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 select_pairs_star(dateList,m_date):
## 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.
##
## Pre-process Inputs
dateList = ptime.yymmdd(sorted(dateList))
m_date = ptime.yymmdd(m_date)
## Get date index
idxList = list(range(len(dateList)))
m_idx = dateList.index(m_date)
pairs = []
for idx in idxList:
if not idx == m_idx:
pairs.append([m_idx,idx])
pairs = pair_sort(pairs)
return pairs
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 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_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 read_pairs_list(date12ListFile, dateList=[]):
'''Read Pairs List file like below:
070311-070426
070311-070611
...
'''
# Read date12 list file
date12List = sorted(list(np.loadtxt(date12ListFile, dtype=str)))
# Get dateList from date12List
if not dateList:
dateList = []
for date12 in date12List:
dates = date12.split('-')
if not dates[0] in dateList: dateList.append(dates[0])
if not dates[1] in dateList: dateList.append(dates[1])
dateList.sort()
date6List = ptime.yymmdd(dateList)
# Get pair index
pairs_idx = []
for date12 in date12List:
dates = date12.split('-')
pair_idx = [date6List.index(dates[0]), date6List.index(dates[1])]
pairs_idx.append(pair_idx)
return pairs_idx
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 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 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 write_pairs_list(pairs,dateList,outName):
dateList6 = ptime.yymmdd(dateList)
fl = open(outName,'w')
for idx in pairs:
date12 = dateList6[idx[0]]+'-'+dateList6[idx[1]]+'\n'
fl.write(date12)
fl.close()
return 1
def write_pairs_list(pairs, dateList, outName):
'''Write pairs list file.'''
dateList6 = ptime.yymmdd(dateList)
fl = open(outName,'w')
for idx in pairs:
date12 = dateList6[idx[0]]+'-'+dateList6[idx[1]]+'\n'
fl.write(date12)
fl.close()
return 1
def manual_select_pairs_to_remove(File):
'''Manually select interferograms to remove'''
print '----------------------------------------------------------------------------'
print 'Manually select interferograms to remove'
print 'Click two dates - points - in the figure to select one pair of interferogram'
print 'repeat until you select all pairs you would like to remove'
print 'then close the figure to continue the program ...'
print '----------------------------------------------------------------------------'
# Display the network
fig = plt.figure()
ax = fig.add_subplot(111)
pairs_idx = pnet.read_igram_pairs(File)
bperp_list = ut.Baseline_timeseries(File)
date8_list = ptime.igram_date_list(File)
ax = pnet.plot_network(ax, pairs_idx, date8_list, bperp_list)
print 'display the network of interferogram of file: ' + File
date12_orig = pnet.get_date12_list(File)
date6_list = ptime.yymmdd(date8_list)
dates_array = np.array(ptime.date_list2vector(date8_list)[0])
dateNum_array = mdates.date2num(dates_array)
bperp_array = np.array(bperp_list)
date_click = []
date12_click = []
def onclick(event):
xClick = event.xdata
yClick = event.ydata
idx = nearest_neighbor(xClick, yClick, dateNum_array, bperp_array)
date6 = date6_list[idx]
print 'click at ' + date6
date_click.append(date6)
if len(date_click) % 2 == 0 and date_click[-2] != date_click[-1]:
[m_date, s_date] = sorted(date_click[-2:])
m_idx = date6_list.index(m_date)
s_idx = date6_list.index(s_date)
date12 = m_date + '-' + s_date
if date12 in date12_orig:
print 'select date12: ' + date12
date12_click.append(date12)
ax.plot([dateNum_array[m_idx], dateNum_array[s_idx]],
[bperp_array[m_idx], bperp_array[s_idx]],
'r',
lw=4)
else:
print date12 + ' is not existed in input file'
plt.draw()
cid = fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
return date12_click
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 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 read_pairs_list(listFile,dateList):
## Read Pairs List file like below:
## 070311-070426
## 070311-070611
## ...
dateList6 = ptime.yymmdd(dateList)
pairs=[]
fl = open(listFile,'r')
lines = []
lines = fl.read().splitlines()
for line in lines:
date12 = line.split('-')
pairs.append([dateList6.index(date12[0]),dateList6.index(date12[1])])
fl.close()
pairs = pair_sort(pairs)
return pairs
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 igram_pairs(igramFile):
## Read Igram file
h5file = h5py.File(igramFile)
k = h5file.keys()
if "interferograms" in k:
k[0] = "interferograms"
elif "coherence" in k:
k[0] = "coherence"
if k[0] not in ["interferograms", "coherence", "wrapped"]:
print "Only interferograms / coherence / wrapped are supported."
sys.exit(1)
dateList = ptime.date_list(igramFile)
dateList6 = ptime.yymmdd(dateList)
pairs = []
igramList = h5file[k[0]].keys()
for igram in igramList:
date12 = h5file[k[0]][igram].attrs["DATE12"].split("-")
pairs.append([dateList6.index(date12[0]), dateList6.index(date12[1])])
return pairs
def read_igram_pairs(igramFile):
## Read Igram file
h5file = h5py.File(igramFile,'r')
k = h5file.keys()
if 'interferograms' in k: k[0] = 'interferograms'
elif 'coherence' in k: k[0] = 'coherence'
if k[0] not in ['interferograms','coherence','wrapped']:
print 'Only interferograms / coherence / wrapped are supported.'; sys.exit(1)
dateList = ptime.date_list(igramFile)
dateList6 = ptime.yymmdd(dateList)
pairs = []
igramList=h5file[k[0]].keys()
for igram in igramList:
date12 = h5file[k[0]][igram].attrs['DATE12'].split('-')
pairs.append([dateList6.index(date12[0]),dateList6.index(date12[1])])
h5file.close()
pairs = pair_sort(pairs)
return pairs
def read_igram_pairs(igramFile):
'''Read pairs index from hdf5 file'''
## Read Igram file
h5file = h5py.File(igramFile,'r')
k = h5file.keys()
if 'interferograms' in k: k[0] = 'interferograms'
elif 'coherence' in k: k[0] = 'coherence'
if k[0] not in ['interferograms','coherence','wrapped']:
print 'Only interferograms / coherence / wrapped are supported.'; sys.exit(1)
dateList = ptime.ifgram_date_list(igramFile)
dateList6 = ptime.yymmdd(dateList)
pairs = []
igramList=h5file[k[0]].keys()
for igram in igramList:
date12 = h5file[k[0]][igram].attrs['DATE12'].split('-')
pairs.append([dateList6.index(date12[0]),dateList6.index(date12[1])])
h5file.close()
pairs = pair_sort(pairs)
return pairs
def main(argv):
## Global Variables
global fontSize, lineWidth, markerColor, markerSize
## Default Values
fontSize = 12
lineWidth = 2
markerColor = "orange"
markerSize = 16
saveFig = "no"
dispFig = "yes"
saveList = "no"
if len(sys.argv) > 2:
try:
opts, args = getopt.getopt(argv, "h:b:f:s:w:l:m:c:o:", ["save", "nodisplay", "list"])
except getopt.GetoptError:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
Usage()
sys.exit()
elif opt == "-b":
baselineFile = arg
elif opt == "-f":
igramsFile = arg
elif opt == "-l":
listFile = arg
elif opt == "-s":
fontSize = int(arg)
elif opt == "-w":
lineWidth = int(arg)
elif opt == "-m":
markerSize = int(arg)
elif opt == "-c":
markerColor = arg
# elif opt == '-o': figName2 = arg; saveFig = 'yes'
elif opt == "--save":
saveFig = "yes"
elif opt == "--nodisplay":
dispFig = "no"
saveFig = "yes"
elif opt == "--list":
saveList = "yes"
try:
igramsFile
except:
try:
baselineFile
except:
Usage()
sys.exit(1)
elif len(sys.argv) == 2:
igramsFile = argv[0]
else:
Usage()
sys.exit(1)
##### Output figure name
figName1 = "BperpHist.pdf"
figName2 = "Network.pdf"
try:
igramsFile
if "Modified" in igramsFile:
figName1 = "BperpHist_Modified.pdf"
figName2 = "Network_Modified.pdf"
except:
pass
############# Read Time and Bperp ################
print "\n******************** Plot Network **********************"
try:
igramsFile
atr = readfile.read_attributes(igramsFile)
k = atr["FILE_TYPE"]
if k not in ["interferograms", "coherence", "wrapped"]:
print "Only interferograms / coherence / wrapped are supported."
sys.exit(1)
print "reading date and perpendicular baseline from " + k
dateList = ptime.date_list(igramsFile)
dateList6 = ptime.yymmdd(dateList)
print "number of acquisitions: " + str(len(dateList))
Bp = ut.Baseline_timeseries(igramsFile)
except:
baselineFile
print "reading date and perpendicular baseline from " + baselineFile
dateList, Bp = pnet.read_baseline_file(baselineFile)[0:2]
dateList6 = ptime.yymmdd(dateList)
############# Read Pairs Info ####################
print "reading pairs info"
try:
listFile
pairs = pnet.read_pairs_list(listFile, dateList)
except:
pairs = pnet.read_igram_pairs(igramsFile)
print "number of pairs : " + str(len(pairs))
if saveList == "yes":
pnet.write_pairs_list(pairs, dateList, "Pairs.list")
print "save pairs info to Pairs.list"
############# Read Unwrapping Error Info #######################
## For simulated interferograms only
## To plot the interferograms with unwrapping errors with a different color
# N_unw_err=0
# try:
# for ifgram in ifgramList:
# if h5file[k[0]][ifgram].attrs['unwrap_error']=='yes':
# N_unw_err=N_unw_err+1
# except: pass
#
# if N_unw_err>0:
# pairs_ue=np.zeros([N_unw_err,2],np.int)
# i=0
# for ifgram in ifgramList:
# if h5file[k[0]][ifgram].attrs['unwrap_error']=='yes':
# date1,date2 = h5file[k[0]][ifgram].attrs['DATE12'].split('-')
# pairs_ue[i][0]=dateList6.index(date1)
# pairs_ue[i][1]=dateList6.index(date2)
# i=i+1
#
############### Fig 1 - Interferogram Network ##################
fig1 = plt.figure(1)
fig1 = plot_network(fig1, pairs, dateList, Bp)
if saveFig == "yes":
plt.savefig(figName2, bbox_inches="tight")
print "save figure to " + figName2
############## Fig 2 - Baseline History ###################
fig2 = plt.figure(2)
fig2 = plot_bperp_hist(fig2, dateList, Bp)
if saveFig == "yes":
plt.savefig(figName1, bbox_inches="tight")
print "save figure to " + figName1
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
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):
##### Read Inputs
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
date12_orig = pnet.get_date12_list(inps.file[0])
#print '\n****************** Network Modification ********************'
if (not inps.reference_file and not inps.template_file and\
not inps.max_temp_baseline and not inps.max_perp_baseline and\
not inps.drop_ifg_index and not inps.drop_date and \
not inps.coherence_file):
# Display network for manually modification when there is no other modification input.
print 'No input found to remove interferogram, continue by display the network to select it manually ...'
inps.disp_network = True
# Update inps if template is input
if inps.template_file:
inps = update_inps_with_template(inps, inps.template_file)
# Convert index : input to continous index list
if inps.drop_ifg_index:
ifg_index = list(inps.drop_ifg_index)
inps.drop_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.drop_ifg_index.append(j)
elif len(index_temp) == 1:
inps.drop_ifg_index.append(int(index))
else:
print 'Unrecoganized input: ' + index
inps.drop_ifg_index = sorted(inps.drop_ifg_index)
if max(inps.drop_ifg_index) > len(date12_orig):
raise Exception('Input index out of range!\n'+\
'input index:'+str(inps.drop_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)
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:'
for date12 in date12_orig:
if date12 not in date12_to_keep:
date12_to_rmv.append(date12)
print date12
# 2.1 Update date12_to_rmv from coherence file
if inps.coherence_file:
print '----------------------------------------------------------------------------'
print 'use coherence-based network modification from coherence file: ' + inps.coherence_file
# Calculate spatial average coherence
if not inps.mask_file:
mask = readfile.read(inps.mask_file)[0]
print 'mask coherence with file: ' + inps.mask_file
else:
mask = None
cohTextFile = os.path.splitext(
inps.coherence_file)[0] + '_spatialAverage.list'
if os.path.isfile(cohTextFile):
print 'average coherence in space has been calculated before and store in file: ' + cohTextFile
print 'read it directly, or delete it and re-run the script to re-calculate the list'
cohTxt = np.loadtxt(cohTextFile, dtype=str)
mean_coherence_list = [float(i) for i in cohTxt[:, 1]]
coh_date12_list = [i for i in cohTxt[:, 0]]
else:
print 'calculating average coherence of each interferogram ...'
mean_coherence_list = ut.spatial_average(inps.coherence_file,
mask,
saveList=True)
coh_date12_list = pnet.get_date12_list(inps.coherence_file)
print 'date12 with average coherence < ' + str(
inps.min_coherence) + ': '
for i in range(len(coh_date12_list)):
if mean_coherence_list[i] < inps.min_coherence:
date12 = coh_date12_list[i]
date12_to_rmv.append(date12)
print date12
# 2.2 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])
for i in range(len(ifg_bperp_list)):
if ifg_bperp_list[i] > inps.max_perp_baseline:
date12 = date12_orig[i]
date12_to_rmv.append(date12)
print date12
# 2.3 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.igram_date_list(inps.file[0])
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
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)
print date12
# 2.4 Update date12_to_rmv from drop_ifg_index
if inps.drop_ifg_index:
print '----------------------------------------------------------------------------'
print 'drop date12/pair with the following index number:'
for index in inps.drop_ifg_index:
date12 = date12_orig[index - 1]
date12_to_rmv.append(date12)
print str(index) + ' ' + date12
# 2.5 Update date12_to_rmv from drop_date
if inps.drop_date:
inps.drop_date = ptime.yymmdd(inps.drop_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs including the following dates: \n' + str(
inps.drop_date)
for i in range(len(date12_orig)):
date1, date2 = date12_orig[i].split('-')
if (date1 in inps.drop_date) or (date2 in inps.drop_date):
date12 = date12_orig[i]
date12_to_rmv.append(date12)
print date12
# 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 = list(set(date12_to_rmv))
date12_to_rmv = sorted(date12_to_rmv)
print '----------------------------------------------------------------------------'
print 'number of interferograms to remove: ' + str(len(date12_to_rmv))
print 'list of interferograms to remove:'
print date12_to_rmv
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)
k = readfile.read_attribute(File)['FILE_TYPE']
# Update Mask File
if k == 'interferograms':
print 'update mask file for input ' + k + ' file based on ' + Modified_File
outFile = 'Modified_Mask.h5'
print 'writing >>> ' + outFile
ut.nonzero_mask(Modified_File, outFile)
elif k == 'coherence':
print 'update average spatial coherence for input ' + k + ' file based on: ' + Modified_File
outFile = 'Modified_average_spatial_coherence.h5'
print 'writing >>> ' + outFile
ut.temporal_average(Modified_File, outFile)
Modified_CoherenceFile = Modified_File
# Plot result
if inps.plot:
print '\nplot modified network and save to file.'
plotCmd = 'plot_network.py ' + Modified_File + ' --coherence ' + Modified_CoherenceFile + ' --nodisplay'
print plotCmd
os.system(plotCmd)
print 'Done.'
return
else:
print 'No interferogram dropped, skip update.'
return
def main(argv):
##### Default
fontSize = 12
lineWidth = 2
markerColor = 'crimson'
markerSize = 16
disp_fig = 'no'
save_fig = 'yes'
save_list = 'yes'
ref_file = 'reference_date.txt'
drop_file = 'drop_date.txt'
##### Check Inputs
if len(sys.argv)>3:
try:
opts, args = getopt.getopt(argv,'h:f:m:o:x:y:',['help','circle='])
except getopt.GetoptError:
print 'Error in reading input options!'; Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"): Usage() ; sys.exit()
elif opt == '-f': File = arg
elif opt == '-m': maskFile = arg
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort()
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort()
elif opt == '--circle' : cir_par = [i for i in arg.split(';')]
#elif opt == '-o': outName = arg
else:
try: File = argv[0]
except: Usage(); sys.exit(1)
try: maskFile = argv[1]
except: pass
try: atr = readfile.read_attributes(File)
except: Usage(); sys.exit(1)
ext = os.path.splitext(File)[1].lower()
FileBase = os.path.basename(File).split(ext)[0]
outNameBase = 'spatialMean_'+FileBase
print '\n*************** Spatial Average ******************'
##### Input File Info
k = atr['FILE_TYPE']
print 'Input file is '+k
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
h5file = h5py.File(File)
epochList = h5file[k].keys();
epochList = sorted(epochList)
epochNum = len(epochList)
print 'number of epoch: '+str(epochNum)
dates,datevector = ptime.date_list2vector(epochList)
##### Mask Info
try:
Mask_orig,Matr = readfile.read(maskFile)
print 'mask file: '+maskFile
Masking = 'yes'
except:
print 'No mask. Use the whole area for ramp estimation.'
Masking = 'no'
Mask_orig=np.ones((length,width))
Mask = np.zeros((length,width))
Mask[:] = Mask_orig[:]
## Bounding Subset
try:
xsub
ysub
ysub,xsub = subset.check_subset_range(ysub,xsub,atr)
Mask[ysub[0]:ysub[1],xsub[0]:xsub[1]] = Mask_orig[ysub[0]:ysub[1],xsub[0]:xsub[1]]*2
#Mask[0:ysub[0],:] = 0
#Mask[ysub[1]:length,:] = 0
#Mask[:,0:xsub[0]] = 0
#Mask[:,xsub[1]:width] = 0
except:
Mask = Mask_orig*2
print 'No subset input.'
## Circle Inputs
try:
cir_par
for i in range(len(cir_par)):
cir_idx = circle_index(atr,cir_par[i])
Mask[cir_idx] = Mask_orig[cir_idx]
print 'Circle '+str(i)+': '+cir_par[i]
except: print 'No circle of interest input.'
## Mask output
idx = Mask == 2
idxNum = float(sum(sum(idx)))
fig = plt.figure()
plt.imshow(Mask,cmap='gray')
plt.savefig(outNameBase+'_mask.png',bbox_inches='tight')
print 'save mask to '+outNameBase+'_mask.png'
#fig.clf()
##### Calculation
meanList = np.zeros(epochNum)
pixPercent = np.zeros(epochNum)
pixT = 0.7
print 'calculating ...'
print ' Date Mean Percentage'
for i in range(epochNum):
epoch = epochList[i]
d = h5file[k].get(epoch)[:]
#d[Mask==0] = np.nan
meanList[i] = np.nanmean(d[idx])
pixPercent[i] = np.sum(d[idx] >= pixT)/idxNum
print epoch+' : %.2f %.1f%%'%(meanList[i],pixPercent[i]*100)
del d
h5file.close()
##### Reference date - Max Value
top3 = sorted(zip(meanList,epochList), reverse=True)[:3]
print '------------ Top 3 Mean ------------------'
print top3
## Write to txt file
fref = open(ref_file,'w')
fref.write(str(top3[0][1])+'\n')
fref.close()
print 'write optimal reference date to '+ref_file
idxMean = meanList == np.nanmax(meanList)
##### Drop dates - mean threshold
#meanT = 0.7
#idxMean = meanList < meanT
#print '------------ Mean Value < '+str(meanT)+' --------'
#print np.array(epochList)[idxMean]
#print meanList[idxMean]
##### Drop dates - good pixel percentage
pixNumT = 0.7
print '------------ Good Pixel Percentage < %.0f%% -------'%(pixNumT*100)
idxPix = pixPercent < pixNumT
dropEpochList = np.array(epochList)[idxPix]
print dropEpochList
print pixPercent[idxPix]
## Write to txt file
fdrop = open(drop_file,'w')
for i in range(len(dropEpochList)):
fdrop.write(str(dropEpochList[i])+'\n')
fdrop.close()
print 'write drop dates to '+drop_file
print '-------------------------------------------'
##### Display
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(211)
ax.plot(dates, meanList, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
#ax.plot([dates[0],dates[-1]],[meanT,meanT], '--b', lw=lineWidth)
#sc = ax.scatter(dates, np.tile(0.5,epochNum), c=meanList, s=22**2, alpha=0.3, vmin=0.0, vmax=1.0)
#ax.scatter(np.array(dates)[idxMean], 0.5, c=meanList[idxMean], s=22**2, alpha=1.0, vmin=0.0, vmax=1.0)
ax = ptime.adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Spatial Average Value', fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
#cbar = plt.colorbar(sc)
#cbar.set_label('Spatial Mean of Normalized Sum Epochs')
ax = fig.add_subplot(212)
ax.plot(dates, pixPercent, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
ax.plot([dates[0],dates[-1]],[pixNumT,pixNumT], '--b', lw=lineWidth)
ax = ptime.adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Percenrage of Pixels with Value > '+str(pixNumT), fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.0f}%'.format(i*100) for i in vals])
if save_fig == 'yes':
plt.savefig(outNameBase+'.png',bbox_inches='tight')
print 'save figure to '+outNameBase+'.png'
if disp_fig == 'yes':
plt.show()
##### Output
if save_list == 'yes':
epochList6 = ptime.yymmdd(epochList)
fl = open(outNameBase+'.txt','w')
for i in range(epochNum):
str_line = epochList6[i]+' %.2f %.2f\n'%(meanList[i],pixPercent[i])
fl.write(str_line)
fl.close()
print 'write data to '+outNameBase+'.txt\n'
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 read_template2inps(template_file, inps=None):
'''Read input template options into Namespace inps'''
if not inps:
inps = cmdLineParse()
template = readfile.read_template(inps.template_file)
key_list = template.keys()
# Coherence-based network modification
prefix = 'pysar.network.'
key = prefix + 'coherenceBased'
if key in key_list and template[key] in ['auto', 'yes']:
inps.coherence_based = True
key = prefix + 'keepMinSpanTree'
if key in key_list and template[key] in ['no']:
inps.keep_mst = False
key = prefix + 'coherenceFile'
if key in key_list:
if template[key] == 'auto':
inps.coherence_file = 'coherence.h5'
else:
inps.coherence_file = template[key]
# find coherence file from input files if inps.coherence_file does not exists.
if inps.coherence_based and not os.path.isfile(inps.coherence_file):
k_list = [readfile.read_attribute(f)['FILE_TYPE'] for f in inps.file]
try:
coh_file_idx = k_list.index('coherence')
except ValueError:
print 'No coherence file found! Can not use coherence-based method without it.'
inps.coherence_file = inps.file[coh_file_idx]
key = prefix + 'minCoherence'
if key in key_list:
if template[key] == 'auto':
inps.min_coherence = 0.7
else:
inps.min_coherence = float(template[key])
key = prefix + 'maskFile'
if key in key_list:
value = template[key]
if value == 'auto':
try:
inps.mask_file = ut.get_file_list(['maskLand.h5',
'mask.h5'])[0]
except:
inps.mask_file = None
elif value == 'no':
inps.mask_file = None
else:
inps.mask_file = value
key = prefix + 'maskAoi.yx'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.aoi_pix_box = None
else:
tmp = [i.strip() for i in value.split(',')]
sub_y = sorted([int(i.strip()) for i in tmp[0].split(':')])
sub_x = sorted([int(i.strip()) for i in tmp[1].split(':')])
inps.aoi_pix_box = (sub_x[0], sub_y[0], sub_x[1], sub_y[1])
key = prefix + 'maskAoi.lalo'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.aoi_geo_box = None
else:
tmp = [i.strip() for i in value.split(',')]
sub_lat = sorted([float(i.strip()) for i in tmp[0].split(':')])
sub_lon = sorted([float(i.strip()) for i in tmp[1].split(':')])
inps.aoi_geo_box = (sub_lon[0], sub_lat[1], sub_lon[1], sub_lat[0])
# Check lookup file
if not inps.lookup_file:
print 'Warning: no lookup table file found! Can not use ' + key + ' option without it.'
print 'skip this option.'
inps.aoi_pix_box = None
## Network Modification based on thresholds
key = prefix + 'tempBaseMax'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.max_temp_baseline = float(value)
key = prefix + 'perpBaseMax'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.max_perp_baseline = float(value)
key = prefix + 'referenceFile'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.reference_file = None
else:
inps.reference_file = value
key = prefix + 'excludeDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.exclude_date = [i for i in value.replace(',', ' ').split()]
key = prefix + 'excludeIfgIndex'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.exclude_ifg_index = [
i for i in value.replace(',', ' ').split()
]
key = prefix + 'startDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.start_date = ptime.yymmdd(value)
key = prefix + 'endDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.end_date = ptime.yymmdd(value)
return inps
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 extract_attribute_interferogram(fname):
'''Read/extract attributes for PySAR from Gamma .unw, .cor and .int file
Inputs:
fname : str, Gamma interferogram filename or path, i.e. /PopoSLT143TsxD/diff_filt_HDR_130118-130129_4rlks.unw
Output:
atr : dict, Attributes dictionary
'''
file_dir = os.path.dirname(fname)
file_basename = os.path.basename(fname)
rsc_file = fname + '.rsc'
#if os.path.isfile(rsc_file):
# return rsc_file
atr = {}
atr['PROCESSOR'] = 'gamma'
atr['INSAR_PROCESSOR'] = 'gamma'
atr['FILE_TYPE'] = os.path.splitext(fname)[1]
## Get info: date12, num of loooks
try:
date12 = str(re.findall('\d{8}[-_]\d{8}', file_basename)[0])
except:
date12 = str(re.findall('\d{6}[-_]\d{6}', file_basename)[0])
m_date, s_date = date12.replace('_', '-').split('-')
atr['DATE12'] = ptime.yymmdd(m_date) + '-' + ptime.yymmdd(s_date)
lks = os.path.splitext(file_basename.split(date12)[1])[0]
## Read .off and .par file
off_file = file_dir + '/*' + date12 + lks + '.off'
m_par_file = [
file_dir + '/*' + m_date + lks + i for i in ['.amp.par', '.ramp.par']
]
s_par_file = [
file_dir + '/*' + s_date + lks + i for i in ['.amp.par', '.ramp.par']
]
try:
off_file = ut.get_file_list(off_file)[0]
except:
print '\nERROR: Can not find .off file, it supposed to be like: ' + off_file
try:
m_par_file = ut.get_file_list(m_par_file)[0]
except:
print '\nERROR: Can not find master date .par file, it supposed to be like: ' + m_par_file
try:
s_par_file = ut.get_file_list(s_par_file)[0]
except:
print '\nERROR: Can not find slave date .par file, it supposed to be like: ' + s_par_file
#print 'read '+m_par_file
#print 'read '+off_file
par_dict = readfile.read_gamma_par(m_par_file)
off_dict = readfile.read_gamma_par(off_file)
#print 'convert Gamma attribute to ROI_PAC style'
atr.update(par_dict)
atr.update(off_dict)
atr = readfile.attribute_gamma2roipac(atr)
## Perp Baseline Info
#print 'extract baseline info from %s, %s and %s file' % (m_par_file, s_par_file, off_file)
atr = get_perp_baseline(m_par_file, s_par_file, off_file, atr)
## LAT/LON_REF1/2/3/4
#print 'extract LAT/LON_REF1/2/3/4 from '+m_par_file
atr = get_lalo_ref(m_par_file, atr)
## Write to .rsc file
#print 'writing >>> '+rsc_file
try:
atr_orig = readfile.read_roipac_rsc(rsc_file)
except:
atr_orig = None
if atr_orig != atr:
print 'merge %s, %s and %s into %s' % (os.path.basename(m_par_file), os.path.basename(s_par_file),\
os.path.basename(off_file), os.path.basename(rsc_file))
writefile.write_roipac_rsc(atr, rsc_file)
return rsc_file
def plot_coherence_matrix(ax,
date12_list,
coherence_list,
date12_list_drop=[],
plot_dict={}):
'''Plot Coherence Matrix of input network
if date12_list_drop is not empty, plot KEPT pairs in the upper triangle and
ALL pairs in the lower triangle.
'''
# 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
coh_mat = coherence_matrix(date12_list, coherence_list)
if date12_list_drop:
# Date Convert
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date6_list = ptime.yymmdd(sorted(list(set(m_dates + s_dates))))
# Set dropped pairs' value to nan, in upper triangle only.
for date12 in date12_list_drop:
idx1, idx2 = [date6_list.index(i) for i in date12.split('-')]
coh_mat[idx1, idx2] = np.nan
im = ax.imshow(coh_mat,
cmap='jet',
vmin=0.0,
vmax=1.0,
interpolation='nearest')
date_num = coh_mat.shape[0]
if date_num < 30:
tick_list = range(0, date_num, 5)
else:
tick_list = range(0, date_num, 10)
ax.get_xaxis().set_ticks(tick_list)
ax.get_yaxis().set_ticks(tick_list)
ax.set_xlabel('Image Number', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Image Number', fontsize=plot_dict['fontsize'])
if plot_dict['disp_title']:
ax.set_title('Coherence Matrix')
# Colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax)
cbar.set_label('Spatial Coherence', fontsize=plot_dict['fontsize'])
# Legend
if date12_list_drop:
ax.plot([], [], label='Upper: used ifgrams')
ax.plot([], [], label='Lower: all ifgrams')
ax.legend(handlelength=0)
return ax
def read_template2inps(templateFile, inps=None):
'''Read network options from template file into Namespace variable inps'''
template_dict = readfile.read_template(templateFile)
if not template_dict:
print 'Empty template: '+templateFile
return None
keyList = template_dict.keys()
if not inps:
inps = cmdLineParse([''])
# Read network option regardless of prefix
for key in keyList:
if 'selectPairs.' in key: template_dict[key.split('selectPairs.')[1]] = template_dict[key]
if 'pysar.network.' in key: template_dict[key.split('pysar.network.')[1]] = template_dict[key]
if 'select.network.' in key: template_dict[key.split('select.network.')[1]] = template_dict[key]
keyList = template_dict.keys()
for key, value in template_dict.iteritems():
if value.lower() in ['off','false','n']: template_dict[key] = 'no'
if value.lower() in ['on', 'true', 'y']: template_dict[key] = 'yes'
# Update inps value if not existed
if not inps.method:
if 'selectMethod' in keyList: inps.method = template_dict['selectMethod']
elif 'method' in keyList: inps.method = template_dict['method']
else: inps.method = 'all'
if not inps.perp_base_max:
if 'perpBaseMax' in keyList: inps.perp_base_max = float(template_dict['perpBaseMax'])
else: inps.perp_base_max = 500.0
if not inps.temp_base_max:
if 'lengthDayMax' in keyList: inps.temp_base_max = float(template_dict['lengthDayMax'])
elif 'tempBaseMax' in keyList: inps.temp_base_max = float(template_dict['tempBaseMax'])
else: inps.temp_base_max = 1800.0
if not inps.temp_base_min:
if 'lengthDayMin' in keyList: inps.temp_base_min = float(template_dict['lengthDayMin'])
elif 'tempBaseMin' in keyList: inps.temp_base_min = float(template_dict['tempBaseMin'])
else: inps.temp_base_min = 0.0
if 'seasonal' in keyList and template_dict['seasonal'].lower() == 'no': inps.keep_seasonal = False
if 'keepSeasonal' in keyList and template_dict['keepSeasonal'].lower() == 'no': inps.keep_seasonal = False
if not inps.dop_overlap_min:
if 'DopOverlapThresh' in keyList: inps.dop_overlap_min = float(template_dict['DopOverlapThresh'])
elif 'dopOverlapThresh' in keyList: inps.dop_overlap_min = float(template_dict['dopOverlapThresh'])
elif 'dopOverlapMin' in keyList: inps.dop_overlap_min = float(template_dict['dopOverlapMin'])
else: inps.dop_overlap_min = 15.0
if not inps.reference_file and 'referenceFile' in keyList: inps.reference_file = template_dict['referenceFile']
if not inps.increment_num:
if 'incrementNum' in keyList: inps.increment_num = int(template_dict['incrementNum'])
else: inps.increment_num = 3
if not inps.temp_perp_list:
if 'dayPerpList' in keyList: inps.temp_perp_list = template_dict['dayPerpList']
elif 'tempPerpList' in keyList: inps.temp_perp_list = template_dict['tempPerpList']
else: inps.temp_perp_list = '16,1600;32,800;48,600;64,200'
inps.temp_perp_list = [[float(j) for j in i.split(',')] for i in inps.temp_perp_list.split(';')]
if not inps.exclude_date and 'excludeDate' in keyList:
ex_date_list = [i for i in template_dict['excludeDate'].split(',')]
inps.exclude_date = ptime.yymmdd(ex_date_list)
if not inps.start_date and 'startDate' in keyList:
inps.start_date = ptime.yyyymmdd(template_dict['startDate'])
if not inps.end_date and 'endDate' in keyList:
inps.end_date = ptime.yyyymmdd(template_dict['endDate'])
if not inps.m_date and 'masterDate' in keyList:
inps.m_date = ptime.yymmdd(template_dict['masterDate'])
return inps
def read_template2inps(templateFile, inps=None):
'''Read network options from template file into Namespace variable inps'''
if not inps:
inps = cmdLineParse()
##Read template file
template = readfile.read_template(templateFile)
key_list = template.keys()
if not template:
print 'Empty template: ' + templateFile
return None
prefix = 'select.network.'
##Extra keys
#extra_key_list = ['masterDate','startDate','endDate']
#for extra_key in extra_key_list:
# if extra_key in key_list:
# template[prefix+extra_key] = template[extra_key]
#Check option prefix
for i in ['selectPairs.']:
if any(i in key for key in key_list):
print '\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
print 'WARNING: un-supported option prefix detected: selectPairs.'
print " Use selectNetwork. instead"
print '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n'
if all(prefix not in key for key in key_list):
print '\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
print 'ERROR: no valid input option deteced in template file!'
print 'Check the template below for supported options:'
print '+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n'
print TEMPLATE
sys.exit(-1)
##Read template dict into inps namespace
key = prefix + 'method'
if key in key_list:
value = template[key]
if value == 'auto':
inps.method = 'all'
else:
inps.method = value
key = prefix + 'referenceFile'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.reference_file = None
else:
inps.reference_file = value
key = prefix + 'perpBaseMax'
if key in key_list:
value = template[key]
if value == 'auto':
inps.perp_base_max = 500.0
elif value == 'no':
inps.perp_base_max = 1e5
else:
inps.perp_base_max = float(value)
key = prefix + 'tempBaseMax'
if key in key_list:
value = template[key]
if value == 'auto':
inps.temp_base_max = 1800.0
elif value == 'no':
inps.temp_base_max = 3.65e5
else:
inps.temp_base_max = float(value)
key = prefix + 'tempBaseMin'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.temp_base_min = 0.0
else:
inps.temp_base_min = float(value)
key = prefix + 'keepSeasonal'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.keep_seasonal = False
else:
inps.keep_seasonal = True
key = prefix + 'dopOverlapMin'
if key in key_list:
value = template[key]
if value == 'auto':
inps.dop_overlap_min = 15.0
elif value == 'no':
inps.dop_overlap_min = 0.0
else:
inps.dop_overlap_min = float(value)
key = 'PLATFORM'
if key in key_list and not inps.sensor:
inps.sensor = template[key]
key = 'COH_COLOR_JUMP'
if key in key_list:
inps.coh_thres = float(template[key])
key = prefix + 'masterDate'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.m_date = None
else:
inps.m_date = ptime.yymmdd(value)
key = prefix + 'startDate'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.start_date = None
else:
inps.start_date = ptime.yyyymmdd(value)
key = prefix + 'endDate'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.end_date = None
else:
inps.end_date = ptime.yyyymmdd(value)
key = prefix + 'excludeDate'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.exclude_date = []
else:
inps.exclude_date = ptime.yyyymmdd([i for i in value.split(',')])
key = prefix + 'incrementNum'
if key in key_list:
value = template[key]
if value in ['auto']:
inps.increment_num = 3
else:
inps.increment_num = int(value)
key = prefix + 'tempPerpList'
if key in key_list:
value = template[key]
if value in ['auto']:
inps.temp_perp_list = '16,1600;32,800;48,600;64,200'
else:
inps.temp_perp_list = value
if isinstance(inps.temp_perp_list, basestring):
inps.temp_perp_list = [[float(j) for j in i.split(',')]
for i in inps.temp_perp_list.split(';')]
return inps
def main(argv):
# Read inputs
inps = cmdLineParse()
inps = read_template2inps(inps.template_file, inps)
log(os.path.basename(sys.argv[0]) + ' ' + inps.template_file)
project_name = os.path.splitext(os.path.basename(inps.template_file))[0]
print 'project name: ' + project_name
if not inps.sensor:
inps.sensor = project_name2sensor(project_name)
# Auto path setting for Miami user
if not inps.baseline_file and pysar.miami_path and 'SCRATCHDIR' in os.environ:
if pysar.miami_path and 'SCRATCHDIR' in os.environ:
try:
inps.baseline_file = glob.glob(
os.getenv('SCRATCHDIR') + '/' + project_name +
'/SLC/bl_list.txt')[0]
except:
inps.baseline_file = None
# Pair selection from reference
if inps.reference_file:
print 'Use pairs info from reference file: ' + inps.reference_file
date12_list = pnet.get_date12_list(inps.reference_file)
date12_list = [i.replace('_', '-') for i in date12_list]
if inps.baseline_file:
date8_list, pbase_list, dop_list = pnet.read_baseline_file(
inps.baseline_file)[0:3]
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Pair selection from temp/perp/dop baseline info
else:
if not inps.baseline_file:
raise Exception('ERROR: No baseline file found!')
# Check start/end/exclude date
date8_list = pnet.read_baseline_file(inps.baseline_file)[0]
inps.exclude_date = ptime.yyyymmdd(inps.exclude_date)
if not inps.exclude_date:
inps.exclude_date = []
else:
print 'input exclude dates: ' + str(inps.exclude_date)
if inps.start_date:
print 'input start date: ' + inps.start_date
inps.exclude_date += [
i for i in date8_list
if float(i) < float(ptime.yyyymmdd(inps.start_date))
]
inps.exclude_date = sorted(inps.exclude_date)
if inps.end_date:
print 'input end date: ' + inps.end_date
inps.exclude_date += [
i for i in date8_list
if float(i) > float(ptime.yyyymmdd(inps.end_date))
]
inps.exclude_date = sorted(inps.exclude_date)
if inps.exclude_date:
print 'exclude dates: '
print inps.exclude_date
# Read baseline list file: bl_list.txt
inps.exclude_date = ptime.yymmdd(inps.exclude_date)
date8_list, pbase_list, dop_list = pnet.read_baseline_file(
inps.baseline_file, inps.exclude_date)[0:3]
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Initial network using input methods
inps.method = inps.method.lower().replace('-', '_')
if inps.method in ['star', 'ps']: inps.method = 'star'
elif inps.method.startswith('seq'): inps.method = 'sequential'
elif inps.method.startswith('hierar'): inps.method = 'hierarchical'
elif inps.method in [
'mst', 'min_spanning_tree', 'minimum_spanning_tree'
]:
inps.method = 'mst'
print 'select method: ' + inps.method
if inps.method == 'all':
date12_list = pnet.select_pairs_all(date6_list)
elif inps.method == 'delaunay':
date12_list = pnet.select_pairs_delaunay(date6_list, pbase_list,
inps.norm)
elif inps.method == 'star':
date12_list = pnet.select_pairs_star(date6_list)
elif inps.method == 'sequential':
date12_list = pnet.select_pairs_sequential(date6_list,
inps.increment_num)
elif inps.method == 'hierarchical':
date12_list = pnet.select_pairs_hierarchical(
date6_list, pbase_list, inps.temp_perp_list)
elif inps.method == 'mst':
date12_list = pnet.select_pairs_mst(date6_list, pbase_list)
else:
raise Exception('Unrecoganized select method: ' + inps.method)
print 'initial number of interferograms: ' + str(len(date12_list))
# Filter pairs (optional) using temp/perp/doppler baseline threshold
if inps.method in ['star', 'hierarchical', 'mst']:
inps.threshold = False
if inps.threshold:
# Temporal baseline
date12_list = pnet.threshold_temporal_baseline(date12_list, inps.temp_base_max,\
inps.keep_seasonal, inps.temp_base_min)
print 'number of interferograms after filtering of <%d, %d> days in temporal baseline: %d'\
% (inps.temp_base_min, inps.temp_base_max, len(date12_list))
if inps.keep_seasonal:
print '\tkeep seasonal pairs, i.e. pairs with temporal baseline == N*years +/- one month'
# Perpendicular spatial baseline
date12_list = pnet.threshold_perp_baseline(date12_list, date6_list,
pbase_list,
inps.perp_base_max)
print 'number of interferograms after filtering of max %d meters in perpendicular baseline: %d'\
% (inps.perp_base_max, len(date12_list))
# Doppler Overlap Percentage
if inps.sensor:
bandwidth_az = pnet.azimuth_bandwidth(inps.sensor)
date12_list = pnet.threshold_doppler_overlap(date12_list, date6_list, dop_list,\
bandwidth_az, inps.dop_overlap_min/100.0)
print 'number of interferograms after filtering of min '+str(inps.dop_overlap_min)+'%'+\
' overlap in azimuth Doppler frequency: '+str(len(date12_list))
# Write ifgram_list.txt
if not date12_list:
print 'WARNING: No interferogram selected!'
return None
# date12_list to date_list
m_dates = [
date12.replace('_', '-').split('-')[0] for date12 in date12_list
]
s_dates = [
date12.replace('_', '-').split('-')[1] for date12 in date12_list
]
try:
print 'number of acquisitions input : ' + str(len(date6_list))
except:
pass
print 'number of acquisitions selected: ' + str(
len(list(set(m_dates + s_dates))))
print 'number of interferograms selected: ' + str(len(date12_list))
# Output directory/filename
if not inps.outfile:
if pysar.miami_path and 'SCRATCHDIR' in os.environ:
inps.out_dir = os.getenv(
'SCRATCHDIR') + '/' + project_name + '/PROCESS'
else:
try:
inps.out_dir = os.path.dirname(
os.path.abspath(inps.reference_file))
except:
inps.out_dir = os.path.dirname(
os.path.abspath(inps.baseline_file))
inps.outfile = inps.out_dir + '/ifgram_list.txt'
inps.outfile = os.path.abspath(inps.outfile)
inps.out_dir = os.path.dirname(inps.outfile)
if not os.path.isdir(inps.out_dir):
os.makedirs(inps.out_dir)
print 'writing >>> ' + inps.outfile
if not inps.baseline_file:
np.savetxt(inps.outfile, date12_list, fmt='%s')
return inps.outfile
## Calculate Bperp, Btemp and predicted coherence
ifgram_num = len(date12_list)
ifgram_pbase_list = []
ifgram_tbase_list = []
for i in range(ifgram_num):
m_date, s_date = date12_list[i].split('-')
m_idx = date6_list.index(m_date)
s_idx = date6_list.index(s_date)
pbase = pbase_list[s_idx] - pbase_list[m_idx]
tbase = tbase_list[s_idx] - tbase_list[m_idx]
ifgram_pbase_list.append(pbase)
ifgram_tbase_list.append(tbase)
try:
inps.coherence_list = pnet.simulate_coherence(
date12_list, inps.baseline_file,
sensor=inps.sensor).flatten().tolist()
inps.cbar_label = 'Simulated coherence'
except:
inps.coherence_list = None
##### Write txt file
fl = open(inps.outfile, 'w')
fl.write('#Interferograms configuration generated by select_network.py\n')
fl.write('# Date12 Btemp(days) Bperp(m) sim_coherence\n')
for i in range(len(date12_list)):
line = '%s %6.0f %6.1f' % (
date12_list[i], ifgram_tbase_list[i], ifgram_pbase_list[i])
if inps.coherence_list:
line += ' %1.4f' % (inps.coherence_list[i])
fl.write(line + '\n')
fl.close()
##### Plot network info
if not inps.disp_fig:
plt.switch_backend('Agg')
out_fig_name = 'BperpHistory.pdf'
print 'plotting baseline history in temp/perp baseline domain to file: ' + out_fig_name
fig2, ax2 = plt.subplots()
ax2 = pnet.plot_perp_baseline_hist(ax2, date8_list, pbase_list)
plt.savefig(inps.out_dir + '/' + out_fig_name, bbox_inches='tight')
out_fig_name = 'Network.pdf'
print 'plotting network / pairs in temp/perp baseline domain to file: ' + out_fig_name
fig1, ax1 = plt.subplots()
ax1 = pnet.plot_network(ax1,
date12_list,
date8_list,
pbase_list,
plot_dict=vars(inps),
print_msg=False)
plt.savefig(inps.out_dir + '/' + out_fig_name, bbox_inches='tight')
out_fig_name = 'CoherenceMatrix.pdf'
if inps.coherence_list:
print 'plotting predicted coherence matrix to file: ' + out_fig_name
fig3, ax3 = plt.subplots()
ax3 = pnet.plot_coherence_matrix(ax3,
date12_list,
inps.coherence_list,
plot_dict=vars(inps))
plt.savefig(inps.out_dir + '/' + out_fig_name, bbox_inches='tight')
if inps.disp_fig:
plt.show()
return inps.outfile
def main(argv):
##### Default
fontSize = 12
lineWidth = 2
markerColor = 'crimson'
markerSize = 16
disp_fig = 'no'
save_fig = 'yes'
save_list = 'yes'
ref_file = 'reference_date.txt'
drop_file = 'drop_date.txt'
##### Check Inputs
if len(sys.argv)>3:
try:
opts, args = getopt.getopt(argv,'h:f:m:o:x:y:',['help','circle='])
except getopt.GetoptError:
print 'Error in reading input options!'; usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"): usage() ; sys.exit()
elif opt == '-f': File = arg
elif opt == '-m': maskFile = arg
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort()
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort()
elif opt == '--circle' : cir_par = [i for i in arg.split(';')]
#elif opt == '-o': outName = arg
else:
try: File = argv[0]
except: usage(); sys.exit(1)
try: maskFile = argv[1]
except: pass
try: atr = readfile.read_attribute(File)
except: usage(); sys.exit(1)
ext = os.path.splitext(File)[1].lower()
FileBase = os.path.basename(File).split(ext)[0]
outNameBase = 'spatialMean_'+FileBase
print '\n*************** Spatial Average ******************'
##### Input File Info
k = atr['FILE_TYPE']
print 'Input file is '+k
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
h5file = h5py.File(File)
epochList = h5file[k].keys()
epochList = sorted(epochList)
epochNum = len(epochList)
print 'number of epoch: '+str(epochNum)
if
dates,datevector = ptime.date_list2vector(epochList)
##### Mask Info
try:
Mask_orig,Matr = readfile.read(maskFile)
print 'mask file: '+maskFile
Masking = 'yes'
except:
print 'No mask. Use the whole area for ramp estimation.'
Masking = 'no'
Mask_orig=np.ones((length,width))
Mask = np.zeros((length,width))
Mask[:] = Mask_orig[:]
## Bounding Subset
try:
xsub
ysub
ysub,xsub = subset.check_subset_range(ysub,xsub,atr)
Mask[ysub[0]:ysub[1],xsub[0]:xsub[1]] = Mask_orig[ysub[0]:ysub[1],xsub[0]:xsub[1]]*2
#Mask[0:ysub[0],:] = 0
#Mask[ysub[1]:length,:] = 0
#Mask[:,0:xsub[0]] = 0
#Mask[:,xsub[1]:width] = 0
except:
Mask = Mask_orig*2
print 'No subset input.'
## Circle Inputs
try:
cir_par
for i in range(len(cir_par)):
cir_idx = circle_index(atr,cir_par[i])
Mask[cir_idx] = Mask_orig[cir_idx]
print 'Circle '+str(i)+': '+cir_par[i]
except: print 'No circle of interest input.'
## Mask output
idx = Mask == 2
idxNum = float(sum(sum(idx)))
fig = plt.figure()
plt.imshow(Mask,cmap='gray')
plt.savefig(outNameBase+'_mask.png',bbox_inches='tight')
print 'save mask to '+outNameBase+'_mask.png'
#fig.clf()
##### Calculation
meanList = np.zeros(epochNum)
pixPercent = np.zeros(epochNum)
pixT = 0.7
print 'calculating ...'
print ' Date Mean Percentage'
for i in range(epochNum):
epoch = epochList[i]
d = h5file[k].get(epoch)[:]
#d[Mask==0] = np.nan
meanList[i] = np.nanmean(d[idx])
pixPercent[i] = np.sum(d[idx] >= pixT)/idxNum
print epoch+' : %.2f %.1f%%'%(meanList[i],pixPercent[i]*100)
del d
h5file.close()
##### Reference date - Max Value
top3 = sorted(zip(meanList,epochList), reverse=True)[:3]
print '------------ Top 3 Mean ------------------'
print top3
## Write to txt file
fref = open(ref_file,'w')
fref.write(str(top3[0][1])+'\n')
fref.close()
print 'write optimal reference date to '+ref_file
idxMean = meanList == np.nanmax(meanList)
##### Drop dates - mean threshold
#meanT = 0.7
#idxMean = meanList < meanT
#print '------------ Mean Value < '+str(meanT)+' --------'
#print np.array(epochList)[idxMean]
#print meanList[idxMean]
##### Drop dates - good pixel percentage
pixNumT = 0.7
print '------------ Good Pixel Percentage < %.0f%% -------'%(pixNumT*100)
idxPix = pixPercent < pixNumT
dropEpochList = np.array(epochList)[idxPix]
print dropEpochList
print pixPercent[idxPix]
## Write to txt file
fdrop = open(drop_file,'w')
for i in range(len(dropEpochList)):
fdrop.write(str(dropEpochList[i])+'\n')
fdrop.close()
print 'write drop dates to '+drop_file
print '-------------------------------------------'
##### Display
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(211)
ax.plot(dates, meanList, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
#ax.plot([dates[0],dates[-1]],[meanT,meanT], '--b', lw=lineWidth)
#sc = ax.scatter(dates, np.tile(0.5,epochNum), c=meanList, s=22**2, alpha=0.3, vmin=0.0, vmax=1.0)
#ax.scatter(np.array(dates)[idxMean], 0.5, c=meanList[idxMean], s=22**2, alpha=1.0, vmin=0.0, vmax=1.0)
ax = ptime.auto_adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Spatial Average Value', fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
#cbar = plt.colorbar(sc)
#cbar.set_label('Spatial Mean of Normalized Sum Epochs')
ax = fig.add_subplot(212)
ax.plot(dates, pixPercent, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
ax.plot([dates[0],dates[-1]],[pixNumT,pixNumT], '--b', lw=lineWidth)
ax = ptime.auto_adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Percenrage of Pixels with Value > '+str(pixNumT), fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.0f}%'.format(i*100) for i in vals])
if save_fig == 'yes':
plt.savefig(outNameBase+'.png',bbox_inches='tight')
print 'save figure to '+outNameBase+'.png'
if disp_fig == 'yes':
plt.show()
##### Output
if save_list == 'yes':
epochList6 = ptime.yymmdd(epochList)
fl = open(outNameBase+'.txt','w')
for i in range(epochNum):
str_line = epochList6[i]+' %.2f %.2f\n'%(meanList[i],pixPercent[i])
fl.write(str_line)
fl.close()
print 'write data to '+outNameBase+'.txt\n'
def main(argv):
inps = cmdLineParse()
if not inps.disp_fig:
plt.switch_backend('Agg')
print '\n******************** Plot Network **********************'
# Output figure name
figName1 = 'BperpHist' + inps.fig_ext
figName2 = 'Network' + inps.fig_ext
if 'Modified' in inps.file:
figName1 = 'BperpHist_Modified' + inps.fig_ext
figName2 = 'Network_Modified' + inps.fig_ext
##### 1. Read Info
# Read dateList and bperpList
ext = os.path.splitext(inps.file)[1]
if ext in ['.h5']:
k = readfile.read_attribute(inps.file)['FILE_TYPE']
print 'reading date and perpendicular baseline from ' + k + ' file: ' + os.path.basename(
inps.file)
if not k in multi_group_hdf5_file:
print 'ERROR: only the following file type are supported:\n' + str(
multi_group_hdf5_file)
sys.exit(1)
Bp = ut.Baseline_timeseries(inps.file)
date8List = ptime.igram_date_list(inps.file)
date6List = ptime.yymmdd(date8List)
else:
print 'reading date and perpendicular baseline from baseline list file: ' + inps.bl_list_file
date8List, Bp = pnet.read_baseline_file(inps.bl_list_file)[0:2]
date6List = ptime.yymmdd(date8List)
print 'number of acquisitions: ' + str(len(date8List))
# Read Pairs Info
print 'reading pairs info from file: ' + inps.file
date12_list = pnet.get_date12_list(inps.file)
pairs_idx = pnet.date12_list2index(date12_list, date6List)
print 'number of pairs : ' + str(len(pairs_idx))
# 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.list'
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]]
coh_date12_list = [i for i in fcoh[:, 0]]
else:
print 'calculating average coherence value from ' + inps.coherence_file
inps.coherence_list = ut.spatial_average(inps.coherence_file,
saveList=True)
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]]
coh_date12_list = [i for i in fcoh[:, 0]]
# Check length of coherence file and input file
if not set(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
##### 2. Plot
# Fig 1 - Baseline History
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111)
ax1 = pnet.plot_perp_baseline_hist(ax1, date8List, Bp, vars(inps))
if inps.save_fig:
fig1.savefig(figName1, bbox_inches='tight')
print 'save figure to ' + figName1
# Fig 2 - Interferogram Network
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111)
ax2 = pnet.plot_network(ax2, pairs_idx, date8List, Bp, vars(inps))
if inps.save_fig:
fig2.savefig(figName2, bbox_inches='tight')
print 'save figure to ' + figName2
if inps.save_list:
txtFile = os.path.splitext(inps.file)[0] + '_date12.list'
np.savetxt(txtFile, date12_list, fmt='%s')
print 'save pairs/date12 info to file: ' + txtFile
if inps.disp_fig:
plt.show()