notebooks/PathLoss_classes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Path Loss Models\n",
    "\n",
    "This notebook illustrates some path loss models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we set the Python path and import some libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we import some pyphysim stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from pyphysim.channels import pathloss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Path Loss Classes Representation in IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "PathLossGeneral (n=3.7, C=120): $PL = 37.0 \\log_{10} (d) + 120$"
      ],
      "text/plain": [
       "<pyphysim.channels.pathloss.PathLossGeneral at 0x7f25fe0b1220>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl_general = pathloss.PathLossGeneral(n=3.7, C=120)\n",
    "pl_general.handle_small_distances_bool = True\n",
    "pl_general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "PathLoss3GPP1: $PL = 37.599999999999994 \\log_{10} (d) + 128.1$"
      ],
      "text/plain": [
       "<pyphysim.channels.pathloss.PathLoss3GPP1 at 0x7f25fe0b1a60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl_3gpp = pathloss.PathLoss3GPP1()\n",
    "pl_3gpp.handle_small_distances_bool = True\n",
    "pl_3gpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "PathLossFreeSpace (n=2, fc=900): $PL = 20 \\log_{10} (d) + 91.5266223748352$"
      ],
      "text/plain": [
       "<pyphysim.channels.pathloss.PathLossFreeSpace at 0x7f25fe0b10a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl_fs = pathloss.PathLossFreeSpace()\n",
    "pl_fs.n = 2\n",
    "pl_fs.fc = 900\n",
    "pl_fs.handle_small_distances_bool = True\n",
    "pl_fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = np.linspace(0.01, 0.5, 100)\n",
    "fig, ax = plt.subplots()\n",
    "pl_general.plot_deterministic_path_loss_in_dB(d,\n",
    "                                              ax,\n",
    "                                              extra_args={'label': 'General'})\n",
    "pl_fs.plot_deterministic_path_loss_in_dB(d,\n",
    "                                         ax,\n",
    "                                         extra_args={'label': 'Free Space'})\n",
    "pl_3gpp.plot_deterministic_path_loss_in_dB(d, ax, extra_args={'label': '3GPP'})\n",
    "ax.grid()\n",
    "ax.set_ylabel('Path Loss (in dB)')\n",
    "ax.set_xlabel('Distance (in Km)')\n",
    "ax.legend(loc=5)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}