ifs loader works, but returns data with dim initial_time and lead_time

mlair/configuration/ifs_settings.py

+"""Settings to access not public era5 data."""
+
+from typing import Tuple
+
+
+def ifs_settings(sampling="daily") -> Tuple[str, str]:
+    """
+    Check for sampling as only hourly resolution is supported by ifs and return path on HPC systems.
+
+    :param sampling: temporal resolution to load data for, only hourly supported (default "daily")
+
+    :return: HPC path
+    """
+    if sampling == "hourly":  # pragma: no branch
+        IFS_DATA_PATH = "."
+        FILE_NAMES = "*.nc"
+    else:
+        raise NameError(f"Given sampling {sampling} is not supported, only hourly sampling can be used.")
+    return IFS_DATA_PATH, FILE_NAMES

mlair/helpers/data_sources/era5.py

@@ -53,7 +53,7 @@ def load_era5(station_name, stat_var, sampling, data_origin):
 
     # check if all requested variables are available
     if set(stat_var).issubset(station_data.columns) is False:
-        missing_variables = set(stat_var).difference(stat_var)
+        missing_variables = set(stat_var).difference(station_data.columns)
         origin = helpers.select_from_dict(data_origin, missing_variables)
         options = f"station={station_name}, origin={origin}"
         raise EmptyQueryResult(f"No data found for variables {missing_variables} and options {options} in JOIN.")

mlair/helpers/data_sources/ifs.py

+"""Methods to load ifs data."""
+__author__ = "Lukas Leufen, Michael Langgut"
+__date__ = "2023-06-07"
+
+import logging
+import os
+import re
+import glob
+from functools import partial
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+from mlair import helpers
+from mlair.configuration.ifs_settings import ifs_settings
+from mlair.configuration.toar_data_v2_settings import toar_data_v2_settings
+from mlair.helpers.data_sources.toar_data_v2 import load_station_information, combine_meta_data, correct_timezone
+from mlair.helpers.data_sources.data_loader import EmptyQueryResult
+from mlair.helpers.meteo import relative_humidity_from_dewpoint
+
+
+def load_ifs(station_name, stat_var, sampling, data_origin):
+
+    # make sure station_name parameter is a list
+    station_name = helpers.to_list(station_name)
+
+    # get data path
+    data_path, file_names = ifs_settings(sampling)
+
+    # correct stat_var values if data is not aggregated (hourly)
+    if sampling == "hourly":
+        stat_var = {key: "values" for key in stat_var.keys()}
+    else:
+        raise ValueError(f"Given sampling {sampling} is not supported, only hourly sampling can be used.")
+
+    # load station meta using toar-data v2 API
+    meta_url_base, headers = toar_data_v2_settings("meta")
+    station_meta = load_station_information(station_name, meta_url_base, headers)
+
+    # sel data for station using sel method nearest
+    logging.info(f"load data for {station_meta['codes'][0]} from IFS")
+    try:
+        lon, lat = station_meta["coordinates"]["lng"], station_meta["coordinates"]["lat"]
+        file_names = sort_ifs_files(data_path)
+        with xr.open_mfdataset(file_names, preprocess=partial(preprocess_ifs_single_file, lon, lat),
+                               concat_dim="initial_time", combine="nested") as data:
+            station_data = data.to_array().T.compute()
+    except OSError as e:
+        logging.info(f"Cannot load ifs data from path {data_path} and filenames {file_names} due to: {e}")
+        return None, None
+
+    if "relhum" in stat_var:
+        relhum = relative_humidity_from_dewpoint(station_data.sel(variable="d2m"), station_data.sel(variable="t2m"))
+        station_data = xr.concat([station_data, relhum.expand_dims({"variable": ["rhw"]})], dim="variable")
+    station_data.coords["variable"] = _rename_ifs_variables(station_data.coords["variable"].values)
+
+    # check if all requested variables are available
+    if set(stat_var).issubset(station_data.coords["variable"].values) is False:
+        missing_variables = set(stat_var).difference(station_data.coords["variable"].values)
+        origin = helpers.select_from_dict(data_origin, missing_variables)
+        options = f"station={station_name}, origin={origin}"
+        raise EmptyQueryResult(f"No data found for variables {missing_variables} and options {options} in JOIN.")
+    else:
+        station_data = station_data.sel(variable=list(stat_var.keys()))
+
+    # convert to local timezone
+    station_data.coords["initial_time"] = correct_timezone(station_data.sel(lead_time=1).to_pandas(), station_meta,
+                                                           sampling).index
+
+    variable_meta = _emulate_meta_data(station_data)
+    meta = combine_meta_data(station_meta, variable_meta)
+    meta = pd.DataFrame.from_dict(meta, orient='index')
+    meta.columns = station_name
+    return station_data, meta
+
+
+def sort_ifs_files(data_path, pattern="sfc_*.nc"):
+    def sort_by_date(file_name):
+        match = re.search(r'(\d{8})_(\d{2})', file_name)
+        if match:
+            return match.group(1), match.group(2)
+    file_names = glob.glob(os.path.join(data_path, pattern))
+    return sorted(file_names, key=sort_by_date)
+
+
+def preprocess_ifs_single_file(lon, lat, ds):
+    """Select lon and lat from data file and transform valid time into lead time."""
+    ds = ds.sel(longitude=lon, latitude=lat, method="nearest", drop=True)
+    return expand_dims_initial_time(ds)
+
+
+def expand_dims_initial_time(ds):
+    """Create lead time from initial time and valid time."""
+    initial_time = ds.time[0]
+    lead_time = (ds.time - initial_time) / np.timedelta64(1, "h")
+    # ds = ds.expand_dims(dim={"initial_time": [initial_time.values], "lead_time": lead_time}, axis=(0, 1))
+    ds.coords["time"] = lead_time
+    ds = ds.rename({"time": "lead_time"})
+    ds = ds.expand_dims(dim={"initial_time": [initial_time.values]}, axis=0)
+    return ds
+
+
+def _emulate_meta_data(station_data):
+    general_meta = {"sampling_frequency": "hourly", "data_origin": "model", "data_origin_type": "model"}
+    roles_meta = {"roles": [{"contact": {"organisation": {"name": "IFS", "longname": "ECMWF"}}}]}
+    variable_meta = {var: {"variable": {"name": var}, **roles_meta, ** general_meta} for var in station_data.coords["variable"].values}
+    return variable_meta
+
+
+def _rename_ifs_variables(ifs_names):
+    mapper = {"sp": "press", "u10": "u", "v10": "v", "t2m": "temp", "d2m": "dew", "blh": "pblheight",
+              "tcc": "cloudcover", "rhw": "relhum"}
+    ifs_names = list(ifs_names)
+    try:
+        join_names = list(map(lambda x: mapper.get(x, x), ifs_names))
+        return join_names
+    except KeyError as e:
+        raise KeyError(f"Cannot map names from ifs to join naming convention: {e}")