def run_deramp(fname, ramp_type, mask_file=None, out_file=None, datasetName=None):
""" Remove ramp from each 2D matrix of input file
Parameters: fname : str, data file to be derampped
ramp_type : str, name of ramp to be estimated.
mask_file : str, file of mask of pixels used for ramp estimation
out_file : str, output file name
datasetName : str, output dataset name, for ifgramStack file type only
Returns: out_file : str, output file name
"""
print('remove {} ramp from file: {}'.format(ramp_type, fname))
if not out_file:
fbase, fext = os.path.splitext(fname)
out_file = '{}_ramp{}'.format(fbase, fext)
start_time = time.time()
atr = readfile.read_attribute(fname)
# mask
if os.path.isfile(mask_file):
mask = readfile.read(mask_file, datasetName='mask')[0]
print('read mask file: '+mask_file)
else:
mask = np.ones((int(atr['LENGTH']), int(atr['WIDTH'])))
print('use mask of the whole area')
# deramping
k = atr['FILE_TYPE']
if k == 'timeseries':
print('reading data ...')
data = readfile.read(fname)[0]
print('estimating phase ramp ...')
data = deramp(data, mask, ramp_type=ramp_type, metadata=atr)[0]
writefile.write(data, out_file, ref_file=fname)
elif k == 'ifgramStack':
obj = ifgramStack(fname)
obj.open(print_msg=False)
if not datasetName:
datasetName = 'unwrapPhase'
with h5py.File(fname, 'a') as f:
ds = f[datasetName]
dsNameOut = '{}_ramp'.format(datasetName)
if dsNameOut in f.keys():
dsOut = f[dsNameOut]
print('access HDF5 dataset /{}'.format(dsNameOut))
else:
dsOut = f.create_dataset(dsNameOut, shape=(obj.numIfgram, obj.length, obj.width),
dtype=np.float32, chunks=True, compression=None)
print('create HDF5 dataset /{}'.format(dsNameOut))
prog_bar = ptime.progressBar(maxValue=obj.numIfgram)
for i in range(obj.numIfgram):
data = ds[i, :, :]
data = deramp(data, mask, ramp_type=ramp_type, metadata=atr)[0]
dsOut[i, :, :] = data
prog_bar.update(i+1, suffix='{}/{}'.format(i+1, obj.numIfgram))
prog_bar.close()
print('finished writing to file: '.format(fname))
# Single Dataset File
else:
data = readfile.read(fname)[0]
data = deramp(data, mask, ramp_type, metadata=atr)[0]
print('writing >>> {}'.format(out_file))
writefile.write(data, out_file=out_file, ref_file=fname)
m, s = divmod(time.time()-start_time, 60)
print('time used: {:02.0f} mins {:02.1f} secs.'.format(m, s))
return out_file
def run_unwrap_error_bridge(ifgram_file, mask_cc_file, bridges, dsNameIn='unwrapPhase',
dsNameOut='unwrapPhase_bridging', ramp_type=None):
"""Run unwrapping error correction with bridging
Parameters: ifgram_file : str, path of ifgram stack file
mask_cc_file : str, path of conn comp mask file
bridges : list of dicts, check bridge_unwrap_error() for details
dsNameIn : str, dataset name of unwrap phase to be corrected
dsNameOut : str, dataset name of unwrap phase to be saved after correction
ramp_type : str, name of phase ramp to be removed during the phase jump estimation
Returns: ifgram_file : str, path of ifgram stack file
"""
print('-'*50)
print('correct unwrapping error in {} with bridging ...'.format(ifgram_file))
# file info
atr = readfile.read_attribute(ifgram_file)
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
ref_y, ref_x = int(atr['REF_Y']), int(atr['REF_X'])
k = atr['FILE_TYPE']
# read mask
print('read mask from file: {}'.format(mask_cc_file))
mask_cc = readfile.read(mask_cc_file, datasetName='mask')[0]
if ramp_type is not None:
print('estimate and remove phase ramp of {} during the correction'.format(ramp_type))
mask4ramp = (mask_cc == mask_cc[ref_y, ref_x])
# correct unwrap error ifgram by ifgram
if k == 'ifgramStack':
date12_list = ifgramStack(ifgram_file).get_date12_list(dropIfgram=False)
num_ifgram = len(date12_list)
shape_out = (num_ifgram, length, width)
# prepare output data writing
print('open {} with r+ mode'.format(ifgram_file))
f = h5py.File(ifgram_file, 'r+')
print('input dataset:', dsNameIn)
print('output dataset:', dsNameOut)
if dsNameOut in f.keys():
ds = f[dsNameOut]
print('access /{d} of np.float32 in size of {s}'.format(d=dsNameOut, s=shape_out))
else:
ds = f.create_dataset(dsNameOut, shape_out, maxshape=(None, None, None),
chunks=True, compression=None)
print('create /{d} of np.float32 in size of {s}'.format(d=dsNameOut, s=shape_out))
# correct unwrap error ifgram by ifgram
prog_bar = ptime.progressBar(maxValue=num_ifgram)
for i in range(num_ifgram):
# read unwrapPhase
date12 = date12_list[i]
unw = np.squeeze(f[dsNameIn][i, :, :])
unw[unw != 0.] -= unw[ref_y, ref_x]
# remove phase ramp before phase jump estimation
if ramp_type is not None:
unw, unw_ramp = deramp(unw, mask4ramp, ramp_type, metadata=atr)
# estimate/correct phase jump
unw_cor = bridge_unwrap_error(unw, mask_cc, bridges)
if ramp_type is not None:
unw_cor += unw_ramp
# write to hdf5 file
ds[i, :, :] = unw_cor
prog_bar.update(i+1, suffix=date12)
prog_bar.close()
ds.attrs['MODIFICATION_TIME'] = str(time.time())
f.close()
print('close {} file.'.format(ifgram_file))
if k == '.unw':
# read data
unw = readfile.read(ifgram_file)[0]
unw[unw != 0.] -= unw[ref_y, ref_x]
# remove phase ramp before phase jump estimation
if ramp_type is not None:
unw, unw_ramp = deramp(unw, mask4ramp, ramp_type, metadata=atr)
# estimate/correct phase jump
unw_cor = bridge_unwrap_error(unw, mask_cc, bridges)
if ramp_type is not None:
unw_cor += unw_ramp
# write to hdf5 file
out_file = '{}_unwCor{}'.format(os.path.splitext(ifgram_file)[0],
os.path.splitext(ifgram_file)[1])
print('writing >>> {}'.format(out_file))
writefile.write(unw_cor, out_file=out_file, ref_file=ifgram_file)
return ifgram_file
def get_boxes4deforming_area(vel_file,
mask_file,
win_size=30,
min_percentage=0.2,
ramp_type='quadratic',
display=False):
"""Get list of boxes to cover the deforming areas.
A pixel is identified as deforming if its velocity exceeds the MAD of the whole image.
Parameters: vel_file : str, path of velocity file
mask_file : str, path of mask file
win_size : int, length and width of the output box
min_percentage : float between 0 and 1, minimum percentage of deforming points in the box
ramp_type : str, type of phase ramps to be removed while evaluating the deformation
display : bool, plot the identification result or not
Returns: box_list : list of t-tuple of int, each indicating (col0, row0, col1, row1)
"""
print('-' * 30)
print('get boxes on deforming areas')
mask = readfile.read(mask_file)[0]
vel, atr = readfile.read(vel_file)
print('removing a {} phase ramp from input velocity before the evaluation'.
format(ramp_type))
vel = deramp(vel, mask, ramp_type=ramp_type,
metadata=atr)[0] #remove ramp before the evaluation
# get deforming pixels
mad = ut.median_abs_deviation_threshold(vel[mask], center=0.,
cutoff=3) #deformation threshold
print('velocity threshold / median abs dev: {:.3f} cm/yr'.format(mad))
vel[mask == 0] = 0
mask_aoi = (vel >= mad) + (vel <= -1. * mad)
print('number of points: {}'.format(np.sum(mask_aoi)))
# get deforming boxes
box_list = []
min_num = min_percentage * (win_size**2)
length, width = vel.shape
num_row = np.ceil(length / win_size).astype(int)
num_col = np.ceil(width / win_size).astype(int)
for i in range(num_row):
r0 = i * win_size
r1 = min([length, r0 + win_size])
for j in range(num_col):
c0 = j * win_size
c1 = min([width, c0 + win_size])
box = (c0, r0, c1, r1)
if np.sum(mask_aoi[r0:r1, c0:c1]) >= min_num:
box_list.append(box)
print('number of boxes : {}'.format(len(box_list)))
if display:
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[12, 8], sharey=True)
vel[mask == 0] = np.nan
axs[0].imshow(vel, cmap='jet')
axs[1].imshow(mask_aoi, cmap='gray')
for box in box_list:
for ax in axs:
rect = Rectangle((box[0], box[1]), (box[2] - box[0]),
(box[3] - box[1]),
linewidth=2,
edgecolor='r',
fill=False)
ax.add_patch(rect)
plt.show()
return box_list