text: # Code adapted from "upfirdn" python library with permission:
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  import numpy as np
  
  from ._upfirdn_apply import _output_len, _apply, mode_enum
  
  __all__ = ['upfirdn', '_output_len']
  
  _upfirdn_modes = [
      'constant', 'wrap', 'edge', 'smooth', 'symmetric', 'reflect',
      'antisymmetric', 'antireflect', 'line',
  ]
  
  
  def _pad_h(h, up):
      """Store coefficients in a transposed, flipped arrangement.
  
      For example, suppose upRate is 3, and the
      input number of coefficients is 10, represented as h[0], ..., h[9].
  
      Then the internal buffer will look like this::
  
         h[9], h[6], h[3], h[0],   // flipped phase 0 coefs
         0,    h[7], h[4], h[1],   // flipped phase 1 coefs (zero-padded)
         0,    h[8], h[5], h[2],   // flipped phase 2 coefs (zero-padded)
  
      """
      h_padlen = len(h) + (-len(h) % up)
      h_full = np.zeros(h_padlen, h.dtype)
      h_full[:len(h)] = h
      h_full = h_full.reshape(-1, up).T[:, ::-1].ravel()
      return h_full
  
  
  def _check_mode(mode):
      mode = mode.lower()
      enum = mode_enum(mode)
      return enum
  
  
  class _UpFIRDn(object):
      def __init__(self, h, x_dtype, up, down):
          """Helper for resampling"""
          h = np.asarray(h)
          if h.ndim != 1 or h.size == 0:
              raise ValueError('h must be 1-D with non-zero length')
          self._output_type = np.result_type(h.dtype, x_dtype, np.float32)
          h = np.asarray(h, self._output_type)
          self._up = int(up)
          self._down = int(down)
          if self._up < 1 or self._down < 1:
              raise ValueError('Both up and down must be >= 1')
          # This both transposes, and "flips" each phase for filtering
          self._h_trans_flip = _pad_h(h, self._up)
          self._h_trans_flip = np.ascontiguousarray(self._h_trans_flip)
  
      def apply_filter(self, x, axis=-1, mode='constant', cval=0):
          """Apply the prepared filter to the specified axis of a N-D signal x"""
          output_len = _output_len(len(self._h_trans_flip), x.shape[axis],
                                   self._up, self._down)
          # Explicit use of np.int64 for output_shape dtype avoids OverflowError
          # when allocating large array on platforms where np.int_ is 32 bits
          output_shape = np.asarray(x.shape, dtype=np.int64)
          output_shape[axis] = output_len
          out = np.zeros(output_shape, dtype=self._output_type, order='C')
          axis = axis % x.ndim
          mode = _check_mode(mode)
          _apply(np.asarray(x, self._output_type),
                 self._h_trans_flip, out,
                 self._up, self._down, axis, mode, cval)
          return out
  
  
  def upfirdn(h, x, up=1, down=1, axis=-1, mode='constant', cval=0):
      """Upsample, FIR filter, and downsample
  
      Parameters
      ----------
      h : array_like
          1-D FIR (finite-impulse response) filter coefficients.
      x : array_like
          Input signal array.
      up : int, optional
          Upsampling rate. Default is 1.
      down : int, optional
          Downsampling rate. Default is 1.
      axis : int, optional
          The axis of the input data array along which to apply the
          linear filter. The filter is applied to each subarray along
          this axis. Default is -1.
      mode : str, optional
          The signal extension mode to use. The set
          ``{"constant", "symmetric", "reflect", "edge", "wrap"}`` correspond to
          modes provided by `numpy.pad`. ``"smooth"`` implements a smooth
          extension by extending based on the slope of the last 2 points at each
          end of the array. ``"antireflect"`` and ``"antisymmetric"`` are
          anti-symmetric versions of ``"reflect"`` and ``"symmetric"``. The mode
          `"line"` extends the signal based on a linear trend defined by the
          first and last points along the ``axis``.
  
          .. versionadded:: 1.4.0
      cval : float, optional
          The constant value to use when ``mode == "constant"``.
  
          .. versionadded:: 1.4.0
  
      Returns
      -------
      y : ndarray
          The output signal array. Dimensions will be the same as `x` except
          for along `axis`, which will change size according to the `h`,
          `up`,  and `down` parameters.
  
      Notes
      -----
      The algorithm is an implementation of the block diagram shown on page 129
      of the Vaidyanathan text [1]_ (Figure 4.3-8d).
  
      .. [1] P. P. Vaidyanathan, Multirate Systems and Filter Banks,
         Prentice Hall, 1993.
  
      The direct approach of upsampling by factor of P with zero insertion,
      FIR filtering of length ``N``, and downsampling by factor of Q is
      O(N*Q) per output sample. The polyphase implementation used here is
      O(N/P).
  
      .. versionadded:: 0.18
  
      Examples
      --------
      Simple operations:
  
      >>> from scipy.signal import upfirdn
      >>> upfirdn([1, 1, 1], [1, 1, 1])   # FIR filter
      array([ 1.,  2.,  3.,  2.,  1.])
      >>> upfirdn([1], [1, 2, 3], 3)  # upsampling with zeros insertion
      array([ 1.,  0.,  0.,  2.,  0.,  0.,  3.,  0.,  0.])
      >>> upfirdn([1, 1, 1], [1, 2, 3], 3)  # upsampling with sample-and-hold
      array([ 1.,  1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.])
      >>> upfirdn([.5, 1, .5], [1, 1, 1], 2)  # linear interpolation
      array([ 0.5,  1. ,  1. ,  1. ,  1. ,  1. ,  0.5,  0. ])
      >>> upfirdn([1], np.arange(10), 1, 3)  # decimation by 3
      array([ 0.,  3.,  6.,  9.])
      >>> upfirdn([.5, 1, .5], np.arange(10), 2, 3)  # linear interp, rate 2/3
      array([ 0. ,  1. ,  2.5,  4. ,  5.5,  7. ,  8.5,  0. ])
  
      Apply a single filter to multiple signals:
  
      >>> x = np.reshape(np.arange(8), (4, 2))
      >>> x
      array([[0, 1],
             [2, 3],
             [4, 5],
             [6, 7]])
  
      Apply along the last dimension of ``x``:
  
      >>> h = [1, 1]
      >>> upfirdn(h, x, 2)
      array([[ 0.,  0.,  1.,  1.],
             [ 2.,  2.,  3.,  3.],
             [ 4.,  4.,  5.,  5.],
             [ 6.,  6.,  7.,  7.]])
  
      Apply along the 0th dimension of ``x``:
  
      >>> upfirdn(h, x, 2, axis=0)
      array([[ 0.,  1.],
             [ 0.,  1.],
             [ 2.,  3.],
             [ 2.,  3.],
             [ 4.,  5.],
             [ 4.,  5.],
             [ 6.,  7.],
             [ 6.,  7.]])
  
      """
      x = np.asarray(x)
      ufd = _UpFIRDn(h, x.dtype, up, down)
      # This is equivalent to (but faster than) using np.apply_along_axis
      return ufd.apply_filter(x, axis, mode, cval)
  