result_analyzer_2007.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "\n",
    "log_file = \"compare_with_ic3ref_hwmcc2007.csv\"\n",
    "\n",
    "# Set display options to avoid truncation\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.expand_frame_repr\", False)\n",
    "pd.set_option(\"max_colwidth\", None)\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "\n",
    "# read csv table from log/compare_with_ic3ref.csv\n",
    "df = pd.read_csv(f\"log/{log_file}\")\n",
    "\n",
    "# Strip out the spaces from column names\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "# Filter and sort dataframe\n",
    "if \"ic3ref\" in log_file:\n",
    "    df = df[(df[\"NN-IC3-bg\"] == 1) & (df[\"IC3ref-bg\"] == 1) | (df[\"NN-IC3-bg\"] == 0) & (df[\"IC3ref-bg\"] == 0)]\n",
    "    df = df.sort_values(by=[\"case name\", \"NN-IC3 Time\"])\n",
    "    # Create a new dataframe that keeps the first duplicate\n",
    "    df_last = df.copy()\n",
    "    df_last.drop_duplicates(subset=\"case name\", keep=\"last\", inplace=True)\n",
    "    df_last = df_last.reset_index(drop=True)\n",
    "    #df.drop_duplicates(subset=\"case name\", keep=\"first\", inplace=True)\n",
    "    df.drop_duplicates(subset=\"case name\", keep=\"last\", inplace=True)\n",
    "    df = df.reset_index(drop=True)\n",
    "    \n",
    "# new a dataframe to store the resu\n",
    "\n",
    "# Time scatter plot\n",
    "plt.scatter(df[\"NN-IC3 Time\"], df[\"IC3ref Time\"], color=\"blue\", label=\"Data Points\")\n",
    "plt.xlabel(\"NN-IC3 Time\")\n",
    "plt.ylabel(\"IC3ref Time\")\n",
    "plt.title(\"Scatter Plot of NN-IC3 Time and IC3ref Time\")\n",
    "\n",
    "# Apply the log scale\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "# Set the axis limits\n",
    "plt.xlim(0.1, 1000)\n",
    "plt.ylim(0.1, 1000)\n",
    "\n",
    "# Diagonal line values\n",
    "diag_min = 0.1\n",
    "diag_max = 1000\n",
    "\n",
    "# Plot the diagonal line\n",
    "plt.plot([diag_min, diag_max], [diag_min, diag_max], color=\"red\", linestyle=\"--\", label=\"Central Line\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Frame scatter plot\n",
    "plt.scatter(df[\"NN-IC3 Frame\"], df[\"IC3ref Frame\"], color=\"blue\", label=\"Data Points\")\n",
    "plt.xlabel(\"NN-IC3 Frame\")\n",
    "plt.ylabel(\"IC3ref Frame\")\n",
    "plt.title(\"Scatter Plot of NN-IC3 Frame and IC3ref Frame\")\n",
    "\n",
    "# Apply the log scale\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "# Set the axis limits\n",
    "plt.xlim(1, 1000)\n",
    "plt.ylim(1, 1000)\n",
    "\n",
    "# Plot the diagonal line\n",
    "plt.plot([diag_min, diag_max], [diag_min, diag_max], color=\"red\", linestyle=\"--\", label=\"Central Line\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def read_clauses(file_path):\n",
    "    with open(file_path, 'r') as f:\n",
    "        lines = [line.strip() for line in f.readlines() if not line.startswith('unsat')]\n",
    "        clauses = [set(map(int, line.split())) for line in lines]\n",
    "    return clauses\n",
    "\n",
    "def coverage_ratio(inv_cti_clauses, predicted_clauses):\n",
    "    # inv_cti_clauses: list of clauses that predicted before filtering\n",
    "    # predicted_clauses: list of clauses that predicted after filtering\n",
    "    \n",
    "    found_clauses = 0\n",
    "    total_clauses = len(inv_cti_clauses)\n",
    "\n",
    "    if total_clauses == 0:\n",
    "        return 0\n",
    "\n",
    "    for inv_cti_clause in inv_cti_clauses:\n",
    "        inv_cti_clause_sorted = sorted(inv_cti_clause)\n",
    "        for predicted_clause in predicted_clauses:\n",
    "            if inv_cti_clause_sorted == sorted(predicted_clause):\n",
    "                found_clauses += 1\n",
    "                break\n",
    "    \n",
    "    # equation: (amount of found clauses that converged) / (total amount of clauses)\n",
    "    return found_clauses / total_clauses\n",
    "\n",
    "base_path = \"/data/guangyuh/coding_env/AIG2INV/AIG2INV_main/case4comp/dataset_hwmcc2007_tip_ic3ref_no_simplification_0-22_comp/\"\n",
    "cases = os.listdir(base_path)\n",
    "\n",
    "results = []\n",
    "\n",
    "for case in cases:\n",
    "    inv_cti_path = os.path.join(base_path, case, f\"{case}_inv_CTI_predicted.cnf\")\n",
    "    predicted_path = os.path.join(base_path, case, f\"{case}_predicted_clauses_after_filtering.cnf\")\n",
    "\n",
    "    inv_cti_clauses = read_clauses(inv_cti_path)\n",
    "    predicted_clauses = read_clauses(predicted_path)\n",
    "\n",
    "    ratio = coverage_ratio(inv_cti_clauses, predicted_clauses)\n",
    "    results.append({\"case\": case, \"coverage_ratio\": ratio})\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Existing DataFrame\n",
    "existing_df = df\n",
    "\n",
    "# Merge DataFrames on the 'case' column\n",
    "merged_df = existing_df.merge(results_df, left_on='case name', right_on='case')\n",
    "\n",
    "# Drop the 'case' column as it's redundant\n",
    "merged_df.drop(columns=['case'], inplace=True)\n",
    "\n",
    "# Rename the 'coverage_ratio' column if necessary\n",
    "merged_df.rename(columns={'coverage_ratio': 'passing ratio'}, inplace=True)\n",
    "\n",
    "# Calculate the time speed up and add it to the DataFrame\n",
    "merged_df['time speed up'] = merged_df['IC3ref Time'] / merged_df['NN-IC3 Time']\n",
    "\n",
    "# Scatter plot\n",
    "plt.scatter(merged_df['passing ratio'], merged_df['time speed up'])\n",
    "plt.xlabel('Passing Ratio')\n",
    "plt.ylabel('Time Speed Up')\n",
    "plt.title('Scatter Plot of Time Speed Up vs. Passing Ratio')\n",
    "# No speed up line\n",
    "x = [0, max(merged_df['passing ratio'])]\n",
    "y = [1, 1]\n",
    "plt.plot(x, y, label='No Speed Up', linestyle='--', color='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#print(merged_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def read_clauses(file_path):\n",
    "    with open(file_path, 'r') as f:\n",
    "        lines = [line.strip() for line in f.readlines() if not line.startswith('unsat')]\n",
    "        clauses = [set(map(int, line.split())) for line in lines]\n",
    "    return clauses\n",
    "\n",
    "def coverage_ratio(inv_cti_clauses, ground_truth_clauses):\n",
    "    # inv_cti_clauses: list of clauses that predicted before filtering\n",
    "    # ground_truth_clauses: list of clauses that predicted after filtering\n",
    "    \n",
    "    found_clauses = 0\n",
    "    total_clauses = len(inv_cti_clauses)\n",
    "\n",
    "    if total_clauses == 0:\n",
    "        return 0\n",
    "\n",
    "    for inv_cti_clause in inv_cti_clauses:\n",
    "        inv_cti_clause_sorted = sorted(inv_cti_clause)\n",
    "        for gt_clause in ground_truth_clauses:\n",
    "            if inv_cti_clause_sorted == sorted(gt_clause):\n",
    "                found_clauses += 1\n",
    "                break\n",
    "\n",
    "    # equation: (amount of found clauses that converged) / (total amount of clauses)\n",
    "    return found_clauses / total_clauses\n",
    "\n",
    "\n",
    "base_path = \"/data/guangyuh/coding_env/AIG2INV/AIG2INV_main/case4comp/dataset_hwmcc2007_tip_ic3ref_no_simplification_0-22_comp/\"\n",
    "ground_truth_base_path = \"/data/guangyuh/coding_env/AIG2INV/AIG2INV_main/ground_truth/hwmcc07-ic3ref_7200-result\"\n",
    "cases = os.listdir(base_path)\n",
    "\n",
    "results = []\n",
    "\n",
    "for case in cases:\n",
    "    ground_truth_path  = os.path.join(ground_truth_base_path, case, \"inv.cnf\")\n",
    "    inv_cti_path  = os.path.join(base_path, case, f\"{case}_predicted_clauses_after_filtering.cnf\")\n",
    "\n",
    "    inv_cti_clauses = read_clauses(inv_cti_path)\n",
    "    ground_truth_clauses = read_clauses(ground_truth_path)\n",
    "\n",
    "    ratio = coverage_ratio(inv_cti_clauses, ground_truth_clauses)\n",
    "    results.append({\"case\": case, \"coverage_ratio\": ratio})\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Existing DataFrame\n",
    "existing_df = df\n",
    "\n",
    "# Merge DataFrames on the 'case' column\n",
    "merged_df = existing_df.merge(results_df, left_on='case name', right_on='case')\n",
    "\n",
    "# Drop the 'case' column as it's redundant\n",
    "merged_df.drop(columns=['case'], inplace=True)\n",
    "\n",
    "# Rename the 'coverage_ratio' column if necessary\n",
    "merged_df.rename(columns={'coverage_ratio': 'prefect prediction ratio'}, inplace=True)\n",
    "\n",
    "# Calculate the time speed up and add it to the DataFrame\n",
    "merged_df['time speed up'] = merged_df['IC3ref Time'] / merged_df['NN-IC3 Time']\n",
    "\n",
    "# Scatter plot\n",
    "plt.scatter(merged_df['prefect prediction ratio'], merged_df['time speed up'])\n",
    "plt.xlabel('Prefect Prediction Ratio')\n",
    "plt.ylabel('Speed Up')\n",
    "plt.title('Scatter Plot of Time Speed Up vs. Prefect Prediction Ratio')\n",
    "# No speed up line\n",
    "x = [0, max(merged_df['prefect prediction ratio'])]\n",
    "y = [1, 1]\n",
    "plt.plot(x, y, label='No Speed Up', linestyle='--', color='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#print(merged_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_speedup_factor(df, x):\n",
    "    # Filter cases where IC3ref Time is greater than x seconds\n",
    "    df_filtered = df[ (df[\"IC3ref Time\"] > x) & (df[\"IC3ref Time\"] < 10000)]\n",
    "    #df_filtered = df[df[\"IC3ref Time\"] > x]\n",
    "    # Calculate the accumulated time for IC3ref and NN-IC3\n",
    "    accumulated_ic3ref_time = df_filtered[\"IC3ref Time\"].sum()\n",
    "    accumulated_nn_ic3_time = df_filtered[\"NN-IC3 Time\"].sum()\n",
    "\n",
    "    # Calculate the speedup factor for the accumulated times\n",
    "    #print(f\"accumulated_ic3ref_time is {accumulated_ic3ref_time}, accumulated_nn_ic3_time is {accumulated_nn_ic3_time}\")\n",
    "    speedup_factor = accumulated_ic3ref_time / accumulated_nn_ic3_time\n",
    "\n",
    "    return speedup_factor\n",
    "\n",
    "# Define the range of x values (thresholds)\n",
    "x_values = np.arange(0, 230, 10)\n",
    "\n",
    "# Calculate the speedup factor for each x value\n",
    "speedup_factors = [calculate_speedup_factor(df, x) for x in x_values]\n",
    "\n",
    "# Create the plot\n",
    "plt.plot(x_values, speedup_factors, color=\"blue\", label=\"NN-IC3 Integraton\")\n",
    "plt.xlabel(\"IC3ref Time Threshold (seconds)\")\n",
    "plt.ylabel(\"Speed up\")\n",
    "#plt.title(\"Speedup Factor by IC3ref Time Threshold\")\n",
    "plt.axhline(y=1, color=\"red\", linestyle=\"--\", label=\"No Speedup Line\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_reduce_factor(df, x):\n",
    "    # Filter cases where IC3ref Time is greater than x seconds\n",
    "    #df_filtered = df[ (df[\"IC3ref Time\"] > x) & (df[\"ABC Time\"] < 20000) ]\n",
    "    df_filtered = df[ df[\"IC3ref Time\"]> x ]\n",
    "    # Calculate the accumulated time for IC3ref and NN-IC3\n",
    "    accumulated_ic3ref_frame = df_filtered[\"IC3ref Frame\"].sum()\n",
    "    accumulated_nn_ic3_frame = df_filtered[\"NN-IC3 Frame\"].sum()\n",
    "\n",
    "    # Calculate the speedup factor for the accumulated times\n",
    "    reduce_factor = accumulated_ic3ref_frame / accumulated_nn_ic3_frame\n",
    "\n",
    "    return reduce_factor\n",
    "\n",
    "# Define the range of x values (thresholds)\n",
    "x_values = np.arange(0, 230, 10)\n",
    "#x_values = np.arange(0, 1000, 10)\n",
    "#x_values = np.arange(0, 3500, 10)\n",
    "\n",
    "# Calculate the speedup factor for each x value\n",
    "reduction_factors = [calculate_reduce_factor(df, x) for x in x_values]\n",
    "\n",
    "# Create the plot\n",
    "plt.plot(x_values, reduction_factors, color=\"blue\", label=\"NN-IC3 Integraton\")\n",
    "plt.xlabel(\"IC3ref Time Threshold (seconds)\")\n",
    "plt.ylabel(\"Frame Reduction\")\n",
    "#plt.title(\"Speedup Factor by ABC Time Threshold\")\n",
    "plt.axhline(y=1, color=\"red\", linestyle=\"--\", label=\"No Reduction Line\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import fileinput\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "log_file = \"compare_with_abc_hwmcc2007.csv\"\n",
    "\n",
    "# Set display options to avoid truncation\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.expand_frame_repr\", False)\n",
    "pd.set_option(\"max_colwidth\", None)\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "\n",
    "# read csv table from log/compare_with_ic3ref.csv\n",
    "df = pd.read_csv(f\"log/{log_file}\")\n",
    "\n",
    "# Strip out the spaces from column names\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "# Filter and sort dataframe\n",
    "if \"abc\" in log_file:\n",
    "    #df = df[(df[\"NN-IC3-bg\"] == 1) & (df[\"IC3ref-bg\"] == 1) | (df[\"NN-IC3-bg\"] == 0) & (df[\"IC3ref-bg\"] == 0)]\n",
    "    df = df.sort_values(by=[\"case name\", \"NN-ABC Time\"])\n",
    "    # Create a new dataframe that keeps the first duplicate\n",
    "    df_last = df.copy()\n",
    "    df_last.drop_duplicates(subset=\"case name\", keep=\"last\", inplace=True)\n",
    "    df_last = df_last.reset_index(drop=True)\n",
    "    #df.drop_duplicates(subset=\"case name\", keep=\"first\", inplace=True)\n",
    "    df.drop_duplicates(subset=\"case name\", keep=\"last\", inplace=True)\n",
    "    df = df.reset_index(drop=True)\n",
    "\n",
    "# Create scatter plot for time\n",
    "plt.scatter(df[\"NN-ABC Time\"], df[\"ABC Time\"], color=\"blue\", label=\"Data Points\")\n",
    "plt.xlabel(\"NN-ABC Time\")\n",
    "plt.ylabel(\"ABC Time\")\n",
    "plt.title(\"Scatter Plot of NN-ABC Time and ABC Time\")\n",
    "plt.plot(df[\"ABC Time\"], df[\"ABC Time\"], color=\"red\", linestyle=\"--\", label=\"Central Line\")\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Create scatter plot for frames\n",
    "plt.scatter(df[\"NN-ABC Frame\"], df[\"ABC Frame\"], color=\"blue\", label=\"Data Points\")\n",
    "plt.xlabel(\"NN-ABC Frame\")\n",
    "plt.ylabel(\"ABC Frame\")\n",
    "plt.title(\"Scatter Plot of NN-ABC Frame and ABC Frame\")\n",
    "plt.plot(df[\"ABC Frame\"], df[\"ABC Frame\"], color=\"red\", linestyle=\"--\", label=\"Central Line\")\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def read_clauses(file_path):\n",
    "    with open(file_path, 'r') as f:\n",
    "        lines = [line.strip() for line in f.readlines() if not line.startswith('unsat')]\n",
    "        clauses = [set(map(int, line.split())) for line in lines]\n",
    "    return clauses\n",
    "\n",
    "def coverage_ratio(inv_cti_clauses, predicted_clauses):\n",
    "    # inv_cti_clauses: list of clauses that predicted before filtering\n",
    "    # predicted_clauses: list of clauses that predicted after filtering\n",
    "    \n",
    "    found_clauses = 0\n",
    "    total_clauses = len(inv_cti_clauses)\n",
    "\n",
    "    if total_clauses == 0:\n",
    "        return 0\n",
    "\n",
    "    for inv_cti_clause in inv_cti_clauses:\n",
    "        inv_cti_clause_sorted = sorted(inv_cti_clause)\n",
    "        for predicted_clause in predicted_clauses:\n",
    "            if inv_cti_clause_sorted == sorted(predicted_clause):\n",
    "                found_clauses += 1\n",
    "                break\n",
    "    \n",
    "    # equation: (amount of found clauses that converged) / (total amount of clauses)\n",
    "    return found_clauses / total_clauses\n",
    "\n",
    "base_path = \"/data/guangyuh/coding_env/AIG2INV/AIG2INV_main/case4comp/dataset_hwmcc2007_tip_abc_no_simplification_0-22_comp/\"\n",
    "cases = os.listdir(base_path)\n",
    "\n",
    "results = []\n",
    "\n",
    "for case in cases:\n",
    "    inv_cti_path = os.path.join(base_path, case, f\"{case}_inv_CTI_predicted.cnf\")\n",
    "    predicted_path = os.path.join(base_path, case, f\"{case}_predicted_clauses_after_filtering.cnf\")\n",
    "\n",
    "    inv_cti_clauses = read_clauses(inv_cti_path)\n",
    "    predicted_clauses = read_clauses(predicted_path)\n",
    "\n",
    "    ratio = coverage_ratio(inv_cti_clauses, predicted_clauses)\n",
    "    results.append({\"case\": case, \"coverage_ratio\": ratio})\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Existing DataFrame\n",
    "existing_df = df\n",
    "\n",
    "# Merge DataFrames on the 'case' column\n",
    "merged_df = existing_df.merge(results_df, left_on='case name', right_on='case')\n",
    "\n",
    "# Drop the 'case' column as it's redundant\n",
    "merged_df.drop(columns=['case'], inplace=True)\n",
    "\n",
    "# Rename the 'coverage_ratio' column if necessary\n",
    "merged_df.rename(columns={'coverage_ratio': 'passing ratio'}, inplace=True)\n",
    "\n",
    "# Calculate the time speed up and add it to the DataFrame\n",
    "merged_df['time speed up'] = merged_df['ABC Time'] / merged_df['NN-ABC Time']\n",
    "\n",
    "# Scatter plot\n",
    "plt.scatter(merged_df['passing ratio'], merged_df['time speed up'])\n",
    "plt.xlabel('Passing Ratio')\n",
    "plt.ylabel('Time Speed Up')\n",
    "plt.title('Scatter Plot of Time Speed Up vs. Passing Ratio')\n",
    "# No speed up line\n",
    "x = [0, max(merged_df['passing ratio'])]\n",
    "y = [1, 1]\n",
    "plt.plot(x, y, label='No Speed Up', linestyle='--', color='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#print(merged_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def read_clauses(file_path):\n",
    "    with open(file_path, 'r') as f:\n",
    "        lines = [line.strip() for line in f.readlines() if not line.startswith('unsat')]\n",
    "        clauses = [set(map(int, line.split())) for line in lines]\n",
    "    return clauses\n",
    "\n",
    "def coverage_ratio(inv_cti_clauses, ground_truth_clauses):\n",
    "    # inv_cti_clauses: list of clauses that predicted before filtering\n",
    "    # ground_truth_clauses: list of clauses that predicted after filtering\n",
    "    \n",
    "    found_clauses = 0\n",
    "    total_clauses = len(inv_cti_clauses)\n",
    "\n",
    "    if total_clauses == 0:\n",
    "        return 0\n",
    "\n",
    "    for inv_cti_clause in inv_cti_clauses:\n",
    "        inv_cti_clause_sorted = sorted(inv_cti_clause)\n",
    "        for gt_clause in ground_truth_clauses:\n",
    "            if inv_cti_clause_sorted == sorted(gt_clause):\n",
    "                found_clauses += 1\n",
    "                break\n",
    "\n",
    "    # equation: (amount of found clauses that converged) / (total amount of clauses)\n",
    "    return found_clauses / total_clauses\n",
    "\n",
    "\n",
    "base_path = \"/data/guangyuh/coding_env/AIG2INV/AIG2INV_main/case4comp/dataset_hwmcc2007_tip_abc_no_simplification_0-22_comp/\"\n",
    "ground_truth_base_path = \"/data/guangyuh/coding_env/AIG2INV/AIG2INV_main/ground_truth/hwmcc07_abc_7200_result\"\n",
    "cases = os.listdir(base_path)\n",
    "\n",
    "results = []\n",
    "\n",
    "for case in cases:\n",
    "    # if file not exist, skip\n",
    "    if not os.path.exists(os.path.join(ground_truth_base_path, case, \"inv.cnf\")):\n",
    "        continue\n",
    "    ground_truth_path  = os.path.join(ground_truth_base_path, case, \"inv.cnf\")\n",
    "    inv_cti_path  = os.path.join(base_path, case, f\"{case}_predicted_clauses_after_filtering.cnf\")\n",
    "\n",
    "    inv_cti_clauses = read_clauses(inv_cti_path)\n",
    "    ground_truth_clauses = read_clauses(ground_truth_path)\n",
    "\n",
    "    ratio = coverage_ratio(inv_cti_clauses, ground_truth_clauses)\n",
    "    results.append({\"case\": case, \"coverage_ratio\": ratio})\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Existing DataFrame\n",
    "existing_df = df\n",
    "\n",
    "# Merge DataFrames on the 'case' column\n",
    "merged_df = existing_df.merge(results_df, left_on='case name', right_on='case')\n",
    "\n",
    "# Drop the 'case' column as it's redundant\n",
    "merged_df.drop(columns=['case'], inplace=True)\n",
    "\n",
    "# Rename the 'coverage_ratio' column if necessary\n",
    "merged_df.rename(columns={'coverage_ratio': 'prefect prediction ratio'}, inplace=True)\n",
    "\n",
    "# Calculate the time speed up and add it to the DataFrame\n",
    "merged_df['time speed up'] = merged_df['ABC Time'] / merged_df['NN-ABC Time']\n",
    "\n",
    "# Scatter plot\n",
    "plt.scatter(merged_df['prefect prediction ratio'], merged_df['time speed up'])\n",
    "plt.xlabel('Prefect Prediction Ratio')\n",
    "plt.ylabel('Speed Up')\n",
    "plt.title('Scatter Plot of Time Speed Up vs. Prefect Prediction Ratio')\n",
    "# No speed up line\n",
    "x = [0, max(merged_df['prefect prediction ratio'])]\n",
    "y = [1, 1]\n",
    "plt.plot(x, y, label='No Speed Up', linestyle='--', color='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#print(merged_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+nHbt2vHGG2/w9ddfs3XrViZOnJhye+vWraNRo0ZA0DdRqHbt2mzYsH1sz+OOO45x48YBMG3aNLKzs4v1fZS30loEhd34NYA84APAgBzgXeDYyKISkUpp8ODBKS/VZGdn061bt5SdyolOO+00vvzyS0444QTcHTOjX7/gMSlDhw5l8eLFVKlShQMOOIBRo0btcrz9+/cnJyeHtm3bMm7cOG677TZOOukktm3bRlZWFg899BBHH300119/PUcddRQNGzakRYsWRXcw7WjIkCGcd9553HfffcVaM507d+auu+4iNzeX6667jmHDhtG3b19ycnKoWbMmTz755C7/DemwZE2QYhXMngNud/d54ftWwNXufn6kkaWQl5fns2bNysSuRfY4ixYt4vDDD890GJXet99+S61atdi6dSvdunWjX79+dOvWLWPxJPt3N7PZ7p6XrH46ncWHFSYBAHefD+SWtZKZPW5mq8xsfil1OplZvpkt0K+VRWRPNWzYMHJzc2nVqhXNmjXjzDPPzHRIOyWdzuJFZvYY8AzB4HPnAovSWG8sMBJ4KlmhmdUFHgZOcfdPzeyn6QQsIrK7KfxV854qnUTQF7gUuDJ8Px14pKyV3H26mTUtpUpP4AV3/zSsv6qUuiIiEpF0Bp3bbGajgFfcfXE57vtQIMvMphE85OZBd0/VeugP9Ado0qRJOYYgIiLpDDr3ayAfeDV8n2tmL5XDvqsBRwCnAycDN5rZockquvtod89z97wGDRqUw65FRKRQOp3FfwTaAd8AuHs+0LQc9l0AvOru37n7GoJLTq3LWEdERMpZOolgq7uvi2Df/wB+aWbVzKwmcBTpdUKLyB6ktGGo0/Hll1/StWtXWrduTYsWLTjttNMiiHK7dIeoTsewYcOSdiR36NChXLZfXtJJBPPNrCdQ1cwOMbM/AzPKWsnMniUYtrq5mRWY2QVmdomZXQLg7osILjfNBWYCj4W3popIJVLWMNRluemmmzjxxBP54IMPWLhwIXfddVc5R1jxZswo8xRaodJJBAOBlsD3wF+BdcCgslZy9x7uvp+7Z7l7Y3cf4+6j3H1UQp173L2Fu7dy9wd27U8Qkd1ZacNQL1++nC5dupCTk0OXLl349NNPS9RZuXIljRs3Lnqfk5MDBEMvHHfccXTr1o0WLVpwySWXsG3bNgBee+012rdvT9u2benevTvffvstALNnz6Zjx44cccQRnHzyyaxcubJoeevWrWnfvj0PPfRQ0b52fBhO165dmTZtGhCMmjp48GDatm1Lly5ddurxkIUP35k2bRqdOnXi7LPP5rDDDqNXr15F4wylijUK6TyPYKO73wB0cvcj3f0P7r45sohEJDqdOpWcCgd627gxeXnhmDhr1pQsS9OAAQMYN25c0QiahS6//HL69OnD3Llz6dWrF1dccUXSdS+44AI6d+7M7bffXuyJYzNnzuTee+9l3rx5fPzxx0Utj9tuu43JkyczZ84c8vLyuO+++9iyZQsDBw5kwoQJzJ49m379+nHDDTcA0LdvX0aMGMHbb7+d9t/03Xff0bZtW+bMmUPHjh3THkJ7R++//z4PPPAACxcu5JNPPuGtt94qNdYolHn7qJl1AB4DagFNzKw1cLG7XxZZVCJSqSQOQ7333nsXLX/77bd54YUXAOjdu3fS5xWcfPLJfPLJJ7z66qtMmjSJNm3aMH9+cBW5Xbt2HHjggQD06NGDN998kxo1arBw4UKOOeYYAH744Qfat2/P4sWLmT9/PieeeCIAP/74I/vttx/r1q3jm2++oWPHjkVxTJo0qcy/qUqVKpxzzjkAnHvuuZx11lm7dGzatWtX1OLJzc1l2bJl1K1bN2msUUnnB2X3E9ze+RKAu39gZsdFFpGIRCe8rJFUzZqll2dnl15ehtKGoS6U6mli9erVo2fPnvTs2ZOuXbsyffp06tevX6K+meHunHjiiTz77LPFyubNm0fLli1LfOv/5ptvUu63WrVqRZebADZvTn0xZFceGg/Jh5V296SxRiWtB9O4+4odFv0YQSwiUoklG4a6Q4cOPPfccwCMGzeOY48tOajx66+/zsaNGwHYsGEDH3/8cdEPS2fOnMnSpUvZtm0b48eP59hjj+Xoo4/mrbfeYsmSJQBs3LiRjz76iObNm7N69eqik+uWLVtYsGABdevWpU6dOrz55ptFcRRq2rQp+fn5bNu2jRUrVjBz5syism3btjFhwgQA/vrXvyaNfVelijUq6SSCFeHlITezvczsanSbp4jsgsGDBxe7e2jEiBE88cQT5OTk8PTTT/Pggw+WWGf27Nnk5eWRk5ND+/btufDCCznyyCMBaN++PUOHDi0a7K1bt240aNCAsWPH0qNHj6Jn/n744YfstddeTJgwgWuvvZbWrVuTm5tbdPfOE088wYABA2jfvn2xS1fHHHMMzZo14xe/+AVXX301bdtufxTLPvvsw4IFCzjiiCN4/fXXuemmm5L+zbfddhuNGzcumtJRWqxRSGcY6mzgQeAEgsTxb+BKd18bWVSl0DDUIumrzMNQT5s2jeHDh/Pyyy9nZP+1atUquhtpd7Ozw1CnM9bQGqBX+YQnIiK7m3TGGjrQzP5pZqvD5wv8w8wOrIjgRERS6dSpU8ZaA8Bu2xrYFen0EfwVeB7YD2gI/A14ttQ1RERkj5FOIjB3f9rdt4ZT4QNqRGQPUFY/oFQuu/LvnU4imGpmQ82sqZkdYGZDgH+ZWT0zq7fTexSRClOjRg3Wrl2rZBAT7s7atWupUaPGTq2Xzg/KzglfL95heT+CloH6C0R2U40bN6agoGCnxsGRPVuNGjXSvk21UDp3DTXb5YhEJKOysrJo1kz/haV0KS8NmdmRZvbzhPd9wjuGRuiSkIhI5VFaH8GjwA8A4dhCdwFPEQxDPTr60EREpCKUdmmoqrt/Fc6fA4x294nARDPLjzwyERGpEKW1CKqaWWGi6AK8nlCWTieziIjsAUo7oT8LvGFma4BNwP8BmNnBBJeHRESkEkiZCNz9djObQvCL4td8+43IVQgeXykiIpVAqZd43P2dJMs+ii4cERGpaGk9mEZERCovJQIRkZhTIhARiTklAhGRmIssEZjZ4+GDbOaXUe9IM/vRzM6OKhYREUktyhbBWOCU0iqYWVXgTwTPQRYRkQyILBG4+3TgqzKqDQQmAquiikNEREqXsT4CM2sEdANGpVG3v5nNMrNZGlddRKR8ZbKz+AHgWnf/sayK7j7a3fPcPa9BgwbRRyYiEiOZHDwuD3jOzACygdPMbKu7/z2DMYmIxE7GEkHik8/MbCzwspKAiEjFiywRmNmzQCcg28wKgD8CWQDuXma/gIiIVIzIEoG799iJuudHFYeIiJROvywWEYk5JQIRkZhTIhARiTklAhGRmFMiEBGJOSUCEZGYUyIQEYk5JQIRkZhTIhARiTklAhGRmFMiEBGJOSUCEZGYUyIQEYk5JQIRkZhTIhARiTklAhGRmFMiEBGJOSUCEZGYUyIQEYk5JQIRkZhTIhARiTklAhGRmFMiEBGJOSUCEZGYiywRmNnjZrbKzOanKO9lZnPDaYaZtY4qFhERSS3KFsFY4JRSypcCHd09B7gVGB1hLCIikkK1qDbs7tPNrGkp5TMS3r4DNI4qFhERSW136SO4AJiUqtDM+pvZLDObtXr16goMS0Sk8st4IjCzzgSJ4NpUddx9tLvnuXtegwYNKi44EZEYiOzSUDrMLAd4DDjV3ddmMhYRkbjKWIvAzJoALwC93f2jTMUhIhJ3kbUIzOxZoBOQbWYFwB+BLAB3HwXcBNQHHjYzgK3unhdVPCIiklyUdw31KKP8QuDCqPYvIiLpyXhnsYiIZJYSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMxFlgjM7HEzW2Vm81OUm5mNMLMlZjbXzNpGFYuIiKQWZYtgLHBKKeWnAoeEU3/gkQhjERGRFKpFtWF3n25mTUupcgbwlLs78I6Z1TWz/dx9ZakbXrwYOnUqvuy3v4XLLoONG+G000quc/75wbRmDZx9dsnySy+Fc86BFSugd++S5YMHw69+Fez74otLlv/hD3DCCZCfD4MGlSy/4w7o0AFmzIDrry9Z/sADkJsLkyfDbbeVLH/0UWjeHP75T7j33pLlTz8N++8P48fDI0ny6YQJkJ0NY8cG045eeQVq1oSHH4bnny9ZPm1a8Dp8OLz8cvGyvfeGSZOC+VtvhSlTipfXrw8TJwbz110Hb79dvLxxY3jmmWB+0KDgGCY69FAYPTqY798fPvqoeHlubnD8AM49FwoKipe3bw933hnM/+Y3sHZt8fIuXeDGG4P5U0+FTZuKl3ftCldfHczv+LkDffb02Qvm98TPXoJM9hE0AlYkvC8Il5VgZv3NbJaZzdqyZUuFBCciEhcWfCGPaONBi+Bld2+VpOxfwJ3u/mb4fgowxN1nl7bNvLw8nzVrVhThiohUWmY2293zkpVlskVQAOyf8L4x8HmGYhERia1MJoKXgD7h3UNHA+vK7B8QEZFyF1lnsZk9C3QCss2sAPgjkAXg7qOAV4DTgCXARqBvVLGIiEhqUd411KOMcgcGRLV/ERFJj35ZLCISc0oEIiIxp0QgIhJzSgQiIjEX6Q/KomBmq4Hlu7h6NrCmHMOpLHRcStIxKUnHpKQ96Zgc4O4NkhXscYngf2Fms1L9si7OdFxK0jEpScekpMpyTHRpSEQk5pQIRERiLm6JYHSmA9hN6biUpGNSko5JSZXimMSqj0BEREqKW4tARER2oEQgIhJzsUkEZnaKmS02syVmNjTT8WSKmS0zs3lmlm9ms8Jl9czsP2b23/D1J5mOM0pm9riZrTKz+QnLUh4DM7su/NwsNrOTMxN19FIcl2Fm9ln4eck3s9MSyir1cTGz/c1sqpktMrMFZnZluLzSfVZikQjMrCrwEHAq0ALoYWYtMhtVRnV299yE+5+HAlPc/RBgSvi+MhsLnLLDsqTHIPyc/A5oGa7zcPh5qozGUvK4ANwffl5y3f0ViM1x2QoMdvfDgaOBAeHfXek+K7FIBEA7YIm7f+LuPwDPAWdkOKbdyRnAk+H8k8CZmQsleu4+Hfhqh8WpjsEZwHPu/r27LyV4fka7ioizoqU4LqlU+uPi7ivdfU44vwFYRPBc9Ur3WYlLImgErEh4XxAuiyMHXjOz2WbWP1z2s8Knw4WvP81YdJmT6hjoswOXm9nc8NJR4WWQWB2X8PnrbYB3qYSflbgkAkuyLK73zR7j7m0JLpMNMLPjMh3Qbi7un51HgIOAXGAlcG+4PDbHxcxqAROBQe6+vrSqSZbtEcckLomgANg/4X1j4PMMxZJR7v55+LoKeJGg6fqlme0HEL6uylyEGZPqGMT6s+PuX7r7j+6+DfgL2y91xOK4mFkWQRIY5+4vhIsr3WclLongPeAQM2tmZnsRdOi8lOGYKpyZ7WNmtQvngZOA+QTH4ryw2nnAPzITYUalOgYvAb8zs+pm1gw4BJiZgfgyovCEF+pG8HmBGBwXMzNgDLDI3e9LKKp0n5XInlm8O3H3rWZ2OfBvoCrwuLsvyHBYmfAz4MXg80014K/u/qqZvQc8b2YXAJ8C3TMYY+TM7FmgE5BtZgXAH4G7SHIM3H2BmT0PLCS4i2SAu/+YkcAjluK4dDKzXIJLHMuAiyE2x+UYoDcwz8zyw2XXUwk/KxpiQkQk5uJyaUhERFJQIhARiTklAhGRmFMiEBGJOSUCEZGYUyKQtJhZNzNzMzssYVlTM9sUjkr5gZnNMLPmCeWnmtmscPTGD81s+A7b7JswquUPCaOi3mVmt5jZCeX8N7wbbv9TM1udsO+mZvZtee4r3N/5ZjZyJ9dZZmbZSZYPM7OrU6wzyMz67Gqc5S3V35BQ/pyZHVKRMUnpYvE7AikXPYA3CX6MNyxh+cfungtgZhcT3Gd9npm1AkYCp7v7h2ZWDeifuEF3fwJ4Ilx3GcGoqGui+gPc/ahwX+cDee5+eWFZ+NuKUplZNXffGlV8uyI8rv2AtpmOZSc8AgwBLsp0IBJQi0DKFI61cgxwAUEiSGVf4Otwfghwu7t/CMGP+tz94Z3Y51gzOzucX2Zmd5jZ22ELo62Z/dvMPjazSxLWucbM3gsHSLt5J/9MzOz2sGXzjpn9LCGO+8xsKvAnMzvIzF4NB+37v8IWkpl1N7P54frTEzbbMKz/XzO7O2FfPcIW0Hwz+1OKeG6wYFz7yUDzZHWA44E5hQnKzK4ws4XhMXguXLaPBQPGvWdm75vZGeHyqmY2PIxjrpkNDJd3CevNC9ernvDvcLOZzQnLCv/2+mb2WrjOo4Rj7oT7/Vd4TOab2TlhzP8HnBAmMdkduLsmTaVOwLnAmHB+BtA2nG8KbALygY8JBiVrEpbNAVrvxD6WAdkJ78cCZyeUXRrO3w/MBWoDDYBV4fKTCB4kbgRfcF4Gjkuxr/OBkTssc+BX4fzdwB8S4ngZqBq+nwIcEs4fBbwezs8DGoXzdRP28wlQB6gBLCcYi6YhwS9SGxC0yl8Hzkw8DsAR4TZrEiTYJcDVSf6Wm4GBCe8/B6rvEMcdwLmFy4CPgH2ASwnG0akWltUL41wBHBoue4pgsLXC2AaG85cBj4XzI4CbwvnTw2OZDfwG+EtCbHUS5v8DHJHpz7amYFKLQNLRg+AZDoSvPRLKPvbggSUHAYMITsZRKBwbah7wrrtvcPfVwGYzq0uQCE4C3idIQocRjPWSrh8ITvgAswmSXKG/ufuPYcuoA/A3C4YceBQoHIvnLWCsmV1EMIxJoSnuvs7dNxMMPXAAcCQwzd1Xe/BNfhyw4yiwvwRedPeNHox4mWpsrP2A1Qnv5wLjzOxcgmEOIDguQ8OYpxGc7JsAJwCjwhhw968IWh5L3f2jcN0nd4itcOC1xGN0HPBMuI1/sb1VOI/gm/+fzOyX7r4uYTurCBKi7AbUNJNSmVl9gssPrczMCU5ybmZDklR/ifCaP7CA4FvtB+UUyvfh67aE+cL31QhaAne6+6O7uP0tHn5VBX6k+P+N78LXKsA3HvaJJHL3S8zsKIJvxPkWjM+TGHfidsvukAg3m0adTQQn9kKnE5yYfw3caGYtw/39xt0XJ65oQcfIjvsoK7bCv2fHY1QiVnf/yMyOAE4D7jSz19z9lrC4Rhi77AbUIpCynA085e4HuHtTd98fWAocm6TusQSXiADuAa43s0MBzKyKmV0VYZz/BvqF39oxs0ZmVq4P2Am/mS81s+7hPszMWofzB7n7u+5+E7CG4sMR7+hdoKOZZVvwKMMewBs71JkOdDOzvS0YMfZXKba1CDg4jKEKsL+7TyXoo6kL1CI4NgPDEz9m1iZc9zXgksJr9WZWD/gQaGpmB4d1eieJbUfTgV7hNk4FfhLONwQ2uvszwHCKd2gfSvBlQXYDahFIWXoQjLaYaCLQE/gTcFB4ycEILq9cCODuc81sEPCsmdUk+Mb4r6iCdPfXzOxw4O3wfPctQd9GeT9boRfwiJn9AcgiuFT2AXCPBbdEGkE/wgcED3NJFutKM7sOmBrWf8Xd/7FDnTlmNp6g/2U5QQdrMpOAp8P5qsAzZlYn3O797v6Nmd0KPADMDZPBMqAr8BjBCXmumW0huJ4/0sz6Elz+qkYwhPuoMo7JzQT/znMIksan4fJfhMdlG7CFoE+CsCN+k4dP+ZLM0+ijIns4M3sRGOLu/810LOkws98D6919TKZjkYAuDYns+YayvdN6T/AN2x/+LrsBtQhERGJOLQIRkZhTIhARiTklAhGRmFMiEBGJOSUCEZGY+3/SUM2pxiib1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_speedup_factor(df, x):\n",
    "    # Filter cases where IC3ref Time is greater than x seconds\n",
    "    df_filtered = df[ (df[\"ABC Time\"] > x) & (df[\"ABC Time\"] < 20000) ]\n",
    "    #df_filtered = df[ df[\"ABC Time\"]> x ]\n",
    "    # Calculate the accumulated time for IC3ref and NN-IC3\n",
    "    accumulated_ic3_abc_time = df_filtered[\"ABC Time\"].sum()\n",
    "    accumulated_nn_ic3_time = df_filtered[\"NN-ABC Time\"].sum()\n",
    "\n",
    "    # Calculate the speedup factor for the accumulated times\n",
    "    speedup_factor = accumulated_ic3_abc_time / accumulated_nn_ic3_time\n",
    "\n",
    "    return speedup_factor\n",
    "\n",
    "# Define the range of x values (thresholds)\n",
    "x_values = np.arange(0, 230, 10)\n",
    "#x_values = np.arange(0, 1000, 10)\n",
    "#x_values = np.arange(0, 3500, 10)\n",
    "\n",
    "# Calculate the speedup factor for each x value\n",
    "speedup_factors = [calculate_speedup_factor(df, x) for x in x_values]\n",
    "\n",
    "# Create the plot\n",
    "plt.plot(x_values, speedup_factors, color=\"blue\", label=\"NN-IC3 Integraton\")\n",
    "plt.xlabel(\"ABC Time Threshold (seconds)\")\n",
    "plt.ylabel(\"Speed up\")\n",
    "#plt.title(\"Speedup Factor by ABC Time Threshold\")\n",
    "plt.axhline(y=1, color=\"red\", linestyle=\"--\", label=\"No Speedup Line\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_reduce_factor(df, x):\n",
    "    # Filter cases where IC3ref Time is greater than x seconds\n",
    "    df_filtered = df[ (df[\"ABC Time\"] > x) & (df[\"ABC Time\"] < 20000) ]\n",
    "    #df_filtered = df[ df[\"ABC Time\"]> x ]\n",
    "    # Calculate the accumulated time for IC3ref and NN-IC3\n",
    "    accumulated_ic3_abc_frame = df_filtered[\"ABC Frame\"].sum()\n",
    "    accumulated_nn_ic3_frame = df_filtered[\"NN-ABC Frame\"].sum()\n",
    "\n",
    "    # Calculate the speedup factor for the accumulated times\n",
    "    reduce_factor = accumulated_ic3_abc_frame / accumulated_nn_ic3_frame\n",
    "\n",
    "    return reduce_factor\n",
    "\n",
    "# Define the range of x values (thresholds)\n",
    "x_values = np.arange(0, 230, 10)\n",
    "#x_values = np.arange(0, 1000, 10)\n",
    "#x_values = np.arange(0, 3500, 10)\n",
    "\n",
    "# Calculate the speedup factor for each x value\n",
    "reduction_factors = [calculate_reduce_factor(df, x) for x in x_values]\n",
    "\n",
    "# Create the plot\n",
    "plt.plot(x_values, reduction_factors, color=\"blue\", label=\"NN-IC3 Integraton\")\n",
    "plt.xlabel(\"ABC Time Threshold (seconds)\")\n",
    "plt.ylabel(\"Frame Reduction\")\n",
    "#plt.title(\"Speedup Factor by ABC Time Threshold\")\n",
    "plt.axhline(y=1, color=\"red\", linestyle=\"--\", label=\"No Reduction Line\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-gpu-aig2inv",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}