def configure(self):
# copy inps to self object
inps = cmd_line_parse(self.iargs)
for key, value in inps.__dict__.items():
setattr(self, key, value)
# copy inps from view.py to self object
view_cmd = get_view_cmd(self.iargs)
self.data_img, atr, inps_view = view.prep_slice(view_cmd)
for key, value in inps_view.__dict__.items():
setattr(self, key, value)
self.offset *= self.disp_scale # due to unit change from view.prep_slice()
# do not update the following setting from view.py
self.file = inps.file
self.dset = inps.dset
self.fig_size = inps.fig_size
# auto figure size
if not self.fig_size:
length, width = int(self.atr['LENGTH']), int(self.atr['WIDTH'])
fig_size = pp.auto_figure_size((length, width), disp_cbar=True)
self.fig_size = [fig_size[0] + fig_size[1], fig_size[1]]
return
def plot(self):
# Figure 1
self.fig = plt.figure(self.figname, figsize=self.fig_size)
# Axes 1 - Image
self.ax_img = self.fig.add_axes([0.05, 0.1, 0.4, 0.8])
view_cmd = self.view_cmd.format(self.img_file)
d_img, atr, inps_img = view.prep_slice(view_cmd)
if all(i is not None for i in self.yx):
inps_img.pts_marker = 'r^'
inps_img.pts_yx = np.array(self.yx).reshape(-1, 2)
# point yx --> lalo for geocoded product
if 'Y_FIRST' in atr.keys():
coord = ut.coordinate(atr)
inps_img.pts_lalo = np.array(coord.radar2geo(self.yx[0], self.yx[1])[0:2]).reshape(-1,2)
inps_img.print_msg = self.print_msg
self.ax_img = view.plot_slice(self.ax_img, d_img, atr, inps_img)[0]
# coordinate info
self.coord = ut.coordinate(atr)
self.fig_coord = inps_img.fig_coord
# Axes 2 - coherence matrix
self.ax_mat = self.fig.add_axes([0.55, 0.125, 0.40, 0.75])
self.colormap = pp.ColormapExt(self.cmap_name, vlist=self.cmap_vlist).colormap
if all(i is not None for i in self.yx):
self.plot_coherence_matrix4pixel(self.yx)
# Link the canvas to the plots.
self.cid = self.fig.canvas.mpl_connect('button_press_event', self.update_coherence_matrix)
if self.disp_fig:
plt.show()
return
def plot(self):
# Figure 1
self.fig = plt.figure(self.figname, figsize=self.fig_size)
# Axes 1 - Image
self.ax_img = self.fig.add_axes([0.05, 0.1, 0.4, 0.8])
view_cmd = self.view_cmd.format(self.img_file)
d_img, atr, inps_img = view.prep_slice(view_cmd)
if self.yx:
inps_img.pts_yx = np.array(self.yx).reshape(-1, 2)
inps_img.pts_marker = 'r^'
inps_img.print_msg = self.print_msg
self.ax_img = view.plot_slice(self.ax_img, d_img, atr, inps_img)[0]
# coordinate info
self.coord = ut.coordinate(atr)
self.fig_coord = inps_img.fig_coord
# Axes 2 - coherence matrix
self.ax_mat = self.fig.add_axes([0.55, 0.125, 0.40, 0.75])
self.colormap = pp.ColormapExt(self.cmap_name,
vlist=self.cmap_vlist).colormap
if self.yx:
self.plot_coherence_matrix4pixel(self.yx)
# Link the canvas to the plots.
self.cid = self.fig.canvas.mpl_connect('button_press_event',
self.update_coherence_matrix)
if self.disp_fig:
plt.show()
return
def plot(self):
# Figure 1
self.fig = plt.figure(self.figname, figsize=self.fig_size)
# Axes 1 - Image
self.ax_img = self.fig.add_axes([0.05, 0.1, 0.4, 0.8])
view_cmd = self.view_cmd.format(self.img_file)
d_img, atr, inps_img = view.prep_slice(view_cmd)
if self.yx:
inps_img.pts_yx = np.array(self.yx).reshape(-1, 2)
inps_img.pts_marker = 'r^'
inps_img.print_msg = self.print_msg
self.ax_img = view.plot_slice(self.ax_img, d_img, atr, inps_img)[0]
# coordinate info
self.coord = ut.coordinate(atr)
self.fig_coord = inps_img.fig_coord
# Axes 2 - coherence matrix
self.ax_mat = self.fig.add_axes([0.55, 0.125, 0.40, 0.75])
if self.yx:
self.plot_coherence_matrix4pixel(self.yx)
# Link the canvas to the plots.
self.cid = self.fig.canvas.mpl_connect('button_press_event', self.update_coherence_matrix)
if self.disp_fig:
plt.show()
return
def get_poly_mask(fname, datasetName, print_msg=True):
"""Get mask of pixels within polygon from interactive selection
Parameters: data : 2D np.array in size of (length, width)
Returns: mask : 2D np.arrat in size of (length, width) in np.bool_ format
"""
# option 1 - Advanced plot using view.prep/plot_slice()
cmd = 'view.py {} '.format(fname)
if datasetName:
cmd += ' {} '.format(datasetName)
## Customized setting for plotting (temporarily for advanced users)
#cmd += ' --dem ./inputs/geometryGeo.h5 --contour-step 100 --contour-smooth 0.0 '
#cmd += ' -u cm -v -6 6 '
d_v, atr, inps = view.prep_slice(cmd)
ax = plt.subplots(figsize=inps.fig_size)[1]
inps.fig_coord = 'radar' #selector works for radar coord plot only
ax, inps, im = view.plot_slice(ax, d_v, atr, inps)[0:3]
## Option 2 - Simple plot with matplotlib
#from mintpy.utils import readfile
#data = readfile.read(fname, datasetName)[0]
#vlim = np.nanmax(np.abs(data))
#vmin, vmax = -vlim, vlim
## for dataset with non-negative values such as elevation
#if np.nanmin(data) > 0:
# vmin = np.nanmin(data)
## plot
#fig, ax = plt.subplots()
#im = ax.imshow(data, cmap='jet', vmin=vmin, vmax=vmax)
#fig.colorbar(im)
selector = SelectFromCollection(ax, im)
plt.show()
selector.disconnect()
if hasattr(selector, 'mask'):
mask = selector.mask
if print_msg:
print('selected polygon: {}'.format(selector.poly_path))
else:
mask = None
if print_msg:
print('no polygon selected.\n')
return mask
pass
if save_fig:
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[8, 3])
# read vel data
cmd = 'view.py {} velocity --mask {} '.format(vel_file, mask_file)
cmd += ' --sub-lon {w} {e} --sub-lat {s} {n} '.format(w=geo_box[0],
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,
