{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'FedOpt_3': {'recall': {'station1': [0.87273, 0.89091, 0.87273, 0.87273, 0.89091, 0.90909, 0.90909, 0.89091, 0.89091, 0.89091], 'station2': [0.94545, 0.94545, 0.94545, 0.96364, 0.94545, 0.98182, 0.98182, 0.94545, 0.94545, 0.94545], 'station3': [0.92727, 0.94545, 0.90909, 0.92727, 0.92727, 0.94545, 0.94545, 0.92727, 0.92727, 0.94545], 'station4': [0.89091, 0.89091, 0.89091, 0.90909, 0.89091, 0.90909, 0.90909, 0.89091, 0.89091, 0.89091]}, 'Precision': {'station1': [0.66667, 0.75385, 0.77419, 0.66667, 0.76562, 0.60976, 0.53191, 0.74242, 0.76562, 0.80328], 'station2': [0.06508, 0.06675, 0.04828, 0.07382, 0.06396, 0.06844, 0.10651, 0.0581, 0.05526, 0.10788], 'station3': [0.02411, 0.02052, 0.28571, 0.39231, 0.40157, 0.23318, 0.02273, 0.33117, 0.32278, 0.56522], 'station4': [0.64474, 0.73134, 0.74242, 0.67568, 0.73134, 0.60976, 0.53191, 0.74242, 0.75385, 0.80328]}, 'F2': {'station1': [0.82192, 0.85965, 0.85106, 0.82192, 0.86268, 0.82781, 0.79618, 0.85664, 0.86268, 0.87189], 'station2': [0.25515, 0.26026, 0.20046, 0.28252, 0.25169, 0.26759, 0.37139, 0.23318, 0.22394, 0.37037], 'station3': [0.10921, 0.09441, 0.63291, 0.72857, 0.73487, 0.58691, 0.10367, 0.68182, 0.6746, 0.83333], 'station4': [0.8277, 0.85366, 0.85664, 0.85034, 0.85366, 0.82781, 0.79618, 0.85664, 0.85965, 0.87189]}}, 'FedProx_3': {'recall': {'station1': [0.87273, 0.96364, 0.87273, 0.85455, 0.83636, 0.89091, 0.90909, 0.89091, 0.89091, 0.90909], 'station2': [0.94545, 0.98182, 0.94545, 0.94545, 0.94545, 0.94545, 0.98182, 0.94545, 0.94545, 0.96364], 'station3': [0.92727, 0.98182, 0.90909, 0.89091, 0.87273, 0.96364, 0.94545, 0.92727, 0.96364, 0.94545], 'station4': [0.89091, 0.96364, 0.89091, 0.89091, 0.89091, 0.89091, 0.90909, 0.89091, 0.89091, 0.90909]}, 'Precision': {'station1': [0.66667, 0.56989, 0.73846, 0.77049, 0.7541, 0.74242, 0.54945, 0.76562, 0.79032, 0.75758], 'station2': [0.06508, 0.03581, 0.04224, 0.08769, 0.05882, 0.05765, 0.08244, 0.04962, 0.05702, 0.04981], 'station3': [0.02411, 0.00813, 0.2, 0.48515, 0.45714, 0.44167, 0.00811, 0.0123, 0.44915, 0.2488], 'station4': [0.64474, 0.56989, 0.71014, 0.74242, 0.75385, 0.72059, 0.54348, 0.76562, 0.76562, 0.73529]}, 'F2': {'station1': [0.82192, 0.84665, 0.84211, 0.8363, 0.81851, 0.85664, 0.80386, 0.86268, 0.86879, 0.87413], 'station2': [0.25515, 0.15625, 0.17919, 0.3198, 0.23551, 0.23173, 0.30857, 0.20505, 0.22968, 0.20639], 'station3': [0.10921, 0.03934, 0.53191, 0.76324, 0.73846, 0.77941, 0.03922, 0.05841, 0.78402, 0.60606], 'station4': [0.8277, 0.84665, 0.84775, 0.85664, 0.85965, 0.85069, 0.80128, 0.86268, 0.86268, 0.86806]}}, 'FaultTolerantFedAvg_3': {'recall': {'station1': [0.87273, 0.90909, 0.87273, 0.81818, 0.8, 0.89091, 0.90909, 0.89091, 0.89091, 0.89091], 'station2': [0.94545, 0.96364, 0.94545, 0.94545, 0.94545, 0.94545, 1.0, 0.94545, 0.94545, 0.94545], 'station3': [0.92727, 0.94545, 0.90909, 0.85455, 0.83636, 0.92727, 0.98182, 0.94545, 0.94545, 0.92727], 'station4': [0.89091, 0.90909, 0.89091, 0.89091, 0.92727, 0.89091, 0.90909, 0.89091, 0.89091, 0.89091]}, 'Precision': {'station1': [0.66667, 0.55556, 0.7619, 0.75, 0.74576, 0.75385, 0.68493, 0.77778, 0.7, 0.71014], 'station2': [0.06508, 0.02956, 0.05497, 0.08037, 0.05231, 0.06242, 0.07463, 0.0628, 0.06641, 0.0625], 'station3': [0.02411, 0.00674, 0.34483, 0.38525, 0.48936, 0.05209, 0.0177, 0.03408, 0.00861, 0.41129], 'station4': [0.64474, 0.55556, 0.73134, 0.73134, 0.76119, 0.75385, 0.67568, 0.77778, 0.7, 0.71014]}, 'F2': {'station1': [0.82192, 0.80645, 0.84806, 0.80357, 0.78853, 0.85965, 0.85324, 0.86572, 0.84483, 0.84775], 'station2': [0.25515, 0.13164, 0.22298, 0.29988, 0.21417, 0.24691, 0.28736, 0.24809, 0.25922, 0.24715], 'station3': [0.10921, 0.03278, 0.68493, 0.68713, 0.73248, 0.21268, 0.08254, 0.14891, 0.04154, 0.74128], 'station4': [0.8277, 0.80645, 0.85366, 0.85366, 0.8885, 0.85965, 0.85034, 0.86572, 0.84483, 0.84775]}}, 'FedAvgM_3': {'recall': {'station1': [0.87273, 0.90909, 0.89091, 0.87273, 0.83636, 0.90909, 0.90909, 0.89091, 0.89091, 0.90909], 'station2': [0.94545, 0.94545, 0.94545, 0.94545, 0.94545, 0.96364, 0.96364, 0.94545, 0.94545, 0.98182], 'station3': [0.92727, 0.94545, 0.92727, 0.94545, 0.87273, 0.94545, 0.98182, 0.92727, 0.96364, 0.96364], 'station4': [0.89091, 0.90909, 0.89091, 0.89091, 0.89091, 0.90909, 0.90909, 0.89091, 0.89091, 0.90909]}, 'Precision': {'station1': [0.66667, 0.33557, 0.75385, 0.73846, 0.62162, 0.5, 0.44248, 0.75385, 0.73134, 0.64103], 'station2': [0.06508, 0.042, 0.06878, 0.14208, 0.07602, 0.08646, 0.14722, 0.0574, 0.5098, 0.06708], 'station3': [0.02411, 0.00528, 0.14783, 0.65, 0.11483, 0.20884, 0.01266, 0.00884, 0.54082, 0.53], 'station4': [0.64474, 0.33557, 0.72059, 0.71014, 0.63636, 0.5, 0.44248, 0.75385, 0.73134, 0.64103]}, 'F2': {'station1': [0.82192, 0.67751, 0.85965, 0.84211, 0.78231, 0.78125, 0.75075, 0.85965, 0.85366, 0.83893], 'station2': [0.25515, 0.17833, 0.26639, 0.44369, 0.28761, 0.31813, 0.4569, 0.23091, 0.80745, 0.26341], 'station3': [0.10921, 0.02584, 0.45133, 0.86667, 0.37618, 0.55437, 0.0602, 0.04259, 0.83333, 0.82813], 'station4': [0.8277, 0.67751, 0.85069, 0.84775, 0.82492, 0.78125, 0.75075, 0.85965, 0.85366, 0.83893]}}, 'FedMedian_3': {'recall': {'station1': [0.85455, 0.98182, 0.87273, 0.87273, 0.87273, 0.89091, 0.90909, 0.89091, 0.90909, 0.89091], 'station2': [0.94545, 1.0, 0.96364, 0.96364, 0.96364, 0.94545, 0.96364, 0.94545, 0.96364, 0.94545], 'station3': [0.89091, 0.98182, 0.94545, 0.90909, 0.92727, 0.92727, 0.94545, 0.94545, 0.96364, 0.92727], 'station4': [0.89091, 0.98182, 0.89091, 0.90909, 0.89091, 0.89091, 0.90909, 0.89091, 0.90909, 0.89091]}, 'Precision': {'station1': [0.72308, 0.09747, 0.72727, 0.69565, 0.78689, 0.71014, 0.5102, 0.72059, 0.69444, 0.75385], 'station2': [0.04444, 0.04277, 0.0724, 0.04637, 0.06386, 0.06295, 0.073, 0.08889, 0.6625, 0.04788], 'station3': [0.33333, 0.07941, 0.53061, 0.06158, 0.6375, 0.67105, 0.01525, 0.4127, 0.53, 0.24171], 'station4': [0.7, 0.09747, 0.7, 0.67568, 0.79032, 0.71014, 0.49505, 0.72059, 0.69444, 0.75385]}, 'F2': {'station1': [0.82456, 0.34884, 0.83916, 0.83045, 0.85409, 0.84775, 0.78616, 0.85069, 0.85616, 0.85965], 'station2': [0.18705, 0.1826, 0.27836, 0.19442, 0.25238, 0.24857, 0.28013, 0.32298, 0.88333, 0.19908], 'station3': [0.66757, 0.3, 0.81761, 0.24225, 0.85, 0.86149, 0.07163, 0.75145, 0.82813, 0.59165], 'station4': [0.84483, 0.34884, 0.84483, 0.85034, 0.86879, 0.84775, 0.77882, 0.85069, 0.85616, 0.85965]}}, 'FedAvg_3': {'recall': {'station1': [0.87273, 0.94545, 0.89091, 0.85455, 0.78182, 0.90909, 0.90909, 0.89091, 0.90909, 0.90909], 'station2': [0.94545, 0.96364, 0.96364, 0.94545, 0.94545, 0.96364, 0.96364, 0.94545, 0.96364, 0.96364], 'station3': [0.92727, 0.98182, 0.92727, 0.90909, 0.81818, 0.94545, 0.94545, 0.92727, 0.96364, 0.92727], 'station4': [0.89091, 0.94545, 0.90909, 0.89091, 0.89091, 0.90909, 0.90909, 0.89091, 0.90909, 0.90909]}, 'Precision': {'station1': [0.66667, 0.57778, 0.72059, 0.71212, 0.74138, 0.80645, 0.74627, 0.72059, 0.61728, 0.73529], 'station2': [0.06508, 0.02925, 0.06642, 0.07293, 0.0486, 0.04223, 0.05086, 0.05274, 0.0555, 0.15014], 'station3': [0.02411, 0.00977, 0.09714, 0.56818, 0.35714, 0.35862, 0.36364, 0.0998, 0.43089, 0.56044], 'station4': [0.64474, 0.56522, 0.68493, 0.69014, 0.73134, 0.76923, 0.72464, 0.72059, 0.60976, 0.73529]}, 'F2': {'station1': [0.82192, 0.83871, 0.85069, 0.82168, 0.77338, 0.88652, 0.87108, 0.85069, 0.83056, 0.86806], 'station2': [0.25515, 0.13041, 0.26031, 0.27867, 0.20155, 0.17966, 0.20998, 0.21559, 0.22553, 0.46248], 'station3': [0.10921, 0.04697, 0.34228, 0.81169, 0.65029, 0.71233, 0.71625, 0.34884, 0.77259, 0.81994], 'station4': [0.8277, 0.83333, 0.85324, 0.84192, 0.85366, 0.87719, 0.86505, 0.85069, 0.82781, 0.86806]}}, 'QFedAvg_3': {'recall': {'station1': [0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091], 'station2': [0.96364, 0.96364, 0.96364, 0.96364, 0.96364, 0.96364, 0.96364, 0.96364, 0.96364, 0.96364], 'station3': [0.92727, 0.92727, 0.92727, 0.92727, 0.92727, 0.92727, 0.92727, 0.92727, 0.92727, 0.92727], 'station4': [0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091, 0.89091]}, 'Precision': {'station1': [0.54444, 0.54444, 0.54444, 0.54444, 0.54444, 0.54444, 0.54444, 0.54444, 0.54444, 0.54444], 'station2': [0.02178, 0.02178, 0.02178, 0.02178, 0.02178, 0.02178, 0.02178, 0.02178, 0.02178, 0.02178], 'station3': [0.22973, 0.22973, 0.22973, 0.22973, 0.22973, 0.22973, 0.22973, 0.22973, 0.22973, 0.22973], 'station4': [0.52688, 0.52688, 0.52688, 0.52688, 0.52688, 0.52688, 0.52688, 0.52688, 0.52688, 0.52688]}, 'F2': {'station1': [0.79032, 0.79032, 0.79032, 0.79032, 0.79032, 0.79032, 0.79032, 0.79032, 0.79032, 0.79032], 'station2': [0.09989, 0.09989, 0.09989, 0.09989, 0.09989, 0.09989, 0.09989, 0.09989, 0.09989, 0.09989], 'station3': [0.57692, 0.57692, 0.57692, 0.57692, 0.57692, 0.57692, 0.57692, 0.57692, 0.57692, 0.57692], 'station4': [0.78275, 0.78275, 0.78275, 0.78275, 0.78275, 0.78275, 0.78275, 0.78275, 0.78275, 0.78275]}}, 'FedTrimmedAvg_3': {'recall': {'station1': [0.87273, 0.90909, 0.87273, 0.89091, 0.81818, 0.96364, 0.90909, 0.89091, 0.89091, 0.90909], 'station2': [0.94545, 0.96364, 0.94545, 0.96364, 0.94545, 0.98182, 0.96364, 0.94545, 0.94545, 0.96364], 'station3': [0.92727, 0.96364, 0.90909, 0.92727, 0.85455, 0.98182, 0.94545, 0.94545, 0.92727, 0.98182], 'station4': [0.89091, 0.90909, 0.89091, 0.90909, 0.89091, 0.96364, 0.90909, 0.89091, 0.89091, 0.90909]}, 'Precision': {'station1': [0.66667, 0.58824, 0.73846, 0.72059, 0.76271, 0.47748, 0.5814, 0.72059, 0.79032, 0.60241], 'station2': [0.06508, 0.0314, 0.05128, 0.06625, 0.05597, 0.07307, 0.05498, 0.05977, 0.0583, 0.05585], 'station3': [0.02411, 0.04223, 0.32258, 0.43966, 0.46535, 0.21012, 0.37143, 0.16508, 0.35417, 0.36735], 'station4': [0.64474, 0.56818, 0.71014, 0.69444, 0.76562, 0.47748, 0.5618, 0.72059, 0.77778, 0.60241]}, 'F2': {'station1': [0.82192, 0.81967, 0.84211, 0.85069, 0.80645, 0.8006, 0.81699, 0.85069, 0.86879, 0.82508], 'station2': [0.25515, 0.13889, 0.2107, 0.2598, 0.22628, 0.28154, 0.22382, 0.23853, 0.23381, 0.22669], 'station3': [0.10921, 0.17966, 0.66667, 0.75893, 0.73209, 0.56604, 0.72222, 0.48598, 0.70055, 0.73569], 'station4': [0.8277, 0.81169, 0.84775, 0.85616, 0.86268, 0.8006, 0.80906, 0.85069, 0.86572, 0.82508]}}}\n"
     ]
    }
   ],
   "source": [
    "# Set the folder path\n",
    "summary_folder = \"./workspace/Jammer_detection/Src/federated_darcy/results/8_way_baseline\"\n",
    "\n",
    "# Initialize an empty dictionary to store the data\n",
    "data_dict = {}\n",
    "\n",
    "# Iterate through files in the folder\n",
    "for file_name in  sorted(os.listdir(summary_folder)):\n",
    "    # Check if the file name contains \"summary_r\"\n",
    "    if \"summary_r\" in file_name:\n",
    "        # Construct the full file path\n",
    "        file_path = os.path.join(summary_folder, file_name)\n",
    "\n",
    "        # Read the file\n",
    "        with open(file_path, 'r') as file:\n",
    "            lines = file.readlines()\n",
    "\n",
    "        # Split lines into sections (each section has 33 lines)\n",
    "        sections = [lines[i:i + 33] for i in range(0, len(lines), 33)]\n",
    "\n",
    "        # Process each section\n",
    "        for section in sections:\n",
    "            # Extract method name from the second line of each section\n",
    "            method_name = section[0].strip()\n",
    "\n",
    "            # Extract lines 3, 5, 7, and 9\n",
    "            relevant_lines = section[2:10:2]\n",
    "\n",
    "            # Process the relevant lines (assuming the data structure is the same)\n",
    "            df = pd.read_csv(StringIO('\\n'.join(relevant_lines)), sep='\\t', header=None)\n",
    "            df = df.dropna(axis=1, how='any')\n",
    "\n",
    "            # Set specified columns as column names\n",
    "            df.columns = [\"line\", \"Accuracy\", \"F1\", \"F2\", \"Precision\", \"Recall\", \"Label\"]\n",
    "\n",
    "            # Drop unnecessary columns\n",
    "            df = df[[\"line\", \"Accuracy\", \"F1\", \"F2\", \"Precision\", \"Recall\"]]\n",
    "\n",
    "            # Create a dictionary for the current method\n",
    "            method_data = {\n",
    "                'recall': {f'station{i + 1}': df.loc[i, 'Recall'] for i in range(len(df))},\n",
    "                'precision': {f'station{i + 1}': df.loc[i, 'Precision'] for i in range(len(df))},\n",
    "                'f2': {f'station{i + 1}': df.loc[i, 'F2'] for i in range(len(df))}\n",
    "            }\n",
    "\n",
    "            # Add the method data to the main dictionary\n",
    "            if method_name in data_dict:\n",
    "                data_dict[method_name].append(method_data)\n",
    "            else:\n",
    "                data_dict[method_name] = [method_data]\n",
    "# Create a dictionary to store the final data\n",
    "final_data_dict = {}\n",
    "\n",
    "# Iterate through methods\n",
    "for method_name, method_data_list in data_dict.items():\n",
    "    # Initialize lists for recall, precision, and f2\n",
    "    recall_list = {f'station{i + 1}': [] for i in range(4)}\n",
    "    precision_list = {f'station{i + 1}': [] for i in range(4)}\n",
    "    f2_list = {f'station{i + 1}': [] for i in range(4)}\n",
    "\n",
    "    # Iterate through method data\n",
    "    for method_data in method_data_list:\n",
    "        # Iterate through stations\n",
    "        for station in recall_list.keys():\n",
    "            # Append values to respective lists\n",
    "            recall_list[station].append(method_data['recall'][station])\n",
    "            precision_list[station].append(method_data['precision'][station])\n",
    "            f2_list[station].append(method_data['f2'][station])\n",
    "\n",
    "    # Add method-wise lists to the final dictionary\n",
    "    final_data_dict[method_name] = {\n",
    "        'recall': recall_list,\n",
    "        'Precision': precision_list,\n",
    "        'F2': f2_list\n",
    "    }\n",
    "\n",
    "# Print the final data dictionary\n",
    "print(final_data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "plt.tight_layout()\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42\n",
    "# save_path = \".. .pdf\" #TODO Path to save the image in \n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 10}\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "# Iterate through methods in data_dict\n",
    "for method_name, method_data_list in data_dict.items():\n",
    "    # Create a list to store recall values for station 1\n",
    "    recall_station1_values = []\n",
    "\n",
    "    # Iterate through method_data_list\n",
    "    for method_data in method_data_list:\n",
    "        # Append recall value for station 1 to the list\n",
    "        recall_station1_values.append(method_data['f2']['station2'])\n",
    "    if method_name.split(\"_\")[0]!=\"QFedAvg\":\n",
    "        # Plot recall values for station 1\n",
    "        plt.plot(range(1, len(recall_station1_values) + 1), recall_station1_values, label=method_name.split(\"_\")[0],linewidth=3.0)\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "# Add labels and legend\n",
    "plt.xlabel('Rounds',fontsize=14)\n",
    "plt.ylabel('F2-score',fontsize=14)\n",
    "plt.yticks(np.arange(0, 1, step=0.1),fontsize=14)\n",
    "plt.xticks(range(1, len(recall_station1_values) + 1),fontsize=14)\n",
    "plt.legend(fontsize=12.0,ncol=2)\n",
    "# plt.title('f2 Values when label 3 added on station 1')\n",
    "plt.savefig(\"./workspace/Jammer_detection/Src/Research_paper/station1_f2.pdf\")\n",
    "\n",
    "##station 4 label 10, station 2 is label 3, station 3 is label 7,station1 is label 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flower_temp_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}