signal.py

#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################

"""
Signal processing functions (fft, convolve, etc).
"""

from .library import *
from .array import *
from .bcast import broadcast

@broadcast
def _scale_pos_axis0(x_curr, x_orig):
    x0 = x_orig[0, 0, 0, 0]
    dx = x_orig[1, 0, 0, 0] - x0
    return((x_curr - x0) / dx)

@broadcast
def _scale_pos_axis1(y_curr, y_orig):
    y0 = y_orig[0, 0, 0, 0]
    dy = y_orig[0, 1, 0, 0] - y0
    return((y_curr - y0) / dy)

def approx1(signal, x, method=INTERP.LINEAR, off_grid=0.0, xp = None, output = None):
    """
    Interpolate along a single dimension.Interpolation is performed along axis 0
    of the input array.

    Parameters
    ----------

    signal: af.Array
            Input signal array (signal = f(x))

    x: af.Array
       The x-coordinates of the interpolation points. The interpolation 
       function is queried at these set of points.

    method: optional: af.INTERP. default: af.INTERP.LINEAR.
            Interpolation method.

    off_grid: optional: scalar. default: 0.0.
            The value used for positions outside the range.

    xp : af.Array
         The x-coordinates of the input data points

    output: None or af.Array
        Optional preallocated output array. If it is a sub-array of an existing af_array,
        only the corresponding portion of the af_array will be overwritten

    Returns
    -------

    output: af.Array
            Values calculated at interpolation points.


    Note
    -----
    This holds applicable when x_input isn't provided:
    The initial measurements are assumed to have taken place at equal steps between [0, N - 1],
    where N is the length of the first dimension of `signal`.
    """

    if output is None:
        output = Array()

        if(xp is not None):
            pos0 = _scale_pos_axis0(x, xp)
        else:
            pos0 = x

        safe_call(backend.get().af_approx1(c_pointer(output.arr), signal.arr, pos0.arr,
                                           method.value, c_float_t(off_grid)))

    else:
        if(xp is not None):
            pos0 = _scale_pos_axis0(x, xp)
        else:
            pos0 = x
        safe_call(backend.get().af_approx1_v2(c_pointer(output.arr), signal.arr, pos0.arr,
                                              method.value, c_float_t(off_grid)))
    return output


def approx1_uniform(signal, x, interp_dim, idx_start, idx_step, method=INTERP.LINEAR, off_grid=0.0, output = None):
    """
    Interpolation on one dimensional signals along specified dimension.

    af_approx1_uniform() accepts the dimension to perform the interpolation along the input.
    It also accepts start and step values which define the uniform range of corresponding indices.

    Parameters
    ----------

    signal: af.Array
            Input signal array (signal = f(x))

    x: af.Array
       The x-coordinates of the interpolation points. The interpolation 
       function is queried at these set of points.

    interp_dim: scalar
        is the dimension to perform interpolation across.

    idx_start: scalar
        is the first index value along interp_dim.

    idx_step: scalar
        is the uniform spacing value between subsequent indices along interp_dim.

    method: optional: af.INTERP. default: af.INTERP.LINEAR.
            Interpolation method.

    off_grid: optional: scalar. default: 0.0.
            The value used for positions outside the range.

    output: None or af.Array
        Optional preallocated output array. If it is a sub-array of an existing af_array,
        only the corresponding portion of the af_array will be overwritten

    Returns
    -------

    output: af.Array
            Values calculated at interpolation points.

    """

    if output is None:
        output = Array()

        safe_call(backend.get().af_approx1_uniform(c_pointer(output.arr), signal.arr, x.arr,
                                           c_dim_t(interp_dim), c_double_t(idx_start), c_double_t(idx_step),
                                           method.value, c_float_t(off_grid)))
    else:
        safe_call(backend.get().af_approx1_uniform_v2(c_pointer(output.arr), signal.arr, x.arr,
                                           c_dim_t(interp_dim), c_double_t(idx_start), c_double_t(idx_step),
                                           method.value, c_float_t(off_grid)))
    return output


def approx2(signal, x, y,
            method=INTERP.LINEAR, off_grid=0.0, xp = None, yp = None, output = None):
    """
    Interpolate along a two dimension.Interpolation is performed along axes 0 and 1
    of the input array.

    Parameters
    ----------

    signal: af.Array
            Input signal array (signal = f(x, y))

    x : af.Array
        The x-coordinates of the interpolation points. The interpolation 
        function is queried at these set of points.


    y : af.Array
        The y-coordinates of the interpolation points. The interpolation 
        function is queried at these set of points.

    method: optional: af.INTERP. default: af.INTERP.LINEAR.
            Interpolation method.

    off_grid: optional: scalar. default: 0.0.
            The value used for positions outside the range.

    xp : af.Array
         The x-coordinates of the input data points. The convention followed is that
         the x-coordinates vary along axis 0

    yp : af.Array
         The y-coordinates of the input data points. The convention followed is that
         the y-coordinates vary along axis 1

    output: None or af.Array
        Optional preallocated output array. If it is a sub-array of an existing af_array,
        only the corresponding portion of the af_array will be overwritten

    Returns
    -------

    output: af.Array
            Values calculated at interpolation points.

    Note
    -----
    This holds applicable when x_input/y_input isn't provided:

    The initial measurements are assumed to have taken place at equal steps between [(0,0) - [M - 1, N - 1]]
    where M is the length of the first dimension of `signal`,
    and N is the length of the second dimension of `signal`.
    """

    if output is None:
        output = Array()

        if(xp is not None):
            pos0 = _scale_pos_axis0(x, xp)
        else:
            pos0 = x

        if(yp is not None):
            pos1 = _scale_pos_axis1(y, yp)
        else:
            pos1 = y

        safe_call(backend.get().af_approx2(c_pointer(output.arr), signal.arr,
                                           pos0.arr, pos1.arr, method.value, c_float_t(off_grid)))
    else:
        if(xp is not None):
            pos0 = _scale_pos_axis0(x, xp)
        else:
            pos0 = x

        if(yp is not None):
            pos1 = _scale_pos_axis1(y, yp)
        else:
            pos1 = y

        safe_call(backend.get().af_approx2_v2(c_pointer(output.arr), signal.arr,
                                              pos0.arr, pos1.arr, method.value, c_float_t(off_grid)))

    return output

def approx2_uniform(signal, pos0, interp_dim0, idx_start0, idx_step0, pos1, interp_dim1, idx_start1, idx_step1,
            method=INTERP.LINEAR, off_grid=0.0, output = None):
    """
    Interpolate along two uniformly spaced dimensions of the input array.
    af_approx2_uniform() accepts two dimensions to perform the interpolation along the input.
    It also accepts start and step values which define the uniform range of corresponding indices.

    Parameters
    ----------

    signal: af.Array
            Input signal array (signal = f(x, y))

    pos0 : af.Array
        positions of the interpolation points along interp_dim0.

    interp_dim0: scalar
        is the first dimension to perform interpolation across.

    idx_start0: scalar
        is the first index value along interp_dim0.

    idx_step0: scalar
        is the uniform spacing value between subsequent indices along interp_dim0.

    pos1 : af.Array
        positions of the interpolation points along interp_dim1.

    interp_dim1: scalar
        is the second dimension to perform interpolation across.

    idx_start1: scalar
        is the first index value along interp_dim1.

    idx_step1: scalar
        is the uniform spacing value between subsequent indices along interp_dim1.

    method: optional: af.INTERP. default: af.INTERP.LINEAR.
            Interpolation method.

    off_grid: optional: scalar. default: 0.0.
            The value used for positions outside the range.

    output: None or af.Array
        Optional preallocated output array. If it is a sub-array of an existing af_array,
        only the corresponding portion of the af_array will be overwritten

    Returns
    -------

    output: af.Array
            Values calculated at interpolation points.

    Note
    -----
    This holds applicable when x_input/y_input isn't provided:

    The initial measurements are assumed to have taken place at equal steps between [(0,0) - [M - 1, N - 1]]
    where M is the length of the first dimension of `signal`,
    and N is the length of the second dimension of `signal`.
    """

    if output is None:
        output = Array()
        safe_call(backend.get().af_approx2_uniform(c_pointer(output.arr), signal.arr,
                                           pos0.arr, c_dim_t(interp_dim0), c_double_t(idx_start0), c_double_t(idx_step0),
                                           pos1.arr, c_dim_t(interp_dim1), c_double_t(idx_start1), c_double_t(idx_step1),
                                           method.value, c_float_t(off_grid)))
    else:
        safe_call(backend.get().af_approx2_uniform_v2(c_pointer(output.arr), signal.arr,
                                           pos0.arr, c_dim_t(interp_dim0), c_double_t(idx_start0), c_double_t(idx_step0),
                                           pos1.arr, c_dim_t(interp_dim1), c_double_t(idx_start1), c_double_t(idx_step1),
                                           method.value, c_float_t(off_grid)))

    return output


def fft(signal, dim0 = None , scale = None):
    """
    Fast Fourier Transform: 1D

    Parameters
    ----------

    signal: af.Array
           A 1 dimensional signal or a batch of 1 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    Returns
    -------

    output: af.Array
            A complex af.Array containing the full output of the fft.

    """

    if dim0 is None:
        dim0 = 0

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft(c_pointer(output.arr), signal.arr, c_double_t(scale), c_dim_t(dim0)))
    return output

def fft2(signal, dim0 = None, dim1 = None , scale = None):
    """
    Fast Fourier Transform: 2D

    Parameters
    ----------

    signal: af.Array
           A 2 dimensional signal or a batch of 2 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    dim1: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim1 is calculated to be the second dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    Returns
    -------

    output: af.Array
            A complex af.Array containing the full output of the fft.

    """
    if dim0 is None:
        dim0 = 0

    if dim1 is None:
        dim1 = 0

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft2(c_pointer(output.arr), signal.arr, c_double_t(scale),
                                    c_dim_t(dim0), c_dim_t(dim1)))
    return output

def fft3(signal, dim0 = None, dim1 = None , dim2 = None, scale = None):
    """
    Fast Fourier Transform: 3D

    Parameters
    ----------

    signal: af.Array
           A 3 dimensional signal or a batch of 3 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    dim1: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim1 is calculated to be the second dimension of `signal`.

    dim2: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim2 is calculated to be the third dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    Returns
    -------

    output: af.Array
            A complex af.Array containing the full output of the fft.

    """
    if dim0 is None:
        dim0 = 0

    if dim1 is None:
        dim1 = 0

    if dim2 is None:
        dim2 = 0

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft3(c_pointer(output.arr), signal.arr, c_double_t(scale),
                                    c_dim_t(dim0), c_dim_t(dim1), c_dim_t(dim2)))
    return output

def ifft(signal, dim0 = None , scale = None):
    """
    Inverse Fast Fourier Transform: 1D

    Parameters
    ----------

    signal: af.Array
           A 1 dimensional signal or a batch of 1 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.0 / (dim0)

    Returns
    -------

    output: af.Array
            A complex af.Array containing the full output of the inverse fft.

    Note
    ----

    The output is always complex.

    """

    if dim0 is None:
        dim0 = signal.dims()[0]

    if scale is None:
        scale = 1.0/float(dim0)

    output = Array()
    safe_call(backend.get().af_ifft(c_pointer(output.arr), signal.arr, c_double_t(scale), c_dim_t(dim0)))
    return output

def ifft2(signal, dim0 = None, dim1 = None , scale = None):
    """
    Inverse Fast Fourier Transform: 2D

    Parameters
    ----------

    signal: af.Array
           A 2 dimensional signal or a batch of 2 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    dim1: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim1 is calculated to be the second dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.0 / (dim0 * dim1)

    Returns
    -------

    output: af.Array
            A complex af.Array containing the full output of the inverse fft.

    Note
    ----

    The output is always complex.

    """

    dims = signal.dims()

    if dim0 is None:
        dim0 = dims[0]

    if dim1 is None:
        dim1 = dims[1]

    if scale is None:
        scale = 1.0/float(dim0 * dim1)

    output = Array()
    safe_call(backend.get().af_ifft2(c_pointer(output.arr), signal.arr, c_double_t(scale),
                                     c_dim_t(dim0), c_dim_t(dim1)))
    return output

def ifft3(signal, dim0 = None, dim1 = None , dim2 = None, scale = None):
    """
    Inverse Fast Fourier Transform: 3D

    Parameters
    ----------

    signal: af.Array
           A 3 dimensional signal or a batch of 3 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    dim1: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim1 is calculated to be the second dimension of `signal`.

    dim2: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim2 is calculated to be the third dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.0 / (dim0 * dim1 * dim2).

    Returns
    -------

    output: af.Array
            A complex af.Array containing the full output of the inverse fft.

    Note
    ----

    The output is always complex.

    """

    dims = signal.dims()

    if dim0 is None:
        dim0 = dims[0]

    if dim1 is None:
        dim1 = dims[1]

    if dim2 is None:
        dim2 = dims[2]

    if scale is None:
        scale = 1.0 / float(dim0 * dim1 * dim2)

    output = Array()
    safe_call(backend.get().af_ifft3(c_pointer(output.arr), signal.arr, c_double_t(scale),
                                     c_dim_t(dim0), c_dim_t(dim1), c_dim_t(dim2)))
    return output

def fft_inplace(signal, scale = None):
    """
    In-place Fast Fourier Transform: 1D

    Parameters
    ----------

    signal: af.Array
           A 1 dimensional signal or a batch of 1 dimensional signals.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    """

    if scale is None:
        scale = 1.0

    safe_call(backend.get().af_fft_inplace(signal.arr, c_double_t(scale)))

def fft2_inplace(signal, scale = None):
    """
    In-place Fast Fourier Transform: 2D

    Parameters
    ----------

    signal: af.Array
           A 2 dimensional signal or a batch of 2 dimensional signals.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    """

    if scale is None:
        scale = 1.0

    safe_call(backend.get().af_fft2_inplace(signal.arr, c_double_t(scale)))

def fft3_inplace(signal, scale = None):
    """
    In-place Fast Fourier Transform: 3D

    Parameters
    ----------

    signal: af.Array
           A 3 dimensional signal or a batch of 3 dimensional signals.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.
    """

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft3_inplace(signal.arr, c_double_t(scale)))

def ifft_inplace(signal, scale = None):
    """
    Inverse In-place Fast Fourier Transform: 1D

    Parameters
    ----------

    signal: af.Array
           A 1 dimensional signal or a batch of 1 dimensional signals.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.0 / (signal.dims()[0])
    """

    if scale is None:
        dim0 = signal.dims()[0]
        scale = 1.0/float(dim0)

    safe_call(backend.get().af_ifft_inplace(signal.arr, c_double_t(scale)))

def ifft2_inplace(signal, scale = None):
    """
    Inverse In-place Fast Fourier Transform: 2D

    Parameters
    ----------

    signal: af.Array
           A 2 dimensional signal or a batch of 2 dimensional signals.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.0 / (signal.dims()[0] * signal.dims()[1])
    """

    dims = signal.dims()

    if scale is None:
        dim0 = dims[0]
        dim1 = dims[1]
        scale = 1.0/float(dim0 * dim1)

    safe_call(backend.get().af_ifft2_inplace(signal.arr, c_double_t(scale)))

def ifft3_inplace(signal, scale = None):
    """
    Inverse In-place Fast Fourier Transform: 3D

    Parameters
    ----------

    signal: af.Array
           A 3 dimensional signal or a batch of 3 dimensional signals.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.0 / (signal.dims()[0] * signal.dims()[1] * signal.dims()[2]).
    """

    dims = signal.dims()

    if scale is None:
        dim0 = dims[0]
        dim1 = dims[1]
        dim2 = dims[2]
        scale = 1.0 / float(dim0 * dim1 * dim2)

    safe_call(backend.get().af_ifft3_inplace(signal.arr, c_double_t(scale)))

def fft_r2c(signal, dim0 = None , scale = None):
    """
    Real to Complex Fast Fourier Transform: 1D

    Parameters
    ----------

    signal: af.Array
           A 1 dimensional signal or a batch of 1 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    Returns
    -------

    output: af.Array
            A complex af.Array containing the non-redundant parts of the full FFT.

    """

    if dim0 is None:
        dim0 = 0

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft_r2c(c_pointer(output.arr), signal.arr, c_double_t(scale), c_dim_t(dim0)))
    return output

def fft2_r2c(signal, dim0 = None, dim1 = None , scale = None):
    """
    Real to Complex Fast Fourier Transform: 2D

    Parameters
    ----------

    signal: af.Array
           A 2 dimensional signal or a batch of 2 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    dim1: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim1 is calculated to be the second dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    Returns
    -------

    output: af.Array
            A complex af.Array containing the non-redundant parts of the full FFT.

    """
    if dim0 is None:
        dim0 = 0

    if dim1 is None:
        dim1 = 0

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft2_r2c(c_pointer(output.arr), signal.arr, c_double_t(scale),
                                        c_dim_t(dim0), c_dim_t(dim1)))
    return output

def fft3_r2c(signal, dim0 = None, dim1 = None , dim2 = None, scale = None):
    """
    Real to Complex Fast Fourier Transform: 3D

    Parameters
    ----------

    signal: af.Array
           A 3 dimensional signal or a batch of 3 dimensional signals.

    dim0: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim0 is calculated to be the first dimension of `signal`.

    dim1: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim1 is calculated to be the second dimension of `signal`.

    dim2: optional: int. default: None.
          - Specifies the size of the output.
          - If None, dim2 is calculated to be the third dimension of `signal`.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1.

    Returns
    -------

    output: af.Array
            A complex af.Array containing the non-redundant parts of the full FFT.

    """
    if dim0 is None:
        dim0 = 0

    if dim1 is None:
        dim1 = 0

    if dim2 is None:
        dim2 = 0

    if scale is None:
        scale = 1.0

    output = Array()
    safe_call(backend.get().af_fft3_r2c(c_pointer(output.arr), signal.arr, c_double_t(scale),
                                        c_dim_t(dim0), c_dim_t(dim1), c_dim_t(dim2)))
    return output

def _get_c2r_dim(dim, is_odd):
    return 2 *(dim - 1) + int(is_odd)

def fft_c2r(signal, is_odd = False, scale = None):
    """
    Real to Complex Fast Fourier Transform: 1D

    Parameters
    ----------

    signal: af.Array
           A 1 dimensional signal or a batch of 1 dimensional signals.

    is_odd: optional: Boolean. default: False.
          - Specifies if the first dimension of output should be even or odd.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1 / (signal.dims()[0]).

    Returns
    -------

    output: af.Array
            A real af.Array containing the full output of the fft.

    """


    if scale is None:
        dim0 = _get_c2r_dim(signal.dims()[0], is_odd)
        scale = 1.0/float(dim0)

    output = Array()
    safe_call(backend.get().af_fft_c2r(c_pointer(output.arr), signal.arr, c_double_t(scale), is_odd))
    return output

def fft2_c2r(signal, is_odd = False, scale = None):
    """
    Real to Complex Fast Fourier Transform: 2D

    Parameters
    ----------

    signal: af.Array
           A 2 dimensional signal or a batch of 2 dimensional signals.

    is_odd: optional: Boolean. default: False.
          - Specifies if the first dimension of output should be even or odd.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1 / (signal.dims()[0] * signal.dims()[1]).

    Returns
    -------

    output: af.Array
            A real af.Array containing the full output of the fft.

    """
    dims = signal.dims()

    if scale is None:
        dim0 = _get_c2r_dim(dims[0], is_odd)
        dim1 = dims[1]
        scale = 1.0/float(dim0 * dim1)

    output = Array()
    safe_call(backend.get().af_fft2_c2r(c_pointer(output.arr), signal.arr, c_double_t(scale), is_odd))
    return output

def fft3_c2r(signal, is_odd = False, scale = None):
    """
    Real to Complex Fast Fourier Transform: 3D

    Parameters
    ----------

    signal: af.Array
           A 3 dimensional signal or a batch of 3 dimensional signals.

    is_odd: optional: Boolean. default: False.
          - Specifies if the first dimension of output should be even or odd.

    scale: optional: scalar. default: None.
          - Specifies the scaling factor.
          - If None, scale is set to 1 / (signal.dims()[0] * signal.dims()[1] * signal.dims()[2]).

    Returns
    -------

    output: af.Array
            A real af.Array containing the full output of the fft.

    """
    dims = signal.dims()

    if scale is None:
        dim0 = _get_c2r_dim(dims[0], is_odd)
        dim1 = dims[1]
        dim2 = dims[2]
        scale = 1.0/float(dim0 * dim1 * dim2)

    output = Array()
    safe_call(backend.get().af_fft3_c2r(c_pointer(output.arr), signal.arr, c_double_t(scale), is_odd))
    return output


def dft(signal, odims=(None, None, None, None), scale = None):

    """
    Non batched Fourier transform.

    This function performs n-dimensional fourier transform depending on the input dimensions.

    Parameters
    ----------

    signal: af.Array
          - A multi dimensional arrayfire array.

    odims: optional: tuple of ints. default: (None, None, None, None).
          - If None, calculated to be `signal.dims()`

    scale: optional: scalar. default: None.
           - Scale factor for the fourier transform.
           - If none, calculated to be 1.0.

    Returns
    -------
    output: af.Array
           - A complex array that is the ouput of n-dimensional fourier transform.

    """

    odims4 = dim4_to_tuple(odims, default=None)

    dims = signal.dims()
    ndims = len(dims)

    if (ndims == 1):
        return fft(signal, dims[0], scale)
    elif (ndims == 2):
        return fft2(signal, dims[0], dims[1], scale)
    else:
        return fft3(signal, dims[0], dims[1], dims[2], scale)

def idft(signal, scale = None, odims=(None, None, None, None)):
    """
    Non batched Inverse Fourier transform.

    This function performs n-dimensional inverse fourier transform depending on the input dimensions.

    Parameters
    ----------

    signal: af.Array
          - A multi dimensional arrayfire array.

    odims: optional: tuple of ints. default: (None, None, None, None).
          - If None, calculated to be `signal.dims()`

    scale: optional: scalar. default: None.
           - Scale factor for the fourier transform.
           - If none, calculated to be 1.0 / signal.elements()

    Returns
    -------
    output: af.Array
           - A complex array that is the ouput of n-dimensional inverse fourier transform.

    Note
    ----

    the output is always complex.

    """

    odims4 = dim4_to_tuple(odims, default=None)

    dims = signal.dims()
    ndims = len(dims)

    if (ndims == 1):
        return ifft(signal, scale, dims[0])
    elif (ndims == 2):
        return ifft2(signal, scale, dims[0], dims[1])
    else:
        return ifft3(signal, scale, dims[0], dims[1], dims[2])

def convolve1(signal, kernel, conv_mode = CONV_MODE.DEFAULT, conv_domain = CONV_DOMAIN.AUTO):
    """
    Convolution: 1D

    Parameters
    -----------

    signal: af.Array
            - A 1 dimensional signal or batch of 1 dimensional signals.

    kernel: af.Array
            - A 1 dimensional kernel or batch of 1 dimensional kernels.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    conv_domain: optional: af.CONV_DOMAIN. default: af.CONV_DOMAIN.AUTO.
            - Specifies the domain in which convolution is performed.
            - af.CONV_DOMAIN.SPATIAL: Performs convolution in spatial domain.
            - af.CONV_DOMAIN.FREQ: Performs convolution in frequency domain.
            - af.CONV_DOMAIN.AUTO: Switches between spatial and frequency based on input size.

    Returns
    --------

    output: af.Array
          - Output of 1D convolution.

    Note
    -----

    Supported batch combinations:

    | Signal    | Kernel    | output    |
    |:---------:|:---------:|:---------:|
    | [m 1 1 1] | [m 1 1 1] | [m 1 1 1] |
    | [m n 1 1] | [m n 1 1] | [m n 1 1] |
    | [m n p 1] | [m n 1 1] | [m n p 1] |
    | [m n p 1] | [m n p 1] | [m n p 1] |
    | [m n p 1] | [m n 1 q] | [m n p q] |
    | [m n 1 p] | [m n q 1] | [m n q p] |

    """
    output = Array()
    safe_call(backend.get().af_convolve1(c_pointer(output.arr), signal.arr, kernel.arr,
                                         conv_mode.value, conv_domain.value))
    return output

def convolve2(signal, kernel, conv_mode = CONV_MODE.DEFAULT, conv_domain = CONV_DOMAIN.AUTO):
    """
    Convolution: 2D

    Parameters
    -----------

    signal: af.Array
            - A 2 dimensional signal or batch of 2 dimensional signals.

    kernel: af.Array
            - A 2 dimensional kernel or batch of 2 dimensional kernels.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    conv_domain: optional: af.CONV_DOMAIN. default: af.CONV_DOMAIN.AUTO.
            - Specifies the domain in which convolution is performed.
            - af.CONV_DOMAIN.SPATIAL: Performs convolution in spatial domain.
            - af.CONV_DOMAIN.FREQ: Performs convolution in frequency domain.
            - af.CONV_DOMAIN.AUTO: Switches between spatial and frequency based on input size.

    Returns
    --------

    output: af.Array
          - Output of 2D convolution.

    Note
    -----

    Supported batch combinations:

    | Signal    | Kernel    | output    |
    |:---------:|:---------:|:---------:|
    | [m n 1 1] | [m n 1 1] | [m n 1 1] |
    | [m n p 1] | [m n 1 1] | [m n p 1] |
    | [m n p 1] | [m n p 1] | [m n p 1] |
    | [m n p 1] | [m n 1 q] | [m n p q] |
    | [m n 1 p] | [m n q 1] | [m n q p] |

    """
    output = Array()
    safe_call(backend.get().af_convolve2(c_pointer(output.arr), signal.arr, kernel.arr,
                                         conv_mode.value, conv_domain.value))
    return output

def convolve2NN(signal, kernel, stride = (1, 1), padding = (0, 0), dilation = (1, 1)):
    """
    This version of convolution is consistent with the machine learning
    formulation that will spatially convolve a filter on 2-dimensions against a
    signal. Multiple signals and filters can be batched against each other.
    Furthermore, the signals and filters can be multi-dimensional however their
    dimensions must match.

    Example:
        Signals with dimensions: d0 x d1 x d2 x Ns
        Filters with dimensions: d0 x d1 x d2 x Nf

        Resulting Convolution:   d0 x d1 x Nf x Ns

    Parameters
    -----------

    signal: af.Array
            - A 2 dimensional signal or batch of 2 dimensional signals.

    kernel: af.Array
            - A 2 dimensional kernel or batch of 2 dimensional kernels.

    stride: tuple of ints. default: (1, 1).
            - Specifies how much to stride along each dimension

    padding: tuple of ints. default: (0, 0).
            - Specifies signal padding along each dimension

    dilation: tuple of ints. default: (1, 1).
            - Specifies how much to dilate kernel along each dimension before convolution

    Returns
    --------

    output: af.Array
          - Convolved 2D array.

    """
    output = Array()
    stride_dim   = dim4(stride[0],   stride[1])
    padding_dim  = dim4(padding[0],  padding[1])
    dilation_dim = dim4(dilation[0], dilation[1])

    safe_call(backend.get().af_convolve2_nn(c_pointer(output.arr), signal.arr, kernel.arr,
                                            2, c_pointer(stride_dim),
                                            2, c_pointer(padding_dim),
                                            2, c_pointer(dilation_dim)))
    return output

def convolve2_separable(col_kernel, row_kernel, signal, conv_mode = CONV_MODE.DEFAULT):
    """
    Convolution: 2D separable convolution

    Parameters
    -----------

    col_kernel: af.Array
            - A column vector to be applied along each column of `signal`

    row_kernel: af.Array
            - A row vector to be applied along each row of `signal`

    signal: af.Array
            - A 2 dimensional signal or batch of 2 dimensional signals.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).
    Returns
    --------

    output: af.Array
          - Output of 2D sepearable convolution.
    """
    output = Array()
    safe_call(backend.get().af_convolve2_sep(c_pointer(output.arr),
                                             col_kernel.arr, row_kernel.arr,signal.arr,
                                             conv_mode.value))
    return output

def convolve3(signal, kernel, conv_mode = CONV_MODE.DEFAULT, conv_domain = CONV_DOMAIN.AUTO):
    """
    Convolution: 3D

    Parameters
    -----------

    signal: af.Array
            - A 3 dimensional signal or batch of 3 dimensional signals.

    kernel: af.Array
            - A 3 dimensional kernel or batch of 3 dimensional kernels.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    conv_domain: optional: af.CONV_DOMAIN. default: af.CONV_DOMAIN.AUTO.
            - Specifies the domain in which convolution is performed.
            - af.CONV_DOMAIN.SPATIAL: Performs convolution in spatial domain.
            - af.CONV_DOMAIN.FREQ: Performs convolution in frequency domain.
            - af.CONV_DOMAIN.AUTO: Switches between spatial and frequency based on input size.

    Returns
    --------

    output: af.Array
          - Output of 3D convolution.

    Note
    -----

    Supported batch combinations:

    | Signal    | Kernel    | output    |
    |:---------:|:---------:|:---------:|
    | [m n p 1] | [m n p 1] | [m n p 1] |
    | [m n p 1] | [m n p q] | [m n p q] |
    | [m n q p] | [m n q p] | [m n q p] |

    """
    output = Array()
    safe_call(backend.get().af_convolve3(c_pointer(output.arr), signal.arr, kernel.arr,
                                         conv_mode.value, conv_domain.value))
    return output

def convolve(signal, kernel, conv_mode = CONV_MODE.DEFAULT, conv_domain = CONV_DOMAIN.AUTO):
    """
    Non batched Convolution.

    This function performs n-dimensional convolution based on input dimensionality.

    Parameters
    -----------

    signal: af.Array
            - An n-dimensional array.

    kernel: af.Array
            - A n-dimensional kernel.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    conv_domain: optional: af.CONV_DOMAIN. default: af.CONV_DOMAIN.AUTO.
            - Specifies the domain in which convolution is performed.
            - af.CONV_DOMAIN.SPATIAL: Performs convolution in spatial domain.
            - af.CONV_DOMAIN.FREQ: Performs convolution in frequency domain.
            - af.CONV_DOMAIN.AUTO: Switches between spatial and frequency based on input size.

    Returns
    --------

    output: af.Array
          - Output of n-dimensional convolution.
    """

    dims = signal.dims()
    ndims = len(dims)

    if (ndims == 1):
        return convolve1(signal, kernel, conv_mode, conv_domain)
    elif (ndims == 2):
        return convolve2(signal, kernel, conv_mode, conv_domain)
    else:
        return convolve3(signal, kernel, conv_mode, conv_domain)

def fft_convolve1(signal, kernel, conv_mode = CONV_MODE.DEFAULT):
    """
    FFT based Convolution: 1D

    Parameters
    -----------

    signal: af.Array
            - A 1 dimensional signal or batch of 1 dimensional signals.

    kernel: af.Array
            - A 1 dimensional kernel or batch of 1 dimensional kernels.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    Returns
    --------

    output: af.Array
          - Output of 1D convolution.

    Note
    -----

    This is same as convolve1(..., conv_mode=af.CONV_MODE.FREQ)

    Supported batch combinations:

    | Signal    | Kernel    | output    |
    |:---------:|:---------:|:---------:|
    | [m 1 1 1] | [m 1 1 1] | [m 1 1 1] |
    | [m n 1 1] | [m n 1 1] | [m n 1 1] |
    | [m n p 1] | [m n 1 1] | [m n p 1] |
    | [m n p 1] | [m n p 1] | [m n p 1] |
    | [m n p 1] | [m n 1 q] | [m n p q] |
    | [m n 1 p] | [m n q 1] | [m n q p] |

    """
    output = Array()
    safe_call(backend.get().af_fft_convolve1(c_pointer(output.arr), signal.arr, kernel.arr,
                                             conv_mode.value))
    return output

def fft_convolve2(signal, kernel, conv_mode = CONV_MODE.DEFAULT):
    """
    FFT based Convolution: 2D

    Parameters
    -----------

    signal: af.Array
            - A 2 dimensional signal or batch of 2 dimensional signals.

    kernel: af.Array
            - A 2 dimensional kernel or batch of 2 dimensional kernels.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    Returns
    --------

    output: af.Array
          - Output of 2D convolution.

    Note
    -----

    This is same as convolve2(..., conv_mode=af.CONV_MODE.FREQ)

    Supported batch combinations:

    | Signal    | Kernel    | output    |
    |:---------:|:---------:|:---------:|
    | [m n 1 1] | [m n 1 1] | [m n 1 1] |
    | [m n p 1] | [m n 1 1] | [m n p 1] |
    | [m n p 1] | [m n p 1] | [m n p 1] |
    | [m n p 1] | [m n 1 q] | [m n p q] |
    | [m n 1 p] | [m n q 1] | [m n q p] |

    """
    output = Array()
    safe_call(backend.get().af_fft_convolve2(c_pointer(output.arr), signal.arr, kernel.arr,
                                             conv_mode.value))
    return output

def fft_convolve3(signal, kernel, conv_mode = CONV_MODE.DEFAULT):
    """
    FFT based Convolution: 3D

    Parameters
    -----------

    signal: af.Array
            - A 3 dimensional signal or batch of 3 dimensional signals.

    kernel: af.Array
            - A 3 dimensional kernel or batch of 3 dimensional kernels.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    Returns
    --------

    output: af.Array
          - Output of 3D convolution.

    Note
    -----

    This is same as convolve3(..., conv_mode=af.CONV_MODE.FREQ)

    Supported batch combinations:

    | Signal    | Kernel    | output    |
    |:---------:|:---------:|:---------:|
    | [m n p 1] | [m n p 1] | [m n p 1] |
    | [m n p 1] | [m n p q] | [m n p q] |
    | [m n q p] | [m n q p] | [m n q p] |

    """
    output = Array()
    safe_call(backend.get().af_fft_convolve3(c_pointer(output.arr), signal.arr, kernel.arr,
                                             conv_mode.value))
    return output

def fft_convolve(signal, kernel, conv_mode = CONV_MODE.DEFAULT):
    """
    Non batched FFT Convolution.

    This function performs n-dimensional convolution based on input dimensionality.

    Parameters
    -----------

    signal: af.Array
            - An n-dimensional array.

    kernel: af.Array
            - A n-dimensional kernel.

    conv_mode: optional: af.CONV_MODE. default: af.CONV_MODE.DEFAULT.
            - Specifies if the output does full convolution (af.CONV_MODE.EXPAND) or
              maintains the same size as input (af.CONV_MODE.DEFAULT).

    Returns
    --------

    output: af.Array
          - Output of n-dimensional convolution.

    Note
    -----

    This is same as convolve(..., conv_mode=af.CONV_MODE.FREQ)

    """
    dims = signal.dims()
    ndims = len(dims)

    if (ndims == 1):
        return fft_convolve1(signal, kernel, conv_mode)
    elif (ndims == 2):
        return fft_convolve2(signal, kernel, conv_mode)
    else:
        return fft_convolve3(signal, kernel, conv_mode)

def fir(B, X):
    """
    Finite impulse response filter.

    Parameters
    ----------

    B : af.Array
        A 1 dimensional array containing the coefficients of the filter.

    X : af.Array
        A 1 dimensional array containing the signal.

    Returns
    -------

    Y : af.Array
        The output of the filter.

    """
    Y = Array()
    safe_call(backend.get().af_fir(c_pointer(Y.arr), B.arr, X.arr))
    return Y

def iir(B, A, X):
    """
    Infinite impulse response filter.

    Parameters
    ----------

    B : af.Array
        A 1 dimensional array containing the feed forward coefficients of the filter.

    A : af.Array
        A 1 dimensional array containing the feed back coefficients of the filter.

    X : af.Array
        A 1 dimensional array containing the signal.

    Returns
    -------

    Y : af.Array
        The output of the filter.

    """
    Y = Array()
    safe_call(backend.get().af_iir(c_pointer(Y.arr), B.arr, A.arr, X.arr))
    return Y

def medfilt(signal, w0 = 3, w1 = 3, edge_pad = PAD.ZERO):
    """
    Apply median filter for the signal.

    Parameters
    ----------
    signal : af.Array
          - A 2 D arrayfire array representing a signal, or
          - A multi dimensional array representing batch of signals.

    w0 : optional: int. default: 3.
          - The length of the filter along the first dimension.

    w1 : optional: int. default: 3.
          - The length of the filter along the second dimension.

    edge_pad : optional: af.PAD. default: af.PAD.ZERO
          - Flag specifying how the median at the edge should be treated.

    Returns
    ---------

    output : af.Array
           - The signal after median filter is applied.

    """
    output = Array()
    safe_call(backend.get().af_medfilt(c_pointer(output.arr),
                                       signal.arr, c_dim_t(w0),
                                       c_dim_t(w1), edge_pad.value))
    return output

def medfilt1(signal, length = 3, edge_pad = PAD.ZERO):
    """
    Apply median filter for the signal.

    Parameters
    ----------
    signal : af.Array
          - A 1 D arrayfire array representing a signal, or
          - A multi dimensional array representing batch of signals.

    length : optional: int. default: 3.
          - The length of the filter.

    edge_pad : optional: af.PAD. default: af.PAD.ZERO
          - Flag specifying how the median at the edge should be treated.

    Returns
    ---------

    output : af.Array
           - The signal after median filter is applied.

    """
    output = Array()
    safe_call(backend.get().af_medfilt1(c_pointer(output.arr), signal.arr, c_dim_t(length), edge_pad.value))
    return output

def medfilt2(signal, w0 = 3, w1 = 3, edge_pad = PAD.ZERO):
    """
    Apply median filter for the signal.

    Parameters
    ----------
    signal : af.Array
          - A 2 D arrayfire array representing a signal, or
          - A multi dimensional array representing batch of signals.

    w0 : optional: int. default: 3.
          - The length of the filter along the first dimension.

    w1 : optional: int. default: 3.
          - The length of the filter along the second dimension.

    edge_pad : optional: af.PAD. default: af.PAD.ZERO
          - Flag specifying how the median at the edge should be treated.

    Returns
    ---------

    output : af.Array
           - The signal after median filter is applied.

    """
    output = Array()
    safe_call(backend.get().af_medfilt2(c_pointer(output.arr),
                                        signal.arr, c_dim_t(w0),
                                        c_dim_t(w1), edge_pad.value))
    return output

def set_fft_plan_cache_size(cache_size):
    """
    Sets plan cache size.

    Parameters
    ----------

    cache_size : scalar
        the number of plans that shall be cached
    """
    safe_call(backend.get().af_set_fft_plan_cache_size(c_size_t(cache_size)))