def get_delay_timeseries(inps, atr):
"""Calculate delay time-series and write it to HDF5 file.
Parameters: inps : namespace, all input parameters
atr : dict, metadata to be saved in trop_file
Returns: trop_file : str, file name of ECMWF.h5
"""
def get_dataset_size(fname):
atr = readfile.read_attribute(fname)
return (atr['LENGTH'], atr['WIDTH'])
# check 1 - existing tropo delay file
if (ut.run_or_skip(out_file=inps.trop_file, in_file=inps.grib_file_list, print_msg=False) == 'skip'
and get_dataset_size(inps.trop_file) == get_dataset_size(inps.geom_file)):
print('{} file exists and is newer than all GRIB files, skip updating.'.format(inps.trop_file))
return
# check 2 - geometry file
if any(i is None for i in [inps.geom_file, inps.ref_yx]):
print('No DEM / incidenceAngle / ref_yx found, skip calculating tropospheric delays.')
if not os.path.isfile(inps.trop_file):
inps.trop_file = None
return
# prepare geometry data
geom_obj = geometry(inps.geom_file)
geom_obj.open()
inps.dem = geom_obj.read(datasetName='height')
inps.inc = geom_obj.read(datasetName='incidenceAngle')
if 'latitude' in geom_obj.datasetNames:
# for dataset in geo OR radar coord with lookup table in radar-coord (isce, doris)
inps.lat = geom_obj.read(datasetName='latitude')
inps.lon = geom_obj.read(datasetName='longitude')
elif 'Y_FIRST' in geom_obj.metadata:
# for geo-coded dataset (gamma, roipac)
inps.lat, inps.lon = ut.get_lat_lon(geom_obj.metadata)
else:
# for radar-coded dataset (gamma, roipac)
inps.lat, inps.lon = ut.get_lat_lon_rdc(geom_obj.metadata)
# calculate phase delay
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
num_date = len(inps.grib_file_list)
date_list = [str(re.findall('\d{8}', i)[0]) for i in inps.grib_file_list]
trop_data = np.zeros((num_date, length, width), np.float32)
print('calcualting delay for each date using PyAPS (Jolivet et al., 2011; 2014) ...')
print('number of grib files used: {}'.format(num_date))
prog_bar = ptime.progressBar(maxValue=num_date)
for i in range(num_date):
grib_file = inps.grib_file_list[i]
trop_data[i] = get_delay(grib_file, inps)
prog_bar.update(i+1, suffix=os.path.basename(grib_file))
prog_bar.close()
# Convert relative phase delay on reference date
inps.ref_date = atr.get('REF_DATE', date_list[0])
print('convert to relative phase delay with reference date: '+inps.ref_date)
inps.ref_idx = date_list.index(inps.ref_date)
trop_data -= np.tile(trop_data[inps.ref_idx, :, :], (num_date, 1, 1))
# Write tropospheric delay to HDF5
atr['REF_Y'] = inps.ref_yx[0]
atr['REF_X'] = inps.ref_yx[1]
ts_obj = timeseries(inps.trop_file)
ts_obj.write2hdf5(data=trop_data,
dates=date_list,
metadata=atr,
refFile=inps.timeseries_file)
return
def calculate_delay_timeseries(inps):
"""Calculate delay time-series and write it to HDF5 file.
Parameters: inps : namespace, all input parameters
Returns: tropo_file : str, file name of ECMWF.h5
"""
def get_dataset_size(fname):
atr = readfile.read_attribute(fname)
shape = (int(atr['LENGTH']), int(atr['WIDTH']))
return shape
# check existing tropo delay file
if (ut.run_or_skip(out_file=inps.tropo_file,
in_file=inps.grib_files,
print_msg=False) == 'skip'
and get_dataset_size(inps.tropo_file) == get_dataset_size(
inps.geom_file)):
print(
'{} file exists and is newer than all GRIB files, skip updating.'.
format(inps.tropo_file))
return
# prepare geometry data
geom_obj = geometry(inps.geom_file)
geom_obj.open()
inps.dem = geom_obj.read(datasetName='height')
inps.inc = geom_obj.read(datasetName='incidenceAngle')
if 'latitude' in geom_obj.datasetNames:
# for dataset in geo OR radar coord with lookup table in radar-coord (isce, doris)
inps.lat = geom_obj.read(datasetName='latitude')
inps.lon = geom_obj.read(datasetName='longitude')
elif 'Y_FIRST' in geom_obj.metadata:
# for geo-coded dataset (gamma, roipac)
inps.lat, inps.lon = ut.get_lat_lon(geom_obj.metadata)
else:
# for radar-coded dataset (gamma, roipac)
inps.lat, inps.lon = ut.get_lat_lon_rdc(geom_obj.metadata)
# calculate phase delay
length, width = int(inps.atr['LENGTH']), int(inps.atr['WIDTH'])
num_date = len(inps.grib_files)
date_list = [str(re.findall('\d{8}', i)[0]) for i in inps.grib_files]
tropo_data = np.zeros((num_date, length, width), np.float32)
print(
'\n------------------------------------------------------------------------------'
)
print(
'calcualting absolute delay for each date using PyAPS (Jolivet et al., 2011; 2014) ...'
)
print('number of grib files used: {}'.format(num_date))
prog_bar = ptime.progressBar(maxValue=num_date)
for i in range(num_date):
grib_file = inps.grib_files[i]
tropo_data[i] = get_delay(grib_file, inps)
prog_bar.update(i + 1, suffix=os.path.basename(grib_file))
prog_bar.close()
# remove metadata related with double reference
# because absolute delay is calculated and saved
for key in ['REF_DATE', 'REF_X', 'REF_Y', 'REF_LAT', 'REF_LON']:
if key in inps.atr.keys():
inps.atr.pop(key)
# Write tropospheric delay to HDF5
ts_obj = timeseries(inps.tropo_file)
ts_obj.write2hdf5(data=tropo_data,
dates=date_list,
metadata=inps.atr,
refFile=inps.timeseries_file)
return inps.tropo_file
def calc_delay_timeseries(inps):
"""Calculate delay time-series and write it to HDF5 file.
Parameters: inps : namespace, all input parameters
Returns: tropo_file : str, file name of ECMWF.h5
"""
def get_dataset_size(fname):
atr = readfile.read_attribute(fname)
shape = (int(atr['LENGTH']), int(atr['WIDTH']))
return shape
def run_or_skip(grib_files, tropo_file, geom_file):
print('update mode: ON')
print('output file: {}'.format(tropo_file))
flag = 'skip'
# check existance and modification time
if ut.run_or_skip(out_file=tropo_file,
in_file=grib_files,
print_msg=False) == 'run':
flag = 'run'
print(
'1) output file either do NOT exist or is NOT newer than all GRIB files.'
)
else:
print('1) output file exists and is newer than all GRIB files.')
# check dataset size in space / time
date_list = [
str(re.findall('\d{8}', os.path.basename(i))[0])
for i in grib_files
]
if (get_dataset_size(tropo_file) != get_dataset_size(geom_file)
or any(i not in timeseries(tropo_file).get_date_list()
for i in date_list)):
flag = 'run'
print(
'2) output file does NOT have the same len/wid as the geometry file {} or does NOT contain all dates'
.format(geom_file))
else:
print(
'2) output file has the same len/wid as the geometry file and contains all dates'
)
# check if output file is fully written
with h5py.File(tropo_file, 'r') as f:
if np.all(f['timeseries'][-1, :, :] == 0):
flag = 'run'
print('3) output file is NOT fully written.')
else:
print('3) output file is fully written.')
# result
print('run or skip: {}'.format(flag))
return flag
if run_or_skip(inps.grib_files, inps.tropo_file, inps.geom_file) == 'skip':
return
## 1. prepare geometry data
geom_obj = geometry(inps.geom_file)
geom_obj.open()
inps.inc = geom_obj.read(datasetName='incidenceAngle')
inps.dem = geom_obj.read(datasetName='height')
# for testing
if inps.custom_height:
print(
'use input custom height of {} m for vertical integration'.format(
inps.custom_height))
inps.dem[:] = inps.custom_height
if 'latitude' in geom_obj.datasetNames:
# for lookup table in radar-coord (isce, doris)
inps.lat = geom_obj.read(datasetName='latitude')
inps.lon = geom_obj.read(datasetName='longitude')
elif 'Y_FIRST' in geom_obj.metadata:
# for lookup table in geo-coded (gamma, roipac) and obs. in geo-coord
inps.lat, inps.lon = ut.get_lat_lon(geom_obj.metadata)
# convert coordinates to lat/lon, e.g. from UTM for ASF HyPP3
if not geom_obj.metadata['Y_UNIT'].startswith('deg'):
inps.lat, inps.lon = ut.to_latlon(inps.atr['OG_FILE_PATH'],
inps.lon, inps.lat)
else:
# for lookup table in geo-coded (gamma, roipac) and obs. in radar-coord
inps.lat, inps.lon = ut.get_lat_lon_rdc(inps.atr)
# mask of valid pixels
mask = np.multiply(inps.inc != 0, ~np.isnan(inps.inc))
## 2. prepare output file
# metadata
atr = inps.atr.copy()
atr['FILE_TYPE'] = 'timeseries'
atr['UNIT'] = 'm'
# remove metadata related with double reference
# because absolute delay is calculated and saved
for key in ['REF_DATE', 'REF_X', 'REF_Y', 'REF_LAT', 'REF_LON']:
if key in atr.keys():
atr.pop(key)
# instantiate time-series
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
num_date = len(inps.grib_files)
date_list = [
str(re.findall('\d{8}', os.path.basename(i))[0])
for i in inps.grib_files
]
dates = np.array(date_list, dtype=np.string_)
ds_name_dict = {
"date": [dates.dtype, (num_date, ), dates],
"timeseries": [np.float32, (num_date, length, width), None],
}
writefile.layout_hdf5(inps.tropo_file, ds_name_dict, metadata=atr)
## 3. calculate phase delay
print(
'\n------------------------------------------------------------------------------'
)
print(
'calculating absolute delay for each date using PyAPS (Jolivet et al., 2011; 2014) ...'
)
print('number of grib files used: {}'.format(num_date))
prog_bar = ptime.progressBar(maxValue=num_date, print_msg=~inps.verbose)
for i in range(num_date):
grib_file = inps.grib_files[i]
# calc tropo delay
tropo_data = get_delay(grib_file,
tropo_model=inps.tropo_model,
delay_type=inps.delay_type,
dem=inps.dem,
inc=inps.inc,
lat=inps.lat,
lon=inps.lon,
mask=mask,
verbose=inps.verbose)
# write tropo delay to file
block = [i, i + 1, 0, length, 0, width]
writefile.write_hdf5_block(inps.tropo_file,
data=tropo_data,
datasetName='timeseries',
block=block,
print_msg=False)
prog_bar.update(i + 1, suffix=os.path.basename(grib_file))
prog_bar.close()
return inps.tropo_file
