{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import struct\n",
"\n",
"import numpy as np\n",
"from scipy import signal, optimize\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import rocof_test_data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"from IPython.display import set_matplotlib_formats\n",
"%matplotlib widget\n",
"#%matplotlib inline\n",
"#set_matplotlib_formats('png', 'pdf')\n",
"#font = {'family' : 'normal',\n",
"# 'weight' : 'normal',\n",
"# 'size' : 10}\n",
"#matplotlib.rc('font', **font)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"fs = 1000 # Hz\n",
"ff = 50 # Hz\n",
"duration = 60 # seconds\n",
"# test_data = rocof_test_data.sample_waveform(rocof_test_data.test_close_interharmonics_and_flicker(),\n",
"# duration=20,\n",
"# sampling_rate=fs,\n",
"# frequency=ff)[0]\n",
"# test_data = rocof_test_data.sample_waveform(rocof_test_data.gen_noise(fmin=10, amplitude=1),\n",
"# duration=20,\n",
"# sampling_rate=fs,\n",
"# frequency=ff)[0]\n",
"\n",
"test_data = []\n",
"test_labels = [ fun.__name__.replace('test_', '') for fun in rocof_test_data.all_tests ] + ['tmp']\n",
"for gen in rocof_test_data.all_tests:\n",
" test_data.append(rocof_test_data.sample_waveform(gen(),\n",
" duration=duration,\n",
" sampling_rate=fs,\n",
" frequency=ff)[0])\n",
"test_data.append(rocof_test_data.sample_waveform(rocof_test_data.gen_chirp(49.8, 50.6, 0.40, dwell_time=0.60), duration=duration, sampling_rate=fs, frequency=ff)[0])\n",
"# d = 10 # seconds\n",
"# test_data = np.sin(2*np.pi * ff * np.linspace(0, d, int(d*fs)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"spr_fmt = f'{fs}Hz' if fs<1000 else f'{fs/1e3:f}'.rstrip('.0') + 'kHz'\n",
"for label, data in zip(test_labels, test_data):\n",
" with open(f'rocof_test_data/rocof_test_{label}_{spr_fmt}.bin', 'wb') as f:\n",
" for sample in data:\n",
" f.write(struct.pack('<f', sample))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"analysis_periods = 10\n",
"window_len = 256 # fs * analysis_periods/ff\n",
"nfft_factor = 8\n",
"sigma = window_len/8 # samples\n",
"quantization_bits = 14\n",
"\n",
"ffts = []\n",
"for item in test_data:\n",
" f, t, Zxx = signal.stft((item * (2**(quantization_bits-1) - 1)).round().astype(np.int16).astype(float),\n",
" fs = fs,\n",
" window=('gaussian', sigma),\n",
" nperseg = window_len,\n",
" nfft = window_len * nfft_factor)\n",
" #boundary = 'zeros')\n",
" ffts.append((f, t, Zxx))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(129, 470)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Zxx.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.90625"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1000/256"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "429b30daffb04963ab07c34115ecb84b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n",
"fig.tight_layout(pad=2, h_pad=0.1)\n",
"\n",
"for fft, ax, label in zip(test_data, ax.flatten(), test_labels):\n",
" ax.plot((item * (2**(quantization_bits-1) - 1)).round())\n",
" \n",
" ax.set_title(label, pad=-20, color='white', bbox=dict(boxstyle=\"square\", ec=(0,0,0,0), fc=(0,0,0,0.8)))\n",
" ax.grid()\n",
" ax.set_ylabel('f [Hz]')\n",
"ax.set_xlabel('simulation time t [s]')\n",
"ax.set_xlim([5000, 5200])\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c2ca60f3c6d489290a770d78195c4fc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-27-31c82486a777>:6: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n",
" ax.pcolormesh(t[1:], f[:250], np.abs(Zxx[:250,1:]))\n"
]
}
],
"source": [
"fig, ax = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n",
"fig.tight_layout(pad=2, h_pad=0.1)\n",
"\n",
"for fft, ax, label in zip(ffts, ax.flatten(), test_labels):\n",
" f, t, Zxx = fft\n",
" ax.pcolormesh(t[1:], f[:250], np.abs(Zxx[:250,1:]))\n",
" ax.set_title(label, pad=-20, color='white')\n",
" ax.grid()\n",
" ax.set_ylabel('f [Hz]')\n",
" ax.set_ylim([30, 75]) # Hz\n",
"ax.set_xlabel('simulation time t [s]')\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 3.90625, 7.8125 , 11.71875, 15.625 , 19.53125,\n",
" 23.4375 , 27.34375, 31.25 , 35.15625, 39.0625 , 42.96875,\n",
" 46.875 , 50.78125, 54.6875 , 58.59375, 62.5 , 66.40625,\n",
" 70.3125 , 74.21875, 78.125 , 82.03125, 85.9375 , 89.84375,\n",
" 93.75 , 97.65625, 101.5625 , 105.46875, 109.375 , 113.28125,\n",
" 117.1875 , 121.09375, 125. , 128.90625, 132.8125 , 136.71875,\n",
" 140.625 , 144.53125, 148.4375 , 152.34375, 156.25 , 160.15625,\n",
" 164.0625 , 167.96875, 171.875 , 175.78125, 179.6875 , 183.59375,\n",
" 187.5 , 191.40625, 195.3125 , 199.21875, 203.125 , 207.03125,\n",
" 210.9375 , 214.84375, 218.75 , 222.65625, 226.5625 , 230.46875,\n",
" 234.375 , 238.28125, 242.1875 , 246.09375, 250. , 253.90625,\n",
" 257.8125 , 261.71875, 265.625 , 269.53125, 273.4375 , 277.34375,\n",
" 281.25 , 285.15625, 289.0625 , 292.96875, 296.875 , 300.78125,\n",
" 304.6875 , 308.59375, 312.5 , 316.40625, 320.3125 , 324.21875,\n",
" 328.125 , 332.03125, 335.9375 , 339.84375, 343.75 , 347.65625,\n",
" 351.5625 , 355.46875, 359.375 , 363.28125, 367.1875 , 371.09375,\n",
" 375. , 378.90625, 382.8125 , 386.71875, 390.625 , 394.53125,\n",
" 398.4375 , 402.34375, 406.25 , 410.15625, 414.0625 , 417.96875,\n",
" 421.875 , 425.78125, 429.6875 , 433.59375, 437.5 , 441.40625,\n",
" 445.3125 , 449.21875, 453.125 , 457.03125, 460.9375 , 464.84375,\n",
" 468.75 , 472.65625, 476.5625 , 480.46875, 484.375 , 488.28125,\n",
" 492.1875 , 496.09375, 500. ])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b361be278748475bbe442f861a99418b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n",
"/usr/lib/python3.9/site-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n"
]
}
],
"source": [
"fig, axs = plt.subplots(len(test_data)-1, figsize=(12, 15), sharex=True)\n",
"axs = axs.flatten()\n",
"\n",
"for fft, label in zip(ffts, test_labels):\n",
" if label in ['noise_loud']: # custom test case, not part of upstream suite\n",
" continue\n",
" ax, *axs = axs\n",
" \n",
" f, f_t, Zxx = fft\n",
" \n",
" n_f, n_t = Zxx.shape\n",
" f_min, f_max = 30, 70 # Hz\n",
" bounds_f = slice(np.argmax(f > f_min), np.argmin(f < f_max))\n",
" \n",
" f_mean = np.zeros(Zxx.shape[1])\n",
" for t in range(1, Zxx.shape[1] - 1):\n",
" frame_f = f[bounds_f]\n",
" frame_step = frame_f[1] - frame_f[0]\n",
" time_step = f_t[1] - f_t[0]\n",
" frame_Z = np.abs(Zxx[bounds_f, t])\n",
" \n",
" def gauss(x, *p):\n",
" A, mu, sigma = p\n",
" return A*np.exp(-(x-mu)**2/(2.*sigma**2))\n",
" \n",
" f_start = frame_f[np.argmax(frame_Z)]\n",
" A_start = np.max(frame_Z)\n",
" p0 = [A_start, f_start, 1.]\n",
" try:\n",
" coeff, var = optimize.curve_fit(gauss, frame_f, frame_Z, p0=p0)\n",
" A, mu, sigma, *_ = coeff\n",
" f_mean[t] = mu\n",
" except RuntimeError:\n",
" f_mean[t] = np.nan\n",
" ax.plot(f_t[1:-1], f_mean[1:-1])\n",
" \n",
" ax.set_title(label, pad=-20, bbox=dict(fc='white', alpha=0.8, ec='none'))\n",
" ax.set_ylabel('f [Hz]')\n",
" ax.grid()\n",
" if not label in ['off_frequency', 'sweep_phase_steps']:\n",
" #ax.set_ylim([49.90, 50.10])\n",
" var = np.var(f_mean[1:-1])\n",
" ax.text(0.5, 0.1, f'σ²={var * 1e3:.3g} mHz²', transform=ax.transAxes, ha='center', bbox=dict(fc='white', alpha=0.8, ec='none'))\n",
" ax.text(0.5, 0.25, f'σ={np.sqrt(var) * 1e3:.3g} mHz', transform=ax.transAxes, ha='center', bbox=dict(fc='white', alpha=0.8, ec='none'))\n",
" else:\n",
" f_min, f_max = min(f_mean[1:-1]), max(f_mean[1:-1])\n",
" delta = f_max - f_min\n",
" ax.set_ylim(f_min - delta * 0.1, f_max + delta * 0.3)\n",
" \n",
"ax.set_xlabel('simulation time t [s]')\n",
"fig.tight_layout(pad=2.2, h_pad=0, w_pad=1)\n",
"fig.savefig('fig_out/freq_meas_rocof_reference.pdf')\n",
"None"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ma-thesis-env",
"language": "python",
"name": "ma-thesis-env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.2"
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"nbformat": 4,
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