def main(argv):
inps = cmdLineParse()
print '\n*************** Spatial Average ******************'
for File in inps.file:
mean_list, date_list = ut.spatial_average(File,
inps.mask_file,
saveList=True)
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
if inps.disp_fig and k == 'timeseries':
dates, datevector = ptime.date_list2vector(date_list)
# plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dates,
mean_list,
'-ko',
lw=2,
ms=16,
alpha=0.7,
mfc='crimson')
ax.set_title('Spatial Average', fontsize=12)
ax = ptime.auto_adjust_xaxis_date(ax, datevector)[0]
ax.set_xlabel('Time [years]', fontsize=12)
ax.set_ylabel('Mean', fontsize=12)
plt.show()
def plot_coherence_history(ax, date12_list, coherence_list, plot_dict={}):
'''Plot min/max Coherence of all interferograms for each date'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
# Get date list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date8_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
dates, datevector = ptime.date_list2vector(date8_list)
bar_width = mode(np.diff(dates).tolist())*3/4
x_list = [i-bar_width/2 for i in dates]
coh_mat = coherence_matrix(date12_list, coherence_list)
ax.bar(x_list, np.nanmax(coh_mat, axis=0), bar_width.days, label='Max Coherence')
ax.bar(x_list, np.nanmin(coh_mat, axis=0), bar_width.days, label='Min Coherence')
if plot_dict['disp_title']:
ax.set_title('Coherence History of All Related Interferograms')
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax.set_ylim([0.0,1.0])
ax.set_xlabel('Time [years]',fontsize=plot_dict['fontsize'])
ax.set_ylabel('Coherence',fontsize=plot_dict['fontsize'])
ax.legend(loc='lower right')
return ax
def read_dis_xy(xsub, ysub, dateList, h5file, unit='cm'):
global ref_date
## Unit and Scale
if unit == 'm': unitFac = 1.0
elif unit == 'mm': unitFac = 1000.0
elif unit == 'km': unitFac = 0.001
else:
unit = 'cm'
unitFac = 100.0 # cm by default
## read displacement
try:
ref_date
dis_ref = h5file['timeseries'].get(ref_date)[ysub[0]:ysub[1] + 1,
xsub[0]:xsub[1] + 1]
except:
pass
dis = []
for date in dateList:
dis0 = h5file['timeseries'].get(date)[ysub[0]:ysub[1] + 1,
xsub[0]:xsub[1] + 1]
try:
dis0 -= dis_ref
except:
pass
dis.append(dis0)
dis = np.array(dis) * unitFac
dis = np.reshape(dis, (len(dateList), -1))
## calculate mean
dis_mean = np.nanmean(dis, 1)
## calculate standard deviation
if (xsub[1] - xsub[0]) * (ysub[1] - ysub[0]) == 1:
dis_std = np.array([0] * len(dateList))
else:
dis_std = np.nanstd(dis, 1)
## display
print 'spatial averaged displacement [' + unit + ']:'
print dis_mean
print 'standard deviation [' + unit + ']:'
print dis_std
## calculate linear velocity
try:
dates, datevector = ptime.date_list2vector(dateList)
dis_slope = stats.linregress(np.array(datevector), dis_mean)
print 'linear velocity [' + unit + '/yr]:'
print str(dis_slope[0]) + '+/-' + str(dis_slope[4])
except:
pass
try:
return dis, dis_mean, dis_std, dis_slope
except:
return dis, dis_mean, dis_std
def manual_select_pairs_to_remove(File):
'''Manually select interferograms to remove'''
print '----------------------------------------------------------------------------'
print 'Manually select interferograms to remove'
print 'Click two dates - points - in the figure to select one pair of interferogram'
print 'repeat until you select all pairs you would like to remove'
print 'then close the figure to continue the program ...'
print '----------------------------------------------------------------------------'
# Display the network
fig = plt.figure()
ax = fig.add_subplot(111)
pairs_idx = pnet.read_igram_pairs(File)
bperp_list = ut.Baseline_timeseries(File)
date8_list = ptime.igram_date_list(File)
ax = pnet.plot_network(ax, pairs_idx, date8_list, bperp_list)
print 'display the network of interferogram of file: ' + File
date12_orig = pnet.get_date12_list(File)
date6_list = ptime.yymmdd(date8_list)
dates_array = np.array(ptime.date_list2vector(date8_list)[0])
dateNum_array = mdates.date2num(dates_array)
bperp_array = np.array(bperp_list)
date_click = []
date12_click = []
def onclick(event):
xClick = event.xdata
yClick = event.ydata
idx = nearest_neighbor(xClick, yClick, dateNum_array, bperp_array)
date6 = date6_list[idx]
print 'click at ' + date6
date_click.append(date6)
if len(date_click) % 2 == 0 and date_click[-2] != date_click[-1]:
[m_date, s_date] = sorted(date_click[-2:])
m_idx = date6_list.index(m_date)
s_idx = date6_list.index(s_date)
date12 = m_date + '-' + s_date
if date12 in date12_orig:
print 'select date12: ' + date12
date12_click.append(date12)
ax.plot([dateNum_array[m_idx], dateNum_array[s_idx]],
[bperp_array[m_idx], bperp_array[s_idx]],
'r',
lw=4)
else:
print date12 + ' is not existed in input file'
plt.draw()
cid = fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
return date12_click
def read_dis(xsub, ysub, dateList, h5file, unit="cm"):
global ref_date
## Unit and Scale
if unit == "m":
unitFac = 1.0
elif unit == "mm":
unitFac = 1000.0
elif unit == "km":
unitFac = 0.001
else:
unit = "cm"
unitFac = 100.0 # cm by default
## read displacement
try:
ref_date
dis_ref = h5file["timeseries"].get(ref_date)[ysub[0] : ysub[1] + 1, xsub[0] : xsub[1] + 1]
except:
pass
dis = []
for date in dateList:
dis0 = h5file["timeseries"].get(date)[ysub[0] : ysub[1] + 1, xsub[0] : xsub[1] + 1]
try:
dis0 -= dis_ref
except:
pass
dis.append(dis0)
dis = np.array(dis) * unitFac
dis = np.reshape(dis, (len(dateList), -1))
## calculate mean
dis_mean = stats.nanmean(dis, 1)
## calculate standard deviation
if (xsub[1] - xsub[0]) * (ysub[1] - ysub[0]) == 1:
dis_std = np.array([0] * len(dateList))
else:
dis_std = stats.nanstd(dis, 1)
## calculate linear velocity
dates, datevector = ptime.date_list2vector(dateList)
dis_slope = stats.linregress(np.array(datevector), dis_mean)
## display
print "spatial averaged displacement [" + unit + "]:"
print dis_mean
print "standard deviation [" + unit + "]:"
print dis_std
print "linear velocity [" + unit + "/yr]:"
print str(dis_slope[0]) + "+/-" + str(dis_slope[4])
return dis, dis_mean, dis_std, dis_slope
def print_timseries_date_info(dateList):
datevector = ptime.date_list2vector(dateList)[1]
print '*************** Date Info ***************'
print 'Start Date: '+dateList[0]
print 'End Date: '+dateList[-1]
print 'Number of acquisitions : '+str(len(dateList))
print 'Standard deviation of acquisition times : '+str(std(datevector))+' years'
print '----------------------'
print 'List of dates:'
print dateList
print '----------------------'
print 'List of dates in years'
print datevector
return
def print_timseries_date_info(dateList):
datevector = ptime.date_list2vector(dateList)[1]
print('*************** Date Info ***************')
print('Start Date: ' + dateList[0])
print('End Date: ' + dateList[-1])
print('Number of acquisitions : %d' % len(dateList))
print('Std. of acquisition times : %.2f yeras' % std(datevector))
print('----------------------')
print('List of dates:')
print(dateList)
print('----------------------')
print('List of dates in years')
print(datevector)
return
def axis_adjust_date_length(ax, dateList, lengthArray):
fontSize = 12
## Date Convert
dates, datevector = ptime.date_list2vector(dateList)
## Date Display
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter("%Y")
## X axis format
ax.fmt_xdata = DateFormatter("%Y-%m-%d %H:%M:%S")
ts = datevector[0] - 0.2
ys = int(ts)
ms = int((ts - ys) * 12)
te = datevector[-1] + 0.3
ye = int(te)
me = int((te - ye) * 12)
if ms > 12:
ys = ys + 1
ms = 1
if me > 12:
ye = ye + 1
me = 1
if ms < 1:
ys = ys - 1
ms = 12
if me < 1:
ye = ye - 1
me = 12
dss = datetime.date(ys, ms, 1)
dee = datetime.date(ye, me, 1)
ax.set_xlim(dss, dee) # using the same xlim with the previous one
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
### Y axis format
length = max(lengthArray) - min(lengthArray)
ax.set_ylim(min(lengthArray) - 0.1 * length, max(lengthArray) + 0.1 * length)
xticklabels = getp(gca(), "xticklabels")
yticklabels = getp(gca(), "yticklabels")
setp(yticklabels, "color", "k", fontsize=fontSize)
setp(xticklabels, "color", "k", fontsize=fontSize)
return ax
def plot_bperp_hist(fig, dateList, bperp):
## Figure Setting
fontSize = 12
markerColor = "orange"
markerSize = 16
lineWidth = 2
## Date Convert
dates, datevector = ptime.date_list2vector(dateList)
## Plot
ax = fig.add_subplot(111)
ax.plot(dates, bperp, "-ko", ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
ax.set_title("Perpendicular Baseline History", fontsize=fontSize)
## axis format
ax = axis_adjust_date_length(ax, dateList, bperp)
ax.set_xlabel("Time [years]", fontsize=fontSize)
ax.set_ylabel("Perpendicular Baseline [m]", fontsize=fontSize)
return fig
def plot_network(fig, pairs_idx, dateList, bperp):
## Plot Temporal-Bperp Network
##
## Inputs
## fig : matplotlib figure
## pairs_idx : pairs info in int index
## dateList : date list in 8 digit string
## bperp : perp baseline array
## Output
## fig : matplotlib figure
## Figure Setting
fontSize = 12
markerColor = "orange"
markerSize = 16
lineWidth = 2
## Date Convert
dates, datevector = ptime.date_list2vector(dateList)
## Ploting
ax = fig.add_subplot(111)
ax.plot(dates, bperp, "o", ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
for i in range(len(pairs_idx)):
ax.plot(
array([dates[pairs_idx[i][0]], dates[pairs_idx[i][1]]]),
array([bperp[pairs_idx[i][0]], bperp[pairs_idx[i][1]]]),
"k",
lw=lineWidth,
)
ax.set_title("Interferogram Network", fontsize=fontSize)
## axis format
ax = axis_adjust_date_length(ax, dateList, bperp)
ax.set_xlabel("Time [years]", fontsize=fontSize)
ax.set_ylabel("Perpendicular Baseline [m]", fontsize=fontSize)
return fig
def main(argv):
inps = cmdLineParse()
print '\n*************** Spatial Average ******************'
if inps.mask_file:
print 'reading mask file: ' + inps.mask_file
mask, mask_atr = readfile.read(inps.mask_file)
else:
mask = None
for File in inps.file:
mean_list = ut.spatial_average(File, mask, saveList=True)
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
if inps.disp_fig and k == 'timeseries':
# Get date list
h5file = h5py.File(File)
dateList = sorted(h5file[k].keys())
h5file.close()
dates, datevector = ptime.date_list2vector(dateList)
# plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dates,
mean_list,
'-ko',
lw=2,
ms=16,
alpha=0.7,
mfc='crimson')
ax.set_title('Spatial Average', fontsize=12)
ax = ptime.auto_adjust_xaxis_date(ax, datevector)[0]
ax.set_xlabel('Time [years]', fontsize=12)
ax.set_ylabel('Mean', fontsize=12)
plt.show()
def main(argv):
inps = cmdLineParse()
#print '\n********** Inversion: Time Series to Velocity ***********'
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
print 'input '+k+' file: '+inps.timeseries_file
if not k == 'timeseries':
sys.exit('ERROR: input file is not timeseries!')
h5file = h5py.File(inps.timeseries_file)
#####################################
## Date Info
dateListAll = sorted(h5file[k].keys())
dateListAll = ptime.yyyymmdd(dateListAll)
yyListAll = ptime.yyyymmdd2years(dateListAll)
print '--------------------------------------------'
print 'Dates from input file: '+str(len(dateListAll))
print dateListAll
# Extrac exclude dates from input arguments
inps.datesNot2include = []
# 1. template_file
if inps.template_file:
inps = update_inps_from_template(inps, inps.template_file)
# 2. ex_date
if inps.ex_date:
for ex_date in inps.ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
inps.datesNot2include += list(set(ex_date) - set(inps.datesNot2include))
# delete dates not existed in input file
inps.datesNot2include = list(set(inps.datesNot2include).intersection(dateListAll))
print 'date excluded:'+str(inps.datesNot2include)
# 3. min_date
if inps.min_date:
inps.min_date = ptime.yyyymmdd(inps.min_date)
print 'minimum date: '+inps.min_date
yy_min = ptime.yyyymmdd2years(inps.min_date)
for i in range(len(dateListAll)):
date = dateListAll[i]
if yyListAll[i] < yy_min and date not in inps.datesNot2include:
print ' remove date: '+date
inps.datesNot2include.append(date)
# 4. max_date
if inps.max_date:
inps.max_date = ptime.yyyymmdd(inps.max_date)
print 'minimum date: '+inps.max_date
yy_max = ptime.yyyymmdd2years(inps.max_date)
for i in range(len(dateListAll)):
date = dateListAll[i]
if yyListAll[i] > yy_max and date not in inps.datesNot2include:
print ' remove date: '+date
inps.datesNot2include.append(date)
# Summary
dateList = sorted(list(set(dateListAll) - set(inps.datesNot2include)))
print '--------------------------------------------'
if len(dateList) == len(dateListAll):
print 'using all dates to calculate the velocity'
else:
print 'Dates used to estimate the velocity: '+str(len(dateList))
print dateList
print '--------------------------------------------'
# Date Aux Info
dates, datevector = ptime.date_list2vector(dateList)
#####################################
## Inversion
# Design matrix
B=np.ones([len(datevector),2])
B[:,0]=datevector
#B1 = np.linalg.pinv(B)
B1 = np.dot(np.linalg.inv(np.dot(B.T,B)),B.T)
B1 = np.array(B1,np.float32)
# Loading timeseries
print "Loading time series file: "+inps.timeseries_file+' ...'
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
dateNum = len(dateList)
timeseries = np.zeros([dateNum,length*width],np.float32)
start_time = time.time()
for i in range(dateNum):
date = dateList[i]
ut.print_progress(i+1, dateNum, prefix='loading:', suffix=date, elapsed_time=time.time()-start_time)
timeseries[i,:] = h5file[k].get(date)[:].flatten()
h5file.close()
# Velocity Inversion
print 'Calculating velocity ...'
x = np.dot(B1,timeseries)
velocity = np.reshape(x[0,:],[length,width])
print 'Calculating rmse ...'
timeseries_linear = np.dot(B,x)
rmse = np.reshape(np.sqrt((np.sum((timeseries_linear-timeseries)**2,0))/dateNum),[length,width])
print 'Calculating the standard deviation of the estimated velocity ...'
residual = timeseries_linear - timeseries
s1 = np.sqrt(np.sum(residual**2,0)/(dateNum-2))
s2 = np.sqrt(np.sum((datevector-np.mean(datevector))**2))
std = np.reshape(s1/s2,[length,width])
# SSt=np.sum((timeseries-np.mean(timeseries,0))**2,0)
# SSres=np.sum(residual**2,0)
# SS_REG=SSt-SSres
# Rsquared=np.reshape(SS_REG/SSt,[length,width])
######################################################
# covariance of the velocities
#####################################
# Output file name
if not inps.outfile:
inps.outfile = 'velocity.h5'
if inps.datesNot2include:
inps.outfile = os.path.splitext(inps.outfile)[0]+'_ex'+os.path.splitext(inps.outfile)[1]
inps.outfile_rmse = 'rmse_'+inps.outfile
inps.outfile_std = 'std_'+inps.outfile
inps.outfile_r2 = 'R2_'+inps.outfile
# Attributes
atr['date1'] = datevector[0]
atr['date2'] = datevector[dateNum-1]
# File Writing
print '--------------------------------------'
atr['FILE_TYPE'] = 'velocity'
print 'writing >>> '+inps.outfile
writefile.write(velocity, atr, inps.outfile)
atr['FILE_TYPE'] = 'rmse'
print 'writing >>> '+inps.outfile_rmse
writefile.write(rmse, atr, inps.outfile_rmse)
atr['FILE_TYPE'] = 'rmse'
print 'writing >>> '+inps.outfile_std
writefile.write(std, atr, inps.outfile_std)
print 'Done.'
return inps.outfile
def plot_perp_baseline_hist(ax, date8_list, pbase_list, plot_dict={}, date8_list_drop=[]):
''' Plot Perpendicular Spatial Baseline History
Inputs
ax : matplotlib axes object
date8_list : list of string, date in YYYYMMDD format
pbase_list : list of float, perp baseline
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
disp_title : bool, show figure title or not, default: True
date8_list_drop : list of string, date dropped in YYYYMMDD format
e.g. ['20080711', '20081011']
Output:
ax : matplotlib axes object
'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
transparency = 0.7
# Date Convert
dates, datevector = ptime.date_list2vector(date8_list)
# Get index of date used and dropped
#date8_list_drop = ['20080711', '20081011'] # for debug
idx_keep = range(len(date8_list))
idx_drop = []
for i in date8_list_drop:
idx = date8_list.index(i)
idx_keep.remove(idx)
idx_drop.append(idx)
# Plot
#ax=fig.add_subplot(111)
# Plot date used
if idx_keep:
x_list = [dates[i] for i in idx_keep]
y_list = [pbase_list[i] for i in idx_keep]
ax.plot(x_list, y_list, '-ko', alpha=transparency, lw=plot_dict['linewidth'], \
ms=plot_dict['markersize'], mfc=plot_dict['markercolor'])
# Plot date dropped
if idx_drop:
x_list = [dates[i] for i in idx_drop]
y_list = [pbase_list[i] for i in idx_drop]
ax.plot(x_list, y_list, 'ko', alpha=transparency, ms=plot_dict['markersize'], mfc='gray')
if plot_dict['disp_title']:
ax.set_title('Perpendicular Baseline History',fontsize=plot_dict['fontsize'])
# axis format
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax = auto_adjust_yaxis(ax, pbase_list, plot_dict['fontsize'])
ax.set_xlabel('Time [years]',fontsize=plot_dict['fontsize'])
ax.set_ylabel('Perpendicular Baseline [m]',fontsize=plot_dict['fontsize'])
return ax
def plot_network(ax, date12_list, date_list, pbase_list, plot_dict={}, date12_list_drop=[]):
'''Plot Temporal-Perp baseline Network
Inputs
ax : matplotlib axes object
date12_list : list of string for date12 in YYMMDD-YYMMDD format
date_list : list of string, for date in YYYYMMDD/YYMMDD format
pbase_list : list of float, perp baseline, len=number of acquisition
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
coherence_list : list of float, coherence value of each interferogram, len = number of ifgrams
coh_date12_list: list of date, corresponding to coherence_list
disp_min/max : float, min/max range of the color display based on coherence_list
colormap : string, colormap name
coh_thres : float, coherence of where to cut the colormap for display
disp_title : bool, show figure title or not, default: True
Output
ax : matplotlib axes object
'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
# For colorful display of coherence
if not 'coherence_list' in keyList: plot_dict['coherence_list'] = None
if not 'disp_min' in keyList: plot_dict['disp_min'] = 0.2
if not 'disp_max' in keyList: plot_dict['disp_max'] = 1.0
if not 'colormap' in keyList: plot_dict['colormap'] = 'RdBu'
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
transparency = 0.7
# Date Convert
date8_list = ptime.yyyymmdd(date_list)
date6_list = ptime.yymmdd(date8_list)
dates, datevector = ptime.date_list2vector(date8_list)
# Index of date12 used and dropped
idx_date12_keep = range(len(date12_list))
idx_date12_drop = []
for i in date12_list_drop:
idx = date12_list.index(i)
idx_date12_keep.remove(idx)
idx_date12_drop.append(idx)
# Index of date used and dropped
date12_list_keep = sorted(list(set(date12_list) - set(date12_list_drop)))
m_dates = [i.split('-')[0] for i in date12_list_keep]
s_dates = [i.split('-')[1] for i in date12_list_keep]
date8_list_keep = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
date8_list_drop = sorted(list(set(date8_list) - set(date8_list_keep)))
idx_date_keep = range(len(date8_list))
idx_date_drop = []
for i in date8_list_drop:
idx = date8_list.index(i)
idx_date_keep.remove(idx)
idx_date_drop.append(idx)
# Ploting
#ax=fig.add_subplot(111)
## Colorbar when conherence is colored
if plot_dict['coherence_list']:
data_min = min(plot_dict['coherence_list'])
data_max = max(plot_dict['coherence_list'])
# Normalize
normalization = False
if normalization:
plot_dict['coherence_list'] = [(coh-data_min) / (data_min-data_min) for coh in plot_dict['coherence_list']]
plot_dict['disp_min'] = data_min
plot_dict['disp_max'] = data_max
print 'showing coherence'
print 'colormap: '+plot_dict['colormap']
print 'display range: '+str([plot_dict['disp_min'], plot_dict['disp_max']])
print 'data range: '+str([data_min, data_max])
# Use lower/upper part of colormap to emphasis dropped interferograms
if 'coh_thres' not in plot_dict.keys() or not plot_dict['coh_thres']:
# Find proper cut percentage so that all keep pairs are blue and drop pairs are red
coh_list_keep = [plot_dict['coherence_list'][i] for i in idx_date12_keep]
coh_list_drop = [plot_dict['coherence_list'][i] for i in idx_date12_drop]
plot_dict['coh_thres'] = min(coh_list_keep)
if coh_list_drop:
plot_dict['coh_thres'] += max(coh_list_drop)
plot_dict['coh_thres'] /= 2
plot_dict['coh_thres'] = round(plot_dict['coh_thres'], -int(np.floor(np.log10(abs(plot_dict['coh_thres'])))))
print 'color jump at '+str(plot_dict['coh_thres'])
c1_num = (plot_dict['coh_thres'] - plot_dict['disp_min']) / (plot_dict['disp_max'] - plot_dict['disp_min'])
c1_num = int(c1_num * 200)
cmap = plt.get_cmap(plot_dict['colormap'])
colors1 = cmap(np.linspace(0.0, 0.3, c1_num))
colors2 = cmap(np.linspace(0.6, 1.0, 200 - c1_num))
cmap = colors.LinearSegmentedColormap.from_list('truncate_RdBu', np.vstack((colors1, colors2)))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
norm = mpl.colors.Normalize(vmin=plot_dict['disp_min'], vmax=plot_dict['disp_max'])
cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm)
cbar.set_label('Spatial Coherence', fontsize=plot_dict['fontsize'])
## Dot - SAR Acquisition
if idx_date_keep:
x_list = [dates[i] for i in idx_date_keep]
y_list = [pbase_list[i] for i in idx_date_keep]
ax.plot(x_list, y_list, 'ko', alpha=0.7, ms=plot_dict['markersize'], mfc=plot_dict['markercolor'])
if idx_date_drop:
x_list = [dates[i] for i in idx_date_drop]
y_list = [pbase_list[i] for i in idx_date_drop]
ax.plot(x_list, y_list, 'ko', alpha=0.7, ms=plot_dict['markersize'], mfc='gray')
## Line - Pair/Interferogram
# interferograms kept
for date12 in date12_list_keep:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if plot_dict['coherence_list']:
coh = plot_dict['coherence_list'][plot_dict['coh_date12_list'].index(date12)]
coh_idx = (coh - plot_dict['disp_min']) / (plot_dict['disp_max'] - plot_dict['disp_min'])
ax.plot(x, y, '-', lw=plot_dict['linewidth'], alpha=transparency, c=cmap(coh_idx))
else:
ax.plot(x, y, '-', lw=plot_dict['linewidth'], alpha=transparency, c='k')
# interferograms dropped
for date12 in date12_list_drop:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if plot_dict['coherence_list']:
coh_idx = (plot_dict['coherence_list'][date12_list.index(date12)] - plot_dict['disp_min']) /\
(plot_dict['disp_max'] - plot_dict['disp_min'])
ax.plot(x, y, '--', lw=plot_dict['linewidth'], alpha=transparency, c=cmap(coh_idx))
else:
ax.plot(x, y, '--', lw=plot_dict['linewidth'], alpha=transparency, c='k')
if plot_dict['disp_title']:
ax.set_title('Interferogram Network', fontsize=plot_dict['fontsize'])
# axis format
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax = auto_adjust_yaxis(ax, pbase_list, plot_dict['fontsize'])
ax.set_xlabel('Time [years]',fontsize=plot_dict['fontsize'])
ax.set_ylabel('Perp Baseline [m]',fontsize=plot_dict['fontsize'])
return ax
def plot_network(ax,
date12_list,
date_list,
pbase_list,
plot_dict={},
date12_list_drop=[]):
'''Plot Temporal-Perp baseline Network
Inputs
ax : matplotlib axes object
date12_list : list of string for date12 in YYMMDD-YYMMDD format
date_list : list of string, for date in YYYYMMDD/YYMMDD format
pbase_list : list of float, perp baseline, len=number of acquisition
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
coherence_list : list of float, coherence value of each interferogram, len = number of ifgrams
disp_min/max : float, min/max range of the color display based on coherence_list
colormap : string, colormap name
coh_thres : float, coherence of where to cut the colormap for display
disp_title : bool, show figure title or not, default: True
disp_drop: bool, show dropped interferograms or not, default: True
Output
ax : matplotlib axes object
'''
# Figure Setting
keyList = plot_dict.keys()
if not 'fontsize' in keyList: plot_dict['fontsize'] = 12
if not 'linewidth' in keyList: plot_dict['linewidth'] = 2
if not 'markercolor' in keyList: plot_dict['markercolor'] = 'orange'
if not 'markersize' in keyList: plot_dict['markersize'] = 16
# For colorful display of coherence
if not 'coherence_list' in keyList: plot_dict['coherence_list'] = None
if not 'disp_min' in keyList: plot_dict['disp_min'] = 0.2
if not 'disp_max' in keyList: plot_dict['disp_max'] = 1.0
if not 'colormap' in keyList: plot_dict['colormap'] = 'RdBu'
if not 'disp_title' in keyList: plot_dict['disp_title'] = True
if not 'coh_thres' in keyList: plot_dict['coh_thres'] = None
if not 'disp_drop' in keyList: plot_dict['disp_drop'] = True
coh_list = plot_dict['coherence_list']
disp_min = plot_dict['disp_min']
disp_max = plot_dict['disp_max']
coh_thres = plot_dict['coh_thres']
transparency = 0.7
# Date Convert
date8_list = ptime.yyyymmdd(sorted(date_list))
date6_list = ptime.yymmdd(date8_list)
dates, datevector = ptime.date_list2vector(date8_list)
## Keep/Drop - date12
date12_list_keep = sorted(list(set(date12_list) - set(date12_list_drop)))
idx_date12_keep = [date12_list.index(i) for i in date12_list_keep]
idx_date12_drop = [date12_list.index(i) for i in date12_list_drop]
if not date12_list_drop:
plot_dict['disp_drop'] = False
## Keep/Drop - date
m_dates = [i.split('-')[0] for i in date12_list_keep]
s_dates = [i.split('-')[1] for i in date12_list_keep]
date8_list_keep = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
date8_list_drop = sorted(list(set(date8_list) - set(date8_list_keep)))
idx_date_keep = [date8_list.index(i) for i in date8_list_keep]
idx_date_drop = [date8_list.index(i) for i in date8_list_drop]
# Ploting
#ax=fig.add_subplot(111)
## Colorbar when conherence is colored
if coh_list:
data_min = min(coh_list)
data_max = max(coh_list)
# Normalize
normalization = False
if normalization:
coh_list = [(coh - data_min) / (data_min - data_min)
for coh in coh_list]
disp_min = data_min
disp_max = data_max
print 'showing coherence'
print 'colormap: ' + plot_dict['colormap']
print 'display range: ' + str([disp_min, disp_max])
print 'data range: ' + str([data_min, data_max])
# Use lower/upper part of colormap to emphasis dropped interferograms
if not coh_thres:
# Find proper cut percentage so that all keep pairs are blue and drop pairs are red
coh_list_keep = [coh_list[i] for i in idx_date12_keep]
coh_list_drop = [coh_list[i] for i in idx_date12_drop]
if coh_list_drop:
coh_thres = max(coh_list_drop)
else:
coh_thres = min(coh_list_keep)
if coh_thres < disp_min:
print 'data range exceed orginal display range, set new display range to: [0.0, %f]' % (
disp_max)
disp_min = 0.0
c1_num = np.ceil(200.0 * (coh_thres - disp_min) /
(disp_max - disp_min)).astype('int')
coh_thres = c1_num / 200.0 * (disp_max - disp_min) + disp_min
cmap = plt.get_cmap(plot_dict['colormap'])
colors1 = cmap(np.linspace(0.0, 0.3, c1_num))
colors2 = cmap(np.linspace(0.6, 1.0, 200 - c1_num))
cmap = colors.LinearSegmentedColormap.from_list(
'truncate_RdBu', np.vstack((colors1, colors2)))
print 'color jump at ' + str(coh_thres)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
norm = mpl.colors.Normalize(vmin=disp_min, vmax=disp_max)
cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm)
cbar.set_label('Average Spatial Coherence',
fontsize=plot_dict['fontsize'])
## Dot - SAR Acquisition
if idx_date_keep:
x_list = [dates[i] for i in idx_date_keep]
y_list = [pbase_list[i] for i in idx_date_keep]
ax.plot(x_list,
y_list,
'ko',
alpha=0.7,
ms=plot_dict['markersize'],
mfc=plot_dict['markercolor'])
if idx_date_drop:
x_list = [dates[i] for i in idx_date_drop]
y_list = [pbase_list[i] for i in idx_date_drop]
ax.plot(x_list,
y_list,
'ko',
alpha=0.7,
ms=plot_dict['markersize'],
mfc='gray')
## Line - Pair/Interferogram
# interferograms dropped
if plot_dict['disp_drop']:
for date12 in date12_list_drop:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if coh_list:
coh = coh_list[date12_list.index(date12)]
coh_idx = (coh - disp_min) / (disp_max - disp_min)
ax.plot(x,
y,
'--',
lw=plot_dict['linewidth'],
alpha=transparency,
c=cmap(coh_idx))
else:
ax.plot(x,
y,
'--',
lw=plot_dict['linewidth'],
alpha=transparency,
c='k')
# interferograms kept
for date12 in date12_list_keep:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbase_list[idx1], pbase_list[idx2]])
if coh_list:
coh = coh_list[date12_list.index(date12)]
coh_idx = (coh - disp_min) / (disp_max - disp_min)
ax.plot(x,
y,
'-',
lw=plot_dict['linewidth'],
alpha=transparency,
c=cmap(coh_idx))
else:
ax.plot(x,
y,
'-',
lw=plot_dict['linewidth'],
alpha=transparency,
c='k')
if plot_dict['disp_title']:
ax.set_title('Interferogram Network', fontsize=plot_dict['fontsize'])
# axis format
ax = ptime.auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'])[0]
ax = auto_adjust_yaxis(ax, pbase_list, plot_dict['fontsize'])
ax.set_xlabel('Time [years]', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Perp Baseline [m]', fontsize=plot_dict['fontsize'])
# Legend
if plot_dict['disp_drop']:
solid_line = mlines.Line2D([], [],
color='k',
ls='solid',
label='Interferograms')
dash_line = mlines.Line2D([], [],
color='k',
ls='dashed',
label='Interferograms dropped')
ax.legend(handles=[solid_line, dash_line])
return ax
def main(argv):
## Default settings
contour_step = 200.0
contour_sigma = 3.0
demShade = "yes"
demContour = "yes"
global markerSize, markderSize2, markerColor, markerColor2, rectColor
global lineWidth, lineWidth2, edgeWidth, fontSize
# global markerColor_ref, markerColor_ref2
markerSize = 16
markerSize2 = 16
markerColor = "crimson" # g
markerColor2 = "lightgray"
markerColor_ref = "white"
markerColor_ref2 = "lightgray"
rectColor = "black"
lineWidth = 0
lineWidth2 = 0
edgeWidth = 1.5
fontSize = 16
global unit, radius, saveFig, dispFig, fig_dpi
fig_dpi = 300
radius = 0
saveFig = "no"
dispFig = "yes"
unit = "cm"
dispDisplacement = "no"
dispOpposite = "no"
dispContour = "only"
smoothContour = "no"
contour_step = 200
showRef = "yes"
vel_alpha = 1.0
zero_start = "yes"
global ref_xsub, ref_ysub, ref_date
global h5timeseries_2, dates_2, dateList_2
global lbound, hbound
############### Check Inputs ##################
if len(sys.argv) < 2:
Usage()
sys.exit(1)
elif len(sys.argv) == 2:
if argv[0] == "-h":
Usage()
sys.exit(1)
elif os.path.isfile(argv[0]):
timeSeriesFile = argv[0]
h5timeseries = h5py.File(timeSeriesFile)
k = h5timeseries.keys()
if not "timeseries" in k:
print "ERROR: Input file is " + k[0] + ".\n\tOnly timeseries is supported.\n"
sys.exit(1)
else:
Usage()
sys.exit(1)
elif len(sys.argv) > 2:
try:
opts, args = getopt.getopt(
argv,
"f:F:v:a:b:s:m:c:w:u:l:h:D:V:t:T:d:r:x:y:X:Y:o:E:",
[
"save",
"nodisplay",
"unit=",
"exclude=",
"ref-date=",
"rect-color=",
"zero-start=",
"zoom-x=",
"zoom-y=",
"zoom-lon",
"zoom-lat",
"lalo=",
"opposite",
"dem-nocontour",
"dem-noshade",
"displacement",
"contour-step=",
"contour-smooth=",
"LALO=",
],
)
except getopt.GetoptError:
Usage()
sys.exit(1)
for opt, arg in opts:
if opt == "-f":
timeSeriesFile = arg
elif opt == "-F":
timeSeriesFile_2 = arg
elif opt == "-v":
velocityFile = arg
elif opt == "-a":
vmin = float(arg)
elif opt == "-b":
vmax = float(arg)
elif opt == "-s":
fontSize = int(arg)
elif opt == "-m":
markerSize = int(arg)
markerSize2 = int(arg)
elif opt == "-c":
markerColor = arg
elif opt == "-w":
lineWidth = int(arg)
elif opt == "-u":
unit = arg
elif opt == "-l":
lbound = float(arg)
elif opt == "-h":
hbound = float(arg)
elif opt == "-D":
demFile = arg
elif opt == "-V":
contour_step = float(arg)
elif opt == "-t":
minDate = arg
elif opt == "-T":
maxDate = arg
elif opt == "-r":
radius = abs(int(arg))
elif opt == "-x":
xsub = [int(i) for i in arg.split(":")]
xsub.sort()
# dispVelFig='no'
elif opt == "-y":
ysub = [int(i) for i in arg.split(":")]
ysub.sort()
# dispVelFig='no'
elif opt == "-X":
ref_xsub = [int(i) for i in arg.split(":")]
ref_xsub.sort()
elif opt == "-Y":
ref_ysub = [int(i) for i in arg.split(":")]
ref_ysub.sort()
# dispVelFig='no'
elif opt == "--contour-step":
contour_step = float(arg)
elif opt == "--contour-smooth":
contour_sigma = float(arg)
elif opt == "--dem-nocontour":
demContour = "no"
elif opt == "--dem-noshade":
demShade = "no"
elif opt == "--displacement":
dispDisplacement = "yes"
elif opt in ["-E", "--exclude"]:
datesNot2show = arg.split(",")
elif opt in "--lalo":
lalosub = [float(i) for i in arg.split(",")]
elif opt in "--LALO":
ref_lalosub = [float(i) for i in arg.split(",")]
elif opt in ["--rect-color"]:
rectColor = arg
elif opt in ["--ref-date"]:
ref_date = ptime.yyyymmdd(arg)
elif opt in ["-u", "--unit"]:
unit = arg.lower()
elif opt == "--save":
saveFig = "yes"
elif opt == "--nodisplay":
dispFig = "no"
saveFig = "yes"
elif opt == "--opposite":
dispOpposite = "yes"
elif opt == "--zero-start":
zero_start = arg.lower()
elif opt == "--zoom-x":
win_x = [int(i) for i in arg.split(":")]
win_x.sort()
elif opt == "--zoom-y":
win_y = [int(i) for i in arg.split(":")]
win_y.sort()
elif opt == "--zoom-lon":
win_lon = [float(i) for i in arg.split(":")]
win_lon.sort()
elif opt == "--zoom-lat":
win_lat = [float(i) for i in arg.split(":")]
win_lat.sort()
##############################################################
## Read time series file info
if not os.path.isfile(timeSeriesFile):
print "\nERROR: Input time series file does not exist: " + timeSeriesFile + "\n"
sys.exit(1)
h5timeseries = h5py.File(timeSeriesFile)
k = h5timeseries.keys()
# read h5 file and its group type
if not "timeseries" in k:
print "ERROR: Input file is " + k[0] + ".\n\tOnly timeseries is supported.\n"
sys.exit(1)
atr = readfile.read_attributes(timeSeriesFile)
dateList1 = h5timeseries["timeseries"].keys()
dateList1 = sorted(dateList1)
dates1, datevector1 = ptime.date_list2vector(dateList1)
print "\n************ Time Series Display - Point *************"
##### Select Check
try:
lalosub
xsub = subset.coord_geo2radar([lalosub[1]], atr, "longitude")
ysub = subset.coord_geo2radar([lalosub[0]], atr, "latitude")
xsub = [xsub]
ysub = [ysub]
if radius == 0:
radius = 3
except:
pass
try:
ref_lalosub
ref_xsub = subset.coord_geo2radar([ref_lalosub[1]], atr, "longitude")
ref_ysub = subset.coord_geo2radar([ref_lalosub[0]], atr, "latitude")
ref_xsub = [ref_xsub]
ref_ysub = [ref_ysub]
if radius == 0:
radius = 3
except:
pass
##############################################################
global dates, dateList, datevector_all, dateListMinMax
print "*******************"
print "All dates existed:"
print dateList1
print "*******************"
## Check exclude date input
try:
datesNot2show
if os.path.isfile(datesNot2show[0]):
try:
datesNot2show = ptime.read_date_list(datesNot2show[0])
except:
print "Can not read date list file: " + datesNot2show[0]
print "dates not to show: " + str(datesNot2show)
except:
datesNot2show = []
## Check Min / Max Date
dateListMinMax = []
try:
minDate
minDate = ptime.yyyymmdd(minDate)
dateListMinMax.append(minDate)
minDateyy = ptime.yyyymmdd2years(minDate)
print "minimum date: " + minDate
for date in dateList1:
yy = ptime.yyyymmdd2years(date)
if yy < minDateyy:
datesNot2show.append(date)
except:
pass
try:
maxDate
maxDate = ptime.yyyymmdd(maxDate)
dateListMinMax.append(maxDate)
maxDateyy = ptime.yyyymmdd2years(maxDate)
print "maximum date: " + maxDate
for date in dateList1:
yy = ptime.yyyymmdd2years(date)
if yy > maxDateyy:
datesNot2show.append(date)
except:
pass
dateListMinMax = sorted(dateListMinMax)
if not dateListMinMax:
print "no min/max date input."
else:
datesMinMax, dateVecMinMax = ptime.date_list2vector(dateListMinMax)
## Finalize Date List
try:
dateList = []
for date in dateList1:
if date not in datesNot2show:
dateList.append(date)
print "--------------------------------------------"
print "dates used to show time series displacements:"
print dateList
print "--------------------------------------------"
except:
dateList = dateList1
print "using all dates to show time series displacement"
## Read Date Info (x axis for time series display)
dates, datevector = ptime.date_list2vector(dateList)
datevector_all = list(datevector)
## Check reference date input
try:
ref_date
if not ref_date in dateList:
print "Reference date - " + ref_date + " - is not included in date list to show."
sys.exit(1)
else:
print "reference date: " + ref_date
except:
if zero_start == "yes":
ref_date = dateList[0]
print "set the 1st date as reference for displacement display."
else:
pass
##############################################################
##### Plot Fig 1 - Velocity / last epoch of time series / DEM
fig = plt.figure(1)
ax = fig.add_subplot(111)
##### Check subset range
width = int(atr["WIDTH"])
length = int(atr["FILE_LENGTH"])
print "file size: " + str(length) + ", " + str(width)
try:
win_y = subset.coord_geo2radar(win_lat, atr, "latitude")
except:
try:
win_y
except:
win_y = [0, length]
try:
win_x = subset.coord_geo2radar(win_lon, atr, "longitude")
except:
try:
win_x
except:
win_x = [0, width]
win_y, win_x = subset.check_subset_range(win_y, win_x, atr)
try:
velocityFile
try:
vel, vel_atr = readfile.read(velocityFile)
except:
vel, vel_atr = readfile.read(timeSeriesFile, velocityFile)
ax.set_title(velocityFile)
print "display: " + velocityFile
except:
vel, vel_atr = readfile.read(timeSeriesFile, dateList1[-1])
ax.set_title("epoch: " + dateList1[-1])
print "display last epoch"
##### show displacement instead of phase
if vel_atr["FILE_TYPE"] in ["interferograms", ".unw"] and dispDisplacement == "yes":
print "show displacement"
phase2range = -float(vel_atr["WAVELENGTH"]) / (4 * np.pi)
vel *= phase2range
else:
dispDisplacement = "no"
## Reference Point
if showRef == "yes":
try:
ax.plot(int(atr["ref_x"]), int(atr["ref_y"]), "ks", ms=6)
except:
pass
if dispOpposite == "yes":
print "show opposite value in figure/map 1"
vel *= -1
## Flip
try:
flip_lr
except:
try:
flip_ud
except:
flip_lr, flip_ud = view.auto_flip_check(atr)
## Status bar
## Geo coordinate
try:
ullon = float(atr["X_FIRST"])
ullat = float(atr["Y_FIRST"])
lon_step = float(atr["X_STEP"])
lat_step = float(atr["Y_STEP"])
lon_unit = atr["Y_UNIT"]
lat_unit = atr["X_UNIT"]
geocoord = "yes"
print "Input file is Geocoded"
except:
geocoord = "no"
def format_coord(x, y):
col = int(x + 0.5)
row = int(y + 0.5)
if col >= 0 and col <= width and row >= 0 and row <= length:
z = vel[row, col]
try:
lon = ullon + x * lon_step
lat = ullat + y * lat_step
return "x=%.1f, y=%.1f, value=%.4f, lon=%.4f, lat=%.4f" % (x, y, z, lon, lat)
except:
return "x=%.1f, y=%.1f, value=%.4f" % (x, y, z)
ax.format_coord = format_coord
## DEM
try:
demFile
dem, demRsc = readfile.read(demFile)
ax = view.plot_dem_yx(ax, dem, demShade, demContour, contour_step, contour_sigma)
vel_alpha = 0.8
except:
print "No DEM file"
try:
img = ax.imshow(vel, vmin=vmin, vmax=vmax, alpha=vel_alpha)
except:
img = ax.imshow(vel, alpha=vel_alpha)
plt.colorbar(img)
## Zoom In (subset)
if flip_lr == "yes":
ax.set_xlim(win_x[1], win_x[0])
else:
ax.set_xlim(win_x[0], win_x[1])
if flip_ud == "yes":
ax.set_ylim(win_y[0], win_y[1])
else:
ax.set_ylim(win_y[1], win_y[0])
## Flip
# if flip_lr == 'yes': fig.gca().invert_xaxis()
# if flip_ud == 'yes': fig.gca().invert_yaxis()
##########################################
##### Plot Fig 2 - Time series plot
# fig2 = plt.figure(num=2,figsize=(12,6))
fig2 = plt.figure(2, figsize=(12, 6))
ax2 = fig2.add_subplot(111)
try:
timeSeriesFile_2
h5timeseries_2 = h5py.File(timeSeriesFile_2)
dateList_2 = h5timeseries_2["timeseries"].keys()
dateList_2 = sorted(dateList_2)
dates_2, datevector_2 = ptime.date_list2vector(dateList_2)
datevector_all += list(set(datevector_2) - set(datevector_all))
datevector_all = sorted(datevector_all)
except:
pass
################################ Plot Code Package <start> #################################
def plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries):
ax2.cla()
print "\n-------------------------------------------------------------------------------"
disp_min = 0
disp_max = 0
############################# Plot Time Series ##############################
global ref_xsub, ref_ysub
##### 1.1 Plot Reference time series
try:
ref_xsub
ref_ysub
ref_xsub, ref_ysub = check_yx(ref_xsub, ref_ysub, radius, ax, rectColor)
print "----------------------------------------------------"
print "Reference Point:"
print "ref_x=" + str(ref_xsub[0]) + ":" + str(ref_xsub[1])
print "ref_y=" + str(ref_ysub[0]) + ":" + str(ref_ysub[1])
print "-----------------------------"
print "Time series with all dates:"
dis1, dis1_mean, dis1_std, dis1_vel = read_dis(ref_xsub, ref_ysub, dateList1, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates1,
dis1_mean,
yerr=dis1_std,
fmt="-ks",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor_ref,
mew=edgeWidth,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis1_mean, dis1_std)
if not len(dateList) == len(dateList1):
print "-----------------------------"
print "Time series with dates of interest:"
dis12, dis12_mean, dis12_std, dis12_vel = read_dis(ref_xsub, ref_ysub, dateList, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates,
dis12_mean,
yerr=dis12_std,
fmt="-ks",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor_ref2,
mew=edgeWidth,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis12_mean, dis12_std)
except:
pass
##### 1.2.0 Read y/x
print "\n----------------------------------------------------"
print "Point of Interest:"
xsub, ysub = check_yx(xsub, ysub, radius, ax, rectColor)
print "x=" + str(xsub[0]) + ":" + str(xsub[1])
print "y=" + str(ysub[0]) + ":" + str(ysub[1])
##### 1.2.1 Plot 2nd time series
try:
timeSeriesFile_2
print "-----------------------------"
print "2nd Time Series:"
dis2, dis2_mean, dis2_std, dis2_vel = read_dis(xsub, ysub, dateList_2, h5timeseries_2, unit)
(_, caps, _) = ax2.errorbar(
dates_2,
dis2_mean,
yerr=dis2_std,
fmt="-ko",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor2,
elinewidth=0,
ecolor="black",
capsize=0,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis2_mean, dis2_std)
except:
pass
##### 1.2.2 Plot 1st time series
print "-----------------------------"
print "Time Series:"
dis, dis_mean, dis_std, dis_vel = read_dis(xsub, ysub, dateList, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates,
dis_mean,
yerr=dis_std,
fmt="-ko",
ms=markerSize,
lw=lineWidth,
alpha=1,
mfc=markerColor,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis_mean, dis_std)
####################### Figure Format #######################
## x axis format
try:
ax2 = ptime.adjust_xaxis_date(ax2, dateVecMinMax, fontSize)
except:
ax2 = ptime.adjust_xaxis_date(ax2, datevector_all, fontSize)
## y axis format
ax2.set_ylabel("Displacement [" + unit + "]", fontsize=fontSize)
try:
lbound
hbound
ax2.set_ylim(lbound, hbound)
except:
disp_buf = 0.2 * (disp_max - disp_min)
ax2.set_ylim(disp_min - disp_buf, disp_max + disp_buf)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(fontSize)
## title
figTitle = "x=" + str(xsub[0]) + ":" + str(xsub[1]) + ", y=" + str(ysub[0]) + ":" + str(ysub[1])
try:
lonc = ullon + (xsub[0] + xsub[1]) / 2.0 * lon_step
latc = ullat + (ysub[0] + ysub[1]) / 2.0 * lat_step
figTitle += ", lalo=" + "%.4f,%.4f" % (latc, lonc)
except:
pass
ax2.set_title(figTitle)
################## Save and Output #####################
if saveFig == "yes":
print "-----------------------------"
Delay = {}
Delay["displacement"] = dis
Delay["unit"] = unit
Delay["time"] = datevector
Delay["velocity"] = dis_vel[0]
Delay["velocity_unit"] = unit + "/yr"
Delay["velocity_std"] = dis_vel[4]
figBase = "x" + str(xsub[0]) + "_" + str(xsub[1] - 1) + "y" + str(ysub[0]) + "_" + str(ysub[1] - 1)
sio.savemat(figBase + "_ts.mat", {"displacement": Delay})
print "saved " + figBase + "_ts.mat"
fig2.savefig(figBase + "_ts.pdf", bbox_inches="tight", transparent=True, dpi=fig_dpi)
print "saved " + figBase + "_ts.pdf"
if dispFig == "no":
fig.savefig(figBase + "_vel.png", bbox_inches="tight", transparent=True, dpi=fig_dpi)
print "saved " + figBase + "_vel.png"
################################ Plot Code Package <end> #################################
########### 1. Plot Time Series with x/y ##########
try:
xsub
ysub
plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries)
except:
print "No x/y input"
pass
########### 2. Plot Time Series with Click ##########
## similar to 1. Plot Time Series with x/y
def onclick(event):
ax2.cla()
xsub = [int(event.xdata)]
ysub = [int(event.ydata)]
plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries)
if dispFig == "yes":
plt.show()
try:
cid = fig.canvas.mpl_connect("button_press_event", onclick)
except:
pass
if dispFig == "yes":
plt.show()
def main(argv):
inps = cmdLineParse()
if inps.template_file:
inps = read_template2inps(inps.template_file)
##### calculate timeseries of residual Root Mean Square
#std_list, date_list = ut.get_residual_std(inps.timeseries_file, inps.mask_file, inps.ramp_type)
rms_list, date_list = ut.get_residual_rms(inps.timeseries_file,
inps.mask_file, inps.ramp_type)
##### reference_date.txt
print '------------------------------------------------------------'
ref_idx = np.argmin(rms_list)
ref_date = date_list[ref_idx]
print 'date with minimum residual RMS: %s - %.4f' % (ref_date,
rms_list[ref_idx])
refTxtFile = 'reference_date.txt'
if (inps.save_reference_date and \
ut.update_file(refTxtFile, [inps.timeseries_file, inps.mask_file, inps.template_file],\
check_readable=False)):
f = open(refTxtFile, 'w')
f.write(ref_date + '\n')
f.close()
print 'save date to file: ' + refTxtFile
##### exclude_date.txt
print '------------------------------------------------------------'
ex_idx_list = [rms_list.index(i) for i in rms_list if i > inps.min_rms]
print 'date(s) with residual RMS > ' + str(inps.min_rms)
exTxtFile = 'exclude_date.txt'
if ex_idx_list:
if (inps.save_exclude_date and \
ut.update_file(exTxtFile, [inps.timeseries_file, inps.mask_file, inps.template_file],\
check_readable=False)):
f = open(exTxtFile, 'w')
for i in ex_idx_list:
print '%s - %.4f' % (date_list[i], rms_list[i])
f.write(date_list[i] + '\n')
f.close()
print 'save date(s) to file: ' + exTxtFile
else:
print 'None.'
##### Plot
fig_name = os.path.dirname(os.path.abspath(inps.timeseries_file))+\
'/rms_'+os.path.splitext(inps.timeseries_file)[0]
if inps.ramp_type != 'no':
fig_name += '_' + inps.ramp_type
fig_name += '.pdf'
if ut.update_file(fig_name, [exTxtFile, refTxtFile, inps.template_file],
check_readable=False):
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
font_size = 12
dates, datevector = ptime.date_list2vector(date_list)
try:
bar_width = ut.mode(np.diff(dates).tolist()) * 3 / 4
except:
bar_width = np.min(np.diff(dates).tolist()) * 3 / 4
x_list = [i - bar_width / 2 for i in dates]
rms_list = [i * 1000. for i in rms_list]
min_rms = inps.min_rms * 1000.
# Plot all dates
ax.bar(x_list, rms_list, bar_width.days)
#ax.bar(x_list, rms_list, bar_width.days)
# Plot reference date
#if inps.save_reference_date:
ax.bar(x_list[ref_idx],
rms_list[ref_idx],
bar_width.days,
label='Reference date')
# Plot exclude dates
#if ex_idx_list and inps.save_exclude_date:
if ex_idx_list:
ex_x_list = [x_list[i] for i in ex_idx_list]
ex_rms_list = [rms_list[i] for i in ex_idx_list]
ax.bar(ex_x_list,
ex_rms_list,
bar_width.days,
color='darkgray',
label='Exclude date(s)')
# Plot min_rms line
ax, xmin, xmax = ptime.auto_adjust_xaxis_date(
ax, datevector, font_size, every_year=inps.tick_year_num)
ax.plot(np.array([xmin, xmax]), np.array([min_rms, min_rms]), '--k')
# axis format
ax = pnet.auto_adjust_yaxis(ax,
rms_list + [min_rms],
font_size,
ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel('Root Mean Square [mm]', fontsize=font_size)
ax.yaxis.set_ticks_position('both')
ax.tick_params(labelsize=font_size)
if inps.save_reference_date or inps.save_exclude_date:
plt.legend(fontsize=font_size)
# save figure
fig.savefig(fig_name, bbox_inches='tight', transparent=True)
print 'save figure to file: ' + fig_name
return
def main(argv):
##### Default
fontSize = 12
lineWidth = 2
markerColor = 'crimson'
markerSize = 16
disp_fig = 'no'
save_fig = 'yes'
save_list = 'yes'
ref_file = 'reference_date.txt'
drop_file = 'drop_date.txt'
##### Check Inputs
if len(sys.argv)>3:
try:
opts, args = getopt.getopt(argv,'h:f:m:o:x:y:',['help','circle='])
except getopt.GetoptError:
print 'Error in reading input options!'; usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"): usage() ; sys.exit()
elif opt == '-f': File = arg
elif opt == '-m': maskFile = arg
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort()
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort()
elif opt == '--circle' : cir_par = [i for i in arg.split(';')]
#elif opt == '-o': outName = arg
else:
try: File = argv[0]
except: usage(); sys.exit(1)
try: maskFile = argv[1]
except: pass
try: atr = readfile.read_attribute(File)
except: usage(); sys.exit(1)
ext = os.path.splitext(File)[1].lower()
FileBase = os.path.basename(File).split(ext)[0]
outNameBase = 'spatialMean_'+FileBase
print '\n*************** Spatial Average ******************'
##### Input File Info
k = atr['FILE_TYPE']
print 'Input file is '+k
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
h5file = h5py.File(File)
epochList = h5file[k].keys()
epochList = sorted(epochList)
epochNum = len(epochList)
print 'number of epoch: '+str(epochNum)
if
dates,datevector = ptime.date_list2vector(epochList)
##### Mask Info
try:
Mask_orig,Matr = readfile.read(maskFile)
print 'mask file: '+maskFile
Masking = 'yes'
except:
print 'No mask. Use the whole area for ramp estimation.'
Masking = 'no'
Mask_orig=np.ones((length,width))
Mask = np.zeros((length,width))
Mask[:] = Mask_orig[:]
## Bounding Subset
try:
xsub
ysub
ysub,xsub = subset.check_subset_range(ysub,xsub,atr)
Mask[ysub[0]:ysub[1],xsub[0]:xsub[1]] = Mask_orig[ysub[0]:ysub[1],xsub[0]:xsub[1]]*2
#Mask[0:ysub[0],:] = 0
#Mask[ysub[1]:length,:] = 0
#Mask[:,0:xsub[0]] = 0
#Mask[:,xsub[1]:width] = 0
except:
Mask = Mask_orig*2
print 'No subset input.'
## Circle Inputs
try:
cir_par
for i in range(len(cir_par)):
cir_idx = circle_index(atr,cir_par[i])
Mask[cir_idx] = Mask_orig[cir_idx]
print 'Circle '+str(i)+': '+cir_par[i]
except: print 'No circle of interest input.'
## Mask output
idx = Mask == 2
idxNum = float(sum(sum(idx)))
fig = plt.figure()
plt.imshow(Mask,cmap='gray')
plt.savefig(outNameBase+'_mask.png',bbox_inches='tight')
print 'save mask to '+outNameBase+'_mask.png'
#fig.clf()
##### Calculation
meanList = np.zeros(epochNum)
pixPercent = np.zeros(epochNum)
pixT = 0.7
print 'calculating ...'
print ' Date Mean Percentage'
for i in range(epochNum):
epoch = epochList[i]
d = h5file[k].get(epoch)[:]
#d[Mask==0] = np.nan
meanList[i] = np.nanmean(d[idx])
pixPercent[i] = np.sum(d[idx] >= pixT)/idxNum
print epoch+' : %.2f %.1f%%'%(meanList[i],pixPercent[i]*100)
del d
h5file.close()
##### Reference date - Max Value
top3 = sorted(zip(meanList,epochList), reverse=True)[:3]
print '------------ Top 3 Mean ------------------'
print top3
## Write to txt file
fref = open(ref_file,'w')
fref.write(str(top3[0][1])+'\n')
fref.close()
print 'write optimal reference date to '+ref_file
idxMean = meanList == np.nanmax(meanList)
##### Drop dates - mean threshold
#meanT = 0.7
#idxMean = meanList < meanT
#print '------------ Mean Value < '+str(meanT)+' --------'
#print np.array(epochList)[idxMean]
#print meanList[idxMean]
##### Drop dates - good pixel percentage
pixNumT = 0.7
print '------------ Good Pixel Percentage < %.0f%% -------'%(pixNumT*100)
idxPix = pixPercent < pixNumT
dropEpochList = np.array(epochList)[idxPix]
print dropEpochList
print pixPercent[idxPix]
## Write to txt file
fdrop = open(drop_file,'w')
for i in range(len(dropEpochList)):
fdrop.write(str(dropEpochList[i])+'\n')
fdrop.close()
print 'write drop dates to '+drop_file
print '-------------------------------------------'
##### Display
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(211)
ax.plot(dates, meanList, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
#ax.plot([dates[0],dates[-1]],[meanT,meanT], '--b', lw=lineWidth)
#sc = ax.scatter(dates, np.tile(0.5,epochNum), c=meanList, s=22**2, alpha=0.3, vmin=0.0, vmax=1.0)
#ax.scatter(np.array(dates)[idxMean], 0.5, c=meanList[idxMean], s=22**2, alpha=1.0, vmin=0.0, vmax=1.0)
ax = ptime.auto_adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Spatial Average Value', fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
#cbar = plt.colorbar(sc)
#cbar.set_label('Spatial Mean of Normalized Sum Epochs')
ax = fig.add_subplot(212)
ax.plot(dates, pixPercent, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
ax.plot([dates[0],dates[-1]],[pixNumT,pixNumT], '--b', lw=lineWidth)
ax = ptime.auto_adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Percenrage of Pixels with Value > '+str(pixNumT), fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.0f}%'.format(i*100) for i in vals])
if save_fig == 'yes':
plt.savefig(outNameBase+'.png',bbox_inches='tight')
print 'save figure to '+outNameBase+'.png'
if disp_fig == 'yes':
plt.show()
##### Output
if save_list == 'yes':
epochList6 = ptime.yymmdd(epochList)
fl = open(outNameBase+'.txt','w')
for i in range(epochNum):
str_line = epochList6[i]+' %.2f %.2f\n'%(meanList[i],pixPercent[i])
fl.write(str_line)
fl.close()
print 'write data to '+outNameBase+'.txt\n'
###########################################################################################
if __name__ == '__main__':
#######Actual code.
inps = cmdLineParse()
# Time Series Info
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
print 'input file is ' + k + ': ' + inps.timeseries_file
if not k == 'timeseries':
raise ValueError('Only timeseries file is supported!')
h5 = h5py.File(inps.timeseries_file, 'r')
dateList = sorted(h5[k].keys())
date_num = len(dateList)
inps.dates, tims = ptime.date_list2vector(dateList)
# Read exclude dates
if inps.ex_date_list:
input_ex_date = list(inps.ex_date_list)
inps.ex_date_list = []
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
inps.ex_date_list += list(
set(ex_date) - set(inps.ex_date_list))
# delete dates not existed in input file
inps.ex_date_list = sorted(
def main(argv):
lineWidth = 2
fontSize = 12
markerColor = 'orange'
markerSize = 16
networkDisplay = 'no'
if len(sys.argv)>2:
try: opts, args = getopt.getopt(argv,"h:f:C:s:w:m:c:t:b:d:l:n:N:T:l:")
except getopt.GetoptError: Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"): Usage(); sys.exit()
elif opt == '-f': File = arg
elif opt == '-C': corFile = arg
elif opt == '-s': fontSize = int(arg)
elif opt == '-w': lineWidth = int(arg)
elif opt == '-m': markerSize = int(arg)
elif opt == '-c': markerColor = arg
elif opt == '-t': temp_thr = float(arg)
elif opt == '-b': base_thr = float(arg)
elif opt == '-d': dates2Rmv = arg
elif opt == '-l': ifgrams_to_rmv = arg
elif opt == '-n': networkDisplay = arg
elif opt == '-N': ifgrams_Number_to_rmv = arg.split()
elif opt == '-T': templateFile = arg
elif opt == '-l': list_fileList = arg.split(',')
try: File
except: Usage() ; sys.exit(1)
elif len(sys.argv)==2:
File = argv[0]
networkDisplay = 'yes'
else: Usage() ; sys.exit(1)
## display network for modification, if no other limit setted
try:
temp_thr
base_trh
dates2Rmv
ifgrams_to_rmv
ifgrams_Number_to_rmv
networkDisplay = 'yes'
except: pass
###########################################################
atr = readfile.read_attributes(File)
k = atr['FILE_TYPE']
print '\n*************** Modify Network ****************'
print 'Input file is '+k
#if h5file.keys()[0] != 'interferograms':
# print 'Input file should be interferograms'; sys.exit(1)
h5file = h5py.File(File)
ifgramList = h5file[k].keys()
ifgramList = sorted(ifgramList)
try: ifgrams_to_rmv
except: ifgrams_to_rmv=[]
###########################################################
##### L - list file of interferograms
try: ifgrams_to_rmv = list_file.read()
except: pass
##### T - templateFile, pysar.dropIfgIndex
try:
templateFile
template = readfile.read_template(templateFile)
drop_ifg_index = template['pysar.drop.ifgIndex'].split(',')
print 'drop interferogram index:'
print drop_ifg_index
try: ifgrams_Number_to_rmv
except: ifgrams_Number_to_rmv = []
for index in drop_ifg_index:
index_temp = [int(i) for i in index.split(':')]; index_temp.sort()
if len(index_temp)==2:
for j in range(index_temp[0],index_temp[1]+1): ifgrams_Number_to_rmv.append(str(j))
elif len(index_temp)==1: ifgrams_Number_to_rmv.append(index)
else: print 'Unrecoganized input: '+index
except: pass
##### N - interferogram number list
try:
for i in ifgrams_Number_to_rmv:
print i+' '+ifgramList[int(i)-1]
ifgrams_to_rmv.append(ifgramList[int(i)-1])
except: pass
##### b - perpendicular baseline limit
try:
base_thr
print 'interferograms with the spatial baseline longer than '+ str(base_thr)+' m is removed'
for ifgram in ifgramList:
Baseline = (float(h5file[k][ifgram].attrs['P_BASELINE_BOTTOM_HDR'])+\
float(h5file[k][ifgram].attrs['P_BASELINE_TOP_HDR']))/2
if abs(Baseline) > base_thr:
if not ifgram in ifgrams_to_rmv: ifgrams_to_rmv.append(ifgram)
except: print 'No Spatial Baseline threshold applied'
##### d - dates to remove
try:
dates2Rmv
print 'interferograms with any of following dates will be removed: '+ dates2Rmv
for ifgram in ifgramList:
date1,date2 = h5file[k][ifgram].attrs['DATE12'].split('-')
if (date1 in dates2Rmv) or (date2 in dates2Rmv):
if not ifgram in ifgrams_to_rmv: ifgrams_to_rmv.append(ifgram)
except: print 'No specific dates selected to remove'
##### t - temporal baseline limit
tbase,dateList,dateDict,dateList6=ut.date_list(h5file)
try:
temp_thr
print 'Applying the temporal baseline threshold with threshold of '+str(temp_thr)+' days'
for ifgram in ifgramList:
date1,date2 = h5file[k][ifgram].attrs['DATE12'].split('-')
ind1 = dateList6.index(date1)
ind2 = dateList6.index(date2)
dt=tbase[ind2]-tbase[ind1]
if dt>temp_thr:
if not ifgram in ifgrams_to_rmv:
ifgrams_to_rmv.append(ifgram)
except:
print 'No Temporal Baseline threshold applied'
############################################################
############################################################
if networkDisplay=='yes':
dates,datevector = ptime.date_list2vector(dateList)
##################################################
Bp = ut.Baseline_timeseries(File)
#############################################################
ifgramList = h5file[k].keys()
igram_pairs=np.zeros([len(ifgramList),2],np.int)
i=0
for ifgram in ifgramList:
date1,date2 = h5file[k][ifgram].attrs['DATE12'].split('-')
igram_pairs[i][0]=dateList6.index(date1)
igram_pairs[i][1]=dateList6.index(date2)
i=i+1
############################################################
import matplotlib.pyplot as plt
fig1 = plt.figure(1)
ax1=fig1.add_subplot(111)
ax1.cla()
# ax1.plot(dates,Bp, 'o',ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
print tbase
ax1.plot(tbase,Bp, 'o',ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
for ni in range(len(ifgramList)):
ax1.plot(array([tbase[igram_pairs[ni][0]],tbase[igram_pairs[ni][1]]]),\
array([Bp[igram_pairs[ni][0]],Bp[igram_pairs[ni][1]]]),'k',lw=4)
# ax1.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')
ax1.set_ylabel('Bperp [m]',fontsize=fontSize)
ax1.set_xlabel('Time [years]',fontsize=fontSize)
ts=datevector[0]+0.2
te=datevector[-1]+0.2
ys=int(ts)
ye=int(te)
ms=int((ts-ys)*12)
me=int((te-ye)*12)
if ms>12: ys =ys+1; ms=1
if me>12: ye =ye+1; me=1
if ms<1: ys =ys-1; ms=12
if me<1: ye =ye-1; me=12
dss=datetime.datetime(ys,ms,1,0,0)
dee=datetime.datetime(ye,me,1,0,0)
ax1.set_ylim(min(Bp)-0.4*abs(min(Bp)),max(Bp)+0.4*max(Bp))
xticklabels = getp(gca(), 'xticklabels')
yticklabels = getp(gca(), 'yticklabels')
setp(yticklabels, 'color', 'k', fontsize=fontSize)
setp(xticklabels, 'color', 'k', fontsize=fontSize)
##########################################
x=[]
y=[]
Master_index_torremove=[]
Slave_index_torremove=[]
a_tbase=array(tbase)
a_Bp=array(Bp)
def onclick(event):
if event.button==1:
print 'click'
xClick = event.xdata
yClick = event.ydata
idx=nearest_neighbor(xClick,yClick, a_tbase, a_Bp)
xr = a_tbase[idx]
yr = a_Bp[idx]
ix=tbase.index(xr)+1
print ix
x.append(xr)
y.append(yr)
if mod(len(x),2)==0:
Master_index_torremove.append(tbase.index(xr))
ax1.plot([x[len(x)-1],x[len(x)-2]],[y[len(x)-1],y[len(x)-2]],'r',lw=4)
else:
Slave_index_torremove.append(tbase.index(xr))
plt.show()
cid = fig1.canvas.mpl_connect('button_press_event', onclick)
plt.show()
print Master_index_torremove
print Slave_index_torremove
if len(Master_index_torremove) == len(Slave_index_torremove):
R=np.vstack((Master_index_torremove,Slave_index_torremove))
else:
R=np.vstack((Master_index_torremove[:-1],Slave_index_torremove))
R=np.vstack((Master_index_torremove,Slave_index_torremove))
R.sort(0)
print R
print dateList6
numIgrams_rmv=np.shape(R)[1]
for ifgram in ifgramList:
date1,date2 = h5file[k][ifgram].attrs['DATE12'].split('-')
for i in range(numIgrams_rmv):
if dateList6[R[0][i]]==date1 and dateList6[R[1][i]]==date2:
ifgrams_to_rmv.append(ifgram)
else:
print 'No network display.'
############################################################
############################################################
print 'Number of interferograms to remove: '+str(len(ifgrams_to_rmv))
print 'List of interferograms to remove:'
print ifgrams_to_rmv
file_modified='Modified_'+File
h5filem = h5py.File(file_modified,'w')
gg = h5filem.create_group(k)
ifgram=ifgramList[0]
unw = h5file[k][ifgram].get(ifgram)
MaskZero=np.ones([unw.shape[0],unw.shape[1]])
print 'writing >>> modified interferogram file ...'
print 'Number of interferograms: '+str(len(ifgramList)-len(ifgrams_to_rmv))
for ifgram in ifgramList:
if not ifgram in ifgrams_to_rmv:
print ifgram
unwSet = h5file[k][ifgram].get(ifgram)
unw = unwSet[0:unwSet.shape[0],0:unwSet.shape[1]]
MaskZero=unw*MaskZero
group = gg.create_group(ifgram)
dset = group.create_dataset(ifgram, data=unw, compression='gzip')
for key, value in h5file[k][ifgram].attrs.iteritems():
group.attrs[key] = value
Mask=np.ones([unwSet.shape[0],unwSet.shape[1]])
Mask[MaskZero==0]=0
h5file.close()
h5filem.close()
####################### Coherence ########################
# updating Coherence file
# convert ifgrams_to_rmv to cor_to_rmv
date12_to_rmv=[]
for igram in ifgrams_to_rmv:
date12_to_rmv.append(igram.split('-sim')[0].split('filt_')[-1])
try:
corFile
h5fileCor=h5py.File(corFile)
corList=h5fileCor['coherence'].keys()
corFile_modified='Modified_'+corFile
h5fileCorm=h5py.File(corFile_modified,'w')
gc = h5fileCorm.create_group('coherence')
print 'writing >>> modified coherence file ...'
for cor in corList:
date12=cor.split('-sim')[0].split('filt_')[-1]
if not date12 in date12_to_rmv:
print cor
unwSet = h5fileCor['coherence'][cor].get(cor)
unw = unwSet[0:unwSet.shape[0],0:unwSet.shape[1]]
group = gc.create_group(cor)
dset = group.create_dataset(cor, data=unw, compression='gzip')
for key, value in h5fileCor['coherence'][cor].attrs.iteritems():
group.attrs[key] = value
h5fileCor.close()
h5fileCorm.close()
except:
print 'No coherence file to be updated.'
############################################################
print 'writing >>> Modified_Mask.h5'
h5mask = h5py.File('Modified_Mask.h5','w')
group = h5mask.create_group('mask')
dset = group.create_dataset(os.path.basename('mask'), data=Mask, compression='gzip')
for key, value in atr.iteritems():
group.attrs[key] = value
h5mask.close()
def main(argv):
## Default settings
contour_step = 200.0
contour_sigma = 3.0
demShade = 'yes'
demContour = 'yes'
global markerSize, markderSize2, markerColor, markerColor2, rectColor
global lineWidth, lineWidth2, edgeWidth, fontSize
#global markerColor_ref, markerColor_ref2
markerSize = 16
markerSize2 = 16
markerColor = 'crimson' # g
markerColor2 = 'lightgray'
markerColor_ref = 'white'
markerColor_ref2 = 'lightgray'
rectColor = 'black'
lineWidth = 0
lineWidth2 = 0
edgeWidth = 1.5
fontSize = 16
global unit, radius, saveFig, dispFig, fig_dpi
fig_dpi = 300
radius = 0
saveFig = 'no'
dispFig = 'yes'
unit = 'cm'
dispDisplacement = 'no'
dispOpposite = 'no'
dispContour = 'only'
smoothContour = 'no'
contour_step = 200
showRef = 'yes'
vel_alpha = 1.0
zero_start = 'yes'
global ref_xsub, ref_ysub, ref_date
global h5timeseries_2, dates_2, dateList_2
global lbound, hbound
############### Check Inputs ##################
if len(sys.argv) < 2:
usage()
sys.exit(1)
elif len(sys.argv) == 2:
if argv[0] == '-h':
usage()
sys.exit(1)
elif os.path.isfile(argv[0]):
timeSeriesFile = argv[0]
h5timeseries = h5py.File(timeSeriesFile)
k = h5timeseries.keys()
if not 'timeseries' in k:
print 'ERROR: Input file is ' + k[
0] + '.\n\tOnly timeseries is supported.\n'
sys.exit(1)
else:
usage()
sys.exit(1)
elif len(sys.argv) > 2:
try: opts, args = getopt.getopt(argv,"f:F:v:a:b:s:m:c:w:u:l:h:D:V:t:T:d:r:x:y:X:Y:o:E:",
['save','nodisplay','unit=','exclude=','ref-date=','rect-color=',\
'zero-start=','zoom-x=','zoom-y=','zoom-lon','zoom-lat','lalo=',\
'opposite','dem-nocontour','dem-noshade','displacement','contour-step=',\
'contour-smooth=','LALO='])
except getopt.GetoptError:
usage()
sys.exit(1)
for opt, arg in opts:
if opt == '-f': timeSeriesFile = arg
elif opt == '-F': timeSeriesFile_2 = arg
elif opt == '-v': velocityFile = arg
elif opt == '-a': vmin = float(arg)
elif opt == '-b': vmax = float(arg)
elif opt == '-s': fontSize = int(arg)
elif opt == '-m':
markerSize = int(arg)
markerSize2 = int(arg)
elif opt == '-c':
markerColor = arg
elif opt == '-w':
lineWidth = int(arg)
elif opt == '-u':
unit = arg
elif opt == '-l':
lbound = float(arg)
elif opt == '-h':
hbound = float(arg)
elif opt == '-D':
demFile = arg
elif opt == '-V':
contour_step = float(arg)
elif opt == '-t':
minDate = arg
elif opt == '-T':
maxDate = arg
elif opt == '-r':
radius = abs(int(arg))
elif opt == '-x':
xsub = sorted([int(i) for i in arg.split(':')])
elif opt == '-y':
ysub = sorted([int(i) for i in arg.split(':')])
elif opt == '-X':
ref_xsub = sorted([int(i) for i in arg.split(':')])
elif opt == '-Y':
ref_ysub = sorted([int(i) for i in arg.split(':')])
elif opt == '--contour-step':
contour_step = float(arg)
elif opt == '--contour-smooth':
contour_sigma = float(arg)
elif opt == '--dem-nocontour':
demContour = 'no'
elif opt == '--dem-noshade':
demShade = 'no'
elif opt == '--displacement':
dispDisplacement = 'yes'
elif opt in ['-E', '--exclude']:
datesNot2show = arg.split(',')
elif opt in '--lalo':
lalosub = [float(i) for i in arg.split(',')]
elif opt in '--LALO':
ref_lalosub = [float(i) for i in arg.split(',')]
elif opt in ['--rect-color']:
rectColor = arg
elif opt in ['--ref-date']:
ref_date = ptime.yyyymmdd(arg)
elif opt in ['-u', '--unit']:
unit = arg.lower()
elif opt == '--save':
saveFig = 'yes'
elif opt == '--nodisplay':
dispFig = 'no'
saveFig = 'yes'
elif opt == '--opposite':
dispOpposite = 'yes'
elif opt == '--zero-start':
zero_start = arg.lower()
elif opt == '--zoom-x':
win_x = sorted([int(i) for i in arg.split(':')])
elif opt == '--zoom-y':
win_y = sorted([int(i) for i in arg.split(':')])
elif opt == '--zoom-lon':
win_lon = sorted([float(i) for i in arg.split(':')])
elif opt == '--zoom-lat':
win_lat = sorted([float(i) for i in arg.split(':')])
##############################################################
## Read time series file info
if not os.path.isfile(timeSeriesFile):
print '\nERROR: Input time series file does not exist: ' + timeSeriesFile + '\n'
sys.exit(1)
h5timeseries = h5py.File(timeSeriesFile, 'r')
k = h5timeseries.keys()
# read h5 file and its group type
if not 'timeseries' in k:
print 'ERROR: Input file is ' + k[
0] + '.\n\tOnly timeseries is supported.\n'
sys.exit(1)
atr = readfile.read_attribute(timeSeriesFile)
dateList1 = sorted(h5timeseries['timeseries'].keys())
dates1, datevector1 = ptime.date_list2vector(dateList1)
print '\n************ Time Series Display - Point *************'
##### Select Check
try:
lalosub
xsub = subset.coord_geo2radar([lalosub[1]], atr, 'longitude')
ysub = subset.coord_geo2radar([lalosub[0]], atr, 'latitude')
xsub = [xsub]
ysub = [ysub]
if radius == 0: radius = 3
except:
pass
try:
ref_lalosub
ref_xsub = subset.coord_geo2radar([ref_lalosub[1]], atr, 'longitude')
ref_ysub = subset.coord_geo2radar([ref_lalosub[0]], atr, 'latitude')
ref_xsub = [ref_xsub]
ref_ysub = [ref_ysub]
if radius == 0: radius = 3
except:
pass
##############################################################
global dates, dateList, datevector_all, dateListMinMax
print '*******************'
print 'All dates existed:'
print dateList1
print '*******************'
## Check exclude date input
try:
datesNot2show
if os.path.isfile(datesNot2show[0]):
try:
datesNot2show = ptime.read_date_list(datesNot2show[0])
except:
print 'Can not read date list file: ' + datesNot2show[0]
print 'dates not to show: ' + str(datesNot2show)
except:
datesNot2show = []
## Check Min / Max Date
dateListMinMax = []
try:
minDate
minDate = ptime.yyyymmdd(minDate)
dateListMinMax.append(minDate)
minDateyy = ptime.yyyymmdd2years(minDate)
print 'minimum date: ' + minDate
for date in dateList1:
yy = ptime.yyyymmdd2years(date)
if yy < minDateyy:
datesNot2show.append(date)
except:
pass
try:
maxDate
maxDate = ptime.yyyymmdd(maxDate)
dateListMinMax.append(maxDate)
maxDateyy = ptime.yyyymmdd2years(maxDate)
print 'maximum date: ' + maxDate
for date in dateList1:
yy = ptime.yyyymmdd2years(date)
if yy > maxDateyy:
datesNot2show.append(date)
except:
pass
dateListMinMax = sorted(dateListMinMax)
if not dateListMinMax: print 'no min/max date input.'
else: datesMinMax, dateVecMinMax = ptime.date_list2vector(dateListMinMax)
## Finalize Date List
try:
dateList = []
for date in dateList1:
if date not in datesNot2show:
dateList.append(date)
print '--------------------------------------------'
print 'dates used to show time series displacements:'
print dateList
print '--------------------------------------------'
except:
dateList = dateList1
print 'using all dates to show time series displacement'
## Read Date Info (x axis for time series display)
dates, datevector = ptime.date_list2vector(dateList)
datevector_all = list(datevector)
## Check reference date input
try:
ref_date
if not ref_date in dateList:
print 'Reference date - ' + ref_date + ' - is not included in date list to show.'
sys.exit(1)
else:
print 'reference date: ' + ref_date
except:
if zero_start == 'yes':
ref_date = dateList[0]
print 'set the 1st date as reference for displacement display.'
else:
pass
##############################################################
##### Plot Fig 1 - Velocity / last epoch of time series / DEM
fig = plt.figure(1)
ax = fig.add_subplot(111)
##### Check subset range
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
print 'file size: ' + str(length) + ', ' + str(width)
try:
win_y = subset.coord_geo2radar(win_lat, atr, 'latitude')
except:
try:
win_y
except:
win_y = [0, length]
try:
win_x = subset.coord_geo2radar(win_lon, atr, 'longitude')
except:
try:
win_x
except:
win_x = [0, width]
win_box = (win_x[0], win_y[0], win_x[1], win_y[1])
win_box = subset.check_box_within_data_coverage(win_box, atr)
try:
velocityFile
try:
vel, vel_atr = readfile.read(velocityFile)
except:
vel, vel_atr = readfile.read(timeSeriesFile, velocityFile)
ax.set_title(velocityFile)
print 'display: ' + velocityFile
except:
vel, vel_atr = readfile.read(timeSeriesFile, dateList1[-1])
ax.set_title('epoch: ' + dateList1[-1])
print 'display last epoch'
##### show displacement instead of phase
if vel_atr['FILE_TYPE'] in ['interferograms', '.unw'
] and dispDisplacement == 'yes':
print 'show displacement'
phase2range = -float(vel_atr['WAVELENGTH']) / (4 * np.pi)
vel *= phase2range
else:
dispDisplacement = 'no'
## Reference Point
if showRef == 'yes':
try:
ax.plot(int(atr['ref_x']), int(atr['ref_y']), 'ks', ms=6)
except:
pass
if dispOpposite == 'yes':
print 'show opposite value in figure/map 1'
vel *= -1
## Flip
try:
flip_lr
except:
try:
flip_ud
except:
flip_lr, flip_ud = view.auto_flip_direction(atr)
## Status bar
## Geo coordinate
try:
ullon = float(atr['X_FIRST'])
ullat = float(atr['Y_FIRST'])
lon_step = float(atr['X_STEP'])
lat_step = float(atr['Y_STEP'])
lon_unit = atr['Y_UNIT']
lat_unit = atr['X_UNIT']
geocoord = 'yes'
print 'Input file is Geocoded'
except:
geocoord = 'no'
def format_coord(x, y):
col = int(x + 0.5)
row = int(y + 0.5)
if col >= 0 and col <= width and row >= 0 and row <= length:
z = vel[row, col]
try:
lon = ullon + x * lon_step
lat = ullat + y * lat_step
return 'x=%.1f, y=%.1f, value=%.4f, lon=%.4f, lat=%.4f' % (
x, y, z, lon, lat)
except:
return 'x=%.1f, y=%.1f, value=%.4f' % (x, y, z)
ax.format_coord = format_coord
## DEM
try:
demFile
dem, demRsc = readfile.read(demFile)
ax = view.plot_dem_yx(ax, dem, demShade, demContour, contour_step,
contour_sigma)
vel_alpha = 0.8
except:
print 'No DEM file'
try:
img = ax.imshow(vel, vmin=vmin, vmax=vmax, alpha=vel_alpha)
except:
img = ax.imshow(vel, alpha=vel_alpha)
plt.colorbar(img)
## Zoom In (subset)
if flip_lr == 'yes': ax.set_xlim(win_box[2], win_box[0])
else: ax.set_xlim(win_box[0], win_box[2])
if flip_ud == 'yes': ax.set_ylim(win_box[1], win_box[3])
else: ax.set_ylim(win_box[3], win_box[1])
## Flip
#if flip_lr == 'yes': fig.gca().invert_xaxis()
#if flip_ud == 'yes': fig.gca().invert_yaxis()
##########################################
##### Plot Fig 2 - Time series plot
#fig2 = plt.figure(num=2,figsize=(12,6))
fig2 = plt.figure(2, figsize=(12, 6))
ax2 = fig2.add_subplot(111)
try:
timeSeriesFile_2
h5timeseries_2 = h5py.File(timeSeriesFile_2)
dateList_2 = sorted(h5timeseries_2['timeseries'].keys())
dates_2, datevector_2 = ptime.date_list2vector(dateList_2)
datevector_all += list(set(datevector_2) - set(datevector_all))
datevector_all = sorted(datevector_all)
except:
pass
################################ Plot Code Package <start> #################################
def plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries):
ax2.cla()
print '\n-------------------------------------------------------------------------------'
disp_min = 0
disp_max = 0
############################# Plot Time Series ##############################
global ref_xsub, ref_ysub
##### 1.1 Plot Reference time series
try:
ref_xsub
ref_ysub
ref_xsub, ref_ysub = check_yx(ref_xsub, ref_ysub, radius, ax,
rectColor)
print '----------------------------------------------------'
print 'Reference Point:'
print 'ref_x=' + str(ref_xsub[0]) + ':' + str(ref_xsub[1])
print 'ref_y=' + str(ref_ysub[0]) + ':' + str(ref_ysub[1])
print '-----------------------------'
print 'Time series with all dates:'
dis1, dis1_mean, dis1_std, dis1_vel = read_dis_xy(
ref_xsub, ref_ysub, dateList1, h5timeseries, unit)
(_, caps, _)=ax2.errorbar(dates1,dis1_mean,yerr=dis1_std,fmt='-ks',\
ms=markerSize2, lw=0, alpha=1,mfc=markerColor_ref,mew=edgeWidth,\
elinewidth=edgeWidth,ecolor='black',capsize=markerSize*0.5)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis1_mean,
dis1_std)
if not len(dateList) == len(dateList1):
print '-----------------------------'
print 'Time series with dates of interest:'
dis12, dis12_mean, dis12_std, dis12_vel = read_dis_xy(
ref_xsub, ref_ysub, dateList, h5timeseries, unit)
(_, caps, _)=ax2.errorbar(dates,dis12_mean,yerr=dis12_std,fmt='-ks',\
ms=markerSize2, lw=0, alpha=1,mfc=markerColor_ref2,mew=edgeWidth,\
elinewidth=edgeWidth,ecolor='black',capsize=markerSize*0.5)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis12_mean,
dis12_std)
except:
pass
##### 1.2.0 Read y/x
print '\n----------------------------------------------------'
print 'Point of Interest:'
xsub, ysub = check_yx(xsub, ysub, radius, ax, rectColor)
print 'x=' + str(xsub[0]) + ':' + str(xsub[1])
print 'y=' + str(ysub[0]) + ':' + str(ysub[1])
##### 1.2.1 Plot 2nd time series
try:
timeSeriesFile_2
print '-----------------------------'
print '2nd Time Series:'
dis2, dis2_mean, dis2_std, dis2_vel = read_dis_xy(
xsub, ysub, dateList_2, h5timeseries_2, unit)
(_, caps, _)=ax2.errorbar(dates_2,dis2_mean,yerr=dis2_std,fmt='-ko',\
ms=markerSize2, lw=0, alpha=1, mfc=markerColor2,\
elinewidth=0,ecolor='black',capsize=0)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis2_mean,
dis2_std)
except:
pass
##### 1.2.2 Plot 1st time series
print '-----------------------------'
print 'Time Series:'
dis, dis_mean, dis_std, dis_vel = read_dis_xy(xsub, ysub, dateList,
h5timeseries, unit)
(_, caps, _)=ax2.errorbar(dates,dis_mean,yerr=dis_std,fmt='-ko',\
ms=markerSize, lw=lineWidth, alpha=1, mfc=markerColor,\
elinewidth=edgeWidth,ecolor='black',capsize=markerSize*0.5)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis_mean, dis_std)
####################### Figure Format #######################
## x axis format
try:
ax2 = ptime.auto_adjust_xaxis_date(ax2, dateVecMinMax, fontSize)
except:
ax2 = ptime.auto_adjust_xaxis_date(ax2, datevector_all, fontSize)
## y axis format
ax2.set_ylabel('Displacement [' + unit + ']', fontsize=fontSize)
try:
lbound
hbound
ax2.set_ylim(lbound, hbound)
except:
disp_buf = 0.2 * (disp_max - disp_min)
ax2.set_ylim(disp_min - disp_buf, disp_max + disp_buf)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(fontSize)
## title
figTitle = 'x=' + str(xsub[0]) + ':' + str(xsub[1]) + ', y=' + str(
ysub[0]) + ':' + str(ysub[1])
try:
lonc = ullon + (xsub[0] + xsub[1]) / 2.0 * lon_step
latc = ullat + (ysub[0] + ysub[1]) / 2.0 * lat_step
figTitle += ', lalo=' + '%.4f,%.4f' % (latc, lonc)
except:
pass
ax2.set_title(figTitle)
################## Save and Output #####################
if saveFig == 'yes':
print '-----------------------------'
Delay = {}
Delay['displacement'] = dis
Delay['unit'] = unit
Delay['time'] = datevector
Delay['velocity'] = dis_vel[0]
Delay['velocity_unit'] = unit + '/yr'
Delay['velocity_std'] = dis_vel[4]
figBase = 'x' + str(xsub[0]) + '_' + str(xsub[1] - 1) + 'y' + str(
ysub[0]) + '_' + str(ysub[1] - 1)
sio.savemat(figBase + '_ts.mat', {'displacement': Delay})
print 'saved ' + figBase + '_ts.mat'
fig2.savefig(figBase + '_ts.pdf',
bbox_inches='tight',
transparent=True,
dpi=fig_dpi)
print 'saved ' + figBase + '_ts.pdf'
if dispFig == 'no':
fig.savefig(figBase + '_vel.png',
bbox_inches='tight',
transparent=True,
dpi=fig_dpi)
print 'saved ' + figBase + '_vel.png'
################################ Plot Code Package <end> #################################
########### 1. Plot Time Series with x/y ##########
try:
xsub
ysub
plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries)
except:
print 'No x/y input'
pass
########### 2. Plot Time Series with Click ##########
## similar to 1. Plot Time Series with x/y
def onclick(event):
ax2.cla()
xsub = [int(event.xdata)]
ysub = [int(event.ydata)]
plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries)
if dispFig == 'yes': plt.show()
try:
cid = fig.canvas.mpl_connect('button_press_event', onclick)
except:
pass
if dispFig == 'yes': plt.show()
def main(argv):
inps = cmdLineParse()
#print '\n********** Inversion: Time Series to Velocity ***********'
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
print 'input ' + k + ' file: ' + inps.timeseries_file
if not k == 'timeseries':
sys.exit('ERROR: input file is not timeseries!')
h5file = h5py.File(inps.timeseries_file)
#####################################
## Date Info
dateListAll = sorted(h5file[k].keys())
print '--------------------------------------------'
print 'Dates from input file: ' + str(len(dateListAll))
print dateListAll
inps.ex_date = get_exclude_date(inps, dateListAll)
dateList = sorted(list(set(dateListAll) - set(inps.ex_date)))
print '--------------------------------------------'
if len(dateList) == len(dateListAll):
print 'using all dates to calculate the velocity'
else:
print 'Dates used to estimate the velocity: ' + str(len(dateList))
print dateList
print '--------------------------------------------'
# Date Aux Info
dates, datevector = ptime.date_list2vector(dateList)
#####################################
## Inversion
# Design matrix
B = np.ones([len(datevector), 2])
B[:, 0] = datevector
#B_inv = np.linalg.pinv(B)
B_inv = np.dot(np.linalg.inv(np.dot(B.T, B)), B.T)
B_inv = np.array(B_inv, np.float32)
# Loading timeseries
print "Loading time series file: " + inps.timeseries_file + ' ...'
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
dateNum = len(dateList)
timeseries = np.zeros([dateNum, length * width], np.float32)
prog_bar = ptime.progress_bar(maxValue=dateNum, prefix='loading: ')
for i in range(dateNum):
date = dateList[i]
timeseries[i, :] = h5file[k].get(date)[:].flatten()
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5file.close()
# Velocity Inversion
print 'Calculating velocity ...'
X = np.dot(B_inv, timeseries)
velocity = np.reshape(X[0, :], [length, width])
print 'Calculating rmse ...'
timeseries_linear = np.dot(B, X)
timeseries_residual = timeseries - timeseries_linear
rmse = np.reshape(np.sqrt((np.sum((timeseries_residual)**2, 0)) / dateNum),
[length, width])
print 'Calculating the standard deviation of the estimated velocity ...'
s1 = np.sqrt(np.sum(timeseries_residual**2, 0) / (dateNum - 2))
s2 = np.sqrt(np.sum((datevector - np.mean(datevector))**2))
std = np.reshape(s1 / s2, [length, width])
# SSt=np.sum((timeseries-np.mean(timeseries,0))**2,0)
# SSres=np.sum(residual**2,0)
# SS_REG=SSt-SSres
# Rsquared=np.reshape(SS_REG/SSt,[length,width])
######################################################
# covariance of the velocities
#####################################
# Output file name
if not inps.outfile:
inps.outfile = 'velocity.h5'
inps.outfile_rmse = os.path.splitext(
inps.outfile)[0] + 'Rmse' + os.path.splitext(inps.outfile)[1]
inps.outfile_std = os.path.splitext(
inps.outfile)[0] + 'Std' + os.path.splitext(inps.outfile)[1]
inps.outfile_r2 = os.path.splitext(
inps.outfile)[0] + 'R2' + os.path.splitext(inps.outfile)[1]
# Attributes
atr['date1'] = datevector[0]
atr['date2'] = datevector[dateNum - 1]
# File Writing
print '--------------------------------------'
atr['FILE_TYPE'] = 'velocity'
print 'writing >>> ' + inps.outfile
writefile.write(velocity, atr, inps.outfile)
#atr['FILE_TYPE'] = 'rmse'
print 'writing >>> ' + inps.outfile_rmse
writefile.write(rmse, atr, inps.outfile_rmse)
#atr['FILE_TYPE'] = 'rmse'
print 'writing >>> ' + inps.outfile_std
writefile.write(std, atr, inps.outfile_std)
print 'Done.\n'
return inps.outfile
def main(argv):
##### Default
fontSize = 12
lineWidth = 2
markerColor = 'crimson'
markerSize = 16
disp_fig = 'no'
save_fig = 'yes'
save_list = 'yes'
ref_file = 'reference_date.txt'
drop_file = 'drop_date.txt'
##### Check Inputs
if len(sys.argv)>3:
try:
opts, args = getopt.getopt(argv,'h:f:m:o:x:y:',['help','circle='])
except getopt.GetoptError:
print 'Error in reading input options!'; Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"): Usage() ; sys.exit()
elif opt == '-f': File = arg
elif opt == '-m': maskFile = arg
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort()
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort()
elif opt == '--circle' : cir_par = [i for i in arg.split(';')]
#elif opt == '-o': outName = arg
else:
try: File = argv[0]
except: Usage(); sys.exit(1)
try: maskFile = argv[1]
except: pass
try: atr = readfile.read_attributes(File)
except: Usage(); sys.exit(1)
ext = os.path.splitext(File)[1].lower()
FileBase = os.path.basename(File).split(ext)[0]
outNameBase = 'spatialMean_'+FileBase
print '\n*************** Spatial Average ******************'
##### Input File Info
k = atr['FILE_TYPE']
print 'Input file is '+k
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
h5file = h5py.File(File)
epochList = h5file[k].keys();
epochList = sorted(epochList)
epochNum = len(epochList)
print 'number of epoch: '+str(epochNum)
dates,datevector = ptime.date_list2vector(epochList)
##### Mask Info
try:
Mask_orig,Matr = readfile.read(maskFile)
print 'mask file: '+maskFile
Masking = 'yes'
except:
print 'No mask. Use the whole area for ramp estimation.'
Masking = 'no'
Mask_orig=np.ones((length,width))
Mask = np.zeros((length,width))
Mask[:] = Mask_orig[:]
## Bounding Subset
try:
xsub
ysub
ysub,xsub = subset.check_subset_range(ysub,xsub,atr)
Mask[ysub[0]:ysub[1],xsub[0]:xsub[1]] = Mask_orig[ysub[0]:ysub[1],xsub[0]:xsub[1]]*2
#Mask[0:ysub[0],:] = 0
#Mask[ysub[1]:length,:] = 0
#Mask[:,0:xsub[0]] = 0
#Mask[:,xsub[1]:width] = 0
except:
Mask = Mask_orig*2
print 'No subset input.'
## Circle Inputs
try:
cir_par
for i in range(len(cir_par)):
cir_idx = circle_index(atr,cir_par[i])
Mask[cir_idx] = Mask_orig[cir_idx]
print 'Circle '+str(i)+': '+cir_par[i]
except: print 'No circle of interest input.'
## Mask output
idx = Mask == 2
idxNum = float(sum(sum(idx)))
fig = plt.figure()
plt.imshow(Mask,cmap='gray')
plt.savefig(outNameBase+'_mask.png',bbox_inches='tight')
print 'save mask to '+outNameBase+'_mask.png'
#fig.clf()
##### Calculation
meanList = np.zeros(epochNum)
pixPercent = np.zeros(epochNum)
pixT = 0.7
print 'calculating ...'
print ' Date Mean Percentage'
for i in range(epochNum):
epoch = epochList[i]
d = h5file[k].get(epoch)[:]
#d[Mask==0] = np.nan
meanList[i] = np.nanmean(d[idx])
pixPercent[i] = np.sum(d[idx] >= pixT)/idxNum
print epoch+' : %.2f %.1f%%'%(meanList[i],pixPercent[i]*100)
del d
h5file.close()
##### Reference date - Max Value
top3 = sorted(zip(meanList,epochList), reverse=True)[:3]
print '------------ Top 3 Mean ------------------'
print top3
## Write to txt file
fref = open(ref_file,'w')
fref.write(str(top3[0][1])+'\n')
fref.close()
print 'write optimal reference date to '+ref_file
idxMean = meanList == np.nanmax(meanList)
##### Drop dates - mean threshold
#meanT = 0.7
#idxMean = meanList < meanT
#print '------------ Mean Value < '+str(meanT)+' --------'
#print np.array(epochList)[idxMean]
#print meanList[idxMean]
##### Drop dates - good pixel percentage
pixNumT = 0.7
print '------------ Good Pixel Percentage < %.0f%% -------'%(pixNumT*100)
idxPix = pixPercent < pixNumT
dropEpochList = np.array(epochList)[idxPix]
print dropEpochList
print pixPercent[idxPix]
## Write to txt file
fdrop = open(drop_file,'w')
for i in range(len(dropEpochList)):
fdrop.write(str(dropEpochList[i])+'\n')
fdrop.close()
print 'write drop dates to '+drop_file
print '-------------------------------------------'
##### Display
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(211)
ax.plot(dates, meanList, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
#ax.plot([dates[0],dates[-1]],[meanT,meanT], '--b', lw=lineWidth)
#sc = ax.scatter(dates, np.tile(0.5,epochNum), c=meanList, s=22**2, alpha=0.3, vmin=0.0, vmax=1.0)
#ax.scatter(np.array(dates)[idxMean], 0.5, c=meanList[idxMean], s=22**2, alpha=1.0, vmin=0.0, vmax=1.0)
ax = ptime.adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Spatial Average Value', fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
#cbar = plt.colorbar(sc)
#cbar.set_label('Spatial Mean of Normalized Sum Epochs')
ax = fig.add_subplot(212)
ax.plot(dates, pixPercent, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
ax.plot([dates[0],dates[-1]],[pixNumT,pixNumT], '--b', lw=lineWidth)
ax = ptime.adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Percenrage of Pixels with Value > '+str(pixNumT), fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.0f}%'.format(i*100) for i in vals])
if save_fig == 'yes':
plt.savefig(outNameBase+'.png',bbox_inches='tight')
print 'save figure to '+outNameBase+'.png'
if disp_fig == 'yes':
plt.show()
##### Output
if save_list == 'yes':
epochList6 = ptime.yymmdd(epochList)
fl = open(outNameBase+'.txt','w')
for i in range(epochNum):
str_line = epochList6[i]+' %.2f %.2f\n'%(meanList[i],pixPercent[i])
fl.write(str_line)
fl.close()
print 'write data to '+outNameBase+'.txt\n'
