package scipy

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val get_py : string -> Py.Object.t

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

val affine_transform : ?offset:[ `F of float | `Sequence of Py.Object.t ] -> ?output_shape:int list -> ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?order:int -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> ?cval:[ `F of float | `I of int | `Bool of bool | `S of string ] -> ?prefilter:bool -> input:[> `Ndarray ] Np.Obj.t -> matrix:[> `Ndarray ] Np.Obj.t -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Apply an affine transformation.

Given an output image pixel index vector ``o``, the pixel value is determined from the input image at position ``np.dot(matrix, o) + offset``.

This does 'pull' (or 'backward') resampling, transforming the output space to the input to locate data. Affine transformations are often described in the 'push' (or 'forward') direction, transforming input to output. If you have a matrix for the 'push' transformation, use its inverse (:func:`numpy.linalg.inv`) in this function.

Parameters ---------- input : array_like The input array. matrix : ndarray The inverse coordinate transformation matrix, mapping output coordinates to input coordinates. If ``ndim`` is the number of dimensions of ``input``, the given matrix must have one of the following shapes:

  • ``(ndim, ndim)``: the linear transformation matrix for each output coordinate.
  • ``(ndim,)``: assume that the 2-D transformation matrix is diagonal, with the diagonal specified by the given value. A more efficient algorithm is then used that exploits the separability of the problem.
  • ``(ndim + 1, ndim + 1)``: assume that the transformation is specified using homogeneous coordinates 1_. In this case, any value passed to ``offset`` is ignored.
  • ``(ndim, ndim + 1)``: as above, but the bottom row of a homogeneous transformation matrix is always ``0, 0, ..., 1``, and may be omitted.

offset : float or sequence, optional The offset into the array where the transform is applied. If a float, `offset` is the same for each axis. If a sequence, `offset` should contain one value for each axis. output_shape : tuple of ints, optional Shape tuple. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. prefilter : bool, optional Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary `float64` array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

Returns ------- affine_transform : ndarray The transformed input.

Notes ----- The given matrix and offset are used to find for each point in the output the corresponding coordinates in the input by an affine transformation. The value of the input at those coordinates is determined by spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode.

.. versionchanged:: 0.18.0 Previously, the exact interpretation of the affine transformation depended on whether the matrix was supplied as a 1-D or a 2-D array. If a 1-D array was supplied to the matrix parameter, the output pixel value at index ``o`` was determined from the input image at position ``matrix * (o + offset)``.

References ---------- .. 1 https://en.wikipedia.org/wiki/Homogeneous_coordinates

val docfiller : Py.Object.t -> Py.Object.t

None

val geometric_transform : ?output_shape:int list -> ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?order:int -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> ?cval:[ `F of float | `I of int | `Bool of bool | `S of string ] -> ?prefilter:bool -> ?extra_arguments:Py.Object.t -> ?extra_keywords:Py.Object.t -> input:[> `Ndarray ] Np.Obj.t -> mapping:[ `Scipy_LowLevelCallable of Py.Object.t | `Callable of Py.Object.t ] -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Apply an arbitrary geometric transform.

The given mapping function is used to find, for each point in the output, the corresponding coordinates in the input. The value of the input at those coordinates is determined by spline interpolation of the requested order.

Parameters ---------- input : array_like The input array. mapping : callable, scipy.LowLevelCallable A callable object that accepts a tuple of length equal to the output array rank, and returns the corresponding input coordinates as a tuple of length equal to the input array rank. output_shape : tuple of ints, optional Shape tuple. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. prefilter : bool, optional Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary `float64` array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input. extra_arguments : tuple, optional Extra arguments passed to `mapping`. extra_keywords : dict, optional Extra keywords passed to `mapping`.

Returns ------- output : ndarray The filtered input.

See Also -------- map_coordinates, affine_transform, spline_filter1d

Notes ----- This function also accepts low-level callback functions with one the following signatures and wrapped in `scipy.LowLevelCallable`:

.. code:: c

int mapping(npy_intp *output_coordinates, double *input_coordinates, int output_rank, int input_rank, void *user_data) int mapping(intptr_t *output_coordinates, double *input_coordinates, int output_rank, int input_rank, void *user_data)

The calling function iterates over the elements of the output array, calling the callback function at each element. The coordinates of the current output element are passed through ``output_coordinates``. The callback function must return the coordinates at which the input must be interpolated in ``input_coordinates``. The rank of the input and output arrays are given by ``input_rank`` and ``output_rank`` respectively. ``user_data`` is the data pointer provided to `scipy.LowLevelCallable` as-is.

The callback function must return an integer error status that is zero if something went wrong and one otherwise. If an error occurs, you should normally set the Python error status with an informative message before returning, otherwise a default error message is set by the calling function.

In addition, some other low-level function pointer specifications are accepted, but these are for backward compatibility only and should not be used in new code.

Examples -------- >>> import numpy as np >>> from scipy.ndimage import geometric_transform >>> a = np.arange(12.).reshape((4, 3)) >>> def shift_func(output_coords): ... return (output_coords0 - 0.5, output_coords1 - 0.5) ... >>> geometric_transform(a, shift_func) array([ 0. , 0. , 0. ], [ 0. , 1.362, 2.738], [ 0. , 4.812, 6.187], [ 0. , 8.263, 9.637])

>>> b = 1, 2, 3, 4, 5 >>> def shift_func(output_coords): ... return (output_coords0 - 3,) ... >>> geometric_transform(b, shift_func, mode='constant') array(0, 0, 0, 1, 2) >>> geometric_transform(b, shift_func, mode='nearest') array(1, 1, 1, 1, 2) >>> geometric_transform(b, shift_func, mode='reflect') array(3, 2, 1, 1, 2) >>> geometric_transform(b, shift_func, mode='wrap') array(2, 3, 4, 1, 2)

val map_coordinates : ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?order:int -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> ?cval:[ `F of float | `I of int | `Bool of bool | `S of string ] -> ?prefilter:bool -> input:[> `Ndarray ] Np.Obj.t -> coordinates:[> `Ndarray ] Np.Obj.t -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Map the input array to new coordinates by interpolation.

The array of coordinates is used to find, for each point in the output, the corresponding coordinates in the input. The value of the input at those coordinates is determined by spline interpolation of the requested order.

The shape of the output is derived from that of the coordinate array by dropping the first axis. The values of the array along the first axis are the coordinates in the input array at which the output value is found.

Parameters ---------- input : array_like The input array. coordinates : array_like The coordinates at which `input` is evaluated. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. prefilter : bool, optional Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary `float64` array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

Returns ------- map_coordinates : ndarray The result of transforming the input. The shape of the output is derived from that of `coordinates` by dropping the first axis.

See Also -------- spline_filter, geometric_transform, scipy.interpolate

Examples -------- >>> from scipy import ndimage >>> a = np.arange(12.).reshape((4, 3)) >>> a array([ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., 8.], [ 9., 10., 11.]) >>> ndimage.map_coordinates(a, [0.5, 2], [0.5, 1], order=1) array( 2., 7.)

Above, the interpolated value of a0.5, 0.5 gives output0, while a2, 1 is output1.

>>> inds = np.array([0.5, 2], [0.5, 4]) >>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3) array( 2. , -33.3) >>> ndimage.map_coordinates(a, inds, order=1, mode='nearest') array( 2., 8.) >>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool) array( True, False, dtype=bool)

val normalize_axis_index : ?msg_prefix:string -> axis:int -> ndim:int -> unit -> int

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

val rotate : ?axes:Py.Object.t -> ?reshape:bool -> ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?order:int -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> ?cval:[ `F of float | `I of int | `Bool of bool | `S of string ] -> ?prefilter:bool -> input:[> `Ndarray ] Np.Obj.t -> angle:float -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Rotate an array.

The array is rotated in the plane defined by the two axes given by the `axes` parameter using spline interpolation of the requested order.

Parameters ---------- input : array_like The input array. angle : float The rotation angle in degrees. axes : tuple of 2 ints, optional The two axes that define the plane of rotation. Default is the first two axes. reshape : bool, optional If `reshape` is true, the output shape is adapted so that the input array is contained completely in the output. Default is True. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. prefilter : bool, optional Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary `float64` array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

Returns ------- rotate : ndarray The rotated input.

Examples -------- >>> from scipy import ndimage, misc >>> import matplotlib.pyplot as plt >>> fig = plt.figure(figsize=(10, 3)) >>> ax1, ax2, ax3 = fig.subplots(1, 3) >>> img = misc.ascent() >>> img_45 = ndimage.rotate(img, 45, reshape=False) >>> full_img_45 = ndimage.rotate(img, 45, reshape=True) >>> ax1.imshow(img, cmap='gray') >>> ax1.set_axis_off() >>> ax2.imshow(img_45, cmap='gray') >>> ax2.set_axis_off() >>> ax3.imshow(full_img_45, cmap='gray') >>> ax3.set_axis_off() >>> fig.set_tight_layout(True) >>> plt.show() >>> print(img.shape) (512, 512) >>> print(img_45.shape) (512, 512) >>> print(full_img_45.shape) (724, 724)

val shift : ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?order:int -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> ?cval:[ `F of float | `I of int | `Bool of bool | `S of string ] -> ?prefilter:bool -> input:[> `Ndarray ] Np.Obj.t -> shift:[ `F of float | `Sequence of Py.Object.t ] -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Shift an array.

The array is shifted using spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode.

Parameters ---------- input : array_like The input array. shift : float or sequence The shift along the axes. If a float, `shift` is the same for each axis. If a sequence, `shift` should contain one value for each axis. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. prefilter : bool, optional Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary `float64` array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

Returns ------- shift : ndarray The shifted input.

val spline_filter : ?order:Py.Object.t -> ?output:Py.Object.t -> ?mode:Py.Object.t -> input:Py.Object.t -> unit -> Py.Object.t

Multidimensional spline filter.

For more details, see `spline_filter1d`.

See Also -------- spline_filter1d : Calculate a 1-D spline filter along the given axis.

Notes ----- The multidimensional filter is implemented as a sequence of 1-D spline filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

Examples -------- We can filter an image using multidimentional splines:

>>> from scipy.ndimage import spline_filter >>> import matplotlib.pyplot as plt >>> orig_img = np.eye(20) # create an image >>> orig_img10, : = 1.0 >>> sp_filter = spline_filter(orig_img, order=3) >>> f, ax = plt.subplots(1, 2, sharex=True) >>> for ind, data in enumerate([orig_img, 'original image'], ... [sp_filter, 'spline filter']): ... axind.imshow(data0, cmap='gray_r') ... axind.set_title(data1) >>> plt.tight_layout() >>> plt.show()

val spline_filter1d : ?order:int -> ?axis:int -> ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> input:[> `Ndarray ] Np.Obj.t -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Calculate a 1-D spline filter along the given axis.

The lines of the array along the given axis are filtered by a spline filter. The order of the spline must be >= 2 and <= 5.

Parameters ---------- input : array_like The input array. order : int, optional The order of the spline, default is 3. axis : int, optional The axis along which the spline filter is applied. Default is the last axis. output : ndarray or dtype, optional The array in which to place the output, or the dtype of the returned array. Default is ``numpy.float64``. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge.

Returns ------- spline_filter1d : ndarray The filtered input.

Notes ----- All functions in `ndimage.interpolation` do spline interpolation of the input image. If using B-splines of `order > 1`, the input image values have to be converted to B-spline coefficients first, which is done by applying this 1-D filter sequentially along all axes of the input. All functions that require B-spline coefficients will automatically filter their inputs, a behavior controllable with the `prefilter` keyword argument. For functions that accept a `mode` parameter, the result will only be correct if it matches the `mode` used when filtering.

See Also -------- spline_filter : Multidimensional spline filter.

Examples -------- We can filter an image using 1-D spline along the given axis:

>>> from scipy.ndimage import spline_filter1d >>> import matplotlib.pyplot as plt >>> orig_img = np.eye(20) # create an image >>> orig_img10, : = 1.0 >>> sp_filter_axis_0 = spline_filter1d(orig_img, axis=0) >>> sp_filter_axis_1 = spline_filter1d(orig_img, axis=1) >>> f, ax = plt.subplots(1, 3, sharex=True) >>> for ind, data in enumerate([orig_img, 'original image'], ... [sp_filter_axis_0, 'spline filter (axis=0)'], ... [sp_filter_axis_1, 'spline filter (axis=1)']): ... axind.imshow(data0, cmap='gray_r') ... axind.set_title(data1) >>> plt.tight_layout() >>> plt.show()

val zoom : ?output:[ `Ndarray of [> `Ndarray ] Np.Obj.t | `Dtype of Np.Dtype.t ] -> ?order:int -> ?mode:[ `Reflect | `Constant | `Nearest | `Mirror | `Wrap ] -> ?cval:[ `F of float | `I of int | `Bool of bool | `S of string ] -> ?prefilter:bool -> input:[> `Ndarray ] Np.Obj.t -> zoom:[ `F of float | `Sequence of Py.Object.t ] -> unit -> [ `ArrayLike | `Ndarray | `Object ] Np.Obj.t

Zoom an array.

The array is zoomed using spline interpolation of the requested order.

Parameters ---------- input : array_like The input array. zoom : float or sequence The zoom factor along the axes. If a float, `zoom` is the same for each axis. If a sequence, `zoom` should contain one value for each axis. output : array or dtype, optional The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. order : int, optional The order of the spline interpolation, default is 3. The order has to be in the range 0-5. mode : 'reflect', 'constant', 'nearest', 'mirror', 'wrap', optional The `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'constant'. Behavior for each valid value is as follows:

'reflect' (`d c b a | a b c d | d c b a`) The input is extended by reflecting about the edge of the last pixel.

'constant' (`k k k k | a b c d | k k k k`) The input is extended by filling all values beyond the edge with the same constant value, defined by the `cval` parameter.

'nearest' (`a a a a | a b c d | d d d d`) The input is extended by replicating the last pixel.

'mirror' (`d c b | a b c d | c b a`) The input is extended by reflecting about the center of the last pixel.

'wrap' (`a b c d | a b c d | a b c d`) The input is extended by wrapping around to the opposite edge. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. prefilter : bool, optional Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary `float64` array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

Returns ------- zoom : ndarray The zoomed input.

Examples -------- >>> from scipy import ndimage, misc >>> import matplotlib.pyplot as plt

>>> fig = plt.figure() >>> ax1 = fig.add_subplot(121) # left side >>> ax2 = fig.add_subplot(122) # right side >>> ascent = misc.ascent() >>> result = ndimage.zoom(ascent, 3.0) >>> ax1.imshow(ascent) >>> ax2.imshow(result) >>> plt.show()

>>> print(ascent.shape) (512, 512)

>>> print(result.shape) (1536, 1536)

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