tropomi no2 reader

monetio/sat/__init__.py

@@ -1,4 +1,5 @@
 from . import (
+    _tropomi_l2_no2, 
     _gridded_eos_mm,
     _modis_l2_mm,
     _omps_nadir_mm,
@@ -11,6 +12,7 @@
 )
 
 __all__ = [
+    "_tropomi_l2_no2",
     "_gridded_eos_mm",
     "_modis_l2_mm",
     "_omps_nadir_mm",

monetio/sat/_tropomi_l2_no2.py

@@ -0,0 +1,141 @@
+# Reading TROPOMI L2 NO2 data
+
+import os
+import sys
+import logging
+from glob import glob
+from collections import OrderedDict
+
+import numpy as np
+import xarray as xr
+
+from netCDF4 import Dataset
+
+#from monetio.hdf.hdfio import hdf_open, hdf_close, hdf_list, hdf_read
+
+def read_dataset(fname, variable_dict):
+    """
+    Parameters
+    __________
+    fname : str
+        Input file path.
+
+    Returns
+    _______
+    xarray.Dataset
+    """
+    print('reading ' + fname)
+
+    ds = xr.Dataset()
+
+    dso = Dataset(fname, "r") 
+
+    longitude = dso.groups['PRODUCT']['longitude']
+    latitude =  dso.groups['PRODUCT']['latitude']
+    start_time = dso.groups['PRODUCT']['time']
+
+    # squeeze 1-dimension
+    longitude = np.squeeze(longitude)
+    latitude = np.squeeze(latitude)
+    start_time = np.squeeze(start_time)
+
+    ds['lon'] = xr.DataArray(longitude)
+    ds['lat'] = xr.DataArray(latitude)
+
+    for varname in variable_dict:
+        print(varname)
+        values = dso.groups['PRODUCT'][varname]
+        # squeeze out 1-dimension
+        values = np.squeeze(values)
+
+        if 'fillvalue' in variable_dict[varname]:
+            fillvalue = variable_dict[varname]['fillvalue']
+            values[:][values[:] == fillvalue] = 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] = xr.DataArray(values)
+
+        if 'quality_flag_min' in variable_dict[varname]: 
+            ds.attrs['quality_flag'] = varname
+            ds.attrs['quality_thresh_min'] = variable_dict[varname]['quality_flag_min']
+
+
+    dso.close()
+
+    return ds
+
+
+def apply_quality_flag(ds):
+    """
+    Parameters
+    __________
+    ds : xarray.Dataset
+    """
+    if 'quality_flag' in ds.attrs:
+        quality_flag = ds[ds.attrs['quality_flag']]
+        quality_thresh_min = ds.attrs['quality_thresh_min']
+
+
+        # apply the quality thresh minimum to all variables in ds
+
+        for varname in ds:
+            print(varname)
+            if varname != ds.attrs['quality_flag']:
+                logging.debug(varname)
+                values = ds[varname].values
+
+                values[quality_flag <= quality_thresh_min] = np.nan
+                ds[varname].values = values
+
+
+def read_trpdataset(fnames, variable_dict, debug=False):
+    """
+    Parameters
+    __________
+    fnames : str
+        Regular expression for input file paths.
+
+    Returns
+    _______
+    xarray.Dataset
+    """
+    if debug:
+        logging_level = logging.DEBUG
+    else:
+        logging_level = logging.INFO
+    logging.basicConfig(stream=sys.stdout, level=logging_level)
+
+    for subpath in fnames.split('/'):
+        if '$' in subpath:
+            envvar = subpath.replace('$', '')
+            envval = os.getenv(envvar)
+            if envval is None:
+                print('environment variable not defined: ' + subpath)
+                exit(1)
+            else:
+                fnames = fnames.replace(subpath, envval)
+
+    print(fnames)
+    files = sorted(glob(fnames))
+    granules = OrderedDict()
+    for file in files:
+        granule = read_dataset(file, variable_dict)
+        apply_quality_flag(granule)
+        granule_str = file.split('/')[-1]
+        granule_info = granule_str.split('____')
+
+        datetime_str = granule_info[1][0:8]
+        granules[datetime_str] = granule
+
+    return granules