def structure_function(data,
lat,
lon,
step=5e3,
min_pair_num=100e3,
print_msg=True):
num_sample = len(data)
distance = np.zeros((num_sample**2))
variance = np.zeros((num_sample**2))
if print_msg:
prog_bar = ptime.progress_bar(maxValue=num_sample)
for i in range(num_sample):
distance[i * num_sample:(i + 1) * num_sample] = get_distance(
lat, lon, i)
variance[i * num_sample:(i + 1) * num_sample] = np.square(data -
data[i])
if print_msg:
prog_bar.update(i + 1, every=10)
if print_msg:
prog_bar.close()
bin_dist, bin_struct_func, bin_struct_func_std = bin_variance(
distance,
variance,
step=step,
min_pair_num=min_pair_num,
print_msg=print_msg)
return bin_dist, bin_struct_func, bin_struct_func_std
def bin_variance(distance,
variance,
step=5e3,
min_pair_num=100e3,
print_msg=True):
x_steps = np.arange(0, np.max(distance), step)
num_step = len(x_steps)
var = np.zeros(x_steps.shape)
var_std = np.zeros(var.shape)
p_num = np.zeros(x_steps.shape)
if print_msg:
prog_bar = ptime.progress_bar(maxValue=num_step)
for i in range(num_step):
x = x_steps[i]
idx = (distance > max(0, x - step / 2.)) * (distance < x + step / 2.)
p_num[i] = np.sum(idx)
var[i] = np.mean(variance[idx])
var_std[i] = np.std(variance[idx])
if print_msg:
prog_bar.update(i + 1, every=10)
if print_msg:
prog_bar.close()
max_step_idx = int(max(np.argwhere(p_num > min_pair_num)))
return x_steps[0:max_step_idx], var[0:max_step_idx], var_std[
0:max_step_idx]
def structure_function(data, lat, lon, step=5e3, min_pair_num=100e3, print_msg=True):
num_sample = len(data)
distance = np.zeros((num_sample**2))
variance = np.zeros((num_sample**2))
if print_msg:
prog_bar = ptime.progress_bar(maxValue=num_sample)
for i in range(num_sample):
distance[i*num_sample:(i+1)*num_sample] = get_distance(lat, lon, i)
variance[i*num_sample:(i+1)*num_sample] = np.square(data - data[i])
if print_msg:
prog_bar.update(i+1, every=10)
if print_msg:
prog_bar.close()
bin_dist, bin_struct_func, bin_struct_func_std = bin_variance(distance, variance,
step=step,
min_pair_num=min_pair_num,
print_msg=print_msg)
return bin_dist, bin_struct_func, bin_struct_func_std
def bin_variance(distance, variance, step=5e3, min_pair_num=100e3, print_msg=True):
x_steps = np.arange(0,np.max(distance),step)
num_step = len(x_steps)
var = np.zeros(x_steps.shape)
var_std = np.zeros(var.shape)
p_num = np.zeros(x_steps.shape)
if print_msg:
prog_bar = ptime.progress_bar(maxValue=num_step)
for i in range(num_step):
x = x_steps[i]
idx = (distance > max(0, x-step/2.)) * (distance < x+step/2.)
p_num[i] = np.sum(idx)
var[i] = np.mean(variance[idx])
var_std[i] = np.std(variance[idx])
if print_msg:
prog_bar.update(i+1, every=10)
if print_msg:
prog_bar.close()
max_step_idx = int(max(np.argwhere(p_num > min_pair_num)))
return x_steps[0:max_step_idx], var[0:max_step_idx], var_std[0:max_step_idx]
def main(argv):
inps = cmdLineParse()
if inps.timeseries_file:
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
if 'ref_y' not in list(atr.keys()) and inps.ref_yx:
print('No reference info found in input file, use input ref_yx: ' +
str(inps.ref_yx))
atr['ref_y'] = inps.ref_yx[0]
atr['ref_x'] = inps.ref_yx[1]
#****reading incidence angle file***/
if os.path.isfile(inps.inc_angle):
inps.inc_angle = readfile.read(inps.inc_angle,
datasetName='incidenceAngle')[0]
inps.inc_angle = np.nan_to_num(inps.inc_angle)
else:
inps.inps.inc_angle = float(inps.inc_angle)
print('incidence angle: ' + str(inps.inc_angle))
cinc = np.cos(inps.inc_angle * np.pi / 180.0)
#****look up file****/
if inps.lookup_file:
inps.lookup_file = ut.get_file_list(
[inps.lookup_file])[0] #'geomap_32rlks_tight.trans'
#****GACOS****/
delay_source = 'GACOS'
# Get weather directory
if not inps.GACOS_dir:
if inps.timeseries_file:
inps.GACOS_dir = os.path.dirname(
os.path.abspath(inps.timeseries_file)) + '/../WEATHER/GACOS'
elif inps.lookup_file:
inps.GACOS_dir = os.path.dirname(os.path.abspath(
inps.lookup_file)) + '/../WEATHER/GACOS'
else:
inps.GACOS_dir = os.path.abspath(os.getcwd())
print('Store weather data into directory: ' + inps.GACOS_dir)
#source_dir=os.path.dirname(os.path.abspath('timeseries_file'))+'/Agung/GACOS/data';print source_dir
#os.makedirs(GACOS_dir) -----------------------------------------------add part to copy/download weather data------#
#----get date list-----#
if not inps.date_list_file:
print('read date list info from: ' + inps.timeseries_file)
h5 = h5py.File(inps.timeseries_file, 'r')
if 'timeseries' in list(h5.keys()):
date_list = sorted(h5[k].keys())
elif k in ['interferograms', 'coherence', 'wrapped']:
ifgram_list = sorted(h5[k].keys())
date12_list = pnet.get_date12_list(inps.timeseries_file)
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
else:
raise ValueError('Un-support input file type:' + k)
h5.close()
else:
date_list = ptime.yyyymmdd(
np.loadtxt(inps.date_list_file, dtype=str, usecols=(0, )).tolist())
print('read date list info from: ' + inps.date_list_file)
#****cheacking availability of delays****/
print('checking availability of delays')
delay_file_list = []
for d in date_list:
if delay_source == 'GACOS':
delay_file = inps.GACOS_dir + '/' + d + '.ztd'
delay_file_list.append(delay_file)
delay_file_existed = ut.get_file_list(delay_file_list)
if len(delay_file_existed) == len(date_list):
print('no missing files')
else:
print('no. of date files found:', len(delay_file_existed))
print('no. of dates:', len(date_list))
#*****Calculating delays***/
print('calculating delays')
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
#initialise delay files
date_num = len(date_list)
trop_ts = np.zeros((date_num, length, width), np.float32)
#reading wrf files for each epoch and getting delay
for i in range(date_num):
delay_file = delay_file_existed[i]
date = date_list[i]
print('calculating delay for date', date)
trop_ts[i] = get_delay(delay_file, atr, inps.lookup_file, cinc)
print('Delays Calculated')
# Convert relative phase delay on reference date
try:
ref_date = atr['ref_date']
except:
ref_date = date_list[0]
print('convert to relative phase delay with reference date: ' + ref_date)
ref_idx = date_list.index(ref_date)
trop_ts -= np.tile(trop_ts[ref_idx, :, :], (date_num, 1, 1))
## Write tropospheric delay to HDF5
tropFile = 'GACOSdelays' + '.h5'
print('writing >>> %s' % (tropFile))
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
print('number of acquisitions: ' + str(date_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
group_trop.create_dataset(date, data=trop_ts[i], compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
# Write Attributes
for key, value in atr.items():
group_trop.attrs[key] = value
h5trop.close()
## Write corrected Time series to HDF5
if k == 'timeseries':
if not inps.out_file:
inps.out_file = os.path.splitext(
inps.timeseries_file)[0] + '_' + 'GACOS' + '.h5'
print('writing trop corrected timeseries file %s' % (inps.out_file))
h5ts = h5py.File(inps.timeseries_file, 'r')
h5tsCor = h5py.File(inps.out_file, 'w')
group_tsCor = h5tsCor.create_group('timeseries')
print('number of acquisitions: ' + str(date_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
print(date)
ts = h5ts['timeseries'].get(date)[:]
group_tsCor.create_dataset(date,
data=ts - trop_ts[i],
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5ts.close()
# Write Attributes
for key, value in atr.items():
group_tsCor.attrs[key] = value
h5tsCor.close()
print('delays written to %s' % (inps.out_file))
print('finished')
return inps.out_file
def main(argv):
inps = cmdLineParse()
if inps.timeseries_file:
inps.timeseries_file=ut.get_file_list([inps.timeseries_file])[0]
atr=readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
if 'ref_y' not in list(atr.keys()) and inps.ref_yx:
print('No reference info found in input file, use input ref_yx: '+str(inps.ref_yx))
atr['ref_y'] = inps.ref_yx[0]
atr['ref_x'] = inps.ref_yx[1]
#****reading incidence angle file***/
if os.path.isfile(inps.inc_angle):
inps.inc_angle=readfile.read(inps.inc_angle, datasetName='incidenceAngle')[0]
inps.inc_angle=np.nan_to_num(inps.inc_angle)
else:
inps.inps.inc_angle = float(inps.inc_angle)
print('incidence angle: '+str(inps.inc_angle))
cinc=np.cos(inps.inc_angle*np.pi/180.0);
#****look up file****/
if inps.lookup_file:
inps.lookup_file = ut.get_file_list([inps.lookup_file])[0] #'geomap_32rlks_tight.trans'
#****GACOS****/
delay_source = 'GACOS'
# Get weather directory
if not inps.GACOS_dir:
if inps.timeseries_file:
inps.GACOS_dir = os.path.dirname(os.path.abspath(inps.timeseries_file))+'/../WEATHER/GACOS'
elif inps.lookup_file:
inps.GACOS_dir = os.path.dirname(os.path.abspath(inps.lookup_file))+'/../WEATHER/GACOS'
else:
inps.GACOS_dir = os.path.abspath(os.getcwd())
print('Store weather data into directory: '+inps.GACOS_dir)
#source_dir=os.path.dirname(os.path.abspath('timeseries_file'))+'/Agung/GACOS/data';print source_dir
#os.makedirs(GACOS_dir) -----------------------------------------------add part to copy/download weather data------#
#----get date list-----#
if not inps.date_list_file:
print('read date list info from: '+inps.timeseries_file)
h5=h5py.File(inps.timeseries_file,'r')
if 'timeseries' in list(h5.keys()):
date_list=sorted(h5[k].keys())
elif k in ['interferograms','coherence','wrapped']:
ifgram_list = sorted(h5[k].keys())
date12_list = pnet.get_date12_list(inps.timeseries_file)
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
else:
raise ValueError('Un-support input file type:'+k)
h5.close()
else:
date_list = ptime.yyyymmdd(np.loadtxt(inps.date_list_file, dtype=str, usecols=(0,)).tolist())
print('read date list info from: '+inps.date_list_file)
#****cheacking availability of delays****/
print('checking availability of delays')
delay_file_list=[]
for d in date_list:
if delay_source == 'GACOS': delay_file = inps.GACOS_dir+'/'+d+'.ztd';
delay_file_list.append(delay_file)
delay_file_existed = ut.get_file_list(delay_file_list)
if len(delay_file_existed)==len(date_list):
print('no missing files')
else:
print('no. of date files found:', len(delay_file_existed));
print('no. of dates:', len(date_list))
#*****Calculating delays***/
print('calculating delays')
length=int(atr['FILE_LENGTH'])
width=int(atr['WIDTH'])
#initialise delay files
date_num=len(date_list)
trop_ts=np.zeros((date_num, length, width), np.float32)
#reading wrf files for each epoch and getting delay
for i in range(date_num):
delay_file=delay_file_existed[i]
date=date_list[i]
print('calculating delay for date',date)
trop_ts[i] =get_delay(delay_file,atr,inps.lookup_file,cinc)
print('Delays Calculated')
# Convert relative phase delay on reference date
try: ref_date = atr['ref_date']
except: ref_date = date_list[0]
print('convert to relative phase delay with reference date: '+ref_date)
ref_idx = date_list.index(ref_date)
trop_ts -= np.tile(trop_ts[ref_idx,:,:], (date_num, 1, 1))
## Write tropospheric delay to HDF5
tropFile = 'GACOSdelays'+'.h5'
print('writing >>> %s' % (tropFile))
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
print('number of acquisitions: '+str(date_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
group_trop.create_dataset(date, data=trop_ts[i], compression='gzip')
prog_bar.update(i+1, suffix=date)
prog_bar.close()
# Write Attributes
for key,value in atr.items():
group_trop.attrs[key] = value
h5trop.close()
## Write corrected Time series to HDF5
if k == 'timeseries':
if not inps.out_file:
inps.out_file = os.path.splitext(inps.timeseries_file)[0]+'_'+'GACOS'+'.h5'
print('writing trop corrected timeseries file %s' % (inps.out_file))
h5ts = h5py.File(inps.timeseries_file, 'r')
h5tsCor = h5py.File(inps.out_file, 'w')
group_tsCor = h5tsCor.create_group('timeseries')
print('number of acquisitions: '+str(date_num))
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i];print(date)
ts = h5ts['timeseries'].get(date)[:]
group_tsCor.create_dataset(date, data=ts-trop_ts[i], compression='gzip')
prog_bar.update(i+1, suffix=date)
prog_bar.close()
h5ts.close()
# Write Attributes
for key,value in atr.items():
group_tsCor.attrs[key] = value
h5tsCor.close()
print('delays written to %s' % (inps.out_file))
print('finished')
return inps.out_file