ANOVA_200ns_heatmaps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "class MeanCalculator:\n",
    "    def __init__(self, directories):\n",
    "        self.directories = directories\n",
    "        self.all_subdirectories = []\n",
    "        self.all_means = []\n",
    "\n",
    "    def extract_mean(self, file_path):\n",
    "        with open(file_path) as f:\n",
    "            # Skip the first 24 lines, which are header information\n",
    "            for i in range(18):\n",
    "                next(f)\n",
    "            data = [float(line.strip().split()[1]) for line in f]\n",
    "            mean = np.mean(data[:1001])\n",
    "        return mean\n",
    "\n",
    "    def process_directory(self, directory):\n",
    "        means = []\n",
    "        subdirectories = []\n",
    "        for subdir, dirs, files in os.walk(directory):\n",
    "            for file in files:\n",
    "                if file == \"rmsd.xvg\":\n",
    "                    file_path = os.path.join(subdir, file)\n",
    "                    mean = self.extract_mean(file_path)\n",
    "                    subdirectory = subdir.split(\"/\")[-1]\n",
    "                    means.append(mean)\n",
    "                    subdirectories.append(subdirectory)\n",
    "        return subdirectories, means\n",
    "\n",
    "    def calculate_means(self):\n",
    "        for directory in self.directories:\n",
    "            subdirectories, means = self.process_directory(directory)\n",
    "            self.all_subdirectories.extend(subdirectories)\n",
    "            self.all_means.extend(means)\n",
    "\n",
    "        self.unique_subdirectories = list(set(self.all_subdirectories))\n",
    "\n",
    "        self.subdir_to_means = {}\n",
    "        for subdir, mean, directory in zip(self.all_subdirectories, self.all_means, self.directories * len(self.unique_subdirectories)):\n",
    "            if subdir not in self.subdir_to_means:\n",
    "                self.subdir_to_means[subdir] = []\n",
    "            self.subdir_to_means[subdir].append(mean)\n",
    "\n",
    "        self.df = pd.DataFrame.from_dict(self.subdir_to_means, orient='index', columns=self.directories)\n",
    "        self.df = self.df.T\n",
    "        self.df.columns = [int(col) for col in self.df.columns]\n",
    "        self.df = self.df.sort_index(axis=1)\n",
    "        self.df.columns = [str(col) for col in self.df.columns]\n",
    "\n",
    "    def get_df(self):\n",
    "        return self.df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100ns.1</th>\n",
       "      <td>0.102290</td>\n",
       "      <td>0.123298</td>\n",
       "      <td>0.101595</td>\n",
       "      <td>0.094797</td>\n",
       "      <td>0.105467</td>\n",
       "      <td>0.117420</td>\n",
       "      <td>0.113608</td>\n",
       "      <td>0.128597</td>\n",
       "      <td>0.088409</td>\n",
       "      <td>0.103129</td>\n",
       "      <td>0.088469</td>\n",
       "      <td>0.120258</td>\n",
       "      <td>0.119245</td>\n",
       "      <td>0.104188</td>\n",
       "      <td>0.119557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100ns.2</th>\n",
       "      <td>0.117608</td>\n",
       "      <td>0.096498</td>\n",
       "      <td>0.087630</td>\n",
       "      <td>0.100609</td>\n",
       "      <td>0.112048</td>\n",
       "      <td>0.104936</td>\n",
       "      <td>0.119513</td>\n",
       "      <td>0.130550</td>\n",
       "      <td>0.100389</td>\n",
       "      <td>0.101461</td>\n",
       "      <td>0.098862</td>\n",
       "      <td>0.097226</td>\n",
       "      <td>0.106650</td>\n",
       "      <td>0.122696</td>\n",
       "      <td>0.119568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100ns.3</th>\n",
       "      <td>0.122667</td>\n",
       "      <td>0.092140</td>\n",
       "      <td>0.131991</td>\n",
       "      <td>0.114084</td>\n",
       "      <td>0.095322</td>\n",
       "      <td>0.103398</td>\n",
       "      <td>0.106636</td>\n",
       "      <td>0.133802</td>\n",
       "      <td>0.097583</td>\n",
       "      <td>0.112788</td>\n",
       "      <td>0.090964</td>\n",
       "      <td>0.101044</td>\n",
       "      <td>0.116765</td>\n",
       "      <td>0.129540</td>\n",
       "      <td>0.126481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                1         2         3         4         5         6         7  \\\n",
       "100ns.1  0.102290  0.123298  0.101595  0.094797  0.105467  0.117420  0.113608   \n",
       "100ns.2  0.117608  0.096498  0.087630  0.100609  0.112048  0.104936  0.119513   \n",
       "100ns.3  0.122667  0.092140  0.131991  0.114084  0.095322  0.103398  0.106636   \n",
       "\n",
       "                8         9        10        11        12        13        14  \\\n",
       "100ns.1  0.128597  0.088409  0.103129  0.088469  0.120258  0.119245  0.104188   \n",
       "100ns.2  0.130550  0.100389  0.101461  0.098862  0.097226  0.106650  0.122696   \n",
       "100ns.3  0.133802  0.097583  0.112788  0.090964  0.101044  0.116765  0.129540   \n",
       "\n",
       "               15  \n",
       "100ns.1  0.119557  \n",
       "100ns.2  0.119568  \n",
       "100ns.3  0.126481  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usage:\n",
    "mean_calculator = MeanCalculator([\"100ns.1\", \"100ns.2\", \"100ns.3\"])\n",
    "mean_calculator.calculate_means()\n",
    "df_2 = mean_calculator.get_df()\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MeanCalculator:\n",
    "    def __init__(self, directories, start=0, end=1001):\n",
    "        self.directories = directories\n",
    "        self.start = start\n",
    "        self.end = end\n",
    "        self.all_subdirectories = []\n",
    "        self.all_means = []\n",
    "\n",
    "    def extract_mean(self, file_path):\n",
    "        with open(file_path) as f:\n",
    "            # Skip the first 18 lines, which are header information\n",
    "            for i in range(18):\n",
    "                next(f)\n",
    "            data = [float(line.strip().split()[1]) for line in f]\n",
    "            mean = np.mean(data[self.start:self.end])\n",
    "        return mean\n",
    "\n",
    "    def process_directory(self, directory):\n",
    "        means = []\n",
    "        subdirectories = []\n",
    "        for subdir, dirs, files in os.walk(directory):\n",
    "            for file in files:\n",
    "                if file == \"rmsd.xvg\":\n",
    "                    file_path = os.path.join(subdir, file)\n",
    "                    mean = self.extract_mean(file_path)\n",
    "                    subdirectory = subdir.split(\"/\")[-1]\n",
    "                    means.append(mean)\n",
    "                    subdirectories.append(subdirectory)\n",
    "        return subdirectories, means\n",
    "\n",
    "    def calculate_means(self):\n",
    "        for directory in self.directories:\n",
    "            subdirectories, means = self.process_directory(directory)\n",
    "            self.all_subdirectories.extend(subdirectories)\n",
    "            self.all_means.extend(means)\n",
    "\n",
    "        self.unique_subdirectories = list(set(self.all_subdirectories))\n",
    "\n",
    "        self.subdir_to_means = {}\n",
    "        for subdir, mean, directory in zip(self.all_subdirectories, self.all_means, self.directories * len(self.unique_subdirectories)):\n",
    "            if subdir not in self.subdir_to_means:\n",
    "                self.subdir_to_means[subdir] = []\n",
    "            self.subdir_to_means[subdir].append(mean)\n",
    "\n",
    "        self.df = pd.DataFrame.from_dict(self.subdir_to_means, orient='index', columns=self.directories)\n",
    "        self.df = self.df.T\n",
    "        self.df.columns = [int(col) for col in self.df.columns]\n",
    "        self.df = self.df.sort_index(axis=1)\n",
    "        self.df.columns = [str(col) for col in self.df.columns]\n",
    "\n",
    "    def get_df(self):\n",
    "        return self.df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100ns.1</th>\n",
       "      <td>0.102290</td>\n",
       "      <td>0.123298</td>\n",
       "      <td>0.101595</td>\n",
       "      <td>0.094797</td>\n",
       "      <td>0.105467</td>\n",
       "      <td>0.117420</td>\n",
       "      <td>0.113608</td>\n",
       "      <td>0.128597</td>\n",
       "      <td>0.088409</td>\n",
       "      <td>0.103129</td>\n",
       "      <td>0.088469</td>\n",
       "      <td>0.120258</td>\n",
       "      <td>0.119245</td>\n",
       "      <td>0.104188</td>\n",
       "      <td>0.119557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100ns.2</th>\n",
       "      <td>0.117608</td>\n",
       "      <td>0.096498</td>\n",
       "      <td>0.087630</td>\n",
       "      <td>0.100609</td>\n",
       "      <td>0.112048</td>\n",
       "      <td>0.104936</td>\n",
       "      <td>0.119513</td>\n",
       "      <td>0.130550</td>\n",
       "      <td>0.100389</td>\n",
       "      <td>0.101461</td>\n",
       "      <td>0.098862</td>\n",
       "      <td>0.097226</td>\n",
       "      <td>0.106650</td>\n",
       "      <td>0.122696</td>\n",
       "      <td>0.119568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100ns.3</th>\n",
       "      <td>0.122667</td>\n",
       "      <td>0.092140</td>\n",
       "      <td>0.131991</td>\n",
       "      <td>0.114084</td>\n",
       "      <td>0.095322</td>\n",
       "      <td>0.103398</td>\n",
       "      <td>0.106636</td>\n",
       "      <td>0.133802</td>\n",
       "      <td>0.097583</td>\n",
       "      <td>0.112788</td>\n",
       "      <td>0.090964</td>\n",
       "      <td>0.101044</td>\n",
       "      <td>0.116765</td>\n",
       "      <td>0.129540</td>\n",
       "      <td>0.126481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                1         2         3         4         5         6         7  \\\n",
       "100ns.1  0.102290  0.123298  0.101595  0.094797  0.105467  0.117420  0.113608   \n",
       "100ns.2  0.117608  0.096498  0.087630  0.100609  0.112048  0.104936  0.119513   \n",
       "100ns.3  0.122667  0.092140  0.131991  0.114084  0.095322  0.103398  0.106636   \n",
       "\n",
       "                8         9        10        11        12        13        14  \\\n",
       "100ns.1  0.128597  0.088409  0.103129  0.088469  0.120258  0.119245  0.104188   \n",
       "100ns.2  0.130550  0.100389  0.101461  0.098862  0.097226  0.106650  0.122696   \n",
       "100ns.3  0.133802  0.097583  0.112788  0.090964  0.101044  0.116765  0.129540   \n",
       "\n",
       "               15  \n",
       "100ns.1  0.119557  \n",
       "100ns.2  0.119568  \n",
       "100ns.3  0.126481  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directories = [\"100ns.1\", \"100ns.2\", \"100ns.3\"]\n",
    "mean_calculator = MeanCalculator(directories, start=0, end=1001)\n",
    "mean_calculator.calculate_means()\n",
    "df = mean_calculator.get_df()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for i in range(20):\n",
    "    start = i * 1001\n",
    "    end = start + 1001\n",
    "    mean_calculator = MeanCalculator(directories, start=start, end=end)\n",
    "    mean_calculator.calculate_means()\n",
    "    df = mean_calculator.get_df()\n",
    "    dfs.append(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100ns.1    0.102290\n",
      "100ns.2    0.117608\n",
      "100ns.3    0.122667\n",
      "Name: 1, dtype: float64\n",
      "100ns.1    0.123298\n",
      "100ns.2    0.096498\n",
      "100ns.3    0.092140\n",
      "Name: 2, dtype: float64\n",
      "100ns.1    0.101595\n",
      "100ns.2    0.087630\n",
      "100ns.3    0.131991\n",
      "Name: 3, dtype: float64\n",
      "100ns.1    0.094797\n",
      "100ns.2    0.100609\n",
      "100ns.3    0.114084\n",
      "Name: 4, dtype: float64\n",
      "100ns.1    0.105467\n",
      "100ns.2    0.112048\n",
      "100ns.3    0.095322\n",
      "Name: 5, dtype: float64\n",
      "100ns.1    0.117420\n",
      "100ns.2    0.104936\n",
      "100ns.3    0.103398\n",
      "Name: 6, dtype: float64\n",
      "100ns.1    0.113608\n",
      "100ns.2    0.119513\n",
      "100ns.3    0.106636\n",
      "Name: 7, dtype: float64\n",
      "100ns.1    0.128597\n",
      "100ns.2    0.130550\n",
      "100ns.3    0.133802\n",
      "Name: 8, dtype: float64\n",
      "100ns.1    0.088409\n",
      "100ns.2    0.100389\n",
      "100ns.3    0.097583\n",
      "Name: 9, dtype: float64\n",
      "100ns.1    0.103129\n",
      "100ns.2    0.101461\n",
      "100ns.3    0.112788\n",
      "Name: 10, dtype: float64\n",
      "100ns.1    0.088469\n",
      "100ns.2    0.098862\n",
      "100ns.3    0.090964\n",
      "Name: 11, dtype: float64\n",
      "100ns.1    0.120258\n",
      "100ns.2    0.097226\n",
      "100ns.3    0.101044\n",
      "Name: 12, dtype: float64\n",
      "100ns.1    0.119245\n",
      "100ns.2    0.106650\n",
      "100ns.3    0.116765\n",
      "Name: 13, dtype: float64\n",
      "100ns.1    0.104188\n",
      "100ns.2    0.122696\n",
      "100ns.3    0.129540\n",
      "Name: 14, dtype: float64\n",
      "100ns.1    0.119557\n",
      "100ns.2    0.119568\n",
      "100ns.3    0.126481\n",
      "Name: 15, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 15):\n",
    "    print(dfs[0].iloc[:, i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a significant difference.\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "f_value, p_value = stats.f_oneway(dfs[0].iloc[:, 7], dfs[1].iloc[:, 7])\n",
    "if p_value < 0.05:\n",
    "    print(f\"There is a significant difference.\")\n",
    "else:\n",
    "    print(f\"There is no significant difference.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant difference in the means of the groups 20ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 5.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 6.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 20ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 10.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 11.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 13.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 14.\n",
      "There is no significant difference in the means of the groups 20ns and 10ns for 15.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 5.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 6.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 30ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 30ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 13.\n",
      "There is no significant difference in the means of the groups 30ns and 10ns for 14.\n",
      "There is a significant difference in the means of the groups 30ns and 10ns for 15\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 5.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 6.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 40ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 40ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 13.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 14.\n",
      "There is no significant difference in the means of the groups 40ns and 10ns for 15.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 5.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 6.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 50ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 50ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 13.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 14.\n",
      "There is no significant difference in the means of the groups 50ns and 10ns for 15.\n",
      "There is a significant difference in the means of the groups 60ns and 10ns for 1\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 5.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 6.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 60ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 60ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 13.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 14.\n",
      "There is no significant difference in the means of the groups 60ns and 10ns for 15.\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 9\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 10\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 70ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 70ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 1\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 80ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 80ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 1\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 5.\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 90ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 90ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 1\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 4.\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 5.\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 9\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 10\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 100ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 100ns and 10ns for 15\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 3.\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 4\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 110ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 110ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 1\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 2\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 13.\n",
      "There is no significant difference in the means of the groups 120ns and 10ns for 14.\n",
      "There is a significant difference in the means of the groups 120ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 1\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 2\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 7.\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 8.\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 130ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 130ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 1\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 2\n",
      "There is no significant difference in the means of the groups 140ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 140ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 140ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 140ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 140ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 140ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 140ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 1\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 2\n",
      "There is no significant difference in the means of the groups 150ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 150ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 6\n",
      "There is no significant difference in the means of the groups 150ns and 10ns for 7.\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 150ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 150ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 150ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 150ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 1\n",
      "There is no significant difference in the means of the groups 160ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 160ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 160ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 6\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 7\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 160ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 160ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 160ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 14\n",
      "There is a significant difference in the means of the groups 160ns and 10ns for 15\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 1\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 6\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 7\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 8\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 9.\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 170ns and 10ns for 14\n",
      "There is no significant difference in the means of the groups 170ns and 10ns for 15.\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 6\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 7\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 180ns and 10ns for 14\n",
      "There is no significant difference in the means of the groups 180ns and 10ns for 15.\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 1.\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 2.\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 6\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 7\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 190ns and 10ns for 14\n",
      "There is no significant difference in the means of the groups 190ns and 10ns for 15.\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 1.\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 2\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 3.\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 4.\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 5\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 6\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 7\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 8\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 9\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 10.\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 11\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 12.\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 13.\n",
      "There is a significant difference in the means of the groups 200ns and 10ns for 14\n",
      "There is no significant difference in the means of the groups 200ns and 10ns for 15.\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for i in range(1,20):\n",
    "    for c in range(0, 15):\n",
    "        f_value, p_value = stats.f_oneway(dfs[0].iloc[:, c], dfs[i].iloc[:, c])\n",
    "        if p_value < 0.05:\n",
    "            print(f\"There is a significant difference in the means of the groups {(i+1)*10}ns and 10ns for {c+1}\")\n",
    "        else:\n",
    "            print(f\"There is no significant difference in the means of the groups {(i+1)*10}ns and 10ns for {c+1}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of significant's over the total: 40.33%\n"
     ]
    },
    {
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   "source": [
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "results = []\n",
    "for i in range(0,20):\n",
    "    row = []\n",
    "    for c in range(0, 15):\n",
    "        f_value, p_value = stats.f_oneway(dfs[0].iloc[:, c], dfs[i].iloc[:, c])\n",
    "        if p_value < 0.05:\n",
    "            row.append(1)\n",
    "        else:\n",
    "            row.append(0)\n",
    "    results.append(row)\n",
    "\n",
    "results = np.array(results)\n",
    "percentage = 100 * np.count_nonzero(results) / results.size\n",
    "print(f\"Percentage of significant's over the total: {percentage:.2f}%\")\n",
    "\n",
    "data = [go.Heatmap(z=results, x=[i for i in range(1,16)], y=[i*10 for i in range(1,21)], colorscale='Viridis')]\n",
    "layout = go.Layout(title='Significance of differences in means', xaxis_title='Variant', yaxis_title='Duration (ns)', xaxis=dict(tickmode='array', tickvals=[i for i in range(1, 16)]), yaxis=dict(tickmode='array', tickvals=[i*10 for i in range(1,21)]))\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "fig.show()\n",
    "fig.write_image(\"significance_10ns.png\")\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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     "text": [
      "Percentage of significant's over the total for 10: 40.33%\n",
      "Percentage of significant's over the total for 20: 15.00%\n",
      "Percentage of significant's over the total for 30: 12.33%\n",
      "Percentage of significant's over the total for 40: 8.00%\n",
      "Percentage of significant's over the total for 50: 7.67%\n",
      "Percentage of significant's over the total for 60: 6.00%\n",
      "Percentage of significant's over the total for 70: 7.00%\n",
      "Percentage of significant's over the total for 80: 4.00%\n",
      "Percentage of significant's over the total for 90: 6.00%\n",
      "Percentage of significant's over the total for 100: 5.33%\n",
      "Percentage of significant's over the total for 110: 5.67%\n",
      "Percentage of significant's over the total for 120: 4.00%\n",
      "Percentage of significant's over the total for 130: 5.67%\n",
      "Percentage of significant's over the total for 140: 6.67%\n",
      "Percentage of significant's over the total for 150: 9.00%\n",
      "Percentage of significant's over the total for 160: 6.67%\n",
      "Percentage of significant's over the total for 170: 9.33%\n",
      "Percentage of significant's over the total for 180: 9.67%\n",
      "Percentage of significant's over the total for 190: 7.00%\n",
      "Percentage of significant's over the total for 200: 8.67%\n"
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       "                            \n",
       "var gd = document.getElementById('f72e9215-3ac6-46e7-8ecd-1d5b8064cb35');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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     "metadata": {},
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    }
   ],
   "source": [
    "import plotly.subplots as sp\n",
    "\n",
    "fig = sp.make_subplots(rows=5, cols=4, subplot_titles=[f\"{i*10} ns\" for i in range(1,21)], \n",
    "                       shared_xaxes=True, shared_yaxes=True, vertical_spacing=0.03, horizontal_spacing=0.03, \n",
    "                       column_width=[0.8]*4, row_heights=[0.2, 0.2, 0.2, 0.2, 0.2], specs=[[{'type': 'heatmap'}]*4]*5, \n",
    "                       print_grid=False, )\n",
    "fig.update_layout(height=1000, width=1200, font=dict(size=10))\n",
    "\n",
    "for i in range(1, 21):\n",
    "    results = []\n",
    "    for j in range(1, 21):\n",
    "        row = []\n",
    "        for c in range(0, 15):\n",
    "            f_value, p_value = stats.f_oneway(dfs[j-1].iloc[:, c], dfs[i-1].iloc[:, c])\n",
    "            if p_value < 0.05:\n",
    "                row.append(1)\n",
    "            else:\n",
    "                row.append(0)\n",
    "        results.append(row)\n",
    "    results = np.array(results)\n",
    "    percentage = 100 * np.count_nonzero(results) / results.size\n",
    "    print(f\"Percentage of significant's over the total for {(i)*10}: {percentage:.2f}%\")\n",
    "    \n",
    "    heatmap = go.Heatmap(z=results, x=[i for i in range(1,16)], y=[(j)*10 for j in range(1,21)], colorscale='Viridis')\n",
    "    row = (i-1) // 4 + 1\n",
    "    col = (i-1) % 4 + 1\n",
    "    fig.add_trace(heatmap, row=row, col=col)\n",
    "    \n",
    "fig.update_xaxes(title_text='Variant', row=5, col=3, title_font=dict(size=20), automargin=True)\n",
    "fig.update_yaxes(title_text='Duration (ns)', row=3, col=1, title_font=dict(size=20))\n",
    "\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of significant's over the total for 10: 40.33%\n",
      "Percentage of significant's over the total for 20: 15.00%\n",
      "Percentage of significant's over the total for 30: 12.33%\n",
      "Percentage of significant's over the total for 40: 8.00%\n",
      "Percentage of significant's over the total for 50: 7.67%\n",
      "Percentage of significant's over the total for 60: 6.00%\n",
      "Percentage of significant's over the total for 70: 7.00%\n",
      "Percentage of significant's over the total for 80: 4.00%\n",
      "Percentage of significant's over the total for 90: 6.00%\n",
      "Percentage of significant's over the total for 100: 5.33%\n",
      "Percentage of significant's over the total for 110: 5.67%\n",
      "Percentage of significant's over the total for 120: 4.00%\n",
      "Percentage of significant's over the total for 130: 5.67%\n",
      "Percentage of significant's over the total for 140: 6.67%\n",
      "Percentage of significant's over the total for 150: 9.00%\n",
      "Percentage of significant's over the total for 160: 6.67%\n",
      "Percentage of significant's over the total for 170: 9.33%\n",
      "Percentage of significant's over the total for 180: 9.67%\n",
      "Percentage of significant's over the total for 190: 7.00%\n",
      "Percentage of significant's over the total for 200: 8.67%\n"
     ]
    },
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     ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a9f8c77d-ea53-4795-951d-60be912ae0e9');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.subplots as sp\n",
    "\n",
    "fig = sp.make_subplots(rows=5, cols=4, subplot_titles=[f\"{i*10} ns\" for i in range(1,21)], \n",
    "                       shared_xaxes=True, shared_yaxes=True, vertical_spacing=0.03, horizontal_spacing=0.03, \n",
    "                       column_width=[0.8]*4, row_heights=[0.2, 0.2, 0.2, 0.2, 0.2], specs=[[{'type': 'heatmap'}]*4]*5, \n",
    "                       print_grid=False, )\n",
    "fig.update_layout(height=1000, width=1200, font=dict(size=10))\n",
    "\n",
    "for i in range(1, 21):\n",
    "    results = []\n",
    "    for j in range(1, 21):\n",
    "        row = []\n",
    "        for c in range(0, 15):\n",
    "            f_value, p_value = stats.f_oneway(dfs[j-1].iloc[:, c], dfs[i-1].iloc[:, c])\n",
    "            if p_value < 0.05:\n",
    "                row.append(1)\n",
    "            else:\n",
    "                row.append(0)\n",
    "        results.append(row)\n",
    "    results = np.array(results)\n",
    "    percentage = 100 * np.count_nonzero(results) / results.size\n",
    "    print(f\"Percentage of significant's over the total for {(i)*10}: {percentage:.2f}%\")\n",
    "    \n",
    "    heatmap = go.Heatmap(z=results, x=[i for i in range(1,16)], y=[(j)*10 for j in range(1,21)], colorscale='Viridis')\n",
    "    row = (i-1) // 4 + 1\n",
    "    col = (i-1) % 4 + 1\n",
    "    fig.add_trace(heatmap, row=row, col=col)\n",
    "    \n",
    "    # Update x-axis properties\n",
    "    fig.update_xaxes(tickmode='array', tickvals=[i for i in range(1,16)], row=row, col=col)\n",
    "    \n",
    "fig.update_xaxes(title_text='Variant', row=5, col=3, title_font=dict(size=20), automargin=True)\n",
    "fig.update_yaxes(title_text='Simulation Chunk (ns)', row=3, col=1, title_font=dict(size=20))\n",
    "\n",
    "fig.show()\n"
   ]
  }
 ],
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