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    ">> EVOLUCIÓN DE LOS MORFO-ÁRBOLES COLOREADOS DE NEWICK\n",
    ">>>>\n",
    ">>> José Rodríguez\n",
    ">>>>\n",
    ">>> jose@ejristos.com\n",
    ">>>>\n",
    ">>> 2 de noviembre de 2026.\n",
    ">>>>\n",
    ">>> Ibagué, Colombia.\n",
    "\n",
    "**Resumen**\n",
    "\n",
    "Un morfoárbol es un árbol que modula la morfogénesis o diferenciación. Funciona como una red de iluminación que ilumina una región donde la intensidad de la luz supera un umbral.  Aquí, una región puede ser iluminada por diversas lámparas coloreadas y todas cuentan. Investigamos la batalla de la Evolución para restaurar una región coloreada dada que fue creada por un árbol coloreado objetivo.\n",
    "\n",
    "# INTRODUCCIÓN\n",
    "\n",
    "Un modelo de morfogénesis debe explicar dos hechos contradictorios. En primer lugar, la estabilidad evolutiva de la forma biológica y, concretamente, por qué las moscas domésticas son iguales en todo el mundo. En segundo lugar, la extrema variabilidad de los detalles morfológicos, concretamente, por qué todos los miembros de una familia son distintos, incluidos los gemelos.  \n",
    "\n",
    "Perseguimos la idea de que la forma biológica es un subproducto, una modulación de la reproducción celular. Esta está representado por un árbol. Los árboles tienen el potencial de explicar ambos hechos. La mutación en las hojas, o los nodos terminales a lo largo de las ramas, crea variabilidad mientras que la recombinación mezcla y distribuye características. Al mismo tiempo, los nodos profundos son inmutables si están limitados por la pleiotropía, es decir, cuando un cambio tiene múltiples efectos que afectan la fitness o aptitud. Para explicar cómo surge la forma biológica, hemos propuesto el modelo de red de iluminación que dice que el árbol reproductivo tiene un subárbol que produce mensajeros que pueden controlar la reproducción o diferenciación siempre que la concentración supere un umbral dado. Cada nodo del subárbol es como una lámpara de una red de iluminación que ilumina su entorno dondequiera que la intensidad de la luz supere un cierto umbral. \n",
    "\n",
    "Según nuestras simulaciones anteriores y lo mejor de nuestra imaginación, la Evolución destruye los morfoárboles. Esto contradice lo que observamos en las familias humanas de que la Evolución es claramente compatible con la morfogénesis. De hecho: \n",
    "1. Los niños son en la mayoría de los detalles diferentes de los padres, también son diferentes unos de otros, pero sin embargo, todos están bien formados y son fácilmente reconocibles como seres humanos estupendos.\n",
    "2. La mayoría de los elementos de la forma biológica permanecen congelados, monomórficos, por ejemplo, todos tenemos dos brazos.\n",
    "\n",
    " En el último trabajo, también observamos que la pleitropía es inútil para crear estabilidad evolutiva de la forma cuando puede ser creada por demasiados árboles. Por eso **decidimos estudiar el efecto de añadir color a las lámparas**. Este representa la gran riqueza de detalles dentro de cada estructura biológica. Por ejemplo, un hueso está vivo y puede destruir y reconstruir partes internas debido a una maquinaria enzimática regulable dentro de él. \n",
    "\n",
    "Nuestros propósitos para esta contribución son: \n",
    "1. Aprender a codificar morfoárboles de colores según la notación o formato de Newick.\n",
    "2. Estudiar la Evolución de los árboles coloreados. En particular, ponemos a prueba nuestra expectativa: la plieotropía permite la estabilidad evolutiva de los rasgos profundos porque los árboles que coinciden con una región coloreada son pocos en número.\n",
    "3. Verificar que la Evolución es impotente frente a la complejidad de la morfogénesis. Damos otra vuelta a la idea de que el número de nodos de un árbol es un predictor del costo evolutivo de emparejar un árbol coloreado. No podemos separar la complejidad de la pleitropía porque si los árboles no fueran un desafío para la Evolución, en poblaciones muy pequeñas, en las que la selección está dominada por la deriva aleatoria, la Evolución desharía sus logros y deben aparecer monstruosidades, una predicción que no se observa en la naturaleza, digamos en moscas alrededor de un arbusto fructífero, o en tortugas a lo largo de los ríos. \n",
    "\n",
    "# CODIFICACIÓN DE MORFO-ÁRBOLES COLOREADOS\n",
    "\n",
    "Nuestra primera tarea para codificar morfoárboles coloreados como cadenas es familiarizarnos con los colores en Python.\n",
    "\n",
    "**Ejercicio.** Dile a tu sistema que busque `colores en python` y selecciona una paleta adecuada para trabajar con ellos. Muestra en un programa cómo hacerlo.\n",
    ">\n",
    "> *Answer.*  En el siguiente programa, vemos cómo trabajar con colores en Python. \n"
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      "Base Colors\n",
      "['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']\n",
      "Tableau Colors\n",
      "['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
      "CSS Colors\n",
      "['aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'rebeccapurple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen']\n"
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",
      "text/plain": [
       "<Figure size 448x134 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 872x838 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program python_colors_m1.py\n",
    "# By matplotlib\n",
    "# https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "\n",
    "def plot_colortable(colors, *, ncols=4, sort_colors=True):\n",
    "\n",
    "    cell_width = 212\n",
    "    cell_height = 22\n",
    "    swatch_width = 48\n",
    "    margin = 12\n",
    "\n",
    "    # Sort colors by hue, saturation, value and name.\n",
    "    if sort_colors is True:\n",
    "        names = sorted(\n",
    "            colors, key=lambda c: tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(c))))\n",
    "    else:\n",
    "        names = list(colors)\n",
    "\n",
    "    print(list(colors))\n",
    "\n",
    "    n = len(names)\n",
    "    nrows = math.ceil(n / ncols)\n",
    "\n",
    "    width = cell_width * ncols + 2 * margin\n",
    "    height = cell_height * nrows + 2 * margin\n",
    "    dpi = 72\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)\n",
    "    fig.subplots_adjust(margin/width, margin/height,\n",
    "                        (width-margin)/width, (height-margin)/height)\n",
    "    ax.set_xlim(0, cell_width * ncols)\n",
    "    ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.)\n",
    "    ax.yaxis.set_visible(False)\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.set_axis_off()\n",
    "\n",
    "    for i, name in enumerate(names):\n",
    "        row = i % nrows\n",
    "        col = i // nrows\n",
    "        y = row * cell_height\n",
    "\n",
    "        swatch_start_x = cell_width * col\n",
    "        text_pos_x = cell_width * col + swatch_width + 7\n",
    "\n",
    "        ax.text(text_pos_x, y, name, fontsize=14,\n",
    "                horizontalalignment='left',\n",
    "                verticalalignment='center')\n",
    "\n",
    "        ax.add_patch(\n",
    "            Rectangle(xy=(swatch_start_x, y-9), width=swatch_width,\n",
    "                      height=18, facecolor=colors[name], edgecolor='0.7')\n",
    "        )\n",
    "\n",
    "    return fig\n",
    "\n",
    "print(\"Base Colors\")\n",
    "plot_colortable(mcolors.BASE_COLORS, ncols=3, sort_colors=False)\n",
    "print(\"Tableau Colors\")\n",
    "plot_colortable(mcolors.TABLEAU_COLORS, ncols=2, sort_colors=False)\n",
    "print(\"CSS Colors\")\n",
    "plot_colortable(mcolors.CSS4_COLORS)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2bcf23-e779-4478-983b-d1d3595412aa",
   "metadata": {},
   "source": [
    "**Ejercicio.** Corra el programa anterior para ver las diversas tablas de colores de Python.\n",
    "\n",
    "**Nuestra paleta.** Hay muchas posibilidades para elegir una paleta, una combinación de colores de trabajo. Algunas elecciones son más evolutivas que otras.  Hemos elegido una paleta que consta de 7 colores de la tabla CSS: gris claro, salmón, trigo, palegreen o verde pálido, azul polvo, púrpura media, rosa. Nuestra selección no es evolutiva. Para aumentar la evolutividad, se podría preferir el formato RGBA, que es evolutivo por rangos. \n",
    "\n",
    " ## Generación de árboles coloridos\n",
    "\n",
    " Usaremos la notación de Newick con el siguiente prototipo para la información de un nodo: `Node89[x=-5.0,y=-9.0,c=3, r=0.94]`, donde:\n",
    " 1. El nombre del nodo es `Node89`, el número correrá de la mano con un procedimiento de enumeración.\n",
    " 2. El par $(x,y)$ denota la posición de las coordenadas de la izquierda inferior de un cuadrado de ancho uno que representa una celda.\n",
    " 3. El color se denota por `c=3`, donde el número representa el color en una lista de colores que se encuentran en nuestra paleta. La nuestra es `['gris claro', 'salmón', 'trigo', 'verde pálido', 'azul polvo', 'púrpura medio', 'rosa']\n",
    " 4. En el centro del cuadrado hay una lámpara con color `c` y con un radio de iluminación dado por `r = 0,94`. Una celda iluminada hereda el color de la lámpara. Si dos lámparas coinciden, se registran ambos colores. \n",
    "\n",
    "**Ejercicio.** Desarrollar el código para un generador de la información de un nodo en notación Newick. \n",
    ">\n",
    "> *Respuesta.* El siguiente programa genera la información de un nodo coloreado.  \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aace7b51-5887-4a0e-8830-55a119fc16ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program colored_node_info_generator_m2.py\n",
    "# This program generates the info of a colored node according to the pattern\n",
    "# Node89[x=-5.0,y=-9.0,c = 3, r=0.94]\n",
    "# where\n",
    "# The name of the node is `Node89`, the number will run hand in  hand with an enumeration procedure.\n",
    "# where x and y are the coordinates of the position of the left-bottom corner of a squared cell,\n",
    "# c denotes the index of a color in the palette,\n",
    "# r is the radius of illumination.\n",
    "# Created on Mon Oct 12 2026 on the Python editor Spyder 6\n",
    "\n",
    "\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import math\n",
    "import copy # For deep copying trees during reproduction and mutatio\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "def node_info_generator_m2():\n",
    "    \"\"\"For each node, info is generated on the model\n",
    "     Node_26[x=3,y=5,c= 3,r=0.85]\n",
    "      \"\"\"\n",
    "   \n",
    "    nx,  ny  = -3,4\n",
    "    max_node_radius = 1.7\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    color_index = 7\n",
    "    counter = 7\n",
    "    info = \"Node\" +  str(counter)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(color_index) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    return info\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "        info = node_info_generator_m2()\n",
    "        print(f\"info = {info}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9c84a3-903f-4fa4-a089-d2dd11c64670",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el programa y decide si hace lo que debe hacer.\n",
    "\n",
    "**Ejercicio.** Reutiliza los programas de la contribución anterior para desarrollar el código para generar un árbol coloreado de morfología.\n",
    ">\n",
    "> *Respuesta.* El siguiente programa genera un árbol coloreado de morfología  en notación Newick. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7c3124f-e7da-4c40-b290-da3b7c78ddb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program colored_tree_generator_m3.py\n",
    "# This program generates a colored morpho-tree as a Newick formula. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "# Created on Mon Oct 12 2026 on the Python editor Spyder 6\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "\n",
    "import random\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    " \n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "# Parameters for the target tree generation\n",
    "tree_params = {\n",
    "  'max_depth': 10, # maximal number of division along a branch\n",
    "  'max_number_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    " \n",
    "def print_parameters_m3(tree_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n A random colored morpho-tree T is generated.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    " \n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m3(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m3(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "\n",
    "def detect_all_groups_m3(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m3(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m3(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m3(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = float(c[0][2:])\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m3(newick_string):\n",
    "    #print(\"extract_all_infos\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m3(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m3(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      \n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m3(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m3(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m3(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m3(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m3(father_center, counter,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator13(father_center, counter,  occupied_squares_set)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m3(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # First, filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l)\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "\n",
    "  else:\n",
    "    #All neighboring places are occupied\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "    info = \"\"\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m3(father_center, numb_children, counter, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  s = \"\"\n",
    "  for i in range(numb_children):\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m3(father_center, counter, occupied_squares_set, max_node_radius)\n",
    "    if not info:\n",
    "      continue\n",
    "    else:\n",
    "     s = info + a + s\n",
    "     counter = counter + 1\n",
    "  s =  \"(\" + s + \")\"\n",
    "  #print(f\"Children: {s}\")\n",
    "  return s, counter, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m3(max_depth, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  actual_depth = random.randint(0,max_depth) # Number of allowed divisions along each branch.\n",
    "  print(f\"Actual depth: {actual_depth}\")\n",
    "  # () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))\n",
    "  #counter of defined nodes\n",
    "  counter = 1\n",
    "  for i in range(actual_depth):\n",
    "      #print(f\"depth: {i}\")\n",
    "      l = len(tree)\n",
    "      #print(f\"i : {i} l: {l}\")\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m3(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement\n",
    "            generated_children, counter, occupied_squares_set = generate_children_m3(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m3(tree, j-1, j+1, generated_children)\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print_parameters_m3(tree_params)\n",
    "    complete_tree_generator_m3(max_depth = tree_params['max_depth'], max_number_children = tree_params['max_number_children'], max_node_radius = tree_params['max_node_radius'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e6c947-9c93-4189-aaec-f82f0dd3297c",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el programa anterior y juzga si las fórmulas generadas son correctas. \n",
    "\n",
    "**Desafío.** Desarrollar un validador para las fórmulas de Newick que son generadas por el programa anterior. \n",
    "\n",
    "**Ejercicio.** Sin variabilidad no hay Evolución. Entonces, el programa anterior la maximiza, pero el costo en tiempo de CPU es inmenso precisamente debido a la gran varianza en algunas observables. Una de ellas es el número total de nodos que esperamos, que predice la complejidad para los árboles coloreados. Para solucionar este problema, sacrifica la profundidad máxima (max_depth), que dice cuántas generaciones se permiten a lo largo de las ramas. En su lugar, introduce el number_of_nodes como un parámetro fundamental y ajusta las funciones correspondientes que generan el árbol.\n",
    ">\n",
    "> *Respuesta.* El siguiente programa introduce el number_of_nodes como un parámetro constante fundamental y ajusta las funciones correspondientes que generan el árbol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32d61111-89d2-468d-81b5-425394f35e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program colored_tree_generator_m4.py\n",
    "# This program generates a colored morpho-tree as a Newick formula,\n",
    "# and draws it. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "\n",
    "\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import math\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "# trace memory allocation \n",
    "#https://www.geeksforgeeks.org/python/monitoring-memory-usage-of-a-running-python-program/\n",
    "# this consumes too much cpu-time by a multiplicative factor, say 5 for 1000 lines.\n",
    "import tracemalloc \n",
    "\n",
    "\n",
    "#Activate  memory monitoring?\n",
    "memory_monitoring = False \n",
    "recombination_of_leaves = False\n",
    "\n",
    "# starting the memory allocation monitoring\n",
    "if memory_monitoring:\n",
    "    tracemalloc.start() \n",
    "start_time = time.time()\n",
    " \n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "# Parameters for the target tree generation\n",
    "tree_params = {\n",
    "  'total_number_of_nodes': 250,\n",
    "  'max_number_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    " \n",
    "def print_parameters_m4(tree_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n A random colored morpho-tree T is generated.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    " \n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m4(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m4(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "#=== COLORED TREE GENERATOR===\n",
    "\n",
    "def detect_all_groups_m4(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m4(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m4(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m4(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = int(float(c[0][2:]))\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m4(newick_string):\n",
    "    #print(\"extract_all_infos_m4\")\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m4(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m4(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m4(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m4(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m4(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m4(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m4(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator_m4(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m4(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # Filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l-1) # color index\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter_of_nodes)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    \n",
    "    # distance2 = distance squared between father and child.\n",
    "    # Test: distance2 must be 2 or  1. \n",
    "    distance2 = (father_center[0]-nx)**2 + (father_center[1]-ny)**2\n",
    "    if distance2 > 2:\n",
    "        print(f\"distance2 > 2: {distance2}\")\n",
    "  #else:\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m4(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children_m4(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  generated_children = \"\"\n",
    "  i = 0\n",
    "  while i in range(numb_children) and counter_of_nodes < total_number_of_nodes:\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m4(father_center, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "    if info == \"\":\n",
    "      generated_children = \"()\"\n",
    "      return generated_children, counter_of_nodes, occupied_squares_set\n",
    "    else:\n",
    "     generated_children = info + a + generated_children\n",
    "     counter_of_nodes +=  1\n",
    "     i += 1\n",
    "     #print(f\"counter_of_nodes at generate_children_m4= {counter_of_nodes}\")    \n",
    "  generated_children =  \"(\" + generated_children + \")\"\n",
    "  #print(f\"Children: {generated_children}\")\n",
    "  return generated_children, counter_of_nodes, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m4(total_number_of_nodes, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  # Initial tree = root. () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))# Root\n",
    "  #counter of defined nodes\n",
    "  counter_of_nodes = 1\n",
    "  while counter_of_nodes < total_number_of_nodes:\n",
    "      l = len(tree)\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Find and replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m4(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement of  () by a list of children\n",
    "            generated_children, counter_of_nodes, occupied_squares_set = generate_children_m4(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m4(tree, j-1, j+1, generated_children)\n",
    "            #print(f\"counter_of_nodes at complete_tree_generator_m4= {counter_of_nodes}\")\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  #print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print_parameters_m4(tree_params)\n",
    "    colored_tree = complete_tree_generator_m4(total_number_of_nodes = tree_params['total_number_of_nodes'], max_number_children = tree_params['max_number_children'], max_node_radius = tree_params['max_node_radius'])\n",
    "    print(f\"\\nfinal colored tree: {colored_tree}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd31401-d8d4-4023-890e-24f74523d94e",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el programa anterior y mira a ver si funciona correctamente.\n",
    "\n",
    "**Ejercicio.** Desarrolla el código para dibujar árboles de morfo coloreados. \n",
    ">\n",
    "> *Respuesta.* El siguiente es el código para dibujar morfo-árboles coloreados. El árbol está representado por las líneas quebradas que conectan centro a centro, es decir, lámpara a lámpara. Cada lámpara tiene un color y un radio de acción, que envuelve un círculo. Cada cuadrado se ilumina cuando al menos un píxel se ilumina. Varias lámparas pueden iluminar la misma plaza. Todos los cuadrados iluminados conforman un área coloreada que es el objetivo de la Evolución en el espacio de los árboles coloreados. La tarea es reproducir el área coloreada con el color correspondiente de cada cuadrado. La fórmula de Newick es el genotipo, el área coloreada es el fenotipo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf2132a7-750f-42aa-ab9d-7e34a7e0793d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Program colored_tree_generator_and_drawer__m5.py\n",
    "# This program generates a colored morpho-tree as a Newick formula,\n",
    "# and draws it. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "\n",
    "\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import math\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "# trace memory allocation \n",
    "#https://www.geeksforgeeks.org/python/monitoring-memory-usage-of-a-running-python-program/\n",
    "# this consumes too much cpu-time by a multiplicative factor, say 5 for 1000 lines.\n",
    "import tracemalloc \n",
    "\n",
    "\n",
    "#Activate  memory monitoring?\n",
    "memory_monitoring = False \n",
    "recombination_of_leaves = False\n",
    "\n",
    "# starting the memory allocation monitoring\n",
    "if memory_monitoring:\n",
    "    tracemalloc.start() \n",
    "start_time = time.time()\n",
    " \n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "# Parameters for the target tree generation\n",
    "tree_params = {\n",
    "  'total_number_of_nodes': 250,\n",
    "  'max_number_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    " \n",
    "def print_parameters_m5(tree_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n A random colored morpho-tree T is generated.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    " \n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m5(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m5(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "#=== COLORED TREE GENERATOR===\n",
    "\n",
    "def detect_all_groups_m5(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m5(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m5(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m5(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = int(float(c[0][2:]))\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m5(newick_string):\n",
    "    #print(\"extract_all_infos_m5\")\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m5(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m5(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m5(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m5(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m5(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m5(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m5(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator_m5(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m5(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # Filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l-1) # color index\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter_of_nodes)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    \n",
    "    # distance2 = distance squared between father and child.\n",
    "    # Test: distance2 must be 2 or  1. \n",
    "    distance2 = (father_center[0]-nx)**2 + (father_center[1]-ny)**2\n",
    "    if distance2 > 2:\n",
    "        print(f\"distance2 > 2: {distance2}\")\n",
    "  #else:\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m5(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children_m5(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  generated_children = \"\"\n",
    "  i = 0\n",
    "  while i in range(numb_children) and counter_of_nodes < total_number_of_nodes:\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m5(father_center, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "    if info == \"\":\n",
    "      generated_children = \"()\"\n",
    "      return generated_children, counter_of_nodes, occupied_squares_set\n",
    "    else:\n",
    "     generated_children = info + a + generated_children\n",
    "     counter_of_nodes +=  1\n",
    "     i += 1\n",
    "     #print(f\"counter_of_nodes at generate_children_m5= {counter_of_nodes}\")    \n",
    "  generated_children =  \"(\" + generated_children + \")\"\n",
    "  #print(f\"Children: {generated_children}\")\n",
    "  return generated_children, counter_of_nodes, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m5(total_number_of_nodes, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  # Initial tree = root. () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))# Root\n",
    "  #counter of defined nodes\n",
    "  counter_of_nodes = 1\n",
    "  while counter_of_nodes < total_number_of_nodes:\n",
    "      l = len(tree)\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Find and replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m5(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement of  () by a list of children\n",
    "            generated_children, counter_of_nodes, occupied_squares_set = generate_children_m5(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m5(tree, j-1, j+1, generated_children)\n",
    "            #print(f\"counter_of_nodes at complete_tree_generator_m5= {counter_of_nodes}\")\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  #print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "# === DRAWER ===\n",
    "\n",
    "def find_branch_m5(newick_string, final_index):\n",
    "  initial_segment = newick_string[:final_index]\n",
    "  #print(f\"initial_segment: {initial_segment}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  branch = \"\"\n",
    "  for i in range(len(initial_segment)-1,-1,-1):\n",
    "    if initial_segment[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if initial_segment[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = initial_segment[i:final_index+1]\n",
    "      #print(f\"Branch to be deleted: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_find_all_groups_m5(newick_string):\n",
    "  \"\"\" All groups are found and one is chosen at random and returned\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  info_words =  r'[a-zA-Z_0-9_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(info_words, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string, 0\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  return groups[start_idx], starts[start_idx]\n",
    "\n",
    "def deletion_of_branch_m5(newick_string ):\n",
    "  group, start =  newick_find_all_groups_m5(newick_string)\n",
    "  #print(f\"Group (main): {group}\")\n",
    "  #print(f\"Start (main): {start}\")\n",
    "  # Does the group have children?\n",
    "  char_before = newick_string[start-1]\n",
    "  #print(f\"char_before: {char_before}\")\n",
    "  if char_before ==  \")\":\n",
    "    #print(\"The group has children\")\n",
    "    #find branch\n",
    "    final_index = start-1\n",
    "    branch = find_branch_m5(newick_string, final_index+1)\n",
    "\n",
    "    branch_and_father = branch + group\n",
    "    #print(f\"  Branch and father to be deleted: {branch_and_father}\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(branch_and_father , \"\")\n",
    "  else:\n",
    "    #print(\"The group has no children\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(group, \"\")\n",
    "\n",
    "  # Cosmetics\n",
    "  newick_string = newick_string.replace(\",,\", \",\")\n",
    "  newick_string = newick_string.replace(\",)\", \")\")\n",
    "  newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "  newick_string = newick_string.replace(\" \", \"\")\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {newick_string}\")\n",
    "  l = len(newick_string)\n",
    "  #print(f\"Length of newick_string: {l}\")\n",
    "  if l == 0 :\n",
    "     newick_string = \"Node_0[a=0,b=-0,x=0,y=0,r=1]\"\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "# =================================================================================\n",
    "# ==== Drawer ===\n",
    "# =================================================================================\n",
    "\n",
    "\n",
    " \n",
    "# colored_coords = get_colored_area_m5(x,y,c,r)\n",
    "def get_colored_area_m5(x,y,c,r):\n",
    "    '''\n",
    "      The illuminated area encloses all unitary squares \n",
    "      that intersects the circles of illumination with the respective color ci.\n",
    "      Squares are denoted by their left bottom corners (xi,yi). These will be found in the set colored_coords.\n",
    "      The radius of illumination is ri\n",
    "    '''\n",
    "    #print(\"get_colored_area_m5\")\n",
    "    #print(f\"x: {x}, y: {y}, c: {c}, r: {r}\")\n",
    "    colored_coords = set()\n",
    "    \n",
    "    for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "        circle_center_x = xi + 0.5\n",
    "        circle_center_y = yi + 0.5\n",
    "        r_sq = ri ** 2\n",
    "        # 1. Add the cell containing the center of the circle and its color\n",
    "        # This covers the case of small circles not intersecting grid lines.\n",
    "        colored_coords.add((math.floor(circle_center_x), math.floor(circle_center_y), ci))\n",
    "\n",
    "        # 2. Horizontal Scan: Check intersections with horizontal grid lines\n",
    "        # Iterate y_line from floor(cy - r) to ciel(cy + r)\n",
    "        # These y_line values represent the y-coordinates of horizontal grid lines.\n",
    "        for y_line in range(math.floor(circle_center_y - ri)-1,\n",
    "                            math.ceil(circle_center_y + ri) + 1):\n",
    "            dy = y_line - circle_center_y\n",
    "            discriminant = r_sq - dy * dy\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dx = math.sqrt(discriminant)\n",
    "                x_min_intersect = circle_center_x - dx\n",
    "                x_max_intersect = circle_center_x + dx\n",
    "\n",
    "                # For each integer x-coordinate that the horizontal segment crosses:\n",
    "                # Add the cells whose bottom-left corner's x-coordinate is in this range.\n",
    "                # This covers cells whose top edge is y_line AND cells whose bottom edge is y_line.\n",
    "                for ix in range(math.floor(x_min_intersect), math.floor(x_max_intersect) + 1):\n",
    "                    colored_coords.add((ix, y_line, ci))\n",
    "                    colored_coords.add((ix, y_line - 1, ci))\n",
    "\n",
    "        # 3. Vertical Scan: Check intersections with vertical grid lines\n",
    "        # Iterate x_line from floor(cx - r) to ciel(cx + r)\n",
    "        # These x_line values represent the x-coordinates of vertical grid lines.\n",
    "        for x_line in range(math.floor(circle_center_x - ri)-1,\n",
    "                            math.ceil(circle_center_x + ri) + 1):\n",
    "            dx = x_line - circle_center_x\n",
    "            discriminant = r_sq - dx * dx\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dy = math.sqrt(discriminant)\n",
    "                y_min_intersect = circle_center_y - dy\n",
    "                y_max_intersect = circle_center_y + dy\n",
    "\n",
    "                # For each integer y-coordinate that the vertical segment crosses:\n",
    "                # Add the cells whose bottom-left corner's y-coordinate is in this range.\n",
    "                # This covers cells whose right edge is x_line AND cells whose left edge is x_line.\n",
    "                for iy in range(math.floor(y_min_intersect), math.floor(y_max_intersect) + 1):\n",
    "                    # Cell (x_line, iy) - its left edge is at x_line\n",
    "                    colored_coords.add((x_line, iy, ci))\n",
    "                    # Cell (x_line - 1, iy) - its right edge is at x_line\n",
    "                    colored_coords.add((x_line - 1, iy, ci))\n",
    "    #print(f\"colored_coords  {colored_coords}\")\n",
    "\n",
    "    return colored_coords\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def plot_colored_area_newick_m5(ax, colored_coords, title,  alpha=0.9):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not colored_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y, c in colored_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=palette[c], alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(d[0] for d in colored_coords) - 1\n",
    "    max_x = max(d[0] for d in colored_coords) + 2\n",
    "    min_y = min(d[1] for d in colored_coords) - 1\n",
    "    max_y = max(d[1] for d in colored_coords) + 2\n",
    "    #print(f\"min_x: {min_x}, max_x: {max_x}, min_y: {min_y}, max_y: {max_y}\")\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "\n",
    "def plot_tree_nodes_and_their_diffusion_circles_m5(x,y,c,r, ax):\n",
    "  for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "    #print(f\"ci at plot_tree_nodes_and_their_diffusion_circles_m5: {ci}\")\n",
    "    # Plot the square representing the cell\n",
    "    rect = patches.Rectangle((xi, yi), 1, 1, facecolor=palette[ci], edgecolor='black', linewidth=1, zorder=1)\n",
    "    ax.add_patch(rect)\n",
    "    #node_label_part = node.value.split('_')[-1] if '_' in node.value else node.value\n",
    "    #ax.text(xi + 0.5, yi + 0.5, node_label_part, ha='center', va='center', color='darkblue', fontsize=7, zorder=2)\n",
    "\n",
    "    # Plot the circle representing the radius of influence\n",
    "    circle = patches.Circle((xi + 0.5, yi + 0.5), ri, color=palette[int(ci)], alpha=0.6, zorder=2)\n",
    "    ax.add_patch(circle)\n",
    "\n",
    "def find_leaves_newick_m5(newick_string):\n",
    "    '''Detect leaves in a Newick string.'''\n",
    "    #print(\"\\n\\nfind_leaves\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "    # Detection of leaves.\n",
    "    # Detect substrings of the form \"(...)\" but without interior parentheses.\n",
    "    pattern_leaves = r'[(][^()]*[)]'\n",
    "    # Leaves may come grouped by bouquets\n",
    "    bouquets_of_leaves = re.findall(pattern_leaves, newick_string )\n",
    "    #print(f\"Bouquets_of_leaves: {bouquets_of_leaves}\")\n",
    "    all_leaves = []\n",
    "    # Decipher each leaf and find the father\n",
    "    for bouquet_of_leaves in bouquets_of_leaves:\n",
    "        bouquet_of_leaves = bouquet_of_leaves[1:-1]\n",
    "        leaves = detect_all_groups_m5(bouquet_of_leaves)\n",
    "        for leaf in leaves:\n",
    "          leaf = leaf.replace(\" \", \"\")\n",
    "          all_leaves.append(leaf)\n",
    "    return all_leaves\n",
    "\n",
    "\n",
    "\n",
    "def connect_leaves_to_father_m5(newick_string, ax, tree_color='lightblue', diff_color='red'):\n",
    "    '''Draw lines connecting leaves to fathers'''\n",
    "    #print(\"\\n\\nconnect_leaves_to_father_m5\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    all_leaves = find_leaves_newick_m5(newick_string)\n",
    "    for leaf in all_leaves:\n",
    "        #Leaf info\n",
    "        [xl,yl,cl,rl] = extract_info_m5(leaf)\n",
    "        #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n",
    "\n",
    "        # Task: Find the father of the leaf.\n",
    "        # It is the node after the last leaf\n",
    "        # Example:\n",
    "        # (Node9[x=-11,y=19.7,r=12.4567],Node8[x=0,y=7,r=15],Node7[x=6,y=16,r=9])Node6[x=17,y=9,r=20]\n",
    "        # The father of Node9, Node8, and Node7 is Node 6.\n",
    "        # The father of a node is found after the first unbalanced right parenthesis.\n",
    "        index_next_right_par = newick_string.find(\")\" , newick_string.find(leaf))\n",
    "        father_with_tail = newick_string[index_next_right_par+1:]\n",
    "        father_group = detect_all_groups_m5(father_with_tail)\n",
    "        if father_group:\n",
    "          [xf,yf,cf,rf]  = extract_info_m5(father_group[0])\n",
    "          #father_center = (xf,yf)\n",
    "          #Draw link\n",
    "          ax.plot([xl + 0.5, xf + 0.5], [yl + 0.5, yf + 0.5], 'k--', linewidth=0.8, zorder = 3)\n",
    "          # Test: distance2 = distance squared between father and child.\n",
    "          # distance2 must be 2 or  1. \n",
    "          distance2 = (xf-xl)**2 + (yf-yl)**2\n",
    "          #print(f\"distance2: {distance2}\")\n",
    "          if distance2 > 2:\n",
    "              print(f\"\\n distance2 > 2: {distance2}\")\n",
    "              print(f\"xl,yl,xf,yf: {xl} {yl} {xf} {yf}\")\n",
    "              print(f\"leaf: {leaf}\")\n",
    "              print(f\"father_with_tail: {father_with_tail}  \")\n",
    "              print(f\"father: {father_group[0]}\")\n",
    "          \n",
    "    # Leaves are erased: each leave is replaced by \"\"\n",
    "    for leaf in all_leaves:\n",
    "          #print(f\"leaf to be erased: {leaf}\")\n",
    "          newick_string = newick_string.replace(leaf + \",\", \"\")\n",
    "          newick_string = newick_string.replace(leaf, \"\")\n",
    "          #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    #is_valid, message = validate_newick_syntax11b(newick_string)\n",
    "    #print(f\"is valid at connect_leaves_to_father11(...) : {is_valid}, message: {message}\")\n",
    "    #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n",
    "    #print(f\"Is the Newick string a valid morpho-tree? : {status} - {message}\")\n",
    "    groups = detect_all_groups_m5(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")    \n",
    "     \n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_links_m5(newick_string, ax,   diff_color='red' ):\n",
    "    '''Draw lines connecting children to father.\n",
    "       1. Each leaf is connected to its father,\n",
    "          and the corresponding stroke is plotted.\n",
    "       2. Leaves are erased.\n",
    "       3. New leaves are detected and connected to their father.\n",
    "       4. And so on.\n",
    "       '''\n",
    "\n",
    "    print(\"draw_tree_links_m5\")\n",
    "    print(f\"Newick String: {newick_string}\")\n",
    "    length_string = len(newick_string)\n",
    "    #print(f\"length_string: {length_string}\")\n",
    "    groups = detect_all_groups_m5(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    # Leaves are recursively found and connected to their father.\n",
    "    counter_of_trials = 0\n",
    "    while number_of_groups > 1:\n",
    "        #print(\"\\n\\ndraw_tree_links11\")\n",
    "        #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "        newick_string, number_of_groups = connect_leaves_to_father_m5(newick_string, ax, tree_color='lightblue', diff_color='red')\n",
    "        counter_of_trials = counter_of_trials + 1\n",
    "        if counter_of_trials > length_string:\n",
    "          break\n",
    "      \n",
    "    # The root is detected and marked with a cross\n",
    "    groups = detect_all_groups_m5(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    # Mark the root with a red cross\n",
    "    if number_of_groups >= 1:\n",
    "      [x0,y0,c0,r0] = extract_info_m5(groups[-1]) #The root is the last group\n",
    "      #print(f\"Root: a: {a0}, b: {b0}, x: {x0}, y: {y0}, r: {r0}\")\n",
    "      ax.plot([x0, x0 +1 ], [y0+ 0.5, y0+ 0.5],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "      ax.plot([x0 + 0.5, x0 +0.5], [y0, y0+ 1],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_newick_m5(newick_string, title):\n",
    "    #print(\"\\n=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    x,y,c,r = extract_all_infos_m5(newick_string)\n",
    "    #print(f\"c= {c}\")\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    #color = palette[c]\n",
    "    #print(f\"color = {color}\")\n",
    "    plot_tree_nodes_and_their_diffusion_circles_m5(x,y,c,r, ax)\n",
    "     \n",
    "    # Illuminated area is calculated\n",
    "    #colored_coords = get_illuminated_area_m5(x,y,r)\n",
    "    colored_coords = get_colored_area_m5(x,y,c,r)\n",
    "    #print(f\"colored cells at draw_tree_newick_m5: {colored_coords}\")\n",
    "    #print(f\"Number of illuminated cells: {len(colored_coords)}\")\n",
    "    # Colored squares are added to the plot\n",
    "    plot_colored_area_newick_m5(ax, colored_coords, title,  alpha=0.3)\n",
    "    # Lines that define the tree are added\n",
    "    draw_tree_links_m5(newick_string, ax, diff_color='red'  )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return colored_coords\n",
    "\n",
    "def draw_colored_squares_m5(colored_coords):\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    title = 'Colored area only'\n",
    "    plot_colored_area_newick_m5(ax, colored_coords, title,  alpha=0.5)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print_parameters_m5(tree_params)\n",
    "    colored_tree = complete_tree_generator_m5(total_number_of_nodes = tree_params['total_number_of_nodes'], max_number_children = tree_params['max_number_children'], max_node_radius = tree_params['max_node_radius'])\n",
    "    #print(f\"\\nfinal colored tree: {colored_tree}\")\n",
    "    number_of_groups = count_groups_m5(colored_tree)\n",
    "    colored_coords = draw_tree_newick_m5(colored_tree, title=f\"Colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    #print(f\"Colored coordinates: {colored_coords}\")\n",
    "    #draw_colored_squares_m5(colored_coords)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8aca7a-833c-4da9-8da2-272d640b5dd6",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el programa para comprobar su funcionamiento.\n",
    ">\n",
    "> *Respuesta.* El programa funciona bien.\n",
    "\n",
    "**Desafío.** El programa de dibujo anterior utiliza una paleta plana o mate y produce dibujos muy bonitos. Pero la visibilidad de las regiones superpuestas no es perfecta. Utilice una paleta vívida y valores alfa variables para definir la transparencia y mejorar la visibilidad de los colores superpuestos.\n",
    "\n",
    "**Observaciones.** Las áreas coloreadas generadas representan la región biológica estructurada a observar. Plantean un problema: ¿cómo vamos a definir la distancia entre dos áreas de este tipo para calcular la aptitud de un árbol coloreado?\n",
    "\n",
    "**Ejercicio.** Proponga una distancia entre las áreas coloreadas.\n",
    ">\n",
    "> *Respuesta.* Proponemos: Cada lámpara produce su conjunto de cuadrados iluminados, que consisten en tripletas denotando x, y-posición y color. Por lo tanto, podemos medir la distancia entre diferentes áreas como la diferencia simétrica de los conjuntos correspondientes de tripletas. \n",
    ">\n",
    "> Implementamos este método a continuación.\n",
    ">\n",
    "**Desafío.** Implementa la siguiente forma alternativa de definir el color de un cuadrado iluminado por diversas lámparas y la métrica subsiguiente entre las áreas iluminadas. El método es:\n",
    ">\n",
    "> Las áreas coloreadas consisten en cuadrados coloreados. Por lo tanto, debemos definir la distancia entre los cuadrados coloreados y sumar sobre todos los cuadrados. Para definir la distancia entre dos cuadrados coloreados, debemos recordar que un cuadrado puede ser iluminado por varias lámparas con diversos colores. Luego, el color de un cuadrado se define según la paleta utilizada. Las nuestras tienen 7 colores tomados de la lista CSS:\n",
    ">\n",
    "> paleta = ['gris claro', 'salmón', 'trigo', 'palegreen', 'azul polvo', 'medio púrpura', 'rosa']\n",
    ">\n",
    "> Por lo tanto, podemos definir el color de un cuadrado por un vector booleano de presencia -ausencia de un color. Por ejemplo, si una lámpara ilumina un cuadrado y su color es gris claro, entonces el color del cuadrado se puede codificar como $[1,0,0,0,0,0]$. Y si dos lámparas, gris claro y rosa, lo iluminan, el color del cuadrado es $[1,0,0,0,0,1]$. Y así sucesivamente.\n",
    "\n",
    "**Desafío.** Incluye intensidades en la definición del color de un cuadrado.\n",
    "\n",
    "**Ejercicio.** Desarrollar el código para la simulación de la Evolución de los árboles coloreados para que su área coloreda coincida con un área coloreada definida por un árbol coloreado objetivo.\n",
    ">\n",
    ">*Respuesta.* El siguiente programa utiliza la Evolución en el espacio de árboles coloreados para que su área coloreda coincida con un área de color objetivo propuesta por un árbol objetivo. Reutilizamos un programa de la contribución anterior y lo combinamos con el dibujador de árboles coloreados, un programa que está más arriba. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a886d7ad-e1de-41ca-be18-777d35039a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\n",
      "PURPOSE: \n",
      " Evolution searches in the space of colored trees \n",
      " to match a target colored area associated to a target tree.\n",
      "EXPERIMENT 1793034770.108517\n",
      "TREE PARAMETERS: {'total_number_of_nodes': 15, 'max_number_of_children': 2, 'max_node_radius': 1.5}\n",
      "\n",
      "Target colored tree: ((((Node13[x=3.0,y=-1.0,c=2,r=0.89],Node12[x=3.0,y=0.0,c=5,r=1.37])Node8[x=2.0,y=-1.0,c=6,r=1.0])Node7[x=1.0,y=0.0,c=6,r=1.16],Node6[x=-1.0,y=-2.0,c=6,r=1.29])Node2[x=0.0,y=-1.0,c=1,r=0.52],(((Node11[x=-1.0,y=0.0,c=4,r=0.66])Node5[x=-1.0,y=-1.0,c=6,r=1.1],(Node10[x=1.0,y=-4.0,c=3,r=1.35],(Node14[x=2.0,y=-2.0,c=5,r=0.59])Node9[x=2.0,y=-3.0,c=3,r=0.51])Node4[x=1.0,y=-3.0,c=1,r=1.03])Node3[x=0.0,y=-2.0,c=1,r=0.66])Node1[x=1.0,y=-1.0,c=5,r=0.78])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n",
      "draw_tree_links_m6\n",
      "Newick String: ((((Node13[x=3.0,y=-1.0,c=2,r=0.89],Node12[x=3.0,y=0.0,c=5,r=1.37])Node8[x=2.0,y=-1.0,c=6,r=1.0])Node7[x=1.0,y=0.0,c=6,r=1.16],Node6[x=-1.0,y=-2.0,c=6,r=1.29])Node2[x=0.0,y=-1.0,c=1,r=0.52],(((Node11[x=-1.0,y=0.0,c=4,r=0.66])Node5[x=-1.0,y=-1.0,c=6,r=1.1],(Node10[x=1.0,y=-4.0,c=3,r=1.35],(Node14[x=2.0,y=-2.0,c=5,r=0.59])Node9[x=2.0,y=-3.0,c=3,r=0.51])Node4[x=1.0,y=-3.0,c=1,r=1.03])Node3[x=0.0,y=-2.0,c=1,r=0.66])Node1[x=1.0,y=-1.0,c=5,r=0.78])Node_0[x=0,y=0,c=3,r=1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(0, -3, 6), (2, 1, 6), (1, -1, 1), (-1, 1, 4), (1, -3, 3), (0, 1, 6), (1, -2, 1), (2, -2, 1), (3, 1, 5), (3, -1, 6), (3, -2, 6), (-2, -3, 6), (-1, -3, 6), (1, -1, 3), (2, -2, 3), (4, -1, 5), (0, -3, 1), (4, 0, 5), (0, -5, 3), (2, -1, 5), (2, -2, 5), (0, -3, 3), (2, 0, 5), (1, -4, 1), (0, 1, 3), (2, -4, 1), (0, 0, 4), (-1, -1, 4), (-1, 1, 3), (0, -1, 6), (0, -2, 6), (2, -4, 3), (4, -1, 2), (4, -2, 2), (-1, 0, 4), (-2, 0, 4), (0, 0, 6), (1, 0, 3), (2, -3, 1), (3, 0, 5), (4, 0, 2), (-1, -2, 6), (-2, -2, 6), (-2, -1, 6), (-1, -1, 6), (1, -2, 5), (1, -1, 5), (2, -1, 2), (2, -2, 2), (-2, 0, 6), (-1, 0, 6), (1, 0, 5), (2, -3, 3), (2, 0, 2), (0, -1, 1), (0, -2, 1), (0, 0, 1), (1, 1, 6), (-1, -1, 1), (2, -3, 5), (-1, -2, 1), (0, -1, 3), (2, 1, 5), (1, -4, 3), (0, 0, 3), (3, -1, 5), (3, 0, 2), (3, -2, 5), (-1, -1, 3), (0, -1, 5), (0, -2, 5), (-1, 0, 3), (0, 0, 5), (2, -5, 3), (0, -4, 1), (4, 1, 5), (1, 1, 3), (3, 0, 6), (0, -4, 3), (1, -1, 6), (1, -2, 6), (2, -1, 6), (2, -2, 6), (3, -2, 2), (3, -1, 2), (1, 0, 6), (2, 0, 6), (1, -3, 1), (3, -3, 3), (1, -5, 3)}\n",
      "Population initialized with 200 individuals.\n",
      "\n",
      "--- Generation 1/500000, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 10001/500000, Best fitness 0.64567 ---\n",
      "\n",
      "--- Generation 20001/500000, Best fitness 0.69492 ---\n",
      "\n",
      "--- Generation 30001/500000, Best fitness 0.69492 ---\n",
      "\n",
      "--- Generation 40001/500000, Best fitness 0.69492 ---\n",
      "\n",
      "--- Generation 50001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 60001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 70001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 80001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 90001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 100001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 110001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 120001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 130001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 140001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 150001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 160001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 170001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 180001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 190001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 200001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 210001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 220001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 230001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 240001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 250001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 260001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 270001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 280001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 290001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 300001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 310001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 320001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 330001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 340001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 350001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 360001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 370001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 380001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 390001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 400001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 410001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 420001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 430001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 440001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 450001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 460001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 470001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 480001/500000, Best fitness 0.70085 ---\n",
      "\n",
      "--- Generation 490001/500000, Best fitness 0.70085 ---\n",
      "draw_tree_links_m6\n",
      "Newick String: ((((Node312[x=3.0,y=-1.0,c=2,r=0.92])Node10[x=2.0,y=-1.0,c=5,r=1.5772260098055142],()Node9[x=3.0,y=0.0,c=2,r=0.6431548065128281])Node5[x=2.0,y=0.0,c=6,r=0.9167750697889563],Node947[x=2.0,y=2.0,c=5,r=0.5697801184711807])Node2[x=1.0,y=1.0,c=2,r=0.5140980750946262],(Node462[x=1.0,y=-4.0,c=3,r=1.17])(Node4[x=1.0,y=0.0,c=6,r=1.5010362402407151],((Node476[x=-1.0,y=-2.0,c=6,r=1.2138471458529592])Node244[x=-1.0,y=-1.0,c=4,r=1.19])(Node7[x=-2.0,y=-1.0,c=6,r=0.6951981515504952],(Node656[x=1.0,y=-3.0,c=1,r=0.86])(Node17[x=0.0,y=-2.0,c=1,r=1.5487078301882613])Node6[x=0.0,y=-1.0,c=5,r=0.6360856349335928])Node3[x=-1.0,y=0.0,c=3,r=0.6624305298457905])Node1[x=0.0,y=1.0,c=0,r=0.6776114033527768])Node_0[x=0,y=0,c=3,r=1.4526759511434415]\n",
      "\n",
      " distance2 > 2: 16.0\n",
      "xl,yl,xf,yf: 1.0 -4.0 1.0 0.0\n",
      "leaf: Node462[x=1.0,y=-4.0,c=3,r=1.17]\n",
      "father_with_tail: (Node4[x=1.0,y=0.0,c=6,r=1.5010362402407151],((Node476[x=-1.0,y=-2.0,c=6,r=1.2138471458529592])Node244[x=-1.0,y=-1.0,c=4,r=1.19])(Node7[x=-2.0,y=-1.0,c=6,r=0.6951981515504952],(Node656[x=1.0,y=-3.0,c=1,r=0.86])(Node17[x=0.0,y=-2.0,c=1,r=1.5487078301882613])Node6[x=0.0,y=-1.0,c=5,r=0.6360856349335928])Node3[x=-1.0,y=0.0,c=3,r=0.6624305298457905])Node1[x=0.0,y=1.0,c=0,r=0.6776114033527768])Node_0[x=0,y=0,c=3,r=1.4526759511434415]  \n",
      "father: Node4[x=1.0,y=0.0,c=6,r=1.5010362402407151]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(0, -3, 6), (2, 1, 6), (1, -1, 1), (-1, 1, 4), (1, -3, 3), (0, 1, 6), (1, -2, 1), (2, -2, 1), (3, 1, 5), (3, -1, 6), (3, -2, 6), (-2, -3, 6), (-1, -3, 6), (1, -1, 3), (2, -2, 3), (4, -1, 5), (0, -3, 1), (4, 0, 5), (0, -5, 3), (2, -1, 5), (2, -2, 5), (0, -3, 3), (2, 0, 5), (1, -4, 1), (0, 1, 3), (2, -4, 1), (0, 0, 4), (-1, -1, 4), (-1, 1, 3), (0, -1, 6), (0, -2, 6), (2, -4, 3), (4, -1, 2), (4, -2, 2), (-1, 0, 4), (-2, 0, 4), (0, 0, 6), (1, 0, 3), (2, -3, 1), (3, 0, 5), (4, 0, 2), (-1, -2, 6), (-2, -2, 6), (-2, -1, 6), (-1, -1, 6), (1, -2, 5), (1, -1, 5), (2, -1, 2), (2, -2, 2), (-2, 0, 6), (-1, 0, 6), (1, 0, 5), (2, -3, 3), (2, 0, 2), (0, -1, 1), (0, -2, 1), (0, 0, 1), (1, 1, 6), (-1, -1, 1), (2, -3, 5), (-1, -2, 1), (0, -1, 3), (2, 1, 5), (1, -4, 3), (0, 0, 3), (3, -1, 5), (3, 0, 2), (3, -2, 5), (-1, -1, 3), (0, -1, 5), (0, -2, 5), (-1, 0, 3), (0, 0, 5), (2, -5, 3), (0, -4, 1), (4, 1, 5), (1, 1, 3), (3, 0, 6), (0, -4, 3), (1, -1, 6), (1, -2, 6), (2, -1, 6), (2, -2, 6), (3, -2, 2), (3, -1, 2), (1, 0, 6), (2, 0, 6), (1, -3, 1), (3, -3, 3), (1, -5, 3)}\n",
      "draw_histories_m6(fitness_historiy, number_of_nodes, population_size)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 6 hours, 0 minutes, 27 seconds\n"
     ]
    }
   ],
   "source": [
    "# Program evolution_of_colored_morpho_trees_m6.py\n",
    "# This program reports the battle of Evolution \n",
    "# in the space of colored morpho-trees \n",
    "# to match a target colored area proposed by a target colored morpho-tree.\n",
    "# We merge the code for Evolution to that for generating and drawing a colored tree.\n",
    "# Edited with Spider 6.\n",
    "\n",
    "# =====Reused:  Program colored_tree_generator_and_drawer_m6.py\n",
    "# This program generates a colored morpho-tree as a Newick formula,\n",
    "# and draws it. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "  \n",
    "  \n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import math\n",
    "import copy # For deep copying trees during reproduction and mutation\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "# trace memory allocation \n",
    "#https://www.geeksforgeeks.org/python/monitoring-memory-usage-of-a-running-python-program/\n",
    "# this consumes too much cpu-time by a multiplicative factor, say 5 for 1000 lines.\n",
    "import tracemalloc \n",
    "\n",
    "\n",
    "#Activate  memory monitoring?\n",
    "memory_monitoring = False \n",
    "recombination_of_leaves = False\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# starting the memory allocation monitoring\n",
    "if memory_monitoring:\n",
    "    tracemalloc.start() \n",
    "\n",
    "\n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "# Parameters for the target tree generation\n",
    "# Parameters for the target tree  \n",
    "target_tree_params = {\n",
    "  'total_number_of_nodes': 15,\n",
    "  'max_number_of_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "# Parameters for the Genetic Algorithm\n",
    "ga_params = {\n",
    "   #'seed': 47423648, #the seed of the target tree is this plus 1.\n",
    "  'pop_size': 200,\n",
    "  'generations': 500000,\n",
    "  'average_number_of_nodes':  target_tree_params['total_number_of_nodes'],\n",
    "  'max_number_of_children': target_tree_params['max_number_of_children'],\n",
    "  'max_node_radius': 1.5,\n",
    "  'mutation_rate': 0.001, \n",
    "  'crossover_rate': 0.001  \n",
    "}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    " \n",
    "\n",
    " \n",
    "def print_parameters_m6(tree_params, ga_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n Evolution searches in the space of colored trees \"\n",
    "        + \"\\n to match a target colored area associated to a target tree.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m6(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m6(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "#=== COLORED TREE GENERATOR===\n",
    "\n",
    "def detect_all_groups_m6(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m6(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m6(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m6(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = int(float(c[0][2:]))\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m6(newick_string):\n",
    "    #print(\"extract_all_infos_m6\")\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m6(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m6(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m6(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m6(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m6(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m6(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m6(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator_m6(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m6(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # Filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l-1) # color index\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter_of_nodes)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    \n",
    "    # distance2 = distance squared between father and child.\n",
    "    # Test: distance2 must be 2 or  1. \n",
    "    distance2 = (father_center[0]-nx)**2 + (father_center[1]-ny)**2\n",
    "    if distance2 > 2:\n",
    "        print(f\"distance2 > 2: {distance2}\")\n",
    "  #else:\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m6(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children_m6(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  generated_children = \"\"\n",
    "  i = 0\n",
    "  while i in range(numb_children) and counter_of_nodes < total_number_of_nodes:\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m6(father_center, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "    if info == \"\":\n",
    "      generated_children = \"()\"\n",
    "      return generated_children, counter_of_nodes, occupied_squares_set\n",
    "    else:\n",
    "     generated_children = info + a + generated_children\n",
    "     counter_of_nodes +=  1\n",
    "     i += 1\n",
    "     #print(f\"counter_of_nodes at generate_children_m6= {counter_of_nodes}\")    \n",
    "  generated_children =  \"(\" + generated_children + \")\"\n",
    "  #print(f\"Children: {generated_children}\")\n",
    "  return generated_children, counter_of_nodes, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m6(total_number_of_nodes, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  # Initial tree = root. () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))# Root\n",
    "  #counter of defined nodes\n",
    "  counter_of_nodes = 1\n",
    "  while counter_of_nodes < total_number_of_nodes:\n",
    "      l = len(tree)\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Find and replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m6(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement of  () by a list of children\n",
    "            generated_children, counter_of_nodes, occupied_squares_set = generate_children_m6(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m6(tree, j-1, j+1, generated_children)\n",
    "            #print(f\"counter_of_nodes at complete_tree_generator_m6= {counter_of_nodes}\")\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  #print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "# === DRAWER ===\n",
    "\n",
    "def find_branch_m6(newick_string, final_index):\n",
    "  initial_segment = newick_string[:final_index]\n",
    "  #print(f\"initial_segment: {initial_segment}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  branch = \"\"\n",
    "  for i in range(len(initial_segment)-1,-1,-1):\n",
    "    if initial_segment[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if initial_segment[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = initial_segment[i:final_index+1]\n",
    "      #print(f\"Branch to be deleted: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_find_all_groups_m6(newick_string):\n",
    "  \"\"\" All groups are found and one is chosen at random and returned\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  info_words =  r'[a-zA-Z_0-9_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(info_words, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string, 0\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  return groups[start_idx], starts[start_idx]\n",
    "\n",
    "def deletion_of_branch_m6(newick_string ):\n",
    "  group, start =  newick_find_all_groups_m6(newick_string)\n",
    "  #print(f\"Group (main): {group}\")\n",
    "  #print(f\"Start (main): {start}\")\n",
    "  # Does the group have children?\n",
    "  char_before = newick_string[start-1]\n",
    "  #print(f\"char_before: {char_before}\")\n",
    "  if char_before ==  \")\":\n",
    "    #print(\"The group has children\")\n",
    "    #find branch\n",
    "    final_index = start-1\n",
    "    branch = find_branch_m6(newick_string, final_index+1)\n",
    "\n",
    "    branch_and_father = branch + group\n",
    "    #print(f\"  Branch and father to be deleted: {branch_and_father}\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(branch_and_father , \"\")\n",
    "  else:\n",
    "    #print(\"The group has no children\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(group, \"\")\n",
    "\n",
    "  # Cosmetics\n",
    "  newick_string = newick_string.replace(\",,\", \",\")\n",
    "  newick_string = newick_string.replace(\",)\", \")\")\n",
    "  newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "  newick_string = newick_string.replace(\" \", \"\")\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {newick_string}\")\n",
    "  l = len(newick_string)\n",
    "  #print(f\"Length of newick_string: {l}\")\n",
    "  if l == 0 :\n",
    "     newick_string = \"Node_0[a=0,b=-0,x=0,y=0,r=1]\"\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "# =================================================================================\n",
    "# ==== Drawer ===\n",
    "# =================================================================================\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "# colored_coords = get_colored_area_m6(x,y,c,r)\n",
    "def get_colored_area_m6(x,y,c,r):\n",
    "    '''\n",
    "      The illuminated area encloses all unitary squares \n",
    "      that intersects the circles of illumination with the respective color ci.\n",
    "      Squares are denoted by their left bottom corners (xi,yi). These will be found in the set colored_coords.\n",
    "      The radius of illumination is ri\n",
    "    '''\n",
    "    #print(\"get_colored_area_m6\")\n",
    "    #print(f\"x: {x}, y: {y}, c: {c}, r: {r}\")\n",
    "    colored_coords = set()\n",
    "    \n",
    "    for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "        circle_center_x = xi + 0.5\n",
    "        circle_center_y = yi + 0.5\n",
    "        r_sq = ri ** 2\n",
    "        # 1. Add the cell containing the center of the circle and its color\n",
    "        # This covers the case of small circles not intersecting grid lines.\n",
    "        colored_coords.add((math.floor(circle_center_x), math.floor(circle_center_y), ci))\n",
    "\n",
    "        # 2. Horizontal Scan: Check intersections with horizontal grid lines\n",
    "        # Iterate y_line from floor(cy - r) to ciel(cy + r)\n",
    "        # These y_line values represent the y-coordinates of horizontal grid lines.\n",
    "        for y_line in range(math.floor(circle_center_y - ri)-1,\n",
    "                            math.ceil(circle_center_y + ri) + 1):\n",
    "            dy = y_line - circle_center_y\n",
    "            discriminant = r_sq - dy * dy\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dx = math.sqrt(discriminant)\n",
    "                x_min_intersect = circle_center_x - dx\n",
    "                x_max_intersect = circle_center_x + dx\n",
    "\n",
    "                # For each integer x-coordinate that the horizontal segment crosses:\n",
    "                # Add the cells whose bottom-left corner's x-coordinate is in this range.\n",
    "                # This covers cells whose top edge is y_line AND cells whose bottom edge is y_line.\n",
    "                for ix in range(math.floor(x_min_intersect), math.floor(x_max_intersect) + 1):\n",
    "                    colored_coords.add((ix, y_line, ci))\n",
    "                    colored_coords.add((ix, y_line - 1, ci))\n",
    "\n",
    "        # 3. Vertical Scan: Check intersections with vertical grid lines\n",
    "        # Iterate x_line from floor(cx - r) to ciel(cx + r)\n",
    "        # These x_line values represent the x-coordinates of vertical grid lines.\n",
    "        for x_line in range(math.floor(circle_center_x - ri)-1,\n",
    "                            math.ceil(circle_center_x + ri) + 1):\n",
    "            dx = x_line - circle_center_x\n",
    "            discriminant = r_sq - dx * dx\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dy = math.sqrt(discriminant)\n",
    "                y_min_intersect = circle_center_y - dy\n",
    "                y_max_intersect = circle_center_y + dy\n",
    "\n",
    "                # For each integer y-coordinate that the vertical segment crosses:\n",
    "                # Add the cells whose bottom-left corner's y-coordinate is in this range.\n",
    "                # This covers cells whose right edge is x_line AND cells whose left edge is x_line.\n",
    "                for iy in range(math.floor(y_min_intersect), math.floor(y_max_intersect) + 1):\n",
    "                    # Cell (x_line, iy) - its left edge is at x_line\n",
    "                    colored_coords.add((x_line, iy, ci))\n",
    "                    # Cell (x_line - 1, iy) - its right edge is at x_line\n",
    "                    colored_coords.add((x_line - 1, iy, ci))\n",
    "    #print(f\"colored_coords  {colored_coords}\")\n",
    "\n",
    "    return colored_coords\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def plot_colored_area_newick_m6(ax, colored_coords, title,  alpha=0.9):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not colored_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y, c in colored_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=palette[c], alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(d[0] for d in colored_coords) - 1\n",
    "    max_x = max(d[0] for d in colored_coords) + 2\n",
    "    min_y = min(d[1] for d in colored_coords) - 1\n",
    "    max_y = max(d[1] for d in colored_coords) + 2\n",
    "    #print(f\"min_x: {min_x}, max_x: {max_x}, min_y: {min_y}, max_y: {max_y}\")\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "\n",
    "def plot_tree_nodes_and_their_diffusion_circles_m6(x,y,c,r, ax):\n",
    "  for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "    #print(f\"ci at plot_tree_nodes_and_their_diffusion_circles_m6: {ci}\")\n",
    "    # Plot the square representing the cell\n",
    "    rect = patches.Rectangle((xi, yi), 1, 1, facecolor=palette[ci], edgecolor='black', linewidth=1, zorder=1)\n",
    "    ax.add_patch(rect)\n",
    "    #node_label_part = node.value.split('_')[-1] if '_' in node.value else node.value\n",
    "    #ax.text(xi + 0.5, yi + 0.5, node_label_part, ha='center', va='center', color='darkblue', fontsize=7, zorder=2)\n",
    "\n",
    "    # Plot the circle representing the radius of influence\n",
    "    circle = patches.Circle((xi + 0.5, yi + 0.5), ri, color=palette[int(ci)], alpha=0.6, zorder=2)\n",
    "    ax.add_patch(circle)\n",
    "\n",
    "def find_leaves_newick_m6(newick_string):\n",
    "    '''Detect leaves in a Newick string.'''\n",
    "    #print(\"\\n\\nfind_leaves\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "    # Detection of leaves.\n",
    "    # Detect substrings of the form \"(...)\" but without interior parentheses.\n",
    "    pattern_leaves = r'[(][^()]*[)]'\n",
    "    # Leaves may come grouped by bouquets\n",
    "    bouquets_of_leaves = re.findall(pattern_leaves, newick_string )\n",
    "    #print(f\"Bouquets_of_leaves: {bouquets_of_leaves}\")\n",
    "    all_leaves = []\n",
    "    # Decipher each leaf and find the father\n",
    "    for bouquet_of_leaves in bouquets_of_leaves:\n",
    "        bouquet_of_leaves = bouquet_of_leaves[1:-1]\n",
    "        leaves = detect_all_groups_m6(bouquet_of_leaves)\n",
    "        for leaf in leaves:\n",
    "          leaf = leaf.replace(\" \", \"\")\n",
    "          all_leaves.append(leaf)\n",
    "    return all_leaves\n",
    "\n",
    "\n",
    "\n",
    "def connect_leaves_to_father_m6(newick_string, ax, tree_color='lightblue', diff_color='red'):\n",
    "    '''Draw lines connecting leaves to fathers'''\n",
    "    #print(\"\\n\\nconnect_leaves_to_father_m6\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    all_leaves = find_leaves_newick_m6(newick_string)\n",
    "    for leaf in all_leaves:\n",
    "        #Leaf info\n",
    "        [xl,yl,cl,rl] = extract_info_m6(leaf)\n",
    "        #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n",
    "\n",
    "        # Task: Find the father of the leaf.\n",
    "        # It is the node after the last leaf\n",
    "        # Example:\n",
    "        # (Node9[x=-11,y=19.7,r=12.4567],Node8[x=0,y=7,r=15],Node7[x=6,y=16,r=9])Node6[x=17,y=9,r=20]\n",
    "        # The father of Node9, Node8, and Node7 is Node 6.\n",
    "        # The father of a node is found after the first unbalanced right parenthesis.\n",
    "        index_next_right_par = newick_string.find(\")\" , newick_string.find(leaf))\n",
    "        father_with_tail = newick_string[index_next_right_par+1:]\n",
    "        father_group = detect_all_groups_m6(father_with_tail)\n",
    "        if father_group:\n",
    "          [xf,yf,cf,rf]  = extract_info_m6(father_group[0])\n",
    "          #father_center = (xf,yf)\n",
    "          #Draw link\n",
    "          ax.plot([xl + 0.5, xf + 0.5], [yl + 0.5, yf + 0.5], 'k--', linewidth=0.8, zorder = 3)\n",
    "          # Test: distance2 = distance squared between father and child.\n",
    "          # distance2 must be 2 or  1. \n",
    "          distance2 = (xf-xl)**2 + (yf-yl)**2\n",
    "          #print(f\"distance2: {distance2}\")\n",
    "          if distance2 > 2:\n",
    "              print(f\"\\n distance2 > 2: {distance2}\")\n",
    "              print(f\"xl,yl,xf,yf: {xl} {yl} {xf} {yf}\")\n",
    "              print(f\"leaf: {leaf}\")\n",
    "              print(f\"father_with_tail: {father_with_tail}  \")\n",
    "              print(f\"father: {father_group[0]}\")\n",
    "          \n",
    "    # Leaves are erased: each leave is replaced by \"\"\n",
    "    for leaf in all_leaves:\n",
    "          #print(f\"leaf to be erased: {leaf}\")\n",
    "          newick_string = newick_string.replace(leaf + \",\", \"\")\n",
    "          newick_string = newick_string.replace(leaf, \"\")\n",
    "          #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    #is_valid, message = validate_newick_syntax11b(newick_string)\n",
    "    #print(f\"is valid at connect_leaves_to_father11(...) : {is_valid}, message: {message}\")\n",
    "    #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n",
    "    #print(f\"Is the Newick string a valid morpho-tree? : {status} - {message}\")\n",
    "    groups = detect_all_groups_m6(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")    \n",
    "     \n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_links_m6(newick_string, ax,   diff_color='red' ):\n",
    "    '''Draw lines connecting children to father.\n",
    "       1. Each leaf is connected to its father,\n",
    "          and the corresponding stroke is plotted.\n",
    "       2. Leaves are erased.\n",
    "       3. New leaves are detected and connected to their father.\n",
    "       4. And so on.\n",
    "       '''\n",
    "\n",
    "    print(\"draw_tree_links_m6\")\n",
    "    print(f\"Newick String: {newick_string}\")\n",
    "    length_string = len(newick_string)\n",
    "    #print(f\"length_string: {length_string}\")\n",
    "    groups = detect_all_groups_m6(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    # Leaves are recursively found and connected to their father.\n",
    "    counter_of_trials = 0\n",
    "    while number_of_groups > 1:\n",
    "        #print(\"\\n\\ndraw_tree_links11\")\n",
    "        #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "        newick_string, number_of_groups = connect_leaves_to_father_m6(newick_string, ax, tree_color='lightblue', diff_color='red')\n",
    "        counter_of_trials = counter_of_trials + 1\n",
    "        if counter_of_trials > length_string:\n",
    "          break\n",
    "      \n",
    "    # The root is detected and marked with a cross\n",
    "    groups = detect_all_groups_m6(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    # Mark the root with a red cross\n",
    "    if number_of_groups >= 1:\n",
    "      [x0,y0,c0,r0] = extract_info_m6(groups[-1]) #The root is the last group\n",
    "      #print(f\"Root: a: {a0}, b: {b0}, x: {x0}, y: {y0}, r: {r0}\")\n",
    "      ax.plot([x0, x0 +1 ], [y0+ 0.5, y0+ 0.5],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "      ax.plot([x0 + 0.5, x0 +0.5], [y0, y0+ 1],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_newick_m6(newick_string, title):\n",
    "    #print(\"\\n=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    x,y,c,r = extract_all_infos_m6(newick_string)\n",
    "    #print(f\"c= {c}\")\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    #color = palette[c]\n",
    "    #print(f\"color = {color}\")\n",
    "    plot_tree_nodes_and_their_diffusion_circles_m6(x,y,c,r, ax)\n",
    "     \n",
    "    # Illuminated area is calculated\n",
    "    #colored_coords = get_illuminated_area_m6(x,y,r)\n",
    "    colored_coords = get_colored_area_m6(x,y,c,r)\n",
    "    #print(f\"colored cells at draw_tree_newick_m6: {colored_coords}\")\n",
    "    #print(f\"Number of illuminated cells: {len(colored_coords)}\")\n",
    "    # Colored squares are added to the plot\n",
    "    plot_colored_area_newick_m6(ax, colored_coords, title,  alpha=0.3)\n",
    "    # Lines that define the tree are added\n",
    "    draw_tree_links_m6(newick_string, ax, diff_color='red'  )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return colored_coords\n",
    "\n",
    "def draw_colored_squares_m6(colored_coords, title):\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    plot_colored_area_newick_m6(ax, colored_coords, title,  alpha=0.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ========== EVOLUTION ==========\n",
    "\n",
    "\n",
    "\n",
    "# --- Evolutionary functions ---\n",
    "\n",
    "def create_initial_population_newick_m6(pop_size=ga_params['pop_size'],  average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'],  max_node_radius=ga_params['max_node_radius'] ):\n",
    "    population = []\n",
    "    for i in range(pop_size):\n",
    "        number_of_nodes = max(average_number_of_nodes + random.randint(-5,5), 5)\n",
    "        #print(f\"Number of nodes: {number_of_nodes}\")\n",
    "        new_tree = complete_tree_generator_m6(number_of_nodes, max_number_of_children, max_node_radius)\n",
    "        population.append(new_tree)\n",
    "        #print(f\"Created tree: {i}\")\n",
    "    return population\n",
    "\n",
    "def get_illuminated_coordinates_m6(newick_string):\n",
    "  \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n",
    "  x,y,c,r = extract_all_infos_m6(newick_string)\n",
    "  # Illuminated area is calculated\n",
    "  illuminated_coords = get_colored_area_m6(x,y,c,r)\n",
    "  ##print(f\"Illuminated cells: {illuminated_coords}\")\n",
    "  ##print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n",
    "  return illuminated_coords\n",
    "\n",
    "def calculate_fitness_illumination_m6(candidate_tree_root, target_illuminated_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness based on the symmetric difference between illuminated areas.\n",
    "    Fitness is 1.0 for a perfect match, and decreases as symmetric difference increases.\n",
    "    Reuse of a previous code by Gemini 2.5 Flash\n",
    "    \"\"\"\n",
    "    if candidate_tree_root is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    candidate_illuminated_area = get_illuminated_coordinates_m6(candidate_tree_root)\n",
    "\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = candidate_illuminated_area.symmetric_difference(target_illuminated_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(candidate_illuminated_area.union(target_illuminated_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "        if not target_illuminated_area and not candidate_illuminated_area:\n",
    "            return 1.0 # Perfect match if both are empty\n",
    "        else:\n",
    "            return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "def calculate_fitness_colored_area_m6(test_tree, target_colored_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness of a colored area. \n",
    "    Fitness is 1.0 for a perfect match, and decreases as distance increases.\n",
    "    \"\"\"\n",
    "    if test_tree is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    test_colored_area = get_colored_area_m6(test_tree)\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = test_colored_area.symmetric_difference(target_colored_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(test_colored_area.union(target_colored_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "         if not target_colored_area and not test_colored_area:\n",
    "             return 1.0 # Perfect match if both are empty\n",
    "         else:\n",
    "             return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "\n",
    "def select_parents_newick(population, fitness_scores, num_parents):\n",
    "    parents = []\n",
    "    # Use roulette wheel selection or tournament selection. Tournament is generally robust.\n",
    "    for _ in range(num_parents):\n",
    "        tournament_size = min(5, len(population))\n",
    "        if tournament_size == 0:\n",
    "            break\n",
    "        tournament_candidates = random.sample(list(zip(population, fitness_scores)), tournament_size)\n",
    "        winner = max(tournament_candidates, key=lambda x: x[1])[0]\n",
    "        parents.append(winner)\n",
    "    return parents\n",
    "\n",
    "def find_recombinant_branch_m6(newick_string):\n",
    "  # A full functional branch is recognized because it is enclosed by ()\n",
    "  # and everything inside is full functional.\n",
    "  # We simply keep an accounting of parentheses pairing\n",
    "  #print(\"\\nfind_branch\")\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  pattern_left_par = r'[(]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_left_par, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  start_idx = starts[start_idx]\n",
    "  #print(f\"start_idx: {start_idx}\")\n",
    "  tail = newick_string[start_idx:]\n",
    "  #print(f\"tail: {tail}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  for i in range(len(tail)):\n",
    "    if tail[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if tail[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = tail[: i+1]\n",
    "      #print(f\"Functional branch: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_position_branch_recombination_m6(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_position_branch_recombination_m6\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  branch1 = find_recombinant_branch_m6(newick_string1)\n",
    "  branch2 = find_recombinant_branch_m6(newick_string2)\n",
    "  #print(f\"\\nbranch1: {branch1}\")\n",
    "  #print(f\"branch2: {branch2}\")\n",
    "  # Branches are interchanged\n",
    "  newick_string1 = newick_string1.replace(branch1, branch2)\n",
    "  newick_string2 = newick_string2.replace(branch2, branch1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "def newick_leaves_recombination_m6(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_leaves_recombination_m6\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  leaves1 = find_leaves_newick_m6(newick_string1)  \n",
    "  leaves2 = find_leaves_newick_m6(newick_string2)\n",
    "  if not leaves1 or not leaves2:\n",
    "    return newick_string1, newick_string2\n",
    "  # Select a leaf at random from each tree\n",
    "  leaf1 = random.choice(leaves1)\n",
    "  leaf2 = random.choice(leaves2)\n",
    "  #print(f\"\\nleaf1: {leaf1}\")\n",
    "  #print(f\"leaf2: {leaf2}\")\n",
    "  # Leaves are interchanged\n",
    "  newick_string1 = newick_string1.replace(leaf1, leaf2)\n",
    "  newick_string2 = newick_string2.replace(leaf2, leaf1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "\n",
    "def find_occupied_squares_m6(newick_string):\n",
    "    \"\"\"Detects all the occupied squares in a tree\"\"\"\n",
    "    occupied_squares_set = set()\n",
    "    groups = detect_all_groups_m6(newick_string)\n",
    "    for group in groups:\n",
    "        [x,y,c,r] = extract_info_m6(group)\n",
    "        occupied_squares_set.add((x, y))\n",
    "    return occupied_squares_set\n",
    "\n",
    "def newick_mutation_of_radius_m6(newick_string):\n",
    "  # The pattern of the radius of illumination, r = 0.2\n",
    "  pattern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_r, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  l = len(groups[start_idx])\n",
    "  #print(f\"l: {l}\")\n",
    "  # Select a new radius\n",
    "  new_radius = random.uniform(0.5, 2)\n",
    "  #print(f\"new_radius: {new_radius}\")\n",
    "\n",
    "  new_info = \"r=\" + str(new_radius)\n",
    "  #print(f\"new_info: {new_info}\")\n",
    "  # Replace the info\n",
    "  mutated_newick_string = replace_str_subsequence_m6(newick_string, starts[start_idx], starts[start_idx] + l, new_info)\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  return mutated_newick_string\n",
    "\n",
    " \n",
    "def newick_find_insertion_patterns_m6(newick_string):\n",
    "  \"\"\" All insertion patterns are found and one is chosen at random\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][])]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  return group, start\n",
    " \n",
    "\n",
    "def insertion_of_branch_m6(newick_string):\n",
    "  #original_newick_string = newick_string\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  # A group together its start site are elected\n",
    "  group, start =  newick_find_insertion_patterns_m6(newick_string)\n",
    "  # String to be inserted\n",
    "  # child_string = \"(Node_m6[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node_m6[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07])Node1[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.95]\"\n",
    "  child_string = complete_tree_generator_m6(max_depth = 2, max_numb_children = 2, max_node_radius= 1.5)\n",
    "  if group:\n",
    "    #print(f\"Group (main): {group}\")\n",
    "    #print(f\"Start (main): {start}\")\n",
    "    # Does the group have children?\n",
    "    #char_before = newick_string[start-1]\n",
    "    #print(f\"char_before: {char_before}\")\n",
    "\n",
    "    newick_string = newick_string.replace(\"])\", \"],\" + child_string + \")\")\n",
    "  else:\n",
    "    # a tree is given as a child to the root\n",
    "    newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "  # Cosmetics\n",
    "  #newick_string = newick_string.replace(\",,\", \",\")\n",
    "\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {original_newick_string}\")\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_left_parenthesis__m6(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns (N  are found and one is chosen at random\n",
    "      The new info will be inserted to the right of the parenthesis\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[(][N]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m60: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m6(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m6(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m6(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \"(\" + child_string +  \")\" + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "def select_one_insertion_pattern_comma__m6(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All  commas are found and one is chosen at random,\n",
    "  the child will be inserted after the comma\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][,]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m60: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m6(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m6(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m6(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+2] + \"(\" + child_string + \")\" + newick_string[start+2:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_parentheses__m6(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns ]) are found and one is chosen at random\n",
    "      The new info will be inserted between ] and ) as a brother\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][)]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m60: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m6(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m6(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m6(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \",\" + child_string + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "def insertion_of_leaf_m6(newick_string, counter, max_illumination_radius):    #original_newick_string = newick_string\n",
    "    \"\"\" All insertion patterns are found and one is chosen at random\n",
    "        for the new info to be inserted \"\"\"\n",
    "    case = random.choice([1, 2, 3])  # Randomly choose between three insertion methods\n",
    "    match case:\n",
    "        case 1:\n",
    "            newick_string = select_one_insertion_pattern_comma__m6(newick_string, counter, max_illumination_radius)\n",
    "        case 2:\n",
    "            newick_string = select_one_insertion_pattern_parentheses__m6(newick_string, counter, max_illumination_radius)\n",
    "        case 3:\n",
    "            newick_string = select_one_insertion_pattern_left_parenthesis__m6(newick_string, counter, max_illumination_radius)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    newick_string = newick_string.replace(\" \", \"\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    return newick_string\n",
    "\n",
    "\n",
    "\n",
    "def deletion_of_leaf_m6(newick_string):\n",
    "    \"\"\"A leaf is deleted at random from the Newick string. \n",
    "        If there are no leaves, the original string is returned.\"\"\"\n",
    "    # All leaves are detected\n",
    "  \n",
    "    leaves = find_leaves_newick_m6(newick_string)\n",
    "    if not leaves:\n",
    "        return newick_string  # No leaves to delete\n",
    "    # Select a leaf at random\n",
    "    leaf_to_delete = random.choice(leaves)\n",
    "    # Remove the selected leaf from the Newick string\n",
    "    newick_string = newick_string.replace(leaf_to_delete, \"\")\n",
    "    # Clean up any potential formatting issues (like double commas)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    return newick_string\n",
    "\n",
    "  \n",
    "\n",
    "def newick_mutation_branches_m6(newick_string):\n",
    "  # Mutation of radius of illumination and of complete branches\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    newick_string = newick_mutation_of_radius_m6(newick_string)\n",
    "    newick_string = insertion_of_branch_m6(newick_string)\n",
    "    newick_string = deletion_of_branch_m6(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "def newick_mutation_leaves_m6(newick_string, ga_params):\n",
    "  \"\"\"Radius is mutated, and only leaves can be inserted or deleted\"\"\"\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    # Mutation of radius of illumination and of complete leaves\n",
    "    newick_string = newick_mutation_of_radius_m6(newick_string)\n",
    "    newick_string = insertion_of_leaf_m6(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n",
    "    newick_string = deletion_of_leaf_m6(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "def plot_illuminated_area_m6(ax, illuminated_coords, title, color='blue', alpha=0.5):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not illuminated_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y in illuminated_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=color, alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(c[0] for c in illuminated_coords) - 1\n",
    "    max_x = max(c[0] for c in illuminated_coords) + 2\n",
    "    min_y = min(c[1] for c in illuminated_coords) - 1\n",
    "    max_y = max(c[1] for c in illuminated_coords) + 2\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "# --- MAIN EVOLUTIONARY SIMULATION LOOP ---\n",
    "\n",
    "def oneEvolutionaryProcess_m6(target_tree_params, ga_params):\n",
    "    # A previous code by Gemini was reused.\n",
    "\n",
    "    # 1. Generate Target Tree and its Illuminated Area\n",
    "    #print(\"\\n--- Generating Target Tree ---\")\n",
    "    #random.seed(ga_params['seed'] + 1) # Use a different seed for target generation\n",
    "    #complete_tree_generator_m6(total_number_of_nodes, max_number_children, max_node_radius)\n",
    "    target_colored_tree = complete_tree_generator_m6(total_number_of_nodes = target_tree_params['total_number_of_nodes'], max_number_children = target_tree_params['max_number_of_children'], max_node_radius = target_tree_params['max_node_radius'])\n",
    "    print(f\"\\nTarget colored tree: {target_colored_tree}\")\n",
    "    number_of_groups = count_groups_m6(target_colored_tree)\n",
    "    print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n",
    "    target_colored_area = draw_tree_newick_m6(target_colored_tree, title=f\"Target colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    print(f\"Colored area in target tree: {target_colored_area}\")\n",
    "    title = 'Target colored area'\n",
    "    draw_colored_squares_m6(target_colored_area, title)\n",
    "    \n",
    "    # length as a string\n",
    "    length_of_target_tree = len(target_colored_tree)\n",
    "\n",
    "\n",
    "    # 2. Initialize Population\n",
    "    #random.seed(ga_params['seed']) # Reset seed for GA population generation\n",
    "    #print(f\"\\n--- Initializing Population of {ga_params['pop_size']} Trees ---\")\n",
    "    population = create_initial_population_newick_m6(\n",
    "        pop_size=ga_params['pop_size'],average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'], max_node_radius=ga_params['max_node_radius']  )\n",
    "         \n",
    "       \n",
    "    print(f\"Population initialized with {len(population)} individuals.\")\n",
    "    #print(f\"Population: {population}\")\n",
    "\n",
    "    best_tree_overall = \"\"\n",
    "    best_fitness_overall = -1.0\n",
    "    history = []\n",
    "\n",
    "    # 3. Evolutionary Loop\n",
    "    #print(f\"\\n--- Starting Evolutionary Loop for {ga_params['generations']} Generations ---\")\n",
    "    for generation in range(ga_params['generations']):\n",
    "        if generation % 10000 == 0:\n",
    "            print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n",
    "        fitness_scores = [calculate_fitness_illumination_m6(tree, target_colored_area)for tree in population]\n",
    "\n",
    "        current_best_fitness = max(fitness_scores)\n",
    "        current_best_tree = population[np.argmax(fitness_scores)]\n",
    "\n",
    "        if current_best_fitness > best_fitness_overall:\n",
    "            best_fitness_overall = current_best_fitness\n",
    "            best_tree_overall = copy.deepcopy(current_best_tree) # Deepcopy to save the best tree\n",
    "\n",
    "        history.append(current_best_fitness)\n",
    "\n",
    "        #print(f\"Generation {generation+1}/{ga_params['generations']}: Best Fitness = {current_best_fitness:.4f} (Overall Best: {best_fitness_overall:.4f})\")\n",
    "\n",
    "        if best_fitness_overall >= 0.999: # Almost perfect match\n",
    "            #print(\"Target illumination achieved (or very close)! Stopping evolution.\")\n",
    "            return target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, history\n",
    "\n",
    "        # Selection\n",
    "        parents = select_parents_newick(population, fitness_scores, ga_params['pop_size'] // 2)\n",
    "\n",
    "        # Create next generation\n",
    "        next_population = []\n",
    "        # Elitism: Keep the best individual from the current generation\n",
    "        if best_tree_overall and current_best_fitness == best_fitness_overall: # Only if it's the current best in this generation\n",
    "            next_population.append(copy.deepcopy(current_best_tree))\n",
    "\n",
    "        while len(next_population) < ga_params['pop_size']:\n",
    "            if len(parents) < 2: # Not enough parents for crossover, fill with new random trees\n",
    "                new_tree =  complete_tree_generator_m6(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'] )\n",
    "                if new_tree:\n",
    "                    next_population.append(new_tree)\n",
    "                continue\n",
    "\n",
    "            # Randomly pick two parents (can be the same parent multiple times with replacement for simplicity)\n",
    "            p1 = random.choice(parents)\n",
    "            p2 = random.choice(parents)\n",
    "            child1, child2 = p1,p2 # No crossover, just pass parents to children for mutation\n",
    "            if recombination_of_leaves:\n",
    "                # Crossover newick_position_branch_recombination_m6\n",
    "                # child1, child2 = newick_position_branch_recombination_m6(p1, p2)\n",
    "                # Crossover newick_leaves_recombination_m6\n",
    "                child1, child2 =newick_leaves_recombination_m6(p1,p2)\n",
    "            # Mutation: Apply mutation to both children\n",
    "            mutated_child1 = newick_mutation_leaves_m6(child1, ga_params)\n",
    "            mutated_child2 = newick_mutation_leaves_m6(child2, ga_params)\n",
    "\n",
    "\n",
    "            next_population.append(mutated_child1)\n",
    "            if len(next_population) < ga_params['pop_size']:\n",
    "                next_population.append(mutated_child2)\n",
    "\n",
    "        population = next_population[:ga_params['pop_size']]\n",
    "        # print(f\"Population size for next generation: {len(population)}\")\n",
    "        # print(f\"Best fitness this generation: {current_best_fitness:.4f}, Overall best fitness: {best_fitness_overall:.4f}\")\n",
    "        # print(f\"Best tree this generation: {current_best_tree}\")\n",
    "        # If you want to see the entire population, uncomment the following line:\n",
    "        # print(f\"Population: {population}\")\n",
    "        # Cutt-off for the length of the Newick string to avoid excessive growth\n",
    "        population = [tree for tree in population if len(tree) <= 2 * length_of_target_tree]  # Allow some growth but limit it\n",
    "        #print(f\"Population size after length cut-off: {len(population)}\")\n",
    "        # If the population is too small, refill\n",
    "        while len(population) < ga_params['pop_size']:\n",
    "            new_tree = complete_tree_generator_m6(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'])\n",
    "            if new_tree:\n",
    "                population.append(new_tree) \n",
    "    #print(\"\\n--- Evolution Finished ---\")\n",
    "    #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n",
    "    return target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, history, number_of_groups\n",
    "    \n",
    "     \n",
    "        \n",
    "  \n",
    "        \n",
    "def draw_history_m6(fitness_history, number_of_nodes, population_size):\n",
    "    print(\"draw_histories_m6(fitness_historiy, number_of_nodes, population_size)\")\n",
    "    # Plot the fitness history \n",
    "    x_range = range(len(fitness_history))\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    plt.plot(x_range, fitness_history, label=f'pop_size {population_size}, Number of nodes {number_of_nodes}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Evolution of the Fitness of the best colored morpho-tree with pop_size = {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()        \n",
    " \n",
    "    \n",
    "# Run an evolutionary process for each value of target tree and GA parameters\n",
    "# Produces a comparative graphic\n",
    "if __name__ == \"__main__\":\n",
    "    print_parameters_m6(target_tree_params, ga_params)\n",
    "    \n",
    "    target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, fitness_history, number_of_groups = oneEvolutionaryProcess_m6(target_tree_params, ga_params)\n",
    "    final_best_colored_area = draw_tree_newick_m6(best_tree_overall, title=f\"Best evolved colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    print(f\"Colored area in target tree: {target_colored_area}\")\n",
    "    title = 'Best evolved colored area'\n",
    "    draw_colored_squares_m6(final_best_colored_area, title)\n",
    "    \n",
    "    \n",
    "    #print(f\"history_m6: {history_m6}\")\n",
    "    #print(f\"number_of_groups_m6 {number_of_groups_m6}\")\n",
    "    draw_history_m6(fitness_history, number_of_nodes = target_tree_params['total_number_of_nodes'], population_size = ga_params['pop_size'])\n",
    "     \n",
    "\n",
    "    end_time = time.time()\n",
    "    Execution_time = end_time - start_time\n",
    "    # print(f\"Execution time: {Execution_time:.2f} seconds\")\n",
    "    # Execution time in hours, minutes and seconds:\n",
    "    hours = int(Execution_time // 3600)\n",
    "    minutes = int((Execution_time % 3600) // 60)\n",
    "    seconds = int(Execution_time % 60)\n",
    "    print(f\"Execution time: {hours} hours, {minutes} minutes, {seconds} seconds\")\n",
    "    if memory_monitoring:\n",
    "        # displaying the allocated memory\n",
    "        print(tracemalloc.get_traced_memory())\n",
    "        # stopping the memory allocation library\n",
    "        tracemalloc.stop()\n",
    "    \n",
    "# End of program evolution_of_colored_morpho_trees_m6.py\n",
    "\n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf42ec5-dc69-4306-a857-ef72d8fd6833",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el programa anterior y redacta tus observaciones.\n",
    ">\n",
    "> *Observación.* En menos de 50 mil generaciones, la Evolución alcanza una meseta y permanece allí hasta la generación 500000 cuando el programa se detiene.\n",
    "\n",
    "**Ejercicio.** Desarrolla el código para estudiar la batalla de la Evolución contra un número creciente de nodos.\n",
    ">\n",
    "> *Respuesta.* El siguiente programa representa la batalla de la Evolución contra un número creciente de nodos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50f45dcd-8bf9-4fc7-b5bc-1a47d38aa323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "\n",
      "Running Evolutionary process for number_of_nodes = 10 and generations = 30001\n",
      "\n",
      "Target colored tree: ((Node6[x=1.0,y=-2.0,c=4,r=1.23])Node2[x=1.0,y=-1.0,c=4,r=1.19],(((Node9[x=1.0,y=-3.0,c=0,r=0.83])Node5[x=0.0,y=-2.0,c=5,r=1.49],(Node8[x=-2.0,y=-1.0,c=6,r=1.11],Node7[x=-1.0,y=-2.0,c=4,r=1.24])Node4[x=-1.0,y=-1.0,c=5,r=1.37])Node3[x=0.0,y=-1.0,c=4,r=1.25])Node1[x=-1.0,y=0.0,c=5,r=0.81])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 10\n",
      "draw_tree_links_m7\n",
      "Newick String: ((Node6[x=1.0,y=-2.0,c=4,r=1.23])Node2[x=1.0,y=-1.0,c=4,r=1.19],(((Node9[x=1.0,y=-3.0,c=0,r=0.83])Node5[x=0.0,y=-2.0,c=5,r=1.49],(Node8[x=-2.0,y=-1.0,c=6,r=1.11],Node7[x=-1.0,y=-2.0,c=4,r=1.24])Node4[x=-1.0,y=-1.0,c=5,r=1.37])Node3[x=0.0,y=-1.0,c=4,r=1.25])Node1[x=-1.0,y=0.0,c=5,r=0.81])Node_0[x=0,y=0,c=3,r=1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(-2, -3, 4), (-1, -3, 4), (0, -3, 0), (-1, -2, 5), (-1, -1, 5), (-2, -1, 5), (-2, -2, 5), (1, -3, 0), (0, -2, 4), (0, -1, 4), (0, 1, 3), (1, -2, 4), (1, -1, 4), (-3, 0, 6), (-3, -1, 6), (-3, -2, 6), (2, -2, 4), (2, -1, 4), (0, 0, 4), (-2, 0, 5), (-1, 0, 5), (1, 0, 4), (0, -4, 0), (1, 1, 3), (0, -2, 0), (0, -1, 3), (0, -3, 5), (-1, -2, 4), (-2, -2, 4), (2, 0, 4), (1, -4, 0), (-2, -1, 4), (1, -3, 5), (-1, -1, 4), (1, -2, 0), (0, 1, 5), (2, -4, 0), (1, -1, 3), (2, -2, 0), (0, 0, 3), (-1, 0, 4), (2, -3, 4), (1, 0, 3), (0, -3, 4), (-1, -3, 5), (-1, -1, 3), (0, -2, 5), (0, -1, 5), (-2, -1, 6), (1, -3, 4), (-2, -2, 6), (-1, -1, 6), (-1, -2, 6), (-2, 1, 5), (-1, 1, 5), (1, -2, 5), (1, -1, 5), (-1, 0, 3), (0, 0, 5), (-2, 0, 6), (-1, 0, 6), (-1, 1, 3), (2, -3, 0)}\n",
      "Population initialized with 200 individuals.\n",
      "\n",
      "--- Generation 1/30001, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/30001, Best fitness 0.55319 ---\n",
      "\n",
      "--- Generation 2001/30001, Best fitness 0.5814 ---\n",
      "\n",
      "--- Generation 3001/30001, Best fitness 0.60227 ---\n",
      "\n",
      "--- Generation 4001/30001, Best fitness 0.64634 ---\n",
      "\n",
      "--- Generation 5001/30001, Best fitness 0.64634 ---\n",
      "\n",
      "--- Generation 6001/30001, Best fitness 0.66265 ---\n",
      "\n",
      "--- Generation 7001/30001, Best fitness 0.66265 ---\n",
      "\n",
      "--- Generation 8001/30001, Best fitness 0.66265 ---\n",
      "\n",
      "--- Generation 9001/30001, Best fitness 0.66265 ---\n",
      "\n",
      "--- Generation 10001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 11001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 12001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 13001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 14001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 15001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 16001/30001, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 17001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 18001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 19001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 20001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 21001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 22001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 23001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 24001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 25001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 26001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 27001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 28001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 29001/30001, Best fitness 0.67073 ---\n",
      "\n",
      "--- Generation 30001/30001, Best fitness 0.67073 ---\n",
      "1\n",
      "\n",
      "Running Evolutionary process for number_of_nodes = 50 and generations = 30002\n",
      "\n",
      "Target colored tree: ((((((((Node28[x=1.0,y=3.0,c=3,r=0.58])Node22[x=1.0,y=2.0,c=2,r=0.63])Node15[x=0.0,y=3.0,c=1,r=0.82],(Node36[x=1.0,y=1.0,c=0,r=0.58])Node14[x=0.0,y=1.0,c=3,r=0.5])Node9[x=-1.0,y=2.0,c=5,r=0.69])Node7[x=-1.0,y=1.0,c=5,r=1.33],((((((((Node45[x=-5.0,y=0.0,c=4,r=1.18])Node35[x=-4.0,y=0.0,c=0,r=1.32])Node27[x=-4.0,y=1.0,c=1,r=1.48],(Node44[x=-4.0,y=2.0,c=2,r=1.36],Node43[x=-3.0,y=0.0,c=4,r=1.32])Node26[x=-3.0,y=1.0,c=2,r=0.69])Node21[x=-3.0,y=2.0,c=5,r=1.22])Node18[x=-2.0,y=3.0,c=1,r=1.26],((Node42[x=-2.0,y=5.0,c=5,r=1.12],Node41[x=-4.0,y=5.0,c=6,r=0.95])Node20[x=-3.0,y=4.0,c=1,r=1.21],((Node34[x=-5.0,y=2.0,c=1,r=1.13],(Node49[x=-5.0,y=5.0,c=1,r=1.47],Node48[x=-5.0,y=4.0,c=5,r=0.53])Node33[x=-4.0,y=4.0,c=2,r=1.39])Node25[x=-4.0,y=3.0,c=3,r=0.68])Node19[x=-3.0,y=3.0,c=6,r=1.29])Node17[x=-2.0,y=4.0,c=1,r=1.3])Node13[x=-1.0,y=3.0,c=5,r=1.47])Node11[x=-2.0,y=2.0,c=0,r=1.49])Node8[x=-2.0,y=1.0,c=6,r=1.17])Node6[x=-2.0,y=0.0,c=0,r=1.2])Node5[x=-1.0,y=0.0,c=1,r=1.17])Node3[x=-2.0,y=-1.0,c=4,r=0.69],(((((((Node40[x=0.0,y=-6.0,c=2,r=0.97])Node32[x=0.0,y=-5.0,c=1,r=1.17],(Node39[x=0.0,y=-3.0,c=4,r=1.05])Node31[x=0.0,y=-4.0,c=0,r=0.81])Node24[x=-1.0,y=-5.0,c=4,r=0.99],((Node38[x=-3.0,y=-3.0,c=6,r=1.13])Node30[x=-2.0,y=-4.0,c=5,r=0.73],((Node47[x=-3.0,y=-7.0,c=2,r=0.75],Node46[x=-4.0,y=-7.0,c=5,r=0.59])Node37[x=-3.0,y=-6.0,c=6,r=0.77])Node29[x=-4.0,y=-5.0,c=6,r=0.55])Node23[x=-3.0,y=-5.0,c=5,r=1.4])Node16[x=-2.0,y=-5.0,c=0,r=0.81])Node12[x=-1.0,y=-4.0,c=3,r=1.26])Node10[x=-2.0,y=-3.0,c=0,r=1.07])Node4[x=-2.0,y=-2.0,c=2,r=0.78])Node2[x=-1.0,y=-2.0,c=4,r=1.15])Node1[x=-1.0,y=-1.0,c=0,r=1.37])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 50\n",
      "draw_tree_links_m7\n",
      "Newick String: ((((((((Node28[x=1.0,y=3.0,c=3,r=0.58])Node22[x=1.0,y=2.0,c=2,r=0.63])Node15[x=0.0,y=3.0,c=1,r=0.82],(Node36[x=1.0,y=1.0,c=0,r=0.58])Node14[x=0.0,y=1.0,c=3,r=0.5])Node9[x=-1.0,y=2.0,c=5,r=0.69])Node7[x=-1.0,y=1.0,c=5,r=1.33],((((((((Node45[x=-5.0,y=0.0,c=4,r=1.18])Node35[x=-4.0,y=0.0,c=0,r=1.32])Node27[x=-4.0,y=1.0,c=1,r=1.48],(Node44[x=-4.0,y=2.0,c=2,r=1.36],Node43[x=-3.0,y=0.0,c=4,r=1.32])Node26[x=-3.0,y=1.0,c=2,r=0.69])Node21[x=-3.0,y=2.0,c=5,r=1.22])Node18[x=-2.0,y=3.0,c=1,r=1.26],((Node42[x=-2.0,y=5.0,c=5,r=1.12],Node41[x=-4.0,y=5.0,c=6,r=0.95])Node20[x=-3.0,y=4.0,c=1,r=1.21],((Node34[x=-5.0,y=2.0,c=1,r=1.13],(Node49[x=-5.0,y=5.0,c=1,r=1.47],Node48[x=-5.0,y=4.0,c=5,r=0.53])Node33[x=-4.0,y=4.0,c=2,r=1.39])Node25[x=-4.0,y=3.0,c=3,r=0.68])Node19[x=-3.0,y=3.0,c=6,r=1.29])Node17[x=-2.0,y=4.0,c=1,r=1.3])Node13[x=-1.0,y=3.0,c=5,r=1.47])Node11[x=-2.0,y=2.0,c=0,r=1.49])Node8[x=-2.0,y=1.0,c=6,r=1.17])Node6[x=-2.0,y=0.0,c=0,r=1.2])Node5[x=-1.0,y=0.0,c=1,r=1.17])Node3[x=-2.0,y=-1.0,c=4,r=0.69],(((((((Node40[x=0.0,y=-6.0,c=2,r=0.97])Node32[x=0.0,y=-5.0,c=1,r=1.17],(Node39[x=0.0,y=-3.0,c=4,r=1.05])Node31[x=0.0,y=-4.0,c=0,r=0.81])Node24[x=-1.0,y=-5.0,c=4,r=0.99],((Node38[x=-3.0,y=-3.0,c=6,r=1.13])Node30[x=-2.0,y=-4.0,c=5,r=0.73],((Node47[x=-3.0,y=-7.0,c=2,r=0.75],Node46[x=-4.0,y=-7.0,c=5,r=0.59])Node37[x=-3.0,y=-6.0,c=6,r=0.77])Node29[x=-4.0,y=-5.0,c=6,r=0.55])Node23[x=-3.0,y=-5.0,c=5,r=1.4])Node16[x=-2.0,y=-5.0,c=0,r=0.81])Node12[x=-1.0,y=-4.0,c=3,r=1.26])Node10[x=-2.0,y=-3.0,c=0,r=1.07])Node4[x=-2.0,y=-2.0,c=2,r=0.78])Node2[x=-1.0,y=-2.0,c=4,r=1.15])Node1[x=-1.0,y=-1.0,c=0,r=1.37])Node_0[x=0,y=0,c=3,r=1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(1, -4, 4), (-1, 3, 1), (-2, 3, 1), (0, 3, 3), (-2, -5, 6), (-6, 1, 1), (0, -4, 0), (-3, 3, 1), (1, 3, 2), (-3, -5, 6), (-1, 3, 5), (-2, 3, 5), (-1, -5, 1), (0, -5, 3), (-3, -4, 6), (-6, 4, 1), (-4, 4, 3), (-2, -1, 2), (-1, -1, 2), (-2, -2, 2), (0, -1, 4), (0, -2, 4), (-1, -2, 2), (-3, 3, 5), (1, -5, 2), (0, 2, 1), (-4, 0, 1), (1, 2, 1), (-4, 1, 2), (-6, 4, 5), (-2, -2, 6), (-3, 3, 0), (1, 3, 1), (-3, -5, 5), (-5, -1, 0), (0, 2, 5), (-2, 5, 5), (-1, 5, 5), (-3, -2, 6), (-2, -6, 4), (-1, -6, 4), (-5, 1, 1), (-6, 1, 4), (2, 3, 3), (-4, 4, 2), (-4, -7, 2), (-2, 6, 5), (-1, 6, 5), (-2, -1, 1), (-1, -1, 1), (0, -1, 3), (-3, -5, 0), (1, -5, 1), (-2, -3, 5), (-1, -3, 5), (-4, 0, 0), (-3, 6, 5), (1, 2, 0), (0, -6, 1), (-4, 1, 1), (-4, 4, 6), (-5, 5, 2), (-1, -7, 2), (1, 1, 3), (-2, -7, 2), (-1, -3, 0), (-2, -3, 0), (-4, 0, 4), (-5, 1, 0), (-5, -7, 5), (-3, 2, 2), (-4, 3, 5), (-4, -8, 5), (-2, 1, 2), (-5, 0, 4), (-2, -7, 6), (-1, -3, 4), (-2, -3, 4), (-3, -1, 0), (-3, -2, 0), (-5, 4, 6), (-2, 0, 5), (-1, 0, 5), (-3, 1, 1), (-4, -5, 5), (-3, -7, 6), (-2, 1, 6), (-1, 1, 6), (-4, -4, 6), (1, 1, 2), (-3, -1, 4), (-6, 2, 1), (-4, 2, 3), (1, 4, 3), (-5, 4, 1), (-2, 0, 0), (-1, 0, 0), (-3, 1, 5), (-3, 4, 1), (-2, 1, 1), (-1, 1, 1), (1, -3, 0), (0, 1, 3), (-2, 4, 6), (-3, 5, 2), (-3, -6, 2), (-5, 4, 5), (-2, 0, 4), (-3, 1, 0), (-3, 4, 5), (-3, -7, 5), (1, -3, 4), (-1, 4, 1), (-2, 4, 1), (-3, 5, 6), (-3, -6, 6), (-3, -3, 2), (0, -4, 4), (-4, 2, 2), (1, -7, 2), (0, 0, 1), (-3, 1, 4), (-2, 3, 0), (-1, 3, 0), (-2, -5, 5), (-1, -5, 5), (-5, 2, 2), (-4, -1, 0), (1, 0, 0), (-2, -4, 6), (-5, 3, 3), (-4, 2, 6), (-1, 0, 3), (0, 0, 5), (-1, -5, 0), (-2, -5, 0), (0, -5, 2), (-4, -1, 4), (-3, -4, 5), (-1, -4, 1), (0, -4, 3), (-4, 2, 1), (-3, 0, 2), (1, 4, 1), (-6, 5, 1), (-5, -1, 4), (-4, -6, 2), (0, 3, 1), (-1, -5, 4), (-2, -5, 4), (-5, 2, 1), (-3, -4, 0), (-4, -7, 6), (-3, 0, 6), (-3, -8, 2), (-4, -6, 6), (0, 3, 5), (0, -5, 1), (-4, 1, 5), (-4, 4, 1), (-2, 2, 0), (-2, -1, 0), (-1, -1, 0), (-2, -2, 0), (-1, -2, 0), (-5, 5, 6), (-4, 5, 2), (-3, 3, 3), (1, -5, 0), (-2, 2, 6), (-1, 2, 6), (1, -4, 1), (-2, -5, 3), (-1, -5, 3), (-5, 1, 4), (-4, 1, 0), (-4, 4, 5), (-4, -7, 5), (-3, 2, 6), (-2, -1, 4), (-2, -2, 4), (-5, 5, 1), (-1, -1, 4), (-1, -2, 4), (-4, 5, 6), (-1, 2, 1), (-2, 2, 1), (0, 2, 3), (0, -5, 0), (-1, -6, 2), (1, 2, 3), (-2, -6, 2), (0, -6, 4), (-4, 1, 4), (-3, 2, 1), (0, -1, 1), (-5, 5, 5), (-3, 3, 2), (1, 3, 3), (-1, 2, 5), (-2, 2, 5), (-2, -3, 3), (-1, -3, 3), (-2, -6, 6), (-3, 2, 5), (-1, -1, 3), (1, -6, 2), (-1, 1, 5), (-2, 1, 5), (-4, -4, 5), (0, -3, 0), (1, -2, 4), (-1, -6, 1), (1, 2, 2), (-3, 2, 0), (-6, 3, 1), (-4, 3, 3), (0, -1, 0), (0, -2, 0), (-2, 1, 0), (-1, 1, 0), (-2, 4, 5), (-1, 4, 5), (-3, 5, 1), (-3, -3, 6), (-2, -3, 2), (-1, -3, 2), (0, -3, 4), (-6, 0, 4), (-2, -6, 5), (-5, 1, 2), (-6, 6, 1), (1, -6, 1), (-2, 1, 4), (0, -7, 2), (-3, 5, 5), (-3, -6, 5), (-2, 5, 1), (1, 1, 0), (-1, 5, 1), (-2, -3, 6), (-3, 6, 6), (-3, -2, 2), (-3, -1, 2), (1, -1, 3), (-2, -6, 0), (-1, -6, 0), (0, -6, 2), (0, 0, 0), (-4, 3, 2), (-4, -8, 2), (0, 1, 1), (-5, 0, 1), (-3, -6, 0), (-3, -3, 5), (0, -3, 3), (-2, -4, 5), (-1, -4, 5), (2, 2, 2), (-5, 3, 2), (-4, 2, 5), (-4, 3, 6), (-1, 1, 3), (0, 1, 5), (-3, -3, 0), (1, 0, 3), (-1, -4, 0), (-2, -4, 0), (-3, 0, 1), (-2, 0, 6), (-1, 0, 6), (-2, -8, 2), (-4, 3, 1), (-3, 1, 2), (-4, -5, 6), (0, 1, 0), (-5, 0, 0), (0, 4, 1), (-1, -4, 4), (-2, -4, 4), (-5, 3, 1), (-5, -5, 6), (-5, 4, 2), (-1, 0, 1), (-2, 0, 1), (0, 0, 3), (-3, 1, 6), (-3, 4, 2), (-3, -7, 2), (-4, -6, 5), (-4, 6, 6), (0, 4, 5), (0, -4, 1), (-5, 3, 5), (2, 1, 0), (-3, 0, 0), (-4, 5, 1), (-4, -3, 6), (-5, 6, 6), (-3, 4, 6), (1, -4, 0), (-2, 3, 6), (-1, -5, 2), (-4, 6, 1), (0, -5, 4), (-6, -1, 4), (-4, -2, 6), (-1, -4, 3), (-2, -4, 3), (-3, 0, 4), (-3, 3, 6), (-5, 6, 1), (-1, 2, 0), (0, 2, 2)}\n",
      "Population initialized with 200 individuals.\n",
      "\n",
      "--- Generation 1/30002, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/30002, Best fitness 0.49664 ---\n",
      "\n",
      "--- Generation 2001/30002, Best fitness 0.52809 ---\n",
      "\n",
      "--- Generation 3001/30002, Best fitness 0.53846 ---\n",
      "\n",
      "--- Generation 4001/30002, Best fitness 0.55333 ---\n",
      "\n",
      "--- Generation 5001/30002, Best fitness 0.58998 ---\n",
      "\n",
      "--- Generation 6001/30002, Best fitness 0.59132 ---\n",
      "\n",
      "--- Generation 7001/30002, Best fitness 0.60407 ---\n",
      "\n",
      "--- Generation 8001/30002, Best fitness 0.60789 ---\n",
      "\n",
      "--- Generation 9001/30002, Best fitness 0.62238 ---\n",
      "\n",
      "--- Generation 10001/30002, Best fitness 0.62238 ---\n",
      "\n",
      "--- Generation 11001/30002, Best fitness 0.62238 ---\n",
      "\n",
      "--- Generation 12001/30002, Best fitness 0.62824 ---\n",
      "\n",
      "--- Generation 13001/30002, Best fitness 0.63033 ---\n",
      "\n",
      "--- Generation 14001/30002, Best fitness 0.63208 ---\n",
      "\n",
      "--- Generation 15001/30002, Best fitness 0.6342 ---\n",
      "\n",
      "--- Generation 16001/30002, Best fitness 0.64554 ---\n",
      "\n",
      "--- Generation 17001/30002, Best fitness 0.64623 ---\n",
      "\n",
      "--- Generation 18001/30002, Best fitness 0.65083 ---\n",
      "\n",
      "--- Generation 19001/30002, Best fitness 0.65083 ---\n",
      "\n",
      "--- Generation 20001/30002, Best fitness 0.65238 ---\n",
      "\n",
      "--- Generation 21001/30002, Best fitness 0.66508 ---\n",
      "\n",
      "--- Generation 22001/30002, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 23001/30002, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 24001/30002, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 25001/30002, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 26001/30002, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 27001/30002, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 28001/30002, Best fitness 0.66748 ---\n",
      "\n",
      "--- Generation 29001/30002, Best fitness 0.66748 ---\n",
      "\n",
      "--- Generation 30001/30002, Best fitness 0.66829 ---\n",
      "2\n",
      "\n",
      "Running Evolutionary process for number_of_nodes = 250 and generations = 30003\n",
      "\n",
      "Target colored tree: ((((((((((((Node222[x=-4.0,y=5.0,c=0,r=1.5])Node187[x=-3.0,y=4.0,c=4,r=0.59])Node151[x=-2.0,y=4.0,c=5,r=1.39])Node124[x=-1.0,y=5.0,c=5,r=1.0])Node102[x=-1.0,y=4.0,c=3,r=0.63])Node82[x=0.0,y=3.0,c=3,r=1.43],((((Node221[x=2.0,y=6.0,c=5,r=1.18],Node220[x=0.0,y=6.0,c=2,r=1.31])Node186[x=1.0,y=5.0,c=6,r=0.91])Node150[x=0.0,y=5.0,c=5,r=0.9],(Node185[x=-1.0,y=3.0,c=3,r=1.39])Node149[x=0.0,y=4.0,c=2,r=0.53])Node123[x=1.0,y=4.0,c=5,r=1.24],(((Node219[x=-2.0,y=3.0,c=5,r=1.29],Node218[x=-2.0,y=2.0,c=6,r=1.48])Node184[x=-1.0,y=2.0,c=3,r=1.24],(Node217[x=-2.0,y=1.0,c=3,r=1.17])Node183[x=-1.0,y=1.0,c=1,r=1.12])Node148[x=0.0,y=2.0,c=2,r=1.48],Node147[x=2.0,y=4.0,c=6,r=0.96])Node122[x=1.0,y=3.0,c=2,r=0.99])Node81[x=2.0,y=3.0,c=0,r=0.8])Node61[x=1.0,y=2.0,c=5,r=0.9])Node35[x=1.0,y=1.0,c=2,r=0.62],((((((Node182[x=7.0,y=1.0,c=6,r=0.5],(Node215[x=9.0,y=1.0,c=1,r=0.8])Node181[x=8.0,y=1.0,c=5,r=1.28])Node146[x=7.0,y=2.0,c=1,r=0.58])Node101[x=6.0,y=3.0,c=6,r=1.29])Node80[x=5.0,y=3.0,c=6,r=0.92])Node60[x=4.0,y=2.0,c=3,r=0.8],((((Node145[x=5.0,y=5.0,c=6,r=1.28],((Node214[x=4.0,y=7.0,c=2,r=1.01],Node213[x=5.0,y=7.0,c=4,r=0.59])Node180[x=4.0,y=6.0,c=3,r=0.86],Node179[x=2.0,y=5.0,c=6,r=0.95])Node144[x=3.0,y=5.0,c=4,r=0.86])Node121[x=4.0,y=4.0,c=6,r=1.15],(((Node212[x=9.0,y=6.0,c=1,r=0.54])Node178[x=8.0,y=5.0,c=4,r=1.44],(Node211[x=7.0,y=5.0,c=6,r=0.65],Node210[x=7.0,y=6.0,c=0,r=0.65])Node177[x=6.0,y=5.0,c=2,r=0.95])Node143[x=7.0,y=4.0,c=6,r=1.27],(Node176[x=8.0,y=4.0,c=1,r=0.82],(Node209[x=9.0,y=3.0,c=5,r=1.27],Node208[x=8.0,y=3.0,c=3,r=0.81])Node175[x=8.0,y=2.0,c=2,r=1.25])Node142[x=7.0,y=3.0,c=4,r=1.45])Node120[x=6.0,y=4.0,c=2,r=0.78])Node100[x=5.0,y=4.0,c=4,r=0.9])Node79[x=4.0,y=3.0,c=3,r=1.32],(((Node207[x=3.0,y=7.0,c=1,r=0.66])Node174[x=3.0,y=6.0,c=6,r=1.48],((Node249[x=7.0,y=7.0,c=1,r=0.72])Node206[x=6.0,y=6.0,c=4,r=0.77])Node173[x=5.0,y=6.0,c=1,r=1.42])Node141[x=4.0,y=5.0,c=5,r=1.46])Node78[x=3.0,y=4.0,c=3,r=1.37])Node59[x=3.0,y=3.0,c=6,r=0.62])Node45[x=3.0,y=2.0,c=2,r=1.02],Node44[x=3.0,y=1.0,c=1,r=0.75])Node34[x=2.0,y=2.0,c=0,r=1.33])Node25[x=2.0,y=1.0,c=1,r=1.39])Node18[x=2.0,y=0.0,c=2,r=1.24])Node4[x=1.0,y=-1.0,c=4,r=0.92],((((((Node58[x=3.0,y=-4.0,c=5,r=1.0])Node43[x=3.0,y=-3.0,c=4,r=0.66],Node42[x=2.0,y=-5.0,c=5,r=1.42])Node24[x=2.0,y=-4.0,c=3,r=0.88],((Node57[x=-1.0,y=-5.0,c=3,r=0.58],(Node77[x=-1.0,y=-6.0,c=2,r=0.85],(Node99[x=-4.0,y=-6.0,c=1,r=1.23],((((Node248[x=-8.0,y=-11.0,c=1,r=0.68],Node247[x=-8.0,y=-9.0,c=4,r=1.43])Node205[x=-7.0,y=-10.0,c=0,r=1.42])Node172[x=-6.0,y=-9.0,c=2,r=1.25],Node171[x=-4.0,y=-9.0,c=6,r=0.71])Node140[x=-5.0,y=-8.0,c=6,r=1.33])Node98[x=-4.0,y=-7.0,c=1,r=1.17])Node76[x=-3.0,y=-6.0,c=5,r=1.13])Node56[x=-2.0,y=-5.0,c=0,r=0.9])Node33[x=-1.0,y=-4.0,c=5,r=1.02],(((((((Node204[x=1.0,y=-11.0,c=2,r=0.88],(Node245[x=0.0,y=-12.0,c=5,r=0.56])Node203[x=1.0,y=-12.0,c=4,r=1.17])Node170[x=0.0,y=-11.0,c=2,r=0.87])Node139[x=1.0,y=-10.0,c=3,r=1.25])Node97[x=0.0,y=-9.0,c=0,r=0.54])Node75[x=1.0,y=-8.0,c=5,r=0.71],((((Node244[x=4.0,y=-11.0,c=3,r=0.5])Node169[x=4.0,y=-10.0,c=4,r=0.9],((Node243[x=3.0,y=-12.0,c=3,r=0.79],Node242[x=3.0,y=-11.0,c=1,r=1.29])Node202[x=2.0,y=-11.0,c=1,r=0.62])Node168[x=2.0,y=-10.0,c=0,r=1.47])Node138[x=3.0,y=-9.0,c=2,r=0.54])Node119[x=2.0,y=-8.0,c=6,r=1.43])Node74[x=1.0,y=-7.0,c=1,r=1.44])Node55[x=0.0,y=-7.0,c=3,r=1.33])Node41[x=1.0,y=-6.0,c=5,r=1.25])Node32[x=0.0,y=-5.0,c=4,r=0.65])Node23[x=0.0,y=-4.0,c=4,r=0.8])Node17[x=1.0,y=-3.0,c=5,r=1.02])Node12[x=2.0,y=-2.0,c=2,r=1.15],((((((Node96[x=-1.0,y=-8.0,c=5,r=0.55],((Node241[x=-1.0,y=-11.0,c=0,r=0.91])Node137[x=0.0,y=-10.0,c=6,r=0.91])Node95[x=1.0,y=-9.0,c=1,r=1.2])Node73[x=0.0,y=-8.0,c=1,r=1.48],(((Node167[x=-3.0,y=-9.0,c=1,r=1.02])Node136[x=-2.0,y=-10.0,c=0,r=0.75])Node117[x=-1.0,y=-9.0,c=4,r=0.59])Node72[x=-2.0,y=-8.0,c=5,r=1.35])Node40[x=-1.0,y=-7.0,c=5,r=0.94])Node31[x=0.0,y=-6.0,c=6,r=0.54])Node22[x=1.0,y=-5.0,c=5,r=1.06])Node16[x=1.0,y=-4.0,c=0,r=0.73],((Node39[x=3.0,y=-1.0,c=0,r=1.26],((((Node116[x=5.0,y=-7.0,c=0,r=1.27])Node94[x=4.0,y=-6.0,c=6,r=0.99],Node93[x=2.0,y=-6.0,c=0,r=1.23])Node71[x=3.0,y=-5.0,c=0,r=1.03],((Node134[x=6.0,y=-4.0,c=6,r=1.45],(((Node240[x=9.0,y=-7.0,c=1,r=1.03])Node201[x=8.0,y=-6.0,c=1,r=1.18],(Node239[x=9.0,y=-9.0,c=4,r=0.76],Node238[x=8.0,y=-7.0,c=1,r=1.48])Node200[x=8.0,y=-8.0,c=5,r=0.8])Node165[x=7.0,y=-7.0,c=1,r=0.9])Node133[x=6.0,y=-6.0,c=2,r=1.16])Node92[x=6.0,y=-5.0,c=3,r=0.79])Node70[x=5.0,y=-5.0,c=4,r=0.93])Node54[x=4.0,y=-4.0,c=1,r=0.83],((((((Node237[x=9.0,y=-5.0,c=6,r=1.12])Node199[x=8.0,y=-5.0,c=3,r=0.55])Node132[x=7.0,y=-6.0,c=5,r=1.12],(((Node236[x=7.0,y=-8.0,c=4,r=1.08])Node198[x=7.0,y=-9.0,c=4,r=1.04])Node164[x=6.0,y=-9.0,c=2,r=1.18],Node163[x=4.0,y=-9.0,c=6,r=0.7])Node131[x=5.0,y=-8.0,c=0,r=0.7])Node114[x=6.0,y=-7.0,c=6,r=1.07])Node91[x=5.0,y=-6.0,c=1,r=0.7],(((((Node235[x=6.0,y=-11.0,c=6,r=1.38])Node196[x=5.0,y=-10.0,c=3,r=0.99],(Node234[x=5.0,y=-11.0,c=5,r=1.2])Node195[x=6.0,y=-10.0,c=6,r=1.04])Node162[x=5.0,y=-9.0,c=5,r=0.74])Node130[x=4.0,y=-8.0,c=1,r=1.49])Node113[x=3.0,y=-7.0,c=0,r=0.97],Node112[x=4.0,y=-7.0,c=6,r=0.56])Node90[x=3.0,y=-6.0,c=0,r=1.14])Node69[x=4.0,y=-5.0,c=5,r=0.87])Node53[x=5.0,y=-4.0,c=5,r=0.76])Node38[x=4.0,y=-3.0,c=4,r=1.48])Node30[x=4.0,y=-2.0,c=4,r=1.07])Node15[x=3.0,y=-2.0,c=5,r=1.39])Node11[x=2.0,y=-3.0,c=0,r=1.26])Node7[x=1.0,y=-2.0,c=6,r=0.99],(((((((Node89[x=7.0,y=-4.0,c=6,r=0.91],(Node160[x=5.0,y=-3.0,c=2,r=0.67])Node88[x=6.0,y=-2.0,c=2,r=1.31])Node68[x=7.0,y=-3.0,c=0,r=0.52])Node52[x=6.0,y=-3.0,c=0,r=1.42])Node29[x=5.0,y=-2.0,c=6,r=1.09])Node21[x=5.0,y=-1.0,c=5,r=1.41])Node14[x=4.0,y=-1.0,c=1,r=0.79])Node10[x=4.0,y=0.0,c=3,r=1.37],(((((Node67[x=5.0,y=0.0,c=2,r=1.29])Node51[x=6.0,y=0.0,c=1,r=0.87],(((((Node233[x=9.0,y=-1.0,c=3,r=1.21],Node232[x=9.0,y=0.0,c=2,r=0.68])Node194[x=8.0,y=-1.0,c=1,r=1.34])Node159[x=8.0,y=0.0,c=6,r=1.34],((Node231[x=10.0,y=-2.0,c=0,r=0.79])Node193[x=9.0,y=-2.0,c=0,r=1.12])Node158[x=8.0,y=-2.0,c=2,r=0.99])Node111[x=7.0,y=-1.0,c=6,r=0.89],Node110[x=7.0,y=-2.0,c=5,r=1.09])Node66[x=6.0,y=-1.0,c=2,r=0.57])Node50[x=7.0,y=0.0,c=1,r=1.4])Node37[x=6.0,y=1.0,c=0,r=1.0])Node28[x=5.0,y=2.0,c=5,r=0.91])Node13[x=5.0,y=1.0,c=6,r=1.34])Node9[x=4.0,y=1.0,c=6,r=0.85])Node6[x=3.0,y=0.0,c=1,r=0.69])Node3[x=2.0,y=-1.0,c=3,r=1.28])Node2[x=1.0,y=0.0,c=1,r=0.64],((((((Node230[x=-3.0,y=-2.0,c=3,r=0.71],Node229[x=-4.0,y=-2.0,c=6,r=0.83])Node49[x=-3.0,y=-3.0,c=4,r=1.1],((Node87[x=-2.0,y=-7.0,c=3,r=1.3],(Node109[x=-3.0,y=-8.0,c=4,r=0.95],(((Node192[x=-3.0,y=-10.0,c=0,r=1.09],(Node228[x=-4.0,y=-11.0,c=0,r=0.76])Node191[x=-5.0,y=-10.0,c=6,r=1.11])Node157[x=-4.0,y=-10.0,c=6,r=0.93])Node129[x=-5.0,y=-9.0,c=5,r=1.05])Node108[x=-4.0,y=-8.0,c=2,r=0.99])Node86[x=-3.0,y=-7.0,c=3,r=0.65])Node65[x=-2.0,y=-6.0,c=5,r=1.26])Node48[x=-3.0,y=-5.0,c=4,r=0.98])Node27[x=-2.0,y=-4.0,c=1,r=1.43],(((((Node190[x=-7.0,y=-7.0,c=3,r=1.35])Node128[x=-6.0,y=-8.0,c=3,r=1.33],Node127[x=-5.0,y=-7.0,c=6,r=0.9])Node107[x=-6.0,y=-7.0,c=2,r=1.16],(((Node227[x=-9.0,y=-5.0,c=2,r=1.49],Node226[x=-9.0,y=-6.0,c=5,r=1.37])Node156[x=-8.0,y=-5.0,c=3,r=1.33])Node126[x=-7.0,y=-6.0,c=5,r=1.42])Node106[x=-6.0,y=-6.0,c=2,r=1.18])Node64[x=-5.0,y=-6.0,c=2,r=1.12])Node47[x=-4.0,y=-5.0,c=5,r=0.85],((Node85[x=-5.0,y=-5.0,c=5,r=1.16])Node63[x=-4.0,y=-4.0,c=0,r=0.94],((Node105[x=-6.0,y=-5.0,c=1,r=1.09])Node84[x=-5.0,y=-4.0,c=6,r=1.01],(((Node225[x=-9.0,y=-4.0,c=0,r=1.05])Node154[x=-8.0,y=-4.0,c=5,r=0.79])Node104[x=-7.0,y=-5.0,c=3,r=0.65],((Node153[x=-5.0,y=-1.0,c=3,r=0.61],((Node224[x=-8.0,y=-3.0,c=1,r=0.87])Node189[x=-7.0,y=-3.0,c=0,r=1.28],(Node223[x=-8.0,y=0.0,c=2,r=1.05])Node188[x=-7.0,y=-1.0,c=0,r=1.13])Node152[x=-6.0,y=-2.0,c=3,r=0.5])Node125[x=-5.0,y=-2.0,c=6,r=1.14])Node103[x=-6.0,y=-3.0,c=0,r=1.14])Node83[x=-6.0,y=-4.0,c=6,r=1.38])Node62[x=-5.0,y=-3.0,c=4,r=1.26])Node46[x=-4.0,y=-3.0,c=2,r=1.22])Node26[x=-3.0,y=-4.0,c=2,r=1.13])Node20[x=-2.0,y=-3.0,c=0,r=0.71],Node19[x=0.0,y=-3.0,c=5,r=1.14])Node8[x=-1.0,y=-2.0,c=3,r=0.72])Node5[x=0.0,y=-2.0,c=3,r=1.1])Node1[x=0.0,y=-1.0,c=1,r=1.12])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 240\n",
      "draw_tree_links_m7\n",
      "Newick String: 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    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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(-4, -6, 5), (-1, 3, 2), (4, -1, 1), (4, -2, 1), (7, 5, 6), (7, -3, 2), (8, -6, 1), (8, -3, 6), (-1, -8, 1), (-2, -8, 1), (7, -2, 2), (7, -1, 2), (-1, -9, 5), (9, 2, 2), (-2, -9, 5), (5, -10, 5), (1, -4, 0), (3, 7, 2), (0, -5, 4), (0, -4, 5), (10, -6, 6), (3, -5, 5), (9, -10, 4), (-6, -6, 2), (-7, -10, 0), (-8, -6, 3), (5, 4, 2), (6, 0, 0), (4, 0, 1), (5, 5, 3), (4, 1, 2), (6, 4, 6), (3, -11, 3), (-1, 6, 2), (-5, -5, 0), (-5, -10, 6), (5, -7, 6), (8, 0, 6), (-2, 3, 5), (-1, 3, 5), (-2, -5, 1), (-1, -5, 1), (4, -11, 4), (7, -3, 5), (0, -1, 4), (0, -2, 4), (2, 7, 1), (7, -11, 6), (1, 3, 6), (0, -9, 1), (9, -7, 5), (3, 7, 5), (6, 6, 6), (-6, -7, 5), (3, -9, 2), (-7, -6, 1), (-8, -10, 0), (-7, -3, 6), (-8, -7, 5), (2, -4, 5), (-4, -3, 4), (2, -12, 1), (-5, -2, 0), (5, 1, 0), (-2, 5, 5), (-1, 5, 5), (5, 4, 5), (6, -9, 3), (8, -5, 3), (-5, -7, 6), (5, -4, 6), (9, 7, 1), (4, -8, 5), (-2, 6, 5), (-1, 6, 5), (-3, 2, 3), (-1, -1, 1), (1, 6, 2), (-1, -2, 1), (5, -11, 3), (7, 0, 5), (-3, 3, 4), (7, -8, 1), (8, -4, 3), (-3, -5, 0), (6, -12, 6), (-9, -5, 3), (7, -7, 2), (1, -1, 2), (1, -2, 2), (0, -6, 1), (3, 2, 2), (-3, -10, 6), (0, -10, 3), (-6, -4, 5), (-7, -8, 2), (-8, -4, 5), (5, 6, 4), (-8, -12, 1), (2, -9, 1), (6, 3, 4), (4, 2, 3), (3, -10, 4), (-7, -7, 3), (4, 3, 4), (4, -5, 0), (-4, -8, 5), (8, -1, 3), (8, -2, 3), (-1, 1, 2), (6, -10, 6), (5, -9, 2), (4, -10, 6), (-1, -3, 4), (-2, -3, 4), (7, -5, 1), (-3, -2, 0), (6, -9, 6), (8, -5, 6), (5, -12, 5), (1, -3, 5), (3, 6, 3), (-10, -4, 2), (2, 2, 0), (1, -10, 1), (2, -6, 1), (-4, -5, 0), (6, -2, 0), (2, -3, 6), (3, -6, 6), (6, 1, 5), (-7, -4, 3), (-5, 0, 3), (4, 6, 4), (4, -1, 0), (4, -2, 0), (2, -10, 3), (6, 2, 6), (6, -6, 2), (-7, -11, 0), (5, 3, 2), (4, 2, 6), (7, 6, 6), (8, -3, 5), (-5, -8, 3), (7, -1, 1), (7, -2, 1), (5, -10, 4), (9, 2, 1), (-3, 4, 5), (8, -1, 6), (8, -2, 6), (5, -9, 5), (-3, -3, 2), (3, 8, 2), (-9, -3, 5), (9, 3, 1), (1, -8, 1), (0, -4, 4), (0, -12, 0), (3, 4, 4), (-6, -6, 1), (3, -3, 1), (3, -4, 5), (9, -9, 4), (-7, -9, 0), (5, 5, 2), (4, 1, 1), (-7, -5, 6), (6, 5, 6), (6, -3, 2), (-5, -4, 0), (6, -11, 3), (8, 0, 5), (-2, -5, 0), (-1, -5, 0), (5, -6, 0), (-5, -9, 6), (9, 5, 1), (4, -11, 3), (8, 1, 6), (-2, -4, 1), (-1, -4, 1), (8, -6, 3), (1, 3, 5), (0, -8, 1), (0, -5, 6), (-9, -6, 3), (2, 4, 3), (3, 0, 1), (-3, -11, 6), (-4, -3, 3), (-7, -10, 2), (-8, -6, 5), (5, 4, 4), (6, 0, 2), (4, 0, 3), (2, -8, 6), (-7, -9, 3), (5, 5, 5), (2, -11, 0), (5, -3, 1), (8, 4, 1), (-2, -2, 0), (-1, -2, 0), (-5, -6, 6), (6, -12, 5), (-2, 2, 6), (-1, 2, 6), (7, 1, 5), (7, -7, 1), (6, -11, 6), (8, -8, 4), (-1, -5, 3), (-2, -5, 3), (-9, -4, 3), (9, 5, 4), (-3, -9, 6), (1, -6, 3), (0, -2, 6), (0, -1, 6), (0, -10, 2), (-6, -3, 0), (1, -5, 4), (0, -9, 3), (-10, -6, 2), (2, 0, 0), (-6, -8, 6), (-7, -7, 2), (-8, -3, 5), (-6, -11, 0), (5, 7, 4), (-8, -11, 1), (4, 3, 3), (3, -9, 4), (-7, -6, 3), (5, 0, 1), (4, 4, 4), (4, -4, 0), (2, -12, 3), (-5, -2, 2), (-2, -3, 3), (-1, -3, 3), (6, -9, 5), (8, -5, 5), (-3, 2, 5), (-2, -2, 3), (-2, -1, 3), (-1, -2, 3), (-1, -1, 3), (5, -11, 5), (-3, -5, 2), (3, 6, 2), (-9, -5, 5), (9, -8, 1), (-6, 0, 0), (9, -5, 6), (1, -1, 4), (1, -2, 4), (1, -10, 0), (0, -6, 3), (-8, 0, 0), (2, 3, 0), (10, -8, 4), (1, -9, 1), (1, -6, 6), (-6, -5, 6), (11, -3, 0), (3, -6, 5), (-4, -4, 0), (2, -2, 6), (2, -1, 6), (6, 2, 5), (6, -6, 1), (-7, -8, 4), (5, 6, 6), (2, -9, 3), (6, 3, 6), (4, 2, 5), (-7, -7, 5), (7, 7, 0), (4, 3, 6), (8, -2, 5), (8, -1, 5), (5, -9, 4), (4, -13, 3), (-3, 5, 5), (-3, -3, 1), (1, 1, 0), (7, -5, 3), (-3, -2, 2), (-2, -6, 0), (-1, -6, 0), (9, 4, 1), (1, -7, 1), (0, -11, 0), (3, 5, 4), (3, -3, 0), (-1, -11, 6), (0, -7, 6), (3, -1, 1), (3, -2, 1), (9, -8, 4), (2, 2, 2), (-7, -5, 5), (-6, -9, 3), (-8, -9, 4), (4, 5, 6), (-4, -5, 2), (6, -2, 2), (6, -1, 2), (-7, -4, 5), (-8, -8, 4), (5, 2, 3), (4, 6, 6), (4, -1, 2), (8, 2, 1), (6, -6, 4), (8, 1, 5), (-2, -4, 0), (-1, -4, 0), (0, 7, 2), (9, 6, 1), (1, 4, 0), (-5, -8, 5), (5, -10, 6), (9, 2, 3), (8, -10, 4), (-3, -3, 4), (-9, -6, 2), (3, 0, 0), (1, -8, 3), (0, -12, 2), (2, 5, 3), (3, 1, 1), (-9, -10, 4), (3, 4, 6), (0, -11, 3), (-8, -5, 0), (-6, -6, 3), (3, -12, 3), (-7, -9, 2), (5, 5, 4), (-4, -2, 2), (5, -3, 0), (4, 1, 3), (-6, -10, 5), (3, -11, 4), (-5, -5, 1), (8, 5, 1), (2, -7, 5), (5, -2, 0), (7, 2, 0), (6, -11, 5), (-2, 3, 6), (-1, 3, 6), (-2, -5, 2), (-1, -5, 2), (4, -11, 5), (7, -3, 6), (8, -7, 4), (-1, -4, 3), (10, 2, 1), (9, 6, 4), (9, -2, 0), (9, -1, 0), (3, 3, 0), (1, -5, 3), (7, -10, 2)}\n",
      "Population initialized with 200 individuals.\n",
      "\n",
      "--- Generation 1/30003, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/30003, Best fitness 0.48769 ---\n",
      "\n",
      "--- Generation 2001/30003, Best fitness 0.52432 ---\n",
      "\n",
      "--- Generation 3001/30003, Best fitness 0.55483 ---\n",
      "\n",
      "--- Generation 4001/30003, Best fitness 0.56947 ---\n",
      "\n",
      "--- Generation 5001/30003, Best fitness 0.59093 ---\n",
      "\n",
      "--- Generation 6001/30003, Best fitness 0.5991 ---\n",
      "\n",
      "--- Generation 7001/30003, Best fitness 0.6085 ---\n",
      "\n",
      "--- Generation 8001/30003, Best fitness 0.61608 ---\n",
      "\n",
      "--- Generation 9001/30003, Best fitness 0.61702 ---\n",
      "\n",
      "--- Generation 10001/30003, Best fitness 0.61707 ---\n",
      "\n",
      "--- Generation 11001/30003, Best fitness 0.61988 ---\n",
      "\n",
      "--- Generation 12001/30003, Best fitness 0.62387 ---\n",
      "\n",
      "--- Generation 13001/30003, Best fitness 0.62521 ---\n",
      "\n",
      "--- Generation 14001/30003, Best fitness 0.62521 ---\n",
      "\n",
      "--- Generation 15001/30003, Best fitness 0.62634 ---\n",
      "\n",
      "--- Generation 16001/30003, Best fitness 0.62634 ---\n",
      "\n",
      "--- Generation 17001/30003, Best fitness 0.62648 ---\n",
      "\n",
      "--- Generation 18001/30003, Best fitness 0.62648 ---\n",
      "\n",
      "--- Generation 19001/30003, Best fitness 0.63063 ---\n",
      "\n",
      "--- Generation 20001/30003, Best fitness 0.63119 ---\n",
      "\n",
      "--- Generation 21001/30003, Best fitness 0.63463 ---\n",
      "\n",
      "--- Generation 22001/30003, Best fitness 0.63616 ---\n",
      "\n",
      "--- Generation 23001/30003, Best fitness 0.63703 ---\n",
      "\n",
      "--- Generation 24001/30003, Best fitness 0.63723 ---\n",
      "\n",
      "--- Generation 25001/30003, Best fitness 0.63851 ---\n",
      "\n",
      "--- Generation 26001/30003, Best fitness 0.64113 ---\n",
      "\n",
      "--- Generation 27001/30003, Best fitness 0.64298 ---\n",
      "\n",
      "--- Generation 28001/30003, Best fitness 0.64298 ---\n",
      "\n",
      "--- Generation 29001/30003, Best fitness 0.6435 ---\n",
      "\n",
      "--- Generation 30001/30003, Best fitness 0.64443 ---\n",
      "3\n",
      "\n",
      "Running Evolutionary process for number_of_nodes = 750 and generations = 30004\n",
      "\n",
      "Target colored tree: 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=5,r=0.81])Node644[x=15.0,y=0.0,c=0,r=0.78])Node571[x=14.0,y=0.0,c=4,r=1.5])Node502[x=13.0,y=0.0,c=6,r=0.7])Node433[x=12.0,y=1.0,c=3,r=1.15])Node369[x=12.0,y=2.0,c=1,r=1.24])Node319[x=11.0,y=2.0,c=6,r=1.26])Node277[x=10.0,y=3.0,c=5,r=0.76])Node235[x=9.0,y=3.0,c=0,r=0.78],(Node318[x=10.0,y=6.0,c=0,r=0.74])Node234[x=9.0,y=5.0,c=1,r=1.37])Node169[x=8.0,y=4.0,c=1,r=0.82])Node143[x=7.0,y=5.0,c=2,r=0.81])Node130[x=6.0,y=4.0,c=3,r=0.81],(((Node233[x=6.0,y=7.0,c=4,r=1.38])Node168[x=6.0,y=6.0,c=1,r=0.91],((((((((Node643[x=12.0,y=12.0,c=6,r=0.9],(Node729[x=13.0,y=13.0,c=5,r=0.57])Node642[x=13.0,y=12.0,c=4,r=1.46])Node570[x=12.0,y=11.0,c=1,r=0.84],((Node728[x=12.0,y=13.0,c=1,r=1.49])Node641[x=11.0,y=12.0,c=5,r=1.25])Node569[x=11.0,y=11.0,c=5,r=0.81])Node501[x=11.0,y=10.0,c=3,r=0.93],Node500[x=9.0,y=10.0,c=6,r=0.75])Node367[x=10.0,y=9.0,c=3,r=1.38])Node317[x=9.0,y=9.0,c=1,r=1.25])Node276[x=8.0,y=8.0,c=2,r=1.46],((Node366[x=5.0,y=8.0,c=3,r=1.29])Node316[x=5.0,y=7.0,c=2,r=0.52],((Node432[x=8.0,y=10.0,c=0,r=0.67],(((Node640[x=8.0,y=14.0,c=5,r=1.13])Node568[x=8.0,y=13.0,c=5,r=0.98],(Node727[x=10.0,y=13.0,c=4,r=1.33],Node726[x=11.0,y=13.0,c=3,r=1.25])Node567[x=10.0,y=12.0,c=3,r=0.98])Node499[x=9.0,y=12.0,c=1,r=1.31],((Node725[x=9.0,y=11.0,c=6,r=1.24],Node724[x=9.0,y=13.0,c=2,r=0.81])Node566[x=8.0,y=12.0,c=1,r=1.47],Node565[x=7.0,y=12.0,c=3,r=1.46])Node498[x=7.0,y=11.0,c=4,r=0.81])Node431[x=8.0,y=11.0,c=5,r=1.26])Node365[x=7.0,y=10.0,c=3,r=1.32])Node315[x=6.0,y=9.0,c=4,r=0.67])Node275[x=6.0,y=8.0,c=6,r=1.05])Node232[x=7.0,y=7.0,c=0,r=0.76],(((Node430[x=12.0,y=5.0,c=4,r=1.26],(((Node723[x=15.0,y=7.0,c=6,r=1.22])Node564[x=14.0,y=6.0,c=0,r=1.5],Node563[x=14.0,y=7.0,c=2,r=0.72])Node497[x=13.0,y=7.0,c=2,r=0.91])Node429[x=12.0,y=6.0,c=5,r=1.09])Node314[x=11.0,y=6.0,c=3,r=0.92],((Node496[x=12.0,y=3.0,c=1,r=0.76])Node364[x=11.0,y=3.0,c=6,r=0.71],(Node428[x=13.0,y=5.0,c=6,r=1.05],((((Node722[x=16.0,y=3.0,c=0,r=0.86],Node721[x=14.0,y=3.0,c=3,r=0.78])Node639[x=15.0,y=3.0,c=2,r=0.6])Node561[x=14.0,y=4.0,c=5,r=0.68],(Node638[x=15.0,y=6.0,c=3,r=1.05],(Node720[x=16.0,y=5.0,c=5,r=0.72],Node719[x=16.0,y=4.0,c=4,r=0.78])Node637[x=15.0,y=4.0,c=1,r=0.69])Node560[x=14.0,y=5.0,c=2,r=0.65])Node495[x=13.0,y=4.0,c=6,r=0.67])Node427[x=13.0,y=3.0,c=3,r=0.92])Node363[x=12.0,y=4.0,c=0,r=0.77])Node313[x=11.0,y=4.0,c=0,r=1.01])Node274[x=10.0,y=5.0,c=0,r=0.91],((((Node494[x=7.0,y=9.0,c=6,r=0.64])Node426[x=8.0,y=9.0,c=0,r=0.92])Node362[x=7.0,y=8.0,c=1,r=0.68])Node312[x=8.0,y=7.0,c=5,r=0.73],(((((Node636[x=15.0,y=8.0,c=0,r=0.57])Node559[x=14.0,y=8.0,c=5,r=1.31],Node558[x=12.0,y=9.0,c=4,r=0.74])Node493[x=13.0,y=9.0,c=0,r=0.74])Node425[x=12.0,y=8.0,c=6,r=1.18],(Node492[x=13.0,y=6.0,c=5,r=0.98])Node424[x=12.0,y=7.0,c=2,r=0.54])Node361[x=11.0,y=8.0,c=1,r=0.75],(((Node557[x=10.0,y=11.0,c=2,r=1.05])Node491[x=10.0,y=10.0,c=0,r=0.59],(((Node718[x=14.0,y=12.0,c=0,r=0.63],Node717[x=14.0,y=11.0,c=4,r=0.67])Node634[x=13.0,y=11.0,c=2,r=1.15])Node556[x=13.0,y=10.0,c=0,r=0.85])Node490[x=12.0,y=10.0,c=2,r=1.41])Node423[x=11.0,y=9.0,c=6,r=1.49],Node422[x=9.0,y=8.0,c=5,r=1.2])Node360[x=10.0,y=8.0,c=6,r=1.33])Node311[x=10.0,y=7.0,c=6,r=1.17])Node273[x=9.0,y=7.0,c=0,r=1.07])Node231[x=9.0,y=6.0,c=3,r=0.53])Node198[x=8.0,y=6.0,c=5,r=1.37])Node167[x=7.0,y=6.0,c=3,r=0.82])Node142[x=6.0,y=5.0,c=6,r=1.23])Node129[x=5.0,y=4.0,c=6,r=0.74])Node101[x=5.0,y=5.0,c=1,r=0.69])Node77[x=5.0,y=6.0,c=1,r=0.62],((Node166[x=2.0,y=5.0,c=4,r=0.76])Node90[x=3.0,y=5.0,c=5,r=0.9],(Node116[x=3.0,y=6.0,c=3,r=0.79])Node89[x=4.0,y=7.0,c=6,r=0.89])Node76[x=4.0,y=6.0,c=4,r=1.04])Node63[x=4.0,y=5.0,c=0,r=0.71])Node54[x=3.0,y=4.0,c=0,r=1.48])Node43[x=4.0,y=3.0,c=0,r=0.66])Node34[x=3.0,y=3.0,c=6,r=1.22],Node33[x=2.0,y=1.0,c=6,r=0.69])Node26[x=2.0,y=2.0,c=2,r=0.8],(Node42[x=0.0,y=1.0,c=0,r=0.7])Node25[x=1.0,y=2.0,c=4,r=0.65])Node18[x=1.0,y=1.0,c=5,r=1.36],(((((Node100[x=5.0,y=0.0,c=4,r=0.79])Node75[x=4.0,y=0.0,c=5,r=1.17])Node41[x=3.0,y=1.0,c=0,r=1.4])Node32[x=4.0,y=1.0,c=4,r=1.39])Node24[x=3.0,y=0.0,c=2,r=1.0])Node17[x=2.0,y=0.0,c=4,r=1.4])Node11[x=1.0,y=0.0,c=6,r=0.58])Node7[x=0.0,y=-1.0,c=2,r=1.4])Node3[x=1.0,y=-2.0,c=6,r=1.29])Node1[x=1.0,y=-1.0,c=4,r=0.97])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 712\n",
      "draw_tree_links_m7\n",
      "Newick String: 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+RudzhXO6UmknWoeWKOtmam3aTH+w3aV/f9GQZR206mtnxSXbMGiFNGRZB62QBpZs49ASde0o28zxP9r+aE2ttN/eYeXKlQ2+nzJlirz55pty+umnp61NAAAAFtI+ovX3v/9dpk6dKmPGjHGXDHX+ld7y4fjjj0930wAAADI7aF144YVy3333yZ133ukuB/7hD39wE8wAAAAyXdqDlud5cvbZZ7sFAACgPUn7HC0AAID2iqAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQEQIWgAAAAQtAACAzMKIFgAAQEQIWgAAAO31bx2qyZMny1NPPSWLFi2SPn36yIknnijbbrttupsFAACQ2SNa//jHP2S33XaT5cuXy7Bhw2TmzJmy4447yquvvprupgEAAGT2iNZdd90lY8eOldtvv919f/7558usWbPkvvvuk9GjR6e7eQAAAJk7otW/f39ZsGBB/feJREIWL14sAwYMSGu7AAAAMn5E689//rNccskl7rLhkCFD3HytoqIi+fWvf73e19TW1rolVF1d7b7W1dW5xUpYy7JmJtemzfQH213695ewXsIPTOum1kwkkrZ119ZLJn3Tuqk1E0EE/bG2pnXtqOqm1uT4v3H2w5bwgiCCNd0Kr7/+uhQXF8sxxxwjQ4cOlffee0/Gjx8v//73v134akpJSYmUlpau83hZWZnk5uZuhFYDAIAfqpqaGpddqqqqJC8vb9MOWoMGDZKjjz7ajWyFTjrpJJk7d668/fbbLR7RKiwslIqKCsnPzzdNrOXl5W6ELTs726xuptamzU33x/DhwyUej4ulZDIpkyZNysh1OGrEIMnKipuOhrzxwUzzuqm1u/mDJSa2tX1JyvLYDBmxrMb00kFCRD7onisdvukqnniGlUUCCWT15pWyVWGuxGN2tZN+INPn1kinbbqIFzduczKQlV9UyYC+fSUWs50N4/u+zJo3T/oXFprW1rqz586V0Z07SJbnmY9ovbpitXntsG4mnbOirK25o6CgoEVBK62XDvVkMmfOHPcpw1Q6svXGG2+s93U5OTluaUw70XolRVk3U2vT5oY0ZGVlRbMrZeI61DCUbRyIoqyrNGRZB62QbhlZlm9n1547NWRZB62QhizLoBXSkGUdtEIahOLGQSu1tnWIUxqEso2DVtS1M/H4H0Xt1tRK62R4PUFpyHrmmWdcwg/T5/PPP+9GCQAAADLZJnF7h+OOO86NYmno+uijj0SvZt5///3pbhoAAEBmB6099thDpk+fLu+//74sXLhQLr74YvdYVMOHAAAAP5igpTp16iSjRo1KdzMAAADa1w1LAQAA2iuCFgAAQEQIWgAAABEhaAEAAESEoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABAe/0TPMlk0v0R6cZisZhbAAAAMlXak8zOO+8sHTt2bLDoH5Q+/vjj0900AACAzA5an376qSQSifplypQp7vETTjgh3U0DAADI7KDV2H333Sf5+fly7LHHprspAAAA7Sdo6YjWQw89JKeffrrk5OSkuzkAAACZPRk+1XPPPScLFy6Us88+u9nn1dbWuiVUXV3tvtbV1bnFSljLsmYm16bNTfeHfqDDWlgzE9dhImHbH2E967qpNX2xrx3WTOj/eXZ1XT0RCWTdDxG1VVgz6dvWDusFyQjavLam7/vmtcOa1rXDeokmPgjWVmFN69phvUw6Z22M82FLeEFTH/lLkzFjxkhlZaX897//bfZ5JSUlUlpaus7jZWVlkpubG2ELAQDAD11NTY0UFxdLVVWV5OXlZUbQmj9/vmy55ZZujtYZZ5zR6hGtwsJCqaiocPO7LBNreXm5FBUVuU9CWsrE2pnc5uHDh0s8HhfrUadJkyZlVO2wbkH2bhLzbAe0/SAhS+o+lG7+YIlJ3HRkaHlshuyYXShxz3a2QzLw5dO6ubLbFt0ly/h2Mgnflw8XLJPNV2wjnmF/BJKUbzp/IYO2XCXWd8DRgZaZczrKDlv0kLhh8aTvy5QFi2Vwl76RrMMZVfNkeNfukdSeVLnMvHZYt9eAAea3MdLRsoWzZsmAvn1Na2vdWfPmyejOHSTL88xHy15dsTqjzi2aOwoKCloUtDaZS4cPPPCAbL755jJ27Njvfa7O32pqDpd2ovVKirJuptbOxDZrWMnKimZzz8TaGrKsg1Z9bYmbBq2QnuisT6QhDVnWQSukIcsyaIW0ufGIZtlqyLIMWhtjHWpd83XoR1TbT71fpP22EdaOYh1qyMo2DlqZeG5pTa1NYjK8Dqr9/e9/l1NOOYVLfwAAoN3YJILWW2+9JbNmzZJzzjkn3U0BAAAws0lcOtxvv/3crR0AAADak01iRAsAAKA9ImgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQEQIWgAAABEhaAEAAESEoAUAANCe/9ahWrhwobz++uvieZ4UFRVJ9+7d090kAACAzB/R+tvf/iaDBw+Whx9+WF566SUZOXKkTJo0Kd3NAgAAyOwRrfHjx8uFF14oTz75pBx77LHusaVLl8qiRYvS3TQAAIDMDlo333yz7LvvvvUhS+Xn57sFAAAgk6U9aL399ttyySWXyLRp0+S///2v9OzZU/bbbz/p0qXLel9TW1vrllB1dbX7WldX5xYrYS3LmplcO5PbnEwmTeum1syk2mE9P0iY1k2t6Yttm8N6ycA3rZtaM+Hb1w5rBsb9EdaLoMn1NZPGxcN6Ua5D99XPjNphXT+ClRjWtK4d1ksEgWnd1JqZeG5pCS8IIui1FtIfHYvF5IADDpCvv/5a9tlnH5k6dap89dVX8swzz8iee+7Z5OtKSkqktLR0ncfLysokNzd3I7QcAAD8UNXU1EhxcbFUVVVJXl7ephu0lAajHj16uIC12WabuceOP/54mTlzpkycOLHFI1qFhYVSUVFheslRE2t5ebn7FGR2drZZ3UytvTHaPHz4cInH46YjOPrBCuu6G6u2X7u9iFjWTkosZ6psVreDeKZ114y0fJs9RXKSW4hn+DmbQHypjS+QHbMLJe7Zfn5HRxY+rZsr+dUDJWb82SBffFma95V03L6DeHHPrG6QDGTV1NUSDOonEjf+PFPSF2/m11IY7yExw772A1/mJheL13WAeMbrMAh8CSpnyS69ekk8Zrx9+L5MXLhQhnftbrrt6XY3qXKZ7F230vyyko4rv5PdSQb07esGMixHtGbNm5dR56woa2vuKCgoaFHQSvulw2222Ua23377+pCltEMuvvhiN+Klt3toLCcnxy2NaSdar6Qo62Zq7SjbrIElKysrY+pGXXtNyLINREpDlnXQ+q52zDRohfREZx20QhqyYlH1R9wzDVr1NGRZB621NGRZBq2QhizroKV0tEBDVpZx0Arpdmdae+1VPT1qRHXk0JBlHTwz9ZwVRe3W1Er77R10Evz//ve/BnNR9J38oEGDmgxZAAAAmSLtI1qXXXaZPPXUU3LQQQfJYYcdJlOmTJF//vOf7nYPAAAAmSztI1qdO3eW9957z00q03tn7bTTTi5saegCAADIZGkf0VKdOnWSc845J93NAAAAaF8jWgAAAO0VQQsAACAiBC0AAICIELQAAAAiQtACAACICEELAAAgIgQtAACAiBC0AAAACFoAAACZhREtAACAiBC0AAAA2uvfOrz33nvljjvuWOcPTb/11ltpaxMAAEC7CFoLFiwQ3/floYceqn8sHo+ntU0AAADtImip3NxcGTZsWLqbAQAA0P6C1pdffikjR46Ujh07yogRI+TnP/+5dO3aNd3NAgAAyOyg1aFDBzn77LPl0EMPlcrKSvn9738vjz76qHz88cdurlZTamtr3RKqrq52X+vq6txiJaxlWTOTa2+MNieTSdO6YT3ruhurtoh17TX1AvO639UMxDeuu6ZeMrCtm1rTN25zas0gGZjWra+XtG9zWNM37uuwXhDBOgxrJv0Ito+1Nd124ttvdwmxF9bUKTmWwnqZdM7aGOfDlvCCILA9CmxAY7Ozs+u/X758uQwaNEh++ctfypVXXtnka0pKSqS0tHSdx8vKytxlSAAAgKjU1NRIcXGxVFVVSV5e3qYdtJoyatQo6devnzz88MMtHtEqLCyUiooKyc/PNw2B5eXlUlRU1CAM/lBrZ3Kbhw8fbv4hCx11mjRpkvQaOkxixrX9ZFIWfjJZ/Nrt9eMhhpWTEsuZKiv9wcZ119TuFJshOcktxDO8c4yOaNXGF8jADj0l5sXMR1q+Wr1IunfqF0ntZSu/ls2HJ8Uz7OogKfLNpLgM6ttbYjHjNvu+zJxXIfGu24hn2OggSEqy8guJdRsknnGbA98Xf/lM2WXLARI3rq0jWhPnzJJte3c3ra11p1Usk8FzVkjc+Ayc9ERmbNlZ+i6qlJhhbd8TmdezqxRk7yYxz/ZimB8kZEndhxl1btHcUVBQ0KKglfZLh02ZP3++DBkyZL3/PScnxy2NaSdar6Qo62Zq7Uxss4asrKxoNncNWdZB6ztaN55BdcWFLMugFdIgFDcOQ6m1rYNWSPOK8XnJ0ZBlHSxCGrK8mOH2sfYqloYs07optC+yIuqPeER9rSHLOmiFNGRZBq36ul6WedDKxHNLa2ql/YaleglQLxeG76Z+97vfyfTp092QHAAAQCZL+4jWFltsITvuuKP7xKEGLh2C++c//+k+hQgAAJDJ0h60zjvvPDn33HNl9uzZbiJ7z549090kAACA9hG0lOd5MmDAgHQ3AwAAwFTa52gBAAC0VwQtAACAiBC0AAAAIkLQAgAAiAhBCwAAICIELQAAgIgQtAAAACJC0AIAAIgIQQsAACAiBC0AAIAfQtCqra2VDz/8UGbOnJnupgAAALSvoHX55ZfLiBEj5Be/+EW6mwIAANB+gtbTTz8tb775powePTrdTQEAAGg/QWvu3Lnyk5/8RB555BHJyclJd3MAAADaR9BKJpNSXFzsLhcOHTo03c0BAAAwkyVpVlJSIrm5uXLJJZe0atK8LqHq6mr3ta6uzi1WwlqWNTO5dia3WQO9tbCmH0Ht72pa146q7nc1A/FNq4b1/MC2bmrNKGsHxl0d1vP9CNq8tmagP8SwvKunXyNoc1gzGUHtZES16+t6pmUb1PSNa4f1/CBhW1i+q5mJ55aW8IIgCCRNJk2aJHvvvbc8/vjj0rt3b/fYpZdeKh06dJA//vGPstNOO7l/NxXOSktL13m8rKzMhTYAAICo1NTUuKtxVVVVkpeXt+kGrfHjx7tPGqaaPn26xGIxGTRokDz77LP1Aez7RrQKCwuloqJC8vPzTRNreXm5FBUVSXZ2tlndTK1Nm5vuj+HDh0s8Hhfr0TJ9I9Kj9/YSi9nV9v2kLK6YKgM79pGYFzMfwflq1XzZJquXxA1rJwNfvkgslJzkFuIZz3bQ0bLa+ALpXNUnktorusyXAd36uGOa5ajTrOXzZfv+vSRuWDccaZk6e6EM7N9FYjG7IRHfD+Sr2VUyrLCrxA3rqqQfyOS5lbJjzy0j6Y9PF82R+A4J8Qx3cR3gS07JkqyqaLbpRJcF0n/RMokFtiNas3t2l4Ls3STmZZmPaC2p+zCjzoeaOwoKCloUtNJ66XD//fd3981KNWbMGOnYsaM88cQT632dTphvatK8dqL1SoqybqbWps0NacjKyopmV9KQZRm06ut6MdMwlCoeUW09IVmflDZGbQ1ZlkErpKHCOliENGRZByIVj6hu1P2hIcsyaG2U7S7QxXIcZc1605BlHbQy8dzSmlppnwwPAADQXqV9MnxjW2+9dZPzsgAAADLNJhe0/vznP6e7CQAAACa4dAgAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQEQIWgAAAO35bx1+/PHH8uyzz8qSJUtkm222kVNOOUW6dOmS7mYBAABk9ojW73//e7ngggskmUxK//795dFHH5UhQ4bInDlz0t00AACAzA5axcXF8s4778i1114rl156qbz22mtSV1fnAhcAAEAmS3vQGjBgQIPvq6urZdWqVdKrV6+0tQkAAKDdzNGaP3++XHPNNfLtt9/Ke++9J1dccYWcdtpp631+bW2tW1LDmdKRMF2shLUsa2ZybdrcdH/oZW9rYU3ft60d1vMD37Ruas2kce2wXiD2bQ5rRlnb921rh/WSxnVTa/p+YFo3rJc0rptaM8r+CIx38bBepNudp//v/s/Emnq6nyfMaobCmpl4PmwJLwgC+y2/lZYvXy5PPvmkVFZWylNPPSU1NTXy3HPPSb9+/Zp8fklJiZSWlq7zeFlZmeTm5m6EFgMAgB+qmpoaN/WpqqpK8vLyNv2glSqRSMguu+wiI0aMkHvvvbfFI1qFhYVSUVEh+fn5pom1vLxcioqKJDs726xuptamzRu/P3x/C+Mr/L7EYgtkoNddYl7MfETrq2CZbO3lS9ywto5ofRkslQ413cQzfIeuAglkde5yqavuZPruP6yenbdSduiRJ/GYZzqCM2VxtezaN25aN6z90bykdFyWL57hdqejLKu6L5UOO2SJFzdeh8lAVk9JSO7iLcULbLfpwPOlpsccGdCzUGKxmOmo5KxFc6XPwmUSMz4D68jT/F7dpUvfpeIZFg98T6rm5csOS32JG7c56YlMyY9l1PlQc0dBQUGLgtYmcekwVVZWlgwdOlSmT5++3ufk5OS4pTHtROuVFGXdTK1Nmzdef6wJWfZTKTVkWYaheoG4uua1A41B+j/rNoeXbrwIgtYaGoasA1FYNyuCukr72TS0rG2mhizroFX/IwLjNqfQkBU3DFr1dYM1SxQ0ZFkGrZCGLOuglYnnltbUSvtk+GeeeabB9zoq9eqrr8qee+6ZtjYBAABYSHvQ0rlYO+ywg5x44oly1FFHuRuWasi6+uqr0900AACANkn7pcO7775bZs+eLRMmTHBDcTfffLNsvfXW6W4WAABA5gctpXeE1wUAAKA9SfulQwAAgPaKoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAABC0AAIDMwogWAABARAhaAAAA7TVoTZw4UU488UTZYostJD8/Xw499FCZNGlSupsFAACQ+UHrmmuukeOPP14+/vhj+fzzz6WwsFBGjx4t8+bNS3fTAAAAMjtoPffcczJ27Fjp1auX9OjRQ+644w5ZsWKFlJeXp7tpAAAAmR20Glu+fLkkk0np0qVLupsCAADQJlmyifnpT3/qLh8ecsgh631ObW2tW0LV1dXua11dnVushLUsa2Zybdq88ftDxDeuvKaeH1jX/a5m0rh2WC+QwLw/1tRc8y97a2omfdvaYT3ruqk1A+1nz66uq6dfk/ZtDmsGnv02Hdb0fdvaYT3fsI/ra6+tGRgXD+slI2hzcm3NTDwftoQXBEEUR5gNcvXVV8ttt90mr732muy+++7rfV5JSYmUlpau83hZWZnk5uZG3EoAAPBDVlNTI8XFxVJVVSV5eXmZEbQ0PN18883y4osvyr777tvsc5sa0dJRsIqKCvfJRcvEqnPFioqKJDs726xuau1RIwZJVlbctHYikZQ3PphpXjuquqm1rft6Y6zDKGvvvG0vicftrvAnk758PG2h9BhQKTHjiQP6Jn3xrK4yZHCtxA03j2RS5LMZOdKhppt4lsMsa0e0Vucul7xO+eJ5th0SBL5Ur1wqXWdvJl5g1+7AC6Sy/7eyT+1iyTIeiUuIJ2/n9JAuvReLFwtMR0OqKnrIwK69JWbczzqS+lVlhXSd1cm0n+v7esBK89ph3e5LekssMO4Pz5dlBRWSX9HDtLbWXdp7sYzu3EGyPNt+TgSBvLpidUYdpzV3FBQUtChobRKXDnV0SkPWCy+88L0hS+Xk5LilMe1E65UUZV2lgSXbOLREXTvKNmfiOoyytoYsy6AV0pAVi2YVupBlGbRCGrI882mlay7haMiyDlohPUHHDE/S4UUsDVnWQSukIcsyaIU0ZMXNE340/ZxSOrJ1qEHIOmhJxLU1ZGUbB61MPE63plbaJ8Nfd911ctNNN7mQNXLkyHQ3BwAAwEzag9Zvf/tb+eabb2S//fYTz/PqF72UCAAAkMnSfulw1apV6W4CAABA+xzRAgAAaK8IWgAAABEhaAEAAESEoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAAtPegNXHiRLn33ntlxowZ6W4KAABA+/ij0u+8845ceumlUltbKx9//LGMGzdOBg8enO5mAQAAZP6IVjKZlFtvvVUmT56c7qYAAAC0rxGtkSNHprsJAAAA7TNobQi9zKhLqLq62n2tq6tzi5WwlmXNxrUTiaR57bCmde2o6qbWtO7rjbEOo6ydTPqmdcN6vm3ZBjWTxptHWC+QQH+Kae01NUWCwL5DwpqBF5i2WuuphHhiLawZ+La1w3q+9olxV/sR9XNYM4raYV3fs9/uwprWtcN6iWBN2y0l1tbMpON0a+p5QRBBr20gz/PcHK1TTz212eeVlJRIaWnpOo+XlZVJbm5uhC0EAAA/dDU1NVJcXCxVVVWSl5fX/oJWUyNahYWFUlFRIfn5+aaJtby8XIqKiiQ7O9usbmrtUSMGSVZW3Hx06I0PZsrO2w+QeDxmOhry8dRZMmLH3pJlWFclkr588GmFDB8+XOLxuOkcwEmTJkmv0b0klmXbZj/hy8JXF0a6fVi3O2xzj21XSsx2sxM/KbJ4Wifz2mHd3O26i2e83QVJX2o+XybxXUS8uPEoTjKQ5ESR/EE9JRYzXIe+L0tnLpKhm2dJ3LNtczII5JNvEtK1cJF4scB0RKtybk8ZXlUpxpud6IDnpC5dZVhBtsRjxv3hBzJ5SZ10+SpPvMAzHdGqGlgtect6iWc8VVrH3qq7L5SB0lVihqOevgTylVTKkG2TYniIrh+1/mxaPNJjqXVtzR0FBQUtCloZeekwJyfHLY1pJ1qvpCjrKg1Z2cZBK6QhyzK0hDRkWQetkLY3K8t+s9SwEsuOps1Rbh9RtVuDkHXQirq2hizroPVdbU+8LPtLcXra05AVi6DdGrKsg1ZIQ1bMMGiFF7HiEZ50NGRlGQetkIYsy6BVX9dFoWi2aa0c9wxrr71Eq6eUCE4rkR9LrWu3plbaP3UIAADQXqV9RGv+/Pnywgsv1H//xhtvyKpVq2S77baTfffdN61tAwAAyOigpdc333vvPffvs846y33V73VYjqAFAAAyWdqD1pAhQ9yf3gEAAGhvmKMFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQEQIWgAAABEhaAEAAESEoAUAABARghYAAEBECFoAAADtOWh99NFHUlxcLPvss4+ceeaZMm3atHQ3CQAAIPOD1ieffCIjR46UgoICKSkpkWQyKXvvvbfMnTs33U0DAADI7KD129/+VnbbbTe57bbbpKioSB588EHp3r273HTTTeluGgAAQGYHrVdffVWOOOKI+u9jsZgcfvjh7nEAAIBMlpXOH/7tt9/KsmXLpE+fPg0e1+/nzJmz3tfV1ta6JVRdXe2+1tXVucVKWMuyZuPaiUTSvHZYM5n0TeuG9RLGdVNr6qVjS2E9P2Hf5rBmlNuHdbvDer79Zldf07p2WC+IYLsLawbJIILaa2r6vvE6XFsvGdi3OawZ+J5YtlrrufpiL6yZ9CPoj7U1A8+2dlgvMO1laVDTl0A73qyuq+eOqWIuubZmlMdS69qtqecFQQR7awtVVlZKt27d5LHHHpMTTjih/nG9jHjllVfKypUrm3ydzuUqLS1d5/GysjLJzc2NtM0AAOCHraamxn2Ir6qqSvLy8jbdEa3OnTtLdna2LF26tMHjS5Yskfz8/PW+7qqrrpLLLruswYhWYWGhHHDAAc2+bkMSa3l5uZs7pu20FNYeNWKQZGXFzUe03vhgpozYsbdkxWOmo04ffFphXje19pBB3SRuWFtH4T6buVx23raXad2w9sfTFmZU7bBur9G9JJYVMx8tW/jqQumx7UqJxW1HtBZP6yTb9imQeMy4n31fps1fIvnb5EvMeB36SV+WfrFU+mX3lJhnV9sPfPm6bpH0WbBMYsZvk3Xgaf4W3WXngiyJx9aMQlmNDH28JCED83qb9kXYH19VV8h2HfMk7tm1ORzh+3xVtezz7VLJWjuiYyEhnry9Wb7kVPUUz3gGj45o1XZZJFv17Wq+DqfPq5TOe3UWL8u2n4NEICveXSHDhw+XeDxuflVj0qRJ5ufx8EpaS6Q1aGmH7rzzzjJhwgS54IIL6h9///33ZZdddlnv63JyctzSmHaidSCKsq7SkJVtHLTqa8dj5oEoyrpKQ4V1aImybqbW1pAVy46mzRqyLINWSEOWddAKaciyDlr1tb2YebhwdQNdrC9IrDmB6gna8iQd0n6IR9AXSkOWddAKaciyDFohDVnWQSu0Zh1a1l5zGVJDlnXQSs0EWVnRxBLr83hraqV9MvzZZ58tTzzxhLvNgxo/fry89tpr7nEAAIBMltYRLXXuuefK1KlT3S0e+vXrJ/PmzZNf//rXctRRR6W7aQAAAJkdtDzPk1tvvVWuvfZaF7K23HJL6dKlS7qbBQAAkPlBK6Q3KdUFAACgvUj7HC0AAID2iqAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQEQIWgAAAAQtAACAzMKIFgAAQHsPWt98841MnjxZli9fnu6mAAAAtI+g9dVXX8lPfvIT2XrrrWX48OHy/PPPp7tJAAAA7SNoTZgwQYYMGSLTpk1Ld1MAAABMZUmanXDCCeluAgAAQPsc0QIAAGivzEe0FixY4JbmbLXVVrL55ptv8M+ora11S6i6utp9raurc4uVsJZlzca1E4mkee2wZiLp29ZdW8+6bmrNpHHtZER1M7V2WM9P2Lc5rOkbb9JhvaQfQT+vrelHsA7Dmn5gWzus53v6/55t7bXlkn5gWjesZ90XqTWTgW2bU2smjPs5rBeIfX+ENdf0uW++DoOEfT8Ha2smk/bnw7Cm9Xm8NfW8ILDdOu+880655557mn3OvffeK7vtttu6jfE8GTdunJx66qnNvr6kpERKS0vXebysrExyc3M3oNUAAAAtU1NTI8XFxVJVVSV5eXkbN2i1RUuDVlMjWoWFhVJRUSH5+fmmibW8vFyKiookOzvbrG5q7dGdO0iWZ/xuKQjk1RWrZS8vIVmGpfVNx7tBlnTe0RcvLqaCpMiKT2MyaFA3icc803dhM2cul2GDu0o8btvPyWQgk2dUyvBtciOpPemLGtl5q86mtbXux9NXSHz3XPEsN46170qTE2qkw+A60+1Dt43VM7JlYJc+EvNi5qMhX1XNl67b9hQvbls7SPpSOW2R9MvuadpubfPXdYukz4JlEjM+euuI1vwtussgyZWY4XHJDwKZKTUyKL+fxGLG69D3ZebSr2WH3nrsiJmPeE6pWC57rloiWRKYjmi917FAcqp6imc8g0dHtGq7LJLeAzpIzPBY6vuBVMxaLZ336hzJsWPFuyvcnQfi8bj5iNakSZPMz+OaOwoKCloUtNI+GX5D5OTkuKUx7UTrQBRlXaUhK9s4aH1Xe81iKhB3ErUOWiENWXHTE96aoXMNK9ZhKKR1syKsHUW79UBpfbCsrx3R9qFhJW4ctEIasqyDVmq7rQOiqxvoYv0+ec02oSErbn1c0vbGYuZhKBSPsLaGLMugFdKQZR20QhqyYhl27IjH45KVFU0ssT6Pt6ZW1qZwo9Lp06fXfz9nzhx341IdmdJRKgAAgEyV9k8dTpkyRc4880y37LzzzvL444+7f99///3pbhoAAECbpH1Ea4899nAjWAAAAO1N2ke0AAAA2iuCFgAAQEQIWgAAABEhaAEAAESEoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAA7fVvHaq33npLxo8fL3V1dbL77rvLmDFj0t0kAACAzA9ahxxyiKxatUpGjhwp8Xhczj33XNl1113l3//+t8RiDLgBAIDMlfagdeONN8pOO+1U//3JJ58sQ4YMkRdffFGOOOKItLYNAACgLdI+ZJQastTWW28tHTp0kAULFqStTQAAAO1iRKuxhx9+WBKJhOy7777rfU5tba1bQtXV1e6rzvHSxUpYy7Jm49qJIDCvHdZMGJcO6wVJ27qpNZO+/hDfrO6aeiLJpH0/hzUzqXZYL7DeOFJqWm8fYT0/sNsuQmHNIGlfO6xp3e6wnu/p/3u2tdeW842PS2E9349gHa6tmYygdlgzYdzPYb3A8FgXCmv6a499VsJ6UR47kkn7k0tY0/o83pp6XhDY7lHPPfecvPTSS80+56c//akbuWps8uTJbq6W/vff/e536319SUmJlJaWrvN4WVmZ5ObmbmDLAQAAvl9NTY0UFxdLVVWV5OXlbdyg9f7778uECROafc6xxx4rffv2bfDYlClT5IADDpBjjjlG7rrrLvE8r1UjWoWFhVJRUSH5+flimVjLy8tl+PDhbqK+dcqeNGmS5C7rJ15gewU38Hyp6f61dJnbVbzA7p1Y4AVSVVgp2/q9JW581TkpvkyLVcigAVtK3PBDEPqOdOasObLtlv1N64a1p82ZLbJblnhZnv07vA8T0q97Z4nFPNN3pV8vWyEy3BOJ27ZZdLRsUiDJoXHb2slA4p8kJatqgHjG252++090mSXx6j6R1E7mzZd+2T0l5sVMR7S+rlsk/eIdJNbMcXLDagfydXK1bLak0PS4pMekbwvmynYdO0rcuM3JIJDPV62SHXp3i2Qfn1KxXIbE+krccB0mA18+8+dJt+X9I9nulnebLSN27C1ZcbvaiaQvH3xaEen5cHiEtYuKiiQ7O9usruaOgoKCFgUt80uHe+yxh1taY+rUqXLggQfK0Ucf/b0hS+Xk5LilMe1Ey44M6YrPyormKqsezKyD1ne1PePaa4akNWRZB62QHiitD5ZR1lUasqyDltJ3QBqyLINWvbh9m9deWFgTsqxDnLtIFjM/KW2M2hqyLIPWd3U986AV9XFJQ5Z10NoY+7iGLMugFdJtTv9nKbwYqSHLMmhtjPNhPMLa1vmgNbXSPhn+888/dyHrqKOOkrvvvvt7QxYAAECm2CTuo/Xtt9+6TxpefPHF9Y8ffvjhbgEAAMhUaQ9aV111lfuUYWN67RMAACCTpT1onX/++eluAgAAQCTSPkcLAACgvSJoAQAARISgBQAAEBGCFgAAQEQIWgAAABEhaAEAAESEoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAADQXoPWwoUL5fLLL5etttpKunbtKiNGjJDHHnss3c0CAADI/D8qffvtt8s222wjr776quTl5ck//vEPKS4ulu7du0tRUVG6mwcAAJC5I1q/+93v5LzzzpP+/ftLt27d5IILLpC+ffvKO++8k+6mAQAAZPaIVqpVq1bJk08+KUuXLpXDDz883c0BAADI/KA1ZcoU2XnnnSWZTErHjh3l3nvvld133329z6+trXVLqLq62n2tq6tzi5WwlrbLWlgz8Hzz2mHNwAtExK7+mnoiScOaobBm0retHdazrptaM0is6RdLYU3ft61dXy8ZiHmrk9/VjqJuEMF2F9aMsrYf2NYO6/mB/XYX1rQ+LoX1khG0OawZ5T6eNF6HYT3dRvyItrtE0rZyWC/K82EywtqW2aC19bwgsN3yr732WrnuuuuafY7Ox9p///0bPJZIJKSyslIef/xxufTSS+Wpp56SI444osnXl5SUSGlp6TqPl5WVSW5ubht/AwAAgPWrqalx88mrqqrc/PKNGrR833dLc+LxuHiet97/fvTRR7vg9fzzz7d4RKuwsFAqKiokPz9fLBNreXm5jNixt2TFY+bvDj74tEI677hKvLhpaQmSIis+7Sh11Z10FVtWluy8lVJXlWtcd23tLjUiS/qJBIZ9re+kC76WQT0KJRazXYe6nc9cPFcKcgZIzDOuHfiypHaWyDcDjKdS+iKbz5IBXq7EmtkHN6hyEMisoEbig+pMt2ndnpMzsyWr21DxYrY7S+AnJbH8E+nUtVA843UYBL6srJwrq6S3+TrsKBXyzcotI5hm68vmneZI15z+pv2hfVFZO1sG5/WVuHE/6+jQjOp5El/RVzzj/tDRoWTnebL1oL4SNzx+6EjZlzPnSa/dN5NYlvF+mAhk4YRvpWjITpIdt9tf6pJJKf/sf+Z1G9QuKpLs7GyxFJ7HrWtr7igoKGhR0DK/dKgns7ae0HSor7n8l5OT45bGtBOtV5LSkGUdtEJ6QrIOWinVIwhEUdbVI1tMPMOgFW5Fuk1aHihTaciyDlop1fWtiX1VzzMPWk4Q3TatIcs6aNXX9mLmQes7WjeWQXXX9IflNh2+9daQZR20QhqyrINWSI8d8QjOARqyrINWSMOQdSCKsm6U5/AoaremVto/dXjSSSfJe++9JytXrpRly5a52z28+OKLcsYZZ6S7aQAAAJk9Gf7888+Xq6++Wj744APJysqSHXbYwX3y8Jhjjkl30wAAADI7aI0aNcotAAAA7U3aLx0CAAC0VwQtAACAiBC0AAAAIkLQAgAAiAhBCwAAICIELQAAgIgQtAAAACJC0AIAAIgIQQsAAICgBQAAkFkY0QIAAPghBK2lS5fKAw88IG+88Ua6mwIAAJD5f1Q61ZlnnimvvPKKHHHEEfyhaQAAkPE2mRGtW265RWpra+XAAw9Md1MAAADaT9CaOHGi3HjjjfLggw+K53npbg4AAED7uHT4zTffyEknnSR33HGH9O7du0Wv0ZEvXULV1dXua11dnVushLUSSV+shTWDpHnplJqBdeWI6qbU9Hzb6t6afvZ9+3UY1vSDCGrX17SuHbbZfh2GNa236bBe4NvvLGHNIIJ1GES8Du3rfldT2+5H0BfJCPo5rBlE0B9hzaTx8SOs5yci2A/X1qxL2u4vYT3rug1qG56/Q2FN69qtqecFge0Rd9KkSfLxxx83+5xDDz1UtthiC/fv0047TTp27Cj33HOP+37MmDHu+yeeeGK9ry8pKZHS0tJ1Hi8rK5Pc3Nw2/w4AAADrU1NTI8XFxVJVVSV5eXmyUYPWU089Jc8880yzz/nlL38p2223nYwfP14OPvhg+dOf/iSbbbZZ/Vyt7Oxs+clPfiLHH3+8bL755i0a0SosLJSKigrJz883Tazl5eWyQ2GBxGMx83c0U+YukdV+9wiu4PrSIbZM8rr2Ec+Lmb4rra6cL3nLeoln3GZ951jdfaEMlK4SE7vLxzo+9pVUyq7VVRIXW/oe7KO8LrJdz64Sj9le8k76gXy+qFK229K2tqs7p1IGbB1IzLhDdHBo1peeee2w7qCu/SRuuD2HoyEzK7+W1cNyReLG0xaSgXSYXCOrREfqLdvtS0epkA7Lo9kPV3dbKAO6FErMsK91hHZW1VwZ3KVvJOtwRtU8yVrRN5L+SHSeJ936LZZYzO5U6fueLP+6h3Ra1jOSNq/svkh26pNjfuz43/xaKRqyk2TH4+YjWuWf/U+Kiorc+d+09trzuHVtzR0FBQUtClrmlw6PO+44t7RE586d5eSTT5YJEyY0uMVDPB53t3jQ0a2mglZOTo5bGtNOtF5JSkOWddD6jtaNpraGLMugVV/XRaFo2qyVTQ/Eay8rxCO8Tq4HM+ug1bC27UlaaRAyPlbWi6q2bhfWJ+mU4vZBK+J9PNL9MKK+jnIdRtofscA0aG2MNkd1XNKQZR20oj6HR1G7NbXSOkdrl112cffNShVeOmz8OAAAQKbZJD51CAAA0B6l/VOHjR100EGRDR0CAAD8oIPWz372s3Q3AQAAwASXDgEAACJC0AIAAIgIQQsAACAiBC0AAICIELQAAAAiQtACAACICEELAAAgIgQtAACAiBC0AAAAIkLQAgAAiAhBCwAAoL3+rcOHHnpI7r777gaPde7cWV588cW0tQkAAKBdBK05c+bI8uXL5a677qp/LCsr7c0CAABos00i0egI1r777pvuZgAAALS/oDVz5kw57LDDpGPHjjJixAi55JJLZLPNNkt3swAAADI7aOllwrFjx8qhhx4qlZWVcsMNN7h5Wx999JHk5uY2+Zra2lq3hKqrq93Xuro6t1gJayV9X6x9V9O+dlgzCGxrh/WCCNoc1vQlEDFst6un/S32wppJP7Cvvbbmmq++eV0/gg4Ja1rXDusljbfnBjWT9uvwu5rW7d4I+6FxX4f1olyHkfaH75nWDetF2Wbr41JYry5pf/CoW1vT8vxdX3ttTevarannBUFgujbuueceefDBB5t9zu233y7Dhw93/161apUbyQotXrxYttpqK7nmmmvk8ssvb/L1JSUlUlpaus7jZWVl6w1nAAAAFmpqaqS4uFiqqqokLy9v4wYtndyuS3OGDh0qXbp0We9/33///WXLLbeUcePGtXhEq7CwUCoqKiQ/P18sE2t5ebls17OvxGMx8xGtzxfNky23zpFY3PjdUjKQOV/WSlJ6GN/Bw5e4LJZOBcvEM74xiL4pXbmku3TpvVi8mN0mGfieVFX0kB1ru0nc+G4mSfHl05zlstmy/uIZ19Z3pd92ny3Vq7qZr8O8jsul6xYDxDPepgPfl8oFs2RQt74SM9xAdDRk5vJ50q3QdtsIt4/lc3vI1lt0knjMMx8B+HLBShlavUrilnVF5JO8jtK1aivxTCvrdpeUyi7TpdOyAeIFdusw8HxZ2X2WDOhZKDHj7c73fZm1aK707dZZYp7xsTQIZN7yFTJQukpMPNOR9q+kUnLzl0dyLK1Z2k1GB6tNL1klRORVr4Mc1LmLZBn3cyII5JUVVVJUVCTZ2dmmtcPzuHVtzR0FBQUtClrmlw41IOnSFosWLZLttttuvf89JyfHLY1pJ1qvJKUhyzpohTRkWQetlOpiuhevPcdpSeuDQ0hPpNYnU6UhyzpohTRkWZ6U1hZdS+vat1tDVixme5IOL4JoyDLdX9YW1u0iZrxthG3WkGUdtELay7Y9vYaGLOugVV870G3avnYsymOp50nMeh2u3UA0ZMVNj6V+5MdSPbnbnw3Fhaxs46AV9Tk8itqtqZX2G5bedNNNbggudMstt8jnn38uJ5xwQlrbBQAAkPGT4fUdzqBBg6RXr16ydOlSSSQSbo7X6NGj0900AACAzA5al112mVx88cUybdo0N5G9f//+Eo9HMxwOAADwgwpa4bXOHXfcMd3NAAAAMJX2OVoAAADtFUELAAAgIgQtAACAiBC0AAAAIkLQAgAAiAhBCwAAICIELQAAgIgQtAAAACJC0AIAAIgIQQsAAOCHELQWLFjgFgAAgPZgkwhaH374oeyyyy6y3XbbyV577SUHHXSQzJ8/P93NAgAAyOyg9dVXX8mBBx7oliVLlrjvr7nmGvniiy/S3TQAAIA2yZI0u+6666R3795yww03SCy2Jvftt99+6W4WAABA5o9ovfzyy3LkkUdKEAQyY8YMWbFiRbqbBAAAsGmOaC1atMgtzRk4cKBsttlm7t/z5s2T6upq2XbbbSWZTMrChQtl5MiRcv/990ufPn2afH1tba1bQvp6VVdX5xYrYa2k74u1sKafDMxrf1fTFzEtv6bNgX131NcMfM+47pp6ybVttxTWDPSrbbPX1HSs2x2uQ9+8stZ0P0FXpmFxV2/turRv89rtw7ffD8OaSeu6a78G5pW/qxl4tj0d1vMjOJaGNf0gMN9dXE231wSmBz5XL+JjacK4blgvsbZPTGsHa2panr9DYU3r2q2p5wU6lGTo9ttvl7vuuqvZ52iI2n333d2/s7OzpVOnTvL222/L0KFDZdmyZVJUVORC1rPPPtvk60tKSqS0tHSdx8vKyiQ3N9foNwEAAFhXTU2NFBcXS1VVleTl5clGDVqtNXjwYNl1113l8ccfr3/szjvvlF/84hfyzTffiOd5LRrRKiwslIqKCsnPzzdNrOXl5VKQvZvEPNvBPz9IyJK6DyOt7cf7i3iGV4cDX2LJ2ZKT3EI846vOOoJTG18gyWR34yvavsTjy2Ror5jEY575iMUnC30pjOVLzLKf147izPWXyorabrqbGlYOpHPOcol1HyxeLG5YVzePpPjLZkiHmm7iGbY5kEBW5y6Xrb18iRv3czLw5ctgqWy/rFriEYw8Te2eJ12+yhMvMOwPL5CqgdWyT7cCyTLuj0Tgy9vLl5jXDuvu16OrZBnvhwk/kDcXV8pmi/uJFxgflzxfvu3xtey1aqlkGV4eSIgn73bMl4LNekZy7Fjy7SLJX9hDYob94Xu+LO21WEZ37iBZTZyX2zqi9eqK1W6QRQdfLIXncevamjsKCgpaFLTSPhleb+XQ+BOG+unDLl26NBmyVE5Ojlsa0060XklKg5B1GNoYtV3IMt6JXVmJmQet72hd+9oasqyDVkgPlNYHy+945sHTVY3FzYNWSEOWF0GbNWRZBy0nEBey4tZvOddubhqyLINWSINQ1toPEJnxI6od1o159m0Ot+kgZh60QhqyLIPWxjh2aMiyDFohDVnZxkEr6nN4FLVbUyvtQeuXv/ylu4eW3tJhzJgxMmXKFPnTn/4kv/rVr9LdNAAAgMz+1KFOjNf5WfqJwwsvvFCeeuop+etf/+ouHQIAAGSytI9oqe23314eeeSRdDcDAACgfY1oAQAAtFcELQAAgIgQtAAAACJC0AIAAIgIQQsAACAiBC0AAICIELQAAAAiQtACAACICEELAACAoAUAAJBZGNECAABor3/r8Prrr5eFCxeu8/iQIUPkvPPOS0ubAAAA2sWIVt++fWXAgAH1S69eveTWW2+V2bNnp7tpAAAAmT2idfrppzf4fty4ceJ5npx11llpaxMAAEC7GNFq7L777pMDDzxQBg8enO6mAAAAZPaIVqrp06fLm2++KY8++mizz6utrXVLqLq62n2tq6tzi5Wwlh8kxFpYM8raEvi2hdfWC8S4boOa1rXX1Ev6gXHd72r61v3coKb+DMv6a9oc+EnDmtKgZmDc5jX1RJIR9HNY07XcM6699mvg2W57Yb2Ett24S1zNCGrX13X7jG2j19TUfonguLS2ZsJ44wjrRXns8I37I6yXCOyPpYm1NS3P36GwpnXt1tTzgsC211544QV5+eWXm33ORRddJFtttdU6j1911VVy7733yrx586RDhw7rfX1JSYmUlpau83hZWZnk5uZuYMsBAAC+X01NjRQXF0tVVZXk5eVt3KD17rvvyvvvv9/sc44//njp169fg8eSyaQUFhbKySefLDfffHOrR7T0tRUVFZKfny+WibW8vFyKiookOzvbrG5q7UF77C2xLNuBRT+RkJnvvyOb7T5IvKy4Wd0gkZRvJ8yU/J23lFjc9qqzn/Rl6cdzZGCHnhLzYqbv7r5avUiSVR3thywkkHiXVbLZsv7iGV+F1xG+b7vPlsJYvnl/zPWXSt9FlRIzfmPqeyLzenY1rx3W3baqWuy25u9GnaZ1yZPc/OVi2M2ODizULO0m26/Kl7jh9pEUX6Z2XCr79egqWTHjkRY/kDcXV0q3+b0kFhhud54vy/sslCHSy7Qvwv74TBbKZov7iWfY5nBE69seX8uuVdWml3/0msNHXfKkYDPb4124jy/5dpF57bDugL59JRYzbrPvy6x58yI911rX1txRUFDQoqBlfulwr732csuGjIRpUDr77LO/97k5OTluaUw70XolRVlXaciKGwetkIYsy6AV0pBlHbTqa3sxiVuf8RwvgqAVVo6ZH+DDpmp/WB+IXd1gzRKFqGrrlmy/Na+hXRzJZufaHDMPF0pDVpbxCS+8rKchyzJoRd0XSvdB8/1wLT1CR3GUjmr/jrK2hqy4+XYX/bnWunZrasU2pUnw++67r7t/FgAAQHuwSUyG1xuWPv/88y5sAQAAtBebxIhWZWWl3HjjjTJ27Nh0NwUAAKB9jWhtu+22bgEAAGhPNokRLQAAgPaIoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQHsNWqtXr5Zf//rXsv3220tBQYHstNNO8sc//lGCIEh30wAAADL7j0pfffXV8tBDD8njjz8uO+ywg7z33nty8sknS3Z2tlx22WXpbh4AAEDmBq13331XjjzySNl///3d92PGjJH99tvPPQ4AAJDJ0n7p8Nhjj5VXX31VvvzyS/f9xIkT5f3333ePAwAAZLK0j2hdfvnlMm/ePNlmm20kKytLfN+X66+/XoqLi9f7mtraWreEqqur3de6ujq3WAlrWdZsXNtPJMxrhzWDRNK0bljPT/qmdVNr+oFt7e/qRTHnb03NQHwRz7pytP3hG7c3taZ17bCe7dbcsKZxNzeomVy7Lq2E9RK+bn+2tdfU1D433u7W1rPui9SagXGbU2taH6XDetb7d2rNyI4dfgRt9v3Iz7XWtVtTzwuMZ53/9re/dZPZm/Piiy/KyJEj3b9LSkrknnvucfO0hg4d6uZonX766XLjjTfKOeec0+Tr9TWlpaXrPF5WVia5ublGvwkAAMC6ampq3IBQVVWV5OXlyUYNWvopQl2a06lTJ4nH4+6ThZ07d5bf/OY3DSa+/+IXv5Ann3xSZsyY0eIRrcLCQqmoqJD8/HzTxFpeXi5FRUVucr6lsPagPfaWWFaW+YjWzPffkV5Dh0ksHrerm0zKwk8my4CdsyUWtx228JOBzPq4TjoPS4pn12QJkiIrJsdlm6xeEvdsr5QnA1++SCyU3GUDxDO+Cq8jWjXdZ8mg5BYSN6yt7/5nxheIXzVIRAw7em31WJeZ0kn7I4iZjiqs7D5LOvdbJsar0I06rfi6uwyeWylx40HPpCcyo7CrdC1cJF7Mrnjge1I5t6f0GdRXYrGY+cjC/JnzJKuyl+k2rdtzoutC8br0F894JQaBL0HVbMla0TeS/TDReZ4Mjvc3PX7osWNGcrbEgmHiGe+Hge7l3mTp1Gu4eDG72oGflJULJ0V6ziqK8FxrXVtzh94poSVBy/zSYYcOHdzSUhq2Gj9fv28u/+Xk5LilMe1E65UUZV2lG2zceKOtrx2Pmwat7+p6EjcOWiENWZZBK6QHSeugFdKDu2WwWFvU0ZBlGbS+o50cQUe7d2/aH/a1dfVZBpa1Vd3/a8iyDlr1PyEWSMyw3eGFGw1ZsXiE23QE252GLOugpYII2xzl8UNDlnXQqq8di4sXy8qoc1Z2hOda69qtqZXWyfCe57lPGd56660yZcoUF64mTJjgLiXqJxEBAAAyWdo/dfi3v/1NRo0a5W7voCNZGrD0uucf/vCHdDcNAAAgsz912K1bNzeCpUsikXCfPAQAAGgP0j6ilYqQBQAA2pNNKmgBAAC0JwQtAACAiBC0AAAAIkLQAgAAiAhBCwAAICIELQAAgIgQtAAAACJC0AIAAIgIQQsAACAiBC0AAICIbBJ/WHDlypUyYcIEWbx4sey0006y9dZbp7tJAAAAmT+i9e6778o222wjl1xyiYwbN0722GMPueKKK9LdLAAAgMwf0Tr11FPlwAMPlAcffNB9P3v2bBk6dKiMGjVKxowZk+7mAQAAZOaI1tKlS2XmzJly3HHH1T/Wv39/2XXXXeWhhx5KZ9MAAAAye0SrW7dukpubK5999pkcffTR7rHVq1fLjBkzpKKiYr2vq62tdUuourrafa2rq3OLlbCWZc3Gtf1Ewrx2WNNPJm3rrq3nJwPTuqk1A9sm19dLBr5t4ZSagfginm1tV1N/xtqvVr6rZ9zRKTUDz7bNYb013W3b0eFmkTRef6k1A98zXYtaT/m+/TYd1gy3PythvSCC/TCsad3m1JrWx4/vjh32+2FYM/Bta4f1ojxn1UV4rrWu3Zp6XhAEpmfNjz/+WD755JNmn1NUVCS9evVy/77hhhukpKTEzdEaOHCgPP744y5oaZBaX9jS55eWlq7zeFlZmQtuAAAAUampqZHi4mKpqqqSvLy8jRu0nnjiCXn66aebfc7VV18tQ4YMqf/+9ddflxdeeME1+NBDD5W33npLnnvuOfnyyy9bPKJVWFjogll+fr5pYi0vL3fBMDs726xuau1Be+wtsaws83cHM99/R/oXFkosFjN9tzt77lzZabueEo/bXnVOJn353+eLJO/bfuIZXtHWd6TVm30tQ2J9Je4Ztznw5TN/nnSb30tigW1t3/NleZ+FMiqnk2R5dsMtiSCQN2pXyrAe3SRuuG2opO/L5MXLZVjfQtParu68ubLNoFUSj5uVXVM7KfLFzI6yfecsiRv2s6sdBDJ1RUK6DVgqsZjdYdb3PVk+K1/6DO4nMeP90E/6Mn/G11IY7yExw/3FD3yZm1wsg7sWRrIfzqicK4XZvUzbXN/uuoWy51YFkmXY14mkL+9NXxLp8d+6dlR1U2tHea61rq25o6CgoEVBy/zS4fHHH++W1jjggAPcErrxxhtl+PDh631+Tk6OWxrTTrReSVHWVbrBxo032vrasZhp0AppyLIOWiENWZZBK6QHd+sDfEhDlnXQCmnIsgxaIQ1CWRFsG2Ft6xDn6sbXLFHQkGUdtEIasmJx+8vtGrKsg1Z9bS9mHloi3w8jarPSkGUZtDbK8T+i2lG2OTvCc6117dbUSvvtHRYtWtTgex3Nev/99+W8885LW5sAAADaxe0d9JKhXm484ogjZP78+XL77bfLVVddJaNHj0530wAAADI7aJ155pnSr18/N69LLwc+//zzss8++6S7WQAAAJkftNRBBx3kFgAAgPYk7XO0AAAA2iuCFgAAQEQIWgAAABEhaAEAAESEoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAmRq0vvjiC7nssstk1KhRUl5e3uRzpk6dKueee64ccsghctFFF8ns2bOjbhYAAEBmB6177rlHjjzySOnTp4+MHz9eFi5cuM5zPv/8c9lzzz0lmUzK+eefLxUVFTJixAj3FQAAIJNFGrR+9KMfuSB1xRVXrPc5v/3tb2WHHXaQ++67T4499lh57LHHJDc3V26++eYomwYAAJDZQat79+7ieV6zz3n55ZflqKOOqv8+KytLxowZ4x4HAADIZFnp/OE1NTWyZMkS6du3b4PH9fvm5mnV1ta6JVRdXe2+1tXVucVKWMuyZuPafiJhXjus6fu+bd219ZJJ27qpNQOxrR3WSwYRtHltTd+zrx3WTASBad2wXtJ420itaV27vm7StGyDmknjfk6t6fvNv9lsrbCeH8F+GNb0jfeXsF6k+2EEtcOaCeO+DutFevw3rh1V3dSaUZ5rrWu3pp4XBC0/wujlvXHjxjX7nFtuuUWGDRu27g/yPPfaU089tf6xyspK6datmzz++OMyduzY+sdvu+02ufLKK2XlypVN/oySkhIpLS1d5/GysjJ32REAACDKgaLi4mKpqqqSvLw8uxGtAw88UAYPHtzscwYMGNDiep07d3aXCpcuXdrgcf1eA9j6XHXVVe6TjKkjWoWFhXLAAQdIfn6+WCZW/aRkUVGRZGdnm9VNrT26cwfJ+p7LqxsyavHqitWyl5eQLMPSiUDk3SBLhvXoJvFYzHzUYvLi5TJ80NYSj8ft6iaTMmnml+Z1U2vv03+wZMVsayf8pLw9e4Z57bDu8OHDo+mPSZNkv369JMtw+0j4vrz59ULJHhoXL267rwTJQOo+ScqgAZ0lHrOtnfQDmTlrhXTbKs+03drm5dOrJXv7nuLFbffDIOlL3dRF8s1qPY5a1vZl8w5LpTbRM4IZK77kZC2STjX9xTOurSPiK3NnS9GQnSTbcH+pSyal/LP/RXpusa69Mc6HRRlUO7yS1hKtCloDBw50ixU90A8dOlQmTpzY4PEJEya4E8H65OTkuKUx7UTrlRRlXaUhK9s4aH1Xe81iKhAXsixPpI23iSzjABBlXaVByDpoRV3b9UdWNDMHsiLaPjSsWAetkIYs66AV0jbHDNsdXsTSkGUdtL6jdWMZVFdcyLIOWiENWZZBa2OcWzLxfJidQbVbUyvtNyz98Y9/LP/85z/d/bbUBx98IK+88op7HAAAIJNFGrQ++ugjd6NSXdR1113n/n3jjTfWP+fCCy+U4447TnbeeWe37LfffvKzn/1Mjj/++CibBgAAkNmfOhw0aJCbuN5Y7969G1zC0En2Orl97ty57jW9evWKslkAAACZH7R0Qns4mvV9+vXr5xYAAID2Iu1ztAAAANorghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQCYHrdraWpk+fbp88803bXoOAABAJok0aH399ddy5ZVXuj8UvfXWW8vTTz+9Qc8BAADIRJEGrfHjx7s/LD1p0qQ2PQcAACATZUVZ/JRTTjF5DgAAQCZiMjwAAMCmMKK1bNkytzSnb9++0qlTJ4mSTpzXJVRdXe2+1tXVucVKWMuyZuPaiSAwrx3WTBiXDuslfd+2cErNZDJpW3dtPeu6qTUTvn3tsKZ17bBetP1hu32E9YKk/b4S1kz69rXDmvoz/AjaHCTt98PvalrXjqrudzVte1ka1Kwz3l/CelGeW6xrb4zzYV0G1W5NPS8IWn6m//Of/yx33nlns8955JFHZI899lj3B3mejBs3Tk499dT1N6YFz1ElJSVSWlq6zuNlZWWSm5vb7GsBAADaoqamRoqLi6Wqqkry8vLsglZbWAatpka0CgsLpaKiQvLz800Ta3l5uRQVFUl2drZZ3Y1Ve3TnDpLleaYjZa+uWJ1R/RHWHT58uMTjcbEewdEPcWRS7Y3R5rxVA8QznJWgowrVHWfJdgPzJR6LmY+kfv7VUvFWbyOe2PZHIEkJOnwhXQcuEy9md5gNfE8qv+ouPbZdKTHbJosOeC6e1kmSNV30iGxYOZB4bpUUrBwiMeN+9iUpSzp9FmntoiE7Sbbh/qIjWuWf/S8jj6WZ1OYoa2vuKCgoaFHQinQyfFRycnLc0ph2ovVKirJu1LU1ZGUbBq1M7g8NFVlZ0WzumVg7yjZryLIMWiENWfF4NNNKNWRZBy2l8UpDVixuF7TCC2QasqyD1nf0uGF/7NAgZB2GNkZtDVmWQSuTj6WZ2OYoaremVqRBa+XKlTJv3rz67xcuXOhuStqlSxfp0aNHi58DAACQiSL91KFeRjj00EPdMnjwYPnrX//q/q1zvVrzHAAAgEwU6YjW3nvv7Uan2vocAACATMR9tAAAACJC0AIAAIgIQQsAACAiBC0AAICIELQAAAAiQtACAACICEELAAAgIgQtAACAiBC0AAAAIkLQAgAAiAhBCwAAIBP/1qFavXq1PPHEEzJx4kQpLi6WXXbZZZ3nTJgwQcaPHy91dXWy++67y0EHHRR1swAAADJ7ROvZZ5+VQYMGyTPPPCM333yzTJ06dZ3nHH300XLRRRfJggULpLKy0oWx448/XoIgiLJpAAAAmT2ipSFLR7J69uwpjz32WJPP+b//+z83ihU67bTTZOjQofKf//xHDj300CibBwAAkLlBa4cddvje56SGLLX99ttLhw4d5Ouvv46wZQAAAO1gjlZr/eMf/3BztfbZZ5/1Pqe2ttYtoerqavdVX6eLlbCWZc2NWTthfPk1rJdJ/RHWSyaTpnVTa2ZS7Y3R5kB807phvaRvWze1ZiD2/RHWDHzPtEe0nvLtm5xS03rqxpp6fgT9HNaMsnad8f4S1svEY2kmtXlj9EdLeEErJkO99NJL8sorrzT7nAsuuEAGDx687g/yPBk3bpyceuqp633tlClTXMA699xz5YYbbljv80pKSqS0tHSdx8vKyiQ3N/d7fw8AAIANVVNT4+aUV1VVSV5ent2IVufOnWWLLbZo9jl62W9DfPHFF1JUVOQmx//hD39o9rlXXXWVXHbZZQ1GtAoLC+WAAw6Q/Px8sUys5eXlrl3Z2dlmdTO1Nm2mP9K53XXqNVy8WFwsBX5SVi6cJIP22FtiWbYD/H4iITPff0eyR3YTL2vNKJSFIBFI3VvLpdfoXhLLsv08k5/wZeGrC81rh3Uz6XgXZW3anPn9EV5Ja4lWHVl0tKm5S3ob6ssvv3QhSW/rcP/990ss1vwOnpOT45bGtBOtV1KUdTO1Nm2mP9KxfWjI8mLRzHbQkBU3DlohDVmeaSBacyFSg1AsO5oPjkdVOxOPHVHWps2Z2x+tqZX2G5ZOnz5dRo0aJaNHj5YHHnjge0MWAABApoh0MryOVN1111313z/66KMyefJk2WOPPWTs2LHusYMPPtgNwRUUFMgvfvGL+ufq47oAAABkqkiDll7eC+d03XjjjfWPd+nSpf7fl1xyiSQSiXVeu/nmm0fZNAAAgMwOWltuuaVcccUVzT7nZz/7WZRNAAAASBsmRAEAAESEoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAEBGCFgAAQCYGrbq6OvnnP/8pBx54oHTt2lUef/zxdZ5TWVkp1157rQwbNkz69evnnvvcc89F2SwAAIDMD1p33HGHPPbYY/KrX/1KqqqqZPXq1es85/e//73k5eXJI488Iu+++64cdthhcvTRR8sbb7wRZdMAAAAilxVl8Z/97Gdy6aWXNvucG264ocH3P//5z+X222+X8ePHy6hRo6JsHgAAQOYGLc/zWvX8IAjkmWeekcWLF0tRUVFk7QIAAMj4oNVSn332mey1115SU1Mj8Xhc7rvvPtl7773X+/za2lq3hKqrq+vnhOliJaxlWTOTa9Nm+iOd213gJ03rptb0Ewnz2mHNIBHod2Z119TT+nY1Q2FN69phvUw63kVZmza3n/5oCS/QYaQWuu666+TGG29s9jk6kX3fffdd9wd5nowbN05OPfXUdf6b7/suLOnEeJ0wf80117g6Bx10UJM/o6SkREpLS9d5vKysTHJzc1v66wAAALSaDgwVFxe7+ec6z9wsaK1atcotzdl8880lKyurVUGrsTFjxrjnP/vssy0e0SosLJSKigrJz88Xy8RaXl7uLmNmZ2eb1c3U2rSZ/mC7S//+wn5If7DdpX9/0dxRUFDQoqDVqkuHHTt2dEvUNKg1F+hycnLc0ph2onVoibJuptamzfQH21369xf2Q/qD7S59+0traqX9hqVnnXWWfPrpp5JMJt0o1f333+8uG+qQHAAAQCaLNGjpLRr0RqW6qPPOO8/9+5JLLql/znHHHefClg696X+79dZb5YEHHpDTTz89yqYBAABk9qcO99lnH5k1a9Y6j6de9jviiCPcotdR9ZJha28JAQAA8IMMWhqcwtGs7xPVPCUAAIB0SfscLQAAgPaKoAUAABARghYAAEBECFoAAAARIWgBAABEhKAFAAAQEYIWAABARAhaAAAAESFoAQAARISgBQAAkMlBa/r06fLEE0/InDlzmn1eVVWVe9577723MZoFAACQuUFr4sSJcsghh8ihhx4qY8eOlTfffLPZ55999tlSXFwsN910U5TNAgAAyPygtXTpUrn00kvlyy+//N7n3nXXXbJo0SI56KCDomwSAADARpMVZfGioqIWPe/TTz+V0tJSd8nwwgsvjLJJAAAA7SNotcTKlSvlpJNOkptvvlm23HLLFr2mtrbWLalzu9SyZctM21ZXVyc1NTVuZC47O/sHXzsT+yMT2xxlbdpMf7DdpX9/YT/M/P5YsWKF+xoEgW3QmjJlinz22WfNPmf//feXHj16tLjmJZdcIsOHD5eTTz65xa+5/vrr3QhYY9tss02LawAAALQ1cHXp0sUuaH3yySfuU4HNGTJkSIuD1ttvvy0PPfSQ/PWvf62vu2DBApc69fvDDz9ccnNz13ndVVddJZdddln995WVldK/f3/3qcbv+4Vbo7q6WgoLC2Xu3LmSl5dnVjdTa9Nm+oPtLv37C/sh/cF2l/79RUeyNGT16dPne5/bqqCll/h0sdKhQwc58sgj5YUXXqh/bN68eRKPx+Uf//iHGx1rKmjl5OS4pTENWdahRWnNKOpmam3aTH+w3aV/f2E/pD/Y7tK7v7R0YCetc7R23333dUbIxowZIx07dvzekTMAAIBNXaRBS2/XkHrvrAkTJrgQNXDgQNl1112j/NEAAADtO2gtXLjQXQJUP/rRj9xlQf1e75W1vqC17777tvqTAXoZ8dprr23ycmJbRFU3U2vTZvqD7S79+wv7If3Bdrdp7C8t5QUt+WwiAAAAWo0/Kg0AABARghYAAEBECFoAAADt9U/wWNBpZm+99ZbMnz9fjjvuOHd/rg15TlP0z/u88sor0qtXLzdRvyk6yf/DDz9099TYZ599Nvg2/9OnT5fPP//c1dlrr70kK8tm9SQSCfeJT/1wwhZbbOFuq6H3KmuL5cuXy3/+858m/5u2XW8g21bJZNK1e8mSJbLHHnu06i8OrI/+Pc1Zs2Y1eKygoMD8j5m/8cYb7ua7ep+4zTbbzGQdTp48WSoqKmTw4MGy/fbbixW90a/ejLh79+6yyy67mE4a1T978dprr7k/r6XrsLV833frTD/BPHToUPe7W1m1apWUl5e7/fXQQw81q6t/7uOjjz5yX3fccUfp27evWW39M2N6rNH9d+edd3bbrrV///vfbnvTDzC1lbZVj2upunbtatrf+tdKvvjiC9lhhx1kq622anM93V51e2usW7ducsghh5jsE5MmTXLb37bbbitbb721WNG/Gzxz5sz647zneRtU59tvv3X7RufOnWX06NFNPkf7SPdNPb7peU/vKNASX3zxhUycONEdD/QOBBv6nKbOF7ru9Nx0wgknNPkcPfZrH+l+o8e6luaANgsy3AMPPBBsu+22wVZbbaWT+oPFixdv0HMaq66uDs4555ygd+/eQa9evYKjjz66yefdfvvtQadOnYIDDjggGDx4cLDNNtsEs2fPbtXvkEgkglNOOSXo0qVLcMQRRwQ77LCDa++sWbOCtvr888+DAQMGuN9df4eBAwe6f8+YMaNNdfX1J554YoNlxIgRrn/Hjx/f5nZ/8sknri+1rcccc4zrj8cff7zNdbWftT9S2/1///d/gaU333zTbRPaF1999VWb6/3nP/8Jtttuu2DPPfd020e3bt2CAw880G2jbTF//vzg8MMPd/2hdYcMGRJsueWWwbvvvtvmNus+dvrpp7v9Jz8/PzjjjDNaXWPZsmXB7rvvHvTt2zc46KCDgtzc3ODqq68OLFx++eVBnz59gsLCQre/WbnpppuCfv36Bfvuu29w8MEHu+3g5z//uUntK664wrX3sMMOc/W1P/TnWfrb3/4WdOjQIdhss81M6p133nmuP1L3N/09LFRWVrq+0O3rqKOOCnbeeWeT2qWlpesc27RPjjzyyDbXvueee9x60/Wn+9zmm28enHTSSe4c0BYrVqxw+0jPnj2DMWPGBIMGDQr22msvtw+1xurVq4OLL77Y7be63+2zzz5NPu/hhx9228jIkSPdcUO3y6lTpzZb+6OPPnJt3HrrrQPP84L7779/g57TlFtuucUdx/T3jsfj6/z3OXPmBIcccog7/2m/6/lE//3hhx8GG0PGB62///3vLky8+OKL6w1RLXlOYwsXLgzuuuuu4Jtvvgl+9KMfNRm0PvvsM7dSH330Ufd9bW1tsPfee7sNvTXuuOMOt8PNnDnTfe/7fnDqqacGRUVFQVudcMIJ7mQV7si6Iw0dOtSdBK1piNEdRNvfFlVVVe7gfOaZZ9a3u6amJnj55ZdN2rghJ/2WWrp0qdvhr7/+etOgpaEopNtv9+7dg9///vdtqjtt2rTg+eefr/8+3O70ANRW+iZB3+Doehs9evQG9bmepDVghoHy9ddfd336xhtvtLl9N954o+vHK6+80jRo6Ylh+fLl9d+//fbb7oSR2s8bSk9udXV1DY5r2h/z5s0LLHz66afu5PrLX/7SNGjp8TMKGnx22mmnBmHi3//+t/nPmTRpkuvnp59+uk119PzQsWPHBvutvqHU2s8880ybamvA1GNm2Bd6nB81apQbLGiNb7/9Nrj11lvdNqzrrqmgpccifQOhgwwqmUy6N2z6RrA5r732mjuW6XFGz5tNhaiWPKcpf/nLX9wxR5/fVNCaMmWKqxvSNo8dO9aFxI0h44NWqCUhqjVBK9X6gta1117r3hWnBouysrIgFou5E25L6clNd4pUulNbHES13ccdd1yDx/RdoL6LsqQ7pu58f/zjH9tcS3fgnJycVr8ba2nQ0nc02r868qShzpK+sy4pKakPBRZBqykaaK+66irzuk8++aRrt2W/bEjQ0gNhXl5ecPPNNzd4fJdddgnOPvtss7ZZB62m6OjAddddZ15XQ5yuKw3MbaWBWPvhiSeecPufZdDS0VcNQBqQU0NoW4QB5YUXXgiidtFFF7l1mBpyN4SGmOzsbHeOSB2J0vOF9ntb6AiZvjFtPMqj61FD14ZYX9DS7UMHBlatWlX/mL4J1vXx5Zdftqh2vAUhqjVBK7S+oNWURx55xLU59feISruYo5UuOq9F5wWkXgfXeSQ6r2Tq1KnrndPVmP5RSr2Dfl1dXf38Lp2rpf73v/+16I9Wrs9vfvMbN9fiiiuucG3TuSN6DV/nYVgqKytz8zrOOOOMNtfSvtD5BXr9/MUXX3Rfhw8f7uYQWdB5X/omY/bs2W7Onv5R8xNPPLHNdW+77TZZvHix/N///Z+bD2hp5cqVbp3pV/3boDqP75JLLhFrOh9R/wBrVH9/s6V03egfg9U5Tql0G9b9LlNMmTLFzdVr/HtsKD2u6DFBa959991y5ZVXyjbbbNPmuj/96U9lxIgR7lhxxx13iKWPP/5Y/va3v7m5rDpH5tZbb5UzzzyzzccIncOqfw/3v//9r5tLq31sMTc0VW1trTzyyCNywQUXtHnOrP7d3ltuucXdPFPXn+5jDz/8sBx//PFy9NFHt6m2niOmTZvW4DE9h+hcqxkzZsh2220nVnT/03llqXM5db9UOv/JYp7cxqDHusa/R1Q2uaD18ssvuwmf66OTQMeOHdvqupWVlW6CsvrXv/7lJvk1pgcsnSDXUrpzN56Mmp+f774+88wz8vXXX6/3tf369asPYhdddJHcd999csQRR7gT/ldffVX/tx71ZzR1oG3OqFGj3GRI1bt3b3cXfv2dv/zyS7eT6MTFnj17rvO6Z5991u2YzR0ojjrqqCb/m7Zf/5t+aKAxnYT/+uuvN9tmPUiGJyOdZLl69WrX7gEDBrg+0BPWXXfdJSeffHKD17399tvur7I355hjjqmfqPn//t//c20Nd67f/e53Lhzqek+dlKqh98knn2y2rk5yHjlypPu3TlT/7W9/K++///73ftBAD4g6GbY5Wjd1ErUGrKefftqFD51grPtAU2Ho+eefd39Rfn06deq03oO6BjjtYz34N6brRCeaNkffdIQH3LYKt/vG4Vr3L92XM4HuS6eeeqrb3/RvuFrQbUf3Zd3mdZvYaaed2lzzn//8p1u3ug1bO+mkk+TPf/6z2+6UBo1zzjnH7dtt2VZ0e9TtX7flb775xr3xGD9+vAtEf/rTn8za/9RTT7nt7ayzzjKppx9i0b7QPtcPBeibXv3ATFtDnAZu3c60vw8++GB3jnjppZeaPIe0ldZrar9UmbJv/utf/5IHH3zQrYeNYZMLWrqzaAJfHx3d2JCgpRvHu+++W39CaSrF6idKWhO0tIbu5KnC73XURD/NtT46YhMGLR1B0AB17733undn+u5E30Xttttusvnmm69zoNUTbnOGDBlSH7ROOeUUN8Kmr9OdWQOMfopEA4dubI1Dro7IrI/uTE0FLT1A6ydEfv/73zf5Oq35fW3WtoVBS0ORbgfaF3rwUFpbD3b6aSX99E9I+1k/+dKcww47rD5oHXjggQ3+21VXXeVq6ydsGget72uzbith0Dr//PNdwP3ggw/couszDK/6O6RuVxp4v6+2vitMDVp6YAv/nJV+akm3H+0zHR1Ipb+HvlteHz24NxW0dARO96uSkpJ1wqzST35+X5tjsZhZ0Ar3z6b2r5Z+uimdNATpvqLbke5n2jcWjj32WLeEIUBHQ3S0ojXHrVQ6Cq3BR48Hzz33nHtM92V9XLc33c7a8klP3SdS/exnP3NvbnSkui3bim4D+ob8gAMOkF/96lf1b7r0mKrHcYtPByp9U6bHy0GDBrW5lu6X+mb6D3/4g1x88cX1n64bNmyYe4Oqx+oNpetfjzn333+/G+3TY9lf/vIXOfzww9c5h7RVc+e9TNg3X331VSkuLnbrQe9AsFEE7UQ65mjppzN0Ynmq8vJy9zPa+qm+xx57zNWZO3dum+roXCedbN94IrB+wtFyDkP//v3dvBoLF154YbDFFls0eEw/zKD9ofNSrBUUFLh5VW3x05/+tMGnlPRTqNpe/WCETlq2pn00fPhwk1pvvfWWm3Ohcw6jsCFztHQ+i861ePDBBxs8rp8cOvbYYzfpOVorV650n5zafvvt3YdqoqRzcHTy8obS+TuNP2Gn8+CysrLcv/V4Zk0/LNLWTwfqJ5B1/9LjQio9bljNh9P5lfpBBj0WW9AJ79rmBQsWNHh8jz32CM4666zAms6X1U846iR8yzla+qlMnbPW1AcG3n///U16jtarr77q+kQ/rLQxccPSNtB3C3opLpxPpR577DH3DrM174B0xCn1cqm+m9SRCq2vlxjbQl+v95lJpd+3tW7jOQw62mT1rl2H0nXUJnV0LfwddPRvQ+lontZtPJKjozX6zr0t9JKIjgCEyzXXXOMev/322+XHP/5xm2rrvbNS6fwyvfTYlr4IvfPOO27E77LLLnOjWZsKvUytoyGpQ/t6uUgv/+uowKZK742kI1k6908vxzV1iX5D6L2BdF9rag5OW7YDnROaut3qoturjlrov9tyfzk9rjW+H5XOEdX5d23d37Rdegku9dirxwvdvy32C/X3v//djeLr1AMLYbtSj8e6vei8tbYej3U70FHU1BEmnQagc+Gs7xWl5yU9JukIYup5T6+i6CXhTdUbb7zhzi06h/aXv/zlRv3Zm9ylw9bSy1a6s+mEy9T5VzqEHG68LXlOU3RoXk/OOtdKb4amBx7daMPhRr2MpQd9nX+hE5P1xnwPPPCAu1zUGlpbL2np/Cyd86VzZHRek268bfXrX/9azj77bBeCdEK5Xmp76KGHmpyHsyG0j3TekF56sBL2q841OO+881z9m266SS688MI2HUT1RKXrXNeXhmGdC6dBSG9up2FjU3Xaaae5yzd6eUBDuPa5bs96aaAttIb2tc4b0cvN4aVJpf3f1DzG1gjr6basl9D0e725oR7sWuKPf/yj7Lfffm4OnU7Uvueee9ycJO0Pq7mgetLTaQVhW1tzM+Om6CUJvex94403NpiX2Nr5n43pnCyd76WXDfUGjjotQU+kRUVF6503mW56XNPJ6rpv6fw9bbPub7pttfVmqDp9QC/562VPnRah87W0P/Ry5PpuVtnakKjHct32rIKKXiLUdaXbyKWXXuou448bN869cdLjXFuDuAZCra0hWfcVDYnXX399q2uF8zx1Co++CQ33DT0/6Qe/dErL6aef7r6//PLL3f598803uzlPzc1PXZQyz1N/Z53PqpcadXsOb2bckuc0dyNqfb6+LmyzBnI9p+qAiB73dR/UWqnHOqubSjfH02EtyWA6mtJUsNENOVwxLXlOU3SUpvHkcF0het0+pCcQfeej8790QqZugBuS6nVj1ROnHlA1EOm7Sqtr6zp5Wid26xwBnRyvByLd6S3oSI7u5KWlpWJ9kNYdV+dphXcntjihaGjTA6hOFtUDkQZcq/kcqXS+hH7iU08sbb2jfRjy9R2kHug0JOp2pttbW7cLDbBN0QnFbfm0q9KJuY1pX2iftJSeRHXuoh6A9U7oOhdOR7va6uqrr25yLqieoNoSMH/yk5+sM2qqdBtr68imjpLpmySd16MnDw0xOrqwoXf/bi6E6qeIdT9pKz1+6n6sI7AajnROY0uDdks/OaZvPHQfCY+bFp8i0zfNOvKhH3CxvHO7BjjdlzUY6AiU3hle36RafKJa26yfRNU3EHvvvbd7Q7Ihf6VE59E1Nc9Tt4nwqoX+HhoS9U2F7o+6r3/fp+ynrj0mNqbbsX6IoaXPacqdd97Z5Ce99bykfazn58bzWUMWx+h2H7QAAAA2VczRAgAAiAhBCwAAICIELQAAgIgQtAAAACJC0AIAAIgIQQsAACAiBC0AAICIELQAAAAiQtACAACICEELAAAgIgQtAACAiBC0AAAAJBr/H2+5NIkoOI81AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(-8, 5, 3), (2, 8, 3), (-10, -3, 6), (-9, -7, 4), (9, -7, 6), (1, -4, 4), (3, 7, 6), (0, -8, 3), (-7, 2, 1), (12, 6, 5), (6, 12, 1), (4, 12, 2), (-10, -10, 3), (2, 4, 5), (13, -3, 4), (8, 15, 5), (-12, 7, 0), (0, -12, 5), (-7, -6, 2), (-13, -5, 1), (5, 8, 4), (-15, 8, 3), (-8, -10, 1), (4, 4, 3), (-8, -7, 6), (2, -4, 6), (-5, 6, 5), (-5, -2, 1), (-5, -1, 1), (5, 1, 1), (7, 12, 3), (4, -3, 0), (8, 8, 1), (-8, -14, 3), (5, 4, 6), (6, 0, 4), (-14, 0, 3), (4, 0, 5), (6, -8, 0), (-16, -8, 6), (3, -12, 6), (7, 13, 4), (11, -10, 5), (5, -3, 3), (4, 1, 6), (-3, 11, 4), (14, -5, 0), (8, 4, 3), (-9, 8, 2), (-15, -4, 6), (-11, 0, 5), (11, -9, 6), (-1, 6, 6), (-2, -2, 2), (-2, -1, 2), (-1, -1, 2), (-1, -2, 2), (10, 12, 4), (-5, -14, 4), (9, 8, 3), (5, -11, 4), (13, 12, 2), (7, 0, 6), (-11, -8, 0), (-3, 3, 5), (-12, -4, 3), (-9, 4, 4), (-3, -5, 1), (7, -8, 2), (0, 2, 1), (10, 5, 1), (-10, 5, 0), (7, -7, 3), (8, -11, 1), (-6, 8, 3), (12, 9, 0), (15, 9, 5), (-8, 8, 3), (-6, 1, 0), (-6, 4, 5), (1, -9, 0), (-2, -13, 6), (1, -6, 5), (-1, -13, 6), (12, 1, 1), (-8, 1, 0), (11, 5, 3), (-10, -7, 3), (12, 4, 6), (9, -11, 3), (2, 7, 5), (3, 3, 3), (6, 11, 4), (11, 6, 4), (-6, -4, 6), (1, -5, 6), (-5, 9, 0), (0, -9, 5), (-2, 13, 0), (-1, 13, 0), (-13, 6, 5), (-13, -2, 1), (12, -3, 3), (2, 0, 2), (14, 6, 0), (-4, 0, 5), (6, 3, 5), (-14, 3, 4), (3, -13, 0), (-2, 9, 2), (-1, 9, 2), (3, -10, 5), (7, 15, 3), (-7, -7, 4), (5, 7, 6), (-15, 7, 5), (-4, 1, 6), (-4, -7, 2), (4, 3, 5), (6, -5, 0), (4, -5, 1), (-16, -5, 6), (-12, 7, 3), (-7, -6, 5), (7, 8, 0), (-5, -3, 3), (5, 0, 3), (-15, 0, 2), (4, 4, 6), (6, -4, 1), (7, 11, 5), (4, -4, 2), (2, -12, 5), (16, 2, 0), (11, -6, 6), (1, 9, 3), (-12, -9, 0), (0, 13, 6), (-5, -10, 0), (0, 5, 2), (7, 4, 2), (-5, -11, 4), (-1, -3, 5), (-2, -3, 5), (9, 11, 3), (11, -13, 3), (-3, 6, 5), (1, 10, 4), (10, 8, 1), (9, 4, 0), (7, -4, 3), (9, 7, 5), (-12, -8, 0), (5, -12, 6), (-18, 0, 6), (13, 11, 4), (0, 6, 2), (-2, -2, 5), (-1, -2, 5), (-2, -10, 1), (-1, -10, 1), (-10, 4, 2), (7, -11, 0), (-7, 8, 2), (9, 8, 6), (9, 0, 2), (-6, 7, 5), (-8, 7, 6), (-9, -8, 2), (3, 6, 4), (6, 13, 3), (3, -2, 0), (3, -1, 0), (-8, 4, 0), (-5, 12, 1), (-6, 8, 6), (-6, 0, 2), (2, 10, 5), (9, -8, 3), (2, 2, 1), (6, 14, 4), (6, 6, 0), (1, -2, 6), (1, -1, 6), (11, 1, 0), (1, -10, 2), (3, 2, 6), (11, 4, 5), (-13, 1, 1), (12, 0, 3), (2, 3, 2), (10, -8, 6), (4, 14, 4), (9, -12, 5), (14, 9, 0), (-4, 3, 5), (-15, 2, 2), (-4, -5, 1), (6, -2, 1), (-14, -2, 0), (-14, -1, 0), (11, -3, 2), (-12, 9, 2), (-6, -13, 4), (-2, 12, 2), (-1, 12, 2), (6, 1, 6), (6, -1, 1), (-7, -4, 4), (-13, -3, 3), (-7, -12, 0), (-8, -8, 3), (-4, -4, 2), (4, 6, 5), (7, 14, 6), (-11, 6, 0), (-6, -12, 5), (14, 1, 1), (8, 10, 4), (-1, 13, 3), (-2, 13, 3), (8, 2, 0), (14, 4, 6), (-7, -11, 1), (11, -11, 3), (15, 0, 4), (5, 3, 3), (-15, 3, 2), (-7, -8, 6), (17, 4, 4), (-8, -12, 5), (-3, 9, 0), (2, -9, 5), (8, 3, 1), (3, -13, 3), (-11, -1, 1), (-11, -2, 1), (14, -3, 3), (15, 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(-9, 10, 2), (-15, -2, 6), (8, -1, 5), (-12, -2, 4), (-12, -1, 4), (-2, 1, 4), (8, -10, 1), (8, -2, 5), (0, 12, 6), (-10, 7, 1), (-9, 11, 3), (-5, -12, 4), (5, -9, 4), (-11, 3, 6), (4, -13, 3), (-3, 5, 5), (8, -9, 2), (1, 9, 4), (16, 1, 5), (1, 1, 0), (-2, -3, 6), (-1, -3, 6), (9, 3, 0), (12, 11, 1), (5, -16, 1), (-3, 6, 6), (7, -5, 3), (-3, -2, 2), (-6, 10, 3), (-3, -1, 2), (5, -13, 6), (-2, -6, 0), (-9, -1, 5), (9, 4, 1), (1, -7, 1), (7, -12, 0), (-7, 7, 2), (9, 7, 6), (-6, 3, 0), (-8, 3, 1), (-6, 6, 5), (-9, -9, 2), (2, 9, 6), (3, 5, 4), (-7, 8, 3), (3, -3, 0), (-3, -15, 4), (-1, -11, 6), (-10, -5, 3), (-13, 0, 2), (13, 3, 6), (13, -5, 2), (6, 13, 4), (11, 8, 4), (11, 0, 0), (-8, 4, 1), (-16, 1, 5), (-10, -4, 4), (2, 10, 6), (-5, 11, 6), (2, 2, 2), (-5, 3, 2), (-15, 6, 1), (-7, -5, 5), (11, 1, 1), (8, 13, 2), (-4, 2, 5), (-14, 5, 4), (11, 4, 6), (6, -3, 1), (-13, 1, 2), (-14, -3, 0), (4, 5, 6), (-5, 4, 3), (-2, 11, 2), (-1, 11, 2), (0, -15, 6), (-4, 3, 6), (2, -6, 3), (-15, 2, 3), (-4, -5, 2), (6, -2, 2), (-14, -2, 1), (14, 8, 5), (-12, 9, 3), (14, 0, 1), (-1, 12, 3), (-2, 12, 3), (-9, 13, 3), (-7, -4, 5), (-7, -12, 1), (11, -12, 3), (-5, 0, 5), (5, 2, 3), (2, -13, 0), (4, 6, 6), (4, -1, 2), (4, -2, 2), (8, 2, 1), (2, -10, 5), (16, 4, 0), (6, -6, 4), (-3, 8, 6), (-12, 1, 4), (8, 1, 5), (15, 0, 5), (1, 11, 3), (-2, -4, 0), (-1, -4, 0), (17, 4, 5), (0, 7, 2), (10, 10, 2), (2, -17, 2), (9, 6, 1), (-11, -2, 2), (-11, -1, 2), (14, -3, 4), (8, -6, 2), (1, 12, 4), (-12, -6, 1), (9, 9, 6), (0, 8, 3), (-5, -8, 5), (-2, 0, 6), (-1, 0, 6), (-2, -8, 2), (-1, -8, 2), (12, 14, 1), (7, -2, 3), (7, -1, 3), (-9, 2, 1), (5, -10, 6), (4, -14, 5), (0, 1, 0), (-9, 1, 5), (8, -10, 4), (10, 7, 5), (7, -9, 0), (-7, 10, 2), (-8, 6, 1), (4, -13, 6), (-4, 10, 0), (-3, -3, 4), (-10, -2, 4), (-10, -1, 4), (-9, -6, 2), (2, 12, 6), (13, 5, 5), (3, 8, 4), (3, 0, 0), (1, -8, 3), (-5, 14, 1), (0, -4, 6), (0, -12, 2), (-6, 10, 6), (13, 6, 6), (-8, 2, 3), (-16, 5, 1), (13, -1, 2), (7, -14, 6), (13, -2, 2), (2, 5, 3), (11, 3, 0), (1, -15, 0), (3, 4, 6), (-7, 7, 5), (10, -9, 1), (-10, -9, 0), (5, 13, 3), (-8, -5, 0), (9, -10, 5), (11, 7, 6), (12, -5, 0), (-17, -4, 6), (10, -13, 3), (3, -12, 3), (-13, 0, 5), (9, -9, 6), (7, 13, 1), (-13, -8, 1), (-5, 2, 4), (-4, 6, 6), (5, 5, 4), (-4, -2, 2), (-4, -1, 2), (-14, 1, 1), (4, 1, 3), (-6, -10, 5), (8, 12, 4), (8, 4, 0), (-14, 4, 6), (15, 3, 6), (-5, 3, 5), (-8, -13, 0), (-15, 6, 4), (-5, -5, 1), (8, 13, 5), (7, 9, 3), (8, 5, 1), (2, -7, 5), (-9, 4, 1), (5, -1, 0), (5, -2, 0), (3, -15, 6), (7, 2, 0), (2, -6, 6), (-5, -13, 2), (-11, 1, 2), (14, 0, 4), (-12, -3, 1), (-15, -7, 6), (6, -11, 5), (0, 11, 3), (-2, 3, 6), (8, -8, 3), (-1, -5, 2), (-2, -5, 2), (-10, 9, 3), (-9, 5, 1), (-4, -13, 6), (9, 5, 3), (-11, -3, 4), (7, -3, 6), (4, -11, 5), (1, 11, 6), (-6, 12, 6), (-12, -7, 3), (-2, -4, 3), (-1, -4, 3), (10, 10, 5), (7, -6, 0), (-10, 2, 0), (-15, -6, 6), (5, -14, 3), (9, -2, 0), (9, -1, 0), (-4, 13, 0), (-3, 0, 4), (8, -14, 1), (-6, 5, 3), (-3, -8, 0), (3, 3, 0), (6, 11, 1), (1, -5, 3), (7, -10, 2), (12, 5, 4)}\n",
      "Population initialized with 200 individuals.\n",
      "\n",
      "--- Generation 1/30004, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/30004, Best fitness 0.51863 ---\n",
      "\n",
      "--- Generation 2001/30004, Best fitness 0.54881 ---\n",
      "\n",
      "--- Generation 3001/30004, Best fitness 0.56616 ---\n",
      "\n",
      "--- Generation 4001/30004, Best fitness 0.58057 ---\n",
      "\n",
      "--- Generation 5001/30004, Best fitness 0.59604 ---\n",
      "\n",
      "--- Generation 6001/30004, Best fitness 0.60608 ---\n",
      "\n",
      "--- Generation 7001/30004, Best fitness 0.61695 ---\n",
      "\n",
      "--- Generation 8001/30004, Best fitness 0.62367 ---\n",
      "\n",
      "--- Generation 9001/30004, Best fitness 0.63052 ---\n",
      "\n",
      "--- Generation 10001/30004, Best fitness 0.6364 ---\n",
      "\n",
      "--- Generation 11001/30004, Best fitness 0.64237 ---\n",
      "\n",
      "--- Generation 12001/30004, Best fitness 0.65021 ---\n",
      "\n",
      "--- Generation 13001/30004, Best fitness 0.6542 ---\n",
      "\n",
      "--- Generation 14001/30004, Best fitness 0.65808 ---\n",
      "\n",
      "--- Generation 15001/30004, Best fitness 0.65889 ---\n",
      "\n",
      "--- Generation 16001/30004, Best fitness 0.66312 ---\n",
      "\n",
      "--- Generation 17001/30004, Best fitness 0.66687 ---\n",
      "\n",
      "--- Generation 18001/30004, Best fitness 0.66841 ---\n",
      "\n",
      "--- Generation 19001/30004, Best fitness 0.67062 ---\n",
      "\n",
      "--- Generation 20001/30004, Best fitness 0.67425 ---\n",
      "\n",
      "--- Generation 21001/30004, Best fitness 0.67513 ---\n",
      "\n",
      "--- Generation 22001/30004, Best fitness 0.67627 ---\n",
      "\n",
      "--- Generation 23001/30004, Best fitness 0.67802 ---\n",
      "\n",
      "--- Generation 24001/30004, Best fitness 0.68026 ---\n",
      "\n",
      "--- Generation 25001/30004, Best fitness 0.68449 ---\n",
      "\n",
      "--- Generation 26001/30004, Best fitness 0.68565 ---\n",
      "\n",
      "--- Generation 27001/30004, Best fitness 0.68901 ---\n",
      "\n",
      "--- Generation 28001/30004, Best fitness 0.6906 ---\n",
      "\n",
      "--- Generation 29001/30004, Best fitness 0.69066 ---\n",
      "\n",
      "--- Generation 30001/30004, Best fitness 0.69217 ---\n",
      "draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program evolution_of_colored_morpho_trees_vs_complexity_m7.py\n",
    "# This program reports the battle of Evolution \n",
    "# in the space of colored morpho-trees \n",
    "# to match a target colored area proposed by a target colored morpho-tree.\n",
    "# We merge the code for Evolution to that for generating and drawing a colored tree.\n",
    "# Edited with Spider 6.\n",
    "\n",
    "# =====Reused:  Program colored_tree_generator_and_drawer_m7.py\n",
    "# This program generates a colored morpho-tree as a Newick formula,\n",
    "# and draws it. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "  \n",
    "  \n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import math\n",
    "import copy # For deep copying trees during reproduction and mutation\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "# trace memory allocation \n",
    "#https://www.geeksforgeeks.org/python/monitoring-memory-usage-of-a-running-python-program/\n",
    "# this consumes too much cpu-time by a multiplicative factor, say 5 for 1000 lines.\n",
    "import tracemalloc \n",
    "\n",
    "\n",
    "#Activate  memory monitoring?\n",
    "memory_monitoring = False \n",
    "recombination_of_leaves = False\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# starting the memory allocation monitoring\n",
    "if memory_monitoring:\n",
    "    tracemalloc.start() \n",
    "start_time = time.time()\n",
    " \n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "\n",
    "# Parameters for the target tree  \n",
    "target_tree_params = {\n",
    "  'total_number_of_nodes': 15,\n",
    "  'max_number_of_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "# Parameters for the Genetic Algorithm\n",
    "ga_params = {\n",
    "   #'seed': 47423648, #the seed of the target tree is this plus 1.\n",
    "  'pop_size': 200,\n",
    "  'generations': 5,\n",
    "  'average_number_of_nodes':  target_tree_params['total_number_of_nodes'],\n",
    "  'max_number_of_children': target_tree_params['max_number_of_children'],\n",
    "  'max_node_radius': 1.5,\n",
    "  'mutation_rate': 0.001, \n",
    "  'crossover_rate': 0.001  \n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "def print_parameters_m7(tree_params, ga_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n Evolution searches in the space of colored trees \"\n",
    "        + \"\\n to match a target colored area associated to a target tree.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    " \n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m7(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m7(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "#=== COLORED TREE GENERATOR===\n",
    "\n",
    "def detect_all_groups_m7(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m7(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m7(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m7(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = int(float(c[0][2:]))\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m7(newick_string):\n",
    "    #print(\"extract_all_infos_m7\")\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m7(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m7(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m7(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m7(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m7(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m7(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m7(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator_m7(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m7(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # Filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l-1) # color index\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter_of_nodes)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    \n",
    "    # distance2 = distance squared between father and child.\n",
    "    # Test: distance2 must be 2 or  1. \n",
    "    distance2 = (father_center[0]-nx)**2 + (father_center[1]-ny)**2\n",
    "    if distance2 > 2:\n",
    "        print(f\"distance2 > 2: {distance2}\")\n",
    "  #else:\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m7(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children_m7(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  generated_children = \"\"\n",
    "  i = 0\n",
    "  while i in range(numb_children) and counter_of_nodes < total_number_of_nodes:\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m7(father_center, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "    if info == \"\":\n",
    "      generated_children = \"()\"\n",
    "      return generated_children, counter_of_nodes, occupied_squares_set\n",
    "    else:\n",
    "     generated_children = info + a + generated_children\n",
    "     counter_of_nodes +=  1\n",
    "     i += 1\n",
    "     #print(f\"counter_of_nodes at generate_children_m7= {counter_of_nodes}\")    \n",
    "  generated_children =  \"(\" + generated_children + \")\"\n",
    "  #print(f\"Children: {generated_children}\")\n",
    "  return generated_children, counter_of_nodes, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m7(total_number_of_nodes, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  # Initial tree = root. () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))# Root\n",
    "  #counter of defined nodes\n",
    "  counter_of_nodes = 1\n",
    "  while counter_of_nodes < total_number_of_nodes:\n",
    "      l = len(tree)\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Find and replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m7(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement of  () by a list of children\n",
    "            generated_children, counter_of_nodes, occupied_squares_set = generate_children_m7(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m7(tree, j-1, j+1, generated_children)\n",
    "            #print(f\"counter_of_nodes at complete_tree_generator_m7= {counter_of_nodes}\")\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  #print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "# === DRAWER ===\n",
    "\n",
    "def find_branch_m7(newick_string, final_index):\n",
    "  initial_segment = newick_string[:final_index]\n",
    "  #print(f\"initial_segment: {initial_segment}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  branch = \"\"\n",
    "  for i in range(len(initial_segment)-1,-1,-1):\n",
    "    if initial_segment[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if initial_segment[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = initial_segment[i:final_index+1]\n",
    "      #print(f\"Branch to be deleted: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_find_all_groups_m7(newick_string):\n",
    "  \"\"\" All groups are found and one is chosen at random and returned\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  info_words =  r'[a-zA-Z_0-9_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(info_words, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string, 0\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  return groups[start_idx], starts[start_idx]\n",
    "\n",
    "def deletion_of_branch_m7(newick_string ):\n",
    "  group, start =  newick_find_all_groups_m7(newick_string)\n",
    "  #print(f\"Group (main): {group}\")\n",
    "  #print(f\"Start (main): {start}\")\n",
    "  # Does the group have children?\n",
    "  char_before = newick_string[start-1]\n",
    "  #print(f\"char_before: {char_before}\")\n",
    "  if char_before ==  \")\":\n",
    "    #print(\"The group has children\")\n",
    "    #find branch\n",
    "    final_index = start-1\n",
    "    branch = find_branch_m7(newick_string, final_index+1)\n",
    "\n",
    "    branch_and_father = branch + group\n",
    "    #print(f\"  Branch and father to be deleted: {branch_and_father}\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(branch_and_father , \"\")\n",
    "  else:\n",
    "    #print(\"The group has no children\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(group, \"\")\n",
    "\n",
    "  # Cosmetics\n",
    "  newick_string = newick_string.replace(\",,\", \",\")\n",
    "  newick_string = newick_string.replace(\",)\", \")\")\n",
    "  newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "  newick_string = newick_string.replace(\" \", \"\")\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {newick_string}\")\n",
    "  l = len(newick_string)\n",
    "  #print(f\"Length of newick_string: {l}\")\n",
    "  if l == 0 :\n",
    "     newick_string = \"Node_0[a=0,b=-0,x=0,y=0,r=1]\"\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "# =================================================================================\n",
    "# ==== Drawer ===\n",
    "# =================================================================================\n",
    "\n",
    "\n",
    " \n",
    "def extract_info11b(group):\n",
    "  '''The info of each group is extracted by tokenizers from the string.'''\n",
    "  patern_a = r'a=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_b = r'b=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  a = re.findall(patern_a, group)\n",
    "  b = re.findall(patern_b, group)\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  a = float(a[0][2:])\n",
    "  b = float(b[0][2:])\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  r = float(r[0][2:])\n",
    "  return [a,b,x,y,r]\n",
    "\n",
    "\n",
    "# colored_coords = get_colored_area_m7(x,y,c,r)\n",
    "def get_colored_area_m7(x,y,c,r):\n",
    "    '''\n",
    "      The illuminated area encloses all unitary squares \n",
    "      that intersects the circles of illumination with the respective color ci.\n",
    "      Squares are denoted by their left bottom corners (xi,yi). These will be found in the set colored_coords.\n",
    "      The radius of illumination is ri\n",
    "    '''\n",
    "    #print(\"get_colored_area_m7\")\n",
    "    #print(f\"x: {x}, y: {y}, c: {c}, r: {r}\")\n",
    "    colored_coords = set()\n",
    "    \n",
    "    for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "        circle_center_x = xi + 0.5\n",
    "        circle_center_y = yi + 0.5\n",
    "        r_sq = ri ** 2\n",
    "        # 1. Add the cell containing the center of the circle and its color\n",
    "        # This covers the case of small circles not intersecting grid lines.\n",
    "        colored_coords.add((math.floor(circle_center_x), math.floor(circle_center_y), ci))\n",
    "\n",
    "        # 2. Horizontal Scan: Check intersections with horizontal grid lines\n",
    "        # Iterate y_line from floor(cy - r) to ciel(cy + r)\n",
    "        # These y_line values represent the y-coordinates of horizontal grid lines.\n",
    "        for y_line in range(math.floor(circle_center_y - ri)-1,\n",
    "                            math.ceil(circle_center_y + ri) + 1):\n",
    "            dy = y_line - circle_center_y\n",
    "            discriminant = r_sq - dy * dy\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dx = math.sqrt(discriminant)\n",
    "                x_min_intersect = circle_center_x - dx\n",
    "                x_max_intersect = circle_center_x + dx\n",
    "\n",
    "                # For each integer x-coordinate that the horizontal segment crosses:\n",
    "                # Add the cells whose bottom-left corner's x-coordinate is in this range.\n",
    "                # This covers cells whose top edge is y_line AND cells whose bottom edge is y_line.\n",
    "                for ix in range(math.floor(x_min_intersect), math.floor(x_max_intersect) + 1):\n",
    "                    colored_coords.add((ix, y_line, ci))\n",
    "                    colored_coords.add((ix, y_line - 1, ci))\n",
    "\n",
    "        # 3. Vertical Scan: Check intersections with vertical grid lines\n",
    "        # Iterate x_line from floor(cx - r) to ciel(cx + r)\n",
    "        # These x_line values represent the x-coordinates of vertical grid lines.\n",
    "        for x_line in range(math.floor(circle_center_x - ri)-1,\n",
    "                            math.ceil(circle_center_x + ri) + 1):\n",
    "            dx = x_line - circle_center_x\n",
    "            discriminant = r_sq - dx * dx\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dy = math.sqrt(discriminant)\n",
    "                y_min_intersect = circle_center_y - dy\n",
    "                y_max_intersect = circle_center_y + dy\n",
    "\n",
    "                # For each integer y-coordinate that the vertical segment crosses:\n",
    "                # Add the cells whose bottom-left corner's y-coordinate is in this range.\n",
    "                # This covers cells whose right edge is x_line AND cells whose left edge is x_line.\n",
    "                for iy in range(math.floor(y_min_intersect), math.floor(y_max_intersect) + 1):\n",
    "                    # Cell (x_line, iy) - its left edge is at x_line\n",
    "                    colored_coords.add((x_line, iy, ci))\n",
    "                    # Cell (x_line - 1, iy) - its right edge is at x_line\n",
    "                    colored_coords.add((x_line - 1, iy, ci))\n",
    "    #print(f\"colored_coords  {colored_coords}\")\n",
    "\n",
    "    return colored_coords\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def plot_colored_area_newick_m7(ax, colored_coords, title,  alpha=0.9):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not colored_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y, c in colored_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=palette[c], alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(d[0] for d in colored_coords) - 1\n",
    "    max_x = max(d[0] for d in colored_coords) + 2\n",
    "    min_y = min(d[1] for d in colored_coords) - 1\n",
    "    max_y = max(d[1] for d in colored_coords) + 2\n",
    "    #print(f\"min_x: {min_x}, max_x: {max_x}, min_y: {min_y}, max_y: {max_y}\")\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "\n",
    "def plot_tree_nodes_and_their_diffusion_circles_m7(x,y,c,r, ax):\n",
    "  for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "    #print(f\"ci at plot_tree_nodes_and_their_diffusion_circles_m7: {ci}\")\n",
    "    # Plot the square representing the cell\n",
    "    rect = patches.Rectangle((xi, yi), 1, 1, facecolor=palette[ci], edgecolor='black', linewidth=1, zorder=1)\n",
    "    ax.add_patch(rect)\n",
    "    #node_label_part = node.value.split('_')[-1] if '_' in node.value else node.value\n",
    "    #ax.text(xi + 0.5, yi + 0.5, node_label_part, ha='center', va='center', color='darkblue', fontsize=7, zorder=2)\n",
    "\n",
    "    # Plot the circle representing the radius of influence\n",
    "    circle = patches.Circle((xi + 0.5, yi + 0.5), ri, color=palette[int(ci)], alpha=0.6, zorder=2)\n",
    "    ax.add_patch(circle)\n",
    "\n",
    "def find_leaves_newick_m7(newick_string):\n",
    "    '''Detect leaves in a Newick string.'''\n",
    "    #print(\"\\n\\nfind_leaves\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "    # Detection of leaves.\n",
    "    # Detect substrings of the form \"(...)\" but without interior parentheses.\n",
    "    pattern_leaves = r'[(][^()]*[)]'\n",
    "    # Leaves may come grouped by bouquets\n",
    "    bouquets_of_leaves = re.findall(pattern_leaves, newick_string )\n",
    "    #print(f\"Bouquets_of_leaves: {bouquets_of_leaves}\")\n",
    "    all_leaves = []\n",
    "    # Decipher each leaf and find the father\n",
    "    for bouquet_of_leaves in bouquets_of_leaves:\n",
    "        bouquet_of_leaves = bouquet_of_leaves[1:-1]\n",
    "        leaves = detect_all_groups_m7(bouquet_of_leaves)\n",
    "        for leaf in leaves:\n",
    "          leaf = leaf.replace(\" \", \"\")\n",
    "          all_leaves.append(leaf)\n",
    "    return all_leaves\n",
    "\n",
    "\n",
    "\n",
    "def connect_leaves_to_father_m7(newick_string, ax, tree_color='lightblue', diff_color='red'):\n",
    "    '''Draw lines connecting leaves to fathers'''\n",
    "    #print(\"\\n\\nconnect_leaves_to_father_m7\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    all_leaves = find_leaves_newick_m7(newick_string)\n",
    "    for leaf in all_leaves:\n",
    "        #Leaf info\n",
    "        [xl,yl,cl,rl] = extract_info_m7(leaf)\n",
    "        #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n",
    "\n",
    "        # Task: Find the father of the leaf.\n",
    "        # It is the node after the last leaf\n",
    "        # Example:\n",
    "        # (Node9[x=-11,y=19.7,r=12.4567],Node8[x=0,y=7,r=15],Node7[x=6,y=16,r=9])Node6[x=17,y=9,r=20]\n",
    "        # The father of Node9, Node8, and Node7 is Node 6.\n",
    "        # The father of a node is found after the first unbalanced right parenthesis.\n",
    "        index_next_right_par = newick_string.find(\")\" , newick_string.find(leaf))\n",
    "        father_with_tail = newick_string[index_next_right_par+1:]\n",
    "        father_group = detect_all_groups_m7(father_with_tail)\n",
    "        if father_group:\n",
    "          [xf,yf,cf,rf]  = extract_info_m7(father_group[0])\n",
    "          #father_center = (xf,yf)\n",
    "          #Draw link\n",
    "          ax.plot([xl + 0.5, xf + 0.5], [yl + 0.5, yf + 0.5], 'k--', linewidth=0.8, zorder = 3)\n",
    "          # Test: distance2 = distance squared between father and child.\n",
    "          # distance2 must be 2 or  1. \n",
    "          distance2 = (xf-xl)**2 + (yf-yl)**2\n",
    "          #print(f\"distance2: {distance2}\")\n",
    "          if distance2 > 2:\n",
    "              print(f\"\\n distance2 > 2: {distance2}\")\n",
    "              print(f\"xl,yl,xf,yf: {xl} {yl} {xf} {yf}\")\n",
    "              print(f\"leaf: {leaf}\")\n",
    "              print(f\"father_with_tail: {father_with_tail}  \")\n",
    "              print(f\"father: {father_group[0]}\")\n",
    "          \n",
    "    # Leaves are erased: each leave is replaced by \"\"\n",
    "    for leaf in all_leaves:\n",
    "          #print(f\"leaf to be erased: {leaf}\")\n",
    "          newick_string = newick_string.replace(leaf + \",\", \"\")\n",
    "          newick_string = newick_string.replace(leaf, \"\")\n",
    "          #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    #is_valid, message = validate_newick_syntax11b(newick_string)\n",
    "    #print(f\"is valid at connect_leaves_to_father11(...) : {is_valid}, message: {message}\")\n",
    "    #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n",
    "    #print(f\"Is the Newick string a valid morpho-tree? : {status} - {message}\")\n",
    "    groups = detect_all_groups_m7(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")    \n",
    "     \n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_links_m7(newick_string, ax,   diff_color='red' ):\n",
    "    '''Draw lines connecting children to father.\n",
    "       1. Each leaf is connected to its father,\n",
    "          and the corresponding stroke is plotted.\n",
    "       2. Leaves are erased.\n",
    "       3. New leaves are detected and connected to their father.\n",
    "       4. And so on.\n",
    "       '''\n",
    "\n",
    "    print(\"draw_tree_links_m7\")\n",
    "    print(f\"Newick String: {newick_string}\")\n",
    "    length_string = len(newick_string)\n",
    "    #print(f\"length_string: {length_string}\")\n",
    "    groups = detect_all_groups_m7(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    # Leaves are recursively found and connected to their father.\n",
    "    counter_of_trials = 0\n",
    "    while number_of_groups > 1:\n",
    "        #print(\"\\n\\ndraw_tree_links11\")\n",
    "        #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "        newick_string, number_of_groups = connect_leaves_to_father_m7(newick_string, ax, tree_color='lightblue', diff_color='red')\n",
    "        counter_of_trials = counter_of_trials + 1\n",
    "        if counter_of_trials > length_string:\n",
    "          break\n",
    "      \n",
    "    # The root is detected and marked with a cross\n",
    "    groups = detect_all_groups_m7(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    # Mark the root with a red cross\n",
    "    if number_of_groups >= 1:\n",
    "      [x0,y0,c0,r0] = extract_info_m7(groups[-1]) #The root is the last group\n",
    "      #print(f\"Root: a: {a0}, b: {b0}, x: {x0}, y: {y0}, r: {r0}\")\n",
    "      ax.plot([x0, x0 +1 ], [y0+ 0.5, y0+ 0.5],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "      ax.plot([x0 + 0.5, x0 +0.5], [y0, y0+ 1],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_newick_m7(newick_string, title):\n",
    "    #print(\"\\n=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    x,y,c,r = extract_all_infos_m7(newick_string)\n",
    "    #print(f\"c= {c}\")\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    #color = palette[c]\n",
    "    #print(f\"color = {color}\")\n",
    "    plot_tree_nodes_and_their_diffusion_circles_m7(x,y,c,r, ax)\n",
    "     \n",
    "    # Illuminated area is calculated\n",
    "    #colored_coords = get_illuminated_area_m7(x,y,r)\n",
    "    colored_coords = get_colored_area_m7(x,y,c,r)\n",
    "    #print(f\"colored cells at draw_tree_newick_m7: {colored_coords}\")\n",
    "    #print(f\"Number of illuminated cells: {len(colored_coords)}\")\n",
    "    # Colored squares are added to the plot\n",
    "    plot_colored_area_newick_m7(ax, colored_coords, title,  alpha=0.3)\n",
    "    # Lines that define the tree are added\n",
    "    draw_tree_links_m7(newick_string, ax, diff_color='red'  )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return colored_coords\n",
    "\n",
    "def draw_colored_squares_m7(colored_coords, title):\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    plot_colored_area_newick_m7(ax, colored_coords, title,  alpha=0.5)\n",
    "\n",
    "\n",
    "# ========== EVOLUTION ==========\n",
    "\n",
    "\n",
    "\n",
    "# --- Evolutionary functions ---\n",
    "\n",
    "def create_initial_population_newick_m7(pop_size=ga_params['pop_size'],  average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'],  max_node_radius=ga_params['max_node_radius'] ):\n",
    "    population = []\n",
    "    for i in range(pop_size):\n",
    "        number_of_nodes = max(average_number_of_nodes + random.randint(-5,5), 5)\n",
    "        #print(f\"Number of nodes: {number_of_nodes}\")\n",
    "        new_tree = complete_tree_generator_m7(number_of_nodes, max_number_of_children, max_node_radius)\n",
    "        population.append(new_tree)\n",
    "        #print(f\"Created tree: {i}\")\n",
    "    return population\n",
    "\n",
    "def get_illuminated_coordinates_m7(newick_string):\n",
    "  \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n",
    "  x,y,c,r = extract_all_infos_m7(newick_string)\n",
    "  # Illuminated area is calculated\n",
    "  illuminated_coords = get_colored_area_m7(x,y,c,r)\n",
    "  ##print(f\"Illuminated cells: {illuminated_coords}\")\n",
    "  ##print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n",
    "  return illuminated_coords\n",
    "\n",
    "def calculate_fitness_illumination_m7(candidate_tree_root, target_illuminated_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness based on the symmetric difference between illuminated areas.\n",
    "    Fitness is 1.0 for a perfect match, and decreases as symmetric difference increases.\n",
    "    Reuse of a previous code by Gemini 2.5 Flash\n",
    "    \"\"\"\n",
    "    if candidate_tree_root is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    candidate_illuminated_area = get_illuminated_coordinates_m7(candidate_tree_root)\n",
    "\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = candidate_illuminated_area.symmetric_difference(target_illuminated_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(candidate_illuminated_area.union(target_illuminated_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "        if not target_illuminated_area and not candidate_illuminated_area:\n",
    "            return 1.0 # Perfect match if both are empty\n",
    "        else:\n",
    "            return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "def calculate_fitness_colored_area_m7(test_tree, target_colored_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness of a colored area. \n",
    "    Fitness is 1.0 for a perfect match, and decreases as distance increases.\n",
    "    \"\"\"\n",
    "    if test_tree is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    test_colored_area = get_colored_area_m7(test_tree)\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = test_colored_area.symmetric_difference(target_colored_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(test_colored_area.union(target_colored_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "         if not target_colored_area and not test_colored_area:\n",
    "             return 1.0 # Perfect match if both are empty\n",
    "         else:\n",
    "             return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "\n",
    "def select_parents_newick(population, fitness_scores, num_parents):\n",
    "    parents = []\n",
    "    # Use roulette wheel selection or tournament selection. Tournament is generally robust.\n",
    "    for _ in range(num_parents):\n",
    "        tournament_size = min(5, len(population))\n",
    "        if tournament_size == 0:\n",
    "            break\n",
    "        tournament_candidates = random.sample(list(zip(population, fitness_scores)), tournament_size)\n",
    "        winner = max(tournament_candidates, key=lambda x: x[1])[0]\n",
    "        parents.append(winner)\n",
    "    return parents\n",
    "\n",
    "def find_recombinant_branch_m7(newick_string):\n",
    "  # A full functional branch is recognized because it is enclosed by ()\n",
    "  # and everything inside is full functional.\n",
    "  # We simply keep an accounting of parentheses pairing\n",
    "  #print(\"\\nfind_branch\")\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  pattern_left_par = r'[(]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_left_par, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  start_idx = starts[start_idx]\n",
    "  #print(f\"start_idx: {start_idx}\")\n",
    "  tail = newick_string[start_idx:]\n",
    "  #print(f\"tail: {tail}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  for i in range(len(tail)):\n",
    "    if tail[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if tail[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = tail[: i+1]\n",
    "      #print(f\"Functional branch: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_position_branch_recombination_m7(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_position_branch_recombination_m7\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  branch1 = find_recombinant_branch_m7(newick_string1)\n",
    "  branch2 = find_recombinant_branch_m7(newick_string2)\n",
    "  #print(f\"\\nbranch1: {branch1}\")\n",
    "  #print(f\"branch2: {branch2}\")\n",
    "  # Branches are interchanged\n",
    "  newick_string1 = newick_string1.replace(branch1, branch2)\n",
    "  newick_string2 = newick_string2.replace(branch2, branch1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "def newick_leaves_recombination_m7(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_leaves_recombination_m7\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  leaves1 = find_newick_leaves_m7(newick_string1)  \n",
    "  leaves2 = find_newick_leaves_m7(newick_string2)\n",
    "  if not leaves1 or not leaves2:\n",
    "    return newick_string1, newick_string2\n",
    "  # Select a leaf at random from each tree\n",
    "  leaf1 = random.choice(leaves1)\n",
    "  leaf2 = random.choice(leaves2)\n",
    "  #print(f\"\\nleaf1: {leaf1}\")\n",
    "  #print(f\"leaf2: {leaf2}\")\n",
    "  # Leaves are interchanged\n",
    "  newick_string1 = newick_string1.replace(leaf1, leaf2)\n",
    "  newick_string2 = newick_string2.replace(leaf2, leaf1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "\n",
    "def find_occupied_squares_m7(newick_string):\n",
    "    \"\"\"Detects all the occupied squares in a tree\"\"\"\n",
    "    occupied_squares_set = set()\n",
    "    groups = detect_all_groups_m7(newick_string)\n",
    "    for group in groups:\n",
    "        [x,y,c,r] = extract_info_m7(group)\n",
    "        occupied_squares_set.add((x, y))\n",
    "    return occupied_squares_set\n",
    "\n",
    "def newick_mutation_of_radius_m7(newick_string):\n",
    "  # The pattern of the radius of illumination, r = 0.2\n",
    "  pattern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_r, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  l = len(groups[start_idx])\n",
    "  #print(f\"l: {l}\")\n",
    "  # Select a new radius\n",
    "  new_radius = random.uniform(0.5, 2)\n",
    "  #print(f\"new_radius: {new_radius}\")\n",
    "\n",
    "  new_info = \"r=\" + str(new_radius)\n",
    "  #print(f\"new_info: {new_info}\")\n",
    "  # Replace the info\n",
    "  mutated_newick_string = replace_str_subsequence_m7(newick_string, starts[start_idx], starts[start_idx] + l, new_info)\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  return mutated_newick_string\n",
    "\n",
    " \n",
    "def newick_find_insertion_patterns_m7(newick_string):\n",
    "  \"\"\" All insertion patterns are found and one is chosen at random\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][])]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  return group, start\n",
    " \n",
    "\n",
    "def insertion_of_branch_m7(newick_string):\n",
    "  #original_newick_string = newick_string\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  # A group together its start site are elected\n",
    "  group, start =  newick_find_insertion_patterns_m7(newick_string)\n",
    "  # String to be inserted\n",
    "  # child_string = \"(Node_m7[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node_m7[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07])Node1[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.95]\"\n",
    "  child_string = complete_tree_generator_m7(max_depth = 2, max_numb_children = 2, max_node_radius= 1.5)\n",
    "  if group:\n",
    "    #print(f\"Group (main): {group}\")\n",
    "    #print(f\"Start (main): {start}\")\n",
    "    # Does the group have children?\n",
    "    #char_before = newick_string[start-1]\n",
    "    #print(f\"char_before: {char_before}\")\n",
    "\n",
    "    newick_string = newick_string.replace(\"])\", \"],\" + child_string + \")\")\n",
    "  else:\n",
    "    # a tree is given as a child to the root\n",
    "    newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "  # Cosmetics\n",
    "  #newick_string = newick_string.replace(\",,\", \",\")\n",
    "\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {original_newick_string}\")\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_left_parenthesis__m7(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns (N  are found and one is chosen at random\n",
    "      The new info will be inserted to the right of the parenthesis\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[(][N]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m70: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m7(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m7(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m7(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \"(\" + child_string +  \")\" + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "def select_one_insertion_pattern_comma__m7(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All  commas are found and one is chosen at random,\n",
    "  the child will be inserted after the comma\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][,]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m70: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m7(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m7(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m7(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+2] + \"(\" + child_string + \")\" + newick_string[start+2:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_parentheses__m7(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns ]) are found and one is chosen at random\n",
    "      The new info will be inserted between ] and ) as a brother\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][)]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m70: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m7(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m7(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m7(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \",\" + child_string + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "def insertion_of_leaf_m7(newick_string, counter, max_illumination_radius):    #original_newick_string = newick_string\n",
    "    \"\"\" All insertion patterns are found and one is chosen at random\n",
    "        for the new info to be inserted \"\"\"\n",
    "    case = random.choice([1, 2, 3])  # Randomly choose between three insertion methods\n",
    "    match case:\n",
    "        case 1:\n",
    "            newick_string = select_one_insertion_pattern_comma__m7(newick_string, counter, max_illumination_radius)\n",
    "        case 2:\n",
    "            newick_string = select_one_insertion_pattern_parentheses__m7(newick_string, counter, max_illumination_radius)\n",
    "        case 3:\n",
    "            newick_string = select_one_insertion_pattern_left_parenthesis__m7(newick_string, counter, max_illumination_radius)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    newick_string = newick_string.replace(\" \", \"\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    return newick_string\n",
    "\n",
    "\n",
    "\n",
    "def deletion_of_leaf_m7(newick_string):\n",
    "    \"\"\"A leaf is deleted at random from the Newick string. \n",
    "        If there are no leaves, the original string is returned.\"\"\"\n",
    "    # All leaves are detected\n",
    "    leaves = find_leaves_newick_m7(newick_string)\n",
    "    if not leaves:\n",
    "        return newick_string  # No leaves to delete\n",
    "    # Select a leaf at random\n",
    "    leaf_to_delete = random.choice(leaves)\n",
    "    # Remove the selected leaf from the Newick string\n",
    "    newick_string = newick_string.replace(leaf_to_delete, \"\")\n",
    "    # Clean up any potential formatting issues (like double commas)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    return newick_string\n",
    "\n",
    "  \n",
    "\n",
    "def newick_mutation_branches_m7(newick_string):\n",
    "  # Mutation of radius of illumination and of complete branches\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    newick_string = newick_mutation_of_radius_m7(newick_string)\n",
    "    newick_string = insertion_of_branch_m7(newick_string)\n",
    "    newick_string = deletion_of_branch_m7(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "def newick_mutation_leaves_m7(newick_string, ga_params):\n",
    "  \"\"\"Radius is mutated, and only leaves can be inserted or deleted\"\"\"\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    # Mutation of radius of illumination and of complete leaves\n",
    "    newick_string = newick_mutation_of_radius_m7(newick_string)\n",
    "    newick_string = insertion_of_leaf_m7(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n",
    "    newick_string = deletion_of_leaf_m7(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "def plot_illuminated_area_m7(ax, illuminated_coords, title, color='blue', alpha=0.5):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not illuminated_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y in illuminated_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=color, alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(c[0] for c in illuminated_coords) - 1\n",
    "    max_x = max(c[0] for c in illuminated_coords) + 2\n",
    "    min_y = min(c[1] for c in illuminated_coords) - 1\n",
    "    max_y = max(c[1] for c in illuminated_coords) + 2\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "# --- MAIN EVOLUTIONARY SIMULATION LOOP ---\n",
    "\n",
    "def oneEvolutionaryProcess_m7(target_tree_params, ga_params):\n",
    "    # A previous code by Gemini was reused.\n",
    "\n",
    "    # 1. Generate Target Tree and its Illuminated Area\n",
    "    #print(\"\\n--- Generating Target Tree ---\")\n",
    "    #random.seed(ga_params['seed'] + 1) # Use a different seed for target generation\n",
    "    target_colored_tree = complete_tree_generator_m7(total_number_of_nodes = target_tree_params['total_number_of_nodes'], max_number_children = target_tree_params['max_number_of_children'], max_node_radius = target_tree_params['max_node_radius'])\n",
    "    print(f\"\\nTarget colored tree: {target_colored_tree}\")\n",
    "    number_of_groups = count_groups_m7(target_colored_tree)\n",
    "    print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n",
    "    target_colored_area = draw_tree_newick_m7(target_colored_tree, title=f\"Target colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    print(f\"Colored area in target tree: {target_colored_area}\")\n",
    "    title = 'Target colored area'\n",
    "    draw_colored_squares_m7(target_colored_area, title)\n",
    "    \n",
    "    # length as a string\n",
    "    length_of_target_tree = len(target_colored_tree)\n",
    "\n",
    "\n",
    "    # 2. Initialize Population\n",
    "    #random.seed(ga_params['seed']) # Reset seed for GA population generation\n",
    "    #print(f\"\\n--- Initializing Population of {ga_params['pop_size']} Trees ---\")\n",
    "    population = create_initial_population_newick_m7(\n",
    "        pop_size=ga_params['pop_size'],average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'], max_node_radius=ga_params['max_node_radius']  )\n",
    "         \n",
    "       \n",
    "    print(f\"Population initialized with {len(population)} individuals.\")\n",
    "    #print(f\"Population: {population}\")\n",
    "\n",
    "    best_tree_overall = \"\"\n",
    "    best_fitness_overall = -1.0\n",
    "    history = []\n",
    "\n",
    "    # 3. Evolutionary Loop\n",
    "    #print(f\"\\n--- Starting Evolutionary Loop for {ga_params['generations']} Generations ---\")\n",
    "    for generation in range(ga_params['generations']):\n",
    "        if generation % 1000 == 0:\n",
    "            print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n",
    "        fitness_scores = [calculate_fitness_illumination_m7(tree, target_colored_area)for tree in population]\n",
    "\n",
    "        current_best_fitness = max(fitness_scores)\n",
    "        current_best_tree = population[np.argmax(fitness_scores)]\n",
    "\n",
    "        if current_best_fitness > best_fitness_overall:\n",
    "            best_fitness_overall = current_best_fitness\n",
    "            best_tree_overall = copy.deepcopy(current_best_tree) # Deepcopy to save the best tree\n",
    "\n",
    "        history.append(current_best_fitness)\n",
    "\n",
    "        #print(f\"Generation {generation+1}/{ga_params['generations']}: Best Fitness = {current_best_fitness:.4f} (Overall Best: {best_fitness_overall:.4f})\")\n",
    "\n",
    "        if best_fitness_overall >= 0.999: # Almost perfect match\n",
    "            #print(\"Target illumination achieved (or very close)! Stopping evolution.\")\n",
    "            return target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, history\n",
    "\n",
    "        # Selection\n",
    "        parents = select_parents_newick(population, fitness_scores, ga_params['pop_size'] // 2)\n",
    "\n",
    "        # Create next generation\n",
    "        next_population = []\n",
    "        # Elitism: Keep the best individual from the current generation\n",
    "        if best_tree_overall and current_best_fitness == best_fitness_overall: # Only if it's the current best in this generation\n",
    "            next_population.append(copy.deepcopy(current_best_tree))\n",
    "\n",
    "        while len(next_population) < ga_params['pop_size']:\n",
    "            if len(parents) < 2: # Not enough parents for crossover, fill with new random trees\n",
    "                new_tree =  complete_tree_generator_m7(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'] )\n",
    "                if new_tree:\n",
    "                    next_population.append(new_tree)\n",
    "                continue\n",
    "\n",
    "            # Randomly pick two parents (can be the same parent multiple times with replacement for simplicity)\n",
    "            p1 = random.choice(parents)\n",
    "            p2 = random.choice(parents)\n",
    "            child1, child2 = p1,p2 # No crossover, just pass parents to children for mutation\n",
    "            if recombination_of_leaves:\n",
    "                # Crossover newick_position_branch_recombination_m7\n",
    "                # child1, child2 = newick_position_branch_recombination_m7(p1, p2)\n",
    "                # Crossover newick_leaves_recombination_m7\n",
    "                child1, child2 =newick_leaves_recombination_m7(p1,p2)\n",
    "            # Mutation: Apply mutation to both children\n",
    "            mutated_child1 = newick_mutation_leaves_m7(child1, ga_params)\n",
    "            mutated_child2 = newick_mutation_leaves_m7(child2, ga_params)\n",
    "\n",
    "\n",
    "            next_population.append(mutated_child1)\n",
    "            if len(next_population) < ga_params['pop_size']:\n",
    "                next_population.append(mutated_child2)\n",
    "\n",
    "        population = next_population[:ga_params['pop_size']]\n",
    "        # print(f\"Population size for next generation: {len(population)}\")\n",
    "        # print(f\"Best fitness this generation: {current_best_fitness:.4f}, Overall best fitness: {best_fitness_overall:.4f}\")\n",
    "        # print(f\"Best tree this generation: {current_best_tree}\")\n",
    "        # If you want to see the entire population, uncomment the following line:\n",
    "        # print(f\"Population: {population}\")\n",
    "        # Cutt-off for the length of the Newick string to avoid excessive growth\n",
    "        population = [tree for tree in population if len(tree) <= 2 * length_of_target_tree]  # Allow some growth but limit it\n",
    "        #print(f\"Population size after length cut-off: {len(population)}\")\n",
    "        # If the population is too small, refill\n",
    "        while len(population) < ga_params['pop_size']:\n",
    "            new_tree = complete_tree_generator_m7(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'])\n",
    "            if new_tree:\n",
    "                population.append(new_tree) \n",
    "    #print(\"\\n--- Evolution Finished ---\")\n",
    "    #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n",
    "    #return target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, history, number_of_groups\n",
    "    return history, number_of_groups\n",
    "     \n",
    "        \n",
    "  \n",
    "        \n",
    "def draw_history_m7(fitness_history, number_of_nodes, population_size):\n",
    "    print(\"draw_histories_m7(fitness_historiy, number_of_nodes, population_size)\")\n",
    "    # Plot the fitness history \n",
    "    x_range = range(len(fitness_history))\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    plt.plot(x_range, fitness_history, label=f'pop_size {population_size}, Number of nodes {number_of_nodes}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Evolution of the Fitness of the best colored morpho-tree with pop_size = {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()        \n",
    " \n",
    "def draw_all_histories_m7(fitness_histories, numbers_of_nodes):\n",
    "    print(\"draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\")\n",
    "    # Plot all the fitness histories together\n",
    "    # By the AI of VSCode. \n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    for i in range(len(fitness_histories)):\n",
    "        # x_range contains all numbers from 0 to the length of the fitness history for the current length of target sequence.\n",
    "        x_range = range(len(fitness_histories[i]))\n",
    "        plt.plot(x_range, fitness_histories[i], label=f'Number of nodes {numbers_of_nodes[i]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title('GA: Fitness of the best morpho-tree over Generations for Different number of nodes')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    " \n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    ''' An Evolutionary process is triggered for each max_depth value.\n",
    "    Only max-depth values are changed, the other parameters remain constant.\n",
    "    The fitness history is stored in a list, \n",
    "    and then all the histories are plotted together.\n",
    "    '''\n",
    "    numbers_of_nodes = [10, 50, 250, 750  ]  # Example max_depth values for different lengths of target sequence\n",
    "    #numbers_of_generations = [71, 72, 73 ] \n",
    "    numbers_of_generations = [30001, 30002, 30003, 30004 ]  # Example numbers of generations for different lengths of target sequence\n",
    "    # List to store the normalized best fitness history for each length of target sequence   \n",
    "    fitness_histories = []# Example numbers of generations for different lengths of target sequence\n",
    "    for i in range(len(numbers_of_nodes)):\n",
    "        print(i)\n",
    "        target_tree_params['total_number_of_nodes'] = numbers_of_nodes[i]\n",
    "        ga_params['generations'] =  numbers_of_generations[i]\n",
    "        \n",
    "        ga_params['average_number_of_nodes' ] =  target_tree_params['total_number_of_nodes']\n",
    "        print(f\"\\nRunning Evolutionary process for number_of_nodes = {target_tree_params['total_number_of_nodes']} and generations = {ga_params['generations']}\")\n",
    "        history, number_of_groups = oneEvolutionaryProcess_m7(target_tree_params, ga_params)\n",
    "        fitness_histories.append(history)\n",
    "        numbers_of_nodes.append(number_of_groups)\n",
    "    draw_all_histories_m7(fitness_histories, numbers_of_nodes)\n",
    "     \n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32b618d-a9d1-4244-945a-938cbf4b6618",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el prgrama y redacta tus observaciones.\n",
    ">\n",
    "> *Observaciones.*\n",
    ">\n",
    "> Nuestra expectativa se cumplió parcialmente: la Evolución resuelve rápidamente el caso de un árbol con 10 nodos, menos rápidamente  el de 50, y aún más lentamente el caso de 250 nodos. Entonces, la complejidad, representada aquí por el número de nodos, curva la Evolución. Pero, según esta tendencia, el caso de 750 nodos debe aplastar a la Evolución. Falso. En realidad, este es el caso más fácil para la Evolución. Por lo tanto, el número de nodos no se clasifica como un descriptor de la complejidad evolutiva. \n",
    "\n",
    "**Ejercicio.** Estudia la batalla de la Evolución contra la escasez de recursos.\n",
    ">\n",
    "> *Respuesta.* El siguiente programa ejecuta un Proceso Evolutivo para diferentes tamaños de población. Según la teoría MAX, los recursos se equiparan con el tamaño de la población."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6ed0732-809e-4c96-8046-21a67f002634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running evolutionary process for pop size= 12 and generations = 70001\n",
      "\n",
      "Target colored tree: ((((((Node12[x=4.0,y=0.0,c=5,r=1.37])Node9[x=3.0,y=-1.0,c=4,r=1.26],(Node11[x=2.0,y=-3.0,c=2,r=0.82],Node10[x=3.0,y=-2.0,c=5,r=0.63])Node8[x=2.0,y=-2.0,c=1,r=0.9])Node5[x=2.0,y=-1.0,c=5,r=0.67],(Node7[x=1.0,y=-2.0,c=0,r=0.72],(Node14[x=-2.0,y=-1.0,c=3,r=0.83],Node13[x=-2.0,y=0.0,c=2,r=1.36])Node6[x=-1.0,y=-1.0,c=0,r=0.53])Node4[x=0.0,y=-2.0,c=4,r=0.6])Node3[x=1.0,y=-1.0,c=4,r=1.44])Node2[x=0.0,y=-1.0,c=0,r=0.97])Node1[x=-1.0,y=0.0,c=5,r=1.41])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(1, -2, 1), (1, -1, 1), (3, -3, 5), (2, -2, 1), (3, 1, 5), (2, -1, 1), (-1, 0, 5), (-2, 0, 5), (3, -2, 4), (-3, -1, 2), (1, -1, 3), (4, -1, 5), (4, -2, 5), (0, 0, 0), (-1, -1, 0), (-1, -2, 0), (-2, -1, 0), (4, 0, 5), (2, -2, 5), (2, -1, 5), (-1, 0, 0), (3, -2, 1), (3, -1, 1), (2, 0, 5), (-2, -1, 2), (1, -3, 0), (-1, -1, 2), (0, -2, 4), (3, -3, 2), (0, -1, 4), (0, 1, 3), (-2, 0, 2), (-1, 0, 2), (0, 0, 4), (5, 1, 5), (-1, -2, 4), (-1, 1, 3), (1, -3, 2), (0, 1, 5), (2, -2, 0), (2, -1, 0), (1, 0, 3), (2, -3, 1), (3, 0, 5), (1, -1, 5), (2, -2, 2), (4, -1, 4), (4, -2, 4), (-3, 1, 2), (4, 0, 4), (-3, 0, 3), (2, -1, 4), (2, -2, 4), (0, -1, 3), (3, -3, 1), (1, -2, 0), (1, -1, 0), (0, 0, 3), (5, -1, 5), (1, 0, 0), (3, -1, 5), (3, -2, 5), (-2, -1, 3), (-2, -2, 3), (-1, -1, 3), (-1, -2, 3), (0, -1, 5), (5, 0, 5), (1, -2, 2), (2, -4, 2), (-2, 1, 5), (-1, 1, 5), (-2, 0, 3), (-1, 0, 3), (0, 0, 5), (2, -3, 0), (3, 0, 4), (0, -3, 0), (-2, -1, 5), (-1, -1, 5), (-3, -1, 3), (-3, -2, 3), (1, -2, 4), (1, -1, 4), (4, 1, 5), (1, 0, 4), (2, -3, 2), (1, 1, 3), (0, -2, 0), (0, -1, 0), (2, 0, 4), (3, -4, 2), (-3, 0, 2), (3, -2, 2), (0, -3, 4), (1, -4, 2), (-2, 1, 2), (1, -3, 1), (-1, 1, 2), (3, -1, 4)}\n",
      "Population initialized with 12 individuals.\n",
      "\n",
      "--- Generation 1/70001, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/70001, Best fitness 0.39597 ---\n",
      "\n",
      "--- Generation 2001/70001, Best fitness 0.45 ---\n",
      "\n",
      "--- Generation 3001/70001, Best fitness 0.46575 ---\n",
      "\n",
      "--- Generation 4001/70001, Best fitness 0.47222 ---\n",
      "\n",
      "--- Generation 5001/70001, Best fitness 0.47368 ---\n",
      "\n",
      "--- Generation 6001/70001, Best fitness 0.47368 ---\n",
      "\n",
      "--- Generation 7001/70001, Best fitness 0.48 ---\n",
      "\n",
      "--- Generation 8001/70001, Best fitness 0.48 ---\n",
      "\n",
      "--- Generation 9001/70001, Best fitness 0.48 ---\n",
      "\n",
      "--- Generation 10001/70001, Best fitness 0.48 ---\n",
      "\n",
      "--- Generation 11001/70001, Best fitness 0.48 ---\n",
      "\n",
      "--- Generation 12001/70001, Best fitness 0.50685 ---\n",
      "\n",
      "--- Generation 13001/70001, Best fitness 0.50685 ---\n",
      "\n",
      "--- Generation 14001/70001, Best fitness 0.51049 ---\n",
      "\n",
      "--- Generation 15001/70001, Best fitness 0.51111 ---\n",
      "\n",
      "--- Generation 16001/70001, Best fitness 0.51111 ---\n",
      "\n",
      "--- Generation 17001/70001, Best fitness 0.51111 ---\n",
      "\n",
      "--- Generation 18001/70001, Best fitness 0.51111 ---\n",
      "\n",
      "--- Generation 19001/70001, Best fitness 0.51128 ---\n",
      "\n",
      "--- Generation 20001/70001, Best fitness 0.51128 ---\n",
      "\n",
      "--- Generation 21001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 22001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 23001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 24001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 25001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 26001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 27001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 28001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 29001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 30001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 31001/70001, Best fitness 0.5188 ---\n",
      "\n",
      "--- Generation 32001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 33001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 34001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 35001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 36001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 37001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 38001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 39001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 40001/70001, Best fitness 0.52593 ---\n",
      "\n",
      "--- Generation 41001/70001, Best fitness 0.53077 ---\n",
      "\n",
      "--- Generation 42001/70001, Best fitness 0.53077 ---\n",
      "\n",
      "--- Generation 43001/70001, Best fitness 0.53077 ---\n",
      "\n",
      "--- Generation 44001/70001, Best fitness 0.53077 ---\n",
      "\n",
      "--- Generation 45001/70001, Best fitness 0.53077 ---\n",
      "\n",
      "--- Generation 46001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 47001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 48001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 49001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 50001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 51001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 52001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 53001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 54001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 55001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 56001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 57001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 58001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 59001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 60001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 61001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 62001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 63001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 64001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 65001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 66001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 67001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 68001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 69001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "--- Generation 70001/70001, Best fitness 0.53906 ---\n",
      "\n",
      "Running evolutionary process for pop size= 25 and generations = 70002\n",
      "\n",
      "Target colored tree: ((((Node11[x=-1.0,y=-3.0,c=2,r=1.14],Node10[x=1.0,y=-3.0,c=5,r=1.23])Node6[x=0.0,y=-2.0,c=3,r=1.25],((Node14[x=3.0,y=-3.0,c=4,r=0.86])Node9[x=3.0,y=-2.0,c=5,r=0.65])Node5[x=2.0,y=-2.0,c=6,r=1.15])Node4[x=1.0,y=-2.0,c=4,r=1.47])Node2[x=1.0,y=-1.0,c=0,r=0.56],(((Node13[x=-1.0,y=2.0,c=5,r=1.26],Node12[x=-1.0,y=3.0,c=6,r=1.24])Node8[x=-2.0,y=2.0,c=1,r=0.9],Node7[x=-2.0,y=0.0,c=0,r=0.78])Node3[x=-1.0,y=1.0,c=6,r=1.16])Node1[x=-1.0,y=0.0,c=4,r=0.51])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(-3, -1, 0), (-1, 1, 4), (1, -3, 3), (-2, 3, 1), (-1, 3, 1), (3, -3, 5), (0, 1, 6), (3, -2, 6), (3, -1, 6), (-3, 1, 1), (1, -2, 3), (1, -3, 5), (1, -1, 3), (-1, 1, 6), (0, 3, 5), (-2, 1, 6), (-3, 3, 1), (4, -2, 5), (0, 2, 6), (-2, -1, 0), (-1, -1, 0), (0, -2, 2), (-2, 3, 5), (-1, 3, 5), (2, -2, 5), (-2, 0, 0), (-1, 0, 0), (-1, 2, 6), (-2, 2, 6), (0, -3, 3), (2, -3, 6), (-2, -2, 2), (-1, -2, 2), (-2, 1, 1), (0, -2, 4), (0, -1, 4), (-1, 1, 1), (0, 1, 3), (-2, 4, 6), (-1, 4, 6), (0, 0, 4), (4, -3, 4), (-1, -1, 4), (-1, 1, 3), (0, 1, 5), (3, -3, 4), (2, -1, 0), (-1, 0, 4), (-2, 2, 1), (-1, 2, 1), (0, 0, 6), (-2, 0, 4), (-3, 1, 0), (1, 0, 3), (0, 4, 6), (0, -4, 2), (1, -3, 4), (3, -3, 6), (1, -2, 5), (2, -4, 5), (4, -2, 4), (-2, 0, 6), (-1, 0, 6), (0, 2, 5), (-3, 2, 1), (-2, -4, 2), (-1, -4, 2), (1, -3, 6), (2, -2, 4), (2, -1, 4), (-1, 2, 5), (-2, 2, 5), (2, -3, 5), (-1, -3, 3), (0, -2, 3), (0, -3, 5), (0, -1, 3), (1, -1, 0), (1, -2, 0), (0, 0, 3), (1, 0, 0), (3, -2, 5), (3, -1, 5), (-1, -2, 3), (-1, -1, 3), (0, -2, 5), (1, -4, 5), (-2, 1, 5), (-1, 1, 5), (4, -4, 4), (-1, 0, 3), (1, -2, 4), (1, -1, 4), (2, -4, 4), (-3, 0, 0), (0, 3, 6), (1, 1, 3), (0, -1, 0), (0, -3, 2), (-2, 1, 0), (-1, 1, 0), (1, -2, 6), (1, -1, 6), (-1, 3, 6), (-2, 3, 6), (2, -2, 6), (2, -1, 6), (2, -3, 4), (-2, -3, 2), (-1, -3, 2), (0, -4, 5), (0, -3, 4), (3, -4, 4), (3, -2, 4)}\n",
      "Population initialized with 25 individuals.\n",
      "\n",
      "--- Generation 1/70002, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/70002, Best fitness 0.3908 ---\n",
      "\n",
      "--- Generation 2001/70002, Best fitness 0.39884 ---\n",
      "\n",
      "--- Generation 3001/70002, Best fitness 0.40571 ---\n",
      "\n",
      "--- Generation 4001/70002, Best fitness 0.45506 ---\n",
      "\n",
      "--- Generation 5001/70002, Best fitness 0.45506 ---\n",
      "\n",
      "--- Generation 6001/70002, Best fitness 0.45506 ---\n",
      "\n",
      "--- Generation 7001/70002, Best fitness 0.47399 ---\n",
      "\n",
      "--- Generation 8001/70002, Best fitness 0.56024 ---\n",
      "\n",
      "--- Generation 9001/70002, Best fitness 0.56024 ---\n",
      "\n",
      "--- Generation 10001/70002, Best fitness 0.56024 ---\n",
      "\n",
      "--- Generation 11001/70002, Best fitness 0.56024 ---\n",
      "\n",
      "--- Generation 12001/70002, Best fitness 0.56024 ---\n",
      "\n",
      "--- Generation 13001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 14001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 15001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 16001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 17001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 18001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 19001/70002, Best fitness 0.56707 ---\n",
      "\n",
      "--- Generation 20001/70002, Best fitness 0.57143 ---\n",
      "\n",
      "--- Generation 21001/70002, Best fitness 0.57764 ---\n",
      "\n",
      "--- Generation 22001/70002, Best fitness 0.57764 ---\n",
      "\n",
      "--- Generation 23001/70002, Best fitness 0.59509 ---\n",
      "\n",
      "--- Generation 24001/70002, Best fitness 0.59509 ---\n",
      "\n",
      "--- Generation 25001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 26001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 27001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 28001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 29001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 30001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 31001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 32001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 33001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 34001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 35001/70002, Best fitness 0.6 ---\n",
      "\n",
      "--- Generation 36001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 37001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 38001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 39001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 40001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 41001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 42001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 43001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 44001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 45001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 46001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 47001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 48001/70002, Best fitness 0.61446 ---\n",
      "\n",
      "--- Generation 49001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 50001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 51001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 52001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 53001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 54001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 55001/70002, Best fitness 0.625 ---\n",
      "\n",
      "--- Generation 56001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 57001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 58001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 59001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 60001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 61001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 62001/70002, Best fitness 0.62987 ---\n",
      "\n",
      "--- Generation 63001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 64001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 65001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 66001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 67001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 68001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 69001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "--- Generation 70001/70002, Best fitness 0.63226 ---\n",
      "\n",
      "Running evolutionary process for pop size= 50 and generations = 70003\n",
      "\n",
      "Target colored tree: (((Node10[x=2.0,y=1.0,c=5,r=0.92],Node9[x=2.0,y=2.0,c=2,r=1.07])Node4[x=1.0,y=1.0,c=4,r=1.49],((Node14[x=-1.0,y=3.0,c=3,r=1.28])Node8[x=0.0,y=2.0,c=2,r=0.7],(Node13[x=0.0,y=3.0,c=0,r=0.94],Node12[x=2.0,y=3.0,c=5,r=0.58])Node7[x=1.0,y=2.0,c=5,r=0.75])Node3[x=0.0,y=1.0,c=5,r=0.87])Node2[x=1.0,y=0.0,c=5,r=0.57],(Node6[x=-1.0,y=0.0,c=0,r=0.99],(Node11[x=-3.0,y=1.0,c=2,r=1.35])Node5[x=-2.0,y=0.0,c=1,r=0.69])Node1[x=-1.0,y=1.0,c=2,r=0.72])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(2, 2, 5), (-1, 4, 0), (0, 3, 3), (1, 3, 0), (3, 1, 5), (3, 3, 2), (-1, 0, 5), (-1, 2, 2), (2, 4, 5), (-2, 2, 2), (0, 2, 4), (1, -1, 3), (-1, 3, 3), (-2, 3, 3), (0, 3, 5), (1, 3, 2), (0, 0, 0), (1, 1, 2), (-2, -1, 0), (-1, -1, 0), (-1, 0, 0), (-2, 0, 0), (0, 0, 2), (1, 1, 4), (2, 0, 5), (2, 2, 2), (-2, 1, 1), (0, 1, 3), (0, 3, 0), (3, 1, 2), (-2, 0, 2), (-1, 0, 2), (0, 0, 4), (2, 2, 4), (-1, 1, 3), (0, 1, 5), (0, 2, 3), (-4, 1, 2), (1, 0, 3), (3, 0, 5), (1, -1, 5), (-3, 0, 1), (1, 4, 0), (3, 2, 5), (-2, 2, 3), (-1, 2, 3), (0, 2, 5), (-3, 1, 2), (1, 0, 5), (0, 1, 0), (-2, 4, 3), (-1, 4, 3), (-4, 2, 2), (1, 2, 5), (3, 3, 5), (-1, 2, 5), (-2, -1, 1), (0, -1, 3), (2, 1, 5), (-1, 3, 0), (0, 3, 2), (-2, 0, 1), (-1, 0, 1), (0, 0, 3), (0, 2, 0), (2, 3, 5), (0, 4, 3), (-1, -1, 3), (-1, 1, 5), (1, 2, 0), (3, 2, 2), (-1, 0, 3), (0, 0, 5), (-4, 0, 2), (1, 2, 2), (1, 0, 4), (1, 1, 3), (0, -1, 0), (2, 1, 2), (2, 0, 4), (-2, 1, 0), (-1, 1, 0), (0, 1, 2), (-3, 0, 2), (1, 2, 4), (1, 3, 5), (2, 3, 2), (0, 4, 0), (-3, 2, 2), (1, 1, 5), (2, 1, 4), (-2, 1, 2), (-1, 1, 2), (0, 1, 4), (-1, 2, 0), (0, 2, 2)}\n",
      "Population initialized with 50 individuals.\n",
      "\n",
      "--- Generation 1/70003, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/70003, Best fitness 0.5137 ---\n",
      "\n",
      "--- Generation 2001/70003, Best fitness 0.57241 ---\n",
      "\n",
      "--- Generation 3001/70003, Best fitness 0.59859 ---\n",
      "\n",
      "--- Generation 4001/70003, Best fitness 0.62069 ---\n",
      "\n",
      "--- Generation 5001/70003, Best fitness 0.64748 ---\n",
      "\n",
      "--- Generation 6001/70003, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 7001/70003, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 8001/70003, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 9001/70003, Best fitness 0.66667 ---\n",
      "\n",
      "--- Generation 10001/70003, Best fitness 0.67188 ---\n",
      "\n",
      "--- Generation 11001/70003, Best fitness 0.67188 ---\n",
      "\n",
      "--- Generation 12001/70003, Best fitness 0.67188 ---\n",
      "\n",
      "--- Generation 13001/70003, Best fitness 0.68033 ---\n",
      "\n",
      "--- Generation 14001/70003, Best fitness 0.68033 ---\n",
      "\n",
      "--- Generation 15001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 16001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 17001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 18001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 19001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 20001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 21001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 22001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 23001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 24001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 25001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 26001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 27001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 28001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 29001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 30001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 31001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 32001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 33001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 34001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 35001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 36001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 37001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 38001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 39001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 40001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 41001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 42001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 43001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 44001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 45001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 46001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 47001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 48001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 49001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 50001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 51001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 52001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 53001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 54001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 55001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 56001/70003, Best fitness 0.68595 ---\n",
      "\n",
      "--- Generation 57001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 58001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 59001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 60001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 61001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 62001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 63001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 64001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 65001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 66001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 67001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 68001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 69001/70003, Best fitness 0.704 ---\n",
      "\n",
      "--- Generation 70001/70003, Best fitness 0.704 ---\n",
      "\n",
      "Running evolutionary process for pop size= 100 and generations = 70004\n",
      "\n",
      "Target colored tree: ((((Node13[x=4.0,y=1.0,c=1,r=1.02],Node12[x=4.0,y=2.0,c=6,r=0.73])Node8[x=3.0,y=2.0,c=2,r=1.35])Node5[x=2.0,y=1.0,c=1,r=0.81],(Node11[x=2.0,y=-2.0,c=0,r=1.23],Node10[x=2.0,y=0.0,c=4,r=0.93])Node4[x=1.0,y=-1.0,c=0,r=0.64])Node2[x=1.0,y=0.0,c=4,r=0.61],((Node7[x=-2.0,y=-3.0,c=6,r=0.51],((Node14[x=1.0,y=-2.0,c=2,r=0.99])Node9[x=0.0,y=-2.0,c=4,r=0.84])Node6[x=-1.0,y=-3.0,c=2,r=1.32])Node3[x=-1.0,y=-2.0,c=1,r=0.9])Node1[x=0.0,y=-1.0,c=0,r=1.1])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(5, 1, 6), (-1, -3, 4), (4, 0, 1), (3, 2, 1), (3, 3, 2), (4, 1, 2), (1, 0, 1), (-2, -3, 6), (-1, -3, 6), (2, 0, 1), (1, -1, 3), (5, 2, 6), (0, 0, 0), (5, 1, 1), (0, -3, 1), (-1, -1, 0), (-1, -2, 0), (0, -2, 2), (4, 2, 2), (3, -3, 0), (0, -1, 2), (3, 3, 6), (-1, 0, 0), (4, 1, 6), (1, 1, 4), (-2, -3, 1), (-1, -3, 1), (-2, -2, 2), (2, 2, 2), (1, -3, 0), (0, -2, 4), (0, -1, 4), (-1, -2, 2), (0, 1, 3), (5, 2, 1), (3, 1, 2), (0, 0, 4), (-1, -2, 4), (-1, -1, 4), (1, -3, 2), (-1, 1, 3), (4, 2, 6), (1, 2, 1), (2, -2, 0), (2, -1, 0), (3, 1, 4), (1, 0, 3), (0, -4, 2), (-2, -2, 6), (1, -3, 4), (2, 1, 1), (2, -2, 2), (3, 1, 6), (2, -1, 2), (-2, -4, 2), (-1, -4, 2), (0, -2, 1), (0, -1, 1), (5, 0, 1), (2, -1, 4), (-1, -2, 1), (-2, -2, 1), (-2, -1, 1), (-1, -1, 1), (0, -1, 3), (1, -2, 0), (1, -1, 0), (3, 1, 1), (0, 0, 3), (4, 1, 1), (1, 0, 0), (-2, -4, 6), (-1, -1, 3), (1, -2, 2), (1, -1, 2), (3, 2, 2), (-1, 0, 3), (2, -3, 0), (1, 1, 1), (3, 0, 4), (5, 3, 6), (4, 2, 1), (1, -1, 4), (1, -2, 4), (4, 3, 2), (3, -2, 0), (3, -1, 0), (1, 0, 4), (2, -3, 2), (1, 1, 3), (0, -1, 0), (0, -3, 2), (0, -2, 0), (2, 1, 2), (2, 0, 4), (2, 2, 1), (3, 2, 6), (2, 3, 2), (-3, -3, 6), (-1, -3, 2), (-2, -3, 2), (0, -3, 4), (2, 1, 4), (4, 3, 6), (3, -1, 4), (3, 0, 1)}\n",
      "Population initialized with 100 individuals.\n",
      "\n",
      "--- Generation 1/70004, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/70004, Best fitness 0.38857 ---\n",
      "\n",
      "--- Generation 2001/70004, Best fitness 0.44693 ---\n",
      "\n",
      "--- Generation 3001/70004, Best fitness 0.47727 ---\n",
      "\n",
      "--- Generation 4001/70004, Best fitness 0.48352 ---\n",
      "\n",
      "--- Generation 5001/70004, Best fitness 0.48864 ---\n",
      "\n",
      "--- Generation 6001/70004, Best fitness 0.48864 ---\n",
      "\n",
      "--- Generation 7001/70004, Best fitness 0.48864 ---\n",
      "\n",
      "--- Generation 8001/70004, Best fitness 0.48864 ---\n",
      "\n",
      "--- Generation 9001/70004, Best fitness 0.48864 ---\n",
      "\n",
      "--- Generation 10001/70004, Best fitness 0.48864 ---\n",
      "\n",
      "--- Generation 11001/70004, Best fitness 0.49153 ---\n",
      "\n",
      "--- Generation 12001/70004, Best fitness 0.49153 ---\n",
      "\n",
      "--- Generation 13001/70004, Best fitness 0.49153 ---\n",
      "\n",
      "--- Generation 14001/70004, Best fitness 0.5 ---\n",
      "\n",
      "--- Generation 15001/70004, Best fitness 0.5 ---\n",
      "\n",
      "--- Generation 16001/70004, Best fitness 0.5 ---\n",
      "\n",
      "--- Generation 17001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 18001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 19001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 20001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 21001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 22001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 23001/70004, Best fitness 0.50877 ---\n",
      "\n",
      "--- Generation 24001/70004, Best fitness 0.53049 ---\n",
      "\n",
      "--- Generation 25001/70004, Best fitness 0.53374 ---\n",
      "\n",
      "--- Generation 26001/70004, Best fitness 0.53374 ---\n",
      "\n",
      "--- Generation 27001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 28001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 29001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 30001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 31001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 32001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 33001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 34001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 35001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 36001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 37001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 38001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 39001/70004, Best fitness 0.53704 ---\n",
      "\n",
      "--- Generation 40001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 41001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 42001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 43001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 44001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 45001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 46001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 47001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 48001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 49001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 50001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 51001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 52001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 53001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 54001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 55001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 56001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 57001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 58001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 59001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 60001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 61001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 62001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 63001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 64001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 65001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 66001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 67001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 68001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 69001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "--- Generation 70001/70004, Best fitness 0.53797 ---\n",
      "\n",
      "Running evolutionary process for pop size= 200 and generations = 70005\n",
      "\n",
      "Target colored tree: (((Node14[x=1.0,y=0.0,c=2,r=1.17],Node13[x=3.0,y=-1.0,c=5,r=0.78])Node3[x=2.0,y=0.0,c=2,r=0.95])Node2[x=1.0,y=-1.0,c=2,r=1.06],(((Node12[x=-2.0,y=0.0,c=2,r=1.38])Node9[x=-2.0,y=-1.0,c=2,r=0.98],(Node11[x=0.0,y=-3.0,c=6,r=0.86])Node8[x=-1.0,y=-3.0,c=1,r=0.59])Node5[x=-1.0,y=-2.0,c=5,r=1.49],((Node10[x=2.0,y=-1.0,c=5,r=1.23])Node7[x=1.0,y=-2.0,c=3,r=1.2],Node6[x=1.0,y=-3.0,c=6,r=0.65])Node4[x=0.0,y=-2.0,c=3,r=0.67])Node1[x=0.0,y=-1.0,c=4,r=0.61])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F17zmNbL0E013bCSJhsuj5aTo3Ne+9rVRkWayBdBwI9krJlrHqX424v3vf79clu9973s7/I22E5XrIejCRzcAv//978t/J8FGw8rNhCL1dDND+7GSr3zlK3I49s1vfnPN0yIBULlfKBpPkcW3ve1tLVu2eo6vyn1IgojKd1UrBRa1h631feNBJbWobNSXv/xlnHDCCWVBSgKXOt5RSTd6Ty3rSNS7no2GSn+NHd2hKOdXv/pVWfbq5JNPbti86Nig80ilpSQ6NmjUhwRl9D2jmyHqfFZZOos6eo2l1v05VhjTuY5u2Cn6S5YQhokTjsgyXQlFdChqdc0118iLJUU2yWJAdRdrjbjRxfLrX/863v3ud0vfHl2U6IS+adMm6SmtFDAkBukCQ4KWhtLpokMne7rQUaSJ/Gk0LYJqxpJwvOKKK2Rd1onqyNJ0qf4mCVoa5iOBREN6UR1JEoX0ebpI0XA8LeNrX/taKSyp5SsJhI997GPjruN0Phvxute9Dp/+9Kel35G6rVHUhqKJTz75pByG/M53viPtHBS9oW1EYuzcc8+VPkBafqrLOvbiHCe0P+lYoGeqnUkeTfr9d7/7Ha6//vq6hunf9KY3ye1G24Dq2dLxFnkkW7VsdCxRU5AvfelLsitWZR3Z8aDhYYr603FA86L6u3STQe1Z//znP0vPL02n1veNx/HHHy9vMGnE5JOf/GT5ddqGdAwRtfhjyatJ9YHpxpSEP92cVqsjO1XoO0vf82i4vrLuM9VYjc4htA3o+KZheHqNPvO///u/UgD+5Cc/KQvuRnDWWWfJ2sUkVM8++2w5vz/+8Y9yWelmMKqPTX+j7x2dM+h7Fnlg6aaVjqVKat2fNM+orTYdU3QepHPEe9/7Xjl9hokTjsgyXQHZB8a2kaSoD0UaKEJKQ9/vete7ZKSEkjPoghpBn3njG99YdbrvfOc7ZQIKCQiKUFIrSrpQUSRo7HAaecho6I/8ZHRxpc9RVObb3/52WcQSl19+uRSydHGk7kEkSieChCFN9wMf+IAUAHQRoYtUpfij5aeLDyVhUGMDEt504SVhWWmtIKFN3c8qqfWzJNrGtv+NuPTSS+U2pqQVagZBkSMSGxRhi4QJiVu6WNJFl7YfrQdtC7qY0r4j0TYZJIjHtiutbGNKz5XQ++j1KOkrWg5KyCMRTaLq3nvvlfuVoqm0jSebVyU0JP6Nb3xDJg9SYg5FzinJsPLYGG86NG96vTLJsNZlm4gvfvGL8iaJjhk6vkgQR1CEvVrST5RQFzVIoGN95cqV8r0070rPaq3vqwbdhNHNEa33y1/+8lHfHfqukYAfm/hUrUUt3ahS5JsEMG1vOlajxCiyddD7xw7rk9il1ynhcDLI607TJOFIn6HhevqdHpFtgCChSN9D8h3fd9998m8f/vCH5fFPwnEy6llWisjTvKgxCUVY6dig44++d7TtKv21tN9pJImsVHSeoWOCxChNc6zYr2V/0rxovejmnRLE6NikeXzrW9+adB0ZZrooVLpg2lNhGKYMRWnowkXR0UqBynQPJGgo8knDt5N5ohmGYZipwxFZhpkGNEQ49l6QohA0jBx50hiGYRiGiQf2yDLMNKBh/89//vPSjkB+Nxpy/MEPfiC9YY2odckwDMMwzPiwkGWYaUCeUvKxUfIE+cTIUkDJFbVkVjMzl1r8swzDMMz0YY8swzAMwzAM05GwR5ZhGIZhGIbpSFjIMgzDMAzDMB1J23tkqfgydf6hepvt3naQYRiGYRiGmR5UDYi6wlG+wWTtvNteyJKIpQ4/DMMwDMMwTPewevXqcle6jhWyFIklqPvI7Nmz0Y3F9SkLnjrUUEeWboPXv3v3P+/77t33BO9/3v98/Hfv939kZEQGMSMN2NFCNrIT0Mr09fWhG0/mqVRKrns3Hsy8/t27/3nfd+++J3j/8/7n4797v/8RtVhKOdmLYRiGYRiG6UhYyDIMwzAMwzAdCQtZhmEYhmEYpiNhIcswDMMwDMN0JCxkGYZhGIZhmI6EhSzDMAzDMAzTkbCQZRiGYRiGYToSFrIMwzAMwzBMR8JClmEYhmEYhulIWMgyDMMwDMMwHQkLWYZhGIZhGKYjYSHLMAzDMAzDdCQsZBmGYRiGYZiOhIUswzAMwzAM05GwkGUYhmEYhmE6EhayDMMwDMMwTEfCQpZhGIZhGIbpSFjIMgzDMAzDMB0JC1mGYRiGYRimI2EhyzAMwzAMw3QkLGQZhmEYhmGYjoSFLMMwDMMwDNORsJBlGIZhGIZhOhIWsgzDMAzDMExHwkKWYRiGYRiG6UhYyDIMwzAMwzAdCQtZhmEYhmEYpiNhIcswDMMwDMN0JCxkGYZhGIZhmI6EhSzDMAzDMAzTkbCQZRiGYRiGYToSFrIMwzAMwzBMR8JClmEYhmEYhulIWMgyDMMwDMMwHQkLWYZhGIZhGKYjYSHLMAzDMAzDdCQsZBmGYRiGYZiOhIUswzAMwzAM05GwkGUYhmEYhmE6EhayDMMwDMMwTEfCQpZhGIZhGIbpSFjIMgzDMAzDMB0JC1mGYRiGYRimI2EhyzAMwzAMw3QkLGQZhmEYhmGYjoSFLMMwDMMwDNORsJBlGIZhGIZhOhIWsgzDMAzDMExHwkKWYRiGYRiG6UhYyDIMwzAMwzAdCQtZhmEYhmEYpiNhIcswDMMwDMN0JCxkGYZhGIZhmI6EhSzDMAzDMAzTkbCQZRiGYRiGYToSFrIMwzAMwzBMR6LHOfFt27bh+uuvx7/+9S+4roujjjoKH/3oR7FgwYI4Z8swDMMwDMN0AbFGZE8++WR4noePf/zj+NSnPoX7778fRx99NIaGhuKcLcMwDMMwDNMFxBqRvf3222FZVvn3ww47DHPmzMFf/vIXvPWtb41z1gzDMAzDMMwMJ9aIbKWIJTRNg6IoCIIgztkyDMMwDMMwXUCsEdmxfOYzn0E6ncZJJ5007nts25aPiJGREflMHlt6dBvROnfjuhO8/t27/3nfd+++J3j/8/6vPA66DT7+3Zq3lSKEEGgCP/jBD3DmmWfi5z//OV73uteN+z7y0l5++eU7vP6Tn/wEqVQq5qVkGIZhGIZhWkk+n8fb3/52DA8Po6+vr/VC9qc//Sne+c534jvf+Y58nohqEdklS5Zg/fr10l/bjXdlN998M1567GHQdQ3dhuf5uOW2B7p+/Q950RJoendVy/O9AA/dvror171y/fd58U5du/5P/Xtt13/3u/3cTyO4hmGgW6/93br+IyMjmDt3bk1CNnZrwU033YR3vetd+Pa3vz2piI18tWO9tQTtyG7cmRF0IjP0pjpB2opuX38SMt0oZrp93YluX/9u/+53+/p3+7W/W9ffqGOdYz07/uIXv8A73vEOfOtb35JilmEYhmEYhmEaRaxC9owzzpCVCr70pS/hwAMPLD9uuOGGOGfLMAzDMAzDdAGxjlfcc889VUttzZ8/P87ZMgzDMAzDMF1ArEJ2//33j3PyDNNQwrzHAJDpj1EOZPSshA96girrITMMwzAM01q610HOdBVCBEDgQgQeEPiA8CACP/xduEBAv7tjRGw1QjGrKDqgGoCqQyk9oGjh7/SsmVCU7k3QYRiGYZhmwEKWmZmi1XcgfDt8eEUIvwgIErBh1FWEoVUoUrSSMiXRKRVqKfo64QwghAP4VCYuQFCeVgmalqJC0RJQ9CQUzZIPFrcMwzAM01hYyDIdj4ysenkIrzBGtJJgJclKAlUrRUxDoao20BoQTalckpmENM3bzSFws+HfaX6Ktl3c6vScCiO5DMMwDMNMCb6KMh2HFIxkE/DyCJyMfJbWgB1Eq9FQwToZZd8szRthAXMlWt6q4laHYqSgGj3yGSrZEdh7yzAMwzC1wkKW6QhIDFLENXBzEG5WRl5l1JUoeVLVNvWkSnFaVdx6EM4IfGd4e7TW7A0jtRSxbdP1YRiGYZh2gYUs09ZQMtaCfgXIroQnHOl/lVFXGpJXrY6NYIbilpLFjJKoJXtEAYGXI+NDKMytAQgt3epFZRiGYZi2hYUs077RV2cIKI5g1/lKmFilGVDUmVf6KhS1JMz1cjIaAgd+foOM4u42X4HmOQAljM2wdWcYhmGY6cBClmmvpC03g8AehHALECTooCFXFOjrt6DIRK2ZTSjSySqhhdURfBfz+xUk84MIHBOelYRnJCBUth0wDMMwDAtZpuVQ/dagOIjAHpJJXBLVkB7RqBBAN0KiVqiGFPJWrwrNd6HlHJhqFq6ZgGulIUjwMgzDMEyXwkKWaWkElqKvQXEbROCUGgmM9r2WS1p1O4qKgCLSQkARAcxiDoZThGslQ0HLEVqGYRimC2EhyzQdStgS9jD84laAar5GGfvs/5wcitIqGgQ1XAgCmIUsdKcgxSyJ2rCxA8MwDMN0ByxkmeYmcTkjCIpbwvJZJFxZwE5d0GoahFChBj6s/AgMJw/HSsMzSdDOfD8xwzAMw7CQZZoC1X8NCpsg3Hz4gkbF/zl6OG0UBYEWVjtQfR+J3DB8uwAn2QNfN1nQMgzDMDMaFrJMrAjhw89vgbC3hd2tqISWbA7AxCVoqVRXMjsI10zCSfayf5ap9sWEGgTSb739OfRfK+P60snWokivNllbAlWteKYScjwKwDBM82Ehy8Qbhc1vROAVoKhaRzcw6BhIaOgGlMCHYedlpQM72cvR2a4WrD4034fuedA8F5rvQZVNOKilMyYQrpWvVyRgRt3p5MuKbAwtFCBQNfi6Dk/X4WvhgwUuwzBxw0KWiT0Kq7CNoOkINUwIU32Xo7PdhBAwXKcsWnUSrTLiGopS+j8Ul+FzUHljWc9NZml6SsWz7rswPLdiPioCTYWnG1Lc0jOJW47cMgzTSFjIMg2Fo7DtZjfg6OxMR0bfXVcKWMOxQ+FaIVpJrMrybI0cDSlNS4riMbHbSpErfdueV36vr2lwTAuuYUphy6KWYZjpwkKWaVhJraCwBQGV1OIobFtHZx0rJb2zLCI6F9X3pHA1HQe660pva6VYbOm+rRC5pYbLpfrHQtoaknkPSeQQaJoUtPRwTEpM5ORPhmHqh4UsM21E4MHPbYBwhmVN2LFNDZj2is5SMwWKlNnpPilymc6yDZh2UQpYleoxk1BUKeGqxeK1pvrHCnyoo5LNrGJBPkjU2mYCjpWQPluGYZha4TMGMy2Eb8PPrkPg5aCoZpjUxbR5dFaB7hahZH3YqX6ZHMa0L9T4wioWpeAj7yshVAVeu4vXSW+stFGiNlXIIVHMwzVN2FYSvsKXJ4ZhJofPFMyUCZws/Nx6iMCGIqOwPDTYEcgkHEUmA5HVwE71wTMTrV4qpop9YOlcBbMzg9Ci6KsWlryaUZREbVCyH5i2LR9JVcOCfkUmj/KlimGY8WAhy0ypQ1dgb0OQ31Tyw3J72U6tO0ulmaiJguO7cBI9nRvhm0EoPpVOy8GwC9BnhWlbXqt9r820H1BimvTT+th1ngJkVyJIzoNi9fPNMsMwOzDDbu2ZZiR1+fkNCHIbwhfYD9vxYpZqgJqFLKz8MBCESUNMC3ZHEMAsZJDKbJU+ZrphzBZF+/tf40AmrenI2wLwXfi5dfBHViCwh+V2YRiGieCILFNffdjsujCpSzWgqHz4zBjfLBQZASQxZacHuBtYU3dAILc9RWEpCS+sv6pHVay6GrkJNItqeUF4BfjZNVCMNNTEXPnMSaUMw7ASYWpCBD783FoEzggndc1EKPNdAXTXhpIbQjHdP6MrGrjw4CguXPhwFE/+7io+6BVX8UCOVFmHVUp8+kfDVypMocOADkPoMKFDF5p8toQBDfVvL821ZRSW/Mqy5mtlwwBWsmWk/15PyJtp4ebgu3lpNdBS86GonKzIMN0MC1mmtvJaVJnAHSl16Zq5AqeriZLAXBsJKWYpMtv5+5pEal6xkVeKyClFZJQCiooDHz4CivSVxGqlaCWin0JJGxKmXEXvEFDpXUKFDhUpkUCvSCElrPJjXHFLNoJiFqZdkBHZUQKWGRd57tE1eU4KioMQXh5acj4Us4+jswzTpbCQZSaPxGbXInAzXJmgGyhFBTXX6VgxS2IzqxQwrGSxTc1IEespPoKwND80qNCFKiOpqiApul201kMogYWcH019WM1iEJlwHlLcaugRScwO+tAv0lLY0nxGRWFVFYIsOixi60LamkjU+o48PylWlqOzDNOlsJBlJvHEsojtOnYQs7Pa3jPrwceIksOQGorXIhz4SiAjpobQkBBGGD2dgmAdj3BqisyYJdFK9gJClIQtPWhZtqoj0oJAQnZ+0cS8ogLL02WDChaw09j+JP51a8rRWUpcBZX2CnwI4VU80w3PWIOyIveVjAiXRLSsmU21bhWVo8EM00JYyDLjVycYZSdobyHDxCtmC+kB6aNtJ0gwUrR1szqEjeoQbMWRr0XRVoqKNlK41grNk4RtJG5pmTzhoxBk8KwZYKWpIuUb2MlJY4GTQkLwabix0dkctPSCURYo6a31bfmewCsCXgEicEo+ZBK0oXCtlK/bDSRj92/pfymWFUDVoOjJsAwhVXGh5DRKhuUoO8M0BT6DMjtA5W2o0UE5sYs9sV0uZm0k8sPSZtAOEUSKdA6qGWxSBzGo5OApXiniacqErHZDDQQSng9F6DKhzlcE8pqLJ1NDeC4xgvluCoucFAa80HrATDM6a2+D8B1oiVlSrAonF4pY2VihJEvp/XRzLp/pMhgeN2qV43vsK2H5L1ESvwIIPAh7mMwl4XtpuqoORU9BNXqgGClOSGOYGGEhy+xAUNwKYQ/Jky+3nO1ySl2XqKUt+TqdZG/LxCzZBzaqg1ivbZOJWwRVEUiIRHsKQEG1YX2oviuXjqoSSL1FEdtAlZFaW/Wx2spgnZlDn2diqd2L+W5S2iCYOjd3hcAU9lZ49rZSZJTsACQuyfpB+2B62zb8PAngMa9HyyDtCiRuh+DbQ6ENgSK2Zi9UPcW1txmmwbCQZUZBUdigsHm7B4xhFGqLSq1D8zJC61mppm4TStLaog5jjbZZJnFRslZSmPK5bRHUYtaVndNCeVVN+ChIBDossh4oAsOGjUcMG7NcC7sW+zDbsVq19B1DKByD0CYQePLnECWMmAYuoJP1gARt/MujlCO8lctHJcOyMmE2KIla1RqQwpZHuxhm+rCQZcoIrwifOnbRxYB8XgwTHRvkA/Q9WIWMzLL3DTP2bSMjlrNtPGo9h4xWkMIvLay2tA+Mgtqreq5sLkFR2MmCq7RehlBg+Cqogu02w8aQvgVz3ARmjejoK5UFYyo3cWlIn4QqCdhyWbSSXUBu2JLI9QqARuc0aqXd3K1YFrbk441Et6yDmwNUE6rVLx/SV8swzJRgIctI6IJADQ9kBIPbzjJVoFapKonZ/DAKPbMgqPZpTFDk9VlzPTL7ZuCrZqkma5sL2LKIdaAEoiYROxaqR9vrq3CVAJvMPDYtFhhBgD2KfZwUVhawbsnzWhF9rRSwZWj706hSAPh2+FmdxGxrbgpCUUs2By2smED1uQubpJVLMXqgJefKaC3DMPXBQpYpVShYj8ArhJm3bZDQw7Rv8heJ2URJzMrElgYncq1Tt2KNtgk2PKi2inSQbLeCCbGI2EoMqkPrGcgFLtYls9J2sHuhHwvcZFdGZ6sL2GritRr0PhKODqi6VivFbISsAqOZpSitLy1dZD9QzH5oyTkcoWWYOmAh2+XICgX5TRCy4QFVKOi+iyQztbJcVj4DO9XXsOQvGYXV1stasFRCKx0k4Pr5ztg9DRSxoywHPpXq0pDTPDye3oqtThp7FLosOksCVpbKoqoD9QjYSugzoq3E7CjrAUVqSdAWt0FQtRhrFoTR1+rFY5iOoIvOhkw1SMCKImX36px4wNSGokjPrOEU4OvGtJO/glIUdrW2CY7ilW0EUdytG0XsWEHb4xtwFR/rzCyG9e6IzioiQG+C6pUVpiFgR00xHEFoMzG7g6CVDR42A/YQ5vVF1RgYhhmPThiwY2KCai5SNFbmVFMyAsPUeuyUxvqtQlYmgU0VBy6e1FfjWX29TO7qEYnO8MI2ScRWQh3Ken0DBZWis9uwLDGMsCbCDEMIqL4P03VhymZpJW9pQ0RnpZgtRn0Q2gYStIpmSIsXdRrbbb4K5NeFlgqGYarSQVcMJhZLgV/kCgXMlJO/qE4qVTKYiiLIKQU8pq+UnbmohWwCZmdFGAVK1QniF7Fjo7N6oGBlYgSPpbbCUaIh95kRhdU9Vz5oA/sUlm941LTUlYvErN9+YrYcodVM2K4A3Ay8kZVhG952XFiGaTEchutmS0HU9KBNhteYDvTLqtQswYbuFOqyGGxVRrBMX4eiYndGSa1qdWLrKLHVaCyhQQsUbDTzKGo+9svPQq8ff0m0WKOwQQDd96AIgaDcAjYug0kpAYwinVHiVRsihbxq0Qkbfm4dFDcDLb2IO4UxTAUddvVgGgFbCpiGUafFgOwDa9TNeFJfBUdxpZWgI0Ws70GlQvctELERlBBH0dkRzcZD6S3YbERe0g6D7Bm+F0ZhR4nYuAmPOxmVDdo4qi3tBpbsTBbYI/BGVoW1cRmGkXTYFYSZLmwpYFplMSARu0LbIP2wJFNSosOsBCVk21kqxi9/ae2yUCtbErPU6pZsBtTqtqMQIrQS+OT2VSAoOtrMESKaH7W09fJhbdc2hjotKnoC8IvwMqsQ2MOtXiSGaQtYyHYZbClg4rYYVCOAkKW1VmubYQqt8/ywJchKQK1nw1/aY/llxzNflzcKTyUHsdbMolO2peFSG9+giVHYHZaiJGbDDmDt7kENvbNkNfCl1cDPb277ZWaYuGEh20UIOvlxlQImRouBWczKiOWo4w4Cz2nrsVbbAlPoMCFT0Ts0m94NO5+2mQgnMZsKwu36dHKo7cWsFLGeC5UEZMtE7NhKBl5bVjKoXtmAWtoqCAqbpKBt92gyw8QJC9kuIigOhckNbZrYwMyEFrY+DDs/RsRuqBCxHZpfKpO7vKZWKJgKqUAvi9l2tRlEIra5ftjaKxnIRwdAZbrINyvsQRazTFfDQraLEryopzdFHmR7RIaJpVGCCsMuyMQvErGrtE1YI+0EHSxiI19si5O76hezg9hktFdnNDXwyyK29ZHY8ZK/7I6JcJJvFqop/bJ+dm3HLDfDNBJWNF2CbH3oO/IOnmHigpJ1SPSZdl7Wh12tbpKF/DtZxIaWglJFhnbSXROQDDQECnlmhzCiOW0TidU9r01FbImyX7b9LQajksCoooEzwpFZpithIdsFkIAN7MHwhNeOFw9mxkVls8EQnlXXSuFndaonttJSEImvDkF6Zn0dturhidQ22C1umlBpJ2hbETvKL+t2jMWgUsxSbXA/t54TwJiugoVsF+BTNJZOzByNZZpAXgvwWHoQrnCRDDrbj91JloLq1QyozqwjxawfW3OB2kpstb+IjVA6zmJQaTMgMRsUtrR6cRimabCQneHIITLZwUvnaCwTOx4CPJ4eREb30OupMpLZsXSgpaBanVmKzG4xClieHJa+5VaIWLVjRGznWgzKYlbREBS3cJ1ZpmtgITvD8YtbIYQHKB3sUWQ6ApIqy5LD2GIUkfZ0KaKoikGztVMju3d1mqWgGrQnrEDDGiuL9Wa+ySLWg9bSOrGNsBiU6gZ3CLKaAQT8/AbuAMZ0BSxkZzByaMzJSO8Ue2OZuCEBS2LJClRoiioFIA3LU2Sr4xCBzLDvREtBNagJBYnzZxPDyKvNEWaa7IDmd6CIrWYx6LC7MdWUdXG97LrQVsYwM5hYhazv+/j973+PU045BQsXLsQvf/nLOGfHjIGyWKkJAg01MUycOIqPZ5JDMlPeItFUGdkMgg6MxnZoJHmSslwF1cOyRPwWA0ru0jxKMOtUEVtpMfA7Lypb6gBG3cr8LCd/MTObWIXs1VdfjW9+85v40Ic+hI0bN6JQqN6+kmk8JGAD8sbKurEdfCFh2h4SRcsTw8hojmyVWkahclxhwlQnGQ0VisZ2aILX5N2/dGw2C1gfZ7OEKLmr+Y7cGOjcqGzYAcwstSUfbPXiMExsxGqcPP/88/GRj3wkzlkw4yCcLEB1Y7mLF9MES8FaKyd9mOSLHY0ifaYUoROa1jHeWKnAZqDxSheqFOnPJkYw4FnltraNRPM8mdzVuZaC6olfshyXbA3bWclfsjV5YTMUI11qbcswM4tYT9Vqqf8601wockDRWIK7eDFx4o5nKYggHaOQvaAkDtscRfgyIiujsTOUZIwWA2kp6Mjkrlqisk7HRWUlVF82cOHnNnbm8jPMJLRdKrtt2/IRMTIyIp9d15WPbiNaZ0/6zWqDfFFwc7JSgQg6+8QVlE682WymKy0S0YVHPrXhvlyTyEpLQY83fmRPLjUtOyX+1OHXjta9SJakpux7gZQiECilTU1D5JkivN5ES0UZffcbPXsrULBJy2Gdb2C23agonUCa7CSUH9GYyUlc12sLTazAh0vds0RzAjTR8S/PgdO1mSvU+SuDIL8VijWATiC65nXjdb9yvbt9/TtSyH7hC1/A5ZdfvsPrt956K1KpFLqVW257oOb3LpmjYPEsBTm7/YTPVMnkOqfLThwMbo7R0zhFHM3H8iVDELaA604sXXQNsAs+ssX651PINCdZzKSzYVKBGwh546BnCnj5Hh/EH5Z/HV5vEq0icEUsflnfElhpZJHcVM0SUj8pE1At2n5oKJ7fHsmCGm0i4SGTD4+PZrFx07aGTCdhAP5gFo+tEXBK5ZE7gZtvvhndTLeufz6f71whe/HFF4/y1VJEdsmSJTj++OMxZ84cdONdCR3ILz32MOikBiZB0BBudoWMJvXNgE5eFIklEav6GaQSnd0lairkiw4CrRdmn4pkqnViqhrLk0PwEkCvb0LRJxFCQiBpAFqfWXN0s1AowhkJUEwVoZrx+2tT0CCgItBKUXBj+7Mwm39TSKMpqhf6M1Wt8VHApKchl3KR6S9iXm56UVlVAZIWlVwT8udG4PtBWODfD6BJFdli6OZGBdK6gBfDzcVYbMeDME0kdQuJZCO++wK64uCIPQZQ9Nr/WuoHPh5+7nHsu8sx0NS2kyqx4wcenlz5H5x00kkwjM6/ltdLNBpfC213dFiWJR9joR3ZjTszgkSsoU++uwI7B5/KxWjWjBiKj9aBRGxvT3sJuWaRdUsZyG20O6kW6TorB1NQLK+GBaPlFwIaAgQ1XpSi9SURq1vxnqpIu1qeIkdwo/VR1O3P0c+tgPa9FsP8NShwEGD9LBsL3TQ0KjExRQxNgLQ2WTIadZxGtiISsVqb5FvQuiUTCrwmCWs5gKEoUBuyURWSsrD0LHwMIBCdERggEduNQrbbtY9Rxzq3x9mBaRgBeWMrBCDDxMEqKwNb9WWlgnpo15qyVgCooiPy0RpK0teQMT1sTE3B81FCUYSMVHZDHpEorW+nnl7pVlJRfJgql+NiZg6xCtnbbrtNNkKgB3H22WfLny+44II4Z9u1CMq2drNhuRiGiYmc6mKjmYflU0xPqV/ItpniUQSQCBSZ5NVtUBSWBPya3jx82hB1I2CoYaSyvfZqTJQizmrJftJ5KBBCg65moSpTv3lhmHYi1nj985//fDz44IM7vN7NSVtxIry8bEsI2WubYeKBRKyjBOitswapKNkLSMwGbVRTluyvpEu8LhSyRMLXkDc8bEvYmFdI1PVZcjxEloJuge7DVFXA79BuGRSVVRUHpjaIokdBps5cD4ZpipA1TbMcjWXiJ3ByoDLkKkdkmZjwyFNp5mRh/XqjsRGqoDqj7SNkKRrbzVBUlnTohnQRcwtWXftVj6KxXSRkZVRWDaOyga90aFRWh67kpaANBDdJYDobHoOeIVDNQWpFyA0QmDih9qZUTD9Rpze2EoXKKbWJ8qFhdV0AHalHGojlqxiyHOSM2usykfylaGyb7MrmQlHZDj5mqDqHogTSYsAwnQ4L2ZmCX6RML9kEgWHigEorUTSWrt9TrTsq7QUQ0mLQDlCFrW5M8hqLEShwVVFX0pfWTd7YaklfdOB0LBSVVWGomUa1r2CYlsFCdoYQuHnZU5sTvZi4GNEcDOsOrGD6N0vtUr3A7HJbQQTdXpCY3ZS24aq17Bshm1x0spRrSNJXB4tZKsWlKi4Mtf2arTBMPbCQnSEIlyJlVGuUL8xMPGwxCtIjq0+j3miEGrQ+CkRJ+hSRZS273V5Q1HwMWpN30aMyqjS03iaB9ZZ6ZTuX0BttqFR4vpt3JNPpdPTXkKkou0XWgjr62DNMvbaCzUZRitipJnltnxaJSBrPb+3F0yhVK2iP2HDriewig4kahCxdObpc+9Dqd3JElqCkL1Wx5YNhOhUWsjMB3wHYVsDESFZzZZKXIRpws1QaNWi1T9ZgBVtlmyjYlnQmqSlbSvJCl0MR2VIJss4lTPrSuKYs08GwkJ0BCN+W/dg7fJyLaWMGdRue0hhbQURLhayg+rHd2QRhIoxAlR3bRkx33PeQcOvWJK8doG3R0VFZ+gIo0Nkny3QwrHxmiJANWyfyVZmJhy16QQ49T9dWUIlCN18tPPHx6Pg4NWUVshfYE9sKmFFJX50MdfrSFBsKxr95YZh2hk9JMwDhFbk3CxMbRcVDRndhBo09XbSycgF5Y7ns1vjbZmvSkb7oHeni2rEzsgxXVFPWh64WWr0oDDMluOhoh8OJXkzcjOgOXMVHT50taWtO+GpBSIuaIDDj2wuoekFB95Hy9B1tBTMgz4sOO89T4BQ1OI4G21bh2Bo8VwXdXwmKTNNKlg5Pcm1Rjj918zItH5YVyGczEcA0/Q7vbhZ+/zQlDxd9rV4YhqkbFrIzJtGLKxYw8ZDTwiHHRtoKois/iVlqktBsGun1nWnogYKiEcguX1WFbAeJNhKlhZyOTMZAdsRAZthEPqejWNDg+apsMUvvCVvN0srRMVlFqFceLlS5QwlFLVUt0DQBwwhgJX2ke1309rryuafPhUGlMToAao6gqUXAl7eYrV4chqkLFrIdjghIyAaA2rhoGcNUMqK5jRWxFUgh2+zNXWpLy4le1Yn2dd7wgTGjze3uByWBOjRoStE6MmRiaJsFx1bheqUyCwqk8IwEqG4G8mcSpbWuWzSQEIpgBX6goJDXkc0Y2LwhKd9D09ONAOm0h/7ZthS3JGz7BhyoapvaC+BDhYsAZqsXh2HqgoVshyMCN6xn2O5XGKYjCSCQ0ZzYIpitqFzAiV6TQ3s7Y+yY/CObIKB9oMMnM2Jg66YENm1IhsLVCUUrnRJJTJJYTaS8hglIaTUod/US2y1epQ1Dz76vwPNUDA8ZGNxqyddJONNyzFtYwJy5NmbNKcIw22Vrkk+WtpGDIGAhy3QWLGQ7nTbokMTMXKh2rKsEMERcYSTRskQvj+/9JrQXZE1PJnxtj8aLsGZqi7UXicShrSa2bk5gw/oUchkDrqtCVQSshI/evuZHPclqQL7a8GdA1wV03QcSo8WtXdSw4pk+rFxGXtsAc+YVMXd+EbPnFZFMtfJcHi67qkzeDINh2g0Wsh2OCDx2NDGx+mN9JUAy0GZMRJbd5DVso0CBqwajEr6kiG2hPzaX0bFuTRprVvQgn9fh+4BOyVcJH6m029a2h+3i1ivHHyjJbN3qtHwYZiDF7E5Lcpi7oCijt62AO3wxnQgL2U4noOG/Nj6DMx1NXg0vvHF6ZJvNhE2rGAlZSYpVhGyzA7KUiLVtUxLrV/di4/qkrCxgGD6SKVcKw05F1YBE0pcP+gqQj3fj2iQ2rUuip9fDTrtkMTB3GEgGzU34kq1qOeGL6SxYyHY6gdf+GRhMx0JRuVhpQQmuNsy1aTuiGxdH277/m3maoaStdaso+tonE7dEoMiqAP2z7LY93U1V5NP6WIlAPnxPQT6n4clHZkE3ejGwIIedd3GR2Ik8uYgZSvgKZNKXYGnAdBAsZDsYQRnfgoUsE28zhLiisa2K+6hceqt9bmTGQJHJlct7sWp5rxR0NMSeTHmdUcaqAeFqTRdI93ry3q6QV7BhVR+2rlOwdoGP3fctYM78+CwUoRs6kM0RhGBpwHQOfLR2MiRiW1RQnukObNWXiVFx0uxastyadmpCVtaPjWWPAJ6rSO/rc0/3IZc1pH2gr9+GosZ1G9XehFFaH3oigPB1bNloYNsWA/MX2dh93yIGZntxzFUmrZGYZZhOgoVsx1csICHLg6VM46GMdRKysUmJFlXWpzwaDspODu11u8JaEEfFAkrYWr86jWef6pd1X3U9QG+/LYfRO6XpQtzohkAi4cOxFaxfncCWDSYWLbWx295F9PQ1utIB1dP1W16ZgmHqgYVsByOoo5c823djzIKJG3LK+dTBKHbVJ5qa6MXfljHbpMovSqm6g2v4MEsZ9FLIRkQ1U6e4H+i0tWl9Esuf6Jd1VikS2NNnQ+OSEuNiWgKG6UlBu2p5EhvWWNh5tyJ236cAK9GI71CpfBi4pCPTWbCQ7Wiigtx8aWbiGVamhgh6zOlRVVuCxjWv0nM3BpzkupcqD1QTr5KK04kGBa4eQNd2/PwOn6k4G9F/E21fu6ji6UcHsHZlj6ytmurp7AoEzbccUA1aD8WCiuVPpLB5vYm9D8pjwWKnIS4zGZFlmA6ChWwnw+d+JkZIxPJt0gwSrZE1oPQsxjuPRL9TEX8IBKXftUmsBXJe0QzHiNuoOMXGdSk89cgAMkMmEikX6V72Y05p/ypAMhVWOsiOaHjwzl7stGsRex+Yn150Vo5Y8IWF6SxYyHY0fMJhmnB08WHWEURCslrENIqURr/XSihEK7t71fCBKstULEVhV6/oQeAr6B0IfbDM9KBt2Dfgwya7wbIEBrcY04vOys/wF57pLFjIMgxTFdFqQ6kQMDIFCNWFbjXmVEUVGHQvjDlNdrnWMwX5XFi1Gcml89BsRCCguAr8Hqs+8VqKfk5/AQDXdjGcycsKoxSRNQwNyYSFQtGG624fgrZMA5ZlIJcvwveDUa9v29yPB+41MTykIpkallFEiAQU6MgVsqNmaVlJqIqKQjEnywtGFW1TyTQCEcC2w30SkUqSPcGD7RS3bxNFRTKRguu5cF0q8B+iqhoSVhKu68D1trdi1TQdlpmQ06BpRTzx7MPYsHUdTnrhq2Ea4++D7TNunQa0yD9r+I2NzjJMh8BClmEmwLZdfPnrP8eFH3yTvFB3E63ugKVninjjQR9s7UIAeNPRl7V0/n9cfgMwKz3qtUi8RlE3qfkavb8U4Bdf/SPedMVvyy+9800n4fovnIsLL/8mfvDzm8uvX3zO23DJeW/HaR/6PP7xnwfKr1/ywY/h4F1OwwVffT3Wblpefv3ys67FkQe8EGd84pVStEZc9/9+hnmzFuAtFx43alFu+vI/sXlwI8763FvKryUTafz8K//Cg0/9F5+8/pzy60sX7o4bLr0Jt9z9R1z7k8+WXz9svxfgM2dfh5v+diP+70/fLr9+8tGvxbmnXYpv/vxL+Nsd29dV13QsnLsT7nr4n/jUB78mBW8nRWeHtho46KhsTKW6GKZ9UER429u2jIyMoL+/H1u2bMGcOXPQbbiuiz/96U84+fgjYeijT6SBk4GfWQVolPU7MxO+MpkMRrI2egwbvT3Jps9/JJPHokPegvUP/Qx9vammzz+TLSDrWrD6NSRTiabOO6e6uKd3I0yhQhdqbDVkPd1AUCVdvZDLI1ibh9Pf2IjsQI0RWbF1BK8+5CJ87NLX45j3ngC1yWPhlRFZ3dB2jL7GnCRX1HwERQ/PW9Mvf68nIkuNDR57aBa2bpiFdFqFUDIQojJSm5DCMB9FZEvrQxFZpcUR2QefvBtf+cEncfZbL8HzD3kJ7nzoVpzw/FdNur2EUBpaMoy2IxW/01QNep3lHKi1b2aYzhkB9js0h0VLtq/vRKiw4QSzYPtz0Wp838cDyx/BAbsdB01t75uIOPADD48990+ccsopMIzuCqJUar/h4WH09fVhIrrv6JhJzFDxyjASRYHbm4Tbp0M0UMh6NQpZp2QtOPLtxyAD4NsX/ADv/sLb0D934pNqQ4Wso0BVhSx9FWv0tdr8Zckns3wDJ5O9SpCYTVa5r0qnEhgZNvDEA3MxtMVCX78jh7yB6jeBJERHURK06WQPggohS+uuKdqO7y8J0WqvG7ohHzu8bpjyMRYSsySe//Dvn+O80y/Di484Wb5OInbj1nUyivvBt1wk39fuRNHZXEbDw/f0IpfJy85gtdyLkSOaYToJttszDFMVisKqpeoFM4WprkmiJwHX8fDJV38Rw1tG0AxIThi6AkOjjkvbM/+bBc1KD+oTNRvXJXHPbfNlbVhqbFB3a9no7QrVrQ3Xu1k8u/opGfX97Dk3lEVsRG+qD2s3rcRnv3XhqOhvO0PbjhomqJrA04+m8Mh/e+A4k2xQeSPBsoDpLPiI7fjd18IMgy6AhlLf9eaT5HO3YUghG3/DymZ22Zpq5WVN1/CR734AO+29KHYxKwWsAEwoMhLbbAEbESgCll/bJYKW79mn+nD/XfNQzOvoG5hmc4PS+lb6gOPkvsfuwMe++j94esVjVS0kFPG9/EPXoGjnO0rMEmQvoMea5xK4//Y+5DIT71MhW2EwTOfAQraDUVStZW0+uwUaQr3hinPlc7dBItYIyN8Y9/HVRCVLX5ca36qVbl7KzyUxe+gJB8Kv8Ic2EmqiRSI26qbWyq82LUKiQsiKcXYV+TGffGRAemLpXT19jSnMX+mgiFPMkoj9wncuwrmnX4r9dj943PdJMXvWtVi6cDcEtNIdhGEK9PR72LJRx3239yEzVE2shltbCBayTGfBQraTUfTSLmQhGxeU1PKhj18jn7sRK9DjsxaUVJpostfbJ79pDatkJcxRz5GYPeOzb8WshQP43XV/bVhkNorC6pGAVcJmFK1FwAomFjWk5554aBaWPdEP0/SRSnuxiM5I0Dd60oViHl/70eVSxI61E1QjlUjjvW+8QHpsf/6373dUZJYi5OSbpSSw++/sxfDg2H1LoX8agWEhy3QWLGQ7GUXliGzMUGb2/95086gM7W4iEWhyiDkOWpVSEtQ4b7vojHoeNY1A4Kn/LmuIzaAchY1q97YNCswJPLIkYh97cDaWP90PK+EjkYz/OyJtIQ06cIazQ7K6wdcv/UVNIrYSx7VxzyP/7jibgUwC6w/rzT5wZx+GtlYmUYatLzgiy3QaLGQ7GCq5pciyJJ01zFUfAlS+NdFlNVzbBTPmYcZmR2OJWoV5ZB+oZiPQNHXantkdorBoH0jS0PKQtaT8mhgtYh9/aBZWPNOLRNKVQrZpy9aA6CzZCd73qVOxdWgzelK9dX+eIrNjPbOd4vCi+AdFZvMZFQ/evT0yG9byoP85Ist0FixkOx3VmLEeWRF4MFUfPQnKXu7OkjAk4E0ZNGnNPo6GluMb6K6oK9Uk6kzEH5fIM7vbQUux4pHVdZ942zMKGxKUSpUlKoRsBJ1unnh4Fp57ug9WwoNlteZGeqrR2cgTe9bbLsGcgal3bIs8s7pmyPJcnQRtt15ZnkvFg3f1Ss+sogQIQAGD7jzXMp0LC9kORyEhO8OgWo6BW4Bw81AVAep42aq+HVTo/ZJz3yafW4GqKuhNKpCVK1uwDVKBLit5BjMoItvIrUhi9rxvvQ+HHH8AnrjrmZois7q0EoT1UdtPwoZ4qpDl19KePjr5KgCefqwfz0oR64ftZlsIfSXqOYTWbVpdTuyq104wXmT2kx+8CksX7Y77n7ijo2wGUsySzWBYxwN3Ua1ZFX5ASa0sZJnOgoVsp6PqbXsxnGoUVrg5alsW/t7i5aFuRf/v/Le3rD0teTEDKsVEXZVcB6rvN1XQpn1DChpfCTq+9FYEDYLXsgWNUhOG6Hkybv7+Pye1GVAUlnp0RUP37YqnBkj6KswxEdm1q9JY9mQ/DJNEbHv4xmv9OlDXrkXzdsaXP3pjQ0TsqGkHPm76y3fx+W9f0HGe2d4BDyNDGh7+7wBsp/uqszCdDwvZDkdRtBkXhYWgCyQlskV1clsHtdx8zbsuk8+tvFD7pXauuudC97ymiVlqT0sJX15cw98tiMhS1QIS0JOd/IxSFD56noyzrjtzXM9s5IelslrtLWG3+4j7ndHrPbjFksldRLIJiV1TQZnATnDeFafD8z3sunjPhs9XVXRc9oGrUbALnSlm+zxs2WTiyQct2VGOYToJFrKdjmaEQ5QVfcw7DSF8CG97FDYUse0xvEX9zv9x2wPyudUIRZVZxWrgw3BdKE2oZUkD4H2eGV9EtgU3KiRiaynBVczbo57raZrwtxv/WV3EtqEfdixhtFhBb4WtIJ/T8Mi9c2AXVKR7XbQr1XyzkSf2zS8/s2rL2kZBntlPffAaKWb/++i/0e7QvTCdQmiQh74MFGFf8aSKO/7i4aHbPTx4W/ignx++w8OT9/t47nEfa5YF2Lg6wLZNAYp50TLbF8NENKaBOdMyFM2i/0LzmoxgdqCVwCuEy99GArZtUag8DomiAIbnwtN1BNQYI0Z6/bCOaihvlI6uIRtBUmwySROUbl6i53rErKIqGNmakes5d05feLPZASK2MtEr7YXHlecqUsSODJnoHbDDr2gbr0rkm6XnR5c90FBP7Pgz3S5mP3/eN8sJYAO9s2GZ0uHeUiJ/MwlXirjK51LHCdmKWA//7tjA1g1APiNgVLoMpOgNVzLa/XSM0+do9fpmKejpV5DuB/pm0+t8HmeaBwvZmdAUgUpwBe0bJamGvIsPXAivWDotsoitGUWRvllpNXA9+LqAH3V5i4F0oJda1VJhnsbMI7oYtkrI+qTUGlW+oIqYJahhwn1/eRBf+N3H0De3D50C+WOpPXGPp4cVCh6ahc0bUkj3OXIYup1F7Fgxu9tOe+Hi/7kSRxxwdLzzq/iZRCxx42++hkIxh0ve+5WWiNko4krCVVrr6efS35SKgiH0UFUBzzNgJhR4LuA6QLqPbDXKuOdvmi65nOwCsD4TKmISt1YCmDVfwez5CgbmKrCSLGqZeOm8EB6zYy1ZPVWKaHYG8iToFcNIrKR9RSyVv7r+C2e3Xx1bisyWRKXmebH6ZslaQGW4HLWxx5hQW7ffvZj1GK3Vuy55HXbeaxEufs30myY0E0cTGHB0mei1clkvVj/bi2TKhU7lFiLa8+s6yk5w7Y8/h55UT+widjzOfftlTffM0h4i8eo6AnZRwLUFXLc0YCcFa/iIeulIMVsSva4btWKmUQhgZJCexbjXHVVTYFoKkmkFvf0KegfoZwHfE1i/IsBj9/i45x8+Hr3Lw5Z1AfxxpsUw04WF7AxA0a1OCJJUiNhCyQ9LZ9H2FbGEaRo44y0vk89tB4nZkqDVAl8mgsUhZjWomOMm4DXYJ0ue31ZRS8KXkTBGPdfd6EDX8LHvfkCK2Rsu+CE6gaiawlzbwsiQgWceH4CmBzArasW2+7km8sQevM9RdZfnmgpinNIblZ7Zm/763ZiXIfS6knB1imGkVEZIS+J1orxZisaKQIXnht8G+gwFlSkqmx2pr/ShpilIpEJR29NH0xLYtFbgkbt93Herj1XPhL5ahmkkbC2YIT7ZKOFLaWOf7HYRSzaI9hawEdlcAce9/gL881dfQU86iXa2GqhBIMWsRwktDd62s7wE1lg5aS8gm0EjaJWtIJw34FFJswn61RpGqfyWocsIbq2QiI2sE1pJzGaH8/L4L2SLSPW26XFE3mFVwAgUDBQM2bnLLmjom1Ul2a1Ntch9j9+5gye20jPbbKSY/dC1UBUVruciCPyG2gxkBNYDPI/E6PZkN7WOrxaJTdumMo7bPyTFrAYUc5BWAWsKh2xkM6AHRWkLWYFlD/tY9bSCRbso2HkPlW0HTENoX9XDTC3hq03pRBFLUILDE8+sLic6tC2lyGwkZht91Z7lWbIUl9uIqGyLE70iHGXimgmFXHHUc80iVpbY2g6J2f45vbjlp3fgoy/7XFvbDMg+Qk0QNj81C1s2JKUvdtzd1IZf4XSyB+edftkOiV1SzMY0z8m+atQ0IWEl8bO/fLthNgOaJUVgKfpKNoJK68BUvlaus2PCKNnuaT7Z4fEtBrWi6QpSvZQQFnp2VzwVyAjtyqcCufwMMx1YyM6khK82FbKdKmI7jhjFrCk09HsWHNXv+ESvCEcNI7PjLUVUT7PWupr6JCW2jnvTC6TN4JI29cySqYDqx/YOJrH8iQHoRjDaFzvqve1nJ/j3fX/DvrsdhGOPOKlpy1xP+bjXn/DOhnhmpQeWvK/2dgE71ZLbZCsIhAK3VKFiLOSXJY9tvRaD8SBvrbQe9IdR5OWP+rjvnz7Wrwy4jBczZVjIzgDaOeErSuxiEdskyp7ZoOEJYHPdcEiU7AWdnOgVQZJctt5owCbSoo5dE5TYimwGJGY/+carEDShDnA9UNMLLVCw7cF5sIsakulJDBWivTyxLaGObVDpmb3mx5+e0qzo/tSxwyoE0xGwlULWsXUE41TwqLQYNDJfja5ZlCRGlRHIM/vU/T4e+6+PQq5NDiqmo2CP7AxBNdLw7UEpHOkk0TZQUlc5sauNlqtGUkkLv/n+5fK5Yyh5ZikBjJqk+RRWacC2n+cm8Wygy6hsIpjeqSPu2rc1oQC2KpCmzK9pQFqC1qaWjl2RmH324VVQVVWKWXpuB2wtgLYljeEnZiHdO4GloI2IRGw9dWIb6ZetdzqRZ3bL4Ab5e637n+55PEeAShrTbmnEIUPeWEpUs4sTf5fpq+oFocXAMIWsVtAoVFVBujessrBplUBmm49d91OxcGkHHHxM29AeZ1Bm2ihGuuSTrSctJV6ErBNLiSJR6KDzoMzzk158uHzuyMis70urQaPsBQvcJByFYrJiWlf+oJ5slBhxSdSM8zcraY56Hg8afa9nbUjM7nX4blj+0EpccOJn28JmQHuUbJDFB+bDMMS4loJK5DtavBufXft03c0OmlHJYDLP7NJFe+Cuh/+JT3/jvEltBlQKS0ZhgzCJq1GnUo1qx7qUhDb5uS2yGOSyiAWqV9szEHp+KTr75P0BPJejs0xtdKa6YHZAUfXQXkBnvTZAUDSw3OygPUTLVBjJ5LHw4DfL504j8qDqvtewdrYLnBR0ocph6KkQdrgqFbBsNYKG0xU4ig5PWLCRQFEkURBp5EQvilq/fBs950SPfJ3+Tu9zYcKDBk0oOyR31cquB+yM+UvmtIVnlqKxwbAF/5lZk1sKKmnR1/vBJ++WnbPedPIZU+rY1Yjkr/HKbtXKwXsdiXwxN6FnVvgqfI/M3KUobIO3dXGSaGxElEhGFoO4BCaNJFJCGBV1oDq0j94t4NW4fEx3w0J2BqGaPWESTYt7X1MZsJnUdjaTjRo3dB4yGYU6gDUo+avPN9Hvm7CnkfQlbQWtOiaECgQGFD8Jxe8BvF7kSaAiAUdY8GDChya3WyET7nd6pqJj9DoJWJdEr0iiKHqQRS9GlBQK9HkYCOo4pVZ6ZknM5oZbc7NE0fWiEPAen4tknV9X0SI7wWe/eSGWr35qWtOZ9rJPcwKVnlkSs35FEIJEsudpCPywlVqjB7QoGusHKhyndqFIX1vy5uZjispWRmdTvcDQZmBo2UIMb+msrpVM8+HbnRlmL5B1ZGUqq9bC5C6bQgkzQsR2PGQxkNn0oiE1ZhUoWGynsU23p1BTNrIVNPn+Wd7d6VACExDU3CBa5rDZfKD4MMWO2iSKs9Kzih0j2rTuJHgpMuuVTqX0igmH5C40+JNunUjMUmmuVF+yZbVj/awBZdlsWMkp3KCI1nhijz70+GlPbzp+2UasduSZ/e+jt0Er+cZJxLqOur0iQQynUEUVKGaNuqLKMipLiV95yA5e47WvbQTUWKGnT2DzFhNP3ZvDngermLdzB+UpME0l9ivKTTfdhIMPPhj9/f048sgj8be//S3uWXYvqhnWlG2lT5aqc3dwcteMhJK/SmW5GuGXnesm0ePrKKj1HWfkRpCdyJp1XNCwf2DJyKvipwFhRrnfgEIPSv0OZKWBerdKOMpLMjaAJh8Urw1FoA0LGaSRRRo2zElLNJGYPen0Y5EdzOHKM7/eVJsBRWNz5I9dMYBkUZvSV7ZZXlm6Sf7trf9Xtyd24mlObdGnaysY65l9yZEvx/rNq3HVDz+JbM6RyV3yCxPDXYKmURtZFcVi/d0K6R6UBHYuE//IH8Vk9KQtmykseyiDjaua0+aX6TxiFbJ///vfcdppp+Hss8/Gk08+ide//vV49atfjYceeijO2XZ3GS6zN4yKtsBeIC0Ffulk06HJXWNJpyz89y/XyeeOpsIvO12LgQ4VS+1eWXPUr/NCK6OxsfcM1ULrgNcL+En5O6UzheK1lPY9drnq6YRUUQt3LBS5JUGrQsgobR5JjKBHWg8msx1QRDbwg6Z6Zh1VwM0YUB+ZD8uaxnERs1d22aonZVesT33o6oaJ2IiprHUcp9e+9Gys27QaX7zxw3D9Ymybk27ACoX6orFjo7Jk6aUWtnFD80v2hNHp5Q9nWcwyVYlVbXzpS1/CKaecgve9731YtGgRLrnkEhx00EH46le/GudsuxpFT8vh32bXlN1uKSj5YmcIVBpn50Xz2qZE0nSgyKDSIL/sAieNPt9CQasvKhurrYCia74FxUsDQXTjEUVeJ15fn0oRjdFiiZQ56jlCqVEshII2kNHwQilKG0Zoa/PMxi1mZTSWvrfPzEJPwZzW/YWI2U7w8aveiyefezS272FdvuAGRmMrp2lofbj4Pdej6OSlmLXdxkcgNS2A56uTltyaiMi9lmtQk4Saas72hG3Gnn0ki81rq7RMZrqa2K4qdIDffvvtOP740T6mE044Qb7OxIOiJ6HoiVIDgiYyQy0FlOi16JC3dHTCVxwWAyr+v0uxV/7s1TAwTwI6tBXEdMoJ9DB5K4h8puNHX8edxNj3RqKpQjxNFI0dX9AG0OHLbU8R2hxSUuJOJGaPPOngWKJ+YysV+BkD2qMLYJqNaD3c+KhspSf2gD0PRVzUs60bv18UuC7VFAZ6kj34xP/cgF0W7dXwGUXHbiE/ud1l0qgsleOymxOVjUikVVmUhyKzw1s5AYxpQrJXJpNBLpfD/PnzR71Ov2/YEBaDroZt2/IRMTISRiVc15WPbiNaZ8+rPQlD6H2Am4eQ1bObYl4DZKmtKLTRuBNwdC53XA8erU+TKdpO+dkwmp8bKQumUw3bRm3SUnaL5nnwSVSSuJUtL1U4xXAIj6JD4XP4MxVOj7oI0c80tGhYAeZYSQyYFgaNInr9CWqthqU0ZGMGuT9rvEBHbwscn2TpeCsEDUmoSJRk49TPEZ4UnWo5WllZtcDoS5WEwNRKbREkaElAOIoBT+hISDftjtElVdNwxqfeLFvj/uKaP+LEtx2DgXl9aCRy/ejm4sm5SNkaRGL6Zftou0TJf404XItOEdf+5LNSxB57xMmx55TJM5e0ZY3zd9loRMB1G1viUPjUWSv8btH0LSuJ0191PhzXwS9u/hZec9w7YZnTTwTU9AC2raGQp/0zvXWgbRT4CjLDPlK98ZR8jGpVk2UtPPkAiR4F+REfzzwwgv1e0INEqsPqe9eBT8GhCg3Qbbh1rHfsV+axQ0H0+0TDEV/4whdw+eWX7/D6rbfeilQqhW7lltseqPm9ugoctFSBquRgNyHvK2ECPZYiu7/Ehaq3yKOqGtufo5+bOfvS1yfwBAJqOVsn9FUrFjTkswYcWwsFq0PPGjIZDfm8LjOkqcyPDNKWhk3Fjin8oUSRz2E5IFULgCU68seulseZ7quy5aWiCflMF056lkXcFWBo2A7nUSeJfALIj7NxEknqWgHZykzIeDOmQ6BRaSKaXFgfVq5y6WcpZCdooFALtO10QdFZVfpmqeJBOiA/5I5TDbwAzzzwHG696Q58/jcXoX9u48RsQQ8gRkwoj8yDacnMooZMt1FB2aHMNgz0zsb1n7gJ6WQPmoFcdtlIZBwUEhclk2ij5knfO6FWpBRs33q+7+Ghp+7AUysexEfP+CpMI2wRPeUEr0DB8IgKr0E1pWmExab4hWXLKghxMZLbPHq+KlDYYuG/t23AwB4boFJ/6BnMzTffjG4kn8+3Xsj29vZK4bl58+iDcNOmTViwYMG4n7v44ovxkY98ZFREdsmSJdKiMGfOHHTjXQkdyC899rC6ukuJwibA3gqoiXijsnS37OXkZcCIYdjYLUViKbqstsKyQAKp9KxEPzd19iQMKRpBFzu1JtGaHTGQGzGQGTIwtM2EXdTguRRljdRoeNUmkSlK12XNAAwSoVKtRWWnto+jbxe2VM6Lorg0PRX+ijnAggKKe2+BMpwo+bOjZm5CXkBN0wd0RdaPVc1STcwadqVPd+Seg4IMyIy5IdZ1aIkEFFUJRx62L/G0oKpx0WHm9CRB0vErPUnoJJGnKWIroWQw+idrz6oqUqIAbYyvnSLxH/v2B/HF934dl5x6JT7/28aIWU8JZEML9YGF0F0ditW4u91IyCrTtBNc+b2Lcd0nfoZ5sxai2dCyj/2q076nGqqUK9kohFAhqNlB+bgavdUSVhoX/891+Px3zsYXb/wIPvquq6YkZul7SMf0yJAGp9BAES4XWoU9bEKt0ytfC77vIDlLo1s99PSMvpnxkwKFbA9S2Z2x+yGp9mrL3sCI7JMr/4OTTjoJhtH8IEqriUbjWypk6cB6/vOfj3/9618455xzRkVWX/jCF477Ocuy5GMstCO7cWdGkIg1KPJUIyI5C547LH2C1PUrLgJKHCoNKsaRthydn0jE6lrzh5Fm9fVg9X03oTedbMnJ0vPHrwtAfrHhQRPD26wxopVGPcL3UKtR3QhgWX7JGhB9OnTJUeTRpxBHzURLs32pEsvnYWSnDII5BWgjpQutKE3bU5B3dRl1kpDAJeFMPdt1qhgnZKC76pYtLaxmmNAqj31NC+0W9PeAju/G3kCRAUCTdgoFmdK5TEaVG5zUpJTsBhSVzSkppFCEUTHkS0JXema/80Fcfc53sfqpddO2GEhLgR4gtTmNzKPzkU574c1HwyObYuqe2O9ehHNPu7QlIjZc9tLxGCV1lZ7o5q1hpwBp3ylF/JXxI56pRA8uec/1uO5nl2Lr0EYsnr9rvTMCOaLovFDMmY0NBoRfP1mfmWxXjT49hqau8Psw9hjVdUUOxmxe42LW/GBG15jtVu1j1LHOsVoLPvzhD+MNb3iDrCX7qle9Cv/7v/+Le++9F1dffXWcs2UILSFb1go3GzrzY0B6l2ZggtdYj+ra9Zux9+47Q2uBkB6L6ygY3GJh66YENq9LoJDXpVeNdsF20erWMPpJQ6g07E81VMkXOPX9pxYNpB5biOxRqxGYHlTqFkSRXhKDYXdNeNJvW4ru+mThji7iSihsEwKaGQrciQ4lRdOgRCMTDRoiHYsvFClkqRzWt1f+EMneZMNFbARNV0cgk7/ySlJGZivFLEFi9oJvvE/+/PBtT2CX/XaacmS2oAWwAhXq3TtDCZRGjpLvWJ+1zkNqw5Y1YWLXaZfixUeeHHuy20RU7m/6ptBNWSMHZMIRDaWmIfmklcbH3vU1+fN9j/8LB+31/Jojs5oeWgpGhs1Yqi2ElvtwhIZGX5qJYalwHQ8rn8ihb44OK9n68zPTGmKtKUQ1Y6+77jpccMEFcmjgK1/5Cn7605/KSC0TLzKKZA1sF5wxEJbbalGz9SaRK9h4wavOks+topDTsXZFGg/fPRu3/WUR7v/PPKx4qleW0EkkffQOuOgbcJHq8aTfsV5xQsPc08VY2wdz1QCCtCsbDGwnFAFElDBG91VkZaBnKXRJ2GYV2NsUFDarKA4r8ApkGRgzk0oRG6PKkbmLMmIWYNvaLSXvbbxQZFZWNVCSsifYePztR//Gxa+5ckqluchSQGXGFqzvR25FL5KpeJJ0QurzYXi+h4Vzd8ZVH/uBFLGtJhThYbcEGrvw6WaxQZCADbt21VmDOfDxm39+D1/6X0oEm7w0l/SrA9JqRKM0cRB5xmnkpRUk0+T/97Hi8XzLW7MzrSP24phUQ3b16tXwPA/Lli3DG9/4xrhnyZSg5ggyzBVDKS4pjoU7o6OxrcRxVKx6rgf//c9C3PGPxXjkv3OxdmVaXgTTvS76ZoXCVTcmjmBOTJjYQpHTyeqsTj4lBanHF0AbsRD02TLaW5KvUqBV/cwYYavoJct1XkFxiIStCSeTgCiGHmFpJyCacMGiqGwhZ+Oioz8MO0frEz+VYtYb59T84ev/BzvvvahuMUv7I68FmGObUJ6YC89TZfQ+Tmot8UR2gnM+9zY4ro0li3ZDqynv69KpjQL/DYvGRpaCKdz/q6qGi8+8HkW7MKmYJZFMEdJ8Tkc+G2+kUlqUSJy3QEeSRz6RUrFlrY0ta5tYC4xpK5pW5X0mFJTvNBRFhZaYIy/8DY/Kkjie4dHYZkObc2TIwJOPDOC2mxfh4XvnYOvmJFQ1QG+/I6OuiZQfy3BwI6Ky0mLw8CIongqRcqU49utI/YnqU0bRWvp84GoQNpX2MsqRrKZAHt/Iw4jmQWKWtlleSVQVgmQzuOg7H5Ri9m8/vq32xge6j5SvYZ/hXmxem4auB02qzKfUVCf2bae8F6bRXj5HmdxHZeMaHo0lS8HUPk+eWdk0wS7gwafGq8cupM2oWFSRGSafYfw7OirH1QoMM0ydXP10XrazZbqP5hfGZJqKYg1AsQchqM4rNUpoALLWol+K8nZBNJYSveKEhuU2bUhizco0tm5OyHJYhhmgp88Jhx/L5QPicwNKr2wDrgHmhj4Ejy9A/uD18D0fgT21U0y5bi2V8poVlicK7FIdV8rzokeMJX+iqGwriBLASMymRWGHPR+JWVVTMbhpGKqqTOiZJV+sFqjYb7gXwdYk8lkdphWnrWA0US3isTy+/MFRnth2REYaGzWxUn3mqSbCVYrZT33gu9A0HRu2rsbsvnkVnlkBw6Bat4pMBI3DFzuuvYB8sk0Zu9iRZEpDbsSTLWwX7x7v+ZppP1jIdkFUVk3MgZ9dK6Oy9Pu0EV5obJxBrWjHo68nhTX3/zyWaZOgWLcmhTUrepDN0rg6lUX1kUx55fuDZgzXlUsmUZvWBlz4rGVzEPQVkN9tEPCo49bUQ8hqnw7VVOQhJw9dmSwW+mdpWJGEbljOK4YNRX3ee5t/UYyqGbgwYCs+EqX87bFilvj19X/BvTc/jC/8rnppLkcNECgC+2R6MNex8NzmhPRLptJNKDA9SfLXbjvtjUve+yUcvv8L0A2Ew++1JXhNBolY4kd//Cpsp4CPvutrMqJNkViKIA8PWrI2dLOQ9gL6Xk4hya8RqBolmylYt7yAeTtZMhGM6R54b3cBitknKxjAb4yHqJuisdRR7e+33VdXZ7XJoFqvTz46gNtvXYgnHp6FQl5DuseT1gFK1mr+Zi2VuWqQGNRIAD68GMr6foi+4pjkr9pRE3QTpkH4FZ8vdxiTdbIQuAoCR0FAySYNjj6l+1L47uofIVHq6tVMQkOGQFGxxm1nS7zr0jeO65mlWrFF1cfOuSSW5ENBvnkdWVWm46ueKts9lGQn+NoPLkfCSraliC2XWpa1lhs0UTo2GxCNHcuH3vyZsmfWDwoysZIisTSq02xozSI7TiuwUioKWR/rV8yAduJMXbCQ7ZaobHJueDGh4qPTLrlF0ZyZL2KJfNHGG/7nk/K5EQlcy5/qw39uWYhnHu+XSSR9Aw7SvV7TS9dUj8qSwGnMctiOBePeXaAMpSD6C+V2kzVDSWA9pRBsNV8sHX5qRZTWCwUtRWsbhaDOSv94AJ4b1vJt9hEfOv8UFKRftjqVntlvfPzH5deppFpe8zHPtrB3pldaMvI5TYqcZtoKRqPg3kdDT+zh+7+wLYvYbxexpeiitN00Lhrb6IMo9Mxeh6KTx6/+cSNGBsNa0s0m6kzeKp8sQRYb3VCwYYUN125+O3OmdbC1oEtQjB4oRlrWlRXUJWqqFxEpYqMGCEytHljyvz73TB8yI1TcOpACtg2v49Oul0peW/KWuoEGJa/BuGcXuEc/CzFQAIaoHmttK62mqUqBgsCtoX1q2BypHKFV/JLlYJo3B16hiCvf8FlZS1brT7XkiFelxUCHAxNWFYtBpZjNZwqy7nEuU4CYa2KWbWL/4T5ope23bXNCtifu62tNdvf9j9+BK8gTe3r7emIJWe5YVPjHVTE9gRZTNLY0cfT1pHHpe69HdiSJTMaDEO602tlOlbDCQ+vsBYSZVJHP+Ni63sHCXZu/DZjWwGqkm+rKUlQ2Ktw5RYQUst1hK5guFPxeuyqFO/65AI/cPwf5vCarD1DZrPbbfOECyVJcU55E+NlisL0jizqYhnHXblByZs2RWcVUoCYUBJWWglqIIrRly4Fa7p5ULztE4UpteptvMaCYrEABE1sMSMz2zuqRlQw+esrngXVFHDTUj0SwPTq3bVNCKrQYOknXRF/PLJz3jk/i2CNe1tJmB7VEYyPkLfs0t5esVBhDNFZWJzDCOs12YTYCrwd/vPM6fPO3Z9VUZzYOaL+2ct9SVJaOb0r6Ei1oKc60BhayXQT5ZMkvSx7XqRSPDmvHdo+tgKCWjvvuubTu1o6DWy3cfdsCPHjPXNlVh2q/9vR6jfPcxWgvmGpMljKWPaHCC0avpLq1B8Y9u0IpmDIyO5mYVdLT7Adb4aEVTslyUKegrSboZYmhFlkMAkWBDSpDNj5kJ3jeO1+AXfZajMtf9SXkNmdHLfvQVmol2vyLO3lib7n7j9hz6X445vCTSq+2pu7oeET7VDbvGN3PI3SxTHWnS2FHx3NjV5aiu7QvabRHtqYuhDcsL3ve+2G7uZaJWSlkW+iTJayEhuyQh+Gtja+fzrQnbXxZZeKIymrJ+VB0a2qJXxRi7LLasT3pJO7+4w3yuRY8V8HTj/Xjnv/Mk6W0kmkPvX1uyz2w9TCVi3bY6lZBwa/eH1vd3AvjzlJklsTseBd2WUO2Sleveil5aGVnJhrudKnygVKXeKTozk777iyfR12oWyFmhYADY9zmEuTizege5voJ/PhLH8c+uy/BW993efmGlRIMyTsZdxOEaiL2iu98DIZe7bgo+UbbKRJb9bCcehRbem2pbqzSWBFL1QkcW5U3zE6FJzZp9eCs139Hitkf/+3/NW6mNS2X/L/cya9VkE+WRnM2rWldN0amubBHtstQNEOKWVmOK/Ch1FFdvxttBY7j4v9+ewve9tqXwjSri7QIuqg8/tAAtm1NwDR9aSPorE0VhkGll6+ui1FYur/g6xNexGRk9s7d4T5vBcRAHhhJQhlTIogqFZQauKMhRI3nZI6iAoW8irI1rpi4hq0CWD1JfPHOq3f4e7BdIzetaiZFyslaQCW5xnplXSWQtWLnOGZoJ1A0fPuqj+KJp1fKm1fyzWaHE3BdFVbCbbqIPff0y3DsESfXXWe2Nb7Y8essh8096pxug0WdqgloqpCVTkaGzKpVAkjMnv2G72A4u0n+HogAatP8JKJ5TUsmwLAUDG104LkBdIPjdTMd3sPdWo7LIouBU7PFQL6vy2wFRNFxce4nrpXPtURhB7cm0NPryj72nSVit1NvCXgq2+UKFU4w+X2xui0N87Y9oW7sg+grQJgV9UxJw1oUwYpB1ET6uIbobPQXz3Zx6w/+Dm/svi9FZZspvaJlsilkXQGV1ypqAXYqJHDo4EDZE6vrGg7af3fc//DTOPENF2D16py8OWmmtWX1hmcnFbGYNBoaL9HNyETD4TICr0y1HlWjkrwoCkuCVCAzomNosLqIjUiYPVgwe3fc//RfcMOv3ts0m0FlwlcroW5fjh1gZFtz6yUzrYGFbNdaDBbUaTGIXPwdqs5iIvTCzsdTjw/I3ykK20k2gomaIzTCUlANJWfBuH0PaM/OhUg68iFft0qtO+OK6JRq0MoyQVG5ripiIFp3p+jiO+d9XT6PJbIYtKLjF1UxIJ9xVvdAi79nJi2rExhix9P5wfvvgV12XoD3XvQRDOe2NWU5H3jiLqzbtBqnnnB6TSI2Qqa1iRaI2MmipiWfbL1iVtoKohlNA6qaIP2wPvlhLWRHjJpHTPbb5RgUneZ7ZlstZKlBAkWGhze3pkIH01xYyHa5xYCoqbasNC12rkBrNLTJnn585kRhx6LUYSkoTmIpqDp9V4N+7y7QH1kMaIHsBKYkmxSVGxudHVNaqdaTYrP9stF8itCQ0X2YvooDhvuwe64H6jhLQZHZb331o1g0dzdc8b3/Qb6Yk/5k3/Lg9RbhzM2huNMwCrttQ27fTcgcvB4jh67DyOFrMXzEGgwftRojR6zFyGFr5evZAzcgt/cWFHYZhL0wA3d2Hl7aRmCQS1dIO8Hnv3UhVqx7ZsprGQnLsClEq3yxY5lCPVk57amvAclg8sKS+yuf06XnnrzO9WyVSs/st353lrQZdEPCF6EZCrZtcBFw9YIZD3tk0e0WgwyC4hCgJCauLRudAGeKUqsRTVXx0mMOk88RlGTx6AOzsW51GkZHemFrilNNesEMLQVaTZaCqnMSCvQnF0HJJOAftRZBfw4oJqA047RU6Z2l2rMBoJYy+usZCpaVDJRQ/MatwUkoBqqDvOZhHvlhh3vQ500cCSf/7FYVeN/FF+GO+/+C4kkbke9xIcgjrAoILRi18GELhqg/cmkiFYfBqKOCfqCavZTM5Ct45JaHcf2NV+P9H/swDj/yCHgjRWhZE0qVSPGk6yoFYHij1Ggv8rgVCib8RNQspPYvejiANcWudmrohfVkFFaDa9N+ntpJJvLMPr7iP7F7ZUN7e5jwRVVMWm0vsAs+csMeemfVPmLEdB4sZLuYyGIgvALg2xCaNa6YnW5HsE4lnUrg19/9TPn3zLCBh++bjW1bEkj1uC0pZ9QMKPo0USBjKpaC8dDWzoI1ux+etRZOYgugu1DtJhUzp+gsRZB8uviGYjYSTqqm4qCXHiKfWy1mAwRw9CJUoaEntxi7ZzX0BZnRy0H1ZjUfg6YrKxiMGC7yuo+iD9hHD+OoYw7B4ObN+NHZ38c7PnUm+mb1Q6X9J4fOp5DIhEgMCwglwD9+8jec8ZUzccip+2EEa6B4KhRHgz6cgDGUhD6UgLE1BdXV6ot4V5RgFU0XsRWfrSv/MZy+MhUBqwnpM83mNORyGsgRoE2zvRh5Zg/f++XYuO1Z/OmuG3DaSZ+NsWlCeyR8aTpQzAnZIIGF7MyGrQVdjrQYpBfRDxSamiDRi4TsjAk71oztuPjCtT+Wz5vWJ/Hf2+dLEdvT58xYERuu1URZ/eHf8r7ZsFI7RiKN5Lq9kVy7J1TXQJDMQahNStRQRlsNZBcnASTSCXz8l5fJ58mIq8YsiUVPdeEYRSS8FBYP74aB7BI4IlWuHTtkuHguncN/Zw/irrnb8Gj/CFam81LIyiShogZlaxLGUAJ96iwogYovnf55ZDflZFR8KiKWkJ8MVKy4/zmIrIIPX/txHH38cVK4asOWFLFCD+AszCC33yYMv2AVtp24DMNHrkFh6RD8lFNTgwxq5UzHWdm/3QIRW2/CVzkZUKmjLqweyHnk8zq2brEwMmxMmNA1FfrS87F1eE2sntlGFh2Z3nKEd0D5DCd8zXRYyDJQjR5oqQXy7FMusTWKFrdraSEkYK+47v/w9BMWHrh7rix7Q+1ltea3M28q4yV80Wt00igG+g6ND6Y8L0uBaqlQPAXm0HyknzsAxtBcCMNBkKCas00I75QSwUjMuo4K31Ph2i5+ecXP5HMtNFrMBoovo7C0/rNy87HT4B5IOb0y6WubZuGpngzunLsV984exNN9WQybLrRAQa+noc/TkfZ1WFTFwNNDsaqEHcA++KXzsGj3xbjy3Z/GyLaRaS3jw7c9iCvOuBzLHnp61GgOzY8ir1rBgD6SCMVt1pSWhuKSYWSOWIttL30WQ8esRH7PrXD7ixOL2pLvcqr+2emI2GrTqU3JKrUlcumBPJ8Uixq2bTUxPGTAq6PmcT1UembjFLMy0a0NLhlUAzozyEJ2psNClpEo1gCUxGxK54YYW41enpG6s2JBdEF58pEBuQV6+twZ5IetH7IU2IEG22+cK0ntUaXJSXjhlU91E0iu3huJ1XtCK6SkmA2sfFMErawvK8KSasWcj19deVPNQrZSzE5XwNp6Hp7mIG33YfHg7piX3QkCmqxUMGIWMaQrWJEMS29ZJF5dDT2eDlNQ2peyQ0SzkkjMHnb8EXV3rBsrYq87/yt4z2c+iH2P2n/S91P0loStEUVsPQXu3JxMMBt68XMYfuEqmXgmfbsTbd+SoK2M0Cpxi9g6KxdMVAmB/qJrVImgFIEthAJ2cNCUtX7jJvLMLlmw/8R5ETMATVdQyPjwS+cWZmbCQpbZ7pdNzYdi9EJ4Y+vLtoHhqQVQhvBD98yRP1sJH6m019UiVlMCuIHWEF/sDkJ2TEYPiTFjeC5Sy/ZDYs3uUBxLitnAnCRyN02kWClVNfDdcGeHrtnaqSzNVV9bCfLBFuBqDhJuGguHdpMP1U9h2HCx1XKQMajklgdV6Ohxe5HyNehVxGsl0iqBHcXsG89/G1L9afzuG7+qOzLrFB18/5PfkiL2+accjXqREVtHD6O1QwkorgpnYRYjz1uDbccvl5UR/LQzaWZ8ZDlAFVFbmSg23Uhsaa71vXXMiEYYfQ0rEfiBgsyIga2bLQwPmXCc5l6KyTN76rEXyJ//cPs1sURm2yEiS5ULSMQWct2Z49EtsJBlyiiKCq1nEVQjGSZ/RWei6LmLVBxZCO6/ey62be7FiS84FclkN31Vtg/fRoSdjdSGi1g57V513HslRWgwti1AevnBSKzfDYqvIUjkShHaeC9OUYK376hTFrOTjWOQKPcVD7ZekDYC00tgwfBSLB7cE6rfg0HLxaDlyKQt6XcNSjUdSMD5oU92Mqj+6LjzDwRWPbWiLpvB0KZBmAkTn/v9V6YkYscVtRSpzZgIkp4s8zV4/LOy9JczLzf+zUuloK1yA1GuEysa9J2o9TCgPDiyclDkVQ+kgCUKRRWDQwa2bLaQzeqyKkEr8QMXT666Ix6bQRsIWSo2Q+W3XLs7gzHdQjddnZkaUFRK/lpMDe8rxGwbnJGaSD6n4f675mLrpgRmz1Fx3umXwjKblEXfVpSG+pVQRuQ9I5Y+6mpKhfAnPsYUX4e5ZTFSyw5GYt1uUO0khGnDT2QhNDeWKC1FLY99+4lQFA2erdbdbjSyGVQTs2ESlyMjsIHqIen0YMHwLli8bS/oXr8UsJSs5VIWu1BgBIp83h5tJD+7VdNykN93vJJi9XpmyU5w0SnnY8vazUimk2g00n6QM2WUlkpBFHcZwsiLVmHk+WvgzsqP/0Fp4w8bXJRvIqS4DbcZrb8UoTEWp5XClVwyGqAb26sP5HK6tA1s2ZyQHblopKcdopVRZDYOz2x41Wh94CNKPPOcNtngTCywkGV2QNGToZiVlQzIZtA9d7O5rI7775pXrkzg+UVc8+NPw3aa1xWnXVBKIpagSKw/hXqgk0LD+DrV+qrx7Z4Jc2soaJMr9wmTwlRfVjkIzIKsdDBtUVv6uJm0cOZXzoaVtmQDDI8is/UKeRKzZd+sgK96Urw6RkFu4f78PBl9XTy4B0x3AMOmjxHThaeEApYisFXnKGuGmbXNn6YxwWJHYnbXA3bHhhXrJvXEnvnp92PuTvMQJ7TWWtGQZbsUW4e98zCGj12JzOFr4fXa439OBl8VeJ4K21XguoqMevqBWi4nF3ldxz5QwyOs7xoJVhGK1tKzLKsbUNKWipEhA1u3kni1MDJiyNfGepXbhcgza5kpDOc2TXt60bHWDmI98gBzRHZmw0KWqX5gmL1hWS46PVetZDBzI7HUdjZsNUvDsh5uvvO38rnbiKJ4BV+X3thY5mFQW9rahWz5c0KFnpmN5Op9kFp+EKwNS8More5K64FP9gO9tvJOo6ZbMf7gFGx874Lr5DPZDKYiZsn3SpFXsg4U9bACQ9LtwbyRnbFk6z6Yl9kZapDEkOViyHTgaoGsPBD6XicigKhByMp1qSFPk8Tsez//Iex9+L547I6Hd4jMUgQ2SuxqhJ2gHlSqRUs+Wk9FYfdBDL3kOWQP2gA/WZGEVxKjJFYpQZMEZbTq9LPnA66nwCFx64Xi1gvC98kbjVH+7OqPCBKk9HnbUZEvqsjlNYxkNQxmdAxndGSyurwhpuoX7SDmao3Mvu8112LewFI8+Mzfm9rOthm4TpveRTANgRsiMOOiWv3yUuANrwhvr1s/UhSrJ5bKa1EvcxKxFY28upao/exUO3fVNA8j7CQwOrmwPjQ7BW1TCuamnREk8vDTI/B6B+GnsvJ3ediS+PR1KCTIg4mToyJ8z8dtP/k73vapM8NlrRCzukXtIEYvM4lmEqoBRYjJv1vqBKUFOlKFWUjafUi4PUgEBigITVFXKplV1Oi91KlXkXVdayMAhB4OnU/YPWrsUk7Of37zL6x66ge46MbL0De7D57jyQjsp39xJRbuthitQNastXUotiY9tPl9tsBeMozk03ORei6sjUsJVCRYJ0OK1vJGGb29lfF+EeR3pXJ8KrJ5raZh9fq3fOvxAw9/v/c7uO2h/8P7X3v91JsmtNOqyyok7bRATKPhyzUz8QFCZbmMdPiLtBjMvBMCedZIxEZ2gm4XsdEFmKwEdowiVqI2rgdpOBydhrl1EVIr9kf6mUOQXL0XjM2LoeV7w9kYDgRFaxPZUhWEAgKDutpR21ZPClFR8Y8Y9bsWwIcHx/dkdQFXL5asAnm4RgG+5sloMYnW/swizNu2OxZt2h/zhnZDT2EOdM+S9XezaoBtpoOi7kOVFoLaxPXYNZ707nIK2/V/Pv+hsmf2rj/djktec4GsUtAqEbvDPqbatIMJ2Wwhd8gGDL1gNQppuyYROxmRxg09thWPeifSoWiq3pQ6s02FIvUTtSlkOh6OyDKTougWBGWrUziKxKxM554Z4Vny0D343znYujkUsWMbHTy67H687OjXw9Br8CPOIBFL8UY/hsSuKjOMDdW1oA7Ng4HQzym9tCRerQL8RB7CLCIgYUsPqlFLIlUt3awpwDN3PCw/J6skJCN1Q2FZSipSodgGTE2D7pswPAuGl4ThJeTPyjgxAl8RKJgunFIVAkriCnV8nXWapUd2ciE7lTJgkWf26xdejZ9f9RO8+cOnySoF7YQUtDkTgR7AXpiF278CiSfmwVweRmcbwV0P/wNLF+6JxfN3kb+3Q/JSM4iaJtzwq/fKigYH7/HSqU2onbRjOy0L03BYyDI1QZnb0E0IrzBjxCx53R5/aBY2b0iip9et2q1r7aaVuOPBf+AVx74BeyzZF90SiZ2JFxiyFWiFXvmoLCIWRlo9CM2DbtqyisDKp5/GN864Foe/5BgMbDoAxqAlhaNK9gR6uAZ8R0eq10e6v7aGCbbuo2C4UiyrQpWR2KgTWNhNM4oB1/C9IhFbw9um+g0lMfuhr5wvLR/0c7sRRUzhqtAGLQRpF/nD1sNdmEHy4YXQhqdXZeT2B/6Kr//007jw3V8uC9ntLRi6Q8x++C0/gqq2376fEt2x27oWFrJM7TUUtfDyL/yZIWafe6YPq1f0IJnyoJXqPI7ldSe8A37g49JrP4TPnHPDjBWzlSKWSmxF1Qpipw2qu8mYqG9A8Q0YnikXZ5c5h+KcS76ElcufgjkyC4ZZpdSVBhSyujx2EunxkwEDRSBfjsJS4Hd0IlckyqQPtCxo6yhY2rB3jUal1Pw2ZOxQfxSdJauBuzgLf9YKJB6bH0Znp3B+ikTsBWd8EYfu+0J0K9MWse1yaaDvVrssCxML7XmmYtqLyh7qmgFFT0eN6VuvQqbIhnVJPPNEv2wTaVoTZ7S+8aQz8PqT3ok7HrwFM5FIOHklERvRjKFU2ZZWtM+Z6ImH78VXPnkudMPAIUe9CDfdeA1ct3qHKSrFRNsuN6zDtatf9H1VIGtR1QLyzlLi1/jSKuxAFbU3LW39cXcB/YGO20mysal+KjqbcnODUnWB8c44VNWAorMkaAuHrUfhsA0TtrutBkWgb7v/L1LEHrbfi3b8ey3L2+kbvBG02TbQ2vSmjGkMHJFlJodsBZW/0p26kQ5tBlSaS565O+dEQTUeH3tgtmzdme6trawWiVm6yA2ObMVQZht222kvdD6hyKH/KRI7unBAnI1gK+biUmF/IUtwNWeO4/PUI/fiio++B+/80EVQa8z4U6ndqKsiO2igb24wKrLvaD7ylgtfoZJak5XT2o60G0RidtwPqVDU4qSiqfznTrzfLCUB1pNsJaOzWROB5cHeewuCHhupe3eCWpi8I91jy+7D3rsejIvO/Gq5/ugOC1STkg1vcDq1csGMQwEMq82UNdNQOkd9MC1DUfUdTsfUzlbRU1RwNmp83hFXSypM/tB9c5DP60j3unVFT+ji9t9Hb8P/u/r9WL76SXQydIENY3phJHZs9Sv5t2Yke9F9RGQUbSFPPXIfrvjoGVLEvvzUt9f8OVpszQjgewqyg6ZsPkAUDU9GYslWUI+IHR2dVaSPu2plMvIxK05NC0iR4/b/Zu5IFIGdSsK5auvQMhbcxRlkj10Jb3Z+UjvBF759LlasfWocERsS7d+JKDdX6MSN3gCiZpDtJB0Nk6XOTIb3LlNbolfV1xXZBUzRSokVbS5mqef8o/fPxhA1POhzpjQEePLRp0qbAXlmO1XMVvphqePR+G3sm3MpCooBFK21l71UTy/ede5lOPnU08pXYE03cMKr3iyfJxWz5M+0VWkzyJmO9MQS6gRWgvrKQUW3HqWp0XdPHb/D1diocVjhoDMol76a5nSk1WAoAX+giNyLVsFZMlw16l/pid1rlwMnnGZNorpZ/vJ2JsZWwPUQ1afWzTZYGCY22FrATI6qhwEGQUOdYwqI06+6Je0H7Z4E9vTj/diwNo10jzutWrFkMyC+9+ur8NlzvjFhBKfdk7omeGNzIrIkDkYCaANay+wEq599Cie+9jQs2W1v2YkrwrISOOvjX6hpOnQIqIaPQm8RqupDE5BVCRqFGNWpKxziltaCMX+vhqaJtu8wVe5A1mCkL3nIQtDrIH/UWiRSDqyn5paTwAp2Hj/6/TXjemJ3WM4a9mnY8pa2eeecFxpNWImj9QcdjWioqgLD4pjdTIb3LjM5MiI78ViZouklq4Eeitk2i85u3pjAymW9MC0fujH95SIxe9kHrpbiftO29Wh3wnhe9aSuqpSTjuInyAUt9MSeMTpKV/GjbRdx/RUXy+fJEJpAMD8PpFwElPjlx3lqJVtBgEApylq/0b6s1k41ErLtSGXTgThPFdI3m7GgBAqKB21Ccf/Ncp9v2rYOSSuFr338l7WJ2JIwqmme2jiWkC6iHe7vA0+AUjqsJEudmQzvXaYmj6w8K01yZqYkMBKz0mogdW97iFnHUfHkIwOyP7qVaED7nxKWmcCDT92N8684ra1tBpVRWOoqVYtADWVvcwiyAUC7RWu+iH37Bz+Ok157evn1ynX2PRf/+MNN8nkyEevPySNIuHI4m1rg+o4Sn5ARNJDmQaiFsHFFKVnPK4naaP9GopY8st0mXquh5g0oriqF7D+3/Bwf+eKbsW14s/weT0bY5rh2v64iqyW0gZLrcjxPwExoMBMsdWYyvHeZGo4So3SoTB6OCH2zVliiq02is8ue6MPQNgs9PfUld9XC4fu9sG09s3VHYaPP0bBos5K9aD75QFYvUPTmXfhXP/f0DiIWUzhKt4tYD4pLYTjyrgoEJGZJ1MaCBmh5KNSNLFqOqPoE7euSsKVorV9anvKbmki1Vq+tvq+l6gX3/upufOeqq3DOJy/GrP65NX0uuo+vdZSifPPQPvcQTSW0V7R6KehmVKB3lt5R9i+mftgjy0yKrFCgUZvafO2fofEcJQX4DkRgt8w7S5aCVc/2ykhsXE1qIs/sZ7/5EXzzk7+GaVQpnt+uXtiqnw1FbNMqkFJANutDn61D2CL26gSpdA9OfM3bJyxIVgujIrElEVtGEbKSgaopjY+IkjjVchO/pTJZjxatSnGIRu3dMVXb2pqNKzfg+x//Nj54/XnY++RdUHxsMxKPz5u0cQLpIH+8ChLV3k81hpXuLMFF26idRgHS/SxzZjockWVqQ09A1GoQmzA6G/aY72RLwXhi9sqPfFeKWNuZ3FPZblHY0dOA/GwzCQYpK6MZdoJ3YfmTD437Hoq4RWLFMEy8+d3nyucd3qcK+LNHR2IrkQEgiog22mIgy26RUsjW/BHZuEFGFEt1UEsP0aBH5TTbGafoYMEuC/HZ330Vhzz/SKiOBnv/zbD32TJpDWPafp5X+3dJRsG7uARXO7gqAqpPrVBFkhnSZpcZFxayTE2oekIK06icST2UvbN6cntHsCYI2jgtBdWYP3sRNmxZg/d96rVNtxlUCth6vLDVEU2zFUR4mz0IR0CJqXB5pSf2uFPePOF7ZRSNiqibFt76nvN2aE9Lm8afVUSQJBFLx3P1ZY7FYiAsMnsCWqbmj2gGNZygxhPoWu7+8x249HUXwnM8zF08r2wzIE9z8YDNcJeM1FS+r1ZkFQs96M7KBaXaxa3GdQR0U0XPAEdkZzosZJmaIGvBdEIMMjqrmVCMHih6okLQxuOfbYaloBoL5+6MVx//tqZ5ZrcL2FIy1xSjsJVEYriZkKXA3+xDjalw+V9/9b9VPbFVl6V0OBYLeXz6I2fI50qCXhtBygkTuybb1iWLQS2F9CdfsDAiqxhb6iptJEW5EUA0Yhk6VMTeeOnXcfolZ0I39R0SwGgfFQ7dMGnTBL/O7VeuFtF6Tdc0ou9OO1hSXTtA/xyDS291ASxkmdrQzFJNmekN0YeC1goFbWV1gwYKWhoCbJalYDybQZQAtmVwY6wClqCEHk9ooYCd5iaMEr1oms3G3eRC+KKh1QtWPPMYHLuIsy+9uiYRS0SbMAh8PHjPbfI5Iki58PuKAImaWrs8lSwG00bQd9ABtG11f9QwO7O713R5/K5HpIg966oLcNCxh1Z9j5oxESRd5I9cJ593+Dt9rQLI80k9UES2G+vJtkOljGjkcNb8HW1BzMyDhSxTc3cvGUmtuKhPh7J/1ugBZLS3cYJ2zco0hgct2figVZEBErMXvecKzBmYPyU7Ri0ClkovuVLAjt+dq/7pNznRq4JgKICf8aE2qFQO2QkuP+cteOTe/0CtowMGbdeqr5s+/IGC3EhKHXViyxaDOoXQjtFYA9C3QVGpr299UBvd7SmA3QF97/Y8dB9c+J1LxxWx5Tqzwxb8WQXkj1gHIUtnVfydrCSRH7gOaL+rHdCMotG0QzTWcwV0Q0H/3Im78jEzAxayTO0Hi9krB7EbKswUNfTfmhShpQ5hyrQ8tI6t4rmn+6BqAbQWe/wP2ed5sN0iLrrqPdO2GchyNiULQVCKwE7UXnbq8xFSHLcKb4MHhUJgSuM8sUe86MS6PhslfCljKxTMLkDoIvTF1o2A75JvGVOEqoD4UIytU/q0bnRXdJDsBN/46NUwEyb2PHTv2jqAZSy4O42gcPDGUclf9SZ6jY3KyiOpS8Ss/N6UkgtbiVMMkOrTkUizxOkGeC8zNSMTtqS9IIinxBcJWumhjTqEhdFH0jW1suq5HmQzBlLp+qNWcZAwk3jeQS+emmeW9Bwlqct2sZEHVpPP8UR5QqFDiWKtgpK+gmIwraQv3/PwjSs+VrMnthp05BmWhQ9e9HnoFkXr8hCmD8WZWgm50BKuIJhqVDYww0oF6sRlt8ajmxK+Ik/sMa89rq7PkeeZPLPOHtvg7DE46m/uFBP2tC67gWiHRC+qVkDH+fwlFteP7RJYyDK1Q95WipqK+ERi6KE1ZMkuxUjL5CU6LWpaqbD7BNeDQl7DimW9MMwAdYwkN9Uzu2LtMxO/udSnPUrmISeH6wEuiaAGeGAngm4YghYkeo2C1nW9C8WYWlR2aOsmaLqOz33rt1MWsQTdOFDZrZNe/RZoswSCVKlCwbRCxZT4NYWbEIqQ0+YwN0050tUtCV/3/u2uST2xE6HauvQ+Fw/YJL3Q8jsR0HdwattN2gtou3eBkG2XRC+KxlJL2rmLWl/Pm2kObXS5Z9odKTKNXllPtpH2gurzCst2eYGG4ZxAoSBK0awwshQVHK9k5fJeFPI6kqn2iMaOFbNnvu58zO4PS/+MoiRc5aMUFaUsd1pf36dSWM1ZRmkraGE0NsJd4yLIBNDSWt12gg+ffgJWP/sUUuneaS0DJbsVcjmc+46XIacNhold0xQj5ahsPdE96Y1NArQM9JgGRiJoauvhVrBg18U462tTE7ERataQpdUKh2yAogcyyWs6pztp6yh9r2c6rU70ouuS5wjM3cniagVdBBdYY+pCMcheQBdiOlk1qYWpoOYGAhrVoyWrgSbkyVI2DyudNLMjurQVmJbf8ojAeJz4wtfIE+13f30Vjj/q5dhjyb7ydSkuygXmW7fwoa2gDYqHe4DzrAPrAAuKqQA7JpJP6Ildsvs+Nc1G+iDJw0iJUFrpoZZe0wLY2QzWPLcMvuFAUIUNlbzbpf1Dz9SsTkbJ69hnipB+S3LO1HScCppvEYq1ZtrHtWltH+aup3xXp9gJCpk8jnvziViy99JpTUsmf2UMuIuyKO42CP+RedObnhZA0QUCj0abMGOh8xeVHGvl+ZeSvDRDwbydORrbTbQ+/MJ0FNTUgKoNIKhBXcSArHHqq3BdDXZRhWNr8FwVzz7VD7uoyWhsObpJF+tWi9oKqwA9yPIw0DOAT1xzFpatfEpms1PklSKwrRSxaqmdZrM7eo2Hv82Hs86BmgiH1SdiZGgrrrzo3ZN6YmWiouFB9BYh5uSAxSPAohFgfgag36kiAZXWSjmA6cuuXWW0AIIK3Bt++DA9iIQn3yMSbvg7CeBJBGK5HFctUVm5L1TAWAdFtTFddJMSIKORjZnniZ2zaG7Dpql4mrSSFPbdDKdnetue9jlFZSUz6/6hTBSxpiBD65ZBwM4HGJhrcBOELoMjskz9SVnmAIL8hjAZq6XhTxKAwNA2U5bcomhsOeOcFquU6V9NCI0qLzXdc29pUuMN2sp5VZTved0JZ0oBe+l1H8Knz/o6di9FZlsJiWzbb6/TQfG5IvQBFVpKR5Cvvm09z0XfwBx85uu/xk677Fk96mr5QMIFUi5g+NvFpoyqUsS3SmMDir6Wov1KoMjH6Olu3+/UrjZ8b+iFlO/1lbBEV7Wbk1KTBE2fIDJathRsA7TNDRn9oO+ElfSRGzFmnIidqid2IrS8Aa/fRnD4emi37AplGrYb1fChOuFNayvFXpzIbmYttBWQpYCisTvvRaOGM+tmjZmY9gi/MB2FavVBobHRoD28qOtWp+C4mhw6DYVspcdUDX8uRTzloyQKopqslRHTKT1KAja0CFTMPyjNvyLiGkVd33DSGXjjyWciV8yi1UTDzU472AoqoJa1xaezEIGAWqWKAdkJPnL6CchlhncQsUININI2sCALLMiE0VYSsRSNdDTA0QFXA6qJTRKXpg8rZeHjP7oWVjKxw7zlfZIoPSKhWxK7VIeUKhzIaK3lleqSiipRWWViEasUAX3VpFHeejCsMGo8U2qb5oYysYjYyGKAYRNi5wzEPtumH5WlG6rSOWKm0eqyWxSNLRYCzFlkoXd2e92QM/HDe5ypG0U1ZNJXYG8DhN7Su1/bVrFxXQomlUYaTxeULhw7XrzlpaoU7Br9x+2vRxOpFrgdb7q1cepL3yGf//vovzGnf37LIrNUF8KJOoO1Gd4mB4Vni0jvnYLii3LBjEpPbLq3f5SABQ0F99qh11VORAVkNK3G9ZN+WQEtMHDIcUfXnB5VPozkzVKpfGjkvSWRS8PVXjRUEHomRTWvrOzgRTaGFVDUQkO96KZs2UwNGpTtLVQ7tDrBot13xkvf9rLY5iE96y7d8PgIDtwEZWUflPzUO0VRTVlND6StROngbT8Wef5TAK2FQtYpChimip33SnI0tgvhiCwztQPHGoBCh0+LC1NuWpdEsaBNuRWt1B2lJKvKR1ARVZWPMX+PLAyNiGyt2bgCl13/QTw7zaYJU4UiyU6b2QoqKay0Ya93oaZU2Rdg1fInyyI28sQKEn69xdDzOqsghaiMuFLktR4RS9HPkp8xn8nhzH1fjHym/qh5OWJbqnZA9oPQV+uH4rYUBadjaxSkbKFDGOsAbbju+U66XCqJ2WDH+XaYneA7l1yPwY1Taw5RC7J2M42i0C9ZE6LXQbDv1mlPU5PJqFTntHO3/7jVClokzqlurGsHmL/UQrqvfc9jTIcLWQr7Z7NZeF57DEUzDUr6MpItS/oiyB+7dlU6rGDQwbdkrzvhnXjdCe9qiZilJK+wU1gbb0ABFJ4pwt3sQUurWLTLbjj3k9dKESsTuCg5a2EWmJ3fLmDpMZUIM0VxKapbuj8rZHPTt1CPErSBtBtI364ayKjs9vWkJiAmhL4RoEdMRDd9osM9sQe+6JD4ZiTC8wshK1MUdIi9t0FQMuA0kB0Ho7qynbgDqhDlJLQqGlvI+kj161iyd6o1C8C0nFivXoODg7jqqquw7777ore3Fz/96U/jnB3TRMhOoCYoS1iBoKr9LWBwi4XMiIFEqjXzbyTkmSUxe8+j/27qfOnaUwzaP4pBHYsf+uMd+OLZ/wNzwMJhLzo+jGySeJ2bA0xvegI2Upy6X/p85TQac4UeJWjJR5v04Gul6GhZxG4GzDWxVtughC9NF/A7rHoBBUTIUhCXJzYi7KRXakASkTMgUi6C/bc0xCur0k3MDIjKRiNSZJlohZB17EC2tN51vxRMq41vxplYiXXP33TTTVi5ciV++9vfxjkbpkXIdrJGumVR2fVrUjKRStdnRmiDxOxbX/E+bB3ejOfWPt2UaKwnFLhtluRVjWXP3Y+rv/5+7L34BfC3BtAGBJQF+dALS4KALATT9fhSMhhlXVeZTiOPMJm7FYkYM4APDUKK2E2A2djkrqrzVyFv/oQfds3rBJ64+1F4jouzrvpIrCI2IorGjnLTk5jdYwiCbp6mASVF6Ulf3qvMBIuBtEy0wFZAlgKnEGDB0gRmL5y6d5npfGIVsu9///vxta99TUZkmRkalU3OlWcy0eQKBvmchs0bklP2xrYz9z12Gy699v3x2gxKYqkYtH8ppuUrH8Q13/4A3vCqC/DiF7wZWXcViun1UHUdqpcIKw9MFxmNrWh4ABJ7SXzxHzfJ50aHSGXMl/LAvCTVLEI+tQJO8rnYRWxEguotl5K+2p17/nwHrj7rSqx6ckVTRBlFGf1q24XsBaYP/6DNYVm36VoMzM62GES5BeSNbXY0lqLz+YyPdL+Opftyua1uh2PxzLRQ9FSpba0Xe9vaSqhSgeOEJbdmGicf/frtntk1T8UyDw0UjVXhtUFL2slIpwbw1lMvxotf/EaII1YjOHgNcmIlCsVNUIQKTWlAF5+ovmyFkFVUFXMWL5DPREOPbqFAE2kIxUNWX468tRq5hI2i4U5bJNWCbgp5Exg04iYgbk/sZaEndo9D9m7KPKklbTVkVDZrAEtGgAW56VsMEp6sZEA1pTtVzFLimi7LyzWXQi6QVoI9D6Vuju19DDPxU5c5znEc+ZiIZDIJTZv6UKVt2/IRMTIyIp9d15WPbiNaZ89r38ijMGcBTgbC9yCovmwjp10Sx07Fvqdhv9Ur5kFRPQQt8ufGDZXmCkSAH/7+Wlz6oetlLdWAkpAagCo9gEDOAdwWJutNxlPL7sPatY/hRUe/HQuX7oTg6OXAoowc4oWtI4+N8Pwi0uYi6EoCvnAg4E8zGrtdURSyWbxnv+Pw3Sf+iVRvT2NWSvZOMKHAhKdmkDefhafmw6YMmo+84cCHj5RtyPcqpSHUcvZZAzGTLop5DT7lt7WhFijmi/jl1f+HD131ERx4zKGx176V34sA8H1Zy2SchVIhemx4e2yBsn76N1Aq1RkOEmFknMpXVfxNiECKZ4EAAZnE2wwhVCiqD1fYUGJYPN8nrRFqicqbO6dI31WBXfZPIj2gwG+TeuaNJlqvbtQ99a53XarjyiuvlI+J+POf/4xjjz0WU+ULX/gCLr/88h1ev/XWW5FKdW9W4i23PYB2ZulcBYtmKcgX44knKQkT0WGdGbFQKBowqEXoDO7gcuqJZ+CVx70dQRBg8+B6LJizU0Omq2qA7QKeHfr02pFnVj6Ea79/Ht708nNgpYrwj1kLQZ7YITPsmFUSdp43iIyfRdJcCFOfLU9pfkA3wnUchVFr2RqG2UslM6cGRY9FUkZhC/oqFKnEFvkLSp5Z1ackMAGbosMQSBf1cq1jupFpNKbhleqaKtIv3U5sWrUB85cuxOd+91UYZpPsL9SQLRDSQzwRoqBD2TUD7TEbCt1UTRNNK8LJJuSNlFIR3SxVuYZukphrr/0jbyoCASPtlBo9xEEoYn3kMJwNI+CBp8J3DKQXDWKjO4hNz2HGc/PNN6Mbyedr96IroknjweSn/OEPf4jTTx+/F/p4EdklS5Zg/fr1mDNnDrrxroQO5Jceexh0vX2TcmTlgtxqwC8C2o6dkKZKIAQ2btoGUyGfYsiqZT149sk+pHvdlpV8aRYUcb7/ydtxzY8uw2UfuB677zw9vzkNltCuGs6QPxJtyTOrHsLVPz4PbzjxLBx/0uvgHrsGyiISsdaEflhT60fSWAhdSyIQrnzURMKrmuRF9WOrRWTrPuTIRygsKNDgasMoGKvga6Nr01JETjfDagIBxeBUActV0VPUpf+LMrPjoJA1kBmyoFPtXKV97ATfv/Qb+OzvvoI5i+bJ15rxPacLoVfTzYwAZhWhP7AYxpPh8k0Xt6iiWGodXFmPlW5ilTbsW0TnEBL8yT5/UuE/HUjE9qXnydbonitQzPmYt7OJPQ+jsottcsDGGJF9cuV/cNJJJ8Ew2j+XodGQ9ps7dy6Gh4fR19c34Xvb7htiWZZ8jIV2ZDfuzAgSsYbedrurAh0BFsLPrpanH9nCthGUxFYiEXZsoduuoa1p2adeN9pX2DcMDzh8/xfhtce/A5/+xln45AdumLKYDdvpKigUwwSTdr0ObN62Bm866Twc+8JXy0issqgADCekH3YiV78rRuA5OSSM+bC02dDVlByeJcvBuBGtUhevWqKxdUVlpS1AkwKWfgpUGwVjNWx9Q9WELplkRK1uFQGN9pIAHCNATnjodfTYLtrJHl/6Df02qf4hPbGf+DrO+toFmLN4XrgdZWZcvAerjHpTxLzG7ez7OsQewzBXLoLSAJ85XfIoTpEbCsuzaVSEQ9ayDaDrervcY0go9uUJoGdAR6onvnMw3TBQJJZELI2yF3MB5u2UwN6H9crzf7fQrdrHqGOdY1VGvu+jUKAWiyEUaaXGCLSA1cQq09lQKS7FGkBQ3AooWiytAvNZFdkRfUYmeU3E6044Q4rQK2+8ANdd/BsYer0nNhFaCmzyuqMteWbVg7CMJI457DUQVBrshashFuWAYatmsUAe2YK7Hra3RUZoQ0GbKEc4BN0ZVBK1sa1Csicto7H0XL8H1pDdxlxtBI6+Ga6+DWJCI6GQpeSi4vKq7G8L2GYAXQToiSkpj5qJpHo9jAyaUqC0coRj0+qNuPHSb0gRe/CxhzZtMJ1cqCRi65mfWjAR9BXhLRyBsW6gIcth9dByAPlhBT4NErTpfTotm24oSFCnvSYQRmIDzF1kYq/DerpKxDK1EeuReNttt2HhwoXykU6ncd5558mfL7jggjhny7QIEq5ach4ULQn4dixVDAa3GHAdBYbZ+uhRK8Ts58/5nhSxRXv7DWLtlgIFhbzStiL2qh+dg5Xrn5C/BwdvhthzGMgYUyqvRbaCorcFw/YzyNgrYPvDUBUVupqUVQ4URQ/DcBSNHaf+rAgCbF23UT7v8LfKHyjpRZjQglSpEgFQNDYgm3hcPhxj8yQithRwpCL8FRnzKnkmSdhYHmwtvmSfRNqDYZBXtnUZX3a+iPlLFuDzf7hKilg0U8RSxDwsiFb75+QxKeAu3dqwKhM090SvQHpAlAVjuxGd0ykS24yhffLESjvBThb2PqIXutGGWYlMy4n1qDjuuONkBHbs47rrrotztkwLIUuBll4gI7JxNErYstEIh8jbU4/FzpyBBVi/eTXO+vxr8eyaJ2uOulG9x3yeuhWhbUWstBMcfiqCXYcRHLwFsDUoznTDUgJuMIKcsxIj9jMouBvgi6KMbmu6CZ3Ep5+GGlhQhAFFUGewsBxSMVfAx054i3zeLlh1qMKAGiSgBWkpXDXpf6WmYiPIW89hJPkQCtZz8LRM7dqo9L6xdV2ldZcSHE0PLiWGxQAleqX6qOzX9k5NzbYTfOLUC+HYDmYvbHYeBEXBpQu5/k8WDHjzMzIy27ilARI9AulZYdJZu5VHk9FYU4EVczSWBHMx78N3DSzc1cLeh7OIZcanvb4lzIxANXqgpebLq2IjGyU4toLBLToMqw3VWBNZNG8JXvXit+Pyb3xoUjEbif5CkUqYoS35132/kiL2+KPeCNHjwD9qY1iKqAEZ4ZX4wkbB2ygF7XDxKWQSTyCXeA62ThFTT4pbEqmaSEAXafkgop9JsKrSjRV6Xov6RuStZ5FJPo7h1APIJh+HbWyAUKe4oZXt9oLySyS4qSSUIpA1yRoRz7GfSHowLR9ek6OyJGK/94mv4x2X/g9Mq7ndmUJLAf0/xc87OoThw5uXafCSAVZKoHdOIBsneE5oOWg1sgycAqR647GNjW12QDumd+et2P3gFNsJmAlp5+whpoNRrFlQyF5Q3AqhqNKwP11IxJKYTfe1X03FVtgMCBKzV330Z5jdP28CX6yCIiV4tRkr1z+JhXN2wXtOvTxM5COH65EbIfpsKFsbV/miGkJ34CYzcGX9zo3l5CyKyqpCl89FJ7RvZK1n4FkJKXYDxZUPmYWolDp0NWrTluwFMumrInOdxCxZeV0tQF73kfYaf9qmdUj1unBtTUaFKYofN0/c85gUsZEntplM1VIwehphYpa3YBjW8vmxNK0wewtQnAScopBD+fR9bsU3mcSl70M2HzAT8d3s+J5AIesjkdaw20FJrMsPxyqamZkBR2SZ+PyyqQVQjD4IrzF+2cGtRtgSkY/aspj92Lu/jFl9c6tsXyEznz03tBS0o53gyhvfi8efvbt8oSJPrNh1BMqICWUc32qjEFHJrWgoX2oSH4FahKdl4eqDcLRBJHoS8tnVh+TrFImVtV9LH2vkcHzUHrVa21gSTbRN8oYPt0GNMcZiJX1YSU/WlY1bxtLxuuehe+Nj37usBSJ2epaCUdMq6vBn5xEk4smgVFSBvtmGjIKS9KbEp1ZEZ8kmTrGIVF880VhpJchRBQ0fAwtM7P+CPsxa0H2Z+szUYEnAxAZFYbWeRVD0xiR/DW/TZJ1NZjsH7HG4jBxefPUZFTYDUU7uyuVGD1W3myf2sH2Pk6+JpAv/sE1hoHPavtgaSLiT1phP9iXxtZU3yOeJaOTmDctwVf9bWOpWIGvEYzEgedIz4MrvGInZOO0E1533FeiGjj0O2Su2+UxEIC0F019HaS+wPHjzRtcFbiQkIHv6dfTPMWCYKjwvjI4262tN520SsskUtQRXY4nC5oZ9KJqC3Q/swQHP70O6jweLmdphIcvEiqIa0HsWA5o5LTHrUmQxq7VFrct2I2ml8PyDji95Zp8IRWygIJuj9o1ouwYP3//dZ8qe2Ah/v20QfQ4w0gSfJClCishOUjvW93w8dsuj8nkyGpdER6IhLL01FunhDRRpMbBj6m+v6wHS/a4c+YgjMTDyxL7kTSe2ZMh4uy+2MfOORg68+WEr9TghEdk/N4zOohSdjbupiawZ6wGGqchobCMJfPLCerK0FkVhDzy6HzvtmYSqtd8IEtPesJBlYocislrPTpQFNmUxmxvR5DC57EDEVLUZnFpqmvDcuuUyEksXoHZicGQzVFXDpe/94SgRK3odiH0GoRTJoxr/RYwiaFLMTpIR7uQdXPumr8rnWmiI8IvKcI2zHWR9WaqnbPixJX4l065M/vLd+mqrTsa9N9/dMk9spYidji+26nRtHf6CEQg9fu++GkVn55qwkqpMwHJJ0Ip4LQUUIW1UuS1aZvLB5skL26Njr8PDKCytF8NMBRayTFNQ9RS0np2nLGZzGU12H2rXIuGtR+CNJ5+BM079sGzT6nrtZyf4xPVvwKoNTyNhpUb9zd9/K0TKA7JNupBRNDYmvTxdG0dYT3Z8ewGhBQo8NUBxgmYO7WgxWLzHzjjnmgtbJ2Jlctf0fbE7TNvWESRceHPisxeMhSKkfXMMGaGl5CuKbkYRWhGHpaABCV6+XxKwIz6MhIo9DurBIcf2Y8HSBEdhmWnBt0BM01CNFNVTgZ9dG4pZjQrT13ZRyYyECpYTWKtR8sQGCp63/2vguAI//tMXZYesXRZNrZ1tXJ7YpQv3HvU30WtD7DkEJS8bszZngWqwFUwVKSKitqrTmY6MyIoJsuUFCrqHhEcxWiU2i8HINhOBENNqZ0x2gsy2EZx42suxePed0DIR24DkrqrTp65rChAMFICN/Q2f/rjzLdkNDEuFUwhkspTjBBBuGLmlIXqlxZYCOR1HwC4G8juR7NGxYKklxSstN8M0Aj6SmKZHZnWKzGoWhF+EmCj0VMHIoM6JXlUJqxNEnliKxNLNway++fjyDz4oS1y1kmx+CFf/5LwdPLERwW4jEAkfyDfpnpq8pfSoQcgqqoJF+yyWz/Uw7UoGEyR8RWhU9kkVcGLyyjbKYhB5YucvXYhWENZfaEyFggkhp0p/Hq2A1opsBn1zDcyaZ5brvFKElmwHFAmt93ikSKw6RUsBiVfXDpAb8WQSF81/zkIT+x7Vh0OPG8DOe6VYxDINhSOyTEs8s3rvUvi5dQjcrLQbUEew8ZAlpDjRa8ftqIR1Ymn75MYkdp1yTFhnlsTshe/8eksis57noic1ID2xC+Ys3eHvQgsQ7DUExYknqlgV8liTP7aGIXMqvfXJOz7bgshsmPAVSrDqhNtLoKj5sPx4/DY0h95ZjmyS4Lmq9KcrUxCxrfTEyuYVgoR4vMeX4mrwZ+VlVYlm+LyrLgNd0A1Fek1TvYBT9GX9WRKVkZil45GEqTwuS3WQq3lY6UYq3a/XZCmg91PlAXqQeEZpOcj6MHuhiVnzt4trhokDFrJMS1A0E1rvEiC3EcIeDCOzJGirnOzyWVNWLUimuBFCBBWspyQMahCRy1cvsUViVlN1OG7jWmjWyrJVD+Fbv/p/uOx9P64qYgmxJAP02cBw8zo6USemKKFqMjzHw10/uwMveMvR0E29eWK23BghTLQZD6pg4GgCnhJAlxHHxqNpVMfUxtCWBHxPlZaDWinmi10hYuX8XE0mEQYpG1ou3mYetUDR1ERKQyIVRlddJ5CCVoparyRWS+eMsaKWSnslUirMpCIFcBSOjwQuPQd+qdNXCRKuVBps9kIDA/MMWSqM2tiyeGWaAQtZpmUoigYtvQiBZiIobB7XN1vIGrImKid6bffD0sW5kKfWsxNfpF929Ony+b7H/4G5s3ZqSmSWROxVPz4HbzzxXPSkxvcMBruMhBfQZvaTJyFb4zCrW3Txo/O/jyNee9SUhOxUxWxUS3Yin6x8H4kOJbQX6DG2ljXNAL0DjvTLUsIliduJ+O9f78KCXRbiJW94KVpB5IkVTRKxEk+DSDkI+optIWTHilorocoHQcLWd8kDG8hnGU31SZyGP2sGVYdR4BSkSaascGk6ZLMhwWomVVhJDek+DaleHcleEs0sXJnWwEKWaX0HsORcKJoFP78BwivKmrNKhWp1HRqWEl2f6BVZCUjU5wsKnDqaCW0eXIvv//6zsdsM1m5aXhax1TyxEcL0IRblgGKTy1BYJGSbO8Q51cjsZL7GsNuXkDVlkx5JtvjWK5EKvbLZERo1oXapE9sJzr76AnSNiI3qyVLCV18RWI+2RiaCWQoMSxvla82N+DKKu/vBaVgJDYIisKUbKxKwUdRV09kiwLQXLGSZtkA1e6FoCfiFTRD2METghU0U5BCvVopOdS8aRUMUwHEoElt/o4OXv+idCEQQu2eWbAQffNMVOHDPoyd8n1iQA5IeMNQ8W0E9iV5tIWZrOObVUtIXrdIkgdJpQUuS7iO/rIJiTodCflllfE/sQce0sMRW3IldbZjwNV2oKQFVEdj78F5Z0othOgmuWsC0DYpmQEsvDpsnaEYYnQ182IUwItuNkFjQyUoggFxORTY79W5d5Jl9xYvehUeX3RmLneCan5wPVVEnFbFEsCgndZosXdQsyN8p+7zW9nZVU7H/8QfI50ZAs621cH1pULdGO62AowZNORb7ZjkwLV8mf1UuH0X0Hrjl3pZ4YpXKZgetErE0V0+DTyW4Ogy7QM01gF33T7OIZToSjsgybWc1UKx+KHpKRmcDewi+m5TJTd2GilB4FV0gn6m/hE41omoGW4fWI1cYwdJF+zTUE0uduyaD4mZicQ6K29z7aEHHUI2JXoSVtnDuLxo/RE5idrKKRlFjhMkgOwFtT2pbm4ypekEl9D3sm+NgeKsF11ahmwGeuPMR7HHo3vjAl85Fs4k24/akrhaO3PgKhOFBqEFzb9CmgV0IZL3YXfZNyvquDNOJdMa3jena6KxI7oR8TpcJJlLYdQGy/5ASIBAa8kUd2aKYchR2PB5Zdju+9IMPTLvO7LLVD9XkiR0FdfFKu4DT5NMP2QrquBlwbRe/v/I38jkOMTvZjUmtdprQXhDE1rJ2LFS5oH+ODcMMcOfv78TVZ38Rq55cgWYTSvhIxLYuEluGxKsmwhbIHYBTDOC5AZbunZS1XbnCANOpsJBl2hY6sfpIYyRHlyxDtpckgUdCbyYLWPpaun4Crp+SNTzj4Lgj3yhtBtNtmtCXno23v+JjtYtYEmgDNmAGLRCy9Qk9z/bwxy/+Tj7HwcSNE8JSR7VArhufEo2aqONIzD5y27/wv5+6AR/48oXY87DpR/anaiVoZlLXRCgUkVU7Q8hSpy0qyUUCdsk+LGKZzoatBUxb4xRLNTUVHa5nQFNdaKoTCj5pd1Ta5kI2FcKlD4e86cLs+Sb8gBKg4l+nyGbw61tuwPmnXVO3nWDZmofx8qPfgfmzl9T1WRKy5OtUmz38GlkL2ohIzEpxVrlsJWsBHROT+cPprYEiZCku6vjVDOyCg59+6Zf42PfOxR6HHALXCW0GStOrErTRDqXjWQ0QWC6aXIujbk+s5wJL9k5h6b4sYpnOhyOyTFtjF8LIS1gYnhKdTDheGq6flEPvdLUPo7Sd5aFVKpab1oMisI7XAz+wmnpxJjF79lu+giDwsWnb6ro8sZaRnNI8RZ/dGvlByV5tepiMjc6Wt0+NPlnCb5KPfO2yDbCSJq79z+fwvJcdiP65NgwrgOeoDfFx1xaFbQMrwVhKrdiE2Z4RWUrIK+Z92RBh6b5JFrHMjIGFLNPWUItFEShj6lYqCAJDDr27fhq+H5ZwImHYzqJWiteyfQDwAwOOXIcUgiZFYauh6wYefuZ2fObb75zUZlCZ2FWPnWAU5I9thTuErAV1RCw1Q8OLTj9WPjeDqKrBdjFY3/FALtm4ue1Xd+P84y/DptVbYJSaRESeWTMRVjOg9rqNIopSR17YoB0FbOUNBe2/NrQWkIjNZ315bO16QFpGY9kTy8wUWMgybQ1FD4jqJ10aYtTgBYlSlDYhxeFoURv/cOdkUaRoWYhA6PBk9DUNz09CCL0tLsyH7vPimjyzW4bWTU/EEmmPwodoOnUme5lJE++4+t3yuZlI0VaK0ErbgVLjZ2IuUUci9przvouP33g25i+ZO+pvJGYH5tpIpD3Z/Yse00W6K8j/285R2LEo7SdkqZVsbtiHaWnY54he7LRHkkUsM6NgjyzT1tQ+VEmRILMU6CPx6EFVffksxaxSOb1IYjaWcIoVHchKHl4S10Ggl6wQ7XvvGHlmr//ZR/H5c34FXTNGRWI1TccLDn7FtOYhS29RI4QGCJ1651yvBnIKDn728R/jLVec1nQxGx47gBeEyVxUrmuiilz0njiFLEVgrz3/e1LEHnHiweOW5qLIrK4L5DK6TFTU9PpvJGWLXlofIeD5YVcprd62aC1DxNuZok48V6CY89E728Beh/Yg3c+XfGbm0b5XVYahy8KUhqApgmOUI5/laK1vyghu6R3lSOno6G1oTRj/EVT9bFQajKZP85GeV5/m3SMjr7Q8nfB1IzF7yXu+J0Vswc6NshOs3vD09GdA1QroQt9sITuF2fmuj9t/dJt8bhUk6DyffNTb3RjjrUpcmzSfKcgI7DfuuXJcETuqA1i/g77ZDhRV7NA4YaIPRlqV1tUvPeLy3MYHJaaKtimvRZ7YuTtZ2P8FfSximRlL+19Zma5m+hcykp+ajNaSBcEtiUspbr2kFLueb8moKQ3zh8OXEywPKKFFk++nCgP0eUo8i0QrTZ/mQ/MLRXOnRJK2M9A7D+s3P4eLr3kt/vPA78qe2Bcf8brGDO/ThV60MBunE1BG/0AiNhJ3lYJWqfRnxiCgyE5w1tGXyCoFsxcO1LzoyZSHWXOLMIwwCWy8jmZSvJZCzbRuXiRg0cG0WMhGfliKxu68Z1LaCUyLL/XMzIXHGZi2Jp6ITChupdCsOv2JZtohQmiaLJq3G172wtPx1zt/NH1PbCWtNCwTUzie1j+1Fov22an8OyV/kdWArAeV0Vrd0mFYBuycjaCig4WRMKCbOorZIkSFojNTJjRdQ2GksENHMRpOL2SKUDW/9FCR6KUbJIF8zpbvI2lCloNkXxK+58PJOcjboQ+YPp/sScB1PLjUGq6EnE6aunK5cCvq49I6USUCu2KdAhHgrz/8J86+6t3yb/VCDRMG5hWRGTRRLOjS+kC2g0qbj4w0d7pwHQN19moV1OCgkAuQSGnYZb8U5i+x2A/LzHhYyDJtTWuscd0hVifjFcecgZcd/U6oo0tGTI9WKRZR/64lYbrH8/bE197wFTgl8UhQJQNKAiP/LFkPIl75sdfg1Redim++6zo8futj5ddP/9oZOOYdL8aVJ38W659aV379nJ9/BAe89EBcfNAFUuRGXHb7ZzBr8Wx8ZPezRi3PdeuvwbY123DZUZ8qv0bi9oYN1+KxW57E1157dfn1pfsuxg13fgG3/PQ/uO78G8uvH3b8gfj0Lz+Kn1/1B/zfF39Tfv2k01+Mc695D7550Q9x84/+XX79rR977aR2gomgjnyUBEY+zeywIevNquSbVUvJbJh5KE2q5btDaa1cIG905u1kYdf900j2tHM1W4ZpHIqgb0AbMzIygv7+fmzZsgVz5sxBt+G6Lv70pz/h5OOPhKF3333H8sdt/PO3w5gzJ92VkYVCIQ9HFCBsir4198Lkeg7+eNv38Mpjz4ShNybZSSQ8eK9fBlAST3Hi41k2ICXBoyrT3/eKgNhpJPy5jooJdHp84p+PYbcj9pA/03I0JSKrUETWhqp5ULWgHJGlz9P0K6GIrOt78LIueooWNAUwVKCnLwGvIiJLc6bpWKnaIrJy2UvrVCu0m5Qq9XHJMuC4KvJDJpyiJt9HgnaybR/44TJrameIMn8gD3PZPCQfWjrtadH6Z7M59PcsKNcKniwKSw0OFlAUlkL1HY4feHjsuX/igN2Og6Z237UvWv9TTjkFhlH7d3CmEGm/4eFh9PX1Tfje7js6mI5iBpyPOxbPc/C7f31LWgwaJWRlW1rZhbcV988T5f2P8wlFwf7HH4js1gyuf9vVOOOG/8GCPRfKv8lKBlV6QpAQrUaiJ1H1dRKiOyCAZG8Sqr5dyMrl0ZSq79cMFVZvCsmK/ZTzqJKAAS1tQKWuX6WEKtoKVsKQj4rZhcuYMsOfx26m0vdwbPOxMYss8cqWAUq6DH+WqALJ2Tb0vI5ixoDvqlA1IZPCZgzS/92ckxbd1BTygXzmKCzTzbADnGlrwo5ezExBoTaettb8EkUkLqYxy/TsHuz9on3w1ddciY3LNqDdkGlsYwOcJRHpBoDtK8h72x9FX4HtA04QlviqbMQQtcsd9ai0GZcSsmi69HmaDk2v4CnIuQqKngLHV8rTrYSmYaY8pCkRLOHLZickaKlj14yA/Mkxt16OOnTlRqg2rIo9D+3Fvkf2spWA6Vo4Isu0NZE9MxrWZTofJW9A9DnNnzF1nNKnpmbp2Dv1sjDhjcTshX+6GPN2nY92Qq1BDEa+1FBgVn//RF+zRhnRyDubmm3DszXYWR2+rcnidopMBuvkCK0CxYnHBkHnQOp06NgBrIQq28wu2i0JM8F3+0x3w0KWaWuMRFhWSNaT5fN1U6EGCMcedqp8bihZozVF48kba0y9JmwkZufuOg+9cyf2bDWKWu/dZMS0QVHNZmVN0NIalg/d8uEWNdgluwFtZ7JTdCqKrTdcwHqOQLEQwDBVWVJr8e5JJNKd4RtmmLhhacC0NVaC7AUCQede1zoW00jg3a+9TD43EiVb8mE2G0+ddkEKElnHvus46Y/9yYU/jNVmUGvvOZkU16Js+UYg7QYJHz1zi0gNOFLE+p6KgPZXB61TeT80SMhKC4ejyfayniewcGkCBx/bj90P6mERyzAVsJBl2hozQRnrpYgs01Qct4gbf/tp+dxIlEErTLtq9hAytb5q0CxlndbeRIye2Si7qsYFFgq0prf9bXz0mfyzPfOKSPY7sqoBeWgDV5XVC9recFDqWKc40xOytK7FjIL8MF2eFSzYxcKBR/djr8N7kO7jQVSGGQsLWaatMS2OyLYK3/dw2wO/kc+NRBmyAJeG+YPme2Qrl6P8UKDW+dAUFa+77E14wVuOjkfM1tGEjIKWlPjfqRHZaoLWSntIzyrA6C1Cp6QwOh4dgA7Fti0YqQYy0Uu16y+VROvk2kB+WEExq8BMC+x8gIM5+63Fnoem0Tfb4BwBhhkHvr1j2hqKfKk6ZTe3ekmYhpExAaoha3pATIkxVe/YfVVq2VDuVa/MWYtGij6nKQrecNmbkEgn4GRtKXDDaYRTmX535RqtBYqA6mkT1hrtSBQqK+bD7HGhqwqcvAo7r0gxK6sDaGEyaLvkgAq6mwiUmq0Fsr6uA3i2Iq1TmgEMLPQxdxcf/QsCCATIP9vYm0iGmYmwkGXaHtX0EORbvRRMo6DIobIxBbHnEJCNZ7sqUQtXGUENfw8CFU6FNpyq0Bz1OUXBKy98jfzxnl/djV0O3gULS3VmZTOA0k9TmlcNAi2ashZzyadWQ/mGyb4AiV7ALShwbAVusULUqhWPli1kAMXVQi/2OJBg9ez/396dgElWlnfD/5+19up19ukZtkEQERU0SBBXEoJL4g5EE1SiRiERNTH6oTKvaFCjfon4mVcxGiN8qHxBTILmhWhIICouiYKCsg0gzD7TW61neb7rfk5VT3dPL9VLVdep8/9dV1FMT0/VqaWr/3XX/dyPBFhDB1nbAXIDIfo3hejbECBTPLKFb7D8dYlEicIgS13PkiBbWuujSB7bdvGy575Zn68284kcghNGdTVxNT8SlwhhTQuv0wOl8qVH1tCVM2NWm8Fq+PU9j+LrV9yAd3/zPVObJjS2MZgKsqrFULuEzgJ9/9kLhKdeEvXRKn3SFU0JtDUD9Yqhe0tDCbZSdW9UajsZbJUdwjqUm6qM61FnIRB40hLRCN2y65qr0L8pqroWhkOk80fCKxEtHYMsdT1pLZAP2qizZDev33v+W9ty2caeXLQxQkpWttirUn21p324rmelzv4++fg9NNq2yOzl73+VvuK/kjmzjTA7/Zqa/biqhUDb3JSgtf5YA2Ybgnm3k/vHSSt9yhZlq1bpMzV0tVNXPGVDhuYn89PDbaM5ejXvMR1a5Xm1P6d7XJtt5dL6YDkK6UKI/GAUXAtDIVZ5EAhRojHIUtczVzD7k5avVq/gmq++C5e+9hNIuXNso7rCEVzGwTTUxtKyg6zRCK/mVECMwuGCAbFmQ2Vl1dCyD33+yzYMvFw2TTCAX/z7z6eqsk1qzkA7R+PB1DcuHrglPNleD/bHLpXsdeHKqXGPSruq36iEeoYOuc1wq0Pn9AejUf5eLOBObd3b2FRi+sOmQ2woCxnTUWgdUMj0hcgUohDrzL1rMRGtAgZZ6npWytchIQwVTDPhv7A7KAwD/PzB7+vzdjAeKSLcXNKBbSntBfKdEt2saTmk1Yq93nUpt4KDXuzyDQOv+MCr9f/vfXCPPrj5Aq2QEC47Ws1ZoV3kLpF/IQE2KW0FSyGhVBZPSTUUmUansmRNaT8IGtVaCbVBtNCq2ZYQTq9sTw+7zTcgJmBaCqYsNLNkwZm8Jslv0gBBCjjhFBNFYw12rSNKMAZZ6np2uq4Xe0ivmcnKRs8wJcg+bT+Q9YGS02KAjUJsswK71GEWejHOkrtQl+cn//RjfOdzt83omZ2tmZXMGS0HjbKgVPkWyOfS62sFpj5Ri+HWltP0O3X+O7hZtdXPkkWeKnUrgAsLeXmBYhcUUUfxFZBi0VrgpqMZktQ7jLIDY1cRKuMvugRKXqhcqT42EoVUYJeTFwyZXyt9sjIqqc3O+9Pz8ezXnqV7ZhebM9uM1RJoJb/KJiALf3/09w7bCtpmej/tYgIzQLqehqn4K5Wo0/hTR11Pfpnk+wy9TSN1jmOncPFL36/P28V8qC+qkrpz11al8urA0CG22UawkmdBc8FXyztmrULPrITZf/rYNxb9/um3zTZnLl476nvlJoTSVtCZOby0MOmRzdZXt4+ciFrD1gKKhUyhGWOoU2zbwTmnv7yt1yHzZI3dWaiRSaiDuh459XemYcAxm20Eq/fot3PB13xh1q/7+jS2dxRDI8Pz/4Pmx9myeEn6wmFCZnbM/Balg5Nbt/XEAlpbzep4xuMoAqK1wIosxUI2b+hf7rLgizqjWivjis+8Sp+3i94e9u510RB5GcXVYJkSYqNgKzFuNR91s+I0Pi7uzHNJwqyTcvDDm+7C1b/94Za2s5XWAjk6uQccw5wRWPXIrdCA67EO0Q1804cd2MjV2riKkIjmxSBLsZDJG7AdGaWz1keSHEqFeGL/Q/q8nYw9WRiPFKAKUa+sbEcqJ30MbQibegvRDvXJTnfma569aM+sPqJGb+b00VyWMvRJ/1mqsR6rsd3CtwJk61m4weILFolo9fEtPcWCzGGUBV9SHORMxt4iVVfrnmFgZBJWPoRVtRqzPtuz45Hhm3oMl5IKcHsz+txzZgF89uJr8IH/+F8w9eymaRrbrU5vjp1aCCZb+8pQ/9CEK2PEVoMC3MBCxrfhBCbc0NJ/tkMzWnTWnA5hKHhWiLoZ6POaFaDieAg6/Gag2zTbPPoq+bU+FKLEYpClWJAQkO8zUZ6Qj5/ZF9hrnENZpO8fRvWpexHWLBhtXv0t7QVB2u/IGK65wuzz/+hFOsRWJ6tI52f2VprzhEMJkxIsi5XUststJJwWain01Vzk6i76aimkfftIxbe5OcD08nDjmpr3lLQ2yLH4ZohJt47xVB2TTh1j6Roqtp+oH8/ADGGFFvI1BlmitcIgS7FR6Dex97GgUalL0G/LNeI6abzzddfo83aSBV0F14Zx7wZ4mybhD5VhHW7vdZpVG0FzekGHF0zJc3dg8wB2/eQh/D9/cA3e9Y0/j+bMNpKpOcdEBan8BVaITM1BznP0OrWytfjYMn15oYGBahoDlTTWlbO6+mrprXqBwAh1IK2bUdV10RCqjlSHpWrbX0ljqJzVVUm5nHG3jgO5Mg6nq5hw6z0faqU/Nu2nkPY44JporTDIUmwUh6I+Wdlu0nHX+mh6n2XZeMoJZ3UkxFoG4NdMZH+8CRPP36Vnyxrl9r08GXUbRmBCWSGMNZpPvP3px+LMVz9b98zKpgnrj9+og9/sbgMRWNHmB/mqq6uxMporG9gLhtmMZ2N9KYfNE3lkPQdS6PXMEFXLRyCbAiwnZDZ6dwND6dmpteboBwU4oYmBagpDlTR8U2E0XcUThUkcyJZ7tgUhNEP0VYrcIphoDTHIUmykswayBQOToyEct8dLPR1kqBC2CmCFASwVwJCKNxQq1Um85ZpX439f+nVk0vloK1XDgG/YCEwLgWHpPy+XjJeaCrGNaRTOvjzS9w2hcuo+mJ4BI2jfnFSz7CDoq3a8vWCuntlP/O5H8cE7Poz8YOaoQ5HQKApVF1aj5SKQRXE6zFqNMHtEoeZiZKyoq69uYOpQWbY9hO0MkwZ076ycmqF2sJLBUCWDsu3jieIEHi9MRH/fIwIj0Bsg5KucVkDU80G2VCohDEMUCoVOXB31KPnFP7jexPihkO0Fy6UAW/mwQ18HV1t5sFQ4FV6PRB0DfuihUi/DCT24gewfrzdPbVxMFGolzPqmPRVu5byVTGgZBoopa0aIbcrcvQFBXw21Y0ZhjKaA0GpfkC3U9PQCvUnCGmiG2VN/6zRk+7IwjJlBTz7ul+PL1VykZo3bisKsqSuzJctH1rN1gN00mdcLt2RBlnzU3/GMPhVq67oKLD24Ow4OYvN4Ho/2jWN3YbInKrSe7SFTzyDPsVtEa6qtKypuvvlm/MZv/AY2bdqELVu24KSTTsK3vvWtdl4l9bjikAnLAoIODLPvtapr2q+irz6KvtoY8t4k0kEFdhjdkaFhNsJo82Tpk2j+v3y9GVrl+4UE4oxfQcGb0JfbVx9Dyq/p65v3WCCVWAmxEpaPDjSyKj931xbY+7Lw+2ttGcE11V4go7istQ1VEmZPOPNEjO8fx1Uv+BD23L9nWl+sQqpu6ZaCufb5kjArkwaedHAQZzy+GdvG+vTXJMDW7GDNe1Tl/UHZ8THh1pAKbJx0cAjPfGIT1pWysd7fpDnJYbA0wLYCol4Ost/+9rdxzTXXYHR0VJ8uvPBCvOIVr8D999/fzqulHpYrGkhlDPi1tT6SmFRfQx85r4SB2qgOrxJcZ4ZWS/9ZtwgYS+iTlF2nDDOqwk4Lt3J9Emr7a6PIeiX959mBJe+aujd2rhDbZFYd5L63FdZECmF/tW1h1ipJs3U0m3VNKaC4voAnP+9kXH3+x7D7/t16cZfjWyiUU/OGJWk3OHnvMI4d7Ye87Rh3a6h3QYCdTRaWlR0PJaeupyWcum8dTjowpFsf4sizPDiBg/5y31ofClHitfVV5LOf/Sye+cxn6jEzcrriiisQBAFuv/32xN/xtDymaWBgvQnfj3E5p90UdFW0WB/XFVKpmIrmx/+thtaUk8b/fckX9Hmr4TZqL7D0xWf9ir5+OQ7dmqCArG0hbZkI9JynhdmjGeT/ayQaldXXnjAr7QUyV1YWfa2Zxs2yLeDVO1+Jsy58Nq4+/+M4/MAhFCdTc25Da4bAyOEinrxvWPfESkCUj/NlW99uJhVaGdkl82hHxos4/YlNGC5lECfyPJRNEAZKfXBCLjMhWmsd/Sl8+OGH4XkeNm/e3MmrpR7TN2TiiYcDhIGCKU2WNEX6XrN+udHTiqVXW6cxDRPDxfX6fEmkWqu3lpUNVkM4YR1O3UNgp2Cni5BCbAs5VnP255D7/ghKz34MQX8V1mh6dT/KVQbMkqsve60Wfc2YH2sAL7/q5dhw/EZsza2HFcoOCDPvLMc3cfyhAQyVM3oKQcnxpg5bz4JtbG/bzSR0+2YNOc/FU/avw8PeqO6f7fAktBXMjjUxUO5f60MhoqUG2WaLwEI2btyIdProCo7v+3jzm9+MU089Feeee+68/75Wq+lT0/j4uD6XACynpGneZt9PZlNo83YHjV5Oke1Tenevek0hFa9izpI1I4lsE7tYzTAd1JDzqzBViEAC7LSlWctJNuVaCa//1MvwD5d/E9nU8lZmyzGEhgU5mlRQAyYPwUvloFqaTRsFS3t3Hrk7t6F05mMIBipRmJWAt0p0kG0u+go6n6Qk1OtCqhnNinV9Gy995blQtRDX/Nnn8fK3no+tJ0Rv/nN1BzsODqFQd1Gxp+2s1TiTkC8VXF93yna/cauKTGDjhIMDSFUs3Nu/f+o2qUDO7ejp2+o7nw6QRWy5Sh6pSgpBG7eGkwXSIpD2nARq3u6k3/4k5p6l3u4lBdkvfOEL+PSnP73g99xwww0488wzj/qBfOMb34h7770X//mf/wnHmX9P6r/8y7/Ezp07j/r6d7/7XWSzWSTVd/7zv5Fk9+/52Yw/+5khlPf2oapqbdnGtNuY6fmDmxmGyFXLSMmAXRlyL4u0lleEncGwjpw3/3+5rOYFBD6cyjiCoI4gnZ97aOqRI9D/le9w9+Zg3r4dpTN/DX+4DHPChemtzjQDwzN1+0KYq8tMpVW5zCUeAQzbbyzsslEouTqMKstEYSCP/+s1H8aHv/5+nLL5GJx4aAgp38Kk4zWql3O0HcjlKRNhLKKsbOwQwAkVRib7kPZc/M/APj1xoale9/SpG8ibnUAFcB7w8PD+xzpynfc9cgeSLOm3/9Zbb0USlcvllr/XUG1+qysX/6Y3vUlPK5AwKpMLFjJXRXZkZAS7d+/G0NAQkviuRJ7IUsVe6A1Ar9/+J7nHwpr2EffEhIFf/MKGbUOfelW1WkHd9YH9k7CP+ohfwTUCZAxPVzzl4/wjVdiVk4rs73/6Fbjusn9cdkVWSH+87UhlLaov64kGUm2SSQhuFqElC66OPm793SkbYXCk9zNM+yg963HUt4/BrNgw5LQKtzlM+fA2lGBE212t+PJavl65Q5wQth0iW7WRKzlTtyeUkWiWiS//5f+L22+8E//0mb/BSVu367msrdzkICZV2ek7kOV9FwdTFdwztBcTYRWB6SKVt5DOdMdHL2WzgnyQxcmV42G2d4kJAj/EvT/YlfjX/qT/7kvq7R8fH8fw8DDGxsZQLBYX/F673SH2kksuwS233NJSiBWpVEqfZpMHMokPZlPSb7+E2KmqnvTJFgAZSzw+ZqC375YosaQtC67MHWuSofOqBhvy8ZN0osrH96ur+cYhuu+Xd+mSPy1ZxdT8s/6PCVgmIHNsa5NQbhbKyR69EYCC/uBWQmyzT9esuSjeuR3l8b2oPOkgwoEarLHUilsNzJoNS8+VrTc2YWh/mJVeVrgBzNBEXymNjIwDM6ddr7TTGAYuv/wN2BFsgF/1UXH8lj+BsCETKWIUZU2gYvoYrmfx9NFN+En/bozJM8AwumJLamnXkOPYEm6A08F3z0l/7eftT+bj7yzhNrf1LeUf//Ef42tf+xq++MUvIpPJYNeuXfq0WJ8t0WLk99qGdVGFr9FKlhxTIbYe9aC26cc442Zxw598Q58vlymBVR6suT740S0QBox6GUa91HIfr54z+9NNKN5+DJyDGQT9NYQZb4VTDQxY4+moR7bNc2XlOJWMyHICGCUH/aMZZOtHKrHTyUSCHYeH8J6LL8bxJ2/H1//l/+DBR1r/SHtVF8Z1gOw+JmO6+ippnHJIFrt1z/HXzBr6gjwGA47cIuombQ2yUoWVdgAJtM973vOmTl/60pfaebWUEIODCmlZ9BUt0E8GpeCqaiPETl/QtfpCFeLAxH59vhxSXTQW7IFtVGclzHoVGPXJJS1Kc/fmUfy345G5Zz2UpRAMVhBKG8YyGXUL5qQLZcvtVe0JsDLmKyXbABsw9ufg7s8hO70KO430wu44PKjPK43JBPc//Ahe9eZ3thxm41SQnR1mh6tZnDw+2BUbJ/jwdSvBZn9D7N4cEPW6tn4+8stf/rKdF08JJ5/urVun8OhjxpGV3z1fia3Dgtf2ECtqXhWXfvGPdFV2OT2y0hurLdaGr9sGFAxPRmAZUG6u5U/2ZcFX/n82IfVoHypP3of61nH4OU8H0uUsBrPGUwhzng6zhr86i8l0pVhW4juh7r81x1Kwx1Pwqzay+blbBaRf9ITRQeTrjg51zfvjvZdeolu2JMze+LlP4vjtI4tevwSv7h/IdXSYrVoetpeLqI+F2Jddu5Xrct/VzDqGgn70hfk1Ow4imlsPL5OhJBgeDvHEbgu+Lz016GlH2gnaH2JXKqrGLuEYdZqLKrPy/7pndgmcQ1nYd2yHt2ESlVP263PfqulpBEtZECbhVSYirMZcWR0epborrQoSYCdcWGNpHbCD0NADG9LuHNVuBWwf68NQLYNSY2FX8yikR/N9l/2RPv/+j3/aWpCV8VXd/XSZd9as9A0df7AfXmEch/Nr89GLZ/iwlYUt/npWY4m6EIMsxVouC/T3KRw82NuLvlz4sDtUiV0NLVdjj6rMhrpnVs/7smWawRL+OQy4ewtw9ubhry+heuxh1EfGEQxWoyBZtWHUZNexhe8/e6JRlXXlWKylh1cJrrrPVuldw8zxlJ5VK2O+piYShEA2HejdvGYbqKaxoZRD1fR1ZXJ2c0YzzAppNZD7erFAG8eqrKhYPvpUCsftKeCnxxyCb3f2NsgIs7pRxyZ/HfLh8id3EFH7xHOja6JpNqxXOgNJVbYXWUEIV4fYaIOBTlrOQq+pauxyMkdjOoEs/tJjupZzETDg7Muj8IMR9N+yA/kfbYZzUCYjhDrU+gMVBNk6QkfGU81xkIEJ+3AmOv5Ftq7Vfa9mqBdvKenPdWXSgIJZtmEfyMJ5oqC32pUqbDPENttgMqmjL9sOTBwz1qdnweqK5CK+9d07WuqZjWOvrGZA71xWqDrYtj/f8X7ZqllFLsxgq7exs1dMRC1jRZZib2BAoa+oMDpqwO65FjaFXK0MU287ujo9m62Svtiv/unNS/535lRLwTJTh0wzCH2YtRKC1Mq2AbXKLjL3rUP6vmGEhbpuOahvmtCbKshM2rDQWNgli69kYkFgRuc+9JzaIF8/EgJ190O0jeyMZBgaMOQkGxlU7ajyG8xfI/ADA+lUCNeZdf8oYNt4EVmv0RfbgsvecBEmJkst9czGtSorbRFVO8DmwxkcztdwuNCZFgMPnr7Ptnmb4aKHP+4hijkGWYo9qW5t3aIwPmFAdrXrpRYD16/C9T2EErQ63FEg2wL/9JGf4LTtz4Al4bIFcoyLTipo6YIsGEEdZr2K0E6t/OJkvNZESp/SDwzpKmpQrCHoqyEoVvUWtUHOQ5j1dOVWpiBIIA0zPpQb6BaBaLMEU/+/tCoYngWzbumWAcjft1At1/tAmEA+IzNJZ/6dbDu7vpxD3QqintYWMuf0NoO/+cJ1+Ov/9Rc91ysrPDuEW7VxzP48xnKHsIo7FM/fUmB62OgPYyBceBg7Ea0tBlnqCX19CgP9CgcOmrBt1RMTDMzAg+OXoRo3ptM3SaYWXHnj+5Y0tWAqxK50w0A9exawqiWEWTvaQGEVySxa+chfTnMt0Ap1iwBQ3zKOyWf+GoautDpRmF0BWeQlvbFzVWO3TBT03NSqTDdYym1phFnfD1Ct1bH/4CGMbN7YU1VZUXF9FMoO1o1lsHeg0vaWgmyY1i0FHLdF1N3YI0s9VJWVrT6VrsrGnlKwq2MwVYBwmbtqrYUlTSpYjGnBCAMdZjtFQotMLpCWBKvkIv2roaiCuwrjn5qTCnKZcM4FXnKqtrj97FHHbchiRxs3/+t38LI3XLakTRPiQqqw8j5i5EAWVhu3EWZLAVG8xOc3JNEiZMva4SGlN0hYaUFwrVn1MkyvikBW78ekuqwXeTUqqatFmSYsv9aYMdt5EmxzP9uoF3/JSK6VVDP1pIJUAGf2ynsFbJ4s6GpvIPNmV+A1L/1tvOrF5867ACy2i74aZIveXM3GhlmV9NUSINQtBev9QbYUEMUEgyz1lC2bQ90jG+vdvqQaK1u2ijXskTAMEyND2/V5a9+/wkVec1By3bKfQL29HyUvRGbR5n+wNeqZXeZCI6nGSndENhMc9XfFuotizUVNqrEr1GwzkDD71vd+SG+ecNT3xOWd0RyUqdfWYdPhDIxV3ppa3qRIS0FfUNALvOJ8PxElCYMs9ZRsNhrHJe0Fca3KWkFNL3RSLS6wapeMm8Fn3nitPu94W8GsqqzhezCCtesZcfbnkPvvTXqHrjCz9ONYaG7scDkLcxWqsbPD7HV/c7X+/8lSGb2k6gS6KjtQWvkiwOkhtmJWkAnTON4bgd3hCSFEtHwMstRzNm8KkU4B1bX5NHrFzHpZL8lpzlRdK17g4f/87BZ93tK0gjZVj6PFbs0tbNdO6qEBZH6xTk8ykBm0rZJxW7alkJujGuv6FoYq2ZZmxi6FPBbrhwfxo5/9HOe88g9ntBnEvb1ANomQ27BhNL1ql1kz6rCVrUNsWq1eQCai9mOQpZ6TSkULv6QKFrSeN7qCGfqwvApC6Y1dY55fxzX/+il93nI1ti1lcD3TS4/iWu4mCatzFAayd29AalcfwmJNj/BaTPPuyGcDvdBrtsFqBm5gom6254l6+qlPxivPP7pnNu4fm9fsEAOTKaSXuPPaXOqGB2WE2O5tRjHsuUHURD2PQZZ60oYNCoMDCpWKEasWA+kF1WGtC4LsUrSrGnvkCqRXNoTh1dp7PYsdRmgi/8OtutWglcVfevMDN5xzFy8xUEnrns925crpPbOvfss79eYJvUAq2E5goL+0tG2MZ/MRwDM8bPLXY10wuGrHR0SdwyBLPUmqX8dsD5FyFWprm31apxSseglKfixjVjBrf5CN/rOWi76azJqN/PdGYI2n9Za384XZZktBIefPuWZP2gpkEwS/TdXY2WH2cx+7EoV8bs4FYLHTGI4xMLn8IBsgQM2sYTjoxwjnxRLFFoMs9fTCr5GtoW4viEOLgeVXYYT+mi/yajINE08/5nR9vhAd0joxXUHenQS+Xgi31uyxNAp3jsCadBEMHB1mp7cUzLXASxTrKTihCa+FFoXVCLNnPPUU7DtwCL/1+2/BQ7se64mqrFRkbd9YVoitmFUMBn04rr4NJn8VEsUWgyz1tDi1GMiq/GiRV3eUY9NuBjtffbU+X1DzeNt+BzcWfXXJuxLnYA6FO7fBKjsIZ4XZxVoKhIzc0jr4cK8bGsDzzjxD98w+FPNNE2TbWsc3Uay4yw6xJ9S3cUIBUcwxyFJPi1OLgWxJq7qop0AWeV1/55cXXezV9raCqStqnAcrn7e6WqRXVsKsUXamKrOLtRRoCijUU6s2cmtZPbN/9C489sQexJXs8iV3r4ziWkqIrZo1DOgQux02d2knij0GWUpUi4HfPRloJiWVRq9rqrFCxm7d8F//sOj4rc4esgGzC1oLpnP25VG8Y7ve0taXBWCGWrClQP+b0ETKtxCs9lT/JYTZ91z6JqwbivcCJ3kbkKvaSwqxUok9sb4dDkMsUU9gkKVEtRjIbFkZy9VtzDCAoYJooVfsdDDJSmoOwzUdwzVfZTb/H9uAsTSs9WWk0gu3P2Q9B7Yy4XegP3a+MPval50HyzRx+Qc/Gts2A6loFyvOopvJefB1iB0K+lmJJeoxcfytSbSsFoPjjwuRzwGVcvft+mWEXmPsVvdUZFvWyUOW+0cevLC7SutySNXH+jD8X9uwvuailK3BX2CTg7Rv66H+a/00tG1LV2WlzSCOYVbeCEifbMo359921qjBMz1s9IfZE0vUgxhkKVEbJZxwfADX7b5dv6a2X+2iIGuZNs49Vap2dnf0yHbhgq9miC1VLL0F7WlDAZ7+wEZsOJhHJeWh5swduJ2w0Xewhg+3BGl57N572SV45UvOjWWYDWVSXQgdZufedraq7+JjvC041tsKi1vPEvWc1rvkiXpAoQAce0yI+x809eIvCbfdoNsWeomUk8Jl571rwe/peO7uwgVf1boJywJOOraEQk4asS2c8tAGZKuH8OimMQRWHZmqM2M3LSfonhpCM8wWcln4XfQGoRXSj2wq46ggGyLUIVa2mz22vhUDYXHNjpGI2qt7Xk2JOmR4WGHL5lAv/OqaxV9d1vMpal4Nn/72J/T5/Jqjtzp1VI1r7ZL7q+4ZCAITx28tY/3AkUVxEq6Oe3wQJz28DmZgYjJTRzjtTkoFll51301h9rI3/T5OPO4YXH/Tv8SmMiv3qDHrjYHs1iUhthDmcFLtOIZYoh7HIEuJI1XEka0K64a7aPGXbtrtomQjRc/Qx613f1ufz2vqkDuZZBt9smtMipfVuoUt66vYvunoXhWpwG46WMBT79+IXNXFZLaGuh1VPJ3AQiif7XehXY89EZ82g8YOXxJkdSuBUUXdrGM4GNAhNqvSa32ERNRmDLKU2MVfxx0boq+oUC6v/WYJ0UYIa3sMsbLGD1gofbFVG8P9dTzpmNKCLRb9k2k8/b5N2LKvCM8OMJmpdfVDHbeeWbkvQxWibJb1SK3j6iPYwfFaRInBHllKLMeRxV8hfnGfgXI5mje7Zmut1jpJL8DzPZRRmvqzLP6S/llpOQgRwAosffyO5cCxXVTrFR0smuRr8neVegVq2tdTThqWaaFcO3LZza/LtrjlehmhGZXc5CzjZvXl1molKMtGYDv6+7PpPILAR807UhU1DBOZVFYfu+cfaY0wTRNpV75en7HRg2XJbUrry5DLmnHstnv04q6yhULWxynHlxacFzt1mzwbJ+1ah+GxHB7cehCBJXONu/Mxb/bMmoaBH//sFzhu+wi6ldyDMrQuNAIMBQPY5m1GRnVJ4zsRdQSDLCWahNcdJwT41a+stQ2zeqwUuoqEzwvOej2++ZOb8PXvXz/1dZlkIIvAPvdv1+jWg6aLzv4DvO6ci3HV//dB/OThH019/U/OfxfOe9qLcfmX3oZHDzwy9fUPXfBRnH7cM/H6T78WlXp56uuf/aMvYF1xPV79iZfOOJ6v/9k/Y//4Prztf79x6muZVA437PwefvrgD7Dz7/546usj64/HNe+8Cd/9yTfxmX/cOfX1p+04Czvf9Le48bvX4oZ/+9upr7/ojJfjslftxOdu/kvc9qObpr5+wQvfigvPfduMEDtZtpFJBzj1hMkFt6Cdq9Vg3eEcipMpODkDsBQ8K4AdWF1XoZUw+xeXXYJuFjYCrAkb6/xBbKj3zVhQR0TJYCjVxaUgAOPj4+jr68OBAwcwNDSEpPE8D7fccgvOP/98OFJCTOjtf3LqeFhGC6WvZRodM/CrX5nwPCCzBmHWndgLM/B1pXE6qQ6GpoJZC3SFbC3o6uW03b2OqsjaHa7IVktQtosg29fRiqwOsRUbmVSAp+6YRH9h+SsF846NwFEYzVX0vFlDGbBCc0YMC+UKDUP3f7Xroe/WPl0RhCF8BVi2TIU48rMvvbDNHdFSykF/JYPJdQ5KA71Vlwn8EPfc+VDiX/uT/rsvqbd/vJH9xsbGUCwuPHWkt37yiZapv09hxwkhfvWAiUoFyGQ6HGYNiSvdGSrm+nhdSJg1TGMqyDal3cycl5OZ5+vZVG7er08PskLezGRTWSgnjSCdnxFEs1Z+jmOXcO0s4TalgTl+Z0Qh1kLajSqxKwmxzctzfQsD41lUUnWUMx58O4AZGjBnBVpq3Ge6Ait1WAVb2Xohl6scmEohtHiPESUVF3sRNQwMKOw4PoRtr8HuX120EcKSNO+kTh9/B68vaieQEBviKSdMYqC48pltzVFcElxzlRQGR7PIVaJg7VtBo+LYnW9sOq1ZgQ2kjUCZeqxWf1iAq9yp+VvN/SWIKHkYZImmGRxUOPGEUC8EK3cwzIaWG8vgshaNSXpvr0V2G1stR9oJQt1OMNTnr9rUg+llV2kryJdSukKbqTl6xqxvK4RmVIFMIrnVypL7QEFq1Lkwi/6wiJRezBXdeXqbXx1kY/pGkIhWjEGWaI7K7Iknhki5navMKlN2fopllu1smlXNgGN35GZNlBs9sSeuTiX2yGU3bsgstm+iOJnGwHgG6Vp0G307hC+BdpWzWjdtyDC7b1dub2gpGKEB13PQFxSQUemjF3PpIGswyBIlGIMs0Tw9syeeGOgtbEslo+2bJkgwU7pPtht2Z1iajmZvWSwm91Obg2wQRiE2n/Fx2okTK+6JnW36Ll9zcXwLxYkU+sczKFRcWMrU4c6TsV1Gb9ZoA1PphW+y7awbWMhO2nAOO0h5Dsx5flWZofzsAAFXexAlFn/8iebRVwROPjnAAw+YGJ8wkE5D98+2Q2hGQVa2Xo0CbYw0Vtd37LpMq633kecbqNQsDPXV9ZzYbHr131wEoYRRFbVJLPB9dmAiVXFRqBuoOj7Kjqd3B/PMUG+DKyeZeNClxdWWqq96coIR9QunPQdpz9I7n1XqdXiLlI2NUKGWNePbY05EK8YgS7SAXBZ48skhHnzIxIGDUWXWPXqx+8oZBpTlwJg2QipWObZz1wY1xxSC1VKtmfACA1vXV/GkY8pw7PbUPlWjT1YPZWjhKiSqZjwHGc/WVdmK46Nm+9FH8Gao/15PPOjyUKv7WaWi3Bj7JSFcqq8p30bKs/TxL4V8t5+K2Rs/IlpVDLJEi5CFXyfuCJFOm3hit6HHc0l1drWLQLLgy/QqsXs8OjmKWm9H2oaFXnITylVLP6Y7Rso4Zkt1auRXuwQqbMxGXsr9Z+hqpZwAV1dma3aAmuOjbgU62OrvalRqFwq2nXjUJLDqnmYdXqMrlWOSnc4kwMpJAvjyLjy6BX6qm6M7EbUbgyxRC0wT2L4tRDZr4OFdZlt2AZMFXzqqdfKj+tUwFWQX+6B85dcjH8av9kIvqbLLjFiZTHDSMWVsGDqyUUI7SXsBlppjZzDghBacuoV83dUjqqTtQCq1dVt6aWWRmI6RUU1XnlYSbhv/v9qP1VGhtXEVcn22HKdv6uAqIXw19mEwA3lTY6CeZkWWKMkYZIlaJNly/TqFdCrAAw9aKJWijROmbTq0IqGTgjLll3wAZdjxyrHN8N3OMp8kTumPXcXWAt83dCW2r+DjlOMmUcwHq3bZi1639EOrxftkWyULwjKenOT+kdmr0eIpaUXwzeBIuNWBc1Y/yNQfp/13+vuTGcc4LahONy20Sm+vHcrJ0P8vAXq1mYFCLWcitGP0po+IVl18flsSdQnZLe/JJwe6b/bwqKGDrEw3WGkRVRYwBU4WTm08Cocx+v3c9iJyo4IY6t3BjFU53krNRBAaugL75OMmkXI7OwtACrKBUrDlcV/19gwDlmx765tITQ1ciMKtzGWNwm00IUB/rfH15tavMzT3vGj06srOY3LZzZ7c6ScrjFoa2k7eACigluNOCERJxyBLtAzSI3vSSSH27DHw68dNPaIrk1Errs6GbhaqPgmoAND9k/EQVRaNto/dUo4Mw18ZP4iqsLJT14nbS9i6vqZbR9aCH4awrU5deRQ0bSlshwacxSY/NArsq9EGsNqkFVg2QahnYvRuj4jagkGWaJkkf2zZrNDXF2DXLhOjYyuvzspCptBOwfIqCHUDZdwWfLWnT1bGkkk1VlovVqMKu36wjhO3lZHPdq6VYC5eGCIlg/87tPhKPy0bkwMW1dFpFEtj+VFbge926xESUaewS55ohfI5mTcb4thjQl3Zk+pssNx8ZBgInFw0YXQt9n9dJiVzUds0h0vP1oWB0EmvqAorGxzYFnDysSU87cSJNQ+xzfYCP1Rtn5AwY+pD3LdT0Pv7AuWiFa9FkUTUFqzIEq1mdbYYYNcjK6vOBk4atmnDCKW50YhVmDXasOe9BFlYDpTt9kQVdrZ6GMDREyvaHDAbF99SNbaLWT7gO4auyBIR8ZWAaBXl87Ors0C9vsTiqlRlUzkZNNXpDWC7b56sXKZCY5HX0v5ZtW5iotR9VdjZpCIbzZRt75uWZvtCczOCWFIKZqhQKVpQnSpjE1FXY0WWqE3V2cGBAI8/YWL/AUO3G6RSSm+u0IrAzcGsV2B6VYQx2bJWKrJHxnCtTlgywwCh7SJ0W2srkKuVLWardQuuHWL7lgq2b6y2ZZvZ1VQLQmTt9j7OEvtkQkGMYywsT342DJT74tM/TkTtxSBL1CYyY/aE40Ns3AA92eDQYUNXZ6XdwLYXH8XlZ/pg+TWY8tF6TITS77la7QVhAGWaCNJSnV78MiXAVmoWHEthy7oqjtlcRSHXfRXY+RZ9yVzZ9ozi6pG2AqnGKoXxflkQyWosEUUYZIk60G7wpBNDjI0bePxxQ/fP1mrRCK+FxnXJ9ALfyiAVlGLzGCnZtEBK0iutyka7LOgQKzt5GYss5JIAaxoKGwbrOHZLBX15P3brgGpBANuOIvvqT5WVEBvvIGvXlZ5SoBd5ERE1MMgSdYCEqv4+WQymdGVWAu3ERJS03FQUaOcKXjUnCxM1pPw6VAzGcemWVln0tdL+RdndzHIRpjJTq9RnX49UYGt1U99vg0UJsFUM9XmxC7DTe2W9UMGxZHHaKgZOdaStIK6MIDr2yUEbqg0LCokovhhkiTpIQtbQoMJAv8LBgwb27jMwPmGgWoXun3Xd2YHWQCmdhVur68Vf0cTR7haGISw973WZtUWp6homQjd39F/Jbk51E55nwrYVhgc8vaHBusF6x0ZYtVPV92GZDkzDmCu/L0vzUQjn2rUrDhRge7LAy0SlEI9+cSLqHAZZojUgEw3WrVMYHlaYmAT27zd1sC2XdIbTfbTNtgPfslCHgzQ8Pfq528Nsc6asodsLltdSoNzcjM0PfFnA5ZkIQwOZVICtGyrYNFTTPbBxrcDORaJmzQ+QsaN2CrWK1di41mNTofRJG5gYdjg3loiOwiBLtIYkhBULcgoxshU4eMjA3r0GSmVD78oaKtmWFajBgQw8sGISZsMghCVzr5YSx6K+BCgnDWVnECqFWs1EvW7qEVp9OR9b1tf0PFjXiWssW1xdtq2Vk2nCX2H8bN77gRnP+8s1TH0bJoZsBE53P+eJaG0wyBJ1CWkr2LRRYcN6hbExA/v2S+sB4FdSqIYmYAbIoATXqHV9mNVV2WavrGo1xAbwzSyqKMKrmHreqZ0GNq0rY9sGH/1FvyfaB1pR8X2kLROObUPpOu0KqrGmVGPjF2QlwkqQ3Z+qwyzk1/pwiCjJQfbw4cM4ePAgRkZGkJLPTIlowbaDgQGlT+nsAew1Qri/9FBXg5gIi3q2qoRZx/BgoXs/Wp/qlV1ggoF82Q8t1EIbnpGCaaWQskJs7i+hL19FdosBN/SQdpa+q1ecyb01Xquh3zahmwzkC8Yyq7Fx7I1VUo218bhXw4FMgE3d+iQnot4Osj/4wQ/w53/+57j33ntRKBSwZ88evP3tb8dHP/rRqH+OiBb+AbVDZPonsTH1BPrcvZjwcxj3spj0MqiGLnxlw5DKJXzYhq/Pu+VHa0ZVdnpwVTY8ZcELbShlwDY8ZFMB1g/XMVwoYSDnwTIVamGAquzoVUUiyTzZicDHgOUsvV+2UY31dTU2hou7lInxsIL7axPoN7JrfURElNQge/fdd+OTn/wkTj/9dP3nu+66C2effTae+tSn4nWve107r5qo51hGiH5nQp9kZb/hV1Hy09jvr8NY2AdPuaggq1cMGUa45uFWQmvNNxFa7lRoFbbpwzF8DDn7sM45gMEBE7mNG2FIKZpmqKsQVeUhCxcyPatxFy5yx8vH8tHc2NhVYxsh1jdD7PZH4bV0g4koydoaZC+55JIZf37Ws56FDRs24OGHH27n1RL1PlMWgaWx3jqMY91dOjRKmJ1URUyGeYwFRYwHhSPhtlGWMxDCbJ6Maf+PcElhV65v6l+q6Zcivbvm1KgwW/lwDQ8b02PotydQsEsomJPIq4MwLRtB/1aEfZu4Gn0BHqR3WBaAmVGYjR7I3mspaITYwAxRcuvwSrIrG5dxENHC2v4qUa1Wcd9996FUKuHGG2/ULQV/+Id/2O6rJep9holJawg1lUUxPIhB4zD6MY5ANk4wDATKRDnM6mAr57XQRVWlUQ3TqKmU/j5P2VMBNEpIUeJt5iQJoxJKm39z5CNuY0YgthEgZZSRNqtIG1WkzDpyZgk5cxI5s4xMPg3LsaBqZRhBgDDTB3/oGKgUF/G0QnbkmgqzzSllxsLjtmK1i9e0SuykW0cY0ykLRNTlQVZ6XOW0kBNOOAF52ZOz4fHHH8fFF1+M0dFR7Nu3T/fHbtu2bd5/X6vV9KlpfHxcn3uep09J07zNSbzt0293ILOoEqjZ4Tge+EBtvmZRA/swiEFjAoOYgAlPz52VEJrCGFLmGIZmfWqve1XhoK5cXbVtnkcVVUO3AejzRpDVJyM6l/DqGHW4Rn3q3Mb8O2r5IVCenEQ+bQCWi+rgFniFjdGqtlCqbnPzGgvEgjCEF8z/fb1KbrcMMKtN+9m3YSJtRBsmzLX7l2UY8FSIspKd4OLBggnHMHE4rGCvPwa/FP2sH5DXfncQYaAQyJMoYZq3Oemv/bz9yX78W2EomVzeos985jP4/Oc/v+D3XHvttTjjjDPmXfx17rnn4iMf+QguvfTSOb/nyiuvxM6dO4/6+vXXX49slk3/RAspVCZxzP7HUaxMIjBM1G0X4Rr2npphqLfXNVWIeq4f2W1PRi2TmwqptHQWDBQtW4c/WRDWvCcl3IqxwNNhNg7ShqVHqu32qnioNrncQWNE1GPK5TIuuugijI2NoVgsrl6QXQ0XXHCBHsV16623tlyRlbFdu3fvxtDQEJL4rkTuK3kD4MgepgnD2x89/qfpaluLd1oYID16AKnRg7DrURU3sB0o2SqsE6u+ZFevwIfle1BSOXTTqPUPo9o3jEK6iKyTgR8uPupf/n5UwnkqncgpJ/LSPFGrzn37ZTyVfBx/5I+6paBuAH4M7irpepA9LQIDGLOASXmvZRxdkf7V/j187eNrP3/3JfB3//j4OIaHh1sKsm3tkZVAOntu7GOPPYbNmzfP+2/k++eaNSsPZBIfzCbe/mQ//ps2boCzlMrqlq1Q8nH84QMI9z0OZ+yw7PMK2A7gptoyIUCFISDBOWhcz7oNMNdvBvqHkG9uNys9nJ4BJ5DtRhdetCQ7e41WqyjkCzCTshPCNGHYCLLz3X7dHxJI70HUouHYsOW1s5vvq+Yxy3MlmwGG+pFz5/659nxfB1m+9iX7tY+PfzIff2cJt7mtQfacc87B61//epx22mmoVCq47rrr8OMf/xgf//jH23m1RCQZUSqwwxtgDq0HSpNQB3ZD7d8DVEpR761hApbdOFlLqnrqD3IksEpYlnP9UbYBpNMwNm+HMbwRRm6OhVwy299RMGSsUvNz5C7OXV1NHi/Zu1eCoWNH96P0ldnyeHbZKDP9fAmihmk51sE+oJjntAoiWrG2Btmbb74Zn/rUp/S0AknXp5xyCu655x69IIyIOkMH1HwBRr4AteUYYPQQVHkSanJMB1z4NaAWRCvhpVIrAVeHS2PamILG3CcJrM3QKkFZQlNxGEa+CCObB/oHYUg1dsEDaoRZ+Ry82V/AMLvMQb0ekE4BG4eiQDs6AVSq0f9LyJXq7Fq2ZegAG0YhVqryA0WgLx89b4iIVkFbX002btyopxQQUXcwZKvXdRuPjNeSgFEpA5VJqHIJanIc8BsfVcvHv7IyXsKQBFwJIo4bhdZMDsjmgEwWRrNtYCnMRpj1GGaXHRDrHiAfy28Yks/hopOE2nI1CrS1elQBlepsp/qjm+R5I60scpxy3VJ97StEx0tEtIr4tpgo6e0H+UJUse30lVvReDGG2WWGWKcRYqeHQwmruQyQTQPVOjBZAkqV6Pv1fW5FwXa1Q60c01T1VW8tB7g2UMgBuWzUTkBE1AZ8dSGitcMwu7x2gmYlNuXO/X0SJDOp6DTgR1VaOVVrQL1RKZ2qtDfaD5a8tZs6UrVvXp60M0h4zaSjUzcvPCOinsAgS0Rri2G2NXoqhB+F1/WD84fY2XQfcz466VYS6YmuRxVbWRwm7Qez587OMe5Lh9WpLxtHgnDGjY5F2hrknOGViDqIQZaIuiPMGo2eWU7FP5oEUC+IWgbWDS7/o3rdSpKNTtPDsfSzNhdl6ZOE28ZKvObCv+Y0hGbPrRyDnpbAqisRrR0GWSLqDrIAzG2E2eTtSDs/vWgK0Wr/of6oCrpa5LLSUtltsbpLRNRlGGSJqHs0RnPp/w2AjCGl2ua+VQmjFHJm4yVaAqwEWVY/iYhm6LKp2USUeJJZHdlWV0FvZCsr7uUj8CSRObCeh1LoI1w3APQXGGKJiObAIEtE3UdaZS2Fn5ZHoWSclPSHeo3V9kmYShAqqEIeP62MQsnqfyIimhODLBF1rYoKooqknGTHMQl5ship1wKt3B4J6s1NDjYOIRwswu+120lEtMrYI0tE3U36QmV0lIx3kh2rSuVobJSsmJcV9HHW3ESguaVsfzFqI5CpALLIi4iIFsQgS0TxIJVKqcwWc1GglQH/EvaaY6HiRG8o0AiwMjlAwqss5pLduoiIqGUMskQUr+qsVGY3uNFg/9HxaLC/BNp2bb+62gFWwqtUYeVY87koxLa6uQEREc3AIEtE8SNhVTYHkC1YJchOloBSJeqhbW6VutRtV9uluZ2rBFiZwtDcaUu2cuWGAkREK8IgS0TxJUFVwqycBnxgsgJMlBoTDsLo76XyKR/fdzLUNntfZWGa0Fu5pqLwms3ErxWCiKhLMcgSUW+QSmez17RWj1oPypVoEoDvRTO9zOZpFYOthNZm1VX6XpuTBqTVQUKrnCTEsvpKRLTqGGSJqDf7aOUkwVaqs9VaFG51P23QmAggAVSqpY1A22xF0Kc5LldNC6361Pxi499KOJZeVzk1r19aHIiIqG0YZImod0nAlGkHcpr+kb+M75JKrYRcHWyDI/NpdVV1rstqXJ60BUi1VSrAElSl0iqXLxMH2DJARNRRDLJElBwSRCV8ymn2jllT7QESaBt/nv7vmiG2WxaRERERgywRkaaDaiOsEhFRLPAVm4iIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYolBloiIiIhiiUGWiIiIiGKJQZaIiIiIYqljQbZcLuO2227D3Xff3amrJCIiIqIe1rEg+7a3vQ3nnXcedu7c2amrJCIiIqIe1pEge9111+EXv/gFXvSiF3Xi6oiIiIgoAdoeZB944AG8+93v1mHWtu12Xx0RERERJURbk2W9XscFF1yAD33oQ9ixY0dL/6ZWq+lT0/j4uD73PE+fkqZ5m5N42wVvf/S4+2GIpGneZs/3kUTN253428/XPiQRX/v5u79VhlJKtfrNDz74IB5++OEFv+f000/HwMCA/v/LL78cu3btwk033aT//JKXvATpdBo33njjvP/+yiuvnLOP9vrrr0c2m231UImIiIgohmRAwEUXXYSxsTEUi8XVC7Jf+tKX8JWvfGXB7/nEJz6B0047DXfddRfOOecc/W+Gh4f1311xxRVwXRcf+MAHcPbZZ+tQ20pFdmRkBLt378bQ0BCS+K701ltvxbnnngvHcZA0vP3Jffz52Cf3sRd8/Pn48/mf3J//8fFxnR1bCbJLai24+OKL9akVvu/rsHrttdfOqOiapomrr75a98zOFWRTqZQ+zSYPZBIfzCbefj7+SX3+87nP535Sn/uCz38+/5P4/HeWcJvb1iN71lln6bmx07XSWkBERERE1Aru7EVEREREsdTReVhPe9rTdI8sEREREVGsguxVV13VyasjIiIioh7G1gIiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiUGWSIiIiKKJQZZIiIiIoolBlkiIiIiiiW7nRd+33334Z577pnxNdd18bKXvaydV0tERERECdDWIHvjjTfiU5/6FJ7//OdPfS2XyzHIEhEREVF3B1mxY8cOHWiJiIiIiGIVZCuVCv71X/8V6XQap556KgYHB9t9lURERESUAG0Psg899BA+9rGPYXR0FPfeey+uuuoqvPOd75z3+2u1mj41jY2N6fNDhw4hiTzPQ7lcxsGDB+E4DpKGtz+5jz8f++Q+9oKPPx9/Pv+T+/M/MTGhz5VSi36voVr5roaf//znOowu5LnPfS7WrVun//+OO+7AKaecgoGBAf3nG264ARdeeCFuu+02vPCFL5zz31955ZXYuXNnq4dERERERD3osccew9atW1cvyEoQXazfVUKohNf5PPWpT9WLv/76r/+6pYqsVHK3b9+ORx99FH19fUia8fFxjIyM6AezWCwiaXj7k/v487FP7mMv+Pjz8efzP7k//0opXZXdvHkzTNNcvdaCCy64QJ9WQsLovn375v37VCqlT3P9uyQ+mE1y23n7+fgnEZ/7/Nnnax9f+5Iqya9/fS0WL9u6IcLevXtn/FneWfzkJz/BGWec0c6rJSIiIqIEaOtir9/93d/Fs571LDz96U/HgQMH8OlPf1q3HbzlLW9p59USERERUQK0tSJ7++2365Fbd955p67GfuQjH8H3vvc95PP5li9D2gw++MEPztlukAS8/Xz8k/r853Ofz/2kPvcFn/98/if5+b8US1rsRURERESUiIosEREREVG7MMgSERERUSwxyBIRERFRLLV9i9p2kdbef/zHf9Rbt73sZS9DUoRhiJ/97Gd6g4ht27bhaU97GpJEtiz88Y9/rM+f8pSnYMuWLUiaX/7yl/jv//5vPPvZz9abhfQi+fn+4Q9/iCeeeAInn3wynvSkJyFp5Hl+//3349xzz8XQ0BCSZM+ePfif//kf5HI5PfVmKQuEe8Hhw4fxox/9CIZh6E2E1q9fjySS58B9992H5zznOYl5rb/55ptRqVRmfE2mPcnCeZqHiqmrr75aua6rtmzZopLi+9//vjrttNPU6aefrl760peqDRs26P/fs2ePSoK/+qu/Ulu3blVnn322+q3f+i2VyWTUn/3Zn6mk+OEPf6he8IIXqB07dsgCTfUP//APqhdNTk6q5z73uWrjxo3q3HPPVfl8Xl166aUqKb71rW/pn+vm4/y9731PJYU89hdddJF+XT/vvPPUM57xDDU0NKS++c1vqqR43/vep0ZGRvTtf85znqNf5z784Q+rpHn00Uf1a4D8DNx0000qKeT3+plnnqle+9rXTp2++tWvrvVhdTXENdBt375dveMd70hUkL399tvVQw89NONF/7jjjlN/8id/opLgi1/8ojp8+PDUn++8805lGIb6l3/5F5UEt912m7r11ltVGIY9HWTf85736J/vAwcO6D//6Ec/UpZlqW984xsqCW688UZ11113qfvvvz9xQXb//v3quuuuU0EQzAh2uVxOlUollQRy++v1+tSfr7/+ev08ePDBB1VS+L6vQ/zHP/7xRAbZXn1tb5fY9ciOjY3hoosuwuc///nEfdx2zjnn4Nhjj536s3zsJh+3yH7ESXDxxRejv79/6s9nnXUWNm7cqD9+SoIXvvCFeNGLXqQ/buxlX/nKV/AHf/AHUz/fp59+Op773OfqryfBK1/5Sjzzmc9EEg0PD+vX9+l7q8v9USqV8MADDyAJ5PZLy1xTs61mcnISSbFz507983/JJZcgiaR97Bvf+IbeCdX3/bU+nK4Xux7ZN7/5zXrHMOkb+8EPfoCkkSr6V7/6VdRqNfzHf/wH9u/fj7/7u79DEv385z/XvXTSK0u90xv4+OOPH/WYSn/Yt7/97TU7Llo7t912G9LpNI4//vjE9cHv27cPn/vc5/Cnf/qnulc2Cf793/8dX/ziF/XtT6qvfe1r+vbLSYo3N9xwA3tkuzXIykIOCWMLOe200/RiDyFVWGn8/vKXv4xe8Mgjj+idzhYilZnpL+ASZOWdmlQoZDFMnBeC/OpXv9LvOBdy9tlnY+vWrUd9XW7/6173Ovzmb/4mXvKSlyCO7r77bh3GF/KCF7wgUQs95BMXMTg4OOPr8hwfHR1do6OitSKvD1Kd+8AHPqA/gUoKWeR300036Td18ombLHhLAtnK/vWvfz2+8IUv6Op8En/mJcT/zu/8jv5/WfT1ile8AhdccIH+fTH9kwrqkiC7d+9eHcoWks1mdZAdHx/HO97xDrzzne/UP+BCHlh5oOXdinzMLKv440S27V3s9ssP8/QgK09kub3NMCerOd/61rfqKm3cPPjgg4vefmmlmB1k5TGXSRWe5+nnQlx/uO+9995Fb3/SViw3t2Oc/TGq/FmqcpQc8vMhv9AvvPBC/MVf/AWSRN6cN9+g33LLLXjxi1+MHTt26N9zveyKK67Apk2bcOjQIf17TqbTiDvuuAOFQkG3V/W6ZogVmUwG73vf+3RbobTWnHjiiWt6bN1qTYOsvMtshrLFSCXypS99qX6nKqfmC52EGgkDMoYobkFWqo1yWi6pULz85S/HZz/7WcT1B3b6D20rqtWqDrHSUvDd735XB/24es1rXqNPdMSGDRv081rGy83+9OK4447jXZUQ8smbfBohAU4+iev1vvCFnH/++foTCglzvR5kTzrpJF2Fbb7Bl2KFkDZCGcGWhCA7W7FY1OfSRsggG/Me2b6+vqNC71VXXYW//du/bTkMx93u3bv1u9XZsyZHRkaQBM0QKy0p3/nOdxJVqUwKqa6fd955uPHGG3HZZZfpACOfxkh/7Pvf//61PjzqUH/o85//fP0m99prr01UiJXWGtd1dSWu6eGHH9a940l4nZdPXaeTUCvz4t/1rnfh937v99DrJKwODAzAto9EM7n98mkU14L0QJAl4PLLL9cvcmeeeab+hS+/3G+99Vb88z//c2JW895+++34+Mc/rquxTfIu9RnPeAZ6nbTiTL/d3//+9/ULnrSe9NIqd3mDKs/x1772tTrQ/P3f/72eziEtNEkgLTfS/y6Pd3Ox065du2asF+jlX+RSiZWqvEzokEUvTc973vP0lJJeL1a8+tWv1p+0yScQv/71r/ViL2khe9WrXrXWh0dtJu2S7373u/Xjv3nzZtx555247rrr8MlPflIX82huhszgQkxJf6S8yH/mM59BEshDJbdZVnXW63XdMyVjitatW4ckePvb346DBw8e9fXf/u3fxhve8Ab0unvuuUeHvNnkF79M8+glUoWSX+DNnb3e9ra3TX3E1uvkNU0qkXO9kev1XQylheQ973nPnH/33ve+V4f5XidvYOTNm7RXyCJHaT+Txz1Jlekm6ZF94xvfqMPdGWecgaS8kZVRg83dO6X9rNffwCY6yBIRERFRcsVzuTcRERERJR6DLBERERHFEoMsEREREcUSgywRERERxRKDLBERERHFEoMsEREREcUSgywRERERxRKDLBERERHFEoMsEREREcUSgywRERERxRKDLBERERHFEoMsERERESGO/n+JG2U9p0wJpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(0, -3, 6), (1, -3, 3), (-1, -3, 6), (-3, -1, 2), (-3, -2, 2), (1, -4, 6), (1, -2, 3), (1, -1, 3), (2, -2, 3), (2, -1, 3), (4, -1, 5), (4, -2, 5), (1, 1, 2), (0, -3, 1), (4, 0, 5), (0, -1, 2), (0, -2, 2), (2, -1, 5), (2, -2, 5), (0, 0, 2), (-2, -3, 1), (-1, -3, 1), (0, -3, 3), (2, -3, 6), (2, 0, 5), (-2, -1, 2), (-1, -1, 2), (-2, -2, 2), (-1, -2, 2), (0, -1, 4), (0, -2, 4), (0, 1, 3), (3, 1, 2), (-2, 0, 2), (-1, 0, 2), (0, 0, 4), (-1, -1, 4), (-1, 1, 3), (0, -2, 6), (1, 0, 3), (3, 0, 5), (-1, -2, 6), (1, -1, 5), (1, -2, 5), (2, -1, 2), (2, -2, 2), (-3, 1, 2), (1, 0, 5), (2, -3, 3), (2, 0, 2), (1, -3, 6), (-1, -2, 1), (0, -4, 6), (0, -2, 3), (0, -3, 5), (0, -1, 3), (0, 0, 3), (3, -1, 5), (3, -2, 5), (3, 0, 2), (-1, -4, 6), (-1, -2, 3), (-2, -3, 5), (-1, -3, 5), (0, -2, 5), (0, -1, 5), (-1, -1, 3), (1, -1, 2), (1, -2, 2), (-1, 0, 3), (1, 0, 2), (-1, -2, 5), (-2, -2, 5), (-2, -1, 5), (-1, -1, 5), (1, -1, 4), (-1, -4, 1), (1, 1, 3), (2, 1, 2), (0, 1, 2), (-3, 0, 2), (1, -2, 6), (3, -1, 2), (-2, 1, 2), (-1, 1, 2)}\n",
      "Population initialized with 200 individuals.\n",
      "\n",
      "--- Generation 1/70005, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 1001/70005, Best fitness 0.53211 ---\n",
      "\n",
      "--- Generation 2001/70005, Best fitness 0.58095 ---\n",
      "\n",
      "--- Generation 3001/70005, Best fitness 0.64706 ---\n",
      "\n",
      "--- Generation 4001/70005, Best fitness 0.65347 ---\n",
      "\n",
      "--- Generation 5001/70005, Best fitness 0.65347 ---\n",
      "\n",
      "--- Generation 6001/70005, Best fitness 0.65347 ---\n",
      "\n",
      "--- Generation 7001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 8001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 9001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 10001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 11001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 12001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 13001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 14001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 15001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 16001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 17001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 18001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 19001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 20001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 21001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 22001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 23001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 24001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 25001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 26001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 27001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 28001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 29001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 30001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 31001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 32001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 33001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 34001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 35001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 36001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 37001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 38001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 39001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 40001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 41001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 42001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 43001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 44001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 45001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 46001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 47001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 48001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 49001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 50001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 51001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 52001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 53001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 54001/70005, Best fitness 0.65657 ---\n",
      "\n",
      "--- Generation 55001/70005, Best fitness 0.71287 ---\n",
      "\n",
      "--- Generation 56001/70005, Best fitness 0.71287 ---\n",
      "\n",
      "--- Generation 57001/70005, Best fitness 0.71287 ---\n",
      "\n",
      "--- Generation 58001/70005, Best fitness 0.71717 ---\n",
      "\n",
      "--- Generation 59001/70005, Best fitness 0.72 ---\n",
      "\n",
      "--- Generation 60001/70005, Best fitness 0.72 ---\n",
      "\n",
      "--- Generation 61001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 62001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 63001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 64001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 65001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 66001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 67001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 68001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 69001/70005, Best fitness 0.73196 ---\n",
      "\n",
      "--- Generation 70001/70005, Best fitness 0.73196 ---\n",
      "draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program evolution_of_colored_morpho_trees_vs_scared_resources_m8.py\n",
    "\n",
    "# This program reports the battle of Evolution \n",
    "# in the space of colored morpho-trees \n",
    "# to match a target colored area proposed by a target colored morpho-tree.\n",
    "# We merge the code for Evolution to that for generating and drawing a colored tree.\n",
    "# Edited with Spider 6.\n",
    "\n",
    "# =====Reused:  Program colored_tree_generator_and_drawer_m8.py\n",
    "# This program generates a colored morpho-tree as a Newick formula,\n",
    "# and draws it. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "  \n",
    "  \n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import math\n",
    "import copy # For deep copying trees during reproduction and mutation\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "# trace memory allocation \n",
    "#https://www.geeksforgeeks.org/python/monitoring-memory-usage-of-a-running-python-program/\n",
    "# this consumes too much cpu-time by a multiplicative factor, say 5 for 1000 lines.\n",
    "import tracemalloc \n",
    "\n",
    "\n",
    "#Activate  memory monitoring?\n",
    "memory_monitoring = False \n",
    "recombination_of_leaves = False\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# starting the memory allocation monitoring\n",
    "if memory_monitoring:\n",
    "    tracemalloc.start() \n",
    "start_time = time.time()\n",
    " \n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "\n",
    "# Parameters for the target tree  \n",
    "target_tree_params = {\n",
    "  'total_number_of_nodes': 15,\n",
    "  'max_number_of_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "# Parameters for the Genetic Algorithm\n",
    "ga_params = {\n",
    "   #'seed': 47423648, #the seed of the target tree is this plus 1.\n",
    "  'pop_size': 200,\n",
    "  'generations': 5,\n",
    "  'average_number_of_nodes':  target_tree_params['total_number_of_nodes'],\n",
    "  'max_number_of_children': target_tree_params['max_number_of_children'],\n",
    "  'max_node_radius': 1.5,\n",
    "  'mutation_rate': 0.001, \n",
    "  'crossover_rate': 0.001  \n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "def print_parameters_m8(tree_params, ga_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n Evolution searches in the space of colored trees \"\n",
    "        + \"\\n to match a target colored area associated to a target tree.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    " \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m8(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m8(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "#=== COLORED TREE GENERATOR===\n",
    "\n",
    "def detect_all_groups_m8(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m8(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m8(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m8(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = int(float(c[0][2:]))\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m8(newick_string):\n",
    "    #print(\"extract_all_infos_m8\")\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m8(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m8(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m8(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m8(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m8(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m8(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m8(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator_m8(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m8(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # Filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l-1) # color index\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter_of_nodes)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    \n",
    "    # distance2 = distance squared between father and child.\n",
    "    # Test: distance2 must be 2 or  1. \n",
    "    distance2 = (father_center[0]-nx)**2 + (father_center[1]-ny)**2\n",
    "    if distance2 > 2:\n",
    "        print(f\"distance2 > 2: {distance2}\")\n",
    "  #else:\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m8(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children_m8(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  generated_children = \"\"\n",
    "  i = 0\n",
    "  while i in range(numb_children) and counter_of_nodes < total_number_of_nodes:\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m8(father_center, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "    if info == \"\":\n",
    "      generated_children = \"()\"\n",
    "      return generated_children, counter_of_nodes, occupied_squares_set\n",
    "    else:\n",
    "     generated_children = info + a + generated_children\n",
    "     counter_of_nodes +=  1\n",
    "     i += 1\n",
    "     #print(f\"counter_of_nodes at generate_children_m8= {counter_of_nodes}\")    \n",
    "  generated_children =  \"(\" + generated_children + \")\"\n",
    "  #print(f\"Children: {generated_children}\")\n",
    "  return generated_children, counter_of_nodes, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m8(total_number_of_nodes, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  # Initial tree = root. () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))# Root\n",
    "  #counter of defined nodes\n",
    "  counter_of_nodes = 1\n",
    "  while counter_of_nodes < total_number_of_nodes:\n",
    "      l = len(tree)\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Find and replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m8(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement of  () by a list of children\n",
    "            generated_children, counter_of_nodes, occupied_squares_set = generate_children_m8(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m8(tree, j-1, j+1, generated_children)\n",
    "            #print(f\"counter_of_nodes at complete_tree_generator_m8= {counter_of_nodes}\")\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  #print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "# === DRAWER ===\n",
    "\n",
    "def find_branch_m8(newick_string, final_index):\n",
    "  initial_segment = newick_string[:final_index]\n",
    "  #print(f\"initial_segment: {initial_segment}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  branch = \"\"\n",
    "  for i in range(len(initial_segment)-1,-1,-1):\n",
    "    if initial_segment[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if initial_segment[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = initial_segment[i:final_index+1]\n",
    "      #print(f\"Branch to be deleted: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_find_all_groups_m8(newick_string):\n",
    "  \"\"\" All groups are found and one is chosen at random and returned\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  info_words =  r'[a-zA-Z_0-9_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(info_words, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string, 0\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  return groups[start_idx], starts[start_idx]\n",
    "\n",
    "def deletion_of_branch_m8(newick_string ):\n",
    "  group, start =  newick_find_all_groups_m8(newick_string)\n",
    "  #print(f\"Group (main): {group}\")\n",
    "  #print(f\"Start (main): {start}\")\n",
    "  # Does the group have children?\n",
    "  char_before = newick_string[start-1]\n",
    "  #print(f\"char_before: {char_before}\")\n",
    "  if char_before ==  \")\":\n",
    "    #print(\"The group has children\")\n",
    "    #find branch\n",
    "    final_index = start-1\n",
    "    branch = find_branch_m8(newick_string, final_index+1)\n",
    "\n",
    "    branch_and_father = branch + group\n",
    "    #print(f\"  Branch and father to be deleted: {branch_and_father}\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(branch_and_father , \"\")\n",
    "  else:\n",
    "    #print(\"The group has no children\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(group, \"\")\n",
    "\n",
    "  # Cosmetics\n",
    "  newick_string = newick_string.replace(\",,\", \",\")\n",
    "  newick_string = newick_string.replace(\",)\", \")\")\n",
    "  newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "  newick_string = newick_string.replace(\" \", \"\")\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {newick_string}\")\n",
    "  l = len(newick_string)\n",
    "  #print(f\"Length of newick_string: {l}\")\n",
    "  if l == 0 :\n",
    "     newick_string = \"Node_0[a=0,b=-0,x=0,y=0,r=1]\"\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "# =================================================================================\n",
    "# ==== Drawer ===\n",
    "# =================================================================================\n",
    "\n",
    "  \n",
    "\n",
    "# colored_coords = get_colored_area_m8(x,y,c,r)\n",
    "def get_colored_area_m8(x,y,c,r):\n",
    "    '''\n",
    "      The illuminated area encloses all unitary squares \n",
    "      that intersects the circles of illumination with the respective color ci.\n",
    "      Squares are denoted by their left bottom corners (xi,yi). These will be found in the set colored_coords.\n",
    "      The radius of illumination is ri\n",
    "    '''\n",
    "    #print(\"get_colored_area_m8\")\n",
    "    #print(f\"x: {x}, y: {y}, c: {c}, r: {r}\")\n",
    "    colored_coords = set()\n",
    "    \n",
    "    for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "        circle_center_x = xi + 0.5\n",
    "        circle_center_y = yi + 0.5\n",
    "        r_sq = ri ** 2\n",
    "        # 1. Add the cell containing the center of the circle and its color\n",
    "        # This covers the case of small circles not intersecting grid lines.\n",
    "        colored_coords.add((math.floor(circle_center_x), math.floor(circle_center_y), ci))\n",
    "\n",
    "        # 2. Horizontal Scan: Check intersections with horizontal grid lines\n",
    "        # Iterate y_line from floor(cy - r) to ciel(cy + r)\n",
    "        # These y_line values represent the y-coordinates of horizontal grid lines.\n",
    "        for y_line in range(math.floor(circle_center_y - ri)-1,\n",
    "                            math.ceil(circle_center_y + ri) + 1):\n",
    "            dy = y_line - circle_center_y\n",
    "            discriminant = r_sq - dy * dy\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dx = math.sqrt(discriminant)\n",
    "                x_min_intersect = circle_center_x - dx\n",
    "                x_max_intersect = circle_center_x + dx\n",
    "\n",
    "                # For each integer x-coordinate that the horizontal segment crosses:\n",
    "                # Add the cells whose bottom-left corner's x-coordinate is in this range.\n",
    "                # This covers cells whose top edge is y_line AND cells whose bottom edge is y_line.\n",
    "                for ix in range(math.floor(x_min_intersect), math.floor(x_max_intersect) + 1):\n",
    "                    colored_coords.add((ix, y_line, ci))\n",
    "                    colored_coords.add((ix, y_line - 1, ci))\n",
    "\n",
    "        # 3. Vertical Scan: Check intersections with vertical grid lines\n",
    "        # Iterate x_line from floor(cx - r) to ciel(cx + r)\n",
    "        # These x_line values represent the x-coordinates of vertical grid lines.\n",
    "        for x_line in range(math.floor(circle_center_x - ri)-1,\n",
    "                            math.ceil(circle_center_x + ri) + 1):\n",
    "            dx = x_line - circle_center_x\n",
    "            discriminant = r_sq - dx * dx\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dy = math.sqrt(discriminant)\n",
    "                y_min_intersect = circle_center_y - dy\n",
    "                y_max_intersect = circle_center_y + dy\n",
    "\n",
    "                # For each integer y-coordinate that the vertical segment crosses:\n",
    "                # Add the cells whose bottom-left corner's y-coordinate is in this range.\n",
    "                # This covers cells whose right edge is x_line AND cells whose left edge is x_line.\n",
    "                for iy in range(math.floor(y_min_intersect), math.floor(y_max_intersect) + 1):\n",
    "                    # Cell (x_line, iy) - its left edge is at x_line\n",
    "                    colored_coords.add((x_line, iy, ci))\n",
    "                    # Cell (x_line - 1, iy) - its right edge is at x_line\n",
    "                    colored_coords.add((x_line - 1, iy, ci))\n",
    "    #print(f\"colored_coords  {colored_coords}\")\n",
    "\n",
    "    return colored_coords\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def plot_colored_area_newick_m8(ax, colored_coords, title,  alpha=0.9):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not colored_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y, c in colored_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=palette[c], alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(d[0] for d in colored_coords) - 1\n",
    "    max_x = max(d[0] for d in colored_coords) + 2\n",
    "    min_y = min(d[1] for d in colored_coords) - 1\n",
    "    max_y = max(d[1] for d in colored_coords) + 2\n",
    "    #print(f\"min_x: {min_x}, max_x: {max_x}, min_y: {min_y}, max_y: {max_y}\")\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "\n",
    "def plot_tree_nodes_and_their_diffusion_circles_m8(x,y,c,r, ax):\n",
    "  for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "    #print(f\"ci at plot_tree_nodes_and_their_diffusion_circles_m8: {ci}\")\n",
    "    # Plot the square representing the cell\n",
    "    rect = patches.Rectangle((xi, yi), 1, 1, facecolor=palette[ci], edgecolor='black', linewidth=1, zorder=1)\n",
    "    ax.add_patch(rect)\n",
    "    #node_label_part = node.value.split('_')[-1] if '_' in node.value else node.value\n",
    "    #ax.text(xi + 0.5, yi + 0.5, node_label_part, ha='center', va='center', color='darkblue', fontsize=7, zorder=2)\n",
    "\n",
    "    # Plot the circle representing the radius of influence\n",
    "    circle = patches.Circle((xi + 0.5, yi + 0.5), ri, color=palette[int(ci)], alpha=0.65, zorder=2)\n",
    "    ax.add_patch(circle)\n",
    "\n",
    "def find_newick_leaves_m8(newick_string):\n",
    "    '''Detect leaves in a Newick string.'''\n",
    "    #print(\"\\n\\nfind_leaves\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "    # Detection of leaves.\n",
    "    # Detect substrings of the form \"(...)\" but without interior parentheses.\n",
    "    pattern_leaves = r'[(][^()]*[)]'\n",
    "    # Leaves may come grouped by bouquets\n",
    "    bouquets_of_leaves = re.findall(pattern_leaves, newick_string )\n",
    "    #print(f\"Bouquets_of_leaves: {bouquets_of_leaves}\")\n",
    "    all_leaves = []\n",
    "    # Decipher each leaf and find the father\n",
    "    for bouquet_of_leaves in bouquets_of_leaves:\n",
    "        bouquet_of_leaves = bouquet_of_leaves[1:-1]\n",
    "        leaves = detect_all_groups_m8(bouquet_of_leaves)\n",
    "        for leaf in leaves:\n",
    "          leaf = leaf.replace(\" \", \"\")\n",
    "          all_leaves.append(leaf)\n",
    "    return all_leaves\n",
    "\n",
    "\n",
    "\n",
    "def connect_leaves_to_father_m8(newick_string, ax, tree_color='lightblue', diff_color='red'):\n",
    "    '''Draw lines connecting leaves to fathers'''\n",
    "\n",
    "    #print(\"\\n\\nconnect_leaves_to_father11\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "    all_leaves = find_newick_leaves_m8(newick_string)\n",
    "    for leaf in all_leaves:\n",
    "        [xl,yl,cl,rl] = extract_info_m8(leaf)\n",
    "        #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n",
    "\n",
    "        # Task: Find the father of the leaf.\n",
    "        # It is the node after the last leaf\n",
    "        # Example:\n",
    "        # (Node9[x=-11,y=19.7,r=12.4567],Node8[x=0,y=7,r=15],Node7[x=6,y=16,r=9])Node6[x=_m8,y=9,r=20]\n",
    "        # The father of Node9, Node8, and Node7 is Node 6.\n",
    "        # The father of a node is found after the first unbalanced right parenthesis.\n",
    "        index_nex_right_par = newick_string.find(\")\", newick_string.find(leaf))\n",
    "        father_with_tail = newick_string[index_nex_right_par+1:]\n",
    "        father_group = detect_all_groups_m8(father_with_tail)\n",
    "        if father_group:\n",
    "          [xf,yf,cf,rf]  = extract_info_m8(father_group[0])\n",
    "          #father_center = (xf,yf)\n",
    "          ax.plot([xl + 0.5, xf + 0.5], [yl + 0.5, yf + 0.5], 'k--', linewidth=0.8, zorder = 3)\n",
    "    # Leaves are erased: each leave is replaced by \"\"\n",
    "    for leaf in all_leaves:\n",
    "          #print(f\"leaf to be erased: {leaf}\")\n",
    "          newick_string = newick_string.replace(leaf + \",\", \"\")\n",
    "          newick_string = newick_string.replace(leaf, \"\")\n",
    "          #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    #is_valid, message = validate_newick_syntax11b(newick_string)\n",
    "    #print(f\"is valid at connect_leaves_to_father11(...) : {is_valid}, message: {message}\")\n",
    "    #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n",
    "    #print(f\"Is the Newick string a valid morpho-tree? : {status} - {message}\")\n",
    "    groups = detect_all_groups_m8(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")    \n",
    "     \n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_links_m8(newick_string, ax,   diff_color='red' ):\n",
    "    '''Draw lines connecting children to father.\n",
    "       1. Each leaf is connected to its father,\n",
    "          and the corresponding stroke is plotted.\n",
    "       2. Leaves are erased.\n",
    "       3. New leaves are detected and connected to their father.\n",
    "       4, And so on.\n",
    "       '''\n",
    "\n",
    "    #print(\"draw_tree_links11\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    length_string = len(newick_string)\n",
    "    #print(f\"length_string: {length_string}\")\n",
    "    groups = detect_all_groups_m8(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    # Leaves are recursively found and connected to their father.\n",
    "    counter_of_trials = 0\n",
    "    while number_of_groups > 1:\n",
    "        #print(\"\\n\\ndraw_tree_links11\")\n",
    "        #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "        newick_string, number_of_groups = connect_leaves_to_father_m8(newick_string, ax, tree_color='lightblue', diff_color='red')\n",
    "        counter_of_trials = counter_of_trials + 1\n",
    "        if counter_of_trials > length_string:\n",
    "          break\n",
    "    # The root is detected and marked with a cross\n",
    "    groups = detect_all_groups_m8(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    # Mark the root with a red cross\n",
    "    if number_of_groups >= 1:\n",
    "      [x0,y0,c0,r0] = extract_info_m8(groups[-1])\n",
    "      #print(f\"Origin: a: {a0}, b: {b0}, x: {x0}, y: {y0}, r: {r0}\")\n",
    "      ax.plot([x0, x0 +1 ], [y0+ 0.5, y0+ 0.5],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "      ax.plot([x0 + 0.5, x0 +0.5], [y0, y0+ 1],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_newick_m8(newick_string, title):\n",
    "    #print(\"\\n=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    x,y,c,r = extract_all_infos_m8(newick_string)\n",
    "    #print(f\"c= {c}\")\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    #color = palette[c]\n",
    "    #print(f\"color = {color}\")\n",
    "    plot_tree_nodes_and_their_diffusion_circles_m8(x,y,c,r, ax)\n",
    "     \n",
    "    # Illuminated area is calculated\n",
    "    #colored_coords = get_illuminated_area_m8(x,y,r)\n",
    "    colored_coords = get_colored_area_m8(x,y,c,r)\n",
    "    #print(f\"colored cells at draw_tree_newick_m8: {colored_coords}\")\n",
    "    #print(f\"Number of illuminated cells: {len(colored_coords)}\")\n",
    "    # Colored squares are added to the plot\n",
    "    plot_colored_area_newick_m8(ax, colored_coords, title,  alpha=0.3)\n",
    "    # Lines that define the tree are added\n",
    "    draw_tree_links_m8(newick_string, ax, diff_color='red'  )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return colored_coords\n",
    "\n",
    "def draw_colored_squares(colored_coords, title):\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    plot_colored_area_newick_m8(ax, colored_coords, title,  alpha=0.5)\n",
    "\n",
    "\n",
    "# ========== EVOLUTION ==========\n",
    "\n",
    "\n",
    "\n",
    "# --- Evolutionary functions ---\n",
    "\n",
    "def create_initial_population_newick_m8(pop_size=ga_params['pop_size'],  average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'],  max_node_radius=ga_params['max_node_radius'] ):\n",
    "    population = []\n",
    "    for i in range(pop_size):\n",
    "        number_of_nodes = max(average_number_of_nodes + random.randint(-5,5), 5)\n",
    "        #print(f\"Number of nodes: {number_of_nodes}\")\n",
    "        new_tree = complete_tree_generator_m8(number_of_nodes, max_number_of_children, max_node_radius)\n",
    "        population.append(new_tree)\n",
    "        #print(f\"Created tree: {i}\")\n",
    "    return population\n",
    "\n",
    "def get_illuminated_coordinates_m8(newick_string):\n",
    "  \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n",
    "  x,y,c,r = extract_all_infos_m8(newick_string)\n",
    "  # Illuminated area is calculated\n",
    "  illuminated_coords = get_colored_area_m8(x,y,c,r)\n",
    "  ##print(f\"Illuminated cells: {illuminated_coords}\")\n",
    "  ##print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n",
    "  return illuminated_coords\n",
    "\n",
    "def calculate_fitness_illumination_m8(candidate_tree_root, target_illuminated_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness based on the symmetric difference between illuminated areas.\n",
    "    Fitness is 1.0 for a perfect match, and decreases as symmetric difference increases.\n",
    "    Reuse of a previous code by Gemini 2.5 Flash\n",
    "    \"\"\"\n",
    "    if candidate_tree_root is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    candidate_illuminated_area = get_illuminated_coordinates_m8(candidate_tree_root)\n",
    "\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = candidate_illuminated_area.symmetric_difference(target_illuminated_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(candidate_illuminated_area.union(target_illuminated_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "        if not target_illuminated_area and not candidate_illuminated_area:\n",
    "            return 1.0 # Perfect match if both are empty\n",
    "        else:\n",
    "            return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "def calculate_fitness_colored_area_m8(test_tree, target_colored_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness of a colored area. \n",
    "    Fitness is 1.0 for a perfect match, and decreases as distance increases.\n",
    "    \"\"\"\n",
    "    if test_tree is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    test_colored_area = get_colored_area_m8(test_tree)\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = test_colored_area.symmetric_difference(target_colored_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(test_colored_area.union(target_colored_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "         if not target_colored_area and not test_colored_area:\n",
    "             return 1.0 # Perfect match if both are empty\n",
    "         else:\n",
    "             return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "\n",
    "def select_parents_newick(population, fitness_scores, num_parents):\n",
    "    parents = []\n",
    "    # Use roulette wheel selection or tournament selection. Tournament is generally robust.\n",
    "    for _ in range(num_parents):\n",
    "        tournament_size = min(5, len(population))\n",
    "        if tournament_size == 0:\n",
    "            break\n",
    "        tournament_candidates = random.sample(list(zip(population, fitness_scores)), tournament_size)\n",
    "        winner = max(tournament_candidates, key=lambda x: x[1])[0]\n",
    "        parents.append(winner)\n",
    "    return parents\n",
    "\n",
    "def find_recombinant_branch_m8(newick_string):\n",
    "  # A full functional branch is recognized because it is enclosed by ()\n",
    "  # and everything inside is full functional.\n",
    "  # We simply keep an accounting of parentheses pairing\n",
    "  #print(\"\\nfind_branch\")\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  pattern_left_par = r'[(]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_left_par, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  start_idx = starts[start_idx]\n",
    "  #print(f\"start_idx: {start_idx}\")\n",
    "  tail = newick_string[start_idx:]\n",
    "  #print(f\"tail: {tail}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  for i in range(len(tail)):\n",
    "    if tail[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if tail[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = tail[: i+1]\n",
    "      #print(f\"Functional branch: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_position_branch_recombination_m8(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_position_branch_recombination_m8\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  branch1 = find_recombinant_branch_m8(newick_string1)\n",
    "  branch2 = find_recombinant_branch_m8(newick_string2)\n",
    "  #print(f\"\\nbranch1: {branch1}\")\n",
    "  #print(f\"branch2: {branch2}\")\n",
    "  # Branches are interchanged\n",
    "  newick_string1 = newick_string1.replace(branch1, branch2)\n",
    "  newick_string2 = newick_string2.replace(branch2, branch1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "def newick_leaves_recombination_m8(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_leaves_recombination_m8\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  leaves1 = find_newick_leaves_m8(newick_string1)  \n",
    "  leaves2 = find_newick_leaves_m8(newick_string2)\n",
    "  if not leaves1 or not leaves2:\n",
    "    return newick_string1, newick_string2\n",
    "  # Select a leaf at random from each tree\n",
    "  leaf1 = random.choice(leaves1)\n",
    "  leaf2 = random.choice(leaves2)\n",
    "  #print(f\"\\nleaf1: {leaf1}\")\n",
    "  #print(f\"leaf2: {leaf2}\")\n",
    "  # Leaves are interchanged\n",
    "  newick_string1 = newick_string1.replace(leaf1, leaf2)\n",
    "  newick_string2 = newick_string2.replace(leaf2, leaf1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "\n",
    "def find_occupied_squares_m8(newick_string):\n",
    "    \"\"\"Detects all the occupied squares in a tree\"\"\"\n",
    "    occupied_squares_set = set()\n",
    "    groups = detect_all_groups_m8(newick_string)\n",
    "    for group in groups:\n",
    "        [x,y,c,r] = extract_info_m8(group)\n",
    "        occupied_squares_set.add((x, y))\n",
    "    return occupied_squares_set\n",
    "\n",
    "def newick_mutation_of_radius_m8(newick_string):\n",
    "  # The pattern of the radius of illumination, r = 0.2\n",
    "  pattern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_r, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  l = len(groups[start_idx])\n",
    "  #print(f\"l: {l}\")\n",
    "  # Select a new radius\n",
    "  new_radius = random.uniform(0.5, 2)\n",
    "  #print(f\"new_radius: {new_radius}\")\n",
    "\n",
    "  new_info = \"r=\" + str(new_radius)\n",
    "  #print(f\"new_info: {new_info}\")\n",
    "  # Replace the info\n",
    "  mutated_newick_string = replace_str_subsequence_m8(newick_string, starts[start_idx], starts[start_idx] + l, new_info)\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  return mutated_newick_string\n",
    "\n",
    " \n",
    "def newick_find_insertion_patterns_m8(newick_string):\n",
    "  \"\"\" All insertion patterns are found and one is chosen at random\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][])]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  return group, start\n",
    " \n",
    "\n",
    "def insertion_of_branch_m8(newick_string):\n",
    "  #original_newick_string = newick_string\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  # A group together its start site are elected\n",
    "  group, start =  newick_find_insertion_patterns_m8(newick_string)\n",
    "  # String to be inserted\n",
    "  # child_string = \"(Node_m8[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node_m8[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07])Node1[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.95]\"\n",
    "  child_string = complete_tree_generator_m8(max_depth = 2, max_numb_children = 2, max_node_radius= 1.5)\n",
    "  if group:\n",
    "    #print(f\"Group (main): {group}\")\n",
    "    #print(f\"Start (main): {start}\")\n",
    "    # Does the group have children?\n",
    "    #char_before = newick_string[start-1]\n",
    "    #print(f\"char_before: {char_before}\")\n",
    "\n",
    "    newick_string = newick_string.replace(\"])\", \"],\" + child_string + \")\")\n",
    "  else:\n",
    "    # a tree is given as a child to the root\n",
    "    newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "  # Cosmetics\n",
    "  #newick_string = newick_string.replace(\",,\", \",\")\n",
    "\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {original_newick_string}\")\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_left_parenthesis__m8(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns (N  are found and one is chosen at random\n",
    "      The new info will be inserted to the right of the parenthesis\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[(][N]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m80: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m8(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m8(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m8(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \"(\" + child_string +  \")\" + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "def select_one_insertion_pattern_comma__m8(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All  commas are found and one is chosen at random,\n",
    "  the child will be inserted after the comma\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][,]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m80: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m8(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m8(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m8(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+2] + \"(\" + child_string + \")\" + newick_string[start+2:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_parentheses__m8(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns ]) are found and one is chosen at random\n",
    "      The new info will be inserted between ] and ) as a brother\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][)]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m80: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m8(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m8(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m8(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \",\" + child_string + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "def insertion_of_leaf_m8(newick_string, counter, max_illumination_radius):    #original_newick_string = newick_string\n",
    "    \"\"\" All insertion patterns are found and one is chosen at random\n",
    "        for the new info to be inserted \"\"\"\n",
    "    case = random.choice([1, 2, 3])  # Randomly choose between three insertion methods\n",
    "    match case:\n",
    "        case 1:\n",
    "            newick_string = select_one_insertion_pattern_comma__m8(newick_string, counter, max_illumination_radius)\n",
    "        case 2:\n",
    "            newick_string = select_one_insertion_pattern_parentheses__m8(newick_string, counter, max_illumination_radius)\n",
    "        case 3:\n",
    "            newick_string = select_one_insertion_pattern_left_parenthesis__m8(newick_string, counter, max_illumination_radius)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    newick_string = newick_string.replace(\" \", \"\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    return newick_string\n",
    "\n",
    "\n",
    "\n",
    "def deletion_of_leaf_m8(newick_string):\n",
    "    \"\"\"A leaf is deleted at random from the Newick string. \n",
    "        If there are no leaves, the original string is returned.\"\"\"\n",
    "    # All leaves are detected\n",
    "    leaves = find_newick_leaves_m8(newick_string)\n",
    "    if not leaves:\n",
    "        return newick_string  # No leaves to delete\n",
    "    # Select a leaf at random\n",
    "    leaf_to_delete = random.choice(leaves)\n",
    "    # Remove the selected leaf from the Newick string\n",
    "    newick_string = newick_string.replace(leaf_to_delete, \"\")\n",
    "    # Clean up any potential formatting issues (like double commas)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    return newick_string\n",
    "\n",
    "  \n",
    "\n",
    "def newick_mutation_branches_m8(newick_string):\n",
    "  # Mutation of radius of illumination and of complete branches\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    newick_string = newick_mutation_of_radius_m8(newick_string)\n",
    "    newick_string = insertion_of_branch_m8(newick_string)\n",
    "    newick_string = deletion_of_branch_m8(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "def newick_mutation_leaves_m8(newick_string, ga_params):\n",
    "  \"\"\"Radius is mutated, and only leaves can be inserted or deleted\"\"\"\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    # Mutation of radius of illumination and of complete leaves\n",
    "    newick_string = newick_mutation_of_radius_m8(newick_string)\n",
    "    newick_string = insertion_of_leaf_m8(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n",
    "    newick_string = deletion_of_leaf_m8(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "def plot_illuminated_area_m8(ax, illuminated_coords, title, color='blue', alpha=0.5):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not illuminated_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y in illuminated_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=color, alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(c[0] for c in illuminated_coords) - 1\n",
    "    max_x = max(c[0] for c in illuminated_coords) + 2\n",
    "    min_y = min(c[1] for c in illuminated_coords) - 1\n",
    "    max_y = max(c[1] for c in illuminated_coords) + 2\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "# --- MAIN EVOLUTIONARY SIMULATION LOOP ---\n",
    "\n",
    "def oneEvolutionaryProcess_m8(target_tree_params, ga_params):\n",
    "    # A previous code by Gemini was reused.\n",
    "\n",
    "    # 1. Generate Target Tree and its Illuminated Area\n",
    "    #print(\"\\n--- Generating Target Tree ---\")\n",
    "    #random.seed(ga_params['seed'] + 1) # Use a different seed for target generation\n",
    "    target_colored_tree = complete_tree_generator_m8(total_number_of_nodes = target_tree_params['total_number_of_nodes'], max_number_children = target_tree_params['max_number_of_children'], max_node_radius = target_tree_params['max_node_radius'])\n",
    "    print(f\"\\nTarget colored tree: {target_colored_tree}\")\n",
    "    number_of_groups = count_groups_m8(target_colored_tree)\n",
    "    print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n",
    "    target_colored_area = draw_tree_newick_m8(target_colored_tree, title=f\"Target colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    print(f\"Colored area in target tree: {target_colored_area}\")\n",
    "    title = 'Target colored area'\n",
    "    draw_colored_squares(target_colored_area, title)\n",
    "    \n",
    "    # length as a string\n",
    "    length_of_target_tree = len(target_colored_tree)\n",
    "\n",
    "\n",
    "    # 2. Initialize Population\n",
    "    #random.seed(ga_params['seed']) # Reset seed for GA population generation\n",
    "    #print(f\"\\n--- Initializing Population of {ga_params['pop_size']} Trees ---\")\n",
    "    population = create_initial_population_newick_m8(\n",
    "        pop_size=ga_params['pop_size'],average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'], max_node_radius=ga_params['max_node_radius']  )\n",
    "         \n",
    "       \n",
    "    print(f\"Population initialized with {len(population)} individuals.\")\n",
    "    #print(f\"Population: {population}\")\n",
    "\n",
    "    best_tree_overall = \"\"\n",
    "    best_fitness_overall = -1.0\n",
    "    history = []\n",
    "\n",
    "    # 3. Evolutionary Loop\n",
    "    #print(f\"\\n--- Starting Evolutionary Loop for {ga_params['generations']} Generations ---\")\n",
    "    for generation in range(ga_params['generations']):\n",
    "        if generation % 1000 == 0:\n",
    "            print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n",
    "        fitness_scores = [calculate_fitness_illumination_m8(tree, target_colored_area)for tree in population]\n",
    "\n",
    "        current_best_fitness = max(fitness_scores)\n",
    "        current_best_tree = population[np.argmax(fitness_scores)]\n",
    "\n",
    "        if current_best_fitness > best_fitness_overall:\n",
    "            best_fitness_overall = current_best_fitness\n",
    "            best_tree_overall = copy.deepcopy(current_best_tree) # Deepcopy to save the best tree\n",
    "\n",
    "        history.append(current_best_fitness)\n",
    "\n",
    "        #print(f\"Generation {generation+1}/{ga_params['generations']}: Best Fitness = {current_best_fitness:.4f} (Overall Best: {best_fitness_overall:.4f})\")\n",
    "\n",
    "        if best_fitness_overall >= 0.999: # Almost perfect match\n",
    "            #print(\"Target illumination achieved (or very close)! Stopping evolution.\")\n",
    "            return target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, history\n",
    "\n",
    "        # Selection\n",
    "        parents = select_parents_newick(population, fitness_scores, ga_params['pop_size'] // 2)\n",
    "\n",
    "        # Create next generation\n",
    "        next_population = []\n",
    "        # Elitism: Keep the best individual from the current generation\n",
    "        if best_tree_overall and current_best_fitness == best_fitness_overall: # Only if it's the current best in this generation\n",
    "            next_population.append(copy.deepcopy(current_best_tree))\n",
    "\n",
    "        while len(next_population) < ga_params['pop_size']:\n",
    "            if len(parents) < 2: # Not enough parents for crossover, fill with new random trees\n",
    "                new_tree =  complete_tree_generator_m8(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'] )\n",
    "                if new_tree:\n",
    "                    next_population.append(new_tree)\n",
    "                continue\n",
    "\n",
    "            # Randomly pick two parents (can be the same parent multiple times with replacement for simplicity)\n",
    "            p1 = random.choice(parents)\n",
    "            p2 = random.choice(parents)\n",
    "            child1, child2 = p1,p2 # No crossover, just pass parents to children for mutation\n",
    "            if recombination_of_leaves:\n",
    "                # Crossover newick_position_branch_recombination_m8\n",
    "                # child1, child2 = newick_position_branch_recombination_m8(p1, p2)\n",
    "                # Crossover newick_leaves_recombination_m8\n",
    "                child1, child2 =newick_leaves_recombination_m8(p1,p2)\n",
    "            # Mutation: Apply mutation to both children\n",
    "            mutated_child1 = newick_mutation_leaves_m8(child1, ga_params)\n",
    "            mutated_child2 = newick_mutation_leaves_m8(child2, ga_params)\n",
    "\n",
    "\n",
    "            next_population.append(mutated_child1)\n",
    "            if len(next_population) < ga_params['pop_size']:\n",
    "                next_population.append(mutated_child2)\n",
    "\n",
    "        population = next_population[:ga_params['pop_size']]\n",
    "        # print(f\"Population size for next generation: {len(population)}\")\n",
    "        # print(f\"Best fitness this generation: {current_best_fitness:.4f}, Overall best fitness: {best_fitness_overall:.4f}\")\n",
    "        # print(f\"Best tree this generation: {current_best_tree}\")\n",
    "        # If you want to see the entire population, uncomment the following line:\n",
    "        # print(f\"Population: {population}\")\n",
    "        # Cutt-off for the length of the Newick string to avoid excessive growth\n",
    "        population = [tree for tree in population if len(tree) <= 2 * length_of_target_tree]  # Allow some growth but limit it\n",
    "        #print(f\"Population size after length cut-off: {len(population)}\")\n",
    "        # If the population is too small, refill\n",
    "        while len(population) < ga_params['pop_size']:\n",
    "            new_tree = complete_tree_generator_m8(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'])\n",
    "            if new_tree:\n",
    "                population.append(new_tree) \n",
    "    #print(\"\\n--- Evolution Finished ---\")\n",
    "    #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n",
    "    #return target_colored_tree, target_colored_area, best_tree_overall, best_fitness_overall, history, number_of_groups\n",
    "    return history, number_of_groups\n",
    "     \n",
    "        \n",
    "  \n",
    "        \n",
    "def draw_history_m8(fitness_history, number_of_nodes, population_size):\n",
    "    print(\"draw_histories_m8(fitness_historiy, number_of_nodes, population_size)\")\n",
    "    # Plot the fitness history \n",
    "    x_range = range(len(fitness_history))\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    plt.plot(x_range, fitness_history, label=f'pop_size {population_size}, Number of nodes {number_of_nodes}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Evolution of the Fitness of the best colored morpho-tree with pop_size = {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()        \n",
    " \n",
    "def draw_all_histories_m8(fitness_histories, numbers_of_nodes, population_sizes):\n",
    "    print(\"draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\")\n",
    "    # Plot all the fitness histories together\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    for i in range(len(fitness_histories)):\n",
    "        # x_range contains all numbers from 0 to the length of the fitness history for the current length of target sequence.\n",
    "        x_range = range(len(fitness_histories[i]))\n",
    "        plt.plot(x_range, fitness_histories[i], label=f'pop_size {population_sizes[i]}, Number of nodes {numbers_of_nodes[i]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title('GA: Fitness of the best morpho-tree over Generations for Different numbers of pop-sizes and nodes')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    " \n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    ''' An Evolutionary process is triggered for each pop-size value.\n",
    "    The number of nodes is not predefined, \n",
    "    but results from interation with random generation.\n",
    "    The fitness history is stored in a list, \n",
    "    and then all the histories are plotted together.\n",
    "    '''\n",
    "    population_sizes = [12, 25, 50, 100, 200]   \n",
    "    numbers_of_generations = [70001, 70002, 70003, 70004, 70005 ] \n",
    "    # List to store the normalized best fitness history for each length of target sequence   \n",
    "    fitness_histories = []\n",
    "    numbers_of_nodes = []  # Example numbers of generations for different lengths of target sequence\n",
    "    for i in range(len(population_sizes)):\n",
    "        ga_params['pop_size'] = population_sizes[i]\n",
    "        ga_params['generations'] =  numbers_of_generations[i]\n",
    "        print(f\"\\nRunning evolutionary process for pop size= {ga_params['pop_size']} and generations = {ga_params['generations']}\")\n",
    "        history_m8, number_of_groups_m8 = oneEvolutionaryProcess_m8(target_tree_params, ga_params)\n",
    "        fitness_histories.append(history_m8)\n",
    "        numbers_of_nodes.append(number_of_groups_m8)\n",
    "    draw_all_histories_m8(fitness_histories, numbers_of_nodes, population_sizes)\n",
    "     \n",
    "     \n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567e2b26-1f8f-4e0d-a357-ca35114860d5",
   "metadata": {},
   "source": [
    "**Ejercicio.** Corre el programa y redacta tus observaciones.\n",
    ">\n",
    "> *Observación.* Las curvas determinadas por los tamaños de población de 12, 25, 50, 200 encajan en la expectativa que dice: más recursos para la Evolución implica una mayor aptitud final que se da por el nivel terminal. Pero, la curva para un tamaño de población de 100 rompe esta regla porque vemos que un tamaño de población de 12 funciona igual que otro de 100. Este tipo de fracasos son comunes.\n",
    "\n",
    "**Ejercicio.** ¿Consideras que el número de nodos del árbol objetivo es una buena medida de complejidad que sirve como predictor del comportamiento de la Evolución en su batalla contra la complejidad?\n",
    ">\n",
    "> No. Una buena medida de la complejidad debe predecir que la complejidad aplasta a la Evolución, y que cuando los recursos se agotan, el nivel disminuye. Lo mismo debe suceder cuando la complejidad crece, aquí el número de nodos. Este no es el caso aquí.  Sin embargo, nuestros éxitos parciales nos llevan a creer que una versión modificada podría funcionar. Se podría intentar aumentar la pleiotropía y disminuir la proporción de árboles que cumplen la función objetivo de encajar un área objetivo. O uno podría unir otros indicadores o descriptores al número de nodos. Tambien podriamos incluir otros generadores de complejidad.\n",
    "\n",
    "**Ejercicio.** Los Procesos Evolutivos vistos alcanzan niveles por debajo del 70% de la tarea que corresponde a la perfección. Dilucidar si esta meseta es un estado transitorio creado en el camino de la Evolución hacia la perfección o si es el estado final de equilibrio del Proceso Evolutivo. Cuando una meseta corresponde a un estado estable, si la aptitud está por debajo de la meseta, tiende a elevarse, pero si está por encima,  tiende a disminuir y se estabiliza en la meseta. \n",
    ">\n",
    "> *Respuesta.* El siguiente programa crea una población perfecta, en la que cada miembro es una copia del árbol objetivo. Se observa su Evolución, si la perfección se mantiene o no, y si degenera hasta un valor de equilibrio en torno a 0,7.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4dd39fa-1ec1-4361-acad-1c82e741ecc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\n",
      "PURPOSE: \n",
      " Evolution searches in the space of colored trees \n",
      " to match a target colored area associated to a target tree.\n",
      "EXPERIMENT 1793129388.4808009\n",
      "TREE PARAMETERS: {'total_number_of_nodes': 15, 'max_number_of_children': 2, 'max_node_radius': 1.5}\n",
      "\n",
      "Target colored tree: ((((Node14[x=1.0,y=0.0,c=5,r=0.63])Node9[x=2.0,y=-1.0,c=5,r=1.4])Node5[x=2.0,y=0.0,c=3,r=1.01],((Node13[x=2.0,y=2.0,c=5,r=1.44])Node8[x=3.0,y=1.0,c=0,r=1.29],(Node12[x=4.0,y=-1.0,c=4,r=1.2],Node11[x=4.0,y=0.0,c=2,r=1.06])Node7[x=3.0,y=0.0,c=0,r=0.5])Node4[x=2.0,y=1.0,c=1,r=1.06])Node2[x=1.0,y=1.0,c=6,r=0.99],(((Node10[x=1.0,y=-2.0,c=3,r=0.93])Node6[x=0.0,y=-1.0,c=4,r=0.79])Node3[x=-1.0,y=0.0,c=5,r=0.9])Node1[x=-1.0,y=-1.0,c=4,r=1.12])Node_0[x=0,y=0,c=3,r=1]\n",
      "Number of groups or nodes in target tree: 15\n",
      "draw_tree_links_m9\n",
      "Newick String: ((((Node14[x=1.0,y=0.0,c=5,r=0.63])Node9[x=2.0,y=-1.0,c=5,r=1.4])Node5[x=2.0,y=0.0,c=3,r=1.01],((Node13[x=2.0,y=2.0,c=5,r=1.44])Node8[x=3.0,y=1.0,c=0,r=1.29],(Node12[x=4.0,y=-1.0,c=4,r=1.2],Node11[x=4.0,y=0.0,c=2,r=1.06])Node7[x=3.0,y=0.0,c=0,r=0.5])Node4[x=2.0,y=1.0,c=1,r=1.06])Node2[x=1.0,y=1.0,c=6,r=0.99],(((Node10[x=1.0,y=-2.0,c=3,r=0.93])Node6[x=0.0,y=-1.0,c=4,r=0.79])Node3[x=-1.0,y=0.0,c=5,r=0.9])Node1[x=-1.0,y=-1.0,c=4,r=1.12])Node_0[x=0,y=0,c=3,r=1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(2, 1, 6), (2, 2, 5), (1, -3, 3), (0, 1, 6), (3, 2, 1), (3, 1, 5), (4, 1, 2), (-1, 0, 5), (1, 0, 1), (-2, 0, 5), (3, 0, 3), (3, -1, 4), (2, 0, 1), (4, 2, 0), (1, -1, 3), (1, -2, 3), (2, -1, 3), (2, -2, 3), (0, 2, 6), (2, 0, 3), (2, 2, 0), (5, 0, 2), (3, 1, 0), (2, -1, 5), (2, -2, 5), (5, -2, 4), (5, -1, 4), (0, -3, 3), (2, 0, 5), (0, -1, 4), (0, -2, 4), (0, 1, 3), (5, 0, 4), (3, 1, 2), (0, 0, 4), (4, 0, 0), (-1, -1, 4), (-1, -2, 4), (-2, -1, 4), (-2, -2, 4), (0, 1, 5), (-1, 1, 3), (1, 2, 1), (4, -1, 2), (-1, 0, 4), (-2, 0, 4), (0, 0, 6), (1, 0, 3), (3, 0, 5), (2, 0, 0), (4, 0, 2), (2, 1, 1), (1, -1, 5), (1, -2, 5), (3, 2, 5), (4, -1, 4), (4, -2, 4), (1, 0, 5), (2, -3, 3), (4, 0, 4), (2, 1, 3), (1, 2, 5), (3, 3, 5), (3, -1, 3), (3, 0, 0), (1, 1, 6), (0, -2, 3), (0, -1, 3), (2, 1, 5), (3, 1, 1), (3, 2, 0), (0, 0, 3), (2, 3, 5), (3, -1, 5), (3, -2, 5), (3, 0, 2), (-1, -1, 3), (0, -1, 5), (2, 2, 6), (-2, 1, 5), (-1, 1, 5), (3, 1, 3), (-1, 0, 3), (0, 0, 5), (1, 1, 1), (3, 0, 4), (-2, -1, 5), (-1, -1, 5), (2, 1, 0), (1, -1, 4), (1, -2, 4), (3, -1, 0), (1, 0, 4), (1, 1, 3), (5, 1, 2), (2, 2, 1), (1, 3, 5), (5, -1, 2), (3, -1, 2), (1, 0, 6), (1, 1, 5), (2, 0, 6), (1, 2, 6), (4, 1, 0), (3, -2, 4), (3, 0, 1)}\n",
      "Population initialized with 70 individuals.\n",
      "\n",
      "--- Generation 1/500000, Best fitness -1.0 ---\n",
      "\n",
      "--- Generation 10001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 20001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 30001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 40001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 50001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 60001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 70001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 80001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 90001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 100001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 110001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 120001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 130001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 140001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 150001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 160001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 170001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 180001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 190001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 200001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 210001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 220001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 230001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 240001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 250001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 260001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 270001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 280001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 290001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 300001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 310001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 320001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 330001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 340001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 350001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 360001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 370001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 380001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 390001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 400001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 410001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 420001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 430001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 440001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 450001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 460001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 470001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 480001/500000, Best fitness 1.0 ---\n",
      "\n",
      "--- Generation 490001/500000, Best fitness 1.0 ---\n",
      "best_tree_overall: ((((Node14[x=1.0,y=0.0,c=5,r=0.63])Node9[x=2.0,y=-1.0,c=5,r=1.4])Node5[x=2.0,y=0.0,c=3,r=1.01],((Node13[x=2.0,y=2.0,c=5,r=1.44])Node8[x=3.0,y=1.0,c=0,r=1.29],(Node12[x=4.0,y=-1.0,c=4,r=1.2],Node11[x=4.0,y=0.0,c=2,r=1.06])Node7[x=3.0,y=0.0,c=0,r=0.5])Node4[x=2.0,y=1.0,c=1,r=1.06])Node2[x=1.0,y=1.0,c=6,r=0.99],(((Node10[x=1.0,y=-2.0,c=3,r=0.93])Node6[x=0.0,y=-1.0,c=4,r=0.79])Node3[x=-1.0,y=0.0,c=5,r=0.9])Node1[x=-1.0,y=-1.0,c=4,r=1.12])Node_0[x=0,y=0,c=3,r=1]\n",
      "best_fitness_overall: 1.0\n",
      "draw_tree_links_m9\n",
      "Newick String: ((((Node14[x=1.0,y=0.0,c=5,r=0.63])Node9[x=2.0,y=-1.0,c=5,r=1.4])Node5[x=2.0,y=0.0,c=3,r=1.01],((Node13[x=2.0,y=2.0,c=5,r=1.44])Node8[x=3.0,y=1.0,c=0,r=1.29],(Node12[x=4.0,y=-1.0,c=4,r=1.2],Node11[x=4.0,y=0.0,c=2,r=1.06])Node7[x=3.0,y=0.0,c=0,r=0.5])Node4[x=2.0,y=1.0,c=1,r=1.06])Node2[x=1.0,y=1.0,c=6,r=0.99],(((Node10[x=1.0,y=-2.0,c=3,r=0.93])Node6[x=0.0,y=-1.0,c=4,r=0.79])Node3[x=-1.0,y=0.0,c=5,r=0.9])Node1[x=-1.0,y=-1.0,c=4,r=1.12])Node_0[x=0,y=0,c=3,r=1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colored area in target tree: {(2, 1, 6), (2, 2, 5), (1, -3, 3), (0, 1, 6), (3, 2, 1), (3, 1, 5), (4, 1, 2), (-1, 0, 5), (1, 0, 1), (-2, 0, 5), (3, 0, 3), (3, -1, 4), (2, 0, 1), (4, 2, 0), (1, -1, 3), (1, -2, 3), (2, -1, 3), (2, -2, 3), (0, 2, 6), (2, 0, 3), (2, 2, 0), (5, 0, 2), (3, 1, 0), (2, -1, 5), (2, -2, 5), (5, -2, 4), (5, -1, 4), (0, -3, 3), (2, 0, 5), (0, -1, 4), (0, -2, 4), (0, 1, 3), (5, 0, 4), (3, 1, 2), (0, 0, 4), (4, 0, 0), (-1, -1, 4), (-1, -2, 4), (-2, -1, 4), (-2, -2, 4), (0, 1, 5), (-1, 1, 3), (1, 2, 1), (4, -1, 2), (-1, 0, 4), (-2, 0, 4), (0, 0, 6), (1, 0, 3), (3, 0, 5), (2, 0, 0), (4, 0, 2), (2, 1, 1), (1, -1, 5), (1, -2, 5), (3, 2, 5), (4, -1, 4), (4, -2, 4), (1, 0, 5), (2, -3, 3), (4, 0, 4), (2, 1, 3), (1, 2, 5), (3, 3, 5), (3, -1, 3), (3, 0, 0), (1, 1, 6), (0, -2, 3), (0, -1, 3), (2, 1, 5), (3, 1, 1), (3, 2, 0), (0, 0, 3), (2, 3, 5), (3, -1, 5), (3, -2, 5), (3, 0, 2), (-1, -1, 3), (0, -1, 5), (2, 2, 6), (-2, 1, 5), (-1, 1, 5), (3, 1, 3), (-1, 0, 3), (0, 0, 5), (1, 1, 1), (3, 0, 4), (-2, -1, 5), (-1, -1, 5), (2, 1, 0), (1, -1, 4), (1, -2, 4), (3, -1, 0), (1, 0, 4), (1, 1, 3), (5, 1, 2), (2, 2, 1), (1, 3, 5), (5, -1, 2), (3, -1, 2), (1, 0, 6), (1, 1, 5), (2, 0, 6), (1, 2, 6), (4, 1, 0), (3, -2, 4), (3, 0, 1)}\n",
      "best_fitness_overall: 1.0\n",
      "draw_histories_m9(fitness_historiy, number_of_nodes, population_size)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 1 hours, 45 minutes, 18 seconds\n"
     ]
    }
   ],
   "source": [
    "# Program evolution_of_perfectcolored_morpho_trees_m9.py\n",
    "# This program reports the battle of Evolution \n",
    "# in the space of colored morpho-trees \n",
    "# to match a target colored area proposed by a target colored morpho-tree.\n",
    "# We merge the code for Evolution to that for generating and drawing a colored tree.\n",
    "# Edited with Spider 6.\n",
    "\n",
    "# =====Reused:  Program colored_tree_generator_and_drawer_m9.py\n",
    "# This program generates a colored morpho-tree as a Newick formula,\n",
    "# and draws it. \n",
    "# Children are located in the immediate neighborhood of the father.\n",
    "# If positions are occupied, no children.\n",
    "# Then, children are generated only when there is place for them.\n",
    "\n",
    "\n",
    "# ====   Grammar_generator =======\n",
    "\n",
    "# This program uses a hidden grammar to generate Newick expressions \n",
    "# that encode for colored morphological trees.\n",
    "# The information of each node is generated on the image of\n",
    "#   Node25[x=19,y=0,c=3,r=14.3]\n",
    "# The rules of the grammar are:\n",
    "# 1. Initial string or seed: `s = ()n`\n",
    "# 2. Generate a random digit: x\n",
    "# 3. Replace in `s`, `()` by x copies of `(()n ` separated by a comma.\n",
    "# 4. Iterate.\n",
    "# 5. Replace each `n`by the info of a node whose values are generated at random but the index of the tag is a counter with the iterative process.\n",
    "# 6. Delete the tag \"()\" that appears in the last appended children.\n",
    "# An ordered set of rules like this is a Generative Grammar.\n",
    "# The grammar is hidden because no n is generated,\n",
    "# because beforehand it is replaced by the info of a node.\n",
    "# With the assistance of Colab and reusing a previous code by Gemini.\n",
    "  \n",
    "  \n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import math\n",
    "import copy # For deep copying trees during reproduction and mutation\n",
    "import re # Required for newick parsing\n",
    "#import itertools # Required for sequential replacement\n",
    "import sys # Import sys module to adjust recursion limit\n",
    "sys.setrecursionlimit(3000)\n",
    "import time\n",
    "#from datetime import datetime, timedelta\n",
    "\n",
    "# trace memory allocation \n",
    "#https://www.geeksforgeeks.org/python/monitoring-memory-usage-of-a-running-python-program/\n",
    "# this consumes too much cpu-time by a multiplicative factor, say 5 for 1000 lines.\n",
    "import tracemalloc \n",
    "\n",
    "\n",
    "#Activate  memory monitoring?\n",
    "memory_monitoring = False \n",
    "recombination_of_leaves = False\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# starting the memory allocation monitoring\n",
    "if memory_monitoring:\n",
    "    tracemalloc.start() \n",
    "\n",
    "\n",
    "palette = ['lightgray', 'salmon', 'wheat', 'palegreen', 'powderblue', 'mediumpurple', 'pink']\n",
    "# Parameters for the target tree generation\n",
    "# Parameters for the target tree  \n",
    "target_tree_params = {\n",
    "  'total_number_of_nodes': 15,\n",
    "  'max_number_of_children': 2,\n",
    "  'max_node_radius': 1.5,\n",
    "}\n",
    "\n",
    "# Parameters for the Genetic Algorithm\n",
    "ga_params = {\n",
    "   #'seed': 47423648, #the seed of the target tree is this plus 1.\n",
    "  'pop_size': 70,\n",
    "  'generations': 500000,\n",
    "  'average_number_of_nodes':  target_tree_params['total_number_of_nodes'],\n",
    "  'max_number_of_children': target_tree_params['max_number_of_children'],\n",
    "  'max_node_radius': 1.5,\n",
    "  'mutation_rate': 0.001, \n",
    "  'crossover_rate': 0.001  \n",
    "}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    " \n",
    "\n",
    " \n",
    "def print_parameters_m9(tree_params, ga_params):\n",
    "    print(\"LAB: EVOLUTION OF NEWICK COLORED MORPHO-TREES\")\n",
    "    print(\"PURPOSE: \"\n",
    "          + \"\\n Evolution searches in the space of colored trees \"\n",
    "        + \"\\n to match a target colored area associated to a target tree.\")\n",
    "    print(f\"EXPERIMENT {start_time}\")\n",
    "    print(f\"TREE PARAMETERS: {tree_params}\")\n",
    "     \n",
    "\n",
    "\n",
    "\n",
    "def replace_str_subsequence_m9(original_str, start_idx, end_idx, new_subsequence):\n",
    "    \"\"\" Replace a subsequence from start_idx to end_idx with another string.\n",
    "        By Google\"\"\"\n",
    "    # Concatenate the pieces together\n",
    "    return original_str[:start_idx] + new_subsequence + original_str[end_idx:]\n",
    "\n",
    "def replace_strig_of_length_m9(original_str, replacement, position, length_to_replace):\n",
    "    \"\"\"\n",
    "    Replaces a subsequence of given length.\n",
    "    :param original_str: The main text string\n",
    "    :param replacement: The new text you want to insert\n",
    "    :param position: The starting index of the old subsequence\n",
    "    :param length_to_replace: The length of the old subsequence being removed\n",
    "    \"\"\"\n",
    "    before = original_str[:position]\n",
    "    after = original_str[position + length_to_replace:]\n",
    "    return before + replacement + after\n",
    "\n",
    "#=== COLORED TREE GENERATOR===\n",
    "\n",
    "def detect_all_groups_m9(newick_string):\n",
    "    \"\"\"Al substrings that encodes for the info of a node, step from father, center and radius are detected.\"\"\"\n",
    "\n",
    "    #print(\"detect_all_groups13(newick_string)\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    # Detect a pattern on the model  info = NodeB_3_x_2[x=2,y=2,c=4,r=3]\n",
    "    #info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n",
    "    #info_words = r'[a-zA-Z_][a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]$'\n",
    "                                                    #info_words = r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    #info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    info_words =  r'[a-zA-Z_0-9_]*\\[x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,c=-?[0-9],r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "    \n",
    "    starts = []\n",
    "    groups = []\n",
    "    for match in re.finditer(info_words, newick_string):\n",
    "        #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "        #print(f\"    starts at index: {match.start()}\")\n",
    "        starts.append(match.start())\n",
    "        groups.append(match.group())\n",
    "    #number_of_groups = len(starts)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    #print(f\"starts of groups: {starts}\")\n",
    "    #print(f\"groups: {groups}\")\n",
    "\n",
    "\n",
    "    return groups\n",
    "\n",
    "def count_groups_m9(newick_string):\n",
    "    \"\"\"The number of groups is counted.\"\"\"\n",
    "    groups = detect_all_groups_m9(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    return number_of_groups\n",
    "\n",
    "def extract_info_m9(group):\n",
    "  \"\"\"The info of each group is extracted by tokenizers from the string.\"\"\"\n",
    "  patern_x = r'x=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_y = r'y=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_c = r'c=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  patern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  x = re.findall(patern_x, group)\n",
    "  y = re.findall(patern_y, group)\n",
    "  c = re.findall(patern_c, group)\n",
    "  r = re.findall(patern_r, group)\n",
    "  x = float(x[0][2:])\n",
    "  y = float(y[0][2:])\n",
    "  c = int(float(c[0][2:]))\n",
    "  r = float(r[0][2:])\n",
    "  return [x,y,c,r]\n",
    "\n",
    "\n",
    "def extract_all_infos_m9(newick_string):\n",
    "    #print(\"extract_all_infos_m9\")\n",
    "    # Groups are detected. A group is the encoding of the parameters of a node in an info.\n",
    "    groups = detect_all_groups_m9(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "\n",
    "    # Info is extracted from each group and\n",
    "    # all infos are kept in corresponding arrays.\n",
    "    x = []\n",
    "    y = []\n",
    "    c = []\n",
    "    r = []\n",
    "    #print(\"\\nInfo is extracted form each group\")\n",
    "    for i in range(number_of_groups):\n",
    "      #print(f\"group[{i}]: {groups[i]}\")\n",
    "      #print(f\"len(group[{i}]): {len(groups[i])}\")\n",
    "      [xi,yi,ci,ri] = extract_info_m9(groups[i])\n",
    "      #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n",
    "      x.append(xi)\n",
    "      y.append(yi)\n",
    "      c.append(ci)\n",
    "      r.append(ri)\n",
    "    return  x,y,c,r\n",
    "\n",
    "\n",
    "def get_neighboring_coordinates_m9(cell):\n",
    "  \"\"\"Immediate neighboring coordinates of a cell are generated\"\"\"\n",
    "  neighbors = []\n",
    "  for dx in [-1, 0, 1]:\n",
    "      for dy in [-1, 0, 1]:\n",
    "          if dx == 0 and dy == 0:\n",
    "              continue  # Exclude the current square itself\n",
    "          neighbors.append((cell[0] + dx, cell[1] + dy))\n",
    "  return neighbors\n",
    "\n",
    " \n",
    "\n",
    "def find_father_center_m9(newick_string, j):\n",
    "  \"\"\" Finds the address of the father of a node at j\"\"\"\n",
    "  #print(\"find_father_center13(s, j)\")\n",
    "  #print(f\"s: {newick_string}\")\n",
    "  #print(f\"j: {j}\")\n",
    "\n",
    "  #Right tail is recruited\n",
    "  tail = newick_string[j+1:]\n",
    "  father_group = detect_all_groups_m9(tail)\n",
    "  if father_group:\n",
    "    #print(f\"Father_group 1: {father_group}\")\n",
    "    [xf,yf,cf,rf] = extract_info_m9(father_group[0])\n",
    "    #print(f\"Info:  x: {xf}, y: {yf}, c: {cf}, r: {rf}\")\n",
    "    father_center = [xf,yf]\n",
    "    return father_center\n",
    "  else:\n",
    "   return \"\"\n",
    "\n",
    "\n",
    "\n",
    "def node_info_generator_m9(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius):\n",
    "  \"\"\"For each node, info is generated on the model\n",
    "     Node_26[a=1,b=-1,x=3,y=5,r=0.85]\n",
    "     by replacing n by the info of a node whose values are generated at random\n",
    "     but the index of the tag is a counter with the iterative process.\n",
    "      \"\"\"\n",
    "  #print(\"node_info_generator_m9(father_center, counter_of_nodes,  occupied_squares_set, max_node_radius)\")\n",
    "  coordinates_neighbors = get_neighboring_coordinates_m9(father_center)\n",
    "  #print(f\"coordinates_neighbors: {coordinates_neighbors}\")\n",
    "  #print(f\"occupied_squares_set: {occupied_squares_set}\")\n",
    "  # Filter for unoccupied grid squares within this tree\n",
    "  available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in occupied_squares_set]\n",
    "  info = \"\" # future info of a node that represents a cell\n",
    "  if available_square_neighbors:\n",
    "    # Choose a random valid candidate\n",
    "    nx,  ny  = random.choice(available_square_neighbors)\n",
    "    occupied_squares_set.add((nx, ny))\n",
    "    l = len(palette)\n",
    "    nc =  random.randint(0, l-1) # color index\n",
    "    nr = random.uniform(0.5, max_node_radius)\n",
    "    nr = round(nr, 2)\n",
    "    info  = \"Node\" +  str(counter_of_nodes)  + \"[\" + \"x=\" + str(nx) + \",\" + \"y=\"  + str(ny) + \",\"  + \"c=\" + str(nc) + \",\" + \"r=\" + str(nr)+ \"]\"\n",
    "    \n",
    "    info = \"()\"+ info\n",
    "    #print(f\"Generated Node info: {info}\")\n",
    "    \n",
    "    # distance2 = distance squared between father and child.\n",
    "    # Test: distance2 must be 2 or  1. \n",
    "    distance2 = (father_center[0]-nx)**2 + (father_center[1]-ny)**2\n",
    "    if distance2 > 2:\n",
    "        print(f\"distance2 > 2: {distance2}\")\n",
    "  #else:\n",
    "    #print(\"All neighboring places are occupied\")\n",
    "  return info, occupied_squares_set\n",
    "\n",
    "\n",
    "def generate_children_m9(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius):\n",
    "  \"\"\" Generate children in the pattern of a Newick Tree.\n",
    "      Output in the form\n",
    "      n,n,n,n \"\"\"\n",
    "  #print(\"generate_children_m9(max_numb_children)\")\n",
    "  #print(f\"Number of children: {numb_children}\")\n",
    "  generated_children = \"\"\n",
    "  i = 0\n",
    "  while i in range(numb_children) and counter_of_nodes < total_number_of_nodes:\n",
    "    if i == 0:\n",
    "      a = \"\"\n",
    "    else:\n",
    "      a = \",\"\n",
    "    info, occupied_squares_set = node_info_generator_m9(father_center, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "    if info == \"\":\n",
    "      generated_children = \"()\"\n",
    "      return generated_children, counter_of_nodes, occupied_squares_set\n",
    "    else:\n",
    "     generated_children = info + a + generated_children\n",
    "     counter_of_nodes +=  1\n",
    "     i += 1\n",
    "     #print(f\"counter_of_nodes at generate_children_m9= {counter_of_nodes}\")    \n",
    "  generated_children =  \"(\" + generated_children + \")\"\n",
    "  #print(f\"Children: {generated_children}\")\n",
    "  return generated_children, counter_of_nodes, occupied_squares_set\n",
    "\n",
    "\n",
    "def complete_tree_generator_m9(total_number_of_nodes, max_number_children, max_node_radius):\n",
    "  \"\"\" A tree is generated step by step\n",
    "   taking care of generating no child where there is no place for it.\"\"\"\n",
    "  #print(\"complete_tree_generator13(...)\")\n",
    "  occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n",
    "  # Initial tree = root. () is used as a marker for insertion of children. \n",
    "  tree = \"()Node_0[x=0,y=0,c=3,r=1]\"\n",
    "  occupied_squares_set.add((0,0))# Root\n",
    "  #counter of defined nodes\n",
    "  counter_of_nodes = 1\n",
    "  while counter_of_nodes < total_number_of_nodes:\n",
    "      l = len(tree)\n",
    "      # String s is scanned starting from the end\n",
    "      for j in range(l-1, 0, -1):\n",
    "        #print(f\"s: {s}\")\n",
    "        #print(f\"s[j-1] s[j]  :  {s[j-1]}{s[j]}\")\n",
    "        # Find and replace () by a list of children\n",
    "        if tree[j-1] == \"(\" and tree[j] == \")\":\n",
    "          father_center = find_father_center_m9(tree, j)\n",
    "          #print(f\"Father center: {father_center}\")\n",
    "          if not father_center:\n",
    "            continue\n",
    "          numb_children = random.randint(0, max_number_children)\n",
    "          if numb_children == 0:\n",
    "            continue\n",
    "          else:\n",
    "            # Replacement of  () by a list of children\n",
    "            generated_children, counter_of_nodes, occupied_squares_set = generate_children_m9(father_center, numb_children, total_number_of_nodes, counter_of_nodes, occupied_squares_set, max_node_radius)\n",
    "            tree = replace_str_subsequence_m9(tree, j-1, j+1, generated_children)\n",
    "            #print(f\"counter_of_nodes at complete_tree_generator_m9= {counter_of_nodes}\")\n",
    "          #print(f\"{i} {j} Tree: {tree}\")\n",
    "  #print(f\"crude tree: {tree}\")\n",
    "  tree = tree.replace(\"()\", \"\")\n",
    "  #print(f\"final colored tree: {tree}\")\n",
    "  return tree\n",
    "\n",
    "# === DRAWER ===\n",
    "\n",
    "def find_branch_m9(newick_string, final_index):\n",
    "  initial_segment = newick_string[:final_index]\n",
    "  #print(f\"initial_segment: {initial_segment}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  branch = \"\"\n",
    "  for i in range(len(initial_segment)-1,-1,-1):\n",
    "    if initial_segment[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if initial_segment[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = initial_segment[i:final_index+1]\n",
    "      #print(f\"Branch to be deleted: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_find_all_groups_m9(newick_string):\n",
    "  \"\"\" All groups are found and one is chosen at random and returned\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  info_words =  r'[a-zA-Z_0-9_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,x=-?[0-9]+(?:\\.[0-9]+)?,y=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(info_words, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string, 0\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  return groups[start_idx], starts[start_idx]\n",
    "\n",
    "def deletion_of_branch_m9(newick_string ):\n",
    "  group, start =  newick_find_all_groups_m9(newick_string)\n",
    "  #print(f\"Group (main): {group}\")\n",
    "  #print(f\"Start (main): {start}\")\n",
    "  # Does the group have children?\n",
    "  char_before = newick_string[start-1]\n",
    "  #print(f\"char_before: {char_before}\")\n",
    "  if char_before ==  \")\":\n",
    "    #print(\"The group has children\")\n",
    "    #find branch\n",
    "    final_index = start-1\n",
    "    branch = find_branch_m9(newick_string, final_index+1)\n",
    "\n",
    "    branch_and_father = branch + group\n",
    "    #print(f\"  Branch and father to be deleted: {branch_and_father}\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(branch_and_father , \"\")\n",
    "  else:\n",
    "    #print(\"The group has no children\")\n",
    "    #delete group\n",
    "    newick_string = newick_string.replace(group, \"\")\n",
    "\n",
    "  # Cosmetics\n",
    "  newick_string = newick_string.replace(\",,\", \",\")\n",
    "  newick_string = newick_string.replace(\",)\", \")\")\n",
    "  newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "  newick_string = newick_string.replace(\" \", \"\")\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {newick_string}\")\n",
    "  l = len(newick_string)\n",
    "  #print(f\"Length of newick_string: {l}\")\n",
    "  if l == 0 :\n",
    "     newick_string = \"Node_0[a=0,b=-0,x=0,y=0,r=1]\"\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "# =================================================================================\n",
    "# ==== Drawer ===\n",
    "# =================================================================================\n",
    "\n",
    "\n",
    "\n",
    "# colored_coords = get_colored_area_m9(x,y,c,r)\n",
    "def get_colored_area_m9(x,y,c,r):\n",
    "    '''\n",
    "      The illuminated area encloses all unitary squares \n",
    "      that intersects the circles of illumination with the respective color ci.\n",
    "      Squares are denoted by their left bottom corners (xi,yi). These will be found in the set colored_coords.\n",
    "      The radius of illumination is ri\n",
    "    '''\n",
    "    #print(\"get_colored_area_m9\")\n",
    "    #print(f\"x: {x}, y: {y}, c: {c}, r: {r}\")\n",
    "    colored_coords = set()\n",
    "    \n",
    "    for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "        circle_center_x = xi + 0.5\n",
    "        circle_center_y = yi + 0.5\n",
    "        r_sq = ri ** 2\n",
    "        # 1. Add the cell containing the center of the circle and its color\n",
    "        # This covers the case of small circles not intersecting grid lines.\n",
    "        colored_coords.add((math.floor(circle_center_x), math.floor(circle_center_y), ci))\n",
    "\n",
    "        # 2. Horizontal Scan: Check intersections with horizontal grid lines\n",
    "        # Iterate y_line from floor(cy - r) to ciel(cy + r)\n",
    "        # These y_line values represent the y-coordinates of horizontal grid lines.\n",
    "        for y_line in range(math.floor(circle_center_y - ri)-1,\n",
    "                            math.ceil(circle_center_y + ri) + 1):\n",
    "            dy = y_line - circle_center_y\n",
    "            discriminant = r_sq - dy * dy\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dx = math.sqrt(discriminant)\n",
    "                x_min_intersect = circle_center_x - dx\n",
    "                x_max_intersect = circle_center_x + dx\n",
    "\n",
    "                # For each integer x-coordinate that the horizontal segment crosses:\n",
    "                # Add the cells whose bottom-left corner's x-coordinate is in this range.\n",
    "                # This covers cells whose top edge is y_line AND cells whose bottom edge is y_line.\n",
    "                for ix in range(math.floor(x_min_intersect), math.floor(x_max_intersect) + 1):\n",
    "                    colored_coords.add((ix, y_line, ci))\n",
    "                    colored_coords.add((ix, y_line - 1, ci))\n",
    "\n",
    "        # 3. Vertical Scan: Check intersections with vertical grid lines\n",
    "        # Iterate x_line from floor(cx - r) to ciel(cx + r)\n",
    "        # These x_line values represent the x-coordinates of vertical grid lines.\n",
    "        for x_line in range(math.floor(circle_center_x - ri)-1,\n",
    "                            math.ceil(circle_center_x + ri) + 1):\n",
    "            dx = x_line - circle_center_x\n",
    "            discriminant = r_sq - dx * dx\n",
    "\n",
    "            if discriminant >= 0:\n",
    "                dy = math.sqrt(discriminant)\n",
    "                y_min_intersect = circle_center_y - dy\n",
    "                y_max_intersect = circle_center_y + dy\n",
    "\n",
    "                # For each integer y-coordinate that the vertical segment crosses:\n",
    "                # Add the cells whose bottom-left corner's y-coordinate is in this range.\n",
    "                # This covers cells whose right edge is x_line AND cells whose left edge is x_line.\n",
    "                for iy in range(math.floor(y_min_intersect), math.floor(y_max_intersect) + 1):\n",
    "                    # Cell (x_line, iy) - its left edge is at x_line\n",
    "                    colored_coords.add((x_line, iy, ci))\n",
    "                    # Cell (x_line - 1, iy) - its right edge is at x_line\n",
    "                    colored_coords.add((x_line - 1, iy, ci))\n",
    "    #print(f\"colored_coords  {colored_coords}\")\n",
    "\n",
    "    return colored_coords\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def plot_colored_area_newick_m9(ax, colored_coords, title,  alpha=0.9):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not colored_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y, c in colored_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=palette[c], alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(d[0] for d in colored_coords) - 1\n",
    "    max_x = max(d[0] for d in colored_coords) + 2\n",
    "    min_y = min(d[1] for d in colored_coords) - 1\n",
    "    max_y = max(d[1] for d in colored_coords) + 2\n",
    "    #print(f\"min_x: {min_x}, max_x: {max_x}, min_y: {min_y}, max_y: {max_y}\")\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "\n",
    "def plot_tree_nodes_and_their_diffusion_circles_m9(x,y,c,r, ax):\n",
    "  for xi,yi,ci,ri in zip(x,y,c,r):\n",
    "    #print(f\"ci at plot_tree_nodes_and_their_diffusion_circles_m9: {ci}\")\n",
    "    # Plot the square representing the cell\n",
    "    rect = patches.Rectangle((xi, yi), 1, 1, facecolor=palette[ci], edgecolor='black', linewidth=1, zorder=1)\n",
    "    ax.add_patch(rect)\n",
    "    #node_label_part = node.value.split('_')[-1] if '_' in node.value else node.value\n",
    "    #ax.text(xi + 0.5, yi + 0.5, node_label_part, ha='center', va='center', color='darkblue', fontsize=7, zorder=2)\n",
    "\n",
    "    # Plot the circle representing the radius of influence\n",
    "    circle = patches.Circle((xi + 0.5, yi + 0.5), ri, color=palette[int(ci)], alpha=0.6, zorder=2)\n",
    "    ax.add_patch(circle)\n",
    "\n",
    "def find_leaves_newick_m9(newick_string):\n",
    "    '''Detect leaves in a Newick string.'''\n",
    "    #print(\"\\n\\nfind_leaves\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "    # Detection of leaves.\n",
    "    # Detect substrings of the form \"(...)\" but without interior parentheses.\n",
    "    pattern_leaves = r'[(][^()]*[)]'\n",
    "    # Leaves may come grouped by bouquets\n",
    "    bouquets_of_leaves = re.findall(pattern_leaves, newick_string )\n",
    "    #print(f\"Bouquets_of_leaves: {bouquets_of_leaves}\")\n",
    "    all_leaves = []\n",
    "    # Decipher each leaf and find the father\n",
    "    for bouquet_of_leaves in bouquets_of_leaves:\n",
    "        bouquet_of_leaves = bouquet_of_leaves[1:-1]\n",
    "        leaves = detect_all_groups_m9(bouquet_of_leaves)\n",
    "        for leaf in leaves:\n",
    "          leaf = leaf.replace(\" \", \"\")\n",
    "          all_leaves.append(leaf)\n",
    "    return all_leaves\n",
    "\n",
    "\n",
    "\n",
    "def connect_leaves_to_father_m9(newick_string, ax, tree_color='lightblue', diff_color='red'):\n",
    "    '''Draw lines connecting leaves to fathers'''\n",
    "    #print(\"\\n\\nconnect_leaves_to_father_m9\")\n",
    "    #print(f\"Newick String: {newick_string}\")\n",
    "    all_leaves = find_leaves_newick_m9(newick_string)\n",
    "    for leaf in all_leaves:\n",
    "        #Leaf info\n",
    "        [xl,yl,cl,rl] = extract_info_m9(leaf)\n",
    "        #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n",
    "\n",
    "        # Task: Find the father of the leaf.\n",
    "        # It is the node after the last leaf\n",
    "        # Example:\n",
    "        # (Node9[x=-11,y=19.7,r=12.4567],Node8[x=0,y=7,r=15],Node7[x=6,y=16,r=9])Node6[x=17,y=9,r=20]\n",
    "        # The father of Node9, Node8, and Node7 is Node 6.\n",
    "        # The father of a node is found after the first unbalanced right parenthesis.\n",
    "        index_next_right_par = newick_string.find(\")\" , newick_string.find(leaf))\n",
    "        father_with_tail = newick_string[index_next_right_par+1:]\n",
    "        father_group = detect_all_groups_m9(father_with_tail)\n",
    "        if father_group:\n",
    "          [xf,yf,cf,rf]  = extract_info_m9(father_group[0])\n",
    "          #father_center = (xf,yf)\n",
    "          #Draw link\n",
    "          ax.plot([xl + 0.5, xf + 0.5], [yl + 0.5, yf + 0.5], 'k--', linewidth=0.8, zorder = 3)\n",
    "          # Test: distance2 = distance squared between father and child.\n",
    "          # distance2 must be 2 or  1. \n",
    "          distance2 = (xf-xl)**2 + (yf-yl)**2\n",
    "          #print(f\"distance2: {distance2}\")\n",
    "          if distance2 > 2:\n",
    "              print(f\"\\n distance2 > 2: {distance2}\")\n",
    "              print(f\"xl,yl,xf,yf: {xl} {yl} {xf} {yf}\")\n",
    "              print(f\"leaf: {leaf}\")\n",
    "              print(f\"father_with_tail: {father_with_tail}  \")\n",
    "              print(f\"father: {father_group[0]}\")\n",
    "          \n",
    "    # Leaves are erased: each leave is replaced by \"\"\n",
    "    for leaf in all_leaves:\n",
    "          #print(f\"leaf to be erased: {leaf}\")\n",
    "          newick_string = newick_string.replace(leaf + \",\", \"\")\n",
    "          newick_string = newick_string.replace(leaf, \"\")\n",
    "          #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    #print(f\"Newick String without leaves: {newick_string}\")\n",
    "    #is_valid, message = validate_newick_syntax11b(newick_string)\n",
    "    #print(f\"is valid at connect_leaves_to_father11(...) : {is_valid}, message: {message}\")\n",
    "    #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n",
    "    #print(f\"Is the Newick string a valid morpho-tree? : {status} - {message}\")\n",
    "    groups = detect_all_groups_m9(newick_string)\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")    \n",
    "     \n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_links_m9(newick_string, ax,   diff_color='red' ):\n",
    "    '''Draw lines connecting children to father.\n",
    "       1. Each leaf is connected to its father,\n",
    "          and the corresponding stroke is plotted.\n",
    "       2. Leaves are erased.\n",
    "       3. New leaves are detected and connected to their father.\n",
    "       4. And so on.\n",
    "       '''\n",
    "\n",
    "    print(\"draw_tree_links_m9\")\n",
    "    print(f\"Newick String: {newick_string}\")\n",
    "    length_string = len(newick_string)\n",
    "    #print(f\"length_string: {length_string}\")\n",
    "    groups = detect_all_groups_m9(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    # Leaves are recursively found and connected to their father.\n",
    "    counter_of_trials = 0\n",
    "    while number_of_groups > 1:\n",
    "        #print(\"\\n\\ndraw_tree_links11\")\n",
    "        #print(f\"Newick String: {newick_string}\")\n",
    "\n",
    "        newick_string, number_of_groups = connect_leaves_to_father_m9(newick_string, ax, tree_color='lightblue', diff_color='red')\n",
    "        counter_of_trials = counter_of_trials + 1\n",
    "        if counter_of_trials > length_string:\n",
    "          break\n",
    "      \n",
    "    # The root is detected and marked with a cross\n",
    "    groups = detect_all_groups_m9(newick_string)\n",
    "    #print(f\"Groups: {groups}\")\n",
    "    number_of_groups = len(groups)\n",
    "    #print(f\"number_of_groups = {number_of_groups}\")\n",
    "    # Mark the root with a red cross\n",
    "    if number_of_groups >= 1:\n",
    "      [x0,y0,c0,r0] = extract_info_m9(groups[-1]) #The root is the last group\n",
    "      #print(f\"Root: a: {a0}, b: {b0}, x: {x0}, y: {y0}, r: {r0}\")\n",
    "      ax.plot([x0, x0 +1 ], [y0+ 0.5, y0+ 0.5],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "      ax.plot([x0 + 0.5, x0 +0.5], [y0, y0+ 1],   color = 'red', linewidth=0.8, zorder = 3)\n",
    "    return newick_string, number_of_groups\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_tree_newick_m9(newick_string, title):\n",
    "    #print(\"\\n=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    #print(\"=========== DRAWING=======\\n\")\n",
    "    x,y,c,r = extract_all_infos_m9(newick_string)\n",
    "    #print(f\"c= {c}\")\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    #color = palette[c]\n",
    "    #print(f\"color = {color}\")\n",
    "    plot_tree_nodes_and_their_diffusion_circles_m9(x,y,c,r, ax)\n",
    "     \n",
    "    # Illuminated area is calculated\n",
    "    #colored_coords = get_illuminated_area_m9(x,y,r)\n",
    "    colored_coords = get_colored_area_m9(x,y,c,r)\n",
    "    #print(f\"colored cells at draw_tree_newick_m9: {colored_coords}\")\n",
    "    #print(f\"Number of illuminated cells: {len(colored_coords)}\")\n",
    "    # Colored squares are added to the plot\n",
    "    plot_colored_area_newick_m9(ax, colored_coords, title,  alpha=0.3)\n",
    "    # Lines that define the tree are added\n",
    "    draw_tree_links_m9(newick_string, ax, diff_color='red'  )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return colored_coords\n",
    "\n",
    "def draw_colored_squares_m9(colored_coords, title):\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    ax.set_aspect('equal')\n",
    "    plot_colored_area_newick_m9(ax, colored_coords, title,  alpha=0.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ========== EVOLUTION ==========\n",
    "\n",
    "\n",
    "\n",
    "# --- Evolutionary functions ---\n",
    "\n",
    "def create_initial_population_newick_m9(pop_size=ga_params['pop_size'],  average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'],  max_node_radius=ga_params['max_node_radius'] ):\n",
    "    population = []\n",
    "    for i in range(pop_size):\n",
    "        number_of_nodes = max(average_number_of_nodes + random.randint(-5,5), 5)\n",
    "        #print(f\"Number of nodes: {number_of_nodes}\")\n",
    "        new_tree = complete_tree_generator_m9(number_of_nodes, max_number_of_children, max_node_radius)\n",
    "        population.append(new_tree)\n",
    "        #print(f\"Created tree: {i}\")\n",
    "    return population\n",
    "\n",
    "def create_initial_perfect_population_newick_m9( target_colored_tree, pop_size ):\n",
    "    population = []\n",
    "    for i in range(pop_size):\n",
    "        \n",
    "        new_tree = copy.copy(target_colored_tree)\n",
    "\n",
    "        population.append(new_tree)\n",
    "        #print(f\"Created tree: {i}\")\n",
    "    return population\n",
    "\n",
    "def get_illuminated_coordinates_m9(newick_string):\n",
    "  \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n",
    "  x,y,c,r = extract_all_infos_m9(newick_string)\n",
    "  # Illuminated area is calculated\n",
    "  illuminated_coords = get_colored_area_m9(x,y,c,r)\n",
    "  ##print(f\"Illuminated cells: {illuminated_coords}\")\n",
    "  ##print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n",
    "  return illuminated_coords\n",
    "\n",
    "def calculate_fitness_illumination_m9(candidate_tree_root, target_illuminated_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness based on the symmetric difference between illuminated areas.\n",
    "    Fitness is 1.0 for a perfect match, and decreases as symmetric difference increases.\n",
    "    Reuse of a previous code by Gemini 2.5 Flash\n",
    "    \"\"\"\n",
    "    if candidate_tree_root is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    candidate_illuminated_area = get_illuminated_coordinates_m9(candidate_tree_root)\n",
    "\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = candidate_illuminated_area.symmetric_difference(target_illuminated_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(candidate_illuminated_area.union(target_illuminated_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "        if not target_illuminated_area and not candidate_illuminated_area:\n",
    "            return 1.0 # Perfect match if both are empty\n",
    "        else:\n",
    "            return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "def calculate_fitness_colored_area_m9(test_tree, target_colored_area):\n",
    "    \"\"\"\n",
    "    Calculates fitness of a colored area. \n",
    "    Fitness is 1.0 for a perfect match, and decreases as distance increases.\n",
    "    \"\"\"\n",
    "    if test_tree is None:\n",
    "        # If the tree generation failed, assign a very low fitness\n",
    "        return 0.0001 # A small non-zero value to avoid division by zero issues later\n",
    "\n",
    "    test_colored_area = get_colored_area_m9(test_tree)\n",
    "    # Symmetric difference: (A U B) - (A intersect B)\n",
    "    # Or, the elements in A but not B, plus elements in B but not A.\n",
    "    symmetric_difference = test_colored_area.symmetric_difference(target_colored_area)\n",
    "\n",
    "    # Union size: used for normalization\n",
    "    union_size = len(test_colored_area.union(target_colored_area))\n",
    "\n",
    "    if union_size == 0:\n",
    "         if not target_colored_area and not test_colored_area:\n",
    "             return 1.0 # Perfect match if both are empty\n",
    "         else:\n",
    "             return 0.0001 # Should not happen if target is non-empty, but handles cases where both are empty but expected non-empty\n",
    "\n",
    "    # Fitness is 1.0 - (len(symmetric_difference) / union_size)\n",
    "    # This gives 1.0 for a perfect match (symmetric_difference_area = 0)\n",
    "    # and approaches 0.0 for large differences.\n",
    "    fitness = 1.0 - (len(symmetric_difference) / union_size)\n",
    "    return max(0.0001, fitness) # Ensure fitness is always positive\n",
    "\n",
    "\n",
    "def select_parents_newick(population, fitness_scores, num_parents):\n",
    "    parents = []\n",
    "    # Use roulette wheel selection or tournament selection. Tournament is generally robust.\n",
    "    for _ in range(num_parents):\n",
    "        tournament_size = min(5, len(population))\n",
    "        if tournament_size == 0:\n",
    "            break\n",
    "        tournament_candidates = random.sample(list(zip(population, fitness_scores)), tournament_size)\n",
    "        winner = max(tournament_candidates, key=lambda x: x[1])[0]\n",
    "        parents.append(winner)\n",
    "    return parents\n",
    "\n",
    "def find_recombinant_branch_m9(newick_string):\n",
    "  # A full functional branch is recognized because it is enclosed by ()\n",
    "  # and everything inside is full functional.\n",
    "  # We simply keep an accounting of parentheses pairing\n",
    "  #print(\"\\nfind_branch\")\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  pattern_left_par = r'[(]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_left_par, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  if number_of_infos == 0: # The root is alone\n",
    "    return newick_string\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  start_idx = starts[start_idx]\n",
    "  #print(f\"start_idx: {start_idx}\")\n",
    "  tail = newick_string[start_idx:]\n",
    "  #print(f\"tail: {tail}\")\n",
    "  # Balance accounting\n",
    "  counter_of_pars = 0\n",
    "  for i in range(len(tail)):\n",
    "    if tail[i] == \"(\":\n",
    "      counter_of_pars = counter_of_pars + 1\n",
    "    if tail[i] == \")\":\n",
    "      counter_of_pars = counter_of_pars - 1\n",
    "    if counter_of_pars == 0:\n",
    "      branch = tail[: i+1]\n",
    "      #print(f\"Functional branch: {branch}\")\n",
    "      break\n",
    "  return branch\n",
    "\n",
    "\n",
    "def newick_position_branch_recombination_m9(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_position_branch_recombination_m9\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  branch1 = find_recombinant_branch_m9(newick_string1)\n",
    "  branch2 = find_recombinant_branch_m9(newick_string2)\n",
    "  #print(f\"\\nbranch1: {branch1}\")\n",
    "  #print(f\"branch2: {branch2}\")\n",
    "  # Branches are interchanged\n",
    "  newick_string1 = newick_string1.replace(branch1, branch2)\n",
    "  newick_string2 = newick_string2.replace(branch2, branch1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "def newick_leaves_recombination_m9(newick_string1, newick_string2):\n",
    "  \"\"\" Recombination of full functional branches\"\"\"\n",
    "  #print(\"\\nnewick_leaves_recombination_m9\")\n",
    "  #print(f\"newick_string1: {newick_string1}\")\n",
    "  #print(f\"newick_string2: {newick_string2}\")\n",
    "  leaves1 = find_leaves_newick_m9(newick_string1)  \n",
    "  leaves2 = find_leaves_newick_m9(newick_string2)\n",
    "  if not leaves1 or not leaves2:\n",
    "    return newick_string1, newick_string2\n",
    "  # Select a leaf at random from each tree\n",
    "  leaf1 = random.choice(leaves1)\n",
    "  leaf2 = random.choice(leaves2)\n",
    "  #print(f\"\\nleaf1: {leaf1}\")\n",
    "  #print(f\"leaf2: {leaf2}\")\n",
    "  # Leaves are interchanged\n",
    "  newick_string1 = newick_string1.replace(leaf1, leaf2)\n",
    "  newick_string2 = newick_string2.replace(leaf2, leaf1)\n",
    "  return newick_string1, newick_string2\n",
    "\n",
    "\n",
    "def find_occupied_squares_m9(newick_string):\n",
    "    \"\"\"Detects all the occupied squares in a tree\"\"\"\n",
    "    occupied_squares_set = set()\n",
    "    groups = detect_all_groups_m9(newick_string)\n",
    "    for group in groups:\n",
    "        [x,y,c,r] = extract_info_m9(group)\n",
    "        occupied_squares_set.add((x, y))\n",
    "    return occupied_squares_set\n",
    "\n",
    "def newick_mutation_of_radius_m9(newick_string):\n",
    "  # The pattern of the radius of illumination, r = 0.2\n",
    "  pattern_r = r'r=-?[0-9]+(?:\\.[0-9]+)?'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(pattern_r, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_infos = len(starts)\n",
    "  #print(f\"number_of_infos = {number_of_infos}\")\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  # Select a start index at random\n",
    "  start_idx = random.randint(0, number_of_infos-1)\n",
    "  #print(f\"selected start_idx: {start_idx}\")\n",
    "  l = len(groups[start_idx])\n",
    "  #print(f\"l: {l}\")\n",
    "  # Select a new radius\n",
    "  new_radius = random.uniform(0.5, 2)\n",
    "  #print(f\"new_radius: {new_radius}\")\n",
    "\n",
    "  new_info = \"r=\" + str(new_radius)\n",
    "  #print(f\"new_info: {new_info}\")\n",
    "  # Replace the info\n",
    "  mutated_newick_string = replace_str_subsequence_m9(newick_string, starts[start_idx], starts[start_idx] + l, new_info)\n",
    "  #print(f\"newick_string: {newick_string}\")\n",
    "  return mutated_newick_string\n",
    "\n",
    " \n",
    "def newick_find_insertion_patterns_m9(newick_string):\n",
    "  \"\"\" All insertion patterns are found and one is chosen at random\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][])]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  return group, start\n",
    " \n",
    "\n",
    "def insertion_of_branch_m9(newick_string):\n",
    "  #original_newick_string = newick_string\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  # A group together its start site are elected\n",
    "  group, start =  newick_find_insertion_patterns_m9(newick_string)\n",
    "  # String to be inserted\n",
    "  # child_string = \"(Node_m9[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node_m9[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07])Node1[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.95]\"\n",
    "  child_string = complete_tree_generator_m9(max_depth = 2, max_numb_children = 2, max_node_radius= 1.5)\n",
    "  if group:\n",
    "    #print(f\"Group (main): {group}\")\n",
    "    #print(f\"Start (main): {start}\")\n",
    "    # Does the group have children?\n",
    "    #char_before = newick_string[start-1]\n",
    "    #print(f\"char_before: {char_before}\")\n",
    "\n",
    "    newick_string = newick_string.replace(\"])\", \"],\" + child_string + \")\")\n",
    "  else:\n",
    "    # a tree is given as a child to the root\n",
    "    newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "  # Cosmetics\n",
    "  #newick_string = newick_string.replace(\",,\", \",\")\n",
    "\n",
    "  # Comparison\n",
    "  #print(f\"Original Newick string: {original_newick_string}\")\n",
    "  #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_left_parenthesis__m9(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns (N  are found and one is chosen at random\n",
    "      The new info will be inserted to the right of the parenthesis\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[(][N]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m90: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m9(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m9(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m9(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \"(\" + child_string +  \")\" + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "def select_one_insertion_pattern_comma__m9(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All  commas are found and one is chosen at random,\n",
    "  the child will be inserted after the comma\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][,]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m90: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m9(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m9(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m9(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+2] + \"(\" + child_string + \")\" + newick_string[start+2:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    "def select_one_insertion_pattern_parentheses__m9(newick_string, counter, max_illumination_radius):\n",
    "  \"\"\" All insertion patterns ]) are found and one is chosen at random\n",
    "      The new info will be inserted between ] and ) as a brother\"\"\"\n",
    "  # The pattern of groups on the image of\n",
    "  # Node4[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07]\n",
    "  insertion_pattern = r'[]][)]'\n",
    "  starts = []\n",
    "  groups = []\n",
    "  for match in re.finditer(insertion_pattern, newick_string):\n",
    "      #print(f\"Match: '{match.group()}' starts at index: {match.start()}\")\n",
    "      starts.append(match.start())\n",
    "      groups.append(match.group())\n",
    "  number_of_patterns = len(starts)\n",
    "  #print(f\"starts: {starts}\")\n",
    "  #print(f\"groups: {groups}\")\n",
    "  start_idx = 0\n",
    "  if groups:\n",
    "    # Select a start index at random\n",
    "    start_idx = random.randint(0, number_of_patterns-1)\n",
    "    group = groups[start_idx]\n",
    "    start = starts[start_idx]\n",
    "    #print(f\"group: {group}\")\n",
    "    #print(f\"start: {start}\")\n",
    "  else:\n",
    "    group = \"\"\n",
    "    start = 0\n",
    "  #print(f\"Group_m90: {group}\")\n",
    "  # String to be inserted\n",
    "  father_center = find_father_center_m9(newick_string, start)\n",
    "  #print(f\"Father center: {father_center}\")\n",
    "  occupied_squares_set = find_occupied_squares_m9(newick_string)\n",
    "  info, occupied_squares_set = node_info_generator_m9(father_center, counter,  occupied_squares_set, max_illumination_radius)\n",
    "  info = info.replace(\"()\", \"\")\n",
    "  child_string = info \n",
    "  if group:\n",
    "      #print(f\"Group (main): {group}\")\n",
    "      #print(f\"Start (main): {start}\")\n",
    "      # The child string is inserted between ] and ) at place start\n",
    "      #print(f\"Child string: {child_string}\")\n",
    "      #print(f\"Newick string before insertion_point: {newick_string[:start+1]}\")\n",
    "      #print(f\"Newick string after insertion_point: {newick_string[start+2:]}\")\n",
    "      newick_string = newick_string[:start+1] + \",\" + child_string + newick_string[start+1:]\n",
    "      #print(f\"Newick string after insertion****: {newick_string}\")\n",
    "  else:\n",
    "      # a node in the info is given as a child to the root\n",
    "      newick_string = \"(\" + child_string + \")\" + newick_string\n",
    "    # Cosmetics\n",
    "    #newick_string = newick_string.replace(\",,\", \",\")\n",
    "  \n",
    "    # Comparison\n",
    "    #print(f\"Original Newick string: {original_newick_string}\")\n",
    "    #print(f\"Final Newick_string: {newick_string}\")\n",
    "  return newick_string\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "def insertion_of_leaf_m9(newick_string, counter, max_illumination_radius):    #original_newick_string = newick_string\n",
    "    \"\"\" All insertion patterns are found and one is chosen at random\n",
    "        for the new info to be inserted \"\"\"\n",
    "    case = random.choice([1, 2, 3])  # Randomly choose between three insertion methods\n",
    "    match case:\n",
    "        case 1:\n",
    "            newick_string = select_one_insertion_pattern_comma__m9(newick_string, counter, max_illumination_radius)\n",
    "        case 2:\n",
    "            newick_string = select_one_insertion_pattern_parentheses__m9(newick_string, counter, max_illumination_radius)\n",
    "        case 3:\n",
    "            newick_string = select_one_insertion_pattern_left_parenthesis__m9(newick_string, counter, max_illumination_radius)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    newick_string = newick_string.replace(\" \", \"\")\n",
    "    newick_string = newick_string.replace(\"()\", \"\")\n",
    "    return newick_string\n",
    "\n",
    "\n",
    "\n",
    "def deletion_of_leaf_m9(newick_string):\n",
    "    \"\"\"A leaf is deleted at random from the Newick string. \n",
    "        If there are no leaves, the original string is returned.\"\"\"\n",
    "    # All leaves are detected\n",
    "  \n",
    "    leaves = find_leaves_newick_m9(newick_string)\n",
    "    if not leaves:\n",
    "        return newick_string  # No leaves to delete\n",
    "    # Select a leaf at random\n",
    "    leaf_to_delete = random.choice(leaves)\n",
    "    # Remove the selected leaf from the Newick string\n",
    "    newick_string = newick_string.replace(leaf_to_delete, \"\")\n",
    "    # Clean up any potential formatting issues (like double commas)\n",
    "    newick_string = newick_string.replace(\",,\", \",\")\n",
    "    newick_string = newick_string.replace(\",)\", \")\")\n",
    "    newick_string = newick_string.replace(\"(,\", \"(\")\n",
    "    return newick_string\n",
    "\n",
    "  \n",
    "\n",
    "def newick_mutation_branches_m9(newick_string):\n",
    "  # Mutation of radius of illumination and of complete branches\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    newick_string = newick_mutation_of_radius_m9(newick_string)\n",
    "    newick_string = insertion_of_branch_m9(newick_string)\n",
    "    newick_string = deletion_of_branch_m9(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "def newick_mutation_leaves_m9(newick_string, ga_params):\n",
    "  \"\"\"Radius is mutated, and only leaves can be inserted or deleted\"\"\"\n",
    "  if random.random() <  ga_params['mutation_rate']:\n",
    "    # Mutation of radius of illumination and of complete leaves\n",
    "    newick_string = newick_mutation_of_radius_m9(newick_string)\n",
    "    newick_string = insertion_of_leaf_m9(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n",
    "    newick_string = deletion_of_leaf_m9(newick_string)\n",
    "  return newick_string\n",
    "\n",
    "\n",
    "def plot_illuminated_area_m9(ax, illuminated_coords, title, color='blue', alpha=0.5):\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    if not illuminated_coords:\n",
    "        ax.set_title(title)\n",
    "        ax.grid(True)\n",
    "        return\n",
    "\n",
    "    for x, y in illuminated_coords:\n",
    "        rect = patches.Rectangle((x, y), 1, 1, facecolor=color, alpha=alpha, edgecolor='black', linewidth=0.5)\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "    min_x = min(c[0] for c in illuminated_coords) - 1\n",
    "    max_x = max(c[0] for c in illuminated_coords) + 2\n",
    "    min_y = min(c[1] for c in illuminated_coords) - 1\n",
    "    max_y = max(c[1] for c in illuminated_coords) + 2\n",
    "\n",
    "    ax.set_xlim(min_x, max_x)\n",
    "    ax.set_ylim(min_y, max_y)\n",
    "    ax.set_xticks(range(int(min_x), int(max_x)))\n",
    "    ax.set_yticks(range(int(min_y), int(max_y)))\n",
    "    ax.grid(True)\n",
    "    ax.set_title(title)\n",
    "\n",
    "# --- MAIN EVOLUTIONARY SIMULATION LOOP ---\n",
    "\n",
    "def oneEvolutionaryProcess_m9(target_tree_params, ga_params):\n",
    "    # A previous code by Gemini was reused.\n",
    "\n",
    "    # 1. Generate Target Tree and its Illuminated Area\n",
    "    #print(\"\\n--- Generating Target Tree ---\")\n",
    "    #random.seed(ga_params['seed'] + 1) # Use a different seed for target generation\n",
    "    #complete_tree_generator_m9(total_number_of_nodes, max_number_children, max_node_radius)\n",
    "    target_colored_tree = complete_tree_generator_m9(total_number_of_nodes = target_tree_params['total_number_of_nodes'], max_number_children = target_tree_params['max_number_of_children'], max_node_radius = target_tree_params['max_node_radius'])\n",
    "    print(f\"\\nTarget colored tree: {target_colored_tree}\")\n",
    "    number_of_groups = count_groups_m9(target_colored_tree)\n",
    "    print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n",
    "    target_colored_area = draw_tree_newick_m9(target_colored_tree, title=f\"Target colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    print(f\"Colored area in target tree: {target_colored_area}\")\n",
    "    title = 'Target colored area'\n",
    "    draw_colored_squares_m9(target_colored_area, title)\n",
    "    \n",
    "    # length as a string\n",
    "    length_of_target_tree = len(target_colored_tree)\n",
    "\n",
    "\n",
    "    # 2. Initialize Population\n",
    "    #random.seed(ga_params['seed']) # Reset seed for GA population generation\n",
    "    #print(f\"\\n--- Initializing Population of {ga_params['pop_size']} Trees ---\")\n",
    "    #population = create_initial_population_newick_m9(\n",
    "    #    pop_size=ga_params['pop_size'],average_number_of_nodes=ga_params['average_number_of_nodes'], max_number_of_children=ga_params['max_number_of_children'], max_node_radius=ga_params['max_node_radius']  )\n",
    "    pop_size  = ga_params['pop_size']\n",
    "    population = create_initial_perfect_population_newick_m9( target_colored_tree, pop_size )\n",
    "     \n",
    "    \n",
    "    print(f\"Population initialized with {len(population)} individuals.\")\n",
    "    #print(f\"Population: {population}\")\n",
    "\n",
    "    best_tree_overall = \"\"\n",
    "    best_fitness_overall = -1.0\n",
    "    fitness_history = []\n",
    "\n",
    "    # 3. Evolutionary Loop\n",
    "    #print(f\"\\n--- Starting Evolutionary Loop for {ga_params['generations']} Generations ---\")\n",
    "    for generation in range(ga_params['generations']):\n",
    "        if generation % 10000 == 0:\n",
    "            print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n",
    "        fitness_scores = [calculate_fitness_illumination_m9(tree, target_colored_area)for tree in population]\n",
    "\n",
    "        current_best_fitness = max(fitness_scores)\n",
    "        current_best_tree = population[np.argmax(fitness_scores)]\n",
    "\n",
    "        if current_best_fitness > best_fitness_overall:\n",
    "            best_fitness_overall = current_best_fitness\n",
    "            best_tree_overall = copy.deepcopy(current_best_tree) # Deepcopy to save the best tree\n",
    "\n",
    "        fitness_history.append(current_best_fitness)\n",
    "\n",
    "        #print(f\"Generation {generation+1}/{ga_params['generations']}: Best Fitness = {current_best_fitness:.4f} (Overall Best: {best_fitness_overall:.4f})\")\n",
    "\n",
    "        if best_fitness_overall >= 1.999: # More than perfect match\n",
    "            #print(\"Target illumination achieved (or very close)! Stopping evolution.\")\n",
    "            return target_colored_area, best_tree_overall, best_fitness_overall, fitness_history, number_of_groups\n",
    "\n",
    "        # Selection\n",
    "        parents = select_parents_newick(population, fitness_scores, ga_params['pop_size'] // 2)\n",
    "\n",
    "        # Create next generation\n",
    "        next_population = []\n",
    "        # Elitism: Keep the best individual from the current generation\n",
    "        if best_tree_overall and current_best_fitness == best_fitness_overall: # Only if it's the current best in this generation\n",
    "            next_population.append(copy.deepcopy(current_best_tree))\n",
    "\n",
    "        while len(next_population) < ga_params['pop_size']:\n",
    "            if len(parents) < 2: # Not enough parents for crossover, fill with new random trees\n",
    "                new_tree =  complete_tree_generator_m9(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'] )\n",
    "                if new_tree:\n",
    "                    next_population.append(new_tree)\n",
    "                continue\n",
    "\n",
    "            # Randomly pick two parents (can be the same parent multiple times with replacement for simplicity)\n",
    "            p1 = random.choice(parents)\n",
    "            p2 = random.choice(parents)\n",
    "            child1, child2 = p1,p2 # No crossover, just pass parents to children for mutation\n",
    "            if recombination_of_leaves:\n",
    "                # Crossover newick_position_branch_recombination_m9\n",
    "                # child1, child2 = newick_position_branch_recombination_m9(p1, p2)\n",
    "                # Crossover newick_leaves_recombination_m9\n",
    "                child1, child2 =newick_leaves_recombination_m9(p1,p2)\n",
    "            # Mutation: Apply mutation to both children\n",
    "            mutated_child1 = newick_mutation_leaves_m9(child1, ga_params)\n",
    "            mutated_child2 = newick_mutation_leaves_m9(child2, ga_params)\n",
    "\n",
    "\n",
    "            next_population.append(mutated_child1)\n",
    "            if len(next_population) < ga_params['pop_size']:\n",
    "                next_population.append(mutated_child2)\n",
    "\n",
    "        population = next_population[:ga_params['pop_size']]\n",
    "        # print(f\"Population size for next generation: {len(population)}\")\n",
    "        # print(f\"Best fitness this generation: {current_best_fitness:.4f}, Overall best fitness: {best_fitness_overall:.4f}\")\n",
    "        # print(f\"Best tree this generation: {current_best_tree}\")\n",
    "        # If you want to see the entire population, uncomment the following line:\n",
    "        # print(f\"Population: {population}\")\n",
    "        # Cutt-off for the length of the Newick string to avoid excessive growth\n",
    "        population = [tree for tree in population if len(tree) <= 2 * length_of_target_tree]  # Allow some growth but limit it\n",
    "        #print(f\"Population size after length cut-off: {len(population)}\")\n",
    "        # If the population is too small, refill\n",
    "        while len(population) < ga_params['pop_size']:\n",
    "            new_tree = complete_tree_generator_m9(target_tree_params['max_depth'], target_tree_params['max_number_of_children'], target_tree_params['max_node_radius'])\n",
    "            if new_tree:\n",
    "                population.append(new_tree) \n",
    "    #print(\"\\n--- Evolution Finished ---\")\n",
    "    #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n",
    "    return target_colored_area, best_tree_overall, best_fitness_overall, fitness_history, number_of_groups\n",
    "    \n",
    "     \n",
    "        \n",
    "  \n",
    "        \n",
    "def draw_history_m9(fitness_history, number_of_nodes, population_size):\n",
    "    print(\"draw_histories_m9(fitness_historiy, number_of_nodes, population_size)\")\n",
    "    # Plot the fitness history \n",
    "    x_range = range(len(fitness_history))\n",
    "    fig, ax = plt.subplots(figsize=(7, 15))\n",
    "    plt.plot(x_range, fitness_history, label=f'pop_size {population_size}, Number of nodes {number_of_nodes}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Evolution of the Fitness of the best colored morpho-tree with pop_size = {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()        \n",
    " \n",
    "    \n",
    "# Run an evolutionary process for each value of target tree and GA parameters\n",
    "# Produces a comparative graphic\n",
    "if __name__ == \"__main__\":\n",
    "    print_parameters_m9(target_tree_params, ga_params)\n",
    "    \n",
    "    target_colored_area, best_tree_overall, best_fitness_overall, fitness_history, number_of_groups = oneEvolutionaryProcess_m9(target_tree_params, ga_params)\n",
    "    print(f\"best_tree_overall: {best_tree_overall}\")\n",
    "    print(f\"best_fitness_overall: {best_fitness_overall}\")\n",
    "    final_best_colored_area = draw_tree_newick_m9(best_tree_overall, title=f\"Best evolved colored-morpho-tree with {number_of_groups} nodes\")\n",
    "    print(f\"Colored area in target tree: {target_colored_area}\")\n",
    "    title = 'Best evolved colored area'\n",
    "    draw_colored_squares_m9(final_best_colored_area, title)\n",
    "    print(f\"best_fitness_overall: {best_fitness_overall}\")\n",
    "    \n",
    "    \n",
    "    #print(f\"history_m9: {history_m9}\")\n",
    "    #print(f\"number_of_groups_m9 {number_of_groups_m9}\")\n",
    "    draw_history_m9(fitness_history, number_of_nodes = target_tree_params['total_number_of_nodes'], population_size = ga_params['pop_size'])\n",
    "     \n",
    "\n",
    "    end_time = time.time()\n",
    "    Execution_time = end_time - start_time\n",
    "    # print(f\"Execution time: {Execution_time:.2f} seconds\")\n",
    "    # Execution time in hours, minutes and seconds:\n",
    "    hours = int(Execution_time // 3600)\n",
    "    minutes = int((Execution_time % 3600) // 60)\n",
    "    seconds = int(Execution_time % 60)\n",
    "    print(f\"Execution time: {hours} hours, {minutes} minutes, {seconds} seconds\")\n",
    "    if memory_monitoring:\n",
    "        # displaying the allocated memory\n",
    "        print(tracemalloc.get_traced_memory())\n",
    "        # stopping the memory allocation library\n",
    "        tracemalloc.stop()\n",
    "    \n",
    "# End of program evolution_of_colored_morpho_trees_m9.py\n"
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    "**Ejercicio.** Ejecuta el programa anterior y redacta tus observaciones.\n",
    ">\n",
    "> *Observación.* Con un tamaño poblacional de 70, cuando la Evolución se inaugura con perfección, esta puede mantenerse durante medio millón de generaciones. Esto es compatible con la creencia de que la meseta es un estado transitorio de la Evolución hacia la perfección. Sin embargo, preferimos creer que la meseta es el estado final de la Evolución cuando comienza con una población aleatoria y baja aptitud porque no se observa ningún cambio durante tan enorme número de generaciones. \n",
    "\n",
    "**Ejercicio.** ¿Consideras que los morfo-árboles coloreados son una solución al rompecabezas de la estabilidad evolutiva de los grandes rasgos de las especies, mientras que la variabilidad de los pequeños detalles es inmensa?\n",
    ">\n",
    "> *Respuesta.* No, porque si lo fuesen, habríamos podido elaborar una definición correcta de la complejidad de un árbol y su relación con la pleiotropía, la estabilidad evolutiva y la inmensa variabilidad de los pequeños detalles.\n",
    "\n",
    "El trabajo continúa.\n",
    "\n",
    "# GRADUACIÓN\n",
    "\n",
    "Seleccionar un tema en el que trabajar:\n",
    "\n",
    "1. Hemos inaugurado la Evolución tanto con árboles aleatorios como perfectos. Amplíe el estudio a la aptitud inicial arbitraria mutando recurrentemente un árbol perfecto.\n",
    "2. Diseñar experimentos digitales claros para verificar o rechazar la idea de que la meseta es el estado de equilibrio del proceso evolutivo.\n",
    "3. Hemos utilizado el número de nodos como nuestro indicador de complejidad evolutiva. Hemos ignorado el efecto de complejidad de los colores. Hagamos algo mejor. ¿Tu elección predice correctamente que la complejidad debe destruir a la  Evolución?\n",
    "4. Estudiar la Evolución de los árboles bicolores. Ampliar el estudio a múltiples colores.\n",
    "5. Hemos dicho que la pleiotropía creada por los diversos patrones de iluminación es ineficaz cuando el número de árboles de solución es inmenso. Pero no hemos podido convertir esta apreciación en un discurso matemático. ¿Te atreverías a hacer eso? \n",
    "6.  En varios programas trabajamos con 15 nodos y una población de 200 individuos, y eso fue todo. ¿No es eso vergonzoso? Haz  algo mejor.\n",
    "\n",
    "# CONCLUSIÓN\n",
    "\n",
    "Hemos intentado hacer del Modelo de Red de Alumbrado de la morfogénesis una propuesta aceptable para explicar la estabilidad evolutiva de la forma de las moscas junto con la inmensa variabilidad de los pequeños detalles. En esta contribución probamos el efecto de añadir un color a las lámparas. Bajo este escenario, la Evolución puede ascender desde árboles de colores aleatorios, con una aptitud alrededor de 0,4, hasta árboles cuya aptitud va en el rango de 0,7. La Evolución también es muy eficiente para conservar la perfección porque, con una población de 70 árboles, se puede mantener la perfección durante medio millón de generaciones. Este modelo predice enormes cantidades de monstruosidades si suponemos que la morfogénesis apareció por la Evolución porque los procesos evolutivos terminan en mesetas muy por debajo de la perfección. El descriptor natural más simple de la complejidad de un árbol es el número de nodos. Este indicador no es un predictor justo del costo evolutivo. Por eso, no hemos podido demostrar que la complejidad destruye a la Evolución. Como consecuencia, debemos rechazar este modelo como la explicación de la morfogénesis. \n"
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