def main(argv):
inps = cmdLineParse()
print '\n*************** Spatial Average ******************'
for File in inps.file:
mean_list, date_list = ut.spatial_average(File,
inps.mask_file,
saveList=True)
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
if inps.disp_fig and k == 'timeseries':
dates, datevector = ptime.date_list2vector(date_list)
# plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dates,
mean_list,
'-ko',
lw=2,
ms=16,
alpha=0.7,
mfc='crimson')
ax.set_title('Spatial Average', fontsize=12)
ax = ptime.auto_adjust_xaxis_date(ax, datevector)[0]
ax.set_xlabel('Time [years]', fontsize=12)
ax.set_ylabel('Mean', fontsize=12)
plt.show()
def plot_coherence_history(ax, date12_list, coherence_list, plot_dict={}):
'''Plot min/max Coherence of all interferograms for each date'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
# Get date list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date8_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
dates, datevector = ptime.date_list2vector(date8_list)
bar_width = mode(np.diff(dates).tolist())*3/4
x_list = [i-bar_width/2 for i in dates]
coh_mat = coherence_matrix(date12_list, coherence_list)
ax.bar(x_list, np.nanmax(coh_mat, axis=0), bar_width.days, label='Max Coherence')
ax.bar(x_list, np.nanmin(coh_mat, axis=0), bar_width.days, label='Min Coherence')
if plot_dict['disp_title']:
ax.set_title('Coherence History of All Related Interferograms')
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax.set_ylim([0.0,1.0])
ax.set_xlabel('Time [years]',fontsize=plot_dict['fontsize'])
ax.set_ylabel('Coherence',fontsize=plot_dict['fontsize'])
ax.legend(loc='lower right')
return ax
def update_timeseries(y, x):
'''Plot point time series displacement at pixel [y, x]'''
global fig_ts, ax_ts, inps
d_ts = []
for date in dateList:
d = h5[k].get(date)[y, x]
if inps.ref_yx:
d -= h5[k].get(date)[inps.ref_yx[0], inps.ref_yx[1]]
d_ts.append(d * inps.unit_fac)
if inps.zero_first:
d_ts -= d_ts[0]
ax_ts.cla()
if inps.error_file:
ax_ts = plot_timeseries_errorbar(ax_ts, d_ts, inps)
else:
ax_ts = plot_timeseries_scatter(ax_ts, d_ts, inps)
ax_ts.set_ylim(inps.ylim)
# Title
title_ts = 'Y = %d, X = %d' % (y, x)
try:
lat = ullat + y * lat_step
lon = ullon + x * lon_step
title_ts += ', lat = %.4f, lon = %.4f' % (lat, lon)
except:
pass
if inps.disp_title:
ax_ts.set_title(title_ts)
ax_ts = ptime.auto_adjust_xaxis_date(ax_ts,
tims,
fontSize=inps.font_size)[0]
ax_ts.set_xlabel('Time', fontsize=inps.font_size)
ax_ts.set_ylabel('Displacement [%s]' % inps.disp_unit,
fontsize=inps.font_size)
fig_ts.canvas.draw()
# Print to terminal
print '\n---------------------------------------'
print title_ts
print d_ts
return d_ts
def main(argv):
inps = cmdLineParse()
print '\n*************** Spatial Average ******************'
if inps.mask_file:
print 'reading mask file: ' + inps.mask_file
mask, mask_atr = readfile.read(inps.mask_file)
else:
mask = None
for File in inps.file:
mean_list = ut.spatial_average(File, mask, saveList=True)
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
if inps.disp_fig and k == 'timeseries':
# Get date list
h5file = h5py.File(File)
dateList = sorted(h5file[k].keys())
h5file.close()
dates, datevector = ptime.date_list2vector(dateList)
# plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dates,
mean_list,
'-ko',
lw=2,
ms=16,
alpha=0.7,
mfc='crimson')
ax.set_title('Spatial Average', fontsize=12)
ax = ptime.auto_adjust_xaxis_date(ax, datevector)[0]
ax.set_xlabel('Time [years]', fontsize=12)
ax.set_ylabel('Mean', fontsize=12)
plt.show()
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 plot_perp_baseline_hist(ax, date8_list, pbase_list, plot_dict={}, date8_list_drop=[]):
''' Plot Perpendicular Spatial Baseline History
Inputs
ax : matplotlib axes object
date8_list : list of string, date in YYYYMMDD format
pbase_list : list of float, perp baseline
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
disp_title : bool, show figure title or not, default: True
date8_list_drop : list of string, date dropped in YYYYMMDD format
e.g. ['20080711', '20081011']
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
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
transparency = 0.7
# Date Convert
dates, datevector = ptime.date_list2vector(date8_list)
# Get index of date used and dropped
#date8_list_drop = ['20080711', '20081011'] # for debug
idx_keep = range(len(date8_list))
idx_drop = []
for i in date8_list_drop:
idx = date8_list.index(i)
idx_keep.remove(idx)
idx_drop.append(idx)
# Plot
#ax=fig.add_subplot(111)
# Plot date used
if idx_keep:
x_list = [dates[i] for i in idx_keep]
y_list = [pbase_list[i] for i in idx_keep]
ax.plot(x_list, y_list, '-ko', alpha=transparency, lw=plot_dict['linewidth'], \
ms=plot_dict['markersize'], mfc=plot_dict['markercolor'])
# Plot date dropped
if idx_drop:
x_list = [dates[i] for i in idx_drop]
y_list = [pbase_list[i] for i in idx_drop]
ax.plot(x_list, y_list, 'ko', alpha=transparency, ms=plot_dict['markersize'], mfc='gray')
if plot_dict['disp_title']:
ax.set_title('Perpendicular Baseline History',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('Perpendicular Baseline [m]',fontsize=plot_dict['fontsize'])
return ax
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 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 main(argv):
inps = cmdLineParse()
if inps.template_file:
inps = read_template2inps(inps.template_file)
##### calculate timeseries of residual Root Mean Square
#std_list, date_list = ut.get_residual_std(inps.timeseries_file, inps.mask_file, inps.ramp_type)
rms_list, date_list = ut.get_residual_rms(inps.timeseries_file,
inps.mask_file, inps.ramp_type)
##### reference_date.txt
print '------------------------------------------------------------'
ref_idx = np.argmin(rms_list)
ref_date = date_list[ref_idx]
print 'date with minimum residual RMS: %s - %.4f' % (ref_date,
rms_list[ref_idx])
refTxtFile = 'reference_date.txt'
if (inps.save_reference_date and \
ut.update_file(refTxtFile, [inps.timeseries_file, inps.mask_file, inps.template_file],\
check_readable=False)):
f = open(refTxtFile, 'w')
f.write(ref_date + '\n')
f.close()
print 'save date to file: ' + refTxtFile
##### exclude_date.txt
print '------------------------------------------------------------'
ex_idx_list = [rms_list.index(i) for i in rms_list if i > inps.min_rms]
print 'date(s) with residual RMS > ' + str(inps.min_rms)
exTxtFile = 'exclude_date.txt'
if ex_idx_list:
if (inps.save_exclude_date and \
ut.update_file(exTxtFile, [inps.timeseries_file, inps.mask_file, inps.template_file],\
check_readable=False)):
f = open(exTxtFile, 'w')
for i in ex_idx_list:
print '%s - %.4f' % (date_list[i], rms_list[i])
f.write(date_list[i] + '\n')
f.close()
print 'save date(s) to file: ' + exTxtFile
else:
print 'None.'
##### Plot
fig_name = os.path.dirname(os.path.abspath(inps.timeseries_file))+\
'/rms_'+os.path.splitext(inps.timeseries_file)[0]
if inps.ramp_type != 'no':
fig_name += '_' + inps.ramp_type
fig_name += '.pdf'
if ut.update_file(fig_name, [exTxtFile, refTxtFile, inps.template_file],
check_readable=False):
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
font_size = 12
dates, datevector = ptime.date_list2vector(date_list)
try:
bar_width = ut.mode(np.diff(dates).tolist()) * 3 / 4
except:
bar_width = np.min(np.diff(dates).tolist()) * 3 / 4
x_list = [i - bar_width / 2 for i in dates]
rms_list = [i * 1000. for i in rms_list]
min_rms = inps.min_rms * 1000.
# Plot all dates
ax.bar(x_list, rms_list, bar_width.days)
#ax.bar(x_list, rms_list, bar_width.days)
# Plot reference date
#if inps.save_reference_date:
ax.bar(x_list[ref_idx],
rms_list[ref_idx],
bar_width.days,
label='Reference date')
# Plot exclude dates
#if ex_idx_list and inps.save_exclude_date:
if ex_idx_list:
ex_x_list = [x_list[i] for i in ex_idx_list]
ex_rms_list = [rms_list[i] for i in ex_idx_list]
ax.bar(ex_x_list,
ex_rms_list,
bar_width.days,
color='darkgray',
label='Exclude date(s)')
# Plot min_rms line
ax, xmin, xmax = ptime.auto_adjust_xaxis_date(
ax, datevector, font_size, every_year=inps.tick_year_num)
ax.plot(np.array([xmin, xmax]), np.array([min_rms, min_rms]), '--k')
# axis format
ax = pnet.auto_adjust_yaxis(ax,
rms_list + [min_rms],
font_size,
ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel('Root Mean Square [mm]', fontsize=font_size)
ax.yaxis.set_ticks_position('both')
ax.tick_params(labelsize=font_size)
if inps.save_reference_date or inps.save_exclude_date:
plt.legend(fontsize=font_size)
# save figure
fig.savefig(fig_name, bbox_inches='tight', transparent=True)
print 'save figure to file: ' + fig_name
return
def update_timeseries(y, x):
'''Plot point time series displacement at pixel [y, x]'''
global fig_ts, ax_ts, inps
d_ts = []
for date in dateList:
d = h5[k].get(date)[y, x]
if inps.ref_yx:
d -= h5[k].get(date)[inps.ref_yx[0], inps.ref_yx[1]]
d_ts.append(d * inps.unit_fac)
if inps.zero_first:
d_ts -= d_ts[inps.zero_idx]
ax_ts.cla()
if inps.error_file:
ax_ts = plot_timeseries_errorbar(ax_ts, d_ts, inps)
else:
ax_ts = plot_timeseries_scatter(ax_ts, d_ts, inps)
ax_ts.set_ylim(inps.ylim)
for tick in ax_ts.yaxis.get_major_ticks():
tick.label.set_fontsize(inps.font_size)
# Title
title_ts = 'Y = %d, X = %d' % (y, x)
try:
lat = ullat + y * lat_step
lon = ullon + x * lon_step
title_ts += ', lat = %.4f, lon = %.4f' % (lat, lon)
except:
pass
if inps.disp_title:
ax_ts.set_title(title_ts)
ax_ts = ptime.auto_adjust_xaxis_date(ax_ts,
tims,
fontSize=inps.font_size)[0]
ax_ts.set_xlabel('Time', fontsize=inps.font_size)
ax_ts.set_ylabel('Displacement [%s]' % inps.disp_unit,
fontsize=inps.font_size)
fig_ts.canvas.draw()
# Print to terminal
print '\n---------------------------------------'
print title_ts
print d_ts
# Slope estimation
if inps.ex_date_list:
tims_kept = [
tims[i] for i in range(date_num) if i not in inps.ex_idx_list
]
d_ts_kept = [
d_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
]
d_slope = stats.linregress(np.array(tims_kept),
np.array(d_ts_kept))
else:
d_slope = stats.linregress(np.array(tims), np.array(d_ts))
print 'linear velocity: %.2f +/- %.2f [%s/yr]' % (
d_slope[0], d_slope[4], inps.disp_unit)
return d_ts
def plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries):
ax2.cla()
print '\n-------------------------------------------------------------------------------'
disp_min = 0
disp_max = 0
############################# Plot Time Series ##############################
global ref_xsub, ref_ysub
##### 1.1 Plot Reference time series
try:
ref_xsub
ref_ysub
ref_xsub, ref_ysub = check_yx(ref_xsub, ref_ysub, radius, ax,
rectColor)
print '----------------------------------------------------'
print 'Reference Point:'
print 'ref_x=' + str(ref_xsub[0]) + ':' + str(ref_xsub[1])
print 'ref_y=' + str(ref_ysub[0]) + ':' + str(ref_ysub[1])
print '-----------------------------'
print 'Time series with all dates:'
dis1, dis1_mean, dis1_std, dis1_vel = read_dis_xy(
ref_xsub, ref_ysub, dateList1, h5timeseries, unit)
(_, caps, _)=ax2.errorbar(dates1,dis1_mean,yerr=dis1_std,fmt='-ks',\
ms=markerSize2, lw=0, alpha=1,mfc=markerColor_ref,mew=edgeWidth,\
elinewidth=edgeWidth,ecolor='black',capsize=markerSize*0.5)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis1_mean,
dis1_std)
if not len(dateList) == len(dateList1):
print '-----------------------------'
print 'Time series with dates of interest:'
dis12, dis12_mean, dis12_std, dis12_vel = read_dis_xy(
ref_xsub, ref_ysub, dateList, h5timeseries, unit)
(_, caps, _)=ax2.errorbar(dates,dis12_mean,yerr=dis12_std,fmt='-ks',\
ms=markerSize2, lw=0, alpha=1,mfc=markerColor_ref2,mew=edgeWidth,\
elinewidth=edgeWidth,ecolor='black',capsize=markerSize*0.5)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis12_mean,
dis12_std)
except:
pass
##### 1.2.0 Read y/x
print '\n----------------------------------------------------'
print 'Point of Interest:'
xsub, ysub = check_yx(xsub, ysub, radius, ax, rectColor)
print 'x=' + str(xsub[0]) + ':' + str(xsub[1])
print 'y=' + str(ysub[0]) + ':' + str(ysub[1])
##### 1.2.1 Plot 2nd time series
try:
timeSeriesFile_2
print '-----------------------------'
print '2nd Time Series:'
dis2, dis2_mean, dis2_std, dis2_vel = read_dis_xy(
xsub, ysub, dateList_2, h5timeseries_2, unit)
(_, caps, _)=ax2.errorbar(dates_2,dis2_mean,yerr=dis2_std,fmt='-ko',\
ms=markerSize2, lw=0, alpha=1, mfc=markerColor2,\
elinewidth=0,ecolor='black',capsize=0)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis2_mean,
dis2_std)
except:
pass
##### 1.2.2 Plot 1st time series
print '-----------------------------'
print 'Time Series:'
dis, dis_mean, dis_std, dis_vel = read_dis_xy(xsub, ysub, dateList,
h5timeseries, unit)
(_, caps, _)=ax2.errorbar(dates,dis_mean,yerr=dis_std,fmt='-ko',\
ms=markerSize, lw=lineWidth, alpha=1, mfc=markerColor,\
elinewidth=edgeWidth,ecolor='black',capsize=markerSize*0.5)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis_mean, dis_std)
####################### Figure Format #######################
## x axis format
try:
ax2 = ptime.auto_adjust_xaxis_date(ax2, dateVecMinMax, fontSize)
except:
ax2 = ptime.auto_adjust_xaxis_date(ax2, datevector_all, fontSize)
## y axis format
ax2.set_ylabel('Displacement [' + unit + ']', fontsize=fontSize)
try:
lbound
hbound
ax2.set_ylim(lbound, hbound)
except:
disp_buf = 0.2 * (disp_max - disp_min)
ax2.set_ylim(disp_min - disp_buf, disp_max + disp_buf)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(fontSize)
## title
figTitle = 'x=' + str(xsub[0]) + ':' + str(xsub[1]) + ', y=' + str(
ysub[0]) + ':' + str(ysub[1])
try:
lonc = ullon + (xsub[0] + xsub[1]) / 2.0 * lon_step
latc = ullat + (ysub[0] + ysub[1]) / 2.0 * lat_step
figTitle += ', lalo=' + '%.4f,%.4f' % (latc, lonc)
except:
pass
ax2.set_title(figTitle)
################## Save and Output #####################
if saveFig == 'yes':
print '-----------------------------'
Delay = {}
Delay['displacement'] = dis
Delay['unit'] = unit
Delay['time'] = datevector
Delay['velocity'] = dis_vel[0]
Delay['velocity_unit'] = unit + '/yr'
Delay['velocity_std'] = dis_vel[4]
figBase = 'x' + str(xsub[0]) + '_' + str(xsub[1] - 1) + 'y' + str(
ysub[0]) + '_' + str(ysub[1] - 1)
sio.savemat(figBase + '_ts.mat', {'displacement': Delay})
print 'saved ' + figBase + '_ts.mat'
fig2.savefig(figBase + '_ts.pdf',
bbox_inches='tight',
transparent=True,
dpi=fig_dpi)
print 'saved ' + figBase + '_ts.pdf'
if dispFig == 'no':
fig.savefig(figBase + '_vel.png',
bbox_inches='tight',
transparent=True,
dpi=fig_dpi)
print 'saved ' + figBase + '_vel.png'