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stack.py
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############################################################
# Program is part of MintPy #
# Copyright (c) 2013, Zhang Yunjun, Heresh Fattahi #
# Author: Zhang Yunjun, Heresh Fattahi, 2018 #
############################################################
# class used for file operation within MintPy
# Recommend import:
# from mintpy.objects import timeseries, ifgramStack, geometry
import os
import time
import itertools
import datetime as dt
import h5py
import numpy as np
from mintpy.utils import ptime, time_func
##------------------ Global Variables ---------------------##
dataTypeDict = {
'bool': np.bool_, 'byte': np.bool_, 'flag': np.bool_,
'int': np.int16, 'int16': np.int16, 'short': np.int16, 'int32': np.int32,
'int64': np.int64, 'long': np.int64,
'float': np.float32, 'float32': np.float32,
'float_': np.float64, 'float64': np.float64,
'complex': np.complex64, 'complex64': np.complex64, 'cpx_float32': np.complex64,
'cfloat': np.complex64, 'cfloat32': np.complex64,
'complex128': np.complex128, 'complex_': np.complex128, 'cpx_float64': np.complex128,
}
timeseriesKeyNames = ['timeseries', 'HDFEOS', 'giantTimeseries']
timeseriesDatasetNames = [
'timeseries',
'raw',
'troposphericDelay',
'topographicResidual',
'ramp',
'displacement',
]
geometryDatasetNames = [
'height',
'latitude',
'longitude',
'rangeCoord',
'azimuthCoord',
'incidenceAngle',
'azimuthAngle',
'slantRangeDistance',
'shadowMask',
'waterMask',
'commonMask',
'bperp',
]
ifgramDatasetNames = [
'unwrapPhase',
'unwrapPhase_bridging_phaseClosure',
'unwrapPhase_bridging',
'unwrapPhase_phaseClosure',
'coherence',
'connectComponent',
'wrapPhase',
'ionoPhase',
'magnitude',
'azimuthOffset',
'azimuthOffsetStd',
'rangeOffset',
'rangeOffsetStd',
'offsetSNR',
'refPhase',
]
datasetUnitDict = {
# interferogram
'unwrapPhase' : 'radian',
'coherence' : '1',
'connectComponent' : '1',
'wrapPhase' : 'radian',
'ionoPhase' : 'radian',
'magnitude' : '1',
# offset
'azimuthOffset' : 'pixel',
'azimuthOffsetStd' : 'pixel',
'rangeOffset' : 'pixel',
'rangeOffsetStd' : 'pixel',
'offsetSNR' : '1',
# geometry
'height' : 'm',
'latitude' : 'degree',
'longitude' : 'degree',
'rangeCoord' : '1',
'azimuthCoord' : '1',
'incidenceAngle' : 'degree',
'azimuthAngle' : 'degree',
'slantRangeDistance' : 'm',
'shadowMask' : '1',
'waterMask' : '1',
'commonMask' : '1',
'bperp' : 'm',
# time-series
'timeseries' : 'm',
'raw' : 'm',
'troposphericDelay' : 'm',
'topographicResidual' : 'm',
'ramp' : 'm',
'displacement' : 'm',
# others
'temporalCoherence' : '1',
'velocity' : 'm/year',
'acceleration' : 'm/year^2',
'mask' : '1',
# giant
'giantTimeseries' : 'mm',
'recons' : 'mm',
'rawts' : 'mm',
'sar_aps' : 'mm',
'igram_aps' : 'mm',
'figram' : 'mm',
'igram' : 'mm',
'cmask' : '1',
'ifgcnt' : '1',
# binary
'unw' : 'radian',
'int' : 'radian',
'flat' : 'radian',
'cor' : '1',
'dem' : 'm',
'hgt' : 'm',
'hgt_sim' : 'm',
'magnitude' : '1',
'intensity' : '1',
}
################################ timeseries class begin ################################
class timeseries:
"""
Time-series object for displacement of a set of SAR images from the same platform and track.
It contains three datasets in root level: date, bperp and timeseries.
File structure: https://mintpy.readthedocs.io/en/latest/api/data_structure/#timeseries
"""
# point get_design_matrix4time_func() to utils.time_func for backward compatibility
get_design_matrix4time_func = time_func.get_design_matrix4time_func
def __init__(self, file=None):
self.file = file
self.name = 'timeseries'
def close(self, print_msg=True):
try:
self.f.close()
if print_msg:
print('close timeseries file: {}'.format(os.path.basename(self.file)))
except:
pass
return None
def open(self, print_msg=True):
if print_msg:
print('open {} file: {}'.format(self.name, os.path.basename(self.file)))
self.get_metadata()
self.get_size()
self.get_date_list()
self.numPixel = self.length * self.width
with h5py.File(self.file, 'r') as f:
try:
self.pbase = f['bperp'][:]
self.pbase -= self.pbase[self.refIndex]
except:
self.pbase = None
# time info
self.dateFormat = ptime.get_date_str_format(self.dateList[0])
self.times = np.array([dt.datetime.strptime(i, self.dateFormat) for i in self.dateList])
# add hh/mm/ss info to the datetime objects
if 'T' not in self.dateFormat or all(i.hour==0 and i.minute==0 for i in self.times):
if 'CENTER_LINE_UTC' in self.metadata.keys():
utc_sec = float(self.metadata['CENTER_LINE_UTC'])
self.times = np.array([i + dt.timedelta(seconds=utc_sec) for i in self.times])
self.tbase = np.array([(i.days + i.seconds / (24 * 60 * 60))
for i in (self.times - self.times[self.refIndex])],
dtype=np.float32)
# list of float for year, 2014.95
self.yearList = [i.year + (i.timetuple().tm_yday-1)/365.25 for i in self.times]
self.sliceList = ['{}-{}'.format(self.name, i) for i in self.dateList]
return None
def get_metadata(self):
with h5py.File(self.file, 'r') as f:
self.metadata = dict(f.attrs)
dates = f['date'][:]
for key, value in self.metadata.items():
try:
self.metadata[key] = value.decode('utf8')
except:
self.metadata[key] = value
# ref_date/index
dateList = [i.decode('utf8') for i in dates]
if 'REF_DATE' not in self.metadata.keys():
self.metadata['REF_DATE'] = dateList[0]
self.refIndex = dateList.index(self.metadata['REF_DATE'])
self.metadata['START_DATE'] = dateList[0]
self.metadata['END_DATE'] = dateList[-1]
return self.metadata
def get_size(self):
with h5py.File(self.file, 'r') as f:
self.numDate, self.length, self.width = f[self.name].shape[-3:]
return self.numDate, self.length, self.width
def get_date_list(self):
with h5py.File(self.file, 'r') as f:
self.dateList = [i.decode('utf8') for i in f['date'][:]]
return self.dateList
def read(self, datasetName=None, box=None, squeeze=True, print_msg=True):
"""Read dataset from timeseries file
Parameters: self : timeseries object
datasetName : (list of) string in YYYYMMDD format
box : tuple of 4 int, indicating x0,y0,x1,y1 of range
Returns: data : 2D or 3D dataset
Examples: from mintpy.objects import timeseries
tsobj = timeseries('timeseries_ERA5_demErr.h5')
data = tsobj.read(datasetName='20161020')
data = tsobj.read(datasetName='20161020', box=(100,300,500,800))
data = tsobj.read(datasetName=['20161020','20161026','20161101'])
data = tsobj.read(box=(100,300,500,800))
"""
if print_msg:
print('reading {} data from file: {} ...'.format(self.name, self.file))
self.open(print_msg=False)
# convert input datasetName into list of dates
if not datasetName or datasetName == 'timeseries':
datasetName = []
elif isinstance(datasetName, str):
datasetName = [datasetName]
datasetName = [i.replace('timeseries', '').replace('-', '') for i in datasetName]
with h5py.File(self.file, 'r') as f:
ds = f[self.name]
if isinstance(ds, h5py.Group): # support for old mintpy files
ds = ds[self.name]
# Get dateFlag - mark in time/1st dimension
dateFlag = np.zeros((self.numDate), dtype=np.bool_)
if not datasetName:
dateFlag[:] = True
else:
for e in datasetName:
dateFlag[self.dateList.index(e)] = True
# Get Index in space/2_3 dimension
if box is None:
box = [0, 0, self.width, self.length]
# read
data = ds[:,
box[1]:box[3],
box[0]:box[2]][dateFlag]
if squeeze and any(i == 1 for i in data.shape):
data = np.squeeze(data)
return data
def write2hdf5(self, data, outFile=None, dates=None, bperp=None, metadata=None, refFile=None, compression=None):
"""
Parameters: data : 3D array of float32
dates : 1D array/list of string in YYYYMMDD format
bperp : 1D array/list of float32 (optional)
metadata : dict
outFile : string
refFile : string
compression : string or None
Returns: outFile : string
Examples:
from mintpy.objects import timeseries
##Generate a new timeseries file
tsobj = timeseries('timeseries.h5')
timeseries.write(data, dates=dateList, bperp=bperp, metadata=atr)
##Generate a timeseries with same attributes and same date/bperp info
tsobj = timeseries('timeseries_demErr.h5')
timeseries.write(data, refFile='timeseries.h5')
"""
if not outFile:
outFile = self.file
if refFile:
refobj = timeseries(refFile)
refobj.open(print_msg=False)
if metadata is None:
metadata = refobj.metadata
if dates is None:
dates = refobj.dateList
if bperp is None:
bperp = refobj.pbase
# get ref file compression type if input compression is None
if compression is None:
with h5py.File(refFile, 'r') as rf:
compression = rf[timeseriesDatasetNames[0]].compression
refobj.close(print_msg=False)
data = np.array(data, dtype=np.float32)
dates = np.array(dates, dtype=np.string_)
bperp = np.array(bperp, dtype=np.float32)
metadata = dict(metadata)
metadata['FILE_TYPE'] = self.name
# directory
outDir = os.path.dirname(os.path.abspath(outFile))
if not os.path.isdir(outDir):
os.makedirs(outDir)
print('create directory: {}'.format(outDir))
# 3D dataset - timeseries
print('create timeseries HDF5 file: {} with w mode'.format(outFile))
with h5py.File(outFile, 'w') as f:
print(('create dataset /timeseries of {t:<10} in size of {s} '
'with compression={c}').format(t=str(data.dtype),
s=data.shape,
c=compression))
f.create_dataset('timeseries',
data=data,
chunks=True,
compression=compression)
# 1D dataset - date / bperp
print('create dataset /dates of {:<10} in size of {}'.format(str(dates.dtype), dates.shape))
f.create_dataset('date', data=dates)
if bperp.shape != ():
print('create dataset /bperp of {:<10} in size of {}'.format(str(bperp.dtype), bperp.shape))
f.create_dataset('bperp', data=bperp)
# Attributes
for key, value in metadata.items():
f.attrs[key] = str(value)
print('finished writing to {}'.format(outFile))
return outFile
def timeseries_std(self, maskFile=None, outFile=None):
"""Calculate the standard deviation (STD) for acquisition of time-series,
output result to a text file.
"""
# Calculate STD
data = self.read()
if maskFile:
mask = singleDataset(maskFile).read()
print('read mask from file: '+maskFile)
data[:, mask == 0] = np.nan
self.std = np.nanstd(data, axis=(1, 2))
# Write text file
header = 'Standard Deviation in space for each acquisition of time-series\n'
header += 'Timeseries file: {}\n'.format(self.file)
header += 'Mask file: {}\n'.format(maskFile)
header += 'Date\t\tSTD (m)'
if not outFile:
outFile = os.path.join(os.path.dirname(os.path.abspath(self.file)),
'std_{}.txt'.format(os.path.splitext(os.path.basename(self.file))[0]))
np.savetxt(outFile, np.hstack((np.array(self.dateList).reshape(-1, 1), self.std.reshape(-1, 1))),
fmt='%s', delimiter='\t', header=header)
print('save timeseries STD to text file: {}'.format(outFile))
return outFile
def timeseries_rms(self, maskFile=None, outFile=None):
"""Calculate the Root Mean Square for each acquisition of time-series
and output result to a text file.
"""
# Get date list
date_list = self.get_date_list()
num_date = len(date_list)
# Get mask
if maskFile and os.path.isfile(maskFile):
print('read mask from file: '+maskFile)
mask = singleDataset(maskFile).read()
# Calculate RMS one date at a time
self.rms = np.zeros(num_date) * np.nan
print('reading {} data from file: {} ...'.format(self.name, self.file))
prog_bar = ptime.progressBar(maxValue=num_date)
for i in range(num_date):
data = self.read(datasetName='{}'.format(date_list[i]), print_msg=False)
if maskFile and os.path.isfile(maskFile):
data[mask == 0] = np.nan
self.rms[i] = np.sqrt(np.nanmean(np.square(data), axis=(0, 1)))
prog_bar.update(i+1, suffix='{}/{}'.format(i+1, num_date))
prog_bar.close()
# Write text file
header = 'Root Mean Square in space for each acquisition of time-series\n'
header += 'Timeseries file: {}\n'.format(self.file)
header += 'Mask file: {}\n'.format(maskFile)
header += 'Date\t\tRMS (m)'
if not outFile:
outFile = os.path.join(os.path.dirname(os.path.abspath(self.file)),
'rms_{}.txt'.format(os.path.splitext(os.path.basename(self.file))[0]))
np.savetxt(outFile, np.hstack((np.array(self.dateList).reshape(-1, 1), self.rms.reshape(-1, 1))),
fmt='%s', delimiter='\t', header=header)
print('save timeseries RMS to text file: {}'.format(outFile))
return outFile
def spatial_average(self, maskFile=None, box=None, reverseMask=False, threshold=None):
self.open(print_msg=False)
data = self.read(box=box)
if maskFile and os.path.isfile(maskFile):
print('read mask from file: '+maskFile)
mask = singleDataset(maskFile).read(box=box)
data[mask == int(reverseMask)] = np.nan
# calculate area ratio if threshold is specified
# percentage of pixels with value above the threshold
if threshold is not None:
data[data > threshold] = 1
data[data <= threshold] = 0
dmean = np.nanmean(data, axis=(1, 2))
return dmean, self.dateList
def temporal_average(self):
print('calculating the temporal average of timeseries file: {}'.format(self.file))
self.open(print_msg=False)
data = self.read(squeeze=False)
dmean = np.nanmean(data, axis=0)
return dmean
def save2bl_list_file(self, out_file='bl_list.txt'):
"""Generate bl_list.txt file from timeseries h5 file."""
self.open(print_msg=False)
date6_list = [i[2:8] for i in self.dateList]
pbase_list = self.pbase.tolist()
print('write baseline list info to file: {}'.format(out_file))
with open(out_file, 'w') as f:
for d, pbase in zip(date6_list, pbase_list):
f.write('{}\t{}\n'.format(d, pbase))
return out_file
################################ timeseries class end ##################################
################################# geometry class begin #################################
class geometry:
""" Geometry object.
File structure: https://mintpy.readthedocs.io/en/latest/api/data_structure/#geometry
"""
def __init__(self, file=None):
self.file = file
self.name = 'geometry'
def close(self, print_msg=True):
try:
self.f.close()
if print_msg:
print('close geometry file: {}'.format(os.path.basename(self.file)))
except:
pass
def open(self, print_msg=True):
if print_msg:
print('open {} file: {}'.format(self.name, os.path.basename(self.file)))
self.get_metadata()
self.get_size()
self.numPixel = self.length * self.width
self.geocoded = False
if 'Y_FIRST' in self.metadata.keys():
self.geocoded = True
with h5py.File(self.file, 'r') as f:
self.datasetNames = [i for i in f.keys() if isinstance(f[i], h5py.Dataset)]
self.sliceList = list(self.datasetNames)
if 'bperp' in f.keys():
self.dateList = [i.decode('utf8') for i in f['date'][:]]
self.numDate = len(self.dateList)
# Update bperp datasetNames
try:
self.sliceList.remove('bperp')
except:
pass
self.sliceList += ['bperp-'+d for d in self.dateList]
else:
self.dateList = None
def get_size(self):
with h5py.File(self.file, 'r') as f:
dsName = [i for i in f.keys() if i in geometryDatasetNames][0]
dsShape = f[dsName].shape
if len(dsShape) == 3:
self.length, self.width = dsShape[1:3]
else:
self.length, self.width = dsShape
return self.length, self.width
def get_metadata(self):
with h5py.File(self.file, 'r') as f:
self.metadata = dict(f.attrs)
for key, value in self.metadata.items():
try:
self.metadata[key] = value.decode('utf8')
except:
self.metadata[key] = value
return self.metadata
def read(self, datasetName=geometryDatasetNames[0], box=None, print_msg=True):
"""Read 2D / 3D dataset with bounding box in space
Parameters: datasetName : (list of) string, to point to specific 2D dataset, e.g.:
height
incidenceAngle
bperp
...
bperp-20161020
bperp-20161026
bperp-...
box : tuple of 4 int, for (x0,y0,x1,y1)
print_msg : bool
Returns: data : 2D or 3D array
Example:
obj = geometry('./inputs/geometryRadar.h5')
obj.read(datasetName='height')
obj.read(datasetName='incidenceAngle')
obj.read(datasetName='bperp')
obj.read(datasetName='bperp-20161020')
obj.read(datasetName=['bperp-20161020',
'bperp-20161026'])
"""
self.open(print_msg=False)
if box is None:
box = (0, 0, self.width, self.length)
if datasetName is None:
datasetName = geometryDatasetNames[0]
elif isinstance(datasetName, str):
datasetName = [datasetName]
with h5py.File(self.file, 'r') as f:
familyName = datasetName[0].split('-')[0]
ds = f[familyName]
if print_msg:
print('reading {:<15} data from file: {} ...'.format(familyName, self.file))
if len(ds.shape) == 1:
data = ds[:]
elif len(ds.shape) == 2:
data = ds[box[1]:box[3], box[0]:box[2]]
else:
# get dateFlag - mark in time/1st dimension
dateFlag = np.zeros((ds.shape[0]), dtype=np.bool_)
datasetName = [i.replace(familyName, '').replace('-', '') for i in datasetName]
if any(not i for i in datasetName):
dateFlag[:] = True
else:
for e in datasetName:
dateFlag[self.dateList.index(e)] = True
# read
data = ds[:,
box[1]:box[3],
box[0]:box[2]][dateFlag]
if any(i == 1 for i in data.shape):
data = np.squeeze(data)
return data
################################# geometry class end ###################################
################################# ifgramStack class begin ##############################
class ifgramStack:
""" Interferograms Stack object.
File structure: https://mintpy.readthedocs.io/en/latest/api/data_structure/#ifgramstack
"""
def __init__(self, file=None):
self.file = file
self.name = 'ifgramStack'
def close(self, print_msg=True):
try:
self.f.close()
if print_msg:
print('close {} file: {}'.format(self.name, os.path.basename(self.file)))
except:
pass
def open(self, print_msg=True):
"""
Time format/rules:
All datetime.datetime objects named with time
All string in YYYYMMDD named with date (following roipac)
"""
if print_msg:
print('open {} file: {}'.format(self.name, os.path.basename(self.file)))
self.get_metadata()
self.get_size()
self.read_datetimes()
self.numPixel = self.length * self.width
# time info
self.date12List = ['{}_{}'.format(i, j) for i, j in zip(self.mDates, self.sDates)]
self.tbaseIfgram = np.array([i.days + i.seconds / (24 * 60 * 60)
for i in (self.sTimes - self.mTimes)],
dtype=np.float32)
with h5py.File(self.file, 'r') as f:
self.dropIfgram = f['dropIfgram'][:]
self.pbaseIfgram = f['bperp'][:]
# get existed datasetNames in the order of ifgramDatasetNames
dsNames = [i for i in f.keys()
if (isinstance(f[i], h5py.Dataset)
and f[i].shape[-2:] == (self.length, self.width))]
self.datasetNames = [i for i in ifgramDatasetNames if i in dsNames]
self.datasetNames += [i for i in dsNames if i not in ifgramDatasetNames]
# Get sliceList for self.read()
self.sliceList = []
for dsName in self.datasetNames:
self.sliceList += ['{}-{}'.format(dsName, i) for i in self.date12List]
# Time in timeseries domain
self.dateList = self.get_date_list(dropIfgram=False)
self.numDate = len(self.dateList)
# Reference pixel
try:
self.refY = int(self.metadata['REF_Y'])
self.refX = int(self.metadata['REF_X'])
except:
self.refY = None
self.refX = None
try:
self.refLat = float(self.metadata['REF_LAT'])
self.refLon = float(self.metadata['REF_LON'])
except:
self.refLat = None
self.refLon = None
def get_metadata(self):
# read metadata from root level
with h5py.File(self.file, 'r') as f:
self.metadata = dict(f.attrs)
dates = f['date'][:].flatten()
# decode metadata
for key, value in self.metadata.items():
try:
self.metadata[key] = value.decode('utf8')
except:
self.metadata[key] = value
# START/END_DATE
dateList = sorted([i.decode('utf8') for i in dates])
self.metadata['START_DATE'] = dateList[0]
self.metadata['END_DATE'] = dateList[-1]
return self.metadata
def get_size(self, dropIfgram=False, datasetName=None):
with h5py.File(self.file, 'r') as f:
# get default datasetName
if datasetName is None:
datasetName = [i for i in ['unwrapPhase', 'rangeOffset', 'azimuthOffset'] if i in f.keys()][0]
# get 3D size
self.numIfgram, self.length, self.width = f[datasetName].shape
# update 1st dimension size
if dropIfgram:
self.numIfgram = np.sum(f['dropIfgram'][:])
return self.numIfgram, self.length, self.width
def read_datetimes(self):
"""Read date1/2 into array of datetime.datetime objects"""
with h5py.File(self.file, 'r') as f:
dates = f['date'][:]
# grab the date string format
self.dateFormat = ptime.get_date_str_format(dates[0, 0])
# convert date from str to datetime.datetime objects
self.mDates = np.array([i.decode('utf8') for i in dates[:, 0]])
self.sDates = np.array([i.decode('utf8') for i in dates[:, 1]])
self.mTimes = np.array([dt.datetime.strptime(i, self.dateFormat) for i in self.mDates])
self.sTimes = np.array([dt.datetime.strptime(i, self.dateFormat) for i in self.sDates])
def read(self, datasetName='unwrapPhase', box=None, print_msg=True, dropIfgram=False):
"""Read 3D dataset with bounding box in space
Parameters: datasetName : string, to point to specific 2D dataset, e.g.:
unwrapPhase
coherence
connectComponent
...
unwrapPhase-20161020_20161026
unwrapPhase-...
coherence-20161020_20161026
...
['unwrapPhase-20161020_20161026',
'unwrapPhase-20161020_20161101',
...]
box : tuple of 4 int, for (x0,y0,x1,y1)
print_msg : bool
Returns: data : 2D or 3D array
Example:
obj = ifgramStack('./inputs/ifgramStack.h5')
obj.read(datasetName='unwrapPhase')
obj.read(datasetName='coherence')
obj.read(datasetName='unwrapPhase-20161020_20161026')
obj.read(datasetName=['unwrapPhase-20161020_20161026',
'unwrapPhase-20161020_20161101'])
"""
self.get_size(dropIfgram=False)
date12List = self.get_date12_list(dropIfgram=False)
# convert input datasetName into list
if datasetName is None:
datasetName = ['unwrapPhase']
elif isinstance(datasetName, str):
datasetName = [datasetName]
with h5py.File(self.file, 'r') as f:
familyName = datasetName[0].split('-')[0]
ds = f[familyName]
if print_msg:
print('reading {} data from file: {} ...'.format(familyName, self.file))
# get dateFlag - mark in time/1st dimension
dateFlag = np.zeros((self.numIfgram), dtype=np.bool_)
datasetName = [i.replace(familyName, '').replace('-', '') for i in datasetName]
if any(not i for i in datasetName):
if dropIfgram:
dateFlag = f['dropIfgram'][:]
else:
dateFlag[:] = True
else:
for e in datasetName:
dateFlag[date12List.index(e)] = True
# get index in space/2-3 dimension
if box is None:
box = (0, 0, self.width, self.length)
# read
data = ds[:,
box[1]:box[3],
box[0]:box[2]][dateFlag]
if any(i == 1 for i in data.shape):
data = np.squeeze(data)
return data
def spatial_average(self, datasetName='coherence', maskFile=None, box=None, useMedian=False,
reverseMask=False, threshold=None):
""" Calculate the spatial average."""
if datasetName is None:
datasetName = 'coherence'
if useMedian:
print('calculating spatial median of {} in file {} ...'.format(datasetName, self.file))
else:
print('calculating spatial mean of {} in file {} ...'.format(datasetName, self.file))
# read mask
if maskFile and os.path.isfile(maskFile):
print('read mask from file: '+maskFile)
mask = singleDataset(maskFile).read(box=box)
else:
maskFile = None
# calculation
with h5py.File(self.file, 'r') as f:
dset = f[datasetName]
numIfgram = dset.shape[0]
dmean = np.zeros((numIfgram), dtype=np.float32)
prog_bar = ptime.progressBar(maxValue=numIfgram)
for i in range(numIfgram):
prog_bar.update(i+1, suffix='{}/{}'.format(i+1, numIfgram))
# read
data = dset[i, box[1]:box[3], box[0]:box[2]]
if maskFile:
data[mask == int(reverseMask)] = np.nan
# ignore ZERO value for coherence
if datasetName == 'coherence':
data[data == 0] = np.nan
# calculate area ratio if threshold is specified
# percentage of pixels with value above the threshold
if threshold is not None:
data[data > threshold] = 1
data[data <= threshold] = 0
if useMedian:
dmean[i] = np.nanmedian(data)
else:
dmean[i] = np.nanmean(data)
prog_bar.close()
return dmean, self.date12List
# Functions considering dropIfgram value
def get_date12_list(self, dropIfgram=True):
with h5py.File(self.file, 'r') as f:
dates = f['date'][:]
if dropIfgram:
dates = dates[f['dropIfgram'][:], :]
mDates = np.array([i.decode('utf8') for i in dates[:, 0]])
sDates = np.array([i.decode('utf8') for i in dates[:, 1]])
date12List = ['{}_{}'.format(i, j) for i, j in zip(mDates, sDates)]
return date12List
def get_drop_date12_list(self):
with h5py.File(self.file, 'r') as f:
dates = f['date'][:]
dates = dates[~f['dropIfgram'][:], :]
mDates = np.array([i.decode('utf8') for i in dates[:, 0]])
sDates = np.array([i.decode('utf8') for i in dates[:, 1]])
date12List = ['{}_{}'.format(i, j) for i, j in zip(mDates, sDates)]
return date12List
def get_date_list(self, dropIfgram=False):
with h5py.File(self.file, 'r') as f:
dates = f['date'][:]
if dropIfgram:
dates = dates[f['dropIfgram'][:], :]
mDates = [i.decode('utf8') for i in dates[:, 0]]
sDates = [i.decode('utf8') for i in dates[:, 1]]
dateList = sorted(list(set(mDates + sDates)))
return dateList
def get_reference_phase(self, unwDatasetName='unwrapPhase', skip_reference=False, dropIfgram=False):
"""Get reference value
Parameters: unwDatasetName : string, unwrapPhase, or unwrapPhase_unwCor
skip_reference : bool, skip reference value (for simulation only)
dropIfgram : bool, skip ifgrams marked as dropped or not
Returns: ref_phase : 1D np.array in size of (num_ifgram,) in float32
"""
self.open(print_msg=False)
if skip_reference:
ref_phase = np.zeros(self.get_size(dropIfgram=dropIfgram)[0], np.float32)
print('skip checking reference pixel info - This is for offset and testing ONLY.')
elif 'REF_Y' not in self.metadata.keys():
raise ValueError('No REF_X/Y found!\nrun reference_point.py to select reference pixel.')
else:
print('reference pixel in y/x: ({}, {}) from dataset: {}'.format(self.refY, self.refX, unwDatasetName))
ref_phase = self.read(datasetName=unwDatasetName,
box=(self.refX, self.refY, self.refX+1, self.refY+1),
dropIfgram=dropIfgram,
print_msg=False)
return ref_phase
def nonzero_mask(self, datasetName=None, print_msg=True, dropIfgram=True):
"""Return the common mask of pixels with non-zero value in dataset of all ifgrams.
Ignoring dropped ifgrams
"""
self.open(print_msg=False)
with h5py.File(self.file, 'r') as f:
if datasetName is None:
datasetName = [i for i in ['connectComponent', 'unwrapPhase']
if i in f.keys()][0]
print('calculate the common mask of pixels with non-zero {} value'.format(datasetName))
dset = f[datasetName]
mask = np.ones(dset.shape[1:3], dtype=np.bool_)
dropIfgramFlag = np.ones(dset.shape[0], dtype=np.bool_)
if dropIfgram:
dropIfgramFlag = self.dropIfgram
num2read = np.sum(dropIfgramFlag)
idx2read = np.where(dropIfgramFlag)[0]
# Loop to save memory usage
prog_bar = ptime.progressBar(maxValue=num2read)
for i in range(num2read):
prog_bar.update(i+1, suffix='{}/{}'.format(i+1, num2read))
data = dset[idx2read[i], :, :]
mask[data == 0.] = 0
mask[np.isnan(data)] = 0
prog_bar.close()
return mask
def temporal_average(self, datasetName='coherence', dropIfgram=True, max_memory=4):
self.open(print_msg=False)
if datasetName is None:
datasetName = 'coherence'
print('calculate the temporal average of {} in file {} ...'.format(datasetName, self.file))
# index of pairs to read
ifgram_flag = np.ones(self.numIfgram, dtype=np.bool_)
if dropIfgram:
ifgram_flag = self.dropIfgram
if np.all(ifgram_flag == 0.):
raise Exception(('ALL interferograms are marked as dropped, '
'can not calculate temporal average.'))
# temporal baseline for phase
# with unit of years (float64 for very short tbase of UAVSAR data)
if 'unwrapPhase' in datasetName:
phase2range = -1 * float(self.metadata['WAVELENGTH']) / (4.0 * np.pi)
tbase = np.array(self.tbaseIfgram, dtype=np.float64) / 365.25
tbase = tbase[ifgram_flag]
with h5py.File(self.file, 'r') as f:
dset = f[datasetName]
# reference value for phase
ref_val = None
if ('unwrapPhase' in datasetName
and self.refY is not None and 0 <= self.refY <= self.width
and self.refX is not None and 0 <= self.refX <= self.length):
ref_val = dset[:, self.refY, self.refX][ifgram_flag]
# get step size and number
ds_size = np.sum(ifgram_flag, dtype=np.int64) * self.length * self.width * 4
num_step = int(np.ceil(ds_size * 3 / (max_memory * 1024**3)))
row_step = int(np.rint(self.length / num_step / 10) * 10)
num_step = int(np.ceil(self.length / row_step))
# calculate lines by lines
dmean = np.zeros(dset.shape[1:3], dtype=np.float32)
prog_bar = ptime.progressBar(maxValue=num_step)
for i in range(num_step):
r0 = i * row_step
r1 = min(r0 + row_step, self.length)
prog_bar.update(i+1, suffix='lines {}/{}'.format(r1, self.length))
# read
data = dset[:, r0:r1, :][ifgram_flag]
# referencing / normalizing for phase
if 'unwrapPhase' in datasetName:
# spatial referencing
if ref_val is not None:
data -= np.tile(ref_val.reshape(-1, 1, 1), (1, data.shape[1], data.shape[2]))
# phase to phase velocity
for j in range(data.shape[0]):
data[j,:,:] *= (phase2range / tbase[j])
# use nanmean to better handle NaN values
dmean[r0:r1, :] = np.nanmean(data, axis=0)
prog_bar.close()
return dmean
def get_max_connection_number(self):
date12_list = self.get_date12_list()
A = self.get_design_matrix4timeseries(date12_list, refDate=0)[0]
num_conn = np.zeros(A.shape[0], dtype=np.int16)
for i in range(A.shape[0]):
Ai = A[i, :]
num_conn[i] = np.where(Ai == 1)[0] - np.where(Ai == -1)[0]
return np.max(num_conn)
# Functions for Unwrap error correction
@staticmethod
def get_design_matrix4triplet(date12_list):
"""Generate the design matrix of ifgram triangle for unwrap error correction using phase closure
Parameters: date12_list : list of string in YYYYMMDD_YYYYMMDD format
Returns: C : 2D np.array in size of (num_tri, num_ifgram) consisting 0, 1, -1
for 3 SAR acquisition in t1, t2 and t3 in time order,
ifg1 for (t1, t2) with 1
ifg2 for (t1, t3) with -1
ifg3 for (t2, t3) with 1
Examples: obj = ifgramStack('./inputs/ifgramStack.h5')
date12_list = obj.get_date12_list(dropIfgram=True)
C = ifgramStack.get_design_matrix4triplet(date12_list)
"""
# Date info
date12_list = list(date12_list)
# Use tuples of the dates, which are hashable for the lookup dict
date12_tuples = [tuple(d.split('_')) for d in date12_list]
# Create an inverse map from tuple(date1, date1) -> index in ifg list
ifg_to_idx = {ifg: idx for idx, ifg in enumerate(date12_tuples)}
# Get the unique SAR dates present in the interferogram list
date_list = sorted(set(itertools.chain.from_iterable(date12_tuples)))
# Start with all possible triplets, narrow down based on ifgs present
closure_list = itertools.combinations(date_list, 3)
M = len(date12_tuples) # Number of igrams, number of rows
C_list = []