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133 lines
3.3 KiB
Python
133 lines
3.3 KiB
Python
"""
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Various useful utilities (duh)
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"""
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import numpy as np
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try:
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import scipy.fft as ft
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except ImportError:
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import numpy.fft as ft
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def sampled_time(sample_rate=1, start=0, end=1, offset=0):
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return offset + np.arange(start, end, 1/sample_rate)
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def rot_vector(phi1=0.12345):
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"""
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Return a unit vector rotated by phi radians.
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"""
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unit = np.array([
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phi1,
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phi1 - np.pi/2
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])
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return np.cos(unit)
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def detect_edges(threshold, data, rising=True, falling=False):
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"""
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Detect rising/falling edges in data, returning the indices
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of the detected edges.
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https://stackoverflow.com/a/50365462
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"""
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mask = np.full(len(data)-1, False)
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if rising:
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mask |= (data[:-1] < threshold) & (data[1:] > threshold)
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if falling:
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mask |= (data[:-1] > threshold) & (data[1:] < threshold)
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return np.flatnonzero(mask)+1
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def sin_delay(f, t, t_delay=0, phase=0):
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return np.sin( 2*np.pi*f*(t - t_delay) + phase )
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def time2phase(time, frequency=1):
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return 2*np.pi*frequency*time
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def phase2time(phase, frequency=1):
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return phase/(2*np.pi*frequency)
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def phase_modulo(phase, low=np.pi):
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"""
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Modulo phase such that it falls within the
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interval $[-low, 2\pi - low)$.
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"""
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return (phase + low) % (2*np.pi) - low
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def time_roll(a, samplerate, time_shift, sample_shift=0, int_func=lambda x: np.rint(x).astype(int), **roll_kwargs):
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"""
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Like np.roll, but use samplerate and time_shift to approximate
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the offset to roll.
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"""
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shift = int_func(time_shift*samplerate + sample_shift)
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return np.roll(a, shift, **roll_kwargs)
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### signal generation
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def fft_bandpass(signal, band, samplerate):
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"""
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Simple bandpassing function employing a FFT.
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Parameters
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----------
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signal : arraylike
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band : tuple(low, high)
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Frequencies for bandpassing
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samplerate : float
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"""
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signal = np.asarray(signal)
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fft = ft.rfft(signal)
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freqs = ft.rfftfreq(signal.size, 1/samplerate)
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fft[(freqs < band[0]) | (freqs > band[1])] = 0
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return ft.irfft(fft, signal.size), (fft, freqs)
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def deltapeak(timelength=1e3, samplerate=1, offset=None, peaklength=1, rng=None):
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"""
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Generate a series of zeroes with a deltapeak.
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If offset is not specified, it puts it at a random location.
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Note: the series is regarded as periodic.
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Parameters
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----------
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timelength : float
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samplerate : float
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offset : float or tuple(float, float)
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Start of the peak
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peaklength : int
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Length of the peak
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"""
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N_samples = int(timelength * samplerate)
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if offset is None:
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offset = (None,None)
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if isinstance(offset, (tuple, list)):
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offset_min = 0 if offset[0] is None else offset[0]
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offset_max = N_samples if offset[-1] is None else offset[-1]
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if 0 < offset_min < 1:
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offset_min *= N_samples
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if 0 < offset_max < 1:
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offset_max *= N_samples
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if rng is not None:
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rand = rng.random(1)
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else:
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rand = np.random.random(1)
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rand = np.asarray(rand)
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offset = (rand * (offset_max - offset_min)+offset_min).astype(int) % N_samples
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position = (offset + np.arange(0, peaklength)).astype(int) % N_samples
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signal = np.zeros(N_samples)
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signal[position] = 1
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return signal, position
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