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 exclude_dates():
global inps, dateList
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(
list(set(inps.ex_date_list).intersection(dateList)))
inps.ex_dates = ptime.date_list2vector(inps.ex_date_list)[0]
inps.ex_idx_list = sorted(
[dateList.index(i) for i in inps.ex_date_list])
print('exclude date:' + str(inps.ex_date_list))
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)
date12_orig = pnet.get_date12_list(File)
bperp_list = ut.perp_baseline_ifgram2timeseries(File)[0].tolist()
date8_list = ptime.ifgram_date_list(File)
ax = pnet.plot_network(ax, date12_orig, date8_list, bperp_list)
print('display the network of interferogram of file: ' + 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_timeseries_info():
global atr, k, h5, dateList, tims, date_num, inps
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)
def plot_coherence_history(ax, date12_list, coherence_list, plot_dict={}):
'''Plot min/max Coherence of all interferograms for each date'''
# Figure Setting
keyList = list(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 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
if inps.ramp_type != 'no':
fig_name = os.path.splitext(
inps.timeseries_file)[0] + '_' + inps.ramp_type + '_rms.pdf'
else:
fig_name = os.path.splitext(inps.timeseries_file)[0] + '_rms.pdf'
if ut.update_file(fig_name, [exTxtFile, refTxtFile, inps.template_file],
check_readable=False):
fig = plt.figure()
ax = fig.add_subplot(111)
font_size = 12
dates, datevector = ptime.date_list2vector(date_list)
bar_width = ut.mode(np.diff(dates).tolist()) * 3 / 4
x_list = [i - bar_width / 2 for i in dates]
# 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:
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)
ax.plot(np.array([xmin, xmax]), np.array([inps.min_rms, inps.min_rms]),
'-')
# axis format
ax = pnet.auto_adjust_yaxis(ax,
rms_list + [inps.min_rms],
font_size,
ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel('Root Mean Square (m)', fontsize=font_size)
ax.yaxis.set_ticks_position('both')
if inps.save_reference_date or inps.save_exclude_date:
plt.legend()
# save figure
fig.savefig(fig_name, bbox_inches='tight')
print('save figure to file: ' + fig_name)
return
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 = list(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]
## 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)
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 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')
# 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')
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
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 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 = list(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 = list(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 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