def _dump(self, file, protocol=2):
if hasattr(file, "write"):
ctx = contextlib_nullcontext(file)
else:
ctx = open(os_fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create an array from binary file data
If file is a string or a path-like object then that file is opened,
else it is assumed to be a file object. The file object must
support random access (i.e. it must have tell and seek methods).
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if dtype is None and formats is None:
raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")
if (shape is None or shape == 0):
shape = (-1,)
elif isinstance(shape, (int, long)):
shape = (shape,)
if isfileobj(fd):
# file already opened
name = 0
else:
# open file
fd = open(os_fspath(fd), 'rb')
name = 1
if (offset > 0):
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod(dtype=nt.intp)
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size // -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod(dtype=nt.intp)
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise IOError("Didn't read as many bytes as expected")
if name:
fd.close()
return _array
def open_memmap(filename,
mode='r+',
dtype=None,
shape=None,
fortran_order=False,
version=None):
"""
Open a .npy file as a memory-mapped array.
This may be used to read an existing file or create a new one.
Parameters
----------
filename : str or path-like
The name of the file on disk. This may *not* be a file-like
object.
mode : str, optional
The mode in which to open the file; the default is 'r+'. In
addition to the standard file modes, 'c' is also accepted to mean
"copy on write." See `memmap` for the available mode strings.
dtype : data-type, optional
The data type of the array if we are creating a new file in "write"
mode, if not, `dtype` is ignored. The default value is None, which
results in a data-type of `float64`.
shape : tuple of int
The shape of the array if we are creating a new file in "write"
mode, in which case this parameter is required. Otherwise, this
parameter is ignored and is thus optional.
fortran_order : bool, optional
Whether the array should be Fortran-contiguous (True) or
C-contiguous (False, the default) if we are creating a new file in
"write" mode.
version : tuple of int (major, minor) or None
If the mode is a "write" mode, then this is the version of the file
format used to create the file. None means use the oldest
supported version that is able to store the data. Default: None
Returns
-------
marray : memmap
The memory-mapped array.
Raises
------
ValueError
If the data or the mode is invalid.
IOError
If the file is not found or cannot be opened correctly.
See Also
--------
memmap
"""
if isfileobj(filename):
raise ValueError("Filename must be a string or a path-like object."
" Memmap cannot use existing file handles.")
if 'w' in mode:
# We are creating the file, not reading it.
# Check if we ought to create the file.
_check_version(version)
# Ensure that the given dtype is an authentic dtype object rather
# than just something that can be interpreted as a dtype object.
dtype = numpy.dtype(dtype)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
d = dict(
descr=dtype_to_descr(dtype),
fortran_order=fortran_order,
shape=shape,
)
# If we got here, then it should be safe to create the file.
with open(os_fspath(filename), mode + 'b') as fp:
_write_array_header(fp, d, version)
offset = fp.tell()
else:
# Read the header of the file first.
with open(os_fspath(filename), 'rb') as fp:
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
offset = fp.tell()
if fortran_order:
order = 'F'
else:
order = 'C'
# We need to change a write-only mode to a read-write mode since we've
# already written data to the file.
if mode == 'w+':
mode = 'r+'
marray = numpy.memmap(filename,
dtype=dtype,
shape=shape,
order=order,
mode=mode,
offset=offset)
return marray
def savetxt(fname,
X,
fmt='%.18e',
delimiter=' ',
newline='\n',
header='',
footer='',
comments='# ',
encoding=None,
progress_callback=None):
"""numpy.savetxt modified to output progress """
# Py3 conversions first
if isinstance(fmt, bytes):
fmt = asstr(fmt)
delimiter = asstr(delimiter)
class WriteWrap:
"""Convert to bytes on bytestream inputs.
"""
def __init__(self, ifh, iencoding):
self.fh = ifh
self.encoding = iencoding
self.do_write = self.first_write
self.write = None
def close(self):
self.fh.close()
def write(self, v):
self.do_write(v)
def write_bytes(self, v):
if isinstance(v, bytes):
self.fh.write(v)
else:
self.fh.write(v.encode(self.encoding))
def write_normal(self, v):
self.fh.write(asunicode(v))
def first_write(self, v):
try:
self.write_normal(v)
self.write = self.write_normal
except TypeError:
# input is probably a bytestream
self.write_bytes(v)
self.write = self.write_bytes
own_fh = False
if isinstance(fname, os_PathLike):
fname = os_fspath(fname)
if isinstance(fname, str):
# datasource doesn't vis_support creating a new file ...
open(fname, 'wt').close()
fh = np.lib._datasource.open(fname, 'wt', encoding=encoding)
own_fh = True
elif hasattr(fname, 'write'):
# wrap to handle byte output streams
fh = WriteWrap(fname, encoding or 'latin1')
else:
raise ValueError('fname must be a string or file handle')
try:
X = np.asarray(X)
# Handle 1-dimensional arrays
if X.ndim == 0 or X.ndim > 2:
raise ValueError("Expected 1D or 2D array, got %dD array instead" %
X.ndim)
elif X.ndim == 1:
# Common case -- 1d array of numbers
if X.dtype.names is None:
X = np.atleast_2d(X).T
ncol = 1
# Complex dtype -- each field indicates a separate column
else:
ncol = len(X.dtype.names)
else:
ncol = X.shape[1]
iscomplex_X = np.iscomplexobj(X)
# `fmt` can be a string with multiple insertion points or a
# list of formats. E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
if type(fmt) in (list, tuple):
if len(fmt) != ncol:
raise AttributeError('fmt has wrong shape. %s' % str(fmt))
iformat = asstr(delimiter).join(map(asstr, fmt))
elif isinstance(fmt, str):
n_fmt_chars = fmt.count('%')
error = ValueError('fmt has wrong number of %% formats: %s' % fmt)
if n_fmt_chars == 1:
if iscomplex_X:
fmt = [
' (%s+%sj)' % (fmt, fmt),
] * ncol
else:
fmt = [
fmt,
] * ncol
iformat = delimiter.join(fmt)
elif iscomplex_X and n_fmt_chars != (2 * ncol):
raise error
elif (not iscomplex_X) and n_fmt_chars != ncol:
raise error
else:
iformat = fmt
else:
raise ValueError('invalid fmt: %r' % (fmt, ))
if len(header) > 0:
header = header.replace('\n', '\n' + comments)
fh.write(comments + header + newline)
if iscomplex_X:
for row in X:
row2 = []
for number in row:
row2.append(number.real)
row2.append(number.imag)
s = iformat % tuple(row2) + newline
fh.write(s.replace('+-', '-'))
else:
length = len(X)
for ind, row in enumerate(X):
try:
v = iformat % tuple(row) + newline
except TypeError:
raise TypeError("Mismatch between array dtype ('%s') and "
"format specifier ('%s')" %
(str(X.dtype), iformat))
fh.write(v)
progress_callback.emit((ind + 1) * 100 / length)
if len(footer) > 0:
footer = footer.replace('\n', '\n' + comments)
fh.write(comments + footer + newline)
finally:
if own_fh:
fh.close()
def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
shape=None, order='C'):
# Import here to minimize 'import numpy' overhead
import mmap
import os.path
try:
mode = mode_equivalents[mode]
except KeyError as e:
if mode not in valid_filemodes:
raise ValueError(
"mode must be one of {!r} (got {!r})"
.format(valid_filemodes + list(mode_equivalents.keys()), mode)
) from None
if mode == 'w+' and shape is None:
raise ValueError("shape must be given")
if hasattr(filename, 'read'):
f_ctx = nullcontext(filename)
else:
f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b')
with f_ctx as fid:
fid.seek(0, 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if bytes % _dbytes:
raise ValueError("Size of available data is not a "
"multiple of the data-type size.")
size = bytes // _dbytes
shape = (size,)
else:
if not isinstance(shape, tuple):
shape = (shape,)
size = np.intp(1) # avoid default choice of np.int_, which might overflow
for k in shape:
size *= k
bytes = int(offset + size*_dbytes)
if mode in ('w+', 'r+') and flen < bytes:
fid.seek(bytes - 1, 0)
fid.write(b'\0')
fid.flush()
if mode == 'c':
acc = mmap.ACCESS_COPY
elif mode == 'r':
acc = mmap.ACCESS_READ
else:
acc = mmap.ACCESS_WRITE
start = offset - offset % mmap.ALLOCATIONGRANULARITY
bytes -= start
array_offset = offset - start
mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
offset=array_offset, order=order)
self._mmap = mm
self.offset = offset
self.mode = mode
if is_pathlib_path(filename):
# special case - if we were constructed with a pathlib.path,
# then filename is a path object, not a string
self.filename = filename.resolve()
elif hasattr(fid, "name") and isinstance(fid.name, str):
# py3 returns int for TemporaryFile().name
self.filename = os.path.abspath(fid.name)
# same as memmap copies (e.g. memmap + 1)
else:
self.filename = None
return self
def open_memmap(filename, mode='r+', dtype=None, shape=None,
fortran_order=False, version=None):
"""
Open a .npy file as a memory-mapped array.
This may be used to read an existing file or create a new one.
Parameters
----------
filename : str or path-like
The name of the file on disk. This may *not* be a file-like
object.
mode : str, optional
The mode in which to open the file; the default is 'r+'. In
addition to the standard file modes, 'c' is also accepted to mean
"copy on write." See `memmap` for the available mode strings.
dtype : data-type, optional
The data type of the array if we are creating a new file in "write"
mode, if not, `dtype` is ignored. The default value is None, which
results in a data-type of `float64`.
shape : tuple of int
The shape of the array if we are creating a new file in "write"
mode, in which case this parameter is required. Otherwise, this
parameter is ignored and is thus optional.
fortran_order : bool, optional
Whether the array should be Fortran-contiguous (True) or
C-contiguous (False, the default) if we are creating a new file in
"write" mode.
version : tuple of int (major, minor) or None
If the mode is a "write" mode, then this is the version of the file
format used to create the file. None means use the oldest
supported version that is able to store the data. Default: None
Returns
-------
marray : memmap
The memory-mapped array.
Raises
------
ValueError
If the data or the mode is invalid.
IOError
If the file is not found or cannot be opened correctly.
See Also
--------
memmap
"""
if isfileobj(filename):
raise ValueError("Filename must be a string or a path-like object."
" Memmap cannot use existing file handles.")
if 'w' in mode:
# We are creating the file, not reading it.
# Check if we ought to create the file.
_check_version(version)
# Ensure that the given dtype is an authentic dtype object rather
# than just something that can be interpreted as a dtype object.
dtype = numpy.dtype(dtype)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
d = dict(
descr=dtype_to_descr(dtype),
fortran_order=fortran_order,
shape=shape,
)
# If we got here, then it should be safe to create the file.
fp = open(os_fspath(filename), mode+'b')
try:
used_ver = _write_array_header(fp, d, version)
# this warning can be removed when 1.9 has aged enough
if version != (2, 0) and used_ver == (2, 0):
warnings.warn("Stored array in format 2.0. It can only be"
"read by NumPy >= 1.9", UserWarning, stacklevel=2)
offset = fp.tell()
finally:
fp.close()
else:
# Read the header of the file first.
fp = open(os_fspath(filename), 'rb')
try:
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
offset = fp.tell()
finally:
fp.close()
if fortran_order:
order = 'F'
else:
order = 'C'
# We need to change a write-only mode to a read-write mode since we've
# already written data to the file.
if mode == 'w+':
mode = 'r+'
marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order,
mode=mode, offset=offset)
return marray
def fromfile(fd,
dtype=None,
shape=None,
offset=0,
formats=None,
names=None,
titles=None,
aligned=False,
byteorder=None):
"""Create an array from binary file data
Parameters
----------
fd : str or file type
If file is a string or a path-like object then that file is opened,
else it is assumed to be a file object. The file object must
support random access (i.e. it must have tell and seek methods).
dtype : data-type, optional
valid dtype for all arrays
shape : int or tuple of ints, optional
shape of each array.
offset : int, optional
Position in the file to start reading from.
formats, names, titles, aligned, byteorder :
If `dtype` is ``None``, these arguments are passed to
`numpy.format_parser` to construct a dtype. See that function for
detailed documentation
Returns
-------
np.recarray
record array consisting of data enclosed in file.
Examples
--------
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> _ = fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if dtype is None and formats is None:
raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")
# NumPy 1.19.0, 2020-01-01
shape = _deprecate_shape_0_as_None(shape)
if shape is None:
shape = (-1, )
elif isinstance(shape, int):
shape = (shape, )
if hasattr(fd, 'readinto'):
# GH issue 2504. fd supports io.RawIOBase or io.BufferedIOBase interface.
# Example of fd: gzip, BytesIO, BufferedReader
# file already opened
ctx = nullcontext(fd)
else:
# open file
ctx = open(os_fspath(fd), 'rb')
with ctx as fd:
if offset > 0:
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned,
byteorder).dtype
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod(dtype=nt.intp)
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size // -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod(dtype=nt.intp)
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise IOError("Didn't read as many bytes as expected")
return _array
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create an array from binary file data
If file is a string or a path-like object then that file is opened,
else it is assumed to be a file object. The file object must
support random access (i.e. it must have tell and seek methods).
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> _ = fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if dtype is None and formats is None:
raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")
if (shape is None or shape == 0):
shape = (-1,)
elif isinstance(shape, (int, long)):
shape = (shape,)
if isfileobj(fd):
# file already opened
name = 0
else:
# open file
fd = open(os_fspath(fd), 'rb')
name = 1
if (offset > 0):
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod(dtype=nt.intp)
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size // -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod(dtype=nt.intp)
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise IOError("Didn't read as many bytes as expected")
if name:
fd.close()
return _array
def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
shape=None, order='C'):
# Import here to minimize 'import numpy' overhead
import mmap
import os.path
try:
mode = mode_equivalents[mode]
except KeyError:
if mode not in valid_filemodes:
raise ValueError("mode must be one of %s" %
(valid_filemodes + list(mode_equivalents.keys())))
if mode == 'w+' and shape is None:
raise ValueError("shape must be given")
if hasattr(filename, 'read'):
f_ctx = contextlib_nullcontext(filename)
else:
f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b')
with f_ctx as fid:
fid.seek(0, 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if bytes % _dbytes:
raise ValueError("Size of available data is not a "
"multiple of the data-type size.")
size = bytes // _dbytes
shape = (size,)
else:
if not isinstance(shape, tuple):
shape = (shape,)
size = np.intp(1) # avoid default choice of np.int_, which might overflow
for k in shape:
size *= k
bytes = long(offset + size*_dbytes)
if mode == 'w+' or (mode == 'r+' and flen < bytes):
fid.seek(bytes - 1, 0)
fid.write(b'\0')
fid.flush()
if mode == 'c':
acc = mmap.ACCESS_COPY
elif mode == 'r':
acc = mmap.ACCESS_READ
else:
acc = mmap.ACCESS_WRITE
start = offset - offset % mmap.ALLOCATIONGRANULARITY
bytes -= start
array_offset = offset - start
mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
offset=array_offset, order=order)
self._mmap = mm
self.offset = offset
self.mode = mode
if is_pathlib_path(filename):
# special case - if we were constructed with a pathlib.path,
# then filename is a path object, not a string
self.filename = filename.resolve()
elif hasattr(fid, "name") and isinstance(fid.name, basestring):
# py3 returns int for TemporaryFile().name
self.filename = os.path.abspath(fid.name)
# same as memmap copies (e.g. memmap + 1)
else:
self.filename = None
return self
def test_os_fspath_strings():
for string_path in (b'/a/b/c.d', u'/a/b/c.d'):
assert_(os_fspath(string_path) == string_path)
def test_os_fspath_strings():
for string_path in (b"/a/b/c.d", u"/a/b/c.d"):
assert_(os_fspath(string_path) == string_path)