def set_initial_map():
global d_v, h5, k, dateList, inps, data_lim
d_v = h5['timeseries'][inps.epoch_num][:] * inps.unit_fac
# Initial Map
print(str(dateList))
d_v = readfile.read(
inps.timeseries_file,
datasetName=dateList[inps.epoch_num])[0] * inps.unit_fac
#d_v = h5[k].get(dateList[inps.epoch_num])[:]*inps.unit_fac
if inps.ref_date:
inps.ref_d_v = readfile.read(
inps.timeseries_file, datasetName=inps.ref_date)[0] * inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim_mat:
inps.ylim_mat = data_lim
print(('Initial data range: ' + str(data_lim)))
print(('Display data range: ' + str(inps.ylim_mat)))
print(('Initial data range: ' + str(data_lim)))
print(('Display data range: ' + str(inps.ylim)))
def set_initial_map():
global d_v, h5, k, dateList, inps, data_lim
d_v = h5['timeseries'][inps.epoch_num][:] * inps.unit_fac
# Initial Map
print(str(dateList))
d_v = readfile.read(inps.timeseries_file, datasetName=dateList[inps.epoch_num])[0] * inps.unit_fac
#d_v = h5[k].get(dateList[inps.epoch_num])[:]*inps.unit_fac
if inps.ref_date:
inps.ref_d_v = readfile.read(inps.timeseries_file, datasetName=inps.ref_date)[0]*inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim_mat:
inps.ylim_mat = data_lim
print(('Initial data range: '+str(data_lim)))
print(('Display data range: '+str(inps.ylim_mat)))
print(('Initial data range: ' + str(data_lim)))
print(('Display data range: ' + str(inps.ylim)))
def time_slider_update(val):
'''Update Displacement Map using Slider'''
global tims, tslider, ax_v, d_v, inps, img, fig_v, h5, k, dateList
timein = tslider.val
idx_nearest = np.argmin(np.abs(np.array(tims) - timein))
ax_v.set_title('N = %d, Time = %s' % (idx_nearest, inps.dates[idx_nearest].strftime('%Y-%m-%d')))
d_v = h5[k][idx_nearest][:] * inps.unit_fac
if inps.ref_date:
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
img.set_data(d_v)
fig_v.canvas.draw()
def time_slider_update(val):
'''Update Displacement Map using Slider'''
global inps.tims, tslider, ax_v, d_v, inps, img, fig_v, h5, k, dateList
timein = tslider.val
idx_nearest = np.argmin(np.abs(np.array(inps.tims) - timein))
ax_v.set_title('N = %d, Time = %s' % (idx_nearest, inps.dates[idx_nearest].strftime('%Y-%m-%d')))
d_v = h5[k][idx_nearest][:] * inps.unit_fac
if inps.ref_date:
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
img.set_data(d_v)
fig_v.canvas.draw()
def estimate_phase_elevation_ratio(dem, ts_data, inps):
"""Estimate phase/elevation ratio for each acquisition of timeseries
Parameters: dem : 2D array in size of ( length, width)
ts_data : 3D array in size of (num_date, length, width)
inps : Namespace
Returns: X : 2D array in size of (poly_num+1, num_date)
"""
num_date = ts_data.shape[0]
# prepare phase and elevation data
print('reading mask from file: ' + inps.mask_file)
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
dem = mask_matrix(np.array(dem), mask)
ts_data = mask_matrix(np.array(ts_data), mask)
# display
# 1. effect of multilooking --> narrow phase range --> better ratio estimation
debug_mode = False
if debug_mode:
import matplotlib.pyplot as plt
d_index = 47 # np.argmax(topo_trop_corr)
data = ts_data[d_index, :, :]
title = inps.date_list[d_index]
plt.figure()
plt.plot(dem[~np.isnan(dem)],
data[~np.isnan(dem)],
'.',
label='Number of Looks = 1')
mli_dem = multilook_data(dem, 8, 8)
mli_data = multilook_data(data, 8, 8)
plt.plot(mli_dem[~np.isnan(mli_dem)],
mli_data[~np.isnan(mli_dem)],
'.',
label='Number of Looks = 8')
plt.legend()
plt.xlabel('Elevation (m)')
plt.ylabel('Range Change (m)')
plt.title(title)
out_file = 'phase_elevation_ratio_{}.png'.format(title)
plt.savefig(out_file, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_file))
plt.show()
print('----------------------------------------------------------')
print(
'Empirical tropospheric delay correction based on phase/elevation ratio (Doin et al., 2009)'
)
print('polynomial order: {}'.format(inps.poly_order))
if inps.num_multilook > 1:
print('number of multilook: {} (multilook data for estimation only)'.
format(inps.num_multilook))
mask = multilook_data(mask, inps.num_multilook, inps.num_multilook)
dem = multilook_data(dem, inps.num_multilook, inps.num_multilook)
ts_data = multilook_data(ts_data, inps.num_multilook,
inps.num_multilook)
if inps.threshold > 0.:
print('correlation threshold: {}'.format(inps.threshold))
mask_nan = ~np.isnan(dem)
dem = dem[mask_nan]
ts_data = ts_data[:, mask_nan]
# calculate correlation coefficient
print('----------------------------------------------------------')
print('calculate correlation of DEM with each acquisition')
topo_trop_corr = np.zeros(num_date, np.float32)
for i in range(num_date):
phase = ts_data[i, :]
cc = 0.
if np.count_nonzero(phase) > 0:
comp_data = np.vstack((dem, phase))
cc = np.corrcoef(comp_data)[0, 1]
topo_trop_corr[i] = cc
print('{}: {:>5.2f}'.format(inps.date_list[i], cc))
topo_trop_corr = np.abs(topo_trop_corr)
print('average correlation magnitude: {:>5.2f}'.format(
np.nanmean(topo_trop_corr)))
# estimate ratio parameter
print('----------------------------------------------------------')
print('estimate phase/elevation ratio')
A = design_matrix(dem=dem, poly_order=inps.poly_order)
X = np.dot(np.linalg.pinv(A), ts_data.T)
X = np.array(X, dtype=np.float32)
X[:, topo_trop_corr < inps.threshold] = 0.
return X
def main():
parser = build_parser()
parseArgs = parser.parse_args()
file_name = parseArgs.file
output_folder = parseArgs.outputDir
should_mask = True
path_name_and_extension = os.path.basename(file_name).split(".")
path_name = path_name_and_extension[0]
# ---------------------------------------------------------------------------------------
# start clock to track how long conversion process takes
start_time = time.clock()
# use h5py to open specified group(s) in the h5 file
# then read datasets from h5 file into memory for faster reading of data
he_obj = HDFEOS(file_name)
he_obj.open(print_msg=False)
displacement_3d_matrix = he_obj.read(datasetName='displacement')
mask = he_obj.read(datasetName='mask')
if should_mask:
print("Masking displacement")
displacement_3d_matrix = mask_matrix(displacement_3d_matrix, mask)
del mask
dates = he_obj.dateList
attributes = dict(he_obj.metadata)
#file = h5py.File(file_name, "r")
#timeseries_group = file["HDFEOS"]["GRIDS"]["timeseries"]
#displacement_3d_matrix = timeseries_group["observation"]["displacement"]
# get attributes (stored at root) of UNAVCO timeseries file
#attributes = dict(file.attrs)
# in timeseries displacement_3d_matrix, there are datasets
# need to get datasets with dates - strings that can be converted to integers
#dates = displacement_3d_matrix.attrs["DATE_TIMESERIES"].split(" ")
# array that stores dates from dates that have been converted to decimal
decimal_dates = []
# read datasets in the group into a dictionary of 2d arrays and intialize decimal dates
timeseries_datasets = {}
num_date = len(dates)
for i in range(num_date):
timeseries_datasets[dates[i]] = np.squeeze(
displacement_3d_matrix[i, :, :])
d = get_date(dates[i])
decimal = get_decimal_date(d)
decimal_dates.append(decimal)
del displacement_3d_matrix
#for displacement_2d_matrix in displacement_3d_matrix:
# dataset = displacement_2d_matrix[:]
# if should_mask:
# print("Masking " + dates[i])
# mask = timeseries_group["quality"]["mask"][:]
# dataset = mask_matrix(dataset, mask)
# timeseries_datasets[dates[i]] = dataset
# d = get_date(dates[i])
# decimal = get_decimal_date(d)
# decimal_dates.append(decimal)
# i += 1
# close h5 file
#file.close()
path_list = path_name.split("/")
folder_name = path_name.split("/")[len(path_list) - 1]
try: # create path for output
os.mkdir(output_folder)
except:
print(output_folder + " already exists")
# read and convert the datasets, then write them into json files and insert into database
convert_data(attributes, decimal_dates, timeseries_datasets, dates,
output_folder, folder_name)
# run tippecanoe command to get mbtiles file
os.chdir(os.path.abspath(output_folder))
os.system(
"tippecanoe *.json -l chunk_1 -x d -pf -pk -Bg -d9 -D12 -g12 -r0 -o " +
folder_name + ".mbtiles")
# ---------------------------------------------------------------------------------------
# check how long it took to read h5 file data and create json files
end_time = time.clock()
print(("time elapsed: " + str(end_time - start_time)))
return
def main():
parser = build_parser()
parseArgs = parser.parse_args()
file_name = parseArgs.file
output_folder = parseArgs.outputDir
should_mask = True
path_name_and_extension = os.path.basename(file_name).split(".")
path_name = path_name_and_extension[0]
# ---------------------------------------------------------------------------------------
# start clock to track how long conversion process takes
start_time = time.clock()
# use h5py to open specified group(s) in the h5 file
# then read datasets from h5 file into memory for faster reading of data
he_obj = HDFEOS(file_name)
he_obj.open(print_msg=False)
displacement_3d_matrix = he_obj.read(datasetName='displacement')
mask = he_obj.read(datasetName='mask')
if should_mask:
print("Masking displacement")
displacement_3d_matrix = mask_matrix(displacement_3d_matrix, mask)
del mask
dates = he_obj.dateList
attributes = dict(he_obj.metadata)
#file = h5py.File(file_name, "r")
#timeseries_group = file["HDFEOS"]["GRIDS"]["timeseries"]
#displacement_3d_matrix = timeseries_group["observation"]["displacement"]
# get attributes (stored at root) of UNAVCO timeseries file
#attributes = dict(file.attrs)
# in timeseries displacement_3d_matrix, there are datasets
# need to get datasets with dates - strings that can be converted to integers
#dates = displacement_3d_matrix.attrs["DATE_TIMESERIES"].split(" ")
# array that stores dates from dates that have been converted to decimal
decimal_dates = []
# read datasets in the group into a dictionary of 2d arrays and intialize decimal dates
timeseries_datasets = {}
num_date = len(dates)
for i in range(num_date):
timeseries_datasets[dates[i]] = np.squeeze(displacement_3d_matrix[i, :, :])
d = get_date(dates[i])
decimal = get_decimal_date(d)
decimal_dates.append(decimal)
del displacement_3d_matrix
#for displacement_2d_matrix in displacement_3d_matrix:
# dataset = displacement_2d_matrix[:]
# if should_mask:
# print("Masking " + dates[i])
# mask = timeseries_group["quality"]["mask"][:]
# dataset = mask_matrix(dataset, mask)
# timeseries_datasets[dates[i]] = dataset
# d = get_date(dates[i])
# decimal = get_decimal_date(d)
# decimal_dates.append(decimal)
# i += 1
# close h5 file
#file.close()
path_list = path_name.split("/")
folder_name = path_name.split("/")[len(path_list)-1]
try: # create path for output
os.mkdir(output_folder)
except:
print(output_folder + " already exists")
# read and convert the datasets, then write them into json files and insert into database
convert_data(attributes, decimal_dates, timeseries_datasets, dates, output_folder, folder_name)
# run tippecanoe command to get mbtiles file
os.chdir(os.path.abspath(output_folder))
os.system("tippecanoe *.json -l chunk_1 -x d -pf -pk -Bg -d9 -D12 -g12 -r0 -o " + folder_name + ".mbtiles")
# ---------------------------------------------------------------------------------------
# check how long it took to read h5 file data and create json files
end_time = time.clock()
print(("time elapsed: " + str(end_time - start_time)))
return
def estimate_phase_elevation_ratio(dem, ts_data, inps):
"""Estimate phase/elevation ratio for each acquisition of timeseries
Parameters: dem : 2D array in size of ( length, width)
ts_data : 3D array in size of (num_date, length, width)
inps : Namespace
Returns: X : 2D array in size of (poly_num+1, num_date)
"""
num_date = ts_data.shape[0]
# prepare phase and elevation data
print('reading mask from file: '+inps.mask_file)
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
dem = mask_matrix(np.array(dem), mask)
ts_data = mask_matrix(np.array(ts_data), mask)
# display
# 1. effect of multilooking --> narrow phase range --> better ratio estimation
debug_mode = False
if debug_mode:
import matplotlib.pyplot as plt
#d_index = np.argmax(topo_trop_corr)
d_index = 47
data = ts_data[d_index, :, :]
title = inps.date_list[d_index]
fig = plt.figure()
plt.plot(dem[~np.isnan(dem)],
data[~np.isnan(dem)],
'.', label='Number of Looks = 1')
mli_dem = multilook_data(dem, 8, 8)
mli_data = multilook_data(data, 8, 8)
plt.plot(mli_dem[~np.isnan(mli_dem)],
mli_data[~np.isnan(mli_dem)],
'.', label='Number of Looks = 8')
plt.legend()
plt.xlabel('Elevation (m)')
plt.ylabel('Range Change (m)')
plt.title(title)
out_file = 'phase_elevation_ratio_{}.png'.format(title)
plt.savefig(out_file, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_file))
#plt.show()
print('----------------------------------------------------------')
print('Empirical tropospheric delay correction based on phase/elevation ratio (Doin et al., 2009)')
print('polynomial order: {}'.format(inps.poly_order))
if inps.num_multilook > 1:
print('number of multilook: {} (multilook data for estimation only)'.format(inps.num_multilook))
mask = multilook_data(mask, inps.num_multilook, inps.num_multilook)
dem = multilook_data(dem, inps.num_multilook, inps.num_multilook)
ts_data = multilook_data(ts_data, inps.num_multilook, inps.num_multilook)
if inps.threshold > 0.:
print('correlation threshold: {}'.format(inps.threshold))
mask_nan = ~np.isnan(dem)
dem = dem[mask_nan]
ts_data = ts_data[:, mask_nan]
# calculate correlation coefficient
print('----------------------------------------------------------')
print('calculate correlation of DEM with each acquisition')
topo_trop_corr = np.zeros(num_date, np.float32)
for i in range(num_date):
phase = ts_data[i, :]
cc = 0.
if np.count_nonzero(phase) > 0:
comp_data = np.vstack((dem, phase))
cc = np.corrcoef(comp_data)[0, 1]
topo_trop_corr[i] = cc
print('{}: {:>5.2f}'.format(inps.date_list[i], cc))
topo_trop_corr = np.abs(topo_trop_corr)
print('average correlation magnitude: {:>5.2f}'.format(np.nanmean(topo_trop_corr)))
# estimate ratio parameter
print('----------------------------------------------------------')
print('estimate phase/elevation ratio')
A = design_matrix(dem=dem, poly_order=inps.poly_order)
X = np.dot(np.linalg.pinv(A), ts_data.T)
X = np.array(X, dtype=np.float32)
X[:, topo_trop_corr < inps.threshold] = 0.
return X
