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writefile.py
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writefile.py
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"""Utilities to write files."""
############################################################
# Program is part of MintPy #
# Copyright (c) 2013, Zhang Yunjun, Heresh Fattahi #
# Author: Zhang Yunjun, Heresh Fattahi, 2013 #
############################################################
# Recommend import:
# from mintpy.utils import writefile
import os
import shutil
import h5py
import numpy as np
from mintpy.utils import readfile
def write(datasetDict, out_file, metadata=None, ref_file=None, compression=None, ds_unit_dict=None, print_msg=True):
""" Write one file.
Parameters: datasetDict - dict of dataset, with key = datasetName and value = 2D/3D array, e.g.:
{'height' : np.ones(( 200,300), dtype=np.int16),
'incidenceAngle': np.ones(( 200,300), dtype=np.float32),
'bperp' : np.ones((80,200,300), dtype=np.float32),
...}
out_file - str, output file name
metadata - dict of attributes
ref_file - str, reference file to get auxliary info
compression - str, compression while writing to HDF5 file, None, "lzf", "gzip"
ds_unit_dict - dict, top-level dataset unit definition
{dname : dunit,
dname : dunit,
...
}
Returns: out_file - strs
Examples: dsDict = dict()
dsDict['velocity'] = np.ones((200,300), dtype=np.float32)
write(datasetDict=dsDict, out_file='velocity.h5', metadata=atr)
"""
vprint = print if print_msg else lambda *args, **kwargs: None
# copy metadata to meta
if metadata:
meta = {key: value for key, value in metadata.items()}
elif ref_file:
meta = readfile.read_attribute(ref_file)
else:
raise ValueError('No metadata or reference file input.')
# convert ndarray input into dict type
if isinstance(datasetDict, np.ndarray):
data = np.array(datasetDict, datasetDict.dtype)
datasetDict = dict()
datasetDict[meta['FILE_TYPE']] = data
# file extension
fbase, fext = os.path.splitext(out_file)
# ignore certain meaningless file extensions
while fext in ['.geo', '.rdr', '.full', '.wgs84']:
fbase, fext = os.path.splitext(fbase)
if not fext:
fext = fbase
fext = fext.lower()
# output file info
out_dir = os.path.dirname(os.path.abspath(out_file))
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
vprint(f'create directory: {out_dir}')
# HDF5 File
if fext in ['.h5', '.he5']:
# grab info from reference h5 file
if ref_file and os.path.splitext(ref_file)[1] in ['.h5', '.he5']:
# compression
if compression is None:
compression = readfile.get_hdf5_compression(ref_file)
# list of auxiliary datasets
shape2d = (int(meta['LENGTH']), int(meta['WIDTH']))
with h5py.File(ref_file, 'r') as fr:
auxDsNames = [i for i in fr.keys()
if (i not in list(datasetDict.keys())
and isinstance(fr[i], h5py.Dataset)
and fr[i].shape[-2:] != shape2d)]
else:
auxDsNames = []
# check required datasets
dsNames = list(datasetDict.keys()) + auxDsNames
if meta['FILE_TYPE'] in ['timeseries', 'ifgramStack']:
if 'date' not in dsNames:
raise Exception("Can not write {} file without 'date' dataset!".format(meta['FILE_TYPE']))
# remove existing file
if os.path.isfile(out_file):
os.remove(out_file)
vprint(f'delete exsited file: {out_file}')
# writing
print(f'create HDF5 file: {out_file} with w mode')
maxDigit = max(len(i) for i in dsNames)
with h5py.File(out_file, 'w') as f:
# 1. write input datasets
for dsName in datasetDict.keys():
data = datasetDict[dsName]
vprint(('create dataset /{d:<{w}} of {t:<10} in size of {s:<20} '
'with compression={c}').format(d=dsName,
w=maxDigit,
t=str(data.dtype),
s=str(data.shape),
c=compression))
ds = f.create_dataset(dsName,
data=data,
chunks=True,
compression=compression)
# 2. Write extra/auxliary datasets from ref_file
if len(auxDsNames) > 0:
with h5py.File(ref_file, 'r') as fr:
for dsName in auxDsNames:
ds = fr[dsName]
vprint(('create dataset /{d:<{w}} of {t:<10} in size of {s:<10} '
'with compression={c}').format(d=dsName,
w=maxDigit,
t=str(ds.dtype),
s=str(ds.shape),
c=compression))
f.create_dataset(dsName,
data=ds[:],
chunks=True,
compression=compression)
# 3. metadata
for key, value in meta.items():
try:
f.attrs[key] = str(value)
except:
f.attrs[key] = str(value.encode('utf-8'))
# write attributes in dataset level
if ds_unit_dict is not None:
for key, value in ds_unit_dict.items():
if key in f.keys() and value is not None:
f[key].attrs['UNIT'] = value
vprint(f'add /{key:<{maxDigit}} attribute: UNIT = {value}')
vprint(f'finished writing to {out_file}')
# ISCE / ROI_PAC GAMMA / Image product
else:
# basic info
key_list = list(datasetDict.keys())
data_list = list(datasetDict.values())
meta['BANDS'] = len(key_list)
meta['INTERLEAVE'] = meta.get('INTERLEAVE', 'BIL').upper()
# data type
meta['DATA_TYPE'] = meta.get('DATA_TYPE', 'float32').lower()
DATA_TYPE_DICT = {'float' : 'float32',
'short' : 'int16',
'byte' : 'int8'}
if meta['DATA_TYPE'] in DATA_TYPE_DICT.keys():
meta['DATA_TYPE'] = DATA_TYPE_DICT[meta['DATA_TYPE']]
# adjust for pre-defined files determined by fext
if fext in ['.unw']:
meta['DATA_TYPE'] = 'float32'
meta['INTERLEAVE'] = 'BIL'
if key_list != ['magnitude', 'phase']:
data_list = [data_list[0], data_list[0]]
meta['BANDS'] = 2
elif fext in ['.cor']:
# remove .hgt as it can be float64 in isce2.
meta['DATA_TYPE'] = 'float32'
meta['INTERLEAVE'] = 'BIL'
if meta.get('PROCESSOR', 'isce') == 'roipac':
data_list = [data_list[0], data_list[0]]
elif fext == '.dem':
meta['DATA_TYPE'] = 'int16'
elif fext in ['.trans']:
# ROI_PAC lookup table
meta['DATA_TYPE'] = 'float32'
meta['INTERLEAVE'] = 'BIL'
elif fext.endswith(('to_rdc', '2_rdc', '2rdc')):
# Gamma lookup table
meta['BANDS'] = 2
meta['DATA_TYPE'] = 'float32'
meta['INTERLEAVE'] = 'BIP'
elif fext in ['.mli', '.flt']:
meta['DATA_TYPE'] = 'float32'
elif fext == '.slc':
# SLC: complex 64 or 32
meta['BANDS'] = 1
elif fext == '.int':
# wrapped interferogram: complex 64
meta['BANDS'] = 1
meta['DATA_TYPE'] = 'complex64'
if key_list == ['magnitude', 'phase']:
data_list[0] = data_list[0] * np.exp(1j * data_list[1])
elif fext == '.msk':
meta['DATA_TYPE'] = 'int8'
data_types = ['bool', 'int8', 'uint8', 'int16', 'float32', 'float64', 'complex32', 'complex64', 'complex128']
if meta['DATA_TYPE'] not in data_types:
msg = 'Un-supported file type "{}" with data type "{}"!'.format(fext, meta['DATA_TYPE'])
msg += f'\nSupported data type list: {data_types}'
raise ValueError(msg)
# write binary file
write_binary(data_list, out_file, data_type=meta['DATA_TYPE'], interleave=meta['INTERLEAVE'])
vprint(f'write file: {out_file}')
# write metadata file
write_roipac_rsc(meta, out_file+'.rsc', print_msg=print_msg)
return out_file
#########################################################################
def layout_hdf5(fname, ds_name_dict=None, metadata=None, ds_unit_dict=None, ref_file=None, compression=None, print_msg=True):
"""Create HDF5 file with defined metadata and (empty) dataset structure
Parameters: fname - str, HDF5 file path
ds_name_dict - dict, dataset structure definition
{dname : [dtype, dshape],
dname : [dtype, dshape, None],
dname : [dtype, dshape, 1/2/3/4D np.ndarray], #for aux data
...
}
metadata - dict, metadata
ds_unit_dict - dict, top-level dataset unit definition
{dname : dunit,
dname : dunit,
...
}
ref_file - str, reference file for the data structure
compression - str, HDF5 compression type
Returns: fname - str, HDF5 file path
Example: layout_hdf5('timeseries_ERA5.h5', ref_file='timeseries.h5')
layout_hdf5('timeseries_ERA5.5h', ds_name_dict, metadata)
# structure for ifgramStack
ds_name_dict = {
"date" : [np.dtype('S8'), (num_ifgram, 2)],
"dropIfgram" : [np.bool_, (num_ifgram,)],
"bperp" : [np.float32, (num_ifgram,)],
"unwrapPhase" : [np.float32, (num_ifgram, length, width)],
"coherence" : [np.float32, (num_ifgram, length, width)],
"connectComponent" : [np.int16, (num_ifgram, length, width)],
}
# structure for geometry
ds_name_dict = {
"height" : [np.float32, (length, width), None],
"incidenceAngle" : [np.float32, (length, width), None],
"slantRangeDistance" : [np.float32, (length, width), None],
}
# structure for timeseries
dates = np.array(date_list, np.bytes_)
ds_name_dict = {
"date" : [np.dtype("S8"), (num_date,), dates],
"bperp" : [np.float32, (num_date,), pbase],
"timeseries" : [np.float32, (num_date, length, width)],
}
"""
vprint = print if print_msg else lambda *args, **kwargs: None
vprint('-'*50)
# get meta from metadata and ref_file
if metadata:
meta = {key: value for key, value in metadata.items()}
elif ref_file:
with h5py.File(ref_file, 'r') as fr:
meta = {key: value for key, value in fr.attrs.items()}
vprint(f'grab metadata from ref_file: {ref_file}')
else:
raise ValueError('No metadata or ref_file found.')
# check ds_name_dict
if ds_name_dict is None:
if not ref_file or not os.path.isfile(ref_file):
raise FileNotFoundError('No ds_name_dict or ref_file found!')
else:
vprint(f'grab dataset structure from ref_file: {ref_file}')
ds_name_dict = {}
fext = os.path.splitext(ref_file)[1]
shape2d = (int(meta['LENGTH']), int(meta['WIDTH']))
if fext in ['.h5', '.he5']:
# copy dset structure from HDF5 file
with h5py.File(ref_file, 'r') as fr:
# in case output mat size is different from the input ref file mat size
shape2d_orig = (int(fr.attrs['LENGTH']), int(fr.attrs['WIDTH']))
for key in fr.keys():
ds = fr[key]
if isinstance(ds, h5py.Dataset):
# auxliary dataset
if ds.shape[-2:] != shape2d_orig:
ds_name_dict[key] = [ds.dtype, ds.shape, ds[:]]
# dataset
else:
ds_shape = list(ds.shape)
ds_shape[-2:] = shape2d
ds_name_dict[key] = [ds.dtype, tuple(ds_shape), None]
else:
# construct dset structure from binary file
ds_names = readfile.get_slice_list(ref_file)
ds_dtype = meta['DATA_TYPE']
for ds_name in ds_names:
ds_name_dict[ds_name] = [ds_dtype, tuple(shape2d), None]
# directory
fdir = os.path.dirname(os.path.abspath(fname))
if not os.path.isdir(fdir):
os.makedirs(fdir)
vprint(f'crerate directory: {fdir}')
# create file
with h5py.File(fname, "w") as f:
vprint(f'create HDF5 file: {fname} with w mode')
# initiate dataset
max_digit = max(len(i) for i in ds_name_dict.keys())
for key in ds_name_dict.keys():
data_type = ds_name_dict[key][0]
data_shape = ds_name_dict[key][1]
# turn ON compression for conn comp
ds_comp = compression
if key in ['connectComponent']:
ds_comp = 'lzf'
# changeable dataset shape
if len(data_shape) == 3:
max_shape = (None, data_shape[1], data_shape[2])
else:
max_shape = data_shape
# create empty dataset
vprint(("create dataset : {d:<{w}} of {t:<25} in size of {s:<20} with "
"compression = {c}").format(d=key,
w=max_digit,
t=str(data_type),
s=str(data_shape),
c=ds_comp))
ds = f.create_dataset(key,
shape=data_shape,
maxshape=max_shape,
dtype=data_type,
chunks=True,
compression=ds_comp)
# write auxliary data
if len(ds_name_dict[key]) > 2 and ds_name_dict[key][2] is not None:
ds[:] = np.array(ds_name_dict[key][2])
# write attributes in root level
for key, value in meta.items():
f.attrs[key] = str(value)
# write attributes in dataset level
if ds_unit_dict is not None:
for key, value in ds_unit_dict.items():
if key in f.keys() and value is not None:
f[key].attrs['UNIT'] = value
vprint(f'add /{key:<{max_digit}} attribute: UNIT = {value}')
vprint(f'close HDF5 file: {fname}')
return fname
def write_hdf5_block(fname, data, datasetName, block=None, mode='a', print_msg=True):
"""Write data to existing HDF5 dataset in disk block by block.
Parameters: data - np.ndarray 1/2/3/4D matrix
datasetName - str, dataset name
block - list of 2/4/6/8 int, for
[d1Start, d1End,
d2Start, d2End,
yStart, yEnd,
xStart, xEnd]
mode - str, open mode
Returns: fname
"""
# default block value
if block is None:
# data shape
if isinstance(data, list):
shape=(len(data),)
else:
shape = data.shape
# set default block as the entire data
if len(shape) ==1:
block = [0, shape[0]]
elif len(shape) == 2:
block = [0, shape[0],
0, shape[1]]
elif len(shape) == 3:
block = [0, shape[0],
0, shape[1],
0, shape[2]]
elif len(shape) == 4:
block = [0, shape[0],
0, shape[1],
0, shape[2],
0, shape[3]]
# write
if print_msg:
print('-'*50)
print(f'open HDF5 file {fname} in {mode} mode')
print(f"writing dataset /{datasetName:<25} block: {block}")
with h5py.File(fname, mode) as f:
if len(block) == 8:
f[datasetName][block[0]:block[1],
block[2]:block[3],
block[4]:block[5],
block[6]:block[7]] = data
elif len(block) == 6:
f[datasetName][block[0]:block[1],
block[2]:block[3],
block[4]:block[5]] = data
elif len(block) == 4:
f[datasetName][block[0]:block[1],
block[2]:block[3]] = data
elif len(block) == 2:
f[datasetName][block[0]:block[1]] = data
if print_msg:
print(f'close HDF5 file {fname}.')
return fname
def remove_hdf5_dataset(fname, datasetNames, print_msg=True):
"""Remove an existing dataset from an HDF5 file.
Parameters: fname : str, HDF5 file name/path
datasetName : (list of) str, dataset name(s)
Returns: fname : str,
Example: remove_hdf5_dataset('./inputs/ifgramStack.h5', 'unwrapPhase_phaseClosure')
remove_hdf5_dataset('./inputs/ifgramStack.h5', ['unwrapPhase_phaseClosure',
'unwrapPhase_bridging'])
"""
vprint = print if print_msg else lambda *args, **kwargs: None
if isinstance(datasetNames, str):
datasetNames = list(datasetNames)
vprint(f'delete {datasetNames} from file {fname}')
# 1. rename the file to a temporary file
temp_file = os.path.join(os.path.dirname(fname), f'tmp_{os.path.basename(fname)}')
shutil.move(fname, temp_file)
vprint(f'move {fname} to {temp_file}')
# 2. write a new file with all data except for the one to be deleted
vprint(f'read HDF5 file: {temp_file} with r mode')
vprint(f'create HDF5 file: {fname} with w mode')
with h5py.File(temp_file, 'r') as fi:
with h5py.File(fname, 'w') as fo:
# datasets
compression = None
maxDigit = max(len(i) for i in list(fi.keys()))
for dsName in [i for i in fi.keys() if i not in datasetNames]:
ds = fi[dsName]
msg = f'create dataset /{dsName:<{maxDigit}} of {str(ds.dtype):<10}'
msg += f' in size of {str(ds.shape):<20} with compression={compression}'
vprint(msg)
fo.create_dataset(dsName, data=ds[:], chunks=True, compression=compression)
# metadata
for key, value in fi.attrs.items():
fo.attrs[key] = str(value)
vprint(f'finished writing to {fname}')
vprint(f'old file is now saved as: {temp_file}. Use rm command to delete it.')
return fname
#########################################################################
def write_roipac_rsc(metadata, out_file, update_mode=False, print_msg=False):
"""Write attribute dict into ROI_PAC .rsc file
Inputs:
metadata : dict, attributes dictionary
out_file : rsc file name, to which attribute is written
update_mode : bool, skip writing if
1) output file existed AND
2) no new metadata key/value
print_msg : bool, print message
Output:
out_file
"""
run = True
if update_mode:
rsc_dict = dict()
if os.path.isfile(out_file):
rsc_dict = readfile.read_roipac_rsc(out_file)
# update .rsc file only if there are new metadata key/value
if set(metadata.items()).issubset(set(rsc_dict.items())):
run = False
if run:
# Convert MintPy attributes to ROI_PAC attributes
if 'LENGTH' in metadata.keys():
metadata['FILE_LENGTH'] = metadata['LENGTH']
# Convert 3.333e-4 to 0.0003333
if 'X_STEP' in metadata.keys():
metadata['X_STEP'] = str(float(metadata['X_STEP']))
metadata['Y_STEP'] = str(float(metadata['Y_STEP']))
metadata['X_FIRST'] = str(float(metadata['X_FIRST']))
metadata['Y_FIRST'] = str(float(metadata['Y_FIRST']))
# writing .rsc file
if print_msg:
print(f'write file: {out_file}')
maxDigit = max([len(key) for key in metadata.keys()]+[2])
with open(out_file, 'w') as f:
for key in sorted(metadata.keys()):
f.write('{k:<{d}} {v}\n'.format(k=str(key),
d=maxDigit,
v=str(metadata[key])))
return out_file
def write_gdal_vrt(meta, out_file):
"""Write GDAL VRT file.
!!! This function is NOT RIGHT. DO NOT USE IT. Keep here as a placeholder ONLY. !!!
It needs more work.
Parameters: meta - dict, dictionary of metadata
out_file - str, VRT file name to which attributes are written
"""
# data type: mintpy to gdal
dtype_dict = {
'int8' : 'Byte',
'int16' : 'Int16',
'float32' : 'Float32',
'float64' : 'Float64',
'complex64' : 'CFloat32',
'complex128': 'CFloat64',
}
# pixel / line / image offset
pixel_offset_dict = {
'int8' : '2',
'int16' : '4',
'float32' : '8',
'float64' : '16',
'complex64' : '16',
'complex128': '32',
}
pixel_offset = int(pixel_offset_dict[meta['DATA_TYPE']])
length, width = int(meta['LENGTH']), int(meta['WIDTH'])
num_band = int(meta['BANDS'])
interleave = meta['INTERLEAVE']
if interleave == 'BIP':
line_offset = pixel_offset * num_band * width
image_offset = pixel_offset
elif interleave == 'BIL':
line_offset = pixel_offset * width * num_band
image_offset = pixel_offset * width
elif interleave == 'BSQ':
line_offset = pixel_offset * width
image_offset = pixel_offset * width * length
else:
raise ValueError(f'un-recognized band interleave type: {interleave}')
# compose VRT file string
ds_str = '<VRTDataset rasterXSize="{w}" rasterYSize="{l}">\n'.format(w=meta['WIDTH'], l=meta['LENGTH'])
for band in range(num_band):
band_str = '''<VRTRasterBand dataType="{d}" band="{b}" subClass="VRTRawRasterBand">
<SourceFilename relativeToVRT="1">{f}</SourceFilename>
<ByteOrder>LSB</ByteOrder>
<ImageOffset>{io}</ImageOffset>
<PixelOffset>{po}</PixelOffset>
<LineOffset>{lo}</LineOffset>
</VRTRasterBand>
'''.format(
d=dtype_dict[meta['DATA_TYPE']],
b=band+1,
f=os.path.basename(out_file[:-4]),
io=image_offset * band,
po=pixel_offset,
lo=line_offset,
)
ds_str += band_str
ds_str += '</VRTDataset>\n'
# write VRT file
with open(out_file, 'w') as f:
f.write(ds_str)
return out_file
def write_isce_xml(meta, fname, print_msg=True):
"""Write XML metadata file in ISCE-2 format
Parameters: meta - dict, attributes dictionary
fname - str, path of data file, not the metadata file
print_msg - bool, print out message
Examples: write_isce_xml(atr, fname='filt_fine.cor')
"""
import isce
import isceobj
# data type
dtype = readfile.DATA_TYPE_NUMPY2ISCE[meta['DATA_TYPE']]
# write ISCE XML and GDAL VRT files
image_type = meta['FILE_TYPE']
if not image_type: img = isceobj.Image.createImage()
elif image_type == '.slc': img = isceobj.Image.createSlcImage()
elif image_type == '.unw': img = isceobj.Image.createUnwImage()
elif image_type == '.int': img = isceobj.Image.createIntImage()
else: img = isceobj.Image.createImage()
img.setFilename(fname)
img.setWidth(int(meta['WIDTH']))
img.setLength(int(meta['LENGTH']))
img.setAccessMode('READ')
img.bands = int(meta.get('BANDS', '1'))
img.dataType = dtype
img.scheme = meta.get('INTERLEAVE', 'BIL')
img.renderHdr()
img.renderVRT()
if print_msg:
print(f'write file: {fname}.xml')
print(f'write file: {fname}.vrt')
return
def write_isce_file(data, out_file, file_type='isce_unw', print_msg=True):
"""write data to file in ISCE format
Parameters: data - 2D np.ndarray, binary data matrix
out_file - str, path of output binary data file
file_type - str, file type
Returns: out_file - str, path of output binary data file
"""
# fix potential typo
file_type = file_type.replace('-', '_')
# write data to binary file
data.tofile(out_file)
if print_msg:
print(f'write file: {out_file}')
# write isce xml metadata file
length, width = data.shape
meta = {
'LENGTH' : length,
'WIDTH' : width,
}
if file_type == 'isce_unw':
meta['FILE_TYPE'] = '.unw'
meta['BANDS'] = 2
meta['DATA_TYPE'] = 'float32'
meta['INTERLEAVE'] = 'BIL'
meta['WIDTH'] = int(width / 2)
elif file_type == 'isce_int':
meta['FILE_TYPE'] = '.int'
meta['BANDS'] = 1
meta['DATA_TYPE'] = 'complex64'
meta['INTERLEAVE'] = 'BIL'
elif file_type == 'isce_cor':
meta['FILE_TYPE'] = '.cor'
meta['BANDS'] = 1
meta['DATA_TYPE'] = 'float32'
meta['INTERLEAVE'] = 'BIL'
elif file_type == 'isce_slc':
meta['FILE_TYPE'] = '.slc'
meta['BANDS'] = 1
meta['DATA_TYPE'] = 'complex64'
meta['INTERLEAVE'] = 'BIP'
else:
raise ValueError(f'un-recognized ISCE file type: {file_type}')
write_isce_xml(meta, out_file, print_msg=print_msg)
return out_file
#########################################################################
def write_binary(data_list, out_file, data_type=None, interleave='BIL'):
"""Write binary file.
Parameters: data_list - list of 2D np.ndarray matrices
out_file - str, path of the output binary file
data_type - str/np.dtype, numpy data type object
interleave - str, band interleave type, BSQ, BIL, BIP
Returns: out_file - str, path of the output binary file
"""
# data type
if data_type:
data_type = data_type.lower()
if data_type != data_list[0].dtype:
data_list = [np.array(x, dtype=data_type) for x in data_list]
# stack multi-band - reshape
interleave = interleave.upper()
if interleave == 'BIP':
data_list = [x.reshape(-1,1) for x in data_list]
elif interleave == 'BSQ':
data_list = [x.reshape(1,-1) for x in data_list]
# stack multi-band - stack
data = data_list[0]
for datai in data_list[1:]:
data = np.hstack((data, datai))
# write to file
data.tofile(out_file)
return out_file
######################## Obsolete functions #############################
def write_float32(*args):
"""Write ROI_PAC rmg format with float32 precision (BIL)
Format of the binary file is same as roi_pac unw, cor, or hgt data.
should rename to write_rmg_float32()
Example:
write_float32(phase, out_file)
write_float32(amp, phase, out_file)
"""
if len(args) == 2:
amp = args[0]
pha = args[0]
out_file = args[1]
elif len(args) == 3:
amp = args[0]
pha = args[1]
out_file = args[2]
else:
print('Error while getting args: support 2/3 args only.')
return
data = np.hstack((amp, pha)).flatten()
data = np.array(data, dtype=np.float32)
data.tofile(out_file)
return out_file
def write_complex_float32(data, out_file):
"""write complex float32 data into file"""
data = np.array(data, dtype=np.complex64)
data.tofile(out_file)
return out_file
#def write_complex_float32(data, out_file):
# """Writes roi_pac .int data"""
# num_pixel = data.size
# F = np.zeros([2 * num_pixel, 1], np.float32)
# id1 = list(range(0, 2 * num_pixel, 2))
# id2 = list(range(1, 2 * num_pixel, 2))
# F[id1] = np.reshape(np.cos(data), (num_pixel, 1))
# F[id2] = np.reshape(np.sin(data), (num_pixel, 1))
# F.tofile(out_file)
# return out_file
def write_complex_int16(data, out_file):
"""Write gamma scomplex data, i.e. .slc file.
data is complex 2-D matrix
real, imagery, real, ...
Write in this way, because numpy does not have complex int16 directly.
"""
num_pixel = data.size
id1 = list(range(0, 2 * num_pixel, 2))
id2 = list(range(1, 2 * num_pixel, 2))
F = np.zeros([2 * num_pixel, 1], np.int16)
F[id1] = np.reshape(np.array(data.real, np.int16), (num_pixel, 1))
F[id2] = np.reshape(np.array(data.imag, np.int16), (num_pixel, 1))
F.tofile(out_file)
return out_file
def write_real_float64(data, out_file):
"""write isce float data, i.e. hgt.rdr file."""
data = np.array(data, dtype=np.float64)
data.tofile(out_file)
return out_file
def write_real_float32(data, out_file):
"""write gamma float data, i.e. .mli file."""
data = np.array(data, dtype=np.float32)
data.tofile(out_file)
return out_file
def write_real_int16(data, out_file):
data = np.array(data, dtype=np.int16)
data.tofile(out_file)
return out_file
def write_byte(data, out_file):
data = np.array(data, dtype=np.byte)
data.tofile(out_file)
return out_file
def write_bool(data, out_file):
data = np.array(data, dtype=np.bool_)
data.tofile(out_file)
return out_file
#########################################################################