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2367 lines
86 KiB
Python
2367 lines
86 KiB
Python
from __future__ import division, absolute_import, print_function
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import sys
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import os
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import re
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import functools
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import itertools
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import warnings
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import weakref
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import contextlib
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from operator import itemgetter, index as opindex
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import numpy as np
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from . import format
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from ._datasource import DataSource
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from numpy.core import overrides
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from numpy.core.multiarray import packbits, unpackbits
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from numpy.core.overrides import set_module
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from numpy.core._internal import recursive
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from ._iotools import (
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LineSplitter, NameValidator, StringConverter, ConverterError,
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ConverterLockError, ConversionWarning, _is_string_like,
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has_nested_fields, flatten_dtype, easy_dtype, _decode_line
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)
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from numpy.compat import (
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asbytes, asstr, asunicode, bytes, basestring, os_fspath, os_PathLike,
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pickle, contextlib_nullcontext
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)
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if sys.version_info[0] >= 3:
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from collections.abc import Mapping
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else:
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from future_builtins import map
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from collections import Mapping
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@set_module('numpy')
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def loads(*args, **kwargs):
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# NumPy 1.15.0, 2017-12-10
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warnings.warn(
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"np.loads is deprecated, use pickle.loads instead",
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DeprecationWarning, stacklevel=2)
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return pickle.loads(*args, **kwargs)
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__all__ = [
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'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
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'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
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'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource'
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]
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array_function_dispatch = functools.partial(
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overrides.array_function_dispatch, module='numpy')
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class BagObj(object):
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"""
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BagObj(obj)
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Convert attribute look-ups to getitems on the object passed in.
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Parameters
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----------
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obj : class instance
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Object on which attribute look-up is performed.
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Examples
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--------
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>>> from numpy.lib.npyio import BagObj as BO
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>>> class BagDemo(object):
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... def __getitem__(self, key): # An instance of BagObj(BagDemo)
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... # will call this method when any
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... # attribute look-up is required
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... result = "Doesn't matter what you want, "
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... return result + "you're gonna get this"
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...
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>>> demo_obj = BagDemo()
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>>> bagobj = BO(demo_obj)
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>>> bagobj.hello_there
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"Doesn't matter what you want, you're gonna get this"
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>>> bagobj.I_can_be_anything
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"Doesn't matter what you want, you're gonna get this"
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"""
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def __init__(self, obj):
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# Use weakref to make NpzFile objects collectable by refcount
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self._obj = weakref.proxy(obj)
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def __getattribute__(self, key):
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try:
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return object.__getattribute__(self, '_obj')[key]
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except KeyError:
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raise AttributeError(key)
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def __dir__(self):
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"""
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Enables dir(bagobj) to list the files in an NpzFile.
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This also enables tab-completion in an interpreter or IPython.
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"""
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return list(object.__getattribute__(self, '_obj').keys())
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def zipfile_factory(file, *args, **kwargs):
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"""
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Create a ZipFile.
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Allows for Zip64, and the `file` argument can accept file, str, or
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pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile
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constructor.
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"""
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if not hasattr(file, 'read'):
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file = os_fspath(file)
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import zipfile
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kwargs['allowZip64'] = True
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return zipfile.ZipFile(file, *args, **kwargs)
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class NpzFile(Mapping):
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"""
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NpzFile(fid)
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A dictionary-like object with lazy-loading of files in the zipped
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archive provided on construction.
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`NpzFile` is used to load files in the NumPy ``.npz`` data archive
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format. It assumes that files in the archive have a ``.npy`` extension,
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other files are ignored.
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The arrays and file strings are lazily loaded on either
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getitem access using ``obj['key']`` or attribute lookup using
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``obj.f.key``. A list of all files (without ``.npy`` extensions) can
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be obtained with ``obj.files`` and the ZipFile object itself using
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``obj.zip``.
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Attributes
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----------
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files : list of str
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List of all files in the archive with a ``.npy`` extension.
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zip : ZipFile instance
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The ZipFile object initialized with the zipped archive.
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f : BagObj instance
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An object on which attribute can be performed as an alternative
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to getitem access on the `NpzFile` instance itself.
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allow_pickle : bool, optional
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Allow loading pickled data. Default: False
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.. versionchanged:: 1.16.3
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Made default False in response to CVE-2019-6446.
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pickle_kwargs : dict, optional
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Additional keyword arguments to pass on to pickle.load.
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These are only useful when loading object arrays saved on
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Python 2 when using Python 3.
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Parameters
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----------
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fid : file or str
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The zipped archive to open. This is either a file-like object
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or a string containing the path to the archive.
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own_fid : bool, optional
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Whether NpzFile should close the file handle.
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Requires that `fid` is a file-like object.
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Examples
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--------
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>>> from tempfile import TemporaryFile
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>>> outfile = TemporaryFile()
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>>> x = np.arange(10)
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>>> y = np.sin(x)
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>>> np.savez(outfile, x=x, y=y)
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>>> _ = outfile.seek(0)
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>>> npz = np.load(outfile)
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>>> isinstance(npz, np.lib.io.NpzFile)
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True
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>>> sorted(npz.files)
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['x', 'y']
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>>> npz['x'] # getitem access
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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>>> npz.f.x # attribute lookup
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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"""
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def __init__(self, fid, own_fid=False, allow_pickle=False,
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pickle_kwargs=None):
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# Import is postponed to here since zipfile depends on gzip, an
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# optional component of the so-called standard library.
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_zip = zipfile_factory(fid)
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self._files = _zip.namelist()
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self.files = []
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self.allow_pickle = allow_pickle
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self.pickle_kwargs = pickle_kwargs
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for x in self._files:
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if x.endswith('.npy'):
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self.files.append(x[:-4])
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else:
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self.files.append(x)
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self.zip = _zip
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self.f = BagObj(self)
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if own_fid:
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self.fid = fid
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else:
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self.fid = None
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.close()
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def close(self):
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"""
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Close the file.
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"""
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if self.zip is not None:
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self.zip.close()
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self.zip = None
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if self.fid is not None:
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self.fid.close()
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self.fid = None
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self.f = None # break reference cycle
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def __del__(self):
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self.close()
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# Implement the Mapping ABC
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def __iter__(self):
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return iter(self.files)
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def __len__(self):
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return len(self.files)
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def __getitem__(self, key):
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# FIXME: This seems like it will copy strings around
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# more than is strictly necessary. The zipfile
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# will read the string and then
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# the format.read_array will copy the string
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# to another place in memory.
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# It would be better if the zipfile could read
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# (or at least uncompress) the data
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# directly into the array memory.
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member = False
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if key in self._files:
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member = True
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elif key in self.files:
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member = True
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key += '.npy'
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if member:
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bytes = self.zip.open(key)
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magic = bytes.read(len(format.MAGIC_PREFIX))
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bytes.close()
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if magic == format.MAGIC_PREFIX:
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bytes = self.zip.open(key)
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return format.read_array(bytes,
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allow_pickle=self.allow_pickle,
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pickle_kwargs=self.pickle_kwargs)
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else:
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return self.zip.read(key)
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else:
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raise KeyError("%s is not a file in the archive" % key)
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if sys.version_info.major == 3:
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# deprecate the python 2 dict apis that we supported by accident in
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# python 3. We forgot to implement itervalues() at all in earlier
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# versions of numpy, so no need to deprecated it here.
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def iteritems(self):
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# Numpy 1.15, 2018-02-20
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warnings.warn(
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"NpzFile.iteritems is deprecated in python 3, to match the "
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"removal of dict.itertems. Use .items() instead.",
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DeprecationWarning, stacklevel=2)
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return self.items()
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def iterkeys(self):
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# Numpy 1.15, 2018-02-20
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warnings.warn(
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"NpzFile.iterkeys is deprecated in python 3, to match the "
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"removal of dict.iterkeys. Use .keys() instead.",
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DeprecationWarning, stacklevel=2)
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return self.keys()
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@set_module('numpy')
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def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
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encoding='ASCII'):
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"""
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Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
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.. warning:: Loading files that contain object arrays uses the ``pickle``
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module, which is not secure against erroneous or maliciously
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constructed data. Consider passing ``allow_pickle=False`` to
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load data that is known not to contain object arrays for the
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safer handling of untrusted sources.
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Parameters
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----------
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file : file-like object, string, or pathlib.Path
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The file to read. File-like objects must support the
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``seek()`` and ``read()`` methods. Pickled files require that the
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file-like object support the ``readline()`` method as well.
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mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
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If not None, then memory-map the file, using the given mode (see
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`numpy.memmap` for a detailed description of the modes). A
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memory-mapped array is kept on disk. However, it can be accessed
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and sliced like any ndarray. Memory mapping is especially useful
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for accessing small fragments of large files without reading the
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entire file into memory.
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allow_pickle : bool, optional
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Allow loading pickled object arrays stored in npy files. Reasons for
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disallowing pickles include security, as loading pickled data can
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execute arbitrary code. If pickles are disallowed, loading object
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arrays will fail. Default: False
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.. versionchanged:: 1.16.3
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Made default False in response to CVE-2019-6446.
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fix_imports : bool, optional
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Only useful when loading Python 2 generated pickled files on Python 3,
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which includes npy/npz files containing object arrays. If `fix_imports`
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is True, pickle will try to map the old Python 2 names to the new names
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used in Python 3.
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encoding : str, optional
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What encoding to use when reading Python 2 strings. Only useful when
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loading Python 2 generated pickled files in Python 3, which includes
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npy/npz files containing object arrays. Values other than 'latin1',
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'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
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data. Default: 'ASCII'
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Returns
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-------
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result : array, tuple, dict, etc.
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Data stored in the file. For ``.npz`` files, the returned instance
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of NpzFile class must be closed to avoid leaking file descriptors.
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Raises
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------
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IOError
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If the input file does not exist or cannot be read.
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ValueError
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The file contains an object array, but allow_pickle=False given.
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See Also
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--------
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save, savez, savez_compressed, loadtxt
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memmap : Create a memory-map to an array stored in a file on disk.
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lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
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Notes
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-----
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- If the file contains pickle data, then whatever object is stored
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in the pickle is returned.
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- If the file is a ``.npy`` file, then a single array is returned.
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- If the file is a ``.npz`` file, then a dictionary-like object is
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returned, containing ``{filename: array}`` key-value pairs, one for
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each file in the archive.
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- If the file is a ``.npz`` file, the returned value supports the
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context manager protocol in a similar fashion to the open function::
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with load('foo.npz') as data:
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a = data['a']
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The underlying file descriptor is closed when exiting the 'with'
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block.
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Examples
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--------
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Store data to disk, and load it again:
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>>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
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>>> np.load('/tmp/123.npy')
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array([[1, 2, 3],
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[4, 5, 6]])
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Store compressed data to disk, and load it again:
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>>> a=np.array([[1, 2, 3], [4, 5, 6]])
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>>> b=np.array([1, 2])
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>>> np.savez('/tmp/123.npz', a=a, b=b)
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>>> data = np.load('/tmp/123.npz')
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>>> data['a']
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array([[1, 2, 3],
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[4, 5, 6]])
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>>> data['b']
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array([1, 2])
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>>> data.close()
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Mem-map the stored array, and then access the second row
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directly from disk:
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>>> X = np.load('/tmp/123.npy', mmap_mode='r')
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>>> X[1, :]
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memmap([4, 5, 6])
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"""
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if encoding not in ('ASCII', 'latin1', 'bytes'):
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# The 'encoding' value for pickle also affects what encoding
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# the serialized binary data of NumPy arrays is loaded
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# in. Pickle does not pass on the encoding information to
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# NumPy. The unpickling code in numpy.core.multiarray is
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# written to assume that unicode data appearing where binary
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# should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
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#
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# Other encoding values can corrupt binary data, and we
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# purposefully disallow them. For the same reason, the errors=
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# argument is not exposed, as values other than 'strict'
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# result can similarly silently corrupt numerical data.
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raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")
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|
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if sys.version_info[0] >= 3:
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pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
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else:
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# Nothing to do on Python 2
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pickle_kwargs = {}
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|
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# TODO: Use contextlib.ExitStack once we drop Python 2
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if hasattr(file, 'read'):
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fid = file
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own_fid = False
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else:
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fid = open(os_fspath(file), "rb")
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own_fid = True
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|
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try:
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# Code to distinguish from NumPy binary files and pickles.
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_ZIP_PREFIX = b'PK\x03\x04'
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_ZIP_SUFFIX = b'PK\x05\x06' # empty zip files start with this
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N = len(format.MAGIC_PREFIX)
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magic = fid.read(N)
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# If the file size is less than N, we need to make sure not
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# to seek past the beginning of the file
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fid.seek(-min(N, len(magic)), 1) # back-up
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if magic.startswith(_ZIP_PREFIX) or magic.startswith(_ZIP_SUFFIX):
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# zip-file (assume .npz)
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# Transfer file ownership to NpzFile
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ret = NpzFile(fid, own_fid=own_fid, allow_pickle=allow_pickle,
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pickle_kwargs=pickle_kwargs)
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own_fid = False
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return ret
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elif magic == format.MAGIC_PREFIX:
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# .npy file
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if mmap_mode:
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return format.open_memmap(file, mode=mmap_mode)
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else:
|
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return format.read_array(fid, allow_pickle=allow_pickle,
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pickle_kwargs=pickle_kwargs)
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else:
|
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# Try a pickle
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if not allow_pickle:
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raise ValueError("Cannot load file containing pickled data "
|
|
"when allow_pickle=False")
|
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try:
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return pickle.load(fid, **pickle_kwargs)
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except Exception:
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raise IOError(
|
|
"Failed to interpret file %s as a pickle" % repr(file))
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finally:
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if own_fid:
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fid.close()
|
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|
|
|
|
def _save_dispatcher(file, arr, allow_pickle=None, fix_imports=None):
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return (arr,)
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|
|
|
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@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.
|
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|
|
Parameters
|
|
----------
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file : file, str, or pathlib.Path
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File or filename to which the data is saved. If file is a file-object,
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then the filename is unchanged. If file is a string or Path, a ``.npy``
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extension will be appended to the file name if it does not already
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have one.
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arr : array_like
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Array data to be saved.
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allow_pickle : bool, optional
|
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Allow saving object arrays using Python pickles. Reasons for disallowing
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pickles include security (loading pickled data can execute arbitrary
|
|
code) and portability (pickled objects may not be loadable on different
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Python installations, for example if the stored objects require libraries
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that are not available, and not all pickled data is compatible between
|
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Python 2 and Python 3).
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Default: True
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fix_imports : bool, optional
|
|
Only useful in forcing objects in object arrays on Python 3 to be
|
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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.
|
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|
|
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()
|
|
|
|
|
|
#####--------------------------------------------------------------------------
|
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#---- --- ASCII functions ---
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#####--------------------------------------------------------------------------
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@set_module('numpy')
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def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
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skip_header=0, skip_footer=0, converters=None,
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missing_values=None, filling_values=None, usecols=None,
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names=None, excludelist=None,
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deletechars=''.join(sorted(NameValidator.defaultdeletechars)),
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replace_space='_', autostrip=False, case_sensitive=True,
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defaultfmt="f%i", unpack=None, usemask=False, loose=True,
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invalid_raise=True, max_rows=None, encoding='bytes'):
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"""
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Load data from a text file, with missing values handled as specified.
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Each line past the first `skip_header` lines is split at the `delimiter`
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character, and characters following the `comments` character are discarded.
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Parameters
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----------
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fname : file, str, pathlib.Path, list of str, generator
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File, filename, list, or generator to read. If the filename
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extension is `.gz` or `.bz2`, the file is first decompressed. Note
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that generators must return byte strings in Python 3k. The strings
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in a list or produced by a generator are treated as lines.
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dtype : dtype, optional
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Data type of the resulting array.
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If None, the dtypes will be determined by the contents of each
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column, individually.
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comments : str, optional
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The character used to indicate the start of a comment.
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All the characters occurring on a line after a comment are discarded
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delimiter : str, int, or sequence, optional
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The string used to separate values. By default, any consecutive
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whitespaces act as delimiter. An integer or sequence of integers
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can also be provided as width(s) of each field.
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skiprows : int, optional
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`skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
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skip_header : int, optional
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The number of lines to skip at the beginning of the file.
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skip_footer : int, optional
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The number of lines to skip at the end of the file.
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converters : variable, optional
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The set of functions that convert the data of a column to a value.
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The converters can also be used to provide a default value
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for missing data: ``converters = {3: lambda s: float(s or 0)}``.
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missing : variable, optional
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`missing` was removed in numpy 1.10. Please use `missing_values`
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instead.
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missing_values : variable, optional
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The set of strings corresponding to missing data.
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filling_values : variable, optional
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The set of values to be used as default when the data are missing.
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usecols : sequence, optional
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Which columns to read, with 0 being the first. For example,
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``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
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names : {None, True, str, sequence}, optional
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If `names` is True, the field names are read from the first line after
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the first `skip_header` lines. This line can optionally be proceeded
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by a comment delimiter. If `names` is a sequence or a single-string of
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comma-separated names, the names will be used to define the field names
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in a structured dtype. If `names` is None, the names of the dtype
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fields will be used, if any.
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excludelist : sequence, optional
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A list of names to exclude. This list is appended to the default list
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['return','file','print']. Excluded names are appended an underscore:
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for example, `file` would become `file_`.
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deletechars : str, optional
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A string combining invalid characters that must be deleted from the
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names.
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defaultfmt : str, optional
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A format used to define default field names, such as "f%i" or "f_%02i".
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autostrip : bool, optional
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Whether to automatically strip white spaces from the variables.
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replace_space : char, optional
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Character(s) used in replacement of white spaces in the variables
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names. By default, use a '_'.
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case_sensitive : {True, False, 'upper', 'lower'}, optional
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If True, field names are case sensitive.
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If False or 'upper', field names are converted to upper case.
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If 'lower', field names are converted to lower case.
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unpack : bool, optional
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If True, the returned array is transposed, so that arguments may be
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unpacked using ``x, y, z = loadtxt(...)``
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usemask : bool, optional
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If True, return a masked array.
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If False, return a regular array.
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loose : bool, optional
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If True, do not raise errors for invalid values.
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invalid_raise : bool, optional
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If True, an exception is raised if an inconsistency is detected in the
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number of columns.
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If False, a warning is emitted and the offending lines are skipped.
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max_rows : int, optional
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The maximum number of rows to read. Must not be used with skip_footer
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at the same time. If given, the value must be at least 1. Default is
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to read the entire file.
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.. versionadded:: 1.10.0
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encoding : str, optional
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Encoding used to decode the inputfile. Does not apply when `fname` is
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a file object. The special value 'bytes' enables backward compatibility
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workarounds that ensure that you receive byte arrays when possible
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and passes latin1 encoded strings to converters. Override this value to
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receive unicode arrays and pass strings as input to converters. If set
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to None the system default is used. The default value is 'bytes'.
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.. versionadded:: 1.14.0
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Returns
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-------
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out : ndarray
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Data read from the text file. If `usemask` is True, this is a
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masked array.
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See Also
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--------
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numpy.loadtxt : equivalent function when no data is missing.
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Notes
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-----
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* When spaces are used as delimiters, or when no delimiter has been given
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as input, there should not be any missing data between two fields.
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* When the variables are named (either by a flexible dtype or with `names`,
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there must not be any header in the file (else a ValueError
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exception is raised).
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* Individual values are not stripped of spaces by default.
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When using a custom converter, make sure the function does remove spaces.
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References
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----------
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.. [1] NumPy User Guide, section `I/O with NumPy
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<https://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
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Examples
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---------
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>>> from io import StringIO
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>>> import numpy as np
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Comma delimited file with mixed dtype
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>>> s = StringIO(u"1,1.3,abcde")
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>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
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... ('mystring','S5')], delimiter=",")
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>>> data
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array((1, 1.3, b'abcde'),
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dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
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Using dtype = None
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>>> _ = s.seek(0) # needed for StringIO example only
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>>> data = np.genfromtxt(s, dtype=None,
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... names = ['myint','myfloat','mystring'], delimiter=",")
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>>> data
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array((1, 1.3, b'abcde'),
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dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
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Specifying dtype and names
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>>> _ = s.seek(0)
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>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
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... names=['myint','myfloat','mystring'], delimiter=",")
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>>> data
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array((1, 1.3, b'abcde'),
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dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
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An example with fixed-width columns
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>>> s = StringIO(u"11.3abcde")
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>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
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... delimiter=[1,3,5])
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>>> data
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array((1, 1.3, b'abcde'),
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dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', 'S5')])
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An example to show comments
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>>> f = StringIO('''
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... text,# of chars
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... hello world,11
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... numpy,5''')
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>>> np.genfromtxt(f, dtype='S12,S12', delimiter=',')
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array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')],
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dtype=[('f0', 'S12'), ('f1', 'S12')])
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"""
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if max_rows is not None:
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if skip_footer:
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raise ValueError(
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"The keywords 'skip_footer' and 'max_rows' can not be "
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"specified at the same time.")
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if max_rows < 1:
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raise ValueError("'max_rows' must be at least 1.")
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if usemask:
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from numpy.ma import MaskedArray, make_mask_descr
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# Check the input dictionary of converters
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user_converters = converters or {}
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if not isinstance(user_converters, dict):
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raise TypeError(
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"The input argument 'converter' should be a valid dictionary "
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"(got '%s' instead)" % type(user_converters))
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if encoding == 'bytes':
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encoding = None
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byte_converters = True
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else:
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byte_converters = False
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# Initialize the filehandle, the LineSplitter and the NameValidator
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try:
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if isinstance(fname, os_PathLike):
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fname = os_fspath(fname)
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if isinstance(fname, basestring):
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fid = np.lib._datasource.open(fname, 'rt', encoding=encoding)
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fid_ctx = contextlib.closing(fid)
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else:
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fid = fname
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fid_ctx = contextlib_nullcontext(fid)
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fhd = iter(fid)
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except TypeError:
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raise TypeError(
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"fname must be a string, filehandle, list of strings, "
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"or generator. Got %s instead." % type(fname))
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with fid_ctx:
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split_line = LineSplitter(delimiter=delimiter, comments=comments,
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autostrip=autostrip, encoding=encoding)
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validate_names = NameValidator(excludelist=excludelist,
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deletechars=deletechars,
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case_sensitive=case_sensitive,
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replace_space=replace_space)
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# Skip the first `skip_header` rows
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for i in range(skip_header):
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next(fhd)
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# Keep on until we find the first valid values
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first_values = None
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try:
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while not first_values:
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first_line = _decode_line(next(fhd), encoding)
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if (names is True) and (comments is not None):
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if comments in first_line:
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first_line = (
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''.join(first_line.split(comments)[1:]))
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first_values = split_line(first_line)
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except StopIteration:
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# return an empty array if the datafile is empty
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first_line = ''
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first_values = []
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warnings.warn('genfromtxt: Empty input file: "%s"' % fname, stacklevel=2)
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# Should we take the first values as names ?
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if names is True:
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fval = first_values[0].strip()
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if comments is not None:
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if fval in comments:
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del first_values[0]
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# Check the columns to use: make sure `usecols` is a list
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if usecols is not None:
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try:
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usecols = [_.strip() for _ in usecols.split(",")]
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except AttributeError:
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try:
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usecols = list(usecols)
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except TypeError:
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usecols = [usecols, ]
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nbcols = len(usecols or first_values)
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# Check the names and overwrite the dtype.names if needed
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if names is True:
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names = validate_names([str(_.strip()) for _ in first_values])
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first_line = ''
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elif _is_string_like(names):
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names = validate_names([_.strip() for _ in names.split(',')])
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elif names:
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names = validate_names(names)
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# Get the dtype
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if dtype is not None:
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dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
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excludelist=excludelist,
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deletechars=deletechars,
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case_sensitive=case_sensitive,
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replace_space=replace_space)
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# Make sure the names is a list (for 2.5)
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if names is not None:
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names = list(names)
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if usecols:
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for (i, current) in enumerate(usecols):
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# if usecols is a list of names, convert to a list of indices
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if _is_string_like(current):
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usecols[i] = names.index(current)
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elif current < 0:
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usecols[i] = current + len(first_values)
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# If the dtype is not None, make sure we update it
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if (dtype is not None) and (len(dtype) > nbcols):
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descr = dtype.descr
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dtype = np.dtype([descr[_] for _ in usecols])
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names = list(dtype.names)
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# If `names` is not None, update the names
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elif (names is not None) and (len(names) > nbcols):
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names = [names[_] for _ in usecols]
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elif (names is not None) and (dtype is not None):
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names = list(dtype.names)
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|
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# Process the missing values ...............................
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# Rename missing_values for convenience
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user_missing_values = missing_values or ()
|
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if isinstance(user_missing_values, bytes):
|
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user_missing_values = user_missing_values.decode('latin1')
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# Define the list of missing_values (one column: one list)
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missing_values = [list(['']) for _ in range(nbcols)]
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# We have a dictionary: process it field by field
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if isinstance(user_missing_values, dict):
|
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# Loop on the items
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for (key, val) in user_missing_values.items():
|
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# Is the key a string ?
|
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if _is_string_like(key):
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try:
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# Transform it into an integer
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key = names.index(key)
|
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except ValueError:
|
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# We couldn't find it: the name must have been dropped
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continue
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# Redefine the key as needed if it's a column number
|
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if usecols:
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try:
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key = usecols.index(key)
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except ValueError:
|
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pass
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# Transform the value as a list of string
|
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if isinstance(val, (list, tuple)):
|
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val = [str(_) for _ in val]
|
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else:
|
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val = [str(val), ]
|
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# Add the value(s) to the current list of missing
|
|
if key is None:
|
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# None acts as default
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for miss in missing_values:
|
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miss.extend(val)
|
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else:
|
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missing_values[key].extend(val)
|
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# We have a sequence : each item matches a column
|
|
elif isinstance(user_missing_values, (list, tuple)):
|
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for (value, entry) in zip(user_missing_values, missing_values):
|
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value = str(value)
|
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if value not in entry:
|
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entry.append(value)
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# We have a string : apply it to all entries
|
|
elif isinstance(user_missing_values, basestring):
|
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user_value = user_missing_values.split(",")
|
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for entry in missing_values:
|
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entry.extend(user_value)
|
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# We have something else: apply it to all entries
|
|
else:
|
|
for entry in missing_values:
|
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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:
|
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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
|