def get_datetime_list(ts_file, date_wise_acq_time=False):
"""Prepare exact datetime for each acquisition in the time-series file.
Parameters: ts_file - str, path of the time-series HDF5 file
date_wise_acq_time - bool, use the exact date-wise acquisition time
Returns: sensingMid - list of datetime.datetime objects
"""
print('\nprepare datetime info for each acquisition')
ts_file = os.path.abspath(ts_file)
date_list = timeseries(ts_file).get_date_list()
proj_dir = os.path.dirname(os.path.dirname(ts_file))
xml_dirs = [os.path.join(proj_dir, i) for i in ['reference', 'secondarys']]
# list of existing dataset names
with h5py.File(ts_file, 'r') as f:
ds_names = [i for i in f.keys() if isinstance(f[i], h5py.Dataset)]
dt_name = 'sensingMid'
if dt_name in ds_names:
# opt 1. read sensingMid if exists
print('read exact datetime info from /{} in file: {}'.format(
dt_name, os.path.basename(ts_file)))
with h5py.File(ts_file, 'r') as f:
sensingMidStr = [i.decode('utf-8') for i in f[dt_name][:]]
# convert string to datetime object
date_str_format = ptime.get_date_str_format(sensingMidStr[0])
sensingMid = [
dt.datetime.strptime(i, date_str_format) for i in sensingMidStr
]
elif date_wise_acq_time and all(os.path.isdir(i) for i in xml_dirs):
# opt 2. read sensingMid in xml files
print(
'read exact datetime info in XML files from ISCE-2/topsStack results in directory:',
proj_dir)
from mintpy.utils import isce_utils
sensingMid = isce_utils.get_sensing_datetime_list(
proj_dir, date_list=date_list)[0]
# plot
plot_sensingMid_variation(sensingMid)
else:
# opt 3. use constant time of the day for all acquisitions
msg = 'Use the same time of the day for all acquisitions from CENTER_LINE_UTC\n'
msg += 'With <= 1 min variation for Sentinel-1A/B for example, this simplication has negligible impact on SET calculation.'
print(msg)
atr = readfile.read_attribute(ts_file)
utc_sec = dt.timedelta(seconds=float(atr['CENTER_LINE_UTC']))
sensingMid = [
dt.datetime.strptime(i, '%Y%m%d') + utc_sec for i in date_list
]
return sensingMid
def main(iargs=None):
inps = cmd_line_parse(iargs)
atr = readfile.read_attribute(inps.file[0])
box = read_inps2box(inps, atr)
num_file = len(inps.file)
# initiate output
dsDict = {}
if num_file == 1:
dsDict[inps.dset_name] = np.zeros((box[3]-box[1], box[2]-box[0]), dtype=inps.data_type)
else:
dsDict[inps.dset_name] = np.zeros((num_file, box[3]-box[1], box[2]-box[0]), dtype=inps.data_type)
# add "date" dataset for timeseries
if inps.dset_name and inps.dset_name == 'timeseries':
date_list = [os.path.basename(os.path.dirname(x)) for x in inps.file]
date_str_format = ptime.get_date_str_format(date_list[0])
dsDict['date'] = np.array(date_list, dtype=np.string_)
# metadata
if inps.metadata:
print('add/update the following metadata:')
for meta_str in inps.metadata:
key, value = meta_str.split('=')
atr[key] = value
print(f'{key} : {value}')
# read
for i, fname in enumerate(inps.file):
print(f'reading file {i+1} / {num_file}: {fname}')
ds_names = readfile.get_dataset_list(fname)
ds_name = inps.dset_name if inps.dset_name in ds_names else None
data = readfile.read(fname, datasetName=ds_name, box=box)[0]
if num_file == 1:
dsDict[inps.dset_name][:] = data
else:
dsDict[inps.dset_name][i, :, :] = data
# write
atr['LENGTH'] = box[3] - box[1]
atr['WIDTH'] = box[2] - box[0]
writefile.write(dsDict, out_file=inps.outfile, metadata=atr)
return inps.outfile
def get_sensing_datetime_list(proj_dir, date_list=None):
"""Get the sensing datetime objects from ISCE stack results.
It assumes the default directory structure from topsStack, as below:
/proj_dir
/reference/IW*.xml
/secondarys
/20150521/IW*.xml
/20150614/IW*.xml
...
/20210113/IW*.xml
Parameters: proj_dir - str, path to the root directory of stack processing
Returns: sensingMid - list of datetime.datetime.obj
sensingStart - list of datetime.datetime.obj
sensingStop - list of datetime.datetime.obj
"""
# determine xml file basename
ref_fname = glob.glob(os.path.join(proj_dir, 'reference', 'IW*.xml'))[0]
fbase = os.path.basename(ref_fname)
# get xml files for all acquisitions
sec_fnames = sorted(glob.glob(os.path.join(proj_dir, 'secondarys', '*', fbase)))
fnames = [ref_fname] + sec_fnames
num_file = len(fnames)
# loop to read file one by one
sensingStart = []
sensingStop = []
for i, fname in enumerate(fnames):
print('[{}/{}] read {}'.format(i+1, num_file, fname))
obj = load_product(fname)
sensingStart.append(obj.bursts[0].sensingStart)
sensingStop.append(obj.bursts[-1].sensingStop)
sensingStart = sorted(sensingStart)
sensingStop = sorted(sensingStop)
# sensingStart/Stop --> sensingMid
sensingMid = [i + (j - i)/2 for i, j in zip(sensingStart, sensingStop)]
# round to the nearest second
print('round sensingStart/Stop/Mid to the nearest second.')
sensingStart = [ptime.round_seconds(i) for i in sensingStart]
sensingStop = [ptime.round_seconds(i) for i in sensingStop]
sensingMid = [ptime.round_seconds(i) for i in sensingMid]
if date_list is not None:
date_str_format = ptime.get_date_str_format(date_list[0])
date_list_out = [i.strftime(date_str_format) for i in sensingMid]
# check possible missing dates
dates_missing = [i for i in date_list if i not in date_list_out]
if dates_missing:
raise ValueError('The following dates are missing:\n{}'.format(dates_missing))
# prune dates not-needed
flag = np.array([i in date_list for i in date_list_out], dtype=np.bool_)
if np.sum(flag) > 0:
sensingMid = np.array(sensingMid)[flag].tolist()
sensingStart = np.array(sensingStart)[flag].tolist()
sensingStop = np.array(sensingStop)[flag].tolist()
dates_removed = np.array(date_list_out)[~flag].tolist()
print('The following dates are not needed and removed:\n{}'.format(dates_removed))
return sensingMid, sensingStart, sensingStop
