{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0335fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import atlas_mpl_style as ampl\n",
    "ampl.use_atlas_style()\n",
    "plt.rcParams['font.family'] = 'DejaVu Sans'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9ad4428",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "\n",
    "with open('all_stats.csv', mode='r', newline='') as file:\n",
    "    csv_reader = csv.reader(file)\n",
    "    next(csv_reader) # skip first row\n",
    "    for row in csv_reader:\n",
    "        rows.append(row)\n",
    "\n",
    "rows = np.array(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0f99b770",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows[:,1] = [int(s.strip() == 'TRUE') for s in rows[:,1]]\n",
    "rows[:,5] = [int(s.strip() == 'TRUE') for s in rows[:,5]]\n",
    "rows[:,9] = [int(s.strip() == 'TRUE') for s in rows[:,9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "04407304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize=(20, 8)) \n",
    "\n",
    "br1 = np.arange(len(rows[:,1])) \n",
    "br2 = [x + barWidth for x in br1] \n",
    "br3 = [x + barWidth for x in br2] \n",
    "\n",
    "plt.bar(br1, rows[:,1], color='#0072B2', width=barWidth, edgecolor='grey', label='Step 1') \n",
    "plt.bar(br2, rows[:,5], color='#009E73', width=barWidth, edgecolor='grey', label='Step 2') \n",
    "plt.bar(br3, rows[:,9], color='#CC79A7', width=barWidth, edgecolor='grey', label='Step 3') \n",
    "\n",
    "plt.xlabel('Model', fontweight='bold', fontsize=25) \n",
    "plt.ylabel('Success Rate', fontweight='bold', fontsize=25) \n",
    "plt.xticks([r + barWidth for r in range(len(rows[:,1]))], rows[:,0], rotation=45, fontsize=8)\n",
    "plt.xlim(-0.5, 11)\n",
    "plt.legend(fontsize=20)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "839db86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "llama_time_1 = llama[:,1].astype(float)\n",
    "llama_time_2 = llama[:,5].astype(float)\n",
    "llama_time_3 = llama[:,9].astype(float)\n",
    "\n",
    "opus_time_1 = opus[:,1].astype(float)\n",
    "opus_time_2 = opus[:,5].astype(float)\n",
    "opus_time_3 = opus[:,9].astype(float)\n",
    "\n",
    "grok_time_1 = grok[:,1].astype(float)\n",
    "grok_time_2 = grok[:,5].astype(float)\n",
    "grok_time_3 = grok[:,9].astype(float)\n",
    "\n",
    "gemini_time_1 = gemini[:,1].astype(float)\n",
    "gemini_time_2 = gemini[:,5].astype(float)\n",
    "gemini_time_3 = gemini[:,9].astype(float)\n",
    "\n",
    "deepseek_time_1 = deepseek[:,1].astype(float)\n",
    "deepseek_time_2 = deepseek[:,5].astype(float)\n",
    "deepseek_time_3 = deepseek[:,9].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "319138bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize=(20, 8)) \n",
    "\n",
    "step1_avg = [np.mean(llama_time_1), np.mean(opus_time_1), np.mean(grok_time_1), np.mean(gemini_time_1), np.mean(deepseek_time_1)] \n",
    "step2_avg = [np.mean(llama_time_2), np.mean(opus_time_2), np.mean(grok_time_2), np.mean(gemini_time_2), np.mean(deepseek_time_2)] \n",
    "step3_avg = [np.mean(llama_time_3), np.mean(opus_time_3), np.mean(grok_time_3), np.mean(gemini_time_3), np.mean(deepseek_time_3)]\n",
    "\n",
    "step1_std = [np.std(llama_time_1), np.std(opus_time_1), np.std(grok_time_1), np.std(gemini_time_1), np.std(deepseek_time_1)] \n",
    "step2_std = [np.std(llama_time_2), np.std(opus_time_2), np.std(grok_time_2), np.std(gemini_time_2), np.std(deepseek_time_2)] \n",
    "step3_std = [np.std(llama_time_3), np.std(opus_time_3), np.std(grok_time_3), np.std(gemini_time_3), np.std(deepseek_time_3)]\n",
    "\n",
    "br1 = np.arange(len(step1)) \n",
    "br2 = [x + barWidth for x in br1] \n",
    "br3 = [x + barWidth for x in br2] \n",
    "\n",
    "plt.bar(br1, step1_avg, yerr=step1_std, color='#0072B2', width=barWidth, edgecolor='grey', label='Step 1') \n",
    "plt.bar(br2, step2_avg, yerr=step2_std, color='#009E73', width=barWidth, edgecolor='grey', label='Step 2') \n",
    "plt.bar(br3, step3_avg, yerr=step3_std, color='#CC79A7', width=barWidth, edgecolor='grey', label='Step 3') \n",
    "\n",
    "plt.xlabel('Model', fontweight='bold', fontsize=25) \n",
    "plt.ylabel('Time (seconds)', fontweight='bold', fontsize=25) \n",
    "plt.xticks([r + barWidth for r in range(len(step1))], ['Llama', 'Opus', 'Grok', 'Gemini', 'Deepseek'])\n",
    "plt.xlim(-0.5, 5)\n",
    "plt.ylim(bottom=0, top=1800)\n",
    "plt.legend(fontsize=20)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ecac6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "llama_calls_1 = llama[:,2].astype(float)\n",
    "llama_calls_2 = llama[:,6].astype(float)\n",
    "llama_calls_3 = llama[:,10].astype(float)\n",
    "\n",
    "opus_calls_1 = opus[:,2].astype(float)\n",
    "opus_calls_2 = opus[:,6].astype(float)\n",
    "opus_calls_3 = opus[:,10].astype(float)\n",
    "\n",
    "grok_calls_1 = grok[:,2].astype(float)\n",
    "grok_calls_2 = grok[:,6].astype(float)\n",
    "grok_calls_3 = grok[:,10].astype(float)\n",
    "\n",
    "gemini_calls_1 = gemini[:,2].astype(float)\n",
    "gemini_calls_2 = gemini[:,6].astype(float)\n",
    "gemini_calls_3 = gemini[:,10].astype(float)\n",
    "\n",
    "deepseek_calls_1 = deepseek[:,2].astype(float)\n",
    "deepseek_calls_2 = deepseek[:,6].astype(float)\n",
    "deepseek_calls_3 = deepseek[:,10].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3beec347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize=(20, 8)) \n",
    "\n",
    "step1_avg = [np.mean(llama_calls_1), np.mean(opus_calls_1), np.mean(grok_calls_1), np.mean(gemini_calls_1), np.mean(deepseek_calls_1)] \n",
    "step2_avg = [np.mean(llama_calls_2), np.mean(opus_calls_2), np.mean(grok_calls_2), np.mean(gemini_calls_2), np.mean(deepseek_calls_2)] \n",
    "step3_avg = [np.mean(llama_calls_3), np.mean(opus_calls_3), np.mean(grok_calls_3), np.mean(gemini_calls_3), np.mean(deepseek_calls_3)]\n",
    "\n",
    "step1_std = [np.std(llama_calls_1), np.std(opus_calls_1), np.std(grok_calls_1), np.std(gemini_calls_1), np.std(deepseek_calls_1)] \n",
    "step2_std = [np.std(llama_calls_2), np.std(opus_calls_2), np.std(grok_calls_2), np.std(gemini_calls_2), np.std(deepseek_calls_2)] \n",
    "step3_std = [np.std(llama_calls_3), np.std(opus_calls_3), np.std(grok_calls_3), np.std(gemini_calls_3), np.std(deepseek_calls_3)]\n",
    "\n",
    "br1 = np.arange(len(step1)) \n",
    "br2 = [x + barWidth for x in br1] \n",
    "br3 = [x + barWidth for x in br2] \n",
    "\n",
    "plt.bar(br1, step1_avg, yerr=step1_std, color='#0072B2', width=barWidth, edgecolor='grey', label='Step 1') \n",
    "plt.bar(br2, step2_avg, yerr=step2_std, color='#009E73', width=barWidth, edgecolor='grey', label='Step 2') \n",
    "plt.bar(br3, step3_avg, yerr=step3_std, color='#CC79A7', width=barWidth, edgecolor='grey', label='Step 3') \n",
    "\n",
    "plt.xlabel('Model', fontweight='bold', fontsize=25) \n",
    "plt.ylabel('# API Calls', fontweight='bold', fontsize=25) \n",
    "plt.xticks([r + barWidth for r in range(len(step1))], ['Llama', 'Opus', 'Grok', 'Gemini', 'Deepseek'])\n",
    "plt.xlim(-0.5, 5)\n",
    "plt.ylim(bottom=0, top=45)\n",
    "plt.legend(fontsize=20)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a4ecd8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "llama_tokens_1 = llama[:,3].astype(float)\n",
    "llama_tokens_2 = llama[:,7].astype(float)\n",
    "llama_tokens_3 = llama[:,11].astype(float)\n",
    "\n",
    "opus_tokens_1 = opus[:,3].astype(float)\n",
    "opus_tokens_2 = opus[:,7].astype(float)\n",
    "opus_tokens_3 = opus[:,11].astype(float)\n",
    "\n",
    "grok_tokens_1 = grok[:,3].astype(float)\n",
    "grok_tokens_2 = grok[:,7].astype(float)\n",
    "grok_tokens_3 = grok[:,11].astype(float)\n",
    "\n",
    "gemini_tokens_1 = gemini[:,3].astype(float)\n",
    "gemini_tokens_2 = gemini[:,7].astype(float)\n",
    "gemini_tokens_3 = gemini[:,11].astype(float)\n",
    "\n",
    "deepseek_tokens_1 = deepseek[:,3].astype(float)\n",
    "deepseek_tokens_2 = deepseek[:,7].astype(float)\n",
    "deepseek_tokens_3 = deepseek[:,11].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c86ccc44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize=(20, 8)) \n",
    "\n",
    "step1_avg = [np.mean(llama_tokens_1), np.mean(opus_tokens_1), np.mean(grok_tokens_1), np.mean(gemini_tokens_1), np.mean(deepseek_tokens_1)] \n",
    "step2_avg = [np.mean(llama_tokens_2), np.mean(opus_tokens_2), np.mean(grok_tokens_2), np.mean(gemini_tokens_2), np.mean(deepseek_tokens_2)] \n",
    "step3_avg = [np.mean(llama_tokens_3), np.mean(opus_tokens_3), np.mean(grok_tokens_3), np.mean(gemini_tokens_3), np.mean(deepseek_tokens_3)]\n",
    "\n",
    "step1_std = [np.std(llama_tokens_1), np.std(opus_tokens_1), np.std(grok_tokens_1), np.std(gemini_tokens_1), np.std(deepseek_tokens_1)] \n",
    "step2_std = [np.std(llama_tokens_2), np.std(opus_tokens_2), np.std(grok_tokens_2), np.std(gemini_tokens_2), np.std(deepseek_tokens_2)] \n",
    "step3_std = [np.std(llama_tokens_3), np.std(opus_tokens_3), np.std(grok_tokens_3), np.std(gemini_tokens_3), np.std(deepseek_tokens_3)]\n",
    "\n",
    "br1 = np.arange(len(step1)) \n",
    "br2 = [x + barWidth for x in br1] \n",
    "br3 = [x + barWidth for x in br2] \n",
    "\n",
    "plt.bar(br1, step1_avg, yerr=step1_std, color='#0072B2', width=barWidth, edgecolor='grey', label='Step 1') \n",
    "plt.bar(br2, step2_avg, yerr=step2_std, color='#009E73', width=barWidth, edgecolor='grey', label='Step 2') \n",
    "plt.bar(br3, step3_avg, yerr=step3_std, color='#CC79A7', width=barWidth, edgecolor='grey', label='Step 3') \n",
    "\n",
    "plt.xlabel('Model', fontweight='bold', fontsize=25) \n",
    "plt.ylabel('Total Tokens', fontweight='bold', fontsize=25) \n",
    "plt.xticks([r + barWidth for r in range(len(step1))], ['Llama', 'Opus', 'Grok', 'Gemini', 'Deepseek'])\n",
    "plt.xlim(-0.5, 5)\n",
    "plt.ylim(bottom=0, top=3e5)\n",
    "plt.legend(fontsize=20)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d4bc1ee",
   "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": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}