package np

Create a memory-map to an array stored in a *binary* file on disk.

Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. NumPy's memmap's are array-like objects. This differs from Python's ``mmap`` module, which uses file-like objects.

This subclass of ndarray has some unpleasant interactions with some operations, because it doesn't quite fit properly as a subclass. An alternative to using this subclass is to create the ``mmap`` object yourself, then create an ndarray with ndarray.__new__ directly, passing the object created in its 'buffer=' parameter.

This class may at some point be turned into a factory function which returns a view into an mmap buffer.

Delete the memmap instance to close the memmap file.

Parameters ---------- filename : str, file-like object, or pathlib.Path instance The file name or file object to be used as the array data buffer. dtype : data-type, optional The data-type used to interpret the file contents. Default is `uint8`. mode : 'r+', 'r', 'w+', 'c', optional The file is opened in this mode:

+------+-------------------------------------------------------------+ | 'r' | Open existing file for reading only. | +------+-------------------------------------------------------------+ | 'r+' | Open existing file for reading and writing. | +------+-------------------------------------------------------------+ | 'w+' | Create or overwrite existing file for reading and writing. | +------+-------------------------------------------------------------+ | 'c' | Copy-on-write: assignments affect data in memory, but | | | changes are not saved to disk. The file on disk is | | | read-only. | +------+-------------------------------------------------------------+

Default is 'r+'. offset : int, optional In the file, array data starts at this offset. Since `offset` is measured in bytes, it should normally be a multiple of the byte-size of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of file are valid; The file will be extended to accommodate the additional data. By default, ``memmap`` will start at the beginning of the file, even if ``filename`` is a file pointer ``fp`` and ``fp.tell() != 0``. shape : tuple, optional The desired shape of the array. If ``mode == 'r'`` and the number of remaining bytes after `offset` is not a multiple of the byte-size of `dtype`, you must specify `shape`. By default, the returned array will be 1-D with the number of elements determined by file size and data-type. order : 'C', 'F', optional Specify the order of the ndarray memory layout: :term:`row-major`, C-style or :term:`column-major`, Fortran-style. This only has an effect if the shape is greater than 1-D. The default order is 'C'.

Attributes ---------- filename : str or pathlib.Path instance Path to the mapped file. offset : int Offset position in the file. mode : str File mode.

Methods ------- flush Flush any changes in memory to file on disk. When you delete a memmap object, flush is called first to write changes to disk before removing the object.

See also -------- lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.

Notes ----- The memmap object can be used anywhere an ndarray is accepted. Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns ``True``.

Memory-mapped files cannot be larger than 2GB on 32-bit systems.

When a memmap causes a file to be created or extended beyond its current size in the filesystem, the contents of the new part are unspecified. On systems with POSIX filesystem semantics, the extended part will be filled with zero bytes.

Examples -------- >>> data = np.arange(12, dtype='float32') >>> data.resize((3,4))

This example uses a temporary file so that doctest doesn't write files to your directory. You would use a 'normal' filename.

>>> from tempfile import mkdtemp >>> import os.path as path >>> filename = path.join(mkdtemp(), 'newfile.dat')

Create a memmap with dtype and shape that matches our data:

>>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4)) >>> fp memmap([0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], dtype=float32)

Write data to memmap array:

>>> fp: = data: >>> fp memmap([ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], dtype=float32)

>>> fp.filename == path.abspath(filename) True

Deletion flushes memory changes to disk before removing the object:

>>> del fp

Load the memmap and verify data was stored:

>>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4)) >>> newfp memmap([ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], dtype=float32)

Read-only memmap:

>>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4)) >>> fpr.flags.writeable False

Copy-on-write memmap:

>>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4)) >>> fpc.flags.writeable True

It's possible to assign to copy-on-write array, but values are only written into the memory copy of the array, and not written to disk:

>>> fpc memmap([ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], dtype=float32) >>> fpc0,: = 0 >>> fpc memmap([ 0., 0., 0., 0.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], dtype=float32)

File on disk is unchanged:

>>> fpr memmap([ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], dtype=float32)

Offset into a memmap:

>>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16) >>> fpo memmap( 4., 5., 6., 7., 8., 9., 10., 11., dtype=float32)

Return selfkey.

Implement iter(self).

Set selfkey to value.

a.all(axis=None, out=None, keepdims=False)

Returns True if all elements evaluate to True.

Refer to `numpy.all` for full documentation.

See Also -------- numpy.all : equivalent function

a.any(axis=None, out=None, keepdims=False)

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

a.argmax(axis=None, out=None)

Return indices of the maximum values along the given axis.

Refer to `numpy.argmax` for full documentation.

See Also -------- numpy.argmax : equivalent function

a.argmin(axis=None, out=None)

Return indices of the minimum values along the given axis of `a`.

Refer to `numpy.argmin` for detailed documentation.

See Also -------- numpy.argmin : equivalent function

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

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

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)

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)

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

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

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

a.conj()

Complex-conjugate all elements.

Refer to `numpy.conjugate` for full documentation.

See Also -------- numpy.conjugate : equivalent function

a.conjugate()

Return the complex conjugate, element-wise.

Refer to `numpy.conjugate` for full documentation.

See Also -------- numpy.conjugate : equivalent function

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.)

See also -------- numpy.copy numpy.copyto

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

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

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

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

a.dot(b, out=None)

Dot product of two arrays.

Refer to `numpy.dot` for full documentation.

See Also -------- numpy.dot : equivalent function

Examples -------- >>> a = np.eye(2) >>> b = np.ones((2, 2)) * 2 >>> a.dot(b) array([2., 2.], [2., 2.])

This array method can be conveniently chained:

>>> a.dot(b).dot(b) array([8., 8.], [8., 8.])

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.

a.dumps()

Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.

Parameters ---------- None

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.)

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)

Write any changes in the array to the file on disk.

For further information, see `memmap`.

Parameters ---------- None

See Also -------- memmap

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) >>> x1, 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.])

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 aargs, 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

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 ``aargs = 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])

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

a.mean(axis=None, dtype=None, out=None, keepdims=False)

Returns the average of the array elements along given axis.

Refer to `numpy.mean` for full documentation.

See Also -------- numpy.mean : equivalent function

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

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 * '<', 'L' - little endian * '>', 'B' - big endian * '=', 'N' - native order * '|', 'I' - ignore (no change to byte order)

The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of `new_order` for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian.

Returns ------- new_arr : array New array object with the dtype reflecting given change to the byte order.

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

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. 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 parititioned 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)

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

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

a.put(indices, values, mode='raise')

Set ``a.flatn = valuesn`` for all `n` in indices.

Refer to `numpy.put` for full documentation.

See Also -------- numpy.put : equivalent function

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.

a.repeat(repeats, axis=None)

Repeat elements of an array.

Refer to `numpy.repeat` for full documentation.

See Also -------- numpy.repeat : equivalent function

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))``.

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])

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

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

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.])

a.setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), 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 (deprecated) UPDATEIFCOPY flags 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, UPDATEIFCOPY, 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);

UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;

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 UPDATEIFCOPY : False >>> y.setflags(write=0, align=0) >>> y.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : False ALIGNED : False WRITEBACKIFCOPY : False UPDATEIFCOPY : False >>> y.setflags(uic=1) Traceback (most recent call last): File '<stdin>', line 1, in <module> ValueError: cannot set WRITEBACKIFCOPY flag to True

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'))

a.squeeze(axis=None)

Remove single-dimensional entries from the shape of `a`.

Refer to `numpy.squeeze` for full documentation.

See Also -------- numpy.squeeze : equivalent function

a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)

Returns the standard deviation of the array elements along given axis.

Refer to `numpy.std` for full documentation.

See Also -------- numpy.std : equivalent function

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

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

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

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 can be produced in either 'C' or 'Fortran', or 'Any' order (the default is 'C'-order). 'Any' order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means 'Fortran' order.

.. versionadded:: 1.9.0

Parameters ---------- order : 'C', 'F', None, optional Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array.

Returns ------- s : bytes Python bytes exhibiting a copy of `a`'s raw data.

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'

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).

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_list0) <class 'numpy.uint32'> >>> a_tolist = a.tolist() >>> a_tolist 1, 2 >>> type(a_tolist0) <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

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

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

a.transpose( *axes)

Returns a view of the array with axes transposed.

For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. `np.atleast2d(a).T` achieves this, as does `a:, np.newaxis`. For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and ``a.shape = (i0, i1, ... in-2, in-1)``, then ``a.transpose().shape = (in-1, in-2, ... i1, i0)``.

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 `a`'s `i`-th axis becomes `a.transpose()`'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 ------- out : ndarray View of `a`, with axes suitably permuted.

See Also -------- 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.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)

Returns the variance of the array elements, along given axis.

Refer to `numpy.var` for full documentation.

See Also -------- numpy.var : equivalent function

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 behavior of the view cannot be predicted just from the superficial appearance of ``a`` (shown by ``print(a)``). It also depends on exactly how ``a`` is stored in memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results.

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

>>> xv0,1 = 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:

>>> x0 = (9, 10) >>> z0 (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:, 0:2 >>> y array([1, 2], [4, 5], 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 array must be C-contiguous >>> z = y.copy() >>> z.view(dtype=('width', np.int16), ('length', np.int16)) array([(1, 2)], [(4, 5)], dtype=('width', '<i2'), ('length', '<i2'))

Attribute filename: get value or raise Not_found if None.

Attribute filename: get value as an option.

Attribute offset: get value or raise Not_found if None.

Attribute offset: get value as an option.

Attribute mode: get value or raise Not_found if None.

Attribute mode: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.