def savez(file, *args, **kwds):
__doc__ = numpy.savez.__doc__
import zipfile
from numpy.lib import format
if isinstance(file, basestring):
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
raise ValueError, "Cannot use un-named variables and keyword %s" % key
namedict[key] = val
zip = zipfile.ZipFile(file, mode="w")
# Place to write temporary .npy files
# before storing them in the zip. We need to path this to have a working
# function in parallel !
import tempfile
direc = tempfile.mkdtemp()
for key, val in namedict.iteritems():
fname = key + '.npy'
filename = os.path.join(direc, fname)
fid = open(filename, 'wb')
format.write_array(fid, numpy.asanyarray(val))
fid.close()
zip.write(filename, arcname=fname)
zip.close()
shutil.rmtree(direc)
def _pickle_array(arr):
arr = arr.view(np.ndarray)
buf = BytesIO()
write_array(buf, arr)
return buf.getvalue()
def roundtrip_truncated(arr):
f = BytesIO()
format.write_array(f, arr)
#BytesIO is one byte short
f2 = BytesIO(f.getvalue()[0:-1])
arr2 = format.read_array(f2)
return arr2
def test_memmap_roundtrip():
# Fixme: test crashes nose on windows.
if not (sys.platform == 'win32' or sys.platform == 'cygwin'):
for arr in basic_arrays + record_arrays:
if arr.dtype.hasobject:
# Skip these since they can't be mmap'ed.
continue
# Write it out normally and through mmap.
nfn = os.path.join(tempdir, 'normal.npy')
mfn = os.path.join(tempdir, 'memmap.npy')
fp = open(nfn, 'wb')
try:
format.write_array(fp, arr)
finally:
fp.close()
fortran_order = (
arr.flags.f_contiguous and not arr.flags.c_contiguous)
ma = format.open_memmap(mfn, mode='w+', dtype=arr.dtype,
shape=arr.shape, fortran_order=fortran_order)
ma[...] = arr
del ma
# Check that both of these files' contents are the same.
fp = open(nfn, 'rb')
normal_bytes = fp.read()
fp.close()
fp = open(mfn, 'rb')
memmap_bytes = fp.read()
fp.close()
yield assert_equal_, normal_bytes, memmap_bytes
# Check that reading the file using memmap works.
ma = format.open_memmap(nfn, mode='r')
del ma
def test_memmap_roundtrip():
# XXX: test crashes nose on windows. Fix this
if not (sys.platform == "win32" or sys.platform == "cygwin"):
for arr in basic_arrays + record_arrays:
if arr.dtype.hasobject:
# Skip these since they can't be mmap'ed.
continue
# Write it out normally and through mmap.
nfn = os.path.join(tempdir, "normal.npy")
mfn = os.path.join(tempdir, "memmap.npy")
fp = open(nfn, "wb")
try:
format.write_array(fp, arr)
finally:
fp.close()
fortran_order = arr.flags.f_contiguous and not arr.flags.c_contiguous
ma = format.open_memmap(mfn, mode="w+", dtype=arr.dtype, shape=arr.shape, fortran_order=fortran_order)
ma[...] = arr
del ma
# Check that both of these files' contents are the same.
fp = open(nfn, "rb")
normal_bytes = fp.read()
fp.close()
fp = open(mfn, "rb")
memmap_bytes = fp.read()
fp.close()
yield assert_equal, normal_bytes, memmap_bytes
# Check that reading the file using memmap works.
ma = format.open_memmap(nfn, mode="r")
# yield assert_array_equal, ma, arr
del ma
def savez(self, *args, **kwds):
import os
import numpy.lib.format as format
namedict = kwds
for val in args:
key = 'arr_%d' % self.i
if key in namedict.keys():
raise ValueError(
"Cannot use un-named variables and keyword %s" % key)
namedict[key] = val
self.i += 1
try:
for key, val in namedict.iteritems():
fname = key + '.npy'
fid = open(self.tmpfile, 'wb')
try:
format.write_array(fid, np.asanyarray(val))
fid.close()
fid = None
self.zip.write(self.tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(self.tmpfile)
def test_read_array_header_2_0():
s = BytesIO()
arr = np.ones((3, 6), dtype=float)
format.write_array(s, arr, version=(2, 0))
s.seek(format.MAGIC_LEN)
shape, fortran, dtype = format.read_array_header_2_0(s)
assert_((shape, fortran, dtype) == ((3, 6), False, float))
def test_read_array_header_2_0():
s = BytesIO()
arr = np.ones((3, 6), dtype=float)
format.write_array(s, arr, version=(2, 0))
s.seek(format.MAGIC_LEN)
shape, fortran, dtype = format.read_array_header_2_0(s)
assert_(s.tell() % format.ARRAY_ALIGN == 0)
assert_((shape, fortran, dtype) == ((3, 6), False, float))
def save(file, iarray, metafile=None, version=(1, 0)):
"""Save a info array to a .npy file and a metadata file.
Similar to the numpy.save function.
Parameters
----------
file: file handle or str
File or file name to write the array to in .npy format.
iarray: InfoArray object or array with similar interface
Array to be written to file with meta data.
metafile: str
File name for the meta data. The `info` attribute of `iarray` will be
written here. Default is None, where the it is
assumed to be the file name associated with `file` with ".meta"
appended.
"""
# Restrict to version (1,0) because we've only written write_header for
# this version.
if version != (1, 0):
raise ValueError("Only version (1,0) is safe from this function.")
# Make sure that the meta data will be representable as a string.
infostring = repr(iarray.info)
try:
safe_eval(infostring)
except SyntaxError:
raise ValueError
# Save the array in .npy format.
if isinstance(file, basestring):
fid = open(file, "wb")
else:
fid = file
npfor.write_array(fid, iarray, version=version)
# Figure out what the filename for the meta data should be.
if metafile is None:
try:
fname = file.name
except AttributeError:
fname = file
metafile = fname + ".meta"
# Save the meta data.
info_fid = open(metafile, 'w')
try:
info_fid.write(infostring)
finally:
info_fid.close()
def save(file, iarray, metafile=None, version=(1, 0)):
"""Save a info array to a .npy file and a metadata file.
Similar to the numpy.save function.
Parameters
----------
file: file handle or str
File or file name to write the array to in .npy format.
iarray: InfoArray object or array with similar interface
Array to be written to file with meta data.
metafile: str
File name for the meta data. The `info` attribute of `iarray` will be
written here. Default is None, where the it is
assumed to be the file name associated with `file` with ".meta"
appended.
"""
# Restrict to version (1,0) because we've only written write_header for
# this version.
if version != (1, 0):
raise ValueError("Only version (1,0) is safe from this function.")
# Make sure that the meta data will be representable as a string.
infostring = repr(iarray.info)
try:
safe_eval(infostring)
except SyntaxError:
raise ValueError
# Save the array in .npy format.
if isinstance(file, basestring):
fid = open(file, "wb")
else:
fid = file
npfor.write_array(fid, iarray, version=version)
# Figure out what the filename for the meta data should be.
if metafile is None:
try:
fname = file.name
except AttributeError:
fname = file
metafile = fname + ".meta"
# Save the meta data.
info_fid = open(metafile, 'w')
try:
info_fid.write(infostring)
finally:
info_fid.close()
def _savez(file, args, kwds, compress):
# Import is postponed to here since zipfile depends on gzip, an optional
# component of the so-called standard library.
import zipfile
# Import deferred for startup time improvement
import tempfile
if isinstance(file, basestring):
if not file.endswith(".npz"):
file = file + ".npz"
namedict = kwds
for i, val in enumerate(args):
key = "arr_%d" % i
if key in namedict.keys():
msg = "Cannot use un-named variables and keyword %s" % key
raise ValueError, msg
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
zip = zipfile_factory(file, mode="w", compression=compression)
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix="-numpy.npy")
os.close(fd)
try:
for key, val in namedict.iteritems():
fname = key + ".npy"
fid = open(tmpfile, "wb")
try:
format.write_array(fid, numpy.asanyarray(val))
fid.close()
fid = None
zip.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zip.close()
def _savez(file, args, kwds, compress):
# Import is postponed to here since zipfile depends on gzip, an optional
# component of the so-called standard library.
import zipfile
# Import deferred for startup time improvement
import tempfile
if isinstance(file, basestring):
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
msg = "Cannot use un-named variables and keyword %s" % key
raise ValueError, msg
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
zip = zipfile_factory(file, mode="w", compression=compression)
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
try:
for key, val in namedict.iteritems():
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
format.write_array(fid, numpy.asanyarray(val))
fid.close()
fid = None
zip.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zip.close()
def test_version_2_0():
f = BytesIO()
# requires more than 2 byte for header
dt = [(("%d" % i) * 100, float) for i in range(500)]
d = np.ones(1000, dtype=dt)
format.write_array(f, d, version=(2, 0))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', UserWarning)
format.write_array(f, d)
assert_(w[0].category is UserWarning)
f.seek(0)
n = format.read_array(f)
assert_array_equal(d, n)
# 1.0 requested but data cannot be saved this way
assert_raises(ValueError, format.write_array, f, d, (1, 0))
def test_read_magic():
s1 = BytesIO()
s2 = BytesIO()
arr = np.ones((3, 6), dtype=float)
format.write_array(s1, arr, version=(1, 0))
format.write_array(s2, arr, version=(2, 0))
s1.seek(0)
s2.seek(0)
version1 = format.read_magic(s1)
version2 = format.read_magic(s2)
assert_(version1 == (1, 0))
assert_(version2 == (2, 0))
assert_(s1.tell() == format.MAGIC_LEN)
assert_(s2.tell() == format.MAGIC_LEN)
def test_unicode_field_names():
# gh-7391
arr = np.array(
[(1, 3), (1, 2), (1, 3), (1, 2)],
dtype=[
('int', int),
(u'\N{CJK UNIFIED IDEOGRAPH-6574}\N{CJK UNIFIED IDEOGRAPH-5F62}',
int)
])
fname = os.path.join(tempdir, "unicode.npy")
with open(fname, 'wb') as f:
format.write_array(f, arr, version=(3, 0))
with open(fname, 'rb') as f:
arr2 = format.read_array(f)
assert_array_equal(arr, arr2)
# notifies the user that 3.0 is selected
with open(fname, 'wb') as f:
with assert_warns(UserWarning):
format.write_array(f, arr, version=None)
def savez(file, version, *args, compress=True, **kwargs):
namedict = kwargs
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
raise ValueError("Cannot use un-named variables and keyword %s" %
key)
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
with zipfile_factory(file, mode="a", compression=compression) as zipf:
# Write file format version
zipf.writestr('version', str(version))
# Write directly to a ZIP file
for key, val in namedict.items():
if isinstance(val, (str, bytes)):
zipf.writestr(key, val)
else:
try:
s = json.dumps(val, indent=4)
except TypeError:
if type(val).__module__ == 'pandas.core.frame':
fname = key + '.parquet'
force_zip64 = val.values.nbytes >= 2**30
with zipf.open(fname, 'w',
force_zip64=force_zip64) as fid:
pq.write_table(pa.Table.from_pandas(val), fid)
else:
fname = key + '.npy'
val = np.asanyarray(val)
force_zip64 = val.nbytes >= 2**30
with zipf.open(fname, 'w',
force_zip64=force_zip64) as fid:
format.write_array(fid, val, allow_pickle=False)
else:
zipf.writestr(key, s)
def savez(self, *args, **kwds):
import os
import numpy.lib.format as fmt
namedict = kwds
for val in args:
key = 'arr_%d' % self._i
if key in namedict.keys():
raise ValueError("Cannot use un-named variables and keyword %s" % key)
namedict[key] = val
self._i += 1
try:
for key, val in namedict.items():
fname = key + '.npy'
fid = open(self.tmpfile, 'wb')
with open(self.tmpfile, 'wb') as fid:
fmt.write_array(fid, np.asanyarray(val), allow_pickle=True)
self.zip.write(self.tmpfile, arcname=fname)
finally:
os.remove(self.tmpfile)
def test_write_version_1_0():
f = StringIO()
arr = np.arange(1)
# These should pass.
format.write_array(f, arr, version=(1, 0))
format.write_array(f, arr)
# These should all fail.
bad_versions = [
(1, 1),
(0, 0),
(0, 1),
(2, 0),
(2, 2),
(255, 255),
]
for version in bad_versions:
try:
format.write_array(f, arr, version=version)
except ValueError:
pass
else:
raise AssertionError(
"we should have raised a ValueError for the bad version %r" %
(version, ))
def _savez(file, args, kwds, compress):
if isinstance(file, basestring):
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
msg = "Cannot use un-named variables and keyword %s" % key
raise ValueError, msg
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
zip = zipfile_factory(file, mode="w", compression=compression)
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
try:
for key, val in namedict.iteritems():
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
format.write_array(fid, numpy.asanyarray(val))
fid.close()
fid = None
zip.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zip.close()
def test_memmap_roundtrip():
# XXX: test crashes nose on windows. Fix this
if not (sys.platform == 'win32' or sys.platform == 'cygwin'):
for arr in basic_arrays + record_arrays:
if arr.dtype.hasobject:
# Skip these since they can't be mmap'ed.
continue
# Write it out normally and through mmap.
nfn = os.path.join(tempdir, 'normal.npy')
mfn = os.path.join(tempdir, 'memmap.npy')
fp = open(nfn, 'wb')
try:
format.write_array(fp, arr)
finally:
fp.close()
fortran_order = (arr.flags.f_contiguous
and not arr.flags.c_contiguous)
ma = format.open_memmap(mfn,
mode='w+',
dtype=arr.dtype,
shape=arr.shape,
fortran_order=fortran_order)
ma[...] = arr
del ma
# Check that both of these files' contents are the same.
fp = open(nfn, 'rb')
normal_bytes = fp.read()
fp.close()
fp = open(mfn, 'rb')
memmap_bytes = fp.read()
fp.close()
yield assert_equal, normal_bytes, memmap_bytes
# Check that reading the file using memmap works.
ma = format.open_memmap(nfn, mode='r')
#yield assert_array_equal, ma, arr
del ma
def test_memmap_roundtrip():
# Fixme: used to crash on windows
if not (sys.platform == "win32" or sys.platform == "cygwin"):
for arr in basic_arrays + record_arrays:
if arr.dtype.hasobject:
# Skip these since they can't be mmap'ed.
continue
# Write it out normally and through mmap.
nfn = os.path.join(tempdir, "normal.npy")
mfn = os.path.join(tempdir, "memmap.npy")
fp = open(nfn, "wb")
try:
format.write_array(fp, arr)
finally:
fp.close()
fortran_order = arr.flags.f_contiguous and not arr.flags.c_contiguous
ma = format.open_memmap(
mfn,
mode="w+",
dtype=arr.dtype,
shape=arr.shape,
fortran_order=fortran_order,
)
ma[...] = arr
del ma
# Check that both of these files' contents are the same.
fp = open(nfn, "rb")
normal_bytes = fp.read()
fp.close()
fp = open(mfn, "rb")
memmap_bytes = fp.read()
fp.close()
assert_equal_(normal_bytes, memmap_bytes)
# Check that reading the file using memmap works.
ma = format.open_memmap(nfn, mode="r")
del ma
def test_version_2_0():
f = BytesIO()
# requires more than 2 byte for header
dt = [(("%d" % i) * 100, float) for i in range(500)]
d = np.ones(1000, dtype=dt)
format.write_array(f, d, version=(2, 0))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings("always", "", UserWarning)
format.write_array(f, d)
assert_(w[0].category is UserWarning)
# check alignment of data portion
f.seek(0)
header = f.readline()
assert_(len(header) % format.ARRAY_ALIGN == 0)
f.seek(0)
n = format.read_array(f)
assert_array_equal(d, n)
# 1.0 requested but data cannot be saved this way
assert_raises(ValueError, format.write_array, f, d, (1, 0))
def test_memmap_roundtrip():
# Fixme: used to crash on windows
if not (sys.platform == 'win32' or sys.platform == 'cygwin'):
for arr in basic_arrays + record_arrays:
if arr.dtype.hasobject:
# Skip these since they can't be mmap'ed.
continue
# Write it out normally and through mmap.
nfn = os.path.join(tempdir, 'normal.npy')
mfn = os.path.join(tempdir, 'memmap.npy')
with open(nfn, 'wb') as fp:
format.write_array(fp, arr)
fortran_order = (arr.flags.f_contiguous
and not arr.flags.c_contiguous)
ma = format.open_memmap(mfn,
mode='w+',
dtype=arr.dtype,
shape=arr.shape,
fortran_order=fortran_order)
ma[...] = arr
del ma
if IS_PYPY:
break_cycles()
# Check that both of these files' contents are the same.
with open(nfn, 'rb') as fp:
normal_bytes = fp.read()
with open(mfn, 'rb') as fp:
memmap_bytes = fp.read()
assert_equal_(normal_bytes, memmap_bytes)
# Check that reading the file using memmap works.
ma = format.open_memmap(nfn, mode='r')
del ma
if IS_PYPY:
break_cycles()
def test_ensemble_matrix_json(client, simple_ensemble, get, post):
from numpy.lib.format import write_array, read_array
ensemble_id = simple_ensemble()
matrix = np.random.rand(5, 8, 13)
# POST
post_url = f"/ensembles/{ensemble_id}/records/mat/matrix"
if post == "json":
resp = client.post(post_url, json=matrix.tolist())
elif post == "numpy":
stream = io.BytesIO()
write_array(stream, matrix)
resp = client.post(
post_url,
data=stream.getvalue(),
headers={"content-type": "application/x-numpy"},
)
else:
raise NotImplementedError()
# GET
get_url = f"/ensembles/{ensemble_id}/records/mat"
if get == "json":
resp = client.get(f"/ensembles/{ensemble_id}/records/mat")
assert resp.json() == matrix.tolist()
elif get == "numpy":
resp = client.get(
f"/ensembles/{ensemble_id}/records/mat",
headers={"accept": "application/x-numpy"},
)
stream = io.BytesIO(resp.content)
assert (read_array(stream) == matrix).all()
else:
raise NotImplementedError()
def test_write_version_1_0():
f = StringIO()
arr = np.arange(1)
# These should pass.
format.write_array(f, arr, version=(1, 0))
format.write_array(f, arr)
# These should all fail.
bad_versions = [(1, 1), (0, 0), (0, 1), (2, 0), (2, 2), (255, 255)]
for version in bad_versions:
try:
format.write_array(f, arr, version=version)
except ValueError:
pass
else:
raise AssertionError("we should have raised a ValueError for the bad version %r" % (version,))
def to_string(arr):
f = StringIO()
format.write_array(f, arr)
s = f.getvalue()
return s
def toSparse(source, idx2label, Format='NPZ'):
"""
Convert intra-chromosomal contact matrices to sparse ones.
Parameters
----------
source : str
Hdf5 file name.
idx2label : dict
A dictionary for conversion between zero-based indices and
string chromosome labels.
Format : {'NPZ', 'HDF5'}
Output format. (Default: HDF5)
"""
lib = h5dict(source, mode='r')
## Uniform numpy-structured-array format
itype = np.dtype({
'names': ['bin1', 'bin2', 'IF'],
'formats': [np.int, np.int, np.float]
})
## Create a Zip file in NPZ case
if Format.upper() == 'NPZ':
output = source.replace('.hm', '-sparse.npz')
Zip = zipfile.ZipFile(output, mode='w', allowZip64=True)
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
if Format.upper() == 'HDF5':
output = source.replace('.hm', '-sparse.hm')
odict = h5dict(output)
log.log(21, 'Sparse Matrices will be saved to %s', output)
log.log(21, 'Only intra-chromosomal matrices will be taken into account')
log.log(21, 'Coverting ...')
for i in lib:
if (i != 'resolution') and (len(set(i.split())) == 1):
# Used for the dict-like key
key = idx2label[int(i.split()[0])]
log.log(21, 'Chromosome %s ...', key)
# 2D-Matrix
H = lib[i]
# Triangle Array
Triu = np.triu(H)
# Sparse Matrix in Memory
x, y = np.nonzero(Triu)
values = Triu[x, y]
sparse = np.zeros(values.size, dtype=itype)
sparse['bin1'] = x
sparse['bin2'] = y
sparse['IF'] = values
if Format.upper() == 'HDF5':
# Really Simple, just like a dictionary
odict[key] = sparse
if Format.upper() == 'NPZ':
# Much more complicated, but we need make a compatible
# file interface for other API
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
write_array(fid, np.asanyarray(sparse))
fid.close()
fid = None
Zip.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
log.log(21, 'Done!')
os.remove(tmpfile)
Zip.close()
def test_write_version():
f = BytesIO()
arr = np.arange(1)
# These should pass.
format.write_array(f, arr, version=(1, 0))
format.write_array(f, arr)
format.write_array(f, arr, version=None)
format.write_array(f, arr)
format.write_array(f, arr, version=(2, 0))
format.write_array(f, arr)
# These should all fail.
bad_versions = [
(1, 1),
(0, 0),
(0, 1),
(2, 2),
(255, 255),
]
for version in bad_versions:
with assert_raises_regex(ValueError,
'we only support format version.*'):
format.write_array(f, arr, version=version)
def _savez(file, args, kwds, compress):
# Import is postponed to here since zipfile depends on gzip, an optional
# component of the so-called standard library.
import zipfile
import tempfile
import shutil
if isinstance(file, basestring):
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = 'arr_%d' % i
if key in namedict.keys():
raise ValueError("Cannot use un-named variables and keyword %s" % key)
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
# use compress keyword if given; only active in savez
if 'compress' in namedict.keys():
if namedict['compress']:
compression = zipfile.ZIP_DEFLATED
del namedict['compress']
# append or update
if 'append' in namedict.keys():
appendit = namedict['append']
del namedict['append']
else:
appendit = False
if 'update' in namedict.keys():
updateit = namedict['update']
del namedict['update']
else:
updateit = False
if appendit and updateit:
raise KeyError("append and update mutually exclusive.")
# check if file exists, otherwise it will be a simple write
if not os.path.isfile(file):
appendit = False
updateit = False
inzipf = []
else:
zipf = zipfile.ZipFile(file, mode="r")
inzipf = zipf.namelist()
inzipf = [ i[:-4] for i in inzipf ]
zipf.close()
allkeys = set(namedict.keys())
allkeys.update(inzipf)
# append if keyword is True
if appendit:
mode = "a"
# check if new arrays already exist in zip file
if len(inzipf) != 0:
for key in namedict:
if key in inzipf:
raise ValueError("array name already in npz-file: %s" % key)
else:
mode = "w"
if not updateit:
# Just add arrays to existing or non-existing file; duplicates were checked before
zipf = zipfile.ZipFile(file, mode=mode, compression=compression, allowZip64=True)
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
try:
for key, val in namedict.items():
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
format.write_array(fid, np.asanyarray(val))
fid.close()
fid = None
zipf.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zipf.close()
else:
# open existing zip file in read mode
zipr = zipfile.ZipFile(file, mode="r")
# open temporary zip file in write mode
tempdir = tempfile.mkdtemp()
try:
tempname = os.path.join(tempdir, 'new.zip')
zipw = zipfile.ZipFile(tempname, mode="w", compression=compression, allowZip64=True)
for key in allkeys:
# if in namedict then write new, else extract it from zipfile
if key in namedict.keys():
# Stage arrays in a temporary file on disk, before writing to zip.
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
try:
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
format.write_array(fid, np.asanyarray(namedict[key]))
fid.close()
fid = None
zipw.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
else:
fname = key + '.npy'
zipr.extract(fname, tempdir)
tmpfile = os.path.join(tempdir, fname)
zipw.write(tmpfile, arcname=fname)
os.remove(tmpfile)
# close both files and move new to old
zipr.close()
zipw.close()
shutil.move(tempname, file)
finally:
shutil.rmtree(tempdir)
def tobytes(array):
fp = BytesIO()
write_array(fp, array, allow_pickle=False)
return fp.getvalue()
def roundtrip(arr):
f = BytesIO()
format.write_array(f, arr)
f2 = BytesIO(f.getvalue())
arr2 = format.read_array(f2, allow_pickle=True)
return arr2
def toSparse(source, csr=False):
"""
Convert intra-chromosomal contact matrices to sparse ones.
Parameters
----------
source : str
Hdf5 file name.
idx2label : dict
A dictionary for conversion between zero-based indices and
string chromosome labels.
csr : bool
Whether to use CSR (Compressed Row Storage) format or not.
"""
import zipfile, tempfile
from numpy.lib.format import write_array
from scipy import sparse
lib = h5dict(source, mode='r')
## Uniform numpy-structured-array format
itype = np.dtype({
'names': ['bin1', 'bin2', 'IF'],
'formats': [np.int, np.int, np.float]
})
## Create a Zip file in NPZ case
if not csr:
output = source.replace('.hm', '-sparse.npz')
else:
output = source.replace('.hm', '-csrsparse.npz')
Zip = zipfile.ZipFile(output, mode='w', allowZip64=True)
fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')
os.close(fd)
log.log(21, 'Sparse Matrices will be saved to %s', output)
log.log(21, 'Only intra-chromosomal matrices will be taken into account')
log.log(21, 'Coverting ...')
count = 0
for i in lib:
if (i != 'resolution') and (i != 'genomeInformation') and (len(
set(i.split())) == 1):
# Used for the dict-like key
key = lib['genomeInformation']['idx2label'][int(i.split()[0])]
log.log(21, 'Chromosome %s ...', key)
# 2D-Matrix
H = lib[i]
if not csr:
# Triangle Array
Triu = np.triu(H)
# Sparse Matrix in Memory
x, y = np.nonzero(Triu)
values = Triu[x, y]
temp = np.zeros(values.size, dtype=itype)
temp['bin1'] = x
temp['bin2'] = y
temp['IF'] = values
else:
temp = sparse.triu(H, format='csr')
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
write_array(fid, np.asanyarray(temp))
fid.close()
fid = None
Zip.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
log.log(21, 'Done!')
count += 1
if count == 0:
log.warning('Empty source file!')
# Other information
for i in ['resolution', 'genomeInformation']:
fname = '.'.join([i, 'npy'])
fid = open(tmpfile, 'wb')
try:
write_array(fid, np.asanyarray(lib[i]))
fid.close()
fid = None
Zip.write(tmpfile, arcname=fname)
finally:
if fid:
fid.close()
os.remove(tmpfile)
Zip.close()
def to_string(arr):
f = StringIO()
format.write_array(f, arr)
s = f.getvalue()
return s
def roundtrip_randsize(arr):
f = BytesIO()
format.write_array(f, arr)
f2 = BytesIOSRandomSize(f.getvalue())
arr2 = format.read_array(f2)
return arr2
def roundtrip(arr):
f = BytesIO()
format.write_array(f, arr)
f2 = BytesIO(f.getvalue())
arr2 = format.read_array(f2)
return arr2
def toSparse(source, idx2label, csr = False):
"""
Convert intra-chromosomal contact matrices to sparse ones.
Parameters
----------
source : str
Hdf5 file name.
idx2label : dict
A dictionary for conversion between zero-based indices and
string chromosome labels.
csr : bool
Whether to use CSR (Compressed Row Storage) format or not.
"""
import zipfile, tempfile
from numpy.lib.format import write_array
from scipy import sparse
lib = h5dict(source, mode = 'r')
## Uniform numpy-structured-array format
itype = np.dtype({'names':['bin1', 'bin2', 'IF'],
'formats':[np.int, np.int, np.float]})
## Create a Zip file in NPZ case
if not csr:
output = source.replace('.hm', '-sparse.npz')
else:
output = source.replace('.hm', '-csrsparse.npz')
Zip = zipfile.ZipFile(output, mode = 'w', allowZip64 = True)
fd, tmpfile = tempfile.mkstemp(suffix = '-numpy.npy')
os.close(fd)
log.log(21, 'Sparse Matrices will be saved to %s', output)
log.log(21, 'Only intra-chromosomal matrices will be taken into account')
log.log(21, 'Coverting ...')
count = 0
for i in lib:
if (i != 'resolution') and (len(set(i.split())) == 1):
# Used for the dict-like key
key = idx2label[int(i.split()[0])]
log.log(21, 'Chromosome %s ...', key)
# 2D-Matrix
H = lib[i]
if not csr:
# Triangle Array
Triu = np.triu(H)
# Sparse Matrix in Memory
x, y = np.nonzero(Triu)
values = Triu[x, y]
temp = np.zeros(values.size, dtype = itype)
temp['bin1'] = x
temp['bin2'] = y
temp['IF'] = values
else:
temp = sparse.triu(H, format = 'csr')
fname = key + '.npy'
fid = open(tmpfile, 'wb')
try:
write_array(fid, np.asanyarray(temp))
fid.close()
fid = None
Zip.write(tmpfile, arcname = fname)
finally:
if fid:
fid.close()
log.log(21, 'Done!')
count += 1
# Store the resolution information
if 'resolution' in lib:
fname = 'resolution.npy'
fid = open(tmpfile, 'wb')
try:
write_array(fid, np.asanyarray(lib['resolution']))
fid.close()
fid = None
Zip.write(tmpfile, arcname = fname)
finally:
if fid:
fid.close()
if count == 0:
log.warning('Empty source file!')
os.remove(tmpfile)
Zip.close()
def toString(data): f= StringIO() format.write_array(f,data) return f.getvalue()
def __init__(self, datasets, chroms=['#','X'], maxsize=4000000, npzpre=None, cache=None):
self.chroms = set(chroms)
data = datasets
self._npzpre = npzpre
if not self._npzpre is None:
self._npzpre = os.path.abspath(os.path.expanduser(npzpre))
for res in data:
for rep in data[res]:
rl = '%dK' % (res//1000)
output = '.'.join([self._npzpre, rl, rep, 'npz'])
if os.path.exists(output):
log.error('The destination npz file will be overriden, reset npz prefix and run again ...')
log.error('Exit ...')
sys.exit(1)
# We don't read data in memory at this point.
# We only construct the mapping for loading convenience
self.data = {}
for res in data:
for rep in data[res]:
if data[res][rep].endswith('.npz'):
lib = np.load(data[res][rep])
for i in lib.files:
if ((not self.chroms) or (i.isdigit() and '#' in self.chroms)
or (i in self.chroms)):
if not i in self.data:
self.data[i] = {res:{rep:lib}}
else:
if res in self.data[i]:
self.data[i][res][rep] = lib
else:
self.data[i][res] = {rep:lib}
else:
Map = self._scanFolder(data[res][rep])
for i in Map:
if not i in self.data:
self.data[i] = {res:{rep:Map[i]}}
else:
if res in self.data[i]:
self.data[i][res][rep] = Map[i]
else:
self.data[i][res] = {rep:Map[i]}
if cache is None:
self._cache = tempfile.gettempdir()
else:
self._cache = os.path.abspath(os.path.expanduser(cache))
if not os.path.isdir(cache):
os.makedirs(cache)
self._intertype = np.dtype({'names':['bin1', 'bin2', 'IF'],
'formats':[np.int, np.int, np.float]})
# Before starting calling, we make cache data under the cache folder
for chrom in self.data:
log.debug('Chrom %s:', chrom)
ms = self.data[chrom]
for res in ms:
for rep in ms[res]:
log.debug(' resolution: %d, %s', res, rep)
if type(ms[res][rep])==str:
tdata = np.loadtxt(ms[res][rep], dtype = self._intertype)
else:
tdata = ms[res][rep][chrom]
work = Chrom(chrom, res, tdata, rep, maxsize)
work.Label = rep
tl = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))
kw = {'suffix':tl, 'dir':self._cache}
fd, tmpfil = tempfile.mkstemp(**kw)
os.close(fd)
with open(tmpfil, 'wb') as output:
log.debug(' Cache Chrom object into %s ...', tmpfil)
cPickle.dump(work, output, protocol = 2)
self.data[chrom][res][rep] = tmpfil
time.sleep(3)
if not self._npzpre is None:
rl = '%dK' % (res//1000)
output = '.'.join([self._npzpre, rl, rep, 'npz'])
if not os.path.exists(output):
Zip = zipfile.ZipFile(output, mode = 'w', allowZip64 = True)
else:
Zip = zipfile.ZipFile(output, mode = 'a', allowZip64 = True)
tl = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))
fd, tmpfile = tempfile.mkstemp(suffix = '.'.join([tl, 'npy']))
os.close(fd)
log.debug(' Save data into %s for accelerating IO next time ...', output)
fname = '.'.join([chrom, 'npy'])
fid = open(tmpfile, 'wb')
try:
write_array(fid, tdata)
fid.close()
fid = None
Zip.write(tmpfile, arcname = fname)
finally:
if fid:
fid.close()
os.remove(tmpfile)
Zip.close()
def roundtrip(arr):
f = BytesIO()
format.write_array(f, arr)
f2 = BytesIO(f.getvalue())
arr2 = format.read_array(f2, allow_pickle=True)
return arr2
def test_write_version():
f = BytesIO()
arr = np.arange(1)
# These should pass.
format.write_array(f, arr, version=(1, 0))
format.write_array(f, arr)
format.write_array(f, arr, version=None)
format.write_array(f, arr)
format.write_array(f, arr, version=(2, 0))
format.write_array(f, arr)
# These should all fail.
bad_versions = [
(1, 1),
(0, 0),
(0, 1),
(2, 2),
(255, 255),
]
for version in bad_versions:
with assert_raises_regex(ValueError,
'we only support format version.*'):
format.write_array(f, arr, version=version)
async def _get_record_data(
record: ds.Record,
accept: Optional[str],
realization_index: Optional[int] = None) -> Response:
type_ = record.record_info.record_type
if type_ == ds.RecordType.f64_matrix:
if realization_index is None:
content = record.f64_matrix.content
else:
content = record.f64_matrix.content[realization_index]
if accept == "application/x-numpy":
from numpy.lib.format import write_array
stream = io.BytesIO()
write_array(stream, np.array(content))
return Response(
content=stream.getvalue(),
media_type="application/x-numpy",
)
if accept == "text/csv":
data = pd.DataFrame(content)
labels = record.f64_matrix.labels
if labels is not None and realization_index is None:
data.columns = labels[0]
data.index = labels[1]
elif labels is not None and realization_index is not None:
# The output is such that rows are realizations. Because
# `content` is a 1d list in this case, it treats each element as
# its own row. We transpose the data so that all of the data
# falls on the same row.
data = data.T
data.columns = labels[0]
data.index = [realization_index]
return Response(
content=data.to_csv().encode(),
media_type="text/csv",
)
else:
return content
if type_ == ds.RecordType.file:
f = record.file
if f.content is not None:
return Response(
content=f.content,
media_type=f.mimetype,
headers={
"Content-Disposition":
f'attachment; filename="{f.filename}"'
},
)
elif f.az_container is not None and f.az_blob is not None:
blob = azure_blob_container.get_blob_client(f.az_blob)
download = await blob.download_blob()
async def chunk_generator() -> AsyncGenerator[bytes, None]:
async for chunk in download.chunks():
yield chunk
return StreamingResponse(
chunk_generator(),
media_type=f.mimetype,
headers={
"Content-Disposition":
f'attachment; filename="{f.filename}"'
},
)
raise NotImplementedError(
f"Getting record data for type {type_} and Accept header {accept} not implemented"
)
def toString(self, data):
f = StringIO()
format.write_array(f, data)
return f.getvalue()