{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = '<path-to>/results'\n",
    "#filename = 'exp1002_uc3_75000_1_totallag.csv'\n",
    "filename = 'exp1002_uc3_50000_2_totallag.csv'\n",
    "warmup_sec = 60\n",
    "threshold = 2000 #slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(os.path.join(directory, filename))\n",
    "\n",
    "input = df.iloc[::3]\n",
    "#print(input)\n",
    "input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']\n",
    "#print(input)\n",
    "#print(input.iloc[0, 'timestamp'])\n",
    "regress = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up\n",
    "#regress = input\n",
    "\n",
    "#input.plot(kind='line',x='timestamp',y='value',color='red')\n",
    "#plt.show()\n",
    "\n",
    "X = regress.iloc[:, 4].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = regress.iloc[:, 3].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(linear_regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.rcParams['axes.facecolor']='w'\n",
    "plt.rcParams['axes.edgecolor']='555555'\n",
    "#plt.rcParams['ytick.color']='black'\n",
    "plt.rcParams['grid.color']='dddddd'\n",
    "plt.rcParams['axes.spines.top']='false'\n",
    "plt.rcParams['axes.spines.right']='false'\n",
    "plt.rcParams['legend.frameon']='true'\n",
    "plt.rcParams['legend.framealpha']='1'\n",
    "plt.rcParams['legend.edgecolor']='1'\n",
    "plt.rcParams['legend.borderpad']='1'\n",
    "\n",
    "\n",
    "#filename = f\"exp{exp_id}_{benchmark}_{dim_value}_{instances}\"\n",
    "\n",
    "\n",
    "t_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 4].values\n",
    "y_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 3].values\n",
    "\n",
    "plt.figure()\n",
    "#plt.figure(figsize=(4, 3))\n",
    "\n",
    "plt.plot(X, Y, c=\"#348ABD\", label=\"observed\")\n",
    "#plt.plot(t_warmup, y_warmup)\n",
    "\n",
    "plt.plot(X, Y_pred, c=\"#E24A33\", label=\"trend\") # color='red')\n",
    "\n",
    "#348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, pos: '%1.0fK' % (x * 1e-3)))\n",
    "plt.ylabel('queued messages')\n",
    "plt.xlabel('seconds since start')\n",
    "plt.legend()\n",
    "#ax.set_ylim(ymin=0)\n",
    "#ax.set_xlim(xmin=0)\n",
    "\n",
    "plt.savefig(\"plot.pdf\", bbox_inches='tight')\n"
   ]
  }
 ],
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