def open(self):
atr = readfile.read_attribute(self.insar_file)
k = atr['FILE_TYPE']
if k == 'timeseries':
ts_obj = timeseries(self.insar_file)
elif k == 'giantTimeseries':
ts_obj = giantTimeseries(self.insar_file)
else:
raise ValueError('Un-supported time-series file: {}'.format(k))
ts_obj.open(print_msg=False)
self.metadata = dict(ts_obj.metadata)
self.num_date = ts_obj.numDate
# remove time info from insar_datetime to be consistent with gps_datetime
self.insar_datetime = np.array([i.replace(hour=0, minute=0, second=0, microsecond=0)
for i in ts_obj.times])
# default start/end
if self.start_date is None:
self.start_date = (ts_obj.times[0] - relativedelta(months=1)).strftime('%Y%m%d')
if self.end_date is None:
self.end_date = (ts_obj.times[-1] + relativedelta(months=1)).strftime('%Y%m%d')
# default min_ref_date
if self.min_ref_date is None:
self.min_ref_date = ts_obj.times[5].strftime('%Y%m%d')
elif self.min_ref_date not in ts_obj.dateList:
raise ValueError('input min_ref_date {} does not exist in insar file {}'.format(
self.min_ref_date, self.insar_file))
self.read_gps()
self.read_insar()
self.calculate_rmse()
return
def read_date_info(inps):
"""Read dates used in the estimation and its related info.
Parameters: inps - Namespace
Returns: inps - Namespace
"""
if inps.key == 'timeseries':
tsobj = timeseries(inps.timeseries_file)
elif inps.key == 'giantTimeseries':
tsobj = giantTimeseries(inps.timeseries_file)
elif inps.key == 'HDFEOS':
tsobj = HDFEOS(inps.timeseries_file)
tsobj.open()
inps.excludeDate = read_exclude_date(inps, tsobj.dateList)
# exclude dates without obs data [for offset time-series only for now]
if os.path.basename(inps.timeseries_file).startswith('timeseriesRg'):
date_list = timeseries(inps.timeseries_file).get_date_list()
data, atr = readfile.read(inps.timeseries_file)
flag = np.nansum(data, axis=(1, 2)) == 0
flag[date_list.index(atr['REF_DATE'])] = 0
if np.sum(flag) > 0:
print('number of empty dates to exclude: {}'.format(np.sum(flag)))
inps.excludeDate += np.array(date_list)[flag].tolist()
inps.excludeDate = sorted(list(set(inps.excludeDate)))
# Date used for estimation inps.dateList
inps.dateList = [i for i in tsobj.dateList if i not in inps.excludeDate]
inps.numDate = len(inps.dateList)
inps.startDate = inps.dateList[0]
inps.endDate = inps.dateList[-1]
print('-' * 50)
print('dates from input file: {}\n{}'.format(tsobj.numDate,
tsobj.dateList))
print('-' * 50)
if len(inps.dateList) == len(tsobj.dateList):
print('using all dates to calculate the velocity')
else:
print('dates used to estimate the velocity: {}\n{}'.format(
inps.numDate, inps.dateList))
print('-' * 50)
# flag array for ts data reading
inps.dropDate = np.array(
[i not in inps.excludeDate for i in tsobj.dateList], dtype=np.bool_)
# output file name
if not inps.outfile:
fbase = os.path.splitext(os.path.basename(inps.timeseries_file))[0]
outname = 'velocity'
if inps.key == 'giantTimeseries':
prefix = os.path.basename(inps.timeseries_file).split('PARAMS')[0]
outname = prefix + outname
elif fbase in ['timeseriesRg', 'timeseriesAz']:
suffix = fbase.split('timeseries')[-1]
outname = outname + suffix
outname += '.h5'
inps.outfile = outname
return inps
def read_date_info(inps):
"""Get inps.excludeDate full list
Inputs:
inps - Namespace,
Output:
inps.excludeDate - list of string for exclude date in YYYYMMDD format
"""
if inps.key == 'timeseries':
tsobj = timeseries(inps.timeseries_file)
elif inps.key == 'giantTimeseries':
tsobj = giantTimeseries(inps.timeseries_file)
elif inps.key == 'HDFEOS':
tsobj = HDFEOS(inps.timeseries_file)
tsobj.open()
inps.excludeDate = read_exclude_date(inps, tsobj.dateList)
# Date used for estimation inps.dateList
inps.dateList = [i for i in tsobj.dateList if i not in inps.excludeDate]
date_list = inps.dateList
dt_list = [dt.strptime(i, '%Y%m%d') for i in date_list]
yr_list = [i.year + (i.timetuple().tm_yday - 1) / 365.25 for i in dt_list]
yr_diff = np.array(yr_list)
yr_diff -= yr_diff[0]
inps.yr_diff = yr_diff
inps.numDate = len(inps.dateList)
print('-' * 50)
print('dates from input file: {}\n{}'.format(tsobj.numDate,
tsobj.dateList))
print('-' * 50)
if len(inps.dateList) == len(tsobj.dateList):
print('using all dates to calculate the velocity')
else:
print('dates used to estimate the velocity: {}\n{}'.format(
inps.numDate, inps.dateList))
print('-' * 50)
# flag array for ts data reading
inps.dropDate = np.array(
[i not in inps.excludeDate for i in tsobj.dateList], dtype=np.bool_)
# output file name
if not inps.outfile:
outname = 'velocity'
if inps.key == 'giantTimeseries':
prefix = os.path.basename(inps.timeseries_file).split('PARAMS')[0]
outname = prefix + outname
outname += '.h5'
inps.outfile = outname
return inps
def open(self):
atr = readfile.read_attribute(self.insar_file)
k = atr['FILE_TYPE']
if k == 'timeseries':
ts_obj = timeseries(self.insar_file)
elif k == 'giantTimeseries':
ts_obj = giantTimeseries(self.insar_file)
ts_obj.open(print_msg=False)
self.metadata = dict(ts_obj.metadata)
self.num_date = ts_obj.numDate
self.insar_datetime = ts_obj.times
self.read_gps()
self.read_insar()
self.calculate_rmse()
return
def open(self):
atr = readfile.read_attribute(self.insar_file)
k = atr['FILE_TYPE']
if k == 'timeseries':
ts_obj = timeseries(self.insar_file)
elif k == 'giantTimeseries':
ts_obj = giantTimeseries(self.insar_file)
ts_obj.open(print_msg=False)
self.metadata = dict(ts_obj.metadata)
self.num_date = ts_obj.numDate
self.insar_datetime = ts_obj.times
self.read_gps()
self.read_insar()
self.calculate_rmse()
return
def print_date_list(fname, disp_ifgram='all', disp_num=False, print_msg=False):
"""Print time/date info of file"""
k = readfile.read_attribute(fname)['FILE_TYPE']
dateList = None
if k in ['timeseries']:
dateList = timeseries(fname).get_date_list()
elif k == 'HDFEOS':
dateList = HDFEOS(fname).get_date_list()
elif k == 'giantTimeseries':
dateList = giantTimeseries(fname).get_date_list()
elif k in ['giantIfgramStack']:
dateList = giantIfgramStack(fname).get_date12_list()
elif k in ['ifgramStack']:
obj = ifgramStack(fname)
obj.open(print_msg=False)
dateListAll = obj.get_date12_list(dropIfgram=False)
dateListKept = obj.get_date12_list(dropIfgram=True)
# show dropped ifgram or not
if disp_ifgram == 'all':
dateList = list(dateListAll)
elif disp_ifgram == 'kept':
dateList = list(dateListKept)
else:
dateList = sorted(list(set(dateListAll) - set(dateListKept)))
else:
print('--date option can not be applied to {} file, ignore it.'.format(
k))
# print list info
if print_msg and dateList is not None:
for d in dateList:
if disp_num:
if k in ['ifgramStack']:
num = dateListAll.index(d)
else:
num = dateList.index(d)
msg = '{}\t{}'.format(d, num)
else:
msg = d
print(msg)
return dateList
def print_date_list(fname, disp_ifgram='all', disp_num=False, print_msg=False):
"""Print time/date info of file"""
k = readfile.read_attribute(fname)['FILE_TYPE']
dateList = None
if k in ['timeseries']:
dateList = timeseries(fname).get_date_list()
elif k == 'HDFEOS':
dateList = HDFEOS(fname).get_date_list()
elif k == 'giantTimeseries':
dateList = giantTimeseries(fname).get_date_list()
elif k in ['giantIfgramStack']:
dateList = giantIfgramStack(fname).get_date12_list()
elif k in ['ifgramStack']:
obj = ifgramStack(fname)
obj.open(print_msg=False)
dateListAll = obj.get_date12_list(dropIfgram=False)
dateListKept = obj.get_date12_list(dropIfgram=True)
# show dropped ifgram or not
if disp_ifgram == 'all':
dateList = list(dateListAll)
elif disp_ifgram == 'kept':
dateList = list(dateListKept)
else:
dateList = sorted(list(set(dateListAll) - set(dateListKept)))
else:
print('--date option can not be applied to {} file, ignore it.'.format(k))
# print list info
if print_msg and dateList is not None:
for d in dateList:
if disp_num:
if k in ['ifgramStack']:
num = dateListAll.index(d)
else:
num = dateList.index(d)
msg = '{}\t{}'.format(d, num)
else:
msg = d
print(msg)
return dateList
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 diff_file(file1, file2, outFile=None, force=False):
"""Subtraction/difference of two input files"""
if not outFile:
fbase, fext = os.path.splitext(file1)
if len(file2) > 1:
raise ValueError(
'Output file name is needed for more than 2 files input.')
outFile = '{}_diff_{}{}'.format(
fbase,
os.path.splitext(os.path.basename(file2[0]))[0], fext)
print('{} - {} --> {}'.format(file1, file2, outFile))
# Read basic info
atr1 = readfile.read_attribute(file1)
k1 = atr1['FILE_TYPE']
atr2 = readfile.read_attribute(file2[0])
k2 = atr2['FILE_TYPE']
print('input files are: {} and {}'.format(k1, k2))
if k1 == 'timeseries':
if k2 not in ['timeseries', 'giantTimeseries']:
raise Exception(
'Input multiple dataset files are not the same file type!')
if len(file2) > 1:
raise Exception(
('Only 2 files substraction is supported for time-series file,'
' {} input.'.format(len(file2) + 1)))
obj1 = timeseries(file1)
obj1.open()
if k2 == 'timeseries':
obj2 = timeseries(file2[0])
unit_fac = 1.
elif k2 == 'giantTimeseries':
obj2 = giantTimeseries(file2[0])
unit_fac = 0.001
obj2.open()
ref_date, ref_y, ref_x = _check_reference(obj1.metadata, obj2.metadata)
# check dates shared by two timeseries files
dateListShared = [i for i in obj1.dateList if i in obj2.dateList]
dateShared = np.ones((obj1.numDate), dtype=np.bool_)
if dateListShared != obj1.dateList:
print('WARNING: {} does not contain all dates in {}'.format(
file2, file1))
if force:
dateExcluded = list(set(obj1.dateList) - set(dateListShared))
print(
'Continue and enforce the differencing for their shared dates only.'
)
print(
'\twith following dates are ignored for differencing:\n{}'.
format(dateExcluded))
dateShared[np.array(
[obj1.dateList.index(i) for i in dateExcluded])] = 0
else:
raise Exception(
'To enforce the differencing anyway, use --force option.')
# consider different reference_date/pixel
data2 = readfile.read(file2[0],
datasetName=dateListShared)[0] * unit_fac
if ref_date:
data2 -= np.tile(data2[dateListShared.index(ref_date), :, :],
(data2.shape[0], 1, 1))
if ref_y and ref_x:
data2 -= np.tile(data2[:, ref_y, ref_x].reshape(-1, 1, 1),
(1, data2.shape[1], data2.shape[2]))
data = obj1.read()
mask = data == 0.
data[dateShared] -= data2
data[mask] = 0. # Do not change zero phase value
del data2
writefile.write(data, out_file=outFile, ref_file=file1)
elif all(i == 'ifgramStack' for i in [k1, k2]):
obj1 = ifgramStack(file1)
obj1.open()
obj2 = ifgramStack(file2[0])
obj2.open()
dsNames = list(set(obj1.datasetNames) & set(obj2.datasetNames))
if len(dsNames) == 0:
raise ValueError('no common dataset between two files!')
dsName = [i for i in ifgramDatasetNames if i in dsNames][0]
# read data
print('reading {} from file {} ...'.format(dsName, file1))
data1 = readfile.read(file1, datasetName=dsName)[0]
print('reading {} from file {} ...'.format(dsName, file2[0]))
data2 = readfile.read(file2[0], datasetName=dsName)[0]
# consider reference pixel
if 'unwrapphase' in dsName.lower():
print('referencing to pixel ({},{}) ...'.format(
obj1.refY, obj1.refX))
ref1 = data1[:, obj1.refY, obj1.refX]
ref2 = data2[:, obj2.refY, obj2.refX]
for i in range(data1.shape[0]):
data1[i, :][data1[i, :] != 0.] -= ref1[i]
data2[i, :][data2[i, :] != 0.] -= ref2[i]
# operation and ignore zero values
data1[data1 == 0] = np.nan
data2[data2 == 0] = np.nan
data = data1 - data2
del data1, data2
data[np.isnan(data)] = 0.
# write to file
dsDict = {}
dsDict[dsName] = data
writefile.write(dsDict, out_file=outFile, ref_file=file1)
# Sing dataset file
else:
data1 = readfile.read(file1)[0]
data = np.array(data1, data1.dtype)
for fname in file2:
data2 = readfile.read(fname)[0]
data = np.array(data, dtype=np.float32) - np.array(
data2, dtype=np.float32)
data = np.array(data, data1.dtype)
print('writing >>> ' + outFile)
writefile.write(data, out_file=outFile, metadata=atr1)
return outFile
def read_attribute(fname,
datasetName=None,
standardize=True,
metafile_ext=None):
"""Read attributes of input file into a dictionary
Parameters: fname : str, path/name of data file
datasetName : str, name of dataset of interest, for file with multiple datasets
e.g. unwrapPhase in ifgramStack.h5
coherence in ifgramStack.h5
height in geometryRadar.h5
latitude in geometryRadar.h5
...
standardize : bool, grab standardized metadata key name
Returns: atr : dict, attributes dictionary
"""
fbase, fext = os.path.splitext(os.path.basename(fname))
fext = fext.lower()
if not os.path.isfile(fname):
msg = 'input file not existed: {}\n'.format(fname)
msg += 'current directory: ' + os.getcwd()
raise Exception(msg)
# HDF5 files
if fext in ['.h5', '.he5']:
f = h5py.File(fname, 'r')
g1_list = [i for i in f.keys() if isinstance(f[i], h5py.Group)]
d1_list = [
i for i in f.keys()
if isinstance(f[i], h5py.Dataset) and f[i].ndim >= 2
]
# FILE_TYPE - k
py2_mintpy_stack_files = ['interferograms', 'coherence',
'wrapped'] #obsolete mintpy format
if any(i in d1_list for i in ['unwrapPhase']):
k = 'ifgramStack'
elif any(i in d1_list for i in ['height', 'latitude', 'azimuthCoord']):
k = 'geometry'
elif any(i in g1_list + d1_list
for i in ['timeseries', 'displacement']):
k = 'timeseries'
elif 'HDFEOS' in g1_list:
k = 'HDFEOS'
elif 'recons' in d1_list:
k = 'giantTimeseries'
elif any(i in d1_list for i in ['igram', 'figram']):
k = 'giantIfgramStack'
elif any(i in g1_list for i in py2_mintpy_stack_files):
k = list(set(g1_list) & set(py2_mintpy_stack_files))[0]
elif len(d1_list) > 0:
k = d1_list[0]
elif len(g1_list) > 0:
k = g1_list[0]
else:
raise ValueError('unrecognized file type: ' + fname)
# metadata dict
if k == 'giantTimeseries':
atr = giantTimeseries(fname).get_metadata()
elif k == 'giantIfgramStack':
atr = giantIfgramStack(fname).get_metadata()
else:
if len(f.attrs) > 0 and 'WIDTH' in f.attrs.keys():
atr = dict(f.attrs)
else:
# grab the list of attrs in HDF5 file
global atr_list
def get_hdf5_attrs(name, obj):
global atr_list
if len(obj.attrs) > 0 and 'WIDTH' in obj.attrs.keys():
atr_list.append(dict(obj.attrs))
atr_list = []
f.visititems(get_hdf5_attrs)
# use the attrs with most items
if atr_list:
num_list = [len(i) for i in atr_list]
atr = atr_list[np.argmax(num_list)]
else:
raise ValueError('No attribute WIDTH found in file:',
fname)
# decode string format
for key, value in atr.items():
try:
atr[key] = value.decode('utf8')
except:
atr[key] = value
# attribute identified by MintPy
# 1. FILE_TYPE
atr['FILE_TYPE'] = str(k)
# 2. DATA_TYPE
ds = None
if datasetName and datasetName in f.keys():
ds = f[datasetName]
else:
# get the 1st dataset
global ds_list
def get_hdf5_dataset(name, obj):
global ds_list
if isinstance(obj, h5py.Dataset) and obj.ndim >= 2:
ds_list.append(obj)
ds_list = []
f.visititems(get_hdf5_dataset)
if ds_list:
ds = ds_list[0]
if ds is not None:
atr['DATA_TYPE'] = str(ds.dtype)
f.close()
# 3. PROCESSOR
if 'INSAR_PROCESSOR' in atr.keys():
atr['PROCESSOR'] = atr['INSAR_PROCESSOR']
if 'PROCESSOR' not in atr.keys():
atr['PROCESSOR'] = 'mintpy'
else:
# get metadata file basename
metafile_base = fname
# for SNAP BEAM-DIMAP format
if fname.endswith('.img') and os.path.isfile(
os.path.splitext(fname)[0] + '.hdr'):
metafile_base = os.path.splitext(fname)[0]
# get existing metadata file extensions
metafile_exts = ['.rsc', '.xml', '.aux.xml', '.par', '.hdr']
if metafile_ext:
metafile_exts = [
i for i in metafile_exts if i.endswith(metafile_ext)
]
metafile_exts = [
i for i in metafile_exts if os.path.isfile(metafile_base + i)
]
if len(metafile_exts) == 0:
raise FileNotFoundError(
'No metadata file found for data file: {}'.format(fname))
atr = {}
# PROCESSOR
if any(i.endswith('.hdr')
for i in metafile_exts) and fname.endswith('.img'):
atr['PROCESSOR'] = 'snap'
elif any(i.endswith('.xml') for i in metafile_exts):
atr['PROCESSOR'] = 'isce'
xml_exts = [i for i in metafile_exts if i.endswith('.xml')]
if len(xml_exts) > 0:
atr.update(read_isce_xml(metafile_base + xml_exts[0]))
elif any(i.endswith('.par') for i in metafile_exts):
atr['PROCESSOR'] = 'gamma'
elif any(i.endswith('.rsc') for i in metafile_exts):
if 'PROCESSOR' not in atr.keys():
atr['PROCESSOR'] = 'roipac'
if 'PROCESSOR' not in atr.keys():
atr['PROCESSOR'] = 'mintpy'
# Read metadata file and FILE_TYPE
metafile0 = metafile_base + metafile_exts[0]
while fext in ['.geo', '.rdr', '.full']:
fbase, fext = os.path.splitext(fbase)
if not fext:
fext = fbase
if metafile0.endswith('.rsc'):
atr.update(read_roipac_rsc(metafile0))
if 'FILE_TYPE' not in atr.keys():
atr['FILE_TYPE'] = fext
elif metafile0.endswith('.xml'):
atr.update(read_isce_xml(metafile0))
if 'FILE_TYPE' not in atr.keys():
atr['FILE_TYPE'] = fext #atr.get('image_type', fext)
elif metafile0.endswith('.par'):
atr.update(read_gamma_par(metafile0))
atr['FILE_TYPE'] = fext
elif metafile0.endswith('.hdr'):
atr.update(read_envi_hdr(metafile0))
fbase = os.path.basename(fname).lower()
if fbase.startswith('unw'):
atr['FILE_TYPE'] = '.unw'
elif fbase.startswith(('coh', 'cor')):
atr['FILE_TYPE'] = '.cor'
elif fbase.startswith('phase_ifg'):
atr['FILE_TYPE'] = '.int'
elif 'dem' in fbase:
atr['FILE_TYPE'] = 'dem'
else:
atr['FILE_TYPE'] = atr['file type']
# UNIT
k = atr['FILE_TYPE'].replace('.', '')
if k == 'ifgramStack':
if datasetName and datasetName in datasetUnitDict.keys():
atr['UNIT'] = datasetUnitDict[datasetName]
else:
atr['UNIT'] = 'radian'
elif 'UNIT' not in atr.keys():
if datasetName and datasetName in datasetUnitDict.keys():
atr['UNIT'] = datasetUnitDict[datasetName]
elif k in datasetUnitDict.keys():
atr['UNIT'] = datasetUnitDict[k]
else:
atr['UNIT'] = '1'
# FILE_PATH
atr['FILE_PATH'] = os.path.abspath(fname)
if standardize:
atr = standardize_metadata(atr)
return atr
def get_slice_list(fname):
"""Get list of 2D slice existed in file (for display)"""
fbase, fext = os.path.splitext(os.path.basename(fname))
fext = fext.lower()
atr = read_attribute(fname)
k = atr['FILE_TYPE']
global slice_list
# HDF5 Files
if fext in ['.h5', '.he5']:
with h5py.File(fname, 'r') as f:
d1_list = [i for i in f.keys() if isinstance(f[i], h5py.Dataset)]
if k == 'timeseries' and k in d1_list:
obj = timeseries(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['geometry'] and k not in d1_list:
obj = geometry(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['ifgramStack']:
obj = ifgramStack(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['HDFEOS']:
obj = HDFEOS(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['giantTimeseries']:
obj = giantTimeseries(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['giantIfgramStack']:
obj = giantIfgramStack(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
else:
## Find slice by walking through the file structure
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
def get_hdf5_2d_dataset(name, obj):
global slice_list
if isinstance(obj, h5py.Dataset) and obj.shape[-2:] == (length,
width):
if obj.ndim == 2:
slice_list.append(name)
else:
warnings.warn(
'file has un-defined {}D dataset: {}'.format(
obj.ndim, name))
slice_list = []
with h5py.File(fname, 'r') as f:
f.visititems(get_hdf5_2d_dataset)
# Binary Files
else:
if fext.lower() in ['.trans', '.utm_to_rdc']:
slice_list = ['rangeCoord', 'azimuthCoord']
elif fbase.startswith('los'):
slice_list = ['incidenceAngle', 'azimuthAngle']
elif atr.get('number_bands', '1') == '2' and 'unw' not in k:
slice_list = ['band1', 'band2']
else:
slice_list = ['']
return slice_list
def diff_file(file1, file2, out_file=None, force=False, max_num_pixel=2e8):
"""calculate/write file1 - file2
Parameters: file1 - str, path of file1
file2 - list of str, path of file2(s)
out_file - str, path of output file
force - bool, overwrite existing output file
max_num_pixel - float, maximum number of pixels for each block
"""
start_time = time.time()
if not out_file:
fbase, fext = os.path.splitext(file1)
if len(file2) > 1:
raise ValueError(
'Output file name is needed for more than 2 files input.')
out_file = '{}_diff_{}{}'.format(
fbase,
os.path.splitext(os.path.basename(file2[0]))[0], fext)
print('{} - {} --> {}'.format(file1, file2, out_file))
# Read basic info
atr1 = readfile.read_attribute(file1)
k1 = atr1['FILE_TYPE']
atr2 = readfile.read_attribute(file2[0])
k2 = atr2['FILE_TYPE']
print('input files are: {} and {}'.format(k1, k2))
if k1 == 'timeseries':
if k2 not in ['timeseries', 'giantTimeseries']:
raise Exception(
'Input multiple dataset files are not the same file type!')
if len(file2) > 1:
raise Exception(
('Only 2 files substraction is supported for time-series file,'
' {} input.'.format(len(file2) + 1)))
atr1 = readfile.read_attribute(file1)
atr2 = readfile.read_attribute(file2[0])
dateList1 = timeseries(file1).get_date_list()
if k2 == 'timeseries':
dateList2 = timeseries(file2[0]).get_date_list()
unit_fac = 1.
elif k2 == 'giantTimeseries':
dateList2 = giantTimeseries(file2[0]).get_date_list()
unit_fac = 0.001
# check reference point
ref_date, ref_y, ref_x = check_reference(atr1, atr2)
# check dates shared by two timeseries files
dateListShared = [i for i in dateList1 if i in dateList2]
dateShared = np.ones((len(dateList1)), dtype=np.bool_)
if dateListShared != dateList1:
print('WARNING: {} does not contain all dates in {}'.format(
file2, file1))
if force:
dateListEx = list(set(dateList1) - set(dateListShared))
print(
'Continue and enforce the differencing for their shared dates only.'
)
print(
'\twith following dates are ignored for differencing:\n{}'.
format(dateListEx))
dateShared[np.array([dateList1.index(i)
for i in dateListEx])] = 0
else:
raise Exception(
'To enforce the differencing anyway, use --force option.')
# instantiate the output file
writefile.layout_hdf5(out_file, ref_file=file1)
# block-by-block IO
length, width = int(atr1['LENGTH']), int(atr1['WIDTH'])
num_box = int(np.ceil(len(dateList1) * length * width / max_num_pixel))
box_list = cluster.split_box2sub_boxes(box=(0, 0, width, length),
num_split=num_box,
dimension='y',
print_msg=True)
if ref_y and ref_x:
ref_box = (ref_x, ref_y, ref_x + 1, ref_y + 1)
ref_val = readfile.read(file2[0],
datasetName=dateListShared,
box=ref_box)[0] * unit_fac
for i, box in enumerate(box_list):
if num_box > 1:
print('\n------- processing patch {} out of {} --------------'.
format(i + 1, num_box))
print('box: {}'.format(box))
# read data2 (consider different reference_date/pixel)
print('read from file: {}'.format(file2[0]))
data2 = readfile.read(
file2[0], datasetName=dateListShared, box=box)[0] * unit_fac
if ref_y and ref_x:
print('* referencing data from {} to y/x: {}/{}'.format(
os.path.basename(file2[0]), ref_y, ref_x))
data2 -= np.tile(ref_val.reshape(-1, 1, 1),
(1, data2.shape[1], data2.shape[2]))
if ref_date:
print('* referencing data from {} to date: {}'.format(
os.path.basename(file2[0]), ref_date))
ref_ind = dateListShared.index(ref_date)
data2 -= np.tile(data2[ref_ind, :, :], (data2.shape[0], 1, 1))
# read data1
print('read from file: {}'.format(file1))
data = readfile.read(file1, box=box)[0]
# apply differencing
mask = data == 0.
data[dateShared] -= data2
data[mask] = 0. # Do not change zero phase value
del data2
# write the block
block = [0, data.shape[0], box[1], box[3], box[0], box[2]]
writefile.write_hdf5_block(out_file,
data=data,
datasetName=k1,
block=block)
elif all(i == 'ifgramStack' for i in [k1, k2]):
obj1 = ifgramStack(file1)
obj1.open()
obj2 = ifgramStack(file2[0])
obj2.open()
dsNames = list(set(obj1.datasetNames) & set(obj2.datasetNames))
if len(dsNames) == 0:
raise ValueError('no common dataset between two files!')
dsName = [i for i in ifgramDatasetNames if i in dsNames][0]
# read data
print('reading {} from file {} ...'.format(dsName, file1))
data1 = readfile.read(file1, datasetName=dsName)[0]
print('reading {} from file {} ...'.format(dsName, file2[0]))
data2 = readfile.read(file2[0], datasetName=dsName)[0]
# consider reference pixel
if 'unwrapphase' in dsName.lower():
print('referencing to pixel ({},{}) ...'.format(
obj1.refY, obj1.refX))
ref1 = data1[:, obj1.refY, obj1.refX]
ref2 = data2[:, obj2.refY, obj2.refX]
for i in range(data1.shape[0]):
data1[i, :][data1[i, :] != 0.] -= ref1[i]
data2[i, :][data2[i, :] != 0.] -= ref2[i]
# operation and ignore zero values
data1[data1 == 0] = np.nan
data2[data2 == 0] = np.nan
data = data1 - data2
del data1, data2
data[np.isnan(data)] = 0.
# write to file
dsDict = {}
dsDict[dsName] = data
writefile.write(dsDict, out_file=out_file, ref_file=file1)
# Sing dataset file
else:
data1 = readfile.read(file1)[0]
data = np.array(data1, data1.dtype)
for fname in file2:
data2 = readfile.read(fname)[0]
data = np.array(data, dtype=np.float32) - np.array(
data2, dtype=np.float32)
data = np.array(data, data1.dtype)
print('writing >>> ' + out_file)
writefile.write(data, out_file=out_file, metadata=atr1)
m, s = divmod(time.time() - start_time, 60)
print('time used: {:02.0f} mins {:02.1f} secs'.format(m, s))
return out_file
def read_attribute(fname, datasetName=None, standardize=True, metafile_ext=None):
"""Read attributes of input file into a dictionary
Parameters: fname : str, path/name of data file
datasetName : str, name of dataset of interest, for file with multiple datasets
e.g. unwrapPhase in ifgramStack.h5
coherence in ifgramStack.h5
height in geometryRadar.h5
latitude in geometryRadar.h5
...
standardize : bool, grab standardized metadata key name
Returns: atr : dict, attributes dictionary
"""
fbase, fext = os.path.splitext(os.path.basename(fname))
fext = fext.lower()
if not os.path.isfile(fname):
msg = 'input file not existed: {}\n'.format(fname)
msg += 'current directory: '+os.getcwd()
raise Exception(msg)
# HDF5 files
if fext in ['.h5', '.he5']:
f = h5py.File(fname, 'r')
g1_list = [i for i in f.keys() if isinstance(f[i], h5py.Group)]
d1_list = [i for i in f.keys() if isinstance(f[i], h5py.Dataset) and f[i].ndim >= 2]
# FILE_TYPE - k
py2_mintpy_stack_files = ['interferograms', 'coherence', 'wrapped'] #obsolete mintpy format
if any(i in d1_list for i in ['unwrapPhase']):
k = 'ifgramStack'
elif any(i in d1_list for i in ['height', 'latitude', 'azimuthCoord']):
k = 'geometry'
elif any(i in g1_list+d1_list for i in ['timeseries', 'displacement']):
k = 'timeseries'
elif 'HDFEOS' in g1_list:
k = 'HDFEOS'
elif 'recons' in d1_list:
k = 'giantTimeseries'
elif any(i in d1_list for i in ['igram', 'figram']):
k = 'giantIfgramStack'
elif any(i in g1_list for i in py2_mintpy_stack_files):
k = list(set(g1_list) & set(py2_mintpy_stack_files))[0]
elif len(d1_list) > 0:
k = d1_list[0]
elif len(g1_list) > 0:
k = g1_list[0]
else:
raise ValueError('unrecognized file type: '+fname)
# metadata dict
if k == 'giantTimeseries':
atr = giantTimeseries(fname).get_metadata()
elif k == 'giantIfgramStack':
atr = giantIfgramStack(fname).get_metadata()
else:
if len(f.attrs) > 0 and 'WIDTH' in f.attrs.keys():
atr = dict(f.attrs)
else:
# grab the list of attrs in HDF5 file
global atr_list
def get_hdf5_attrs(name, obj):
global atr_list
if len(obj.attrs) > 0 and 'WIDTH' in obj.attrs.keys():
atr_list.append(dict(obj.attrs))
atr_list = []
f.visititems(get_hdf5_attrs)
# use the attrs with most items
if atr_list:
num_list = [len(i) for i in atr_list]
atr = atr_list[np.argmax(num_list)]
else:
raise ValueError('No attribute WIDTH found in file:', fname)
# decode string format
for key, value in atr.items():
try:
atr[key] = value.decode('utf8')
except:
atr[key] = value
# attribute identified by MintPy
# 1. FILE_TYPE
atr['FILE_TYPE'] = str(k)
# 2. DATA_TYPE
ds = None
if datasetName and datasetName in f.keys():
ds = f[datasetName]
else:
# get the 1st dataset
global ds_list
def get_hdf5_dataset(name, obj):
global ds_list
if isinstance(obj, h5py.Dataset) and obj.ndim >= 2:
ds_list.append(obj)
ds_list = []
f.visititems(get_hdf5_dataset)
if ds_list:
ds = ds_list[0]
if ds is not None:
atr['DATA_TYPE'] = str(ds.dtype)
f.close()
# 3. PROCESSOR
if 'INSAR_PROCESSOR' in atr.keys():
atr['PROCESSOR'] = atr['INSAR_PROCESSOR']
if 'PROCESSOR' not in atr.keys():
atr['PROCESSOR'] = 'mintpy'
else:
# get existing metadata file extensions
metafile_exts = ['.rsc', '.xml', '.aux.xml', '.par', '.hdr']
if metafile_ext:
metafile_exts = [i for i in metafile_exts if i.endswith(metafile_ext)]
metafile_exts = [i for i in metafile_exts if os.path.isfile(fname+i)]
if len(metafile_exts) == 0:
raise FileNotFoundError('No metadata file found for data file: {}'.format(fname))
atr = {}
# PROCESSOR
if any(i.endswith(('.xml', '.hdr')) for i in metafile_exts):
atr['PROCESSOR'] = 'isce'
xml_exts = [i for i in metafile_exts if i.endswith('.xml')]
if len(xml_exts) > 0:
atr.update(read_isce_xml(fname+xml_exts[0]))
elif any(i.endswith('.par') for i in metafile_exts):
atr['PROCESSOR'] = 'gamma'
elif any(i.endswith('.rsc') for i in metafile_exts):
if 'PROCESSOR' not in atr.keys():
atr['PROCESSOR'] = 'roipac'
if 'PROCESSOR' not in atr.keys():
atr['PROCESSOR'] = 'mintpy'
# Read metadata file and FILE_TYPE
metafile0 = fname + metafile_exts[0]
while fext in ['.geo', '.rdr', '.full']:
fbase, fext = os.path.splitext(fbase)
if not fext:
fext = fbase
if metafile0.endswith('.rsc'):
atr.update(read_roipac_rsc(metafile0))
if 'FILE_TYPE' not in atr.keys():
atr['FILE_TYPE'] = fext
elif metafile0.endswith('.xml'):
atr.update(read_isce_xml(metafile0))
if 'FILE_TYPE' not in atr.keys():
atr['FILE_TYPE'] = fext #atr.get('image_type', fext)
elif metafile0.endswith('.par'):
atr.update(read_gamma_par(metafile0))
atr['FILE_TYPE'] = fext
elif metafile0.endswith('.hdr'):
atr.update(read_envi_hdr(metafile0))
atr['FILE_TYPE'] = atr['file type']
# UNIT
k = atr['FILE_TYPE'].replace('.', '')
if k == 'ifgramStack':
if datasetName and datasetName in datasetUnitDict.keys():
atr['UNIT'] = datasetUnitDict[datasetName]
else:
atr['UNIT'] = 'radian'
elif 'UNIT' not in atr.keys():
if datasetName and datasetName in datasetUnitDict.keys():
atr['UNIT'] = datasetUnitDict[datasetName]
elif k in datasetUnitDict.keys():
atr['UNIT'] = datasetUnitDict[k]
else:
atr['UNIT'] = '1'
# FILE_PATH
atr['FILE_PATH'] = os.path.abspath(fname)
if standardize:
atr = standardize_metadata(atr)
return atr
def get_slice_list(fname):
"""Get list of 2D slice existed in file (for display)"""
fbase, fext = os.path.splitext(os.path.basename(fname))
fext = fext.lower()
atr = read_attribute(fname)
k = atr['FILE_TYPE']
global slice_list
# HDF5 Files
if fext in ['.h5', '.he5']:
with h5py.File(fname, 'r') as f:
d1_list = [i for i in f.keys() if isinstance(f[i], h5py.Dataset)]
if k == 'timeseries' and k in d1_list:
obj = timeseries(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['geometry'] and k not in d1_list:
obj = geometry(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['ifgramStack']:
obj = ifgramStack(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['HDFEOS']:
obj = HDFEOS(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['giantTimeseries']:
obj = giantTimeseries(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
elif k in ['giantIfgramStack']:
obj = giantIfgramStack(fname)
obj.open(print_msg=False)
slice_list = obj.sliceList
else:
## Find slice by walking through the file structure
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
def get_hdf5_2d_dataset(name, obj):
global slice_list
if isinstance(obj, h5py.Dataset) and obj.shape[-2:] == (length, width):
if obj.ndim == 2:
slice_list.append(name)
else:
warnings.warn('file has un-defined {}D dataset: {}'.format(obj.ndim, name))
slice_list = []
with h5py.File(fname, 'r') as f:
f.visititems(get_hdf5_2d_dataset)
# Binary Files
else:
if fext.lower() in ['.trans', '.utm_to_rdc']:
slice_list = ['rangeCoord', 'azimuthCoord']
elif fbase.startswith('los'):
slice_list = ['incidenceAngle', 'azimuthAngle']
elif atr.get('number_bands', '1') == '2' and 'unw' not in k:
slice_list = ['band1', 'band2']
else:
slice_list = ['']
return slice_list
def diff_file(file1, file2, outFile=None, force=False):
"""Subtraction/difference of two input files"""
if not outFile:
fbase, fext = os.path.splitext(file1)
if len(file2) > 1:
raise ValueError('Output file name is needed for more than 2 files input.')
outFile = '{}_diff_{}{}'.format(fbase, os.path.splitext(os.path.basename(file2[0]))[0], fext)
print('{} - {} --> {}'.format(file1, file2, outFile))
# Read basic info
atr1 = readfile.read_attribute(file1)
k1 = atr1['FILE_TYPE']
atr2 = readfile.read_attribute(file2[0])
k2 = atr2['FILE_TYPE']
print('input files are: {} and {}'.format(k1, k2))
if k1 == 'timeseries':
if k2 not in ['timeseries', 'giantTimeseries']:
raise Exception('Input multiple dataset files are not the same file type!')
if len(file2) > 1:
raise Exception(('Only 2 files substraction is supported for time-series file,'
' {} input.'.format(len(file2)+1)))
obj1 = timeseries(file1)
obj1.open()
if k2 == 'timeseries':
obj2 = timeseries(file2[0])
unit_fac = 1.
elif k2 == 'giantTimeseries':
obj2 = giantTimeseries(file2[0])
unit_fac = 0.001
obj2.open()
ref_date, ref_y, ref_x = _check_reference(obj1.metadata, obj2.metadata)
# check dates shared by two timeseries files
dateListShared = [i for i in obj1.dateList if i in obj2.dateList]
dateShared = np.ones((obj1.numDate), dtype=np.bool_)
if dateListShared != obj1.dateList:
print('WARNING: {} does not contain all dates in {}'.format(file2, file1))
if force:
dateExcluded = list(set(obj1.dateList) - set(dateListShared))
print('Continue and enforce the differencing for their shared dates only.')
print('\twith following dates are ignored for differencing:\n{}'.format(dateExcluded))
dateShared[np.array([obj1.dateList.index(i) for i in dateExcluded])] = 0
else:
raise Exception('To enforce the differencing anyway, use --force option.')
# consider different reference_date/pixel
data2 = readfile.read(file2[0], datasetName=dateListShared)[0] * unit_fac
if ref_date:
data2 -= np.tile(data2[obj2.dateList.index(ref_date), :, :],
(data2.shape[0], 1, 1))
if ref_y and ref_x:
data2 -= np.tile(data2[:, ref_y, ref_x].reshape(-1, 1, 1),
(1, data2.shape[1], data2.shape[2]))
data = obj1.read()
mask = data == 0.
data[dateShared] -= data2
data[mask] = 0. # Do not change zero phase value
del data2
writefile.write(data, out_file=outFile, ref_file=file1)
elif all(i == 'ifgramStack' for i in [k1, k2]):
obj1 = ifgramStack(file1)
obj1.open()
obj2 = ifgramStack(file2[0])
obj2.open()
dsNames = list(set(obj1.datasetNames) & set(obj2.datasetNames))
if len(dsNames) == 0:
raise ValueError('no common dataset between two files!')
dsName = [i for i in ifgramDatasetNames if i in dsNames][0]
# read data
print('reading {} from file {} ...'.format(dsName, file1))
data1 = readfile.read(file1, datasetName=dsName)[0]
print('reading {} from file {} ...'.format(dsName, file2[0]))
data2 = readfile.read(file2[0], datasetName=dsName)[0]
# consider reference pixel
if 'unwrapphase' in dsName.lower():
print('referencing to pixel ({},{}) ...'.format(obj1.refY, obj1.refX))
ref1 = data1[:, obj1.refY, obj1.refX]
ref2 = data2[:, obj2.refY, obj2.refX]
for i in range(data1.shape[0]):
data1[i,:][data1[i, :] != 0.] -= ref1[i]
data2[i,:][data2[i, :] != 0.] -= ref2[i]
# operation and ignore zero values
data1[data1 == 0] = np.nan
data2[data2 == 0] = np.nan
data = data1 - data2
del data1, data2
data[np.isnan(data)] = 0.
# write to file
dsDict = {}
dsDict[dsName] = data
writefile.write(dsDict, out_file=outFile, ref_file=file1)
# Sing dataset file
else:
data1 = readfile.read(file1)[0]
data = np.array(data1, data1.dtype)
for fname in file2:
data2 = readfile.read(fname)[0]
data = np.array(data, dtype=np.float32) - np.array(data2, dtype=np.float32)
data = np.array(data, data1.dtype)
print('writing >>> '+outFile)
writefile.write(data, out_file=outFile, metadata=atr1)
return outFile
