mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-22 03:23:34 +01:00
118 lines
3 KiB
Python
118 lines
3 KiB
Python
"""
|
|
Various useful utilities (duh)
|
|
"""
|
|
|
|
import numpy as np
|
|
import scipy.fft as ft
|
|
|
|
def sampled_time(sample_rate=1, start=0, end=1, offset=0):
|
|
return offset + np.arange(start, end, 1/sample_rate)
|
|
|
|
def rot_vector(phi1=0.12345):
|
|
"""
|
|
Return a unit vector rotated by phi radians.
|
|
"""
|
|
|
|
unit = np.array([
|
|
phi1,
|
|
phi1 - np.pi/2
|
|
])
|
|
|
|
return np.cos(unit)
|
|
|
|
def detect_edges(threshold, data, rising=True, falling=False):
|
|
"""
|
|
Detect rising/falling edges in data, returning the indices
|
|
of the detected edges.
|
|
|
|
https://stackoverflow.com/a/50365462
|
|
"""
|
|
|
|
mask = np.full(len(data)-1, False)
|
|
|
|
if rising:
|
|
mask |= (data[:-1] < threshold) & (data[1:] > threshold)
|
|
|
|
if falling:
|
|
mask |= (data[:-1] > threshold) & (data[1:] < threshold)
|
|
|
|
return np.flatnonzero(mask)+1
|
|
|
|
def sin_delay(f, t, t_delay=0, phase=0):
|
|
return np.sin( 2*np.pi*f*(t - t_delay) + phase )
|
|
|
|
def time2phase(time, frequency=1):
|
|
return 2*np.pi*frequency*time
|
|
|
|
def phase2time(phase, frequency=1):
|
|
return phase/(2*np.pi*frequency)
|
|
|
|
def phase_modulo(phase, low=np.pi):
|
|
"""
|
|
Modulo phase such that it falls within the
|
|
interval $[-low, 2\pi - low)$.
|
|
"""
|
|
return (phase + low) % (2*np.pi) - low
|
|
|
|
def time_roll(a, samplerate, time_shift, sample_shift=0, int_func=lambda x: np.rint(x).astype(int), **roll_kwargs):
|
|
"""
|
|
Like np.roll, but use samplerate and time_shift to approximate
|
|
the offset to roll.
|
|
"""
|
|
shift = int_func(time_shift*samplerate + sample_shift)
|
|
return np.roll(a, shift, **roll_kwargs)
|
|
|
|
### signal generation
|
|
def fft_bandpass(signal, band, samplerate):
|
|
"""
|
|
Simple bandpassing function employing a FFT.
|
|
|
|
Parameters
|
|
----------
|
|
signal : arraylike
|
|
band : tuple(low, high)
|
|
Frequencies for bandpassing
|
|
samplerate : float
|
|
"""
|
|
signal = np.asarray(signal)
|
|
|
|
fft = ft.rfft(signal)
|
|
freqs = ft.rfftfreq(signal.size, 1/samplerate)
|
|
fft[(freqs < band[0]) | (freqs > band[1])] = 0
|
|
|
|
return ft.irfft(fft, signal.size), (fft, freqs)
|
|
|
|
def deltapeak(timelength=1e3, samplerate=1, offset=None, peaklength=1):
|
|
"""
|
|
Generate a series of zeroes with a deltapeak.
|
|
|
|
If offset is not specified, it puts it at a random location.
|
|
|
|
Note: the series is regarded as periodic.
|
|
|
|
Parameters
|
|
----------
|
|
timelength : float
|
|
samplerate : float
|
|
offset : float or tuple(float, float)
|
|
Start of the peak
|
|
peaklength : int
|
|
Length of the peak
|
|
"""
|
|
|
|
N_samples = int(timelength * samplerate)
|
|
if offset is None:
|
|
offset = (None,None)
|
|
|
|
if isinstance(offset, (tuple, list)):
|
|
offset_min = 0 if offset[0] is None else offset[0]
|
|
offset_max = N_samples if offset[-1] is None else offset[-1]
|
|
|
|
offset = (np.random.random(1)*(offset_max - offset_min)+offset_min).astype(int) % N_samples
|
|
|
|
position = (offset + np.arange(0, peaklength)).astype(int) % N_samples
|
|
|
|
signal = np.zeros(N_samples)
|
|
signal[position] = 1
|
|
|
|
return signal, position
|