def _adjust_ts_axis(ax, inps):
ax.tick_params(which='both', direction='in', labelsize=inps.font_size, bottom=True, top=True, left=True, right=True)
ax = pp.auto_adjust_xaxis_date(ax, inps.yearList, fontsize=inps.font_size)[0]
ax.set_xlabel('Time [years]', fontsize=inps.font_size)
ax.set_ylabel('Displacement [{}]'.format(inps.disp_unit), fontsize=inps.font_size)
ax.set_ylim(inps.ylim)
return ax
def pixel_ts(lat, lon, font_size, fig_dpi):
'''
format : pixel_ts(lat, lon, font_size, fig_dpi)
plots the time-series at a pixel
'''
dates, dis = ut.read_timeseries_lalo(lat, lon, ts_file=ts_file, lookup_file=geom_file)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[6, 3])
ax.scatter(dates, dis, marker='^', s=6**2, facecolors='none', edgecolors='k', linewidth=1.)
# axis format
pp.auto_adjust_xaxis_date(ax, dates, fontsize=font_size)
ax.tick_params(which='both', direction='in', labelsize=font_size, bottom=True, top=True, left=True, right=True)
ax.set_xlabel('Time [yr]', fontsize=font_size)
ax.set_ylabel('LOS displacement [m]', fontsize=font_size)
# output
out_file = proj_dir+ '/pic/lat' + str(lat) + 'lon' + str(lon) + '.png'
plt.savefig(out_file, bbox_inches='tight', dpi=fig_dpi)
print('save to file: '+out_file)
def main(iargs=None):
inps = cmd_line_parse(iargs)
print('\n*************** Spatial Average ******************')
mean_list, date_list = ut.spatial_average(inps.file,
datasetName=inps.datasetName,
maskFile=inps.mask_file,
saveList=True)
atr = readfile.read_attribute(inps.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, '-o')#, lw=2, ms=16, alpha=0.7) #, mfc='crimson')
ax.set_title('Spatial Average', fontsize=12)
ax = pp.auto_adjust_xaxis_date(ax, datevector)[0]
ax.set_xlabel('Time [years]', fontsize=12)
ax.set_ylabel('Mean', fontsize=12)
plt.show()
return
def plot_point_timeseries(self, yx):
"""Plot point displacement time-series at pixel [y, x]
Parameters: yx : list of 2 int
Returns: ts_dis : 1D np.array in size of (num_date,) for the 1st file
m_strs : list of strings for the est. time func. param. for the 1st file
"""
ax = self.ax_pts
ax.cla()
# plot scatter in different size for different files
num_file = len(self.ts_data)
if num_file <= 2: ms_step = 4
elif num_file == 3: ms_step = 3
elif num_file == 4: ms_step = 2
elif num_file >= 5: ms_step = 1
# get local Y/X coord for the subsetted and multilooked 3D data cube
(y, x) = subset_and_multilook_yx(yx, self.pix_box, self.multilook_num)
for i in range(num_file-1, -1, -1):
# get displacement data
ts_dis = self.ts_data[i][:, y, x]
# fit time func
m_strs, ts_fit, ts_fit_lim = fit_time_func(
model=self.model,
date_list=self.date_list,
ts_dis=ts_dis,
unit_fac=self.unit_fac,
G_fit=self.G_fit,
seconds=self.seconds)
if self.zero_first:
off = ts_dis[self.zero_idx]
ts_dis -= off
ts_fit -= off
if self.offset:
ts_dis += self.offset * (num_file - 1 - i)
ts_fit += self.offset * (num_file - 1 - i)
# plot
if not np.all(np.isnan(ts_dis)):
ppar = argparse.Namespace()
ppar.label = self.file_label[i]
ppar.ms = self.marker_size - ms_step * (num_file - 1 - i)
ppar.mfc = pp.mplColors[num_file - 1 - i] if self.mask[y, x] != 0 else 'gray'
self.ts_plot_func(ax, ts_dis, self, ppar)
# plot model prediction
if self.plot_model:
fpar = argparse.Namespace()
fpar.linewidth = 3
fpar.color = 'C1' if num_file == 1 else ppar.mfc
fpar.fontsize = self.font_size
if not self.plot_model_conf_int:
ts_fit_lim = None
plot_ts_fit(ax, ts_fit, self, fpar,
m_strs=m_strs,
ts_fit_lim=ts_fit_lim)
# axis format
ax.tick_params(which='both', direction='in', labelsize=self.font_size, bottom=True, top=True, left=True, right=True)
pp.auto_adjust_xaxis_date(ax, self.yearList, fontsize=self.font_size)
ax.set_ylabel('Displacement [{}]'.format(self.disp_unit), fontsize=self.font_size)
ax.set_ylim(self.ylim)
if self.tick_right:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
# title
title = get_ts_title(yx[0], yx[1], self.coord)
title += ' (masked out)' if self.mask[y, x] == 0 else ''
if self.disp_title:
ax.set_title(title, fontsize=self.font_size)
# legend
if len(self.ts_data) > 1:
ax.legend()
# Print to terminal
vprint('\n---------------------------------------')
vprint(title)
float_formatter = lambda x: [float('{:.2f}'.format(i)) for i in x]
vprint(float_formatter(ts_dis))
if not np.all(np.isnan(ts_dis)):
# min/max displacement
vprint('time-series range: [{:.2f}, {:.2f}] {}'.format(np.nanmin(ts_dis), np.nanmax(ts_dis), self.disp_unit))
# time func param
vprint('time function parameters:')
for m_str in m_strs:
vprint(f' {m_str}')
# update figure
self.fig_pts.canvas.draw()
return ts_dis, m_strs
def update_timeseries(y, x, plot_number, data_only=False):
'''Plot point time series displacement at pixel [y, x]
Inputs:
y : int, y coordinate to update
x : int, x coordinate to update
plot_number : int, plot number (1/2) to update
data_only : bool, compute and return data only, or set remainder of plot variables
Outputs:
d_ts : [float], timeseries data at x, y point
'''
global fig_ts, ax_ts, second_plot_axis, inps, dateList, h5, k, inps, tims, fig_v, date_num, d_ts
set_scatter_coords(plot_number, x, y)
if plot_number == 1:
axis = ax_ts
else:
axis = second_plot_axis
d_ts = []
for i, date in enumerate(dateList):
d = h5['timeseries'][i][y, x]
if inps.ref_yx:
d -= h5['timeseries'][i][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]
# Returns computed data without setting any plot or figure parameters
if data_only:
return d_ts
axis.cla()
if inps.error_file:
axis = plot_timeseries_errorbar(ax_ts, d_ts, inps)
else:
axis, scatter = plot_timeseries_scatter(axis, d_ts, inps, plot_number)
scatter.set_label('2')
if inps.ylim:
axis.set_ylim(inps.ylim)
for tick in axis.yaxis.get_major_ticks():
tick.label.set_fontsize(inps.font_size)
# Title
title_ts = set_axis_title(x, y)
if inps.disp_title:
axis.set_title(title_ts)
axis = pp.auto_adjust_xaxis_date(axis, tims, fontsize=inps.font_size)[0]
axis.set_xlabel('Time', fontsize=inps.font_size)
axis.set_ylabel('Displacement [%s]' % inps.disp_unit,
fontsize=inps.font_size)
fig_v.canvas.draw()
# Print to terminal
print('\n---------------------------------------')
print(title_ts)
print(d_ts)
# Slope estimation
estimate_slope()
return d_ts
def plot_rms_bar(ax,
date_list,
rms,
cutoff=3.,
font_size=12,
tick_year_num=1,
legend_loc='best',
disp_legend=True,
disp_side_plot=True,
disp_thres_text=False,
ylabel=r'Residual Phase $\hat \phi_{resid}$ RMS [mm]'):
""" Bar plot Phase Residual RMS
Parameters: ax : Axes object
date_list : list of string in YYYYMMDD format
rms : 1D np.array of float for RMS value in mm
cutoff : cutoff value of MAD outlier detection
tick_year_num : int, number of years per major tick
legend_loc : 'upper right' or (0.5, 0.5)
Returns: ax : Axes object
"""
dates, datevector = ptime.date_list2vector(date_list)
dates = np.array(dates)
try:
bar_width = min(ut.most_common(np.diff(dates).tolist(), k=2)) * 3 / 4
except:
bar_width = np.min(np.diff(dates).tolist()) * 3 / 4
rms = np.array(rms)
# Plot all dates
ax.bar(dates, rms, bar_width.days, color=pp.mplColors[0])
# Plot reference date
ref_idx = np.argmin(rms)
ax.bar(dates[ref_idx],
rms[ref_idx],
bar_width.days,
color=pp.mplColors[1],
label='Reference date')
# Plot exclude dates
rms_threshold = ut.median_abs_deviation_threshold(rms,
center=0.,
cutoff=cutoff)
ex_idx = rms > rms_threshold
if not np.all(ex_idx == False):
ax.bar(dates[ex_idx],
rms[ex_idx],
bar_width.days,
color='darkgray',
label='Exclude date')
# Plot rms_threshold line
(ax, xmin, xmax) = pp.auto_adjust_xaxis_date(ax,
datevector,
font_size,
every_year=tick_year_num)
ax.plot(np.array([xmin, xmax]),
np.array([rms_threshold, rms_threshold]),
'--k',
label='Median Abs Dev * {}'.format(cutoff))
# axis format
ax = pp.auto_adjust_yaxis(ax,
np.append(rms, rms_threshold),
font_size,
ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel(ylabel, fontsize=font_size)
ax.tick_params(which='both',
direction='in',
labelsize=font_size,
bottom=True,
top=True,
left=True,
right=True)
# 2nd axes for circles
if disp_side_plot:
divider = make_axes_locatable(ax)
ax2 = divider.append_axes("right", "10%", pad="2%")
ax2.plot(np.ones(rms.shape, np.float32) * 0.5,
rms,
'o',
mfc='none',
color=pp.mplColors[0])
ax2.plot(np.ones(rms.shape, np.float32)[ref_idx] * 0.5,
rms[ref_idx],
'o',
mfc='none',
color=pp.mplColors[1])
if not np.all(ex_idx == False):
ax2.plot(np.ones(rms.shape, np.float32)[ex_idx] * 0.5,
rms[ex_idx],
'o',
mfc='none',
color='darkgray')
ax2.plot(np.array([0, 1]), np.array([rms_threshold, rms_threshold]),
'--k')
ax2.set_ylim(ax.get_ylim())
ax2.set_xlim([0, 1])
ax2.tick_params(which='both',
direction='in',
labelsize=font_size,
bottom=True,
top=True,
left=True,
right=True)
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticklabels([])
if disp_legend:
ax.legend(loc=legend_loc, frameon=False, fontsize=font_size)
# rms_threshold text
if disp_thres_text:
ymin, ymax = ax.get_ylim()
yoff = (ymax - ymin) * 0.1
if (rms_threshold - ymin) > 0.5 * (ymax - ymin):
yoff *= -1.
ax.annotate('Median Abs Dev * {}'.format(cutoff),
xy=(xmin + (xmax - xmin) * 0.05, rms_threshold + yoff),
color='k',
xycoords='data',
fontsize=font_size)
return ax
def plot_rms_bar(ax, date_list, rms, cutoff=3., font_size=12,
tick_year_num=1, legend_loc='best',
disp_legend=True, disp_side_plot=True, disp_thres_text=True,
ylabel=r'Residual Phase $\hat \phi_{resid}$ RMS [mm]'):
""" Bar plot Phase Residual RMS
Parameters: ax : Axes object
date_list : list of string in YYYYMMDD format
rms : 1D np.array of float for RMS value in mm
cutoff : cutoff value of MAD outlier detection
tick_year_num : int, number of years per major tick
legend_loc : 'upper right' or (0.5, 0.5)
Returns: ax : Axes object
"""
dates, datevector = ptime.date_list2vector(date_list)
dates = np.array(dates)
try:
bar_width = min(ut.most_common(np.diff(dates).tolist(), k=2))*3/4
except:
bar_width = np.min(np.diff(dates).tolist())*3/4
rms = np.array(rms)
# Plot all dates
ax.bar(dates, rms, bar_width.days, color=pp.mplColors[0])
# Plot reference date
ref_idx = np.argmin(rms)
ax.bar(dates[ref_idx], rms[ref_idx], bar_width.days, color=pp.mplColors[1], label='Reference date')
# Plot exclude dates
rms_threshold = ut.median_abs_deviation_threshold(rms, center=0., cutoff=cutoff)
ex_idx = rms > rms_threshold
if not np.all(ex_idx==False):
ax.bar(dates[ex_idx], rms[ex_idx], bar_width.days, color='darkgray', label='Exclude date')
# Plot rms_threshold line
(ax, xmin, xmax) = pp.auto_adjust_xaxis_date(ax, datevector, font_size, every_year=tick_year_num)
ax.plot(np.array([xmin, xmax]), np.array([rms_threshold, rms_threshold]), '--k', label='RMS threshold')
# axis format
ax = pp.auto_adjust_yaxis(ax, np.append(rms, rms_threshold), font_size, ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel(ylabel, fontsize=font_size)
ax.tick_params(which='both', direction='in', labelsize=font_size,
bottom=True, top=True, left=True, right=True)
# 2nd axes for circles
if disp_side_plot:
divider = make_axes_locatable(ax)
ax2 = divider.append_axes("right", "10%", pad="2%")
ax2.plot(np.ones(rms.shape, np.float32) * 0.5, rms, 'o', mfc='none', color=pp.mplColors[0])
ax2.plot(np.ones(rms.shape, np.float32)[ref_idx] * 0.5, rms[ref_idx], 'o', mfc='none', color=pp.mplColors[1])
if not np.all(ex_idx==False):
ax2.plot(np.ones(rms.shape, np.float32)[ex_idx] * 0.5, rms[ex_idx], 'o', mfc='none', color='darkgray')
ax2.plot(np.array([0, 1]), np.array([rms_threshold, rms_threshold]), '--k')
ax2.set_ylim(ax.get_ylim())
ax2.set_xlim([0, 1])
ax2.tick_params(which='both', direction='in', labelsize=font_size,
bottom=True, top=True, left=True, right=True)
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticklabels([])
if disp_legend:
ax.legend(loc=legend_loc, frameon=False, fontsize=font_size)
# rms_threshold text
if disp_thres_text:
ymin, ymax = ax.get_ylim()
yoff = (ymax - ymin) * 0.1
if (rms_threshold - ymin) > 0.5 * (ymax - ymin):
yoff *= -1.
ax.annotate('Median Abs Dev * {}'.format(cutoff),
xy=(xmin + (xmax-xmin)*0.05, rms_threshold + yoff ),
color='k', xycoords='data', fontsize=font_size)
return ax
def update_timeseries(y, x, plot_number, data_only=False):
'''Plot point time series displacement at pixel [y, x]
Inputs:
y : int, y coordinate to update
x : int, x coordinate to update
plot_number : int, plot number (1/2) to update
data_only : bool, compute and return data only, or set remainder of plot variables
Outputs:
d_ts : [float], timeseries data at x, y point
'''
global fig_ts, ax_ts, second_plot_axis, inps, dateList, h5, k, inps, tims, fig_v, date_num, d_ts
set_scatter_coords(plot_number, x, y)
if plot_number == 1:
axis = ax_ts
else:
axis = second_plot_axis
d_ts = []
for i, date in enumerate(dateList):
d = h5['timeseries'][i][y, x]
if inps.ref_yx:
d -= h5['timeseries'][i][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]
# Returns computed data without setting any plot or figure parameters
if data_only:
return d_ts
axis.cla()
if inps.error_file:
axis = plot_timeseries_errorbar(ax_ts, d_ts, inps)
else:
axis, scatter = plot_timeseries_scatter(axis, d_ts, inps, plot_number)
scatter.set_label('2')
if inps.ylim:
axis.set_ylim(inps.ylim)
for tick in axis.yaxis.get_major_ticks():
tick.label.set_fontsize(inps.font_size)
# Title
title_ts = set_axis_title(x, y)
if inps.disp_title:
axis.set_title(title_ts)
axis = pp.auto_adjust_xaxis_date(axis, tims, fontsize=inps.font_size)[0]
axis.set_xlabel('Time', fontsize=inps.font_size)
axis.set_ylabel('Displacement [%s]' % inps.disp_unit, fontsize=inps.font_size)
fig_v.canvas.draw()
# Print to terminal
print('\n---------------------------------------')
print(title_ts)
print(d_ts)
# Slope estimation
estimate_slope()
return d_ts
n=geo_box[1],
e=geo_box[2],
s=geo_box[3])
cmd += ' -c bwr_r -v -1.0 1.0 --cbar-loc bottom --cbar-nbins 5 --cbar-ext both --cbar-size 5% '
cmd += ' --dem {} --dem-nocontour '.format(dem_file)
cmd += ' --lalo-step 0.2 --lalo-loc 1 0 1 0 --scalebar 0.3 0.80 0.05 --notitle --fontsize 12 '
cmd += ' --noverbose'
d_v, atr, inps = view.prep_slice(cmd)
ax[0], inps, im, cbar = view.plot_slice(ax[0], d_v, atr, inps)
ax[0].plot(lon, lat, "k^", mfc='none', mew=1.,
ms=6) # point of interest
cbar.set_label("LOS Velocity [cm/yr]", fontsize=font_size)
ax[1].scatter(dates, d_ts, marker='o', s=4**2) # , color='k')
pp.auto_adjust_xaxis_date(ax[1], obj.yearList, fontsize=font_size)
ax[1].set_xlabel('Time [years]', fontsize=font_size)
ax[1].set_ylabel('LOS Displacement [cm]', fontsize=font_size)
ax[1].yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax[1].tick_params(which='both',
direction='in',
labelsize=font_size,
bottom=True,
top=True,
left=True,
right=True)
ax[1].set_title(cur_point, fontsize=font_size)
fig.subplots_adjust(wspace=-0.4)
fig.tight_layout()
#plt.savefig('{}_POI.png'.format(proj_name), bbox_inches='tight', transparent=True, dpi=fig_dpi)
