def ref_date_file(inFile, ref_date, outFile=None):
'''Change input file reference date to a different one.'''
if not outFile:
outFile = os.path.splitext(inFile)[0]+'_refDate.h5'
# Input file type
atr = readfile.read_attribute(inFile)
k = atr['FILE_TYPE']
if not k in ['timeseries']:
print('Input file is '+k+', only timeseries is supported.')
return None
# Input reference date
h5 = h5py.File(inFile, 'r')
date_list = sorted(h5[k].keys())
h5.close()
date_num = len(date_list)
try: ref_date_orig = atr['ref_date']
except: ref_date_orig = date_list[0]
ref_date = ptime.yyyymmdd(ref_date)
print('input reference date: '+ref_date)
if not ref_date in date_list:
print('Input reference date was not found!\nAll dates available: '+str(date_list))
return None
if ref_date == ref_date_orig:
print('Same reference date chosen as existing reference date.')
print('Copy %s to %s' % (inFile, outFile))
shutil.copy2(inFile, outFile)
return outFile
# Referencing in time
h5 = h5py.File(inFile, 'r')
ref_data = h5[k].get(ref_date)[:]
print('writing >>> '+outFile)
h5out = h5py.File(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)[:]
dset = group.create_dataset(date, data=data-ref_data, compression='gzip')
prog_bar.update(i+1, suffix=date)
prog_bar.close()
h5.close()
## Update attributes
atr = ref_date_attribute(atr, ref_date, date_list)
for key,value in atr.items():
group.attrs[key] = value
h5out.close()
return outFile
def main(argv):
try:
timeseries_file = argv[0]
except:
usage() ; sys.exit(1)
# Basic info
atr = readfile.read_attribute(timeseries_file)
k = atr['FILE_TYPE']
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
##### Read time-series
print("loading time series: " + timeseries_file)
h5 = h5py.File(timeseries_file)
date_list = sorted(h5[k].keys())
date_num = len(date_list)
pixel_num = length*width
tbase = np.array(ptime.date_list2tbase(date_list)[0], np.float32)
prog_bar = ptime.progress_bar(maxValue=date_num)
timeseries = np.zeros((date_num, pixel_num),np.float32)
for i in range(date_num):
date = date_list[i]
d = h5[k].get(date)[:]
timeseries[i,:] = d.flatten(0)
prog_bar.update(i+1, suffix=date)
prog_bar.close()
del d
h5.close()
##### Calculate 1st and 2nd temporal derivatives
print("calculating temporal 1st derivative ... ")
timeseries_1st = np.zeros((date_num-1,pixel_num),np.float32)
for i in range(date_num-1):
timeseries_1st[i][:] = timeseries[i+1][:] - timeseries[i][:]
print("calculating temporal 2nd derivative")
timeseries_2nd = np.zeros((date_num-2,pixel_num),np.float32)
for i in range(date_num-2):
timeseries_2nd[i][:] = timeseries_1st[i+1][:] - timeseries_1st[i][:]
##### Write 1st and 2nd temporal derivatives
outfile1 = os.path.splitext(timeseries_file)[0]+'_1stDerivative.h5'
print('writing >>> '+outfile1)
h5out = h5py.File(outfile1, 'w')
group = h5out.create_group(k)
prog_bar = ptime.progress_bar(maxValue=date_num-1)
for i in range(date_num-1):
date = date_list[i+1]
dset = group.create_dataset(date, data=np.reshape(timeseries_1st[i][:],[length,width]), compression='gzip')
prog_bar.update(i+1, suffix=date)
for key,value in atr.items():
group.attrs[key] = value
prog_bar.close()
h5out.close()
outfile2 = os.path.splitext(timeseries_file)[0]+'_2ndDerivative.h5'
print('writing >>> '+outfile2)
h5out = h5py.File(outfile2, 'w')
group = h5out.create_group(k)
prog_bar = ptime.progress_bar(maxValue=date_num-2)
for i in range(date_num-2):
date = date_list[i+2]
dset = group.create_dataset(date, data=np.reshape(timeseries_2nd[i][:],[length,width]), compression='gzip')
prog_bar.update(i+1, suffix=date)
for key,value in atr.items():
group.attrs[key] = value
prog_bar.close()
h5out.close()
print('Done.')
return outfile1, outfile2
def modify_file_date12_list(File,
date12_to_rmv,
mark_attribute=False,
outFile=None):
'''Update multiple group hdf5 file using date12 to remove
Inputs:
File - multi_group HDF5 file, i.e. unwrapIfgram.h5, coherence.h5
date12_to_rmv - list of string indicating interferograms in YYMMDD-YYMMDD format
mark_attribute- bool, if True, change 'drop_ifgram' attribute only; otherwise, write
resutl to a new file
outFile - string, output file name
Output:
outFile - string, output file name, if mark_attribute=True, outFile = File
'''
k = readfile.read_attribute(File)['FILE_TYPE']
print(
'----------------------------------------------------------------------------'
)
print('file: ' + File)
if mark_attribute:
print(
"set drop_ifgram to 'yes' for all interferograms to remove, and 'no' for all the others."
)
h5 = h5py.File(File, 'r+')
ifgram_list = sorted(h5[k].keys())
for ifgram in ifgram_list:
if h5[k][ifgram].attrs['DATE12'] in date12_to_rmv:
h5[k][ifgram].attrs['drop_ifgram'] = 'yes'
else:
h5[k][ifgram].attrs['drop_ifgram'] = 'no'
h5.close()
outFile = File
else:
date12_orig = pnet.get_date12_list(File)
date12_to_write = sorted(list(set(date12_orig) - set(date12_to_rmv)))
print('number of interferograms in file : ' +
str(len(date12_orig)))
print('number of interferograms to keep/write: ' +
str(len(date12_to_write)))
print('list of interferograms to keep/write: ')
print(date12_to_write)
date12Num = len(date12_to_write)
if not outFile:
outFile = 'Modified_' + os.path.basename(File)
print('writing >>> ' + outFile)
h5out = h5py.File(outFile, 'w')
gg = h5out.create_group(k)
h5 = h5py.File(File, 'r')
igramList = sorted(h5[k].keys())
date12_list = ptime.list_ifgram2date12(igramList)
prog_bar = ptime.progress_bar(maxValue=date12Num, prefix='writing: ')
for i in range(date12Num):
date12 = date12_to_write[i]
idx = date12_orig.index(date12)
igram = igramList[idx]
data = h5[k][igram].get(igram)[:]
group = gg.create_group(igram)
dset = group.create_dataset(igram, data=data, compression='gzip')
for key, value in h5[k][igram].attrs.items():
group.attrs[key] = value
group.attrs['drop_ifgram'] = 'no'
prog_bar.update(i + 1, suffix=date12_list[i])
prog_bar.close()
h5.close()
h5out.close()
print('finished writing >>> ' + outFile)
return outFile
def subset_file(File, subset_dict_input, outFile=None):
'''Subset file with
Inputs:
File : str, path/name of file
outFile : str, path/name of output file
subset_dict : dict, subsut parameter, including the following items:
subset_x : list of 2 int, subset in x direction, default=None
subset_y : list of 2 int, subset in y direction, default=None
subset_lat : list of 2 float, subset in lat direction, default=None
subset_lon : list of 2 float, subset in lon direction, default=None
fill_value : float, optional. filled value for area outside of data coverage. default=None
None/not-existed to subset within data coverage only.
tight : bool, tight subset or not, for lookup table file, i.e. geomap*.trans
Outputs:
outFile : str, path/name of output file;
outFile = 'subset_'+File, if File is in current directory;
outFile = File, if File is not in the current directory.
'''
# Input File Info
try:
atr_dict = readfile.read_attribute(File)
except:
return None
width = int(atr_dict['WIDTH'])
length = int(atr_dict['FILE_LENGTH'])
k = atr_dict['FILE_TYPE']
print('subset ' + k + ' file: ' + File + ' ...')
subset_dict = subset_dict_input.copy()
# Read Subset Inputs into 4-tuple box in pixel and geo coord
pix_box, geo_box = subset_input_dict2box(subset_dict, atr_dict)
# if fill_value exists and not None, subset data and fill assigned value for area out of its coverage.
# otherwise, re-check subset to make sure it's within data coverage and initialize the matrix with np.nan
outfill = False
if 'fill_value' in list(subset_dict.keys()) and subset_dict['fill_value']:
outfill = True
else:
outfill = False
if not outfill:
pix_box = check_box_within_data_coverage(pix_box, atr_dict)
subset_dict['fill_value'] = np.nan
geo_box = box_pixel2geo(pix_box, atr_dict)
data_box = (0, 0, width, length)
print('data range in y/x: ' + str(data_box))
print('subset range in y/x: ' + str(pix_box))
print('data range in lat/lon: ' + str(box_pixel2geo(data_box, atr_dict)))
print('subset range in lat/lon: ' + str(geo_box))
if pix_box == data_box:
print('Subset range == data coverage, no need to subset. Skip.')
return File
# Calculate Subset/Overlap Index
pix_box4data, pix_box4subset = get_box_overlap_index(data_box, pix_box)
########################### Data Read and Write ######################
# Output File Name
if not outFile:
if os.getcwd() == os.path.dirname(os.path.abspath(File)):
if 'tight' in list(subset_dict.keys()) and subset_dict['tight']:
outFile = os.path.splitext(
File)[0] + '_tight' + os.path.splitext(File)[1]
else:
outFile = 'subset_' + os.path.basename(File)
else:
outFile = os.path.basename(File)
print('writing >>> ' + outFile)
##### Multiple Dataset File
if k in ['timeseries', 'interferograms', 'wrapped', 'coherence']:
##### Open Input File
h5file = h5py.File(File, 'r')
epochList = sorted(h5file[k].keys())
epochNum = len(epochList)
if k in multi_dataset_hdf5_file:
print('number of acquisitions: ' + str(epochNum))
else:
print('number of interferograms: ' + str(epochNum))
##### Open Output File
h5out = h5py.File(outFile)
group = h5out.create_group(k)
prog_bar = ptime.progress_bar(maxValue=epochNum)
## Loop
if k == 'timeseries':
for i in range(epochNum):
epoch = epochList[i]
dset = h5file[k].get(epoch)
data_overlap = dset[pix_box4data[1]:pix_box4data[3],
pix_box4data[0]:pix_box4data[2]]
data = np.ones(
(pix_box[3] - pix_box[1],
pix_box[2] - pix_box[0])) * subset_dict['fill_value']
data[pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = data_overlap
dset = group.create_dataset(epoch, data=data, compression='gzip')
prog_bar.update(i + 1, suffix=epoch)
atr_dict = subset_attribute(atr_dict, pix_box)
for key, value in atr_dict.items():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
date12_list = ptime.list_ifgram2date12(epochList)
for i in range(epochNum):
epoch = epochList[i]
dset = h5file[k][epoch].get(epoch)
atr_dict = h5file[k][epoch].attrs
data_overlap = dset[pix_box4data[1]:pix_box4data[3],
pix_box4data[0]:pix_box4data[2]]
data = np.ones(
(pix_box[3] - pix_box[1],
pix_box[2] - pix_box[0])) * subset_dict['fill_value']
data[pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = data_overlap
atr_dict = subset_attribute(atr_dict, pix_box, print_msg=False)
gg = group.create_group(epoch)
dset = gg.create_dataset(epoch, data=data, compression='gzip')
for key, value in atr_dict.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
##### Single Dataset File
elif k in ['.jpeg', '.jpg', '.png', '.ras', '.bmp']:
data, atr_dict = readfile.read(File, pix_box)
atr_dict = subset_attribute(atr_dict, pix_box)
writefile.write(data, atr_dict, outFile)
elif k == '.trans':
rg_overlap, az_overlap, atr_dict = readfile.read(File, pix_box4data)
rg = np.ones((pix_box[3] - pix_box[1],
pix_box[2] - pix_box[0])) * subset_dict['fill_value']
rg[pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = rg_overlap
az = np.ones((pix_box[3] - pix_box[1],
pix_box[2] - pix_box[0])) * subset_dict['fill_value']
az[pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = az_overlap
atr_dict = subset_attribute(atr_dict, pix_box)
writefile.write(rg, az, atr_dict, outFile)
else:
data_overlap, atr_dict = readfile.read(File, pix_box4data)
data = np.ones((pix_box[3] - pix_box[1],
pix_box[2] - pix_box[0])) * subset_dict['fill_value']
data[pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = data_overlap
atr_dict = subset_attribute(atr_dict, pix_box)
writefile.write(data, atr_dict, outFile)
##### End Cleaning
try:
prog_bar.close()
h5file.close()
h5out.close()
except:
pass
return outFile
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 list(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 list(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)[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)[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.items():
group.attrs[key] = value
prog_bar.close()
h5out.close()
h5.close()
print('Done.')
return inps.outfile
def remove_surface(File, surf_type, maskFile=None, outFile=None, ysub=None):
start = time.time()
atr = readfile.read_attribute(File)
# Output File Name
if not outFile:
outFile = os.path.splitext(
File)[0] + '_' + surf_type + os.path.splitext(File)[1]
if maskFile:
Mask = readfile.read(maskFile)[0]
print('read mask file: ' + maskFile)
else:
Mask = np.ones((int(atr['FILE_LENGTH']), int(atr['WIDTH'])))
print('use mask of the whole area')
##### Input File Info
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
print('Input file is ' + k)
print('remove ramp type: ' + surf_type)
## Multiple Datasets File
if k in ['interferograms', 'coherence', 'wrapped', 'timeseries']:
h5file = h5py.File(File, 'r')
epochList = sorted(h5file[k].keys())
epoch_num = len(epochList)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5flat = h5py.File(outFile, 'w')
group = h5flat.create_group(k)
print('writing >>> ' + outFile)
if k in ['timeseries']:
print('number of acquisitions: ' + str(len(epochList)))
for i in range(epoch_num):
epoch = epochList[i]
data = h5file[k].get(epoch)[:]
if not ysub:
data_n, ramp = remove_data_surface(data, Mask, surf_type)
else:
data_n = remove_data_multiple_surface(data, Mask, surf_type,
ysub)
dset = group.create_dataset(epoch, data=data_n, compression='gzip')
prog_bar.update(i + 1, suffix=epoch)
for key, value in h5file[k].attrs.items():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print('number of interferograms: ' + str(len(epochList)))
date12_list = ptime.list_ifgram2date12(epochList)
for i in range(epoch_num):
epoch = epochList[i]
data = h5file[k][epoch].get(epoch)[:]
if not ysub:
data_n, ramp = remove_data_surface(data, Mask, surf_type)
else:
data_n = remove_data_multiple_surface(data, Mask, surf_type,
ysub)
gg = group.create_group(epoch)
dset = gg.create_dataset(epoch, data=data_n, compression='gzip')
for key, value in h5file[k][epoch].attrs.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
## Single Dataset File
else:
data, atr = readfile.read(File)
print('Removing ' + surf_type + ' from ' + k)
if not ysub:
data_n, ramp = remove_data_surface(data, Mask, surf_type)
else:
data_n = remove_data_multiple_surface(data, Mask, surf_type, ysub)
print('writing >>> ' + outFile)
writefile.write(data_n, atr, outFile)
try:
h5file.close()
h5flat.close()
prog_bar.close()
except:
pass
print('Remove ' + surf_type + ' took ' + str(time.time() - start) +
' secs')
return outFile
def geocode_file_with_geo_lut(fname,
lut_file=None,
method='nearest',
fill_value=np.nan,
fname_out=None):
'''Geocode file using ROI_PAC/Gamma lookup table file.
Related module: scipy.interpolate.RegularGridInterpolator
Inputs:
fname : string, file to be geocoded
lut_file : string, optional, lookup table file genereated by ROIPAC or Gamma
i.e. geomap_4rlks.trans from ROI_PAC
sim_150911-150922.UTM_TO_RDC from Gamma
method : string, optional, interpolation/resampling method, supporting nearest, linear
fill_value : value used for points outside of the interpolation domain.
If None, values outside the domain are extrapolated.
fname_out : string, optional, output geocoded filename
Output:
fname_out : string, optional, output geocoded filename
'''
start = time.time()
## Default Inputs and outputs
if not fname_out:
fname_out = 'geo_' + fname
# Default lookup table file:
atr_rdr = readfile.read_attribute(fname)
if not lut_file:
if atr_rdr['INSAR_PROCESSOR'] == 'roipac':
lut_file = ['geomap*lks_tight.trans', 'geomap*lks.trans']
elif atr_rdr['INSAR_PROCESSOR'] == 'gamma':
lut_file = ['sim*_tight.UTM_TO_RDC', 'sim*.UTM_TO_RDC']
try:
lut_file = ut.get_file_list(lut_file)[0]
except:
lut_file = None
if not lut_file:
sys.exit(
'ERROR: No lookup table file found! Can not geocoded without it.')
## Original coordinates: row/column number in radar file
print('------------------------------------------------------')
print('geocoding file: ' + fname)
len_rdr = int(atr_rdr['FILE_LENGTH'])
wid_rdr = int(atr_rdr['WIDTH'])
pts_rdr = (np.arange(len_rdr), np.arange(wid_rdr))
## New coordinates: data value in lookup table
print('reading lookup table file: ' + lut_file)
rg, az, atr_lut = readfile.read(lut_file)
len_geo = int(atr_lut['FILE_LENGTH'])
wid_geo = int(atr_lut['WIDTH'])
# adjustment if input radar file has been subseted.
if 'subset_x0' in list(atr_rdr.keys()):
x0 = float(atr_rdr['subset_x0'])
y0 = float(atr_rdr['subset_y0'])
rg -= x0
az -= y0
print(
'\tinput radar coord file has been subsetted, adjust lookup table value'
)
# extract pixels only available in radar file (get ride of invalid corners)
idx = (az > 0.0) * (az <= len_rdr) * (rg > 0.0) * (rg <= wid_rdr)
pts_geo = np.hstack((az[idx].reshape(-1, 1), rg[idx].reshape(-1, 1)))
del az, rg
print('geocoding using scipy.interpolate.RegularGridInterpolator ...')
data_geo = np.empty((len_geo, wid_geo)) * fill_value
k = atr_rdr['FILE_TYPE']
##### Multiple Dataset File
if k in multi_group_hdf5_file + multi_dataset_hdf5_file:
h5 = h5py.File(fname, 'r')
epoch_list = sorted(h5[k].keys())
epoch_num = len(epoch_list)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5out = h5py.File(fname_out, 'w')
group = h5out.create_group(k)
print('writing >>> ' + fname_out)
if k == 'timeseries':
print('number of acquisitions: ' + str(epoch_num))
for i in range(epoch_num):
date = epoch_list[i]
data = h5[k].get(date)[:]
RGI_func = RGI(pts_rdr,
data,
method,
bounds_error=False,
fill_value=fill_value)
data_geo.fill(fill_value)
data_geo[idx] = RGI_func(pts_geo)
dset = group.create_dataset(date,
data=data_geo,
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
print('update attributes')
atr = geocode_attribute_with_geo_lut(atr_rdr, atr_lut)
for key, value in atr.items():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print('number of interferograms: ' + str(epoch_num))
date12_list = ptime.list_ifgram2date12(epoch_list)
for i in range(epoch_num):
ifgram = epoch_list[i]
data = h5[k][ifgram].get(ifgram)[:]
RGI_func = RGI(pts_rdr,
data,
method,
bounds_error=False,
fill_value=fill_value)
data_geo.fill(fill_value)
data_geo[idx] = RGI_func(pts_geo)
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram,
data=data_geo,
compression='gzip')
atr = geocode_attribute_with_geo_lut(h5[k][ifgram].attrs,
atr_lut,
print_msg=False)
for key, value in atr.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
h5.close()
h5out.close()
##### Single Dataset File
else:
print('reading ' + fname)
data = readfile.read(fname)[0]
RGI_func = RGI(pts_rdr,
data,
method,
bounds_error=False,
fill_value=fill_value)
data_geo.fill(fill_value)
data_geo[idx] = RGI_func(pts_geo)
print('update attributes')
atr = geocode_attribute_with_geo_lut(atr_rdr, atr_lut)
print('writing >>> ' + fname_out)
writefile.write(data_geo, atr, fname_out)
del data_geo
s = time.time() - start
m, s = divmod(s, 60)
h, m = divmod(m, 60)
print('Time used: %02d hours %02d mins %02d secs' % (h, m, s))
return fname_out
def unwrap_error_correction_bridging(ifgram_file, mask_file, y_list, x_list, ramp_type='plane',\
ifgram_cor_file=None, save_cor_deramp_file=False):
'''Unwrapping error correction with bridging.
Inputs:
ifgram_file : string, name/path of interferogram(s) to be corrected
mask_file : string, name/path of mask file to mark different patches
y/x_list : list of int, bonding points in y/x
ifgram_cor_file : string, optional, output file name
save_cor_deramp_file : bool, optional
Output:
ifgram_cor_file
Example:
y_list = [235, 270, 350, 390]
x_list = [880, 890, 1200, 1270]
unwrap_error_correction_bridging('unwrapIfgram.h5', 'mask_all.h5', y_list, x_list, 'quadratic')
'''
##### Mask and Ramp
mask = readfile.read(mask_file)[0]
ramp_mask = mask == 1
print('estimate phase ramp during the correction')
print('ramp type: '+ramp_type)
##### Bridge Info
# Check
for i in range(len(x_list)):
if mask[y_list[i],x_list[i]] == 0:
print('\nERROR: Connecting point (%d,%d) is out of masked area! Select them again!\n' % (y_list[i],x_list[i]))
sys.exit(1)
print('Number of bridges: '+str(len(x_list)/2))
print('Bonding points coordinates:\nx: '+str(x_list)+'\ny: '+str(y_list))
# Plot Connecting Pair of Points
plot_bonding_points = False
if plot_bonding_points:
point_yx = ''
line_yx = ''
n_bridge = len(x)/2
for i in range(n_bridge):
pair_yx = str(y[2*i])+','+str(x[2*i])+','+str(y[2*i+1])+','+str(x[2*i+1])
if not i == n_bridge-1:
point_yx += pair_yx+','
line_yx += pair_yx+';'
else:
point_yx += pair_yx
line_yx += pair_yx
try:
plot_cmd = 'view.py --point-yx="'+point_yx+'" --line-yx="'+line_yx+\
'" --nodisplay -o bonding_points.png -f '+maskFile
print(plot_cmd)
os.system(plot_cmd)
except: pass
# Basic info
ext = os.path.splitext(ifgram_file)[1]
atr = readfile.read_attribute(ifgram_file)
k = atr['FILE_TYPE']
try:
ref_y = int(atr['ref_y'])
ref_x = int(atr['ref_x'])
except:
sys.exit('ERROR: Can not find ref_y/x value, input file is not referenced in space!')
# output file name
if not ifgram_cor_file:
ifgram_cor_file = os.path.splitext(ifgram_file)[0]+'_unwCor'+ext
ifgram_cor_deramp_file = os.path.splitext(ifgram_cor_file)[0]+'_'+ramp_type+ext
##### HDF5 file
if ext == '.h5':
##### Read
h5 = h5py.File(ifgram_file,'r')
ifgram_list = sorted(h5[k].keys())
ifgram_num = len(ifgram_list)
h5out = h5py.File(ifgram_cor_file,'w')
group = h5out.create_group(k)
print('writing >>> '+ifgram_cor_file)
if save_cor_deramp_file:
h5out_deramp = h5py.File(ifgram_cor_deramp_file,'w')
group_deramp = h5out_deramp.create_group(k)
print('writing >>> '+ifgram_cor_deramp_file)
##### Loop
print('Number of interferograms: '+str(ifgram_num))
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
date12_list = ptime.list_ifgram2date12(ifgram_list)
for i in range(ifgram_num):
ifgram = ifgram_list[i]
data = h5[k][ifgram].get(ifgram)[:]
data -= data[ref_y, ref_x]
data_deramp, ramp = rm.remove_data_surface(data, ramp_mask, ramp_type)
data_derampCor = bridging_data(data_deramp, mask, x_list, y_list)
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram, data=data_derampCor-ramp, compression='gzip')
for key, value in h5[k][ifgram].attrs.items():
gg.attrs[key]=value
if save_cor_deramp_file:
gg_deramp = group_deramp.create_group(ifgram)
dset = gg_deramp.create_dataset(ifgram, data=data_derampCor, compression='gzip')
for key, value in h5[k][ifgram].attrs.items():
gg_deramp.attrs[key]=value
prog_bar.update(i+1, suffix=date12_list[i])
prog_bar.close()
h5.close()
h5out.close()
try: h5out_deramp.close()
except: pass
#### .unw file
elif ext == '.unw':
print('read '+ifgram_file)
data = readfile.read(ifgram_file)[0]
data -= data[ref_y, ref_x]
data_deramp,ramp = rm.remove_data_surface(data,ramp_mask,ramp_type)
data_derampCor = bridging_data(data_deramp,mask,x_list,y_list)
data_cor = data_derampCor - ramp
print('writing >>> '+ifgram_cor_file)
ifgram_cor_file = writefile.write(data_cor, atr, ifgram_cor_file)
if save_cor_deramp_file:
print('writing >>> '+ifgram_cor_deramp_file)
ifgram_cor_deramp_file = writefile.write(data_derampCor, atr, ifgram_cor_deramp_file)
else:
sys.exit('Un-supported file type: '+ext)
return ifgram_cor_file, ifgram_cor_deramp_file
def main(argv):
##### Inputs
try:
ifgram_file = argv[0]
timeseries_file = argv[1]
except:
usage()
sys.exit(1)
try:
outfile = argv[2]
except:
outfile = 'reconstructed_' + ifgram_file
atr = readfile.read_attribute(timeseries_file)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
##### Read time-series file
print('loading timeseries ...')
h5ts = h5py.File(timeseries_file, 'r')
date_list = sorted(h5ts['timeseries'].keys())
date_num = len(date_list)
timeseries = np.zeros((date_num, length * width))
print('number of acquisitions: ' + str(date_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
d = h5ts['timeseries'].get(date)[:]
timeseries[i, :] = d.flatten(0)
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5ts.close()
del d
range2phase = -4 * np.pi / float(atr['WAVELENGTH'])
timeseries = range2phase * timeseries
##### Estimate interferograms from timeseries
print(
'estimating interferograms from timeseries using design matrix from input interferograms'
)
A, B = ut.design_matrix(ifgram_file)
p = -1 * np.ones([A.shape[0], 1])
Ap = np.hstack((p, A))
estData = np.dot(Ap, timeseries)
del timeseries
##### Write interferograms file
print('writing >>> ' + outfile)
h5 = h5py.File(ifgram_file, 'r')
ifgram_list = sorted(h5['interferograms'].keys())
ifgram_num = len(ifgram_list)
date12_list = ptime.list_ifgram2date12(ifgram_list)
h5out = h5py.File(outfile, 'w')
group = h5out.create_group('interferograms')
print('number of interferograms: ' + str(ifgram_num))
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
for i in range(ifgram_num):
ifgram = ifgram_list[i]
data = np.reshape(estData[i, :], (length, width))
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram, data=data, compression='gzip')
for key, value in h5['interferograms'][ifgram].attrs.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
prog_bar.close()
h5.close()
h5out.close()
print('Done.')
return outfile
def multilook_file(infile, lks_y, lks_x, outfile=None):
lks_y = int(lks_y)
lks_x = int(lks_x)
## input file info
atr = readfile.read_attribute(infile)
k = atr['FILE_TYPE']
print('multilooking ' + k + ' file ' + infile)
print('number of looks in y / azimuth direction: %d' % lks_y)
print('number of looks in x / range direction: %d' % lks_x)
## output file name
if not outfile:
if os.getcwd() == os.path.dirname(os.path.abspath(infile)):
ext = os.path.splitext(infile)[1]
outfile = os.path.splitext(infile)[0] + '_' + str(
lks_y) + 'alks_' + str(lks_x) + 'rlks' + ext
else:
outfile = os.path.basename(infile)
print('writing >>> ' + outfile)
###############################################################################
## Read/Write multi-dataset files
if k in ['interferograms', 'coherence', 'wrapped', 'timeseries']:
h5 = h5py.File(infile, 'r')
epochList = sorted(h5[k].keys())
epoch_num = len(epochList)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5out = h5py.File(outfile, 'w')
group = h5out.create_group(k)
if k in ['interferograms', 'coherence', 'wrapped']:
date12_list = ptime.list_ifgram2date12(epochList)
print('number of interferograms: ' + str(len(epochList)))
for i in range(epoch_num):
epoch = epochList[i]
data = h5[k][epoch].get(epoch)[:]
atr = h5[k][epoch].attrs
data_mli = multilook_matrix(data, lks_y, lks_x)
atr_mli = multilook_attribute(atr,
lks_y,
lks_x,
print_msg=False)
gg = group.create_group(epoch)
dset = gg.create_dataset(epoch,
data=data_mli,
compression='gzip')
for key, value in atr_mli.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
elif k == 'timeseries':
print('number of acquisitions: ' + str(len(epochList)))
for i in range(epoch_num):
epoch = epochList[i]
data = h5[k].get(epoch)[:]
data_mli = multilook_matrix(data, lks_y, lks_x)
dset = group.create_dataset(epoch,
data=data_mli,
compression='gzip')
prog_bar.update(i + 1, suffix=epoch)
atr = h5[k].attrs
atr_mli = multilook_attribute(atr, lks_y, lks_x)
for key, value in atr_mli.items():
group.attrs[key] = value
h5.close()
h5out.close()
prog_bar.close()
## Read/Write single-dataset files
elif k == '.trans':
rg, az, atr = readfile.read(infile)
rgmli = multilook_matrix(rg, lks_y, lks_x)
#rgmli *= 1.0/lks_x
azmli = multilook_matrix(az, lks_y, lks_x)
#azmli *= 1.0/lks_y
atr = multilook_attribute(atr, lks_y, lks_x)
writefile.write(rgmli, azmli, atr, outfile)
else:
data, atr = readfile.read(infile)
data_mli = multilook_matrix(data, lks_y, lks_x)
atr = multilook_attribute(atr, lks_y, lks_x)
writefile.write(data_mli, atr, outfile)
return outfile
def filter_file(fname, filter_type, filter_par=None, fname_out=None):
'''Filter 2D matrix with selected filter
Inputs:
fname : string, name/path of file to be filtered
filter_type : string, filter type
filter_par : string, optional, parameter for low/high pass filter
for low/highpass_avg, it's kernel size in int
for low/highpass_gaussain, it's sigma in float
Output:
fname_out : string, optional, output file name/path
'''
# Basic info
atr = readfile.read_attribute(fname)
k = atr['FILE_TYPE']
try:
ref_yx = [int(atr['ref_y']), int(atr['ref_x'])]
except:
ref_yx = None
filter_type = filter_type.lower()
MSG = 'filtering ' + k + ' file: ' + fname + ' using ' + filter_type + ' filter'
if filter_type.endswith('avg'):
if not filter_par:
filter_par = 5
MSG += ' with kernel size of %d' % int(filter_par)
elif filter_type.endswith('gaussian'):
if not filter_par:
filter_par = 3.0
MSG += ' with sigma of %.1f' % filter_par
print(MSG)
if not fname_out:
ext = os.path.splitext(fname)[1]
fname_out = os.path.splitext(fname)[0] + '_' + filter_type + ext
##### Multiple Dataset File
if k in multi_group_hdf5_file + multi_dataset_hdf5_file:
h5 = h5py.File(fname, 'r')
epoch_list = sorted(h5[k].keys())
epoch_num = len(epoch_list)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5out = h5py.File(fname_out, 'w')
group = h5out.create_group(k)
print('writing >>> ' + fname_out)
if k == 'timeseries':
print('number of acquisitions: ' + str(epoch_num))
for i in range(epoch_num):
date = epoch_list[i]
data = h5[k].get(date)[:]
data_filt = filter_data(data, filter_type, filter_par)
if ref_yx:
data_filt -= data_filt[ref_yx[0], ref_yx[1]]
dset = group.create_dataset(date,
data=data_filt,
compression='gzip')
prog_bar.update(i + 1, suffix=date)
for key, value in atr.items():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print('number of interferograms: ' + str(epoch_num))
date12_list = ptime.list_ifgram2date12(epoch_list)
for i in range(epoch_num):
ifgram = epoch_list[i]
data = h5[k][ifgram].get(ifgram)[:]
data_filt = filter_data(data, filter_type, filter_par)
if ref_yx and k in ['interferograms']:
data_filt -= data_filt[ref_yx[0], ref_yx[1]]
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram,
data=data_filt,
compression='gzip')
for key, value in h5[k][ifgram].attrs.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
h5.close()
h5out.close()
prog_bar.close()
##### Single Dataset File
else:
data, atr = readfile.read(fname)
data_filt = filter_data(data, filter_type, filter_par)
if ref_yx and k in ['.unw', 'velocity']:
data_filt -= data_filt[ref_yx[0], ref_yx[1]]
print('writing >>> ' + fname_out)
writefile.write(data_filt, atr, fname_out)
return fname_out
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 list(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.items():
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.items():
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):
try:
timeseriesFile = argv[0]
except:
usage()
sys.exit(1)
try:
outname = argv[1]
except:
outname = 'sum_' + timeseriesFile
##### Read Timeseries
atr = readfile.read_attribute(timeseriesFile)
k = atr['FILE_TYPE']
print("loading time series: " + timeseriesFile)
h5timeseries = h5py.File(timeseriesFile)
dateList = sorted(h5timeseries['timeseries'].keys())
date_num = len(dateList)
print('number of acquisitions: %d' % date_num)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
D = np.zeros((date_num, length * width), np.float32)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = dateList[i]
d = h5timeseries['timeseries'].get(date)[:]
D[i][:] = d.flatten(0)
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5timeseries.close()
##### Calculate Sum
print('calculating epochs sum ...')
sumD = np.zeros(D.shape)
prog_bar.reset()
for i in range(date_num):
sumD[j, :] = np.sum(np.abs(D - D[j, :]), 0) / date_num
prog_bar.update(i + 1)
prog_bar.close()
## Normalize to 0 and 1
## with high atmosphere equal to 0 and no atmosphere equal to 1
sumD -= np.max(sumD, 0)
sumD *= -1
sumD /= np.max(sumD, 0)
sumD[np.isnan(sumD)] = 1
##### Write sum epochs file
print('writing to >>> ' + outname)
h5sum = h5py.File(outname, 'w')
group = h5sum.create_group('timeseries')
prog_bar.reset()
for i in range(date_num):
date = dateList[i]
d = np.reshape(sumD[i][:], [length, width])
dset = group.create_dataset(date, data=d, compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
for key, value in atr.items():
group.attrs[key] = value
h5sum.close()
print('Done.')
def file_operation(fname, operator, operand, fname_out=None):
'''Mathmathic operation of file'''
# Basic Info
atr = readfile.read_attribute(fname)
k = atr['FILE_TYPE']
print('input is '+k+' file: '+fname)
print('operation: file %s %f' % (operator, operand))
# default output filename
if not fname_out:
if operator in ['+','plus', 'add', 'addition']: suffix = 'plus'
elif operator in ['-','minus', 'substract','substraction']: suffix = 'minus'
elif operator in ['*','times', 'multiply', 'multiplication']: suffix = 'multiply'
elif operator in ['/','obelus','divide', 'division']: suffix = 'divide'
elif operator in ['^','pow','power']: suffix = 'pow'
ext = os.path.splitext(fname)[1]
fname_out = os.path.splitext(fname)[0]+'_'+suffix+str(operand)+ext
##### Multiple Dataset HDF5 File
if k in multi_group_hdf5_file+multi_dataset_hdf5_file:
h5 = h5py.File(fname,'r')
epoch_list = sorted(h5[k].keys())
epoch_num = len(epoch_list)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5out = h5py.File(fname_out,'w')
group = h5out.create_group(k)
print('writing >>> '+fname_out)
if k == 'timeseries':
print('number of acquisitions: '+str(epoch_num))
for i in range(epoch_num):
date = epoch_list[i]
data = h5[k].get(date)[:]
data_out = data_operation(data, operator, operand)
dset = group.create_dataset(date, data=data_out, compression='gzip')
prog_bar.update(i+1, suffix=date)
for key,value in atr.items():
group.attrs[key] = value
elif k in ['interferograms','wrapped','coherence']:
print('number of interferograms: '+str(epoch_num))
date12_list = ptime.list_ifgram2date12(epoch_list)
for i in range(epoch_num):
ifgram = epoch_list[i]
data = h5[k][ifgram].get(ifgram)[:]
data_out = data_operation(data, operator, operand)
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram, data=data_out, compression='gzip')
for key, value in h5[k][ifgram].attrs.items():
gg.attrs[key] = value
prog_bar.update(i+1, suffix=date12_list[i])
h5.close()
h5out.close()
prog_bar.close()
##### Duo datasets non-HDF5 File
elif k in ['.trans']:
rg, az, atr = readfile.read(fname)
rg_out = data_operation(rg, operator, operand)
az_out = data_operation(az, operator, operand)
print('writing >>> '+fname_out)
writefile.write(rg_out, az_out, atr, fname_out)
##### Single Dataset File
else:
data, atr = readfile.read(fname)
data_out = data_operation(data, operator, operand)
print('writing >>> '+fname_out)
writefile.write(data_out, atr, fname_out)
return fname_out
def main(argv):
inps = cmdLineParse()
#print '\n********** Inversion: Time Series to Velocity ***********'
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
print('input ' + k + ' file: ' + inps.timeseries_file)
if not k == 'timeseries':
sys.exit('ERROR: input file is not timeseries!')
h5file = h5py.File(inps.timeseries_file)
#####################################
## Date Info
dateListAll = sorted(h5file[k].keys())
print('--------------------------------------------')
print('Dates from input file: ' + str(len(dateListAll)))
print(dateListAll)
inps.ex_date = get_exclude_date(inps, dateListAll)
dateList = sorted(list(set(dateListAll) - set(inps.ex_date)))
print('--------------------------------------------')
if len(dateList) == len(dateListAll):
print('using all dates to calculate the velocity')
else:
print('Dates used to estimate the velocity: ' + str(len(dateList)))
print(dateList)
print('--------------------------------------------')
# Date Aux Info
dates, datevector = ptime.date_list2vector(dateList)
#####################################
## Inversion
# Design matrix
B = np.ones([len(datevector), 2])
B[:, 0] = datevector
#B_inv = np.linalg.pinv(B)
B_inv = np.dot(np.linalg.inv(np.dot(B.T, B)), B.T)
B_inv = np.array(B_inv, np.float32)
# Loading timeseries
print("Loading time series file: " + inps.timeseries_file + ' ...')
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
dateNum = len(dateList)
timeseries = np.zeros([dateNum, length * width], np.float32)
prog_bar = ptime.progress_bar(maxValue=dateNum, prefix='loading: ')
for i in range(dateNum):
date = dateList[i]
timeseries[i, :] = h5file[k].get(date)[:].flatten()
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5file.close()
# Velocity Inversion
print('Calculating velocity ...')
X = np.dot(B_inv, timeseries)
velocity = np.reshape(X[0, :], [length, width])
print('Calculating rmse ...')
timeseries_linear = np.dot(B, X)
timeseries_residual = timeseries - timeseries_linear
rmse = np.reshape(np.sqrt((np.sum((timeseries_residual)**2, 0)) / dateNum),
[length, width])
print('Calculating the standard deviation of the estimated velocity ...')
s1 = np.sqrt(np.sum(timeseries_residual**2, 0) / (dateNum - 2))
s2 = np.sqrt(np.sum((datevector - np.mean(datevector))**2))
std = np.reshape(s1 / s2, [length, width])
# SSt=np.sum((timeseries-np.mean(timeseries,0))**2,0)
# SSres=np.sum(residual**2,0)
# SS_REG=SSt-SSres
# Rsquared=np.reshape(SS_REG/SSt,[length,width])
######################################################
# covariance of the velocities
#####################################
# Output file name
if not inps.outfile:
inps.outfile = 'velocity.h5'
inps.outfile_rmse = os.path.splitext(
inps.outfile)[0] + 'Rmse' + os.path.splitext(inps.outfile)[1]
inps.outfile_std = os.path.splitext(
inps.outfile)[0] + 'Std' + os.path.splitext(inps.outfile)[1]
inps.outfile_r2 = os.path.splitext(
inps.outfile)[0] + 'R2' + os.path.splitext(inps.outfile)[1]
# Attributes
atr['date1'] = datevector[0]
atr['date2'] = datevector[dateNum - 1]
# File Writing
print('--------------------------------------')
atr['FILE_TYPE'] = 'velocity'
print('writing >>> ' + inps.outfile)
writefile.write(velocity, atr, inps.outfile)
#atr['FILE_TYPE'] = 'rmse'
print('writing >>> ' + inps.outfile_rmse)
writefile.write(rmse, atr, inps.outfile_rmse)
#atr['FILE_TYPE'] = 'rmse'
print('writing >>> ' + inps.outfile_std)
writefile.write(std, atr, inps.outfile_std)
print('Done.\n')
return inps.outfile
def seed_file_reference_value(File, outName, refList, ref_y='', ref_x=''):
## Seed Input File with reference value in refList
print('Reference value: ')
print(refList)
##### IO Info
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
print('file type: ' + k)
##### Multiple Dataset File
if k in ['timeseries', 'interferograms', 'wrapped', 'coherence']:
##### Input File Info
h5file = h5py.File(File, 'r')
epochList = sorted(h5file[k].keys())
epochNum = len(epochList)
##### Check Epoch Number
if not epochNum == len(refList):
print('\nERROR: Reference value has different epoch number'+\
'from input file.')
print('Reference List epoch number: ' + str(refList))
print('Input file epoch number: ' + str(epochNum))
sys.exit(1)
##### Output File Info
h5out = h5py.File(outName, 'w')
group = h5out.create_group(k)
print('writing >>> ' + outName)
prog_bar = ptime.progress_bar(maxValue=epochNum, prefix='seeding: ')
## Loop
if k == 'timeseries':
print('number of acquisitions: ' + str(epochNum))
for i in range(epochNum):
epoch = epochList[i]
data = h5file[k].get(epoch)[:]
data -= refList[i]
dset = group.create_dataset(epoch, data=data, compression='gzip')
prog_bar.update(i + 1, suffix=epoch)
atr = seed_attributes(atr, ref_x, ref_y)
for key, value in atr.items():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print('number of interferograms: ' + str(epochNum))
date12_list = ptime.list_ifgram2date12(epochList)
for i in range(epochNum):
epoch = epochList[i]
#print epoch
data = h5file[k][epoch].get(epoch)[:]
atr = h5file[k][epoch].attrs
data -= refList[i]
atr = seed_attributes(atr, ref_x, ref_y)
gg = group.create_group(epoch)
dset = gg.create_dataset(epoch, data=data, compression='gzip')
for key, value in atr.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
##### Single Dataset File
else:
print('writing >>> ' + outName)
data, atr = readfile.read(File)
data -= refList
atr = seed_attributes(atr, ref_x, ref_y)
writefile.write(data, atr, outName)
##### End & Cleaning
try:
prog_bar.close()
h5file.close()
h5out.close()
except:
pass
return outName
def add_files(fname_list, fname_out=None):
'''Generate sum of all input files
Inputs:
fname_list - list of string, path/name of input files to be added
fname_out - string, optional, path/name of output file
Output:
fname_out - string, path/name of output file
Example:
'mask_all.h5' = add_file(['mask_1.h5','mask_2.h5','mask_3.h5'], 'mask_all.h5')
'''
# Default output file name
ext = os.path.splitext(fname_list[0])[1]
if not fname_out:
fname_out = os.path.splitext(fname_list[0])[0]
for i in range(1, len(fname_list)):
fname_out += '_plus_' + os.path.splitext(
os.path.basename(fname_list[i]))[0]
fname_out += ext
# Basic Info
atr = readfile.read_attribute(fname_list[0])
k = atr['FILE_TYPE']
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
print('First input file is ' + atr['PROCESSOR'] + ' ' + k)
## Multi-dataset/group file
if k in multi_group_hdf5_file + multi_dataset_hdf5_file:
# File Type Check
for i in range(1, len(fname_list)):
ki = readfile.read_attribute(fname_list[i])['FILE_TYPE']
if (k in multi_dataset_hdf5_file and ki in multi_dataset_hdf5_file
or k in multi_group_hdf5_file
and ki in multi_group_hdf5_file):
pass
else:
print('Input files structure are not the same: ' + k +
' v.s. ' + ki)
sys.exit(1)
print('writing >>> ' + fname_out)
h5out = h5py.File(fname_out, 'w')
group = h5out.create_group(k)
h5 = h5py.File(fname_list[0], 'r')
epoch_list = sorted(h5[k].keys())
epoch_num = len(epoch_list)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
if k in multi_dataset_hdf5_file:
print('number of acquisitions: %d' % epoch_num)
for i in range(epoch_num):
epoch = epoch_list[i]
data = np.zeros((length, width))
for fname in fname_list:
h5file = h5py.File(fname, 'r')
d = h5file[k].get(epoch)[:]
data = add_matrix(data, d)
dset = group.create_dataset(epoch, data=data, compression='gzip')
prog_bar.update(i + 1, suffix=epoch)
for key, value in atr.items():
group.attrs[key] = value
h5out.close()
h5.close()
prog_bar.close()
elif k in multi_group_hdf5_file:
print('number of interferograms: %d' % epoch_num)
date12_list = ptime.list_ifgram2date12(epoch_list)
for i in range(epoch_num):
epoch = epoch_list[i]
data = np.zeros((length, width))
for fname in fname_list:
h5file = h5py.File(fname, 'r')
temp_k = list(h5file.keys())[0]
temp_epoch_list = sorted(h5file[temp_k].keys())
d = h5file[temp_k][temp_epoch_list[i]].get(
temp_epoch_list[i])[:]
data = add_matrix(data, d)
gg = group.create_group(epoch)
dset = gg.create_dataset(epoch, data=data, compression='gzip')
for key, value in h5[k][epoch].attrs.items():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
h5out.close()
h5.close()
prog_bar.close()
## Single dataset files
else:
data = np.zeros((length, width))
for fname in fname_list:
print('loading ' + fname)
d, r = readfile.read(fname)
data = add_matrix(data, d)
print('writing >>> ' + fname_out)
writefile.write(data, atr, fname_out)
return fname_out
def unwrap_error_correction_phase_closure(ifgram_file, mask_file, ifgram_cor_file=None):
'''Correct unwrapping errors in network of interferograms using phase closure.
Inputs:
ifgram_file - string, name/path of interferograms file
mask_file - string, name/path of mask file to mask the pixels to be corrected
ifgram_cor_file - string, optional, name/path of corrected interferograms file
Output:
ifgram_cor_file
Example:
'unwrapIfgram_unwCor.h5' = unwrap_error_correction_phase_closure('Seeded_unwrapIfgram.h5','mask.h5')
'''
print('read mask from file: '+mask_file)
mask = readfile.read(mask_file)[0].flatten(1)
atr = readfile.read_attribute(ifgram_file)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
k = atr['FILE_TYPE']
pixel_num = length*width
# Check reference pixel
try:
ref_y = int(atr['ref_y'])
ref_x = int(atr['ref_x'])
except:
sys.exit('ERROR: Can not find ref_y/x value, input file is not referenced in space!')
h5 = h5py.File(ifgram_file,'r')
ifgram_list = sorted(h5[k].keys())
ifgram_num = len(ifgram_list)
##### Prepare curls
curls, Triangles, C = ut.get_triangles(h5)
curl_num = np.shape(curls)[0]
print('Number of triangles: '+ str(curl_num))
curl_file='curls.h5'
if not os.path.isfile(curl_file):
print('writing >>> '+curl_file)
ut.generate_curls(curl_file, h5, Triangles, curls)
thr=0.50
curls = np.array(curls); n1=curls[:,0]; n2=curls[:,1]; n3=curls[:,2]
print('reading interferograms...')
print('Number of interferograms: '+ str(ifgram_num))
data = np.zeros((ifgram_num,pixel_num),np.float32)
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
for ni in range(ifgram_num):
ifgram = ifgram_list[ni]
d = h5[k][ifgram].get(ifgram)[:].flatten(1)
data[ni,:] = d
prog_bar.update(ni+1)
prog_bar.close()
print('reading curls ...')
print('number of culrs: '+str(curl_num))
h5curl = h5py.File(curl_file,'r')
curl_list = sorted(h5curl[k].keys())
curl_data = np.zeros((curl_num, pixel_num),np.float32)
prog_bar = ptime.progress_bar(maxValue=curl_num)
for ni in range(curl_num):
d = h5curl[k][curl_list[ni]].get(curl_list[ni])[:].flatten(1)
curl_data[ni,:] = d.flatten(1)
prog_bar.update(ni+1)
prog_bar.close()
h5curl.close()
print('estimating unwrapping error pixel by pixel ...')
EstUnwrap = np.zeros((ifgram_num,pixel_num),np.float32)
prog_bar = ptime.progress_bar(maxValue=pixel_num)
for ni in range(pixel_num):
if mask[ni]==1:
dU = data[:,ni]
unwCurl = np.array(curl_data[:,ni])
ind = np.abs(unwCurl)>=thr; N1 =n1[ind]; N2 =n2[ind]; N3 =n3[ind]
indC = np.abs(unwCurl)< thr; Nc1=n1[indC]; Nc2=n2[indC]; Nc3=n3[indC]
N =np.hstack([N1, N2, N3]); UniN =np.unique(N)
Nc=np.hstack([Nc1,Nc2,Nc3]); UniNc=np.unique(Nc)
inter = list(set(UniNc) & set(UniN)) # intersetion
UniNc = list(UniNc)
for x in inter:
UniNc.remove(x)
D = np.zeros([len(UniNc),ifgram_num])
for i in range(len(UniNc)):
D[i,UniNc[i]]=1
AAA = np.vstack([-2*np.pi*C,D])
AAAA = np.vstack([AAA,0.25*np.eye(ifgram_num)])
##########
# with Tikhonov regularization:
LLL = list(np.dot(C,dU)) + list(np.zeros(np.shape(UniNc)[0])) + list(np.zeros(ifgram_num))
ind = np.isnan(AAAA)
M1 = pinv(AAAA)
M = np.dot(M1,LLL)
EstUnwrap[:,ni] = np.round(M[0:ifgram_num])*2.0*np.pi
prog_bar.update(ni+1, suffix='%s/%d' % (ni,pixel_num))
prog_bar.close()
dataCor = data + EstUnwrap
##### Output
if not ifgram_cor_file:
ifgram_cor_file = os.path.splitext(ifgram_file)[0]+'_unwCor.h5'
print('writing >>> '+ifgram_cor_file)
h5unwCor = h5py.File(ifgram_cor_file,'w')
gg = h5unwCor.create_group(k)
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
for i in range(ifgram_num):
ifgram = ifgram_list[i]
group = gg.create_group(ifgram)
dset = group.create_dataset(ifgram, data=np.reshape(dataCor[i,:],[width,length]).T, compression='gzip')
for key, value in h5[k][ifgram].attrs.items():
group.attrs[key] = value
prog_bar.update(i+1)
prog_bar.close()
h5unwCor.close()
h5.close()
return ifgram_cor_file
def main(argv):
inps = cmdLineParse()
# Basic info
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
if k not in ['timeseries']:
sys.exit('ERROR: only timeseries file supported, input is ' + k +
' file!')
h5 = h5py.File(inps.timeseries_file, 'r')
date_list = sorted(h5[k].keys())
date_num = len(date_list)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
pixel_num = length * width
tbase = np.array(ptime.date_list2tbase(date_list)[0], np.float32).reshape(
(date_num, 1))
tbase /= 365.25
# Read timeseries
print('loading time-series ...')
timeseries = np.zeros((date_num, pixel_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
d = h5[k].get(date)[:]
timeseries[i, :] = d.flatten(0)
prog_bar.update(i + 1, suffix=date)
del d
h5.close()
prog_bar.close()
# Smooth timeseries with moving window in time
print(
'smoothing time-series using moving gaussian window with size of %.1f years'
% inps.time_win)
timeseries_filt = np.zeros((date_num, pixel_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
# Weight from Gaussian (normal) distribution in time
t_diff = tbase[i] - tbase
weight = np.exp(-0.5 * (t_diff**2) / (inps.time_win**2))
weight /= np.sum(weight)
weightMat = np.tile(weight, (1, pixel_num))
# Smooth the current acquisition - moving window in time one by one
timeseries_filt[i, :] = np.sum(timeseries * weightMat, 0)
prog_bar.update(i + 1, suffix=date)
del weightMat
del timeseries
prog_bar.close()
# Write smoothed timeseries file
try:
ref_date = atr['ref_date']
except:
ref_date = date_list[0]
ref_date_idx = date_list.index(ref_date)
print('reference date: ' + ref_date)
print('reference date index: ' + str(ref_date_idx))
ref_data = np.reshape(timeseries_filt[ref_date_idx, :], [length, width])
if not inps.outfile:
inps.outfile = os.path.splitext(inps.timeseries_file)[0] + '_smooth.h5'
print('writing >>> ' + inps.outfile)
print('number of acquisitions: ' + str(date_num))
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 = np.reshape(timeseries_filt[i, :], [length, width])
dset = group.create_dataset(date,
data=data - ref_data,
compression='gzip')
prog_bar.update(i + 1, suffix=date)
for key, value in atr.items():
group.attrs[key] = value
h5out.close()
prog_bar.close()
print('Done.')
return inps.outfile