def read_exclude_date(ex_date_list, date_list_all):
"""Read exclude dates info
Parameters: ex_date_list : list of string, date in YYMMDD or YYYYMMDD format,
or text file with date in it
date_list_all : list of string, date in YYYYMMDD format
Returns: drop_date : 1D array of bool in size of (num_date,)
"""
# Read exclude date input
if ex_date_list:
tempList = []
for d in ex_date_list:
if os.path.isfile(d):
tempList += ptime.read_date_list(d)
else:
tempList.append(d)
ex_date_list = sorted(ptime.yyyymmdd(tempList))
print(('exclude the following dates for DEM error estimation:'
' ({})\n{}').format(len(ex_date_list), ex_date_list))
else:
ex_date_list = []
# convert to mark array
drop_date = np.array([i not in ex_date_list for i in date_list_all],
dtype=np.bool_)
return drop_date
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 read_exclude_date(input_ex_date, dateListAll):
# default value
ex_date_list = []
ex_dates = []
ex_flag = np.ones((len(dateListAll)), np.bool_)
ex_date_list = ptime.read_date_list(input_ex_date, date_list_all=dateListAll)
if ex_date_list:
ex_dates = ptime.date_list2vector(ex_date_list)[0]
for i in ex_date_list:
ex_flag[dateListAll.index(i)] = False
vprint('exclude date:'+str(ex_date_list))
return ex_date_list, ex_dates, ex_flag
def read_exclude_date(ex_date_list, date_list_all, print_msg=True):
"""Read exclude dates info
Parameters: ex_date_list : list of string, date in YYMMDD or YYYYMMDD format,
or text file with date in it
date_list_all : list of string, date in YYYYMMDD format
Returns: drop_date : 1D array of bool in size of (num_date,)
"""
# Read exclude date input
ex_date_list = ptime.read_date_list(ex_date_list)
if ex_date_list and print_msg:
print(('exclude the following dates for DEM error estimation:'
' ({})\n{}').format(len(ex_date_list), ex_date_list))
# convert to mark array
drop_date = np.array([i not in ex_date_list for i in date_list_all],
dtype=np.bool_)
return drop_date, ex_date_list
def read_ref_date(inps):
if not inps.refDate:
print('No reference date input, skip this step.')
return inps.timeseries_file
elif os.path.isfile(inps.refDate):
print('read reference date from file: ' + inps.refDate)
inps.refDate = ptime.read_date_list(inps.refDate)[0]
inps.refDate = ptime.yyyymmdd(inps.refDate)
print('input reference date: {}'.format(inps.refDate))
# check input reference date
date_list = timeseries(inps.timeseries_file[0]).get_date_list()
if inps.refDate not in date_list:
msg = 'input reference date: {} is not found.'.format(inps.refDate)
msg += '\nAll available dates:\n{}'.format(date_list)
raise Exception(msg)
return inps.refDate
def read_exclude_date(inps, dateListAll):
# Merge ex_date/startDate/endDate into ex_date
yy_list_all = ptime.yyyymmdd2years(dateListAll)
exDateList = []
# 1. template_file
if inps.template_file:
print('read option from template file: ' + inps.template_file)
inps = read_template2inps(inps.template_file, inps)
# 2. ex_date
input_ex_date = list(inps.excludeDate)
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)]
exDateList += list(set(ex_date) - set(exDateList))
# delete dates not existed in input file
exDateList = list(set(exDateList).intersection(dateListAll))
print('exclude date:' + str(exDateList))
# 3. startDate
if inps.startDate:
print('start date: ' + inps.startDate)
yy_min = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.startDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] < yy_min and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
# 4. endDate
if inps.endDate:
print('end date: ' + inps.endDate)
yy_max = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.endDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] > yy_max and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
exDateList = list(set(exDateList))
return exDateList
def read_ref_date(inps):
if not inps.refDate:
print('No reference date input, skip this step.')
return inps.timeseries_file
elif inps.refDate.lower() == 'minrms':
print('-' * 50)
print('auto choose reference date based on minimum residual RMS')
rms_list, date_list = ut.get_residual_rms(inps.resid_file,
inps.maskFile,
inps.ramp_type)[0:2]
ref_idx = np.argmin(rms_list)
inps.refDate = date_list[ref_idx]
print('date with minimum residual RMS: %s - %.4f' %
(inps.refDate, rms_list[ref_idx]))
print('-' * 50)
elif os.path.isfile(inps.refDate):
print('read reference date from file: ' + inps.refDate)
inps.refDate = ptime.read_date_list(inps.refDate)[0]
return inps.refDate
def read_exclude_date(input_ex_date, dateListAll):
ex_date_list = []
ex_dates = []
ex_flag = np.ones((len(dateListAll)), np.bool_)
if input_ex_date:
input_ex_date = list(input_ex_date)
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)]
ex_date_list += list(set(ex_date) - set(ex_date_list))
# delete dates not existed in input file
ex_date_list = sorted(
list(set(ex_date_list).intersection(dateListAll)))
ex_dates = ptime.date_list2vector(ex_date_list)[0]
for i in ex_date_list:
ex_flag[dateListAll.index(i)] = False
print('exclude date:' + str(ex_date_list))
return ex_date_list, ex_dates, ex_flag
def read_exclude_date(inps, dateListAll):
# Merge ex_date/startDate/endDate into ex_date
yy_list_all = ptime.yyyymmdd2years(dateListAll)
exDateList = []
# 1. template_file
if inps.template_file:
print('read option from template file: ' + inps.template_file)
inps = read_template2inps(inps.template_file, inps)
# 2. ex_date
exDateList += ptime.read_date_list(list(inps.excludeDate),
date_list_all=dateListAll)
if exDateList:
print('exclude date:' + str(exDateList))
# 3. startDate
if inps.startDate:
print('start date: ' + inps.startDate)
yy_min = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.startDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] < yy_min and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
# 4. endDate
if inps.endDate:
print('end date: ' + inps.endDate)
yy_max = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.endDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] > yy_max and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
exDateList = list(set(exDateList))
return exDateList
def main(iargs=None):
# Actual code.
inps = cmd_line_parse(iargs)
# 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 in ['timeseries', 'GIANT_TS']:
raise ValueError('Only timeseries file is supported!')
obj = timeseries(inps.timeseries_file)
obj.open()
h5 = h5py.File(inps.timeseries_file, 'r')
if k in ['GIANT_TS']:
dateList = [
dt.fromordinal(int(i)).strftime('%Y%m%d')
for i in h5['dates'][:].tolist()
]
else:
dateList = obj.dateList
date_num = len(dateList)
inps.dates, inps.yearList = 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(
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))
# Zero displacement for 1st acquisition
if inps.zero_first:
if inps.ex_date_list:
inps.zero_idx = min(
list(set(range(date_num)) - set(inps.ex_idx_list)))
else:
inps.zero_idx = 0
# File Size
length = int(atr['LENGTH'])
width = int(atr['WIDTH'])
print('data size in [y0,y1,x0,x1]: [%d, %d, %d, %d]' %
(0, length, 0, width))
try:
ullon = float(atr['X_FIRST'])
ullat = float(atr['Y_FIRST'])
lon_step = float(atr['X_STEP'])
lat_step = float(atr['Y_STEP'])
lrlon = ullon + width * lon_step
lrlat = ullat + length * lat_step
print('data size in [lat0,lat1,lon0,lon1]: [%.4f, %.4f, %.4f, %.4f]' %
(lrlat, ullat, ullon, lrlon))
except:
pass
# Initial Pixel Coord
if inps.lalo and 'Y_FIRST' in atr.keys():
y = int((inps.lalo[0] - ullat) / lat_step + 0.5)
x = int((inps.lalo[1] - ullon) / lon_step + 0.5)
inps.yx = [y, x]
if inps.ref_lalo and 'Y_FIRST' in atr.keys():
y = int((inps.ref_lalo[0] - ullat) / lat_step + 0.5)
x = int((inps.ref_lalo[1] - ullon) / lon_step + 0.5)
inps.ref_yx = [y, x]
# Display Unit
if inps.disp_unit == 'cm':
inps.unit_fac = 100.0
elif inps.disp_unit == 'm':
inps.unit_fac = 1.0
elif inps.disp_unit == 'dm':
inps.unit_fac = 10.0
elif inps.disp_unit == 'mm':
inps.unit_fac = 1000.0
elif inps.disp_unit == 'km':
inps.unit_fac = 0.001
else:
raise ValueError('Un-recognized unit: ' + inps.disp_unit)
if k in ['GIANT_TS']:
print('data unit: mm')
inps.unit_fac *= 0.001
else:
print('data unit: m')
print('display unit: ' + inps.disp_unit)
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(atr)
else:
inps.flip_ud = False
inps.left_lr = False
# Mask file
if not inps.mask_file:
if os.path.basename(inps.timeseries_file).startswith('geo_'):
file_list = ['geo_maskTempCoh.h5']
else:
file_list = ['maskTempCoh.h5', 'mask.h5']
try:
inps.mask_file = ut.get_file_list(file_list)[0]
except:
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
mask[mask != 0] = 1
print('load mask from file: ' + inps.mask_file)
except:
mask = None
print('No mask used.')
# Initial Map
d_v = readfile.read(
inps.timeseries_file,
datasetName=dateList[inps.epoch_num])[0] * inps.unit_fac
if inps.ref_date:
inps.ref_d_v = readfile.read(
inps.timeseries_file, datasetName=inps.ref_date)[0] * inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim_mat:
inps.ylim_mat = data_lim
print('Initial data range: ' + str(data_lim))
print('Display data range: ' + str(inps.ylim_mat))
# Fig 1 - Cumulative Displacement Map
if not inps.disp_fig:
plt.switch_backend('Agg')
fig_v = plt.figure('Cumulative Displacement')
# Axes 1
#ax_v = fig_v.add_subplot(111)
# ax_v.set_position([0.125,0.25,0.75,0.65])
# This works on OSX. Original worked on Linux.
# rect[left, bottom, width, height]
ax_v = fig_v.add_axes([0.125, 0.25, 0.75, 0.65])
if inps.dem_file:
dem = readfile.read(inps.dem_file, datasetName='height')[0]
ax_v = pp.plot_dem_yx(ax_v, dem)
img = ax_v.imshow(d_v,
cmap=inps.colormap,
clim=inps.ylim_mat,
interpolation='nearest')
# Reference Pixel
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
ax_v.plot(inps.ref_yx[1], inps.ref_yx[0], 'ks', ms=6)
else:
try:
ax_v.plot(int(atr['REF_X']), int(atr['REF_Y']), 'ks', ms=6)
except:
pass
# Initial Pixel
if inps.yx:
ax_v.plot(inps.yx[1], inps.yx[0], 'ro', markeredgecolor='black')
ax_v.set_xlim(0, np.shape(d_v)[1])
ax_v.set_ylim(np.shape(d_v)[0], 0)
# Status Bar
def format_coord(x, y):
col = int(x + 0.5)
row = int(y + 0.5)
if 0 <= col < width and 0 <= row < length:
z = d_v[row, col]
try:
lon = ullon + x * lon_step
lat = ullat + y * lat_step
return 'x=%.0f, y=%.0f, value=%.4f, lon=%.4f, lat=%.4f' % (
x, y, z, lon, lat)
except:
return 'x=%.0f, y=%.0f, value=%.4f' % (x, y, z)
ax_v.format_coord = format_coord
# Title and Axis Label
ax_v.set_title(
'N = %d, Time = %s' %
(inps.epoch_num, inps.dates[inps.epoch_num].strftime('%Y-%m-%d')))
if not 'Y_FIRST' in atr.keys():
ax_v.set_xlabel('Range')
ax_v.set_ylabel('Azimuth')
# Flip axis
if inps.flip_lr:
ax_v.invert_xaxis()
print('flip map left and right')
if inps.flip_ud:
ax_v.invert_yaxis()
print('flip map up and down')
# Colorbar
cbar = fig_v.colorbar(img, orientation='vertical')
cbar.set_label('Displacement [%s]' % inps.disp_unit)
# Axes 2 - Time Slider
ax_time = fig_v.add_axes([0.125, 0.1, 0.6, 0.07],
facecolor='lightgoldenrodyellow',
yticks=[])
tslider = Slider(ax_time,
'Years',
inps.yearList[0],
inps.yearList[-1],
valinit=inps.yearList[inps.epoch_num])
tslider.ax.bar(inps.yearList,
np.ones(len(inps.yearList)),
facecolor='black',
width=0.01,
ecolor=None)
tslider.ax.set_xticks(
np.round(
np.linspace(inps.yearList[0], inps.yearList[-1], num=5) * 100) /
100)
def time_slider_update(val):
"""Update Displacement Map using Slider"""
timein = tslider.val
idx_nearest = np.argmin(np.abs(np.array(inps.yearList) - timein))
ax_v.set_title(
'N = %d, Time = %s' %
(idx_nearest, inps.dates[idx_nearest].strftime('%Y-%m-%d')))
d_v = h5[dateList[idx_nearest]][:] * inps.unit_fac
if inps.ref_date:
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
img.set_data(d_v)
fig_v.canvas.draw()
tslider.on_changed(time_slider_update)
# Fig 2 - Time Series Displacement - Point
fig_ts = plt.figure('Time series - point', figsize=inps.fig_size)
ax_ts = fig_ts.add_subplot(111)
# Read Error List
inps.error_ts = None
if inps.error_file:
error_fileContent = np.loadtxt(inps.error_file,
dtype=bytes).astype(str)
inps.error_ts = error_fileContent[:, 1].astype(
np.float) * inps.unit_fac
if inps.ex_date_list:
e_ts = inps.error_ts[:]
inps.ex_error_ts = np.array([e_ts[i] for i in inps.ex_idx_list])
inps.error_ts = np.array([
e_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
])
def plot_timeseries_errorbar(ax, dis_ts, inps):
dates = list(inps.dates)
d_ts = dis_ts[:]
if inps.ex_date_list:
# Update displacement time-series
dates = sorted(list(set(inps.dates) - set(inps.ex_dates)))
ex_d_ts = np.array([dis_ts[i] for i in inps.ex_idx_list])
d_ts = np.array([
dis_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
])
# Plot excluded dates
(_, caps, _) = ax.errorbar(inps.ex_dates,
ex_d_ts,
yerr=inps.ex_error_ts,
fmt='-o',
color='gray',
ms=inps.marker_size,
lw=0,
alpha=1,
mfc='gray',
elinewidth=inps.edge_width,
ecolor='black',
capsize=inps.marker_size * 0.5)
for cap in caps:
cap.set_markeredgewidth(inps.edge_width)
# Plot kept dates
(_, caps, _) = ax.errorbar(dates,
d_ts,
yerr=inps.error_ts,
fmt='-o',
ms=inps.marker_size,
lw=0,
alpha=1,
elinewidth=inps.edge_width,
ecolor='black',
capsize=inps.marker_size * 0.5)
for cap in caps:
cap.set_markeredgewidth(inps.edge_width)
return ax
def plot_timeseries_scatter(ax, dis_ts, inps):
dates = list(inps.dates)
d_ts = dis_ts[:]
if inps.ex_date_list:
# Update displacement time-series
dates = sorted(list(set(inps.dates) - set(inps.ex_dates)))
ex_d_ts = np.array([dis_ts[i] for i in inps.ex_idx_list])
d_ts = np.array([
dis_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
])
# Plot excluded dates
ax.scatter(inps.ex_dates,
ex_d_ts,
s=inps.marker_size**2,
color='gray') # color='crimson'
# Plot kept dates
ax.scatter(dates, d_ts, s=inps.marker_size**2)
return ax
def update_timeseries(ax_ts, y, x):
"""Plot point time series displacement at pixel [y, x]"""
d_ts = read_timeseries_yx(
inps.timeseries_file, y, x, ref_yx=inps.ref_yx) * inps.unit_fac
# for date in dateList:
# d = h5[k].get(date)[y,x]
# if inps.ref_yx:
# d -= h5[k].get(date)[inps.ref_yx[0], inps.ref_yx[1]]
# d_ts.append(d*inps.unit_fac)
if inps.zero_first:
d_ts -= d_ts[inps.zero_idx]
ax_ts.cla()
if inps.error_file:
ax_ts = plot_timeseries_errorbar(ax_ts, d_ts, inps)
else:
ax_ts = plot_timeseries_scatter(ax_ts, d_ts, inps)
if inps.ylim:
ax_ts.set_ylim(inps.ylim)
for tick in ax_ts.yaxis.get_major_ticks():
tick.label.set_fontsize(inps.font_size)
# Title
title_ts = 'Y = %d, X = %d' % (y, x)
try:
lat = ullat + y * lat_step
lon = ullon + x * lon_step
title_ts += ', lat = %.4f, lon = %.4f' % (lat, lon)
except:
pass
if inps.disp_title:
ax_ts.set_title(title_ts)
ax_ts = pp.auto_adjust_xaxis_date(ax_ts,
inps.yearList,
fontSize=inps.font_size)[0]
ax_ts.set_xlabel('Time', fontsize=inps.font_size)
ax_ts.set_ylabel('Displacement [%s]' % inps.disp_unit,
fontsize=inps.font_size)
fig_ts.canvas.draw()
# Print to terminal
print('\n---------------------------------------')
print(title_ts)
print(d_ts)
# Slope estimation
if inps.ex_date_list:
inps.yearList_kept = [
inps.yearList[i] for i in range(date_num)
if i not in inps.ex_idx_list
]
d_ts_kept = [
d_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
]
d_slope = stats.linregress(np.array(inps.yearList_kept),
np.array(d_ts_kept))
else:
d_slope = stats.linregress(np.array(inps.yearList), np.array(d_ts))
print('linear velocity: %.2f +/- %.2f [%s/yr]' %
(d_slope[0], d_slope[4], inps.disp_unit))
return d_ts
# Initial point time series plot
if inps.yx:
d_ts = update_timeseries(ax_ts, inps.yx[0], inps.yx[1])
else:
d_ts = np.zeros(len(inps.yearList))
ax_ts = plot_timeseries_scatter(ax_ts, d_ts, inps)
def plot_timeseries_event(event):
"""Event function to get y/x from button press"""
if event.inaxes != ax_v:
return
ii = int(event.ydata + 0.5)
jj = int(event.xdata + 0.5)
d_ts = update_timeseries(ax_ts, ii, jj)
# Output
if inps.save_fig and inps.yx:
print('save info for pixel ' + str(inps.yx))
if not inps.fig_base:
inps.fig_base = 'y%d_x%d' % (inps.yx[0], inps.yx[1])
# TXT - point time series
outName = inps.fig_base + '_ts.txt'
header_info = 'timeseries_file=' + inps.timeseries_file
header_info += '\ny=%d, x=%d' % (inps.yx[0], inps.yx[1])
try:
lat = ullat + inps.yx[0] * lat_step
lon = ullon + inps.yx[1] * lon_step
header_info += '\nlat=%.6f, lon=%.6f' % (lat, lon)
except:
pass
if inps.ref_yx:
header_info += '\nreference pixel: y=%d, x=%d' % (inps.ref_yx[0],
inps.ref_yx[1])
else:
header_info += '\nreference pixel: y=%s, x=%s' % (atr['REF_Y'],
atr['REF_X'])
header_info += '\nunit=m/yr'
np.savetxt(outName,
list(zip(np.array(dateList),
np.array(d_ts) / inps.unit_fac)),
fmt='%s',
delimiter=' ',
header=header_info)
print('save time series displacement in meter to ' + outName)
# Figure - point time series
outName = inps.fig_base + '_ts.pdf'
fig_ts.savefig(outName,
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save time series plot to ' + outName)
# Figure - map
outName = inps.fig_base + '_' + dateList[inps.epoch_num] + '.png'
fig_v.savefig(outName,
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save map plot to ' + outName)
# Final linking of the canvas to the plots.
cid = fig_v.canvas.mpl_connect('button_press_event', plot_timeseries_event)
if inps.disp_fig:
plt.show()
fig_v.canvas.mpl_disconnect(cid)
