNP threshold': snp_thr})\\n\\n # Initialisation function, it reads the summary stats file, filters the SNPs,\\n # creates the output files\\n\\n logging.info(\\\"Start Analysis\\\")\\n\\n snps = s.Snps()\\n # read table\\n logging.info(\\\"Reading SNP file: %s,\\\\n\\\\t with %s delimiter\\\"%(input_snp_filename, sep))\\n snps.read_table(input_snp_filename, separator=sep)\\n # generate chi squared stats\\n snps.generate_stats()\\n # update the summary stats\\n snps.update_summary()\\n output_logger.info(\\\" Sample size \\\" + str(snps.n_patients) + \\\"\\\\n\\\")\\n\\n\\n\\n snps.apply_filter_table(s.baghera_filter)\\n snps.update_summary()\\n output_logger.info(\\\"After baghera init filter.\\\\n\\\\t Number of SNPs: %s\\\\n\\\\t Number of genes: %s\\\\n\\\" \\\\\\n %(str(snps.n_snps), str(snps.n_genes)) )\\n\\n # Non coding SNPs are assigned to a dummy gene, such that the regression is done on the entire SNPs' set\\n snps.rename_non_annotated(name='NonCoding')\\n\\n if chromosome != \\\"all\\\":\\n snps.apply_filter_table(snps.cut_single_chrom, **{'chromosome': chromosome})\\n output_logger.info(\\n \\\"Analysis restricted to chr %s\\\" %str(chromosome) )\\n\\n snps.update_summary()\\n output_logger.info(\\\"Analysis. Number of SNPs: %s\\\\n, Number of genes: %s\\\\n\\\" \\\\\\n %(str(snps.n_snps), str(snps.n_genes)) )\\n\\n # Creates the genes table with the number of SNPs for each gene and the basic stats values\\n genes=g.Genes()\\n genes.initialise_genes(snps.table.copy(), snps_thr=snp_thr)\\n\\n output_logger.info(\\\"Output gene table initialised:\\\\nNumber of genes: %s\\\\n\\\" \\\\\\n %(str(genes.n_genes)) )\\n\\n snps.set_non_annotated(genes.cut_genes, 'NonCoding')\\n\\n if model == 'gamma':\\n result = gr.analyse_gamma(snps, output_summary_filename, output_logger,\\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept,\\n )\\n else:\\n result = gr.analyse_normal(snps, output_summary_filename, output_logger,\\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept,\\n )\\n\\n logging.info(\\\"Saving genes table\\\")\\n genes.table = genes.table.merge(\\n result, left_index=False, left_on=\\\"name\\\", right_on=\\\"name\\\")\\n\\n k = genes.table.n_snps / float(N_1kG)\\n genes.table[\\\"h2g\\\"] = genes.table.bg_mean.astype(\\\"float\\\") * k\\n\\n genes.table = genes.table.sort_values(by=[\\\"P\\\", \\\"bg_median\\\"])\\n\\n genes.save_table(output_genes_filename)\\n\\n non_coding = genes.table[genes.table.name == \\\"NonCoding\\\"]\\n h2g_tot = np.sum(genes.table[\\\"h2g\\\"].values) - non_coding[\\\"h2g\\\"].values\\n\\n output_logger.info(\\\" Non coding heritability : \\\" +\\n str(non_coding[\\\"h2g\\\"].values) + \\\"\\\\n\\\")\\n output_logger.info(\\\" Coding heritability : \\\" + str(h2g_tot) + \\\"\\\\n\\\")\",\n \"def plot_likelihood(expected_posts_per_month, probability):\\n bar_amount = max(10, int(5 * expected_posts_per_month * probability)) # at least 10 bars, not too long of a tail\\n print(\\\"Generating likelihood plot\\\")\\n distribution = [binom.pmf(option, expected_posts_per_month, probability) for option in range(bar_amount)]\\n plt.bar(range(bar_amount), distribution)\\n plt.xlabel(\\\"occurrences\\\")\\n plt.ylabel(\\\"likelihood\\\")\\n plt.title(\\\"Likelihood of word occurences next month\\\")\\n plt.show()\",\n \"def annotate_ISM(data_df, REFERENCE, position_list, reference_genbank_name=\\\"data/covid-19-genbank.gb\\\"):\\n seq_list = data_df['sequence'].values.tolist()\\n \\n seq_index = []\\n index = 0\\n for base in REFERENCE[1]:\\n if base == '-':\\n seq_index.append(index)\\n else:\\n index += 1\\n seq_index.append(index)\\n reference_local_index_map = np.array(seq_index)\\n mapped_reference_index = []\\n for index, entropy in position_list:\\n mapped_reference_index.append((index, reference_local_index_map[index], entropy))\\n REFERENCE_ISM = ''.join([REFERENCE[1][item[0]] for item in position_list])\\n logging.info('Reference ISM: {}.'.format(REFERENCE_ISM))\\n \\n gene_dict = load_gene_dict(reference_genbank_name)\\n reference_raw = REFERENCE[1].replace('-', '')\\n res = OrderedDict()\\n res['Ref position'] = []\\n res['Entropy'] = []\\n res['Gene'] = []\\n res['Is silent'] = []\\n res['AA position'] = []\\n for align_index, ref_index, entropy in mapped_reference_index:\\n codon, codon_idx, name, codon_pos = find_SNP(ref_index, gene_dict, reference_raw)\\n base_freq = Counter([item[align_index] for item in seq_list]).most_common()\\n for alt_base, count in base_freq:\\n if alt_base != reference_raw[ref_index-1]:\\n break\\n if codon is None:\\n if_silence = True\\n else:\\n alt_codon = list(codon)\\n alt_codon[codon_idx] = alt_base\\n alt_codon = ''.join(alt_codon)\\n ref_aa = translate(codon)\\n ism_aa = translate(alt_codon)\\n if ref_aa == ism_aa:\\n if_silence = True\\n else:\\n if_silence = False\\n res['Ref position'].append(ref_index)\\n res['Entropy'].append(entropy)\\n if name is None:\\n name = 'Non-coding'\\n res['Gene'].append(name)\\n res['Is silent'].append(if_silence)\\n if codon_pos is None:\\n res['AA position'].append('NaN')\\n else:\\n res['AA position'].append('{}{}{}'.format(ref_aa, codon_pos, ism_aa))\\n annotation_df = pd.DataFrame.from_dict(res)\\n return annotation_df\",\n \"def gw_heritability(\\n input_snp_filename: \\\"Data Input, use the SNPs file from dataParse\\\",\\n output_summary_filename: 'output file for the genomewide results summary, use .csv',\\n logger_filename: 'file for the logger, use a txt',\\n sweeps: \\\"number of samples for each chain\\\" = 1000,\\n burnin: \\\"number of burnin samples\\\" = 1000,\\n n_chains: \\\"number of chains of the sampler\\\" = 4,\\n n_cores: \\\"number of parallel cores to use\\\" = 4,\\n N_1kG: \\\"number of SNPs onwhich the LD-score is calculates\\\" = 1290028,\\n chromosome: \\\"chromosome on which the analysis is run\\\" = \\\"all\\\",\\n sep: \\\"separator for the input files, use t for tab separated (not \\\\t)\\\" = \\\",\\\",\\n model: 'regression model'='normal',\\n fix_intercept = False,\\n ):\\n\\n # Initialisation of the logger\\n output_logger = log.setup_logger(\\\"output_logger\\\", logger_filename)\\n log.initialise_log(output_logger,\\n 'genome-wide regression, model: %s' %model,\\n [input_snp_filename],\\n [output_summary_filename],\\n sweeps,\\n burnin,\\n chromosome = str(chromosome),\\n other_params_diz = {'chains': n_chains, 'cores': n_cores})\\n\\n # Initialisation function, it reads the summary stats file, filters the SNPs,\\n # creates the output files\\n\\n logging.info(\\\"Start Analysis\\\")\\n\\n snps = s.Snps()\\n # read table\\n snps.read_table(input_snp_filename, separator=sep)\\n # generate chi squared stats\\n snps.generate_stats()\\n # update the summary stats\\n snps.update_summary()\\n output_logger.info(\\\" Sample size \\\" + str(snps.n_patients) + \\\"\\\\n\\\")\\n\\n\\n snps.apply_filter_table(s.baghera_filter)\\n snps.update_summary()\\n output_logger.info(\\\"After baghera init filter.\\\\nNumber of SNPs: %s\\\\nNumber of genes: %s\\\\n\\\" \\\\\\n %(str(snps.n_snps), str(snps.n_genes)) )\\n\\n # Non coding SNPs are assigned to a dummy gene, such that the regression is done on the entire SNPs' set\\n snps.rename_non_annotated(name='NonCoding')\\n\\n if chromosome != \\\"all\\\":\\n snps.apply_filter_table(snps.cut_single_chrom, **{'chromosome': chromosome})\\n output_logger.info(\\n \\\"Analysis restricted to chr %s\\\" %str(chromosome) )\\n\\n snps.update_summary()\\n output_logger.info(\\\"Analysis. Number of SNPs: %s\\\\n, Number of genes: %s\\\\n\\\" \\\\\\n %(str(snps.n_snps), str(snps.n_genes)) )\\n\\n\\n if model =='normal':\\n [intercept, slope] = heritability.gw_normal(snps, output_summary_filename, output_logger,\\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept)\\n elif model=='gamma':\\n [intercept, slope] = heritability.gw_normal(snps, output_summary_filename, output_logger,\\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept)\\n else:\\n logging.info('Normal model by default')\\n [intercept, slope] = heritability.gw_normal(snps, output_summary_filename, output_logger,\\n sweeps, burnin, n_chains, n_cores, N_1kG, fix_intercept)\\n logging.info(\\\"Analysis complete\\\")\",\n \"def log_likelihood_grad_rew(self, data, reward_model, bias_params):\",\n \"def gene_finder(dna, threshold):\\n\\n # YOUR IMPLEMENTATION HERE\",\n \"def process_observations(message, agent):\\n if not message:\\n print(\\\"Message is empty\\\");\\n # return None\\n else:\\n # # Check if joint values are in the expected order and size.\\n if message.joint_names != agent['joint_order']:\\n # Check that the message is of same size as the expected message.\\n if len(message.joint_names) != len(agent['joint_order']):\\n raise MSG_INVALID_JOINT_NAMES_DIFFER\\n\\n # Check that all the expected joint values are present in a message.\\n if not all(map(lambda x,y: x in y, message.joint_names,\\n raise MSG_INVALID_JOINT_NAMES_DIFFER\\n print(\\\"Joints differ\\\")\\n return np.array(message.actual.positions) # + message.actual.velocities\\n\\ndef get_jacobians(state, scara_chain, jac_solver):\\n \\\"\\\"\\\"\\n Produce a Jacobian from the urdf that maps from joint angles to x, y, z.\\n This makes a 6x6 matrix from 6 joint angles to x, y, z and 3 angles.\\n The angles are roll, pitch, and yaw (not Euler angles) and are not needed.\\n Returns a repackaged Jacobian that is 3x6.\\n \\\"\\\"\\\"\\n # Initialize a Jacobian for scara_chain.getNrOfJoints() joint angles by 3 cartesian coords and 3 orientation angles\\n jacobian = Jacobian(scara_chain.getNrOfJoints())\\n # Initialize a joint array for the present self.scara_chain.getNrOfJoints() joint angles.\\n angles = JntArray(scara_chain.getNrOfJoints())\\n # Construct the joint array from the most recent joint angles.\\n for i in range(scara_chain.getNrOfJoints()):\\n angles[i] = state[i]\\n # Update the jacobian by solving for the given angles.observation_callback\\n jac_solver.JntToJac(angles, jacobian)\\n # Initialize a numpy array to store the Jacobian.\\n J = np.array([[jacobian[i, j] for j in range(jacobian.columns())] for i in range(jacobian.rows())])\\n # Only want the cartesian position, not Roll, Pitch, Yaw (RPY) Angles\\n ee_jacobians = J\\n return ee_jacobians\",\n \"def compute_probabilities():\\n global total_spam_words, total_ham_words\\n total_words = total_spam_words+total_ham_words\\n unique_words = len(all_dict)\\n print(\\\"Training Set Description: \\\")\\n len_ham = len(ham_file_list)\\n len_spam = len(spam_file_list)\\n print(\\\"SPAM EMAILS: \\\",len_spam)\\n print(\\\"HAM EMAILS: \\\",len_ham)\\n print(\\\"Total words: \\\",total_words)\\n print(\\\"Training...\\\")\\n \\n spam_probability = math.log((len_spam)/(len_spam+len_ham))\\n ham_probability = math.log((len_ham)/(len_spam+len_ham))\\n \\n \\n \\n output_file = open(\\\"nbmodel.txt\\\", \\\"w+\\\", encoding=\\\"latin-1\\\")\\n output_file.write(\\\"model_params \\\"+str(spam_probability)+\\\" \\\"+str(ham_probability)+\\\"\\\\n\\\")\\n \\n nbmodel = {}\\n nbmodel[\\\"model_params\\\"] = (spam_probability,ham_probability)\\n for word in all_dict.keys():\\n spam_count = 1\\n if word in spam_dict:\\n spam_count+= spam_dict[word]\\n \\n word_spam_probability = math.log(spam_count / (total_spam_words+unique_words))\\n \\n ham_count = 1\\n if word in ham_dict:\\n ham_count+= ham_dict[word]\\n \\n word_ham_probability = math.log(ham_count / (total_ham_words+unique_words))\\n \\n output_file.write(word+\\\" \\\"+str(word_spam_probability)+\\\" \\\"+str(word_ham_probability)+\\\"\\\\n\\\")\\n nbmodel[word] = (word_spam_probability, word_ham_probability) \\n \\n print(\\\"nbmodel.txt generated successfully...\\\")\\n print(\\\"SPAM Probability: \\\",spam_probability)\\n print(\\\"HAM Probability: \\\",ham_probability)\\n output_file.close()\",\n \"def compute_probability_for(fixation):\\n probabilities = np.zeros(Number_of_locs) #MOD Number_of_locs deleted\\n for possible_target_location in xrange(Number_of_locs): #MOD Number_of_locs deleted\\n probabilities[possible_target_location] = integrate.quad(\\n integral_function,\\n -np.inf, np.inf,\\n args=(possible_target_location,Dprime_map[fixation]),\\n epsabs=0,\\n limit=100,\\n full_output=1\\n )[0] #MOD Dprime_map deleted\\n return np.sum(Post_probs * probabilities) #MOD Post_probs deleted\",\n \"def prob(self, sequence):\\n prob = 1\\n for event, context in self.extract_ngrams(sequence):\\n prob *= self.cond_prob(event, context)\\n return prob\",\n \"def loglikelihood(self):\\n raise NotImplementedError(\\\"To be implemented\\\")\",\n \"def giveMotevoParamFile(genome, wmlen, inter_dir, tag, aligned, ufemodel_path, ATfreq, GCfreq, emprior, bgorder, bgprior):\\n\\n ##UFE_models from genome_dict are not used anymore\\n #UFEmodel_hg19 is UFE model for mammal species\\n genome_dict = {}\\n genome_dict['hg19'] = ['((((hg19:0.032973,rheMac2:0.057695):0.09821,mm9:0.352605):0.020666,(bosTau6:0.186713,(equCab2:0.107726,canFam2:0.150374):0.010431):0.032764):0.156024,monDom5:0.425899);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFEmodel_hg19']\\n genome_dict['hg18'] = ['((((hg18:0.032973,rheMac2:0.057695):0.09821,mm9:0.352605):0.020666,(bosTau3:0.186713,(equCab1:0.107726,canFam2:0.150374):0.010431):0.032764):0.156024,monDom4:0.425899);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFE_mammals']\\n #genome_dict['dm3'] = ['((((((dm3:0.059,droSim1:0.075):0.041,(droYak2:0.104,droEre2:0.107):0.054):0.120,droAna3:0.377):0.072,dp4:0.397):0.061,droWil1:0.536):0.020,((droVir3:0.196,droMoj3:0.255):0.073,droGri2:0.291):0.337);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFEmodel_dm3']\\n genome_dict['dm3'] = ['((((((dm3:0.059,droSim1:0.075):0.041,(droYak2:0.104,droEre2:0.107):0.054):0.120,droAna3:0.377):0.072,dp4:0.397):0.061,droWil1:0.536):0.020,((droVir3:0.196,droMoj3:0.255):0.073,droGri2:0.291):0.337);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/dm3UFEparallel/UFEmodel_dm3']\\n genome_dict['mm9'] = ['((((hg19:0.032973,rheMac2:0.057695):0.09821,mm9:0.352605):0.020666,(bosTau7:0.186713,(equCab2:0.107726,canFam2:0.150374):0.010431):0.032764):0.156024,monDom5:0.425899);', '/import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/MotEvo_v1.0/UFEmodels/UFEmodel_mm9']\\n\\n\\n sitefilepath = os.path.join(inter_dir, 'sites_' + tag)\\n priorfilepath = os.path.join(inter_dir, 'priors_' + tag)\\n loglikfile = os.path.join(inter_dir, 'loglik_' + tag)\\n\\n\\n print '\\\\nCreate motevo parameter file %s' %tag\\n print 'aligned', aligned\\n if aligned:\\n motevo_params = '\\\\n'.join(['refspecies %s' %genome,\\n 'TREE %s' %genome_dict[genome][0],\\n 'Mode TFBS',\\n 'EMprior %s' %emprior,\\n 'priordiff %s' %0.05,\\n 'UFEwmprior %s' %200,\\n 'UFEwmfile %s' %ufemodel_path,\\n 'UFEwmlen %s' %wmlen,\\n 'UFEprint %s' %0,\\n 'markovorderBG %s' %bgorder,\\n 'bgprior %s' %bgprior,\\n 'bg A %s' %ATfreq,\\n 'bg T %s' %ATfreq,\\n 'bg G %s' %GCfreq,\\n 'bg C %s' %GCfreq,\\n 'restrictparses %s' %0,\\n 'sitefile %s' %sitefilepath,\\n 'priorfile %s' %priorfilepath,\\n 'printsiteals %s' %0,\\n 'minposterior %f' %0.0,\\n 'loglikfile %s' %loglikfile])\\n else:\\n motevo_params = '\\\\n'.join(['refspecies %s' %genome,\\n 'TREE (%s: 1)' %genome,\\n 'Mode TFBS',\\n 'EMprior %s' %emprior,\\n 'priordiff %s' %0.05,\\n 'markovorderBG %s' %bgorder,\\n 'bgprior %s' %bgprior,\\n 'bg A %s' %ATfreq,\\n 'bg T %s' %ATfreq,\\n 'bg G %s' %GCfreq,\\n 'bg C %s' %GCfreq,\\n 'restrictparses %s' %0,\\n 'sitefile %s' %sitefilepath,\\n 'priorfile %s' %priorfilepath,\\n 'printsiteals %s' %0,\\n 'minposterior %f' %0.0,\\n 'loglikfile %s' %loglikfile]) \\n\\n params_path = os.path.join(inter_dir, 'motevo_TFBS_params_' + tag)\\n pf = open(params_path, 'w')\\n pf.write(motevo_params)\\n return (params_path, sitefilepath, priorfilepath, loglikfile)\",\n \"def log_prob_of_file(filepath, model):\\n vocab = set(counts_un.keys())\\n tot = 0\\n count = 0\\n prev_prev = \\\"<s>\\\\n\\\"\\n prev = \\\"<s>\\\\n\\\"\\n with open(filepath) as f:\\n for line in f:\\n count += 2\\n line = line.strip()+\\\"\\\\n\\\"\\n tri_prob = model.get_trigram_prob(prev_prev, prev, line)\\n tot += math.log(tri_prob)\\n prev_prev = prev\\n prev = line \\n for line in [\\\"</s>\\\\n\\\", \\\"</s>\\\\n\\\"]:\\n tri_prob = model.get_trigram_prob(prev_prev, prev, line)\\n tot += math.log(tri_prob)\\n prev_prev = prev\\n prev = line \\n return tot, count\",\n \"def __compute_log_likelihood(self, outputs, data, boolean):\\n end_loc, pi_loc, mu_loc, sigma_loc, rho_loc = outputs\\n\\n mu1_loc, mu2_loc = mu_loc[:, :, :20], mu_loc[:, :, 20:]\\n sig1_loc, sig2_loc = (\\n sigma_loc[:, :, :20] + 10e-10,\\n sigma_loc[:, :, 20:] + 10e-10,\\n )\\n\\n x1_loc = data[:, 1:, 1].unsqueeze(2).to(self.__device)\\n x2_loc = data[:, 1:, 2].unsqueeze(2).to(self.__device)\\n x3_loc = data[:, 1:, 0].to(self.__device)\\n\\n end_loc = end_loc[:, :-1, -1].to(self.__device)\\n pi_loc = pi_loc[:, :-1, :].to(self.__device)\\n mu1_loc = mu1_loc[:, :-1, :].to(self.__device)\\n mu2_loc = mu2_loc[:, :-1, :].to(self.__device)\\n sig1_loc = sig1_loc[:, :-1, :].to(self.__device)\\n sig2_loc = sig2_loc[:, :-1, :].to(self.__device)\\n rho_loc = rho_loc[:, :-1, :].to(self.__device)\\n\\n boolean = boolean[:, :-1].to(self.__device)\\n\\n gauss = pi_loc * self.__bivariate_gaussian(\\n sig1_loc, sig2_loc, mu1_loc, mu2_loc, x1_loc, x2_loc, rho_loc\\n )\\n gauss = torch.sum(gauss, dim=2).to(self.__device)\\n\\n log_lik = torch.sum(\\n -boolean * torch.log(gauss + 10e-10)\\n - boolean * torch.log(end_loc + 10e-10) * (x3_loc)\\n - boolean * torch.log(1 - end_loc + 10e-10) * (1 - x3_loc)\\n )\\n\\n return log_lik\",\n \"def __compute_log_likelihood(self, outputs, data, boolean):\\n end_loc, pi_loc, mu_loc, sigma_loc, rho_loc = outputs\\n\\n mu1_loc, mu2_loc = mu_loc[:, :, :20], mu_loc[:, :, 20:]\\n sig1_loc, sig2_loc = (\\n sigma_loc[:, :, :20] + 10e-10,\\n sigma_loc[:, :, 20:] + 10e-10,\\n )\\n\\n x1_loc = data[:, 1:, 1].unsqueeze(2).to(self.__device)\\n x2_loc = data[:, 1:, 2].unsqueeze(2).to(self.__device)\\n x3_loc = data[:, 1:, 0].to(self.__device)\\n\\n end_loc = end_loc[:, :-1, -1].to(self.__device)\\n pi_loc = pi_loc[:, :-1, :].to(self.__device)\\n mu1_loc = mu1_loc[:, :-1, :].to(self.__device)\\n mu2_loc = mu2_loc[:, :-1, :].to(self.__device)\\n sig1_loc = sig1_loc[:, :-1, :].to(self.__device)\\n sig2_loc = sig2_loc[:, :-1, :].to(self.__device)\\n rho_loc = rho_loc[:, :-1, :].to(self.__device)\\n\\n boolean = boolean[:, :-1].to(self.__device)\\n\\n gauss = pi_loc * self.__bivariate_gaussian(\\n sig1_loc, sig2_loc, mu1_loc, mu2_loc, x1_loc, x2_loc, rho_loc\\n )\\n gauss = torch.sum(gauss, dim=2).to(self.__device)\\n\\n log_lik = torch.sum(\\n -boolean * torch.log(gauss + 10e-10)\\n - boolean * torch.log(end_loc + 10e-10) * (x3_loc)\\n - boolean * torch.log(1 - end_loc + 10e-10) * (1 - x3_loc)\\n )\\n\\n return log_lik\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n N, M = log_emlik.shape\\n backward_prob = np.zeros((N,M))\\n\\n backward_prob[N-1, :] = 0.0\\n\\n for i in range(N-2,-1,-1):\\n for k in range(M):\\n # probability of transitioning from k to state l * probability of emitting symbol at state l at ts i+1 * recursive backward probability\\n backward_prob[i,k] = logsumexp(log_transmat[k,:] + log_emlik[i+1,:] + backward_prob[i+1,:])\\n\\n return backward_prob\",\n \"def sentence_logprob(self, sentence):\\n grams = get_ngrams(sentence, 3)\\n p = 1\\n\\n for gram in grams:\\n p *= np.longfloat(self.smoothed_trigram_probability(gram))\\n\\n return np.log2(p)\",\n \"def dna_probability(dna:str, gc:float, return_log=False) -> float:\\n at = (1 - gc) / 2.0\\n gc /= 2.0\\n\\n p = 1\\n for l in dna:\\n if l in \\\"AT\\\":\\n p *= at\\n elif l in \\\"CG\\\":\\n p *= gc\\n else:\\n raise ValueError(\\\"You should use dna string.\\\")\\n if return_log:\\n return math.log(p, 10)\\n else:\\n return p\",\n \"def likelihood(params,data):\\n spec, isnflux, igalflux = data\\n chi2=0\\n modflux = (params[0]*isnflux + params[1]*igalflux)\\n chi2 += sum((spec.flux - modflux)**2)/((0.05*sum(spec.var)**2)/2.0)\\n return np.exp(-chi2/2.0)\",\n \"def test_GA():\\n\\tgenerationSize = 150\\n\\tmutationProb = 0.01\\n\\tgenerations = 500\\n\\tX = []\\n\\tT = []\\n\\tY = [] \\n\\tfitnesses = [0]*generationSize\\n\\tfor i in range(DATA_POINTS_NUM):\\n\\t\\tX.append((i - DATA_POINTS_NUM/2)*0.1)\\n\\t\\tT.append(polynomi_3N(REFERENCE, X[-1]))\\n\\t\\tY.append(0)\\n\\t\\n\\tga = GA.GA(generationSize, 4, mutationProb)\\n\\tgenomes = ga.seedGenomes()\\n\\t#plot initial genomes\\n\\tplt.figure(1)\\n\\tplt.title('Initial genomes')\\n\\tfor i in range(len(genomes)):\\n\\t\\tGenome = prescale(genomes[i])\\n\\t\\tprint Genome\\n\\t\\tfor j in range(DATA_POINTS_NUM):\\n\\t\\t\\tY[j] = (polynomi_3N(Genome, X[j]))\\n\\t\\tfitnesses[i] = calculate_fitness(T, Y)\\n\\t\\tplt.plot(X,Y, 'b-')\\n\\tplt.plot(X,T, 'r-')\\n\\t\\t\\n\\t\\n\\t#live and learn\\n\\tfor k in range(generations):\\n\\t\\tprint \\\".\\\",\\n\\t\\tfor i in range(len(genomes)):\\n\\t\\t\\tGenome = prescale(genomes[i])\\n\\t\\t\\tfor j in range(DATA_POINTS_NUM):\\n\\t\\t\\t\\tY[j] = (polynomi_3N(Genome,X[j]))\\n\\t\\t\\tfitnesses[i] = calculate_fitness(T, Y)\\n\\t\\tga.fitnessUpdate(fitnesses)\\n\\t\\tgenomes = ga.createNextGeneration()\\n\\t\\t\\n\\t#plot final genomes\\n\\tplt.figure(2)\\n\\tplt.title('Final genomes')\\n\\tprint \\\"\\\\nfinal Genomes\\\"\\n\\tfor i in range(len(genomes)):\\n\\t\\tGenome = prescale(genomes[i])\\n\\t\\tfor j in range(DATA_POINTS_NUM):\\n\\t\\t\\tY[j] = (polynomi_3N(Genome,X[j]))\\n\\t\\tprint \\\"fit:%5.1f [%7.4f, %7.4f, %7.4f, %7.4f]\\\"%\\\\\\n\\t\\t (calculate_fitness(T, Y), Genome[0],\\n\\t\\t Genome[1], Genome[2], Genome[3])\\n\\t\\tplt.plot(X,Y, 'b-')\\n\\tplt.plot(X,T, 'r-')\\n\\t\\t\\n\\t#plot progress\\n\\tP = []\\n\\thistory = ga.generations[:]\\n\\tfor f in history:\\n\\t\\t#f[1].sort()\\n\\t\\tP.append(max(f[1]))\\n\\tplt.figure(3)\\n\\tplt.title('progress')\\n\\tplt.plot(P)\\n\\tplt.show()\\n\\t\\n\\t#print the result:\\t\\n\\tbestGene = fitnesses.index(max(fitnesses))\\n\\tG = prescale(genomes[bestGene])\\n print \\\"\\\"\\n\\tprint \\\"And the result is:\\\"\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(A, G[0], abs(A - G[0]))\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(B, G[1], abs(B - G[1]))\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(C, G[2], abs(C - G[2]))\\n\\tprint \\\"%.4f => %.4f (%.4f)\\\"%(D, G[3], abs(D - G[3]))\",\n \"def return_likelihood(self, params):\\n\\n self._y_pred = self._int_spec(self._x, **params) * self._atten\\n\\n # Add the cascade\\n if self._casc is not None:\\n params_casc = deepcopy(params)\\n\\n # apply the weights\\n if self._cr_spec is not None:\\n # add units to the parameters where neccessary\\n params_casc['Prefactor_CR'] *= u.Unit(\\\"TeV-1 cm-2 s-1\\\")\\n params_casc['Scale_CR'] *= u.Unit(\\\"eV\\\").to(\\\"eV\\\") * u.eV\\n params_casc['Emin_CR'] *= u.Unit(\\\"eV\\\").to(\\\"eV\\\") * u.eV\\n params_casc['Emax_CR'] *= u.Unit(\\\"eV\\\").to(\\\"eV\\\") * u.eV\\n self._casc.apply_spectral_weights(lambda x: self._cr_spec(x, **params_casc),\\n smooth=True)\\n\\n else:\\n # add units to the parameters where neccessary\\n params_casc['Prefactor'] *= u.Unit(\\\"TeV-1 cm-2 s-1\\\")\\n params_casc['Scale'] *= u.Unit(\\\"TeV\\\").to(\\\"eV\\\") * u.eV\\n params_casc['Cutoff'] *= u.Unit(\\\"TeV\\\").to(\\\"eV\\\") * u.eV\\n self._casc.apply_spectral_weights(lambda x: self._int_spec(x, **params_casc),\\n smooth=True)\\n\\n # and get the flux in the ON region\\n spec_halo = self._casc.get_obs_spectrum(\\n region=self._on_region\\n )\\n # convert the units back\\n flux_unit_conversion = (spec_halo.quantity.unit).to(\\\"TeV-1 cm-2 s-1\\\")\\n\\n # either add directly if energy bins are the same or use 1D interpolation\\n if self._interp_casc:\\n m = spec_halo.data[:, 0, 0] > 0.\\n if not np.sum(m):\\n raise ValueError(\\\"Predicted cascade flux is zero!\\\")\\n interp = interp1d(np.log(spec_halo.geom.get_axis_by_name('energy').center.to(\\\"TeV\\\").value[m]),\\n np.log(spec_halo.data[:, 0, 0][m] * flux_unit_conversion),\\n fill_value='extrapolate', kind='cubic'\\n )\\n self._y_pred += np.exp(interp(np.log(self._x)))\\n\\n else:\\n self._y_pred += spec_halo.data[:, 0, 0] * flux_unit_conversion\\n\\n if self._cov_inv is None:\\n self._llh = -1. * ((self._y - self._y_pred) ** 2. / self._dy ** 2.).sum()\\n else:\\n self._llh = -1. * np.dot(self._y - self._y_pred, np.dot(self._cov_inv, self._y - self._y_pred))\\n\\n # add contribution from profile likelihood\\n if self._llh_fermi_interp is not None:\\n # change parameters to the values over which grid was interpolated\\n params_llh = deepcopy(params)\\n params_llh['Prefactor'] *= u.Unit(\\\"TeV-1 cm-2 s-1\\\").to(\\\"MeV-1 cm-2 s-1\\\")\\n params_llh['Index'] *= -1.\\n self._llh_fermi = 2. * self._llh_fermi_interp([params_llh['Cutoff'],\\n -1. * params_llh['Index'],\\n np.log10(params_llh['Prefactor'])])[0]\\n else:\\n self._llh_fermi = 0\\n\\n return -1. * (self._llh + self._llh_fermi)\",\n \"def get_log_likelihood(response_probability, observed_response):\\n \\n return np.log(response_probability[observed_response])\",\n \"def test_relevance_with_itself():\\n state = gen_state_cgpm(get_data_separated)\\n assert np.allclose(state.relevance_probability(2, [2], 1), 1.0)\",\n \"def p(self) -> Probability:\\n ...\",\n \"def _trajectory_prob(self, trajectory):\\n\\n # Accumulate probabilities in log space for numerical stability\\n logprob = np.log(self._starting_prob(trajectory[0][0]))\\n logprob += np.sum([\\n np.log(self._transition_prob(s1, a, s2))\\n for (s1, a), (s2, _) in zip(trajectory[:-1], trajectory[1:])\\n ])\\n\\n return np.exp(logprob)\",\n \"def log_probability(self, sequence):\\n sequence = self._transform(sequence)\\n\\n T = len(sequence)\\n\\n if T > 0 and sequence[0][_TAG]:\\n last_state = sequence[0][_TAG]\\n p = self._priors.logprob(last_state) + self._output_logprob(\\n last_state, sequence[0][_TEXT]\\n )\\n for t in range(1, T):\\n state = sequence[t][_TAG]\\n p += self._transitions[last_state].logprob(\\n state\\n ) + self._output_logprob(state, sequence[t][_TEXT])\\n last_state = state\\n return p\\n else:\\n alpha = self._forward_probability(sequence)\\n p = logsumexp2(alpha[T - 1])\\n return p\",\n \"def _create_log_likelihood(self, individual):\\n # Get individuals data\\n times = []\\n observations = []\\n mask = self._data[self._id_key] == individual\\n data = self._data[mask][\\n [self._time_key, self._obs_key, self._value_key]]\\n for output in self._mechanistic_model.outputs():\\n # Mask data for observable\\n observable = self._output_observable_dict[output]\\n mask = data[self._obs_key] == observable\\n temp_df = data[mask]\\n\\n # Filter times and observations for non-NaN entries\\n mask = temp_df[self._value_key].notnull()\\n temp_df = temp_df[[self._time_key, self._value_key]][mask]\\n mask = temp_df[self._time_key].notnull()\\n temp_df = temp_df[mask]\\n\\n # Collect data for output\\n times.append(temp_df[self._time_key].to_numpy())\\n observations.append(temp_df[self._value_key].to_numpy())\\n\\n # # Count outputs that were measured\\n # # TODO: copy mechanistic model and update model outputs.\\n # # (Useful for e.g. control group and dose group training)\\n # n_measured_outputs = 0\\n # for output_measurements in observations:\\n # if len(output_measurements) > 0:\\n # n_measured_outputs += 1\\n\\n # Create log-likelihood and set ID to individual\\n log_likelihood = chi.LogLikelihood(\\n self._mechanistic_model, self._error_models, observations, times)\\n log_likelihood.set_id(individual)\\n\\n return log_likelihood\",\n \"def probabilities_of_structures(sequence, structure_list, react=None):\\n ensemble_energy = get_ens_energy(sequence, react = react)\\n energies = [get_stru_energy(x, sequence, react = react) for x in structure_list]\\n probabilities = [energy_to_proba(ensemble_energy, x) for x in energies]\\n #probabilities = normalize(probabilities, norm='l1').tolist()[0]\\n return [(stru,proba) for stru,proba in zip(structure_list,probabilities)]\",\n \"def sentence_logprob(self, sentence):\\n line = get_ngrams(sentence,3)\\n log_por = 0.0\\n for item in line:\\n raw_por = self.smoothed_trigram_probability(item)\\n log_por = log_por+math.log2(raw_por)\\n\\n return float(log_por)\",\n \"def _logprob(self, sample):\\n return 0, 0\",\n \"def log_probability(self, samples):\\n pass\",\n \"def calc_likelihood(par_num, par_rng):\\n\\n likelihoods = np.zeros(np.size(par_rng))\\n\\n trivial_prior = trivial_prior_class()\\n\\n pipe = pipeline(observables_generator=hammu12,\\n likelihood=likelihood,\\n prior=trivial_prior,\\n optimizer_class=Hamiltonian_Monte_Carlo)\\n\\n parameters = [0]*hammu12.get_parameter_dimension()\\n for par_val in par_rng:\\n parameters[par_num] = par_val\\n likelihoods[par_val-par_rng[0]] = pipe._calc_posterior(parameters)\\n\\n np.save('data%s_RM' % (par_num), likelihoods)\",\n \"def compute_joint_probability(token_list, token_probabilities, use_log_prob=False):\\n\\n log_prob = 0\\n\\n for word in token_list:\\n\\n # do not allow zero probabilites\\n assert word in token_probabilities\\n\\n if use_log_prob:\\n log_prob += token_probabilities[word]\\n else:\\n log_prob += log10(token_probabilities[word])\\n\\n if use_log_prob:\\n return log_prob\\n\\n return 10**log_prob\",\n \"def MAP(cpts, obs, terms):\\r\\n\\r\\n # a list to store the computed probabilities\\r\\n all_sums = []\\r\\n # initialize all terms to false\\r\\n for value in range(len(terms)):\\r\\n terms[value] = [terms[value], '0']\\r\\n search_array = terms + obs\\r\\n # if all terms are being watched, just call MPE\\r\\n if len(search_array) == len(cpts):\\r\\n return MPE(cpts, obs)\\r\\n # we need to know what terms we aren't interested in so we start with \\r\\n # or terms and observations and note the variables that appear in CPT but\\r\\n # not in those\\r\\n dont_count = []\\r\\n for var in cpts:\\r\\n if [var[0], '0'] not in search_array and [var[0], '1'] not in search_array:\\r\\n dont_count.append(var[0])\\r\\n terms.append([var[0],'1'])\\r\\n # sort the terms to ensure correct ordering\\r\\n terms.sort()\\r\\n # creates a list of all possible bit strings\\r\\n # just an easy way to create all possible truth assignments\\r\\n seq = [\\\"\\\".join(seq) for seq in itertools.product(\\\"01\\\", repeat=len(terms))]\\r\\n # loop through all possible truth assignments\\r\\n for j in range(len(seq)):\\r\\n # we initialize at probability = 100%\\r\\n chance = 1\\r\\n # assign the truth values\\r\\n for k in range(len(seq[j])):\\r\\n terms[k][1] = seq[j][k]\\r\\n # this computes the probability using the chaining rule\\r\\n for i in range(len(terms)):\\r\\n new_terms = terms[:-i-1] + obs\\r\\n new_terms.sort()\\r\\n chance *= probability(cpts,terms[-i-1], new_terms)\\r\\n # add the probabilities to our list\\r\\n all_sums.append(chance)\\r\\n combine = []\\r\\n # note all variables which weren't in obs or Vs\\r\\n for i in dont_count:\\r\\n combine.append(terms.index([i,'1']))\\r\\n # this will store the final probabilities\\r\\n final_array = [0] * len(seq)\\r\\n # another complicated looking loop, it just serves to combine probabilities\\r\\n # for example, if we have a CPT with x_1, x_2, x_3, x_4 and we observe \\r\\n # x_1 to be true and have Vs = [x_3, x_4] then we need to combine the \\r\\n # probabilities that are the same except for x_2 = true vs false\\r\\n for loc in combine:\\r\\n for sequence in range(len(seq)):\\r\\n for alt_sequence in range(sequence+1,len(seq)):\\r\\n if (seq[sequence][:loc] + seq[sequence][loc+1:]) == (seq[alt_sequence][:loc] + seq[alt_sequence][loc+1:]):\\r\\n final_array[sequence] = all_sums[sequence] + all_sums[alt_sequence]\\r\\n\\r\\n # get the truth assignment for the highest probability\\r\\n location = seq[final_array.index(max(final_array))]\\r\\n truth_assignment = []\\r\\n # place the truth assignment in a more readable fashion\\r\\n for value in range(len(terms)):\\r\\n if terms[value] in search_array:\\r\\n if location[value] == '0':\\r\\n truth_assignment.append(terms[value][0]+ ' = False')\\r\\n else:\\r\\n truth_assignment.append(terms[value][0]+ ' = True')\\r\\n return (truth_assignment)\",\n \"def random_strings(sequence, GC_array):\\r\\n\\r\\n AT = 0\\r\\n GC = 0\\r\\n\\r\\n for nt in sequence:\\r\\n if nt == \\\"A\\\" or nt == \\\"T\\\":\\r\\n AT += 1\\r\\n elif nt == \\\"G\\\" or nt == \\\"C\\\":\\r\\n GC += 1\\r\\n\\r\\n probabilities = []\\r\\n\\r\\n #Calculate probability of G = probability of C = %GC / 2\\r\\n #Calculate probability of A = probability of T = (1 - %GC) / 2\\r\\n\\r\\n #For each consecutive base in provided sequence:\\r\\n #1. Convert total probability to logarithm using math.log(probability, base=10)\\r\\n #2. Total probability to be multiplied by probability of specifically that base\\r\\n\\r\\n for i in range(len(GC_array)):\\r\\n prob = (AT * math.log10((1 - GC_array[i])/2)) + (GC * math.log10(GC_array[i]/2))\\r\\n\\r\\n probabilities.append('%0.3f' % prob)\\r\\n\\r\\n print(*probabilities, sep= \\\" \\\")\",\n \"def log_marg_likelihood(self):\\n self.A = np.linalg.inv(self.Sn)\\n term1 = self.t - self.design_matrix@self.mn\\n self.Evidence_mN = (self.beta/2)*np.linalg.norm(term1)+ (self.alpha/2)*self.mn.T@self.mn\\n A_abs = np.linalg.eigvals(self.A)\\n A_abs = np.prod(A_abs)\\n\\n self.marg_lik = ((self.p)/2)*np.log(self.alpha) + (self.n/2)*np.log(self.beta) - self.Evidence_mN - (1/2)*np.log(A_abs) - (self.n/2)*np.log(2*np.pi)\\n\\n return self.marg_lik\",\n \"def Log_OB_S1(xref,x):\\n\\n nX = np.shape(x)\\n\\n m = nX[0]\\n n = nX[1]\\n t = nX[2]\\n\\n G = np.zeros((n,t))\\n Gv = np.zeros((m,n,t))\\n\\n for r in range(t):\\n\\n # Correct for permuations\\n\\n #Xout,PiA= CorrectPerm(xref,x[:,:,r])\\n Xout = dp(x[:,:,r])\\n\\n for q in range(n):\\n\\n a = np.sum(Xout[:,q]*xref[:,q])/np.sqrt(np.sum(xref[:,q]**2)*np.sum(Xout[:,q]**2)) # Should have unit L2 norm\\n if a > 1:\\n a = 1\\n if a < -1:\\n a = -1\\n G[q,r] = np.arccos(a) # Computing the angles\\n v = Xout[:,q] - a*xref[:,q]\\n Gv[:,q,r] = v / (1e-12 + np.linalg.norm(v)) # Unit vector in the tangent subspace\\n\\n return G,Gv\",\n \"def _log_likelihood_poisson(self, df, dfo, n_bins=10):\\n cond = df[\\\"selected_jig\\\"].values == 1\\n range = parameter_ranges['uae'], parameter_ranges['rec']\\n\\n uae_obs = dfo[\\\"mueff_av\\\"].values\\n rec_obs = dfo[\\\"rec_arcsec\\\"].values\\n obs, xedges, yedges = np.histogram2d(uae_obs, rec_obs, range=range, bins=n_bins)\\n\\n uae_mod = df[\\\"uae_obs_jig\\\"].values[cond]\\n rec_mod = df[\\\"rec_obs_jig\\\"].values[cond]\\n model, _, _ = np.histogram2d(uae_mod, rec_mod, range=range, bins=n_bins, density=True)\\n\\n # Rescale model by number of observations\\n model = model.astype(\\\"float\\\") * dfo.shape[0]\\n\\n # Calculate Poisson probability for each bin\\n obs = obs.reshape(-1).astype(\\\"float\\\")\\n model = model.reshape(-1)\\n probs = stats.poisson(mu=model).pmf(obs)\\n\\n # Return overall log likelihood\\n return np.log(probs).sum()\",\n \"def GoAnnot(prots, gos, onlyProts=False):\\r\\n with resources.open_text(\\\"autoprot.data\\\",\\\"Homo_sapiens.gene_info\\\") as d:\\r\\n geneInfo = pd.read_csv(d, sep='\\\\t')\\r\\n with resources.open_text(\\\"autoprot.data\\\",\\\"gene2go_alt\\\") as d:\\r\\n gene2go = pd.read_csv(d, sep='\\\\t')\\r\\n prots = pd.DataFrame(pd.Series([str(i).upper().split(';')[0] for i in prots]), columns=[\\\"Gene names\\\"])\\r\\n prots = prots.merge(geneInfo[[\\\"Symbol\\\", \\\"GeneID\\\"]], left_on=\\\"Gene names\\\", right_on=\\\"Symbol\\\", how='inner')\\r\\n \\r\\n prots = prots.merge(gene2go[[\\\"GeneID\\\", \\\"GO_ID\\\", \\\"GO_term\\\"]], on=\\\"GeneID\\\", how='inner')\\r\\n if onlyProts == True:\\r\\n for idx, go in enumerate(gos):\\r\\n if idx == 0:\\r\\n redProts = prots[\\\"Symbol\\\"][prots[\\\"GO_term\\\"].str.contains(go)]\\r\\n else:\\r\\n redProts = redProts.append(prots[\\\"Symbol\\\"][prots[\\\"GO_term\\\"].str.contains(go)])\\r\\n return redProts.drop_duplicates()\\r\\n else: \\r\\n for idx, go in enumerate(gos):\\r\\n if idx == 0:\\r\\n redProts = prots[prots[\\\"GO_term\\\"]==go]\\r\\n else:\\r\\n redProts = redProts.append(prots[prots[\\\"GO_term\\\"]==go])\\r\\n return redProts.drop_duplicates()\",\n \"def calculate_likelihood_probability(measurement, predicted_measurement, covariance):\\n \\n return None\",\n \"def compute_correspondence_likelihoods(self, measurement,\\r\\n number_of_landmarks,\\r\\n Qt_measurement_covariance):\\r\\n likelihoods = []\\r\\n for i in range(number_of_landmarks):\\r\\n likelihoods.append(\\r\\n self.compute_weight(measurement, i, Qt_measurement_covariance))\\r\\n \\r\\n return likelihoods\",\n \"def label_propagate_probabilistic(embeddings, seeds_map, normalize=True, **kwargs):\\n words = embeddings.iw\\n M = transition_matrix(embeddings, **kwargs)\\n teleport_set_map={}\\n for seed_key, seed_list in seeds_map.items():\\n teleport_set_map[seed_key]=teleport_set(words, seed_list)\\n def update_seeds(r):\\n idm= np.eye(len(seeds_map))\\n for seed_key, w_indices in teleport_set_map.items():\\n r[w_indices] = idm[seed_key]\\n r /= np.sum(r, axis=1)[:, np.newaxis]\\n r = run_iterative(M, np.random.random((M.shape[0], len(seeds_map))), update_seeds, **kwargs)\\n polarities={}\\n for i, w in enumerate(words):\\n polarities[w]=Counter()\\n for seed_key in seeds_map:\\n polarities[w][seed_key]=r[i][seed_key]\\n if normalize:\\n polarities[w]=normalize_counter(polarities[w])\\n return polarities\",\n \"def build_mm_df(sralist):\\n\\n def convert_to_codon(nts_array):\\n \\\"\\\"\\\"\\n pysam output is in nucleotides resolution, but scikit_curated_df uses codon resolution.\\n This function converts nucleotide arrays to codon length (nts to codon resolution):\\n \\\"\\\"\\\"\\n \\n nts_array = np.array(nts_array)\\n codon_array = np.sum( np.reshape(A, (int(np.floor(nts_array[1]/3)),3) ), 1)/3.\\n\\n return codon_array\\n\\n\\n def compute_mm(mmdata):\\n \\\"\\\"\\\"\\n get per gene average multi-mapping score\\n \\\"\\\"\\\"\\n\\n mm_df = pd.DataFrame(columns=['ORF', 'MM'])\\n counter = 0\\n\\n for gene in mmdata.keys():\\n current_matrix = mmdata[gene]\\n current_avrg = np.mean( np.sum(current_matrix, 1) / current_matrix.shape[1] )\\n mm_df.loc[counter] = [gene, current_avrg]\\n counter += 1\\n\\n return mm_df\\n\\n\\n mm_mat = {}\\n mm_pct = {}\\n\\n N = len(sralist)\\n\\n for ix, dataset in enumerate(sralist):\\n samfile = pysam.AlignmentFile(TMP_DIR+'/ambiguous_reads/'+dataset+'_STAR_transcriptome_multi_mapped_sorted.bam', 'rb')\\n genes_list = list(samfile.references)\\n print(ix, dataset)\\n\\n for geneID in genes_list:\\n # count the coverage of genomic positions by reads in region.\\n # Returns: four array.arrays of the same length in order A C G T\\n # The coverage is computed per-base [ACGT]\\n cov = samfile.count_coverage(geneID, read_callback='nofilter')\\n # Summ all 4 arrays\\n cov_sum = np.sum(cov, axis=0)\\n #print(geneID, cov_sum)\\n codon_cov = convert_to_codon(cov_sum)\\n codon_bool = np.asarray([1 if i > 0 else 0 for i in codon_cov])\\n \\n M = len(codon_bool)\\n\\n if ix == 0:\\n \\tmm_mat[geneID] = np.zeros((N,M)) * np.nan\\n \\n current_matrix = mm_mat[geneID]\\n current_matrix[ix,:] = np.copy(codon_bool)\\n mm_mat[geneID] = current_matrix\\n\\n\\n mm_avrg = compute_mm(mm_mat)\\n #mm_avrg.to_json('yeast_mm.json')\\n #mm_avrg.to_csv('yeast_mm.txt', header=True, index=False, sep='\\\\t')\\n\\n \\n mm_profile = {}\\n theta_mm = 5\\n for orf in mm_mat.keys():\\n current_mat = mm_mat[orf]\\n current_bool = np.sum(current_mat, 0) <= theta_mm\\n mm_profile[orf] = current_bool\\n\\n with open('../data/processed/mm_consensus.pkl', 'wb') as f_mm:\\n pickle.dump(mm_profile, f_mm)\\n\\n\\n return mm_mat, mm_avrg, mm_profile\",\n \"def ICA_log_likelihood(X, model):\\n\\n # TODO: YOUR CODE HERE\",\n \"def ref_lamanno(\\n fasta_path,\\n gtf_path,\\n cdna_path,\\n intron_path,\\n index_path,\\n t2g_path,\\n cdna_t2c_path,\\n intron_t2c_path,\\n temp_dir='tmp',\\n overwrite=False,\\n):\\n results = {}\\n if not os.path.exists(index_path) or overwrite:\\n fasta_path = decompress_file(fasta_path, temp_dir=temp_dir)\\n sorted_fasta_path, fasta_chromosomes = sort_fasta(\\n fasta_path, os.path.join(temp_dir, SORTED_FASTA_FILENAME)\\n )\\n gtf_path = decompress_file(gtf_path, temp_dir=temp_dir)\\n sorted_gtf_path, gtf_chromosomes = sort_gtf(\\n gtf_path, os.path.join(temp_dir, SORTED_GTF_FILENAME)\\n )\\n logger.info('Splitting genome into cDNA at {}'.format(cdna_path))\\n chromosomes = check_chromosomes(fasta_chromosomes, gtf_chromosomes)\\n cdna_fasta_path = generate_cdna_fasta(\\n sorted_fasta_path,\\n sorted_gtf_path,\\n cdna_path,\\n chromosomes=chromosomes\\n )\\n results.update({'cdna_fasta': cdna_fasta_path})\\n logger.info(\\n 'Creating cDNA transcripts-to-capture at {}'.format(cdna_t2c_path)\\n )\\n cdna_t2c_result = create_t2c(cdna_fasta_path, cdna_t2c_path)\\n results.update({'cdna_t2c': cdna_t2c_result['t2c']})\\n logger.info('Splitting genome into introns at {}'.format(intron_path))\\n intron_fasta_path = generate_intron_fasta(\\n sorted_fasta_path,\\n sorted_gtf_path,\\n intron_path,\\n chromosomes=chromosomes\\n )\\n results.update({'intron_fasta': intron_fasta_path})\\n logger.info(\\n 'Creating intron transcripts-to-capture at {}'.\\n format(cdna_t2c_path)\\n )\\n intron_t2c_result = create_t2c(intron_fasta_path, intron_t2c_path)\\n results.update({'intron_t2c': intron_t2c_result['t2c']})\\n logger.info('Concatenating cDNA and intron FASTAs')\\n combined_path = concatenate_files(\\n cdna_fasta_path,\\n intron_fasta_path,\\n out_path=os.path.join(temp_dir, COMBINED_FILENAME),\\n temp_dir=temp_dir\\n )\\n t2g_result = create_t2g_from_fasta(combined_path, t2g_path)\\n results.update(t2g_result)\\n index_result = kallisto_index(combined_path, index_path)\\n results.update(index_result)\\n else:\\n logger.info(\\n 'Skipping kallisto index because {} already exists. Use the --overwrite flag to overwrite.'\\n .format(index_path)\\n )\\n\\n return results\",\n \"def calories_protein(og, fg):\\n\\n return 0.994 * fg * real_extract(og, fg)\",\n \"def bayesian_info_criterion(log_likelihood, n_params, n_samples):\\n return n_params * np.log(n_samples) - 2.0 * log_likelihood\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64158255","0.61762154","0.6171898","0.61255485","0.6108559","0.60751355","0.5888883","0.581115","0.57994837","0.5773769","0.57539636","0.5743196","0.57412934","0.57062215","0.5637121","0.5604803","0.56041193","0.55022335","0.5498393","0.543617","0.53924245","0.5376682","0.5366281","0.53575003","0.5351058","0.5326567","0.53247386","0.532451","0.5315191","0.53046423","0.53007615","0.52895355","0.5286242","0.5279394","0.5272649","0.5256475","0.52495486","0.5248279","0.5241967","0.523703","0.52352494","0.5231489","0.5223748","0.522235","0.52157533","0.52153933","0.5211854","0.52102554","0.52069914","0.52068955","0.52005404","0.51990205","0.5191093","0.5184872","0.5178004","0.515485","0.5150197","0.5142383","0.51392275","0.5138804","0.5128439","0.51066357","0.51057214","0.5103622","0.5102116","0.5100729","0.5100422","0.5100422","0.5098312","0.50956213","0.50889736","0.5086658","0.50860214","0.50852585","0.50837","0.5080683","0.5075263","0.5074123","0.50736403","0.50516945","0.5051418","0.5050595","0.50469905","0.5041392","0.50409913","0.50378263","0.5036027","0.50352263","0.50314987","0.5027727","0.50216466","0.50213647","0.50196993","0.5014496","0.50070405","0.49968448","0.49965647","0.49908167","0.49891657","0.49828002"],"string":"[\n \"0.64158255\",\n \"0.61762154\",\n \"0.6171898\",\n \"0.61255485\",\n \"0.6108559\",\n \"0.60751355\",\n \"0.5888883\",\n \"0.581115\",\n \"0.57994837\",\n \"0.5773769\",\n \"0.57539636\",\n \"0.5743196\",\n \"0.57412934\",\n \"0.57062215\",\n \"0.5637121\",\n \"0.5604803\",\n \"0.56041193\",\n \"0.55022335\",\n \"0.5498393\",\n \"0.543617\",\n \"0.53924245\",\n \"0.5376682\",\n \"0.5366281\",\n \"0.53575003\",\n \"0.5351058\",\n \"0.5326567\",\n \"0.53247386\",\n \"0.532451\",\n \"0.5315191\",\n \"0.53046423\",\n \"0.53007615\",\n \"0.52895355\",\n \"0.5286242\",\n \"0.5279394\",\n \"0.5272649\",\n \"0.5256475\",\n \"0.52495486\",\n \"0.5248279\",\n \"0.5241967\",\n \"0.523703\",\n \"0.52352494\",\n \"0.5231489\",\n \"0.5223748\",\n \"0.522235\",\n \"0.52157533\",\n \"0.52153933\",\n \"0.5211854\",\n \"0.52102554\",\n \"0.52069914\",\n \"0.52068955\",\n \"0.52005404\",\n \"0.51990205\",\n \"0.5191093\",\n \"0.5184872\",\n \"0.5178004\",\n \"0.515485\",\n \"0.5150197\",\n \"0.5142383\",\n \"0.51392275\",\n \"0.5138804\",\n \"0.5128439\",\n \"0.51066357\",\n \"0.51057214\",\n \"0.5103622\",\n \"0.5102116\",\n \"0.5100729\",\n \"0.5100422\",\n \"0.5100422\",\n \"0.5098312\",\n \"0.50956213\",\n \"0.50889736\",\n \"0.5086658\",\n \"0.50860214\",\n \"0.50852585\",\n \"0.50837\",\n \"0.5080683\",\n \"0.5075263\",\n \"0.5074123\",\n \"0.50736403\",\n \"0.50516945\",\n \"0.5051418\",\n \"0.5050595\",\n \"0.50469905\",\n \"0.5041392\",\n \"0.50409913\",\n \"0.50378263\",\n \"0.5036027\",\n \"0.50352263\",\n \"0.50314987\",\n \"0.5027727\",\n \"0.50216466\",\n \"0.50213647\",\n \"0.50196993\",\n \"0.5014496\",\n \"0.50070405\",\n \"0.49968448\",\n \"0.49965647\",\n \"0.49908167\",\n \"0.49891657\",\n \"0.49828002\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6003137"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":99,"cells":{"query":{"kind":"string","value":"Fit an ellipse to an object. It returns the rotated rectangle in which the ellipse is inscribed."},"document":{"kind":"string","value":"def __CalculateEllipse(self, contour):\r\n if len(contour) > 5:\r\n return cv2.fitEllipse(contour)\r\n\r\n return cv2.minAreaRect(contour)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fit_ellipse(x,y):\r\n \r\n def fit(x,y):\r\n x = x[:,np.newaxis]\r\n y = y[:,np.newaxis]\r\n D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))\r\n S = np.dot(D.T,D)\r\n C = np.zeros([6,6])\r\n C[0,2] = C[2,0] = 2; C[1,1] = -1\r\n E, V = np.linalg.eig(np.dot(np.linalg.inv(S), C))\r\n n = np.argmax(np.abs(E))\r\n a = V[:,n]\r\n return a\r\n \r\n def ellipse_center(a):\r\n b,c,d,f,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[0]\r\n num = b*b-a*c\r\n x0=(c*d-b*f)/num\r\n y0=(a*f-b*d)/num\r\n return np.array([x0,y0])\r\n \r\n def ellipse_angle_of_rotation(a):\r\n b,c,a = a[1]/2, a[2], a[0]\r\n return 0.5*np.arctan(2*b/(a-c))\r\n \r\n def ellipse_axis_length(a):\r\n b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]\r\n up = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)\r\n down1=(b*b-a*c)*( (c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\r\n down2=(b*b-a*c)*( (a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\r\n res1=np.sqrt(up/down1)\r\n res2=np.sqrt(up/down2)\r\n return np.array([res1, res2])\r\n \r\n a = fit(x,y)\r\n center = ellipse_center(a)\r\n theta = ellipse_angle_of_rotation(a)\r\n [R1,R2] = ellipse_axis_length(a)\r\n\r\n return R1, R2, center, theta","def fitEllipse(x,y):\r\n x = x[:,np.newaxis]\r\n y = y[:,np.newaxis]\r\n D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))\r\n S = np.dot(D.T,D)\r\n C = np.zeros([6,6])\r\n C[0,2] = C[2,0] = 2; C[1,1] = -1\r\n E, V = eig(np.dot(inv(S), C))\r\n n = np.argmax(np.abs(E))\r\n a = V[:,n]\r\n return a","def create_ellipse(self, ratio):\n circ = Point(self.center).buffer(1.0)\n ell = affinity.scale(circ, float(\n self.lengths[0]*ratio), float(self.lengths[1]*ratio))\n ellr = affinity.rotate(ell, self.angle)\n return ellr","def ellipse(self):\n f = self.img\n x = self.x\n y = self.y\n x2 = self.x2\n y2 = self.y2\n xy = self.xy\n self.a2 = (x2+y2) + sqrt(((x2-y2)/2.)**2 + xy**2)\n self.b2 = (x2+y2) - sqrt(((x2-y2)/2.)**2 + xy**2)\n self.a = sqrt(self.a2)\n self.b = sqrt(self.b2)\n tan2theta = 2* (xy/(x2-y2))\n self.theta = arctan(tan2theta)/2.\n denominator = sqrt(((x2-y2)/2)**2+xy**2)\n self.cxx = y2/denominator\n self.cyy = x2/denominator\n self.cxy = -2*xy/denominator","def to_ellipse(self, factor=1.0):\n self._check_initialized()\n vals, vecs = sp.linalg.eigh(self.covariance)\n order = vals.argsort()[::-1]\n vals, vecs = vals[order], vecs[:, order]\n angle = np.arctan2(*vecs[:, 0][::-1])\n width, height = factor * np.sqrt(vals)\n return angle, width, height","def fitEllipsetoContour(poly):\n from skimage.measure import EllipseModel\n\n model = EllipseModel()\n success=model.estimate(poly)\n\n if success:\n yc, xc, a, b, theta=model.params # xc, yc, a, b, theta <- in radiand\n # convert theta to degree\n theta=np.degrees(theta)\n theta=90.-theta\n # make sure major is larger than minor and theta is for major\n if a<b:\n [a,b]=[b,a]\n theta=theta-90.\n if theta<0.: theta=theta+180.\n params=(xc, yc, a, b, theta)\n return params\n else:\n return (0, 0, 0, 0, 0)","def polarization_ellipse(self):\n self.ellipse = {}\n self.ellipse['d_lin'] = sqrt(self.Q**2 + self.U**2)/self.I\n self.ellipse['d_cir'] = abs(self.V)/self.I\n self.ellipse['d'] = sqrt(self.Q**2 + self.U**2 + self.V**2)/self.I\n if self.Q:\n self.ellipse['theta'] = 0.5*atan(self.U/self.Q)\n else:\n self.ellipse['theta'] = float('NaN')\n self.logger.debug(\"polarization_ellipse: theta = %f\",\n self.ellipse['theta'])\n\n if (self.Q**2 + self.U**2):\n self.ellipse['beta'] = 0.5*atan(self.V/sqrt(self.Q**2 + self.U**2))\n if self.V:\n self.ellipse['eccen'] = tan(self.ellipse['beta'])\n else:\n self.ellipse['eccen'] = 0.\n else:\n self.ellipse['beta'] = pi/4\n self.ellipse['eccen'] = 1.\n self.logger.debug(\"polarization_ellipse: beta = %f\",\n self.ellipse['beta'])\n self.logger.debug(\"polarization_ellipse: eccen = %f\",\n self.ellipse['eccen'])","def drawFitEllipse(img, cnt):\n\td = fitEllipse(cnt)\n\tcv2.ellipse(img, d[\"center\"], (d[\"major\"], d[\"minor\"]), d[\"angle\"])","def _proc_ellipse(self, tokens, filled):\n\n component = Ellipse(pen=self.pen,\n x_origin=tokens[\"x0\"],\n y_origin=tokens[\"y0\"],\n e_width=tokens[\"w\"],\n e_height=tokens[\"h\"],\n filled=filled)\n\n return component","def ellipseDesc(lps):\r\n unit = 100 #units in QualiTree are in [mm], hence Pgl is in [dm] ?\r\n\r\n if isinstance(lps, pgl.Translated):\r\n cx, cy, cz = lps.translation\r\n else:\r\n print\"missing Translated from the bounding ellipse as a Translated(Rotated(Scaled(Sphere)))\"\r\n\r\n ori = lps.geometry\r\n\r\n if isinstance(ori, pgl.Oriented):\r\n rotMat = ori.transformation().getMatrix3()\r\n az, el, roll = rotMat.eulerAnglesZYX()\r\n else:\r\n print\"missing Oriented from the bounding ellipse as a Translated(Rotated(Scaled(Sphere)))\"\r\n az = 0\r\n \r\n scal = ori.geometry\r\n\r\n if isinstance(scal, pgl.Scaled):\r\n scMat = scal.transformation().getMatrix()\r\n rx, ry, rz, rt = scMat.getDiagonal()\r\n else:\r\n print\"missing Scaled from the bounding ellipse as a Translated(Rotated(Scaled(Sphere)))\"\r\n rx=ry=rz=1\r\n\r\n #x1, y1, z1 #Conversion repère MappleT (m) à reprère Qualitree (q) : Xq=Xm Yq=Zm Zq=-Ym. \r\n #Due to change of coordinate axis, rotation needs - pi <-- apparently not !\r\n #return cx*unit, cz*unit, -cy*unit, rx*unit, rz*unit, ry*unit, az-3.1415927\r\n\r\n return cx*unit, cz*unit, -cy*unit, rx*unit, rz*unit, ry*unit, az","def _draw_ellipse(data, obj, draw_options):\n if isinstance(obj, mpl.patches.Circle):\n # circle specialization\n return _draw_circle(data, obj, draw_options)\n x, y = obj.center\n ff = data[\"float format\"]\n\n if obj.angle != 0:\n draw_options.append(\n f\"rotate around={{{obj.angle:{ff}}:(axis cs:{x:{ff}},{y:{ff}})}}\"\n )\n\n do = \",\".join(draw_options)\n content = (\n f\"\\\\draw[{do}] (axis cs:{x:{ff}},{y:{ff}}) ellipse \"\n f\"({0.5 * obj.width:{ff}} and {0.5 * obj.height:{ff}});\\n\"\n )\n content += _patch_legend(obj, draw_options, \"area legend\")\n\n return data, content","def drawEllipse(img, center, axes, angle, startAngle=0, endAngle=360, color = (0,0,255), fill = -1):\n\tcv2.ellipse(img, center, axes, angle, startAngle, endAngle, color, fill)","def DrawEllipseRect(*args, **kwargs):\n return _gdi_.DC_DrawEllipseRect(*args, **kwargs)","def ellipse_dist_ratio_poly(self, theta, lwr):\n\n \"\"\"\n\n Params for FWD fit\n array([ 9.99999989e-01, 8.10852195e+07, 1.95444928e+00, 7.96543026e-02])\n this one is un-needed, since it's approximation y = 1\n\n Params for FWD_DIAG fit\n array([-0.00650758, 0.57761793, 0.35369061, 1.87834152])\n\n Params for ORTHOG fit\n array([-0.02014989, 5.7007518 , -0.83345416, 0.97711175])\n\n Params for BCK_DIAG fit\n array([-0.01608705, 9.44079769, -0.92071169, 0.89094967])\n\n Params for BCK fit\n array([ -0.01451187, 10.92674105, -0.93514904, 0.87868538])\n \"\"\"\n\n #fitting function\n def f(x,params):\n return params[0] + (1.0 / (params[1]*(x+params[2])**params[3]))\n\n #force float math, in case theta is an integer\n theta = float(theta)\n\n #into an angle index form:\n t = abs(int(4.0*theta/np.pi))\n\n if (t == 0) or (t == 8):\n return 1.0\n elif (t == 1) or (t == 7):\n #forward diagonal\n return f(lwr, [-0.00650758, 0.57761793, 0.35369061, 1.87834152])\n elif (t == 2) or (t == 6):\n #orthogonal\n return f(lwr, [-0.02014989, 5.7007518 , -0.83345416, 0.97711175])\n elif (t == 3) or (t == 5):\n #backward diagonal\n return f(lwr, [-0.01608705, 9.44079769, -0.92071169, 0.89094967])\n elif t == 4:\n #backward\n return f(lwr, [ -0.01451187, 10.92674105, -0.93514904, 0.87868538])\n else:\n #hmmm... TODO\n return 0.0","def plot_ellipse(center, covariance, alpha, color):\n # eigenvalues and eigenvector of matrix covariance\n eigenvalues, eigenvector = np.linalg.eigh(covariance)\n order = eigenvalues.argsort()[::-1]\n eigenvector = eigenvector[:, order]\n\n # Calculate Angle of ellipse\n angle = np.degrees(np.arctan2(*eigenvector[:, 0][::-1]))\n\n # Calculate with, height\n width, height = 4 * np.sqrt(eigenvalues[order])\n\n # Ellipse Object\n ellipse = Ellipse(xy=center, width=width, height=height, angle=angle,\n alpha=alpha, color=color)\n\n ax = plt.gca()\n ax.add_artist(ellipse)\n\n return ellipse","def add_ellipse(self, x, y, w, h, fill_style=None, outline_style=None):\n if w < 1 or h < 1:\n return\n fill_style = self.__prepare_style(fill_style, self.style.char)\n outline_style = self.__prepare_style(outline_style, 'o')\n # Bresenham's algorithm to plot ellipse is used\n a = w\n b = h - 1\n eight_a_square = 8 * a * a\n eight_b_square = 8 * b * b\n x_change = 4 * b * b * (1.0 - a)\n y_change = 4 * a * a * ((b & 1) + 1)\n ellipse_error = x_change + y_change + (b & 1) * a * a\n x0 = x\n x1 = x0 + w - 1\n y0 = y + h / 2\n y1 = y0 - (b & 1)\n outline_points = []\n while x0 <= x1:\n # add fill\n if x0 > x and x0 < x + w - 1:\n self.add_line(int(x0), int(y0), int(x0), int(y1), fill_style)\n self.add_line(int(x1), int(y0), int(x1), int(y1), fill_style)\n outline_points.append((int(x1), int(y0)))\n outline_points.append((int(x0), int(y0)))\n outline_points.append((int(x0), int(y1)))\n outline_points.append((int(x1), int(y1)))\n two_ellipse_error = 2 * ellipse_error\n if two_ellipse_error <= y_change:\n y0 += 1\n y1 -= 1\n y_change += eight_a_square\n ellipse_error += y_change\n if two_ellipse_error >= x_change or 2 * ellipse_error > y_change:\n x0 += 1\n x1 -= 1\n x_change += eight_b_square\n ellipse_error += x_change\n while y0 - y1 <= b:\n self.add_point(int(x0 - 1), int(y0), outline_style)\n self.add_point(int(x1 + 1), int(y0), outline_style)\n self.add_point(int(x0 - 1), int(y1), outline_style)\n self.add_point(int(x1 + 1), int(y1), outline_style)\n y0 += 1\n y1 -= 1\n # draw outline over fill\n for outline_point in outline_points:\n px, py = outline_point\n self.add_point(px, py, outline_style)","def _getEllipseSize(self, pointInEllipse):\n x = abs(self.center[0] - pointInEllipse[0])\n y = abs(self.center[1] - pointInEllipse[1])\n if x == 0 or y == 0:\n return x, y\n # Ellipse definitions\n # e: eccentricity\n # a: length fron center to bounding box width\n # b: length fron center to bounding box height\n # Equations\n # (1) b < a\n # (2) For x,y a point in the ellipse: x^2/a^2 + y^2/b^2 = 1\n # (3) b = a * sqrt(1-e^2)\n # (4) e = sqrt(a^2 - b^2) / a\n\n # The eccentricity of the ellipse defined by a,b=x,y is the same\n # as the one we are searching for.\n swap = x < y\n if swap:\n x, y = y, x\n e = math.sqrt(x**2 - y**2) / x\n # From (2) using (3) to replace b\n # a^2 = x^2 + y^2 / (1-e^2)\n a = math.sqrt(x**2 + y**2 / (1.0 - e**2))\n b = a * math.sqrt(1 - e**2)\n if swap:\n a, b = b, a\n return a, b","def tilted_ellipse(s, pos1, pos2, size_x, size_y, color, angle):\n surface = pygame.Surface((150, 150), pygame.SRCALPHA, 32).convert_alpha()\n ellipse(surface, color, (0, 0, size_x, size_y))\n surface2 = pygame.transform.rotate(surface, angle)\n return s.blit(surface2, (pos1, pos2))","def ellipse(x,y,a,b):\n return ((x/float(a))**2 + (y/float(b))**2)","def draw_ellipse_outline(center_x, center_y, width, height, color,\n border_width=1, tilt_angle=0):\n\n num_segments = 128\n\n GL.glEnable(GL.GL_BLEND)\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\n GL.glEnable(GL.GL_LINE_SMOOTH)\n GL.glHint(GL.GL_LINE_SMOOTH_HINT, GL.GL_NICEST)\n GL.glHint(GL.GL_POLYGON_SMOOTH_HINT, GL.GL_NICEST)\n\n GL.glLoadIdentity()\n GL.glTranslatef(center_x, center_y, 0)\n GL.glRotatef(tilt_angle, 0, 0, 1)\n GL.glLineWidth(border_width)\n\n # Set color\n if len(color) == 4:\n GL.glColor4ub(color[0], color[1], color[2], color[3])\n elif len(color) == 3:\n GL.glColor4ub(color[0], color[1], color[2], 255)\n\n GL.glBegin(GL.GL_LINE_LOOP)\n for segment in range(num_segments):\n theta = 2.0 * 3.1415926 * segment / num_segments\n\n x = width * math.cos(theta)\n y = height * math.sin(theta)\n\n GL.glVertex3f(x, y, 0.5)\n\n GL.glEnd()\n GL.glLoadIdentity()","def fit_ellipse(*args, equatorial_radius, dequatorial_radius=0, center_f=0, dcenter_f=0, center_g=0,\n dcenter_g=0, oblateness=0, doblateness=0, position_angle=0, dposition_angle=0,\n loop=10000000, number_chi=10000, dchi_min=None, verbose=False, ellipse_error=0, sigma_result=1):\n from sora.extra import ChiSquare\n from sora.config.visuals import progressbar_show\n from astropy.coordinates import Angle\n from .core import Occultation\n\n v = {'dcenter_f': dcenter_f, 'dcenter_g': dcenter_g, 'doblateness': doblateness, 'dposition_angle': dposition_angle,\n 'dequatorial_radius': dequatorial_radius, 'ellipse_error': ellipse_error, 'sigma_result': sigma_result,\n 'dchi_min': dchi_min}\n for key, item in v.items():\n if item is not None and item < 0:\n raise ValueError(\"{} must be a positive number.\".format(key))\n\n values = []\n chord_name = []\n if len(args) == 0:\n raise ValueError('No occultation have been given as input.')\n for occ in args:\n if not isinstance(occ, Occultation):\n raise TypeError('Given argument must be an Occultation object.')\n for name, chord in occ.chords.items():\n if chord.status() == 'positive':\n if chord.is_able['immersion']:\n f, g, vf, vg = chord.get_fg(time='immersion', vel=True)\n err = np.linalg.norm([vf, vg])*chord.lightcurve.immersion_err\n values.append([f, g, err])\n chord_name.append(name + '_immersion')\n if chord.is_able['emersion']:\n f, g, vf, vg = chord.get_fg(time='emersion', vel=True)\n err = np.linalg.norm([vf, vg])*chord.lightcurve.emersion_err\n values.append([f, g, err])\n chord_name.append(name + '_emersion')\n\n controle_f0 = Time.now()\n f0_chi = np.array([])\n g0_chi = np.array([])\n a_chi = np.array([])\n obla_chi = np.array([])\n posang_chi = np.array([])\n chi2_best = np.array([])\n\n while len(f0_chi) < number_chi:\n progressbar_show(len(f0_chi), number_chi, prefix='Ellipse fit:')\n chi2 = np.zeros(loop)\n f0 = center_f + dcenter_f*(2*np.random.random(loop) - 1)\n g0 = center_g + dcenter_g*(2*np.random.random(loop) - 1)\n a = equatorial_radius + dequatorial_radius*(2*np.random.random(loop) - 1)\n obla = oblateness + doblateness*(2*np.random.random(loop) - 1)\n obla[obla < 0], obla[obla > 1] = 0, 1\n phi_deg = position_angle + dposition_angle*(2*np.random.random(loop) - 1)\n controle_f1 = Time.now()\n\n for fi, gi, si in values:\n b = a - a*obla\n phi = phi_deg*(np.pi/180.0)\n dfi = fi-f0\n dgi = gi-g0\n theta = np.arctan2(dgi, dfi)\n ang = theta+phi\n r_model = (a*b)/np.sqrt((a*np.sin(ang))**2 + (b*np.cos(ang))**2)\n f_model = f0 + r_model*np.cos(theta)\n g_model = g0 + r_model*np.sin(theta)\n chi2 += ((fi - f_model)**2 + (gi - g_model)**2)/(si**2 + ellipse_error**2)\n\n controle_f2 = Time.now()\n if dchi_min is not None:\n region = np.where(chi2 < chi2.min() + dchi_min)[0]\n else:\n region = np.arange(len(chi2))\n chi2_best = np.append(chi2_best, chi2[region])\n if verbose:\n print('Elapsed time: {:.3f} seconds.'.format((controle_f2 - controle_f1).sec))\n print(len(chi2[region]), len(chi2_best))\n f0_chi = np.append(f0_chi, f0[region])\n g0_chi = np.append(g0_chi, g0[region])\n a_chi = np.append(a_chi, a[region])\n obla_chi = np.append(obla_chi, obla[region])\n posang_chi = np.append(posang_chi, phi_deg[region])\n\n progressbar_show(number_chi, number_chi, prefix='Ellipse fit:')\n chisquare = ChiSquare(chi2_best, len(values), center_f=f0_chi, center_g=g0_chi, equatorial_radius=a_chi,\n oblateness=obla_chi, position_angle=posang_chi)\n controle_f4 = Time.now()\n if verbose:\n print('Total elapsed time: {:.3f} seconds.'.format((controle_f4 - controle_f0).sec))\n\n result_sigma = chisquare.get_nsigma(sigma=sigma_result)\n a = result_sigma['equatorial_radius'][0]\n f0 = result_sigma['center_f'][0]\n g0 = result_sigma['center_g'][0]\n obla = result_sigma['oblateness'][0]\n phi_deg = result_sigma['position_angle'][0]\n radial_dispersion = np.array([])\n error_bar = np.array([])\n position_angle_point = np.array([])\n\n for fi, gi, si in values:\n b = a - a*obla\n phi = phi_deg*(np.pi/180.0)\n dfi = fi-f0\n dgi = gi-g0\n r = np.sqrt(dfi**2 + dgi**2)\n theta = np.arctan2(dgi, dfi)\n ang = theta+phi\n r_model = (a*b)/np.sqrt((a*np.sin(ang))**2 + (b*np.cos(ang))**2)\n radial_dispersion = np.append(radial_dispersion, r - r_model)\n error_bar = np.append(error_bar, si)\n position_angle_point = np.append(position_angle_point, Angle(90*u.deg - theta*u.rad).wrap_at(360 * u.deg).degree)\n\n for occ in args:\n if isinstance(occ, Occultation):\n occ.fitted_params = {i: result_sigma[i] for i in ['equatorial_radius', 'center_f', 'center_g',\n 'oblateness', 'position_angle']}\n occ.chi2_params = {'chord_name': chord_name, 'radial_dispersion': radial_dispersion,\n 'position_angle': position_angle_point, 'radial_error': error_bar,\n 'chi2_min': chisquare.get_nsigma(sigma=sigma_result)['chi2_min'],\n 'nparam': chisquare.nparam, 'npts': chisquare.npts}\n return chisquare","def ellipse(self, arg, fill='', outline=''):\n pass","def fitRectangle(self):\n \n #TODO MAKE SOMETHING MORE GENERIC!!\n \n fA, (fXg, fYg) = self.getArea_and_CenterOfMass()\n \n x1,y1, x2,y2 = self.getBoundingBox()\n #build a rectangle with same \"width\" as the polygon... is-it good enough??\n w = x2 - x1\n \n #but this width should not lead to go out of the bounding box!\n fW = min(w, (x2-fXg)*2, (fXg-x1)*2)\n \n #same area\n fH = fA / fW\n \n x1,y1, x2,y2 = [ int(round(v)) for v in [ fXg - fW/2.0, fYg - fH/2\n , fXg + fW/2.0, fYg + fH/2 ]]\n \n return x1,y1, x2,y2","def get_ellipse_mask(ellipse, img_shape, offset=0):\n # create image\n mask = np.zeros((img_shape[0], img_shape[1]), dtype=np.uint8)\n\n try:\n # fill ellipse\n draw_ellipse(mask, ellipse, (255, 255, 255), -1)\n except Exception as ex:\n logging.getLogger(\"StrainDetection\").warning(\"Unable to create ellipse mask: {}\".format(ex))\n mask += 255 # make mask white to include everything\n return mask\n\n # dilate/erode by given offset, if necessary\n if offset != 0:\n operation = cv.MORPH_DILATE\n if offset < 0:\n operation = cv.MORPH_ERODE # if offset is negative --> erode\n\n # create kernel\n n = 2 * abs(offset) + 1\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (n, n))\n\n # perform morphological operation\n mask = cv.morphologyEx(mask, operation, kernel)\n\n return mask","def ellipsoid(center, radii, rotation, scales=None, shape=None, minarea=0):\n center = np.array(center)\n radii = np.array(radii)\n rotation = np.array(rotation)\n assert center.shape == (3,)\n assert radii.shape == (3,)\n assert 0 < radii.max(), \"radii should contain at least one positive value\"\n assert rotation.shape == (3, 3)\n if scales is None:\n scales = (1.,) * 3\n scales = np.array(scales)\n assert scales.shape == (3,)\n\n scaled_center = center / scales\n\n # The upper_left_bottom and lower_right_top corners of the smallest cuboid\n # containing the ellipsoid.\n factor = np.array([\n [i, j, k] for k in (-1, 1) for j in (-1, 1) for i in (-1, 1)]).T\n while True:\n radii_rot = np.abs(\n np.diag(1. / scales).dot(rotation.dot(np.diag(radii).dot(factor)))\n ).max(axis=1)\n # In the original scikit-image code, ceil and floor were replaced.\n # https://github.com/scikit-image/scikit-image/blob/master/skimage/draw/draw.py#L127\n upper_left_bottom = np.floor(scaled_center - radii_rot).astype(int)\n lower_right_top = np.ceil(scaled_center + radii_rot).astype(int)\n\n if shape is not None:\n # Constrain upper_left and lower_ight by shape boundary.\n upper_left_bottom = np.maximum(\n upper_left_bottom, np.array([0, 0, 0]))\n lower_right_top = np.minimum(\n lower_right_top, np.array(shape[:3]) - 1)\n\n bounding_shape = lower_right_top - upper_left_bottom + 1\n\n d_lim, r_lim, c_lim = np.ogrid[0:float(bounding_shape[0]),\n 0:float(bounding_shape[1]),\n 0:float(bounding_shape[2])]\n d_org, r_org, c_org = scaled_center - upper_left_bottom\n d_rad, r_rad, c_rad = radii\n rotation_inv = np.linalg.inv(rotation)\n conversion_matrix = rotation_inv.dot(np.diag(scales))\n d, r, c = (d_lim - d_org), (r_lim - r_org), (c_lim - c_org)\n distances = (\n ((d * conversion_matrix[0, 0] +\n r * conversion_matrix[0, 1] +\n c * conversion_matrix[0, 2]) / d_rad) ** 2 +\n ((d * conversion_matrix[1, 0] +\n r * conversion_matrix[1, 1] +\n c * conversion_matrix[1, 2]) / r_rad) ** 2 +\n ((d * conversion_matrix[2, 0] +\n r * conversion_matrix[2, 1] +\n c * conversion_matrix[2, 2]) / c_rad) ** 2\n )\n if distances.size < minarea:\n old_radii = radii.copy()\n radii *= 1.1\n print('Increase radii from ({}) to ({})'.format(old_radii, radii))\n else:\n break\n distance_thresh = 1\n while True:\n dd, rr, cc = np.nonzero(distances < distance_thresh)\n if len(dd) < minarea:\n distance_thresh *= 1.1\n else:\n break\n dd.flags.writeable = True\n rr.flags.writeable = True\n cc.flags.writeable = True\n dd += upper_left_bottom[0]\n rr += upper_left_bottom[1]\n cc += upper_left_bottom[2]\n return dd, rr, cc","def ci95_ellipse(data, type=\"pop\"):\n\n # Build and fit PCA model\n pca = PCA()\n pca.fit(data)\n coeff = pca.components_\n score = pca.transform(data)\n eigvals = pca.explained_variance_\n\n # Calculate rotation angle\n phi = math.atan2(coeff[0, 1], coeff[0, 0])\n\n # This angle is between -pi and pi.\n # Let's shift it such that the angle is between 0 and 2pi\n if phi < 0:\n phi += 2 * math.pi\n\n # Get the coordinates of the data mean\n n = len(data)\n m = np.mean(data, axis=0)\n x0 = m[0]\n y0 = m[1]\n\n # Get the 95% confidence interval error ellipse\n # inverse of the chi-square cumulative distribution for p = 0.05 & 2 d.f. = 5.9915\n chisquare_val = 5.9915\n if type is \"pop\":\n a = math.sqrt(chisquare_val * eigvals[0])\n b = math.sqrt(chisquare_val * eigvals[1])\n elif type is \"mean\":\n a = math.sqrt(chisquare_val * eigvals[0] / n)\n b = math.sqrt(chisquare_val * eigvals[1] / n)\n else:\n raise ValueError(\"type has to be 'pop' or 'mean'.\")\n\n # the ellipse in x and y coordinates\n theta_grid = np.linspace(0, 2 * math.pi, num=100)\n ellipse_x_r = a * np.cos(theta_grid)\n ellipse_y_r = b * np.sin(theta_grid)\n\n # Define a rotation matrix\n R = np.array([[np.cos(phi), np.sin(phi)], [-np.sin(phi), np.cos(phi)]])\n # let's rotate the ellipse to some angle phi\n r_ellipse = np.dot(np.vstack((ellipse_x_r, ellipse_y_r)).T, R)\n\n # Draw the error ellipse\n x = r_ellipse[:, 0] + x0\n y = r_ellipse[:, 1] + y0\n ellipse = np.stack((x, y), axis=1)\n\n outside = []\n for i in range(len(score)):\n metric = (score[i, 0] / a) ** 2 + (score[i, 1] / b) ** 2\n if metric > 1:\n outside.append(1)\n else:\n outside.append(0)\n\n return ellipse, outside","def ellipse(radii = (10,5), angle_resolution = 2.5, layer = 0):\n D = Device(name = 'ellipse')\n a = radii[0]\n b = radii[1]\n t = np.linspace(0, 360, int(np.ceil(360/angle_resolution) + 1)) * pi/180\n r = a*b / (sqrt((b*cos(t))**2 + (a*sin(t))**2))\n xpts = r*cos(t)\n ypts = r*sin(t)\n D.add_polygon(points = (xpts, ypts), layer = layer)\n return D","def ellipse2pathd(ellipse):\n\n cx = ellipse.get('cx', 0)\n cy = ellipse.get('cy', 0)\n rx = ellipse.get('rx', None)\n ry = ellipse.get('ry', None)\n r = ellipse.get('r', None)\n\n if r is not None:\n rx = ry = float(r)\n else:\n rx = float(rx)\n ry = float(ry)\n\n cx = float(cx)\n cy = float(cy)\n\n d = ''\n d += 'M' + str(cx - rx) + ',' + str(cy)\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\n\n return d","def ellipse(self, x, y, radiusx, radiusy, rotation=0, startangle=0, endangle=2 * pi, anticlockwise=False):\n self._impl.ellipse(x, y, radiusx, radiusy, rotation, startangle, endangle, anticlockwise)","def fitEllipse(cnt):\n\t(x,y), (MA, ma), angle = cv2.fitEllipse(cnt)\n\treturn {\"center\":(x,y), \"major\":MA, \"minor\":ma, \"angle\":angle}","def ellipse2pathd(ellipse):\n\n cx = ellipse.get('cx', None)\n cy = ellipse.get('cy', None)\n rx = ellipse.get('rx', None)\n ry = ellipse.get('ry', None)\n r = ellipse.get('r', None)\n\n if r is not None:\n rx = ry = float(r)\n else:\n rx = float(rx)\n ry = float(ry)\n\n cx = float(cx)\n cy = float(cy)\n\n d = ''\n d += 'M' + str(cx - rx) + ',' + str(cy)\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\n\n return d","def ellipse2pathd(ellipse):\n\n cx = ellipse.get('cx', None)\n cy = ellipse.get('cy', None)\n rx = ellipse.get('rx', None)\n ry = ellipse.get('ry', None)\n r = ellipse.get('r', None)\n\n if r is not None:\n rx = ry = float(r)\n else:\n rx = float(rx)\n ry = float(ry)\n\n cx = float(cx)\n cy = float(cy)\n\n d = ''\n d += 'M' + str(cx - rx) + ',' + str(cy)\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\n\n return d","def DrawEllipseRect(*args, **kwargs):\n return _gdi_.PseudoDC_DrawEllipseRect(*args, **kwargs)","def draw_ellipse(self, color, position, size=None,\n border_width=0, anchor='topleft'):\n if size is None:\n rect = spyral.Rect(position)\n else:\n rect = spyral.Rect(position, size)\n offset = self._calculate_offset(anchor, rect.size)\n pygame.draw.ellipse(self._surf, color,\n (rect.pos + offset, rect.size), border_width)\n self._version += 1\n spyral.util.scale_surface.clear(self._surf)\n return self","def get_eccentricity(self, ellipse):\r\n a = ellipse.get_width()\r\n b = ellipse.get_height()\r\n if b > a:\r\n a, b = b, a\r\n c = np.sqrt(a**2 - b**2)\r\n return fdiv(c, a)","def r_ellipse(self,xc=None,yc=None):\n x = self.x\n y = self.y\n if xc == None:\n xc = self.x1\n if yc == None:\n yc = self.y1\n self.rel = sqrt(self.cxx*(x-xc)**2 +\n\t\t self.cyy*(y-yc)**2 +\n\t\t self.cxy*(x-xc)*(y-yc)\n\t\t )","def draw_ellipse_filled(center_x, center_y,\n width, height, color, tilt_angle=0):\n\n num_segments = 128\n\n GL.glEnable(GL.GL_BLEND)\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\n GL.glEnable(GL.GL_LINE_SMOOTH)\n GL.glHint(GL.GL_LINE_SMOOTH_HINT, GL.GL_NICEST)\n GL.glHint(GL.GL_POLYGON_SMOOTH_HINT, GL.GL_NICEST)\n\n GL.glLoadIdentity()\n GL.glTranslatef(center_x, center_y, 0)\n GL.glRotatef(tilt_angle, 0, 0, 1)\n\n # Set color\n if len(color) == 4:\n GL.glColor4ub(color[0], color[1], color[2], color[3])\n elif len(color) == 3:\n GL.glColor4ub(color[0], color[1], color[2], 255)\n\n GL.glBegin(GL.GL_TRIANGLE_FAN)\n\n GL.glVertex3f(0, 0, 0.5)\n\n for segment in range(num_segments + 1):\n theta = 2.0 * 3.1415926 * segment / num_segments\n\n x = width * math.cos(theta)\n y = height * math.sin(theta)\n\n GL.glVertex3f(x, y, 0.5)\n\n GL.glEnd()\n GL.glLoadIdentity()","def cartesian_to_ellipse(center, angle, lengths):\n xInd, yInd = np.mgrid[:512, :512]\n major = max(lengths)/np.mean(lengths)\n minor = min(lengths)/np.mean(lengths)\n xInd, yInd = xInd - center[0], yInd - center[1]\n xInd, yInd = rotate(xInd, yInd, angle=-angle)\n xInd, yInd = xInd*minor, yInd*major\n xInd, yInd = rotate(xInd, yInd, angle=angle)\n return xInd, yInd","def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0,\n **kwargs):\n ellipse = Ellipse(xy=centroid, width=length, height=width,\n angle=np.degrees(angle), fill=False, **kwargs)\n self.axes.add_patch(ellipse)\n self.update()\n return ellipse","def render_ellipse_filled(shape, center_x, center_y, color, angle=0):\n # Set color\n if len(color) == 4:\n GL.glColor4ub(shape.color[0], shape.color[1], shape.color[2],\n shape.color[3])\n GL.glEnable(GL.GL_BLEND)\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\n elif len(color) == 3:\n GL.glDisable(GL.GL_BLEND)\n GL.glColor4ub(shape.color[0], shape.color[1], shape.color[2], 255)\n\n GL.glBindBuffer(GL.GL_ARRAY_BUFFER, shape.vbo_id)\n GL.glVertexPointer(2, GL.GL_FLOAT, 0, 0)\n\n GL.glLoadIdentity()\n GL.glTranslatef(center_x, center_y, 0)\n if angle:\n GL.glRotatef(angle, 0, 0, 1)\n\n GL.glDrawArrays(GL.GL_TRIANGLE_FAN, 0, shape.size)","def draw_ellipse(mat, radius_x, radius_y, angle, color=(0, 0, 255), thickness=1):\n cv2.ellipse(mat, (radius_x, radius_y), angle, 0, 0, color, thickness=thickness)","def box_ellipse(A, r):\n A = np.array(A)\n A = A.transpose().dot(A)\n size = len(A)\n widths = []\n for i in range(size):\n setperp = [k for k in range(i)] + [k for k in range(i + 1, size)]\n v = A[i, setperp]\n A22 = A[setperp][:, setperp]\n try:\n Aperpinv = np.linalg.inv(A22)\n gamma = Aperpinv.dot(v)\n gamma = gamma.dot(v)\n widths.append(r / np.sqrt(A[i, i] - gamma))\n except np.linalg.linalg.LinAlgError:\n widths.append(1.0e300)\n return widths","def draw_ellipse(image, bounds, width=1, outline: Color = 'white', antialias=4):\n\n # Use a single channel image (mode='L') as mask.\n # The size of the mask can be increased relative to the imput image\n # to get smoother looking results.\n mask = Image.new(size=[int(dim * antialias) for dim in image.size], mode='L', color='black')\n draw = ImageDraw.Draw(mask)\n\n # draw outer shape in white (color) and inner shape in black (transparent)\n for offset, fill in (-7, 'white'), (width, 'black'):\n left, top = [(value + offset) * antialias for value in bounds[:2]]\n right, bottom = [(value - offset) * antialias for value in bounds[2:]]\n draw.ellipse([left, top, right, bottom], fill=fill)\n\n # downsample the mask using PIL.Image.LANCZOS\n # (a high-quality downsampling filter).\n mask = mask.resize(image.size, Image.LANCZOS)\n # paste outline color to input image through the mask\n image.paste(outline, mask=mask)","def ellipse_pt(th, x_c, y_c, a, b, rot):\n x = x_c + (a * cos(th) * cos(rot) - b * sin(th) * sin(rot))\n y = y_c + (a * cos(th) * sin(rot) - b * sin(th) * cos(rot))\n return x, y","def r_ellipse(radius=5, xc=[0., 0.], q=0.5, pa=0, re=1., gf_header=None, comp='2', verbose=True, nstep=256, psf_offset=[1, 1], **kwargs):\n if gf_header is not None:\n xx = gf_header_key(gf_header, comp+'_XC')\n yy = gf_header_key(gf_header, comp+'_YC')\n xc = np.array([xx, yy])\n mag = gf_header_key(gf_header, comp+'_MAG')\n\n if comp+'_N' in gf_header:\n n = gf_header_key(gf_header, comp+'_N')\n q = gf_header_key(gf_header, comp+'_AR')\n pa = gf_header_key(gf_header, comp+'_PA')\n re = gf_header_key(gf_header, comp+'_RE')\n else:\n n = 1.\n q = 1.\n pa = 0.\n re = 0.01\n\n if verbose:\n print(f'xc:{xc}, q:{q}, pa:{pa}')\n\n phi = np.linspace(0, 2*np.pi, nstep)\n xp = np.array([np.cos(phi), q*np.sin(phi)]).T*radius\n\n theta = -(np.pi/2 + pa/180*np.pi) # + np.pi\n\n _rot = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])\n\n x0 = xp.dot(_rot) + np.atleast_1d(xc)\n xell, yell = (x0 - 1 - np.atleast_1d(psf_offset)).T\n\n return xell, yell","def drawEllipse(self,id,x,y,rx,ry):\n if id in self.elements.keys():\n print(\"The id for the ellipse has been registered! Please use another one\")\n return\n try:\n self.checkInBound(x,0); self.checkInBound(x+rx,0); self.checkInBound(x-rx,0)\n except AssertionError as e:\n print(\"The ellipse set would be longer than the x axis of canvas!\")\n return\n try:\n self.checkInBound(y,0); self.checkInBound(y+ry,0); self.checkInBound(y-ry,0)\n except AssertionError as e:\n print(\"The ellipse set would be longer than the y axis of canvas!\")\n return\n ellipseEle=Ellipse(id,self.h-1-y,x,ry,rx,self.drawColor)\n self.elements[id]=ellipseEle\n self.sync=False","def fill_ellipse(self, x0, y0, a, b, color):\n a2 = a * a\n b2 = b * b\n twoa2 = a2 + a2\n twob2 = b2 + b2\n x = 0\n y = b\n px = 0\n py = twoa2 * y\n # Plot initial points\n self.line(x0, y0 - y, x0, y0 + y, color)\n # Region 1\n p = round(b2 - (a2 * b) + (0.25 * a2))\n while px < py:\n x += 1\n px += twob2\n if p < 0:\n p += b2 + px\n else:\n y -= 1\n py -= twoa2\n p += b2 + px - py\n self.line(x0 + x, y0 - y, x0 + x, y0 + y, color)\n self.line(x0 - x, y0 - y, x0 - x, y0 + y, color)\n # Region 2\n p = round(b2 * (x + 0.5) * (x + 0.5) +\n a2 * (y - 1) * (y - 1) - a2 * b2)\n while y > 0:\n y -= 1\n py -= twoa2\n if p > 0:\n p += a2 - py\n else:\n x += 1\n px += twob2\n p += a2 - py + px\n self.line(x0 + x, y0 - y, x0 + x, y0 + y, color)\n self.line(x0 - x, y0 - y, x0 - x, y0 + y, color)","def in_ellipse(x,y,a,b):\n return ellipse(x,y,a,b) <= 1","def scatter_ellipse(axis_ob, x, y, w, h, c='b', a=0.0, alpha=0.5):\r\n if not axis_ob._hold:\r\n axis_ob.cla()\r\n\r\n if not iterable(a):\r\n a = [a] * len(x)\r\n\r\n if not iterable(alpha):\r\n alpha = [alpha] * len(x)\r\n if len(c) != len(x):\r\n raise ValueError('c and x are not equal lengths')\r\n if len(w) != len(x):\r\n raise ValueError('w and x are not equal lengths')\r\n\r\n if len(h) != len(x):\r\n raise ValueError('h and x are not equal lengths')\r\n if len(a) != len(x):\r\n raise ValueError('a and x are not equal lengths')\r\n # if len(alpha)!=len(x):\r\n # raise ValueError, 'alpha and x are not equal lengths'\r\n patches = []\r\n for thisX, thisY, thisW, thisH, thisC, thisA, thisAl in \\\r\n zip(x, y, w, h, c, a, alpha):\r\n ellip = Ellipse((thisX, thisY), width=thisW, height=thisH,\r\n angle=thisA)\r\n\r\n ellip.set_facecolor(thisC)\r\n ellip.set_alpha(thisAl)\r\n axis_ob.add_patch(ellip)\r\n patches.append(ellip)\r\n axis_ob.autoscale_view()\r\n return axis_ob","def ellipseToPath(self,node):\n cx = float(node['cx'])\n cy = float(node['cy'])\n rx = 0\n ry = 0\n if 'rx' in node:\n rx = float(node['rx'])\n if 'ry' in node:\n ry = float(node['ry'])\n\n d ='M %f,%f '%(cx-rx,cy)\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx,cy-ry)\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx+rx,cy)\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx,cy+ry)\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx-rx,cy)\n\n return d","def smallest_ellipse(points, tol = 0.001):\n points = np.asmatrix(points)\n N, d = points.shape\n Q = np.column_stack((points, np.ones(N))).T\n err = tol+1.0\n u = np.ones(N)/N\n \n while err > tol:\n # assert u.sum() == 1 # invariant\n X = Q * np.diag(u) * Q.T\n M = np.diag(Q.T * la.inv(X) * Q)\n jdx = np.argmax(M)\n step_size = (M[jdx]-d-1.0)/((d+1)*(M[jdx]-1.0))\n new_u = (1-step_size)*u\n new_u[jdx] += step_size\n err = la.norm(new_u-u)\n u = new_u\n c = u*points\n A = la.inv(points.T*np.diag(u)*points - c.T*c)/d\n return np.asarray(A), np.squeeze(np.asarray(c))","def create_ellipse(home_location, work_location, spread):\n\tif work_location is None:\n\t\treturn None\n\telse:\n\t\ta = home_location.distance(work_location)\n\t\tb = a * float(spread)\n\t\tpoint_list = []\n\t\tazimuth = math.atan2(work_location.y - home_location.y, work_location.x - home_location.x)\n\t\tro = (math.pi / 200)\n\n\t\tfor t in range(0, 401):\n\t\t\tx = home_location.x + (a * math.cos(t * ro) * math.cos(azimuth) - b * math.sin(t * ro) * math.sin(azimuth))\n\t\t\ty = home_location.y + (b * math.sin(t * ro) * math.cos(azimuth) + a * math.cos(t * ro) * math.sin(azimuth))\n\t\t\tpoint_list.append([Point(x, y).x, Point(x, y).y])\n\t\treturn Polygon(point_list)","def create_ellipse(width, height, color):\n num_segments = 64\n\n data = []\n\n for segment in range(num_segments + 1):\n theta = 2.0 * 3.1415926 * segment / num_segments\n\n x = width * math.cos(theta)\n y = height * math.sin(theta)\n\n data.extend([x, y])\n\n vbo_id = GL.GLuint()\n\n GL.glGenBuffers(1, ctypes.pointer(vbo_id))\n\n v2f = data\n data2 = (GL.GLfloat * len(v2f))(*v2f)\n\n GL.glBindBuffer(GL.GL_ARRAY_BUFFER, vbo_id)\n GL.glBufferData(GL.GL_ARRAY_BUFFER, ctypes.sizeof(data2), data2,\n GL.GL_STATIC_DRAW)\n\n shape = VertexBuffer(vbo_id, len(v2f) // 2, width, height, color)\n return shape","def getEllipticalKernel(size = (5,5)):\n\treturn cv2.getStructuringElement(cv2.MORPH_ELLIPSE, size)","def draw_probe_ellipse(xy, covar, alpha, color=None, **kwargs):\n \n b24ac = scipy.sqrt(pow(covar[0,0] - covar[1,1],2) + 4*pow(covar[0,1],2))\n c2inv = chi2.ppf(alpha, 2.)\n \n a = scipy.real(scipy.sqrt(c2inv*.5*(covar[0,0] + covar[1,1] + b24ac)))\n b = scipy.real(scipy.sqrt(c2inv*.5*(covar[0,0] + covar[1,1] - b24ac)))\n\n if covar[0,0] != covar[1,1]:\n theta = .5*scipy.arctan(2*covar[0,1]/(covar[0,0] - covar[1,1]))\n print(theta)\n else:\n theta = scipy.sign(covar[0,1])*scipy.pi/4\n \n if covar[1,1] > covar[0,0]:\n swap = a\n a = b\n b = swap\n\n ellipse = Ellipse(xy, 2*a, 2*b, angle=theta*180./scipy.pi, edgecolor=color, fill=False, **kwargs)\n plt.gca().add_patch(ellipse)\n return ellipse","def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0, **kwargs):\n ellipse = Ellipse(\n x=centroid[0],\n y=centroid[1],\n width=length,\n height=width,\n angle=angle,\n fill_color=None,\n **kwargs,\n )\n glyph = self.figure.add_glyph(ellipse)\n self._annotations.append(glyph)\n self.update()\n return ellipse","def construct_by_ellipse(a_xx, h_xy, b_yy, g_x, f_y, d, focal_length):\n gamma = - focal_length\n a = gamma**2 * a_xx\n b = gamma**2 * b_yy\n c = d\n d = gamma**2 * d\n f = -gamma*(f_y)\n g = -gamma*(g_x)\n h = gamma**2 * h_xy\n #Not needed\n u = gamma**2 * g_x\n v = gamma**2 * f_y\n w = -gamma*(d)\n return ConeCamera(a, b, c, f, g, h)","def proc_filled_ellipse(self, tokens):\n\n return self._proc_ellipse(tokens, filled=True)","def plot_cov_ellipse(ellipses, cov, pos=[0.0, 0.0], nstds=[0.0,1.0,2.0], **kwargs):\n def eigsorted(cov):\n vals, vecs = _np.linalg.eigh(cov)\n order = vals.argsort()[::-1]\n return vals[order], vecs[:,order]\n\n\n vals, vecs = eigsorted(cov)\n theta = _np.degrees(_np.arctan2(*vecs[:,0][::-1]))\n\n # Width and height are \"full\" widths, not radius\n sigma_max = 0.5\n alpha = min(0.8, _np.prod(sigma_max /_np.sqrt(vals)))\n for i,e in enumerate(ellipses):\n sigma = nstds[i]\n width, height = 2 * sigma * _np.sqrt(vals)\n #ellipses[i].center = pos\n e.set_alpha(alpha)\n if sigma > 0.1: #if this is below, then treat ellipse as a center circle and do not modify size at all\n e.width = width\n e.height= height\n e.angle = theta\n e.center = pos\n e.set(**kwargs)\n\n# e.fill=True\n# e.set_linewidth(0.0)\n\n\n return ellipses","def draw_ellipse(self, color, position, size, border_width = 0, anchor= 'topleft'):\n # We'll try to make sure that everything is okay later\n \n color = spyral.color._determine(color)\n offset = self._calculate_offset(anchor, size)\n pygame.draw.ellipse(self._surf, color, (position + offset, size), border_width)","def geom_ellipse(mean: np.array, cov: np.array, data: np.array=None, q: float=0.95, **kwargs):\n\n # Radius that covers q-fraction of white gaussian noise \n r = np.sqrt(stats.chi2.ppf(q=q, df=2))\n \n # Eigen-directions of a covariance matrix\n try:\n L, W = np.linalg.eigh(cov)\n except:\n return geom_path(aes(x = 'x', y = 'y'), data = DataFrame(columns=['x', 'y']))\n \n # Properly scaled eigen-directions\n W[0, :] = W[0, :] * r * np.sqrt(L[0]) \n W[1, :] = W[1, :] * r * np.sqrt(L[1]) \n \n theta = np.linspace(0, 2 * np.pi, 100)\n \n return geom_path(aes(x = 'x', y = 'y'), data = DataFrame()\n .assign(x = mean[0] + np.sin(theta) * W[0, 0] + np.cos(theta) * W[1, 0])\n .assign(y = mean[1] + np.sin(theta) * W[0, 1] + np.cos(theta) * W[1, 1]), **kwargs)","def extractOblateEllipse(kperp,kpar,aniso):\n\n if aniso > 1.:\n #print(\"Swapping axis for oblate ellipse\")\n aniso = 1. / aniso\n\n # Define the eccentricity of the ellipse\n e = np.sqrt( 1. - aniso**2. )\n\n\n # the oblate surface area\n surface = 2. * np.pi * kperp**2. * ( 1. + ( (1. - e**2.) / e ) * np.arctanh(e) )\n\n return surface","def getEllipse(self, xc, Sigma, nSigma=2):\n\n if nla.det(Sigma) == 0:\n return None\n\n w, v = nla.eig(Sigma)\n D = np.diag(w, 0)\n\n theta = np.linspace(0, 2*np.pi, 100, endpoint=True)\n circle = nSigma*np.vstack((np.cos(theta), np.sin(theta)))\n\n el = sla.sqrtm(D)\n el = el.dot(circle)\n el = v.dot(el)\n\n XY = xc + el\n\n return XY","def _save_parameters(self):\n\n # eigenvectors are the coefficients of an ellipse in general form\n # a*x^2 + 2*b*x*y + c*y^2 + 2*d*x + 2*f*y + g = 0 [eqn. 15) from (**) or (***)\n a = self.coef[0, 0]\n b = self.coef[1, 0]/2.\n c = self.coef[2, 0]\n d = self.coef[3, 0]/2.\n f = self.coef[4, 0]/2.\n g = self.coef[5, 0]\n\n # finding center of ellipse [eqn.19 and 20] from (**)\n x0 = (c*d-b*f)/(b**2.-a*c)\n y0 = (a*f-b*d)/(b**2.-a*c)\n\n # Find the semi-axes lengths [eqn. 21 and 22] from (**)\n numerator = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)\n denominator1 = (b*b-a*c) * \\\n ((c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\n denominator2 = (b*b-a*c) * \\\n ((a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\n width = np.sqrt(numerator/denominator1)\n height = np.sqrt(numerator/denominator2)\n\n # angle of counterclockwise rotation of major-axis of ellipse to x-axis [eqn. 23] from (**)\n # or [eqn. 26] from (***).\n phi = .5*np.arctan((2.*b)/(a-c))\n\n self._center = [x0, y0]\n self._width = width\n self._height = height\n self._phi = phi","def ellipse_bounds(P, level, n=100):\n # Round up to multiple of 2\n n += n % 2\n\n # Principal axes of ellipsoid\n eigval, eigvec = np.linalg.eig(P)\n eigvec *= np.sqrt(level / eigval)\n\n # set zero angle at maximum x\n angle = np.linspace(0, 2 * np.pi, n)[:, None]\n angle += np.arctan(eigvec[0, 1] / eigvec[0, 0])\n\n # Compute positions\n pos = np.cos(angle) * eigvec[:, 0] + np.sin(angle) * eigvec[:, 1]\n n /= 2\n\n # Return x-position (symmetric) and upper/lower bounds\n return pos[:n, 0], pos[:n, 1], pos[:n - 1:-1, 1]","def test_ellipse_draw():\n with TestingCanvas():\n ellipse = visuals.Ellipse(pos=(0., 0.), radius=(0.4, 0.3),\n color=(0, 0, 1, 1))\n ellipse.transform = transforms.STTransform(scale=(2.0, 3.0))\n ellipse.draw()\n assert_image_equal(\"screenshot\", 'visuals/ellipse1.png')\n\n gloo.clear()\n ellipse = visuals.Ellipse(pos=(0., 0.), radius=(0.4, 0.3),\n color=(0, 0, 1, 1),\n border_color=(1, 0, 0, 1))\n ellipse.transform = transforms.STTransform(scale=(2.0, 3.0))\n ellipse.draw()\n assert_image_equal(\"screenshot\", 'visuals/ellipse2.png')\n\n gloo.clear()\n ellipse = visuals.Ellipse(pos=(0., 0.), radius=(0.4, 0.3),\n border_color=(1, 0, 0, 1))\n ellipse.transform = transforms.STTransform(scale=(2.0, 3.0))\n ellipse.draw()\n assert_image_equal(\"screenshot\", 'visuals/ellipse3.png')","def draw_oval(display, coord, box_size, color, bg_color):\n left, top = coord\n half = int(box_size * 0.5)\n quarter = int(box_size * 0.25)\n pygame.draw.ellipse(display, color, (left, top + quarter, box_size, half))","def fitEllipseDirect(points):\r\n x = points[:, 0]; y = points[:, 1];\r\n # Build design matrix\r\n D = np.vstack((x*x, x*y, y*y, x, y, np.ones(x.shape)))\r\n # Build scatter matrix\r\n S = D.dot(D.T)\r\n # Build constraint matrix\r\n C = np.zeros((6, 6))\r\n C[0, 2]= +2; C[1, 1]= -1; C[2, 0]= +2;\r\n # Solve generalised eigenvalue system C*a == l*S*a\r\n geval, gevec = linalg.eig(S, C)\r\n # Find the eigenvector with the only pozitive eigenvalue\r\n geval = np.real(geval)\r\n i = np.argmax((geval>0) * np.isfinite(geval))\r\n if not np.isfinite(geval[i]):\r\n raise linalg.LinAlgError(\r\n \"Eigenvalue calculation failed to return a valid answer.\" +\r\n \"\\nEigenvalues:\\n\" + str(geval) + '\\n')\r\n theVec = np.real(gevec[:, i])\r\n # That vector has the parameters of the ellipse\r\n return tuple(theVec.flatten())","def draw_ellipse(position, covariance, ax=None, **kwargs):\r\n # Convert covariance to principal axes\r\n if covariance.shape == (2, 2):\r\n U, s, Vt = np.linalg.svd(covariance)\r\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\r\n width, height = 2 * np.sqrt(s)\r\n else:\r\n angle = 0\r\n width, height = 2 * np.sqrt(covariance)\r\n \r\n # Draw the Ellipse\r\n for nsig in range(1, 4):\r\n ax.add_patch(Ellipse(position, nsig * width, nsig * height, \r\n angle, **kwargs))","def run_ellipse(img, redo=False):\n # Reading data and mask\n outfile = \"ellipse.txt\"\n if os.path.exists(outfile) and not redo:\n return\n data = make_masked_img(img)\n # Preparing ellipse fitting\n\n geometry = EllipseGeometry(x0=213, y0=235, sma=25, eps=0.3,\n pa=np.deg2rad(-50))\n ellipse = Ellipse(data, geometry)\n isolist = ellipse.fit_image(fflag=0.01, maxsma=200, maxrit=104)\n # isolist = ellipse.fit_image(fflag=0.01, maxsma=20)\n table = isolist.to_table()[1:]\n table.write(outfile, format=\"ascii\", overwrite=True)\n # Producing image\n model_image = build_ellipse_model(data.shape, isolist)\n residual = data - model_image\n fig, (ax1, ax2, ax3) = plt.subplots(figsize=(14, 5), nrows=1, ncols=3)\n fig.subplots_adjust(left=0.04, right=0.98, bottom=0.02, top=0.98)\n ax1.imshow(data, origin='lower')\n ax1.set_title('Data')\n\n smas = np.linspace(5, 200, 10)\n for sma in smas:\n iso = isolist.get_closest(sma)\n x, y, = iso.sampled_coordinates()\n ax1.plot(x, y, color='C1')\n ax2.imshow(model_image, origin='lower')\n ax2.set_title('Ellipse Model')\n ax3.imshow(residual, origin='lower')\n ax3.set_title('Residual')\n plt.savefig(\"ellipse.png\", dpi=250)\n plt.show()","def draw_ellipse(position, covariance, ax=None, **kwargs):\n ax = ax or plt.gca()\n # Convert covariance to principal axes\n if covariance.shape == (2, 2):\n U, s, Vt = np.linalg.svd(covariance)\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n width, height = 2 * np.sqrt(s)\n else:\n angle = 0\n width, height = 2 * np.sqrt(covariance)\n \n # Draw the Ellipse\n for nsig in range(1, 4):\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n angle, **kwargs))","def ellipse_orientation(S):\n return 1/2 * np.arctan2(S[..., 2], S[..., 1])","def draw_ellipse(position, covariance, ax=None, **kwargs):\n ax = ax or plt.gca()\n \n # Convert covariance to principal axes\n if covariance.shape == (2, 2):\n U, s, Vt = np.linalg.svd(covariance)\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n width, height = 2 * np.sqrt(s)\n else:\n angle = 0\n width, height = 2 * np.sqrt(covariance)\n \n # Draw the Ellipse\n for nsig in range(1, 4):\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n angle, **kwargs))","def proc_unfilled_ellipse(self, tokens):\n\n return self._proc_ellipse(tokens, filled=False)","def transform(self, heraldry: Image.Image) -> Image.Image:\n heraldry = heraldry.convert('RGBA')\n orig_width, orig_height = heraldry.size\n\n new_height = int(self.height_change * orig_height)\n ell_img = Image.new('RGBA', (orig_width, new_height))\n draw = ImageDraw.Draw(ell_img)\n\n top_left = (0, int(self.ell_start * orig_height))\n bot_right = (orig_width - 1, new_height - 1)\n draw.ellipse((*top_left, *bot_right), fill = self.fill_col)\n ell_img.paste(heraldry, (0, 0), heraldry)\n\n return ell_img","def __DrawEllipse(self, image, rectangule, color):\n cv2.ellipse(image, rectangule, color, 2)\n points = cv2.boxPoints(rectangule)\n for i in range(4):\n cv2.line(image, tuple(np.array(points[i], np.int32)),\n tuple(np.array(points[(i + 1) % 4], np.int32)), color, 2)","def area_ellipse(radius_x: float, radius_y: float) -> float:\r\n if radius_x < 0 or radius_y < 0:\r\n raise ValueError(\"area_ellipse() only accepts non-negative values\")\r\n return pi * radius_x * radius_y","def getMinVolEllipse(P, tolerance=0.01):\n (N, d) = np.shape(P)\n d = float(d)\n\n # Q will be our working array\n Q = np.vstack([np.copy(P.T), np.ones(N)]) \n QT = Q.T\n \n # initializations\n err = 1.0 + tolerance\n u = (1.0 / N) * np.ones(N)\n\n # Khachiyan Algorithm\n while err > tolerance:\n V = np.dot(Q, np.dot(np.diag(u), QT))\n M = np.diag(np.dot(QT , np.dot(linalg.inv(V), Q))) # M the diagonal vector of an NxN matrix\n j = np.argmax(M)\n maximum = M[j]\n step_size = (maximum - d - 1.0) / ((d + 1.0) * (maximum - 1.0))\n new_u = (1.0 - step_size) * u\n new_u[j] += step_size\n err = np.linalg.norm(new_u - u)\n u = new_u\n\n # center of the ellipse \n center = np.dot(P.T, u)\n\n # the A matrix for the ellipse\n A = linalg.inv(\n np.dot(P.T, np.dot(np.diag(u), P)) - \n np.array([[a * b for b in center] for a in center])\n ) / d\n \n # Get the values we'd like to return\n U, s, rotation = linalg.svd(A)\n radii = 1.0/np.sqrt(s)\n\n rot_err = linalg.norm(np.identity(3)-abs(rotation))\n if(rot_err > 0.05):\n \tradii = np.array([radii[1],radii[0],radii[2]])\n return radii","def draw_ellipse(position, covariance, ax=None, **kwargs):\n ax = ax or plt.gca()\n\n # Convert covariance to principal axes\n if covariance.shape == (2, 2):\n U, s, Vt = np.linalg.svd(covariance)\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n width, height = 2 * np.sqrt(s)\n else:\n angle = 0\n width, height = 2 * np.sqrt(covariance)\n\n # Draw the Ellipse\n for nsig in range(1, 4):\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n angle, **kwargs))","def draw_ellipse(frame, coordinate, line_color=(124, 0, 0), radius=1, normalized=True):\n\n x1 = coordinate[0]\n y1 = coordinate[1]\n\n if normalized:\n h = frame.shape[0]\n w = frame.shape[1]\n\n x1 = int(x1 * w)\n y1 = int(y1 * h)\n\n cv.circle(frame, (x1, y1), radius=radius, color=line_color, thickness=1)","def draw_ellipse(position, covariance, ax=None, num_contours=5, **kwargs):\n ax = ax or plt.gca()\n \n # Convert covariance to principal axes\n U, s, Vt = np.linalg.svd(covariance)\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n width, height = 2 * np.sqrt(s)\n \n # Draw the Ellipse\n for nsig in range(1, num_contours):\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n angle, **kwargs))","def get_ellipse_coords(a=0.0, b=0.0, x=0.0, y=0.0, angle=0.0, k=2):\n pts = np.zeros((360*k+1, 2))\n\n beta = -angle * np.pi/180.0\n sin_beta = np.sin(beta)\n cos_beta = np.cos(beta)\n alpha = np.radians(np.r_[0.:360.:1j*(360*k+1)])\n \n sin_alpha = np.sin(alpha)\n cos_alpha = np.cos(alpha)\n \n pts[:, 0] = x + (a * cos_alpha * cos_beta - b * sin_alpha * sin_beta)\n pts[:, 1] = y + (a * cos_alpha * sin_beta + b * sin_alpha * cos_beta)\n\n return pts","def ellipse_to_rectangle(n, locations):\n major_axis_radius, minor_axis_radius, angle, center_x, center_y = locations\n pt = []\n for i in range(n):\n pt1 = (int(center_x[i]) - int(minor_axis_radius[i]), int(center_y[i]) - int(major_axis_radius[i]))\n pt2 = (int(center_x[i]) + int(minor_axis_radius[i]), int(center_y[i]) + int(major_axis_radius[i]))\n pt.append([pt1, pt2])\n return pt","def get_ellipse_coords(a=0.0, b=0.0, x=0.0, y=0.0, angle=0.0, k=2):\n pts = np.zeros((360*k+1, 2))\n\n beta = -angle * np.pi/180.0\n sin_beta = np.sin(beta)\n cos_beta = np.cos(beta)\n alpha = np.radians(np.r_[0.:360.:1j*(360*k+1)])\n\n sin_alpha = np.sin(alpha)\n cos_alpha = np.cos(alpha)\n\n pts[:, 0] = x + (a * cos_alpha * cos_beta - b * sin_alpha * sin_beta)\n pts[:, 1] = y + (a * cos_alpha * sin_beta + b * sin_alpha * cos_beta)\n\n return pts","def calcualte_ellipse_radii(guess, eccentricity = 0, perimeter = 2 * np.pi*1):\n return fsolve(ellipse_radii_test, guess, args = (eccentricity, perimeter))","def DrawEllipse(*args, **kwargs):\n return _gdi_.DC_DrawEllipse(*args, **kwargs)","def get_cov_ellipse(cov, centre, nstd, **kwargs):\n #WZN\n\n # Find and sort eigenvalues and eigenvectors into descending order\n eigvals, eigvecs = np.linalg.eigh(cov)\n order = eigvals.argsort()[::-1]\n eigvals, eigvecs = eigvals[order], eigvecs[:, order]\n\n # The anti-clockwise angle to rotate our ellipse by \n vx, vy = eigvecs[:,0][0], eigvecs[:,0][1]\n theta = np.arctan2(vy, vx)\n\n # Width and height of ellipse to draw\n width, height = 2 * nstd * np.sqrt(eigvals)\n return Ellipse(xy=centre, width=width, height=height,\n angle=np.degrees(theta), **kwargs)","def DrawEllipsePointSize(*args, **kwargs):\n return _gdi_.DC_DrawEllipsePointSize(*args, **kwargs)","def visualize_result(img, ellipse, radii=None, angles=None, reference_ellipse=None, strain=None, marker_size=8,\n title=\"\"):\n if img is None or ellipse is None:\n return img\n\n img = np.copy(img) # copy so we don't alter the original\n\n blue = (255, 0, 0)\n green = (0, 200, 0)\n red = (0, 0, 255)\n black = (0, 0, 0)\n strain_colors = ((0, 127, 255), (127, 0, 255))\n\n # draw the reference ellipse\n if reference_ellipse is not None:\n draw_ellipse(img, reference_ellipse, green, 1, True, 1)\n\n # draw the fitted ellipse\n draw_ellipse(img, ellipse, blue, 2, True, 1)\n\n # draw radial strain\n if radii is not None and angles is not None:\n assert len(radii) == len(angles)\n # duplicate colors to make sure we have enough for each radius\n strain_colors = strain_colors * int(np.ceil(len(radii) / len(strain_colors)))\n center, diam, angle = ellipse\n for r, a, c in zip(radii, angles, strain_colors):\n p1, p2 = draw_diameter(img, center, r, a, c, 1)\n draw_cross(img, p1, marker_size, a, c, 2)\n draw_cross(img, p2, marker_size, a, c, 2)\n\n # draw text\n font = cv.FONT_HERSHEY_SIMPLEX\n scale = 1.5\n margin = 20\n cv.putText(img, title, (margin, 60), font, scale, black, 3)\n if reference_ellipse is not None:\n cv.putText(img, \"Detected ellipse\", (margin, 120), font, scale, blue, 2)\n cv.putText(img, \"Reference ellipse\", (margin, 180), font, scale, green, 2)\n\n if strain is not None:\n cv.putText(img, \"Strain: {:.1f} %\".format(strain), (margin, 240), font, scale, black, 2)\n\n return img","def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\n if x.size != y.size:\n raise ValueError(\"x and y must be the same size\")\n\n cov = np.cov(x, y)\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\n # Using a special case to obtain the eigenvalues of this\n # two-dimensionl dataset.\n ell_radius_x = np.sqrt(1 + pearson)\n ell_radius_y = np.sqrt(1 - pearson)\n ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n facecolor=facecolor, **kwargs)\n\n # Calculating the stdandard deviation of x from\n # the squareroot of the variance and multiplying\n # with the given number of standard deviations.\n scale_x = np.sqrt(cov[0, 0]) * n_std\n mean_x = np.mean(x)\n\n # calculating the stdandard deviation of y ...\n scale_y = np.sqrt(cov[1, 1]) * n_std\n mean_y = np.mean(y)\n\n transf = transforms.Affine2D() \\\n .rotate_deg(45) \\\n .scale(scale_x, scale_y) \\\n .translate(mean_x, mean_y)\n\n ellipse.set_transform(transf + ax.transData)\n return ax.add_patch(ellipse)","def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\n if x.size != y.size:\n raise ValueError(\"x and y must be the same size\")\n\n cov = np.cov(x, y)\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\n # Using a special case to obtain the eigenvalues of this\n # two-dimensionl dataset.\n ell_radius_x = np.sqrt(1 + pearson)\n ell_radius_y = np.sqrt(1 - pearson)\n ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n facecolor=facecolor, **kwargs)\n\n # Calculating the stdandard deviation of x from\n # the squareroot of the variance and multiplying\n # with the given number of standard deviations.\n scale_x = np.sqrt(cov[0, 0]) * n_std\n mean_x = np.mean(x)\n\n # calculating the stdandard deviation of y ...\n scale_y = np.sqrt(cov[1, 1]) * n_std\n mean_y = np.mean(y)\n\n transf = transforms.Affine2D() \\\n .rotate_deg(45) \\\n .scale(scale_x, scale_y) \\\n .translate(mean_x, mean_y)\n\n ellipse.set_transform(transf + ax.transData)\n return ax.add_patch(ellipse)","def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\n if x.size != y.size:\n raise ValueError(\"x and y must be the same size\")\n\n cov = np.cov(x, y)\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\n # Using a special case to obtain the eigenvalues of this\n # two-dimensionl dataset.\n ell_radius_x = np.sqrt(1 + pearson)\n ell_radius_y = np.sqrt(1 - pearson)\n ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\n facecolor=facecolor, **kwargs)\n\n # Calculating the stdandard deviation of x from\n # the squareroot of the variance and multiplying\n # with the given number of standard deviations.\n scale_x = np.sqrt(cov[0, 0]) * n_std\n mean_x = np.mean(x)\n\n # calculating the stdandard deviation of y ...\n scale_y = np.sqrt(cov[1, 1]) * n_std\n mean_y = np.mean(y)\n\n transf = transforms.Affine2D() \\\n .rotate_deg(45) \\\n .scale(scale_x, scale_y) \\\n .translate(mean_x, mean_y)\n\n ellipse.set_transform(transf + ax.transData)\n return ax.add_patch(ellipse)","def minimum_rotated_rectangle(self): # -> BaseGeometry:\n ...","def DrawEllipse(*args, **kwargs):\n return _gdi_.GraphicsContext_DrawEllipse(*args, **kwargs)","def generate_ellipse(R1,R2,center,theta,N=100):\r\n t = np.linspace(0.0,2.0*np.pi,N)\r\n x = R1*np.cos(t)*np.cos(theta) - R2*np.sin(t)*np.sin(theta) + center[0]\r\n y = R1*np.cos(t)*np.sin(theta) + R2*np.sin(t)*np.cos(theta) + center[1]\r\n return x,y","def constructByParamEllipse(x_center, y_center, maj, min, rot, radius_3d_circle, focal_length=1):\n e = ImpEllipse.construct_by_param(x_center, y_center, maj, min, rot)\n\n return Double3DCircle.construct_by_ImpEllipse(e, radius_3d_circle, focal_length)","def determine_bounding_box_of_rotated_box(self, box, rotation_matrix):\n\n # top left, top right, bottom left, bottom right\n p1, p2, p3, p4 = box_points(box)\n\n # rotate all the points of the box\n tp1 = calc_rotate_point_with_rotation_matrix(p1, rotation_matrix)\n tp2 = calc_rotate_point_with_rotation_matrix(p2, rotation_matrix)\n tp3 = calc_rotate_point_with_rotation_matrix(p3, rotation_matrix)\n tp4 = calc_rotate_point_with_rotation_matrix(p4, rotation_matrix)\n\n # figure out which point has the furthest x distance, and the furthest y distance\n dx1 = abs(tp1[0] - tp4[0])\n dx2 = abs(tp2[0] - tp3[0])\n dy1 = abs(tp1[1] - tp4[1])\n dy2 = abs(tp2[1] - tp3[1])\n # the width and the height is the max distance between x and y\n w, h = max(dx1, dx2), max(dy1, dy2)\n\n # x and y is the min x, and min y among all points\n x = min(tp1[0], tp2[0], tp3[0], tp4[0])\n y = min(tp1[1], tp2[1], tp3[1], tp4[1])\n\n return (x, y, w, h)","def get_exterior(self, x, y, x1, x2, bottom, head_y):\n fx1 = x+(x-x1)*8\n fx2 = x+(x-x2)*8\n # compute bounding ellipse; and intersection with body outline\n cv2.ellipse(self.ellipse_finder, ((x/mscale,y/mscale), ((fx1-fx2)/mscale, (2*(bottom-head_y))/mscale), 0), 255,-1 )\n intersection = np.bitwise_and(255-self.ellipse_finder, self.median_finder)\n # find external blobs\n im2, out_contours, out_hierarchy = cv2.findContours(intersection,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n return out_contours, out_hierarchy, fx1-fx2","def _gen_ellipse(twiss, ep=1, num=100):\n a, b, c = twiss\n\n t = np.linspace(0, 2 * np.pi, num)\n t0 = np.arctan(a)\n x = np.sqrt(b * ep) * np.cos(t)\n y = np.sqrt(c * ep) * np.sin(t - t0)\n\n return np.vstack([x, y])","def eclipse_compensate(aLocation):\n EQUATOR_RADIUS = 6378137.0 # equator radius, or \"spherical\" earth\n POLAR_RADIUS = 6356725.0 # ploar radius\n EP_DIFF = EQUATOR_RADIUS - POLAR_RADIUS # rad-diff between equator and pole\n \n # assuming linear slope from equator to pole\n r_center = POLAR_RADIUS + EP_DIFF * (1.0 - abs(aLocation.lat)/90) # the ring thru earth center\n r_level = r_center * math.cos(math.radians(aLocation.lat)) # the ring thru latitude level\n \n return [r_center, r_level]"],"string":"[\n \"def fit_ellipse(x,y):\\r\\n \\r\\n def fit(x,y):\\r\\n x = x[:,np.newaxis]\\r\\n y = y[:,np.newaxis]\\r\\n D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))\\r\\n S = np.dot(D.T,D)\\r\\n C = np.zeros([6,6])\\r\\n C[0,2] = C[2,0] = 2; C[1,1] = -1\\r\\n E, V = np.linalg.eig(np.dot(np.linalg.inv(S), C))\\r\\n n = np.argmax(np.abs(E))\\r\\n a = V[:,n]\\r\\n return a\\r\\n \\r\\n def ellipse_center(a):\\r\\n b,c,d,f,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[0]\\r\\n num = b*b-a*c\\r\\n x0=(c*d-b*f)/num\\r\\n y0=(a*f-b*d)/num\\r\\n return np.array([x0,y0])\\r\\n \\r\\n def ellipse_angle_of_rotation(a):\\r\\n b,c,a = a[1]/2, a[2], a[0]\\r\\n return 0.5*np.arctan(2*b/(a-c))\\r\\n \\r\\n def ellipse_axis_length(a):\\r\\n b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]\\r\\n up = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)\\r\\n down1=(b*b-a*c)*( (c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\\r\\n down2=(b*b-a*c)*( (a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\\r\\n res1=np.sqrt(up/down1)\\r\\n res2=np.sqrt(up/down2)\\r\\n return np.array([res1, res2])\\r\\n \\r\\n a = fit(x,y)\\r\\n center = ellipse_center(a)\\r\\n theta = ellipse_angle_of_rotation(a)\\r\\n [R1,R2] = ellipse_axis_length(a)\\r\\n\\r\\n return R1, R2, center, theta\",\n \"def fitEllipse(x,y):\\r\\n x = x[:,np.newaxis]\\r\\n y = y[:,np.newaxis]\\r\\n D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))\\r\\n S = np.dot(D.T,D)\\r\\n C = np.zeros([6,6])\\r\\n C[0,2] = C[2,0] = 2; C[1,1] = -1\\r\\n E, V = eig(np.dot(inv(S), C))\\r\\n n = np.argmax(np.abs(E))\\r\\n a = V[:,n]\\r\\n return a\",\n \"def create_ellipse(self, ratio):\\n circ = Point(self.center).buffer(1.0)\\n ell = affinity.scale(circ, float(\\n self.lengths[0]*ratio), float(self.lengths[1]*ratio))\\n ellr = affinity.rotate(ell, self.angle)\\n return ellr\",\n \"def ellipse(self):\\n f = self.img\\n x = self.x\\n y = self.y\\n x2 = self.x2\\n y2 = self.y2\\n xy = self.xy\\n self.a2 = (x2+y2) + sqrt(((x2-y2)/2.)**2 + xy**2)\\n self.b2 = (x2+y2) - sqrt(((x2-y2)/2.)**2 + xy**2)\\n self.a = sqrt(self.a2)\\n self.b = sqrt(self.b2)\\n tan2theta = 2* (xy/(x2-y2))\\n self.theta = arctan(tan2theta)/2.\\n denominator = sqrt(((x2-y2)/2)**2+xy**2)\\n self.cxx = y2/denominator\\n self.cyy = x2/denominator\\n self.cxy = -2*xy/denominator\",\n \"def to_ellipse(self, factor=1.0):\\n self._check_initialized()\\n vals, vecs = sp.linalg.eigh(self.covariance)\\n order = vals.argsort()[::-1]\\n vals, vecs = vals[order], vecs[:, order]\\n angle = np.arctan2(*vecs[:, 0][::-1])\\n width, height = factor * np.sqrt(vals)\\n return angle, width, height\",\n \"def fitEllipsetoContour(poly):\\n from skimage.measure import EllipseModel\\n\\n model = EllipseModel()\\n success=model.estimate(poly)\\n\\n if success:\\n yc, xc, a, b, theta=model.params # xc, yc, a, b, theta <- in radiand\\n # convert theta to degree\\n theta=np.degrees(theta)\\n theta=90.-theta\\n # make sure major is larger than minor and theta is for major\\n if a<b:\\n [a,b]=[b,a]\\n theta=theta-90.\\n if theta<0.: theta=theta+180.\\n params=(xc, yc, a, b, theta)\\n return params\\n else:\\n return (0, 0, 0, 0, 0)\",\n \"def polarization_ellipse(self):\\n self.ellipse = {}\\n self.ellipse['d_lin'] = sqrt(self.Q**2 + self.U**2)/self.I\\n self.ellipse['d_cir'] = abs(self.V)/self.I\\n self.ellipse['d'] = sqrt(self.Q**2 + self.U**2 + self.V**2)/self.I\\n if self.Q:\\n self.ellipse['theta'] = 0.5*atan(self.U/self.Q)\\n else:\\n self.ellipse['theta'] = float('NaN')\\n self.logger.debug(\\\"polarization_ellipse: theta = %f\\\",\\n self.ellipse['theta'])\\n\\n if (self.Q**2 + self.U**2):\\n self.ellipse['beta'] = 0.5*atan(self.V/sqrt(self.Q**2 + self.U**2))\\n if self.V:\\n self.ellipse['eccen'] = tan(self.ellipse['beta'])\\n else:\\n self.ellipse['eccen'] = 0.\\n else:\\n self.ellipse['beta'] = pi/4\\n self.ellipse['eccen'] = 1.\\n self.logger.debug(\\\"polarization_ellipse: beta = %f\\\",\\n self.ellipse['beta'])\\n self.logger.debug(\\\"polarization_ellipse: eccen = %f\\\",\\n self.ellipse['eccen'])\",\n \"def drawFitEllipse(img, cnt):\\n\\td = fitEllipse(cnt)\\n\\tcv2.ellipse(img, d[\\\"center\\\"], (d[\\\"major\\\"], d[\\\"minor\\\"]), d[\\\"angle\\\"])\",\n \"def _proc_ellipse(self, tokens, filled):\\n\\n component = Ellipse(pen=self.pen,\\n x_origin=tokens[\\\"x0\\\"],\\n y_origin=tokens[\\\"y0\\\"],\\n e_width=tokens[\\\"w\\\"],\\n e_height=tokens[\\\"h\\\"],\\n filled=filled)\\n\\n return component\",\n \"def ellipseDesc(lps):\\r\\n unit = 100 #units in QualiTree are in [mm], hence Pgl is in [dm] ?\\r\\n\\r\\n if isinstance(lps, pgl.Translated):\\r\\n cx, cy, cz = lps.translation\\r\\n else:\\r\\n print\\\"missing Translated from the bounding ellipse as a Translated(Rotated(Scaled(Sphere)))\\\"\\r\\n\\r\\n ori = lps.geometry\\r\\n\\r\\n if isinstance(ori, pgl.Oriented):\\r\\n rotMat = ori.transformation().getMatrix3()\\r\\n az, el, roll = rotMat.eulerAnglesZYX()\\r\\n else:\\r\\n print\\\"missing Oriented from the bounding ellipse as a Translated(Rotated(Scaled(Sphere)))\\\"\\r\\n az = 0\\r\\n \\r\\n scal = ori.geometry\\r\\n\\r\\n if isinstance(scal, pgl.Scaled):\\r\\n scMat = scal.transformation().getMatrix()\\r\\n rx, ry, rz, rt = scMat.getDiagonal()\\r\\n else:\\r\\n print\\\"missing Scaled from the bounding ellipse as a Translated(Rotated(Scaled(Sphere)))\\\"\\r\\n rx=ry=rz=1\\r\\n\\r\\n #x1, y1, z1 #Conversion repère MappleT (m) à reprère Qualitree (q) : Xq=Xm Yq=Zm Zq=-Ym. \\r\\n #Due to change of coordinate axis, rotation needs - pi <-- apparently not !\\r\\n #return cx*unit, cz*unit, -cy*unit, rx*unit, rz*unit, ry*unit, az-3.1415927\\r\\n\\r\\n return cx*unit, cz*unit, -cy*unit, rx*unit, rz*unit, ry*unit, az\",\n \"def _draw_ellipse(data, obj, draw_options):\\n if isinstance(obj, mpl.patches.Circle):\\n # circle specialization\\n return _draw_circle(data, obj, draw_options)\\n x, y = obj.center\\n ff = data[\\\"float format\\\"]\\n\\n if obj.angle != 0:\\n draw_options.append(\\n f\\\"rotate around={{{obj.angle:{ff}}:(axis cs:{x:{ff}},{y:{ff}})}}\\\"\\n )\\n\\n do = \\\",\\\".join(draw_options)\\n content = (\\n f\\\"\\\\\\\\draw[{do}] (axis cs:{x:{ff}},{y:{ff}}) ellipse \\\"\\n f\\\"({0.5 * obj.width:{ff}} and {0.5 * obj.height:{ff}});\\\\n\\\"\\n )\\n content += _patch_legend(obj, draw_options, \\\"area legend\\\")\\n\\n return data, content\",\n \"def drawEllipse(img, center, axes, angle, startAngle=0, endAngle=360, color = (0,0,255), fill = -1):\\n\\tcv2.ellipse(img, center, axes, angle, startAngle, endAngle, color, fill)\",\n \"def DrawEllipseRect(*args, **kwargs):\\n return _gdi_.DC_DrawEllipseRect(*args, **kwargs)\",\n \"def ellipse_dist_ratio_poly(self, theta, lwr):\\n\\n \\\"\\\"\\\"\\n\\n Params for FWD fit\\n array([ 9.99999989e-01, 8.10852195e+07, 1.95444928e+00, 7.96543026e-02])\\n this one is un-needed, since it's approximation y = 1\\n\\n Params for FWD_DIAG fit\\n array([-0.00650758, 0.57761793, 0.35369061, 1.87834152])\\n\\n Params for ORTHOG fit\\n array([-0.02014989, 5.7007518 , -0.83345416, 0.97711175])\\n\\n Params for BCK_DIAG fit\\n array([-0.01608705, 9.44079769, -0.92071169, 0.89094967])\\n\\n Params for BCK fit\\n array([ -0.01451187, 10.92674105, -0.93514904, 0.87868538])\\n \\\"\\\"\\\"\\n\\n #fitting function\\n def f(x,params):\\n return params[0] + (1.0 / (params[1]*(x+params[2])**params[3]))\\n\\n #force float math, in case theta is an integer\\n theta = float(theta)\\n\\n #into an angle index form:\\n t = abs(int(4.0*theta/np.pi))\\n\\n if (t == 0) or (t == 8):\\n return 1.0\\n elif (t == 1) or (t == 7):\\n #forward diagonal\\n return f(lwr, [-0.00650758, 0.57761793, 0.35369061, 1.87834152])\\n elif (t == 2) or (t == 6):\\n #orthogonal\\n return f(lwr, [-0.02014989, 5.7007518 , -0.83345416, 0.97711175])\\n elif (t == 3) or (t == 5):\\n #backward diagonal\\n return f(lwr, [-0.01608705, 9.44079769, -0.92071169, 0.89094967])\\n elif t == 4:\\n #backward\\n return f(lwr, [ -0.01451187, 10.92674105, -0.93514904, 0.87868538])\\n else:\\n #hmmm... TODO\\n return 0.0\",\n \"def plot_ellipse(center, covariance, alpha, color):\\n # eigenvalues and eigenvector of matrix covariance\\n eigenvalues, eigenvector = np.linalg.eigh(covariance)\\n order = eigenvalues.argsort()[::-1]\\n eigenvector = eigenvector[:, order]\\n\\n # Calculate Angle of ellipse\\n angle = np.degrees(np.arctan2(*eigenvector[:, 0][::-1]))\\n\\n # Calculate with, height\\n width, height = 4 * np.sqrt(eigenvalues[order])\\n\\n # Ellipse Object\\n ellipse = Ellipse(xy=center, width=width, height=height, angle=angle,\\n alpha=alpha, color=color)\\n\\n ax = plt.gca()\\n ax.add_artist(ellipse)\\n\\n return ellipse\",\n \"def add_ellipse(self, x, y, w, h, fill_style=None, outline_style=None):\\n if w < 1 or h < 1:\\n return\\n fill_style = self.__prepare_style(fill_style, self.style.char)\\n outline_style = self.__prepare_style(outline_style, 'o')\\n # Bresenham's algorithm to plot ellipse is used\\n a = w\\n b = h - 1\\n eight_a_square = 8 * a * a\\n eight_b_square = 8 * b * b\\n x_change = 4 * b * b * (1.0 - a)\\n y_change = 4 * a * a * ((b & 1) + 1)\\n ellipse_error = x_change + y_change + (b & 1) * a * a\\n x0 = x\\n x1 = x0 + w - 1\\n y0 = y + h / 2\\n y1 = y0 - (b & 1)\\n outline_points = []\\n while x0 <= x1:\\n # add fill\\n if x0 > x and x0 < x + w - 1:\\n self.add_line(int(x0), int(y0), int(x0), int(y1), fill_style)\\n self.add_line(int(x1), int(y0), int(x1), int(y1), fill_style)\\n outline_points.append((int(x1), int(y0)))\\n outline_points.append((int(x0), int(y0)))\\n outline_points.append((int(x0), int(y1)))\\n outline_points.append((int(x1), int(y1)))\\n two_ellipse_error = 2 * ellipse_error\\n if two_ellipse_error <= y_change:\\n y0 += 1\\n y1 -= 1\\n y_change += eight_a_square\\n ellipse_error += y_change\\n if two_ellipse_error >= x_change or 2 * ellipse_error > y_change:\\n x0 += 1\\n x1 -= 1\\n x_change += eight_b_square\\n ellipse_error += x_change\\n while y0 - y1 <= b:\\n self.add_point(int(x0 - 1), int(y0), outline_style)\\n self.add_point(int(x1 + 1), int(y0), outline_style)\\n self.add_point(int(x0 - 1), int(y1), outline_style)\\n self.add_point(int(x1 + 1), int(y1), outline_style)\\n y0 += 1\\n y1 -= 1\\n # draw outline over fill\\n for outline_point in outline_points:\\n px, py = outline_point\\n self.add_point(px, py, outline_style)\",\n \"def _getEllipseSize(self, pointInEllipse):\\n x = abs(self.center[0] - pointInEllipse[0])\\n y = abs(self.center[1] - pointInEllipse[1])\\n if x == 0 or y == 0:\\n return x, y\\n # Ellipse definitions\\n # e: eccentricity\\n # a: length fron center to bounding box width\\n # b: length fron center to bounding box height\\n # Equations\\n # (1) b < a\\n # (2) For x,y a point in the ellipse: x^2/a^2 + y^2/b^2 = 1\\n # (3) b = a * sqrt(1-e^2)\\n # (4) e = sqrt(a^2 - b^2) / a\\n\\n # The eccentricity of the ellipse defined by a,b=x,y is the same\\n # as the one we are searching for.\\n swap = x < y\\n if swap:\\n x, y = y, x\\n e = math.sqrt(x**2 - y**2) / x\\n # From (2) using (3) to replace b\\n # a^2 = x^2 + y^2 / (1-e^2)\\n a = math.sqrt(x**2 + y**2 / (1.0 - e**2))\\n b = a * math.sqrt(1 - e**2)\\n if swap:\\n a, b = b, a\\n return a, b\",\n \"def tilted_ellipse(s, pos1, pos2, size_x, size_y, color, angle):\\n surface = pygame.Surface((150, 150), pygame.SRCALPHA, 32).convert_alpha()\\n ellipse(surface, color, (0, 0, size_x, size_y))\\n surface2 = pygame.transform.rotate(surface, angle)\\n return s.blit(surface2, (pos1, pos2))\",\n \"def ellipse(x,y,a,b):\\n return ((x/float(a))**2 + (y/float(b))**2)\",\n \"def draw_ellipse_outline(center_x, center_y, width, height, color,\\n border_width=1, tilt_angle=0):\\n\\n num_segments = 128\\n\\n GL.glEnable(GL.GL_BLEND)\\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\\n GL.glEnable(GL.GL_LINE_SMOOTH)\\n GL.glHint(GL.GL_LINE_SMOOTH_HINT, GL.GL_NICEST)\\n GL.glHint(GL.GL_POLYGON_SMOOTH_HINT, GL.GL_NICEST)\\n\\n GL.glLoadIdentity()\\n GL.glTranslatef(center_x, center_y, 0)\\n GL.glRotatef(tilt_angle, 0, 0, 1)\\n GL.glLineWidth(border_width)\\n\\n # Set color\\n if len(color) == 4:\\n GL.glColor4ub(color[0], color[1], color[2], color[3])\\n elif len(color) == 3:\\n GL.glColor4ub(color[0], color[1], color[2], 255)\\n\\n GL.glBegin(GL.GL_LINE_LOOP)\\n for segment in range(num_segments):\\n theta = 2.0 * 3.1415926 * segment / num_segments\\n\\n x = width * math.cos(theta)\\n y = height * math.sin(theta)\\n\\n GL.glVertex3f(x, y, 0.5)\\n\\n GL.glEnd()\\n GL.glLoadIdentity()\",\n \"def fit_ellipse(*args, equatorial_radius, dequatorial_radius=0, center_f=0, dcenter_f=0, center_g=0,\\n dcenter_g=0, oblateness=0, doblateness=0, position_angle=0, dposition_angle=0,\\n loop=10000000, number_chi=10000, dchi_min=None, verbose=False, ellipse_error=0, sigma_result=1):\\n from sora.extra import ChiSquare\\n from sora.config.visuals import progressbar_show\\n from astropy.coordinates import Angle\\n from .core import Occultation\\n\\n v = {'dcenter_f': dcenter_f, 'dcenter_g': dcenter_g, 'doblateness': doblateness, 'dposition_angle': dposition_angle,\\n 'dequatorial_radius': dequatorial_radius, 'ellipse_error': ellipse_error, 'sigma_result': sigma_result,\\n 'dchi_min': dchi_min}\\n for key, item in v.items():\\n if item is not None and item < 0:\\n raise ValueError(\\\"{} must be a positive number.\\\".format(key))\\n\\n values = []\\n chord_name = []\\n if len(args) == 0:\\n raise ValueError('No occultation have been given as input.')\\n for occ in args:\\n if not isinstance(occ, Occultation):\\n raise TypeError('Given argument must be an Occultation object.')\\n for name, chord in occ.chords.items():\\n if chord.status() == 'positive':\\n if chord.is_able['immersion']:\\n f, g, vf, vg = chord.get_fg(time='immersion', vel=True)\\n err = np.linalg.norm([vf, vg])*chord.lightcurve.immersion_err\\n values.append([f, g, err])\\n chord_name.append(name + '_immersion')\\n if chord.is_able['emersion']:\\n f, g, vf, vg = chord.get_fg(time='emersion', vel=True)\\n err = np.linalg.norm([vf, vg])*chord.lightcurve.emersion_err\\n values.append([f, g, err])\\n chord_name.append(name + '_emersion')\\n\\n controle_f0 = Time.now()\\n f0_chi = np.array([])\\n g0_chi = np.array([])\\n a_chi = np.array([])\\n obla_chi = np.array([])\\n posang_chi = np.array([])\\n chi2_best = np.array([])\\n\\n while len(f0_chi) < number_chi:\\n progressbar_show(len(f0_chi), number_chi, prefix='Ellipse fit:')\\n chi2 = np.zeros(loop)\\n f0 = center_f + dcenter_f*(2*np.random.random(loop) - 1)\\n g0 = center_g + dcenter_g*(2*np.random.random(loop) - 1)\\n a = equatorial_radius + dequatorial_radius*(2*np.random.random(loop) - 1)\\n obla = oblateness + doblateness*(2*np.random.random(loop) - 1)\\n obla[obla < 0], obla[obla > 1] = 0, 1\\n phi_deg = position_angle + dposition_angle*(2*np.random.random(loop) - 1)\\n controle_f1 = Time.now()\\n\\n for fi, gi, si in values:\\n b = a - a*obla\\n phi = phi_deg*(np.pi/180.0)\\n dfi = fi-f0\\n dgi = gi-g0\\n theta = np.arctan2(dgi, dfi)\\n ang = theta+phi\\n r_model = (a*b)/np.sqrt((a*np.sin(ang))**2 + (b*np.cos(ang))**2)\\n f_model = f0 + r_model*np.cos(theta)\\n g_model = g0 + r_model*np.sin(theta)\\n chi2 += ((fi - f_model)**2 + (gi - g_model)**2)/(si**2 + ellipse_error**2)\\n\\n controle_f2 = Time.now()\\n if dchi_min is not None:\\n region = np.where(chi2 < chi2.min() + dchi_min)[0]\\n else:\\n region = np.arange(len(chi2))\\n chi2_best = np.append(chi2_best, chi2[region])\\n if verbose:\\n print('Elapsed time: {:.3f} seconds.'.format((controle_f2 - controle_f1).sec))\\n print(len(chi2[region]), len(chi2_best))\\n f0_chi = np.append(f0_chi, f0[region])\\n g0_chi = np.append(g0_chi, g0[region])\\n a_chi = np.append(a_chi, a[region])\\n obla_chi = np.append(obla_chi, obla[region])\\n posang_chi = np.append(posang_chi, phi_deg[region])\\n\\n progressbar_show(number_chi, number_chi, prefix='Ellipse fit:')\\n chisquare = ChiSquare(chi2_best, len(values), center_f=f0_chi, center_g=g0_chi, equatorial_radius=a_chi,\\n oblateness=obla_chi, position_angle=posang_chi)\\n controle_f4 = Time.now()\\n if verbose:\\n print('Total elapsed time: {:.3f} seconds.'.format((controle_f4 - controle_f0).sec))\\n\\n result_sigma = chisquare.get_nsigma(sigma=sigma_result)\\n a = result_sigma['equatorial_radius'][0]\\n f0 = result_sigma['center_f'][0]\\n g0 = result_sigma['center_g'][0]\\n obla = result_sigma['oblateness'][0]\\n phi_deg = result_sigma['position_angle'][0]\\n radial_dispersion = np.array([])\\n error_bar = np.array([])\\n position_angle_point = np.array([])\\n\\n for fi, gi, si in values:\\n b = a - a*obla\\n phi = phi_deg*(np.pi/180.0)\\n dfi = fi-f0\\n dgi = gi-g0\\n r = np.sqrt(dfi**2 + dgi**2)\\n theta = np.arctan2(dgi, dfi)\\n ang = theta+phi\\n r_model = (a*b)/np.sqrt((a*np.sin(ang))**2 + (b*np.cos(ang))**2)\\n radial_dispersion = np.append(radial_dispersion, r - r_model)\\n error_bar = np.append(error_bar, si)\\n position_angle_point = np.append(position_angle_point, Angle(90*u.deg - theta*u.rad).wrap_at(360 * u.deg).degree)\\n\\n for occ in args:\\n if isinstance(occ, Occultation):\\n occ.fitted_params = {i: result_sigma[i] for i in ['equatorial_radius', 'center_f', 'center_g',\\n 'oblateness', 'position_angle']}\\n occ.chi2_params = {'chord_name': chord_name, 'radial_dispersion': radial_dispersion,\\n 'position_angle': position_angle_point, 'radial_error': error_bar,\\n 'chi2_min': chisquare.get_nsigma(sigma=sigma_result)['chi2_min'],\\n 'nparam': chisquare.nparam, 'npts': chisquare.npts}\\n return chisquare\",\n \"def ellipse(self, arg, fill='', outline=''):\\n pass\",\n \"def fitRectangle(self):\\n \\n #TODO MAKE SOMETHING MORE GENERIC!!\\n \\n fA, (fXg, fYg) = self.getArea_and_CenterOfMass()\\n \\n x1,y1, x2,y2 = self.getBoundingBox()\\n #build a rectangle with same \\\"width\\\" as the polygon... is-it good enough??\\n w = x2 - x1\\n \\n #but this width should not lead to go out of the bounding box!\\n fW = min(w, (x2-fXg)*2, (fXg-x1)*2)\\n \\n #same area\\n fH = fA / fW\\n \\n x1,y1, x2,y2 = [ int(round(v)) for v in [ fXg - fW/2.0, fYg - fH/2\\n , fXg + fW/2.0, fYg + fH/2 ]]\\n \\n return x1,y1, x2,y2\",\n \"def get_ellipse_mask(ellipse, img_shape, offset=0):\\n # create image\\n mask = np.zeros((img_shape[0], img_shape[1]), dtype=np.uint8)\\n\\n try:\\n # fill ellipse\\n draw_ellipse(mask, ellipse, (255, 255, 255), -1)\\n except Exception as ex:\\n logging.getLogger(\\\"StrainDetection\\\").warning(\\\"Unable to create ellipse mask: {}\\\".format(ex))\\n mask += 255 # make mask white to include everything\\n return mask\\n\\n # dilate/erode by given offset, if necessary\\n if offset != 0:\\n operation = cv.MORPH_DILATE\\n if offset < 0:\\n operation = cv.MORPH_ERODE # if offset is negative --> erode\\n\\n # create kernel\\n n = 2 * abs(offset) + 1\\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (n, n))\\n\\n # perform morphological operation\\n mask = cv.morphologyEx(mask, operation, kernel)\\n\\n return mask\",\n \"def ellipsoid(center, radii, rotation, scales=None, shape=None, minarea=0):\\n center = np.array(center)\\n radii = np.array(radii)\\n rotation = np.array(rotation)\\n assert center.shape == (3,)\\n assert radii.shape == (3,)\\n assert 0 < radii.max(), \\\"radii should contain at least one positive value\\\"\\n assert rotation.shape == (3, 3)\\n if scales is None:\\n scales = (1.,) * 3\\n scales = np.array(scales)\\n assert scales.shape == (3,)\\n\\n scaled_center = center / scales\\n\\n # The upper_left_bottom and lower_right_top corners of the smallest cuboid\\n # containing the ellipsoid.\\n factor = np.array([\\n [i, j, k] for k in (-1, 1) for j in (-1, 1) for i in (-1, 1)]).T\\n while True:\\n radii_rot = np.abs(\\n np.diag(1. / scales).dot(rotation.dot(np.diag(radii).dot(factor)))\\n ).max(axis=1)\\n # In the original scikit-image code, ceil and floor were replaced.\\n # https://github.com/scikit-image/scikit-image/blob/master/skimage/draw/draw.py#L127\\n upper_left_bottom = np.floor(scaled_center - radii_rot).astype(int)\\n lower_right_top = np.ceil(scaled_center + radii_rot).astype(int)\\n\\n if shape is not None:\\n # Constrain upper_left and lower_ight by shape boundary.\\n upper_left_bottom = np.maximum(\\n upper_left_bottom, np.array([0, 0, 0]))\\n lower_right_top = np.minimum(\\n lower_right_top, np.array(shape[:3]) - 1)\\n\\n bounding_shape = lower_right_top - upper_left_bottom + 1\\n\\n d_lim, r_lim, c_lim = np.ogrid[0:float(bounding_shape[0]),\\n 0:float(bounding_shape[1]),\\n 0:float(bounding_shape[2])]\\n d_org, r_org, c_org = scaled_center - upper_left_bottom\\n d_rad, r_rad, c_rad = radii\\n rotation_inv = np.linalg.inv(rotation)\\n conversion_matrix = rotation_inv.dot(np.diag(scales))\\n d, r, c = (d_lim - d_org), (r_lim - r_org), (c_lim - c_org)\\n distances = (\\n ((d * conversion_matrix[0, 0] +\\n r * conversion_matrix[0, 1] +\\n c * conversion_matrix[0, 2]) / d_rad) ** 2 +\\n ((d * conversion_matrix[1, 0] +\\n r * conversion_matrix[1, 1] +\\n c * conversion_matrix[1, 2]) / r_rad) ** 2 +\\n ((d * conversion_matrix[2, 0] +\\n r * conversion_matrix[2, 1] +\\n c * conversion_matrix[2, 2]) / c_rad) ** 2\\n )\\n if distances.size < minarea:\\n old_radii = radii.copy()\\n radii *= 1.1\\n print('Increase radii from ({}) to ({})'.format(old_radii, radii))\\n else:\\n break\\n distance_thresh = 1\\n while True:\\n dd, rr, cc = np.nonzero(distances < distance_thresh)\\n if len(dd) < minarea:\\n distance_thresh *= 1.1\\n else:\\n break\\n dd.flags.writeable = True\\n rr.flags.writeable = True\\n cc.flags.writeable = True\\n dd += upper_left_bottom[0]\\n rr += upper_left_bottom[1]\\n cc += upper_left_bottom[2]\\n return dd, rr, cc\",\n \"def ci95_ellipse(data, type=\\\"pop\\\"):\\n\\n # Build and fit PCA model\\n pca = PCA()\\n pca.fit(data)\\n coeff = pca.components_\\n score = pca.transform(data)\\n eigvals = pca.explained_variance_\\n\\n # Calculate rotation angle\\n phi = math.atan2(coeff[0, 1], coeff[0, 0])\\n\\n # This angle is between -pi and pi.\\n # Let's shift it such that the angle is between 0 and 2pi\\n if phi < 0:\\n phi += 2 * math.pi\\n\\n # Get the coordinates of the data mean\\n n = len(data)\\n m = np.mean(data, axis=0)\\n x0 = m[0]\\n y0 = m[1]\\n\\n # Get the 95% confidence interval error ellipse\\n # inverse of the chi-square cumulative distribution for p = 0.05 & 2 d.f. = 5.9915\\n chisquare_val = 5.9915\\n if type is \\\"pop\\\":\\n a = math.sqrt(chisquare_val * eigvals[0])\\n b = math.sqrt(chisquare_val * eigvals[1])\\n elif type is \\\"mean\\\":\\n a = math.sqrt(chisquare_val * eigvals[0] / n)\\n b = math.sqrt(chisquare_val * eigvals[1] / n)\\n else:\\n raise ValueError(\\\"type has to be 'pop' or 'mean'.\\\")\\n\\n # the ellipse in x and y coordinates\\n theta_grid = np.linspace(0, 2 * math.pi, num=100)\\n ellipse_x_r = a * np.cos(theta_grid)\\n ellipse_y_r = b * np.sin(theta_grid)\\n\\n # Define a rotation matrix\\n R = np.array([[np.cos(phi), np.sin(phi)], [-np.sin(phi), np.cos(phi)]])\\n # let's rotate the ellipse to some angle phi\\n r_ellipse = np.dot(np.vstack((ellipse_x_r, ellipse_y_r)).T, R)\\n\\n # Draw the error ellipse\\n x = r_ellipse[:, 0] + x0\\n y = r_ellipse[:, 1] + y0\\n ellipse = np.stack((x, y), axis=1)\\n\\n outside = []\\n for i in range(len(score)):\\n metric = (score[i, 0] / a) ** 2 + (score[i, 1] / b) ** 2\\n if metric > 1:\\n outside.append(1)\\n else:\\n outside.append(0)\\n\\n return ellipse, outside\",\n \"def ellipse(radii = (10,5), angle_resolution = 2.5, layer = 0):\\n D = Device(name = 'ellipse')\\n a = radii[0]\\n b = radii[1]\\n t = np.linspace(0, 360, int(np.ceil(360/angle_resolution) + 1)) * pi/180\\n r = a*b / (sqrt((b*cos(t))**2 + (a*sin(t))**2))\\n xpts = r*cos(t)\\n ypts = r*sin(t)\\n D.add_polygon(points = (xpts, ypts), layer = layer)\\n return D\",\n \"def ellipse2pathd(ellipse):\\n\\n cx = ellipse.get('cx', 0)\\n cy = ellipse.get('cy', 0)\\n rx = ellipse.get('rx', None)\\n ry = ellipse.get('ry', None)\\n r = ellipse.get('r', None)\\n\\n if r is not None:\\n rx = ry = float(r)\\n else:\\n rx = float(rx)\\n ry = float(ry)\\n\\n cx = float(cx)\\n cy = float(cy)\\n\\n d = ''\\n d += 'M' + str(cx - rx) + ',' + str(cy)\\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\\n\\n return d\",\n \"def ellipse(self, x, y, radiusx, radiusy, rotation=0, startangle=0, endangle=2 * pi, anticlockwise=False):\\n self._impl.ellipse(x, y, radiusx, radiusy, rotation, startangle, endangle, anticlockwise)\",\n \"def fitEllipse(cnt):\\n\\t(x,y), (MA, ma), angle = cv2.fitEllipse(cnt)\\n\\treturn {\\\"center\\\":(x,y), \\\"major\\\":MA, \\\"minor\\\":ma, \\\"angle\\\":angle}\",\n \"def ellipse2pathd(ellipse):\\n\\n cx = ellipse.get('cx', None)\\n cy = ellipse.get('cy', None)\\n rx = ellipse.get('rx', None)\\n ry = ellipse.get('ry', None)\\n r = ellipse.get('r', None)\\n\\n if r is not None:\\n rx = ry = float(r)\\n else:\\n rx = float(rx)\\n ry = float(ry)\\n\\n cx = float(cx)\\n cy = float(cy)\\n\\n d = ''\\n d += 'M' + str(cx - rx) + ',' + str(cy)\\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\\n\\n return d\",\n \"def ellipse2pathd(ellipse):\\n\\n cx = ellipse.get('cx', None)\\n cy = ellipse.get('cy', None)\\n rx = ellipse.get('rx', None)\\n ry = ellipse.get('ry', None)\\n r = ellipse.get('r', None)\\n\\n if r is not None:\\n rx = ry = float(r)\\n else:\\n rx = float(rx)\\n ry = float(ry)\\n\\n cx = float(cx)\\n cy = float(cy)\\n\\n d = ''\\n d += 'M' + str(cx - rx) + ',' + str(cy)\\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\\n\\n return d\",\n \"def DrawEllipseRect(*args, **kwargs):\\n return _gdi_.PseudoDC_DrawEllipseRect(*args, **kwargs)\",\n \"def draw_ellipse(self, color, position, size=None,\\n border_width=0, anchor='topleft'):\\n if size is None:\\n rect = spyral.Rect(position)\\n else:\\n rect = spyral.Rect(position, size)\\n offset = self._calculate_offset(anchor, rect.size)\\n pygame.draw.ellipse(self._surf, color,\\n (rect.pos + offset, rect.size), border_width)\\n self._version += 1\\n spyral.util.scale_surface.clear(self._surf)\\n return self\",\n \"def get_eccentricity(self, ellipse):\\r\\n a = ellipse.get_width()\\r\\n b = ellipse.get_height()\\r\\n if b > a:\\r\\n a, b = b, a\\r\\n c = np.sqrt(a**2 - b**2)\\r\\n return fdiv(c, a)\",\n \"def r_ellipse(self,xc=None,yc=None):\\n x = self.x\\n y = self.y\\n if xc == None:\\n xc = self.x1\\n if yc == None:\\n yc = self.y1\\n self.rel = sqrt(self.cxx*(x-xc)**2 +\\n\\t\\t self.cyy*(y-yc)**2 +\\n\\t\\t self.cxy*(x-xc)*(y-yc)\\n\\t\\t )\",\n \"def draw_ellipse_filled(center_x, center_y,\\n width, height, color, tilt_angle=0):\\n\\n num_segments = 128\\n\\n GL.glEnable(GL.GL_BLEND)\\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\\n GL.glEnable(GL.GL_LINE_SMOOTH)\\n GL.glHint(GL.GL_LINE_SMOOTH_HINT, GL.GL_NICEST)\\n GL.glHint(GL.GL_POLYGON_SMOOTH_HINT, GL.GL_NICEST)\\n\\n GL.glLoadIdentity()\\n GL.glTranslatef(center_x, center_y, 0)\\n GL.glRotatef(tilt_angle, 0, 0, 1)\\n\\n # Set color\\n if len(color) == 4:\\n GL.glColor4ub(color[0], color[1], color[2], color[3])\\n elif len(color) == 3:\\n GL.glColor4ub(color[0], color[1], color[2], 255)\\n\\n GL.glBegin(GL.GL_TRIANGLE_FAN)\\n\\n GL.glVertex3f(0, 0, 0.5)\\n\\n for segment in range(num_segments + 1):\\n theta = 2.0 * 3.1415926 * segment / num_segments\\n\\n x = width * math.cos(theta)\\n y = height * math.sin(theta)\\n\\n GL.glVertex3f(x, y, 0.5)\\n\\n GL.glEnd()\\n GL.glLoadIdentity()\",\n \"def cartesian_to_ellipse(center, angle, lengths):\\n xInd, yInd = np.mgrid[:512, :512]\\n major = max(lengths)/np.mean(lengths)\\n minor = min(lengths)/np.mean(lengths)\\n xInd, yInd = xInd - center[0], yInd - center[1]\\n xInd, yInd = rotate(xInd, yInd, angle=-angle)\\n xInd, yInd = xInd*minor, yInd*major\\n xInd, yInd = rotate(xInd, yInd, angle=angle)\\n return xInd, yInd\",\n \"def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0,\\n **kwargs):\\n ellipse = Ellipse(xy=centroid, width=length, height=width,\\n angle=np.degrees(angle), fill=False, **kwargs)\\n self.axes.add_patch(ellipse)\\n self.update()\\n return ellipse\",\n \"def render_ellipse_filled(shape, center_x, center_y, color, angle=0):\\n # Set color\\n if len(color) == 4:\\n GL.glColor4ub(shape.color[0], shape.color[1], shape.color[2],\\n shape.color[3])\\n GL.glEnable(GL.GL_BLEND)\\n GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\\n elif len(color) == 3:\\n GL.glDisable(GL.GL_BLEND)\\n GL.glColor4ub(shape.color[0], shape.color[1], shape.color[2], 255)\\n\\n GL.glBindBuffer(GL.GL_ARRAY_BUFFER, shape.vbo_id)\\n GL.glVertexPointer(2, GL.GL_FLOAT, 0, 0)\\n\\n GL.glLoadIdentity()\\n GL.glTranslatef(center_x, center_y, 0)\\n if angle:\\n GL.glRotatef(angle, 0, 0, 1)\\n\\n GL.glDrawArrays(GL.GL_TRIANGLE_FAN, 0, shape.size)\",\n \"def draw_ellipse(mat, radius_x, radius_y, angle, color=(0, 0, 255), thickness=1):\\n cv2.ellipse(mat, (radius_x, radius_y), angle, 0, 0, color, thickness=thickness)\",\n \"def box_ellipse(A, r):\\n A = np.array(A)\\n A = A.transpose().dot(A)\\n size = len(A)\\n widths = []\\n for i in range(size):\\n setperp = [k for k in range(i)] + [k for k in range(i + 1, size)]\\n v = A[i, setperp]\\n A22 = A[setperp][:, setperp]\\n try:\\n Aperpinv = np.linalg.inv(A22)\\n gamma = Aperpinv.dot(v)\\n gamma = gamma.dot(v)\\n widths.append(r / np.sqrt(A[i, i] - gamma))\\n except np.linalg.linalg.LinAlgError:\\n widths.append(1.0e300)\\n return widths\",\n \"def draw_ellipse(image, bounds, width=1, outline: Color = 'white', antialias=4):\\n\\n # Use a single channel image (mode='L') as mask.\\n # The size of the mask can be increased relative to the imput image\\n # to get smoother looking results.\\n mask = Image.new(size=[int(dim * antialias) for dim in image.size], mode='L', color='black')\\n draw = ImageDraw.Draw(mask)\\n\\n # draw outer shape in white (color) and inner shape in black (transparent)\\n for offset, fill in (-7, 'white'), (width, 'black'):\\n left, top = [(value + offset) * antialias for value in bounds[:2]]\\n right, bottom = [(value - offset) * antialias for value in bounds[2:]]\\n draw.ellipse([left, top, right, bottom], fill=fill)\\n\\n # downsample the mask using PIL.Image.LANCZOS\\n # (a high-quality downsampling filter).\\n mask = mask.resize(image.size, Image.LANCZOS)\\n # paste outline color to input image through the mask\\n image.paste(outline, mask=mask)\",\n \"def ellipse_pt(th, x_c, y_c, a, b, rot):\\n x = x_c + (a * cos(th) * cos(rot) - b * sin(th) * sin(rot))\\n y = y_c + (a * cos(th) * sin(rot) - b * sin(th) * cos(rot))\\n return x, y\",\n \"def r_ellipse(radius=5, xc=[0., 0.], q=0.5, pa=0, re=1., gf_header=None, comp='2', verbose=True, nstep=256, psf_offset=[1, 1], **kwargs):\\n if gf_header is not None:\\n xx = gf_header_key(gf_header, comp+'_XC')\\n yy = gf_header_key(gf_header, comp+'_YC')\\n xc = np.array([xx, yy])\\n mag = gf_header_key(gf_header, comp+'_MAG')\\n\\n if comp+'_N' in gf_header:\\n n = gf_header_key(gf_header, comp+'_N')\\n q = gf_header_key(gf_header, comp+'_AR')\\n pa = gf_header_key(gf_header, comp+'_PA')\\n re = gf_header_key(gf_header, comp+'_RE')\\n else:\\n n = 1.\\n q = 1.\\n pa = 0.\\n re = 0.01\\n\\n if verbose:\\n print(f'xc:{xc}, q:{q}, pa:{pa}')\\n\\n phi = np.linspace(0, 2*np.pi, nstep)\\n xp = np.array([np.cos(phi), q*np.sin(phi)]).T*radius\\n\\n theta = -(np.pi/2 + pa/180*np.pi) # + np.pi\\n\\n _rot = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])\\n\\n x0 = xp.dot(_rot) + np.atleast_1d(xc)\\n xell, yell = (x0 - 1 - np.atleast_1d(psf_offset)).T\\n\\n return xell, yell\",\n \"def drawEllipse(self,id,x,y,rx,ry):\\n if id in self.elements.keys():\\n print(\\\"The id for the ellipse has been registered! Please use another one\\\")\\n return\\n try:\\n self.checkInBound(x,0); self.checkInBound(x+rx,0); self.checkInBound(x-rx,0)\\n except AssertionError as e:\\n print(\\\"The ellipse set would be longer than the x axis of canvas!\\\")\\n return\\n try:\\n self.checkInBound(y,0); self.checkInBound(y+ry,0); self.checkInBound(y-ry,0)\\n except AssertionError as e:\\n print(\\\"The ellipse set would be longer than the y axis of canvas!\\\")\\n return\\n ellipseEle=Ellipse(id,self.h-1-y,x,ry,rx,self.drawColor)\\n self.elements[id]=ellipseEle\\n self.sync=False\",\n \"def fill_ellipse(self, x0, y0, a, b, color):\\n a2 = a * a\\n b2 = b * b\\n twoa2 = a2 + a2\\n twob2 = b2 + b2\\n x = 0\\n y = b\\n px = 0\\n py = twoa2 * y\\n # Plot initial points\\n self.line(x0, y0 - y, x0, y0 + y, color)\\n # Region 1\\n p = round(b2 - (a2 * b) + (0.25 * a2))\\n while px < py:\\n x += 1\\n px += twob2\\n if p < 0:\\n p += b2 + px\\n else:\\n y -= 1\\n py -= twoa2\\n p += b2 + px - py\\n self.line(x0 + x, y0 - y, x0 + x, y0 + y, color)\\n self.line(x0 - x, y0 - y, x0 - x, y0 + y, color)\\n # Region 2\\n p = round(b2 * (x + 0.5) * (x + 0.5) +\\n a2 * (y - 1) * (y - 1) - a2 * b2)\\n while y > 0:\\n y -= 1\\n py -= twoa2\\n if p > 0:\\n p += a2 - py\\n else:\\n x += 1\\n px += twob2\\n p += a2 - py + px\\n self.line(x0 + x, y0 - y, x0 + x, y0 + y, color)\\n self.line(x0 - x, y0 - y, x0 - x, y0 + y, color)\",\n \"def in_ellipse(x,y,a,b):\\n return ellipse(x,y,a,b) <= 1\",\n \"def scatter_ellipse(axis_ob, x, y, w, h, c='b', a=0.0, alpha=0.5):\\r\\n if not axis_ob._hold:\\r\\n axis_ob.cla()\\r\\n\\r\\n if not iterable(a):\\r\\n a = [a] * len(x)\\r\\n\\r\\n if not iterable(alpha):\\r\\n alpha = [alpha] * len(x)\\r\\n if len(c) != len(x):\\r\\n raise ValueError('c and x are not equal lengths')\\r\\n if len(w) != len(x):\\r\\n raise ValueError('w and x are not equal lengths')\\r\\n\\r\\n if len(h) != len(x):\\r\\n raise ValueError('h and x are not equal lengths')\\r\\n if len(a) != len(x):\\r\\n raise ValueError('a and x are not equal lengths')\\r\\n # if len(alpha)!=len(x):\\r\\n # raise ValueError, 'alpha and x are not equal lengths'\\r\\n patches = []\\r\\n for thisX, thisY, thisW, thisH, thisC, thisA, thisAl in \\\\\\r\\n zip(x, y, w, h, c, a, alpha):\\r\\n ellip = Ellipse((thisX, thisY), width=thisW, height=thisH,\\r\\n angle=thisA)\\r\\n\\r\\n ellip.set_facecolor(thisC)\\r\\n ellip.set_alpha(thisAl)\\r\\n axis_ob.add_patch(ellip)\\r\\n patches.append(ellip)\\r\\n axis_ob.autoscale_view()\\r\\n return axis_ob\",\n \"def ellipseToPath(self,node):\\n cx = float(node['cx'])\\n cy = float(node['cy'])\\n rx = 0\\n ry = 0\\n if 'rx' in node:\\n rx = float(node['rx'])\\n if 'ry' in node:\\n ry = float(node['ry'])\\n\\n d ='M %f,%f '%(cx-rx,cy)\\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx,cy-ry)\\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx+rx,cy)\\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx,cy+ry)\\n d+='A %f,%f 0 0 1 %f,%f'%(rx,ry,cx-rx,cy)\\n\\n return d\",\n \"def smallest_ellipse(points, tol = 0.001):\\n points = np.asmatrix(points)\\n N, d = points.shape\\n Q = np.column_stack((points, np.ones(N))).T\\n err = tol+1.0\\n u = np.ones(N)/N\\n \\n while err > tol:\\n # assert u.sum() == 1 # invariant\\n X = Q * np.diag(u) * Q.T\\n M = np.diag(Q.T * la.inv(X) * Q)\\n jdx = np.argmax(M)\\n step_size = (M[jdx]-d-1.0)/((d+1)*(M[jdx]-1.0))\\n new_u = (1-step_size)*u\\n new_u[jdx] += step_size\\n err = la.norm(new_u-u)\\n u = new_u\\n c = u*points\\n A = la.inv(points.T*np.diag(u)*points - c.T*c)/d\\n return np.asarray(A), np.squeeze(np.asarray(c))\",\n \"def create_ellipse(home_location, work_location, spread):\\n\\tif work_location is None:\\n\\t\\treturn None\\n\\telse:\\n\\t\\ta = home_location.distance(work_location)\\n\\t\\tb = a * float(spread)\\n\\t\\tpoint_list = []\\n\\t\\tazimuth = math.atan2(work_location.y - home_location.y, work_location.x - home_location.x)\\n\\t\\tro = (math.pi / 200)\\n\\n\\t\\tfor t in range(0, 401):\\n\\t\\t\\tx = home_location.x + (a * math.cos(t * ro) * math.cos(azimuth) - b * math.sin(t * ro) * math.sin(azimuth))\\n\\t\\t\\ty = home_location.y + (b * math.sin(t * ro) * math.cos(azimuth) + a * math.cos(t * ro) * math.sin(azimuth))\\n\\t\\t\\tpoint_list.append([Point(x, y).x, Point(x, y).y])\\n\\t\\treturn Polygon(point_list)\",\n \"def create_ellipse(width, height, color):\\n num_segments = 64\\n\\n data = []\\n\\n for segment in range(num_segments + 1):\\n theta = 2.0 * 3.1415926 * segment / num_segments\\n\\n x = width * math.cos(theta)\\n y = height * math.sin(theta)\\n\\n data.extend([x, y])\\n\\n vbo_id = GL.GLuint()\\n\\n GL.glGenBuffers(1, ctypes.pointer(vbo_id))\\n\\n v2f = data\\n data2 = (GL.GLfloat * len(v2f))(*v2f)\\n\\n GL.glBindBuffer(GL.GL_ARRAY_BUFFER, vbo_id)\\n GL.glBufferData(GL.GL_ARRAY_BUFFER, ctypes.sizeof(data2), data2,\\n GL.GL_STATIC_DRAW)\\n\\n shape = VertexBuffer(vbo_id, len(v2f) // 2, width, height, color)\\n return shape\",\n \"def getEllipticalKernel(size = (5,5)):\\n\\treturn cv2.getStructuringElement(cv2.MORPH_ELLIPSE, size)\",\n \"def draw_probe_ellipse(xy, covar, alpha, color=None, **kwargs):\\n \\n b24ac = scipy.sqrt(pow(covar[0,0] - covar[1,1],2) + 4*pow(covar[0,1],2))\\n c2inv = chi2.ppf(alpha, 2.)\\n \\n a = scipy.real(scipy.sqrt(c2inv*.5*(covar[0,0] + covar[1,1] + b24ac)))\\n b = scipy.real(scipy.sqrt(c2inv*.5*(covar[0,0] + covar[1,1] - b24ac)))\\n\\n if covar[0,0] != covar[1,1]:\\n theta = .5*scipy.arctan(2*covar[0,1]/(covar[0,0] - covar[1,1]))\\n print(theta)\\n else:\\n theta = scipy.sign(covar[0,1])*scipy.pi/4\\n \\n if covar[1,1] > covar[0,0]:\\n swap = a\\n a = b\\n b = swap\\n\\n ellipse = Ellipse(xy, 2*a, 2*b, angle=theta*180./scipy.pi, edgecolor=color, fill=False, **kwargs)\\n plt.gca().add_patch(ellipse)\\n return ellipse\",\n \"def add_ellipse(self, centroid, length, width, angle, asymmetry=0.0, **kwargs):\\n ellipse = Ellipse(\\n x=centroid[0],\\n y=centroid[1],\\n width=length,\\n height=width,\\n angle=angle,\\n fill_color=None,\\n **kwargs,\\n )\\n glyph = self.figure.add_glyph(ellipse)\\n self._annotations.append(glyph)\\n self.update()\\n return ellipse\",\n \"def construct_by_ellipse(a_xx, h_xy, b_yy, g_x, f_y, d, focal_length):\\n gamma = - focal_length\\n a = gamma**2 * a_xx\\n b = gamma**2 * b_yy\\n c = d\\n d = gamma**2 * d\\n f = -gamma*(f_y)\\n g = -gamma*(g_x)\\n h = gamma**2 * h_xy\\n #Not needed\\n u = gamma**2 * g_x\\n v = gamma**2 * f_y\\n w = -gamma*(d)\\n return ConeCamera(a, b, c, f, g, h)\",\n \"def proc_filled_ellipse(self, tokens):\\n\\n return self._proc_ellipse(tokens, filled=True)\",\n \"def plot_cov_ellipse(ellipses, cov, pos=[0.0, 0.0], nstds=[0.0,1.0,2.0], **kwargs):\\n def eigsorted(cov):\\n vals, vecs = _np.linalg.eigh(cov)\\n order = vals.argsort()[::-1]\\n return vals[order], vecs[:,order]\\n\\n\\n vals, vecs = eigsorted(cov)\\n theta = _np.degrees(_np.arctan2(*vecs[:,0][::-1]))\\n\\n # Width and height are \\\"full\\\" widths, not radius\\n sigma_max = 0.5\\n alpha = min(0.8, _np.prod(sigma_max /_np.sqrt(vals)))\\n for i,e in enumerate(ellipses):\\n sigma = nstds[i]\\n width, height = 2 * sigma * _np.sqrt(vals)\\n #ellipses[i].center = pos\\n e.set_alpha(alpha)\\n if sigma > 0.1: #if this is below, then treat ellipse as a center circle and do not modify size at all\\n e.width = width\\n e.height= height\\n e.angle = theta\\n e.center = pos\\n e.set(**kwargs)\\n\\n# e.fill=True\\n# e.set_linewidth(0.0)\\n\\n\\n return ellipses\",\n \"def draw_ellipse(self, color, position, size, border_width = 0, anchor= 'topleft'):\\n # We'll try to make sure that everything is okay later\\n \\n color = spyral.color._determine(color)\\n offset = self._calculate_offset(anchor, size)\\n pygame.draw.ellipse(self._surf, color, (position + offset, size), border_width)\",\n \"def geom_ellipse(mean: np.array, cov: np.array, data: np.array=None, q: float=0.95, **kwargs):\\n\\n # Radius that covers q-fraction of white gaussian noise \\n r = np.sqrt(stats.chi2.ppf(q=q, df=2))\\n \\n # Eigen-directions of a covariance matrix\\n try:\\n L, W = np.linalg.eigh(cov)\\n except:\\n return geom_path(aes(x = 'x', y = 'y'), data = DataFrame(columns=['x', 'y']))\\n \\n # Properly scaled eigen-directions\\n W[0, :] = W[0, :] * r * np.sqrt(L[0]) \\n W[1, :] = W[1, :] * r * np.sqrt(L[1]) \\n \\n theta = np.linspace(0, 2 * np.pi, 100)\\n \\n return geom_path(aes(x = 'x', y = 'y'), data = DataFrame()\\n .assign(x = mean[0] + np.sin(theta) * W[0, 0] + np.cos(theta) * W[1, 0])\\n .assign(y = mean[1] + np.sin(theta) * W[0, 1] + np.cos(theta) * W[1, 1]), **kwargs)\",\n \"def extractOblateEllipse(kperp,kpar,aniso):\\n\\n if aniso > 1.:\\n #print(\\\"Swapping axis for oblate ellipse\\\")\\n aniso = 1. / aniso\\n\\n # Define the eccentricity of the ellipse\\n e = np.sqrt( 1. - aniso**2. )\\n\\n\\n # the oblate surface area\\n surface = 2. * np.pi * kperp**2. * ( 1. + ( (1. - e**2.) / e ) * np.arctanh(e) )\\n\\n return surface\",\n \"def getEllipse(self, xc, Sigma, nSigma=2):\\n\\n if nla.det(Sigma) == 0:\\n return None\\n\\n w, v = nla.eig(Sigma)\\n D = np.diag(w, 0)\\n\\n theta = np.linspace(0, 2*np.pi, 100, endpoint=True)\\n circle = nSigma*np.vstack((np.cos(theta), np.sin(theta)))\\n\\n el = sla.sqrtm(D)\\n el = el.dot(circle)\\n el = v.dot(el)\\n\\n XY = xc + el\\n\\n return XY\",\n \"def _save_parameters(self):\\n\\n # eigenvectors are the coefficients of an ellipse in general form\\n # a*x^2 + 2*b*x*y + c*y^2 + 2*d*x + 2*f*y + g = 0 [eqn. 15) from (**) or (***)\\n a = self.coef[0, 0]\\n b = self.coef[1, 0]/2.\\n c = self.coef[2, 0]\\n d = self.coef[3, 0]/2.\\n f = self.coef[4, 0]/2.\\n g = self.coef[5, 0]\\n\\n # finding center of ellipse [eqn.19 and 20] from (**)\\n x0 = (c*d-b*f)/(b**2.-a*c)\\n y0 = (a*f-b*d)/(b**2.-a*c)\\n\\n # Find the semi-axes lengths [eqn. 21 and 22] from (**)\\n numerator = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)\\n denominator1 = (b*b-a*c) * \\\\\\n ((c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\\n denominator2 = (b*b-a*c) * \\\\\\n ((a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\\n width = np.sqrt(numerator/denominator1)\\n height = np.sqrt(numerator/denominator2)\\n\\n # angle of counterclockwise rotation of major-axis of ellipse to x-axis [eqn. 23] from (**)\\n # or [eqn. 26] from (***).\\n phi = .5*np.arctan((2.*b)/(a-c))\\n\\n self._center = [x0, y0]\\n self._width = width\\n self._height = height\\n self._phi = phi\",\n \"def ellipse_bounds(P, level, n=100):\\n # Round up to multiple of 2\\n n += n % 2\\n\\n # Principal axes of ellipsoid\\n eigval, eigvec = np.linalg.eig(P)\\n eigvec *= np.sqrt(level / eigval)\\n\\n # set zero angle at maximum x\\n angle = np.linspace(0, 2 * np.pi, n)[:, None]\\n angle += np.arctan(eigvec[0, 1] / eigvec[0, 0])\\n\\n # Compute positions\\n pos = np.cos(angle) * eigvec[:, 0] + np.sin(angle) * eigvec[:, 1]\\n n /= 2\\n\\n # Return x-position (symmetric) and upper/lower bounds\\n return pos[:n, 0], pos[:n, 1], pos[:n - 1:-1, 1]\",\n \"def test_ellipse_draw():\\n with TestingCanvas():\\n ellipse = visuals.Ellipse(pos=(0., 0.), radius=(0.4, 0.3),\\n color=(0, 0, 1, 1))\\n ellipse.transform = transforms.STTransform(scale=(2.0, 3.0))\\n ellipse.draw()\\n assert_image_equal(\\\"screenshot\\\", 'visuals/ellipse1.png')\\n\\n gloo.clear()\\n ellipse = visuals.Ellipse(pos=(0., 0.), radius=(0.4, 0.3),\\n color=(0, 0, 1, 1),\\n border_color=(1, 0, 0, 1))\\n ellipse.transform = transforms.STTransform(scale=(2.0, 3.0))\\n ellipse.draw()\\n assert_image_equal(\\\"screenshot\\\", 'visuals/ellipse2.png')\\n\\n gloo.clear()\\n ellipse = visuals.Ellipse(pos=(0., 0.), radius=(0.4, 0.3),\\n border_color=(1, 0, 0, 1))\\n ellipse.transform = transforms.STTransform(scale=(2.0, 3.0))\\n ellipse.draw()\\n assert_image_equal(\\\"screenshot\\\", 'visuals/ellipse3.png')\",\n \"def draw_oval(display, coord, box_size, color, bg_color):\\n left, top = coord\\n half = int(box_size * 0.5)\\n quarter = int(box_size * 0.25)\\n pygame.draw.ellipse(display, color, (left, top + quarter, box_size, half))\",\n \"def fitEllipseDirect(points):\\r\\n x = points[:, 0]; y = points[:, 1];\\r\\n # Build design matrix\\r\\n D = np.vstack((x*x, x*y, y*y, x, y, np.ones(x.shape)))\\r\\n # Build scatter matrix\\r\\n S = D.dot(D.T)\\r\\n # Build constraint matrix\\r\\n C = np.zeros((6, 6))\\r\\n C[0, 2]= +2; C[1, 1]= -1; C[2, 0]= +2;\\r\\n # Solve generalised eigenvalue system C*a == l*S*a\\r\\n geval, gevec = linalg.eig(S, C)\\r\\n # Find the eigenvector with the only pozitive eigenvalue\\r\\n geval = np.real(geval)\\r\\n i = np.argmax((geval>0) * np.isfinite(geval))\\r\\n if not np.isfinite(geval[i]):\\r\\n raise linalg.LinAlgError(\\r\\n \\\"Eigenvalue calculation failed to return a valid answer.\\\" +\\r\\n \\\"\\\\nEigenvalues:\\\\n\\\" + str(geval) + '\\\\n')\\r\\n theVec = np.real(gevec[:, i])\\r\\n # That vector has the parameters of the ellipse\\r\\n return tuple(theVec.flatten())\",\n \"def draw_ellipse(position, covariance, ax=None, **kwargs):\\r\\n # Convert covariance to principal axes\\r\\n if covariance.shape == (2, 2):\\r\\n U, s, Vt = np.linalg.svd(covariance)\\r\\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\\r\\n width, height = 2 * np.sqrt(s)\\r\\n else:\\r\\n angle = 0\\r\\n width, height = 2 * np.sqrt(covariance)\\r\\n \\r\\n # Draw the Ellipse\\r\\n for nsig in range(1, 4):\\r\\n ax.add_patch(Ellipse(position, nsig * width, nsig * height, \\r\\n angle, **kwargs))\",\n \"def run_ellipse(img, redo=False):\\n # Reading data and mask\\n outfile = \\\"ellipse.txt\\\"\\n if os.path.exists(outfile) and not redo:\\n return\\n data = make_masked_img(img)\\n # Preparing ellipse fitting\\n\\n geometry = EllipseGeometry(x0=213, y0=235, sma=25, eps=0.3,\\n pa=np.deg2rad(-50))\\n ellipse = Ellipse(data, geometry)\\n isolist = ellipse.fit_image(fflag=0.01, maxsma=200, maxrit=104)\\n # isolist = ellipse.fit_image(fflag=0.01, maxsma=20)\\n table = isolist.to_table()[1:]\\n table.write(outfile, format=\\\"ascii\\\", overwrite=True)\\n # Producing image\\n model_image = build_ellipse_model(data.shape, isolist)\\n residual = data - model_image\\n fig, (ax1, ax2, ax3) = plt.subplots(figsize=(14, 5), nrows=1, ncols=3)\\n fig.subplots_adjust(left=0.04, right=0.98, bottom=0.02, top=0.98)\\n ax1.imshow(data, origin='lower')\\n ax1.set_title('Data')\\n\\n smas = np.linspace(5, 200, 10)\\n for sma in smas:\\n iso = isolist.get_closest(sma)\\n x, y, = iso.sampled_coordinates()\\n ax1.plot(x, y, color='C1')\\n ax2.imshow(model_image, origin='lower')\\n ax2.set_title('Ellipse Model')\\n ax3.imshow(residual, origin='lower')\\n ax3.set_title('Residual')\\n plt.savefig(\\\"ellipse.png\\\", dpi=250)\\n plt.show()\",\n \"def draw_ellipse(position, covariance, ax=None, **kwargs):\\n ax = ax or plt.gca()\\n # Convert covariance to principal axes\\n if covariance.shape == (2, 2):\\n U, s, Vt = np.linalg.svd(covariance)\\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\\n width, height = 2 * np.sqrt(s)\\n else:\\n angle = 0\\n width, height = 2 * np.sqrt(covariance)\\n \\n # Draw the Ellipse\\n for nsig in range(1, 4):\\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\\n angle, **kwargs))\",\n \"def ellipse_orientation(S):\\n return 1/2 * np.arctan2(S[..., 2], S[..., 1])\",\n \"def draw_ellipse(position, covariance, ax=None, **kwargs):\\n ax = ax or plt.gca()\\n \\n # Convert covariance to principal axes\\n if covariance.shape == (2, 2):\\n U, s, Vt = np.linalg.svd(covariance)\\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\\n width, height = 2 * np.sqrt(s)\\n else:\\n angle = 0\\n width, height = 2 * np.sqrt(covariance)\\n \\n # Draw the Ellipse\\n for nsig in range(1, 4):\\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\\n angle, **kwargs))\",\n \"def proc_unfilled_ellipse(self, tokens):\\n\\n return self._proc_ellipse(tokens, filled=False)\",\n \"def transform(self, heraldry: Image.Image) -> Image.Image:\\n heraldry = heraldry.convert('RGBA')\\n orig_width, orig_height = heraldry.size\\n\\n new_height = int(self.height_change * orig_height)\\n ell_img = Image.new('RGBA', (orig_width, new_height))\\n draw = ImageDraw.Draw(ell_img)\\n\\n top_left = (0, int(self.ell_start * orig_height))\\n bot_right = (orig_width - 1, new_height - 1)\\n draw.ellipse((*top_left, *bot_right), fill = self.fill_col)\\n ell_img.paste(heraldry, (0, 0), heraldry)\\n\\n return ell_img\",\n \"def __DrawEllipse(self, image, rectangule, color):\\n cv2.ellipse(image, rectangule, color, 2)\\n points = cv2.boxPoints(rectangule)\\n for i in range(4):\\n cv2.line(image, tuple(np.array(points[i], np.int32)),\\n tuple(np.array(points[(i + 1) % 4], np.int32)), color, 2)\",\n \"def area_ellipse(radius_x: float, radius_y: float) -> float:\\r\\n if radius_x < 0 or radius_y < 0:\\r\\n raise ValueError(\\\"area_ellipse() only accepts non-negative values\\\")\\r\\n return pi * radius_x * radius_y\",\n \"def getMinVolEllipse(P, tolerance=0.01):\\n (N, d) = np.shape(P)\\n d = float(d)\\n\\n # Q will be our working array\\n Q = np.vstack([np.copy(P.T), np.ones(N)]) \\n QT = Q.T\\n \\n # initializations\\n err = 1.0 + tolerance\\n u = (1.0 / N) * np.ones(N)\\n\\n # Khachiyan Algorithm\\n while err > tolerance:\\n V = np.dot(Q, np.dot(np.diag(u), QT))\\n M = np.diag(np.dot(QT , np.dot(linalg.inv(V), Q))) # M the diagonal vector of an NxN matrix\\n j = np.argmax(M)\\n maximum = M[j]\\n step_size = (maximum - d - 1.0) / ((d + 1.0) * (maximum - 1.0))\\n new_u = (1.0 - step_size) * u\\n new_u[j] += step_size\\n err = np.linalg.norm(new_u - u)\\n u = new_u\\n\\n # center of the ellipse \\n center = np.dot(P.T, u)\\n\\n # the A matrix for the ellipse\\n A = linalg.inv(\\n np.dot(P.T, np.dot(np.diag(u), P)) - \\n np.array([[a * b for b in center] for a in center])\\n ) / d\\n \\n # Get the values we'd like to return\\n U, s, rotation = linalg.svd(A)\\n radii = 1.0/np.sqrt(s)\\n\\n rot_err = linalg.norm(np.identity(3)-abs(rotation))\\n if(rot_err > 0.05):\\n \\tradii = np.array([radii[1],radii[0],radii[2]])\\n return radii\",\n \"def draw_ellipse(position, covariance, ax=None, **kwargs):\\n ax = ax or plt.gca()\\n\\n # Convert covariance to principal axes\\n if covariance.shape == (2, 2):\\n U, s, Vt = np.linalg.svd(covariance)\\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\\n width, height = 2 * np.sqrt(s)\\n else:\\n angle = 0\\n width, height = 2 * np.sqrt(covariance)\\n\\n # Draw the Ellipse\\n for nsig in range(1, 4):\\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\\n angle, **kwargs))\",\n \"def draw_ellipse(frame, coordinate, line_color=(124, 0, 0), radius=1, normalized=True):\\n\\n x1 = coordinate[0]\\n y1 = coordinate[1]\\n\\n if normalized:\\n h = frame.shape[0]\\n w = frame.shape[1]\\n\\n x1 = int(x1 * w)\\n y1 = int(y1 * h)\\n\\n cv.circle(frame, (x1, y1), radius=radius, color=line_color, thickness=1)\",\n \"def draw_ellipse(position, covariance, ax=None, num_contours=5, **kwargs):\\n ax = ax or plt.gca()\\n \\n # Convert covariance to principal axes\\n U, s, Vt = np.linalg.svd(covariance)\\n angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\\n width, height = 2 * np.sqrt(s)\\n \\n # Draw the Ellipse\\n for nsig in range(1, num_contours):\\n ax.add_patch(Ellipse(position, nsig * width, nsig * height,\\n angle, **kwargs))\",\n \"def get_ellipse_coords(a=0.0, b=0.0, x=0.0, y=0.0, angle=0.0, k=2):\\n pts = np.zeros((360*k+1, 2))\\n\\n beta = -angle * np.pi/180.0\\n sin_beta = np.sin(beta)\\n cos_beta = np.cos(beta)\\n alpha = np.radians(np.r_[0.:360.:1j*(360*k+1)])\\n \\n sin_alpha = np.sin(alpha)\\n cos_alpha = np.cos(alpha)\\n \\n pts[:, 0] = x + (a * cos_alpha * cos_beta - b * sin_alpha * sin_beta)\\n pts[:, 1] = y + (a * cos_alpha * sin_beta + b * sin_alpha * cos_beta)\\n\\n return pts\",\n \"def ellipse_to_rectangle(n, locations):\\n major_axis_radius, minor_axis_radius, angle, center_x, center_y = locations\\n pt = []\\n for i in range(n):\\n pt1 = (int(center_x[i]) - int(minor_axis_radius[i]), int(center_y[i]) - int(major_axis_radius[i]))\\n pt2 = (int(center_x[i]) + int(minor_axis_radius[i]), int(center_y[i]) + int(major_axis_radius[i]))\\n pt.append([pt1, pt2])\\n return pt\",\n \"def get_ellipse_coords(a=0.0, b=0.0, x=0.0, y=0.0, angle=0.0, k=2):\\n pts = np.zeros((360*k+1, 2))\\n\\n beta = -angle * np.pi/180.0\\n sin_beta = np.sin(beta)\\n cos_beta = np.cos(beta)\\n alpha = np.radians(np.r_[0.:360.:1j*(360*k+1)])\\n\\n sin_alpha = np.sin(alpha)\\n cos_alpha = np.cos(alpha)\\n\\n pts[:, 0] = x + (a * cos_alpha * cos_beta - b * sin_alpha * sin_beta)\\n pts[:, 1] = y + (a * cos_alpha * sin_beta + b * sin_alpha * cos_beta)\\n\\n return pts\",\n \"def calcualte_ellipse_radii(guess, eccentricity = 0, perimeter = 2 * np.pi*1):\\n return fsolve(ellipse_radii_test, guess, args = (eccentricity, perimeter))\",\n \"def DrawEllipse(*args, **kwargs):\\n return _gdi_.DC_DrawEllipse(*args, **kwargs)\",\n \"def get_cov_ellipse(cov, centre, nstd, **kwargs):\\n #WZN\\n\\n # Find and sort eigenvalues and eigenvectors into descending order\\n eigvals, eigvecs = np.linalg.eigh(cov)\\n order = eigvals.argsort()[::-1]\\n eigvals, eigvecs = eigvals[order], eigvecs[:, order]\\n\\n # The anti-clockwise angle to rotate our ellipse by \\n vx, vy = eigvecs[:,0][0], eigvecs[:,0][1]\\n theta = np.arctan2(vy, vx)\\n\\n # Width and height of ellipse to draw\\n width, height = 2 * nstd * np.sqrt(eigvals)\\n return Ellipse(xy=centre, width=width, height=height,\\n angle=np.degrees(theta), **kwargs)\",\n \"def DrawEllipsePointSize(*args, **kwargs):\\n return _gdi_.DC_DrawEllipsePointSize(*args, **kwargs)\",\n \"def visualize_result(img, ellipse, radii=None, angles=None, reference_ellipse=None, strain=None, marker_size=8,\\n title=\\\"\\\"):\\n if img is None or ellipse is None:\\n return img\\n\\n img = np.copy(img) # copy so we don't alter the original\\n\\n blue = (255, 0, 0)\\n green = (0, 200, 0)\\n red = (0, 0, 255)\\n black = (0, 0, 0)\\n strain_colors = ((0, 127, 255), (127, 0, 255))\\n\\n # draw the reference ellipse\\n if reference_ellipse is not None:\\n draw_ellipse(img, reference_ellipse, green, 1, True, 1)\\n\\n # draw the fitted ellipse\\n draw_ellipse(img, ellipse, blue, 2, True, 1)\\n\\n # draw radial strain\\n if radii is not None and angles is not None:\\n assert len(radii) == len(angles)\\n # duplicate colors to make sure we have enough for each radius\\n strain_colors = strain_colors * int(np.ceil(len(radii) / len(strain_colors)))\\n center, diam, angle = ellipse\\n for r, a, c in zip(radii, angles, strain_colors):\\n p1, p2 = draw_diameter(img, center, r, a, c, 1)\\n draw_cross(img, p1, marker_size, a, c, 2)\\n draw_cross(img, p2, marker_size, a, c, 2)\\n\\n # draw text\\n font = cv.FONT_HERSHEY_SIMPLEX\\n scale = 1.5\\n margin = 20\\n cv.putText(img, title, (margin, 60), font, scale, black, 3)\\n if reference_ellipse is not None:\\n cv.putText(img, \\\"Detected ellipse\\\", (margin, 120), font, scale, blue, 2)\\n cv.putText(img, \\\"Reference ellipse\\\", (margin, 180), font, scale, green, 2)\\n\\n if strain is not None:\\n cv.putText(img, \\\"Strain: {:.1f} %\\\".format(strain), (margin, 240), font, scale, black, 2)\\n\\n return img\",\n \"def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\\n if x.size != y.size:\\n raise ValueError(\\\"x and y must be the same size\\\")\\n\\n cov = np.cov(x, y)\\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\\n # Using a special case to obtain the eigenvalues of this\\n # two-dimensionl dataset.\\n ell_radius_x = np.sqrt(1 + pearson)\\n ell_radius_y = np.sqrt(1 - pearson)\\n ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\\n facecolor=facecolor, **kwargs)\\n\\n # Calculating the stdandard deviation of x from\\n # the squareroot of the variance and multiplying\\n # with the given number of standard deviations.\\n scale_x = np.sqrt(cov[0, 0]) * n_std\\n mean_x = np.mean(x)\\n\\n # calculating the stdandard deviation of y ...\\n scale_y = np.sqrt(cov[1, 1]) * n_std\\n mean_y = np.mean(y)\\n\\n transf = transforms.Affine2D() \\\\\\n .rotate_deg(45) \\\\\\n .scale(scale_x, scale_y) \\\\\\n .translate(mean_x, mean_y)\\n\\n ellipse.set_transform(transf + ax.transData)\\n return ax.add_patch(ellipse)\",\n \"def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\\n if x.size != y.size:\\n raise ValueError(\\\"x and y must be the same size\\\")\\n\\n cov = np.cov(x, y)\\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\\n # Using a special case to obtain the eigenvalues of this\\n # two-dimensionl dataset.\\n ell_radius_x = np.sqrt(1 + pearson)\\n ell_radius_y = np.sqrt(1 - pearson)\\n ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\\n facecolor=facecolor, **kwargs)\\n\\n # Calculating the stdandard deviation of x from\\n # the squareroot of the variance and multiplying\\n # with the given number of standard deviations.\\n scale_x = np.sqrt(cov[0, 0]) * n_std\\n mean_x = np.mean(x)\\n\\n # calculating the stdandard deviation of y ...\\n scale_y = np.sqrt(cov[1, 1]) * n_std\\n mean_y = np.mean(y)\\n\\n transf = transforms.Affine2D() \\\\\\n .rotate_deg(45) \\\\\\n .scale(scale_x, scale_y) \\\\\\n .translate(mean_x, mean_y)\\n\\n ellipse.set_transform(transf + ax.transData)\\n return ax.add_patch(ellipse)\",\n \"def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\\n if x.size != y.size:\\n raise ValueError(\\\"x and y must be the same size\\\")\\n\\n cov = np.cov(x, y)\\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\\n # Using a special case to obtain the eigenvalues of this\\n # two-dimensionl dataset.\\n ell_radius_x = np.sqrt(1 + pearson)\\n ell_radius_y = np.sqrt(1 - pearson)\\n ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,\\n facecolor=facecolor, **kwargs)\\n\\n # Calculating the stdandard deviation of x from\\n # the squareroot of the variance and multiplying\\n # with the given number of standard deviations.\\n scale_x = np.sqrt(cov[0, 0]) * n_std\\n mean_x = np.mean(x)\\n\\n # calculating the stdandard deviation of y ...\\n scale_y = np.sqrt(cov[1, 1]) * n_std\\n mean_y = np.mean(y)\\n\\n transf = transforms.Affine2D() \\\\\\n .rotate_deg(45) \\\\\\n .scale(scale_x, scale_y) \\\\\\n .translate(mean_x, mean_y)\\n\\n ellipse.set_transform(transf + ax.transData)\\n return ax.add_patch(ellipse)\",\n \"def minimum_rotated_rectangle(self): # -> BaseGeometry:\\n ...\",\n \"def DrawEllipse(*args, **kwargs):\\n return _gdi_.GraphicsContext_DrawEllipse(*args, **kwargs)\",\n \"def generate_ellipse(R1,R2,center,theta,N=100):\\r\\n t = np.linspace(0.0,2.0*np.pi,N)\\r\\n x = R1*np.cos(t)*np.cos(theta) - R2*np.sin(t)*np.sin(theta) + center[0]\\r\\n y = R1*np.cos(t)*np.sin(theta) + R2*np.sin(t)*np.cos(theta) + center[1]\\r\\n return x,y\",\n \"def constructByParamEllipse(x_center, y_center, maj, min, rot, radius_3d_circle, focal_length=1):\\n e = ImpEllipse.construct_by_param(x_center, y_center, maj, min, rot)\\n\\n return Double3DCircle.construct_by_ImpEllipse(e, radius_3d_circle, focal_length)\",\n \"def determine_bounding_box_of_rotated_box(self, box, rotation_matrix):\\n\\n # top left, top right, bottom left, bottom right\\n p1, p2, p3, p4 = box_points(box)\\n\\n # rotate all the points of the box\\n tp1 = calc_rotate_point_with_rotation_matrix(p1, rotation_matrix)\\n tp2 = calc_rotate_point_with_rotation_matrix(p2, rotation_matrix)\\n tp3 = calc_rotate_point_with_rotation_matrix(p3, rotation_matrix)\\n tp4 = calc_rotate_point_with_rotation_matrix(p4, rotation_matrix)\\n\\n # figure out which point has the furthest x distance, and the furthest y distance\\n dx1 = abs(tp1[0] - tp4[0])\\n dx2 = abs(tp2[0] - tp3[0])\\n dy1 = abs(tp1[1] - tp4[1])\\n dy2 = abs(tp2[1] - tp3[1])\\n # the width and the height is the max distance between x and y\\n w, h = max(dx1, dx2), max(dy1, dy2)\\n\\n # x and y is the min x, and min y among all points\\n x = min(tp1[0], tp2[0], tp3[0], tp4[0])\\n y = min(tp1[1], tp2[1], tp3[1], tp4[1])\\n\\n return (x, y, w, h)\",\n \"def get_exterior(self, x, y, x1, x2, bottom, head_y):\\n fx1 = x+(x-x1)*8\\n fx2 = x+(x-x2)*8\\n # compute bounding ellipse; and intersection with body outline\\n cv2.ellipse(self.ellipse_finder, ((x/mscale,y/mscale), ((fx1-fx2)/mscale, (2*(bottom-head_y))/mscale), 0), 255,-1 )\\n intersection = np.bitwise_and(255-self.ellipse_finder, self.median_finder)\\n # find external blobs\\n im2, out_contours, out_hierarchy = cv2.findContours(intersection,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\\n return out_contours, out_hierarchy, fx1-fx2\",\n \"def _gen_ellipse(twiss, ep=1, num=100):\\n a, b, c = twiss\\n\\n t = np.linspace(0, 2 * np.pi, num)\\n t0 = np.arctan(a)\\n x = np.sqrt(b * ep) * np.cos(t)\\n y = np.sqrt(c * ep) * np.sin(t - t0)\\n\\n return np.vstack([x, y])\",\n \"def eclipse_compensate(aLocation):\\n EQUATOR_RADIUS = 6378137.0 # equator radius, or \\\"spherical\\\" earth\\n POLAR_RADIUS = 6356725.0 # ploar radius\\n EP_DIFF = EQUATOR_RADIUS - POLAR_RADIUS # rad-diff between equator and pole\\n \\n # assuming linear slope from equator to pole\\n r_center = POLAR_RADIUS + EP_DIFF * (1.0 - abs(aLocation.lat)/90) # the ring thru earth center\\n r_level = r_center * math.cos(math.radians(aLocation.lat)) # the ring thru latitude level\\n \\n return [r_center, r_level]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72764295","0.65389484","0.6258115","0.6085874","0.6078846","0.59491014","0.5820374","0.5784566","0.561725","0.55927783","0.5559429","0.5536257","0.5470644","0.5456046","0.54508764","0.54230434","0.5402299","0.53830856","0.5359062","0.53571004","0.53560513","0.53382266","0.5328916","0.5314603","0.5278285","0.526421","0.5252613","0.5245298","0.5244697","0.5243301","0.5242532","0.5242532","0.5236282","0.5232601","0.52291036","0.51700604","0.51643294","0.5152434","0.5149357","0.5147671","0.5136441","0.5118137","0.5099325","0.5098919","0.50835234","0.50798863","0.5063783","0.50600314","0.50450844","0.5044488","0.502462","0.50230616","0.5020567","0.50108546","0.5007209","0.5006757","0.4990369","0.4988743","0.49806115","0.49620646","0.4955084","0.49350673","0.49310857","0.49072263","0.49036583","0.49018162","0.48816252","0.48798743","0.4878113","0.486852","0.48643646","0.4854467","0.48396644","0.48237053","0.48216644","0.48196954","0.48155782","0.47958115","0.478994","0.477515","0.4756133","0.4749394","0.47141978","0.47078654","0.47029993","0.47008288","0.47006717","0.4695882","0.46900976","0.4689779","0.4689779","0.4689779","0.46845865","0.46709248","0.4670893","0.46501812","0.4625324","0.46126327","0.46106678","0.46102318"],"string":"[\n \"0.72764295\",\n \"0.65389484\",\n \"0.6258115\",\n \"0.6085874\",\n \"0.6078846\",\n \"0.59491014\",\n \"0.5820374\",\n \"0.5784566\",\n \"0.561725\",\n \"0.55927783\",\n \"0.5559429\",\n \"0.5536257\",\n \"0.5470644\",\n \"0.5456046\",\n \"0.54508764\",\n \"0.54230434\",\n \"0.5402299\",\n \"0.53830856\",\n \"0.5359062\",\n \"0.53571004\",\n \"0.53560513\",\n \"0.53382266\",\n \"0.5328916\",\n \"0.5314603\",\n \"0.5278285\",\n \"0.526421\",\n \"0.5252613\",\n \"0.5245298\",\n \"0.5244697\",\n \"0.5243301\",\n \"0.5242532\",\n \"0.5242532\",\n \"0.5236282\",\n \"0.5232601\",\n \"0.52291036\",\n \"0.51700604\",\n \"0.51643294\",\n \"0.5152434\",\n \"0.5149357\",\n \"0.5147671\",\n \"0.5136441\",\n \"0.5118137\",\n \"0.5099325\",\n \"0.5098919\",\n \"0.50835234\",\n \"0.50798863\",\n \"0.5063783\",\n \"0.50600314\",\n \"0.50450844\",\n \"0.5044488\",\n \"0.502462\",\n \"0.50230616\",\n \"0.5020567\",\n \"0.50108546\",\n \"0.5007209\",\n \"0.5006757\",\n \"0.4990369\",\n \"0.4988743\",\n \"0.49806115\",\n \"0.49620646\",\n \"0.4955084\",\n \"0.49350673\",\n \"0.49310857\",\n \"0.49072263\",\n \"0.49036583\",\n \"0.49018162\",\n \"0.48816252\",\n \"0.48798743\",\n \"0.4878113\",\n \"0.486852\",\n \"0.48643646\",\n \"0.4854467\",\n \"0.48396644\",\n \"0.48237053\",\n \"0.48216644\",\n \"0.48196954\",\n \"0.48155782\",\n \"0.47958115\",\n \"0.478994\",\n \"0.477515\",\n \"0.4756133\",\n \"0.4749394\",\n \"0.47141978\",\n \"0.47078654\",\n \"0.47029993\",\n \"0.47008288\",\n \"0.47006717\",\n \"0.4695882\",\n \"0.46900976\",\n \"0.4689779\",\n \"0.4689779\",\n \"0.4689779\",\n \"0.46845865\",\n \"0.46709248\",\n \"0.4670893\",\n \"0.46501812\",\n \"0.4625324\",\n \"0.46126327\",\n \"0.46106678\",\n \"0.46102318\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5605659"},"document_rank":{"kind":"string","value":"9"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":82011,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3Mjg4NzYzMCwic3ViIjoiL2RhdGFzZXRzL2J1bnlhbWluZXJnZW4vQ29ybnN0YWNrLVB5dGhvbi1WMS1GaWx0ZXJlZCIsImV4cCI6MTc3Mjg5MTIzMCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.D0z2_Co8-BpND8lC-uTrtfKornidva6NP0crZhiDspxsN6Mw7Tf_nlPJ4dgy1iI--GC18ynQWXDInBhQFi3-BQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":82011,"numBytesParquet":1978359412}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 33 521 | document stringlengths 8 49.6k | metadata dict | negatives listlengths 5 101 | negative_scores listlengths 5 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real | def circulant_multiplication(u, a):
return real(ifft(fft(a)*fft(u))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def covar(fx,cx):\n \n fx = np.array(fx)\n cx = np.array(cx)\n \n shape_fx = fx.shape\n shape_cx = cx.shape\n \n \n if shape_fx[1] != shape_cx[0]:\n print('-----------------------------------------')\n print(\"Shapes of fx and cx cannot be multiplied:\")\n print(shap... | [
"0.650418",
"0.650212",
"0.6441079",
"0.6313763",
"0.6310517",
"0.62949276",
"0.62782884",
"0.62631303",
"0.61975265",
"0.6096459",
"0.608041",
"0.606508",
"0.6038961",
"0.6011421",
"0.60068315",
"0.59920776",
"0.59303707",
"0.58836865",
"0.5879482",
"0.58772385",
"0.58575416... | 0.6389226 | 3 |
Compute the matrixvector product y = Tu where T is a Toeplitz matrix All matrices are real | def toeplitz_multiplication(u, c, r=None):
n = len(u)
if r is None:
r = c
u1 = zeros((2*n))
u1[0:n] = u
c = np.concatenate((c, [0], r[-1:0:-1]))
y1 = circulant_multiplication(u1, c)
return y1[0:n] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrix_vector_prod(m,u):\n each_product = []\n for v in m:\n each_product.append(dot_prod(v, u))\n return each_product",
"def matmul(x, y):\n if len(list(y.size())) == 2:\n # if one of them is a vector (i.e. wanting to do MV mult)\n z = torch.zeros(2, x.size()[1], dtype=torch... | [
"0.7003199",
"0.6513981",
"0.64759356",
"0.6454179",
"0.6377554",
"0.6326698",
"0.6245358",
"0.620894",
"0.6208685",
"0.61977005",
"0.6195611",
"0.61694974",
"0.6168602",
"0.6134469",
"0.6106113",
"0.60868716",
"0.6082444",
"0.60823506",
"0.6070701",
"0.60688484",
"0.6063607"... | 0.63380134 | 5 |
Read in labels from digitStruct.mat file to create a dict of image file name and corresponding labels | def read_labels(digitstruct_file):
labels = dict()
for dsObj in tdqm(yieldNextDigitStruct(digitstruct_file), ncols=50):
image_labels = []
for bbox in dsObj.bboxList:
image_labels.append(bbox.label)
labels[dsObj.name] = image_labels
return labels | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_imagenet_as_dict(self):\n real_file_path = os.path.realpath(self.map_file)\n if not os.path.exists(real_file_path):\n raise IOError(\"map file {} not exists\".format(self.map_file))\n\n label_dict = {}\n with open(real_file_path) as fp:\n line = fp.readlin... | [
"0.7442581",
"0.67145514",
"0.6680717",
"0.66700083",
"0.6651974",
"0.6599294",
"0.65706545",
"0.6568262",
"0.65624034",
"0.65466106",
"0.6527709",
"0.65229243",
"0.65100825",
"0.6500305",
"0.649048",
"0.6466592",
"0.6466018",
"0.6442053",
"0.6429563",
"0.6409631",
"0.6398935... | 0.8415674 | 0 |
ref CLRS pg326, solution to the basic supply chain problem using the book notation for variables name | def fastestWay( a, t, e, x, n ):
import pdb;pdb.set_trace()
f1.append( ( e[0] , 1 ) )
f2.append( ( e[1] , 2 ) )
for i in xrange(n):
f11 = f1[i][0]+a[0][i]
f12 = f2[i][0]+a[1][i]+t[1][i+1]
f22 = f2[i][0]+a[1][i]
f21 = f1[i][0]+a[0][i]+t[0][i+1]
f1.append( ( min( f11, f12 ), 1 ) if f11 < f12 else ( min( f11, f12 ), 2 ) )
f2.append( ( min( f21, f22 ), 2 ) if f22 < f21 else ( min( f22, f21 ), 1 ) )
f1x, f2x = f1[n][0]+x[0], f2[n][0]+x[1]
return ( min( f1x, f2x ) , f1 ) if f1x < f2x else ( min( f1x, f2x ), f2 ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exercise_b2_39():\r\n pass",
"def exercise_b2_113():\r\n pass",
"def exercise_b2_93():\r\n pass",
"def exercise_b2_98():\r\n pass",
"def exercise_b2_27():\r\n pass",
"def exercise_b2_106():\r\n pass",
"def exercise_b2_53():\r\n pass",
"def exercise_b2_43():\r\n pass",
"d... | [
"0.5789567",
"0.5612758",
"0.56002617",
"0.5582453",
"0.5527549",
"0.5454671",
"0.5450963",
"0.5440792",
"0.5437476",
"0.54072136",
"0.5388782",
"0.53822136",
"0.53622717",
"0.5361482",
"0.53562933",
"0.5325314",
"0.5280684",
"0.52761763",
"0.5207379",
"0.5178423",
"0.5170255... | 0.0 | -1 |
This function computes the fundamental matrix by computing the SVD of Ax = 0 ; 8point algorithm | def computeFundamentalMatrix(pts1, pts2):
A = np.empty((8, 9))
for i in range(len(pts1)-1):
x1 = pts1[i][0]
x2 = pts2[i][0]
y1 = pts1[i][1]
y2 = pts2[i][1]
A[i] = np.array([x1 * x2, x2 * y1, x2,
y2 * x1, y2 * y1, y2,
x1, y1, 1])
# Compute F matrix by evaluating SVD
U, S, V = np.linalg.svd(A)
F = V[-1].reshape(3, 3)
# Constrain the F matrix to rank 2
U1, S1, V1 = np.linalg.svd(F)
# print('Old S', S)
# S[2] = 0
S2 = np.array([[S1[0], 0, 0], [0, S1[1], 0], [0, 0, 0]])
# print('New S', S)
F = np.dot(np.dot(U1, S2), V1)
return F | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def svd0(A):\n M,N = A.shape\n if M>N: return sla.svd(A, full_matrices=True)\n else: return sla.svd(A, full_matrices=False)",
"def invert_L1_svd():",
"def visualize_svd():",
"def svd(self):\n U, s, Vh = la.svd(self)\n S = np.zeros(self.shape)\n np.fill_diagonal(S, s)\n return... | [
"0.66048837",
"0.6466162",
"0.6259937",
"0.6250825",
"0.62505597",
"0.62274474",
"0.6104567",
"0.6089218",
"0.6025379",
"0.5982765",
"0.597328",
"0.590215",
"0.58907986",
"0.58582675",
"0.58575904",
"0.584388",
"0.58408606",
"0.58376825",
"0.581499",
"0.58008623",
"0.5792866"... | 0.68444854 | 0 |
Leverages the 8point algorithm and implement RANSAC algorithm to find the inliers and the best fundamental matrix | def getInlierRANSAC(pts1, pts2):
# global finalFundamentalMatrix
iterations = 50
threshold = 0.01
max_count = 0
n = len(pts1)
finalFundamentalMatrix = np.zeros((3, 3))
for i in range(iterations):
count = 0
idx = random.sample(range(n - 1), 8)
left_pts = pts1[idx]
right_pts = pts2[idx]
F = computeFundamentalMatrix(left_pts, right_pts)
left_feature_inlier = []
right_feature_inlier = []
# print("Sample index: ", len(idx))
for j in range(0, n):
homogeneous_right = np.array([pts2[j, 0], pts2[j, 1], 1])
homogeneous_left = np.array([pts1[j, 0], pts1[j, 1], 1])
fit = np.dot(homogeneous_right.T, np.dot(F, homogeneous_left))
# print("Fit for iteration ", i," ", np.abs(fit))
if np.abs(fit) < threshold:
left_feature_inlier.append(pts1[j])
right_feature_inlier.append(pts2[j])
count = count + 1
# print('Inlier count', count)
inlier_Left = np.array(left_feature_inlier)
inlier_Right = np.array(right_feature_inlier)
if count > max_count:
max_count = count
finalFundamentalMatrix = F
final_inlier_Left = inlier_Left
final_inlier_Right = inlier_Right
return finalFundamentalMatrix, final_inlier_Left, final_inlier_Right | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ransac(data, hypothesis, metric, sample_size, num_iter, inlier_thresh):\n N,d = data.shape\n best_frac, best_hypothesis, best_mask = 0, None, None\n for i in range(num_iter):\n js = np.random.choice(N,size=sample_size,replace=False)\n hypothesis_elements = data[js,:]\n H = hypothe... | [
"0.6203597",
"0.5916464",
"0.5894118",
"0.5867515",
"0.5715989",
"0.56956524",
"0.56905115",
"0.5686345",
"0.56403846",
"0.55984086",
"0.5590803",
"0.5577823",
"0.5559308",
"0.5542196",
"0.5525735",
"0.55202436",
"0.55189615",
"0.55174667",
"0.5481329",
"0.5479639",
"0.546803... | 0.699575 | 0 |
=========================================================== DateFormatedSQL(x) =========================================================== this function converts the the date read from a list to a datetime format | def DateFormatedSQL(x):
x=[i[0] for i in x]
x1=[]
for i in x:
if len(i)==19:
x1.append(datetime.datetime(int(i[:4]),int(i[5:7]),int(i[8:10]),int(i[11:13]),int(i[14:16]),int(i[17:18]) ))
# elif len(i)==13:
# x1.append(datetime.datetime(int(i[:4]),int(i[5:7]),int(i[8:10]),int(i[11:13]),int(0),int(0) ))
# else:
# x1.append(datetime.datetime(int(i[:4]),int(i[5:7]),int(i[8:10]),int(0),int(0),int(0) ))
# del i,x
return x1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list_d_t_t:\n data_output.append([datetime.datetime.strptime(row[0] + \" \" + row[1], \"%Y-%m-%d %H:%M:%S\"),\n datetime.datetime.strptime(row[0] + \" \" + row[2], \"%Y-%m-%d %H:%M:%S\")]... | [
"0.66734904",
"0.65000284",
"0.6259414",
"0.59757656",
"0.5600508",
"0.5579302",
"0.5578522",
"0.5551475",
"0.5513122",
"0.54512274",
"0.5435365",
"0.52705914",
"0.52298",
"0.5214014",
"0.5199284",
"0.51939476",
"0.5177129",
"0.5144611",
"0.51139647",
"0.5111645",
"0.5084982"... | 0.7940835 | 0 |
=========================================================== dateformated(x) =========================================================== this function converts the the date read from a list to a datetime format | def DateFormated(x):
x1=[]
for i in x:
if len(i)==19:
x1.append(datetime.datetime(int(i[:4]),int(i[5:7]),int(i[8:10]),int(i[11:13]),int(i[14:16]),int(i[17:18]) ))
# elif len(i)==13:
# x1.append(datetime.datetime(int(i[:4]),int(i[5:7]),int(i[8:10]),int(i[11:13]),int(0),int(0) ))
# else:
# x1.append(datetime.datetime(int(i[:4]),int(i[5:7]),int(i[8:10]),int(0),int(0),int(0) ))
# del i,x
return x1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list_d_t_t:\n data_output.append([datetime.datetime.strptime(row[0] + \" \" + row[1], \"%Y-%m-%d %H:%M:%S\"),\n datetime.datetime.strptime(row[0] + \" \" + row[2], \"%Y-%m-%d %H:%M:%S\")]... | [
"0.73220545",
"0.6644235",
"0.64673054",
"0.63785565",
"0.6323779",
"0.63159305",
"0.6256937",
"0.6174311",
"0.5986844",
"0.58866596",
"0.5878423",
"0.58775616",
"0.58339506",
"0.579193",
"0.57800907",
"0.57769805",
"0.5757611",
"0.57572365",
"0.57409817",
"0.5732213",
"0.572... | 0.75249213 | 0 |
Mimic the & operator in R. This has to have Expression objects to be involved to work | def _op_and_(self, left: Any, right: Any) -> Any:
if isinstance(left, list):
# induce an intersect with Collection
return Intersect(left, right)
left, right = _recycle_left_right(left, right)
left = Series(left).fillna(False)
right = Series(right).fillna(False)
return left & right | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AND(f, g):\n def _and(x):\n return f(x) & g(x)\n return _and",
"def and_(a, b):",
"def __and__(self, other):\n return self.fam.c_binop('and', self, other)",
"def __and__(self, obj):\n return self._boolean_operation(obj, operator.__and__)",
"def _and(cls, arg1, arg2):\n ... | [
"0.6913351",
"0.6844835",
"0.6834847",
"0.68041515",
"0.6614185",
"0.6585983",
"0.65602845",
"0.65299505",
"0.6528684",
"0.6510841",
"0.6501651",
"0.64942497",
"0.6466398",
"0.6432646",
"0.639087",
"0.6376711",
"0.6334177",
"0.6331774",
"0.63316846",
"0.6288898",
"0.62308896"... | 0.62697506 | 20 |
Mimic the & operator in R. This has to have Expression objects to be involved to work | def _op_or_(self, left: Any, right: Any) -> Any:
if isinstance(left, list):
return Collection(left, right)
left, right = _recycle_left_right(left, right)
left = Series(left).fillna(False)
right = Series(right).fillna(False)
return left | right | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AND(f, g):\n def _and(x):\n return f(x) & g(x)\n return _and",
"def and_(a, b):",
"def __and__(self, other):\n return self.fam.c_binop('and', self, other)",
"def __and__(self, obj):\n return self._boolean_operation(obj, operator.__and__)",
"def _and(cls, arg1, arg2):\n ... | [
"0.6913351",
"0.6844835",
"0.6834847",
"0.68041515",
"0.6614185",
"0.6585983",
"0.65602845",
"0.65299505",
"0.6528684",
"0.6510841",
"0.6501651",
"0.64942497",
"0.6466398",
"0.6432646",
"0.639087",
"0.6376711",
"0.6334177",
"0.6331774",
"0.63316846",
"0.6288898",
"0.62697506"... | 0.0 | -1 |
type + sequence_number + key_size + key + value_size + value 1bit 63bit 32bit varlength 32bit varlength | def __init__(self, key, sequence_number, type=KeyType.PUT, value=None):
assert key is not None
assert sequence_number >= 0
self.type = type
self.sequence_number = sequence_number
self.key = key
self.value = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_meta_chunk(key, value):\n bkey = key.encode(\"utf-8\")\n bvalue = value.encode(\"utf-8\")\n return (wozardry.to_uint32(len(bkey) + len(bvalue) + 2) + bkey + b'\\x09' + bvalue + b'\\x0A').hex()",
"def _pack_dict( self, table, pad = False ) :\r\n\r\n keys, values = zip( *table... | [
"0.5809457",
"0.5597021",
"0.55033654",
"0.5478504",
"0.5475797",
"0.5431724",
"0.54011375",
"0.53711766",
"0.535695",
"0.53174025",
"0.53174025",
"0.5285025",
"0.5257738",
"0.52197987",
"0.5217525",
"0.52163196",
"0.51897675",
"0.51784354",
"0.5164822",
"0.5161085",
"0.51330... | 0.48401326 | 43 |
Find all occurences of val on list lo Returns a list of indices of val on lo. | def findall(lo,val):
u = []
i = -1
while( i < len(lo)-1):
try:
i = lo.index(val,i+1)
u.append(i)
except:
i += 1
return u | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findall(l, o):\n return [i for i, u in enumerate(l) if u==o]",
"def getIndexes(self, val):\n # Find where this value is listed. \n valNdx = (self.values == val).nonzero()[0]\n \n # If this value is not actually in those listed, then we \n # must return empty indexes\n ... | [
"0.70793176",
"0.7071109",
"0.66156113",
"0.6319743",
"0.6250533",
"0.620535",
"0.62024206",
"0.619781",
"0.6169729",
"0.60985184",
"0.6077737",
"0.6067603",
"0.5907215",
"0.5842827",
"0.58379203",
"0.5817054",
"0.58101517",
"0.5786621",
"0.577902",
"0.5734879",
"0.5654707",
... | 0.8087934 | 0 |
Generate an identity key pair. Clients should only do this once, at install time. the generated IdentityKeyPair. | def generateIdentityKeyPair():
keyPair = Curve.generateKeyPair()
publicKey = IdentityKey(keyPair.getPublicKey())
serialized = '0a21056e8936e8367f768a7bba008ade7cf58407bdc7a6aae293e2c' \
'b7c06668dcd7d5e12205011524f0c15467100dd603e0d6020f4d293' \
'edfbcd82129b14a88791ac81365c'
serialized = binascii.unhexlify(serialized.encode())
identityKeyPair = IdentityKeyPair(publicKey, keyPair.getPrivateKey())
return identityKeyPair
# return IdentityKeyPair(serialized=serialized) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_key_pair(self, keysize, cb):\n\n def gen_key_pair_pub_cb(data, ctx):\n if not data:\n warning('keymanagement: Could not generate a key pair\\n')\n cb(None, None)\n else:\n cb(ctx, data)\n\n def gen_key_pair_priv_cb(data, ctx):... | [
"0.71120954",
"0.68204904",
"0.65850353",
"0.6553852",
"0.65218014",
"0.6443924",
"0.6378423",
"0.63755655",
"0.6358998",
"0.6324435",
"0.63147116",
"0.6309187",
"0.62952316",
"0.6251726",
"0.61868006",
"0.61828184",
"0.61474425",
"0.6133282",
"0.61287004",
"0.60888094",
"0.6... | 0.8180875 | 0 |
Test pointwise arithmetic with stencil offsets across a single functions with buffering dimension in indexed expression format | def test_indexed_buffered(self, expr, result):
i, j, l = dimify('i j l')
a = symbol(name='a', dimensions=(i, j, l), value=2., mode='indexed').base
fa = a.function
eqn = eval(expr)
Operator(eqn)(fa)
assert np.allclose(fa.data[1, 1:-1, 1:-1], result[1:-1, 1:-1], rtol=1e-12) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_indexed_stencil(self, expr, result):\n j, l = dimify('j l')\n a = symbol(name='a', dimensions=(j, l), value=0., mode='indexed').base\n fa = a.function\n b = symbol(name='b', dimensions=(j, l), value=2., mode='indexed').base\n fb = b.function\n\n eqn = eval(expr)\n... | [
"0.70109046",
"0.5731927",
"0.5725207",
"0.5534058",
"0.5485873",
"0.54508567",
"0.5420271",
"0.5402067",
"0.53938526",
"0.5361206",
"0.53606236",
"0.5359261",
"0.5359202",
"0.5320887",
"0.5293664",
"0.5291382",
"0.5265721",
"0.52385396",
"0.523383",
"0.52290106",
"0.52282166... | 0.5903518 | 1 |
You can override this method if you want to change the format of outputs (e.g., storing gradients) | def update_output(self, ):
input_ids, outputs, grads, adv_tokens = self.batch_output
probs = softmax(outputs, dim=-1)
probs, labels = torch.max(probs, dim=-1)
tokens = [
self.tokenizer.convert_ids_to_tokens(input_ids_)
for input_ids_ in input_ids
]
embedding_grads = grads.sum(dim=2)
# norm for each sequence
norms = torch.norm(embedding_grads, dim=1, p=2) # need check hyperparameter
# normalizing
for i, norm in enumerate(norms):
embedding_grads[i] = torch.abs(embedding_grads[i]) / norm
batch_output = []
# check probs, labels shape
labels = torch.reshape(labels, (1, -1))
probs = torch.reshape(probs, (1, -1))
iterator = zip(tokens, probs, embedding_grads, labels)
for example_tokens, example_prob, example_grad, example_label in iterator:
example_dict = dict()
# as we do it by batches we has a padding so we need to remove it
example_tokens = [t for t in example_tokens if t != self.tokenizer.pad_token]
example_dict['tokens'] = example_tokens
example_dict['grad'] = example_grad.cpu().tolist()[:len(example_tokens)]
example_dict['label'] = example_label.cpu().tolist()[:len(example_tokens)] # example_label.item()
example_dict['prob'] = example_prob.cpu().tolist()[:len(example_tokens)] # example_prob.item()
batch_output.append(example_dict)
return batch_output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def out(self, inputs):",
"def _save_grad_output(self, mod, grad_input, grad_output):\n if mod.training:\n self.state[mod][\"gy\"] = grad_output[0] * grad_output[0].size(0)",
"def _output_update(self):\n self._outputtype = self.inputs.outputtype",
"def outputs(self):\n return s... | [
"0.6842611",
"0.63906467",
"0.62235004",
"0.62234104",
"0.62234104",
"0.62234104",
"0.62234104",
"0.6200916",
"0.61630815",
"0.6138572",
"0.6102943",
"0.6075281",
"0.6048927",
"0.60099345",
"0.60045785",
"0.59973353",
"0.5995878",
"0.59723157",
"0.5926936",
"0.5917695",
"0.59... | 0.0 | -1 |
If USE_PATH is True rely on PATH to look for binaries. Otherwise ../src/ is used by default. | def binary_location(cmd, USE_PATH=False):
if USE_PATH:
return cmd
else:
return os.path.join(BIN_PREFIX, cmd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linkpath(srcdir, pkg):\n home = os.getenv('HOME')\n if srcdir:\n rval = '{}/{}'.format(srcdir, pkg)\n else:\n rval = '{}/bin/{}'.format(home, pkg)\n return rval",
"def binary_location(cmd, USE_PATH=False):\n return os.path.join(BIN_PREFIX, cmd)",
"def set_path():\n impor... | [
"0.64583826",
"0.6119704",
"0.60500836",
"0.5732794",
"0.5658576",
"0.56554246",
"0.5512849",
"0.5505444",
"0.54935056",
"0.5483672",
"0.5481498",
"0.5455168",
"0.5439266",
"0.5433778",
"0.54287785",
"0.5424196",
"0.5423426",
"0.5394256",
"0.53781176",
"0.5329684",
"0.5325822... | 0.6302872 | 1 |
Remove the user from 'workers' or 'prospects', if applicable. user A TcsUser instance to remove from workers | def removeWorker(self, user):
if user == self.owner:
return None
# Without these queries, there's no way to tell if anything actually gets removed.
# Calling remove() on a user that is not in the set does not raise an error.
if self.workers.filter(pk=user.id).exists():
self.workers.remove(user)
return self
if self.prospects.filter(pk=user.id).exists():
self.prospects.remove(user)
return self
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_candidate(self, user):\n self.assignment_related_users.filter(user=user).delete()\n inform_changed_data(self)",
"def remove(self, user):\n self.packet.send_room([\"rp\", user.get_int_id(self.rooms),\n user.data.id], user.room)\n self.rooms[user... | [
"0.6894938",
"0.68542784",
"0.685336",
"0.67998946",
"0.6638343",
"0.6393456",
"0.63634795",
"0.6293484",
"0.6282741",
"0.6280759",
"0.6247314",
"0.62332475",
"0.6231962",
"0.6227879",
"0.61912465",
"0.6178004",
"0.6158775",
"0.6148308",
"0.6134449",
"0.6126714",
"0.6120114",... | 0.7817377 | 0 |
Unfreeze a FrozenDict. Makes a mutable copy of a `FrozenDict` mutable by transforming it into (nested) dict. | def unfreeze(x: Union[FrozenDict, Dict[str, Any]]) -> Dict[Any, Any]:
if isinstance(x, FrozenDict):
# deep copy internal state of a FrozenDict
# the dict branch would also work here but
# it is much less performant because jax.tree_util.tree_map
# uses an optimized C implementation.
return jax.tree_util.tree_map(lambda y: y, x._dict) # type: ignore
elif isinstance(x, dict):
ys = {}
for key, value in x.items():
ys[key] = unfreeze(value)
return ys
else:
return x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _prepare_freeze(xs: Any) -> Any:\n if isinstance(xs, FrozenDict):\n # we can safely ref share the internal state of a FrozenDict\n # because it is immutable.\n return xs._dict # pylint: disable=protected-access\n if not isinstance(xs, dict):\n # return a leaf as is.\n return xs\n # recursive... | [
"0.678866",
"0.63871247",
"0.63075334",
"0.6243246",
"0.61387753",
"0.61324066",
"0.5833921",
"0.56678116",
"0.5610028",
"0.5581237",
"0.547987",
"0.528664",
"0.5166779",
"0.51488274",
"0.5131935",
"0.513004",
"0.5127286",
"0.51143354",
"0.5108335",
"0.5104038",
"0.50658894",... | 0.80311483 | 0 |
This method is used to postprocess the form data. By default, it returns the raw `form.data` dictionary. | def process_step(self, form):
#print(form.data)
#print(form.data)
#print(self)
institution = {}
inst_list = []
if self.steps.current == '1':
institution['institution'] = form.data['1-0-institution']
institution['date_from'] = form.data['1-0-date_from']
institution['date_to'] = form.data['1-0-date_to']
inst_list.append(institution)
inst_keys = dict(form.data.lists())
#Create dictionary dynamically for the other institutions incase more than two institutions are entered
if inst_keys.get('1-NaN-institution') and type(inst_keys.get('1-NaN-institution')) is list:
inst_list2 = []
#Add institutions
for i,insti in enumerate(inst_keys.get('1-NaN-institution')):
inst_i = {}
#print(i)
date_from = inst_keys['1-NaN-date_from'][i]
date_to = inst_keys['1-NaN-date_to'][i]
course_duration = inst_keys['1-NaN-course_duration'][i]
inst_i['institution'] = insti
inst_i['date_from'] = date_from
inst_i['date_to'] = date_to
inst_list2.append(inst_i)
#print(inst_list2)
inst_list.extend(inst_list2)
#Create dictionary dynamically for the other institutions incase more than two institutions are entered
if inst_keys.get('1-NaN-institution') and type(inst_keys.get('1-NaN-institution')) is not list:
inst_0 = {}
inst_0['institution'] = form.data['1-NaN-institution']
inst_0['date_from'] = form.data['1-NaN-date_from']
inst_0['date_to'] = form.data['1-NaN-date_to']
inst_0['course_duration'] = form.data['1-NaN-course_duration']
#inst_0['achievements'] = ''
inst_list.append(inst_0)
#Add the entered information to a session object
self.request.session['institution'] = inst_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_post_data(self):\n if not self._form_data:\n self._form_data = async_to_sync(self.request.form)()\n return self._form_data",
"def get_form_data(self) -> dict:\n with logging.LogCall(__file__, \"get_form_data\", self.__class__):\n return self.serialize()",
"def... | [
"0.79507184",
"0.76475173",
"0.738958",
"0.73285085",
"0.7258168",
"0.6955681",
"0.6792374",
"0.65343493",
"0.65144867",
"0.6490603",
"0.6459408",
"0.6415936",
"0.63819605",
"0.63031137",
"0.6298642",
"0.6277921",
"0.62373143",
"0.6232082",
"0.62192225",
"0.60904664",
"0.6056... | 0.0 | -1 |
Ensure that awsmarketplace actions from all the different awsmarketplace SAR pages are present in the IAM definition. | def test_services_with_multiple_pages_aws_marketplace(self):
# Overlap: AWS Marketplace, Marketplace Catalog, and AWS Marketplace Entitlement service, AWS Marketplace Image Building Service, AWS Marketplace Metering Service, AWS Marketplace Private Marketplace, and AWS Marketplace Procurement Systems
# AWS Marketplace: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplace.html
self.assertTrue("aws-marketplace:AcceptAgreementApprovalRequest" in self.all_actions)
# AWS Marketplace Catalog: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplacecatalog.html
self.assertTrue("aws-marketplace:CancelChangeSet" in self.all_actions)
# AWS Marketplace Entitlement Service: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplaceentitlementservice.html
self.assertTrue("aws-marketplace:GetEntitlements" in self.all_actions)
# AWS Marketplace Image Building Service: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplaceimagebuildingservice.html
self.assertTrue("aws-marketplace:DescribeBuilds" in self.all_actions)
# AWS Marketplace Metering Service: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplacemeteringservice.html
self.assertTrue("aws-marketplace:BatchMeterUsage" in self.all_actions)
# AWS Marketplace Private Marketplace: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplaceprivatemarketplace.html
self.assertTrue("aws-marketplace:AssociateProductsWithPrivateMarketplace" in self.all_actions)
# AWS Marketplace Procurement Systems: https://docs.aws.amazon.com/service-authorization/latest/reference/list_awsmarketplaceprocurementsystemsintegration.html
self.assertTrue("aws-marketplace:DescribeProcurementSystemConfiguration" in self.all_actions)
results = get_actions_for_service("aws-marketplace")
actions = [
"aws-marketplace:AcceptAgreementApprovalRequest",
"aws-marketplace:BatchMeterUsage",
"aws-marketplace:CancelAgreementRequest",
"aws-marketplace:CancelChangeSet",
"aws-marketplace:CompleteTask",
"aws-marketplace:DescribeAgreement",
"aws-marketplace:DescribeBuilds",
"aws-marketplace:DescribeChangeSet",
"aws-marketplace:DescribeEntity",
"aws-marketplace:DescribeProcurementSystemConfiguration",
"aws-marketplace:DescribeTask",
"aws-marketplace:GetAgreementApprovalRequest",
"aws-marketplace:GetAgreementRequest",
"aws-marketplace:GetAgreementTerms",
"aws-marketplace:GetEntitlements",
"aws-marketplace:ListAgreementApprovalRequests",
"aws-marketplace:ListAgreementRequests",
"aws-marketplace:ListBuilds",
"aws-marketplace:ListChangeSets",
"aws-marketplace:ListEntities",
"aws-marketplace:ListTasks",
"aws-marketplace:MeterUsage",
"aws-marketplace:PutProcurementSystemConfiguration",
"aws-marketplace:RegisterUsage",
"aws-marketplace:RejectAgreementApprovalRequest",
"aws-marketplace:ResolveCustomer",
"aws-marketplace:SearchAgreements",
"aws-marketplace:StartBuild",
"aws-marketplace:StartChangeSet",
"aws-marketplace:Subscribe",
"aws-marketplace:Unsubscribe",
"aws-marketplace:UpdateAgreementApprovalRequest",
"aws-marketplace:UpdateTask",
"aws-marketplace:ViewSubscriptions",
]
for action in actions:
self.assertTrue(action in results) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_actions_with_arn_type_and_access_level_case_3(self):\n desired_output = [\n 's3:PutAccountPublicAccessBlock',\n 's3:PutAccessPointPublicAccessBlock'\n ]\n output = get_actions_with_arn_type_and_access_level(\n # \"ram\", \"resource-share\", \"Write... | [
"0.58062184",
"0.57554114",
"0.5695702",
"0.5673701",
"0.5447731",
"0.5414008",
"0.53181934",
"0.5090683",
"0.5057209",
"0.49688128",
"0.49623215",
"0.49491638",
"0.49443293",
"0.49354288",
"0.4900723",
"0.48934472",
"0.48312107",
"0.48288488",
"0.47950754",
"0.4768656",
"0.4... | 0.66148233 | 0 |
Called when a mouse button is pressed in the widget. Adjust method signature as appropriate for callback. | def button_press_event(self, widget, event):
x, y = event.x, event.y
# x, y = coordinates where the button was pressed
self.last_win_x, self.last_win_y = x, y
button = 0
# Prepare a button mask with bits set as follows:
# left button: 0x1
# middle button: 0x2
# right button: 0x4
# Others can be added as appropriate
self.logger.debug("button down event at %dx%d, button=%x" % (x, y, button))
data_x, data_y = self.check_cursor_location()
return self.make_ui_callback('button-press', button, data_x, data_y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_mouse_press(self, x, y, button):\n\n pass",
"def ev_mousebuttondown(self, event: MouseButtonDown) -> None:",
"def handle_mouse_press(self, event):",
"def mouse_press_event(self, x: int, y: int, button: int):\n pass",
"def on_mouse_press(self, x, y, button, key_modifiers):\r\n pa... | [
"0.8186688",
"0.7803107",
"0.7685904",
"0.7667033",
"0.7550329",
"0.75264764",
"0.74540734",
"0.74537903",
"0.7434162",
"0.71306",
"0.71141076",
"0.7086629",
"0.70717835",
"0.70475805",
"0.70216006",
"0.70136315",
"0.69730556",
"0.69179136",
"0.69159424",
"0.69106615",
"0.690... | 0.786055 | 1 |
Called when a mouse button is released after being pressed. Adjust method signature as appropriate for callback. | def button_release_event(self, widget, event):
x, y = event.x, event.y
# x, y = coordinates where the button was released
self.last_win_x, self.last_win_y = x, y
button = 0
# prepare button mask as in button_press_event()
data_x, data_y = self.check_cursor_location()
return self.make_ui_callback('button-release', button, data_x, data_y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mouse_release_event(self, x: int, y: int, button: int):\n pass",
"def on_mouse_release(self, x, y, button):\n pass",
"def on_mouse_release(self, x, y, button, key_modifiers):\r\n pass",
"def OnMouseUp(self, evt):\n self.ReleaseMouse()",
"def emitReleaseEvent(self, clickLocat... | [
"0.80996853",
"0.80075884",
"0.7933358",
"0.74254155",
"0.7286587",
"0.7269564",
"0.72388613",
"0.72388613",
"0.72174096",
"0.71852064",
"0.7037113",
"0.7027572",
"0.7019888",
"0.69756335",
"0.6968941",
"0.6932969",
"0.69013923",
"0.68389016",
"0.6834489",
"0.68217915",
"0.68... | 0.7786435 | 3 |
Called when a mouse cursor is moving in the widget. Adjust method signature as appropriate for callback. | def motion_notify_event(self, widget, event):
x, y = event.x, event.y
# x, y = coordinates of cursor
self.last_win_x, self.last_win_y = x, y
button = 0
# prepare button mask as in button_press_event()
data_x, data_y = self.check_cursor_location()
return self.make_ui_callback('motion', button, data_x, data_y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mouse_move_callback(self, event):\n # TODO drag and drop figuriek\n print(\"moving at \", event.x + self.offset_x, event.y + self.offset_y)",
"def on_mouse_motion(self, x, y, delta_x, delta_y):\r\n pass",
"def __mouseMoved(self, x, y):\n # Are we on the bounding box?\n if... | [
"0.7342505",
"0.7273285",
"0.7090941",
"0.70634186",
"0.7034477",
"0.69676363",
"0.6943248",
"0.68984425",
"0.669294",
"0.659831",
"0.65874934",
"0.6549803",
"0.6516948",
"0.6500888",
"0.6497365",
"0.6471458",
"0.6461945",
"0.64583486",
"0.6448528",
"0.6439387",
"0.63827825",... | 0.64577484 | 18 |
Called when a drop (drag/drop) event happens in the widget. Adjust method signature as appropriate for callback. | def drop_event(self, widget, event):
# make a call back with a list of URLs that were dropped
#self.logger.debug("dropped filename(s): %s" % (str(paths)))
#self.make_ui_callback('drag-drop', paths)
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dropEvent(self, de):\n # dragging a track\n if hasattr(Globals.dragObject, \"trackFrame\"):\n de.accept()\n trackFrame = Globals.dragObject.trackFrame\n oldParent = trackFrame.parentWidget()\n if oldParent:\n args = (trackFrame, self, old... | [
"0.72224736",
"0.7178574",
"0.7033632",
"0.69308865",
"0.69137365",
"0.68517953",
"0.66538286",
"0.660371",
"0.6481881",
"0.6399347",
"0.6314507",
"0.63019335",
"0.6220606",
"0.6072313",
"0.60482043",
"0.6036351",
"0.59223694",
"0.59200025",
"0.58687717",
"0.58277893",
"0.573... | 0.81672764 | 0 |
Creates a data folder containing a 100class subset of ImageNet, then creates a zipped copy of it | def zip_imagenet100c():
#First make sure the directory we are given is correct!
if not os.path.isdir(DATA_SRC_ROOT):
raise Exception("Bad filepath given")
#create the destiantion directories if they don't exist
if not os.path.isdir(IMAGENET100_DIR):
os.mkdir(IMAGENET100_DIR)
#grab the subset wnids for the 100 class-subset
with open(IMAGENET100_CLASSES) as f:
subset_wnids = f.readlines()
subset_wnids = [x.strip() for x in subset_wnids] #list of the 100 WNIDs we grab
#Grab the names of all of the folders inside the root data source
#Structure is distortion/sub_distortion/level/wnids
for distortion in os.listdir(DATA_SRC_ROOT):
if distortion != "meta.bin":
print(distortion)
folder_path = os.path.join(DATA_SRC_ROOT, distortion)
if not os.path.isdir(folder_path):
continue
for sub_distortion in os.listdir(folder_path):
print(sub_distortion)
subfolder_path = os.path.join(folder_path, sub_distortion)
if not os.path.isdir(subfolder_path):
continue
for level in os.listdir(subfolder_path):
print(level)
level_path = os.path.join(subfolder_path, level)
#grab the correcrt validation d9recotires
for wnid in os.listdir(level_path):
wnid_path = os.path.join(level_path, wnid)
if not os.path.isdir(wnid_path):
continue
if wnid in subset_wnids:
dest_path = os.path.join(IMAGENET100_DIR, distortion, sub_distortion, level, wnid)
shutil.copytree(wnid_path, dest_path)
#copy the metadata bin file
meta_file = os.path.join(DATA_SRC_ROOT, 'meta.bin')
meta_dest = os.path.join(IMAGENET100_DIR, 'meta.bin')
shutil.copy(meta_file, meta_dest)
#Zip the destinatio file
shutil.make_archive(ZIP_PATH + '/ImageNet100C', 'tar', IMAGENET100_DIR) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dataset(data_folder: str, dataset_file: str, targets_file: str = os.path.join('data', 'targets.pkl')):\n files = sorted(glob.glob(os.path.join(data_folder, '**/*.jpg'), recursive=True))\n images = []\n crop_sizes = []\n crop_centers = []\n targets = []\n for image in tqdm(files, desc='... | [
"0.6529704",
"0.6481513",
"0.6435279",
"0.6418067",
"0.63397163",
"0.6227401",
"0.62035155",
"0.61983734",
"0.6165103",
"0.61225355",
"0.612228",
"0.60964",
"0.60848254",
"0.6073827",
"0.60563177",
"0.6047265",
"0.60234",
"0.6019958",
"0.60174394",
"0.6004536",
"0.5983722",
... | 0.7347322 | 0 |
Given a wav file, use Praat to return a dictionary containing pitch (in Hz) at each millisecond. | def praat_analyze_pitch(audio_file):
praatpath = path.abspath('Praat.app/Contents/MacOS/Praat') # locate Praat executable
pl = PraatLoader(praatpath=praatpath) # create instance of PraatLoader object
praat_output = pl.run_script('pitch.praat', audio_file) # run pitch script in Praat
pitch_data = pl.read_praat_out(praat_output) # turn Praat's output into Python dict
return pitch_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_pitch_from_sound_file(filename: str, samplerate: int = DEFAULT_SAMPLE_RATE):\n if os.path.isfile(filename) is False:\n raise Exception('File not found with filename = %s' % filename)\n\n print(\"====> reading pitch from sound file\")\n win_s = 4096 // DOWN_SAMPLE # fft size\n hop_s = 5... | [
"0.70273054",
"0.70135546",
"0.68500084",
"0.6821027",
"0.6812894",
"0.6796246",
"0.65991676",
"0.6327907",
"0.6325302",
"0.62763965",
"0.62052196",
"0.61778903",
"0.60919714",
"0.60821027",
"0.60718834",
"0.60426664",
"0.59239006",
"0.59204537",
"0.5904547",
"0.58931905",
"0... | 0.6584172 | 7 |
Checks whether the given ISBN13 code is valid. >>> isISBN13('9789743159664') True >>> isISBN13('9787954527409') False >>> isISBN13('8799743159665') False | def isISBN13(code):
# helper function for computing ISBN-10 check digit
def check_digit(code):
# compute check digit
check = sum((3 if i % 2 else 1) * int(code[i]) for i in range(12))
# convert check digit into a single digit
return str((10 - check) % 10)
# check whether given code is a string
if not isinstance(code, str):
return False
# check whether given code contains 10 characters
if len(code) != 13:
return False
# check whether first nine characters of given code are digits
if not code[:12].isdigit():
return False
# check the check digit
return check_digit(code) == code[-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_isbn_13(isbn13):\r\n isbn13 = re.sub(r'[^0-9X]', '', isbn13.replace('x', 'X'))\r\n if len(isbn13) != 13: return False\r\n if isbn13[0:3] not in ('978', '979'): return False\r\n return False if isbn_13_check_digit(isbn13[:-1]) != isbn13[-1] else True",
"def isISBN(code, isbn13=True):\n\n ret... | [
"0.8294502",
"0.8156061",
"0.7813687",
"0.7593365",
"0.7539352",
"0.7096975",
"0.70109516",
"0.6998053",
"0.6760535",
"0.6702344",
"0.6442007",
"0.64231825",
"0.6403078",
"0.6313602",
"0.6305555",
"0.6260889",
"0.6107894",
"0.5969835",
"0.5896946",
"0.57390374",
"0.56742215",... | 0.86895674 | 0 |
Parse OpenSSLstyle foo.0, foo.1, ... subscripted options. Returns a list of values matching the specified option name. | def multiget(self, option, section = None):
matches = []
if section is None:
section = self.default_section
if self.cfg.has_option(section, option):
matches.append((-1, self.get(option, section = section)))
for key, value in self.cfg.items(section):
s = key.rsplit(".", 1)
if len(s) == 2 and s[0] == option and s[1].isdigit():
matches.append((int(s[1]), self.get(option, section = section)))
matches.sort()
return [match[1] for match in matches] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_option(self, name):\r\n if not isinstance(name, str):\r\n name = \" \".join(name)\r\n lines = self.sendAndRecv(\"GETCONF %s\\r\\n\" % name)\r\n\r\n r = []\r\n for _,line,_ in lines:\r\n try:\r\n key, val = line.split(\"=\", 1)\r\n r.append((key,val))\r\n except Valu... | [
"0.5681802",
"0.5620391",
"0.5565046",
"0.541359",
"0.53751975",
"0.5340231",
"0.5283279",
"0.5276728",
"0.52554685",
"0.5251639",
"0.51984483",
"0.5196909",
"0.519201",
"0.5174309",
"0.5122386",
"0.51024044",
"0.51020473",
"0.5095427",
"0.5034037",
"0.5030856",
"0.50224656",... | 0.5834238 | 0 |
namespaces typecodes representing global elements with literal encoding. typeCode typecode representing an element. namespaceURI namespace literal True/False | def _globalElement(self, typeCode, namespaceURI, literal):
if literal:
typeCode.oname = '%(prefix)s:%(name)s xmlns:%(prefix)s="%(namespaceURI)s"' \
%{'prefix':self._getPrefix(namespaceURI), 'name':typeCode.oname, 'namespaceURI':namespaceURI} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def XmlTypeNamespace(self) -> str:",
"def is_namespace_type(self):\n raise exceptions.NotImplementedError()",
"def GetNamespaces(self):\n return list(self.type_namespaces_map.values())",
"def element_type(self) -> global___Type:",
"def patch_well_known_namespaces(etree_module):\n etree_module.... | [
"0.66442066",
"0.5593534",
"0.5443724",
"0.5412149",
"0.5365882",
"0.5329653",
"0.5311929",
"0.5237586",
"0.5178215",
"0.5165827",
"0.5055696",
"0.5044016",
"0.5007665",
"0.4926739",
"0.48959085",
"0.48641986",
"0.48631665",
"0.4855509",
"0.48434836",
"0.48183277",
"0.4792356... | 0.6797826 | 0 |
Returns a True if a customer identifier does not belongs to dataframe used to build classifier model. | def is_customer_out_sample(self, customerID):
listCustomer = list(self._df_invoice_line_out_sample.CustomerID.unique())
is_flag = customerID in listCustomer
return is_flag | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_customer(self) -> bool:\n return self.customer_id is not None",
"def has_customer(self):\n return self.customer is not None",
"def is_customer(self):\n return self.user_type == 'C'",
"def is_label_dataframe(label, df):\n\n setdiff = set(label) - set(df.columns.tolist())\n\n ... | [
"0.661681",
"0.6462838",
"0.59257627",
"0.5741564",
"0.5628499",
"0.55574435",
"0.5537199",
"0.5509296",
"0.5507439",
"0.5461328",
"0.5405652",
"0.53825015",
"0.5370098",
"0.5365358",
"0.5328702",
"0.53143424",
"0.529039",
"0.52698815",
"0.52663475",
"0.52634853",
"0.52370775... | 0.63775516 | 2 |
Returns RFM score from dataframe given from parameter. RFM score is computed from local RFM matrix threshold. | def get_rfm(self, df):
df_tmp, df_RFM, df_RFM_threshold, day_now \
= p5_util.p5_df_rfm_build(df, df_RFM_threshold=self.df_RFM_quantiles
,day_now = self._day_now)
RFM = df_RFM.RFM.iloc[0]
return str(RFM) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_rfm_score(dataframe):\n\n dataframe[\"recency_score\"] = pd.qcut(dataframe['recency'].rank(method=\"first\"), 5, labels=[5, 4, 3, 2, 1])\n dataframe[\"frequency_score\"] = pd.cut(dataframe['frequency'], bins=[0, 4, 8, 13, 17, 20], labels=[1, 2, 3, 4, 5])\n dataframe[\"RFM_SCORE\"] = (dataframe[... | [
"0.6958095",
"0.61751866",
"0.60585284",
"0.5860262",
"0.58369166",
"0.5765395",
"0.57476515",
"0.5725581",
"0.5717504",
"0.5708911",
"0.5702308",
"0.5689124",
"0.5686297",
"0.5685354",
"0.56756556",
"0.5671112",
"0.5645583",
"0.5639483",
"0.56138635",
"0.56126195",
"0.559187... | 0.60182154 | 3 |
Returns the data representation of the timer, will be used to send it over the web socket. | def get_list_data(self):
key = 'timer'
if self.repeated:
key += '_repeat'
return '%s %s' % (key, self.data.get_list_data()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_timer_data(self):\n return {\"sRef\": self._timer_service_ref_entry.get_text(),\n \"begin\": int(datetime.strptime(self._timer_begins_entry.get_text(), self._TIME_STR).timestamp()),\n \"end\": int(datetime.strptime(self._timer_ends_entry.get_text(), self._TIME_STR).time... | [
"0.7045231",
"0.6424909",
"0.63322824",
"0.61530703",
"0.61145544",
"0.6017771",
"0.592645",
"0.59186465",
"0.59073746",
"0.5903025",
"0.5891447",
"0.58581436",
"0.5799672",
"0.5785056",
"0.575003",
"0.5743803",
"0.5738986",
"0.5706574",
"0.5649952",
"0.5620999",
"0.5616731",... | 0.63464636 | 2 |
Executing every forest in collection, activating their networks. By the way collecting data about best fitness function. | def execute(self):
process_list = []
forests_queue = Queue(self.power)
iterational = 0
print '| |-starting evaluation, training and validation'
for one_forest in self._forests:
process_list.append(
Process(target=main_async_method,
args=(forests_queue, copy(one_forest.to_portal()), iterational, self.settings)))
iterational += 1
for proc in process_list:
proc.start()
for proc in process_list:
proc.join()
for smth in range(forests_queue.qsize()):
tmp = forests_queue.get()
self._forests[tmp['place']].fitness = tmp['fitness']
fitness_summ = sum(map(lambda forest: forest.fitness, self._forests))
fss = map(lambda x: x.fitness, self._forests)
print 'avg = ', str(sum(fss) / len(fss)), 'max = ', max(fss)
self.roulet = map(lambda x: x.fitness / fitness_summ, self._forests) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mutate(self):\n for forest in self._forests:\n forest.mutate(self._fullInput)",
"def run(self, num_iterations = 50, **kwargs):\n \n #setup system\n self.cost_calculator = t.CostCalculator(self.suppliers_allcards, self.all_ensembles_dict)\n bounds = np.array(self.... | [
"0.66012484",
"0.62504995",
"0.61932045",
"0.6135642",
"0.6134355",
"0.61263996",
"0.6049594",
"0.5867963",
"0.5865974",
"0.5832648",
"0.581788",
"0.5802029",
"0.57808244",
"0.5779419",
"0.5708533",
"0.56901664",
"0.5634386",
"0.56181246",
"0.558044",
"0.553923",
"0.55354095"... | 0.7236054 | 0 |
Expects a list of signals and a list of bkgs (Dataset objects), and a cut_function and cut_values. | def roccurve(signals, bkgs, cut_function, cut_values):
eff_sig, n_pass_sig, n_total_sig = get_eff(svjflatanalysis.iterate(signals), cut_function, cut_values)
eff_bkg, n_pass_bkg, n_total_bkg = get_eff(svjflatanalysis.iterate(bkgs), cut_function, cut_values)
return eff_sig, eff_bkg, n_pass_sig, n_pass_bkg, n_total_sig, n_total_bkg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cuts(data, args, verbose):\n\n if args['experiment']['cut_finding'] == CutFinding.features:\n\n values = (data.xs == True).T\n return Cuts(values=values)\n\n if args['experiment']['cut_finding'] == CutFinding.binning:\n\n values, names = binning(xs=data.xs,\n ... | [
"0.55629873",
"0.5544333",
"0.54654413",
"0.53966707",
"0.5293295",
"0.5175403",
"0.5154368",
"0.50937045",
"0.5059277",
"0.5027049",
"0.50096345",
"0.49881732",
"0.49795693",
"0.4978986",
"0.49496424",
"0.4933744",
"0.4921186",
"0.49105307",
"0.49013457",
"0.48807377",
"0.48... | 0.6186934 | 0 |
Audit the commit for proper endofline characters. The UNIX type EOL is the only allowed EOL character. | def audit_eol(self):
# Regex's....
re_commit = re.compile("^\xff(.+)\xff$")
re_filename = re.compile("^diff --(cc |git a\/.+ b\/)(.+)$")
blocked_eol = re.compile(r"(?:\r\n|\n\r|\r)$")
# Bool to allow special files such as vcards to bypass the check
eol_allowed = False
# Do EOL audit!
process = get_change_diff( self.repository, ["-p"] )
for line in process.stdout:
commit_change = re.match( re_commit, line )
if commit_change:
commit = commit_change.group(1)
continue
file_change = re.match( re_filename, line )
if file_change:
filename = file_change.group(2)
eol_violation = False
eol_allowed = False
# Check if it's an allowed mimetype
# First - check with the mimetypes system, to see if it can tell
guessed_type, _ = mimetypes.guess_type(filename)
if guessed_type in self.ALLOWED_EOL_MIMETYPES:
eol_allowed = True
continue
# Second check: by file extension
# NOTE: This uses the FIRST dot as extension
splitted_filename = filename.split(os.extsep)
# Check if there's an extension or not
# NOTE This assumes that files use dots for extensions only!
if len(splitted_filename) > 1:
extension = splitted_filename[1]
if extension in self.ALLOWED_EOL_EXTENSIONS:
eol_allowed = True
continue
# Unless they added it, ignore it
if not line.startswith("+"):
continue
if re.search( blocked_eol, line ) and not eol_violation:
# Is this an allowed filename?
if eol_allowed:
continue
# Failure has been found... handle it
eol_violation = True
self.__log_failure(commit, "End of Line Style (non-Unix): " + filename); | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eol(self):\n if self.current not in EOL:\n self.on_parser_error(\"EOL expected\")\n self.maybe_eol()",
"def _output_commit_line(self): # noqa: C901, E501 pylint: disable=too-many-branches\n seen_this = False\n chars_written = 0\n for i in range(self.num_columns ... | [
"0.6151211",
"0.6065239",
"0.57468516",
"0.5741316",
"0.5723494",
"0.5639385",
"0.5574638",
"0.5561204",
"0.5554823",
"0.55486727",
"0.553186",
"0.5530341",
"0.55275774",
"0.5481987",
"0.54660696",
"0.540383",
"0.5398025",
"0.5388231",
"0.53565466",
"0.53498983",
"0.5348075",... | 0.83223575 | 0 |
Takes a user and a group name, and returns `True` if the user is in that group. | def is_in_group(user, group_name):
return is_in_group_user_id(user.id, group_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_user_in_group(user, group):\n users = group.get_users()\n if user in users:\n return True\n return False",
"def is_in_group(user, group_name):\n return user.groups.filter(name__exact=group_name).exists()",
"def is_in_group(user, group_name):\n return Group.objects.get(name=group_na... | [
"0.90404195",
"0.89909446",
"0.8921993",
"0.88956964",
"0.8691311",
"0.8651488",
"0.85226196",
"0.8471016",
"0.8424121",
"0.83981097",
"0.83976525",
"0.8325658",
"0.8266118",
"0.8199991",
"0.81931895",
"0.7539715",
"0.7505139",
"0.7332905",
"0.726839",
"0.7217727",
"0.7149777... | 0.90948594 | 0 |
Score vector of model. Default implementation sums score_obs. The gradient of loglike with respect to each parameter. | def score(self, params, *args, **kwargs):
try:
# If an analytic score_obs is available, try this first before
# falling back to numerical differentiation below
return self.score_obs(params, *args, **kwargs).sum(0)
except NotImplementedError:
# Fallback in case a `loglike` is implemented but `loglikeobs`
# is not.
approx_func = (approx_fprime_cs
if self._use_approx_cs else approx_fprime)
return approx_func(params, self.loglike, args=args, kwargs=kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def score(self, s):\n fv = s.feature_vector\n product = fv.dot(self.params.T)[0, 0]\n return s.score(lmwt=self.lmwt) + product",
"def svm_loss(scores, y):\r\n\r\n N = scores.shape[0]\r\n\r\n # Compute svm data loss\r\n correct_class_scores = scores[range(N), y]\r\n margins = np.m... | [
"0.6385058",
"0.6369366",
"0.6337341",
"0.62241894",
"0.6202223",
"0.6085212",
"0.6069365",
"0.5963406",
"0.59437096",
"0.59082556",
"0.5902111",
"0.58229357",
"0.5803482",
"0.57961464",
"0.5775284",
"0.5754475",
"0.5751942",
"0.5720772",
"0.5715828",
"0.57079184",
"0.5697270... | 0.6393054 | 0 |
Evaluate the size expected from the FP relation for a given velocity dispersion and Vband apparent magnitude | def get_effective_radius(self, vel_disp, m_V):
log_vel_disp = np.log10(vel_disp)
log_R_eff = self.a*log_vel_disp + self.b*m_V + self.c + np.random.randn()*self.intrinsic_scatter
R_eff = 10**log_R_eff
return R_eff | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fluxRatio_fromVmag(Vmag):\n fluxRatio = 10.**(-0.4*Vmag)\n return fluxRatio",
"def width_v_a(model: SingleRhNeutrinoModel) -> float:\n u = 0.5 * np.tan(2 * model.theta)\n return 9 * ALPHA_EM * GF**2 / (256 * np.pi**4) * model.mx**5 * u**2",
"def testCalspecMags(self):\n std = MKIDStd.MKI... | [
"0.6291265",
"0.5848869",
"0.5809261",
"0.5802869",
"0.57336414",
"0.5729062",
"0.569543",
"0.56734866",
"0.5662906",
"0.56628376",
"0.56435436",
"0.56359833",
"0.5614053",
"0.5611046",
"0.55810857",
"0.55808634",
"0.5556087",
"0.55491066",
"0.55242413",
"0.5521659",
"0.55142... | 0.0 | -1 |
Parse name and seed for uci regression data. E.g. yacht_2 is the yacht dataset with seed 2. | def _parse_uci_regression_dataset(name_str):
pattern_string = "(?P<name>[a-z]+)_(?P<seed>[0-9]+)"
pattern = re.compile(pattern_string)
matched = pattern.match(name_str)
if matched:
name = matched.group("name")
seed = matched.group("seed")
return name, seed
return None, None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_uci_regression_dataset(name,\n split_seed,\n train_fraction=0.9,\n data_dir=\"uci_datasets\"):\n path = os.path.join(data_dir,\n _UCI_REGRESSION_FILENAMES[UCIRegressionDatasets(name)])\n dat... | [
"0.6082497",
"0.5352037",
"0.5263292",
"0.5021193",
"0.49601898",
"0.48798177",
"0.4859456",
"0.48388806",
"0.48280886",
"0.48230565",
"0.48205665",
"0.4811698",
"0.4808677",
"0.4778711",
"0.477406",
"0.4759555",
"0.47592923",
"0.47398236",
"0.47390524",
"0.4733845",
"0.47287... | 0.730633 | 0 |
Converts a string of text into a numerical vector of features based on the word embedding LTM. | def vectorize(self,text):
lv_active = set()
words = word_tokenize(text)
for word in words:
if word in self.tree:
ancestors = self.tree.word_ancestors(word)
lv_active.update(ancestors)
return self.nl.isin(lv_active).values | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_vector(text, model, idf, is_tokenized=False):\n if not is_tokenized: text= text.split() # splits the text by space and returns a list of words\n vec = np.zeros(300) # creates an empty vector of 300 dimensions\n for word in text: # iterates over the sentence\n if (word in model) & (word in id... | [
"0.70021975",
"0.6557391",
"0.65419817",
"0.65404963",
"0.6515521",
"0.64831823",
"0.6474055",
"0.64408255",
"0.64127994",
"0.6378021",
"0.63599265",
"0.63383466",
"0.6331677",
"0.6309186",
"0.63056415",
"0.6245646",
"0.6237467",
"0.62303",
"0.62136185",
"0.61973566",
"0.6189... | 0.0 | -1 |
Creates a Tensor for use as an Embedding initialization from the source vocabulary and predefined word embeddings. | def get_pretrained_embeddings(source_vocab,embed_df):
num_tokens = len(source_vocab)
embedding_dim = embed_df.shape[1]
weights = np.zeros((num_tokens,embedding_dim),dtype=np.float32)
for idx in range(num_tokens):
token = source_vocab.lookup_index(idx)
if token in embed_df.index:
weights[idx,:] = embed_df.loc[token]
else:
weights[idx,:] = np.random.randn(1,embedding_dim)
embed_tensor = torch.FloatTensor(weights)
return embed_tensor | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_embedding_layer(inputs_, vocab_size, embed_size):\n embedding = tf.Variable(tf.random_uniform((vocab_size, embed_size), -1, 1))\n embed = tf.nn.embedding_lookup(embedding, inputs_)\n \n return embed",
"def init_word_embed(config):\n embedding_mat_val = np.load(config.wordembed_params)\n ... | [
"0.74340177",
"0.7306449",
"0.72952855",
"0.70558393",
"0.7044115",
"0.68895066",
"0.67812735",
"0.6750932",
"0.67492104",
"0.67476356",
"0.67427427",
"0.6717155",
"0.6711093",
"0.6680487",
"0.6676144",
"0.6672202",
"0.6647878",
"0.6621589",
"0.66202843",
"0.65980107",
"0.658... | 0.66311824 | 17 |
Retrieve the dictform of all of the transactions in a given bar or for the whole simulation. | def transactions(self, dt=None):
if dt is None:
# flatten the by-day transactions
return [
txn
for by_day in itervalues(self._processed_transactions)
for txn in by_day
]
return self._processed_transactions.get(dt, []) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transaction_data(self):\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))",
"def transaction_base() -> Dict[str, Any]:\n return {\n \"first_name\": \"Donald\",\n \"last_name\": \"Duck\",\n \"company\": \"Duck Co\",\n \"email\": \... | [
"0.63048834",
"0.602332",
"0.5960751",
"0.56639814",
"0.56584823",
"0.5563182",
"0.5531879",
"0.55191296",
"0.55072176",
"0.5477383",
"0.54718333",
"0.5458796",
"0.5426807",
"0.54197216",
"0.541453",
"0.5338547",
"0.5327534",
"0.53225285",
"0.53161556",
"0.5279584",
"0.526199... | 0.0 | -1 |
Add 1 to the Partition.file_counter if self._subarray is an instance of FileArray and not a temporary FileArray. | def _increment_file_counter(self):
self._add_to_file_counter(1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ... | [
"0.83802605",
"0.6079216",
"0.60073787",
"0.5972287",
"0.5871502",
"0.58649766",
"0.58356875",
"0.5813897",
"0.5656929",
"0.5650024",
"0.5628978",
"0.5588129",
"0.5575472",
"0.55536973",
"0.55466735",
"0.55383646",
"0.5529106",
"0.5502226",
"0.54975855",
"0.5481056",
"0.54359... | 0.71174276 | 1 |
Subtract 1 from the Partition.file_counter if self._subarray is an instance of FileArray and not a temporary FileArray. | def _decrement_file_counter(self):
self._add_to_file_counter(-1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_to_file_counter(self, i):\n # subarray = getattr(self, '_subarray', None)\n subarray = self._subarray\n\n if subarray is None:\n return\n\n try:\n if isinstance(subarray, FileArray) and not isinstance(\n subarray, CachedArray\n ... | [
"0.7747842",
"0.63648546",
"0.6072333",
"0.60351396",
"0.5918244",
"0.57059807",
"0.56674904",
"0.5664642",
"0.56031275",
"0.5573989",
"0.5520654",
"0.5453089",
"0.5448838",
"0.54281026",
"0.5422772",
"0.5378375",
"0.5307265",
"0.5244931",
"0.52217174",
"0.5220032",
"0.521629... | 0.6648334 | 1 |
Add the lock files listed in lock_files to the list of lock files managed by other ranks. | def _update_lock_files(self, lock_files):
_, _lock_file, _other_lock_files = _temporary_files[
self._subarray._partition_file
]
_other_lock_files.update(set(lock_files))
if _lock_file in _other_lock_files:
# If the lock file managed by this rank is in the list of
# lock files managed by other ranks, remove it from there
_other_lock_files.remove(_lock_file) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def LockFiles(self, entries):\n self._model.lock(entries)",
"def add_mock_files(self, file_list):\n self._mock_file_list.extend(file_list)",
"def thread_file_list(self):\n # Establish connection for this thread\n connection = self.connect()\n\n # Set working directory on serv... | [
"0.5858453",
"0.5857905",
"0.5442941",
"0.5427439",
"0.5369485",
"0.534254",
"0.5242692",
"0.5233011",
"0.52133423",
"0.51889026",
"0.51700175",
"0.51625514",
"0.5118121",
"0.5110031",
"0.50916535",
"0.5077471",
"0.5056525",
"0.5047517",
"0.50415224",
"0.50407803",
"0.5031041... | 0.785469 | 0 |
If we don't define this, it will use the regular dictionary __iter__ which does not call SortedDictionary.keys(). | def __iter__(self):
for each in list(self.keys()):
yield each | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n return self.ordered_keys.__iter__()",
"def __iter__(self):\n return iter(self.keys())",
"def __iter__(self):\n if self._len_keys == 1:\n yield from self._dict.keys()\n else:\n for key in self._dict.keys():\n yield tuple(sort... | [
"0.8112754",
"0.7881003",
"0.7754839",
"0.76607627",
"0.764876",
"0.7564836",
"0.7504256",
"0.75019187",
"0.74793774",
"0.7474488",
"0.74243957",
"0.7364466",
"0.7364466",
"0.73356414",
"0.7253679",
"0.72350866",
"0.7233559",
"0.7106118",
"0.7090561",
"0.7053434",
"0.69872856... | 0.7525102 | 6 |
Create a CourseGraph, fetching unitary weights and edge weights from database, creating CourseNodes for each course, and | def __init__(self, database, session, max_suggestions=5, max_courses=30, cache_mult=4):
self._nodes = dict() # dict with courseid keys, CourseNode vals
self._max_suggestions = max_suggestions
self._max_courses = max_courses
self._cache_mult = cache_mult
db = database
# Get dict mapping courses to unitary weights
unitary_dict = db.get_unitary_dict(session)
# Get dict mapping courses to adjacent courses and weights
edge_dict = db.get_edges_dict(session)
# Create CourseNodes
for courseid in unitary_dict:
courseNode = CourseGraph.CourseNode(courseid=courseid, edges=dict(), popularity=unitary_dict[courseid])
self._nodes[courseid] = courseNode
# Create course edge dict for each CourseNode
for courseid in edge_dict:
node = self._nodes[courseid] # get node of interest
adj_courses = edge_dict[courseid] # get inner dict {otherid: edge_weight}
for otherid in adj_courses:
other_node = self._nodes[otherid]
node.addEdge(other_node, adj_courses[otherid]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _CreateGraph(self):\n self.nodes = []\n self.edges = []\n for i, r in self.airports.set_index('airport_id').iterrows():\n self.nodes.append((i,r.to_dict()))\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\n self.edges.append((i[0],i[1],r.to... | [
"0.610946",
"0.6068693",
"0.5970327",
"0.59139115",
"0.5910554",
"0.59065074",
"0.5842171",
"0.58402723",
"0.5831642",
"0.5821705",
"0.58180076",
"0.5813589",
"0.5796841",
"0.57905227",
"0.5715409",
"0.5708784",
"0.56940794",
"0.5655929",
"0.5635845",
"0.5629975",
"0.56149757... | 0.7083467 | 0 |
Process a full set of images, with parallelization if multiple CPU threads are available on this machine | def _process_images(
raw_image_paths: pd.Series,
raw_images_dir: str,
ROI_definitions: Dict[str, Tuple],
flat_field_filepath_or_none: Union[str, None],
save_ROIs: bool,
save_dark_frame_corrected_images: bool,
save_flat_field_corrected_images: bool,
) -> Tuple[pd.DataFrame, pd.DataFrame]:
def _process_image_local(raw_image_path):
""" Version of process_image with all of the local configuration variables packed in.
Also encapsulates the opening of the image.
"""
return process_image(
original_rgb_image=raw.open.as_rgb(raw_image_path),
original_image_filepath=raw_image_path,
raw_images_dir=raw_images_dir,
ROI_definitions=ROI_definitions,
flat_field_filepath_or_none=flat_field_filepath_or_none,
save_ROIs=save_ROIs,
save_dark_frame_corrected_image=save_dark_frame_corrected_images,
save_flat_field_corrected_image=save_flat_field_corrected_images,
)
with ThreadPoolExecutor() as executor:
# We want identical warnings to be shown only for the first image they occur on (the default),
# but we also want subsequent calls to process_experiment to start with a fresh warning store
# so that warnings don't stop showing after the first run.
# catch_warnings gives us this fresh warning store.
with warnings.catch_warnings():
# process_image returns roi_summary_data df, image_diagnostics df -> this will be a list of 2-tuples
roi_summary_data_and_image_diagnostics_dfs_for_files = list(
tqdm(
executor.map(_process_image_local, raw_image_paths),
total=len(raw_image_paths),
)
)
roi_summary_data_for_files, image_diagnostics_for_files = zip(
*roi_summary_data_and_image_diagnostics_dfs_for_files
)
roi_summary_data_for_all_files = _stack_dataframes(roi_summary_data_for_files)
image_diagnostics_for_all_files = _stack_serieses(image_diagnostics_for_files)
return roi_summary_data_for_all_files, image_diagnostics_for_all_files | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scan_images_multiprocessed(images, clf, processes, vstep=15, hstep=15, dnum=5):\n pool = Pool(processes=processes) # start 4 worker processes\n results = []\n for i in range(0, processes):\n begin = i * int(len(images) / processes)\n if i == processes - 1:\n end = len(images)... | [
"0.7019778",
"0.701473",
"0.68229294",
"0.6802597",
"0.67542833",
"0.6570712",
"0.6545006",
"0.65141946",
"0.6469355",
"0.6455748",
"0.6437736",
"0.6373732",
"0.6337872",
"0.6320698",
"0.6302606",
"0.6255835",
"0.6229472",
"0.62148833",
"0.6202636",
"0.61866295",
"0.61764526"... | 0.56445336 | 94 |
Return the serializer instance that should be used for validating and deserializing input, and for serializing output. | def get_serializer_in(self, *args, **kwargs):
serializer_class = self.get_serializer_class_in()
kwargs['context'] = self.get_serializer_context()
return serializer_class(*args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSerializer():",
"def get_serializer(self, *args, **kwargs):\n serializer_class = self.get_serializer_class()\n kwargs['context'] = self.get_serializer_context()\n return serializer_class(*args, **kwargs)",
"def serializer_for(self, obj):\n # 1-NULL serializer\n if obj ... | [
"0.72009987",
"0.7115752",
"0.69366866",
"0.6924428",
"0.68449354",
"0.6820688",
"0.67195165",
"0.6692233",
"0.6686074",
"0.65120816",
"0.6473344",
"0.6473284",
"0.6444412",
"0.6443525",
"0.6418904",
"0.63524395",
"0.6348317",
"0.63381505",
"0.6311651",
"0.63109744",
"0.62789... | 0.67095476 | 7 |
Description When is given a directory name that exist Expected Result Shows log that directory was found | def test_has_directory_log(self, check_fn_true, caplog):
#setup
records = caplog.records
has_directory = extractor.make_has_directory(os.path.isdir)
directory_path = "./data/observed"
#when
test1 = has_directory(directory_path)
#result
assert len(records) == 1
assert records[0].message == f"It was found directory {directory_path}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_doesnt_have_directory_log(self, check_fn_false, caplog):\n\n #setup\n records = caplog.records\n has_directory = extractor.make_has_directory(os.path.isdir)\n directory_path = \"./data/tests\"\n \n #when\n test2 = has_directory(directory_path)\n\n #r... | [
"0.74612963",
"0.66516936",
"0.6648649",
"0.6637668",
"0.6592905",
"0.65451527",
"0.6507091",
"0.6476768",
"0.6445445",
"0.64397144",
"0.641731",
"0.6323137",
"0.6317305",
"0.6291415",
"0.6287829",
"0.6264551",
"0.62638617",
"0.62398607",
"0.6232754",
"0.6224753",
"0.62079227... | 0.7359447 | 1 |
Create a new websocket and connect its input and output to the subprocess with the specified PID. | async def websocket_handler(self, request, ws):
if self.repl_mgr is None:
return sanic.response.HTTPResponse(status=404)
log.info('initiating websocket')
await self.repl_mgr.process_websocket(ws)
log.info('terminating websocket') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def launch_web_socket(vnc_port, web_socket_port, server):\n\n path = os.path.abspath(os.path.dirname(__file__))\n ws = os.path.join(path, \"../../webConsole/bin/websockify.py\")\n\n web_socket_path = os.path.abspath(ws)\n\n cmd = \"%s %s:%s %s:%s --idle-timeout=120 &\" % (web_socket_path, server, vnc_p... | [
"0.61489266",
"0.6004585",
"0.58952093",
"0.5711386",
"0.56981546",
"0.5681631",
"0.5521888",
"0.55072397",
"0.5495588",
"0.54655325",
"0.5456767",
"0.5442702",
"0.539571",
"0.53936225",
"0.5373037",
"0.5360796",
"0.53407484",
"0.53166866",
"0.52801496",
"0.5262081",
"0.52579... | 0.0 | -1 |
Creates a group for a given node list. So far this is only an AiiDA verdi command. | def create_group(name, nodes, description=None):
group, created = Group.get_or_create(name=name)
if created:
print('Group created with PK={} and name {}'.format(group.pk, group.name))
else:
print('Group with name {} and pk {} already exists. Do you want to add nodes?[y/n]'.format(group.name, group.pk))
answer = raw_input()
if answer.strip().lower() == 'y':
pass
else:
return
nodes2 = []
nodes2_pks = []
for node in nodes:
try:
node = int(node)
except ValueError:
pass
nodes2_pks.append(node)
try:
nodes2.append(load_node(node))
except:# NotExistentError:
pass
group.add_nodes(nodes2)
print('added nodes: {} to group {} {}'.format(nodes2_pks, group.name, group.pk))
if description:
group.description = description
return group | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_new_group(self, a, b):\n self.groups[self.group_id] = set([a,b])\n self.node_id[a] = self.node_id[b] = self.group_id\n self.group_id += 1",
"def create(self, context=None):\n values = self.obj_get_changes()\n db_nodegroup = self.dbapi.create_nodegroup(values)\n ... | [
"0.7485883",
"0.72396135",
"0.71599764",
"0.66984046",
"0.66795355",
"0.65447414",
"0.6374939",
"0.6371427",
"0.63429767",
"0.63429767",
"0.6336112",
"0.6318668",
"0.631537",
"0.63136333",
"0.6290033",
"0.6238642",
"0.6233603",
"0.62333775",
"0.616521",
"0.61182487",
"0.61024... | 0.7037025 | 3 |
returns a list of node uuids for a given group as, name, pk, uuid or group object | def get_nodes_from_group(group, return_format='uuid'):
from aiida.orm import Group
from aiida.common.exceptions import NotExistent
nodes = []
g_nodes = []
try:
group_pk = int(group)
except ValueError:
group_pk = None
group_name = group
if group_pk is not None:
try:
str_group = Group(dbgroup=group_pk)
except NotExistent:
str_group = None
message = ('You have to provide a valid pk for a Group '
'or a Group name. Reference key: "group".'
'given pk= {} is not a valid group'
'(or is your group name integer?)'.format(group_pk))
print(message)
elif group_name is not None:
try:
str_group = Group.get_from_string(group_name)
except NotExistent:
str_group = None
message = ('You have to provide a valid pk for a Group or a Group name.'
'given group name= {} is not a valid group'
'(or is your group name integer?)'.format(group_name))
print(message)
elif isinstance(group, Group):
str_group = group
else:
str_group = None
print('I could not handle given input, either Group, pk, or group name please.')
return nodes
g_nodes = str_group.nodes
for node in g_nodes:
if return_format == 'uuid':
nodes.append(node.uuid)
elif return_format == 'pk':
nodes.append(node.pk)
return nodes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getGroup(group: int, name=\"\") -> list:\n groups = mongo.db.groups.find({'id':group},{'_id':0})\n userID_list = []\n user_list = []\n for entry in groups:\n if entry[\"id\"] == group:\n userID_list = userID_list + entry[\"members\"]\n if len(userID_list) != 0:\n for ent... | [
"0.6142465",
"0.5999166",
"0.59815145",
"0.58869386",
"0.5741488",
"0.5735394",
"0.5697292",
"0.5640951",
"0.563847",
"0.56142646",
"0.55772024",
"0.5554826",
"0.5521682",
"0.5469941",
"0.5396493",
"0.5393709",
"0.53740793",
"0.5341698",
"0.5340419",
"0.5333693",
"0.53306866"... | 0.73784983 | 0 |
Creates an interior node with the given operator (a token), and left and right operands (other nodes). | def __init__(self, opToken, leftOper, rightOper):
self.operator = opToken
self.leftOperand = leftOper
self.rightOperand = rightOper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __create_internal_node_by_operator(operator: PatternStructure, sliding_window: timedelta, parent: Node = None):\n operator_type = operator.get_top_operator()\n if operator_type == SeqOperator:\n return SeqNode(sliding_window, parent)\n if operator_type == AndOperator:\n ... | [
"0.6768396",
"0.6402284",
"0.6212105",
"0.6038182",
"0.6029708",
"0.59814626",
"0.59771395",
"0.5969799",
"0.57881653",
"0.57876146",
"0.57825124",
"0.57195956",
"0.56935775",
"0.56912225",
"0.56751347",
"0.559061",
"0.5583933",
"0.5532599",
"0.54641896",
"0.5460819",
"0.5419... | 0.66272795 | 1 |
Write a custom auth property where we grab the auth token and put it in the headers | def authenticate(self):
#it's weird i have to do this here, but the code makes this not simple
auth_json={'email':self.user, 'password':self.password}
#send a post with no auth. prevents an infinite loop
auth_response = self.post('/auth', data = json.dumps(auth_json), auth =
None)
_token = auth_response.json['token']
self._token = _token
self._wrapped.auth = SpringAuth(_token) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, r):\n r.headers[\"x-aims-auth-token\"] = self._token\n return r",
"def auth_header_value(self):\n return f\"token {self.API_TOKEN}\"",
"def authorization(self):\n return {'auth-token': '{token}'.format(token=self.token)}",
"def authorization(self):\n retu... | [
"0.7161087",
"0.7057166",
"0.6970341",
"0.6970341",
"0.6921795",
"0.68667036",
"0.67996633",
"0.6784417",
"0.6764053",
"0.66727406",
"0.66410804",
"0.6630715",
"0.66150844",
"0.6613414",
"0.6583362",
"0.65666574",
"0.6546947",
"0.6528426",
"0.6521229",
"0.6509429",
"0.6488572... | 0.5789005 | 80 |
if the person removed is an owner of flockr, check that they were actually removed from flockr | def test_channel_leave_normal_case_owner():
clear()
leaver = auth_register('leaver@gmail.com', '123abc!@#', 'first', 'last')
user = auth_register('user@gmail.com', '123abc!@#', 'first', 'last')
userchannel_id = channels_create(user['token'], 'userchannel', True)
channel_join(leaver['token'], userchannel_id['channel_id'])
channel_addowner(leaver['token'], userchannel_id['channel_id'], leaver['u_id'])
channel_leave(leaver['token'], userchannel_id['channel_id'])
randChannel_details = channel_details(user['token'], userchannel_id['channel_id'])
assert(randChannel_details['owner_members'] == [
{
'u_id' : user['u_id'],
'name_first' : 'first',
'name_last' : 'last',
'profile_img_url': ''
}
]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_channel_removeowner_owner_flockr_not_member():\n clear()\n register_first_result = auth_register('randemail@gmail.com', 'password1234', 'Jane', 'Citizen')\n register_second_result = auth_register('randemail2@gmail.com', 'password1234', 'Jane', 'Citizen')\n register_third_result = auth_register... | [
"0.6447291",
"0.6402777",
"0.637274",
"0.61781394",
"0.6064277",
"0.59293276",
"0.58794194",
"0.58277196",
"0.58173835",
"0.5780744",
"0.5778175",
"0.5695273",
"0.5692215",
"0.56889635",
"0.56681126",
"0.5662332",
"0.5635315",
"0.56060123",
"0.5600414",
"0.5581912",
"0.557282... | 0.5255025 | 65 |
Needs a ATOM.atom instance as argument. Returns the names of the framework atoms bound to that atom. | def get_framework_neighbours(atom, useH=True):
neighbourlist = []
for atom2 in atom.partner[:5]:
#if not 'H(' in atom2.name and np.linalg.norm(atom.cart-atom2.cart)<=1.6:
if np.linalg.norm(atom.cart - atom2.cart) <= float(covalence_radius[atom.element]) + float(
covalence_radius[atom2.element]) + .1:
if not 'H' == atom2.element or useH:
neighbourlist.append(atom2)
return neighbourlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_atom_labels(self, full=False):\n import numpy\n\n labels = self.get_attr(\"atom_labels\")\n if full:\n return labels\n return numpy.array(labels)[self._get_equivalent_atom_list()].tolist()",
"def getAtomNames(self):\n return self._raw_data['ATOM_NAME']",
"d... | [
"0.5786468",
"0.5760434",
"0.5465059",
"0.54395527",
"0.5285232",
"0.52612984",
"0.52286106",
"0.5170458",
"0.5095022",
"0.5048037",
"0.5035679",
"0.5034012",
"0.49822837",
"0.49687123",
"0.4942803",
"0.49194297",
"0.49104977",
"0.49047342",
"0.4869437",
"0.48587328",
"0.4852... | 0.45080042 | 97 |
Returns the rotation matrix equivalent of the given quaternion. This function is used by the get_refined_rotation() function. | def get_rotation_matrix_from_quaternion(q):
R = np.matrix([[q[0] * q[0] + q[1] * q[1] - q[2] * q[2] - q[3] * q[3],
2 * (q[1] * q[2] - q[0] * q[3]),
2 * (q[1] * q[3] + q[0] * q[2])],
[2 * (q[2] * q[1] + q[0] * q[3]),
q[0] * q[0] - q[1] * q[1] + q[2] * q[2] - q[3] * q[3],
2 * (q[2] * q[3] - q[0] * q[1])],
[2 * (q[3] * q[1] - q[0] * q[2]),
2 * (q[3] * q[2] + q[0] * q[1]),
q[0] * q[0] - q[1] * q[1] - q[2] * q[2] + q[3] * q[3]]])
return R | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quaternion_to_rotation_matrix(quaternion):\n\n q_w, q_x, q_y, q_z = quaternion\n sqw, sqx, sqy, sqz = np.square(quaternion)\n norm = (sqx + sqy + sqz + sqw)\n rotation_matrix = np.zeros((3, 3))\n\n # division of square length if quaternion is not already normalized\n rotation_matrix[0, 0] = (... | [
"0.8077829",
"0.79751414",
"0.7973847",
"0.797261",
"0.79455817",
"0.79306656",
"0.79097867",
"0.780534",
"0.7727341",
"0.77201724",
"0.77022475",
"0.7419111",
"0.74067664",
"0.7311962",
"0.7208562",
"0.7142397",
"0.71323454",
"0.71113116",
"0.70910096",
"0.7048041",
"0.69940... | 0.8253612 | 0 |
Returns a count of the number of unique metrics currently recorded for apdex, time and value metrics. | def metrics_count(self):
return len(self.__stats_table) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_of_running_metrics(self):\n try:\n return len(self.get_classads(\"OSGRSV==\\\"metrics\\\"\"))\n except TypeError:\n self.rsv.log(\"ERROR\", \"Classad parsing failed, unable to count running metrics\")",
"def metric_data_count(self):\n\n if not self.__settings... | [
"0.6411366",
"0.6283042",
"0.62296087",
"0.6220417",
"0.61111647",
"0.6047972",
"0.60304636",
"0.5969979",
"0.59513307",
"0.5878805",
"0.5827968",
"0.5753369",
"0.5703473",
"0.56953144",
"0.56933933",
"0.56933933",
"0.5690032",
"0.5668702",
"0.56455106",
"0.5636696",
"0.56353... | 0.6419223 | 0 |
Run the script at given path catching exceptions. This function should only be used internally by Pyto. | def runScriptAtPath(path):
sys.argv = [path]
for arg in PytoClasses.Python.shared.args:
sys.argv.append(str(arg))
def run() -> None:
os.system = PytoClasses.Python.shared.system
directory = os.path.expanduser(os.path.dirname(path))
sys.path.insert(0, directory)
try:
global __script__
spec = importlib.util.spec_from_file_location("__main__", path)
__script__ = importlib.util.module_from_spec(spec)
spec.loader.exec_module(__script__)
PytoClasses.Python.shared.values = [item for item in dir(__script__) if not item.startswith("__")]
except SystemExit:
print("SystemExit")
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
extracts = traceback.extract_tb(sys.exc_info()[2])
count = len(extracts)
lineNumber = -1
fileName = path
for i, extract in enumerate(extracts):
if extract[0] == fileName:
lineNumber = extract[1]
break
count -= 1
if (type(e) == SyntaxError): # The last word in a `SyntaxError` exception is the line number
lineNumber = [int(s) for s in (str(e)[:-1]).split() if s.isdigit()][-1]
PytoClasses.Python.shared.errorType = exc_type.__name__
PytoClasses.Python.shared.errorReason = str(e)
PytoClasses.EditorViewController.visible.showErrorAtLine(lineNumber)
print(traceback.format_exc(limit=-count))
sys.path.remove(directory)
PytoClasses.ReviewHelper.shared.launches = PytoClasses.ReviewHelper.shared.launches+1
PytoClasses.ReviewHelper.shared.requestReview()
PytoClasses.Python.shared.isScriptRunning = False
thread = threading.Thread(target=run, args=())
def loop():
while PytoClasses.Python.shared.isScriptRunning:
time.sleep(1)
ignoredThreads.append(thread)
raise Exception("Stopped script!")
def runLoop():
try:
loop()
except:
pass
thread.start()
runLoop()
return __script__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runScript(path=None):\n if path:\n exec(compile(open(path, \"rb\").read(), path, 'exec'))",
"def do_exec(self, arg):\n self.run_file(arg['path'])",
"def _run_file(file_path, globals_):\n script_name = os.path.basename(file_path)\n\n sys.path = (_PATHS.script_paths(script_name) +\n ... | [
"0.72653073",
"0.6912623",
"0.68354243",
"0.67718136",
"0.67703193",
"0.65939707",
"0.6413898",
"0.63070154",
"0.61949843",
"0.61937946",
"0.6164765",
"0.6151639",
"0.60480416",
"0.60265625",
"0.60191596",
"0.6006073",
"0.5994495",
"0.59873414",
"0.5959248",
"0.58620226",
"0.... | 0.7851395 | 0 |
Fast translation, rotation & scale in 2D using np.einsum in case input is not a single point | def fast_TRS_2d(input, transform_matrix, input_is_point=False):
if input_is_point:
return np.delete(np.dot(transform_matrix, np.insert(input, 2, 1)), 2)
else:
return np.delete(np.einsum('jk,ik->ij', transform_matrix, np.insert(input, 2, 1, axis=1)), 2, 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transAffine2D( iScale=(1, 1), iTrans=(0, 0), iRot=0, iShear=(0, 0) ): \n iRot = iRot * np.pi / 180\n oMatScale = np.matrix( ((iScale[0],0,0),(0,iScale[1],0),(0,0,1)) )\n oMatTrans = np.matrix( ((1,0,iTrans[0]),(0,1,iTrans[1]),(0,0,1)) )\n oMatRot = np.matrix( ((np.cos(iRot),-np.sin(iRot),0),\\\n... | [
"0.65614295",
"0.634234",
"0.61342865",
"0.61295545",
"0.61271703",
"0.59918904",
"0.5945617",
"0.5937629",
"0.5931673",
"0.59223235",
"0.5891538",
"0.58714736",
"0.5866033",
"0.58649784",
"0.58559966",
"0.5835464",
"0.5825817",
"0.58026475",
"0.5768168",
"0.575795",
"0.57447... | 0.5372871 | 64 |
Filter multiple iterable at once, selecting values at index i such that func(iterables[0][i], iterables[1][i], ...) is True | def sync_filter(func, *iterables):
return tuple(zip(*tuple(i for i in zip(*iterables) if func(*i)))) or ((),) * len(
iterables
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filtern(func: Callable, iterable: Iterable):\n return next(filter(func, iterable))",
"def cfilter(func,iterable):\n result = []\n\n for i in iterable:\n\n if func(i) == True:\n result.append(i)\n\n return result",
"def filter(function, iterable):\n\n if function is bool:\n ... | [
"0.7254669",
"0.70515805",
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"0.5971072",
"0.59694105",
"0.5959575",
"0.5932348",
"0.5888483",
"0.5888037"... | 0.781177 | 0 |
This will log a user out and redirect them to log in again via the AuthN server. | def logout_redirect(request):
logout(request)
# Build the URL
login_url = furl(login_redirect_url(request, next_url=request.build_absolute_uri()))
# Check for branding
if hasattr(settings, 'SCIAUTH_BRANDING'):
logger.debug('SciAuth branding passed')
# Encode it and pass it
branding = base64.urlsafe_b64encode(json.dumps(settings.SCIAUTH_BRANDING).encode('utf-8')).decode('utf-8')
login_url.query.params.add('branding', branding)
# Set the URL and purge cookies
response = redirect(login_url.url)
response.delete_cookie('DBMI_JWT', domain=dbmi_settings.JWT_COOKIE_DOMAIN)
logger.debug('Redirecting to: {}'.format(login_url.url))
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logout():\n logout_user()\n return redirect(url_for('auth.index'))",
"def signout():\n session.pop('oauth2_state', None)\n session.pop('oauth2_token', None)\n session.pop('discord_user', None)\n return redirect('/')",
"def logout_user(request):\r\n # We do not log here, because we have... | [
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"0.71333206",
"0.71177167",
"0.7101278",
"0.709512",
"0.7080758",
"0.706229... | 0.0 | -1 |
Given the positions of a list of the indices, create a unique key to register the position. | def placementKey( geo):
def diagcmp( xyA, xyB):
"""
Compare two positions based on x + y. If x + y is the same for the
two, compare based on x.
"""
return cmp(xyA[0] + xyA[1], xyB[0] + xyB[1]) or cmp(xyA[0], xyB[0])
sorted = [ tuple(geo[i]) for i in xrange(geo.shape[0]) ]
sorted.sort( diagcmp)
return hash(tuple(sorted)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_new_key(idx, key, d):\n\n new_key = \"%s_%d\" % (key, idx)\n if new_key in d:\n return make_new_key(idx + 1, key, d)\n return new_key",
"def _make_key(self):\n all_position_values = (chromosome_sort_key(self.chromosome), self.min_position, self.max_position, \n ... | [
"0.6364943",
"0.59690577",
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"0.5483186",
"0.5469... | 0.53552693 | 35 |
Updates the environment according to action and returns a `TimeStep`. See `step(self, action)` docstring for more details. | def _step(self, action: types.NestedArray) -> ts.TimeStep: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def step(self, action: types.NestedArray) -> ts.TimeStep:\n if self._current_time_step is None or self.should_reset(\n self._current_time_step\n ):\n return self.reset()\n\n self._current_time_step = self._step(action)\n return self._current_time_step",
"def step(\n self,\n action: ... | [
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"0.6996414",
"0.699088",
"0.6952764",
"0.6945095",
"0.69395... | 0.70150334 | 15 |
Starts a new sequence, returns the first `TimeStep` of this sequence. See `reset(self)` docstring for more details | def _reset(self) -> ts.TimeStep: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self) -> ts.TimeStep:\n self._current_time_step = self._reset()\n return self._current_time_step",
"def start(self):\n\t\tself._start = time.clock()\n\t\tif self._initial is None:\n\t\t\tself._initial = self._start\n\t\treturn self",
"def get_first_step(self):\n return self.get_step_by_i... | [
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"0.53334737",
"0.5326875",
"0.532644... | 0.5530532 | 14 |
r"""Calculate the cold plasma dispersion surfaces according to equation 2.64 in Plasma Waves by Swanson (2nd ed.) | def disp_surf_calc(kc_x_max, kc_z_max, m_i, wp_e):
# Make vectors of the wave numbers
kc_z = np.linspace(1e-6, kc_z_max, 35)
kc_x = np.linspace(1e-6, kc_x_max, 35)
# Turn those vectors into matrices
kc_x_mat, kc_z_mat = np.meshgrid(kc_x, kc_z)
# Find some of the numbers that appear later in the calculations
kc_ = np.sqrt(kc_x_mat ** 2 + kc_z_mat ** 2) # Absolute value of k
theta_ = np.arctan2(kc_x_mat, kc_z_mat) # The angle between k and B
wc_i = 1 / m_i # The ion gyro frequency
wp_i = wp_e / np.sqrt(m_i) # The ion plasma frequency
wp_ = np.sqrt(wp_e ** 2 + wp_i ** 2) # The total plasma frequency
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# For every k_perp and k_par, turn the dispersion relation into a
# polynomial equation and solve it.
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# The polynomial coefficients are calculated
pol_koeff_8 = -2 * kc_ ** 2
pol_koeff_8 -= (1 + wc_i ** 2 + 3 * wp_ ** 2) * np.ones(kc_.shape)
pol_koeff_6 = (2 * kc_ ** 2 + wp_ ** 2) * (1 + wc_i ** 2 + 2 * wp_ ** 2)
pol_koeff_6 += kc_ ** 4 + (wp_ ** 2 + wc_i) ** 2
pol_koeff_4 = -kc_ ** 4 * (1 + wc_i ** 2 + wp_ ** 2)
pol_koeff_4 -= 2 * kc_ ** 2 * (wp_ ** 2 + wc_i) ** 2
pol_koeff_4 -= (kc_ * wp_) ** 2 * (1 + wc_i ** 2 - wc_i) * (
1 + np.cos(theta_) ** 2)
pol_koeff_4 -= wp_ ** 2 * (wp_ ** 2 + wc_i) ** 2
pol_koeff_2 = kc_ ** 4 * (wp_ ** 2 * (1 + wc_i ** 2 - wc_i) * np.cos(
theta_) ** 2 + wc_i * (wp_ ** 2 + wc_i))
pol_koeff_2 += kc_ ** 2 * wp_ ** 2 * wc_i * (wp_ ** 2 + wc_i) * (
1 + np.cos(theta_) ** 2)
pol_koeff_0 = -kc_ ** 4 * wc_i ** 2 * wp_ ** 2 * np.cos(theta_) ** 2
w_final = np.zeros((10, len(kc_z), len(kc_x)))
# For each k, solve the equation
for k_z, k_x in itertools.product(range(len(kc_z)), range(len(kc_x))):
disp_polynomial = [1, 0, pol_koeff_8[k_z, k_x], 0,
pol_koeff_6[k_z, k_x], 0, pol_koeff_4[k_z, k_x],
0, pol_koeff_2[k_z, k_x], 0, pol_koeff_0[k_z, k_x]]
# theoretically should be real (A. Tjulin)
w_temp = np.real(np.roots(disp_polynomial))
# We need to sort the answers to get nice surfaces.
w_final[:, k_z, k_x] = np.sort(w_temp)
n2_ = kc_ ** 2 / w_final ** 2
v_ph_c = np.sqrt(1. / n2_)
va_c = 1 / (wp_e * np.sqrt(m_i))
v_ph_va = v_ph_c / va_c
diel_tensor = _calc_diel(kc_, w_final, theta_, wp_e, wp_i, wc_i)
e_x, e_y, e_z, e_per, e_tot, e_pol = _calc_e(diel_tensor)
e_par = (kc_x_mat * e_x + kc_z_mat * e_z) / kc_
b_x, b_y, b_z, b_par, b_per, b_pol, b_tot = _calc_b(kc_x_mat, kc_z_mat,
w_final, e_x, e_y, e_z)
dk_x, dk_z = [kc_x_mat[1], kc_z_mat[1]]
dw_x, dw_z = [np.zeros(w_final.shape) for _ in range(2)]
dw_x[:, :, 1:] = np.diff(w_final, axis=2)
dw_z[:, 1:, :] = np.diff(w_final, axis=1)
v_x, v_z = [dw_ / dk for dw_, dk in zip([dw_x, dw_z], [dk_x, dk_z])]
s_par, s_tot = _calc_s(e_x, e_y, e_z, b_x, b_y, b_z)
# Compute ion and electron velocities
v_ex, v_ey, v_ez, v_ix, v_iy, v_iz = _calc_vei(m_i, wc_i, w_final,
e_x, e_y, e_z)
# Ratio of parallel and perpendicular to B speed
vepar_perp = v_ez * np.conj(v_ez)
vepar_perp /= (v_ex * np.conj(v_ex) + v_ey * np.conj(v_ey))
vipar_perp = v_iz * np.conj(v_iz)
vipar_perp /= (v_ix * np.conj(v_ix) + v_iy * np.conj(v_iy))
# Total particle speeds
v_e2 = v_ex * np.conj(v_ex) + v_ey * np.conj(v_ey) + v_ez * np.conj(v_ez)
v_i2 = v_ix * np.conj(v_ix) + v_iy * np.conj(v_iy) + v_iz * np.conj(v_iz)
# Ion and electron energies
m_e = -1
en_e = 0.5 * m_e * v_e2
en_i = 0.5 * m_i * v_i2
# Ratio of particle and field energy densities
ratio_part_field = _calc_part2fields(wp_e, en_e, en_i, e_tot, b_tot)
# Continuity equation
dn_e_n, dn_i_n, dne_dni = _calc_continuity(kc_x_mat, kc_z_mat, w_final,
v_ex, v_ez, v_ix, v_iz)
dn_e_n_db_b = dn_e_n / b_tot
dn_i_n_db_b = dn_i_n / b_tot
dn_e_n_dbpar_b = dn_e_n / b_par
dn_i_n_dbpar_b = dn_i_n / b_par
dn_e = dn_e_n * wp_e ** 2
k_dot_e = e_x * kc_x_mat + e_z * kc_z_mat
k_dot_e = np.sqrt(k_dot_e * np.conj(k_dot_e))
# Build output dict
extra_param = {"Degree of electromagnetism": np.log10(b_tot / e_tot),
"Degree of longitudinality": np.abs(e_par) / e_tot,
"Degree of parallelity E": e_z / e_tot,
"Degree of parallelity B": np.sqrt(
b_z * np.conj(b_z)) / b_tot,
"Ellipticity E": e_pol, "Ellipticity B": b_pol,
"E_part/E_field": np.log10(ratio_part_field),
"v_g": np.sqrt(v_x ** 2 + v_z ** 2),
"v_ph/v_a": np.log10(v_ph_va),
"E_e/E_i": np.log10(en_e / en_i),
"v_e/v_i": np.log10(np.sqrt(v_e2 / v_i2)),
"v_epara/v_eperp": np.log10(vepar_perp),
"v_ipara/v_iperp": np.log10(vipar_perp),
"dn_e/dn_i": np.log10(dne_dni),
"(dn_e/n)/ (dB/B)": np.log10(dn_e_n_db_b),
"(dn_i/n)/(dB/B)": np.log10(dn_i_n_db_b),
"(dn_i/n)/(dBpar/B)": np.log10(dn_i_n_dbpar_b),
"(dn_e/n)/(dB/B)": np.log10(dn_e / k_dot_e),
"(dn_e/n)/(dBpar /B)": np.log10(dn_e_n_dbpar_b),
" Spar/Stot": s_par / s_tot}
for k, v in zip(extra_param.keys(), extra_param.values()):
extra_param[k] = np.transpose(np.real(v), [0, 2, 1])
kx_ = np.transpose(kc_x_mat)
kz_ = np.transpose(kc_z_mat)
wf_ = np.transpose(w_final, [0, 2, 1])
return kx_, kz_, wf_, extra_param | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_mixing_coefficients_surf(self):\n [Ly,N] = self.b.shape\n z_u_w = self.grid_dict['z_u_w']\n\n # SET UP NEW MIXING COEFFICIENT ARRAYS\n self.Kv_surf = np.zeros([Ly,N+1])\n self.Kt_surf = np.zeros([Ly,N+1])\n \n self.ghat = np.zeros([Ly,N+1])\n \n\n ... | [
"0.6823951",
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"0.5858084",
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"0.56355387",
"0.56164163",
"0.561608",
"0.56118447",
"0.5599761",
... | 0.7354267 | 0 |
Test of cooking the same product twice. Test passed if second cooking of same product raise ValueError | def test_cook_twice(cook_not_busy, product_for_cook):
cook_not_busy.cook_dish(product_for_cook)
with pytest.raises(ValueError):
cook_not_busy.cook_dish(product_for_cook) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checker(self, product):\n for item in self.instock:\n if item == product:\n return True\n return False",
"def test_buyTicket_AlreadySold():\n assert not testUser2.buyTicket(testTicket1)\n assert testTicket1 in testUser1.inventory\n assert testTicket1 not in te... | [
"0.6690166",
"0.63332933",
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"0.585461",
"0.5827044",
"0.5807381",
"0.58039653",
"0.579343",... | 0.7491278 | 0 |
Signs a transaction (in format of build_tx) with the given node, and returns the decoderawtransactiontype result again. | def sign (self, node, tx):
signed = node.signrawtransactionwithwallet (tx["hex"])
res = node.decoderawtransaction (signed["hex"])
res.update (signed)
return res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sign_transaction():\n data = request.get_json()\n\n try:\n tx = Transaction.from_dict(data)\n except TypeError:\n response = dict(message='Improper transaction json provided.')\n status_code = 400\n return jsonify(response), status_code\n\n signature = tx.sign(node.walle... | [
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"0.5552044",
"0.54918426",
"0.5468147",
"0.54531074",
"0.54221904",
"0.536... | 0.77226585 | 0 |
Return a list of the ids of outer divs with the specified text in a child element. | def ids_of_outer_divs_with_inner_text(self, child_text):
return self.q(css='div.outer').filter(
lambda el: child_text in [inner.text for inner in el.find_elements_by_css_selector('div.inner')]
).attrs('id') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_ids(self):\n page = r.get(self.url)\n tree = html.fromstring(page.content)\n ids_elements = tree.xpath(\"//div[@id='selectedcontent']/div/ul/li/a\")\n return [self._e_to_id(e) for e in ids_elements]",
"def get_child_ids(id,conn):\n\n child_ids = ('WITH RECURSIVE children AS '\n ... | [
"0.55548036",
"0.5479126",
"0.54318756",
"0.54250884",
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"0.512827",
"0.5109152",
"0.5081774",
"0.5059653",
"0.5021192",
... | 0.877173 | 0 |
Wait for scripts to finish and then return the contents of the ``output`` div on the page. | def output(self):
return super(RequireJSPage, self).output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wait(self):\n\t\tself.wait_window(self)\n\t\treturn self.result",
"def waitUntilFinished():",
"def waitUntilFinished():",
"def waitUntilFinished():",
"def waitUntilFinished():",
"def trigger_output(self):\n\n EmptyPromise(self.q(css='div#ready').is_present, \"Click ready\").fulfill()\n ... | [
"0.58988714",
"0.5896934",
"0.5896934",
"0.5896934",
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"0.5818085",
"0.5690976",
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"0.5146636",
"0.51380193",
"0.51380193",
"0.51380193",
"0.51380193",
"0.51246... | 0.0 | -1 |
x Position x on the map y Position y on the map theta Direction on the map | def scan(self, x, y, theta):
# create ray list
max_theta = theta + self.fov/2.0
min_theta = theta - self.fov/2.0
thetas = np.arange(min_theta,
max_theta,
self.theta_inc,
dtype=np.float32)
self.input_vector[:, 0] = x
self.input_vector[:, 1] = y
self.input_vector[:, 2] = thetas
# run ray marching
self.scan_method.calc_range_many(self.input_vector,
self.output_vector)
return self.output_vector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def theta(self):\n return atan2(self.y, self.x)",
"def theta(self):\n return float(np.arctan2(self.y, self.x))",
"def xy(self,theta,phi):\n dist=great_circle_distance(self.theta0,theta,self.phi0,phi)\n [yt,xt]=np.unravel_index(np.argmin(dist),dist.shape)\n return xt,yt",
"d... | [
"0.691251",
"0.683143",
"0.6619195",
"0.6595212",
"0.63898385",
"0.628581",
"0.6238489",
"0.61594254",
"0.61381114",
"0.61379653",
"0.61337817",
"0.6127042",
"0.6115421",
"0.6079736",
"0.60695887",
"0.6060793",
"0.6034483",
"0.60037607",
"0.60031086",
"0.59396416",
"0.5934096... | 0.0 | -1 |
Saves ciphers or keys or any text to the given file path; more efficient than manual saving. | def save(string, file):
save_file = open(file, 'w')
save_file.write(string)
save_file.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_file(path, text):\n with path.open(mode='w') as f_stream:\n f_stream.write(text)",
"def store_file(text: str, file_path: str) -> None:\n with open(file=file_path, mode='w', encoding='utf8') as f:\n f.write(text)",
"def save_text_file(text, path):\n os.makedirs(os.path.dirname(pa... | [
"0.68838364",
"0.6661782",
"0.65330315",
"0.6515433",
"0.6431692",
"0.64074653",
"0.6158728",
"0.61471856",
"0.6121656",
"0.6058847",
"0.6049562",
"0.60482824",
"0.60414743",
"0.6010596",
"0.598768",
"0.59727114",
"0.59727114",
"0.59727114",
"0.59499484",
"0.5907642",
"0.5892... | 0.55175346 | 68 |
Prompts for confirmation (yes/no question). If the user aborts the input by sending a interrupt signal this | def confirm(
text: str,
default: bool = False,
abort: bool = False,
prompt_suffix: str = ": ",
show_default: bool = True,
err: bool = False,
):
prompt = _build_prompt(text, prompt_suffix, show_default, "Y/n" if default else "y/N")
while True:
try:
value = _prompt(prompt, err=err, hide_input=False).lower().strip()
except (KeyboardInterrupt, EOFError):
raise click.Abort()
if value in ('y', "yes"):
rv = True
elif value in ('n', "no"):
rv = False
elif value == '':
rv = default
else:
click.echo("Error: invalid input", err=err)
continue
break
if abort and not rv:
raise click.Abort()
return rv | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def confirmation(self, question, answer):\n confirm_flag = False\n while confirm_flag not in ['y', 'n']:\n confirm_flag = raw_input(question + ' [y/n]: ')\n if confirm_flag == 'y':\n print answer\n elif confirm_flag == 'n':\n print 'The u... | [
"0.7569344",
"0.7401378",
"0.72859263",
"0.7283516",
"0.7266474",
"0.719268",
"0.71785426",
"0.7086956",
"0.70464075",
"0.7021681",
"0.6976385",
"0.69672275",
"0.69638824",
"0.6946273",
"0.6888155",
"0.6809539",
"0.6780994",
"0.67785114",
"0.66649485",
"0.6664532",
"0.6661782... | 0.62040895 | 47 |
Prompts a user for input. If the user aborts the input by sending an interrupt signal, this | def choice(
options: Union[List[str], Mapping[str, str]],
text: str = '',
default: Optional[str] = None,
prompt_suffix: str = ": ",
show_default: bool = True,
err: bool = False,
start_index: int = 0
) -> Union[str, int]:
# TODO: completer for numbers?
type_: click.ParamType
if isinstance(options, Mapping):
# (Y/I/N/O/D/Z) [default=N]
text = f"{text} ({'/'.join(options.keys())})"
type_ = click.STRING
for choice, descripton in options.items():
click.echo(f" {choice} : {descripton}")
else:
type_ = click.IntRange(start_index, len(options) + 1 - start_index)
for idx, descripton in enumerate(options):
idx += start_index
click.echo(f" [{idx}] {descripton}")
if default is not None and show_default:
text += f" [default={default}]"
while True:
selection = prompt(
text=text,
default=default,
type=type_,
prompt_suffix=prompt_suffix,
show_default=False,
err=err,
)
if isinstance(options, Mapping):
selection = selection.strip().upper()
if selection not in options:
click.echo(f"Please enter a valid option.")
else:
return selection
else:
return selection - start_index | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pause(question='PRESS ENTER TO CONTINUE ...'):\n try: input(question)\n except KeyboardInterrupt:\n global shutDown\n shutDown = True\n except: pass",
"def wait_for_user_input():\n\n input(\"Pulse ENTER para continuar...\")",
"def cont():\n\n try:\n input = raw_input()\n... | [
"0.7103245",
"0.6709058",
"0.6595114",
"0.6572123",
"0.65524346",
"0.6543129",
"0.65140307",
"0.6374105",
"0.63740534",
"0.631756",
"0.6300449",
"0.628559",
"0.6279168",
"0.6268421",
"0.62003326",
"0.61359113",
"0.6018758",
"0.6006392",
"0.5985524",
"0.5977991",
"0.5903809",
... | 0.0 | -1 |
Calculates the next state of a given 'board' following the classic rules of Conway's Game Of Life | def original(arr):
height = np.shape(arr)[0]
width = np.shape(arr)[1]
result = np.array(arr)
for row in range(height):
for col in range(width):
neighbors = 0
val = result[row][col]
for i in range(-1, 2):
for j in range(-1, 2):
if i == 0 and j == 0: # The cell itself cannot be counted as a neighbor
continue
if row + i < 0 or col + j < 0 or row + i > height or col + j > width: # Out of bounds
continue
with suppress(IndexError):
if arr[row + i][col + j] == 1:
neighbors += 1
if neighbors == 3 and val == 0: # Cell becomes alive
result[row][col] = 1
elif neighbors > 3 and val == 1 or neighbors < 2 and val == 1: # Cell dies
result[row][col] = 0
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_next_board_state(self):\n new_board_state = np.zeros_like(self.board_state)\n\n for x in range(self.board_size[0]):\n for y in range(self.board_size[0]):\n new_board_state[x][y] = self.next_state_of_cell(x,y)\n \n self.set_state(new_board_state)",... | [
"0.8105419",
"0.74080914",
"0.7388347",
"0.73284864",
"0.7323322",
"0.72788733",
"0.7252915",
"0.7215804",
"0.72030765",
"0.72030765",
"0.7184667",
"0.70937914",
"0.70153326",
"0.7014133",
"0.7008801",
"0.7003514",
"0.7002293",
"0.69419354",
"0.693517",
"0.69348997",
"0.69085... | 0.0 | -1 |
Solve tile in column zero on specified row (> 1) Updates puzzle and returns a move string | def solve_col0_tile(self, target_row):
move_str = 'ur'
self.update_puzzle(move_str)
cur_row, cur_col = self.current_position(target_row, 0)
if cur_row == target_row and cur_col == 0:
move_str += 'r' * (self._width - 2)
else:
move_str += self.position_tile(target_row-1, 1, cur_row, cur_col)
move_str += 'ruldrdlurdluurddlur'
move_str += 'r' * (self._width - 2)
self.update_puzzle(move_str[2:])
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_puzzle(self):\n\n move_str = \"\"\n \n # Move zero tile to bottom right corner tile of puzzle.\n zero_pos = self.current_position(0,0) \n vert_dist = (self.get_height() - 1) - zero_pos[0]\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\n... | [
"0.80090946",
"0.8002715",
"0.79543203",
"0.7940283",
"0.7870043",
"0.7847743",
"0.7818097",
"0.77486104",
"0.774018",
"0.7731879",
"0.77268267",
"0.7707487",
"0.77027786",
"0.76879483",
"0.7634697",
"0.76258874",
"0.7590558",
"0.7560754",
"0.7522215",
"0.75079787",
"0.750233... | 0.777891 | 7 |
Solve the tile in row zero at the specified column Updates puzzle and returns a move string | def solve_row0_tile(self, target_col):
move_str = 'ld'
self.update_puzzle(move_str)
cur_row, cur_col = self.current_position(0, target_col)
if cur_row == 0 and cur_col == target_col:
return move_str
else:
move_str += self.position_tile(1, target_col-1, cur_row, cur_col)
move_str += 'urdlurrdluldrruld'
self.update_puzzle(move_str[2:])
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_col0_tile(self, target_row):\r\n moves_str = \"\"\r\n # move the zero tile from (i,0) to (i−1,1) \r\n # using the move string \"ur\"\r\n moves_str += \"ur\"\r\n temp_grid = Puzzle(self._height, self._width, self._grid)\r\n temp_grid.update_puzzle(moves_str)\r\n ... | [
"0.7689822",
"0.76361793",
"0.7602665",
"0.759841",
"0.7547982",
"0.75370145",
"0.75116783",
"0.7487752",
"0.7478812",
"0.74497175",
"0.7435299",
"0.7407118",
"0.7397174",
"0.73776317",
"0.73732615",
"0.7324699",
"0.72756827",
"0.72559106",
"0.7206573",
"0.71551996",
"0.71403... | 0.745573 | 9 |
Solve the tile in row one at the specified column Updates puzzle and returns a move string | def solve_row1_tile(self, target_col):
cur_row, cur_col = self.current_position(1, target_col)
move_str = self.position_tile(1, target_col, cur_row, cur_col, need_ld=False)
self.update_puzzle(move_str)
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_row1_tile(self, target_col):\r\n moves_str = \"\"\r\n current_row, current_col = self.current_position(1, target_col)\r\n zero_row, zero_col = self.current_position(0, 0)\r\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\r\n moves_str += ... | [
"0.7611342",
"0.7530595",
"0.7489633",
"0.74769557",
"0.7346029",
"0.7312759",
"0.72872084",
"0.72763294",
"0.72613305",
"0.72458375",
"0.72417307",
"0.7236731",
"0.72223794",
"0.7210395",
"0.71957016",
"0.71925163",
"0.7142041",
"0.71206504",
"0.70840454",
"0.7064091",
"0.70... | 0.74047565 | 4 |
Solve the upper left 2x2 part of the puzzle Updates the puzzle and returns a move string | def solve_2x2(self):
cur_row, cur_col = self.current_position(0, 0)
move_str = 'u' * cur_row + 'l' * cur_col
self.update_puzzle(move_str)
if self.check_2x2_solved():
return move_str
else:
while not self.check_2x2_solved():
move_str += 'rdlu'
self.update_puzzle('rdlu')
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_puzzle(self):\n\n move_str = \"\"\n \n # Move zero tile to bottom right corner tile of puzzle.\n zero_pos = self.current_position(0,0) \n vert_dist = (self.get_height() - 1) - zero_pos[0]\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\n... | [
"0.86358064",
"0.84391785",
"0.820359",
"0.81158245",
"0.8079695",
"0.7876614",
"0.7855013",
"0.77959996",
"0.7629816",
"0.75706357",
"0.7446708",
"0.71919894",
"0.70876247",
"0.6987711",
"0.6876523",
"0.686447",
"0.686447",
"0.686447",
"0.6857152",
"0.6857152",
"0.6825898",
... | 0.8153259 | 3 |
This function will return data about the number of triangles on each vertex in a file inputs | def get_triangles_per_vertex(my_core, native_ranges):
t_p_v_data = []
tri_dimension = 2
for vertex in native_ranges[types.MBVERTEX]:
t_p_v_data.append(my_core.get_adjacencies(vertex, tri_dimension).size())
return np.array(t_p_v_data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_triangles(self, file):\n self.nibble(80)\n return struct.unpack(\"@i\", self.nibble(4))[0]",
"def get_num_vertices(triangles):\n return numpy.amax(numpy.reshape(triangles, -1)) + 1",
"def count_cells(fpath):\n cells = []\n for i in range(40):\n fname = f\"{fpath}/Mesh2d_... | [
"0.7772032",
"0.68836695",
"0.6398507",
"0.6369426",
"0.6362666",
"0.6353974",
"0.6323438",
"0.6275137",
"0.6170703",
"0.6165812",
"0.6144632",
"0.613628",
"0.61066425",
"0.6011404",
"0.59843576",
"0.59520715",
"0.5947087",
"0.594126",
"0.5935394",
"0.59270984",
"0.59270984",... | 0.64086694 | 2 |
This function will return data about the number of triangles on each surface in a file inputs | def get_triangles_per_surface(my_core, entity_ranges):
t_p_s = {}
for surface in entity_ranges['Surfaces']:
t_p_s[surface] = my_core.get_entities_by_type(
surface, types.MBTRI).size()
return t_p_s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_triangles(self, file):\n self.nibble(80)\n return struct.unpack(\"@i\", self.nibble(4))[0]",
"def get_num_vertices(triangles):\n return numpy.amax(numpy.reshape(triangles, -1)) + 1",
"def count_cells(fpath):\n cells = []\n for i in range(40):\n fname = f\"{fpath}/Mesh2d_... | [
"0.78216827",
"0.65307426",
"0.64875203",
"0.6224915",
"0.6100354",
"0.60187995",
"0.59512895",
"0.5924314",
"0.59203315",
"0.5906817",
"0.59059787",
"0.5882943",
"0.5867339",
"0.585497",
"0.58549577",
"0.5812365",
"0.57997227",
"0.57795846",
"0.57746047",
"0.57551175",
"0.57... | 0.6084134 | 5 |
Open a PCAP, seek to a packet offset, then get all packets belonging to the same connection | def packets_for_stream(fobj, offset):
pcap = dpkt.pcap.Reader(fobj)
pcapiter = iter(pcap)
ts, raw = pcapiter.next()
fobj.seek(offset)
for p in next_connection_packets(pcapiter, linktype=pcap.datalink()):
yield p | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_packets(pcap):\n\n # For each packet in the pcap process the contents\n for timestamp, buf, hdr_len in pcap:\n \n # Unpack the Ethernet frame (mac src/dst, ethertype)\n eth = dpkt.ethernet.Ethernet(buf)\n # print('Ethernet Frame: ', mac_addr(eth.src), mac_addr(eth.dst), ... | [
"0.5710354",
"0.5529603",
"0.5436237",
"0.5389503",
"0.5370659",
"0.5341635",
"0.5319951",
"0.5312948",
"0.5296589",
"0.52821374",
"0.5232271",
"0.5231817",
"0.5208476",
"0.52023923",
"0.51755023",
"0.5116855",
"0.5112408",
"0.5110333",
"0.5092085",
"0.5089987",
"0.5086152",
... | 0.69073343 | 0 |
gathers selected cards in order to take action on selected cards (either discarding them or preparing them) | def gatherSelected(self):
self.selected_list = []
for element in self.hand_info:
if element.status == 1:
self.selected_list.append(element)
return self.selected_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pick(self, pack, cards_owned, draft_info):\n pass",
"def card_sel(\n self, num=1, **kwargs\n ): # pylint: disable=too-many-locals, too-many-branches\n selectfrom = self.card_selSource(**kwargs)\n force = kwargs[\"force\"] if \"force\" in kwargs else False\n showdesc = k... | [
"0.6779038",
"0.64504594",
"0.6393454",
"0.6248788",
"0.6065166",
"0.60555077",
"0.60298246",
"0.6014514",
"0.6014514",
"0.5978343",
"0.5904059",
"0.5888115",
"0.5883593",
"0.5855397",
"0.58428776",
"0.584109",
"0.5830821",
"0.582095",
"0.5810005",
"0.5807255",
"0.5788884",
... | 0.5896568 | 11 |
Flattened array of ints, specifying the index of this object. This has to account for shaped parameters! | def _raveled_index(self):
return np.r_[:self.size] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _idxs_postformat_array(self):\n self.idxs = np.array(self.idxs)",
"def flatten_idx(idx, axis=-1):\n idx = numpy.asanyarray(idx)\n if not idx.dtype.kind in ('i', 'u'):\n idx = idx.astype(int)\n preshape = idx.shape[:axis]\n postshape = idx.shape[axis:]\n stride = int(numpy.product... | [
"0.65175533",
"0.62058824",
"0.5974461",
"0.59025675",
"0.58936995",
"0.58421",
"0.58367753",
"0.57612103",
"0.5670626",
"0.5655063",
"0.5648909",
"0.56180966",
"0.5589195",
"0.55857176",
"0.55857176",
"0.5580362",
"0.55702096",
"0.5560747",
"0.544082",
"0.5435741",
"0.541882... | 0.53458685 | 27 |
Validate the response that came back from the API, return True if it's good, False if bad | def _validate_response(self, response):
# Check for unexpected response - all should be JSON dicts that have
# already been deserialised
if not isinstance(response, types.DictionaryType):
self.message(
"\t\t[!] ERROR - Unexpected value returned from the API: '%s'" %
(response))
return False
# Check for valid errors
if "error" in response and "msg" in response:
self.message(
"\t\t[!] ERROR - %s (%s)" %
(response["msg"], response["timestamp"]))
return False
# Is this a valid response message
if "msg" in response:
return True
# Catch all...dictionary returned but does not contain expected keys?
# Who know's what's going on here?!
else:
self.message(
"\t\t[!] ERROR - Unexpected dictionary response returned from the API: '%s'" %
(response))
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(self, response):\n return response[\"status_code\"] == 1",
"def is_valid_response(self, response):\r\n if response.status_code in VALID_CODES:\r\n return True\r\n return False",
"def validate_response(self, response):\n pass",
"def validate_response(respons... | [
"0.8024064",
"0.78625363",
"0.77986264",
"0.7515456",
"0.74777824",
"0.74465626",
"0.73384",
"0.7265737",
"0.7262182",
"0.72441804",
"0.7198832",
"0.712405",
"0.7028976",
"0.701251",
"0.69981617",
"0.69959635",
"0.6970569",
"0.6948191",
"0.6897628",
"0.68025744",
"0.68025744"... | 0.76538676 | 3 |
estimate an MxF user factor matrix and an FxN item factor matrix from the MxN rating matrix | def factor_mat(all_dat, f_num, iterations, regularization):
# get # of users and # of items
[u_num, i_num] = all_dat.shape
# init user factors and item factors with random values
u_fac = np.matrix(np.random.rand(u_num, f_num)) # MxF
i_fac = np.matrix(np.random.rand(i_num, f_num)) # NxF
# calculate the preference matrix
preference = cal_preference(all_dat)
# calculate the confidence matrix
confidence = cal_confidence(all_dat)
# recalculate the user factors and item factors using the alternating least square method
for itr in range(iterations):
u_fac = alternate_ls(u_num, i_fac, preference, confidence, regularization)
#print itr, "u_fac"
i_fac = alternate_ls(i_num, u_fac, preference.T, confidence.T, regularization)
#print itr, "i_fac"
# save the output
df = pd.DataFrame(u_fac)
df.to_csv("tmp/u_fac.tmp", index=False, header=False, sep='\t', encoding='utf-8')
df = pd.DataFrame(i_fac.T)
df.to_csv("tmp/i_fac.tmp", index=False, header=False, sep='\t', encoding='utf-8')
# an MxF user factor matrix and an FxN item factor matrix
return [u_fac, i_fac.T] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_matrix(self):\n\n self.matrix = np.zeros((len(self.users), len(self.items)))\n\n for user in self.train_set['users']:\n for item in self.train_set['feedback'][user]:\n self.matrix[self.user_to_user_id[user]][self.item_to_item_id[item]] = \\\n se... | [
"0.6282623",
"0.62621325",
"0.60587424",
"0.6045789",
"0.6040702",
"0.6002245",
"0.5951851",
"0.59179777",
"0.59059614",
"0.58943605",
"0.589419",
"0.587945",
"0.5858747",
"0.58264637",
"0.5798391",
"0.57919794",
"0.574544",
"0.5737683",
"0.5693354",
"0.5668427",
"0.56560904"... | 0.72323316 | 0 |
Return list of choices for combo box, each a tuple of edit text and any annotation text | def getEditChoices(self, currentText=''):
return [(text, '') for text in self.formatList] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_choices(self):\n raise NotImplementedError()",
"def get_choices(self):\n raise NotImplementedError()",
"def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n return [(int(code), name) for name, code in cls.__members__.items()]",
"def getEditChoices(self,... | [
"0.64435107",
"0.64435107",
"0.6406375",
"0.6369448",
"0.6282898",
"0.61602914",
"0.61550856",
"0.6096663",
"0.6079173",
"0.6074226",
"0.599768",
"0.5979722",
"0.5946701",
"0.59085536",
"0.58665997",
"0.5852769",
"0.5851758",
"0.5840527",
"0.58387506",
"0.5816007",
"0.5803215... | 0.6981332 | 0 |
Return list of choices for combo box, each a tuple of edit text and any annotation text | def getEditChoices(self, currentText=''):
currentChoices, valid = self.sortedChoices(currentText)
nonChoices = [text for text in self.formatList
if text not in currentChoices]
results = []
for choice in nonChoices: # menu entries to add a choice
allChoices = currentChoices + [choice]
allChoices = [text for text in self.formatList
if text in allChoices]
results.append((self.editSep.join(allChoices),
'(%s %s)' % (_('add'), choice)))
if currentChoices:
results.append((None, None)) # separator
for choice in currentChoices: # menu entries to remove a choice
allChoices = currentChoices[:]
allChoices.remove(choice)
allChoices = [text for text in self.formatList
if text in allChoices]
results.append((self.editSep.join(allChoices),
'(%s %s)' % (_('remove'), choice)))
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getEditChoices(self, currentText=''):\n return [(text, '') for text in self.formatList]",
"def get_choices(self):\n raise NotImplementedError()",
"def get_choices(self):\n raise NotImplementedError()",
"def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n ... | [
"0.6981332",
"0.64435107",
"0.64435107",
"0.6406375",
"0.6282898",
"0.61602914",
"0.61550856",
"0.6096663",
"0.6079173",
"0.6074226",
"0.599768",
"0.5979722",
"0.5946701",
"0.59085536",
"0.58665997",
"0.5852769",
"0.5851758",
"0.5840527",
"0.58387506",
"0.5816007",
"0.5803215... | 0.6369448 | 4 |
Return list of choices for combo box, each a tuple of edit text and any annotation text | def getEditChoices(self, currentText=''):
format = globalref.options.strData('EditDateFormat', True)
today = GenDate().dateStr(format)
yesterday = (GenDate() - 1).dateStr(format)
tomorrow = (GenDate() + 1).dateStr(format)
return [(today, '(%s)' % _('today')),
(yesterday, '(%s)' % _('yesterday')),
(tomorrow, '(%s)' % _('tomorrow'))] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getEditChoices(self, currentText=''):\n return [(text, '') for text in self.formatList]",
"def get_choices(self):\n raise NotImplementedError()",
"def get_choices(self):\n raise NotImplementedError()",
"def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n ... | [
"0.6981832",
"0.64423245",
"0.64423245",
"0.6404376",
"0.63695693",
"0.62808853",
"0.615837",
"0.61527145",
"0.60969144",
"0.60806596",
"0.6074747",
"0.59950155",
"0.59769976",
"0.59458613",
"0.59082",
"0.5853147",
"0.5850785",
"0.58412063",
"0.58367765",
"0.58151263",
"0.580... | 0.5867416 | 15 |
Note this initialization command will start spawning traffic and select the specified human demonstrators for imitation learning | def initialize_element(self):
init_command = {
"StartLearning": True,
"AgentID": 1854
}
msg = json.dumps(init_command).encode('unicode_escape')
self.socket_control.send(msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def starting_tests(self):\n# disable menus during testing, because their message loop seems to interfere\n# with the natlink message loop which waits for recognitionMimic to\n# finish\n self.testing = 1",
"def initMana():\n run(\"chariot-me -i\")",
"def starting_tests(self):\n# disable menus during tes... | [
"0.63875884",
"0.61016667",
"0.60508204",
"0.60147184",
"0.5999624",
"0.5965473",
"0.59024185",
"0.587887",
"0.58662325",
"0.5809647",
"0.58030933",
"0.5800339",
"0.5754827",
"0.574139",
"0.5726314",
"0.5693041",
"0.56924486",
"0.5661824",
"0.5648796",
"0.5642869",
"0.5640304... | 0.5699686 | 15 |
Ensure that a folder exists and create it if it doesn't, including any parent folders, as necessary. | def create_folder(target_folder):
try:
os.makedirs(target_folder)
except OSError as e:
pass
return os.path.exists(target_folder) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ensure_folder(*arg):\n if len(arg) == 0:\n raise Exception(\"No input to ensure_folder\")\n path = get_dir(Path(*arg))\n path.mkdir(parents=True, exist_ok=True)",
"def create_folder_if_needed(path):\n if os.path.exists(path):\n print(\"{} dir exists\".format(path))\n else:\n ... | [
"0.82459253",
"0.8094383",
"0.8059282",
"0.788112",
"0.7779735",
"0.7752992",
"0.77248335",
"0.77168036",
"0.77027094",
"0.76686084",
"0.7639529",
"0.76316696",
"0.76276046",
"0.7627525",
"0.7594649",
"0.7567482",
"0.7552197",
"0.75503033",
"0.7529557",
"0.7526078",
"0.75254"... | 0.7339701 | 36 |
Sends a POST request every second to the monitoring server indicating that the process is still running. | def post_heartbeat(host, name, auth=None):
data = {'name': name, 'status': 'ok'}
try:
response = post('{host}/monitoring/heartbeat'.format(host=host), data, auth)
except urllib2.URLError:
print("Failed to send heartbeat.", file=sys.stderr)
else:
if response.strip() != 'ok':
print('POST got response {response}'.format(response=response), file=sys.stderr)
timer = threading.Timer(1.0, post_heartbeat, args=(host, name, auth))
# set the thread as a daemon to exit the program cleanly
# when the main thread finishes
timer.daemon = True
timer.start() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def periodic_timer(self):\n while self.running:\n self.sendStatusQuery()\n time.sleep(REPORT_INTERVAL)",
"def keep_alive():\r\n app = Flask(\"\")\r\n @app.route(\"/\")\r\n def home():\r\n return \"Your bot is now alive!\"\r\n\r\n def run():\r\n app.run(host=\"0.... | [
"0.6335293",
"0.6263304",
"0.6176471",
"0.61680967",
"0.6098956",
"0.6098956",
"0.6098956",
"0.6034498",
"0.5934776",
"0.5786157",
"0.5784672",
"0.5767236",
"0.57396203",
"0.57379746",
"0.5700311",
"0.5679326",
"0.5662139",
"0.56477326",
"0.5647195",
"0.56367123",
"0.5632368"... | 0.57290554 | 14 |
Gets the contact_list of this MessagingCampaign. The contact list that this messaging campaign will send messages for. | def contact_list(self):
return self._contact_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_contacts(self):\n return self.contacts",
"def get_contacts(self):\n\n\t\treturn self.__contacts",
"def GetContactList(self):\n\t\tfeeds = []\n\t\tfeed = self.client.GetContacts()\n\t\tfeeds.append(feed)\n\t\tnext = feed.GetNextLink()\n\t\twhile next:\n\t\t\tfeed = self.client.GetContacts(uri=ne... | [
"0.72631705",
"0.71663344",
"0.7121245",
"0.6652589",
"0.65349084",
"0.64175344",
"0.63972867",
"0.6386485",
"0.63770306",
"0.63157517",
"0.61904186",
"0.61765337",
"0.6132532",
"0.6121634",
"0.6121634",
"0.602559",
"0.6003591",
"0.59747976",
"0.5964907",
"0.5926964",
"0.5926... | 0.8081953 | 0 |
Sets the contact_list of this MessagingCampaign. The contact list that this messaging campaign will send messages for. | def contact_list(self, contact_list):
self._contact_list = contact_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def receiveContactList(self, contactList):",
"def set_contacts(self, contacts):\n\n\t\tif contacts is not None and not isinstance(contacts, list):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: contacts EXPECTED TYPE: list', None, None)\n\t\t\n\t\tself.__contacts = contacts\n\t\tself.__key_modified['... | [
"0.6209503",
"0.6202445",
"0.588594",
"0.588594",
"0.5883504",
"0.5845821",
"0.5795876",
"0.5775629",
"0.55796677",
"0.5562263",
"0.5562263",
"0.5559013",
"0.5479643",
"0.5460591",
"0.53779536",
"0.5320598",
"0.53073066",
"0.5305575",
"0.52854943",
"0.5274175",
"0.5192591",
... | 0.83265656 | 0 |
Gets the errors of this MessagingCampaign. A list of current error conditions associated with this messaging campaign. | def errors(self):
return self._errors | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_errors(self):\n return [result for result in self.values() if result.outcome == Result.ERROR]",
"def Errors(self):\n return self._get_attribute('errors')",
"def getErrorsList(self):\n return self.__errors",
"def errors (self):\n return self._errors",
"def errors (self):\... | [
"0.7164323",
"0.70850545",
"0.7084039",
"0.7073318",
"0.7073318",
"0.7032286",
"0.69743216",
"0.6840167",
"0.6840085",
"0.6816689",
"0.68060446",
"0.67511207",
"0.66765666",
"0.6626607",
"0.65734386",
"0.65553755",
"0.65435886",
"0.6522403",
"0.6402078",
"0.63631475",
"0.6350... | 0.7075878 | 3 |
Sets the errors of this MessagingCampaign. A list of current error conditions associated with this messaging campaign. | def errors(self, errors):
self._errors = errors | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def errors(self, errors):\n\n self._errors = errors",
"def validation_errors(self, validation_errors):\n self._validation_errors = validation_errors",
"def add_errors(self, errors):\n self.errors = merge_errors(self.errors, errors)",
"def errors (self):\n return self._errors",
"... | [
"0.7063868",
"0.6316049",
"0.62133807",
"0.5995449",
"0.5995449",
"0.5993789",
"0.5955976",
"0.58611673",
"0.5738866",
"0.57359105",
"0.56898946",
"0.56828547",
"0.56606567",
"0.56536406",
"0.5637317",
"0.5577304",
"0.5540206",
"0.5453656",
"0.54534554",
"0.54445463",
"0.5427... | 0.7172415 | 0 |
Calculate matrix of number of edits to convert every subset of y to every subset of x | def distance_matrix(self, x, y, keyboard_weight=None):
# create distance matrix
size_x = len(x) + 1
size_y = len(y) + 1
dist_matrix = np.zeros((size_x, size_y))
for i in range(size_x):
dist_matrix[i, 0] = i
for j in range(size_y):
dist_matrix[0, j] = j
## fill distance matrix
# no keyboard weight
if not keyboard_weight:
for i in range(1, size_x):
for j in range(1, size_y):
# if letters are same
if x[i-1] == y[j-1]:
dist_matrix[i, j] = dist_matrix[i-1, j-1]
# if letters are different
else:
subs = dist_matrix[i-1, j-1] + 1
delete = dist_matrix[i-1, j] + 1
insert = dist_matrix[i, j-1] + 1
dist_matrix[i, j] = min(subs, delete, insert)
# manhattan keyboard weight
elif keyboard_weight == "manhattan":
for i in range(1, size_x):
for j in range(1, size_y):
# if letters are same
if x[i-1] == y[j-1]:
dist_matrix[i, j] = dist_matrix[i-1, j-1]
# if letters are different
else:
dist = self.key_distance(x[i-1], y[j-1], keyboard_weight)
subs_weight = dist * self.manhattan_coef
subs = dist_matrix[i-1, j-1] + subs_weight
delete = dist_matrix[i-1, j] + 1
insert = dist_matrix[i, j-1] + 1
dist_matrix[i, j] = min(subs, delete, insert)
# euclidean keyboard weight
elif keyboard_weight == "euclidean":
for i in range(1, size_x):
for j in range(1, size_y):
# if letters are same
if x[i-1] == y[j-1]:
dist_matrix[i, j] = dist_matrix[i-1, j-1]
# if letters are different
else:
dist = self.key_distance(x[i-1], y[j-1], keyboard_weight)
subs_weight = dist * self.euclidean_coef
subs = dist_matrix[i-1, j-1] + subs_weight
delete = dist_matrix[i-1, j] + 1
insert = dist_matrix[i, j-1] + 1
dist_matrix[i, j] = min(subs, delete, insert)
return dist_matrix | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def topsolutions(self):\n answers = []\n for y in xrange(0, self.y):\n answer = self.retrieve(y,self.y)\n i = 0\n for x in xrange(0,y):\n answer -= self.retrieve(y,x)*answers[i]\n i += 1\n answers.append(answer)\n return... | [
"0.5772924",
"0.573736",
"0.57085884",
"0.5653021",
"0.56054556",
"0.54707366",
"0.5380854",
"0.53584445",
"0.5358038",
"0.53385925",
"0.5314144",
"0.5285138",
"0.52835494",
"0.52652776",
"0.52577674",
"0.5253505",
"0.5253083",
"0.524562",
"0.5237841",
"0.52344394",
"0.521122... | 0.0 | -1 |
A function for generating reaction likelihoods for a given genome according to the Probabilistic Annotation algorithm as | def generate_reaction_probabilities(fasta_file, template_model_file, genome_id=None):
if genome_id is None:
# Use fasta_file name minus extension. worker uses only for file names and logging
genome_id = '.'.join(fasta_file.split('.')[0:-1])
# Create a worker for running the algorithm.
worker = ProbAnnotationWorker(genome_id)
try:
template_model = _load_template_file(template_model_file)
# Run blast using the fasta file.
blast_result_file = worker.runBlast(fasta_file)
# Calculate roleset probabilities.
rolestring_tuples = worker.rolesetProbabilitiesMarble(blast_result_file)
# Calculate per-gene role probabilities.
role_probs = worker.rolesetProbabilitiesToRoleProbabilities(rolestring_tuples)
# Calculate whole cell role probabilities.
total_role_probs = worker.totalRoleProbabilities(role_probs)
# Calculate complex probabilities.
complex_probs = worker.complexProbabilities(total_role_probs, complexesToRequiredRoles=_complex_to_roles_dict(template_model))
# Calculate reaction probabilities.
rxn_probs = worker.reactionProbabilities(complex_probs, rxnsToComplexes=_reactions_to_complexes_dict(template_model))
# Store in dictionary for better serialization
return ReactionProbabilities([{'reaction': r[0], 'probability': r[1], 'type': r[2], 'complexes': _deserialize_cplx(r[3], worker.config['separator']), 'gpr': r[4]} for r in rxn_probs])
finally:
worker.cleanup() # worker creates lots of temporary and intermediate files. Allow it to clean up | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculateLogJointProbabilities(self, datum):\n logJoint = util.Counter()\n \n \"*** YOUR CODE HERE ***\"\n \n # -- OUR CODE HERE\n \n \n import math\n for label in self.legalLabels:\n sumThing = 0.0\n for pixel in self.conditionalProb[label]:\n if datum[pixel] is 1:\... | [
"0.64158255",
"0.61762154",
"0.6171898",
"0.61255485",
"0.6108559",
"0.60751355",
"0.5888883",
"0.581115",
"0.57994837",
"0.5773769",
"0.57539636",
"0.5743196",
"0.57412934",
"0.57062215",
"0.5637121",
"0.5604803",
"0.56041193",
"0.55022335",
"0.5498393",
"0.543617",
"0.53924... | 0.6003137 | 6 |
Fit an ellipse to an object. It returns the rotated rectangle in which the ellipse is inscribed. | def __CalculateEllipse(self, contour):
if len(contour) > 5:
return cv2.fitEllipse(contour)
return cv2.minAreaRect(contour) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_ellipse(x,y):\r\n \r\n def fit(x,y):\r\n x = x[:,np.newaxis]\r\n y = y[:,np.newaxis]\r\n D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))\r\n S = np.dot(D.T,D)\r\n C = np.zeros([6,6])\r\n C[0,2] = C[2,0] = 2; C[1,1] = -1\r\n E, V = np.linalg.eig... | [
"0.72764295",
"0.65389484",
"0.6258115",
"0.6085874",
"0.6078846",
"0.59491014",
"0.5820374",
"0.5784566",
"0.561725",
"0.55927783",
"0.5559429",
"0.5536257",
"0.5470644",
"0.5456046",
"0.54508764",
"0.54230434",
"0.5402299",
"0.53830856",
"0.5359062",
"0.53571004",
"0.535605... | 0.5605659 | 9 |
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