More BER curves, initial FEC experiements
This commit is contained in:
jaseg
parent
36951a5f19
commit
9b39be3180
5 changed files with 316 additions and 64 deletions
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lab-windows/data/dsss_experiments_res-2020-02-20-19-51-21.json
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lab-windows/data/dsss_experiments_res-2020-02-20-20-43-32.json
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lab-windows/data/dsss_experiments_res-2020-02-20-21-36-42.json
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"/usr/lib64/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
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" out=out, **kwargs)\n",
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"/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
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" return _methods._mean(a, axis=axis, dtype=dtype,\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
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" keepdims=keepdims)\n",
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"/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide\n",
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" arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
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"/usr/lib64/python3.7/site-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
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"/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",
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" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
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"/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",
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" arrmean = um.true_divide(\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
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" return _methods._mean(a, axis=axis, dtype=dtype,\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/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",
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" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
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"/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",
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" arrmean = um.true_divide(\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
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" return _methods._mean(a, axis=axis, dtype=dtype,\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/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",
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" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
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"/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",
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" arrmean = um.true_divide(\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
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" return _methods._mean(a, axis=axis, dtype=dtype,\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/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",
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" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
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"/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",
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" arrmean = um.true_divide(\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
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" return _methods._mean(a, axis=axis, dtype=dtype,\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/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",
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" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
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"/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",
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" arrmean = um.true_divide(\n",
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"/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",
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" ret = ret.dtype.type(ret / rcount)\n",
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"/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
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" return _methods._mean(a, axis=axis, dtype=dtype,\n",
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"/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,
|
||||
|
|
|
|||
179
lab-windows/fec_experiments.ipynb
Normal file
179
lab-windows/fec_experiments.ipynb
Normal file
|
|
@ -0,0 +1,179 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import matplotlib\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import pyldpc"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib widget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 92,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"H, G = pyldpc.make_ldpc(1024, 6, 8, systematic=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 95,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1024, 261)"
|
||||
]
|
||||
},
|
||||
"execution_count": 95,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"n, k = G.shape\n",
|
||||
"G.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 94,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-94-bc8d2a92108c>: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=(15, 6))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "96882c0b2fb3492a8fbcd68d4c1db371",
|
||||
"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(figsize=(15, 6))\n",
|
||||
"fig.tight_layout()\n",
|
||||
"ax.matshow(H)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-96-9621d966b0c0>:6: 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=(15, 3))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "961f48f481054e9da3c60ff5414e4ab8",
|
||||
"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.image.AxesImage at 0x7f4bcd779e50>"
|
||||
]
|
||||
},
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_data = np.random.randint(0, 2, k)\n",
|
||||
"\n",
|
||||
"d = np.dot(G, test_data) % 2\n",
|
||||
"x = (-1) ** d\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(figsize=(15, 3))\n",
|
||||
"ax.matshow(x.reshape((-1, 64)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 97,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 97,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.all(np.equal(pyldpc.get_message(G, pyldpc.decode(H, x, 3)), test_data))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue