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()[source]¶
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)[source]¶
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)[source]¶
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)