def read_patmosx_geoobj_hdf(patmosx_hdf, filename, cross, SETTINGS):
"""Read geolocation and time info from filename."""
cloudproducts = AllImagerData()
name = 'latitude'
temp = patmosx_hdf.select(name).get().astype(np.float)
offset = patmosx_hdf.select(name).attributes()['add_offset']
gain = patmosx_hdf.select(name).attributes()['scale_factor']
latitude_v = temp * gain + offset
name = 'longitude'
temp = patmosx_hdf.select(name).get().astype(np.float)
offset = patmosx_hdf.select(name).attributes()['add_offset']
gain = patmosx_hdf.select(name).attributes()['scale_factor']
longitude_v = temp * gain + offset
cloudproducts.latitude = np.repeat(latitude_v[:, np.newaxis], len(longitude_v), axis=1)
cloudproducts.longitude = np.repeat(longitude_v[np.newaxis, :], len(latitude_v), axis=0)
cloudproducts.nodata = -999
date_time_start = cross.time
date_time_end = cross.time + timedelta(seconds=SETTINGS['SAT_ORBIT_DURATION'])
cloudproducts.sec1970_start = calendar.timegm(date_time_start.timetuple())
cloudproducts.sec1970_end = calendar.timegm(date_time_end.timetuple())
frac_hour = patmosx_hdf.select('scan_line_time').get().astype(np.float)
if np.ma.is_masked(frac_hour):
frac_hour = frac_hour.data
seconds = frac_hour*60*60.0
time_s = patmosx_hdf.attributes()['time_coverage_start']
dt_obj = datetime.strptime(time_s, "%Y-%m-%dT%H:%M:%SZ")
time_sec_1970 = calendar.timegm(dt_obj.timetuple())
cloudproducts.time = seconds + time_sec_1970
do_some_geo_obj_logging(cloudproducts)
return cloudproducts
def read_maia_geoobj(maia_h5, filename):
"""Read geolocation and time info from filename
"""
cloudproducts = AllImagerData()
cloudproducts.latitude = 0.0001 * maia_h5['DATA']['Latitude'].value
cloudproducts.longitude = 0.0001 * maia_h5['DATA']['Longitude'].value
cloudproducts.nodata = 999
cloudproducts.longitude[cloudproducts.longitude ==
-9999] = cloudproducts.nodata
cloudproducts.latitude[cloudproducts.latitude ==
-9999] = cloudproducts.nodata
# viiCT_npp_GL_20150711_S211124_E211248_ASC_D_La-40_Lo-108_19188.h5
# viiCT_npp_DB_20120817_S035411_E035535_DES_N_La052_Lo-027_00001.h5
sl_ = os.path.basename(filename).split('_')
date_time_start = datetime.strptime(sl_[3] + sl_[4], '%Y%m%dS%H%M%S')
date_time_end = datetime.strptime(sl_[3] + sl_[5], '%Y%m%dE%H%M%S')
cloudproducts.sec1970_start = calendar.timegm(date_time_start.timetuple())
cloudproducts.sec1970_end = calendar.timegm(date_time_end.timetuple())
cloudproducts.num_of_lines = cloudproducts.latitude.shape[0]
cloudproducts = create_imager_time(cloudproducts, values={})
do_some_geo_obj_logging(cloudproducts)
return cloudproducts
def read_pps_geoobj_nc(pps_nc):
"""Read geolocation and time info from netcdf file."""
all_imager_obj = AllImagerData()
longitude = pps_nc.variables['lon'][::]
if np.ma.is_masked(longitude):
all_imager_obj.longitude = pps_nc.variables['lon'][::].data
all_imager_obj.longitude[longitude.mask] = -999.0
else:
all_imager_obj.longitude = longitude
latitude = pps_nc.variables['lat'][::]
if np.ma.is_masked(latitude):
all_imager_obj.latitude = pps_nc.variables['lat'][::].data
all_imager_obj.latitude[latitude.mask] = -999.0
else:
all_imager_obj.latitude = latitude
all_imager_obj.nodata = pps_nc.variables['lon']._FillValue
all_imager_obj.num_of_lines = all_imager_obj.latitude.shape[0]
# import pdb
# pdb.set_trace()
time_temp = pps_nc.variables['time'].units # to 1970 s
seconds = np.float64(pps_nc.variables['time'][::]) # from PPS often 0
if 'seconds' in time_temp:
if 'T' in time_temp:
time_obj = time.strptime(time_temp, 'seconds since %Y-%m-%dT%H:%M:%S+00:00')
elif time_temp == u'seconds since 1970-01-01':
time_obj = time.strptime(time_temp, 'seconds since %Y-%m-%d')
else:
time_obj = time.strptime(time_temp, 'seconds since %Y-%m-%d %H:%M:%S.%f +00:00')
sec_since_1970 = calendar.timegm(time_obj)
all_imager_obj.sec1970_start = (sec_since_1970 +
np.float64(np.min(pps_nc.variables['time_bnds'][::])) +
seconds)
all_imager_obj.sec1970_end = (sec_since_1970 +
np.float64(np.max(pps_nc.variables['time_bnds'][::])) +
seconds)
else:
try:
all_imager_obj.sec1970_start = calendar.timegm(time.strptime(pps_nc.variables['lon'].start_time,
'%Y-%m-%d %H:%M:%S.%f'))
all_imager_obj.sec1970_end = calendar.timegm(time.strptime(pps_nc.variables['lon'].end_time,
'%Y-%m-%d %H:%M:%S.%f'))
except:
all_imager_obj.sec1970_start = calendar.timegm(time.strptime(pps_nc.start_time, '%Y-%m-%d %H:%M:%S'))
all_imager_obj.sec1970_end = calendar.timegm(time.strptime(pps_nc.end_time, '%Y-%m-%d %H:%M:%S'))
# print type(all_imager_obj.sec1970_start)
all_imager_obj.sec1970_start = np.float64(all_imager_obj.sec1970_start)
all_imager_obj.sec1970_end = np.float64(all_imager_obj.sec1970_end)
do_some_geo_obj_logging(all_imager_obj)
return all_imager_obj
def oca_read_all(filename):
if 'MYD' in os.path.basename(filename):
cloudproducts = oca_read_all_nc_modis(filename)
logger.info("No timeinfo in OCA-MODIS file calculate from filename + 5min")
time_info = get_satid_datetime_orbit_from_fname_oca(filename)
cloudproducts.sec1970_start = calendar.timegm(time_info["date_time"].timetuple())
cloudproducts.sec1970_end = cloudproducts.sec1970_start + 60*5
cloudproducts = create_imager_time(cloudproducts, values={})
cloudproducts.instrument = 'modis'
do_some_geo_obj_logging(cloudproducts)
return(cloudproducts)
return oca_read_all_nc(filename)
def read_oca_geoobj(oca_nc, filename):
"""Read geolocation and time info from filename."""
cloudproducts = AllImagerData()
# import pdb;pdb.set_trace()
cloudproducts.longitude, cloudproducts.nodata = scale_oca_var(oca_nc['data']['measurement_data']['longitude'])
cloudproducts.latitude, cloudproducts.nodata = scale_oca_var(oca_nc['data']['measurement_data']['latitude'])
cloudproducts.num_of_lines = cloudproducts.longitude.shape[0]
cloudproducts.nodata = -999
# sensing_start_time_utc and sensing_end_time_utc,
stime = getattr(oca_nc, "sensing_start_time_utc")
etime = getattr(oca_nc, "sensing_end_time_utc")
cloudproducts.sec1970_start = calendar.timegm(
time.strptime(stime, '%Y%m%d%H%M%S.%f'))
cloudproducts.sec1970_end = calendar.timegm(
time.strptime(etime, '%Y%m%d%H%M%S.%f'))
cloudproducts = create_imager_time(cloudproducts, values={})
do_some_geo_obj_logging(cloudproducts)
return cloudproducts
def read_patmosx_geoobj(patmosx_nc, filename, cross, SETTINGS):
"""Read geolocation and time info from filename."""
cloudproducts = AllImagerData()
latitude_v = patmosx_nc.variables['latitude'][:].astype(np.float)
longitude_v = patmosx_nc.variables['longitude'][:].astype(np.float)
cloudproducts.latitude = np.repeat(latitude_v[:, np.newaxis], len(longitude_v), axis=1)
cloudproducts.longitude = np.repeat(longitude_v[np.newaxis, :], len(latitude_v), axis=0)
cloudproducts.nodata = -999
date_time_start = cross.time
date_time_end = cross.time + timedelta(seconds=SETTINGS['SAT_ORBIT_DURATION'])
cloudproducts.sec1970_start = calendar.timegm(date_time_start.timetuple())
cloudproducts.sec1970_end = calendar.timegm(date_time_end.timetuple())
frac_hour = patmosx_nc.variables['scan_line_time'][0, :, :].astype(np.float)
if np.ma.is_masked(frac_hour):
frac_hour = frac_hour.data
seconds = frac_hour*60*60.0
cloudproducts.time = seconds + patmosx_nc.variables['time']
do_some_geo_obj_logging(cloudproducts)
return cloudproducts
def read_cci_geoobj(cci_nc):
"""Read geolocation and time info from filename
"""
cloudproducts = AllImagerData()
logger.debug("Min lon: %s, max lon: %d",
np.min(cci_nc.variables['lon'][::]),
np.max(cci_nc.variables['lon'][::]))
# cci_nc.variables['lon'].add_offset
# cloudproducts.longitude = cci_nc.variables['lon'][::]
cloudproducts.nodata = -999.0
in_fillvalue = cci_nc.variables['lon']._FillValue
cloudproducts.longitude = cci_nc.variables['lon'][::]
cloudproducts.longitude[cci_nc.variables['lon'][::] == in_fillvalue] = cloudproducts.nodata
cloudproducts.latitude = cci_nc.variables['lon'][::]
cloudproducts.latitude[cci_nc.variables['lon'][::] == in_fillvalue] = cloudproducts.nodata
np.where(
np.logical_and(
np.greater_equal(cci_nc.variables['lon'][::],
cci_nc.variables['lon'].valid_min),
np.less_equal(cci_nc.variables['lon'][::],
cci_nc.variables['lon'].valid_max)),
cci_nc.variables['lon'][::],
cloudproducts.nodata)
# cloudproducts.latitude = cci_nc.variables['lat'][::]
cloudproducts.latitude = np.where(
np.logical_and(
np.greater_equal(cci_nc.variables['lat'][::],
cci_nc.variables['lat'].valid_min),
np.less_equal(cci_nc.variables['lat'][::],
cci_nc.variables['lat'].valid_max)),
cci_nc.variables['lat'][::],
cloudproducts.nodata)
# For pps these are calculated in calipso.py, but better read them
# from file because time are already available on arrays in the netcdf files
# dsec = calendar.timegm((1993, 1, 1, 0, 0, 0, 0, 0, 0)) # TAI to UTC
time_temp = daysafter4713bc_to_sec1970(cci_nc.variables['time'][::])
cloudproducts.time = time_temp[::] # [:, 0] # time_temp[:, 0]
cloudproducts.sec1970_start = np.min(cloudproducts.time)
cloudproducts.sec1970_end = np.max(cloudproducts.time)
do_some_geo_obj_logging(cloudproducts)
return cloudproducts
def read_pps_geoobj_h5(filename):
"""Read geolocation and time info from filename hdf5."""
h5file = h5py.File(filename, 'r')
all_imager_obj = AllImagerData()
in_fillvalue1 = h5file['where/lon/what'].attrs['nodata']
in_fillvalue2 = h5file['where/lon/what'].attrs['missingdata']
all_imager_obj.nodata = -999.0
gain = h5file['where/lon/what'].attrs['gain']
intercept = h5file['where/lon/what'].attrs['offset']
all_imager_obj.longitude = h5file['where/lon']['data'].value*gain + intercept
all_imager_obj.latitude = h5file['where/lat']['data'].value*gain + intercept
all_imager_obj.longitude[h5file['where/lon']['data'].value == in_fillvalue1] = all_imager_obj.nodata
all_imager_obj.latitude[h5file['where/lat']['data'].value == in_fillvalue1] = all_imager_obj.nodata
all_imager_obj.longitude[h5file['where/lon']['data'].value == in_fillvalue2] = all_imager_obj.nodata
all_imager_obj.latitude[h5file['where/lat']['data'].value == in_fillvalue2] = all_imager_obj.nodata
all_imager_obj.num_of_lines = all_imager_obj.latitude.shape[0]
all_imager_obj.sec1970_start = h5file['how'].attrs['startepochs']
all_imager_obj.sec1970_end = h5file['how'].attrs['endepochs']
do_some_geo_obj_logging(all_imager_obj)
return all_imager_obj
