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@mgeier
Last active May 12, 2016 14:13
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Possible Distance Attenuation Behaviours for the SSR"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"plt.rcParams['figure.figsize'] = 12, 4 # inch\n",
"plt.rcParams['axes.grid'] = True"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r = np.linspace(0, 20, num=200)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def proposal1(r, r_flat, exponent, r_ref):\n",
" return r_ref ** exponent / np.maximum(r, r_flat) ** exponent"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def proposal2(r, r_flat, exponent):\n",
" return (np.maximum(r, r_flat) / r_flat) ** -exponent"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def db(x):\n",
" with np.errstate(divide='ignore'):\n",
" return 20 * np.log10(np.abs(x))\n",
"\n",
"\n",
"def plot_decay(r, data):\n",
" plt.plot(r, db(data))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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SX/4S+OsnRCVoyfeWaOORjbr25WtV31gf+DeB51F7B69ibMKrGJsINv02LEtO/fKiRdLP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\nSAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb7fc20a828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_decay(r, proposal1(r, 0.1, 0.5, 3))\n",
"plot_decay(r, proposal1(r, 0.1, 1, 3))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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WdETSsIDHhko61tYLFyxYoKysLEmSz+fT1KlTlZ2dLamp94nz0J8PTByoKZVT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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb7c9b78f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_decay(r, proposal2(r, 0.1, 0.5))\n",
"plot_decay(r, proposal2(r, 0.1, 1))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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