def main(iargs=None):
inps = cmd_line_parse(iargs)
plt.switch_backend('Agg') # Backend setting
# Read data
k = readfile.read_attribute(inps.file)['FILE_TYPE']
if k == 'timeseries' and inps.dset and '_' in inps.dset:
inps.ref_date, inps.dset = inps.dset.split('_')
else:
inps.ref_date = None
data, atr = readfile.read(inps.file, datasetName=inps.dset)
if k == 'timeseries' and inps.ref_date:
data -= readfile.read(inps.file, datasetName=inps.ref_date)[0]
# mask
mask = pp.read_mask(inps.file,
mask_file=inps.mask_file,
datasetName=inps.dset,
print_msg=True)[0]
if mask is not None:
data = np.ma.masked_where(mask == 0., data)
if inps.zero_mask:
print('masking pixels with zero value')
data = np.ma.masked_where(data == 0., data)
# Data Operation - Display Unit & Rewrapping
(data, inps.disp_unit, inps.disp_scale,
inps.wrap) = pp.scale_data4disp_unit_and_rewrap(
data,
metadata=atr,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range)
if inps.wrap:
inps.vlim = inps.wrap_range
# Output filename
inps.fig_title = pp.auto_figure_title(inps.file,
datasetNames=inps.dset,
inps_dict=vars(inps))
if not inps.outfile:
inps.outfile = '{}.kmz'.format(inps.fig_title)
inps.outfile = os.path.abspath(inps.outfile)
# 2. Generate Google Earth KMZ
write_kmz_file(data, metadata=atr, out_file=inps.outfile, inps=inps)
return inps.outfile
def configure(self):
inps = cmd_line_parse(self.iargs)
inps, self.atr = check_input_file_info(inps)
inps = update_inps_with_file_metadata(inps, self.atr)
# copy inps to self object
for key, value in inps.__dict__.items():
setattr(self, key, value)
# read mask
self.msk, self.mask_file = pp.read_mask(self.file,
mask_file=self.mask_file,
datasetName=self.dset[0],
box=self.pix_box,
print_msg=self.print_msg)
return
def main(iargs=None):
inps = cmd_line_parse(iargs)
plt.switch_backend('Agg') # Backend setting
# Read data
data, atr = readfile.read(inps.file, datasetName=inps.dset)
# mask
mask = pp.read_mask(inps.file, mask_file=inps.mask_file, datasetName=inps.dset, print_msg=True)[0]
if mask is not None:
data = np.ma.masked_where(mask == 0., data)
# Data Operation - Display Unit & Rewrapping
(data,
inps.disp_unit,
inps.disp_scale,
inps.wrap) = pp.scale_data4disp_unit_and_rewrap(data,
metadata=atr,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range)
if inps.wrap:
inps.vlim = inps.wrap_range
# Output filename
inps.fig_title = pp.auto_figure_title(inps.file,
datasetNames=inps.dset,
inps_dict=vars(inps))
if not inps.outfile:
inps.outfile = '{}.kmz'.format(inps.fig_title)
inps.outfile = os.path.abspath(inps.outfile)
# 2. Generate Google Earth KMZ
write_kmz_file(data,
metadata=atr,
out_file=inps.outfile,
inps=inps)
return inps.outfile
def prep_slice(cmd, auto_fig=False):
"""Prepare data from command line as input, for easy call plot_slice() externally
Parameters: cmd : string, command to be run in terminal
Returns: data : 2D np.ndarray, data to be plotted
atr : dict, metadata
inps : namespace, input argument for plot setup
Example:
fig, ax = plt.subplots(figsize=[4, 3])
geo_box = (-91.670, -0.255, -91.370, -0.515) # W, N, E, S
cmd = 'view.py geo_velocity.h5 velocity --mask geo_maskTempCoh.h5 '
cmd += '--sub-lon {w} {e} --sub-lat {s} {n} '.format(w=geo_box[0], n=geo_box[1], e=geo_box[2], s=geo_box[3])
cmd += '-c jet -v -3 10 --cbar-loc bottom --cbar-nbins 3 --cbar-ext both --cbar-size 5% '
cmd += '--dem srtm1.dem --dem-nocontour '
cmd += '--lalo-step 0.2 --lalo-loc 1 0 1 0 --scalebar 0.3 0.80 0.05 --notitle --fontsize 12 '
d_v, atr ,inps = view.prep_slice(cmd)
ax, inps, im, cbar = view.plot_slice(ax, d_v, atr, inps)
plt.show()
"""
inps = cmd_line_parse(cmd.split()[1:])
vprint(cmd)
inps, atr = check_input_file_info(inps)
inps = update_inps_with_file_metadata(inps, atr)
inps.msk, inps.mask_file = pp.read_mask(inps.file,
mask_file=inps.mask_file,
datasetName=inps.dset[0],
box=inps.pix_box,
print_msg=inps.print_msg)
# read data
data, atr = read(inps.file,
datasetName=inps.dset[0],
box=inps.pix_box,
print_msg=inps.print_msg)
# reference in time
if inps.ref_date:
data -= read(inps.file,
datasetName=inps.ref_date,
box=inps.pix_box,
print_msg=False)[0]
# reference in space for unwrapPhase
if (inps.key in ['ifgramStack']
and inps.dset[0].split('-')[0] == 'unwrapPhase'
and 'REF_Y' in atr.keys()):
ref_y, ref_x = int(atr['REF_Y']), int(atr['REF_X'])
ref_data = read(inps.file,
datasetName=inps.dset[0],
box=(ref_x, ref_y, ref_x + 1, ref_y + 1),
print_msg=False)[0]
data[data != 0.] -= ref_data
# masking
if inps.zero_mask:
data = np.ma.masked_where(data == 0., data)
if inps.msk is not None:
data = np.ma.masked_where(inps.msk == 0., data)
data, inps = update_data_with_plot_inps(data, atr, inps)
# matplotlib.Axes
if auto_fig == True:
fig, ax = plt.subplots(figsize=[i / 2.0 for i in inps.fig_size],
num='Figure')
return data, atr, inps, ax
else:
return data, atr, inps
def read_timeseries_data(inps):
"""Read data of time-series files
Parameters: inps : Namespace of input arguments
Returns: ts_data : list of 3D np.array in size of (num_date, length, width)
mask : 2D np.array in size of (length, width)
inps : Namespace of input arguments
"""
## read list of 3D time-series
ts_data = []
for fname in inps.file:
msg = f'reading timeseries from file {fname}'
msg += f' with step of {inps.multilook_num} by {inps.multilook_num}' if inps.multilook_num > 1 else ''
vprint(msg)
data, atr = readfile.read(fname,
datasetName=inps.date_list,
box=inps.pix_box,
xstep=inps.multilook_num,
ystep=inps.multilook_num)
if inps.ref_yx and inps.ref_yx != (int(atr.get('REF_Y', -1)), int(atr.get('REF_X', -1))):
(ry, rx) = subset_and_multilook_yx(inps.ref_yx, inps.pix_box, inps.multilook_num)
ref_phase = data[:, ry, rx]
data -= np.tile(ref_phase.reshape(-1, 1, 1), (1, data.shape[-2], data.shape[-1]))
vprint('reference to pixel: {}'.format(inps.ref_yx))
if inps.ref_idx is not None:
vprint('reference to date: {}'.format(inps.date_list[inps.ref_idx]))
data -= np.tile(data[inps.ref_idx, :, :], (inps.num_date, 1, 1))
# Display Unit
(data,
inps.disp_unit,
inps.unit_fac) = pp.scale_data2disp_unit(data,
metadata=atr,
disp_unit=inps.disp_unit)
ts_data.append(data)
## mask file: input mask file + non-zero ts pixels - ref_point
mask = pp.read_mask(inps.file[0],
mask_file=inps.mask_file,
datasetName='displacement',
box=inps.pix_box,
xstep=inps.multilook_num,
ystep=inps.multilook_num,
print_msg=inps.print_msg)[0]
if mask is None:
mask = np.ones(ts_data[0].shape[-2:], np.bool_)
ts_stack = np.nansum(ts_data[0], axis=0)
mask[np.isnan(ts_stack)] = False
# keep all-zero value for unwrapError time-series
if atr['UNIT'] not in ['cycle']:
mask[ts_stack == 0.] = False
del ts_stack
# do not mask the reference point
if inps.ref_yx and inps.ref_yx != (int(atr.get('REF_Y', -1)), int(atr.get('REF_X', -1))):
(ry, rx) = subset_and_multilook_yx(inps.ref_yx, inps.pix_box, inps.multilook_num)
mask[ry, rx] = True
## default vlim
inps.dlim = [np.nanmin(ts_data[0]), np.nanmax(ts_data[0])]
if not inps.vlim:
inps.cmap_lut, inps.vlim = pp.auto_adjust_colormap_lut_and_disp_limit(ts_data[0],
num_multilook=10,
print_msg=inps.print_msg)
vprint('data range: {} {}'.format(inps.dlim, inps.disp_unit))
vprint('display range: {} {}'.format(inps.vlim, inps.disp_unit))
## default ylim
num_file = len(inps.file)
if not inps.ylim:
ts_data_mli = multilook_data(np.squeeze(ts_data[-1]), 4, 4)
if inps.zero_first:
ts_data_mli -= np.tile(ts_data_mli[inps.zero_idx, :, :], (inps.num_date, 1, 1))
ymin, ymax = (np.nanmin(ts_data_mli[inps.ex_flag != 0]),
np.nanmax(ts_data_mli[inps.ex_flag != 0]))
ybuffer = (ymax - ymin) * 0.05
inps.ylim = [ymin - ybuffer, ymax + ybuffer]
if inps.offset:
inps.ylim[1] += inps.offset * (num_file - 1)
del ts_data_mli
return ts_data, mask, inps
def read_timeseries_data(inps):
"""Read data of time-series files
Parameters: inps : Namespace of input arguments
Returns: ts_data : list of 3D np.array in size of (num_date, length, width)
mask : 2D np.array in size of (length, width)
inps : Namespace of input arguments
"""
# read list of 3D time-series
ts_data = []
for fname in inps.file:
vprint('reading timeseries from file {} ...'.format(fname))
data, atr = readfile.read(fname,
datasetName=inps.date_list,
box=inps.pix_box)
try:
ref_phase = data[:, inps.ref_yx[0] - inps.pix_box[1],
inps.ref_yx[1] - inps.pix_box[0]]
data -= np.tile(ref_phase.reshape(-1, 1, 1),
(1, data.shape[-2], data.shape[-1]))
vprint('reference to pixel: {}'.format(inps.ref_yx))
except:
pass
vprint('reference to date: {}'.format(inps.date_list[inps.ref_idx]))
data -= np.tile(data[inps.ref_idx, :, :], (inps.num_date, 1, 1))
# Display Unit
(data, inps.disp_unit,
inps.unit_fac) = pp.scale_data2disp_unit(data,
metadata=atr,
disp_unit=inps.disp_unit)
ts_data.append(data)
# Mask file: input mask file + non-zero ts pixels - ref_point
mask = np.ones(ts_data[0].shape[-2:], np.bool_)
msk = pp.read_mask(inps.file[0],
mask_file=inps.mask_file,
datasetName='displacement',
box=inps.pix_box,
print_msg=inps.print_msg)[0]
mask[msk == 0.] = False
del msk
ts_stack = np.sum(ts_data[0], axis=0)
mask[ts_stack == 0.] = False
mask[np.isnan(ts_stack)] = False
del ts_stack
#do not mask the reference point
try:
mask[inps.ref_yx[0] - inps.pix_box[1],
inps.ref_yx[1] - inps.pix_box[0]] = True
except:
pass
#vprint('masking data')
#ts_mask = np.tile(mask, (inps.num_date, 1, 1))
#for i in range(len(ts_data)):
# ts_data[i][ts_mask == 0] = np.nan
# try:
# ts_data[i][:, inps.ref_yx[0], inps.ref_yx[1]] = 0. # keep value on reference pixel
# except:
# pass
#del ts_mask
# default vlim
inps.dlim = [np.nanmin(ts_data[0]), np.nanmax(ts_data[0])]
ts_data_mli = multilook_data(np.squeeze(ts_data[0]), 10, 10)
if not inps.vlim:
inps.vlim = [
np.nanmin(ts_data_mli[inps.ex_flag != 0]),
np.nanmax(ts_data_mli[inps.ex_flag != 0])
]
vprint('data range: {} {}'.format(inps.dlim, inps.disp_unit))
vprint('display range: {} {}'.format(inps.vlim, inps.disp_unit))
# default ylim
num_file = len(inps.file)
if not inps.ylim:
ts_data_mli = multilook_data(np.squeeze(ts_data[-1]), 4, 4)
if inps.zero_first:
ts_data_mli -= np.tile(ts_data_mli[inps.zero_idx, :, :],
(inps.num_date, 1, 1))
ymin, ymax = (np.nanmin(ts_data_mli[inps.ex_flag != 0]),
np.nanmax(ts_data_mli[inps.ex_flag != 0]))
ybuffer = (ymax - ymin) * 0.05
inps.ylim = [ymin - ybuffer, ymax + ybuffer]
if inps.offset:
inps.ylim[1] += inps.offset * (num_file - 1)
del ts_data_mli
return ts_data, mask, inps
def main(iargs=None):
inps = cmd_line_parse(iargs)
# 1. Read metadata and data
k = readfile.read_attribute(inps.file)['FILE_TYPE']
if k == 'timeseries' and inps.dset and '_' in inps.dset:
inps.ref_date, inps.dset = inps.dset.split('_')
else:
inps.ref_date = None
atr = readfile.read_attribute(inps.file, datasetName=inps.dset)
# pix_box
inps.pix_box = subset.subset_input_dict2box(vars(inps), atr)[0]
inps.pix_box = ut.coordinate(atr).check_box_within_data_coverage(
inps.pix_box)
data_box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
print('data coverage in y/x: {}'.format(data_box))
print('subset coverage in y/x: {}'.format(inps.pix_box))
atr = attr.update_attribute4subset(atr, inps.pix_box)
# read data
data = readfile.read(inps.file, datasetName=inps.dset, box=inps.pix_box)[0]
if k == 'timeseries' and inps.ref_date:
data -= readfile.read(inps.file,
datasetName=inps.ref_date,
box=inps.pix_box)[0]
# mask
mask = pp.read_mask(inps.file,
mask_file=inps.mask_file,
datasetName=inps.dset,
box=inps.pix_box)[0]
if mask is not None:
print('masking out pixels with zero value in file: {}'.format(
inps.mask_file))
data[mask == 0] = np.nan
if inps.zero_mask:
print('masking out pixels with zero value')
data[data == 0] = np.nan
del mask
# Data Operation - Display Unit & Rewrapping
(data, inps.disp_unit, inps.disp_scale,
inps.wrap) = pp.scale_data4disp_unit_and_rewrap(
data,
metadata=atr,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range)
if inps.wrap:
inps.vlim = inps.wrap_range
# 2. Generate Google Earth KMZ
# 2.1 Common settings
# disp min/max and colormap
cmap_lut = 256
if not inps.vlim:
cmap_lut, inps.vlim = pp.auto_adjust_colormap_lut_and_disp_limit(data)
inps.colormap = pp.auto_colormap_name(atr, inps.colormap)
inps.colormap = pp.ColormapExt(inps.colormap, cmap_lut).colormap
inps.norm = colors.Normalize(vmin=inps.vlim[0], vmax=inps.vlim[1])
# Output filename
inps.fig_title = pp.auto_figure_title(inps.file,
datasetNames=inps.dset,
inps_dict=vars(inps))
if not inps.outfile:
inps.outfile = '{}.kmz'.format(inps.fig_title)
inps.outfile = os.path.abspath(inps.outfile)
# 2.2 Write KMZ file
if 'Y_FIRST' in atr.keys():
# create ground overlay KML for file in geo-coord
write_kmz_overlay(
data,
meta=atr,
out_file=inps.outfile,
inps=inps,
)
else:
# create placemark KML for file in radar-coord
write_kmz_placemark(
data,
meta=atr,
out_file=inps.outfile,
geom_file=inps.geom_file,
inps=inps,
)
return inps.outfile
