def plot_coherence_matrix4pixel(self, yx):
"""Plot coherence matrix for one pixel
Parameters: yx : list of 2 int
"""
# read coherence
box = (yx[1], yx[0], yx[1]+1, yx[0]+1)
coh = readfile.read(self.ifgram_file, datasetName='coherence', box=box)[0]
# prep metadata
plotDict = {}
plotDict['fig_title'] = 'Y = {}, X = {}'.format(yx[0], yx[1])
plotDict['colormap'] = self.colormap
plotDict['disp_legend'] = False
# plot
coh_mat = pp.plot_coherence_matrix(self.ax_mat,
date12List=self.date12_list,
cohList=coh.tolist(),
date12List_drop=self.ex_date12_list,
plot_dict=plotDict)[1]
self.fig.canvas.draw()
# status bar
def format_coord(x, y):
row, col = int(y+0.5), int(x+0.5)
date12 = sorted([self.date_list[row], self.date_list[col]])
date12 = ['{}-{}-{}'.format(i[0:4], i[4:6], i[6:8]) for i in date12]
return 'x={}, y={}, v={:.3f}'.format(date12[0], date12[1], coh_mat[row, col])
self.ax_mat.format_coord = format_coord
# info
vprint('-'*30)
vprint('pixel: yx = {}'.format(yx))
vprint('min/max coherence: {:.2f} / {:.2f}'.format(np.min(coh), np.max(coh)))
return
def main(iargs=None):
inps = cmd_line_parse(iargs)
inps = read_network_info(inps)
# Plot
if not inps.disp_fig:
plt.switch_backend('Agg')
inps.cbar_label = 'Average Spatial Coherence'
figNames = [i+inps.fig_ext for i in ['BperpHistory', 'CoherenceMatrix', 'CoherenceHistory', 'Network']]
# Fig 1 - Baseline History
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_perp_baseline_hist(ax,
inps.dateList,
inps.pbaseList,
vars(inps),
inps.dateList_drop)
if inps.save_fig:
fig.savefig(figNames[0], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[0]))
if inps.cohList is not None:
# Fig 2 - Coherence Matrix
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_coherence_matrix(ax,
inps.date12List,
inps.cohList,
inps.date12List_drop,
plot_dict=vars(inps))[0]
if inps.save_fig:
fig.savefig(figNames[1], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[1]))
# Fig 3 - Min/Max Coherence History
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_coherence_history(ax,
inps.date12List,
inps.cohList,
plot_dict=vars(inps))
if inps.save_fig:
fig.savefig(figNames[2], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[2]))
# Fig 4 - Interferogram Network
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_network(ax,
inps.date12List,
inps.dateList,
inps.pbaseList,
vars(inps),
inps.date12List_drop)
if inps.save_fig:
fig.savefig(figNames[3], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[3]))
if inps.disp_fig:
print('showing ...')
plt.show()
def main(iargs=None):
inps = cmd_line_parse(iargs)
# Plot
inps.cbar_label = 'Average Spatial Coherence'
figNames = [i+'.pdf' for i in ['BperpHistory', 'CoherenceMatrix', 'CoherenceHistory', 'Network']]
# Fig 1 - Baseline History
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_perp_baseline_hist(ax,
inps.dateList,
inps.pbaseList,
vars(inps),
inps.dateList_drop)
if inps.save_fig:
fig.savefig(figNames[0], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[0]))
if inps.cohList is not None:
# Fig 2 - Coherence Matrix
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_coherence_matrix(ax,
inps.date12List,
inps.cohList,
inps.date12List_drop,
plot_dict=vars(inps))[0]
if inps.save_fig:
fig.savefig(figNames[1], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[1]))
# Fig 3 - Min/Max Coherence History
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_coherence_history(ax,
inps.date12List,
inps.cohList,
plot_dict=vars(inps))
if inps.save_fig:
fig.savefig(figNames[2], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[2]))
# Fig 4 - Interferogram Network
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_network(ax,
inps.date12List,
inps.dateList,
inps.pbaseList,
vars(inps),
inps.date12List_drop)
if inps.save_fig:
fig.savefig(figNames[3], bbox_inches='tight', transparent=True, dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[3]))
if inps.disp_fig:
print('showing ...')
plt.show()
def plot_coherence_matrix4pixel(self, yx):
"""Plot coherence matrix for one pixel
Parameters: yx : list of 2 int
"""
self.ax_mat.cla()
# read coherence
box = (yx[1], yx[0], yx[1]+1, yx[0]+1)
coh = readfile.read(self.ifgram_file, datasetName='coherence', box=box)[0]
# ex_date for pixel-wise masking during network inversion
ex_date12_list = self.ex_date12_list[:] #local copy
if self.min_coh_used > 0.:
ex_date12_list += np.array(self.date12_list)[coh < self.min_coh_used].tolist()
ex_date12_list = sorted(list(set(ex_date12_list)))
# prep metadata
plotDict = {}
plotDict['fig_title'] = 'Y = {}, X = {}'.format(yx[0], yx[1])
# display temporal coherence value of the pixel
if self.tcoh_file:
tcoh = self.tcoh[yx[0], yx[1]]
plotDict['fig_title'] += ', tcoh = {:.2f}'.format(tcoh)
plotDict['colormap'] = self.colormap
plotDict['cmap_vlist'] = self.cmap_vlist
plotDict['disp_legend'] = False
# plot
coh_mat = pp.plot_coherence_matrix(self.ax_mat,
date12List=self.date12_list,
cohList=coh.tolist(),
date12List_drop=ex_date12_list,
plot_dict=plotDict)[1]
self.ax_mat.annotate('ifgrams\navailable', xy=(0.05, 0.05), xycoords='axes fraction', fontsize=12)
self.ax_mat.annotate('ifgrams\nused', ha='right', xy=(0.95, 0.85), xycoords='axes fraction', fontsize=12)
# status bar
def format_coord(x, y):
row, col = int(y+0.5), int(x+0.5)
date12 = sorted([self.date_list[row], self.date_list[col]])
date12 = ['{}-{}-{}'.format(i[0:4], i[4:6], i[6:8]) for i in date12]
return 'x={}, y={}, v={:.3f}'.format(date12[0], date12[1], coh_mat[row, col])
self.ax_mat.format_coord = format_coord
# info
msg = 'pixel in yx = {}, '.format(tuple(yx))
msg += 'min/max spatial coherence: {:.2f} / {:.2f}, '.format(np.min(coh), np.max(coh))
if self.tcoh_file:
msg += 'temporal coherence: {:.2f}'.format(tcoh)
vprint(msg)
self.fig.canvas.draw()
return
def plot_network_info(inps):
if not inps.disp_fig:
plt.switch_backend('Agg')
out_fig_name = os.path.join(inps.out_dir, 'network{}'.format(inps.figext))
log('plot network / pairs to file: ' + os.path.basename(out_fig_name))
fig1, ax1 = plt.subplots(figsize=inps.fig_size)
ax1 = pp.plot_network(ax1,
inps.date12_list,
inps.date_list,
inps.pbase_list,
p_dict=vars(inps),
print_msg=False)
plt.savefig(out_fig_name, bbox_inches='tight', dpi=inps.figdpi)
out_fig_name = os.path.join(inps.out_dir,
'bperpHistory{}'.format(inps.figext))
log('plot baseline history to file: ' + os.path.basename(out_fig_name))
fig2, ax2 = plt.subplots(figsize=inps.fig_size)
ax2 = pp.plot_perp_baseline_hist(ax2, inps.date_list, inps.pbase_list)
plt.savefig(out_fig_name, bbox_inches='tight', dpi=inps.figdpi)
out_fig_name = os.path.join(inps.out_dir,
'coherenceMatrix{}'.format(inps.figext))
if inps.cohList:
log('plot predicted coherence matrix to file: ' +
os.path.basename(out_fig_name))
fig3, ax3 = plt.subplots(figsize=inps.fig_size)
ax3 = pp.plot_coherence_matrix(ax3,
inps.date12_list,
inps.cohList,
p_dict=vars(inps))[0]
plt.savefig(out_fig_name, bbox_inches='tight', dpi=inps.figdpi)
if inps.disp_fig:
plt.show()
return
def main(iargs=None):
inps = cmd_line_parse(iargs)
# read / calculate
inps = read_network_info(inps)
# Plot settings
inps = check_colormap(inps)
if inps.dsetName == 'coherence':
inps.ds_name = 'Coherence'
figNames = [
i + '.pdf' for i in
['bperpHistory', 'coherenceMatrix', 'coherenceHistory', 'network']
]
elif inps.dsetName == 'offsetSNR':
inps.ds_name = 'SNR'
figNames = [
i + '.pdf'
for i in ['bperpHistory', 'SNRMatrix', 'SNRHistory', 'network']
]
inps.cbar_label = 'Average Spatial {}'.format(inps.ds_name)
# Fig 1 - Baseline History
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_perp_baseline_hist(ax, inps.dateList, inps.pbaseList,
vars(inps), inps.dateList_drop)
if inps.save_fig:
fig.savefig(figNames[0],
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[0]))
if inps.cohList is not None:
# Fig 2 - Coherence Matrix
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_coherence_matrix(ax,
inps.date12List,
inps.cohList,
inps.date12List_drop,
p_dict=vars(inps))[0]
if inps.save_fig:
fig.savefig(figNames[1],
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[1]))
# Fig 3 - Min/Max Coherence History
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_coherence_history(ax,
inps.date12List,
inps.cohList,
p_dict=vars(inps))
if inps.save_fig:
fig.savefig(figNames[2],
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[2]))
# Fig 4 - Interferogram Network
fig, ax = plt.subplots(figsize=inps.fig_size)
ax = pp.plot_network(ax, inps.date12List, inps.dateList, inps.pbaseList,
vars(inps), inps.date12List_drop)
if inps.save_fig:
fig.savefig(figNames[3],
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save figure to {}'.format(figNames[3]))
if inps.disp_fig:
print('showing ...')
plt.show()
else:
plt.close()
return
