package scipy

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0)

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. dtype : data-type, optional The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. 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 pre-pended to the shape as needed to meet this requirement.

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])

concatenate((a1, a2, ...), axis=0, out=None)

Join a sequence of arrays along an existing axis.

Parameters ---------- a1, a2, ... : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default). axis : int, optional The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0. out : ndarray, optional If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.

Returns ------- res : ndarray The concatenated array.

See Also -------- ma.concatenate : Concatenate function that preserves input masks. array_split : Split an array into multiple sub-arrays of equal or near-equal size. split : Split array into a list of multiple sub-arrays of equal size. hsplit : Split array into multiple sub-arrays horizontally (column wise). vsplit : Split array into multiple sub-arrays vertically (row wise). dsplit : Split array into multiple sub-arrays along the 3rd axis (depth). stack : Stack a sequence of arrays along a new axis. block : Assemble arrays from blocks. hstack : Stack arrays in sequence horizontally (column wise). vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third dimension). column_stack : Stack 1-D arrays as columns into a 2-D array.

Notes ----- When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are *not* preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead.

Examples -------- >>> a = np.array([1, 2], [3, 4]) >>> b = np.array([5, 6]) >>> np.concatenate((a, b), axis=0) array([1, 2], [3, 4], [5, 6]) >>> np.concatenate((a, b.T), axis=1) array([1, 2, 5], [3, 4, 6]) >>> np.concatenate((a, b), axis=None) array(1, 2, 3, 4, 5, 6)

This function will not preserve masking of MaskedArray inputs.

>>> a = np.ma.arange(3) >>> a1 = np.ma.masked >>> b = np.arange(2, 5) >>> a masked_array(data=0, --, 2, mask=False, True, False, fill_value=999999) >>> b array(2, 3, 4) >>> np.concatenate(a, b) masked_array(data=0, 1, 2, 2, 3, 4, mask=False, fill_value=999999) >>> np.ma.concatenate(a, b) masked_array(data=0, --, 2, 2, 3, 4, mask=False, True, False, False, False, False, fill_value=999999)

Calculate the n-th discrete difference along the given axis.

The first difference is given by ``outi = ai+1 - ai`` along the given axis, higher differences are calculated by using `diff` recursively.

Parameters ---------- a : array_like Input array n : int, optional The number of times values are differenced. If zero, the input is returned as-is. axis : int, optional The axis along which the difference is taken, default is the last axis. prepend, append : array_like, optional Values to prepend or append to `a` along axis prior to performing the difference. Scalar values are expanded to arrays with length 1 in the direction of axis and the shape of the input array in along all other axes. Otherwise the dimension and shape must match `a` except along axis.

.. versionadded:: 1.16.0

Returns ------- diff : ndarray The n-th differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. The type of the output is the same as the type of the difference between any two elements of `a`. This is the same as the type of `a` in most cases. A notable exception is `datetime64`, which results in a `timedelta64` output array.

See Also -------- gradient, ediff1d, cumsum

Notes ----- Type is preserved for boolean arrays, so the result will contain `False` when consecutive elements are the same and `True` when they differ.

For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly:

>>> u8_arr = np.array(1, 0, dtype=np.uint8) >>> np.diff(u8_arr) array(255, dtype=uint8) >>> u8_arr1,... - u8_arr0,... 255

If this is not desirable, then the array should be cast to a larger integer type first:

>>> i16_arr = u8_arr.astype(np.int16) >>> np.diff(i16_arr) array(-1, dtype=int16)

Examples -------- >>> x = np.array(1, 2, 4, 7, 0) >>> np.diff(x) array( 1, 2, 3, -7) >>> np.diff(x, n=2) array( 1, 1, -10)

>>> x = np.array([1, 3, 6, 10], [0, 5, 6, 8]) >>> np.diff(x) array([2, 3, 4], [5, 1, 2]) >>> np.diff(x, axis=0) array([-1, 2, 0, -2])

>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64) >>> np.diff(x) array(1, 1, dtype='timedelta64D')

Return a new array of given shape and type, filled with ones.

Parameters ---------- shape : int or sequence 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.

Returns ------- out : ndarray Array of ones with the given shape, dtype, and order.

See Also -------- ones_like : Return an array of ones with shape and type of input. empty : Return a new uninitialized array. zeros : Return a new array setting values to zero. full : Return a new array of given shape filled with value.

Examples -------- >>> np.ones(5) array(1., 1., 1., 1., 1.)

>>> np.ones((5,), dtype=int) array(1, 1, 1, 1, 1)

>>> np.ones((2, 1)) array([1.], [1.])

>>> s = (2,2) >>> np.ones(s) array([1., 1.], [1., 1.])

Return a contiguous flattened array.

A 1-D array, containing the elements of the input, is returned. A copy is made only if needed.

As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input)

Parameters ---------- a : array_like Input array. The elements in `a` are read in the order specified by `order`, and packed as a 1-D array. order : 'C','F', 'A', 'K', optional

The elements of `a` are read using this index order. 'C' means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. 'A' means to read the elements in Fortran-like index order if `a` is Fortran *contiguous* in memory, C-like order otherwise. 'K' means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, 'C' index order is used.

Returns ------- y : array_like y is an array of the same subtype as `a`, with shape ``(a.size,)``. Note that matrices are special cased for backward compatibility, if `a` is a matrix, then y is a 1-D ndarray.

See Also -------- ndarray.flat : 1-D iterator over an array. ndarray.flatten : 1-D array copy of the elements of an array in row-major order. ndarray.reshape : Change the shape of an array without changing its data.

Notes ----- In row-major, C-style order, in two dimensions, the row index varies the slowest, and the column index the quickest. This can be generalized to multiple dimensions, where row-major order implies that the index along the first axis varies slowest, and the index along the last quickest. The opposite holds for column-major, Fortran-style index ordering.

When a view is desired in as many cases as possible, ``arr.reshape(-1)`` may be preferable.

Examples -------- It is equivalent to ``reshape(-1, order=order)``.

>>> x = np.array([1, 2, 3], [4, 5, 6]) >>> np.ravel(x) array(1, 2, 3, 4, 5, 6)

>>> x.reshape(-1) array(1, 2, 3, 4, 5, 6)

>>> np.ravel(x, order='F') array(1, 4, 2, 5, 3, 6)

When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:

>>> np.ravel(x.T) array(1, 4, 2, 5, 3, 6) >>> np.ravel(x.T, order='A') array(1, 2, 3, 4, 5, 6)

When ``order`` is 'K', it will preserve orderings that are neither 'C' nor 'F', but won't reverse axes:

>>> a = np.arange(3)::-1; a array(2, 1, 0) >>> a.ravel(order='C') array(2, 1, 0) >>> a.ravel(order='K') array(2, 1, 0)

>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a array([[ 0, 2, 4], [ 1, 3, 5]], [[ 6, 8, 10], [ 7, 9, 11]]) >>> a.ravel(order='C') array( 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11) >>> a.ravel(order='K') array( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)

zeros(shape, dtype=float, order='C')

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.

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'))