Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a7c199d-36fb-4a58-9925-a39d1cf22af2",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from utils import get_all_elements_in_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ead4018e-ae48-4630-9f46-e8155bb9a86e",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ASP', 'Tilsets', 'SolveTime', 'GroundTime', 'Hash', 'Dim'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data_dir = \"platformer\"\n",
    "data = pd.read_csv(\"asp.csv\")\n",
    "# asp = pd.read_csv(\"asp.csv\")\n",
    "# asp[\"GroundTime\"].mean()  \n",
    "        \n",
    "# sample_time_as_sec = asp[\"SampleTime\"].astype(float) / 1e09\n",
    "# cumlative_cost = asp[\"GroundTime\"] / 1e09 +  sample_time_as_sec.cumsum()\n",
    "# iter_num_samples =  pd.Series(range(len(sample_time_as_sec)))\n",
    "# line = cumlative_cost / iter_num_samples\n",
    "\n",
    "\n",
    "# for el in get_all_elements_in_dir(data_dir):\n",
    "#     data = pd.read_csv(f\"{data_dir}/{el}\", dtype=\"str\")\n",
    "#     compile_time_as_sec =data[\"CompileTime\"].astype(float) / 1e09\n",
    "#     sample_time_as_sec =data[\"SampleTime\"].astype(float) / 1e09\n",
    "#     cumlative_cost = compile_time_as_sec + sample_time_as_sec.cumsum()\n",
    "#     iter_num_samples =  pd.Series(range(len(sample_time_as_sec)))\n",
    "#     plot_data = cumlative_cost / iter_num_samples\n",
    "#     print(iter_num_samples)\n",
    "#     t = plot_data[100:].plot(xlabel=\"Number of Samples\", ylabel=\"Time to generate per number of samples\")\n",
    "#     line[100:].plot()\n",
    "#     t.get_figure().savefig(\"temp-1.png\")\n",
    "# data = pd.read_csv(\"asp.csv\")\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b741459-62fe-4e91-8adc-f03cb56211d3",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1016000.0 $\\pm$ (125537.88177183714)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:7: SyntaxWarning: invalid escape sequence '\\p'\n",
      "<>:7: SyntaxWarning: invalid escape sequence '\\p'\n",
      "/tmp/ipykernel_514258/4035758240.py:7: SyntaxWarning: invalid escape sequence '\\p'\n",
      "  print(f\"{mean} $\\pm$ ({std})\")\n"
     ]
    }
   ],
   "source": [
    "# t = data[data[\"Dimension\"] == \"2\"][\"CompileTime\"].astype(float) + data[data[\"Dimension\"] == \"2\"][\"ComputeProbs\"].astype(float)  \n",
    "# t = data[data[\"Dimension\"] == \"2\"][\"SampleTime\"].astype(float) \n",
    "# t = data[\"ComputeProbs\"].astype(float) + data[\"CompileTime\"].astype(float)\n",
    "t = data[\"SolveTime\"].astype(float)\n",
    "mean = t.mean()\n",
    "std =  t.std()\n",
    "print(f\"{mean} $\\pm$ ({std})\")\n",
    "\n",
    "# len(data[\"Hash\"].unique())\n",
    "# data[\"Hash\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d57f7b27-40f4-460c-b43b-825b76931693",
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0           inf\n",
      "1      0.025522\n",
      "2      0.019834\n",
      "3      0.004386\n",
      "4      0.003926\n",
      "         ...   \n",
      "888    0.001015\n",
      "889    0.001015\n",
      "890    0.001015\n",
      "891    0.001015\n",
      "892    0.001015\n",
      "Length: 893, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(plot_data)\n",
    "t = plot_data.plot(xlabel=\"Number of Samples\", ylabel=\"Time to generate per number of samples\", ylim=(0,0.2))\n",
    "t.get_figure().savefig(\"linegonedown.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "/home/a3madkour/miniconda3/envs/omega/bin/python",
    "-m",
    "ipykernel_launcher",
    "-f",
    "{connection_file}"
   ],
   "display_name": "omega",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": {
    "debugger": true
   },
   "name": "omega"
  },
  "name": "Untitled.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}