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 main():
parser = build_parser()
parseArgs = parser.parse_args()
file_name = parseArgs.file
output_folder = parseArgs.outputDir
should_mask = True
path_name_and_extension = os.path.basename(file_name).split(".")
path_name = path_name_and_extension[0]
# ---------------------------------------------------------------------------------------
# start clock to track how long conversion process takes
start_time = time.clock()
# use h5py to open specified group(s) in the h5 file
# then read datasets from h5 file into memory for faster reading of data
he_obj = HDFEOS(file_name)
he_obj.open(print_msg=False)
displacement_3d_matrix = he_obj.read(datasetName='displacement')
mask = he_obj.read(datasetName='mask')
if should_mask:
print("Masking displacement")
displacement_3d_matrix = mask_matrix(displacement_3d_matrix, mask)
del mask
dates = he_obj.dateList
attributes = dict(he_obj.metadata)
#file = h5py.File(file_name, "r")
#timeseries_group = file["HDFEOS"]["GRIDS"]["timeseries"]
#displacement_3d_matrix = timeseries_group["observation"]["displacement"]
# get attributes (stored at root) of UNAVCO timeseries file
#attributes = dict(file.attrs)
# in timeseries displacement_3d_matrix, there are datasets
# need to get datasets with dates - strings that can be converted to integers
#dates = displacement_3d_matrix.attrs["DATE_TIMESERIES"].split(" ")
# array that stores dates from dates that have been converted to decimal
decimal_dates = []
# read datasets in the group into a dictionary of 2d arrays and intialize decimal dates
timeseries_datasets = {}
num_date = len(dates)
for i in range(num_date):
timeseries_datasets[dates[i]] = np.squeeze(
displacement_3d_matrix[i, :, :])
d = get_date(dates[i])
decimal = get_decimal_date(d)
decimal_dates.append(decimal)
del displacement_3d_matrix
#for displacement_2d_matrix in displacement_3d_matrix:
# dataset = displacement_2d_matrix[:]
# if should_mask:
# print("Masking " + dates[i])
# mask = timeseries_group["quality"]["mask"][:]
# dataset = mask_matrix(dataset, mask)
# timeseries_datasets[dates[i]] = dataset
# d = get_date(dates[i])
# decimal = get_decimal_date(d)
# decimal_dates.append(decimal)
# i += 1
# close h5 file
#file.close()
path_list = path_name.split("/")
folder_name = path_name.split("/")[len(path_list) - 1]
try: # create path for output
os.mkdir(output_folder)
except:
print(output_folder + " already exists")
# read and convert the datasets, then write them into json files and insert into database
convert_data(attributes, decimal_dates, timeseries_datasets, dates,
output_folder, folder_name)
# run tippecanoe command to get mbtiles file
os.chdir(os.path.abspath(output_folder))
os.system(
"tippecanoe *.json -l chunk_1 -x d -pf -pk -Bg -d9 -D12 -g12 -r0 -o " +
folder_name + ".mbtiles")
# ---------------------------------------------------------------------------------------
# check how long it took to read h5 file data and create json files
end_time = time.clock()
print(("time elapsed: " + str(end_time - start_time)))
return
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 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 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 main():
parser = build_parser()
parseArgs = parser.parse_args()
file_name = parseArgs.file
output_folder = parseArgs.outputDir
should_mask = True
path_name_and_extension = os.path.basename(file_name).split(".")
path_name = path_name_and_extension[0]
# ---------------------------------------------------------------------------------------
# start clock to track how long conversion process takes
start_time = time.clock()
# use h5py to open specified group(s) in the h5 file
# then read datasets from h5 file into memory for faster reading of data
he_obj = HDFEOS(file_name)
he_obj.open(print_msg=False)
displacement_3d_matrix = he_obj.read(datasetName='displacement')
mask = he_obj.read(datasetName='mask')
if should_mask:
print("Masking displacement")
displacement_3d_matrix = mask_matrix(displacement_3d_matrix, mask)
del mask
dates = he_obj.dateList
attributes = dict(he_obj.metadata)
#file = h5py.File(file_name, "r")
#timeseries_group = file["HDFEOS"]["GRIDS"]["timeseries"]
#displacement_3d_matrix = timeseries_group["observation"]["displacement"]
# get attributes (stored at root) of UNAVCO timeseries file
#attributes = dict(file.attrs)
# in timeseries displacement_3d_matrix, there are datasets
# need to get datasets with dates - strings that can be converted to integers
#dates = displacement_3d_matrix.attrs["DATE_TIMESERIES"].split(" ")
# array that stores dates from dates that have been converted to decimal
decimal_dates = []
# read datasets in the group into a dictionary of 2d arrays and intialize decimal dates
timeseries_datasets = {}
num_date = len(dates)
for i in range(num_date):
timeseries_datasets[dates[i]] = np.squeeze(displacement_3d_matrix[i, :, :])
d = get_date(dates[i])
decimal = get_decimal_date(d)
decimal_dates.append(decimal)
del displacement_3d_matrix
#for displacement_2d_matrix in displacement_3d_matrix:
# dataset = displacement_2d_matrix[:]
# if should_mask:
# print("Masking " + dates[i])
# mask = timeseries_group["quality"]["mask"][:]
# dataset = mask_matrix(dataset, mask)
# timeseries_datasets[dates[i]] = dataset
# d = get_date(dates[i])
# decimal = get_decimal_date(d)
# decimal_dates.append(decimal)
# i += 1
# close h5 file
#file.close()
path_list = path_name.split("/")
folder_name = path_name.split("/")[len(path_list)-1]
try: # create path for output
os.mkdir(output_folder)
except:
print(output_folder + " already exists")
# read and convert the datasets, then write them into json files and insert into database
convert_data(attributes, decimal_dates, timeseries_datasets, dates, output_folder, folder_name)
# run tippecanoe command to get mbtiles file
os.chdir(os.path.abspath(output_folder))
os.system("tippecanoe *.json -l chunk_1 -x d -pf -pk -Bg -d9 -D12 -g12 -r0 -o " + folder_name + ".mbtiles")
# ---------------------------------------------------------------------------------------
# check how long it took to read h5 file data and create json files
end_time = time.clock()
print(("time elapsed: " + str(end_time - start_time)))
return