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7080 lines
204 KiB
7080 lines
204 KiB
"""
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This is only meant to add docs to objects defined in C-extension modules.
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The purpose is to allow easier editing of the docstrings without
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requiring a re-compile.
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NOTE: Many of the methods of ndarray have corresponding functions.
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If you update these docstrings, please keep also the ones in
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core/fromnumeric.py, core/defmatrix.py up-to-date.
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"""
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from numpy.core.function_base import add_newdoc
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from numpy.core.overrides import array_function_like_doc
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###############################################################################
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#
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# flatiter
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#
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# flatiter needs a toplevel description
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#
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###############################################################################
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add_newdoc('numpy.core', 'flatiter',
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"""
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Flat iterator object to iterate over arrays.
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A `flatiter` iterator is returned by ``x.flat`` for any array `x`.
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It allows iterating over the array as if it were a 1-D array,
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either in a for-loop or by calling its `next` method.
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Iteration is done in row-major, C-style order (the last
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index varying the fastest). The iterator can also be indexed using
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basic slicing or advanced indexing.
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See Also
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--------
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ndarray.flat : Return a flat iterator over an array.
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ndarray.flatten : Returns a flattened copy of an array.
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Notes
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-----
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A `flatiter` iterator can not be constructed directly from Python code
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by calling the `flatiter` constructor.
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Examples
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--------
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>>> x = np.arange(6).reshape(2, 3)
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>>> fl = x.flat
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>>> type(fl)
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<class 'numpy.flatiter'>
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>>> for item in fl:
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... print(item)
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...
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0
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1
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2
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3
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4
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5
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>>> fl[2:4]
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array([2, 3])
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""")
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# flatiter attributes
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add_newdoc('numpy.core', 'flatiter', ('base',
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"""
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A reference to the array that is iterated over.
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Examples
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--------
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>>> x = np.arange(5)
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>>> fl = x.flat
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>>> fl.base is x
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True
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"""))
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add_newdoc('numpy.core', 'flatiter', ('coords',
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"""
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An N-dimensional tuple of current coordinates.
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Examples
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--------
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>>> x = np.arange(6).reshape(2, 3)
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>>> fl = x.flat
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>>> fl.coords
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(0, 0)
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>>> next(fl)
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0
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>>> fl.coords
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(0, 1)
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"""))
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add_newdoc('numpy.core', 'flatiter', ('index',
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"""
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Current flat index into the array.
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Examples
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--------
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>>> x = np.arange(6).reshape(2, 3)
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>>> fl = x.flat
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>>> fl.index
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0
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>>> next(fl)
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0
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>>> fl.index
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1
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"""))
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# flatiter functions
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add_newdoc('numpy.core', 'flatiter', ('__array__',
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"""__array__(type=None) Get array from iterator
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"""))
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add_newdoc('numpy.core', 'flatiter', ('copy',
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"""
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copy()
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Get a copy of the iterator as a 1-D array.
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Examples
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--------
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>>> x = np.arange(6).reshape(2, 3)
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>>> x
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array([[0, 1, 2],
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[3, 4, 5]])
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>>> fl = x.flat
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>>> fl.copy()
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array([0, 1, 2, 3, 4, 5])
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"""))
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###############################################################################
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#
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# nditer
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#
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###############################################################################
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add_newdoc('numpy.core', 'nditer',
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"""
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nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)
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Efficient multi-dimensional iterator object to iterate over arrays.
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To get started using this object, see the
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:ref:`introductory guide to array iteration <arrays.nditer>`.
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Parameters
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----------
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op : ndarray or sequence of array_like
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The array(s) to iterate over.
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flags : sequence of str, optional
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Flags to control the behavior of the iterator.
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* ``buffered`` enables buffering when required.
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* ``c_index`` causes a C-order index to be tracked.
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* ``f_index`` causes a Fortran-order index to be tracked.
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* ``multi_index`` causes a multi-index, or a tuple of indices
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with one per iteration dimension, to be tracked.
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* ``common_dtype`` causes all the operands to be converted to
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a common data type, with copying or buffering as necessary.
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* ``copy_if_overlap`` causes the iterator to determine if read
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operands have overlap with write operands, and make temporary
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copies as necessary to avoid overlap. False positives (needless
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copying) are possible in some cases.
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* ``delay_bufalloc`` delays allocation of the buffers until
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a reset() call is made. Allows ``allocate`` operands to
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be initialized before their values are copied into the buffers.
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* ``external_loop`` causes the ``values`` given to be
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one-dimensional arrays with multiple values instead of
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zero-dimensional arrays.
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* ``grow_inner`` allows the ``value`` array sizes to be made
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larger than the buffer size when both ``buffered`` and
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``external_loop`` is used.
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* ``ranged`` allows the iterator to be restricted to a sub-range
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of the iterindex values.
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* ``refs_ok`` enables iteration of reference types, such as
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object arrays.
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* ``reduce_ok`` enables iteration of ``readwrite`` operands
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which are broadcasted, also known as reduction operands.
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* ``zerosize_ok`` allows `itersize` to be zero.
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op_flags : list of list of str, optional
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This is a list of flags for each operand. At minimum, one of
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``readonly``, ``readwrite``, or ``writeonly`` must be specified.
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* ``readonly`` indicates the operand will only be read from.
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* ``readwrite`` indicates the operand will be read from and written to.
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* ``writeonly`` indicates the operand will only be written to.
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* ``no_broadcast`` prevents the operand from being broadcasted.
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* ``contig`` forces the operand data to be contiguous.
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* ``aligned`` forces the operand data to be aligned.
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* ``nbo`` forces the operand data to be in native byte order.
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* ``copy`` allows a temporary read-only copy if required.
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* ``updateifcopy`` allows a temporary read-write copy if required.
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* ``allocate`` causes the array to be allocated if it is None
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in the ``op`` parameter.
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* ``no_subtype`` prevents an ``allocate`` operand from using a subtype.
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* ``arraymask`` indicates that this operand is the mask to use
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for selecting elements when writing to operands with the
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'writemasked' flag set. The iterator does not enforce this,
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but when writing from a buffer back to the array, it only
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copies those elements indicated by this mask.
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* ``writemasked`` indicates that only elements where the chosen
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``arraymask`` operand is True will be written to.
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* ``overlap_assume_elementwise`` can be used to mark operands that are
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accessed only in the iterator order, to allow less conservative
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copying when ``copy_if_overlap`` is present.
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op_dtypes : dtype or tuple of dtype(s), optional
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The required data type(s) of the operands. If copying or buffering
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is enabled, the data will be converted to/from their original types.
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order : {'C', 'F', 'A', 'K'}, optional
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Controls the iteration order. 'C' means C order, 'F' means
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Fortran order, 'A' means 'F' order if all the arrays are Fortran
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contiguous, 'C' order otherwise, and 'K' means as close to the
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order the array elements appear in memory as possible. This also
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affects the element memory order of ``allocate`` operands, as they
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are allocated to be compatible with iteration order.
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Default is 'K'.
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casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
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Controls what kind of data casting may occur when making a copy
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or buffering. Setting this to 'unsafe' is not recommended,
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as it can adversely affect accumulations.
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* 'no' means the data types should not be cast at all.
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* 'equiv' means only byte-order changes are allowed.
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* 'safe' means only casts which can preserve values are allowed.
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* 'same_kind' means only safe casts or casts within a kind,
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like float64 to float32, are allowed.
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* 'unsafe' means any data conversions may be done.
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op_axes : list of list of ints, optional
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If provided, is a list of ints or None for each operands.
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The list of axes for an operand is a mapping from the dimensions
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of the iterator to the dimensions of the operand. A value of
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-1 can be placed for entries, causing that dimension to be
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treated as `newaxis`.
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itershape : tuple of ints, optional
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The desired shape of the iterator. This allows ``allocate`` operands
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with a dimension mapped by op_axes not corresponding to a dimension
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of a different operand to get a value not equal to 1 for that
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dimension.
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buffersize : int, optional
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When buffering is enabled, controls the size of the temporary
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buffers. Set to 0 for the default value.
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Attributes
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----------
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dtypes : tuple of dtype(s)
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The data types of the values provided in `value`. This may be
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different from the operand data types if buffering is enabled.
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Valid only before the iterator is closed.
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finished : bool
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Whether the iteration over the operands is finished or not.
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has_delayed_bufalloc : bool
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If True, the iterator was created with the ``delay_bufalloc`` flag,
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and no reset() function was called on it yet.
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has_index : bool
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If True, the iterator was created with either the ``c_index`` or
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the ``f_index`` flag, and the property `index` can be used to
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retrieve it.
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has_multi_index : bool
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If True, the iterator was created with the ``multi_index`` flag,
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and the property `multi_index` can be used to retrieve it.
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index
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When the ``c_index`` or ``f_index`` flag was used, this property
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provides access to the index. Raises a ValueError if accessed
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and ``has_index`` is False.
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iterationneedsapi : bool
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Whether iteration requires access to the Python API, for example
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if one of the operands is an object array.
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iterindex : int
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An index which matches the order of iteration.
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itersize : int
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Size of the iterator.
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itviews
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Structured view(s) of `operands` in memory, matching the reordered
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and optimized iterator access pattern. Valid only before the iterator
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is closed.
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multi_index
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When the ``multi_index`` flag was used, this property
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provides access to the index. Raises a ValueError if accessed
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accessed and ``has_multi_index`` is False.
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ndim : int
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The dimensions of the iterator.
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nop : int
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The number of iterator operands.
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operands : tuple of operand(s)
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The array(s) to be iterated over. Valid only before the iterator is
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closed.
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shape : tuple of ints
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Shape tuple, the shape of the iterator.
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value
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Value of ``operands`` at current iteration. Normally, this is a
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tuple of array scalars, but if the flag ``external_loop`` is used,
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it is a tuple of one dimensional arrays.
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Notes
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-----
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`nditer` supersedes `flatiter`. The iterator implementation behind
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`nditer` is also exposed by the NumPy C API.
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The Python exposure supplies two iteration interfaces, one which follows
|
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the Python iterator protocol, and another which mirrors the C-style
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do-while pattern. The native Python approach is better in most cases, but
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if you need the coordinates or index of an iterator, use the C-style pattern.
|
|
|
|
Examples
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|
--------
|
|
Here is how we might write an ``iter_add`` function, using the
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Python iterator protocol:
|
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>>> def iter_add_py(x, y, out=None):
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... addop = np.add
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... it = np.nditer([x, y, out], [],
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... [['readonly'], ['readonly'], ['writeonly','allocate']])
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... with it:
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|
... for (a, b, c) in it:
|
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... addop(a, b, out=c)
|
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... return it.operands[2]
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Here is the same function, but following the C-style pattern:
|
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>>> def iter_add(x, y, out=None):
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... addop = np.add
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... it = np.nditer([x, y, out], [],
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... [['readonly'], ['readonly'], ['writeonly','allocate']])
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... with it:
|
|
... while not it.finished:
|
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... addop(it[0], it[1], out=it[2])
|
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... it.iternext()
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... return it.operands[2]
|
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|
Here is an example outer product function:
|
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>>> def outer_it(x, y, out=None):
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... mulop = np.multiply
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... it = np.nditer([x, y, out], ['external_loop'],
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... [['readonly'], ['readonly'], ['writeonly', 'allocate']],
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... op_axes=[list(range(x.ndim)) + [-1] * y.ndim,
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... [-1] * x.ndim + list(range(y.ndim)),
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... None])
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... with it:
|
|
... for (a, b, c) in it:
|
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... mulop(a, b, out=c)
|
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... return it.operands[2]
|
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>>> a = np.arange(2)+1
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>>> b = np.arange(3)+1
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>>> outer_it(a,b)
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array([[1, 2, 3],
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[2, 4, 6]])
|
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|
|
Here is an example function which operates like a "lambda" ufunc:
|
|
|
|
>>> def luf(lamdaexpr, *args, **kwargs):
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... '''luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)'''
|
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... nargs = len(args)
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... op = (kwargs.get('out',None),) + args
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... it = np.nditer(op, ['buffered','external_loop'],
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... [['writeonly','allocate','no_broadcast']] +
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... [['readonly','nbo','aligned']]*nargs,
|
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... order=kwargs.get('order','K'),
|
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... casting=kwargs.get('casting','safe'),
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... buffersize=kwargs.get('buffersize',0))
|
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... while not it.finished:
|
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... it[0] = lamdaexpr(*it[1:])
|
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... it.iternext()
|
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... return it.operands[0]
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>>> a = np.arange(5)
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>>> b = np.ones(5)
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>>> luf(lambda i,j:i*i + j/2, a, b)
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array([ 0.5, 1.5, 4.5, 9.5, 16.5])
|
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If operand flags ``"writeonly"`` or ``"readwrite"`` are used the
|
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operands may be views into the original data with the
|
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`WRITEBACKIFCOPY` flag. In this case `nditer` must be used as a
|
|
context manager or the `nditer.close` method must be called before
|
|
using the result. The temporary data will be written back to the
|
|
original data when the `__exit__` function is called but not before:
|
|
|
|
>>> a = np.arange(6, dtype='i4')[::-2]
|
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>>> with np.nditer(a, [],
|
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... [['writeonly', 'updateifcopy']],
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... casting='unsafe',
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... op_dtypes=[np.dtype('f4')]) as i:
|
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... x = i.operands[0]
|
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... x[:] = [-1, -2, -3]
|
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... # a still unchanged here
|
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>>> a, x
|
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(array([-1, -2, -3], dtype=int32), array([-1., -2., -3.], dtype=float32))
|
|
|
|
It is important to note that once the iterator is exited, dangling
|
|
references (like `x` in the example) may or may not share data with
|
|
the original data `a`. If writeback semantics were active, i.e. if
|
|
`x.base.flags.writebackifcopy` is `True`, then exiting the iterator
|
|
will sever the connection between `x` and `a`, writing to `x` will
|
|
no longer write to `a`. If writeback semantics are not active, then
|
|
`x.data` will still point at some part of `a.data`, and writing to
|
|
one will affect the other.
|
|
|
|
Context management and the `close` method appeared in version 1.15.0.
|
|
|
|
""")
|
|
|
|
# nditer methods
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('copy',
|
|
"""
|
|
copy()
|
|
|
|
Get a copy of the iterator in its current state.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.arange(10)
|
|
>>> y = x + 1
|
|
>>> it = np.nditer([x, y])
|
|
>>> next(it)
|
|
(array(0), array(1))
|
|
>>> it2 = it.copy()
|
|
>>> next(it2)
|
|
(array(1), array(2))
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('operands',
|
|
"""
|
|
operands[`Slice`]
|
|
|
|
The array(s) to be iterated over. Valid only before the iterator is closed.
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('debug_print',
|
|
"""
|
|
debug_print()
|
|
|
|
Print the current state of the `nditer` instance and debug info to stdout.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('enable_external_loop',
|
|
"""
|
|
enable_external_loop()
|
|
|
|
When the "external_loop" was not used during construction, but
|
|
is desired, this modifies the iterator to behave as if the flag
|
|
was specified.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('iternext',
|
|
"""
|
|
iternext()
|
|
|
|
Check whether iterations are left, and perform a single internal iteration
|
|
without returning the result. Used in the C-style pattern do-while
|
|
pattern. For an example, see `nditer`.
|
|
|
|
Returns
|
|
-------
|
|
iternext : bool
|
|
Whether or not there are iterations left.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('remove_axis',
|
|
"""
|
|
remove_axis(i, /)
|
|
|
|
Removes axis `i` from the iterator. Requires that the flag "multi_index"
|
|
be enabled.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('remove_multi_index',
|
|
"""
|
|
remove_multi_index()
|
|
|
|
When the "multi_index" flag was specified, this removes it, allowing
|
|
the internal iteration structure to be optimized further.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('reset',
|
|
"""
|
|
reset()
|
|
|
|
Reset the iterator to its initial state.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'nested_iters',
|
|
"""
|
|
nested_iters(op, axes, flags=None, op_flags=None, op_dtypes=None, \
|
|
order="K", casting="safe", buffersize=0)
|
|
|
|
Create nditers for use in nested loops
|
|
|
|
Create a tuple of `nditer` objects which iterate in nested loops over
|
|
different axes of the op argument. The first iterator is used in the
|
|
outermost loop, the last in the innermost loop. Advancing one will change
|
|
the subsequent iterators to point at its new element.
|
|
|
|
Parameters
|
|
----------
|
|
op : ndarray or sequence of array_like
|
|
The array(s) to iterate over.
|
|
|
|
axes : list of list of int
|
|
Each item is used as an "op_axes" argument to an nditer
|
|
|
|
flags, op_flags, op_dtypes, order, casting, buffersize (optional)
|
|
See `nditer` parameters of the same name
|
|
|
|
Returns
|
|
-------
|
|
iters : tuple of nditer
|
|
An nditer for each item in `axes`, outermost first
|
|
|
|
See Also
|
|
--------
|
|
nditer
|
|
|
|
Examples
|
|
--------
|
|
|
|
Basic usage. Note how y is the "flattened" version of
|
|
[a[:, 0, :], a[:, 1, 0], a[:, 2, :]] since we specified
|
|
the first iter's axes as [1]
|
|
|
|
>>> a = np.arange(12).reshape(2, 3, 2)
|
|
>>> i, j = np.nested_iters(a, [[1], [0, 2]], flags=["multi_index"])
|
|
>>> for x in i:
|
|
... print(i.multi_index)
|
|
... for y in j:
|
|
... print('', j.multi_index, y)
|
|
(0,)
|
|
(0, 0) 0
|
|
(0, 1) 1
|
|
(1, 0) 6
|
|
(1, 1) 7
|
|
(1,)
|
|
(0, 0) 2
|
|
(0, 1) 3
|
|
(1, 0) 8
|
|
(1, 1) 9
|
|
(2,)
|
|
(0, 0) 4
|
|
(0, 1) 5
|
|
(1, 0) 10
|
|
(1, 1) 11
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core', 'nditer', ('close',
|
|
"""
|
|
close()
|
|
|
|
Resolve all writeback semantics in writeable operands.
|
|
|
|
.. versionadded:: 1.15.0
|
|
|
|
See Also
|
|
--------
|
|
|
|
:ref:`nditer-context-manager`
|
|
|
|
"""))
|
|
|
|
|
|
###############################################################################
|
|
#
|
|
# broadcast
|
|
#
|
|
###############################################################################
|
|
|
|
add_newdoc('numpy.core', 'broadcast',
|
|
"""
|
|
Produce an object that mimics broadcasting.
|
|
|
|
Parameters
|
|
----------
|
|
in1, in2, ... : array_like
|
|
Input parameters.
|
|
|
|
Returns
|
|
-------
|
|
b : broadcast object
|
|
Broadcast the input parameters against one another, and
|
|
return an object that encapsulates the result.
|
|
Amongst others, it has ``shape`` and ``nd`` properties, and
|
|
may be used as an iterator.
|
|
|
|
See Also
|
|
--------
|
|
broadcast_arrays
|
|
broadcast_to
|
|
broadcast_shapes
|
|
|
|
Examples
|
|
--------
|
|
|
|
Manually adding two vectors, using broadcasting:
|
|
|
|
>>> x = np.array([[1], [2], [3]])
|
|
>>> y = np.array([4, 5, 6])
|
|
>>> b = np.broadcast(x, y)
|
|
|
|
>>> out = np.empty(b.shape)
|
|
>>> out.flat = [u+v for (u,v) in b]
|
|
>>> out
|
|
array([[5., 6., 7.],
|
|
[6., 7., 8.],
|
|
[7., 8., 9.]])
|
|
|
|
Compare against built-in broadcasting:
|
|
|
|
>>> x + y
|
|
array([[5, 6, 7],
|
|
[6, 7, 8],
|
|
[7, 8, 9]])
|
|
|
|
""")
|
|
|
|
# attributes
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('index',
|
|
"""
|
|
current index in broadcasted result
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([[1], [2], [3]])
|
|
>>> y = np.array([4, 5, 6])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.index
|
|
0
|
|
>>> next(b), next(b), next(b)
|
|
((1, 4), (1, 5), (1, 6))
|
|
>>> b.index
|
|
3
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('iters',
|
|
"""
|
|
tuple of iterators along ``self``'s "components."
|
|
|
|
Returns a tuple of `numpy.flatiter` objects, one for each "component"
|
|
of ``self``.
|
|
|
|
See Also
|
|
--------
|
|
numpy.flatiter
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> row, col = b.iters
|
|
>>> next(row), next(col)
|
|
(1, 4)
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('ndim',
|
|
"""
|
|
Number of dimensions of broadcasted result. Alias for `nd`.
|
|
|
|
.. versionadded:: 1.12.0
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.ndim
|
|
2
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('nd',
|
|
"""
|
|
Number of dimensions of broadcasted result. For code intended for NumPy
|
|
1.12.0 and later the more consistent `ndim` is preferred.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.nd
|
|
2
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('numiter',
|
|
"""
|
|
Number of iterators possessed by the broadcasted result.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.numiter
|
|
2
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('shape',
|
|
"""
|
|
Shape of broadcasted result.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.shape
|
|
(3, 3)
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('size',
|
|
"""
|
|
Total size of broadcasted result.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.size
|
|
9
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'broadcast', ('reset',
|
|
"""
|
|
reset()
|
|
|
|
Reset the broadcasted result's iterator(s).
|
|
|
|
Parameters
|
|
----------
|
|
None
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> y = np.array([[4], [5], [6]])
|
|
>>> b = np.broadcast(x, y)
|
|
>>> b.index
|
|
0
|
|
>>> next(b), next(b), next(b)
|
|
((1, 4), (2, 4), (3, 4))
|
|
>>> b.index
|
|
3
|
|
>>> b.reset()
|
|
>>> b.index
|
|
0
|
|
|
|
"""))
|
|
|
|
###############################################################################
|
|
#
|
|
# numpy functions
|
|
#
|
|
###############################################################################
|
|
|
|
add_newdoc('numpy.core.multiarray', 'array',
|
|
"""
|
|
array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0,
|
|
like=None)
|
|
|
|
Create an array.
|
|
|
|
Parameters
|
|
----------
|
|
object : array_like
|
|
An array, any object exposing the array interface, an object whose
|
|
``__array__`` method returns an array, or any (nested) sequence.
|
|
If object is a scalar, a 0-dimensional array containing object is
|
|
returned.
|
|
dtype : data-type, optional
|
|
The desired data-type for the array. If not given, NumPy will try to use
|
|
a default ``dtype`` that can represent the values (by applying promotion
|
|
rules when necessary.)
|
|
copy : bool, optional
|
|
If true (default), then the object is copied. Otherwise, a copy will
|
|
only be made if ``__array__`` returns a copy, if obj is a nested
|
|
sequence, or if a copy is needed to satisfy any of the other
|
|
requirements (``dtype``, ``order``, etc.).
|
|
order : {'K', 'A', 'C', 'F'}, optional
|
|
Specify the memory layout of the array. If object is not an array, the
|
|
newly created array will be in C order (row major) unless 'F' is
|
|
specified, in which case it will be in Fortran order (column major).
|
|
If object is an array the following holds.
|
|
|
|
===== ========= ===================================================
|
|
order no copy copy=True
|
|
===== ========= ===================================================
|
|
'K' unchanged F & C order preserved, otherwise most similar order
|
|
'A' unchanged F order if input is F and not C, otherwise C order
|
|
'C' C order C order
|
|
'F' F order F order
|
|
===== ========= ===================================================
|
|
|
|
When ``copy=False`` and a copy is made for other reasons, the result is
|
|
the same as if ``copy=True``, with some exceptions for 'A', see the
|
|
Notes section. The default order is 'K'.
|
|
subok : bool, optional
|
|
If True, then sub-classes will be passed-through, otherwise
|
|
the returned array will be forced to be a base-class array (default).
|
|
ndmin : int, optional
|
|
Specifies the minimum number of dimensions that the resulting
|
|
array should have. Ones will be prepended to the shape as
|
|
needed to meet this requirement.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
An array object satisfying the specified requirements.
|
|
|
|
See Also
|
|
--------
|
|
empty_like : Return an empty array with shape and type of input.
|
|
ones_like : Return an array of ones with shape and type of input.
|
|
zeros_like : Return an array of zeros with shape and type of input.
|
|
full_like : Return a new array with shape of input filled with value.
|
|
empty : Return a new uninitialized array.
|
|
ones : Return a new array setting values to one.
|
|
zeros : Return a new array setting values to zero.
|
|
full : Return a new array of given shape filled with value.
|
|
|
|
|
|
Notes
|
|
-----
|
|
When order is 'A' and ``object`` is an array in neither 'C' nor 'F' order,
|
|
and a copy is forced by a change in dtype, then the order of the result is
|
|
not necessarily 'C' as expected. This is likely a bug.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.array([1, 2, 3])
|
|
array([1, 2, 3])
|
|
|
|
Upcasting:
|
|
|
|
>>> np.array([1, 2, 3.0])
|
|
array([ 1., 2., 3.])
|
|
|
|
More than one dimension:
|
|
|
|
>>> np.array([[1, 2], [3, 4]])
|
|
array([[1, 2],
|
|
[3, 4]])
|
|
|
|
Minimum dimensions 2:
|
|
|
|
>>> np.array([1, 2, 3], ndmin=2)
|
|
array([[1, 2, 3]])
|
|
|
|
Type provided:
|
|
|
|
>>> np.array([1, 2, 3], dtype=complex)
|
|
array([ 1.+0.j, 2.+0.j, 3.+0.j])
|
|
|
|
Data-type consisting of more than one element:
|
|
|
|
>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
|
|
>>> x['a']
|
|
array([1, 3])
|
|
|
|
Creating an array from sub-classes:
|
|
|
|
>>> np.array(np.mat('1 2; 3 4'))
|
|
array([[1, 2],
|
|
[3, 4]])
|
|
|
|
>>> np.array(np.mat('1 2; 3 4'), subok=True)
|
|
matrix([[1, 2],
|
|
[3, 4]])
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'asarray',
|
|
"""
|
|
asarray(a, dtype=None, order=None, *, like=None)
|
|
|
|
Convert the input to an array.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input data, in any form that can be converted to an array. This
|
|
includes lists, lists of tuples, tuples, tuples of tuples, tuples
|
|
of lists and ndarrays.
|
|
dtype : data-type, optional
|
|
By default, the data-type is inferred from the input data.
|
|
order : {'C', 'F', 'A', 'K'}, optional
|
|
Memory layout. 'A' and 'K' depend on the order of input array a.
|
|
'C' row-major (C-style),
|
|
'F' column-major (Fortran-style) memory representation.
|
|
'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
|
|
'K' (keep) preserve input order
|
|
Defaults to 'K'.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
Array interpretation of `a`. No copy is performed if the input
|
|
is already an ndarray with matching dtype and order. If `a` is a
|
|
subclass of ndarray, a base class ndarray is returned.
|
|
|
|
See Also
|
|
--------
|
|
asanyarray : Similar function which passes through subclasses.
|
|
ascontiguousarray : Convert input to a contiguous array.
|
|
asfarray : Convert input to a floating point ndarray.
|
|
asfortranarray : Convert input to an ndarray with column-major
|
|
memory order.
|
|
asarray_chkfinite : Similar function which checks input for NaNs and Infs.
|
|
fromiter : Create an array from an iterator.
|
|
fromfunction : Construct an array by executing a function on grid
|
|
positions.
|
|
|
|
Examples
|
|
--------
|
|
Convert a list into an array:
|
|
|
|
>>> a = [1, 2]
|
|
>>> np.asarray(a)
|
|
array([1, 2])
|
|
|
|
Existing arrays are not copied:
|
|
|
|
>>> a = np.array([1, 2])
|
|
>>> np.asarray(a) is a
|
|
True
|
|
|
|
If `dtype` is set, array is copied only if dtype does not match:
|
|
|
|
>>> a = np.array([1, 2], dtype=np.float32)
|
|
>>> np.asarray(a, dtype=np.float32) is a
|
|
True
|
|
>>> np.asarray(a, dtype=np.float64) is a
|
|
False
|
|
|
|
Contrary to `asanyarray`, ndarray subclasses are not passed through:
|
|
|
|
>>> issubclass(np.recarray, np.ndarray)
|
|
True
|
|
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
|
|
>>> np.asarray(a) is a
|
|
False
|
|
>>> np.asanyarray(a) is a
|
|
True
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'asanyarray',
|
|
"""
|
|
asanyarray(a, dtype=None, order=None, *, like=None)
|
|
|
|
Convert the input to an ndarray, but pass ndarray subclasses through.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input data, in any form that can be converted to an array. This
|
|
includes scalars, lists, lists of tuples, tuples, tuples of tuples,
|
|
tuples of lists, and ndarrays.
|
|
dtype : data-type, optional
|
|
By default, the data-type is inferred from the input data.
|
|
order : {'C', 'F', 'A', 'K'}, optional
|
|
Memory layout. 'A' and 'K' depend on the order of input array a.
|
|
'C' row-major (C-style),
|
|
'F' column-major (Fortran-style) memory representation.
|
|
'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
|
|
'K' (keep) preserve input order
|
|
Defaults to 'C'.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray or an ndarray subclass
|
|
Array interpretation of `a`. If `a` is an ndarray or a subclass
|
|
of ndarray, it is returned as-is and no copy is performed.
|
|
|
|
See Also
|
|
--------
|
|
asarray : Similar function which always returns ndarrays.
|
|
ascontiguousarray : Convert input to a contiguous array.
|
|
asfarray : Convert input to a floating point ndarray.
|
|
asfortranarray : Convert input to an ndarray with column-major
|
|
memory order.
|
|
asarray_chkfinite : Similar function which checks input for NaNs and
|
|
Infs.
|
|
fromiter : Create an array from an iterator.
|
|
fromfunction : Construct an array by executing a function on grid
|
|
positions.
|
|
|
|
Examples
|
|
--------
|
|
Convert a list into an array:
|
|
|
|
>>> a = [1, 2]
|
|
>>> np.asanyarray(a)
|
|
array([1, 2])
|
|
|
|
Instances of `ndarray` subclasses are passed through as-is:
|
|
|
|
>>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
|
|
>>> np.asanyarray(a) is a
|
|
True
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ascontiguousarray',
|
|
"""
|
|
ascontiguousarray(a, dtype=None, *, like=None)
|
|
|
|
Return a contiguous array (ndim >= 1) in memory (C order).
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
dtype : str or dtype object, optional
|
|
Data-type of returned array.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
Contiguous array of same shape and content as `a`, with type `dtype`
|
|
if specified.
|
|
|
|
See Also
|
|
--------
|
|
asfortranarray : Convert input to an ndarray with column-major
|
|
memory order.
|
|
require : Return an ndarray that satisfies requirements.
|
|
ndarray.flags : Information about the memory layout of the array.
|
|
|
|
Examples
|
|
--------
|
|
Starting with a Fortran-contiguous array:
|
|
|
|
>>> x = np.ones((2, 3), order='F')
|
|
>>> x.flags['F_CONTIGUOUS']
|
|
True
|
|
|
|
Calling ``ascontiguousarray`` makes a C-contiguous copy:
|
|
|
|
>>> y = np.ascontiguousarray(x)
|
|
>>> y.flags['C_CONTIGUOUS']
|
|
True
|
|
>>> np.may_share_memory(x, y)
|
|
False
|
|
|
|
Now, starting with a C-contiguous array:
|
|
|
|
>>> x = np.ones((2, 3), order='C')
|
|
>>> x.flags['C_CONTIGUOUS']
|
|
True
|
|
|
|
Then, calling ``ascontiguousarray`` returns the same object:
|
|
|
|
>>> y = np.ascontiguousarray(x)
|
|
>>> x is y
|
|
True
|
|
|
|
Note: This function returns an array with at least one-dimension (1-d)
|
|
so it will not preserve 0-d arrays.
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'asfortranarray',
|
|
"""
|
|
asfortranarray(a, dtype=None, *, like=None)
|
|
|
|
Return an array (ndim >= 1) laid out in Fortran order in memory.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Input array.
|
|
dtype : str or dtype object, optional
|
|
By default, the data-type is inferred from the input data.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
The input `a` in Fortran, or column-major, order.
|
|
|
|
See Also
|
|
--------
|
|
ascontiguousarray : Convert input to a contiguous (C order) array.
|
|
asanyarray : Convert input to an ndarray with either row or
|
|
column-major memory order.
|
|
require : Return an ndarray that satisfies requirements.
|
|
ndarray.flags : Information about the memory layout of the array.
|
|
|
|
Examples
|
|
--------
|
|
Starting with a C-contiguous array:
|
|
|
|
>>> x = np.ones((2, 3), order='C')
|
|
>>> x.flags['C_CONTIGUOUS']
|
|
True
|
|
|
|
Calling ``asfortranarray`` makes a Fortran-contiguous copy:
|
|
|
|
>>> y = np.asfortranarray(x)
|
|
>>> y.flags['F_CONTIGUOUS']
|
|
True
|
|
>>> np.may_share_memory(x, y)
|
|
False
|
|
|
|
Now, starting with a Fortran-contiguous array:
|
|
|
|
>>> x = np.ones((2, 3), order='F')
|
|
>>> x.flags['F_CONTIGUOUS']
|
|
True
|
|
|
|
Then, calling ``asfortranarray`` returns the same object:
|
|
|
|
>>> y = np.asfortranarray(x)
|
|
>>> x is y
|
|
True
|
|
|
|
Note: This function returns an array with at least one-dimension (1-d)
|
|
so it will not preserve 0-d arrays.
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'empty',
|
|
"""
|
|
empty(shape, dtype=float, order='C', *, like=None)
|
|
|
|
Return a new array of given shape and type, without initializing entries.
|
|
|
|
Parameters
|
|
----------
|
|
shape : int or tuple of int
|
|
Shape of the empty array, e.g., ``(2, 3)`` or ``2``.
|
|
dtype : data-type, optional
|
|
Desired output data-type for the array, e.g, `numpy.int8`. Default is
|
|
`numpy.float64`.
|
|
order : {'C', 'F'}, optional, default: 'C'
|
|
Whether to store multi-dimensional data in row-major
|
|
(C-style) or column-major (Fortran-style) order in
|
|
memory.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
Array of uninitialized (arbitrary) data of the given shape, dtype, and
|
|
order. Object arrays will be initialized to None.
|
|
|
|
See Also
|
|
--------
|
|
empty_like : Return an empty array with shape and type of input.
|
|
ones : Return a new array setting values to one.
|
|
zeros : Return a new array setting values to zero.
|
|
full : Return a new array of given shape filled with value.
|
|
|
|
|
|
Notes
|
|
-----
|
|
`empty`, unlike `zeros`, does not set the array values to zero,
|
|
and may therefore be marginally faster. On the other hand, it requires
|
|
the user to manually set all the values in the array, and should be
|
|
used with caution.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.empty([2, 2])
|
|
array([[ -9.74499359e+001, 6.69583040e-309],
|
|
[ 2.13182611e-314, 3.06959433e-309]]) #uninitialized
|
|
|
|
>>> np.empty([2, 2], dtype=int)
|
|
array([[-1073741821, -1067949133],
|
|
[ 496041986, 19249760]]) #uninitialized
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'scalar',
|
|
"""
|
|
scalar(dtype, obj)
|
|
|
|
Return a new scalar array of the given type initialized with obj.
|
|
|
|
This function is meant mainly for pickle support. `dtype` must be a
|
|
valid data-type descriptor. If `dtype` corresponds to an object
|
|
descriptor, then `obj` can be any object, otherwise `obj` must be a
|
|
string. If `obj` is not given, it will be interpreted as None for object
|
|
type and as zeros for all other types.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'zeros',
|
|
"""
|
|
zeros(shape, dtype=float, order='C', *, like=None)
|
|
|
|
Return a new array of given shape and type, filled with zeros.
|
|
|
|
Parameters
|
|
----------
|
|
shape : int or tuple of ints
|
|
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
|
|
dtype : data-type, optional
|
|
The desired data-type for the array, e.g., `numpy.int8`. Default is
|
|
`numpy.float64`.
|
|
order : {'C', 'F'}, optional, default: 'C'
|
|
Whether to store multi-dimensional data in row-major
|
|
(C-style) or column-major (Fortran-style) order in
|
|
memory.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
Array of zeros with the given shape, dtype, and order.
|
|
|
|
See Also
|
|
--------
|
|
zeros_like : Return an array of zeros with shape and type of input.
|
|
empty : Return a new uninitialized array.
|
|
ones : Return a new array setting values to one.
|
|
full : Return a new array of given shape filled with value.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.zeros(5)
|
|
array([ 0., 0., 0., 0., 0.])
|
|
|
|
>>> np.zeros((5,), dtype=int)
|
|
array([0, 0, 0, 0, 0])
|
|
|
|
>>> np.zeros((2, 1))
|
|
array([[ 0.],
|
|
[ 0.]])
|
|
|
|
>>> s = (2,2)
|
|
>>> np.zeros(s)
|
|
array([[ 0., 0.],
|
|
[ 0., 0.]])
|
|
|
|
>>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
|
|
array([(0, 0), (0, 0)],
|
|
dtype=[('x', '<i4'), ('y', '<i4')])
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'set_typeDict',
|
|
"""set_typeDict(dict)
|
|
|
|
Set the internal dictionary that can look up an array type using a
|
|
registered code.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'fromstring',
|
|
"""
|
|
fromstring(string, dtype=float, count=-1, *, sep, like=None)
|
|
|
|
A new 1-D array initialized from text data in a string.
|
|
|
|
Parameters
|
|
----------
|
|
string : str
|
|
A string containing the data.
|
|
dtype : data-type, optional
|
|
The data type of the array; default: float. For binary input data,
|
|
the data must be in exactly this format. Most builtin numeric types are
|
|
supported and extension types may be supported.
|
|
|
|
.. versionadded:: 1.18.0
|
|
Complex dtypes.
|
|
|
|
count : int, optional
|
|
Read this number of `dtype` elements from the data. If this is
|
|
negative (the default), the count will be determined from the
|
|
length of the data.
|
|
sep : str, optional
|
|
The string separating numbers in the data; extra whitespace between
|
|
elements is also ignored.
|
|
|
|
.. deprecated:: 1.14
|
|
Passing ``sep=''``, the default, is deprecated since it will
|
|
trigger the deprecated binary mode of this function. This mode
|
|
interprets `string` as binary bytes, rather than ASCII text with
|
|
decimal numbers, an operation which is better spelt
|
|
``frombuffer(string, dtype, count)``. If `string` contains unicode
|
|
text, the binary mode of `fromstring` will first encode it into
|
|
bytes using utf-8, which will not produce sane results.
|
|
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
arr : ndarray
|
|
The constructed array.
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If the string is not the correct size to satisfy the requested
|
|
`dtype` and `count`.
|
|
|
|
See Also
|
|
--------
|
|
frombuffer, fromfile, fromiter
|
|
|
|
Examples
|
|
--------
|
|
>>> np.fromstring('1 2', dtype=int, sep=' ')
|
|
array([1, 2])
|
|
>>> np.fromstring('1, 2', dtype=int, sep=',')
|
|
array([1, 2])
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'compare_chararrays',
|
|
"""
|
|
compare_chararrays(a1, a2, cmp, rstrip)
|
|
|
|
Performs element-wise comparison of two string arrays using the
|
|
comparison operator specified by `cmp_op`.
|
|
|
|
Parameters
|
|
----------
|
|
a1, a2 : array_like
|
|
Arrays to be compared.
|
|
cmp : {"<", "<=", "==", ">=", ">", "!="}
|
|
Type of comparison.
|
|
rstrip : Boolean
|
|
If True, the spaces at the end of Strings are removed before the comparison.
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
The output array of type Boolean with the same shape as a and b.
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If `cmp_op` is not valid.
|
|
TypeError
|
|
If at least one of `a` or `b` is a non-string array
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array(["a", "b", "cde"])
|
|
>>> b = np.array(["a", "a", "dec"])
|
|
>>> np.compare_chararrays(a, b, ">", True)
|
|
array([False, True, False])
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'fromiter',
|
|
"""
|
|
fromiter(iter, dtype, count=-1, *, like=None)
|
|
|
|
Create a new 1-dimensional array from an iterable object.
|
|
|
|
Parameters
|
|
----------
|
|
iter : iterable object
|
|
An iterable object providing data for the array.
|
|
dtype : data-type
|
|
The data-type of the returned array.
|
|
|
|
.. versionchanged:: 1.23
|
|
Object and subarray dtypes are now supported (note that the final
|
|
result is not 1-D for a subarray dtype).
|
|
|
|
count : int, optional
|
|
The number of items to read from *iterable*. The default is -1,
|
|
which means all data is read.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
The output array.
|
|
|
|
Notes
|
|
-----
|
|
Specify `count` to improve performance. It allows ``fromiter`` to
|
|
pre-allocate the output array, instead of resizing it on demand.
|
|
|
|
Examples
|
|
--------
|
|
>>> iterable = (x*x for x in range(5))
|
|
>>> np.fromiter(iterable, float)
|
|
array([ 0., 1., 4., 9., 16.])
|
|
|
|
A carefully constructed subarray dtype will lead to higher dimensional
|
|
results:
|
|
|
|
>>> iterable = ((x+1, x+2) for x in range(5))
|
|
>>> np.fromiter(iterable, dtype=np.dtype((int, 2)))
|
|
array([[1, 2],
|
|
[2, 3],
|
|
[3, 4],
|
|
[4, 5],
|
|
[5, 6]])
|
|
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'fromfile',
|
|
"""
|
|
fromfile(file, dtype=float, count=-1, sep='', offset=0, *, like=None)
|
|
|
|
Construct an array from data in a text or binary file.
|
|
|
|
A highly efficient way of reading binary data with a known data-type,
|
|
as well as parsing simply formatted text files. Data written using the
|
|
`tofile` method can be read using this function.
|
|
|
|
Parameters
|
|
----------
|
|
file : file or str or Path
|
|
Open file object or filename.
|
|
|
|
.. versionchanged:: 1.17.0
|
|
`pathlib.Path` objects are now accepted.
|
|
|
|
dtype : data-type
|
|
Data type of the returned array.
|
|
For binary files, it is used to determine the size and byte-order
|
|
of the items in the file.
|
|
Most builtin numeric types are supported and extension types may be supported.
|
|
|
|
.. versionadded:: 1.18.0
|
|
Complex dtypes.
|
|
|
|
count : int
|
|
Number of items to read. ``-1`` means all items (i.e., the complete
|
|
file).
|
|
sep : str
|
|
Separator between items if file is a text file.
|
|
Empty ("") separator means the file should be treated as binary.
|
|
Spaces (" ") in the separator match zero or more whitespace characters.
|
|
A separator consisting only of spaces must match at least one
|
|
whitespace.
|
|
offset : int
|
|
The offset (in bytes) from the file's current position. Defaults to 0.
|
|
Only permitted for binary files.
|
|
|
|
.. versionadded:: 1.17.0
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
See also
|
|
--------
|
|
load, save
|
|
ndarray.tofile
|
|
loadtxt : More flexible way of loading data from a text file.
|
|
|
|
Notes
|
|
-----
|
|
Do not rely on the combination of `tofile` and `fromfile` for
|
|
data storage, as the binary files generated are not platform
|
|
independent. In particular, no byte-order or data-type information is
|
|
saved. Data can be stored in the platform independent ``.npy`` format
|
|
using `save` and `load` instead.
|
|
|
|
Examples
|
|
--------
|
|
Construct an ndarray:
|
|
|
|
>>> dt = np.dtype([('time', [('min', np.int64), ('sec', np.int64)]),
|
|
... ('temp', float)])
|
|
>>> x = np.zeros((1,), dtype=dt)
|
|
>>> x['time']['min'] = 10; x['temp'] = 98.25
|
|
>>> x
|
|
array([((10, 0), 98.25)],
|
|
dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])
|
|
|
|
Save the raw data to disk:
|
|
|
|
>>> import tempfile
|
|
>>> fname = tempfile.mkstemp()[1]
|
|
>>> x.tofile(fname)
|
|
|
|
Read the raw data from disk:
|
|
|
|
>>> np.fromfile(fname, dtype=dt)
|
|
array([((10, 0), 98.25)],
|
|
dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])
|
|
|
|
The recommended way to store and load data:
|
|
|
|
>>> np.save(fname, x)
|
|
>>> np.load(fname + '.npy')
|
|
array([((10, 0), 98.25)],
|
|
dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'frombuffer',
|
|
"""
|
|
frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None)
|
|
|
|
Interpret a buffer as a 1-dimensional array.
|
|
|
|
Parameters
|
|
----------
|
|
buffer : buffer_like
|
|
An object that exposes the buffer interface.
|
|
dtype : data-type, optional
|
|
Data-type of the returned array; default: float.
|
|
count : int, optional
|
|
Number of items to read. ``-1`` means all data in the buffer.
|
|
offset : int, optional
|
|
Start reading the buffer from this offset (in bytes); default: 0.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
|
|
See also
|
|
--------
|
|
ndarray.tobytes
|
|
Inverse of this operation, construct Python bytes from the raw data
|
|
bytes in the array.
|
|
|
|
Notes
|
|
-----
|
|
If the buffer has data that is not in machine byte-order, this should
|
|
be specified as part of the data-type, e.g.::
|
|
|
|
>>> dt = np.dtype(int)
|
|
>>> dt = dt.newbyteorder('>')
|
|
>>> np.frombuffer(buf, dtype=dt) # doctest: +SKIP
|
|
|
|
The data of the resulting array will not be byteswapped, but will be
|
|
interpreted correctly.
|
|
|
|
This function creates a view into the original object. This should be safe
|
|
in general, but it may make sense to copy the result when the original
|
|
object is mutable or untrusted.
|
|
|
|
Examples
|
|
--------
|
|
>>> s = b'hello world'
|
|
>>> np.frombuffer(s, dtype='S1', count=5, offset=6)
|
|
array([b'w', b'o', b'r', b'l', b'd'], dtype='|S1')
|
|
|
|
>>> np.frombuffer(b'\\x01\\x02', dtype=np.uint8)
|
|
array([1, 2], dtype=uint8)
|
|
>>> np.frombuffer(b'\\x01\\x02\\x03\\x04\\x05', dtype=np.uint8, count=3)
|
|
array([1, 2, 3], dtype=uint8)
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'from_dlpack',
|
|
"""
|
|
from_dlpack(x, /)
|
|
|
|
Create a NumPy array from an object implementing the ``__dlpack__``
|
|
protocol. Generally, the returned NumPy array is a read-only view
|
|
of the input object. See [1]_ and [2]_ for more details.
|
|
|
|
Parameters
|
|
----------
|
|
x : object
|
|
A Python object that implements the ``__dlpack__`` and
|
|
``__dlpack_device__`` methods.
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
|
|
References
|
|
----------
|
|
.. [1] Array API documentation,
|
|
https://data-apis.org/array-api/latest/design_topics/data_interchange.html#syntax-for-data-interchange-with-dlpack
|
|
|
|
.. [2] Python specification for DLPack,
|
|
https://dmlc.github.io/dlpack/latest/python_spec.html
|
|
|
|
Examples
|
|
--------
|
|
>>> import torch
|
|
>>> x = torch.arange(10)
|
|
>>> # create a view of the torch tensor "x" in NumPy
|
|
>>> y = np.from_dlpack(x)
|
|
""")
|
|
|
|
add_newdoc('numpy.core', 'fastCopyAndTranspose',
|
|
"""
|
|
fastCopyAndTranspose(a)
|
|
|
|
.. deprecated:: 1.24
|
|
|
|
fastCopyAndTranspose is deprecated and will be removed. Use the copy and
|
|
transpose methods instead, e.g. ``arr.T.copy()``
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'correlate',
|
|
"""cross_correlate(a,v, mode=0)""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'arange',
|
|
"""
|
|
arange([start,] stop[, step,], dtype=None, *, like=None)
|
|
|
|
Return evenly spaced values within a given interval.
|
|
|
|
``arange`` can be called with a varying number of positional arguments:
|
|
|
|
* ``arange(stop)``: Values are generated within the half-open interval
|
|
``[0, stop)`` (in other words, the interval including `start` but
|
|
excluding `stop`).
|
|
* ``arange(start, stop)``: Values are generated within the half-open
|
|
interval ``[start, stop)``.
|
|
* ``arange(start, stop, step)`` Values are generated within the half-open
|
|
interval ``[start, stop)``, with spacing between values given by
|
|
``step``.
|
|
|
|
For integer arguments the function is roughly equivalent to the Python
|
|
built-in :py:class:`range`, but returns an ndarray rather than a ``range``
|
|
instance.
|
|
|
|
When using a non-integer step, such as 0.1, it is often better to use
|
|
`numpy.linspace`.
|
|
|
|
See the Warning sections below for more information.
|
|
|
|
Parameters
|
|
----------
|
|
start : integer or real, optional
|
|
Start of interval. The interval includes this value. The default
|
|
start value is 0.
|
|
stop : integer or real
|
|
End of interval. The interval does not include this value, except
|
|
in some cases where `step` is not an integer and floating point
|
|
round-off affects the length of `out`.
|
|
step : integer or real, optional
|
|
Spacing between values. For any output `out`, this is the distance
|
|
between two adjacent values, ``out[i+1] - out[i]``. The default
|
|
step size is 1. If `step` is specified as a position argument,
|
|
`start` must also be given.
|
|
dtype : dtype, optional
|
|
The type of the output array. If `dtype` is not given, infer the data
|
|
type from the other input arguments.
|
|
${ARRAY_FUNCTION_LIKE}
|
|
|
|
.. versionadded:: 1.20.0
|
|
|
|
Returns
|
|
-------
|
|
arange : ndarray
|
|
Array of evenly spaced values.
|
|
|
|
For floating point arguments, the length of the result is
|
|
``ceil((stop - start)/step)``. Because of floating point overflow,
|
|
this rule may result in the last element of `out` being greater
|
|
than `stop`.
|
|
|
|
Warnings
|
|
--------
|
|
The length of the output might not be numerically stable.
|
|
|
|
Another stability issue is due to the internal implementation of
|
|
`numpy.arange`.
|
|
The actual step value used to populate the array is
|
|
``dtype(start + step) - dtype(start)`` and not `step`. Precision loss
|
|
can occur here, due to casting or due to using floating points when
|
|
`start` is much larger than `step`. This can lead to unexpected
|
|
behaviour. For example::
|
|
|
|
>>> np.arange(0, 5, 0.5, dtype=int)
|
|
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
|
|
>>> np.arange(-3, 3, 0.5, dtype=int)
|
|
array([-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
|
|
|
|
In such cases, the use of `numpy.linspace` should be preferred.
|
|
|
|
The built-in :py:class:`range` generates :std:doc:`Python built-in integers
|
|
that have arbitrary size <python:c-api/long>`, while `numpy.arange`
|
|
produces `numpy.int32` or `numpy.int64` numbers. This may result in
|
|
incorrect results for large integer values::
|
|
|
|
>>> power = 40
|
|
>>> modulo = 10000
|
|
>>> x1 = [(n ** power) % modulo for n in range(8)]
|
|
>>> x2 = [(n ** power) % modulo for n in np.arange(8)]
|
|
>>> print(x1)
|
|
[0, 1, 7776, 8801, 6176, 625, 6576, 4001] # correct
|
|
>>> print(x2)
|
|
[0, 1, 7776, 7185, 0, 5969, 4816, 3361] # incorrect
|
|
|
|
See Also
|
|
--------
|
|
numpy.linspace : Evenly spaced numbers with careful handling of endpoints.
|
|
numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions.
|
|
numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
|
|
:ref:`how-to-partition`
|
|
|
|
Examples
|
|
--------
|
|
>>> np.arange(3)
|
|
array([0, 1, 2])
|
|
>>> np.arange(3.0)
|
|
array([ 0., 1., 2.])
|
|
>>> np.arange(3,7)
|
|
array([3, 4, 5, 6])
|
|
>>> np.arange(3,7,2)
|
|
array([3, 5])
|
|
|
|
""".replace(
|
|
"${ARRAY_FUNCTION_LIKE}",
|
|
array_function_like_doc,
|
|
))
|
|
|
|
add_newdoc('numpy.core.multiarray', '_get_ndarray_c_version',
|
|
"""_get_ndarray_c_version()
|
|
|
|
Return the compile time NPY_VERSION (formerly called NDARRAY_VERSION) number.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', '_reconstruct',
|
|
"""_reconstruct(subtype, shape, dtype)
|
|
|
|
Construct an empty array. Used by Pickles.
|
|
|
|
""")
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'set_string_function',
|
|
"""
|
|
set_string_function(f, repr=1)
|
|
|
|
Internal method to set a function to be used when pretty printing arrays.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'set_numeric_ops',
|
|
"""
|
|
set_numeric_ops(op1=func1, op2=func2, ...)
|
|
|
|
Set numerical operators for array objects.
|
|
|
|
.. deprecated:: 1.16
|
|
|
|
For the general case, use :c:func:`PyUFunc_ReplaceLoopBySignature`.
|
|
For ndarray subclasses, define the ``__array_ufunc__`` method and
|
|
override the relevant ufunc.
|
|
|
|
Parameters
|
|
----------
|
|
op1, op2, ... : callable
|
|
Each ``op = func`` pair describes an operator to be replaced.
|
|
For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace
|
|
addition by modulus 5 addition.
|
|
|
|
Returns
|
|
-------
|
|
saved_ops : list of callables
|
|
A list of all operators, stored before making replacements.
|
|
|
|
Notes
|
|
-----
|
|
.. warning::
|
|
Use with care! Incorrect usage may lead to memory errors.
|
|
|
|
A function replacing an operator cannot make use of that operator.
|
|
For example, when replacing add, you may not use ``+``. Instead,
|
|
directly call ufuncs.
|
|
|
|
Examples
|
|
--------
|
|
>>> def add_mod5(x, y):
|
|
... return np.add(x, y) % 5
|
|
...
|
|
>>> old_funcs = np.set_numeric_ops(add=add_mod5)
|
|
|
|
>>> x = np.arange(12).reshape((3, 4))
|
|
>>> x + x
|
|
array([[0, 2, 4, 1],
|
|
[3, 0, 2, 4],
|
|
[1, 3, 0, 2]])
|
|
|
|
>>> ignore = np.set_numeric_ops(**old_funcs) # restore operators
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'promote_types',
|
|
"""
|
|
promote_types(type1, type2)
|
|
|
|
Returns the data type with the smallest size and smallest scalar
|
|
kind to which both ``type1`` and ``type2`` may be safely cast.
|
|
The returned data type is always considered "canonical", this mainly
|
|
means that the promoted dtype will always be in native byte order.
|
|
|
|
This function is symmetric, but rarely associative.
|
|
|
|
Parameters
|
|
----------
|
|
type1 : dtype or dtype specifier
|
|
First data type.
|
|
type2 : dtype or dtype specifier
|
|
Second data type.
|
|
|
|
Returns
|
|
-------
|
|
out : dtype
|
|
The promoted data type.
|
|
|
|
Notes
|
|
-----
|
|
Please see `numpy.result_type` for additional information about promotion.
|
|
|
|
.. versionadded:: 1.6.0
|
|
|
|
Starting in NumPy 1.9, promote_types function now returns a valid string
|
|
length when given an integer or float dtype as one argument and a string
|
|
dtype as another argument. Previously it always returned the input string
|
|
dtype, even if it wasn't long enough to store the max integer/float value
|
|
converted to a string.
|
|
|
|
.. versionchanged:: 1.23.0
|
|
|
|
NumPy now supports promotion for more structured dtypes. It will now
|
|
remove unnecessary padding from a structure dtype and promote included
|
|
fields individually.
|
|
|
|
See Also
|
|
--------
|
|
result_type, dtype, can_cast
|
|
|
|
Examples
|
|
--------
|
|
>>> np.promote_types('f4', 'f8')
|
|
dtype('float64')
|
|
|
|
>>> np.promote_types('i8', 'f4')
|
|
dtype('float64')
|
|
|
|
>>> np.promote_types('>i8', '<c8')
|
|
dtype('complex128')
|
|
|
|
>>> np.promote_types('i4', 'S8')
|
|
dtype('S11')
|
|
|
|
An example of a non-associative case:
|
|
|
|
>>> p = np.promote_types
|
|
>>> p('S', p('i1', 'u1'))
|
|
dtype('S6')
|
|
>>> p(p('S', 'i1'), 'u1')
|
|
dtype('S4')
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'c_einsum',
|
|
"""
|
|
c_einsum(subscripts, *operands, out=None, dtype=None, order='K',
|
|
casting='safe')
|
|
|
|
*This documentation shadows that of the native python implementation of the `einsum` function,
|
|
except all references and examples related to the `optimize` argument (v 0.12.0) have been removed.*
|
|
|
|
Evaluates the Einstein summation convention on the operands.
|
|
|
|
Using the Einstein summation convention, many common multi-dimensional,
|
|
linear algebraic array operations can be represented in a simple fashion.
|
|
In *implicit* mode `einsum` computes these values.
|
|
|
|
In *explicit* mode, `einsum` provides further flexibility to compute
|
|
other array operations that might not be considered classical Einstein
|
|
summation operations, by disabling, or forcing summation over specified
|
|
subscript labels.
|
|
|
|
See the notes and examples for clarification.
|
|
|
|
Parameters
|
|
----------
|
|
subscripts : str
|
|
Specifies the subscripts for summation as comma separated list of
|
|
subscript labels. An implicit (classical Einstein summation)
|
|
calculation is performed unless the explicit indicator '->' is
|
|
included as well as subscript labels of the precise output form.
|
|
operands : list of array_like
|
|
These are the arrays for the operation.
|
|
out : ndarray, optional
|
|
If provided, the calculation is done into this array.
|
|
dtype : {data-type, None}, optional
|
|
If provided, forces the calculation to use the data type specified.
|
|
Note that you may have to also give a more liberal `casting`
|
|
parameter to allow the conversions. Default is None.
|
|
order : {'C', 'F', 'A', 'K'}, optional
|
|
Controls the memory layout of the output. 'C' means it should
|
|
be C contiguous. 'F' means it should be Fortran contiguous,
|
|
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
|
|
'K' means it should be as close to the layout of the inputs as
|
|
is possible, including arbitrarily permuted axes.
|
|
Default is 'K'.
|
|
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
|
|
Controls what kind of data casting may occur. Setting this to
|
|
'unsafe' is not recommended, as it can adversely affect accumulations.
|
|
|
|
* 'no' means the data types should not be cast at all.
|
|
* 'equiv' means only byte-order changes are allowed.
|
|
* 'safe' means only casts which can preserve values are allowed.
|
|
* 'same_kind' means only safe casts or casts within a kind,
|
|
like float64 to float32, are allowed.
|
|
* 'unsafe' means any data conversions may be done.
|
|
|
|
Default is 'safe'.
|
|
optimize : {False, True, 'greedy', 'optimal'}, optional
|
|
Controls if intermediate optimization should occur. No optimization
|
|
will occur if False and True will default to the 'greedy' algorithm.
|
|
Also accepts an explicit contraction list from the ``np.einsum_path``
|
|
function. See ``np.einsum_path`` for more details. Defaults to False.
|
|
|
|
Returns
|
|
-------
|
|
output : ndarray
|
|
The calculation based on the Einstein summation convention.
|
|
|
|
See Also
|
|
--------
|
|
einsum_path, dot, inner, outer, tensordot, linalg.multi_dot
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 1.6.0
|
|
|
|
The Einstein summation convention can be used to compute
|
|
many multi-dimensional, linear algebraic array operations. `einsum`
|
|
provides a succinct way of representing these.
|
|
|
|
A non-exhaustive list of these operations,
|
|
which can be computed by `einsum`, is shown below along with examples:
|
|
|
|
* Trace of an array, :py:func:`numpy.trace`.
|
|
* Return a diagonal, :py:func:`numpy.diag`.
|
|
* Array axis summations, :py:func:`numpy.sum`.
|
|
* Transpositions and permutations, :py:func:`numpy.transpose`.
|
|
* Matrix multiplication and dot product, :py:func:`numpy.matmul` :py:func:`numpy.dot`.
|
|
* Vector inner and outer products, :py:func:`numpy.inner` :py:func:`numpy.outer`.
|
|
* Broadcasting, element-wise and scalar multiplication, :py:func:`numpy.multiply`.
|
|
* Tensor contractions, :py:func:`numpy.tensordot`.
|
|
* Chained array operations, in efficient calculation order, :py:func:`numpy.einsum_path`.
|
|
|
|
The subscripts string is a comma-separated list of subscript labels,
|
|
where each label refers to a dimension of the corresponding operand.
|
|
Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
|
|
is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label
|
|
appears only once, it is not summed, so ``np.einsum('i', a)`` produces a
|
|
view of ``a`` with no changes. A further example ``np.einsum('ij,jk', a, b)``
|
|
describes traditional matrix multiplication and is equivalent to
|
|
:py:func:`np.matmul(a,b) <numpy.matmul>`. Repeated subscript labels in one
|
|
operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent
|
|
to :py:func:`np.trace(a) <numpy.trace>`.
|
|
|
|
In *implicit mode*, the chosen subscripts are important
|
|
since the axes of the output are reordered alphabetically. This
|
|
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
|
|
``np.einsum('ji', a)`` takes its transpose. Additionally,
|
|
``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
|
|
``np.einsum('ij,jh', a, b)`` returns the transpose of the
|
|
multiplication since subscript 'h' precedes subscript 'i'.
|
|
|
|
In *explicit mode* the output can be directly controlled by
|
|
specifying output subscript labels. This requires the
|
|
identifier '->' as well as the list of output subscript labels.
|
|
This feature increases the flexibility of the function since
|
|
summing can be disabled or forced when required. The call
|
|
``np.einsum('i->', a)`` is like :py:func:`np.sum(a, axis=-1) <numpy.sum>`,
|
|
and ``np.einsum('ii->i', a)`` is like :py:func:`np.diag(a) <numpy.diag>`.
|
|
The difference is that `einsum` does not allow broadcasting by default.
|
|
Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
|
|
order of the output subscript labels and therefore returns matrix
|
|
multiplication, unlike the example above in implicit mode.
|
|
|
|
To enable and control broadcasting, use an ellipsis. Default
|
|
NumPy-style broadcasting is done by adding an ellipsis
|
|
to the left of each term, like ``np.einsum('...ii->...i', a)``.
|
|
To take the trace along the first and last axes,
|
|
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
|
|
product with the left-most indices instead of rightmost, one can do
|
|
``np.einsum('ij...,jk...->ik...', a, b)``.
|
|
|
|
When there is only one operand, no axes are summed, and no output
|
|
parameter is provided, a view into the operand is returned instead
|
|
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
|
|
produces a view (changed in version 1.10.0).
|
|
|
|
`einsum` also provides an alternative way to provide the subscripts
|
|
and operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
|
|
If the output shape is not provided in this format `einsum` will be
|
|
calculated in implicit mode, otherwise it will be performed explicitly.
|
|
The examples below have corresponding `einsum` calls with the two
|
|
parameter methods.
|
|
|
|
.. versionadded:: 1.10.0
|
|
|
|
Views returned from einsum are now writeable whenever the input array
|
|
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
|
|
have the same effect as :py:func:`np.swapaxes(a, 0, 2) <numpy.swapaxes>`
|
|
and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
|
|
of a 2D array.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.arange(25).reshape(5,5)
|
|
>>> b = np.arange(5)
|
|
>>> c = np.arange(6).reshape(2,3)
|
|
|
|
Trace of a matrix:
|
|
|
|
>>> np.einsum('ii', a)
|
|
60
|
|
>>> np.einsum(a, [0,0])
|
|
60
|
|
>>> np.trace(a)
|
|
60
|
|
|
|
Extract the diagonal (requires explicit form):
|
|
|
|
>>> np.einsum('ii->i', a)
|
|
array([ 0, 6, 12, 18, 24])
|
|
>>> np.einsum(a, [0,0], [0])
|
|
array([ 0, 6, 12, 18, 24])
|
|
>>> np.diag(a)
|
|
array([ 0, 6, 12, 18, 24])
|
|
|
|
Sum over an axis (requires explicit form):
|
|
|
|
>>> np.einsum('ij->i', a)
|
|
array([ 10, 35, 60, 85, 110])
|
|
>>> np.einsum(a, [0,1], [0])
|
|
array([ 10, 35, 60, 85, 110])
|
|
>>> np.sum(a, axis=1)
|
|
array([ 10, 35, 60, 85, 110])
|
|
|
|
For higher dimensional arrays summing a single axis can be done with ellipsis:
|
|
|
|
>>> np.einsum('...j->...', a)
|
|
array([ 10, 35, 60, 85, 110])
|
|
>>> np.einsum(a, [Ellipsis,1], [Ellipsis])
|
|
array([ 10, 35, 60, 85, 110])
|
|
|
|
Compute a matrix transpose, or reorder any number of axes:
|
|
|
|
>>> np.einsum('ji', c)
|
|
array([[0, 3],
|
|
[1, 4],
|
|
[2, 5]])
|
|
>>> np.einsum('ij->ji', c)
|
|
array([[0, 3],
|
|
[1, 4],
|
|
[2, 5]])
|
|
>>> np.einsum(c, [1,0])
|
|
array([[0, 3],
|
|
[1, 4],
|
|
[2, 5]])
|
|
>>> np.transpose(c)
|
|
array([[0, 3],
|
|
[1, 4],
|
|
[2, 5]])
|
|
|
|
Vector inner products:
|
|
|
|
>>> np.einsum('i,i', b, b)
|
|
30
|
|
>>> np.einsum(b, [0], b, [0])
|
|
30
|
|
>>> np.inner(b,b)
|
|
30
|
|
|
|
Matrix vector multiplication:
|
|
|
|
>>> np.einsum('ij,j', a, b)
|
|
array([ 30, 80, 130, 180, 230])
|
|
>>> np.einsum(a, [0,1], b, [1])
|
|
array([ 30, 80, 130, 180, 230])
|
|
>>> np.dot(a, b)
|
|
array([ 30, 80, 130, 180, 230])
|
|
>>> np.einsum('...j,j', a, b)
|
|
array([ 30, 80, 130, 180, 230])
|
|
|
|
Broadcasting and scalar multiplication:
|
|
|
|
>>> np.einsum('..., ...', 3, c)
|
|
array([[ 0, 3, 6],
|
|
[ 9, 12, 15]])
|
|
>>> np.einsum(',ij', 3, c)
|
|
array([[ 0, 3, 6],
|
|
[ 9, 12, 15]])
|
|
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
|
|
array([[ 0, 3, 6],
|
|
[ 9, 12, 15]])
|
|
>>> np.multiply(3, c)
|
|
array([[ 0, 3, 6],
|
|
[ 9, 12, 15]])
|
|
|
|
Vector outer product:
|
|
|
|
>>> np.einsum('i,j', np.arange(2)+1, b)
|
|
array([[0, 1, 2, 3, 4],
|
|
[0, 2, 4, 6, 8]])
|
|
>>> np.einsum(np.arange(2)+1, [0], b, [1])
|
|
array([[0, 1, 2, 3, 4],
|
|
[0, 2, 4, 6, 8]])
|
|
>>> np.outer(np.arange(2)+1, b)
|
|
array([[0, 1, 2, 3, 4],
|
|
[0, 2, 4, 6, 8]])
|
|
|
|
Tensor contraction:
|
|
|
|
>>> a = np.arange(60.).reshape(3,4,5)
|
|
>>> b = np.arange(24.).reshape(4,3,2)
|
|
>>> np.einsum('ijk,jil->kl', a, b)
|
|
array([[ 4400., 4730.],
|
|
[ 4532., 4874.],
|
|
[ 4664., 5018.],
|
|
[ 4796., 5162.],
|
|
[ 4928., 5306.]])
|
|
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
|
|
array([[ 4400., 4730.],
|
|
[ 4532., 4874.],
|
|
[ 4664., 5018.],
|
|
[ 4796., 5162.],
|
|
[ 4928., 5306.]])
|
|
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
|
|
array([[ 4400., 4730.],
|
|
[ 4532., 4874.],
|
|
[ 4664., 5018.],
|
|
[ 4796., 5162.],
|
|
[ 4928., 5306.]])
|
|
|
|
Writeable returned arrays (since version 1.10.0):
|
|
|
|
>>> a = np.zeros((3, 3))
|
|
>>> np.einsum('ii->i', a)[:] = 1
|
|
>>> a
|
|
array([[ 1., 0., 0.],
|
|
[ 0., 1., 0.],
|
|
[ 0., 0., 1.]])
|
|
|
|
Example of ellipsis use:
|
|
|
|
>>> a = np.arange(6).reshape((3,2))
|
|
>>> b = np.arange(12).reshape((4,3))
|
|
>>> np.einsum('ki,jk->ij', a, b)
|
|
array([[10, 28, 46, 64],
|
|
[13, 40, 67, 94]])
|
|
>>> np.einsum('ki,...k->i...', a, b)
|
|
array([[10, 28, 46, 64],
|
|
[13, 40, 67, 94]])
|
|
>>> np.einsum('k...,jk', a, b)
|
|
array([[10, 28, 46, 64],
|
|
[13, 40, 67, 94]])
|
|
|
|
""")
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# Documentation for ndarray attributes and methods
|
|
#
|
|
##############################################################################
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# ndarray object
|
|
#
|
|
##############################################################################
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray',
|
|
"""
|
|
ndarray(shape, dtype=float, buffer=None, offset=0,
|
|
strides=None, order=None)
|
|
|
|
An array object represents a multidimensional, homogeneous array
|
|
of fixed-size items. An associated data-type object describes the
|
|
format of each element in the array (its byte-order, how many bytes it
|
|
occupies in memory, whether it is an integer, a floating point number,
|
|
or something else, etc.)
|
|
|
|
Arrays should be constructed using `array`, `zeros` or `empty` (refer
|
|
to the See Also section below). The parameters given here refer to
|
|
a low-level method (`ndarray(...)`) for instantiating an array.
|
|
|
|
For more information, refer to the `numpy` module and examine the
|
|
methods and attributes of an array.
|
|
|
|
Parameters
|
|
----------
|
|
(for the __new__ method; see Notes below)
|
|
|
|
shape : tuple of ints
|
|
Shape of created array.
|
|
dtype : data-type, optional
|
|
Any object that can be interpreted as a numpy data type.
|
|
buffer : object exposing buffer interface, optional
|
|
Used to fill the array with data.
|
|
offset : int, optional
|
|
Offset of array data in buffer.
|
|
strides : tuple of ints, optional
|
|
Strides of data in memory.
|
|
order : {'C', 'F'}, optional
|
|
Row-major (C-style) or column-major (Fortran-style) order.
|
|
|
|
Attributes
|
|
----------
|
|
T : ndarray
|
|
Transpose of the array.
|
|
data : buffer
|
|
The array's elements, in memory.
|
|
dtype : dtype object
|
|
Describes the format of the elements in the array.
|
|
flags : dict
|
|
Dictionary containing information related to memory use, e.g.,
|
|
'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
|
|
flat : numpy.flatiter object
|
|
Flattened version of the array as an iterator. The iterator
|
|
allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
|
|
assignment examples; TODO).
|
|
imag : ndarray
|
|
Imaginary part of the array.
|
|
real : ndarray
|
|
Real part of the array.
|
|
size : int
|
|
Number of elements in the array.
|
|
itemsize : int
|
|
The memory use of each array element in bytes.
|
|
nbytes : int
|
|
The total number of bytes required to store the array data,
|
|
i.e., ``itemsize * size``.
|
|
ndim : int
|
|
The array's number of dimensions.
|
|
shape : tuple of ints
|
|
Shape of the array.
|
|
strides : tuple of ints
|
|
The step-size required to move from one element to the next in
|
|
memory. For example, a contiguous ``(3, 4)`` array of type
|
|
``int16`` in C-order has strides ``(8, 2)``. This implies that
|
|
to move from element to element in memory requires jumps of 2 bytes.
|
|
To move from row-to-row, one needs to jump 8 bytes at a time
|
|
(``2 * 4``).
|
|
ctypes : ctypes object
|
|
Class containing properties of the array needed for interaction
|
|
with ctypes.
|
|
base : ndarray
|
|
If the array is a view into another array, that array is its `base`
|
|
(unless that array is also a view). The `base` array is where the
|
|
array data is actually stored.
|
|
|
|
See Also
|
|
--------
|
|
array : Construct an array.
|
|
zeros : Create an array, each element of which is zero.
|
|
empty : Create an array, but leave its allocated memory unchanged (i.e.,
|
|
it contains "garbage").
|
|
dtype : Create a data-type.
|
|
numpy.typing.NDArray : An ndarray alias :term:`generic <generic type>`
|
|
w.r.t. its `dtype.type <numpy.dtype.type>`.
|
|
|
|
Notes
|
|
-----
|
|
There are two modes of creating an array using ``__new__``:
|
|
|
|
1. If `buffer` is None, then only `shape`, `dtype`, and `order`
|
|
are used.
|
|
2. If `buffer` is an object exposing the buffer interface, then
|
|
all keywords are interpreted.
|
|
|
|
No ``__init__`` method is needed because the array is fully initialized
|
|
after the ``__new__`` method.
|
|
|
|
Examples
|
|
--------
|
|
These examples illustrate the low-level `ndarray` constructor. Refer
|
|
to the `See Also` section above for easier ways of constructing an
|
|
ndarray.
|
|
|
|
First mode, `buffer` is None:
|
|
|
|
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
|
|
array([[0.0e+000, 0.0e+000], # random
|
|
[ nan, 2.5e-323]])
|
|
|
|
Second mode:
|
|
|
|
>>> np.ndarray((2,), buffer=np.array([1,2,3]),
|
|
... offset=np.int_().itemsize,
|
|
... dtype=int) # offset = 1*itemsize, i.e. skip first element
|
|
array([2, 3])
|
|
|
|
""")
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# ndarray attributes
|
|
#
|
|
##############################################################################
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_interface__',
|
|
"""Array protocol: Python side."""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__',
|
|
"""Array priority."""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__',
|
|
"""Array protocol: C-struct side."""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack__',
|
|
"""a.__dlpack__(*, stream=None)
|
|
|
|
DLPack Protocol: Part of the Array API."""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__dlpack_device__',
|
|
"""a.__dlpack_device__()
|
|
|
|
DLPack Protocol: Part of the Array API."""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('base',
|
|
"""
|
|
Base object if memory is from some other object.
|
|
|
|
Examples
|
|
--------
|
|
The base of an array that owns its memory is None:
|
|
|
|
>>> x = np.array([1,2,3,4])
|
|
>>> x.base is None
|
|
True
|
|
|
|
Slicing creates a view, whose memory is shared with x:
|
|
|
|
>>> y = x[2:]
|
|
>>> y.base is x
|
|
True
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('ctypes',
|
|
"""
|
|
An object to simplify the interaction of the array with the ctypes
|
|
module.
|
|
|
|
This attribute creates an object that makes it easier to use arrays
|
|
when calling shared libraries with the ctypes module. The returned
|
|
object has, among others, data, shape, and strides attributes (see
|
|
Notes below) which themselves return ctypes objects that can be used
|
|
as arguments to a shared library.
|
|
|
|
Parameters
|
|
----------
|
|
None
|
|
|
|
Returns
|
|
-------
|
|
c : Python object
|
|
Possessing attributes data, shape, strides, etc.
|
|
|
|
See Also
|
|
--------
|
|
numpy.ctypeslib
|
|
|
|
Notes
|
|
-----
|
|
Below are the public attributes of this object which were documented
|
|
in "Guide to NumPy" (we have omitted undocumented public attributes,
|
|
as well as documented private attributes):
|
|
|
|
.. autoattribute:: numpy.core._internal._ctypes.data
|
|
:noindex:
|
|
|
|
.. autoattribute:: numpy.core._internal._ctypes.shape
|
|
:noindex:
|
|
|
|
.. autoattribute:: numpy.core._internal._ctypes.strides
|
|
:noindex:
|
|
|
|
.. automethod:: numpy.core._internal._ctypes.data_as
|
|
:noindex:
|
|
|
|
.. automethod:: numpy.core._internal._ctypes.shape_as
|
|
:noindex:
|
|
|
|
.. automethod:: numpy.core._internal._ctypes.strides_as
|
|
:noindex:
|
|
|
|
If the ctypes module is not available, then the ctypes attribute
|
|
of array objects still returns something useful, but ctypes objects
|
|
are not returned and errors may be raised instead. In particular,
|
|
the object will still have the ``as_parameter`` attribute which will
|
|
return an integer equal to the data attribute.
|
|
|
|
Examples
|
|
--------
|
|
>>> import ctypes
|
|
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
|
|
>>> x
|
|
array([[0, 1],
|
|
[2, 3]], dtype=int32)
|
|
>>> x.ctypes.data
|
|
31962608 # may vary
|
|
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
|
|
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
|
|
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
|
|
c_uint(0)
|
|
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
|
|
c_ulong(4294967296)
|
|
>>> x.ctypes.shape
|
|
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
|
|
>>> x.ctypes.strides
|
|
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('data',
|
|
"""Python buffer object pointing to the start of the array's data."""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('dtype',
|
|
"""
|
|
Data-type of the array's elements.
|
|
|
|
.. warning::
|
|
|
|
Setting ``arr.dtype`` is discouraged and may be deprecated in the
|
|
future. Setting will replace the ``dtype`` without modifying the
|
|
memory (see also `ndarray.view` and `ndarray.astype`).
|
|
|
|
Parameters
|
|
----------
|
|
None
|
|
|
|
Returns
|
|
-------
|
|
d : numpy dtype object
|
|
|
|
See Also
|
|
--------
|
|
ndarray.astype : Cast the values contained in the array to a new data-type.
|
|
ndarray.view : Create a view of the same data but a different data-type.
|
|
numpy.dtype
|
|
|
|
Examples
|
|
--------
|
|
>>> x
|
|
array([[0, 1],
|
|
[2, 3]])
|
|
>>> x.dtype
|
|
dtype('int32')
|
|
>>> type(x.dtype)
|
|
<type 'numpy.dtype'>
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('imag',
|
|
"""
|
|
The imaginary part of the array.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.sqrt([1+0j, 0+1j])
|
|
>>> x.imag
|
|
array([ 0. , 0.70710678])
|
|
>>> x.imag.dtype
|
|
dtype('float64')
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemsize',
|
|
"""
|
|
Length of one array element in bytes.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1,2,3], dtype=np.float64)
|
|
>>> x.itemsize
|
|
8
|
|
>>> x = np.array([1,2,3], dtype=np.complex128)
|
|
>>> x.itemsize
|
|
16
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('flags',
|
|
"""
|
|
Information about the memory layout of the array.
|
|
|
|
Attributes
|
|
----------
|
|
C_CONTIGUOUS (C)
|
|
The data is in a single, C-style contiguous segment.
|
|
F_CONTIGUOUS (F)
|
|
The data is in a single, Fortran-style contiguous segment.
|
|
OWNDATA (O)
|
|
The array owns the memory it uses or borrows it from another object.
|
|
WRITEABLE (W)
|
|
The data area can be written to. Setting this to False locks
|
|
the data, making it read-only. A view (slice, etc.) inherits WRITEABLE
|
|
from its base array at creation time, but a view of a writeable
|
|
array may be subsequently locked while the base array remains writeable.
|
|
(The opposite is not true, in that a view of a locked array may not
|
|
be made writeable. However, currently, locking a base object does not
|
|
lock any views that already reference it, so under that circumstance it
|
|
is possible to alter the contents of a locked array via a previously
|
|
created writeable view onto it.) Attempting to change a non-writeable
|
|
array raises a RuntimeError exception.
|
|
ALIGNED (A)
|
|
The data and all elements are aligned appropriately for the hardware.
|
|
WRITEBACKIFCOPY (X)
|
|
This array is a copy of some other array. The C-API function
|
|
PyArray_ResolveWritebackIfCopy must be called before deallocating
|
|
to the base array will be updated with the contents of this array.
|
|
FNC
|
|
F_CONTIGUOUS and not C_CONTIGUOUS.
|
|
FORC
|
|
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
|
|
BEHAVED (B)
|
|
ALIGNED and WRITEABLE.
|
|
CARRAY (CA)
|
|
BEHAVED and C_CONTIGUOUS.
|
|
FARRAY (FA)
|
|
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
|
|
|
|
Notes
|
|
-----
|
|
The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),
|
|
or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag
|
|
names are only supported in dictionary access.
|
|
|
|
Only the WRITEBACKIFCOPY, WRITEABLE, and ALIGNED flags can be
|
|
changed by the user, via direct assignment to the attribute or dictionary
|
|
entry, or by calling `ndarray.setflags`.
|
|
|
|
The array flags cannot be set arbitrarily:
|
|
|
|
- WRITEBACKIFCOPY can only be set ``False``.
|
|
- ALIGNED can only be set ``True`` if the data is truly aligned.
|
|
- WRITEABLE can only be set ``True`` if the array owns its own memory
|
|
or the ultimate owner of the memory exposes a writeable buffer
|
|
interface or is a string.
|
|
|
|
Arrays can be both C-style and Fortran-style contiguous simultaneously.
|
|
This is clear for 1-dimensional arrays, but can also be true for higher
|
|
dimensional arrays.
|
|
|
|
Even for contiguous arrays a stride for a given dimension
|
|
``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``
|
|
or the array has no elements.
|
|
It does *not* generally hold that ``self.strides[-1] == self.itemsize``
|
|
for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for
|
|
Fortran-style contiguous arrays is true.
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('flat',
|
|
"""
|
|
A 1-D iterator over the array.
|
|
|
|
This is a `numpy.flatiter` instance, which acts similarly to, but is not
|
|
a subclass of, Python's built-in iterator object.
|
|
|
|
See Also
|
|
--------
|
|
flatten : Return a copy of the array collapsed into one dimension.
|
|
|
|
flatiter
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.arange(1, 7).reshape(2, 3)
|
|
>>> x
|
|
array([[1, 2, 3],
|
|
[4, 5, 6]])
|
|
>>> x.flat[3]
|
|
4
|
|
>>> x.T
|
|
array([[1, 4],
|
|
[2, 5],
|
|
[3, 6]])
|
|
>>> x.T.flat[3]
|
|
5
|
|
>>> type(x.flat)
|
|
<class 'numpy.flatiter'>
|
|
|
|
An assignment example:
|
|
|
|
>>> x.flat = 3; x
|
|
array([[3, 3, 3],
|
|
[3, 3, 3]])
|
|
>>> x.flat[[1,4]] = 1; x
|
|
array([[3, 1, 3],
|
|
[3, 1, 3]])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
|
|
"""
|
|
Total bytes consumed by the elements of the array.
|
|
|
|
Notes
|
|
-----
|
|
Does not include memory consumed by non-element attributes of the
|
|
array object.
|
|
|
|
See Also
|
|
--------
|
|
sys.getsizeof
|
|
Memory consumed by the object itself without parents in case view.
|
|
This does include memory consumed by non-element attributes.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.zeros((3,5,2), dtype=np.complex128)
|
|
>>> x.nbytes
|
|
480
|
|
>>> np.prod(x.shape) * x.itemsize
|
|
480
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('ndim',
|
|
"""
|
|
Number of array dimensions.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3])
|
|
>>> x.ndim
|
|
1
|
|
>>> y = np.zeros((2, 3, 4))
|
|
>>> y.ndim
|
|
3
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('real',
|
|
"""
|
|
The real part of the array.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.sqrt([1+0j, 0+1j])
|
|
>>> x.real
|
|
array([ 1. , 0.70710678])
|
|
>>> x.real.dtype
|
|
dtype('float64')
|
|
|
|
See Also
|
|
--------
|
|
numpy.real : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('shape',
|
|
"""
|
|
Tuple of array dimensions.
|
|
|
|
The shape property is usually used to get the current shape of an array,
|
|
but may also be used to reshape the array in-place by assigning a tuple of
|
|
array dimensions to it. As with `numpy.reshape`, one of the new shape
|
|
dimensions can be -1, in which case its value is inferred from the size of
|
|
the array and the remaining dimensions. Reshaping an array in-place will
|
|
fail if a copy is required.
|
|
|
|
.. warning::
|
|
|
|
Setting ``arr.shape`` is discouraged and may be deprecated in the
|
|
future. Using `ndarray.reshape` is the preferred approach.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 3, 4])
|
|
>>> x.shape
|
|
(4,)
|
|
>>> y = np.zeros((2, 3, 4))
|
|
>>> y.shape
|
|
(2, 3, 4)
|
|
>>> y.shape = (3, 8)
|
|
>>> y
|
|
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
|
|
[ 0., 0., 0., 0., 0., 0., 0., 0.],
|
|
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
|
|
>>> y.shape = (3, 6)
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in <module>
|
|
ValueError: total size of new array must be unchanged
|
|
>>> np.zeros((4,2))[::2].shape = (-1,)
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in <module>
|
|
AttributeError: Incompatible shape for in-place modification. Use
|
|
`.reshape()` to make a copy with the desired shape.
|
|
|
|
See Also
|
|
--------
|
|
numpy.shape : Equivalent getter function.
|
|
numpy.reshape : Function similar to setting ``shape``.
|
|
ndarray.reshape : Method similar to setting ``shape``.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('size',
|
|
"""
|
|
Number of elements in the array.
|
|
|
|
Equal to ``np.prod(a.shape)``, i.e., the product of the array's
|
|
dimensions.
|
|
|
|
Notes
|
|
-----
|
|
`a.size` returns a standard arbitrary precision Python integer. This
|
|
may not be the case with other methods of obtaining the same value
|
|
(like the suggested ``np.prod(a.shape)``, which returns an instance
|
|
of ``np.int_``), and may be relevant if the value is used further in
|
|
calculations that may overflow a fixed size integer type.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
|
|
>>> x.size
|
|
30
|
|
>>> np.prod(x.shape)
|
|
30
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('strides',
|
|
"""
|
|
Tuple of bytes to step in each dimension when traversing an array.
|
|
|
|
The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
|
|
is::
|
|
|
|
offset = sum(np.array(i) * a.strides)
|
|
|
|
A more detailed explanation of strides can be found in the
|
|
"ndarray.rst" file in the NumPy reference guide.
|
|
|
|
.. warning::
|
|
|
|
Setting ``arr.strides`` is discouraged and may be deprecated in the
|
|
future. `numpy.lib.stride_tricks.as_strided` should be preferred
|
|
to create a new view of the same data in a safer way.
|
|
|
|
Notes
|
|
-----
|
|
Imagine an array of 32-bit integers (each 4 bytes)::
|
|
|
|
x = np.array([[0, 1, 2, 3, 4],
|
|
[5, 6, 7, 8, 9]], dtype=np.int32)
|
|
|
|
This array is stored in memory as 40 bytes, one after the other
|
|
(known as a contiguous block of memory). The strides of an array tell
|
|
us how many bytes we have to skip in memory to move to the next position
|
|
along a certain axis. For example, we have to skip 4 bytes (1 value) to
|
|
move to the next column, but 20 bytes (5 values) to get to the same
|
|
position in the next row. As such, the strides for the array `x` will be
|
|
``(20, 4)``.
|
|
|
|
See Also
|
|
--------
|
|
numpy.lib.stride_tricks.as_strided
|
|
|
|
Examples
|
|
--------
|
|
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
|
|
>>> y
|
|
array([[[ 0, 1, 2, 3],
|
|
[ 4, 5, 6, 7],
|
|
[ 8, 9, 10, 11]],
|
|
[[12, 13, 14, 15],
|
|
[16, 17, 18, 19],
|
|
[20, 21, 22, 23]]])
|
|
>>> y.strides
|
|
(48, 16, 4)
|
|
>>> y[1,1,1]
|
|
17
|
|
>>> offset=sum(y.strides * np.array((1,1,1)))
|
|
>>> offset/y.itemsize
|
|
17
|
|
|
|
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
|
|
>>> x.strides
|
|
(32, 4, 224, 1344)
|
|
>>> i = np.array([3,5,2,2])
|
|
>>> offset = sum(i * x.strides)
|
|
>>> x[3,5,2,2]
|
|
813
|
|
>>> offset / x.itemsize
|
|
813
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
|
|
"""
|
|
View of the transposed array.
|
|
|
|
Same as ``self.transpose()``.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[1, 2], [3, 4]])
|
|
>>> a
|
|
array([[1, 2],
|
|
[3, 4]])
|
|
>>> a.T
|
|
array([[1, 3],
|
|
[2, 4]])
|
|
|
|
>>> a = np.array([1, 2, 3, 4])
|
|
>>> a
|
|
array([1, 2, 3, 4])
|
|
>>> a.T
|
|
array([1, 2, 3, 4])
|
|
|
|
See Also
|
|
--------
|
|
transpose
|
|
|
|
"""))
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# ndarray methods
|
|
#
|
|
##############################################################################
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array__',
|
|
""" a.__array__([dtype], /)
|
|
|
|
Returns either a new reference to self if dtype is not given or a new array
|
|
of provided data type if dtype is different from the current dtype of the
|
|
array.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_finalize__',
|
|
"""a.__array_finalize__(obj, /)
|
|
|
|
Present so subclasses can call super. Does nothing.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_prepare__',
|
|
"""a.__array_prepare__(array[, context], /)
|
|
|
|
Returns a view of `array` with the same type as self.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_wrap__',
|
|
"""a.__array_wrap__(array[, context], /)
|
|
|
|
Returns a view of `array` with the same type as self.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__copy__',
|
|
"""a.__copy__()
|
|
|
|
Used if :func:`copy.copy` is called on an array. Returns a copy of the array.
|
|
|
|
Equivalent to ``a.copy(order='K')``.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__class_getitem__',
|
|
"""a.__class_getitem__(item, /)
|
|
|
|
Return a parametrized wrapper around the `~numpy.ndarray` type.
|
|
|
|
.. versionadded:: 1.22
|
|
|
|
Returns
|
|
-------
|
|
alias : types.GenericAlias
|
|
A parametrized `~numpy.ndarray` type.
|
|
|
|
Examples
|
|
--------
|
|
>>> from typing import Any
|
|
>>> import numpy as np
|
|
|
|
>>> np.ndarray[Any, np.dtype[Any]]
|
|
numpy.ndarray[typing.Any, numpy.dtype[typing.Any]]
|
|
|
|
See Also
|
|
--------
|
|
:pep:`585` : Type hinting generics in standard collections.
|
|
numpy.typing.NDArray : An ndarray alias :term:`generic <generic type>`
|
|
w.r.t. its `dtype.type <numpy.dtype.type>`.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__',
|
|
"""a.__deepcopy__(memo, /)
|
|
|
|
Used if :func:`copy.deepcopy` is called on an array.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__reduce__',
|
|
"""a.__reduce__()
|
|
|
|
For pickling.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('__setstate__',
|
|
"""a.__setstate__(state, /)
|
|
|
|
For unpickling.
|
|
|
|
The `state` argument must be a sequence that contains the following
|
|
elements:
|
|
|
|
Parameters
|
|
----------
|
|
version : int
|
|
optional pickle version. If omitted defaults to 0.
|
|
shape : tuple
|
|
dtype : data-type
|
|
isFortran : bool
|
|
rawdata : string or list
|
|
a binary string with the data (or a list if 'a' is an object array)
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('all',
|
|
"""
|
|
a.all(axis=None, out=None, keepdims=False, *, where=True)
|
|
|
|
Returns True if all elements evaluate to True.
|
|
|
|
Refer to `numpy.all` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.all : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('any',
|
|
"""
|
|
a.any(axis=None, out=None, keepdims=False, *, where=True)
|
|
|
|
Returns True if any of the elements of `a` evaluate to True.
|
|
|
|
Refer to `numpy.any` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.any : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmax',
|
|
"""
|
|
a.argmax(axis=None, out=None, *, keepdims=False)
|
|
|
|
Return indices of the maximum values along the given axis.
|
|
|
|
Refer to `numpy.argmax` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.argmax : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmin',
|
|
"""
|
|
a.argmin(axis=None, out=None, *, keepdims=False)
|
|
|
|
Return indices of the minimum values along the given axis.
|
|
|
|
Refer to `numpy.argmin` for detailed documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.argmin : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort',
|
|
"""
|
|
a.argsort(axis=-1, kind=None, order=None)
|
|
|
|
Returns the indices that would sort this array.
|
|
|
|
Refer to `numpy.argsort` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.argsort : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('argpartition',
|
|
"""
|
|
a.argpartition(kth, axis=-1, kind='introselect', order=None)
|
|
|
|
Returns the indices that would partition this array.
|
|
|
|
Refer to `numpy.argpartition` for full documentation.
|
|
|
|
.. versionadded:: 1.8.0
|
|
|
|
See Also
|
|
--------
|
|
numpy.argpartition : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('astype',
|
|
"""
|
|
a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
|
|
|
|
Copy of the array, cast to a specified type.
|
|
|
|
Parameters
|
|
----------
|
|
dtype : str or dtype
|
|
Typecode or data-type to which the array is cast.
|
|
order : {'C', 'F', 'A', 'K'}, optional
|
|
Controls the memory layout order of the result.
|
|
'C' means C order, 'F' means Fortran order, 'A'
|
|
means 'F' order if all the arrays are Fortran contiguous,
|
|
'C' order otherwise, and 'K' means as close to the
|
|
order the array elements appear in memory as possible.
|
|
Default is 'K'.
|
|
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
|
|
Controls what kind of data casting may occur. Defaults to 'unsafe'
|
|
for backwards compatibility.
|
|
|
|
* 'no' means the data types should not be cast at all.
|
|
* 'equiv' means only byte-order changes are allowed.
|
|
* 'safe' means only casts which can preserve values are allowed.
|
|
* 'same_kind' means only safe casts or casts within a kind,
|
|
like float64 to float32, are allowed.
|
|
* 'unsafe' means any data conversions may be done.
|
|
subok : bool, optional
|
|
If True, then sub-classes will be passed-through (default), otherwise
|
|
the returned array will be forced to be a base-class array.
|
|
copy : bool, optional
|
|
By default, astype always returns a newly allocated array. If this
|
|
is set to false, and the `dtype`, `order`, and `subok`
|
|
requirements are satisfied, the input array is returned instead
|
|
of a copy.
|
|
|
|
Returns
|
|
-------
|
|
arr_t : ndarray
|
|
Unless `copy` is False and the other conditions for returning the input
|
|
array are satisfied (see description for `copy` input parameter), `arr_t`
|
|
is a new array of the same shape as the input array, with dtype, order
|
|
given by `dtype`, `order`.
|
|
|
|
Notes
|
|
-----
|
|
.. versionchanged:: 1.17.0
|
|
Casting between a simple data type and a structured one is possible only
|
|
for "unsafe" casting. Casting to multiple fields is allowed, but
|
|
casting from multiple fields is not.
|
|
|
|
.. versionchanged:: 1.9.0
|
|
Casting from numeric to string types in 'safe' casting mode requires
|
|
that the string dtype length is long enough to store the max
|
|
integer/float value converted.
|
|
|
|
Raises
|
|
------
|
|
ComplexWarning
|
|
When casting from complex to float or int. To avoid this,
|
|
one should use ``a.real.astype(t)``.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1, 2, 2.5])
|
|
>>> x
|
|
array([1. , 2. , 2.5])
|
|
|
|
>>> x.astype(int)
|
|
array([1, 2, 2])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap',
|
|
"""
|
|
a.byteswap(inplace=False)
|
|
|
|
Swap the bytes of the array elements
|
|
|
|
Toggle between low-endian and big-endian data representation by
|
|
returning a byteswapped array, optionally swapped in-place.
|
|
Arrays of byte-strings are not swapped. The real and imaginary
|
|
parts of a complex number are swapped individually.
|
|
|
|
Parameters
|
|
----------
|
|
inplace : bool, optional
|
|
If ``True``, swap bytes in-place, default is ``False``.
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray
|
|
The byteswapped array. If `inplace` is ``True``, this is
|
|
a view to self.
|
|
|
|
Examples
|
|
--------
|
|
>>> A = np.array([1, 256, 8755], dtype=np.int16)
|
|
>>> list(map(hex, A))
|
|
['0x1', '0x100', '0x2233']
|
|
>>> A.byteswap(inplace=True)
|
|
array([ 256, 1, 13090], dtype=int16)
|
|
>>> list(map(hex, A))
|
|
['0x100', '0x1', '0x3322']
|
|
|
|
Arrays of byte-strings are not swapped
|
|
|
|
>>> A = np.array([b'ceg', b'fac'])
|
|
>>> A.byteswap()
|
|
array([b'ceg', b'fac'], dtype='|S3')
|
|
|
|
``A.newbyteorder().byteswap()`` produces an array with the same values
|
|
but different representation in memory
|
|
|
|
>>> A = np.array([1, 2, 3])
|
|
>>> A.view(np.uint8)
|
|
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
|
|
0, 0], dtype=uint8)
|
|
>>> A.newbyteorder().byteswap(inplace=True)
|
|
array([1, 2, 3])
|
|
>>> A.view(np.uint8)
|
|
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
|
|
0, 3], dtype=uint8)
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('choose',
|
|
"""
|
|
a.choose(choices, out=None, mode='raise')
|
|
|
|
Use an index array to construct a new array from a set of choices.
|
|
|
|
Refer to `numpy.choose` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.choose : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('clip',
|
|
"""
|
|
a.clip(min=None, max=None, out=None, **kwargs)
|
|
|
|
Return an array whose values are limited to ``[min, max]``.
|
|
One of max or min must be given.
|
|
|
|
Refer to `numpy.clip` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.clip : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('compress',
|
|
"""
|
|
a.compress(condition, axis=None, out=None)
|
|
|
|
Return selected slices of this array along given axis.
|
|
|
|
Refer to `numpy.compress` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.compress : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('conj',
|
|
"""
|
|
a.conj()
|
|
|
|
Complex-conjugate all elements.
|
|
|
|
Refer to `numpy.conjugate` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.conjugate : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate',
|
|
"""
|
|
a.conjugate()
|
|
|
|
Return the complex conjugate, element-wise.
|
|
|
|
Refer to `numpy.conjugate` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.conjugate : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('copy',
|
|
"""
|
|
a.copy(order='C')
|
|
|
|
Return a copy of the array.
|
|
|
|
Parameters
|
|
----------
|
|
order : {'C', 'F', 'A', 'K'}, optional
|
|
Controls the memory layout of the copy. 'C' means C-order,
|
|
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
|
|
'C' otherwise. 'K' means match the layout of `a` as closely
|
|
as possible. (Note that this function and :func:`numpy.copy` are very
|
|
similar but have different default values for their order=
|
|
arguments, and this function always passes sub-classes through.)
|
|
|
|
See also
|
|
--------
|
|
numpy.copy : Similar function with different default behavior
|
|
numpy.copyto
|
|
|
|
Notes
|
|
-----
|
|
This function is the preferred method for creating an array copy. The
|
|
function :func:`numpy.copy` is similar, but it defaults to using order 'K',
|
|
and will not pass sub-classes through by default.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
|
|
|
|
>>> y = x.copy()
|
|
|
|
>>> x.fill(0)
|
|
|
|
>>> x
|
|
array([[0, 0, 0],
|
|
[0, 0, 0]])
|
|
|
|
>>> y
|
|
array([[1, 2, 3],
|
|
[4, 5, 6]])
|
|
|
|
>>> y.flags['C_CONTIGUOUS']
|
|
True
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumprod',
|
|
"""
|
|
a.cumprod(axis=None, dtype=None, out=None)
|
|
|
|
Return the cumulative product of the elements along the given axis.
|
|
|
|
Refer to `numpy.cumprod` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.cumprod : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumsum',
|
|
"""
|
|
a.cumsum(axis=None, dtype=None, out=None)
|
|
|
|
Return the cumulative sum of the elements along the given axis.
|
|
|
|
Refer to `numpy.cumsum` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.cumsum : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('diagonal',
|
|
"""
|
|
a.diagonal(offset=0, axis1=0, axis2=1)
|
|
|
|
Return specified diagonals. In NumPy 1.9 the returned array is a
|
|
read-only view instead of a copy as in previous NumPy versions. In
|
|
a future version the read-only restriction will be removed.
|
|
|
|
Refer to :func:`numpy.diagonal` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.diagonal : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('dot'))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('dump',
|
|
"""a.dump(file)
|
|
|
|
Dump a pickle of the array to the specified file.
|
|
The array can be read back with pickle.load or numpy.load.
|
|
|
|
Parameters
|
|
----------
|
|
file : str or Path
|
|
A string naming the dump file.
|
|
|
|
.. versionchanged:: 1.17.0
|
|
`pathlib.Path` objects are now accepted.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('dumps',
|
|
"""
|
|
a.dumps()
|
|
|
|
Returns the pickle of the array as a string.
|
|
pickle.loads will convert the string back to an array.
|
|
|
|
Parameters
|
|
----------
|
|
None
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('fill',
|
|
"""
|
|
a.fill(value)
|
|
|
|
Fill the array with a scalar value.
|
|
|
|
Parameters
|
|
----------
|
|
value : scalar
|
|
All elements of `a` will be assigned this value.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([1, 2])
|
|
>>> a.fill(0)
|
|
>>> a
|
|
array([0, 0])
|
|
>>> a = np.empty(2)
|
|
>>> a.fill(1)
|
|
>>> a
|
|
array([1., 1.])
|
|
|
|
Fill expects a scalar value and always behaves the same as assigning
|
|
to a single array element. The following is a rare example where this
|
|
distinction is important:
|
|
|
|
>>> a = np.array([None, None], dtype=object)
|
|
>>> a[0] = np.array(3)
|
|
>>> a
|
|
array([array(3), None], dtype=object)
|
|
>>> a.fill(np.array(3))
|
|
>>> a
|
|
array([array(3), array(3)], dtype=object)
|
|
|
|
Where other forms of assignments will unpack the array being assigned:
|
|
|
|
>>> a[...] = np.array(3)
|
|
>>> a
|
|
array([3, 3], dtype=object)
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten',
|
|
"""
|
|
a.flatten(order='C')
|
|
|
|
Return a copy of the array collapsed into one dimension.
|
|
|
|
Parameters
|
|
----------
|
|
order : {'C', 'F', 'A', 'K'}, optional
|
|
'C' means to flatten in row-major (C-style) order.
|
|
'F' means to flatten in column-major (Fortran-
|
|
style) order. 'A' means to flatten in column-major
|
|
order if `a` is Fortran *contiguous* in memory,
|
|
row-major order otherwise. 'K' means to flatten
|
|
`a` in the order the elements occur in memory.
|
|
The default is 'C'.
|
|
|
|
Returns
|
|
-------
|
|
y : ndarray
|
|
A copy of the input array, flattened to one dimension.
|
|
|
|
See Also
|
|
--------
|
|
ravel : Return a flattened array.
|
|
flat : A 1-D flat iterator over the array.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[1,2], [3,4]])
|
|
>>> a.flatten()
|
|
array([1, 2, 3, 4])
|
|
>>> a.flatten('F')
|
|
array([1, 3, 2, 4])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('getfield',
|
|
"""
|
|
a.getfield(dtype, offset=0)
|
|
|
|
Returns a field of the given array as a certain type.
|
|
|
|
A field is a view of the array data with a given data-type. The values in
|
|
the view are determined by the given type and the offset into the current
|
|
array in bytes. The offset needs to be such that the view dtype fits in the
|
|
array dtype; for example an array of dtype complex128 has 16-byte elements.
|
|
If taking a view with a 32-bit integer (4 bytes), the offset needs to be
|
|
between 0 and 12 bytes.
|
|
|
|
Parameters
|
|
----------
|
|
dtype : str or dtype
|
|
The data type of the view. The dtype size of the view can not be larger
|
|
than that of the array itself.
|
|
offset : int
|
|
Number of bytes to skip before beginning the element view.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.diag([1.+1.j]*2)
|
|
>>> x[1, 1] = 2 + 4.j
|
|
>>> x
|
|
array([[1.+1.j, 0.+0.j],
|
|
[0.+0.j, 2.+4.j]])
|
|
>>> x.getfield(np.float64)
|
|
array([[1., 0.],
|
|
[0., 2.]])
|
|
|
|
By choosing an offset of 8 bytes we can select the complex part of the
|
|
array for our view:
|
|
|
|
>>> x.getfield(np.float64, offset=8)
|
|
array([[1., 0.],
|
|
[0., 4.]])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('item',
|
|
"""
|
|
a.item(*args)
|
|
|
|
Copy an element of an array to a standard Python scalar and return it.
|
|
|
|
Parameters
|
|
----------
|
|
\\*args : Arguments (variable number and type)
|
|
|
|
* none: in this case, the method only works for arrays
|
|
with one element (`a.size == 1`), which element is
|
|
copied into a standard Python scalar object and returned.
|
|
|
|
* int_type: this argument is interpreted as a flat index into
|
|
the array, specifying which element to copy and return.
|
|
|
|
* tuple of int_types: functions as does a single int_type argument,
|
|
except that the argument is interpreted as an nd-index into the
|
|
array.
|
|
|
|
Returns
|
|
-------
|
|
z : Standard Python scalar object
|
|
A copy of the specified element of the array as a suitable
|
|
Python scalar
|
|
|
|
Notes
|
|
-----
|
|
When the data type of `a` is longdouble or clongdouble, item() returns
|
|
a scalar array object because there is no available Python scalar that
|
|
would not lose information. Void arrays return a buffer object for item(),
|
|
unless fields are defined, in which case a tuple is returned.
|
|
|
|
`item` is very similar to a[args], except, instead of an array scalar,
|
|
a standard Python scalar is returned. This can be useful for speeding up
|
|
access to elements of the array and doing arithmetic on elements of the
|
|
array using Python's optimized math.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.random.seed(123)
|
|
>>> x = np.random.randint(9, size=(3, 3))
|
|
>>> x
|
|
array([[2, 2, 6],
|
|
[1, 3, 6],
|
|
[1, 0, 1]])
|
|
>>> x.item(3)
|
|
1
|
|
>>> x.item(7)
|
|
0
|
|
>>> x.item((0, 1))
|
|
2
|
|
>>> x.item((2, 2))
|
|
1
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemset',
|
|
"""
|
|
a.itemset(*args)
|
|
|
|
Insert scalar into an array (scalar is cast to array's dtype, if possible)
|
|
|
|
There must be at least 1 argument, and define the last argument
|
|
as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster
|
|
than ``a[args] = item``. The item should be a scalar value and `args`
|
|
must select a single item in the array `a`.
|
|
|
|
Parameters
|
|
----------
|
|
\\*args : Arguments
|
|
If one argument: a scalar, only used in case `a` is of size 1.
|
|
If two arguments: the last argument is the value to be set
|
|
and must be a scalar, the first argument specifies a single array
|
|
element location. It is either an int or a tuple.
|
|
|
|
Notes
|
|
-----
|
|
Compared to indexing syntax, `itemset` provides some speed increase
|
|
for placing a scalar into a particular location in an `ndarray`,
|
|
if you must do this. However, generally this is discouraged:
|
|
among other problems, it complicates the appearance of the code.
|
|
Also, when using `itemset` (and `item`) inside a loop, be sure
|
|
to assign the methods to a local variable to avoid the attribute
|
|
look-up at each loop iteration.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.random.seed(123)
|
|
>>> x = np.random.randint(9, size=(3, 3))
|
|
>>> x
|
|
array([[2, 2, 6],
|
|
[1, 3, 6],
|
|
[1, 0, 1]])
|
|
>>> x.itemset(4, 0)
|
|
>>> x.itemset((2, 2), 9)
|
|
>>> x
|
|
array([[2, 2, 6],
|
|
[1, 0, 6],
|
|
[1, 0, 9]])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('max',
|
|
"""
|
|
a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
|
|
|
|
Return the maximum along a given axis.
|
|
|
|
Refer to `numpy.amax` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.amax : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('mean',
|
|
"""
|
|
a.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
|
|
|
|
Returns the average of the array elements along given axis.
|
|
|
|
Refer to `numpy.mean` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.mean : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
|
|
"""
|
|
a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
|
|
|
|
Return the minimum along a given axis.
|
|
|
|
Refer to `numpy.amin` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.amin : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
|
|
"""
|
|
arr.newbyteorder(new_order='S', /)
|
|
|
|
Return the array with the same data viewed with a different byte order.
|
|
|
|
Equivalent to::
|
|
|
|
arr.view(arr.dtype.newbytorder(new_order))
|
|
|
|
Changes are also made in all fields and sub-arrays of the array data
|
|
type.
|
|
|
|
|
|
|
|
Parameters
|
|
----------
|
|
new_order : string, optional
|
|
Byte order to force; a value from the byte order specifications
|
|
below. `new_order` codes can be any of:
|
|
|
|
* 'S' - swap dtype from current to opposite endian
|
|
* {'<', 'little'} - little endian
|
|
* {'>', 'big'} - big endian
|
|
* {'=', 'native'} - native order, equivalent to `sys.byteorder`
|
|
* {'|', 'I'} - ignore (no change to byte order)
|
|
|
|
The default value ('S') results in swapping the current
|
|
byte order.
|
|
|
|
|
|
Returns
|
|
-------
|
|
new_arr : array
|
|
New array object with the dtype reflecting given change to the
|
|
byte order.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('nonzero',
|
|
"""
|
|
a.nonzero()
|
|
|
|
Return the indices of the elements that are non-zero.
|
|
|
|
Refer to `numpy.nonzero` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.nonzero : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('prod',
|
|
"""
|
|
a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
|
|
|
|
Return the product of the array elements over the given axis
|
|
|
|
Refer to `numpy.prod` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.prod : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('ptp',
|
|
"""
|
|
a.ptp(axis=None, out=None, keepdims=False)
|
|
|
|
Peak to peak (maximum - minimum) value along a given axis.
|
|
|
|
Refer to `numpy.ptp` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.ptp : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
|
|
"""
|
|
a.put(indices, values, mode='raise')
|
|
|
|
Set ``a.flat[n] = values[n]`` for all `n` in indices.
|
|
|
|
Refer to `numpy.put` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.put : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
|
|
"""
|
|
a.ravel([order])
|
|
|
|
Return a flattened array.
|
|
|
|
Refer to `numpy.ravel` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.ravel : equivalent function
|
|
|
|
ndarray.flat : a flat iterator on the array.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('repeat',
|
|
"""
|
|
a.repeat(repeats, axis=None)
|
|
|
|
Repeat elements of an array.
|
|
|
|
Refer to `numpy.repeat` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.repeat : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('reshape',
|
|
"""
|
|
a.reshape(shape, order='C')
|
|
|
|
Returns an array containing the same data with a new shape.
|
|
|
|
Refer to `numpy.reshape` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.reshape : equivalent function
|
|
|
|
Notes
|
|
-----
|
|
Unlike the free function `numpy.reshape`, this method on `ndarray` allows
|
|
the elements of the shape parameter to be passed in as separate arguments.
|
|
For example, ``a.reshape(10, 11)`` is equivalent to
|
|
``a.reshape((10, 11))``.
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('resize',
|
|
"""
|
|
a.resize(new_shape, refcheck=True)
|
|
|
|
Change shape and size of array in-place.
|
|
|
|
Parameters
|
|
----------
|
|
new_shape : tuple of ints, or `n` ints
|
|
Shape of resized array.
|
|
refcheck : bool, optional
|
|
If False, reference count will not be checked. Default is True.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If `a` does not own its own data or references or views to it exist,
|
|
and the data memory must be changed.
|
|
PyPy only: will always raise if the data memory must be changed, since
|
|
there is no reliable way to determine if references or views to it
|
|
exist.
|
|
|
|
SystemError
|
|
If the `order` keyword argument is specified. This behaviour is a
|
|
bug in NumPy.
|
|
|
|
See Also
|
|
--------
|
|
resize : Return a new array with the specified shape.
|
|
|
|
Notes
|
|
-----
|
|
This reallocates space for the data area if necessary.
|
|
|
|
Only contiguous arrays (data elements consecutive in memory) can be
|
|
resized.
|
|
|
|
The purpose of the reference count check is to make sure you
|
|
do not use this array as a buffer for another Python object and then
|
|
reallocate the memory. However, reference counts can increase in
|
|
other ways so if you are sure that you have not shared the memory
|
|
for this array with another Python object, then you may safely set
|
|
`refcheck` to False.
|
|
|
|
Examples
|
|
--------
|
|
Shrinking an array: array is flattened (in the order that the data are
|
|
stored in memory), resized, and reshaped:
|
|
|
|
>>> a = np.array([[0, 1], [2, 3]], order='C')
|
|
>>> a.resize((2, 1))
|
|
>>> a
|
|
array([[0],
|
|
[1]])
|
|
|
|
>>> a = np.array([[0, 1], [2, 3]], order='F')
|
|
>>> a.resize((2, 1))
|
|
>>> a
|
|
array([[0],
|
|
[2]])
|
|
|
|
Enlarging an array: as above, but missing entries are filled with zeros:
|
|
|
|
>>> b = np.array([[0, 1], [2, 3]])
|
|
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
|
|
>>> b
|
|
array([[0, 1, 2],
|
|
[3, 0, 0]])
|
|
|
|
Referencing an array prevents resizing...
|
|
|
|
>>> c = a
|
|
>>> a.resize((1, 1))
|
|
Traceback (most recent call last):
|
|
...
|
|
ValueError: cannot resize an array that references or is referenced ...
|
|
|
|
Unless `refcheck` is False:
|
|
|
|
>>> a.resize((1, 1), refcheck=False)
|
|
>>> a
|
|
array([[0]])
|
|
>>> c
|
|
array([[0]])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('round',
|
|
"""
|
|
a.round(decimals=0, out=None)
|
|
|
|
Return `a` with each element rounded to the given number of decimals.
|
|
|
|
Refer to `numpy.around` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.around : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted',
|
|
"""
|
|
a.searchsorted(v, side='left', sorter=None)
|
|
|
|
Find indices where elements of v should be inserted in a to maintain order.
|
|
|
|
For full documentation, see `numpy.searchsorted`
|
|
|
|
See Also
|
|
--------
|
|
numpy.searchsorted : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('setfield',
|
|
"""
|
|
a.setfield(val, dtype, offset=0)
|
|
|
|
Put a value into a specified place in a field defined by a data-type.
|
|
|
|
Place `val` into `a`'s field defined by `dtype` and beginning `offset`
|
|
bytes into the field.
|
|
|
|
Parameters
|
|
----------
|
|
val : object
|
|
Value to be placed in field.
|
|
dtype : dtype object
|
|
Data-type of the field in which to place `val`.
|
|
offset : int, optional
|
|
The number of bytes into the field at which to place `val`.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
|
|
See Also
|
|
--------
|
|
getfield
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.eye(3)
|
|
>>> x.getfield(np.float64)
|
|
array([[1., 0., 0.],
|
|
[0., 1., 0.],
|
|
[0., 0., 1.]])
|
|
>>> x.setfield(3, np.int32)
|
|
>>> x.getfield(np.int32)
|
|
array([[3, 3, 3],
|
|
[3, 3, 3],
|
|
[3, 3, 3]], dtype=int32)
|
|
>>> x
|
|
array([[1.0e+000, 1.5e-323, 1.5e-323],
|
|
[1.5e-323, 1.0e+000, 1.5e-323],
|
|
[1.5e-323, 1.5e-323, 1.0e+000]])
|
|
>>> x.setfield(np.eye(3), np.int32)
|
|
>>> x
|
|
array([[1., 0., 0.],
|
|
[0., 1., 0.],
|
|
[0., 0., 1.]])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('setflags',
|
|
"""
|
|
a.setflags(write=None, align=None, uic=None)
|
|
|
|
Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY,
|
|
respectively.
|
|
|
|
These Boolean-valued flags affect how numpy interprets the memory
|
|
area used by `a` (see Notes below). The ALIGNED flag can only
|
|
be set to True if the data is actually aligned according to the type.
|
|
The WRITEBACKIFCOPY and flag can never be set
|
|
to True. The flag WRITEABLE can only be set to True if the array owns its
|
|
own memory, or the ultimate owner of the memory exposes a writeable buffer
|
|
interface, or is a string. (The exception for string is made so that
|
|
unpickling can be done without copying memory.)
|
|
|
|
Parameters
|
|
----------
|
|
write : bool, optional
|
|
Describes whether or not `a` can be written to.
|
|
align : bool, optional
|
|
Describes whether or not `a` is aligned properly for its type.
|
|
uic : bool, optional
|
|
Describes whether or not `a` is a copy of another "base" array.
|
|
|
|
Notes
|
|
-----
|
|
Array flags provide information about how the memory area used
|
|
for the array is to be interpreted. There are 7 Boolean flags
|
|
in use, only four of which can be changed by the user:
|
|
WRITEBACKIFCOPY, WRITEABLE, and ALIGNED.
|
|
|
|
WRITEABLE (W) the data area can be written to;
|
|
|
|
ALIGNED (A) the data and strides are aligned appropriately for the hardware
|
|
(as determined by the compiler);
|
|
|
|
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced
|
|
by .base). When the C-API function PyArray_ResolveWritebackIfCopy is
|
|
called, the base array will be updated with the contents of this array.
|
|
|
|
All flags can be accessed using the single (upper case) letter as well
|
|
as the full name.
|
|
|
|
Examples
|
|
--------
|
|
>>> y = np.array([[3, 1, 7],
|
|
... [2, 0, 0],
|
|
... [8, 5, 9]])
|
|
>>> y
|
|
array([[3, 1, 7],
|
|
[2, 0, 0],
|
|
[8, 5, 9]])
|
|
>>> y.flags
|
|
C_CONTIGUOUS : True
|
|
F_CONTIGUOUS : False
|
|
OWNDATA : True
|
|
WRITEABLE : True
|
|
ALIGNED : True
|
|
WRITEBACKIFCOPY : False
|
|
>>> y.setflags(write=0, align=0)
|
|
>>> y.flags
|
|
C_CONTIGUOUS : True
|
|
F_CONTIGUOUS : False
|
|
OWNDATA : True
|
|
WRITEABLE : False
|
|
ALIGNED : False
|
|
WRITEBACKIFCOPY : False
|
|
>>> y.setflags(uic=1)
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in <module>
|
|
ValueError: cannot set WRITEBACKIFCOPY flag to True
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('sort',
|
|
"""
|
|
a.sort(axis=-1, kind=None, order=None)
|
|
|
|
Sort an array in-place. Refer to `numpy.sort` for full documentation.
|
|
|
|
Parameters
|
|
----------
|
|
axis : int, optional
|
|
Axis along which to sort. Default is -1, which means sort along the
|
|
last axis.
|
|
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
|
|
Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
|
|
and 'mergesort' use timsort under the covers and, in general, the
|
|
actual implementation will vary with datatype. The 'mergesort' option
|
|
is retained for backwards compatibility.
|
|
|
|
.. versionchanged:: 1.15.0
|
|
The 'stable' option was added.
|
|
|
|
order : str or list of str, optional
|
|
When `a` is an array with fields defined, this argument specifies
|
|
which fields to compare first, second, etc. A single field can
|
|
be specified as a string, and not all fields need be specified,
|
|
but unspecified fields will still be used, in the order in which
|
|
they come up in the dtype, to break ties.
|
|
|
|
See Also
|
|
--------
|
|
numpy.sort : Return a sorted copy of an array.
|
|
numpy.argsort : Indirect sort.
|
|
numpy.lexsort : Indirect stable sort on multiple keys.
|
|
numpy.searchsorted : Find elements in sorted array.
|
|
numpy.partition: Partial sort.
|
|
|
|
Notes
|
|
-----
|
|
See `numpy.sort` for notes on the different sorting algorithms.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[1,4], [3,1]])
|
|
>>> a.sort(axis=1)
|
|
>>> a
|
|
array([[1, 4],
|
|
[1, 3]])
|
|
>>> a.sort(axis=0)
|
|
>>> a
|
|
array([[1, 3],
|
|
[1, 4]])
|
|
|
|
Use the `order` keyword to specify a field to use when sorting a
|
|
structured array:
|
|
|
|
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
|
|
>>> a.sort(order='y')
|
|
>>> a
|
|
array([(b'c', 1), (b'a', 2)],
|
|
dtype=[('x', 'S1'), ('y', '<i8')])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('partition',
|
|
"""
|
|
a.partition(kth, axis=-1, kind='introselect', order=None)
|
|
|
|
Rearranges the elements in the array in such a way that the value of the
|
|
element in kth position is in the position it would be in a sorted array.
|
|
All elements smaller than the kth element are moved before this element and
|
|
all equal or greater are moved behind it. The ordering of the elements in
|
|
the two partitions is undefined.
|
|
|
|
.. versionadded:: 1.8.0
|
|
|
|
Parameters
|
|
----------
|
|
kth : int or sequence of ints
|
|
Element index to partition by. The kth element value will be in its
|
|
final sorted position and all smaller elements will be moved before it
|
|
and all equal or greater elements behind it.
|
|
The order of all elements in the partitions is undefined.
|
|
If provided with a sequence of kth it will partition all elements
|
|
indexed by kth of them into their sorted position at once.
|
|
|
|
.. deprecated:: 1.22.0
|
|
Passing booleans as index is deprecated.
|
|
axis : int, optional
|
|
Axis along which to sort. Default is -1, which means sort along the
|
|
last axis.
|
|
kind : {'introselect'}, optional
|
|
Selection algorithm. Default is 'introselect'.
|
|
order : str or list of str, optional
|
|
When `a` is an array with fields defined, this argument specifies
|
|
which fields to compare first, second, etc. A single field can
|
|
be specified as a string, and not all fields need to be specified,
|
|
but unspecified fields will still be used, in the order in which
|
|
they come up in the dtype, to break ties.
|
|
|
|
See Also
|
|
--------
|
|
numpy.partition : Return a partitioned copy of an array.
|
|
argpartition : Indirect partition.
|
|
sort : Full sort.
|
|
|
|
Notes
|
|
-----
|
|
See ``np.partition`` for notes on the different algorithms.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([3, 4, 2, 1])
|
|
>>> a.partition(3)
|
|
>>> a
|
|
array([2, 1, 3, 4])
|
|
|
|
>>> a.partition((1, 3))
|
|
>>> a
|
|
array([1, 2, 3, 4])
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze',
|
|
"""
|
|
a.squeeze(axis=None)
|
|
|
|
Remove axes of length one from `a`.
|
|
|
|
Refer to `numpy.squeeze` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.squeeze : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('std',
|
|
"""
|
|
a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
|
|
|
|
Returns the standard deviation of the array elements along given axis.
|
|
|
|
Refer to `numpy.std` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.std : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('sum',
|
|
"""
|
|
a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
|
|
|
|
Return the sum of the array elements over the given axis.
|
|
|
|
Refer to `numpy.sum` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.sum : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('swapaxes',
|
|
"""
|
|
a.swapaxes(axis1, axis2)
|
|
|
|
Return a view of the array with `axis1` and `axis2` interchanged.
|
|
|
|
Refer to `numpy.swapaxes` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.swapaxes : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('take',
|
|
"""
|
|
a.take(indices, axis=None, out=None, mode='raise')
|
|
|
|
Return an array formed from the elements of `a` at the given indices.
|
|
|
|
Refer to `numpy.take` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.take : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('tofile',
|
|
"""
|
|
a.tofile(fid, sep="", format="%s")
|
|
|
|
Write array to a file as text or binary (default).
|
|
|
|
Data is always written in 'C' order, independent of the order of `a`.
|
|
The data produced by this method can be recovered using the function
|
|
fromfile().
|
|
|
|
Parameters
|
|
----------
|
|
fid : file or str or Path
|
|
An open file object, or a string containing a filename.
|
|
|
|
.. versionchanged:: 1.17.0
|
|
`pathlib.Path` objects are now accepted.
|
|
|
|
sep : str
|
|
Separator between array items for text output.
|
|
If "" (empty), a binary file is written, equivalent to
|
|
``file.write(a.tobytes())``.
|
|
format : str
|
|
Format string for text file output.
|
|
Each entry in the array is formatted to text by first converting
|
|
it to the closest Python type, and then using "format" % item.
|
|
|
|
Notes
|
|
-----
|
|
This is a convenience function for quick storage of array data.
|
|
Information on endianness and precision is lost, so this method is not a
|
|
good choice for files intended to archive data or transport data between
|
|
machines with different endianness. Some of these problems can be overcome
|
|
by outputting the data as text files, at the expense of speed and file
|
|
size.
|
|
|
|
When fid is a file object, array contents are directly written to the
|
|
file, bypassing the file object's ``write`` method. As a result, tofile
|
|
cannot be used with files objects supporting compression (e.g., GzipFile)
|
|
or file-like objects that do not support ``fileno()`` (e.g., BytesIO).
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist',
|
|
"""
|
|
a.tolist()
|
|
|
|
Return the array as an ``a.ndim``-levels deep nested list of Python scalars.
|
|
|
|
Return a copy of the array data as a (nested) Python list.
|
|
Data items are converted to the nearest compatible builtin Python type, via
|
|
the `~numpy.ndarray.item` function.
|
|
|
|
If ``a.ndim`` is 0, then since the depth of the nested list is 0, it will
|
|
not be a list at all, but a simple Python scalar.
|
|
|
|
Parameters
|
|
----------
|
|
none
|
|
|
|
Returns
|
|
-------
|
|
y : object, or list of object, or list of list of object, or ...
|
|
The possibly nested list of array elements.
|
|
|
|
Notes
|
|
-----
|
|
The array may be recreated via ``a = np.array(a.tolist())``, although this
|
|
may sometimes lose precision.
|
|
|
|
Examples
|
|
--------
|
|
For a 1D array, ``a.tolist()`` is almost the same as ``list(a)``,
|
|
except that ``tolist`` changes numpy scalars to Python scalars:
|
|
|
|
>>> a = np.uint32([1, 2])
|
|
>>> a_list = list(a)
|
|
>>> a_list
|
|
[1, 2]
|
|
>>> type(a_list[0])
|
|
<class 'numpy.uint32'>
|
|
>>> a_tolist = a.tolist()
|
|
>>> a_tolist
|
|
[1, 2]
|
|
>>> type(a_tolist[0])
|
|
<class 'int'>
|
|
|
|
Additionally, for a 2D array, ``tolist`` applies recursively:
|
|
|
|
>>> a = np.array([[1, 2], [3, 4]])
|
|
>>> list(a)
|
|
[array([1, 2]), array([3, 4])]
|
|
>>> a.tolist()
|
|
[[1, 2], [3, 4]]
|
|
|
|
The base case for this recursion is a 0D array:
|
|
|
|
>>> a = np.array(1)
|
|
>>> list(a)
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: iteration over a 0-d array
|
|
>>> a.tolist()
|
|
1
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('tobytes', """
|
|
a.tobytes(order='C')
|
|
|
|
Construct Python bytes containing the raw data bytes in the array.
|
|
|
|
Constructs Python bytes showing a copy of the raw contents of
|
|
data memory. The bytes object is produced in C-order by default.
|
|
This behavior is controlled by the ``order`` parameter.
|
|
|
|
.. versionadded:: 1.9.0
|
|
|
|
Parameters
|
|
----------
|
|
order : {'C', 'F', 'A'}, optional
|
|
Controls the memory layout of the bytes object. 'C' means C-order,
|
|
'F' means F-order, 'A' (short for *Any*) means 'F' if `a` is
|
|
Fortran contiguous, 'C' otherwise. Default is 'C'.
|
|
|
|
Returns
|
|
-------
|
|
s : bytes
|
|
Python bytes exhibiting a copy of `a`'s raw data.
|
|
|
|
See also
|
|
--------
|
|
frombuffer
|
|
Inverse of this operation, construct a 1-dimensional array from Python
|
|
bytes.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
|
|
>>> x.tobytes()
|
|
b'\\x00\\x00\\x01\\x00\\x02\\x00\\x03\\x00'
|
|
>>> x.tobytes('C') == x.tobytes()
|
|
True
|
|
>>> x.tobytes('F')
|
|
b'\\x00\\x00\\x02\\x00\\x01\\x00\\x03\\x00'
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('tostring', r"""
|
|
a.tostring(order='C')
|
|
|
|
A compatibility alias for `tobytes`, with exactly the same behavior.
|
|
|
|
Despite its name, it returns `bytes` not `str`\ s.
|
|
|
|
.. deprecated:: 1.19.0
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('trace',
|
|
"""
|
|
a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
|
|
|
|
Return the sum along diagonals of the array.
|
|
|
|
Refer to `numpy.trace` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.trace : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose',
|
|
"""
|
|
a.transpose(*axes)
|
|
|
|
Returns a view of the array with axes transposed.
|
|
|
|
Refer to `numpy.transpose` for full documentation.
|
|
|
|
Parameters
|
|
----------
|
|
axes : None, tuple of ints, or `n` ints
|
|
|
|
* None or no argument: reverses the order of the axes.
|
|
|
|
* tuple of ints: `i` in the `j`-th place in the tuple means that the
|
|
array's `i`-th axis becomes the transposed array's `j`-th axis.
|
|
|
|
* `n` ints: same as an n-tuple of the same ints (this form is
|
|
intended simply as a "convenience" alternative to the tuple form).
|
|
|
|
Returns
|
|
-------
|
|
p : ndarray
|
|
View of the array with its axes suitably permuted.
|
|
|
|
See Also
|
|
--------
|
|
transpose : Equivalent function.
|
|
ndarray.T : Array property returning the array transposed.
|
|
ndarray.reshape : Give a new shape to an array without changing its data.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[1, 2], [3, 4]])
|
|
>>> a
|
|
array([[1, 2],
|
|
[3, 4]])
|
|
>>> a.transpose()
|
|
array([[1, 3],
|
|
[2, 4]])
|
|
>>> a.transpose((1, 0))
|
|
array([[1, 3],
|
|
[2, 4]])
|
|
>>> a.transpose(1, 0)
|
|
array([[1, 3],
|
|
[2, 4]])
|
|
|
|
>>> a = np.array([1, 2, 3, 4])
|
|
>>> a
|
|
array([1, 2, 3, 4])
|
|
>>> a.transpose()
|
|
array([1, 2, 3, 4])
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('var',
|
|
"""
|
|
a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
|
|
|
|
Returns the variance of the array elements, along given axis.
|
|
|
|
Refer to `numpy.var` for full documentation.
|
|
|
|
See Also
|
|
--------
|
|
numpy.var : equivalent function
|
|
|
|
"""))
|
|
|
|
|
|
add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
|
|
"""
|
|
a.view([dtype][, type])
|
|
|
|
New view of array with the same data.
|
|
|
|
.. note::
|
|
Passing None for ``dtype`` is different from omitting the parameter,
|
|
since the former invokes ``dtype(None)`` which is an alias for
|
|
``dtype('float_')``.
|
|
|
|
Parameters
|
|
----------
|
|
dtype : data-type or ndarray sub-class, optional
|
|
Data-type descriptor of the returned view, e.g., float32 or int16.
|
|
Omitting it results in the view having the same data-type as `a`.
|
|
This argument can also be specified as an ndarray sub-class, which
|
|
then specifies the type of the returned object (this is equivalent to
|
|
setting the ``type`` parameter).
|
|
type : Python type, optional
|
|
Type of the returned view, e.g., ndarray or matrix. Again, omission
|
|
of the parameter results in type preservation.
|
|
|
|
Notes
|
|
-----
|
|
``a.view()`` is used two different ways:
|
|
|
|
``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
|
|
of the array's memory with a different data-type. This can cause a
|
|
reinterpretation of the bytes of memory.
|
|
|
|
``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
|
|
returns an instance of `ndarray_subclass` that looks at the same array
|
|
(same shape, dtype, etc.) This does not cause a reinterpretation of the
|
|
memory.
|
|
|
|
For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
|
|
bytes per entry than the previous dtype (for example, converting a regular
|
|
array to a structured array), then the last axis of ``a`` must be
|
|
contiguous. This axis will be resized in the result.
|
|
|
|
.. versionchanged:: 1.23.0
|
|
Only the last axis needs to be contiguous. Previously, the entire array
|
|
had to be C-contiguous.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
|
|
|
|
Viewing array data using a different type and dtype:
|
|
|
|
>>> y = x.view(dtype=np.int16, type=np.matrix)
|
|
>>> y
|
|
matrix([[513]], dtype=int16)
|
|
>>> print(type(y))
|
|
<class 'numpy.matrix'>
|
|
|
|
Creating a view on a structured array so it can be used in calculations
|
|
|
|
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
|
|
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
|
|
>>> xv
|
|
array([[1, 2],
|
|
[3, 4]], dtype=int8)
|
|
>>> xv.mean(0)
|
|
array([2., 3.])
|
|
|
|
Making changes to the view changes the underlying array
|
|
|
|
>>> xv[0,1] = 20
|
|
>>> x
|
|
array([(1, 20), (3, 4)], dtype=[('a', 'i1'), ('b', 'i1')])
|
|
|
|
Using a view to convert an array to a recarray:
|
|
|
|
>>> z = x.view(np.recarray)
|
|
>>> z.a
|
|
array([1, 3], dtype=int8)
|
|
|
|
Views share data:
|
|
|
|
>>> x[0] = (9, 10)
|
|
>>> z[0]
|
|
(9, 10)
|
|
|
|
Views that change the dtype size (bytes per entry) should normally be
|
|
avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
|
|
|
|
>>> x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
|
|
>>> y = x[:, ::2]
|
|
>>> y
|
|
array([[1, 3],
|
|
[4, 6]], dtype=int16)
|
|
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
|
|
Traceback (most recent call last):
|
|
...
|
|
ValueError: To change to a dtype of a different size, the last axis must be contiguous
|
|
>>> z = y.copy()
|
|
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
|
|
array([[(1, 3)],
|
|
[(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])
|
|
|
|
However, views that change dtype are totally fine for arrays with a
|
|
contiguous last axis, even if the rest of the axes are not C-contiguous:
|
|
|
|
>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
|
|
>>> x.transpose(1, 0, 2).view(np.int16)
|
|
array([[[ 256, 770],
|
|
[3340, 3854]],
|
|
<BLANKLINE>
|
|
[[1284, 1798],
|
|
[4368, 4882]],
|
|
<BLANKLINE>
|
|
[[2312, 2826],
|
|
[5396, 5910]]], dtype=int16)
|
|
|
|
"""))
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# umath functions
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.umath', 'frompyfunc',
|
|
"""
|
|
frompyfunc(func, /, nin, nout, *[, identity])
|
|
|
|
Takes an arbitrary Python function and returns a NumPy ufunc.
|
|
|
|
Can be used, for example, to add broadcasting to a built-in Python
|
|
function (see Examples section).
|
|
|
|
Parameters
|
|
----------
|
|
func : Python function object
|
|
An arbitrary Python function.
|
|
nin : int
|
|
The number of input arguments.
|
|
nout : int
|
|
The number of objects returned by `func`.
|
|
identity : object, optional
|
|
The value to use for the `~numpy.ufunc.identity` attribute of the resulting
|
|
object. If specified, this is equivalent to setting the underlying
|
|
C ``identity`` field to ``PyUFunc_IdentityValue``.
|
|
If omitted, the identity is set to ``PyUFunc_None``. Note that this is
|
|
_not_ equivalent to setting the identity to ``None``, which implies the
|
|
operation is reorderable.
|
|
|
|
Returns
|
|
-------
|
|
out : ufunc
|
|
Returns a NumPy universal function (``ufunc``) object.
|
|
|
|
See Also
|
|
--------
|
|
vectorize : Evaluates pyfunc over input arrays using broadcasting rules of numpy.
|
|
|
|
Notes
|
|
-----
|
|
The returned ufunc always returns PyObject arrays.
|
|
|
|
Examples
|
|
--------
|
|
Use frompyfunc to add broadcasting to the Python function ``oct``:
|
|
|
|
>>> oct_array = np.frompyfunc(oct, 1, 1)
|
|
>>> oct_array(np.array((10, 30, 100)))
|
|
array(['0o12', '0o36', '0o144'], dtype=object)
|
|
>>> np.array((oct(10), oct(30), oct(100))) # for comparison
|
|
array(['0o12', '0o36', '0o144'], dtype='<U5')
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.umath', 'geterrobj',
|
|
"""
|
|
geterrobj()
|
|
|
|
Return the current object that defines floating-point error handling.
|
|
|
|
The error object contains all information that defines the error handling
|
|
behavior in NumPy. `geterrobj` is used internally by the other
|
|
functions that get and set error handling behavior (`geterr`, `seterr`,
|
|
`geterrcall`, `seterrcall`).
|
|
|
|
Returns
|
|
-------
|
|
errobj : list
|
|
The error object, a list containing three elements:
|
|
[internal numpy buffer size, error mask, error callback function].
|
|
|
|
The error mask is a single integer that holds the treatment information
|
|
on all four floating point errors. The information for each error type
|
|
is contained in three bits of the integer. If we print it in base 8, we
|
|
can see what treatment is set for "invalid", "under", "over", and
|
|
"divide" (in that order). The printed string can be interpreted with
|
|
|
|
* 0 : 'ignore'
|
|
* 1 : 'warn'
|
|
* 2 : 'raise'
|
|
* 3 : 'call'
|
|
* 4 : 'print'
|
|
* 5 : 'log'
|
|
|
|
See Also
|
|
--------
|
|
seterrobj, seterr, geterr, seterrcall, geterrcall
|
|
getbufsize, setbufsize
|
|
|
|
Notes
|
|
-----
|
|
For complete documentation of the types of floating-point exceptions and
|
|
treatment options, see `seterr`.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.geterrobj() # first get the defaults
|
|
[8192, 521, None]
|
|
|
|
>>> def err_handler(type, flag):
|
|
... print("Floating point error (%s), with flag %s" % (type, flag))
|
|
...
|
|
>>> old_bufsize = np.setbufsize(20000)
|
|
>>> old_err = np.seterr(divide='raise')
|
|
>>> old_handler = np.seterrcall(err_handler)
|
|
>>> np.geterrobj()
|
|
[8192, 521, <function err_handler at 0x91dcaac>]
|
|
|
|
>>> old_err = np.seterr(all='ignore')
|
|
>>> np.base_repr(np.geterrobj()[1], 8)
|
|
'0'
|
|
>>> old_err = np.seterr(divide='warn', over='log', under='call',
|
|
... invalid='print')
|
|
>>> np.base_repr(np.geterrobj()[1], 8)
|
|
'4351'
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.umath', 'seterrobj',
|
|
"""
|
|
seterrobj(errobj, /)
|
|
|
|
Set the object that defines floating-point error handling.
|
|
|
|
The error object contains all information that defines the error handling
|
|
behavior in NumPy. `seterrobj` is used internally by the other
|
|
functions that set error handling behavior (`seterr`, `seterrcall`).
|
|
|
|
Parameters
|
|
----------
|
|
errobj : list
|
|
The error object, a list containing three elements:
|
|
[internal numpy buffer size, error mask, error callback function].
|
|
|
|
The error mask is a single integer that holds the treatment information
|
|
on all four floating point errors. The information for each error type
|
|
is contained in three bits of the integer. If we print it in base 8, we
|
|
can see what treatment is set for "invalid", "under", "over", and
|
|
"divide" (in that order). The printed string can be interpreted with
|
|
|
|
* 0 : 'ignore'
|
|
* 1 : 'warn'
|
|
* 2 : 'raise'
|
|
* 3 : 'call'
|
|
* 4 : 'print'
|
|
* 5 : 'log'
|
|
|
|
See Also
|
|
--------
|
|
geterrobj, seterr, geterr, seterrcall, geterrcall
|
|
getbufsize, setbufsize
|
|
|
|
Notes
|
|
-----
|
|
For complete documentation of the types of floating-point exceptions and
|
|
treatment options, see `seterr`.
|
|
|
|
Examples
|
|
--------
|
|
>>> old_errobj = np.geterrobj() # first get the defaults
|
|
>>> old_errobj
|
|
[8192, 521, None]
|
|
|
|
>>> def err_handler(type, flag):
|
|
... print("Floating point error (%s), with flag %s" % (type, flag))
|
|
...
|
|
>>> new_errobj = [20000, 12, err_handler]
|
|
>>> np.seterrobj(new_errobj)
|
|
>>> np.base_repr(12, 8) # int for divide=4 ('print') and over=1 ('warn')
|
|
'14'
|
|
>>> np.geterr()
|
|
{'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
|
|
>>> np.geterrcall() is err_handler
|
|
True
|
|
|
|
""")
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# compiled_base functions
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.multiarray', 'add_docstring',
|
|
"""
|
|
add_docstring(obj, docstring)
|
|
|
|
Add a docstring to a built-in obj if possible.
|
|
If the obj already has a docstring raise a RuntimeError
|
|
If this routine does not know how to add a docstring to the object
|
|
raise a TypeError
|
|
""")
|
|
|
|
add_newdoc('numpy.core.umath', '_add_newdoc_ufunc',
|
|
"""
|
|
add_ufunc_docstring(ufunc, new_docstring)
|
|
|
|
Replace the docstring for a ufunc with new_docstring.
|
|
This method will only work if the current docstring for
|
|
the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)
|
|
|
|
Parameters
|
|
----------
|
|
ufunc : numpy.ufunc
|
|
A ufunc whose current doc is NULL.
|
|
new_docstring : string
|
|
The new docstring for the ufunc.
|
|
|
|
Notes
|
|
-----
|
|
This method allocates memory for new_docstring on
|
|
the heap. Technically this creates a mempory leak, since this
|
|
memory will not be reclaimed until the end of the program
|
|
even if the ufunc itself is removed. However this will only
|
|
be a problem if the user is repeatedly creating ufuncs with
|
|
no documentation, adding documentation via add_newdoc_ufunc,
|
|
and then throwing away the ufunc.
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'get_handler_name',
|
|
"""
|
|
get_handler_name(a: ndarray) -> str,None
|
|
|
|
Return the name of the memory handler used by `a`. If not provided, return
|
|
the name of the memory handler that will be used to allocate data for the
|
|
next `ndarray` in this context. May return None if `a` does not own its
|
|
memory, in which case you can traverse ``a.base`` for a memory handler.
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'get_handler_version',
|
|
"""
|
|
get_handler_version(a: ndarray) -> int,None
|
|
|
|
Return the version of the memory handler used by `a`. If not provided,
|
|
return the version of the memory handler that will be used to allocate data
|
|
for the next `ndarray` in this context. May return None if `a` does not own
|
|
its memory, in which case you can traverse ``a.base`` for a memory handler.
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', '_get_madvise_hugepage',
|
|
"""
|
|
_get_madvise_hugepage() -> bool
|
|
|
|
Get use of ``madvise (2)`` MADV_HUGEPAGE support when
|
|
allocating the array data. Returns the currently set value.
|
|
See `global_state` for more information.
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', '_set_madvise_hugepage',
|
|
"""
|
|
_set_madvise_hugepage(enabled: bool) -> bool
|
|
|
|
Set or unset use of ``madvise (2)`` MADV_HUGEPAGE support when
|
|
allocating the array data. Returns the previously set value.
|
|
See `global_state` for more information.
|
|
""")
|
|
|
|
add_newdoc('numpy.core._multiarray_tests', 'format_float_OSprintf_g',
|
|
"""
|
|
format_float_OSprintf_g(val, precision)
|
|
|
|
Print a floating point scalar using the system's printf function,
|
|
equivalent to:
|
|
|
|
printf("%.*g", precision, val);
|
|
|
|
for half/float/double, or replacing 'g' by 'Lg' for longdouble. This
|
|
method is designed to help cross-validate the format_float_* methods.
|
|
|
|
Parameters
|
|
----------
|
|
val : python float or numpy floating scalar
|
|
Value to format.
|
|
|
|
precision : non-negative integer, optional
|
|
Precision given to printf.
|
|
|
|
Returns
|
|
-------
|
|
rep : string
|
|
The string representation of the floating point value
|
|
|
|
See Also
|
|
--------
|
|
format_float_scientific
|
|
format_float_positional
|
|
""")
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# Documentation for ufunc attributes and methods
|
|
#
|
|
##############################################################################
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# ufunc object
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core', 'ufunc',
|
|
"""
|
|
Functions that operate element by element on whole arrays.
|
|
|
|
To see the documentation for a specific ufunc, use `info`. For
|
|
example, ``np.info(np.sin)``. Because ufuncs are written in C
|
|
(for speed) and linked into Python with NumPy's ufunc facility,
|
|
Python's help() function finds this page whenever help() is called
|
|
on a ufunc.
|
|
|
|
A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.
|
|
|
|
**Calling ufuncs:** ``op(*x[, out], where=True, **kwargs)``
|
|
|
|
Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.
|
|
|
|
The broadcasting rules are:
|
|
|
|
* Dimensions of length 1 may be prepended to either array.
|
|
* Arrays may be repeated along dimensions of length 1.
|
|
|
|
Parameters
|
|
----------
|
|
*x : array_like
|
|
Input arrays.
|
|
out : ndarray, None, or tuple of ndarray and None, optional
|
|
Alternate array object(s) in which to put the result; if provided, it
|
|
must have a shape that the inputs broadcast to. A tuple of arrays
|
|
(possible only as a keyword argument) must have length equal to the
|
|
number of outputs; use None for uninitialized outputs to be
|
|
allocated by the ufunc.
|
|
where : array_like, optional
|
|
This condition is broadcast over the input. At locations where the
|
|
condition is True, the `out` array will be set to the ufunc result.
|
|
Elsewhere, the `out` array will retain its original value.
|
|
Note that if an uninitialized `out` array is created via the default
|
|
``out=None``, locations within it where the condition is False will
|
|
remain uninitialized.
|
|
**kwargs
|
|
For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.
|
|
|
|
Returns
|
|
-------
|
|
r : ndarray or tuple of ndarray
|
|
`r` will have the shape that the arrays in `x` broadcast to; if `out` is
|
|
provided, it will be returned. If not, `r` will be allocated and
|
|
may contain uninitialized values. If the function has more than one
|
|
output, then the result will be a tuple of arrays.
|
|
|
|
""")
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# ufunc attributes
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('identity',
|
|
"""
|
|
The identity value.
|
|
|
|
Data attribute containing the identity element for the ufunc, if it has one.
|
|
If it does not, the attribute value is None.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.add.identity
|
|
0
|
|
>>> np.multiply.identity
|
|
1
|
|
>>> np.power.identity
|
|
1
|
|
>>> print(np.exp.identity)
|
|
None
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('nargs',
|
|
"""
|
|
The number of arguments.
|
|
|
|
Data attribute containing the number of arguments the ufunc takes, including
|
|
optional ones.
|
|
|
|
Notes
|
|
-----
|
|
Typically this value will be one more than what you might expect because all
|
|
ufuncs take the optional "out" argument.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.add.nargs
|
|
3
|
|
>>> np.multiply.nargs
|
|
3
|
|
>>> np.power.nargs
|
|
3
|
|
>>> np.exp.nargs
|
|
2
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('nin',
|
|
"""
|
|
The number of inputs.
|
|
|
|
Data attribute containing the number of arguments the ufunc treats as input.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.add.nin
|
|
2
|
|
>>> np.multiply.nin
|
|
2
|
|
>>> np.power.nin
|
|
2
|
|
>>> np.exp.nin
|
|
1
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('nout',
|
|
"""
|
|
The number of outputs.
|
|
|
|
Data attribute containing the number of arguments the ufunc treats as output.
|
|
|
|
Notes
|
|
-----
|
|
Since all ufuncs can take output arguments, this will always be (at least) 1.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.add.nout
|
|
1
|
|
>>> np.multiply.nout
|
|
1
|
|
>>> np.power.nout
|
|
1
|
|
>>> np.exp.nout
|
|
1
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('ntypes',
|
|
"""
|
|
The number of types.
|
|
|
|
The number of numerical NumPy types - of which there are 18 total - on which
|
|
the ufunc can operate.
|
|
|
|
See Also
|
|
--------
|
|
numpy.ufunc.types
|
|
|
|
Examples
|
|
--------
|
|
>>> np.add.ntypes
|
|
18
|
|
>>> np.multiply.ntypes
|
|
18
|
|
>>> np.power.ntypes
|
|
17
|
|
>>> np.exp.ntypes
|
|
7
|
|
>>> np.remainder.ntypes
|
|
14
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('types',
|
|
"""
|
|
Returns a list with types grouped input->output.
|
|
|
|
Data attribute listing the data-type "Domain-Range" groupings the ufunc can
|
|
deliver. The data-types are given using the character codes.
|
|
|
|
See Also
|
|
--------
|
|
numpy.ufunc.ntypes
|
|
|
|
Examples
|
|
--------
|
|
>>> np.add.types
|
|
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
|
|
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
|
|
'GG->G', 'OO->O']
|
|
|
|
>>> np.multiply.types
|
|
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
|
|
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
|
|
'GG->G', 'OO->O']
|
|
|
|
>>> np.power.types
|
|
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
|
|
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
|
|
'OO->O']
|
|
|
|
>>> np.exp.types
|
|
['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
|
|
|
|
>>> np.remainder.types
|
|
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
|
|
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('signature',
|
|
"""
|
|
Definition of the core elements a generalized ufunc operates on.
|
|
|
|
The signature determines how the dimensions of each input/output array
|
|
are split into core and loop dimensions:
|
|
|
|
1. Each dimension in the signature is matched to a dimension of the
|
|
corresponding passed-in array, starting from the end of the shape tuple.
|
|
2. Core dimensions assigned to the same label in the signature must have
|
|
exactly matching sizes, no broadcasting is performed.
|
|
3. The core dimensions are removed from all inputs and the remaining
|
|
dimensions are broadcast together, defining the loop dimensions.
|
|
|
|
Notes
|
|
-----
|
|
Generalized ufuncs are used internally in many linalg functions, and in
|
|
the testing suite; the examples below are taken from these.
|
|
For ufuncs that operate on scalars, the signature is None, which is
|
|
equivalent to '()' for every argument.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.core.umath_tests.matrix_multiply.signature
|
|
'(m,n),(n,p)->(m,p)'
|
|
>>> np.linalg._umath_linalg.det.signature
|
|
'(m,m)->()'
|
|
>>> np.add.signature is None
|
|
True # equivalent to '(),()->()'
|
|
"""))
|
|
|
|
##############################################################################
|
|
#
|
|
# ufunc methods
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('reduce',
|
|
"""
|
|
reduce(array, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)
|
|
|
|
Reduces `array`'s dimension by one, by applying ufunc along one axis.
|
|
|
|
Let :math:`array.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then
|
|
:math:`ufunc.reduce(array, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
|
|
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
|
|
ufunc to each :math:`array[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
|
|
For a one-dimensional array, reduce produces results equivalent to:
|
|
::
|
|
|
|
r = op.identity # op = ufunc
|
|
for i in range(len(A)):
|
|
r = op(r, A[i])
|
|
return r
|
|
|
|
For example, add.reduce() is equivalent to sum().
|
|
|
|
Parameters
|
|
----------
|
|
array : array_like
|
|
The array to act on.
|
|
axis : None or int or tuple of ints, optional
|
|
Axis or axes along which a reduction is performed.
|
|
The default (`axis` = 0) is perform a reduction over the first
|
|
dimension of the input array. `axis` may be negative, in
|
|
which case it counts from the last to the first axis.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
If this is None, a reduction is performed over all the axes.
|
|
If this is a tuple of ints, a reduction is performed on multiple
|
|
axes, instead of a single axis or all the axes as before.
|
|
|
|
For operations which are either not commutative or not associative,
|
|
doing a reduction over multiple axes is not well-defined. The
|
|
ufuncs do not currently raise an exception in this case, but will
|
|
likely do so in the future.
|
|
dtype : data-type code, optional
|
|
The type used to represent the intermediate results. Defaults
|
|
to the data-type of the output array if this is provided, or
|
|
the data-type of the input array if no output array is provided.
|
|
out : ndarray, None, or tuple of ndarray and None, optional
|
|
A location into which the result is stored. If not provided or None,
|
|
a freshly-allocated array is returned. For consistency with
|
|
``ufunc.__call__``, if given as a keyword, this may be wrapped in a
|
|
1-element tuple.
|
|
|
|
.. versionchanged:: 1.13.0
|
|
Tuples are allowed for keyword argument.
|
|
keepdims : bool, optional
|
|
If this is set to True, the axes which are reduced are left
|
|
in the result as dimensions with size one. With this option,
|
|
the result will broadcast correctly against the original `array`.
|
|
|
|
.. versionadded:: 1.7.0
|
|
initial : scalar, optional
|
|
The value with which to start the reduction.
|
|
If the ufunc has no identity or the dtype is object, this defaults
|
|
to None - otherwise it defaults to ufunc.identity.
|
|
If ``None`` is given, the first element of the reduction is used,
|
|
and an error is thrown if the reduction is empty.
|
|
|
|
.. versionadded:: 1.15.0
|
|
|
|
where : array_like of bool, optional
|
|
A boolean array which is broadcasted to match the dimensions
|
|
of `array`, and selects elements to include in the reduction. Note
|
|
that for ufuncs like ``minimum`` that do not have an identity
|
|
defined, one has to pass in also ``initial``.
|
|
|
|
.. versionadded:: 1.17.0
|
|
|
|
Returns
|
|
-------
|
|
r : ndarray
|
|
The reduced array. If `out` was supplied, `r` is a reference to it.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.multiply.reduce([2,3,5])
|
|
30
|
|
|
|
A multi-dimensional array example:
|
|
|
|
>>> X = np.arange(8).reshape((2,2,2))
|
|
>>> X
|
|
array([[[0, 1],
|
|
[2, 3]],
|
|
[[4, 5],
|
|
[6, 7]]])
|
|
>>> np.add.reduce(X, 0)
|
|
array([[ 4, 6],
|
|
[ 8, 10]])
|
|
>>> np.add.reduce(X) # confirm: default axis value is 0
|
|
array([[ 4, 6],
|
|
[ 8, 10]])
|
|
>>> np.add.reduce(X, 1)
|
|
array([[ 2, 4],
|
|
[10, 12]])
|
|
>>> np.add.reduce(X, 2)
|
|
array([[ 1, 5],
|
|
[ 9, 13]])
|
|
|
|
You can use the ``initial`` keyword argument to initialize the reduction
|
|
with a different value, and ``where`` to select specific elements to include:
|
|
|
|
>>> np.add.reduce([10], initial=5)
|
|
15
|
|
>>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initial=10)
|
|
array([14., 14.])
|
|
>>> a = np.array([10., np.nan, 10])
|
|
>>> np.add.reduce(a, where=~np.isnan(a))
|
|
20.0
|
|
|
|
Allows reductions of empty arrays where they would normally fail, i.e.
|
|
for ufuncs without an identity.
|
|
|
|
>>> np.minimum.reduce([], initial=np.inf)
|
|
inf
|
|
>>> np.minimum.reduce([[1., 2.], [3., 4.]], initial=10., where=[True, False])
|
|
array([ 1., 10.])
|
|
>>> np.minimum.reduce([])
|
|
Traceback (most recent call last):
|
|
...
|
|
ValueError: zero-size array to reduction operation minimum which has no identity
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('accumulate',
|
|
"""
|
|
accumulate(array, axis=0, dtype=None, out=None)
|
|
|
|
Accumulate the result of applying the operator to all elements.
|
|
|
|
For a one-dimensional array, accumulate produces results equivalent to::
|
|
|
|
r = np.empty(len(A))
|
|
t = op.identity # op = the ufunc being applied to A's elements
|
|
for i in range(len(A)):
|
|
t = op(t, A[i])
|
|
r[i] = t
|
|
return r
|
|
|
|
For example, add.accumulate() is equivalent to np.cumsum().
|
|
|
|
For a multi-dimensional array, accumulate is applied along only one
|
|
axis (axis zero by default; see Examples below) so repeated use is
|
|
necessary if one wants to accumulate over multiple axes.
|
|
|
|
Parameters
|
|
----------
|
|
array : array_like
|
|
The array to act on.
|
|
axis : int, optional
|
|
The axis along which to apply the accumulation; default is zero.
|
|
dtype : data-type code, optional
|
|
The data-type used to represent the intermediate results. Defaults
|
|
to the data-type of the output array if such is provided, or the
|
|
data-type of the input array if no output array is provided.
|
|
out : ndarray, None, or tuple of ndarray and None, optional
|
|
A location into which the result is stored. If not provided or None,
|
|
a freshly-allocated array is returned. For consistency with
|
|
``ufunc.__call__``, if given as a keyword, this may be wrapped in a
|
|
1-element tuple.
|
|
|
|
.. versionchanged:: 1.13.0
|
|
Tuples are allowed for keyword argument.
|
|
|
|
Returns
|
|
-------
|
|
r : ndarray
|
|
The accumulated values. If `out` was supplied, `r` is a reference to
|
|
`out`.
|
|
|
|
Examples
|
|
--------
|
|
1-D array examples:
|
|
|
|
>>> np.add.accumulate([2, 3, 5])
|
|
array([ 2, 5, 10])
|
|
>>> np.multiply.accumulate([2, 3, 5])
|
|
array([ 2, 6, 30])
|
|
|
|
2-D array examples:
|
|
|
|
>>> I = np.eye(2)
|
|
>>> I
|
|
array([[1., 0.],
|
|
[0., 1.]])
|
|
|
|
Accumulate along axis 0 (rows), down columns:
|
|
|
|
>>> np.add.accumulate(I, 0)
|
|
array([[1., 0.],
|
|
[1., 1.]])
|
|
>>> np.add.accumulate(I) # no axis specified = axis zero
|
|
array([[1., 0.],
|
|
[1., 1.]])
|
|
|
|
Accumulate along axis 1 (columns), through rows:
|
|
|
|
>>> np.add.accumulate(I, 1)
|
|
array([[1., 1.],
|
|
[0., 1.]])
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('reduceat',
|
|
"""
|
|
reduceat(array, indices, axis=0, dtype=None, out=None)
|
|
|
|
Performs a (local) reduce with specified slices over a single axis.
|
|
|
|
For i in ``range(len(indices))``, `reduceat` computes
|
|
``ufunc.reduce(array[indices[i]:indices[i+1]])``, which becomes the i-th
|
|
generalized "row" parallel to `axis` in the final result (i.e., in a
|
|
2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
|
|
`axis = 1`, it becomes the i-th column). There are three exceptions to this:
|
|
|
|
* when ``i = len(indices) - 1`` (so for the last index),
|
|
``indices[i+1] = array.shape[axis]``.
|
|
* if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
|
|
simply ``array[indices[i]]``.
|
|
* if ``indices[i] >= len(array)`` or ``indices[i] < 0``, an error is raised.
|
|
|
|
The shape of the output depends on the size of `indices`, and may be
|
|
larger than `array` (this happens if ``len(indices) > array.shape[axis]``).
|
|
|
|
Parameters
|
|
----------
|
|
array : array_like
|
|
The array to act on.
|
|
indices : array_like
|
|
Paired indices, comma separated (not colon), specifying slices to
|
|
reduce.
|
|
axis : int, optional
|
|
The axis along which to apply the reduceat.
|
|
dtype : data-type code, optional
|
|
The type used to represent the intermediate results. Defaults
|
|
to the data type of the output array if this is provided, or
|
|
the data type of the input array if no output array is provided.
|
|
out : ndarray, None, or tuple of ndarray and None, optional
|
|
A location into which the result is stored. If not provided or None,
|
|
a freshly-allocated array is returned. For consistency with
|
|
``ufunc.__call__``, if given as a keyword, this may be wrapped in a
|
|
1-element tuple.
|
|
|
|
.. versionchanged:: 1.13.0
|
|
Tuples are allowed for keyword argument.
|
|
|
|
Returns
|
|
-------
|
|
r : ndarray
|
|
The reduced values. If `out` was supplied, `r` is a reference to
|
|
`out`.
|
|
|
|
Notes
|
|
-----
|
|
A descriptive example:
|
|
|
|
If `array` is 1-D, the function `ufunc.accumulate(array)` is the same as
|
|
``ufunc.reduceat(array, indices)[::2]`` where `indices` is
|
|
``range(len(array) - 1)`` with a zero placed
|
|
in every other element:
|
|
``indices = zeros(2 * len(array) - 1)``,
|
|
``indices[1::2] = range(1, len(array))``.
|
|
|
|
Don't be fooled by this attribute's name: `reduceat(array)` is not
|
|
necessarily smaller than `array`.
|
|
|
|
Examples
|
|
--------
|
|
To take the running sum of four successive values:
|
|
|
|
>>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
|
|
array([ 6, 10, 14, 18])
|
|
|
|
A 2-D example:
|
|
|
|
>>> x = np.linspace(0, 15, 16).reshape(4,4)
|
|
>>> x
|
|
array([[ 0., 1., 2., 3.],
|
|
[ 4., 5., 6., 7.],
|
|
[ 8., 9., 10., 11.],
|
|
[12., 13., 14., 15.]])
|
|
|
|
::
|
|
|
|
# reduce such that the result has the following five rows:
|
|
# [row1 + row2 + row3]
|
|
# [row4]
|
|
# [row2]
|
|
# [row3]
|
|
# [row1 + row2 + row3 + row4]
|
|
|
|
>>> np.add.reduceat(x, [0, 3, 1, 2, 0])
|
|
array([[12., 15., 18., 21.],
|
|
[12., 13., 14., 15.],
|
|
[ 4., 5., 6., 7.],
|
|
[ 8., 9., 10., 11.],
|
|
[24., 28., 32., 36.]])
|
|
|
|
::
|
|
|
|
# reduce such that result has the following two columns:
|
|
# [col1 * col2 * col3, col4]
|
|
|
|
>>> np.multiply.reduceat(x, [0, 3], 1)
|
|
array([[ 0., 3.],
|
|
[ 120., 7.],
|
|
[ 720., 11.],
|
|
[2184., 15.]])
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('outer',
|
|
r"""
|
|
outer(A, B, /, **kwargs)
|
|
|
|
Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
|
|
|
|
Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
|
|
``op.outer(A, B)`` is an array of dimension M + N such that:
|
|
|
|
.. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
|
|
op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
|
|
|
|
For `A` and `B` one-dimensional, this is equivalent to::
|
|
|
|
r = empty(len(A),len(B))
|
|
for i in range(len(A)):
|
|
for j in range(len(B)):
|
|
r[i,j] = op(A[i], B[j]) # op = ufunc in question
|
|
|
|
Parameters
|
|
----------
|
|
A : array_like
|
|
First array
|
|
B : array_like
|
|
Second array
|
|
kwargs : any
|
|
Arguments to pass on to the ufunc. Typically `dtype` or `out`.
|
|
See `ufunc` for a comprehensive overview of all available arguments.
|
|
|
|
Returns
|
|
-------
|
|
r : ndarray
|
|
Output array
|
|
|
|
See Also
|
|
--------
|
|
numpy.outer : A less powerful version of ``np.multiply.outer``
|
|
that `ravel`\ s all inputs to 1D. This exists
|
|
primarily for compatibility with old code.
|
|
|
|
tensordot : ``np.tensordot(a, b, axes=((), ()))`` and
|
|
``np.multiply.outer(a, b)`` behave same for all
|
|
dimensions of a and b.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.multiply.outer([1, 2, 3], [4, 5, 6])
|
|
array([[ 4, 5, 6],
|
|
[ 8, 10, 12],
|
|
[12, 15, 18]])
|
|
|
|
A multi-dimensional example:
|
|
|
|
>>> A = np.array([[1, 2, 3], [4, 5, 6]])
|
|
>>> A.shape
|
|
(2, 3)
|
|
>>> B = np.array([[1, 2, 3, 4]])
|
|
>>> B.shape
|
|
(1, 4)
|
|
>>> C = np.multiply.outer(A, B)
|
|
>>> C.shape; C
|
|
(2, 3, 1, 4)
|
|
array([[[[ 1, 2, 3, 4]],
|
|
[[ 2, 4, 6, 8]],
|
|
[[ 3, 6, 9, 12]]],
|
|
[[[ 4, 8, 12, 16]],
|
|
[[ 5, 10, 15, 20]],
|
|
[[ 6, 12, 18, 24]]]])
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('at',
|
|
"""
|
|
at(a, indices, b=None, /)
|
|
|
|
Performs unbuffered in place operation on operand 'a' for elements
|
|
specified by 'indices'. For addition ufunc, this method is equivalent to
|
|
``a[indices] += b``, except that results are accumulated for elements that
|
|
are indexed more than once. For example, ``a[[0,0]] += 1`` will only
|
|
increment the first element once because of buffering, whereas
|
|
``add.at(a, [0,0], 1)`` will increment the first element twice.
|
|
|
|
.. versionadded:: 1.8.0
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
The array to perform in place operation on.
|
|
indices : array_like or tuple
|
|
Array like index object or slice object for indexing into first
|
|
operand. If first operand has multiple dimensions, indices can be a
|
|
tuple of array like index objects or slice objects.
|
|
b : array_like
|
|
Second operand for ufuncs requiring two operands. Operand must be
|
|
broadcastable over first operand after indexing or slicing.
|
|
|
|
Examples
|
|
--------
|
|
Set items 0 and 1 to their negative values:
|
|
|
|
>>> a = np.array([1, 2, 3, 4])
|
|
>>> np.negative.at(a, [0, 1])
|
|
>>> a
|
|
array([-1, -2, 3, 4])
|
|
|
|
Increment items 0 and 1, and increment item 2 twice:
|
|
|
|
>>> a = np.array([1, 2, 3, 4])
|
|
>>> np.add.at(a, [0, 1, 2, 2], 1)
|
|
>>> a
|
|
array([2, 3, 5, 4])
|
|
|
|
Add items 0 and 1 in first array to second array,
|
|
and store results in first array:
|
|
|
|
>>> a = np.array([1, 2, 3, 4])
|
|
>>> b = np.array([1, 2])
|
|
>>> np.add.at(a, [0, 1], b)
|
|
>>> a
|
|
array([2, 4, 3, 4])
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('resolve_dtypes',
|
|
"""
|
|
resolve_dtypes(dtypes, *, signature=None, casting=None, reduction=False)
|
|
|
|
Find the dtypes NumPy will use for the operation. Both input and
|
|
output dtypes are returned and may differ from those provided.
|
|
|
|
.. note::
|
|
|
|
This function always applies NEP 50 rules since it is not provided
|
|
any actual values. The Python types ``int``, ``float``, and
|
|
``complex`` thus behave weak and should be passed for "untyped"
|
|
Python input.
|
|
|
|
Parameters
|
|
----------
|
|
dtypes : tuple of dtypes, None, or literal int, float, complex
|
|
The input dtypes for each operand. Output operands can be
|
|
None, indicating that the dtype must be found.
|
|
signature : tuple of DTypes or None, optional
|
|
If given, enforces exact DType (classes) of the specific operand.
|
|
The ufunc ``dtype`` argument is equivalent to passing a tuple with
|
|
only output dtypes set.
|
|
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
|
|
The casting mode when casting is necessary. This is identical to
|
|
the ufunc call casting modes.
|
|
reduction : boolean
|
|
If given, the resolution assumes a reduce operation is happening
|
|
which slightly changes the promotion and type resolution rules.
|
|
`dtypes` is usually something like ``(None, np.dtype("i2"), None)``
|
|
for reductions (first input is also the output).
|
|
|
|
.. note::
|
|
|
|
The default casting mode is "same_kind", however, as of
|
|
NumPy 1.24, NumPy uses "unsafe" for reductions.
|
|
|
|
Returns
|
|
-------
|
|
dtypes : tuple of dtypes
|
|
The dtypes which NumPy would use for the calculation. Note that
|
|
dtypes may not match the passed in ones (casting is necessary).
|
|
|
|
See Also
|
|
--------
|
|
numpy.ufunc._resolve_dtypes_and_context :
|
|
Similar function to this, but returns additional information which
|
|
give access to the core C functionality of NumPy.
|
|
|
|
Examples
|
|
--------
|
|
This API requires passing dtypes, define them for convenience:
|
|
|
|
>>> int32 = np.dtype("int32")
|
|
>>> float32 = np.dtype("float32")
|
|
|
|
The typical ufunc call does not pass an output dtype. `np.add` has two
|
|
inputs and one output, so leave the output as ``None`` (not provided):
|
|
|
|
>>> np.add.resolve_dtypes((int32, float32, None))
|
|
(dtype('float64'), dtype('float64'), dtype('float64'))
|
|
|
|
The loop found uses "float64" for all operands (including the output), the
|
|
first input would be cast.
|
|
|
|
``resolve_dtypes`` supports "weak" handling for Python scalars by passing
|
|
``int``, ``float``, or ``complex``:
|
|
|
|
>>> np.add.resolve_dtypes((float32, float, None))
|
|
(dtype('float32'), dtype('float32'), dtype('float32'))
|
|
|
|
Where the Python ``float`` behaves samilar to a Python value ``0.0``
|
|
in a ufunc call. (See :ref:`NEP 50 <NEP50>` for details.)
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('_resolve_dtypes_and_context',
|
|
"""
|
|
_resolve_dtypes_and_context(dtypes, *, signature=None, casting=None, reduction=False)
|
|
|
|
See `numpy.ufunc.resolve_dtypes` for parameter information. This
|
|
function is considered *unstable*. You may use it, but the returned
|
|
information is NumPy version specific and expected to change.
|
|
Large API/ABI changes are not expected, but a new NumPy version is
|
|
expected to require updating code using this functionality.
|
|
|
|
This function is designed to be used in conjunction with
|
|
`numpy.ufunc._get_strided_loop`. The calls are split to mirror the C API
|
|
and allow future improvements.
|
|
|
|
Returns
|
|
-------
|
|
dtypes : tuple of dtypes
|
|
call_info :
|
|
PyCapsule with all necessary information to get access to low level
|
|
C calls. See `numpy.ufunc._get_strided_loop` for more information.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core', 'ufunc', ('_get_strided_loop',
|
|
"""
|
|
_get_strided_loop(call_info, /, *, fixed_strides=None)
|
|
|
|
This function fills in the ``call_info`` capsule to include all
|
|
information necessary to call the low-level strided loop from NumPy.
|
|
|
|
See notes for more information.
|
|
|
|
Parameters
|
|
----------
|
|
call_info : PyCapsule
|
|
The PyCapsule returned by `numpy.ufunc._resolve_dtypes_and_context`.
|
|
fixed_strides : tuple of int or None, optional
|
|
A tuple with fixed byte strides of all input arrays. NumPy may use
|
|
this information to find specialized loops, so any call must follow
|
|
the given stride. Use ``None`` to indicate that the stride is not
|
|
known (or not fixed) for all calls.
|
|
|
|
Notes
|
|
-----
|
|
Together with `numpy.ufunc._resolve_dtypes_and_context` this function
|
|
gives low-level access to the NumPy ufunc loops.
|
|
The first function does general preparation and returns the required
|
|
information. It returns this as a C capsule with the version specific
|
|
name ``numpy_1.24_ufunc_call_info``.
|
|
The NumPy 1.24 ufunc call info capsule has the following layout::
|
|
|
|
typedef struct {
|
|
PyArrayMethod_StridedLoop *strided_loop;
|
|
PyArrayMethod_Context *context;
|
|
NpyAuxData *auxdata;
|
|
|
|
/* Flag information (expected to change) */
|
|
npy_bool requires_pyapi; /* GIL is required by loop */
|
|
|
|
/* Loop doesn't set FPE flags; if not set check FPE flags */
|
|
npy_bool no_floatingpoint_errors;
|
|
} ufunc_call_info;
|
|
|
|
Note that the first call only fills in the ``context``. The call to
|
|
``_get_strided_loop`` fills in all other data.
|
|
Please see the ``numpy/experimental_dtype_api.h`` header for exact
|
|
call information; the main thing to note is that the new-style loops
|
|
return 0 on success, -1 on failure. They are passed context as new
|
|
first input and ``auxdata`` as (replaced) last.
|
|
|
|
Only the ``strided_loop``signature is considered guaranteed stable
|
|
for NumPy bug-fix releases. All other API is tied to the experimental
|
|
API versioning.
|
|
|
|
The reason for the split call is that cast information is required to
|
|
decide what the fixed-strides will be.
|
|
|
|
NumPy ties the lifetime of the ``auxdata`` information to the capsule.
|
|
|
|
"""))
|
|
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# Documentation for dtype attributes and methods
|
|
#
|
|
##############################################################################
|
|
|
|
##############################################################################
|
|
#
|
|
# dtype object
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype',
|
|
"""
|
|
dtype(dtype, align=False, copy=False, [metadata])
|
|
|
|
Create a data type object.
|
|
|
|
A numpy array is homogeneous, and contains elements described by a
|
|
dtype object. A dtype object can be constructed from different
|
|
combinations of fundamental numeric types.
|
|
|
|
Parameters
|
|
----------
|
|
dtype
|
|
Object to be converted to a data type object.
|
|
align : bool, optional
|
|
Add padding to the fields to match what a C compiler would output
|
|
for a similar C-struct. Can be ``True`` only if `obj` is a dictionary
|
|
or a comma-separated string. If a struct dtype is being created,
|
|
this also sets a sticky alignment flag ``isalignedstruct``.
|
|
copy : bool, optional
|
|
Make a new copy of the data-type object. If ``False``, the result
|
|
may just be a reference to a built-in data-type object.
|
|
metadata : dict, optional
|
|
An optional dictionary with dtype metadata.
|
|
|
|
See also
|
|
--------
|
|
result_type
|
|
|
|
Examples
|
|
--------
|
|
Using array-scalar type:
|
|
|
|
>>> np.dtype(np.int16)
|
|
dtype('int16')
|
|
|
|
Structured type, one field name 'f1', containing int16:
|
|
|
|
>>> np.dtype([('f1', np.int16)])
|
|
dtype([('f1', '<i2')])
|
|
|
|
Structured type, one field named 'f1', in itself containing a structured
|
|
type with one field:
|
|
|
|
>>> np.dtype([('f1', [('f1', np.int16)])])
|
|
dtype([('f1', [('f1', '<i2')])])
|
|
|
|
Structured type, two fields: the first field contains an unsigned int, the
|
|
second an int32:
|
|
|
|
>>> np.dtype([('f1', np.uint64), ('f2', np.int32)])
|
|
dtype([('f1', '<u8'), ('f2', '<i4')])
|
|
|
|
Using array-protocol type strings:
|
|
|
|
>>> np.dtype([('a','f8'),('b','S10')])
|
|
dtype([('a', '<f8'), ('b', 'S10')])
|
|
|
|
Using comma-separated field formats. The shape is (2,3):
|
|
|
|
>>> np.dtype("i4, (2,3)f8")
|
|
dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])
|
|
|
|
Using tuples. ``int`` is a fixed type, 3 the field's shape. ``void``
|
|
is a flexible type, here of size 10:
|
|
|
|
>>> np.dtype([('hello',(np.int64,3)),('world',np.void,10)])
|
|
dtype([('hello', '<i8', (3,)), ('world', 'V10')])
|
|
|
|
Subdivide ``int16`` into 2 ``int8``'s, called x and y. 0 and 1 are
|
|
the offsets in bytes:
|
|
|
|
>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
|
|
dtype((numpy.int16, [('x', 'i1'), ('y', 'i1')]))
|
|
|
|
Using dictionaries. Two fields named 'gender' and 'age':
|
|
|
|
>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
|
|
dtype([('gender', 'S1'), ('age', 'u1')])
|
|
|
|
Offsets in bytes, here 0 and 25:
|
|
|
|
>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
|
|
dtype([('surname', 'S25'), ('age', 'u1')])
|
|
|
|
""")
|
|
|
|
##############################################################################
|
|
#
|
|
# dtype attributes
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('alignment',
|
|
"""
|
|
The required alignment (bytes) of this data-type according to the compiler.
|
|
|
|
More information is available in the C-API section of the manual.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> x = np.dtype('i4')
|
|
>>> x.alignment
|
|
4
|
|
|
|
>>> x = np.dtype(float)
|
|
>>> x.alignment
|
|
8
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('byteorder',
|
|
"""
|
|
A character indicating the byte-order of this data-type object.
|
|
|
|
One of:
|
|
|
|
=== ==============
|
|
'=' native
|
|
'<' little-endian
|
|
'>' big-endian
|
|
'|' not applicable
|
|
=== ==============
|
|
|
|
All built-in data-type objects have byteorder either '=' or '|'.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> dt = np.dtype('i2')
|
|
>>> dt.byteorder
|
|
'='
|
|
>>> # endian is not relevant for 8 bit numbers
|
|
>>> np.dtype('i1').byteorder
|
|
'|'
|
|
>>> # or ASCII strings
|
|
>>> np.dtype('S2').byteorder
|
|
'|'
|
|
>>> # Even if specific code is given, and it is native
|
|
>>> # '=' is the byteorder
|
|
>>> import sys
|
|
>>> sys_is_le = sys.byteorder == 'little'
|
|
>>> native_code = '<' if sys_is_le else '>'
|
|
>>> swapped_code = '>' if sys_is_le else '<'
|
|
>>> dt = np.dtype(native_code + 'i2')
|
|
>>> dt.byteorder
|
|
'='
|
|
>>> # Swapped code shows up as itself
|
|
>>> dt = np.dtype(swapped_code + 'i2')
|
|
>>> dt.byteorder == swapped_code
|
|
True
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('char',
|
|
"""A unique character code for each of the 21 different built-in types.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> x = np.dtype(float)
|
|
>>> x.char
|
|
'd'
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('descr',
|
|
"""
|
|
`__array_interface__` description of the data-type.
|
|
|
|
The format is that required by the 'descr' key in the
|
|
`__array_interface__` attribute.
|
|
|
|
Warning: This attribute exists specifically for `__array_interface__`,
|
|
and passing it directly to `np.dtype` will not accurately reconstruct
|
|
some dtypes (e.g., scalar and subarray dtypes).
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> x = np.dtype(float)
|
|
>>> x.descr
|
|
[('', '<f8')]
|
|
|
|
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
|
|
>>> dt.descr
|
|
[('name', '<U16'), ('grades', '<f8', (2,))]
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('fields',
|
|
"""
|
|
Dictionary of named fields defined for this data type, or ``None``.
|
|
|
|
The dictionary is indexed by keys that are the names of the fields.
|
|
Each entry in the dictionary is a tuple fully describing the field::
|
|
|
|
(dtype, offset[, title])
|
|
|
|
Offset is limited to C int, which is signed and usually 32 bits.
|
|
If present, the optional title can be any object (if it is a string
|
|
or unicode then it will also be a key in the fields dictionary,
|
|
otherwise it's meta-data). Notice also that the first two elements
|
|
of the tuple can be passed directly as arguments to the ``ndarray.getfield``
|
|
and ``ndarray.setfield`` methods.
|
|
|
|
See Also
|
|
--------
|
|
ndarray.getfield, ndarray.setfield
|
|
|
|
Examples
|
|
--------
|
|
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
|
|
>>> print(dt.fields)
|
|
{'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('flags',
|
|
"""
|
|
Bit-flags describing how this data type is to be interpreted.
|
|
|
|
Bit-masks are in `numpy.core.multiarray` as the constants
|
|
`ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,
|
|
`NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation
|
|
of these flags is in C-API documentation; they are largely useful
|
|
for user-defined data-types.
|
|
|
|
The following example demonstrates that operations on this particular
|
|
dtype requires Python C-API.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])
|
|
>>> x.flags
|
|
16
|
|
>>> np.core.multiarray.NEEDS_PYAPI
|
|
16
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('hasobject',
|
|
"""
|
|
Boolean indicating whether this dtype contains any reference-counted
|
|
objects in any fields or sub-dtypes.
|
|
|
|
Recall that what is actually in the ndarray memory representing
|
|
the Python object is the memory address of that object (a pointer).
|
|
Special handling may be required, and this attribute is useful for
|
|
distinguishing data types that may contain arbitrary Python objects
|
|
and data-types that won't.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('isbuiltin',
|
|
"""
|
|
Integer indicating how this dtype relates to the built-in dtypes.
|
|
|
|
Read-only.
|
|
|
|
= ========================================================================
|
|
0 if this is a structured array type, with fields
|
|
1 if this is a dtype compiled into numpy (such as ints, floats etc)
|
|
2 if the dtype is for a user-defined numpy type
|
|
A user-defined type uses the numpy C-API machinery to extend
|
|
numpy to handle a new array type. See
|
|
:ref:`user.user-defined-data-types` in the NumPy manual.
|
|
= ========================================================================
|
|
|
|
Examples
|
|
--------
|
|
>>> dt = np.dtype('i2')
|
|
>>> dt.isbuiltin
|
|
1
|
|
>>> dt = np.dtype('f8')
|
|
>>> dt.isbuiltin
|
|
1
|
|
>>> dt = np.dtype([('field1', 'f8')])
|
|
>>> dt.isbuiltin
|
|
0
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('isnative',
|
|
"""
|
|
Boolean indicating whether the byte order of this dtype is native
|
|
to the platform.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('isalignedstruct',
|
|
"""
|
|
Boolean indicating whether the dtype is a struct which maintains
|
|
field alignment. This flag is sticky, so when combining multiple
|
|
structs together, it is preserved and produces new dtypes which
|
|
are also aligned.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('itemsize',
|
|
"""
|
|
The element size of this data-type object.
|
|
|
|
For 18 of the 21 types this number is fixed by the data-type.
|
|
For the flexible data-types, this number can be anything.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> arr = np.array([[1, 2], [3, 4]])
|
|
>>> arr.dtype
|
|
dtype('int64')
|
|
>>> arr.itemsize
|
|
8
|
|
|
|
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
|
|
>>> dt.itemsize
|
|
80
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('kind',
|
|
"""
|
|
A character code (one of 'biufcmMOSUV') identifying the general kind of data.
|
|
|
|
= ======================
|
|
b boolean
|
|
i signed integer
|
|
u unsigned integer
|
|
f floating-point
|
|
c complex floating-point
|
|
m timedelta
|
|
M datetime
|
|
O object
|
|
S (byte-)string
|
|
U Unicode
|
|
V void
|
|
= ======================
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> dt = np.dtype('i4')
|
|
>>> dt.kind
|
|
'i'
|
|
>>> dt = np.dtype('f8')
|
|
>>> dt.kind
|
|
'f'
|
|
>>> dt = np.dtype([('field1', 'f8')])
|
|
>>> dt.kind
|
|
'V'
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('metadata',
|
|
"""
|
|
Either ``None`` or a readonly dictionary of metadata (mappingproxy).
|
|
|
|
The metadata field can be set using any dictionary at data-type
|
|
creation. NumPy currently has no uniform approach to propagating
|
|
metadata; although some array operations preserve it, there is no
|
|
guarantee that others will.
|
|
|
|
.. warning::
|
|
|
|
Although used in certain projects, this feature was long undocumented
|
|
and is not well supported. Some aspects of metadata propagation
|
|
are expected to change in the future.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> dt = np.dtype(float, metadata={"key": "value"})
|
|
>>> dt.metadata["key"]
|
|
'value'
|
|
>>> arr = np.array([1, 2, 3], dtype=dt)
|
|
>>> arr.dtype.metadata
|
|
mappingproxy({'key': 'value'})
|
|
|
|
Adding arrays with identical datatypes currently preserves the metadata:
|
|
|
|
>>> (arr + arr).dtype.metadata
|
|
mappingproxy({'key': 'value'})
|
|
|
|
But if the arrays have different dtype metadata, the metadata may be
|
|
dropped:
|
|
|
|
>>> dt2 = np.dtype(float, metadata={"key2": "value2"})
|
|
>>> arr2 = np.array([3, 2, 1], dtype=dt2)
|
|
>>> (arr + arr2).dtype.metadata is None
|
|
True # The metadata field is cleared so None is returned
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('name',
|
|
"""
|
|
A bit-width name for this data-type.
|
|
|
|
Un-sized flexible data-type objects do not have this attribute.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> x = np.dtype(float)
|
|
>>> x.name
|
|
'float64'
|
|
>>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])
|
|
>>> x.name
|
|
'void640'
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('names',
|
|
"""
|
|
Ordered list of field names, or ``None`` if there are no fields.
|
|
|
|
The names are ordered according to increasing byte offset. This can be
|
|
used, for example, to walk through all of the named fields in offset order.
|
|
|
|
Examples
|
|
--------
|
|
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
|
|
>>> dt.names
|
|
('name', 'grades')
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('num',
|
|
"""
|
|
A unique number for each of the 21 different built-in types.
|
|
|
|
These are roughly ordered from least-to-most precision.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> dt = np.dtype(str)
|
|
>>> dt.num
|
|
19
|
|
|
|
>>> dt = np.dtype(float)
|
|
>>> dt.num
|
|
12
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('shape',
|
|
"""
|
|
Shape tuple of the sub-array if this data type describes a sub-array,
|
|
and ``()`` otherwise.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> dt = np.dtype(('i4', 4))
|
|
>>> dt.shape
|
|
(4,)
|
|
|
|
>>> dt = np.dtype(('i4', (2, 3)))
|
|
>>> dt.shape
|
|
(2, 3)
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('ndim',
|
|
"""
|
|
Number of dimensions of the sub-array if this data type describes a
|
|
sub-array, and ``0`` otherwise.
|
|
|
|
.. versionadded:: 1.13.0
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.dtype(float)
|
|
>>> x.ndim
|
|
0
|
|
|
|
>>> x = np.dtype((float, 8))
|
|
>>> x.ndim
|
|
1
|
|
|
|
>>> x = np.dtype(('i4', (3, 4)))
|
|
>>> x.ndim
|
|
2
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('str',
|
|
"""The array-protocol typestring of this data-type object."""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('subdtype',
|
|
"""
|
|
Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and
|
|
None otherwise.
|
|
|
|
The *shape* is the fixed shape of the sub-array described by this
|
|
data type, and *item_dtype* the data type of the array.
|
|
|
|
If a field whose dtype object has this attribute is retrieved,
|
|
then the extra dimensions implied by *shape* are tacked on to
|
|
the end of the retrieved array.
|
|
|
|
See Also
|
|
--------
|
|
dtype.base
|
|
|
|
Examples
|
|
--------
|
|
>>> x = numpy.dtype('8f')
|
|
>>> x.subdtype
|
|
(dtype('float32'), (8,))
|
|
|
|
>>> x = numpy.dtype('i2')
|
|
>>> x.subdtype
|
|
>>>
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('base',
|
|
"""
|
|
Returns dtype for the base element of the subarrays,
|
|
regardless of their dimension or shape.
|
|
|
|
See Also
|
|
--------
|
|
dtype.subdtype
|
|
|
|
Examples
|
|
--------
|
|
>>> x = numpy.dtype('8f')
|
|
>>> x.base
|
|
dtype('float32')
|
|
|
|
>>> x = numpy.dtype('i2')
|
|
>>> x.base
|
|
dtype('int16')
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('type',
|
|
"""The type object used to instantiate a scalar of this data-type."""))
|
|
|
|
##############################################################################
|
|
#
|
|
# dtype methods
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('newbyteorder',
|
|
"""
|
|
newbyteorder(new_order='S', /)
|
|
|
|
Return a new dtype with a different byte order.
|
|
|
|
Changes are also made in all fields and sub-arrays of the data type.
|
|
|
|
Parameters
|
|
----------
|
|
new_order : string, optional
|
|
Byte order to force; a value from the byte order specifications
|
|
below. The default value ('S') results in swapping the current
|
|
byte order. `new_order` codes can be any of:
|
|
|
|
* 'S' - swap dtype from current to opposite endian
|
|
* {'<', 'little'} - little endian
|
|
* {'>', 'big'} - big endian
|
|
* {'=', 'native'} - native order
|
|
* {'|', 'I'} - ignore (no change to byte order)
|
|
|
|
Returns
|
|
-------
|
|
new_dtype : dtype
|
|
New dtype object with the given change to the byte order.
|
|
|
|
Notes
|
|
-----
|
|
Changes are also made in all fields and sub-arrays of the data type.
|
|
|
|
Examples
|
|
--------
|
|
>>> import sys
|
|
>>> sys_is_le = sys.byteorder == 'little'
|
|
>>> native_code = '<' if sys_is_le else '>'
|
|
>>> swapped_code = '>' if sys_is_le else '<'
|
|
>>> native_dt = np.dtype(native_code+'i2')
|
|
>>> swapped_dt = np.dtype(swapped_code+'i2')
|
|
>>> native_dt.newbyteorder('S') == swapped_dt
|
|
True
|
|
>>> native_dt.newbyteorder() == swapped_dt
|
|
True
|
|
>>> native_dt == swapped_dt.newbyteorder('S')
|
|
True
|
|
>>> native_dt == swapped_dt.newbyteorder('=')
|
|
True
|
|
>>> native_dt == swapped_dt.newbyteorder('N')
|
|
True
|
|
>>> native_dt == native_dt.newbyteorder('|')
|
|
True
|
|
>>> np.dtype('<i2') == native_dt.newbyteorder('<')
|
|
True
|
|
>>> np.dtype('<i2') == native_dt.newbyteorder('L')
|
|
True
|
|
>>> np.dtype('>i2') == native_dt.newbyteorder('>')
|
|
True
|
|
>>> np.dtype('>i2') == native_dt.newbyteorder('B')
|
|
True
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('__class_getitem__',
|
|
"""
|
|
__class_getitem__(item, /)
|
|
|
|
Return a parametrized wrapper around the `~numpy.dtype` type.
|
|
|
|
.. versionadded:: 1.22
|
|
|
|
Returns
|
|
-------
|
|
alias : types.GenericAlias
|
|
A parametrized `~numpy.dtype` type.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
|
|
>>> np.dtype[np.int64]
|
|
numpy.dtype[numpy.int64]
|
|
|
|
See Also
|
|
--------
|
|
:pep:`585` : Type hinting generics in standard collections.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('__ge__',
|
|
"""
|
|
__ge__(value, /)
|
|
|
|
Return ``self >= value``.
|
|
|
|
Equivalent to ``np.can_cast(value, self, casting="safe")``.
|
|
|
|
See Also
|
|
--------
|
|
can_cast : Returns True if cast between data types can occur according to
|
|
the casting rule.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('__le__',
|
|
"""
|
|
__le__(value, /)
|
|
|
|
Return ``self <= value``.
|
|
|
|
Equivalent to ``np.can_cast(self, value, casting="safe")``.
|
|
|
|
See Also
|
|
--------
|
|
can_cast : Returns True if cast between data types can occur according to
|
|
the casting rule.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('__gt__',
|
|
"""
|
|
__ge__(value, /)
|
|
|
|
Return ``self > value``.
|
|
|
|
Equivalent to
|
|
``self != value and np.can_cast(value, self, casting="safe")``.
|
|
|
|
See Also
|
|
--------
|
|
can_cast : Returns True if cast between data types can occur according to
|
|
the casting rule.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'dtype', ('__lt__',
|
|
"""
|
|
__lt__(value, /)
|
|
|
|
Return ``self < value``.
|
|
|
|
Equivalent to
|
|
``self != value and np.can_cast(self, value, casting="safe")``.
|
|
|
|
See Also
|
|
--------
|
|
can_cast : Returns True if cast between data types can occur according to
|
|
the casting rule.
|
|
|
|
"""))
|
|
|
|
##############################################################################
|
|
#
|
|
# Datetime-related Methods
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.multiarray', 'busdaycalendar',
|
|
"""
|
|
busdaycalendar(weekmask='1111100', holidays=None)
|
|
|
|
A business day calendar object that efficiently stores information
|
|
defining valid days for the busday family of functions.
|
|
|
|
The default valid days are Monday through Friday ("business days").
|
|
A busdaycalendar object can be specified with any set of weekly
|
|
valid days, plus an optional "holiday" dates that always will be invalid.
|
|
|
|
Once a busdaycalendar object is created, the weekmask and holidays
|
|
cannot be modified.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
weekmask : str or array_like of bool, optional
|
|
A seven-element array indicating which of Monday through Sunday are
|
|
valid days. May be specified as a length-seven list or array, like
|
|
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
|
|
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
|
|
weekdays, optionally separated by white space. Valid abbreviations
|
|
are: Mon Tue Wed Thu Fri Sat Sun
|
|
holidays : array_like of datetime64[D], optional
|
|
An array of dates to consider as invalid dates, no matter which
|
|
weekday they fall upon. Holiday dates may be specified in any
|
|
order, and NaT (not-a-time) dates are ignored. This list is
|
|
saved in a normalized form that is suited for fast calculations
|
|
of valid days.
|
|
|
|
Returns
|
|
-------
|
|
out : busdaycalendar
|
|
A business day calendar object containing the specified
|
|
weekmask and holidays values.
|
|
|
|
See Also
|
|
--------
|
|
is_busday : Returns a boolean array indicating valid days.
|
|
busday_offset : Applies an offset counted in valid days.
|
|
busday_count : Counts how many valid days are in a half-open date range.
|
|
|
|
Attributes
|
|
----------
|
|
Note: once a busdaycalendar object is created, you cannot modify the
|
|
weekmask or holidays. The attributes return copies of internal data.
|
|
weekmask : (copy) seven-element array of bool
|
|
holidays : (copy) sorted array of datetime64[D]
|
|
|
|
Examples
|
|
--------
|
|
>>> # Some important days in July
|
|
... bdd = np.busdaycalendar(
|
|
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
|
|
>>> # Default is Monday to Friday weekdays
|
|
... bdd.weekmask
|
|
array([ True, True, True, True, True, False, False])
|
|
>>> # Any holidays already on the weekend are removed
|
|
... bdd.holidays
|
|
array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('weekmask',
|
|
"""A copy of the seven-element boolean mask indicating valid days."""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('holidays',
|
|
"""A copy of the holiday array indicating additional invalid days."""))
|
|
|
|
add_newdoc('numpy.core.multiarray', 'normalize_axis_index',
|
|
"""
|
|
normalize_axis_index(axis, ndim, msg_prefix=None)
|
|
|
|
Normalizes an axis index, `axis`, such that is a valid positive index into
|
|
the shape of array with `ndim` dimensions. Raises an AxisError with an
|
|
appropriate message if this is not possible.
|
|
|
|
Used internally by all axis-checking logic.
|
|
|
|
.. versionadded:: 1.13.0
|
|
|
|
Parameters
|
|
----------
|
|
axis : int
|
|
The un-normalized index of the axis. Can be negative
|
|
ndim : int
|
|
The number of dimensions of the array that `axis` should be normalized
|
|
against
|
|
msg_prefix : str
|
|
A prefix to put before the message, typically the name of the argument
|
|
|
|
Returns
|
|
-------
|
|
normalized_axis : int
|
|
The normalized axis index, such that `0 <= normalized_axis < ndim`
|
|
|
|
Raises
|
|
------
|
|
AxisError
|
|
If the axis index is invalid, when `-ndim <= axis < ndim` is false.
|
|
|
|
Examples
|
|
--------
|
|
>>> normalize_axis_index(0, ndim=3)
|
|
0
|
|
>>> normalize_axis_index(1, ndim=3)
|
|
1
|
|
>>> normalize_axis_index(-1, ndim=3)
|
|
2
|
|
|
|
>>> normalize_axis_index(3, ndim=3)
|
|
Traceback (most recent call last):
|
|
...
|
|
AxisError: axis 3 is out of bounds for array of dimension 3
|
|
>>> normalize_axis_index(-4, ndim=3, msg_prefix='axes_arg')
|
|
Traceback (most recent call last):
|
|
...
|
|
AxisError: axes_arg: axis -4 is out of bounds for array of dimension 3
|
|
""")
|
|
|
|
add_newdoc('numpy.core.multiarray', 'datetime_data',
|
|
"""
|
|
datetime_data(dtype, /)
|
|
|
|
Get information about the step size of a date or time type.
|
|
|
|
The returned tuple can be passed as the second argument of `numpy.datetime64` and
|
|
`numpy.timedelta64`.
|
|
|
|
Parameters
|
|
----------
|
|
dtype : dtype
|
|
The dtype object, which must be a `datetime64` or `timedelta64` type.
|
|
|
|
Returns
|
|
-------
|
|
unit : str
|
|
The :ref:`datetime unit <arrays.dtypes.dateunits>` on which this dtype
|
|
is based.
|
|
count : int
|
|
The number of base units in a step.
|
|
|
|
Examples
|
|
--------
|
|
>>> dt_25s = np.dtype('timedelta64[25s]')
|
|
>>> np.datetime_data(dt_25s)
|
|
('s', 25)
|
|
>>> np.array(10, dt_25s).astype('timedelta64[s]')
|
|
array(250, dtype='timedelta64[s]')
|
|
|
|
The result can be used to construct a datetime that uses the same units
|
|
as a timedelta
|
|
|
|
>>> np.datetime64('2010', np.datetime_data(dt_25s))
|
|
numpy.datetime64('2010-01-01T00:00:00','25s')
|
|
""")
|
|
|
|
|
|
##############################################################################
|
|
#
|
|
# Documentation for `generic` attributes and methods
|
|
#
|
|
##############################################################################
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
"""
|
|
Base class for numpy scalar types.
|
|
|
|
Class from which most (all?) numpy scalar types are derived. For
|
|
consistency, exposes the same API as `ndarray`, despite many
|
|
consequent attributes being either "get-only," or completely irrelevant.
|
|
This is the class from which it is strongly suggested users should derive
|
|
custom scalar types.
|
|
|
|
""")
|
|
|
|
# Attributes
|
|
|
|
def refer_to_array_attribute(attr, method=True):
|
|
docstring = """
|
|
Scalar {} identical to the corresponding array attribute.
|
|
|
|
Please see `ndarray.{}`.
|
|
"""
|
|
|
|
return attr, docstring.format("method" if method else "attribute", attr)
|
|
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('T', method=False))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('base', method=False))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('data',
|
|
"""Pointer to start of data."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('dtype',
|
|
"""Get array data-descriptor."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('flags',
|
|
"""The integer value of flags."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('flat',
|
|
"""A 1-D view of the scalar."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('imag',
|
|
"""The imaginary part of the scalar."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('itemsize',
|
|
"""The length of one element in bytes."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('nbytes',
|
|
"""The length of the scalar in bytes."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('ndim',
|
|
"""The number of array dimensions."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('real',
|
|
"""The real part of the scalar."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('shape',
|
|
"""Tuple of array dimensions."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('size',
|
|
"""The number of elements in the gentype."""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('strides',
|
|
"""Tuple of bytes steps in each dimension."""))
|
|
|
|
# Methods
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('all'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('any'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('argmax'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('argmin'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('argsort'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('astype'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('byteswap'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('choose'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('clip'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('compress'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('conjugate'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('copy'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('cumprod'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('cumsum'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('diagonal'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('dump'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('dumps'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('fill'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('flatten'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('getfield'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('item'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('itemset'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('max'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('mean'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('min'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic', ('newbyteorder',
|
|
"""
|
|
newbyteorder(new_order='S', /)
|
|
|
|
Return a new `dtype` with a different byte order.
|
|
|
|
Changes are also made in all fields and sub-arrays of the data type.
|
|
|
|
The `new_order` code can be any from the following:
|
|
|
|
* 'S' - swap dtype from current to opposite endian
|
|
* {'<', 'little'} - little endian
|
|
* {'>', 'big'} - big endian
|
|
* {'=', 'native'} - native order
|
|
* {'|', 'I'} - ignore (no change to byte order)
|
|
|
|
Parameters
|
|
----------
|
|
new_order : str, optional
|
|
Byte order to force; a value from the byte order specifications
|
|
above. The default value ('S') results in swapping the current
|
|
byte order.
|
|
|
|
|
|
Returns
|
|
-------
|
|
new_dtype : dtype
|
|
New `dtype` object with the given change to the byte order.
|
|
|
|
"""))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('nonzero'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('prod'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('ptp'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('put'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('ravel'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('repeat'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('reshape'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('resize'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('round'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('searchsorted'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('setfield'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('setflags'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('sort'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('squeeze'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('std'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('sum'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('swapaxes'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('take'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('tofile'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('tolist'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('tostring'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('trace'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('transpose'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('var'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'generic',
|
|
refer_to_array_attribute('view'))
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'number', ('__class_getitem__',
|
|
"""
|
|
__class_getitem__(item, /)
|
|
|
|
Return a parametrized wrapper around the `~numpy.number` type.
|
|
|
|
.. versionadded:: 1.22
|
|
|
|
Returns
|
|
-------
|
|
alias : types.GenericAlias
|
|
A parametrized `~numpy.number` type.
|
|
|
|
Examples
|
|
--------
|
|
>>> from typing import Any
|
|
>>> import numpy as np
|
|
|
|
>>> np.signedinteger[Any]
|
|
numpy.signedinteger[typing.Any]
|
|
|
|
See Also
|
|
--------
|
|
:pep:`585` : Type hinting generics in standard collections.
|
|
|
|
"""))
|
|
|
|
##############################################################################
|
|
#
|
|
# Documentation for scalar type abstract base classes in type hierarchy
|
|
#
|
|
##############################################################################
|
|
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'number',
|
|
"""
|
|
Abstract base class of all numeric scalar types.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'integer',
|
|
"""
|
|
Abstract base class of all integer scalar types.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'signedinteger',
|
|
"""
|
|
Abstract base class of all signed integer scalar types.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'unsignedinteger',
|
|
"""
|
|
Abstract base class of all unsigned integer scalar types.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'inexact',
|
|
"""
|
|
Abstract base class of all numeric scalar types with a (potentially)
|
|
inexact representation of the values in its range, such as
|
|
floating-point numbers.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'floating',
|
|
"""
|
|
Abstract base class of all floating-point scalar types.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'complexfloating',
|
|
"""
|
|
Abstract base class of all complex number scalar types that are made up of
|
|
floating-point numbers.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'flexible',
|
|
"""
|
|
Abstract base class of all scalar types without predefined length.
|
|
The actual size of these types depends on the specific `np.dtype`
|
|
instantiation.
|
|
|
|
""")
|
|
|
|
add_newdoc('numpy.core.numerictypes', 'character',
|
|
"""
|
|
Abstract base class of all character string scalar types.
|
|
|
|
""")
|
|
|