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Editing File: /home/ubuntu/.local/lib/python3.10/site-packages/resampy/interpn.py

#!/usr/bin/env python
"""Numba implementation of resampler"""

from numba import guvectorize, jit, prange

def _resample_loop(x, t_out, interp_win, interp_delta, num_table, scale, y):

index_step = int(scale * num_table)
    time_register = 0.0

n = 0
    frac = 0.0
    index_frac = 0.0
    offset = 0
    eta = 0.0
    weight = 0.0

nwin = interp_win.shape[0]
    n_orig = x.shape[0]
    n_out = t_out.shape[0]

for t in prange(n_out):
        time_register = t_out[t]

# Grab the top bits as an index to the input buffer
        n = int(time_register)

# Grab the fractional component of the time index
        frac = scale * (time_register - n)

# Offset into the filter
        index_frac = frac * num_table
        offset = int(index_frac)

# Interpolation factor
        eta = index_frac - offset

# Compute the left wing of the filter response
        i_max = min(n + 1, (nwin - offset) // index_step)
        for i in range(i_max):

weight = (
                interp_win[offset + i * index_step]
                + eta * interp_delta[offset + i * index_step]
            )
            y[t] += weight * x[n - i]

# Invert P
        frac = scale - frac

# Offset into the filter
        index_frac = frac * num_table
        offset = int(index_frac)

# Interpolation factor
        eta = index_frac - offset

# Compute the right wing of the filter response
        k_max = min(n_orig - n - 1, (nwin - offset) // index_step)
        for k in range(k_max):
            weight = (
                interp_win[offset + k * index_step]
                + eta * interp_delta[offset + k * index_step]
            )
            y[t] += weight * x[n + k + 1]

_resample_loop_p = jit(nopython=True, nogil=True, parallel=True)(_resample_loop)
_resample_loop_s = jit(nopython=True, nogil=True, parallel=False)(_resample_loop)

@guvectorize(
    "(n),(m),(p),(p),(),()->(m)",
    nopython=True,
)
def resample_f_p(x, t_out, interp_win, interp_delta, num_table, scale, y):
    _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)

@guvectorize(
    "(n),(m),(p),(p),(),()->(m)",
    nopython=True,
)
def resample_f_s(x, t_out, interp_win, interp_delta, num_table, scale, y):
    _resample_loop_s(x, t_out, interp_win, interp_delta, num_table, scale, y)

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