def drive_velocity_gmtsar(intf_files,
nsbas_min_intfs,
smoothing,
wavelength,
rowref,
colref,
outdir,
signal_spread_file,
baseline_file=None,
coh_files=None):
# GMTSAR DRIVING VELOCITIES
signal_spread_file = outdir + "/" + signal_spread_file
intf_tuple = rmd.reader(intf_files)
coh_tuple = None
if coh_files is not None:
coh_tuple = rmd.reader(coh_files)
signal_spread_data = rwr.read_grd(signal_spread_file)
velocities = nsbas.Velocities(intf_tuple,
nsbas_min_intfs,
smoothing,
wavelength,
rowref,
colref,
signal_spread_data,
baseline_file=baseline_file,
coh_tuple=coh_tuple)
rwr.produce_output_netcdf(intf_tuple.xvalues, intf_tuple.yvalues,
velocities, 'mm/yr', outdir + '/velo_nsbas.grd')
rwr.produce_output_plot(outdir + '/velo_nsbas.grd', 'LOS Velocity',
outdir + '/velo_nsbas.png', 'velocity (mm/yr)')
return
def combine_all_files(datestr, input_dirs, output_dir):
print("\nCombining files for date %s" % datestr)
xdata, ydata, zdata0 = netcdf_read_write.read_grd_xyz(input_dirs[0] + "/" +
datestr + ".grd")
xdata, ydata, zdata1 = netcdf_read_write.read_grd_xyz(input_dirs[1] + "/" +
datestr + ".grd")
zdata_total = np.zeros(np.shape(zdata0))
for j in range(len(ydata)):
if np.mod(j, 200) == 0:
print(j)
for k in range(len(xdata)):
vector = [zdata0[j][k], zdata1[j][k]]
# , zdata2[j][k], zdata3[j][k], zdata4[j][k], zdata5[j][k], zdata6[j][k] ];
zdata_total[j][k] = np.sum(vector)
output_file = output_dir + "/" + datestr + ".grd"
output_plot = output_dir + "/" + datestr + ".png"
netcdf_read_write.produce_output_netcdf(xdata, ydata, zdata_total, "mm",
output_file)
netcdf_read_write.produce_output_plot(output_file,
datestr,
output_plot,
"mm",
aspect=1.0,
invert_yaxis=True,
vmin=-50,
vmax=100)
return
def stack_corr_for_ref_unwrapped_isce(intf_files,
rowref,
colref,
ts_output_dir,
label=""):
# WE MAKE THE SIGNAL SPREAD FOR THE 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)
rwr.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 remove_plane(filename, planefile, outfile, m1, m2, m3):
xvalues, yvalues, zvalues = rwr.read_grd_xyz(filename)
i, j = 0, 0
new_z = np.zeros((len(yvalues), len(xvalues)))
for z in np.nditer(zvalues):
new_z[i, j] = zvalues[i, j] - (m1 + m2 * xvalues[j] + m3 * yvalues[i])
j += 1
if j == len(xvalues):
j = 0
i += 1
if i == len(yvalues):
i = 0
print(new_z[0, 0])
new_z = np.flipud(new_z)
print(new_z[0, 0])
rwr.produce_output_netcdf(xvalues, yvalues, np.flipud(new_z),
'unwrapped phase', outfile)
rwr.flip_if_necessary(outfile)
outfile1 = outfile.replace("no_ramp.grd", "no_ramp_comparison.png")
fr = netcdf.netcdf_file(outfile, 'r')
xread = fr.variables['x']
yread = fr.variables['y']
zread = fr.variables['z']
zread_copy = zread[:][:].copy()
fr2 = netcdf.netcdf_file(filename, 'r')
xread2 = fr2.variables['x']
yread2 = fr2.variables['y']
zread2 = fr2.variables['z']
zread2_copy = zread2[:][:].copy()
fig = plt.figure(figsize=(7, 10))
ax1 = plt.subplot(121)
old = ax1.imshow(zread2_copy, aspect=1.2)
ax1.invert_xaxis()
ax1.get_xaxis().set_ticks([])
ax1.get_yaxis().set_ticks([])
ax1.set_xlabel("Range", fontsize=16)
ax1.set_ylabel("Azimuth", fontsize=16)
ax2 = plt.subplot(122)
new = ax2.imshow(zread_copy,
aspect=1.2,
vmin=np.nanmin(zread_copy),
vmax=np.nanmax(zread2_copy))
ax2.invert_xaxis()
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticks([])
ax2.set_xlabel("Range", fontsize=16)
cb = plt.colorbar(new)
cb.set_label('unwrapped phase', size=16)
plt.savefig(outfile1)
plt.close()
rwr.produce_output_plot(outfile, 'Ramp removed',
outfile.replace('grd', 'png'), 'unwraped phase')
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.
rwr.produce_output_netcdf(mytuple.xvalues, mytuple.yvalues, a, 'Percentage', output_file)
rwr.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_xyz(intfs[0]);
a = np.add(np.zeros(np.shape(zdata)), 100);
rwr.produce_output_netcdf(xdata, ydata, a, 'Percentage', output_filename, dtype=np.float32)
rwr.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 drive_coseismic_stack_gmtsar(intf_files, wavelength, rowref, colref,
outdir):
intf_tuple = rmd.reader(intf_files)
average_coseismic = get_avg_coseismic(intf_tuple, rowref, colref,
wavelength)
rwr.produce_output_netcdf(intf_tuple.xvalues, intf_tuple.yvalues,
average_coseismic, 'mm',
outdir + '/coseismic.grd')
rwr.produce_output_plot(outdir + '/coseismic.grd', 'LOS Displacement',
outdir + '/coseismic.png', 'displacement (mm)')
return
def drive_velocity_simple_stack(intfs, wavelength, rowref, colref, outdir):
signal_spread_data = rwr.read_grd(outdir + "/signalspread.nc")
intf_tuple = rmd.reader(intfs)
velocities, x, y = velocity_simple_stack(intf_tuple, wavelength, rowref,
colref, signal_spread_data, 25)
# last argument is signal threshold (< 100%). lower signal threshold allows for more data into the stack.
rwr.produce_output_netcdf(x, y, velocities, 'mm/yr',
outdir + '/velo_simple_stack.grd')
rwr.produce_output_plot(outdir + '/velo_simple_stack.grd', 'LOS Velocity ',
outdir + '/velo_simple_stack.png',
'velocity (mm/yr)')
return
def test_read_write(filename):
# A TEST OF READ/WRITE FUNCTIONS
# Step 1: Read ISCE interferogram as phase
# Step 2: Write it as ISCE format data
# Step 3: Read ISCE interferogram again
# Step 4: Write as .grd
# Step 5: make plot.
# RESULT: PRETTY GOOD! There is a flipup between the ISCE/GMTSAR conventions,
# but it might never really be a problem.
# Step 1: read phase
slc = isce_read_write.read_phase_data(filename)
print("Shape of slc is ", np.shape(slc))
isce_read_write.plot_complex_data(filename,
aspect=1 / 10,
outname="original.png")
# Step 2: write ISCE data file
ny, nx = np.shape(slc)
# dtype = 'FLOAT';
isce_written = "isce_written_phase.phase"
isce_read_write.write_isce_data(slc,
nx,
ny,
dtype="FLOAT",
filename=isce_written)
# Step 3: read phase again.
phase = isce_read_write.read_scalar_data(isce_written + ".vrt")
print("Shape of phase is ", np.shape(phase))
isce_read_write.plot_scalar_data("isce_written_phase.phase",
colormap='rainbow',
aspect=1 / 10,
outname="isce_written_phase.png")
# Step 4: write that phase as grd.
netcdfname = "netcdf_written_phase.grd"
xdata = np.arange(0, nx)
ydata = np.arange(0, ny)
phase = np.flipud(phase)
# THIS SEEMS TO BE NECESSARY TO SWITCH BETWEEN CONVENTIONS. GRD PLOTS ARE UPSIDE DOWN FROM ISCE.
netcdf_read_write.produce_output_netcdf(xdata, ydata, phase, "radians",
netcdfname)
# Step 5: look at what's inside;
netcdf_read_write.produce_output_plot(netcdfname,
"phase",
"grdstyle_phase.png",
"radians",
aspect=1 / 10)
return
def make_vels_from_ts_grids(ts_dir, geocoded=False):
if geocoded:
filelist = glob.glob(ts_dir + "/publish/*_ll.grd")
mydata = rmd.reader_from_ts(filelist, "lon", "lat", "z")
# put these if using geocoded values
else:
filelist = glob.glob(ts_dir + "/????????.grd")
mydata = rmd.reader_from_ts(filelist)
vel = nsbas.Velocities_from_TS(mydata)
rwr.produce_output_netcdf(mydata.xvalues, mydata.yvalues, vel, 'mm/yr',
ts_dir + '/velo_nsbas.grd')
rwr.produce_output_plot(ts_dir + '/velo_nsbas.grd', 'LOS Velocity',
ts_dir + '/velo_nsbas.png', 'velocity (mm/yr)')
return
def drive_coseismic_stack_isce(intf_files, wavelength, rowref, colref, outdir):
intf_tuple = rmd.reader_isce(intf_files)
average_coseismic = get_avg_coseismic(intf_tuple, rowref, colref,
wavelength)
rwr.produce_output_netcdf(intf_tuple.xvalues, intf_tuple.yvalues,
average_coseismic, 'mm',
outdir + '/coseismic.grd')
rwr.produce_output_plot(outdir + '/coseismic.grd',
'LOS Displacement',
outdir + '/coseismic.png',
'displacement (mm)',
aspect=1 / 8,
invert_yaxis=False,
vmin=-50,
vmax=200)
return
def make_corrections_isce(config_params):
if config_params.startstage > 1: # if we're starting after, we don't do this.
return
if config_params.endstage < 1: # if we're ending before, we don't do this.
return
print("Start Stage 1 - optional atm corrections")
# For ISCE, we might want to re-make all the interferograms and unwrap them in custom fashion.
# This operates on files in the Igram directory, no need to move directories yourself.
if config_params.solve_unwrap_errors:
unwrapping_isce_custom.main_function(config_params.rlks,
config_params.alks,
config_params.filt,
config_params.xbounds,
config_params.ybounds,
config_params.cor_cutoff_mask)
# WE ALSO MAKE THE SIGNAL SPREAD FOR FULL IMAGES
cor_value = 0.5
filepathslist = glob.glob("../Igrams/????????_????????/filt*.cor")
# *** This may change
cor_data = readmytupledata.reader_isce(filepathslist)
a = stack_corr.stack_corr(cor_data, cor_value)
rwr.produce_output_netcdf(
cor_data.xvalues, cor_data.yvalues, a, 'Percentage',
config_params.ts_output_dir + '/signalspread_full.nc')
rwr.produce_output_plot(
config_params.ts_output_dir + '/signalspread_full.nc',
'Signal Spread above cor=' + str(cor_value),
config_params.ts_output_dir + '/signalspread_full.png',
'Percentage of coherence',
aspect=1 / 4,
invert_yaxis=False)
print("End Stage 1 - optional atm corrections\n")
return
for f in range(len(store)):
ref_values.append(store[f, yvalue, xvalue])
print(len(ref_values))
i,j,f = 0,0,0
while f < len(mytuple.zvalues):
store[f, i ,j] = store[f, i ,j] - ref_values[f]
j+=1
if j==len(mytuple.xvalues):
j=0
i+=1
if i == len(mytuple.yvalues):
i=0
print('Referencing phases in file ' + str(f+1) + ' out of ' + str(len(mytuple.zvalues)))
f+=1
return store
if __name__ == "__main__":
myfiles = glob.glob("intf_all_remote/???????_???????/unwrap.grd")
d=rmd.reader(myfiles)
store = phase_ref(d, 621, 32)
for i in range(len(myfiles)):
print('Dealing with file ' + str(i+1))
temp = myfiles[i].split('/')[-1]
stem = myfiles[i][0:-len(temp)]
rwr.produce_output_netcdf(d.xvalues, d.yvalues, store[i], 'Radians', stem + 'unwrap_ref.grd')
rwr.flip_if_necessary(stem + 'unwrap_ref.grd')
rwr.produce_output_plot(stem + 'unwrap_ref.grd', 'Referenced Unwrapped Phase', stem + 'unwrap_ref.png', 'unwrapped phase')
if __name__ == "__main__":
myfiles_no_ramp, remove_ramp_flag, wls_flag, myfiles_phase, manual_remove, signal_spread_file, wavelength, nsbas_good_num, smoothing, outfile = configure(
)
datatuple, signal_spread_data, dates, date_pairs, coherence_cube = inputs(
myfiles_no_ramp, remove_ramp_flag, myfiles_phase, signal_spread_file,
manual_remove, 15, wls_flag)
vel = compute(coherence_cube, datatuple, nsbas_good_num,
signal_spread_data, dates, date_pairs, smoothing, wavelength,
outfile, wls_flag)
rwr.produce_output_netcdf(datatuple.xvalues, datatuple.yvalues, vel,
'velocity', outfile)
rwr.flip_if_necessary(outfile)
rwr.produce_output_plot(
outfile, 'Reasonable WNSBAS - smoothing factor: ' + str(smoothing),
"Stacking/NSBAS/velocity_weighted50NSBAS_reasonable_smooth1.png",
'velocity in mm/yr')
# x,y,vel = rwr.read_grd_xyz(outfile)
# signal = rwr.read_grd(signal_spread_file)
# updated_vel = np.zeros((np.shape(vel)))
# i, j,c = 0, 0, 0
# for v in np.nditer(vel):
# print(c)
# if signal[i,j] < 65 and signal[i,j] > 50:
# updated_vel[i,j] = np.nan
# else:
# updated_vel[i,j] = vel[i,j]
# j+=1
# c+=1
# if j==len(x):
def outputs(xdata, ydata, number_of_datas, zdim, vel, out_dir):
netcdf_read_write.produce_output_netcdf(xdata, ydata, number_of_datas, 'coherent_intfs', out_dir+'/number_of_datas.grd');
netcdf_read_write.flip_if_necessary(out_dir+'/number_of_datas.grd');
netcdf_read_write.produce_output_plot(out_dir+'/number_of_datas.grd', "Number of Coherent Intfs (Total = "+str(zdim)+")", out_dir+'/number_of_coherent_intfs.eps', 'intfs');
geocode(out_dir+'/number_of_datas.grd',out_dir);
netcdf_read_write.produce_output_netcdf(xdata,ydata, vel, 'mm/yr', out_dir+'/vel.grd');
netcdf_read_write.flip_if_necessary(out_dir+'/vel.grd');
geocode(out_dir+'/vel.grd',out_dir);
# Visualizing the velocity field in a few different ways.
zdata2=np.reshape(vel, [len(xdata)*len(ydata), 1])
zdata2=sentinel_utilities.remove_nans_array(zdata2);
plt.figure();
plt.hist(zdata2,bins=80);
plt.gca().set_yscale('log');
plt.title('Pixels by Velocity: mean=%.2fmm/yr, sdev=%.2fmm/yr' % (np.mean(zdata2), np.std(zdata2)) )
plt.ylabel('Number of Pixels');
plt.xlabel('LOS velocity (mm/yr)')
plt.grid('on');
plt.savefig(out_dir+'/velocity_hist_log.png');
plt.close();
plt.figure();
plt.gca().set_yscale('linear');
plt.title('Pixels by Velocity: mean=%.2fmm/yr, sdev=%.2fmm/yr' % (np.mean(zdata2), np.std(zdata2)) )
plt.hist(zdata2,bins=80);
plt.ylabel('Number of Pixels');
plt.xlabel('LOS velocity (mm/yr)')
plt.grid('on');
plt.savefig(out_dir+'/velocity_hist_lin.png');
plt.close();
plt.figure(figsize=(8,10));
plt.imshow(vel,aspect=0.5,cmap='jet',vmin=-30, vmax=30);
plt.gca().invert_yaxis()
plt.gca().invert_xaxis()
plt.gca().get_xaxis().set_ticks([]);
plt.gca().get_yaxis().set_ticks([]);
plt.title("Velocity");
plt.gca().set_xlabel("Range",fontsize=16);
plt.gca().set_ylabel("Azimuth",fontsize=16);
cb = plt.colorbar();
cb.set_label("mm/yr", size=16);
plt.savefig(out_dir+"/vel_cutoff.png");
plt.close();
plt.figure(figsize=(8,10));
plt.imshow(vel,aspect=0.5,cmap='jet',vmin=-150, vmax=150);
plt.gca().invert_yaxis()
plt.gca().invert_xaxis()
plt.gca().get_xaxis().set_ticks([]);
plt.gca().get_yaxis().set_ticks([]);
plt.title("Velocity");
plt.gca().set_xlabel("Range",fontsize=16);
plt.gca().set_ylabel("Azimuth",fontsize=16);
cb = plt.colorbar();
cb.set_label("mm/yr", size=16);
plt.savefig(out_dir+"/vel.png");
plt.close();
return;
content_1.append('intf_all_remote/' + content[i] + '/unwrap.grd')
m1, m2, m3 = plane_fitter(
content_1[i], content_1[i].replace('unwrap', 'unwrap_model'))
model.append(content_1[i].replace('unwrap', 'unwrap_model'))
remove_plane(content_1[i], model[i],
model[i].replace('model', 'no_ramp'), m1, m2, m3)
temp1 = ['intf_all_remote/' + content[i] + '/unwrap_no_ramp.grd']
d = rmd.reader(temp1)
store = phr.phase_ref(d, 621, 32)
temp = temp1[0].split('/')[-1]
stem = temp1[0][0:-len(temp)]
rwr.produce_output_netcdf(d.xvalues, d.yvalues, store[0], 'Radians',
stem + 'unwrap_ref_corrected.grd')
rwr.flip_if_necessary(stem + 'unwrap_ref_corrected.grd')
rwr.produce_output_plot(stem + 'unwrap_ref_corrected.grd',
'Referenced and Corrected Unwrapped Phase',
stem + 'unwrap_ref_corrected.png',
'unwrapped phase')
print('Done with file ' + str(i + 1))
# if __name__=="__main__":
# grdname="intf_all_remote/2018281_2018305/unwrap.grd"
# remove_trend2d(grdname,4);
# remove_trend2d(grdname,3);
# remove_trend2d(grdname,6);
# outfile = "classic_unwrapped_N4_good.grd"
# rwr.produce_output_plot(outfile, 'Ramp removed', 'Ramp_remove.png', 'phase')
# # BASH Version
# # Define name and number of coefficients in planar fit.
# grdname="unwrap.grd";
# order="4"
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_xyz(intfs[0]);
a = np.add(np.zeros(np.shape(zdata)), 100);
rwr.produce_output_netcdf(xdata, ydata, a, 'Percentage', output_filename, dtype=np.float32)
rwr.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 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.
rwr.produce_output_netcdf(mytuple.xvalues, mytuple.yvalues, a, 'Percentage', output_file)
rwr.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;
if __name__ == "__main__":
myfiles = glob.glob("intf_all_remote/???????_???????/corr.grd")
mytuple = rmd.reader(myfiles)
a = stack_corr(mytuple, 0.1)
rwr.produce_output_netcdf(mytuple.xvalues, mytuple.yvalues, a, 'Percentage', 'signalspread.nc')
rwr.produce_output_plot('signalspread.nc', 'Signal Spread', 'signalspread.png', 'Percentage of coherence')
# for x in range(len(xfinal_ratioed)):
# pixel_box = z[(yfinal_ratioed[x]-50):(yfinal_ratioed[x]+51),(xfinal_ratioed[x]-50):(xfinal_ratioed[x]+51) ]
# print(np.shape(pixel_box))
# for v in np.nditer(pixel_box):
# if np.isnan(v) == False:
# counter.append(v)
# print(len(counter))
x1, y1, z1 = rwr.read_grd_xyz('signalspread_please_test.nc')
x2, y2, z2 = rwr.read_grd_xyz(filenames[-2][0])
thing1 = z1[1200:1424, 400:660]
thing2 = z2[1200:1424, 400:660]
rwr.produce_output_netcdf(x1[400:660], y1[1200:1424], thing1, 'signal',
'possible_fault_info.grd')
rwr.flip_if_necessary('possible_fault_info.grd')
rwr.produce_output_plot('possible_fault_info.grd', '',
'possible_fault_info.png', '%')
rwr.produce_output_netcdf(x2[400:660], y2[1200:1424], thing2, 'velocity',
'possible_fault_info_vel.grd')
rwr.flip_if_necessary('possible_fault_info_vel.grd')
[x, y] = np.meshgrid(x1[400:660], y1[1200:1424])
[x_, y_] = np.meshgrid(x2[400:660], y2[1200:1424])
fig = plt.figure(figsize=(18, 10))
ax1 = plt.subplot(121)
image1 = ax1.contourf(x,
y,
thing1,
cmap='jet',
vmin=np.nanmin(thing1),
vmax=np.nanmax(thing1))
a=np.zeros((len(mytuple.yvalues), len(mytuple.xvalues)))
i,j = 0,0
for z in np.nditer(mytuple.zvalues):
if z >= cutoff:
a[i,j] = a[i,j] + 1
j+=1
if j== len(mytuple.xvalues):
j=0
i+=1
if i == len(mytuple.yvalues):
i=0
i,j = 0,0
for n in np.nditer(a):
a[i,j] = (a[i,j]/(len(mytuple.filepaths)))*100
j+=1
if j== len(mytuple.xvalues):
j=0
i+=1
if i == len(mytuple.yvalues):
i=0
return a
if __name__ == "__main__":
myfiles = glob.glob("intf_all_remote/???????_???????/corr.grd")
mytuple=rmd.reader(myfiles)
a=stack_corr(mytuple, 0.1)
rwr.produce_output_netcdf(mytuple.xvalues, mytuple.yvalues, a, 'Percentage', 'signalspread_please_test.nc')
rwr.flip_if_necessary('signalspread_please_test.nc')
rwr.produce_output_plot('signalspread_please_test.nc', 'Signal Spread', 'signalspread_please_test.png', 'Percentage of coherence (out of 288 images)' )
j] = (wavelength / (4 * (np.pi))) * ((np.sum(phases)) /
(np.sum(times)))
phases, times = [], []
c += 1
print('Done with ' + str(c) + ' out of ' +
str(len(mytuple.xvalues) * len(mytuple.yvalues)) + ' pixels')
f = 0
j += 1
if j == len(mytuple.xvalues):
j = 0
i += 1
if i == len(mytuple.yvalues):
i = 0
return velocities, mytuple.xvalues, mytuple.yvalues
if __name__ == "__main__":
ramps, outfile_stem, myfiles, myfiles_no_ramp, remove_ramp = configure()
myfiles_new = inputs(ramps, myfiles, myfiles_no_ramp, remove_ramp)
velocities, x, y = velocity_simple_stack(myfiles_new, 56, 1, 50)
rwr.produce_output_netcdf(
x, y, velocities, 'mm/yr',
outfile_stem + 'velo_prof_reasonable50_remastered.grd')
rwr.flip_if_necessary(outfile_stem +
'velo_prof_reasonable50_remastered.grd')
rwr.produce_output_plot(
outfile_stem + 'velo_prof_reasonable50_remastered.grd',
'Velocity Profile Reasonable (15 images removed)',
outfile_stem + 'velo_prof_reasonable50_remastered.png',
'velocity (mm/yr)')