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IMSClient: fetch and cache IMS weather data from station 43 (Sde Boker).
Resamples 10min data to 15min for alignment with sensor data.
"""
import os
import time
from pathlib import Path
from typing import Optional
import pandas as pd
import requests
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
pass
def _parse_ims_date(d: str) -> str:
"""Convert YYYY-MM-DD to IMS format YYYY/MM/DD."""
return d.replace("-", "/")
class IMSClient:
"""Fetch IMS API data for a station and cache to Data/ims/."""
def __init__(
self,
token: Optional[str] = None,
station_id: Optional[int] = None,
cache_dir: Optional[Path] = None,
channel_map: Optional[dict[int, str]] = None,
):
from config import settings
self.token = (token or os.environ.get("IMS_API_TOKEN", "")).strip()
if not self.token:
raise ValueError(
"IMS API token is required. Set IMS_API_TOKEN in .env, "
"in Streamlit Secrets, or pass token= to IMSClient."
)
self.station_id = station_id or settings.IMS_STATION_ID
self.cache_dir = cache_dir or settings.IMS_CACHE_DIR
self.channel_map = channel_map or settings.IMS_CHANNEL_MAP.copy()
self._base = f"{settings.IMS_BASE_URL}/{self.station_id}/data"
self._stations_url = settings.IMS_BASE_URL
def get_station_metadata(self, station_id: Optional[int] = None) -> dict:
"""
Fetch station metadata from IMS API (name, location, monitors/channels).
Returns dict with 'stationId', 'name', 'monitors' (list of {channelId, name, units, ...}).
"""
sid = station_id or self.station_id
url = f"{self._stations_url}/{sid}"
headers = {"Authorization": f"ApiToken {self.token}"}
r = requests.get(url, headers=headers, timeout=30)
r.raise_for_status()
return r.json()
def list_channels(self, station_id: Optional[int] = None) -> list[dict]:
"""Return list of channel descriptors for the station (channelId, name, units, active)."""
meta = self.get_station_metadata(station_id)
monitors = meta.get("monitors", meta.get("channelGroups", []))
# Flatten if nested; IMS may return list of { channelId, name, ... }
out = []
for m in monitors:
if isinstance(m, dict):
out.append({
"channelId": m.get("channelId", m.get("id")),
"name": m.get("name", m.get("channelName", "")),
"units": m.get("units", ""),
"active": m.get("active", True),
})
return out
def fetch_channel(
self,
channel_id: int,
from_date: str,
to_date: str,
) -> pd.DataFrame:
"""
Fetch one channel for date range. Dates as YYYY-MM-DD.
Returns DataFrame with timestamp_utc and one value column.
"""
from_f = _parse_ims_date(from_date)
to_f = _parse_ims_date(to_date)
url = f"{self._base}/{channel_id}?from={from_f}&to={to_f}"
headers = {"Authorization": f"ApiToken {self.token}"}
r = requests.get(url, headers=headers, timeout=120)
r.raise_for_status()
if not r.text or not r.text.strip():
return pd.DataFrame()
try:
raw = r.json()
except Exception:
return pd.DataFrame()
data = raw.get("data", raw) if isinstance(raw, dict) else raw
if not isinstance(data, list):
data = []
col_name = self.channel_map.get(channel_id, f"channel_{channel_id}")
rows = []
for item in data:
dt = item.get("datetime")
# IMS returns Israel time (Asia/Jerusalem); parse and convert to UTC
if isinstance(dt, str):
ts = pd.to_datetime(dt)
if ts.tzinfo is None:
ts = ts.tz_localize("Asia/Jerusalem").tz_convert("UTC")
else:
ts = ts.tz_convert("UTC")
else:
continue
ch_list = item.get("channels", [])
val = None
for ch in ch_list:
if ch.get("id") == channel_id and ch.get("status") == 1:
val = ch.get("value")
break
rows.append({"timestamp_utc": ts, col_name: val})
df = pd.DataFrame(rows)
if not df.empty:
df = df.dropna(subset=[col_name])
df = df.set_index("timestamp_utc").sort_index()
return df
def fetch_all_channels(
self,
from_date: str,
to_date: str,
delay_seconds: float = 0.5,
) -> pd.DataFrame:
"""Fetch all configured channels and merge on timestamp_utc."""
out = None
for ch_id, col_name in self.channel_map.items():
df = self.fetch_channel(ch_id, from_date, to_date)
if df.empty:
continue
df = df.rename(columns={c: c for c in df.columns})
if out is None:
out = df
else:
out = out.join(df, how="outer")
time.sleep(delay_seconds)
if out is None:
return pd.DataFrame()
out = out.reset_index()
return out
def resample_to_15min(self, df: pd.DataFrame) -> pd.DataFrame:
"""Resample 10min IMS data to 15min (mean). Expects timestamp_utc column."""
if df.empty or "timestamp_utc" not in df.columns:
return df
d = df.set_index("timestamp_utc")
d = d.resample("15min").mean().dropna(how="all")
return d.reset_index()
def load_cached(self, cache_path: Optional[Path] = None) -> pd.DataFrame:
"""Load merged IMS data from cache file if it exists."""
path = cache_path or (self.cache_dir / "ims_merged_15min.csv")
if not path.exists():
return pd.DataFrame()
df = pd.read_csv(path)
if "timestamp_utc" in df.columns:
df["timestamp_utc"] = pd.to_datetime(df["timestamp_utc"], utc=True)
return df
def fetch_and_cache(
self,
from_date: str,
to_date: str,
cache_path: Optional[Path] = None,
chunk_days: Optional[int] = 60,
) -> pd.DataFrame:
"""
Fetch all channels for the date range, resample to 15min, save to cache.
If chunk_days is set, split the range into chunks to avoid API empty responses.
"""
path = cache_path or (self.cache_dir / "ims_merged_15min.csv")
path.parent.mkdir(parents=True, exist_ok=True)
from datetime import datetime, timedelta
start = datetime.strptime(from_date, "%Y-%m-%d").date()
end = datetime.strptime(to_date, "%Y-%m-%d").date()
if start > end:
start, end = end, start
if chunk_days is None or (end - start).days <= chunk_days:
df = self.fetch_all_channels(from_date, to_date)
else:
chunks = []
d = start
while d < end:
chunk_end = min(d + timedelta(days=chunk_days), end)
from_s = d.strftime("%Y-%m-%d")
to_s = chunk_end.strftime("%Y-%m-%d")
try:
df_chunk = self.fetch_all_channels(from_s, to_s)
if not df_chunk.empty:
chunks.append(df_chunk)
except Exception:
pass # skip failed chunk, continue
d = chunk_end
df = pd.concat(chunks, ignore_index=True) if chunks else pd.DataFrame()
if not df.empty and "timestamp_utc" in df.columns:
df = df.drop_duplicates(subset=["timestamp_utc"]).sort_values("timestamp_utc")
if df.empty:
return df
df = self.resample_to_15min(df)
df.to_csv(path, index=False)
return df
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