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authorjaseg <git-bigdata-wsl-arch@jaseg.de>2021-04-09 18:38:02 +0200
committerjaseg <git-bigdata-wsl-arch@jaseg.de>2021-04-09 18:38:57 +0200
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+#!/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[ ]:
+
+
+get_ipython().run_line_magic('matplotlib', 'notebook')
+
+
+# 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]
+