{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train = df_samples = pd.read_excel(r'hepatitisBC.xlsx', engine='openpyxl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def sum_col(x):\n",
    "    if  any(x)==1:\n",
    "        return x.values.sum()\n",
    "\n",
    "ndf = train.groupby('prognosis',as_index=False).agg(lambda x:(sum_col(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ndf2=ndf.fillna(0)\n",
    "df=ndf2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "disease_df=df\n",
    "symptom_df=df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Hepatitis B': {'itching': 1.0, 'fatigue': 0.5, 'lethargy': 1.0, 'yellowish_skin': 0.5, 'dark_urine': 1.0, 'nausea': 0.0, 'loss_of_appetite': 0.5, 'abdominal_pain': 1.0, 'yellow_urine': 1.0, 'yellowing_of_eyes': 0.5263157894736842, 'malaise': 1.0, 'family_history': 0.0, 'receiving_blood_transfusion': 1.0, 'receiving_unsterile_injections': 1.0}, 'Hepatitis C': {'itching': 0.0, 'fatigue': 0.5, 'lethargy': 0.0, 'yellowish_skin': 0.5, 'dark_urine': 0.0, 'nausea': 1.0, 'loss_of_appetite': 0.5, 'abdominal_pain': 0.0, 'yellow_urine': 0.0, 'yellowing_of_eyes': 0.47368421052631576, 'malaise': 0.0, 'family_history': 1.0, 'receiving_blood_transfusion': 0.0, 'receiving_unsterile_injections': 0.0}}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Calculate the total count of cases for each symptom in both diseases\n",
    "total_counts = symptom_df.sum(axis=0)[1:]\n",
    "\n",
    "# Create a dictionary to store the conditional probabilities\n",
    "p_Symptom_given_Disease = {}\n",
    "\n",
    "# Calculate the conditional probabilities for each symptom given each disease\n",
    "for disease in symptom_df['prognosis']:\n",
    "    p_Symptom_given_Disease[disease] = {}\n",
    "    for symptom in symptom_df.columns[1:]:\n",
    "        if total_counts[symptom] != 0:\n",
    "            p_Symptom_given_Disease[disease][symptom] = symptom_df[symptom_df['prognosis'] == disease][symptom].sum() / total_counts[symptom]\n",
    "\n",
    "# Convert to the desired dictionary format\n",
    "output_dict = {}\n",
    "for disease in symptom_df['prognosis']:\n",
    "    output_dict[disease] = p_Symptom_given_Disease[disease]\n",
    "\n",
    "# Print the conditional probabilities in the desired format\n",
    "print(output_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyvis in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (0.3.2)\n",
      "Requirement already satisfied: jsonpickle>=1.4.1 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from pyvis) (3.0.2)\n",
      "Requirement already satisfied: jinja2>=2.9.6 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from pyvis) (3.1.2)\n",
      "Requirement already satisfied: networkx>=1.11 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from pyvis) (2.6.3)\n",
      "Requirement already satisfied: ipython>=5.3.0 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from pyvis) (7.34.0)\n",
      "Requirement already satisfied: matplotlib-inline in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (0.1.6)\n",
      "Requirement already satisfied: jedi>=0.16 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (0.18.2)\n",
      "Requirement already satisfied: pickleshare in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (0.7.5)\n",
      "Requirement already satisfied: setuptools>=18.5 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (65.5.0)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (3.0.36)\n",
      "Requirement already satisfied: decorator in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (5.1.1)\n",
      "Requirement already satisfied: backcall in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (0.2.0)\n",
      "Requirement already satisfied: pygments in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (2.13.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (0.4.6)\n",
      "Requirement already satisfied: traitlets>=4.2 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from ipython>=5.3.0->pyvis) (5.7.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from jinja2>=2.9.6->pyvis) (2.1.1)\n",
      "Requirement already satisfied: importlib-metadata in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from jsonpickle>=1.4.1->pyvis) (5.1.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from jedi>=0.16->ipython>=5.3.0->pyvis) (0.8.3)\n",
      "Requirement already satisfied: wcwidth in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.3.0->pyvis) (0.2.5)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from importlib-metadata->jsonpickle>=1.4.1->pyvis) (3.11.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4 in c:\\users\\dr.asif waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages (from importlib-metadata->jsonpickle>=1.4.1->pyvis) (4.4.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pyvis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "disease_symptom_graph.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800px\"\n",
       "            src=\"disease_symptom_graph.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1d7df63bb08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "from pyvis.network import Network\n",
    "import random\n",
    "# Create dictionaries for P(Disease) and P(Symptom | Disease)\n",
    "num_diseases = 2\n",
    "equal_probability = 1 / num_diseases\n",
    "disease_names = ['Hepatitis B', 'Hepatitis C']\n",
    "disease_prior_probabilities = {disease: equal_probability for disease in disease_names}\n",
    "\n",
    "disease_symptom_clusters = output_dict\n",
    "\n",
    "# Create a graph\n",
    "G = nx.Graph()\n",
    "\n",
    "# Define a color mapping function for diseases\n",
    "def get_random_color():\n",
    "    return \"#{:02x}{:02x}{:02x}\".format(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n",
    "\n",
    "# Calculate P(Disease | Symptoms) using Naive Bayes formula for all diseases\n",
    "results = {}\n",
    "sum_prob = 0.0  # To calculate the sum of probabilities for normalization\n",
    "\n",
    "for disease in disease_prior_probabilities:\n",
    "    query_prob = disease_prior_probabilities[disease]\n",
    "    valid_symptoms = {\n",
    "        symptom: disease_symptom_clusters[disease][symptom]\n",
    "        for symptom in disease_symptom_clusters[disease] if disease_symptom_clusters[disease][symptom] != 0\n",
    "    }\n",
    "    \n",
    "    if valid_symptoms:\n",
    "        for symptom, symptom_prob in valid_symptoms.items():\n",
    "            query_prob *= symptom_prob\n",
    "        results[disease] = query_prob\n",
    "        sum_prob += query_prob\n",
    "\n",
    "# Normalize the probabilities for diseases\n",
    "for disease, probability in results.items():\n",
    "    results[disease] = probability / sum_prob\n",
    "\n",
    "# Add disease nodes with prior probabilities and assign colors dynamically\n",
    "disease_colors = {}\n",
    "for disease, probability in disease_prior_probabilities.items():\n",
    "    disease_color = get_random_color()\n",
    "    disease_colors[disease] = disease_color\n",
    "    label = f\"{disease}\\nPrior: {probability:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "    G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add symptom nodes with conditional probabilities and assign colors based on associated disease colors\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    disease_color = disease_colors[disease]\n",
    "    valid_symptoms = {\n",
    "        symptom: symptom_prob\n",
    "        for symptom, symptom_prob in symptoms.items() if symptom_prob != 0\n",
    "    }\n",
    "    \n",
    "    for symptom, probability in valid_symptoms.items():\n",
    "        label = f\"{symptom}\\n{probability:.3f}\"\n",
    "        G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add edges between disease nodes and associated symptom nodes\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    for symptom, symptom_prob in symptoms.items():\n",
    "        if symptom_prob != 0:\n",
    "            disease_label = f\"{disease}\\nPrior: {disease_prior_probabilities[disease]:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "            symptom_label = f\"{symptom}\\n{symptom_prob:.3f}\"\n",
    "            G.add_edge(disease_label, symptom_label)\n",
    "\n",
    "# Create a Network object\n",
    "nt = Network(notebook=True, width=\"100%\", height=\"800px\")\n",
    "\n",
    "# Show the graph\n",
    "nt.from_nx(G)\n",
    "nt.show(\"disease_symptom_graph.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest Posterior Probability: 0.5\n",
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "disease_symptom_graph.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800px\"\n",
       "            src=\"disease_symptom_graph.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1d7ce61d048>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "from pyvis.network import Network\n",
    "import random\n",
    "# Create dictionaries for P(Disease) and P(Symptom | Disease)\n",
    "num_diseases = 2\n",
    "equal_probability = 1 / num_diseases\n",
    "disease_names = ['Hepatitis B', 'Hepatitis C']\n",
    "disease_prior_probabilities = {disease: equal_probability for disease in disease_names}\n",
    "disease_symptom_clusters = output_dict\n",
    "\n",
    "# Symptoms in evidence\n",
    "evidence_symptoms = ['loss_of_appetite','nausea','family_history']\n",
    "\n",
    "# Create a graph\n",
    "G = nx.Graph()\n",
    "\n",
    "# Define a color mapping function for diseases\n",
    "def get_random_color():\n",
    "    return \"#{:02x}{:02x}{:02x}\".format(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n",
    "\n",
    "# Calculate P(Disease | Symptoms) using Naive Bayes formula for all diseases based on evidence symptoms\n",
    "results = {}\n",
    "sum_prob = 0.0  # To calculate the sum of probabilities for normalization\n",
    "\n",
    "for disease in disease_prior_probabilities:\n",
    "    query_prob = disease_prior_probabilities[disease]\n",
    "    valid_symptoms = {\n",
    "        symptom: disease_symptom_clusters[disease][symptom]\n",
    "        for symptom in evidence_symptoms if symptom in disease_symptom_clusters[disease] and disease_symptom_clusters[disease][symptom] != 0\n",
    "    }\n",
    "    \n",
    "    if valid_symptoms:\n",
    "        for symptom, symptom_prob in valid_symptoms.items():\n",
    "            query_prob *= symptom_prob\n",
    "        results[disease] = query_prob\n",
    "        sum_prob += query_prob\n",
    "\n",
    "# Normalize the probabilities for diseases\n",
    "for disease, probability in results.items():\n",
    "    results[disease] = probability / sum_prob\n",
    "\n",
    "# Calculate the highest posterior probability\n",
    "highest_posterior = max(results.values())\n",
    "\n",
    "# Print the highest posterior probability\n",
    "print(\"Highest Posterior Probability:\", highest_posterior)\n",
    "\n",
    "# Add disease nodes with prior probabilities and assign colors dynamically\n",
    "disease_colors = {}\n",
    "for disease, probability in disease_prior_probabilities.items():\n",
    "    disease_color = get_random_color()\n",
    "    disease_colors[disease] = disease_color\n",
    "    label = f\"{disease}\\nPrior: {probability:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "    G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add symptom nodes with conditional probabilities and assign colors based on associated disease colors\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    if any(symptom in evidence_symptoms for symptom in symptoms):\n",
    "        disease_color = disease_colors[disease]\n",
    "        valid_symptoms = {\n",
    "            symptom: symptom_prob\n",
    "            for symptom, symptom_prob in symptoms.items() if symptom in evidence_symptoms and symptom_prob != 0\n",
    "        }\n",
    "\n",
    "        for symptom, probability in valid_symptoms.items():\n",
    "            label = f\"{symptom}\\n{probability:.3f}\"\n",
    "            G.add_node(label, color=disease_color)\n",
    "\n",
    "# Add edges between disease nodes and associated symptom nodes\n",
    "for disease, symptoms in disease_symptom_clusters.items():\n",
    "    if any(symptom in evidence_symptoms for symptom in symptoms):\n",
    "        for symptom, symptom_prob in symptoms.items():\n",
    "            if symptom in evidence_symptoms and symptom_prob != 0:\n",
    "                disease_label = f\"{disease}\\nPrior: {disease_prior_probabilities[disease]:.3f}\\nPosterior: {results[disease]:.4f}\"\n",
    "                symptom_label = f\"{symptom}\\n{symptom_prob:.3f}\"\n",
    "                G.add_edge(disease_label, symptom_label)\n",
    "\n",
    "# Create a Network object\n",
    "nt = Network(notebook=True, width=\"100%\", height=\"800px\")\n",
    "\n",
    "# Show the graph\n",
    "nt.from_nx(G)\n",
    "nt.show(\"disease_symptom_graph.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import time\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pgmpy.estimators import BayesianEstimator\n",
    "from pgmpy.models import BayesianModel\n",
    "from pgmpy.inference import VariableElimination\n",
    "from pgmpy.factors.discrete import State\n",
    "from pgmpy.sampling import BayesianModelSampling\n",
    "from pgmpy.sampling import GibbsSampling\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cleanup_nums = {\"prognosis\":     {\"Hepatitis B\": 0, \"Hepatitis C\": 1}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train2 = train.replace(cleanup_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "architecture= [(\"itching\", \"prognosis\"),\n",
    "                   \n",
    "                      \n",
    "                       (\"fatigue\", \"prognosis\"),\n",
    "                       (\"lethargy\", \"prognosis\"),\n",
    "                    \n",
    "                       (\"yellowish_skin\", \"prognosis\"),\n",
    "                       (\"dark_urine\", \"prognosis\"),\n",
    "                       (\"nausea\", \"prognosis\"),\n",
    "                       (\"loss_of_appetite\", \"prognosis\"),\n",
    "                       (\"abdominal_pain\", \"prognosis\"),\n",
    "                     \n",
    "                     \n",
    "                       (\"yellow_urine\", \"prognosis\"),\n",
    "                       (\"yellowing_of_eyes\", \"prognosis\"),\n",
    "                      \n",
    "                       (\"malaise\", \"prognosis\"),\n",
    "             \n",
    "                       (\"family_history\", \"prognosis\"),\n",
    "                       (\"receiving_blood_transfusion\", \"prognosis\"),\n",
    "                       (\"receiving_unsterile_injections\", \"prognosis\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = BayesianModel(architecture)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The nodes of the net are: ['itching', 'prognosis', 'fatigue', 'lethargy', 'yellowish_skin', 'dark_urine', 'nausea', 'loss_of_appetite', 'abdominal_pain', 'yellow_urine', 'yellowing_of_eyes', 'malaise', 'family_history', 'receiving_blood_transfusion', 'receiving_unsterile_injections']\n",
      "The edges of the net are: [('itching', 'prognosis'), ('fatigue', 'prognosis'), ('lethargy', 'prognosis'), ('yellowish_skin', 'prognosis'), ('dark_urine', 'prognosis'), ('nausea', 'prognosis'), ('loss_of_appetite', 'prognosis'), ('abdominal_pain', 'prognosis'), ('yellow_urine', 'prognosis'), ('yellowing_of_eyes', 'prognosis'), ('malaise', 'prognosis'), ('family_history', 'prognosis'), ('receiving_blood_transfusion', 'prognosis'), ('receiving_unsterile_injections', 'prognosis')]\n"
     ]
    }
   ],
   "source": [
    "nodes = list(model.nodes())\n",
    "edges = list(model.edges())\n",
    "\n",
    "print('The nodes of the net are: {}'.format(nodes))\n",
    "print('The edges of the net are: {}'.format(edges))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DAG = nx.DiGraph()\n",
    "DAG.add_edges_from(edges)\n",
    "\n",
    "fig = plt.figure(figsize = (16,12))\n",
    "pos = nx.spring_layout(DAG) \n",
    "\n",
    "nx.draw_networkx_nodes(DAG, pos = pos, node_color = 'red')\n",
    "nx.draw_networkx_labels(DAG, pos = pos)\n",
    "nx.draw_networkx_edges(DAG, pos = pos, edge_color = 'black', width = 1.5, arrows = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check model: True\n",
      "\n",
      "CPT of itching:\n",
      "+------------+-------+\n",
      "| itching(0) | 0.524 |\n",
      "+------------+-------+\n",
      "| itching(1) | 0.476 |\n",
      "+------------+-------+ \n",
      "\n",
      "CPT of prognosis:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "\n",
    "# disable text wrapping in output cell\n",
    "display(HTML(\"\"))\n",
    "\n",
    "model.cpds = []\n",
    "model.fit(  data=train2\n",
    "          , estimator=BayesianEstimator\n",
    "          , prior_type=\"BDeu\"\n",
    "          , equivalent_sample_size=10\n",
    "          ,complete_samples_only=False)\n",
    "\n",
    "print(f'Check model: {model.check_model()}\\n')\n",
    "for cpd in model.get_cpds():\n",
    "    print(f'CPT of {cpd.variable}:')\n",
    "    print(cpd, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pgmpy.inference import VariableElimination\n",
    "inference =VariableElimination(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "BMS_inference = BayesianModelSampling(model)\n",
    "GS_inference = GibbsSampling(model)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Dictionary of the discrete value to let the method access at CPTs\n",
    "value_dict = {\n",
    "    True: 1,\n",
    "    False: 0,\n",
    "    '0-15': 0,\n",
    "    '16-45': 1,\n",
    "    '46-90': 2,\n",
    "    '1': 0,\n",
    "    '2': 1,\n",
    "    '3': 2,\n",
    "    '4': 3,\n",
    "    '5': 4,\n",
    "    'sv': 0,\n",
    "    '<=5': 1,\n",
    "    '5.5-6.5': 2,\n",
    "    '7-9.5': 3,\n",
    "    '>=10': 4\n",
    "}\n",
    "\n",
    "def discretization(x, value):\n",
    "    '''\n",
    "    Function that helps to discretize for likelihood weighting and rejection sampling\n",
    "    x     : unknown variable\n",
    "    value : value to analyze for the unknown variable based on the value of the query variable\n",
    "    '''\n",
    "    if x == value_dict[value]:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def prob_LW(samples, variable, query_value):\n",
    "    '''\n",
    "    Function that calculates the likelihood weighting probability\n",
    "    samples     : sample from which to extract the probability value\n",
    "    variable    : variable of the query\n",
    "    query_value : value of the query variable\n",
    "    '''\n",
    "    discretize = np.vectorize(discretization)\n",
    "    samples_thresholded = discretize(samples[variable], query_value)\n",
    "    return round(np.sum(np.dot(samples_thresholded, samples['_weight'])) / np.sum(samples['_weight']), 2)\n",
    "\n",
    "def prob_RS(samples, variable, query_value):\n",
    "    '''\n",
    "    Function that calculates the rejection sampling probability\n",
    "    samples     : sample from which to extract the probability value\n",
    "    variable    : variable of the query\n",
    "    query_value : value of the query variable\n",
    "    '''\n",
    "    discretize = np.vectorize(discretization)\n",
    "    samples_rejection = discretize(samples[variable], query_value)\n",
    "    return np.mean(samples_rejection, axis=0)\n",
    "\n",
    "def get_state_index(var, var_val):\n",
    "    '''\n",
    "    Function that returns the index of a given value for a variable\n",
    "    '''\n",
    "    return inference.query([var], show_progress=False).state_names.get(var).index(var_val)\n",
    "\n",
    "def prob_GS(samples, query_variable, query_evidence, query_value):\n",
    "    \"\"\"\n",
    "    Computes the probability of Gibbs Sampling given the samples\n",
    "    it will call a pgmpy query function building the string for the requested query\n",
    "    \"\"\"\n",
    "    gs_query = \"\"\n",
    "    for evidence, value in query_evidence.items():\n",
    "        gs_query += evidence + \" == \" + str(get_state_index(evidence, value))\n",
    "        if evidence != list(query_evidence.keys())[-1]:\n",
    "            # add the '&' except for the last element\n",
    "            gs_query += \" & \"\n",
    "\n",
    "    # check denominator to avoid division by 0\n",
    "    if samples.query(gs_query).shape[0] == 0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return (samples.query(query_variable[0] + \" == \" + str(get_state_index(query_variable[0], query_value)) + \" & \" + gs_query).shape[0] \n",
    "              / samples.query(gs_query).shape[0])\n",
    "\n",
    "#Absolute error function\n",
    "def absolute_error(exact_value, approx_value):\n",
    "    '''\n",
    "    Function that computes the absolute error between the approximated and exact probabilities\n",
    "    exact_value   : reference probability value of the query\n",
    "    approx_value  : approximate probability value of the query\n",
    "    '''\n",
    "    return np.absolute(exact_value - approx_value)\n",
    "\n",
    "#Relative error function\n",
    "def relative_error(exact_value, approx_value):\n",
    "    '''\n",
    "    Function that computes the absolute error between the approximated and exact probabilities\n",
    "    exact_value   : reference probability value of the query\n",
    "    approx_value  : approximate probability value of the query\n",
    "    '''\n",
    "    return np.absolute(((exact_value - approx_value) / approx_value))\n",
    "\n",
    "def query_preprocessing(query_variable, query_evidence, query_value):\n",
    "    '''\n",
    "    Function that returns the processed query variable, evidence to adapt them to the methods\n",
    "    of likelihood weighting and rejection sampling\n",
    "\n",
    "    query_variable: list with one unknown variable of the query\n",
    "    query_evidence: dict representing the evidence of the query\n",
    "    query_value   : value of the query var to find in the query\n",
    "    '''\n",
    "    evidence = []\n",
    "\n",
    "    for (evidence_var, evidence_value) in query_evidence.items():\n",
    "        state = State(evidence_var, evidence_value)\n",
    "        evidence.append(state)\n",
    "\n",
    "    query_prob = highest_posterior\n",
    "    variables = query_variable[0]\n",
    "\n",
    "    return query_prob, evidence, variables\n",
    "\n",
    "def run_experiment(sample_size, query_variable, query_evidence, query_value):\n",
    "    '''\n",
    "    Function that return the result of a sampling experiment using likelihood weighting,\n",
    "    rejection sampling and Gibbs sampling\n",
    "\n",
    "    sample_size    : size of the sample for the experiment\n",
    "    query_variable : list with one unknown variable of the query\n",
    "    query_evidence : dict representing the evidence of the query\n",
    "    query_value    : value of the query var to find in the query\n",
    "    '''\n",
    "\n",
    "    # Rounding precision\n",
    "    precision = 3\n",
    "\n",
    "    # Preprocessing for the LW and RS methods\n",
    "    query_prob, evidence, variable = query_preprocessing(query_variable, query_evidence, query_value)\n",
    "\n",
    "    t0_LW = time.time()\n",
    "    samples_LW = BMS_inference.likelihood_weighted_sample(evidence=evidence, size=sample_size, return_type='recarray')\n",
    "    approx_prob_LW = prob_LW(samples_LW, variable, query_value)\n",
    "    time_LW = round(time.time() - t0_LW, precision)\n",
    "\n",
    "    t0_RS = time.time()\n",
    "    samples_RS = BMS_inference.rejection_sample(evidence=evidence, size=sample_size, return_type='recarray', show_progress=False)\n",
    "    approx_prob_RS = prob_RS(samples_RS, variable, query_value)\n",
    "    time_RS = round(time.time() - t0_RS, precision)\n",
    "\n",
    "    t0_GS = time.time()\n",
    "    samples_GS = GS_inference.sample(size=sample_size, seed=37)\n",
    "    approx_prob_GS = prob_GS(samples_GS, query_variable, query_evidence, query_value)\n",
    "    time_GS = round(time.time() - t0_GS, precision)\n",
    "\n",
    "    return np.array([(sample_size,\n",
    "                      approx_prob_RS, absolute_error(query_prob, approx_prob_RS), relative_error(query_prob, approx_prob_RS), time_RS,\n",
    "                      approx_prob_LW, absolute_error(query_prob, approx_prob_LW), relative_error(query_prob, approx_prob_LW), time_LW,\n",
    "                      approx_prob_GS, absolute_error(query_prob, approx_prob_GS), relative_error(query_prob, approx_prob_GS), time_GS)],\n",
    "                    dtype=[('sample_size', '<i8'),\n",
    "                           ('approx_prob_RS', '<f8'), ('abs_error_RS', '<f8'), ('rel_error_RS', '<f8'), ('time_RS', '<f8'),\n",
    "                           ('approx_prob_LW', '<f8'), ('abs_error_LW', '<f8'), ('rel_error_LW', '<f8'), ('time_LW', '<f8'),\n",
    "                           ('approx_prob_GS', '<f8'), ('abs_error_GS', '<f8'), ('rel_error_GS', '<f8'), ('time_GS', '<f8')])\n",
    "\n",
    "def approximate_inference(q, query_var, query_evidence, query_value, starting_size=2.5, final_size=5, experiments=10):  \n",
    "    '''\n",
    "    Function that create the result of the different approximate inference methods using\n",
    "    different sample size values\n",
    "\n",
    "    query_var     : list with one unknown variable of the query\n",
    "    query_evidence: dict representing the evidence of the query\n",
    "    query_value   : value of the query var to find in the query\n",
    "    starting_size : exponent of the number of start sample points that are 10^(starting_size)\n",
    "    final_size    : exponent of the number of final sample points that are 10^(final_size)\n",
    "    experiments   : number of sampling experiments\n",
    "    '''\n",
    "    results = np.array([], dtype=[('sample_size', '<i8'),\n",
    "                                  ('approx_prob_RS', '<f8'), ('abs_error_RS', '<f8'), ('rel_error_RS', '<f8'), ('time_RS', '<f8'),\n",
    "                                  ('approx_prob_LW', '<f8'), ('abs_error_LW', '<f8'), ('rel_error_LW', '<f8'), ('time_LW', '<f8'),\n",
    "                                  ('approx_prob_GS', '<f8'), ('abs_error_GS', '<f8'), ('rel_error_GS', '<f8'), ('time_GS', '<f8')])\n",
    "\n",
    "    for size in np.logspace(starting_size, final_size, num=experiments, dtype='<i8'):\n",
    "        results = np.append(results, run_experiment(size, query_var, query_evidence, query_value))\n",
    "\n",
    "    globals()[f\"query{q}\"] = pd.DataFrame(results)\n",
    "    globals()[f\"query{q}\"].to_csv(f\"query{q}.csv\")\n",
    "    \n",
    "    print(\"\\nResults during sampling:\")\n",
    "    display(pd.DataFrame(results))\n",
    "    print(\"\\n\")\n",
    "    \n",
    "    plot_results(results, query_evidence, query_var, query_value)\n",
    "\n",
    "# Ensure plot_results function is defined elsewhere in your code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set the desired figure size\n",
    "figure_width = 80  # Further increased to make the plots broader\n",
    "figure_height = 40  # Increased height for better aspect ratio\n",
    "\n",
    "# Adjust font sizes globally\n",
    "plt.rcParams.update({\n",
    "    'font.size': 60,  # Increased font size for general text\n",
    "    'legend.fontsize': 58,  # Increased font size for legend\n",
    "    'xtick.labelsize': 64,  # Increased font size for x-axis ticks\n",
    "    'ytick.labelsize': 64  # Increased font size for y-axis ticks\n",
    "})\n",
    "\n",
    "def plot_results(results, query_evidence, query_variable, query_value):\n",
    "    '''\n",
    "    Function to plot the result of the approximate inference using colorblind-friendly schemes, distinct markers, and thicker lines.\n",
    "    '''\n",
    "    query_prob, evidence, var = query_preprocessing(query_variable, query_evidence, query_value)\n",
    "    \n",
    "    plt.figure(figsize=(figure_width, figure_height))\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Approximate probability values\", fontsize=70)\n",
    "    plt.semilogx(results['sample_size'], results['approx_prob_LW'], 'go-', label=\"Likelihood Weighting\", markersize=48, marker='o', linestyle=':', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['approx_prob_RS'], 'bs-', label=\"Rejection Sampling\", markersize=48, marker='s', linestyle='-.', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['approx_prob_GS'], 'c^-', label=\"Gibbs Sampling\", markersize=48, marker='^', linestyle='--', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], query_prob * np.ones(len(results['sample_size'])), 'k--', label=\"Reference value\", linewidth=16)\n",
    "    plt.xlabel(\"Sample size\", fontsize=68)\n",
    "    plt.ylabel(\"Approximate probability value\", fontsize=68)\n",
    "    plt.xticks(fontsize=64)\n",
    "    plt.yticks(fontsize=64)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=64)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('output_plot1.png', dpi=300)\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(figure_width, figure_height))\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Trend of absolute error\", fontsize=70)\n",
    "    plt.semilogx(results['sample_size'], results['abs_error_LW'], 'go-', label=\"Likelihood Weighting\", markersize=48, marker='o', linestyle=':', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['abs_error_RS'], 'bs-', label=\"Rejection Sampling\", markersize=48, marker='s', linestyle='-.', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['abs_error_GS'], 'c^-', label=\"Gibbs Sampling\", markersize=48, marker='^', linestyle='--', linewidth=16)\n",
    "    plt.xlabel(\"Sample size\", fontsize=68)\n",
    "    plt.ylabel(\"Absolute error value\", fontsize=68)\n",
    "    plt.xticks(fontsize=64)\n",
    "    plt.yticks(fontsize=64)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=64)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('output_plot2.png', dpi=300)\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(figure_width, figure_height))\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Trend of relative error\", fontsize=70)\n",
    "    plt.semilogx(results['sample_size'], results['rel_error_LW'], 'go-', label=\"Likelihood Weighting\", markersize=48, marker='o', linestyle=':', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['rel_error_RS'], 'bs-', label=\"Rejection Sampling\", markersize=48, marker='s', linestyle='-.', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['rel_error_GS'], 'c^-', label=\"Gibbs Sampling\", markersize=48, marker='^', linestyle='--', linewidth=16)\n",
    "    plt.xlabel(\"Sample size\", fontsize=68)\n",
    "    plt.ylabel(\"Relative error value\", fontsize=68)\n",
    "    plt.xticks(fontsize=64)\n",
    "    plt.yticks(fontsize=64)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=64)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('output_plot3.png', dpi=300)\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(figure_width, figure_height))\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.title(\"Trend of the sampling time\", fontsize=70)\n",
    "    plt.semilogx(results['sample_size'], results['time_LW'], 'go-', label=\"Likelihood Weighting\", markersize=48, marker='o', linestyle=':', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['time_RS'], 'bs-', label=\"Rejection Sampling\", markersize=48, marker='s', linestyle='-.', linewidth=16)\n",
    "    plt.semilogx(results['sample_size'], results['time_GS'], 'c^-', label=\"Gibbs Sampling\", markersize=48, marker='^', linestyle='--', linewidth=16)\n",
    "    plt.xlabel(\"Sample size\", fontsize=68)\n",
    "    plt.ylabel(\"Seconds\", fontsize=68)\n",
    "    plt.xticks(fontsize=64)\n",
    "    plt.yticks(fontsize=64)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=64)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('output_plot4.png', dpi=300)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(disease|'nausea':True, 'loss_of_appetite':True,'family_history':True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.94it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.99it/s]\n",
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      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.99it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.95it/s]\n",
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      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.95it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.92it/s]\n",
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      "100%|█████████████████████████████████████████████████████████████████████████████| 4083/4083 [00:06<00:00, 667.46it/s]\n",
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      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.75it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.51it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 14676/14676 [00:21<00:00, 668.27it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.64it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:06<00:00,  2.38it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 27824/27824 [00:41<00:00, 663.89it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:05<00:00,  2.59it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:07<00:00,  2.02it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 52748/52748 [01:19<00:00, 666.86it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:06<00:00,  2.29it/s]\n",
      "Generating for node: prognosis: 100%|██████████████████████████████████████████████████| 15/15 [00:09<00:00,  1.50it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 99999/99999 [02:30<00:00, 665.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results during sampling:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_size</th>\n",
       "      <th>approx_prob_RS</th>\n",
       "      <th>abs_error_RS</th>\n",
       "      <th>rel_error_RS</th>\n",
       "      <th>time_RS</th>\n",
       "      <th>approx_prob_LW</th>\n",
       "      <th>abs_error_LW</th>\n",
       "      <th>rel_error_LW</th>\n",
       "      <th>time_LW</th>\n",
       "      <th>approx_prob_GS</th>\n",
       "      <th>abs_error_GS</th>\n",
       "      <th>rel_error_GS</th>\n",
       "      <th>time_GS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>316</td>\n",
       "      <td>0.531646</td>\n",
       "      <td>0.031646</td>\n",
       "      <td>0.059524</td>\n",
       "      <td>10.142</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>5.348</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>66.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>599</td>\n",
       "      <td>0.494157</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>0.011824</td>\n",
       "      <td>10.136</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>67.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1136</td>\n",
       "      <td>0.494718</td>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.010676</td>\n",
       "      <td>10.249</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>5.038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>66.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2154</td>\n",
       "      <td>0.500929</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.001854</td>\n",
       "      <td>10.375</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>5.279</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>73.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4084</td>\n",
       "      <td>0.503183</td>\n",
       "      <td>0.003183</td>\n",
       "      <td>0.006326</td>\n",
       "      <td>10.366</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>5.304</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>72.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7742</td>\n",
       "      <td>0.503617</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>0.007181</td>\n",
       "      <td>10.449</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.094</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>79.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14677</td>\n",
       "      <td>0.505757</td>\n",
       "      <td>0.005757</td>\n",
       "      <td>0.011384</td>\n",
       "      <td>11.482</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>87.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27825</td>\n",
       "      <td>0.503863</td>\n",
       "      <td>0.003863</td>\n",
       "      <td>0.007668</td>\n",
       "      <td>12.058</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.712</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>107.258</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52749</td>\n",
       "      <td>0.502645</td>\n",
       "      <td>0.002645</td>\n",
       "      <td>0.005261</td>\n",
       "      <td>13.342</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>144.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100000</td>\n",
       "      <td>0.502150</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.004282</td>\n",
       "      <td>16.730</td>\n",
       "      <td>0.50</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>7.361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>222.035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_size  approx_prob_RS  abs_error_RS  rel_error_RS  time_RS  \\\n",
       "0          316        0.531646      0.031646      0.059524   10.142   \n",
       "1          599        0.494157      0.005843      0.011824   10.136   \n",
       "2         1136        0.494718      0.005282      0.010676   10.249   \n",
       "3         2154        0.500929      0.000929      0.001854   10.375   \n",
       "4         4084        0.503183      0.003183      0.006326   10.366   \n",
       "5         7742        0.503617      0.003617      0.007181   10.449   \n",
       "6        14677        0.505757      0.005757      0.011384   11.482   \n",
       "7        27825        0.503863      0.003863      0.007668   12.058   \n",
       "8        52749        0.502645      0.002645      0.005261   13.342   \n",
       "9       100000        0.502150      0.002150      0.004282   16.730   \n",
       "\n",
       "   approx_prob_LW  abs_error_LW  rel_error_LW  time_LW  approx_prob_GS  \\\n",
       "0            0.51          0.01      0.019608    5.348             1.0   \n",
       "1            0.50          0.00      0.000000    5.189             1.0   \n",
       "2            0.46          0.04      0.086957    5.038             1.0   \n",
       "3            0.49          0.01      0.020408    5.279             1.0   \n",
       "4            0.49          0.01      0.020408    5.304             1.0   \n",
       "5            0.50          0.00      0.000000    5.094             1.0   \n",
       "6            0.50          0.00      0.000000    5.862             1.0   \n",
       "7            0.50          0.00      0.000000    5.712             1.0   \n",
       "8            0.50          0.00      0.000000    6.188             1.0   \n",
       "9            0.50          0.00      0.000000    7.361             1.0   \n",
       "\n",
       "   abs_error_GS  rel_error_GS  time_GS  \n",
       "0           0.5           0.5   66.977  \n",
       "1           0.5           0.5   67.288  \n",
       "2           0.5           0.5   66.912  \n",
       "3           0.5           0.5   73.071  \n",
       "4           0.5           0.5   72.065  \n",
       "5           0.5           0.5   79.023  \n",
       "6           0.5           0.5   87.357  \n",
       "7           0.5           0.5  107.258  \n",
       "8           0.5           0.5  144.098  \n",
       "9           0.5           0.5  222.035  "
      ]
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      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:26: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"go-\" (-> marker='o'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:27: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"bs-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:27: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bs-\" (-> marker='s'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"c^-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"c^-\" (-> marker='^'). The keyword argument will take precedence.\n"
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rr93u71evXp3NmzdnypQphdcykQDqAw44oPA6JrvnP//5uemmm3a4zl133ZXDDjusTRUBAAAAk5EAapp19f1XZ9n6nc/1AQAAQDPq9WR4OFm/Plm3rvH/o79+9M9e97rE6RHtNXNm2RUwWa1fn8yYUXYV3cV47Q4DA42w8ZkzG8uWr7f1s4l8PX162T0CAAAAAKBsgoo7k8cVAAAAAAAAAAAAAAAAoPMIoO4gT37yk3e6zgMPPJBDDjmk8FoeeOCBna4zkXq73ZFHHrnTdZYuXdqGSgAAAIDJavPm5DvfKbsKJqsLb76w7BIAAACosBtuSG65Zdvh0dsLkn701+vXJ2NjE7utpz1NAHW7zZpVdgVMVuvXJ/vsU3YV3UUAdef4xCeSY499YmD0wEDS21t2dQAAAAAAdBpBxZ3J4woAAAAAALBjU6fWM29erewyAAAAAAAAAHZJrV6v18sugtb49Kc/nXe+8507XOc73/lOXv7ylxdey4knnpgrr7xyu7/fc889s2rVqsLrmOz+8z//M7/+67++w3XOPffcvOMd79it21m7dm322GOPPPzww5k9e/ZutQXAY913331ll9AR9t5770ydOrXsMpIkmzZtyooVK8ouoyMceOCBZZfwiHXr1uXhhx8uu4xJb8qUKZk/f37ZZTxi5cqV2bhxY9llTHozZ87MnDlzyi7jEQ8++GDGJppIw3bttddeGRgYKLuMJMno6GiWLVtWdhkdYd99901fX98Tfn7ZZUkbpoMeZyhJ83M/z3lO8rKXNZZnPzvp6Xns72+6KVmwYPcqnBx6k+y307Wuuy45+uhiKjjyc0fmlpW3NL7ZmGTzxP92xpQZedmTX5ZTDzs1Lz3kpZk9zbxTkkyfPj1z584tu4xHLF++PJs378IDyzbtsccemVWhZDbzEa1hPqIzmY/oLCMjI/nBD36Q3/qt30p/f3/Z5SQxH9EqM2fOzJ13zskxx5RdyRYPJil3PmLx4uRZzyq1hN1mPqL9xsaSoaFGEOzQULJhw86XR6+7rb8bGXn8reyb5InzEUX48peT3/7t7f9+aGiobeeirF/fCMQuwn//d2NupCy9vb3Zb7/GfMS11zZCUMu1Jsn6sovoANOTtG8+4oYbkuc+d/u/b+d8xPXXJ69+dTFt33ZbMm1aMW1PxKPnI774xeSMM8qrpWHyzkf09DSCngcGGv/PmLHzrwcGGt9v6+9mzEiafYnSLfMR3TPPv8WO5yOKnOd/PPMRreH8iM7k/IjOZD6iM23v/IgytHM+opM9ej6iCtasWZP1681H7I7GMX975yN2bnl26cSXFuqEOf4tnB/RmbplPqLbOD+i81RhPmLz2Obcs+aeDA4N5s7778yyNcuyauOqrN64Oqs2rsqq4VWN///3Z2uG10y47dveflum9ZU46f44P1v2syz8ysJC2r7u96/L/BmNx7IK8xF3rr4zh33msMY365KMt67ty8+4PE/Z6ymta3A3rdm4Js/6u2IOzC78zQtzzP6NE12qMB8xNj6WKf/flIyPjrf87dYvvvaL2Xv9y7psnj/Z0fkR7Zrn/9rPv5ZNY5uyaeOmbFi7oWXtnnDACdl/9v4ta293jY2P5Zu3fLOQtl96yEuz5/Q9k1RjPmLdpnWP9HXD2g3ZtHFTy9pe8NQFldq3Lt+wPD9a+qOWt1tLLa992msf+b4Kn9e4c/WdueLeK5IkD698OGOj257rHx0bzS9+/os8/RlPT1/vzuc7B/oHctpTTmtprbvr5sGb84sVv2h5u/Omz8uLD3nxI99XYT7i6vuvzq0rb02SrHqodXPCB80+KMceUPoJWo/xk3t/kgfXP9jydg/f6/A8c59nPvJ9FeYjFt26KKuHV2dk80jWrV7Xsnafs89zctheh7WsvVb471v+O5vHWz8/+vjjiLLnI8bGx/Jv//NvSZKNGzZm4/rWvY/+6OOIKli/aX2W3LmkkLYffRzx1ovemouXXZzR8dFCbmuXDSV5wrnMxejt6c1pTzktf//Kv2/PDSa5Z809ue7B61re7uOPI6owH/E/y/8nNy67MUmyZsWajI+3ZkLi8ccRVXDDgzfkrjV3tbzdxx9HVGE+4nt3fy/3rb0vY6NjeXhl6+aEH38cUQWX3XlZHt7U+nnvxx9HVOH8CPMRu898RHnMR+w+8xGdOx+xf47Ney78SH5y308yXpXj/XaY3pf09qS3py8vPeSlOf//fCwHH9zeEsxH7D7zEeVp13xEFc6PWLZ+WS6989Ikybo16zKyqTUTEo8/jqgC8xG7z3yE+Yh2MR+x+7plPuLe2+bkr9/2skf9ZGLXs2yfNWnn9SPafS6/+YjdZz6ic+cjnB9RHvMRu898RHnMR+w+8xFzWn47u6KI+YgnHvOXpRqfXWvnNXsAirYrWbICqDvIokWL8spXvnKH6/zd3/1d3vKWtxRey/z583f4Qf9jjz02V199deF1THaXXHJJTjttxxNcH/nIR/Jnf/Znu3U7AqgBAACgc7373cnf/E3ZVTRv/vzk9NOThQuTU05J9tijERpTnSC68hX1JtfNgzfnaee1Jk2rv6c/L33yS/PqI16dXzniV3Lg7Gq8QQgATG4jIyNZvHhxFixYUJkAalrHcf9jObmt89XrW8Oi169P1q174tfb+tmO1u20/Lm/+7ukDae8TMj4eNLbW0zbl1+evOQlxbS9q26+ubigbTrbD3+YvPCFZVfR8POfJ88s6DPeK1Yk8+YV0/au+s//TH7918uuon1mzkxmzWr8/+ivt/WziXw9dWpSq5Xdq+7ieP+xHO8DANBpHPM/lmN+ADrFjctuzFH/cFQhbd/3rvtywOwDCmm7GfesuSeHfPqQQtr+n7f+T54x/xmFtN2MtZvWZo+/2qOQtn/4ph/mhQdX5E2jJOP18Uz58JSM1VtzoctH+9ZvfiuvOuJVLW93d+z9ib0zODTY8na/+Jov5iV7/k6OOCIZHm5585POtGnJLbek7QEVwOTnHFgAJoPv3f29vPSLLy27jNJ87/9+LycdclLZZQAAQCEc7zveBwCgMzmX/7Gcyw8AQKdxzP9YjvmBTrIrWbJ9baqJNnjuc5+703X+53/+p/A6li9fvsPw6WRitZL0FnW1YgAAAKBrLF5cdgW7Z/ny5ItfbCx9fckLXpA85zllV9UdvnnzN1vW1sj4SJbcsSRL7liSty1+W37nOb+TL77miy1rHwAAoJ3q9WTz5tYFRW/5Wb1eds+qbf36sivYqqcnmTEj2bCh9W2vW9f6Nps1a1bZFTBZVWm8zpxZXNvr1lUngLrIfu6u6dMnFgI90cDo6dMbz8MAAAAAALTXvIHiJsVXblxZqQDqIvtaRCDw7pg1ZVb6e/ozMj7S8rar1teeWk/mDszN8g3LW9521fqaNLbjIuoaHBrMwc9phC6vWFHPr/7Hr2bpmqVJuumkh1qeNOdJ+c/X/2f23rsmfBoAgI510pNOyuFzD89tK29LvYuO+Wup5fC5h+fFT3px2aUAAEBhHO873gcAAAAAAAAmJwHUHeSAAw7I/Pnzs3z59j/0eP311xdex3XXXbfTdY466qjC6+gEGyZwpeKBgYE2VAIAAABMRnfc0biwU6cYHU2+//3GQvG+eUvrAqgf79A5hxbWNgAAwOONjk48EHqiQdKjo2X3qvtUKdA2aYSgFhFAXaV+VjnQlmqrUpB6kdtxlcZrqwLjp0yZWAj0RIOkZ85MentbUxsAAACTx7x5ybRpyfBw2ZWUb9q0xv0BABM1NDKUlUMrMzg0mMP2Oiyzp84uu6RHzJ0+t7C2qxbeO9A/kGl90zI82voDmqr1tVarZd7AvDy4/sGWt121viaNUOZuCqCeqD2n7Zl5A/Myd2Bu5g3MayzT5239+lHLlrD4gw9ODj64lj8ee3nOuuisrgun+OMFv5tjjqmVXQoAABSqVqvl7ce9PWdddFbZpbTd249/e2o1x/wAAHQux/uO9wEAAAAAAIDJSQB1hzn66KNz8cUXb/f3P/3pTzMyMpL+/v7Carj66qt3us4xxxxT2O13kgceeGCn68xq1dVDAQAAgI6zaFHZFTBZPbjuwVx535WFtf/qI19dWNsAAMDkNj6eDA3tXjj0478WctIZqhRomxQXalulQNsZM8qugMmqSttxkadWVamfc+cmxxyz7RDoXfl6ypSyewIAAEAnOPjg5JZbksHqZfC13bx5jfsDgO60aXRTVm5shElPdNk4uvGRv//OG7+Tlx/68hJ78FjT+6dnRv+MbBjZ0PK2qxbeuyWU+b6197W87ar1NUnXBVAXoYp9PfHAEzN76uzMnT53m0HSW5a9pu+Vvp7mLzvyxue8Me/9znsLCWyvqml90/KGZ7+h7DIAAKAtHPMDAEDncrwPAAAAAAAAMPkIoO4wJ5100g4DqIeGhnLllVfmRS96UWE1fPe7393h72fNmpWjjz66sNvvJLfffvtO1znooIPaUAkAAAAwGS1eXHYFTFbfvvXbqadeSNtP2uNJec4+zymkbQAAYHL4sz9Lbrxx26HSG1p/rXI6RJWCXpPiAqirFLTd15dMn55s3LjzdeHRqjRep0xJ+vuTkZHWt12l8XrEEcm115ZdBQAAAGx18MGClwHoHn/+3T/P0oeXPiFMet3m3ZtIrmKg7byBednwcOcHUCfpugDqIlSxr3Onzy2k3Sr29eOv+Hhbbmf21Nk58+gzc94152WsPtaW2yxTb603Zx5zZmZPnV12KQD8/+z9eXyddZ03/r/PSZq1LW2TtnRJaCk0AVlboEACiEpBtsQZna86OK6DtwM66njrzP0VGHDm64wjiuNyKw7zwxl0xnGZBKQswrCYAAUKiCwJexPoAgnd06RZzu+PSrVCaZucK+ckeT55nAdpel2v6/1Jzkk/bU7OC4BRYc8PAADjl/0+AAAAAAAAwNijgHqcOeOMM+Jv/uZv3vSYX/7yl4kVUG/dujXuu+++Nz3mrW99axQWuuvti719LCMiFi5cOAqTAAAAAGPNtm0Rd96Z6ykYq5rbmxPLbqxtjFQqlVg+AACQ/+69199Z2X/5VGgbETFlSjK5+bbOyZMVULP/8qmYOWLn/XjDhv07p7h453lTpuz+/99/e+7cZOYFAAAAAGBs+Y/H/iOe2/Bc1nPzsdC2sqwyVm9anfXcfF1rEvJxrRVlE6eUObHP6/b8W+to+tLbvhQ/eeInsX7b+hjKDOV6nMSkU+mYVT4rrjj9ilyPAgAAo8qeHwAAxi/7fQAAAAAAAICxRQvwOHPsscdGZWVldHXt+YcUf/azn8UVVyTzDe/m5ubYsWPHmx5zxhlnJHLt8aarqyvuv//+Nz1m8uTJcfDBB4/SRAAAAMBYcvvtEX19uZ6CsWhL35a47bnbEstvqGlILBsAAMarF16IyGR2Fnpu3brztj9vn3RSxH/8R65X8TtJFfcyvuVjoW0S8q2AesqUiFdeyfUUjDX5dj++8MKI/v43LpF+o/dNnhwxaVKupwYAAAAAYKyoLKucUAXUScjHtVaUJlTKnIdFxZWlE+fzmq378KT0pKgsq9x1O2rWUVnJHaumFk+NaxuvjTOvOzPXoyRqKDMU1zZeG1OLp+Z6FAAAGFX2/AAAMH7Z7wMAwPhSWRlRUhLR25vrSXKvpGTnxwMAAABgvFFAPc6k0+n44z/+4/je9763x2OeeOKJeOSRR+KYY47J+vX/Yy+vWP3afOzdf/7nf8bAwMCbHnPCCSdEQUHBKE0EAAAAjCUrVuR6AsaqW569JXYM7kgke3rJ9DjloFMSyQYAgPFspN9mr67OzhzZklRxL+NbvhXaJlWkPlGKthnf8u3x+g//kOsJAAAAAADYF5lMJrbs2BJdPV17vJ2+4PR435Hvy/Wou5lQpcxlCZUy5+FaJ9LndaKvtSBVEBVlFbsVSleWVu7+69/eXjtuStGUSKVSOVhB/lq+aHl89NiPxrWPXBuDmcFcj5N1BamC+PCxH47li5bnehQAAMgJe34AABi/7PcBAGD8qK6OaG+P6PqDp25dcdcVcUP7DTE0Dvf86VRBnF97flxy6iW7vb+yMv9ebwcAAAAgGxRQj0N/+qd/+qYF1BER3/zmN+Oaa67J6nWfeeaZuOmmm970mNNPPz3mzp2b1euOR/39/fH1r399r8e94x3vGIVpAAAAgLGotDRi2rSIjRtzPQljTVNbU2LZ5y4+NwrT/lkaAOD3ZTJ7vw0Njez38yVjf67xzDO5/syML/lWaJtUcS/jW77dj5MqZs634l4F1JSU7Py6PXnyztve3p4yJeKII3I9NQAAAAAAuZbJZGJb/7bo7unec6H09t1/3d3THf1D/W+aW5AqyLsC6orSCVTKXDqxi4qzwVpz6/ya82NxxeLdSqWnFk+NdCqd69HGha+d+bVY8fSKWL9tfQxlhnI9TtakU+mYVT4rrlx+Za5HAQCAnLLnBwCA8ct+HwAAxo/q6tcXL//bWz4dtd/67rjc888unx0/+MRfxtTiXE8DAADJq6zc+Zpgvb25niT3Skp2fjwAJiJNH+NQfX19HHzwwfHcc8/t8Zgf/vCHccUVV8S8efOydt2vfvWrMTT05v9g+Gd/9mdZu954dtVVV73p5+81f/InfzIK0wAAAABj0de/HvFP/xRx770RK1ZE3HhjxG9+k+upyHf9g/1x49M3JpbfWNuYWDYA+y6fy2nHU0ZJScQFF+T6s/07N9wQ8T//k/0y4eOOi/jsZ3O9ut/56EcjHnoof+4H+3IMjAaFtowHE+V+nG/rVBg/thQW7l4E/Ubl0PtTJF1evjMTAAAAAAD215/f8OdxzcPXZD23a3v+FdpOpPJeax25ibTW7u3dieSOxCEzDolDZhyS6zHGranFU+PaxmvjzOvOzPUoWTWUGYprG6+NqcVTcz0KAADklD0/AACMX/b7AAAwvtnzAwDA+FBdHdHeHtH1Bj+acm/nvXHxiotGf6iEfevsb8dJVSe97v2VlTs/HgATkZeHHIdSqVR85jOfiU9+8pN7PKavry/++q//Ov793/89K9d8/PHH41/+5V/e9Ji5c+fGe9/73qxcbzx75JFH4tJLL93rcfX19bFo0aJRmAgAAAAYqwoLI045Zefty1+O6OiIuOmmnWXUt98e0dOT6wnJN3evvjs29m5MJLuksCTOXDS+nnTIvhvtss98KRRNKqO+PuLgg3P9Wd1py5aIv//7va9zfz8umUzEP/5jxMyZuV7hTnfdFfGFL+TX/WC4GYyeWbPyq4D6V7+KuOqq7Of29ORXAfXTT0c88kiup4D8k2+FtgqoGY58ux8nVcy8ZUsyucPl8ZqcVGr45dB7Oq+4ONerAgAAAACAnaaVTEskdyKV91prblnryG3s3Rj9g/0xqWBSIvnkp+WLlsflb708LrvzslyPkjWXv/XyWL5oea7HAACAvGDPDwAA45f9PgAAjG/2/AAAMD5UV79x8fKSJSdF97TGcbfnv+i015dPA0x0CqjHqY985CPxt3/7t9Hd3b3HY374wx/G+973vjj77LNHdK3+/v746Ec/GoODg2963F/+5V9GUVHRiK71mlQqtddjnn/++ViwYMGwrzE4OBgFBQXDPn84Vq9eHeeff3709vbu9djPf/7zozARAAAAMJ5UV0d8/OM7b729O8s0b7xx5+2553I9HcN1//0R3d0jLy6dNi3ihoGmxOZ8x8HviPKi8n0+/uc/j3j00eyVs772+6edFvHe9ya2zP32vvdFrFkz9gps9+cYsu+66/KngLqnZ2dRdBK++MX8KaDesCFi5cpcT8FYk29fA/fh22vDMlHWCWNdvhXaJlXcy/iWb/fjpIqZJ0rR9lhUVjaycug/fLu01N4FAAAAAICR6x/sj607tsb00um5HmU3yntHzlpzK6m1bt2xNfoG+qK4sDiR/OEYzlrLJ5VHZVnl624VpRW7/XpfXhOA8eeSUy+JDds3xFUrr8r1KCP2mRM/E5ecekmuxwAAgLxizw8AAOOX/T4AAIxv9vwAADC+2fMDTAwKqMepsrKy+OIXvxif+cxn9nhMJpOJD37wg3HvvffGIYccMuxrfeYzn4mVe2ldmDdvXlx00UXDvkYu/PCHP4xf/OIXcfnll8dhhx2W+PUeffTROO+886Kzs3Ovxy5btizOPffcxGcCAAAAxq+Skogzz9x5+8Y3Ip56amcR9YoVEXffHdHfn+sJ2Vef+ER2co49NhNdH2jOTtgbaKxp3K/jf/aziB/9KPtzpNO7F1A/vPbh+O6D343G2sZ428K3jfoL+q1cGfH886N6ScaBfCp7TfK1ISfKOhm/8uk+HKGAGia6bdsihoZ27ofzQVLFvYxvW7fu/HMnX77WT5QC6rH6eC0qyl5R9OTJEeXlEQUFuV4VAAAAAADj3eDQYGzo3RBdPV1veOve3v26923s3RiHVR4WT1z0RK7H342i4pHr6umKTCaTVwW+Sa11+8D26OnvibJJZYnkD0dSa42I6N7eHXOnzE0sf3/NLJ8Z86fO371MuvT3SqXLKl5XMl06qTTXY5PHUqlUXHnmlbFlx5a45uFrcj3OsH302I/GlcuvzKuvwwAAkA/s+QEAYPyy3wcAgPHNnh8AAMY3e36AiUEB9Th28cUXx9VXXx1PPvnkHo/p6uqKt7/97XHrrbdGTU3NfuUPDQ3FF77whfj2t7+912O/8pWvRHl5+X7l59rQ0FD85Cc/iZ/97GdxzjnnxKc//ek4/fTTs76pyGQy8Z3vfCe+8IUvxLZt2/Z6fEFBQXz3u9+1uQEAAACyJpWKqKnZefvsZyM2b4647badZdQrVkSsXZvrCRkNPTu2R+fmzkSyU5GKcxefu3/njFJJ5k+e+Elc/dDVcfVDV8fkosnxzkPeGQ01DXH2oWfH9NLpyQzxe/KlAJCxJZ/KXidKAbXHKsORT/fhiOTux0NDyeQOl2+hwZ719ORPkeyUKbmegLFocDCiry+ipCTXk+yU1P14y5ZkcodrNL5upNNvXgS9v0XS5eU7C6gBAAAAACDfPN39dDS1Nf2uQHp7V3T3/K5U+tXtr0Ym9v8JBxOplLm7p3vClDL3DfbFtv5tMbkoT77RGwmXMvd0R9kBE6OAuqunK68KqKsPqI7OzyTzHGImrnQqHVefd3VMKZoSV628Ktfj7LfPnPiZ+Oryr+bVnzcAAJBP7PkBAGD8st8HAIDxzZ4fAADGN3t+gPFPAfU4VlhYGN/73vfi9NNPj8HBwT0e19HREccff3x84xvfiA996EP79Afn888/H5/4xCfilltu2euxy5cvj/e///37NXs+GRoaihtuuCFuuOGGWLBgQVxwwQXR0NAQS5cuHdEmo7+/P5qbm+OKK66I3/zmN/t83pe+9KU45phjhn1dAAAAgL2ZOjXij/5o521oKOKRRyJuvHFnGfXKlflXpkh2bOzdlFj2yVUnx+zJs/frnNEqoG5qa9r19tYdW+MnT/wkfvLET6IwXRhvXfDWaKhpiIaahqg6oCqReXwfk+HIp6/DSd6H86nU1mOV4cinx2rE6P3Zmmser7BnW7bkTwF1vszB2LNlS/4UUCd1P966NZnc4Xqjou3y8uGVQ++pVLqkxJ/hAAAAAIzM2i1rY9XaVfHchudie//26B3ojR2DO6KooChKCkuidFJpHDz94Fg6Z2nMmTIn1+MCE1hbV1t8/rbPZz331e2vxlBmKNKpdNazhyup8t7+of7YsmNLTC2emkj+cCRdVDxRCqi7eroSe67mcFSUViSWnY+l8ZCEdCodXzvzazG9dHpcdudlkU6lYyiTR08O/QOvzXf5Wy+PS069xAtWAQDAXtjzAwDA+GW/DwAA45s9PwAAjG/2/ADjmwLqce6UU06JSy+9NC677LI3PW7Lli3xkY98JK666qq48MIL49xzz42DDjpot2N6enrivvvuix/96Edx3XXXRV9f316vP2fOnPj3f//3Ea0hn7zwwgvxd3/3d/F3f/d3MWvWrKivr48TTzwxjjzyyKipqYmqqqooLHzjh9XQ0FA8//zzsWrVqvjlL38ZN9xwQ6xfv36/rv/ud787/vqv/zobSwEAAADYJ+l0xJIlO2+XXBLxyisRN9+8s4z65psjNm7M9YRky6bezYllN9Y27vc56YReB/P3C22f6n4qnux68g2PGxgaiNueuy1ue+62+ORNn4wlc5ZEY01jNNY2xhGzjsjaNyF9L5PhyKey1yTvwxNlnYxf+VSiHqGAGsivUlsF1AzX1q0RM2fmeoqd3qiYORvy6bEaEfHnfx7xnvf8rjS6rCy5v7MDAAAAwL54ZdsrsfKllbFqzap4YM0Dcf9L98crPa9ExM4fMk+n0pGK333TKBOZGMoM7frh+JllM+OEeSfE8XOPj6Vzl8ayectiZnme/MMjMO4lVd47mBmMTb2bYnrp9ETyhyPpouKJVEC9YNqCxPL3V0XZxCllLp1UGuWTymNb/7as5KUiFTNKZ0RlWWVk8u0JN5CgVCoVl552aZw4/8T4UNOH4uVtL8dgZjDXY71OQaogZpXPimsbr43li5bnehwAABgz7PkBAGD8st8HAIDxzZ4fAADGN3t+gPFLAfUE8MUvfjEefPDBuOGGG/Z67KOPPhoXX3xxXHzxxTF9+vSYPXt2FBcXx8aNG+PFF1+MwcF93wCUlJTEj3/845g1a9ZIxs9bL7/8cvz85z+Pn//857vel0qlYsaMGTFjxowoLi6OoqKi6Ovri82bN8fatWtjYGBg2Nc777zz4oc//GHWim0AAAAAhmPmzIgPfGDnbWAg4r77Im68ceftN7/J9XSMxMJpB8dnzr06mtqb4rbnbosdgzuylt1Q07Df54xGSWZzW/M+n/fQ2ofiobUPxaV3XhoLpy2MxtrGaKhpiLrquihMD/+f2v1zH8ORT6W2SRbP5dNrbHqsMhz5dB+OUEAN5FepbVLFvYx/+XQ/TqJIvbAworw8or8/YtKk7OcPx8yZ+VP6DcD4UVkZUVIS0dub60lyr6Rk58cDAAB4c5lMJu5afVd8+/5vx3+3/XcMZgajIFWwq1z6Nb9fNL0nr/S8Ejc9c1Pc/MzNu3LeVfuuuOiEi+K0g07zs1swBm3v3x5dPV3Rvb07unq6drt99qTPTqii4olUQH3w9IMTy99fM0pnJJadb6XMRQVFMbV4amzu25z17Hxba8TO+/G2TW9cQD29ZHpUllVGZVllVJRV7Hy7tHLX+/7wNq1kWhSkC0Z5BZA/li9aHm0Xt8Vf3fpX8S8P/UukU+m97t1Hw2tzfPjYD8eVy6/Mq30DAACMJfb8AAAwftnvAwDA+GbPDwAA45s9P8D4o4B6Akin0/GTn/wkzjnnnLj99tv3+bwNGzbEhg0bhnXNSZMmxc9+9rM45ZRThnX+WJXJZKK7uzu6u7uzmvuRj3wk/u///b9RVFSU1VwAAACAkSgsjKiv33n78pcjOjsjVqzYWUZ9++0RPT25npD9kY5J8edL/zz+fOmfx5a+LXHLs7dEU1tT/OKpX8Smvk3Dzj185uFxaMWh+33eaJRkNrU3DSvj+Y3Px9fv+3p8/b6vR0VpRZxXc1401DTE8kXLo2xS2X5lec1ihiOfyl6TvA9PlHUyfuXTfThCATUQsWVLrif4nSSKe5kY8ul+PGXK726TJ++87e3tvf1+UZE/ywCYGKqrI9rbI7qy0CezdcfWuPGpG+M/HvuP6NzUEQXpwhgcGhh58B94Lbd62kHxvre8N85efE5MLhr5xraycufHAwAAeGOb+zbHv/363+KfV/5zPP3q01GYLozBzGBExK7/D8fv/3D8YGYwmtqb4qdP/jQWVyyOT53wqfjA0R/ww+qQI30Dfa8rku7u+b1fb+96Xcl0T/+en6z43iPem1eP56RLmYfzXL2kTC+ZHqlIRSay/8SCfCsqLkwXxvSS6bGhd3g/k/1m8m2tETvvxxOlgPqSUy+Jwczg68qkZ5TOiMK0l2mA/TW1eGp8/7zvx3sOf098qOlD8fK2l0e0rx+pglRBzCqfFdc2XhvLFy3P2RwAADBe2PMDAMD4Zb8PAADjmz0/AACMb/b8AOOLn2ycIIqLi+P666+PD33oQ/GTn/wk0WtNnz49/vM//zOWL/cH80hNnjw5rrzyyrjwwgtzPeJRCKIAAQAASURBVAoAAADAXlVVRXz84ztvvb0Rd921s4z6xhsjnnsu19OxN79fHjmleEq8+/B3x7sPf3f0D/bH3avvjqa2pmhub47OzZ37ldtQ0zCseZIuyVy/dX3c23nviPO6t3fHtY9cG9c+cm2UFpbGGYvOiMaaxvjA0R/YpxcXVCzGcORT2asCatizfLoPR0ycAup0OtcTQP7aujXXE/zOlCm5noCxKp/ux0ceGbE5+6+rDwATRnX1yIqXN/dtjkv+55K4+qGro2+gL6I8IsozkdSPtryW2xmPxFeeb45vdBbHhUsujC+97Ut5VWIFAADjxev2/L81MDSQyPVey326++n45E2fjM/98nP2/DCKevp74ojvHBFdPV2xZceWrGbnW6HtASUHREGqIJEX6Mi3tRakC2JG6Yzo3t6d9ex8W2vEzlLmiVJAXVFaEc9tyP4Tg/NxrR9d8tFcjwDj0vJFy6Pt4ra49I5L4+pVV0fvQG9ERGQi+SejpWLnE+lKCkviwqUXxhWnX2HPDwAAWWbPDwAA45f9PgAAjG/2/AAAML7Z8wOMD15+ewIpKyuL//qv/4ovf/nLUVRUlMg1jj322Fi5cqXy6SxobGyMJ554Qvk0AAAAMCaVlESceWbEP/9zxDPPRLS1RVx5ZcTb3x4xaVKup+ON7Kk8clLBpHj7wW+Pb579zVj96dWx6sJVccmpl8RRs4/ap9zG2sZhzZN0SeYNT92Q9W9sbh/YHte3Xx9/e9ffRkGqYJ/OUWrLcORT2asCatizfLoPR0ycAmqPV9izfCrunTw51xOQ74qKIioqIg46KOItb4k48cSId7xDeTkAsNOtz94atd+qjW8/8O3oHeiNzG//Gw2vXat3oDe+/cC3o/ZbtXHrs7eOyrUBAGCisOeHiae0sDRe3Pxi1sunI/Kv0DadSkdFWUUi2fm21oidpcxJyMe1+ryOXD6uFUjO1OKpcdVZV8W6z62Lb77zm3FoxaEREVGYLkzkeq/lHlpxaHzr7G/Fus+ti6vOusoLVgEAQELs+QEAYPyy3wcAgPHNnh8AAMY3e36AsS+Zr9jktb/+67+OP/qjP4pPfvKTceut2XkBjGnTpsXf/u3fxsUXXxwFBftWrJLvlixZEn/yJ38Sd911V6xfv35UrllYWBgNDQ3xf/7P/4klS5aMyjUBAAAAkpZKRdTU7Lx99rMRmzdH3H57xI03RqxYEbF2ba4nJGLfyiNTqVQsmbMklsxZElecfkU8v+H5aG5vjqa2pvhVx69iKDO02/Fzp8yN4+YeN6x5ki7JbGprSuYCEdFQ0xCpfVyAkkyGI5/KXhVQw57l0304QgE1ELEl+6/XP2wKqMeXdHpnMfTkyb/7/++//Ubve7Njy8t3FlADAPyhzX2b47O3fDauefiaSKfSr/s36dE2mBmM9dvWx5nXnRkfPfaj8bUzv+aHWwAAYATs+WHiSqVSUVlWGWu3Zv/JhPlYaFtZVhkvb3s567n5utb27vas5+brWpMwntY6KT0pKssq93hbMsfPVcNENLV4alx0wkXxF8f/Rdy9+u749gPfjp8/+fMYzAxGYaowhmJoWH83SKfSkY50DGQGoiBVEO+qfVdcdPxFcepBp+7z87wBAICRs+cHAIDxy34fAADGN3t+AAAY3+z5AcYuBdQT1OLFi+OWW26JBx98MK666qpoamqKbdu27XfOW97ylvj4xz8eH/7wh2PyOHuF6KOOOip+/OMfR0TEE088EXfccUfcd9998fDDD0d7e3sMDAxk5TolJSVRV1cX559/frzvfe+LmTNnZiUXAAAAIF9NnRrxrnftvGUyEY88srOM+sYbI1auzL8Sw4liOB/3hdMXxqdP/HR8+sRPR1dPV9z41I3R1N4UtzxzS2wf2B7nLz4/0qn0sOZJsiRz646tcdtztyVzgYhorG3c52PTw/vwMMHl09dJBdSwZ/l0H45I7s+cody+7v/reLzCnm3dmusJfmfSpIji4oi+vlxPMjGVlw+vHHpPpdIlJb7+AgDJu/XZW+NDTR/aVc6U6yK617w2x7WPXBsrnl4R1zZeG8sXLc/xVAAAMPbY88PwZTKZ2LJjS3T1dL3prXt79663//5tfx8fW/KxXI++m4lUQF1RWpFIbj6uVSnzyHVv704kdyQqyyqjIFUQFWUVu5dIl75xsfRrx00pmuKFYoA9SqVScdqC0+K0BadFV09XrHxxZTy45sF4cM2DsfKllfFKzysR8dsXo0qlIxW/+3qSiUwMZX734lYzy2bGsnnL4ri5x8Vxc4+LZfOXJfZ1GgAA2Df2/AAAMH7Z7wMAwPhmzw8AAOPbcPb8Q0NDkU6n7fkBckQB9QR33HHHxXXXXRd9fX1x1113xd133x2PP/54tLW1RXd3d2zdujV27NgRkydPjilTpkR1dXW85S1viWOPPTbOOuusWLhwYU7mzozyK/Yffvjhcfjhh8dFF10UERF9fX3x2GOPxWOPPRarV6+Ozs7O6OzsjJdeeik2b94cPT090dPTE729vVFQUBAlJSVRXl4es2bNirlz58aCBQviiCOOiKOOOiqOP/74KCkpGdX1AAAAAOSLVCri2GN33r74xYhXXom45ZadZdQ33xyxcWOuJ5w4RvpPbpVllfHBYz4YHzzmg9HT3xO3PXdbLJi2YNh5SRZQ3/LMLdE3mEzT3bSSaXFK9Sn7fLzXUmQ48qnUNsn7cD6V2nqsMhz59FiNSPbP1nzi8Qp7lk8F1BE7i4sVUO9dScnIyqH/8O2ysoh0OterAgDYd5lMJr5095fisjsvi3QqnTcldH9oMDMY67etjzOvOzMuf+vlccmplyjTAQCAfWD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      "text/plain": [
       "<Figure size 8000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:44: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"go-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:44: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"go-\" (-> marker='o'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:45: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"bs-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:45: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bs-\" (-> marker='s'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:46: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"c^-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:46: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"c^-\" (-> marker='^'). The keyword argument will take precedence.\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 8000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:61: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"go-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:61: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"go-\" (-> marker='o'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:62: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"bs-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:62: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bs-\" (-> marker='s'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:63: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"c^-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:63: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"c^-\" (-> marker='^'). The keyword argument will take precedence.\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 8000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:78: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"go-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:78: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"go-\" (-> marker='o'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:79: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"bs-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:79: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bs-\" (-> marker='s'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:80: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"c^-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "C:\\Users\\Dr.asif Waghan\\anaconda3\\envs\\my_phdproject_env\\lib\\site-packages\\ipykernel_launcher.py:80: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"c^-\" (-> marker='^'). The keyword argument will take precedence.\n"
     ]
    },
    {
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sy7J9wRSt7MYHb4zDZh4W1y65VkgdAAD8N4EVAADQ3pzIFgAAWp9gOgAAaC+OzwcAgPaWZVlcc981seyeZVGtVJM5XudgtawWW7q3xJJblsT7X/H+uOrlV9m2BwCgtLp6u2LF2hVRy2pFlzItalktVqxZER847wMxb9a8ossBAIBc7OrZFUtXLk16X30zVCvVWLpyaax71zrb9wDAuJ7peyb29O8ZMaR4KsHFz5373HjxUS8uur1hrv/W9bFm85oR655M3wffvvTFS+O9v/Teotsb5rdv++1Yu3lt08f96Ks/mkQA9bbubXH7uttLsw8/b6mFKeYZfJplWVLHQ5Up5DWvkMwUe83rfe2vpRVoG1GuOazXqUstlDmiPO9rXj+DI/RapDzDtvvr/dMeQB0RcdaxZ8UdD9/R1vvvx1OtVOPsY88uugyAQgigBgDI2TX3XbMvjK7VXbf6ulgwZ0Fcfe7VRZcCAACFE1gBAADty4lsAQCgPQimAwCA9uP4fAAAaF/1rB6X33X5vm3+VI/ZGTJU37J7lsWOvTviI0s+4th8AABK6ZPf/WT0DvQWXca06hnoiVu+d0u888x3Fl0KAADk4uq7r46t3VuT31c/VfWsHlu7t8bVd18d1736uqLLAaANZVk2bljvZIKLO6ud8cJFLyy6rWHu/cm98YWHvjDhAOIJ3adei59f9PPxd6/+u6LbG+ZPv/yn8bEHPtb0cX/3F343PvVbn2r6uFNxz6P3xMp1K5s+7pO7n2z6mFOV17EeqWxLr35itfDpJkotTHFGNZ/gyCyyqGW16KykE2kl5HXqhDIXa2a1PL3m9bMpxV5LNYdL0mu1Uo3OamcM1AeaPnZqveYZyly2Xmd3zs5t/NGcecyZcefDd07786akEpV4yTEvKboMgEKk820dAKANrdq4Kpbds6zoMppq2T3L4qXHvTQuOPGCoksBAIDCCKwAAID25US2AADQPgTTAQBAe3F8PgAAtK96Vo+LP39xyx6jf93q62J33+5Y8boVjt0BAKBUsixrm+NzJuv61dfHJS+5xN/nAgDQdrp6u2LF2hWlCamrZbVYsWZFfOC8D8S8WfOKLgdgTFmWRRbZhEKL61k9jpx7ZHRUO4oue58nup6If/2vf21aGPPQ9YiIT77+kwV3N9yn/+vT8bv/9rtNH/f5hz8/fvI/f9L0cafigScfiA9/88NNH7e7v7vpY05VuwcVH6hMvXZU8vk5mUqva55cEx2VjtJs3+etbGGKndV0Iq1mdAi0nSq9FkuvU6fXYpXt53AeAdT9tf6mjzkVeW5H9NfL02tRc/iMY84o/TZ+LasJoAZKK51v6wAAbWZXz65YunJpVCvVZH7Z1wzVSjWWrlwa6961zgFyAACUlsAKAABoT05kCwAA7UMwHQAAtBfH5wMAQPvKsiwuv+vylj1mZ8iND94Yh808LK5dcq0AOgAASuPeR++NDU9vKLqMaZdFFg899VB84aEvxDmLz4mIiEqlEpWo7Ps+MHR96N9ZHbOSCnwCAIDRfPK7n4zegd6iy5hWPQM9ccv3bol3nvnOokuBQtSz+rDvtCnIsixu/f6tkwopnmhw8WVnXxbHzz++6Bb3ebzr8XjJipdMKHB5ssdPPnH5E3HMYcfkVPnkPbrr0bjyS1c2fdxKVJILoM5rfUoxXKpMQcVl6jWvUOYyzeFUen3gyQcii6zoMtpGaiGZuQZH1voj8skabUiZQl7L1OuMarkCbfOQWshrRMnmcMlCmfOQYq9l+dnUjqHMo2nHXs8+9uzoqHQk872jCB2Vjjj7uLOLLgOgEAKoAQBycsWqK2Jr99Ykf4E7FfWsHlu7t8YVq66IT7zuE0WXAwAA005gBQAAtCcnsgUAgPYhmA4AANqP4/MBAKB9XXPfNXH96uuLLqMprlt9XSyYsyCuPvfqoksBAKCFZFkWtawWA/WBGKgPxOzO2dFZTecUgT0DPXHpf14aA9lA1Or76xyoD8TqJ1ZHJSqlDa/4jX/9jQnf9+633B2vOP4V+RUzSdv3bI+T/v6kiBg7PPvg5SPddvBj7n7L3fH8+c8voKuRPfTUQ/H2/3h7RIwfFH7wbeM95rbfvi3Xk1VP1ve2fC/+8dv/GBFT7+3A2w6deWj8+cv/vKOOLawAAOhuSURBVICORvf9Ld+Pr2362oR6OXj5WI95zpznxK/97K8V0NHofrzjx/FY12OTft/G6/Xw2YcnFboXEfFM3zPRV+treN4evHzotmql6u+sACYoy7K22Wc/Wdevvj4uecklPjNa1P1P3B879u4YNcC30eDi844/L8489syi2xvmVZ98Vfz0mZ9OKHB5ordHRHzmjZ+Ji37+ooK7G+7Nt785l3EveuFFyW0Lb+neksu4tXpaIUR5hbxmkUWWZUn9DC9TUHGZeu2o5hTKnNi6GlGu97Xde73/ifuTqaUd9NfSCnptxzDF0ZQp+FSvU6fXYul16vRarLL0mlfQdkR6vbbjNtOiuYvi9ae8PlauXxkD9YFCaihSZ7UzXn/K62PhIQuLLgWgEOkcXQoA0EZWbVzV8gEVY6lltbhh7Q1x0QsvElAHAECpCKwAAID25US2AADQPgTTAQBAe3F8PgAAtK9VG1fFsnuWFV1GUy27Z1m89LiX2r4HABjHwaHLBwcbD9QHhi0/6tCj4jlznlN02cO84dNviB09O4bXPEIfB/dy8H1r2fBwkS/83hfitSe9tqCunq2e1eN/f+d/F10GTVar12JHz45cxk7t5Ma7e3fH1zZ9LZexsyyt8PVHdjwSH//2x5s+7qJDFiUXQH3vo/fGu+94d9PHPe2o05ILoP6nNf8UH/rGh5o+7m+c/Bvx77/7700fdyr+6At/FLd+/9amj3vpmZfGx17zsaaPOxW/9Znfim8+9s0ph2sf/JjLX3p5vOOMdxTQ0ejesvItsXn35hHrPvD6wX0feH2k+1521mXxK8//lQI6Gt0fr/rjqGW1CfVw4PXxen/LqW9JKiRzb//e+NR/fSoiJvZ+HXh9vPsu+ZklcejMQ6e7pVF193XHD7b9ICKa814eeN+TjjgpqZDMgfpA9NX64r5H74sNT28oupxpl0UWDz31UPz9/X8fv3jkLzYU6Lv48MVx3gvOK7qVYf7yvr+Mex+9d1+NI9XdSFjzxadfHH/1q39VdHvDvOfO98S3Hv9W08f98Ks+nFwA9brt6+LxrsebPm5qf4Mz9PMyi+Z/70qt17yCTyPK12tHJZ9w4Ea0e6DtgfJ63YUyFyuvsG29Tq/NuzfHtj3bii6jrQhTLE5evQ5978nrZ0EjyhJ8GlGuXmd05BP0mmKveb2v/fX+XMadijLN4dze15r3tSgd1Y7oqHQ865iVZkit1zmdc+K0o06LmR0zY0bHjJjZMXPEy4zq5JcVGYB86VmXxm0/uq2w5y/SQH0gLj3z0qLLACiMAGoAgCZr10C6gwmoAwCgjARWAABAe3IiWwAAaB+C6QAAoL04Ph8AANpXu27v274HAMaSZdm4YcRjhRYffJ8XLHhBnLLwlKLbGuY3//U34/tbvj9ij2OFLo/nH17zD3HJmZfkVHVjvrbpa7F9z/amj5taeG9KwT2tbChwMBV5BHgNSSlIMaJcvealLH1GpNlrakHnecpzfU3Ntu5tsfmZzU0f96m9TzV9zKn62qNfix/v/HHTx73w5AubPuZUXbf6uly25V5x/CuSCqDu6u2Kt/3H23IZe+NlG5MKoF63fV2cfcPZuYzd+xe9uQaFTdZ/PvSfceGnLyy6jMK95873NPzYN/zcG5ILoP7+1u/Hlx/5ctPHfabvmaaPOVV5fXdN8XdYuQW95hA2NFXVSjWXulLrtUwB1HnuZ6plteiIdPZjCWWeutTmb0S5Pm/yel/LNIdT6HXN5jVFl9B2UgtTzCvQNiK9XvP8Dt1f7xdAXRC9Tl2Kvc6oCtueKqHMxSpbr3sH9o64rBKVmNU5a2LhzAeFOj9nznOmuZOxLZizINb+j7VFl9F05z7/3DjpiJNiw1MbSvV71kpU4qQjToqXP//lRZcCUBgB1AAATXb13Ve3ZSDdwYYC6q6+++q47tXXFV0OAADkTmAFAAC0JyeyBQCA9tGu2/cHs70PAECZOD4fAADa1xWrrmjL7f2h7fsrVl0Rn3jdJ4ouBwCStqtnV+zu2z1q+PJkgpoPvO+JC06MV534qqLbG+YNn35D/Pv6f2/6ts/7fvl98cFXfrCpY07VE11P5BIwmFooc0REZzWf0/ilFoKUV59lk2KobV5SC9vOU2q9liqouEy95nSy8NTmb0R+72uSvZbpfc2r1zJ9tpao19SUKbChTL3mJcXfdwjJnLrUvqNHlCvoNa8A6tR6zTuUOSVlCtsu27qah9Tmb4RemyHFOdzO20yP7Hik7f/edrqlFhyZZyhz2Xqd3Tk7t/EnS3jv1PXXhfcWSa9Tp9dilanX7/7Rd4eFRx8YMt1RzW9/Bc1RqVTisrMui3ff8e6iS5l2l519md+fAaXmiE4AgCbq6u2KFWtXJPlL2zzUslqsWLMiPnDeB5zMFgCAtiawAgAA2pcT2QIAQPsQTAcAAO3F8fkAANC+Vm1cFTc+eGPRZeSmltXihrU3xEUvvCguOPGCossBoMU8ufvJ+MnOn0w6dHm8+y4+fHFccuYlRbc3zF989S/iYw98rOnj/t4v/F5yAdQR+QQN1Orp7T/N6wS0ZQqgTq3XPIOByiS1QNBShfeWqVeBtlOWZK95hTIneEJuQcVTV6ZeU1SWz5w8g4pT+zmc53tapl7LIsW/YxCSOXUpvq957XtJtdc8guNS26cmlLk5Uus1r3U1xZ/BZfq5VKrPm5xCmVOcw+38vu7t39v258+cbqkFR5YpgDqvUOaI9HotUyjzjA5h21PVX0vvfS1TeK9epy7FXt9z9ntiZ8/OfUHM+8KZDwhqftay6sjLDnxMXtvYU/GzR/xs0SUwRW9+8Zvjyi9dGT0DPUWXMm1md86OP3jRHxRdBkChBFADADTRJ7/7yegd6C26jGnVM9ATt3zvlnjnme8suhQAAMiNwAoAAGhPTmQLAADtQzAdAAC0H8fnAwBAe9rVsyuWrlza9ie4rVaqsXTl0lj3rnX25QM0waZdm+Ibm74xauDyZAOah25fMHtBfOw1zQ9Anopbv3dr/MmX/6Tp477seS9LLoBaUPHU6bVYeZ0cOLVeK5VKdFY7k6uLqck1ODKx8NNS9VqmoOIS9ZqX1IJPy6ZMQa+5ra8JzuHcQtQT6zXXUObEfg6Xajsix17LIrVA24j2DlM8mPDTqTOHiyOUuTlSm8Nlmb8R5eo1t2DxxOZvRLne19w+WxP4W9eegZ7kvlO2utRCMssUQK3XqRuoD0Q9q+e6/TlZZQq01evUJdlrNade6+n1euKCE+NFz33R5AOYR7l9KJz5Rc99UdGtPUtqx1fBWObNmhcXn35xLH9geRLfQfLWUemIi8+42LH5QOkJoAYAaJIsy+L61dcXXUYhrl99fVzykkuSO5ARAACaQWAFAAC0JyeyBQCA9iKYDgAA2ovj8x2fDwBA+7r67qtja/fWtj5mJ2LwpMlbu7fG1XdfHde9+rqiywHa1BNdT8Qt37tlwqHLEw1urmW1+Obbvll0e8N86/FvxZs+96amj3vcvOOSC6DOK7w3xfCCMgUV63XqUvwbz7K9rynW1UrKtN8/tUCTXEMyE+s1L2XpMyLNXssUCCqoeOpK1WuZPlsT6zXXUOYE53BeytRrWaT4ew+hzFOX4j41gaBTl1qvQpmbI7X3tSzzN8LP4GYo0/uaYq/tvM2UYlBnq0vtNZ1RnZHb2P31/tzGboQA6ubor/XHrM5ZuY0/WaUKKtbrlKXY64yO8X8OHxjAPKNjYiHNLzoyvVDmf3jtPxRdAjCKa86/Jj77w8/Glu4tSXwPyUu1Uo0j5x4ZHzjvA0WXAlA4AdQAAE1y76P3xoanNxRdxrTLIouHnnoo7nv0vjj3+HNzf75NuzbFV3/81X0HTVYqlahEZd+/E71t6GDaoeuzO2fHq3/m1bnXPxnburfF412PP6vWRvo98LZZnbPimMOOKaapUQztiDqwVgCAVAisAACA9uREtgAA0D4E0wmmAwCg/Tg+f3qOzwcAgOnW1dsVK9auSDI8MA+1rBYr1qyID5z3gZg3a17R5UDTZVkWA/WB6Kh25BoYMFk79u6IP//qn086dHki933gHQ/EsfOOLbrFfR7reiz+11f+Vy5j17N6Uu9rRyWfE7+nGKZatkDbPOi1WHqdujL1WiapBQzmGspcomOZUuu1VEHFZeo1r1DmxOZvhKDiZkix11IFi5eo17LIc5spNWXqNS8p/r1yNQSCTlWK72te+0rL1Gtqv6sUytwcqb2vuc3fBH8G+7k0dWXqNbV1NaK9tyPyDLEtq9RCmYeO18hjvqUW9Jp3KHNK8g7bFkBdjDL1OpFQ5kak2OtfvPwv4rKzL3tWgPRQ2PSM6owk99sD7WXerHlx04U3xZJblhRdSq7qWT1uuvAmx+QDhABqAICmWX7/8uisdib5Rzx566x2xvIHlk/LCa7Wbl4bb/33tzZ93CPnHhlbrtzS9HGn4tM/+HS8+453N33c048+PdZcvKbp407Fn33lz+JD3/jQs26faMj20H0PXv7rJ/16fOq3PjVNXUzM+770vvjPDf855dD0g2/7rZ/7rXjvL723mKZGcc2918S6p9aN+z4N/Tfs/8d4ry885cK44MQLCuhodP/07X+KnT07J/x+TTQ4/vwXnB8/e8TPFtPUKO56+K7IImtonRzrdXjhohcmtcO2Vq/Flu7Bz4XJrIsTeW9ndc5K6qQLwNgEVgisAACgPTmRLQAAqRuoD0RfrS+yLIsssqhn9X3XR7utntWHLR/pthnVGfG8w59XdHvD/HDbD2P99vVj9jHaazB0/3Xb15U6mO6qu6+Kvzz/L4suZ5gVa1bEf239rxGXjXdysrFOIPmqE14Vv3nKb06ptmb7y/v+Mjbt2jSh+07mxGxv+sU3xXkvOK/RsnLxri++K3b37R5x2Vi9jXdS0Pe+9L1x+tGnT6m2Zuqv9cebb3/zqMvH6me89/iDr/xgnLDghIZra7bNuzfHJf95yYTuO9mTu9584c1x+OzDGykrF9/b8r1435ffN+ryRufwjOqM+MKbvjCl2prtSxu/FP/f1/+/UZc3OoePnXds3PqGW6dUW7Pd/N2b4x+//Y8Tuu9k5vBpR50W//Daf2i0rFx88OsfjM/+8LMjLpvKZ+trfuY18Ve/+ldTqq3Z3vXFd8U9P7lnxGXjvY9jvRbvOP0dyR1n+Zv/+pvxg60/mNB9JzOH/99z/99484tH/ywrwqn/eGo8tfepEZeN9r6Ndv+yeMNn3hCnLDxl3/GHf/9rfx+nHnVq0WXts6tnV1y56soRj3me6r9XvuzKeM6c5xTd4j5P7Xlq8PjvcequVqqT7vWCEy9I6pjS7r7u2LZnW9Pey6HXpFqpxpwZc4puDwBIxCe/+8noHegtuoxp1TPQE7d875Z455nvLLoUmmQodHm8sOIj5x4Zh806rOhyh/mNT/1G9NX6Jh26PNr9h05g/LW3fi3OWXxOwd3t1zPQEx//9sdzGTu1E5nmGQY6UB9I6oTkQl6nrky9pniMcEdViPpUlanXMoXIlElqf6+bZ3jv0LkWUiGoeOpSm78R5eo1L6mtqxHlCu8tU69lUpbPnDyDilP7OZzrNlNivT699+miS2h5ZdofUY/iwxQPVqZA0LyON0pxDrdzIOiBhDI3R1ne19T6jNBrM6S2rkaU631t58D42Z2zS/NddbqkdsxAxGCobc9AT9PHTa3XvEOZU6LXqUutz4jBv0XNQ4q9nrP4nLj65VcPC2I+OJx537IRgpvHelxqjjnsmDjmsGOKLgMgLjjxgnjbaW+Lm75zU5LfMaeqo9IRbz3trcll1AAURQA1AEATbOveFrevu70tv0hPxEB9ID73o8/F9j3bY+EhC4sup23keYBtakbrdegX1PuWT/Il2du/dypl5eKxrsfiB9smdrLAyUjphHJDvvTIl+Jrm77W9HGff/jzk9u598FvfDB+svMnTR/3ltffklwA9a9/6tdz+ePce5feGy9//subPm6jtu/ZHsdee2wuY2+8bGNSJ5P+zk+/E6/65KsiYnIB6RO57Qfv/EHM6Mjnl9uN+Mamb8Sye5Y1FJR+8PIDb5s/a37c+Js3FtDR6L726Nfi9nW3j1r7ZMLUD7ztmMOOibef/vZimhrFmifXxH9t/a9Jv2/jvddHH3p0nH3c2XHvo/eWOrDivkfvi3OPP3fan3+gPhBZlo35vgEAwFQ4kS0ATN5ooa9D12d2zMztpCSNGKgPxNrNa5sS2Hvwbee/4PykThK+5ZktsWLNinGDehvp/bpXX5fUH2F987Fvxh9/6Y8nHEo80RDjw2cdHt+++NtFtzfMDWtvmHAg6GT84pG/GN+75HtNH3cqbv3erfHXX//rostoaZ/87ieTC6D+wkNfiM8/9Pmmj3vYzMOSC6D+3I8+Fw/+9MGmj3vqUacmF0D96R98Orbv2d70cS964UVJBVDXs3p8+gefzmXs/3XO/8pl3EZ193fHv6//91zGTu2P1p/e+3Tc+fCdTR83pW2lIVu6t8TdP7m76eOeuODEpo85VU90PRHffPybTR93Vsespo85VY/teizWbl7b9HF/ftHPN33Mqdq0a1Mux1lu7d7a9DGn6rFdj8XGHRubPm5Xb1fTx5yqJ3Y/kct2RDt7eu/T8X8f+7/7/j+193VP/5644cEbchn7Hae/I6kA6kd2PBJvWfmWXMbu+4u+qHakE0B918a74rc+81tNH/e5c58bP73yp00fdyr++cF/jqvvuXrEY2erleqkA7eHHvPzi34+bn79zUW3N8ynvv+p+MKGL4zdxyjh4WP1/aLnvij+8LQ/LLq9Yb78yJfj4acfnlQfE+n9xAUnxouPenHR7Q3zk50/iT39e5o2h4f+nTtzbhwy45Ci2wPaWJZlcf3q64suoxDXr74+LnnJJS19PPx4ocvjBRUPXU5YcEIcP//4otsZ5tW3vDoe3fXohPuY6Amxb33DrfGmX3xTztVPzp0P3xn99f6mj5vCibMPlOfxC6mFnwqgnrrU3tMIQcXNkGSvlZx6zRLstUTvq/WVVua8Ns0x9HfQ7S7FPvMKzilVry28n6IdlCn8yRyeutR6zTWUOcGfw3lJrdeNTzf/OKaySTFMsUzBkWUKBM0tWLxE72tqvQplbo6y9JrizyW9Tl1q8zfC500zpNDrnBlzkqijnaT2N3MRAqibQa/FySuU+cBz2qZiwZwFcdIRJ40bvrwvdLk6dhDz0GMPn3140a09yzmLz4lzFp9TdBkApXPtkmvjixu+GFu6t7TV94BqpRpHzj0yPnLBR4ouBSAZjuYEAGiC1U+sTvKX0tOpltVi9eOr47UnvTbX58nrDzpSOwgzwh85NENKv9wZ4n2duhTf17yUqdfUlOmPdPpr/aU5sefW7q3xlR9/penjHjn3yKaPOVUP/vTB+Ltv/V3Txz3j6DOSC6D+9A8+HX/7f/+26eP+5sm/GSt/d2Usv395dFY7k/xj/bx1Vjtj+QPLCwmgPu9fzouvb/r6uPcbLUh8tHDxvzz/L+PyX7o8v8Ib8MqbXxmPdz0+Ytj2gdcP7unA6yPd9wOv+EAs+ZklBXQ0ut+57XeiVq9Nub9hj4tKvO+c98VJR5xUQEcje6bvmfjw//1wRIz9Hh28fCLv61tPfWtSB3g9vffp+OqPvxoRY9c9kf4PXn7+C87P9Q9GJmt37+54YvcTDfVy8PIDb+uodiS5LQHQ7pzItrVPZAsp2LF3R3T3d48YbjqRwNfRQmAXzV2U3AmW73z4znii64lJhbhOJPT13OPPTe4PRd5753vjid1PTDqIeLz3+E/P+dP4tZ/9taLbG+aYjxyzbw5Pps/xfOnNX4pXnvDKaehgYrp6u+LsG87OZewfXfqjOGXWKbmM3YifPvPTuPqeq3MZ+29f9bdJnUx6R8+O+MZj32j6uPNnz2/6mFOV1++AU/w9mO3TqXus67HYvmd7LDxkYdGlAAAAOUntWOFcTyad2PfEUvWa199rJNZnRMTuvt3xeNfjTR83peNdhqzdvDb+z/f/T9PHvfCUC5MLoL5h7Q3x6R98uunjvvusd8dHf+2jTR93Kv7gc3+Qy77SD73yQ/Env/wnTR93Kk77p9PisV2PNRwMP9p9PvyqD+f+d3CT9cbPvDHqWb3hAPHRQuX/+GV/HD97xM8W3d4+3X3d8U9r/mnCPU0mTP7CUy6MQ2ceWnSL+3T3dcePtv9oau/nKP8uPnxxkp+x47n30Xtjw9Mbii5j2mWRxUNPPRR/842/iZ9b9HMjBhyfvPDkeNnzXlZ0qcO85tbXxL2P3jvp0OXx/PX5fx1/+it/2pSxmuXhpx+OjTuaH66S4t+idFY7cwmgTq3XvEOZU6LXqUstQD2iXOG9ep26FOdwXoE5ZXpfy9RrmaS2rz9PqX1nLdW+fud6mbIUe3XOrakr1ftapl4TnMN5Sa3XvN7TFOXZa2rr65buLUWX0PJSDPTILRA0xf0RQjKnLMVe83pfU5vDQpmbI7X3tUw/l/Q6danN34hybUe082frCQtOSKKOdpJaeG9EfmHFqfVaqlDmjnxCmSPS63Wi72slKjGrc9a44c0HhjXXslp0VtL5Heerf+bVsf5d64suA4A2Nm/WvLjpwptiyS1pnZN8qupZPW668KaYN2te0aUAJCOdbzoAAC1szZNroqPSUeoQ6o5KR3z7yW/nH0DtAPEpK1WviR00HVGu97VMHBA/damtr7ke+J9ar/5YcspSe0/zlNp7mqdKpRLburfF7etuL+12/kB9ID73o88VElgx0Z/DQwFIB9wwphRPuvDw0w/Ho7sebfq4T+19quljTtVtP7wtl4NQl566NKkA6t29u+P9974/l7F/8+TfTCqAeuPTG+Oiz16Uy9j9V/UndULeLz/y5XjDZ97Q9HGfO/e58dMrf9r0cafiY/d/LN5713ufFaZ94PVGQuNPO+q0+PL/8+XpbmdM133ruviX7/7LpHqdyH1/ZfGvxF+e/5fT3c6Ylt+/PL71xLfG7mmUHscKUn/F8a+IN77wjQV0NLpbv3drPN71eEPv59C/tVotfrD9B/HYmseis3PwV+ovPe6lcepRpxbS02i++uOvxt7+vWP2cvBtEeO/1ycfcXIsmruogI5Gt2nXpoiY3M+dsX5efeOxb5T6RLb3PXpfnHv8uYXWsq17W3z7yW83JcT2wNtmVGfE77/o9wvt7WDffOyb8ZkffGbyQbbj3Od5854Xf/Oqvym6vWH+fvXfx8e//fGG3rux7v+rL/jV+MxFnym6vWHee9d741+++y9NH/cdp78jVrxuRdPHnYoP/98Px1d+/JWmj/uBV3wguQDqOx6+I9Y/1fw/1HnrM29t+phTtev/Z+/fo+Qq63zx/7OrOukknXQIuRJugUCMIGIIJOhIQtRpNJEjqHxHZ2AE4fD1EAQRjpevBlZwZhwdwjcH5XxnUJdxeZlRZzg440LBcYajP44nEtTRUaPAgBc44aKRADGX7q7fHxgEkvQlVbv20/W8Xi4XoXun9ufDfqpq165nP++dj8f23dtb/ripfV9S5rXb1Hot8zN0amHFZR3X1I5pRHnX5FPsNaXrQGNVIxqx8ZcbkwvMAQAAWie1uVtZzbPMqdeM5lmWtZheir3mdFz12rwUe/31b39dypzIbTu3tfwxm/U/Nv+PUl6fznvxeUkFUD++8/G48vYrS3ns+y+/P6kA6h8++sNY+vGlpTx2/5r+0kL9DsSXNn8p3vQPb9pnWPazg+Gf3PVk1aVW6j1ff89+f/e2xW9LLoB658DOUr5bTvEejZyCI8taED21XssMyExtDJf5fpDLcU2tz4i8ehVU3Dy9VkuvjEZy1/rLXGcgwetMuchlTZCINOdDliW1+bxlymkMlyXFXnMZwzkFFZcptV5zGb8R6QVQjUUphimWde0lxRDDTg6OfD6BoM1L7bgKZW6NXI5ran1GeF1qhZyOa4q9lhYsnsB3y4sPWVx1CR0nxc9OuQRQ12v1KKIo5bN6ar2WGba9e3B3aY99II6beVzcffHd+wySfnbIdJnnkQDQSfrm98Xa09fGNXdcU3UpLbP29LXRN7+v6jIAkmI2JwBAC9z10F1ZTRDcl0Y0YtNDm8rfjwniTUux15yCxcuS5HHN6Pma0+JNuTxfcwplLlNqY7i016UEj2lWN0uW2OvGBzcmMXGvSgONgUoCK7y3Ni/FXsuS4utwWVLrNadrITn1OjA4UMoiNY/vfLzlj9msB7c9GN/b8r2WP+7BEw9u+WM265s//2Z8/oefb/nj1opacgHU/9+m/y/u/MWdrXmwX/7+jx961YeSC6B+65feGj97/Gctf9zPvf5z8eYT3tzyx23G0f/t6OzPzVulq9YVN951Y+UB1Hc9dFes+lzrP2dM7Z6aXAD19x/+fqzfuL7lj/uiWS9KLoD60e2Pxo8f+3HLHzfFhd9zCgQtrdcEz7HL6jXFm2BLC/BN7LiW+Tk6p15TG8NZvS5l8lyNyOt6ZlmKKGLTQ5sEUAMAQAdL7bNTTvMss+rVPMum6bVaem2eXquVS6+lBnCkds6UUdhI/2B/7OjfUXUZY1pOAYN6rVYuvZYZkJlTr6kt6p/L+I0or9cU5+M6rs3LqdfUXpciHFdGx+fW6qQ4T7ssKc4RLIt7tJuX2nM1whhuhRSPa1lS67XU75YTe23KqteMziNSC9sai1K7Bycir+DInMJPBYs3L7Veywyg1mt1yu61zMcfLa9LzUux17KOa4rfV3XycT1kyiExc9LMeHT7o1WX0jFS/OyUSwB1xNO97hzY2fLHTS2UefL4yfGul70rxtXH7TeQea+fj3DbKd1Tqm7vOXrG98RJh5xUdRkA0FHWLFsTW3+7tZT1D9vtilOviDXL1lRdBkByzOYEAGiBbz/47SS+0KvSYGMwNj64sfT95DRpOqdec+Imh+alOIZzWeQnIp9gcQsaMdbldLNkmb3e/dDdUS/qSU5UbJd6Ue+owIoU31vLkmKvubznZHXO75ypaUk+V51HNC3FXsui12pl9Z6TUa9l6x/sj5t/fHM8tv2xmDFpRmV15BSm2Mk3mj1fTsdVr80zhquVS4h6medweq1Oac/VxPqMKO88IsVeU7xGMtY0ohGbHtpUdRkAAECJUvvsZM5AayTXa0bfo+u1eXqtVk69lvW9Uoq95nJcSw2lSO2cKaMAjhS/Ax5r+hv5BAzmFKaYYihFLsdVAHVrpNZrvVbOIvep9RmRz3M1Iq9ejeHm6bVaAqg7T4pzXMuSy3WXMqV23SUis/s+M1nrpUyOa7Vy6TXFedplKfV79ASfr2VIbfxGpPk5ZKxJ8fuS0kKZE1yPSfhp81LsNZfw06IoooiilM+vqfVaZmhyar2WNX4j0gug9rrUvNTGb0Rex7XTe11y6JL4yr1fSaaesS7FUOZxtXGlPO7ugbRCmSMijp52dOwa2DVk6PJQIc37+92C6Quqbu05Jo2bFB/6ww9VXQYAMEYVRRHrzlgXT+x6Ij7x3U9UXc4Bu3DRhbGub1023x0BjIbZnAAATfo/T/yfeHT7o1WXkYRHtz8aW57cEnMmz6m6lI5gMnHzUpxg6+aV5iV5XDO62SynmwjLktrz1eJNzUvyuer9pmlFUcRdD92V/eteVYEVzoU7Uy6vwxYdbo3U3nNyGb9lSu2YlinFXnM6p8mqV+f9HKCBxkBs/OXGWLVgVWU15BJ8GqHXVkiyV+GnTUuxV2O4eamdi5V583xqN9iWeQ6X2nEt7bmaWJ8R5fWa2viNyOsaSZk2Prix6hIAAIASpfbZyZyB1kiuV99BNk2v1dJr85LstaTX4ZQW4S1basc1q3mWGQVwpPhdy1iTYlBxWQtnpxhiIjiyean1WuaC/qn1mlMAdVm9NqKRXFBDLs/ViPJ6TfG91XFtXoq95hQ2UlaIek5S+yxXplw+o6d4TK310rysejWGK5XTNbWcxnBZUuvV+G1easc0wjl/K+T0WS7Fe1M6PTjy2XIKFs/tuJZxDFLrtexQ5pSUGrY9OFDqd0Sj5XWpeamN34i8ziNKu9afyPdVp8w9Jb5671erLqNjpBjKPL4+flTbDhnO/KxA51k9s0qs+sD8aPWPqi4BAGBMqBW1uOnMm2LK+CmxfuP6qssZtStOvSKu67suye9TAFKQztVhAIAx6u7/c3fVJSTl7ofuLjW0wgTx5mXVa4IXhBzX5iV5XDO62awsqfWa1U0OFm9qWmrjN8L7TSsUUcS3H/x2kpMU22mwMVhJYIUx3LwUey1Laq/DOS06XKZcxnCKxzTFcLyy6LV5OY3hJHvN6Dwip8/p7VAv6rHpoU2VBlDndFNdLiGvEXn1WtoYjgTHcE5Br8Zw01J7Hc4qlLnEc7jUPjuV1Wtq4zcir9elFD9zjUWPbn80tjy5JeZMnlN1KaVKcQzTvNTeb8qUVa+ZPF8d086UU685cVwZ61IKQIow97BT+Q6yeak9VyMc11bQa7VymQtS5vtNaq9NOc2zzOlzSE7nTGVJMUwxp+BIvTYvtcXfy3z/S+24CqBujYHBgajV0zlvKmuR+9SOaYTX4FZIsdeyQilS7DWn45pSoM1Yldq1l1Kv9WfyGT21YxqR11ovdCZjuHkp9prL9cNSr38n9p5jzkDzUuszImJcbVzVJYx5Kd6bkst9ZBHlXY9I7fp3RF6BoLmN4YGBzg+gLjuUOSVl9pracc3pdSmnXksLFk/suRrR+e83i+cuTvKcZqzaNbCr6hL28tnXfzZ2D+5+bpD0PkKmu2pdSV47AgCgHLWiFtefcX1MmzgtrrnjmqgVtWQ+p+zLnvrWnr421ixb49wVYAhmcwIANOk/tv5H8h+U26VW1OK+rfeVuo+cJojnskBKRF4TbB1X2LfUnq9Z3eRg8aampXZMaY0d/Tvi0e2PVl1GEqoIrHDO1LwUey1LTq/DqfWa0zl/Vr1mdM6k1+bptTPldB6Rq0Y0YtNDmyqtQZhi81I8P9Fr85Lstazna4K9lnVjaIq95jKGcwplzmkhAs/V5qXYa2rhJ2PZ3Q/dHasWrKq6jIjI6/NlVr1mco3EMe1MOfWak5yerznJ6bhm1avX4aalNl7MPWxeis+LnOYyuYeheVkdV71WKpfXYaEUrZFLr6n1GZHmnI2xJqeAwRQXk84pJDOXUNuiKKKr1lVKXan1Wtb4jUgvWKXssO1x9XRClgTaNi+nXlN7rkZEdBUlHddGPsc1pzGck9SuveR0PaIsqR3T3GR1nTSn60wJznMtS1bHNZdr/WVe/07wtaksOfWampSujYxVqd2DE1HeNeEUr/V3enDks+XUa2nB4hmN4dR6zeleyDK/w0mt11zGb4ReWyG18RtR4jlTIt/hLD10adSLejL1VKGIIt778vfGQRMO2iuUeXx9fIyr7x3WvK8A53H1cTFl/JSq29nLiXNOrLoEAAASVRRFXL386jj1sFPj/FvOj0eeeiTJzwb1oh6zembFhrM2RN/8vqrLAUie2ZwAAE367e7fCqD+nVpRix39O0rdR06TpsuSYq8WmGheise1LCn26iaH5qU28T+nhRFz4gbC5uXU69YdW0t53LEqpcCKZqT23hrhPKITZdVrRudMuTxXI5xHtEKKvZZFr9XK6T2H1hpsDMbGBzdWWkNOYYpZhW3rtWlJ9lrW81WvlcplDJd5DqfX6niuNi+nXnNTK2px39b7qi4DAAAoSWqfnQRHNq/MhTQPVFbzI8rqNcXvlh3Xpum1WrnM8XG/Rmvk0mtqfUakOWdjrMkpYFCv1cqt1zLqSm3x97JDmVOi1+alNn4jygvgSO2YRuT3GlwGvVZLAHXzUrz2koucPrfmtDZTVtdJc7rO5Lg2LcXjWpacek1Nqdf6E3u+5vRcnTVpVtUljHkpXnvJKTgyp14FizcvxeOaS69CmVsjtaCoXMZvRF69dnoo87MNdVyLKKJeq0etqEWtqEW9ePrP+/rZs39eL+oxc9LMNnaxfzN7ZsbZC8+OW35yS5LfP5Stq9YVZy88O/78lX9edSkAAFCZvvl9sfnSzXHl7VfGx7/z8WQytvbUccGiC2Jd37ro7e6tuiSAMcFsTgCAJu3o35HcZMWqFFGUH0Cd4ELIZclp0mlZUuzVGG5eksc1lwWNcpr4b2FERiG1YxrhprpW2L5rezJfglWtisCKnM6ZcmGxwNZIrdec5HLOT2skec6U0Xl/Vr1mct6f0zFtp0e3PxpbntwScybPqWT/OYUp5nQDYU69ClFvXpLHNadeMxnDZZ7X6LU6pZ1HJNZnRHnvrSn26hpJa9SKWunzdgAAgOqk9tkpp/kR5kQ3L6teE/u+NcJxbQW9Viub+RHu12iJXHpNrc+INOdsjDUpLvArTLF5KS6c7bg2L7VehTK3Ri69ptZnhF5bIadeUwxBclwZy0r9jJ7YdaZcrjFF+F6jFbLqNcExXJYUj2tOUpy/XAbX+jtTTq/B86bNq7qEMS/F+8hyuu9TKHPzUjyuOfVa1hhOrVehzK2R2nHNZfxGtKbXPUHFzw4unjhuYguqa62T554cb1v8tn0GK4/kZ/sLaz689/CqW9vLDa+5If7fV/+/e/VSK2odc01h9ZLV8fc//vuqy6hE/2B/rD5lddVlAABA5Xq7e+NjZ34szjnunDj/lvPjkaceqfR6Sr2ox6yeWbHhrA3RN7+vsjoAxiKzOQEAmrRrYFfVJSRlZ//OUh/fxP/mpTjpVK/NM4arlcvNZjlN/C9Tas/XXMZvRF4LGnm/ad5AY0AA9e9UEVhhDDcvxV7Lktp7joURO5Nzpubl1GuKHNfmpdhrWVI7j8jpudpudz90d6xasKqSfQtTbJ5eq1XaGE7wNS+n45pTr7mM4TLPa/RandKeq4n1GZFZrxl95ipTEYUA6g6T4vMVgM7j/aYzOa6dKbXPTuZHNC+1PiPy+r41qwAOx7Vpeq1WLvMsBT61Ri69ptZnRJrfi441OQUM6rVaem1ear3mFMpcVnhBRHq95jJ+I0oMKk4saCSivDGc03FNsdd6LZ/jWubrcC5S+zwnJLN5qR3TiLy+1yhLTr2mKKfrTDk9X3P5XsO1/tbIpdfU+oyIOGjCQVWXMOYleT2ipM+tKfaaU1BxTr2WFiye0RhOrVehzK2RWq+zembFWQvP2m/g8DM/G2Vw8fj6+Kpb28sbjntDLDty2T7721/fz/55EUWS54L7smrBqsrWJGm3FAPAW235kctjwfQFcc+v7snqfoMiilgwfUEsO3JZ1aUAAEAy+ub3xeZLN8fV/3p13HT3Tc+sVdOOzwp7vt+b0DUhLl58cVy74tro7e4tfb8AnUYANQBAk1L8MrpK3V3dVZdwQFKbSByR1+JvOU2wzWUyfERmYzijm83Kklqvxm/zUjumEXn1Wpac3m8GG4NJ9luFTgqsSPGY5nJ+mNXNvm6WbFpq4zc3OZ0z5dRrWVLsNafjmtP7K61XK2px39b7Ktt/Wc+p1G6UjCjv3CbF61d6bV6KYziXoOIIY7gVUhvDOS1EUOZn6dTOO3MZvxGZ9ZrgZ66xamf/zqpLACABvmvpTM6ZgNReB8r8fJlar1nNdfd9a9NSPBcr6/ma1XHVa6VymadW6ntrYr2WGsCRSa+p9RmR5veiY01qC79HlBiS2UgvTFFIZvOS7DWToNecAqj12rzU+ozQayvotVqlhagPpnd+WObrcC5S+zyXU0hmWVI7prnJ6XpEVt9XZXJNOCKz45rJ9xqlrjOQ4BguS069pia1+0fGohTv18gpqLi0XiO9XgWLNy+rMZxYr121rnjpYS8dMqx3xD97XrjvwRMPrrq95zhi6hFx51vvHHVA8Uh+PqFrQtXtPceJc06M//FH/6PqMtqit7tXMBZjUlEUcdmSy+LtX3l71aW03WVLL0vuGgIAAFStt7s31r96fVy74tr49L99Om749g3x01/9NLpqXaXM0drzuMdOPzYuX3p5nPvic32+BmiC2ZwAAE2a0DUhq8noQ2lEo/RJCDlNmi5Lir3mNOk0l8nwEXn1WpbUJsPndJNDTgsalSXFPt1o1pnKer7m9N9wJNodWJHTf/9czplyCmXOSS7jNyKvXsuS0zlTkr3mdO0lp+OaSa85jd92qhW12NG/o7L9ZxXyWtL7fYrPDb02L8ley3q+JthraWHbCfaayxjOKZQ5q7DtTMZvRF69ljmGc9Pd1V11CQAAQEmS+w7HnNKmpdZnRD7fQUbkdW+KMdw8vVYrl7lbOc2zzKnXsr5nSa3PiDS/fxhrcgpT1Gu1hGQ2L7XjWmYYaGrHtdReEwtWKStAphGNGGwMJvU9fS7P1Yi8XoPLGsM5Hdeces1Jip/ncpHivQZlcf27eVn16ppwpXI6rmXJqdfU5HRNOKfnak7nTGVJ7R6ciIh6Uc5n9Jx6TfHaSy5BxRFPB/j+tv+3LQnvffbPjjroqKpb28unz/509A/2D1n3UD/bX1jzrJ5ZVbf2HBO6JsT/uvB/VV1GW0wcNzFedvjLqi4D4BnnnXheXPW1qypdb6bdJnRNiHNffG7VZQAAQLJ6u3tj9ZLVcckpl8Q3fvaNuPGuG+PmH98cA42B6Cq6YjAGD+i6Ya2oRS1q0d/oj3pRj7MXnh2rT1kdy45cluR3twBjjdmcAABNmjhuYpITZaow2BgsP4DapNOm5TTpVK+dKcVec5k4XWooc2IXe3Na0CiX8RvhRrNWSLHXsqS0iEYK2h1YYQw3L7VzplLPI/TacVLsM6fPcs4Pm5fi+01OYzgnuYzhnF6X2qmIotoAamGKTUvxuZFVsHhOvZb1fNVrpXIZw2We1+i1Ojm9t+bUq8+XrdGIRunzdgAAxrIUrx/CaCT3HU6Jz6nU5m4JamheVr0meJ2jrDGc2nM1whhuhSR7zWTeS05zD7PqNZPxG5Hm9w9jTU4BgykGNZQVSpHTcdVrdco8N0+t1zKDT3PrdXx9fGmPP1q5PFcj9NoKeq3WqmNXxbEHHxtdta5n/l+v1eMff/KPcc+v7onByG/tnXpRj1cd/apY/+r10Wg0ohGNZ/4ZEc/5WUTEgukLqix3L3OnzI0vv/nLw9b97N/v62f7+jtTxk+poKP9mz9tfvzlK//ygHoZ6vcpBrMfPe3o+JMT/iQimuvt+T+bO3luBd0Mbfbk2XHi7BMjYnS9DNf/IZMPqaCboe0JkovYu+5mpHhNuCwpXlPLSS7XD63N1Bq59JpanxHmV7VCite/y7p+mGKvE8dNjCnjpzQVULyvn6f2WS4i4pKTL4nXHvvaUQUujyS4eO6U9M77v/zHX666hLZZtWBV1SUA0OF6u3vj4pMujhvvujEGGumdz7VavajHxYsvjt7u3qpLAQCA5BVFEcvnLY/l85bHY9sfi42/3BibHtoUmx7aFBsf3BiPbn80IuKZ64lFFDE4OBi1Wi0a0YjBxu+DqmdOmhlLD10aJ889OU6ee3IsPWxpzJg0o8r2ADpOerPWAADGmKOnHS2A+ncGG4Mxf9r8Uvdh0YXmpTjptCw59ZqinMZwLos35XIzR0RmCxplMn7LlNoxjXDO1Ar1op7V695QqgisMIYZjdTec9ws2ZlyOmfKqdec5HRcc+q1LKmdM7lhvTw7+3dWtu9cgk8jyus1xe+mSgvJTPB1IKcQ9ZzGcGmvTSke10zGcJnnNXqtTlbP1Zx6zegzV5kGG4NZBFCnOIZpXoqf58qSVa+ZPF8d086UU68pSu06dZmy6tV5f9NSGy9ZzY8QVNy0rHpNbPxG5PU9ujHcvCR7zWQMZ/XemlOvmYzfCJ+lWyHFgMGcwhT12rycek1xIe6uWlcpxyC14yqAujUGBgci6qU9/KjtCVJstdSOaYTX4FZIsdfXL3x9HHvwsVEv6s8JZn52QPM+f76P7Z+97fSJ06tubS9XvuzKff585bErY8WnVrS5mjQMNAbivS9/byycsbDqUg5Iz/iebMKtjjzoyHj3y99ddRlt8fIjXh4vP+LlVZfRFheddFFcdNJFVZfRFjeuujFuXHXjfn9/oMHq9VpCJ4a/89ev/et4cteTEbH/up/95+F63PPnFIPFr+u7Ln6z4zej6u/Zf97f33vpYS9tdyvDevcfvDu2797ekmP57G2PnnZ0u1sZUq2oxRuPe+MB9zPU35syfkq72xnS+Pr4WDhj4Yh6He04LusetdSk+L0cvzeuNi7qtfozQb0jDfk9YuoRVZe+l9OOPC3Wnr52r/qbDS6e2TOz6tb2cv0Z18f1Z1xfdRlt8ZpjX1N1CQDAGPWBV3wgvvijL8bDTz2c5JoPrVIrajGrZ1Zcu+LaqksBAIAxZ8akGbFqwarnzCnZ8uSWuPuhu+O+rffFjv4d8dTOp+JHP/1RHLfguOjp7okJXRNi/rT5sXju4pgzeU6F1QPkQQA1AECTFh+yuOoSkrJ4brn/PSy60LwUJ53qtXk5jeEU5bJ4U1ahzDktaJTJ+I3Ia9HnsqQ2fiPKe23qqnV19KS40eikwIqcxnBqveb0GpzVOVMm47dMqR3TCOeHrZBir2VJsdecjmsu76+59FmF7q7uyvZd1iIQKX6OyyXkNaK8XlM8rsZw84zhauUyhsv8fJnaOYpem5danxGZ9ZrgZ66xaLAxGPOnza+6jGfkdFyz6jWT67eOaWfKqdec5PR8BUjtvSyr+RGCipuWYq9lXc9MbfxGZPY9ekZjOKteMxnDWb235tRrJuM3Is3vH8aaFMMUcwqOLC2oeDDNoOIy5HRcU+01hwDqMsPxUutV2HbzUuszonW97gku3hNW3DO+pyWP20pnLzw7jjn4mGEDl0cb0Dy+Pr7q1vZy5gvOjDNfcGbVZVRq+ZHLY8H0BXHPr+7J6rNBEUUsmL4glh25rOpSAKIoiqevGaV32WjUDus9rOoS2uZVR7+q6hLa5l1/8K6qS2iL7q7u+OI5X6y6jLaYd9C8+PHqH1ddRlusmLciBq4eeE5o9mhDt/f3+9Sc+YIz43v/9/fiqtuvin994F9joJHe9c2yddW64uyFZ8cXzvlC1aW0zMuPeHm8/IiXV10GAACJ6O3ujQ1nbYgzPnNG1aWUarAxGBvO2hC93b1VlwIAAB1hzuQ5zwmk3r17d9z6xK2x8rSVMW7cuAorA8iTAGoAgCYdMuWQmDlpZjy6/dGqS6nczEkzY87kOVWXcUBSXHQhJ6lOhi2DhUOal2SvGS3eVJaces1Fisc0p+dqigE+ZSnr/Wby+MnJBeZUpYrACmO4eamdM+W0WGCZUnvPyWX8RuT1ubUsqY3fiMzODzMaw1kd14x6pfUa0YgJXRMq239O46ysc5sUX9v12jy9VqusUOYUe83luJb5+TK161dljd+I9Hot+7imdJ5S2nM1sWMaUe4Yzs3iuYurLgEAAChJat+ll3ktKKXP5xElfi+X2DGNyGwuSEbft2bVa05jOKdeMxnDWb235tRrJuM3Is3vH8aaFEMyywp6TbHXnMJP64Xj2iy9Vkcoc2uM1V6fH7o8XFBxiqHMrzn2NfGN878x6tDlZ29bL+pJng8+34lzTowT55xYdRm0SVEUcdmSy+LtX3l71aW03WVLLxsTz0kAAA5cURRPf4eWwWnfwRMPjoMnHhzvW/a++Of7/7nqcirRP9gfq09ZXXUZAABQqr75fXHhogtjw/c2xEBjoOpyWq5e1OOCRRdE3/y+qksBAAAAKIUAagCAFlhy6JL4yr1fyTqgrlbUYumhS0vfT2kLpCR4Q09WvVpMo2l67UypLd5U6iI/qfWaUUhmTov8ZLXQWk69ljSGp0+aXsrjjlXtDqzIaQyXJbVzpqwWC0wsbIvWyOnzjfPD5qX4fpPTGC5Lksc1kzGc0+tSOw02BqsNoM4pOLKkWlJ8bui1eSn2Wlooc4K95hT0mssYLvP9ILW5AWW+tybXa4nHtRGNpM6Hy+o1tWMakdfnyzLNnDQz5kyeU3UZAABASVL77JTVPEvz+puWZK+ZfN8akVmvOY3hnHrNZAxn9d6aU6+ZjN8I82dbYWAwvQV9cwm0jdBrK6S4KLUQ9ealdlxzCmXeExa/r0DikQQwD7VNWc+NA/Wyw18WD1z+QEeELg9nzuQ5vtenY5134nlx1deuih39O6oupW0mdE2Ic198btVlAABAyy0/cnksmL4g7vnVPVld/y+iiAXTF8SyI5dVXQoAAJTu+jOuj1vvuTUefurhJO87PlC1ohazembFur51VZcCAAAAUBoB1AAALXDK3FPiq/d+teoyKlVEESfPPbn0/aS2aHmZSlsMJqMFJvTamVLsNZdJ0lm9BgvJbFqSz9WcFlrLqNey9IzriZmTZsaj2x+tupTKdVJgRYpjOKf311xktTBiRu83WZ0zZdRrWVIcw2VJ8bjm8hk9J45pOQYbgzF/2vzK9p9TcGRZ4b0p3sBVWnhvgq8DWfUq/LRpSR7XnMZwFKXUlVqvZb+3pqTM9/lGoxEJnUaUdh6R2jGNSPPz5Vi09NClVZcAAACUKLXPTjnNsyzremZqxzQirzkDWc17yejeFM/X5iXZaybP1zLfW8u63nygyvyuMLnjmsn4jTAnuhVSCz6NyCuUeU/Qa6v1N9LrNafjqtfmpdZrV60rjph6xF5BxfsKYN5fCPP+tp0+cXrV7T3HMQcfE4NXDyb5vt9qE8dNjCMPOrLqMoAm9Xb3xsUnXRw33nVjDDQGqi6ndPWiHhcvvjh6u3urLgUAAFquKIq4bMll8favvL3qUtrusqWXZXE9BgAAert7Y8NZG+KMz5xRdSktNdgYjA1nbXD9HgAAAOhoAqgBAFpg8dzFWdwAM5SBxkB7AqhzWiClrF4TnNiY02IaZUmx1xQXLi9LLmM4p8UCBUc2L7VjWqbUjmmZUuy1zNemJYcuia/c+5UkA5HapVbUKgmsyGkBslw+42T13prTOVMm47dMqR3TMqXYq/ebzpTTZ5xces3pudpui+curmzfOQVHZhXyWlZ4b4KvA1n1mtMYLqvXFI9rTmO4KEqpK7VeS39vTUiZIRmpvTZ5XWI0iijaMm8HAACoTmqfnbKaC5LJ93IRmfWa0byXrO5NyalXz9empfZ8LfW9NbHjWuo8y9SOaybjNyK971nGotRCXiPKC7RN8X7sXMJ7I/TaCgOD6Y3hPzj8D2Lbzm2jDl3e18+e/RjHzTyu6taeY3x9fPzsHT+ruoy2SO0cDmAkPvCKD8QXf/TFePiphzv6vtxaUYtZPbPi2hXXVl0KAACU5rwTz4urvnZV7OjfUXUpbTOha0Kc++Jzqy4DAADapm9+X6w9fW1cc8c1VZfSMmtPXxt98/uqLgMAAACgVAKoAQBaYOmhS6Ne1JO86bld6kU9lh5WfjBdb3dvHHvwsRHx9KIEjUbjmX/u62d7Fi4Y7mfTJ00vvfZUpLjARFlS7DXFxbzLYkGj5qU2hrNaLFBwZNNSO6YReS1olFWvJb7fnDL3lPjqvV8t5fHHiqoCK7J6bcrknCmn99YypTiGy5DiMfVZrjN5v2leks/XnI5rRr3SejMnzYw5k+dUtv8ygyMHG4NRj3ppjz9aOYW8lnVcU7zOUVavKS62l9MYLq1XY7hSuYSol3lunlOvqY3hnF6XfA5pXiMaAqgBAKDDpfbZKae5IDl9L5dVr75Hb1pOx7XM7zcPlOPavNSer1mFMpf4XWFqz9dcxm9Emt8BjzU5hffqtVo59fraY18bJ8w6YdShy8Ntf+KcE6tubS+3vOmWqksAgOjt7o0NZ22IMz5zRtWllGqwMRgbztoQvd29VZcCAACl6e3ujYtPujhuvOvGLNbXrBf1uHjxxc7zAQDIzppla2Lrb7fG+o3rqy6laVecekWsWbam6jIAAAAASieAGgCgBWb2zIyzF54dt/zkliRvkC1bV60rzl54dsyYNKP0fb3+ha+P17/w9aXvJwXvefl74sqXXhkRzw3Wjhg+UHuovzO+Pr6Cbob2npe/J9666K0j7m+o0PFn/2zulLnVNDSEy5ZeFmctPGtUx3MkP3vpYS+tpqEhvOlFb4rTtp02ojH6nJ8NM473hNCn5KWHvTSe2PVE02P2+T+b0j2lmoaG0NvdO+LXm+H6e7YUF78pS2qLN+XE4k3NS7LXEhfQWzx3cRY3wQxloDFQTQB1RmO4LDm936TWa04L6GXVa04LtubUa0bvNzkd17KkeFzLklqvKYbjjXW1ohZLD11aaQ1ZhWRmEnwaUV6vqYWBRjiurZBir0KZm5ficS2KIsooK7XPxGWGKaQ2hsv8HJLacS3tuZpYnxERPeN7YlbPrCiiiFpRi6IooogiiuJ3//67P+/vZwONgbj31/dW3Ualiihi6WHVnuc/32lHnBYTx03c7++HG+P7+9zwkjkvaaasUpy54Mw4YfYJ+/39sL3u5/cvnPHCpuoqw5+++E/jiV1P7Pf3Q/U61GfBeQfNa6aslqsVtbhsyWVDbjNUP0P9d5jVM+uA6yrDQRMOine97F0j3n40n+knjZt0ICWV5oipRwy7iMiBjOHUgq0iIk6cc2L82Yo/G3KbAxnDUydMbaquMqw4akVc94fXjXj7kY7hQ6cceqAlleac486JhTMW7vf3B/remuJ8vEtOuSRWHbtqv78f7jju77/FUO/XVVl7+tr49W9/PeLtRzqGT5l7yoGWVJqbXntT7BzYud/f7+u4rfvWurjrobuS+2zaDrWiFi+a9aI478XnPTP/MrXziGkTpsX5Lzl/n/Nlm/1nWWFoB2rSuEkxf9r8lvY52BhM8r3V9+jNS/F7dMe1eY5rtXI5rmVeu03tuJb5nVIuvaY2fiPS/K5wrEnx3uScgorL6nVgML17Us457pznhDLvK3T5QAKaD5lySNWt7WXtirVVlwAA2em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      "text/plain": [
       "<Figure size 8000x4000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"P(disease|'nausea':True, 'loss_of_appetite':True,'family_history':True\")\n",
    "\n",
    "query_var = ['prognosis']\n",
    "query_evidence = { 'nausea':1, 'loss_of_appetite':1,'family_history':1}\n",
    "value=True\n",
    "q=1\n",
    "approximate_inference(q,query_var,query_evidence,value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}