def output_manager_coseismic(x, y, average_coseismic, outdir):
rwr.produce_output_netcdf(x, y, average_coseismic, 'mm',
outdir + '/coseismic.grd')
netcdf_plots.produce_output_plot(outdir + '/coseismic.grd',
'LOS Displacement',
outdir + '/coseismic.png', 'disp (mm)')
return
def stack_corr_for_ref_unwrapped_isce(intf_files,
rowref,
colref,
ts_output_dir,
label=""):
# WE MAKE SIGNAL SPREAD FOR CUT IMAGES
cor_files = [
i.replace("fully_processed.unwrappedphase", "cut.cor")
for i in intf_files
]
# get for isce
netcdfname = ts_output_dir + '/signalspread_cut_ref' + label + '.nc'
cor_value = np.nan
cor_data = readmytupledata.reader_isce(cor_files)
a = stack_corr.stack_corr(cor_data, cor_value)
rwr.produce_output_netcdf(cor_data.xvalues, cor_data.yvalues, a,
'Percentage', netcdfname)
netcdf_plots.produce_output_plot(
netcdfname,
'Signal Spread above cor=' + str(cor_value),
ts_output_dir + '/signalspread_cut_ref' + label + '.png',
'Percentage of coherence',
aspect=1 / 4,
invert_yaxis=False,
dot_points=[[colref], [rowref]])
signal_spread_ref = a[rowref, colref]
print("Signal Spread of the reference pixel = %.2f " % signal_spread_ref)
if signal_spread_ref < 50:
print(
"WARNING: Your reference pixel has very low coherence. Consider picking a different one."
)
print("STOPPING ON PURPOSE.")
sys.exit(0)
return
def drive_signal_spread_isce(corr_files, cutoff, output_dir, output_filename):
cor_data = rmd.reader_isce(corr_files);
a = stack_corr(cor_data, cutoff);
netcdf_read_write.produce_output_netcdf(cor_data.xvalues, cor_data.yvalues, a, 'Percentage', output_dir+'/' +
output_filename);
netcdf_plots.produce_output_plot(output_dir + '/' + output_filename, 'Signal Spread above cor=' + str(cutoff),
output_dir + '/signalspread_full.png', 'Percentage of coherence', aspect=1 / 4,
invert_yaxis=False);
return;
def drive_signal_spread_calculation(corr_files, cutoff, output_dir, output_filename):
print("Making stack_corr")
output_file = output_dir + "/" + output_filename
mytuple = rmd.reader(corr_files)
a = stack_corr(mytuple, cutoff) # if unwrapped files, we use Nan to show when it was unwrapped successfully.
netcdf_read_write.produce_output_netcdf(mytuple.xvalues, mytuple.yvalues, a, 'Percentage', output_file)
netcdf_plots.produce_output_plot(output_file, 'Signal Spread', output_dir + '/signalspread.png',
'Percentage of coherence (out of ' + str(len(corr_files)) + ' images)',
aspect=1.2);
return;
def dummy_signal_spread(intfs, output_dir, output_filename):
""" Make a perfect signal spread for passing to other applications """
print("Making a dummy signal spread that matches interferograms' dimensions (perfect 100).");
output_filename = output_dir + "/" + output_filename;
[xdata, ydata, zdata] = netcdf_read_write.read_netcdf4(intfs[0]);
a = np.add(np.zeros(np.shape(zdata)), 100);
netcdf_read_write.produce_output_netcdf(xdata, ydata, a, 'Percentage', output_filename, dtype=np.float32)
netcdf_plots.produce_output_plot(output_filename, 'Signal Spread', output_dir + '/signalspread.png',
'Percentage of coherence (out of ' + str(len(intfs)) + ' images)', aspect=1.2);
return;
def output_manager_simple_stack(x, y, velocities, rowref, colref,
signal_spread_data, outdir):
rwr.produce_output_netcdf(x, y, velocities, 'mm/yr',
outdir + '/velo_simple_stack.grd')
netcdf_plots.produce_output_plot(outdir + '/velo_simple_stack.grd',
'LOS Velocity ',
outdir + '/velo_simple_stack.png',
'velocity (mm/yr)')
stacking_utilities.report_on_refpixel(rowref, colref, signal_spread_data,
outdir)
return
def make_vel_unc_from_ts_grids(ts_slice_files, outdir):
"""
Given existing TS grid files, create an estimate of velocity uncertainty.
I have called this function from outside of the program. Can be called separately.
"""
mydata = rmd.reader_from_ts(ts_slice_files)
# read filelist of time series grids
unc = velo_uncertainties.empirical_uncertainty(mydata)
rwr.produce_output_netcdf(mydata.xvalues, mydata.yvalues, unc, 'mm/yr',
outdir + '/velo_unc.grd')
netcdf_plots.produce_output_plot(outdir + '/velo_unc.grd',
'LOS Uncertainty',
outdir + '/velo_unc.png',
'Uncertainty (mm/yr)')
return
def make_vels_from_ts_grids(param_dictionary, ts_slice_files):
"""
Given existing TS grid files, create an estimate of velocity.
"""
mydata = rmd.reader_from_ts(ts_slice_files)
# read filelist of time series grids
vel = nsbas.Velocities_from_TS(mydata)
rwr.produce_output_netcdf(
mydata.xvalues, mydata.yvalues, vel, 'mm/yr',
param_dictionary["ts_output_dir"] + '/velo_nsbas.grd')
netcdf_plots.produce_output_plot(
param_dictionary["ts_output_dir"] + '/velo_nsbas.grd', 'LOS Velocity',
param_dictionary["ts_output_dir"] + '/velo_nsbas.png',
'velocity (mm/yr)')
return
def write_output_metrics(param_dict, intf_tuple, metrics):
"""Unpack the dictionary that contains output metrics (if any), write into files. """
if param_dict["dem_error"]:
gridshape = np.shape(metrics)
Kz_grid = np.zeros(gridshape)
for i in range(gridshape[0]):
for j in range(gridshape[1]):
if "Kz_error" in metrics[i][j].keys():
Kz_grid[i][j] = metrics[i][j]["Kz_error"]
rwr.produce_output_netcdf(
intf_tuple.xvalues, intf_tuple.yvalues, Kz_grid, 'm',
param_dict["ts_output_dir"] + '/kz_error.grd')
netcdf_plots.produce_output_plot(
param_dict["ts_output_dir"] + '/kz_error.grd', 'DEM Error',
param_dict["ts_output_dir"] + '/kz_error.png', 'DEM Error (m)')
return
def drive_velocity(param_dict, intf_files, coh_files):
intf_tuple, coh_tuple, baseline_tuple = param_dict["reader"](
intf_files, coh_files, param_dict["baseline_file"],
param_dict["ts_type"], param_dict["dem_error"])
[_, _, signal_spread_tuple
] = rwr.read_any_grd(param_dict["signal_spread_filename"])
velocities, metrics = nsbas.Velocities(param_dict, intf_tuple,
signal_spread_tuple, baseline_tuple,
coh_tuple)
rwr.produce_output_netcdf(intf_tuple.xvalues, intf_tuple.yvalues,
velocities, 'mm/yr',
param_dict["ts_output_dir"] + '/velo_nsbas.grd')
netcdf_plots.produce_output_plot(
param_dict["ts_output_dir"] + '/velo_nsbas.grd', 'LOS Velocity',
param_dict["ts_output_dir"] + '/velo_nsbas.png', 'velocity (mm/yr)')
return
