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PCQRSCANER/venv/Lib/site-packages/numpy/lib/npyio.py

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2019-12-22 21:51:47 +01:00
from __future__ import division, absolute_import, print_function
import sys
import os
import re
import functools
import itertools
import warnings
import weakref
import contextlib
from operator import itemgetter, index as opindex
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core import overrides
from numpy.core.multiarray import packbits, unpackbits
from numpy.core.overrides import set_module
from numpy.core._internal import recursive
from ._iotools import (
LineSplitter, NameValidator, StringConverter, ConverterError,
ConverterLockError, ConversionWarning, _is_string_like,
has_nested_fields, flatten_dtype, easy_dtype, _decode_line
)
from numpy.compat import (
asbytes, asstr, asunicode, bytes, basestring, os_fspath, os_PathLike,
pickle, contextlib_nullcontext
)
if sys.version_info[0] >= 3:
from collections.abc import Mapping
else:
from future_builtins import map
from collections import Mapping
@set_module('numpy')
def loads(*args, **kwargs):
# NumPy 1.15.0, 2017-12-10
warnings.warn(
"np.loads is deprecated, use pickle.loads instead",
DeprecationWarning, stacklevel=2)
return pickle.loads(*args, **kwargs)
__all__ = [
'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource'
]
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
class BagObj(object):
"""
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
--------
>>> from numpy.lib.npyio import BagObj as BO
>>> class BagDemo(object):
... 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)
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)
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. It assumes that files in the archive have a ``.npy`` extension,
other files are ignored.
The arrays and file strings are lazily loaded on either
getitem access using ``obj['key']`` or attribute lookup using
``obj.f.key``. A list of all files (without ``.npy`` extensions) can
be obtained with ``obj.files`` and the ZipFile object itself using
``obj.zip``.
Attributes
----------
files : list of str
List of all files in the archive with a ``.npy`` extension.
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
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
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 when using Python 3.
Parameters
----------
fid : file or str
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
--------
>>> 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.io.NpzFile)
True
>>> sorted(npz.files)
['x', 'y']
>>> npz['x'] # getitem access
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> npz.f.x # attribute lookup
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
def __init__(self, fid, own_fid=False, allow_pickle=False,
pickle_kwargs=None):
# Import is postponed to here since zipfile depends on gzip, an
# optional component of the so-called standard library.
_zip = zipfile_factory(fid)
self._files = _zip.namelist()
self.files = []
self.allow_pickle = allow_pickle
self.pickle_kwargs = pickle_kwargs
for x in self._files:
if x.endswith('.npy'):
self.files.append(x[:-4])
else:
self.files.append(x)
self.zip = _zip
self.f = BagObj(self)
if own_fid:
self.fid = fid
else:
self.fid = None
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):
# 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.
member = False
if key in self._files:
member = True
elif key in self.files:
member = True
key += '.npy'
if member:
bytes = self.zip.open(key)
magic = bytes.read(len(format.MAGIC_PREFIX))
bytes.close()
if magic == format.MAGIC_PREFIX:
bytes = self.zip.open(key)
return format.read_array(bytes,
allow_pickle=self.allow_pickle,
pickle_kwargs=self.pickle_kwargs)
else:
return self.zip.read(key)
else:
raise KeyError("%s is not a file in the archive" % key)
if sys.version_info.major == 3:
# deprecate the python 2 dict apis that we supported by accident in
# python 3. We forgot to implement itervalues() at all in earlier
# versions of numpy, so no need to deprecated it here.
def iteritems(self):
# Numpy 1.15, 2018-02-20
warnings.warn(
"NpzFile.iteritems is deprecated in python 3, to match the "
"removal of dict.itertems. Use .items() instead.",
DeprecationWarning, stacklevel=2)
return self.items()
def iterkeys(self):
# Numpy 1.15, 2018-02-20
warnings.warn(
"NpzFile.iterkeys is deprecated in python 3, to match the "
"removal of dict.iterkeys. Use .keys() instead.",
DeprecationWarning, stacklevel=2)
return self.keys()
@set_module('numpy')
def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
encoding='ASCII'):
"""
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. 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
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
fix_imports : bool, optional
Only useful when loading Python 2 generated pickled files on Python 3,
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 in Python 3, 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'
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
------
IOError
If the input file does not exist or cannot be read.
ValueError
The file contains an object array, but allow_pickle=False given.
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, 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
--------
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'")
if sys.version_info[0] >= 3:
pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
else:
# Nothing to do on Python 2
pickle_kwargs = {}
# TODO: Use contextlib.ExitStack once we drop Python 2
if hasattr(file, 'read'):
fid = file
own_fid = False
else:
fid = open(os_fspath(file), "rb")
own_fid = True
try:
# 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 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) or magic.startswith(_ZIP_SUFFIX):
# zip-file (assume .npz)
# Transfer file ownership to NpzFile
ret = NpzFile(fid, own_fid=own_fid, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
own_fid = False
return ret
elif magic == format.MAGIC_PREFIX:
# .npy file
if mmap_mode:
return format.open_memmap(file, mode=mmap_mode)
else:
return format.read_array(fid, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
else:
# Try a pickle
if not allow_pickle:
raise ValueError("Cannot load file containing pickled data "
"when allow_pickle=False")
try:
return pickle.load(fid, **pickle_kwargs)
except Exception:
raise IOError(
"Failed to interpret file %s as a pickle" % repr(file))
finally:
if own_fid:
fid.close()
def _save_dispatcher(file, arr, allow_pickle=None, fix_imports=None):
return (arr,)
@array_function_dispatch(_save_dispatcher)
def save(file, arr, allow_pickle=True, fix_imports=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 file name 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
Python 2 and Python 3).
Default: True
fix_imports : bool, optional
Only useful in forcing objects in object arrays on Python 3 to be
pickled in a Python 2 compatible way. If `fix_imports` is True, pickle
will try to map the new Python 3 names to the old module names used in
Python 2, so that the pickle data stream is readable with Python 2.
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`.
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> np.save(outfile, x)
>>> _ = outfile.seek(0) # Only needed here to simulate closing & reopening file
>>> np.load(outfile)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""
own_fid = False
if hasattr(file, 'write'):
fid = file
else:
file = os_fspath(file)
if not file.endswith('.npy'):
file = file + '.npy'
fid = open(file, "wb")
own_fid = True
if sys.version_info[0] >= 3:
pickle_kwargs = dict(fix_imports=fix_imports)
else:
# Nothing to do on Python 2
pickle_kwargs = None
try:
arr = np.asanyarray(arr)
format.write_array(fid, arr, allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
finally:
if own_fid:
fid.close()
def _savez_dispatcher(file, *args, **kwds):
for a in args:
yield a
for v in kwds.values():
yield v
@array_function_dispatch(_savez_dispatcher)
def savez(file, *args, **kwds):
"""
Save several arrays into a single file in uncompressed ``.npz`` format.
If arguments are passed in with no keywords, the corresponding variable
names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword
arguments are given, the corresponding variable names, in the ``.npz``
file will match the keyword names.
Parameters
----------
file : str or file
Either the file name (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 file name if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
know the names of the arrays outside `savez`, the arrays will be saved
with names "arr_0", "arr_1", and so on. These arguments can be any
expression.
kwds : Keyword arguments, optional
Arrays to save to the file. Arrays will be saved in the file with the
keyword names.
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 `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
--------
>>> 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 here 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)
def _savez_compressed_dispatcher(file, *args, **kwds):
for a in args:
yield a
for v in kwds.values():
yield v
@array_function_dispatch(_savez_compressed_dispatcher)
def savez_compressed(file, *args, **kwds):
"""
Save several arrays into a single file in compressed ``.npz`` format.
If keyword arguments are given, then filenames are taken from the keywords.
If arguments are passed in with no keywords, then stored file names are
arr_0, arr_1, etc.
Parameters
----------
file : str or file
Either the file name (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 file name if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Since it is not possible for Python to
know the names of the arrays outside `savez`, the arrays will be saved
with names "arr_0", "arr_1", and so on. These arguments can be any
expression.
kwds : Keyword arguments, optional
Arrays to save to the file. Arrays will be saved in the file with the
keyword names.
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 `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
--------
>>> 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)
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 = '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
zipf = zipfile_factory(file, mode="w", compression=compression)
if sys.version_info >= (3, 6):
# Since Python 3.6 it is possible to write directly to a ZIP file.
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)
else:
# Stage arrays in a temporary file on disk, before writing to zip.
# Import deferred for startup time improvement
import tempfile
# Since target file might be big enough to exceed capacity of a global
# temporary directory, create temp file side-by-side with the target file.
file_dir, file_prefix = os.path.split(file) if _is_string_like(file) else (None, 'tmp')
fd, tmpfile = tempfile.mkstemp(prefix=file_prefix, dir=file_dir, 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),
allow_pickle=allow_pickle,
pickle_kwargs=pickle_kwargs)
fid.close()
fid = None
zipf.write(tmpfile, arcname=fname)
except IOError as exc:
raise IOError("Failed to write to %s: %s" % (tmpfile, exc))
finally:
if fid:
fid.close()
finally:
os.remove(tmpfile)
zipf.close()
def _getconv(dtype):
""" Find the correct dtype converter. Adapted from matplotlib """
def floatconv(x):
x.lower()
if '0x' in x:
return float.fromhex(x)
return float(x)
typ = dtype.type
if issubclass(typ, np.bool_):
return lambda x: bool(int(x))
if issubclass(typ, np.uint64):
return np.uint64
if issubclass(typ, np.int64):
return np.int64
if issubclass(typ, np.integer):
return lambda x: int(float(x))
elif issubclass(typ, np.longdouble):
return np.longdouble
elif issubclass(typ, np.floating):
return floatconv
elif issubclass(typ, complex):
return lambda x: complex(asstr(x).replace('+-', '-'))
elif issubclass(typ, np.bytes_):
return asbytes
elif issubclass(typ, np.unicode_):
return asunicode
else:
return asstr
# amount of lines loadtxt reads in one chunk, can be overridden for testing
_loadtxt_chunksize = 50000
@set_module('numpy')
def loadtxt(fname, dtype=float, comments='#', delimiter=None,
converters=None, skiprows=0, usecols=None, unpack=False,
ndmin=0, encoding='bytes', max_rows=None):
"""
Load data from a text file.
Each row in the text file must have the same number of values.
Parameters
----------
fname : file, str, or pathlib.Path
File, filename, or generator to read. If the filename extension is
``.gz`` or ``.bz2``, the file is first decompressed. Note that
generators should return byte strings for Python 3k.
dtype : data-type, optional
Data-type of the resulting array; default: float. If this is a
structured data-type, the resulting array will be 1-dimensional, and
each row will be interpreted as an element of the array. In this
case, the number of columns used must match the number of fields in
the data-type.
comments : str or sequence of str, optional
The characters or list of characters used to indicate the start of a
comment. None implies no comments. For backwards compatibility, byte
strings will be decoded as 'latin1'. The default is '#'.
delimiter : str, optional
The string used to separate values. For backwards compatibility, byte
strings will be decoded as 'latin1'. The default is whitespace.
converters : dict, optional
A dictionary mapping column number to a function that will parse the
column string into the desired value. 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 (but see also
`genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``.
Default: None.
skiprows : int, optional
Skip the first `skiprows` lines, including comments; default: 0.
usecols : int or sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
The default, None, results in all columns being read.
.. versionchanged:: 1.11.0
When a single column has to be read it is possible to use
an integer instead of a tuple. E.g ``usecols = 3`` reads the
fourth column the same way as ``usecols = (3,)`` would.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
ndmin : int, optional
The returned array will have at least `ndmin` dimensions.
Otherwise mono-dimensional axes will be squeezed.
Legal values: 0 (default), 1 or 2.
.. versionadded:: 1.6.0
encoding : str, optional
Encoding used to decode the inputfile. Does not apply to input streams.
The special value 'bytes' enables backward compatibility workarounds
that ensures you receive byte arrays as results if possible and passes
'latin1' encoded strings to converters. Override this value to receive
unicode arrays and pass strings as input to converters. If set to None
the system default is used. The default value is 'bytes'.
.. versionadded:: 1.14.0
max_rows : int, optional
Read `max_rows` lines of content after `skiprows` lines. The default
is to read all the lines.
.. versionadded:: 1.16.0
Returns
-------
out : ndarray
Data read from the text file.
See Also
--------
load, fromstring, fromregex
genfromtxt : Load data with missing values handled as specified.
scipy.io.loadmat : reads MATLAB data files
Notes
-----
This function aims to be a fast reader for simply formatted files. The
`genfromtxt` function provides more sophisticated handling of, e.g.,
lines with missing values.
.. versionadded:: 1.10.0
The strings produced by the Python float.hex method can be used as
input for floats.
Examples
--------
>>> from io import StringIO # StringIO behaves like a file object
>>> c = StringIO(u"0 1\\n2 3")
>>> np.loadtxt(c)
array([[0., 1.],
[2., 3.]])
>>> d = StringIO(u"M 21 72\\nF 35 58")
>>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
... 'formats': ('S1', 'i4', 'f4')})
array([(b'M', 21, 72.), (b'F', 35, 58.)],
dtype=[('gender', 'S1'), ('age', '<i4'), ('weight', '<f4')])
>>> c = StringIO(u"1,0,2\\n3,0,4")
>>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
>>> x
array([1., 3.])
>>> y
array([2., 4.])
"""
# Type conversions for Py3 convenience
if comments is not None:
if isinstance(comments, (basestring, bytes)):
comments = [comments]
comments = [_decode_line(x) for x in comments]
# Compile regex for comments beforehand
comments = (re.escape(comment) for comment in comments)
regex_comments = re.compile('|'.join(comments))
if delimiter is not None:
delimiter = _decode_line(delimiter)
user_converters = converters
if encoding == 'bytes':
encoding = None
byte_converters = True
else:
byte_converters = False
if usecols is not None:
# Allow usecols to be a single int or a sequence of ints
try:
usecols_as_list = list(usecols)
except TypeError:
usecols_as_list = [usecols]
for col_idx in usecols_as_list:
try:
opindex(col_idx)
except TypeError as e:
e.args = (
"usecols must be an int or a sequence of ints but "
"it contains at least one element of type %s" %
type(col_idx),
)
raise
# Fall back to existing code
usecols = usecols_as_list
fown = False
try:
if isinstance(fname, os_PathLike):
fname = os_fspath(fname)
if _is_string_like(fname):
fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
fencoding = getattr(fh, 'encoding', 'latin1')
fh = iter(fh)
fown = True
else:
fh = iter(fname)
fencoding = getattr(fname, 'encoding', 'latin1')
except TypeError:
raise ValueError('fname must be a string, file handle, or generator')
# input may be a python2 io stream
if encoding is not None:
fencoding = encoding
# we must assume local encoding
# TODO emit portability warning?
elif fencoding is None:
import locale
fencoding = locale.getpreferredencoding()
# not to be confused with the flatten_dtype we import...
@recursive
def flatten_dtype_internal(self, dt):
"""Unpack a structured data-type, and produce re-packing info."""
if dt.names is None:
# If the dtype is flattened, return.
# If the dtype has a shape, the dtype occurs
# in the list more than once.
shape = dt.shape
if len(shape) == 0:
return ([dt.base], None)
else:
packing = [(shape[-1], list)]
if len(shape) > 1:
for dim in dt.shape[-2::-1]:
packing = [(dim*packing[0][0], packing*dim)]
return ([dt.base] * int(np.prod(dt.shape)), packing)
else:
types = []
packing = []
for field in dt.names:
tp, bytes = dt.fields[field]
flat_dt, flat_packing = self(tp)
types.extend(flat_dt)
# Avoid extra nesting for subarrays
if tp.ndim > 0:
packing.extend(flat_packing)
else:
packing.append((len(flat_dt), flat_packing))
return (types, packing)
@recursive
def pack_items(self, items, packing):
"""Pack items into nested lists based on re-packing info."""
if packing is None:
return items[0]
elif packing is tuple:
return tuple(items)
elif packing is list:
return list(items)
else:
start = 0
ret = []
for length, subpacking in packing:
ret.append(self(items[start:start+length], subpacking))
start += length
return tuple(ret)
def split_line(line):
"""Chop off comments, strip, and split at delimiter. """
line = _decode_line(line, encoding=encoding)
if comments is not None:
line = regex_comments.split(line, maxsplit=1)[0]
line = line.strip('\r\n')
if line:
return line.split(delimiter)
else:
return []
def read_data(chunk_size):
"""Parse each line, including the first.
The file read, `fh`, is a global defined above.
Parameters
----------
chunk_size : int
At most `chunk_size` lines are read at a time, with iteration
until all lines are read.
"""
X = []
line_iter = itertools.chain([first_line], fh)
line_iter = itertools.islice(line_iter, max_rows)
for i, line in enumerate(line_iter):
vals = split_line(line)
if len(vals) == 0:
continue
if usecols:
vals = [vals[j] for j in usecols]
if len(vals) != N:
line_num = i + skiprows + 1
raise ValueError("Wrong number of columns at line %d"
% line_num)
# Convert each value according to its column and store
items = [conv(val) for (conv, val) in zip(converters, vals)]
# Then pack it according to the dtype's nesting
items = pack_items(items, packing)
X.append(items)
if len(X) > chunk_size:
yield X
X = []
if X:
yield X
try:
# Make sure we're dealing with a proper dtype
dtype = np.dtype(dtype)
defconv = _getconv(dtype)
# Skip the first `skiprows` lines
for i in range(skiprows):
next(fh)
# Read until we find a line with some values, and use
# it to estimate the number of columns, N.
first_vals = None
try:
while not first_vals:
first_line = next(fh)
first_vals = split_line(first_line)
except StopIteration:
# End of lines reached
first_line = ''
first_vals = []
warnings.warn('loadtxt: Empty input file: "%s"' % fname, stacklevel=2)
N = len(usecols or first_vals)
dtype_types, packing = flatten_dtype_internal(dtype)
if len(dtype_types) > 1:
# We're dealing with a structured array, each field of
# the dtype matches a column
converters = [_getconv(dt) for dt in dtype_types]
else:
# All fields have the same dtype
converters = [defconv for i in range(N)]
if N > 1:
packing = [(N, tuple)]
# By preference, use the converters specified by the user
for i, conv in (user_converters or {}).items():
if usecols:
try:
i = usecols.index(i)
except ValueError:
# Unused converter specified
continue
if byte_converters:
# converters may use decode to workaround numpy's old behaviour,
# so encode the string again before passing to the user converter
def tobytes_first(x, conv):
if type(x) is bytes:
return conv(x)
return conv(x.encode("latin1"))
converters[i] = functools.partial(tobytes_first, conv=conv)
else:
converters[i] = conv
converters = [conv if conv is not bytes else
lambda x: x.encode(fencoding) for conv in converters]
# read data in chunks and fill it into an array via resize
# over-allocating and shrinking the array later may be faster but is
# probably not relevant compared to the cost of actually reading and
# converting the data
X = None
for x in read_data(_loadtxt_chunksize):
if X is None:
X = np.array(x, dtype)
else:
nshape = list(X.shape)
pos = nshape[0]
nshape[0] += len(x)
X.resize(nshape, refcheck=False)
X[pos:, ...] = x
finally:
if fown:
fh.close()
if X is None:
X = np.array([], dtype)
# Multicolumn data are returned with shape (1, N, M), i.e.
# (1, 1, M) for a single row - remove the singleton dimension there
if X.ndim == 3 and X.shape[:2] == (1, 1):
X.shape = (1, -1)
# Verify that the array has at least dimensions `ndmin`.
# Check correctness of the values of `ndmin`
if ndmin not in [0, 1, 2]:
raise ValueError('Illegal value of ndmin keyword: %s' % ndmin)
# Tweak the size and shape of the arrays - remove extraneous dimensions
if X.ndim > ndmin:
X = np.squeeze(X)
# and ensure we have the minimum number of dimensions asked for
# - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0
if X.ndim < ndmin:
if ndmin == 1:
X = np.atleast_1d(X)
elif ndmin == 2:
X = np.atleast_2d(X).T
if unpack:
if len(dtype_types) > 1:
# For structured arrays, return an array for each field.
return [X[field] for field in dtype.names]
else:
return X.T
else:
return X
def _savetxt_dispatcher(fname, X, fmt=None, delimiter=None, newline=None,
header=None, footer=None, comments=None,
encoding=None):
return (X,)
@array_function_dispatch(_savetxt_dispatcher)
def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='',
footer='', comments='# ', encoding=None):
"""
Save an array to a text file.
Parameters
----------
fname : filename or file handle
If the filename ends in ``.gz``, the file is automatically saved in
compressed gzip format. `loadtxt` understands gzipped files
transparently.
X : 1D or 2D array_like
Data to be saved to a text file.
fmt : str or sequence of strs, optional
A single format (%10.5f), a sequence of formats, or a
multi-format string, e.g. 'Iteration %d -- %10.5f', in which
case `delimiter` is ignored. For complex `X`, the legal options
for `fmt` are:
* a single specifier, `fmt='%.4e'`, resulting in numbers formatted
like `' (%s+%sj)' % (fmt, fmt)`
* a full string specifying every real and imaginary part, e.g.
`' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns
* a list of specifiers, one per column - in this case, the real
and imaginary part must have separate specifiers,
e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
delimiter : str, optional
String or character separating columns.
newline : str, optional
String or character separating lines.
.. versionadded:: 1.5.0
header : str, optional
String that will be written at the beginning of the file.
.. versionadded:: 1.7.0
footer : str, optional
String that will be written at the end of the file.
.. versionadded:: 1.7.0
comments : str, optional
String that will be prepended to the ``header`` and ``footer`` strings,
to mark them as comments. Default: '# ', as expected by e.g.
``numpy.loadtxt``.
.. versionadded:: 1.7.0
encoding : {None, str}, optional
Encoding used to encode the outputfile. Does not apply to output
streams. If the encoding is something other than 'bytes' or 'latin1'
you will not be able to load the file in NumPy versions < 1.14. Default
is 'latin1'.
.. versionadded:: 1.14.0
See Also
--------
save : Save an array to a binary file in NumPy ``.npy`` format
savez : Save several arrays into an uncompressed ``.npz`` archive
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
Further explanation of the `fmt` parameter
(``%[flag]width[.precision]specifier``):
flags:
``-`` : left justify
``+`` : Forces to precede result with + or -.
``0`` : Left pad the number with zeros instead of space (see width).
width:
Minimum number of characters to be printed. The value is not truncated
if it has more characters.
precision:
- For integer specifiers (eg. ``d,i,o,x``), the minimum number of
digits.
- For ``e, E`` and ``f`` specifiers, the number of digits to print
after the decimal point.
- For ``g`` and ``G``, the maximum number of significant digits.
- For ``s``, the maximum number of characters.
specifiers:
``c`` : character
``d`` or ``i`` : signed decimal integer
``e`` or ``E`` : scientific notation with ``e`` or ``E``.
``f`` : decimal floating point
``g,G`` : use the shorter of ``e,E`` or ``f``
``o`` : signed octal
``s`` : string of characters
``u`` : unsigned decimal integer
``x,X`` : unsigned hexadecimal integer
This explanation of ``fmt`` is not complete, for an exhaustive
specification see [1]_.
References
----------
.. [1] `Format Specification Mini-Language
<https://docs.python.org/library/string.html#format-specification-mini-language>`_,
Python Documentation.
Examples
--------
>>> x = y = z = np.arange(0.0,5.0,1.0)
>>> np.savetxt('test.out', x, delimiter=',') # X is an array
>>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays
>>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation
"""
# Py3 conversions first
if isinstance(fmt, bytes):
fmt = asstr(fmt)
delimiter = asstr(delimiter)
class WriteWrap(object):
"""Convert to unicode in py2 or to bytes on bytestream inputs.
"""
def __init__(self, fh, encoding):
self.fh = fh
self.encoding = encoding
self.do_write = self.first_write
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 _is_string_like(fname):
# datasource doesn't support creating a new file ...
open(fname, 'wt').close()
fh = np.lib._datasource.open(fname, 'wt', encoding=encoding)
own_fh = True
# need to convert str to unicode for text io output
if sys.version_info[0] == 2:
fh = WriteWrap(fh, encoding or 'latin1')
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))
format = asstr(delimiter).join(map(asstr, fmt))
elif isinstance(fmt, basestring):
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
format = 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:
format = 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 = format % tuple(row2) + newline
fh.write(s.replace('+-', '-'))
else:
for row in X:
try:
v = format % tuple(row) + newline
except TypeError:
raise TypeError("Mismatch between array dtype ('%s') and "
"format specifier ('%s')"
% (str(X.dtype), format))
fh.write(v)
if len(footer) > 0:
footer = footer.replace('\n', '\n' + comments)
fh.write(comments + footer + newline)
finally:
if own_fh:
fh.close()
@set_module('numpy')
def fromregex(file, regexp, dtype, encoding=None):
"""
Construct an array from a text file, using regular expression parsing.
The returned array is always a structured array, and is constructed from
all matches of the regular expression in the file. Groups in the regular
expression are converted to fields of the structured array.
Parameters
----------
file : str or file
File name or file object to read.
regexp : str or regexp
Regular expression used to parse the file.
Groups in the regular expression correspond to fields in the dtype.
dtype : dtype or list of dtypes
Dtype for the structured array.
encoding : str, optional
Encoding used to decode the inputfile. Does not apply to input streams.
.. versionadded:: 1.14.0
Returns
-------
output : ndarray
The output array, containing the part of the content of `file` that
was matched by `regexp`. `output` is always a structured array.
Raises
------
TypeError
When `dtype` is not a valid dtype for a structured array.
See Also
--------
fromstring, loadtxt
Notes
-----
Dtypes for structured arrays can be specified in several forms, but all
forms specify at least the data type and field name. For details see
`doc.structured_arrays`.
Examples
--------
>>> f = open('test.dat', 'w')
>>> _ = f.write("1312 foo\\n1534 bar\\n444 qux")
>>> f.close()
>>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything]
>>> output = np.fromregex('test.dat', regexp,
... [('num', np.int64), ('key', 'S3')])
>>> output
array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')],
dtype=[('num', '<i8'), ('key', 'S3')])
>>> output['num']
array([1312, 1534, 444])
"""
own_fh = False
if not hasattr(file, "read"):
file = np.lib._datasource.open(file, 'rt', encoding=encoding)
own_fh = True
try:
if not isinstance(dtype, np.dtype):
dtype = np.dtype(dtype)
content = file.read()
if isinstance(content, bytes) and isinstance(regexp, np.unicode):
regexp = asbytes(regexp)
elif isinstance(content, np.unicode) and isinstance(regexp, bytes):
regexp = asstr(regexp)
if not hasattr(regexp, 'match'):
regexp = re.compile(regexp)
seq = regexp.findall(content)
if seq and not isinstance(seq[0], tuple):
# Only one group is in the regexp.
# Create the new array as a single data-type and then
# re-interpret as a single-field structured array.
newdtype = np.dtype(dtype[dtype.names[0]])
output = np.array(seq, dtype=newdtype)
output.dtype = dtype
else:
output = np.array(seq, dtype=dtype)
return output
finally:
if own_fh:
file.close()
#####--------------------------------------------------------------------------
#---- --- ASCII functions ---
#####--------------------------------------------------------------------------
@set_module('numpy')
def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
skip_header=0, skip_footer=0, converters=None,
missing_values=None, filling_values=None, usecols=None,
names=None, excludelist=None,
deletechars=''.join(sorted(NameValidator.defaultdeletechars)),
replace_space='_', autostrip=False, case_sensitive=True,
defaultfmt="f%i", unpack=None, usemask=False, loose=True,
invalid_raise=True, max_rows=None, encoding='bytes'):
"""
Load data from a text file, with missing values handled as specified.
Each line past the first `skip_header` lines is split at the `delimiter`
character, and characters following the `comments` character are discarded.
Parameters
----------
fname : file, str, pathlib.Path, list of str, generator
File, filename, list, or generator to read. If the filename
extension is `.gz` or `.bz2`, the file is first decompressed. Note
that generators must return byte strings in Python 3k. The strings
in a list or produced by a generator are treated as lines.
dtype : dtype, optional
Data type of the resulting array.
If None, the dtypes will be determined by the contents of each
column, individually.
comments : str, optional
The character used to indicate the start of a comment.
All the characters occurring on a line after a comment are discarded
delimiter : str, int, or sequence, optional
The string used to separate values. By default, any consecutive
whitespaces act as delimiter. An integer or sequence of integers
can also be provided as width(s) of each field.
skiprows : int, optional
`skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
skip_header : int, optional
The number of lines to skip at the beginning of the file.
skip_footer : int, optional
The number of lines to skip at the end of the file.
converters : variable, optional
The set of functions that convert the data of a column to a value.
The converters can also be used to provide a default value
for missing data: ``converters = {3: lambda s: float(s or 0)}``.
missing : variable, optional
`missing` was removed in numpy 1.10. Please use `missing_values`
instead.
missing_values : variable, optional
The set of strings corresponding to missing data.
filling_values : variable, optional
The set of values to be used as default when the data are missing.
usecols : sequence, optional
Which columns to read, with 0 being the first. For example,
``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
names : {None, True, str, sequence}, optional
If `names` is True, the field names are read from the first line after
the first `skip_header` lines. This line can optionally be proceeded
by a comment delimiter. If `names` is a sequence or a single-string of
comma-separated names, the names will be used to define the field names
in a structured dtype. If `names` is None, the names of the dtype
fields will be used, if any.
excludelist : sequence, optional
A list of names to exclude. This list is appended to the default list
['return','file','print']. Excluded names are appended an underscore:
for example, `file` would become `file_`.
deletechars : str, optional
A string combining invalid characters that must be deleted from the
names.
defaultfmt : str, optional
A format used to define default field names, such as "f%i" or "f_%02i".
autostrip : bool, optional
Whether to automatically strip white spaces from the variables.
replace_space : char, optional
Character(s) used in replacement of white spaces in the variables
names. By default, use a '_'.
case_sensitive : {True, False, 'upper', 'lower'}, optional
If True, field names are case sensitive.
If False or 'upper', field names are converted to upper case.
If 'lower', field names are converted to lower case.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``
usemask : bool, optional
If True, return a masked array.
If False, return a regular array.
loose : bool, optional
If True, do not raise errors for invalid values.
invalid_raise : bool, optional
If True, an exception is raised if an inconsistency is detected in the
number of columns.
If False, a warning is emitted and the offending lines are skipped.
max_rows : int, optional
The maximum number of rows to read. Must not be used with skip_footer
at the same time. If given, the value must be at least 1. Default is
to read the entire file.
.. versionadded:: 1.10.0
encoding : str, optional
Encoding used to decode the inputfile. Does not apply when `fname` is
a file object. The special value 'bytes' enables backward compatibility
workarounds that ensure that you receive byte arrays when possible
and passes latin1 encoded strings to converters. Override this value to
receive unicode arrays and pass strings as input to converters. If set
to None the system default is used. The default value is 'bytes'.
.. versionadded:: 1.14.0
Returns
-------
out : ndarray
Data read from the text file. If `usemask` is True, this is a
masked array.
See Also
--------
numpy.loadtxt : equivalent function when no data is missing.
Notes
-----
* When spaces are used as delimiters, or when no delimiter has been given
as input, there should not be any missing data between two fields.
* When the variables are named (either by a flexible dtype or with `names`,
there must not be any header in the file (else a ValueError
exception is raised).
* Individual values are not stripped of spaces by default.
When using a custom converter, make sure the function does remove spaces.
References
----------
.. [1] NumPy User Guide, section `I/O with NumPy
<https://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
Examples
---------
>>> from io import StringIO
>>> import numpy as np
Comma delimited file with mixed dtype
>>> s = StringIO(u"1,1.3,abcde")
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
Using dtype = None
>>> _ = s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
Specifying dtype and names
>>> _ = s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
An example with fixed-width columns
>>> s = StringIO(u"11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
>>> data
array((1, 1.3, b'abcde'),
dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', 'S5')])
An example to show comments
>>> f = StringIO('''
... text,# of chars
... hello world,11
... numpy,5''')
>>> np.genfromtxt(f, dtype='S12,S12', delimiter=',')
array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')],
dtype=[('f0', 'S12'), ('f1', 'S12')])
"""
if max_rows is not None:
if skip_footer:
raise ValueError(
"The keywords 'skip_footer' and 'max_rows' can not be "
"specified at the same time.")
if max_rows < 1:
raise ValueError("'max_rows' must be at least 1.")
if usemask:
from numpy.ma import MaskedArray, make_mask_descr
# Check the input dictionary of converters
user_converters = converters or {}
if not isinstance(user_converters, dict):
raise TypeError(
"The input argument 'converter' should be a valid dictionary "
"(got '%s' instead)" % type(user_converters))
if encoding == 'bytes':
encoding = None
byte_converters = True
else:
byte_converters = False
# Initialize the filehandle, the LineSplitter and the NameValidator
try:
if isinstance(fname, os_PathLike):
fname = os_fspath(fname)
if isinstance(fname, basestring):
fid = np.lib._datasource.open(fname, 'rt', encoding=encoding)
fid_ctx = contextlib.closing(fid)
else:
fid = fname
fid_ctx = contextlib_nullcontext(fid)
fhd = iter(fid)
except TypeError:
raise TypeError(
"fname must be a string, filehandle, list of strings, "
"or generator. Got %s instead." % type(fname))
with fid_ctx:
split_line = LineSplitter(delimiter=delimiter, comments=comments,
autostrip=autostrip, encoding=encoding)
validate_names = NameValidator(excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Skip the first `skip_header` rows
for i in range(skip_header):
next(fhd)
# Keep on until we find the first valid values
first_values = None
try:
while not first_values:
first_line = _decode_line(next(fhd), encoding)
if (names is True) and (comments is not None):
if comments in first_line:
first_line = (
''.join(first_line.split(comments)[1:]))
first_values = split_line(first_line)
except StopIteration:
# return an empty array if the datafile is empty
first_line = ''
first_values = []
warnings.warn('genfromtxt: Empty input file: "%s"' % fname, stacklevel=2)
# Should we take the first values as names ?
if names is True:
fval = first_values[0].strip()
if comments is not None:
if fval in comments:
del first_values[0]
# Check the columns to use: make sure `usecols` is a list
if usecols is not None:
try:
usecols = [_.strip() for _ in usecols.split(",")]
except AttributeError:
try:
usecols = list(usecols)
except TypeError:
usecols = [usecols, ]
nbcols = len(usecols or first_values)
# Check the names and overwrite the dtype.names if needed
if names is True:
names = validate_names([str(_.strip()) for _ in first_values])
first_line = ''
elif _is_string_like(names):
names = validate_names([_.strip() for _ in names.split(',')])
elif names:
names = validate_names(names)
# Get the dtype
if dtype is not None:
dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
excludelist=excludelist,
deletechars=deletechars,
case_sensitive=case_sensitive,
replace_space=replace_space)
# Make sure the names is a list (for 2.5)
if names is not None:
names = list(names)
if usecols:
for (i, current) in enumerate(usecols):
# if usecols is a list of names, convert to a list of indices
if _is_string_like(current):
usecols[i] = names.index(current)
elif current < 0:
usecols[i] = current + len(first_values)
# If the dtype is not None, make sure we update it
if (dtype is not None) and (len(dtype) > nbcols):
descr = dtype.descr
dtype = np.dtype([descr[_] for _ in usecols])
names = list(dtype.names)
# If `names` is not None, update the names
elif (names is not None) and (len(names) > nbcols):
names = [names[_] for _ in usecols]
elif (names is not None) and (dtype is not None):
names = list(dtype.names)
# Process the missing values ...............................
# Rename missing_values for convenience
user_missing_values = missing_values or ()
if isinstance(user_missing_values, bytes):
user_missing_values = user_missing_values.decode('latin1')
# Define the list of missing_values (one column: one list)
missing_values = [list(['']) for _ in range(nbcols)]
# We have a dictionary: process it field by field
if isinstance(user_missing_values, dict):
# Loop on the items
for (key, val) in user_missing_values.items():
# Is the key a string ?
if _is_string_like(key):
try:
# Transform it into an integer
key = names.index(key)
except ValueError:
# We couldn't find it: the name must have been dropped
continue
# Redefine the key as needed if it's a column number
if usecols:
try:
key = usecols.index(key)
except ValueError:
pass
# Transform the value as a list of string
if isinstance(val, (list, tuple)):
val = [str(_) for _ in val]
else:
val = [str(val), ]
# Add the value(s) to the current list of missing
if key is None:
# None acts as default
for miss in missing_values:
miss.extend(val)
else:
missing_values[key].extend(val)
# We have a sequence : each item matches a column
elif isinstance(user_missing_values, (list, tuple)):
for (value, entry) in zip(user_missing_values, missing_values):
value = str(value)
if value not in entry:
entry.append(value)
# We have a string : apply it to all entries
elif isinstance(user_missing_values, basestring):
user_value = user_missing_values.split(",")
for entry in missing_values:
entry.extend(user_value)
# We have something else: apply it to all entries
else:
for entry in missing_values:
entry.extend([str(user_missing_values)])
# Process the filling_values ...............................
# Rename the input for convenience
user_filling_values = filling_values
if user_filling_values is None:
user_filling_values = []
# Define the default
filling_values = [None] * nbcols
# We have a dictionary : update each entry individually
if isinstance(user_filling_values, dict):
for (key, val) in user_filling_values.items():
if _is_string_like(key):
try:
# Transform it into an integer
key = names.index(key)
except ValueError:
# We couldn't find it: the name must have been dropped,
continue
# Redefine the key if it's a column number and usecols is defined
if usecols:
try:
key = usecols.index(key)
except ValueError:
pass
# Add the value to the list
filling_values[key] = val
# We have a sequence : update on a one-to-one basis
elif isinstance(user_filling_values, (list, tuple)):
n = len(user_filling_values)
if (n <= nbcols):
filling_values[:n] = user_filling_values
else:
filling_values = user_filling_values[:nbcols]
# We have something else : use it for all entries
else:
filling_values = [user_filling_values] * nbcols
# Initialize the converters ................................
if dtype is None:
# Note: we can't use a [...]*nbcols, as we would have 3 times the same
# ... converter, instead of 3 different converters.
converters = [StringConverter(None, missing_values=miss, default=fill)
for (miss, fill) in zip(missing_values, filling_values)]
else:
dtype_flat = flatten_dtype(dtype, flatten_base=True)
# Initialize the converters
if len(dtype_flat) > 1:
# Flexible type : get a converter from each dtype
zipit = zip(dtype_flat, missing_values, filling_values)
converters = [StringConverter(dt, locked=True,
missing_values=miss, default=fill)
for (dt, miss, fill) in zipit]
else:
# Set to a default converter (but w/ different missing values)
zipit = zip(missing_values, filling_values)
converters = [StringConverter(dtype, locked=True,
missing_values=miss, default=fill)
for (miss, fill) in zipit]
# Update the converters to use the user-defined ones
uc_update = []
for (j, conv) in user_converters.items():
# If the converter is specified by column names, use the index instead
if _is_string_like(j):
try:
j = names.index(j)
i = j
except ValueError:
continue
elif usecols:
try:
i = usecols.index(j)
except ValueError:
# Unused converter specified
continue
else:
i = j
# Find the value to test - first_line is not filtered by usecols:
if len(first_line):
testing_value = first_values[j]
else:
testing_value = None
if conv is bytes:
user_conv = asbytes
elif byte_converters:
# converters may use decode to workaround numpy's old behaviour,
# so encode the string again before passing to the user converter
def tobytes_first(x, conv):
if type(x) is bytes:
return conv(x)
return conv(x.encode("latin1"))
user_conv = functools.partial(tobytes_first, conv=conv)
else:
user_conv = conv
converters[i].update(user_conv, locked=True,
testing_value=testing_value,
default=filling_values[i],
missing_values=missing_values[i],)
uc_update.append((i, user_conv))
# Make sure we have the corrected keys in user_converters...
user_converters.update(uc_update)
# Fixme: possible error as following variable never used.
# miss_chars = [_.missing_values for _ in converters]
# Initialize the output lists ...
# ... rows
rows = []
append_to_rows = rows.append
# ... masks
if usemask:
masks = []
append_to_masks = masks.append
# ... invalid
invalid = []
append_to_invalid = invalid.append
# Parse each line
for (i, line) in enumerate(itertools.chain([first_line, ], fhd)):
values = split_line(line)
nbvalues = len(values)
# Skip an empty line
if nbvalues == 0:
continue
if usecols:
# Select only the columns we need
try:
values = [values[_] for _ in usecols]
except IndexError:
append_to_invalid((i + skip_header + 1, nbvalues))
continue
elif nbvalues != nbcols:
append_to_invalid((i + skip_header + 1, nbvalues))
continue
# Store the values
append_to_rows(tuple(values))
if usemask:
append_to_masks(tuple([v.strip() in m
for (v, m) in zip(values,
missing_values)]))
if len(rows) == max_rows:
break
# Upgrade the converters (if needed)
if dtype is None:
for (i, converter) in enumerate(converters):
current_column = [itemgetter(i)(_m) for _m in rows]
try:
converter.iterupgrade(current_column)
except ConverterLockError:
errmsg = "Converter #%i is locked and cannot be upgraded: " % i
current_column = map(itemgetter(i), rows)
for (j, value) in enumerate(current_column):
try:
converter.upgrade(value)
except (ConverterError, ValueError):
errmsg += "(occurred line #%i for value '%s')"
errmsg %= (j + 1 + skip_header, value)
raise ConverterError(errmsg)
# Check that we don't have invalid values
nbinvalid = len(invalid)
if nbinvalid > 0:
nbrows = len(rows) + nbinvalid - skip_footer
# Construct the error message
template = " Line #%%i (got %%i columns instead of %i)" % nbcols
if skip_footer > 0:
nbinvalid_skipped = len([_ for _ in invalid
if _[0] > nbrows + skip_header])
invalid = invalid[:nbinvalid - nbinvalid_skipped]
skip_footer -= nbinvalid_skipped
#
# nbrows -= skip_footer
# errmsg = [template % (i, nb)
# for (i, nb) in invalid if i < nbrows]
# else:
errmsg = [template % (i, nb)
for (i, nb) in invalid]
if len(errmsg):
errmsg.insert(0, "Some errors were detected !")
errmsg = "\n".join(errmsg)
# Raise an exception ?
if invalid_raise:
raise ValueError(errmsg)
# Issue a warning ?
else:
warnings.warn(errmsg, ConversionWarning, stacklevel=2)
# Strip the last skip_footer data
if skip_footer > 0:
rows = rows[:-skip_footer]
if usemask:
masks = masks[:-skip_footer]
# Convert each value according to the converter:
# We want to modify the list in place to avoid creating a new one...
if loose:
rows = list(
zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)]
for (i, conv) in enumerate(converters)]))
else:
rows = list(
zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)]
for (i, conv) in enumerate(converters)]))
# Reset the dtype
data = rows
if dtype is None:
# Get the dtypes from the types of the converters
column_types = [conv.type for conv in converters]
# Find the columns with strings...
strcolidx = [i for (i, v) in enumerate(column_types)
if v == np.unicode_]
if byte_converters and strcolidx:
# convert strings back to bytes for backward compatibility
warnings.warn(
"Reading unicode strings without specifying the encoding "
"argument is deprecated. Set the encoding, use None for the "
"system default.",
np.VisibleDeprecationWarning, stacklevel=2)
def encode_unicode_cols(row_tup):
row = list(row_tup)
for i in strcolidx:
row[i] = row[i].encode('latin1')
return tuple(row)
try:
data = [encode_unicode_cols(r) for r in data]
except UnicodeEncodeError:
pass
else:
for i in strcolidx:
column_types[i] = np.bytes_
# Update string types to be the right length
sized_column_types = column_types[:]
for i, col_type in enumerate(column_types):
if np.issubdtype(col_type, np.character):
n_chars = max(len(row[i]) for row in data)
sized_column_types[i] = (col_type, n_chars)
if names is None:
# If the dtype is uniform (before sizing strings)
base = {
c_type
for c, c_type in zip(converters, column_types)
if c._checked}
if len(base) == 1:
uniform_type, = base
(ddtype, mdtype) = (uniform_type, bool)
else:
ddtype = [(defaultfmt % i, dt)
for (i, dt) in enumerate(sized_column_types)]
if usemask:
mdtype = [(defaultfmt % i, bool)
for (i, dt) in enumerate(sized_column_types)]
else:
ddtype = list(zip(names, sized_column_types))
mdtype = list(zip(names, [bool] * len(sized_column_types)))
output = np.array(data, dtype=ddtype)
if usemask:
outputmask = np.array(masks, dtype=mdtype)
else:
# Overwrite the initial dtype names if needed
if names and dtype.names is not None:
dtype.names = names
# Case 1. We have a structured type
if len(dtype_flat) > 1:
# Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])]
# First, create the array using a flattened dtype:
# [('a', int), ('b1', int), ('b2', float)]
# Then, view the array using the specified dtype.
if 'O' in (_.char for _ in dtype_flat):
if has_nested_fields(dtype):
raise NotImplementedError(
"Nested fields involving objects are not supported...")
else:
output = np.array(data, dtype=dtype)
else:
rows = np.array(data, dtype=[('', _) for _ in dtype_flat])
output = rows.view(dtype)
# Now, process the rowmasks the same way
if usemask:
rowmasks = np.array(
masks, dtype=np.dtype([('', bool) for t in dtype_flat]))
# Construct the new dtype
mdtype = make_mask_descr(dtype)
outputmask = rowmasks.view(mdtype)
# Case #2. We have a basic dtype
else:
# We used some user-defined converters
if user_converters:
ishomogeneous = True
descr = []
for i, ttype in enumerate([conv.type for conv in converters]):
# Keep the dtype of the current converter
if i in user_converters:
ishomogeneous &= (ttype == dtype.type)
if np.issubdtype(ttype, np.character):
ttype = (ttype, max(len(row[i]) for row in data))
descr.append(('', ttype))
else:
descr.append(('', dtype))
# So we changed the dtype ?
if not ishomogeneous:
# We have more than one field
if len(descr) > 1:
dtype = np.dtype(descr)
# We have only one field: drop the name if not needed.
else:
dtype = np.dtype(ttype)
#
output = np.array(data, dtype)
if usemask:
if dtype.names is not None:
mdtype = [(_, bool) for _ in dtype.names]
else:
mdtype = bool
outputmask = np.array(masks, dtype=mdtype)
# Try to take care of the missing data we missed
names = output.dtype.names
if usemask and names:
for (name, conv) in zip(names, converters):
missing_values = [conv(_) for _ in conv.missing_values
if _ != '']
for mval in missing_values:
outputmask[name] |= (output[name] == mval)
# Construct the final array
if usemask:
output = output.view(MaskedArray)
output._mask = outputmask
if unpack:
return output.squeeze().T
return output.squeeze()
def ndfromtxt(fname, **kwargs):
"""
Load ASCII data stored in a file and return it as a single array.
.. deprecated:: 1.17
ndfromtxt` is a deprecated alias of `genfromtxt` which
overwrites the ``usemask`` argument with `False` even when
explicitly called as ``ndfromtxt(..., usemask=True)``.
Use `genfromtxt` instead.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function.
"""
kwargs['usemask'] = False
# Numpy 1.17
warnings.warn(
"np.ndfromtxt is a deprecated alias of np.genfromtxt, "
"prefer the latter.",
DeprecationWarning, stacklevel=2)
return genfromtxt(fname, **kwargs)
def mafromtxt(fname, **kwargs):
"""
Load ASCII data stored in a text file and return a masked array.
.. deprecated:: 1.17
np.mafromtxt is a deprecated alias of `genfromtxt` which
overwrites the ``usemask`` argument with `True` even when
explicitly called as ``mafromtxt(..., usemask=False)``.
Use `genfromtxt` instead.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function to load ASCII data.
"""
kwargs['usemask'] = True
# Numpy 1.17
warnings.warn(
"np.mafromtxt is a deprecated alias of np.genfromtxt, "
"prefer the latter.",
DeprecationWarning, stacklevel=2)
return genfromtxt(fname, **kwargs)
def recfromtxt(fname, **kwargs):
"""
Load ASCII data from a file and return it in a record array.
If ``usemask=False`` a standard `recarray` is returned,
if ``usemask=True`` a MaskedRecords array is returned.
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function
Notes
-----
By default, `dtype` is None, which means that the data-type of the output
array will be determined from the data.
"""
kwargs.setdefault("dtype", None)
usemask = kwargs.get('usemask', False)
output = genfromtxt(fname, **kwargs)
if usemask:
from numpy.ma.mrecords import MaskedRecords
output = output.view(MaskedRecords)
else:
output = output.view(np.recarray)
return output
def recfromcsv(fname, **kwargs):
"""
Load ASCII data stored in a comma-separated file.
The returned array is a record array (if ``usemask=False``, see
`recarray`) or a masked record array (if ``usemask=True``,
see `ma.mrecords.MaskedRecords`).
Parameters
----------
fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
--------
numpy.genfromtxt : generic function to load ASCII data.
Notes
-----
By default, `dtype` is None, which means that the data-type of the output
array will be determined from the data.
"""
# Set default kwargs for genfromtxt as relevant to csv import.
kwargs.setdefault("case_sensitive", "lower")
kwargs.setdefault("names", True)
kwargs.setdefault("delimiter", ",")
kwargs.setdefault("dtype", None)
output = genfromtxt(fname, **kwargs)
usemask = kwargs.get("usemask", False)
if usemask:
from numpy.ma.mrecords import MaskedRecords
output = output.view(MaskedRecords)
else:
output = output.view(np.recarray)
return output
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