diff --git a/abm.ipynb b/abm.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2fc9e0a5ed86d1e84d46e567908dcb034fc63881
--- /dev/null
+++ b/abm.ipynb
@@ -0,0 +1,1061 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "a52a20df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import math\n",
+    "from enum import Enum\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "f5e81fd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class StudentStrategy(Enum):\n",
+    "    H = 1\n",
+    "    L = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "3891b100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Student:\n",
+    "\n",
+    "    def __init__(self, student_strategy: int) -> None:\n",
+    "        \"\"\"\n",
+    "        In the beginning of the course:\n",
+    "            every student has a specific type H or L;\n",
+    "            every student has a mark 0;\n",
+    "            every student has no performance measure π, set it as 0.\n",
+    "\n",
+    "        :param student_strategy: Student strategy, H or L\n",
+    "        :param group_number: Every student will have a group, so set a group number. -1 means no group.\n",
+    "        \"\"\"\n",
+    "        self.student_strategy = student_strategy\n",
+    "        self._mark = 0\n",
+    "        # self.group_number = group_number\n",
+    "\n",
+    "    @property\n",
+    "    def mark(self) -> int:\n",
+    "        return self._mark\n",
+    "\n",
+    "    @mark.setter\n",
+    "    def mark(self, m: (int, float)):\n",
+    "        self._mark = m\n",
+    "\n",
+    "    @property\n",
+    "    def measure(self) -> (int, float):\n",
+    "        \"\"\"Calculate the π\"\"\"\n",
+    "        return self.mark - a * float(self.student_strategy)\n",
+    "\n",
+    "    def imitate_strategy(self, reference_student) -> None:\n",
+    "        if self.measure >= reference_student.measure:\n",
+    "            return\n",
+    "\n",
+    "        \"\"\"\n",
+    "        Imitate the reference student's strategy in the next semester with a probability\n",
+    "        that is proportional to the difference in the performance measure π.\n",
+    "        \"\"\"\n",
+    "        # The range of diff is (0, reference_student.measure]\n",
+    "        diff = reference_student.measure - self.measure\n",
+    "        random_number = random.uniform(0, H)\n",
+    "        # random_number = random.random()\n",
+    "        self.student_strategy = reference_student.student_strategy if random_number <= diff else self.student_strategy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "09d595cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Group:\n",
+    "\n",
+    "    \"\"\"\n",
+    "    Every group has n students.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, size: int) -> None:\n",
+    "        self.size = size\n",
+    "        self.__students = list()\n",
+    "        self.hard_students_number = 0\n",
+    "        self.lazy_students_number = 0\n",
+    "\n",
+    "    @property\n",
+    "    def students(self):\n",
+    "        return self.__students\n",
+    "\n",
+    "    @property\n",
+    "    def effort(self):\n",
+    "        return H * self.hard_students_number + L * self.lazy_students_number\n",
+    "\n",
+    "    def set_mark(self):\n",
+    "        mark = self.effort / self.size\n",
+    "        for student in self.students:\n",
+    "            student.mark = mark\n",
+    "\n",
+    "    def add_students(self, student: Student) -> None:\n",
+    "        if len(self.__students) >= self.size:\n",
+    "            raise Exception(\"Students is enough\")\n",
+    "        self.__students.append(student)\n",
+    "        if student.student_strategy == H:\n",
+    "            self.hard_students_number += 1\n",
+    "        else:\n",
+    "            self.lazy_students_number += 1\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return \"hard_students_number: {},\\tlazy_students_number: {}\".format(\n",
+    "            self.hard_students_number, self.lazy_students_number\n",
+    "        )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "0b08df55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Model:\n",
+    "\n",
+    "    def __init__(self, population: int):\n",
+    "        self.population = population\n",
+    "        self.students = []\n",
+    "\n",
+    "    @property\n",
+    "    def student_composition(self) -> list:\n",
+    "        \"\"\"\n",
+    "        [hard_student_number, lazy_student_number]\n",
+    "        \"\"\"\n",
+    "        if not self.students:\n",
+    "            return [1, 1]\n",
+    "        res = [0, 0]\n",
+    "        for student in self.students:\n",
+    "            if student.student_strategy == H:\n",
+    "                res[0] += 1\n",
+    "            else:\n",
+    "                res[1] += 1\n",
+    "        return res\n",
+    "\n",
+    "    def init_students(self, hard_number: int, lazy_number: int):\n",
+    "        for _ in range(hard_number):\n",
+    "            self.students.append(Student(H))\n",
+    "        for _ in range(lazy_number):\n",
+    "            self.students.append(Student(L))\n",
+    "\n",
+    "    def group(self, size):\n",
+    "        group_number = math.ceil(population / size)\n",
+    "        random.shuffle(self.students)\n",
+    "        groups = []\n",
+    "        i = 0\n",
+    "        for _ in range(group_number):\n",
+    "            g = Group(size)\n",
+    "            for _ in range(size):\n",
+    "                if i <= len(self.students) - 1:\n",
+    "                    g.add_students(self.students[i])\n",
+    "                    i += 1\n",
+    "            groups.append(g)\n",
+    "        return groups\n",
+    "\n",
+    "    def imitate_strategy(self):\n",
+    "        for student in self.students:\n",
+    "            new_students = self.students[::]\n",
+    "            new_students.remove(student)\n",
+    "            rand_n = random.randint(1, self.population - 1)\n",
+    "            try:\n",
+    "                student.imitate_strategy(new_students[rand_n - 1])\n",
+    "            except Exception:\n",
+    "                print(rand_n, len(new_students))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "b30cfe29",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5               \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 20\n",
+    "# initial composition of the population\n",
+    "hard_number = int(population / 2)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "f501f686",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5               \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 50\n",
+    "# initial composition of the population\n",
+    "prop_h = 0.9\n",
+    "hard_number = int(population * prop_h)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "1cf75955",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5               \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 50\n",
+    "# initial composition of the population\n",
+    "hard_number = 49\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "18bb37d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0           \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 500\n",
+    "# initial composition of the population\n",
+    "hard_number = int(population / 2)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "fig = plt.figure(figsize = (12, 10))\n",
+    "fig_i = 221\n",
+    "\n",
+    "ll = [\"a\", \"b\", \"c\", \"d\"]\n",
+    "\n",
+    "for a in [0, 0.01, 0.2, 10]:\n",
+    "    ax1 = fig.add_subplot(fig_i)\n",
+    "    \n",
+    "    model = Model(population)\n",
+    "    model.init_students(hard_number, lazy_number)\n",
+    "    groups = model.group(n)\n",
+    "    hard_students = [hard_number]\n",
+    "    lazy_students = [lazy_number]\n",
+    "    for i in range(courses_number):\n",
+    "        # print(i, model.student_composition)\n",
+    "        # random grouping\n",
+    "        groups = model.group(n)\n",
+    "        for group in groups:\n",
+    "            # mark for every group\n",
+    "            group.set_mark()\n",
+    "        model.imitate_strategy()\n",
+    "        hard_students.append(model.student_composition[0])\n",
+    "        lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "    time = list(range(courses_number + 1))\n",
+    "    \n",
+    "    ax1.grid()\n",
+    "    ax1.plot(time, hard_students, label=\"hard students\")\n",
+    "    ax1.plot(time, lazy_students, label=\"lazy students\")\n",
+    "    ax1.set_xlabel(\"Course number\")\n",
+    "    ax1.set_ylabel(\"Population\")\n",
+    "    ax1.set_title(\"({}) N={}, n={}, H={}, L={}, a={}\".format(ll[0], population, n, H, L, a))\n",
+    "    ll.pop(0)\n",
+    "    ax1.legend()\n",
+    "    if 0 in hard_students:\n",
+    "        no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "        plt.annotate(\n",
+    "            \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "            no_hard_students_pos,\n",
+    "            (hard_students.index(0), 4),\n",
+    "            arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "        )\n",
+    "    fig_i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "fa1587ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = -1         \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 50\n",
+    "# initial composition of the population\n",
+    "prop_h = 0.5\n",
+    "hard_number = int(population * prop_h)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "cf3f478d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5       \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 500\n",
+    "# initial composition of the population\n",
+    "hard_number = int(population / 2)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "fig = plt.figure(figsize = (12, 10))\n",
+    "fig_i = 221\n",
+    "\n",
+    "ll = [\"a\", \"b\", \"c\", \"d\"]\n",
+    "\n",
+    "for n in [1, 2, 4, 50]:\n",
+    "    ax1 = fig.add_subplot(fig_i)\n",
+    "    \n",
+    "    model = Model(population)\n",
+    "    model.init_students(hard_number, lazy_number)\n",
+    "    groups = model.group(n)\n",
+    "    hard_students = [hard_number]\n",
+    "    lazy_students = [lazy_number]\n",
+    "    for i in range(courses_number):\n",
+    "        # print(i, model.student_composition)\n",
+    "        # random grouping\n",
+    "        groups = model.group(n)\n",
+    "        for group in groups:\n",
+    "            # mark for every group\n",
+    "            group.set_mark()\n",
+    "        model.imitate_strategy()\n",
+    "        hard_students.append(model.student_composition[0])\n",
+    "        lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "    time = list(range(courses_number + 1))\n",
+    "    \n",
+    "    ax1.grid()\n",
+    "    ax1.plot(time, hard_students, label=\"hard students\")\n",
+    "    ax1.plot(time, lazy_students, label=\"lazy students\")\n",
+    "    ax1.set_xlabel(\"Course number\")\n",
+    "    ax1.set_ylabel(\"Population\")\n",
+    "    ax1.set_title(\"({}) N={}, n={}, H={}, L={}, a={}\".format(ll[0], population, n, H, L, a))\n",
+    "    ll.pop(0)\n",
+    "    ax1.legend()\n",
+    "    if 0 in hard_students:\n",
+    "        no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "        plt.annotate(\n",
+    "            \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "            no_hard_students_pos,\n",
+    "            (hard_students.index(0), 4),\n",
+    "            arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "        )\n",
+    "    fig_i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "6a510f31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 2        \n",
+    "H = 5\n",
+    "L = 4\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 50\n",
+    "# initial composition of the population\n",
+    "prop_h = 0.5\n",
+    "hard_number = int(population * prop_h)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "f191b1d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5       \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 1000\n",
+    "# initial composition of the population\n",
+    "hard_number = int(population / 2)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "fig = plt.figure(figsize = (12, 10))\n",
+    "fig_i = 221\n",
+    "\n",
+    "ll = [\"a\", \"b\", \"c\", \"d\"]\n",
+    "\n",
+    "for k in [(0.3, 0), (10, 5), (10, 8), (10, 9.8)]:\n",
+    "    H, L = k\n",
+    "    ax1 = fig.add_subplot(fig_i)\n",
+    "    \n",
+    "    model = Model(population)\n",
+    "    model.init_students(hard_number, lazy_number)\n",
+    "    groups = model.group(n)\n",
+    "    hard_students = [hard_number]\n",
+    "    lazy_students = [lazy_number]\n",
+    "    for i in range(courses_number):\n",
+    "        # print(i, model.student_composition)\n",
+    "        # random grouping\n",
+    "        groups = model.group(n)\n",
+    "        for group in groups:\n",
+    "            # mark for every group\n",
+    "            group.set_mark()\n",
+    "        model.imitate_strategy()\n",
+    "        hard_students.append(model.student_composition[0])\n",
+    "        lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "    time = list(range(courses_number + 1))\n",
+    "    \n",
+    "    ax1.grid()\n",
+    "    ax1.plot(time, hard_students, label=\"hard students\")\n",
+    "    ax1.plot(time, lazy_students, label=\"lazy students\")\n",
+    "    ax1.set_xlabel(\"Course number\")\n",
+    "    ax1.set_ylabel(\"Population\")\n",
+    "    ax1.set_title(\"({}) N={}, n={}, H={}, L={}, a={}\".format(ll[0], population, n, H, L, a))\n",
+    "    ll.pop(0)\n",
+    "    ax1.legend()\n",
+    "    if 0 in hard_students:\n",
+    "        no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "        plt.annotate(\n",
+    "            \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "            no_hard_students_pos,\n",
+    "            (hard_students.index(0), 4),\n",
+    "            arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "        )\n",
+    "    fig_i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "3eb3a86e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5        \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 1\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 200\n",
+    "# initial composition of the population\n",
+    "prop_h = 0.5\n",
+    "hard_number = int(population * prop_h)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    n = 5 if i % 2 == 0 else 1\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "# plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "f4886dbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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T1ZOmxM/Lt/B169jyD9TXLnwteBlbMxbo+xZ4utfKCPre/ZCujn/W09nJ1wuylJrN/hcYLs+F999+R6rPbZhhGfa3LxHOteB9qThj9r+dxxSI5MCfv80TfzJWqP/I5gKkbOktuaQexLULcJe8VVPWoGl6gXv1pKvjb8nagf0Vpr0mjbaRtYV7rVDVI5NVDhcjOeHTTvk7SnfAbEmfasG9ocSfAUyLeFaJX2jELdH7cVVPYOJ3MQarVD2idJJpSAi+gMvU8Qcl/trM8tufYQ7KaRBctsiwNXr1yM9hb+7b7Kfq4c893YG3JQN1C2YLoY4/IxiNIJvGXVGqa4kUwIk8qFrGzVNCqeoRGmmmRCx79QQl/r1uzGsFyGRNxl6VgvcyZGFjn0j8PqqedIm8RRJ/5oNGKPFnAOJk9e1bukFl91r/m2Y8DvznLPfzYiPeMl9fBZiJ8SpdVY+bF1BE8RpFCSOoEfGtn9n/swx0/I01wCIXQ/r9w+0zr0VvqK9b/Rnw4Gg9ImgqATw8HrizD1DpsRJTxG2lzlALiUbgX8cC0+62H//iYeCF8+CJt67xz5MPvq9e6gxvUflX4NUf6b+3L7WOp0s862cAfx/nXEks23xaAzKV+N+6BvjwDy3M20fi52VZ8ZE6JIsbvKT2p0/X292OFToX3D9Ct/cs/1B/Z26RfGu3APcdAuxQLMI0EOr4M4Igpe5apV7lKuPdG4FVlR5JCml+fr/uyZFJ5zOJPKiqx4WAlRK/kGbQBStznrP/N716eAcKIPFvnut+rno9sOEb6/9rlwt5CXWa8lt9tez2JXon2rFUn7VU3umfP8dX/7D/r9+ml+0TKTb8lN8Cy97zTisIaYnqtq2L7Ocq77QGQ/FcusS/6lO9/a74ML37OPamS61Dxx9Q3TjnWWD6Ay3M3E/iF8qWzrP0EtDWfq5/f/MEsPRdoHod8OUjwDu/1N9Z9Xr1fYv/p4dnl9urgFDVkwFIbuwtmHKZEF9ENE//zmRRV9qqHhcdv0qyEQeTTNcdcFVPOn78ecU+F4hlFd6NWKdI3Mg/CUTjAQqqQLzI/j9bEVXdIA6+Xu9TPJduh+bPItNYUXtzMdX+oOMH1EKTG4IM1EyznBvyhDboNmjEO+jfHvtd7FeqHiIqIKIZRDSPiBYR0R+N47cR0UYimmt8TstVGXRIxJ8NFzqxA6VL/OJAZKp6Ar5YN4lfNRUU0/Qqm5cUKHv1BJEYeUN2g9gZxfTETmVb35Ch4TJeaP+f6z2DRYnfSzIUy5HuFJ63tUx1zXuT+PepVw+Hj6oHUDtGuCHQc2fWxkoi8bsNGvwaj82YciHx53IHriYAxzHG6ogoDuBzIuJz6vsZY/fkMG8BssSfBeIXG3GUe+YEJH4lSQeU4ExvBKkDqzq0OMB5Eb9Xh9RSOumn5cfvNzi4SPxixzAl20TmZJUnDUB+74exlnnHUFDiF951uqqeSJozRBl7U9WzLyV+vzYotvlIGhQYRGhkLDOJ32M3v0gO9uvImcTPdPBhLG589vq6fUeAo2w8xJZI/NsFoyNPJ9UMTH/QPeb9io+A58+1jKHzXrDr7VUdWmxoXMpc+h6wwFgwNf0hPT8vYs0kVo8fubhOv0WJX9igJlPCkGce4vtRlXHeS8A3T+qGZS0FTLkVqEsjtr9YLzeC2DATeO8m4TruXWLkp4rRv20J8Mol+rvj5c40Smq6g6imAR/dZl+NnGgA3rtF7TCwaS7w+QPWvba0Wkj8Vet1oy/TgBUfA7OfBeY8Dyz/yHmtm6pn5Sf6farZZRAEmZl/8ziwZ5f+e/0MS7fv1ia4zZGretZ/47hkf5P4QURRALMAHATgEcbY10R0KoBriOgSADMB3MAYcwR6J6IrAVwJAGVlZaisrMyoDCNT9oe2ZNECbNnVw/OeCum/nHe/tSsw0Pi9cdMm9AawZvUqrIF/GSd8cRnyjd+bNm7AAQDWr1uNvhse0vOqeMtZnspzHMdmvfccxhq/F3+7CFt3dTfP1dXVYfWO5Rhg/F8wdxZ2boiiolL3Xqnc2Q0Vlb8DAEyb+AqOcanr4XW7UR2txo6FizAcwMxvvkFdR+8VmEV1a3CYx/m58+abPFIhHJ/+2WdI5JUAAEZU1aIrgPnz5qCuuAri+kq3dlBXZ58qL1u1FpuarGs775qLUcbvT6d+AmYMLmYZ3rzavHbeyNswav7fben5tb/BW7bhAOP3qhVLsS5hXc/zWPfBI+gn3DN3zmxUrUnqZZv/d+xY+hUWjrjVlm7fdW/gwFVvYdfmddjV5VAcBGDbpjVwa8Fe5Yykmsx3HaQ/FdeuwrhZ96N6/ruYc+jfAAC9N7yDQSsex7pNm7HqwB/Zrj+28rsgaKhMjETvjUsxSDg3b94c7F7vP6OqcCnfmNm/QmnNUkSGdgM+tT8juc90274QwwFs37EDi4R0Kiq/CwDY1v1I8/ktWPQtdm617FIVcIKXpbTqW4xxKZ94X2rui4gCttXqu3duQ2dF2vjgNwCAmqqdmF1ZiYpKpzdhXVMqY/5zQ06JnzGWAjCaiDoBeIOIhgP4J4DboYuPtwO4F8CPFfc+BuAxABg3bhyrqKjIqAw1s+yNbcjgAzFkrE9alfa/jrynfQMYIdp7l3UDNgHl5QNQHqSMX1qS0AE9y4DNQN+ePYANLnkpygMAYw85CJit/x465GAMHW3dV1lZiQEd+gBr9P8jhg4CDqkw06mosH4fM/FoQAjZb8v/q2YU9huEngeOBBYB48YeChww2rt+m+frw7kLRo8eDQw4xlGvoyYcDnQs0/9sehTYBYw8ZCjQaxQgeC+6tQO5Yww+6EAMPly4dmkjMF//eewxE4FYnqMMHKNGDDev9cvXRN3bgLHR28B+fTFQvN7Io1/f3oDg3DF6xCHAQRXAsmZgPtCtc6kzn89mAauALl06o8uBA4GVQI/SDoCLF6JnOZvrzXcdqD9tKAZmAaXFRdb1Xy0BVgD9evVAPzmNSl3SrpgwFpj9LSDs/T5q+HBgcIA8K13KtzgO1AAdOhTJdzivXbgLWAR0797dfs5Iu0fXLubzGzFylL1clc4imWmsZMBclzyF+6KKWW3nogKgypk2R0lJia1fmhj7IyQ6jg32vtLAXvHqYYxVQa/SKYyxrYyxFGNMA/A4gPE5zt3+Nys6fmEay1UIQadjos+9OXXPID54Y5WQjp+OX9IJ24yqLuVmDGiq1bddzJmOX4BNxy+saM5Uxy+r9EQdfy70zTbjrosOXlYxmc/ew/3Qpl4wrss0JEbaqh5F2G+vfaLzO+rfjTWZ6fi9VClGfswvoiYg1DOAV086zySobUVlMPbt4y5lTcf4nAZy6dXT3ZD0QUSFAE4AsISIegmXfRdAhtvgBIXc2bKs4+cvNKgOU2y4vDMkMujI4jaIqsar0vGrrndr+M31evnyS9LU8ft0JLeOq9K7toT4HfHPReLPgXeLWC83fa6bvcmMHeOxEE9LWdd5eIB4Il3jLi+fqAf3In5uzGyqycyrx8sAnw7x87z8FnAJ6QZCUM8w1XP2MN4CcC9rOu6maSCXEn8vAFOJaD6AbwB8yBh7B8DfiGiBcXwSgF/msAwKP/4sBS/j4MSvkmh2rgTWTAcWvSnEABdeJO/ImUj8Kz9Rl4djzefC78+A7cJ2cTwsrVgGGTzERUEJTGlk5yrgq0f1Z7jgNbXvsR+pLnzd5T6jDs31lhE71aROb8MsayHU5nlqo7hjxyOBVDIxNC77wD2/NdPtKy/dJFe5LrzOXn7n3xr6ay0pEL+HoLDMJQoqY8D8l9Xn6rbrq9XlRYtK4ueutor3UmvoulQS/+L/uZeZI1PvM4410/U+Z+YdQOIX24nfIjNx5vztW8DS94EtCy2HCfM6RX8OIvGrBowcSfw50/EzxuYDpi1EPH5xrvJUQ5aysuzHz0dyVcP8+6HW72FnA+f+W9dZ89WCZkf2kQZERPN1QtwmhItVkaO4UnDBq/awy2JoBleJ3yhTXrElib7zC13aZCndKHXopcCZD9nv8xMqZz6pv4Mz7cZT8/mJA1qyUd0ZnjhO/76tGviXYS+QjeLy+7B5QWUg8b9wLnDrdiu/24TQE89IS1FcVT1pSvz1O6z9XVMJ61l4tZcXzgVu3QbE8u3Hl0/RV6Sr8J8zgW1GOGmxXpxAbaoelz0kxDASTTVOEp3/EnDiH4GOPd3L7kWOxvv09HDh7+HMh92vAaQAhqLax2dwESX+Vy7xvlZGEOFObPscQWY4GaAdrNyVDmRj5a7YWBqq9G8/Mqk2rLeFhm2/xzArnXRUPbF8YNDJ9kaqyptpwKgL/NNzk6REaY8LTlzFwGOr8zrJ+fpBtVkJz0+U+lLJzN0A5QG+SSS0TNVHAXS8xT29VT2FXYCfGS57jiiRkoRqm6UIai9DJbJo2K/U+ajUF17xmtw2D+d9RZylkouqp05wRW2sUT8DP9uEVyBFI79Aro3mznQee+4WGwOQTfr3SbslO+8FUfWoZjz7m46/9SDHEj8POubXaPjIzaUGpmWm6onE9I84gKmk4mSztUm6F9xI0Eb8UjPh96g6YRBSVRqjjfxEctWyaNwVic8vTTdduO9aDdLXdbipE5mmP0tZT87TlYlKfL6ppFUuQ+WgRfLU+ajyVwXyE8ulTMd4F0F0/CKpN1XrZZB95P0GTq/BKR3i13xUPc17rBmRWA9fib8FxO/LOwTECxSHQ+LPCLnR8YvEX2Uc82k08qpLm7EuHVVPXO/EYkNSGneb/MMnAO7lNolfiNUj56ckjAAGRJUulXdWkRxSzdkjflGa9Buk3aQzP4mPIvr78SV+ri7hdXYh/qQ0CPJnawgPmtviI1X+XgTiSvxGuVT5yO1GJP7GGiPOkqRu8iN+L4nfyE+5itWxN67PRiuJejXx+7WLlhC/H4isxaAiQok/UwTw6mmuT2+v1KA6fhFcaua6VJvEr5gC71qtr9DctUpKJ6p/uCGNp1W1TjfgphLoUL9er6ccr8avLhxV6yy1jpfEryLwTCV+HkZZJC0/rx6vdya+j8ZqezhkvzLWuzjJ+3l1cOLfsxOo2aQoU1Iifknib6oDqjeq89u+xKqTQYKM3IhfINjqDbpbbia6Yl6uSFT3IqvdYl9tXLfNWqUqttOVn+jvLipL/Alg3VdArbQiunaLnr4Yslv8vX2pqV6MqCRnt9hV62eo22jDbotkN84y7tHsbUSFXSu9z7cUKm4KJf5MEUDif/Z7ekx91yTc/K/FawIQf/Mei6yYIPGLqp6azbqk99Bo4I2r9Fj0Imo3OaUApgH/PAp45DDgw99j/DdG/PhYvv+SdFVdHhgBPKuvcrTF4zfzY1a+MuRjqlmH6r43r9bJ3ybx+xC/1zsT3/MjR+ibfpv5+7wrNyOoX4RPiuiRRVdNBe4bqihT0hocAKuu/HvVVOD+YcL1koQp7U/sKvGL5Hj/IcBTp2QmOfKBJxIDHhgF3HuwQPxJ4J5BwN+M9eFL37XuW/OZLuDwKKscX/8LeOpk4N7B9uP3HgzcO8Tupso3OV/+IfCI1QeUxC+3Yf5+q9cD6792Xt9YbUn8s/8N1O8EPr8PePIE57UiVk71Pt8ikNr+GEr8mcERq0dFJOu/sv/vN0G6x2UqKcLPFYwidgnOJvELqoWmWv0D6A1N1dBlKUBLWdNkviE7AMQK/CWGIPpdh8TPiV81AErPqu944AZJkuL3lfYF8kut49Ubc6PqqZWk70zTDCrxu5YpoV/DQ1fz9+w2W5RVCztX2P6moh2A65fAAVm42bowM8mR69zzOljGcTe/d3mAr9moP4uffGEd2+ixpDvZKL0zY0a73V6/iKYYfJkG10WJbgZlUQ3VVK3PRPwge0q54Verg10ngsilr4dePdlBWsYhl3uUhBdAx2/zINBcBpCE1cncRnuVxM8hdvBonr/E77vgKk0dv3wsEnO68InB6coOEfKCnbRE3/V04bUwx41o/fYSUOmoRcJREb8oEKQM4o8X6s+FD9ZudZSJX9IBJ2MdgKLucEBp3M2A+Hn5xHzdDKdyHep36hK/rXw+sXoCbNZCTKXqSbm7ZbqpJWNCnYLu1RB0sVeHLsGus4Gy994CoM0TfyCJX4aD6AOkEUTHLzdIZYjmhCVpuRkJZenNRvzCK40V+Dccv+ehkvhFA7VfeqqBRzTkFnVVp81/54L4synxi/lQxGmg06SBjEj/5JdYEn9Q4pdsNslYkVOPDqjfTyaSI2+HQVwe5To0Vetls7mC+pQhgFDmquN39c3nxC/dJ0r8QVfkttgV3G/gS9Mo3wK0eeJ3Gnc9GpfmIskGWX7uq+OP2qUlUcdvSztpSVpum6TLrnluxBHLd3dNNPOTBznp+kjMqePnuucgXj1KjxAu8SeADt3st+5L4vd7Viq1gS3URMReX75nMEcqYT3LghLLiOwQLOzeOyZiIvETUlGF+x/PB7APHEElR/EZqFyVzfoGUH9G4t5upDICrNlw1fGLz9lWFqOc8mxNlPj9vI00l8EjXXjOvpl6YAlVPZlCbqAMuHuQvjEy/5jn+IIaqQGK4Q94GjIWvm55OKggSvzRuB7rfZXTWPT9Rz5FU32VezqAQuJ3Iaxonj9x/lO2Z8gSe9TZ+PjUmO+v+9cB+kbmqvtVm9uLqp6CUvs5G1EqVD0zn3amp8KCV4F3b1Kfc3smnfp6pyl6mnCI5ZUl/jt72YPpca8eQL/u27cMlZ/0/jhhz3nWftxGHMwj7pFRpsnXW8f+4wz3q8QfheDBfEYyV9iL+YNfO++5rVT9TKNxe1uV97u+rRSYfIP1P4DEP3j5v5wHZz4F3CW8O5XEL8+ebYEWfSR+lgK++Lu7t1dQeNp/UvaBpaSPUbYMwrkEQJsnfocfP0vpG2+rYLrLSY24Rlqh6tZAvTbuEHX8Kn9dA3FKoaZOetl9JM8eLx2/ONBFM9j7RpaUVX78srdJwy5rEJCf9zYjps6lQqwWboxLNdsNZgR/iX/2fwJUwsCMx9TH3STLI37inZ4YGM9MS1b1SJ27fof9Wk7WZcOtY3J74s+XBz07+5/GtQElTv4MxZhMgeGyK5oDXjp+41wk5j7T4O3kmyeE/DJcpf3Nk1LaAYg/2QiceLvx20/iT7m3JRk/VXgRcfDopSowYdZyxgPAOGOvg0wjsfqgzRO/Y+WulwTsJvHLKwrd0pDd10QQWfd5TPliSCGRkBriIWdLaXkRv1iemL/6QoaD+FU6fg8CkvPjBtPO5UIeKSsfmSj9iD8bU1/TOK0oq+zRJYKHqhAh69Hl+thCUCSs8vccYZVFrmNSUKX1GAb0P8q6X4Veo+z/s7FIkefvftL+17Ydadz6dntfSkN5lsJz2CR+pr4mGgcGGrPUIBJ/0J26Snq5nyvo5H5O7BNDzrAGiZD4M4MjVo/fHrOAwlBVo75OhpceVdMCSfwxpNCckBppTNLlOiR+RUM3r0uT+GXSEGP1cHhtY+jmxy9HJeUdX3wWTMq/JV49XnAb4POKPN+NmvgliV8e/G2bqwvEb25gr7D1mMTP7CEe3Ag9T5IkW0r8bv1AVUbzv1BP/gwiHsTfpLBfBfDqUUKur1LiV+xJwQ28fiuytTSI30swKezkfk6U+KMxYRP2kPgzRAYSvyx5BJX4vTbr1hJWuh6NKI4kGpulRir7SLutpJURiaVPnDIZenn1qPJ2SOjGM7HtQ+BC/ICk41cZd1uwbbNJttxYJ81u8oqcg6wIJfHLOv6AEj8ncz+Jn0hY6etC6HJMpkw3Y+fwczMFnFKySFDc0yiqaDscKntJxhK/9B6VOn6F2oqrGf1CMbBU8JmmlxeObM8SoQmeSZG4QPwZ7r3gg5xuvdgaoNTxu+HVy4BL3nKSnyzxu8a38Uh7+RTg+N8bhXIfIGJI4enPVuCvIn/IukFZ4rflK/qVRzNQ9UjkQhF4Ro386Dbr98ZZwEvnq8smlpmlrE2lxUHweWlv4RWKjbTT2RwbAJ482fodzTMWCrl4aeR3tHt7yOCx8QHLKaBQMIaqiP+FH1i/RR0///5LH30hmwgxkB8E4neT5OV1EmltLqIgvbsPAi6f4k38cnwpN4nfzYVRDFnOIfef2zyIUoSjfypsXo5Bk1mD/PxXlI4WVrkUBng3eA0QogebDJvEH7fUQum294Bo88QfyO2MY1WlcY2PxO8WVM2vcST4Enh3qSCGFGKQ8u8yADj5L/rv3mOBZe/bz7t19EgsfSkqkI5fkCi/EOLxf/JnZ3pmiFyhzloKqDK8fXqPdd4jQib/boPUy/DdIK7KjsQBNLpL/L1GqyX+cT/WPUdUEA2+FPW286SaLV988XmIeyeI5ZKjeYrv8rR7AN4MT7oDKOmt2yg++LX1fop72sMlqyAan8X8P7nD+z4vvTgf/GL5ASRlgtlHs7Ulpi3ipuTOWVBqzTb4gOk3Q2IpOHjEDV7q3gNGA2XD9D7VZ7zuZPLJn/VvUccfiQMDjgWO/4O+58U3C4LlnQbagaonDT9+8xbhnvxSp0ThFkXQr+Hy0Awe5BBHChFZPRXNAyb8VP/0O9xqXIf/RO/sbg03qKqHGw8BFx2/7NXjkp9qJqPaBi+VsI6LRl+OPocBBx6nzoPHNep3pPq8FzghqXT8sQIjQqJiWf4JfwyWvluERY5kM0wJ2IsQxZ25KKJWH/Q93Pqd3xE49lfAMMNlk78fL1dAX9908m47Xnpx3r6jed7qTwB2L6IsEb9q052UYDjlIAIGn+oU7GTVqpZKX+I/+HTnOaYBR16re4/1GQscfCrwk8/1/sclfooY60EiwMTrnQscs4ScET8RFRDRDCKaR0SLiOiPxvEuRPQhES03vjv7pdWicsgvLEjse7EBFnZyNozGGqdBDfAnWZ63l3GXFBK/rD7gRMClQTcdZdBFOzYDZACJ31UnqujkpieTKPEnBOlGUcZIzP0Zmc/QQ7J2g0n8LhI/oI7HEjQviqhX0nKkmgUdv0fXExcSitE8/crEnxnfrUtlQOXw0n0DxroTL+L3MCDzZ8AH06DIliHfFhNLkvj5jI4fLyhxuunKoTvcFluqwN9vniI4oevMPGr58XvNGLOIXEr8TQCOY4yNAjAawClEdASAWwB8zBgbBOBj438OkQHxi5J7hy723ZsAXeJXWeh947wbBjAPcogjiahK4hfByZIb/tyMrYGJX4qI6cgrqMSvaE6aQtWTEnzXVaRGUXfdJjciZqL75J3KDBkhrvY02omK+IN2RlXIBhGpJqeOXwVT4vcgflWZooItoLkuoOuyW1gQP4nfQz3Cy+ZlL1HBr/94Gd5FqCR+Xk8z7IXxvvNLnEZ72ViupaHq4QOdKiqtW/0oakj8ycwEmgyQM+JnOrhJOm58GICzAPzbOP5vAGfnqgyALPGT7xZov39rIZjY4As72yX+Pbv0yIMqn9x3bwTe/42ly5cRROJX6fjl60XS2LPTvlBni6APDEqO4rRdtYDLS8dvK5dK1aOQ7Juq9ZWQbmX0UpkkGvSOIumYP1obwIWRl+HrR5HSGP47+W3hpBfxBxxAibwHiaQg8Xt5f2gpYNYz+opxV4lfdYxL/M1WnHkXpF68APe98Rn2NLrp6v2I3+N584WMXoOgCn6qUlVAOhWWvGP9/ug2fdEfL688eBSUOFVi3KNGLJe8L4YfVAH/vCT+qnXAlvk5M+bKyGkuRBQFMAvAQQAeYYx9TURljLHNAMAY20xEPVzuvRLAlQBQVlaGysrKjMpwlNCYNIqhoWY3ijyu/8+Xa/Hb4kbw7r+lTkNZYw0+nToVIEKXnbMwEsDORD649q0xvysKmnbqMd/XfYnlO5qwsc93UCGlvXzRPAwCUF27B9xfYVfn0eiye655TVSh4//sy6+Rilml7rtuLQ4EsH79engFGfh65iwc7nGeY1GPs3DIjrsBALNnTIfob1H55Sx02LMR4trhRGM9VPS2Y+dOcL+F3Z1GolPVAswbfiuqKitBWgLHihcby98/nfa5/TiA3VXVaGqIQrUtd/2uzSgEIbJ2unksGe2A5xY34w6jT2sURURBInuaEugAAEvewSOvf4yfL/+NeU7TNEyrrET5+s0oF+5ZcvA12PLpp453qcLaolFIrV2PgS7ntWQjamtrMaeyEj03L8cQl+vmzpmF0fN+BwCoqqnB3GnTHPl/+fVM1CULbP2CtCSOBbBq5TJoq9fgILg/i+jKjzAitQv/3PQD3OA4C+zYtQt5zVVgJUNRWrPYWZfmBnep0QhTsXv515hXWen57KpLhqC0Rg+9vHHDevT2uLZGK0CJx3kl6rYAb1+LBcN/gxEAVq/fjAEAdu7ahQWVlei7cQcOlG6Z1+N76NfI0LlqIQBg7qeTMZqfG/kHjJpv2Xya46XIS+gagT2FB2CG8T7yaBwOjxQgqlkD6+pVK7BWq3QU8eAtW9ALANZ8huZ4Kb6QuK6uri5j/nNDTomfMZYCMJqIOgF4g4iGp3HvYwAeA4Bx48axioqKjMqQFMLTR/IKUVQQt7whXBAT9K89Dz4M2FqJiiMP06eASxuABUDXw84BPtDjixdU3AB8YJHIoPI+GHR0BVBpT3dQeW9gBVDapRtgTCK6HDAQEIifAMQk4p947HH2yIzT5wOrgL59+wKK/c45Dj/iSGCGy8n+RwEGeR5y9OnA8BHAK5fg0CEDgDnGNQOORcWk44Ady4Fv+MMpQJzUkmC3bj0AY9bc+eJngK4Hmh0GqSQwzXnPsZOOcxzv3KUrUNobECNgnPss8MrFKIok9Olw0pKeYgVFQD2QYoTosTciMu1uZfk6FBUDhnA3eOghwCLrXIQIFRUVQHQ2IIQXGnLObzAkr8jxLh24bDL6lx+tz2RcwrFHWAqlpZ31fOZsBMRtCjp0A374LPD0qRg9cgQwTz/cqVMX/fppMZvEOOHoY1A581vY+gVjwDRgYL8+uqphJRDpOcIKpyEhHwn06dUdUISg6datO1Cb1Ms1/ES79xaACPN3Ge3ctbtevkrFyS4DgYJOKO3QFTCIv3evMkCxcRlHSVk5ULscOPqXwOf3++YvYsSwIcBCYMBBg4E1QNfOxnuYuQpY9W/btaMqzga6XA8seA14/XKMHnaQ+T5Gfe96QCD+vJ9UAg+NAQB0uOx1VJQJG+kc0h94wnJSGNCvLwaoeKzqVcBwvsorKILMdZWVlY5jLcVe8ephjFVBf/2nANhKRL0AwPh2CZyTHdhUPdF4sNCqooTU0ViCzT15VKEG5Om925SOq5nEeyVpLAINUUqBiXp1h6onoMGMFGoaM824/bp8Q5YSF9aY9wr5xfLdjbte5VKpS0ixuxe/Vq4z95dv2O0aqyhKHoHLANs0OpFy0dnKqp6g+n2etp96Q17AJR7n6h+xTfDnI6sA3FRkEWPPXzMgoHd5CiNuumuyvIqUCKDz9g35HbX3FT8dP18AlYk6xPSRl55HvmIOwd85r7vXJvC2MOhy25EXWgbwWvJyDsgiAuVCRPkAzgFQLt7DGPuTxz3dASQYY1VEVAjgBAB/BfA2gEsB3GV8v+WWRnYgEn9eoFWNNh0/b2yNNUDJAYLOWnh08stya8Am8QuNT1qSrUv8KXunyHQzhkhM12mq7BoioUWiuq4TsEeTFI3IHLEC9apLGTJhKAnepfmpQh9wY3qqyaGn1d8Xf88eg4+gV0+kXFYEy503qLGN18WPlNyMu6K/vhi6wI343crF2zhPw6P8DITCmMcqdG5czhSeK2INxwSbz73PQMEXMkZieojqdCJXcpuQ/DxUq2n5Nfx9qILzcYi2Gvkdyc8uyMK6veTVE3R4eQtANXR9fdCt5nsB+Leh548AeIUx9g4RfQngFSK6HMA6AD/wSqSlcEj8AWJfkEjcRsOor92Fz7ZtwSkqSUp+WfU7sHv2W3D4qc4yIkuKDaTJviQ7AoYomLNTZIJITCcyFfHbyh+ztkBsqLKOmyQlSfwuqNu6EqZJKwhhuA1oqhWw8Q4WqSn2IyhCo3++wn1JN4lf9uMPOrvidfEbKJIu5SSyjonbZ7oZg90GmGgMWPSmtT7CpzwFq6aoTyx5B+g+NIfED+c6E7/2zo2uFNXdJdMhfk7eZrsXvHoc5eISfxDi93g+DuIPIvG3Lq+ePoyxHzLG/sYYu5d/vG5gjM1njI1hjI1kjA3nswPG2E7G2PGMsUHGt0cQ+5aDxOkVnwb7gd/TudxsbI99uABXPzcL22oU6hr5ZX3zODq/fYkzXe4WKi4ikUiZoCHKJX438Abl11EiMeC4W9XnHKoeg7Jtqh5hvQCHh0td8W7BCBiEMDl5DT7FflzlyUJkeUooopMeGTEU9rE8YMI16vw6HmD+bE5pWKcJXiLH6cZUeZerwBAXLXmB7zsrk0LtZuudi/p0fp3sGumWTzRPDyO+1thDwmMGEoGGibtedy/r9sX6c5ejw3ph6JlA/6P130f81P06Iqeqx2ugKOpuvRumqdv14R5htfkKZYfELxB/rEBXKZozCxfiH3SS/j1wkr2fyl5H8jsedrZ7+ThamR//F0Q0IqclyQVSSZBoKI3EAhE/MQ2YeCNw3TyTZLbX6tJFkodMFjuU1/T+mF85j/UcDpz/kv5bmt6aqh7ySDNo5L5IBDjsCuBCRecWCTwSEQJWCVKUSsefjk+7G469xX7NBS8Dt1UD3zU22YhE1TpwXmZHrCLNcoEddDJw8p/19H75rf264h7AMfrmLIlkCmtYT6zvMEy/9uhf6NdwCXDAsfpxjmu83SM9VT3nvaC43sXm4ThmPCNORl0G6uVy3Y9ZyN/LxgPJieDoX6ovoogeVuPnc61jXQepr/3FAt1A/aPJehlH+kzmSSJ+LzXsTSusurGUHkbj4jet899/Gjj1Lvf7ua8+HzCZQuI/50ngppXWIGtK/JJseuGrev0ueVN4PyXORVvis7+tGuh7mHv5OFqTjh/A0QAuI6LV0FU9BN1Vf2TOSpYNyMvK5cVOLohA0G0a32RMDUkVWtlreubWwTmBSsQfgYYoNLBI1F1bzSVfX+LnZKTo/KLKJhKzVBzi4peI/Rnox4L6tHsQv9m4JXUL72iq1cIkDE4KiT/C0/KanQh+9slUEvmUgBaRJHwuAcoL/fw6pJdxV6UeUz0fZVsRiMXtPhFyWG6P9QIxEmeMLq2Nz9zENu62oUgQoUDUzcuCmK9qiNtAVLYvH2OzSfweEn80bk+Tt/8gOn7VDDfIYj0ZrUzHf2pOS5EryA0pEvW1rJszhIhdzRExN+/gxO/h1SNAo6hzWhWJWkQi6f10Hb/mvcAnsMRv5KFKy7bzVdT6n1BI/GKjDqyD9PLw4YOefIKTt4KwROKXCJKBWWsfbMQvkzCZnTmZTCEPSaRkoubPViZ+vw5phiJWXKdSjymlew/iNwnKR4UmChI+En/ctlDQhThNLySR+BWLk4BgbSO/2E78QSV+wHo+/B5bqO+gxC+/b6EuDvViAB2/fK3tWAbE35p0/IyxtQA6AfiO8elkHGvdkBtSgIfagduupZG8S2IT8tGMugaFd4CX54TqpdskfjvxEzGU0xZvHXnaEr8P8XPVSiQmEb9Cx5+NDSn485KNc6LHlErVwzutYz8CN+JXSPxGnZLJBPKRgBaRpHG+1F42iPu1nUi6xJ+mxM+9T3wlfoH4WUo92+PFkleIq2DuESwSv8syqiBtQ1wVG4lKEr/PTlgRSViybe7jTfyN1cZiBXMA40KGh1BjvI/GGsU+DGa+vCyqfq5QlfphL63cDUT8RHQdgOcB9DA+zxHRtbksWFbAG1LMmM6r4mdIeCRuGNb4uzJe/i8TT+Du+L/wwpfG0u2AOn5NJQkUlDgDhoFny3BUdJH3BgxdBujfA45xvwawOoYqvERUUvUAOkGJZFy3zZ6OeK0fgkg58sBsxu5XRASlqL6kHdBDOgtbJJJN1SPaI+Sykvk+tVQKcSTBZEm+yFh7PPhk+3FVvctGOM+rZgYq4vciebnMgEVsu11Wh3GI7UkMCqdANAjx830TxL4jhzQwEwwgrQ7g67TJWIgnzMrdQp0UG2u4ZeK3xcvyJv491dv0vsj7zsBJzovkd2e0+3ov4ufPZdiZznPmJkQ+xF8uRMet3ex9bZYQdA5yOYDDGWO/Z4z9HsARAP4vd8XKEnjwse88CNy43NPjoqa/bqmviBpL9Ey3cOsRTYwscAZQAzwbvIYocMs668Cl7+hGRrkRG4iAoZlF0XSQIqwrR2kf4IalpqFSxtLBPwNuXGFJe2WHOC8SyciU6vLsEj8PKidO7dOJW+MGt0B5ou5WpeoRcfGbwDUz9TC3IvGL5ZPLEM0z02WaHhqDyRJxQSlwwzJrI27zXsU7/tFk67eXjl+lE3dbuOZ23Yjv69++sePlHdCkNH/2jfnTpurhwtH4q+zXVxttN14AVPyaJ6oWovzUYdcvBoafY/3PK7Z7kbmpVH4+20if9xmjnXQfAvQ0zIyyxP+DZ2x/C5BAClGg64HA9UuAo65z5uMi8echie2R7sAt65335Bfr3HKqYrW4asaswihh8yKviKpZRFDiJ8AmHqSQ1vxlH4FL/HkddLJ1jT0ONBdLUUKYU3WwB/kW8duiYHrr+G2LRHoM1b8VEn8KEdO4m+zoFbUE+iYSMmmV6RExtEgMKBZcy1QkI+q/OeHECqTIhkZnEjt5Nrx6AhG/wrgrIl6gb8oSK9SJnxSqHhlRS4VESOkGe9X1HcucxlxVveNFMLuBl6rHLQJpoGOSV48fZAKU61fax/xpk/h5fePy/s5C2fnaAKapA6b5SfwlB9jtRgUl9si3qq0tAWuGwdu7qeMnnfz1Qtnv6dDV9jcfzdC4p1xJL5eBV63jjyOJPZEiuyFYRHEPtfE/qI7fpm5KM7BdhgiqUHoawNdE9Ibx/2wAT+akRNmEHIPbI1xDSnafVBB/A8u3OouN+N0fI5PHVlklIKSjIYI4UogSg5ZJAzD09kq7guNa0Z2Tq3ryJN12EPWJCzKS+AVVj0PH7+Z1EgGBqb16ZETiksTPEFj2URK6EXKCMR/iD6jW8bousLeHRIAqW4mBOAnEz/uGIwS4mC9/By4DZpC1G+J9+aX2c8kGIw8XtY0s8YvpyTMdaSFelBia/J6hIyyGnnYMKWc/DoJMjLseCySziUC9mDF2HxFVQnfrJAA/YozN8b6rFcBcpm00Zg+JX3MQv9No04A8y/fZRvzuLzYlS3GcGPi3oOphIBRAH6yYbHQMApPMAzQ0sYPzMkbz7VNN1RL6wMYnDxJwW3Epxuj3U/WYx/V8TEOln1HZlBoNVU/QTumqhpEkfhW5qCT5dP34g3p7OFQ97jMnm3FX3OhbhK2dcJJlwUheBbE8Kgm6oMQ9JIjpx68IaSHPdBR7ATCvtTGA64ZHMdKgZaLg2F+Nu0RUYnx3AbAGwHMAngWw1jjWusENR5wQPVa6anD6hgOwNdRa1gFRLiUViAEZ3F+s5pD4eRwQYTGKea1F/BlJ/EbDZUE6pbhC1VT1SOEdFIJXKkiQO8CbGIQVtDYUl+nfnfr7q3o4DL3wn+NPeV8H6M/H6My/mHsG+kW224PhZYJeo/TvnEn8fGDJlPjdZ042VQ/ff7ZTP/v1thXenGSNTeAzgVhHVZwcOX8RPGCiuDl9l4H6N1c99TQM7nGnAVpLV+IX+oiWSeiKTCT+HsP8r8kC/IaXFwCcAT1Gj7SjCRjgGnq8dcAkfoNExRd77M3Ap3/Vf1/6PzQv+NJ+r4L4V7FeiEJDgkUR7zPWutaD5BzEL0v8QkdliKCADOKP5AM//cp/6nfdPKBmkz6ofcO1b4ry/HKRHgM/mq+nKeqMTYk/LhkPhVf+ky+BWD7qvnkRpSs+wCrqi4FMYezi8OpkR/9CN4oNrLAfH36Ors8ddDIwU9IkUgS45G3gP5L3RM1G53X2A1Y9InEHuQaW+AHgx1OAN38C7Fqpb4QNABe9Dmxb7O3HL+Zx7n/sx/juS8qyCwi6olN2iRQ9cC583VY+PntNnfhnRMf+WN+wffApOiG/ZBgc3Yi/xRI/OT2nAOCkPyP1/LmIphQePgcdj/kjfoeRx/zCOnb09bqBd9CJ+v8f/BvYNMfy3hGg+UnT8rvrNtj82TJVTxrP6syH0s8nA3jWhjF2hvE9gDE2UPgMYIy1btIHrJW7XOIX9doHn2b9HnCMcyqnOTtjBBpi0JzqGw843DllCc5F1ZOK5umG4C4+j7lzOdD/SMm1UyGql/YBDhgDlA3TPRtEX2yu/ohI7nXi9Nm4L9n5IOsYj8migpdqIhrXN5zmhm4OIn0D6kjESYIRwyNDhhTkznmfGEU1zyEBayyNTtnvcCs8dLlR9w5d7O54qgFPHGz4++TlLOysvo7D3LM4Q4mfq1PKJwKDTrCd4qqeVNlw/ZkffKr+DvoK2+6Iz1PUp2cavE0kQZWbccde2NRD3prHwq6u4+yDYDQGHHyKlW7XA3UPKEX5UuQn8cuqHsKOzqMBZEr83J0zjXvdXGWzjKB+/B8HOdbqYEr8htQcVXiyGEjJxG90IFFtEjMCqDmkeI9wso5rOaJOfaVm0/FnoOpJR7Jw28DbtqDMOYAw4l4xzNu1s6W6SpWqR46cCTjXO8j3yQvtHKt+05Rc/UJlq1R0qnUQXDIXfdGF6zRZMMh0RScf4BVqTk78TCZEtxWx5nGGjFU9ohSsaq/RGBLZ2HNdRfx+Cg7FM07E9JlxWjNDR1lanwOk55MgogIAHQB0I6LOsN52CQAXRW0rAid+Thg233Ufyc9o8AwRs9JRSiHGUgp7gPvikaR8LYfCj5+BUEhc4s/cuk9+y9cBNXHJZK1IhxnXEJg3ube0sSuDtCmeSToSv8JonLbRzlTLuBG/4pnwdQksZZWHl9tF4mfRfN3gakr8GQ6kXOJXrIo1JX5HyGdxRaxoSM2ycVeFSBzNWSF+Z/kcnnuOvJ3nm2L6GpaMbEFm/9nPiB/AVQB+AZ3kZ8GqQQ2AR3JXrCxBNu52EOzRASV+DWTK7OdE9VC3NSR5I3i5c7o1dH5PQYm5UIqBUEC6d4WvIUqB2qYUOgJoDLAgU9UxdjZq5j7CAFDf1OzYn5iBS/xAfYJ57l/cIsgLhETi79DNOi57b3gRf16xwwMrfYlf8DxSQSnxR/TrUymBPI10SgT5SSDgJMURBbC1phFlQMsl/qRlu2EgEJjpzukgRKEcTYkkrOFW9KDJjMyaUkA+gD0JDcp19NG4+14JLUQyXa8eAM0G8StX4AeFbYVx64Dnk2CMPQjgQSK6ljH2971UpuwhJRl3T/4zMOdZ/TfJU35ZfaN3CpWqxpSQfvoVsP5r3ZPguFuBpe8BG+3he5PMuPaqacDWRdaJeKG+2u+g43V1xdZF0N78FeJIGnmkL+Et31qLQwGsrUnZNkwPivVVSRvxb69tdBC72AGqm3TiX1o0FgfX+4QtThfDzkbirZ8jnjRWD3Pi58+M44fPAQ8IoRNk4udEPeBY4JDv6huMiKfT1d2axO82kxPIo//Reiz7aNwg/iZrwBh8qh6eesLPrBWoQpqNLI58AOt21unEH1QQuOozPbTFWz/T/ysk/hQIMUGNx+RZqVCOusZmi/hNYUGS+Cdco14drsCGqkYcCGBbTaO+qb1o3AaASBzJdOwuAZBERLfNuc2+hbxlJEjnjowk/uLuwMl/AYZ4rMLnuPR/e23VLhDcj//vxkbpwwAUCMf/k6uCZQVmrB6jyAWluitY9XqH5OeY8gsSvwyTLHoMtQyUx9wEHHMT1vx+MMoj1i7hphtYr1GW6x/H4Vdav3uNAnvzZsSMFagZGZOMsmbabTSJzFTppIQw1Qmm/95ReCAa+kzE6KUPZJizAtEYlgy5FiMWGjHWOdGIzwwAOvXD8rLTMGjru8Z10nPjg//Jd9pW7nJkrOpxlfgF8ug1EhhvRDbh+ZpunzFgkhECYdJvzFsakY8CNCFp6N3NhWlBJf5eI/UPJ36+SErw1tLbr7Bi3GFPsZ5RBAodv+zOedQv7CvFPSGH0YgDSfsK4ha72EpgRn0zkfhTRj/MyJ0TACZ4bEYjwi/uVpYR1Lj7BwB/Nz6TAPwNgCIqke2evkQ0lYgWE9EiI9AbiOg2ItpIRHONz2le6bQIyWa9Edn0vEajlvTXbgZbFQGnPB6bfM5XyhCzBJl610wWjLR0gizPMkiRYkpQ9TQbxB+LRdOXnIOUJ2CaCdE4KXdQPvhzyVee6aWrq1aFBBYhpmcL+SsRvwuaSJevmyERfxZ1/PK7cq41sZ6RbSMjN6+eNDYPcTxuxUrhbBM/b0dJPzlXZRcw2ntmgljrRdA39n0AowDMYYz9iIjKADzhc08SwA2MsdlE1BHALCL60Dh3P2PsnsyKnAaSjdAicURtIQe4t4Tdy8HR2AziV5GPq8FWkU5Q8gJ0so8aHT0jIm2hQVWTvDvUxG/l0azpvyMUzWxlow+yQvwcXNftkPjTVfVI+zV4QVwrEZC4G6kApazGJH6iNCV+RxmcOn75XTmes82FWV6+A4P4hcPp2KNMpwkjAble0XjW2xLPKxGY7iyYxN8Sr55WiKC1aWCMaQCSxmrebfBZvMUY28wYm238rgWwGIBP5LHsYummnWhk9oaVMKrMpC0Y5cbWnDCMrB5SgApdqMZ+LQveYBgiiBk6/owkfh6uJkPZX85RTfyWV0+TQfyasVNwthHUx74ZltToKsEbJFzfbK9TutJl0mg3LICxLxmzLCRcWGAeYUMAoIF0tWSTQ+Lnq7LTJCBD4tc8JH4n8VvPRGwDfNBvTqbsLpdBI7bCKZs0aLJ9IfuqHl6/RAZtlN/bXiX+mUTUCcDj0L176gDMCJoJEZUDGAPgawBHAbiGiC4BMBP6rMARj5WIrgRwJQCUlZWhsrIyaHYmdm9Pok7rg1rh3pdqLsEFqZew4euN+DbxI/Sh7RhSWYnqTVswFkADy0MhNePWDRNwWmUlmhrqIK8vTGpwLU8F2d0Ll65Yha3Nwco+lMEMCbFk6XJsrk9Pyos16Z07kUgEel4Vxje/NtYobezCNEc61ZuW4SAAjGmo2qMPUlU1tWhuspdVlf/MxA/AQDgs4Lus2brNMz2OhhorzMTUT6chIhDRf5pvwpnR6Sj5dBoiRFi1qRrfZx1QQvo91TW1abWtF+uvxPmpN7HryyXoUrhSeU1+ahgmRL/FfUu6YHyDnvYLe67C+ak3UfXFYnQuXOGafq+UTjD1SZ38ks3NqKysRDKlIZE6DK/heFxslLeurs617DMS5yIChpHTvkYiNR5vaCfgIuPaMYxso/ysOfNQvGar7f4K4zsCZuaxZFsCY7QheGH7aUgkEvgHFqAyNQrNn36JvJg7MfK0KisrUbt1FQYA0DS9bc1rPAHHaV9gRGQNAGBqZSVojxXL6d7E9zFWqKNXnd3y1Yz61jUmlfe+0vxzHBedi26Kc/W7dUEuYbyHfYF06hwUQY273ELxKBG9D6CEMTY/yL1EVAzgdQC/YIzVENE/AdwOXSV9O4B7AfxYkedjAB4DgHHjxrGKioog2dlw554y3Dx9FZYJ9/5pFnBJ7a/x0YQj8ZsvdeJaU1GBr6oWA1uBRuRhRmoIon1GoaJiJHbv3qUPVwI0isG1PJX2vwMHDcbICcHKvqXSkvgHHTwEww4Ldh/HN7P+DjQDeXGP8omo1L/4tbNnPwQ0A08kT8UVsfcQIXKks2hWDFgGRIjAiroBTUBxSSlQUgYIe1Kr8r/sfX1gWRPwXX6xfQ6wwz09jqlL3gKMMevoiRXIi1vEf9n79fhEG4MFE45Gx4I42JJtGDn/Cfw9/hC+E/0KxR1LcWQabeu3X2m4rOFgfHb4UejbRb2xT/n7twIJ4LcDhqLiGH1ifOPnCUxpOBgzjp6IHh0VG7MYWPZpBGCGG2EzkB+PoqKiAg3NKQz9UCfEJ43yVlZWuj4X/qxnHH4kxn/0C3TMj+EJ49qqqXaSHjlmLPqVDzL/M8bMtkHEzDzYkm344ezf49iu3bG9tgnlNfom8osmHoOifA8qMdKqqKjA6kWFwGKAInq9rv8sgfsbz8CaggsAABOPORYz5z8JNAEvJyvw99T3bO3Fq85u+fIZTjS/CBNd2uW72hHKdjlt1QdALZCXF8NhGXBQNpBWnQPCbwGXq1cgER3KVTke18Shk/7zjLH/AgBjbKtw/nEA77jc3mLkxyJISD7t3Ec45uLPnYcEkoiCjDmpptCaeOn4Hdey4NdqgnE31QKXtozvNOwajYbqJKJQ9SQFz6E9pKsyiKWc6yCygKA6/ibhGbut/altTKJjQRwp44Xyd5iuSi0a4e3CX51W12TZkZKqhqQAT3cP0428/B0kNLeaeaO2US+DKJE7HBAkdaTGYLbwiPBEmVEWIrsjQdC62WHUK2WvV1JjJlFnS9fP02mJqsd3T9/9DH4S/70e5xiA49xOks6cTwJYzBi7TzjeizHG9xf7LoCFAcuaNvKierdJpjTEovoL5B0/IikbuUtbHEmkEDENwirjXzINfV86xl0GMjd6aZlXT4aNlBM/04lfpeO3dPkM9aRLvPHUHtN2kk0E9YhqYlYzlvtnXiyC5qRmEmDKuCDVQuJ3IzsmFKBeIP6UIXD48bfGmK6WMIife9VkuqiJ1ztfIH65zg6dv8YE4hdaFTc3ENnqmUwFH5Tkpy0TfyKlmTlmS9dv6fi96Y4xZgp8HFxAyNRu1lrh2VsZY5M8Pq6kb+AoABcDOE5y3fwbES0govnQXUN/mZ2qOJEf16vXlNQbV3NSw8Yqfbqckt05DQk7j1JIIAqjfyuJgUtIW2sa8cUKXRfxwaItuO/DZY5r+SCxsaoBX6+y7zD06bLt2FnXZJ7TVwm33I8/YxiLk7hhUTWAJJjVERpID1urNdbi8xW7HNe2BIwxzFxXbfsPAJVLt2FnnRVIrmpPMxZusXTCsiSebwz4L85YB8YYNIOw+TtM16vHbBcuxC8OCOt27cGny7Yb+RkzDYP5GWP437xNaE5qeGvuRsxbX2WUX7+3XpL4g5LrxqoGfCW0s9pG3ZjsLfFLA4HwDEXi10zitw+w7y3cgi9XeuxLK4BJ70ce0JIpS+JPR2jyAu/DzbIhWYJqMDdnQxlK/FOXbEPVHp/tMgGs2FaLBRuqfa/LFgLp+A1DrANeC7gYY59DzUTvBitay5FndPrmpIaifODeKUvNcynpJYsEkBJVPQovCj76n/H3z7G9tgkfXX8srnpWX7l6vaS+5Q1n0j2VaE5qWHOXvoqvKZnCpU/NwPDeJVi2pQ7NKQ3TCsiMkZ7J6sWvSk7G+NoPsaFwiP/FCswrHI9D93yOuZoegfPl/O/j59I1KUHVsyHWHwDw2q4BYEiBjxeztYMyWjks4uPF27B6ZyO4w47GdIPgZU9/gyE9O+L9X+gLXn7+0lz0TUVMEUYm/lhUL+8zX6zB0Qd1EyR+7q2R3nPmKkJZcOAQiWzKt1sx5dutWPHnU01S4e1u2vIduPbFObj4iP549qu16JAXxYLbLDeC1UyPPT+7eCIGA0gEVKec+sA01DRaMw1L4rdI75Hk2bg9/rRVZtm9U2NIsghipOFfkR/CWApmPlsiMtU+AHDrm/qknbdtGXO0gxBFCiMBNBfrWz++nnc2boJFtq8kj8Up0RlIaFrGszEZCRbFS6lJOD6qa6SbfegumWKIS2NDs9kP0yf+qj3N+NEz32D8gC545aoJnteecN80AO7PMNsI6tVzmPC7AMDxAGYDaNUrd/ONt8gl/tU7LK8VmSDEUARJRE1VkEoiLIDuPbO9Vpc89zS7b07Cp4rNSXlKq+e/ans9mlOWeocTfyaBqpYWjUV54wv4SZ5/gDfGnJT3SYdTcMfGMUghivLGFzCopNhB/OKAtDx+MA5sfBYpRPG9yDTz+Pea/4Q16Rffhl31zTaJT2PMfI9LtlhL2zdVNaBMaMYyP4pS3Pa6JnTI099HKkM9coSrelxUL80KyTzFmGVbML4bDePTt5t1r5E9zSlzNgAAC1P9cWDTszixU2+ch+ASv0j6gFrifzZ1Iv4v+g76RfTZSJLJMwCGg5qeAwB075hvEj+X1gnpCcDfbf4TAH0np2ReR7Nt3SRc86vkVfhV8ip8kWJoiBSZ+bQEg5r08CyTonMBWAsO3ZDQNBRK6sU9Sb52IX3it/p4nc+Vex9BvXquFf8TUSn0nbhaNUSJH7AGAMAp8YsGrhSLIMolSEV/K2BNtv+yvcCWrkvz5fmLi8sYI8QMd07ZKB0IabRN0YDHkUwxm15dVS+u4iAwpATpTJ6Wq/Sl6SDFmI2UNcbQpHgoRfkxJGw6fum9Cu85kdKgMbsaIX0dv1UeFVQErWlWOfjzK8rTy1zdkLBdx9GQIqQQNSOLJLKo4wfsAk1Sk1Q9wjOLCKdsOv6MSuNv40imGBoiuu2oAzV5X+wBuR0AdluQW94y9hivh1zdBtxhqgVboXkgUyXaHgCDfK/ax7B0/IYU7UX8gsSfECR+lSqgEMEbpCxNmccNghC5USShphaEpg1CZUlFD0xqmq08Kt7mfUMnftGc7DQQtgQaY7bBhDG1NN0xP2Yz2jkk/pRI/Aw8Cf5eWZoqtajBxG7GXRVBi2ohfl9hnp5O1Z6E8jqeDoEbkzNrEHwGwFVeHGJbkyXhlI34xcFX/yYK5tWkgnifipwTmoY9hsRfBJe9mQPAto0A9yDykfhVg/aeJHMmGBCWZ2DrY/6gsXr+R0RvG5/JAJYCeCu3RWs5uMTfZEr8lsS4SlD7rN+1x13Hr3hpHaDYFs4FMvHzxs4JICqIVOIg05wCdtY1oXqP90rPmsYEtlQ3Yv2uPTa9q4y6piS21TSaaYqDIC9TIsUckiEHv2+3UR4CsGKb9Qxlid/LxY8xhnnrq7Cr3mn04uo4TWMOVc/OOuf1RflRm5ue/L5EwkymNFOatQyIwYl/c3UDEsZzW79LXwDWlEyZDgOA00sFsBPpbsnQVyNI/KrBkvNuUK8eebDmqp6lW2rNMgP2trbNeK782W+qElb5ikQNwSMuQy7j6THYZ+Ac9U1JrK3TB/IiRT/bWq/ZBgzGmE2FW92QwMKN1bZyc0HNT+JX2VHW7fY3zLqBlzMd3k/HQ6olCCrx3wPdtfNeAHcCOIYxdkvOSpUlyDp+kYx+/uIc8/fEv01FncCvSdGrR0X81ITlW4OFUJVtBLwMnCBE4tck4h97x0cY9acpnukffdcnOOIvH+t1aHLXD518/zSMv/NjjL3jI4y5fQqmLt1unrP0z5o5WAKwqWrG3vERDvvzR/hgpU5y7yXGYIfgXSOrTFQEyPHCjHU465HpOPT2D23HP122HZPuqcRbczcipTHUMCvkgcZ0Y7qMbsX5NqOd7G0j/k1qzJSqeXnT8eqZ8JdPTJ38dS/NBQD84qW5OOquT8xnqKq3WKYLHtdXA/LLxFmMpjF8oo0GAGxnpQCsd+D1PEXIAzd3Kd29J4GJf5tqEos4qF73ygK8M38TJt1TiU+WbMV3HraeszjgmNXInPdt7+OjxVsd5+98dzFm7dI9JJazPrZzs9ftxs2fNeC5r9aax16YsQ6T7qnErLW6V9k1L8zGGX//HJurrUGji7Ga3m8WKpPuup17TB0/eeyy5wazraXB/H99f0na+WSCoDr+T4moJ4Dx0N+5eq16K4Os4xdJVoY43U0h4rtQZ1N1MKlfnl5yzwHeoURytal6Akp4ojGPGwxVTVSUSjWmE8KIxifAAMzUGGJR/TkV5kXNNOXH1ZzSgPwSHNb4CHbBvhmNTKBepV+4sUZ5nA+m89ZXo0/nQsxmljbR7T306FiANR6qHhGJlGZ2/rMP7QvMT1/VI2PKtzp5JTUN0UjUxSXQeUxVnxRjeCD5fTyfPAFb0QWAc4boh6i8PkVqDLwszbBCbDBEsGSz/uzldyMOOLwssh9/OrCkYKac8S3ZUosqNginNN2FVYZnE8eKbTqBz9tQjYuNY7PW7gagO0mM7d8Fny3X3atF2wlHTPNW0cpquprGhLBYM/36mo8ojVt5+XONoKqeK6DH5vke9EidXxGRI8xCawP3ZOAqnnjEvbqiSiaJmOfKXcBOil6ShLzYi6/A5CoIm3FXkvjTBR/ggnBEc1JDLTqgDh1MEmpKaigQ/NmUOn6NYTs6OxZXyRK/l4BUKPvMGRAHW03a5cnNb15jzGbc9ZKudOOuYVw1Qgx4qcds+SjyZ4yZRMbbgOy95Xav2zENEZP09TyssgeBbJCXnwf/K7s2chuALPWKA45l3M2OxF/b6PSG01VfhCWsn21wAqxnZhvcTLuDvd4qNVIe8yZ+2Y6i2RwM0q+xloHEn9kq6PQR1J3zJgBjGGM7AYCIugL4AsBTuSpYNsCnvbwzygYuEc2CZ0MSEU9VD2DvYF4vS/aY4JI+ly5sqh4heJaH1sYVvLEHkcZEe4dIWqWFVmcj0wAqGB1d6irr+L0aOzdsyoiZq2I15zoLl+QYY0hQMOJPppiwcpuXIVhHU4VMEMvE32dwid+Zh+o6PjAFXrkrNXE34m9i9jj4cT47lvIRBxzNJvE7sw7iySXq+Gsa7VI5X2XtBtMuFhWFJR1+K4IBIO4j8cvPOKUxa+VuBjMc/o7TudOr/tlEUAXnBgCiUrsWwPrsFye7yDclfk787tVNaHbjrhmyweU9iO3byyAjx/XhOldL1WOdEw2NexLpNwBuKAwq8Zv5apbEn6+Q+O1usOpyOSR+j7zdJX4rrIZcBzdC1xiQEuLxe/XPZkHV47YhjxtUxJvUrPACpp1E0RZU9ypVPcpZgJVXEDhCkTgGUK7qsct8mkv5RfUHbwcEdfmDuJyKM53axiSKheBunTt4R6PldYkJwhJXb8qTeRWB5jNv9aw8WOieZZmrekxHgjQGjdZG/BsBfG3snvUHAF8BWEFE1xPR9bkrXsvAVys+/MkKlN8y2VVdAABvLbBCACdZxCQ91UtrZlFTGga8Jf4EIxx+50fm/4l/m4qqPc2mBOnm1fPyrE2uae6qb0b5LZPx6kz72Mv1+K8vT6D8lsmmpL5mR70jDdWahuakhsK41SS4FChOyd0kT8euTh6NvcCF+GPC4ijHAjvp/82vzUf5LZPx8NQVtp2V/CR+U2rlTKHZp1Z7mpMov2Uynvp8teNeGTe+Ot8cNzgxq1xO9yj0dspZgKLP8/K+Pnuj86QCsl1GzuaQP3yg5y+9r0ZjBviEVO+SAv3ZNic1/Pq/C8zjG3Y7XS39BqfyWybjSyGcRF1jEh0LROJXbFQP4KInvjbSt8+S1+3cg/cWbgEAW38EgEueckaN36R1xtaaRpTfMhlvzXU+T7kfpzQrfMmq5k444b5PHfc0JlIov2UyHv3Uafbkz95PEJu2zHK0iMdaumwtGIIS/0oAb8Ia9t4CsBlAR+PTKsF1/EsNo6EYLVGG2BGSiJkvjTHgu01/xJ5YKQBg+iG3oaLpfsfCII6JTffjtKY7rbRYFFtr7FPM3XsSJpG46fhLCt1X3y4z6vPqrA3K81VNdrXD1hqnpGOT+E0df8omjfcq1b0rxLq62TPqmT1WhRcBu6kD+KrYlBBTZ2LT/Ti96U4wBgzpqTe1fl064GVh0Eu6GHdl6ZXB8uMnvvOTtBPbNuNd/efLNbbjKlXP/+ZZg7Ml8Tvr3aBYeKZSx3mphPj1B3Yvclwjwk/iN487Ftyp0zv24B4A7KvT611WqgfRT78tPLNEym5TchMIPjfiYck6/q21Vrv20jCd3HQXHsm7DA8nz8JSY9X3qzOdfUeW+FMaw0xtMO7Kuwa3Jy42jcsiuFD0xGerHOdMtZaPxC+2o/5dvN9vthDUq+ePAGBsocgYY61vDbICsmublxHWTvwRs8NpjGEOGwStqAyorsbuTsOxCc02IhA7+3pWZks3ofAaSWnMdwGXl38574RFed5Bp1Ka7kGkIhRZ4tc0hkSKoVBIkwnPwKyPyzOshRSb3qOt87p3lGK4c4k/Jbhd8udpGXyd7zHpouOXDXyMCfFmYkYEUmlHLE7SMgn56dgt241zgFCtOFZpB1Xtk5OGyhCqgjioFsajrgNw0BhF4hoPjnoXA1QQO4R5L9MHirigr/fazAVw6vi9ZvAilrJ++LB0JJI1VY73Y48y6pxlJhHDFyWnYltNtTJtPsNSquky8OMPasRvKYJ69QwnojnQQygvIqJZRHRIbovWcsgNKZHSHFNhDlEXn0LUCuYlvTXeThMCqXhNcVVbL2qMmQTqpurxUvXxztPBa/MLiGECnOdk4ucqigIhmBe/X3wCbjr+WmYnfq8+yTuwLKWZi5U0pvTHt7yWZOK3dMPifbK+lMGqkynxp+wuhVwtUygNqn4d0lqf4T3ImuUM6OLJj3EXWz8SsdmMmFNlZp4LONmXVVmA+8w5yOIjceagacxUJwJOQU0Gb3sxYWbI4RU2BQC6FukDfUISuMTZmNyPeVsRHR5keHn/BfXqsdkL95JXT1BVz2MArmeM9WeM9QdwA/RtGFs15IaU1JirgTfFROKPmARiSgTG2+G3ix3cy6ilkrZFid+2JF4gfi/bblCJ34wIqbJTCGSkaRY5FQhpmg1XaIxuUl0tCm3//dwqAafKx5ToUwrjrmYFaXOG21CrelQSv+nVY0r86qBmsgHar0NyUlJK/AFdPNVSo71cfiQiCjaawkhungtojOf5ie/djfiDRBDlAyuDsz/6Eb+l4zciqwrZ+YWF6mwQv+y15GW/4v2mk4vtAVD3Eeuc/TsIWtvK3SLG2FT+hzFWCWDvKKNagFg0YmveKY3ZVqaKsMWFiUTx2LRV+PV/55svLRXX9cuRiE4ytlAAHhL/Qx8vdxzbUt2Iy/89U0/PRvxWGbyI35T48/wl/s+X78ClCkOX6M6Z1DRzIBAlfrXnhrpg9dB1/Dxev1dbf+Cj5bb0H5mqG99/+fI8vdyMOQYrjTFXiV/06vl69U68PmsDJv7tE5x0v90YV9OQwIPG++ASf1VtPY7+6yeYskg3EnIi+GLlTpuBUFzprYLlzul8Pv/3n5mOY0p3Tg91AS/Xmp17cO6jX7rqjcXXI0YFdaQrdf1/VqrXZGqMYcPuPTju3krzmErXDQA3vToPj366Evd/uAzlt0w2F1e5IaUxiB7WXqqe61+Za9PxT1+xAxc+Ye2Jes0Lc5QGW44uXOKXBuFawaX0/o+W47zHvjTJl+fXSZL4H/5kOcpvmYz7PlxmXlPblFR6BXGU3zLZFjLDDZkG40sXQYl/FRH9jojKjc+tAFbnsmDZQtQ2jdJcfflF4m9EAZpTGl6csR4NhoSyZtLDwPG/R32pvprUS+L/7pjeeG38q/hN4nJlXq8IhskCwYtGExeReawo5eQS91iXAOgd69oXZyvPiVKoxpjpZlpc4JSexQas8loB9NWff43+H05v1g3bQfSvnJTu/mCp7ThTEJbGLEnTS8e/uz6BG16dh/W7GhwhiteJHS+qE0EMKWzY3YCfPq8/J3HqL3pbLNio1vHKdUkkvevNDdTywNajY775nM8Y2ctxn2gnmLFml3IWAVgDc+9OhTabhgy+OGpR/mjXspZ37QCNAc9+tTYQIX2xcifuem+JObj+8X+LPK9PapptC1Q3oQwA/jt7IxqTfJYMXPeScyDmYTRU4MQvD8xiG1m8uQZfrdpl7dbGiV9yM71nir7h0kMfL7cN4LIThSwD/PuLNa7l48g0GF+6CEr8PwbQHcB/jU83AD/KVaGyCXEKmEjadYoiRB1/nTCZ4Y2goEsfYOINiEX168TRXZ4i9u/aAd8/7SS8kDredvyKowc47hU3qRaDjTVrPnPXAEhpzNUvWJxSpjSrniUC8Zs6flF94jEVWVV+Hpaxvo573OCmPlGVuymZclX1aALxe63Etd0X0TtznPR683bisAsE9fPnxO/Tcbm+WE6XyCrfOYdaMWrcDNpuknwipeGKowfgh4f1Nf+r0GAs4Mof+V3l+ZMPKUOHvFjGoRkAf9WNptltXFGPlfWApeJMMaZ0kfUCXyPgperh4O+QP3svHb/bhjzi/RxRH0ENSGOhXgvht9l6AYCrARwEYAGAGxhj3uEirXv7Qt+opSf08DGPMcYeJKIuAF4GUA59b4ZzGWPec8IWwEb8Kc21MYoSPw8LC1irC7m/sbnnqkj8UmePuViQucFQlJ6KBHWNOPiI/JrSmGecITckNc1DMmS26/iUV9RnqoxTzYaBXMU7YjuXCVhFIO5hGJzPlEcpjUfJmXckBmP/Gk99qq0jGqqeOOwdX35esueJG0wdv88CHF4EJ5ELK2PF1dzmAi5pluNCEMkUQzxmxZpyk9QboLsLu8Wv0Zi+KKoltkZ/Lx0N+XGr/XsI/AB0v39AbzfpEj/30uLvh9uXahuddMafLe/injp+D1dnucm78YIIP8EhW/CT+P8NYBx00j8VwN1ppJ2EPlAMBXAEgJ8R0TAAtwD4mDE2CMDHxv+cQXzUzSkN8QDEzzcRByy/bpn4mz1UPW6Si0X81ssVVU82d05B1eN0QVMm70BKYx5x4+3GXT7lFaUbcXk9R2Mi5dqhxawcxllFMZIaUw8IjDlUJjzoVmlhnqODie/YS0K13WeoevIM4ucLgGSJP6gE5hWyQYQ1mNqP6+oto2i2GPjOwRdQEwRjundWPEKm7cjNWNhg7GkZ19Qx7xlrWTA2wFuC5+o80cblJ9xw6TwTzxc+05f7kkriN4nfqLs4C5bh5T7skPgDbEy0tyR+P+Ifxhi7iDH2L+jB2Y4JmjBjbDNjbLbxuxbAYgC9AZwFfUCB8X12uoVOB+KjTqQ011FXDDomEv+mqgYQWZI5v3/+hirzmo+l8LKuEr8hdYgv100itxl6HUYj5S0OeK1b+Hr1LvP3rj3N+N1b+r6pIvFPX7HTCERm3deU1Dx0sdaFz321Fj97YbZpCHQ1MrrMHGRiu+s9PVxtpw5xx/Ra/Pf3T1a4z+oUxO+U+O2SpJ+BkuOnz8/GizPWeerCoxFy9QLZWd9s2mNEvrTcKf0lfl6/eNTaQW6di0Gx0djMPZZyi1+jx91pTmn416fOxUl+0jzgPftZs3MP5m2odt30RYVdxl4G/3AxRHvBigMlq3qcEv/O+ib85b3FuPsDvc2JThTyNoo3vz7f/C0+pz3NSdz57mLbtX6qLGDvGXf9FnCZT4Uxlsx0Kz0iKgcwBsDXAMoYY5uNNDcTUQ+Xe64EcCUAlJWVobKyMrO8wcDpvymhIdGolnCaEcOHqbEY17UZm3eVmMcXr92EgigwbZruIbKrQW/Mc1ZuMa/5YuVOIy9gQGkEZQ1rUVm5zpHHutUrAAA7dleZxz781hmTHLBL/5XTPkfHPOv/ipV6B1i/3jtc0pdfO715VPj9azOxvVav1+IFdqPZex9VYneT1RgZg2O1K8f2HdZy/CeNpf/TlmzG348rcuhWOT5RvNedu3YhUWdva3zjHK1pD1IpDX07RrDeKPOu+mas7z0Rd28eCcB9MK3bo5Ng1wLCZ+sZ+uYfjLtqztfTZRoqKyuxYpXdr/+iJ7/GM6f4O7Dtqm/Gg+8vxIQD3LtUlBiqqmtQWVmJxeudhMNXeC+YNw+T+sYwdX0Su6uq8MnUqY5Z3mfTv0ChtsfWL3go7/VrV6OsSCeZ3S4b+bycqsBlneZimhH/X8b2HTtR18wwr1r9LCMB4tPnN1f79lu+KhcAtmx2D1MCABu2exvY3XDGwDiWfKsLNktX6OS8e9cuVFZWYsFyZ2jom1/8Cst2W/XbsMwi9+PutXuKfbXKEqBen70B3+mhCwqvLWvG16vtz37d2tWorHR6Hm3erL/37oWEPU1NjmdWV1eXMf+5wY/4RxERD9BNAAqN/wR9BW+J+63GTUTFAF4H8AvGWE3QwYMx9hj09QMYN24cq6ioCHSfI/+PJltpAujepRTralVSHOH/EjdgzS9OR+zPHwHN+suIFHREl+ImiPn/bc5H0KIRQNoa7t3rJmJoL+GRvD/Zdn708GHAwrnoUFwMVKtj0ovl4Rh/xASUlVghEeanlgPLl6Fv377oun0jjhncHW/M0RvUkJ4dzc3IDx07Dvj8M598gMIORUCNfs8R48cDX0wzzx151FHYVtsEfG4dKy4sQE2zMwxEly5dgO3bbceSLIqKigrda+hDPU5Mx4IYfjbpINz13hIcffQxwJT3bfeUlHbS47Zs2QIZPbt1xuqanRjYqyvW11p5rRxxI97e6IxJJCKeVwDsacDDFx+OCQd2xcZxFVh21ycA9Ng9FRUV+KzuW2DVatt9E485Fnj/Xc+0AaCwQwf07dcLWLFCeb4gHkPHkmJUVByFjV+vBRYtVF439tAxuKq8Cy5+8mvUNyVx9MQJwAfv2a4ZN/5wrF34ja1dVjckgA+nYPCgg3DuYX3x9znum/h8cccPkR+7ACO31AIzpznOd+nSBbHGJFCtnvEU5sfR6DKoDOpRjOXb6tDrgANQUTHC0Q/c0LdPH2DdGtfz9akITGOOB04cVmYKVPf+YBTOGdsHny/fAcz6GmUH9AFWrkbXrl1QUTEelTWLkLdmnc1bLREtBGC1paMmHIHyb7/Gmp3+7pj8fUzZvQBYZRf+Bh10ICqOPdBxz9vb5qJ3/S6cfEhPvDJzPWSuq6ysdBxrKTyJnzFhVVMGIKI4dNJ/njH2X+PwViLqZUj7vQBsc0+h5ZDHGbfIkCJEXVzVnmZbICkA6FgQxzZF/Bu/qSqfMvq5/MmQ9c6it01Ss2+XKOpJg+5763Y/YKyilcRNt2m+KjfVhjbxaMQWgtmRjsK4y1EYj9oWYqUDniavoqiS479UXlBBl9FrjLnObAAgLxY1deZexbcZd5naJVMdBVQz8onYnAaUeRht1c29mcEZ8E2Em3ccYKlTZLWZH/x0/EENuqo9JXg9GyWvtJrGBDoXxW3xtOqlBWrRCPmGm+bgoakbFGV10/FrhvNGPEqtK2RDJiD9ST0JYDFj7D7h1NsALjV+X4oc790rV1D0m3e9R3g/VQ0JBfHHHD7igL9XAh900rXcuy0M0QwCDKTT9kC+sGhL9mDRNOZQM7gSvyI7k/g1+zF+XFXGpKa5EqjKQB4U8h4IKqJRkVVQY6LGmOfKy/xYxHOlJwcniAiRucJVhkoXzI/FIrpXT7FHSA+eh9vmRIw5I16K8NLx83eTbojhTDzXVCgQysYHOD5QNUpxk2obk+hUaPfaURJ/wLx5H5DT4OmokGL6uViUWt1GLJngKAAXA1hARHONY78BcBeAV4jocgDrAPwgh2Vw7NAQpHGJElfVngQ6Ftj9eOX/ZlY+UkGmpJVIMUxdug2xCKFv5w4mWT771RokUszWCeW4H0T+XkAz1lh6SjmkhUrid5P2VJKpSfCixC8Qv2r7vcWba12JhQ+eonE6KORQEaLEz+0CKrJ6baa3LYVD07zfbV4sYg6AXqEX+LOJkOH9oiD51TvqzdUmiZSGD7/dKpCc/l2cH3MNryBLwjIYnLNlW128JP4Ul/g1X68g0TXYb8YcFCqJnz+T+Rt0OwHP6dOl2zGyT6nt/npJWo8Q+YaE4EgxhggIXwnhp610dEeQvFgE5V2LUN2QwNvzNmHltjpESB+wU4anGxHhg0VbcMyg7sEyThM5I37G2OdwbozDcbzL8axDH6utxheE+OVrZInfLUaOn7sWn22oOjIARJW75eqeRT965hvz/1XHDgQgSrDuEn8sQml5CsSluqd8JP7x5V3MgePCw/s59gzlnVmU7KNRMmOvT19hvx7QV8/yFbRdi/KwUxgcxL2Dg6BjQcx02ZM3uJcHueVba5WG4dv+922gvJgQfE8FfQ2Cv6rHIn79ej5oioP4z16YbRqd7/twmS3kAn8/WxTqSA4++MmrUjnOP6wv/vPlWtf7vSR+c2+CpObrgWZztMqS/kEczEiS+HmIdgDYUdeE5pTmK2Wno+qRg+qJWLG9ztaWxLY5uKzYHJwSKYaFm6pw1bOzcPER/XF8p0BZp4WcqXpaC2Se93qBc39/onGPN/FHXAYPL4ml4uDu5vJ0t2ZWSDrBTRv/T9vxqga7VCyrCcQOY98gRvOcrqug0vE7iF/I8MUrjzB/nzK8Fx6/ZJyybKLkFyXCxEHdzPQ5nrx0HL4z6gDz/zGDu+PB88bY0pOlMT/M+d2JVl2MAVCl4weAHXXNnuqJkw+xQm6vuet0rP7Laai8scI8pjGnC+NTl1nPIx6NWKGujXovuO0k/GyS3eDHSZWIoGliiA51d12+1e5iGAvgNsihivc0+3cn4tQRvRy7WomQo5eKbUKMW+Q2s/nkhmMdx2TBac1dp+NXpxzsXggDskpLTIe/YlmFKerhLzy8H44Z7C5ZRyl4L/LS4m6ptg/E4hqCCJEpiCQ1DVuNa7fVeu8alinaPPHL8JLKI8IUW0Rxvl0qckvDSyjQ9a76bzcJowC6gUnLs+9tIxukZG2C24CjaXCfc7mVUyKXlMYcq3DzbDpUuSzqdG0hdCOkXFATi0bs8dmj5GlgDAKxPvy58+clD3INiaSrKyhgxXvhICKbWkFjzgVzov0kFo04FmRFhcVW1j0Ro5z6dXy25KZekfXJQVYae4ELOl6CjOwk0SHf+s/tHAnFTmpWHs6ZhkqgcrNBiJDVVeJ7lXX8Ivi7zo9H4fXIIpHgGy96qfD4IkQVohEyBZFEipkCjp+RPlO0eeIPSkyAReh+qh7XmP4eU8ZYgNWUhdAlexa3+43LHgJy43KbxegSf3qQpWDV/rciAcl5y0XhnhjyQh2L+CX7gdDRY5GIgwyyoQU2PVqktPc0pzwlftXWgGJb0RhzBLETDe9xYQEXHwh1/bG9HHkm8eubmvOZips+Xt4Ry211elDwd+M1O5ZVPWJUV67uSqY0V/uS3KeAYCtbVZBnOGKb4b9U4di5Id/LXgHo7zioo4RX7B4/4ufPPZnSzLhE4oCaTbR54pebkpuaBrDrVkXIS7bd0vDy6NCYFW/HTedeaEj8iNs3NVmyxe7zLzdCVylbWhIfBDK5JDWngc5LvyuTBSf+mgaLnKJEplQqE62452g8FnGUvyUhBMz8jQfmKGtTCsu31apuAeCU+AH74KExZxsQJf5ohEzJkYcCiZBzVsPviUR0nfRmY9rvpsJZttVe5iBSchB4CUnyexEHQP4MVI4BHCpPNK++6QV5hiMOIKaOX0pbYwzrDL/8fB9PvwgFJ36VNw+H2AdUefC+t2pHvRl6vchns6VM0eaJX4YXEfJz8jVdivKV18nwiuKnMes+N8+PSmMVpVbYxXb8FWl/UOeuYGJDt44nUiywN4IqLcBF4ld0WlFKFdG7k75By2kPWQvJiPQOGY+SQ0IWp+RxhapnYhpeDhUHq69145fXZ29wXekKqIN12WItMeYIpSC2CVHH/5ER5sNL1VNndP5z//UlAF31pYKsCuREOKZfJ9e6BEE6Erg41vD2klBsqMPBCZnbeoDM3TmPG2Jf/C8OIJaO395mo0T4iRGKOz+AxD/JpS3J+N4/vnA9pwoPIebBB+wfPPolJi/QVzGHqp4MIbelKBHm/v5ELLjtJHx+8yR89WvLwcjNv1smEFWHGNe/M3qUFDiOc9glfj3C5WHlnW3X/Cl5MRb88CsUdOyiSsJKS5b4FVNbno9fVxKNj4DbZt2SxG90lLISfUCc9/uTMOvWExz5A8DQXh0hgz+HeDRiRlyU0wZ0yVWWyn954mCP2liYMLArHr1orPKcm2TJXf3coArWJUrhmmZX9Xx0/bHoUmwNFjHBqycWIYzr39kgfnua/BnIcfltUaXJffbD1RpPXXqYeezu74/0qpqJmcZ7BCxpUzR8XnvcQY57vvntCUphKJnSbJJyT6l/zPjt8TZngCADzQ8Pdg6+N550MKb80gojppL45ZmsKLz4SfxRIvzujGG+ZQNgzs5U8IzjJEj8ALBwoz7LD7LgNBPk0o+/VUBuS5GIJbnJBibeAWVikAcC1Uy6d+dC50EBIvFrTCfNQmk0TyIGdDwAHQ2iLcqLKr1YHNEpXfpLU1LzdUPr29muVpLrrht37eCdhoeRKBVcAtUDhx2iwU3247dJ/DEnKQaVCnt1KnBsmO5WRuu4d5qqBVG2PZMFfTwAHNSj2LZgKBaxjLu1jUkceZAu7crPnP/vIs0wxE1iNAa47HluDhyiHr1nqbtQIqKroM7i9x8g3NtVoe7q3jFfTfyafe9kWV/do6O9TEFUPR0UjBWLkq0diwK8m8Qvqhjzot7kGokQIiCUleTbVvimC69NVojUdgiv/SVagjYv8Tt0/B5EyElSnlE7iF+Rhp+04ghBq9DtAnoj5gOSmy5dVvXYtm8UTjUHWUDj09lU7py8E6meg5ycyuAtdkZP4o86dfxB4RX7PFMjokoytOv4mUONJ6uueN+vbUz6es/I+TVIq04bkuqtFbmdRCSSoDpqUVDg7VBsI1EXtYhchaK8KBIpzd5WfYoQxBlJ5bHEV71y2FU9lpAhQpyZ+Un8HOm4yarg9QoYnHYIwOnBly20fYlf+h+ESORr/P4D/gTKmH0AISIlAcWjZBKCayhjD+OuPT54yndxit/zSGnMIR5YthDFDdIxpcRv3JgXJSfxi8bdFhC/VwhctyT9FrqpJEM5ro68gEt857FoBIwxNCZSaE5pKOHE6lIe2dtENoTvSapXGqsIym+TdhXMdig8F7cBVX5PnYvykEzZJX6/MgSZzalkoQiRrVy2fkXqtJtsEn9A4m+hm6x/+i7vLQfZtn2JX1b1KB5iL2kaPHd9lec9qgYaROIXJXhxNaAYPyg/FjVVCgf1KFanJfUfsdMN6Ga5gjYnNV9JL0i55f4acelMclkA3eOk/JbJyms2VTfaVlKWFsbtOv4oeS4i8oKXL7sbwbjtJ8zhJxmmGPOMQa+7c1p7B/P37C7x2weaztIq24YEw/DbPnDcJxNZx/xYRpIjt2nsEWYa/NnJM0lZGOlWnI+kpmH8nR+bx/p2sasVZagG6x4ldscK1SuQXWLF9+tG6uKA2SEviv5d/UNvB+Vfub0HAlMPLJkEIwyCdifxq0bVt645Cqu3W2FYZSnZz1cd8Jf4NcZQnB9DYTyKhkQKEbIItENeDI0JXfLt07kQRIQXrjgcQ3uVYPGWGlzw+NdmOt2K8x2djIjw2tUTUNuUxPjyLjgwtgsPzm5CU1Lz9Ct2q8vLVx6B2sYkrvjPTCPsg9qYrFb12I+pdKKqweaaSQdhdN9OmCcMurGIt8T/wv8dbns2tjyE9/H5zZPww399ZYZ78JtF3HTywY4N4AF/yZAxhqSmYUTvUvzprEMc57lxl6uDuCDA1RCj+nbCbd+xjIhyfhcf0R8PfWKFfE5oamIQCeT1n0xA704dHMJMEBSYGwdp+PSmCtQ0JE3X0Wgkgveum2jWYegBJeaeCb07FWJkn1Ks3mEPk/3z4wfhyAO74Vhplew71x6NjgUxWxk/v3kSAODMUQcgPxbB1c/pHjhq4pf/WwfGSQ4U/HpO/OMHdEGPkgL8+rQhGNG7FDe8Os/1eRzYvThQaOZMwMCUbrgpjQE5sO+2O4lfNar26FiAwwd2DZymSFzc9c5vtsinuRMO1POJCG58hbagUvqxIw/qhs5FeTjywG62dCKkN4YeHS1JKErAuPIumHRwDxTlxzCmRwzRCKExkfIN0KaSfg8f2NU0BiYVxl1efRWBBtHMqK45+ZCeAOy62DyFH78I+dmIEKf+fTp3sLkN+s0i+ndVS6Z+Er/GdHVRebcijOnnJBzux8/Jmj973oYOKC2w3Sfmd1CPYlMiVgW+EyE+w7H9u6BnaUFGqh5O6kmNoX/XIozoU2r2n1iEMLRXCQ7srs9KDxXKff74vohHI441DYXxKH5ScSCGHWDfxmN471L071pkM0b3MYy1RIRThvcy11Co9OByG+b/9UHDyZq9Oxeai7e+N6Y3AH2mfZIQkkOFQ/s732m2wFwk/kzeWxC0feKX/mdjcYso3ZcYftp+KhMumOUJhlHeQIOEiuZIanrQLrFzq2YbedFIoPjl7h4uBrko4q24hTzQz/lmqVaVmS6eoo4/85ANstpAJBX/fRPUIpafxM+leRU58Xz5XrOANThxgnXseyDkFxPcPvlxN62SqpxeKgO3x8FJU5xhksu7FwMXdiyIIxZ1Bgf0iuHP73OD1eac5+QZOe8PbjXuEI+ZZbMZrn0am1v482xB9Y5ypepp+8QvNYpsGGhE4uALdPxUPfwFcikuSlaoVznglRcSKQ2axmwEqXLZzI9HzGXfXnAbB/lzSmnOsM68qirCCBLFUEW8PD/RDhJT+PEHhazjF0nFb5B2y1PWuctgTH8/bgSnR9u0VImWxB817xcRsw2CVvgK/ozcbNHpSo6uNgYzH+tertuXSbKD4OrasSCGeCSiWJzn/dxVYRw4+CAZC9Ac+Pt182gryIuaqh5xZugnELSE+L28zAB9kFJt3uKnqs0UbZ/4pf9+UkcQiO+QG8CChmTm+t3l22rNhtYhHtzUkkhp+HjJNputQukWGiHHil8V3Bo779iqCIvcu0Xl1x6EposU8Ues+DneK3eDwiveUqaeQkGCn22taXIVLiKk7z9wo6FHtsJD69+ycCe2KTFNTvx/n6P2KfeaUangtuJctVmOGTdISk9Mg0v8MvwGTi+J3wqz4ZmEca3+7S7xR82Q1dE0JP4gG8y7wW8hFhPCb4vw2rCnJWj7xl1Zx58pkwgQGwhf3ahqNK//ZALW7NiDTVUNOPewvgCAGat3A9A7Ob8nHYmfL8/vWpQHbuZTEZkeblc3GI/r3xk3nHQwzn/8KwDAlccMxGPT9E2n3QYs3lAbmlOmOuv3ZwzDqh11uHziAKQYw2VHljvu8yPVIwZ2wZ3fHeE4zt/LEQOtVcv5sagyvUcvOtQMJ/zkpeP0d7xlsTI9DlF9k4m273dnDEN+LGrlJ+DeH4zCU9NXY9EmfbWlm3DBZxJz1lXZymjV0d7Juwt2nLhg6HZTRQHAracPVS5cO+WQnrhkQn9ljP3Xrp6Ahz9ZgfED7CvGze0xhamFbJ/gmHiQZUPpWBBzPP9RfTvZFoKp0MGDHPlA4iVg3XfuKPTv2gGruKNGgBmRKGj4CW+iveDGkwYjEiEM6tERCzZU2YzuKhTkRVHrEceHATheCj0B5M6Pv91J/CqvnrTTFBoI72QqVc/Y/l1wztg+uPb4QeYqV1F6NlU9GSzLFpfzq8ayk4ZZhqpzxvYxjcoA8KuTrRjnbkTNJeTaxqQ5ZT60f2fccfYIFOfHcP2Jg5VBy/yI/5cnDEbX4nzHcU4kXYvzzZj8ebGIckA9ZXgvM4zA8UPLcNwQp1FOfs/2UNLpD/6XHz3ANb9zxvbBYeUWabrNDJyBzfQy8aOycEdEuGaSHiIhJsx+vNrLFRMHKo/HohHceLI6tv3A7sW474ejcd74fvbyqiR+F+KPRMh0i+5YEHP0h59WHOirtvOSqK3tKN3v/96hfTC2fxfL5dSF+e1Svr0OQct3zXGD8NOKg3DisDJcf5L9uapUQkH6uKsffw6QM+InoqeIaBsRLRSO3UZEG4lorvE5LVf5W3na/7c0Vjlglww48QddDSq+yCASnBtKhKm1qkOJjUhuiLZ45S4toCgvBiI9sBQvcpDJkt9jcGvGohTGvUHi0UjaQebM9KTCylEycwl3Hb/9vynxc9WEopObkm7EsgllGrEx7UitCu+hlIuOH7DWJ5QUxB39IUj/8NKhW+sHfJPxfb+2xV5pTP+C6vhVz9lf1aM+vj8ad58BcIri+P2MsdHG590c5g9AIfFnw6tHSJRv7Bw0pKxtC0K+gCsD4hd1qm4rgDlkSUocKFy9eiL6Zt01jUlTEg2yD5Ev8bu0Y7EOXKrUpdzMSFru/PY9BDJK0hPie3WbVToWE3K9NdQ6fsA+iLREUNDvT+96y84jEH/KaRTl4F5kHQtiri6WXvCajfu5sIrgz8ntUlvolDToIKiOX1VGvz7uVqv9zrjLGJsGIP0dsbMMp6qn5b1eJHlzm7yA94rvkY9BmXgLiBK/aiwTBziVLzOHV4csKYjbVD1BCNOPqN08LVTx3MXNa9KFQ+IXXGYzjdXjBbGDuoVPluvCy8gPq56MWA8+YGcaqjfdbTh5G0oJwcUsVY+7y2hxfswZ5qSFsyw3A7gKfrODTGd8Xv1IhMogW+DXx10K+8LX67C2Jr3tRoNgXxh3ryGiSwDMBHADY2y36iIiuhLAlQBQVlaGysrKjDJjWgoiLa9YthSVdSvdb1BAznvNaiu+zOZNetzsNWvWoLJyk29aVw6P4r5Z+pR48+bN+vfGjRjTI4qjDogp63nBkDy8sKQZHWJ6fBYA2Lx0rnl+yeIlqKy2jEt1dXVYv3W1dX7RAsS3LcY5g+KoT+j1Gd09iqN6x/DZZ9Nc66olGrF242YsYNsBALNmzcSO5d6Nf32ttzVq7rx5aN7gTOOrL79AUVx/T9t36B4X3y5cgNq19g7j1g7q6uogvufFS5ejsmmN+X/pLqvzfDbtU4d67IR+MSQ1oLw0gqb1izCgNIKUBqwz6uPX/jZstDxs1q1ZjcrKjY5rCursxxbMn4fEhiiakgwDSyM4oVutIx/ancIBxYQB8RqsXKEbj2t3Ozeo5/AqZ7NgpA3SnxIaw6BOEZzdt9m8vkcTQ/+SCIbFtqCycpvt+p+Pyce0DUl8/tk0rFprjz2/cP58sE3BZypy+b7bN4XG+ghKqQEXDs3H1noNUQK6FEYc1y7aoneS7Tu2285dODQPm+s17Nyx3Tw2d94CRCTHALeyrKxKOY6pMLZHBF9vSaFnEWFLvf7M62qqlNee3D+GD9YmUV+nv/srR+bjsfl2b61t1Q0Z858b9jbx/xPA7dCFm9sB3Avgx6oLGWOPAXgMAMaNG8cqKioyyvDOr98DYJHRiEOGoULY0FuJ9+2xNuS8l0VWAsuWAAC6l/UC1q/HwYMGoqLCGatcxlEpDffNeg8A0Ld3b2D9Whw4oB8ePXmI6z0VAO4E8OxXa/G7NxfinEP74PQTR+FnH+vlPESqU2VlJQaX9AWW62U8bOwYHDGwK8Rq8N/JlAZMeU9Z15I509C1WwccckhvYM5sjD/sMAztZV91KWPZ1lpg+jTX86NGjcJR3ANEeM7HHjPRdA/917KvgJ07ceiY0Rhc1hGo/EhZPhF6x7BCBPTuP8D2PjqtrwJmTAcATJo0ybrRKMPjPznJNhiccgJw/StzsW72Rs98OSZvnwds0N1nBw86CBWikdXI45fnnoDOvVfjtv99CwAYd+gYjDOMwiefACUqAFz5Xf33M9NXA0u+Rf++B2D6pnXq6z3K2ZzUgA/fC1QfjhOPcx4782T3sl5v/F7/1VpgsWnew5gxozxXWpswnpVcvgoAl0N/z38+swJeaFy4GZg7G127dkNFhRXvn9913UtzgC26kDZ02CGoGCHse/C+M84OL0u3jdXAV58ry8fvu2RCfxTGo/h6yypcMnEwnv9qHTZWNaBHt65YtHM7ZJx33Bh88PQ36NW9CyoqDkcFgMfm28tQ3KEw8PsKir3q1cMY28oYSzHGNACPAxi/N/MHsmPcFaexCWMaHDjCn2KaGXRtAb9Xvlztx28PfeAGzx3JIoSUZk2vA4Vj8Dmv8moC7OoXcWVrphoCeTGM3/vJdKEYh03V4/G88wR1QbrqD3n1d7rIhW3DDZkYd7MH7tWjhsqeFARB1MQEmK7Ge4QNE9zsF01GADwv428WHBEd2KvET0TilkLfBbDQ7dqs5Sn9z45xVyB+Y/ocVE8vEgwni6DEb3nXSPpTH+OuV9m89yDWidotXxWaPKJTAnZVppiaqHflg2ks6tyIPCjkcBVBY65nCrFeXm0s37YyOb26JUxvp8yeSab2kkwgN+lce1KJMG0mAXT86bhLBuEOIjIXKNYLK+fd3hnfW9drLU8uokHnTNVDRC9Cn111I6INAP4AoIKIRkMfjNcAuCpX+XM4XOiyIvFbv3kY3qCGHxHcCBS0I/NGGkRS9HLnDIpoJGLbMDtIKWsb/cNEcBCR2TtFErQkfmegr6BoSLhv6ZgLiF49Xu9TDs2dDhKCm2sm2Jsyd7aNu+nALye7I0Fw4g/ST4lcJH6XQYOHvO7gYbDPBfHn0qvnfMZYL8ZYnDHWhzH2JGPsYsbYCMbYSMbYmYyxzbnK3w0HdPLeIhHQowt6QWw4Z4/RdevyqscgSGnpSfwm8ZP6uAiRSIMOSn272J9NlPTBiaceRPo+sLt3XPODe1p78F53/CDzt0gMFx/RH4AeJVP0XpI31fbC6SPsdpwuRXnIj0VwzqF9bMfPHn1AVgYFUdXTR9rO8rghPczFbmJM+nRnn0N66vaVkX06ZVTGvarqkd05A2bev2sHjC9Pvy+poSZ1sa2lE3EzyOLPU4f3wpHGYsnTR/bC/00cAMDan1rG4QZvnD3a3e6Yi9lSmw/ZwJ/ZScPK8ND5Y1z3YRXxl++NxF++N9J1QwWRACcN6YE1d52eUdlMyTYo8XNXOqkTKbffE9Is8YiBIuKzX9ktedEIGRuxBHfn7FFSgNNG9MS7C7Y4zsnP6efHD8J9Hy5zXPeDcX3xg3HW4Jvu81VdX5Qfw4LbTnZIbQ+cNwYPnOed3j0/GOWbJ3839/5glG2VNAA8dZm16fnovp3M3+mONycMK8OyO0512BCOH9IDHy/Z5nKXhZbaMdJBJn78APDpTZP8L/IBr6ebFocLRccO7m7buMgPblFXAWeb4/+PGdwdlx01AA99vFx53+Cyjr7te79S9bQWcN/l0sJ4INIPArERtyTMM5cS3fy+ndfr33IHVkr8QpqqoGhBYBG//j+ojjhTP/NcoyVBtvzAB990nnU6q0Y5VHXIzRKfliGIHSpX4Dm5PRcrIGB6ZWpJuJeWqJhz0WzbfKwe3gq8Iv+lC7G9tER3Keqyg8AtJK5KDS6mmWmDjUYIKZaejh/IPKTA/gxLDRe8PWQjYGBrRaYSfzZgGXe9Fwum23dbQt4tERD3Kx1/awHvkF6xvtNFtqQXU8cfdCm45qLjV6p6suO2monErwrX3NbhFrXSC9kiQzeC25dwBqTbe3l7rYQGBLfoNPtxS8i7JYNGSPwZ4NudujicTeLPRoftVpxnEX/A9IYYi6dG97UbpFTxPNL1/BjVp9RxLGaoerQ0dPwAMNYwmIn6U7ftDEf0dua7P4Jv3dnPZ0NxEftK4j/qoODbjGYKuY8U52dvxu0Hrt51Gw95e56zXhk0ACUFMWU8pBapaxT9cULA7V5D424LcKbfat000FJd8axbT0B+PIpfvDQHQPAXe+zg7ph20yT0M0j0pGFlmPLtVqWOvziNgW7Gb49HR0XHNHX8xv+gxD9pSA9Mu2kSencuxObqBhTEo64LVF65agJqmxLKc/sTrpw4EKeP6GXz2vFDOu/IC+nI+zN+c7zNUypX4DxXEI/gjZ8eZe7hvFfg007PGt0bj3+2Gjvrmh3nZv/uRMSj+t7IO+uabaHHWzJQy8LdK1dNwEiFsKVCaNxtATorYsdnipa6//F49GIUyqDoJ0jOfKMOlaqnYxrqlh4d1Z1S3ypQ9OpJv5yya6OMwrxoWhvRtFZEIpQW6QOZ7cPQUvQo2TsEzGWRIw/s5hvmI2dlcDnetVjnAtWqXZHoZW+4lnhFyRJ/n86FgZ1N9vuVu/sS2dhykcNvC7mgsHT2mTUo1dZ4HNkwZseihKRNx9/iJEMIyJZ7ZStU8ZvhJfZFmzG9elweTDYdPYJCtrmlozUIdfytBNlaBaq5eOkEBR8wVIsPs2HTiBBB01ha8fhD7H20Qt4X7EJ7v8345Vm0D2aYsudeOqvpQ+JvJchW3JdMPEFE9Omsr7TtqlBjceOUm1E1CLg758sz1wPIjqfQ/oTexirvLkV7X0JMB63Rq4e5rDLfG+hk2DDc1Ix8YMhUOMrExierc4OkUWDwTC4Gzzav479rYiGGjTo0o3s/+MUxSoNmtiT+lMtK3KD40VED0Ku0EKeN6Ok4R0R44f8Ox0E9ijMuXzRCSKYY8qKEvFhEuVdua8JH1x+LpmT2Nq34+fGDMKxXCSYdHDxURBBko5yVN1bgnv9OxzurWqdh3FL17H3mH9W3Ex67eKy5L7MKr149wRzY08FrV08IFPZFhryoMQiHvPWzo7GzvgnN67Mfy7LNE3/PokjGsU3EuDIiClqJxB+NEE4f2cv1fKD4517pG8bd2sYkjvXoRK0FLRnkVIhHIzh1hPvzzRTZKGd5tyIMKG29E/ZMFrRlEycd4hSGRByWYTygcRneJ88ugkjxZSX5OLhnR1SuzyhLT7TeltOKkRfNknHXkIr25qrGdMDdOWsbk1ldBxEiO2ilzQZAens4tAdk0n9yGdE07M0ZIHs6fn1xWWsn/oZEInCgNz8kEgls2LABjY2NWUmPo7S0FIsXe2+h19Zw5MCuGN6HoSAWwSVD9ZlJS59BQUEB+vTpg3i8Ze+b7WOJv7UhE0+iXG5eExJ/BsiWjr/ACJecjc1hcoFohLCzXl/kkmmgNxkbNmxAx44dUV5enlWjVW1tLTp2VKvm2iq27arBlj0aSgriqGlMIBohDD0g85XQjDHs3LkTGzZswIABA1pUNk74+2KtQmtEJhJ/LgXCkPgzQKcOcVx17ECMytB2wPH3C8bglW82YGiv1klYorSWrUU4jY2NWSf99oqCmL74rmtxHor2xFqsjiMidO3aFdu3O/eGTRenDu+Jn1YciKuOPbDFabUFFMSjuPX0oWhMpHBIwDAluZwthcSfAYgIvz51aIvT6VVaiOtOGOR/4T6CuEQ9mzF1QtLPDogIPY2VuHwVdzbSzAZi0Qh+dcqQrKTVVnDFxIFpXZ9LiT9nOgYieoqIthHRQuFYFyL6kIiWG9/Bt78JsdchNrxMtpYMESJE5sil6S+XyuVnAJwiHbsFwMeMsUEAPjb+h2ilEL0KcrmJyd7EmjVrMHz48KymWVwc3D3zgQcewJ49e3Kah4xnnnkGmzZtyvj+EPsGuZwZ53LP3WkAdkmHzwLwb+P3vwGcnav8Q7Qc9n172wbxtxTJZPDN5FXIlPhbgpD4Q8jY2zr+Mr7BOmNsMxG5LokkoisBXAkAZWVlqKyszCjDurq6jO/dX5GtOq9ba4Wt/XL6Z1mJH19aWora2loAwF+nrMSSrXUtThPQPVKICEPKinHzSe4Gxbq6OiQSCVx22WX4+uuv0atXL7z00ksoLCzEM888g6effhqJRAIDBw7EY489hg4dOuDqq69G586dMX/+fIwaNQpXXnklLr/8ciSTSZxwwgkAYNaJo76+Hpdeeik2bdqEVCqFX/3qV9i2bRs2bdqEY489Fl27dsXkyZPRq1cvbN68GQDw5ptv4v3338ejjz6KNWvWuObx4IMP4r///S+amprwne98B7/97W+xdu1anHPOOZgwYYKtXh988AFmzpyJ888/H4WFhfjoo49w11134d1330UsFsNxxx2HP//5z7ayNzY2tto+0576M69nLurcao27jLHHADwGAOPGjWMVFRUZpVNZWYlM791fka06z08tB1bqm6EfP6kiK1PPxYsXm26X8bw4otlaDJdKIRqNIp4X93TrLC4uxsqVK/Hyyy9j9OjROPfcczFlyhRcdNFFuOCCC3DttdcCAG699Va88soruPbaaxGPx7FmzRpMnToV0WgUZ555Jq655hpccskleOSRRwDAkeeUKVPQr18/fPDBBwCA6upqlJaW4h//+Ac+/fRTdOtmrarm9xYWFiIe18v/29/+VpnHlClTsG7dOsyaNQs1NTW48MILMWfOHPTr109Zr4svvhhPPvkk7rnnHowbNw67du3C5MmTsWTJEhARqqqqHGUvKCjAmDFjWvhGcoN20Z/fnwwAZj1zUee9TfxbiaiXIe33ArBtL+cfIg2Ye5NSbvSNf/jOIVlLKx0//gEDBmD06NEAgLFjx2LNmjUAgIULF+LWW29FVVUV6urqcPLJJ5v3/OAHPzAHqenTp+P1118HAFx88cW4+eabHXmMGDECN954I26++WacccYZmDhxYlr1cctjypQpmDJlCsaMGQNN07Bnzx4sX74c/fr1c62XiJKSEhQUFOCKK67A6aefjjPOOCOtcoVoG9jbitu3AVxq/L4UwFt7Of8QaYATf2tdYJYp8vMt18doNGrq7S+77DI8/PDDWLBgAf7whz/YVhcXFRXZ0vAbCAcPHoxZs2ZhxIgR+PWvf40//elPyuvEdOTVzKo8GGP49a9/jblz52L69OlYsWIFLr/8cs96iYjFYpgxYwbOOeccvPnmmzjlFNn/IkR7QC7dOV8E8CWAg4loAxFdDuAuACcS0XIAJxr/Q7RS8CXj7cXtvra2Fr169UIikcDzzz/vet1RRx2Fl156CQBcr9u0aRM6dOiAiy66CDfeeCNmz54NQFfXiPaAsrIyLF68GJqm4Y033vDN4+STT8ZTTz2FujrdNrJx40Zs2+Y9cRbzrKurQ3V1NU477TQ88MADmDt3rue9IdomcqbqYYyd73Lq+FzlGSK74BJ/U1LbxyXZO7j99ttx+OGHo3///hgxYoTDYMvx4IMP4oILLsCDDz6Ic845R3nNggULcNNNNyESiSAej+Of//wnAODKK6/Eqaeeil69emHq1Km46667cMYZZ6Bv374YPny4SehueZx00klYvHgxJkyYAE3TUFJSgueee87TVnLZZZfh6quvRmFhId577z2cddZZaGxsBGMM999/f6aPK8T+DGbsqdqaP2PHjmWZYurUqRnfu78iW3V+Zvpq1v/md9jPX5ydlfQYY+zbb7/NWloiampqcpJua0au6pyrd5QNtIf+3P/md1j/m98x/7ekzgBmMgWnti3lbYisItJGdfwhQrR3hD06hCtigldPiBAh2g5C4g/hCs73YUz1ECHaFkLiD+GKcBelECH2ProU5eU8j1a7cjfEvgffNzUMoxwixN7DpzdV5NyTLiT+EK4wBP5Qxx8ixF5Ex4I4cr01U6jqCeEK1kb3TW1JiONMkGl0zPLycuzYsSOjPN988018++23Gd0bou0jJP4QrtA0rurZxwXZz7EvwiKHxB/CC6GqJ4QrLFVPjpj/vVuALQuyklRhKglEY0DPEcCpwSKB1NXV4ayzzsLu3buRSCRwxx134KyzzsKjjz6KRx99FIAeVbO8vBwXXXQRFi5caK50ffzxx7F48WLcd999ZnqpVAqXX345Zs6cCSLCj3/8Y/Tt2xczZ87EhRdeiMLCQnz55ZcYOnQoZs6ciW7dumHmzJm48cYbUVlZiZ07d+L888/H9u3bMX78eHPGBQDPPfccHnroITQ3N+Pwww/HP/7xDwD67OW6667DO++8g8LCQrz11ltYuXIl3n77bXz66ae444478Prrr2Py5Ml49NFHEYvFMGzYMDMcRIj2iVDiD+GKtu7VU1BQgDfeeAOzZ8/G1KlTccMNN4Axhquvvhpz587FN998gz59+uD666/Heeedh7fffhuJRAIA8PTTT+NHP/qRLb25c+di48aNWLhwIRYsWIAf/ehH+P73v49x48bh+eefx9y5c1FYWOhanj/+8Y84+uijMWfOHJx55plYt24dAD2U9csvv4zp06dj7ty5iEajZvye+vp6HHHEEZg3bx6OOeYYPP744zjyyCNx5pln4u6778bcuXNx4IEH4q677sKcOXMwf/58c1AL0X4RSvwhXMElTkKOmD+gZB4EDWmEZeZgjOE3v/kNpk2bhkgkgo0bN2Lr1q3o2bMnAOC6667Dcccdh+985zsAgOOOOw7vvPMOhg4dikQigREjRtjSGzhwIFatWoVrr70Wp59+Ok466aS0yjNt2jT897//BQCcfvrp6NxZ35L6448/xqxZs3DYYYfpdW1oQI8e+h5GeXl5ZmjlsWPH4sMPP1SmPXLkSFx44YU4++yzcfbZZ6dVrhBtDyHxh3AF1zS0Va+e559/Htu3b8esWbMQj8dRXl5uhkZ+5plnsHbtWjz88MPm9VdccQXuvPNODBkyxCHtA0Dnzp0xb948fPDBB3jkkUfwyiuv4KmnnnJcF4vFoGm6u17QUMyXXnop/vKXv9iO19bWIh6Pm/e4hWIGgMmTJ2PatGl4++23cfvtt2PRokWIxcLu314RqnpCuIL78UfaKPNXV1ejR48eiMfjmDp1KtauXQsAmDVrFu655x4899xziAhxig4//HCsX78eL7zwAs4/3xl8dseOHdA0Deeccw5uv/1211DM5eXlmDVrFgCYm60AwDHHHGOqcN577z3s3r0bAHD88cfjtddeM8Mv79q1yyyrG8Q8NU3D+vXrMWnSJPztb38zN5oJ0X4REn8IV8SievPIi7bNZnLhhRdi5syZpg5+yJAhAICHH34Yu3btwqRJkzB69GhcccUV5j3nnnsujjrqKFMNI2Ljxo2oqKjA6NGjcdlll5kSOg+LPHr0aDQ0NOAPf/gDrrvuOkycONEWTvkPf/gDpk2bhkMPPdTcuhEAhg0bhjvuuAMnnXQSRo4ciRNPPNHcp9cN5513Hu6++26MGTMGy5cvx0UXXYQRI0ZgzJgx+OUvf4lOnTq19PGF2J+hCtnZ2j5hWOb0kK06NzQn2Z8nf8vqGhNZSY+x/T8s8+mnn84++uijvZKXH8KwzO0DYVjmEHsVBfEofnPaUBTlh7rgqqoqDB48GIWFhTj++HAvoRD7N/ZJjyaiNQBqAaQAJBlj4/ZFOUKECIpOnTph2bJl+7oYIUJkBftSlJvEGMtsPXqI/RqMsTDwWysFExaNhWi7CFU9IfYqCgoKsHPnzpBgWiEYY9i5cycKCgr2dVFC5Bi0LzogEa0GsBt6VIB/McYeU1xzJYArAaCsrGxspkvM6+rq9npQrn2N1lxnIkJRUZHn5uCZoD3OInJR51Qqhfr6+lY7MLfmtp0rtKTOkyZNmqVUpassvrn+ADjA+O4BYB6AY7yuD7160kNY5/aBsM7tA23Gq4cxtsn43gbgDQDj90U5QoQIEaI9Yq8TPxEVEVFH/hvASQAW7u1yhAgRIkR7xb7w6ikD8Iahm4wBeIEx9v4+KEeIECFCtEvsE+NuuiCi7QC8g5O4oxuA9uY2Gta5fSCsc/tAS+rcnzHWXT64XxB/S0BEM1k7WyAW1rl9IKxz+0Au6hz68YcIESJEO0NI/CFChAjRztAeiN+xOKwdIKxz+0BY5/aBrNe5zev4Q4QIESKEHe1B4g8RIkSIEAJC4g8RIkSIdoY2TfxEdAoRLSWiFUR0y74uTzZARH2JaCoRLSaiRUR0nXG8CxF9SETLje/Owj2/Np7BUiI6ed+VvmUgoigRzSGid4z/bbrORNSJiF4joiXG+57QDur8S6NdLySiF4mooK3VmYieIqJtRLRQOJZ2HYloLBEtMM49ROlE7FMF8GkLHwBRACsBDASQBz0Y3LB9Xa4s1KsXgEON3x0BLAMwDMDfANxiHL8FwF+N38OMuucDGGA8k+i+rkeGdb8ewAsA3jH+t+k6A/g3gCuM33kAOrXlOgPoDWA1gELj/ysALmtrdQZwDIBDASwUjqVdRwAzAEwAQADeA3Bq0DK0ZYl/PIAVjLFVjLFmAC8BOGsfl6nFYIxtZozNNn7XAlgMvcOcBZ0oYHyfbfw+C8BLjLEmxthqACuwHwbFI6I+AE4H8IRwuM3WmYhKoBPEkwDAGGtmjFWhDdfZQAxAIRHFAHQAsAltrM6MsWkAdkmH06ojEfUCUMIY+5Lpo8B/hHt80ZaJvzeA9cL/DcaxNgMiKgcwBsDXAMoYY5sBfXCAHvIaaDvP4QEAvwKgCcfacp0HAtgO4GlDvfWEEdSwzdaZMbYRwD0A1gHYDKCaMTYFbbjOAtKtY2/jt3w8ENoy8av0XW3Gd5WIigG8DuAXjLEar0sVx/ar50BEZwDYxhibFfQWxbH9qs7QJd9DAfyTMTYGQD10FYAb9vs6G3rts6CrNA4AUEREF3ndoji2X9U5ANzq2KK6t2Xi3wCgr/C/D/Rp434PIopDJ/3nGWP/NQ5vNaZ/ML63GcfbwnM4CsCZRLQGusruOCJ6Dm27zhsAbGCMfW38fw36QNCW63wCgNWMse2MsQSA/wI4Em27zhzp1nGD8Vs+Hghtmfi/ATCIiAYQUR6A8wC8vY/L1GIYlvsnASxmjN0nnHobwKXG70sBvCUcP4+I8oloAIBB0I1C+w0YY79mjPVhjJVDf4+fMMYuQtuu8xYA64noYOPQ8QC+RRuuM3QVzxFE1MFo58dDt2G15TpzpFVHQx1US0RHGM/qEuEef+xrC3eOreenQfd6WQngt/u6PFmq09HQp3TzAcw1PqcB6ArgYwDLje8uwj2/NZ7BUqRh+W+NHwAVsLx62nSdAYwGMNN4128C6NwO6vxHAEugb870LHRvljZVZwAvQrdhJKBL7pdnUkcA44zntBLAwzAiMQT5hCEbQoQIEaKdoS2rekKECBEihAIh8YcIESJEO0NI/CFChAjRzhASf4gQIUK0M4TEHyJEiBDtDCHxh9hvQEQ9ieglIlpJRN8S0btENHhflytXIKI1RNRtX5cjRNtDSPwh9gsYi1TeAFDJGDuQMTYMwG8AlGUh7WhL02htMIKchQihREj8IfYXTAKQYIw9yg8wxuYyxj4jHXcbMdwXENEPAYCIKnjsfuP/w0R0mfF7DRH9nog+B/ADIvq5MYuYT0QvGdcUGbHTvzECpTmiuxp5VJIVN/95HhddlNiJaBwRVRq/byOifxPRFOOa7xHR34yyv2+E5OC4iYhmGJ+DjPu7E9HrRrm+IaKjhHQfI6Ip0KM1hgihRCgVhNhfMByAW5C270Ff5ToKQDcA3xDRtABpNjLGjgYAItoEYABjrImIOhnnfws9PMSPjWMziOgjxli9lM4YAIdAj5UyHXpsoc998j4Q+mA2DMCXAM5hjP2KiN6AHn76TeO6GsbYeCK6BHqE0jMAPAjgfsbY50TUD8AHAIYa148FcDRjrCFA/UO0U4QSf4i2gKMBvMgYSzHGtgL4FMBhAe57Wfg9H8DzRjTIpHHsJAC3ENFcAJUACgD0U6QzgzG2gTGmQQ+hUR4g7/eYHohsAfRNg943ji+Q7n9R+J5g/D4BwMNGud4GUEJEHY1zb4ekH8IPocQfYn/BIgDfdznntuVcEnbhpkA6L0rup0Pf+ORMAL8jokOMdM9hjC31KVuT8DsFq1+J+ct5NwEAY0wjogSzYqdosPdLpvgdATBBJnhDwyTPRkKEcCCU+EPsL/gEQD4R/R8/QESHEdGxAKYB+CHpe/J2h07gMwCsBTDMiGxYCj3aowNEFAHQlzE2FfpmL50AFENXoVwr6OzHpFnmNdBVLwBwTpr3cvxQ+P7S+D0FwDX8AiIanWHaIdopQok/xH4Bxhgjou8CeICIbgHQCJ1YfwGd+CdA35uUAfgV08Mag4hega7GWQ5gjkvyUQDPGYMDQdefVxHR7dD16vMN8l8DXcceFH8E8CQR/Qb6LmmZIJ+IvoYupJ1vHPs5gEeIaD70PjwNwNUZph+iHSKMzhkiRIgQ7QyhqidEiBAh2hlC4g8RIkSIdoaQ+EOECBGinSEk/hAhQoRoZwiJP0SIECHaGULiDxEiRIh2hpD4Q4QIEaKd4f8B5u0rjiwJ1SgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "class Student:\n",
+    "\n",
+    "    def __init__(self, student_strategy: int) -> None:\n",
+    "        \"\"\"\n",
+    "        In the beginning of the course:\n",
+    "            every student has a specific type H or L;\n",
+    "            every student has a mark 0;\n",
+    "            every student has no performance measure π, set it as 0.\n",
+    "\n",
+    "        :param student_strategy: Student strategy, H or L\n",
+    "        :param group_number: Every student will have a group, so set a group number. -1 means no group.\n",
+    "        \"\"\"\n",
+    "        self.student_strategy = student_strategy\n",
+    "        self._mark = 0\n",
+    "        # self.group_number = group_number\n",
+    "\n",
+    "    @property\n",
+    "    def mark(self) -> int:\n",
+    "        return self._mark\n",
+    "\n",
+    "    @mark.setter\n",
+    "    def mark(self, m: (int, float)):\n",
+    "        self._mark = m\n",
+    "\n",
+    "    @property\n",
+    "    def measure(self) -> (int, float):\n",
+    "        \"\"\"Calculate the π\"\"\"\n",
+    "        return self.mark - a * float(self.student_strategy)\n",
+    "\n",
+    "    def imitate_strategy(self, reference_student) -> None:\n",
+    "        if self.measure >= reference_student.measure:\n",
+    "            return\n",
+    "\n",
+    "        \"\"\"\n",
+    "        Imitate the reference student's strategy in the next semester with a probability\n",
+    "        that is proportional to the difference in the performance measure π.\n",
+    "        \"\"\"\n",
+    "        if self.mark < 0.4:\n",
+    "            r = random.uniform(0, 100)\n",
+    "            if r <= 50:\n",
+    "                self.student_strategy = H\n",
+    "                return\n",
+    "        # The range of diff is (0, reference_student.measure]\n",
+    "        diff = reference_student.measure - self.measure\n",
+    "        random_number = random.uniform(0, H)\n",
+    "        # random_number = random.random()\n",
+    "        self.student_strategy = reference_student.student_strategy if random_number <= diff else self.student_strategy\n",
+    "\n",
+    "        \n",
+    "a = 0.5        \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 5\n",
+    "# total students number\n",
+    "population = 50\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 1000\n",
+    "# initial composition of the population\n",
+    "prop_h = 0.5\n",
+    "hard_number = int(population * prop_h)\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "83919192",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = 0.5               \n",
+    "H = 1\n",
+    "L = 0\n",
+    "# group number\n",
+    "n = 1\n",
+    "# total students number\n",
+    "population = 100\n",
+    "# courses number, year = times / 2\n",
+    "courses_number = 300\n",
+    "# initial composition of the population\n",
+    "hard_number = int(population / 2)\n",
+    "hard_number = 99\n",
+    "lazy_number = population - hard_number\n",
+    "\n",
+    "model = Model(population)\n",
+    "model.init_students(hard_number, lazy_number)\n",
+    "groups = model.group(n)\n",
+    "hard_students = [hard_number]\n",
+    "lazy_students = [lazy_number]\n",
+    "for i in range(courses_number):\n",
+    "    # print(i, model.student_composition)\n",
+    "    # random grouping\n",
+    "    groups = model.group(n)\n",
+    "    for group in groups:\n",
+    "        # mark for every group\n",
+    "        group.set_mark()\n",
+    "    model.imitate_strategy()\n",
+    "    hard_students.append(model.student_composition[0])\n",
+    "    lazy_students.append(model.student_composition[1])\n",
+    "\n",
+    "time = list(range(courses_number + 1))\n",
+    "plt.figure()\n",
+    "plt.grid()\n",
+    "plt.plot(time, hard_students, label=\"hard students\")\n",
+    "plt.plot(time, lazy_students, label=\"lazy students\")\n",
+    "plt.xlabel(\"Course number\")\n",
+    "plt.ylabel(\"Population\")\n",
+    "plt.title(\"N={}, n={}, H={}, L={}, a={}\".format(population, n, H, L, a))\n",
+    "plt.legend()\n",
+    "if 0 in hard_students:\n",
+    "    no_hard_students_pos = (hard_students.index(0), 0)\n",
+    "    plt.annotate(\n",
+    "        \"hard_students=0 \" + str(no_hard_students_pos),\n",
+    "        no_hard_students_pos,\n",
+    "        (hard_students.index(0), 4),\n",
+    "        arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\")\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4a9ad9e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73ccc5c5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}