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intrig-unicamp · Feb 3, 2022 · 6c5c0f9 · 6c5c0f9
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diff --git a/iNotebook/Interactive_Control.ipynb b/iNotebook/Interactive_Control.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import *\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=Warning)\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"QoS.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Video', 'Window', 'ExpID', 'Case', 'Algo', 'timeslot sec',\n",
+       "       'timeslot mil', 'IAT', 'IPG Avg', 'IPG Avg GT100', 'IAT GT100',\n",
+       "       'Throughput', 'Total Packets', 'Total Packet GT100', '10p Length',\n",
+       "       '20p Length', '30p Length', '40p Length', '50p Length', '60p Length',\n",
+       "       '70p Length', '80p Length', '90p Length', '10p TP', '20p TP', '30p TP',\n",
+       "       '40p TP', '50p TP', '60p TP', '70p TP', '80p TP', '90p TP',\n",
+       "       'Single EMA', 'CUSUM', 'Double EMA', 'P1203', 'P1203C', 'P1203N'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = []\n",
+    "for n in range(1, 65):\n",
+    "    exp.append(n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def interactive_contol(Case, Algorithm, Target, Experiment):\n",
+    "    \n",
+    "    if Algorithm=='Rate Based':\n",
+    "        CASE= df[(df['Algo']=='conventional')]\n",
+    "        LABEL='Conventional'\n",
+    "    elif Algorithm=='Buffer Based':\n",
+    "        CASE= df[(df['Algo']=='bba')]\n",
+    "        LABEL='BBA'\n",
+    "    else:\n",
+    "        CASE= df[(df['Algo']=='elastic')]\n",
+    "        LABEL='Elastic'\n",
+    "    \n",
+    "    CASEID = CASE[( CASE['ExpID']==Experiment) & (CASE['Case']==Case)]\n",
+    "\n",
+    "    plt.style.use('classic')\n",
+    "    fig = plt.figure(figsize=(10,5))\n",
+    "    with plt.style.context('Solarize_Light2'):\n",
+    "        fig.set_facecolor('white')\n",
+    "        plt.rcParams['axes.facecolor'] = 'white'\n",
+    "        plt.plot(CASEID['timeslot sec'], CASEID[Target], label=LABEL, c='blue', mfc='red',marker='o',mec='k',markersize=12)\n",
+    "        plt.title(LABEL, fontsize=11)\n",
+    "        plt.xlabel('Time/s', fontsize=12, color='black')\n",
+    "        plt.ylabel(Target, fontsize=12, color='black')\n",
+    "        plt.legend(loc='best',frameon=False)\n",
+    "        plt.grid(axis='y', c='#D3D3D3')\n",
+    "        plt.grid(axis='x', c='#D3D3D3')\n",
+    "        plt.tick_params(axis='x',  colors='black')\n",
+    "        plt.tick_params(axis='y', colors='black')\n",
+    "    plt.show()\n",
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82361099e1d94241aea4742995e83882",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='Case', options=('Driving-8', 'Driving-9', 'Driving-10'), value='Dr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.interactive_contol(Case, Algorithm, Target, Experiment)>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(interactive_contol, Case=['Driving-8','Driving-9','Driving-10'],Algorithm=['Buffer Based','Hybrid','Rate Based'], Target=['IPG Avg', 'IPG Avg GT100', 'IAT GT100',\n",
+    "       'Throughput', 'Total Packets', 'Total Packet GT100', '10p Length',\n",
+    "       '20p Length', '30p Length', '40p Length', '50p Length', '60p Length',\n",
+    "       '70p Length', '80p Length', '90p Length', '10p TP', '20p TP', '30p TP',\n",
+    "       '40p TP', '50p TP', '60p TP', '70p TP', '80p TP', '90p TP','IAT', \n",
+    "       'Single EMA', 'CUSUM', 'Double EMA', 'P1203'], Experiment=exp)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}