Core functionality

Filters

Filter construction and loading.

resampy provides two pre-computed resampling filters which are tuned for either high-quality or fast calculation. These filters are constructed by the create_filters.py script.

• kaiser_best :

  > Parameters for kaiser_best: > —————————————- > beta = 12.9846 > roll = 0.917347 > # zeros = 50 > precision = 13 > attenuation = -120.0 > —————————————-

• kaiser_fast :

  > Parameters for kaiser_fast: > —————————————- > beta = 9.90322 > roll = 0.868212 > # zeros = 24 > precision = 9 > attenuation = -93.0 > —————————————-

These filters can be used by calling resample as follows:

>>> # High-quality
>>> resampy.resample(x, sr_orig, sr_new, filter='kaiser_best')  
>>> # Fast calculation
>>> resampy.resample(x, sr_orig, sr_new, filter='kaiser_fast')  

It is also possible to construct custom filters as follows:

>>> resampy.resample(x, sr_orig, sr_new, filter='sinc_window',
...                  **kwargs)                                  

where **kwargs are additional parameters to sinc_window.

resampy.filters.clear_cache()

Clear the filter cache.

Calling this function will ensure that packaged filters are reloaded upon the next usage.

resampy.filters.get_filter(name_or_function, **kwargs)

Retrieve a window given its name or function handle.

Parameters

name_or_functionstr or callable

If a function, returns name_or_function(**kwargs).

If a string, and it matches the name of one of the defined filter functions, the corresponding function is called with **kwargs.

If a string, and it matches the name of a pre-computed filter, the corresponding filter is retrieved, and kwargs is ignored.

Valid pre-computed filter names are:

• ‘kaiser_fast’

• ‘kaiser_best’

**kwargs

Additional keyword arguments passed to name_or_function (if callable)

Returns

half_windownp.ndarray

The right wing of the interpolation filter

precisionint > 0

The number of samples between zero-crossings of the filter

rollofffloat > 0

The roll-off frequency of the filter as a fraction of Nyquist

Raises

NotImplementedError

If name_or_function cannot be found as a filter.

resampy.filters.sinc_window(num_zeros=64, precision=9, window=None, rolloff=0.945)

Construct a windowed sinc interpolation filter

Parameters

num_zerosint > 0

The number of zero-crossings to retain in the sinc filter

precisionint > 0

The number of filter coefficients to retain for each zero-crossing

windowcallable

The window function. By default, uses a Hann window.

rollofffloat > 0

The roll-off frequency (as a fraction of nyquist)

Returns

interp_window: np.ndarray [shape=(num_zeros * num_table + 1)]

The interpolation window (right-hand side)

num_bits: int

The number of bits of precision to use in the filter table

rollofffloat > 0

The roll-off frequency of the filter, as a fraction of Nyquist

Raises

TypeError

if window is not callable or None

ValueError

if num_zeros < 1, precision < 1, or rolloff is outside the range (0, 1].

Examples

>>> import scipy, scipy.signal
>>> import resampy
>>> np.set_printoptions(threshold=5, suppress=False)
>>> # A filter with 10 zero-crossings, 32 samples per crossing, and a
>>> # Hann window for tapering.
>>> halfwin, prec, rolloff = resampy.filters.sinc_window(num_zeros=10, precision=5,
...                                                      window=scipy.signal.hann)
>>> halfwin
array([  9.450e-01,   9.436e-01, ...,  -7.455e-07,  -0.000e+00])
>>> prec
32
>>> rolloff
0.945

>>> # Or using sinc-window filter construction directly in resample
>>> y = resampy.resample(x, sr_orig, sr_new, filter='sinc_window',
...                      num_zeros=10, precision=5,
...                      window=scipy.signal.hann)