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 plot_bar4date_rms(inps):
inps.figName = os.path.splitext(inps.rmsFile)[0] + '.pdf'
if ut.update_file(inps.figName,
[inps.exDateFile, inps.refDateFile, 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(inps.dateList)
try:
bar_width = ut.most_common(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]
inps.rmsList = [i * 1000. for i in inps.rmsList]
min_rms = inps.min_rms * 1000.
# Plot all dates
ax.bar(x_list, inps.rmsList, bar_width.days)
# Plot reference date
ax.bar(x_list[inps.refDateIndex],
inps.rmsList[inps.refDateIndex],
bar_width.days,
label='Reference date')
# Plot exclude dates
if inps.exIdxList:
ex_x_list = [x_list[i] for i in inps.exIdxList]
inps.exRmsList = [inps.rmsList[i] for i in inps.exIdxList]
ax.bar(ex_x_list,
inps.exRmsList,
bar_width.days,
color='darkgray',
label='Exclude date(s)')
# Plot min_rms line
ax, xmin, xmax = pp.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 = pp.auto_adjust_yaxis(ax,
inps.rmsList + [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)
plt.legend(fontsize=font_size)
# save figure
fig.savefig(inps.figName, bbox_inches='tight', transparent=True)
print('save figure to file: ' + inps.figName)
return inps
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 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
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)
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, inps.tims, fig_v, inps.num_date, 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, inps.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