TEMPO NO2 reader

monetio/sat/__init__.py

@@ -4,6 +4,7 @@
     _mopitt_l3_mm,
     _omps_l3_mm,
     _omps_nadir_mm,
+    _tempo_l2_no2_mm,
     _tropomi_l2_no2_mm,
     goes,
     modis_ornl,
@@ -13,6 +14,7 @@
 )
 
 __all__ = [
+    "_tempo_l2_no2_mm",
     "_gridded_eos_mm",
     "_modis_l2_mm",
     "_mopitt_l3_mm",

monetio/sat/_tempo_l2_no2_mm.py

@@ -0,0 +1,146 @@
+"""TEMPO L2 NO2 data file reader.
+
+History:
+
+
+"""
+
+import logging
+import os
+import sys
+from collections import OrderedDict
+from glob import glob
+from pathlib import Path
+
+import numpy as np
+import xarray as xr
+from netCDF4 import Dataset
+
+
+def _open_one_dataset(fname, variable_dict):
+    """Read locally stored INTELSAT TEMPO NO2 level 2
+    Parameters
+    ----------
+    fname: string
+        fname is local path to netcdf4 (HDF5) file
+
+    variable_dict: dict
+    Returns
+    -------
+    ds: xarray dataset
+    """
+    print("reading " + fname)
+
+    ds = xr.Dataset()
+
+    dso = Dataset(fname, "r")
+    lon_var = dso.groups["geolocation"]["longitude"]
+    lat_var = dso.groups["geolocation"]["latitude"]
+    time_var = dso.groups["geolocation"]["time"]
+
+    ds["lon"] = (
+        ("x", "y"),
+        lon_var[:].squeeze(),
+        {"long_name": lon_var.long_name, "units": lon_var.units},
+    )
+    ds["lat"] = (
+        ("x", "y"),
+        lat_var[:].squeeze(),
+        {"long_name": lat_var.long_name, "units": lat_var.units},
+    )
+    ds["time"] = (
+        ("time",),
+        time_var[:].squeeze(),
+        {"long_name": time_var.long_name, "units": time_var.units},
+    )
+    ds["lon"] = ds["lon"].fillna(-999.99)
+    ds["lat"] = ds["lat"].fillna(-999.99)
+    ds = ds.set_coords(["time", "lon", "lat"])
+
+    ds.attrs["reference_time_string"] = dso.time_coverage_start
+    ds.attrs["granule_number"] = dso.granule_num
+    ds.attrs["scan_num"] = dso.scan_num
+
+    for varname in variable_dict:
+        values_var = dso.groups["product"][varname]
+        values = values_var[:].squeeze()
+        fv = values_var.getncattr("_FillValue")
+        if not np.isfinite(fv):
+            values[:][values[:] == fv] = np.nan
+
+        if "scale" in variable_dict[varname]:
+            values[:] = variable_dict[varname]["scale"] * values[:]
+
+        if "minimum" in variable_dict[varname]:
+            minimum = variable_dict[varname]["minimum"]
+            values[:][values[:] < minimum] = np.nan
+
+        if "maximum" in variable_dict[varname]:
+            maximum = variable_dict[varname]["maximum"]
+            values[:][values[:] > maximum] = np.nan
+
+        ds[varname] = (("x", "y"), values, values_var.__dict__)
+
+        if "quality_flag_max" in variable_dict[varname]:
+            ds.attrs["quality_flag"] = varname
+            ds.attrs["quality_thresh_max"] = variable_dict[varname]["quality_flag_max"]
+
+    dso.close()
+
+    return ds
+
+    # time_var = dso.groups["geolocation"]["time"]
+
+
+def apply_quality_flag(ds):
+    """Mask variables in place based on quality flag
+    Parameters
+    ----------
+    ds: xr.Dataset
+    """
+    if "quality_flag" in ds.attrs:
+        quality_flag = ds[ds.attrs["quality_flag"]]
+        quality_thresh_max = ds.attrs["quality_thresh_max"]
+
+        # Apply the quality thersh maximum to all variables in ds
+        for varname in ds:
+            if varname != ds.attrs["quality_flag"]:
+                logging.debug(varname)
+                values = ds[varname].values
+                values[quality_flag > quality_thresh_max] = np.nan
+
+
+def open_dataset(fnames, variable_dict, debug=False):
+    if debug:
+        logging_level = logging.DEBUG
+        logging.basicConfig(strea=sys.stdout, level=logging_level)
+
+    if isinstance(fnames, Path):
+        fnames = fnames.as_posix()
+    if isinstance(variable_dict, str):
+        variable_dict = {variable_dict: {}}
+    elif isinstance(variable_dict, dict):
+        pass
+    else:  # Assume sequence
+        variable_dict = {varname: {} for varname in variable_dict}
+
+    for subpath in fnames.split("/"):
+        if "$" in subpath:
+            envvar = subpath.replace("$", "")
+            envval = os.getenv(envvar)
+            if envval is None:
+                raise Exception("Environment variable not defined: " + subpath)
+            else:
+                fnames = fnames.replace(subpath, envval)
+
+    print(fnames)
+    files = sorted(glob(fnames))
+
+    granules = OrderedDict()
+
+    for file in files:
+        granule = _open_one_dataset(file, variable_dict)
+        apply_quality_flag(granule)
+        granules[granule.attrs["reference_time_string"]] = granule
+
+    return granules