Prettify fft comparisons

1 files changed, 81 insertions, 28 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": {