More BER curves, initial FEC experiements

1 files changed, 134 insertions, 64 deletions

diff --git a/lab-windows/dsss_experiments-ber.ipynb b/lab-windows/dsss_experiments-ber.ipynb
index ce87548..a12b5a4 100644
--- a/lab-windows/dsss_experiments-ber.ipynb
+++ b/lab-windows/dsss_experiments-ber.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
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@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
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    "source": [
@@ -153,13 +153,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
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-       "model_id": "8a090f2d31104ec695a0c68bf39555d9",
+       "model_id": "c5fba7c7cec24f09a4f23f7c7d87eb90",
        "version_major": 2,
        "version_minor": 0
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@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,13 +366,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b961e7d9571748fd8a58a5579f95743d",
+       "model_id": "5106e62521e246a0b0766cce3f9e556f",
        "version_major": 2,
        "version_minor": 0
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@@ -386,10 +386,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7275b7807450>]"
+       "[<matplotlib.lines.Line2D at 0x7fc200d359d0>]"
       ]
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-     "execution_count": 26,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -397,6 +397,7 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(12, 9))\n",
     "\n",
+    "duration = 64\n",
     "decimation = 100\n",
     "extra_dec = 10\n",
     "nbits = 5\n",
@@ -443,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,13 +454,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
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+       "model_id": "15e67925b1bf47b5bbde289b353ed201",
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        "version_minor": 0
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@@ -473,12 +474,12 @@
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+       "model_id": "6432dbccc7db43d7a227384bca772716",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
+       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
       ]
      },
      "metadata": {},
@@ -490,22 +491,22 @@
      "text": [
       "nbits=5\n",
       "nbits=6\n",
-      "signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
+      "signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
       "signal_amplitude=0.00015: ser=0.98958 ±0.00737, br=5.85938\n",
       "signal_amplitude=0.00022: ser=0.96875 ±0.01276, br=17.57812\n",
+      "signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
       "signal_amplitude=0.00032: ser=0.97396 ±0.00737, br=14.64844\n",
-      "signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
       "signal_amplitude=0.00068: ser=0.94792 ±0.01949, br=29.29688\n",
       "signal_amplitude=0.00100: ser=0.78125 ±0.02552, br=123.04688\n",
       "signal_amplitude=0.00147: ser=0.28646 ±0.03683, br=401.36719\n",
       "signal_amplitude=0.00215: ser=0.06250 ±0.03375, br=527.34375\n",
       "signal_amplitude=0.00316: ser=0.00521 ±0.00737, br=559.57031\n",
-      "signal_amplitude=0.00010: ser=0.98958 ±0.00737, br=3.51562\n",
       "signal_amplitude=0.00015: ser=0.97917 ±0.01473, br=7.03125\n",
+      "signal_amplitude=0.00010: ser=0.98958 ±0.00737, br=3.51562\n",
       "signal_amplitude=0.00022: ser=0.98438 ±0.00000, br=5.27344\n",
       "signal_amplitude=0.00032: ser=0.98438 ±0.00000, br=5.27344\n",
-      "signal_amplitude=0.00046: ser=0.97917 ±0.01949, br=7.03125\n",
       "signal_amplitude=0.00068: ser=0.68229 ±0.09051, br=107.22656\n",
+      "signal_amplitude=0.00046: ser=0.97917 ±0.01949, br=7.03125\n",
       "signal_amplitude=0.00100: ser=0.15104 ±0.05156, br=286.52344\n",
       "signal_amplitude=0.00147: ser=0.01562 ±0.00000, br=332.22656\n",
       "signal_amplitude=0.00215: ser=0.01562 ±0.00000, br=332.22656\n",
@@ -518,10 +519,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7275bfe3b490>"
+       "<matplotlib.legend.Legend at 0x7fc23a223550>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,13 +595,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e654922b013420d83e47b27c43e18dc",
+       "model_id": "bda9f8070b0142928936a7752f318e97",
        "version_major": 2,
        "version_minor": 0
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@@ -614,10 +615,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7275bd43da50>"
+       "<matplotlib.legend.Legend at 0x7fc20046b730>"
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+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
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@@ -650,13 +651,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "23d3975515a0462e94a01e7b3c5dd1a6",
+       "model_id": "33b1f777dda149eda3a2f031087372c9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -671,15 +672,95 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/lib64/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
-      "  out=out, **kwargs)\n",
-      "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  keepdims=keepdims)\n",
-      "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
-      "/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  arrmean = um.true_divide(\n",
+      "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
       "  ret = ret.dtype.type(ret / rcount)\n"
      ]
     }
@@ -780,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -789,13 +870,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "62259928c3a24739a4b03c9b9a83859a",
+       "model_id": "406505452bb84597a6c39bc12267dcc6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -810,7 +891,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log10\n"
+      "<ipython-input-25-50856f807a2a>:20: 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"
      ]
     }
    ],
@@ -877,7 +959,7 @@
     "        for amp, ser, std in sorted(sers):\n",
     "            if last_ser > SER_TH and ser < SER_TH:\n",
     "                icp = last_amp + (SER_TH - last_ser) / (ser - last_ser) * (amp - last_amp)\n",
-    "                ic_std = abs(last_amp - amp) / 2# np.sqrt(np.mean(last_std**2 + std**2))\n",
+    "                ic_std = (abs(last_amp - amp) / 2) + np.sqrt(np.mean(last_std**2 + std**2))\n",
     "                intercepts[dec] = (icp, ic_std)\n",
     "                break\n",
     "            last_amp, last_ser = amp, ser\n",
@@ -892,7 +974,6 @@
     "    data = np.array([ intercepts[dec] for dec in x ])\n",
     "    y = data[:,0]\n",
     "    std = data[:,1]\n",
-    "    \n",
     "    ax.set_xlim([min(x), max(x)])\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",
@@ -930,20 +1011,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "41.6"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "13 * 2**5  / 10"
    ]
@@ -951,9 +1021,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "winlabenv",
+   "display_name": "labenv",
    "language": "python",
-   "name": "winlabenv"
+   "name": "labenv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -965,7 +1035,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.1"
   }
  },
  "nbformat": 4,