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"""
IO related functions.
"""
import contextlib
import functools
import itertools
import operator
import os
import pickle
import re
import warnings
import weakref
from collections.abc import Mapping
from operator import itemgetter
import numpy as np
from numpy._core import overrides
from numpy._core._multiarray_umath import _load_from_filelike
from numpy._core.multiarray import packbits, unpackbits
from numpy._core.overrides import finalize_array_function_like, set_module
from numpy._utils import asbytes, asunicode
from . import format
from ._datasource import DataSource # noqa: F401
from ._format_impl import _MAX_HEADER_SIZE
from ._iotools import (
ConversionWarning,
ConverterError,
ConverterLockError,
LineSplitter,
NameValidator,
StringConverter,
_decode_line,
_is_string_like,
easy_dtype,
flatten_dtype,
has_nested_fields,
)
__all__ = [
'savetxt', 'loadtxt', 'genfromtxt', 'load', 'save', 'savez',
'savez_compressed', 'packbits', 'unpackbits', 'fromregex'
]
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
class BagObj:
"""
BagObj(obj)
Convert attribute look-ups to getitems on the object passed in.
Parameters
----------
obj : class instance
Object on which attribute look-up is performed.
Examples
--------
>>> import numpy as np
>>> from numpy.lib._npyio_impl import BagObj as BO
>>> class BagDemo:
... def __getitem__(self, key): # An instance of BagObj(BagDemo)
... # will call this method when any
... # attribute look-up is required
... result = "Doesn't matter what you want, "
... return result + "you're gonna get this"
...
>>> demo_obj = BagDemo()
>>> bagobj = BO(demo_obj)
>>> bagobj.hello_there
"Doesn't matter what you want, you're gonna get this"
>>> bagobj.I_can_be_anything
"Doesn't matter what you want, you're gonna get this"
"""
def __init__(self, obj):
# Use weakref to make NpzFile objects collectable by refcount
self._obj = weakref.proxy(obj)
def __getattribute__(self, key):
try:
return object.__getattribute__(self, '_obj')[key]
except KeyError:
raise AttributeError(key) from None
def __dir__(self):
"""
Enables dir(bagobj) to list the files in an NpzFile.
This also enables tab-completion in an interpreter or IPython.
"""
return list(object.__getattribute__(self, '_obj').keys())
def zipfile_factory(file, *args, **kwargs):
"""
Create a ZipFile.
Allows for Zip64, and the `file` argument can accept file, str, or
pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile
constructor.
"""
if not hasattr(file, 'read'):
file = os.fspath(file)
import zipfile
kwargs['allowZip64'] = True
return zipfile.ZipFile(file, *args, **kwargs)
@set_module('numpy.lib.npyio')
class NpzFile(Mapping):
"""
NpzFile(fid)
A dictionary-like object with lazy-loading of files in the zipped
archive provided on construction.
`NpzFile` is used to load files in the NumPy ``.npz`` data archive
format. Files containing arrays in ``.npy`` format are loaded as arrays;
other files are returned as bytes.
The arrays and bytes are lazily loaded on either
getitem access using ``obj['key']`` or attribute lookup using
``obj.f.key``. Full file names, including the ``.npy`` suffix, can also
be used for getitem access and membership tests. A list of all file names,
with the ``.npy`` suffix removed where present, can be obtained with
``obj.files`` and the ZipFile object itself using ``obj.zip``.
Attributes
----------
files : list of str
List of all file names in the archive, with the ``.npy`` suffix removed
where present.
zip : ZipFile instance
The ZipFile object initialized with the zipped archive.
f : BagObj instance
An object on which attribute can be performed as an alternative
to getitem access on the `NpzFile` instance itself.
allow_pickle : bool, optional
Allow loading pickled data. Default: False
pickle_kwargs : dict, optional
Additional keyword arguments to pass on to pickle.load.
These are only useful when loading object arrays saved on
Python 2.
max_header_size : int, optional
Maximum allowed size of the header. Large headers may not be safe
to load securely and thus require explicitly passing a larger value.
See :py:func:`ast.literal_eval()` for details.
This option is ignored when `allow_pickle` is passed. In that case
the file is by definition trusted and the limit is unnecessary.
Parameters
----------
fid : file, str, or pathlib.Path
The zipped archive to open. This is either a file-like object
or a string containing the path to the archive.
own_fid : bool, optional
Whether NpzFile should close the file handle.
Requires that `fid` is a file-like object.
Examples
--------
>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
>>> np.savez(outfile, x=x, y=y)
>>> _ = outfile.seek(0)
>>> npz = np.load(outfile)
>>> isinstance(npz, np.lib.npyio.NpzFile)
True
>>> npz
NpzFile 'object' with keys: x, y
>>> sorted(npz.files)
['x', 'y']
>>> npz['x'] # getitem access
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> npz['x.npy'] # full file name
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> 'x.npy' in npz
True
>>> npz.f.x # attribute lookup
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
# Make __exit__ safe if zipfile_factory raises an exception
zip = None
fid = None
_MAX_REPR_ARRAY_COUNT = 5
def __init__(self, fid, own_fid=False, allow_pickle=False,
pickle_kwargs=None, *,
max_header_size=_MAX_HEADER_SIZE):
# Import is postponed to here since zipfile depends on gzip, an
# optional component of the so-called standard library.
_zip = zipfile_factory(fid)
_files = _zip.namelist()
self.files = [name.removesuffix(".npy") for name in _files]
self._files = dict(zip(self.files, _files))
self._files.update(zip(_files, _files))
self.allow_pickle = allow_pickle
self.max_header_size = max_header_size
self.pickle_kwargs = pickle_kwargs
self.zip = _zip
self.f = BagObj(self)
if own_fid:
self.fid = fid
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def close(self):
"""
Close the file.
"""
if self.zip is not None:
self.zip.close()
self.zip = None
if self.fid is not None:
self.fid.close()
self.fid = None
self.f = None # break reference cycle
def __del__(self):
self.close()
# Implement the Mapping ABC
def __iter__(self):
return iter(self.files)
def __len__(self):
return len(self.files)
def __getitem__(self, key):
try:
key = self._files[key]
except KeyError:
raise KeyError(f"{key} is not a file in the archive") from None
else:
with self.zip.open(key) as bytes:
magic = bytes.read(len(format.MAGIC_PREFIX))
bytes.seek(0)
if magic == format.MAGIC_PREFIX:
# FIXME: This seems like it will copy strings around
# more than is strictly necessary. The zipfile
# will read the string and then
# the format.read_array will copy the string
# to another place in memory.
# It would be better if the zipfile could read
# (or at least uncompress) the data
# directly into the array memory.
return format.read_array(
bytes,
allow_pickle=self.allow_pickle,
pickle_kwargs=self.pickle_kwargs,
max_header_size=self.max_header_size
)
else:
return bytes.read()
def __contains__(self, key):
return (key in self._files)
def __repr__(self):
# Get filename or default to `object`
if isinstance(self.fid, str):
filename = self.fid
else:
filename = getattr(self.fid, "name", "object")
# Get the name of arrays
array_names = ', '.join(self.files[:self._MAX_REPR_ARRAY_COUNT])
if len(self.files) > self._MAX_REPR_ARRAY_COUNT:
array_names += "..."
return f"NpzFile {filename!r} with keys: {array_names}"
# Work around problems with the docstrings in the Mapping methods
# They contain a `->`, which confuses the type annotation interpretations
# of sphinx-docs. See gh-25964
def get(self, key, default=None, /):
"""
D.get(k,[,d]) returns D[k] if k in D, else d. d defaults to None.
"""
return Mapping.get(self, key, default)
def items(self):
"""
D.items() returns a set-like object providing a view on the items
"""
return Mapping.items(self)
def keys(self):
"""
D.keys() returns a set-like object providing a view on the keys
"""
return Mapping.keys(self)
def values(self):
"""
D.values() returns a set-like object providing a view on the values
"""
return Mapping.values(self)
@set_module('numpy')
def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
encoding='ASCII', *, max_header_size=_MAX_HEADER_SIZE):
"""
Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
.. warning:: Loading files that contain object arrays uses the ``pickle``
module, which is not secure against erroneous or maliciously
constructed data. Consider passing ``allow_pickle=False`` to
load data that is known not to contain object arrays for the
safer handling of untrusted sources.
Parameters
----------
file : file-like object, string, or pathlib.Path
The file to read. File-like objects must support the
``seek()`` and ``read()`` methods and must always
be opened in binary mode. Pickled files require that the
file-like object support the ``readline()`` method as well.
mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, then memory-map the file, using the given mode (see
`numpy.memmap` for a detailed description of the modes). A
memory-mapped array is kept on disk. However, it can be accessed
and sliced like any ndarray. Memory mapping is especially useful
for accessing small fragments of large files without reading the
entire file into memory.
allow_pickle : bool, optional
Allow loading pickled object arrays stored in npy files. Reasons for
disallowing pickles include security, as loading pickled data can
execute arbitrary code. If pickles are disallowed, loading object
arrays will fail. Default: False
fix_imports : bool, optional
Only useful when loading Python 2 generated pickled files,
which includes npy/npz files containing object arrays. If `fix_imports`
is True, pickle will try to map the old Python 2 names to the new names
used in Python 3.
encoding : str, optional
What encoding to use when reading Python 2 strings. Only useful when
loading Python 2 generated pickled files, which includes
npy/npz files containing object arrays. Values other than 'latin1',
'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
data. Default: 'ASCII'
max_header_size : int, optional
Maximum allowed size of the header. Large headers may not be safe
to load securely and thus require explicitly passing a larger value.
See :py:func:`ast.literal_eval()` for details.
This option is ignored when `allow_pickle` is passed. In that case
the file is by definition trusted and the limit is unnecessary.
Returns
-------
result : array, tuple, dict, etc.
Data stored in the file. For ``.npz`` files, the returned instance
of NpzFile class must be closed to avoid leaking file descriptors.
Raises
------
OSError
If the input file does not exist or cannot be read.
UnpicklingError
If ``allow_pickle=True``, but the file cannot be loaded as a pickle.
ValueError
The file contains an object array, but ``allow_pickle=False`` given.
EOFError
When calling ``np.load`` multiple times on the same file handle,
if all data has already been read
See Also
--------
save, savez, savez_compressed, loadtxt
memmap : Create a memory-map to an array stored in a file on disk.
lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
Notes
-----
- If the file contains pickle data, then whatever object is stored
in the pickle is returned.
- If the file is a ``.npy`` file, then a single array is returned.
- If the file is a ``.npz`` file, then a dictionary-like object is
returned, containing ``{filename: array}`` key-value pairs, one for
each file in the archive.
- If the file is a ``.npz`` file, then arrays are read lazily on
key access, so the file should not be modified before then.
- If the file is a ``.npz`` file, the returned value supports the
context manager protocol in a similar fashion to the open function::
with load('foo.npz') as data:
a = data['a']
The underlying file descriptor is closed when exiting the 'with'
block.
Examples
--------
>>> import numpy as np
Store data to disk, and load it again:
>>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
>>> np.load('/tmp/123.npy')
array([[1, 2, 3],
[4, 5, 6]])
Store compressed data to disk, and load it again:
>>> a=np.array([[1, 2, 3], [4, 5, 6]])
>>> b=np.array([1, 2])
>>> np.savez('/tmp/123.npz', a=a, b=b)
>>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
[4, 5, 6]])
>>> data['b']
array([1, 2])
>>> data.close()
Mem-map the stored array, and then access the second row
directly from disk:
>>> X = np.load('/tmp/123.npy', mmap_mode='r')
>>> X[1, :]
memmap([4, 5, 6])
"""
if encoding not in ('ASCII', 'latin1', 'bytes'):
# The 'encoding' value for pickle also affects what encoding
# the serialized binary data of NumPy arrays is loaded
# in. Pickle does not pass on the encoding information to
# NumPy. The unpickling code in numpy._core.multiarray is
# written to assume that unicode data appearing where binary
# should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
#
# Other encoding values can corrupt binary data, and we
# purposefully disallow them. For the same reason, the errors=
# argument is not exposed, as values other than 'strict'
# result can similarly silently corrupt numerical data.
raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")
pickle_kwargs = {'encoding': encoding, 'fix_imports': fix_imports}
with contextlib.ExitStack() as stack:
if hasattr(file, 'read'):
fid = file
own_fid = False
else:
fid = stack.enter_context(open(os.fspath(file), "rb"))
own_fid = True
# Code to distinguish from NumPy binary files and pickles.
_ZIP_PREFIX = b'PK\x03\x04'
_ZIP_SUFFIX = b'PK\x05\x06' # empty zip files start with this
N = len(format.MAGIC_PREFIX)
magic = fid.read(N)
if not magic:
raise EOFError("No data left in file")
# If the file size is less than N, we need to make sure not
# to seek past the beginning of the file
fid.seek(-min(N, len(magic)), 1) # back-up
if magic.startswith((_ZIP_PREFIX, _ZIP_SUFFIX)):
# zip-file (assume .npz)
ret = NpzFile(fid, own_fid=own_fid, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs,
max_header_size=max_header_size)
# Transfer file ownership to ret
stack.pop_all()
return ret
elif magic == format.MAGIC_PREFIX:
# .npy file
if mmap_mode:
if allow_pickle:
max_header_size = 2**64
return format.open_memmap(file, mode=mmap_mode,
max_header_size=max_header_size)
else:
return format.read_array(fid, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs,
max_header_size=max_header_size)
else:
# Try a pickle
if not allow_pickle:
raise ValueError(
"This file contains pickled (object) data. If you trust "
"the file you can load it unsafely using the "
"`allow_pickle=` keyword argument or `pickle.load()`.")
try:
return pickle.load(fid, **pickle_kwargs)
except Exception as e:
raise pickle.UnpicklingError(
f"Failed to interpret file {file!r} as a pickle") from e
def _save_dispatcher(file, arr, allow_pickle=None):
return (arr,)
@array_function_dispatch(_save_dispatcher)
def save(file, arr, allow_pickle=True):
"""
Save an array to a binary file in NumPy ``.npy`` format.
Parameters
----------
file : file, str, or pathlib.Path
File or filename to which the data is saved. If file is a file-object,
then the filename is unchanged. If file is a string or Path,
a ``.npy`` extension will be appended to the filename if it does not
already have one.
arr : array_like
Array data to be saved.
allow_pickle : bool, optional
Allow saving object arrays using Python pickles. Reasons for
disallowing pickles include security (loading pickled data can execute
arbitrary code) and portability (pickled objects may not be loadable
on different Python installations, for example if the stored objects
require libraries that are not available, and not all pickled data is
compatible between different versions of Python).
Default: True
See Also
--------
savez : Save several arrays into a ``.npz`` archive
savetxt, load
Notes
-----
For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
Any data saved to the file is appended to the end of the file.
Examples
--------
>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> np.save(outfile, x)
>>> _ = outfile.seek(0) # Only needed to simulate closing & reopening file
>>> np.load(outfile)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> with open('test.npy', 'wb') as f:
... np.save(f, np.array([1, 2]))
... np.save(f, np.array([1, 3]))
>>> with open('test.npy', 'rb') as f:
... a = np.load(f)
... b = np.load(f)
>>> print(a, b)
# [1 2] [1 3]
"""
if hasattr(file, 'write'):
file_ctx = contextlib.nullcontext(file)
else:
file = os.fspath(file)
if not file.endswith('.npy'):
file = file + '.npy'
file_ctx = open(file, "wb")
with file_ctx as fid:
arr = np.asanyarray(arr)
format.write_array(fid, arr, allow_pickle=allow_pickle)
def _savez_dispatcher(file, *args, allow_pickle=True, **kwds):
yield from args
yield from kwds.values()
@array_function_dispatch(_savez_dispatcher)
def savez(file, *args, allow_pickle=True, **kwds):
"""Save several arrays into a single file in uncompressed ``.npz`` format.
Provide arrays as keyword arguments to store them under the
corresponding name in the output file: ``savez(fn, x=x, y=y)``.
If arrays are specified as positional arguments, i.e., ``savez(fn,
x, y)``, their names will be `arr_0`, `arr_1`, etc.
Parameters
----------
file : file, str, or pathlib.Path
Either the filename (string) or an open file (file-like object)
where the data will be saved. If file is a string or a Path, the
``.npz`` extension will be appended to the filename if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Please use keyword arguments (see
`kwds` below) to assign names to arrays. Arrays specified as
args will be named "arr_0", "arr_1", and so on.
allow_pickle : bool, optional
Allow saving object arrays using Python pickles. Reasons for
disallowing pickles include security (loading pickled data can execute
arbitrary code) and portability (pickled objects may not be loadable
on different Python installations, for example if the stored objects
require libraries that are not available, and not all pickled data is
compatible between different versions of Python).
Default: True
kwds : Keyword arguments, optional
Arrays to save to the file. Each array will be saved to the
output file with its corresponding keyword name.
Returns
-------
None
See Also
--------
save : Save a single array to a binary file in NumPy format.
savetxt : Save an array to a file as plain text.
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
The ``.npz`` file format is a zipped archive of files named after the
variables they contain. The archive is not compressed and each file
in the archive contains one variable in ``.npy`` format. For a
description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
When opening the saved ``.npz`` file with `load` a `~lib.npyio.NpzFile`
object is returned. This is a dictionary-like object which can be queried
for its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.
Keys passed in `kwds` are used as filenames inside the ZIP archive.
Therefore, keys should be valid filenames; e.g., avoid keys that begin with
``/`` or contain ``.``.
When naming variables with keyword arguments, it is not possible to name a
variable ``file``, as this would cause the ``file`` argument to be defined
twice in the call to ``savez``.
Examples
--------
>>> import numpy as np
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
Using `savez` with \\*args, the arrays are saved with default names.
>>> np.savez(outfile, x, y)
>>> _ = outfile.seek(0) # Only needed to simulate closing & reopening file
>>> npzfile = np.load(outfile)
>>> npzfile.files
['arr_0', 'arr_1']
>>> npzfile['arr_0']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Using `savez` with \\**kwds, the arrays are saved with the keyword names.
>>> outfile = TemporaryFile()
>>> np.savez(outfile, x=x, y=y)
>>> _ = outfile.seek(0)
>>> npzfile = np.load(outfile)
>>> sorted(npzfile.files)
['x', 'y']
>>> npzfile['x']
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
_savez(file, args, kwds, False, allow_pickle=allow_pickle)
def _savez_compressed_dispatcher(file, *args, allow_pickle=True, **kwds):
yield from args
yield from kwds.values()
@array_function_dispatch(_savez_compressed_dispatcher)
def savez_compressed(file, *args, allow_pickle=True, **kwds):
"""
Save several arrays into a single file in compressed ``.npz`` format.
Provide arrays as keyword arguments to store them under the
corresponding name in the output file: ``savez_compressed(fn, x=x, y=y)``.
If arrays are specified as positional arguments, i.e.,
``savez_compressed(fn, x, y)``, their names will be `arr_0`, `arr_1`, etc.
Parameters
----------
file : file, str, or pathlib.Path
Either the filename (string) or an open file (file-like object)
where the data will be saved. If file is a string or a Path, the
``.npz`` extension will be appended to the filename if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Please use keyword arguments (see
`kwds` below) to assign names to arrays. Arrays specified as
args will be named "arr_0", "arr_1", and so on.
allow_pickle : bool, optional
Allow saving object arrays using Python pickles. Reasons for
disallowing pickles include security (loading pickled data can execute
arbitrary code) and portability (pickled objects may not be loadable
on different Python installations, for example if the stored objects
require libraries that are not available, and not all pickled data is
compatible between different versions of Python).
Default: True
kwds : Keyword arguments, optional
Arrays to save to the file. Each array will be saved to the
output file with its corresponding keyword name.
Returns
-------
None
See Also
--------
numpy.save : Save a single array to a binary file in NumPy format.
numpy.savetxt : Save an array to a file as plain text.
numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
numpy.load : Load the files created by savez_compressed.
Notes
-----
The ``.npz`` file format is a zipped archive of files named after the
variables they contain. The archive is compressed with
``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable
in ``.npy`` format. For a description of the ``.npy`` format, see
:py:mod:`numpy.lib.format`.
When opening the saved ``.npz`` file with `load` a `~lib.npyio.NpzFile`
object is returned. This is a dictionary-like object which can be queried
for its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.
Examples
--------
>>> import numpy as np
>>> test_array = np.random.rand(3, 2)
>>> test_vector = np.random.rand(4)
>>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector)
>>> loaded = np.load('/tmp/123.npz')
>>> print(np.array_equal(test_array, loaded['a']))
True
>>> print(np.array_equal(test_vector, loaded['b']))
True
"""
_savez(file, args, kwds, True, allow_pickle=allow_pickle)
def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None):
# Import is postponed to here since zipfile depends on gzip, an optional
# component of the so-called standard library.
import zipfile
if not hasattr(file, 'write'):
file = os.fspath(file)
if not file.endswith('.npz'):
file = file + '.npz'
namedict = kwds
for i, val in enumerate(args):
key = f'arr_{i}'
if key in namedict.keys():
raise ValueError(
f"Cannot use un-named variables and keyword {key}")
namedict[key] = val
if compress:
compression = zipfile.ZIP_DEFLATED
else:
compression = zipfile.ZIP_STORED
zipf = zipfile_factory(file, mode="w", compression=compression)
try:
for key, val in namedict.items():
fname = key + '.npy'
val = np.asanyarray(val)
# always force zip64, gh-10776
with zipf.open(fname, 'w', force_zip64=True) as fid:
format.write_array(fid, val,
allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
finally:
zipf.close()
def _ensure_ndmin_ndarray_check_param(ndmin):
"""Just checks if the param ndmin is supported on
_ensure_ndmin_ndarray. It is intended to be used as
verification before running anything expensive.
e.g. loadtxt, genfromtxt
"""
# Check correctness of the values of `ndmin`
if ndmin not in [0, 1, 2]:
raise ValueError(f"Illegal value of ndmin keyword: {ndmin}")
def _ensure_ndmin_ndarray(a, *, ndmin: int):
"""This is a helper function of loadtxt and genfromtxt to ensure
proper minimum dimension as requested
ndim : int. Supported values 1, 2, 3
^^ whenever this changes, keep in sync with
_ensure_ndmin_ndarray_check_param
"""
# Verify that the array has at least dimensions `ndmin`.
# Tweak the size and shape of the arrays - remove extraneous dimensions
if a.ndim > ndmin:
a = np.squeeze(a)
# and ensure we have the minimum number of dimensions asked for
# - has to be in this order for the odd case ndmin=1, a.squeeze().ndim=0
if a.ndim < ndmin:
if ndmin == 1:
a = np.atleast_1d(a)
elif ndmin == 2:
a = np.atleast_2d(a).T
return a
# amount of lines loadtxt reads in one chunk, can be overridden for testing
_loadtxt_chunksize = 50000
def _check_nonneg_int(value, name="argument"):
try:
operator.index(value)
except TypeError:
raise TypeError(f"{name} must be an integer") from None
if value < 0:
raise ValueError(f"{name} must be nonnegative")
def _preprocess_comments(iterable, comments, encoding):
"""
Generator that consumes a line iterated iterable and strips out the
multiple (or multi-character) comments from lines.
This is a pre-processing step to achieve feature parity with loadtxt
(we assume that this feature is a niche feature).
"""
for line in iterable:
if isinstance(line, bytes):
# Need to handle conversion here, or the splitting would fail
line = line.decode(encoding)
for c in comments:
line = line.split(c, 1)[0]
yield line
# The number of rows we read in one go if confronted with a parametric dtype
_loadtxt_chunksize = 50000
def _read(fname, *, delimiter=',', comment='#', quote='"',
imaginary_unit='j', usecols=None, skiplines=0,
max_rows=None, converters=None, ndmin=None, unpack=False,
dtype=np.float64, encoding=None):
r"""
Read a NumPy array from a text file.
This is a helper function for loadtxt.
Parameters
----------
fname : file, str, or pathlib.Path
The filename or the file to be read.
delimiter : str, optional
Field delimiter of the fields in line of the file.
Default is a comma, ','. If None any sequence of whitespace is
considered a delimiter.
comment : str or sequence of str or None, optional
Character that begins a comment. All text from the comment
character to the end of the line is ignored.
Multiple comments or multiple-character comment strings are supported,
but may be slower and `quote` must be empty if used.
Use None to disable all use of comments.
quote : str or None, optional
Character that is used to quote string fields. Default is '"'
(a double quote). Use None to disable quote support.
imaginary_unit : str, optional
Character that represent the imaginary unit `sqrt(-1)`.
Default is 'j'.
usecols : array_like, optional
A one-dimensional array of integer column numbers. These are the
columns from the file to be included in the array. If this value
is not given, all the columns are used.
skiplines : int, optional
Number of lines to skip before interpreting the data in the file.
max_rows : int, optional
Maximum number of rows of data to read. Default is to read the
entire file.
converters : dict or callable, optional
A function to parse all columns strings into the desired value, or
a dictionary mapping column number to a parser function.
E.g. if column 0 is a date string: ``converters = {0: datestr2num}``.
Converters can also be used to provide a default value for missing
data, e.g. ``converters = lambda s: float(s.strip() or 0)`` will
convert empty fields to 0.
Default: None
ndmin : int, optional
Minimum dimension of the array returned.
Allowed values are 0, 1 or 2. Default is 0.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = read(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
dtype : numpy data type
A NumPy dtype instance, can be a structured dtype to map to the
columns of the file.
encoding : str, optional
Encoding used to decode the inputfile. The special value 'bytes'
(the default) enables backwards-compatible behavior for `converters`,
ensuring that inputs to the converter functions are encoded
bytes objects. The special value 'bytes' has no additional effect if
``converters=None``. If encoding is ``'bytes'`` or ``None``, the
default system encoding is used.
Returns
-------
ndarray
NumPy array.
"""
# Handle special 'bytes' keyword for encoding
byte_converters = False
if encoding == 'bytes':
encoding = None
byte_converters = True
if dtype is None:
raise TypeError("a dtype must be provided.")
dtype = np.dtype(dtype)
read_dtype_via_object_chunks = None
if dtype.kind in 'SUM' and dtype in {
np.dtype("S0"), np.dtype("U0"), np.dtype("M8"), np.dtype("m8")}:
# This is a legacy "flexible" dtype. We do not truly support
# parametric dtypes currently (no dtype discovery step in the core),
# but have to support these for backward compatibility.
read_dtype_via_object_chunks = dtype
dtype = np.dtype(object)
if usecols is not None:
# Allow usecols to be a single int or a sequence of ints, the C-code
# handles the rest
try:
usecols = list(usecols)
except TypeError:
usecols = [usecols]
_ensure_ndmin_ndarray_check_param(ndmin)
if comment is None:
comments = None
else:
# assume comments are a sequence of strings
if "" in comment:
raise ValueError(
"comments cannot be an empty string. Use comments=None to "
"disable comments."
)
comments = tuple(comment)
comment = None
if len(comments) == 0:
comments = None # No comments at all
elif len(comments) == 1:
# If there is only one comment, and that comment has one character,
# the normal parsing can deal with it just fine.
if isinstance(comments[0], str) and len(comments[0]) == 1:
comment = comments[0]
comments = None
# Input validation if there are multiple comment characters
elif delimiter in comments:
raise TypeError(
f"Comment characters '{comments}' cannot include the "
f"delimiter '{delimiter}'"
)
# comment is now either a 1 or 0 character string or a tuple:
if comments is not None:
# Note: An earlier version support two character comments (and could
# have been extended to multiple characters, we assume this is
# rare enough to not optimize for.
if quote is not None:
raise ValueError(
"when multiple comments or a multi-character comment is "
"given, quotes are not supported. In this case quotechar "