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Diffstat (limited to 'lab-windows/rocof_test_data.py')
| -rw-r--r-- | lab-windows/rocof_test_data.py | 174 |
1 files changed, 0 insertions, 174 deletions
diff --git a/lab-windows/rocof_test_data.py b/lab-windows/rocof_test_data.py deleted file mode 100644 index ccb19a0..0000000 --- a/lab-windows/rocof_test_data.py +++ /dev/null @@ -1,174 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ROCOF test waveform library -# -# This is a re-implementation of the ROCOF test waveforms described in https://zenodo.org/record/3559798 -# -# **This file is exported as a python module and loaded from other notebooks here, so please make sure to re-export when changing it.** - -# In[ ]: - - -import math -import itertools - -import numpy as np -from scipy import signal -from matplotlib import pyplot as plt - - -# In[ ]: - - -def sample_waveform(generator, duration:"s"=10, sampling_rate:"sp/s"=10000, frequency:"Hz"=50): - samples = int(duration*sampling_rate) - phases = np.linspace(0, 2*np.pi, 6, endpoint=False) - omega_t = np.linspace(phases, phases + 2*np.pi*duration*frequency, samples) - fundamental = np.sin(omega_t) - return generator(omega_t, fundamental, sampling_rate=sampling_rate, duration=duration, frequency=frequency).swapaxes(0, 1) - - -# In[ ]: - - -def gen_harmonics(amplitudes, phases=[]): - return lambda omega_t, fundamental, **_: fundamental + np.sum([ - a*np.sin((p if p else 0) + i*omega_t) - for i, (a, p) in enumerate(itertools.zip_longest(amplitudes, phases), start=2) - ], axis=0) - -def test_harmonics(): - return gen_harmonics([0.02, 0.05, 0.01, 0.06, 0.005, 0.05, 0.005, 0.015, 0.005, 0.035, 0.005, 0.003]) - - -# In[ ]: - - -def gen_interharmonic(amplitudes, ih=[], ih_phase=[]): - def gen(omega_t, fundamental, **_): - return fundamental + np.sum([ - a*np.sin(omega_t * ih + (p if p else 0)) - for a, ih, p in itertools.zip_longest(amplitudes, ih, ih_phase) - ], axis=0) - return gen - -def test_interharmonics(): - return gen_interharmonic([0.1], [15.01401], [np.pi]) - - -# In[ ]: - - -def gen_noise(amplitude=0.2, fmax:'Hz'=4.9e3, fmin:'Hz'=100, filter_order=6): - def gen(omega_t, fundamental, sampling_rate, **_): - noise = np.random.normal(0, amplitude, fundamental.shape) - b, a = signal.butter(filter_order, - [fmin, min(fmax, sampling_rate//2-1)], - btype='bandpass', - fs=sampling_rate) - return fundamental + signal.lfilter(b, a, noise, axis=0) - return gen - -def test_noise(): - return gen_noise() - -def test_noise_loud(): - return gen_noise(amplitude=0.5, fmin=10) - - -# In[406]: - - -def gen_steps(size_amplitude=0.1, size_phase=0.1*np.pi, steps_per_sec=1): - def gen(omega_t, fundamental, duration, **_): - n = int(steps_per_sec * duration) - indices = np.random.randint(0, len(omega_t), n) - amplitudes = np.random.normal(1, size_amplitude, (n, 6)) - phases = np.random.normal(0, size_phase, (n, 6)) - amplitude = np.ones(omega_t.shape) - for start, end, a, p in zip(indices, indices[1:], amplitudes, phases): - omega_t[start:end] += p - amplitude[start:end] = a - return amplitude*np.sin(omega_t) - return gen - -def test_amplitude_steps(): - return gen_steps(size_amplitude=0.4, size_phase=0) - -def test_phase_steps(): - return gen_steps(size_amplitude=0, size_phase=0.1) - -def test_amplitude_and_phase_steps(): - return gen_steps(size_amplitude=0.2, size_phase=0.07) - - -# In[418]: - - -def step_gen(shape, stdev, duration, steps_per_sec=1.0, mean=0.0): - samples, channels = shape - n = int(steps_per_sec * duration) - indices = np.random.randint(0, samples, n) - phases = np.random.normal(mean, stdev, (n, 6)) - amplitude = np.ones((samples, channels)) - out = np.zeros(shape) - for start, end, a in zip(indices, indices[1:], amplitude): - out[start:end] = a - return out - -def gen_chirp(fmin, fmax, period, dwell_time=1.0, amplitude=None, phase_steps=None): - def gen(omega_t, fundamental, sampling_rate, duration, **_): - samples = int(duration*sampling_rate) - phases = np.linspace(0, 2*np.pi, 6, endpoint=False) - - c = (fmax-fmin)/period - t = np.linspace(0, duration, samples) - - x = np.repeat(np.reshape(2*np.pi*fmin*t, (-1,1)), 6, axis=1) - data = (phases + x)[:int(sampling_rate*dwell_time)] - current_phase = 2*np.pi*fmin*dwell_time - direction = 'up' - - for idx in range(int(dwell_time*sampling_rate), samples, int(2*period*sampling_rate)): - t1 = np.linspace(0, period, int(period*sampling_rate)) - t2 = np.linspace(0, period, int(period*sampling_rate)) - chirp_phase = np.hstack(( - 2*np.pi*(c/2 * t1**2 + fmin * t1), - 2*np.pi*(-c/2 * t2**2 + fmax * t2 - (c/2 * period**2 + fmin * period)) - )) - chirp_phase = np.repeat(np.reshape(chirp_phase, (-1, 1)), 6, axis=1) - new = phases + chirp_phase + current_phase - current_phase = chirp_phase[-1] - data = np.vstack((data, new)) - - data = data[:len(fundamental)] - - if phase_steps: - (step_amplitude, steps_per_sec) = phase_steps - steps = step_gen(data.shape, step_amplitude, duration, steps_per_sec) - data += steps - - if amplitude is None: - return np.sin(data) - else: - return fundamental + amplitude*np.sin(data) - return gen - -def test_close_interharmonics_and_flicker(): - return gen_chirp(90.0, 150.0, 10, 1, amplitude=0.1) - -def test_off_frequency(): -# return gen_chirp(48.0, 52.0, 0.25, 1) - return gen_chirp(48.0, 52.0, 10, 1) - -def test_sweep_phase_steps(): - return gen_chirp(48.0, 52.0, 10, 1, phase_steps=(0.1, 1)) -# return gen_chirp(48.0, 52.0, 0.25, 1, phase_steps=(0.1, 1)) - - -# In[ ]: - - -all_tests = [test_harmonics, test_interharmonics, test_noise, test_noise_loud, test_amplitude_steps, test_phase_steps, test_amplitude_and_phase_steps, test_close_interharmonics_and_flicker, test_off_frequency, test_sweep_phase_steps] - |