62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
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import numpy as np
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from scipy.signal import butter, lfilter, decimate
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def anti_alias(data, sample_rate, max_frequency, order=5):
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FILTER_HEADROOM = 1.2
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nyquist_rate = 2 * max_frequency
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downsample_factor = 1
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if sample_rate > nyquist_rate:
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filtered_data = lowpass_filter(
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data,
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max_frequency * FILTER_HEADROOM,
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sample_rate,
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order
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)
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downsample_factor = int(sample_rate / nyquist_rate)
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filtered_data = decimate(filtered_data, downsample_factor)
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sample_rate = sample_rate // downsample_factor
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else:
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filtered_data = data
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return filtered_data, sample_rate, downsample_factor
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def smoothing_filter(data, window_size=256, mode='same'):
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window = np.ones(window_size) / window_size
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return np.convolve(data, window, mode=mode)
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# Stolen from selcald
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def note(freq, length, amp=1.0, rate=44100):
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if freq == 0:
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data = np.zeros(int(length * rate))
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else:
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t = np.linspace(0, length, int(length * rate))
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data = np.sin(2 * np.pi * freq * t) * amp
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return data
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# These originally came from https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass,
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# but they've been copied around the internet so many times that ChatGPT now produces them verbatim.
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def butter_bandpass(lowcut, highcut, fs, order=5):
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nyquist = 0.5 * fs
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low = lowcut / nyquist
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high = highcut / nyquist
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b, a = butter(order, [low, high], btype='band')
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return b, a
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def bandpass_filter(data, lowcut, highcut, fs, order=5):
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b, a = butter_bandpass(lowcut, highcut, fs, order=order)
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y = lfilter(b, a, data)
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return y
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def butter_lowpass(cutoff, fs, order=5):
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nyquist = 0.5 * fs
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normal_cutoff = cutoff / nyquist
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b, a = butter(order, normal_cutoff, btype='low', analog=False)
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return b, a
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def lowpass_filter(data, cutoff, fs, order=5):
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b, a = butter_lowpass(cutoff, fs, order=order)
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y = lfilter(b, a, data)
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return y |