def main(argv):
try:
file = argv[0]
alks = float(argv[1])
rlks = float(argv[2])
except:
Usage()
sys.exit(1)
ext = os.path.splitext(file)[1]
outName = file.split('.')[0] + '_a' + str(int(alks)) + 'lks_r' + str(
int(rlks)) + 'lks' + ext
if ext == '.int' or ext == '.slc':
a, p, r = readfile.read_complex64(file)
plks = multilook(p, alks, rlks)
alks = multilook(a, alks, rlks)
r['FILE_LENGTH'] = str(dlks.shape[0])
r['WIDTH'] = str(dlks.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == '.unw' or ext == '.cor' or ext == '.hgt':
a, p, r = readfile.read_float32(file)
plks = multilook(p, alks, rlks)
alks = multilook(a, alks, rlks)
writefile.write_float32(plks, outName)
r['FILE_LENGTH'] = str(dlks.shape[0])
r['WIDTH'] = str(dlks.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == ('.dem'):
d, r = readfile.read_dem(file)
dlks = multilook(d, alks, rlks)
print 'writing ' + outName
writefile.write_dem(dlks, outName)
r['FILE_LENGTH'] = str(dlks.shape[0])
r['WIDTH'] = str(dlks.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext in ['.jpeg', 'jpg', 'png']:
import Image
im = Image.open(file)
width = im.size[0] / int(rlks)
height = im.size[1] / int(alks)
imlks = im.resize((width, height), Image.NEAREST)
print 'writing ' + outName
imlks.save(outName)
try:
r = readfile.read_rsc_file(file + '.rsc')
except:
sys.exit(1)
r['FILE_LENGTH'] = str(height)
r['WIDTH'] = str(width)
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == ('.h5'):
h5file = h5py.File(file, 'r')
# outName=file.split('.')[0]+'_a'+str(int(alks))+'lks_r'+str(int(rlks))+'lks.h5'
h5file_lks = h5py.File(outName, 'w')
if 'interferograms' in h5file.keys():
print 'Multilooking the interferograms'
gg = h5file_lks.create_group('interferograms')
igramList = h5file['interferograms'].keys()
for igram in igramList:
print igram
unw = h5file['interferograms'][igram].get(igram)
unwlks = multilook(unw, alks, rlks)
group = gg.create_group(igram)
dset = group.create_dataset(igram,
data=unwlks,
compression='gzip')
for key, value in h5file['interferograms'][
igram].attrs.iteritems():
group.attrs[key] = value
group.attrs['WIDTH'] = unwlks.shape[1]
group.attrs['FILE_LENGTH'] = unwlks.shape[0]
try:
group.attrs['Y_STEP'] = alks * float(group.attrs['Y_STEP'])
group.attrs['X_STEP'] = rlks * float(group.attrs['X_STEP'])
except:
group.attrs['AZIMUTH_PIXEL_SIZE'] = alks * float(
group.attrs['AZIMUTH_PIXEL_SIZE'])
group.attrs['RANGE_PIXEL_SIZE'] = rlks * float(
group.attrs['RANGE_PIXEL_SIZE'])
dset1 = h5file['mask'].get('mask')
mask = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
masklks = multilook(mask, alks, rlks)
group = h5file_lks.create_group('mask')
dset = group.create_dataset('mask',
data=masklks,
compression='gzip')
elif 'timeseries' in h5file.keys():
print 'Multilooking the time-series'
group = h5file_lks.create_group('timeseries')
dateList = h5file['timeseries'].keys()
for d in dateList:
print d
unw = h5file['timeseries'].get(d)
unwlks = multilook(unw, alks, rlks)
dset = group.create_dataset(d, data=unwlks, compression='gzip')
for key, value in h5file['timeseries'].attrs.iteritems():
group.attrs[key] = value
group.attrs['WIDTH'] = unwlks.shape[1]
group.attrs['FILE_LENGTH'] = unwlks.shape[0]
try:
group.attrs['Y_STEP'] = alks * float(group.attrs['Y_STEP'])
group.attrs['X_STEP'] = rlks * float(group.attrs['X_STEP'])
except:
group.attrs['AZIMUTH_PIXEL_SIZE'] = alks * float(
group.attrs['AZIMUTH_PIXEL_SIZE'])
group.attrs['RANGE_PIXEL_SIZE'] = rlks * float(
group.attrs['RANGE_PIXEL_SIZE'])
try:
dset1 = h5file['mask'].get('mask')
Mask = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
Masklks = multilook(Mask, alks, rlks)
group = h5file_lks.create_group('mask')
dset = group.create_dataset('mask',
data=Masklks,
compression='gzip')
except:
print 'Multilooked file does not include the maske'
elif 'temporal_coherence' in h5file.keys(
) or 'velocity' in h5file.keys() or 'mask' in h5file.keys():
k = h5file.keys()
print 'multi looking the ' + k[0]
group = h5file_lks.create_group(k[0])
dset1 = h5file[k[0]].get(k[0])
Mask = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
Masklks = multilook(Mask, alks, rlks)
dset = group.create_dataset(k[0], data=Masklks, compression='gzip')
for key, value in h5file[k[0]].attrs.iteritems():
group.attrs[key] = value
try:
group.attrs['Y_STEP'] = alks * float(group.attrs['Y_STEP'])
group.attrs['X_STEP'] = rlks * float(group.attrs['X_STEP'])
except:
group.attrs['AZIMUTH_PIXEL_SIZE'] = alks * float(
group.attrs['AZIMUTH_PIXEL_SIZE'])
group.attrs['RANGE_PIXEL_SIZE'] = rlks * float(
group.attrs['RANGE_PIXEL_SIZE'])
group.attrs['WIDTH'] = Masklks.shape[1]
group.attrs['FILE_LENGTH'] = Masklks.shape[0]
h5file.close()
h5file_lks.close()
def main(argv):
#default settings
markerSize=16
markerSize2=16
markerColor='g'
markerColor2='red'
lineWidth=2
fontSize=16
unit='cm'
Save_timeseries='no'
dispTsFig='yes'
dispVelFig='yes'
dispContour='only'
contour_step=200
smoothContour='no'
radius=0;
edgeWidth=1.5
fig_dpi=300
if len(sys.argv)>2:
try:
opts, args = getopt.getopt(argv,"f:F:v:a:b:s:m:c:w:u:l:h:S:D:C:V:t:T:d:r:x:y:P:p:")
except getopt.GetoptError:
Usage() ; sys.exit(1)
for opt,arg in opts:
if opt == '-f': timeSeriesFile = arg
elif opt == '-F': timeSeriesFile_2 = arg
elif opt == '-v': velocityFile = arg
elif opt == '-a': vmin = float(arg)
elif opt == '-b': vmax = float(arg)
elif opt == '-s': fontSize = int(arg)
elif opt == '-m': markerSize=int(arg); markerSize2=int(arg)
elif opt == '-S': Save_timeseries=arg
elif opt == '-c': markerColor=arg
elif opt == '-w': lineWidth=int(arg)
elif opt == '-u': unit=arg
elif opt == '-l': lbound=float(arg)
elif opt == '-h': hbound=float(arg)
elif opt == '-D': demFile=arg
elif opt == '-C': dispContour=arg
elif opt == '-V': contour_step=float(arg)
elif opt == '-t': minDate=arg
elif opt == '-T': maxDate=arg
elif opt == '-d': datesNot2show = arg.split()
elif opt == '-r': radius=abs(int(arg))
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort(); dispVelFig='no'
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort(); dispVelFig='no'
elif opt == '-P': dispTsFig=arg
elif opt == '-p': dispVelFig=arg
elif len(sys.argv)==2:
if argv[0]=='-h':
Usage(); sys.exit(1)
elif os.path.isfile(argv[0]):
timeSeriesFile = argv[0]
h5timeseries = h5py.File(timeSeriesFile)
if not 'timeseries' in h5timeseries.keys():
print 'ERROR'
Usage(); sys.exit(1)
else: Usage(); sys.exit(1)
elif len(sys.argv)<2:
Usage(); sys.exit(1)
if unit in ('m','M'): unitFac=1
elif unit in ('cm','Cm','CM'): unitFac=100
elif unit in ('mm','Mm','MM','mM'): unitFac=1000
else:
print 'Warning:'
print 'wrong unit input!'
print 'cm is considered to display the displacement'
##############################################################
# Read time series file info
if not os.path.isfile(timeSeriesFile):
Usage();sys.exit(1)
h5timeseries = h5py.File(timeSeriesFile)
if not 'timeseries' in h5timeseries.keys():
Usage(); sys.exit(1)
dateList1 = h5timeseries['timeseries'].keys()
##############################################################
# Dates to show time series plot
import matplotlib.dates as mdates
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
print '*******************'
print 'All dates existed:'
print dateList1
print '*******************'
try:
datesNot2show
print 'dates not to show: '+str(datesNot2show)
except: datesNot2show=[]
try:
minDate
minDateyy=yyyymmdd2years(minDate)
print 'minimum date: '+minDate
for date in dateList1:
yy=yyyymmdd2years(date)
if yy < minDateyy:
datesNot2show.append(date)
except: pass
try:
maxDate
maxDateyy=yyyymmdd2years(maxDate)
print 'maximum date: '+maxDate
for date in dateList1:
yy=yyyymmdd2years(date)
if yy > maxDateyy:
datesNot2show.append(date)
except: pass
try:
dateList=[]
for date in dateList1:
if date not in datesNot2show:
dateList.append(date)
print '--------------------------------------------'
print 'dates used to show time series displacements:'
print dateList
print '--------------------------------------------'
except:
dateList=dateList1
print 'using all dates to show time series displacement'
###################################################################
# Date info
dateIndex={}
for ni in range(len(dateList)):
dateIndex[dateList[ni]]=ni
tbase=[]
d1 = datetime.datetime(*time.strptime(dateList[0],"%Y%m%d")[0:5])
for ni in range(len(dateList)):
d2 = datetime.datetime(*time.strptime(dateList[ni],"%Y%m%d")[0:5])
diff = d2-d1
tbase.append(diff.days)
dates=[]
for ni in range(len(dateList)):
d = datetime.datetime(*time.strptime(dateList[ni],"%Y%m%d")[0:5])
dates.append(d)
datevector=[]
for i in range(len(dates)):
datevector.append(np.float(dates[i].year) + np.float(dates[i].month-1)/12 + np.float(dates[i].day-1)/365)
datevector2=[round(i,2) for i in datevector]
###########################################
# Plot Fig 1 - Velocity / last epoch of time series / DEM
import matplotlib.pyplot as plt
if dispVelFig in ('yes','Yes','y','Y','YES'):
fig = plt.figure()
ax=fig.add_subplot(111)
try:
velocityFile
h5file=h5py.File(velocityFile,'r')
k=h5file.keys()
dset= h5file[k[0]].get(k[0])
print 'display: ' + k[0]
except:
dset = h5timeseries['timeseries'].get(h5timeseries['timeseries'].keys()[-1])
print 'display: last epoch of timeseries'
#DEM/contour option
try:
demFile
import _readfile as readfile
if os.path.basename(demFile).split('.')[1]=='hgt': amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem': dem,demRsc = readfile.read_dem(demFile)
if dispContour in ('no','No','n','N','NO','yes','Yes','y','Y','YES'):
print 'show DEM as basemap'
cmap_dem=plt.get_cmap('gray')
import _pysar_utilities as ut
plt.imshow(ut.hillshade(dem,50.0),cmap=cmap_dem)
if dispContour in ('only','Only','o','O','ONLY','yes','Yes','y','Y','YES'):
print 'show contour'
if smoothContour in ('yes','Yes','y','Y','YES'):
import scipy.ndimage as ndimage
dem=ndimage.gaussian_filter(dem,sigma=10.0,order=0)
contour_sequence=np.arange(-6000,9000,contour_step)
plt.contour(dem,contour_sequence,origin='lower',colors='black',alpha=0.5)
except: print 'No DEM file'
try: img=ax.imshow(dset,vmin=vmin,vmax=vmax)
except: img=ax.imshow(dset)
import matplotlib.patches as patches # need for draw rectangle of points selected on VelFig
##########################################
# Plot Fig 2 - Time series plot
import scipy.stats as stats
fig2 = plt.figure(2)
ax2=fig2.add_subplot(111)
try:
timeSeriesFile_2
h5timeseries_2=h5py.File(timeSeriesFile_2)
print 'plot 2nd time series'
except: pass
########### Plot Time Series with x/y ##########
try:
xsub
ysub
try: xmin=xsub[0]; xmax=xsub[1]+1; print 'x='+str(xsub[0])+':'+str(xsub[1])
except: xmin=xsub[0]-radius; xmax=xsub[0]+radius+1; print 'x='+str(xsub[0])+'+/-'+str(radius)
try: ymin=ysub[0]; ymax=ysub[1]+1; print 'y='+str(ysub[0])+':'+str(ysub[1])
except: ymin=ysub[0]-radius; ymax=ysub[0]+radius+1; print 'y='+str(ysub[0])+'+/-'+str(radius)
try:
fig
rectSelect=patches.Rectangle((xmin,ymin),radius*2+1,radius*2+1,fill=False,lw=edgeWidth)
ax.add_patch(rectSelect)
except: pass
Dis=[]
for date in dateList: Dis.append(h5timeseries['timeseries'].get(date)[ymin:ymax,xmin:xmax])
Dis0=array(Dis)
dis=Dis0*unitFac
dis=reshape(dis,(len(dateList),-1))
dis_mean=stats.nanmean(dis,1)
if (xmax-xmin)*(ymax-ymin)==1: dis_std=[0]*len(dateList)
else: dis_std=stats.nanstd(dis,1)
(_, 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)
print dis_mean
# x axis format
ax2.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')
if unitFac==100: ax2.set_ylabel('Displacement [cm]',fontsize=fontSize)
elif unitFac==1000: ax2.set_ylabel('Displacement [mm]',fontsize=fontSize)
else: ax2.set_ylabel('Displacement [m]' ,fontsize=fontSize)
ax2.set_xlabel('Time [years]',fontsize=fontSize)
ax2.set_title('x='+str(xmin)+':'+str(xmax-1)+', y='+str(ymin)+':'+str(ymax-1))
ax2.xaxis.set_major_locator(years)
ax2.xaxis.set_major_formatter(yearsFmt)
ax2.xaxis.set_minor_locator(months)
datemin = datetime.date(int(datevector[0]),1,1)
datemax = datetime.date(int(datevector[-1])+1,1,1)
ax2.set_xlim(datemin, datemax)
# y axis format
try:
lbound
hbound
ax2.set_ylim(lbound,hbound)
except:
ax2.set_ylim(nanmin(dis_mean-dis_std)-0.4*abs(nanmin(dis_mean)),\
nanmax(dis_mean+dis_std)+0.4*abs(nanmax(dis_mean)))
for tick in ax2.xaxis.get_major_ticks(): tick.label.set_fontsize(fontSize)
for tick in ax2.yaxis.get_major_ticks(): tick.label.set_fontsize(fontSize)
#fig2.autofmt_xdate() #adjust x overlap by rorating, may enble again
if Save_timeseries in ('yes','Yes','Y','y','YES'):
import scipy.io as sio
Delay={}
Delay['displacement']=Dis0
Delay['unit']='m'
Delay['time']=datevector
tsNameBase='ts_x'+str(xmin)+'_'+str(xmax-1)+'y'+str(ymin)+'_'+str(ymax-1)
sio.savemat(tsNameBase+'.mat', {'displacement': Delay})
print 'saved data to '+tsNameBase+'.mat'
plt.savefig(tsNameBase+'.pdf',dpi=fig_dpi)
print 'saved plot to '+tsNameBase+'.pdf'
***********************************************************************
'''
try:
demFile=sys.argv[1]
File=sys.argv[2]
except:
Usage()
sys.exit(1)
if os.path.basename(demFile).split('.')[1]=='hgt':
amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem':
dem,demRsc = readfile.read_dem(demFile)
#amp,dem,demRsc = readfile.read_float32(demFile)
h5data = h5py.File(File)
dset = h5data['velocity'].get('velocity')
data = dset[0:dset.shape[0],0:dset.shape[1]]
try:
suby=sys.argv[3].split(':')
subx=sys.argv[4].split(':')
data = data[int(suby[0]):int(suby[1]),int(subx[0]):int(subx[1])]
dem = dem[int(suby[0]):int(suby[1]),int(subx[0]):int(subx[1])]
except:
print 'no subset'
dem=dem.flatten(1)
def main(argv):
try:
opts, args = getopt.getopt(argv, "h:f:t:p:")
except getopt.GetoptError:
Usage()
sys.exit(1)
if opts == []:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
Usage()
sys.exit()
elif opt == "-f":
file = arg
elif opt == "-t":
filtType = arg
elif opt == "-p":
par = arg
# try:
# file=argv[0]
# alks=float(argv[1])
# rlks=float(argv[2])
# except:
# Usage();sys.exit(1)
ext = os.path.splitext(file)[1]
outName = file.split(".")[0] + "_" + filtType + ext
try:
par
except:
par = []
print "+++++++++++++++++++++++++++"
print "Filter type : " + filtType
print "parameters : " + str(par)
print "+++++++++++++++++++++++++++"
###############################################
if ext == ".int" or ext == ".slc":
a, p, r = readfile.read_complex64(file)
plks = multilook(p, alks, rlks)
alks = multilook(a, alks, rlks)
r["FILE_LENGTH"] = str(dlks.shape[0])
r["WIDTH"] = str(dlks.shape[1])
r["XMAX"] = str(int(r["WIDTH"]) - 1)
r["YMAX"] = str(int(r["FILE_LENGTH"]) - 1)
try:
r["Y_STEP"] = str(float(r["Y_STEP"]) * alks)
r["X_STEP"] = str(float(r["X_STEP"]) * rlks)
except:
Geo = 0
f = open(outName + ".rsc", "w")
for k in r.keys():
f.write(k + " " + r[k] + "\n")
f.close()
elif ext == ".unw" or ext == ".cor" or ext == ".hgt":
a, p, r = readfile.read_float32(file)
plks = multilook(p, alks, rlks)
alks = multilook(a, alks, rlks)
writefile.write_float32(plks, outName)
r["FILE_LENGTH"] = str(dlks.shape[0])
r["WIDTH"] = str(dlks.shape[1])
r["XMAX"] = str(int(r["WIDTH"]) - 1)
r["YMAX"] = str(int(r["FILE_LENGTH"]) - 1)
try:
r["Y_STEP"] = str(float(r["Y_STEP"]) * alks)
r["X_STEP"] = str(float(r["X_STEP"]) * rlks)
except:
Geo = 0
f = open(outName + ".rsc", "w")
for k in r.keys():
f.write(k + " " + r[k] + "\n")
f.close()
elif ext == (".dem"):
d, r = readfile.read_dem(file)
dlks = multilook(d, alks, rlks)
print "writing " + outName
writefile.write_dem(dlks, outName)
r["FILE_LENGTH"] = str(dlks.shape[0])
r["WIDTH"] = str(dlks.shape[1])
r["XMAX"] = str(int(r["WIDTH"]) - 1)
r["YMAX"] = str(int(r["FILE_LENGTH"]) - 1)
try:
r["Y_STEP"] = str(float(r["Y_STEP"]) * alks)
r["X_STEP"] = str(float(r["X_STEP"]) * rlks)
except:
Geo = 0
f = open(outName + ".rsc", "w")
for k in r.keys():
f.write(k + " " + r[k] + "\n")
f.close()
elif ext in [".jpeg", "jpg", "png"]:
import Image
im = Image.open(file)
width = im.size[0] / int(rlks)
height = im.size[1] / int(alks)
imlks = im.resize((width, height), Image.NEAREST)
print "writing " + outName
imlks.save(outName)
try:
r = readfile.read_rsc_file(file + ".rsc")
except:
sys.exit(1)
r["FILE_LENGTH"] = str(height)
r["WIDTH"] = str(width)
r["XMAX"] = str(int(r["WIDTH"]) - 1)
r["YMAX"] = str(int(r["FILE_LENGTH"]) - 1)
try:
r["Y_STEP"] = str(float(r["Y_STEP"]) * alks)
r["X_STEP"] = str(float(r["X_STEP"]) * rlks)
except:
Geo = 0
f = open(outName + ".rsc", "w")
for k in r.keys():
f.write(k + " " + r[k] + "\n")
f.close()
elif ext == (".h5"):
h5file = h5py.File(file, "r")
# outName=file.split('.')[0]+'_a'+str(int(alks))+'lks_r'+str(int(rlks))+'lks.h5'
h5file_lks = h5py.File(outName, "w")
if "interferograms" in h5file.keys():
print "Filtering the interferograms in space"
gg = h5file_lks.create_group("interferograms")
igramList = h5file["interferograms"].keys()
for igram in igramList:
print igram
unwSet = h5file["interferograms"][igram].get(igram)
unw = unwSet[0 : unwSet.shape[0], 0 : unwSet.shape[1]]
unw = filter(unw, filtType, par)
group = gg.create_group(igram)
dset = group.create_dataset(igram, data=unw, compression="gzip")
for key, value in h5file["interferograms"][igram].attrs.iteritems():
group.attrs[key] = value
dset1 = h5file["mask"].get("mask")
mask = dset1[0 : dset1.shape[0], 0 : dset1.shape[1]]
group = h5file_lks.create_group("mask")
dset = group.create_dataset("mask", data=mask, compression="gzip")
elif "timeseries" in h5file.keys():
print "Filtering the time-series"
group = h5file_lks.create_group("timeseries")
dateList = h5file["timeseries"].keys()
for d in dateList:
print d
dset1 = h5file["timeseries"].get(d)
data = dset1[0 : dset1.shape[0], 0 : dset1.shape[1]]
data = filter(data, filtType, par)
dset = group.create_dataset(d, data=data, compression="gzip")
for key, value in h5file["timeseries"].attrs.iteritems():
group.attrs[key] = value
try:
dset1 = h5file["mask"].get("mask")
Mask = dset1[0 : dset1.shape[0], 0 : dset1.shape[1]]
# Masklks=multilook(Mask,alks,rlks)
group = h5file_lks.create_group("mask")
dset = group.create_dataset("mask", data=Mask, compression="gzip")
except:
print "Filterd file does not include the maske"
elif "temporal_coherence" in h5file.keys() or "velocity" in h5file.keys() or "mask" in h5file.keys():
k = h5file.keys()
print "filtering the " + k[0]
group = h5file_lks.create_group(k[0])
dset1 = h5file[k[0]].get(k[0])
data = dset1[0 : dset1.shape[0], 0 : dset1.shape[1]]
data = filter(data, filtType, par)
dset = group.create_dataset(k[0], data=data, compression="gzip")
for key, value in h5file[k[0]].attrs.iteritems():
group.attrs[key] = value
h5file.close()
h5file_lks.close()
def main(argv):
try:
File = argv[0]
demFile=argv[1]
p=int(argv[2])
except:
Usage() ; sys.exit(1)
try:
baseline_error=argv[3]
except:
baseline_error='range_and_azimuth'
##################################
h5file = h5py.File(File)
dateList = h5file['timeseries'].keys()
##################################
try:
maskFile=argv[4]
h5Mask = h5py.File(maskFile,'r')
kMask=h5Mask.keys()
dset1 = h5Mask[kMask[0]].get(kMask[0])
Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
except:
dset1 = h5file['mask'].get('mask')
Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
# try:
# maskFile=argv[3]
# except:
# maskFile='Mask.h5'
# try:
# baseline_error=argv[4]
# except:
# baseline_error='range_and_azimuth'
print baseline_error
##################################
# h5Mask = h5py.File(maskFile)
# kMask=h5Mask.keys()
# dset1 = h5Mask[kMask[0]].get(kMask[0])
# Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
Mask=Mask.flatten(1)
ndx= Mask !=0
##################################
# h5file = h5py.File(File)
# dateList = h5file['timeseries'].keys()
##################################
nt=float(h5file['timeseries'].attrs['LOOK_REF1'])
ft=float(h5file['timeseries'].attrs['LOOK_REF2'])
sy,sx=np.shape(dset1)
npixel=sx*sy
lookangle=np.tile(np.linspace(nt,ft,sx),[sy,1])
lookangle=lookangle.flatten(1)*np.pi/180.0
Fh=-np.sin(lookangle)
Fv=-np.cos(lookangle)
print 'Looking for azimuth pixel size'
try:
daz=float(h5file['timeseries'].attrs['AZIMUTH_PIXEL_SIZE'])
except:
print'''
ERROR!
The attribute AZIMUTH_PIXEL_SIZE was not found!
Possible cause of error: Geo coordinate.
This function works only in radar coordinate system.
'''
sys.exit(1)
lines=np.tile(np.arange(0,sy,1),[1,sx])
lines=lines.flatten(1)
rs=lines*daz
if baseline_error=='range_and_azimuth':
A = np.zeros([npixel,4])
A[:,0]=Fh
A[:,1]=Fh*rs
A[:,2]=Fv
A[:,3]=Fv*rs
num_base_par=4
elif baseline_error=='range':
A = np.zeros([npixel,2])
A[:,0]=Fh
A[:,1]=Fv
num_base_par=2
###########################################
yref=int(h5file['timeseries'].attrs['ref_y'])
xref=int(h5file['timeseries'].attrs['ref_x'])
###########################################
if os.path.basename(demFile).split('.')[1]=='hgt':
amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem':
dem,demRsc = readfile.read_dem(demFile)
dem=dem-dem[yref][xref]
dem=dem.flatten(1)
###################################################
if p==1:
# A=np.vstack((dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem,np.ones(len(dem)))).T
elif p==2:
# A=np.vstack((dem[ndx]**2,dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem**2,dem,np.ones(len(dem)))).T
elif p==3:
# A = np.vstack((dem[ndx]**3,dem[ndx]**2,dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem**3,dem**2,dem,np.ones(len(dem)))).T
print np.shape(A)
Ainv=np.linalg.pinv(A)
###################################################
Bh=[]
Bv=[]
Bhrate=[]
Bvrate=[]
Be=np.zeros([len(dateList),num_base_par+p+1])
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
for i in range(1,len(dateList)):
dset = h5file['timeseries'].get(dateList[i])
data = dset[0:dset.shape[0],0:dset.shape[1]]
L = data.flatten(1)
M=np.hstack((A,B))
Berror=np.dot(np.linalg.pinv(M[ndx]),L[ndx])
Bh.append(Berror[0])
Bhrate.append(Berror[1])
Bv.append(Berror[2])
Bvrate.append(Berror[3])
Be[i,:]=Berror
print Berror
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
print 'baseline error mean std'
print ' bh : ' +str(np.mean(Bh)) + ' , '+str(np.std(Bh))
print ' bh rate : ' +str(np.mean(Bhrate)) + ' , '+str(np.std(Bhrate))
print ' bv : ' +str(np.mean(Bv)) + ' , '+str(np.std(Bv))
print ' bv rate : ' +str(np.mean(Bvrate)) + ' , '+str(np.std(Bvrate))
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
# plt.hist(Bh,bins=8,normed=True)
# formatter = FuncFormatter(to_percent)
# Set the formatter
# plt.gca().yaxis.set_major_formatter(formatter)
# plt.show()
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
# print 'Estimating Baseline error from each differences ...'
# print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
orbEffect=np.zeros([len(dateList),sy,sx])
for i in range(1,len(dateList)):
effect=np.dot(M,Be[i,:])
effect=np.reshape(effect,[sx,sy]).T
# orbEffect[i,:,:]=orbEffect[i-1,:,:]+effect
# orbEffect[i,:,:]=orbEffect[i,:,:]-orbEffect[i,yref,xref]
orbEffect[i,:,:]=effect - effect[yref,xref]
del effect
print 'Correctiing the time series '
outName=File.replace('.h5','')+'_BaseTropCor.h5'
h5orbCor=h5py.File(outName,'w')
group = h5orbCor.create_group('timeseries')
for i in range(len(dateList)):
dset1 = h5file['timeseries'].get(dateList[i])
data = dset1[0:dset1.shape[0],0:dset1.shape[1]] - orbEffect[i,:,:]
dset = group.create_dataset(dateList[i], data=data, compression='gzip')
for key,value in h5file['timeseries'].attrs.iteritems():
group.attrs[key] = value
dset1 = h5file['mask'].get('mask')
group=h5orbCor.create_group('mask')
dset = group.create_dataset('mask', data=dset1, compression='gzip')
h5file.close()
h5orbCor.close()
def main(argv):
color_map='jet'
disp_opposite='no'
try:
opts, args = getopt.getopt(argv,"h:f:d:o:x:y:m:M:i:c:")
except getopt.GetoptError:
Usage() ; sys.exit(1)
if opts==[]:
Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"):
Usage()
sys.exit()
elif opt == '-f':
File = arg
elif opt == '-d':
demFile=arg
elif opt=='-m':
Vmin=float(arg)
elif opt=='-M':
Vmax=float(arg)
elif opt == '-x':
winx=arg.split(':')
elif opt == '-y':
winy = arg.split(':')
elif opt == '-o':
outName = arg
elif opt == '-i':
disp_opposite = arg
elif opt == '-c':
color_map=arg
h5file=h5py.File(File,'r')
k=h5file.keys()
print k[0]
# ccmap=plt.get_cmap(color_map)
################################################
cdict1 = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.0),
(0.6, 1.0, 1.0),
(0.8, 1.0, 1.0),
(1.0, 0.5, 0.5)),
'green': ((0.0, 0.0, 0.0),
(0.2, 0.0, 0.0),
(0.4, 1.0, 1.0),
(0.6, 1.0, 1.0),
(0.8, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.5, .5),
(0.2, 1.0, 1.0),
(0.4, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0),)
}
if color_map =='pysar_hsv':
ccmap = LinearSegmentedColormap('BlueRed1', cdict1)
else:
ccmap=plt.get_cmap(color_map)
print 'colormap is : '+ color_map
################################################
dset = h5file[k[0]].get(k[0])
data=dset[0:dset.shape[0],0:dset.shape[1]]
if disp_opposite in('yes','Yes','Y','y','YES'):
data=-1*data
try:
xref=h5file[k[0]].attrs['ref_x']
yref=h5file[k[0]].attrs['ref_y']
except:
print 'No reference point'
try:
ullon=float(h5file[k[0]].attrs['X_FIRST'])
ullat=float(h5file[k[0]].attrs['Y_FIRST'])
lon_step=float(h5file[k[0]].attrs['X_STEP'])
lat_step=float(h5file[k[0]].attrs['Y_STEP'])
lon_unit=h5file[k[0]].attrs['Y_UNIT']
lat_unit=h5file[k[0]].attrs['X_UNIT']
llcrnrlon=ullon
llcrnrlat=ullat+lat_step*data.shape[0]
urcrnrlon=ullon+lon_step*data.shape[1]
urcrnrlat=ullat
geocoord='yes'
print 'Input file is Geocoded'
except:
geocoord='no'
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat,
resolution='l', area_thresh=1., projection='cyl',suppress_ticks=False,ax=ax)
print demFile
demFile
if os.path.basename(demFile).split('.')[1]=='hgt':
amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem':
dem,demRsc = readfile.read_dem(demFile)
#################################################################
try:
winx
wx=[int(i) for i in win_x.split()]
dem=dem[:,wx[0]:wx[1]]
data=data[:,wx[0]:wx[1]]
ullon=float(h5file[k[0]].attrs['X_FIRST'])+wx[0]
llcrnrlon=ullon
urcrnrlon=ullon+lon_step*data.shape[1]
except:
print ''
try:
winy
wy=[int(i) for i in winy.split()]
dem=dem[wy[0]:wy[1],:]
data=data[wy[0]:wy[1],:]
except:
print ''
################################################################
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat,
resolution='l', area_thresh=1., projection='cyl',suppress_ticks=False,ax=ax)
cmap_dem=plt.get_cmap('gray')
m.imshow(ut.hillshade(np.flipud(dem),50.0),cmap=cmap_dem)
try:
im=m.imshow(np.flipud(data),vmin=Vmin,vmax=Vmax,cmap=ccmap)
# cb = m.colorbar(im,"right", size="5%", pad='2%')
except:
im=m.imshow(np.flipud(data))
# cb = m.colorbar(im,"right", size="5%", pad='2%')
# m.bluemarble()
# cb = m.colorbar(im,"right", size="5%", pad='2%')
# parallels = np.arange(31.,34,0.5)
# m.drawparallels(parallels,labels=[1,0,0,1],linewidth=0.0)
# meridians = np.arange(-115.,-112.,0.5)
# m.drawmeridians(meridians,labels=[1,0,0,1],linewidth=0.0)
# m.drawmapscale()
# m = Basemap(llcrnrlon=-110.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
# projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
# resolution ='l',area_thresh=1000.)
# m.drawcoastlines()
# m.drawcountries()
# m.drawmapboundary(fill_color='#99ffff')
# m.fillcontinents(color='#cc9966',lake_color='#99ffff')
# m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])
# m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])
# plt.title('Atlantic Hurricane Tracks (Storms Reaching Category 4, 1851-2004)')
try:
figName = outName
except:
outName=os.path.basename(File).replace('.h5','')
figName = outName + '.png'
plt.savefig(figName,pad_inches=0.0)
# plt.show()
h5file.close()
def main(argv):
try:
opts, args = getopt.getopt(argv,"h:D:O:G:S:f:m:M:l:u:s:c:e:d:r:p:w:i:j:t:R:a:b:k:x:y:")
except getopt.GetoptError:
Usage() ; sys.exit(1)
flip_lr='no'
flip_ud='no'
disp_geo = 'no'
font_size=8
color_map='jet'
figs_rows=5
figs_cols=8
rewrapping='yes'
allData2display='yes'
Wspace = 0.1
Hspace = 0.1
title = 'out'
# title = 'None'
showRef = 'yes'
ref_color='k'
ref_symbol='s'
ref_size =10
dip_opposite = 'no'
saveFig='no'
if opts==[]:
Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"):
Usage()
sys.exit()
elif opt == '-f':
File = arg
elif opt == '-D':
demFile=arg
elif opt == '-m':
min = float(arg)
elif opt == '-M':
max = float(arg)
elif opt == '-l':
flip_lr = arg
elif opt == '-u':
flip_ud = arg
elif opt == '-s':
font_size = int(arg)
elif opt == '-c':
color_map = arg
elif opt == '-e':
epoch_number = int(arg)
allData2display='no'
elif opt == '-d':
epoch_date = arg
allData2display='no'
elif opt == '-r':
figs_rows = int(arg)
elif opt == '-p':
figs_cols = int(arg)
elif opt == '-w':
rewrapping = arg
elif opt == '-i':
Wspace = float(arg)
elif opt == '-j':
Hspace = float(arg)
elif opt == '-t':
title = arg
elif opt == '-R':
showRef = arg
elif opt == '-a':
ref_color = arg
elif opt == '-b':
ref_symbol = arg
elif opt == 'k':
ref_size=int(arg)
elif opt == '-x':
win_x = arg
elif opt == '-y':
win_y = arg
elif opt == '-G':
disp_geo = arg
elif opt == '-O':
dip_opposite=arg
elif opt=='-S':
saveFig=arg
h5file=h5py.File(File,'r')
k=h5file.keys()
print k
if color_map == 'hsv':
################################################
cdict1 = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.0),
(0.6, 1.0, 1.0),
(0.8, 1.0, 1.0),
(1.0, 0.5, 0.5)),
'green': ((0.0, 0.0, 0.0),
(0.2, 0.0, 0.0),
(0.4, 1.0, 1.0),
(0.6, 1.0, 1.0),
(0.8, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.5, .5),
(0.2, 1.0, 1.0),
(0.4, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0),)
}
ccmap = LinearSegmentedColormap('BlueRed1', cdict1)
################################################
else:
ccmap=plt.get_cmap(color_map)
####################################################################
####################################################################
# if k[0]=='velocity' or k[0]=='temporal_coherence' or k[0]=='rmse':
if len(k)==1 and k[0] in ('dem','velocity','mask','temporal_coherence','rmse'):
dset = h5file[k[0]].get(k[0])
data=dset[0:dset.shape[0],0:dset.shape[1]]
if dip_opposite in('yes','Yes','Y','y','YES'):
data=-1*data
try:
xref=h5file[k[0]].attrs['ref_x']
yref=h5file[k[0]].attrs['ref_y']
except:
print 'No reference point'
# Yunjun, Mar 2015
try:
xref=xref-h5file[k[0]].attrs['subset_x0']
yref=yref-h5file[k[0]].attrs['subset_y0']
except:
print 'No subset'
try:
ullon=float(h5file[k[0]].attrs['X_FIRST'])
ullat=float(h5file[k[0]].attrs['Y_FIRST'])
lon_step=float(h5file[k[0]].attrs['X_STEP'])
lat_step=float(h5file[k[0]].attrs['Y_STEP'])
lon_unit=h5file[k[0]].attrs['Y_UNIT']
lat_unit=h5file[k[0]].attrs['X_UNIT']
llcrnrlon=ullon
llcrnrlat=ullat+lat_step*data.shape[0]
urcrnrlon=ullon+lon_step*data.shape[1]
urcrnrlat=ullat
geocoord='yes'
print 'Input file is Geocoded'
except:
geocoord='no'
try:
win_x
wx=[int(i) for i in win_x.split()]
data=data[:,wx[0]:wx[1]]
xref = xref-wx[0]
except:
print 'No subste in x direction'
try:
win_y
wy=[int(i) for i in win_y.split()]
data=data[wy[0]:wy[1],:]
yref = yref-wy[0]
except:
print 'No subset in y direction'
try:
min
except:
min=np.nanmin(data)
try:
max
except:
max=np.nanmax(data)
if flip_lr=='yes':
data=np.fliplr(data)
xref=np.shape(data)[1]-xref-1
if flip_ud=='yes':
data=np.flipud(data)
yref=np.shape(data)[0]-yref-1
try:
demFile
# amp,dem,demRsc = readfile.read_float32(demFile)
if os.path.basename(demFile).split('.')[1]=='hgt':
amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem':
dem,demRsc = readfile.read_dem(demFile)
try:
win_x
wx=[int(i) for i in win_x.split()]
dem=dem[:,wx[0]:wx[1]]
except:
print ''
try:
win_y
wy=[int(i) for i in win_y.split()]
dem=dem[wy[0]:wy[1],:]
except:
print ''
if flip_lr=='yes':
dem=np.fliplr(dem)
if flip_ud=='yes':
dem=np.flipud(dem)
cmap_dem=plt.get_cmap('gray')
if disp_geo in ('yes','Yes','Y','y','YES') and geocoord in ('yes','Yes','Y','y','YES'):
print 'display geo'
# from mpl_toolkits.basemap import Basemap
# m = Basemap(llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat,resolution='f', area_thresh=1., projection='cyl')
# m.imshow(ut.hillshade(dem,50.0), interpolation='nearest', origin='upper')
# m.drawcoastlines(color='w',linewidth=0.8)
# m.drawmapboundary() # draw a line around the map region
# m.drawrivers()
# m.drawparallels(numpy.arange(int(d1.min()), int(d1.max()), 1),linewidth=0.2,labels=[1,0,0,0])
# m.drawmeridians(numpy.arange(int(d0.min()), int(d0.max()), 1),linewidth=0.2,labels=[0,0,0,1])
else:
print 'Not GEO'
plt.imshow(ut.hillshade(dem,50.0),cmap=cmap_dem)
except:
print 'No DEM file'
plt.imshow(data,cmap=ccmap, vmin=min, vmax=max)
plt.colorbar()
if k[0]=='velocity':
plt.title('Velocity (m/yr)',fontsize=font_size)
figName='velocity.pdf'
elif k[0]=='temporal_coherence':
plt.title('Temporal coherence',fontsize=font_size)
figName='temporal_coherence.pdf'
elif k[0]=='dem':
plt.title('DEM error',fontsize=font_size)
figName='DEM_error.pdf'
elif k[0]=='rmse':
plt.title('RMSE (m/yr)',fontsize=font_size)
figName='rmse.pdf'
elif k[0]=='mask':
plt.title('Pixels with no valid value.',fontsize=font_size)
figName='mask.pdf'
if showRef=='yes':
try:
refPoint=ref_color+ref_symbol
plt.plot(xref,yref,refPoint,ms=ref_size)
except:
print 'No reference point'
plt.xlim(0,np.shape(data)[1])
plt.ylim(np.shape(data)[0],0)
if saveFig=='yes':
plt.savefig(figName)
plt.show()
# plt.savefig('fig.pdf')
# fig = plt.figure()
# ax.imshow(data,vmin=min, vmax=max)
# ax.xaxis.label.set_fontsize(40)
####################################################################
####################################################################
if 'timeseries' in k and allData2display=='yes':
if rewrapping=='yes':
print 'rewrapping'
dateList=h5file['timeseries'].keys()
nfigs=figs_rows*figs_cols
ligram = len(dateList)
range2phase=4*np.pi/float(h5file['timeseries'].attrs['WAVELENGTH'])
# range2phase=4*np.pi/0.056
print 'number of timeseries epochs to display:'+ str(ligram)
kk=int(ligram/nfigs)+1
ii=0
for j in range(1,kk):
fig = plt.figure(j)
ii=(j-1)*nfigs+1
for i in range(ii,ii+nfigs):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
dset=h5file['timeseries'].get(dateList[i-1])
data = dset[0:dset.shape[0],0:dset.shape[1]]
data=range2phase*data
# data=np.angle(np.exp(1j*data))
data=rewrap(data)
ax.imshow(data,cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(dateList[i-1],fontsize=font_size)
elif title=='in':
add_inner_title(ax, dateList[i-1], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
figName=k[0]+'_'+str(j)+'.pdf'
if saveFig in ['yes','Yes','y','YES']:
plt.savefig(figName)
fig = plt.figure(kk)
ii=(kk-1)*nfigs+1
for i in range(ii,ligram+1):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
dset=h5file['timeseries'].get(dateList[i-1])
data = dset[0:dset.shape[0],0:dset.shape[1]]
data=range2phase*data
# data=np.angle(np.exp(1j*data))
data=rewrap(data)
ax.imshow(data,cmap=ccmap)
ax.xaxis.label.set_fontsize(20)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(dateList[i-1],fontsize=font_size)
elif title =='in':
add_inner_title(ax, dateList[i-1], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
figName=k[0]+'_'+str(kk)+'.pdf'
if saveFig in ['yes','Yes','y','YES']:
plt.savefig(figName)
plt.show()
else:
print 'No rewrapping'
dateList=h5file['timeseries'].keys()
nfigs=figs_rows*figs_cols
ligram = len(dateList)
print 'number of timeseries epochs to display:'+ str(ligram)
kk=int(ligram/nfigs)+1
ii=0
for j in range(1,kk):
fig = plt.figure(j)
ii=(j-1)*nfigs+1
for i in range(ii,ii+nfigs):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
data=h5file['timeseries'].get(dateList[i-1])
try:
im=ax.imshow(data,cmap=ccmap,vmin=min,vmax=max)
# print 'here'
except:
im=ax.imshow(data,cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(dateList[i-1],fontsize=font_size)
elif title=='in':
add_inner_title(ax, dateList[i-1], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
fig = plt.figure(kk)
ii=(kk-1)*nfigs+1
for i in range(ii,ligram+1):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
data=h5file['timeseries'].get(dateList[i-1])
try:
im=ax.imshow(data,cmap=ccmap,vmin=min,vmax=max)
except:
im=ax.imshow(data,cmap=ccmap)
ax.xaxis.label.set_fontsize(20)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(dateList[i-1],fontsize=font_size)
if title=='in':
add_inner_title(ax, dateList[i-1], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
plt.show()
####################################################################
####################################################################
elif 'timeseries' in k and allData2display=='no':
dateList=h5file['timeseries'].keys()
try:
epoch_number
except:
epoch_number=dateList.index(epoch_date)
range2phase=4*np.pi/float(h5file['timeseries'].attrs['WAVELENGTH'])
# range2phase=4*np.pi/0.056
dset=h5file['timeseries'].get(dateList[epoch_number])
data = dset[0:dset.shape[0],0:dset.shape[1]]
if rewrapping=='yes':
data=range2phase*data
# data=np.angle(np.exp(1j*data))
data=rewrap(data)
try:
min
except:
min=np.nanmin(data)
try:
max
except:
max=np.nanmax(data)
plt.imshow(data,cmap=ccmap,vmin=min,vmax=max)
plt.colorbar()
plt.show()
################################################################
################################################################
if k[0]in('interferograms','coherence','wrapped') and allData2display=='yes':
if k[0] in ('coherence','wrapped'):
rewrapping='no'
# color_map = 'gray'
# ccmap=plt.get_cmap(color_map)
if rewrapping=='yes':
ifgramList=h5file[k[0]].keys()
nfigs=figs_rows*figs_cols
ligram = len(ifgramList)
print 'number of '+k[0]+' to display:'+ str(ligram)
kk=int(ligram/nfigs)+1
ii=0
for j in range(1,kk):
fig = plt.figure(j)
ii=(j-1)*nfigs+1
for i in range(ii,ii+nfigs):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
dset = h5file[k[0]][ifgramList[i-1]].get(ifgramList[i-1])
data = dset[0:dset.shape[0],0:dset.shape[1]]
data=np.angle(np.exp(1j*data))
ax.imshow(data,cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(h5file[k[0]][ifgramList[i-1]].attrs['DATE12'],fontsize=font_size)
elif title=='in':
add_inner_title(ax, h5file[k[0]][ifgramList[i-1]].attrs['DATE12'], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
fig = plt.figure(kk)
ii=(kk-1)*nfigs+1
for i in range(ii,ligram+1):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
dset = h5file[k[0]][ifgramList[i-1]].get(ifgramList[i-1])
data = dset[0:dset.shape[0],0:dset.shape[1]]
data=np.angle(np.exp(1j*data))
ax.imshow(data,cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(h5file[k[0]][ifgramList[i-1]].attrs['DATE12'],fontsize=font_size)
elif title=='in':
add_inner_title(ax, h5file[k[0]][ifgramList[i-1]].attrs['DATE12'], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
plt.show()
else:
ifgramList=h5file[k[0]].keys()
nfigs=figs_rows*figs_cols
ligram = len(ifgramList)
print 'number of '+k[0]+' to display:'+ str(ligram)
kk=int(ligram/nfigs)+1
ii=0
for j in range(1,kk):
fig = plt.figure(j)
ii=(j-1)*nfigs+1
for i in range(ii,ii+nfigs):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
print 'loading '+ifgramList[i-1]
dset = h5file[k[0]][ifgramList[i-1]].get(ifgramList[i-1])
data = dset[0:dset.shape[0],0:dset.shape[1]]
try:
ax.imshow(data,vmin=min,vmax=max,cmap=ccmap)
except:
ax.imshow(data,cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(h5file[k[0]][ifgramList[i-1]].attrs['DATE12'],fontsize=font_size)
elif title=='in':
add_inner_title(ax, h5file[k[0]][ifgramList[i-1]].attrs['DATE12'], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
fig = plt.figure(kk)
ii=(kk-1)*nfigs+1
for i in range(ii,ligram+1):
ax = fig.add_subplot(figs_rows,figs_cols,i-ii+1)
print 'loading '+ifgramList[i-1]
dset = h5file[k[0]][ifgramList[i-1]].get(ifgramList[i-1])
data = dset[0:dset.shape[0],0:dset.shape[1]]
#data = h5file[k[0]][ifgramList[i-1]].get(ifgramList[i-1])
try:
ax.imshow(data,vmin=min,vmax=max,cmap=ccmap)
except:
ax.imshow(data,cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title=='out':
ax.set_title(h5file[k[0]][ifgramList[i-1]].attrs['DATE12'],fontsize=font_size)
elif title=='in':
add_inner_title(ax, h5file[k[0]][ifgramList[i-1]].attrs['DATE12'], loc=1)
fig.subplots_adjust(wspace=Wspace,hspace=Hspace)
plt.show()
####################################################################
####################################################################
elif k[0]in('interferograms','coherence','wrapped') and allData2display=='no':
if k[0] in ('coherence','wrapped'):
rewrapping=='no'
ifgramList=h5file[k[0]].keys()
try:
epoch_number
except:
for i in range(len(ifgramList)):
if epoch_date in ifgramList[i]:
epoch_number = i
dset = h5file[k[0]][ifgramList[epoch_number]].get(ifgramList[epoch_number])
data = dset[0:dset.shape[0],0:dset.shape[1]]
if rewrapping=='yes':
data=np.angle(np.exp(1j*data))
if dip_opposite in('yes','Yes','Y','y','YES'):
data=-1*data
#DEM basemap
try:
demFile
if os.path.basename(demFile).split('.')[1]=='hgt':
amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem':
dem,demRsc = readfile.read_dem(demFile)
try:
win_x
wx=[int(i) for i in win_x.split()]
dem=dem[:,wx[0]:wx[1]]
except:
print ''
try:
win_y
wy=[int(i) for i in win_y.split()]
dem=dem[wy[0]:wy[1],:]
except:
print ''
if flip_lr=='yes':
dem=np.fliplr(dem)
if flip_ud=='yes':
dem=np.flipud(dem)
cmap_dem=plt.get_cmap('gray')
if disp_geo in ('yes','Yes','Y','y','YES') and geocoord in ('yes','Yes','Y','y','YES'):
print 'display geo'
else:
print 'Not GEO'
plt.imshow(ut.hillshade(dem,50.0),cmap=cmap_dem)
except:
print 'No DEM file'
try:
plt.imshow(data,cmap=ccmap,vmin=min,vmax=max)
except:
plt.imshow(data,cmap=ccmap)
plt.colorbar()
# plt.title(h5file[k[0]][ifgramList[epoch_number]].attrs['DATE12'],fontsize=font_size)
plt.title(ifgramList[epoch_number],fontsize=font_size)
plt.show()
################################################################
################################################################
h5file.close()
***********************************************************************
'''
try:
demFile = sys.argv[1]
File = sys.argv[2]
except:
Usage()
sys.exit(1)
if os.path.basename(demFile).split('.')[1] == 'hgt':
amp, dem, demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1] == 'dem':
dem, demRsc = readfile.read_dem(demFile)
#amp,dem,demRsc = readfile.read_float32(demFile)
h5data = h5py.File(File)
dset = h5data['velocity'].get('velocity')
data = dset[0:dset.shape[0], 0:dset.shape[1]]
try:
suby = sys.argv[3].split(':')
subx = sys.argv[4].split(':')
data = data[int(suby[0]):int(suby[1]), int(subx[0]):int(subx[1])]
dem = dem[int(suby[0]):int(suby[1]), int(subx[0]):int(subx[1])]
except:
print 'no subset'
dem = dem.flatten(1)
def main(argv):
try:
opts, args = getopt.getopt(argv, "h:f:t:p:")
except getopt.GetoptError:
Usage()
sys.exit(1)
if opts == []:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
Usage()
sys.exit()
elif opt == '-f':
file = arg
elif opt == '-t':
filtType = arg
elif opt == '-p':
par = arg
# try:
# file=argv[0]
# alks=float(argv[1])
# rlks=float(argv[2])
# except:
# Usage();sys.exit(1)
ext = os.path.splitext(file)[1]
outName = file.split('.')[0] + '_' + filtType + ext
try:
par
except:
par = []
print '+++++++++++++++++++++++++++'
print 'Filter type : ' + filtType
print 'parameters : ' + str(par)
print '+++++++++++++++++++++++++++'
###############################################
if ext == '.int' or ext == '.slc':
a, p, r = readfile.read_complex64(file)
plks = multilook(p, alks, rlks)
alks = multilook(a, alks, rlks)
r['FILE_LENGTH'] = str(dlks.shape[0])
r['WIDTH'] = str(dlks.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == '.unw' or ext == '.cor' or ext == '.hgt':
a, p, r = readfile.read_float32(file)
plks = multilook(p, alks, rlks)
alks = multilook(a, alks, rlks)
writefile.write_float32(plks, outName)
r['FILE_LENGTH'] = str(dlks.shape[0])
r['WIDTH'] = str(dlks.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == ('.dem'):
d, r = readfile.read_dem(file)
dlks = multilook(d, alks, rlks)
print 'writing ' + outName
writefile.write_dem(dlks, outName)
r['FILE_LENGTH'] = str(dlks.shape[0])
r['WIDTH'] = str(dlks.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext in ['.jpeg', 'jpg', 'png']:
import Image
im = Image.open(file)
width = im.size[0] / int(rlks)
height = im.size[1] / int(alks)
imlks = im.resize((width, height), Image.NEAREST)
print 'writing ' + outName
imlks.save(outName)
try:
r = readfile.read_rsc_file(file + '.rsc')
except:
sys.exit(1)
r['FILE_LENGTH'] = str(height)
r['WIDTH'] = str(width)
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP'] = str(float(r['Y_STEP']) * alks)
r['X_STEP'] = str(float(r['X_STEP']) * rlks)
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == ('.h5'):
h5file = h5py.File(file, 'r')
# outName=file.split('.')[0]+'_a'+str(int(alks))+'lks_r'+str(int(rlks))+'lks.h5'
h5file_lks = h5py.File(outName, 'w')
if 'interferograms' in h5file.keys():
print 'Filtering the interferograms in space'
gg = h5file_lks.create_group('interferograms')
igramList = h5file['interferograms'].keys()
for igram in igramList:
print igram
unwSet = h5file['interferograms'][igram].get(igram)
unw = unwSet[0:unwSet.shape[0], 0:unwSet.shape[1]]
unw = filter(unw, filtType, par)
group = gg.create_group(igram)
dset = group.create_dataset(igram,
data=unw,
compression='gzip')
for key, value in h5file['interferograms'][
igram].attrs.iteritems():
group.attrs[key] = value
dset1 = h5file['mask'].get('mask')
mask = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
group = h5file_lks.create_group('mask')
dset = group.create_dataset('mask', data=mask, compression='gzip')
elif 'timeseries' in h5file.keys():
print 'Filtering the time-series'
group = h5file_lks.create_group('timeseries')
dateList = h5file['timeseries'].keys()
for d in dateList:
print d
dset1 = h5file['timeseries'].get(d)
data = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
data = filter(data, filtType, par)
dset = group.create_dataset(d, data=data, compression='gzip')
for key, value in h5file['timeseries'].attrs.iteritems():
group.attrs[key] = value
try:
dset1 = h5file['mask'].get('mask')
Mask = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
# Masklks=multilook(Mask,alks,rlks)
group = h5file_lks.create_group('mask')
dset = group.create_dataset('mask',
data=Mask,
compression='gzip')
except:
print 'Filterd file does not include the maske'
elif 'temporal_coherence' in h5file.keys(
) or 'velocity' in h5file.keys() or 'mask' in h5file.keys():
k = h5file.keys()
print 'filtering the ' + k[0]
group = h5file_lks.create_group(k[0])
dset1 = h5file[k[0]].get(k[0])
data = dset1[0:dset1.shape[0], 0:dset1.shape[1]]
data = filter(data, filtType, par)
dset = group.create_dataset(k[0], data=data, compression='gzip')
for key, value in h5file[k[0]].attrs.iteritems():
group.attrs[key] = value
h5file.close()
h5file_lks.close()
def main(argv):
#outName='subsetIgrams.h5'
try:
opts, args = getopt.getopt(argv,"h:f:x:y:o:l:L:")
except getopt.GetoptError:
print 'Error while getting args'
Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"):
Usage()
sys.exit()
elif opt == '-f':
File = arg
elif opt=='-y':
ysub=[int(i) for i in arg.split(':')]
ysub.sort()
elif opt=='-x':
xsub = [int(i) for i in arg.split(':')]
xsub.sort()
elif opt=='-o':
outName=arg
elif opt=='-l':
Latsub=[float(i) for i in arg.split(':')]
Latsub.sort()
elif opt=='-L':
Lonsub = [float(i) for i in arg.split(':')]
Lonsub.sort()
#####################################################
try:
File
xsub
ysub
except:
try:
File
Latsub
Lonsub
except:
Usage();sys.exit(1)
try:
outName
except:
outName='subset_'+File
ext = os.path.splitext(File)[1]
if ext == '.h5':
try:
h5file=h5py.File(File,'r')
except:
Usage() ; sys.exit(1)
k=h5file.keys()
# convert LatLon to xy for geocoded file
try:
Latsub
Lonsub
if 'X_FIRST' in h5file[k[0]].attrs.keys():
xsub=[0]*2
ysub=[0]*2
xsub[0]=int((Lonsub[0]-float(h5file[k[0]].attrs['X_FIRST']))/float(h5file[k[0]].attrs['X_STEP']))
xsub[1]=int((Lonsub[1]-float(h5file[k[0]].attrs['X_FIRST']))/float(h5file[k[0]].attrs['X_STEP']))
ysub[0]=int((Latsub[1]-float(h5file[k[0]].attrs['Y_FIRST']))/float(h5file[k[0]].attrs['Y_STEP']))
ysub[1]=int((Latsub[0]-float(h5file[k[0]].attrs['Y_FIRST']))/float(h5file[k[0]].attrs['Y_STEP']))
print 'Subseting geocoded',ext,' file with Latitude and Longitude...'
elif 'X_FIRST' in h5file[k[0]][h5file[k[0]].keys()[0]].attrs.keys(): # for geocoded interferograms/coherence
igramList=h5file[k[0]].keys()
xsub=[0]*2
ysub=[0]*2
xsub[0]=int((Lonsub[0]-float(h5file[k[0]][igramList[0]].attrs['X_FIRST']))/float(h5file[k[0]][igramList[0]].attrs['X_STEP']))
xsub[1]=int((Lonsub[1]-float(h5file[k[0]][igramList[0]].attrs['X_FIRST']))/float(h5file[k[0]][igramList[0]].attrs['X_STEP']))
ysub[0]=int((Latsub[1]-float(h5file[k[0]][igramList[0]].attrs['Y_FIRST']))/float(h5file[k[0]][igramList[0]].attrs['Y_STEP']))
ysub[1]=int((Latsub[0]-float(h5file[k[0]][igramList[0]].attrs['Y_FIRST']))/float(h5file[k[0]][igramList[0]].attrs['Y_STEP']))
print 'Subseting geocoded',ext,' file with Latitude and Longitude...'
else:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage() ; sys.exit(1)
except:
Geo=0
# k=h5file.keys()
if 'interferograms' in k:
igramList=h5file['interferograms'].keys()
h5out=h5py.File(outName,'w')
gg=h5out.create_group('interferograms')
for igram in igramList:
print igram
dset1=h5file['interferograms'][igram].get(igram)
group=gg.create_group(igram)
dset=group.create_dataset(igram, data=dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]], compression='gzip')
for key, value in h5file['interferograms'][igram].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH']=shape(dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]])[0]
group.attrs['WIDTH']=shape(dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]])[1]
group.attrs['subset_x0']=xsub[0]
group.attrs['subset_x1']=xsub[1]
group.attrs['subset_y0']=ysub[0]
group.attrs['subset_y1']=ysub[1]
if 'X_FIRST' in h5file['interferograms'][igram].attrs.keys():
group.attrs['X_FIRST']=float(h5file['interferograms'][igram].attrs['X_FIRST']) + xsub[0]*float(h5file['interferograms'][igram].attrs['X_STEP'])
group.attrs['Y_FIRST']=float(h5file['interferograms'][igram].attrs['Y_FIRST']) + ysub[0]*float(h5file['interferograms'][igram].attrs['Y_STEP'])
gm=h5out.create_group('mask')
try:
Mset=h5file['mask'].get('mask')
dset=gm.create_dataset('mask', data=Mset[ysub[0]:ysub[1],xsub[0]:xsub[1]], compression='gzip')
except:
print 'No group for mask found! It may cause problem in other processing steps.'
try:
Cset=h5file['meanCoherence'].get('meanCoherence')
gm=h5out.create_group('meanCoherence')
dset=gm.create_dataset('meanCoherence', data=Cset[ysub[0]:ysub[1],xsub[0]:xsub[1]], compression='gzip')
except:
print 'No average coherence found in the File'
elif k[0] in ('coherence','wrapped'):
corList=h5file[k[0]].keys()
h5out=h5py.File(outName,'w')
gg=h5out.create_group(k[0])
for cor in corList:
print cor
dset1=h5file[k[0]][cor].get(cor)
group=gg.create_group(cor)
dset=group.create_dataset(cor, data=dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]], compression='gzip')
for key, value in h5file[k[0]][cor].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH']=shape(dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]])[0]
group.attrs['WIDTH']=shape(dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]])[1]
group.attrs['subset_x0']=xsub[0]
group.attrs['subset_x1']=xsub[1]
group.attrs['subset_y0']=ysub[0]
group.attrs['subset_y1']=ysub[1]
if 'X_FIRST' in h5file[k[0]][cor].attrs.keys():
group.attrs['X_FIRST']=float(h5file[k[0]][cor].attrs['X_FIRST']) + xsub[0]*float(h5file[k[0]][cor].attrs['X_STEP'])
group.attrs['Y_FIRST']=float(h5file[k[0]][cor].attrs['Y_FIRST']) + ysub[0]*float(h5file[k[0]][cor].attrs['Y_STEP'])
elif 'timeseries' in h5file.keys():
dateList=h5file['timeseries'].keys()
h5out=h5py.File(outName,'w')
group=h5out.create_group('timeseries')
for d in dateList:
print d
dset1=h5file['timeseries'].get(d)
dset=group.create_dataset(d, data=dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]], compression='gzip')
for key, value in h5file['timeseries'].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH']=shape(dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]])[0]
group.attrs['WIDTH']=shape(dset1[ysub[0]:ysub[1],xsub[0]:xsub[1]])[1]
group.attrs['subset_x0']=xsub[0]
group.attrs['subset_x1']=xsub[1]
group.attrs['subset_y0']=ysub[0]
group.attrs['subset_y1']=ysub[1]
if 'X_FIRST' in h5file['timeseries'].attrs.keys():
group.attrs['X_FIRST']=float(h5file['timeseries'].attrs['X_FIRST']) + xsub[0]*float(h5file['timeseries'].attrs['X_STEP'])
group.attrs['Y_FIRST']=float(h5file['timeseries'].attrs['Y_FIRST']) + ysub[0]*float(h5file['timeseries'].attrs['Y_STEP'])
h5file.close()
h5out.close()
elif 'temporal_coherence' in h5file.keys() or 'velocity' in h5file.keys() or 'mask' in h5file.keys() or 'rmse' in h5file.keys():
print 'writing >>> ' +outName
dset=h5file[k[0]].get(k[0])
data=dset[ysub[0]:ysub[1],xsub[0]:xsub[1]]
hfout=h5py.File(outName,'w')
group= hfout.create_group(k[0])
group.create_dataset(k[0],data=data,compression='gzip')
for key,value in h5file[k[0]].attrs.iteritems():
group.attrs[key]=value
group.attrs['FILE_LENGTH']=data.shape[0]
group.attrs['WIDTH']=data.shape[1]
group.attrs['XMIN']=0
group.attrs['XMAX']=data.shape[1]-1
group.attrs['YMIN']=0
group.attrs['YMAX']=data.shape[0]-1
group.attrs['subset_x0']=xsub[0]
group.attrs['subset_x1']=xsub[1]
group.attrs['subset_y0']=ysub[0]
group.attrs['subset_y1']=ysub[1]
if 'X_FIRST' in h5file[k[0]].attrs.keys():
group.attrs['X_FIRST']=float(h5file[k[0]].attrs['X_FIRST']) + xsub[0]*float(h5file[k[0]].attrs['X_STEP'])
group.attrs['Y_FIRST']=float(h5file[k[0]].attrs['Y_FIRST']) + ysub[0]*float(h5file[k[0]].attrs['Y_STEP'])
h5file.close()
hfout.close()
elif ext in ['.unw','.cor','.hgt']:
a,p,r = readfile.read_float32(File)
try:
Latsub
Lonsub
try:
r['X_FIRST']
xsub=[0]*2
ysub=[0]*2
xsub[0]=int((Lonsub[0]-float(r['X_FIRST']))/float(r['X_STEP']))
xsub[1]=int((Lonsub[1]-float(r['X_FIRST']))/float(r['X_STEP']))
ysub[0]=int((Latsub[1]-float(r['Y_FIRST']))/float(r['Y_STEP']))
ysub[1]=int((Latsub[0]-float(r['Y_FIRST']))/float(r['Y_STEP']))
print 'Subseting geocoded',ext,' file with Latitude and Longitude...'
except:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage() ; sys.exit(1)
except:
Geo=0
a=a[ysub[0]:ysub[1],xsub[0]:xsub[1]]
p=p[ysub[0]:ysub[1],xsub[0]:xsub[1]]
print 'writing >>> '+outName
writefile.write_float32(p,outName)
r['FILE_LENGTH']=str(p.shape[0])
r['WIDTH']=str(p.shape[1])
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
r['subset_x0']=str(xsub[0])
r['subset_x1']=str(xsub[1])
r['subset_y0']=str(ysub[0])
r['subset_y1']=str(ysub[1])
try:
r['Y_FIRST']=str(float(r['Y_FIRST'])+ysub[0]*float(r['Y_STEP']))
r['X_FIRST']=str(float(r['X_FIRST'])+xsub[0]*float(r['X_STEP']))
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
elif ext== '.dem':
d,r = readfile.read_dem(File)
try:
Latsub
Lonsub
# print Latsub
try:
r['X_FIRST']
xsub=[0]*2
ysub=[0]*2
xsub[0]=int((Lonsub[0]-float(r['X_FIRST']))/float(r['X_STEP']))
xsub[1]=int((Lonsub[1]-float(r['X_FIRST']))/float(r['X_STEP']))
ysub[0]=int((Latsub[1]-float(r['Y_FIRST']))/float(r['Y_STEP']))
ysub[1]=int((Latsub[0]-float(r['Y_FIRST']))/float(r['Y_STEP']))
print 'Subseting',ext,' file with Latitude and Longitude...'
except:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage() ; sys.exit(1)
except:
Geo=0
d=d[ysub[0]:ysub[1],xsub[0]:xsub[1]]
print 'writing >>> '+outName
writefile.write_dem(d,outName)
r['FILE_LENGTH']=str(d.shape[0])
r['WIDTH']=str(d.shape[1])
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
r['subset_x0']=str(xsub[0])
r['subset_x1']=str(xsub[1])
r['subset_y0']=str(ysub[0])
r['subset_y1']=str(ysub[1])
try:
r['Y_FIRST']=str(float(r['Y_FIRST'])+ysub[0]*float(r['Y_STEP']))
r['X_FIRST']=str(float(r['X_FIRST'])+xsub[0]*float(r['X_STEP']))
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
elif ext in ['.jpeg','jpg','png']:
import Image
im = Image.open(File)
try:
r=readfile.read_rsc_file(File+'.rsc')
except:
sys.exit(1)
try:
Latsub
Lonsub
try:
r['X_FIRST']
xsub=[0]*2
ysub=[0]*2
xsub[0]=int((Lonsub[0]-float(r['X_FIRST']))/float(r['X_STEP']))
xsub[1]=int((Lonsub[1]-float(r['X_FIRST']))/float(r['X_STEP']))
ysub[0]=int((Latsub[1]-float(r['Y_FIRST']))/float(r['Y_STEP']))
ysub[1]=int((Latsub[0]-float(r['Y_FIRST']))/float(r['Y_STEP']))
print 'Subseting geocoded',ext,' file with Latitude and Longitude...'
except:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage() ; sys.exit(1)
except:
Geo=0
box = (xsub[0],ysub[0],xsub[1],ysub[1])
output_img = im.crop(box)
print 'writing >>> '+outName
output_img.save(outName)
# try:
# r=readfile.read_rsc_file(File+'.rsc')
# except:
# sys.exit(1)
r['FILE_LENGTH']=str(ysub[1]-ysub[0])
r['WIDTH']=str(xsub[1]-xsub[0])
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
r['subset_x0']=str(xsub[0])
r['subset_x1']=str(xsub[1])
r['subset_y0']=str(ysub[0])
r['subset_y1']=str(ysub[1])
try:
r['Y_FIRST']=str(float(r['Y_FIRST'])+ysub[0]*float(r['Y_STEP']))
r['X_FIRST']=str(float(r['X_FIRST'])+xsub[0]*float(r['X_STEP']))
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
def main(argv):
try:
opts, args = getopt.getopt(
argv, "h:D:O:G:S:f:m:M:l:u:s:c:e:d:r:p:w:i:j:t:R:a:b:k:x:y:")
except getopt.GetoptError:
Usage()
sys.exit(1)
flip_lr = 'no'
flip_ud = 'no'
disp_geo = 'no'
font_size = 8
color_map = 'jet'
figs_rows = 5
figs_cols = 8
rewrapping = 'yes'
allData2display = 'yes'
Wspace = 0.1
Hspace = 0.1
title = 'out'
# title = 'None'
showRef = 'yes'
ref_color = 'k'
ref_symbol = 's'
ref_size = 10
dip_opposite = 'no'
saveFig = 'no'
if opts == []:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
Usage()
sys.exit()
elif opt == '-f':
File = arg
elif opt == '-D':
demFile = arg
elif opt == '-m':
min = float(arg)
elif opt == '-M':
max = float(arg)
elif opt == '-l':
flip_lr = arg
elif opt == '-u':
flip_ud = arg
elif opt == '-s':
font_size = int(arg)
elif opt == '-c':
color_map = arg
elif opt == '-e':
epoch_number = int(arg)
allData2display = 'no'
elif opt == '-d':
epoch_date = arg
allData2display = 'no'
elif opt == '-r':
figs_rows = int(arg)
elif opt == '-p':
figs_cols = int(arg)
elif opt == '-w':
rewrapping = arg
elif opt == '-i':
Wspace = float(arg)
elif opt == '-j':
Hspace = float(arg)
elif opt == '-t':
title = arg
elif opt == '-R':
showRef = arg
elif opt == '-a':
ref_color = arg
elif opt == '-b':
ref_symbol = arg
elif opt == 'k':
ref_size = int(arg)
elif opt == '-x':
win_x = arg
elif opt == '-y':
win_y = arg
elif opt == '-G':
disp_geo = arg
elif opt == '-O':
dip_opposite = arg
elif opt == '-S':
saveFig = arg
h5file = h5py.File(File, 'r')
k = h5file.keys()
print k
if color_map == 'hsv':
################################################
cdict1 = {
'red': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (0.6, 1.0, 1.0),
(0.8, 1.0, 1.0), (1.0, 0.5, 0.5)),
'green': ((0.0, 0.0, 0.0), (0.2, 0.0, 0.0), (0.4, 1.0, 1.0),
(0.6, 1.0, 1.0), (0.8, 0.0, 0.0), (1.0, 0.0, 0.0)),
'blue': (
(0.0, 0.5, .5),
(0.2, 1.0, 1.0),
(0.4, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0),
)
}
ccmap = LinearSegmentedColormap('BlueRed1', cdict1)
################################################
else:
ccmap = plt.get_cmap(color_map)
####################################################################
####################################################################
# if k[0]=='velocity' or k[0]=='temporal_coherence' or k[0]=='rmse':
if len(k) == 1 and k[0] in ('dem', 'velocity', 'mask',
'temporal_coherence', 'rmse'):
dset = h5file[k[0]].get(k[0])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
if dip_opposite in ('yes', 'Yes', 'Y', 'y', 'YES'):
data = -1 * data
try:
xref = h5file[k[0]].attrs['ref_x']
yref = h5file[k[0]].attrs['ref_y']
except:
print 'No reference point'
# Yunjun, Mar 2015
try:
xref = xref - h5file[k[0]].attrs['subset_x0']
yref = yref - h5file[k[0]].attrs['subset_y0']
except:
print 'No subset'
try:
ullon = float(h5file[k[0]].attrs['X_FIRST'])
ullat = float(h5file[k[0]].attrs['Y_FIRST'])
lon_step = float(h5file[k[0]].attrs['X_STEP'])
lat_step = float(h5file[k[0]].attrs['Y_STEP'])
lon_unit = h5file[k[0]].attrs['Y_UNIT']
lat_unit = h5file[k[0]].attrs['X_UNIT']
llcrnrlon = ullon
llcrnrlat = ullat + lat_step * data.shape[0]
urcrnrlon = ullon + lon_step * data.shape[1]
urcrnrlat = ullat
geocoord = 'yes'
print 'Input file is Geocoded'
except:
geocoord = 'no'
try:
win_x
wx = [int(i) for i in win_x.split()]
data = data[:, wx[0]:wx[1]]
xref = xref - wx[0]
except:
print 'No subste in x direction'
try:
win_y
wy = [int(i) for i in win_y.split()]
data = data[wy[0]:wy[1], :]
yref = yref - wy[0]
except:
print 'No subset in y direction'
try:
min
except:
min = np.nanmin(data)
try:
max
except:
max = np.nanmax(data)
if flip_lr == 'yes':
data = np.fliplr(data)
xref = np.shape(data)[1] - xref - 1
if flip_ud == 'yes':
data = np.flipud(data)
yref = np.shape(data)[0] - yref - 1
try:
demFile
# amp,dem,demRsc = readfile.read_float32(demFile)
if os.path.basename(demFile).split('.')[1] == 'hgt':
amp, dem, demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1] == 'dem':
dem, demRsc = readfile.read_dem(demFile)
try:
win_x
wx = [int(i) for i in win_x.split()]
dem = dem[:, wx[0]:wx[1]]
except:
print ''
try:
win_y
wy = [int(i) for i in win_y.split()]
dem = dem[wy[0]:wy[1], :]
except:
print ''
if flip_lr == 'yes':
dem = np.fliplr(dem)
if flip_ud == 'yes':
dem = np.flipud(dem)
cmap_dem = plt.get_cmap('gray')
if disp_geo in ('yes', 'Yes', 'Y', 'y',
'YES') and geocoord in ('yes', 'Yes', 'Y', 'y',
'YES'):
print 'display geo'
# from mpl_toolkits.basemap import Basemap
# m = Basemap(llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat,resolution='f', area_thresh=1., projection='cyl')
# m.imshow(ut.hillshade(dem,50.0), interpolation='nearest', origin='upper')
# m.drawcoastlines(color='w',linewidth=0.8)
# m.drawmapboundary() # draw a line around the map region
# m.drawrivers()
# m.drawparallels(numpy.arange(int(d1.min()), int(d1.max()), 1),linewidth=0.2,labels=[1,0,0,0])
# m.drawmeridians(numpy.arange(int(d0.min()), int(d0.max()), 1),linewidth=0.2,labels=[0,0,0,1])
else:
print 'Not GEO'
plt.imshow(ut.hillshade(dem, 50.0), cmap=cmap_dem)
except:
print 'No DEM file'
plt.imshow(data, cmap=ccmap, vmin=min, vmax=max)
plt.colorbar()
if k[0] == 'velocity':
plt.title('Velocity (m/yr)', fontsize=font_size)
figName = 'velocity.pdf'
elif k[0] == 'temporal_coherence':
plt.title('Temporal coherence', fontsize=font_size)
figName = 'temporal_coherence.pdf'
elif k[0] == 'dem':
plt.title('DEM error', fontsize=font_size)
figName = 'DEM_error.pdf'
elif k[0] == 'rmse':
plt.title('RMSE (m/yr)', fontsize=font_size)
figName = 'rmse.pdf'
elif k[0] == 'mask':
plt.title('Pixels with no valid value.', fontsize=font_size)
figName = 'mask.pdf'
if showRef == 'yes':
try:
refPoint = ref_color + ref_symbol
plt.plot(xref, yref, refPoint, ms=ref_size)
except:
print 'No reference point'
plt.xlim(0, np.shape(data)[1])
plt.ylim(np.shape(data)[0], 0)
if saveFig == 'yes':
plt.savefig(figName)
plt.show()
# plt.savefig('fig.pdf')
# fig = plt.figure()
# ax.imshow(data,vmin=min, vmax=max)
# ax.xaxis.label.set_fontsize(40)
####################################################################
####################################################################
if 'timeseries' in k and allData2display == 'yes':
if rewrapping == 'yes':
print 'rewrapping'
dateList = h5file['timeseries'].keys()
nfigs = figs_rows * figs_cols
ligram = len(dateList)
range2phase = 4 * np.pi / float(
h5file['timeseries'].attrs['WAVELENGTH'])
# range2phase=4*np.pi/0.056
print 'number of timeseries epochs to display:' + str(ligram)
kk = int(ligram / nfigs) + 1
ii = 0
for j in range(1, kk):
fig = plt.figure(j)
ii = (j - 1) * nfigs + 1
for i in range(ii, ii + nfigs):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
dset = h5file['timeseries'].get(dateList[i - 1])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
data = range2phase * data
# data=np.angle(np.exp(1j*data))
data = rewrap(data)
ax.imshow(data, cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(dateList[i - 1], fontsize=font_size)
elif title == 'in':
add_inner_title(ax, dateList[i - 1], loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
figName = k[0] + '_' + str(j) + '.pdf'
if saveFig in ['yes', 'Yes', 'y', 'YES']:
plt.savefig(figName)
fig = plt.figure(kk)
ii = (kk - 1) * nfigs + 1
for i in range(ii, ligram + 1):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
dset = h5file['timeseries'].get(dateList[i - 1])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
data = range2phase * data
# data=np.angle(np.exp(1j*data))
data = rewrap(data)
ax.imshow(data, cmap=ccmap)
ax.xaxis.label.set_fontsize(20)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(dateList[i - 1], fontsize=font_size)
elif title == 'in':
add_inner_title(ax, dateList[i - 1], loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
figName = k[0] + '_' + str(kk) + '.pdf'
if saveFig in ['yes', 'Yes', 'y', 'YES']:
plt.savefig(figName)
plt.show()
else:
print 'No rewrapping'
dateList = h5file['timeseries'].keys()
nfigs = figs_rows * figs_cols
ligram = len(dateList)
print 'number of timeseries epochs to display:' + str(ligram)
kk = int(ligram / nfigs) + 1
ii = 0
for j in range(1, kk):
fig = plt.figure(j)
ii = (j - 1) * nfigs + 1
for i in range(ii, ii + nfigs):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
data = h5file['timeseries'].get(dateList[i - 1])
try:
im = ax.imshow(data, cmap=ccmap, vmin=min, vmax=max)
# print 'here'
except:
im = ax.imshow(data, cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(dateList[i - 1], fontsize=font_size)
elif title == 'in':
add_inner_title(ax, dateList[i - 1], loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
fig = plt.figure(kk)
ii = (kk - 1) * nfigs + 1
for i in range(ii, ligram + 1):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
data = h5file['timeseries'].get(dateList[i - 1])
try:
im = ax.imshow(data, cmap=ccmap, vmin=min, vmax=max)
except:
im = ax.imshow(data, cmap=ccmap)
ax.xaxis.label.set_fontsize(20)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(dateList[i - 1], fontsize=font_size)
if title == 'in':
add_inner_title(ax, dateList[i - 1], loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
plt.show()
####################################################################
####################################################################
elif 'timeseries' in k and allData2display == 'no':
dateList = h5file['timeseries'].keys()
try:
epoch_number
except:
epoch_number = dateList.index(epoch_date)
range2phase = 4 * np.pi / float(
h5file['timeseries'].attrs['WAVELENGTH'])
# range2phase=4*np.pi/0.056
dset = h5file['timeseries'].get(dateList[epoch_number])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
if rewrapping == 'yes':
data = range2phase * data
# data=np.angle(np.exp(1j*data))
data = rewrap(data)
try:
min
except:
min = np.nanmin(data)
try:
max
except:
max = np.nanmax(data)
plt.imshow(data, cmap=ccmap, vmin=min, vmax=max)
plt.colorbar()
plt.show()
################################################################
################################################################
if k[0] in ('interferograms', 'coherence',
'wrapped') and allData2display == 'yes':
if k[0] in ('coherence', 'wrapped'):
rewrapping = 'no'
# color_map = 'gray'
# ccmap=plt.get_cmap(color_map)
if rewrapping == 'yes':
ifgramList = h5file[k[0]].keys()
nfigs = figs_rows * figs_cols
ligram = len(ifgramList)
print 'number of ' + k[0] + ' to display:' + str(ligram)
kk = int(ligram / nfigs) + 1
ii = 0
for j in range(1, kk):
fig = plt.figure(j)
ii = (j - 1) * nfigs + 1
for i in range(ii, ii + nfigs):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
dset = h5file[k[0]][ifgramList[i - 1]].get(ifgramList[i -
1])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
data = np.angle(np.exp(1j * data))
ax.imshow(data, cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(
h5file[k[0]][ifgramList[i - 1]].attrs['DATE12'],
fontsize=font_size)
elif title == 'in':
add_inner_title(
ax,
h5file[k[0]][ifgramList[i - 1]].attrs['DATE12'],
loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
fig = plt.figure(kk)
ii = (kk - 1) * nfigs + 1
for i in range(ii, ligram + 1):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
dset = h5file[k[0]][ifgramList[i - 1]].get(ifgramList[i - 1])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
data = np.angle(np.exp(1j * data))
ax.imshow(data, cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(h5file[k[0]][ifgramList[i -
1]].attrs['DATE12'],
fontsize=font_size)
elif title == 'in':
add_inner_title(
ax,
h5file[k[0]][ifgramList[i - 1]].attrs['DATE12'],
loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
plt.show()
else:
ifgramList = h5file[k[0]].keys()
nfigs = figs_rows * figs_cols
ligram = len(ifgramList)
print 'number of ' + k[0] + ' to display:' + str(ligram)
kk = int(ligram / nfigs) + 1
ii = 0
for j in range(1, kk):
fig = plt.figure(j)
ii = (j - 1) * nfigs + 1
for i in range(ii, ii + nfigs):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
print 'loading ' + ifgramList[i - 1]
dset = h5file[k[0]][ifgramList[i - 1]].get(ifgramList[i -
1])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
try:
ax.imshow(data, vmin=min, vmax=max, cmap=ccmap)
except:
ax.imshow(data, cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(
h5file[k[0]][ifgramList[i - 1]].attrs['DATE12'],
fontsize=font_size)
elif title == 'in':
add_inner_title(
ax,
h5file[k[0]][ifgramList[i - 1]].attrs['DATE12'],
loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
fig = plt.figure(kk)
ii = (kk - 1) * nfigs + 1
for i in range(ii, ligram + 1):
ax = fig.add_subplot(figs_rows, figs_cols, i - ii + 1)
print 'loading ' + ifgramList[i - 1]
dset = h5file[k[0]][ifgramList[i - 1]].get(ifgramList[i - 1])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
#data = h5file[k[0]][ifgramList[i-1]].get(ifgramList[i-1])
try:
ax.imshow(data, vmin=min, vmax=max, cmap=ccmap)
except:
ax.imshow(data, cmap=ccmap)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticks([])
if title == 'out':
ax.set_title(h5file[k[0]][ifgramList[i -
1]].attrs['DATE12'],
fontsize=font_size)
elif title == 'in':
add_inner_title(
ax,
h5file[k[0]][ifgramList[i - 1]].attrs['DATE12'],
loc=1)
fig.subplots_adjust(wspace=Wspace, hspace=Hspace)
plt.show()
####################################################################
####################################################################
elif k[0] in ('interferograms', 'coherence',
'wrapped') and allData2display == 'no':
if k[0] in ('coherence', 'wrapped'):
rewrapping == 'no'
ifgramList = h5file[k[0]].keys()
try:
epoch_number
except:
for i in range(len(ifgramList)):
if epoch_date in ifgramList[i]:
epoch_number = i
dset = h5file[k[0]][ifgramList[epoch_number]].get(
ifgramList[epoch_number])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
if rewrapping == 'yes':
data = np.angle(np.exp(1j * data))
if dip_opposite in ('yes', 'Yes', 'Y', 'y', 'YES'):
data = -1 * data
#DEM basemap
try:
demFile
if os.path.basename(demFile).split('.')[1] == 'hgt':
amp, dem, demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1] == 'dem':
dem, demRsc = readfile.read_dem(demFile)
try:
win_x
wx = [int(i) for i in win_x.split()]
dem = dem[:, wx[0]:wx[1]]
except:
print ''
try:
win_y
wy = [int(i) for i in win_y.split()]
dem = dem[wy[0]:wy[1], :]
except:
print ''
if flip_lr == 'yes':
dem = np.fliplr(dem)
if flip_ud == 'yes':
dem = np.flipud(dem)
cmap_dem = plt.get_cmap('gray')
if disp_geo in ('yes', 'Yes', 'Y', 'y',
'YES') and geocoord in ('yes', 'Yes', 'Y', 'y',
'YES'):
print 'display geo'
else:
print 'Not GEO'
plt.imshow(ut.hillshade(dem, 50.0), cmap=cmap_dem)
except:
print 'No DEM file'
try:
plt.imshow(data, cmap=ccmap, vmin=min, vmax=max)
except:
plt.imshow(data, cmap=ccmap)
plt.colorbar()
# plt.title(h5file[k[0]][ifgramList[epoch_number]].attrs['DATE12'],fontsize=font_size)
plt.title(ifgramList[epoch_number], fontsize=font_size)
plt.show()
################################################################
################################################################
h5file.close()
def main(argv):
maskThr=0.7
try:
opts, args = getopt.getopt(argv,"f:d:p:m:M:t:")
except getopt.GetoptError:
Usage() ; sys.exit(1)
for opt,arg in opts:
if opt == '-f':
timeSeriesFile = arg
elif opt == '-d':
demFile = arg
elif opt == '-p':
p = int(arg)
elif opt == '-m':
maskFile=arg
print maskFile
elif opt == '-M':
maskThr=float(arg)
elif opt == '-t':
corThr=float(arg)
try:
timeSeriesFile
demFile
except:
Usage() ; sys.exit(1)
try:
p
except:
p=1
try:
maskFile
except:
maskFile='Mask.h5'
print 'Mask file: ' + maskFile
###################################################
h5Mask=h5py.File(maskFile)
kMask=h5Mask.keys()
dset = h5Mask[kMask[0]].get(kMask[0])
Mask = dset[0:dset.shape[0],0:dset.shape[1]]
# Mask[600:dset.shape[0],:]=0
Mask=Mask.flatten(1)
print maskFile
print maskThr
if kMask[0]=='mask':
ndx = Mask !=0
elif kMask[0]=='temporal_coherence':
ndx = Mask >maskThr
else:
print 'Mask file not recognized!'
Usage();sys.exit(1)
h5Mask.close()
###################################################
h5timeseries = h5py.File(timeSeriesFile)
yref=h5timeseries['timeseries'].attrs['ref_y']
xref=h5timeseries['timeseries'].attrs['ref_x']
###################################################
if os.path.basename(demFile).split('.')[1]=='hgt':
amp,dem,demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1]=='dem':
dem,demRsc = readfile.read_dem(demFile)
dem=dem-dem[yref][xref]
# try:
print 'considering the look angle of each resolution cell...'
near_LA=float(h5timeseries['timeseries'].attrs['LOOK_REF1'])
far_LA=float(h5timeseries['timeseries'].attrs['LOOK_REF2'])
Length,Width=np.shape(dem)
LA=np.linspace(near_LA,far_LA,Width)
LA=np.tile(LA,[Length,1])
dem=dem/np.cos(LA*np.pi/180.0)
# except:
# print 'Look angle is not considered'
dem=dem.flatten(1)
print np.shape(dem)
###################################################
if p==1:
A=np.vstack((dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem,np.ones(len(dem)))).T
elif p==2:
A=np.vstack((dem[ndx]**2,dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem**2,dem,np.ones(len(dem)))).T
elif p==3:
A = np.vstack((dem[ndx]**3,dem[ndx]**2,dem[ndx],np.ones(len(dem[ndx])))).T
B = np.vstack((dem**3,dem**2,dem,np.ones(len(dem)))).T
print np.shape(A)
Ainv=np.linalg.pinv(A)
###################################################
print 'Estimating the tropospheric effect using the differences of the subsequent epochs and DEM'
dateList = h5timeseries['timeseries'].keys()
nrows,ncols=np.shape(h5timeseries['timeseries'].get(dateList[0]))
PAR_EPOCH_DICT_2={}
par_diff_Dict={}
Correlation_Dict={}
Correlation_Dict[dateList[0]]=0
Correlation_diff_Dict={}
for i in range(len(dateList)-1):
dset1 = h5timeseries['timeseries'].get(dateList[i])
dset2 = h5timeseries['timeseries'].get(dateList[i+1])
data1 = dset1[0:dset1.shape[0],0:dset1.shape[1]]
data2 = dset2[0:dset2.shape[0],0:dset2.shape[1]]
d = dset2[0:dset2.shape[0],0:dset2.shape[1]] - dset1[0:dset1.shape[0],0:dset1.shape[1]]
del dset1
del dset2
d=d.flatten(1)
data1=data1.flatten(1)
data2=data2.flatten(1)
##############################
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
print 'correlation of dem with :'
print '******************************'
print dateList[i]
C1=np.zeros([2,len(dem[ndx])])
C1[0][:]=dem[ndx]
C1[1][:]=data1[ndx]
print np.corrcoef(C1)[0][1]
# Correlation_Dict[dateList[i]]=np.corrcoef(C1)[0][1]
# print '******************************'
print dateList[i+1]
C2=np.zeros([2,len(dem[ndx])])
C2[0][:]=dem[ndx]
C2[1][:]=data2[ndx]
print np.corrcoef(C2)[0][1]
Correlation_Dict[dateList[i+1]]=np.corrcoef(C2)[0][1]
# print '******************************'
print dateList[i]+'-'+dateList[i+1]
C=np.zeros([2,len(dem[ndx])])
C[0][:]=dem[ndx]
C[1][:]=d[ndx]
print np.corrcoef(C)[0][1]
print '******************************'
Correlation_diff_Dict[dateList[i]+'-'+dateList[i+1]]=np.corrcoef(C)[0][1]
##############################
# try:
# if np.corrcoef(C)[0][1] >= corThr:
# par=np.dot(Ainv,d[ndx])
# else:
# par=[0,0]
# except:
par=np.dot(Ainv,d[ndx])
# del d
par_diff_Dict[dateList[i]+'-'+dateList[i+1]]=par
# print par
try:
if np.abs(np.corrcoef(C2)[0][1]) >= corThr:
PAR2=np.dot(Ainv,data2[ndx])
else:
#PAR2=[0,0]
PAR2=list(np.zeros(p+1))
except:
PAR2=np.dot(Ainv,data2[ndx])
PAR_EPOCH_DICT_2[dateList[i+1]]=PAR2
# print PAR2
###################################################
print'****************************************'
print 'Correlation of DEM with each time-series epoch:'
average_phase_height_cor=0
for date in dateList:
print date + ' : '+str(Correlation_Dict[date])
# print date + ' : ' + str(Correlation_Dict[date])
average_phase_height_cor=average_phase_height_cor+np.abs(Correlation_Dict[date])
print'****************************************'
print'****************************************'
print ''
print 'Average Correlation of DEM with time-series epochs: ' + str(average_phase_height_cor/(len(dateList)-1))
print ''
print '****************************************'
print'****************************************'
# print 'Correlation of DEM with epoch differences'
# for key , value in Correlation_diff_Dict.iteritems():
# print key+' : '+str(value)
# print'****************************************'
# print 'Estimated parameters for each time-series epoch:'
# for date in dateList:
# print date + ' : ' + str(PAR_EPOCH_DICT_2[date])
# print'****************************************'
###################################################
#fig=plt.figure(1)
#ax = fig.add_subplot(3,1,1)
#ax.plot(dem[ndx],data1[ndx],'o',ms=1)
#ax = fig.add_subplot(3,1,2)
#ax.plot(dem[ndx],data2[ndx],'o',ms=1)
#ax = fig.add_subplot(3,1,3)
#ax.plot(dem[ndx],d[ndx],'o',ms=1)
#plt.show()
###################################################
# print par_diff_Dict
par_epoch_Dict={}
par_epoch_Dict[dateList[1]]=par_diff_Dict[dateList[0]+'-'+dateList[1]]
for i in range(2,len(dateList)):
par_epoch_Dict[dateList[i]]=par_epoch_Dict[dateList[i-1]]+par_diff_Dict[dateList[i-1]+'-'+dateList[i]]
print '###############################'
#for key , value in PAR_EPOCH_DICT_2.iteritems():
# print key+' : '+str(value) +' | '+str(par_epoch_Dict[key])
# print PAR_EPOCH_DICT_2
# print par_epoch_Dict
print '###############################'
yref=h5timeseries['timeseries'].attrs['ref_y']
xref=h5timeseries['timeseries'].attrs['ref_x']
print 'removing the tropospheric delay from each epoch and writing '+timeSeriesFile.split('.')[0]+'_tropCor.h5:'
h5tropCor = h5py.File(timeSeriesFile.split('.')[0]+'_tropCor.h5','w')
group = h5tropCor.create_group('timeseries')
dset = group.create_dataset(dateList[0], data=h5timeseries['timeseries'].get(dateList[0]), compression='gzip')
for date in dateList:
if not date in h5tropCor['timeseries']:
dset = h5timeseries['timeseries'].get(date)
data = dset[0:dset.shape[0],0:dset.shape[1]]
# par=par_epoch_Dict[date]
par=PAR_EPOCH_DICT_2[date]
# print np.shape(B)
# print np.shape(par)
tropo_effect=np.reshape(np.dot(B,par),[dset.shape[1],dset.shape[0]]).T
tropo_effect=tropo_effect-tropo_effect[yref][xref]
dset = group.create_dataset(date, data=data-tropo_effect, compression='gzip')
for key,value in h5timeseries['timeseries'].attrs.iteritems():
group.attrs[key] = value
dset1 = h5timeseries['mask'].get('mask')
group=h5tropCor.create_group('mask')
dset = group.create_dataset('mask', data=dset1, compression='gzip')
h5tropCor.close()
h5timeseries.close()
def main(argv):
#outName='subsetIgrams.h5'
try:
opts, args = getopt.getopt(argv, "h:f:x:y:o:l:L:")
except getopt.GetoptError:
print 'Error while getting args'
Usage()
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
Usage()
sys.exit()
elif opt == '-f':
File = arg
elif opt == '-y':
ysub = [int(i) for i in arg.split(':')]
ysub.sort()
elif opt == '-x':
xsub = [int(i) for i in arg.split(':')]
xsub.sort()
elif opt == '-o':
outName = arg
elif opt == '-l':
Latsub = [float(i) for i in arg.split(':')]
Latsub.sort()
elif opt == '-L':
Lonsub = [float(i) for i in arg.split(':')]
Lonsub.sort()
#####################################################
try:
File
xsub
ysub
except:
try:
File
Latsub
Lonsub
except:
Usage()
sys.exit(1)
try:
outName
except:
outName = 'subset_' + File
ext = os.path.splitext(File)[1]
if ext == '.h5':
try:
h5file = h5py.File(File, 'r')
except:
Usage()
sys.exit(1)
k = h5file.keys()
# convert LatLon to xy for geocoded file
try:
Latsub
Lonsub
if 'X_FIRST' in h5file[k[0]].attrs.keys():
xsub = [0] * 2
ysub = [0] * 2
xsub[0] = int(
(Lonsub[0] - float(h5file[k[0]].attrs['X_FIRST'])) /
float(h5file[k[0]].attrs['X_STEP']))
xsub[1] = int(
(Lonsub[1] - float(h5file[k[0]].attrs['X_FIRST'])) /
float(h5file[k[0]].attrs['X_STEP']))
ysub[0] = int(
(Latsub[1] - float(h5file[k[0]].attrs['Y_FIRST'])) /
float(h5file[k[0]].attrs['Y_STEP']))
ysub[1] = int(
(Latsub[0] - float(h5file[k[0]].attrs['Y_FIRST'])) /
float(h5file[k[0]].attrs['Y_STEP']))
print 'Subseting geocoded', ext, ' file with Latitude and Longitude...'
elif 'X_FIRST' in h5file[k[0]][h5file[k[0]].keys()[0]].attrs.keys(
): # for geocoded interferograms/coherence
igramList = h5file[k[0]].keys()
xsub = [0] * 2
ysub = [0] * 2
xsub[0] = int(
(Lonsub[0] -
float(h5file[k[0]][igramList[0]].attrs['X_FIRST'])) /
float(h5file[k[0]][igramList[0]].attrs['X_STEP']))
xsub[1] = int(
(Lonsub[1] -
float(h5file[k[0]][igramList[0]].attrs['X_FIRST'])) /
float(h5file[k[0]][igramList[0]].attrs['X_STEP']))
ysub[0] = int(
(Latsub[1] -
float(h5file[k[0]][igramList[0]].attrs['Y_FIRST'])) /
float(h5file[k[0]][igramList[0]].attrs['Y_STEP']))
ysub[1] = int(
(Latsub[0] -
float(h5file[k[0]][igramList[0]].attrs['Y_FIRST'])) /
float(h5file[k[0]][igramList[0]].attrs['Y_STEP']))
print 'Subseting geocoded', ext, ' file with Latitude and Longitude...'
else:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage()
sys.exit(1)
except:
Geo = 0
# k=h5file.keys()
if 'interferograms' in k:
igramList = h5file['interferograms'].keys()
h5out = h5py.File(outName, 'w')
gg = h5out.create_group('interferograms')
for igram in igramList:
print igram
dset1 = h5file['interferograms'][igram].get(igram)
group = gg.create_group(igram)
dset = group.create_dataset(igram,
data=dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]],
compression='gzip')
for key, value in h5file['interferograms'][
igram].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH'] = shape(dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]])[0]
group.attrs['WIDTH'] = shape(dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]])[1]
group.attrs['subset_x0'] = xsub[0]
group.attrs['subset_x1'] = xsub[1]
group.attrs['subset_y0'] = ysub[0]
group.attrs['subset_y1'] = ysub[1]
if 'X_FIRST' in h5file['interferograms'][igram].attrs.keys():
group.attrs['X_FIRST'] = float(
h5file['interferograms']
[igram].attrs['X_FIRST']) + xsub[0] * float(
h5file['interferograms'][igram].attrs['X_STEP'])
group.attrs['Y_FIRST'] = float(
h5file['interferograms']
[igram].attrs['Y_FIRST']) + ysub[0] * float(
h5file['interferograms'][igram].attrs['Y_STEP'])
gm = h5out.create_group('mask')
try:
Mset = h5file['mask'].get('mask')
dset = gm.create_dataset('mask',
data=Mset[ysub[0]:ysub[1],
xsub[0]:xsub[1]],
compression='gzip')
except:
print 'No group for mask found! It may cause problem in other processing steps.'
try:
Cset = h5file['meanCoherence'].get('meanCoherence')
gm = h5out.create_group('meanCoherence')
dset = gm.create_dataset('meanCoherence',
data=Cset[ysub[0]:ysub[1],
xsub[0]:xsub[1]],
compression='gzip')
except:
print 'No average coherence found in the File'
elif k[0] in ('coherence', 'wrapped'):
corList = h5file[k[0]].keys()
h5out = h5py.File(outName, 'w')
gg = h5out.create_group(k[0])
for cor in corList:
print cor
dset1 = h5file[k[0]][cor].get(cor)
group = gg.create_group(cor)
dset = group.create_dataset(cor,
data=dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]],
compression='gzip')
for key, value in h5file[k[0]][cor].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH'] = shape(dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]])[0]
group.attrs['WIDTH'] = shape(dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]])[1]
group.attrs['subset_x0'] = xsub[0]
group.attrs['subset_x1'] = xsub[1]
group.attrs['subset_y0'] = ysub[0]
group.attrs['subset_y1'] = ysub[1]
if 'X_FIRST' in h5file[k[0]][cor].attrs.keys():
group.attrs['X_FIRST'] = float(
h5file[k[0]][cor].attrs['X_FIRST']) + xsub[0] * float(
h5file[k[0]][cor].attrs['X_STEP'])
group.attrs['Y_FIRST'] = float(
h5file[k[0]][cor].attrs['Y_FIRST']) + ysub[0] * float(
h5file[k[0]][cor].attrs['Y_STEP'])
elif 'timeseries' in h5file.keys():
dateList = h5file['timeseries'].keys()
h5out = h5py.File(outName, 'w')
group = h5out.create_group('timeseries')
for d in dateList:
print d
dset1 = h5file['timeseries'].get(d)
dset = group.create_dataset(d,
data=dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]],
compression='gzip')
for key, value in h5file['timeseries'].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH'] = shape(dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]])[0]
group.attrs['WIDTH'] = shape(dset1[ysub[0]:ysub[1],
xsub[0]:xsub[1]])[1]
group.attrs['subset_x0'] = xsub[0]
group.attrs['subset_x1'] = xsub[1]
group.attrs['subset_y0'] = ysub[0]
group.attrs['subset_y1'] = ysub[1]
if 'X_FIRST' in h5file['timeseries'].attrs.keys():
group.attrs['X_FIRST'] = float(
h5file['timeseries'].attrs['X_FIRST']) + xsub[0] * float(
h5file['timeseries'].attrs['X_STEP'])
group.attrs['Y_FIRST'] = float(
h5file['timeseries'].attrs['Y_FIRST']) + ysub[0] * float(
h5file['timeseries'].attrs['Y_STEP'])
h5file.close()
h5out.close()
elif 'temporal_coherence' in h5file.keys(
) or 'velocity' in h5file.keys() or 'mask' in h5file.keys(
) or 'rmse' in h5file.keys():
print 'writing >>> ' + outName
dset = h5file[k[0]].get(k[0])
data = dset[ysub[0]:ysub[1], xsub[0]:xsub[1]]
hfout = h5py.File(outName, 'w')
group = hfout.create_group(k[0])
group.create_dataset(k[0], data=data, compression='gzip')
for key, value in h5file[k[0]].attrs.iteritems():
group.attrs[key] = value
group.attrs['FILE_LENGTH'] = data.shape[0]
group.attrs['WIDTH'] = data.shape[1]
group.attrs['XMIN'] = 0
group.attrs['XMAX'] = data.shape[1] - 1
group.attrs['YMIN'] = 0
group.attrs['YMAX'] = data.shape[0] - 1
group.attrs['subset_x0'] = xsub[0]
group.attrs['subset_x1'] = xsub[1]
group.attrs['subset_y0'] = ysub[0]
group.attrs['subset_y1'] = ysub[1]
if 'X_FIRST' in h5file[k[0]].attrs.keys():
group.attrs['X_FIRST'] = float(
h5file[k[0]].attrs['X_FIRST']) + xsub[0] * float(
h5file[k[0]].attrs['X_STEP'])
group.attrs['Y_FIRST'] = float(
h5file[k[0]].attrs['Y_FIRST']) + ysub[0] * float(
h5file[k[0]].attrs['Y_STEP'])
h5file.close()
hfout.close()
elif ext in ['.unw', '.cor', '.hgt']:
a, p, r = readfile.read_float32(File)
try:
Latsub
Lonsub
try:
r['X_FIRST']
xsub = [0] * 2
ysub = [0] * 2
xsub[0] = int(
(Lonsub[0] - float(r['X_FIRST'])) / float(r['X_STEP']))
xsub[1] = int(
(Lonsub[1] - float(r['X_FIRST'])) / float(r['X_STEP']))
ysub[0] = int(
(Latsub[1] - float(r['Y_FIRST'])) / float(r['Y_STEP']))
ysub[1] = int(
(Latsub[0] - float(r['Y_FIRST'])) / float(r['Y_STEP']))
print 'Subseting geocoded', ext, ' file with Latitude and Longitude...'
except:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage()
sys.exit(1)
except:
Geo = 0
a = a[ysub[0]:ysub[1], xsub[0]:xsub[1]]
p = p[ysub[0]:ysub[1], xsub[0]:xsub[1]]
print 'writing >>> ' + outName
writefile.write_float32(p, outName)
r['FILE_LENGTH'] = str(p.shape[0])
r['WIDTH'] = str(p.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
r['subset_x0'] = str(xsub[0])
r['subset_x1'] = str(xsub[1])
r['subset_y0'] = str(ysub[0])
r['subset_y1'] = str(ysub[1])
try:
r['Y_FIRST'] = str(
float(r['Y_FIRST']) + ysub[0] * float(r['Y_STEP']))
r['X_FIRST'] = str(
float(r['X_FIRST']) + xsub[0] * float(r['X_STEP']))
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext == '.dem':
d, r = readfile.read_dem(File)
try:
Latsub
Lonsub
# print Latsub
try:
r['X_FIRST']
xsub = [0] * 2
ysub = [0] * 2
xsub[0] = int(
(Lonsub[0] - float(r['X_FIRST'])) / float(r['X_STEP']))
xsub[1] = int(
(Lonsub[1] - float(r['X_FIRST'])) / float(r['X_STEP']))
ysub[0] = int(
(Latsub[1] - float(r['Y_FIRST'])) / float(r['Y_STEP']))
ysub[1] = int(
(Latsub[0] - float(r['Y_FIRST'])) / float(r['Y_STEP']))
print 'Subseting', ext, ' file with Latitude and Longitude...'
except:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage()
sys.exit(1)
except:
Geo = 0
d = d[ysub[0]:ysub[1], xsub[0]:xsub[1]]
print 'writing >>> ' + outName
writefile.write_dem(d, outName)
r['FILE_LENGTH'] = str(d.shape[0])
r['WIDTH'] = str(d.shape[1])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
r['subset_x0'] = str(xsub[0])
r['subset_x1'] = str(xsub[1])
r['subset_y0'] = str(ysub[0])
r['subset_y1'] = str(ysub[1])
try:
r['Y_FIRST'] = str(
float(r['Y_FIRST']) + ysub[0] * float(r['Y_STEP']))
r['X_FIRST'] = str(
float(r['X_FIRST']) + xsub[0] * float(r['X_STEP']))
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
elif ext in ['.jpeg', 'jpg', 'png']:
import Image
im = Image.open(File)
try:
r = readfile.read_rsc_file(File + '.rsc')
except:
sys.exit(1)
try:
Latsub
Lonsub
try:
r['X_FIRST']
xsub = [0] * 2
ysub = [0] * 2
xsub[0] = int(
(Lonsub[0] - float(r['X_FIRST'])) / float(r['X_STEP']))
xsub[1] = int(
(Lonsub[1] - float(r['X_FIRST'])) / float(r['X_STEP']))
ysub[0] = int(
(Latsub[1] - float(r['Y_FIRST'])) / float(r['Y_STEP']))
ysub[1] = int(
(Latsub[0] - float(r['Y_FIRST'])) / float(r['Y_STEP']))
print 'Subseting geocoded', ext, ' file with Latitude and Longitude...'
except:
print 'Not geocoded file, cannot be subseted with LatLon.'
Usage()
sys.exit(1)
except:
Geo = 0
box = (xsub[0], ysub[0], xsub[1], ysub[1])
output_img = im.crop(box)
print 'writing >>> ' + outName
output_img.save(outName)
# try:
# r=readfile.read_rsc_file(File+'.rsc')
# except:
# sys.exit(1)
r['FILE_LENGTH'] = str(ysub[1] - ysub[0])
r['WIDTH'] = str(xsub[1] - xsub[0])
r['XMAX'] = str(int(r['WIDTH']) - 1)
r['YMAX'] = str(int(r['FILE_LENGTH']) - 1)
r['subset_x0'] = str(xsub[0])
r['subset_x1'] = str(xsub[1])
r['subset_y0'] = str(ysub[0])
r['subset_y1'] = str(ysub[1])
try:
r['Y_FIRST'] = str(
float(r['Y_FIRST']) + ysub[0] * float(r['Y_STEP']))
r['X_FIRST'] = str(
float(r['X_FIRST']) + xsub[0] * float(r['X_STEP']))
except:
Geo = 0
f = open(outName + '.rsc', 'w')
for k in r.keys():
f.write(k + ' ' + r[k] + '\n')
f.close()
def main(argv):
color_map = 'jet'
disp_opposite = 'no'
try:
opts, args = getopt.getopt(argv, "h:f:d:o:x:y:m:M:i:c:")
except getopt.GetoptError:
Usage()
sys.exit(1)
if opts == []:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt in ("-h", "--help"):
Usage()
sys.exit()
elif opt == '-f':
File = arg
elif opt == '-d':
demFile = arg
elif opt == '-m':
Vmin = float(arg)
elif opt == '-M':
Vmax = float(arg)
elif opt == '-x':
winx = arg.split(':')
elif opt == '-y':
winy = arg.split(':')
elif opt == '-o':
outName = arg
elif opt == '-i':
disp_opposite = arg
elif opt == '-c':
color_map = arg
h5file = h5py.File(File, 'r')
k = h5file.keys()
print k[0]
# ccmap=plt.get_cmap(color_map)
################################################
cdict1 = {
'red': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (0.6, 1.0, 1.0),
(0.8, 1.0, 1.0), (1.0, 0.5, 0.5)),
'green': ((0.0, 0.0, 0.0), (0.2, 0.0, 0.0), (0.4, 1.0, 1.0),
(0.6, 1.0, 1.0), (0.8, 0.0, 0.0), (1.0, 0.0, 0.0)),
'blue': (
(0.0, 0.5, .5),
(0.2, 1.0, 1.0),
(0.4, 1.0, 1.0),
(0.5, 0.0, 0.0),
(1.0, 0.0, 0.0),
)
}
if color_map == 'pysar_hsv':
ccmap = LinearSegmentedColormap('BlueRed1', cdict1)
else:
ccmap = plt.get_cmap(color_map)
print 'colormap is : ' + color_map
################################################
dset = h5file[k[0]].get(k[0])
data = dset[0:dset.shape[0], 0:dset.shape[1]]
if disp_opposite in ('yes', 'Yes', 'Y', 'y', 'YES'):
data = -1 * data
try:
xref = h5file[k[0]].attrs['ref_x']
yref = h5file[k[0]].attrs['ref_y']
except:
print 'No reference point'
try:
ullon = float(h5file[k[0]].attrs['X_FIRST'])
ullat = float(h5file[k[0]].attrs['Y_FIRST'])
lon_step = float(h5file[k[0]].attrs['X_STEP'])
lat_step = float(h5file[k[0]].attrs['Y_STEP'])
lon_unit = h5file[k[0]].attrs['Y_UNIT']
lat_unit = h5file[k[0]].attrs['X_UNIT']
llcrnrlon = ullon
llcrnrlat = ullat + lat_step * data.shape[0]
urcrnrlon = ullon + lon_step * data.shape[1]
urcrnrlat = ullat
geocoord = 'yes'
print 'Input file is Geocoded'
except:
geocoord = 'no'
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
m = Basemap(llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
resolution='l',
area_thresh=1.,
projection='cyl',
suppress_ticks=False,
ax=ax)
print demFile
demFile
if os.path.basename(demFile).split('.')[1] == 'hgt':
amp, dem, demRsc = readfile.read_float32(demFile)
elif os.path.basename(demFile).split('.')[1] == 'dem':
dem, demRsc = readfile.read_dem(demFile)
#################################################################
try:
winx
wx = [int(i) for i in win_x.split()]
dem = dem[:, wx[0]:wx[1]]
data = data[:, wx[0]:wx[1]]
ullon = float(h5file[k[0]].attrs['X_FIRST']) + wx[0]
llcrnrlon = ullon
urcrnrlon = ullon + lon_step * data.shape[1]
except:
print ''
try:
winy
wy = [int(i) for i in winy.split()]
dem = dem[wy[0]:wy[1], :]
data = data[wy[0]:wy[1], :]
except:
print ''
################################################################
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
m = Basemap(llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
resolution='l',
area_thresh=1.,
projection='cyl',
suppress_ticks=False,
ax=ax)
cmap_dem = plt.get_cmap('gray')
m.imshow(ut.hillshade(np.flipud(dem), 50.0), cmap=cmap_dem)
try:
im = m.imshow(np.flipud(data), vmin=Vmin, vmax=Vmax, cmap=ccmap)
# cb = m.colorbar(im,"right", size="5%", pad='2%')
except:
im = m.imshow(np.flipud(data))
# cb = m.colorbar(im,"right", size="5%", pad='2%')
# m.bluemarble()
# cb = m.colorbar(im,"right", size="5%", pad='2%')
# parallels = np.arange(31.,34,0.5)
# m.drawparallels(parallels,labels=[1,0,0,1],linewidth=0.0)
# meridians = np.arange(-115.,-112.,0.5)
# m.drawmeridians(meridians,labels=[1,0,0,1],linewidth=0.0)
# m.drawmapscale()
# m = Basemap(llcrnrlon=-110.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
# projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
# resolution ='l',area_thresh=1000.)
# m.drawcoastlines()
# m.drawcountries()
# m.drawmapboundary(fill_color='#99ffff')
# m.fillcontinents(color='#cc9966',lake_color='#99ffff')
# m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])
# m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])
# plt.title('Atlantic Hurricane Tracks (Storms Reaching Category 4, 1851-2004)')
try:
figName = outName
except:
outName = os.path.basename(File).replace('.h5', '')
figName = outName + '.png'
plt.savefig(figName, pad_inches=0.0)
# plt.show()
h5file.close()
def main(argv):
try:
file=argv[0]
alks=float(argv[1])
rlks=float(argv[2])
except:
Usage();sys.exit(1)
ext = os.path.splitext(file)[1]
outName=file.split('.')[0]+'_a'+str(int(alks))+'lks_r'+str(int(rlks))+'lks'+ext
if ext == '.int' or ext == '.slc':
a,p,r = readfile.read_complex64(file)
plks=multilook(p,alks,rlks)
alks=multilook(a,alks,rlks)
r['FILE_LENGTH']=str(dlks.shape[0])
r['WIDTH']=str(dlks.shape[1])
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP']=str(float(r['Y_STEP'])*alks)
r['X_STEP']=str(float(r['X_STEP'])*rlks)
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
elif ext == '.unw' or ext == '.cor' or ext == '.hgt':
a,p,r = readfile.read_float32(file)
plks=multilook(p,alks,rlks)
alks=multilook(a,alks,rlks)
writefile.write_float32(plks,outName)
r['FILE_LENGTH']=str(dlks.shape[0])
r['WIDTH']=str(dlks.shape[1])
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP']=str(float(r['Y_STEP'])*alks)
r['X_STEP']=str(float(r['X_STEP'])*rlks)
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
elif ext == ('.dem'):
d,r = readfile.read_dem(file)
dlks=multilook(d,alks,rlks)
print 'writing '+outName
writefile.write_dem(dlks,outName)
r['FILE_LENGTH']=str(dlks.shape[0])
r['WIDTH']=str(dlks.shape[1])
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP']=str(float(r['Y_STEP'])*alks)
r['X_STEP']=str(float(r['X_STEP'])*rlks)
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
elif ext in ['.jpeg','jpg','png']:
import Image
im = Image.open(file)
width = im.size[0] / int(rlks)
height = im.size[1] / int(alks)
imlks = im.resize((width, height), Image.NEAREST)
print 'writing ' + outName
imlks.save(outName)
try:
r=readfile.read_rsc_file(file+'.rsc')
except:
sys.exit(1)
r['FILE_LENGTH']=str(height)
r['WIDTH']=str(width)
r['XMAX']=str(int(r['WIDTH']) - 1)
r['YMAX']=str(int(r['FILE_LENGTH']) - 1)
try:
r['Y_STEP']=str(float(r['Y_STEP'])*alks)
r['X_STEP']=str(float(r['X_STEP'])*rlks)
except:
Geo=0
f = open(outName+'.rsc','w')
for k in r.keys():
f.write(k+' '+r[k]+'\n')
f.close()
elif ext == ('.h5'):
h5file=h5py.File(file,'r')
# outName=file.split('.')[0]+'_a'+str(int(alks))+'lks_r'+str(int(rlks))+'lks.h5'
h5file_lks=h5py.File(outName,'w')
if 'interferograms' in h5file.keys():
print 'Multilooking the interferograms'
gg = h5file_lks.create_group('interferograms')
igramList=h5file['interferograms'].keys()
for igram in igramList:
print igram
unw = h5file['interferograms'][igram].get(igram)
unwlks=multilook(unw,alks,rlks)
group = gg.create_group(igram)
dset = group.create_dataset(igram, data=unwlks, compression='gzip')
for key, value in h5file['interferograms'][igram].attrs.iteritems():
group.attrs[key] = value
group.attrs['WIDTH']=unwlks.shape[1]
group.attrs['FILE_LENGTH']=unwlks.shape[0]
try:
group.attrs['Y_STEP']=alks*float(group.attrs['Y_STEP'])
group.attrs['X_STEP']=rlks*float(group.attrs['X_STEP'])
except:
group.attrs['AZIMUTH_PIXEL_SIZE']=alks*float(group.attrs['AZIMUTH_PIXEL_SIZE'])
group.attrs['RANGE_PIXEL_SIZE']=rlks*float(group.attrs['RANGE_PIXEL_SIZE'])
dset1=h5file['mask'].get('mask')
mask=dset1[0:dset1.shape[0],0:dset1.shape[1]]
masklks=multilook(mask,alks,rlks)
group=h5file_lks.create_group('mask')
dset = group.create_dataset('mask', data=masklks, compression='gzip')
elif 'timeseries' in h5file.keys():
print 'Multilooking the time-series'
group = h5file_lks.create_group('timeseries')
dateList=h5file['timeseries'].keys()
for d in dateList:
print d
unw = h5file['timeseries'].get(d)
unwlks=multilook(unw,alks,rlks)
dset = group.create_dataset(d, data=unwlks, compression='gzip')
for key,value in h5file['timeseries'].attrs.iteritems():
group.attrs[key] = value
group.attrs['WIDTH']=unwlks.shape[1]
group.attrs['FILE_LENGTH']=unwlks.shape[0]
try:
group.attrs['Y_STEP']=alks*float(group.attrs['Y_STEP'])
group.attrs['X_STEP']=rlks*float(group.attrs['X_STEP'])
except:
group.attrs['AZIMUTH_PIXEL_SIZE']=alks*float(group.attrs['AZIMUTH_PIXEL_SIZE'])
group.attrs['RANGE_PIXEL_SIZE']=rlks*float(group.attrs['RANGE_PIXEL_SIZE'])
try:
dset1 = h5file['mask'].get('mask')
Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
Masklks=multilook(Mask,alks,rlks)
group=h5file_lks.create_group('mask')
dset = group.create_dataset('mask', data=Masklks, compression='gzip')
except:
print 'Multilooked file does not include the maske'
elif 'temporal_coherence' in h5file.keys() or 'velocity' in h5file.keys() or 'mask' in h5file.keys():
k=h5file.keys()
print 'multi looking the '+ k[0]
group=h5file_lks.create_group(k[0])
dset1 = h5file[k[0]].get(k[0])
Mask = dset1[0:dset1.shape[0],0:dset1.shape[1]]
Masklks=multilook(Mask,alks,rlks)
dset = group.create_dataset(k[0], data=Masklks, compression='gzip')
for key , value in h5file[k[0]].attrs.iteritems():
group.attrs[key]=value
try:
group.attrs['Y_STEP']=alks*float(group.attrs['Y_STEP'])
group.attrs['X_STEP']=rlks*float(group.attrs['X_STEP'])
except:
group.attrs['AZIMUTH_PIXEL_SIZE']=alks*float(group.attrs['AZIMUTH_PIXEL_SIZE'])
group.attrs['RANGE_PIXEL_SIZE']=rlks*float(group.attrs['RANGE_PIXEL_SIZE'])
group.attrs['WIDTH']=Masklks.shape[1]
group.attrs['FILE_LENGTH']=Masklks.shape[0]
h5file.close()
h5file_lks.close()