def main(argv):
inps = cmdLineParse()
inps.outfile = os.path.abspath(inps.outfile)
atr = readfile.read_attribute(inps.file[0])
k = atr['FILE_TYPE']
if not inps.sensor:
inps.sensor = get_mission_name(atr)
print 'Sensor name: %s' % (inps.sensor)
m_date_list = []
s_date_list = []
bperp_list = []
inps.file = ut.get_file_list(inps.file, abspath=True)
if os.path.splitext(inps.file[0])[1] not in ['.h5', '.he5']:
ifgramNum = len(inps.file)
print 'Number of interferograms: %d' % (ifgramNum)
for fname in inps.file:
try:
date12 = str(
re.findall('\d{8}[-_]\d{8}',
os.path.basename(fname))[0]).replace('_', '-')
except:
date12 = str(
re.findall('\d{6}[-_]\d{6}',
os.path.basename(fname))[0]).replace('_', '-')
m_date, s_date = date12.split('-')
bperp = readfile.read_attribute(fname)['P_BASELINE_TOP_HDR']
m_date_list.append(m_date)
s_date_list.append(s_date)
bperp_list.append(bperp)
else:
h5 = h5py.File(inps.file[0], 'r')
ifgram_list = ut.check_drop_ifgram(h5)
date12_list = ptime.list_ifgram2date12(ifgram_list)
m_date_list = [date12.split('-')[0] for date12 in date12_list]
s_date_list = [date12.split('-')[1] for date12 in date12_list]
for ifgram in ifgram_list:
bperp = h5[k][ifgram].attrs['P_BASELINE_TOP_HDR']
bperp_list.append(bperp)
ifgramNum = len(ifgram_list)
fout = '{0} {1} {2:<15} {3}\n'
fl = open(inps.outfile, 'w')
for i in range(ifgramNum):
fl.write(
fout.format(m_date_list[i], s_date_list[i], bperp_list[i],
inps.sensor))
fl.close()
print 'write to %s' % (inps.outfile)
return
def temporal_coherence(timeseriesFile, ifgramFile):
'''Calculate temporal coherence based on input timeseries file and interferograms file
Inputs:
timeseriesFile - string, path of time series file
ifgramFile - string, path of interferograms file
Output:
temp_coh - 2D np.array, temporal coherence in float32
'''
# Basic Info
atr_ts = readfile.read_attribute(timeseriesFile)
length = int(atr_ts['FILE_LENGTH'])
width = int(atr_ts['WIDTH'])
pixel_num = length * width
# Read time series data
h5timeseries = h5py.File(timeseriesFile, 'r')
date_list = sorted(h5timeseries['timeseries'].keys())
date_num = len(date_list)
print "load time series: " + timeseriesFile
print 'number of acquisitions: ' + str(date_num)
timeseries = np.zeros((date_num, pixel_num), np.float32)
prog_bar = ptime.progress_bar(maxValue=date_num, prefix='loading: ')
for i in range(date_num):
date = date_list[i]
d = h5timeseries['timeseries'].get(date)[:]
timeseries[i][:] = d.flatten(0)
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5timeseries.close()
# Convert displacement from meter to radian
range2phase = -4 * np.pi / float(atr_ts['WAVELENGTH'])
timeseries *= range2phase
# interferograms data
print "interferograms file: " + ifgramFile
atr_ifgram = readfile.read_attribute(ifgramFile)
h5ifgram = h5py.File(ifgramFile, 'r')
ifgram_list = sorted(h5ifgram['interferograms'].keys())
ifgram_list = ut.check_drop_ifgram(h5ifgram, atr_ifgram, ifgram_list)
ifgram_num = len(ifgram_list)
# Design matrix
date12_list = ptime.list_ifgram2date12(ifgram_list)
A1, B = ut.design_matrix(ifgramFile, date12_list)
A0 = -1 * np.ones([ifgram_num, 1])
A = np.hstack((A0, A1))
# Get reference pixel
try:
ref_x = int(atr_ts['ref_x'])
ref_y = int(atr_ts['ref_y'])
print 'find reference pixel in y/x: [%d, %d]' % (ref_y, ref_x)
except ValueError:
print 'No ref_x/y found! Can not calculate temporal coherence without it.'
print 'calculating temporal coherence interferogram by interferogram ...'
print 'number of interferograms: ' + str(ifgram_num)
temp_coh = np.zeros(pixel_num, dtype=np.float32) + 0j
prog_bar = ptime.progress_bar(maxValue=ifgram_num, prefix='calculating: ')
for i in range(ifgram_num):
ifgram = ifgram_list[i]
# read interferogram
data = h5ifgram['interferograms'][ifgram].get(ifgram)[:]
data -= data[ref_y, ref_x]
data = data.flatten(0)
# calculate difference between observed and estimated data
dataEst = np.dot(A[i, :], timeseries)
dataDiff = data - dataEst
temp_coh += np.exp(1j * dataDiff)
prog_bar.update(i + 1, suffix=date12_list[i])
prog_bar.close()
del timeseries, data, dataEst, dataDiff
h5ifgram.close()
temp_coh = np.array((np.absolute(temp_coh) / ifgram_num).reshape(
(length, width)),
dtype=np.float32)
return temp_coh
def main(argv):
inps = cmdLineParse()
if not inps.disp_fig:
plt.switch_backend('Agg')
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
##### 1. Read Info
# Read dateList and bperpList
ext = os.path.splitext(inps.file)[1]
if ext in ['.h5']:
atr = readfile.read_attribute(inps.file)
k = atr['FILE_TYPE']
print 'reading date and perpendicular baseline from ' + k + ' file: ' + os.path.basename(
inps.file)
if not k in multi_group_hdf5_file:
raise ValueError('only the following file type are supported:\n' +
str(multi_group_hdf5_file))
if not inps.coherence_file and k == 'coherence':
inps.coherence_file = inps.file
pbase_list = ut.perp_baseline_ifgram2timeseries(inps.file)[0]
date8_list = ptime.ifgram_date_list(inps.file)
else:
print 'reading date and perpendicular baseline from baseline list file: ' + inps.bl_list_file
date8_list, pbase_list = pnet.read_baseline_file(
inps.bl_list_file)[0:2]
print 'number of acquisitions : ' + str(len(date8_list))
# Read Pairs Info
print 'reading pairs info from file: ' + inps.file
date12_list = pnet.get_date12_list(inps.file)
print 'number of interferograms: ' + str(len(date12_list))
# Read drop_ifgram
date8_list_drop = []
date12_list_drop = []
if ext in ['.h5', '.he5']:
h5 = h5py.File(inps.file, 'r')
ifgram_list_all = sorted(h5[k].keys())
ifgram_list_keep = ut.check_drop_ifgram(h5)
date12_list_keep = ptime.list_ifgram2date12(ifgram_list_keep)
# Get date12_list_drop
date12_list_drop = sorted(
list(set(date12_list) - set(date12_list_keep)))
print 'number of interferograms marked as dropped: ' + str(
len(date12_list_drop))
print 'number of interferograms marked as kept : ' + str(
len(date12_list_keep))
# Get date_list_drop
m_dates = [i.split('-')[0] for i in date12_list_keep]
s_dates = [i.split('-')[1] for i in date12_list_keep]
date8_list_keep = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
date8_list_drop = sorted(list(set(date8_list) - set(date8_list_keep)))
print 'number of acquisitions marked as dropped: ' + str(
len(date8_list_drop))
# Read Coherence List
inps.coherence_list = None
if inps.coherence_file and os.path.isfile(inps.coherence_file):
if inps.mask_file and not os.path.isfile(inps.mask_file):
inps.mask_file = None
inps.coherence_list, inps.coh_date12_list = ut.spatial_average(inps.coherence_file, inps.mask_file, \
saveList=True, checkAoi=False)
if all(np.isnan(inps.coherence_list)):
print 'WARNING: all coherence value are nan! Do not use this and continue.'
inps.coherence_list = None
# Check subset of date12 info between input file and coherence file
if not set(inps.coh_date12_list) >= set(date12_list):
print 'WARNING: not every pair/date12 from input file is in coherence file'
print 'turn off the color plotting of interferograms based on coherence'
inps.coherence_list = None
elif set(inps.coh_date12_list) > set(date12_list):
print 'extract coherence value for all pair/date12 in input file'
inps.coherence_list = [
inps.coherence_list[inps.coh_date12_list.index(i)]
for i in date12_list
]
#inps.coh_thres = 0.7
##### 2. Plot
inps.cbar_label = 'Average spatial coherence'
# Fig 1 - Baseline History
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_perp_baseline_hist(ax, date8_list, pbase_list, vars(inps),
date8_list_drop)
figName = 'BperpHistory' + inps.fig_ext
if inps.save_fig:
fig.savefig(figName, bbox_inches='tight')
print 'save figure to ' + figName
# Fig 2 - Coherence Matrix
if inps.coherence_list:
figName = 'CoherenceMatrix' + inps.fig_ext
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_coherence_matrix(ax, date12_list, inps.coherence_list,\
date12_list_drop, plot_dict=vars(inps))
if inps.save_fig:
fig.savefig(figName, bbox_inches='tight', dpi=150)
print 'save figure to ' + figName
# Fig 3 - Min/Max Coherence History
if inps.coherence_list:
figName = 'CoherenceHistory' + inps.fig_ext
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_coherence_history(ax, date12_list, inps.coherence_list)
if inps.save_fig:
fig.savefig(figName, bbox_inches='tight')
print 'save figure to ' + figName
# Fig 4 - Interferogram Network
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_network(ax, date12_list, date8_list, pbase_list, vars(inps),
date12_list_drop)
figName = 'Network' + inps.fig_ext
if inps.save_fig:
fig.savefig(figName, bbox_inches='tight')
print 'save figure to ' + figName
if inps.save_list:
txtFile = os.path.splitext(inps.file)[0] + '_date12_list.txt'
np.savetxt(txtFile, date12_list, fmt='%s')
print 'save pairs/date12 info to file: ' + txtFile
if inps.disp_fig:
plt.show()
def main(argv):
inps = cmdLineParse()
if not inps.disp_fig:
plt.switch_backend('Agg')
#print '\n******************** Plot Network **********************'
##### 1. Read Info
# Read dateList and bperpList
ext = os.path.splitext(inps.file)[1]
if ext in ['.h5']:
atr = readfile.read_attribute(inps.file)
k = atr['FILE_TYPE']
print 'reading date and perpendicular baseline from '+k+' file: '+os.path.basename(inps.file)
if not k in multi_group_hdf5_file:
raise ValueError('only the following file type are supported:\n'+str(multi_group_hdf5_file))
pbase_list = ut.perp_baseline_ifgram2timeseries(inps.file)[0]
date8_list = ptime.ifgram_date_list(inps.file)
else:
print 'reading date and perpendicular baseline from baseline list file: '+inps.bl_list_file
date8_list, pbase_list = pnet.read_baseline_file(inps.bl_list_file)[0:2]
print 'number of acquisitions : '+str(len(date8_list))
# Read Pairs Info
print 'reading pairs info from file: '+inps.file
date12_list = pnet.get_date12_list(inps.file)
print 'number of interferograms: '+str(len(date12_list))
# Read drop_ifgram
date8_list_drop = []
date12_list_drop = []
if ext in ['.h5','.he5']:
h5 = h5py.File(inps.file, 'r')
ifgram_list_all = sorted(h5[k].keys())
ifgram_list_keep = ut.check_drop_ifgram(h5, atr, ifgram_list_all)
date12_list_keep = ptime.list_ifgram2date12(ifgram_list_keep)
# Get date12_list_drop
date12_list_drop = sorted(list(set(date12_list) - set(date12_list_keep)))
print 'number of interferograms marked as dropped: '+str(len(date12_list_drop))
# Get date_list_drop
m_dates = [i.split('-')[0] for i in date12_list_keep]
s_dates = [i.split('-')[1] for i in date12_list_keep]
date8_list_keep = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
date8_list_drop = sorted(list(set(date8_list) - set(date8_list_keep)))
print 'number of acquisitions marked as dropped: '+str(len(date8_list_drop))
# Read Coherence List
inps.coherence_list = None
if inps.coherence_file and os.path.isfile(inps.coherence_file):
ext = os.path.splitext(inps.coherence_file)[1]
if ext in ['.h5']:
listFile = os.path.splitext(inps.coherence_file)[0]+'_spatialAverage.txt'
if os.path.isfile(listFile):
print 'reading coherence value from existed '+listFile
fcoh = np.loadtxt(listFile, dtype=str)
inps.coherence_list = [float(i) for i in fcoh[:,1]]
inps.coh_date12_list = [i for i in fcoh[:,0]]
else:
print 'calculating average coherence value from '+inps.coherence_file
if inps.mask_file:
mask = readfile.read(inps.mask_file)[0]
else:
mask = None
inps.coherence_list = ut.spatial_average(inps.coherence_file, mask, saveList=True)
inps.coh_date12_list = pnet.get_date12_list(inps.coherence_file)
else:
print 'reading coherence value from '+inps.coherence_file
fcoh = np.loadtxt(inps.coherence_file, dtype=str)
inps.coherence_list = [float(i) for i in fcoh[:,1]]
inps.coh_date12_list = [i for i in fcoh[:,0]]
# Check length of coherence file and input file
if not set(inps.coh_date12_list) == set(date12_list):
print 'WARNING: input coherence list has different pairs/date12 from input file'
print 'turn off the color plotting of interferograms based on coherence'
inps.coherence_list = None
#inps.coh_thres = 0.7
##### 2. Plot
# Fig 1 - Baseline History
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_perp_baseline_hist(ax, date8_list, pbase_list, vars(inps), date8_list_drop)
figName = 'BperpHistory'+inps.fig_ext
if inps.save_fig:
fig.savefig(figName,bbox_inches='tight')
print 'save figure to '+figName
# Fig 2 - Coherence Matrix
if inps.coherence_list:
figName = 'CoherenceMatrix'+inps.fig_ext
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_coherence_matrix(ax, date12_list, inps.coherence_list)
if inps.save_fig:
fig.savefig(figName, bbox_inches='tight')
print 'save figure to '+figName
# Fig 3 - Min/Max Coherence History
if inps.coherence_list:
figName = 'CoherenceHistory'+inps.fig_ext
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_coherence_history(ax, date12_list, inps.coherence_list)
if inps.save_fig:
fig.savefig(figName, bbox_inches='tight')
print 'save figure to '+figName
# Fig 4 - Interferogram Network
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
ax = pnet.plot_network(ax, date12_list, date8_list, pbase_list, vars(inps), date12_list_drop)
figName = 'Network'+inps.fig_ext
if inps.save_fig:
fig.savefig(figName,bbox_inches='tight')
print 'save figure to '+figName
if inps.save_list:
txtFile = os.path.splitext(inps.file)[0]+'_date12_list.txt'
np.savetxt(txtFile, date12_list, fmt='%s')
print 'save pairs/date12 info to file: '+txtFile
if inps.disp_fig:
plt.show()
def main(argv):
##### Read Inputs
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
date12_orig = pnet.get_date12_list(inps.file[0])
print 'input file(s) to be modified: ' + str(inps.file)
print 'number of interferograms: ' + str(len(date12_orig))
atr = readfile.read_attribute(inps.file[0])
# Update inps if template is input
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
if all(not i for i in [inps.reference_file, inps.max_temp_baseline, inps.max_perp_baseline,\
inps.exclude_ifg_index, inps.exclude_date, inps.coherence_based,\
inps.start_date, inps.end_date, inps.reset]):
# Display network for manually modification when there is no other modification input.
print 'No input option found to remove interferogram'
if inps.template_file:
print 'Keep all interferograms by enable --reset option'
inps.reset = True
else:
print 'To manually modify network, please use --manual option '
return
if inps.reset:
print '----------------------------------------------------------------------------'
for file in inps.file:
reset_pairs(file)
mean_coh_txt_file = os.path.splitext(
inps.coherence_file)[0] + '_spatialAverage.txt'
if os.path.isfile(mean_coh_txt_file):
rmCmd = 'rm ' + mean_coh_txt_file
print rmCmd
os.system(rmCmd)
return
# Convert index : input to continous index list
if inps.exclude_ifg_index:
ifg_index = list(inps.exclude_ifg_index)
inps.exclude_ifg_index = []
for index in ifg_index:
index_temp = [int(i) for i in index.split(':')]
index_temp.sort()
if len(index_temp) == 2:
for j in range(index_temp[0], index_temp[1] + 1):
inps.exclude_ifg_index.append(j)
elif len(index_temp) == 1:
inps.exclude_ifg_index.append(int(index))
else:
print 'Unrecoganized input: ' + index
inps.exclude_ifg_index = sorted(inps.exclude_ifg_index)
if max(inps.exclude_ifg_index) > len(date12_orig):
raise Exception('Input index out of range!\n'+\
'input index:'+str(inps.exclude_ifg_index)+'\n'+\
'index range of file: '+str(len(date12_orig)))
##### Get date12_to_rmv
date12_to_rmv = []
# 1. Update date12_to_rmv from reference file
if inps.reference_file:
date12_to_keep = pnet.get_date12_list(inps.reference_file,
check_drop_ifgram=True)
print '----------------------------------------------------------------------------'
print 'use reference pairs info from file: ' + inps.reference_file
print 'number of interferograms in reference: ' + str(
len(date12_to_keep))
print 'date12 not in reference file:'
date12_to_rmv_temp = []
for date12 in date12_orig:
if date12 not in date12_to_keep:
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.1 Update date12_to_rmv from coherence file
if inps.coherence_based and os.path.isfile(inps.coherence_file):
print '----------------------------------------------------------------------------'
print 'use coherence-based network modification from coherence file: ' + inps.coherence_file
# check mask AOI in lalo
if inps.aoi_geo_box and inps.lookup_file:
print 'input AOI in (lon0, lat1, lon1, lat0): ' + str(
inps.aoi_geo_box)
inps.aoi_pix_box = subset.bbox_geo2radar(inps.aoi_geo_box, atr,
inps.lookup_file)
if inps.aoi_pix_box:
# check mask AOI within the data coverage
inps.aoi_pix_box = subset.check_box_within_data_coverage(
inps.aoi_pix_box, atr)
print 'input AOI in (x0,y0,x1,y1): ' + str(inps.aoi_pix_box)
# Calculate spatial average coherence
coh_list, coh_date12_list = ut.spatial_average(inps.coherence_file, inps.mask_file,\
inps.aoi_pix_box, saveList=True)
# MST network
if inps.keep_mst:
print 'Get minimum spanning tree (MST) of interferograms with inverse of coherence.'
print 'date12 with 1) average coherence < ' + str(
inps.min_coherence) + ' AND 2) not in MST network: '
mst_date12_list = pnet.threshold_coherence_based_mst(
coh_date12_list, coh_list)
else:
print 'date12 with average coherence < ' + str(inps.min_coherence)
mst_date12_list = []
date12_to_rmv_temp = []
for i in range(len(coh_date12_list)):
date12 = coh_date12_list[i]
if coh_list[
i] < inps.min_coherence and date12 not in mst_date12_list:
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.2 Update date12_to_rmv from temp baseline threshold
if inps.max_temp_baseline:
print '----------------------------------------------------------------------------'
print 'Drop pairs with temporal baseline > ' + str(
inps.max_temp_baseline) + ' days'
date8_list = ptime.ifgram_date_list(inps.file[0])
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date1, date2 = date12_orig[i].split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
t_diff = tbase_list[idx2] - tbase_list[idx1]
if t_diff > inps.max_temp_baseline:
date12 = date12_orig[i]
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print 'number of pairs to drop: %d' % (len(date12_to_rmv_temp))
print date12_to_rmv_temp
# 2.3 Update date12_to_rmv from perp baseline threshold
if inps.max_perp_baseline:
print '----------------------------------------------------------------------------'
print 'Drop pairs with perpendicular spatial baseline > ' + str(
inps.max_perp_baseline) + ' meters'
ifg_bperp_list = pnet.igram_perp_baseline_list(inps.file[0])
date12_to_rmv_temp = []
for i in range(len(ifg_bperp_list)):
if abs(ifg_bperp_list[i]) > inps.max_perp_baseline:
date12 = date12_orig[i]
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print 'number of pairs to drop: %d' % (len(date12_to_rmv_temp))
print date12_to_rmv_temp
# 2.4 Update date12_to_rmv from exclude_ifg_index
if inps.exclude_ifg_index:
print '----------------------------------------------------------------------------'
print 'drop date12/pair with the following index number:'
for index in inps.exclude_ifg_index:
date12 = date12_orig[index - 1]
date12_to_rmv.append(date12)
print str(index) + ' ' + date12
# 2.5 Update date12_to_rmv from exclude_date
if inps.exclude_date:
inps.exclude_date = ptime.yymmdd(inps.exclude_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs including the following dates: \n' + str(
inps.exclude_date)
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date1, date2 = date12_orig[i].split('-')
if (date1 in inps.exclude_date) or (date2 in inps.exclude_date):
date12 = date12_orig[i]
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.6 Update date12_to_rmv from start_date
if inps.start_date:
inps.start_date = ptime.yymmdd(inps.start_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs with date earlier than start-date: ' + inps.start_date
min_date = int(ptime.yyyymmdd(inps.start_date))
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date12 = date12_orig[i]
if any(
int(j) < min_date
for j in ptime.yyyymmdd(date12.split('-'))):
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.7 Update date12_to_rmv from end_date
if inps.end_date:
inps.end_date = ptime.yymmdd(inps.end_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs with date earlier than end-date: ' + inps.end_date
max_date = int(ptime.yyyymmdd(inps.end_date))
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date12 = date12_orig[i]
if any(
int(j) > max_date
for j in ptime.yyyymmdd(date12.split('-'))):
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 3. Manually drop pairs
if inps.disp_network:
date12_click = manual_select_pairs_to_remove(inps.file[0])
for date12 in list(date12_click):
if date12 not in date12_orig:
date12_click.remove(date12)
print 'date12 selected to remove:'
print date12_click
date12_to_rmv += date12_click
# 4. drop duplicate date12 and sort in order
date12_to_rmv = sorted(list(set(date12_to_rmv)))
date12_keep = sorted(list(set(date12_orig) - set(date12_to_rmv)))
print '----------------------------------------------------------------------------'
print 'number of interferograms to remove: ' + str(len(date12_to_rmv))
print 'number of interferograms kept : ' + str(len(date12_keep))
##### Calculated date12_to_drop v.s. existing date12_to_drop
# Get list of date12 of interferograms already been marked to drop
k = readfile.read_attribute(inps.file[0])['FILE_TYPE']
h5 = h5py.File(inps.file[0], 'r')
ifgram_list_all = sorted(h5[k].keys())
ifgram_list_keep = ut.check_drop_ifgram(h5, print_msg=False)
ifgram_list_dropped = sorted(
list(set(ifgram_list_all) - set(ifgram_list_keep)))
date12_list_dropped = ptime.list_ifgram2date12(ifgram_list_dropped)
h5.close()
if date12_to_rmv == date12_list_dropped and inps.mark_attribute:
print 'Calculated date12 to drop is the same as exsiting marked input file, skip update file attributes.'
date12_to_rmv = []
##### Update date12 to drop
if date12_to_rmv:
##### Update Input Files with date12_to_rmv
Modified_CoherenceFile = 'Modified_coherence.h5'
for File in inps.file:
Modified_File = modify_file_date12_list(File, date12_to_rmv,
inps.mark_attribute)
k = readfile.read_attribute(File)['FILE_TYPE']
# Update Mask File
if k == 'interferograms' and inps.update_aux:
print 'update mask file for input ' + k + ' file based on ' + Modified_File
inps.mask_file = 'mask.h5'
print 'writing >>> ' + inps.mask_file
ut.nonzero_mask(Modified_File, inps.mask_file)
elif k == 'coherence' and inps.update_aux:
inps.coherence_file = Modified_File
print 'update average spatial coherence for input ' + k + ' file based on: ' + Modified_File
outFile = 'averageSpatialCoherence.h5'
print 'writing >>> ' + outFile
ut.temporal_average(Modified_File, outFile)
# Touch spatial average txt file of coherence if it's existed
coh_spatialAverage_file = os.path.splitext(
Modified_File)[0] + '_spatialAverage.txt'
if os.path.isfile(coh_spatialAverage_file):
touchCmd = 'touch ' + coh_spatialAverage_file
print touchCmd
os.system(touchCmd)
# Plot result
if inps.plot:
print '\nplot modified network and save to file.'
plotCmd = 'plot_network.py ' + inps.coherence_file + ' --coherence ' + inps.coherence_file + ' --nodisplay'
if inps.template_file:
plotCmd += ' --template ' + inps.template_file
print plotCmd
os.system(plotCmd)
print 'Done.'
return
def ifgram_inversion(ifgramFile='unwrapIfgram.h5', coherenceFile='coherence.h5', meta=None):
'''Implementation of the SBAS algorithm.
modified from sbas.py written by scott baker, 2012
Inputs:
ifgramFile - string, HDF5 file name of the interferograms
coherenceFile - string, HDF5 file name of the coherence
meta - dict, including the following options:
weight_function
chunk_size - float, max number of data (ifgram_num*row_num*col_num)
to read per loop; to control the memory
Output:
timeseriesFile - string, HDF5 file name of the output timeseries
tempCohFile - string, HDF5 file name of temporal coherence
Example:
meta = dict()
meta['weight_function'] = 'variance'
meta['chunk_size'] = 0.5e9
meta['timeseriesFile'] = 'timeseries_var.h5'
meta['tempCohFile'] = 'temporalCoherence_var.h5'
ifgram_inversion('unwrapIfgram.h5', 'coherence.h5', meta)
'''
if 'tempCohFile' not in meta.keys():
meta['tempCohFile'] = 'temporalCoherence.h5'
meta['timeseriesStdFile'] = 'timeseriesDecorStd.h5'
total = time.time()
if not meta:
meta = vars(cmdLineParse())
if meta['update_mode'] and not ut.update_file(meta['timeseriesFile'], ifgramFile):
return meta['timeseriesFile'], meta['tempCohFile']
##### Basic Info
# length/width
atr = readfile.read_attribute(ifgramFile)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
meta['length'] = length
meta['width'] = width
# ifgram_list
h5ifgram = h5py.File(ifgramFile,'r')
ifgram_list = sorted(h5ifgram['interferograms'].keys())
#if meta['weight_function'] in ['no','uniform']:
# ifgram_list = ut.check_drop_ifgram(h5ifgram)
ifgram_list = ut.check_drop_ifgram(h5ifgram)
meta['ifgram_list'] = ifgram_list
ifgram_num = len(ifgram_list)
# date12_list/date8_list/tbase_diff
date12_list = ptime.list_ifgram2date12(ifgram_list)
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date8_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
date_num = len(date8_list)
meta['date8_list'] = date8_list
meta['date12_list'] = date12_list
tbase_list = ptime.date_list2tbase(date8_list)[0]
tbase_diff = np.diff(tbase_list).reshape((-1,1))
meta['tbase_diff'] = tbase_diff
print 'number of interferograms: %d' % (ifgram_num)
print 'number of acquisitions : %d' % (date_num)
print 'number of columns: %d' % (width)
print 'number of lines : %d' % (length)
##### ref_y/x/value
try:
ref_x = int(atr['ref_x'])
ref_y = int(atr['ref_y'])
print 'reference pixel in y/x: [%d, %d]' % (ref_y, ref_x)
ref_value = np.zeros((ifgram_num,1), np.float32)
for j in range(ifgram_num):
ifgram = ifgram_list[j]
dset = h5ifgram['interferograms'][ifgram].get(ifgram)
ref_value[j] = dset[ref_y,ref_x]
meta['ref_y'] = ref_y
meta['ref_x'] = ref_x
meta['ref_value'] = ref_value
except:
if meta['skip_ref']:
meta['ref_value'] = 0.0
print 'skip checking reference pixel info - This is for SIMULATION ONLY.'
else:
print 'ERROR: No ref_x/y found! Can not invert interferograms without reference in space.'
print 'run seed_data.py '+ifgramFile+' --mark-attribute for a quick referencing.'
sys.exit(1)
h5ifgram.close()
##### Rank of Design matrix for weighted inversion
A, B = ut.design_matrix(ifgramFile, date12_list)
print '-------------------------------------------------------------------------------'
if meta['weight_function'] in ['no','uniform']:
print 'generic least square inversion with min-norm phase velocity'
print ' based on Berardino et al. (2002, IEEE-TGRS)'
print ' OLS for pixels with fully connected network'
print ' SVD for pixels with partially connected network'
if np.linalg.matrix_rank(A) < date_num-1:
print 'WARNING: singular design matrix! Inversion result can be biased!'
print 'continue using its SVD solution on all pixels'
else:
print 'weighted least square (WLS) inversion with min-norm phase, pixelwise'
if np.linalg.matrix_rank(A) < date_num-1:
print 'ERROR: singular design matrix!'
print ' Input network of interferograms is not fully connected!'
print ' Can not invert the weighted least square solution.'
print 'You could try:'
print ' 1) Add more interferograms to make the network fully connected:'
print ' a.k.a., no multiple subsets nor network islands'
print " 2) Use '-w no' option for non-weighted SVD solution."
sys.exit(-1)
print '-------------------------------------------------------------------------------'
##### Invert time-series phase
##Check parallel environment
if meta['weight_function'] in ['no','uniform']:
meta['parallel'] = False
if meta['parallel']:
num_cores, meta['parallel'], Parallel, delayed = ut.check_parallel(1000, print_msg=False)
##Split into chunks to reduce memory usage
r_step = meta['chunk_size']/ifgram_num/width #split in lines
if meta['weight_function'] not in ['no','uniform']: #more memory usage (coherence) for WLS
r_step /= 2.0
if meta['parallel']:
r_step /= num_cores
r_step = int(ceil_to_1(r_step))
meta['row_step'] = r_step
chunk_num = int((length-1)/r_step)+1
if chunk_num > 1:
print 'maximum chunk size: %.1E' % (meta['chunk_size'])
print 'split %d lines into %d patches for processing' % (length, chunk_num)
print ' with each patch up to %d lines' % (r_step)
if meta['parallel']:
print 'parallel processing using %d cores ...' % (min([num_cores,chunk_num]))
##Computing the inversion
box_list = []
for i in range(chunk_num):
r0 = i*r_step
r1 = min([length, r0+r_step])
box = (0,r0,width,r1)
box_list.append(box)
box_num = len(box_list)
if not meta['parallel']:
timeseries = np.zeros((date_num, length, width), np.float32)
timeseriesStd = np.zeros((date_num, length, width), np.float32)
tempCoh = np.zeros((length, width), np.float32)
for i in range(box_num):
if box_num > 1:
print '\n------- Processing Patch %d out of %d --------------' % (i+1, box_num)
box = box_list[i]
ts, tcoh, tsStd = ifgram_inversion_patch(ifgramFile, coherenceFile, meta, box)
tempCoh[box[1]:box[3],box[0]:box[2]] = tcoh
timeseries[:,box[1]:box[3],box[0]:box[2]] = ts
timeseriesStd[:,box[1]:box[3],box[0]:box[2]] = tsStd
else:
##Temp file list
meta['ftemp_base'] = 'timeseries_temp_'
temp_file_list = [meta['ftemp_base']+str(i)+'.h5' for i in range(chunk_num)]
##Computation
Parallel(n_jobs=num_cores)(delayed(ifgram_inversion_patch)\
(ifgramFile, coherenceFile, meta, box) for box in box_list)
##Concatenate temp files
print 'concatenating temporary timeseries files ...'
timeseries = np.zeros((date_num, length, width), np.float32)
tempCoh = np.zeros((length, width), np.float32)
rmCmd = 'rm'
for i in range(chunk_num):
fname = temp_file_list[i]
box = box_list[i]
print 'reading '+fname
h5temp = h5py.File(fname, 'r')
dset = h5temp['timeseries'].get('timeseries')
timeseries[:,box[1]:box[3],box[0]:box[2]] = dset[0:-1,:,:]
tempCoh[box[1]:box[3],box[0]:box[2]] = dset[-1,:,:]
h5temp.close()
rmCmd += ' '+fname
print rmCmd
os.system(rmCmd)
print 'converting phase to range'
phase2range = -1*float(atr['WAVELENGTH'])/(4.*np.pi)
timeseries *= phase2range
timeseriesStd *= abs(phase2range)
##### Calculate time-series attributes
print 'calculating perpendicular baseline timeseries'
pbase, pbase_top, pbase_bottom = ut.perp_baseline_ifgram2timeseries(ifgramFile, ifgram_list)
pbase = str(pbase.tolist()).translate(None,'[],') # convert np.array into string separated by white space
pbase_top = str(pbase_top.tolist()).translate(None,'[],')
pbase_bottom = str(pbase_bottom.tolist()).translate(None,'[],')
atr['P_BASELINE_TIMESERIES'] = pbase
atr['P_BASELINE_TOP_TIMESERIES'] = pbase_top
atr['P_BASELINE_BOTTOM_TIMESERIES'] = pbase_bottom
atr['ref_date'] = date8_list[0]
atr['FILE_TYPE'] = 'timeseries'
atr['UNIT'] = 'm'
##### Output
## 1. Write time-series file
meta['timeseriesFile'] = write_timeseries_hdf5_file(timeseries, date8_list, atr,\
timeseriesFile=meta['timeseriesFile'])
if not np.all(timeseriesStd == 0.):
meta['timeseriesStdFile'] = write_timeseries_hdf5_file(timeseriesStd, date8_list, atr,\
timeseriesFile=meta['timeseriesStdFile'])
## 2. Write Temporal Coherence File
print 'writing >>> '+meta['tempCohFile']
atr['FILE_TYPE'] = 'temporal_coherence'
atr['UNIT'] = '1'
meta['tempCohFile'] = writefile.write(tempCoh, atr, meta['tempCohFile'])
print 'Time series inversion took ' + str(time.time()-total) +' secs\nDone.'
return meta['timeseriesFile'], meta['tempCohFile']
def ifgram_inversion_patch(ifgramFile, coherenceFile, meta, box=None):
'''
Inputs:
ifgramFile - string, interferograms hdf5 file
coherenceFile - string, coherence hdf5 file
box - 4-tuple, left, upper, right, and lower pixel coordinate of area of interest
meta - dict, including the following attributes:
#Interferograms
length/width - int, file size for each interferogram
ifgram_list - list of string, interferogram dataset name
date12_list - list of string, YYMMDD-YYMMDD
ref_value - np.array in size of (ifgram_num, 1)
reference pixel coordinate in row/column number
ref_y/x - int, reference pixel coordinate in row/column number
#Time-series
date8_list - list of string in YYYYMMDD
tbase_diff - np.array in size of (date_num-1, 1), differential temporal baseline
#Inversion
weight_function - no, fim, var, coh
Outputs:
ts - 3D np.array in size of (date_num, row_num, col_num)
temp_coh - 2D np.array in size of (row_num, col_num)
tsStd - 3D np.array in size of (date_num, row_num, col_num)
'''
##### Get patch size/index
if not box:
box = (0,0,meta['width'],meta['length'])
c0,r0,c1,r1 = box
print 'processing %8d/%d lines ...' % (r1, meta['length'])
## Initiate output data matrixs
row_num = r1-r0
col_num = c1-c0
pixel_num = row_num * col_num
date_num = len(meta['date8_list'])
ts = np.zeros((date_num, pixel_num), np.float32)
tsStd = np.zeros((date_num, pixel_num), np.float32)
temp_coh = np.zeros(pixel_num, np.float32)
##### Mask for pixels to invert
mask = np.ones(pixel_num, np.bool_)
## 1 - Water Mask
if meta['water_mask_file']:
print 'skip pixels on water with mask from file: %s' % (os.path.basename(meta['water_mask_file']))
try: waterMask = readfile.read(meta['water_mask_file'], epoch='waterMask')[0][r0:r1,c0:c1].flatten()
except: waterMask = readfile.read(meta['water_mask_file'], epoch='mask')[0][r0:r1,c0:c1].flatten()
mask *= np.array(waterMask, np.bool_)
## 2 - Mask for Zero Phase in ALL ifgrams
print 'skip pixels with zero/nan value in all interferograms'
ifgram_stack = ut.get_file_stack(ifgramFile)[r0:r1,c0:c1].flatten()
mask *= ~np.isnan(ifgram_stack)
mask *= ifgram_stack != 0.
## Invert pixels on mask 1+2
pixel_num2inv = np.sum(mask)
pixel_idx2inv = np.where(mask)[0]
print 'number of pixels to invert: %s out of %s' % (pixel_num2inv, pixel_num)
if pixel_num2inv < 1:
ts = ts.reshape(date_num, row_num, col_num)
temp_coh = temp_coh.reshape(row_num, col_num)
tsStd = tsStd.reshape(date_num, row_num, col_num)
return ts, temp_coh, tsStd
##### Read interferograms
ifgram_num = len(meta['ifgram_list'])
ifgram_data = np.zeros((ifgram_num, pixel_num), np.float32)
date12_list = meta['date12_list']
if meta['skip_zero_phase']:
print 'skip zero phase value (masked out and filled during phase unwrapping)'
atr = readfile.read_attribute(ifgramFile)
h5ifgram = h5py.File(ifgramFile,'r')
for j in range(ifgram_num):
ifgram = meta['ifgram_list'][j]
d = h5ifgram['interferograms'][ifgram].get(ifgram)[r0:r1,c0:c1].flatten()
if meta['skip_zero_phase']:
d[d != 0.] -= meta['ref_value'][j]
else:
d -= meta['ref_value'][j]
ifgram_data[j] = d
sys.stdout.write('\rreading interferograms %s/%s ...' % (j+1, ifgram_num))
sys.stdout.flush()
print ' '
h5ifgram.close()
#ifgram_data -= meta['ref_value']
## 3 - Mask for Non-Zero Phase in ALL ifgrams (share one B in sbas inversion)
maskAllNet = np.all(ifgram_data, axis=0)
maskAllNet *= mask
maskPartNet = mask ^ maskAllNet
##### Design matrix
A,B = ut.design_matrix(ifgramFile, date12_list)
try: ref_date = str(np.loadtxt('reference_date.txt', dtype=str))
except: ref_date = meta['date8_list'][0]
#print 'calculate decorrelation noise covariance with reference date = %s' % (ref_date)
refIdx = meta['date8_list'].index(ref_date)
timeIdx = [i for i in range(date_num)]
timeIdx.remove(refIdx)
Astd = ut.design_matrix(ifgramFile, date12_list, referenceDate=ref_date)[0]
##### Inversion
if meta['weight_function'] in ['no','uniform']:
if np.sum(maskAllNet) > 0:
print 'inverting pixels with valid phase in all ifgrams with OLS (%.0f pixels) ...' % (np.sum(maskAllNet))
ts1, tempCoh1 = network_inversion_sbas(B, ifgram_data[:,maskAllNet], meta['tbase_diff'], skipZeroPhase=False)
ts[1:,maskAllNet] = ts1
temp_coh[maskAllNet] = tempCoh1
if np.sum(maskPartNet) > 0:
print 'inverting pixels with valid phase in part of ifgrams with SVD ...'
pixel_num2inv = np.sum(maskPartNet)
pixel_idx2inv = np.where(maskPartNet)[0]
prog_bar = ptime.progress_bar(maxValue=pixel_num2inv)
for i in range(pixel_num2inv):
idx = pixel_idx2inv[i]
ts1, tempCoh1 = network_inversion_sbas(B, ifgram_data[:,idx], meta['tbase_diff'], meta['skip_zero_phase'])
ts[1:, idx] = ts1.flatten()
temp_coh[idx] = tempCoh1
prog_bar.update(i+1, every=100, suffix=str(i+1)+'/'+str(pixel_num2inv)+' pixels')
prog_bar.close()
else:
##### Read coherence
coh_data = np.zeros((ifgram_num, pixel_num), np.float32)
h5coh = h5py.File(coherenceFile,'r')
coh_list = sorted(h5coh['coherence'].keys())
coh_list = ut.check_drop_ifgram(h5coh)
for j in range(ifgram_num):
ifgram = coh_list[j]
d = h5coh['coherence'][ifgram].get(ifgram)[r0:r1,c0:c1]
d[np.isnan(d)] = 0.
coh_data[j] = d.flatten()
sys.stdout.write('\rreading coherence %s/%s ...' % (j+1, ifgram_num))
sys.stdout.flush()
print ' '
h5coh.close()
##### Calculate Weight matrix
weight = np.array(coh_data, np.float64)
L = int(atr['ALOOKS']) * int(atr['RLOOKS'])
epsilon = 1e-4
if meta['weight_function'].startswith('var'):
print 'convert coherence to weight using inverse of phase variance'
print ' with phase PDF for distributed scatterers from Tough et al. (1995)'
weight = 1.0 / coherence2phase_variance_ds(weight, L, print_msg=True)
elif meta['weight_function'].startswith(('lin','coh','cor')):
print 'use coherence as weight directly (Perissin & Wang, 2012; Tong et al., 2016)'
weight[weight < epsilon] = epsilon
elif meta['weight_function'].startswith(('fim','fisher')):
print 'convert coherence to weight using Fisher Information Index (Seymour & Cumming, 1994)'
weight = coherence2fisher_info_index(weight, L)
else:
print 'Un-recognized weight function: %s' % meta['weight_function']
sys.exit(-1)
##### Weighted Inversion pixel by pixel
print 'inverting time series ...'
prog_bar = ptime.progress_bar(maxValue=pixel_num2inv)
for i in range(pixel_num2inv):
idx = pixel_idx2inv[i]
ts1, tempCoh1, tsStd1 = network_inversion_wls(A, ifgram_data[:,idx], weight[:,idx], Astd=Astd,\
skipZeroPhase=meta['skip_zero_phase'])
ts[1:, idx] = ts1.flatten()
temp_coh[idx] = tempCoh1
tsStd[timeIdx, idx] = tsStd1.flatten()
prog_bar.update(i+1, every=100, suffix=str(i+1)+'/'+str(pixel_num2inv)+' pixels')
prog_bar.close()
ts = ts.reshape(date_num, row_num, col_num)
temp_coh = temp_coh.reshape(row_num, col_num)
tsStd = tsStd.reshape(date_num, row_num, col_num)
##Write to temp hdf5 files for parallel processing
if meta['parallel']:
fname = meta['ftemp_base']+str(int(r0/meta['row_step']))+'.h5'
print 'writing >>> '+fname
h5temp = h5py.File(fname, 'w')
group = h5temp.create_group('timeseries')
dset = group.create_dataset('timeseries', shape=(date_num+1, row_num, col_num), dtype=np.float32)
dset[0:-1,:,:] = ts
dset[1,:,:] = temp_coh
h5temp.close()
return
else:
return ts, temp_coh, tsStd
def timeseries_inversion(ifgramFile='unwrapIfgram.h5',
coherenceFile='coherence.h5',
inps_dict=None):
'''Implementation of the SBAS algorithm.
modified from sbas.py written by scott baker, 2012
Inputs:
ifgramFile - string, HDF5 file name of the interferograms
coherenceFile - string, HDF5 file name of the coherence
inps_dict - dict, including the following options:
weight_function
min_coherence
max_coherence
Output:
timeseriesFile - string, HDF5 file name of the output timeseries
tempCohFile - string, HDF5 file name of temporal coherence
'''
total = time.time()
if not inps_dict:
inps_dict = vars(cmdLineParse())
weight_func = inps_dict['weight_function']
min_coh = inps_dict['min_coherence']
max_coh = inps_dict['max_coherence']
# Basic Info
atr = readfile.read_attribute(ifgramFile)
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
pixel_num = length * width
h5ifgram = h5py.File(ifgramFile, 'r')
ifgram_list = sorted(h5ifgram['interferograms'].keys())
if inps_dict['weight_function'] == 'no':
ifgram_list = ut.check_drop_ifgram(h5ifgram, atr, ifgram_list)
ifgram_num = len(ifgram_list)
# Convert ifgram_list to date12/8_list
date12_list = ptime.list_ifgram2date12(ifgram_list)
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date8_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
date_num = len(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
tbase_diff = np.diff(tbase_list).reshape((date_num - 1, 1))
print 'number of interferograms: ' + str(ifgram_num)
print 'number of acquisitions : ' + str(date_num)
print 'number of pixels: ' + str(pixel_num)
# Reference pixel in space
try:
ref_x = int(atr['ref_x'])
ref_y = int(atr['ref_y'])
print 'reference pixel in y/x: [%d, %d]' % (ref_y, ref_x)
except:
print 'ERROR: No ref_x/y found! Can not inverse interferograms without reference in space.'
print 'run seed_data.py ' + ifgramFile + ' --mark-attribute for a quick referencing.'
sys.exit(1)
##### Read Interferograms
print 'reading interferograms ...'
ifgram_data = np.zeros((ifgram_num, pixel_num), np.float32)
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
for j in range(ifgram_num):
ifgram = ifgram_list[j]
d = h5ifgram['interferograms'][ifgram].get(ifgram)[:]
#d[d != 0.] -= d[ref_y, ref_x]
d -= d[ref_y, ref_x]
ifgram_data[j] = d.flatten()
prog_bar.update(j + 1, suffix=date12_list[j])
h5ifgram.close()
prog_bar.close()
#####---------------------- Inversion ----------------------#####
# Design matrix
A, B = ut.design_matrix(ifgramFile, date12_list)
if weight_func == 'no':
print 'generalized inversion using SVD (Berardino et al., 2002, IEEE-TGRS)'
print 'inversing time series ...'
B_inv = np.array(np.linalg.pinv(B), np.float32)
ts_rate = np.dot(B_inv, ifgram_data)
ts1 = ts_rate * np.tile(tbase_diff, (1, pixel_num))
ts0 = np.array([0.] * pixel_num, np.float32)
ts_data = np.vstack((ts0, np.cumsum(ts1, axis=0)))
del ts_rate, ts0, ts1
# Temporal coherence
print 'calculating temporal coherence (Tizzani et al., 2007, RSE)'
temp_coh = np.zeros((1, pixel_num), np.float32) + 0j
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
for i in range(ifgram_num):
ifgram_est = np.dot(A[i, :], ts_data[1:, :])
ifgram_diff = ifgram_data[i, :] - ifgram_est
temp_coh += np.exp(1j * ifgram_diff)
prog_bar.update(i + 1, suffix=date12_list[i])
prog_bar.close()
del ifgram_data, ifgram_est, ifgram_diff
temp_coh = np.array((np.absolute(temp_coh) / ifgram_num).reshape(
(length, width)),
dtype=np.float32)
else:
print 'weighted least square (WLS) inversion using coherence pixel by pixel'
if np.linalg.matrix_rank(A) < date_num - 1:
print 'ERROR: singular design matrix!'
print ' Input network of interferograms is not fully connected!'
print ' Can not inverse the weighted least square solution.'
print 'You could try:'
print ' 1) Add more interferograms to make the network fully connected:'
print ' a.k.a., no multiple subsets nor network islands'
print " 2) Use '-w no' option for non-weighted SVD solution."
sys.exit(-1)
pixel_mask = np.ones(pixel_num, np.bool_)
print 'reading coherence: ' + os.path.basename(coherenceFile)
h5coh = h5py.File(coherenceFile, 'r')
coh_list = sorted(h5coh['coherence'].keys())
coh_data = np.zeros((ifgram_num, pixel_num), np.float32)
prog_bar = ptime.progress_bar(maxValue=ifgram_num)
for j in range(ifgram_num):
ifgram = coh_list[j]
d = h5coh['coherence'][ifgram].get(ifgram)[:].flatten()
d[np.isnan(d)] = 0.
pixel_mask[d == 0.] = 0
coh_data[j] = d
prog_bar.update(j + 1, suffix=date12_list[j])
h5coh.close()
prog_bar.close()
# Get mask of valid pixels to inverse
print 'skip pixels with zero coherence in at least one interferogram'
print 'skip pixels with zero phase in all interferograms'
ifgram_stack = ut.get_file_stack(ifgramFile).flatten()
pixel_mask[ifgram_stack == 0.] = 0
pixel_num2inv = np.sum(pixel_mask)
pixel_idx2inv = np.where(pixel_mask)[0]
ifgram_data = ifgram_data[:, pixel_mask]
coh_data = coh_data[:, pixel_mask]
print 'number of pixels to inverse: %d' % (pixel_num2inv)
##### Calculate Weight matrix
weight = coh_data
if weight_func.startswith('var'):
print 'convert coherence to weight using inverse of variance: x**2/(1-x**2) from Hanssen (2001, for 4.2.32)'
weight[weight > 0.999] = 0.999
if weight_func == 'variance-max-coherence':
print 'constrain the max coherence to %f' % max_coh
weight[weight > max_coh] = max_coh
weight = np.square(weight)
weight *= 1. / (1. - weight)
if weight_func == 'variance-log':
print 'use log(1/variance)+1 as weight'
weight = np.log(weight + 1)
elif weight_func.startswith('lin'):
print 'use coherence as weight directly (Tong et al., 2016, RSE)'
elif weight_func.startswith('norm'):
print 'convert coherence to weight using CDF of normal distribution: N(%f, %f)' % (
mu, std)
mu = (min_coh + max_coh) / 2.0
std = (max_coh - min_coh) / 6.0
chunk_size = 1000
chunk_num = int(pixel_num2inv / chunk_size) + 1
prog_bar = ptime.progress_bar(maxValue=chunk_num)
for i in range(chunk_num):
i0 = (i - 1) * chunk_size
i1 = min([pixel_num2inv, i0 + chunk_size])
weight[:, i0:i1] = norm.cdf(weight[:, i0:i1], mu, std)
prog_bar.update(i + 1, every=10)
prog_bar.close()
#weight = norm.cdf(weight, mu, std)
else:
print 'Un-recognized weight function: %s' % weight_func
sys.exit(-1)
##### Weighted Inversion pixel by pixel
print 'inversing time series ...'
ts_data = np.zeros((date_num, pixel_num), np.float32)
temp_coh = np.zeros(pixel_num, np.float32)
prog_bar = ptime.progress_bar(maxValue=pixel_num2inv)
for i in range(pixel_num2inv):
# Inverse timeseries
ifgram_pixel = ifgram_data[:, i]
weight_pixel = weight[:, i]
W = np.diag(weight_pixel)
ts = np.linalg.inv(A.T.dot(W).dot(A)).dot(
A.T).dot(W).dot(ifgram_pixel)
ts_data[1:, pixel_idx2inv[i]] = ts
# Calculate weighted temporal coherence
ifgram_diff = ifgram_pixel - np.dot(A, ts)
temp_coh_pixel = np.abs(
np.sum(np.multiply(weight_pixel, np.exp(1j * ifgram_diff)),
axis=0)) / np.sum(weight_pixel)
temp_coh[pixel_idx2inv[i]] = temp_coh_pixel
prog_bar.update(i + 1, every=2000, suffix=str(i + 1) + ' pixels')
prog_bar.close()
del ifgram_data, weight
#####---------------------- Outputs ----------------------#####
## 1.1 Convert time-series phase to displacement
print 'converting phase to range'
phase2range = -1 * float(atr['WAVELENGTH']) / (4. * np.pi)
ts_data *= phase2range
## 1.2 Write time-series data matrix
timeseriesFile = 'timeseries.h5'
print 'writing >>> ' + timeseriesFile
print 'number of acquisitions: ' + str(date_num)
h5timeseries = h5py.File(timeseriesFile, 'w')
group = h5timeseries.create_group('timeseries')
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date8_list[i]
dset = group.create_dataset(date,
data=ts_data[i].reshape(length, width),
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
## 1.3 Write time-series attributes
print 'calculating perpendicular baseline timeseries'
pbase, pbase_top, pbase_bottom = ut.perp_baseline_ifgram2timeseries(
ifgramFile, ifgram_list)
pbase = str(pbase.tolist()).translate(
None, '[],') # convert np.array into string separated by white space
pbase_top = str(pbase_top.tolist()).translate(None, '[],')
pbase_bottom = str(pbase_bottom.tolist()).translate(None, '[],')
atr['P_BASELINE_TIMESERIES'] = pbase
atr['P_BASELINE_TOP_TIMESERIES'] = pbase_top
atr['P_BASELINE_BOTTOM_TIMESERIES'] = pbase_bottom
atr['ref_date'] = date8_list[0]
atr['FILE_TYPE'] = 'timeseries'
atr['UNIT'] = 'm'
for key, value in atr.iteritems():
group.attrs[key] = value
h5timeseries.close()
del ts_data
## 2. Write Temporal Coherence File
tempCohFile = 'temporalCoherence.h5'
print 'writing >>> ' + tempCohFile
atr['FILE_TYPE'] = 'temporal_coherence'
atr['UNIT'] = '1'
writefile.write(temp_coh.reshape(length, width), atr, tempCohFile)
print 'Time series inversion took ' + str(time.time() -
total) + ' secs\nDone.'
return timeseriesFile, tempCohFile
def ifgram_inversion_patch(ifgramFile, coherenceFile, meta, box=None):
'''
Inputs:
ifgramFile - string, interferograms hdf5 file
coherenceFile - string, coherence hdf5 file
box - 4-tuple, left, upper, right, and lower pixel coordinate of area of interest
meta - dict, including the following attributes:
#Interferograms
length/width - int, file size for each interferogram
ifgram_list - list of string, interferogram dataset name
date12_list - list of string, YYMMDD-YYMMDD
ref_value - np.array in size of (ifgram_num, 1)
reference pixel coordinate in row/column number
ref_y/x - int, reference pixel coordinate in row/column number
#Time-series
date8_list - list of string in YYYYMMDD
tbase_diff - np.array in size of (date_num-1, 1), differential temporal baseline
#Inversion
weight_function -
'''
##### Get patch size/index
if not box:
box = (0, 0, meta['width'], meta['length'])
c0, r0, c1, r1 = box
print 'processing %8d/%d lines ...' % (r1, meta['length'])
## Initiate output data matrixs
row_num = r1 - r0
col_num = c1 - c0
pixel_num = row_num * col_num
date_num = len(meta['date8_list'])
ts = np.zeros((date_num, pixel_num), np.float32)
temp_coh = np.zeros(pixel_num, np.float32)
##### Get mask of non-zero pixels
print 'skip pixels with zero/nan value in all interferograms'
ifgram_stack = ut.get_file_stack(ifgramFile)[r0:r1, c0:c1].flatten()
mask = ~np.isnan(ifgram_stack)
mask[ifgram_stack == 0.] = 0
pixel_num2inv = np.sum(mask)
pixel_idx2inv = np.where(mask)[0]
print 'number of pixels to inverse: %d' % (pixel_num2inv)
if pixel_num2inv < 1:
ts = ts.reshape(date_num, row_num, col_num)
temp_coh = temp_coh.reshape(row_num, col_num)
return ts, temp_coh
##### Read interferograms
ifgram_num = len(meta['ifgram_list'])
ifgram_data = np.zeros((ifgram_num, pixel_num2inv), np.float32)
date12_list = meta['date12_list']
atr = readfile.read_attribute(ifgramFile)
h5ifgram = h5py.File(ifgramFile, 'r')
for j in range(ifgram_num):
ifgram = meta['ifgram_list'][j]
d = h5ifgram['interferograms'][ifgram].get(ifgram)[r0:r1, c0:c1]
ifgram_data[j] = d.flatten()[mask]
sys.stdout.write('\rreading interferograms %s/%s ...' %
(j + 1, ifgram_num))
sys.stdout.flush()
print ' '
h5ifgram.close()
ifgram_data -= meta['ref_value']
##### Design matrix
A, B = ut.design_matrix(ifgramFile, date12_list)
B_inv = np.array(np.linalg.pinv(B), np.float32)
##### Inverse
if meta['weight_function'] in ['no', 'uniform']:
print 'inversing time-series ...'
ts[1:, mask] = network_inversion_sbas(B,
ifgram_data,
meta['tbase_diff'],
B_inv=B_inv)
print 'calculating temporal coherence ...'
temp_coh[mask] = temporal_coherence(A, ts[1:, mask], ifgram_data)
else:
##### Read coherence
coh_data = np.zeros((ifgram_num, pixel_num2inv), np.float32)
h5coh = h5py.File(coherenceFile, 'r')
coh_list = sorted(h5coh['coherence'].keys())
coh_list = ut.check_drop_ifgram(h5coh)
for j in range(ifgram_num):
ifgram = coh_list[j]
d = h5coh['coherence'][ifgram].get(ifgram)[r0:r1, c0:c1]
d[np.isnan(d)] = 0.
coh_data[j] = d.flatten()[mask]
sys.stdout.write('\rreading coherence %s/%s ...' %
(j + 1, ifgram_num))
sys.stdout.flush()
print ' '
h5coh.close()
##### Calculate Weight matrix
weight = coh_data
if meta['weight_function'].startswith('var'):
print 'convert coherence to weight using inverse of phase variance'
print ' with phase PDF for distributed scatterers from Tough et al. (1995)'
L = int(atr['ALOOKS']) * int(atr['RLOOKS'])
lineStr = ' number of multilooks L=%d' % L
if L > 80:
L = 80
lineStr += ', use L=80 to avoid dividing by 0 in calculation with Negligible effect'
print lineStr
weight = 1.0 / coherence2phase_variance_ds(weight, L)
elif meta['weight_function'].startswith(('lin', 'coh', 'cor')):
print 'use coherence as weight directly (Perissin & Wang, 2012; Tong et al., 2016)'
epsilon = 1e-4
weight[weight < epsilon] = epsilon
else:
print 'Un-recognized weight function: %s' % meta['weight_function']
sys.exit(-1)
##### Weighted Inversion pixel by pixel
print 'inversing time series ...'
prog_bar = ptime.progress_bar(maxValue=pixel_num2inv)
for i in range(pixel_num2inv):
ts[1:, pixel_idx2inv[i]] = network_inversion_wls(
A, ifgram_data[:, i], weight[:, i])[0].flatten()
prog_bar.update(i + 1,
every=100,
suffix=str(i + 1) + '/' + str(pixel_num2inv) +
' pixels')
prog_bar.close()
print 'calculating temporal coherence ...'
#temp_coh[mask] = temporal_coherence(A, ts[1:,mask], ifgram_data, weight)
temp_coh[mask] = temporal_coherence(A, ts[1:, mask], ifgram_data)
ts = ts.reshape(date_num, row_num, col_num)
temp_coh = temp_coh.reshape(row_num, col_num)
##Write to temp hdf5 files for parallel processing
if meta['parallel']:
fname = meta['ftemp_base'] + str(int(r0 / meta['row_step'])) + '.h5'
print 'writing >>> ' + fname
h5temp = h5py.File(fname, 'w')
group = h5temp.create_group('timeseries')
dset = group.create_dataset('timeseries',
shape=(date_num + 1, row_num, col_num),
dtype=np.float32)
dset[0:-1, :, :] = ts
dset[1, :, :] = temp_coh
h5temp.close()
return
else:
return ts, temp_coh