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Copy file name to clipboardExpand all lines: shared-bindings/ulab/filter/__init__.pyi
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@@ -17,3 +17,20 @@ def convolve(r, c=None):
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Convolution is most time-efficient when both inputs are of float type."""
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defsosfilt(sos : ulab.array, x : ulab.array, *, xi : Optional[ulab.array] =None) ->Union[ulab.array, Tuple[ulab.array, ulab.array]]:
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"""
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:param ulab.array sos: Array of second-order filter coefficients, must have shape (n_sections, 6). Each row corresponds to a second-order section, with the first three columns providing the numerator coefficients and the last three providing the denominator coefficients.
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:param ulab.array x: The data to be filtered
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:param ulab.array zi: Optional initial conditions for the filter
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:return: If ``xi`` is not specified, the filter result alone is returned. If ``xi`` is specified, the return value is a 2-tuple of the filter result and the final filter conditions.
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Filter data along one dimension using cascaded second-order sections.
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Filter a data sequence, x, using a digital IIR filter defined by sos.
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The filter function is implemented as a series of second-order filters with direct-form II transposed structure. It is designed to minimize numerical precision errors for high-order filters.
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Filter coefficients can be generated by using scipy's filter generators such as ``signal.ellip(..., output='sos')``."""
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