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 cmd_line_parse(iargs=None):
parser = create_parser()
inps = parser.parse_args(args=iargs)
if not inps.disp_fig:
inps.save_fig = True
if not inps.disp_fig:
plt.switch_backend('Agg')
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
else:
inps.template = {}
if not os.path.isfile(inps.maskFile):
inps.maskFile = None
inps = read_network_info(inps)
# colormap
if inps.cohList is not None and '--cmap-vlist' not in sys.argv:
key = 'mintpy.network.minCoherence'
if key in inps.template.keys():
inps.cmap_vlist[1] = float(inps.template[key])
else:
idx_drop = [inps.date12List.index(i) for i in inps.date12List_drop]
coh_drop = [inps.cohList[i] for i in idx_drop]
inps.cmap_vlist[1] = max(coh_drop)
print('max coherence of excluded interferograms: {}'.format(max(coh_drop)))
inps.colormap = pp.ColormapExt(inps.cmap_name, vlist=inps.cmap_vlist).colormap
return inps
def check_colormap(inps):
"""Check input colormaps."""
# adjust color jump if no --cmap-vlist is input
if inps.cohList is not None and '--cmap-vlist' not in sys.argv:
# use template value
key = 'mintpy.network.minCoherence'
if key in inps.template.keys():
inps.cmap_vlist[1] = max(0.01, float(inps.template[key]))
# calculate from dropped interferograms, if there is any
elif len(inps.date12List_drop) > 0:
idx_drop = [inps.date12List.index(i) for i in inps.date12List_drop]
coh_drop = [inps.cohList[i] for i in idx_drop]
inps.cmap_vlist[1] = max(coh_drop)
print('max coherence of excluded interferograms: {}'.format(
max(coh_drop)))
# update default cmap_vlist[0] value if the manually input cmap_vlist[1] is too small
if inps.cmap_vlist[1] <= inps.cmap_vlist[0]:
inps.cmap_vlist[0] = min(0, inps.cmap_vlist[1])
# in case the manually input list is not in order
inps.cmap_vlist = sorted(inps.cmap_vlist)
inps.colormap = pp.ColormapExt(inps.cmap_name,
vlist=inps.cmap_vlist).colormap
return inps
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 cmd_line_parse(iargs=None):
parser = create_parser()
inps = parser.parse_args(args=iargs)
# check input file type
if inps.file.endswith(('.h5', '.he5')):
k = readfile.read_attribute(inps.file)['FILE_TYPE']
if k != 'ifgramStack':
raise ValueError('input HDF5 file is NOT ifgramStack.')
if not inps.disp_fig:
inps.save_fig = True
if not inps.disp_fig:
plt.switch_backend('Agg')
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
else:
inps.template = {}
if not os.path.isfile(inps.maskFile):
inps.maskFile = None
inps = read_network_info(inps)
# colormap
# adjust color jump if no --cmap-vlist is input
if inps.cohList is not None and '--cmap-vlist' not in sys.argv:
# use template value
key = 'mintpy.network.minCoherence'
if key in inps.template.keys():
inps.cmap_vlist[1] = max(0.01, float(inps.template[key]))
# calculate from dropped interferograms, if there is any
elif len(inps.date12List_drop) > 0:
idx_drop = [inps.date12List.index(i) for i in inps.date12List_drop]
coh_drop = [inps.cohList[i] for i in idx_drop]
inps.cmap_vlist[1] = max(coh_drop)
print('max coherence of excluded interferograms: {}'.format(
max(coh_drop)))
# update default cmap_vlist[0] value if the manually input cmap_vlist[1] is too small
if inps.cmap_vlist[1] <= inps.cmap_vlist[0]:
inps.cmap_vlist[0] = min(0, inps.cmap_vlist[1])
# in case the manually input list is not in order
inps.cmap_vlist = sorted(inps.cmap_vlist)
inps.colormap = pp.ColormapExt(inps.cmap_name,
vlist=inps.cmap_vlist).colormap
return inps
def write_kmz_file(data, metadata, out_file, inps=None):
""" Generate Google Earth KMZ file for input data matrix.
Inputs:
data - 2D np.array in int/float, data matrix to write
out_file - string, output file name
metadata - dict, containing the following attributes:
WIDTH/LENGTH : required, file size
X/Y_FIRST/STEP : required, for lat/lon spatial converage
ref_x/y : optional, column/row number of reference pixel
PROJECT_NAME : optional, for KMZ folder name
inps - Namespace, optional, input options for display
Output:
kmz_file - string, output KMZ filename
Example:
from mintpy.utils import readfile, plot as pp
from mintpy import save_kmz
fname = 'geo_velocity_masked.h5'
data, atr = readfile.read(fname)
out_file = pp.auto_figure_title(fname, None)+'.kmz'
save_kmz.write_kmz_file(data, atr, out_file)
"""
if not inps:
inps = cmd_line_parse()
# disp min/max and colormap
cmap_lut = 256
if not inps.vlim:
cmap_lut, inps.vlim = pp.auto_adjust_colormap_lut_and_disp_limit(data)
inps.colormap = pp.auto_colormap_name(metadata, inps.colormap)
inps.colormap = pp.ColormapExt(inps.colormap, cmap_lut).colormap
west, east, south, north = ut.four_corners(metadata)
# 2.1 Make PNG file - Data
print('plotting data ...')
# Figure size
if not inps.fig_size:
plot_shape = [east - west, north - south]
fig_scale = min(pp.min_figsize_single / min(plot_shape),
pp.max_figsize_single / max(plot_shape),
pp.max_figsize_height / plot_shape[1])
inps.fig_size = [2. * i * fig_scale for i in plot_shape]
print('create figure in size: ' + str(inps.fig_size))
fig = plt.figure(figsize=inps.fig_size, frameon=False)
ax = fig.add_axes([0., 0., 1., 1.])
ax.set_axis_off()
# Plot - data matrix
ax.imshow(data,
cmap=inps.colormap,
vmin=inps.vlim[0],
vmax=inps.vlim[1],
aspect='auto',
interpolation='nearest')
# Plot - reference pixel
if inps.disp_ref_pixel:
try:
xref = int(metadata['REF_X'])
yref = int(metadata['REF_Y'])
ax.plot(xref,
yref,
inps.ref_marker,
color=inps.ref_marker_color,
ms=inps.ref_marker_size)
print('show reference point')
except:
inps.disp_ref_pixel = False
print('Cannot find reference point info!')
width = int(metadata['WIDTH'])
length = int(metadata['LENGTH'])
ax.set_xlim([0, width])
ax.set_ylim([length, 0])
out_name_base = os.path.splitext(out_file)[0]
data_png_file = out_name_base + '.png'
print('writing {} with dpi={}'.format(data_png_file, inps.fig_dpi))
plt.savefig(data_png_file,
pad_inches=0.0,
transparent=True,
dpi=inps.fig_dpi)
# 2.3 Generate KML file
proj_name = metadata.get('PROJECT_NAME', 'InSAR_MintPy')
doc = KML.kml(KML.Folder(KML.name(proj_name)))
# Add data png file
img_name = os.path.splitext(os.path.basename(data_png_file))[0]
img = KML.GroundOverlay(
KML.name(img_name),
KML.Icon(KML.href(os.path.basename(data_png_file))),
KML.altitudeMode('clampToGround'),
KML.LatLonBox(KML.north(str(north)), KML.east(str(east)),
KML.south(str(south)), KML.west(str(west))))
doc.Folder.append(img)
# Add colorbar png file
cbar_file = '{}_cbar.png'.format(out_name_base)
cbar_overlay = generate_cbar_element(cbar_file,
vmin=inps.vlim[0],
vmax=inps.vlim[1],
unit=inps.disp_unit,
cmap=inps.colormap,
loc=inps.cbar_loc,
nbins=inps.cbar_bin_num,
label=inps.cbar_label)
doc.Folder.append(cbar_overlay)
kmlstr = etree.tostring(doc, pretty_print=True).decode('utf8')
# Write KML file
kml_file = '{}.kml'.format(out_name_base)
print('writing ' + kml_file)
with open(kml_file, 'w') as f:
f.write(kmlstr)
# 2.4 Generate KMZ file, by
# 1) go to the directory of kmz file
run_dir = os.path.abspath(os.getcwd())
os.chdir(os.path.abspath(os.path.dirname(out_file)))
# 2) zip all data files
kmz_file = '{}.kmz'.format(out_name_base)
with ZipFile(kmz_file, 'w') as fz:
for fname in [kml_file, data_png_file, cbar_file]:
fz.write(os.path.relpath(fname))
os.remove(fname)
# 3) go back to the running directory
os.chdir(run_dir)
print('merged all files to {}'.format(kmz_file))
return kmz_file
def main(iargs=None):
inps = cmd_line_parse(iargs)
# 1. Read metadata and data
k = readfile.read_attribute(inps.file)['FILE_TYPE']
if k == 'timeseries' and inps.dset and '_' in inps.dset:
inps.ref_date, inps.dset = inps.dset.split('_')
else:
inps.ref_date = None
atr = readfile.read_attribute(inps.file, datasetName=inps.dset)
# pix_box
inps.pix_box = subset.subset_input_dict2box(vars(inps), atr)[0]
inps.pix_box = ut.coordinate(atr).check_box_within_data_coverage(
inps.pix_box)
data_box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
print('data coverage in y/x: {}'.format(data_box))
print('subset coverage in y/x: {}'.format(inps.pix_box))
atr = attr.update_attribute4subset(atr, inps.pix_box)
# read data
data = readfile.read(inps.file, datasetName=inps.dset, box=inps.pix_box)[0]
if k == 'timeseries' and inps.ref_date:
data -= readfile.read(inps.file,
datasetName=inps.ref_date,
box=inps.pix_box)[0]
# mask
mask = pp.read_mask(inps.file,
mask_file=inps.mask_file,
datasetName=inps.dset,
box=inps.pix_box)[0]
if mask is not None:
print('masking out pixels with zero value in file: {}'.format(
inps.mask_file))
data[mask == 0] = np.nan
if inps.zero_mask:
print('masking out pixels with zero value')
data[data == 0] = np.nan
del mask
# Data Operation - Display Unit & Rewrapping
(data, inps.disp_unit, inps.disp_scale,
inps.wrap) = pp.scale_data4disp_unit_and_rewrap(
data,
metadata=atr,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range)
if inps.wrap:
inps.vlim = inps.wrap_range
# 2. Generate Google Earth KMZ
# 2.1 Common settings
# disp min/max and colormap
cmap_lut = 256
if not inps.vlim:
cmap_lut, inps.vlim = pp.auto_adjust_colormap_lut_and_disp_limit(data)
inps.colormap = pp.auto_colormap_name(atr, inps.colormap)
inps.colormap = pp.ColormapExt(inps.colormap, cmap_lut).colormap
inps.norm = colors.Normalize(vmin=inps.vlim[0], vmax=inps.vlim[1])
# Output filename
inps.fig_title = pp.auto_figure_title(inps.file,
datasetNames=inps.dset,
inps_dict=vars(inps))
if not inps.outfile:
inps.outfile = '{}.kmz'.format(inps.fig_title)
inps.outfile = os.path.abspath(inps.outfile)
# 2.2 Write KMZ file
if 'Y_FIRST' in atr.keys():
# create ground overlay KML for file in geo-coord
write_kmz_overlay(
data,
meta=atr,
out_file=inps.outfile,
inps=inps,
)
else:
# create placemark KML for file in radar-coord
write_kmz_placemark(
data,
meta=atr,
out_file=inps.outfile,
geom_file=inps.geom_file,
inps=inps,
)
return inps.outfile