def get_fwi_from_api(date: str):
"""Call the CDS API and return all fwi variables as a dataframe with geo coordinates and departments.
When calling the API we get a zip file that must be extracted (in a tmp directory), then handle
each queried variable which is in a separate netcdf file. A dataframe is created with all the variables
and then finally we join codes and departments with geopandas.
Args:
date (str)
Returns:
pd.DataFrame
"""
year, month, day = date.split("-")
date_concat = date.replace("-", "")
with tempfile.TemporaryDirectory() as tmp:
call_fwi(tmp, year, month, day)
file = zipfile.ZipFile(
os.path.join(tmp, f"fwi_{year}_{month}_{day}.zip"))
file.extractall(path=os.path.join(tmp, f"fwi_{year}_{month}_{day}"))
df0 = pd.DataFrame({})
for var_name in ["BUI", "DC", "DMC", "DSR", "FFMC", "FWI", "ISI"]:
var_path = os.path.join(
tmp,
f"fwi_{year}_{month}_{day}/ECMWF_FWI_{var_name}_{date_concat}_1200_hr_v3.1_int.nc",
)
nc = Dataset(var_path, "r")
lats = nc.variables["latitude"][:]
var = nc.variables[var_name.lower()][:]
nc.close()
lons = np.arange(-180, 180.25, 0.25)
var_cyclic = np.ma.hstack([var[0][:, 720:], var[0][:, :721]])
lon2d, lat2d = np.meshgrid(lons, lats)
df = pd.DataFrame({
"latitude": lat2d.flatten(),
"longitude": lon2d.flatten(),
var_name.lower(): var_cyclic.flatten(),
})
df = df.dropna(subset=[var_name.lower()])
df = df.reset_index(drop=True)
if var_name == "BUI":
df0 = pd.concat([df0, df], axis=1)
else:
df0 = pd.merge(df0,
df,
on=["latitude", "longitude"],
how="inner")
geo_data = gpd.GeoDataFrame(
df0,
geometry=gpd.points_from_xy(df0["longitude"], df0["latitude"]),
crs="EPSG:4326",
)
geo_masks = get_french_geom()
geo_df = gpd.sjoin(geo_masks, geo_data, how="inner")
return geo_df.drop(["index_right", "geometry"], axis=1)
def test_get_french_geom(self):
fr_geom = masks.get_french_geom()
self.assertIsInstance(fr_geom, GeoDataFrame)
self.assertTrue(
all(
v1 == v2 for v1, v2 in zip(fr_geom.columns, ["code", "nom", "geometry"])
)
)