Fix frequency measurement simulation

author: jaseg <git-bigdata-wsl-arch@jaseg.de> 2020-03-04 17:10:31 +0100
committer: jaseg <git-bigdata-wsl-arch@jaseg.de> 2020-03-04 17:10:31 +0100
commit: d9b26d16c063aec32b70287ff861a1f23642a9f2 (patch)
tree: 6259bd8509ea2e442e21c4d68c61098cdf7ae19d /lab-windows
parent: 4d80111cad6891cd0e1d5da59e83222d39933e41 (diff)
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15 files changed, 922 insertions, 0 deletions
diff --git a/lab-windows/Phase Measurement Prototype.ipynb b/lab-windows/Phase Measurement Prototype.ipynb
new file mode 100644
index 0000000..11f6226
--- /dev/null
+++ b/lab-windows/Phase Measurement Prototype.ipynb
@@ -0,0 +1,396 @@
+{
+ "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": [
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+    "\n",
+    "#gen = rocof_test_data.gen_noise(fmin=10, amplitude=1)\n",
+    "# gen = rocof_test_data.gen_noise(fmin=60, amplitude=0.2)\n",
+    "# gen = rocof_test_data.test_harmonics()\n",
+    "# gen = rocof_test_data.gen_interharmonic(*rocof_test_data.test_interharmonics)\n",
+    "# gen = rocof_test_data.test_amplitude_steps()\n",
+    "# gen = rocof_test_data.test_amplitude_and_phase_steps()\n",
+    "test_data = []\n",
+    "test_labels = [ fun.__name__.replace('test_', '') for fun in rocof_test_data.all_tests ]\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",
+    "# d = 10 # seconds\n",
+    "# test_data = np.sin(2*np.pi * ff * np.linspace(0, d, int(d*fs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analysis_periods = 10\n",
+    "window_len = 256 # fs * analysis_periods/ff\n",
+    "nfft_factor = 1\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": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(129, 470)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Zxx.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.90625"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1000/256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-61-530955947ba4>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30b7e529e3f94c1b8241c6b6760b7e69",
+       "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": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-76-31c82486a777>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e0976ade81c4990a10fd1182f0f20d5",
+       "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(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": 62,
+   "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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-63-888d30b0f7d6>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, axs = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cef58f5753084c54a648418e134775d8",
+       "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, axs = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n",
+    "fig.tight_layout(pad=2.2, h_pad=0, w_pad=1)\n",
+    "\n",
+    "for fft, ax, label in zip(ffts, axs.flatten(), test_labels):\n",
+    "    f, f_t, Zxx = fft\n",
+    "    \n",
+    "    n_f, n_t = Zxx.shape\n",
+    "    # start, stop = 180, 220\n",
+    "    # start, stop = 90, 110\n",
+    "    # start, stop = 15, 35\n",
+    "    # bounds_f = slice(start // 4 * nfft_factor, stop // 4 * nfft_factor)\n",
+    "    f_min, f_max = 30, 70 # Hz\n",
+    "    bounds_f = slice(np.argmax(f > f_min), np.argmin(f < f_max))\n",
+    "    \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",
+    "        #if t == 10:\n",
+    "        #    axs[-1].plot(frame_f, frame_Z)\n",
+    "        frame_Z = np.abs(Zxx[bounds_f, t])\n",
+    "        # frame_f = f[180:220]\n",
+    "        # frame_Z = np.abs(Zxx[180:220, 40])\n",
+    "        # frame_f = f[15:35]\n",
+    "        # frame_Z = np.abs(Zxx[15:35, 40])\n",
+    "        # plt.plot(frame_f, frame_Z)\n",
+    "\n",
+    "        # peak_f = frame_f[np.argmax(frame)]\n",
+    "        # plt.axvline(peak_f, color='red')\n",
+    "\n",
+    "#         def gauss(x, *p):\n",
+    "#             A, mu, sigma, o = p\n",
+    "#             return A*np.exp(-(x-mu)**2/(2.*sigma**2)) + o\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",
+    "            # plt.plot(frame_f, gauss(frame_f, *coeff))\n",
+    "            #print(coeff)\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",
+    "#     b, a = signal.butter(3,\n",
+    "#                          1/5, # Hz\n",
+    "#                          btype='lowpass',\n",
+    "#                          fs=1/time_step)\n",
+    "#     filtered = signal.lfilter(b, a, f_mean[1:-1], axis=0)\n",
+    "#     ax.plot(f_t[1:-1], filtered)\n",
+    "    \n",
+    "    ax.set_title(label, pad=-20)\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')\n",
+    "        ax.text(0.5, 0.25, f'σ={np.sqrt(var) * 1e3:.3g} mHz', transform=ax.transAxes, ha='center')\n",
+    "#         ax.text(0.5, 0.2, f'filt. σ²={np.var(filtered) * 1e3:.3g} mHz', transform=ax.transAxes, ha='center')\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",
+    "None"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "labenv",
+   "language": "python",
+   "name": "labenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lab-windows/gauss_derivations.ipynb b/lab-windows/gauss_derivations.ipynb
new file mode 100644
index 0000000..6e0dcd8
--- /dev/null
+++ b/lab-windows/gauss_derivations.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\[\\tag{${\\it \\%o}_{2}$}f\\left(a , \\mu , \\sigma , x\\right):=a\\,\\exp \\left(\\frac{-\\left(x-\\mu\\right)^2}{2\\,\\sigma^2}\\right)\\]"
+      ],
+      "text/plain": [
+       "                                                         2\n",
+       "                                               - (x - mu)\n",
+       "(%o2)              f(a, mu, sigma, x) := a exp(-----------)\n",
+       "                                                       2\n",
+       "                                                2 sigma"
+      ],
+      "text/x-maxima": [
+       "f(a,mu,sigma,x):=a*exp((-(x-mu)^2)/(2*sigma^2))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(a, mu, sigma, x) := a*exp(-(x-mu)^2/(2*sigma^2));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\[\\tag{${\\it \\%o}_{11}$}-\\frac{a\\,\\left(x-\\mu\\right)\\,e^ {- \\frac{\\left(x-\\mu\\right)^2}{2\\,\\sigma^2} }}{\\sigma^2}\\]"
+      ],
+      "text/plain": [
+       "                                                   2\n",
+       "                                           (x - mu)\n",
+       "                                         - ---------\n",
+       "                                                  2\n",
+       "                                           2 sigma\n",
+       "                            a (x - mu) %e\n",
+       "(%o11)                    - ------------------------\n",
+       "                                          2\n",
+       "                                     sigma"
+      ],
+      "text/x-maxima": [
+       "-(a*(x-mu)*%e^-((x-mu)^2/(2*sigma^2)))/sigma^2"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(f(a, mu, sigma, x), x);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\[\\tag{${\\it \\%o}_{12}$}e^ {- \\frac{\\left(x-\\mu\\right)^2}{2\\,\\sigma^2} }\\]"
+      ],
+      "text/plain": [
+       "                                             2\n",
+       "                                     (x - mu)\n",
+       "                                   - ---------\n",
+       "                                            2\n",
+       "                                     2 sigma\n",
+       "(%o12)                           %e"
+      ],
+      "text/x-maxima": [
+       "%e^-((x-mu)^2/(2*sigma^2))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(f(a, mu, sigma, x), a);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\[\\tag{${\\it \\%o}_{13}$}\\frac{a\\,\\left(x-\\mu\\right)\\,e^ {- \\frac{\\left(x-\\mu\\right)^2}{2\\,\\sigma^2} }}{\\sigma^2}\\]"
+      ],
+      "text/plain": [
+       "                                                  2\n",
+       "                                          (x - mu)\n",
+       "                                        - ---------\n",
+       "                                                 2\n",
+       "                                          2 sigma\n",
+       "                           a (x - mu) %e\n",
+       "(%o13)                     ------------------------\n",
+       "                                         2\n",
+       "                                    sigma"
+      ],
+      "text/x-maxima": [
+       "(a*(x-mu)*%e^-((x-mu)^2/(2*sigma^2)))/sigma^2"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(f(a, mu, sigma, x), mu);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\[\\tag{${\\it \\%o}_{14}$}\\frac{a\\,\\left(x-\\mu\\right)^2\\,e^ {- \\frac{\\left(x-\\mu\\right)^2}{2\\,\\sigma^2} }}{\\sigma^3}\\]"
+      ],
+      "text/plain": [
+       "                                                   2\n",
+       "                                           (x - mu)\n",
+       "                                         - ---------\n",
+       "                                                  2\n",
+       "                                     2     2 sigma\n",
+       "                           a (x - mu)  %e\n",
+       "(%o14)                     -------------------------\n",
+       "                                         3\n",
+       "                                    sigma"
+      ],
+      "text/x-maxima": [
+       "(a*(x-mu)^2*%e^-((x-mu)^2/(2*sigma^2)))/sigma^3"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(f(a, mu, sigma, x), sigma);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Maxima",
+   "language": "maxima",
+   "name": "maxima"
+  },
+  "language_info": {
+   "codemirror_mode": "maxima",
+   "file_extension": ".mac",
+   "mimetype": "text/x-maxima",
+   "name": "maxima",
+   "pygments_lexer": "maxima",
+   "version": "5.43.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lab-windows/impl_test_out.json b/lab-windows/impl_test_out.json
new file mode 100644
index 0000000..c25eba8
--- /dev/null
+++ b/lab-windows/impl_test_out.json
@@ -0,0 +1 @@
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+\ No newline at end of file
diff --git a/lab-windows/rocof_test_data.py b/lab-windows/rocof_test_data.py
new file mode 100644
index 0000000..ccb19a0
--- /dev/null
+++ b/lab-windows/rocof_test_data.py
@@ -0,0 +1,174 @@
+#!/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]
+
diff --git a/lab-windows/rocof_test_data/rocof_test_amplitude_and_phase_steps_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_amplitude_and_phase_steps_1kHz.bin
new file mode 100644
index 0000000..008a752
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_amplitude_and_phase_steps_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_amplitude_steps_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_amplitude_steps_1kHz.bin
new file mode 100644
index 0000000..0240e23
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_amplitude_steps_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_close_interharmonics_and_flicker_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_close_interharmonics_and_flicker_1kHz.bin
new file mode 100644
index 0000000..6633639
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_close_interharmonics_and_flicker_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_harmonics_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_harmonics_1kHz.bin
new file mode 100644
index 0000000..c57fc34
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_harmonics_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_interharmonics_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_interharmonics_1kHz.bin
new file mode 100644
index 0000000..ab0b9e2
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_interharmonics_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_noise_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_noise_1kHz.bin
new file mode 100644
index 0000000..c59cf96
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_noise_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_noise_loud_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_noise_loud_1kHz.bin
new file mode 100644
index 0000000..58ab5b6
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_noise_loud_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_off_frequency_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_off_frequency_1kHz.bin
new file mode 100644
index 0000000..a4f128e
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_off_frequency_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_phase_steps_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_phase_steps_1kHz.bin
new file mode 100644
index 0000000..2faebce
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_phase_steps_1kHz.bin
diff --git a/lab-windows/rocof_test_data/rocof_test_sweep_phase_steps_1kHz.bin b/lab-windows/rocof_test_data/rocof_test_sweep_phase_steps_1kHz.bin
new file mode 100644
index 0000000..6c73b99
--- /dev/null
+++ b/lab-windows/rocof_test_data/rocof_test_sweep_phase_steps_1kHz.bin
diff --git a/lab-windows/scratch.ipynb b/lab-windows/scratch.ipynb
new file mode 100644
index 0000000..b35c76d
--- /dev/null
+++ b/lab-windows/scratch.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "712481c28d9d4e1d874a66d31c3e8bff",
+       "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"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0, 64)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "a = np.array([-00.000732, -00.000352, -00.000666, -00.000202, -00.000706, -00.000006, -00.000597, -00.002039, 000.050663, -00.644566, 004.456614, -16.817095, 034.654587, -39.021217, 024.007816, -08.070650, 001.478795, -00.150260, 000.006110, -00.002328, -00.002322, -00.002426, -00.002177, -00.002452, -00.002333, -00.002438, -00.002342, -00.002396, -00.001979, -00.003049, -00.001720, -00.002686, -00.002168, -00.002507, -00.001868, -00.002899, -00.002017, -00.001952, -00.003255, -00.001080, -00.003335, -00.001575, -00.002704, -00.001872, -00.002735, -00.001983, -00.002191, -00.002478, -00.002155, -00.002203, -00.002328, -00.002206, -00.002443, -00.001770, -00.002718, -00.002004, -00.002378, -00.002112, -00.002122, -00.002691, -00.001679, -00.002690, -00.001946, -00.002232])\n",
+    "b = np.array([-00.002734, -00.001325, -00.002220, -00.003693, -00.004907, -00.006454, -00.007737, 000.004823, -00.363143, 004.688968, -33.795303, 130.992630, -274.092651, 309.377991, -188.427826, 061.912941, -10.974002, 001.053608, -00.048927, 000.007710, 000.007010, 000.006493, 000.007234, 000.006725, 000.006938, 000.006694, 000.006356, 000.006173, 000.006333, 000.005684, 000.005697, 000.005575, 000.005101, 000.005693, 000.004319, 000.005344, 000.004673, 000.003566, 000.006213, 000.002719, 000.004850, 000.003755, 000.004243, 000.003419, 000.003960, 000.003498, 000.003297, 000.003877, 000.002836, 000.003487, 000.003144, 000.002824, 000.003355, 000.002528, 000.002975, 000.003012, 000.002137, 000.003112, 000.002416, 000.002512, 000.002084, 000.003008, 000.001837, 000.002351])\n",
+    "ax.plot([3.906250*i for i in range(len(a))], np.sqrt(a**2 + b**2))\n",
+    "a2 = ax.twiny()\n",
+    "a2.set_xlim([0, len(a)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4024377df494eac935fd487026edc8b",
+       "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"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f2eb410f280>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "d = [50.000839,50.000839,50.000832,50.000824,50.000839,50.000832,50.000839,50.000824,50.000847,50.000824,50.000824,50.000839,50.000832,50.000839,50.000824,50.000824,50.000839,50.000824,50.000824,50.000835,50.000816,50.000832,50.000847,50.000832,50.000835,50.000824,50.000824,50.000832,50.000832,50.000843,50.000824,50.000832,50.000832,50.000832,50.000828,50.000832,50.000832,50.000824,50.000816,50.000835,50.000843,50.000824,50.000824,50.000832,50.000832,50.000847,50.000824,50.000824,50.000824,50.000835,50.000835,50.000851,50.000824,50.000824,50.000832,50.000828,50.000828,50.000824,50.000832,50.000835,50.000835,50.000832,50.000847,50.000824,50.000832,50.000839,50.000839,50.000824,50.000832,50.000832,50.000832,50.000835,50.000816,50.000820,50.000824,50.000832,50.000824,50.000832,50.000835,50.000832,50.000816,50.000820,50.000839,50.000839,50.000824,50.000839,50.000820,50.000820,50.000839,50.000832,50.000835,50.000828,50.000824,50.000839,50.000839,50.000839,50.000816,50.000832,50.000824,50.000832,50.000832,50.000839,50.000824,50.000832,50.000828,50.000832,50.000828,50.000835,50.000832,50.000843,50.000839,50.000820,50.000832,50.000835,50.000824,50.000824,50.000828,50.000820,50.000820,50.000828,50.000832,50.000832,50.000828,50.000835,50.000839,50.000820,50.000832,50.000832,50.000824,50.000832,50.000832,50.000839,50.000839,50.000816,50.000828,50.000832,50.000839,50.000824,50.000824,50.000824,50.000835,50.000824,50.000832,50.000839,50.000835,50.000832,50.000828,50.000835,50.000828,50.000828,50.000824,50.000824,50.000839,50.000832,50.000824,50.000832,50.000832,50.000820,50.000851,50.000824,50.000824,50.000839,50.000824,50.000839,50.000832,50.000835,50.000820,50.000832,50.000839,50.000832,50.000832,50.000824,50.000832,50.000824,50.000832,50.000839,50.000839,50.000832,50.000816,50.000835,50.000854,50.000824,50.000816,50.000832,50.000832,50.000835,50.000816,50.000832,50.000824,50.000832,50.000832,50.000832,50.000824,50.000832,50.000824,50.000835,50.000832,50.000835,50.000832,50.000832,50.000828,50.000839,50.000824,50.000839,50.000824,50.000824,50.000839,50.000816,50.000839,50.000816,50.000832,50.000839,50.000839,50.000832,50.000824,50.000832,50.000820,50.000824,50.000835,50.000824,50.000835,50.000832,50.000824,50.000824,50.000820,50.000839,50.000816,50.000832,50.000832,50.000832,50.000824,50.000847,50.000824,50.000839]\n",
+    "\n",
+    "ax.plot(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('impl_test_out.json') as f:\n",
+    "    impl_measurements = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd23cf23221e4e14aaafdd58bb9416d9",
+       "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, axs = plt.subplots(len(impl_measurements), figsize=(8, 20), sharex=True)\n",
+    "fig.tight_layout()\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "for (label, data), ax in zip(impl_measurements.items(), axs):\n",
+    "    ax.set_title(label)\n",
+    "    ax.plot(data[1:-1])\n",
+    "    mean = np.mean(data[1:-1])\n",
+    "    rms = np.sqrt(np.mean(np.square(data[1:-1] - mean)))\n",
+    "    ax.text(0.2, 0.2, f'mean={mean:.3}Hz, rms={rms*1e3:.3}mHz', ha='center', va='center', transform=ax.transAxes,\n",
+    "            bbox=dict(boxstyle=\"square\", ec=(0,0,0,0), fc=(1,1,1,0.8)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "labenv",
+   "language": "python",
+   "name": "labenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
author	jaseg <git-bigdata-wsl-arch@jaseg.de>	2020-03-04 17:10:31 +0100
committer	jaseg <git-bigdata-wsl-arch@jaseg.de>	2020-03-04 17:10:31 +0100
commit	d9b26d16c063aec32b70287ff861a1f23642a9f2 (patch)
tree	6259bd8509ea2e442e21c4d68c61098cdf7ae19d /lab-windows
parent	4d80111cad6891cd0e1d5da59e83222d39933e41 (diff)
download	master-thesis-d9b26d16c063aec32b70287ff861a1f23642a9f2.tar.gz master-thesis-d9b26d16c063aec32b70287ff861a1f23642a9f2.tar.bz2 master-thesis-d9b26d16c063aec32b70287ff861a1f23642a9f2.zip