instruction_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from statistics import mean\n",
    "import pandas as pd\n",
    "import json\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from statistics import mean\n",
    "import pandas as pd\n",
    "import json\n",
    "    \n",
    "def get_traces_stat(dir_path):\n",
    "    \"\"\" This function read all files, of type csv with specific structure. The samples (voltage) are filtered and stored in a data frame,\n",
    "        where the trigger value is above the threshold.\n",
    "\n",
    "        Then the mean value of the filtered data will be stored in a dict, and this operation will be repeated for all files in the specified\n",
    "        directory. At the end, the items in the dict are sorted in increasing order (since we are waiting file name's ending with a number representing\n",
    "        the number of measurement).\n",
    "\n",
    "        The best practice while using this function, is to get the average of a desired instruction that have several measurements in a single directory.\n",
    "\n",
    "    \"\"\"\n",
    "    traces_stat = {}\n",
    "\n",
    "    for filename in os.listdir(dir_path):\n",
    "        relative_path = os.path.join(dir_path, filename)\n",
    "\n",
    "        # checking if it is a file\n",
    "        if os.path.isfile(relative_path) and relative_path.endswith('.csv'):\n",
    "\n",
    "            # read data\n",
    "            df = pd.read_csv(relative_path, skiprows=10)\n",
    "            df.columns = ['time', 'data', 'trig']\n",
    "            rslt_df= df[df['trig'] > 2.5] # filter samples where trigger is active (High)\n",
    "            # rslt_df= rslt_df.iloc[50:100, ::]\n",
    "\n",
    "            # calculate stats\n",
    "            traces_stat[filename.replace('.csv', '')] =  rslt_df['data'].mean()\n",
    "    \n",
    "    # sort dictionary keys\n",
    "    traces_stat = dict( sorted(traces_stat.items(), key=lambda x: int(x[0].split('_')[-1])) )\n",
    "    \n",
    "    return traces_stat\n",
    "\n",
    "def get_all_traces_stat(dir_path, unit = \"uA\"):\n",
    "    \"\"\"The following function will iterate over several instruction file to apply the 'get_traces_stat()' function.\n",
    "        It will automate the capture of the average of all instructions stored in one file. Taken into consederation that you can add custom \n",
    "        statistical parameters to the called function 'get_traces_stat()' to get detailed info.\n",
    "\n",
    "        The average values corresponds either to the voltage on the shunt resistor, or to the current passing through the shunt resistor. \n",
    "        \n",
    "        Also, you can specify the desired unit using unit parameter.\n",
    "        Valid options for the unit parameter are: 'A', 'mA', 'uA', 'V', 'mV', 'uV'\n",
    "        \"\"\"\n",
    "    all_traces_stat = {}\n",
    "\n",
    "    # Measured data is in Voltage (V), and R_shunt = 0.1 Ohms\n",
    "    if unit == \"uA\":\n",
    "        scalar = 1e7\n",
    "        key = \"current\"\n",
    "    elif unit == \"A\":\n",
    "        scalar = 1e1\n",
    "        key = \"current\"\n",
    "    elif unit == \"mA\":\n",
    "        key = \"current\"\n",
    "        scalar = 1e4\n",
    "    elif unit == \"mV\":\n",
    "        key = \"voltage\"\n",
    "        scalar = 1e3\n",
    "    elif unit == \"uV\":\n",
    "        key = \"voltage\"\n",
    "        scalar = 1e6\n",
    "    elif unit == \"V\":\n",
    "        key = \"voltage\"\n",
    "        scalar = 1\n",
    "\n",
    "    for sub_dir_name in os.listdir(dir_path):\n",
    "        relative_path = os.path.join(dir_path, sub_dir_name)\n",
    "        # checking if it is a dir\n",
    "        if os.path.isdir(relative_path):\n",
    "            all_traces_stat[sub_dir_name]= {\n",
    "                key : mean(get_traces_stat(relative_path).values())*scalar, # key could be \"current\" or \"voltage\"\n",
    "                \"cycles\": 1\n",
    "            }\n",
    "\n",
    "    return all_traces_stat\n",
    "\n",
    "def save_stats_as_file(stat_dict, file_path):\n",
    "    with open(file_path, 'w') as j_file:\n",
    "        json.dump(stat_dict, j_file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_traces_mean = get_all_traces_stat(dir_path=\"traces_bank_current_trigger/sampling_freq_100MHz\", unit='uV')\n",
    "save_stats_as_file(stat_dict= all_traces_mean,  file_path= \"energy_files/voltage_instruction_data.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_traces_mean = get_all_traces_stat(dir_path=\"traces_bank_current_trigger/sampling_freq_100MHz\")\n",
    "save_stats_as_file(stat_dict= all_traces_mean,  file_path= \"results/instructions_data.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5l1xySdq3b59CoZAbb7wxRxxxRJ555pl06dKlKqcCbKF69OiRG264IR06dMiiRYty/vnnZ7/99stzzz23wS+D/+tf/5oHHnggxx13XO65557MnTs33/3ud/P+++9n1KhR5TUTJ07MsGHDcu655+aJJ57I6aefnrp16+aEE04o9hSpJCWFQqFQ3U1sjlasWJHGjRvn7bffTqNGjaq7HQAAAIAt1ujRozN16tTMnj17o+onTZqUH/3oR3nxxRfLtwfaGNtvv31+9KMfZfDgwZ+yU4B/Wr58eVq1apXx48dv8PfKbrvtlvfeey/z5s1L7dq1kyTjx4/Pj370oyxatChJUrdu3XTv3j2PPvpo+Xmnn356nnjiicycObM4E2GjbWxuYCszAAAAAGq8yt4e6F+tXbs2t956a1atWpWePXtW1RSAz5ltt902u+22W+bOnbvB482bN89uu+1WHsokSadOnbJ48eKsWbOmvKZz584VzuvUqdMn/g6k5hPMAAAAAFCjrd8eaNq0aZk4cWLmzZuX/fbbL++8884G6//617/m17/+ddauXZt77rkn5513XsaNG5cLL7ywQt2zzz6bbbbZJvXq1cupp56au+666yN/AAX4tFauXJlXXnklzZs33+DxfffdN3Pnzs26devKx15++eU0b948devWLa956aWXKpz38ssvp1WrVlXXOFXOVmafkq3MAAAAAKpHZWwPlHz4XTQLFizI22+/nV//+tf5xS9+kYcfflg4A3wqZ511Vg477LC0atUqCxcuzKhRozJ79uw8//zzadq0aQYOHJiddtopY8eOTZK8+uqr6dKlS0444YR873vfy5w5c/Ktb30rp59+en74wx8mSZ544on06tUr559/fo4++ug8/vjjOemkk3LNNdfkuOOOq87psgEbmxt8/LekAQAAAEANtDHbA2211VYfuz3Q+jvR69atm3bt2iVJunXrlieeeCJXXHFFfvazn1X9JIAtzmuvvZYBAwbkrbfeStOmTdO7d+889thjadq0aZJkwYIFqVXrn5tYtWzZMvfdd1/OPPPMdO3aNTvttFPOOOOMfP/73y+v2WeffXLXXXdl+PDhGTNmTNq0aZMJEyYIZTZzghkAAACg0owePTrnn39+hbEOHTrkxRdf3GD9DTfckEGDBlUYq1evXt57773y93feeWcmTZqUp556KsuWLcszzzyTvfbaq9J7Z/Oxfnug448/foPH991330yePDnr1q0r/yPov28PtCHr1q3L6tWrq6RnYMt36623fuLxhx566CNjPXv2zGOPPfaJ533961/P17/+9c/SGjWM75gBAAAAKlWXLl2yaNGi8tcjjzzyifWNGjWqUP+3v/2twvFVq1ald+/eufTSS6uybWqws846Kw8//HDmz5+fRx99NEceeWRq166dAQMGJEkGDhyY4cOHl9d/5zvfybJly3LGGWfk5Zdfzu9+97tcfPHFOe2008prhg8fnj/84Q+ZP39+nn322QwfPjwPPfSQu9ABqHKemAEAAAAqVZ06dVJWVrbR9SUlJZ9Yv/6piPnz53/W1thMVcX2QEuXLs3AgQOzaNGiNG7cOF27ds19992Xr371q0WfHwCfL4IZAAAAoFLNmTMnLVq0SGlpaXr27JmxY8dml112+dj6lStXplWrVlm3bl323nvvXHzxxenSpUsRO6amq4rtga699trP2hYAfCq2MgMAAAAqTY8ePXLDDTdk2rRpmThxYubNm5f99tsv77zzzgbrO3TokOuuuy6/+c1v8stf/jLr1q1Lr1698tprrxW5cwCA4igpFAqF6m5ic7RixYo0btw4b7/9dho1alTd7QAAAECNtHz58rRq1Srjx4/P4MGD/2P9+++/n06dOmXAgAG54IILKhybP39+2rRpk2eeeSZ77bVXFXUMAPDpbGxuYCszAACAz6nRo0fn/PPPrzDWoUOHvPjii//x3FtvvTUDBgzIEUcckalTp5aP33nnnZk0aVKeeuqpLFu2zB/Qybbbbpvddtstc+fO3aj6rbbaKl/4whc2uh4AaoLWP/hddbdQVPMvObS6W9is2coMAADgc6xLly5ZtGhR+euRRx75j+fMnz8/Z511Vvbbb7+PHFu1alV69+6dSy+9tCraZTO0cuXKvPLKK2nevPlG1a9duzbPPvvsRtcDAGxuBDPAZmP06NEpKSmp8OrYsePH1t95553p3r17tt122zRo0CB77bVXbr755go1S5YsyYknnpgWLVpk6623zsEHH5w5c+ZU9VQAAGqMOnXqpKysrPzVpEmTT6xfu3ZtjjvuuJx//vlp27btR44ff/zxGTlyZPr06VNVLVPDnXXWWXn44Yczf/78PProoznyyCNTu3btDBgwIEkycODADB8+vLx+zJgx+f3vf5+//vWvefrpp/M///M/+dvf/pZvf/vb5TXLli3L7Nmz8/zzzydJXnrppcyePTuLFy8u7uQAACqBrcyAzUqXLl1y//33l7+vU+fjf41tv/32+eEPf5iOHTumbt26ufvuuzNo0KDsuOOO6du3bwqFQvr165etttoqv/nNb9KoUaOMHz8+ffr0yfPPP58GDRoUY0oAANVqzpw5adGiRUpLS9OzZ8+MHTs2u+yyy8fWjxkzJjvuuGMGDx6cP/7xj0XslM3Fa6+9lgEDBuStt95K06ZN07t37zz22GNp2rRpkmTBggWpVeuf94n+/e9/z0knnZTFixdnu+22S7du3fLoo4+mc+fO5TW//e1vM2jQoPL3xx57bJJk1KhRGT16dHEmRrWxPRBVZVO39Lzzzjtz8cUXZ+7cuXn//ffTvn37/O///m+OP/748pqSkpINnnvZZZfl7LPPrrzmgc2aYAbYrKy/o3NjHHjggRXen3HGGbnxxhvzyCOPpG/fvpkzZ04ee+yxPPfcc+nSpUuSZOLEiSkrK8stt9xS4Q49AIAtUY8ePXLDDTekQ4cOWbRoUc4///zst99+ee6559KwYcOP1D/yyCO59tprM3v27OI3y2bj1ltv/cTjDz30UIX3l19+eS6//PJPPOfEE0/MiSee+Bk7A/ioyrwBNEkWLVpU4Zx77703gwcPzlFHHVU1EwA2S7YyAzYr6+/obNu2bY477rgsWLBgo84rFAqZMWNGXnrppey///5JktWrVydJSktLy+tq1aqVevXqbdTe6mwZqmKLvH916qmnpqSkJBMmTKiC7oHPk6r4fXXiiSd+5JoHH3xwVU+FGuSQQw5J//7907Vr1/Tt2zf33HNPli9fnttuu+0jte+8806OP/74/PznP/+P250BwOZiU7b0PPDAA3PkkUemU6dO2XXXXXPGGWeka9euFf6G8K/XKisry29+85scdNBBG9z+E/j88sQMsNnY1Ds6k+Ttt9/OTjvtlNWrV6d27dr56U9/mq9+9atJko4dO2aXXXbJ8OHD87Of/SwNGjTI5Zdfntdee+0jd7iwZavsO6TWu+uuu/LYY4+lRYsWVdY78PlSFb+vDj744Fx//fXl7+vVq1c1zbNZ2HbbbbPbbrtl7ty5Hzn2yiuvZP78+TnssMPKx9atW5fkw7X40ksvZddddy1arwBQGTZ1S8/1CoVCHnjggbz00ku59NJLN1izZMmS/O53v8uNN95Y2W0DmznBDLDZOOSQQ8r/u2vXrunRo0datWqV2267LYMHD97gOQ0bNszs2bOzcuXKzJgxI8OGDUvbtm1z4IEHZquttsqdd96ZwYMHZ/vtt0/t2rXTp0+fHHLIISkUCsWaFjVAZW6Rt97rr7+e733ve7nvvvty6KH2iAYqR1X8vqpXr95GX5Mt38qVK/PKK69U2Ct/vY4dO+bZZ5+tMDZixIi88847ueKKK9KyZctitQkAlaKybwD9dzfeeGMaNmyY//7v/67KaQCbIcEMsNn6pDs616tVq1batWuXJNlrr73ywgsvZOzYseV/rOrWrVtmz56dt99+O2vWrEnTpk3To0ePdO/evRhToIao7Duk1q1bl+OPPz5nn312+fcXAVSGqrij86GHHsqOO+6Y7bbbLl/+8pdz4YUXZocddqiqKVDDnHXWWTnssMPSqlWrLFy4MKNGjUrt2rUzYMCAJMnAgQOz0047ZezYsSktLc3uu+9e4fxtt902SSqML1u2LAsWLMjChQuTJC+99FKSf27tAgA1RWXfAPrvrrvuuhx33HEVtlAHSAQzwGbsk+7o/Djr1q0r/26Zf9W4ceMkH/7B68knn8wFF1xQaX1Ss1XFHVKXXnpp6tSpk9NPP71Y0wA+B6ri99XBBx+c//7v/06bNm3yyiuv5Nxzz80hhxySmTNnpnbt2sWaGtXotddey4ABA/LWW2+ladOm6d27dx577LE0bdo0SbJgwYLUqrVpX03629/+NoMGDSp/f+yxxyZJRo0aldGjR1da7wBQ2SrjBtD1/vjHP+all17KlClTqrJlYDMlmAE2G5tyR2eSjB07Nt27d8+uu+6a1atX55577snNN9+ciRMnll/z9ttvT9OmTbPLLrvk2WefzRlnnJF+/frla1/7WrXMkeKr7DuknnrqqVxxxRV5+umnU1JSUqxpAJ8DVXFH5/o/mCfJHnvska5du2bXXXfNQw89lK985StVOh9qhltvvfUTjz/00EOfePyGG274yNiJJ56YE0888dM3xWat9Q9+V90tFNX8S2xZC1uSyrwB9Nprr023bt2y5557VmaLwBZCMANsNjb1js5Vq1blu9/9bl577bXUr18/HTt2zC9/+cscc8wx5TWLFi3KsGHDsmTJkjRv3jwDBw7MeeedV/S5UXN81juk/vjHP2bp0qUVthZau3Zt/vd//zcTJkzI/Pnzq3oKwOdEZd7RuV7btm3TpEmTzJ07VzADAGzxquIG0CRZsWJFbr/99owbN67ocwI2D4IZYLOxqXd0Xnjhhbnwwgs/8ZzTTz/ddlNU8FnvkDr++OPTp0+fCsf79u2b448/vsK2LgCfVWXe0bn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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmp_stat = get_traces_stat(\"traces_bank_current_trigger/sampling_freq_100MHz/CMP\")\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.title(f\"CMP average\")\n",
    "plt.bar(cmp_stat.keys(), cmp_stat.values(), width=0.4, align= 'edge')\n",
    "for i, v in enumerate(cmp_stat.values()):\n",
    "    plt.text(i, v+0.0001, str(round(v*1e3, 2)))\n",
    "plt.xlabel(\"instruction\")\n",
    "plt.ylabel(\"Voltage\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mit_vector = {\n",
    "    'ADD': 1749.85,\n",
    "    'ADDi': 1750.45,\n",
    "    'AND': 1749.85,\n",
    "    'ANDi': 1748.59,\n",
    "    'ASR': 1747.34,\n",
    "    'EOR': 1745.74,\n",
    "    'LDRb': 2453.45,\n",
    "    'LDRh': 2465.97,\n",
    "    'LDRw': 2434.12,\n",
    "    'LSL': 1747.28,\n",
    "    'LSR': 1744.38,\n",
    "    'MUL': 1757.36,\n",
    "    'OR': 1744.44,\n",
    "    'ORi': 1745.47,\n",
    "    'SDIV': 2942.86,\n",
    "    'SLLi': 1926.32,\n",
    "    'SMULL': 1757.98,\n",
    "    'SRAi': 1969.52,\n",
    "    'SRLi': 1972.56,\n",
    "    'STRb': 1838.31,\n",
    "    'STRh': 2896.19,\n",
    "    'STRw': 2727.06,\n",
    "    'SUB': 1744.99,\n",
    "    'UDIV': 1476.47,\n",
    "    'UMULL': 1758.07,\n",
    "    'XORi': 1745.51\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_traces_mean= get_all_traces_stat(dir_path=\"traces_bank_current_trigger/sampling_freq_100MHz\")\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.title(f\"AD2 average\")\n",
    "plt.bar(all_traces_mean.keys(), [d['power'] for d in all_traces_mean.values()], width=0.4, align= 'edge')\n",
    "for i, v in enumerate([d['power'] for d in all_traces_mean.values()]):\n",
    "    plt.text(i, v + 1e3, str(round(v, 2)))\n",
    "plt.xlabel(\"instruction\")\n",
    "plt.ylabel(\"Current u-amp\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 3))\n",
    "plt.title(f\"MIT average\")\n",
    "plt.bar(mit_vector.keys(), mit_vector.values(), width=0.4, align= 'edge')\n",
    "for i, v in enumerate(mit_vector.values()):\n",
    "    plt.text(i, v+1e2, str(round(v, 2)))\n",
    "plt.xlabel(\"instruction\")\n",
    "plt.ylabel(\"Current u-amp\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prof5 Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "instructions = ['mul', 'add', 'and', 'jal', 'bne', 'mv']\n",
    "\n",
    "x = np.arange(len(instructions))\n",
    "\n",
    "y_min = [1.15, 1.15, 1.14, 1.11, 1.10, 1.14]\n",
    "y_avg = [3.09, 2.80, 2.66, 2.27, 2.46, 1.61]\n",
    "y_max = [7.69, 6.89, 7.06, 5.58, 6.09, 4.13]\n",
    "\n",
    "w = 0.22\n",
    "width = 0.2\n",
    "\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(x-w, y_max, width=width, color='b', align='center')\n",
    "ax.bar(x, y_avg, width=width, color='g', align='center')\n",
    "ax.bar(x+w, y_min, width=width, color='r', align='center')\n",
    "\n",
    "plt.title('Power Dissipation in E24 Core')\n",
    "plt.xticks(x, instructions)\n",
    "plt.ylabel('power (mW)')\n",
    "plt.legend(['max', 'avg', 'min'])\n",
    "\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": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}