Add convert_ccast_sdr.py and convert_ccast_geo.py

cris-viirs/convert_ccast_geo.py

+
+"""convert_ccast_geo"""
+
+import sys
+
+import h5py
+import numpy as np
+from scipy.io import loadmat
+
+from collocation import earthrad, geod2geoc, ll2v, v2ll
+
+def main():
+
+    if len(sys.argv) != 2:
+        print 'usage: python %s <ccast_geo_file>' % sys.argv[0]
+        sys.exit(1)
+    filename = sys.argv[1]
+
+    mat = loadmat(filename)
+    lat = twist(mat['fovLat'].transpose())
+    lon = twist(mat['fovLon'].transpose())
+
+    scpos = scpos_from_latlon(lat, lon)
+
+    output_filename = filename[:-4] + '.h5'
+    write_output(output_filename, lat, lon, scpos)
+
+def twist(a):
+
+    num_scans, num_fors = a.shape
+    assert num_scans % 3 == 0
+    num_scans /= 3
+    assert num_fors % 3 == 0
+    num_fors /= 3
+    assert num_fors == 30
+
+    a = a.reshape([num_scans, 3, num_fors, 3])
+    a = a.transpose([0, 2, 1, 3])
+    a = a.reshape([num_scans, num_fors, 9])
+
+    return a
+
+def scpos_from_latlon(lat, lon):
+
+    # take FOV 5 from the middle 2 FORs of each scan line
+    lat = lat[:,14:16,4]
+    lon = lon[:,14:16,4]
+
+    # see which scan lines have NaNs for either of the middle 2 FOVs
+    nans = np.logical_or(np.isnan(lat), np.isnan(lon)).any(axis=1)
+
+    # set up our output array (num_scans by 3)
+    output = np.empty([len(lat), 3], np.float32)
+
+    # loop over each scan line
+    for lat_pair, lon_pair, has_nan, out in zip(lat, lon, nans, output):
+
+        # any NaNs? then just set to fill
+        if has_nan:
+            out[:] = -9999.0
+            continue
+
+        # find the position vector of the center of the scan
+        vecs = []
+        for i in range(2):
+            lat_geoc = geod2geoc(lat_pair[i])
+            vecs.append(ll2v(lat_geoc, lon_pair[i], earthrad(lat_geoc)))
+        gnd_pos = (vecs[0] + vecs[1]) / 2
+
+        # find the offset vector from the ground point to the satellite
+        # using a nominal alitutde of 824 km
+        vecs = []
+        for i in range(2):
+            vecs.append(ll2v(lat_pair[i], lon_pair[i], 824))
+        sat_off = (vecs[0] + vecs[1]) / 2
+
+        # add the two to get the satellite position vector
+        sat_pos = gnd_pos + sat_off
+
+        # set our output values; they're in meters
+        out[:] = [1000.0 * val for val in [sat_pos.x, sat_pos.y, sat_pos.z]]
+
+    return output
+
+def write_output(filename, lat, lon, scpos):
+
+    f = h5py.File(filename, 'w')
+
+    g = f.create_group('All_Data')
+    g = g.create_group('CrIS-SDR-GEO_All')
+
+    g.create_dataset('Latitude', lat.shape, np.float32, lat)
+    g.create_dataset('Longitude', lon.shape, np.float32, lon)
+    g.create_dataset('SCPosition', data=scpos)
+
+if __name__ == '__main__':
+    main()
+

cris-viirs/convert_ccast_sdr.py

+
+"""convert_ccast_sdr"""
+
+import sys
+
+import h5py
+from scipy.io import loadmat
+
+def main():
+
+    if len(sys.argv) != 2:
+        print 'usage: python %s <ccast_sdr_file>' % sys.argv[0]
+        sys.exit(1)
+    filename = sys.argv[1]
+
+    mat = loadmat(filename)
+    lw = mat['ES_RealLW'].transpose()
+    mw = mat['ES_RealMW'].transpose()
+    sw = mat['ES_RealSW'].transpose()
+
+    output_filename = filename[:-4] + '.h5'
+
+    f = h5py.File(output_filename, 'w')
+
+    g = f.create_group('All_Data')
+    g = g.create_group('CrIS-SDR_All')
+
+    g.create_dataset('ES_RealLW', data=lw)
+    g.create_dataset('ES_RealMW', data=mw)
+    g.create_dataset('ES_RealSW', data=sw)
+
+if __name__ == '__main__':
+    main()
+