def main(argv):
try:
File = argv[0]
atr = readfile.read_attribute(File)
except:
usage()
sys.exit(1)
try:
outFile = argv[1]
except:
outFile = 'incidenceAngle.h5'
# Calculate look angle
angle = ut.incidence_angle(atr, dimension=2)
# Geo coord
if 'Y_FIRST' in atr.keys():
print 'Input file is geocoded, only center incident angle is calculated: '
print angle
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
angle_mat = np.zeros((length, width), np.float32)
angle_mat[:] = angle
angle = angle_mat
print 'writing >>> ' + outFile
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 'degree'
try:
atr.pop('ref_date')
except:
pass
writefile.write(angle, atr, outFile)
return outFile
def main(argv):
try:
File = argv[0]
atr = readfile.read_attribute(File)
except:
usage()
sys.exit(1)
try:
outFile = argv[1]
except:
outFile = 'incidenceAngle.h5'
# Calculate look angle
angle = ut.incidence_angle(atr, dimension=2)
# Geo coord
if 'Y_FIRST' in atr.keys():
print 'Input file is geocoded, only center incident angle is calculated: '
print angle
return angle
# Radar coord
else:
print 'writing >>> ' + outFile
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 'degree'
writefile.write(angle, atr, outFile)
return outFile
def main(argv):
try:
File = argv[0]
atr = readfile.read_attribute(File)
except:
usage(); sys.exit(1)
try: outFile = argv[1]
except: outFile = 'incidence_angle.h5'
#print '\n*************** Generate Incidence Angle *****************'
##### Calculate look angle
angle = ut.incidence_angle(atr)
##### Output
print 'writing >>> '+outFile
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 'degree'
writefile.write(angle, atr, outFile)
def main(argv):
inps = cmdLineParse()
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
print '*******************************************************************************'
print 'Downloading weather model data ...'
## Get Grib Source
if inps.weather_model in ['ECMWF', 'ERA-Interim']:
inps.grib_source = 'ECMWF'
elif inps.weather_model == 'ERA':
inps.grib_source = 'ERA'
elif inps.weather_model == 'MERRA':
inps.grib_source = 'MERRA'
elif inps.weather_model == 'NARR':
inps.grib_source = 'NARR'
else:
raise Reception('Unrecognized weather model: ' + inps.weather_model)
print 'grib source: ' + inps.grib_source
## Get Acquisition time
inps.hour = closest_weather_product_time(atr['CENTER_LINE_UTC'],
inps.grib_source)
print 'Time of cloest vailable product: ' + inps.hour
if not os.path.isdir(inps.grib_source):
print 'making directory: ' + inps.grib_source
os.mkdir(inps.grib_source)
## Loop to download
inps.grib_file_list = []
h5timeseries = h5py.File(inps.timeseries_file, 'r')
dateList = sorted(h5timeseries['timeseries'].keys())
for d in dateList:
print[d]
if inps.grib_source == 'ECMWF':
grib_file = './ECMWF/ERA-Int_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'ERA':
grib_file = './ERA/ERA_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'MERRA':
grib_file = './MERRA/merra-' + d + '-' + inps.hour + '.hdf'
elif inps.grib_source == 'NARR':
grib_file = './NARR/narr-a_221_' + d + '_' + inps.hour + '00_000.grb'
inps.grib_file_list.append(grib_file)
if os.path.isfile(grib_file):
print grib_file + ' already exists.'
else:
if inps.grib_source == 'ECMWF':
pa.ECMWFdload([d], inps.hour, './' + inps.grib_source + '/')
elif inps.grib_source == 'ERA':
pa.ERAdload([d], inps.hour, './' + inps.grib_source + '/')
elif inps.grib_source == 'MERRA':
pa.MERRAdload([d], inps.hour, './' + inps.grib_source + '/')
elif inps.grib_source == 'NARR':
pa.NARRdload([d], inps.hour, './' + inps.grib_source + '/')
print '*******************************************************************************'
print 'Calcualting delay for each epoch.'
## Get Incidence angle: to map the zenith delay to the slant delay
if inps.incidence_angle:
if os.path.isfile(inps.incidence_angle):
inps.incidence_angle = readfile.read(inps.incidence_angle)[0]
else:
inps.incidence_angle = float(inps.incidence_angle)
print 'incidence angle: ' + str(inps.incidence_angle)
else:
print 'calculating incidence angle ...'
inps.incidence_angle = ut.incidence_angle(atr)
inps.incidence_angle = inps.incidence_angle * np.pi / 180.0
## Create delay hdf5 file
tropFile = inps.grib_source + '.h5'
print 'writing >>> ' + tropFile
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
## Create tropospheric corrected timeseries hdf5 file
if not inps.out_file:
ext = os.path.splitext(inps.timeseries_file)[1]
inps.out_file = os.path.splitext(
inps.timeseries_file)[0] + '_' + inps.grib_source + '.h5'
print 'writing >>> ' + inps.out_file
h5timeseries_tropCor = h5py.File(inps.out_file, 'w')
group_tropCor = h5timeseries_tropCor.create_group('timeseries')
## Calculate phase delay on reference date
if 'ref_date' in atr.keys():
ref_idx = dateList.index(atr['ref_date'])
else:
ref_idx = 0
print 'calculating phase delay on reference date: ' + dateList[ref_idx]
phs_ref = get_delay(inps.grib_file_list[ref_idx], atr, vars(inps))
## Loop to calculate phase delay on the other dates
for i in range(len(inps.grib_file_list)):
# Get phase delay
grib_file = inps.grib_file_list[i]
if not i == ref_idx:
print dateList[i]
phs = get_delay(grib_file, atr, vars(inps))
else:
phs = np.copy(phs_ref)
# Get relative phase delay in time
phs -= phs_ref
# Write dataset
print 'writing hdf5 file ...'
data = h5timeseries['timeseries'].get(dateList[i])[:]
dset = group_tropCor.create_dataset(dateList[i],
data=data + phs,
compression='gzip')
dset = group_trop.create_dataset(dateList[i],
data=phs,
compression='gzip')
## Write Attributes
for key, value in atr.iteritems():
group_tropCor.attrs[key] = value
group_trop.attrs[key] = value
h5timeseries.close()
h5timeseries_tropCor.close()
h5trop.close()
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
##### 1. Extract the common area of two input files
# Basic info
atr1 = readfile.read_attribute(inps.file[0])
atr2 = readfile.read_attribute(inps.file[1])
if any('X_FIRST' not in i for i in [atr1, atr2]):
sys.exit('ERROR: Not all input files are geocoded.')
k1 = atr1['FILE_TYPE']
print 'Input 1st file is ' + k1
# Common AOI in lalo
west, east, south, north = get_overlap_lalo(atr1, atr2)
lon_step = float(atr1['X_STEP'])
lat_step = float(atr1['Y_STEP'])
width = int(round((east - west) / lon_step))
length = int(round((south - north) / lat_step))
# Read data in common AOI: LOS displacement, heading angle, incident angle
u_los = np.zeros((2, width * length))
heading = []
incidence = []
for i in range(len(inps.file)):
fname = inps.file[i]
print '---------------------'
print 'reading ' + fname
atr = readfile.read_attribute(fname)
[x0, x1] = subset.coord_geo2radar([west, east], atr, 'lon')
[y0, y1] = subset.coord_geo2radar([north, south], atr, 'lat')
V = readfile.read(fname, (x0, y0, x1, y1))[0]
u_los[i, :] = V.flatten(0)
heading_angle = float(atr['HEADING'])
if heading_angle < 0.:
heading_angle += 360.
print 'heading angle: ' + str(heading_angle)
heading_angle *= np.pi / 180.
heading.append(heading_angle)
inc_angle = float(ut.incidence_angle(atr, dimension=0))
#print 'incidence angle: '+str(inc_angle)
inc_angle *= np.pi / 180.
incidence.append(inc_angle)
##### 2. Project displacement from LOS to Horizontal and Vertical components
# math for 3D: cos(theta)*Uz - cos(alpha)*sin(theta)*Ux + sin(alpha)*sin(theta)*Uy = Ulos
# math for 2D: cos(theta)*Uv - sin(alpha-az)*sin(theta)*Uh = Ulos #Uh_perp = 0.0
# This could be easily modified to support multiple view geometry (e.g. two adjcent tracks from asc & desc) to resolve 3D
# Design matrix
A = np.zeros((2, 2))
for i in range(len(inps.file)):
A[i, 0] = np.cos(incidence[i])
A[i, 1] = np.sin(incidence[i]) * np.sin(heading[i] - inps.azimuth)
A_inv = np.linalg.pinv(A)
u_vh = np.dot(A_inv, u_los)
u_v = np.reshape(u_vh[0, :], (length, width))
u_h = np.reshape(u_vh[1, :], (length, width))
##### 3. Output
# Attributes
atr = atr1.copy()
atr['WIDTH'] = str(width)
atr['FILE_LENGTH'] = str(length)
atr['X_FIRST'] = str(west)
atr['Y_FIRST'] = str(north)
atr['X_STEP'] = str(lon_step)
atr['Y_STEP'] = str(lat_step)
print '---------------------'
outname = inps.outfile[0]
print 'writing vertical component to file: ' + outname
writefile.write(u_v, atr, outname)
outname = inps.outfile[1]
print 'writing horizontal component to file: ' + outname
writefile.write(u_h, atr, outname)
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
if inps.timeseries_file:
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
if inps.dem_file:
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
# Convert DEM to ROIPAC format
if os.path.splitext(inps.dem_file)[1] in ['.h5']:
print 'convert DEM file to ROIPAC format'
dem, atr_dem = readfile.read(inps.dem_file)
if 'Y_FIRST' in atr_dem.keys():
atr_dem['FILE_TYPE'] = '.dem'
else:
atr_dem['FILE_TYPE'] = '.hgt'
outname = os.path.splitext(
inps.dem_file)[0] + '4pyaps' + atr_dem['FILE_TYPE']
inps.dem_file = writefile.write(dem, atr_dem, outname)
print '*******************************************************************************'
print 'Downloading weather model data ...'
## Get Grib Source
if inps.weather_model in ['ECMWF', 'ERA-Interim']:
inps.grib_source = 'ECMWF'
elif inps.weather_model == 'ERA':
inps.grib_source = 'ERA'
elif inps.weather_model == 'MERRA':
inps.grib_source = 'MERRA'
elif inps.weather_model == 'NARR':
inps.grib_source = 'NARR'
else:
raise Reception('Unrecognized weather model: ' + inps.weather_model)
print 'grib source: ' + inps.grib_source
## Grib data directory
if not inps.weather_dir:
if inps.timeseries_file:
inps.weather_dir = os.path.dirname(
os.path.abspath(inps.timeseries_file)) + '/../WEATHER'
elif inps.dem_file:
inps.weather_dir = os.path.dirname(os.path.abspath(
inps.dem_file)) + '/../WEATHER'
else:
inps.weather_dir = os.path.abspath(os.getcwd())
print 'Store weather data into directory: ' + inps.weather_dir
grib_dir = inps.weather_dir + '/' + inps.grib_source
if not os.path.isdir(grib_dir):
print 'making directory: ' + grib_dir
os.makedirs(grib_dir)
## Get Acquisition time
if not inps.hour:
inps.hour = closest_weather_product_time(atr['CENTER_LINE_UTC'],
inps.grib_source)
print 'Time of cloest available product: ' + inps.hour
## Get grib file list and date list
inps.grib_file_list = []
if not inps.date_list_file:
h5timeseries = h5py.File(inps.timeseries_file, 'r')
dateList = sorted(h5timeseries['timeseries'].keys())
h5timeseries.close()
print 'read date list info from: ' + inps.timeseries_file
else:
dateList = ptime.yyyymmdd(
np.loadtxt(inps.date_list_file, dtype=str, usecols=(0, )).tolist())
print 'read date list info from: ' + inps.date_list_file
for d in dateList:
if inps.grib_source == 'ECMWF':
grib_file = grib_dir + '/ERA-Int_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'ERA':
grib_file = grib_dir + '/ERA_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'MERRA':
grib_file = grib_dir + '/merra-' + d + '-' + inps.hour + '.hdf'
elif inps.grib_source == 'NARR':
grib_file = grib_dir + '/narr-a_221_' + d + '_' + inps.hour + '00_000.grb'
inps.grib_file_list.append(grib_file)
## Get date list to download
grib_file_existed = ut.get_file_list(inps.grib_file_list)
if grib_file_existed:
grib_filesize_mode = ut.mode(
[os.path.getsize(i) for i in grib_file_existed])
grib_file_corrupted = [
i for i in grib_file_existed
if os.path.getsize(i) != grib_filesize_mode
]
print 'number of grib files existed : %d' % len(grib_file_existed)
print 'file size mode: %d' % grib_filesize_mode
if grib_file_corrupted:
print '------------------------------------------------------------------------------'
print 'corrupted grib files detected! Delete them and re-download...'
print 'number of grib files corrupted : %d' % len(
grib_file_corrupted)
for i in grib_file_corrupted:
rmCmd = 'rm ' + i
print rmCmd
os.system(rmCmd)
grib_file_existed.remove(i)
print '------------------------------------------------------------------------------'
grib_file2download = sorted(
list(set(inps.grib_file_list) - set(grib_file_existed)))
date_list2download = [
str(re.findall('\d{8}', i)[0]) for i in grib_file2download
]
print 'number of grib files to download: %d' % len(date_list2download)
print '------------------------------------------------------------------------------\n'
## Download grib file using PyAPS
if inps.grib_source == 'ECMWF':
pa.ECMWFdload(date_list2download, inps.hour, grib_dir)
elif inps.grib_source == 'ERA':
pa.ERAdload(date_list2download, inps.hour, grib_dir)
elif inps.grib_source == 'MERRA':
pa.MERRAdload(date_list2download, inps.hour, grib_dir)
elif inps.grib_source == 'NARR':
pa.NARRdload(date_list2download, inps.hour, grib_dir)
if inps.download:
print 'Download completed, exit as planned.'
return
print '*******************************************************************************'
print 'Calcualting delay for each epoch.'
## Get Incidence angle: to map the zenith delay to the slant delay
if inps.incidence_angle:
if os.path.isfile(inps.incidence_angle):
inps.incidence_angle = readfile.read(inps.incidence_angle)[0]
else:
inps.incidence_angle = float(inps.incidence_angle)
print 'incidence angle: ' + str(inps.incidence_angle)
else:
print 'calculating incidence angle ...'
inps.incidence_angle = ut.incidence_angle(atr)
inps.incidence_angle = inps.incidence_angle * np.pi / 180.0
## Create delay hdf5 file
tropFile = inps.grib_source + '.h5'
print 'writing >>> ' + tropFile
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
## Create tropospheric corrected timeseries hdf5 file
if not inps.out_file:
ext = os.path.splitext(inps.timeseries_file)[1]
inps.out_file = os.path.splitext(
inps.timeseries_file)[0] + '_' + inps.grib_source + '.h5'
print 'writing >>> ' + inps.out_file
h5timeseries_tropCor = h5py.File(inps.out_file, 'w')
group_tropCor = h5timeseries_tropCor.create_group('timeseries')
## Calculate phase delay on reference date
if 'ref_date' in atr.keys():
ref_idx = dateList.index(atr['ref_date'])
else:
ref_idx = 0
print 'calculating phase delay on reference date: ' + dateList[ref_idx]
phs_ref = get_delay(inps.grib_file_list[ref_idx], atr, vars(inps))
## Loop to calculate phase delay on the other dates
h5timeseries = h5py.File(inps.timeseries_file, 'r')
for i in range(len(inps.grib_file_list)):
# Get phase delay
grib_file = inps.grib_file_list[i]
if not i == ref_idx:
print dateList[i]
phs = get_delay(grib_file, atr, vars(inps))
else:
phs = np.copy(phs_ref)
# Get relative phase delay in time
phs -= phs_ref
# Write dataset
print 'writing hdf5 file ...'
data = h5timeseries['timeseries'].get(dateList[i])[:]
dset = group_tropCor.create_dataset(dateList[i],
data=data - phs,
compression='gzip')
dset = group_trop.create_dataset(dateList[i],
data=phs,
compression='gzip')
## Write Attributes
for key, value in atr.iteritems():
group_tropCor.attrs[key] = value
group_trop.attrs[key] = value
h5timeseries.close()
h5timeseries_tropCor.close()
h5trop.close()
# Delete temporary DEM file in ROI_PAC format
if '4pyaps' in inps.dem_file:
rmCmd = 'rm ' + inps.dem_file + ' ' + inps.dem_file + '.rsc '
print rmCmd
os.system(rmCmd)
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
suffix = '_demErr'
if not inps.outfile:
inps.outfile = os.path.splitext(
inps.timeseries_file)[0] + suffix + os.path.splitext(
inps.timeseries_file)[1]
# 1. template_file
if inps.template_file:
print 'read option from template file: ' + inps.template_file
inps = read_template2inps(inps.template_file, inps)
# Read Time Series
print "loading time series: " + inps.timeseries_file
atr = readfile.read_attribute(inps.timeseries_file)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
h5 = h5py.File(inps.timeseries_file)
date_list = sorted(h5['timeseries'].keys())
date_num = len(date_list)
print 'number of acquisitions: ' + str(date_num)
# Exclude date info
#inps.ex_date = ['20070115','20100310']
if inps.ex_date:
inps = get_exclude_date(inps, date_list)
if inps.ex_date:
inps.ex_flag = np.array([i not in inps.ex_date for i in date_list])
timeseries = np.zeros((len(date_list), length * width), np.float32)
prog_bar = ptime.progress_bar(maxValue=date_num, prefix='loading: ')
for i in range(date_num):
date = date_list[i]
d = h5['timeseries'].get(date)[:]
timeseries[i][:] = d.flatten('F')
prog_bar.update(i + 1, suffix=date)
del d
h5.close()
prog_bar.close()
# Perpendicular Baseline
print 'read perpendicular baseline'
try:
inps.pbase = ut.perp_baseline_timeseries(atr, dimension=0)
if inps.pbase.shape[1] > 1:
print '\tconsider P_BASELINE variation in azimuth direction'
else:
pbase = inps.pbase
except:
print '\tCannot find P_BASELINE_TIMESERIES from timeseries file.'
print '\tTrying to calculate it from interferograms file'
if inps.ifgram_file:
inps.pbase = np.array(
ut.perp_baseline_ifgram2timeseries(
inps.ifgram_file)[0]).reshape(date_num, 1)
else:
message = 'No interferogram file input!\n'+\
'Can not correct for DEM residula without perpendicular base info!'
raise Exception(message)
# Temporal Baseline
print 'read temporal baseline'
inps.tbase = np.array(ptime.date_list2tbase(date_list)[0]).reshape(
date_num, 1)
# Incidence angle (look angle in the paper)
if inps.incidence_angle:
if os.path.isfile(inps.incidence_angle):
print 'reading incidence angle from file: ' + inps.incidence_angle
inps.incidence_angle = readfile.read(inps.incidence_angle)[0]
else:
try:
inps.incidence_angle = np.array(float(inps.incidence_angle))
print 'use input incidence angle : ' + str(
inps.incidence_angle)
except:
raise ValueError('Can not read input incidence angle: ' +
str(inps.incidence_angle))
else:
print 'calculate incidence angle using attributes of time series file'
if inps.pbase.shape[1] > 1:
inps.incidence_angle = ut.incidence_angle(atr, dimension=2)
else:
inps.incidence_angle = ut.incidence_angle(atr, dimension=1)
inps.incidence_angle *= np.pi / 180.0
# Range distance
if inps.range_dis:
if os.path.isfile(inps.range_dis):
print 'reading range distance from file: ' + inps.range_dis
inps.range_dis = readfile.read(inps.range_dis)[0]
else:
try:
inps.range_dis = np.array(float(inps.range_dis))
print 'use input range distance : ' + str(inps.range_dis)
except:
raise ValueError('Can not read input incidence angle: ' +
str(inps.range_dis))
else:
print 'calculate range distance using attributes from time series file'
if inps.pbase.shape[1] > 1:
inps.range_dis = ut.range_distance(atr, dimension=2)
else:
inps.range_dis = ut.range_distance(atr, dimension=1)
# Design matrix - temporal deformation model using tbase
print '-------------------------------------------------'
if inps.phase_velocity:
print 'using phase velocity history'
A1 = np.ones((date_num - 1, 1))
A2 = (inps.tbase[1:date_num] + inps.tbase[0:date_num - 1]) / 2.0
A3 = (inps.tbase[1:date_num]**3 - inps.tbase[0:date_num - 1]**
3) / np.diff(inps.tbase, axis=0) / 6.0
#A3 = (inps.tbase[1:date_num]**2 + inps.tbase[1:date_num]*inps.tbase[0:date_num-1] +\
# inps.tbase[0:date_num-1]**2) / 6.0
else:
print 'using phase history'
A1 = np.hstack((np.ones((date_num, 1)), inps.tbase))
A2 = inps.tbase**2 / 2.0
A3 = inps.tbase**3 / 6.0
# Polynomial order of model
print "temporal deformation model's polynomial order = " + str(
inps.poly_order)
if inps.poly_order == 1: A_def = A1
elif inps.poly_order == 2: A_def = np.hstack((A1, A2))
elif inps.poly_order == 3: A_def = np.hstack((A1, A2, A3))
# step function
if inps.step_date:
print "temporal deformation model's step function step at " + inps.step_date
step_yy = ptime.yyyymmdd2years(inps.step_date)
yy_list = ptime.yyyymmdd2years(date_list)
flag_array = np.array(yy_list) >= step_yy
A_step = np.zeros((date_num, 1))
A_step[flag_array] = 1.0
A_def = np.hstack((A_def, A_step))
# Heresh's original code for phase history approach
#A_def = np.hstack((A2,A1,np.ones((date_num,1))))
print '-------------------------------------------------'
##---------------------------------------- Loop for L2-norm inversion -----------------------------------##
delta_z_mat = np.zeros([length, width], dtype=np.float32)
resid_n = np.zeros([A_def.shape[0], length * width], dtype=np.float32)
constC = np.zeros([length, width], dtype=np.float32)
#delta_a_mat = np.zeros([length, width])
if inps.incidence_angle.ndim == 2 and inps.range_dis.ndim == 2:
print 'inversing using L2-norm minimization (unweighted least squares)'\
' pixel by pixel: %d loops in total' % (length*width)
prog_bar = ptime.progress_bar(maxValue=length * width,
prefix='calculating: ')
for i in range(length * width):
row = i % length
col = i / length
range_dis = inps.range_dis[row, col]
inc_angle = inps.incidence_angle[row, col]
# Consider P_BASELINE variation within one interferogram
if inps.pbase.shape[1] > 1:
pbase = inps.pbase[:, row].reshape(date_num, 1)
# Design matrix - DEM error using pbase, range distance and incidence angle
A_delta_z = pbase / (range_dis * np.sin(inc_angle))
if inps.phase_velocity:
pbase_v = np.diff(pbase, axis=0) / np.diff(inps.tbase, axis=0)
A_delta_z_v = pbase_v / (range_dis * np.sin(inc_angle))
A = np.hstack((A_delta_z_v, A_def))
else:
A = np.hstack((A_delta_z, A_def))
# L-2 norm inversion
if inps.ex_date:
A_inv = np.linalg.pinv(A[inps.ex_flag, :])
else:
A_inv = np.linalg.pinv(A)
# Get unknown parameters X = [delta_z, vel, acc, delta_acc, ...]
ts_dis = timeseries[:, i]
if inps.phase_velocity:
ts_dis = np.diff(ts_dis, axis=0) / np.diff(inps.tbase, axis=0)
if inps.ex_date:
X = np.dot(A_inv, ts_dis[inps.ex_flag])
else:
X = np.dot(A_inv, ts_dis)
# Residual vector n
resid_n[:, i] = ts_dis - np.dot(A, X)
# Update DEM error / timeseries matrix
delta_z = X[0]
delta_z_mat[row, col] = delta_z
if inps.update_timeseries:
timeseries[:, i] -= np.dot(A_delta_z, delta_z).flatten()
prog_bar.update(i + 1, every=length * width / 100)
prog_bar.close()
elif inps.incidence_angle.ndim == 1 and inps.range_dis.ndim == 1:
print 'inversing using L2-norm minimization (unweighted least squares)'\
' column by column: %d loops in total' % (width)
prog_bar = ptime.progress_bar(maxValue=width, prefix='calculating: ')
for i in range(width):
range_dis = inps.range_dis[i]
inc_angle = inps.incidence_angle[i]
# Design matrix - DEM error using pbase, range distance and incidence angle
A_delta_z = pbase / (range_dis * np.sin(inc_angle))
if inps.phase_velocity:
pbase_v = np.diff(pbase, axis=0) / np.diff(inps.tbase, axis=0)
A_delta_z_v = pbase_v / (range_dis * np.sin(inc_angle))
A = np.hstack((A_delta_z_v, A_def))
else:
A = np.hstack((A_delta_z, A_def))
# L-2 norm inversion
if inps.ex_date:
A_inv = np.linalg.pinv(A[inps.ex_flag, :])
else:
A_inv = np.linalg.pinv(A)
# Get unknown parameters X = [delta_z, vel, acc, delta_acc, ...]
ts_dis = timeseries[:, i * length:(i + 1) * length]
if inps.phase_velocity:
ts_dis = np.diff(ts_dis, axis=0) / np.diff(inps.tbase, axis=0)
if inps.ex_date:
X = np.dot(A_inv, ts_dis[inps.ex_flag, :])
else:
X = np.dot(A_inv, ts_dis)
# Residual vector n
resid_n[:, i * length:(i + 1) * length] = ts_dis - np.dot(A, X)
constC[:, i] = X[1].reshape((1, length))
# Update DEM error / timeseries matrix
delta_z = X[0].reshape((1, length))
delta_z_mat[:, i] = delta_z
if inps.update_timeseries:
timeseries[:, i * length:(i + 1) * length] -= np.dot(
A_delta_z, delta_z)
prog_bar.update(i + 1, every=width / 100)
prog_bar.close()
elif inps.incidence_angle.ndim == 0 and inps.range_dis.ndim == 0:
print 'inversing using L2-norm minimization (unweighted least squares) for the whole area'
# Design matrix - DEM error using pbase, range distance and incidence angle
A_delta_z = pbase / (inps.range_dis * np.sin(inps.incidence_angle))
if inps.phase_velocity:
pbase_v = np.diff(pbase, axis=0) / np.diff(inps.tbase, axis=0)
A_delta_z_v = pbase_v / (inps.range_dis *
np.sin(inps.incidence_angle))
A = np.hstack((A_delta_z_v, A_def))
else:
A = np.hstack((A_delta_z, A_def))
# L-2 norm inversion
if inps.ex_date:
A_inv = np.linalg.pinv(A[inps.ex_flag, :])
else:
A_inv = np.linalg.pinv(A)
# Get unknown parameters X = [delta_z, vel, acc, delta_acc, ...]
if inps.phase_velocity:
timeseries = np.diff(timeseries, axis=0) / np.diff(inps.tbase,
axis=0)
if inps.ex_date:
X = np.dot(A_inv, timeseries[inps.ex_flag, :])
else:
X = np.dot(A_inv, timeseries)
# Residual vector n
resid_n = ts_dis - np.dot(A, X)
# Update DEM error / timeseries matrix
delta_z_mat = X[0].reshape((1, length * width))
if inps.update_timeseries:
timeseries -= np.dot(A_delta_z, delta_z_mat)
delta_z_mat = np.reshape(delta_z_mat, [length, width], order='F')
else:
print 'ERROR: Script only support same dimension for both incidence angle and range distance matrix.'
print 'dimension of incidence angle: ' + str(inps.incidence_angle.ndim)
print 'dimension of range distance: ' + str(inps.range_dis.ndim)
sys.exit(1)
##------------------------------------------------ Output --------------------------------------------##
# DEM error file
if 'Y_FIRST' in atr.keys():
dem_error_file = 'demGeo_error.h5'
else:
dem_error_file = 'demRadar_error.h5'
#if inps.phase_velocity: suffix = '_pha_poly'+str(inps.poly_order)
#else: suffix = '_vel_poly'+str(inps.poly_order)
#dem_error_file = os.path.splitext(dem_error_file)[0]+suffix+os.path.splitext(dem_error_file)[1]
print 'writing >>> ' + dem_error_file
atr_dem_error = atr.copy()
atr_dem_error['FILE_TYPE'] = 'dem'
atr_dem_error['UNIT'] = 'm'
writefile.write(delta_z_mat, atr_dem_error, dem_error_file)
## Phase Constant C = resid_n[0,:]
#atrC = atr.copy()
#atrC['FILE_TYPE'] = 'mask'
#atrC['UNIT'] = 'm'
#writefile.write(constC, atrC, 'constD.h5')
## Corrected DEM file
#if inps.dem_file:
# inps.dem_outfile = os.path.splitext(inps.dem_file)[0]+suffix+os.path.splitext(inps.dem_file)[1]
# print '--------------------------------------'
# print 'writing >>> '+inps.dem_outfile
# dem, atr_dem = readfile.read(inps.dem_file)
# writefile.write(dem+delta_z_mat, atr_dem, inps.dem_outfile)
#outfile = 'delta_acc.h5'
#print 'writing >>> '+outfile
#atr_dem_error = atr.copy()
#atr_dem_error['FILE_TYPE'] = 'velocity'
#atr_dem_error['UNIT'] = 'm/s'
#writefile.write(delta_a_mat, atr_dem_error, outfile)
#print '**************************************'
# Corrected Time Series
if inps.update_timeseries:
print 'writing >>> ' + inps.outfile
print 'number of dates: ' + str(len(date_list))
h5out = h5py.File(inps.outfile, 'w')
group = h5out.create_group('timeseries')
prog_bar = ptime.progress_bar(maxValue=date_num, prefix='writing: ')
for i in range(date_num):
date = date_list[i]
d = np.reshape(timeseries[i][:], [length, width], order='F')
dset = group.create_dataset(date, data=d, compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
for key, value in atr.iteritems():
group.attrs[key] = value
h5out.close()
outFile = os.path.splitext(inps.outfile)[0] + 'InvResid.h5'
print 'writing >>> ' + outFile
print 'number of dates: ' + str(A_def.shape[0])
h5out = h5py.File(outFile, 'w')
group = h5out.create_group('timeseries')
prog_bar = ptime.progress_bar(maxValue=A_def.shape[0], prefix='writing: ')
for i in range(A_def.shape[0]):
date = date_list[i]
d = np.reshape(resid_n[i][:], [length, width], order='F')
dset = group.create_dataset(date, data=d, compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
# Attribute
for key, value in atr.iteritems():
group.attrs[key] = value
if A_def.shape[0] == date_num:
group.attrs['UNIT'] = 'm'
else:
group.attrs['UNIT'] = 'm/yr'
h5out.close()
return
def main(argv):
inps = cmdLineParse()
##### Check default input arguments
# default output filename
if not inps.outfile:
inps.outfile = os.path.splitext(
inps.timeseries_file)[0] + '_tropHgt.h5'
# Basic info
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
pix_num = length * width
# default DEM file
if not inps.dem_file:
if 'X_FIRST' in atr.keys():
inps.dem_file = ['demGeo_tight.h5', 'demGeo.h5']
else:
inps.dem_file = ['demRadar.h5']
try:
inps.dem_file = ut.get_file_list(inps.dem_file)[0]
except:
inps.dem_file = None
sys.exit('ERROR: No DEM file found!')
# default Mask file
if not inps.mask_file:
if 'X_FIRST' in atr.keys():
inps.mask_file = 'geo_maskTempCoh.h5'
else:
inps.mask_file = 'maskTempCoh.h5'
if not os.path.isfile(inps.mask_file):
inps.mask_file = None
sys.exit('ERROR: No mask file found!')
##### Read Mask
print 'reading mask from file: ' + inps.mask_file
mask = readfile.read(inps.mask_file, epoch='mask')[0].flatten(1)
ndx = mask != 0
msk_num = np.sum(ndx)
print 'total pixel number: %d' % pix_num
print 'estimating using pixel number: %d' % msk_num
##### Read DEM
print 'read DEM from file: ' + inps.dem_file
dem = readfile.read(inps.dem_file, epoch='height')[0]
ref_y = int(atr['ref_y'])
ref_x = int(atr['ref_x'])
dem -= dem[ref_y, ref_x]
print 'considering the incidence angle of each pixel ...'
inc_angle = ut.incidence_angle(atr, dimension=2)
dem *= 1.0 / np.cos(inc_angle * np.pi / 180.0)
##### Design matrix for elevation v.s. phase
dem = dem.flatten(1)
if inps.poly_order == 1:
A = np.vstack((dem[ndx], np.ones(msk_num))).T
B = np.vstack((dem, np.ones(pix_num))).T
elif inps.poly_order == 2:
A = np.vstack((dem[ndx]**2, dem[ndx], np.ones(msk_num))).T
B = np.vstack((dem**2, dem, np.ones(pix_num))).T
elif inps.poly_order == 3:
A = np.vstack((dem[ndx]**3, dem[ndx]**2, dem[ndx], np.ones(msk_num))).T
B = np.vstack((dem**3, dem**2, dem, np.ones(pix_num))).T
print 'polynomial order: %d' % inps.poly_order
A_inv = np.linalg.pinv(A)
##### Calculate correlation coefficient
print 'Estimating the tropospheric effect between the differences of the subsequent epochs and DEM'
h5 = h5py.File(inps.timeseries_file)
date_list = sorted(h5[k].keys())
date_num = len(date_list)
print 'number of acquisitions: ' + str(date_num)
try:
ref_date = atr['ref_date']
except:
ref_date = date_list[0]
print '----------------------------------------------------------'
print 'correlation of DEM with each time-series epoch:'
corr_array = np.zeros(date_num)
par_dict = {}
for i in range(date_num):
date = date_list[i]
if date == ref_date:
cc = 0.0
par = np.zeros(inps.poly_order + 1)
else:
data = h5[k].get(date)[:].flatten(1)
C = np.zeros((2, msk_num))
C[0, :] = dem[ndx]
C[1, :] = data[ndx]
cc = np.corrcoef(C)[0, 1]
corr_array[i] = cc
if inps.threshold and np.abs(cc) < inps.threshold:
par = np.zeros(inps.poly_order + 1)
else:
par = np.dot(A_inv, data[ndx])
print '%s: %.2f' % (date, cc)
par_dict[date] = par
average_phase_height_corr = np.nansum(np.abs(corr_array)) / (date_num - 1)
print '----------------------------------------------------------'
print 'Average Correlation of DEM with time-series epochs: %.2f' % average_phase_height_corr
# Correlation of DEM with Difference of subsequent epochs (Not used for now)
corr_diff_dict = {}
par_diff_dict = {}
for i in range(date_num - 1):
date1 = date_list[i]
date2 = date_list[i + 1]
date12 = date1 + '-' + date2
data1 = h5[k].get(date1)[:].flatten(1)
data2 = h5[k].get(date2)[:].flatten(1)
data_diff = data2 - data1
C_diff = np.zeros((2, msk_num))
C_diff[0, :] = dem[ndx]
C_diff[1, :] = data_diff[ndx]
cc_diff = np.corrcoef(C_diff)[0, 1]
corr_diff_dict[date12] = cc_diff
par = np.dot(A_inv, data_diff[ndx])
par_diff_dict[date12] = par
##### Correct and write time-series file
print '----------------------------------------------------------'
print 'removing the stratified tropospheric delay from each epoch'
print 'writing >>> ' + inps.outfile
h5out = h5py.File(inps.outfile, 'w')
group = h5out.create_group(k)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
data = h5[k].get(date)[:]
if date != ref_date:
par = par_dict[date]
trop_delay = np.reshape(np.dot(B, par), [width, length]).T
trop_delay -= trop_delay[ref_y, ref_x]
data -= trop_delay
dset = group.create_dataset(date, data=data, compression='gzip')
prog_bar.update(i + 1, suffix=date)
for key, value in atr.iteritems():
group.attrs[key] = value
prog_bar.close()
h5out.close()
h5.close()
print 'Done.'
return inps.outfile
def main(argv):
inps = cmdLineParse()
if inps.timeseries_file:
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
if inps.dem_file:
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
# Convert DEM to ROIPAC format
if os.path.splitext(inps.dem_file)[1] in ['.h5']:
print 'convert DEM file to ROIPAC format'
dem, atr_dem = readfile.read(inps.dem_file)
if 'Y_FIRST' in atr_dem.keys():
atr_dem['FILE_TYPE'] = '.dem'
else:
atr_dem['FILE_TYPE'] = '.hgt'
outname = os.path.splitext(inps.dem_file)[0]+'4pyaps'+atr_dem['FILE_TYPE']
inps.dem_file = writefile.write(dem, atr_dem, outname)
print '*******************************************************************************'
print 'Downloading weather model data ...'
## Get Grib Source
if inps.weather_model in ['ECMWF','ERA-Interim']: inps.grib_source = 'ECMWF'
elif inps.weather_model == 'ERA' : inps.grib_source = 'ERA'
elif inps.weather_model == 'MERRA': inps.grib_source = 'MERRA'
elif inps.weather_model == 'NARR' : inps.grib_source = 'NARR'
else: raise Reception('Unrecognized weather model: '+inps.weather_model)
print 'grib source: '+inps.grib_source
# Get weather directory
if not inps.weather_dir:
if inps.timeseries_file:
inps.weather_dir = os.path.dirname(os.path.abspath(inps.timeseries_file))+'/../WEATHER'
elif inps.dem_file:
inps.weather_dir = os.path.dirname(os.path.abspath(inps.dem_file))+'/../WEATHER'
else:
inps.weather_dir = os.path.abspath(os.getcwd())
print 'Store weather data into directory: '+inps.weather_dir
# Get date list to download
if not inps.date_list_file:
h5timeseries = h5py.File(inps.timeseries_file, 'r')
dateList = sorted(h5timeseries['timeseries'].keys())
h5timeseries.close()
print 'read date list info from: '+inps.timeseries_file
else:
dateList = ptime.yyyymmdd(np.loadtxt(inps.date_list_file, dtype=str, usecols=(0,)).tolist())
print 'read date list info from: '+inps.date_list_file
# Get Acquisition time - hour
if not inps.hour:
inps.hour = closest_weather_product_time(atr['CENTER_LINE_UTC'], inps.grib_source)
print 'Time of cloest available product: '+inps.hour
## Download data using PyAPS
inps.grib_file_list = dload_grib(dateList, inps.hour, inps.weather_model, inps.weather_dir)
if inps.download:
print 'Download completed, exit as planned.'
return
print '*******************************************************************************'
print 'Calcualting delay for each epoch.'
## Get Incidence angle: to map the zenith delay to the slant delay
if inps.incidence_angle:
if os.path.isfile(inps.incidence_angle):
inps.incidence_angle = readfile.read(inps.incidence_angle)[0]
else:
inps.incidence_angle = float(inps.incidence_angle)
print 'incidence angle: '+str(inps.incidence_angle)
else:
print 'calculating incidence angle ...'
inps.incidence_angle = ut.incidence_angle(atr)
inps.incidence_angle = inps.incidence_angle*np.pi/180.0
## Create delay hdf5 file
tropFile = inps.grib_source+'.h5'
print 'writing >>> '+tropFile
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
## Create tropospheric corrected timeseries hdf5 file
if not inps.out_file:
ext = os.path.splitext(inps.timeseries_file)[1]
inps.out_file = os.path.splitext(inps.timeseries_file)[0]+'_'+inps.grib_source+'.h5'
print 'writing >>> '+inps.out_file
h5timeseries_tropCor = h5py.File(inps.out_file, 'w')
group_tropCor = h5timeseries_tropCor.create_group('timeseries')
## Calculate phase delay on reference date
try: ref_date = atr['ref_date']
except: ref_date = dateList[0]
print 'calculating phase delay on reference date: '+ref_date
ref_date_grib_file = None
for fname in inps.grib_file_list:
if ref_date in fname:
ref_date_grib_file = fname
phs_ref = get_delay(ref_date_grib_file, atr, vars(inps))
## Loop to calculate phase delay on the other dates
h5timeseries = h5py.File(inps.timeseries_file, 'r')
for i in range(len(inps.grib_file_list)):
grib_file = inps.grib_file_list[i]
date = re.findall('\d{8}', grib_file)[0]
# Get phase delay
if date != ref_date:
print 'calculate phase delay on %s from file %s' % (date, os.path.basename(grib_file))
phs = get_delay(grib_file, atr, vars(inps))
else:
phs = np.copy(phs_ref)
# Get relative phase delay in time
phs -= phs_ref
# Write dataset
print 'writing to HDF5 files ...'
data = h5timeseries['timeseries'].get(date)[:]
dset = group_tropCor.create_dataset(date, data=data-phs, compression='gzip')
dset = group_trop.create_dataset(date, data=phs, compression='gzip')
## Write Attributes
for key,value in atr.iteritems():
group_tropCor.attrs[key] = value
group_trop.attrs[key] = value
h5timeseries.close()
h5timeseries_tropCor.close()
h5trop.close()
# Delete temporary DEM file in ROI_PAC format
if '4pyaps' in inps.dem_file:
rmCmd = 'rm '+inps.dem_file+' '+inps.dem_file+'.rsc '
print rmCmd
os.system(rmCmd)
print 'Done.'
return