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 analyze_rms(date_list, rms_list, inps):
# reference date
ref_idx = np.argmin(rms_list)
print('-' * 50 + '\ndate with min RMS: {} - {:.4f}'.format(
date_list[ref_idx], rms_list[ref_idx]))
ref_date_file = 'reference_date.txt'
if ut.run_or_skip(
out_file=ref_date_file,
in_file=[inps.timeseries_file, inps.mask_file, inps.template_file],
check_readable=False) == 'run':
with open(ref_date_file, 'w') as f:
f.write(date_list[ref_idx] + '\n')
print('save date to file: ' + ref_date_file)
# exclude date(s) - outliers
try:
rms_threshold = ut.median_abs_deviation_threshold(rms_list,
center=0.,
cutoff=inps.cutoff)
except:
# equivalent calculation using numpy assuming Gaussian distribution
rms_threshold = np.median(rms_list) / .6745 * inps.cutoff
ex_idx = [rms_list.index(i) for i in rms_list if i > rms_threshold]
print(('-' * 50 + '\ndate(s) with RMS > {} * median RMS'
' ({:.4f})'.format(inps.cutoff, rms_threshold)))
ex_date_file = 'exclude_date.txt'
if ex_idx:
# print
for i in ex_idx:
print('{} - {:.4f}'.format(date_list[i], rms_list[i]))
# save to text file
with open(ex_date_file, 'w') as f:
for i in ex_idx:
f.write(date_list[i] + '\n')
print('save date(s) to file: ' + ex_date_file)
else:
print('None.')
if os.path.isfile(ex_date_file):
rmCmd = 'rm {}'.format(ex_date_file)
print(rmCmd)
os.system(rmCmd)
# plot bar figure and save
fig_file = os.path.splitext(inps.rms_file)[0] + '.pdf'
fig, ax = plt.subplots(figsize=inps.fig_size)
print('create figure in size:', inps.fig_size)
ax = plot_rms_bar(ax,
date_list,
np.array(rms_list) * 1000.,
cutoff=inps.cutoff)
fig.savefig(fig_file, bbox_inches='tight', transparent=True)
print('save figure to file: ' + fig_file)
return inps
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 analyze_rms(date_list, rms_list, inps):
# reference date
ref_idx = np.argmin(rms_list)
print('-'*50+'\ndate with min RMS: {} - {:.4f}'.format(date_list[ref_idx],
rms_list[ref_idx]))
ref_date_file = 'reference_date.txt'
if ut.run_or_skip(out_file=ref_date_file,
in_file=[inps.timeseries_file, inps.mask_file, inps.template_file],
check_readable=False) == 'run':
with open(ref_date_file, 'w') as f:
f.write(date_list[ref_idx]+'\n')
print('save date to file: '+ref_date_file)
# exclude date(s) - outliers
try:
rms_threshold = ut.median_abs_deviation_threshold(rms_list, center=0., cutoff=inps.cutoff)
except:
# equivalent calculation using numpy assuming Gaussian distribution
rms_threshold = np.median(rms_list) / .6745 * inps.cutoff
ex_idx = [rms_list.index(i) for i in rms_list if i > rms_threshold]
print(('-'*50+'\ndate(s) with RMS > {} * median RMS'
' ({:.4f})'.format(inps.cutoff, rms_threshold)))
ex_date_file = 'exclude_date.txt'
if ex_idx:
# print
for i in ex_idx:
print('{} - {:.4f}'.format(date_list[i], rms_list[i]))
# save to text file
with open(ex_date_file, 'w') as f:
for i in ex_idx:
f.write(date_list[i]+'\n')
print('save date(s) to file: '+ex_date_file)
else:
print('None.')
if os.path.isfile(ex_date_file):
rmCmd = 'rm {}'.format(ex_date_file)
print(rmCmd)
os.system(rmCmd)
# plot bar figure and save
fig_file = os.path.splitext(inps.rms_file)[0]+'.pdf'
fig, ax = plt.subplots(figsize=inps.fig_size)
print('create figure in size:', inps.fig_size)
ax = plot_rms_bar(ax, date_list, np.array(rms_list)*1000., cutoff=inps.cutoff)
fig.savefig(fig_file, bbox_inches='tight', transparent=True)
print('save figure to file: '+fig_file)
return inps
def plot_rms_bar(ax,
date_list,
rms,
cutoff=3.,
font_size=12,
tick_year_num=1,
legend_loc='best',
disp_legend=True,
disp_side_plot=True,
disp_thres_text=False,
ylabel=r'Residual Phase $\hat \phi_{resid}$ RMS [mm]'):
""" Bar plot Phase Residual RMS
Parameters: ax : Axes object
date_list : list of string in YYYYMMDD format
rms : 1D np.array of float for RMS value in mm
cutoff : cutoff value of MAD outlier detection
tick_year_num : int, number of years per major tick
legend_loc : 'upper right' or (0.5, 0.5)
Returns: ax : Axes object
"""
dates, datevector = ptime.date_list2vector(date_list)
dates = np.array(dates)
try:
bar_width = min(ut.most_common(np.diff(dates).tolist(), k=2)) * 3 / 4
except:
bar_width = np.min(np.diff(dates).tolist()) * 3 / 4
rms = np.array(rms)
# Plot all dates
ax.bar(dates, rms, bar_width.days, color=pp.mplColors[0])
# Plot reference date
ref_idx = np.argmin(rms)
ax.bar(dates[ref_idx],
rms[ref_idx],
bar_width.days,
color=pp.mplColors[1],
label='Reference date')
# Plot exclude dates
rms_threshold = ut.median_abs_deviation_threshold(rms,
center=0.,
cutoff=cutoff)
ex_idx = rms > rms_threshold
if not np.all(ex_idx == False):
ax.bar(dates[ex_idx],
rms[ex_idx],
bar_width.days,
color='darkgray',
label='Exclude date')
# Plot rms_threshold line
(ax, xmin, xmax) = pp.auto_adjust_xaxis_date(ax,
datevector,
font_size,
every_year=tick_year_num)
ax.plot(np.array([xmin, xmax]),
np.array([rms_threshold, rms_threshold]),
'--k',
label='Median Abs Dev * {}'.format(cutoff))
# axis format
ax = pp.auto_adjust_yaxis(ax,
np.append(rms, rms_threshold),
font_size,
ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel(ylabel, fontsize=font_size)
ax.tick_params(which='both',
direction='in',
labelsize=font_size,
bottom=True,
top=True,
left=True,
right=True)
# 2nd axes for circles
if disp_side_plot:
divider = make_axes_locatable(ax)
ax2 = divider.append_axes("right", "10%", pad="2%")
ax2.plot(np.ones(rms.shape, np.float32) * 0.5,
rms,
'o',
mfc='none',
color=pp.mplColors[0])
ax2.plot(np.ones(rms.shape, np.float32)[ref_idx] * 0.5,
rms[ref_idx],
'o',
mfc='none',
color=pp.mplColors[1])
if not np.all(ex_idx == False):
ax2.plot(np.ones(rms.shape, np.float32)[ex_idx] * 0.5,
rms[ex_idx],
'o',
mfc='none',
color='darkgray')
ax2.plot(np.array([0, 1]), np.array([rms_threshold, rms_threshold]),
'--k')
ax2.set_ylim(ax.get_ylim())
ax2.set_xlim([0, 1])
ax2.tick_params(which='both',
direction='in',
labelsize=font_size,
bottom=True,
top=True,
left=True,
right=True)
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticklabels([])
if disp_legend:
ax.legend(loc=legend_loc, frameon=False, fontsize=font_size)
# rms_threshold text
if disp_thres_text:
ymin, ymax = ax.get_ylim()
yoff = (ymax - ymin) * 0.1
if (rms_threshold - ymin) > 0.5 * (ymax - ymin):
yoff *= -1.
ax.annotate('Median Abs Dev * {}'.format(cutoff),
xy=(xmin + (xmax - xmin) * 0.05, rms_threshold + yoff),
color='k',
xycoords='data',
fontsize=font_size)
return ax
def detect_unwrap_error(ifgram_file, mask_file, mask_cc_file='maskConnComp.h5', unwDatasetName='unwrapPhase',
cutoff=1., min_num_pixel=1e4):
"""Detect unwrapping error based on phase closure and extract coherent conn comps
based on its histogram distribution
Check:
https://en.wikipedia.org/wiki/Otsu%27s_method
from skimage.filters import threshold_otsu
Parameters: ifgram_file : string, path of ifgram stack file
mask_file : string, path of mask file, e.g. waterMask.h5, maskConnComp.h5
mask_cc_file: string, path of mask file for coherent conn comps
cutoff : float, cutoff value for the mean number of nonzero phase closure
to be selected as coherent conn comps candidate
min_num_pixel : float, min number of pixels left after morphology operation
to be determined as coherent conn comps
Returns: mask_cc_file : string, path of mask file for coherent conn comps
"""
print('-'*50)
print('detect unwraping error based on phase closure')
obj = ifgramStack(ifgram_file)
obj.open(print_msg=False)
C = obj.get_design_matrix4triplet(obj.get_date12_list(dropIfgram=False))
num_nonzero_closure = get_nonzero_phase_closure(ifgram_file, unwDatasetName=unwDatasetName)
# get histogram of num_nonzero_phase_closure
mask = readfile.read(mask_file)[0]
mask *= num_nonzero_closure != 0.
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[12, 4])
num4disp = np.array(num_nonzero_closure, dtype=np.float32)
num4disp[mask == 0] = np.nan
im = ax[0].imshow(num4disp)
ax[0].set_xlabel('Range [pix.]')
ax[0].set_ylabel('Azimuth [pix.]')
ax[0] = pp.auto_flip_direction(obj.metadata, ax=ax[0], print_msg=False)
cbar = fig.colorbar(im, ax=ax[0])
cbar.set_label('number of non-zero phase closure')
print('2. extract coherent conn comps with unwrap error based on histogram distribution')
max_nonzero_closure = int(np.max(num_nonzero_closure[mask]))
bin_value, bin_edge = ax[1].hist(num_nonzero_closure[mask].flatten(),
range=(0, max_nonzero_closure),
log=True,
bins=max_nonzero_closure)[0:2]
ax[1].set_xlabel('number of non-zero phase closure')
ax[1].set_ylabel('number of pixels')
if 'Closure' not in unwDatasetName:
print('eliminate pixels with number of nonzero phase closure < 5% of total phase closure number')
print('\twhich can be corrected using phase closure alone.')
bin_value[:int(C.shape[0]*0.05)] = 0.
bin_value_thres = ut.median_abs_deviation_threshold(bin_value, cutoff=cutoff)
print('median abs deviation cutoff value: {}'.format(cutoff))
plt.plot([0, max_nonzero_closure], [bin_value_thres, bin_value_thres])
out_img = 'numUnwErr_stat.png'
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save unwrap error detection result to {}'.format(out_img))
# histogram --> candidates of coherence conn comps --> mask_cc
# find pixel clusters sharing similar number of non-zero phase closure
print('searching connected components with more than {} pixels'.format(min_num_pixel))
bin_label, n_bins = ndimage.label(bin_value > bin_value_thres)
mask_cc = np.zeros(num_nonzero_closure.shape, dtype=np.int16)
# first conn comp - reference conn comp with zero non-zero phase closure
num_cc = 1
mask_cc1 = num_nonzero_closure == 0.
mask_cc1s = ut.get_all_conn_components(mask_cc1, min_num_pixel=min_num_pixel)
for mask_cc1 in mask_cc1s:
mask_cc += mask_cc1
# other conn comps - target conn comps to be corrected for unwrap error
for i in range(n_bins):
idx = np.where(bin_label == i+1)[0]
mask_cci0 = np.multiply(num_nonzero_closure >= bin_edge[idx[0]],
num_nonzero_closure < bin_edge[idx[-1]+1])
mask_ccis = ut.get_all_conn_components(mask_cci0, min_num_pixel=min_num_pixel)
if mask_ccis:
for mask_cci in mask_ccis:
num_cc += 1
mask_cc += mask_cci * num_cc
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[8, 4])
im = ax[0].imshow(mask_cci0)
im = ax[1].imshow(mask_cci)
fig.savefig('mask_cc{}.png'.format(num_cc),
bbox_inches='tight', transparent=True, dpi=300)
# save to hdf5 file
num_bridge = num_cc - 1
atr = dict(obj.metadata)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 1
writefile.write(mask_cc, out_file=mask_cc_file, metadata=atr)
# plot and save figure to img file
out_img = '{}.png'.format(os.path.splitext(mask_cc_file)[0])
fig, ax = plt.subplots(figsize=[6, 8])
im = ax.imshow(mask_cc)
ax = pp.auto_flip_direction(atr, ax=ax, print_msg=False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax, ticks=np.arange(num_bridge+2))
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_img))
return mask_cc_file
def plot_rms_bar(ax, date_list, rms, cutoff=3., font_size=12,
tick_year_num=1, legend_loc='best',
disp_legend=True, disp_side_plot=True, disp_thres_text=True,
ylabel=r'Residual Phase $\hat \phi_{resid}$ RMS [mm]'):
""" Bar plot Phase Residual RMS
Parameters: ax : Axes object
date_list : list of string in YYYYMMDD format
rms : 1D np.array of float for RMS value in mm
cutoff : cutoff value of MAD outlier detection
tick_year_num : int, number of years per major tick
legend_loc : 'upper right' or (0.5, 0.5)
Returns: ax : Axes object
"""
dates, datevector = ptime.date_list2vector(date_list)
dates = np.array(dates)
try:
bar_width = min(ut.most_common(np.diff(dates).tolist(), k=2))*3/4
except:
bar_width = np.min(np.diff(dates).tolist())*3/4
rms = np.array(rms)
# Plot all dates
ax.bar(dates, rms, bar_width.days, color=pp.mplColors[0])
# Plot reference date
ref_idx = np.argmin(rms)
ax.bar(dates[ref_idx], rms[ref_idx], bar_width.days, color=pp.mplColors[1], label='Reference date')
# Plot exclude dates
rms_threshold = ut.median_abs_deviation_threshold(rms, center=0., cutoff=cutoff)
ex_idx = rms > rms_threshold
if not np.all(ex_idx==False):
ax.bar(dates[ex_idx], rms[ex_idx], bar_width.days, color='darkgray', label='Exclude date')
# Plot rms_threshold line
(ax, xmin, xmax) = pp.auto_adjust_xaxis_date(ax, datevector, font_size, every_year=tick_year_num)
ax.plot(np.array([xmin, xmax]), np.array([rms_threshold, rms_threshold]), '--k', label='RMS threshold')
# axis format
ax = pp.auto_adjust_yaxis(ax, np.append(rms, rms_threshold), font_size, ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel(ylabel, fontsize=font_size)
ax.tick_params(which='both', direction='in', labelsize=font_size,
bottom=True, top=True, left=True, right=True)
# 2nd axes for circles
if disp_side_plot:
divider = make_axes_locatable(ax)
ax2 = divider.append_axes("right", "10%", pad="2%")
ax2.plot(np.ones(rms.shape, np.float32) * 0.5, rms, 'o', mfc='none', color=pp.mplColors[0])
ax2.plot(np.ones(rms.shape, np.float32)[ref_idx] * 0.5, rms[ref_idx], 'o', mfc='none', color=pp.mplColors[1])
if not np.all(ex_idx==False):
ax2.plot(np.ones(rms.shape, np.float32)[ex_idx] * 0.5, rms[ex_idx], 'o', mfc='none', color='darkgray')
ax2.plot(np.array([0, 1]), np.array([rms_threshold, rms_threshold]), '--k')
ax2.set_ylim(ax.get_ylim())
ax2.set_xlim([0, 1])
ax2.tick_params(which='both', direction='in', labelsize=font_size,
bottom=True, top=True, left=True, right=True)
ax2.get_xaxis().set_ticks([])
ax2.get_yaxis().set_ticklabels([])
if disp_legend:
ax.legend(loc=legend_loc, frameon=False, fontsize=font_size)
# rms_threshold text
if disp_thres_text:
ymin, ymax = ax.get_ylim()
yoff = (ymax - ymin) * 0.1
if (rms_threshold - ymin) > 0.5 * (ymax - ymin):
yoff *= -1.
ax.annotate('Median Abs Dev * {}'.format(cutoff),
xy=(xmin + (xmax-xmin)*0.05, rms_threshold + yoff ),
color='k', xycoords='data', fontsize=font_size)
return ax
def detect_unwrap_error(ifgram_file,
mask_file,
mask_cc_file='maskConnComp.h5',
unwDatasetName='unwrapPhase',
cutoff=1.,
min_num_pixel=1e4):
"""Detect unwrapping error based on phase closure and extract coherent conn comps
based on its histogram distribution
Check:
https://en.wikipedia.org/wiki/Otsu%27s_method
from skimage.filters import threshold_otsu
Parameters: ifgram_file : string, path of ifgram stack file
mask_file : string, path of mask file, e.g. waterMask.h5, maskConnComp.h5
mask_cc_file: string, path of mask file for coherent conn comps
cutoff : float, cutoff value for the mean number of nonzero phase closure
to be selected as coherent conn comps candidate
min_num_pixel : float, min number of pixels left after morphology operation
to be determined as coherent conn comps
Returns: mask_cc_file : string, path of mask file for coherent conn comps
"""
print('-' * 50)
print('detect unwraping error based on phase closure')
obj = ifgramStack(ifgram_file)
obj.open(print_msg=False)
C = obj.get_design_matrix4triplet(obj.get_date12_list(dropIfgram=False))
num_nonzero_closure = get_nonzero_phase_closure(
ifgram_file, unwDatasetName=unwDatasetName)
# get histogram of num_nonzero_phase_closure
mask = readfile.read(mask_file)[0]
mask *= num_nonzero_closure != 0.
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[12, 4])
num4disp = np.array(num_nonzero_closure, dtype=np.float32)
num4disp[mask == 0] = np.nan
im = ax[0].imshow(num4disp)
ax[0].set_xlabel('Range [pix.]')
ax[0].set_ylabel('Azimuth [pix.]')
ax[0] = pp.auto_flip_direction(obj.metadata, ax=ax[0], print_msg=False)
cbar = fig.colorbar(im, ax=ax[0])
cbar.set_label('number of non-zero phase closure')
print(
'2. extract coherent conn comps with unwrap error based on histogram distribution'
)
max_nonzero_closure = int(np.max(num_nonzero_closure[mask]))
bin_value, bin_edge = ax[1].hist(num_nonzero_closure[mask].flatten(),
range=(0, max_nonzero_closure),
log=True,
bins=max_nonzero_closure)[0:2]
ax[1].set_xlabel('number of non-zero phase closure')
ax[1].set_ylabel('number of pixels')
if 'Closure' not in unwDatasetName:
print(
'eliminate pixels with number of nonzero phase closure < 5% of total phase closure number'
)
print('\twhich can be corrected using phase closure alone.')
bin_value[:int(C.shape[0] * 0.05)] = 0.
bin_value_thres = ut.median_abs_deviation_threshold(bin_value,
cutoff=cutoff)
print('median abs deviation cutoff value: {}'.format(cutoff))
plt.plot([0, max_nonzero_closure], [bin_value_thres, bin_value_thres])
out_img = 'numUnwErr_stat.png'
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save unwrap error detection result to {}'.format(out_img))
# histogram --> candidates of coherence conn comps --> mask_cc
# find pixel clusters sharing similar number of non-zero phase closure
print('searching connected components with more than {} pixels'.format(
min_num_pixel))
bin_label, n_bins = ndimage.label(bin_value > bin_value_thres)
mask_cc = np.zeros(num_nonzero_closure.shape, dtype=np.int16)
# first conn comp - reference conn comp with zero non-zero phase closure
num_cc = 1
mask_cc1 = num_nonzero_closure == 0.
mask_cc1s = ut.get_all_conn_components(mask_cc1,
min_num_pixel=min_num_pixel)
for mask_cc1 in mask_cc1s:
mask_cc += mask_cc1
# other conn comps - target conn comps to be corrected for unwrap error
for i in range(n_bins):
idx = np.where(bin_label == i + 1)[0]
mask_cci0 = np.multiply(num_nonzero_closure >= bin_edge[idx[0]],
num_nonzero_closure < bin_edge[idx[-1] + 1])
mask_ccis = ut.get_all_conn_components(mask_cci0,
min_num_pixel=min_num_pixel)
if mask_ccis:
for mask_cci in mask_ccis:
num_cc += 1
mask_cc += mask_cci * num_cc
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[8, 4])
im = ax[0].imshow(mask_cci0)
im = ax[1].imshow(mask_cci)
fig.savefig('mask_cc{}.png'.format(num_cc),
bbox_inches='tight',
transparent=True,
dpi=300)
# save to hdf5 file
num_bridge = num_cc - 1
atr = dict(obj.metadata)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 1
writefile.write(mask_cc, out_file=mask_cc_file, metadata=atr)
# plot and save figure to img file
out_img = '{}.png'.format(os.path.splitext(mask_cc_file)[0])
fig, ax = plt.subplots(figsize=[6, 8])
im = ax.imshow(mask_cc)
ax = pp.auto_flip_direction(atr, ax=ax, print_msg=False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax, ticks=np.arange(num_bridge + 2))
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_img))
return mask_cc_file
