Prettify fft comparisons

3 files changed, 180 insertions, 64 deletions

diff --git a/lab-windows/dsss_experiments-ber.ipynb b/lab-windows/dsss_experiments-ber.ipynb
index d4a2992..91c1daf 100644
--- a/lab-windows/dsss_experiments-ber.ipynb
+++ b/lab-windows/dsss_experiments-ber.ipynb
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 221,
    "metadata": {},
    "outputs": [
     {
@@ -116,7 +116,7 @@
     }
    ],
    "source": [
-    "with open('/mnt/c/Users/jaseg/shared/raw_freq.bin', 'rb') as f:\n",
+    "with open('data/raw_freq.bin', 'rb') as f:\n",
     "    mains_noise = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
     "    print('mean:', np.mean(mains_noise), 'len:', len(mains_noise))\n",
     "    mains_noise -= np.mean(mains_noise)"
@@ -610,13 +610,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 220,
    "metadata": {},
    "outputs": [
     {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-220-44ad44d9c4c6>: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(figsize=(12, 9))\n"
+     ]
+    },
+    {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3bcd7f07ef26458da90f748fd74addfb",
+       "model_id": "f63ab2c387f948b29d992a3edd04f4fa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -630,10 +638,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f23d0aa37f0>"
+       "<matplotlib.legend.Legend at 0x7f00ecacf3a0>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -645,7 +653,7 @@
     "#    ser, std = np.mean(sers), np.std(sers)\n",
     "#    results = { nbits: [ res.get() for res in series ] for nbits, series in results.items() }\n",
     "\n",
-    "with open(f'/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-19-19-30-05.json', 'r') as f:\n",
+    "with open(f'data/dsss_experiments_res-2020-02-19-19-30-05.json', 'r') as f:\n",
     "    results = json.load(f)\n",
     "\n",
     "for nbits, series in results.items():\n",
@@ -666,21 +674,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 218,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-36-8e813d331cd8>: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",
+      "<ipython-input-218-eb5258414ca6>: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, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c05f590a7c0040628c5c5d4032bb7c8e",
+       "model_id": "6651827b5eca4a0ba46effefd605f06c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -795,9 +803,9 @@
     "\n",
     "results = []\n",
     "for fn in [\n",
-    "    '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-12-18-35.json',\n",
-    "    '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-12-26-07.json',\n",
-    "    '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-12-29-02.json'\n",
+    "    'data/dsss_experiments_res-2020-02-20-12-18-35.json',\n",
+    "    'data/dsss_experiments_res-2020-02-20-12-26-07.json',\n",
+    "    'data/dsss_experiments_res-2020-02-20-12-29-02.json'\n",
     "]:\n",
     "    with open(fn, 'r') as f:\n",
     "        results += json.load(f)\n",
@@ -893,21 +901,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 268,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-35-cafaa6062c72>: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, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n"
+      "<ipython-input-268-fd9510d5c128>: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, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05], 'hspace': 0.4})\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fa93fb31355840148f941dab8b60f51c",
+       "model_id": "be4678ec02394b81b8aee83c759e1009",
        "version_major": 2,
        "version_minor": 0
       },
@@ -922,21 +930,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-35-cafaa6062c72>:16: RuntimeWarning: divide by zero encountered in log10\n",
+      "<ipython-input-268-fd9510d5c128>:17: RuntimeWarning: divide by zero encountered in log10\n",
       "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n"
      ]
     }
    ],
    "source": [
-    "fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n",
+    "fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05], 'hspace': 0.4})\n",
     "empty.axis('off')\n",
     "#fig.tight_layout()\n",
     "\n",
     "results = []\n",
+    "\n",
     "for fn in [\n",
-    "    '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-14-10-13.json',\n",
-    "    '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-13-21-57.json',\n",
-    "    '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-13-23-47.json',\n",
+    "    'data/dsss_experiments_res-2020-02-20-14-10-13.json',\n",
+    "    'data/dsss_experiments_res-2020-02-20-13-21-57.json',\n",
+    "    'data/dsss_experiments_res-2020-02-20-13-23-47.json',\n",
     "]:\n",
     "    with open(fn, 'r') as f:\n",
     "        results += json.load(f)\n",
@@ -953,16 +962,27 @@
     "    stds = np.array([ np.std(values)  for values in data ])\n",
     "    decimation_sers[decimation] = list(zip(amps, sers, stds))\n",
     "    \n",
+    "    amps = [ amp*1000 for amp in amps ]\n",
     "    l, = ax.plot(amps, np.clip(sers, 0, 1), label=f'decimation={decimation}', color=cm_func(decimation))\n",
     "    ax.fill_between(amps, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.2)\n",
     "    ax.axhline(0.5, color='gray', ls=(0, (3, 4)), lw=0.8)\n",
     "ax.grid()\n",
-    "ax.set_xlabel('Amplitude in mHz')\n",
+    "ax.set_xlabel('Amplitude [mHz]')\n",
     "ax.set_ylabel('Symbol error rate')\n",
     "\n",
     "norm = matplotlib.colors.Normalize(vmin=np.log10(min(decimations)), vmax=np.log10(max(decimations)))\n",
-    "cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', label=\"Decimation\", ticks=[np.log10(d) for d in decimations])\n",
-    "cb1.ax.set_yticklabels([f'{d:.1f}' for d in decimations])\n",
+    "yticks = [np.log10(d) for d in decimations]\n",
+    "cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', ticks=yticks)\n",
+    "cb1t = cbar_ax.twinx()\n",
+    "cb1t.set_ylim(cbar_ax.get_ylim())\n",
+    "cb1t.set_yticks(yticks)\n",
+    "\n",
+    "cbar_ax.set_yticklabels([f'{d/sampling_rate:.1f}' for d in decimations])\n",
+    "cbar_ax.set_ylabel(\"chip duration [s]\", labelpad=-70)\n",
+    "\n",
+    "cb1t.set_yticklabels([f'{d/sampling_rate * 2**nbits:.1f}' for d in decimations])\n",
+    "cb1t.set_ylabel(\"symbol duration [s]\")\n",
+    "\n",
     "\n",
     "def plot_intercepts(ax, SER_TH = 0.5):\n",
     "    intercepts = {}\n",
@@ -988,8 +1008,10 @@
     "    std = data[:,1]\n",
     "    \n",
     "    ax.set_xlim([min(x), max(x)])\n",
-    "    l = ax.plot(x, y, label='Amplitude at SER=0.5', color='orange')\n",
-    "    ax.legend(loc=3)\n",
+    "    y = [ v*1000 if v is not None else v for v in y ]\n",
+    "    l = ax.plot(x, y, label='Amplitude at SER=0.5 [mHz]', color='orange')\n",
+    "    #ax.legend(loc=3)\n",
+    "    ax.set_ylabel('Amplitude at SER=0.5 [mHz]')\n",
     "    ax.grid()\n",
     "    \n",
     "    x, y, std = zip(*[ (le_x, le_y, le_std) for le_x, le_y, le_std in zip(x, y, std) if le_y is not None ])\n",
@@ -1002,12 +1024,43 @@
     "    ax.set_ylim([min(y)*0.9, max(y)*1.1])\n",
     "    ax.set_xscale('log')\n",
     "    ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, _: '{:g}'.format(x)))\n",
-    "    ax.set_xticks([1, 2, 5, 10, 20, 50])\n",
+    "    xticks = [1, 2, 5, 10, 20, 50]\n",
+    "    ax.set_xticks(xticks)\n",
+    "    ax.set_xticklabels([ f'{x/sampling_rate:.1f}' for x in xticks ])\n",
     "    ax.set_xlim([1, 60])\n",
+    "    ax.set_xlabel('chip duration [s]')\n",
+    "    \n",
+    "    axt = ax.twiny()\n",
+    "    axt.set_xlim(ax.get_xlim())\n",
+    "    axt.set_xscale('log')\n",
+    "    axt.set_xticks(xticks)\n",
+    "    axt.set_xticklabels([ f'{x/sampling_rate * 2**nbits:.1f}' for x in xticks ])\n",
+    "    axt.set_xlabel('symbol duration [s]')\n",
+    "    \n",
     "    return l\n",
     "\n",
     "l1 = plot_intercepts(intercept_ax)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "41.6"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "13 * 2**5  / 10"
+   ]
   }
  ],
  "metadata": {
diff --git a/lab-windows/grid_frequency_spectra.ipynb b/lab-windows/grid_frequency_spectra.ipynb
index 3d375ef..7b187f5 100644
--- a/lab-windows/grid_frequency_spectra.ipynb
+++ b/lab-windows/grid_frequency_spectra.ipynb
@@ -141,13 +141,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-72-51d3a7cc1678>:20: 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()\n"
+     ]
+    },
+    {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd94bc97a276400db0539b703c4eeeac",
+       "model_id": "2991d932b113496a9135d569f9577abe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -161,10 +169,10 @@
     {
      "data": {
       "text/plain": [
-       "(1.6666666666666667e-05, 0.5)"
+       "(5e-07, 0.02)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,8 +202,14 @@
     "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n",
     "ax.set_xlabel('T in s')\n",
     "ax.set_ylabel('Amplitude Δf')\n",
+    "\n",
+    "for i, t in enumerate([45, 60, 600, 1200, 1800, 3600]):\n",
+    "    ax.axvline(1/t, color='red', alpha=0.5)\n",
+    "    ax.annotate(f'{t} s', xy=(1/t, 3e-3), xytext=(-15, 0), xycoords='data', textcoords='offset pixels', rotation=90)\n",
+    "#ax.text(1/60, 10,'60 s', ha='left')\n",
     "ax.grid()\n",
-    "ax.set_xlim([1/60000, 0.5])"
+    "ax.set_xlim([1/60000, 0.5])\n",
+    "ax.set_ylim([5e-7, 2e-2])"
    ]
   },
   {
diff --git a/lab-windows/grid_scope.ipynb b/lab-windows/grid_scope.ipynb
index 9f53906..6ce34fb 100644
--- a/lab-windows/grid_scope.ipynb
+++ b/lab-windows/grid_scope.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db = sqlite3.connect('/mnt/c/Users/jaseg/shared/waveform-raspi.sqlite3')"
+    "db = sqlite3.connect('data/waveform-raspi.sqlite3')"
    ]
   },
   {
@@ -100,7 +100,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f960454ab93244db97e039ae7ebd02ee",
+       "model_id": "55690b4f59d54454b83deb5200c05f09",
        "version_major": 2,
        "version_minor": 0
       },
@@ -177,7 +177,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "264a9f8478e449289c1592c9595dc6cc",
+       "model_id": "cf5c2e35008740379508eaaedfcf3b31",
        "version_major": 2,
        "version_minor": 0
       },
@@ -246,7 +246,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c2382eb8e124ef9b29546ecaed3e155",
+       "model_id": "efa9db02cdfc4f6c86d1eadd4769d097",
        "version_major": 2,
        "version_minor": 0
       },
@@ -279,7 +279,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d29e8ee5bf7f4e94a1749239aec24d6d",
+       "model_id": "e4122530c1444f75aca410ad51d4a733",
        "version_major": 2,
        "version_minor": 0
       },
@@ -356,7 +356,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47c40f28b5a34a94b4a631fd62107521",
+       "model_id": "a36a994f6f484a6cba81b6e680c9eee1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -412,7 +412,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a4cb62296df496bad37d93547d3c26a",
+       "model_id": "e4116959544f44f6a4b898419a5d51db",
        "version_major": 2,
        "version_minor": 0
       },
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -509,21 +509,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-81-a751e13723ea>:17: 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(figsize=(9,5))\n"
-     ]
-    },
-    {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3809a1a83b5844e3906f1d74bcd15b5d",
+       "model_id": "09069e2c9cb740d5b58846371aeea0f2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -548,7 +540,7 @@
        "5.0"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,21 +577,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-156-ddde6af5dee1>:20: 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()\n"
-     ]
-    },
-    {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d137ae59ed7947ce8e3f7295e102f2f0",
+       "model_id": "b812e4b240a642398f09bb3cff8f5cee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -616,7 +600,7 @@
        "(1.6666666666666667e-05, 0.5)"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -649,6 +633,71 @@
     "ax.grid()\n",
     "ax.set_xlim([1/60000, 0.5])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c835382368b6423ab5446835c5785afd",
+       "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": [
+       "(5e-07, 0.02)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Number of samplepoints\n",
+    "N = len(data)\n",
+    "# sample spacing\n",
+    "T = 1.0 / 10.0\n",
+    "x = np.linspace(0.0, N*T, N)\n",
+    "yf = scipy.fftpack.fft(data)\n",
+    "xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n",
+    "\n",
+    "yf = 2.0/N * np.abs(yf[:N//2])\n",
+    "\n",
+    "average_from = lambda val, start, average_width: np.hstack([val[:start], [ np.mean(val[i:i+average_width]) for i in range(start, len(val), average_width) ]])\n",
+    "\n",
+    "average_width = 6\n",
+    "average_start = 20\n",
+    "yf = average_from(yf, average_start, average_width)\n",
+    "xf = average_from(xf, average_start, average_width)\n",
+    "yf = average_from(yf, 70, 4)\n",
+    "xf = average_from(xf, 70, 4)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.loglog(xf, yf)\n",
+    "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n",
+    "ax.set_xlabel('T in s')\n",
+    "ax.set_ylabel('Amplitude Δf')\n",
+    "\n",
+    "for i, t in enumerate([45, 60, 600, 1200, 1800, 3600]):\n",
+    "    ax.axvline(1/t, color='red', alpha=0.5)\n",
+    "    ax.annotate(f'{t} s', xy=(1/t, 3e-5), xytext=(-15, 0), xycoords='data', textcoords='offset pixels', rotation=90)\n",
+    "#ax.text(1/60, 10,'60 s', ha='left')\n",
+    "ax.grid()\n",
+    "ax.set_xlim([1/60000, 0.5])\n",
+    "ax.set_ylim([5e-7, 2e-2])"
+   ]
   }
  ],
  "metadata": {