filtration¶
Filtration of signals.
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dsplab.filtration.
butter_filter
(xdata, sample_rate, freqs, order, btype='band')[source]¶ Butterworth filter.
Parameters: - xdata (array_like) – Signal values.
- sample_rate (float) – Sampling frequency (Hz).
- freqs (array_like) – One or two frequencies.
- order (integer) – Order of filter.
- btype (str ('band' | 'lowpass')) – Type of filter.
Returns: filtered signal.
Return type: np.array
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dsplab.filtration.
find_butt_bandpass_order
(band, sample_rate)[source]¶ Calculate the order of Butterworth bandpass filter using minimization of metric between ideal and real frequency response.
Parameters: - band (array_like) – Pair of frequencies. Bounds of bandpass (Hz).
- sample_rate (float) – Sample rate (Hz).
Returns: Order of filter.
Return type: integer
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dsplab.filtration.
haar_one_step
(xdata, tdata, denominator=2)[source]¶ One cascade of Haar transform.
Parameters: - xdata (array_like) – Signal values.
- tdata (array_like) – Time values.
- denominator (integer) – Denominator used in Haar transform (default is 2).
Returns: - np.array – Scaled signal values.
- np.array. – Details of x
- np.array. – Decimated time values
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dsplab.filtration.
haar_scaling
(xdata, tdata, steps_number)[source]¶ Scaling with Haar transform.
Parameters: - xdata (array_like) – Signal values.
- tdata (array_like) – Time values.
- steps_number (integer) – Number of cascades.
Returns: - np.array – Scaled signal values.
- np.array – Decimated time values.
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dsplab.filtration.
stupid_bandpass_filter
(xdata, sample_rate, bandpass)[source]¶ Return low-pass filtered signal.
Parameters: - xdata (array_like) – Signal values.
- sample_rate (float) – Sampling frequency.
- bandpass (np.array of 2 floats) – Bounds of bandpass (Hz).
Returns: Filteres signal.
Return type: np.array
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dsplab.filtration.
stupid_lowpass_filter
(xdata, sample_rate, cutoff)[source]¶ Return low-pass filtered signal.
Parameters: - xdata (array_like) – Signal values.
- sample_rate (float) – Sampling frequency.
- cutoff (float) – Cutoff frequency.
Returns: Filteres signal.
Return type: np.array
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dsplab.filtration.
trend_smooth
(xdata, sample_rate=1, tdata=None, cut_off=0.5)[source]¶ Calculate trend of signal using smoothing filter.
Parameters: - xdata (array_like) – Signal values.
- tdata (array_like) – Time values.
- cut_off (float) – The frequencies lower than this are trend’s frequencies.
Returns: - np.array – Trend values.
- np.array – Time values.