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BagObj(obj)
Convert attribute look-ups to getitems on the object passed in.
Parameters
----------
obj : class instance
Object on which attribute look-up is performed.
Examples
--------
>>> from numpy.lib.npyio import BagObj as BO
>>> class BagDemo:
... def __getitem__(self, key): # An instance of BagObj(BagDemo)
... # will call this method when any
... # attribute look-up is required
... result = "Doesn't matter what you want, "
... return result + "you're gonna get this"
...
>>> demo_obj = BagDemo()
>>> bagobj = BO(demo_obj)
>>> bagobj.hello_there
"Doesn't matter what you want, you're gonna get this"
>>> bagobj.I_can_be_anything
"Doesn't matter what you want, you're gonna get this"
c 8 t j | | _ d S N)weakrefproxy_obj)selfobjs c/builddir/build/BUILD/cloudlinux-venv-1.0.10/venv/lib64/python3.11/site-packages/numpy/lib/npyio.py__init__zBagObj.__init__H s M#&& c t | d | S # t $ r t | d w xY w)Nr3 )object__getattribute__KeyErrorAttributeErrorr4 keys r6 r; zBagObj.__getattribute__L sM 0**488= = 0 0 0 %%4 / 0s # >c v t t | d S )z
Enables dir(bagobj) to list the files in an NpzFile.
This also enables tab-completion in an interpreter or IPython.
r3 )listr: r; keysr4 s r6 __dir__zBagObj.__dir__R s. F++D&99>>@@AAAr8 N)__name__
__module__ __qualname____doc__r7 r; rD r8 r6 r. r. * sP :' ' '0 0 0 B B B B Br8 r. c t t | d st | } ddl}d|d< |j | g|R i |S )z
Create a ZipFile.
Allows for Zip64, and the `file` argument can accept file, str, or
pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile
constructor.
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NpzFile(fid)
A dictionary-like object with lazy-loading of files in the zipped
archive provided on construction.
`NpzFile` is used to load files in the NumPy ``.npz`` data archive
format. It assumes that files in the archive have a ``.npy`` extension,
other files are ignored.
The arrays and file strings are lazily loaded on either
getitem access using ``obj['key']`` or attribute lookup using
``obj.f.key``. A list of all files (without ``.npy`` extensions) can
be obtained with ``obj.files`` and the ZipFile object itself using
``obj.zip``.
Attributes
----------
files : list of str
List of all files in the archive with a ``.npy`` extension.
zip : ZipFile instance
The ZipFile object initialized with the zipped archive.
f : BagObj instance
An object on which attribute can be performed as an alternative
to getitem access on the `NpzFile` instance itself.
allow_pickle : bool, optional
Allow loading pickled data. Default: False
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
pickle_kwargs : dict, optional
Additional keyword arguments to pass on to pickle.load.
These are only useful when loading object arrays saved on
Python 2 when using Python 3.
max_header_size : int, optional
Maximum allowed size of the header. Large headers may not be safe
to load securely and thus require explicitly passing a larger value.
See :py:func:`ast.literal_eval()` for details.
This option is ignored when `allow_pickle` is passed. In that case
the file is by definition trusted and the limit is unnecessary.
Parameters
----------
fid : file or str
The zipped archive to open. This is either a file-like object
or a string containing the path to the archive.
own_fid : bool, optional
Whether NpzFile should close the file handle.
Requires that `fid` is a file-like object.
Examples
--------
>>> from tempfile import TemporaryFile
>>> outfile = TemporaryFile()
>>> x = np.arange(10)
>>> y = np.sin(x)
>>> np.savez(outfile, x=x, y=y)
>>> _ = outfile.seek(0)
>>> npz = np.load(outfile)
>>> isinstance(npz, np.lib.npyio.NpzFile)
True
>>> npz
NpzFile 'object' with keys x, y
>>> sorted(npz.files)
['x', 'y']
>>> npz['x'] # getitem access
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> npz.f.x # attribute lookup
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
N Fmax_header_sizec t | }| | _ g | _ || _ || _ || _ | j D ]T}| d r#| j |d d :| j | U|| _ t | | _ |r || _ d S d S )N.npy)
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Close the file.
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Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.
.. warning:: Loading files that contain object arrays uses the ``pickle``
module, which is not secure against erroneous or maliciously
constructed data. Consider passing ``allow_pickle=False`` to
load data that is known not to contain object arrays for the
safer handling of untrusted sources.
Parameters
----------
file : file-like object, string, or pathlib.Path
The file to read. File-like objects must support the
``seek()`` and ``read()`` methods and must always
be opened in binary mode. Pickled files require that the
file-like object support the ``readline()`` method as well.
mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
If not None, then memory-map the file, using the given mode (see
`numpy.memmap` for a detailed description of the modes). A
memory-mapped array is kept on disk. However, it can be accessed
and sliced like any ndarray. Memory mapping is especially useful
for accessing small fragments of large files without reading the
entire file into memory.
allow_pickle : bool, optional
Allow loading pickled object arrays stored in npy files. Reasons for
disallowing pickles include security, as loading pickled data can
execute arbitrary code. If pickles are disallowed, loading object
arrays will fail. Default: False
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
fix_imports : bool, optional
Only useful when loading Python 2 generated pickled files on Python 3,
which includes npy/npz files containing object arrays. If `fix_imports`
is True, pickle will try to map the old Python 2 names to the new names
used in Python 3.
encoding : str, optional
What encoding to use when reading Python 2 strings. Only useful when
loading Python 2 generated pickled files in Python 3, which includes
npy/npz files containing object arrays. Values other than 'latin1',
'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
data. Default: 'ASCII'
max_header_size : int, optional
Maximum allowed size of the header. Large headers may not be safe
to load securely and thus require explicitly passing a larger value.
See :py:func:`ast.literal_eval()` for details.
This option is ignored when `allow_pickle` is passed. In that case
the file is by definition trusted and the limit is unnecessary.
Returns
-------
result : array, tuple, dict, etc.
Data stored in the file. For ``.npz`` files, the returned instance
of NpzFile class must be closed to avoid leaking file descriptors.
Raises
------
OSError
If the input file does not exist or cannot be read.
UnpicklingError
If ``allow_pickle=True``, but the file cannot be loaded as a pickle.
ValueError
The file contains an object array, but ``allow_pickle=False`` given.
EOFError
When calling ``np.load`` multiple times on the same file handle,
if all data has already been read
See Also
--------
save, savez, savez_compressed, loadtxt
memmap : Create a memory-map to an array stored in a file on disk.
lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
Notes
-----
- If the file contains pickle data, then whatever object is stored
in the pickle is returned.
- If the file is a ``.npy`` file, then a single array is returned.
- If the file is a ``.npz`` file, then a dictionary-like object is
returned, containing ``{filename: array}`` key-value pairs, one for
each file in the archive.
- If the file is a ``.npz`` file, the returned value supports the
context manager protocol in a similar fashion to the open function::
with load('foo.npz') as data:
a = data['a']
The underlying file descriptor is closed when exiting the 'with'
block.
Examples
--------
Store data to disk, and load it again:
>>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
>>> np.load('/tmp/123.npy')
array([[1, 2, 3],
[4, 5, 6]])
Store compressed data to disk, and load it again:
>>> a=np.array([[1, 2, 3], [4, 5, 6]])
>>> b=np.array([1, 2])
>>> np.savez('/tmp/123.npz', a=a, b=b)
>>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
[4, 5, 6]])
>>> data['b']
array([1, 2])
>>> data.close()
Mem-map the stored array, and then access the second row
directly from disk:
>>> X = np.load('/tmp/123.npy', mmap_mode='r')
>>> X[1, :]
memmap([4, 5, 6])
)r latin1r z.encoding must be 'ASCII', 'latin1', or 'bytes')encoding fix_importsrK FrbTs PKs PKzNo data left in filer )rf r_ r` rX Nl )moderX r} z@Cannot load file containing pickled data when allow_pickle=FalsezFailed to interpret file z as a pickle)
ValueErrordict
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Save an array to a binary file in NumPy ``.npy`` format.
Parameters
----------
file : file, str, or pathlib.Path
File or filename to which the data is saved. If file is a file-object,
then the filename is unchanged. If file is a string or Path, a ``.npy``
extension will be appended to the filename if it does not already
have one.
arr : array_like
Array data to be saved.
allow_pickle : bool, optional
Allow saving object arrays using Python pickles. Reasons for disallowing
pickles include security (loading pickled data can execute arbitrary
code) and portability (pickled objects may not be loadable on different
Python installations, for example if the stored objects require libraries
that are not available, and not all pickled data is compatible between
Python 2 and Python 3).
Default: True
fix_imports : bool, optional
Only useful in forcing objects in object arrays on Python 3 to be
pickled in a Python 2 compatible way. If `fix_imports` is True, pickle
will try to map the new Python 3 names to the old module names used in
Python 2, so that the pickle data stream is readable with Python 2.
See Also
--------
savez : Save several arrays into a ``.npz`` archive
savetxt, load
Notes
-----
For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
Any data saved to the file is appended to the end of the file.
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])
>>> with open('test.npy', 'wb') as f:
... np.save(f, np.array([1, 2]))
... np.save(f, np.array([1, 3]))
>>> with open('test.npy', 'rb') as f:
... a = np.load(f)
... b = np.load(f)
>>> print(a, b)
# [1 2] [1 3]
writerZ wb)r r_ r` N) rM r nullcontextr ra r~ np
asanyarrayr write_arrayr )rP r r_ r file_ctxre s r6 r' r' s z tW $ )$//}}V$$ !&=Dd## HSmC 3,)-+)F)F)F H H H HH H H H H H H H H H H H H H H H H Hs !;B))B-0B-c / N K |E d {V | E d {V d S r0 valuesrP rQ kwdss r6 _savez_dispatcherr & = OOOOOOO{{}} r8 c * t | ||d dS )a Save several arrays into a single file in uncompressed ``.npz`` format.
Provide arrays as keyword arguments to store them under the
corresponding name in the output file: ``savez(fn, x=x, y=y)``.
If arrays are specified as positional arguments, i.e., ``savez(fn,
x, y)``, their names will be `arr_0`, `arr_1`, etc.
Parameters
----------
file : str or file
Either the filename (string) or an open file (file-like object)
where the data will be saved. If file is a string or a Path, the
``.npz`` extension will be appended to the filename if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Please use keyword arguments (see
`kwds` below) to assign names to arrays. Arrays specified as
args will be named "arr_0", "arr_1", and so on.
kwds : Keyword arguments, optional
Arrays to save to the file. Each array will be saved to the
output file with its corresponding keyword name.
Returns
-------
None
See Also
--------
save : Save a single array to a binary file in NumPy format.
savetxt : Save an array to a file as plain text.
savez_compressed : Save several arrays into a compressed ``.npz`` archive
Notes
-----
The ``.npz`` file format is a zipped archive of files named after the
variables they contain. The archive is not compressed and each file
in the archive contains one variable in ``.npy`` format. For a
description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.
When opening the saved ``.npz`` file with `load` a `NpzFile` object is
returned. This is a dictionary-like object which can be queried for
its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.
Keys passed in `kwds` are used as filenames inside the ZIP archive.
Therefore, keys should be valid filenames; e.g., avoid keys that begin with
``/`` or contain ``.``.
When naming variables with keyword arguments, it is not possible to name a
variable ``file``, as this would cause the ``file`` argument to be defined
twice in the call to ``savez``.
Examples
--------
>>> 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])
FN_savezr s r6 r( r( + s h 4tU#####r8 c / N K |E d {V | E d {V d S r0 r r s r6 _savez_compressed_dispatcherr r r8 c * t | ||d dS )a
Save several arrays into a single file in compressed ``.npz`` format.
Provide arrays as keyword arguments to store them under the
corresponding name in the output file: ``savez(fn, x=x, y=y)``.
If arrays are specified as positional arguments, i.e., ``savez(fn,
x, y)``, their names will be `arr_0`, `arr_1`, etc.
Parameters
----------
file : str or file
Either the filename (string) or an open file (file-like object)
where the data will be saved. If file is a string or a Path, the
``.npz`` extension will be appended to the filename if it is not
already there.
args : Arguments, optional
Arrays to save to the file. Please use keyword arguments (see
`kwds` below) to assign names to arrays. Arrays specified as
args will be named "arr_0", "arr_1", and so on.
kwds : Keyword arguments, optional
Arrays to save to the file. Each array will be saved to the
output file with its corresponding keyword name.
Returns
-------
None
See Also
--------
numpy.save : Save a single array to a binary file in NumPy format.
numpy.savetxt : Save an array to a file as plain text.
numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
numpy.load : Load the files created by savez_compressed.
Notes
-----
The ``.npz`` file format is a zipped archive of files named after the
variables they contain. The archive is compressed with
``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable
in ``.npy`` format. For a description of the ``.npy`` format, see
:py:mod:`numpy.lib.format`.
When opening the saved ``.npz`` file with `load` a `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
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Read a NumPy array from a text file.
Parameters
----------
fname : str or file object
The filename or the file to be read.
delimiter : str, optional
Field delimiter of the fields in line of the file.
Default is a comma, ','. If None any sequence of whitespace is
considered a delimiter.
comment : str or sequence of str or None, optional
Character that begins a comment. All text from the comment
character to the end of the line is ignored.
Multiple comments or multiple-character comment strings are supported,
but may be slower and `quote` must be empty if used.
Use None to disable all use of comments.
quote : str or None, optional
Character that is used to quote string fields. Default is '"'
(a double quote). Use None to disable quote support.
imaginary_unit : str, optional
Character that represent the imaginay unit `sqrt(-1)`.
Default is 'j'.
usecols : array_like, optional
A one-dimensional array of integer column numbers. These are the
columns from the file to be included in the array. If this value
is not given, all the columns are used.
skiplines : int, optional
Number of lines to skip before interpreting the data in the file.
max_rows : int, optional
Maximum number of rows of data to read. Default is to read the
entire file.
converters : dict or callable, optional
A function to parse all columns strings into the desired value, or
a dictionary mapping column number to a parser function.
E.g. if column 0 is a date string: ``converters = {0: datestr2num}``.
Converters can also be used to provide a default value for missing
data, e.g. ``converters = lambda s: float(s.strip() or 0)`` will
convert empty fields to 0.
Default: None
ndmin : int, optional
Minimum dimension of the array returned.
Allowed values are 0, 1 or 2. Default is 0.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = read(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
dtype : numpy data type
A NumPy dtype instance, can be a structured dtype to map to the
columns of the file.
encoding : str, optional
Encoding used to decode the inputfile. The special value 'bytes'
(the default) enables backwards-compatible behavior for `converters`,
ensuring that inputs to the converter functions are encoded
bytes objects. The special value 'bytes' has no additional effect if
``converters=None``. If encoding is ``'bytes'`` or ``None``, the
default system encoding is used.
Returns
-------
ndarray
NumPy array.
Examples
--------
First we create a file for the example.
>>> s1 = '1.0,2.0,3.0\n4.0,5.0,6.0\n'
>>> with open('example1.csv', 'w') as f:
... f.write(s1)
>>> a1 = read_from_filename('example1.csv')
>>> a1
array([[1., 2., 3.],
[4., 5., 6.]])
The second example has columns with different data types, so a
one-dimensional array with a structured data type is returned.
The tab character is used as the field delimiter.
>>> s2 = '1.0\t10\talpha\n2.3\t25\tbeta\n4.5\t16\tgamma\n'
>>> with open('example2.tsv', 'w') as f:
... f.write(s2)
>>> a2 = read_from_filename('example2.tsv', delimiter='\t')
>>> a2
array([(1. , 10, b'alpha'), (2.3, 25, b'beta'), (4.5, 16, b'gamma')],
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Load data from a text file.
Parameters
----------
fname : file, str, pathlib.Path, list of str, generator
File, filename, list, or generator to read. If the filename
extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note
that generators must return bytes or strings. The strings
in a list or produced by a generator are treated as lines.
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 or None, 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 character used to separate the values. For backwards compatibility,
byte strings will be decoded as 'latin1'. The default is whitespace.
.. versionchanged:: 1.23.0
Only single character delimiters are supported. Newline characters
cannot be used as the delimiter.
converters : dict or callable, optional
Converter functions to customize value parsing. If `converters` is
callable, the function is applied to all columns, else it must be a
dict that maps column number to a parser function.
See examples for further details.
Default: None.
.. versionchanged:: 1.23.0
The ability to pass a single callable to be applied to all columns
was added.
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` rows of content after `skiprows` lines. The default is
to read all the rows. Note that empty rows containing no data such as
empty lines and comment lines are not counted towards `max_rows`,
while such lines are counted in `skiprows`.
.. versionadded:: 1.16.0
.. versionchanged:: 1.23.0
Lines containing no data, including comment lines (e.g., lines
starting with '#' or as specified via `comments`) are not counted
towards `max_rows`.
quotechar : unicode character or None, optional
The character used to denote the start and end of a quoted item.
Occurrences of the delimiter or comment characters are ignored within
a quoted item. The default value is ``quotechar=None``, which means
quoting support is disabled.
If two consecutive instances of `quotechar` are found within a quoted
field, the first is treated as an escape character. See examples.
.. versionadded:: 1.23.0
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
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.
Each row in the input text file must have the same number of values to be
able to read all values. If all rows do not have same number of values, a
subset of up to n columns (where n is the least number of values present
in all rows) can be read by specifying the columns via `usecols`.
.. 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("0 1\n2 3")
>>> np.loadtxt(c)
array([[0., 1.],
[2., 3.]])
>>> d = StringIO("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', '>> c = StringIO("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.])
The `converters` argument is used to specify functions to preprocess the
text prior to parsing. `converters` can be a dictionary that maps
preprocessing functions to each column:
>>> s = StringIO("1.618, 2.296\n3.141, 4.669\n")
>>> conv = {
... 0: lambda x: np.floor(float(x)), # conversion fn for column 0
... 1: lambda x: np.ceil(float(x)), # conversion fn for column 1
... }
>>> np.loadtxt(s, delimiter=",", converters=conv)
array([[1., 3.],
[3., 5.]])
`converters` can be a callable instead of a dictionary, in which case it
is applied to all columns:
>>> s = StringIO("0xDE 0xAD\n0xC0 0xDE")
>>> import functools
>>> conv = functools.partial(int, base=16)
>>> np.loadtxt(s, converters=conv)
array([[222., 173.],
[192., 222.]])
This example shows how `converters` can be used to convert a field
with a trailing minus sign into a negative number.
>>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94')
>>> def conv(fld):
... return -float(fld[:-1]) if fld.endswith(b'-') else float(fld)
...
>>> np.loadtxt(s, converters=conv)
array([[ 10.01, -31.25],
[ 19.22, 64.31],
[-17.57, 63.94]])
Using a callable as the converter can be particularly useful for handling
values with different formatting, e.g. floats with underscores:
>>> s = StringIO("1 2.7 100_000")
>>> np.loadtxt(s, converters=float)
array([1.e+00, 2.7e+00, 1.e+05])
This idea can be extended to automatically handle values specified in
many different formats:
>>> def conv(val):
... try:
... return float(val)
... except ValueError:
... return float.fromhex(val)
>>> s = StringIO("1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2")
>>> np.loadtxt(s, delimiter=",", converters=conv, encoding=None)
array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00])
Note that with the default ``encoding="bytes"``, the inputs to the
converter function are latin-1 encoded byte strings. To deactivate the
implicit encoding prior to conversion, use ``encoding=None``
>>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94')
>>> conv = lambda x: -float(x[:-1]) if x.endswith('-') else float(x)
>>> np.loadtxt(s, converters=conv, encoding=None)
array([[ 10.01, -31.25],
[ 19.22, 64.31],
[-17.57, 63.94]])
Support for quoted fields is enabled with the `quotechar` parameter.
Comment and delimiter characters are ignored when they appear within a
quoted item delineated by `quotechar`:
>>> s = StringIO('"alpha, #42", 10.0\n"beta, #64", 2.0\n')
>>> dtype = np.dtype([("label", "U12"), ("value", float)])
>>> np.loadtxt(s, dtype=dtype, delimiter=",", quotechar='"')
array([('alpha, #42', 10.), ('beta, #64', 2.)],
dtype=[('label', '>> s = StringIO('"alpha, #42" 10.0\n"beta, #64" 2.0\n')
>>> dtype = np.dtype([("label", "U12"), ("value", float)])
>>> np.loadtxt(s, dtype=dtype, delimiter=None, quotechar='"')
array([('alpha, #42', 10.), ('beta, #64', 2.)],
dtype=[('label', '>> s = StringIO('"Hello, my name is ""Monty""!"')
>>> np.loadtxt(s, dtype="U", delimiter=",", quotechar='"')
array('Hello, my name is "Monty"!', dtype='>> d = StringIO("1 2\n2 4\n3 9 12\n4 16 20")
>>> np.loadtxt(d, usecols=(0, 1))
array([[ 1., 2.],
[ 2., 4.],
[ 3., 9.],
[ 4., 16.]])
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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
`_,
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
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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 : path or file
Filename or file object to read.
.. versionchanged:: 1.22.0
Now accepts `os.PathLike` implementations.
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; must be a structured datatype.
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
`basics.rec`.
Examples
--------
>>> from io import StringIO
>>> text = StringIO("1312 foo\n1534 bar\n444 qux")
>>> regexp = r"(\d+)\s+(...)" # match [digits, whitespace, anything]
>>> output = np.fromregex(text, regexp,
... [('num', np.int64), ('key', 'S3')])
>>> output
array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')],
dtype=[('num', '>> output['num']
array([1312, 1534, 444])
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Examples
--------
>>> from io import StringIO
>>> import numpy as np
Comma delimited file with mixed dtype
>>> s = StringIO(u"1,1.3,abcde")
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
dtype=[('myint', '>> _ = s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
dtype=[('myint', '>> _ = s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
dtype=[('myint', '>> s = StringIO(u"11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
>>> data
array((1, 1.3, b'abcde'),
dtype=[('intvar', '>> f = StringIO('''
... text,# of chars
... hello world,11
... numpy,5''')
>>> np.genfromtxt(f, dtype='S12,S12', delimiter=',')
array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')],
dtype=[('f0', 'S12'), ('f1', 'S12')])
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Parameters
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See Also
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numpy.genfromtxt : generic function
Notes
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