def test_filter(self):
shape = (32 * 1024 + 783,)
chunks = (4 * 1024 + 23,)
dtype = np.int64
data = np.arange(shape[0])
fname = "tmp_test_filters.h5"
f = h5py.File(fname)
h5.create_dataset(
f,
b"range",
shape,
dtype,
chunks,
filter_pipeline=(32008, 32000),
filter_flags=(h5z.FLAG_MANDATORY, h5z.FLAG_MANDATORY),
filter_opts=None,
)
f["range"][:] = data
f.close()
f = h5py.File(fname, "r")
d = f["range"][:]
self.assertTrue(np.all(d == data))
f.close()
def test_with_block_size(self):
shape = (128 * 1024 + 783, )
chunks = (4 * 1024 + 23, )
dtype = np.int64
data = np.arange(shape[0])
fname = "tmp_test_filters.h5"
f = h5py.File(fname)
h5.create_dataset(
f,
b"range",
shape,
dtype,
chunks,
filter_pipeline=(32008, 32000),
filter_flags=(h5z.FLAG_MANDATORY, h5z.FLAG_MANDATORY),
filter_opts=((680, ), ()),
)
f["range"][:] = data
f.close()
#os.system('h5dump -H -p tmp_test_filters.h5')
f = h5py.File(fname, 'r')
d = f['range'][:]
self.assertTrue(np.all(d == data))
f.close()
def test_filter(self):
shape = (32 * 1024 + 783, )
chunks = (4 * 1024 + 23, )
dtype = np.int64
data = np.arange(shape[0])
fname = "tmp_test_filters.h5"
f = h5py.File(fname, "w")
h5.create_dataset(
f,
b"range",
shape,
dtype,
chunks,
filter_pipeline=(32008, 32000),
filter_flags=(h5z.FLAG_MANDATORY, h5z.FLAG_MANDATORY),
filter_opts=None,
)
f["range"][:] = data
f.close()
f = h5py.File(fname, "r")
d = f["range"][:]
self.assertTrue(np.all(d == data))
f.close()
def test_with_lz4_compression(self):
shape = (128 * 1024 + 783, )
chunks = (4 * 1024 + 23, )
dtype = np.int64
data = np.arange(shape[0])
fname = "tmp_test_filters.h5"
f = h5py.File(fname, "w")
h5.create_dataset(
f,
b"range",
shape,
dtype,
chunks,
filter_pipeline=(32008, ),
filter_flags=(h5z.FLAG_MANDATORY, ),
filter_opts=((0, h5.H5_COMPRESS_LZ4), ),
)
f["range"][:] = data
f.close()
# os.system('h5dump -H -p tmp_test_filters.h5')
f = h5py.File(fname, "r")
d = f["range"][:]
self.assertTrue(np.all(d == data))
f.close()
def test_with_compression(self):
shape = (128 * 1024 + 783,)
chunks = (4 * 1024 + 23,)
dtype = np.int64
data = np.arange(shape[0])
fname = "tmp_test_filters.h5"
f = h5py.File(fname)
h5.create_dataset(
f,
b"range",
shape,
dtype,
chunks,
filter_pipeline=(32008,),
filter_flags=(h5z.FLAG_MANDATORY,),
filter_opts=((0, h5.H5_COMPRESS_LZ4),),
)
f["range"][:] = data
f.close()
# os.system('h5dump -H -p tmp_test_filters.h5')
f = h5py.File(fname, "r")
d = f["range"][:]
self.assertTrue(np.all(d == data))
f.close()
def __write_to_hdf5_light(self, filename_out, *args, **kwargs):
""" Write data to HDF5 file in one go.
Args:
filename_out (str): Name of output file
"""
block_size = 0
with h5py.File(filename_out, 'w') as h5:
h5.attrs[b'CLASS'] = b'FILTERBANK'
h5.attrs[b'VERSION'] = b'1.0'
if HAS_BITSHUFFLE:
bs_compression = bitshuffle.h5.H5FILTER
bs_compression_opts = (block_size,
bitshuffle.h5.H5_COMPRESS_LZ4)
else:
bs_compression = None
bs_compression_opts = None
logger.warning(
"Warning: bitshuffle not found. No compression applied.")
dset = h5.create_dataset(
'data',
data=self.data,
# compression='lzf')
compression=bs_compression,
compression_opts=bs_compression_opts)
dset_mask = h5.create_dataset(
'mask',
shape=self.file_shape,
# compression='lzf',
compression=bs_compression,
compression_opts=bs_compression_opts,
dtype='uint8')
dset.dims[0].label = b"frequency"
dset.dims[1].label = b"feed_id"
dset.dims[2].label = b"time"
dset_mask.dims[0].label = b"frequency"
dset_mask.dims[1].label = b"feed_id"
dset_mask.dims[2].label = b"time"
# Copy over header information as attributes
for key, value in self.header.items():
dset.attrs[key] = value
def __write_to_hdf5_light(self, filename_out, *args, **kwargs):
""" Write data to HDF5 file in one go.
Args:
filename_out (str): Name of output file
"""
block_size = 0
with h5py.File(filename_out, 'w') as h5:
h5.attrs[b'CLASS'] = b'FILTERBANK'
h5.attrs[b'VERSION'] = b'1.0'
if HAS_BITSHUFFLE:
bs_compression = bitshuffle.h5.H5FILTER
bs_compression_opts = (block_size, bitshuffle.h5.H5_COMPRESS_LZ4)
else:
bs_compression = None
bs_compression_opts = None
logger.warning("Warning: bitshuffle not found. No compression applied.")
dset = h5.create_dataset('data',
data=self.data,
# compression='lzf')
compression=bs_compression,
compression_opts=bs_compression_opts)
dset_mask = h5.create_dataset('mask',
shape=self.file_shape,
# compression='lzf',
compression=bs_compression,
compression_opts=bs_compression_opts,
dtype='uint8')
dset.dims[0].label = b"frequency"
dset.dims[1].label = b"feed_id"
dset.dims[2].label = b"time"
dset_mask.dims[0].label = b"frequency"
dset_mask.dims[1].label = b"feed_id"
dset_mask.dims[2].label = b"time"
# Copy over header information as attributes
for key, value in self.header.items():
dset.attrs[key] = value
def create_compressed(hgroup, name, data, **kwargs):
"""
Add a compressed dataset to a given group.
Use bitshuffle compression and LZ4 to compress a dataset.
hgroup: h5py group in which to add dataset
name: name of dataset
data: data to write
chunks: chunk size
"""
# Check explicitly for bitshuffle, as it is not part of h5py
compression = ''
if 'compression' in kwargs:
compression = kwargs['compression']
#print name, shape, dtype, chunks
if compression == 'bitshuffle' and USE_BITSHUFFLE:
if 'chunks' not in kwargs:
kwargs['chunks'] = guess_chunk(data.shape)
chunks = kwargs['chunks']
#print "Creating bitshuffled dataset %s" % hgroup
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008, ),
filter_flags=(h5z.FLAG_MANDATORY, ),
filter_opts=((0, h5.H5_COMPRESS_LZ4), ),
)
else:
#print "Creating dataset %s" % hgroup
hgroup.create_dataset(name, data.shape, data.dtype, **kwargs)
hgroup[name][:] = data
return hgroup[name]
def test_with_block_size(self):
shape = (128 * 1024 + 783,)
chunks = (4 * 1024 + 23,)
dtype = np.int64
data = np.arange(shape[0])
fname = "tmp_test_filters.h5"
f = h5py.File(fname)
h5.create_dataset(f, "range", shape, dtype, chunks,
filter_pipeline=(32008, 32000),
filter_flags=(h5z.FLAG_MANDATORY, h5z.FLAG_MANDATORY),
filter_opts=((680,), ()),
)
f["range"][:] = data
f.close()
#os.system('h5dump -H -p tmp_test_filters.h5')
f = h5py.File(fname, 'r')
d = f['range'][:]
self.assertTrue(np.all(d == data))
f.close()
def create_compressed(hgroup, name, data, **kwargs):
"""
Add a compressed dataset to a given group.
Use bitshuffle compression and LZ4 to compress a dataset.
hgroup: h5py group in which to add dataset
name: name of dataset
data: data to write
chunks: chunk size
"""
# Check explicitly for bitshuffle, as it is not part of h5py
compression = ''
if 'compression' in kwargs:
compression = kwargs['compression']
#print name, shape, dtype, chunks
if compression == 'bitshuffle' and USE_BITSHUFFLE:
if 'chunks' not in kwargs:
kwargs['chunks'] = guess_chunk(data.shape)
chunks = kwargs['chunks']
#print "Creating bitshuffled dataset %s" % hgroup
h5.create_dataset(hgroup, name, data.shape, data.dtype, chunks,
filter_pipeline=(32008,),
filter_flags=(h5z.FLAG_MANDATORY,),
filter_opts=((0, h5.H5_COMPRESS_LZ4),),
)
else:
#print "Creating dataset %s" % hgroup
hgroup.create_dataset(name, data.shape, data.dtype, **kwargs)
hgroup[name][:] = data
return hgroup[name]
def create_compressed(hgroup, name, data, **kwargs):
"""
Add a compressed dataset to a given group.
Use bitshuffle compression and LZ4 to compress a dataset.
hgroup: h5py group in which to add dataset
name: name of dataset
data: data to write
chunks: chunk size
"""
# Parse keyword arguments that we need to check
compression = ''
if 'compression' in kwargs:
compression = kwargs['compression']
if compression is None:
compression = ''
if 'chunks' not in kwargs:
kwargs['chunks'] = guess_chunk(data.shape)
chunks = kwargs['chunks']
#print name, shape, dtype, chunks
if compression.startswith('quinoa') and USE_BITSHUFFLE:
q = 4
do_dither = True
try:
cparts = compression.split('_')
q = int(cparts[1])
do_dither = bool(cparts[2])
#print cparts
except:
pass
if data.ndim == 2:
print("QUINOA: scaling %s " % name)
qdata = quinoa.quinoa_scale(data,
q=q,
subtractive_dither=do_dither)
data = qdata["data"]
#data = quinoa.quinoa_unscale(qdata)
#dtype = "int32"
for key in qdata:
if key != 'data':
print("QUINOA: %s: %s" % (key, qdata[key]))
#print "Creating bitshuffled dataset %s" % hgroup
print(data.dtype)
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008, ),
filter_flags=(h5z.FLAG_MANDATORY, ),
filter_opts=((0, h5.H5_COMPRESS_LZ4), ),
)
elif compression == 'couscous' and USE_BITSHUFFLE:
qdata = quinoa.couscous_scale(data)
data = qdata["data"]
#for key in qdata:
# if key != 'data':
# print "COUSCOUS: %s: %s" % (key, qdata[key])
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008, ),
filter_flags=(h5z.FLAG_MANDATORY, ),
filter_opts=((0, h5.H5_COMPRESS_LZ4), ),
)
elif compression == 'bitshuffle' and USE_BITSHUFFLE:
#print "Creating bitshuffled dataset %s" % hgroup
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008, ),
filter_flags=(h5z.FLAG_MANDATORY, ),
filter_opts=((0, h5.H5_COMPRESS_LZ4), ),
)
else:
#print "Creating dataset %s" % hgroup
hgroup.create_dataset(name, data.shape, data.dtype, **kwargs)
hgroup[name][:] = data
return hgroup[name]
OUT_FILE = "bitshuffle/tests/data/regression_%s.h5" % bitshuffle.__version__
DTYPES = ['a1', 'a2', 'a3', 'a4', 'a6', 'a8', 'a10']
f = h5py.File(OUT_FILE, 'w')
g_comp = f.create_group("compressed")
g_orig = f.create_group("origional")
for dtype in DTYPES:
for rep in ['a', 'b', 'c']:
dset_name = "%s_%s" % (dtype, rep)
dtype = np.dtype(dtype)
n_elem = 3 * BLOCK_SIZE + random.randint(0, BLOCK_SIZE)
shape = (n_elem, )
chunks = shape
data = random.randint(0, 255, n_elem * dtype.itemsize)
data = data.astype(np.uint8).view(dtype)
g_orig.create_dataset(dset_name, data=data)
h5.create_dataset(g_comp,
dset_name,
shape,
dtype,
chunks=chunks,
filter_pipeline=FILTER_PIPELINE,
filter_opts=FILTER_OPTS)
g_comp[dset_name][:] = data
f.close()
def cmd_tool(args=None):
""" Command line tool for converting guppi raw into HDF5 versions of guppi raw """
from argparse import ArgumentParser
if not HAS_BITSHUFFLE:
print("Error: the bitshuffle library is required to run this script.")
exit()
parser = ArgumentParser(
description="Command line utility for creating HDF5 Raw files.")
parser.add_argument('filename', type=str, help='Name of filename to read')
args = parser.parse_args()
fileroot = args.filename.split('.0000.raw')[0]
filelist = glob.glob(fileroot + '*.raw')
filelist = sorted(filelist)
# Read first file
r = GuppiRaw(filelist[0])
header, data = r.read_next_data_block()
dshape = data.shape #r.read_next_data_block_shape()
print(dshape)
n_blocks_total = 0
for filename in filelist:
print(filename)
r = GuppiRaw(filename)
n_blocks_total += r.n_blocks
print(n_blocks_total)
full_dshape = np.concatenate(((n_blocks_total, ), dshape))
# Create h5py file
h5 = h5py.File(fileroot + '.h5', 'w')
h5.attrs['CLASS'] = 'GUPPIRAW'
block_size = 0 # This is chunk block size
dset = h5.create_dataset(
'data',
shape=full_dshape,
#compression=bitshuffle.h5.H5FILTER,
#compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),
dtype=data.dtype)
h5_idx = 0
for filename in filelist:
print("\nReading %s header..." % filename)
r = GuppiRaw(filename)
h5 = h5py.File(filename + '.h5', 'w')
header, data = r.read_next_data_block()
for ii in range(0, r.n_blocks):
t0 = time.time()
print("Reading block %i of %i" % (h5_idx + 1, full_dshape[0]))
header, data = r.read_next_data_block()
t1 = time.time()
t2 = time.time()
print("Writing block %i of %i" % (h5_idx + 1, full_dshape[0]))
dset[h5_idx, :] = data
t3 = time.time()
print("Read: %2.2fs, Write %2.2fs" % ((t1 - t0), (t3 - t2)))
h5_idx += 1
# Copy over header information as attributes
for key, value in header.items():
dset.attrs[key] = value
h5.close()
t1 = time.time()
print("Conversion time: %2.2fs" % (t1 - t0))
def cmd_tool(args=None):
""" Command line utility for creating HDF5 blimpy files. """
from argparse import ArgumentParser
parser = ArgumentParser(description="Command line utility for creating HDF5 Filterbank files.")
parser.add_argument('dirname', type=str, help='Name of directory to read')
args = parser.parse_args()
if not HAS_BITSHUFFLE:
print("Error: the bitshuffle library is required to run this script.")
exit()
filelist = glob.glob(os.path.join(args.dirname, '*.fil'))
for filename in filelist:
if not os.path.exists(filename + '.h5'):
t0 = time.time()
print("\nReading %s header..." % filename)
fb = Filterbank(filename, load_data=False)
data_shape = (fb.n_ints_in_file, fb.header['nifs'], fb.header['nchans'])
data_dtype = fb.data.dtype
print(data_dtype)
print("Creating new dataset, %s" % str(data_shape))
block_size = 0
h5 = h5py.File(filename + '.h5', 'w')
h5.attrs['CLASS'] = 'FILTERBANK'
dset = h5.create_dataset('data',
shape=data_shape,
compression=bitshuffle.h5.H5FILTER,
compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),
dtype=data_dtype)
dset_mask = h5.create_dataset('mask',
shape=data_shape,
compression=bitshuffle.h5.H5FILTER,
compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),
dtype='uint8')
dset.dims[0].label = "frequency"
dset.dims[1].label = "feed_id"
dset.dims[2].label = "time"
dset_mask.dims[0].label = "frequency"
dset_mask.dims[1].label = "feed_id"
dset_mask.dims[2].label = "time"
# Copy over header information as attributes
for key, value in fb.header.items():
dset.attrs[key] = value
filesize = os.path.getsize(filename)
if filesize >= MAX_SIZE:
n_int_per_read = int(filesize / MAX_SIZE / 2)
print("Filling in with data over %i reads..." % n_int_per_read)
for ii in range(0, n_int_per_read):
print("Reading %i of %i" % (ii + 1, n_int_per_read))
#print ii*n_int_per_read, (ii+1)*n_int_per_read
fb = Filterbank(filename, t_start=ii*n_int_per_read, t_stop=(ii+1) * n_int_per_read)
dset[ii*n_int_per_read:(ii+1)*n_int_per_read] = fb.data[:]
else:
fb = Filterbank(filename)
print(dset.shape, " -> ", fb.data.shape)
dset[:] = fb.data[:]
h5.close()
t1 = time.time()
print("Conversion time: %2.2fs" % (t1- t0))
BLOCK_SIZE = 64 # Smallish such that datasets have many blocks but are small.
FILTER_PIPELINE = [h5.H5FILTER,]
FILTER_OPTS = [(BLOCK_SIZE, h5.H5_COMPRESS_LZ4)]
OUT_FILE = "bitshuffle/tests/data/regression_%s.h5" % bitshuffle.__version__
DTYPES = ['a1', 'a2', 'a3', 'a4', 'a6', 'a8', 'a10']
f = h5py.File(OUT_FILE, 'w')
g_comp = f.create_group("compressed")
g_orig = f.create_group("origional")
for dtype in DTYPES:
for rep in ['a', 'b', 'c']:
dset_name = "%s_%s" % (dtype, rep)
dtype = np.dtype(dtype)
n_elem = 3 * BLOCK_SIZE + random.randint(0, BLOCK_SIZE)
shape = (n_elem,)
chunks = shape
data = random.randint(0, 255, n_elem * dtype.itemsize)
data = data.astype(np.uint8).view(dtype)
g_orig.create_dataset(dset_name, data=data)
h5.create_dataset(g_comp, dset_name, shape, dtype, chunks=chunks,
filter_pipeline=FILTER_PIPELINE, filter_opts=FILTER_OPTS)
g_comp[dset_name][:] = data
f.close()
dset_name = "%s_%s" % (dtype, rep)
dtype = np.dtype(dtype)
n_elem = 3 * BLOCK_SIZE + random.randint(0, BLOCK_SIZE)
shape = (n_elem, )
chunks = shape
data = random.randint(0, 255, n_elem * dtype.itemsize)
data = data.astype(np.uint8).view(dtype)
g_orig.create_dataset(dset_name, data=data)
# Create LZ4 compressed data
h5.create_dataset(
g_comp_lz4,
bytes(dset_name, "utf-8"),
shape,
dtype,
chunks=chunks,
filter_pipeline=FILTER_PIPELINE,
filter_flags=(h5z.FLAG_MANDATORY, ),
filter_opts=FILTER_OPTS[0],
)
g_comp_lz4[dset_name][:] = data
# Create ZSTD compressed data
h5.create_dataset(
g_comp_zstd,
bytes(dset_name, "utf-8"),
shape,
dtype,
chunks=chunks,
filter_pipeline=FILTER_PIPELINE,
filter_flags=(h5z.FLAG_MANDATORY, ),
def cmd_tool(args=None):
""" Command line utility for creating HDF5 blimpy files. """
from argparse import ArgumentParser
parser = ArgumentParser(
description="Command line utility for creating HDF5 Filterbank files.")
parser.add_argument('dirname', type=str, help='Name of directory to read')
args = parser.parse_args()
if not HAS_BITSHUFFLE:
print("Error: the bitshuffle library is required to run this script.")
exit()
filelist = glob.glob(os.path.join(args.dirname, '*.fil'))
for filename in filelist:
if not os.path.exists(filename + '.h5'):
t0 = time.time()
print("\nReading %s header..." % filename)
fb = Filterbank(filename, load_data=False)
data_shape = (fb.n_ints_in_file, fb.header['nifs'],
fb.header['nchans'])
data_dtype = fb.data.dtype
print(data_dtype)
print("Creating new dataset, %s" % str(data_shape))
block_size = 0
h5 = h5py.File(filename + '.h5', 'w')
h5.attrs['CLASS'] = 'FILTERBANK'
dset = h5.create_dataset(
'data',
shape=data_shape,
compression=bitshuffle.h5.H5FILTER,
compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),
dtype=data_dtype)
dset_mask = h5.create_dataset(
'mask',
shape=data_shape,
compression=bitshuffle.h5.H5FILTER,
compression_opts=(block_size, bitshuffle.h5.H5_COMPRESS_LZ4),
dtype='uint8')
dset.dims[0].label = "frequency"
dset.dims[1].label = "feed_id"
dset.dims[2].label = "time"
dset_mask.dims[0].label = "frequency"
dset_mask.dims[1].label = "feed_id"
dset_mask.dims[2].label = "time"
# Copy over header information as attributes
for key, value in fb.header.items():
dset.attrs[key] = value
filesize = os.path.getsize(filename)
if filesize >= MAX_SIZE:
n_int_per_read = int(filesize / MAX_SIZE / 2)
print("Filling in with data over %i reads..." % n_int_per_read)
for ii in range(0, n_int_per_read):
print("Reading %i of %i" % (ii + 1, n_int_per_read))
#print ii*n_int_per_read, (ii+1)*n_int_per_read
fb = Filterbank(filename,
t_start=ii * n_int_per_read,
t_stop=(ii + 1) * n_int_per_read)
dset[ii * n_int_per_read:(ii + 1) *
n_int_per_read] = fb.data[:]
else:
fb = Filterbank(filename)
print(dset.shape, " -> ", fb.data.shape)
dset[:] = fb.data[:]
h5.close()
t1 = time.time()
print("Conversion time: %2.2fs" % (t1 - t0))
def __write_to_hdf5_heavy(self, filename_out, *args, **kwargs):
""" Write data to HDF5 file.
Args:
filename_out (str): Name of output file
"""
block_size = 0
#Note that a chunk is not a blob!!
chunk_dim = self.__get_chunk_dimensions()
blob_dim = self.__get_blob_dimensions(chunk_dim)
n_blobs = self.container.calc_n_blobs(blob_dim)
with h5py.File(filename_out, 'w') as h5:
h5.attrs[b'CLASS'] = b'FILTERBANK'
h5.attrs[b'VERSION'] = b'1.0'
if HAS_BITSHUFFLE:
bs_compression = bitshuffle.h5.H5FILTER
bs_compression_opts = (block_size, bitshuffle.h5.H5_COMPRESS_LZ4)
else:
bs_compression = None
bs_compression_opts = None
logger.warning("Warning: bitshuffle not found. No compression applied.")
dset = h5.create_dataset('data',
shape=self.selection_shape,
chunks=chunk_dim,
compression=bs_compression,
compression_opts=bs_compression_opts,
dtype=self.data.dtype)
dset_mask = h5.create_dataset('mask',
shape=self.selection_shape,
chunks=chunk_dim,
compression=bs_compression,
compression_opts=bs_compression_opts,
dtype='uint8')
dset.dims[0].label = b"frequency"
dset.dims[1].label = b"feed_id"
dset.dims[2].label = b"time"
dset_mask.dims[0].label = b"frequency"
dset_mask.dims[1].label = b"feed_id"
dset_mask.dims[2].label = b"time"
# Copy over header information as attributes
for key, value in self.header.items():
dset.attrs[key] = value
if blob_dim[self.freq_axis] < self.selection_shape[self.freq_axis]:
logger.info('Using %i n_blobs to write the data.'% n_blobs)
for ii in range(0, n_blobs):
logger.info('Reading %i of %i' % (ii + 1, n_blobs))
bob = self.container.read_blob(blob_dim,n_blob=ii)
#-----
#Using channels instead of frequency.
c_start = self.container.chan_start_idx + ii*blob_dim[self.freq_axis]
t_start = self.container.t_start + (c_start/self.selection_shape[self.freq_axis])*blob_dim[self.time_axis]
t_stop = t_start + blob_dim[self.time_axis]
# Reverse array if frequency axis is flipped
# if self.header['foff'] < 0:
# c_stop = self.selection_shape[self.freq_axis] - (c_start)%self.selection_shape[self.freq_axis]
# c_start = c_stop - blob_dim[self.freq_axis]
# else:
c_start = (c_start)%self.selection_shape[self.freq_axis]
c_stop = c_start + blob_dim[self.freq_axis]
#-----
logger.debug(t_start,t_stop,c_start,c_stop)
dset[t_start:t_stop,0,c_start:c_stop] = bob[:]
else:
logger.info('Using %i n_blobs to write the data.'% n_blobs)
for ii in range(0, n_blobs):
logger.info('Reading %i of %i' % (ii + 1, n_blobs))
bob = self.container.read_blob(blob_dim,n_blob=ii)
t_start = self.container.t_start + ii*blob_dim[self.time_axis]
#This prevents issues when the last blob is smaller than the others in time
if (ii+1)*blob_dim[self.time_axis] > self.n_ints_in_file:
t_stop = self.n_ints_in_file
else:
t_stop = (ii+1)*blob_dim[self.time_axis]
dset[t_start:t_stop] = bob[:]
def create_compressed(hgroup, name, data, **kwargs):
"""
Add a compressed dataset to a given group.
Use bitshuffle compression and LZ4 to compress a dataset.
hgroup: h5py group in which to add dataset
name: name of dataset
data: data to write
chunks: chunk size
"""
# Parse keyword arguments that we need to check
compression = ""
if "compression" in kwargs:
compression = kwargs["compression"]
if compression is None:
compression = ""
if "chunks" not in kwargs:
kwargs["chunks"] = guess_chunk(data.shape)
chunks = kwargs["chunks"]
# print name, shape, dtype, chunks
if compression.startswith("quinoa") and USE_BITSHUFFLE:
q = 4
do_dither = True
try:
cparts = compression.split("_")
q = int(cparts[1])
do_dither = bool(cparts[2])
# print cparts
except:
pass
if data.ndim == 2:
print "QUINOA: scaling %s " % name
qdata = quinoa.quinoa_scale(data, q=q, subtractive_dither=do_dither)
data = qdata["data"]
# data = quinoa.quinoa_unscale(qdata)
# dtype = "int32"
for key in qdata:
if key != "data":
print "QUINOA: %s: %s" % (key, qdata[key])
# print "Creating bitshuffled dataset %s" % hgroup
print data.dtype
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008,),
filter_flags=(h5z.FLAG_MANDATORY,),
filter_opts=((0, h5.H5_COMPRESS_LZ4),),
)
elif compression == "couscous" and USE_BITSHUFFLE:
qdata = quinoa.couscous_scale(data)
data = qdata["data"]
# for key in qdata:
# if key != 'data':
# print "COUSCOUS: %s: %s" % (key, qdata[key])
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008,),
filter_flags=(h5z.FLAG_MANDATORY,),
filter_opts=((0, h5.H5_COMPRESS_LZ4),),
)
elif compression == "bitshuffle" and USE_BITSHUFFLE:
# print "Creating bitshuffled dataset %s" % hgroup
h5.create_dataset(
hgroup,
name,
data.shape,
data.dtype,
chunks,
filter_pipeline=(32008,),
filter_flags=(h5z.FLAG_MANDATORY,),
filter_opts=((0, h5.H5_COMPRESS_LZ4),),
)
else:
# print "Creating dataset %s" % hgroup
hgroup.create_dataset(name, data.shape, data.dtype, **kwargs)
hgroup[name][:] = data
return hgroup[name]
def __write_to_hdf5_heavy(self, filename_out, *args, **kwargs):
""" Write data to HDF5 file.
Args:
filename_out (str): Name of output file
"""
block_size = 0
#Note that a chunk is not a blob!!
chunk_dim = self.__get_chunk_dimensions()
blob_dim = self.__get_blob_dimensions(chunk_dim)
n_blobs = self.container.calc_n_blobs(blob_dim)
with h5py.File(filename_out, 'w') as h5:
h5.attrs[b'CLASS'] = b'FILTERBANK'
h5.attrs[b'VERSION'] = b'1.0'
if HAS_BITSHUFFLE:
bs_compression = bitshuffle.h5.H5FILTER
bs_compression_opts = (block_size, bitshuffle.h5.H5_COMPRESS_LZ4)
else:
bs_compression = None
bs_compression_opts = None
logger.warning("Warning: bitshuffle not found. No compression applied.")
dset = h5.create_dataset('data',
shape=self.selection_shape,
chunks=chunk_dim,
compression=bs_compression,
compression_opts=bs_compression_opts,
dtype=self.data.dtype)
dset_mask = h5.create_dataset('mask',
shape=self.selection_shape,
chunks=chunk_dim,
compression=bs_compression,
compression_opts=bs_compression_opts,
dtype='uint8')
dset.dims[0].label = b"frequency"
dset.dims[1].label = b"feed_id"
dset.dims[2].label = b"time"
dset_mask.dims[0].label = b"frequency"
dset_mask.dims[1].label = b"feed_id"
dset_mask.dims[2].label = b"time"
# Copy over header information as attributes
for key, value in self.header.items():
dset.attrs[key] = value
if blob_dim[self.freq_axis] < self.selection_shape[self.freq_axis]:
logger.info('Using %i n_blobs to write the data.'% n_blobs)
for ii in range(0, n_blobs):
logger.info('Reading %i of %i' % (ii + 1, n_blobs))
bob = self.container.read_blob(blob_dim,n_blob=ii)
#-----
#Using channels instead of frequency.
c_start = self.container.chan_start_idx + ii*blob_dim[self.freq_axis]
t_start = self.container.t_start + (c_start/self.selection_shape[self.freq_axis])*blob_dim[self.time_axis]
t_stop = t_start + blob_dim[self.time_axis]
# Reverse array if frequency axis is flipped
# if self.header['foff'] < 0:
# c_stop = self.selection_shape[self.freq_axis] - (c_start)%self.selection_shape[self.freq_axis]
# c_start = c_stop - blob_dim[self.freq_axis]
# else:
c_start = (c_start)%self.selection_shape[self.freq_axis]
c_stop = c_start + blob_dim[self.freq_axis]
#-----
logger.debug(t_start,t_stop,c_start,c_stop)
dset[t_start:t_stop,0,c_start:c_stop] = bob[:]
else:
logger.info('Using %i n_blobs to write the data.'% n_blobs)
for ii in range(0, n_blobs):
logger.info('Reading %i of %i' % (ii + 1, n_blobs))
bob = self.container.read_blob(blob_dim,n_blob=ii)
t_start = self.container.t_start + ii*blob_dim[self.time_axis]
#This prevents issues when the last blob is smaller than the others in time
if (ii+1)*blob_dim[self.time_axis] > self.n_ints_in_file:
t_stop = self.n_ints_in_file
else:
t_stop = (ii+1)*blob_dim[self.time_axis]
dset[t_start:t_stop] = bob[:]
