def update_inps_with_file_metadata(inps, metadata):
# Subset
# Convert subset input into bounding box in radar / geo coordinate
# geo_box = None if atr is not geocoded.
coord = ut.coordinate(metadata)
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(
vars(inps), metadata)
inps.pix_box = coord.check_box_within_data_coverage(inps.pix_box)
inps.geo_box = coord.box_pixel2geo(inps.pix_box)
# Out message
data_box = (0, 0, inps.width, inps.length)
vprint('data coverage in y/x: ' + str(data_box))
vprint('subset coverage in y/x: ' + str(inps.pix_box))
vprint('data coverage in lat/lon: ' + str(coord.box_pixel2geo(data_box)))
vprint('subset coverage in lat/lon: ' + str(inps.geo_box))
vprint(
'------------------------------------------------------------------------'
)
# DEM contour display
if max(inps.pix_box[3] - inps.pix_box[1],
inps.pix_box[2] - inps.pix_box[0]) > 2e3:
inps.disp_dem_contour = False
if inps.dem_file:
vprint(
'area exceed 2000 pixels, turn off default DEM contour display'
)
# Multilook, if too many subplots in one figure for less memory and faster speed
if inps.multilook_num > 1:
inps.multilook = True
# Colormap
inps.colormap = pp.check_colormap_input(metadata,
inps.colormap,
datasetName=inps.dset[0],
cmap_lut=inps.cmap_lut,
print_msg=inps.print_msg)
# Reference Point
# Convert ref_lalo if existed, to ref_yx, and use ref_yx for the following
# ref_yx is referenced to input data coverage, not subseted area for display
if inps.ref_lalo and inps.geo_box:
inps.ref_yx = [
coord.lalo2yx(inps.ref_lalo[0], coord_type='lat'),
coord.lalo2yx(inps.ref_lalo[1], coord_type='lon')
]
vprint('input reference point in lat/lon: {}'.format(inps.ref_lalo))
vprint('input reference point in y /x : {}'.format(inps.ref_yx))
# ref_lalo
if inps.ref_yx and inps.geo_box:
inps.ref_lalo = [
coord.yx2lalo(inps.ref_yx[0], coord_type='y'),
coord.yx2lalo(inps.ref_yx[1], coord_type='x')
]
elif 'REF_LAT' in metadata.keys():
inps.ref_lalo = [
float(metadata['REF_LAT']),
float(metadata['REF_LON'])
]
else:
inps.ref_lalo = None
# Points of interest
inps = pp.read_point2inps(inps, coord)
# Unit and Wrap
inps.disp_unit, inps.wrap = pp.check_disp_unit_and_wrap(
metadata,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range,
print_msg=inps.print_msg)
# Min / Max - Display
if not inps.vlim:
if (any(i in inps.key.lower() for i in ['coherence', '.cor'])
or (inps.key == 'ifgramStack'
and inps.dset[0].split('-')[0] in ['coherence'])
or inps.dset[0] == 'cmask'):
inps.vlim = [0.0, 1.0]
elif inps.key in ['.int'] or inps.wrap:
inps.vlim = inps.wrap_range
# Transparency - Alpha
if not inps.transparency:
# Auto adjust transparency value when showing shaded relief DEM
if inps.dem_file and inps.disp_dem_shade:
inps.transparency = 0.8
else:
inps.transparency = 1.0
# Flip Left-Right / Up-Down
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(
metadata, print_msg=inps.print_msg)
# Figure Title
if not inps.fig_title:
inps.fig_title = pp.auto_figure_title(metadata['FILE_PATH'],
datasetNames=inps.dset,
inps_dict=vars(inps))
vprint('figure title: ' + inps.fig_title)
# Figure output file name
if not inps.outfile:
inps.outfile = ['{}{}'.format(inps.fig_title, inps.fig_ext)]
inps = update_figure_setting(inps)
return inps
def read_init_info(inps):
# Time Series Info
atr = readfile.read_attribute(inps.file[0])
inps.key = atr['FILE_TYPE']
if inps.key == 'timeseries':
obj = timeseries(inps.file[0])
elif inps.key == 'giantTimeseries':
obj = giantTimeseries(inps.file[0])
elif inps.key == 'HDFEOS':
obj = HDFEOS(inps.file[0])
else:
raise ValueError('input file is {}, not timeseries.'.format(inps.key))
obj.open(print_msg=inps.print_msg)
inps.seconds = atr.get('CENTER_LINE_UTC', 0)
if not inps.file_label:
inps.file_label = []
for fname in inps.file:
fbase = os.path.splitext(os.path.basename(fname))[0]
fbase = fbase.replace('timeseries', '')
inps.file_label.append(fbase)
# default mask file
if not inps.mask_file and 'msk' not in inps.file[0]:
dir_name = os.path.dirname(inps.file[0])
if 'Y_FIRST' in atr.keys():
inps.mask_file = os.path.join(dir_name, 'geo_maskTempCoh.h5')
else:
inps.mask_file = os.path.join(dir_name, 'maskTempCoh.h5')
if not os.path.isfile(inps.mask_file):
inps.mask_file = None
## date info
inps.date_list = obj.dateList
inps.num_date = len(inps.date_list)
if inps.start_date:
inps.date_list = [i for i in inps.date_list if int(i) >= int(inps.start_date)]
if inps.end_date:
inps.date_list = [i for i in inps.date_list if int(i) <= int(inps.end_date)]
inps.num_date = len(inps.date_list)
inps.dates, inps.yearList = ptime.date_list2vector(inps.date_list)
(inps.ex_date_list,
inps.ex_dates,
inps.ex_flag) = read_exclude_date(inps.ex_date_list, inps.date_list)
# reference date/index
if not inps.ref_date:
inps.ref_date = atr.get('REF_DATE', None)
if inps.ref_date:
inps.ref_idx = inps.date_list.index(inps.ref_date)
else:
inps.ref_idx = None
# date/index of interest for initial display
if not inps.idx:
if (not inps.ref_idx) or (inps.ref_idx < inps.num_date / 2.):
inps.idx = inps.num_date - 2
else:
inps.idx = 2
# Display Unit
(inps.disp_unit,
inps.unit_fac) = pp.scale_data2disp_unit(metadata=atr, disp_unit=inps.disp_unit)[1:3]
# Map info - coordinate unit
inps.coord_unit = atr.get('Y_UNIT', 'degrees').lower()
# Read Error List
inps.ts_plot_func = plot_ts_scatter
inps.error_ts = None
inps.ex_error_ts = None
if inps.error_file:
# assign plot function
inps.ts_plot_func = plot_ts_errorbar
# read error file
error_fc = np.loadtxt(inps.error_file, dtype=bytes).astype(str)
inps.error_ts = error_fc[:, 1].astype(np.float)*inps.unit_fac
# update error file with exlcude date
if inps.ex_date_list:
e_ts = inps.error_ts[:]
inps.ex_error_ts = e_ts[inps.ex_flag == 0]
inps.error_ts = e_ts[inps.ex_flag == 1]
# Zero displacement for 1st acquisition
if inps.zero_first:
inps.zero_idx = min(0, np.min(np.where(inps.ex_flag)[0]))
# default lookup table file and coordinate object
if not inps.lookup_file:
inps.lookup_file = ut.get_lookup_file('./inputs/geometryRadar.h5')
inps.coord = ut.coordinate(atr, inps.lookup_file)
## size and lalo info
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(vars(inps), atr)
inps.pix_box = inps.coord.check_box_within_data_coverage(inps.pix_box)
inps.geo_box = inps.coord.box_pixel2geo(inps.pix_box)
data_box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
vprint('data coverage in y/x: '+str(data_box))
vprint('subset coverage in y/x: '+str(inps.pix_box))
vprint('data coverage in lat/lon: '+str(inps.coord.box_pixel2geo(data_box)))
vprint('subset coverage in lat/lon: '+str(inps.geo_box))
vprint('------------------------------------------------------------------------')
# calculate multilook_num
# ONLY IF:
# inps.multilook is True (no --nomultilook input) AND
# inps.multilook_num ==1 (no --multilook-num input)
# Note: inps.multilook is used for this check ONLY
# Note: multilooking is only applied to the 3D data cubes and their related operations:
# e.g. spatial indexing, referencing, etc. All the other variables are in the original grid
# so that users get the same result as the non-multilooked version.
if inps.multilook and inps.multilook_num == 1:
inps.multilook_num = pp.auto_multilook_num(inps.pix_box, inps.num_date,
max_memory=inps.maxMemory,
print_msg=inps.print_msg)
## reference pixel
if not inps.ref_lalo and 'REF_LAT' in atr.keys():
inps.ref_lalo = (float(atr['REF_LAT']), float(atr['REF_LON']))
if inps.ref_lalo:
# set longitude to [-180, 180)
if inps.coord_unit.lower().startswith('deg') and inps.ref_lalo[1] >= 180.:
inps.ref_lalo[1] -= 360.
# ref_lalo --> ref_yx if not set in cmd
if not inps.ref_yx:
inps.ref_yx = inps.coord.geo2radar(inps.ref_lalo[0], inps.ref_lalo[1], print_msg=False)[0:2]
# use REF_Y/X if ref_yx not set in cmd
if not inps.ref_yx and 'REF_Y' in atr.keys():
inps.ref_yx = (int(atr['REF_Y']), int(atr['REF_X']))
# ref_yx --> ref_lalo if in geo-coord
# for plotting purpose only
if inps.ref_yx and 'Y_FIRST' in atr.keys():
inps.ref_lalo = inps.coord.radar2geo(inps.ref_yx[0], inps.ref_yx[1], print_msg=False)[0:2]
# do not plot native reference point if it's out of the coverage due to subset
if (inps.ref_yx and 'Y_FIRST' in atr.keys()
and inps.ref_yx == (int(atr.get('REF_Y',-999)), int(atr.get('REF_X',-999)))
and not ( inps.pix_box[0] <= inps.ref_yx[1] < inps.pix_box[2]
and inps.pix_box[1] <= inps.ref_yx[0] < inps.pix_box[3])):
inps.disp_ref_pixel = False
print('the native REF_Y/X is out of subset box, thus do not display')
## initial pixel coord
if inps.lalo:
inps.yx = inps.coord.geo2radar(inps.lalo[0], inps.lalo[1], print_msg=False)[0:2]
try:
inps.lalo = inps.coord.radar2geo(inps.yx[0], inps.yx[1], print_msg=False)[0:2]
except:
inps.lalo = None
## figure settings
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(atr, print_msg=inps.print_msg)
# Transparency - Alpha
if not inps.transparency:
# Auto adjust transparency value when showing shaded relief DEM
if inps.dem_file and inps.disp_dem_shade:
inps.transparency = 0.7
else:
inps.transparency = 1.0
## display unit ans wrap
# if wrap_step == 2*np.pi (default value), set disp_unit_img = radian;
# otherwise set disp_unit_img = disp_unit
inps.disp_unit_img = inps.disp_unit
if inps.wrap:
inps.range2phase = -4. * np.pi / float(atr['WAVELENGTH'])
if 'cm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 100.
elif 'mm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1000.
elif 'm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1.
else:
raise ValueError('un-recognized display unit: {}'.format(inps.disp_unit))
if (inps.wrap_range[1] - inps.wrap_range[0]) == 2*np.pi:
inps.disp_unit_img = 'radian'
inps.vlim = inps.wrap_range
inps.cbar_label = 'Displacement [{}]'.format(inps.disp_unit_img)
## fit a suite of time func to the time series
inps.model, inps.num_param = ts2vel.read_inps2model(inps, date_list=inps.date_list)
# dense TS for plotting
inps.date_list_fit = ptime.get_date_range(inps.date_list[0], inps.date_list[-1])
inps.dates_fit = ptime.date_list2vector(inps.date_list_fit)[0]
inps.G_fit = time_func.get_design_matrix4time_func(
date_list=inps.date_list_fit,
model=inps.model,
seconds=inps.seconds)
return inps, atr
def read_init_info(inps):
# Time Series Info
ts_file0 = inps.file[0]
atr = readfile.read_attribute(ts_file0)
inps.key = atr['FILE_TYPE']
if inps.key == 'timeseries':
obj = timeseries(ts_file0)
elif inps.key == 'giantTimeseries':
obj = giantTimeseries(ts_file0)
elif inps.key == 'HDFEOS':
obj = HDFEOS(ts_file0)
else:
raise ValueError('input file is {}, not timeseries.'.format(inps.key))
obj.open(print_msg=inps.print_msg)
if not inps.file_label:
inps.file_label = [str(i) for i in list(range(len(inps.file)))]
# default mask file
if not inps.mask_file and 'masked' not in ts_file0:
dir_name = os.path.dirname(ts_file0)
if 'Y_FIRST' in atr.keys():
inps.mask_file = os.path.join(dir_name, 'geo_maskTempCoh.h5')
else:
inps.mask_file = os.path.join(dir_name, 'maskTempCoh.h5')
if not os.path.isfile(inps.mask_file):
inps.mask_file = None
# date info
inps.date_list = obj.dateList
inps.num_date = len(inps.date_list)
if inps.start_date:
inps.date_list = [
i for i in inps.date_list if int(i) >= int(inps.start_date)
]
if inps.end_date:
inps.date_list = [
i for i in inps.date_list if int(i) <= int(inps.end_date)
]
inps.num_date = len(inps.date_list)
inps.dates, inps.yearList = ptime.date_list2vector(inps.date_list)
(inps.ex_date_list, inps.ex_dates,
inps.ex_flag) = read_exclude_date(inps.ex_date_list, inps.date_list)
# initial display index
#if atr['REF_DATE'] in inps.date_list:
# inps.ref_idx = inps.date_list.index(atr['REF_DATE'])
#else:
# inps.ref_idx = 0
if inps.ref_date:
inps.ref_idx = inps.date_list.index(inps.ref_date)
else:
inps.ref_idx = 3
if not inps.idx:
if inps.ref_idx < inps.num_date / 2.:
inps.idx = inps.num_date - 3
else:
inps.idx = 3
# Display Unit
(inps.disp_unit,
inps.unit_fac) = pp.scale_data2disp_unit(metadata=atr,
disp_unit=inps.disp_unit)[1:3]
# Map info - coordinate unit
inps.coord_unit = atr.get('Y_UNIT', 'degrees').lower()
# Read Error List
inps.ts_plot_func = plot_ts_scatter
inps.error_ts = None
inps.ex_error_ts = None
if inps.error_file:
# assign plot function
inps.ts_plot_func = plot_ts_errorbar
# read error file
error_fc = np.loadtxt(inps.error_file, dtype=bytes).astype(str)
inps.error_ts = error_fc[:, 1].astype(np.float) * inps.unit_fac
# update error file with exlcude date
if inps.ex_date_list:
e_ts = inps.error_ts[:]
inps.ex_error_ts = e_ts[inps.ex_flag == 0]
inps.error_ts = e_ts[inps.ex_flag == 1]
# Zero displacement for 1st acquisition
if inps.zero_first:
inps.zero_idx = min(0, np.min(np.where(inps.ex_flag)[0]))
# default lookup table file
if not inps.lookup_file:
inps.lookup_file = ut.get_lookup_file('./inputs/geometryRadar.h5')
inps.coord = ut.coordinate(atr, inps.lookup_file)
# size and lalo info
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(vars(inps), atr)
inps.pix_box = inps.coord.check_box_within_data_coverage(inps.pix_box)
inps.geo_box = inps.coord.box_pixel2geo(inps.pix_box)
data_box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
vprint('data coverage in y/x: ' + str(data_box))
vprint('subset coverage in y/x: ' + str(inps.pix_box))
vprint('data coverage in lat/lon: ' +
str(inps.coord.box_pixel2geo(data_box)))
vprint('subset coverage in lat/lon: ' + str(inps.geo_box))
vprint(
'------------------------------------------------------------------------'
)
# reference pixel
if not inps.ref_lalo and 'REF_LAT' in atr.keys():
inps.ref_lalo = (float(atr['REF_LAT']), float(atr['REF_LON']))
if inps.ref_lalo:
if inps.ref_lalo[1] > 180.:
inps.ref_lalo[1] -= 360.
inps.ref_yx = inps.coord.geo2radar(inps.ref_lalo[0],
inps.ref_lalo[1],
print_msg=False)[0:2]
if not inps.ref_yx and 'REF_Y' in atr.keys():
inps.ref_yx = [int(atr['REF_Y']), int(atr['REF_X'])]
# Initial Pixel Coord
if inps.lalo:
inps.yx = inps.coord.geo2radar(inps.lalo[0],
inps.lalo[1],
print_msg=False)[0:2]
try:
inps.lalo = inps.coord.radar2geo(inps.yx[0],
inps.yx[1],
print_msg=False)[0:2]
except:
inps.lalo = None
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(
atr, print_msg=inps.print_msg)
# Transparency - Alpha
if not inps.transparency:
# Auto adjust transparency value when showing shaded relief DEM
if inps.dem_file and inps.disp_dem_shade:
inps.transparency = 0.7
else:
inps.transparency = 1.0
# display unit ans wrap
# if wrap_step == 2*np.pi (default value), set disp_unit_img = radian;
# otherwise set disp_unit_img = disp_unit
inps.disp_unit_img = inps.disp_unit
if inps.wrap:
inps.range2phase = -4. * np.pi / float(atr['WAVELENGTH'])
if 'cm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 100.
elif 'mm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1000.
elif 'm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1.
else:
raise ValueError('un-recognized display unit: {}'.format(
inps.disp_unit))
if (inps.wrap_range[1] - inps.wrap_range[0]) == 2 * np.pi:
inps.disp_unit_img = 'radian'
inps.vlim = inps.wrap_range
inps.cbar_label = 'Displacement [{}]'.format(inps.disp_unit_img)
return inps, atr
def main(iargs=None):
inps = cmd_line_parse(iargs)
# translate input options
processor = isce_utils.get_processor(inps.metaFile)
src_box, geom_src_dir = read_vrt_info(os.path.join(inps.geomDir,
'lat.vrt'))
# metadata
meta = prepare_metadata(inps.metaFile, geom_src_dir, box=src_box)
# subset - read pix_box for fringe file
pix_box = subset.subset_input_dict2box(vars(inps), meta)[0]
pix_box = ut.coordinate(meta).check_box_within_data_coverage(pix_box)
print('input subset in y/x: {}'.format(pix_box))
# subset - update src_box for isce file and meta
src_box = (pix_box[0] + src_box[0], pix_box[1] + src_box[1],
pix_box[2] + src_box[0], pix_box[3] + src_box[1])
meta = attr.update_attribute4subset(meta, pix_box)
print(
'input subset in y/x with respect to the VRT file: {}'.format(src_box))
## output directory
for dname in [inps.outDir, os.path.join(inps.outDir, 'inputs')]:
os.makedirs(dname, exist_ok=True)
## output filename
ts_file = os.path.join(inps.outDir, 'timeseries.h5')
tcoh_file = os.path.join(inps.outDir, 'temporalCoherence.h5')
ps_mask_file = os.path.join(inps.outDir, 'maskPS.h5')
if 'Y_FIRST' in meta.keys():
geom_file = os.path.join(inps.outDir, 'inputs/geometryGeo.h5')
else:
geom_file = os.path.join(inps.outDir, 'inputs/geometryRadar.h5')
## 1 - time-series (from fringe)
prepare_timeseries(outfile=ts_file,
unw_file=inps.unwFile,
metadata=meta,
processor=processor,
baseline_dir=inps.baselineDir,
box=pix_box)
## 2 - temporal coherence and mask for PS (from fringe)
prepare_temporal_coherence(outfile=tcoh_file,
infile=inps.cohFile,
metadata=meta,
box=pix_box)
prepare_ps_mask(outfile=ps_mask_file,
infile=inps.psMaskFile,
metadata=meta,
box=pix_box)
## 3 - geometry (from SLC stacks before fringe, e.g. ISCE2)
prepare_geometry(outfile=geom_file,
geom_dir=geom_src_dir,
box=src_box,
metadata=meta)
return ts_file, tcoh_file, ps_mask_file, geom_file
def main(iargs=None):
inps = cmd_line_parse(iargs)
print('\n-------------------READ INPUTS -------------------')
print('Read metadata from file: {}'.format(inps.file))
attr = readfile.read_attribute(inps.file)
#Extract subset if defined
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(vars(inps), attr)
# output filename
if not inps.outfile:
inps.outfile = attr['PROJECT_NAME']
# date1/2
if attr['FILE_TYPE'] in ['timeseries', 'HDFEOS']:
date1, date2 = inps.dset.split('_')
inps.dset = date2
elif attr['FILE_TYPE'] == 'ifgramStack':
date1, date2 = inps.dset.split('-')[1].split('_')
else:
# velocity and *.unw files
date1, date2 = ptime.yyyymmdd(attr['DATE12'].replace('_',
'-').split('-'))
if inps.dset.startswith('step'):
date1 = inps.dset.split('step')[-1]
date2 = date1
print('First InSAR date: {}'.format(date1))
print('Second InSAR date: {}'.format(date2))
# read data
print('Read {} from file: {}'.format(inps.dset, inps.file))
dis, attr = readfile.read(inps.file,
datasetName=inps.dset,
box=inps.pix_box)
if attr['FILE_TYPE'] == 'timeseries':
print('Read {} from file: {}'.format(date1, inps.file))
dis -= readfile.read(inps.file, datasetName=date1, box=inps.pix_box)[0]
# mask data
if inps.mask_file is not None:
mask = readfile.read(inps.mask_file, box=inps.pix_box)[0]
print('Set data to NaN for pixels with zero value in file: {}'.format(
inps.mask_file))
dis[mask == 0] = np.nan
# read geometry incidence / azimuth angle
print('\nread incidence / azimuth angle from file: {}'.format(
inps.geom_file))
inc_angle = readfile.read(inps.geom_file,
datasetName='incidenceAngle',
box=inps.pix_box)[0]
az_angle = readfile.read(inps.geom_file,
datasetName='azimuthAngle',
box=inps.pix_box)[0]
print('Mean satellite incidence angle: {0:.2f}°'.format(
np.nanmean(inc_angle)))
print('Mean satellite heading angle: {0:.2f}°\n'.format(
90 - np.nanmean(az_angle)))
# Update attributes
if inps.subset_lat != None or inps.subset_x != None:
attr = attribute.update_attribute4subset(attr, inps.pix_box)
# create kite container
scene = mintpy2kite(dis,
attr,
date1,
date2,
inc_angle,
az_angle,
out_file=inps.outfile)
return scene
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
