def read_template2inps(template_file, inps=None):
'''Read input template file into inps.ex_date'''
if not inps:
inps = cmdLineParse()
template = readfile.read_template(template_file)
key_list = template.keys()
# Read template option
prefix = 'pysar.velocity.'
key = prefix + 'excludeDate'
if key in key_list:
value = template[key]
if value == 'auto':
inps.ex_date = ['exclude_date.txt']
elif value == 'no':
inps.ex_date = []
else:
inps.ex_date = value.replace(',', ' ').split()
key = prefix + 'startDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.min_date = ptime.yyyymmdd(value)
key = prefix + 'endDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.max_date = ptime.yyyymmdd(value)
return inps
def select_pairs_star(date_list, m_date=None, pbase_list=[]):
'''Select Star-like network/interferograms/pairs, it's a single master network, similar to PS approach.
Usage:
m_date : master date, choose it based on the following cretiria:
1) near the center in temporal and spatial baseline
2) prefer winter season than summer season for less temporal decorrelation
Reference:
Ferretti, A., C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE TGRS, 39(1), 8-20.
'''
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
# Select master date if not existed
if not m_date:
m_date = select_master_date(date8_list, pbase_list)
print 'auto select master date: '+m_date
# Check input master date
m_date8 = ptime.yyyymmdd(m_date)
if m_date8 not in date8_list:
print 'Input master date is not existed in date list!'
print 'Input master date: '+str(m_date8)
print 'Input date list: '+str(date8_list)
m_date8 = None
# Generate star/ps network
m_idx = date8_list.index(m_date8)
date12_idx_list = [sorted([m_idx, s_idx]) for s_idx in range(len(date8_list)) if s_idx is not m_idx]
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]] for idx in date12_idx_list]
return date12_list
def read_template2inps(template_file, inps=None):
'''Read input template file into inps.ex_date'''
if not inps:
inps = cmdLineParse()
template = readfile.read_template(template_file)
key_list = template.keys()
# Read template option
prefix = 'pysar.topographicResidual.'
key = prefix+'polyOrder'
if key in key_list:
value = template[key]
if value == 'auto':
inps.poly_order = 2
else:
inps.poly_order = int(value)
key = prefix+'excludeDate'
if key in key_list:
value = template[key]
if value not in ['auto','no']:
value = value.replace(',',' ').split()
value = ptime.yyyymmdd(value)
inps.ex_date += value
key = prefix+'stepFuncDate'
if key in key_list:
value = template[key]
if value not in ['auto','no']:
value = value.replace(',',' ').split()
value = ptime.yyyymmdd(value)
inps.step_date += value
return inps
def get_exclude_date(inps, date_list_all):
'''Get inps.ex_date full list
Inputs:
inps - Namespace,
date_list_all - list of string for all available date in YYYYMMDD format
Output:
inps.ex_date - list of string for exclude date in YYYYMMDD format
'''
yy_list_all = ptime.yyyymmdd2years(date_list_all)
# 1. template_file
if inps.template_file:
print 'read option from template file: ' + inps.template_file
inps = read_template2inps(inps.template_file, inps)
# 2. ex_date
input_ex_date = list(inps.ex_date)
inps.ex_date = []
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
inps.ex_date += list(set(ex_date) - set(inps.ex_date))
# delete dates not existed in input file
inps.ex_date = list(set(inps.ex_date).intersection(date_list_all))
print 'exclude date:' + str(inps.ex_date)
# 3. min_date
if inps.min_date:
print 'start date: ' + inps.min_date
yy_min = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.min_date))
for i in range(len(date_list_all)):
date = date_list_all[i]
if yy_list_all[i] < yy_min and date not in inps.ex_date:
print ' remove date: ' + date
inps.ex_date.append(date)
# 4. max_date
if inps.max_date:
print 'end date: ' + inps.max_date
yy_max = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.max_date))
for i in range(len(date_list_all)):
date = date_list_all[i]
if yy_list_all[i] > yy_max and date not in inps.ex_date:
print ' remove date: ' + date
inps.ex_date.append(date)
return inps.ex_date
def read_baseline_file(baselineFile,exDateList=[]):
## Read bl_list.txt without dates listed in exDateList
##
## Examples:
## date8List, perpBaseList, dopList, prfList, slcDirList = read_baseline_file(baselineFile)
## date8List, perpBaseList, dopList, prfList, slcDirList = read_baseline_file(baselineFile,['080520','100726'])
## date8List, perpBaseList = read_baseline_file(baselineFile)[0:2]
## Read baseline file into lines
fb = open(baselineFile)
lines = []
for line in fb.xreadlines():
l = str.replace(line,'\n','').strip()
lines.append(l)
fb.close()
## Read each line and put the values into arrays
date6List = []
perpBaseList = []
dopplerList = []
prfList = []
slcDirList = []
for line in lines:
c = line.split() # splits on white space
date = c[0]
if not date in exDateList:
date6List.append(date)
perpBaseList.append(float(c[1]))
dopplerList.append(np.array([float(c[2]), float(c[3]), float(c[4])]))
prfList.append(float(c[5]))
slcDirList.append(c[6])
date8List = ptime.yyyymmdd(date6List)
return date8List, perpBaseList, dopplerList, prfList, slcDirList
def select_master_date(date_list, pbase_list=[]):
'''Select super master date based on input temporal and/or perpendicular baseline info.
Return master date in YYYYMMDD format.
'''
date8_list = ptime.yyyymmdd(date_list)
if not pbase_list:
# Choose date in the middle
m_date8 = date8_list[int(len(date8_list)/2)]
else:
# Get temporal baseline list
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_list)-min(pbase_list)) / (max(tbase_list)-min(tbase_list))
tbase_list = [tbase*temp2perp_scale for tbase in tbase_list]
# Get distance matrix
ttMat1, ttMat2 = np.meshgrid(np.array(tbase_list), np.array(tbase_list))
ppMat1, ppMat2 = np.meshgrid(np.array(pbase_list), np.array(pbase_list))
ttMat = np.abs(ttMat1 - ttMat2) # temporal distance matrix
ppMat = np.abs(ppMat1 - ppMat2) # spatial distance matrix
disMat = np.sqrt(np.square(ttMat) + np.square(ppMat)) # 2D distance matrix in temp/perp domain
# Choose date minimize the total distance of temp/perp baseline
disMean = np.mean(disMat, 0)
m_idx = np.argmin(disMean)
m_date8 = date8_list[m_idx]
return m_date8
def coherence_matrix(date12_list, coh_list):
'''Return coherence matrix based on input date12 list and its coherence
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format
coh_list - list of float, average coherence for each interferograms
Output:
coh_matrix - 2D np.array with dimension length = date num
np.nan value for interferograms non-existed.
1.0 for diagonal elements
'''
# Get date list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date6_list = ptime.yymmdd(sorted(ptime.yyyymmdd(list(set(m_dates + s_dates)))))
date_num = len(date6_list)
coh_mat = np.zeros([date_num, date_num])
coh_mat[:] = np.nan
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
coh = coh_list[date12_list.index(date12)]
coh_mat[idx1, idx2] = coh #symmetric
coh_mat[idx2, idx1] = coh
#for i in range(date_num): # diagonal value
# coh_mat[i, i] = 1.0
return coh_mat
def threshold_doppler_overlap(date12_list, date_list, dop_list, bandwidth_az, dop_overlap_min=0.15):
'''Remove pairs/interoferogram with doppler overlap larger than critical value
Inputs:
date12_list : list of string, for date12 in YYMMDD-YYMMDD format
date_list : list of string, for date in YYMMDD/YYYYMMDD format, optional
dop_list : list of list of 3 float, for centroid Doppler frequency
bandwidth_az : float, bandwidth in azimuth direction
dop_overlap_min : float, minimum overlap of azimuth Doppler frequency
Outputs:
date12_list : list of string, for date12 in YYMMDD-YYMMDD format
'''
if not date12_list: return []
# Get date6_list
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
if not len(date_list) == len(pbase_list):
print 'ERROR: number of existing dates is not equal to number of perp baseline!'
print 'date list is needed for threshold filtering!'
print 'skip filtering.'
return date12_list
date6_list = ptime.yymmdd(date_list)
# Threshold
date12_list_out = []
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
dop_overlap = calculate_doppler_overlap(dop_list[idx1], dop_list[idx2], bandwidth_az)
if dop_overlap >= dop_overlap_min:
date12_list_out.append(date12)
return date12_list_out
def threshold_temporal_baseline(date12_list, btemp_max, keep_seasonal=True, btemp_min=0.0):
'''Remove pairs/interferograms out of min/max/seasonal temporal baseline limits
Inputs:
date12_list : list of string for date12 in YYMMDD-YYMMDD format
btemp_max : float, maximum temporal baseline
btemp_min : float, minimum temporal baseline
keep_seasonal : keep interferograms with seasonal temporal baseline
Output:
date12_list_out : list of string for date12 in YYMMDD-YYMMDD format
Example:
date12_list = threshold_temporal_baseline(date12_list, 200)
date12_list = threshold_temporal_baseline(date12_list, 200, False)
'''
if not date12_list: return []
# Get date list and tbase list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date8_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Threshold
date12_list_out = []
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
tbase = int(abs(tbase_list[idx1] - tbase_list[idx2]))
if btemp_min <= tbase <= btemp_max:
date12_list_out.append(date12)
elif keep_seasonal and tbase/30 in [11,12]:
date12_list_out.append(date12)
return date12_list_out
def select_pairs_delaunay(date_list, pbase_list, norm=True):
'''Select Pairs using Delaunay Triangulation based on temporal/perpendicular baselines
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
norm : normalize temporal baseline to perpendicular baseline
Key points
1. Define a ratio between perpendicular and temporal baseline axis units (Pepe and Lanari, 2006, TGRS).
2. Pairs with too large perpendicular / temporal baseline or Doppler centroid difference should be removed
after this, using a threshold, to avoid strong decorrelations (Zebker and Villasenor, 1992, TGRS).
Reference:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Zebker, H. A., and J. Villasenor (1992), Decorrelation in interferometric radar echoes, IEEE TGRS, 30(5), 950-959.
'''
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
if norm:
temp2perp_scale = (max(pbase_list)-min(pbase_list)) / (max(tbase_list)-min(tbase_list))
tbase_list = [tbase*temp2perp_scale for tbase in tbase_list]
# Generate Delaunay Triangulation
date12_idx_list = Triangulation(tbase_list, pbase_list).edges.tolist()
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]] for idx in date12_idx_list]
return date12_list
def plot_coherence_history(ax, date12_list, coherence_list, plot_dict={}):
'''Plot min/max Coherence of all interferograms for each date'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
# Get date list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date8_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
dates, datevector = ptime.date_list2vector(date8_list)
bar_width = mode(np.diff(dates).tolist())*3/4
x_list = [i-bar_width/2 for i in dates]
coh_mat = coherence_matrix(date12_list, coherence_list)
ax.bar(x_list, np.nanmax(coh_mat, axis=0), bar_width.days, label='Max Coherence')
ax.bar(x_list, np.nanmin(coh_mat, axis=0), bar_width.days, label='Min Coherence')
if plot_dict['disp_title']:
ax.set_title('Coherence History of All Related Interferograms')
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax.set_ylim([0.0,1.0])
ax.set_xlabel('Time [years]',fontsize=plot_dict['fontsize'])
ax.set_ylabel('Coherence',fontsize=plot_dict['fontsize'])
ax.legend(loc='lower right')
return ax
def select_pairs_sequential(date_list, increment_num=2):
'''Select Pairs in a Sequential way:
For each acquisition, find its increment_num nearest acquisitions in the past time.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
Reference:
Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, IEEE TGRS, 51(7), 4249-4259.
'''
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date_idx_list = list(range(len(date6_list)))
# Get pairs index list
date12_idx_list = []
for date_idx in date_idx_list:
for i in range(increment_num):
if date_idx - i - 1 >= 0:
date12_idx_list.append([date_idx - i - 1, date_idx])
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [
date6_list[idx[0]] + '-' + date6_list[idx[1]]
for idx in date12_idx_list
]
return date12_list
def threshold_coherence_based_mst(date12_list, coh_list):
'''Return a minimum spanning tree of network based on the coherence inverse.
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format
coh_list - list of float, average coherence for each interferogram
Output:
mst_date12_list - list of string in YYMMDD-YYMMDD format, for MST network of interferograms
'''
# coh_list --> coh_mat --> weight_mat
coh_mat = coherence_matrix(date12_list, coh_list)
mask = ~np.isnan(coh_mat)
wei_mat = np.zeros(coh_mat.shape)
wei_mat[:] = np.inf
wei_mat[mask] = 1/coh_mat[mask]
# MST path based on weight matrix
wei_mat_csr = csr_matrix(wei_mat)
mst_mat_csr = minimum_spanning_tree(wei_mat_csr)
# Get date6_list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date6_list = ptime.yymmdd(sorted(ptime.yyyymmdd(list(set(m_dates + s_dates)))))
# Convert MST index matrix into date12 list
[s_idx_list, m_idx_list] = [date_idx_array.tolist() for date_idx_array in find(mst_mat_csr)[0:2]]
mst_date12_list = []
for i in range(len(m_idx_list)):
idx = sorted([m_idx_list[i], s_idx_list[i]])
date12 = date6_list[idx[0]]+'-'+date6_list[idx[1]]
mst_date12_list.append(date12)
return mst_date12_list
def select_master_interferogram(date12_list, date_list, pbase_list, m_date=None):
'''Select reference interferogram based on input temp/perp baseline info
If master_date is specified, select its closest slave_date; otherwise, choose the closest pair
among all pairs as master interferogram.
Example:
master_date12 = pnet.select_master_ifgram(date12_list, date_list, pbase_list)
'''
pbase_array = np.array(pbase_list, dtype='float64')
# Get temporal baseline
date8_list = ptime.yyyymmdd(date_list)
date6_list = ptime.yymmdd(date8_list)
tbase_array = np.array(ptime.date_list2tbase(date8_list)[0], dtype='float64')
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_array)-min(pbase_array)) / (max(tbase_array)-min(tbase_array))
tbase_array *= temp2perp_scale
# Calculate sqrt of temp/perp baseline for input pairs
idx1 = np.array([date6_list.index(date12.split('-')[0]) for date12 in date12_list])
idx2 = np.array([date6_list.index(date12.split('-')[1]) for date12 in date12_list])
base_distance = np.sqrt((tbase_array[idx2] - tbase_array[idx1])**2 + (pbase_array[idx2] - pbase_array[idx1])**2)
# Get master interferogram index
if not m_date:
# Choose pair with shortest temp/perp baseline
m_date12_idx = np.argmin(base_distance)
else:
m_date = ptime.yymmdd(m_date)
# Choose pair contains m_date with shortest temp/perp baseline
m_date12_idx_array = np.array([date12_list.index(date12) for date12 in date12_list if m_date in date12])
min_base_distance = np.min(base_distance[m_date12_idx_array])
m_date12_idx = np.where(base_distance == min_base_distance)[0][0]
m_date12 = date12_list[m_date12_idx]
return m_date12
def ref_date_file(inFile, ref_date, outFile=None):
'''Change input file reference date to a different one.'''
if not outFile:
outFile = os.path.splitext(inFile)[0] + '_refDate.h5'
# Input file type
atr = readfile.read_attribute(inFile)
k = atr['FILE_TYPE']
if not k in ['timeseries']:
print 'Input file is ' + k + ', only timeseries is supported.'
return None
# Input reference date
h5 = h5py.File(inFile, 'r')
date_list = sorted(h5[k].keys())
h5.close()
date_num = len(date_list)
try:
ref_date_orig = atr['ref_date']
except:
ref_date_orig = date_list[0]
ref_date = ptime.yyyymmdd(ref_date)
print 'input reference date: ' + ref_date
if not ref_date in date_list:
print 'Input reference date was not found!\nAll dates available: ' + str(
date_list)
return None
if ref_date == ref_date_orig:
print 'Same reference date chosen as existing reference date.'
print 'Copy %s to %s' % (inFile, outFile)
shutil.copy2(inFile, outFile)
return outFile
# Referencing in time
h5 = h5py.File(inFile, 'r')
ref_data = h5[k].get(ref_date)[:]
print 'writing >>> ' + outFile
h5out = h5py.File(outFile, 'w')
group = h5out.create_group(k)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
data = h5[k].get(date)[:]
dset = group.create_dataset(date,
data=data - ref_data,
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5.close()
## Update attributes
atr = ref_date_attribute(atr, ref_date, date_list)
for key, value in atr.iteritems():
group.attrs[key] = value
h5out.close()
return outFile
def ref_date_attribute(atr_in, ref_date, date_list):
'''Update attribute dictionary for reference date'''
ref_date = ptime.yyyymmdd(ref_date)
date_list = ptime.yyyymmdd(date_list)
ref_index = date_list.index(ref_date)
atr = dict()
for key, value in atr_in.iteritems():
atr[key] = str(value)
# Update ref_date
atr['ref_date'] = ref_date
print 'update ref_date'
# Update Bperp time series
try:
pbase = np.array(
[float(i) for i in atr['P_BASELINE_TIMESERIES'].split()])
pbase -= pbase[ref_index]
atr['P_BASELINE_TIMESERIES'] = str(pbase.tolist()).translate(
None, '[],')
print 'update P_BASELINE_TIMESERIES'
except:
pass
try:
pbase_top = np.array(
[float(i) for i in atr['P_BASELINE_TOP_TIMESERIES'].split()])
pbase_bottom = np.array(
[float(i) for i in atr['P_BASELINE_BOTTOM_TIMESERIES'].split()])
pbase_top -= pbase_top[ref_index]
pbase_bottom -= pbase_bottom[ref_index]
atr['P_BASELINE_TOP_TIMESERIES'] = str(pbase_top.tolist()).translate(
None, '[],')
atr['P_BASELINE_BOTTOM_TIMESERIES'] = str(
pbase_bottom.tolist()).translate(None, '[],')
print 'update P_BASELINE_TOP/BOTTOM_TIMESERIES'
except:
pass
return atr
def select_pairs_all(date_list):
'''Select All Possible Pairs/Interferograms
Input : date_list - list of date in YYMMDD/YYYYMMDD format
Output: date12_list - list date12 in YYMMDD-YYMMDD format
Reference:
Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti (2002), A new algorithm for surface deformation monitoring
based on small baseline differential SAR interferograms, IEEE TGRS, 40(11), 2375-2383.
'''
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date12_list = list(itertools.combinations(date6_list, 2))
date12_list = [date12[0]+'-'+date12[1] for date12 in date12_list]
return date12_list
def date12_list2index(date12_list, date_list=[]):
'''Convert list of date12 string into list of index'''
# Get dateList from date12List
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = list(set(m_dates + s_dates))
date6_list = ptime.yymmdd(sorted(ptime.yyyymmdd(date_list)))
# Get pair index
pairs_idx = []
for date12 in date12_list:
dates = date12.split('-')
pair_idx = [date6_list.index(dates[0]), date6_list.index(dates[1])]
pairs_idx.append(pair_idx)
return pairs_idx
def get_exclude_date(inps, date_list_all):
input_ex_date = list(inps.ex_date)
inps.ex_date = []
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
inps.ex_date += list(set(ex_date) - set(inps.ex_date))
# delete dates not existed in input file
inps.ex_date = sorted(
list(set(inps.ex_date).intersection(date_list_all)))
print 'Exclude date for DEM error estimation:'
print inps.ex_date
return inps
def read_baseline_file(baselineFile, exDateList=[]):
'''Read bl_list.txt without dates listed in exDateList
# Date Bperp dop0/PRF dop1/PRF dop2/PRF PRF slcDir
070106 0.0 0.03 0.0000000 0.00000000000 2155.2 /scratch/KyushuT422F650AlosA/SLC/070106/
070709 2631.9 0.07 0.0000000 0.00000000000 2155.2 /scratch/KyushuT422F650AlosA/SLC/070709/
070824 2787.3 0.07 0.0000000 0.00000000000 2155.2 /scratch/KyushuT422F650AlosA/SLC/070824/
...
Examples:
date8List, perpBaseList, dopList, prfList, slcDirList = read_baseline_file(baselineFile)
date8List, perpBaseList, dopList, prfList, slcDirList = read_baseline_file(baselineFile,['080520','100726'])
date8List, perpBaseList = read_baseline_file(baselineFile)[0:2]
'''
exDateList = ptime.yymmdd(exDateList)
if not exDateList: exDateList = []
## Read baseline file into lines
fb = open(baselineFile)
lines = []
for line in fb.xreadlines():
l = str.replace(line,'\n','').strip()
lines.append(l)
fb.close()
## Read each line and put the values into arrays
date6List = []
perpBaseList = []
dopplerList = []
slcDirList = []
for line in lines:
c = line.split() # splits on white space
date = c[0]
if not date in exDateList:
date6List.append(date)
perpBaseList.append(float(c[1]))
dop = np.array([float(c[2]), float(c[3]), float(c[4])])
prf = float(c[5])
dop *= prf
dopplerList.append(dop)
slcDirList.append(c[6])
date8List = ptime.yyyymmdd(date6List)
return date8List, perpBaseList, dopplerList, slcDirList
def select_pairs_mst(date_list, pbase_list):
'''Select Pairs using Minimum Spanning Tree technique
Connection Cost is calculated using the baseline distance in perp and scaled temporal baseline (Pepe and Lanari,
2006, TGRS) plane.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
References:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Perissin D., Wang T. (2012), Repeat-pass SAR interferometry with partially coherent targets. IEEE TGRS. 271-280
'''
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_list) - min(pbase_list)) / (max(tbase_list) -
min(tbase_list))
tbase_list = [tbase * temp2perp_scale for tbase in tbase_list]
# Get weight matrix
ttMat1, ttMat2 = np.meshgrid(np.array(tbase_list), np.array(tbase_list))
ppMat1, ppMat2 = np.meshgrid(np.array(pbase_list), np.array(pbase_list))
ttMat = np.abs(ttMat1 - ttMat2) # temporal distance matrix
ppMat = np.abs(ppMat1 - ppMat2) # spatial distance matrix
weightMat = np.sqrt(
np.square(ttMat) +
np.square(ppMat)) # 2D distance matrix in temp/perp domain
weightMat = csr_matrix(weightMat) # compress sparse row matrix
# MST path based on weight matrix
mstMat = minimum_spanning_tree(weightMat)
# Convert MST index matrix into date12 list
[s_idx_list, m_idx_list
] = [date_idx_array.tolist() for date_idx_array in find(mstMat)[0:2]]
date12_list = [
date6_list[m_idx_list[i]] + '-' + date6_list[s_idx_list[i]]
for i in range(len(m_idx_list))
]
return date12_list
def main(argv):
try:
File = argv[0]
atr = readfile.read_attribute(File)
epoch = argv[1]
except:
usage()
sys.exit(1)
try:
outFile = argv[2]
except:
outFile = None
# Calculate look angle
pbase = ut.perp_baseline_timeseries(atr, dimension=1)
if pbase.shape[1] == 1:
print pbase
return pbase
k = atr['FILE_TYPE']
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
h5 = h5py.File(File, 'r')
epochList = sorted(h5[k].keys())
epoch = ptime.yyyymmdd(epoch)
epoch_idx = epochList.index(epoch)
pbase_y = pbase[epoch_idx, :].reshape(length, 1)
pbase_xy = np.tile(pbase_y, (1, width))
if not outFile:
outFile = 'perpBaseline_' + epoch + '.h5'
print 'writing >>> ' + outFile
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 'm'
writefile.write(pbase_xy, atr, outFile)
return outFile
def threshold_perp_baseline(date12_list,
date_list,
pbase_list,
pbase_max,
pbase_min=0.0):
'''Remove pairs/interoferogram out of [pbase_min, pbase_max]
Inputs:
date12_list : list of string for date12 in YYMMDD-YYMMDD format
date_list : list of string for date in YYMMDD/YYYYMMDD format, optional
pbase_list : list of float for perpendicular spatial baseline
pbase_max : float, maximum perpendicular baseline
pbase_min : float, minimum perpendicular baseline
Output:
date12_list_out : list of string for date12 in YYMMDD-YYMMDD format
Example:
date12_list = threshold_perp_baseline(date12_list, date_list, pbase_list, 500)
'''
if not date12_list: return []
# Get date6_list
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
if not len(date_list) == len(pbase_list):
print 'ERROR: number of existing dates is not equal to number of perp baseline!'
print 'date list is needed for threshold filtering!'
print 'skip filtering.'
return date12_list
date6_list = ptime.yymmdd(date_list)
# Threshold
date12_list_out = []
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
pbase = abs(pbase_list[idx1] - pbase_list[idx2])
if pbase_min <= pbase <= pbase_max:
date12_list_out.append(date12)
return date12_list_out
def check_exclude_date(exDateIn, dateList):
'''Read exclude dates info
Inputs:
exDateIn - list of string, date in YYMMDD or YYYYMMDD format,
or text file with date in it
dateList - list of string, date in YYYYMMDD format
Output:
exDateOut - list of string, date in YYYYMMDD format
'''
if not exDateIn:
return []
exDateOut = []
for exDate in exDateIn:
if os.path.isfile(exDate):
exDate = ptime.read_date_list(exDate)
else:
exDate = [ptime.yyyymmdd(exDate)]
exDateOut += exDate
exDateOut = sorted(list(set(exDateOut).intersection(dateList)))
print 'Exclude date for DEM error estimation:'
print exDateOut
return exDateOut
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, epoch='mask')[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(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(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.iteritems():
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
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):
try:
File = argv[0]
except:
usage()
sys.exit(1)
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
h5file = h5py.File(File, 'r')
print '\n************* Output to ROI_PAC format ***************'
if k == 'velocity':
dset = h5file['velocity'].get('velocity')
data = dset[0:dset.shape[0], 0:dset.shape[1]]
print "converting velocity to a 1 year interferogram."
wvl = float(h5file[k].attrs['WAVELENGTH'])
data = (-4 * pi / wvl) * data
outname = File.split('.')[0] + '.unw'
writefile.write(data, atr, outname)
elif k == 'timeseries':
dateList = h5file['timeseries'].keys()
## Input
if len(sys.argv) == 2:
print 'No input date specified >>> continue with the last date'
dateList = h5file['timeseries'].keys()
d = dateList[-1]
elif len(sys.argv) == 3:
d = sys.argv[2]
elif len(sys.argv) == 4:
ds = sorted(sys.argv[2:4])
d_ref = ds[0]
d = ds[1]
else:
usage()
sys.exit(1)
d = ptime.yyyymmdd(d)
try:
d_ref = ptime.yyyymmdd(d_ref)
except:
pass
## Data
print 'reading ' + d + ' ... '
data = h5file['timeseries'].get(d)[:]
try:
print 'reading ' + d_ref + ' ... '
data_ref = h5file['timeseries'].get(d_ref)[:]
data = data - data_ref
except:
pass
wvl = float(atr['WAVELENGTH'])
data *= -4 * pi / wvl
## outName
try:
master_d = d_ref
except:
try:
master_d = atr['ref_date']
except:
master_d = atr['DATE']
if len(master_d) == 8: master_d = master_d[2:8]
if len(d) == 8: d = d[2:8]
outname = master_d + '_' + d + '.unw'
## Attributes
atr['FILE_TYPE'] = '.unw'
atr['P_BASELINE_TIMESERIES'] = '0.0'
atr['UNIT'] = 'radian'
atr['DATE'] = master_d
atr['DATE12'] = master_d + '-' + d
## Writing
writefile.write(data, atr, outname)
elif k in ['interferograms', 'coherence', 'wrapped']:
## Check input
igramList = h5file[k].keys()
try:
d = sys.argv[2]
for i in range(len(igramList)):
if d in igramList[i]:
igram = igramList[i]
except:
igram = igramList[-1]
print 'No input date specified >>> continue with the last date'
## Read and Write
print 'reading ' + igram + ' ... '
data = h5file[k][igram].get(igram)[:]
atr = h5file[k][igram].attrs
outname = igram
print 'writing >>> ' + outname
writefile.write(data, atr, outname)
else:
dset = h5file[k].get(k)
data = dset[0:dset.shape[0], 0:dset.shape[1]]
if k == 'temporal_coherence': outname = File.split('.')[0] + '.cor'
else: outname = File.split('.')[0] + '.unw'
writefile.write(data, atr, outname)
h5file.close()
return
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 plot_network(ax, date12_list, date_list, pbase_list, plot_dict={}, date12_list_drop=[]):
'''Plot Temporal-Perp baseline Network
Inputs
ax : matplotlib axes object
date12_list : list of string for date12 in YYMMDD-YYMMDD format
date_list : list of string, for date in YYYYMMDD/YYMMDD format
pbase_list : list of float, perp baseline, len=number of acquisition
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
coherence_list : list of float, coherence value of each interferogram, len = number of ifgrams
coh_date12_list: list of date, corresponding to coherence_list
disp_min/max : float, min/max range of the color display based on coherence_list
colormap : string, colormap name
coh_thres : float, coherence of where to cut the colormap for display
disp_title : bool, show figure title or not, default: True
Output
ax : matplotlib axes object
'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
# For colorful display of coherence
if not 'coherence_list' in keyList: plot_dict['coherence_list'] = None
if not 'disp_min' in keyList: plot_dict['disp_min'] = 0.2
if not 'disp_max' in keyList: plot_dict['disp_max'] = 1.0
if not 'colormap' in keyList: plot_dict['colormap'] = 'RdBu'
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
transparency = 0.7
# Date Convert
date8_list = ptime.yyyymmdd(date_list)
date6_list = ptime.yymmdd(date8_list)
dates, datevector = ptime.date_list2vector(date8_list)
# Index of date12 used and dropped
idx_date12_keep = range(len(date12_list))
idx_date12_drop = []
for i in date12_list_drop:
idx = date12_list.index(i)
idx_date12_keep.remove(idx)
idx_date12_drop.append(idx)
# Index of date used and dropped
date12_list_keep = sorted(list(set(date12_list) - set(date12_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)))
idx_date_keep = range(len(date8_list))
idx_date_drop = []
for i in date8_list_drop:
idx = date8_list.index(i)
idx_date_keep.remove(idx)
idx_date_drop.append(idx)
# Ploting
#ax=fig.add_subplot(111)
## Colorbar when conherence is colored
if plot_dict['coherence_list']:
data_min = min(plot_dict['coherence_list'])
data_max = max(plot_dict['coherence_list'])
# Normalize
normalization = False
if normalization:
plot_dict['coherence_list'] = [(coh-data_min) / (data_min-data_min) for coh in plot_dict['coherence_list']]
plot_dict['disp_min'] = data_min
plot_dict['disp_max'] = data_max
print 'showing coherence'
print 'colormap: '+plot_dict['colormap']
print 'display range: '+str([plot_dict['disp_min'], plot_dict['disp_max']])
print 'data range: '+str([data_min, data_max])
# Use lower/upper part of colormap to emphasis dropped interferograms
if 'coh_thres' not in plot_dict.keys() or not plot_dict['coh_thres']:
# Find proper cut percentage so that all keep pairs are blue and drop pairs are red
coh_list_keep = [plot_dict['coherence_list'][i] for i in idx_date12_keep]
coh_list_drop = [plot_dict['coherence_list'][i] for i in idx_date12_drop]
plot_dict['coh_thres'] = min(coh_list_keep)
if coh_list_drop:
plot_dict['coh_thres'] += max(coh_list_drop)
plot_dict['coh_thres'] /= 2
plot_dict['coh_thres'] = round(plot_dict['coh_thres'], -int(np.floor(np.log10(abs(plot_dict['coh_thres'])))))
print 'color jump at '+str(plot_dict['coh_thres'])
c1_num = (plot_dict['coh_thres'] - plot_dict['disp_min']) / (plot_dict['disp_max'] - plot_dict['disp_min'])
c1_num = int(c1_num * 200)
cmap = plt.get_cmap(plot_dict['colormap'])
colors1 = cmap(np.linspace(0.0, 0.3, c1_num))
colors2 = cmap(np.linspace(0.6, 1.0, 200 - c1_num))
cmap = colors.LinearSegmentedColormap.from_list('truncate_RdBu', np.vstack((colors1, colors2)))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
norm = mpl.colors.Normalize(vmin=plot_dict['disp_min'], vmax=plot_dict['disp_max'])
cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm)
cbar.set_label('Spatial Coherence', fontsize=plot_dict['fontsize'])
## Dot - SAR Acquisition
if idx_date_keep:
x_list = [dates[i] for i in idx_date_keep]
y_list = [pbase_list[i] for i in idx_date_keep]
ax.plot(x_list, y_list, 'ko', alpha=0.7, ms=plot_dict['markersize'], mfc=plot_dict['markercolor'])
if idx_date_drop:
x_list = [dates[i] for i in idx_date_drop]
y_list = [pbase_list[i] for i in idx_date_drop]
ax.plot(x_list, y_list, 'ko', alpha=0.7, ms=plot_dict['markersize'], mfc='gray')
## Line - Pair/Interferogram
# interferograms kept
for date12 in date12_list_keep:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if plot_dict['coherence_list']:
coh = plot_dict['coherence_list'][plot_dict['coh_date12_list'].index(date12)]
coh_idx = (coh - plot_dict['disp_min']) / (plot_dict['disp_max'] - plot_dict['disp_min'])
ax.plot(x, y, '-', lw=plot_dict['linewidth'], alpha=transparency, c=cmap(coh_idx))
else:
ax.plot(x, y, '-', lw=plot_dict['linewidth'], alpha=transparency, c='k')
# interferograms dropped
for date12 in date12_list_drop:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if plot_dict['coherence_list']:
coh_idx = (plot_dict['coherence_list'][date12_list.index(date12)] - plot_dict['disp_min']) /\
(plot_dict['disp_max'] - plot_dict['disp_min'])
ax.plot(x, y, '--', lw=plot_dict['linewidth'], alpha=transparency, c=cmap(coh_idx))
else:
ax.plot(x, y, '--', lw=plot_dict['linewidth'], alpha=transparency, c='k')
if plot_dict['disp_title']:
ax.set_title('Interferogram Network', fontsize=plot_dict['fontsize'])
# axis format
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax = auto_adjust_yaxis(ax, pbase_list, plot_dict['fontsize'])
ax.set_xlabel('Time [years]',fontsize=plot_dict['fontsize'])
ax.set_ylabel('Perp Baseline [m]',fontsize=plot_dict['fontsize'])
return ax
def main(argv):
## Default settings
contour_step = 200.0
contour_sigma = 3.0
demShade = "yes"
demContour = "yes"
global markerSize, markderSize2, markerColor, markerColor2, rectColor
global lineWidth, lineWidth2, edgeWidth, fontSize
# global markerColor_ref, markerColor_ref2
markerSize = 16
markerSize2 = 16
markerColor = "crimson" # g
markerColor2 = "lightgray"
markerColor_ref = "white"
markerColor_ref2 = "lightgray"
rectColor = "black"
lineWidth = 0
lineWidth2 = 0
edgeWidth = 1.5
fontSize = 16
global unit, radius, saveFig, dispFig, fig_dpi
fig_dpi = 300
radius = 0
saveFig = "no"
dispFig = "yes"
unit = "cm"
dispDisplacement = "no"
dispOpposite = "no"
dispContour = "only"
smoothContour = "no"
contour_step = 200
showRef = "yes"
vel_alpha = 1.0
zero_start = "yes"
global ref_xsub, ref_ysub, ref_date
global h5timeseries_2, dates_2, dateList_2
global lbound, hbound
############### Check Inputs ##################
if len(sys.argv) < 2:
Usage()
sys.exit(1)
elif len(sys.argv) == 2:
if argv[0] == "-h":
Usage()
sys.exit(1)
elif os.path.isfile(argv[0]):
timeSeriesFile = argv[0]
h5timeseries = h5py.File(timeSeriesFile)
k = h5timeseries.keys()
if not "timeseries" in k:
print "ERROR: Input file is " + k[0] + ".\n\tOnly timeseries is supported.\n"
sys.exit(1)
else:
Usage()
sys.exit(1)
elif len(sys.argv) > 2:
try:
opts, args = getopt.getopt(
argv,
"f:F:v:a:b:s:m:c:w:u:l:h:D:V:t:T:d:r:x:y:X:Y:o:E:",
[
"save",
"nodisplay",
"unit=",
"exclude=",
"ref-date=",
"rect-color=",
"zero-start=",
"zoom-x=",
"zoom-y=",
"zoom-lon",
"zoom-lat",
"lalo=",
"opposite",
"dem-nocontour",
"dem-noshade",
"displacement",
"contour-step=",
"contour-smooth=",
"LALO=",
],
)
except getopt.GetoptError:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt == "-f":
timeSeriesFile = arg
elif opt == "-F":
timeSeriesFile_2 = arg
elif opt == "-v":
velocityFile = arg
elif opt == "-a":
vmin = float(arg)
elif opt == "-b":
vmax = float(arg)
elif opt == "-s":
fontSize = int(arg)
elif opt == "-m":
markerSize = int(arg)
markerSize2 = int(arg)
elif opt == "-c":
markerColor = arg
elif opt == "-w":
lineWidth = int(arg)
elif opt == "-u":
unit = arg
elif opt == "-l":
lbound = float(arg)
elif opt == "-h":
hbound = float(arg)
elif opt == "-D":
demFile = arg
elif opt == "-V":
contour_step = float(arg)
elif opt == "-t":
minDate = arg
elif opt == "-T":
maxDate = arg
elif opt == "-r":
radius = abs(int(arg))
elif opt == "-x":
xsub = [int(i) for i in arg.split(":")]
xsub.sort()
# dispVelFig='no'
elif opt == "-y":
ysub = [int(i) for i in arg.split(":")]
ysub.sort()
# dispVelFig='no'
elif opt == "-X":
ref_xsub = [int(i) for i in arg.split(":")]
ref_xsub.sort()
elif opt == "-Y":
ref_ysub = [int(i) for i in arg.split(":")]
ref_ysub.sort()
# dispVelFig='no'
elif opt == "--contour-step":
contour_step = float(arg)
elif opt == "--contour-smooth":
contour_sigma = float(arg)
elif opt == "--dem-nocontour":
demContour = "no"
elif opt == "--dem-noshade":
demShade = "no"
elif opt == "--displacement":
dispDisplacement = "yes"
elif opt in ["-E", "--exclude"]:
datesNot2show = arg.split(",")
elif opt in "--lalo":
lalosub = [float(i) for i in arg.split(",")]
elif opt in "--LALO":
ref_lalosub = [float(i) for i in arg.split(",")]
elif opt in ["--rect-color"]:
rectColor = arg
elif opt in ["--ref-date"]:
ref_date = ptime.yyyymmdd(arg)
elif opt in ["-u", "--unit"]:
unit = arg.lower()
elif opt == "--save":
saveFig = "yes"
elif opt == "--nodisplay":
dispFig = "no"
saveFig = "yes"
elif opt == "--opposite":
dispOpposite = "yes"
elif opt == "--zero-start":
zero_start = arg.lower()
elif opt == "--zoom-x":
win_x = [int(i) for i in arg.split(":")]
win_x.sort()
elif opt == "--zoom-y":
win_y = [int(i) for i in arg.split(":")]
win_y.sort()
elif opt == "--zoom-lon":
win_lon = [float(i) for i in arg.split(":")]
win_lon.sort()
elif opt == "--zoom-lat":
win_lat = [float(i) for i in arg.split(":")]
win_lat.sort()
##############################################################
## Read time series file info
if not os.path.isfile(timeSeriesFile):
print "\nERROR: Input time series file does not exist: " + timeSeriesFile + "\n"
sys.exit(1)
h5timeseries = h5py.File(timeSeriesFile)
k = h5timeseries.keys()
# read h5 file and its group type
if not "timeseries" in k:
print "ERROR: Input file is " + k[0] + ".\n\tOnly timeseries is supported.\n"
sys.exit(1)
atr = readfile.read_attributes(timeSeriesFile)
dateList1 = h5timeseries["timeseries"].keys()
dateList1 = sorted(dateList1)
dates1, datevector1 = ptime.date_list2vector(dateList1)
print "\n************ Time Series Display - Point *************"
##### Select Check
try:
lalosub
xsub = subset.coord_geo2radar([lalosub[1]], atr, "longitude")
ysub = subset.coord_geo2radar([lalosub[0]], atr, "latitude")
xsub = [xsub]
ysub = [ysub]
if radius == 0:
radius = 3
except:
pass
try:
ref_lalosub
ref_xsub = subset.coord_geo2radar([ref_lalosub[1]], atr, "longitude")
ref_ysub = subset.coord_geo2radar([ref_lalosub[0]], atr, "latitude")
ref_xsub = [ref_xsub]
ref_ysub = [ref_ysub]
if radius == 0:
radius = 3
except:
pass
##############################################################
global dates, dateList, datevector_all, dateListMinMax
print "*******************"
print "All dates existed:"
print dateList1
print "*******************"
## Check exclude date input
try:
datesNot2show
if os.path.isfile(datesNot2show[0]):
try:
datesNot2show = ptime.read_date_list(datesNot2show[0])
except:
print "Can not read date list file: " + datesNot2show[0]
print "dates not to show: " + str(datesNot2show)
except:
datesNot2show = []
## Check Min / Max Date
dateListMinMax = []
try:
minDate
minDate = ptime.yyyymmdd(minDate)
dateListMinMax.append(minDate)
minDateyy = ptime.yyyymmdd2years(minDate)
print "minimum date: " + minDate
for date in dateList1:
yy = ptime.yyyymmdd2years(date)
if yy < minDateyy:
datesNot2show.append(date)
except:
pass
try:
maxDate
maxDate = ptime.yyyymmdd(maxDate)
dateListMinMax.append(maxDate)
maxDateyy = ptime.yyyymmdd2years(maxDate)
print "maximum date: " + maxDate
for date in dateList1:
yy = ptime.yyyymmdd2years(date)
if yy > maxDateyy:
datesNot2show.append(date)
except:
pass
dateListMinMax = sorted(dateListMinMax)
if not dateListMinMax:
print "no min/max date input."
else:
datesMinMax, dateVecMinMax = ptime.date_list2vector(dateListMinMax)
## Finalize Date List
try:
dateList = []
for date in dateList1:
if date not in datesNot2show:
dateList.append(date)
print "--------------------------------------------"
print "dates used to show time series displacements:"
print dateList
print "--------------------------------------------"
except:
dateList = dateList1
print "using all dates to show time series displacement"
## Read Date Info (x axis for time series display)
dates, datevector = ptime.date_list2vector(dateList)
datevector_all = list(datevector)
## Check reference date input
try:
ref_date
if not ref_date in dateList:
print "Reference date - " + ref_date + " - is not included in date list to show."
sys.exit(1)
else:
print "reference date: " + ref_date
except:
if zero_start == "yes":
ref_date = dateList[0]
print "set the 1st date as reference for displacement display."
else:
pass
##############################################################
##### Plot Fig 1 - Velocity / last epoch of time series / DEM
fig = plt.figure(1)
ax = fig.add_subplot(111)
##### Check subset range
width = int(atr["WIDTH"])
length = int(atr["FILE_LENGTH"])
print "file size: " + str(length) + ", " + str(width)
try:
win_y = subset.coord_geo2radar(win_lat, atr, "latitude")
except:
try:
win_y
except:
win_y = [0, length]
try:
win_x = subset.coord_geo2radar(win_lon, atr, "longitude")
except:
try:
win_x
except:
win_x = [0, width]
win_y, win_x = subset.check_subset_range(win_y, win_x, atr)
try:
velocityFile
try:
vel, vel_atr = readfile.read(velocityFile)
except:
vel, vel_atr = readfile.read(timeSeriesFile, velocityFile)
ax.set_title(velocityFile)
print "display: " + velocityFile
except:
vel, vel_atr = readfile.read(timeSeriesFile, dateList1[-1])
ax.set_title("epoch: " + dateList1[-1])
print "display last epoch"
##### show displacement instead of phase
if vel_atr["FILE_TYPE"] in ["interferograms", ".unw"] and dispDisplacement == "yes":
print "show displacement"
phase2range = -float(vel_atr["WAVELENGTH"]) / (4 * np.pi)
vel *= phase2range
else:
dispDisplacement = "no"
## Reference Point
if showRef == "yes":
try:
ax.plot(int(atr["ref_x"]), int(atr["ref_y"]), "ks", ms=6)
except:
pass
if dispOpposite == "yes":
print "show opposite value in figure/map 1"
vel *= -1
## Flip
try:
flip_lr
except:
try:
flip_ud
except:
flip_lr, flip_ud = view.auto_flip_check(atr)
## Status bar
## Geo coordinate
try:
ullon = float(atr["X_FIRST"])
ullat = float(atr["Y_FIRST"])
lon_step = float(atr["X_STEP"])
lat_step = float(atr["Y_STEP"])
lon_unit = atr["Y_UNIT"]
lat_unit = atr["X_UNIT"]
geocoord = "yes"
print "Input file is Geocoded"
except:
geocoord = "no"
def format_coord(x, y):
col = int(x + 0.5)
row = int(y + 0.5)
if col >= 0 and col <= width and row >= 0 and row <= length:
z = vel[row, col]
try:
lon = ullon + x * lon_step
lat = ullat + y * lat_step
return "x=%.1f, y=%.1f, value=%.4f, lon=%.4f, lat=%.4f" % (x, y, z, lon, lat)
except:
return "x=%.1f, y=%.1f, value=%.4f" % (x, y, z)
ax.format_coord = format_coord
## DEM
try:
demFile
dem, demRsc = readfile.read(demFile)
ax = view.plot_dem_yx(ax, dem, demShade, demContour, contour_step, contour_sigma)
vel_alpha = 0.8
except:
print "No DEM file"
try:
img = ax.imshow(vel, vmin=vmin, vmax=vmax, alpha=vel_alpha)
except:
img = ax.imshow(vel, alpha=vel_alpha)
plt.colorbar(img)
## Zoom In (subset)
if flip_lr == "yes":
ax.set_xlim(win_x[1], win_x[0])
else:
ax.set_xlim(win_x[0], win_x[1])
if flip_ud == "yes":
ax.set_ylim(win_y[0], win_y[1])
else:
ax.set_ylim(win_y[1], win_y[0])
## Flip
# if flip_lr == 'yes': fig.gca().invert_xaxis()
# if flip_ud == 'yes': fig.gca().invert_yaxis()
##########################################
##### Plot Fig 2 - Time series plot
# fig2 = plt.figure(num=2,figsize=(12,6))
fig2 = plt.figure(2, figsize=(12, 6))
ax2 = fig2.add_subplot(111)
try:
timeSeriesFile_2
h5timeseries_2 = h5py.File(timeSeriesFile_2)
dateList_2 = h5timeseries_2["timeseries"].keys()
dateList_2 = sorted(dateList_2)
dates_2, datevector_2 = ptime.date_list2vector(dateList_2)
datevector_all += list(set(datevector_2) - set(datevector_all))
datevector_all = sorted(datevector_all)
except:
pass
################################ Plot Code Package <start> #################################
def plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries):
ax2.cla()
print "\n-------------------------------------------------------------------------------"
disp_min = 0
disp_max = 0
############################# Plot Time Series ##############################
global ref_xsub, ref_ysub
##### 1.1 Plot Reference time series
try:
ref_xsub
ref_ysub
ref_xsub, ref_ysub = check_yx(ref_xsub, ref_ysub, radius, ax, rectColor)
print "----------------------------------------------------"
print "Reference Point:"
print "ref_x=" + str(ref_xsub[0]) + ":" + str(ref_xsub[1])
print "ref_y=" + str(ref_ysub[0]) + ":" + str(ref_ysub[1])
print "-----------------------------"
print "Time series with all dates:"
dis1, dis1_mean, dis1_std, dis1_vel = read_dis(ref_xsub, ref_ysub, dateList1, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates1,
dis1_mean,
yerr=dis1_std,
fmt="-ks",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor_ref,
mew=edgeWidth,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis1_mean, dis1_std)
if not len(dateList) == len(dateList1):
print "-----------------------------"
print "Time series with dates of interest:"
dis12, dis12_mean, dis12_std, dis12_vel = read_dis(ref_xsub, ref_ysub, dateList, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates,
dis12_mean,
yerr=dis12_std,
fmt="-ks",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor_ref2,
mew=edgeWidth,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis12_mean, dis12_std)
except:
pass
##### 1.2.0 Read y/x
print "\n----------------------------------------------------"
print "Point of Interest:"
xsub, ysub = check_yx(xsub, ysub, radius, ax, rectColor)
print "x=" + str(xsub[0]) + ":" + str(xsub[1])
print "y=" + str(ysub[0]) + ":" + str(ysub[1])
##### 1.2.1 Plot 2nd time series
try:
timeSeriesFile_2
print "-----------------------------"
print "2nd Time Series:"
dis2, dis2_mean, dis2_std, dis2_vel = read_dis(xsub, ysub, dateList_2, h5timeseries_2, unit)
(_, caps, _) = ax2.errorbar(
dates_2,
dis2_mean,
yerr=dis2_std,
fmt="-ko",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor2,
elinewidth=0,
ecolor="black",
capsize=0,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis2_mean, dis2_std)
except:
pass
##### 1.2.2 Plot 1st time series
print "-----------------------------"
print "Time Series:"
dis, dis_mean, dis_std, dis_vel = read_dis(xsub, ysub, dateList, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates,
dis_mean,
yerr=dis_std,
fmt="-ko",
ms=markerSize,
lw=lineWidth,
alpha=1,
mfc=markerColor,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis_mean, dis_std)
####################### Figure Format #######################
## x axis format
try:
ax2 = ptime.adjust_xaxis_date(ax2, dateVecMinMax, fontSize)
except:
ax2 = ptime.adjust_xaxis_date(ax2, datevector_all, fontSize)
## y axis format
ax2.set_ylabel("Displacement [" + unit + "]", fontsize=fontSize)
try:
lbound
hbound
ax2.set_ylim(lbound, hbound)
except:
disp_buf = 0.2 * (disp_max - disp_min)
ax2.set_ylim(disp_min - disp_buf, disp_max + disp_buf)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(fontSize)
## title
figTitle = "x=" + str(xsub[0]) + ":" + str(xsub[1]) + ", y=" + str(ysub[0]) + ":" + str(ysub[1])
try:
lonc = ullon + (xsub[0] + xsub[1]) / 2.0 * lon_step
latc = ullat + (ysub[0] + ysub[1]) / 2.0 * lat_step
figTitle += ", lalo=" + "%.4f,%.4f" % (latc, lonc)
except:
pass
ax2.set_title(figTitle)
################## Save and Output #####################
if saveFig == "yes":
print "-----------------------------"
Delay = {}
Delay["displacement"] = dis
Delay["unit"] = unit
Delay["time"] = datevector
Delay["velocity"] = dis_vel[0]
Delay["velocity_unit"] = unit + "/yr"
Delay["velocity_std"] = dis_vel[4]
figBase = "x" + str(xsub[0]) + "_" + str(xsub[1] - 1) + "y" + str(ysub[0]) + "_" + str(ysub[1] - 1)
sio.savemat(figBase + "_ts.mat", {"displacement": Delay})
print "saved " + figBase + "_ts.mat"
fig2.savefig(figBase + "_ts.pdf", bbox_inches="tight", transparent=True, dpi=fig_dpi)
print "saved " + figBase + "_ts.pdf"
if dispFig == "no":
fig.savefig(figBase + "_vel.png", bbox_inches="tight", transparent=True, dpi=fig_dpi)
print "saved " + figBase + "_vel.png"
################################ Plot Code Package <end> #################################
########### 1. Plot Time Series with x/y ##########
try:
xsub
ysub
plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries)
except:
print "No x/y input"
pass
########### 2. Plot Time Series with Click ##########
## similar to 1. Plot Time Series with x/y
def onclick(event):
ax2.cla()
xsub = [int(event.xdata)]
ysub = [int(event.ydata)]
plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries)
if dispFig == "yes":
plt.show()
try:
cid = fig.canvas.mpl_connect("button_press_event", onclick)
except:
pass
if dispFig == "yes":
plt.show()
def plot_network(ax,
date12_list,
date_list,
pbase_list,
plot_dict={},
date12_list_drop=[]):
'''Plot Temporal-Perp baseline Network
Inputs
ax : matplotlib axes object
date12_list : list of string for date12 in YYMMDD-YYMMDD format
date_list : list of string, for date in YYYYMMDD/YYMMDD format
pbase_list : list of float, perp baseline, len=number of acquisition
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
coherence_list : list of float, coherence value of each interferogram, len = number of ifgrams
disp_min/max : float, min/max range of the color display based on coherence_list
colormap : string, colormap name
coh_thres : float, coherence of where to cut the colormap for display
disp_title : bool, show figure title or not, default: True
disp_drop: bool, show dropped interferograms or not, default: True
Output
ax : matplotlib axes object
'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
# For colorful display of coherence
if not 'coherence_list' in keyList: plot_dict['coherence_list'] = None
if not 'disp_min' in keyList: plot_dict['disp_min'] = 0.2
if not 'disp_max' in keyList: plot_dict['disp_max'] = 1.0
if not 'colormap' in keyList: plot_dict['colormap'] = 'RdBu'
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
if not 'coh_thres' in keyList: plot_dict['coh_thres'] = None
if not 'disp_drop' in keyList: plot_dict['disp_drop'] = True
coh_list = plot_dict['coherence_list']
disp_min = plot_dict['disp_min']
disp_max = plot_dict['disp_max']
coh_thres = plot_dict['coh_thres']
transparency = 0.7
# Date Convert
date8_list = ptime.yyyymmdd(sorted(date_list))
date6_list = ptime.yymmdd(date8_list)
dates, datevector = ptime.date_list2vector(date8_list)
## Keep/Drop - date12
date12_list_keep = sorted(list(set(date12_list) - set(date12_list_drop)))
idx_date12_keep = [date12_list.index(i) for i in date12_list_keep]
idx_date12_drop = [date12_list.index(i) for i in date12_list_drop]
if not date12_list_drop:
plot_dict['disp_drop'] = False
## Keep/Drop - date
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)))
idx_date_keep = [date8_list.index(i) for i in date8_list_keep]
idx_date_drop = [date8_list.index(i) for i in date8_list_drop]
# Ploting
#ax=fig.add_subplot(111)
## Colorbar when conherence is colored
if coh_list:
data_min = min(coh_list)
data_max = max(coh_list)
# Normalize
normalization = False
if normalization:
coh_list = [(coh - data_min) / (data_min - data_min)
for coh in coh_list]
disp_min = data_min
disp_max = data_max
print 'showing coherence'
print 'colormap: ' + plot_dict['colormap']
print 'display range: ' + str([disp_min, disp_max])
print 'data range: ' + str([data_min, data_max])
# Use lower/upper part of colormap to emphasis dropped interferograms
if not coh_thres:
# Find proper cut percentage so that all keep pairs are blue and drop pairs are red
coh_list_keep = [coh_list[i] for i in idx_date12_keep]
coh_list_drop = [coh_list[i] for i in idx_date12_drop]
if coh_list_drop:
coh_thres = max(coh_list_drop)
else:
coh_thres = min(coh_list_keep)
if coh_thres < disp_min:
print 'data range exceed orginal display range, set new display range to: [0.0, %f]' % (
disp_max)
disp_min = 0.0
c1_num = np.ceil(200.0 * (coh_thres - disp_min) /
(disp_max - disp_min)).astype('int')
coh_thres = c1_num / 200.0 * (disp_max - disp_min) + disp_min
cmap = plt.get_cmap(plot_dict['colormap'])
colors1 = cmap(np.linspace(0.0, 0.3, c1_num))
colors2 = cmap(np.linspace(0.6, 1.0, 200 - c1_num))
cmap = colors.LinearSegmentedColormap.from_list(
'truncate_RdBu', np.vstack((colors1, colors2)))
print 'color jump at ' + str(coh_thres)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
norm = mpl.colors.Normalize(vmin=disp_min, vmax=disp_max)
cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm)
cbar.set_label('Average Spatial Coherence',
fontsize=plot_dict['fontsize'])
## Dot - SAR Acquisition
if idx_date_keep:
x_list = [dates[i] for i in idx_date_keep]
y_list = [pbase_list[i] for i in idx_date_keep]
ax.plot(x_list,
y_list,
'ko',
alpha=0.7,
ms=plot_dict['markersize'],
mfc=plot_dict['markercolor'])
if idx_date_drop:
x_list = [dates[i] for i in idx_date_drop]
y_list = [pbase_list[i] for i in idx_date_drop]
ax.plot(x_list,
y_list,
'ko',
alpha=0.7,
ms=plot_dict['markersize'],
mfc='gray')
## Line - Pair/Interferogram
# interferograms dropped
if plot_dict['disp_drop']:
for date12 in date12_list_drop:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if coh_list:
coh = coh_list[date12_list.index(date12)]
coh_idx = (coh - disp_min) / (disp_max - disp_min)
ax.plot(x,
y,
'--',
lw=plot_dict['linewidth'],
alpha=transparency,
c=cmap(coh_idx))
else:
ax.plot(x,
y,
'--',
lw=plot_dict['linewidth'],
alpha=transparency,
c='k')
# interferograms kept
for date12 in date12_list_keep:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if coh_list:
coh = coh_list[date12_list.index(date12)]
coh_idx = (coh - disp_min) / (disp_max - disp_min)
ax.plot(x,
y,
'-',
lw=plot_dict['linewidth'],
alpha=transparency,
c=cmap(coh_idx))
else:
ax.plot(x,
y,
'-',
lw=plot_dict['linewidth'],
alpha=transparency,
c='k')
if plot_dict['disp_title']:
ax.set_title('Interferogram Network', fontsize=plot_dict['fontsize'])
# axis format
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax = auto_adjust_yaxis(ax, pbase_list, plot_dict['fontsize'])
ax.set_xlabel('Time [years]', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Perp Baseline [m]', fontsize=plot_dict['fontsize'])
# Legend
if plot_dict['disp_drop']:
solid_line = mlines.Line2D([], [],
color='k',
ls='solid',
label='Interferograms')
dash_line = mlines.Line2D([], [],
color='k',
ls='dashed',
label='Interferograms dropped')
ax.legend(handles=[solid_line, dash_line])
return ax
def correct_lod_file(File, outFile=None):
# Check Sensor Type
print 'input file: ' + File
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
platform = atr['PLATFORM']
print 'platform: ' + platform
if not platform.lower() in ['env', 'envisat']:
print 'No need to correct LOD for ' + platform
sys.exit(1)
# Output Filename
if not outFile:
ext = os.path.splitext(File)[1]
outFile = os.path.splitext(File)[0] + '_LODcor' + ext
# Get LOD phase ramp from empirical model
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
range_resolution = float(atr['RANGE_PIXEL_SIZE'])
r = np.linspace(0, width - 1, width)
R = range_resolution * r * (3.87e-7)
Ramp = np.tile(R, [length, 1])
yref = int(atr['ref_y'])
xref = int(atr['ref_x'])
Ramp -= Ramp[yref][xref]
# Correct LOD Ramp for Input File
if k in multi_group_hdf5_file + multi_dataset_hdf5_file:
h5 = h5py.File(File, 'r')
epochList = sorted(h5[k].keys())
h5out = h5py.File(outFile, 'w')
group = h5out.create_group(k)
if k in ['interferograms', 'wrapped']:
print 'number of interferograms: ' + str(len(epochList))
wvl = float(atr['WAVELENGTH'])
Ramp *= -4 * np.pi / wvl
for epoch in epochList:
print epoch
data = h5[k][epoch].get(epoch)[:]
atr = h5[k][epoch].attrs
dates = ptime.yyyymmdd(atr['DATE12'].split('-'))
dates = ptime.yyyymmdd2years(dates)
dt = date[1] - date[0]
data -= Ramp * dt
gg = group.create_group(epoch)
dset = gg.create_dataset(epoch, data=data, compression='gzip')
for key, value in atr.iteritems():
gg.attrs[key] = value
elif k == 'timeseries':
print 'number of acquisitions: ' + str(len(epochList))
tbase = [
float(dy) / 365.25
for dy in ptime.date_list2tbase(epochList)[0]
]
for i in range(len(epochList)):
epoch = epochList[i]
print epoch
data = h5[k].get(epoch)[:]
data -= Ramp * tbase[i]
dset = group.create_dataset(epoch,
data=data,
compression='gzip')
for key, value in atr.iteritems():
group.attrs[key] = value
else:
print 'No need to correct for LOD for ' + k + ' file'
sys.exit(1)
h5.close()
h5out.close()
else:
data, atr = readfile.read(File)
data -= Ramp
writefile.write(data, atr, outFile)
return outFile
def main(argv):
try: File=argv[0]
except: Usage();sys.exit(1)
atr = readfile.read_attributes(File)
k = atr['FILE_TYPE']
h5file=h5py.File(File,'r')
print '\n************* Output to ROI_PAC format ***************'
if k == 'velocity':
dset = h5file['velocity'].get('velocity')
data = dset[0:dset.shape[0],0:dset.shape[1]]
print "converting velocity to a 1 year interferogram."
wvl=float(h5file[k].attrs['WAVELENGTH'])
data=(-4*pi/wvl)*data
outname=File.split('.')[0]+'.unw'
writefile.write(data,atr,outname)
elif k == 'timeseries':
dateList=h5file['timeseries'].keys()
## Input
if len(sys.argv)==2:
print 'No input date specified >>> continue with the last date'
dateList=h5file['timeseries'].keys()
d=dateList[-1]
elif len(sys.argv)==3:
d=sys.argv[2]
elif len(sys.argv)==4:
ds=sys.argv[2:4]; ds.sort()
d_ref = ds[0]
d = ds[1]
else: Usage(); sys.exit(1)
d = ptime.yyyymmdd(d)
try: d_ref = ptime.yyyymmdd(d_ref)
except: pass
## Data
print 'reading '+d+' ... '
data = h5file['timeseries'].get(d)[:]
try:
print 'reading '+d_ref+' ... '
data_ref = h5file['timeseries'].get(d_ref)[:]
data = data - data_ref
except: pass
wvl=float(atr['WAVELENGTH'])
data *= -4*pi/wvl
## outName
try: master_d = d_ref
except:
try: master_d = atr['ref_date']
except: master_d = atr['DATE']
if len(master_d)==8: master_d=master_d[2:8]
if len(d)==8: d=d[2:8]
outname = master_d+'_'+d+'.unw'
## Attributes
atr['FILE_TYPE'] = '.unw'
atr['P_BASELINE_TIMESERIES'] = '0.0'
atr['UNIT'] = 'radian'
atr['DATE'] = master_d
atr['DATE12'] = master_d+'-'+d
## Writing
writefile.write(data,atr,outname)
elif k in ['interferograms','coherence','wrapped']:
## Check input
igramList=h5file[k].keys()
try:
d = sys.argv[2]
for i in range(len(igramList)):
if d in igramList[i]:
igram = igramList[i]
except:
igram = igramList[-1]; print 'No input date specified >>> continue with the last date'
## Read and Write
print 'reading '+igram+' ... '
dset = h5file[k][igram].get(igram)
data = dset[0:dset.shape[0],0:dset.shape[1]]
outname = igram
print 'writing >>> '+ outname
writefile.write_float32(data,outname)
f = open(outname+'.rsc','w')
for key , value in h5file[k][igram].attrs.iteritems():
f.write(key+' '+str(value)+'\n')
f.close()
else:
dset = h5file[k].get(k)
data = dset[0:dset.shape[0],0:dset.shape[1]]
if k == 'temporal_coherence': outname=File.split('.')[0]+'.cor'
else: outname=File.split('.')[0]+'.unw'
writefile.write(data,atr,outname)
h5file.close()
