def get_boxes4deforming_area(vel_file, mask_file, step=2, num_pixel=30**2, 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)
"""
win_size = int(np.sqrt(num_pixel) * step)
print('-'*30)
print('get boxes on deforming areas with step: {} pixels'.format(step))
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]),
width=(box[2]-box[0]),
height=(box[3]-box[1]),
linewidth=2, edgecolor='r', fill=False)
ax.add_patch(rect)
plt.show()
return box_list
def calculate_ramp(data, atr, inps):
"""calcualte ramp from data file"""
ramp_type = inps.surface_type
mask = readfile.read(inps.mask)[0]
ramp = deramp(data, mask, ramp_type=ramp_type, metadata=atr)[1]
ramp[mask == False] = np.nan
return ramp
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
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_deramp(fname,
ramp_type,
mask_file=None,
out_file=None,
datasetName=None,
save_ramp_coeff=False):
""" 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
save_ramp_coeff : bool, save the estimated ramp coefficients to text file
Returns: out_file : str, output file name
"""
start_time = time.time()
atr = readfile.read_attribute(fname)
k = atr['FILE_TYPE']
length = int(atr['LENGTH'])
width = int(atr['WIDTH'])
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)
if k == 'ifgramStack':
out_file = fname
# mask
if os.path.isfile(mask_file):
mask = readfile.read(mask_file)[0]
print('read mask file: ' + mask_file)
else:
mask = np.ones((length, width), dtype=np.bool_)
print('use mask of the whole area')
# write coefficient of specified surface function fit
coeff_file = None
if save_ramp_coeff:
fbase = os.path.splitext(os.path.basename(fname))[0]
coeff_file = os.path.join(os.path.dirname(fbase),
'rampCoeff_{}.txt'.format(fbase))
with open(coeff_file, 'w') as f:
f.write('# input file: {}\n'.format(fname))
f.write('# output file: {}\n'.format(out_file))
f.write('# ramp type: {}\n'.format(ramp_type))
# deramping
if k == 'timeseries':
# write HDF5 file with defined metadata and (empty) dataset structure
writefile.layout_hdf5(out_file, ref_file=fname, print_msg=True)
print('estimating phase ramp one date at a time ...')
date_list = timeseries(fname).get_date_list()
num_date = len(date_list)
prog_bar = ptime.progressBar(maxValue=num_date)
for i in range(num_date):
if coeff_file:
# prepend epoch name to line of coefficients
with open(coeff_file, 'a') as f:
f.write('{} '.format((date_list[i])))
# read
data = readfile.read(fname, datasetName=date_list[i])[0]
# deramp
data = deramp(data,
mask,
ramp_type=ramp_type,
metadata=atr,
coeff_file=coeff_file)[0]
# write
writefile.write_hdf5_block(out_file,
data,
datasetName='timeseries',
block=[i, i + 1, 0, length, 0, width],
print_msg=False)
prog_bar.update(i + 1, suffix='{}/{}'.format(i + 1, num_date))
prog_bar.close()
print('finished writing to file: {}'.format(out_file))
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, length, 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):
if coeff_file:
# prepend IFG date12 to line of coefficients
with open(coeff_file, 'a') as f:
f.write('{} '.format(str(obj.date12List[i])))
# read
data = ds[i, :, :]
# deramp
data = deramp(data,
mask,
ramp_type=ramp_type,
metadata=atr,
coeff_file=coeff_file)[0]
# write
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:
if coeff_file:
# prepend file-type to line of coefficients
with open(coeff_file, 'a') as f:
f.write('{} '.format(atr['FILE_TYPE']))
# read
data = readfile.read(fname)[0]
# deramp
data = deramp(data,
mask,
ramp_type=ramp_type,
metadata=atr,
coeff_file=coeff_file)[0]
# write
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_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
"""
start_time = time.time()
atr = readfile.read_attribute(fname)
k = atr['FILE_TYPE']
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)
if k == 'ifgramStack':
out_file = fname
# mask
if os.path.isfile(mask_file):
mask = readfile.read(mask_file)[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
if k == 'timeseries':
# initialize dataset structure
ts_obj = timeseries(fname)
ts_obj.open(print_msg=False)
date_list = ts_obj.dateList
num_date = len(date_list)
date_dtype = np.dtype('S{}'.format(len(date_list[0])))
dsNameDict = {
"date": (date_dtype, (num_date, )),
"bperp": (np.float32, (num_date, )),
"timeseries":
(np.float32, (num_date, ts_obj.length, ts_obj.width)),
}
# write HDF5 file with defined metadata and (empty) dataset structure
writefile.layout_hdf5(out_file, dsNameDict, atr, print_msg=False)
# write date time-series
date_list_utf8 = [dt.encode('utf-8') for dt in date_list]
writefile.write_hdf5_block(out_file,
date_list_utf8,
datasetName='date',
print_msg=False)
# write bperp time-series
bperp = ts_obj.pbase
writefile.write_hdf5_block(out_file,
bperp,
datasetName='bperp',
print_msg=False)
print('estimating phase ramp one date at a time ...')
prog_bar = ptime.progressBar(maxValue=num_date)
for i in range(num_date):
# read
dsName = 'timeseries-{}'.format(date_list[i])
data = readfile.read(fname, datasetName=dsName)[0]
# deramp
data = deramp(data, mask, ramp_type=ramp_type, metadata=atr)[0]
# write
writefile.write_hdf5_block(
out_file,
data,
datasetName='timeseries',
block=[i, i + 1, 0, ts_obj.length, 0, ts_obj.width],
print_msg=False)
prog_bar.update(i + 1, suffix='{}/{}'.format(i + 1, num_date))
prog_bar.close()
print('finished writing to file: {}'.format(out_file))
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_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
