dow : string, tuple, or array_like, optional
    Desired window to use to design the low-pass filter, or the FIR filter
    coefficients to employ. See below for details.
padtype : string, optional
    `constant`, `line`, `mean`, `median`, `maximum`, `minimum` or any of
    the other signal extension modes supported by `scipy.signal.upfirdn`.
    Changes assumptions on values beyond the boundary. If `constant`,
    assumed to be `cval` (default zero). If `line` assumed to continue a
    linear trend defined by the first and last points. `mean`, `median`,
    `maximum` and `minimum` work as in `np.pad` and assume that the values
    beyond the boundary are the mean, median, maximum or minimum
    respectively of the array along the axis.

    .. versionadded:: 1.4.0
cval : float, optional
    Value to use if `padtype='constant'`. Default is zero.

    .. versionadded:: 1.4.0

Returns
-------
resampled_x : array
    The resampled array.

See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample : Resample up or down using the FFT method.

Notes
-----
This polyphase method will likely be faster than the Fourier method
in `scipy.signal.resample` when the number of samples is large and
prime, or when the number of samples is large and `up` and `down`
share a large greatest common denominator. The length of the FIR
filter used will depend on ``max(up, down) // gcd(up, down)``, and
the number of operations during polyphase filtering will depend on
the filter length and `down` (see `scipy.signal.upfirdn` for details).

The argument `window` specifies the FIR low-pass filter design.

If `window` is an array_like it is assumed to be the FIR filter
coefficients. Note that the FIR filter is applied after the upsampling
step, so it should be designed to operate on a signal at a sampling
frequency higher than the original by a factor of `up//gcd(up, down)`.
This function's output will be centered with respect to this array, so it
is best to pass a symmetric filter with an odd number of samples if, as
is usually the case, a zero-phase filter is desired.

For any other type of `window`, the functions `scipy.signal.get_window`
and `scipy.signal.firwin` are called to generate the appropriate filter
coefficients.

The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * down / float(up)``.

Examples
--------
By default, the end of the resampled data rises to meet the first
sample of the next cycle for the FFT method, and gets closer to zero
for the polyphase method:

>>> import numpy as np
>>> from scipy import signal
>>> import matplotlib.pyplot as plt

>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f_fft = signal.resample(y, 100)
>>> f_poly = signal.resample_poly(y, 100, 20)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)

>>> plt.plot(xnew, f_fft, 'b.-', xnew, f_poly, 'r.-')
>>> plt.plot(x, y, 'ko-')
>>> plt.plot(10, y[0], 'bo', 10, 0., 'ro')  # boundaries
>>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best')
>>> plt.show()

This default behaviour can be changed by using the padtype option:

>>> N = 5
>>> x = np.linspace(0, 1, N, endpoint=False)
>>> y = 2 + x**2 - 1.7*np.sin(x) + .2*np.cos(11*x)
>>> y2 = 1 + x**3 + 0.1*np.sin(x) + .1*np.cos(11*x)
>>> Y = np.stack([y, y2], axis=-1)
>>> up = 4
>>> xr = np.linspace(0, 1, N*up, endpoint=False)

>>> y2 = signal.resample_poly(Y, up, 1, padtype='constant')
>>> y3 = signal.resample_poly(Y, up, 1, padtype='mean')
>>> y4 = signal.resample_poly(Y, up, 1, padtype='line')

>>> for i in [0,1]:
...     plt.figure()
...     plt.plot(xr, y4[:,i], 'g.', label='line')
...     plt.plot(xr, y3[:,i], 'y.', label='mean')
...     plt.plot(xr, y2[:,i], 'r.', label='constant')
...     plt.plot(x, Y[:,i], 'k-')
...     plt.legend()
>>> plt.show()

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