def manual_select_start_end_point(File):
'''Manual Select Start/End Point in display figure.'''
print('reading ' + File + ' ...')
data, atr = readfile.read(File)
print('displaying ' + File + ' ...')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(data)
xc = []
yc = []
print('please click on start and end point of the desired profile')
print('then close the figure to continue')
def onclick(event):
if event.button == 1:
xcc, ycc = int(event.xdata), int(event.ydata)
xc.append(xcc)
yc.append(ycc)
print('x = ' + str(xcc) + '\ny = ' + str(ycc))
ax.plot(xcc, ycc, 'ro')
cid = fig.canvas.mpl_connect('button_release_event', onclick)
plt.show()
start_yx = [yc[0], xc[0]]
end_yx = [yc[1], xc[1]]
return start_yx, end_yx
def transect_list(fileList, inps):
'''Get transection along input line from file list
Inputs:
fileList : list of str, path of files to get transect
inps : Namespace including the following items:
start/end_lalo
start/end_yx
interpolation
Outputs:
transectList : list of N*2 matrix containing distance and its value
atrList : list of attribute dictionary, for each input file
'''
transectList = []
atrList = []
for File in fileList:
print('reading ' + File)
data, atr = readfile.read(File)
if inps.start_lalo and inps.end_lalo:
transect = transect_lalo(data, atr, inps.start_lalo, inps.end_lalo,
inps.interpolation)
else:
transect = transect_yx(data, atr, inps.start_yx, inps.end_yx,
inps.interpolation)
transectList.append(transect)
atrList.append(atr)
return transectList, atrList
def pick_file():
global attributes, starting_upper_lim
if h5_file.get() == "":
filename = filedialog.askopenfilename(initialdir="/", title="Select file",
filetypes=(("jpeg files", "*.h5"), ("all files", "*.*")))
frame.filename = filename
h5_file.set(frame.filename)
h5_file_short.set(filename.split("/")[-1])
pick_h5_file_button.config(text="Cancel")
atr = readfile.read_attribute(h5_file.get())
file_type = atr['FILE_TYPE']
if file_type not in readfile.multi_group_hdf5_file + readfile.multi_dataset_hdf5_file + ['HDFEOS']:
data, attributes = readfile.read(h5_file.get())
max = numpy.amax(data)
starting_upper_lim = max * 2
update_sliders("m")
y_lim_upper.set(max)
set_variables_from_attributes()
return frame.filename
else:
h5_file.set("")
h5_file_short.set("No File Selected")
pick_h5_file_button.config(text="Select .h5 File")
def set_dem_file():
global ax_v, inps, img
if inps.dem_file:
dem = readfile.read(inps.dem_file)[0]
ax_v = view.plot_dem_yx(ax_v, dem)
img = ax_v.imshow(d_v,
cmap=inps.colormap,
clim=inps.ylim,
interpolation='nearest')
def main(argv):
try:
demFile = argv[0]
File = argv[1]
except:
usage()
sys.exit(1)
dem, demRsc = readfile.read(demFile)
data, atr = readfile.read(File)
print('Input file is ' + atr['FILE_TYPE'])
# Subset
try:
y0, y1 = [int(i) for i in argv[2].split(':')]
x0, x1 = [int(i) for i in argv[3].split(':')]
data = data[y0:y1, x0:x1]
dem = dem[y0:y1, x0:x1]
except:
pass
# Calculation
dem = dem.flatten(1)
data = data.flatten(1)
ndx = ~np.isnan(data)
C1 = np.zeros([2, len(dem[ndx])])
C1[0][:] = dem[ndx]
C1[1][:] = data[ndx]
# Display
print('-------------------------------------------')
print('Correlation with the DEM: %.2f' % np.corrcoef(C1)[0][1])
print('-------------------------------------------')
print('DEM info:')
print(' Max height difference: %.2f m' %
(np.max(dem[ndx]) - np.min(dem[ndx])))
print(' Average height: %.2f m' % np.mean(dem[ndx]))
print(' Height Std: %.2f m' % np.std(dem[ndx]))
return
def load_single_dataset_hdf5(file_type, infile, outfile, extra_meta_dict=dict()):
'''Convert ROI_PAC .dem / .hgt file to hdf5 file
Based on load_dem.py written by Emre Havazli
Inputs:
file_type : string, group name of hdf5 file, i.e. dem, mask
infile : string, input ROI_PAC file name
outfile : string, output hdf5 file name
extra_meta_dict : dict, extra attributes to output file
Output:
outfile : string, output hdf5 file name
'''
atr = readfile.read_attribute(infile)
if ut.update_file(outfile, infile):
if (os.path.dirname(infile) == os.path.dirname(outfile) and \
os.path.splitext(infile)[1] == os.path.splitext(outfile)[1]):
print(infile+' already in working directory with recommended format, no need to re-load.')
outfile = infile
else:
# Read input file
print('loading file: '+infile)
data = readfile.read(infile)[0]
# Write output file - data
print('writing >>> '+outfile)
h5 = h5py.File(outfile, 'w')
group = h5.create_group(file_type)
dset = group.create_dataset(file_type, data=data, compression='gzip')
# Write output file - attributes
for key, value in atr.items():
group.attrs[key] = value
try: group.attrs['PROJECT_NAME'] = extra_meta_dict['project_name']
except: pass
key = 'INSAR_PROCESSOR'
if key not in list(atr.keys()):
try: atr[key] = extra_meta_dict['insar_processor']
except: pass
h5.close()
#if (os.path.abspath(infile) != os.path.abspath(outfile) and \
# os.path.dirname(infile) == os.path.dirname(outfile)):
# print 'remove the duplicated, obsolete '+atr['FILE_TYPE']+' file in the same directory'
# rmCmd = 'rm '+infile
# print rmCmd
# os.system(rmCmd)
return outfile
def select_max_coherence_yx(cohFile, mask=None, min_coh=0.85):
'''Select pixel with coherence > min_coh in random'''
print('\n---------------------------------------------------------')
print('select pixel with coherence > ' + str(min_coh) + ' in random')
print('use coherence file: ' + cohFile)
coh, coh_atr = readfile.read(cohFile)
if not mask is None:
coh[mask == 0] = 0.0
coh_mask = coh >= min_coh
y, x = random_select_reference_yx(coh_mask, print_msg=False)
#y, x = np.unravel_index(np.argmax(coh), coh.shape)
print('y/x: ' + str([y, x]))
print('---------------------------------------------------------')
return y, x
def set_mask():
global mask, inps, atr
if not inps.mask_file:
if 'X_FIRST' in atr.keys():
file_list = ['geo_maskTempCoh.h5']
else:
file_list = ['maskTempCoh.h5', 'mask.h5']
try:
inps.mask_file = ut.get_file_list(file_list)[0]
except:
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file)[0]
mask[mask != 0] = 1
print('load mask from file: ' + inps.mask_file)
except:
mask = None
print('No mask used.')
def main(argv):
inps = cmdLineParse()
##### 1. Read data
atr = readfile.read_attribute(inps.file)
k = atr['FILE_TYPE']
print('Input file is ' + k)
# Check: file in geo coord
if 'X_FIRST' not in list(atr.keys()):
sys.exit('ERROR: Input file is not geocoded.')
# Check: epoch is required for multi_dataset/group files
if not inps.epoch and k in multi_group_hdf5_file + multi_dataset_hdf5_file:
print("No date/date12 input.\nIt's required for " + k + " file")
sys.exit(1)
# Read data
data, atr = readfile.read(inps.file, (), inps.epoch)
# Output filename
if not inps.outfile:
inps.outfile = pview.auto_figure_title(inps.file, inps.epoch,
vars(inps))
# Data Operation - Display Unit & Rewrapping
data, inps.disp_unit, inps.wrap = pview.scale_data4disp_unit_and_rewrap(
data, atr, inps.disp_unit, inps.wrap)
if inps.wrap:
inps.ylim = [-np.pi, np.pi]
##### 2. Generate Google Earth KMZ
kmz_file = write_kmz_file(data, atr, inps.outfile, inps)
print('Done.')
return
def main(argv):
inps = cmdLineParse()
##### 1. Read data
atr = readfile.read_attribute(inps.file)
k = atr['FILE_TYPE']
print('Input file is '+k)
# Check: file in geo coord
if 'X_FIRST' not in list(atr.keys()):
sys.exit('ERROR: Input file is not geocoded.')
# Check: epoch is required for multi_dataset/group files
if not inps.epoch:
if k in multi_group_hdf5_file:
print("No date/date12 input.\nIt's required for "+k+" file")
sys.exit(1)
elif k in multi_dataset_hdf5_file:
print('No input date ..., continue to convert the last date of time-series.')
h5 = h5py.File(inps.file, 'r')
date_list = sorted(h5[k].keys())
h5.close()
inps.epoch = date_list[-1]
# Read data
data, atr = readfile.read(inps.file, (), inps.epoch)
# Output filename
if not inps.outfile:
inps.outfile = pview.auto_figure_title(inps.file, inps.epoch, vars(inps))
inps.outfile = os.path.splitext(inps.outfile)[0]+'.grd'
##### 2. Write GMT .grd file
inps.outfile = write_grd_file(data, atr, inps.outfile)
print('Done.')
return inps.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()
inps.file = ut.get_file_list(inps.file)
atr = readfile.read_attribute(inps.file[0])
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
if inps.reset:
print(
'----------------------------------------------------------------------------'
)
for file in inps.file:
remove_reference_pixel(file)
return
##### Check Input Coordinates
# Read ref_y/x/lat/lon from reference/template
# priority: Direct Input > Reference File > Template File
if inps.template_file:
print('reading reference info from template: ' + inps.template_file)
inps = read_seed_template2inps(inps.template_file, inps)
if inps.reference_file:
print('reading reference info from reference: ' + inps.reference_file)
inps = read_seed_reference2inps(inps.reference_file, inps)
## Do not use ref_lat/lon input for file in radar-coord
#if not 'X_FIRST' in atr.keys() and (inps.ref_lat or inps.ref_lon):
# print 'Lat/lon reference input is disabled for file in radar coord.'
# inps.ref_lat = None
# inps.ref_lon = None
# Convert ref_lat/lon to ref_y/x
if inps.ref_lat and inps.ref_lon:
if 'X_FIRST' in list(atr.keys()):
inps.ref_y = subset.coord_geo2radar(inps.ref_lat, atr, 'lat')
inps.ref_x = subset.coord_geo2radar(inps.ref_lon, atr, 'lon')
else:
# Convert lat/lon to az/rg for radar coord file using geomap*.trans file
inps.ref_y, inps.ref_x = ut.glob2radar(np.array(inps.ref_lat), np.array(inps.ref_lon),\
inps.trans_file, atr)[0:2]
print('Input reference point in lat/lon: ' +
str([inps.ref_lat, inps.ref_lon]))
print('Input reference point in y/x : ' + str([inps.ref_y, inps.ref_x]))
# Do not use ref_y/x outside of data coverage
if (inps.ref_y and inps.ref_x
and not (0 <= inps.ref_y <= length and 0 <= inps.ref_x <= width)):
inps.ref_y = None
inps.ref_x = None
print('WARNING: input reference point is OUT of data coverage!')
print('Continue with other method to select reference point.')
# Do not use ref_y/x in masked out area
if inps.ref_y and inps.ref_x and inps.mask_file:
print('mask: ' + inps.mask_file)
mask = readfile.read(inps.mask_file)[0]
if mask[inps.ref_y, inps.ref_x] == 0:
inps.ref_y = None
inps.ref_x = None
print('WARNING: input reference point is in masked OUT area!')
print('Continue with other method to select reference point.')
##### Select method
if inps.ref_y and inps.ref_x:
inps.method = 'input-coord'
elif inps.coherence_file:
if os.path.isfile(inps.coherence_file):
inps.method = 'max-coherence'
else:
inps.coherence_file = None
if inps.method == 'manual':
inps.parallel = False
print('Parallel processing is disabled for manual seeding method.')
##### Seeding file by file
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
if len(inps.file) == 1:
seed_file_inps(inps.file[0], inps, inps.outfile)
elif inps.parallel:
#num_cores = min(multiprocessing.cpu_count(), len(inps.file))
#print 'parallel processing using %d cores ...'%(num_cores)
Parallel(n_jobs=num_cores)(delayed(seed_file_inps)(file, inps)
for file in inps.file)
else:
for File in inps.file:
seed_file_inps(File, inps)
print('Done.')
return
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 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 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 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 match_two_files(File1, File2, outName=None, manual_match=False, disp_fig=False):
'''Match two geocoded files by estimating their offset.
Better for two files with common area overlaping.
'''
# Read Input Files
V1, atr1 = readfile.read(File1)
V2, atr2 = readfile.read(File2)
k = atr1['FILE_TYPE']
print('---------------------------')
print('matching 2 '+k+' files:\n'+File1+'\n'+File2)
# Get Coverage Info
# Boundary Info - 2 Input Files
West1,East1,North1,South1,width1,length1 = corners(atr1)
West2,East2,North2,South2,width2,length2 = corners(atr2)
# Boundary Info - Output File
print('finding the corners of the whole area')
West = min(West1, West2)
East = max(East1, East2)
North = max(North1,North2)
South = min(South1,South2)
lon_step = float(atr1['X_STEP'])
lat_step = float(atr1['Y_STEP'])
width = int(round((East - West )/lon_step + 1.0))
length = int(round((South - North)/lat_step + 1.0))
# Get Index of Input Files in Output Files
lon_seq = np.arange(West, West +width *lon_step, lon_step)
lat_seq = np.arange(North, North+length*lat_step, lat_step)
indx1 = nearest(West1, lon_seq)[0]
indy1 = nearest(North1, lat_seq)[0]
indx2 = nearest(West2, lon_seq)[0]
indy2 = nearest(North2, lat_seq)[0]
# Estimate Offset of overlaping area
VV1 = np.zeros([length,width])
VV2 = np.zeros([length,width])
VV1[:,:] = np.nan
VV2[:,:] = np.nan
VV1[indy1:indy1+length1, indx1:indx1+width1] = V1
VV2[indy2:indy2+length2, indx2:indx2+width2] = V2
if not manual_match:
VV_diff = VV2 - VV1
offset = np.nansum(VV_diff) / np.sum(np.isfinite(VV_diff))
if np.isnan(offset):
print('**************************************************')
print('WARNING:')
print('')
print('No common area found between two velocity maps')
print('At least one common pixel is required.')
print('No matching applied. ')
print('Continue with manual matching ...')
print(' by selecting two line from each dataset to calculate the offset')
print('**************************************************')
manual_matching = True
if manual_match:
offset = manual_offset_estimate(V1, V2)
# Adjust File2 value using offset
if np.isnan(offset):
print('**************************************************')
print('WARNING:')
print('')
print('No offset is estimated and no matching applied.')
print('Continue to merge two input files without any adjustment.')
print('**************************************************')
else:
print('Average offset between two velocity in the common area is: ' + str(offset))
V2 = V2 - offset
# Get merged data matrix value
indv2 = np.isfinite(V2)
VV = np.zeros([length,width])
VV[:,:] = np.nan
VV[indy1:indy1+length1, indx1:indx1+width1] = V1
VV[indy2:indy2+length2, indx2:indx2+width2][indv2] = V2[indv2]
# Write Output File
if not outName:
ext = os.path.splitext(File1)[1]
outName = os.path.splitext(os.path.basename(File1))[0]+'_'+\
os.path.splitext(os.path.basename(File2))[0]+ext
print('writing >>> '+outName)
atr = atr1.copy()
atr['WIDTH'] = width
atr['FILE_LENGTH'] = length
atr['X_FIRST'] = West
atr['Y_FIRST'] = North
writefile.write(VV, atr, outName)
# Display
fig_size = [16.0,16.0]
fig = plt.figure(figsize=fig_size)
print('plotting result ...')
fig=plt.subplot(2,2,1); plt.imshow(VV1); plt.title(File1); plt.colorbar()
fig=plt.subplot(2,2,2); plt.imshow(VV2); plt.title(File2); plt.colorbar()
fig=plt.subplot(2,2,3); plt.imshow(VV); plt.title(outName); plt.colorbar()
fig=plt.subplot(2,2,4); plt.imshow(VV_diff); plt.title('Offset'); plt.colorbar()
plt.tight_layout()
plt.savefig(outName+'.png', bbox_inches='tight', transparent=True, dpi=150)
print('save figure to '+outName+'.png')
if disp_fig:
print('showing ...')
plt.show()
return outName
def main(argv):
try:
if argv[0] in ['-h', '--help']:
usage()
sys.exit(1)
else:
File = argv[0]
except:
usage()
sys.exit(1)
try:
maskFile = argv[1]
except:
pass
##################################
h5file = h5py.File(File)
dateList = list(h5file['timeseries'].keys())
##################################
##### Read Mask File
## Priority:
## Input mask file > pysar.mask.file > existed Modified_Mask.h5 > existed Mask.h5
try:
maskFile
except:
if os.path.isfile('Modified_Mask.h5'): maskFile = 'Modified_Mask.h5'
elif os.path.isfile('Mask.h5'): maskFile = 'Mask.h5'
else:
print('No mask found!')
sys.exit(1)
try:
Mask, Matr = readfile.read(maskFile)
print('mask: ' + maskFile)
except:
print('Can not open mask file: ' + maskFile)
sys.exit(1)
##################################
Mask = Mask.flatten(1)
ndx = Mask != 0
##################################
nt = float(h5file['timeseries'].attrs['LOOK_REF1'])
ft = float(h5file['timeseries'].attrs['LOOK_REF2'])
sy, sx = np.shape(dset1)
npixel = sx * sy
lookangle = np.tile(np.linspace(nt, ft, sx), [sy, 1])
lookangle = lookangle.flatten(1) * np.pi / 180.0
Fh = -np.sin(lookangle)
Fv = -np.cos(lookangle)
try:
daz = float(h5file['timeseries'].attrs['AZIMUTH_PIXEL_SIZE'])
except:
print('''
ERROR!
The attribute AZIMUTH_PIXEL_SIZE was not found!
Possible cause of error: Geo coordinate.
This function works only in radar coordinate system.
''')
sys.exit(1)
lines = np.tile(np.arange(0, sy, 1), [1, sx])
lines = lines.flatten(1)
rs = lines * daz
A = np.zeros([npixel, 4])
A[:, 0] = Fh
A[:, 1] = Fh * rs
A[:, 2] = Fv
A[:, 3] = Fv * rs
Bh = []
Bv = []
Bhrate = []
Bvrate = []
Be = np.zeros([len(dateList), 4])
try:
excludedDates = argv[2]
except:
excludedDates = []
print(
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
)
for i in range(1, len(dateList)):
if not dateList[i] in excludedDates:
dset = h5file['timeseries'].get(dateList[i])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
L = data.flatten(1)
Berror = np.dot(np.linalg.pinv(A[ndx]), L[ndx])
Bh.append(Berror[0])
Bhrate.append(Berror[1])
Bv.append(Berror[2])
Bvrate.append(Berror[3])
Be[i, :] = Berror
else:
print(
str(dateList[i]) +
' is not considered for Baseline Error estimation')
print(
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
)
print('baseline error mean std')
print(' bh : ' + str(np.mean(Bh)) + ' , ' +
str(np.std(Bh)))
print(' bh rate : ' + str(np.mean(Bhrate)) + ' , ' +
str(np.std(Bhrate)))
print(' bv : ' + str(np.mean(Bv)) + ' , ' +
str(np.std(Bv)))
print(' bv rate : ' + str(np.mean(Bvrate)) + ' , ' +
str(np.std(Bvrate)))
print(
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
)
print('bh error of each epoch:')
print(Bh)
print(
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
)
print('bv error of each epoch:')
print(Bv)
# plt.hist(Bh,bins=8,normed=True)
# formatter = FuncFormatter(to_percent)
# Set the formatter
# plt.gca().yaxis.set_major_formatter(formatter)
# plt.show()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
print('Estimating Baseline error from each differences ...')
Bedif = np.zeros([len(dateList), 4])
for i in range(1, len(dateList)):
dset1 = h5file['timeseries'].get(dateList[i - 1])
data1 = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
dset2 = h5file['timeseries'].get(dateList[i])
data2 = dset2[0:dset2.shape[0], 0:dset2.shape[1]]
data = data2 - data1
L = data.flatten(1)
Berrord = np.dot(np.linalg.pinv(A[ndx]), L[ndx])
Bedif[i, :] = Berrord
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
yref = int(h5file['timeseries'].attrs['ref_y'])
xref = int(h5file['timeseries'].attrs['ref_x'])
orbEffect = np.zeros([len(dateList), sy, sx])
for i in range(1, len(dateList)):
effect = np.dot(A, Be[i, :])
effect = np.reshape(effect, [sx, sy]).T
# orbEffect[i,:,:]=orbEffect[i-1,:,:]+effect
# orbEffect[i,:,:]=orbEffect[i,:,:]-orbEffect[i,yref,xref]
orbEffect[i, :, :] = effect - effect[yref, xref]
del effect
print('Correctiing the time series ')
outName = File.replace('.h5', '') + '_baselineCor.h5'
h5orbCor = h5py.File(outName, 'w')
group = h5orbCor.create_group('timeseries')
for i in range(len(dateList)):
dset1 = h5file['timeseries'].get(dateList[i])
data = dset1[0:dset1.shape[0], 0:dset1.shape[1]] - orbEffect[i, :, :]
dset = group.create_dataset(dateList[i], data=data, compression='gzip')
for key, value in h5file['timeseries'].attrs.items():
group.attrs[key] = value
try:
dset1 = h5file['mask'].get('mask')
group = h5orbCor.create_group('mask')
dset = group.create_dataset('mask', data=dset1, compression='gzip')
except:
pass
h5file.close()
h5orbCor.close()
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 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 list(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.items():
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 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 main(argv):
inps = cmdLineParse()
print('\n**************** Transect *********************')
print('number of file: ' + str(len(inps.file)))
print(inps.file)
##### Start / End Point Input
# 1. lonlat file
if inps.lola_file:
inps.start_lalo, inps.end_lalo = read_lonlat_file(inps.lola_file)
# 2. Manually select in window display
if (not inps.start_yx or not inps.end_yx) and (not inps.start_lalo
or not inps.end_lalo):
print('No input yx/lalo found.')
print('Continue with manually select start and end point.')
inps.start_yx, inps.end_yx = manual_select_start_end_point(
inps.file[0])
# Message
if inps.start_lalo and inps.end_lalo:
print('Start point: lat = ' + str(inps.start_lalo[0]) + ', lon = ' +
str(inps.start_lalo[1]))
print('End point: lat = ' + str(inps.end_lalo[0]) + ', lon = ' +
str(inps.end_lalo[1]))
else:
print('Start point: y = ' + str(inps.start_yx[0]) + ', x = ' +
str(inps.start_yx[1]))
print('End point: y = ' + str(inps.end_yx[0]) + ', x = ' +
str(inps.end_yx[1]))
##### Get Transection/Profiles Data
print('extract transect from input files ...')
transectList, atrList = transect_list(inps.file, inps)
if inps.dem:
demTransectList, demAtrList = transect_list([inps.dem], inps)
print('profile length: ' + str(transectList[0][-1, 0] / 1000.0) + ' km')
##### Plot
# Figure 1 - Profile line in the 1st input file
print('plot profile line in the 1st input file')
fig0 = plt.figure()
ax0 = fig0.add_axes([0.1, 0.1, 0.8, 0.8])
data0, atr0 = readfile.read(inps.file[0])
ax0.imshow(data0)
if inps.start_lalo and inps.end_lalo:
[y0, y1] = sub.coord_geo2radar([inps.start_lalo[0], inps.end_lalo[0]],
atr0, 'lat')
[x0, x1] = sub.coord_geo2radar([inps.start_lalo[1], inps.end_lalo[1]],
atr0, 'lon')
else:
[y0, y1] = [inps.start_yx[0], inps.end_yx[0]]
[x0, x1] = [inps.start_yx[1], inps.end_yx[1]]
ax0.plot([x0, x1], [y0, y1], 'ro-')
ax0.set_xlim(0, np.shape(data0)[1])
ax0.set_ylim(np.shape(data0)[0], 0)
ax0.set_title('Transect Line in ' + inps.file[0])
# Status bar
def format_coord(x, y):
col = int(x)
row = int(y)
if 0 <= col < data0.shape[1] and 0 <= row < data0.shape[0]:
z = data0[row, col]
if 'X_FIRST' in list(atr0.keys()):
lat = sub.coord_radar2geo(row, atr0, 'row')
lon = sub.coord_radar2geo(col, atr0, 'col')
return 'lon=%.4f, lat=%.4f, x=%.0f, y=%.0f, value=%.4f' % (
lon, lat, x, y, z)
else:
return 'x=%.0f, y=%.0f, value=%.4f' % (x, y, z)
else:
return 'x=%.0f, y=%.0f' % (x, y)
ax0.format_coord = format_coord
# Figure 2 - Transections/Profiles
print('plot profiles')
fig, ax = plt.subplots(figsize=inps.fig_size)
# Plot 2.1 - Input Files
if not inps.disp_unit:
inps.disp_unit = atrList[0]['UNIT']
inps.disp_scale, inps.disp_unit = get_scale_from_disp_unit(
inps.disp_unit, atrList[0]['UNIT'])
value_min = 0
value_max = 0
for i in range(len(inps.file)):
# Profile Color based on Asc/Desc direction
if atrList[i]['ORBIT_DIRECTION'][0].upper() == 'A':
p_color = 'crimson'
else:
p_color = 'royalblue'
# Plot
distance = transectList[i][:, 0] / 1000.0 # km
value = transectList[i][:, 1] * inps.disp_scale - inps.disp_offset * i
ax.plot(distance,
value,
'.',
color=p_color,
markersize=inps.marker_size)
# Y Stat
value_min = np.nanmin([value_min, np.nanmin(value)])
value_max = np.nanmax([value_max, np.nanmax(value)])
# Y axis
if not inps.disp_min:
inps.disp_min = np.floor(value_min - (value_max - value_min) * 1.2 /
len(inps.file))
if not inps.disp_max:
inps.disp_max = np.ceil(value_max)
ax.set_ylim(inps.disp_min, inps.disp_max)
ax.set_ylabel('Mean LOS Velocity (' + inps.disp_unit + ')',
fontsize=inps.font_size)
# X axis
ax.set_xlabel('Distance (km)', fontsize=inps.font_size)
ax.tick_params(which='both', direction='out', labelsize=inps.font_size)
# Plot 2.2 - DEM
if inps.dem:
ax2 = ax.twinx()
distance = demTransectList[0][:, 0] / 1000.0 # km
value = demTransectList[0][:, 1] / 1000.0 # km
ax2.fill_between(distance, 0, value, facecolor='gray')
# Y axis - display DEM in the bottom
value_min = np.nanmin(value)
value_max = np.nanmax(value)
if not inps.dem_disp_min:
inps.dem_disp_min = np.floor(value_min * 2.0) / 2.0
if not inps.dem_disp_max:
inps.dem_disp_max = np.ceil((value_max + (value_max - value_min) *
(len(inps.file) + 0.0)) * 2.0) / 2.0
ax2.set_ylim(inps.dem_disp_min, inps.dem_disp_max)
## Show lower part of yaxis
#dem_tick = ax2.yaxis.get_majorticklocs()
#dem_tick = dem_tick[:len(dem_tick)/2]
#ax2.set_yticks(dem_tick)
ax2.set_ylabel('Elevation (km)', fontsize=inps.font_size)
ax2.tick_params(which='both',
direction='out',
labelsize=inps.font_size)
## X axis - Shared
distanceMax = np.nanmax(transectList[0][:, 0] / 1000.0) # in km
plt.xlim(0, distanceMax)
plt.tight_layout()
##### Output
if not inps.outfile:
figBase = 'transect_x' + str(x0) + 'y' + str(y0) + '_x' + str(
x1) + 'y' + str(y1)
else:
figBase, inps.outfile_ext = os.path.splitext(inps.outfile)
if not inps.outfile_ext:
inps.outfile_ext = '.png'
if inps.save_fig:
print('writing >>> ' + figBase + inps.outfile_ext)
fig0.savefig(inps.file[-1] + inps.outfile_ext,
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
fig.savefig(figBase + inps.outfile_ext,
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('writing >>> ' + figBase + '.mat')
transect_mat = {}
for i in range(len(inps.file)):
project = atrList[i]['PROJECT_NAME']
transect_mat[project] = transectList[i]
if inps.dem:
transect_mat['elevation'] = demTransectList[0]
sio.savemat(figBase + '.mat', {'transection': transect_mat})
# Display
if inps.disp_fig:
print('showing ...')
plt.show()
return
def main(argv):
inps = cmdLineParse()
# Input File Info
atr = readfile.read_attribute(inps.file)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
k = atr['FILE_TYPE']
print('Input file is '+k+': '+inps.file)
# default output filename
if not inps.outfile:
if k == 'temporal_coherence':
inps.outfile = 'maskTempCoh.h5'
else:
inps.outfile = 'mask.h5'
if inps.file.startswith('geo_'):
inps.outfile = 'geo_'+inps.outfile
##### Mask: Non-zero
if inps.nonzero:
print('generate mask for all pixels with non-zero value')
inps.outfile = ut.nonzero_mask(inps.file, inps.outfile)
return inps.outfile
##### Mask: Threshold
print('create initial mask with the same size as the input file and all = 1')
mask = np.ones((length, width), dtype=np.float32)
data, atr = readfile.read(inps.file, inps.epoch)
# min threshold
if inps.vmin:
mask[data<inps.vmin] = 0
print('all pixels with value < %s = 0' % str(inps.vmin))
# max threshold
if inps.vmax:
mask[data>inps.vmax] = 0
print('all pixels with value > %s = 0' % str(inps.vmax))
# nan value
mask[np.isnan(data)] = 0
print('all pixels with nan value = 0')
# subset in Y
if inps.subset_y:
y0,y1 = sorted(inps.subset_y)
mask[0:y0,:] = 0
mask[y1:length,:] = 0
print('all pixels with y OUT of [%d, %d] = 0' % (y0,y1))
# subset in x
if inps.subset_x:
x0,x1 = sorted(inps.subset_x)
mask[:,0:x0] = 0
mask[:,x1:width] = 0
print('all pixels with x OUT of [%d, %d] = 0' % (x0,x1))
## Write mask file
print('writing >>> '+inps.outfile)
atr['FILE_TYPE'] = 'mask'
writefile.write(mask, atr, inps.outfile)
return inps.outfile
def mask_file(File, maskFile, outFile=None, inps_dict=None):
''' Mask input File with maskFile
Inputs:
File/maskFile - string,
inps_dict - dictionary including the following options:
subset_x/y - list of 2 ints, subset in x/y direction
thr - float, threshold/minValue to generate mask
Output:
outFile - string
'''
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
print('masking ' + k + ' file: ' + File + ' ...')
# Read maskFile
atrm = readfile.read_attribute(maskFile)
km = atrm['FILE_TYPE']
if km not in multi_group_hdf5_file + multi_dataset_hdf5_file:
print('reading mask file: ' + maskFile)
mask = readfile.read(maskFile)[0]
if inps_dict:
mask = update_mask(mask, inps_dict)
if not outFile:
outFile = os.path.splitext(File)[0] + '_masked' + os.path.splitext(
File)[1]
if k in ['timeseries', 'interferograms', 'wrapped', 'coherence']:
h5file = h5py.File(File, 'r')
epochList = sorted(h5file[k].keys())
h5out = h5py.File(outFile, 'w')
print('writing >>> ' + outFile)
##### Multiple Dataset File
if k == 'timeseries':
print('number of acquisitions: ' + str(len(epochList)))
group = h5out.create_group(k)
for d in epochList:
print(d)
unw = h5file[k].get(d)[:]
unw = mask_matrix(unw, mask)
dset = group.create_dataset(d, data=unw, compression='gzip')
for key, value in atr.items():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print('number of interferograms: ' + str(len(epochList)))
gg = h5out.create_group(k)
# Mask multi group file with multi group coherence file
if km == 'coherence':
h5mask = h5py.File(maskFile, 'r')
cohList = sorted(h5mask[km].keys())
if len(cohList) != len(epochList):
sys.exit('ERROR: cohERROR: erence mask file has different\
number of interferograms than input file!')
for i in range(len(epochList)):
igram = epochList[i]
print(igram)
unw = h5file[k][igram].get(igram)[:]
if km == 'coherence':
coh = cohList[i]
print(coh)
mask = h5mask[km][coh].get(coh)[:]
if not inps_dict:
mask = update_mask(mask, inps_dict)
unw = mask_matrix(unw, mask)
group = gg.create_group(igram)
dset = group.create_dataset(igram, data=unw, compression='gzip')
for key, value in h5file[k][igram].attrs.items():
group.attrs[key] = value
##### Single Dataset File
else:
unw, atr = readfile.read(File)
unw = mask_matrix(unw, mask)
print('writing >>> ' + outFile)
writefile.write(unw, atr, outFile)
try:
h5file.close()
except:
pass
try:
h5out.close()
except:
pass
try:
h5mask.close()
except:
pass
return outFile
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
print('number of input files: ' + str(len(inps.file)))
print(inps.file)
#print '\n**************** Subset *********************'
atr = readfile.read_attribute(inps.file[0])
##### Convert All Inputs into subset_y/x/lat/lon
# Input Priority: subset_y/x/lat/lon > reference > template > tight
if not inps.subset_x and not inps.subset_y and not inps.subset_lat and not inps.subset_lon:
# 1. Read subset info from Reference File
if inps.reference:
ref_atr = readfile.read_attribute(inps.reference)
pix_box, geo_box = get_coverage_box(ref_atr)
print('using subset info from ' + inps.reference)
# 2. Read subset info from template options
elif inps.template_file:
pix_box, geo_box = read_subset_template2box(inps.template_file)
print('using subset info from ' + inps.template_file)
# 3. Use subset from tight info
elif inps.tight:
if atr['FILE_TYPE'] == '.trans':
# Non-zero area in geomap_*.trans file, accurate
trans_rg, trans_atr = readfile.read(inps.file[0], (), 'range')
idx_row, idx_col = np.nonzero(trans_rg)
pix_box = (np.min(idx_col) - 10, np.min(idx_row) - 10,
np.max(idx_col) + 10, np.max(idx_row) + 10)
geo_box = box_pixel2geo(pix_box, trans_atr)
else:
print(
'ERROR: --tight option only works for geomap_*.trans file.\n'
)
inps.tight = False
sys.exit(1)
## from LAT/LON_REF*, which is not accurate
#lats = [atr['LAT_REF1'], atr['LAT_REF3'], atr['LAT_REF4'], atr['LAT_REF2']]
#lons = [atr['LON_REF1'], atr['LON_REF3'], atr['LON_REF4'], atr['LON_REF2']]
#lats = [float(i) for i in lats]
#lons = [float(i) for i in lons]
#lalo_buff = min([max(lats)-min(lats), max(lons)-min(lons)]) * 0.05
#geo_box = (min(lons)-lalo_buff, max(lats)+lalo_buff, max(lons)+lalo_buff, min(lats)-lalo_buff)
#pix_box = None
#if not inps.fill_value: inps.fill_value = np.nan
#print 'using subset info from scene footprint - LAT/LON_REF1/2/3/4'
else:
raise Exception('No subset inputs found!')
# Update subset_y/x/lat/lon
inps = subset_box2inps(inps, pix_box, geo_box)
##### --bbox option
if inps.trans_file:
## Seperate files in radar and geo coord
rdrFileList = []
geoFileList = []
for File in inps.file:
atr = readfile.read_attribute(File)
if 'X_FIRST' in list(atr.keys()):
geoFileList.append(File)
else:
rdrFileList.append(File)
## Calculate bbox
rdrFile = rdrFileList[0]
atr_rdr = readfile.read_attribute(rdrFile)
if inps.subset_lat and inps.subset_lon:
print('use subset input in lat/lon')
print('calculate corresponding bounding box in radar coordinate.')
geo_box = (inps.subset_lon[0], inps.subset_lat[1],
inps.subset_lon[1], inps.subset_lat[0])
pix_box = bbox_geo2radar(geo_box, atr_rdr, inps.trans_file)
else:
print('use subset input in y/x')
print('calculate corresponding bounding box in geo coordinate.')
pix_box = (inps.subset_x[0], inps.subset_y[0], inps.subset_x[1],
inps.subset_y[1])
geo_box = bbox_radar2geo(pix_box, atr_rdr, inps.trans_file)
print('geo box: ' + str(geo_box))
print('pixel box: ' + str(pix_box))
## Subset files
inps.fill_value = 0
print('--------------------------------------------')
print('subseting dataset in geo coord geo_box: ' + str(geo_box))
inps = subset_box2inps(inps, None, geo_box)
subset_file_list(geoFileList, inps)
print('--------------------------------------------')
print('subseting dataset in radar coord pix_box: ' + str(pix_box))
inps = subset_box2inps(inps, pix_box, None)
subset_file_list(rdrFileList, inps)
else:
##### Subset files
subset_file_list(inps.file, inps)
print('Done.')
return
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):
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):
try:
File = argv[0]
demFile = argv[1]
p = int(argv[2])
except:
usage()
sys.exit(1)
try:
baseline_error = argv[3]
except:
baseline_error = 'range_and_azimuth'
print(baseline_error)
##################################
h5file = h5py.File(File)
dateList = list(h5file['timeseries'].keys())
##################################
try:
maskFile = argv[4]
except:
if os.path.isfile('Modified_Mask.h5'): maskFile = 'Modified_Mask.h5'
elif os.path.isfile('Mask.h5'): maskFile = 'Mask.h5'
else:
print('No mask found!')
sys.exit(1)
try:
Mask, Matr = readfile.read(maskFile)
print('mask: ' + maskFile)
except:
print('Can not open mask file: ' + maskFile)
sys.exit(1)
#try:
# maskFile=argv[4]
# h5Mask = h5py.File(maskFile,'r')
# kMask=h5Mask.keys()
# dset1 = h5Mask[kMask[0]].get(kMask[0])
# Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
#except:
# dset1 = h5file['mask'].get('mask')
# Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
##################################
Mask = Mask.flatten(1)
ndx = Mask != 0
##################################
# h5file = h5py.File(File)
# dateList = h5file['timeseries'].keys()
##################################
nt = float(h5file['timeseries'].attrs['LOOK_REF1'])
ft = float(h5file['timeseries'].attrs['LOOK_REF2'])
sy, sx = np.shape(dset1)
npixel = sx * sy
lookangle = np.tile(np.linspace(nt, ft, sx), [sy, 1])
lookangle = lookangle.flatten(1) * np.pi / 180.0
Fh = -np.sin(lookangle)
Fv = -np.cos(lookangle)
print('Looking for azimuth pixel size')
try:
daz = float(h5file['timeseries'].attrs['AZIMUTH_PIXEL_SIZE'])
except:
print('''
ERROR!
The attribute AZIMUTH_PIXEL_SIZE was not found!
Possible cause of error: Geo coordinate.
This function works only in radar coordinate system.
''')
sys.exit(1)
lines = np.tile(np.arange(0, sy, 1), [1, sx])
lines = lines.flatten(1)
rs = lines * daz
if baseline_error == 'range_and_azimuth':
A = np.zeros([npixel, 4])
A[:, 0] = Fh
A[:, 1] = Fh * rs
A[:, 2] = Fv
A[:, 3] = Fv * rs
num_base_par = 4
elif baseline_error == 'range':
A = np.zeros([npixel, 2])
A[:, 0] = Fh
A[:, 1] = Fv
num_base_par = 2
###########################################
yref = int(h5file['timeseries'].attrs['ref_y'])
xref = int(h5file['timeseries'].attrs['ref_x'])
###########################################
if os.path.basename(demFile).split('.')[1] == 'hgt':
amp, dem, demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1] == 'dem':
dem, demRsc = readfile.read_real_int16(demFile)
dem = dem - dem[yref][xref]
dem = dem.flatten(1)
###################################################
if p == 1:
# A=np.vstack((dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem, np.ones(len(dem)))).T
elif p == 2:
# A=np.vstack((dem[ndx]**2,dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem**2, dem, np.ones(len(dem)))).T
elif p == 3:
# A = np.vstack((dem[ndx]**3,dem[ndx]**2,dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem**3, dem**2, dem, np.ones(len(dem)))).T
print(np.shape(A))
Ainv = np.linalg.pinv(A)
###################################################
Bh = []
Bv = []
Bhrate = []
Bvrate = []
Be = np.zeros([len(dateList), num_base_par + p + 1])
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
for i in range(1, len(dateList)):
dset = h5file['timeseries'].get(dateList[i])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
L = data.flatten(1)
M = np.hstack((A, B))
Berror = np.dot(np.linalg.pinv(M[ndx]), L[ndx])
Bh.append(Berror[0])
Bhrate.append(Berror[1])
Bv.append(Berror[2])
Bvrate.append(Berror[3])
Be[i, :] = Berror
print(Berror)
print(
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
)
print('baseline error mean std')
print(' bh : ' + str(np.mean(Bh)) + ' , ' +
str(np.std(Bh)))
print(' bh rate : ' + str(np.mean(Bhrate)) + ' , ' +
str(np.std(Bhrate)))
print(' bv : ' + str(np.mean(Bv)) + ' , ' +
str(np.std(Bv)))
print(' bv rate : ' + str(np.mean(Bvrate)) + ' , ' +
str(np.std(Bvrate)))
print(
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
)
# plt.hist(Bh,bins=8,normed=True)
# formatter = FuncFormatter(to_percent)
# Set the formatter
# plt.gca().yaxis.set_major_formatter(formatter)
# plt.show()
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
# print 'Estimating Baseline error from each differences ...'
orbEffect = np.zeros([len(dateList), sy, sx])
for i in range(1, len(dateList)):
effect = np.dot(M, Be[i, :])
effect = np.reshape(effect, [sx, sy]).T
# orbEffect[i,:,:]=orbEffect[i-1,:,:]+effect
# orbEffect[i,:,:]=orbEffect[i,:,:]-orbEffect[i,yref,xref]
orbEffect[i, :, :] = effect - effect[yref, xref]
del effect
print('Correctiing the time series ')
outName = File.replace('.h5', '') + '_baseTropCor.h5'
h5orbCor = h5py.File(outName, 'w')
group = h5orbCor.create_group('timeseries')
for i in range(len(dateList)):
dset1 = h5file['timeseries'].get(dateList[i])
data = dset1[0:dset1.shape[0], 0:dset1.shape[1]] - orbEffect[i, :, :]
dset = group.create_dataset(dateList[i], data=data, compression='gzip')
for key, value in h5file['timeseries'].attrs.items():
group.attrs[key] = value
dset1 = h5file['mask'].get('mask')
group = h5orbCor.create_group('mask')
dset = group.create_dataset('mask', data=dset1, compression='gzip')
h5file.close()
h5orbCor.close()
return
def main(argv):
inps = cmdLineParse()
atr = readfile.read_attribute(inps.velocity_file)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
# Check subset input
if inps.subset_y:
inps.subset_y = sorted(inps.subset_y)
print('subset in y/azimuth direction: ' + str(inps.subset_y))
else:
inps.subset_y = [0, length]
if inps.subset_x:
inps.subset_x = sorted(inps.subset_x)
print('subset in x/range direction: ' + str(inps.subset_x))
else:
inps.subset_x = [0, width]
y0, y1 = inps.subset_y
x0, x1 = inps.subset_x
# Read velocity/rate
velocity = readfile.read(inps.velocity_file)[0]
print('read velocity file: ' + inps.velocity_file)
k = 'interferograms'
h5 = h5py.File(inps.ifgram_file, 'r')
ifgram_list = sorted(h5[k].keys())
ifgram_num = len(ifgram_list)
date12_list = ptime.list_ifgram2date12(ifgram_list)
print('number of interferograms: ' + str(ifgram_num))
##### Select interferograms with unwrapping error
if inps.percentage > 0.0:
mask = readfile.read(inps.mask_file)[0]
print('read mask for pixels with unwrapping error from file: ' +
inps.mask_file)
unw_err_ifgram_num = int(np.rint(inps.percentage * ifgram_num))
unw_err_ifgram_idx = random.sample(list(range(ifgram_num)),
unw_err_ifgram_num)
unw_err_ifgram_list = [ifgram_list[i] for i in unw_err_ifgram_idx]
unw_err_date12_list = [date12_list[i] for i in unw_err_ifgram_idx]
print(
'randomly choose the following %d interferograms with unwrapping error'
% unw_err_ifgram_num)
print(unw_err_date12_list)
unit_unw_err = 2.0 * np.pi * mask
else:
unw_err_ifgram_list = []
###### Generate simulated interferograms
m_dates = ptime.yyyymmdd([i.split('-')[0] for i in date12_list])
s_dates = ptime.yyyymmdd([i.split('-')[1] for i in date12_list])
range2phase = -4.0 * np.pi / float(atr['WAVELENGTH'])
print('writing simulated interferograms file: ' + inps.outfile)
h5out = h5py.File(inps.outfile, 'w')
group = h5out.create_group('interferograms')
for i in range(ifgram_num):
ifgram = ifgram_list[i]
# Get temporal baseline in years
t1 = datetime.datetime(*time.strptime(m_dates[i], "%Y%m%d")[0:5])
t2 = datetime.datetime(*time.strptime(s_dates[i], "%Y%m%d")[0:5])
dt = (t2 - t1)
dt = float(dt.days) / 365.25
# Simuated interferograms with unwrap error
unw = velocity * dt * range2phase
if ifgram in unw_err_ifgram_list:
rand_int = random.sample(list(range(1, 10)), 1)[0]
unw += rand_int * unit_unw_err
print(ifgram + ' - add unwrapping error of %d*2*pi' % rand_int)
else:
print(ifgram)
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram,
data=unw[y0:y1, x0:x1],
compression='gzip')
for key, value in h5[k][ifgram].attrs.items():
gg.attrs[key] = value
if ifgram in unw_err_ifgram_list:
gg.attrs['unwrap_error'] = 'yes'
else:
gg.attrs['unwrap_error'] = 'no'
gg.attrs['FILE_LENGTH'] = y1 - y0
gg.attrs['WIDTH'] = x1 - x0
h5.close()
h5out.close()
print('Done.')
return inps.outfile
