{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ ">> EVOLUTION OF NEWICK MORPHO-TREES. \n", ">>>>\n", ">>> José Rodríguez\n", ">>>>\n", ">>> jose@ejristos.com\n", ">>>>\n", ">>> 10 Octuber 2026.\n", ">>>>\n", ">>> Bogotá, Colombia.\n", "\n", "**Abstract**\n", "\n", "The Lighting Network Model of Morphogenesis says that the biological form is attained by modulation of the process of reproduction by means of messenger molecules. Its functioning is modelled by a tree that is similar to a lighting network, whose lamps illuminate the immediate surrounding. We already have seen that the natural encoding of the morphological trees as tree classes is extremely CPU-expensive. So, the purpose for this contribution is to explore the encoding of trees as strings, according to the Newick Notation.\n", "\n", "# INTRODUCTION\n", "\n", "Two simple facts of Nature might serve as test for our modelling of morphogenesis. \n", "\n", "1. Morphogenesis is plainly compatible with Evolution. In fact, all individuals in a human family are different, as predicted by mutation, and the characteristics of the clan are distributed diversely among their members. For instance, Patrick has the nose of the paternal grandfather and Mary the lips of the grandmother of the maternal line. Recombination and gene regulation by epigenetic markers are the candidates to explain this. In general, we see no monsters.\n", "2. Domestic flies are the same all over the Earth. \n", "\n", "The two conditions conform a **squared circle puzzle** that can be expressed by a simple question: How can we explain the tremendous evolutionary stability of the form of flies all over the world in the face of living Evolution?\n", "\n", "**Contest.** Propose a model of morphogenesis that solves the squared-circle puzzle: it must be evolvable, i.e., plainly compatible with Evolution, but also must explain the evolutionary stability of the form of flies that are the same all over the world.\n", ">\n", "> *Answer.* We have inaugurated this concourse in [the previous contribution](https://drive.google.com/file/d/1RxHqtlZGUFg7FAWQ0HLso5ZFZRi2O7nm/view?usp=sharing), in which we proposed the Lighting Model of Morphogenesis. In this model, biological form naturally results from reproduction, which is described by a tree. Specialized cells, conforming a subtree, expel a messenger that modulates the differentiation process wherever its concentration surpasses a given threshold. So, a node or cell of that subtree is similar to a lamp that satisfactorily illuminates its surroundings up to a certain radius. In the same way and to fix ideas, to generate a region with specific form, we need a subtree whose nodes irradiate messengers. If the concentration of the messenger is below a certain threshold, cells stop their division and/or differentiation.\n", ">\n", "> Does the lighting model of morphogenesis has any hope of solving the squared-circle puzzle?\n", ">\n", "> Yes. Evolution can continuously work over the leaves, the terminal nodes of branches, but cannot touch inner nodes, near the root of zygote, because everything would be damaged. This claiming is equivalent to demanding pleiotropy in the tree: changes in inner nodes that have many effects are lethal, but those in the leaves that have no side effects are welcome.\n", "\n", "A tree is a special graph, composed of nodes and links. So, it is natural to encode our trees using the Node class. Although we are looking for new options, we cannot lose contact with the Node class because maybe a mix approach could do it better. Besides, the concepts behind our morpho-trees are independent of its encoding. Or, fundamentals concepts appear everywhere, inclusively in the graphic representations.\n", "\n", "**Example.** The following program uses the Node class to depict a morpho-tree as understood in the lighting network model. The Node class is here represented by the `SpatialTreeNode_Diffusion` class. \n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "executionInfo": { "elapsed": 439, "status": "ok", "timestamp": 1788795761296, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "3kFfZOHFu1YZ", "outputId": "a7bc3ead-a49e-4efe-aa9e-6b23bf09799f" }, "source": [ "# Program lighting_network.py\n", "# The program generates a lighting network based on a tree\n", "# and with lamps of different power.\n", "# This is a simulation of the generation of form in morphogenesis.\n", "# The lamps represent the nodes of the tree that can expel chemical messengers.\n", "# The circles represent the regions in which\n", "# the density of the messengers is high enough to affect reached cells.\n", "# An affected cell can have a license to divide,\n", "# or, if a previous cellular matrix exists,\n", "# it can acquire specific characteristics proper of cell differentiation.\n", "# Trees are encoding along the Node class in the direction of graphs, nodes and links. \n", "# By Gemini.\n", "\n", "import random\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "import numpy as np\n", "\n", "# Global set to store all occupied coordinates for unique cell placement\n", "# (No longer directly used for overlap check within tree generation, but might be useful for plotting limits)\n", "occupied_coords_diffusion = set()\n", "\n", "class SpatialTreeNode_Diffusion:\n", " def __init__(self, value, x, y, radius, occupied_squares_set, diffusion_nodes_info):\n", " self.value = value\n", " self.x = x\n", " self.y = y\n", " self.radius = radius\n", " self.children = []\n", "\n", " self.occupied_squares_set = occupied_squares_set\n", " self.diffusion_nodes_info = diffusion_nodes_info # List of (x, y, radius) for all nodes in the tree\n", "\n", " self.occupied_squares_set.add((x, y)) # Add current node's (x,y) square to the set\n", " self.diffusion_nodes_info.append((x, y, radius)) # Add current node's (x,y,radius) to the list\n", "\n", " def add_child(self, child_node):\n", " if not isinstance(child_node, SpatialTreeNode_Diffusion):\n", " raise TypeError(\"Child must be an instance of SpatialTreeNode_Diffusion\")\n", " self.children.append(child_node)\n", "\n", " def get_neighboring_coordinates(self):\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((self.x + dx, self.y + dy))\n", " return neighbors\n", "\n", " def _is_diffusion_overlapping(self, potential_nx, potential_ny, potential_radius, overlap_factor):\n", " \"\"\"\n", " Checks if a potential new node's diffusion circle overlaps \"too much\" with existing nodes.\n", " \"Too much overlap\" is defined by the overlap_factor.\n", " \"\"\"\n", " potential_center_x, potential_center_y = potential_nx + 0.5, potential_ny + 0.5\n", "\n", " for ex, ey, er in self.diffusion_nodes_info:\n", " # Skip checking against self if self is already in diffusion_nodes_info\n", " if (ex, ey) == (self.x, self.y):\n", " continue\n", "\n", " existing_center_x, existing_center_y = ex + 0.5, ey + 0.5\n", "\n", " # Calculate distance between centers\n", " distance = np.sqrt((potential_center_x - existing_center_x)**2 + (potential_center_y - existing_center_y)**2)\n", "\n", " # Check for significant overlap\n", " # If distance is less than a fraction of the sum of radii, it's too much overlap.\n", " if distance < overlap_factor * (potential_radius + er):\n", " return True # Significant overlap detected\n", " return False\n", "\n", " def add_child_in_random_neighbor(self, child_value_prefix, max_node_radius, overlap_factor):\n", " coordinates_neighbors = self.get_neighboring_coordinates()\n", "\n", " # First, filter for unoccupied grid squares\n", " available_square_neighbors = [coord for coord in coordinates_neighbors if coord not in self.occupied_squares_set]\n", "\n", " valid_child_candidates = [] # Store (nx, ny, child_radius) for candidates that pass overlap check\n", "\n", " for nx, ny in available_square_neighbors:\n", " child_radius = random.uniform(0.5, max_node_radius) # Generate potential radius for this child\n", " if not self._is_diffusion_overlapping(nx, ny, child_radius, overlap_factor):\n", " valid_child_candidates.append((nx, ny, child_radius))\n", "\n", " if not valid_child_candidates:\n", " return None # No available neighbors that satisfy both square and diffusion overlap conditions\n", "\n", " # Choose a random valid candidate\n", " chosen_nx, chosen_ny, chosen_child_radius = random.choice(valid_child_candidates)\n", "\n", " child_value = f\"{child_value_prefix}_{len(self.children) + 1}\"\n", " child_node = SpatialTreeNode_Diffusion(child_value, chosen_nx, chosen_ny, chosen_child_radius, self.occupied_squares_set, self.diffusion_nodes_info)\n", " self.add_child(child_node)\n", " return child_node\n", "\n", " def __repr__(self):\n", " return f\"SpatialTreeNode_Diffusion(value={self.value}, a={self.x}, b={self.y}, R={self.radius:.2f}, children={[child.value for child in self.children]})\"\n", "\n", "\n", "def generate_random_diffusion_tree(\n", " max_depth, max_children, max_node_radius, current_depth=0, parent_node=None, node_prefix=\"Node\",\n", " occupied_squares_set=None, diffusion_nodes_info=None, overlap_factor=0.7\n", "):\n", " if occupied_squares_set is None:\n", " occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n", " if diffusion_nodes_info is None:\n", " diffusion_nodes_info = [] # Initialize a new list for this tree's node (x,y,radius) information\n", "\n", " if parent_node is None:\n", " # Root node at (0,0) with a random initial radius\n", " node_value = f\"{node_prefix}_Root_{random.randint(0, 99)}\"\n", " initial_radius = random.uniform(0.5, max_node_radius)\n", " current_node = SpatialTreeNode_Diffusion(node_value, 0, 0, initial_radius, occupied_squares_set, diffusion_nodes_info)\n", " else:\n", " # Children are added by the parent's method, which handles their coordinates and radius\n", " current_node = parent_node.add_child_in_random_neighbor(f\"{node_prefix}_{current_depth}\", max_node_radius, overlap_factor)\n", " if current_node is None:\n", " return None # Branch terminated if no available neighbor\n", "\n", " if current_depth < max_depth:\n", " # Ensure at least two children if max_children >= 2, else at least one if max_children = 1, else 0\n", " num_children = random.randint(min(max_children, 2) if max_children > 0 else 0, max_children)\n", " for _ in range(num_children):\n", " generate_random_diffusion_tree(\n", " max_depth, max_children, max_node_radius, current_depth + 1, current_node,\n", " node_prefix, occupied_squares_set, diffusion_nodes_info, overlap_factor\n", " )\n", " return current_node\n", "\n", "# --- Plotting functions for Diffusion Trees ---\n", "def plot_diffusion_tree_nodes(node, ax):\n", " if node is None:\n", " return\n", "\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((node.x, node.y), 1, 1, facecolor='lightblue', edgecolor='black', linewidth=1, zorder=1)\n", " ax.add_patch(rect)\n", " ax.text(node.x + 0.5, node.y + 0.5, node.value.split('_')[-1], ha='center', va='center', color='darkblue', fontsize=7, zorder=2)\n", "\n", " # Plot the circle representing the radius of influence\n", " circle = patches.Circle((node.x + 0.5, node.y + 0.5), node.radius, color='red', alpha=0.2, zorder=0) # Zorder 0 to be behind squares\n", " ax.add_patch(circle)\n", "\n", " # Draw lines connecting parent to children\n", " for child in node.children:\n", " ax.plot([node.x + 0.5, child.x + 0.5], [node.y + 0.5, child.y + 0.5], 'k--', linewidth=0.8, zorder=3)\n", " plot_diffusion_tree_nodes(child, ax)\n", "\n", "def print_diffusion_tree(node, level=0):\n", " if node is None:\n", " return\n", " indent = \" \" * level\n", " print(f\"{indent}- {node.value} (a={node.x}, b={node.y}, R={node.radius:.2f})\")\n", " for child in node.children:\n", " print_diffusion_tree(child, level + 1)\n", "\n", "# --- Example Usage ---\n", "if __name__ == \"__main__\":\n", " # Clear the global occupied_coords for each run (still useful for total count but tree logic manages its own)\n", "\n", " # Parameters to modify\n", " occupied_coords_diffusion.clear()\n", " my_seed = 175\n", " my_sees = 1275\n", " random.seed(my_seed) # For reproducibility\n", " MAX_DEPTH = 5 # Increased for a denser tree\n", " MAX_CHILDREN_PER_NODE = 3\n", " MAX_NODE_RADIUS = 5.0 # Max radius of influence for nodes\n", " OVERLAP_FACTOR = 0.5 # Increased to allow more overlap for a denser tree\n", "\n", " print(f\"Generating a random diffusion tree with max_depth={MAX_DEPTH}, max_children={MAX_CHILDREN_PER_NODE}, max_node_radius={MAX_NODE_RADIUS}, overlap_factor={OVERLAP_FACTOR}\")\n", " root_diffusion_tree = generate_random_diffusion_tree(\n", " max_depth=MAX_DEPTH,\n", " max_children=MAX_CHILDREN_PER_NODE,\n", " max_node_radius=MAX_NODE_RADIUS,\n", " overlap_factor=OVERLAP_FACTOR\n", " # occupied_squares_set and diffusion_nodes_info are initialized within the function if None\n", " )\n", "\n", " if root_diffusion_tree:\n", " print(\"\\nGenerated Diffusion Tree Structure:\")\n", " print_diffusion_tree(root_diffusion_tree)\n", "\n", " # Access the diffusion_nodes_info from the root node to get all (x,y,radius) for plotting limits\n", " all_node_coords_radii = root_diffusion_tree.diffusion_nodes_info\n", " all_x = [info[0] for info in all_node_coords_radii]\n", " all_y = [info[1] for info in all_node_coords_radii]\n", " all_radii_values = [info[2] for info in all_node_coords_radii]\n", "\n", " # Plotting the tree with diffusion circles\n", " fig, ax = plt.subplots(figsize=(10, 10))\n", " ax.set_aspect('equal', adjustable='box')\n", "\n", " plot_diffusion_tree_nodes(root_diffusion_tree, ax)\n", "\n", " # Set plot limits based on occupied cells and radii\n", " if all_x and all_y:\n", " min_x_val = min(all_x) - 1\n", " max_x_val = max(all_x) + 2\n", " min_y_val = min(all_y) - 1\n", " max_y_val = max(all_y) + 2\n", "\n", " # Adjust limits to include max radius if it extends beyond cell coords\n", " if all_radii_values:\n", " # Consider the maximum extent of any circle: center + radius\n", " max_extent_x = max([x + 0.5 + r for x, y, r in all_node_coords_radii])\n", " min_extent_x = min([x + 0.5 - r for x, y, r in all_node_coords_radii])\n", " max_extent_y = max([y + 0.5 + r for x, y, r in all_node_coords_radii])\n", " min_extent_y = min([y + 0.5 - r for x, y, r in all_node_coords_radii])\n", "\n", " min_x_val = min(min_x_val, min_extent_x - 0.5) # Add some padding\n", " max_x_val = max(max_x_val, max_extent_x + 0.5)\n", " min_y_val = min(min_y_val, min_extent_y - 0.5)\n", " max_y_val = max(max_y_val, max_extent_y + 0.5)\n", "\n", " ax.set_xlim(min_x_val, max_x_val)\n", " ax.set_ylim(min_y_val, max_y_val)\n", "\n", " # Set sensible ticks\n", " ax.set_xticks(range(int(min_x_val) - 1, int(max_x_val) + 1))\n", " ax.set_yticks(range(int(min_y_val) - 1, int(max_y_val) + 1))\n", "\n", " ax.grid(True)\n", " ax.set_title(\"Random Spatial Tree with Diffusion Circles (Overlap Restricted)\")\n", " plt.xlabel(\"X Coordinate\")\n", " plt.ylabel(\"Y Coordinate\")\n", " plt.show()\n", " else:\n", " print(\"Could not generate a diffusion tree.\")" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "Dj08ZIOl7m2C" }, "source": [ "**Exercise.** Run the program and observe the drawing. The graphic shows how a lighting network is depicted and understood in our works. This must be independent of the encoding format. Notice the blue squares where a node or lamp resides, the links among nodes that form a tree, the circles of illumination that are centered at the nodes or lamps. Each circle is the region of illumination of a lamp that represents the region of influence of a center that expels messengers that control reproduction or differentiation of surrounding cells wherever its concentration is higher than a certain threshold. The overall red region is the form dictated by this morpho-tree.\n", "\n", "**Detailed interpretation.** Let us understand the output of the previous program:\n", "1. All squares are of width one and represent a real or potential cell. Each blue square denotes a cell of the morpho-tree. These expel a messenger that induces specific cell differentiation in surrounding cells wherever its concentration is above a given threshold. The area of influence is marked by red circles that are centered at the center of the square.\n", "2. Every cell has its own position, determined by a couple $(x,y)$. The zygote, or primordial cell from which the organism results by various generations of cellular reproduction, is at the square $(0,0)$. The couple $(x,y)$ represents a square that extends to the right and upwards. Thus, the square corresponding to $(0,0)$ extends from 0 to 1 in the horizontal direction, and from 0 to 1 in the vertical direction.\n", "3. Reproduction of a cell produces a daughter that occupies one of the eight circumventing squares. Because the position of cells are kept in their corresponding genomes, Evolution through recombination breaks the rule that a child and father must be neighbors. This rule is natural because every organism is a connected unit.\n", "4. Each square is occupied by only one cell. Evolution breaks this rule, and we tolerated this because we invoked apoptosis, or cellular suicide, as a mechanism that operates in silence to control overcrowding.\n", "5. To follow the morpho-tree, we need to keep in mind that the descendant is marked by linear strokes from center of the father to center of the child. The root of the tree is at $(0,0)$. Complex trees conform very intricate labyrinths.\n", "\n", "The incompatibility of the Lightning Model with Evolution forces us to react somehow. Some options are:\n", "\n", "1. To abandon the Lighting Model.\n", "2. Try to make it evolvable, i.e., compatible with Evolution, as much as possible.\n", "3. To reformulate Evolution. \n", "\n", "We like the Lighting Model so, we keep it for further study. We used the Node class (nodes and links) in the previous contribution for the encoding of our morpho-trees. The Node class is natural but tremendously expensive: tiny exercises in Evolution demand half an hour of computing by Colab. Thus, it is mandatory to research other options. That is why, we study here the possibility to encode trees as strings or sequences of chars or letters that lack every explicit connection with the node class.\n", "\n", "## Newick Notation \n", "\n", "The challenge of encoding trees, in a format that is suitable for the study of Evolution, as strings without any appeal to the node class has been successfully solved, and the solution is known as the [Newick format or Newick Notation](https://en.wikipedia.org/w/index.php?title=Newick_format&oldid=1375769084). \n", "\n", "**Remark** In the notation used in the Node class, the genome or information associated to a Node or controlling cell is denoted in the image of $Node123[x = x_o, y = y_o, r = r_o]$, where the couple $(x_o,y_o)$ denotes the position of the lower left corner of the occupied square, and $r=r_o$ is the radius of illumination or influence of the messenger center. Additionally to this information, we will also use a redundant couple $(a,b)$, which instead of position denotes movement. For the moment, let us think just of movement.\n", "\n", "The notation is very intuitive. Some examples will suffice to understand it. Let us consider three instances:\n", "\n", "1. The string\n", " >$father[a = 0, b=0, r = 2.1]$\n", "\n", " > denotes the root with name $father$. Its genome consists of $[a = 0, b=0, r = 2.1]$. The interpretation is: beginning from the origin of coordinates $(0,0)$, one adds the step $(a=0, b=0)$ to acquire the position $(0,0)$. From there, a lamp will irradiate light up to a circle of radius $2.1$. We see that the genomes contain not the position but the steps to recover it beginning with the position of the father. Thus, the genomes do not contain data, they content instructions to do things, i.e., software that must be properly decoded and run.\n", "\n", "2. The string\n", "\n", " > $(child[a = -1, b = 1, r = 0.5])father[a = 0, b=0, r = 2.1]$\n", "\n", " denotes a father and its child. The string must be read from right to left. The father is located at $(0,0)$ with radius of action $2.1$, while the child is at $(0-1,0+1) = (-1,1)$ and its radius of action is $0.5$.\n", "\n", "3. The string\n", "\n", " > $(child1[a = 1, b=0, r = 1.3], child0[a = -1, b = 1, r = 0.5])father[a = 0, b=0, r = 2.1]$\n", "\n", " denotes a father with two children. The father is located at $(0,0)$ with radius of action $2.1$. The first child is at $ (-1,1)$ and its radius of action is $0.5$. A second child is located at $(0+1,0+0) = (1,0)$ and its radius of action is $1.3$.\n", "\n", "**Exercise.** Add a child to child1 from the third example. It must be moved upwards east and with radius of action $0.9$. Calculate its position.\n", "> *Answer.* The direction upwards east is denoted $(1,1)$. If we add a child to child1 from the third example, we get:\n", "\n", "> $((grandchild0[a = 1, b = 1, r = 0.9]) child1[a= 1, b=0, r = 1.3], child0[a = -1, b = 1, r = 0.5])father[a = 0, b=0, r = 2.1]$\n", "\n", "The position of the grandchild is obtained by beginning with $(0,0)$, the position of the root, and adding the steps given by its ancestors, child1 and granchild0: $(0,0) + (1,0) + (1,1) = (2,1)$.\n", "\n", "\n", "\n", "**Warnings.** We have two invitations to be humble and to work hard: \n", "\n", "1. Gemini has explicitly expressed that the Newick Notation might result extremely cumbersome for the study of the Evolution of trees.\n", "2. On our part, we expect a fierce battle of Evolution with complexity because the Evolution of morpho-trees encloses a very cumbersome type of Evolution: the Evolution of software! Let us see this in some detail.\n", ">\n", "> Software is composed of verbal instructions to make things, say, to cook. In general, products are highly dependent on the type and order of the instructions to be applied. For instance, we cannot cook the flour first to then add water. We must first add water to the flour to then cook a carefully mixed ensemble. Therefore, the Evolution of software to be useful must burn immense quantities of CPU-time and memory. \n", ">\n", "> Where are concretely the instructions to build a morpho-tree?\n", ">\n", "> When a cell divides, the child must occupy one the other eight neighboring squares. The eight possibilities are denoted by $(a,b)$, where $a$ and $b$ can be $-1, 0, 1$. The case $(0,0)$ is excluded because it corresponds with the father. Thus, if $(a,b) = (-1,1)$, the child must occupy the neighboring cell that is located left upwards from the father, or, northwest. Hence, we can encode cellular division by given directly the final position of children or by expressing the instruction that contains the step that each child must give away from its father to occupy its corresponding square. For a while we will use the movement information $(a,b)$, while $(x,y)$ will be reserved to denote position. Below, we include these two options in the encoding, but we will be attentive to detect and use the best alternative.\n", "\n", "From these considerations, it is not at all clear that the Newick Notation will make programs to run faster. Nevertheless, our creativity and resilience must be tested with experiment to know what they give of themselves.\n", "\n", "Our path to arrive to the simulation of Evolution of morpho-trees using Newick strings alone is very long and difficult. So, let us go step by step. Our first task is to abstract the pattern, architecture, or topology of a tree in Newick Notation.\n", "\n", "\n", "### Schemes and grammars\n", "\n", "\n", "So, let us consider the following Newick string that encodes for a morpho-tree:\n", "\n", "> I = ((Target_4[a=-2,b=2,r=0.86])Target_3[a=-1,b=1,r=0.81], (Target_2[a=1,b=-2,r=2.45])Target_1[a=0,b=-1,r=2.04])Target_Root_0[a=0,b=0,r=0.85]\n", "\n", "All nodes have the same structure, which is modelled on the following *movement info of a node*\n", ">\n", "> Target_Root_0[a=0,b=0,r=0.85].\n", "\n", "Let us replace each node of I by the dummy variable n. We get\n", "\n", "> I = ((n)n,(n)n)n\n", "\n", "This is the **scheme or pattern** of the tree. We see that the root of the tree is in the right extreme\n", ">\n", "> I = ( )n and his family is (n)n,(n)n\n", ">\n", "> The root has two children,\n", ">\n", "> I = ( n, n)n\n", ">\n", "> Each child apports a grandchild\n", ">\n", "> I = ((n)n,(n)n)n\n", ">\n", "> In conclusion: This is a family with a father, two children and one grandchild from each child.\n", "\n", "**Exercise.** Specify the family composition of the following tree:\n", ">\n", "> tree_2: (((Node57[a=16,b=13,r=9],Node50[a=11,b=19,r=18])Node73[a=17,b=5,r=7],(Node86[a=19,b=2,r=18],Node46[a=16,b=10,r=18])Node58[a=4,b=4,r=15],(Node10[a=9,b=0,r=12],Node42[a=7,b=14,r=12])Node86[a=2,b=19,r=7])Node4[a=19,b=3,r=8])Node83[a=13,b=17,r=18]\n", ">\n", "> *Answer.* The corresponding pattern is\n", ">\n", "> tree_2: (((n,n)n,(n,n)n,(n,n)n)n)n\n", ">\n", "> The father is in the right extreme ()n, and his family is (((n,n)n,(n,n)n,(n,n)n)n) with only one son, whose grand-family is (n,n)n,(n,n)n,(n,n)n. In the grand-family there are three children, each of which has two children.\n", ">\n", "> Family composition: a father, one son with three children, each one with two children. Or, a father with one son, who has three children, each one with two sons.\n", "\n", "+\n", "Let us show that the strings that represent a morpho-tree in Newick Notation can be generated automatically following a set of substitution rules that work over an initial string that eventually could be void. Let us notice that all nodes follow exactly the same construction: `tag_index[a_value, b_value, r_radius]`. So, we will refer to it generically as `n`. The proposed rules are:\n", "\n", "\n", "1. Initial string or seed: `s = ()n`\n", "2. Generate a random digit: x, which is the number of children\n", "3. Replace in `s`, `()` by x copies of `()n ` separated by a comma, we get a number of children x.\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 that advances with the iterative process.\n", "6. Delete the tag \"()\" that appears in the last appended children and that was used for recursion.\n", "\n", "An ordered set of rules like this is an algorithm that is called a **Generative grammar**.\n", "\n", "**Challenge.** Research the web over grammars in Computing Science.\n", "\n", "**Exercise.** Implement in Python the aforementioned grammar to build random Newick strings.\n", ">\n", "> *Answer.* The following program in Python implements the aforementioned grammar and produces random Newick strings.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 53, "status": "ok", "timestamp": 1788800163369, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "HjhM-ycjy9xw", "outputId": "97b27772-99d1-4ad3-a329-cae8c3243162" }, "source": [ "# Program grammar_generator_of_Newick_morpho_trees.py\n", "# This program uses a grammar to generate Newick expressions that encode for morphological trees.\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", "# With the assitance of Colab.\n", "\n", "import random\n", "import numpy as np\n", "import math\n", "import re # Required for newick parsing if needed, though not directly used in this generator\n", "import itertools # Required for sequential replacement\n", "\n", "\n", "\n", "def replace_str_subsequence(original_str, start_idx, end_idx, new_subsequence):\n", " ''' Replace a subsequence of a string 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", "\n", "def generate_children(numb_children):\n", " ''' Generate children in the pattern form of a Newick Tree'''\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 == numb_children-1:\n", " s = s + \"()n\"\n", " else:\n", " s = s + \"()n,\"\n", " s = \"(\" + s + \")\"\n", " #print(f\"Children: {s}\")\n", " return s\n", "\n", "\n", "def newick_pattern_generator(max_depth, max_numb_children):\n", " ''' Generates the pattern of a tree in Newick Notation\n", " Outputs strings of the form\n", " ((()n)n)n\n", " '''\n", " #print(\"newick_pattern_generator(max_depth, max_numb_children)\")\n", " actual_depth = random.randint(0, max_depth)\n", " print(f\"Depth: {actual_depth}\")\n", " if actual_depth == 0:\n", " return \"()n\"\n", " s = \"()n\"\n", " for i in range(actual_depth):\n", " l = len(s)\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", " if s[j-1] == \"(\" and s[j] == \")\":\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replace () by a list of children\n", " s = replace_str_subsequence(s, j-1, j+1, generate_children(numb_children))\n", " #print(f\"{i} {j} Tree: {s}\")\n", " return \"(\" + s + \")n\"\n", "\n", "\n", "def node_info_generator(counter):\n", " '''For each node, info is generated on the model\n", " Node_26[x=0,y=0,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", " x = random.randint(0, 20)\n", " y = random.randint(0, 20)\n", " r = random.randint(0, 20)\n", " s = \"Node\" + str(counter) + \"[\" + \"a=\" + str(x) + \",\" + \"b=\" + str(y) + \",\" + \"r=\" + str(r) + \"]\"\n", " return s\n", "\n", "\n", "# Generate a sample of Newick strings\n", "if __name__ == \"__main__\":\n", " print(\"Generated Well-Formed trees in Newick Notation:\\n\" + \"=\"*47)\n", " max_depth = 4\n", " max_numb_children = 4\n", " print(f\"max_depth: {max_depth}\")\n", " print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 6):\n", " counter = 0\n", " s = newick_pattern_generator(max_depth, max_numb_children)\n", " print(f\"tree {i}: {s}\")\n", "\n", " l = len(s)\n", " # String s is scanned starting from the end\n", " for j in range(l-1, 0, -1):\n", " if s[j] == \"n\":\n", " # In s replace each n by a different info\n", " s = replace_str_subsequence(s, j, j+1, node_info_generator(counter))\n", " counter = counter + 1\n", " s = s.replace(\"()\", \"\")\n", " print(f\"tree {i}: {s}\")\n" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "LA8Iq4-mzOWq" }, "source": [ "**Exercise.** Run the previous program and study the code to see how it works. The working of the generator grammar must be understood to follow programs below.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "0QFnicBGy0b4" }, "source": [ "\n", "**Task.** Develop the code for an interpreter of the Newick Notation. It must accept a correct formula and draw the corresponding tree.\n", "\n", "The task is difficult. So, we divide it in various subtasks. Divide and conquer is a usual strategy in Computing Science.\n", "\n", "To efficiently interpret a formula, one needs to understand it. In programming, this is done with **tokenizers** that form the machinery to recognize patterns. The first pattern recognizes substrings of the type $[a = 0, b=0, r = 2.1]$ that convey the information to position a node or cell in the morpho-tree.\n", "\n", "### Tokenizers\n", "\n", "Let us get acquainted with tokenizers, a powerful machinery for the recognizing of patterns in strings.\n", "\n", "**Exercise.** Let your AI assistant develop a program that shows how a tokenizer functions for Newick strings. The program must not use the Node class.\n", ">\n", "> *Answer.* Gemini answered:\n", ">\n", ">> Sure, I can create a tokenizer for Newick strings that doesn't use the Node class. This approach focuses purely on the syntactic structure of the string, making it efficient for validation.\n", ">>\n", ">> The tokenizer will break down the Newick string into its fundamental components (like parentheses, commas, node labels, and metadata elements). In a future, a validator function will analyze this sequence of tokens to ensure it conforms to the expected Newick grammar, without building any tree objects in memory.\n", ">\n", "> Gemini proposed the following program that divides the genome or info or information associated to a node into predefined tokens, or sub-strings that are sub-patterns: \n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 52, "status": "ok", "timestamp": 1788798880204, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "TNSLVV_xmjdE", "outputId": "d19342f9-432f-42ea-a9ab-c36990c2dc23", "ExecuteTime": { "end_time": "2026-10-08T13:06:12.028084Z", "start_time": "2026-10-08T13:06:11.930605Z" } }, "source": [ "# Program newick_tokenizer.py\n", "# Adaptation of an example by Gemini 2.5 Flash\n", "\n", "import re\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- info = NodeB_3_x_2[x=2,y=2,r=3]\n", "\n", "# WARNING: the order of items in a tokenizer is important\n", "# # because the list is processed from top to botton!\n", "TOKEN_SPECIFICATION_NEWICK = [\n", "\n", " ('LPAREN', r'\\('), # Left Parenthesis\n", " ('RPAREN', r'\\)'), # Right Parenthesis\n", " ('COMMA', r','), # Comma separator for children\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata\n", " ('EQUALS', r'='), # Equals sign in metadata\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative)\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (a, b, r)\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines\n", " ('MISMATCH', r'.'), # Any other illegal character\n", "]\n", "\n", "def tokenize_newick(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\n", " Adapted from a code by Gemini\"\"\"\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " try:\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " except SyntaxError as e:\n", " return str(e)\n", " if not tokens:\n", " return \"Empty Newick string.\"\n", " else:\n", " return tokens\n", "\n", "if __name__ == \"__main__\":\n", " #Correct Newick string\n", " newick_str_m = \"Target_1_1[a=-2,b=2,r=0.86]\"\n", " tokens_m = tokenize_newick(newick_str_m)\n", " print(f\"\\nnewick_str: {newick_str_m}\")\n", " print(f\"Tokens: {tokens_m}\")\n", " # Correct Newick string\n", " newick_str_m = \"Node8[a=0.1,b=-7.3,r=15.143]\"\n", " tokens_m = tokenize_newick(newick_str_m)\n", " print(f\"\\nnewick_str: {newick_str_m}\")\n", " print(f\"Tokens: {tokens_m}\")\n", " # Incorrect Newick string+\n", " newick_str_m = \"Node8[a=0,,1,b=-7.3,r=15.143$\"\n", " tokens_m = tokenize_newick(newick_str_m)\n", " print(f\"\\nnewick_str: {newick_str_m}\")\n", " print(f\"Tokens: {tokens_m}\")\n", " # Incorrect Newick string\n", " newick_str_m = \"Node8[a=0,,1,b=-7.3,r=15.143))))))\"\n", " tokens_m = tokenize_newick(newick_str_m)\n", " print(f\"\\nnewick_str: {newick_str_m}\")\n", " print(f\"Tokens: {tokens_m}\")\n" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "newick_str: Target_1_1[a=-2,b=2,r=0.86]\n", "Tokens: [('LETTERS', 'Target_1_1'), ('LBRACKET', '['), ('LETTERS', 'a'), ('EQUALS', '='), ('NUMBER', '-2'), ('COMMA', ','), ('LETTERS', 'b'), ('EQUALS', '='), ('NUMBER', '2'), ('COMMA', ','), ('LETTERS', 'r'), ('EQUALS', '='), ('NUMBER', '0.86'), ('RBRACKET', ']')]\n", "\n", "newick_str: Node8[a=0.1,b=-7.3,r=15.143]\n", "Tokens: [('LETTERS', 'Node8'), ('LBRACKET', '['), ('LETTERS', 'a'), ('EQUALS', '='), ('NUMBER', '0.1'), ('COMMA', ','), ('LETTERS', 'b'), ('EQUALS', '='), ('NUMBER', '-7.3'), ('COMMA', ','), ('LETTERS', 'r'), ('EQUALS', '='), ('NUMBER', '15.143'), ('RBRACKET', ']')]\n", "\n", "newick_str: Node8[a=0,,1,b=-7.3,r=15.143$\n", "Tokens: Newick Grammar Violation: Illegal character '$' in 'Node8[a=0,,1,b=-7.3,r=15.143$'\n", "\n", "newick_str: Node8[a=0,,1,b=-7.3,r=15.143))))))\n", "Tokens: [('LETTERS', 'Node8'), ('LBRACKET', '['), ('LETTERS', 'a'), ('EQUALS', '='), ('NUMBER', '0'), ('COMMA', ','), ('COMMA', ','), ('NUMBER', '1'), ('COMMA', ','), ('LETTERS', 'b'), ('EQUALS', '='), ('NUMBER', '-7.3'), ('COMMA', ','), ('LETTERS', 'r'), ('EQUALS', '='), ('NUMBER', '15.143'), ('RPAREN', ')'), ('RPAREN', ')'), ('RPAREN', ')'), ('RPAREN', ')'), ('RPAREN', ')'), ('RPAREN', ')')]\n" ] } ], "execution_count": 2 }, { "cell_type": "markdown", "metadata": { "id": "TY_lPYpQqN3v" }, "source": [ "**Exercise.** Play with the program to understand how it works.\n", "\n", "**Exercise.** Use a tokenizer to develop a validator of Newick strings. It must discriminate valid expression from invalid ones. A formula contains the info of various cells. A substring with the info of a single cell is called a *group of letters with the information of a node* or simply, a *group*.\n", ">\n", "> *Answer.* With the help of Gemini and Google, we propose the following code for the validator if Newick strings:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 52, "status": "ok", "timestamp": 1788798494011, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "Fcx2NfgcrDhS", "outputId": "04d1d2ca-13d8-4110-9e13-723d0025734b" }, "source": [ "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string is divided in tokens, substrings of predefined content,\n", "# and all groups or substrings that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# This is done so:\n", "# Each specification couple has an index,\n", "# shown to the end of the comments at the right.\n", "# A correct group has a very precise sequences of indexes:\n", "# 73756275627564\n", "# So, a test for correctness reduces to the comparison of two numbers.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each group or info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this simplifying algorithm is the void sting \"\",\n", "# and its length is zero.\n", "\n", "\n", "\n", "# With the help of Gemini and Google.\n", "\n", "import re\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- info = NodeB_3_x_2[a=2,b=2,r=3]\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", "('LPAREN', r'\\('), # Left Parenthesis, 0\n", "('RPAREN', r'\\)'), # Right Parenthesis, 1\n", "('COMMA', r','), # Comma separator for children, 2\n", "('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", "('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", "('EQUALS', r'='), # Equals sign in metadata, 5\n", "('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", "('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", "('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", "('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", "('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick2(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=2,b=2,r=3]\n", "def examine_subsequence2(tokens, j):\n", " print(\"\\nexamine_subsequence2\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " print(f\"tracker: {tracker}\")\n", " print(f\"Encoding of info: {tracker2}\")\n", " if tracker2 == \"73756275627564\":\n", " print(\"Pattern : 73756275627564\")\n", " print(\"Valid Newick sub-string at examine_subsequence2(tokens, j)\")\n", " return True, \"Valid Newick sub-string at examine_subsequence2(tokens, j).\"\n", " else:\n", " print(\"Invalid Newick sub-string at examine_subsequence2(tokens, j)\")\n", " return False, \"Invalid Newick sub-string at examine_subsequence2(tokens, j).\"\n", "\n", "def replace_at_position2(original_str, replacement, position, length_to_replace):\n", " \"\"\"\n", " Replaces a subsequence at a given position.\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 simplify2(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " print(\"\\nsimplify(text)\")\n", " print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[x=2,y=2,r=3]\n", " info_words = r'[\\a-zA-Z_0-9_[_=_,\\] ]+'\n", " info_words = r'[a-zA-Z_0-9_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n", " starts = []\n", " groups = []\n", " for match in re.finditer(info_words, text):\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_infos = len(starts)\n", " print(f\"number_of_infos = {number_of_infos}\")\n", " print(f\"starts of infos: {starts}\")\n", " print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " new_s = new_s.replace(\"(n)n,\", \"\")\n", " new_s = new_s.replace(\"(n)n\", \"\")\n", " new_s = new_s.replace(\"(n),\", \"\")\n", " new_s = new_s.replace(\"(n)\", \"\")\n", "\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "def validate_newick_syntax2(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " print(\"\\nValidating Newick syntax\")\n", " try:\n", " tokens = tokenize_newick2(newick_str)\n", " print(f\"newick_str: {newick_str}\")\n", " print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " # Stack to track parentheses and tree structure\n", " paren_stack = [] # Stores 'LPAREN'\n", " bracket_stack = [] # Stores 'LBRACKET'\n", " node_count_in_group = 0 # Counts nodes/subtrees within a (...,...,...) group\n", " expect_node_label_or_lparen = True\n", " expect_metadata_key = False\n", " expect_equals_after_key = False\n", " expect_value_after_equals = False\n", " expect_comma_or_rbracket_after_value = False\n", " is_valid2 = False\n", " l = len(tokens)\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 14: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 14:\n", " print(f\"Length of string: {l}\")\n", " for j in range(l-12):\n", " # print(f\"{j} tokens[j]: {tokens[j]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid2, message2 = examine_subsequence2(tokens, j)\n", " if is_valid2:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid3, message3 = simplify2(newick_str)\n", " if is_valid3:\n", " return True, \"Valid Newick info-substrings at validate_newick_syntax2(newick_str).\"\n", " else:\n", " return False, \"Invalid Newick string validate_newick_syntax2(newick_str).\"\n", "\n", "# --- TEST SUITE (copied from previous cell and adapted) ---\n", "if __name__ == \"__main__\":\n", " print(\"--- Newick Grammar Validator Results ---\")\n", " # Re-use the examples from the previous `newick_expression_validator.py`\n", " # and ensure they work with this tokenizer-based validator.\n", " # Valid Newick Strings (from previous notebook state)\n", " valid_newick_1 = \"((Target_1_1[a=-2,b=2,r=0.86])Target_0_1[a=-1,b=1,r=0.81],(Target_2_1[a=1,b=-2,r=2.45])Target_0_2[a=0,b=-1,r=2.04])Target_Root_26[a=0,b=0,r=0.85]\"\n", " valid_newick_2 = \"((Node4[a=1,b=0,r=9],Node3[a=5,b=20,r=11],Node2[a=15,b=11,r=15])Node1[a=4,b=8,r=0])Node0[a=20,b=8,r=17]\"\n", " valid_newick_3 = \"(((Node9[a=11,b=19,r=12],Node8[a=0,b=7,r=15],Node7[a=6,b=16,r=9])Node6[a=17,b=9,r=20],Node5[a=19,b=15,r=10],(Node4[a=15,b=14,r=1])Node3[a=11,b=13,r=19],Node2[a=18,b=17,r=11])Node1[a=16,b=1,r=12])Node0[a=10,b=4,r=1]\"\n", " valid_newick_4 = \"NodeA[a=1,b=1,r=5]\" # Single node with metadata\n", " valid_newick_5 = \"(NodeB[a=2,b=2,r=3])NodeA[a=1,b=1,r=5]\" # Simple tree\n", " valid_newick_6 = \"NodeC;\" # Single node with semicolon\n", " valid_newick_7 = \"(NodeD,NodeE)NodeF;\" # Tree with semicolon\n", " valid_newick_8 = \"NodeA\" # Single node without metadata\n", " valid_newick_9 = \"((A,B)C,(D,E)F)G;\" # Classic Newick, no metadata\n", " # Invalid Newick Strings\n", " invalid_newick_1 = \"(NodeA,NodeB\" # Missing closing parenthesis\n", " invalid_newick_2 = \"NodeA[a=1,b=1]\" # Missing r-metadata, or malformed if r is optional\n", " invalid_newick_3 = \"(NodeA,NodeB)[a=1,b=1,r=1]\" # Node label after children missing\n", " invalid_newick_4 = \"((NodeX[a=1,b=1,r=1])NodeY[a=2,b=2,r=2])\" # Missing root label\n", " invalid_newick_5 = \"(NodeA[a=1,b=1,r=1],)NodeC[a=3,b=3,r=3]\" # Trailing comma\n", " invalid_newick_6 = \"(NodeA,NodeB)C D\" # Extra token after valid tree\n", " invalid_newick_7 = \"[a=1]NodeA\" # Metadata before node label\n", " invalid_newick_8 = \"NodeA[a=1,b=1r=1]\" # Missing comma in metadata\n", " invalid_newick_9 = \"(\" # Unclosed parenthesis\n", " invalid_newick_10 = \"NodeA(NodeB)\" # Parentheses in wrong place\n", "\n", " test_cases_newick = {\n", " \"Valid Newick Strings\": [\n", " valid_newick_1,\n", " valid_newick_2,\n", " valid_newick_3,\n", " valid_newick_4,\n", " valid_newick_5,\n", " valid_newick_6,\n", " valid_newick_7,\n", " valid_newick_8,\n", " valid_newick_9\n", " ],\n", " \"Invalid Newick Strings\": [\n", " invalid_newick_1,\n", " invalid_newick_2,\n", " invalid_newick_3,\n", " invalid_newick_4,\n", " invalid_newick_5,\n", " invalid_newick_6,\n", " invalid_newick_7,\n", " invalid_newick_8,\n", " invalid_newick_9,\n", " invalid_newick_10\n", " ]\n", " }\n", "\n", " for category, newick_strings in test_cases_newick.items():\n", " print(f\"\\n--- {category} ---\")\n", " for i, nw_str in enumerate(newick_strings):\n", " print(\"\\nString \", i+1)\n", " is_valid, message = validate_newick_syntax2(nw_str)\n", " print(f\"is valid at main : {is_valid}, message: {message}\")\n", " status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " print(f\" String {i+1}: {status} - {message}\")\n", " #print(f\" '{nw_str}'\") # Uncomment to see the string itself\n", "\n" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "HzAWJOovJrXe" }, "source": [ "**Exercise.** Run the program and test it with formulas of your own invention.\n", "\n", "**Protest.** A test of some few formulas is not enough to inspire confidence.\n", "\n", "**Exercise.** Carry out a massive test to verify that each formula is indeed correct.\n", ">\n", ">*Answer.* In the following program we have a generator of Newick formulas, each one of which is immediately tested by a validator." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 166, "status": "ok", "timestamp": 1788893545141, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "pWVlMuydLPg8", "outputId": "82068ccc-451d-47e8-e320-10524de06d2e" }, "source": [ "# Program massive_test_of_Newick_morpho_trees.py\n", "# This program uses a grammar to generate Newick expressions that encode for morphological trees.\n", "# Each generated formula is tested with a validator to verify its correctness.\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,\n", "# 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", "# With the assitance of Colab.\n", "\n", "import random\n", "import numpy as np\n", "import math\n", "import re # Regular expressions package. Required for newick parsing\n", "import itertools # Required for sequential replacement\n", "\n", "\n", "# === Generator===\n", "\n", "def replace_str_subsequence3(original_str, start_idx, end_idx, new_subsequence):\n", " ''' Replace a subsequence of a string 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", "\n", "def generate_children3(numb_children):\n", " ''' Generate children in the pattern form of a Newick Tree'''\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 == numb_children-1:\n", " s = s + \"()n\"\n", " else:\n", " s = s + \"()n,\"\n", " s = \"(\" + s + \")\"\n", " #print(f\"Children: {s}\")\n", " return s\n", "\n", "\n", "def newick_pattern_generator3(max_depth, max_numb_children):\n", " ''' Generates the pattern of a tree in Newick Notation\n", " Outputs strings of the form\n", " ((()n)n)n\n", " '''\n", " #print(\"newick_pattern_generator(max_depth, max_numb_children)\")\n", " actual_depth = random.randint(0, max_depth)\n", " #print(f\"Depth: {actual_depth}\")\n", " if actual_depth == 0:\n", " return \"()n\"\n", " s = \"()n\"\n", " for i in range(actual_depth):\n", " l = len(s)\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", " if s[j-1] == \"(\" and s[j] == \")\":\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replace () by a list of children\n", " s = replace_str_subsequence3(s, j-1, j+1, generate_children3(numb_children))\n", " #print(f\"{i} {j} Tree: {s}\")\n", " return \"(\" + s + \")n\"\n", "\n", "\n", "def node_info_generator3(counter):\n", " '''For each node, info is generated on the model\n", " Node_26[x=0,y=0,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", " x = random.randint(0, 20)\n", " y = random.randint(0, 20)\n", " r = random.randint(0, 20)\n", " s = \"Node\" + str(counter) + \"[\" + \"a=\" + str(x) + \",\" + \"b=\" + str(y) + \",\" + \"r=\" + str(r) + \"]\"\n", " return s\n", "\n", "def morpho_tree_generator3(max_depth, max_numb_children):\n", " ''' Generates a Newcick formula that repreents a morpho-tree.\n", " '''\n", " counter = 0\n", " s = newick_pattern_generator3(max_depth, max_numb_children)\n", " #print(f\"tree {i} at line 341: {s}\")\n", "\n", " l = len(s)\n", " # String s is scanned starting from the end\n", " for j in range(l-1, 0, -1):\n", " if s[j] == \"n\":\n", " # In s replace each n by a different info\n", " s = replace_str_subsequence3(s, j, j+1, node_info_generator3(counter))\n", " counter = counter + 1\n", " s = s.replace(\"()\", \"\")\n", " return s\n", "\n", "# === VAlidator ===\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# With the help of Gemini and Google.\n", "\n", "import re\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- info = NodeB_3_x_2[a=2,b=2,r=3]\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick3(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=2,b=2,r=3]\n", "def examine_subsequence3(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"tracker: {tracker}\")\n", " #print(f\"Encoding of info: {tracker2}\")\n", " if tracker2 == \"73756275627564\":\n", " #print(\"Pattern : 73756275627564\")\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def replace_at_position3(original_str, replacement, position, length_to_replace):\n", " \"\"\"\n", " Replaces a subsequence at a given position.\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 simplify3(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " #print(\"\\nsimplify(text)\")\n", " #print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,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_]*\\[a=-?[0-9]+(?:\\.[0-9]+)?,b=-?[0-9]+(?:\\.[0-9]+)?,r=-?[0-9]+(?:\\.[0-9]+)?\\]'\n", " starts = []\n", " groups = []\n", " for match in re.finditer(info_words, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position3(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " #print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "def validate_newick_syntax3(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " print(\"Validating Newick syntax\")\n", " try:\n", " tokens = tokenize_newick3(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " # Stack to track parentheses and tree structure\n", " paren_stack = [] # Stores 'LPAREN'\n", " bracket_stack = [] # Stores 'LBRACKET'\n", " node_count_in_group = 0 # Counts nodes/subtrees within a (...,...,...) group\n", " expect_node_label_or_lparen = True\n", " expect_metadata_key = False\n", " expect_equals_after_key = False\n", " expect_value_after_equals = False\n", " expect_comma_or_rbracket_after_value = False\n", " is_valid2 = False\n", " l = len(tokens)\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 14: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 14:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-12):\n", " # print(f\"{j} tokens[j]: {tokens[j]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid2, message2 = examine_subsequence3(tokens, j)\n", " if is_valid2:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid3, message3 = simplify3(newick_str)\n", " if is_valid3:\n", " return True, \"Valid Newick morpho-tree.\"\n", " else:\n", " return False, \"Invalid Newick morpho-tree.\"\n", "\n", "\n", "\n", "\n", "#=== Main ===\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "if __name__ == \"__main__\":\n", " print(\"Program massive_test_of_Newick_morpho_trees.py\")\n", " print(\"Generated Well-Formed Newick formulas of morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " print(f\"max_depth: {max_depth}\")\n", " print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " s = morpho_tree_generator3(max_depth, max_numb_children)\n", " print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax3(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " print(f\"Morpho-tree {i}:: {status} - {message}\")\n" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "qQxn9O6DfRQi" }, "source": [ "**Exercise.** Run the program and express your conclusion: Does our generator produce correct Newick formulas that represent morpho-trees?\n", ">\n", "> *Answer.* Generated formulas pass the massive test offered by the validator.\n", "\n", "**Complaints.** Two complaints might arise:\n", "1. The validator does not include a test for the rule that to be a morpho-tree, the father and the child must be neighbors one to another.\n", "2. The validator successfully can reverse the work of the generator. But, this does not prove that the generated formulas encode for a morpho-tree.\n", ">\n", "> *Answer.* The first protest is right but not the second because everything is thoroughly tested. Anyway, the best that we can do to help in the perception of the formulas is to draw them. So, the drawing transfers the responsibility for validation to the Beholder.\n", "\n", "## Drawing a Newick morpho-tree.\n", "\n", "It happens that the drawing task is hard. It could be eased at least a bit if we include in the genomes of each cell its position, besides its step from the father. Because of bugs committed by the developer at development time, we need the help of a validator for this new pattern of genome that is built on the image of `NodeB_3_x_2[a=0,b=1,x=2,y=2,r=3]`. In this nomenclature, we include the step given by the child away from the father, which is denoted by $(a,b)$, and the resultant position of the child, that is denoted by $(x,y)$.\n", "\n", "**Exercise.** Develop a validator for Newick formulas that work over morpho-trees whose genomes or groups are built on the image of `NodeB_3_x_2[a=0,b=1,x=2,y=2,r=3]`, where the step that is given by the child from its father is denoted by $(a,b)$ and the acquired position of the child by $(x,y)$.\n", ">\n", ">*Answer.* The code for a validator of Newick strings whose groups or infos of each cell are built on the image of `NodeB_3_x_2[a=0,b=1,x=2,y=2,r=3.1]` is included in the program that is the answer to the next exercise. Our validator accepts every correct formula but is fault-tolerant because it also accepts some ill formulas of the style of `NodeB_3_x_2[a=0,b=1,x=2,y=2,r=3.1],,`\n", "\n", "**Exercise.** Develop the code for the generator of Newick formulas to include the rule that the father and its child must be neighbors. Test your code and allow the Beholder to see the tree. So, the Beholder is the final judge for correctness.\n", ">\n", "> *Answer.* The following program contains:\n", "1. Generated trees observe the rule that the child and father shall be neighbors.\n", "2. A syntactic validator is used mainly to detect bugs that give rise to invalid strings. \n", "3. The supposed tree is drawn to give to the Beholder the capacity to test the correctness of the generated formulas. Each child is joint to its father by a linear stroke that goes from center to center. So, the Beholder can see what is generated and whether the drawing corresponds to a morpho-tree. The tree can have self intersecting strokes without being ill because a void can be filled by the child of a more distant node. This causes trees to be entangled. Entanglement seems to be the universal signature of Evolution. To understand the tree, begin with the root, marked by a red cross, and follow a path defined by the strokes that connect center to center.\n", "3. As before, we include in the genome of each cell or node of the tree the step that separates the child from the father. It is denoted by $(a,b)$. New: We also include the position of the left-bottom corner of each cell, denoted by $(x,y)$. To keep track of the position allows drawing the tree more easily. Nevertheless, we know that the position is not compatible with Evolution.\n", "4. The radius of action of each lamp is represented by a red circle.\n", "5. A cell or square is illuminated if at least a pixel is illuminated. Illuminated cells are refilled in a pale purpure color.\n", "6. We imagine that in morphogenesis, cells do not go to occupied space. Otherwise, we shall invoke apoptosis, or cellular suicide, to resolve conflicts. Else, we must keep an accounting of the occupied cells and command children to go to void space only. The accounting is done by means of a set that keeps the coordinates of the occupied squares.\n", "\n", "The code is the following:\n", "\n", "jjjjjjjj jose" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "executionInfo": { "elapsed": 722, "status": "ok", "timestamp": 1789393355379, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "mNH4C_kkk6ZB", "outputId": "afbabae8-dcf7-4181-9e68-320620d05c02" }, "source": [ "# Program morpho_tree_generator_and_drawer.py\n", "# This program generates a morpho tree and drwas it.\n", "# It builds morpho trees of predefined number of trees.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 10,\n", " 'max_number_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': 100,\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001, \n", " 'crossover_rate': 0.001 \n", "}\n", "\n", " \n", "def print_parameters4(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"INITIAL TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"INITIAL GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_generator_and_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence4(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_length4(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_groups4(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[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups4(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups4(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info4(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", "def extract_all_infos4(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_groups4(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info4(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates4(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_center4(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_groups4(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info4(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator4(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_coordinates4(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children4(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_generator4(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_generator4(max_depth, max_numb_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 = max_depth - random.randint(0,5) # 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[a=0,b=0,x=0,y=0,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_center4(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children4(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence4(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 tree: {tree}\")\n", " return tree\n", "\n", "# =================================================================================\n", "# ==== Drawer ===\n", "# =================================================================================\n", "\n", "def get_neighboring_coordinates4(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", "\n", "def detect_all_groups4(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_groups11b(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " '''\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " '''\n", " return groups\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", "def extract_all_infos4(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_groups4(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info4(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "def find_father_center4(newick_string, j):\n", " ''' Finds the address of the father of a node at j'''\n", " #print(\"find_father_center11(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_groups4(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info4(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def get_illuminated_area4(x,y,r):\n", " '''\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " '''\n", " #print(\"get_illuminated_area11\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick4(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles4(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves_newick4(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", " # Dectect 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", " # Descipher 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_groups4(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father4(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_leaves_newick4(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info4(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=4],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_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_groups4(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info4(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", " groups = detect_all_groups4(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_links4(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_groups4(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_father4(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_groups4(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", " [a0,b0,x0,y0,r0] = extract_info4(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_newick4(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos4(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 4))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles4(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", " \n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area4(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick4(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links4(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "if __name__ == \"__main__\":\n", " # Example Newick string\n", " # newick_string = \"((((((()Node63[a=1.0,b=-1.0,x=5.0,y=-3.0,r=1.26])Node52[a=1.0,b=-1.0,x=4.0,y=-2.0,r=0.7])Node21[a=1.0,b=1.0,x=3.0,y=-1.0,r=1.9613197090647045],((()Node219[a=0.0,b=1.0,x=-2.0,y=3.0,r=1.06])((((Node46[a=-1.0,b=-1.0,x=-2.0,y=2.0,r=0.71])Node125[a=0.0,b=1.0,x=-1.0,y=3.0,r=1.37])Node103[a=-1.0,b=1.0,x=-1.0,y=2.0,r=1.36],(((Node198[a=-1.0,b=1.0,x=-4.0,y=1.0,r=0.75])Node45[a=-1.0,b=0.0,x=-3.0,y=0.0,r=0.58])Node124[a=-1.0,b=-1.0,x=-2.0,y=0.0,r=1.6749306080259698],((((()Node913[a=-1.0,b=0.0,x=-4.0,y=3.0,r=0.59]))Node698[a=0.0,b=1.0,x=-3.0,y=3.0,r=0.84])()Node600[a=-1.0,b=1.0,x=-3.0,y=2.0,r=1.25])()Node123[a=0.0,b=-1.0,x=-1.0,y=0.0,r=1.33])Node102[a=-1.0,b=0.0,x=-1.0,y=1.0,r=1.33])Node51[a=-1.0,b=1.0,x=0.0,y=1.0,r=0.54],(((()Node122[a=-1.0,b=0.0,x=0.0,y=3.0,r=1.17])Node81[a=-1.0,b=1.0,x=1.0,y=3.0,r=0.85])Node62[a=0.0,b=1.0,x=2.0,y=2.0,r=1.07],(((()Node43[a=-1.0,b=1.0,x=3.0,y=5.0,r=0.54683679559608],Node42[a=0.0,b=-1.0,x=4.0,y=3.0,r=1.03])Node101[a=1.0,b=1.0,x=4.0,y=4.0,r=0.5748403340449614])Node80[a=0.0,b=1.0,x=3.0,y=3.0,r=1.22],()Node79[a=1.0,b=0.0,x=4.0,y=2.0,r=1.36])Node61[a=1.0,b=1.0,x=3.0,y=2.0,r=0.5295884877970901])Node50[a=1.0,b=1.0,x=2.0,y=1.0,r=1.21])Node29[a=0.0,b=1.0,x=1.0,y=0.0,r=1.43])Node20[a=-1.0,b=1.0,x=1.0,y=-1.0,r=1.1])Node8[a=1.0,b=0.0,x=2.0,y=-2.0,r=0.64])Node3[a=1.0,b=-1.0,x=1.0,y=-2.0,r=1.37],((((((()Node100[a=0.0,b=1.0,x=-2.0,y=-2.0,r=1.05],((Node40[a=0.0,b=1.0,x=-4.0,y=-1.0,r=0.733489119496699],(Node193[a=-1.0,b=1.0,x=-6.0,y=0.0,r=0.5820889162363478])Node149[a=-1.0,b=1.0,x=-5.0,y=-1.0,r=0.81])Node120[a=-1.0,b=0.0,x=-4.0,y=-2.0,r=1.36],()Node119[a=0.0,b=1.0,x=-3.0,y=-1.0,r=1.16])Node99[a=-1.0,b=1.0,x=-3.0,y=-2.0,r=1.47])Node49[a=-1.0,b=1.0,x=-2.0,y=-3.0,r=1.26],()((()Node118[a=0.0,b=-1.0,x=-3.0,y=-4.0,r=1.0],((()Node367[a=-1.0,b=-1.0,x=-7.0,y=-3.0,r=1.8764650499221425])()Node147[a=-1.0,b=0.0,x=-5.0,y=-3.0,r=0.82],Node146[a=-1.0,b=1.0,x=-5.0,y=-2.0,r=1.13])Node117[a=-1.0,b=0.0,x=-4.0,y=-3.0,r=1.13])Node78[a=-1.0,b=1.0,x=-3.0,y=-3.0,r=1.0329473493338008])Node48[a=-1.0,b=0.0,x=-2.0,y=-4.0,r=0.76])Node28[a=-1.0,b=0.0,x=-1.0,y=-4.0,r=1.46])Node14[a=1.0,b=-1.0,x=0.0,y=-4.0,r=1.06])Node7[a=0.0,b=-1.0,x=-1.0,y=-3.0,r=0.89])Node5[a=0.0,b=-1.0,x=-1.0,y=-2.0,r=0.71],(((((((()Node47[a=-1.0,b=1.0,x=4.0,y=-4.0,r=0.6058398421466735],((()Node441[a=0.0,b=-1.0,x=8.0,y=0.0,r=1.2970045651723827])((((()Node145[a=1.0,b=1.0,x=8.0,y=1.0,r=0.52],()Node144[a=0.0,b=1.0,x=7.0,y=1.0,r=0.96])Node116[a=1.0,b=1.0,x=7.0,y=0.0,r=1.23571244728331],()Node14[a=1.0,b=0.0,x=7.0,y=-1.0,r=0.95])Node98[a=1.0,b=1.0,x=6.0,y=-1.0,r=1.37],(()Node114[a=1.0,b=0.0,x=7.0,y=-2.0,r=0.62])Node97[a=1.0,b=0.0,x=6.0,y=-2.0,r=1.04])Node77[a=1.0,b=1.0,x=5.0,y=-2.0,r=1.01])Node59[a=-1.0,b=1.0,x=4.0,y=-3.0,r=0.62],(((Node185[a=1.0,b=0.0,x=9.0,y=-5.0,r=1.25],()Node476[a=1.0,b=-1.0,x=9.0,y=-6.0,r=0.87])Node142[a=1.0,b=-1.0,x=8.0,y=-5.0,r=1.7146586553405185])Node96[a=1.0,b=1.0,x=7.0,y=-4.0,r=1.49],((Node183[a=1.0,b=-1.0,x=8.0,y=-6.0,r=0.54])Node141[a=1.0,b=-1.0,x=7.0,y=-5.0,r=0.518535704237459],(()Node303[a=1.0,b=-1.0,x=9.0,y=-4.0,r=0.69])(Node181[a=1.0,b=0.0,x=8.0,y=-3.0,r=1.16])Node140[a=1.0,b=1.0,x=7.0,y=-3.0,r=1.44])Node95[a=0.0,b=1.0,x=6.0,y=-4.0,r=1.1])Node58[a=1.0,b=-1.0,x=6.0,y=-5.0,r=0.72])Node46[a=0.0,b=1.0,x=5.0,y=-4.0,r=0.79])Node36[a=1.0,b=0.0,x=5.0,y=-5.0,r=1.02])Node27[a=1.0,b=-1.0,x=4.0,y=-5.0,r=0.71])Node19[a=1.0,b=0.0,x=3.0,y=-4.0,r=0.83],(Node57[a=0.0,b=-1.0,x=3.0,y=-6.0,r=0.83])Node18[a=1.0,b=-1.0,x=3.0,y=-5.0,r=1.36])Node13[a=0.0,b=-1.0,x=2.0,y=-4.0,r=1.956337326453025],((((((()Node94[a=1.0,b=1.0,x=2.0,y=3.0,r=1.5714886013535794])Node76[a=1.0,b=0.0,x=1.0,y=2.0,r=1.760704619465394])Node56[a=-1.0,b=1.0,x=0.0,y=2.0,r=1.43])Node45[a=-1.0,b=1.0,x=1.0,y=1.0,r=0.71])Node26[a=0.0,b=1.0,x=2.0,y=0.0,r=0.81],(((()Node55[a=1.0,b=0.0,x=5.0,y=-1.0,r=0.68])Node44[a=0.0,b=-1.0,x=4.0,y=-1.0,r=1.39])Node35[a=1.0,b=0.0,x=4.0,y=0.0,r=1.5450652146942652])Node25[a=1.0,b=1.0,x=3.0,y=0.0,r=0.8])Node17[a=-1.0,b=1.0,x=2.0,y=-1.0,r=0.84])Node12[a=1.0,b=1.0,x=3.0,y=-2.0,r=0.59])Node10[a=1.0,b=0.0,x=2.0,y=-3.0,r=1.31],((((((()Node74[a=0.0,b=1.0,x=-3.0,y=-5.0,r=0.97],(((()Node139[a=0.0,b=1.0,x=-5.0,y=-4.0,r=0.85])Node113[a=0.0,b=1.0,x=-5.0,y=-5.0,r=1.29],(Node138[a=-1.0,b=1.0,x=-7.0,y=-4.0,r=1.09])Node112[a=-1.0,b=1.0,x=-6.0,y=-5.0,r=0.69])Node92[a=-1.0,b=0.0,x=-5.0,y=-6.0,r=1.9891193529247717])Node73[a=-1.0,b=0.0,x=-4.0,y=-6.0,r=0.9])Node43[a=-1.0,b=-1.0,x=-3.0,y=-6.0,r=0.93])Node34[a=-1.0,b=0.0,x=-2.0,y=-5.0,r=1.7667232954270577],(()Node707[a=0.0,b=-1.0,x=-8.0,y=-11.0,r=1.2418125160543307])((((((Node176[a=-1.0,b=1.0,x=-8.0,y=-8.0,r=0.73])Node137[a=-1.0,b=-1.0,x=-7.0,y=-9.0,r=1.3],Node136[a=-1.0,b=0.0,x=-7.0,y=-8.0,r=0.59])Node111[a=-1.0,b=-1.0,x=-6.0,y=-8.0,r=0.53],(Node175[a=-1.0,b=1.0,x=-7.0,y=-6.0,r=1.8710079054235957])Node110[a=-1.0,b=0.0,x=-6.0,y=-7.0,r=0.81])Node91[a=-1.0,b=0.0,x=-5.0,y=-7.0,r=0.64])Node72[a=-1.0,b=0.0,x=-4.0,y=-7.0,r=1.35],(Node90[a=-1.0,b=0.0,x=-5.0,y=-8.0,r=1.48],(()Node109[a=-1.0,b=0.0,x=-6.0,y=-9.0,r=1.8236264836746388])Node89[a=-1.0,b=-1.0,x=-5.0,y=-9.0,r=0.94])Node71[a=-1.0,b=-1.0,x=-4.0,y=-8.0,r=0.91])Node42[a=-1.0,b=-1.0,x=-3.0,y=-7.0,r=0.58],(()Node46[a=-1.0,b=-1.0,x=-2.0,y=-7.0,r=0.93])()Node41[a=1.0,b=0.0,x=-1.0,y=-6.0,r=1.39])Node33[a=-1.0,b=-1.0,x=-2.0,y=-6.0,r=1.12])Node24[a=-1.0,b=0.0,x=-1.0,y=-5.0,r=1.47])Node16[a=-1.0,b=-1.0,x=0.0,y=-5.0,r=0.67],((Node32[a=0.0,b=-1.0,x=1.0,y=-6.0,r=1.9638918299741654],(((()Node841[a=-1.0,b=-1.0,x=-7.0,y=-11.0,r=0.58])((((()Node135[a=-1.0,b=-1.0,x=-5.0,y=-11.0,r=0.85])Node108[a=0.0,b=-1.0,x=-4.0,y=-10.0,r=1.0],(()Node630[a=-1.0,b=-1.0,x=-6.0,y=-13.0,r=0.77])(()Node134[a=-1.0,b=-1.0,x=-6.0,y=-11.0,r=1.0])Node107[a=-1.0,b=-1.0,x=-5.0,y=-10.0,r=1.14])Node88[a=-1.0,b=-1.0,x=-4.0,y=-9.0,r=0.65])Node69[a=-1.0,b=0.0,x=-3.0,y=-8.0,r=0.87],(((Node170[a=1.0,b=-1.0,x=-3.0,y=-12.0,r=1.951238469851274],(()Node686[a=-1.0,b=-1.0,x=-4.0,y=-12.0,r=1.5])Node169[a=1.0,b=0.0,x=-3.0,y=-11.0,r=0.91])Node133[a=-1.0,b=-1.0,x=-4.0,y=-11.0,r=1.33])Node87[a=-1.0,b=-1.0,x=-3.0,y=-10.0,r=1.08],Node86[a=-1.0,b=0.0,x=-3.0,y=-9.0,r=1.33])Node68[a=0.0,b=-1.0,x=-2.0,y=-9.0,r=0.99])Node54[a=-1.0,b=-1.0,x=-2.0,y=-8.0,r=0.72])Node40[a=-1.0,b=-1.0,x=-1.0,y=-7.0,r=1.45],(()Node951[a=-1.0,b=-1.0,x=0.0,y=-11.0,r=1.7392926307253513])((()Node132[a=1.0,b=-1.0,x=2.0,y=-9.0,r=1.25],()Node131[a=1.0,b=0.0,x=2.0,y=-8.0,r=1.45])Node67[a=1.0,b=-1.0,x=1.0,y=-8.0,r=1.41],((()Node193[a=-1.0,b=0.0,x=-4.0,y=-14.0,r=1.22])()Node370[a=-1.0,b=-1.0,x=-3.0,y=-14.0,r=0.94])((()Node139[a=0.0,b=-1.0,x=-2.0,y=-13.0,r=1.33])(((Node166[a=0.0,b=-1.0,x=-2.0,y=-12.0,r=0.65],((()Node192[a=-1.0,b=0.0,x=0.0,y=-14.0,r=0.88])((()Node527[a=1.0,b=-1.0,x=1.0,y=-14.0,r=0.85])((()Node591[a=1.0,b=-1.0,x=0.0,y=-13.0,r=1.04])()Node169[a=0.0,b=-1.0,x=-1.0,y=-12.0,r=1.0099492850296317]))Node421[a=1.0,b=-1.0,x=-1.0,y=-11.0,r=1.29])Node165[a=0.0,b=1.0,x=-2.0,y=-10.0,r=1.23])Node130[a=-1.0,b=-1.0,x=-2.0,y=-11.0,r=1.45])Node106[a=0.0,b=-1.0,x=-1.0,y=-10.0,r=1.2592804255064793],Node105[a=1.0,b=0.0,x=0.0,y=-9.0,r=1.23])Node85[a=0.0,b=-1.0,x=-1.0,y=-9.0,r=1.49])Node66[a=-1.0,b=-1.0,x=-1.0,y=-8.0,r=1.38])Node39[a=0.0,b=-1.0,x=0.0,y=-7.0,r=1.13])Node31[a=-1.0,b=-1.0,x=0.0,y=-6.0,r=1.07])Node23[a=-1.0,b=0.0,x=1.0,y=-5.0,r=0.73],(((()Node952[a=1.0,b=-1.0,x=2.0,y=-10.0,r=1.18])(()Node84[a=-1.0,b=-1.0,x=0.0,y=-8.0,r=0.91])Node38[a=-1.0,b=0.0,x=1.0,y=-7.0,r=1.38],(((Node129[a=-1.0,b=-1.0,x=3.0,y=-8.0,r=1.27],()Node128[a=0.0,b=-1.0,x=4.0,y=-8.0,r=1.35])Node65[a=0.0,b=-1.0,x=4.0,y=-7.0,r=0.98],((((Node162[a=1.0,b=0.0,x=8.0,y=-7.0,r=1.2685669416870335],()Node283[a=1.0,b=-1.0,x=8.0,y=-8.0,r=0.9586343616711437])Node127[a=1.0,b=-1.0,x=7.0,y=-7.0,r=0.81],(()Node189[a=0.0,b=-1.0,x=6.0,y=-12.0,r=0.94])((()Node78[a=-1.0,b=-1.0,x=6.0,y=-11.0,r=0.66])((()Node537[a=0.0,b=-1.0,x=7.0,y=-11.0,r=1.22])()Node887[a=0.0,b=-1.0,x=7.0,y=-10.0,r=0.94])()Node473[a=0.0,b=-1.0,x=7.0,y=-9.0,r=0.81])()Node126[a=0.0,b=-1.0,x=6.0,y=-7.0,r=1.19],()Node93[a=1.0,b=0.0,x=7.0,y=-6.0,r=0.74])Node104[a=1.0,b=0.0,x=6.0,y=-6.0,r=1.4108167024543916])Node83[a=0.0,b=1.0,x=5.0,y=-6.0,r=1.32],((((()Node745[a=-1.0,b=-1.0,x=3.0,y=-14.0,r=0.62]))Node919[a=-1.0,b=-1.0,x=4.0,y=-13.0,r=1.4])()Node772[a=0.0,b=-1.0,x=5.0,y=-12.0,r=1.28])(()Node798[a=1.0,b=0.0,x=5.0,y=-11.0,r=0.8])(((()Node41[a=-1.0,b=-1.0,x=4.0,y=-11.0,r=0.96]))Node4[a=0.0,b=-1.0,x=5.0,y=-10.0,r=1.03])()Node82[a=1.0,b=-1.0,x=6.0,y=-8.0,r=0.96])Node64[a=1.0,b=-1.0,x=5.0,y=-7.0,r=1.37])Node53[a=1.0,b=1.0,x=4.0,y=-6.0,r=0.5])Node37[a=1.0,b=0.0,x=3.0,y=-7.0,r=1.28])Node30[a=0.0,b=-1.0,x=2.0,y=-7.0,r=0.75])Node22[a=0.0,b=-1.0,x=2.0,y=-6.0,r=0.83])Node4[a=1.0,b=-1.0,x=2.0,y=-5.0,r=1.41])Node11[a=1.0,b=-1.0,x=1.0,y=-4.0,r=0.68])Node9[a=-1.0,b=0.0,x=0.0,y=-3.0,r=1.224188604200087])Node6[a=1.0,b=-1.0,x=1.0,y=-3.0,r=0.95])Node4[a=1.0,b=-1.0,x=0.0,y=-2.0,r=0.82])Node2[a=-1.0,b=0.0,x=-1.0,y=-1.0,r=0.53])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.62])Node_0[a=0,b=0,x=0,y=0,r=1]\"\n", " # Draw the tree and get the illuminated coordinates\n", " newick_string = complete_tree_generator4(max_depth = 40, max_numb_children = 2, max_node_radius = 1.2)\n", " number_of_groups = count_groups4(newick_string)\n", " illuminated_coords = draw_tree_newick4(newick_string, title=f\"Morpho-tree with {number_of_groups} nodes\")\n", " print(f\"Illuminated coordinates: {illuminated_coords}\")" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "76buB0jj5n0Q" }, "source": [ "**Exercise.** Run the previous program, contemplate the output and try to assess whether the depicted tree passes as morpho-tree.\n", ">\n", "> *Answer.* To the best of our censuring power, the tree passes as a morpho-tree. It is astonishing that the number of intersection lines is low. This is due to the rule of `one cell in its square`. This creates a sensation of order amidst the disorder proper of randomness.\n", "\n", "Let us implement now the prerequisites of Evolution.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "rKMAK5d9oUyf" }, "source": [ "# BUILDING BLOCKS OF EVOLUTION.\n", "\n", "Let us implement mutation. The first task is to change the radius of illumination of a given node.\n", "\n", "**Exercise.** Implement mutation of the radius of cells for Newick strings.\n", ">\n", "> *Answer.* In a Newick string that encodes for a morpho-tree we select a node and change its radius. The code follows.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 22, "status": "ok", "timestamp": 1789401206525, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "UW1JcW9HKlWt", "outputId": "4191e0db-ca8e-471c-9f6d-4d84d2199447" }, "source": [ " # Program radius_mutation_newick_strings.py\n", " # This program finds a random node in a Newick string that encodes for a morpho-tree and change its radius.\n", "\n", "import random\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "import numpy as np\n", "from numpy import array as Array\n", "import copy # For deep copying trees during reproduction and mutation\n", "import sys # Import sys module to adjust recursion limit\n", "import re # Required for newick parsing\n", "import itertools # Required for sequential replacement\n", "# With the assistance of Colab\n", "\n", "def replace_str_subsequence6(original_str, start_idx, end_idx, new_subsequence):\n", " ''' Replace a subsequence of a string 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", "\n", "\n", "def newick_mutation_of_radius6(newick_string):\n", " # The pattern of the radius of illumination\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_subsequence6(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", "if __name__ == \"__main__\":\n", " newick_string = \"(Node3[a=-1.0,b=0.0,x=-1.0,y=0.0,r=0.5],Node2[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.67],(Node5[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node4[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])Node_0[a=0,b=0,x=0,y=0,r=1] \"\n", " print(f\"Newick_string: {newick_string}\")\n", " mutated_newick_string = newick_mutation_of_radius6(newick_string)\n", " # Comparison\n", " print(f\"Original Newick string: {newick_string}\")\n", " print(f\"Mutated Newick_string: {mutated_newick_string}\")" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "ehgNhGwyhklc" }, "source": [ "**Observation.** The mutation procedure that changes the radius of illumination works well.\n", "\n", "**Criticism.** To enable Evolution with a mutation that only changes the radius of illumination converts it in a lame process. The reason is the following: mutation to be perfect must connect every state with every possible state, here every string with every possible string. But the mutation of radius cannot change the architecture of a tree, so trees with other architectures are inaccessible, and Evolution would fail to chase a random target. One might think that recombination could heal this deficiency. That is false because recombination can only mix what is already at hand. Recombination creates nothing new, and hence, very large population sizes would be needed to get acceptable outputs.\n", ">\n", "> *Answer.* The criticism is right.\n", "\n", "**Exercise.** Implement mutation of architecture of Newick morpho-trees.Is this type of mutation evolvable? In this case, evolvability means to make the mutation of architecture compatible with the occupation rule that says that one square must be occupied by only one cell, and that the tree remains as a whole.\n", ">\n", "> *Answer.* To have a full-fledged Evolution, it is enough to enable mutation with change of radius, insertion of a node and deletion of a node. Our expectancy is that random mutation of architecture will produce most surely unviable products because the occupation rule would be violated. Or, to see that Evolution walks, very large populations must be used. This would trigger high cost in memory and computing time. Deletion of a node is always evolvable. But insertion is not. To avoid problems, we execute a proposed insertion of a node in the only case when the target square is void.\n", ">\n", "> Our code to delete a node follows:\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 51, "status": "ok", "timestamp": 1789490802735, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "V9I5Ayz8BsJe", "outputId": "da307a12-7a81-41ed-8e5c-b2a4a58fa350" }, "source": [ " # Program group_deletion_newick_strings.py\n", " # To include the insertion and deletion of nodes as part of mutation\n", " # is enough to change the architecture.\n", " # The problem is that if a node with children is deleted,\n", " # the children would get orphan, and the form tore apart.\n", " # So, the deletion of a father is accompanied by that of the children,\n", "\n", "\n", "import random\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "import numpy as np\n", "from numpy import array as Array\n", "import copy # For deep copying trees during reproduction and mutation\n", "import sys # Import sys module to adjust recursion limit\n", "import re # Required for newick parsing\n", "import itertools # Required for sequential replacement\n", "# With the assistance of Colab\n", "\n", "def replace_str_subsequence7(original_str, start_idx, end_idx, new_subsequence):\n", " ''' Replace a subsequence of a string 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", "\n", "def newick_find_all_groups7(newick_string):\n", " ''' All groups 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", " 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", " #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", "\n", "\n", "\n", "def find_branch7(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", "def deletion_of_branch7(newick_string ):\n", " group, start = newick_find_all_groups7(original_newick_string)\n", " print(f\"Group (main): {group}\")\n", " print(f\"Start (main): {start}\")\n", " # Does the group have children?\n", " char_before = original_newick_string[start-1]\n", " print(f\"char_before: {char_before}\")\n", " newick_string = original_newick_string\n", " if char_before == \")\":\n", " print(\"The group has children\")\n", " #find branch\n", " final_index = start-1\n", " branch = find_branch7(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: {original_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", "if __name__ == \"__main__\":\n", " original_newick_string = \"(Node3[a=-1.0,b=0.0,x=-1.0,y=0.0,r=0.5],Node2[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.67],(Node5[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node4[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])Node_0[a=0,b=0,x=0,y=0,r=1] \"\n", " print(f\"Original Newick_string: {original_newick_string}\")\n", " group, start = newick_find_all_groups7(original_newick_string)\n", " final_newick_string = deletion_of_branch7(original_newick_string )\n", " print(f\"Final Newick_string: {final_newick_string}\")\n", "\n" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "YVAEvLyFPtdZ" }, "source": [ "**Observation.** The deletion of a node works well. If a father is deleted, its children also.\n", "\n", "**Exercise.** Develop the code for the insertion of a leaf into a Newick string.\n", ">\n", "> *Answer.* The following program detects all possible places that are appropriate for the insertion of leaves inside a Newick string, chooses one of those positions and inserts a leaf. Three modalities are offered. The code follows:" ] }, { "cell_type": "code", "metadata": {}, "source": [ "# Program insertion_of_leaves.py\n", "# Three modalities of insertion of leaves.\n", "# The code also includes functions for extracting information from the Newick string, \n", "# detecting groups, finding neighboring coordinates.\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", "\n", "\n", "def extract_info110(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", "def detect_all_groups110(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_groups11(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", " return groups\n", "\n", "def get_neighboring_coordinates110(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_occupied_squares110(newick_string):\n", " '''Detects all the occupied squares in a tree'''\n", " occupied_squares_set = set()\n", " groups = detect_all_groups110(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info110(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def find_father_center110(newick_string, j):\n", " ''' Finds the address of the father of a node at j'''\n", " #print(\"find_father_center11(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_groups110(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info110(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator110(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", " The node and father must be neighbors, \n", " and the node must be in an unoccupied square. \n", " The occupied squares are stored in a set.\n", " '''\n", " #print(\"node_info_generator11(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates110(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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", "def select_one_insertion_pattern_left_parenthesis_110(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 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", " 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\"Group110: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center110(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares110(newick_string)\n", " info, occupied_squares_set = node_info_generator110(father_center, counter, occupied_squares_set, max_illumination_radius)\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_110(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\"Group110: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center110(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares110(newick_string)\n", " info, occupied_squares_set = node_info_generator110(father_center, counter, occupied_squares_set, max_illumination_radius)\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+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_110(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\"Group110: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center110(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares110(newick_string)\n", " info, occupied_squares_set = node_info_generator110(father_center, counter, occupied_squares_set, max_illumination_radius)\n", " child_string = info \n", " print(f\"Child string: {child_string}\")\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", " \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_leaf110(newick_string, counter, max_illumination_radius): #original_newick_string = newick_string\n", " ''' All insertion patterns ]) are found and one is chosen at random\n", " The new info will be inserted between ] and )'''\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_110(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_110(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_110(newick_string, counter, max_illumination_radius)\n", " \n", " return newick_string\n", "\n", "if __name__ == \"__main__\":\n", " # Example usage of the functions\n", " #newick_string = \"Node1[a=0.0,b=0.0,x=0.0,y=0.0,r=1.4]\"\n", " #newick_string = \"(Node2[a=-1.0,b=1.0,x=-1.0,y=1.0,r=0.68])Node1[a=0.0,b=0.0,x=0.0,y=0.0,r=1.4]\"\n", " #newick_string = \"(Node2[a=-1.0,b=1.0,x=-1.0,y=1.0,r=0.68],Node3[a=1.0,b=1.0,x=1.0,y=1.0,r=0.68])Node1[a=0.0,b=0.0,x=0.0,y=0.0,r=1.4]\"\n", " original_newick_string = \"(Node2[a=-1.0,b=1.0,x=-1.0,y=1.0,r=0.68],Node3[a=1.0,b=1.0,x=1.0,y=1.0,r=0.68],Node4[a=1.0,b=-1.0,x=1.0,y=-1.0,r=2.18])Node1[a=0.0,b=0.0,x=0.0,y=0.0,r=1.0]\"\n", " #newick_string = \"((((Node31[a=-1.0,b=-1.0,x=-4.0,y=-1.0,r=1.11],Node23[a=-1.0,b=1.0,x=1.0,y=3.0,r=1.0])Node13[a=1.0,b=0.0,x=2.0,y=2.0,r=1.05],Node12[a=-1.0,b=1.0,x=0.0,y=3.0,r=0.77])Node7[a=1.0,b=1.0,x=1.0,y=2.0,r=1.23],((Node22[a=-1.0,b=1. x=-3. y=3.\n", " #newick_string = \"((((Node3１[a=-１．０,b=-１．０,x=-４．０,y=-１．０,r=１．１１],Node2３[a=-１．０,b=１．０,x=１．０,y=３．０,r=１．０])Node１３[a=１．０,b=０．０,x=２．０,y=２．０,r=１．０５],Node１２[a=-１．０,b=\"\n", " \n", " newick_string = insertion_of_leaf110(original_newick_string, counter=6, max_illumination_radius=3.0)\n", " print(f\"Original Newick string: {original_newick_string}\")\n", " print(f\"After inserting a leaf: {newick_string}\")\n", " " ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-10-02T13:55:44.075850Z", "start_time": "2026-10-02T13:55:44.034434Z" } }, "source": [], "outputs": [], "execution_count": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise.** Develop the code for the insertion of a branch into a Newick string.\n", ">\n", "> *Answer.* The following program detects all appropriate places for the insertion of a branch inside a Newick string, chooses one, and inserts a branch. The code follows:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 40, "status": "ok", "timestamp": 1789491134200, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "xVo5NrGrQR9W", "outputId": "f0dc6f5e-9f58-402a-c19d-5cb7a8eaa3ad", "ExecuteTime": { "end_time": "2026-10-08T16:04:25.200397Z", "start_time": "2026-10-08T16:04:25.105828Z" } }, "source": [ " # Program branch_insertion_newick_strings.py\n", " # The following program detects all possible places\n", " # that are appropriate for the insertion of a branch inside a Newick string,\n", " # chooses one of those positions and inserts a branch.\n", " # Criterion: all insertion places are marked by the right pair ]).\n", " # If no such a marker exists, the string has only one node, the root.\n", " # In that case, synthezise a tree and give it as child to the root.\n", "\n", "\n", "import random\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "import numpy as np\n", "from numpy import array as Array\n", "import copy # For deep copying trees during reproduction and mutation\n", "import sys # Import sys module to adjust recursion limit\n", "import re # Required for newick parsing\n", "import itertools # Required for sequential replacement\n", "# With the assistance of Colab\n", "\n", "def replace_str_subsequence8(original_str, start_idx, end_idx, new_subsequence):\n", " ''' Replace a subsequence of a string 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", "\n", "def newick_find_all_groups8(newick_string):\n", " ''' All groups 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", " #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", " 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", "def insertion_of_branch8(newick_string, child_string):\n", " # A group together its start site are elected\n", " group, start = newick_find_all_groups8(original_newick_string)\n", " newick_string = original_newick_string\n", " # String to be inserted\n", " child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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", " if group:\n", " print(f\"Group (main): {group}\")\n", " print(f\"Start (main): {start}\")\n", " # Does the group have children?\n", " char_before = original_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", "if __name__ == \"__main__\":\n", " #original_newick_string = \"(Node3[a=-1.0,b=0.0,x=-1.0,y=0.0,r=0.5],Node2[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.67],(Node5[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node4[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])Node_0[a=0,b=0,x=0,y=0,r=1]\"\n", " original_newick_string = \"Node_0[a=0,b=0,x=0,y=0,r=1]\"\n", " #print(f\"Original Newick_string: {original_newick_string}\")\n", " # A group together its start site are elected\n", " group, start = newick_find_all_groups8(original_newick_string)\n", " newick_string = original_newick_string\n", " # String to be inserted\n", " child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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", " newick_string = insertion_of_branch8(newick_string, child_string)\n", " print(f\"Final Newick_string: {newick_string}\")\n", "\n", "\n" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original Newick string: Node_0[a=0,b=0,x=0,y=0,r=1]\n", "Final Newick_string: ((Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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])Node_0[a=0,b=0,x=0,y=0,r=1]\n", "Final Newick_string: ((Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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])Node_0[a=0,b=0,x=0,y=0,r=1]\n" ] } ], "execution_count": 5 }, { "cell_type": "markdown", "metadata": { "id": "exboT3lV1d2H" }, "source": [ "**Observation.** The program inserts a complex item into a former tree.\n", "\n", "**Protest.** The double occupation problem was not revised.\n", ">\n", "> *Answer.* Right, we will do that when programming Evolution.\n", "\n", "**Exercise.** Develop the code for recombination. Do you see a future for this operation?\n", ">\n", "> *Answer.* Recombination is expected to be very traumatic because of multiple instances of the double occupation problem, and of fragmentation of recombinants. So, we see no future for this operation. Anyway, to program the interchange of branches in Newick Strings is rather simple. The code follows:\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 14, "status": "ok", "timestamp": 1789477137068, "user": { "displayName": "jose rodriguez", "userId": "08454562216435392883" }, "user_tz": 300 }, "id": "G3LMAooCBEyf", "outputId": "350112eb-954e-4147-d5cb-413c315eca41" }, "source": [ "# Program newick_strings_recombination.py\n", "# We hint recombination with the capability of\n", "# recombining full-functional branches and\n", "# use small to moderate population sizes.\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", "from numpy import array as Array\n", "import copy # For deep copying trees during reproduction and mutation\n", "import sys # Import sys module to adjust recursion limit\n", "import re # Required for newick parsing\n", "import itertools # Required for sequential replacement\n", "# With the assistance of Colab\n", "\n", "\n", "def find_branch9(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_right_par = r'[(]'\n", " starts = []\n", " groups = []\n", " for match in re.finditer(pattern_right_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", "def newick_position_recombination9(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " print(\"\\nnewick_recombination14\")\n", " print(f\"newick_string1: {newick_string1}\")\n", " print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_branch9(newick_string1)\n", " branch2 = find_branch9(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", "\n", "if __name__ == \"__main__\":\n", "\n", " newick_string1 = \"(Node3[a=-1.0,b=0.0,x=-1.0,y=0.0,r=0.5],Node2[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.67],(Node5[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node4[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])Node_0[a=0,b=0,x=0,y=0,r=1]\"\n", " newick_string2 = \"(Node12[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node11[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.07])Node10[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.95]\"\n", "\n", " #newick_string1 = \"(((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],Node5[x=19,y=15,r=10],(Node4[x=15,y=14,r=1])Node3[x=11,y=13,r=19],Node2[x=18,y=17,r=11])Node1[x=16,y=1,r=12])Node0[x=10,y=4,r=1]\"\n", " #print(f\"Initial newick_string1: {newick_string1}\")\n", " #newick_string2 = \"((Node2[x=18,y=17,r=11])Node1[x=16,y=1,r=12])Node0[x=10,y=4,r=1]\"\n", " #print(f\"Initial newick_string2: {newick_string2}\")\n", " #draw_newick_string14(newick_string1)\n", " #draw_newick_string14(newick_string2)\n", " print(\"Recombination\")\n", " newick_recombinant1, newick_recombinant2 = newick_position_recombination9(newick_string1, newick_string2)\n", " print(f\"newick_recombinant1: {newick_recombinant1}\")\n", " print(f\"newick_recombinant2: {newick_recombinant2}\")\n", " print(\"Done\")\n", "\n", " #print(f\"\\nfinal newick_string1: {newick_string1}\")\n", " #print(f\"final newick_string2: {newick_string2}\")\n", " #draw_newick_string14(newick_string1)\n", " #draw_newick_string14(newick_string2)\n", " #print(\"Done\")\n" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": { "id": "8G-hK8OGKNXM" }, "source": [ "*Observation.* The code is simpler than expected.\n", "\n", "It might be good to burn another option for recombination.\n", "\n", "**Challenge.** With the hope of diminishing the damage caused by cross-over, recombine not position but the instructions for children to step from the father associated to each node. Accept only those moves that are not problematic." ] }, { "cell_type": "code", "metadata": { "id": "d7AK_dHGPU4B", "ExecuteTime": { "end_time": "2026-10-02T13:55:44.254732Z", "start_time": "2026-10-02T13:55:44.242910Z" } }, "source": [], "outputs": [], "execution_count": 11 }, { "cell_type": "markdown", "metadata": { "id": "lSbwgatTPVrp" }, "source": [ "## Fitness evaluation\n", "\n", "**Exercise.** Develop the code to assess the fitness or performance of a Newick morpho-tree. The procedure is the following: a target tree is defined and its illuminated area determined and marked, it is the target illuminated area. To assess the fitness of a morpho-tree, the corresponding illuminated area is determined and then compared with the target illuminated area. The number of cells in the symmetric difference of those areas is the error, from which the fitness is calculated. When the error is zero, that means perfection, the fitness is one, and when the error is infinite, the fitness is zero.\n", ">\n", "> *Answer.* The code to assess the fitness of a Newick morpho-tree follows is the same that we used in the previous contribution with the Node class. Thus, we paste it directly over the programs below that simulate Evolution.\n" ] }, { "cell_type": "code", "metadata": { "id": "XPlyvyUjPn-9", "ExecuteTime": { "end_time": "2026-10-02T13:55:44.265018Z", "start_time": "2026-10-02T13:55:44.258820Z" } }, "source": [], "outputs": [], "execution_count": 11 }, { "cell_type": "markdown", "metadata": { "id": "FK2lcAAPPolV" }, "source": [ "# EVOLUTION OF NEWICK MORPHO-TREES\n", "\n", "We go step by step. Our purpose for the first attempt is just to see that Evolution works, that the fitness functions grows with time. Thus, we do not care for the rule that father and children must be neighbors, nor that two cells cannot occupy the same square, nor that trees end in dispersed pieces.\n", "\n", "**Exercise.** Develop the code for the Evolution of Newick morpho-trees.\n", ">\n", "> *Answer.* The following is the code for our first attempt of simulating the Evolution of morpho-trees in Newick Notation. " ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-10-02T18:50:46.628942Z", "start_time": "2026-10-02T18:46:32.752350Z" } }, "source": [ "# Program evolution_of_newick_morphological_trees_basic.py\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\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", "# 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", "#Activate memory monitoring?\n", "memory_monitoring = False \n", "\n", "\n", "# starting the memory allocation monitoring\n", "if memory_monitoring:\n", " tracemalloc.start() \n", "start_time = time.time()\n", "\n", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 7,\n", " 'max_number_children': 3,\n", " 'max_node_radius': 1.5,\n", "}\n", "\n", "# Parameters for the Genetic Algorithm\n", "ga_params = {\n", " 'seed': 12345, \n", " 'pop_size': 200,\n", " 'generations': 30,\n", " 'max_depth': 8,\n", " 'max_number_children': 3,\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001,#size of trees overgrows\n", " 'crossover_rate': 0.001 #size of trees overgrows\n", "}\n", "\n", "# This is an upper bound to the number of nodes in a tree, \n", "# which is used to limit the size of the trees during Evolution.\n", "max_number_of_groups = ga_params['max_depth'] * ga_params['max_number_children']/2 # This is an upper bound to the number of nodes in a tree, which is used to limit the size of the trees during Evolution.\n", "inhibit_insertion_of_branches = False # If True, the insertion of branches is inhibited during mutation\n", "\n", "\n", "def print_parameters10(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES-BASIC\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence10(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_length10(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_groups10(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_groups10(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " '''\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " '''\n", " return groups\n", "\n", "def extract_info10(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", "\n", "def get_neighboring_coordinates10(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", "\n", "\n", "def find_father_center10(newick_string, j):\n", " ''' Finds the address of the father of a node at j'''\n", " #print(\"find_father_center10(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_groups10(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info10(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "\n", "def node_info_generator10(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_generator10(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates10(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children10(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_generator10(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_generator10(max_depth, max_numb_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_generator10(...)\")\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. \n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center10(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children10(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence10(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# With the help of Gemini and Google.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick10(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence10(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " # 7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def simplify10(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length10(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax10(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick10(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " #print(f\"Length of tokens: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid10, message10 = examine_subsequence10(tokens, j)\n", " if is_valid10:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid10, message10 = simplify10(newick_str)\n", " if is_valid10:\n", " return True, \"Valid morpho-tree.\"\n", " else:\n", " return False, \"Invalid morpho-tree.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "def test_validator10(max_depth, max_numb_children, max_node_radius):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax10(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax10(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " p, s = complete_tree_generator10(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax10(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " #print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area10(x,y,r):\n", " '''\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " '''\n", " #print(\"get_illuminated_area10\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick10(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles10(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "\n", "def connect_leaves_to_father10(newick_string, ax, tree_color='lightblue', diff_color='red'):\n", " '''Draw lines connecting leaves to father'''\n", "\n", " #print(\"\\n\\nconnect_leaves_to_father10\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " # Detection of leaves.\n", " # Dectect 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", " #number_of_bouquets = len(bouquets_of_leaves)\n", " #print(f\"number_of_bouquets = {number_of_bouquets}\")\n", " all_leaves = []\n", " # Descipher each leaf and find the father\n", " for bouquet_of_leaves in bouquets_of_leaves:\n", " #print(f\"\\nbouquet_of_leaves1: {bouquet_of_leaves}\")\n", " bouquet_of_leaves = bouquet_of_leaves[1:-1]\n", " #print(f\"\\nbouquet_of_leaves2: {bouquet_of_leaves}\")\n", " #Bouquet could be composed of various leaves.\n", " leaves = detect_all_groups10(bouquet_of_leaves)\n", "\n", " #print(f\"\\nLeaves: {leaves}\")\n", " #number_of_leaves = len(leaves)\n", " #print(f\"number_of_leaves = {number_of_leaves}\")\n", " centers_leaves = []\n", " father_group = []\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " #print(f\"\\nloaf: {loaf}\")\n", " all_leaves.append(loaf)\n", " [al,bl,xl,yl,rl] = extract_info10(loaf)\n", " #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n", " centers_leaves.append((xl,yl))\n", "\n", " # Find the father of the bouquet of leaves.\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", "\n", " # We find the index of the last leaf in newick_string\n", " # Find the last loaf in bouquet\n", " last_leaf = leaves[-1]\n", " # last_leaf = last_leaf.replace(\" \", \"\")\n", " #print(f\"\\nlast_leaf: {last_leaf}\")\n", " index_last_leaf = newick_string.find(last_leaf)\n", " #print(f\"index_last_leaf: {index_last_leaf}\")\n", " length_of_last_leaf = len(last_leaf)\n", " #print(f\"length_of_last_leaf: {length_of_last_leaf}\")\n", " #Find the father of the leaves. It is the node after the last brother\n", " father_with_tail = newick_string[index_last_leaf+ length_of_last_leaf:]\n", " #print(f\"Father with tail: {father_with_tail}\")\n", " father_group = detect_all_groups10(father_with_tail)\n", " if father_group:\n", " #print(f\"Father_group: {father_group}\")\n", " [al,bl,xl,yl,rl] = extract_info10(father_group[0])\n", " #print(f\"Info: a: {al}, b: {bl}, x: {xl}, y: {yl}, r: {rl}\")\n", " father_center = (xl,yl)\n", " #print(f\"father_center: {father_center}\")\n", " # Draw a line from each center in centers_leaves to father_center\n", " for center_leaf in centers_leaves:\n", " ax.plot([center_leaf[0] + 0.5, father_center[0] + 0.5], [center_leaf[1] + 0.5, father_center[1] + 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_syntax10(newick_string)\n", " #print(f\"is valid at connect_leaves_to_father10(...) : {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_groups10(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "def connect_children_to_father10(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(\"connect_children_to_father10\")\n", " #print(f\"Newick String: {newick_string}\")\n", " length_string = len(newick_string)\n", " #print(f\"length_string: {length_string}\")\n", " groups = detect_all_groups10(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\\nconnect_children_to_father10\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " newick_string, number_of_groups = connect_leaves_to_father10(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_groups10(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", " [a0,b0,x0,y0,r0] = extract_info10(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", "def extract_all_infos10(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_groups10(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info10(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "\n", "\n", "def draw_tree_newick10(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos10(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles10(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area10(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick10(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " connect_children_to_father10(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree10(target_tree_params, ga_params):\n", " #tree_info_generator10(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] )\n", " # Parameters for the target tree generation\n", "\n", "\n", " max_depth = max_depth=ga_params['max_depth'] #20 # MAximal number of cell divisions along each branch\n", " max_numb_children = ga_params['max_number_children']\n", " max_node_radius = ga_params['max_node_radius'] # MAximal radius of illuminated circle\n", "\n", " # test_validator(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children}\")\n", " #print(f\"max_node_radius: {max_node_radius} \\n\"+ \"=\"*20)\n", "\n", " my_seed = 243 # For reproducibility\n", " #print(f\"Seed: {my_seed}\")\n", " random.seed(my_seed)\n", " newick_tree = complete_tree_generator10(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax10(newick_tree)\n", " illuminated_coords = set()\n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos10(newick_tree)\n", " illuminated_coords = get_illuminated_area10(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " #is_valid, message = validate_newick_syntax10(newick_tree)\n", " #print(f\"is valid at main : {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", " return newick_tree, illuminated_coords\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick10(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator10(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "\n", "def get_illuminated_coordinates10(newick_string):\n", " '''Coordinates of illuminated cells are extracted from the Newick string'''\n", " a,b,x,y,r = extract_all_infos10(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area10(x,y,r)\n", " ##print(f\"Illuminated cells: {illuminated_coords}\")\n", " ##print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " return illuminated_coords\n", "\n", "\n", "\n", "def calculate_fitness_illumination10(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_coordinates10(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", "\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_branch10(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_right_par = r'[(]'\n", " starts = []\n", " groups = []\n", " for match in re.finditer(pattern_right_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", "def newick_position_recombination10(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_recombination14\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch10(newick_string1)\n", " branch2 = find_recombinant_branch10(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", "\n", "def newick_mutation_of_radius10(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_subsequence10(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_patterns10(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", "def insertion_of_branch10(newick_string, child_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_patterns10(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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_generator10(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", "\n", "def newick_find_all_groups10(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", " number_of_groups = len(groups)\n", " if number_of_groups > max_number_of_groups:\n", " inhibit_insertion_of_branches = True\n", " else:\n", " inhibit_insertion_of_branches = False\n", " \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", " \n", "\n", "\n", "def find_branch10(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", "def deletion_of_branch10(newick_string ):\n", " group, start = newick_find_all_groups10(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_branch10(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", "def newick_mutation10(newick_string):\n", " # Mutation of radius of illumination\n", " if random.random() < ga_params['mutation_rate']:\n", " newick_string = newick_mutation_of_radius10(newick_string)\n", " if inhibit_insertion_of_branches == False:\n", " newick_string = insertion_of_branch10(newick_string, newick_string)\n", " newick_string = deletion_of_branch10(newick_string)\n", " return newick_string\n", "\n", "def plot_illuminated_area10(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 run_evolution_newick_morpho_trees10(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_tree, target_illuminated_area = generate_and_test_morpho_tree10(target_tree_params, ga_params)\n", " #print(f\"target_tree: {target_tree} \\n\\n\")\n", " #draw_newick_string10(target_tree, \"Target Tree\")\n", " # target_area = get_illuminated_coordinates10(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick10(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\n", " #print(f\"Population initialized with {len(population)} individuals.\")\n", " #print(f\"Population: {population}\")\n", "\n", "\n", "\n", " best_tree_overall = None\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", " print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n", " fitness_scores = [calculate_fitness_illumination10(tree, target_illuminated_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_tree, target_illuminated_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_generator10(target_tree_params['max_depth'], target_tree_params['max_number_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", "\n", " # Crossover newick_position_recombination10\n", " child1, child2 = newick_position_recombination10(p1, p2)\n", "\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation10(child1)\n", " mutated_child2 = newick_mutation10(child2)\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", "\n", " #print(\"\\n--- Evolution Finished ---\")\n", " #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n", "\n", " return target_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history\n", "\n", "\n", "# Generate the Newick string of a morpho-tree and draw it\n", "if __name__ == \"__main__\":\n", " #generate_test_and_draw_morpho_tree()\n", " print_parameters10(target_tree_params, ga_params)\n", " # Run the evolution loop\n", " target_tree, target_illuminated_area, evolved_best_tree, evolved_best_fitness, history = run_evolution_newick_morpho_trees10(target_tree_params, ga_params)\n", " \n", " title = \"Target Tree\"\n", " draw_tree_newick10(target_tree, title)\n", " title = \"Evolved Best Tree\"\n", " draw_tree_newick10(evolved_best_tree, title)\n", "\n", " # Plot Symmetric Difference Area\n", " evolved_best_area = get_illuminated_coordinates10(evolved_best_tree)\n", " symmetric_diff_area = target_illuminated_area.symmetric_difference(evolved_best_area )\n", " ##print(f\"Symmetric Difference Area: {symmetric_diff_area}\")\n", " ##print(f\"Number of cells in symmetric difference area: {len(symmetric_diff_area)}\")\n", "\n", "\n", " fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "\n", "\n", " plot_illuminated_area10(axes[0], target_illuminated_area, title=f\"Target Area (number of cells: {len(target_illuminated_area)})\")\n", " plot_illuminated_area10(axes[1], evolved_best_area, title=f\"Evolved Best Area (number of cells: {len(evolved_best_area)})\")\n", " plot_illuminated_area10(axes[2], symmetric_diff_area, title=f\"Symmetric Difference (Error Area: {len(symmetric_diff_area)})\")\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", " # Plot Fitness History\n", " plt.figure(figsize=(10, 6))\n", " plt.plot(history, marker='o', linestyle='-', color='blue')\n", " plt.title('Best Fitness Progression Over Generations')\n", " plt.xlabel('Generation')\n", " plt.ylabel('Fitness')\n", " plt.grid(True)\n", " plt.show()\n", "\n", " print(f\"Evolved best tree: {evolved_best_tree}\")\n", " print(f\"Evolved best fitness: {evolved_best_fitness:.2}\")\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" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB: EVOLUTION OF NEWICK MORPHO-TREES-BASIC\n", "PURPOSE: \n", " A random morpho-tree T is generated.\n", " Evolution tries to restore the tree T\n", " beginning from a population of random trees.\n", "EXPERIMENT 1790966792.834267\n", "TARGET TREE PARAMETERS: {'max_depth': 7, 'max_number_children': 3, 'max_node_radius': 1.5}\n", "GENETIC ALGORITHM PARAMETERS: {'seed': 12345, 'pop_size': 200, 'generations': 30, 'max_depth': 8, 'max_number_children': 3, 'max_node_radius': 1.5, 'mutation_rate': 0.001, 'crossover_rate': 0.001}\n", "\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 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7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "tracker2: 7375627562756275627564\n", "\n", "--- Generation 1/30, Best fitness -1.0 ---\n", "\n", "--- Generation 2/30, Best fitness 0.78632 ---\n", "\n", "--- Generation 3/30, Best fitness 0.81731 ---\n", "\n", "--- Generation 4/30, Best fitness 0.83962 ---\n", "\n", "--- Generation 5/30, Best fitness 0.86667 ---\n", "\n", "--- Generation 6/30, Best fitness 0.89623 ---\n", "\n", "--- Generation 7/30, Best fitness 0.91509 ---\n", "\n", "--- Generation 8/30, Best fitness 0.93458 ---\n", "\n", "--- Generation 9/30, Best fitness 0.95192 ---\n", "\n", "--- Generation 10/30, Best fitness 0.95192 ---\n", "\n", "--- Generation 11/30, Best fitness 0.96154 ---\n", "\n", "--- Generation 12/30, Best fitness 0.96154 ---\n", "\n", "--- Generation 13/30, Best fitness 0.96154 ---\n", "\n", "--- Generation 14/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 15/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 16/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 17/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 18/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 19/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 20/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 21/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 22/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 23/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 24/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 25/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 26/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 27/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 28/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 29/30, Best fitness 0.97087 ---\n", "\n", "--- Generation 30/30, Best 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" 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Evolved best tree: ((((Node23[a=-1.0,b=0.0,x=-2.0,y=0.0,r=1.17],Node22[a=-1.0,b=1.0,x=-2.0,y=1.0,r=1.08])Node13[a=-1.0,b=1.0,x=-1.0,y=0.0,r=1.28])Node8[a=1.0,b=0.0,x=0.0,y=-1.0,r=1.27])Node3[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=1.37],(((Node21[a=1.0,b=0.0,x=0.0,y=-3.0,r=1.31],Node20[a=-1.0,b=0.0,x=-2.0,y=-3.0,r=0.61])Node12[a=-1.0,b=-1.0,x=-1.0,y=-3.0,r=0.97])Node6[a=-1.0,b=-1.0,x=0.0,y=-2.0,r=1.38],((Node30[a=1.0,b=1.0,x=3.0,y=-2.0,r=1.42],Node29[a=1.0,b=-1.0,x=3.0,y=-4.0,r=0.83],Node28[a=1.0,b=0.0,x=3.0,y=-3.0,r=0.56])Node17[a=1.0,b=-1.0,x=2.0,y=-3.0,r=0.73],Node16[a=1.0,b=0.0,x=2.0,y=-2.0,r=0.68],(Node27[a=0.0,b=1.0,x=0.0,y=-2.0,r=0.72],Node26[a=-1.0,b=1.0,x=-1.0,y=-2.0,r=1.29],Node25[a=-1.0,b=-1.0,x=-1.0,y=-4.0,r=0.89])Node15[a=-1.0,b=-1.0,x=0.0,y=-3.0,r=1.39])Node5[a=1.0,b=1.0,x=2.0,y=0.0,r=0.65])Node2[a=1.0,b=-1.0,x=1.0,y=-1.0,r=0.85],((((Node33[a=1.0,b=0.0,x=4.0,y=-1.0,r=1.05],((Node34[a=-1.0,b=-1.0,x=-3.0,y=-4.0,r=0.68],Node33[a=0.0,b=-1.0,x=-2.0,y=-4.0,r=0.82])Node16[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=1.25],Node15[a=0.0,b=1.0,x=-1.0,y=-1.0,r=1.15],(Node9[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=1.27])Node14[a=-1.0,b=1.0,x=-2.0,y=-1.0,r=1.49])Node32[a=1.0,b=1.0,x=4.0,y=0.0,r=1.23],(Node50[a=0.0,b=-1.0,x=3.0,y=-3.0,r=1.33],Node49[a=1.0,b=-1.0,x=4.0,y=-3.0,r=1.4],Node48[a=1.0,b=1.0,x=4.0,y=-1.0,r=0.72])Node31[a=1.0,b=-1.0,x=4.0,y=-2.0,r=0.54])Node15[a=1.0,b=0.0,x=3.0,y=-1.0,r=1.28],(Node30[a=1.0,b=-1.0,x=3.0,y=-3.0,r=0.83])Node14[a=0.0,b=-1.0,x=2.0,y=-2.0,r=0.8],((Node53[a=0.0,b=1.0,x=2.0,y=1.0,r=0.61],Node52[a=-1.0,b=0.0,x=1.0,y=0.0,r=0.54],Node51[a=-1.0,b=1.0,x=1.0,y=1.0,r=1.17])Node29[a=-1.0,b=0.0,x=2.0,y=0.0,r=0.76],(Node50[a=-1.0,b=1.0,x=3.0,y=2.0,r=1.18],Node49[a=1.0,b=0.0,x=5.0,y=1.0,r=0.86],Node48[a=-1.0,b=0.0,x=3.0,y=1.0,r=1.05])Node28[a=1.0,b=1.0,x=4.0,y=1.0,r=1.06])Node13[a=1.0,b=1.0,x=3.0,y=0.0,r=0.73])Node6[a=1.0,b=0.0,x=2.0,y=-1.0,r=0.82],(((Node47[a=-1.0,b=-1.0,x=-3.0,y=-4.0,r=0.61],Node46[a=-1.0,b=0.0,x=-3.0,y=-3.0,r=0.92])Node27[a=-1.0,b=0.0,x=-2.0,y=-3.0,r=1.36])Node12[a=-1.0,b=-1.0,x=-1.0,y=-3.0,r=1.12])Node5[a=-1.0,b=-1.0,x=0.0,y=-2.0,r=1.11])Node2[a=1.0,b=-1.0,x=1.0,y=-1.0,r=1.23],(((Node26[a=1.0,b=1.0,x=0.0,y=2.0,r=0.7],(Node45[a=1.0,b=-1.0,x=0.0,y=1.0,r=1.11])Node25[a=0.0,b=1.0,x=-1.0,y=2.0,r=0.54])Node11[a=0.0,b=1.0,x=-1.0,y=1.0,r=1.38],(((Node17[a=-1.0,b=1.0,x=-2.0,y=4.0,r=1.16])Node11[a=0.0,b=-1.0,x=-2.0,y=-2.0,r=0.94],(Node23[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=0.78],Node22[a=1.0,b=0.0,x=0.0,y=-2.0,r=1.41],Node21[a=0.0,b=-1.0,x=-1.0,y=-3.0,r=1.26])Node10[a=1.0,b=-1.0,x=-1.0,y=-2.0,r=1.2])Node4[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=1.31])Node10[a=-1.0,b=0.0,x=-2.0,y=0.0,r=0.52],(((Node23[a=-1.0,b=0.0,x=1.0,y=2.0,r=1.4],((Node30[a=-1.0,b=0.0,x=-3.0,y=0.0,r=1.33])Node14[a=-1.0,b=-1.0,x=-2.0,y=0.0,r=1.19],(Node29[a=-1.0,b=1.0,x=-3.0,y=3.0,r=0.99],Node28[a=-1.0,b=-1.0,x=-3.0,y=1.0,r=1.03])Node13[a=-1.0,b=1.0,x=-2.0,y=2.0,r=0.51],(((Node21[a=-1.0,b=-1.0,x=0.0,y=-3.0,r=0.72],(Node37[a=1.0,b=0.0,x=3.0,y=-2.0,r=0.78])Node20[a=1.0,b=0.0,x=2.0,y=-2.0,r=1.02],(Node36[a=0.0,b=-1.0,x=0.0,y=-2.0,r=1.18],Node35[a=-1.0,b=1.0,x=-1.0,y=0.0,r=0.83],Node34[a=-1.0,b=-1.0,x=-1.0,y=-2.0,r=1.37])Node19[a=-1.0,b=1.0,x=0.0,y=-1.0,r=1.23])Node10[a=-1.0,b=-1.0,x=1.0,y=-2.0,r=1.13],(((Node17[a=-1.0,b=1.0,x=-2.0,y=4.0,r=1.16])Node11[a=0.0,b=-1.0,x=-2.0,y=-2.0,r=0.94],(Node23[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=0.78],Node22[a=1.0,b=0.0,x=0.0,y=-2.0,r=1.41],Node21[a=0.0,b=-1.0,x=-1.0,y=-3.0,r=1.26])Node10[a=1.0,b=-1.0,x=-1.0,y=-2.0,r=1.2])Node4[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=1.31])Node9[a=1.0,b=0.0,x=3.0,y=-1.0,r=1.23])Node5[a=1.0,b=-1.0,x=2.0,y=-1.0,r=0.81],(((Node36[a=-1.0,b=1.0,x=-2.0,y=2.0,r=1.04])Node19[a=0.0,b=1.0,x=-1.0,y=1.0,r=0.69],Node18[a=-1.0,b=1.0,x=-2.0,y=1.0,r=0.88],(Node35[a=-1.0,b=1.0,x=-3.0,y=1.0,r=0.98])Node17[a=-1.0,b=0.0,x=-2.0,y=0.0,r=0.65])Node16[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=1.25],Node15[a=0.0,b=1.0,x=-1.0,y=-1.0,r=1.15],(Node32[a=0.0,b=-1.0,x=-2.0,y=-2.0,r=0.91],Node31[a=-1.0,b=1.0,x=-3.0,y=0.0,r=0.83],Node30[a=-1.0,b=-1.0,x=-3.0,y=-2.0,r=1.48])Node14[a=-1.0,b=1.0,x=-2.0,y=-1.0,r=1.49])Node4[a=0.0,b=1.0,x=1.0,y=1.0,r=0.67],((Node7[a=-1.0,b=1.0,x=0.0,y=1.0,r=0.68],Node6[a=1.0,b=0.0,x=2.0,y=0.0,r=1.37],Node5[a=1.0,b=1.0,x=2.0,y=1.0,r=0.67])Node2[a=1.0,b=0.0,x=1.0,y=0.0,r=0.9],(Node4[a=0.0,b=1.0,x=-1.0,y=2.0,r=0.65],Node3[a=-1.0,b=1.0,x=-2.0,y=2.0,r=0.61])Node1[a=-1.0,b=1.0,x=-1.0,y=1.0,r=0.77])Node3[a=0.0,b=-1.0,x=1.0,y=-1.0,r=0.86])Node12[a=1.0,b=1.0,x=0.0,y=2.0,r=0.62])Node22[a=1.0,b=0.0,x=3.0,y=2.0,r=0.71],Node21[a=-1.0,b=1.0,x=1.0,y=3.0,r=1.42])Node10[a=1.0,b=1.0,x=2.0,y=2.0,r=1.0])Node21[a=1.0,b=-1.0,x=-1.0,y=-2.0,r=1.0],Node20[a=-1.0,b=1.0,x=-3.0,y=0.0,r=1.38])Node9[a=-1.0,b=-1.0,x=-2.0,y=-1.0,r=1.01])Node4[a=-1.0,b=1.0,x=-1.0,y=0.0,r=0.73],(((Node39[a=1.0,b=1.0,x=3.0,y=-2.0,r=0.68])Node19[a=1.0,b=0.0,x=2.0,y=-3.0,r=1.39],Node18[a=0.0,b=-1.0,x=1.0,y=-4.0,r=0.9])Node8[a=0.0,b=-1.0,x=1.0,y=-3.0,r=0.6],(((Node26[a=1.0,b=1.0,x=0.0,y=2.0,r=0.7],(Node17[a=1.0,b=0.0,x=1.0,y=1.0,r=1.04],Node16[a=1.0,b=1.0,x=1.0,y=2.0,r=1.33],Node15[a=-1.0,b=1.0,x=-1.0,y=2.0,r=1.5])Node25[a=0.0,b=1.0,x=-1.0,y=2.0,r=0.54])Node11[a=0.0,b=1.0,x=-1.0,y=1.0,r=1.38],(((Node17[a=-1.0,b=1.0,x=-2.0,y=4.0,r=1.16])Node11[a=0.0,b=-1.0,x=-2.0,y=-2.0,r=0.94],(Node23[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=0.78],Node22[a=1.0,b=0.0,x=0.0,y=-2.0,r=1.41],Node21[a=0.0,b=-1.0,x=-1.0,y=-3.0,r=1.26])Node10[a=1.0,b=-1.0,x=-1.0,y=-2.0,r=1.2])Node4[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=1.31])Node10[a=-1.0,b=0.0,x=-2.0,y=0.0,r=0.52],((((Node36[a=-1.0,b=1.0,x=-2.0,y=2.0,r=1.04])Node19[a=0.0,b=1.0,x=-1.0,y=1.0,r=0.69],Node18[a=-1.0,b=1.0,x=-2.0,y=1.0,r=0.88],(Node35[a=-1.0,b=1.0,x=-3.0,y=1.0,r=0.98])Node17[a=-1.0,b=0.0,x=-2.0,y=0.0,r=0.65])Node16[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=1.25],Node15[a=0.0,b=1.0,x=-1.0,y=-1.0,r=1.15],(Node32[a=0.0,b=-1.0,x=-2.0,y=-2.0,r=0.91],Node31[a=-1.0,b=1.0,x=-3.0,y=0.0,r=0.83],Node30[a=-1.0,b=-1.0,x=-3.0,y=-2.0,r=1.48])Node14[a=-1.0,b=1.0,x=-2.0,y=-1.0,r=1.49])Node7[a=-1.0,b=0.0,x=-1.0,y=-2.0,r=0.91],(((((Node23[a=-1.0,b=0.0,x=-2.0,y=0.0,r=1.17],Node22[a=-1.0,b=1.0,x=-2.0,y=1.0,r=1.08])Node13[a=-1.0,b=1.0,x=-1.0,y=0.0,r=1.28])Node8[a=1.0,b=0.0,x=0.0,y=-1.0,r=1.27])Node3[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=1.37],(((Node21[a=1.0,b=0.0,x=0.0,y=-3.0,r=1.31],Node20[a=-1.0,b=0.0,x=-2.0,y=-3.0,r=0.61])Node12[a=-1.0,b=-1.0,x=-1.0,y=-3.0,r=0.97])Node6[a=-1.0,b=-1.0,x=0.0,y=-2.0,r=1.38],(((Node13[a=1.0,b=1.0,x=3.0,y=1.0,r=0.7])Node6[a=1.0,b=1.0,x=2.0,y=0.0,r=0.94])Node7[a=-1.0,b=0.0,x=1.0,y=0.0,r=1.2])Node5[a=1.0,b=1.0,x=2.0,y=0.0,r=0.65])Node2[a=1.0,b=-1.0,x=1.0,y=-1.0,r=0.85],(((Node18[a=1.0,b=1.0,x=0.0,y=3.0,r=1.45])Node11[a=-1.0,b=0.0,x=-1.0,y=2.0,r=1.22],(Node17[a=-1.0,b=1.0,x=-2.0,y=4.0,r=1.16])Node10[a=-1.0,b=1.0,x=-1.0,y=3.0,r=0.6],(Node16[a=1.0,b=0.0,x=2.0,y=2.0,r=0.97],Node15[a=1.0,b=-1.0,x=2.0,y=1.0,r=1.4],Node14[a=-1.0,b=-1.0,x=0.0,y=1.0,r=1.16])Node9[a=1.0,b=0.0,x=1.0,y=2.0,r=0.69])Node4[a=-1.0,b=1.0,x=0.0,y=2.0,r=1.21])Node1[a=1.0,b=1.0,x=1.0,y=1.0,r=1.49])Node13[a=-1.0,b=0.0,x=0.0,y=-3.0,r=0.53],Node12[a=1.0,b=-1.0,x=2.0,y=-4.0,r=0.58],((Node38[a=1.0,b=0.0,x=4.0,y=-1.0,r=1.15],Node37[a=1.0,b=1.0,x=4.0,y=0.0,r=0.63])Node20[a=1.0,b=0.0,x=3.0,y=-1.0,r=0.59],(Node36[a=1.0,b=0.0,x=3.0,y=-2.0,r=0.57],Node35[a=1.0,b=-1.0,x=3.0,y=-3.0,r=1.02])Node19[a=0.0,b=-1.0,x=2.0,y=-2.0,r=1.2],(Node34[a=1.0,b=1.0,x=2.0,y=0.0,r=1.32])Node18[a=-1.0,b=0.0,x=1.0,y=-1.0,r=0.78])Node11[a=0.0,b=-1.0,x=1.0,y=-4.0,r=0.62])Node6[a=1.0,b=-1.0,x=1.0,y=-3.0,r=0.88],Node5[a=1.0,b=0.0,x=1.0,y=-2.0,r=1.16])Node9[a=-1.0,b=-1.0,x=-2.0,y=-1.0,r=1.01])Node17[a=-1.0,b=-1.0,x=-1.0,y=-4.0,r=1.24],(((Node23[a=-1.0,b=0.0,x=1.0,y=2.0,r=1.4],(Node40[a=1.0,b=-1.0,x=4.0,y=1.0,r=1.41],Node39[a=1.0,b=0.0,x=4.0,y=2.0,r=1.46])Node22[a=1.0,b=0.0,x=3.0,y=2.0,r=0.71],Node21[a=-1.0,b=1.0,x=1.0,y=3.0,r=1.42])Node10[a=1.0,b=1.0,x=2.0,y=2.0,r=1.0])Node7[a=0.0,b=1.0,x=1.0,y=1.0,r=1.0],((Node38[a=1.0,b=0.0,x=4.0,y=-1.0,r=1.15],Node37[a=1.0,b=1.0,x=4.0,y=0.0,r=0.63])Node20[a=1.0,b=0.0,x=3.0,y=-1.0,r=0.59],(Node36[a=1.0,b=0.0,x=3.0,y=-2.0,r=0.57],Node35[a=1.0,b=-1.0,x=3.0,y=-3.0,r=1.02])Node19[a=0.0,b=-1.0,x=2.0,y=-2.0,r=1.2],(Node23[a=-1.0,b=0.0,x=1.0,y=2.0,r=1.4],(Node40[a=1.0,b=-1.0,x=4.0,y=1.0,r=1.41],Node39[a=1.0,b=0.0,x=4.0,y=2.0,r=1.46])Node22[a=1.0,b=0.0,x=3.0,y=2.0,r=0.71],Node21[a=-1.0,b=1.0,x=1.0,y=3.0,r=1.42])Node18[a=-1.0,b=0.0,x=1.0,y=-1.0,r=0.78])Node6[a=1.0,b=-1.0,x=2.0,y=-1.0,r=0.76])Node16[a=0.0,b=-1.0,x=0.0,y=-4.0,r=1.0])Node7[a=-1.0,b=-1.0,x=0.0,y=-3.0,r=1.46])Node3[a=1.0,b=-1.0,x=1.0,y=-2.0,r=1.17])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.95])Node1[a=1.0,b=1.0,x=1.0,y=1.0,r=1.49])Node_0[a=0,b=-0,x=0,y=0,r=1]\n", "Evolved best fitness: 0.97\n", "Execution time: 0 hours, 4 minutes, 13 seconds\n" ] } ], "execution_count": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Observation.* Fitness grows with generations, a fact that shows that Evolution works. Nevertheless, this program has nothing to do with morphogenesis because children are located distantly away from fathers. Besides, many branches invade the territory of others, and the final graph looks more like an entanglement than a tree. Thus, our model is not compatible with Evolution and has no future. It is not evolvable. Additionally, we have the problem with overgrowing of trees that causes the program to run the slower, the higher is the number of executed generations.\n", "\n", "**Intrigue.** Can evolvability of morpho-trees be engineered?\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# EVOLVABLE MODELS\n", "\n", "Our considered trees are not evolvable because they are torn apart by recombination. Let us present two evolvable models, one in relation with polar coordinates and the other with a fractal.\n", "\n", "## The pie model\n", "\n", "A bar char in polar coordinates that is appropriate for our purpose is depicted by the following program:" ] }, { "cell_type": "code", "metadata": {}, "source": [ "# Program pie.py\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "# Example \n", "# Bar char in polar axis\n", "# of Matplot library usage to create a polar bar chart.\n", "# Adapted with the help of the AI of Visual Studio Code.\n", "\n", "# Fixing random state for reproducibility\n", "np.random.seed(19680801)\n", "\n", "# Compute pie slices\n", "N = 20\n", "theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)\n", "radii = 10 * np.random.rand(N)\n", "#width = np.pi / 4 * np.random.rand(N) #overlapping bars\n", "width = theta[1] - theta[0] # Set width to the difference between consecutive theta values\n", "colors = plt.colormaps[\"viridis\"](radii / 10.)\n", "\n", "ax = plt.subplot(projection='polar')\n", "ax.bar(theta, radii, width=width, bottom=0.0, color=colors, alpha=0.5)\n", "\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise.** Run the program and figure out that this is an ordinary bar chart that has been rolled up around the origin covering the complete angular span.\n", "\n", "**Exercise.** Interpret the pie char in terms of morphology. Prove that this char leads to a model of morphogenesis that is evolvable to the full.\n", ">\n", "> *Answer.* We see that a pie chart can have very complex outer silhouette a fact that says that very intricate geometries can be modelled with this type of char. All we need is to control the number of slices, the width, and the radius of each slice as a function of the angular position of each slice. In the simplest model, all slices have the same width but varying radius. In morphology, each slice is a product of a single cell whose descendants inherit the same information: the radius of the slice. This is interpreted as the number of cell divisions that are allowed along the corresponding radius. When that number is attained, the outer cells stop reproduction. The genomes of cells contain the number of allowed reproductions before stopping.\n", ">\n", "> This model is evolvable because neither mutation nor recombination have collateral effects. These operations modify each radius without affecting that of other slices. In this sense, each slice is isolated. \n", "\n", "**Exercise.** Relate the pie model with trees. Do you expect pleiotropy? If this model does not observe pleiotropy, the evolutionary stability of the form of flies cannot be explained. \n", ">\n", "> *Answer.* A pie with n slices is indeed a tree whose root is the origin, and the root has n children, each one of which defines a clan that fully occupy each slice and nothing else. Pleiotropy is not expected because each slice is isolated from the others. But, pleiotropy might be engineered by augmenting the width of slices to overlap one with another. \n", "\n", "\n", "The pie model is good to explain how to generate a region in which every point can be connected to the origin by a radius that is whole. Such a region is called a *Star Domain*. But regions with holes, such as a vertebra cannot be explained. To fill in this gap, the next model might help.\n", "\n", "# The quadric cross fractal\n", "\n", "This is a self similar plot that is based on a square. The following code presents how it is drawn recursively. " ] }, { "cell_type": "code", "metadata": {}, "source": [ "# Program quadric.py\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as patches\n", "# By Google on the prompt: \n", "\n", "# \"Draw the Vicsek Cross Fractal (Quadric-Cross-Fractal) using Matplotlib in Python.\" \n", "# The fractal isdrawn recursively, and the depth of recursion should be adjustable. \n", "# The base case should draw a filled square, \n", "# and the recursive case should draw the cross pattern \n", "# by dividing the square into 9 smaller squares \n", "# and drawing the cross in the center and the middle of each edge.\"\n", "\n", "# You can use the following command to install the required libraries \n", "# if they are not already installed:\n", "# pip install matplotlib\n", "\n", "def draw_vicsek_cross(ax, x, y, size, depth):\n", " \"\"\"\n", " Zeichnet das Quadric-Cross-Fraktal (Vicsek-Kreuz) rekursiv.\n", " \n", " Parameters:\n", " ax: Das Matplotlib-Achsen-Objekt.\n", " x, y: Die Koordinaten der linken unteren Ecke des aktuellen Quadrats.\n", " size: Die Seitenlänge des aktuellen Quadrats.\n", " depth: Die aktuelle Rekursionstiefe (0 ist das Basis-Quadrat).\n", " \"\"\"\n", " if depth == 0:\n", " # Basis-Fall: Zeichne ein ausgefülltes Quadrat\n", " rect = patches.Rectangle((x, y), size, size, facecolor='darkblue', edgecolor='none')\n", " ax.add_patch(rect)\n", " else:\n", " # Berechne die Größe der 9 kleineren Unterquadrate (3x3)\n", " new_size = size / 3\n", " \n", " # Das Kreuz besteht aus 5 Positionen im 3x3 Gitter:\n", " # (Spalte, Zeile) -> 0=links/unten, 1=mitte, 2=rechts/oben\n", " cross_positions = [\n", " (1, 1), # Zentrum\n", " (1, 0), # Unten-Mitte\n", " (1, 2), # Oben-Mitte\n", " (0, 1), # Links-Mitte\n", " (2, 1) # Rechts-Mitte\n", " ]\n", " \n", " # Rekursiver Aufruf für jede der 5 Teilflächen des Kreuzes\n", " for dx, dy in cross_positions:\n", " draw_vicsek_cross(ax, x + dx * new_size, y + dy * new_size, new_size, depth - 1)\n", "\n", "def plot_fractal(depth):\n", " fig, ax = plt.subplots(figsize=(8, 8))\n", " \n", " # Startparameter für das Hauptquadrat\n", " start_x, start_y = 0, 0\n", " total_size = 1.0\n", " \n", " # Fraktal zeichnen\n", " draw_vicsek_cross(ax, start_x, start_y, total_size, depth)\n", " \n", " # Achsen-Layout optimieren\n", " ax.set_xlim(0, total_size)\n", " ax.set_ylim(0, total_size)\n", " ax.set_aspect('equal')\n", " ax.axis('off') # Blendet die Achsenlinien aus\n", " \n", " plt.title(f\"Quadric Cross Fraktal (Tiefe {depth})\", fontsize=14, fontweight='bold')\n", " plt.show()\n", "\n", "# Generiert das Fraktal. \n", "# Hinweis: Tiefen über 5 können aufgrund der exponentiellen Zunahme der Quadrate rechenintensiv werden.\n", "plot_fractal(depth=0)\n", "plot_fractal(depth=1)\n", "plot_fractal(depth=2)\n", "plot_fractal(depth=3)\n", "plot_fractal(depth=4)\n", "plot_fractal(depth=5)" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise.** Run the program to see how the quadric fractal is constructed iteratively.\n", "\n", "**Exercise.** Fix a recursion depth, say 4, and explain how this frozen structure can be enlivened to be a model of morphogenesis.\n", ">\n", "> *Answer.* Everything is frozen in the fractal for a given depth of recursion. To fit whatever silhouette, one needs to give to strategic branches an appropriate length, whose value must go inside the genome of each cell. The fractal is a tree that expel messengers to control the reproduction of neighboring cells. The radius of action of each center must also be included in the genome.\n", "\n", "**Exercise.** Decide: Would you keep working with the previous morpho-trees, or would you prefer to explore these evolvable models?\n", ">\n", "> *Answer.* We prefer to continue working with oridnary trees because of four reasons:\n", "> 1. Morpho-trees can eventually be used to produce pleiotropy, which is a simple machinery to explain the evolutionary stability of the morphology of flies.\n", " > 2. Morpho-trees are in principle capable of explaining such complex structures like a vertebra or the pancreas, which includes islands that generate specific products, say insulin or components for the pancreatic juice.\n", " > 3. Morpho-trees can explain the tremedous variability of details inside species and races, while predicting evolutionaery stability of deep characterictics.\n", " > 4. Evolvable trees cannot explain the stability of flies because easy as it might be to gain perfection, it is also easy to lost it when selection fails, for instance in small groups, such as those in which flies love to exist.\n", "\n", "\n", "\n", "So, let us continue working.\n", "\n", "**Example.** In the following program, we see some improvements that have been added to the simulation of Evolution of morpho-trees in Newick nottion:\n", " 1. Mutation has been inbuilt with the power\n", " to have children in the immediate neighborhood of the father and\n", " to change radius and the topology of trees\n", " by means of insertion and deletion of leaves.\n", " 2. Recombination has been enabled with the power\n", " to exchange leaves between trees.\n", " We expect a good proportion of the population to\n", " be able to successfully recombine, mutate and reproduce.\n", " 3. A cut-off for the length of the Newick string has been introduced to avoid overgrowth of trees.\n", " 4. The seed of the random number generator has been fixed to make the results reproducible." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-10-08T18:34:57.650754Z", "start_time": "2026-10-08T18:30:34.294651Z" } }, "source": [ "# Program evolution_of_newick_morphological_trees11.py\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\n", "# \n", "# In this version:\n", "# 1. Mutation has been inbuilt with the power\n", "# to have children in the immediate neighborhood of the father and \n", "# to change radius and the topology of trees\n", "# by means of insertion and deletion of leaves.\n", "# 2. Recombination has been enabled with the power \n", "# to exchange leaves between trees.\n", "# We expect a good proportion of the population to \n", "# be able to successfully recombine, mutate and reproduce.\n", "# 3. A cut-off for the length of the Newick string has been introduced to avoid overgrowth of trees.\n", "# 4. The seed of the random number generator has been fixed to make the results reproducible.\n", "# We reuse programs made with the help of Colab and Gemini 2.5 Flash.\n", "# With the assistance of the AI of Visual Studio Code.\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", "# 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 = True\n", "\n", "# starting the memory allocation monitoring\n", "if memory_monitoring:\n", " tracemalloc.start() \n", "start_time = time.time()\n", "\n", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 7,\n", " 'max_number_children': 3,\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': 5001,\n", " 'max_depth': 8,\n", " 'max_number_children': 3,\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001, \n", " 'crossover_rate': 0.001 \n", "}\n", "\n", " \n", "def print_parameters11(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence11(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_length11(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_groups11(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_groups11(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " '''\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " '''\n", " return groups\n", "\n", "def count_groups11(newick_string):\n", " '''The number of groups is counted.'''\n", " groups = detect_all_groups11(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info11(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", "def extract_all_infos11(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_groups11(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info11(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates11(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_center11(newick_string, j):\n", " ''' Finds the address of the father of a node at j'''\n", " #print(\"find_father_center11(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_groups11(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info11(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator11(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_generator11(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates11(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children11(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_generator11(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_generator11(max_depth, max_numb_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_generator11(...)\")\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. \n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center11(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children11(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence11(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# Connectivity issues are not checked.\n", "\n", "# A bug in the drawing procedure has been corrected, and the drawing of trees is now correct.\n", "# The bug was related to the function that finds the father of a node.\n", "\n", "# With the help of Gemini, Google and the AI of Visual Studio Code.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick11(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence11(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " #print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " # 7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def simplify11(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length11(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax11(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick11(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " #print(f\"Length of tokens: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid11, message11 = examine_subsequence11(tokens, j)\n", " if is_valid11:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid11, message11 = simplify11(newick_str)\n", " if is_valid11:\n", " return True, \"Valid morpho-tree.\"\n", " else:\n", " return False, \"Invalid morpho-tree.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "def test_validator11(max_depth, max_numb_children, max_node_radius):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax11(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax11(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " p, s = complete_tree_generator11(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax11(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " #print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area11(x,y,r):\n", " '''\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " '''\n", " #print(\"get_illuminated_area11\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick11(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles11(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves(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", " # Dectect 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", " # Descipher 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_groups11(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father11(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_leaves(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info11(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_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_groups11(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info11(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_syntax11(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_groups11(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "\n", "def draw_tree_links11(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_groups11(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_father11(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_groups11(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", " [a0,b0,x0,y0,r0] = extract_info11(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_newick11(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos11(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles11(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area11(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick11(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links11(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree11(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " newick_tree = complete_tree_generator11(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator11(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax11(newick_tree)\n", " illuminated_coords = set()\n", " \n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos11(newick_tree)\n", " illuminated_coords = get_illuminated_area11(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " '''\n", " is_valid, message = validate_newick_syntax11(newick_tree)\n", " print(f\"is the Newick String valid?: {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", " '''\n", " return newick_tree, illuminated_coords\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick11(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator11(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "def get_illuminated_coordinates11(newick_string):\n", " '''Coordinates of illuminated cells are extracted from the Newick string'''\n", " a,b,x,y,r = extract_all_infos11(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area11(x,y,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_illumination11(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_coordinates11(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", "\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_branch11(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_recombination11(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_position_branch_recombination11\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch11(newick_string1)\n", " branch2 = find_recombinant_branch11(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_recombination11(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_leaves_recombination11\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " leaves1 = find_leaves(newick_string1)\n", " leaves2 = find_leaves(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_squares11(newick_string):\n", " '''Detects all the occupied squares in a tree'''\n", " occupied_squares_set = set()\n", " groups = detect_all_groups11(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info11(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def newick_mutation_of_radius11(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_subsequence11(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_patterns11(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_branch11(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_patterns11(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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_generator11(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_11(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 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", " 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\"Group110: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center11(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares11(newick_string)\n", " info, occupied_squares_set = node_info_generator11(father_center, counter, occupied_squares_set, max_illumination_radius)\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_11(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\"Group110: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center11(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares11(newick_string)\n", " info, occupied_squares_set = node_info_generator11(father_center, counter, occupied_squares_set, max_illumination_radius)\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+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_11(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\"Group110: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center11(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares11(newick_string)\n", " info, occupied_squares_set = node_info_generator11(father_center, counter, occupied_squares_set, max_illumination_radius)\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_leaf11(newick_string, counter, max_illumination_radius): #original_newick_string = newick_string\n", " ''' All insertion patterns ]) are found and one is chosen at random\n", " The new info will be inserted between ] and )'''\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_11(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_11(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_11(newick_string, counter, max_illumination_radius)\n", " \n", " return newick_string\n", "\n", "\n", "\n", "def deletion_of_leaf11(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_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", "def find_branch11(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_groups11(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", "\n", "def deletion_of_branch11(newick_string ):\n", " group, start = newick_find_all_groups11(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_branch11(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", "def newick_mutation_branches11(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_radius11(newick_string)\n", " newick_string = insertion_of_branch11(newick_string)\n", " newick_string = deletion_of_branch11(newick_string)\n", " return newick_string\n", "\n", "def newick_mutation_leaves11(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_radius11(newick_string)\n", " newick_string = insertion_of_leaf11(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n", " newick_string = deletion_of_leaf11(newick_string)\n", " return newick_string\n", "\n", "\n", "def plot_illuminated_area11(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 run_evolution_newick_morpho_trees11(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_tree, target_illuminated_area = generate_and_test_morpho_tree11( ga_params)#926\n", " print(f\"target_tree: {target_tree} \\n\\n\")\n", " #number_of_groups = count_groups11(target_tree)\n", " #print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " length_of_target_tree = len(target_tree)\n", " #draw_newick_string11(target_tree, \"Target Tree\")\n", " # target_area = get_illuminated_coordinates11(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick11(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\n", " #print(f\"Population initialized with {len(population)} individuals.\")\n", " #print(f\"Population: {population}\")\n", "\n", "\n", "\n", " best_tree_overall = None\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_illumination11(tree, target_illuminated_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_tree, target_illuminated_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_generator11(target_tree_params['max_depth'], target_tree_params['max_number_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_recombination11\n", " # child1, child2 = newick_position_branch_recombination11(p1, p2)\n", " # Crossover newick_leaves_recombination11\n", " child1, child2 =newick_leaves_recombination11(p1,p2)\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation_leaves11(child1, ga_params)\n", " mutated_child2 = newick_mutation_leaves11(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_generator11(target_tree_params['max_depth'], target_tree_params['max_number_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", "\n", " return target_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history\n", "\n", "\n", "# Generate the Newick string of a morpho-tree and draw it\n", "if __name__ == \"__main__\":\n", " print_parameters11(target_tree_params, ga_params)\n", " # Run the evolution loop\n", " target_tree, target_illuminated_area, evolved_best_tree, evolved_best_fitness, history = run_evolution_newick_morpho_trees11(target_tree_params, ga_params)\n", " number_of_groups = count_groups11(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " title = \"Target Tree. Number of groups or nodes: \" + str(number_of_groups)\n", " draw_tree_newick11(target_tree, title)\n", " title = \"Evolved Best Tree\"\n", " draw_tree_newick11(evolved_best_tree, title)\n", "\n", " # Plot Symmetric Difference Area\n", " evolved_best_area = get_illuminated_coordinates11(evolved_best_tree)\n", " symmetric_diff_area = target_illuminated_area.symmetric_difference(evolved_best_area )\n", " ##print(f\"Symmetric Difference Area: {symmetric_diff_area}\")\n", " ##print(f\"Number of cells in symmetric difference area: {len(symmetric_diff_area)}\")\n", "\n", "\n", " fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "\n", "\n", " plot_illuminated_area11(axes[0], target_illuminated_area, title=f\"Target Area (number of cells: {len(target_illuminated_area)})\")\n", " plot_illuminated_area11(axes[1], evolved_best_area, title=f\"Evolved Best Area (number of cells: {len(evolved_best_area)})\")\n", " plot_illuminated_area11(axes[2], symmetric_diff_area, title=f\"Symmetric Difference (Error Area: {len(symmetric_diff_area)})\")\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", " # Plot Fitness History\n", " plt.figure(figsize=(10, 6))\n", " plt.plot(history, marker='o', linestyle='-', color='blue')\n", " plt.title('Best Fitness Progression Over Generations')\n", " plt.xlabel('Generation')\n", " plt.ylabel('Fitness')\n", " plt.grid(True)\n", " plt.show()\n", "\n", " print(f\"Evolved best tree: {evolved_best_tree}\")\n", " print(f\"Evolved best fitness: {evolved_best_fitness:.2}\")\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" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB: EVOLUTION OF NEWICK MORPHO-TREES\n", "PURPOSE: \n", " A random morpho-tree T is generated.\n", " Evolution tries to restore the tree T\n", " beginning from a population of random trees.\n", "EXPERIMENT 1791484234.459203\n", "RECOMBINATION OF LEAVES: True\n", "SEED FOR THE GENETIC ALGORITHM: 47423648\n", "TARGET TREE PARAMETERS: {'max_depth': 7, 'max_number_children': 3, 'max_node_radius': 1.5}\n", "GENETIC ALGORITHM PARAMETERS: {'seed': 47423648, 'pop_size': 200, 'generations': 5001, 'max_depth': 8, 'max_number_children': 3, 'max_node_radius': 1.5, 'mutation_rate': 0.001, 'crossover_rate': 0.001}\n", "\n", "target_tree: (((Node32[a=-1.0,b=-1.0,x=1.0,y=-1.0,r=0.7])Node18[a=1.0,b=-1.0,x=2.0,y=0.0,r=0.9],(Node31[a=1.0,b=-1.0,x=2.0,y=1.0,r=0.79],Node30[a=1.0,b=0.0,x=2.0,y=2.0,r=1.16],Node29[a=1.0,b=1.0,x=2.0,y=3.0,r=1.44])Node17[a=0.0,b=1.0,x=1.0,y=2.0,r=1.32])Node2[a=1.0,b=1.0,x=1.0,y=1.0,r=0.94],(((Node16[a=1.0,b=-1.0,x=0.0,y=-2.0,r=1.39],(Node28[a=1.0,b=1.0,x=1.0,y=0.0,r=1.44],Node27[a=1.0,b=-1.0,x=1.0,y=-2.0,r=1.44])Node15[a=1.0,b=0.0,x=0.0,y=-1.0,r=1.3],(Node26[a=0.0,b=-1.0,x=-2.0,y=-3.0,r=0.84],Node25[a=1.0,b=0.0,x=-1.0,y=-2.0,r=1.47])Node14[a=-1.0,b=-1.0,x=-2.0,y=-2.0,r=0.91])Node7[a=0.0,b=-1.0,x=-1.0,y=-1.0,r=0.72])Node4[a=-1.0,b=-1.0,x=-1.0,y=0.0,r=1.3],(((Node24[a=1.0,b=-1.0,x=-2.0,y=-1.0,r=0.64],Node23[a=0.0,b=-1.0,x=-3.0,y=-1.0,r=1.29],Node22[a=-1.0,b=-1.0,x=-4.0,y=-1.0,r=1.37])Node13[a=-1.0,b=-1.0,x=-3.0,y=0.0,r=1.0],(Node21[a=-1.0,b=-1.0,x=-4.0,y=0.0,r=0.97],Node20[a=-1.0,b=0.0,x=-4.0,y=1.0,r=1.04])Node12[a=-1.0,b=0.0,x=-3.0,y=1.0,r=0.79],Node11[a=-1.0,b=1.0,x=-3.0,y=2.0,r=0.76])Node6[a=-1.0,b=-1.0,x=-2.0,y=1.0,r=0.86],((Node19[a=-1.0,b=1.0,x=-3.0,y=3.0,r=1.27])Node10[a=-1.0,b=1.0,x=-2.0,y=2.0,r=1.27],Node9[a=-1.0,b=-1.0,x=-2.0,y=0.0,r=1.11],Node8[a=1.0,b=1.0,x=0.0,y=2.0,r=0.72])Node5[a=0.0,b=-1.0,x=-1.0,y=1.0,r=0.79])Node3[a=-1.0,b=1.0,x=-1.0,y=2.0,r=1.48])Node1[a=0.0,b=1.0,x=0.0,y=1.0,r=1.31])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "\n", "--- Generation 1/5001, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/5001, Best fitness 0.94203 ---\n", "\n", "--- Generation 2001/5001, Best fitness 0.94203 ---\n", "\n", "--- Generation 3001/5001, Best fitness 0.94203 ---\n", "\n", "--- Generation 4001/5001, Best fitness 0.94203 ---\n", "\n", "--- Generation 5001/5001, Best fitness 0.94203 ---\n", "Number of groups or nodes in target tree: 33\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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" 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" 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Evolved best tree: ((((Node31[a=-1.0,b=-1.0,x=-4.0,y=-1.0,r=1.11],Node23[a=-1.0,b=1.0,x=1.0,y=3.0,r=1.0])Node13[a=1.0,b=0.0,x=2.0,y=2.0,r=1.05],Node12[a=-1.0,b=1.0,x=0.0,y=3.0,r=0.77])Node7[a=1.0,b=1.0,x=1.0,y=2.0,r=1.23],((Node22[a=-1.0,b=1.0,x=-3.0,y=3.0,r=0.72],Node19[a=-1.0,b=-1.0,x=-4.0,y=1.0,r=1.15],Node16[a=0.0,b=-1.0,x=-2.0,y=-3.0,r=1.09])Node11[a=-1.0,b=1.0,x=-2.0,y=2.0,r=0.85])Node6[a=-1.0,b=0.0,x=-1.0,y=1.0,r=0.73])Node3[a=0.0,b=1.0,x=0.0,y=1.0,r=1.4],((Node19[a=-1.0,b=-1.0,x=-3.0,y=-1.0,r=1.27],Node34[a=0.0,b=1.0,x=2.0,y=3.0,r=0.62])Node10[a=-1.0,b=0.0,x=-2.0,y=0.0,r=1.04],(Node20[a=1.0,b=1.0,x=-2.0,y=3.0,r=0.6],Node16[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.97],Node15[a=0.0,b=-1.0,x=-1.0,y=-2.0,r=0.77])Node9[a=0.0,b=-1.0,x=-1.0,y=-1.0,r=1.23])Node2[a=-1.0,b=0.0,x=-1.0,y=0.0,r=0.94],(Node5[a=-1.0,b=-1.0,x=0.0,y=-2.0,r=1.34],((Node14[a=0.0,b=-1.0,x=2.0,y=0.0,r=1.36])Node8[a=1.0,b=1.0,x=2.0,y=1.0,r=0.51])Node4[a=0.0,b=1.0,x=1.0,y=0.0,r=1.38])Node1[a=1.0,b=-1.0,x=1.0,y=-1.0,r=0.8])Node_0[a=0,b=0,x=0,y=0,r=1]\n", "Evolved best fitness: 0.94\n", "Execution time: 0 hours, 4 minutes, 23 seconds\n" ] } ], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise.** Run the previous program, in which recombination is off. Turn recombination on (line 49). Compare the outputs.\n", ">\n", "> *Answer.* Recombination speeds the process 20 times. But the final tree is very disconnected. So, we do not accept that programmed recombination could have something to do with life on Earth. In our case, disconnections happen because it is not censured anyhow. All that imports is the final shape of the illuminated region. \n", "\n", "**Exercise.** The variability of the offspring in every human family points to a high activity of recombination but in most cases no monster is produced. How can this be? Formulate a testable prediction.\n", ">\n", "> *Answer.* Apart from many small and diverse details, the genome of the human being is frozen: we all have two legs and tow arms, two eyes, two ears, one mouth, one head. So, the regions of our genomes that encode for the form and shape are mostly monomorphic. Thus, every type of variable items are somehow disjoint from the others. This is not the case in our simulations, because every tree can have every kind of drastic changes by cumulating effect of insertion and deletion of leaves. One can assume that the present state of the genomes have been attained by the Evolutionary Process. From this a prediction arises: the prehistory and history of the human genome must be filled in every kind of monstrosities. You can go to the museum and test this prediction by direct observation of fossils.\n", "\n", "**Exercise.** The previous program has been made with the intention of implementing error free mutation. This consists of change of the radius of illumination and insertion and deletion of leaves (only leaves). Nevertheless, if recombination is turned off, not every produced tree is perfect, say, it has a disconnected colored square that does not belong in the tree. This is a bug that contradicts the intention of the design. Correct it.\n", ">\n", "> *Answer.* If a defective tree is found, we indeed do not know who is the culprit: the mutation procedure or the drawing machinery. We must analyze as the formula as the drawing. Thus, we present the following program in which a Newick formula that produced a defective tree is plotted. \n", ">\n", "> The formula is: \n", ">\n", "> 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e89[a=-1.0,b=-1.0,x=-5.0,y=-9.0,r=0.94])Node71[a=-1.0,b=-1.0,x=-4.0,y=-8.0,r=0.91])Node42[a=-1.0,b=-1.0,x=-3.0,y=-7.0,r=0.58],(()Node46[a=-1.0,b=-1.0,x=-2.0,y=-7.0,r=0.93])()Node41[a=1.0,b=0.0,x=-1.0,y=-6.0,r=1.39])Node33[a=-1.0,b=-1.0,x=-2.0,y=-6.0,r=1.12])Node24[a=-1.0,b=0.0,x=-1.0,y=-5.0,r=1.47])Node16[a=-1.0,b=-1.0,x=0.0,y=-5.0,r=0.67],((Node32[a=0.0,b=-1.0,x=1.0,y=-6.0,r=1.9638918299741654],(((()Node841[a=-1.0,b=-1.0,x=-7.0,y=-11.0,r=0.58])((((()Node135[a=-1.0,b=-1.0,x=-5.0,y=-11.0,r=0.85])Node108[a=0.0,b=-1.0,x=-4.0,y=-10.0,r=1.0],(()Node630[a=-1.0,b=-1.0,x=-6.0,y=-13.0,r=0.77])(()Node134[a=-1.0,b=-1.0,x=-6.0,y=-11.0,r=1.0])Node107[a=-1.0,b=-1.0,x=-5.0,y=-10.0,r=1.14])Node88[a=-1.0,b=-1.0,x=-4.0,y=-9.0,r=0.65])Node69[a=-1.0,b=0.0,x=-3.0,y=-8.0,r=0.87],(((Node170[a=1.0,b=-1.0,x=-3.0,y=-12.0,r=1.9512384698512715],(()Node686[a=-1.0,b=-1.0,x=-4.0,y=-12.0,r=1.5])Node169[a=1.0,b=0.0,x=-3.0,y=-11.0,r=0.91])Node133[a=-1.0,b=-1.0,x=-4.0,y=-11.0,r=1.33])Node87[a=-1.0,b=-1.0,x=-3.0,y=-10.0,r=1.08],Node86[a=-1.0,b=0.0,x=-3.0,y=-9.0,r=1.33])Node68[a=0.0,b=-1.0,x=-2.0,y=-9.0,r=0.99])Node54[a=-1.0,b=-1.0,x=-2.0,y=-8.0,r=0.72])Node40[a=-1.0,b=-1.0,x=-1.0,y=-7.0,r=1.45],(()Node951[a=-1.0,b=-1.0,x=0.0,y=-11.0,r=1.7392926307253513])((()Node132[a=1.0,b=-1.0,x=2.0,y=-9.0,r=1.25],()Node131[a=1.0,b=0.0,x=2.0,y=-8.0,r=1.45])Node67[a=1.0,b=-1.0,x=1.0,y=-8.0,r=1.41],((()Node193[a=-1.0,b=0.0,x=-4.0,y=-14.0,r=1.22])()Node370[a=-1.0,b=-1.0,x=-3.0,y=-14.0,r=0.94])((()Node139[a=0.0,b=-1.0,x=-2.0,y=-13.0,r=1.33])(((Node166[a=0.0,b=-1.0,x=-2.0,y=-12.0,r=0.65],((()Node192[a=-1.0,b=0.0,x=0.0,y=-14.0,r=0.88])((()Node527[a=1.0,b=-1.0,x=1.0,y=-14.0,r=0.85])((()Node591[a=1.0,b=-1.0,x=0.0,y=-13.0,r=1.04])()Node169[a=0.0,b=-1.0,x=-1.0,y=-12.0,r=1.0099492850296317]))Node421[a=1.0,b=-1.0,x=-1.0,y=-11.0,r=1.29])Node165[a=0.0,b=1.0,x=-2.0,y=-10.0,r=1.23])Node130[a=-1.0,b=-1.0,x=-2.0,y=-11.0,r=1.45])Node106[a=0.0,b=-1.0,x=-1.0,y=-10.0,r=1.2592804255064793],Node105[a=1.0,b=0.0,x=0.0,y=-9.0,r=1.23])Node85[a=0.0,b=-1.0,x=-1.0,y=-9.0,r=1.49])Node66[a=-1.0,b=-1.0,x=-1.0,y=-8.0,r=1.38])Node39[a=0.0,b=-1.0,x=0.0,y=-7.0,r=1.13])Node31[a=-1.0,b=-1.0,x=0.0,y=-6.0,r=1.07])Node23[a=-1.0,b=0.0,x=1.0,y=-5.0,r=0.73],(((()Node952[a=1.0,b=-1.0,x=2.0,y=-10.0,r=1.18])(()Node84[a=-1.0,b=-1.0,x=0.0,y=-8.0,r=0.91])Node38[a=-1.0,b=0.0,x=1.0,y=-7.0,r=1.38],(((Node129[a=-1.0,b=-1.0,x=3.0,y=-8.0,r=1.27],()Node128[a=0.0,b=-1.0,x=4.0,y=-8.0,r=1.35])Node65[a=0.0,b=-1.0,x=4.0,y=-7.0,r=0.98],((((Node162[a=1.0,b=0.0,x=8.0,y=-7.0,r=1.2685669416870335],()Node283[a=1.0,b=-1.0,x=8.0,y=-8.0,r=0.9586343616711437])Node127[a=1.0,b=-1.0,x=7.0,y=-7.0,r=0.81],(()Node189[a=0.0,b=-1.0,x=6.0,y=-12.0,r=0.94])((()Node78[a=-1.0,b=-1.0,x=6.0,y=-11.0,r=0.66])((()Node537[a=0.0,b=-1.0,x=7.0,y=-11.0,r=1.22])()Node887[a=0.0,b=-1.0,x=7.0,y=-10.0,r=0.94])()Node473[a=0.0,b=-1.0,x=7.0,y=-9.0,r=0.81])()Node126[a=0.0,b=-1.0,x=6.0,y=-7.0,r=1.19],()Node93[a=1.0,b=0.0,x=7.0,y=-6.0,r=0.74])Node104[a=1.0,b=0.0,x=6.0,y=-6.0,r=1.4108167024543916])Node83[a=0.0,b=1.0,x=5.0,y=-6.0,r=1.32],((((()Node745[a=-1.0,b=-1.0,x=3.0,y=-14.0,r=0.62]))Node919[a=-1.0,b=-1.0,x=4.0,y=-13.0,r=1.15])()Node772[a=0.0,b=-1.0,x=5.0,y=-12.0,r=1.28])(()Node798[a=1.0,b=0.0,x=5.0,y=-11.0,r=0.8])(((()Node151[a=-1.0,b=-1.0,x=4.0,y=-11.0,r=0.96]))Node15[a=0.0,b=-1.0,x=5.0,y=-10.0,r=1.03])()Node82[a=1.0,b=-1.0,x=6.0,y=-8.0,r=0.96])Node64[a=1.0,b=-1.0,x=5.0,y=-7.0,r=1.37])Node53[a=1.0,b=1.0,x=4.0,y=-6.0,r=0.5])Node37[a=1.0,b=0.0,x=3.0,y=-7.0,r=1.28])Node30[a=0.0,b=-1.0,x=2.0,y=-7.0,r=0.75])Node22[a=0.0,b=-1.0,x=2.0,y=-6.0,r=0.83])Node15[a=1.0,b=-1.0,x=2.0,y=-5.0,r=1.41])Node11[a=1.0,b=-1.0,x=1.0,y=-4.0,r=0.68])Node9[a=-1.0,b=0.0,x=0.0,y=-3.0,r=1.224188604200087])Node6[a=1.0,b=-1.0,x=1.0,y=-3.0,r=0.95])Node4[a=1.0,b=-1.0,x=0.0,y=-2.0,r=0.82])Node2[a=-1.0,b=0.0,x=-1.0,y=-1.0,r=0.53])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.62])Node_0[a=0,b=0,x=0,y=0,r=1]\n", "\n", "How can we understand this unfathomable mystery? \n", "\n", "We might draw it. The code follows:\n", "\n" ] }, { "cell_type": "code", "metadata": {}, "source": [ "# Program morpho_tree_drawer.py\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", "\n", "def get_neighboring_coordinates11b(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", "\n", "def detect_all_groups11b(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_groups11b(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " '''\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " '''\n", " return groups\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", "def extract_all_infos11b(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_groups11b(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info11b(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "def find_father_center11b(newick_string, j):\n", " ''' Finds the address of the father of a node at j'''\n", " #print(\"find_father_center11(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_groups11b(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info11b(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def get_illuminated_area11b(x,y,r):\n", " '''\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " '''\n", " #print(\"get_illuminated_area11\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick11b(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles11b(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves_newick11b(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", " # Dectect 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", " # Descipher 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_groups11b(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father11b(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_leaves_newick11b(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info11b(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_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_groups11b(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info11b(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", " groups = detect_all_groups11b(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_links11b(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_groups11b(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_father11b(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_groups11b(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", " [a0,b0,x0,y0,r0] = extract_info11b(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_newick11b(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos11b(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles11b(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", " \n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area11b(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick11b(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links11b(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "if __name__ == \"__main__\":\n", " # Example Newick string\n", " newick_string = \"((((((()Node63[a=1.0,b=-1.0,x=5.0,y=-3.0,r=1.26])Node52[a=1.0,b=-1.0,x=4.0,y=-2.0,r=0.7])Node21[a=1.0,b=1.0,x=3.0,y=-1.0,r=1.9613197090647045],((()Node219[a=0.0,b=1.0,x=-2.0,y=3.0,r=1.06])((((Node156[a=-1.0,b=-1.0,x=-2.0,y=2.0,r=0.71])Node125[a=0.0,b=1.0,x=-1.0,y=3.0,r=1.37])Node103[a=-1.0,b=1.0,x=-1.0,y=2.0,r=1.36],(((Node198[a=-1.0,b=1.0,x=-4.0,y=1.0,r=0.75])Node155[a=-1.0,b=0.0,x=-3.0,y=0.0,r=0.58])Node124[a=-1.0,b=-1.0,x=-2.0,y=0.0,r=1.6749306080259698],((((()Node913[a=-1.0,b=0.0,x=-4.0,y=3.0,r=0.59]))Node698[a=0.0,b=1.0,x=-3.0,y=3.0,r=0.84])()Node600[a=-1.0,b=1.0,x=-3.0,y=2.0,r=1.25])()Node123[a=0.0,b=-1.0,x=-1.0,y=0.0,r=1.33])Node102[a=-1.0,b=0.0,x=-1.0,y=1.0,r=1.33])Node51[a=-1.0,b=1.0,x=0.0,y=1.0,r=0.54],(((()Node122[a=-1.0,b=0.0,x=0.0,y=3.0,r=1.17])Node81[a=-1.0,b=1.0,x=1.0,y=3.0,r=0.85])Node62[a=0.0,b=1.0,x=2.0,y=2.0,r=1.07],(((()Node153[a=-1.0,b=1.0,x=3.0,y=5.0,r=0.515683679559608],Node152[a=0.0,b=-1.0,x=4.0,y=3.0,r=1.03])Node101[a=1.0,b=1.0,x=4.0,y=4.0,r=0.5748403340449614])Node80[a=0.0,b=1.0,x=3.0,y=3.0,r=1.22],()Node79[a=1.0,b=0.0,x=4.0,y=2.0,r=1.36])Node61[a=1.0,b=1.0,x=3.0,y=2.0,r=0.5295884877970901])Node50[a=1.0,b=1.0,x=2.0,y=1.0,r=1.21])Node29[a=0.0,b=1.0,x=1.0,y=0.0,r=1.43])Node20[a=-1.0,b=1.0,x=1.0,y=-1.0,r=1.1])Node8[a=1.0,b=0.0,x=2.0,y=-2.0,r=0.64])Node3[a=1.0,b=-1.0,x=1.0,y=-2.0,r=1.37],((((((()Node100[a=0.0,b=1.0,x=-2.0,y=-2.0,r=1.05],((Node150[a=0.0,b=1.0,x=-4.0,y=-1.0,r=0.7334891191596699],(Node193[a=-1.0,b=1.0,x=-6.0,y=0.0,r=0.5820889162363478])Node149[a=-1.0,b=1.0,x=-5.0,y=-1.0,r=0.81])Node120[a=-1.0,b=0.0,x=-4.0,y=-2.0,r=1.36],()Node119[a=0.0,b=1.0,x=-3.0,y=-1.0,r=1.16])Node99[a=-1.0,b=1.0,x=-3.0,y=-2.0,r=1.47])Node49[a=-1.0,b=1.0,x=-2.0,y=-3.0,r=1.26],()((()Node118[a=0.0,b=-1.0,x=-3.0,y=-4.0,r=1.0],((()Node367[a=-1.0,b=-1.0,x=-7.0,y=-3.0,r=1.8764650499221425])()Node147[a=-1.0,b=0.0,x=-5.0,y=-3.0,r=0.82],Node146[a=-1.0,b=1.0,x=-5.0,y=-2.0,r=1.13])Node117[a=-1.0,b=0.0,x=-4.0,y=-3.0,r=1.13])Node78[a=-1.0,b=1.0,x=-3.0,y=-3.0,r=1.0329473493338008])Node48[a=-1.0,b=0.0,x=-2.0,y=-4.0,r=0.76])Node28[a=-1.0,b=0.0,x=-1.0,y=-4.0,r=1.46])Node14[a=1.0,b=-1.0,x=0.0,y=-4.0,r=1.06])Node7[a=0.0,b=-1.0,x=-1.0,y=-3.0,r=0.89])Node5[a=0.0,b=-1.0,x=-1.0,y=-2.0,r=0.71],(((((((()Node47[a=-1.0,b=1.0,x=4.0,y=-4.0,r=0.6058398421466735],((()Node441[a=0.0,b=-1.0,x=8.0,y=0.0,r=1.2970045651723827])((((()Node145[a=1.0,b=1.0,x=8.0,y=1.0,r=0.52],()Node144[a=0.0,b=1.0,x=7.0,y=1.0,r=0.96])Node116[a=1.0,b=1.0,x=7.0,y=0.0,r=1.235712154728331],()Node115[a=1.0,b=0.0,x=7.0,y=-1.0,r=0.95])Node98[a=1.0,b=1.0,x=6.0,y=-1.0,r=1.37],(()Node114[a=1.0,b=0.0,x=7.0,y=-2.0,r=0.62])Node97[a=1.0,b=0.0,x=6.0,y=-2.0,r=1.04])Node77[a=1.0,b=1.0,x=5.0,y=-2.0,r=1.01])Node59[a=-1.0,b=1.0,x=4.0,y=-3.0,r=0.62],(((Node185[a=1.0,b=0.0,x=9.0,y=-5.0,r=1.25],()Node476[a=1.0,b=-1.0,x=9.0,y=-6.0,r=0.87])Node142[a=1.0,b=-1.0,x=8.0,y=-5.0,r=1.7146586553405185])Node96[a=1.0,b=1.0,x=7.0,y=-4.0,r=1.49],((Node183[a=1.0,b=-1.0,x=8.0,y=-6.0,r=0.54])Node141[a=1.0,b=-1.0,x=7.0,y=-5.0,r=0.5185357042371559],(()Node303[a=1.0,b=-1.0,x=9.0,y=-4.0,r=0.69])(Node181[a=1.0,b=0.0,x=8.0,y=-3.0,r=1.16])Node140[a=1.0,b=1.0,x=7.0,y=-3.0,r=1.44])Node95[a=0.0,b=1.0,x=6.0,y=-4.0,r=1.1])Node58[a=1.0,b=-1.0,x=6.0,y=-5.0,r=0.72])Node46[a=0.0,b=1.0,x=5.0,y=-4.0,r=0.79])Node36[a=1.0,b=0.0,x=5.0,y=-5.0,r=1.02])Node27[a=1.0,b=-1.0,x=4.0,y=-5.0,r=0.71])Node19[a=1.0,b=0.0,x=3.0,y=-4.0,r=0.83],(Node57[a=0.0,b=-1.0,x=3.0,y=-6.0,r=0.83])Node18[a=1.0,b=-1.0,x=3.0,y=-5.0,r=1.36])Node13[a=0.0,b=-1.0,x=2.0,y=-4.0,r=1.9563373261553025],((((((()Node94[a=1.0,b=1.0,x=2.0,y=3.0,r=1.5714886013535794])Node76[a=1.0,b=0.0,x=1.0,y=2.0,r=1.7607046194653915])Node56[a=-1.0,b=1.0,x=0.0,y=2.0,r=1.43])Node45[a=-1.0,b=1.0,x=1.0,y=1.0,r=0.71])Node26[a=0.0,b=1.0,x=2.0,y=0.0,r=0.81],(((()Node55[a=1.0,b=0.0,x=5.0,y=-1.0,r=0.68])Node44[a=0.0,b=-1.0,x=4.0,y=-1.0,r=1.39])Node35[a=1.0,b=0.0,x=4.0,y=0.0,r=1.5450652146942652])Node25[a=1.0,b=1.0,x=3.0,y=0.0,r=0.8])Node17[a=-1.0,b=1.0,x=2.0,y=-1.0,r=0.84])Node12[a=1.0,b=1.0,x=3.0,y=-2.0,r=0.59])Node10[a=1.0,b=0.0,x=2.0,y=-3.0,r=1.31],((((((()Node74[a=0.0,b=1.0,x=-3.0,y=-5.0,r=0.97],(((()Node139[a=0.0,b=1.0,x=-5.0,y=-4.0,r=0.85])Node113[a=0.0,b=1.0,x=-5.0,y=-5.0,r=1.29],(Node138[a=-1.0,b=1.0,x=-7.0,y=-4.0,r=1.09])Node112[a=-1.0,b=1.0,x=-6.0,y=-5.0,r=0.69])Node92[a=-1.0,b=0.0,x=-5.0,y=-6.0,r=1.9891193529247717])Node73[a=-1.0,b=0.0,x=-4.0,y=-6.0,r=0.9])Node43[a=-1.0,b=-1.0,x=-3.0,y=-6.0,r=0.93])Node34[a=-1.0,b=0.0,x=-2.0,y=-5.0,r=1.7667232954270577],(()Node707[a=0.0,b=-1.0,x=-8.0,y=-11.0,r=1.2418125160543307])((((((Node176[a=-1.0,b=1.0,x=-8.0,y=-8.0,r=0.73])Node137[a=-1.0,b=-1.0,x=-7.0,y=-9.0,r=1.3],Node136[a=-1.0,b=0.0,x=-7.0,y=-8.0,r=0.59])Node111[a=-1.0,b=-1.0,x=-6.0,y=-8.0,r=0.53],(Node175[a=-1.0,b=1.0,x=-7.0,y=-6.0,r=1.8710079054235957])Node110[a=-1.0,b=0.0,x=-6.0,y=-7.0,r=0.81])Node91[a=-1.0,b=0.0,x=-5.0,y=-7.0,r=0.64])Node72[a=-1.0,b=0.0,x=-4.0,y=-7.0,r=1.35],(Node90[a=-1.0,b=0.0,x=-5.0,y=-8.0,r=1.48],(()Node109[a=-1.0,b=0.0,x=-6.0,y=-9.0,r=1.8236264836746388])Node89[a=-1.0,b=-1.0,x=-5.0,y=-9.0,r=0.94])Node71[a=-1.0,b=-1.0,x=-4.0,y=-8.0,r=0.91])Node42[a=-1.0,b=-1.0,x=-3.0,y=-7.0,r=0.58],(()Node46[a=-1.0,b=-1.0,x=-2.0,y=-7.0,r=0.93])()Node41[a=1.0,b=0.0,x=-1.0,y=-6.0,r=1.39])Node33[a=-1.0,b=-1.0,x=-2.0,y=-6.0,r=1.12])Node24[a=-1.0,b=0.0,x=-1.0,y=-5.0,r=1.47])Node16[a=-1.0,b=-1.0,x=0.0,y=-5.0,r=0.67],((Node32[a=0.0,b=-1.0,x=1.0,y=-6.0,r=1.9638918299741654],(((()Node841[a=-1.0,b=-1.0,x=-7.0,y=-11.0,r=0.58])((((()Node135[a=-1.0,b=-1.0,x=-5.0,y=-11.0,r=0.85])Node108[a=0.0,b=-1.0,x=-4.0,y=-10.0,r=1.0],(()Node630[a=-1.0,b=-1.0,x=-6.0,y=-13.0,r=0.77])(()Node134[a=-1.0,b=-1.0,x=-6.0,y=-11.0,r=1.0])Node107[a=-1.0,b=-1.0,x=-5.0,y=-10.0,r=1.14])Node88[a=-1.0,b=-1.0,x=-4.0,y=-9.0,r=0.65])Node69[a=-1.0,b=0.0,x=-3.0,y=-8.0,r=0.87],(((Node170[a=1.0,b=-1.0,x=-3.0,y=-12.0,r=1.9512384698512715],(()Node686[a=-1.0,b=-1.0,x=-4.0,y=-12.0,r=1.5])Node169[a=1.0,b=0.0,x=-3.0,y=-11.0,r=0.91])Node133[a=-1.0,b=-1.0,x=-4.0,y=-11.0,r=1.33])Node87[a=-1.0,b=-1.0,x=-3.0,y=-10.0,r=1.08],Node86[a=-1.0,b=0.0,x=-3.0,y=-9.0,r=1.33])Node68[a=0.0,b=-1.0,x=-2.0,y=-9.0,r=0.99])Node54[a=-1.0,b=-1.0,x=-2.0,y=-8.0,r=0.72])Node40[a=-1.0,b=-1.0,x=-1.0,y=-7.0,r=1.45],(()Node951[a=-1.0,b=-1.0,x=0.0,y=-11.0,r=1.7392926307253513])((()Node132[a=1.0,b=-1.0,x=2.0,y=-9.0,r=1.25],()Node131[a=1.0,b=0.0,x=2.0,y=-8.0,r=1.45])Node67[a=1.0,b=-1.0,x=1.0,y=-8.0,r=1.41],((()Node193[a=-1.0,b=0.0,x=-4.0,y=-14.0,r=1.22])()Node370[a=-1.0,b=-1.0,x=-3.0,y=-14.0,r=0.94])((()Node139[a=0.0,b=-1.0,x=-2.0,y=-13.0,r=1.33])(((Node166[a=0.0,b=-1.0,x=-2.0,y=-12.0,r=0.65],((()Node192[a=-1.0,b=0.0,x=0.0,y=-14.0,r=0.88])((()Node527[a=1.0,b=-1.0,x=1.0,y=-14.0,r=0.85])((()Node591[a=1.0,b=-1.0,x=0.0,y=-13.0,r=1.04])()Node169[a=0.0,b=-1.0,x=-1.0,y=-12.0,r=1.0099492850296317]))Node421[a=1.0,b=-1.0,x=-1.0,y=-11.0,r=1.29])Node165[a=0.0,b=1.0,x=-2.0,y=-10.0,r=1.23])Node130[a=-1.0,b=-1.0,x=-2.0,y=-11.0,r=1.45])Node106[a=0.0,b=-1.0,x=-1.0,y=-10.0,r=1.2592804255064793],Node105[a=1.0,b=0.0,x=0.0,y=-9.0,r=1.23])Node85[a=0.0,b=-1.0,x=-1.0,y=-9.0,r=1.49])Node66[a=-1.0,b=-1.0,x=-1.0,y=-8.0,r=1.38])Node39[a=0.0,b=-1.0,x=0.0,y=-7.0,r=1.13])Node31[a=-1.0,b=-1.0,x=0.0,y=-6.0,r=1.07])Node23[a=-1.0,b=0.0,x=1.0,y=-5.0,r=0.73],(((()Node952[a=1.0,b=-1.0,x=2.0,y=-10.0,r=1.18])(()Node84[a=-1.0,b=-1.0,x=0.0,y=-8.0,r=0.91])Node38[a=-1.0,b=0.0,x=1.0,y=-7.0,r=1.38],(((Node129[a=-1.0,b=-1.0,x=3.0,y=-8.0,r=1.27],()Node128[a=0.0,b=-1.0,x=4.0,y=-8.0,r=1.35])Node65[a=0.0,b=-1.0,x=4.0,y=-7.0,r=0.98],((((Node162[a=1.0,b=0.0,x=8.0,y=-7.0,r=1.2685669416870335],()Node283[a=1.0,b=-1.0,x=8.0,y=-8.0,r=0.9586343616711437])Node127[a=1.0,b=-1.0,x=7.0,y=-7.0,r=0.81],(()Node189[a=0.0,b=-1.0,x=6.0,y=-12.0,r=0.94])((()Node78[a=-1.0,b=-1.0,x=6.0,y=-11.0,r=0.66])((()Node537[a=0.0,b=-1.0,x=7.0,y=-11.0,r=1.22])()Node887[a=0.0,b=-1.0,x=7.0,y=-10.0,r=0.94])()Node473[a=0.0,b=-1.0,x=7.0,y=-9.0,r=0.81])()Node126[a=0.0,b=-1.0,x=6.0,y=-7.0,r=1.19],()Node93[a=1.0,b=0.0,x=7.0,y=-6.0,r=0.74])Node104[a=1.0,b=0.0,x=6.0,y=-6.0,r=1.4108167024543916])Node83[a=0.0,b=1.0,x=5.0,y=-6.0,r=1.32],((((()Node745[a=-1.0,b=-1.0,x=3.0,y=-14.0,r=0.62]))Node919[a=-1.0,b=-1.0,x=4.0,y=-13.0,r=1.15])()Node772[a=0.0,b=-1.0,x=5.0,y=-12.0,r=1.28])(()Node798[a=1.0,b=0.0,x=5.0,y=-11.0,r=0.8])(((()Node151[a=-1.0,b=-1.0,x=4.0,y=-11.0,r=0.96]))Node15[a=0.0,b=-1.0,x=5.0,y=-10.0,r=1.03])()Node82[a=1.0,b=-1.0,x=6.0,y=-8.0,r=0.96])Node64[a=1.0,b=-1.0,x=5.0,y=-7.0,r=1.37])Node53[a=1.0,b=1.0,x=4.0,y=-6.0,r=0.5])Node37[a=1.0,b=0.0,x=3.0,y=-7.0,r=1.28])Node30[a=0.0,b=-1.0,x=2.0,y=-7.0,r=0.75])Node22[a=0.0,b=-1.0,x=2.0,y=-6.0,r=0.83])Node15[a=1.0,b=-1.0,x=2.0,y=-5.0,r=1.41])Node11[a=1.0,b=-1.0,x=1.0,y=-4.0,r=0.68])Node9[a=-1.0,b=0.0,x=0.0,y=-3.0,r=1.224188604200087])Node6[a=1.0,b=-1.0,x=1.0,y=-3.0,r=0.95])Node4[a=1.0,b=-1.0,x=0.0,y=-2.0,r=0.82])Node2[a=-1.0,b=0.0,x=-1.0,y=-1.0,r=0.53])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.62])Node_0[a=0,b=0,x=0,y=0,r=1]\"\n", " # Draw the tree and get the illuminated coordinates\n", " illuminated_coords = draw_tree_newick11b(newick_string, title=\"Example of disconnected Morpho-tree\")\n", " print(f\"Illuminated coordinates: {illuminated_coords}\")" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations.** By looking at the plot, one sees at least three types of connectivity defects:\n", "1. Isolated cells, for instance `(-6, -13)`. According to the formula, this corresponds with the info `(()Node630[a=-1.0,b=-1.0,x=-6.0,y=-13.0,r=0.77])(()Node134`. According to the formula, the node630 is also isolated. Hence, this is not an error of the drawing machinery. It is an error caused by ill programmed Evolution. \n", "2. The father and the child are not neighbors. Such is the case of the cell `(-3,-14)`, whose info is `)()Node370[a=-1.0,b=-1.0,x=-3.0,y=-14.0,r=0.94])((()Node139`, whose father is, according to the plot, (-2,-12), with info `(Node166[a=0.0,b=-1.0,x=-2.0,y=-12.0,r=0.65], `. According to the formula, the node370 is also isolated. This is a bug of an ill programmed Evolution. \n", "3. Unlinked cell: The cell `(-7,-3)`, with info `(()Node367[a=-1.0,b=-1.0,x=-7.0,y=-3.0,r=1.8764650499221425])()Node147` is by side of `(-7,-4)`, with info `(Node138[a=-1.0,b=1.0,x=-7.0,y=-4.0,r=1.09])Node112` but is not linked to it. According to the formula, the node367 is also isolated. This is a bug of an ill programmed Evolution. \n", ">\n", "> We find no reason of blaming the drawing machinery as the cause of the errors. Hence, the errors are caused by ill programmed Evolution. In this case, recombination is turned off, so the culprit is mutation, which consists of insertion and deletion of a leaf. \n", ">\n", "> By looking at the code, we found that a modality of insertion inserts a node after the left parenthesis `( `. But this is valid for `((((`and this would produce an isolated info, if this is inserted somewhere in the middle. To correct the bug, left parenthesis as criterion for a leaf, was changed by `(N` which means that a node begins just after the left parenthesis. Additionally, the bug was seemingly propitiated by the substring `()` that was used in the constructive process of Newick strings to enable recursion. So, this was erased in line 1318 and below together with other possible causes of error. The code follows: it is a clone of the previous one with two exceptions. First: the disconnection bug was presumably resolved, and second: there is no random seed, so one can rerun it and receive different outputs." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-10-05T15:11:06.660439Z", "start_time": "2026-10-05T15:10:18.333441Z" } }, "source": [ "# Program evolution_of_newick_morphological_trees12.py\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\n", "# \n", "# In this version:\n", "# 1. Mutation has been inbuilt with the power\n", "# to have children in the immediate neighborhood of the father and \n", "# to change radius and the topology of trees\n", "# by means of insertion and deletion of leaves.\n", "# 2. Recombination has been enabled with the power \n", "# to exchange leaves between trees.\n", "# We expect a good proportion of the population to \n", "# be able to successfully recombine, mutate and reproduce.\n", "# 3. A cut-off for the length of the Newick string has been introduced \n", "# to avoid overgrowth of trees.\n", "# 4. The seed for the random number generator has been canceled \n", "# to allow different histories for different runs.\n", "#\n", "# A bug was corrected in the function that inserts a node.\n", "# The criterion for insertion was changed from \"a leaf is just after a left parenthesis\" \n", "# to \"a leaf begins with `N` that is preceded by a left parenthesis\".\n", "\n", "\n", "\n", "\n", "\n", "# We reuse pieces of code made with the help of Colab and Gemini 2.5 Flash.\n", "# With the assistance of the AI of Visual Studio Code.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 30,\n", " 'max_number_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': 100,\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001, \n", " 'crossover_rate': 0.001 \n", "}\n", "\n", " \n", "def print_parameters12(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence12(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_length12(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_groups12(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_groups12(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups12(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups12(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info12(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", "def extract_all_infos12(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_groups12(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info12(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates12(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_center12(newick_string, j):\n", " \"\"\" Finds the address of the father of a node at j\"\"\"\n", " #print(\"find_father_center12(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_groups12(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info12(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator12(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_generator12(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates12(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children12(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_generator12(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_generator12(max_depth, max_numb_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_generator12(...)\")\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. \n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center12(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children12(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence12(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# Connectivity issues are not checked.\n", "\n", "# A bug in the drawing procedure has been corrected, and the drawing of trees is now correct.\n", "# The bug was related to the function that finds the father of a node.\n", "\n", "# With the help of Gemini, Google and the AI of Visual Studio Code.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick12(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence12(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " #print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " # 7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def simplify12(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length12(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax12(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick12(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " #print(f\"Length of tokens: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid12, message12 = examine_subsequence12(tokens, j)\n", " if is_valid12:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid12, message12 = simplify12(newick_str)\n", " if is_valid12:\n", " return True, \"Valid morpho-tree.\"\n", " else:\n", " return False, \"Invalid morpho-tree.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "def test_validator12(max_depth, max_numb_children, max_node_radius):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax12(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax12(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " p, s = complete_tree_generator12(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax12(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " #print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area12(x,y,r):\n", " \"\"\"\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " \"\"\"\n", " #print(\"get_illuminated_area12\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick12(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles12(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves(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", " # Dectect 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", " # Descipher 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_groups12(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father12(newick_string, ax, tree_color='lightblue', diff_color='red'):\n", " \"\"\"Draw lines connecting leaves to fathers\"\"\"\n", "\n", " #print(\"\\n\\nconnect_leaves_to_father12\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " all_leaves = find_leaves(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info12(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_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_groups12(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info12(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_syntax12(newick_string)\n", " #print(f\"is valid at connect_leaves_to_father12(...) : {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_groups12(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "\n", "def draw_tree_links12(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_links12\")\n", " #print(f\"Newick String: {newick_string}\")\n", " length_string = len(newick_string)\n", " #print(f\"length_string: {length_string}\")\n", " groups = detect_all_groups12(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_links12\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " newick_string, number_of_groups = connect_leaves_to_father12(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_groups12(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", " [a0,b0,x0,y0,r0] = extract_info12(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_newick12(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos12(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles12(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area12(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick12(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links12(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree12(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " newick_tree = complete_tree_generator12(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator12(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax12(newick_tree)\n", " illuminated_coords = set()\n", " \n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos12(newick_tree)\n", " illuminated_coords = get_illuminated_area12(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " \"\"\"\n", " is_valid, message = validate_newick_syntax12(newick_tree)\n", " print(f\"is the Newick String valid?: {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", " \"\"\"\n", " return newick_tree, illuminated_coords\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick12(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator12(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "def get_illuminated_coordinates12(newick_string):\n", " \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n", " a,b,x,y,r = extract_all_infos12(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area12(x,y,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_illumination12(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_coordinates12(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", "\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_branch12(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_recombination12(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_position_branch_recombination12\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch12(newick_string1)\n", " branch2 = find_recombinant_branch12(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_recombination12(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_leaves_recombination12\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " leaves1 = find_leaves(newick_string1)\n", " leaves2 = find_leaves(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_squares12(newick_string):\n", " \"\"\"Detects all the occupied squares in a tree\"\"\"\n", " occupied_squares_set = set()\n", " groups = detect_all_groups12(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info12(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def newick_mutation_of_radius12(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_subsequence12(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_patterns12(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_branch12(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_patterns12(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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_generator12(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_12(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\"Group120: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center12(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares12(newick_string)\n", " info, occupied_squares_set = node_info_generator12(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_12(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\"Group120: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center12(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares12(newick_string)\n", " info, occupied_squares_set = node_info_generator12(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_12(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\"Group120: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center12(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares12(newick_string)\n", " info, occupied_squares_set = node_info_generator12(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_leaf12(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_12(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_12(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_12(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_leaf12(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_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", "def find_branch12(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_groups12(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", "\n", "def deletion_of_branch12(newick_string ):\n", " group, start = newick_find_all_groups12(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_branch12(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", "def newick_mutation_branches12(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_radius12(newick_string)\n", " newick_string = insertion_of_branch12(newick_string)\n", " newick_string = deletion_of_branch12(newick_string)\n", " return newick_string\n", "\n", "def newick_mutation_leaves12(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_radius12(newick_string)\n", " newick_string = insertion_of_leaf12(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n", " newick_string = deletion_of_leaf12(newick_string)\n", " return newick_string\n", "\n", "\n", "def plot_illuminated_area12(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 run_evolution_newick_morpho_trees12(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_tree, target_illuminated_area = generate_and_test_morpho_tree12( ga_params)#926\n", " print(f\"target_tree: {target_tree} \\n\\n\")\n", " number_of_groups = count_groups12(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " length_of_target_tree = len(target_tree)\n", " #draw_newick_string12(target_tree, \"Target Tree\")\n", " # target_area = get_illuminated_coordinates12(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick12(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\n", " #print(f\"Population initialized with {len(population)} individuals.\")\n", " #print(f\"Population: {population}\")\n", "\n", "\n", "\n", " best_tree_overall = None\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", " generation = 0\n", " for generation in range(ga_params['generations']):\n", " print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n", " fitness_scores = [calculate_fitness_illumination12(tree, target_illuminated_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_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, generation\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_generator12(target_tree_params['max_depth'], target_tree_params['max_number_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_recombination12\n", " # child1, child2 = newick_position_branch_recombination12(p1, p2)\n", " # Crossover newick_leaves_recombination12\n", " child1, child2 =newick_leaves_recombination12(p1,p2)\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation_leaves12(child1, ga_params)\n", " mutated_child2 = newick_mutation_leaves12(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_generator12(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'])\n", " if new_tree:\n", " population.append(new_tree) \n", " generation += 1 # Increment generation count after the loop\n", " #print(\"\\n--- Evolution Finished ---\")\n", " #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n", "\n", " return target_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, generation\n", "\n", "\n", "# Generate the Newick string of a morpho-tree and draw it\n", "if __name__ == \"__main__\":\n", " print_parameters12(target_tree_params, ga_params)\n", " # Run the evolution loop\n", " target_tree, target_illuminated_area, evolved_best_tree, evolved_best_fitness, history, generation = run_evolution_newick_morpho_trees12(target_tree_params, ga_params)\n", " number_of_groups = count_groups12(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " title = \"Target Tree. Number of groups or nodes: \" + str(number_of_groups)\n", " draw_tree_newick12(target_tree, title)\n", " title = \"Evolved Best Tree\"\n", " draw_tree_newick12(evolved_best_tree, title)\n", "\n", " # Plot Symmetric Difference Area\n", " evolved_best_area = get_illuminated_coordinates12(evolved_best_tree)\n", " symmetric_diff_area = target_illuminated_area.symmetric_difference(evolved_best_area )\n", " ##print(f\"Symmetric Difference Area: {symmetric_diff_area}\")\n", " ##print(f\"Number of cells in symmetric difference area: {len(symmetric_diff_area)}\")\n", "\n", "\n", " fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "\n", "\n", " plot_illuminated_area12(axes[0], target_illuminated_area, title=f\"Target Area (number of cells: {len(target_illuminated_area)})\")\n", " plot_illuminated_area12(axes[1], evolved_best_area, title=f\"Evolved Best Area (number of cells: {len(evolved_best_area)})\")\n", " plot_illuminated_area12(axes[2], symmetric_diff_area, title=f\"Symmetric Difference (Error Area: {len(symmetric_diff_area)})\")\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", " # Plot Fitness History\n", " plt.figure(figsize=(10, 6))\n", " plt.plot(history, marker='o', linestyle='-', color='blue')\n", " plt.title('Best Fitness Progression Over Generations')\n", " plt.xlabel('Generation')\n", " plt.ylabel('Fitness')\n", " plt.grid(True)\n", " plt.show()\n", "\n", " \n", " print(f\"Evolved best tree: {evolved_best_tree}\")\n", " print(f\"Evolved best fitness: {evolved_best_fitness:.2}\")\n", " print(f\"Generation: {generation}\")\n", " print(f\"Number of nodes in target tree: {number_of_groups}\")\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_newick_morphological_trees12.py" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB: EVOLUTION OF NEWICK MORPHO-TREES\n", "PURPOSE: \n", " A random morpho-tree T is generated.\n", " Evolution tries to restore the tree T\n", " beginning from a population of random trees.\n", "EXPERIMENT 1791213018.4772859\n", "RECOMBINATION OF LEAVES: False\n", "TARGET TREE PARAMETERS: {'max_depth': 30, 'max_number_children': 2, 'max_node_radius': 1.5}\n", "GENETIC ALGORITHM PARAMETERS: {'pop_size': 200, 'generations': 100, 'max_depth': 30, 'max_number_children': 2, 'max_node_radius': 1.5, 'mutation_rate': 0.001, 'crossover_rate': 0.001}\n", "\n", "target_tree: (((((Node19[a=1.0,b=0.0,x=4.0,y=-1.0,r=0.51])Node12[a=1.0,b=-1.0,x=3.0,y=-1.0,r=0.61])Node8[a=1.0,b=-1.0,x=2.0,y=0.0,r=1.05])Node6[a=1.0,b=0.0,x=1.0,y=1.0,r=1.3],((Node14[a=1.0,b=1.0,x=0.0,y=2.0,r=1.16])Node11[a=0.0,b=-1.0,x=-1.0,y=1.0,r=0.87],Node10[a=0.0,b=1.0,x=-1.0,y=3.0,r=0.85])Node5[a=-1.0,b=1.0,x=-1.0,y=2.0,r=1.14])Node2[a=0.0,b=1.0,x=0.0,y=1.0,r=0.57],(((Node18[a=0.0,b=-1.0,x=-2.0,y=-2.0,r=1.49],Node17[a=-1.0,b=-1.0,x=-3.0,y=-2.0,r=0.79])Node7[a=0.0,b=-1.0,x=-2.0,y=-1.0,r=0.96])Node4[a=-1.0,b=0.0,x=-2.0,y=0.0,r=0.86],(((Node16[a=-1.0,b=1.0,x=-5.0,y=3.0,r=1.28],Node15[a=0.0,b=1.0,x=-4.0,y=3.0,r=0.67])Node13[a=-1.0,b=1.0,x=-4.0,y=2.0,r=1.15])Node9[a=-1.0,b=0.0,x=-3.0,y=1.0,r=0.65])Node3[a=-1.0,b=1.0,x=-2.0,y=1.0,r=1.18])Node1[a=-1.0,b=0.0,x=-1.0,y=0.0,r=0.96])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 20\n", "\n", "--- Generation 1/100, Best fitness -1.0 ---\n", "\n", "--- Generation 2/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 3/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 4/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 5/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 6/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 7/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 8/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 9/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 10/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 11/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 12/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 13/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 14/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 15/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 16/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 17/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 18/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 19/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 20/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 21/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 22/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 23/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 24/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 25/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 26/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 27/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 28/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 29/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 30/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 31/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 32/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 33/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 34/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 35/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 36/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 37/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 38/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 39/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 40/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 41/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 42/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 43/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 44/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 45/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 46/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 47/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 48/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 49/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 50/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 51/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 52/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 53/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 54/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 55/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 56/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 57/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 58/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 59/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 60/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 61/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 62/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 63/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 64/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 65/100, Best fitness 0.59375 ---\n", "\n", "--- Generation 66/100, Best fitness 0.6 ---\n", "\n", "--- Generation 67/100, Best fitness 0.6 ---\n", "\n", "--- Generation 68/100, Best fitness 0.6 ---\n", "\n", "--- Generation 69/100, Best fitness 0.6 ---\n", "\n", "--- Generation 70/100, Best fitness 0.6 ---\n", "\n", "--- Generation 71/100, Best fitness 0.60938 ---\n", "\n", "--- Generation 72/100, Best fitness 0.60938 ---\n", "\n", "--- Generation 73/100, Best fitness 0.60938 ---\n", "\n", "--- Generation 74/100, Best fitness 0.60938 ---\n", "\n", "--- Generation 75/100, Best fitness 0.60938 ---\n", "\n", "--- Generation 76/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 77/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 78/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 79/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 80/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 81/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 82/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 83/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 84/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 85/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 86/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 87/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 88/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 89/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 90/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 91/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 92/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 93/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 94/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 95/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 96/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 97/100, Best fitness 0.64062 ---\n", "\n", "--- Generation 98/100, Best fitness 0.67692 ---\n", "\n", "--- Generation 99/100, Best fitness 0.67692 ---\n", "\n", "--- Generation 100/100, Best fitness 0.67692 ---\n", "Number of groups or nodes in target tree: 20\n" ] }, { "data": { "text/plain": [ "
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" 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yS/5ONptFuVyev5RKpV6+REVRFEVRFCWl9LQi+8lPfgpzc3PzP3/rW9/Cz3/2UzzlyWfik586d9HfefGLXohXvOKsQ24/9dQ7oF6vYxQ59thjMMocc/RxCPylT36GnaOOOmpk3/8ov3ei71///qO8/R9z9JEYZUb52F8oFJIhZNtFLLnppptx6aWX4fannLLk73zggx/COR/56PzPrMhedOH5uOiii1GpVDCqnH/+BRhVuCP//fnXYFTxgwwuvHA03/8ov3ei71///qO8/ZMLLxrdY98oH/vL5XJyPLILGR8fh+95S/674zhyURRFURRFUZSBeWSfcMYZsCxr/ufHPvYxuO1tT8T3f/CDXj6toiiKoiiKMgL0tCJ79DFH4ze//iWuvuYabNy4EUcecQTe+KY347zzftjLp1UURVEURVFGgJ4K2f/4j3fg7LPPkSpsvd7AlVdeiWq12sunVBRFURRFUUaEnntkZ2Zm8Otf/6bXT6MoiqIoiqKMGD31yCqKoiiKoihKr1AhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSFbKKoiiKoihKKlEhqyiKoiiKoqQSe9AvQFGU3mOEoZy1WoiuzbafrTCUa2PB2S3v0yIwon8N4p/5Lx4Mud3ndXwfXvNnX5609YiKoiiK0htUyCrKkEBBmkEIOxamretseKh45XVoANSqFKWtCzkgX6P7GG3/2JKmRnwR0cv/iB9noah1DEMErx8LXtcw4MYCWFEURVHWiwpZRUm5aM2EAQphgGxLvBohgtAQYdkuKl0KV6mWUp3GQrITPdnpfUQ48z9jIU0RHURiV4Rv/Lo8w4ADA3XDhCsXFbdKslYuDpywhfNrFdGJmq40KErSUCGrKAnHDAPkw2AZ0Rot81MQNgwTIUXqIAqeUnWV/4A3f9uCuyAS4LkwQCn0RU8vJ24VpavEJ4C8WGG0OsHvFE/AuHIhJ1zx9SG/Gp8Ucht321YaeM3tV1caFGUwqJBVlATCg2suDFEIfWytVXG410yWaF0jYSxYHWNlcUtBO9GsoxT4aBqmiAVFWQ200+Riscrtq8CTv1jMyvYYV1z9eNvkdSCb2aHbGrdTs7WtxisNUr01QvjxyVgTJhrcxuOTMW7r6hVXlN6iQlZRkkB8wBUxF0QV2KwRyAGSh9xKCkXresUtq85jThM7AwdOaKBumqgZlvhumyoQlCXgSkUkWn2UQnrEA/na0F4jlX/DQG1N289BG+hBmLGVJi/PCTnpdMNI1FZNS07EuM3Kd1hRlK6iQlZRBlwtygU+ynEFlgdDF0CTB1vDlgOma1kjdwDk++WBv5bJYgYmMkYoldkN8ODClM+nEguEhgqEkYffpWJc0S8EgXyvWF2laJ1rnQT2kGCRkzF+lymoy34QNz6a8lpq3G71RExRuoYKWUXpI/Tf0ePKSyReo0ArHgDpD/UNjXY+9EOL/IdsVqvGjW5ZBNjiR58dxWzVMKViW2dtbMRE/yjDamsx8DFOHzm4ghGdBHI7GPSSPqu/nmEddNK6OfAQBD4qpomKYcl3XhM8FGV9qJBVlD5Ajx4rRuOBL7aBMK4WzfahWjRssMGmDoqA6MSAAmFD4GFTANQME7OmJRYE9dQOKXH1tRz6KAeBVD4pXlm5h5nM7xLFal2aGSPrQznwMQFffO4zpoWqbq+KsmZUyCpKr2D1FVwS9+SAy4pRo09LnaMCP0daCxqxqGWlezubw0ITcxQIpiX/PujqnNId+PcdDzw5IeTJYJ0e1LjqmRZ4gsVqLLfXPEJs8100DB/TpiW380RNUZTOUSGrKF3GiitGY4GHUlx9pYCdoudV6amoZSW2xiVnBNgYeFKppdChqGW1Vpdx00kuFrATQRTwRitJ2gVf2Fal5SrNtsCRlYYp05b3p9uqonSGHlkVpYsH2wN+PR9uyMqLHpAGARtr6DvmSQW718dYpYWJGfHSRk1iSvIxgwCbfFcELDNfeTIyjJYRWgwaoYECAuwImqjCwn5TD8+K0gn6TVGULi130j5gxn69aUasJ9SvN0r4raXneBl3a+DBi5ttZk1bBISSTHhSuLlRw5xU1dnsly4LwaqJvd+NeEVnp+9IjjJPxtJefVaUXqJCVlHWSD62D7SWO9mB7KkwSiZtXlo7brobCwLMmPTS2lqhTRAUbhsCFxsCH5nAxzS/UyMk5ML45Ivb6YTTxA7fwT7TltguRVEORYWsoqwh8idqOAmGerlzWOHJxlwsaDfMC9rIR8tpYspgq7CbAk8qkhVWKO3MSInYhdtpxc5I4gmrs1Mh/bMZrc4qygJUyCpKhzA2p1WB5ZjKWltOpJJOoTBrRNFozPdklZad46zQ6lJunwlDTIQ+NvueDI0dtSrsksSTwXjStYn7nRDYZ9niAVcUJUKFrKJ0sNTJzEp2wHP6FgVsXQXs0MAq7IwRNettC1xMhAGm4igk7RzvPRwWsDFwsTFgrqqBpn63Fj3pmuHI5tBHxg+x17IloUNRFBWyirLsAZYCdjLwUWB4OUyp2CnDCZMMODqU2b87Akc6x6dNW5InNPe3N7Aavsn3MBl6mNOhAMvCbXDWsGRU8zbPxV4rlJ+1cq2MOlqRVZRFKLIC63sog7FNcQqBLnUOP61sz9AQnyYbbTjAYsrKSDOf0t1myS2+K6kf04alJwsdQqsBP7vDfBe2GUrurJ5oKaOMCllFWVCFnQg8WeYEQszoAXY0iYcr1KUqHyDvOdhvWhLZpXaD9VOIRSwbJ5ntqyeJq4OxcR4CbA49mEGIfWZGxawysqiQVZQFVVj60LicPPS5lcqKsNLFJW8Krm2hi6IfDG91NgzBd8UkDrZZtbdacTpdAANyerfOlQmK2K2+K7YCJkUoa/fNVsMQm0KPfxwVs8rIokJWGXkWVmGn1HemLIBd4pzUNizVWTYwMnkjw2u+pzCQgwEHehghrylkKV8jKG0DilkjErOuNGWZcMHkDgMO793BZ5GPRSy78FXErh9+9pXQUjGrjDQqZJWRRquwykhUZ8MQOYTRGGUK1yAQIWsaIfwwEqcUqV78Pilao7pshBGLXF540MgGAcbhiyOAv09h20rzaC6Rq8zn1kpsb8VsEBjRaNuUnmApylpQIauMJFqFVUahOstqazRC2ZfoOBshXHkPnHQWpzF08LJb1oJ52v6T4paieDIIsClujqyZpsSXUeTzOZjB3PLEaiW2V2LWlDxkZiDPGHpoV0YH3dqVkUOrsMpQV2fDUCLESoGHchAgZwRwQlZLTfg9eH0U8BSvTYrb2LJA4TwJX56TqQ+l0EcxZPNkAj6fIfbMNtgA5nti+dCRtsqooEJWGcnpQeqFVbpdnc15jkxdGmS2Jz2orelzfAUcMDDN6ly/Xo5hiIhyWegNQ+RDH7f2migjwI1GBpZlio1B6Q20ddihjy2Bh5tjH7OiDDt928rvfvfT8JUvfwmvfMVZ/XpKRTnISrA5cCV70TGACrulE7gUrKS0Omta8A3I9rUx8ETE9RNWhjf7DnZ4johYVkJnTWugo0zFUgBDfLhMAdkED9sDF2OBL99HpXc5szJ22XelqU9Rhp2+VGQ3btiA97z7XfD9ALe+za378ZSKMk+0U/cwodODlB7C6pePAFtCF5k423OxpqeuEnJsaYBNAf2nIaps2EpIbBybuzYELlyYcWUwRB4htoYOKqEtU9OaejLZE2jnmAwDGf27x8zoSbsy1PTldP2973sPPv3pz+Cqv17Vj6dTlHnY6LLNdyUbltODei4sFIy6T3EWllRFt/mOLPX3sgq7NXCx3Xfk5xmT23cylpJZcZ0MPFhhJPAjogazOVjiU+fnM+F7Wp3tBeLhNmW8Nm0vijLM9Hyv95znPBuFQgH/9eGze/1UinJwpUpmkjvz04N0jKPSD9j8xO2NIpZirST5xN2Fj7ndd0UwU7AkosmsjXLAwSIBqou8LsZ6MdGAPtotcKXTnnm2SndhegE/Y1pduCqlKMNKT60FJ598Ml74gufjIQ99GMIOd1TZbFYuLUqlUg9foTKMGHE1iAdI5lvyoKko/d0Io1SDQkjB6WBvvJS+Wl92GPhwHaftBg7v8MVvSmmyX7btZIkUWgrGAhdzMjhhaYHNlstGCIyFDcAwJf+Uo1fb8T0fnsvAsNEkDNb3t2WuL7cX7g/VYqAMKz0TshSgH/7wh/Cv//YG3HjjTR3/3otf9EK8YpGGsFNPvQPq9TpGkWOOPg6Bn8GocvQxR8MPOnv/ZhBg0mmg7Dho2Ba8IRiBedSROzCqDMN7z/g+bhf4mM3kMJvNITDNjkXskUcehlqtOr9cP+42MeG6aJom3ARu23yNG5wG8n6Amm13Hhfme7JiMp3Nom4d+K4fccRmNJujud8nO3dulmujw21mqX3iCb6H/fkSapl0HUeOOXYHTGt0T2SOPfYYjCqFQmHwQvZe97wndu7Ygac99alyISeceIKcYTK94PkveCH27NlzyO994IMfwjkf+ehBgviiC8/HRRddjEqlglGEIvb351+DUYUi9sILr+ioEsRO3bkwwKxhJjKgfq1ceunVGFWG4b2ze5xL7YzmYkRXJ2kCrMRSxG55+DHYetQmjM1UUarU0ChkEVjJE7GkUG1gbLqCeiEHmKv7/mUcF8f6AeYmSmgU87j6t1fjqstuwKYHHYFNR27CqLHv+n246rzr8Ze/7IK9TgFaDHzJEN5lZcVykBYs08WFF12AUeb880fz/ZfL5cEL2d///vc488lPOei2173utXAdF+9697sxOzu76O85jiMXRVkNrRnu9MNOUySkaGetDD8ybQmmNB3afojdVqbjjE+K2FsdVkYhZ6Bx3BaMWcnyw7YwPR9jt0wBW0oo5w/Yw1aD7XjY3HRR3TyBfdsnsO+yWRGxh916G0aRfbihK49TY+MXfMkYnm6reCvKMNAzIbt/agq//vVvDrptamoKzWbzkNsVZb3JBDrDXUk6XDqfFTEbSN4sxexCT+hifm9WYkXEjhUQJlTEkmy1Acvx0Czn1/wYXjY6JBX3zaJQ14JG1+BI4pCRXL5En2l6izJMJHevqCgdwBifw3wHts5wV9JAPDxBorN8d/l4LvHEOihXqokXsabrIT9Xi4ToOldD+BhBxsJ4tY5cwJYwpRvwpCknaS76mSrDRV9H1P77v/8HgnV2YSpKu++LYsBEGE3qUpSUQDHLeLhWZXax+KzxMMCE66CRz6GcYBHbqsZSzLpjxa48npdjiH+ISceB7elQ225RMwzJlq0OeOqboqRWyF5++eX9fDplREQsd8qKkkYxWxYx64iYrbXFxHGlYXPoSnyVbyd7+6adgNVYd42+2KVoMIoxCFCcrcHwg0RXpNMCxSsj4XgStU8/T2VI0D2Dkjp4kKeINVTEKimHKwlMNNjie+L1Jvx5k++BC/ROQtMJ2snUm9LoFWS6XxepWjby9SZyc1EEmbJ+6oYRNR3qEAplSFAhq6RSxLISW9NKrDIkYjbT5plleH0xDFBJwe6ZldJctQG/ByK21SDn5DIoTNeQqTV78hyjWJXNIZB9qaIMA8nfUypKDCtWrFyxYqV2AmXYbAYUs0d5DbEaVNgwlYIIObvpwGq6kae1R4i1wgAKs1UY6pftCk5oYDzwJRVDUdKOClklVTmxPNhrY5cyjFQNUwZ6bAg9WGIsSDhhKE1eodl70e0Uc7BpMaiO7pSvbicYsOpfSNh4Y0VZCypklcRjBz62xCKWlStFGUZKsUfWD4ENgSvjXpMMhxdka82uN3ktimHAy2dRmK3DckZ3ZGm34NRDbm2lQO0FSvpRIaskGh7MJ5oNsRXMaVyMMqTQLjMRejLtiysOrJZtDLxEL/1SUBpB/9IE6MM1PA/ZilZlu0HTMOTkidueoqQZFbJKcglDbAxclDwXszp2VhliSoGHXBigIZYCQ2wGE2GAsufK9yBxhCEy1QaCPkeDuYUc8pWGeHOV9eHAQDYMZbtTlDSjQlZJLBOhj42Bj7plS/eyogwjrIiNI0CTu+N4Ow9gyJAETvbKJXBUq+X6yDhez9IKloLC2fB9EdHKOontBa3YN0VJKypklcQOPNjse2gYBnxTN1NleKGQ4OjQ5oIGL9cAfMNEea4Ku9FMnq3AD/pekSX0yuaqTZiOjlpdL25sL0iyhUVRVkIVgpI4uNS1JfAQIhTPoKIMLWGIUuhBJNkiqw5NyxKfeHGqkijhlmk0o7SCAcAqsOV6yDS16Wu98OSJ+9s8VMgq6UVVgpK4ZdbNcUKBZsUqww4FRDEM0VhmV9zIZWE3HBSn5qQKOmi4tG83PHjZ/toK2vGzdhTFFQz+80gzLctWVu0FSopRIaskBiNu7iprQoEyIuQYSh/HIS2JYaBZKsgErfxMZeDNX5YXwPQ8BAMcn+tlMrDrDmw3OVXqtMJTAW34UtKMClklMXA058bQi2K2tLlLGXZoGUAAt5PhB6YBp5hHYaaK/GytH69u6ZficSJU9JoGRWjxuUPYTRWy3fDJ5sNQfbJKalEhqySCMpu7Ag9VmPBVxCojQBahCAjGIHVCYJvRUIDpCjK1wTV/ma6HMAEhImw0k89BG5XWhQcDNuPU1CerpBQVssrAoR92U+BJl7arzV3KiMCkAgPh8raCBUjclWFIZdbwBjOViX7dQdoKWvi0FziuNH4p6/gcDQM2q9t6QqCkFBWyymAJQ7EUsNmgbgz+4Kgo/YLbPPNiV4tTyMKuN5Gfrfa9GimNXp4v1eFBw9fA10PPrrJ+tCKrpJXB742UkYaNXZOBL5OMFGVkCEPkEciy7qoxDLjFHAozNWT7PCzB9IMoPzYBFdkWpjuYyvQwwU9QkwuUtKLqQUmEpUB9scoo0VrKXZOQjf2hoWUi32eLgeGHMIJwoI1e7YSWpeNquwBr2hm1FigpRYWsMhjUUqCMMBnGWCFc1wncICwGBvNGk6Fh5wW95bqaJ7vez1F8stF+WVHShgpZZSCopUAZ9cEf61aEA7AYmKzGJkjsBKYJM4gsD8o6PkcRA6EKAiWVqJBV+o5aCpRRp1tFzX5bDMRWkKCSbECLA327CRLXaYRNh8wGVkGgpBHdbpX+opYCRYEdBl0rbPbTYiAZsgnxxwomBVioFdl14sdWF14UJW2okFX6iloKFIWigb7ELn0S/bQYhCHCBA4skUljyppp1dmT95dVlJVRIav0DbUUKEoEK1/d1F79shiYrPgmTu0ky7ebSgxDhaySWqRRUVH6wXjgIxcGmDGTk0GpKIOBQra7ipAWg1yljly1jsZEuSuP6TkevLbJWVnHlYOG03ThtzVY2RkLmYyNJqd+iY82grfx3xp1B2Gb2MzmMrAsE/UFo3ZzuQwM05D7N5suHN9DoxH9bhiEcls7hWJOXkejWkerFs3fzxVy8rr5+lsYpolcIXvIe7I46jaXgcv31HYSYGds3Hj1TTj81jthmlrzUZSkokJW6QsUsBOBj1oClyUVZSgwDPi5LPKzdTiFPILs+nfvP/rcj/GDc8+b//me9z8Fz/inB+OLn/g+fvnji+dvf9jj741HnHEfnPOfX8GfLrl6/vanPO9huNcDTsV//PMncNPf9s7f/uLXPwkn3eE4vO557xPR2uLf3v1cbNg0jpf/4zsPvIhLf4P3nPsqTO2bxZvPOmf+5nwhi/f+96vxpz9fj3e/+2vztx921GF49adeifN/cAG+/J9fmb/91ne+NZ77zmcf8p7u+tC74gmvejy+9v7/xe++87v52x/4lAfgkp/9Acfd/licftZjVcwqSkIxtu84PNFrMuVyGVf85c+49W1ORKVSwShyx1NPw+/PvwZpZovvyASv2TVUY08++VhceumBg+OoMcrvv1fvnQ1ClHlmK3ZIOrZDicVqNby0n3KFcWc38zZZh/RhIDSi21jDk/reKk7Stvou8qG/7Fhmz3Nx3HHbceyTTsKWY7d0/Ni5uRpqk2U0Nox1/DtLvoYF1cvx6TkU/RBVy+x5RfaqX12F6V/dhKMfdyIOP3HHkhVZe7qC/RMluMV81yuylZkK/utlZ+NWpx7fdzF7yxU344qPX4y//GUX7AyTh3tIGGIiDHCDnUM9QVMW73LnY3DhRb/BqHLnO98J559/AUaR8iq0n1ZklZ5TCAOMBwFqCdpBKqMDRasMIJDrUFYHsvFkLYpXkZ9GiJDKNG574f+2ZJfRfh3y/kbUXRRE96Oo5WCDBgw4hgnPMGRil7uMuKUE7NXahJfPoTBXh1vKw8+uTwDZWVsu8z/Xm0CtKUJ0MXL57KK3s3q6GBSiS91OUZu1bOTzWRj0cFrGovenIC6UCrAW/BuFKC8rvacWFLO8tDO5ZRIveO/zRMz++TeX46R73BbDSvs2ryhpQoWs0lvCEOMBD+shPBWySp+gSM2GoVQ9C7Fo5VjYqLIKuIaBplRXuWXGknK1ytKIqrh8BD72eMg8U/8gcVuFiaZpohlXc1tQ5LIK3As162cs2I0mcrM11DZPdPWxGb2VvMxWA0EPz5EpZs/6yMuQL+UxN1VBaaI4dDYDvpvou6Ao6UOFrNJTilKN9VFVEav0GApXXgqhj3xcdW0XrbUehLTw4E8hxQXp5rwopT2BY2hDbAg9Cel0DRN1GGgYViSgDROGcWB5u9u4hTxy1Qaccl4qtN0itO14KEIyMPxQxHXYY2FJEUs+97bPYeNhG4fOMxsJ2egETFHSxvB8E5XEwcoNG7ykQqVNXkqPtrFCEGCL72K77+CwsIlS6MMzgFnDRIXCEWafD9AUtrQaRM9fMVi3ZcXWx7awKa9zInCRFT3YG1EY2KashuTm6l2NppJJWglCqsOm2bds2ye8+gxcedFV+Oq7v4YgGJ6xuNxCuWLQ+9lwitJ9VMgqPa3GjoVajVW6T0YsKz62By62Bw7KoS+VzlnYqBoWPNm1JUV0GXBiUcvXR5HNwSBsgKQAL4VBTyYquYUccpUGMl0ckpCoqV48gAUBAstAYPXnUNbyzFLM/uzLP8ewYLU8slpwUFKIWguUnlVKNgSedHO3ewMVZT3QOlAOPBGCXLp3DMQVz/RsYxTZM4aBohnIyV4xdOCEFiowUDOtrlWPOSCBwpMpBm4+I5XLdT9mwpbTOTK3ySavPr4uitkXvvf5yEmGrS+NaGm3GXB/7aT8PSijiwpZpSdQaBQRyPKuonSjAlsKfFmeZ2NV3TBQlzpSOuEJngMDlgGxPtDPuzEMUA5CzBlm1xI+3HwO2Vod2UYRzhIJAash8qMaMPxAhPKgMViRzS2eiNBLJrZETXTfPPtbaFQaqffM8pvUO8e2ovSW9H7zlMRixtVYLmjqUpWyrm0pCDHhe9gWONgUerIsPzdvHUgxBu0GxrylgP/NyjL7xjeHLrYEnlRr15sQEFoGQstCfrYKdMHTGViWXMy2DNlB49uDO6G59+n3GgrPLL9NjI5TlDSiW67SdXgALsBHXTcvZa2EIYpBgM2NGraErsi9WcOCO0TbFJdyJYKrDcZ0zcGChUCGJky6Tdiuu77nyWcl/zXbWN/jEFZhGe9l+oNvCzI9X1IUBilk2z2z3/zwt5BK4pMl5h8rShpRa4HSXcIQY4EPl+HyCWsMUdIBM18nAk9sBGYYYEamXw3ftsQqLNM8uIKx0EcuSQuGhZ2+j/GpivhcnWJBKqyrJo6nylTrXbEXeBxUsGAi1yCwHQ9uLtOVUbzdELOz++aQRvjpuTLAY/i+Y8poMDzlDSURMHye8UcNXaZS1liFPSxwMSHjW000bB5mh/MA2xIPbFpbDJ4L8v2zI78wVUFx3yzs5toSCLxcFtm6A8tZf1U24LSsBPxJDN+fH0s7aChmjzzhCFz6f5fhK+/5aqpsBvSc84RKhaySVlTIKl2lJFO8NDdWWR0cH7sp8HBY4MAKA2l4Gvpwdg5pMCzYK9hgvYwNp5iH7bgo7Z2T6ixWOZSAubJs0MpwxOw6CWwr8r4PcDCCNJuZJrxcshYVj7jN4bji/CtT5ZnlCgibJ5cap6woSUeFrNLVHWI5CNDQHaKyykitLYGLybgKWx9SK8FiMPtWXDgrNXWZBtxiDkEmqs4WpiswvNUJJT9rI1epw/DW52+lJ5Vi1lrn46yHTNOVKrPP6nCCaPfMUsyGiRvneyj8tjV1BU1JMSpkla5RDH3kjEC7X5WO4VSurYGLQhhFtQ19FXYRnyxH5+ZkQOjK+LYdjZ+dq6O0fw6W23lokpe1YTmeiMD1Nny5ueyqnrurhKHYCjh+N4lVxJaY3X7cdsmYTTLiz9bEAiXlqJBVuoIRT1pi17WirEi8vVDE0kpQGaEq7CH2AtOEJWOVOqveseGLIo5+2dK+mc4nd7H6a1nIdmFsrVfIwhiQtcB2ffHpMo0hqVDM3usx98S+m/bjux//XmJtBjmEUo1tjOJ3TxkaVMgqXYEVNV44X15RVp765mNz4EouLH2iowwTCigmKCpWlUNbyoPlNDaBZSuNjn6NldRMw1lz01h7dZe+W9Ptv73AarpojBUQDjB2q1Ms28RFP744sZ5ZDhphfnESK9uK0imqOpSuwKQCndWtdDq6eCNcmV7l6C5Iorc45IGiYrWVUi+fRZixUNzPJrCVK62s5nLoQmad8VmsiHoDsBfw+ejPdQvrjxEbhGc2SWK2NWyjmeKJZIpCdAtW1k02DDAWBFHnq6Isc+DcKCLWQxXWyPlhl0OixgwT+dVUZdtSDfy8jTzzZisri1nmwOaqTZjrFKFsPuv3hC+74aAxXoCfzSAttMSs67jwnOQMguVYZE7z0lU0Je2okFXWTTHwJYvQ085XZYVK7CQ8VFTELl6VNRnFFa5pLC2bwIJ8BvmpaiRml7tvxpbKZmadk768bEYav/plL7CbbuSNTUh27GrF7Jn//CRYGQu/+fZvE1GZzcW2goXDOBQlbaiQVdbf5BUyqUB3hsryInaDVmKXhSOdq4aFYocJBovGYuVtFChmaTNYKYqrur6mLz4GJ4Wt12/bEUEowxwa46VoIENKqc3U8OPP/WTgNgPmNvMvXzOT7zNWlJVQIausCzao5MJA0wqUJWE6wSR8tROsQBhXZQNO+wrXKmZtBDkb+emqTPJazo7Apqn1elzZcEbXba+HI2RrDanENhi5lWLGNo4lwjObDwM5aaqrvUcZAlTIKuuCIpbo8pSyGKUgwMbQkwOmemJXhukFM7CQX6PFoCVSQ9tAfnpOpoEtBi0BRhDAbq7TJ5vLyIXL/r3Cdl3AstCYLAFD0JjU8szuumoXpm6e6v8LCENkjFBOmjStQBkG0r9XUAaHTPLykZz2BSVJ0IO3MY7Y8nRX0zFV05Jc3bVaDAgTBTj5qzBdhbnEBC5mymZqncV2LYlpwhkrwO5RegED+zOOj/p4Ud7TsEAx+9IPvwSbdmzCtX+8tq+VWTYUNmBJg+FCePLEz9yKL3Z8ba7jxEpRek16zUZKIrpeKVZ0vKGykEwsYtkEyOYupXO4ujFjWtgAAznfX1eqQLbaQH6mitqGMRlzuzALNuN4kl6wHt+pm89K4xetCn6ui2kCYYii76JezMMYK2DY4NQvphh84T++hONufyxOP+uxMPtQcWa1/xbTEo8sV9QoVmllKfAaIUz+S6xZucXIfxq85qpKlLDBtAPPMODCkGtFGSRakVXWDEVsxgh0R6YcBCs3k4EnPjwJW1dWjWuYmDJtERLWEhXVFaHQKORlWEKuUjvkn5nHanieTMpaD3yc5lghGn3bxapdvumibtmojhWHwlKwGHbWxvPe9Zy+eGZb38tc4GMsDHCE7+AIr4nDgya2yPeV20EoK2xcRXENSBMvr3lbgBA2Amnc3OE7OJy/G182+S7GmF6jVVtlAPR072DbNs4880k499xP4pvf/Dre97734JRTTunlUyp9pBj68EI9G1cOhge0cXgy8GAkx852CVa+Zu1sVDVdo5jlAASOk83P1BcdZcsGM7u+vuEIpFnOSz5tt7yy2VoTgWViNpOVCWLDTPvQhCvOv6Lrj09xye/kNr+JI/2mrJLYYSDCtGqYmDZszJqWJBhwdc2NL4xTZLXVi39mFbZuWOKtnTYtzMrtXNaNUkl2Bk0c7jex0XflJLabJzWKMjBrwb/8y+vl+hOf+BQqlTk86pGPxP/+z1fxqEc/Fn/4wx96+dRKj+HOsRAEaOqykrKgSj8Z+hLuz+57ZX3ULAvVcgEbGg7cQlZ8rWupmFquj/xcLW4EOyAMg2wGmUYThh9IA9ha4etqTJRQ2j0DLxeuq4koQ2FtGJgtFeCMSDwUxewrPvZy5Ao5zO6fRXmyvL4HDEPxwhYCH2X4yIasWgXYb9i42crC78Z+myKX1gL57+g5mWKziUNPAk9SESh6eUKrzcBKaoXsW9/6NrjsOI254IIL8dCHPRT3v//9VMimHHqruNNi9qWitC9d0n1XV19sdzAM8Yg2xovIz9bWLGbdfEb8stlqHc2J0vztXsZCtuJKusF6x746hRyyBQpjZ82PRRHLOK/qpnE0c7sxSlDEks++5XPYvGMz7v3we67pcQqhL5F3xTA6lWyAI5ANjCHEjGF3R8QuhmFIDGPTiHJquWI37vvRygIvpq2CVukJPVUh7SKW3P72t8emjRtx0UUX9/JplT7AnaUsdmpFVonh8mWZQw/UF9tdTEMGAbDiyWzYNY2WZfUsn0V+doHFwKBsjIYNrBdWdJvjJZheIBXe1UKhjVjEctDCqPKk1z1RbAbf/+x5CFexPE/xyGX9bb6LQhigBhNzhiW2AIraGqy+fTcpliu0IBimNI8dFrrY7jvyuhQldakFRx99ND7x8Y+hWCxg8+bNeNWrX4Of/exnS94/m83KpUWpdKB6oCQDRrGUQs7p1qXjfhAGAbwFJ4VJrNCPBS6qYHNI97xxgRfA85L93ntFECcWTO2aOrBcXGugWJkW76ubXX1CQLbhwL95GnOTY+JBJRnHRXDjDGY2TSBckGywajjpb7qCfH2vVJI7wQhC5FnFzViojJfg1qP3PXPTjFzvu34fRo0zXnI6PvMfn0cw1oDvr/x9YvVzMq7CzhlmXHWNRGMmBLwwwF4rAyfof1jiHIAKoxrhYYvbxH5YmOmwOpuEUb5K8um5kL3xxhvx/Be8EBPj43jEIx6Ot/y/N+PKK6/CJZdcsuj9X/yiF+IVrzjrkNtPPfUOqNeXH7s4rBx11FHwgy7G2qyTnOdha72Cmp2RZpFec9SROzDKInbnzs2o1w/tOk8MHEHrOij5LqpWd7fTHTs2oumsM+s0xRxz9A5c8/MbMYUb52/L+x7GXRecAVZfbai9RFp5mMlkUbWz8yem+cDDnlx3PKl2EGBzsyGxTc4KNggzDFD0fVQtG3OZDNwFz3/88UfgqvNuwD7cgFHjHkedgtvc+ihce/WNEtXFy2Kf35jrouy5IgwbpoXN7fejgPRdzGRyyGUGn8ObCXycGPioWba8puYK28fhh28HcNe+xJIlkWOOPhKjSqHQeeSesX3H4X1tLfzSl76AmekZPPs5z+24InvRhefj1rc5EZVKBaPIHU89Db8//xokhYnAw1bflU7XfnDyycfi0kuvxijCSuxtbrMTmx50BDYduQlJhLFLG/bNopnLzFf5ugErkVM/vxH2CWUceepRGDVa7//KK/4Gc8EBPxsyBslHgWIWUXd5p2QZbg9gt2nP/x49lTdbGWnQ6QZjoS/7CMavLVV5Y24pu+dnDEuixha73+1vfzz+8IerMIr4vofb3vYoXBneCCtn4++f/CAYbRVzVtdLlbp8/+S7Zx/6t8s3mnBtG7MbDlTgBw0r8LlGU4ogjFarlQsIFxGqe67egz3fugaXXXY9Mm2aYJS4y52PwYUX/QajSLlcxhV/+XNH2q/vAxGmpqYwOTGx5L87jiMXJdnLyLrg018oYg+79TYkDlZ8dk8jM56FU+psKXk1sBI5tnUcW47dglGE758i1rYPrnTz+zcVhvACD+NhAKbgcQxwJ9VZ/m4pDDAeN+AQMzRRNGw0re4cEuqhLYkLE6EvPs122AxIryS9m/sMG1XDgMnLIo9j2RbsTHJWowbB351+b3zp/V/FL771y/mhCdlKHaX9cwgn89IAWF6sWuv5sBsuKodNzjeSJQn6srfVHdQmS6hvHF80NWMPklPAUZJLT0/RXvXKV2BycnL+57+7973xwAc8AD/4wXm9fFqlx53pnAyj01wUwuajTK0JN5+8A+VITACzMthjZWTCUjkMpFLbCYzNYyg+JzkRfp/z6GL2p2FI1ih99GwMlZtAb70vInbWsHCLmUGVlTj12i/L2Iax+ZzZb53zbeQoYvfNIjANmd622Odn+KEkQNQni+tOo+gVfjYj+cPF6aq8n7U0CCpKzyuyN950E3543vfFsJ3P58Xj8573vBcf/8Qn9dNPKTzwMUO2oQcfJQiQm60BlinNR8pgYGavY2ZQlEEUAcqBL0KVFc+lYP5nDgFKgYdpk0I4GivMA0K32oH4/Mwt3Ro4GA9dwDCla54RUHXuP3QfsuqhCe6uvSjunYGfsZceBxyGyNYaaJYLknaRZBglx9fJMcp83dXNE+vKM1ZGk54K2c9+9nNy2b59G1zXw969e3v5dEofoIjlJBdfI5ZGnmzDRabeQLPUuSlf6V11tmLZqDNRRGLQIkHrGgYc1kIXEY0Uk+UwRA2h3KeIoKtClnFQlCSeYSEX+rjJyGDONPvSIDqMHFbIoThZxm8uvAp/vvwGPOlZD4G5SMoEY8w4za3OoQopaJKicG2MFSQnmSc3jF9TMaushr5s5TfddLOK2CGqyOrgQYXVk0ylHjVprDeySekajF2atWzsNjPYZ2bgw5CoPMYzZRaMDWVcPjM+8wGX/pknG52orgcmINA6MMFGNFbYDAuX23n82c7D6mIs26hBcVrcNyuV2GNOOgqX/+EafO4j30EQHPyZchgFq5y1DWUEmb63wKwd05yvzBb2zzJ3a9CvSEkRyT9dUxKFNnophJOgsnV6Y0ezmzjpeHEgPQXtbstGFRZM8dGyYhvI95gVU1oKShyIgFCawETsrtIzz9+hrYHilU1kFMh7TBu77GyUhGDZ2GdlMW3Y0pjWNR/uiMCc3+L+WUkdoJ1gw6ZxnPWmp+Ivl10rYrY1NMFyPRhegNrGMXi59H0vZaBG7Jkt0GqgKB2SolM2ZeBoo5cSYzccGEGgS4AJh8v4HBdctyyplmYRIiuxXaGsrlDMlkMXHkPzTWAi9FALDLEqtMtNI656sIJrhdE1C/FBSC8ux5Ka2G9acs0xpQvtA3y8fZYNyw9FzM7y0dRisCL8mxXn6jDKGbjlAxaelpi97KKrpPfEcn1YDRd1TkXrQXpIv2A1mc1pxakKMk1NL1I6Q4Ws0jE8CGqjl2J4PvJzdfgprPqMMhKYDwMNy8Rs3NhFv3s98MQXOxeayPM7DgMLVqxFybLSSi+tx255meAW5dfyv6NJUsvD+zNhgfmyTEyYUzG7IkXPRa7uwFlkShrF7L0feEfsvX4PfvWjC/GQZz0Ezlj6/ep+1pYVn/JMVYS8oqyEClmlY7TRS2kFsZuuJ542JaUYhohXphe4VkZ8rfvNrAhZ2gFYWZW7sUoWX8R0sM4qqmOY2B2LWa3MLk+eU7s8F07OXtKHTjsB4xB/d+FV2P/x7+H0s06XTN600yzmZGxx2dWqrLIy6pFVOkYbvRTZDmpNWQLUpeHhgA1hRiyceM3xBayw8uLF1zJ1q0sCqSVm64aJCT6nVt0OgZXIycCHGUIavBbDdjyxExSO34Hnv+/5uPKiv+Kr7/6axF2mHjZ/5TKY8Bw5yVKU5VAhq3SMNnoprMRyJKaX1cWcYcI1IPFbPn20fRAOFLO3WBmZ+kUxS6+ucoBy4KEc+uJtXgwOO+B3kZ7YxngRk1s3SM4sRazvRQMo0o4I+BDYGLhqMVCWRYWs0hna6KWwCuT6MDxv0bnuSnpxYSIXhuBcC1Zm+/KccWWWTWL0zPZDQKelYLCBIpYNdwur4GEok71C00Bly6SI2FalnEMTnvDqM6T561ff+PVQVGbrli1JG+NBt9KNlWFEhazS8YbCTuX07xqV9WA1mhpoP6T2AkZwsTIqpyh9qpDStrDHzOAW0xYh3RpnO8owysyKq9YLx87m5uoyDpoiVsbTLkJtroaffvFnQ2EzoJDnlDqt2ivLoUJW6XhDYZ555KJTRhHOQmd2bJBdYjSmkmpY86Ifkf7MvtbbDQPTVgY3WUzBMDAW+CPrm2VDLaeyMV3ioNsdD9laXSqwlc3j8Jf5Do5vHBebwZUXXTUUYpafRR4csJHu96H0DhWySkdItYajafXzGlkYiWM6PryM2gqGEVa+KKSyMlq2/0KyaloiZjkNbAK+LLGPGhRr/PxpuyAU9Hn6Yf0AtU0TMuwg7MDWQ5sBxeyNV9+E6d0zSDWMeAujExwdpqEshgpZpSMoYrmxDPOcdB40eCDPtF2yC37mv49qtYih6xKTP8TbwCjDvy4FbA7R0vYgYOwX479uNrMyeKHouiPzfeP7LIeeTFsjtFoUfRfNXBZzWyciPyxHQncIxexLPvQibNy2AVf/4epUV2YbholSGA3yUJSFaOux0hEyJj3ls9Lp/7Pja7MlzilQJQQ+uo0/t8u0Vo5mC8nTjKceMQiey7G+YYp3mEJAIoti79+wYTouwlUcSJW0EUVsyXeB4nFAmzBPlmcMW0Tt8bYt1dlmaKIev75hhRm+bLRrwpTEAgchprJ5lCbLKKxx+Agbv1zHxRff+WUcf8pxOP2sx8JM4XeY+1Mefugfrlvpe/1Kb9EtQukIij6E6TmIUKzm4rnyk76Hbb6DHYGDHX4T20IHWwNHYl0mQwaKswLFRrYQjhEtsbYu7Bxu/5n/zvuxcsXfmwh9bAwcbAlcbA+b8vg7fEeej88bzbWPmmhSDU8CXE0rGHa4lUbWgmRU4fbni1Kd5enlZMjvUnqriitRbu0vEKJiWLjZzKBqZyShYD1kshk8/93PTb1ntmFE9gKuiilKO1qRVTpCbAVJXtahcI2ja8RnhkB2eKYRyrhNVktbM+Gl5rrmY4MhPmFZ/lvkMQwj8hJnEL0OSXoIWKWNnr9mRPPonZTZNEzOcnd9eNroNdRwGy2za14E4+C90FKdNW3xzY4FHiaC6OSxxkrjgq7+NFsKxgMfW0IP06aNfSaHRRjwuhg51fLMfvjl5+DKC6/Cbe58a6QNpjhMwpP9KvOHFaWFClmlIzJhkLjEAi5/snqRCwIJc6eI5Svk7r87onX18PkoWuUQZBwsbpmTWQxZz412ylWYkaiNpyclGcv3Yfg+AuksV4YVbrtcwk1apisnjE1ZGVRMS0QfBS2jumSlJKWWA67SUJTxIBwYwH7Txi1mpmcnuBSzZ3305cgVspjZM4OxTWOpsxn4oSErYXMJOMlSkoMKWaWzDUWitwaPdPEiRCHwUYzFYRgf6GriVU3eAW1e3MYvjfKai6UbQk9Eg1RqYUq1lstnSazUml7810/ga1NWSWwdkEg9SSOJrjkOldvmeOBih28iaKt4hq0LT7pgyL6AXnGuTvA7x+t+bLeswu6zTBG09EuOhwEm4cMNaAPia0749skGUrElRetbrDTPmRbsMMCWwOv5Z0gRG4YhPvOWz2LrEVtT55nlfr5Aa4F4uBP+t1b6hgpZpSPYEDXIg4QdVy/YBMFrHgZY1ay0qq4pggd+yYmUlx01m42HPCizwmTKshn9gRS4ScEI/EQKbGUF4gZHNnCxAsgTv+wh4rVlG4paG8eNAJsDD9VFlrZ5D6OtC7Jd1PLeFJP8XlJwsBmyVysN/J40LROz8X6Bk5/KrCLHJ4as0iZG1MYNpfTK20YozVysvjJuLNoPGNjK1Y4+vRw2gD35X87Ef73sbPHMpknMurGHm9u0zvpSWqiQVTpawjcHkSEb+17HnSa2B474XpknWE1o5XVtRAd8zrqnsMghwGGBLwc7vs9ZOTgP3ptsOp5WQNJAXPFjVBytNq1EDq6okMDg9ziqqvIEkEvaC09QMkF0kjjHiuyyYrBV2Y32D3yuQhDAEl96a7uOKqV8vGYPfK0UymyM4gkto5nygS/jbksy2CESPi1h3e+VI/4dpHHOAJzQkCoyV13qC16PrDKFYV9fY8szSzH7y//5Fe59+r2QBriyVULUA+GpvUCJUSGrdD7Vq187WqmyRFXKYuij7Dgiome540pKlaUHUFg0wGosK+CBNLWUfA+bnCayDWegy2mmHyLU2JvEnmiKaAoCFHiQj088KVKlyZDblZRSO9t2gvjAwO/9slVCI67Gtp9UtnzhsYCWpsdY3DqhKSsNVTPyhnfV20rBDEOimaZb3nmu4AQcrMB81tiCFC9Pe92sFrdF+PGEgcKVX1UK6GnTRD1u8FxKqMrvhqHYivoJxewL3/d8FMeK8FwPpmUmvzLLzyiMVuiGppahrBsVssqKRLWbg/NUewUrE+xOpoWAsHpRy9hwExEI1D/4flmlDYwAOd/HxP45lPbMwBkrwM1n+ytoGR9Ga0HSD3IjRNToGEijYyGuwBL6sNnxHq6j8slvu7XONQ9WeaUiyx/iB7IlycPHuO+LkGSFlpXURtsSezfgc/PxKJpnDEtEvkwsi8VtLj5RlmSGWHRKPnTcdNX+zluGi9bbiFanoiq0Ia85RBhXnz1Wsc0D1opO7Q18bRTBzKPuNxObJ+T6q+/9GgIvSIXNgH+npDUjKoNFhaySCKRqEniyLEjhTAHL5c9RhwfWum2jmc9gU72JXLWB5lhBLt4aQ9JXixGEcgm0IpuI9JB8EMjyKr8zISudYKNj95oEuc2xqtvtbx+FXqvhUfy6CLA1CBDQjxs3PUnDZjdP0pj9jCj/uda6qVVBbRuMEg1LaY3nje+3YCAKrxvxe3DbhqAE8XOstcLL1zJo89ADnnz/1HhmeZJQ1IYvpQ0VssqKLNyhdxNWkrj8RxuBOS9gk7sTHRSshjqlPAw/QK5SR7baQGOsAKdcgN/jbFc+pwjZjJ5YDILIQxlIlz6tAxRg9J5WKJx6UMXjqSRF3UHlyC5D0VcH/aKRqKWFiGH3DZiY5SqMaUllsxeEbeJ28TtEe7rWP8/v93q0CsK/56CFbLtn9tsf+Q4e8byHI6nQHtKaxKh1WYWokFUGg/jWGEHlipiVAHA176/8sVkmmuUCTM9HYaaKXLWJxkQJjXJ+VXPYV4MM5JWDuwrZvhKEKPgetgYeSj49gaEIvUaPq2UtUcUTy378zVsNW4ij9fh+vcBHxTSR8wfQmx4L1n6JS37Ogxay7WK2XmkgychnpRZZpQ0tfSkr0u1DGZfvGO/DMbGEcVOeboqrIrAtNMeKMr6yuG8Gpb2zsBxxJCpph0vf9SbGpivY4DSl+kTrAJff+zE4g1Uudvz3HSP2tZpsjoIMPthcr2KL7wz1aNokQTG7/ZhtuOC8C/Hld30loeNsxZ2sp9XKPCpklY6rcus+tomNgPFSjlgJ6IdjhUlZO14uA6dYQK7WwNgt08jP1oAuHXw4/efC8y5EZboiFZBwBAuyV/35etRrzf48GX2aDQelfbNyYpLxPdStKFN4EBm+g/xzR41TFhzTwmTgY4fnYKPP1ZskCqv10a9G2tVw3B2OxZ9+/Wf5/ieN1jqBrBQpiloLlH7BKixDyylg6e/TWdndI7QMsRvYTVeqs1bDQWOytC7v7MzeGfzXy8+GnbXx48//BK952WORLzLVd7Q475u/xdxMFS9+/ZNQ6OH7t5queJ8zFM3s+C9k4eSyAx1CkQSZ4JsmZk1LBCxXcVilnTbZGGYnfqxzmrn5mptRr9RR3lBGUoV/2gbhKL1Dy2HKirR2GmvdbRQDdie7WoXtc3WWwmit5Ao53O1hd8XLznkJDjtiC/7znV9CZaaKUeNZL3sM8oUsPvDWz/emMhuEyM3VpAqbrTfh5bNwizmNOluAG1sOmKiwLXCx3Xek+W0YSJr7/PLfXY5z3/DfeMKrz8AJdz0BySMJp1hKklAhq/SOMMSENKu4MMNAqrAaqdWf6iwrecydzc9UVmU1mN4zjW+e/S1k8hnc/0n3QyaTwVNfewa279iEn3z/fIwamayN57/6DBGzv/7pJV2vwpb2z6IwVZFoM6eYT8TQCekGl9z5JMmrCA4WmGI2bBhgh+9gk+9K6kGaYUpEUj5p3/fxzQ9/C2e86gycev87IIkcqMgqSoSmFig9qRowOHwy8LAh9CWRwNVEgr7i5TMiior752C6PuqTZYS2taKI/fDLz8Gxpxwr89hbmLaN5zz/EfCzNmrVBnzPx9hECaMkZl/wmifAsk3MzlSRydjrsxmwClutIzdbhxkEImBlHFRCmI+dSs5LOpi48c2O7QaFMMB+05bIrjSSFNfv3l17sXnnZrz8nJeJpSipyGYZDfhSFGHwp/9K4lntDoNxWlsCDxtDBp2bIzeVKyn4GUtEEhvAyiukGrSL2Me/4vSDAtGZYWvaJiwD+NG3fov3vvmz4hsdJeyMJeL+21/+xbpsBgdVYe0oGzhJIrZ1Ekofe9KFAhvCpg3zoOosX3va4PuwEmAneNez3o2brrk50SIW8QALTlLr28h0JfGowlC6PmKWVgIGnM+qlSAZubNjBWQaTZT3TEfNRIswu28OJ552wiEitvUYXPrmUISHPu7e2LZz80iKWfK4pz1wzZ5Zfvb0wmbqjpxgBJlkCgaKqtS4T1mdNTlYwZDq7GG+m7rxpZxUFWWjhgP1xNJOwOitpGOH0ehyRWmhW4PSkYeLh4aVajRs6mLmIzuMOUM9iR67kYRz38sFEaLlvTMHNYGxEnvpLy7FkSccgUe/6FFLjqYMTArZAJZl4hkvfbSI2c+c8x2Msmf2K+ee19kvhVFDF20eJIlV2Hb4bY/m7KWrqjljmCiHPrb50Yl0WmB10W1NU+szjNb79Js/m2hP7EIsjmVWIau0kcySgJIoKGLZPLTcoY0idnPApetQ/GtK8nALOdgNRnTNys+31JtiJ7jVHY/Hyfc+ecUBDEYjGmDRErPVuRrCMITD6Kh8FqMmZptNB0EQwnM9ZHOZJcf75mUCWwNe1k5sFbYdU4RVcrybncJ9FFeBOPJ6m+dirxXKz70aLdstOLuMVo4MQvnvftGsN1GeLOPVn3wlJrZMIC3jmoPQkCq2orTQiqzS0QGC9Y2l5GmJldjARWhw1KyK2KQ3gVGUNq7YhbNf+mHxxD72ZY9Z8fdEyAYHTmQoZscny7jgV3/CO17/qZGzGVDMlseK+Pl5F4jNYjGbgel6KE7NIV+pw81nUyFiiWmEkVBIqViomJyABrEZbAw8ET+JRiaaGbD7+DppJ3j7U98hWbFpEbGEVetWBVtRWqiQVTqCB7bFGilKcSWWcT2NgbcsKJ3mzV506TU44fideMpzHrqknaCdKBbq0L//qaedOBye2SCE6fmwHE8GS3DCll2PL4340nRFnLLKyvuTe97vDot6ZnnfyA/bRDMhsVqdIBOTQqCR8hNSTkPjWN+toSsn2UmP6GoaVt/2ni1P7CNf8AgUygWkCRGyLKyk9CRL6Q3pKBEoA4dnwOYSdoLACFXEpoSpfbMSn3Xvh91VJoBZUxXU2BC2wgHNt8048yY8qFLXshl84n3/K2Lutf/+DJgJFm0UoaZcfBGudtOT/5aqXRBK1Vkqz1RzsahrxVCJ55uNOfS3moZMnWKl+qUvfATe/4Fv4Jx3fAkv/dcnIxNXYg0/hFNKl1CgfYhLt80hqHiJbzY0sCGI0qv3mpllBRC3AYrJpdz9tFrwkcRy0WUhdVDDVw9F2t+u3DXf2JUWT+zCRJzZDk68ldFChazSETwAtO9emd1IERtCRWyaROy73/hp3OnuJ+LRZ94ffj4LI/bMMmLLWSYb1bdtEW0UgbxupyVmr7vqRhGx9M2259AOFIoTx4PleshQuHs+DC+QxjXR5aYpl8A0EGbMSKgutazO9xXGQpeP6wewXQ95P8Brn3w/XLt7Bhv+tkfSCZjZ25go9lyY9KLixWV5TtIaBhjRNAsTkxSzIbDHtGXbpB+VS/lMOGj9t9UmZhc2tobxLVx5YvNry9fKz4kn+RSiDu+zxr81f5fDHvKyP+3N9sLv5Y5jt+M573wOjrnd0Ugb8t2LPydFaUeFrNIRrES0dq/ZMIy8ZwhRS/kS5KiJ2Fvf9ig88on3O8gzm2mEKO6fRWBNwMst3rRFYRZkLBGCC4VsS8wee5vDsev63fjsOd/B81/9+MENTQhC2I4rubmZuisilsKVy/u+bSHksIi1VHVikRvGv7qwGeq4XBbXXvJXfPbHl+B1T7ovSq4vz+cW6I+14NMjm3BRy2ij5pBN4OOfi6fcR3oNbDMMVAxbQiP4Dv340uoDoFBlNXphTVYsF3G1ljmmWYQoBPzZlz+pHzcgNQ1DoqEotuRxOvx7s1Awa5gSIdYwemMn+MkXfobn/uezUyliiYh8w9ToLeUQ9NRG6QgJszEgXrMNgSeVDBWx6eHrn/+JiNgnP/dhMBdEP7ERyfB8FKcq4gFdCieXkfstB/2yGzaND8Qzywlm2Uod5T0zcinMVGVyFsW6Uy5IagMbrtYkYleAorlQqeOYw7fAzGfwxs//FHO+B8v3UZiuoLhvTuK36LkVj21SMeJl7pTrWEYAMr1gq+9ie9DEYYEnApSTBhnRVYGBadPCnGnJRDCKT0Y60Y4QtE5Y2i68zY8rsJGYsqSpbNa0MGNY4sfl6lQx8KXJ7HCviSN8BxsYBRb4HQ1q4OvgCUS3m75antjTHn63jvzwSSUXhpgxLR2EoBxCerdqpa9wZ84l1UnflQMBJ3YpyWd2uiKe2DOf/dBFRWwLZpva9aaILnDZfRHCjL1iB3h7zizFLEfa9pQwlMaq/HRFBj5wahY9r04hJ/5UNrb1Qri2Q+sCRTPovR0r4NVPeyAK+Qze8qkfomoacEt5Ge/LyjAr3+X9s8jO1eTnJCFbRsCu8Gj5PG1w26Ro3OK7Mulra+CgEPpSLZ2DiTnTxl7TRhGBCNpuphlQ6LIKy+EMFFvcP7J6uynwcLjvyGVyhWEN0e+bYtvqFldccEWqPbHtJya0X+ggBGUx1FqgdATrcDwolMBhB6xtpO9AN6p2ggc/6u649wPvuPyd6fErFmRYAkfbNibKhyyD8/bQMKWJKeS82hXE7K9+fDHyhaV9t+uF4175ejkpi/UwL2PDyxfRTwzfR262KtVgN/YYZzO2iNlfXPRXZGMbBm0NnpWNvLWej/xsHWG1Kb5kt5hFYA9+V8xvNU9YmzBTlSFrhwEKYYhy6IkIZLMaq6aLNXZxv8VhLRPw4AVRVbYXdg8K27o0B0YnW1wW3xrwOX3MmaZkbddYDFjw3Ny3TqB7InvLEVtx5uvPxMn3uh3SPjGSJwg6CEFZDC2rKR3BhAJWMej7Gib/3Ch4Yu95/1M7+h2KUy6/F6aqyFUOraRSKPq5TEeVRIrZez/ojhK6/qkPfr2rNgNWQKMK7Ayy9aZUXQcy8jUIRZCyIkwfbDsUsw+4620wW23gg1/8GWrxMAkKF3pl3VJOxv5y4ldp35xUaJmiMOilWzYa+UxkQPLhEvy4WAccbAkc8fdWYElVdLl0AlabqzCxIfQw1o9xtpITa4oQaxrAROBjp+9ge2sCWZtopbhl1ZHCd712gvP++zxs2DqZehFLWwaFCv+uirIYKmSVFcmFgXi9uBNWEZt82J189ju/sqQndjnYyBVkbYmPYgbqQUiyQRaWy8Xnzsjms3AcryueWXpLWf0s7Z1BYbYmy/XOoDJaOXa2Wke22ohE7BLCqZDLYLpSx1s//v0DYjaGDWC0HXD8L99PiZ85s2jbBk/0E8NgRiersXHEVIKFzRindwUONoeeVGDnDFvEYsfNVaAn1pCm1W4u5a8EPbj05bIqXAp97PRogXDnX4NMJzMtOaloF7hr8cRuPnwLhoE82I9hoq5yRVkCFbLKsnCJayObVgxWC/oX2q2sDZcd+oaBZ73s0asWsS1Y4ZTl0JnaIY1JfpZhRXGebAewMvvMlz4Gh+3ctC4xy4EEjAkrTlekksnc28XSE/oF7Qy0Nbj5jAj8pWjZDOiZXUzMtgQtPcoswvEEojBTgen11z9LP6cXcjmeI1MTOtUr9sAeFjg4LHAlTmsOa19uduTwFyWwMImln9B6MBd7accDHzs8B5t9VpWjEd+sytLLu1YRm3ZPbAszDKTBeNq01xxtpgw/KmSVZRkPfYzDkwoCvXNJn5Az6naCt7zyo/jrX27Alm0b1yRiW9C7ma01kKvUDrrdy2YQZC3YbueLzy0xe+Ipx8Jb5fI5T6RKtQZKe2cls7VZKgx81Ks0l81WEViWxJKtREvMHn/EFvjLJBZ4WVuqu6yEi92A3t8+fd9YAWSFks+WxGEIFHgUnBSwDMWfQZQesF7BzZNzNmAxiWUQ+zY/FrSM3NpIQes7sgK2z7CibNslGi+Xwqk7QyNiScn3MGPYcvxRlKXQrUNZknwYSDWWSzo8G2blg3PYleR6Yo8/4Qgcc6vD1/+ARuyXna4eZDHgMj4TAUync3tBS8w+7mkPxOTGMfzo27/rqDJL4brBaaJca0ijGV/PoCuFUXNXJO5pbegUitmnP/I0lAo5fONnly5amSVMWHBplzANlOeqGHP7M16VcpxVQCNpwxDaqrCTYbQvYvRVN7cDVkWZxDIReGtezu/KFDKDhYJAxCwba1ltLnb4mliJvfrSa3D7+9x+aERstuHAMS1M9aghTxkeErTHUpIEK0EbaClAOD9J5aAxikpiaNSd+cautdoJFoPikTBaqt1i4OVzsgy+Fi9nGIS4+i9/W9FmQPE8NlNFLvRRz3GgwOC7+rndZysNyYwV+8Ua8IMAf7hq15I2g/n7ZWzJ7c0HvlQiWYXsFfyO004g2Q9xsH9ivLAOM2AjMU9ht1wT11qhVYZidiL0URzkvs0wJEuWftCNTGKgtSAEsk23IzvB7N5ZDAs8YeQUwelMNlknVkoi0S1EWdFS0IIHOwns1s8sUeQLWTzhGX/fVRHbbjFgA1K7xYC+UI63zayyKkvMOJprSc8sm6jmajJAgEfxupWcaVi0FOSqDXj5pZu7ViJjWyt6Ztursw3TlClSFLNcIelVtJEE+sfDEFb/V+1NpBYzWMddpydV2IVwv8ZPl1Xfbg8kWC38G7A6y68yBXyRiRZLJIW0e2LvcL9TMCwwNaVeKqDG77+irIAKWeUQcgssBS1a88U5j11Jhp3gPW/8NGam5nC7U4/vuohd0mJgRs1WjMFaCy3P7M6jtuKGa2858FR+gPxMVSaMiZUgu7aqZ68qRPm5ukSUrTcloeWZnRwrYNfu6RWe2IjyRilmfQ8ln3FN6Bqtd1I3I0+mS0E34BMH7n+2BBy84qFm2z2pwi4GP2emB4wP0GIwj4zSjaaO2Z6P8Zv2A/zbL+CPv/rTUHliCfczXs5GdayQmJNYJdno6Y5yMGEoXjEuNx4ygjYOS+dybwL7QUY2J3aMwwt6CEUll9PpDeU4WxG3uYwsf1uuP29BWA0yNOElj5b/ZnPaYVsnsTWIIq2cwoAitVawFNAX3Bp6sF4oZl/1tAfKf1/21xtx7M7NKPKzXQL60+3WRKoAMh61G99BNjqxaYr5sWUEaAy4tkE/7KbQhcX3yMErfRUyBywGjTAaOztoHNvGzIZxbGs62HjDHkwdsQWhZeGvl1yN7cduw+kveyyGCa56mF6AucMm4VemBv1ylJSQoKOFkgQY+cLA7qgKdCja8DV4pvfP9cQTuxxuPkoxYCc9YdZss5SD3Vx6abxT/u+8i/C+N30GzVumBpcL22NLwXKZv9/6xR9XtBm0GoIahiHL32WpGq7/+W0jinuiiBt0o1eZ42UDl2GqUfj9AIRkkiwG7VaeqcO3yCS7yb/tweW//hM+9tqP4bo/XY9hgifLdsNFdfM4nHJh0C9HSRHJOmIog6/G+r4cH5daztOGr8FTKOZwnwffqW8ilkRL6pbETiGOBGJ1ko0yrKCsFdoJnnPG32Hn5gm8+SPfwUz10Ilig8QIAmQr3bEULPr4hoGznny/jjyziL+XrKBOhr4Iv/WIWXpvXYmxMmTE7yAbvfheNgeuDGOQWK0B0rIYyOebELxCDlNHbsGfz78S577x03jCKx6HE+92AoYF2pQyDQeVLeNoTJQG/XKUlNHTPcbOnTvx5je9Eef94Hv4yY9/iPe859048sgje/mUyjoocecd+rK8thTa8DVYO8GXPvkD2LaFBz7itL6J2BZOPgu73kSO06dkcEIWzXIemcaCCWCrGfFKT2yjiec9+yHYvm0jfvLzy5AkOPgg03QlP7dXtA9N+MVFf13x/oeI2bU+LwLMGlbcwBkOrNGrFItYPwEiNsJA3TCk4VUmbCUEJ5vF577zOzz7qQ/A/Y7fIb7t4anEOqhuUhGrJNAj+18f+gC+/vVv4Itf+pIEgb/qla/A//7P1/DABz0Y+/fv7+VTK2uI25oMPJmxvlyzx3zDVxhF9ij9YWa6gnPf8yWxExjLTJLqKaYhMVi5OfpYc1Gm7FhBhK3p+aubtMV0gkpNpmPRTmCaJp7/zH+QCmWl2kCwyiD4XkChQDuFeIB7XKmkmH3NPz4ItmViaraG3AoZtSJmAfFz+iGF1+q2iRwCNBFNkCL8PtM21O9GL+alJkvERrBSnYePUuChaQ2+6XD39bux5YgteMnZL0Xe82BfvwcbbtiD/UdsoeEcaYW2HV4qm2MRmwBfspI+errnePRjTscnPvkp/PGPf8Lll1+OF734Jdi8eRPue9/79PJplTVQlmpsNNN6WeKGL53w1T+qThOf+Mg3++qJXQo2edmNpnhGW1XZBquysXe2U7K1JgrTNfGdMmaq1QDG9/a98y7Ef7znayJoBwnfk+V60tTWl+ezLRHyX/7RRWIzqK+QH0ox6xmQEaerGbHKrYeDhhmtRwFJ2NzZ70avTByxRZIkYluw2Ws8DAZelb36smvwnue+Fzf99SbYGRteIY+pI7fKcvzkrn0w+jzOuHsNlHX5fs1tnURjsqwiVlkzPd17sJGhnVwuJztq101CUqHSHjzOaiylSCddwtrw1V8anosTThy8iBUMA34mg9xcFUY8bpaNGRzVyoNSJ3AZkZaCIGMtWsV9zCNOE5vBf33uh6h5g9lXsMIsDV4UsX2uEv3Tw+8mNoP3ff2XaK6wfMzvooUoMsoKO4+3qiMK3m9h9rnRiyfCm+Jxs4ekoyQEH6Z4h8cGGMd1y/Q+fP2cb0jE1o7jdxzsmT18s7yu8p5ZZGrJ8pYvBzNxCzM1+LkMZrdtRHO8OOiXpKScvsZvvfa1r8bU1BR+9rOfL3mfbDYrlxalkhq/ew0bG3iZ7fBAxpnsbMqgAO7HUiSnQXkDEjSDpBl4mHZrOK64HcffcQf2XbsXiSAMUag1MbOnhmYcRzU9V0N5toY6f15mm6BAHJupoOkH4rldijMefBd84vM/wY9vug4Puqo7kVeroVBvolRvorHMa+wlTzzlNvj4jy/AeXtvwslBFt4yVbe5MESJy+DwMGXay56MmtRjoY9pKwMnrobaIdBAgJrpSjJCv2xMeb4OPh/zxJYg8IKBfvfnQu4fPWSsYNX2jfXieE2c/9ercK9H3hM7Dt+GW664+ZD7ZJoO8rVZePaMfO0ahRyChKV+zBOGyDccOSdgRmy9GSCYWXolZ8/Ve/r68pT00jch+8xnPgNPOOMMPO0f/wmzs0uP0nvxi16IV7zirENuP/XUO6Ber2MUOeqoo+AHPfJphSE2Nuoo+C7qdofPwWpKoyZzwZ0eT16hiGV1rtGoj5yd4LtXXYrtY5M45ugduObnN2IKNyIp5H0PrmlibzYvwpXbwianASsI0VhqmwhDmdRU5bAN+vpWOAl6UHEnNh2zEftv2Y89u3ch2ycvoBUEyDhN7Gf1eVB+ZABP2HAkNm3dhOk9++GGAbKMpFpGHO4MAsxaNuqZpb/HRc9D3bZg2wdOOPi35Ez7fHb5k5BuUXYdbHAdmdq0eYXn27FjI5rOYKuNBc/FNtvG/kx/Ph/i+hTPNh608y64edc+XPHxi5f9m/oGPc6W5PCKfzphE7HsIEAhiL73M5lcx6/vhBOORrmcEw/9KHLccUfCtEaviEMKhc4j2PqytT/taU/Fv7z+n/Gc5z4Pv/jF/y173w988EM45yMfPagie9GF5+Oiiy5GpVLBKBL4GVx44TU9eWyOvdzpORLBs5ronXHfxebQx1yPlwVZjaGI3fB3O7Bh5waMSmPX1+mJPeVo/N1pJ+OaX9yIK6/4G8wENXWwslcMA9xsZeYrVaXQx1bfE78jvZsL4f29wMXf0FlTUcB564aB6zb7+M3l1+AFZz4Q5VIevSZXa6BUaaBZGEw1tsXNl9+EmWuncEm5iUtv2oOXPuqeKOSWFqkcTnFYGKIyXowsEQvI0PoRAnOTZWxos3QUqg25bVMfPlvbcTExNYfANBd9je1cf9F1uObyGwf+3Tf9AFnHxdSmCXgrNOF1yxP7g099D4999qNw8//txp/+dB2s5YQfYxNDH/thyUjjjaEv+3XaTur0QA+wgYo+6EIYwjeAKViYMVc3qY0i9uJLfodRhSL2/PMvwChSLnc+6Kfn38qnPvUpeNMb34DnPe8FOO+8H654f8dx5KL0h0LgS6PHapcUXdNC6HtSCerH9B0eyLYcuwWjwF9+9DeceMqx4omlnWAaN4mItTutmPcJO/QxZphw467uJjKo+q5Mn4psKge2Czv2RIZmBibnyHfw+K3F9Hve80TsqlbwkS//BK95+WMxPlbsaW5sad8ssLGEYh9Ey3LM7p7D3LU1PPjuJ+DaH1bwoe/+Fq9/5t8vOwEsU2tiLJ9BbXIsMr7GGH6ITL2B2sZx2OX8gtubyGzfII17PSUIUN47i0w501Hg/dSuKUxdXknEdz83V0NxQxkNfq495PLfXY5vfPSbkSf28G244v92i4i1l6myk2YYYksY4EYri92GKTGKbALcFAaSRBOdXPZH0PKYkAeb5AAHFuZME1XTlhNefu9Xc6QZ1Uqssjp6upWceeaTRMQ+97nPx/d/8INePpWyxoYLGce4hh1cE4Y0h7D7WeleTuwtN+3HvR5wKp7yvAQ0dq0AtxsmXbBhp8VsfMBi9fUgS0Hgyf2aa/AZWnE0Fy0m7/rA1xEEvdvmGAW01rG7vUwzaOXMvuszPzqkibYdjhC2647Ehh30GPUGnFIeTulgv7GkMuTsFauj3SA/V0e22pC4tbTBdI08xxR32NC4Fm686kac+4b/FhF76v3vsLrXxxU1YD4FYtq08Tcri11WVsYZ52X0OHNxg541rvFkdSzwJemBjXJcrfmbncVeK9t3f7EyWvR07/X/3vwmSSl4xzvejnfg7fO3n332R3D2Oef08qmVDqDYYJ7kNFZ/0ObScA3RXHIn2XorNSKWY2fveLcT8Zin3F++N0nHg4kifGkUdGOLCQ+o04aNw0LWYqIDWh6hCF6GzK8VRnNRzF53w24R+BRzXf+MwlAGPojtIWGff2towq7d0/K+l3z/zPrNZuKRuhkEti3inAkR0h2+4Hcs10VtjNXb3goNvgZOhZOl+YSfoC2GT19xIxqO0eyB6Offc9ux2/C8dz0XR932qDU9BlMoJsIAk4Er4pHbMccPV0MTM2Yoq2/cX1NoGiFkFC8zwZkHLvJ3Fds8G31ZxKB4lQHHBuCFBuZMS+xmFK79WKlTlJ4L2buddo9Fd7bValU//UETV8nc0FjzgYXLVRuMOJpGd1rrFrHMiX3UmfdDmuBJDJcx58IDB66aaWIqtLEJLqphlHMaGEy4WJ9Yopg99uhtuO6GPfjUZ3+Ml7/wEV21GbASazc9+AO2FCwnZo/ZuRl/uvomfOEHF+K1//SgRW0GrCZnqp7YDLyiAdP1Uds8fmgeLm1BMOD3cGpZ63m4NG/6IdxCsuwxHcOkFtuSAR6S1NFF4U87wY8++2M8/93PW7OIbb3GKosLQYCq2ZayIMMz2ABmYja0pXhBAZqL/avMIC5xZS0Wt2HbRX69zSQkhiEDMoSDJ638rvM44MoKnSG+XEXpNz3dY+/dm5C4IGXROesM+l5PEDl3Wozi4pIxd2TK2vjmF3+WiGEHa8FhVZYd9WI3OcCMacEOQqnMcnoTK0Pd4vAdm7Bp45gMTeimZ5YVN1aavKTGF8Ucf8QWZDOWDE1YyjNLu0B2rgHT91HZugFOHJPWju36CLJWT8fvyvM0XLEUuPmUitgYj9GQtYZUZd1CrmsitmUnMLuw3VFcGmGUK1w3M4cUGNhoVWMNtXVzXFltVVe5Lzfl9OaA77Bd2NJOxn09n8dhyq4WMJQEkOw9ttIzKGIzRrCqDtKFcGfWgCWiWFk9M1MVeK6PJz7zH1IpYgnzhIl479rgAW7WsMSHzUN+Nw94LZsBPbMUs7V6u4Ree5MXMzm9BHljV7IZ0DNLMess4ttklijTF1gFby7hSTUdF24+GjXcM+JRxPKaVjPCOIGEVjwFrdGdZuQrLrhyzZ7YlSwGXAU5yKe+FBxQZJgylIL+9n1WBnvYMGZlcXN8uSX+mbfTe1s1LfG6q4hVkoIK2RGlGPriaVovXL5K9+FpcHaC//y3c/HLH1+MbC6TShHbwo+3p4VNJDlE2ZYchco4oG7SErMPvv8dkO9Ctz1tBRzWkBax1RKzD7zbbaQZbLHxupxdHzLtIp7Atlh3OSdE9RImIuSqTWlCGwZoO8nxxClY//Z82FFb8eTXn9lVEUtYnOCrY3MX/8aKMuyokB1BuIRUCAI0u1Al42OwHsTKm7J6T+y9H3TH1H9stJiwwt8uVXgALYceqoaJKUZuweh65Z5i9j73uh3q9SY+/PHvYXYuqvytBVYn2QCTJq83xez97nxrTM/V8f4v/BS1uFLI1IIwY6G+sSxeTrt2aMWaoj20bXi9FO5SjW2A58s9rfr2EaY7WE1P8nDXYyf47ie+h4nNE7jdvW6HXsCqLL3rbMRUlGFnOPYuyqrgMnDW6I6vlY9BMcsGAqWz7uSPvOurqfXELoYPA7YRSNNIC+ZIsgrLmDZW7febtvjvehHXls9n4fuB2AzWJGaDEJmGC99O5+6wVMhittqIbAZTFcAyUJ8owqdQzVnI1psiXBemCDhMNehhYxufgxXZYanGCvH3lU2B6/HEbjt6G3oJEwv4TaOYVZRhJ517bmVdsPkm6Fb1yTBQMeyoa1CrssviOp6keDzzZY8ZGhHbgl3M3K5aFLn0Gh5oBqnGYjbbAzG70DO7WjFreZGtgBFLaaRlMyhlbbz5sz/GvmxGGpMI47dML5BGtnkY3RUEcEudj4BcC5laIxqYMiTV2BaMMuPJwWr3d+2NXd22EywGTyDLAQcTaJFBGW6Gaw+jrAi7skshvYtGV3eYfDxt+lreTvCWV34EV/7pOmzeOjlUIpawKs8KbNT5HEglaGEiRsWw5sVstkdi9vYnHb3swIBFf9f1RNj1Oku1lxS9AK8983447vgdcBekEAQZMxqQEA+SkIixXAbuMuNu1wtPDHKM/+p1tNfA7AUcnLG6qqzvBX0Tsa1mXBtRfqyiDDPp3XMra4ICgkKD0SndbC6Yhan2ghU8scefeCSOO+FIDCMMVedBk9XWA2OPD93GKGb3mRnYIZvBut8A9oTT7yVxXN/5wQWdVWY5BKHhIEyprSAa4uAAtgF/yzie+MT7SprBN7/7e/EOEy+Tgd3wkIl9nZbjolkq9LRSmmGUGSekJTSTdz3wc+OJT6f2AlZir7zwKpx0j9v2TcS2n2BySAILGIoyrKR0762sFZnEYoRdj05pmJzjZGjT1wKcpjvf2DVsdoKDMeTUyA4ClBDAXWbXQpvBPtMW60GpB8ueHGF79bW3dGQzoCChtcC3UmgroLe31owauybL83YC+oUvv2IX3vn+r0dilpPQOPek6UZNXhkbbqGHvlVW5St1BGk9OeiA0DRhNZ2O7QSV6QoGAQch0FrQ7dQQRUkSw7unURaF1dhejKpnUw9D79nko0RwiZvRWk961kOGXMRGcLMqhZznHq5Y8WdX9V4rI/YDillzQJ5Z0w9g+EHqfJz0vUojVTGHGkVs5sASfiZj46UveDgK+ey8mKWvM8cRq7UmmoVsT6d5Mcos43iHThIbIhjTZtNasEyls9+e2EWRpq9Q0wuUoSZde29l3TCOhcvAXUfmepsyFUabvmI7wRs+Lde3PeXYoRexhFaC8XgZk13TK0Gxy8ost5si/J6I2SOP2IK/3bhveSHLbTZFsVucyMVqYHOsgMZ4edHs22wsZjdMlnDz7mlJMDCaLmzHg1tafEBCt6B1gScHacnkXQuBZUXZw+7S/tO//P6KwYrYtng8nixqpqwyrAzvKbNyCBQY9MhyJnYvYHWNE1/yHH07wiNr23NiJzaMYVRgFNvmwMfsKpbpGd01ZdrwAnr5fHgGq/tm18Tsc5/+9/LfXGrfsX3DIeNsTcdLz4Qi8fO6IrobE2U4pdyyApxi9sXPfZj892V/ug63mygiLBd62uRFMo0mwiE/caNtwqj7sHwfwYLD6FUX/xXbjtmGR73wkUgCXC0bo70AIeojvF9WhhetyI4QFLH0yPakIhtnF84ZFjIjbC+YmZobEU/sInDQBoL5sbWdwvvPmFETGCe9d9tqQH72yz8eajOgX9z1ZJxr0qGPN1uN/bAbSnDK+Y6ryLS4/ODHl+AtnzoPNcfrafWZHlxm8npD2OR1KIZUZRfaCT7+uo/j+j9dj6TQOlHLaXqBMqQkfw+udA2KWLMHjV4Lo7ikg31Eu2QLxTzu+w93GT0Ry0pcvI2tzVoSWVP2mDbmDFMsMKzsd4tnPe2Bh3hmpdGLS+BJjt3iKkrTheH6MuSgOjkGb5UjeZld/NJnPhiFQg7/+d6vwZmp9uzlcqmd/t1hthW0YNWZE+EW88Te9u4nIkkwX6GoDV/KkJLgPbjSbRiP1Gt96Rpm1PQ1YjtN2gk+/7HvwjRNPOBhdx05EYu2McXcztaKF1sNWJ3lFlT0XJi+39UGsJ//8k+paPSyfE+izHzLlHGzzlhxzTFhBT/EC1/6aBGzl/zkEvQKLrWD3/202DW61PAV+AG+94nvJ8ITu5R/nStyGsOlDCOjsP6jtCcW9MEjVTMtjIdex00/w+SJNRMqivpBlo2ERjd2KqzOGqiaNrbbGWQcH5lqA14+uy7R2RKzPMmYna0h03RQpvhO2DbK5XkJ3A9CzNkZFMZLyKyyCtuOiHU2Yk6U8NKXPBqNwzZg955p5It55Lvc+MXXzWiqUYCWFFoLdv/1Rmw5bgde/MEXwUpoJdqLY7ho3qFnVlGGidHY4ygCQ+j7USdlQ0HNsEYi8qVdxI6inWCeuJHQM8yunR2zijSTyWFmY1lipjL1KD7K8MN1iVkutX/vRxfhLR/6JqZr0dCAxERq1ZpyzUSCmXIJNTuzbg8vhxO4pVw0kSprSxXxvHN/iI+85mNoVBvdbUZz3FR4jrsBTw4uu/iveM+LPoRdV+5KrIglLCjQVpYZUcuXMtyMxh5HEXg23pcKKSd9mbYUuoZ9KatWbeCUu9xmtEVsvLRjx9UefgoSadUlmJFa2ziG6pYJeLkMMvVGLGjXfqJ0+iPvjp1bJvBv//1DzFTqGHQF1qaAdTw4xRzqm8pojpfEUtCNx6YnvtUcxtgois1Hv/iRyBdzXRWzfC5eRsEfSy77w1/xoQ9+HU96+WNx+K0PR9IJQkNW5RRl2FAhOyJQUDLjtV+7MVZlmWBQHNIEA1Zif/uLS7HzyK143NMeONIilnDbskJGcLW2tS5jGDKNqrppHLUtkyJoGUWVrdRheaubed+qzL70iffFzs0T+OQ3foO+w+ql64llghVYNxawjYnSQcMN1gvH7zbLeXjxAARaM+gNzto2nv6WfxIx+52Pfbcrz2UGYZQfOwIV2Wqljk+8/+t4xjP+Hne698lIA9wTc9VEUYYN9ciOCDy0GLQW9MsPyCgu00LZ96PIr4T5ELthJ7jNSUfjrve6nSxVjzqyffE/6MWMhazfkycy4BSycPIZEYJW3UGu2kC2wZGolkyT6tRHS2n38sffG3NxRFW96aKY7+Ho1lbV0vUlMSHM2iJc/XxGBhb0IrIrtK2DBiAEJiOjIsGZyWZEzPquL81KruMiV8it+fn4nqL/GO7vQ73aQKlcwBve+zxsMQ1UW+874XDfr9YCZRgZ/lNnRbBYMUPYG3GxBByOMEuvbF+ftX+e2DOf81AVsW0VWf5fENsKer5jMQypMjYnSqhsnURt47iINrsZVWnpp6VoXBKK7SAUX+NYMYefXnAl/vXD3+q+zYBZtbQO0N/L6qsfwM/aqG8aR2XjBJxyoScillgNB41y/qBRsbQZUMSa8RIzxSwbvn7+1V/gnFd9dF02Az6uEQ63iP3jRX/Fv774v1CZrWF8oiS3rcez3U+C+YJGOl6vonSKCtkRgSKWf+x+TzGqmJZ0zA6LN+vyS6/BrU8a8cauRTDZ/N/qhu6zN5qVWHpA57ZMoHJYJGppPZAhAhS11QbsZixs49fFgzkvrQlUf3fq8di5ZRJv/uh31ydmg1i4Nl3x8YqXNwjEFlHfOB5ZIzaNw5UEht5tP6xWBxkLbmFBKoG83xBGcPDf556Pvse6PbMikLjsM8Qi9px3fQVPfObfozweTYiThIaUDBrwe2X7UZQBo9v0qMDjizGYOd+syuZT7pVlJfamv+3F3e97Cp6iIvYQeIAM20TMQCS+YUj1kaK2unkcc6zUbplEY7yI0LJEyHI6lohbemsbTVheEAlPAC99wn1EzL79U+chWCD0DiIIxdcqv+d6kWitNiPB3HBFuPqWJc9biyuvjcmyiNm+NEKxCtxw0SwXRMwuxkIh27IZUMx++s2fWdPTGrQyYDjZdf1uEbFPe/7Dced7nnTgH5gGsFzlP0GEnEQWFzUUZZhQj6zSl6rsWBBIziiFbVrtBHe46wk4/akPUDvBIvDgmLRTFanUUjgWsmi2/Kl+NHmKy+75uUZcnQxgBYDtB3jlo07DtbdMI1dvIpBJeO2SPKpmMnYJrKYaUUUusE14pbyI5dA24Jv06Q6uWm87HvycBWdhNXYZIdsuZvfcsEd+DoJABnx0iskl9iH0x/KkZscRW3DWG5+Ko4/fgbTSN9uPovQZ3aZHhG5HIq0GZovOwIyqsinzZ7V7Yh/z5PsP+uWkAhZmkyhnKGw53tUp5SXeipFec5snpGLKCm5t6ziaWyex43ZH44/VJl517g9xS8YWS4BcNo2hFt+PFoHKpklUYqsAva6suDJxYJAiVry/jodGubTMFLClXx/F7I7jduDKC6/Ch19+9iptBr0dfz0oO8E7/+VTImYXE7G0BNNWkwYGtCinKD1HK7IjxCD3t5zSNB4EyIchGinanX7nq/+nww46wGj735Qc1yMMQ1IO+JoDWXiN2HHcdmzaMoGP/c8v8U9Pvj/GygWkAdoc/EIW7grpAys1/Bx9u6OQyWXwi//5P9z7Mfeav53Ncbzd5fO0LanbcUNZs+lI9btFJmPDzlho1B1JhmiRpYeZo3cXDKTI5TIwTEPu32y6cHwPjUb0u2EQym3tFIo5+H4Ap+12pojkeVLh+nA5QjaGnvZcPivpDIFYQOiTDmDzdtuGy+dpOvHt0e98+WPfxemPOA3jUxVJfOC2IvFizOQ1TVh8LPHJBnyCZT9TRVF6gwpZpS/4nNJk2NgaumiKnzIdYvaMpz8YFv2V2tjVAamSsB2Nsz3nk9/H29/9Vbz2rNMTL2bZPU/7RG2itGJVeKXvHyuzz3jL0/HDz/4Ir3/4v87ffteH3hVPeNXj8bX3/y9+953fzd/+4H98EB7z8LvhQ+//Ov542bXztz/leQ/DvR5wKv7jnz8hHvMWL379k3DSHY7D6573PhGtLf7t3c/Fhk3jePk/vvPAi7n0N3jPua+S1ZE3n3XO/M0Uq+/971dLA+YH3vr5+du3H74Zb3jP8/Cbn/8Bnzn72/O3n3S7o/Gqlz4a3/+fX+J/vvP7+dvvd9fb4LlPuA/O+dLP8ZPfXj5/+6MefEfc7ZRjcdfbHimClTGCkSUjqm3yZIC3u7mcnMIxjYIVf9+m3cSCT1uLiltF6TkqZEeEuFd7oK+hYpgoGiZKQYCKkY7pP6woKauTsOk4RelMzD736X+PD3/8e6kQs5x4RtuEs44s2IWwCvqmr70Bdjb6HrTGsD72JY/Go17wiIMrsrNVvPAlj0Izlz3k+/Oatz3jkIos+fezX7poRZbC9apfXYXpX92Eox93oojWbTs2ye0LOeHkYw663Ygr0/e5463wd//5nAO+aAPIVhp49APuiIfc/9QosYK5upYFJ2vjyU95AJ74pPvOP47r+njRqz6Khz7srjCWyBfO1Ex4Wc60g0SsZauN6LvAii0zfHNZeKwOZ20Eui9RlJ6gR2mlb7AKNG3ayIeOxHG5KWz8Ulb239E3GKbDNL5iFblVmaWY/f0FV+L+97k9kgjHzlI4NRkL1cnqQQd38VwPP/zMj3C/J95Xsmbbob2Al4Mf00A+m4FZPFRIU4guBq0BS91OUZu1bOTzWbELGJax6P35NyrSquDECRKNZjR0ggNgMhYCpkXEdgA3fuuLtcFlRay3HxIPtj0sioz9NeHnMgelZUtWr+cjV60jP1eLGgJzWZngRlEr2b59XpWKWhVT8N1UlFWiQnaUSIDbn0MSpgwLW0MfsymyGCjL4xsmDOZpSjN/eg6VFDsrvVoKpRc86yFiL9m7fxa5bCZRlVmxFDie5Oe2Dz9YGubndv8kkoJusTSEniFL+x4yDQfZGkf9Ukoygo35ubnOBH0XXsNigpReWj8WuITCmuOCpWJrGpIjLA2CeTYH9md1is/iw0hcuoiirBcVsiMCxxOGy+x4+8mcaaMQhKmyGCidRPvEzV6hgSDh5yfSoEOh02GKRssj/f0fXoQ/XX5DomwG85aCJaqbByHvl41K3X8dnKzWjxNlVjtZgZaBE3VHGrb8TAZOMdPVfRstB/e550lyvSRhuGRWbztSGY7vJ6+fE+hqTfHVNkt5qdT62QUV7i7DIwBXSyhmFWWY0LXdERIaQRyInRSLAcXOsEz8GnX4t+SI2gM/J/xgaVLMGTKmdjU88fR7Y/u2jeKZnev2ONs+WAoMJuCx+76Diiz9sGzuavliV4KfZy+hAOQwi/KeaZT3zEgllhPcnFJBBGG3T9BpNXjm0x4YWw6WeE2RIXZVj8tqLYVrsxwZHIrTFYzfPIXS3mnx2fYqotCKjwG6x1WGDRWyIwLPwqOFt2Qs+7YsBkUG0qcsW1ZZfGoQty4zRQfL0LRWLRpanlmK2S997ZdIgqWgMV7q0FIQx26ZZkeWHnpgmVBwiBd2CWRQRA++yhxYwSV5Ctji/jkRj1LFLOREFPYKx/Hw8f/+oVwvTbj212AYIsQ5gY2+WTaije2eQnHfnFRsuw33/fJOkn6SqSirRIXsKFVkjWRUZNstBnNMMdCqbOrx58PhA9nW0nBqwniktXg6W2L2zDP+TjJMq6saGjAgS0EMl+FpK5DmpxVgVuwX3/llue4ERn6x2svKaTeg6M77HsamqyjumxUBy/dL8dcPMeb7Pn72yz/K9XJI+kE3ptBR0OYyyFfqGLtlCuWZKmzm03YJKwRcFbHKEKJCdkRgBSZI2B9cLQbDVvE3kIlPmNIwfZ6VNFb71gLFbCGfxU9+fine9q6v9N1msOqUghg2RPmW3VG+KQceMCu2ffDBcgSWJRezC0KWFcnxuTo2OE1Yvt9XAdsxchIUifeuPST/puU8vIyNQrWOLc06JnwPZhdWrfgXT+OIcEVZCd2qRwiejXdjh9hN1GIwHFC8ujCQRRCL2AQJjiUQAbLOl3m/vzu5757ZtVgKWjBP1Sv0pqlIOvUzFLJrP41hNTc3W0VpzwyyroeGxSzWhAnYtoqxTPrqgThkY1ijmJfx3ptDD1sDB/n1rFxxmEN8DFCUYUOF7AjhxR7GpNGyGJTVYpBqmqzIhmEqqrEkqqSt78De7pl9/4e/dVDof5IsBe0Edu/Calg1pVheaxWW/tDCdFUEcTOXGWg8n21bePTD7ybXi8GiQEhbcA+nd3mmKfvGfBBim+9gwnfXVIyw4/0/TzYVZdjQ+K0RwmOoOJIHD1ZTpo0sXKk6NHT5K5V49Ed60QEzDXAZnNse7QXrESMtMXvLnmkJ7qdvlrf1AsZNBTl71ZaCViWX75cisRM4rYujZ2VqV4fI9CpjDWkE1Tpycw2pcjrFfH8yYFeAU8ke+4jTlvx3w/cRWPaK44DXC/9mVdOShJfNoY9CEGDazKxqP8nZY9z/q5BVhpEkFuiUHsGd2OAPD4tD79Y+w5YdrkZypRMRsAYrskndyg6GDU+hbXalOYnCdce2jbj8il14479/oSc2A9uNuufr4+VVWwqIGfjyfingO3q+rI2//6cHz4+n7dTjKScHHX6mHBRQmK5IFZbTrxhLlQQRS5pNF+943//I9WIYXgA3379aECchziGqzh7mOxjjAJIOq7NcKamzuq3WAmUIUSE7QvCMnGLDSphPtkXNtLDPsFBAkDgvr7IylFkuzKTokM48nXZ3mpNa3Oq47Ths62TXPbNs0jKbLuoTJbhLjHtdCU7Bot+007ioZt3BOa/6qFx3ipexJNPV6qBBTKwE++eQqzakCivV3AQRBAEu+9P1cr2kR7aHNo3lqrM8WdwcOJgMvI7iCy2DQjZJmTWK0j1UyI4QDgwRs5kEhyPNmjamDBtl7qpVzKYKrrByUluaTkLE07mO5qTlPLMUs8tnkHYGrQ8cb0oR65Ry63ocj6NbOyQMAlxx/hVy3TGmCaeQg+Uu/5lyKhcjtSzXk0zY1Jz9LEgsYBV5UE2yDVhiNdgceLCX+c7x39zQhKPVWGVISdYpsNJbDAMNw8AYD0xJPW7EU78yQSjNX1MpEkWjDg+YFdOCyROlBIxC7nQpvNvndS0xe+ElVy87FaojwhC5hivNXc2x4po/UzZghZYlwfu9xmfKQLjENhCEyFVqyM/UZKStW1xbdXnQWEEgItbnUI0BwaLEXGhiLPQkb3a/mRGBu5Bs3HfAQoaiDCNakR0xuKMzUxDltN+00TQMlFIxI0ohFkLMwZKInyRX/Q9p+DLXnie7nJi9yx2Px9R0Be/+4DfWbDMo+D7crI3GRGldVUvLdeFlM2vy1q4WimU+zyFV2SBEfrYapRJk7SgXNsGw2esZT32AXC/a6GXb4jke9L6S3zn6ZrcEUbPsQvgp046QhhNLRVkLSdc0ygg1fC1sbGCFgeNOs2xqUBIPj5MN05Ilz2xKKumthq+1RkatxPhYUaqya/HMZj0XgWmgOlaMKsfr9NhKI9UqxAybvB7/ysetqtmLsPLr5rOwHPcQEZufrYnHN2l+2MVg7NZ973W7ReO3TLe/jV7LYhiyEsIVEYrZQpuYZT8Ef1qsUqsow4Ju3SPa8LWcpyop1EXM2rDDQA7ESnKhnSAMIw921TCluaTTjupBwsYnN5vp2fa10DPbqZi1PE/ismbsjEx5Wg8tW4GbX10FlLFbpz3sbquK32rhlnLRKTO9pBSxMy0Rm5PXkgYaDQeve+On5fogZLsOEWSSVVGuxv70zW1ilte8vZGK8oWirA0VsiMGfVI0/bPWmQYqMDFrZcQn2K0Z7kr3oZWAEWo85NOPx0s+JfYCL5/turVgMTF7lzvdaslw/Xa4nVsNF9ViHg1r/WLJbjpwCln42dU9VrPexDv+6T/lerUwHYEXu+5Eldi5lohNzyGHwy123bT/kCEXlh/I5K31nmD0glqbmM0HvjRgzqmtQBly0rNXUbq6DJUJU/R6M1nUyzlka424W1hJGsyprLIuaxixb89MzckS/Zxcuu9l1Z9i9tEPuxuyGRtf/p9fLlmZZRU2W2uiMVZALb/2hIJ5glBEulMqrPpXwyDELdfdIterhukF5TyKU5W2SuxwHG44HtjLZQfuj11OzNJSsCNwpJGxprYCZchJ5jdR6Sktv1Ra4q0kO3GshGa5gFy1rmI2cURz3Nt9ePU46zINFhY2e9GLmqk3ZUm/dbFdd/7SfjsvInrXeFJ1y56ZRW0GFLEcP9scK6A5Xu5KskjGceHlc6u2FXQDfkbZRlO6+4dFxM7HmCW8UY12glIQwAp9PcgrQ0/y1kaUnkO/FEVHDmFqvFNsyqltHOPgKBGzTVaY0pY9OcS2AiYVtOdUslmPmbITgY+5QQWxh6EMOzDmr1l+5TU914FMlTLZTigjUpvIT83BLeTnf739RI8nU9GN8Q3c9kzeThHMwQomQpMJCLzdQGjGAplV6rbKXctm8OGPf0/E7GvPOh1j5YKI4kytIVXM+kS5O2NPw1CGIDQ2M/Ggv0KSJwWsxNYmSsiuwZqQBLLZDF75kkfL9UK/MVMXkgxXQ2ZoM4CBjYGLPWbmwDasKENGsr+NSk/gDq1imNgYeGikaN/GAwjFLMlXG2iU8xopkwByYYhZw4K/4EDJyJ+x0JdlzoX/1nViscrhBpFI9ZBxPBieL5VOitf2EicF57zotKIQf/o5aTGYD7lvf8mxpjXmr5mnwepcCMv3YDW5hN8SvlFYvjy+ZSAwLbg5RjXxYsAwLBGzn/rcj7Fv/xzGinmxzTArtmsiVryxXAK3xR+74mfHz6lN8MsQBs/HC974VJRdD1alLoJ9XqDzv5fw+zKtgBO7uJ9pTpZhs4rten2J/uomPOm4/UlHHXSb6XnijeVEuCSTD0NplJ01LWwMPXhBFGmoEVzKMJKuPYvSNdiMI4frlATXt6DQEDEbBMhXVMwmAdMI0Vik6sptjNXYidCXrMuuPidHti4QrSLGJEYrFLFFgSrCNGuuXL23QhF9FvNBFwu5j3/9gFTtpGkrhMnqb+AjP+seELgZCkELz3/0PeR1XvSry3DM7Y6BOVnunu9SqrEuapsnDom6YgXadlz57Hgf/rfFzy2uXrdzlyO3AlNz7b8dnQTIeF9bIqjYve9zNG3GFgFc2D8nGbJNnmjSclIqRNmxKROy9XoTL33tJ/C+tz8DhXgiGrex9Qym6AdZBLJCwhNJ8auHJjYFniSKzBrp+hsoSifoVj2i0FrApeAsu81TYi84SMxuGgf2zaqYHTAWD5ocf7mEUGTHdNn3kQl5cDXXJ8w8X5bKOa7VbkZCrF20skoW0Lu4FpFhGHBLeWT2z3EUUleECiurvghei+oighXcIBa3M47YCX594V/x+R9ejFe/6vEobZ6Ax/eRWZ/wzzRdGUcr418l95SeXx9WoylL/Zbjz392/D6xyhjkogp1i3qtidc9733497NfigIzaOVNsfIMmPRf0kc83RTxy8fhcrvdcMVWUOH3M8Yt5pGrNUQwcyhDmmiP3uL2J9PI8kmeRhZK5NYeIyNilniSJhJgk+/Jd5CxhooyTOgWPaL4sb2Ay8JphFUmitlmMYdcVdMMBkUhDOXA2DpoLoSRXLQd5NeQYMAlbg7DKNSaKO+ZRXn3DIr7ZpGpOyIonFIOTrkgQf9svpGhAesQoJxIxe2qp5nFpiGC27O5zAvUtkzgWc99KHZs24B3vPPL8K65GWN7plHaM4N8w4mGgaz2OxqEIlxZEaXYpPd3/JYplG/ZL75VGKYIXFYWaWeIPrvFq9aN+oIMVYPVWEMqsBR0/Pz5OPzvXKWO8V17kKk2MbZvFtlKQz5LPnajXIBJ8Zzi1BGr6cLJc5hDcm0F/J7VDUuSaRaujnDy3kbflXguRRkmeipkc7kcTj/9sfjG1/8HV//1Sjz6UY/q5dMpq6Rq2lKLNVMuZh1WfDTNoO8YcVoBm7qWg1VZVoIWG5+52GAF3m888LAtcLGp2UBptireRC5Nt4SreBS7vLxLDzaFCiuXvYRL+xTjHBrQGC/ByGXxvGc/FNt3bMJvr9ol7812PExUatjoNDAxNYfcXE3yYDsRgvTb8i+TqTYwdtMUitMVaZZk6gc/Pwr2bjdK0h/MKnlzvCSpCxzhWtzPk49p5GaqcpLB5ITMwuECKYE2ETpDvFZlOoFQqDJWccawF/Wks3BRho+JwBvI61OUVFoLnvKUJ+MOp5yCt7zlbfjf//0azCGKYBkG6oimMLWmv6QRHiCrm8YQGpCKkKYZ9I8cgnj4wfKiiEub04aNraEDJ4xyZhdCiwsD3EtyMI4EL8OpGpaFRjGHUp+WczkcIag2RGz2IjKqJWKdcg6NsdL8c7Cx6AXPeghM08DNe2ZQKGRRz+fQNC0J4C9MVeTUwc9n0CzGVdRFKoPZuZpUXfk+KFY5Drbn0VdhKEKbFVemLhDmrCIXTSgrzNSkKkvrAd8/T0oCVqQTTi6Xwdve8BS5ppeYnz2HPCSVYhglFVSW+D6y+a4WmtgQ+DJKWi0GyrDQ073Jxz/+iV4+vLJe2Ahg2hgLmqlr+lo0zYBidq4RzXJPeFfxMEAr6R5GTnWw3cyxImvYkmLQavxiRZfWllLoi0WB1VhpUok9fJ4R9D0yiEvmbPqSjv+Vuv1XCZfZrYYnYi8SsQe/N4pY8v0fXYTLr/gbnnifO8n7d3IZqaSyGstM29K+WXmdDi0Vhaz4Tk0OUpitYHzPDBoTRdTZOLZOAUsB92/vfq5cLweFOe09/N4thA1hftkWQUvLQabpINtwUOH3NeGFDcMwsGlDOVq1cn2JEkvqPjIPP7LxrJBMwPvkQl8sBjdZ2UVPKhUlbSR7T6L0HE59acCSTNk0I2J2wzjqE0U5sNLPpvQOG4E0CXZc1TEMTIvFwBDhWg59bA08bA1clMIAzVjADrzx0DCkSUrsNl203NiMsHL8SGSOHypi2znz8X+H7ds24tzv/hp1r207Ng2pdHLpnukG+bk6yjdPY+KGvZi8YTfGb56Wx57bsrErVVjDNLBh07hcLwUFXmG2KtaF5Z6TgtbhoIdSAXa9ibFbpkTcJr3R67kvOxtupYEgyxOcTKItBVz1WMqrvpjFgPYdRRkGEre+k81m5dKiVCoN9PUMO/RSzRomNgcemgk+Od93/b7O7hiGUXPQXFWqDQ6XOFPM1K4pJDWnksuYnRw4hTCUnQ29lDt9R36vaRpyIrWSXJy5aQb9hBFS5dmaRHG5XeiyzzRcEaF1Nli5ATBVW/F3znjwXfCRc8/DL3fvwrabjljydRYqdYzNVRG6ASpZG/vrHuqzTUkh6IaQe+sbPonXv+npyC9h7eDz+5W62D86ZX8jwMZ9U/D/NoW5iRIcTh1bzNO5L4r92nXZ3/q+DZBG0wFTq3f99iq4k+OoFTvcB3WJud2zK96Hqxo8EZxexlKwEFb566GJycBHzbSkSqsoaSZxQvbFL3ohXvGKsw65/dRT74B6ffH55MPOUUcdBT/oXTUg4/vYWq/A4VjRPk8AWokwCLBz52Zcdd712IcbOv69vO9hwnHEb1m10h0EfszRO5HPF5atjPUTpgkUfA9780XstFa2cGTZ+OM6KPoewiBELvDk9+scELDM34X33bplA2747U2Ywc3oJznfx6TbFC/hWu0NjKXKcZwpE0LsLJxVVkkfMnkUtp24BTf95kbc4v8N+TZfqRWEKHsOGkGA3QZgh/QTMz3iZjmB42dbN224i2XidojjRxW7a7/yZ2T5HVqAHQTY5DRwC6vYq9xv7PJ9aeSjG3omm0XdWnxbOOLwbbjhqpsxc1UV/cZwHFBKvvaiPdhXag5k33jM0TuQy+ZhLpEvXHSj71E5m8eOVe4fyq6DY7J5zOQOTLNLGsccuwOmNbqra8ceewxGlUKhkF4h+4EPfgjnfOSjB1VkL7rwfFx00cWoVNjwMHoEfgYXXnhNT59ji+/IGTonwSSRv/xl16p/JxsG2MQGIvoyOUo0pWKWIvYvf+lcxPca2gLYHLh7BT8e/a/lwMN46GM/m7cMEz4MWbbnVDk2p1T5+yv8Xa69djf6DUXoptCXbWgtTTGcZlZAiBroW1xF5bqNgAMfDAOXmjP440234B8fcneU8jlkPA/FegMZL0AzY2Os6aJWyMMoZJE1DFiBjw2OB59jnXM5ONkMvDUMWmjSnnMpMHmP7Yv6ZMu1Bkr1Buq5tXXy+7QY1Orgmls9n0etkEPQJsZ2X7UbN/ztZkzcbRsmtk+g3wQzVeDKS7DpkSeivG3jQFZjrvn5jfjrX2+CbR/6+XO/xjQQjp9tHw/dKTzJp899l52VXPEkYpkuLrzoAowy558/mu+/XC6nV8g6jiMXpb9ETV8B7JAVpGTt1AzThJ1ZfUWa1Z79YYgg8DAZ+pLSsK5Q/gGRlEpsy4/XitRaSoBm2cAV+CJgaRxoCdgWPKGYMS3YAcVegPoyU7L43u0Bdbg3QgtF30WG9odVCAWKWOZ5cqoZv1f8xNbyDloOxoc94q6o/uD3+MLPLsDrX/AIHOYHYtVgjFa27sIpZtEYP3S0LT2oVt0Vf6dEbxXyq5ocxoEIOAc4/h7HHxiI0HrspovynhlJIlhzY2UQIj9bRX62jsCIoq0aE6WDHm9m380iYjcduQn9Jjcd2Sk2nXQE3IkiBsEUblwyMYQRaxw7uxYRS7gvLISRV3aPlW4LljLapO+orvQExijNmiaKKc2UXc4DzJ39XiNqaOsky1RZGka1ccABTwoWYochJn0Ph/kONoQeHCPKmG0Xse0H0WkReQZyCf2bNOJmtvwqGiEp4rmdTRkWZky2xK0fyzTx/Gf+A3ZsmcQHP/pdhBSxzLtlskLOloEEizWPtRqseMLB+K6xfdMypEAyUTsgX8jiPee+Sq4Pgu+xwi0gXF86iGlEQxnKOUkcsesO8tPMDO5tjm9HBCEMJzqVaK8SJ2UEbSYMsc/MrDtCix71CQ4dSeh3UFEGLmTvcY+7yyAEXsi73/Wf8t///u9v6+XTKmuE0S0uDFlOHSZYTZuxMrIEJ80Ra5mWpMg42gAGKgssBbQKjAd+NMAg9KS6xmqkt8LuhSdPU6Yt9dhEbnPM3RSrTbjy0JAQKAaBCLJ9pi3DRrpJzvXwqkfdHc9/1N3hs/Gq0gBsE80FFczFYDwXJ3hRVcuQgn0zyLLausJ7okd5at+sXLfDYQ0y0KELjZRMOuBQCFZjDSOU8cP5mSoMGT88ODjilyKPOI6bKBHLk6W9ZgaVLnh26d8mY5pgoKSYnq7Z/frXv8GJt73dIbd7CY9dGVXYvcolX8YisZo2bHBsI3fcGwJX8kzrYfSz0hms1rM7mlFZ7ekFk4zQgo8m/aCrHKxRMyxOTMVGaSwKEtdB3eTQEFjiC+ZrXc5KUDfN+ZPBbmK5HgpzNalgbt2xCX/643X41HkX4jUveRSKnaYTsCGLPtcsM3JdFPfOwJbxsgWZmLaUR/bNZ50jVdl2awErp0bYvYERFOIUszwZoJC1a03kLRPGgLQshzbAD+aHOyRNxLISK9aeLlE3DJSDANNm8r5/itIJPd1quQTWbDYPufh+ApaOlEWhp49CZViX4FkF3G1mMWXYIj6KoVZnOyGDAF6rGhtXYVs2Ai5LcsgBhexaYOPYfsuWs+rE2QyYOBA3ax3y2pjewIYZRAJ/ysx0XcSy6axAO4DnR3aCuoOTjtuOrds24K0f/AbmKqtMcqGgzWfhFnLIVuoo7Z1BttroeIWCy/4cgcsqbzcRMTtRkgEPZhhPKGuuXDXuBZl6U0RskqZ4ceBBqxLb7YZc9kTQ5FPkSpWipBA9/VIOghVKLvdS5PEgOozQNztlZXCzFXX7jsEXf6eyFKFM3mK1lZ8Xq7AcZLAJLtzYB0uv63qgmOWSPHdISfPrcXvhCR4rry2LAf+bVWh+HvssLvN2xw/bDr9/ZddF1vVEVGVqDmBbcDaN43nPfqgMTXj7e74Gx139CherqTItDAaK+2ZRnKpI5XclWM2lmA26kFO7mJitT5TRGC9IxNt4rS5ji/uJ3XQQxL7jRET2hSHG5PtgyAl4Nyux7bB4MR4GK1toFCWBJC61QBk89DeWZDk16rweVuoUZqYpXbtMNaAVkHFJiTiAJQjGZLGphGKTVdiJOI1grgsCth0u3bOxZjLwUAoC1BL0d2BrEwU7LQat98wqbK0HArZFEQEKgQ/XtpCpu/DyGfHE+rYlvmI2gF1y2bXIrkNU0m5g2JZUZyniuMTP0bet78BBjV5BiEy10dPxzy3PLCeF8QRqg9uE3afmL1oKDDdAffM4goyFfCGyU/B6EC5ZGczhu7gOBmZNRmQZPW1snJAVqkC2c0VJE1qRVQ4hiCuWbOxJZBNOF9Hq7MoNXtxJ1GFiU9jdKuxS1g9WZtloU0pShcgw5LXRp8iGwV5VYVtwhYBxeHz3Gc+XZq36ZFlEbAvLMnHHU47Fvv1z+M8P/O/qbQYrVGfpi33vf7963h9rO640QXXbVnAIpgFnrIjpsRJsP8TY1Jx4VvtlKXBi8e7Hz9m67ieW6yNfd1C1M9htZnoqYgXDAMMsxrQRVkkhKmSVRWEFbsq0pCo0rBaDhdVZ9c4uJBp/6TOVIPTnvbBuj3cbjOZiZBorvlnfk+D2gcJEgjAAE65usHKY5Uz7Hn4luKw+5nsoxsFltVIejXFGbC3+uU9OlJDPZfH2d391zWK2VZ1t985aszX88aKr5oUcG7GEPsVRNbNZ7M4XYHk+yvvnYPawSXgxS4EbpxW0rvtCGIpn2XJcVMaLmMrk5GS7X/t8bueMj1OUNKFCVlkS5mDOxRaDUWCx6uzARdQAKQe+NDjljbCnVdglVwVMG7PZvCyj02owiBMqVkbLTGQwDOyxMrjFzIpXmAf8Xr0eNiBuDj3UYGEmk0WD0VnLiEdWZmkzEM/sOsVse3XWunkKH3jbF+AwpcAPkG04SyYc9IqmZWF24xhCyZl1kekgNmy1UCibboDGWEksBYOCVVjm83q5LCpbJ1FjPnAf7TXc/7HpK3ENl4qyAipklSUZJYvBYtXZ/QY76dls4UtzzyhBMbUpYE6BgRlWIAewq+BBvG5H+b8tq0G/TiysMBoByr//fsOSbvFG7J/mIAf6ebs+PESyaH1sDByJweNzUsh19HpjMXu3O98a2ez6xSarsxLXxW1hpgKr4cJ0/IOsDf3CyWXhStJCJppmVm2KzaEbUKBbDQf1DbE3eABwQAWr4KzC1jaOobJlQirjg8BNYLOloqyEClllWUbNYtBenZiW6mwWM4YlKQ4UNonxbPYQNhjt9F2JktotPtDBNl05sW+W+Zmt6myvTiwY/UTBTC8sK6+3WMzsjE7m2k/wps0owaFbB307TkFglFfdsMUX6a3yY6eYfeRD7yqTwD7/lV+sqzJLWuNs2eA1fsuUjLztl61gIX4+A9sL0BwvorYhypwV8beGxIaDmtfqTRnf2yyxCn0wRlwNbV13nSCyEWRqDfFAzx02icYERw0P7rAs27SMKh/+/ZwyPKiQVTqyGMzAlniWUZuIJSIqFrTs2qeg52Sw4RS0oXTlH+k70sV8i82ml2QkB4RxnisFXsUwkaPw6+KJhQjYIEABTOowsdvKYJpicoldZGvELllPlbi98ksbD7c3Rn2tJzmDwosNYOu1GfBxdm7fKFO38nM15Co1ZFo+2T7DcbvM0qVPl41glS2T0vzGimqu0liTfzZbb8LN56JhDIsI9BwnqLVdd7MCSwGdq9ZlOlrlsA2obp6Alx9MFbYdmexoqL1ASRcqZJUVYQVqn2VLdTbKNBw92LG+x7TFP8sJTqyecfl9WKrUjNNi7NVOv4m6aeAmOxO3GiULaQSzMiI0K7Ak37awDkHLxir6XQvwUTMjAUtvrtPBrlFG7Bo2cmGUK7t6AcvGmgOVX0bBWfE0sfXQLc9sPp/Fv7/xqTI9zM8wGs1CfqqCLMfjDmC752hejtY1XR8Bx/OOF1GloB0vwKSgnatHDWkLRuouBkfsBhkTjQ2l+crzQjzXP+h6vcgwiUpdBkowh7eydQMqWyfgtsWdDRzDkI+P3ylFSQsqZJWOBcReixWq7i2npg56NsU/m8HNZlaW4coIoiloKRa0rAZuDDxsCV2J2aKYW6oSmRSacbIBX2sDlgxpYEW14+poyPG6gZyQNAxDHmevaUc+2FUIySors3G394pimsvhcSU5G1d+b5mv/BpyYiTOzy5ompaYPf7Y7Zieqa7pMTzPx0//7zKEDUeEIgVXyHzVmQpyczWpLPYT+nMpBturwhTYjcmyVGirm8ZE4FLssilMbAeL/E1a/tr6xNiyzWutUerrGanOijFfLz8v2/XFxjC3bQNmt06KnQBm8r5n3K/R/z0sJ+nK8KMDEZSOqcdidrvvwEMg4nYU4TI3m33qYdSANB56YrtwQkOqaf3sNF4v9GRGAwh8qULuj4VhKmC2Kww0TEMig+jtLdIeEfgyoc6BIasJ7cj4UwSy4+OMeVZUuV2vOY3BYFi9DSuANAVWWdte8JysvrLyyls56nY/x/ly7G3byUI+np7GE4luQTH79Kc8QP77dxdciRNvczjGJJGgM1zXwyc+/SPc+zaHz78nikl+VvnZuohbdvovVdHsBUwVoCWguSDJgUMamLbgFPPIOC7smoNM05HGMPn3rAXPtmH7fjT0YGMZbvuwhy5CsW07HgzfR2ia8HMZ+ZzYrOb3OoO3C/CkiieGGYTyHVKUpKNCVlkV9CnuDW1sDV3MhUbfMg6TiPg2Y0HLilw59KTahiAKz6eYSrqI3RB4MgaUdUw2MNF/mjqM6ASiaZmYC0OptNLrS3FoBoFso1wulZ2dQUuAIVO5KGC70cjG7UD8sgEkb7cSmpI5y+ortRbredweWL1tLPGcuXgUa9CDTSYMQ/z+wqvw9W//Fq896/RViVliOd5B07xY9WSHf4YiMYQs8fdy2lc7XoajepsiVplkcAimESUc5LNo+AFs14PZdJFrOCjMsirqorZhTBqqGHfls7FqHQ1sknrgBzBdTyZxkdC2ZaSwWxyDl6MtY32e534jMVxh1PDlpOdlKyOMClll1fCgbQdcjvYxs0gFatTgjp/h/RQwTDcocrSkDBBgRcOMPI8J+4yysZ0gG3DkaogqM4OleSlZr3Mtfws25VVDUyLjxiRtwkM2bmShNYSjb1l1ktz39b7dMJT6Ne0ZrLZyeAQtGhyaUDFNsUAwg5ZDfZeCk8IK/P0effZs2nreM/4eH/7498Qzu1oxy5zVMI7iOkgwUszWm7IE3Zgo9UfMmgyEC0U4YoUmLIpV18rK/VgVzdo2GhNFuPkMMg1PrAfRkIcDS+iyL4svvhNZGLKOAyvOrzUW+G/5HOLXLeYQ5LJSseYl6EIE2qBhRVZR0kD6v21K3+HOfr+ZkUPzROBjVsXs/OdSh4U6Q9xDS5a6KaRYpfND/pt5yFL3IMjFIjbDKqAR5eZOJyBmq1tYcVXWNELMmhZ2GTmpivLtZUJ2ZIdiL6DXm/eNDteG2ClZEY2G0B4KPx1ZWA+pc1oqOJSmOIpY5u0yIowWA1boGdvWyd+bJxWs3tZ66EtueWYpZv9w2bW452knrvg7pmni5BOOEGtEsJiXk2K2EInZfAg0JvsjZtkola07UWRWB9VUu+nC9ALM7tiE5lgk4Bv863k+LFoN/MgPSqsEuN04nlhQMkFZ7muNlWVsLW0CgW0jNI1IwJqIrAN8zwn0uq4Hrg/oYAQlLaiQVdZc+WJgO3f4FGoqZg+G/mHXMlGJRRXFDYUTPy9WaMV7NgBRmz1IxLJCyZMSO5EJBatCxGkkUOnhrJrWgaX8ts+5Hv8nhQurqLxQqPFmLqdShh2cGnsALhzzRIVTzgKxCESJHmwdkgpv/DxzoSWWjUl6ZkNzRfuN/C1YF+/xn4Bi9oXPfohUaG+6ZQrlUn7Zymwul8E/P+9hKExV4CwlGFuV2VoT+en+iFlGcbGSansevBU8p2y04vJ/bfO4NFq1E9r0zS79Ws2pilzPsNq8IRK1owJPzOiT5fdk1FfclOSjQlZZM348tpOn7ypmFyeIG8NqoTnfkMQqLWPM2GDPDmEuKffjYLFQxDbmRWw6q0k8yHL5M/KihlGSAWzUOaxghROFMP7cD04JWETUsEy7yr9NEH+u/PtOhh6aoSn5sEtBW0G/TiRa4f7n/fhi/OXKXcvaDNjs9fVv/w5PvOttVnrQWMw6yM9Ue24zCC0DBk8KGYu1lJDliU2tKRVTZrRKQsAqaU8tSOc3ZO1we+TKhVhm0n6Sqww9o/b9VLqMF4tZekTZua+RLcs0JDGqycrgJjOLm82MjCGlrmAEFCu2shzeo8ibdk9sZCcwZFpW2kSsOV/h9lBiTdTgwA5LPlNeZpjHStHYrRODNT5OZL+xscfMithmtNZiMC8h06ro9pEnn3GfFXNmGb/11fMuhMMl95WgmC1kpVKam63B8Hob0cfPl9aARV+KH8qQBPpimdO6FhE76vjxtpmuvYMyquh2qnRFzDKHk8HuEz0UY8P0edVMS6KubqSotXLYZ1gykpSTw5h+wEDybo2JpJDa0BKxMgI1ymBNi4jl50AhSPHKyVv8nPYaNm6yIvHKz5GfZ+ISNNgEaEbDDvja2AC4MNdU7A1h/yqyiw1N+J9v/nbZ+9IT2hEtzyxtBnPVnubMMvIr23QPfQlegGytjmY5j8qmcXi53kRsDTs8UTBjT7iiJB21FihdtRkwgWZj6GEuZPxUOoTSIOEydJ3d9JaJmdjnmYuFG5stipyy47ny31ziW22zWGtiFy0NPCTVTDvxnlg2YLUEXsl1xbNKrystGkwAGJS/eK1EQzQMbAzcOJ7rQBNY62Sl1/7Y5cSsz2EHrodG0z3YZtAS3av5rGPPLPNbA8tEc6zYk79VYEXDEXhp2RiY3Wo1HdQmy2iOl6Qhaz1YljV/PYpyjg2N9PQneFehKIIKWaW7YpZTigIDmwIPTixAlA6JRRqzG+dgSfMRRe12OytCJ9/WZc+GJi5HLy9uQ0wEnlx4Py7BzyYsnWBetMo7isYSUGSzak27yv58ATda2dSfFNHuQJuBy79H6MENo0a0Qb8rillevnvehfi/X//pIM+sbZp44J1uBTOzSr+racDLZ2VkbEi/crn7S/tMUbB8L7IyBfTDNvhmUN08KdXYbojnTBw5xusDs8RGhyBkVXYUJbySNga9H1WGjJY3kMupMk8+DnpXVg/FG7vvZ3J5sSBQ0HE5fa+ZlUEMIm6lccybH8aQa1k7whBjgY+tckJhYJ+MQh2giGXeqkRfMdfVl9fL182JV4y8omjl+7rJymGXlcUuMyuvuW5nUi9i20/06EvebWalykVftM3vRwK0woPvf4dDPLM528ILH3kasmuYRsVs1SCXkdGs7SNlu5ony5MgenIrdbE0zG2diOK1ulQBdmPrQut69Ii+s4qSdIbjCKEki3hs582xCBkPfG0CWyfSZW+YskxN3yVHBS8nbrf4Ho53G9LdLSNYQ0i2Kau6HBTAJe2W4F3fC4seh4+XjR+ftgiOiaVQo1jldRnBkqL1xli08n3xtYpwTZF1YFVwGpxpSbMfPeXjUpHubWPUaj2zH/rod+U2p+niQ//7azSWaKpaiShfleNsq7DiBICuwSpsvSlTx2qbxlHZMtF1P6zv+wddjxo86eVKiaIkHbUWKD2DnsabLQObfC6n+qgg+WNb08TCCCnaEYy4ErvBqOP6TBa3mNGyvIyclC98lJUq13F+qmT7tx5zfjzAwYXC9p85W+nA/aOLHw8SoFzxEDWu8Xlb/xZZIQ7OWx1leFKy1zQwYbnY6IcoBTzhWL0Huhdidt/+uXkh+8OLrsJTH3Xamh/Ty2Vgc2DCdA31LmXMWo4Lq+nLGNrqpnGJ+1K6j9pjlbSgQlbpuTeQiQZuAGwIOOEKElSvYqY3sDP+sMDFlJHBLjvXduJwQEDIFKO2eB25bvX1tO6zyGOHC65bU7BkMEB8reHpq0AGUrA6bUgWbjkMWLNGY4CT8ihmt26ZwKV/ug6f/9LPuvKY9MsyySBXMVEfL3U0jWsx2NiVabjwbRP1jSWZUBGswfagKMpwoUJW6dsUMB60mWU6GQZane0yVlyJLcOXpeubDHvJkwWKJC6WHpRcoEXSgVZnK4xkQ/Q3LIUBPISoDtCfeNvbHIF8vktL9Qabv3LIVBtiN5Bc106EOj3Vvo98rYHivjmJAXOKWbjFgqQW5Oo1ZGoNSUfoBaYTWQqKjo+gyqG2/SVXb6Lge+u3/6wD3S0oaUCFrNIfDANViU8yMRm4Wp3tIsyczdDuCsjnu28ZEaskFE5ag4GmaUhWbkvQZunPHICQueqam3DdDXtw9xOPQKYLdgBO4+KAgvxMTaZtMaJrJXJNB2+4/EKAlwHygqe8faDPf5fxW6E5oOdWh6ySBlTIKn3FW7Q6y7gl7TtcLZKzGgYiXmcMQ2KdpowDGaVK+nyIrJbXYKFhmshaAbZx5n3dke58P2uLCOwHR+7cjLOe8zDcfcsE3C6Nm6U/1gxC5Cs1BFkLvm0vaSGwmq7YXv75hFOx5fSTMHGrbYecnDGtgB5ZZ4kRu+uF1d7nnvFWnPOl18Mt9n862O6rbsFVn74U1Stv1gO1oiyDClll4NXZ8SCQqmLUsa4irBMbARMIyJRpSQrAlsATC4eeEKQLTlfLiNHjYHgywhSK/dk8tkyUULYt2HVHtJyXsRFkerPr/sNl1+L3F12FZz71gbj9iUci3Dfb1a4fL2uLxSBbaaDOJq3W953pDY4L0/URWrQfFDA9UcLNuSqK4yUUFxkza/q+iNhej6CliB3EmNtmIYe6NbjVlYUNn4qSVLQMpgy2Osv4JTuLWdNCIYyWVHXE7eLwc2GsFauwjKpiM9ceKysNXhS20kSnpG+m/TJqgYKWgqa6ZUIuzWIeph9INdJmvmkQdlXEvv+cb+OkE448MG1MNFR35UzLL5upO1EDV60pk8BgmqhvKKOydRL1jWU0M5kVRZw2F/YOfvJJGp6iKEuhFVll4HDCUcPMSJPSuB/lj/L4TGGmB6ooZYCDJfhlZfWVE7pqcfJDMfAxGfjSLKS+2PThxjPtO2uYysql6fqwmw5yIgabEp/GAQR+xl7zWNaWiH3W0x6E0+5ya7lNbAyGASMIEXbrHCkMYQQBLN/H+C1TqGwel+qrU8rBy2bFS9vp41BqdXx/ZdXwk9UVMiUNqJBVkkHsDaxZJuZCC5OBh/EwkLguMxh8YPygLAQyYMDzohgz0z5I3PPfOQqYn45aCtIJK15he5BvJ7+TseBkCnCKediuJ0MBmNXKCi2/K+ziZzqAZLZ2uCxdKuXx7H98EO5250jEEm5nFLM8kVpPTZbC1fRDmG40UpavqzFWhOkFcAtZVDeOrTqSS8S1ZSLQVYiewXMXrcgqaUCFrJJI/ywrjsXYapALfEwEPhpcZh2BQP1oQlYkHvhZ7MsXZYLXwiauDYErn9G0HsxTC33N8xXG1W7XpiEDB3hplvOwXB+W60ljGEWt3eT4iRCBaUaiL760Pw8rsdVaE3e/621w3DHbDnl8ZgWzQWs1otWgaOU1T0ClmmsCtimv0c9mRMjywvtl6g2Z0LVaD6rlB9EYXFvtNL2C1pLRnGmmpA0VskoiYTWIIo4VyD2FEvaaNsZDHxvgoxkYYkcYpu58Vqo4WjaHEM3QxH7TRo0jW2FgeyZzyHtVS8Fw4MZ5vswFWJdoYDZw1pYLRSG9p/TSynVctbU8H3aDs+Cik6SL/vI3fOBTP8Bzzryf3IePwaxWTmGLHjOyFxj8t1jMcjtlNVTssxSq8c/io+X/U7QyaitjwcvkENg2QgrOWEy3Q1sAm9Y4wlZe+yoa2Pi+nEJWfLVKD+CJNP+eQ7SPVYYXFbJKsjEMOJaN/VYGM6EtFcjxwJMpSDxwugbgwDxQ2UoRtAZkEUgGLGFqw37xv3LE69LvRy0FwwNrpvxbZ8Lw4AEV66RV9UQunnzFaW4UtrG4veySq/HBT/0Az37qA3G3Ox4XVV19VlHjjTHOrjUdT5qx5mMLxItNwWrAZ4XXtqJqL4UqL0uI1iXffy6DbKWO3FwdtQ3ljqvShu/Dz3VpYIOypK1AK7JKGlAhq6QGilU2O80ZJgoIkQsD6eIvMLonDKS65RgG3ATbD/g6c2GITCxiGEE2bZpw2LHd4WjSicBTS8FQDUIwMRH6vR2jZBgHidtrdk/jaS94BE6+50mo8N+DyA4gFdbwgJDNlvIIpuz5rFbxzcaV2259x9xCXsQsG9mcDgYltF4bfcBKb7AQwhdrQTL3o4rSjgpZJZ0z6sE59SamDUuW4zmrvhj6KAQBikYAFmy9uOuWgnEQNgTGZdmgyI6ueex3QlOGQew1TRGxq/X8UrzTL1zVlIKhgduBafSn9vWnS67G1m0b8PePvsehfljzUGHoBiH8an1Vy/6rRSwGtiWRYm5u5eQCWiRoSWAmrdIbuL9yU7rSpYweuidQ0k3cANY0gDlYsM2oUpsJOWSBS/cUuBSSjPSiqI0qu5QNdPqJfFjPzppLtrIUF4rPkcv+PAjwIeX5+PpEcJuSPCAV43U0Z9FWwcen/UAZDpqyihBNautl3NEfL/orznnXV/D0Fz8Kmw/b0NHvSBXXZGNWNKigV4jFII4Tc8rLN37R79ss5RFqRbZn0Ooyo/5jJSWokFWGCqnAisizMB1XRTNxVbQlbvkzd9SR+AwlkJ4dutLcED/OYn3aLYkhjS5tzeYtL5mE1xuGiFaKVTe2OXSrGswoLk5Bq2mVZKhgdBpPdvj39eR0qHci9mnPfzhOvdsJHf8eUwFYIaW31u+hkBXrAxu/qjW4+ax4bZe8axDAK3RgQVDWDFeP1nPCrSj9RIWsMtQEbRXblrhl5zV30SJiY7Hb+pkVVd6VIveQx5J+76ii2y5emeNKLxmve9blG4ZSjeUriIS6MkwwoWNMGhjXEMO1AmEY4sff+Z2I2Dvf86TV/TJPDLMZicjqtfmBVoFchXFcDTTHiovehxFjTELgmF6lN0jWb7yapChpQPcGyshBscmD8nwjQwr210W0vLFaJRlGGCfHaj693k4XN8jr/nojDj/qMLzwdU+EucqhAy0YjWVUGug5/F7mM5FXNr94xZUTzerjRQTqj+2tP7bVNKsoKUCPioqSdMIQE/QotgfoK0MFq19zMJGTun737ATvesOncfWVu9YsYkk0IexAWkAvYaXVdH0ZlLAQDlDgl0BtBb0lE59M6b5GSQsqZBUl4RR85uZqNXbYqZuWWFZob+mmJ/ZWJx65rsdiYgG9ssyf7QesyuYrDWTcg58v03TgFnLioVV6B6MBq4skWChKUlEhqygJ96uV3cifOEyTzJRDYcMXc5JpI1kPTtPFp8/+1to8sYvA4QaMxaI/tR/4tg3D85FvMmPkQDWWyQnN8WJiM6KHAfYLBPG2qChpQT2yivL/27sT+LjKev/j33POzGRPoQtLC6UbyA4tCFyhKCoIsomAyhURwQUFN4qifxcQUK8rAgUERFkUEOFe0SvIchUQEGlpkb1QdmjpkqRL1lnO+b9+TxaaLiFpM5k5cz5vHZJMk8xM5syc73nO7/k9ZazOOi3k82pnx5IIrX6g+kKhuxXXRvz8ypZWjdq8XudceJpqBrO4wCDZYgVWuzpS7PaquzqVCbvDc7qzS9na6u5laVE0bons3v7WQExw2AWUqyhSQ1hwtWqseZ6sUdnqjegRYOUE53z5MjUvXzmsIfat8oJgxMoL3G1FkarCgmv9ZUvnuk4GjMYWvT621RvcCoNAuSDIAmXKluGtiwrqol4tUVb5gesta31lh1oTe+LnDtfosaOG/T6F6cB1LwhyGzNOvHFyqcDVh1e3WzuuGuUYjS16GZPpYiEExAxBFihTtWF3g7CQHUuiWIht8VJKW0vZQUz8Wr6kRVf2TOwajprYDbGJVl5h+LoqvJ18EKg2n5MXSR2NdYzGjlBZQSexADFDjSxQhlI9ZQWdnOJLJDu9m/Z8TSkMPAJayBfccrPf/NGp2mrC2KLeJ1tG1p3yz+XdKlzFZqUFtjxJ6Hvy0syiL7aqKNJS65zBew5ihhFZoAzVRgVVeaGyTPJKJlvq2A/c85/pym6wnOD8s65ULpsveog1FmKztVVKZXMjUh9elc1pZTrj/hbpzvX/DTA8Um55ZM+1gAPihiALlBk7ndwYFpg5nHC21v3KTMaNTAZdufXWxB5x/IFKj+AqV7YYQeT5RS8xyLRn1ZVJuSAb+Z5SHV1Fvb2kq7FJXr6tLkckQPyw1QJlpsZabkUhtWpQZ5BSa0OdUtl8X8eAhc++1rfYQTFrYtcnn0kpX50u6qhsqjOrMO2rtbZaoee7SV/VbV3ysyM30SxpB84WBFo9RmMRTyMWZFMpynGBwbBOBTbFhxY4MB21VeoYVat0R9aNhG47aUud9rXjRzzEOp7n+rm6EdkiLFmb6sp2T+7arF75nn2Gtf7y8nllKC8oimqF6vB8dwHiqOhb7kknfULzHp2rl15cqH89/JCOOurIYt8kEFuZKFRDaDsW+jiih+e5YDf3hUW69qI/qioVaOc9ppTsz2OTvgpVaaXWKncYlhBbiNSxef06y9AW0mll2jqlcOS6JiSC1SIr0ko/4MAZsVXUIPuBQw7Red87V9/69rc1ddoOuviS2Zp9ycXaa68ZxbxZILasd2hakWvBBPR6du5zuupnt2jH/XZUVXt2xBYm2NCStV31NW6hAoXRsJUT9IZYm1C2vpIG+570MIfnpKuNQrUroKwAsVbUveWnP3Oq7rzrLt1xx1+VzWb1u9/doMf+/W+desopxbxZILbqwlDsqrGml556Wdeec50+8rWPaJcj9lXHZt1lBmtPABtJ2Zoq5arSSm+go8KgRZHS7V2yhskdoxuUrate//f5doYiUjASHRMSVBtr494tfoqWW4i1ogbZGdOn618P/6vfdQ899E/NmDG9mDcLxFI6Ct2IbBdlBVhDTUONPnb2RzX9vXtKvq+OzRvUNrZRQb6gdKlm8/uesg018kJbPnYjT/eHkaraulTIBGof3bjekdh1ygvas0WpzU2iuih0/YrbOPuDmCvaDKz6+nrV1NSoqam53/VNTU0aO3bDPQ8zmYy79KqrqyvWXQTKriF5xgvV7jExslcUhsrnkjkKt6prpV5d5mu6ttWY8WO05Lk3+/27TX6qX92mVK6gzmprUzXC5ShhpIZVrcpkV6hrrZrWt2NL3aazBTXXZtReV6NC67odCVYvXeU+trzR4j5aYM505bSiuV35EViQoarnIGHpwiXqqhk4ZBdD06tNRfvd1tLNtpYVforaWMRe0d8N/LXeXO3raIAj6i+ecbpmzTpzneunT99THR0dSqLJkyYqySZNnlDquzAiNu9sV10up/Z0ut/1200cr6SG2AkTxqqjo11Js2RFk55b+Jymj99BTXe/pia9tt7vS4WhGvJZ1eXzru9sNhjZFkpVhYJGZ7vUFXiuVdZgTmdXF0KFntSaSqkjSA94WnvypPF66f5FatEi93V9Pqfmqiq1Bf1fI8VQ07Oq2sLrn1BHUJqDy2nTJqqmplbeMB+k1OZy6kil1FBdW9ZL/06eMl5+kMwDWTNlymQlVU1NzaC/t2ivztbWVncZM3ZMv+vHjhmjJUuXbvDnLpl9qa648qp+I7Lz583V/PmPud+XVPPmP6okmzfvJVWyIIq0baFLNoVnfat5PfHEi0oaG4m1EDvm4G01ZmL/95FK9uKTL2nuFffpwKNnKvVGSk8//YqCgYKULWesUJuFBWWiSO2ep/xIhZMo0mZRwV1W2xjfhm63Z3a83b82L9AqPxhUCU1VplovvLC43+SkTvlaMgJB1u7zOxu3V9vzb5Ys7FmIffKpl4f1d1ZFoYJIWpzKqLPMywoCP5f4fd/cucnc99fX1w/6e4t6mDlnzlzNPGB//epXV/ddd+CBMzXnkTkb/BmbFGYXIEls52I7+ZVlvmMpBQuxW+6wlZJiwePP66Nnf1Tjt9lKz139mAuxqbVG6ddm56ryrnVbQQ1RwZ027pCvwggEsI4oUl2YU72i9QajtAux3cstr/ACtXu+QnmD2vn4KV+p1FuPvaBQdZFUE6SUG4HHZsUFpSz0Ge6RWBsRr1GoN/102YdYYLCKuiVfdvnlOuigg/TJT56k8eO31le+8mXtsMMOuvKqt0ZcAXQHWTdlpoxP86G4FsxZoGWvL9OBx83sntg1RFZa0Byk9WaQ0SovcIHFQq1NIiwmC8s2wmqjfHZmwdiiBlbzXW+h2pOavEBL/bRr82QhdqNvS77SXvdBH4auPgq1Simt8qnDR+UoapB98MGHdNrnv6CP/+cJuuuuO/W+971XJ33yZD3zzLPFvFkgVmyUpCEKR+50MMrOs488q2u+e60Wv9h/QtfG6PJ8NfkpLQoyavECBZ7UGBVUGxWUKlIAtNHfVfLciHBdoaBaFZS3AOuntcQCrJ9WYRMC7JrCyFN1VLo+unE+WLbnoCVgghcqS9EPy26//Q53AbB+3bWDIW1wEhxie/vE7n7gbsPzSz1PXfLUFfhabaeTo9AFWQsztZFUiOQOnPLyNr78IIrcDiSlyC3iEfmeclH372wOUuqSr2iYwuuarLa2VqFWRNGIlE5UAutSQEkBKhXnF4ASsxpCm2s+0OxtVCbr4PLgbf90IXZjygkGwwLrai/QagVKRaE75W/9iqsV9kz8ieTGaSPP/u9G7ezr3rFbr+8SyY96TuN59jM2qUwuuK5SoGyQ0eIo1Ngw70ZNoyJtzzl57n5b5SzjsoPQc8bHFj6gpACViCALlJiNxrJDTp5Xn3lV46eN1ykXnCxvhA5ibOljC59tdujkRlS7R1NTFlCjsGeENXQHVr33yAKtVdnmFXQHV89zda69I7r9SmI8X748jQuzKkSbMNo7IM+FaVcmwcHf27IQa4seNPtpesaiIhFkgVLqOe1LfWwyywk+/cNTNXXPqaW5E71B1H1u/1mrB+2a9bRDCIyrPV8pL6XRUUGtkV+UMw0WrLsnsTHzfiC1odUqe1oepHmPQcXiXQAoITcCFkVKbsvvZNfElizEDoYF0N7LEH9upZ3G9gI36asYS8rayLCVRmDgMz02Or7MT7sJgEClYusGSshO62YUuZExVL5cNqdbfn5rUWtiy4GNwlpNpvUqtZZPxaiTtQPAYnVhiDurha6JIi33A7X5I7vaGzDSKC0ASsh2xC7CUutX8VYuX6lRY0fpa785S1U1Vap0dkrb2m+NU071YUGtwxiobEKazcK3HVj3QrJY8z2lPoq0zE+5kXGg0jEiC5RQVVRgoldCygl+dNJP1PxmcyJCbC9bzWu5n1bW91QXFooz4Qv9Q6wKWu6n1GwhlgNkJABBFigV1wYpYhJGQmpijz/rOI3earSSpqsnzOZ8z43MDv+EL/SWE9QpVJNHiEWyEGSBEmGiV+VrWdKia8+5vuJrYgcbZrO9YXYYRlKZ8PUWC/RWTrDcS7m/c7F6+ALliAIaoMQTvWx5T1SefC6vzbfcXGde9RWN22acks7CrM2gH2M1s1FB7VGwSa25bMKXLSZip9OT3L7O2vfZ32Gpn3IT7CgnQNKwBwVKxFZU8jyauldqOcFPTvmZcl05QuxaNbPL/IxavZTqbbLWJozM2qIMtqBDYufkWz1sWLBF1vRmkCHEIrEIskCJBIrcUp6oxJrY63Xopz6gdJUtpIp1uxmk1OQFqlbB9TvdGNbvw149ft9iusnhRZEao9AdGLyZymi1dYRI8Kg0ko3SAqBE/N417lExXn7q5Z6a2OMTXRP7dqykYIWfUiHyNTrMKWWlBjauMsQwZmc0fHsRJSjD2Sh2Q1TQSquHDVLKsdgBEo4gC5TsxUeQrTQTpk3Qpy44WTvstX2p70r58zyt9gI3QrtZmFNjVFBbFKgwhDBrZzR8O7eeBFGkuih0pRTd7bXSRVn+F4gbSguAErEJGgX2QxVTTnD9+b9TKpMixA5RR0/dbIuXcosc1EZD6WoQuVrzJIzCbhaFbvR1UZBxnQkIsUA3gixQCjbbuqfOD5VRE7vr/rvIY4Rso9iorDXwX2ItujxPjSoMKqDa68dqzSt6FDYsqDYK1eQHWhxkupecZTsD+lBaAJToCNImqbC8ZrwtmLOAmtjh4nlq9wJlI1+NYV6jooJq8jlXS76h0Uc7o2Ft7CpRlfWGzefcKOwSP6U2q4UlwALrIMgCJWCjSDZJhdOD8dY4plEf+8ZHtce7dy/1Xam40dkOBZoYpFSrgquFtX7Lazf67x6R7R65rJSQZ4sb1EaRG5luzlS7UoKh1A0DSUOQBUog6BmRtV6YiGc5wYqlK7XfEftq6ylbl/ruVB7Pgqun5qoaLfEzaozybqZ+rifQ9obWQs+kSTvDEVbAErMWYPPy3GQua6k1uqqaEAu8DWpkgRKwEGsvPpaSjG9NbFVtVanvSjLKDfzAhVm75DxPDQpVFxV62td57sxGbBdFiCJXQtAYFlQVSSv8QItSGTUHadpqAYPEiCxQAq5jUFLaBlVgiKVP7MiyA75WL1B75LslWW2JW5sA5YdRT5lOvEoL7P66pWW9SJ0KtNwPXGC3ZXwBDA1BFiiB7l1ufHa86Na0qJkQW0JWU97mBWqLfFUpUl0hry2ivOty0BFG6vI85ex1VYah1sKr3Wdru2ds8tYyP6V2z6d8ANgEBFkAGER3glHjNtP+H3oXf6ty4HnqkqeuIK0o9NXkp125Tk0YqtYLFYZyk6Wy8kpavmP9X20J3nRPDa8tKWujr51+4GqAyzFwA3HDeQygBGz3RWVBfMoJrvnudVr6ypJS3xWsh83ot4lRS4KMXk9V6Q0/42pNPXlqjEKNCgtqCAvuVL4Fy8EvtjD0EVcLrbU9t7dZlFeV6z7ga0mQdvfttSCjliDtFoEgxALDgxFZoESokI1XTezutNgqP57nDgi9NVp35a30QIGao0jV6g6vNqGqxgXNSHX2yrPWdz2vQetFa5PGrAOCdRGJNvDa9NaYpGmTy7zI6nN7rvciFSK7bU+dXqBOGw32fDc5zUaFCa1A8RBkgRLo3l2i3P3rL49QExvjetp2i5q9KTeK3OIJdrFwayuHpXpqVlO9bbyi7rX2el+f9nnvK9X+Jez5vbaQSd73XT2ujQjbx3zPR3pDAyOLIAuUAEvTxsNJ536CZWcrRU/QzLnP1/q3qHektftj34/0C7LdI7Y2ckvbPKB8EGQBYAM8JuOUN9dLdhjKdLye0VY6iQCxw2QvoATcjpcJywAAbBKCLAAglvrKX0t8PwCUDkEWKBl2vwCvIgCbgiALlIDV43kRtQXApu7ArK7VXk8AkokgC5SAtehxO98iNWcHksD6ujJJC0g2gixQArbzLchzDdUBbMKIrNfdEgtAMhFkgRKwEOvKC6iTBTZ5RJaVs4DkIsgCJdC7QhAjssDG8yMpS69fINEIskAJ2MpAYc+IEoCN34HlacgMJBpBFigRG0myESUAG8f6fhQYkQUSjSALlLBOlhcgsPEsw9rrCEBysR8FSiTveeyCgU3gRd315gCSiyALlIg1cucFCGykqKeHLKUFQKKxHwVKpOCxSC2wsVI95Tn0kAWSjSALlEhOnptxnWJ1L2DIUopceY69jgAkF0EWKGWQ9Ty3QwYwNHYA2GFlBZQWAIlGkAVKxfPcjpgRWWDjdl5Zj10YkHS8CwAlZDtiXoTAEEWRKyjIMRoLJB77UKDE5QVU+AFDn+hl9eXUxwIgyAIlZCNKTPgChoaJXgB6EWSBEmLCFzB0TPQC0IsgC5QSE76AIWOiF4BeBFmgxJjwBQwBE70ArIEgC5RBnayb8MXCCMCg6mOtJIeJXgBGLMimUilts802qqmp4a8OrKVLvrKepwwLIwBvqyqK1On5ytFDFkCxg+yWW26ps8/+uh5++CE98q9/6rDDDuWPDqyl4Hlq83y3gwYwsLSkNp+TiQC6FfXd4IAD9ldnZ6cOO+zwYt4MEHsdXqDAI8gCAwmiyLWr62I0FsAafaWL5tZb/7uYvx6oGF2ep2zkK6VQeXbSwHpZ+Y2V4XSxjAiAHpyfAcqA1ftZ3R/lBcCGpaNIrXagx9K0ADZmRLaxsdFdBrJs2TJ1dXVpY2UyGXfpVVdXt9G/C4gTq/trLBRKfTfKTtOrTUqipD7uDfF62m51UR8LYGOD7AknfEynfOrkAb/n8184XfPmzdfG+uIZp2vWrDPXuX769D3V0dGhJJoyZbKSbLvttlMhtCkelS1dKGiLjlZ1+YHCNXbW200crySKwlATJozVwrtfVZNeUxJNmzZRNTW18hIa3tbc9u31YUG2sbZOYULKb6ZOnaDAzymppk6dKD9I7uNP8r6/ZghdroYUZK+44kp3KaZLZl+qK668qt+I7Px5czV//mNqbW1VUs2d+6iSKiykNW/eS6p4UaRtCllXJ2uTv9b0xBMvKqkWLHhDSWUh9smnXlaS9W77DWFBK/xAy4O3zthVOgux8+Y/rKSyEJvkfZ9J6uOvr68vj8leGyObzboLkDie5+r/tggtyJb6zpQHG4lMpSt/NH5DkjoSu44oku91d/cAgBELslVVVRo3blzf16NHj3YLI7S1taqlZUUxbxqIJSsriMK8/ChSyIQW4K1uBbTdArAeRT3c33233XTrLTe7y2uvvabPfPpU9/nnTzutmDcLxFaHuhdHqInCUt8VoGxUR5FWeYHyHNwBGMkR2Tlz52rf/d5VzJsAKovnabWfUkPY5U6n0mYISWeLINhSIe0+ZQUA1kUBFlBm2q2nrAJVud03kGx2dqLNC9TJIggA1oMgC5SZgudpFYsjAO6shJ02XG2jsZQVAFgPgixQhuw0aihPKSsvABIqExbU4fnuLAUArA/vDkAZ6vJ8tfpM+kKyZcLQnZ2ggweADSHIAmWq1QvcC9SW5gSSJhWFKthoLJO8AAyAIAuUKTul2un57vQqkDQ1UaSOVEpZygoADIAgC5QpO5260g+UDukpi2SxBUFscbv2VHJXdQMwOARZoIxZ26GsH6iaBRKQIHVR6EprOoOyW0UdQJkhyAJlzFYyas1UqVph9wIJQAIWQLDRWDsbQcstAG+HIAuUufYgpQ4FqrEwC1S42ijUKj+g5RaAQSHIAmUu9H21+Cll6GCACmd9k208dpWfYjQWwKAQZIEYaLU2RPLdaBVQ2aOxvuvYAQCDwbsFEAOR57lR2VTPjG6g0qSjUPne0VgAGCSCLBATtkznai9wM7qBSlMbRW6Cl61qBwCDxTsGEKNRWdvRez0zu4FKURWFysrTalbxAjBEBFkgZqOyNqObWllUjChy2/MKP1CO0VgAQ0SQBeLE87TCT6kgz41iAXFX37P4wWpqYwFsBIIsEDNWQ9gcpFxfWY8SA8RYKgpdqUyzHZx59hkADA1BFoihVV6gVUq50SwglqJI9VHkQmw7tbEANhJBFojpxC8blaXEAHFlB2Ftnq+VlBQA2AQEWSCmKDFAXFFSAGC4EGSBGKPEALFDSQGAYUSQBWKMEgPEDSUFAIYTQRaIOUoMEBeUFAAYbgRZoEJKDFYqpUbrYkBLLpQh30oKFNKlAMCwIsgCFVJi0BSk1OH5aqAlF8qyLjbUCi/lFvQAgOFCkAUqhC3vuTxIu5ZcNYRZlBE7uLKDrCY/rZCFDwAMI4IsUEE6esJsOoqUJsyiDNREBeXlue0yT4gFMMwIskCFWe0HWu6nVKfQ1SUCpWIHU+lILsR2euxuAAw/3lmACmR1iM0ek79QOkEUqVaRlvkptbIELYAiIcgCldpf1k+rlclfKAEvilxdbIt102ByF4AiIsgCFargddcl2iSw2rBQ6ruDBLEQu9rz3cGUHVQBQLEQZIEKXyzBTu16dDLACGkIC267cx00CLEAiowgC1S4dj/Q0iCtIJKq6WSAIodYOwNg21uWyV0ARgCdqYEEsMk2doJ3y0JWUsgMcgy7+rDg2mtZiKVDAYCRwogskKC2XBYyMlGkKkZmMYzqwoIrI7Dty3oZA8BIYUQWSJBVPTPItyzk3Mis1TICmzoSa6vJWYht9wL+mABGFEEWSGiY3aKQk0eZATY1xPaMxBJiAZQCwzFAQsPskiCjVGRLiIalvjuI6cSuvOe77YgQC6BUCLJAgmtmlwRp+ZHoM4vBs8UOwoLrSmDbDzWxAEqJIAskvJvBm0Faoa0AZosmRFGp7xLKmB9FGhWFLrxaiKU7AYBSI8gCCdfeE2YtnFhIsbACrC0VhWpUQSvcSH6GiYIAygJBFoALJW8GGRdSLKykqZvFGmwhjboo0lIvrWV+2vWLBYByQNcCAI7NPreQkgs9jQ3zSqn7FDISLIpUH4UK5WlxkNFq2x4IsQDKCHspAH0iz1OLn3Kjs17U3V6Jutlk8qJIjVHolpx9M5VxkwMJsQDKDSOyAPrzPK32Anf6eGwh5+pmV8l3IRfJkLLOBFFBK72UlgcpF2YBoBzx7gRgvayswCaBrfICjVKBZW0ToiYqqDYKtdz1Gk4TYgGUNYIsgA2ykTgLM296aQVRdxN8O+WMyhNYa62woFC+q4dd7ltbNkbhAZQ3SgsADMhKClYGaXX5gTYv5DVKeXVEPu2XKmwUNh1JLX6gFT6lBADigyALYFA6e0oN2kNPo8OCq6Fs9aidjfsorHUlsOd2cZByzycTugDECUEWwKAxOls5GIUFUAkIsgCGZXS23fNdL1qU/wpdtrgBo7AAKsGIBNlx48YpnU5p8eI3FTFRBKi40dlRYd5NFCpEUhvlBmVbRmDdCCJ5avYDraQWFkAFKGqQPe64Y/XlL31RDQ0NKhRCeZ6n755zjv73f/9SzJsFMIJsZK/TT7vesxZmG6OCclF3+y56z5aeH0Wq61lyeJUfaJWfcs8ZAFSCogbZd+ywg0765Ml66aWX3def/vSpmn3JxXr22QVauHBhMW8awEjyPLV7gQuvq6NAm9kIrQrqijx1WJc/Sg5GnNcTYC2y2kHGqiBQO88FgApT1MPy7//gh30h1lx99a/dx332eWcxbxZAidgIbKsfaFGQ0WI/43qSbqaCqntGBDEST0Kk2rDgVmSzkVd7Llw9s8cSswAqz4hO9po2bZoymYzeeP2NkbxZACPMGumv8lJq8wLVRwVtHua1WVhQl2cjtB4jtEUqIbADhowXudC6zLe/P+UdACpbaqiTtrbYYtyA32MjsO3t7etcbwH2Zz/7iebPf0z/eOCBDf68fZ9detXV1Q3lLgIoI9bFYGVvoA3zaoxCbRaFyvfU0NLlYNOlo1DVUeROr1nniOU9AZZVuQAkwZCC7OEf/KA+/vETBvyeWWd9XY8//ni/64Ig0OWXXaotxo3TMR8+VmG44dOMXzzjdM2adeY610+fvqc6OjqURFOmTFaSTZ40UUk2afIEVQo/ClVVKKg2l1V1Ia8gjNQVBMr566+j3W7ieCXZhh6/1b9mwoLSYai856sjlVJ7KqOGINAWFVSPPHXqBAV+Tkk1depE+UFyH3/S931Jfvw1NTXFCbLXXHutuwyFhdjLLputXXfdRcce9xHXgmsgl8y+VFdceVW/Edn58+a6kdzW1lYl1dy5jyrJ5s1P+OOf95Iqip0GV3ctp3U5qFboJoZ1rmck8YknXlSSrfn4U1Gkmp4JXPa3Wul3T7DLVmgXAgux8+Y/rKSyEJv0934efzKf//r6+vKokbUQe+mll2jPPfZwIfb1119/25/JZrPuAqCCeZ465akz8LUySrn+po1h3i2XqkjKeZ66rJYWLrxmolBpK9WQp9V+oNaeDhGUDwBIuqIG2Yt+caHee9BB+tKXvqJRoxo1atTO7vqlS5dp2bJlxbxpADFhdbLWHmq156tWoarC0AVaax1Vn8u6UVsbccxX0CnzAUWR0opUFUXu8dvHLs93ixjYKKwL+En5WwBAKYPspEmT9Morr2jWrK/2u/6663+r66//bTFvGkAce9EqUHsQaEUUqUqRxlfXuhBnk5nqVVA+8pT1PGUrLMy5mlfZyGukQHJh1dqYLa+u1eupTMU9XgCIRZA94sijivnrAVRwP1orPWhNZ7QoVeVm5tvIZE1UcCO1o3pKEPKSG6nNy4tPB4Qocm+8KUWubMCCq7HAvsL31eEF7nN7XBPSmYqtfwWA2PWRBYCNkfN85TypVYGabbTW+qW60++9AddCYWjZtrzC7QZCq903u4+rfL8vtHaKmlcAGCqCLIBYsXBqDf/b1zgtbzWlblLUBsJt5P7nqdAz2msfrQlgKM993KjT9j29W311B1T3ec919tvso912ridQrxla7Tq7UC4AAJuGIAsg1iyYWg1p1tN6w62FzMB9Hro3PKtDtZBp/9YdQqM1ftl6P+3rn2Afo54voqg7CPeGYxsJtn64vSPB1mGg4HUHWUIrABQHQRZARYfb9fHWCLhrjqT2/XtPjHXBtfd39sRZ+zpcY2TXAmvv1wCAkUWQBZA4vSOoNnrqkEEBIJaYDgsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiKXUSNzIpEmTNHr05nr55VfU3Nw8EjcJAACAClfUIHvAAfvr3HPOUTqd0urVrdppp51022236WtfP1uFQqGYNw0AAIAKV9QgO3r0aH3qlFP12muvua+nTp2iu++6U3PmzNGNN/2+mDcNAACAClfUIPunP/2539cvvPCiWlpWaOy4ccW8WQAAACRA0Wtka2pqtPfee6m2plYfPPwwrVy5UjfeeNMGvz+TybhLr7q6umLfRQAAAFR6kN1uu+00ceK2A37P448/4cJqrzFjxuiM07+gxsZRmjx5ki67/Jdqamra4M9/8YzTNWvWmetcP336nuro6FASTZkyWUmW9Mc/edJEJdXkKeMV+DklVdIf/9SpE+UHyX38SX/v4/En9/mvqakpTpDdd999dOyHjxnwe7533vn9guzrr7+uj37sP/tqZG+77Y8KCwXNvvSy9f78JbMv1RVXXtVvRHb+vLmaP/8xtba2Kqnmzn1USZb0xz9vfjIfv4WYpD52w+PPJf61n/T3Ph5/Mp//+vr64gTZm2/+g7tsLKuRve/e+zTzwJkbDLLZbNZdAAAAgJItiNDQ0LDOdVOmTFHT8g2XFgAAAAAln+x18+9v0j333KOnnn5GNdXV+tAxR7s62VlnnVXMmwUAAEACFHVE9rjjP6L29nZ9+MMf0sEHv1/z5s3X/gccqKeffqaYNwsAAIAEKOqIbFtbmy7/5RXFvAkAAAAkVFFHZAEAAIBiIcgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGKJIAsAAIBYIsgCAAAglgiyAAAAiCWCLAAAAGJpxIJsQ0ODDj/8g5oxY/pI3SQAAAAq2IgF2Z/+9Me6dPYlOu20z43UTQIAAKCCjUiQPfHEj2vsmLH6xz8eGImbAwAAQAIUPci+4x3v0Jlf/aq+9KUvK4zCYt8cAAAAEiJVzF9eXV2tyy+7VOedd77eWLRoUD+TyWTcpVddXV2/j0lUU1Oj+vp6JRWPP7nPP899cp97w/PP88/2n8zXf90QMt+QguwOO+yg7befNuD3/POfD6u5udl9fv5539OTTz2pP95226Bv44tnnK5Zs85c5/r58+YO5a4CAAAg5oG2tbV1+ILsTjvuqMOP+OCA3/P88wtdkH3n3nvruOOO1Te/+S3XrcBsMW4L5fI59/Xf/u9v6ujsXOfnL5l9qa648qp+D8JC7PQZe6utrU1Jw+Pn+U/q9s+2z7af1G3fsP2z/Sd5++99DSxZskRvZ0hB9rY//cldBiOby+nue+7Re993UN91W221pQphqKOPPsqN3K4vyGazWXdZmz2Rb5fKKxmPn+c/qds/2z7bflK3fcP2z/af1O2/dZCPu2g1sv/+97/12c+e1u+6a6/9jbq6uta5HgAAABgqVvYCAABALBW1a8Ha5jwyR7l8fkg/Y2UGP/vZz9dbbpAEPH6e/6Ru/2z7bPtJ3fYN2z/bf5K3/6Hwth6/TTSknwAAAADKAKUFAAAAiCWCLAAAAGKJIAsAAIBYGtHJXsMlCAJNn76nRo0apXnz5qmlZYWSYOutt9I+79xnnev/euedrq1Zktjyx4cccrCampr04IMPKSnS6bR2221XjR49Wi+++KJefPElJUljY6N233039/nTTz2t5pYWJc1uu+2mKZMn6/5/3F+x732pVEp77723RjU26vEnHtfixW8qafbdd19tteWWuv2OO5TL5ZQkEyZM0E477egWV3riiScT9/jHjRunXXfdRZ2dnXryyae0evXqUt+lsha7ILvrLrvoiit+qTAs6Lnnnte553xXF1zwA915112qdDNmzNBFF12oO+74a7/r//b3vycuyNryx7Zy3KOPzktMkD30Ax/Qt7/zLS1bukyrW1drn3e+Uw8//C997rTPJ+L5P/fcc3TkEUdo4cLnlUqlXaD9/vd/qGuuvVZJ8J73vEdnf/1rqm+o19QpU3TEkUeppWW+KvGA/aYbb1A6ndHrb7yuvWbM0A9++F+6+upfKwmOP/44t1S7HwTugGXXXXdPzAHbVlttpZ/8+EfafvtpWvDcc5q03XZu0OKzn/u8601f6WyQ7qc/+bHe9a7/0LPPPquxY8dp6tQp+ta3v6Nbb/3vUt+9shWrIGsjsL/97XVuBNKWvo2iSDU1NdprrxlKio6ODn3+C6cryY488ggXYm6//Q5tueWWSgpb3vmoI4/u26nZm/4D/7hPH/3oR3Tddder0r3wwgt61/4H9IX2Y4/9sH5x4c/1wAMPaOELL6jS1dbW6Bvf/H9auXKlHnrwH6pU37/gAvcYjz3uI24k7uijjtLs2Rfr/vv/oeeff16Vzvd8feqUU7XtthN1w+8q/3W9pvq6Ol3969/o3nvvdV97nqdLZ1/inv+ZM9+tJARZG5j66pmz+q77yle+rB//6Ef605/+nLiR6YqskT3hYx9za++ed94FLsT2BrsHHnhQSeH7vt594IFudGb8+K2VNNtuu63O+965Ov2ML7llkJPk//7vb/1GZmwNalvCz978k+D663/bb+T59r/c7t74d9p5JyWBHbhV+qiUlY68//3v029+c23fTtuWRV+ydKk+9KGjlQS/v/lmvfDCi0oiOyDtDbHG9vN3/PWv7gyEDVpVOusZ++c//+86B/DpdEqZTKZk96vcxWpEdr/99tWjjz7qPn/vew9yOzWrH7Gj96SwU6pnfPF0d9Q+Y8Z03XjT7/Wtb327L9hXMqubu+yy2frFRRdr4cKFSqKGhga996CD3OjcoYcdqpdeelm/u+FGJdHMmTPdxwULniv1XcEwsVPK9jp/dsGz/a5f8OwC7bTjjvydE2jmAQfolVdecYNWSbH77ru7shIbrDrxxBN13vkXqK2trdR3q2yVNMjaaeH99t13wO956umn+0LLmLFjFIWh7rzzDr3y8ivabLPNXP3IrLO+5kYr4ljQvvdeew34PY/9+9/uRWwWLnxBMw88UIsWLe7b2P/4P7dq4fPP69e/uUZxM2XKZO22a/fEnQ15ZM4jfRM9rD7Qiv+vvfY6VYIdd9xR79hhhwG/54EHH3QT2nrV19fp0EM/4EaubNLXLbf+t9rb2xVHu+26q6ZMmTLg9/z93nu1atWq9b53/PCHP9DNf/iDnnsunkHWSqK2mbDNgN9jtf824SMpGhoa3ccVK/pPYmtpaXGlNEiWgw9+v0444WP6wulnKEl22Xknvfs979bkSZOUzXa5fT/KNMiOGzvW7ZQHYkdhvUG2q7NL++67j4448ui+U2xfO2uWq5P7+9/vjd0R24Tx49/28Te3NPcF2QULFvT7t8cff1x33XW3Dj744FgG2e222+5tH/+rr73qguz222+vz33us/re985zNXNm4rbbasyY0e7re++7L3Yj8zb69HaP/+lnnukXZO1v0VsjbWHur3f8Re1tbfrpz36uuNl55531nvcMXPc299FH1wmy1rHhpht/5yZ9nX32NxVXe+6xh5uZP5D77r8/UUHWdtqmrrZ/uYyVlCVhQiPe8h//sZ9+efll+tGPf7LO6fZKZ2da7WJOPeVT+vXVV+mAmW8NYqGMguyTTz01pIlLFui22WZCvzqxv9x+u7761a9o2tSpeuLJJxUnj8yZ4y6bwtpy2MhmHNnBh10Gw7pU/OUvt/fb8U+YMF61tbUuDM5/7LHYBVl7c96UN2irkbWODfvsu25LtrjUAtplKEZvvrn+cPPvtWz5cp188imxDjc2qcUueIudaes9W7XmBD57358zt7usDJVvv/3203XXXqOLL5mt2bMvVZLZWbfzzz9Pe+yxB0G2Empk7777Hlfwb90LekPL9tO2dx8XLa78I5UttthCS5cu7fvait/f/e4Dde+996nS2eSHtQ96Lrzw59p2m20S08Vh7effTXTaaSc98eQTSgILsTff/Hs1NTfpkyedrI4EjVQmxRuLFunZZxfoiCMOd6PRxgYpdtllF/3Xj35c6ruHEbDPPvvo+uuu0aWXXa6LLro4UX9zO9tkZTVhGPZd19s3m9HYCgmy1nbroYf+qRtv+J1uuPFGVyN72uc+q19ecWW/06+V6rvf6Z7UZadbrTH+f55wggqFgi78xS9KfdcwAn599a/c2QirG6+prtZRRx3pSisuvviSiv/7Wxuem266QVtvvbV+9aurdcghh/T9mwX5JCwMYaU4Vo4wdtzYvslu226zbb95BJXg3HO/p+uuu8bN4LYZ26eceor+9re/u64dSbDLLjtr2tRp2rmnG4dN6mxrbes3X6BSWQnZb6+/Vs8884xeevGlvjIyc/c998R2PsBQ+uR/4xtnu9r4xYsXuzkEnzjxRP3hllsqvmPJpvC2Hr9NrKa72yiU9c185957a9XqVbrvvvvdm1xSHHbYodr/Xe9yO3arn7zllltjfXp1U5x00ic0ZvRoXfiLi5QEdvDy4WOO0fQZeyqfy7uG4fb8x602fGPYTPZLLl7/82wTvgZbohJnM2ce4A5e1/Y///NH3XX33aq0HfrxHzlOjY2jNH/+fN1ww43K5/NKgmOO+ZAOOfjgda6/8qqrNH/+Y6pktmLnZz/zmfX+23e+e46WL1+uSjdp0iQd86GjNXHiRNd2zs64Pvzww6W+W2UtdkEWAAAAiN2CCAAAAEAvgiwAAABiiSALAACAWCLIAgAAIJYIsgAAAIglgiwAAABiiSALAACAWCLIAgAAIJYIsgAAAIglgiwAAABiiSALAACAWCLIAgAAQHH0/wHOWNIg0T0edgAAAABJRU5ErkJggg==" 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JKfn7R+rvhIaEnNY2fv31V3eH9/z2XV005W7+tXLVKhk/foL06tVbwsPD5cqTmhD5skxx+vvvv/WFvMrSeLILL7xQ0tKO6YBapaNWF9Vt27bNs1zbnIQkvvj1l+xjpvoY5XfM8hyPlSvljTfG6z5hqqnY5ZdfXuhlcotfvVo/d+zY0WO6qg1R40+til8lxWHz5s26f6IaPPjkBBUuDz/0kD7GqobSRdUIq5sQqnmZOk6q/9znX3zpEUwU9jgWpswqoFPJM3InjShISZSlOLYZHZNds6aS6+SW+/vGRdX2hnjJgnqmz5/CnssAig/BFYAzTl3kqQxo6s76v//+666ZUZm1VEpilXLbldlK3ZVt2rSpTvigqIv3+/vfJ+Xi4tzba3zuuRJXtqxsydXsTXWkV/07VNOfkqT+TsOGDSTkNAIslblNJbMY8uQT0jnXBZtK0KGadD34YHYqZzXO0U033eiR9U8Nxqq49t2XZUqKam7Wpk0bGXB/f/fd9G7dukqXLtfJlHdOpJaeMPFNnT1QZXFzUamjVYZDX62Kj5cvv/xKhg8bKp2uys6Qp6jz5vLLL9Pjcim3336b3HjDDR5Nt9q3yw7sXE3ofFnGG5UCW13AqnTxKlOiomqPXnl5rA5mJ4yfKMVFNYV1JVBwZcV09XUb/8br+v/i0Ucf08lcclPDAKiyTJn8tm5aenK/Pl+PY1E8/MhgXRM7Z/ZMnYzBlXDGVZvZulWrEi9LcWxz5YqVun9gv773epzXdevVzbPsrp27dMp2X74PSuL8Keq5DKD40CwQQInLnYFPJTRQFyhq3JVXXxvnsZy6637oUKI89NAD8sQTj+saDtU8ZvPmLTJ5yhS9zJ8b/pQLO3aU779fot9nZGZKTHS0bg44dsxY97amTp2mL6ZWrlgme/ft0+O9nO44V9688vKrMmXKJPlt/To5ePA/j3GuCuOhhx+R++/vr7MiTpzwhu5jpAJD1V9iwsTsi6wVy1fIgIEDdFpyFajoviLHj+t05q6mV74sU1Lef/8DMcSQBx8cpANC1QRMVbi98upr8nqufj7vvPOuxMTEyLPPjpDhw4fpu+xqXdXkTV1wnypxg4saP2jgwAEyevQLeoynQ4cO6WOmLlrVuFmK6t83cOD98tJLL+omcSqVvKq5eeqpp3X6cl+Xyc8dd/aRUc+N1KnlVV86db6qsdlUbYHKKFdcVK2gys6oLsTffWeyrslUtb4VK1aS+PhV0rVbd4mPz64JyW3Hjp2yfPkK3Txsw4YNXpuF+nIci0L1r7rm2i76ZshzI5/VfcLU/7QKINTnr8ozbNhwWbVqVYmW5XS3qZoCDh02XIYNHSI9e/XU0374/gcZOXKUzoSZm2qOO3nSW7J+/Vo5dPCgxzhXZ+L8OZ1zGUDxMKpWq3GirQkAFCMVSOXuW2OKKYmHEn1KGFG2bFndkVwlVFAX396ou/bqDrGq/crdbC43dUFXpkxpfdFfUBMgV1m379iRb2Y91bRINU08ua+ICharVq2ScyFz3N1BXQ2om5yc4pHhS1E1aurO+T///JNvmdVYTypZR377Va1aVV2WxMTsAZmLukxuKrGGyjSmmvjlRwVrNapX1/3fTm6mmZs6Hg5HiE5ckd/xVHfnVUZD9fnpC8Dhw/QA0med3dCdRlp9LqVLx+gg4VTNTENDQ055zI4eTXMPYl3YZfL7/F3HRZUhJTnZ6/mtxupKSkrOcy6o4xQamv+5UFDyCtXUy3XsCqICCVXTe/jIkTx/vzDHMb998FVsbKx+JCUlFfgZnE5ZTnU8T2f/1P9G1SpV9HFUga36H65dq5ZuLph7wF/1+VSrWlUioyLlWNoxd1/RM33++HK+Ayh+BFcAAEtQtQBZWU655trr/F0UAACKhD5XAIAzSnXYHzjgft0HSFE1AKq/mepb9/akSXwaAADbos8VAOCMUlkho2NiZMWKZXIsLU03/VN97VQq/fxSjQMAYAc0CwQA+I3qc+VK2AAAgN1RcwUA8BuCKgBAIKHPFQAAAAAUA2quTtFcRaVbBQAAABDcoqOjT9niguCqgMBq7Zr4kvhcAAAAANhQi5atCwywCK7y4aqxUgfQCrVXLVo0l7Vr1/m7GLAZzhtw7oDvHFgdv1Www7mjaq1Uxcup4gKCq1NQB1ClDfY3Naq7FcoBe+G8AecO+M6B1fFbhUA6d0hoAQAAAADFgOAKAAAAAIqB35sFxsbGyrXXXiMVK1aUTRs3ycJvv/VpvaZNm0qH9u0l7ViazJ//tfz3338e8+vUqSMXX3yR3n5CQoJ8880COXr0aAntBQAAAIBg59eaq+rVq8uSJYuke7duEhcXJ6NGjZT3pk0VwzAKXO/FF1+QWTOnS61aNaVWzZoyY/onUq9eXff8bt26yvffLZbzW7eWqKgoueOO22Xprz9LrVq1zsBeAQAAAAhGfq25emr4MNmzZ490695DsrKyZNq09+SnH7+X67t0kS+/+srrOrfddqv06N5dOl99jfz111962hvjJ0hERIR7mYED7pfp02fI0GHD9fvQ0FAdXPXufbOMHv3SGdo7AAAAAMHEbzVXISEhcuWVV8inn36mAytl165dsnzFCrn6ms75rnd3n7vkiy++cAdWypEjR+TAgQPu90lJSWI4TuyaqglzOBxy5PDhEtsfAAAAAMEt1J9NAlWTvZ07dnpM37Fjh7Rq1Srf/PINGjSQiW++JZdccok0a9ZU9u/bL98u+lYSE08EToMffVzGvDRaJk16SxJ2J0jr1q3k++9/kGnvvZ9vecLDw/Uj998CAAAAAMsHV6VKldLPySnJHtOTk5KlVFT2vJOVKVNaP/e56y6dnGJVfLzc1PVGefrpp6RXr5vl9z/+0PMrVqwgVapU1qMnp2dkiMMRIjVr1tB/89ixY163PWjgABk8+BGvg5OpHPr+lrtPGcB5A75zYEX8VoHzBoH6naMqhSwdXLlGNy5duozH9DKxZeToUe8jH6emZmf7U8FO127d3dNnTP9Yhg4dIjf3vkX3r3r7rTflgw8+lJdfedXdLHDBN/Nl+LCh8sjgR71ue/yEiTJp8pQ8ozCrUZ+tMjhZfPxqfxcBNsR5A84d8J0Dq+O3ClY/d2JiYqzd50qlR1e1T/VPijjr1asnW7du87qO6kulaqPWrFnjMX316jVSt24d/bpSpYpSoUIFWbUq3j3fNE1Zs3adnHtuo3zLk56eroMo18MV/AEAAACApYMrp9MpCxd+K926ddO1TUr9+vXkgjZtZN78+e7lOnfqJH3uutP9/qu5c6VNm/M9ttXmgjayecsW/Xrfvv2SnJwsbdu19Uieofpd5Re0AQAAAICtU7E//8IL8vlnn8qXX3wu63/7TQdSCxYu1IMCu1xxxeXSsmVLmTrtPf3+1VfHyZw5s2Tu3C91NWDLFs2lZs1a0r1HD3fQNmz4UzqhRcMGDWTX339Lxw7tpVxcORkz9mW/7SsAAACAwObX4GrPnr1y2eVXytVXd5aKFSro/lA//PCDxzLfLFgga9et80i7fu21XeSqq66UGtVryDvvTpXFi5d4JJ2YM+dTWbZsuXTo0F4PTjzu9TdkyZLv8k1mAQAAAAC2Dq4U1bdJBUP5UUGRt/5R8+adaDqYX5+uWbNmF0sZAQAAAORlmg5JT28jzqzK4gjZL+HhK8UwnCU2z+r8HlwBAAAAsJ+0tM6SdORZcTqruac5HHukTOwz+nVxz4uKWiBWR3AFAAAAoNCB1eHESXmmO51VcqYbqg6qmOf1s3yA5bdsgQAAAADsRzXbU7VL3sMJR06AVNzzVI3WCP23rczapQMAAABgKbo/lG62l18oYeQKlIprnkOczur6b1sZwRUAAAAAn6lEE8H4t31BcAUAAADAZyqDXzD+bV8QXAEAAADwmUqNrjL4ieSXHt3Mk5Ti9Oc5xeFI0H/bygiuAAAAAPhMjTnlSpueN8By5gqQinOeSJnYEZYf74rgCgAAAEChqJToZeP6icNxyDO4cOzV08vG9RWHY1+xzrN6GnaFca4AAAAAFJoKdgw5JomJH4rD8Y+UjXtEN9tz1S5FRn6bnVkwq7LuK1Uc86yO4AoAAABAkZhSKjuoCN0jERHLPeapgOjkaac7z+poFggAAACgSEyztH42jGSOIMEVAAAAgKIynTHZwZUjlYNIcAUAAACgqJxmdnDloOZKo1kgAAAAgNOruTJSOIIEVwAAAACKysypuTIcBFcEVwAAAACKzJmT0MJBzZVGs0AAAAAARWI6o/UzNVfZCK4AAAAAFAmp2D0RXAEAAAAoEmdOQguHQSp2gisAAAAAxZDQgkGECa4AAAAAnH5wRUILjWaBAAAAAE6zWSCp2AmuAAAAABSJaYaLSIR+TbbAbNRcAQAAAChyGnaFZoHZCK4AAAAAFHkAYcM4Kobh5AgSXAEAAAAoCtPMGUDYIFOgCzVXAAAAAArNdLpqrkhm4UJwBQAAAKDQnDlp2B0OBhB2IbgCAAAAcBpjXNEs0IXgCgAAAEChmTljXJGG/QSCKwAAAABFzhbIAMInEFwBAAAAKPI4VyS0OIHgCgAAAEChma5xrhz0uXIhuAIAAABQ9GyBBtkCXQiuAAAAABS9WSA1V24EVwAAAACK3iyQQYTdCK4AAAAAFJozJxU7zQJPILgCAAAAUPRBhGkW6EZwBQAAAKDowRXNAt0IrgAAAACcRrPAFI5eDoIrAAAAAIVimrmbBRJcuRBcAQAAACgcM1JEQvVLw2AQYReCKwAAAACF4sxJwy7iFMM4ytHLQXAFAAAAoFBMM2cAYSNFDIOD50JwBQAAAKBQTCcDCHtDcAUAAACgUJw5ySwcJLPwQHAFAAAAoFAY48o7gisAAAAARRrjijTsngiuAAAAABSKmZMtkAGEPRFcAQAAACgU03kiWyBOILgCAAAAUKRxrgwHAwjnRnAFAAAAoEgJLRxGKkcuF4IrAAAAAIViuhJaGNRc5UZwBQAAAKBI41yRLdATwRUAAACAItVckS3QE8EVAAAAgKINIuwgW2BuBFcAAAAAitYskFTsHgiuAAAAABSK6WQQYW8IrgAAAAAUimnmDCJMs0APBFcAAAAAfGaahpiuQYRJxW694CosLEzKxcUVer1SpUpJRETEaS8DAAAAoHC1VorDwSDClgmuDMOQp59+SjZt3CArViyT1fGrpHPnzqdcr1WrljJ/3lz5/bd1smrlcnl57BgdRBV2GQAAAABFyxQokiEixzh8Vgmu7uvXV3r26CHX/+8GadCwkbz55pvy9lsT5ayzzsp3nXPOOUdmzZwhP/zwg5zTqLGc17ylrFi5UurXr1+oZQAAAAAUntN5IlOgYXAELRNc3XnnHTJ9+nTZsOFPMU1T3p06TRIS9sitt96S7zpPPvG4bPjzTxn78iuSkZGh15s9e478/vvvhVoGAAAAQNFrrhwks7BOcFW+fHmpWbOmrFoV7zF95apV0qL5eV7XCQkJkQsvvFDmz/9av4+LK6ubFhZ2GQAAAACnOYAwY1xZK7hSDh065DE98dAhKZczz9s6UVGRUrZsWVm29Bf55eefZetfm2XMS6MlKjLS52W8CQ8Pl5iYGPcjOvpERz0AAAAAJzULdCRzSE4SKn7idDr1c0ioZxFCQ0PFmZXldR3VvE+5pffN0rVbD/nrr7+kXr26Mmf2bDmaliYjRjzr0zLeDBo4QAYPfiTP9BYtmktaWpr4m9oHgPMGfOfAyvitAudNcPjn76ayLlGkbNkQad26VVB850RFRVk7uNq3b59+rlSposf0ihUruued7ODBg3Ls2DH54osvddCkbN++Qz799FPp3LmTDpx8Wcab8RMmyqTJU9zvVc3V2jXxsnbtOklJSREriI9f7e8iwIY4b8C5A75zYHX8VtlLakpT/ZyctMfvn138Gfr7qmWbpZsFqoDljz/+kIsuvNCjv1THjh1k+fIV7mmxsbFSoUIFd23XypUr8+xcdEyMpKam+ryMN+np6bpMrkdBywIAAADByukaQJhmgdbKFvjauNele/duOjtgo0bnyKuvvqynv//Bh+5lnho+TKdVd3n1tdfl+uu76BTuqiqwa9ebpGeP7vLRx58UahkAAAAAp5Et0KAywjLNApVvvlkggx54UO65u4/c37+/bNq0SW7q2t0jycXhw4fl338PuN+rWqk+fe6RAQPvlwceGCQJexLk0Ucfl8+/+KJQywAAAAAoPNM9zhUJLSwVXClffTVXP/Iz6vkX8kz76eef9aMgviwDAAAAoHCcrlTsjHNlrWaBAAAAAOzFdGb3uXIwzlUeBFcAAAAAfGaa2ePBUnOVF8EVAAAAgMI3C6TPVR4EVwAAAACK0CyQbIEnI7gCAAAAUIRmgWQLPBnBFQAAAIDCDyJMQos8CK4AAAAA+MQ0Q0TMqOxAglTseRBcAQAAAPCJmZPMQjHoc5UHwRUAAAAAnzidruDqmBhGBkftJARXAAAAAHxi5vS3okmgdwRXAAAAAAqXKZBkFl4RXAEAAADwiTNnjCvSsHtHcAUAAACgUAktGEDYO4IrAAAAAD4xna5mgQwg7A3BFQAAAIDCDSDMGFdeEVwBAAAAKGSzwBSOmBcEVwAAAAB8YroSWhBceUVwBQAAAMAnTlcqdpoFekVwBQAAAKBQNVcOElp4RXAFAAAAoFB9rgxHKkfMC4IrAAAAAD5xuoIraq68IrgCAAAA4BPTSbbAghBcAQAAAChks0BSsXtDcAUAAACgkM0CCa68IbgCAAAAcEqmmatZoCOZI+YFwRUAAAAAH0SISLh+ZRhkC/SG4AoAAADAKTmd2QMIKwRX3hFcAQAAADgl0yzt7m9lGE6OmBcEVwAAAAB8zxRIk8B8EVwBAAAAOCVnTjILg2QW+SK4AgAAAOBzzZWDNOz5IrgCAAAAcEqm80SfK3hHcAUAAADglJxmdrZAw0FwlR+CKwAAAACFaBbIAML5IbgCAAAAUIhmgQwgnB+CKwAAAACnRLPAUyO4AgAAAOBzzRXNAvNHcAUAAADA90GEHTQLzA/BFQAAAIBTcrqCKxJa5IvgCgAAAMApmU4GET4VgisAAAAAhWgWyDhX+SG4AgAAAFCIZoEEV/khuAIAAADge7NAB4MI54fgCgAAAECBTDNXs0AGEc4XwRUAAACAAplmlIiE6NdkC8wfwRUAAACAAplm9gDCIlliGGkcrXwQXAEAAAAo0IkmgSliGBys/BBcAQAAACiQMyeZBWnYC0ZwBQAAAMCnmisHadgLRHAFAAAAoECmM7vPFWNcFYzgCgAAAECBnGa0fqZZYMEIrgAAAAD4lC3QYTCAcEEIrgAAAAAUyHQltGAA4QIRXAEAAAAokNOVit1BzVVBCK4AAAAA+FRzRbbAghFcAQAAAPBtEGFHCkeqAARXAAAAAHxrFsg4VwUiuAIAAABQIJoF+obgCgAAAIBPqdhpFlgwgisAAAAAvg0iTLPAAoUWPBsAAAB2YZoOSU9vI86syuII2S/h4SvFMJw+zffXvITdbeX48bBi2aZV9/FMzSvJv+nMKqdfZ2bWlfDwVR5/EycQXAEAAASAtLTOknTkWXE6q7mnORx7pEzsMxIVtaDA+Yq/5h06WDzbtPI+nol5Jb3/ppkdXCUdGSspyQ+7/yY8GVWr1TDFjxo1Okduu/VWqVCxgmzauEneeXeqJCUlFbhOSEiIdO/WTTp0aC9paWkyfcYMWbt2nddlr7zyCrn11ltk4cJv5ZNPpvtcrpiYGNmyeaM0aNhIUlL8n3KydetWEh+/2t/FgM1w3oBzB3znBAd1UX04cZKXXh/ZtQulot+Wo6n35TtfxFD1E7aeFwz7aLX9LxvXz68BVuszeH3sa2zg1z5XzZs3l3lz54rhcMiiRYvlkksuli8+/0wiIyPzXSciIkJmz5op993XV5YtX64fw4cNk6ZNmuRZtnq1avLC86PkvGbNpGHDBiW8NwAAAGeearKlaha8X9plvz+a2i/nItnbfKOAde0yLxj20Wr7r2qxRujzDxZpFjhkyBPy008/yZAhQ/V7Vbu0On6l3Nyrp0x7732v6zwwaKCcffZZcvHFl8qhxEQ97YsvvpRSpUrlqd16880J8uqrr0mfPnedgb0BAAA483Q/mVzNufI61cWvEQDzgmEfrbb/DnE6q+vzLyJi+Sn+fvDwW6ipaqDatW0rX3/zjXuaag74yy+/yiWXXpLvej179pA5n37mDqwU0zQlNTXVY7nHHntU/v33P5k+Y2YJ7QEAAID/qQQEAOdfkNdcVa9eTUJDQ2Xv3r0e0/fs2SPt27fzuk7ZsmWlWrVqsmHDBhk44H5p2qyp7N+3Xwdbv/32m3u5jh07SLduXeXKK6/yuTzh4eH64RIdnZ1uEgAAwMpUZjeA8y/Ig6uwsOxARiWkyE29D8sV5OQWFZXdF2vokCEyf/58mTdvvrRo0ULmzf1S7rmnr3y7aJGUK1dOXh83Th5++BFJTDzsc3kGDRwggwc/kmd6ixbN85TRH+rVq+vvIsCGOG/AuQO+cwKfaWbJ4kX/ybFj5fJplOTMSUjgrT+O3kIBTcDsMi8Y9tFq+++UyMiD0r59lhhGKwn065yoqChrB1dJSUfctVG5xcXFSdIR79kCj+RMX7NmjTz1dHZqyLlz50nlSpVk4KABOri6vksXiY4uJXff00c/lJo1a8pVMTFSp04dufPOProZ4cnGT5gokyZP8ai5WrsmXmchtEK2QIVsgeC8Ad85sDp+q/wjMmqIHDs2Keci21smuUk5meS8z8++eLb3vGDYR6vtf2TUUFm9epX4U/wZzBZo6T5Xe/fuk0OHDsm5557rMb1x43Plzz//9LrO0aNHZfuOHbJr1y6P6bv+/lsqlK+gXy9eskQGDnpAPvzwI/cjMTFRNm3arF97C6yU9PR0HUS5Hif34QIAALAqlQ5bpcV2OPZ5THc49urpsbEvFji/bFxf288Lhn202v4zzpXFsgWqvlK9b+4lH32kAqDDctGFF0qzZs3kmREj3cv0uetOOeuss2TosOH6/YwZM6Vnj+7y2rjXdRCksgR2uuoqWblqpZ6/e/du/cjticcfk7//+VsWL15yhvcQAADgzFAXupGR38q+vRvUfXaJjX1Iokp9Lobh9JivswtmVdZ9tcLDV7rn+2tendptZeeu5cWyTavu45ma54/9h4WCqzFjxsq55zaSn3/6UbZv3y6NGzeRMWNflhUrVriXUTVbLVu2dL9/++1J0qRxY1m29BfZuGmTNGzQQLZu3SbPPnsiIAMAAAhOqoVOdt+QiMif8lwAq/f5pc3217zqNTJk777VxbJNf+6HFeb562/CIsGVaubXo0cvadToHKlYoaJs3rJF9u/3zHjz7tSpMnvOp+73mZmZcl//+6Vu3TpSo0YNSUhIkO3bdxT4d4YNf1o3DQQAAAhkpqmyHYfo1w6H9z7sAAI0uHLZuHGTbJRN+c7zZseOnfrhi5Urs5sMAgAABDLTWSbn1TExjON+Lg0QfPyW0AIAAADFy2lmB1fUWgH+QXAFAAAQIJzOWP1s0CQQ8AuCKwAAgABhumquDPpbAf5AcAUAABAgnM7S+plmgYB/EFwBAAAEWEILg5orwC8IrgAAAAKE08zuc0XNFeAfBFcAAACBVnNFQgvALwiuAAAAAi0VO80CAb8guAIAAAgQ1FwB/kVwBQAAECCcOc0CqbkC/IPgCgAAIMCaBRqOI/4uChCUCK4AAAAChOkkWyDgTwRXAAAAAcJpZg8izDhXgH8QXAEAAAQA0zyR0MLhSPZ3cYCgRHAFAAAQAEyzlIiE6tcOgz5XgD8QXAEAAAQAMyeZhUi6iHHMz6UBghPBFQAAQABw5kpmYRj+Lg0QnAiuAAAAAmkAYSPJ30UBghbBFQAAQACNcaVqrgD4B8EVAABAAHC6aq4IrgC/IbgCAAAIoIQWZAoE/IfgCgAAIKBqrhjjCvAXgisAAIAA4B5AmIQWgN8QXAEAAARQQgv6XAH+Q3AFAAAQSDVXJLQA/IbgCgAAIID6XJHQAvAfgisAAIAAYJqx+plmgYD/EFwBAAAEVM0VgwgD/kJwBQAAEEDjXFFzBfgPwRUAAIDNmWaumisSWgB+Q3AFAABgd2akiITplwbNAgG/IbgCAACwOWdOMguRTDGMo34uDRC8CK4AAABsztUkUPW3Mgx/lwYIXgRXAAAAAZLMgkyBgH8RXAEAANgcySwAayC4AgAAsDnTmTOAsHHE30UBghrBFQAAgM05Xc0CScMO+BXBFQAAgM2ZuRJaAPAfgisAAIBA6XNlJPu7KEBQI7gCAACwOdMsrZ+puQL8i+AKAADA5pw5CS0cJLQA/IrgCgAAIEASWlBzBfgXwRUAAECAJLQgWyAQIMFVdHR0cW0KAAAARUpoQbZAwHbB1VlnnSXDhg5xvx/57Aj5a8smWbsmXho3Prc4ywcAAIBTMGkWCNg3uBrxzNPy088/69f16tWVm2/uJbfdfod88eVXMnzYsOIuIwAAAPJhmrlqrhxHOE6AH4UWZaXWrVvJvX1X69eXXHyxLFz4rSxZ8p2sWhUvy5f9WtxlBAAAQL4iRSRCvzIY5wqwX83V8ePHpVKlSvr1pZdeKr/+mh1QhYWFSUZGRvGWEAAAAPly1VqJZIlhpHKkALvVXKlaqnfemSy///a7XHBBG3no4Uf09As7dpSffspuLggAAICSZzpzBhA2ksUwTA45YLeaq2HDhsu33y6SkNAQuePOu+TgwYN6epsL2sirr71W3GUEAADAKca4or8VYNOaq7Rjx2Ts2JfzpGIfOpRkFgAAAGeS0xmrnxlAGPA/UrEDAAAEQBp2xrgC/I9U7AAAAAGQ0IKaK8D/SMUOAABgY6ZrjCsjyd9FAYIeqdgBAAACIqEFwRXgb6RiBwAACICaK5oFAv5HKnYAAIAAyBZIs0AggFKxK6RiBwAAOLNMM2cQYZoFAvasuVJCQ0Pl/NatpWvXm9zT4uLKFle5AAAAUIhsgdRcATYNrqpXqyYLF3wjM2dOl/FvvO6ePnbMGLnyyiuKs3wAAADwIaEFNVeAXce5GvGMrF69Whqec67H9LcnTZKBA+4vrrIBAADA11TsjiMcK8COfa7atWsrF110iWRkZHhM37hxkzRt2rTQ2zvrrLOkYsUK8tdfW+W///7zaZ3w8HBp0qSxpKWl6b97ssjISGnYsKFkZWbK1m3b5NixY4UuFwAAgF0SWhiMcwXYM7iKiIgQp2nq106n0z29cuVKkpbmexATFRUl774zRZo3P0927tylg6HXXntNJkx8s8D1brrpRnlu5EjZs3ePDq6yMrPk3r793IHZkCefkF69esru3Qn6b6jAbcjQYTJv3vyi7C4AAIAlmWaEuqWsXzPOFWDT4GrpsmVy2223yvjxE8TMCbJUEPPU8OHy8y8/+7ydxx57VOrVqysdO14khxIT5eKLL5bpn3wkK1eukpWrVnld58ILO8rr416TAQMHyVdfzdXTmjdvLmVjY3VwZRiGJCcnS7v2HeXo0aN6/v3975M3Xh8n8fGrZd++fUXZZQAAAMtxOrMzBYo4xTBS/FwaAEXqc/XcyFFy7z13yxeff6qDmUmT3pJlS3+RFi2ay4svvuTzdrp36yrTZ8zUgZXy448/yu+//y7du3fLd52HH3pIvvv+e3dgpaxbt043/VNUsKdqvlyBlTJr9hzdTPDccxvxiQMAgIBhupJZGMliGNk3vAHYLLhSgcyll10hP/z4kyxatFgiIyLko48+liuu7CS7du3yaRtVq1aR8uXLyx9//OEx/Y8/Nsi553omysjdHLFVq5by3XffS7ly5aRt27ZSt26dU/6t1q1b6+ft23cU2IcrJibG/YiOjvZpPwAAAPyehp0xrgD7NgtUDh48KOPGnUjDXlhlymR3vjx8+LDH9MTERIktmz3vZHFxcRIWFiYtW7aQBwYN0oFcgwZny7Zt2+Tue/p6TYZRqVIlGTVqpMycNVt27tyZb3kGDRwggwc/kme6qo1T/br8TTWfBDhvwHcOrIzfqjPvwIHmcvA/kejoTGndupXYEecN7HDuqC5QJRpcqeaA1apWlbJeBg7esOHPU66fkZGun1VzvZMLnpGePe9k6TnTW7ZsKZdedrkkJSXpGqZ5c7+UZ55+SgY98KDH8uXi4mT6Jx/Ljh07ZMiQoQWWZ/yEiTJp8hT3e7XdtWviZe3adZKSYo02zKrPGMB5A75zYGX8Vp1ZaWlV9fOxY/tsfeztXHYEx7kTExNTcsGVujMyccJ4qVmzptf51ap7n57bnj17JTMzU6pVq+YxvWrVqvLP7t1e1zl06JBOVrHo20U6sFJSU1PlmwUL5cYb/uexbFxcWT3IsaoZu/32O0+Zil0Fbq7gDQAAwE5jXDGAMGANRQquXnh+lPzw44/y9tuT5Mjhog1Yp4Kd5StWSOdOnWT27Dl6WunSpXU2wBdfHO1erkGDBhJbpoysio/X77//4Yc8QV2tmjXlwIED7vdly5aVmTOmS1JSstx62+2WaNYHAABQYn2uGOMKsG9wVa9ePbmpa/fTbi43evRLMmf2LBn13EhZvXqN3H77bbJnzx6ZPn2Ge5m+996jmwFedvkV+v3LY1+Rr776Qp5++ilZtWqVtGzRQrp0uU7uvKuPnq/6ZM2Y/rFUrlxZxr3+hlxwwQXubW3evEn27iUVOwAACAxOM2cAYUfRbnYDsEBwtWPnTqlQocJpB1dr1qyV66+/QQdV11/fRZYtXy6TJ0+RtFxN+DZt3iyZWZkemQqvubaL9LnrTunerZskJCTINdde5+7npTIKHjx4SD9u6d3b4+9NnvIOwRUAAAi4ZoEOI9nfRQFQ1OBKNQd8eewYGf7U0zoDn2sgYZfjx4/7vK0/NmyQx594Mt/577zzbp5p6m8+/cwIr8urgO+WW2/z+e8DAADYldM1zhWp2AH7Blfj38hOwf7dkkVFTmgBAACAYqq5IrgC7Btc3XDDTcVfEgAAABQKCS2AAAiu7rrrTul//wCv8956c6KsXLXqdMsFAACAUzDN0vqZhBaANTiKstL//nd9vvNU5j4AAACUPKczO1sgzQIBG9ZclSlTxutrxeFwyPnnt5b9+0+MNwUAAICSY7oSWjDOFWC/4GrTxg1eX7tkZWXJ88+/WDwlAwAAQL5MM0xMs5R+Tc0VYMPgqvPV1+rnBd/Md792yczMkD179srhw4eLt4QAAADIw+nM7m+lGIxzBdgvuPrtt9/0c5sL2snu3btLqkwAAADwuUlgshiGk+MF2Cm4KhcXp58PJSbK0dRU93tv1DIAAAAo+WQWDCAM2DC4+uOP39wDBLte54dBhAEAAM7QAMIkswDsF1xdeVUnr68BAABw5jlzmgWSzAKwYXC1YcOfMmf2LOnWvYd+rXTu1EkWLFxYkuUDAACAF2ZOQgvSsAM2HUS4fft2Hu+nTn2nuMsDAAAAHzhN1wDCRzhegB2DKwAAAFirzxUJLQDrILgCAACwIScJLQB7j3OlXHhhxwLfKz///MvplQoAAAA+JbQwHMkcKcCuwdXMGdMLfK+Qih0AAOBMpWKnzxVgy+CqVevzS64kAAAAKELNVRJHDbBjcLV3776SKwkAAAB8Zjpd2QIJrgCrIKEFAACADZHQArAegisAAAAbMs2cQYSpuQIsg+AKAADAZkwzREwzRr9mEGHAOgiuAAAAbFprpRgGqdgBqyC4AgAAsBlnTjILw0gVw8jyd3EA5CC4AgAAsOkYV4ZBpkDASgiuAAAAbDrGFWnYAWshuAIAALBpGnbDccTfRQFQ1EGEAQAA4Mk0HZKe3kacWZXFEbJfwsNXimE4S3ReRnqLnD+usgY63PMA+BfBFQAAQBGlpXWWpCPPitNZzT3N4dgjZWKf0a9Lel5GRis5sH+ZnhcVtYDPEfAzgisAAIAiBlaHEyflme50VsmZbqiqpTM4rx8BFuBn9LkCAAAoJNUUT9Ugeb+ccuQEQWdynqrRGqHLBcB/+A8EAAAoJN0fSjfNy+9SysgVDJ2JeQ5xOqvrcgHwH4IrAACAQlKJJqzIquUCggXBFQAAQGEvoEL2W/KYWbVcQLAguAIAACgklRpdZfATyS8Fupkn8UTJznOKw5GgywXAfwiuAAAACkmNK+VKjZ434HHmmuY8Q/NEysSOYLwrwM8IrgAAAIpAjStVtuygvBdXjr1SNq6flI3rKw7HvjM2j3GuAP9jnCsAAIAiCglN0Bn8DCNRysQ+JSEh+3XTPFWzpURGfpudWTCrsu4PVdLzAPgXwRUAAEARZaSfp5/Dw+OlVKkv88xXQU9ExHKv65bEPAD+RbNAAACAIkrPyA6uwsLXcwwBEFwBAACcbs1VWBjBFQCCKwAAgCJxOstIVlZ9/Tqc4AoAwRUAAEDRZGQ01c8hIX+LIySRwwiAZoEAAABFQZNAACcjoQUAAEARZLiSWYT9xvEDoBFcAQAAFAGZAgGcjOAKAACgkLKyKogzq7pKayFhYb9z/ABoBFcAAABFbBIYErpNHI4Ujh8AjeAKAACgiMksSMEOIDeCKwAAgCIns2DwYAAnEFwBAAAUgmmq4KqZfh0WTnAF4ASCKwAAgELIyqouTmcFVX8lYWF/cuwAuBFcAQAAFKFJYGjYJjGM4xw7AG4EVwAAAEUIrkhmAeBkBFcAAABFyBRIMgsAJyO4AgAA8JFpGpKR0VS/JpkFgJMRXAEAAPgoK7OemGYZESNNQkO3cNwAeCC4AgAA8FG6a3yr0D/EMLI4bgA8EFwBAAAUdvBgxrcC4AXBFQAAQCGTWZApEIA3BFcAAAA+MM1QychorF9TcwXAG4IrAAAAH2RmNhSRSDGMIxISspNjBiCPUPGza665Wu7u00cqVqwgGzdtkjFjxsq2bdsLXKdcuXIyaNBA6dihvaSlpclHH38is2bNPu3tAgAA6zNNh6SntxFnVmVxhOyX8PCVYhjOU847nXXVvLSjN+rXISF/i4ihpvpl/wFYl1+Dq05XXSVvvTlRRox4VuJXr5F+fe+Vzz6dI5deerkcSkzMN7CaP+8rHSg9OWSoDq7uuvNO2bljp6xctarI2wUAANaXltZZko48K05nNfc0h2OPlIl9Rr/Ob15U1IIir3vyvMzMpnJg/zL3dgHAEsHVww8/KJ9++plMe+99/f6hhx+RdWtXy2233yavv/6G13Uef/xR/dzn7nskPT1dv37s8SfEMIzT2i4AALA2FRwdTpyUZ7rTWSVnet7aJNe89PS35WjqfUVat+B5/QiwAPi/z1V0dLQ0a9ZMfvjxR/e0zMxM+eWXX6V9u7b5rnfdddfJZ5997g6sXEzTPK3tAgAA61LN8lTtkffLF/XeKGCeyNHUfjnLFHbdgrebdGSELhsAePumOGOqVq2qnw8c+Ndj+oEDB6RKlSr5NgksFxcne/ftk/FvvC4//vCdzJw5Xbp163pa21XCw8MlJibG/VBBGgAAsAbdF0o3y8vv0kUFQSdasXhS64QUML+gdQvertNZXZcNAPzaLNDhyP5yzMrM9JiuapkcIeoL0HsApAwfNlSeG/W8vDF+grRs0UJeGj1aSsfE6GaARdmuMmjgABk8+JE801u0aK77dflbvXp1/V0E2BDnDTh3ECjfOQm728qhg2JJdWq3leo1MvxdDNvitwp2OHeioqKsHVwdOnRIP8fFxXlMjytXTg4d9P7tmZiYKFlZWfLNNwvk448/0dP++usvad6iudx8cy8dXBVlu8r4CRNl0uQp7veq5mrtmnhZu3adpKSkiBXEx6/2dxFgQ5w34NxBIHznHD8eJla1c9dy2buP3+jTwW8VrH7uqJZtlm4W+N9//8nu3buldetWHtPPP7+1rFu/3us6x48flz///FOSkpM8picnJUlkZGSRt6uoPlwqiHI9UlNTT2PvAABAcVJp0VX2PpETadU9mQWkRlfrZBZx3YK363Ak6LIBgOLXHpjvv/+B9O59szRsqAblE7njjtulZo0a8vHH093LDB3ypHzy8Ufu91Onvic3/O9/Ur9+Pf2+du3a0rVrV1m0aHGhtgsAAOxDjTflSoueN0hy5gqAvM0TKRU9uYjrFrzdMrEjPMbRAhDc/JqK/c233tZJJr75ep6uOVI1UwMGDpItW7Z4JLHInYhi5qxZUrVaVfl6/jy9vGq+N3PWbHlpzNhCbRcAANhL9phS/byMR7VXBzlKfvPUuuHha4u07qm2CwAuRtVqNfw+vLhq0lemTBn5999/3SnVXeLiykp4eITs37/fY3poaKjuV6WaAZ68ji/b9aVd5ZbNG6VBw0aW6HOlmjnSHhmcN+A7B1Z2pn6rsrLKyYH92U3948r1koiIZe7aI5UWXWcWzKosjpD9usle7pqlguYXdR5OD9c4sMO542ts4NeaK5djx47phzeJiYe9TlfZ/1TQVNTtAgAAezLN7I7lhnFUIiN/9ZinAp6IiOX5rlvQ/KLOAwAXRr0DAAC2Ypql9bNhJPu7KADggeAKAADYitNZRj8bDs/swQDgbwRXAADAVsyc4MpBzRUAiyG4AgAAtuJ0NQt00CwQgLUQXAEAAFsxndnBlcOgWSAAayG4AgAAtuI06XMFwJoIrgAAgE1rrmgWCMBaCK4AAICt0OcKgFURXAEAAFuhzxUAqyK4AgAAtuI0Y/Uz2QIBWA3BFQAAsBX6XAGwKoIrizNNhxw/3lYSdl+on9V7AACC2Yk+V6RiB2Atof4uAPKXltZZko48K05nNTl0MHuaw7FHysQ+I1FRCzh0AIDgrrliEGEAFkM1iIUDq8OJk8TprOIxXb1X09V8AACCepwrUrEDsBiCKwtSTf9UjZX3jyj7fdKRETQRBAAEHdMMFTGj9GsHzQIBWAzBlQWlp7fRTQHz/3gc4nRW18sBABCMTQIVaq4AWA3BlQU5syoX63IAAARek8BUMYwsfxcHADwQXFmQI2R/sS4HAECg1VxRawXAigiuLCg8fKXOCijizGcJpzgcCXo5AACCseaK/lYArIjgyoIMw6nTrWc7OcDKfl8mdoReDgCAYGI6yRQIwLoIrixKjWNVNq6fOBz7PKY7HHv1dMa5AgAE8wDCjHEFwIoIrixMBVCVKrfTTQCVMmWelkqV2xNYAQCCFn2uAFgZwZXFqaZ/ISH/6tchof/QFBAAENRO1Fwd8XdRACAPgisbMHIGSXTmtDMHACBY0ecKgJURXNmAw8i+O2fm3K0DACDYa64MR7K/iwIAeRBc2armKtbfRQEAwBJ9rkjFDsCKCK5swGEkeTSFAAAg2Me5YhBhAFZEcGUDrrtzrh8UAACCletGI6nYAVgRwZWNmgVScwUACHbOnGaB1FwBsCKCKxs1CyRbIAAg2LmSO9HnCoAVEVzZqebKJKEFACC4uW40UnMFwIoIrmyAmisAANRNxjARicz+bcy58QgAVkJwZadU7CS0AAAEMVd/K4WaKwBWRHBlA667c2psD9P0d2kAAPAP052GPUUMw8nHAMByCK5swDCO5LwKE9OM8nNpAADw7wDC1FoBsCqCKxswjDQxjEz9mqQWAIBg5WoeT38rAFZFcGUDhiESFpaqX5OOHQAQrBjjCoDVEVzZhCu4YiBhAECw97lyOJL9XRQA8IrgyiZCXTVXZAwEAAQp+lwBsDqCK5sICz2qn6m5AgAEK6eZndCCPlcArIrgyibcfa7MWH8XBQAAv3DdYHSN/wgAVkNwZbs+VycGUAQAIChrrgz6XAGwJoIru/W5yrlrBwBA0Pa5IqEFAIsiuLJds0CCKwBAsKdip1kgAGsiuLIJUrEDAIKdmdPvmFTsAKyK4MomwkJJaAEACG4MIgzA6giubIKaKwBAsDNJxQ7A4giubCI0LHucKxJaAACCFTVXAKyO4MpuNVcktAAABCHTDBeRSP2aQYQBWBXBld2yBTrLiGn6uzQAAPin1koxjBQOPwBLIriyibAw1w/JiTt3AAAEW38rw0gWw3D6uzgA4BXBlU2EhBwTkSz9mn5XAIBg4/rtU8EVAFgVwZVNGMaJQRMJrgAAwZspkOAKgHURXNmIqwMvSS0AAMFbc5X9WwgAVkRwZSOG44h+puYKABBsXDcWqbkCYGUEVzbiyGlnbjpj/V0UAAD8M8YVzQIBWBjBlY0YOc0CnYx1BQAIMmZOcOWgWSAACyO4shHXD4qZ0+4cAIBgaxboutEIAFZEcGXDhBbOnIxJAAAEW7NAVxN5ALAigisbJrSg5goAELSDCNPnCoCFEVzZsFmgk4QWAICgrbmiWSAA6wr1dwFKlSolV115pVSoWEE2bdokv/zya4HLX3zxxdK0SWOPaQcPHpTpM2Z6TKtSpYp07NhBYmNjJSFhj3z33XeSnp4uduZqZ844VwCAYGOa2ZlyqbkCYGV+rblSAdCSxd/Kfff1lYYNGsiE8W/IW29OLHCdq6/uJD16dJcyZcq4H9ExMR7LdOlynSz99We59pprpHatWjJ48MPyy88/SrVqVSUg+lyR0AIAEGTocwXADvxaczV06JNy5MgR6XL9DZKRkSFnn322DrauvrqzfPPNgnzX27R5s7zw4uh85z/80EMyZ86n8vgTT+r34eHhsnTpL3LrrbfKmDFjxa5co9KTih0AELx9rmgWCMC6/FZz5XA45OrOnWX27Dk6sFL++usvWblylXS57rpT1njd3ecu6d69m9SrVzfP/GPHjnk0AczMzJSszEw5evSoBELNFQktAABBO4gw2QIBWJjfgqvq1atLdHS0bN223WP6tu3b5Oyzzypw3ZjoGKl/1llyzdVXy5LFi+TBBx/wmP/4409Iq1at5LVXX5HBjzwsM2Z8IqtXr5GpU6flu01VuxUTE+N+qLJZtuaKZoEAgCBimhEiEpFzo5FU7ACsy2/NAl3BS3KyZ/V+0pEkKVUq/8Dmvfc+kCefHOp+f801V8vkSW/Lr7/+KvHxq/W0iMgIiYyMkPIVyktKSorExcVJclKyhIWF5bvdQQMHyODBj+SZ3qJFc0lLSxN/UzV0GRmlZME36l2ktGjRVkJCsmv8gILOG6Co3zmAVc6b48diZd9e9cop55/fQAzDLJG/A//g+wZ2OHeioqKsHVy5muiVjvEcELd0mdJyNC3/5nsqo2BuX3/9jfz777/SoUMHHVyFhITIO1Mmy6xZs+XF0S/pZdS0bxcukGFDh7j7YZ1s/ISJMmnyFI/gb+2aeFm7dp0O0Kxg3bo1+odFVTjGx2+VkJD//F0k2IDrpgPAuQO7fudkZmZfQBlGiqxeHV/s24f/8VsFq587qmWbpZsFJiQkSFraMalTt47H9Lp16sj27Z5NBU8ly+mU6FKl9OvKlStJ5cqVZenSZSfmZ2XJylWrpFmzpvluQ/XRUkGU65GamipWo+7UuZoGko4dABAsXM3hScMOwOr8FlypgGfxksXS9aYbdXILpVatWtK2bVv55usTmQIvvfQSublXz+zCOhzSoEGDPONeVataVZYtX67f79u3X9eKtWrV0r2MYRjSovl5smPHTrE70rEDAII1U6CDZBYALM6vqdhHjXpBvvryc5k1c4asW7dOj0/1008/y5dffeVeRo1V1bJlSz1IsAqSXh/3mq712rxli1SvXk2u79JFpk17T77//ge9vNPplGdHPicjnx0hdevWlb///lsuvLCj1KhRQwYO9Ex8YUfumitn9mCKAAAET80VadgBWJtfg6t//vlHLrv8Crn++uulYoUKMvK5UboPlWme6Ki65Lvv9LhWrtqua669TtdmNWnSRPeHmjRpsmzc6NkP68MPP5JlS5fpoEols/jo40/0dq3Sd6pYaq7M7B8aAAACnZmThp2aKwBW59fgSklMPCzvv/9BvvNPHkxYBV7fffe9fhRk67Zt+hFoXO3NSccOAAgWrhuK9LkCYHV+63OFonG4mwVScwUACA7UXAGwC4IrmzEcR/QzzQIBAMHCmZPQwvUbCABWRXBl25orEloAAIKDq7UGfa4AWB3BlW0TWngOvgwAQODXXGX3OwYAqyK4shlXGlr6XAEAggV9rgDYBcGVTZsFki0QABAsTHe2QMa5AmBtBFc24+rM6/qhAQAg0LluKNLnCoDVEVzZjMM9zhUJLQAAwcGkzxUAmyC4shnD1SyQmisAQBAwTXVDsbRH03gAsCqCK5tmCxQzSkwz3N/FAQCghEWKSPbvHdkCAVgdwZXNGMaJNLSuO3kAAASqE791TjGMFD+XBgAKRnBlM4ahflxy0rEz1hUAIFj6WxnJYhimv4sDAAUiuLLzQMIktQAABEumQAYQBmADBFc25K65yvnBAQAg0AcQzt0sHgCsiuDKzjVXZAwEAAQ4128dySwA2AHBlQ25Rqin5goAECw1V6RhB2AHBFc25PqBoeYKABDoqLkCYCcEVzauuSKhBQAgeGqu6HMFwPoIrmxcc0WzQABAoHO6UrHn3FgEACsjuLJzzRUJLQAAAc51I5E+VwDsgODKxtkCXU0lAAAI/JormgUCsD6CKxsyjCP6mZorAECgo88VADshuLJ1zVWsv4sCAECJos8VADshuLJzKvacdugAAAQq141EsgUCsAOCKxtytTs3SWgBAAhw9LkCYCcEVzbkcGT3uTLNaDHNUH8XBwCAEmGaufpckYodgA0QXNmQkWsgRTIGAgAClhkpImF5fvsAwKoIrmzIMLLEMFL0a6dJUgsAQGA6kRVX/e6l+rk0AHBqBFc2ZZDUAgAQ4FytM1StlWGY/i4OAJwSwZXd07HnDK4IAECgJrNwMIAwAJsguLIpIye4Ih07ACBQmTlDjtDfCoBdEFzZlMPIyRjIWFcAgABFzRUAuyG4svlYVyS0AAAEfs1VdmsNALA6giubcuT80FBzBQAI9GyBrhuKAGB1BFc2T2hxIk0tAACBxT2AMGNcAbCJUH8XAKeX0KIoNVem6ZD09DbizKosjpD9Eh6+UgzDeVrzSmq7dplntfK45iXsbivHj4cF9D4G+2dcUvtYnOeOXeZZrTx23P/iPm8yMs92J29S73OXFQCsiODKplwJLQqbLTAtrbMkHXlWnM5qJ7bl2CNlYp/Rr4syLypqQYls1y7zrLz/hw4G/j6eiXnBsP8n72NxnTt2mRcMn/GZ2P+SOm+OHbtRDuy/wF1WALAqo2q1GozK50VMTIxs2bxRGjRsJCkpKeJvrVu3kvj41e73aWnXyOHESRIevkLKV+jm0zbUD6daJ2+LUNedQEPdKyz0vFLRb8vR1PuKfbt2mRcM+x8M+xjs+x8M+8j+B8bnXzauHwFWgDn5Ggew4rnja2xAnyu797nyseZKNadQdwFz1j55azk/ZEWZJ3I0tW8JbNcu84Jh/4NhH4N9/4NhH9n/wPj8VY3WCP2bBgBWRLNAm3KlpfU1oYVu056r6YWXLRZxnvqBc5TAdu0yLxj2Pxj2Mdj3Pxj2kf0PjM/fIU5ndf2bFhGxvIDlAMA/uPVj85orXxNaqM7CAAAEAn7TAFgVwZXNa65Ms7SYZsgpl1dZmAAACAT8pgGwKoIrm3LkGlBRBVinotLbqixMJzoLn0x1HDaLME9tL7MEtmuXecGw/8Gwj8G+/8Gwj+x/YHz+TnE4EvRvGgBYEcGVTRlGhhjGUZ+TWqixQbLT23pry+7M9UPmLOQ8lWVqchHXDYR5wbD/wbCPwb7/wbCP7H9gfP4iZWJHMN4VAMsiuAqEpoE+9rtSY4OEha/KM93h2KtT25aN6ysOx75Cz4uNfVE/F2XdQJgXDPsfDPsY7PsfDPvI/gfG5884VwCsjHGubDrOlfLvgcWSmdlQypXvKRERS0+5jaysinJgv8quFC5lYh8Th5Gm262r5hWuUe9VeludWTCrcqHmnc66gTDPauVxzatTu63s3LU8oPcx2D/jktrH4jx37DLPauWx4/6fqfMGgYVxrhBI41wRXJ3mAfTnyfPfv59JRsb5Ehd3r0T6MGJ9cvIDkpL8mISFxUuFijeWYGlhFfxggXMHfOfA6vitQiAFVzQLDISBhH0Y60plFDyaeot+HR39fomXDQAAAAg2BFc2ZriCKx/6XB0/dqUeRNjhOCiRUV+fgdIBAAAAwYXgysYcRrLPCS1SU2/Xz1GlpothpJd42QAAAIBgQ3AVCDVXp2gWmJlRX9LTL9RpbEuV+ugMlQ4AAAAILgRXNuYwjvhUc5V69Fb9HBGxREJDE85I2QAAAIBgE+rvAqDojJxmgRmZDeX48bZeU9hmZtaQo6m99DQSWQAAAAAlh+DKptLSOkty8hP6dWZGMzl0cLY4HHukTOwzelrSkWd1AosTMsXpLOWn0gIAAACBj+DKpoHV4cRJqu7KY7rTWSXXdPOktULk8OG3RQxGtwcAAABKAn2ubEY191O1UtkMLx+nkc9Hmz096cgIvQ0AAAAAxYurbJtR/aiym/vl99EZXoIuF4c4ndX1NgAAAAAUL4Irm3FmVbbENgAAAAB4IriyGUfIfktsAwAAAIAngiubUenWVVZANSCwd6aXZBYuTnE4EvQ2AAAAABQvgiubUeNYudKt5w2wnLkCK2/zRMrEjnCPhQUAAAAgAIOr0FDfssKHh4dLqVKlPB6RkZEFrmMY+SV4sKeoqAVSNq6fOBz7PKY7HHv19LJxffOdp9YFAAAAEIDjXD300INyzz13S2yZMrJ9+3YZ/tTT8vPPv+S7/MiRI6T3zTfL8ePH3dO2bPlLrr2ui8dyjRqdI08/9ZS0bXuBpKamyqxZs+WlMWM91rMzFSRFRn6bnT0wq7LuR6Wa+7lqpQqaBwAAACDAaq7uvOMO6X9fP7n33r5ydoNz5PMvvpT3pk2T2rVrF7jegoUL9fKux8mBVb16deXzzz6Vv/76S5o2ay4tW50vCXv2yLnnNpJAooKliIjlElXqS/2cO3gqaB4AAACAAAuu7rn3bpk+Y6YsW7Zcjh07JuPGvS4HD/4nt9926ynXDQkJyXfeE088Ljt37pSnnxkhKSkpkp6eLu++O1XWrl1XzHsAAAAAAH4OruLiykq9unVl+fIVHtOXLV8uLVu2LHDdKy6/XLZv+0s2b/pTPvzgfTmrfn33PIfDIZdfdpnMnTvvlEEYAAAAANg+uKpQoaJ+PnTwoMf0gwcPSYUK5fNdb8vmLXLrbbdL/bMayKWXXS4ZGRkye/ZMKVu2rJ5fvnx5neQiJDRUFn27QAdh69etkeHDhkpYWFiBiTJiYmLcj+jo6GLbVwAAAACBz+8JLQyHZ3ynap5MM79xmkSmTnvP/XrPnr0y6IEH5bf1a+X667vIBx986M4MeF+/vnL7HXdKfPxqOe+88+Tjjz6QrKwseXH0S163O2jgABk8+JE801u0aC5paWnib6ofGcB5A75zYGX8VoHzBoH6nRMVFWXt4Gr//v36+eRaqgrly8v+Awd83o7KBLh33z6pXauWfn/o0CHdx+qzzz7XgZWyfv16mTlzlnTqdFW+wdX4CRNl0uQp7veq5mrtmnjdT0v127IC1/4AnDfgOwdWxW8VOG8QiN85qmWbpZsFJiUlyaZNm6Vjx47uaarWqUOH9rJy5SqP5noFjWOl+m5Vr1ZN9uzZo99nZmbK6tVrJCzcswmg2o5qQpgfFZCpIMr1UEEbAAAAANgiW+CEiROlZ4/uukmfCpBGPTdSB1KqeZ/LC8+Pkq/nz3MHSNM/+VgHYBUqVNDN/d6ZMkUSEw/Lp5997l7njTfGy4033CCdO3fWfbCuvPIK6dmzh8yaPccv+wkAAAAg8Pm1z5VquqfaLz7y8ENSoWJF2bRxk/Ts1dvdZFBRg/66+jyp2qXX3xgvA+7vL02aNJYjR47IipUr5f4BA+Xw4cPudX786Sd58KGH5aEHB0mNGjUkISFBnn/hRXn//Q/8sp8AAAAAAp/fE1p8/PEn+pGfYcOf8ni/fPly/TiVb75ZoB8AAAAAEPDNAgEAAAAgUBBcAQAAAEAxILgCAAAAgGJAcAUAAAAAgZDQwurUYMJWoLIq+jp4GcB5A75z4A/8VoHzBoH6neNrTEBwdYoDuHZNfPF9KgAAAABsHSOkpKTkO9+oWq2GeUZLZCOVK1eW1NRUS3yIKshr0bK1JcoDe+C8AecO+M6B1fFbBTudO+pv5h6P1xtqrgpwqoN3pqkTp6BIGeC8Ad858Dd+q8B5g0D9zvHl75DQAgAAAACKAcEVAAAAABQDgisbSE9Pl1deeVU/A5w34DsHVsRvFThvcCZZ9TuHhBYAAAAAUAyouQIAAACAYkBwBQAAAADFgOAKAAAAAIoBwZXFRUZGStOmTaVOnTr+LgosrGrVKtKo0TkSFRWV7zJlypSRZs2a6WWB3OrVqyttzj9foiIj8xyYkJAQOffcRtKgQQMOGjzOi3POOUeqV6+e71GpUaOG/s4pVaoURw5ahQoV9DVNzZo1+a1CvuLiyurfpHLlyuW7TMWKFeW8887Ty57OMiWBQYQtrHPnzvLaqy/LwUOHpHy5crJlyxa566675VBior+LBou48sor5MknnpDYsrFyOPGw1K5dSyZNmiwvv/Kqx3L39esnjz32qPzzz9/6R+3bbxfJAw8+JBkZGX4rO6wTWH09f54Ovi+97ArZvHmze17z5s3lnSmTxDRNCQsLk+TkZP0dtHXbNr+WGf514w03yMiRI+RIUpJkpKfL3//8IwMHPqDPD0Xd5Jk86W1p2/YC2bt3r1SrVk2eeuppmT5jJh9dkFLnxFtvTpQOHdrLtm3b9Dnx77//Sb/7+svWrVvdy/FbFdzq168ngwYNlIsuvEiqVKksAwc9IJ999rnHMoZhyOjRL0j3bt1k165dUrt2bZk0eYq89NKYQi1Tkqi5sihVuzBxwhvy6mvjpGPHi6RV6zb67t8LLz7v76LBQmrVrCn97x8grVu3kSuuvEpuve0OGThwgHTpcp17mbZt28rw4UOlT5+75ZJLL5eLLrpE2rdvJwMH3O/XssP/wsPD9QXPhx9+5HWeCqyWfPednN+mrbRo2Vq2bt0mb739pl/KCmu45JJL5I03xsnw4U/r3yYVkE+d+p6UL1/evcywoUOkfv36csEF7eSiiy+Vx594UsaMeUkaNmzo17LDf/r16yutW7eSDh0vks5XX6uvaRITE+X5USPdy/BbhbPPOluWLl0m7Tt0zPdg3HH77XJ9ly5y1VWd9fdP9x49pf99/eTqqzsXapmSRHBlUTf8739y7NhxmTp1mn5/9OhRmTzlHbm6c2d9hxlQ3p06TddouqxYsUK/V9XpLr169ZA1a9fKjz/9pN8n7Nkjc+Z8Kr169eQgBrmnhg+TP//cKHPnzc8z7+KLL9J3l8eNe12/V7VXb4wfL43PPVc39UJweuzRwbJgwUL58quv3NN+/PFH2blzp7u5YLduXeX9Dz5wt7JQd553794tPXt091u54V+q9c3ff/8jBw4c0O9Vq4nffvtNyuUKyvmtwoKFC2XWrNly7NixfA+GOk/mzZvvbkGxevUa+eWXXz2uaXxZpiQRXFlUkyZNZOPGjZKVleWetnbtOt00R7VzB7yJjY3V1d+uCx19LjVuIr//9rvHcmvXrdPNA9XyCN4mpZdddpkMf+ppr/ObNG6sL4T27t3nnrZ+/W/idDqlSZPGZ7CksIrSpUvLeec1k0WLF+vXqu+M6kOTm2qarG4Aqgvn3NS505jzJmhNe+89qVixgm6efuGFHeXWW2+RG2+8QV4e+4p7GX6rcCoOh0NfA//2u+c1zbp16/T54+syJY0+VxZVtmxZXWWem+t9XNkz2zEP9jFmzGg5cuSIzJo9xz2tbFz+55I6z9TyCC5VqlSRsWNekj533yOpqalel/F23qjA6siRJL6DglSlShX1hUuLFi1k6JAnZf/+/VKvXj1ZunSpDBj4gKSkpOjvFCUx8bDHuqoWS/XvQ3DauXOXfPLJdOnb91654vLLdK24qklYsXKlexl+q3AqMTExusm6t2sa13ePL8uUNGquLCozM0MiIiPyZA5U0jPS/VQqWNmo50ZKhw4d5M67+rg7liuZGRkSEeH9XMrgXApKo198QeLjV0toSKhuQtr43EZ6uqqRqls3OzNpZkamRETkzR4YGRkh6SRCCUoZGZn6uX27trr/ZqfO10i79h2lUaNG8uQTj3ssc/J3jspEmZ5OAp1g9cTjj8ktt/R2nzetz79ASkWXkvemveteht8qnIorCZe3axrX9Ywvy5Q0giuL2r07QapW8UyZ7UqhnZCwx0+lglWNGPGM3HjjjXJzr966D83J55KqqchNvU9PT5cDB/49wyWFFag7eBUqlJehQ5/Ujz597tLT+957j1x3XXYyFNVHRjXjUTUVLuqun8r6lZCQ4Leyw3/27dunL1zmzp0nhw9n10z9999/ug/WBRe00e8Tdu/Wz1W9fOck7OG8CVZXXHGFfP31N7q2Uzl+/LhMnz5T2rRp465N4LcKp5KWliaHDh3y/v2Sc23syzIljeDKon766Wd9NzD3GCKdrrpK9uzdK3/99ZdfywZreeaZp3W60V4395bf//gjz/yffv5ZJydQ1eQunTpdJcuWLZfMzOy7zAguDz8yWG64sav7MfjR7FqHBx58WMaPn6Bf//zzLxIdHS0Xduzo8R2kgvLly5b7rezwH/XZL1u2LM/NmqpVq8rBgwfdzf/++OMPueqqK93zVd9OFXyp3zUEp4OHDurzJLfq1arqIMvV0oLfKvhCfY+oPsMu6gbg5Zdfrs+fwixTkuhzZVHfLlok8fHxMvXdKTLu9Tekdq3auq3y4Ecf01m7AFfmrnvu7iMjRjyrm924sgSquzauLDnvv/+B3HpLb3nnnck65Xa7du30BXPXbmTuQv7U+TNjxkx55ZWx8vwLL0pkRIQ89dRweevtSYy1F8RGvzRWZs2crse2Wr9uvbS5oI2+WXPbbXe4l3lx9Evy/nvTdA3nH39s0Gm41VgzKkspgpNK16+Gdhjy5BM61XbdunXlkUcekQ8+/NCduIvfKpQuXVoa5UraVr9ePX1dc+Dff92Jul4b97p8PX+uvPTSi7J48RLp2vUmiYmJ1mN8uviyTEkyqlarwZW6Ram7xvf3v09atWopyckpMnv2HB10AS4vjx0jZ51VP88B+XXpMhk79mX3e3WnecD9/aVBwwZyYP8BmTptms4+CbgGbnzl5bEy6IGH5J9//nEfFJVW+6677pRLL7lEMrMyZeGChfLJ9BkctCDXtEkT6XN3H6lWtYr8889u+eDDj/JkB1Rj6d1+220SF1dWB1gTJ75JUB7k1EVyz549pHr1anLoUKIeQ0+l6c99w5jfquDWrFkzGfnsM3mmL1q0WCa++Zb7vcoG2K/vvVKtejXZsWOHvPnm2/L33397rOPLMiWF4AoAAAAAigF9rgAAAACgGBBcAQAAAEAxILgCAAAAgGJAcAUAAAAAxYDgCgAAAACKAcEVAAAAABQDgisAAAAAKAYEVwAAnMEBeNVgqgCAwBTq7wIAAJBbgwYNpE7t2nL8+HHZuWuX7Nq1y5YHqFmzZhIRHi6r4uPd03r16ilVq1aVlatW+bVsAICSQXAFALCENm3ayEujX5C4uDj57fffxRBD6tSpLUePHpVnRz4nS5cuEzvp3buXlCtXziO4+u233+Xvf/7xa7kAACWH4AoA4HdNmzaVGdM/lnffnSovjRkrmZmZ7nnnnHOOVKtWNc86arqq4UrYs0c2bNggTqfTPa9Fi+ZiGIZs/HOjNGnSREpFR8vatWslKSmpSNvZvHmLtG7VSkLDQmXJku/k7LPPlnMbNdLLJCUnyaZNm2Tv3n15thkdEyP/u/56Pe3XpUvlzz//lKioKI+/73A4pGXLFlKhQkXZsWOHbN682WN+YfYFAOBfBFcAAL8b8uQTsm37dnnhxdFimqbHPBW4qIdLdHS0THr7LTnnnIbyxx8bpF69upKSkip33HmX/Pvvv3qZ22+/XRqf20giIiJk9+4EqVSpolSqVEm6du0uW7dtK/R2okqVkr93/S1bt23VwVX9+vWlc+dOepmyZcvK+ee3ltdeGycT33xLTzv77LOkZs1aEhER7l5u0+bNeZoFVqhQQT75+ENdW6cCOBVk/fTzz3L//QPdQZ4v+wIAsAaCKwCAX4WGhkq7dm3lzbfezhNYefP008P1cu3ad5SMjAxdqzN50tvy9FPDZdADD7qXUwFQp87XyNatW/X7mTOnS//+98ngRx8r1HYaNmwo11x7nWzY8Kd72oIFC/TDpUnjxjJ37pcyd958+fvvv2Xu3HnSoUN73Syw//0D8t2XYUOHSFaWUy66+FJJS0uTmjVryqJvF8jNN/eSjz/+xOd9AQBYA8EVAMCvVM2PqpVJSEjwmH7xxRdL2dhY/frwkSPy448/SkhIiHS96SZ5//0P5KqrrtQBkXrs3btXrr32Wo/1VR8tVzCirFi+Qi666EL9ujDbWbFypUdg5RITE6Ob6VWoUF5CHCGSkpIizZo11cGVr7p0uU6eeHKIDqyUf/75Rz7/4kv53/+u9wiuCtoXAIB1EFwBAPwqNTVVP5eLi/OY3qF9O12T06RJYzl27JgOrlQzulKlSknjxudKtWrVPJZftswz4cXhw4c93qenp0tERKR+XZjtHNh/IE+Zr7nmannl5bE6k2FCwh697fDwcKlQvoLP+12+fHldBtXcMLddO3fJxRdd5PO+AACsg+AKAOBXqtZGJXFo0aKFx3TV/0p59tlnpH27dvq1qh1SfZE+/mS6bnpXVIXZjremiqOee07Gjn1Zpk57zz3tj9/Xi2H4XoYjR47oxB1l48p6TFfvDx065PuGAACWwSDCAAC/mzx5inTqdJVcdOGFp6zlio+Pl1tvvSXPvCpVqvj8905nOyq7X7lycbJt23b3NNVnTPWvyu1o6lHd3DE/KrBat26dXHP11R7bvrpzJ8bBAgCbouYKAOB302fMlHr16slHH30gX375laxbv16Sk5Oldq1acu0110j86tXuZYcMHSazZs6Q2bNnyrx583UA07FjB928bvhTT/v8N4u6HVXjtXjxEhk5coRMnvKObt53z9199Hhcua1f/5tOTHHHHbfL4cTDOhX7ydT4XbNmzpSsrCxZu26dXN/lOp2+fcL4CT7vBwDAOgiuAACW8PwLL8qs2XN0fyaVfe94+nGdevzue/rK+vXr3ctt3LhJLrn0cunZo7s0b95cEg8dknffnab7ZLmocaAchmfjjC1b/pLomB9PezvKoEEPyO133C6tW7eWxMRE6d37Vrmp602yffsO9zJfzZ0rkZGR0qpVSyldurROxX7yIMKrV6+Rzldfo8vQps35snTZcul//0A5lJhYqH0BAFiDUbVajVPnvQUAAAAAFIg+VwAAAABQDAiuAAAAAKAYEFwBAAAAQDEguAIAAACAYkBwBQAAAADFgOAKAAAAAIoBwRUAAAAAFAOCKwAAAAAoBgRXAAAAAFAMCK4AAAAAoBgQXAEAAABAMSC4AgAAAAA5ff8Ho2E2155OIKgAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Evolved best tree: (((Node6[a=-1.0,b=1.0,x=-2.0,y=2.0,r=0.97],(()Node8[a=-1.0,b=-1.0,x=-2.0,y=-1.0,r=1.6471840104761717],(Node898[a=-1.0,b=-1.0,x=-4.0,y=1.0,r=1.21])(Node10[a=-1.0,b=1.0,x=-3.0,y=2.0,r=1.9722911115144293])Node7[a=-1.0,b=1.0,x=-2.0,y=1.0,r=0.8375737439077295])Node5[a=0.0,b=-1.0,x=-1.0,y=0.0,r=0.62])Node3[a=-1.0,b=0.0,x=-1.0,y=1.0,r=1.14],((Node842[a=-1.0,b=0.0,x=-1.0,y=3.0,r=0.65])Node72[a=0.0,b=-1.0,x=0.0,y=3.0,r=0.93])Node2[a=1.0,b=1.0,x=1.0,y=2.0,r=0.6])Node1[a=0.0,b=1.0,x=0.0,y=1.0,r=0.82])Node_0[a=0,b=0,x=0,y=0,r=1]\n", "Evolved best fitness: 0.68\n", "Generation: 100\n", "Number of nodes in target tree: 20\n", "Execution time: 0 hours, 0 minutes, 48 seconds\n" ] } ], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations.** The Author was unable to find an error in the new version. So, we proceed further on. Moreover:\n", "1. To clearly perceive that the number of nodes is a measure of complexity, one needs large difference among the compared numbers of nodes, say of various hundreds. \n", "2. One gets the impression that globular target trees are easier to resolve by Evolution than trees with a rich structure of holes and large arms. \n", "3. Our measure of fitness does not seem to be perfect because in large globules, there could be too many cells in the symmetric difference of target and test trees, but nevertheless, the fitness is high. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# THE COST OF EVOLUTION\n", "\n", "Our purpose now is to study the cost of Evolution dependning on complexity. The cost is measured in employed chromosomes, that are given by the population size times the number of generations to achieve a given proportion of the fitness for a perfect solution that is one. Complexity is here measured by the number of nodes of the target tree.\n", "\n", "Two questions are important for us: \n", " 1. Does complexity smash Evolution? \n", " 2. There is a sign of pleiotropy? \n", "\n", "In this research these questions are ordinary because we always have found that when complexity increases, the maximal fitness decreases. Moreover, Evolution is very bad to overcome pliotropy.\n", "\n", "We have moreover an ethical reason to ask these questions because we have found in various cases that the fitness grows to its final value in th first 100 generations and remains invariable until generation 5000. Two possible hypotheses compete for being the correct explanation. First: the final observed value is the equilibrium value that is determined by the balance of resources and complexity. Second, the final value is not the equilibrium point, but rather a transient ecstasy state caused by plieotropy.\n", "\n", "In consequence, let us study the battle of Evolution against complexity in the case of Newick morphological trees to shed a light over the two posed questions. We have two complementary experiments. In the first, we increase the complexity, in the second we decrease resources. The first follows.\n", "\n", "**Exercise.** Program Evolution to battle diverse degrees of complexity.\n", ">\n", "> *Answer.* The following program depicts the dynamics of the fitness along the number of generations for different values of complexity to see whether complexity smashes Evolution. The inference of pleiotropy may arise from the increasing of fitness by jumps, and from a very frustrated advance of the fitness function.\n", ">\n", "> Our expectation: Complexity, given by the number of nodes in the target tree, must smash Evolution. We expect that the higher the complexity, the retarded the growth of the fitness and the lower the equilibrium state that operates as maximal value.\n", ">\n", "> Obtained results were inconsistent, sometimes the expectation was more or less fulfilled, most times not. \n", ">\n", "> What could be the cause of so much disorder?\n", ">\n", "> In our opinion, the most influent cause of disorder was that a great difference could exist between the number of nodes in the target tree, that defines the task that Evolution must solve, and the range of the number of nodes in the test trees. For instance: max-depth in the target tree: 40, number of nodes in the target tree: around 1000. Number of nodes in the test tree: 5. This may happen because the actual value of the depth for each tree is random but below the max-depth. So, test trees must pick up many fluctuations to grow enough to fit into the target tree. This can be done but takes time. As a consequence, the solution is retarded. Now, if the actual value of the number of nodes in the test trees is in the same range as that value in the test trees, we expect that the problem would be easier, i.e., solved earlier. Nevertheless, Evolution demands variability and so, it must be kept alive.\n", ">\n", "> The implementation of our homogenizing criterion, in the following program, was the following: the actual depth, the number of allowed cellular divisions along each branch, is the max depth plus a random value in the interval (-5,5). Line 332. The following code tests our ideas:\n", "\n" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-10-05T20:24:04.717922Z", "start_time": "2026-10-05T15:15:27.502286Z" } }, "source": [ "# Program evolution_of_newick_morphological_trees13.py\n", "# Evolution tries to restore three target morpho-trees\n", "# with different number of nodes.\n", "#\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\n", "# \n", "# In this version:\n", "# 1. Mutation has been inbuilt with the power\n", "# to have children in the immediate neighborhood of the father and \n", "# to change radius and the topology of trees\n", "# by means of insertion and deletion of leaves.\n", "# 2. Recombination has been enabled with the power \n", "# to exchange leaves between trees.\n", "# We expect a good proportion of the population to \n", "# be able to successfully recombine, mutate and reproduce.\n", "# 3. A cut-off for the length of the Newick string has been introduced \n", "# to avoid overgrowth of trees.\n", "# 4. The seed for the random number generator has been canceled \n", "# to allow different histories for different runs.\n", "#\n", "# A bug was corrected in the function that inserts a node.\n", "# The criterion for insertion was changed from \"a leaf is just after a left parenthesis\" \n", "# to \"a leaf begins with `N` that is preceded by a left parenthesis\".\n", "\n", "\n", "\n", "\n", "\n", "# We reuse pieces of code made with the help of Colab and Gemini 2.5 Flash.\n", "# With the assistance of the AI of Visual Studio Code.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 10,\n", " 'max_number_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': 100,\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001, \n", " 'crossover_rate': 0.001 \n", "}\n", "\n", " \n", "def print_parameters13(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"INITIAL TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"INITIAL GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence13(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_length13(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_groups13(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[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups13(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups13(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info13(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", "def extract_all_infos13(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_groups13(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info13(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates13(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_center13(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_groups13(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info13(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator13(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_coordinates13(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children13(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_generator13(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_generator13(max_depth, max_numb_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 = max_depth + random.randint(-5,5) # Number of allowed divisions along each branch.\n", " #print(f\"Actual depth: {actual_depth}\")\n", " # () is used as a marker for insertion. \n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center13(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children13(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence13(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# Connectivity issues are not checked.\n", "\n", "# A bug in the drawing procedure has been corrected, and the drawing of trees is now correct.\n", "# The bug was related to the function that finds the father of a node.\n", "\n", "# With the help of Gemini, Google and the AI of Visual Studio Code.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick13(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence13(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " #print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " # 7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def simplify13(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length13(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax13(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick13(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " #print(f\"Length of tokens: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid13, message13 = examine_subsequence13(tokens, j)\n", " if is_valid13:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid13, message13 = simplify13(newick_str)\n", " if is_valid13:\n", " return True, \"Valid morpho-tree.\"\n", " else:\n", " return False, \"Invalid morpho-tree.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "def test_validator13(max_depth, max_numb_children, max_node_radius):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax13(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax13(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " p, s = complete_tree_generator13(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax13(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " #print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area13(x,y,r):\n", " \"\"\"\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " \"\"\"\n", " #print(\"get_illuminated_area13\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick13(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles13(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves(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", " # Dectect 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", " # Descipher 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_groups13(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father13(newick_string, ax, tree_color='lightblue', diff_color='red'):\n", " \"\"\"Draw lines connecting leaves to fathers\"\"\"\n", "\n", " #print(\"\\n\\nconnect_leaves_to_father13\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " all_leaves = find_leaves(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info13(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_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_groups13(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info13(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_syntax13(newick_string)\n", " #print(f\"is valid at connect_leaves_to_father13(...) : {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_groups13(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "\n", "def draw_tree_links13(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_links13\")\n", " #print(f\"Newick String: {newick_string}\")\n", " length_string = len(newick_string)\n", " #print(f\"length_string: {length_string}\")\n", " groups = detect_all_groups13(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_links13\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " newick_string, number_of_groups = connect_leaves_to_father13(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_groups13(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", " [a0,b0,x0,y0,r0] = extract_info13(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_newick13(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos13(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles13(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area13(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick13(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links13(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree13(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " newick_tree = complete_tree_generator13(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator13(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax13(newick_tree)\n", " illuminated_coords = set()\n", " \n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos13(newick_tree)\n", " illuminated_coords = get_illuminated_area13(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " \"\"\"\n", " is_valid, message = validate_newick_syntax13(newick_tree)\n", " print(f\"is the Newick String valid?: {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", " \"\"\"\n", " return newick_tree, illuminated_coords\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick13(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator13(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "def get_illuminated_coordinates13(newick_string):\n", " \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n", " a,b,x,y,r = extract_all_infos13(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area13(x,y,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_illumination13(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_coordinates13(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", "\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_branch13(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_recombination13(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_position_branch_recombination13\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch13(newick_string1)\n", " branch2 = find_recombinant_branch13(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_recombination13(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_leaves_recombination13\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " leaves1 = find_leaves(newick_string1)\n", " leaves2 = find_leaves(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_squares13(newick_string):\n", " \"\"\"Detects all the occupied squares in a tree\"\"\"\n", " occupied_squares_set = set()\n", " groups = detect_all_groups13(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info13(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def newick_mutation_of_radius13(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_subsequence13(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_patterns13(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_branch13(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_patterns13(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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_generator13(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_13(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\"Group130: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center13(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares13(newick_string)\n", " info, occupied_squares_set = node_info_generator13(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_13(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\"Group130: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center13(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares13(newick_string)\n", " info, occupied_squares_set = node_info_generator13(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_13(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\"Group130: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center13(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares13(newick_string)\n", " info, occupied_squares_set = node_info_generator13(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_leaf13(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_13(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_13(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_13(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_leaf13(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_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", "def find_branch13(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_groups13(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", "\n", "def deletion_of_branch13(newick_string ):\n", " group, start = newick_find_all_groups13(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_branch13(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", "def newick_mutation_branches13(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_radius13(newick_string)\n", " newick_string = insertion_of_branch13(newick_string)\n", " newick_string = deletion_of_branch13(newick_string)\n", " return newick_string\n", "\n", "def newick_mutation_leaves13(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_radius13(newick_string)\n", " newick_string = insertion_of_leaf13(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n", " newick_string = deletion_of_leaf13(newick_string)\n", " return newick_string\n", "\n", "\n", "def plot_illuminated_area13(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 oneEvolutionaryProcess13(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_tree, target_illuminated_area = generate_and_test_morpho_tree13( ga_params)#926\n", " print(f\"target_tree: {target_tree} \\n\\n\")\n", " number_of_groups = count_groups13(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " length_of_target_tree = len(target_tree)\n", " #draw_newick_string13(target_tree, \"Target Tree\")\n", " # target_area = get_illuminated_coordinates13(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick13(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\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_illumination13(tree, target_illuminated_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 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_generator13(target_tree_params['max_depth'], target_tree_params['max_number_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_recombination13\n", " # child1, child2 = newick_position_branch_recombination13(p1, p2)\n", " # Crossover newick_leaves_recombination13\n", " child1, child2 =newick_leaves_recombination13(p1,p2)\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation_leaves13(child1, ga_params)\n", " mutated_child2 = newick_mutation_leaves13(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_generator13(target_tree_params['max_depth'], target_tree_params['max_number_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_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, number_of_groups\n", " \n", " return history, number_of_groups\n", " \n", "\n", "def draw_all_histories13(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", " 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-treeover Generations for Different number of nodes')\n", " plt.grid(True)\n", " plt.legend()\n", " plt.show()\n", "\n", " \n", "def main13(target_tree_params, ga_params):\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", " max_depth_values = [10, 25, 40 ] # Example max_depth values for different lengths of target sequence\n", " numbers_of_generations = [7001, 7002, 7003 ] # 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 = []\n", " numbers_of_nodes = [] # Example numbers of generations for different lengths of target sequence\n", " for i in range(len(max_depth_values)):\n", " target_tree_params['max_depth'] = max_depth_values[i]\n", " ga_params['generations'] = numbers_of_generations[i]\n", " ga_params['max_depth'] = max_depth_values[i]\n", " print(f\"\\nRunning evolutionary process for max_depth = {target_tree_params['max_depth']} and generations = {ga_params['generations']}\")\n", " history, number_of_groups = oneEvolutionaryProcess13(target_tree_params, ga_params)\n", " fitness_histories.append(history)\n", " numbers_of_nodes.append(number_of_groups)\n", " draw_all_histories13(fitness_histories, numbers_of_nodes)\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_parameters13(target_tree_params, ga_params)\n", " main13(target_tree_params, ga_params)\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_newick_morphological_trees13.py" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB: EVOLUTION OF NEWICK MORPHO-TREES\n", "PURPOSE: \n", " A random morpho-tree T is generated.\n", " Evolution tries to restore the tree T\n", " beginning from a population of random trees.\n", "EXPERIMENT 1791213327.6287968\n", "RECOMBINATION OF LEAVES: False\n", "INITIAL TARGET TREE PARAMETERS: {'max_depth': 10, 'max_number_children': 2, 'max_node_radius': 1.5}\n", "INITIAL GENETIC ALGORITHM PARAMETERS: {'pop_size': 70, 'generations': 100, 'max_depth': 10, 'max_number_children': 2, 'max_node_radius': 1.5, 'mutation_rate': 0.001, 'crossover_rate': 0.001}\n", "\n", "\n", "Running evolutionary process for max_depth = 10 and generations = 7001\n", "target_tree: ((((Node15[a=1.0,b=-1.0,x=3.0,y=0.0,r=1.09])Node10[a=1.0,b=1.0,x=2.0,y=1.0,r=0.67],(Node14[a=0.0,b=1.0,x=1.0,y=2.0,r=1.48],Node13[a=1.0,b=1.0,x=2.0,y=2.0,r=0.62])Node9[a=0.0,b=1.0,x=1.0,y=1.0,r=0.61])Node6[a=0.0,b=1.0,x=1.0,y=0.0,r=0.51],((Node12[a=-1.0,b=0.0,x=-2.0,y=-2.0,r=0.66],(Node17[a=-1.0,b=1.0,x=-3.0,y=-2.0,r=1.33])Node11[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=1.4])Node8[a=-1.0,b=0.0,x=-1.0,y=-2.0,r=0.81])Node5[a=-1.0,b=-1.0,x=0.0,y=-2.0,r=1.39])Node2[a=1.0,b=-1.0,x=1.0,y=-1.0,r=1.46],(Node4[a=1.0,b=0.0,x=0.0,y=1.0,r=0.66],((Node16[a=-1.0,b=1.0,x=-3.0,y=2.0,r=1.33])Node7[a=-1.0,b=-1.0,x=-2.0,y=1.0,r=0.69])Node3[a=0.0,b=1.0,x=-1.0,y=2.0,r=0.57])Node1[a=-1.0,b=1.0,x=-1.0,y=1.0,r=0.9])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 18\n", "\n", "--- Generation 1/7001, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7001, Best fitness 0.87302 ---\n", "\n", "--- Generation 2001/7001, Best fitness 0.96552 ---\n", "\n", "--- Generation 3001/7001, Best fitness 0.96552 ---\n", "\n", "--- Generation 4001/7001, Best fitness 0.96552 ---\n", "\n", "--- Generation 5001/7001, Best fitness 0.96552 ---\n", "\n", "--- Generation 6001/7001, Best fitness 0.96552 ---\n", "\n", "--- Generation 7001/7001, Best fitness 0.96552 ---\n", "\n", "Running evolutionary process for max_depth = 25 and generations = 7002\n", "target_tree: (((((((((((((((Node374[a=-1.0,b=1.0,x=-4.0,y=6.0,r=1.41])Node317[a=0.0,b=1.0,x=-3.0,y=5.0,r=0.94],(Node373[a=1.0,b=0.0,x=-1.0,y=4.0,r=1.34])Node316[a=1.0,b=0.0,x=-2.0,y=4.0,r=0.54])Node272[a=1.0,b=1.0,x=-3.0,y=4.0,r=0.7],((Node372[a=1.0,b=0.0,x=-1.0,y=2.0,r=1.37])Node315[a=1.0,b=-1.0,x=-2.0,y=2.0,r=0.99],Node314[a=1.0,b=0.0,x=-2.0,y=3.0,r=1.01])Node271[a=1.0,b=0.0,x=-3.0,y=3.0,r=0.59])Node232[a=0.0,b=1.0,x=-4.0,y=3.0,r=1.14],((Node371[a=0.0,b=1.0,x=-4.0,y=5.0,r=1.24])Node313[a=1.0,b=1.0,x=-4.0,y=4.0,r=1.33],Node312[a=0.0,b=1.0,x=-5.0,y=4.0,r=1.26])Node231[a=-1.0,b=1.0,x=-5.0,y=3.0,r=0.86])Node186[a=1.0,b=1.0,x=-4.0,y=2.0,r=0.92])Node94[a=-1.0,b=1.0,x=-5.0,y=1.0,r=0.6])Node57[a=-1.0,b=-1.0,x=-4.0,y=0.0,r=1.07])Node29[a=0.0,b=1.0,x=-3.0,y=1.0,r=1.26])Node23[a=0.0,b=1.0,x=-3.0,y=0.0,r=0.71])Node15[a=-1.0,b=1.0,x=-3.0,y=-1.0,r=1.43],(Node22[a=-1.0,b=0.0,x=-4.0,y=-2.0,r=0.56],(((((((((Node311[a=1.0,b=1.0,x=-7.0,y=5.0,r=1.08])Node230[a=0.0,b=1.0,x=-8.0,y=4.0,r=0.92],(((Node370[a=-1.0,b=1.0,x=-9.0,y=7.0,r=0.58])Node310[a=1.0,b=1.0,x=-8.0,y=6.0,r=1.19])Node270[a=0.0,b=1.0,x=-9.0,y=5.0,r=0.91],(Node369[a=0.0,b=1.0,x=-10.0,y=6.0,r=1.39],Node368[a=1.0,b=1.0,x=-9.0,y=6.0,r=1.34])Node269[a=-1.0,b=1.0,x=-10.0,y=5.0,r=1.21])Node229[a=-1.0,b=1.0,x=-9.0,y=4.0,r=0.97])Node185[a=-1.0,b=1.0,x=-8.0,y=3.0,r=1.36])Node151[a=1.0,b=1.0,x=-7.0,y=2.0,r=1.0],(Node228[a=-1.0,b=1.0,x=-9.0,y=3.0,r=0.81],Node227[a=1.0,b=1.0,x=-7.0,y=3.0,r=1.4])Node150[a=0.0,b=1.0,x=-8.0,y=2.0,r=0.52])Node116[a=-1.0,b=0.0,x=-8.0,y=1.0,r=1.11])Node93[a=0.0,b=1.0,x=-7.0,y=1.0,r=1.21])Node76[a=-1.0,b=1.0,x=-7.0,y=0.0,r=1.1])Node37[a=-1.0,b=0.0,x=-6.0,y=-1.0,r=1.32],(((((Node184[a=-1.0,b=1.0,x=-11.0,y=1.0,r=1.21],((Node309[a=-1.0,b=-1.0,x=-13.0,y=0.0,r=1.28])Node226[a=-1.0,b=1.0,x=-12.0,y=1.0,r=0.83],(Node268[a=-1.0,b=1.0,x=-13.0,y=1.0,r=1.11],Node267[a=-1.0,b=-1.0,x=-13.0,y=-1.0,r=1.41])Node225[a=-1.0,b=0.0,x=-12.0,y=0.0,r=0.55])Node183[a=-1.0,b=0.0,x=-11.0,y=0.0,r=0.9])Node149[a=-1.0,b=1.0,x=-10.0,y=0.0,r=0.96],(Node182[a=-1.0,b=0.0,x=-11.0,y=-1.0,r=1.09],(Node224[a=-1.0,b=1.0,x=-12.0,y=-1.0,r=1.27])Node181[a=-1.0,b=-1.0,x=-11.0,y=-2.0,r=0.8])Node148[a=-1.0,b=0.0,x=-10.0,y=-1.0,r=1.31])Node115[a=-1.0,b=1.0,x=-9.0,y=-1.0,r=1.28],((Node223[a=0.0,b=-1.0,x=-10.0,y=-3.0,r=1.43])Node180[a=-1.0,b=0.0,x=-10.0,y=-2.0,r=1.11])Node114[a=-1.0,b=0.0,x=-9.0,y=-2.0,r=0.91])Node75[a=-1.0,b=1.0,x=-8.0,y=-2.0,r=1.17])Node48[a=-1.0,b=-1.0,x=-7.0,y=-3.0,r=1.24],(((((((Node367[a=-1.0,b=1.0,x=-11.0,y=5.0,r=1.36])Node266[a=1.0,b=1.0,x=-10.0,y=4.0,r=0.8])Node222[a=-1.0,b=1.0,x=-11.0,y=3.0,r=1.22],((Node366[a=-1.0,b=0.0,x=-12.0,y=4.0,r=0.73])Node308[a=-1.0,b=1.0,x=-11.0,y=4.0,r=1.21])Node221[a=0.0,b=1.0,x=-10.0,y=3.0,r=1.16])Node179[a=-1.0,b=1.0,x=-10.0,y=2.0,r=0.55])Node147[a=-1.0,b=1.0,x=-9.0,y=1.0,r=1.29],(((((Node365[a=-1.0,b=-1.0,x=-14.0,y=2.0,r=1.31],Node364[a=0.0,b=1.0,x=-13.0,y=4.0,r=1.06])Node307[a=-1.0,b=0.0,x=-13.0,y=3.0,r=1.24])Node265[a=-1.0,b=1.0,x=-12.0,y=3.0,r=0.93],(Node363[a=-1.0,b=0.0,x=-13.0,y=2.0,r=0.55])Node264[a=-1.0,b=0.0,x=-12.0,y=2.0,r=1.4])Node220[a=-1.0,b=1.0,x=-11.0,y=2.0,r=1.02],Node219[a=1.0,b=1.0,x=-9.0,y=2.0,r=0.75])Node178[a=-1.0,b=1.0,x=-10.0,y=1.0,r=1.3])Node146[a=-1.0,b=0.0,x=-9.0,y=0.0,r=1.25])Node92[a=-1.0,b=1.0,x=-8.0,y=0.0,r=0.87])Node74[a=0.0,b=1.0,x=-7.0,y=-1.0,r=0.54],Node73[a=-1.0,b=1.0,x=-8.0,y=-1.0,r=1.31])Node47[a=-1.0,b=0.0,x=-7.0,y=-2.0,r=0.83])Node36[a=-1.0,b=-1.0,x=-6.0,y=-2.0,r=1.01])Node28[a=-1.0,b=0.0,x=-5.0,y=-1.0,r=0.97],((Node46[a=1.0,b=0.0,x=-5.0,y=-3.0,r=1.28],Node45[a=0.0,b=-1.0,x=-6.0,y=-4.0,r=1.43])Node35[a=-1.0,b=-1.0,x=-6.0,y=-3.0,r=0.84])Node27[a=-1.0,b=-1.0,x=-5.0,y=-2.0,r=1.41])Node21[a=-1.0,b=1.0,x=-4.0,y=-1.0,r=1.13])Node14[a=-1.0,b=0.0,x=-3.0,y=-2.0,r=1.2])Node10[a=-1.0,b=-1.0,x=-2.0,y=-2.0,r=0.64],(((((((((Node362[a=-1.0,b=1.0,x=-8.0,y=5.0,r=0.82])Node263[a=-1.0,b=1.0,x=-7.0,y=4.0,r=0.92],((Node361[a=-1.0,b=1.0,x=-7.0,y=6.0,r=0.97],Node360[a=0.0,b=1.0,x=-6.0,y=6.0,r=0.59])Node306[a=0.0,b=1.0,x=-6.0,y=5.0,r=0.53])Node262[a=0.0,b=1.0,x=-6.0,y=4.0,r=1.39])Node218[a=0.0,b=1.0,x=-6.0,y=3.0,r=1.13])Node177[a=0.0,b=1.0,x=-6.0,y=2.0,r=0.64],Node176[a=1.0,b=1.0,x=-5.0,y=2.0,r=1.09])Node91[a=-1.0,b=1.0,x=-6.0,y=1.0,r=0.98],Node90[a=-1.0,b=0.0,x=-6.0,y=0.0,r=0.52])Node72[a=-1.0,b=-1.0,x=-5.0,y=0.0,r=1.09])Node44[a=-1.0,b=-1.0,x=-4.0,y=1.0,r=1.42])Node34[a=-1.0,b=1.0,x=-3.0,y=2.0,r=1.22])Node20[a=0.0,b=1.0,x=-2.0,y=1.0,r=0.56])Node9[a=-1.0,b=1.0,x=-2.0,y=0.0,r=0.96])Node6[a=1.0,b=0.0,x=-1.0,y=-1.0,r=1.35])Node4[a=-1.0,b=1.0,x=-2.0,y=-1.0,r=1.04],(((((((Node71[a=0.0,b=-1.0,x=-5.0,y=-6.0,r=0.54],((((Node217[a=-1.0,b=-1.0,x=-8.0,y=-10.0,r=1.41])Node145[a=0.0,b=-1.0,x=-7.0,y=-9.0,r=0.94],(((Node359[a=-1.0,b=-1.0,x=-8.0,y=-12.0,r=1.39])Node216[a=0.0,b=-1.0,x=-7.0,y=-11.0,r=1.21],((Node305[a=-1.0,b=-1.0,x=-10.0,y=-13.0,r=0.7],Node304[a=1.0,b=-1.0,x=-8.0,y=-13.0,r=0.65])Node261[a=-1.0,b=-1.0,x=-9.0,y=-12.0,r=1.45])Node215[a=-1.0,b=-1.0,x=-8.0,y=-11.0,r=1.26])Node175[a=1.0,b=-1.0,x=-7.0,y=-10.0,r=0.82])Node144[a=-1.0,b=-1.0,x=-8.0,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0,r=1.08])Node153[a=1.0,b=-1.0,x=1.0,y=-8.0,r=1.08])Node118[a=1.0,b=0.0,x=0.0,y=-7.0,r=0.53],((((Node275[a=1.0,b=0.0,x=4.0,y=-11.0,r=0.56],(Node321[a=0.0,b=-1.0,x=4.0,y=-13.0,r=0.95],Node320[a=1.0,b=0.0,x=5.0,y=-12.0,r=0.78])Node274[a=1.0,b=-1.0,x=4.0,y=-12.0,r=0.66])Node234[a=1.0,b=-1.0,x=3.0,y=-11.0,r=1.36],((Node319[a=1.0,b=0.0,x=5.0,y=-10.0,r=1.33],Node318[a=1.0,b=-1.0,x=5.0,y=-11.0,r=1.11])Node273[a=1.0,b=0.0,x=4.0,y=-10.0,r=1.08])Node233[a=1.0,b=0.0,x=3.0,y=-10.0,r=0.53])Node187[a=1.0,b=-1.0,x=2.0,y=-10.0,r=1.01])Node152[a=1.0,b=-1.0,x=1.0,y=-9.0,r=1.08])Node117[a=1.0,b=-1.0,x=0.0,y=-8.0,r=0.9])Node77[a=-1.0,b=-1.0,x=-1.0,y=-7.0,r=0.86])Node58[a=1.0,b=-1.0,x=0.0,y=-6.0,r=0.6])Node49[a=0.0,b=-1.0,x=-1.0,y=-5.0,r=0.77])Node30[a=-1.0,b=-1.0,x=-1.0,y=-4.0,r=0.68])Node24[a=-1.0,b=0.0,x=0.0,y=-3.0,r=1.44])Node16[a=1.0,b=-1.0,x=1.0,y=-3.0,r=1.3])Node11[a=1.0,b=1.0,x=0.0,y=-2.0,r=0.64])Node7[a=1.0,b=1.0,x=-1.0,y=-3.0,r=1.37])Node5[a=0.0,b=-1.0,x=-2.0,y=-4.0,r=0.55])Node3[a=-1.0,b=-1.0,x=-2.0,y=-3.0,r=0.78])Node2[a=-1.0,b=-1.0,x=-1.0,y=-2.0,r=1.33])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=1.49])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 375\n", "\n", "--- Generation 1/7002, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7002, Best fitness 0.74687 ---\n", "\n", "--- Generation 2001/7002, Best fitness 0.77373 ---\n", "\n", "--- Generation 3001/7002, Best fitness 0.77901 ---\n", "\n", "--- Generation 4001/7002, Best fitness 0.79582 ---\n", "\n", "--- Generation 5001/7002, Best fitness 0.80357 ---\n", "\n", "--- Generation 6001/7002, Best fitness 0.8075 ---\n", "\n", "--- Generation 7001/7002, Best fitness 0.81281 ---\n", "\n", "Running evolutionary process for max_depth = 40 and generations = 7003\n", "target_tree: 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x=-12.0,y=23.0,r=0.6],Node1773[a=0.0,b=-1.0,x=-12.0,y=21.0,r=1.2])Node1640[a=-1.0,b=0.0,x=-12.0,y=22.0,r=0.9])Node1500[a=-1.0,b=1.0,x=-11.0,y=22.0,r=0.58])Node1385[a=1.0,b=1.0,x=-10.0,y=21.0,r=1.26],(((((((((((Node3559[a=-1.0,b=1.0,x=-19.0,y=29.0,r=0.82])Node3217[a=-1.0,b=1.0,x=-18.0,y=28.0,r=0.81])Node3045[a=-1.0,b=0.0,x=-17.0,y=27.0,r=1.1])Node2707[a=1.0,b=1.0,x=-16.0,y=27.0,r=0.54])Node2542[a=-1.0,b=1.0,x=-17.0,y=26.0,r=1.48])Node2384[a=0.0,b=1.0,x=-16.0,y=25.0,r=0.5])Node2225[a=-1.0,b=1.0,x=-16.0,y=24.0,r=1.42],(Node2383[a=-1.0,b=1.0,x=-16.0,y=23.0,r=1.01])Node2224[a=0.0,b=-1.0,x=-15.0,y=22.0,r=1.04])Node2071[a=-1.0,b=1.0,x=-15.0,y=23.0,r=1.45])Node1772[a=0.0,b=1.0,x=-14.0,y=22.0,r=1.24],((Node2070[a=-1.0,b=0.0,x=-17.0,y=20.0,r=0.83],(((Node2706[a=-1.0,b=0.0,x=-19.0,y=23.0,r=1.15],((Node3044[a=0.0,b=1.0,x=-20.0,y=25.0,r=0.95],((Node3394[a=-1.0,b=1.0,x=-21.0,y=27.0,r=1.32])Node3216[a=1.0,b=1.0,x=-20.0,y=26.0,r=0.69],((Node3558[a=-1.0,b=1.0,x=-23.0,y=28.0,r=1.16])Node3393[a=-1.0,b=1.0,x=-22.0,y=27.0,r=1.37])Node3215[a=0.0,b=1.0,x=-21.0,y=26.0,r=0.91])Node3043[a=-1.0,b=1.0,x=-21.0,y=25.0,r=1.19])Node2873[a=-1.0,b=0.0,x=-20.0,y=24.0,r=1.12])Node2705[a=-1.0,b=1.0,x=-19.0,y=24.0,r=0.78])Node2541[a=-1.0,b=1.0,x=-18.0,y=23.0,r=0.77])Node2223[a=0.0,b=1.0,x=-17.0,y=22.0,r=1.05],(Node2382[a=0.0,b=-1.0,x=-18.0,y=21.0,r=0.62])Node2222[a=-1.0,b=1.0,x=-18.0,y=22.0,r=0.82])Node2069[a=-1.0,b=1.0,x=-17.0,y=21.0,r=1.32])Node1918[a=-1.0,b=0.0,x=-16.0,y=20.0,r=1.25])Node1771[a=-1.0,b=-1.0,x=-15.0,y=20.0,r=1.29])Node1639[a=-1.0,b=1.0,x=-14.0,y=21.0,r=0.98])Node1499[a=-1.0,b=1.0,x=-13.0,y=20.0,r=1.06],((((Node2381[a=0.0,b=-1.0,x=-16.0,y=21.0,r=0.51],((((Node3214[a=0.0,b=1.0,x=-18.0,y=27.0,r=0.51],((Node3557[a=1.0,b=1.0,x=-19.0,y=28.0,r=1.21],Node3556[a=-1.0,b=1.0,x=-21.0,y=28.0,r=1.49])Node3392[a=-1.0,b=1.0,x=-20.0,y=27.0,r=1.37],Node3391[a=0.0,b=1.0,x=-19.0,y=27.0,r=0.71])Node3213[a=-1.0,b=0.0,x=-19.0,y=26.0,r=1.26])Node2872[a=-1.0,b=1.0,x=-18.0,y=26.0,r=1.43])Node2704[a=0.0,b=1.0,x=-17.0,y=25.0,r=1.17])Node2540[a=0.0,b=1.0,x=-17.0,y=24.0,r=0.71],((Node3042[a=-1.0,b=0.0,x=-19.0,y=25.0,r=1.29])Node2703[a=0.0,b=1.0,x=-18.0,y=25.0,r=0.51])Node2539[a=-1.0,b=1.0,x=-18.0,y=24.0,r=1.03])Node2380[a=-1.0,b=1.0,x=-17.0,y=23.0,r=0.6])Node2221[a=-1.0,b=1.0,x=-16.0,y=22.0,r=0.72])Node1917[a=-1.0,b=1.0,x=-15.0,y=21.0,r=0.96])Node1638[a=-1.0,b=1.0,x=-14.0,y=20.0,r=0.54])Node1498[a=-1.0,b=0.0,x=-13.0,y=19.0,r=1.2])Node1384[a=-1.0,b=-1.0,x=-12.0,y=19.0,r=1.11])Node1280[a=-1.0,b=1.0,x=-11.0,y=20.0,r=1.17],((Node1637[a=1.0,b=1.0,x=-11.0,y=21.0,r=1.16],(((Node2220[a=-1.0,b=1.0,x=-15.0,y=24.0,r=1.36])Node2068[a=-1.0,b=1.0,x=-14.0,y=23.0,r=1.24],(Node2219[a=-1.0,b=1.0,x=-14.0,y=24.0,r=1.1])Node2067[a=0.0,b=1.0,x=-13.0,y=23.0,r=0.76])Node1770[a=0.0,b=1.0,x=-13.0,y=22.0,r=0.92])Node1636[a=-1.0,b=1.0,x=-13.0,y=21.0,r=0.59])Node1383[a=-1.0,b=1.0,x=-12.0,y=20.0,r=0.79])Node1279[a=-1.0,b=0.0,x=-11.0,y=19.0,r=0.51])Node1182[a=-1.0,b=1.0,x=-10.0,y=19.0,r=0.66])Node1081[a=-1.0,b=1.0,x=-9.0,y=18.0,r=0.85])Node977[a=0.0,b=1.0,x=-8.0,y=17.0,r=1.35])Node882[a=0.0,b=1.0,x=-8.0,y=16.0,r=1.42],(((((Node1635[a=-1.0,b=1.0,x=-14.0,y=19.0,r=1.34])Node1497[a=-1.0,b=1.0,x=-13.0,y=18.0,r=1.19],Node1496[a=0.0,b=1.0,x=-12.0,y=18.0,r=1.19])Node1382[a=-1.0,b=0.0,x=-12.0,y=17.0,r=0.75])Node1181[a=-1.0,b=0.0,x=-11.0,y=17.0,r=1.14],Node1180[a=-1.0,b=1.0,x=-11.0,y=18.0,r=0.74])Node1080[a=-1.0,b=1.0,x=-10.0,y=17.0,r=1.22])Node881[a=-1.0,b=1.0,x=-9.0,y=16.0,r=1.01])Node795[a=0.0,b=1.0,x=-8.0,y=15.0,r=0.92],(((((((Node1769[a=-1.0,b=1.0,x=-15.0,y=19.0,r=1.24])Node1634[a=-1.0,b=1.0,x=-14.0,y=18.0,r=1.41])Node1495[a=-1.0,b=1.0,x=-13.0,y=17.0,r=1.15])Node1278[a=-1.0,b=0.0,x=-12.0,y=16.0,r=1.17])Node1179[a=-1.0,b=1.0,x=-11.0,y=16.0,r=0.93])Node1079[a=0.0,b=-1.0,x=-10.0,y=15.0,r=1.47])Node976[a=-1.0,b=1.0,x=-10.0,y=16.0,r=1.45])Node794[a=-1.0,b=1.0,x=-9.0,y=15.0,r=0.76])Node713[a=0.0,b=1.0,x=-8.0,y=14.0,r=1.09])Node641[a=-1.0,b=1.0,x=-8.0,y=13.0,r=1.17])Node504[a=1.0,b=1.0,x=-7.0,y=12.0,r=0.66])Node424[a=-1.0,b=1.0,x=-8.0,y=11.0,r=0.86])Node367[a=-1.0,b=1.0,x=-7.0,y=10.0,r=1.26])Node249[a=-1.0,b=-1.0,x=-6.0,y=9.0,r=0.91])Node206[a=-1.0,b=1.0,x=-5.0,y=10.0,r=0.91])Node164[a=-1.0,b=1.0,x=-4.0,y=9.0,r=0.63],(((((((Node793[a=-1.0,b=0.0,x=-7.0,y=14.0,r=0.96],(Node880[a=-1.0,b=-1.0,x=-8.0,y=12.0,r=0.52])Node792[a=-1.0,b=-1.0,x=-7.0,y=13.0,r=1.05])Node640[a=0.0,b=1.0,x=-6.0,y=14.0,r=0.57])Node576[a=-1.0,b=1.0,x=-6.0,y=13.0,r=0.65])Node503[a=-1.0,b=1.0,x=-5.0,y=12.0,r=1.43])Node423[a=-1.0,b=1.0,x=-4.0,y=11.0,r=0.71],(Node502[a=-1.0,b=-1.0,x=-5.0,y=9.0,r=0.76])Node422[a=-1.0,b=0.0,x=-4.0,y=10.0,r=1.07])Node302[a=0.0,b=1.0,x=-3.0,y=10.0,r=0.71])Node248[a=1.0,b=1.0,x=-3.0,y=9.0,r=1.14])Node163[a=-1.0,b=0.0,x=-4.0,y=8.0,r=1.04])Node137[a=0.0,b=1.0,x=-3.0,y=8.0,r=1.1])Node110[a=-1.0,b=0.0,x=-3.0,y=7.0,r=0.78])Node94[a=-1.0,b=1.0,x=-2.0,y=7.0,r=1.27])Node79[a=-1.0,b=1.0,x=-1.0,y=6.0,r=1.48])Node39[a=-1.0,b=1.0,x=0.0,y=5.0,r=0.99])Node20[a=1.0,b=1.0,x=1.0,y=4.0,r=1.24],(Node30[a=1.0,b=0.0,x=1.0,y=2.0,r=0.91])Node19[a=0.0,b=-1.0,x=0.0,y=2.0,r=0.66])Node12[a=1.0,b=1.0,x=0.0,y=3.0,r=1.37])Node5[a=-1.0,b=1.0,x=-1.0,y=2.0,r=0.64])Node3[a=1.0,b=0.0,x=0.0,y=1.0,r=0.52])Node1[a=-1.0,b=1.0,x=-1.0,y=1.0,r=1.12])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 3726\n", "\n", "--- Generation 1/7003, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7003, Best fitness 0.88463 ---\n", "\n", "--- Generation 2001/7003, Best fitness 0.89139 ---\n", "\n", "--- Generation 3001/7003, Best fitness 0.89304 ---\n", "\n", "--- Generation 4001/7003, Best fitness 0.89628 ---\n", "\n", "--- Generation 5001/7003, Best fitness 0.89728 ---\n", "\n", "--- Generation 6001/7003, Best fitness 0.89968 ---\n", "\n", "--- Generation 7001/7003, Best fitness 0.90041 ---\n", "draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Execution time: 5 hours, 8 minutes, 36 seconds\n" ] } ], "execution_count": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations from three experiments.**\n", "1. Three problems were given to Evolution, the first with 18 nodes, the second with 375 and the third with 3726. The easiest one for Evolution was that with 18 nodes, followed by that with 3726. At last, the problem with 375 resulted to be the most difficult for Evolution during a period of observation of 7000 generations.\n", "2. No difference of the fitness behaviour over generations for 1273 and 2073 nodes. These curves were both over the curve of 69 nodes during the registered 7000 generations. This contradicts our expectation.\n", "3. There is no difference between the fitness curves of 11 and 454 nodes. And, the complex case, with 3254 nodes, resulted to be the easiest one for Evolution. This is a frontal contradiction against our expectation that said: when complexity, given by the number of nodes grows, the fitness curve must be delayed and tend to a lower equilibrium value. This type of result is the norm for globular morpho-trees.\n", "\n", "**Exercise.** Study the effect of decreasing resources over the silhouette and relative position of the fitness function over generations. \n", ">\n", "> *Answer.* In the following program we depict the fitness function over generations for various values of population sizes." ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-10-06T11:49:13.239480Z", "start_time": "2026-10-06T11:30:26.587757Z" } }, "source": [ "# Program evolution_of_newick_morphological_trees14.py\n", "\n", "# To measure the effect of decreasing resources,\n", "# we gave to Evolution a problem that must be solved with\n", "# various population sizes. Concretely:\n", "# Evolution uses different population sizes\n", "# to restore various target morpho-trees with different number of nodes.\n", "#\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\n", "# \n", "# In this version:\n", "# 1. Mutation has been inbuilt with the power\n", "# to have children in the immediate neighborhood of the father and \n", "# to change radius and the topology of trees\n", "# by means of insertion and deletion of leaves.\n", "# 2. Recombination has been enabled with the power \n", "# to exchange leaves between trees.\n", "# We expect a good proportion of the population to \n", "# be able to successfully recombine, mutate and reproduce.\n", "# 3. A cut-off for the length of the Newick string has been introduced \n", "# to avoid overgrowth of trees.\n", "# 4. The seed for the random number generator has been canceled \n", "# to allow different histories for different runs.\n", "#\n", "# A bug was corrected in the function that inserts a node.\n", "# The criterion for insertion was changed from \"a leaf is just after a left parenthesis\" \n", "# to \"a leaf begins with `N` that is preceded by a left parenthesis\".\n", "#\n", "# Purpose: to study the effect of the decreasing of resources over Evolution.\n", "# To that aim, we depict various fitness functions for diverse\n", "# values of population sizes.\n", "\n", "\n", "\n", "\n", "# We reuse pieces of code made with the help of Colab and Gemini 2.5 Flash.\n", "# With the assistance of the AI of Visual Studio Code.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 10,\n", " 'max_number_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': 100,\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001, \n", " 'crossover_rate': 0.001 \n", "}\n", "\n", " \n", "def print_parameters14(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"INITIAL TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"INITIAL GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence14(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_length14(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_groups14(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_groups14(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups14(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups14(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info14(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", "def extract_all_infos14(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_groups14(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info14(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates14(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_center14(newick_string, j):\n", " \"\"\" Finds the address of the father of a node at j\"\"\"\n", " #print(\"find_father_center14(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_groups14(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info14(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator14(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_generator14(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates14(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children14(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_generator14(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_generator14(max_depth, max_numb_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_generator14(...)\")\n", " occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n", " actual_depth = max_depth + random.randint(-5,5) # Number of allowed divisions along each branch.\n", " #print(f\"Actual depth: {actual_depth}\")\n", " # () is used as a marker for insertion. \n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center14(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children14(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence14(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# Connectivity issues are not checked.\n", "\n", "# A bug in the drawing procedure has been corrected, and the drawing of trees is now correct.\n", "# The bug was related to the function that finds the father of a node.\n", "\n", "# With the help of Gemini, Google and the AI of Visual Studio Code.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick14(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence14(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " #print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " #7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def simplify14(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length14(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax14(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick14(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " #print(f\"Length of tokens: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid14, message14 = examine_subsequence14(tokens, j)\n", " if is_valid14:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid14, message14 = simplify14(newick_str)\n", " if is_valid14:\n", " return True, \"Valid morpho-tree.\"\n", " else:\n", " return False, \"Invalid morpho-tree.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "def test_validator14(max_depth, max_numb_children, max_node_radius):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax14(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax14(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " p, s = complete_tree_generator14(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax14(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " #print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area14(x,y,r):\n", " \"\"\"\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " \"\"\"\n", " #print(\"get_illuminated_area14\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick14(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles14(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves(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", " # Dectect 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", " # Descipher 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_groups14(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father14(newick_string, ax, tree_color='lightblue', diff_color='red'):\n", " \"\"\"Draw lines connecting leaves to fathers\"\"\"\n", "\n", " #print(\"\\n\\nconnect_leaves_to_father14\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " all_leaves = find_leaves(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info14(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_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_groups14(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info14(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_syntax14(newick_string)\n", " #print(f\"is valid at connect_leaves_to_father14(...) : {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_groups14(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "\n", "def draw_tree_links14(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_links14\")\n", " #print(f\"Newick String: {newick_string}\")\n", " length_string = len(newick_string)\n", " #print(f\"length_string: {length_string}\")\n", " groups = detect_all_groups14(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_links14\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " newick_string, number_of_groups = connect_leaves_to_father14(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_groups14(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", " [a0,b0,x0,y0,r0] = extract_info14(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_newick14(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos14(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles14(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area14(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick14(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links14(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree14(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " newick_tree = complete_tree_generator14(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator14(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax14(newick_tree)\n", " illuminated_coords = set()\n", " \n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos14(newick_tree)\n", " illuminated_coords = get_illuminated_area14(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " \"\"\"\n", " is_valid, message = validate_newick_syntax14(newick_tree)\n", " print(f\"is the Newick String valid?: {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", " \"\"\"\n", " return newick_tree, illuminated_coords\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick14(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator14(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "def get_illuminated_coordinates14(newick_string):\n", " \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n", " a,b,x,y,r = extract_all_infos14(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area14(x,y,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_illumination14(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_coordinates14(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", "\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_branch14(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_recombination14(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_position_branch_recombination14\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch14(newick_string1)\n", " branch2 = find_recombinant_branch14(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_recombination14(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_leaves_recombination14\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " leaves1 = find_leaves(newick_string1)\n", " leaves2 = find_leaves(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_squares14(newick_string):\n", " \"\"\"Detects all the occupied squares in a tree\"\"\"\n", " occupied_squares_set = set()\n", " groups = detect_all_groups14(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info14(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def newick_mutation_of_radius14(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_subsequence14(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_patterns14(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_branch14(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_patterns14(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node15[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node14[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_generator14(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_14(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\"Group140: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center14(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares14(newick_string)\n", " info, occupied_squares_set = node_info_generator14(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_14(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\"Group140: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center14(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares14(newick_string)\n", " info, occupied_squares_set = node_info_generator14(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_14(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\"Group140: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center14(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares14(newick_string)\n", " info, occupied_squares_set = node_info_generator14(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_leaf14(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_14(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_14(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_14(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_leaf14(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_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", "def find_branch14(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_groups14(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", "\n", "def deletion_of_branch14(newick_string ):\n", " group, start = newick_find_all_groups14(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_branch14(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", "def newick_mutation_branches14(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_radius14(newick_string)\n", " newick_string = insertion_of_branch14(newick_string)\n", " newick_string = deletion_of_branch14(newick_string)\n", " return newick_string\n", "\n", "def newick_mutation_leaves14(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_radius14(newick_string)\n", " newick_string = insertion_of_leaf14(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n", " newick_string = deletion_of_leaf14(newick_string)\n", " return newick_string\n", "\n", "\n", "def plot_illuminated_area14(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 evolutionaryProcess14(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_tree, target_illuminated_area = generate_and_test_morpho_tree14( ga_params)#926\n", " print(f\"target_tree: {target_tree} \\n\\n\")\n", " number_of_groups = count_groups14(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " length_of_target_tree = len(target_tree)\n", " #draw_newick_string14(target_tree, \"Target Tree\")\n", " # target_area = get_illuminated_coordinates14(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick14(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\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_illumination14(tree, target_illuminated_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 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_generator14(target_tree_params['max_depth'], target_tree_params['max_number_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_recombination14\n", " # child1, child2 = newick_position_branch_recombination14(p1, p2)\n", " # Crossover newick_leaves_recombination14\n", " child1, child2 =newick_leaves_recombination14(p1,p2)\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation_leaves14(child1, ga_params)\n", " mutated_child2 = newick_mutation_leaves14(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_generator14(target_tree_params['max_depth'], target_tree_params['max_number_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_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, number_of_groups\n", " \n", " return history, number_of_groups\n", " \n", "\n", "def draw_all_histories14(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", " # By the AI of VSCode. \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", "def main14(target_tree_params, ga_params):\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 = [7001, 7002, 7003, 7004, 7005 ] # 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 = []\n", " numbers_of_nodes = [] # Example numbers of generations for different lengths of target sequence\n", " for i in range(len(population_sizes)):\n", " target_tree_params['pop_size'] = population_sizes[i]\n", " ga_params['generations'] = numbers_of_generations[i]\n", " print(f\"\\nRunning evolutionary process for max_depth = {target_tree_params['max_depth']} and generations = {ga_params['generations']}\")\n", " history14, number_of_groups14 = evolutionaryProcess14(target_tree_params, ga_params)\n", " fitness_histories.append(history14)\n", " numbers_of_nodes.append(number_of_groups14)\n", " draw_all_histories14(fitness_histories, numbers_of_nodes, population_sizes)\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_parameters14(target_tree_params, ga_params)\n", " main14(target_tree_params, ga_params)\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_newick_morphological_trees14.py" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB: EVOLUTION OF NEWICK MORPHO-TREES\n", "PURPOSE: \n", " A random morpho-tree T is generated.\n", " Evolution tries to restore the tree T\n", " beginning from a population of random trees.\n", "EXPERIMENT 1791286226.727942\n", "RECOMBINATION OF LEAVES: False\n", "INITIAL TARGET TREE PARAMETERS: {'max_depth': 10, 'max_number_children': 2, 'max_node_radius': 1.5}\n", "INITIAL GENETIC ALGORITHM PARAMETERS: {'pop_size': 70, 'generations': 100, 'max_depth': 10, 'max_number_children': 2, 'max_node_radius': 1.5, 'mutation_rate': 0.001, 'crossover_rate': 0.001}\n", "\n", "\n", "Running evolutionary process for max_depth = 10 and generations = 7001\n", "target_tree: ((((Node5[a=-1.0,b=1.0,x=1.0,y=0.0,r=0.69],(Node6[a=0.0,b=-1.0,x=1.0,y=-3.0,r=1.4])Node4[a=-1.0,b=-1.0,x=1.0,y=-2.0,r=0.58])Node3[a=0.0,b=-1.0,x=2.0,y=-1.0,r=0.65])Node2[a=1.0,b=1.0,x=2.0,y=0.0,r=1.4])Node1[a=1.0,b=-1.0,x=1.0,y=-1.0,r=0.62])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 7\n", "\n", "--- Generation 1/7001, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7001, Best fitness 0.80769 ---\n", "\n", "--- Generation 2001/7001, Best fitness 0.80769 ---\n", "\n", "--- Generation 3001/7001, Best fitness 0.80769 ---\n", "\n", "--- Generation 4001/7001, Best fitness 0.80769 ---\n", "\n", "--- Generation 5001/7001, Best fitness 0.80769 ---\n", "\n", "--- Generation 6001/7001, Best fitness 0.80769 ---\n", "\n", "--- Generation 7001/7001, Best fitness 0.80769 ---\n", "\n", "Running evolutionary process for max_depth = 10 and generations = 7002\n", "target_tree: (((((Node27[a=1.0,b=-1.0,x=1.0,y=-2.0,r=1.07])Node19[a=-1.0,b=-1.0,x=0.0,y=-1.0,r=1.43],((((Node63[a=0.0,b=-1.0,x=4.0,y=-3.0,r=1.19])Node50[a=0.0,b=-1.0,x=4.0,y=-2.0,r=1.23])Node37[a=1.0,b=1.0,x=4.0,y=-1.0,r=0.93])Node26[a=1.0,b=-1.0,x=3.0,y=-2.0,r=0.51])Node18[a=1.0,b=-1.0,x=2.0,y=-1.0,r=0.74])Node13[a=1.0,b=-1.0,x=1.0,y=0.0,r=1.35],(((Node49[a=1.0,b=0.0,x=2.0,y=3.0,r=1.29],Node48[a=1.0,b=1.0,x=2.0,y=4.0,r=0.9])Node36[a=0.0,b=1.0,x=1.0,y=3.0,r=0.78])Node17[a=0.0,b=1.0,x=1.0,y=2.0,r=0.84])Node12[a=1.0,b=0.0,x=1.0,y=1.0,r=0.86])Node6[a=1.0,b=0.0,x=0.0,y=1.0,r=1.47],((((Node35[a=-1.0,b=1.0,x=-1.0,y=4.0,r=0.91])Node25[a=0.0,b=1.0,x=0.0,y=3.0,r=1.48])Node11[a=1.0,b=0.0,x=0.0,y=2.0,r=1.47])Node9[a=1.0,b=0.0,x=-1.0,y=2.0,r=0.8],(((((Node62[a=0.0,b=-1.0,x=-3.0,y=5.0,r=1.39],Node61[a=-1.0,b=-1.0,x=-4.0,y=5.0,r=1.36])Node47[a=-1.0,b=1.0,x=-3.0,y=6.0,r=1.17],(Node60[a=1.0,b=-1.0,x=0.0,y=5.0,r=0.53],Node59[a=-1.0,b=0.0,x=-2.0,y=6.0,r=1.18])Node46[a=1.0,b=1.0,x=-1.0,y=6.0,r=1.31])Node34[a=0.0,b=1.0,x=-2.0,y=5.0,r=1.01],(Node45[a=1.0,b=1.0,x=0.0,y=4.0,r=0.75])Node33[a=1.0,b=-1.0,x=-1.0,y=3.0,r=1.44])Node24[a=1.0,b=0.0,x=-2.0,y=4.0,r=1.11])Node16[a=-1.0,b=1.0,x=-3.0,y=4.0,r=0.57],((Node44[a=-1.0,b=0.0,x=-5.0,y=1.0,r=1.41])Node23[a=-1.0,b=-1.0,x=-4.0,y=1.0,r=0.59],(((Node58[a=-1.0,b=-1.0,x=-6.0,y=1.0,r=1.15],Node57[a=-1.0,b=1.0,x=-6.0,y=3.0,r=1.0])Node43[a=-1.0,b=0.0,x=-5.0,y=2.0,r=1.48],(Node56[a=-1.0,b=-1.0,x=-6.0,y=2.0,r=1.2])Node42[a=-1.0,b=1.0,x=-5.0,y=3.0,r=1.25])Node32[a=-1.0,b=1.0,x=-4.0,y=2.0,r=1.09])Node22[a=0.0,b=-1.0,x=-3.0,y=1.0,r=1.15])Node15[a=-1.0,b=-1.0,x=-3.0,y=2.0,r=0.69])Node8[a=0.0,b=1.0,x=-2.0,y=3.0,r=1.42])Node5[a=-1.0,b=1.0,x=-2.0,y=2.0,r=1.48])Node2[a=-1.0,b=1.0,x=-1.0,y=1.0,r=0.5],((((((((Node41[a=-1.0,b=-1.0,x=1.0,y=-5.0,r=0.83],(Node55[a=-1.0,b=-1.0,x=0.0,y=-5.0,r=0.94],Node54[a=1.0,b=-1.0,x=2.0,y=-5.0,r=1.24])Node40[a=-1.0,b=0.0,x=1.0,y=-4.0,r=0.98])Node31[a=0.0,b=-1.0,x=2.0,y=-4.0,r=1.46],(Node53[a=-1.0,b=0.0,x=0.0,y=-3.0,r=0.87])Node30[a=-1.0,b=0.0,x=1.0,y=-3.0,r=1.3])Node21[a=0.0,b=-1.0,x=2.0,y=-3.0,r=0.59],((Node39[a=0.0,b=-1.0,x=3.0,y=-5.0,r=0.73])Node29[a=0.0,b=-1.0,x=3.0,y=-4.0,r=1.44],((Node52[a=0.0,b=1.0,x=5.0,y=-3.0,r=1.28],Node51[a=0.0,b=-1.0,x=5.0,y=-5.0,r=0.84])Node38[a=1.0,b=0.0,x=5.0,y=-4.0,r=0.95])Node28[a=1.0,b=-1.0,x=4.0,y=-4.0,r=1.11])Node20[a=1.0,b=-1.0,x=3.0,y=-3.0,r=1.19])Node14[a=1.0,b=-1.0,x=2.0,y=-2.0,r=1.42])Node10[a=1.0,b=1.0,x=1.0,y=-1.0,r=1.44])Node7[a=1.0,b=-1.0,x=0.0,y=-2.0,r=0.51])Node4[a=1.0,b=-1.0,x=-1.0,y=-1.0,r=0.6])Node3[a=-1.0,b=0.0,x=-2.0,y=0.0,r=0.71])Node1[a=-1.0,b=0.0,x=-1.0,y=0.0,r=1.45])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 64\n", "\n", "--- Generation 1/7002, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7002, Best fitness 0.72973 ---\n", "\n", "--- Generation 2001/7002, Best fitness 0.82143 ---\n", "\n", "--- Generation 3001/7002, Best fitness 0.82609 ---\n", "\n", "--- Generation 4001/7002, Best fitness 0.84058 ---\n", "\n", "--- Generation 5001/7002, Best fitness 0.84058 ---\n", "\n", "--- Generation 6001/7002, Best fitness 0.84058 ---\n", "\n", "--- Generation 7001/7002, Best fitness 0.84058 ---\n", "\n", "Running evolutionary process for max_depth = 10 and generations = 7003\n", "target_tree: (((((((((Node56[a=-1.0,b=1.0,x=-7.0,y=-3.0,r=1.24])Node28[a=1.0,b=1.0,x=-6.0,y=-4.0,r=0.89])Node17[a=-1.0,b=1.0,x=-7.0,y=-5.0,r=1.16])Node15[a=-1.0,b=-1.0,x=-6.0,y=-6.0,r=1.42])Node11[a=-1.0,b=-1.0,x=-5.0,y=-5.0,r=1.37])Node8[a=-1.0,b=-1.0,x=-4.0,y=-4.0,r=1.34])Node5[a=-1.0,b=-1.0,x=-3.0,y=-3.0,r=1.06])Node3[a=-1.0,b=-1.0,x=-2.0,y=-2.0,r=0.84],(((((((((Node55[a=-1.0,b=0.0,x=-5.0,y=-8.0,r=0.89])Node45[a=1.0,b=-1.0,x=-4.0,y=-8.0,r=1.13],(Node54[a=-1.0,b=1.0,x=-7.0,y=-7.0,r=1.09])Node44[a=-1.0,b=-1.0,x=-6.0,y=-8.0,r=0.66])Node37[a=0.0,b=-1.0,x=-5.0,y=-7.0,r=0.81],Node36[a=-1.0,b=-1.0,x=-6.0,y=-7.0,r=1.22])Node27[a=-1.0,b=-1.0,x=-5.0,y=-6.0,r=1.3],(Node35[a=1.0,b=1.0,x=-2.0,y=-5.0,r=1.25],(Node53[a=0.0,b=-1.0,x=-2.0,y=-8.0,r=0.52])Node34[a=1.0,b=-1.0,x=-2.0,y=-7.0,r=1.28])Node26[a=1.0,b=-1.0,x=-3.0,y=-6.0,r=0.92])Node21[a=-1.0,b=-1.0,x=-4.0,y=-5.0,r=1.12])Node14[a=-1.0,b=0.0,x=-3.0,y=-4.0,r=1.18],(((((Node52[a=1.0,b=0.0,x=-2.0,y=-9.0,r=1.36],Node51[a=1.0,b=-1.0,x=-2.0,y=-10.0,r=1.44])Node43[a=0.0,b=-1.0,x=-3.0,y=-9.0,r=1.24],(Node50[a=0.0,b=-1.0,x=-4.0,y=-10.0,r=0.91])Node42[a=-1.0,b=-1.0,x=-4.0,y=-9.0,r=0.58])Node33[a=1.0,b=-1.0,x=-3.0,y=-8.0,r=0.88])Node25[a=0.0,b=-1.0,x=-4.0,y=-7.0,r=1.34],(((Node49[a=1.0,b=0.0,x=0.0,y=-6.0,r=1.36])Node41[a=1.0,b=0.0,x=-1.0,y=-6.0,r=0.64],(Node48[a=0.0,b=-1.0,x=-1.0,y=-8.0,r=1.03])Node40[a=1.0,b=-1.0,x=-1.0,y=-7.0,r=0.82])Node32[a=1.0,b=1.0,x=-2.0,y=-6.0,r=1.39])Node24[a=1.0,b=-1.0,x=-3.0,y=-7.0,r=0.56])Node20[a=-1.0,b=-1.0,x=-4.0,y=-6.0,r=0.94])Node13[a=-1.0,b=-1.0,x=-3.0,y=-5.0,r=0.85])Node10[a=-1.0,b=-1.0,x=-2.0,y=-4.0,r=1.43])Node7[a=-1.0,b=0.0,x=-1.0,y=-3.0,r=0.77],(((((((Node47[a=1.0,b=0.0,x=4.0,y=-4.0,r=0.99])Node31[a=1.0,b=-1.0,x=3.0,y=-4.0,r=1.36])Node23[a=1.0,b=1.0,x=2.0,y=-3.0,r=0.8],(((Node46[a=1.0,b=0.0,x=0.0,y=-5.0,r=1.13])Node39[a=0.0,b=-1.0,x=-1.0,y=-5.0,r=0.91])Node30[a=-1.0,b=0.0,x=-1.0,y=-4.0,r=0.81])Node22[a=-1.0,b=0.0,x=0.0,y=-4.0,r=1.16])Node19[a=0.0,b=-1.0,x=1.0,y=-4.0,r=0.71],((Node38[a=-1.0,b=1.0,x=2.0,y=-2.0,r=1.29])Node29[a=1.0,b=1.0,x=3.0,y=-3.0,r=0.79])Node18[a=1.0,b=-1.0,x=2.0,y=-4.0,r=0.76])Node16[a=0.0,b=-1.0,x=1.0,y=-3.0,r=1.46])Node12[a=1.0,b=-1.0,x=1.0,y=-2.0,r=0.52])Node9[a=1.0,b=1.0,x=0.0,y=-1.0,r=1.3])Node6[a=-1.0,b=1.0,x=-1.0,y=-2.0,r=0.91])Node4[a=0.0,b=-1.0,x=0.0,y=-3.0,r=1.09])Node2[a=1.0,b=-1.0,x=0.0,y=-2.0,r=1.31])Node1[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=1.44])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 57\n", "\n", "--- Generation 1/7003, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7003, Best fitness 0.71338 ---\n", "\n", "--- Generation 2001/7003, Best fitness 0.71795 ---\n", "\n", "--- Generation 3001/7003, Best fitness 0.71975 ---\n", "\n", "--- Generation 4001/7003, Best fitness 0.72152 ---\n", "\n", "--- Generation 5001/7003, Best fitness 0.72152 ---\n", "\n", "--- Generation 6001/7003, Best fitness 0.72611 ---\n", "\n", "--- Generation 7001/7003, Best fitness 0.72611 ---\n", "\n", "Running evolutionary process for max_depth = 10 and generations = 7004\n", "target_tree: (((Node8[a=1.0,b=1.0,x=-1.0,y=1.0,r=0.55])Node5[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.41],(((Node19[a=1.0,b=-1.0,x=-2.0,y=-3.0,r=1.14],Node18[a=-1.0,b=1.0,x=-4.0,y=-1.0,r=0.71])Node14[a=0.0,b=-1.0,x=-3.0,y=-2.0,r=1.48],(Node17[a=1.0,b=0.0,x=-1.0,y=-2.0,r=0.87],Node16[a=-1.0,b=-1.0,x=-3.0,y=-3.0,r=1.0])Node13[a=1.0,b=-1.0,x=-2.0,y=-2.0,r=0.85])Node10[a=-1.0,b=0.0,x=-3.0,y=-1.0,r=0.65])Node4[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=1.02])Node2[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.71],(((Node15[a=1.0,b=0.0,x=2.0,y=1.0,r=0.79])Node7[a=0.0,b=1.0,x=1.0,y=1.0,r=0.7],((Node12[a=1.0,b=0.0,x=4.0,y=-1.0,r=0.63],Node11[a=-1.0,b=-1.0,x=2.0,y=-2.0,r=1.11])Node9[a=1.0,b=0.0,x=3.0,y=-1.0,r=1.15])Node6[a=1.0,b=-1.0,x=2.0,y=-1.0,r=1.23])Node3[a=1.0,b=1.0,x=1.0,y=0.0,r=0.64])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=1.22])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 20\n", "\n", "--- Generation 1/7004, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7004, Best fitness 0.85 ---\n", "\n", "--- Generation 2001/7004, Best fitness 0.88333 ---\n", "\n", "--- Generation 3001/7004, Best fitness 0.96429 ---\n", "\n", "--- Generation 4001/7004, Best fitness 0.96429 ---\n", "\n", "--- Generation 5001/7004, Best fitness 0.96429 ---\n", "\n", "--- Generation 6001/7004, Best fitness 0.96429 ---\n", "\n", "--- Generation 7001/7004, Best fitness 0.96429 ---\n", "\n", "Running evolutionary process for max_depth = 10 and generations = 7005\n", "target_tree: (((((Node39[a=0.0,b=1.0,x=-2.0,y=3.0,r=0.72])Node20[a=0.0,b=1.0,x=-2.0,y=2.0,r=1.32])Node8[a=-1.0,b=1.0,x=-2.0,y=1.0,r=1.01])Node4[a=-1.0,b=-1.0,x=-1.0,y=0.0,r=1.33],(((((Node38[a=0.0,b=-1.0,x=2.0,y=-1.0,r=0.81])Node26[a=1.0,b=-1.0,x=2.0,y=0.0,r=1.46],(Node37[a=1.0,b=-1.0,x=3.0,y=0.0,r=0.51],Node36[a=-1.0,b=-1.0,x=1.0,y=0.0,r=1.32])Node25[a=1.0,b=0.0,x=2.0,y=1.0,r=1.16])Node19[a=1.0,b=-1.0,x=1.0,y=1.0,r=1.09],((Node35[a=0.0,b=1.0,x=-2.0,y=5.0,r=0.52],Node34[a=-1.0,b=-1.0,x=-3.0,y=3.0,r=0.76])Node24[a=-1.0,b=1.0,x=-2.0,y=4.0,r=0.67])Node18[a=-1.0,b=1.0,x=-1.0,y=3.0,r=1.28])Node12[a=1.0,b=1.0,x=0.0,y=2.0,r=1.22])Node7[a=0.0,b=-1.0,x=-1.0,y=1.0,r=0.57],(((Node33[a=1.0,b=1.0,x=3.0,y=5.0,r=1.12],Node32[a=0.0,b=1.0,x=2.0,y=5.0,r=1.31])Node17[a=1.0,b=0.0,x=2.0,y=4.0,r=0.88],Node16[a=-1.0,b=1.0,x=0.0,y=5.0,r=1.09])Node11[a=1.0,b=1.0,x=1.0,y=4.0,r=0.72],((Node23[a=1.0,b=0.0,x=2.0,y=3.0,r=1.25],(Node31[a=-1.0,b=0.0,x=-1.0,y=4.0,r=0.8],Node30[a=-1.0,b=1.0,x=-1.0,y=5.0,r=1.36])Node22[a=-1.0,b=1.0,x=0.0,y=4.0,r=1.48])Node15[a=0.0,b=1.0,x=1.0,y=3.0,r=0.97],((Node29[a=1.0,b=-1.0,x=4.0,y=1.0,r=0.56])Node21[a=1.0,b=0.0,x=3.0,y=2.0,r=1.49])Node14[a=1.0,b=0.0,x=2.0,y=2.0,r=1.34])Node10[a=1.0,b=-1.0,x=1.0,y=2.0,r=0.52])Node6[a=1.0,b=1.0,x=0.0,y=3.0,r=1.46])Node3[a=-1.0,b=1.0,x=-1.0,y=2.0,r=0.98])Node2[a=0.0,b=1.0,x=0.0,y=1.0,r=0.52],((((Node28[a=1.0,b=-1.0,x=-2.0,y=-4.0,r=1.31],Node27[a=-1.0,b=0.0,x=-4.0,y=-3.0,r=1.28])Node13[a=0.0,b=-1.0,x=-3.0,y=-3.0,r=1.26])Node9[a=-1.0,b=0.0,x=-3.0,y=-2.0,r=0.81])Node5[a=-1.0,b=-1.0,x=-2.0,y=-2.0,r=0.94])Node1[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=0.72])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 40\n", "\n", "--- Generation 1/7005, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7005, Best fitness 0.74783 ---\n", "\n", "--- Generation 2001/7005, Best fitness 0.77982 ---\n", "\n", "--- Generation 3001/7005, Best fitness 0.80952 ---\n", "\n", "--- Generation 4001/7005, Best fitness 0.8381 ---\n", "\n", "--- Generation 5001/7005, Best fitness 0.86275 ---\n", "\n", "--- Generation 6001/7005, Best fitness 0.86275 ---\n", "\n", "--- Generation 7001/7005, Best fitness 0.86275 ---\n", "draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Execution time: 0 hours, 18 minutes, 46 seconds\n" ] } ], "execution_count": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations from three experiments.** As usually, the relations are entangled at the beginning, thus, we pay attention only to the final behaviour.\n", ">\n", "> First Experiment:\n", ">> 1. The problem with 64 nodes and a population size of 25 was easier for Evolution than another with 57 nodes and a population size of 50. This is contrary to our expectations that predcits the cost of a problem in proportion to the number of nodes and in inverse relation to availbale resources.\n", ">\n", "> Second Experiment:\n", ">> 1. The number of nodes is not a good indicator of complexity because a problem with 8 nodes was easier than others with 11, 16, and 70.\n", ">> 2. The fitness rises by jumps with periods of ecstasy that are the larger the more advanced is the process.\n", ">\n", "> Third Experiment:\n", ">> 1. The results are incompatible with our expectations and criteria. According to our presuppositions, the easiest case is that with the minimum number of nodes, 23, which is solved by the most powerful team, a population size of 200. In consequence, the corresponding fitness curve must be the upper one because it is solved straightforwardly by Evolution. But, the graph shows that it is the lowest one.\n", ">>\n", ">> 2. The fitness advances by jumps and the periods of ecstasy are the larger the advanced is the Evolutionary Process. A possible explanation is that the fitness grows by making changes. Tiny productive changes are burnt at first and then, the size of changes must grow and that is why they become scarce. This can be explained also by pleiotropy.\n", "\n", "\n", "\n", "It is clear that we lack a correct description of complexity for morpho-trees. If we had one, results would show that Evolution is smashed by complexity. We have failed. But, before throwing our presuppositions to the garbage, let us make a last effort that reflects the idea that there must be hidden variables that mesh everything.\n", "\n", "\n", "**Exercise.** Previous observations have induced us to believe that morpho-trees fall into two different complexity evolutionary classes: easy and difficult. Easy trees are globular. This is possibly due to the immense number of trees that can approximately generate the same frontier for the case of globular trees. Most generated trees are globular. On the other hand, complex trees have a rich distribution of holes and arms. To test this hypothesis, we need in first place, a procedure to create trees with a rich structure. Make a try.\n", ">\n", "> *Answer.* The following program creates trees with the following procedure: an ordinary target tree, presumably globular, is trimmed, i.e., subject to a number of branch deletions. Resultant trees must have a rich structure.\n", "\n" ] }, { "cell_type": "code", "metadata": {}, "source": [ "# Program morpho_trimmed_tree_generator_and_drawer.py\n", "# This program generates a globular morpho tree,\n", "# trims it, by deleting at random a good number of branches, and draws it.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 10,\n", " 'max_number_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': 100,\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001, \n", " 'crossover_rate': 0.001 \n", "}\n", "\n", " \n", "def print_parameters15(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"INITIAL TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"INITIAL GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,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 grammmar 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", "\n", "def replace_str_subsequence15(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_length15(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_groups15(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[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups15(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups15(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info15(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", "def extract_all_infos15(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_groups15(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info15(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates15(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_center15(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_groups15(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info15(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator15(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_coordinates15(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children15(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_generator15(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_generator15(max_depth, max_numb_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 = max_depth - random.randint(0,5) # 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[a=0,b=0,x=0,y=0,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_center15(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children15(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence15(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 tree: {tree}\")\n", " return tree\n", "\n", "\n", "def find_branch15(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_groups15(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_branch15(newick_string ):\n", " group, start = newick_find_all_groups15(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_branch15(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", "def get_neighboring_coordinates15(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", "\n", "def detect_all_groups15(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_groups11b(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " '''\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " '''\n", " return groups\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", "def extract_all_infos15(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_groups15(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info15(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "def find_father_center15(newick_string, j):\n", " ''' Finds the address of the father of a node at j'''\n", " #print(\"find_father_center11(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_groups15(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info15(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def get_illuminated_area15(x,y,r):\n", " '''\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " '''\n", " #print(\"get_illuminated_area11\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick4(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles15(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves_newick15(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", " # Dectect 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", " # Descipher 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_groups15(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father15(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_leaves_newick15(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info15(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_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_groups15(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info15(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", " groups = detect_all_groups15(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_links15(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_groups15(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_father15(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_groups15(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", " [a0,b0,x0,y0,r0] = extract_info15(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_newick15(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos15(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 15))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles15(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", " \n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area15(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick4(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links15(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "if __name__ == \"__main__\":\n", " # Example Newick string\n", " # newick_string = \"((((((()Node63[a=1.0,b=-1.0,x=5.0,y=-3.0,r=1.26])Node52[a=1.0,b=-1.0,x=4.0,y=-2.0,r=0.7])Node21[a=1.0,b=1.0,x=3.0,y=-1.0,r=1.9613197090647045],((()Node219[a=0.0,b=1.0,x=-2.0,y=3.0,r=1.06])((((Node156[a=-1.0,b=-1.0,x=-2.0,y=2.0,r=0.71])Node125[a=0.0,b=1.0,x=-1.0,y=3.0,r=1.37])Node103[a=-1.0,b=1.0,x=-1.0,y=2.0,r=1.36],(((Node198[a=-1.0,b=1.0,x=-4.0,y=1.0,r=0.75])Node155[a=-1.0,b=0.0,x=-3.0,y=0.0,r=0.58])Node124[a=-1.0,b=-1.0,x=-2.0,y=0.0,r=1.6749306080259698],((((()Node913[a=-1.0,b=0.0,x=-4.0,y=3.0,r=0.59]))Node698[a=0.0,b=1.0,x=-3.0,y=3.0,r=0.84])()Node600[a=-1.0,b=1.0,x=-3.0,y=2.0,r=1.25])()Node123[a=0.0,b=-1.0,x=-1.0,y=0.0,r=1.33])Node102[a=-1.0,b=0.0,x=-1.0,y=1.0,r=1.33])Node51[a=-1.0,b=1.0,x=0.0,y=1.0,r=0.54],(((()Node122[a=-1.0,b=0.0,x=0.0,y=3.0,r=1.17])Node81[a=-1.0,b=1.0,x=1.0,y=3.0,r=0.85])Node62[a=0.0,b=1.0,x=2.0,y=2.0,r=1.07],(((()Node153[a=-1.0,b=1.0,x=3.0,y=5.0,r=0.515683679559608],Node152[a=0.0,b=-1.0,x=4.0,y=3.0,r=1.03])Node101[a=1.0,b=1.0,x=4.0,y=4.0,r=0.5748403340449614])Node80[a=0.0,b=1.0,x=3.0,y=3.0,r=1.22],()Node79[a=1.0,b=0.0,x=4.0,y=2.0,r=1.36])Node61[a=1.0,b=1.0,x=3.0,y=2.0,r=0.5295884877970901])Node50[a=1.0,b=1.0,x=2.0,y=1.0,r=1.21])Node29[a=0.0,b=1.0,x=1.0,y=0.0,r=1.43])Node20[a=-1.0,b=1.0,x=1.0,y=-1.0,r=1.1])Node8[a=1.0,b=0.0,x=2.0,y=-2.0,r=0.64])Node3[a=1.0,b=-1.0,x=1.0,y=-2.0,r=1.37],((((((()Node100[a=0.0,b=1.0,x=-2.0,y=-2.0,r=1.05],((Node150[a=0.0,b=1.0,x=-4.0,y=-1.0,r=0.7334891191596699],(Node193[a=-1.0,b=1.0,x=-6.0,y=0.0,r=0.5820889162363478])Node149[a=-1.0,b=1.0,x=-5.0,y=-1.0,r=0.81])Node120[a=-1.0,b=0.0,x=-4.0,y=-2.0,r=1.36],()Node119[a=0.0,b=1.0,x=-3.0,y=-1.0,r=1.16])Node99[a=-1.0,b=1.0,x=-3.0,y=-2.0,r=1.47])Node49[a=-1.0,b=1.0,x=-2.0,y=-3.0,r=1.26],()((()Node118[a=0.0,b=-1.0,x=-3.0,y=-4.0,r=1.0],((()Node367[a=-1.0,b=-1.0,x=-7.0,y=-3.0,r=1.8764650499221425])()Node147[a=-1.0,b=0.0,x=-5.0,y=-3.0,r=0.82],Node146[a=-1.0,b=1.0,x=-5.0,y=-2.0,r=1.13])Node117[a=-1.0,b=0.0,x=-4.0,y=-3.0,r=1.13])Node78[a=-1.0,b=1.0,x=-3.0,y=-3.0,r=1.0329473493338008])Node48[a=-1.0,b=0.0,x=-2.0,y=-4.0,r=0.76])Node28[a=-1.0,b=0.0,x=-1.0,y=-4.0,r=1.46])Node14[a=1.0,b=-1.0,x=0.0,y=-4.0,r=1.06])Node7[a=0.0,b=-1.0,x=-1.0,y=-3.0,r=0.89])Node5[a=0.0,b=-1.0,x=-1.0,y=-2.0,r=0.71],(((((((()Node47[a=-1.0,b=1.0,x=4.0,y=-4.0,r=0.6058398421466735],((()Node441[a=0.0,b=-1.0,x=8.0,y=0.0,r=1.2970045651723827])((((()Node145[a=1.0,b=1.0,x=8.0,y=1.0,r=0.52],()Node144[a=0.0,b=1.0,x=7.0,y=1.0,r=0.96])Node116[a=1.0,b=1.0,x=7.0,y=0.0,r=1.235712154728331],()Node115[a=1.0,b=0.0,x=7.0,y=-1.0,r=0.95])Node98[a=1.0,b=1.0,x=6.0,y=-1.0,r=1.37],(()Node114[a=1.0,b=0.0,x=7.0,y=-2.0,r=0.62])Node97[a=1.0,b=0.0,x=6.0,y=-2.0,r=1.04])Node77[a=1.0,b=1.0,x=5.0,y=-2.0,r=1.01])Node59[a=-1.0,b=1.0,x=4.0,y=-3.0,r=0.62],(((Node185[a=1.0,b=0.0,x=9.0,y=-5.0,r=1.25],()Node476[a=1.0,b=-1.0,x=9.0,y=-6.0,r=0.87])Node142[a=1.0,b=-1.0,x=8.0,y=-5.0,r=1.7146586553405185])Node96[a=1.0,b=1.0,x=7.0,y=-4.0,r=1.49],((Node183[a=1.0,b=-1.0,x=8.0,y=-6.0,r=0.54])Node141[a=1.0,b=-1.0,x=7.0,y=-5.0,r=0.5185357042371559],(()Node303[a=1.0,b=-1.0,x=9.0,y=-4.0,r=0.69])(Node181[a=1.0,b=0.0,x=8.0,y=-3.0,r=1.16])Node140[a=1.0,b=1.0,x=7.0,y=-3.0,r=1.44])Node95[a=0.0,b=1.0,x=6.0,y=-4.0,r=1.1])Node58[a=1.0,b=-1.0,x=6.0,y=-5.0,r=0.72])Node46[a=0.0,b=1.0,x=5.0,y=-4.0,r=0.79])Node36[a=1.0,b=0.0,x=5.0,y=-5.0,r=1.02])Node27[a=1.0,b=-1.0,x=4.0,y=-5.0,r=0.71])Node19[a=1.0,b=0.0,x=3.0,y=-4.0,r=0.83],(Node57[a=0.0,b=-1.0,x=3.0,y=-6.0,r=0.83])Node18[a=1.0,b=-1.0,x=3.0,y=-5.0,r=1.36])Node13[a=0.0,b=-1.0,x=2.0,y=-4.0,r=1.9563373261553025],((((((()Node94[a=1.0,b=1.0,x=2.0,y=3.0,r=1.5714886013535794])Node76[a=1.0,b=0.0,x=1.0,y=2.0,r=1.7607046194653915])Node56[a=-1.0,b=1.0,x=0.0,y=2.0,r=1.43])Node45[a=-1.0,b=1.0,x=1.0,y=1.0,r=0.71])Node26[a=0.0,b=1.0,x=2.0,y=0.0,r=0.81],(((()Node55[a=1.0,b=0.0,x=5.0,y=-1.0,r=0.68])Node44[a=0.0,b=-1.0,x=4.0,y=-1.0,r=1.39])Node35[a=1.0,b=0.0,x=4.0,y=0.0,r=1.5450652146942652])Node25[a=1.0,b=1.0,x=3.0,y=0.0,r=0.8])Node17[a=-1.0,b=1.0,x=2.0,y=-1.0,r=0.84])Node12[a=1.0,b=1.0,x=3.0,y=-2.0,r=0.59])Node10[a=1.0,b=0.0,x=2.0,y=-3.0,r=1.31],((((((()Node74[a=0.0,b=1.0,x=-3.0,y=-5.0,r=0.97],(((()Node139[a=0.0,b=1.0,x=-5.0,y=-4.0,r=0.85])Node113[a=0.0,b=1.0,x=-5.0,y=-5.0,r=1.29],(Node138[a=-1.0,b=1.0,x=-7.0,y=-4.0,r=1.09])Node112[a=-1.0,b=1.0,x=-6.0,y=-5.0,r=0.69])Node92[a=-1.0,b=0.0,x=-5.0,y=-6.0,r=1.9891193529247717])Node73[a=-1.0,b=0.0,x=-4.0,y=-6.0,r=0.9])Node43[a=-1.0,b=-1.0,x=-3.0,y=-6.0,r=0.93])Node34[a=-1.0,b=0.0,x=-2.0,y=-5.0,r=1.7667232954270577],(()Node707[a=0.0,b=-1.0,x=-8.0,y=-11.0,r=1.2418125160543307])((((((Node176[a=-1.0,b=1.0,x=-8.0,y=-8.0,r=0.73])Node137[a=-1.0,b=-1.0,x=-7.0,y=-9.0,r=1.3],Node136[a=-1.0,b=0.0,x=-7.0,y=-8.0,r=0.59])Node111[a=-1.0,b=-1.0,x=-6.0,y=-8.0,r=0.53],(Node175[a=-1.0,b=1.0,x=-7.0,y=-6.0,r=1.8710079054235957])Node110[a=-1.0,b=0.0,x=-6.0,y=-7.0,r=0.81])Node91[a=-1.0,b=0.0,x=-5.0,y=-7.0,r=0.64])Node72[a=-1.0,b=0.0,x=-4.0,y=-7.0,r=1.35],(Node90[a=-1.0,b=0.0,x=-5.0,y=-8.0,r=1.48],(()Node109[a=-1.0,b=0.0,x=-6.0,y=-9.0,r=1.8236264836746388])Node89[a=-1.0,b=-1.0,x=-5.0,y=-9.0,r=0.94])Node71[a=-1.0,b=-1.0,x=-4.0,y=-8.0,r=0.91])Node42[a=-1.0,b=-1.0,x=-3.0,y=-7.0,r=0.58],(()Node46[a=-1.0,b=-1.0,x=-2.0,y=-7.0,r=0.93])()Node41[a=1.0,b=0.0,x=-1.0,y=-6.0,r=1.39])Node33[a=-1.0,b=-1.0,x=-2.0,y=-6.0,r=1.12])Node24[a=-1.0,b=0.0,x=-1.0,y=-5.0,r=1.47])Node16[a=-1.0,b=-1.0,x=0.0,y=-5.0,r=0.67],((Node32[a=0.0,b=-1.0,x=1.0,y=-6.0,r=1.9638918299741654],(((()Node841[a=-1.0,b=-1.0,x=-7.0,y=-11.0,r=0.58])((((()Node135[a=-1.0,b=-1.0,x=-5.0,y=-11.0,r=0.85])Node108[a=0.0,b=-1.0,x=-4.0,y=-10.0,r=1.0],(()Node630[a=-1.0,b=-1.0,x=-6.0,y=-13.0,r=0.77])(()Node134[a=-1.0,b=-1.0,x=-6.0,y=-11.0,r=1.0])Node107[a=-1.0,b=-1.0,x=-5.0,y=-10.0,r=1.14])Node88[a=-1.0,b=-1.0,x=-4.0,y=-9.0,r=0.65])Node69[a=-1.0,b=0.0,x=-3.0,y=-8.0,r=0.87],(((Node170[a=1.0,b=-1.0,x=-3.0,y=-12.0,r=1.9512384698512715],(()Node686[a=-1.0,b=-1.0,x=-4.0,y=-12.0,r=1.5])Node169[a=1.0,b=0.0,x=-3.0,y=-11.0,r=0.91])Node133[a=-1.0,b=-1.0,x=-4.0,y=-11.0,r=1.33])Node87[a=-1.0,b=-1.0,x=-3.0,y=-10.0,r=1.08],Node86[a=-1.0,b=0.0,x=-3.0,y=-9.0,r=1.33])Node68[a=0.0,b=-1.0,x=-2.0,y=-9.0,r=0.99])Node54[a=-1.0,b=-1.0,x=-2.0,y=-8.0,r=0.72])Node40[a=-1.0,b=-1.0,x=-1.0,y=-7.0,r=1.45],(()Node951[a=-1.0,b=-1.0,x=0.0,y=-11.0,r=1.7392926307253513])((()Node132[a=1.0,b=-1.0,x=2.0,y=-9.0,r=1.25],()Node131[a=1.0,b=0.0,x=2.0,y=-8.0,r=1.45])Node67[a=1.0,b=-1.0,x=1.0,y=-8.0,r=1.41],((()Node193[a=-1.0,b=0.0,x=-4.0,y=-14.0,r=1.22])()Node370[a=-1.0,b=-1.0,x=-3.0,y=-14.0,r=0.94])((()Node139[a=0.0,b=-1.0,x=-2.0,y=-13.0,r=1.33])(((Node166[a=0.0,b=-1.0,x=-2.0,y=-12.0,r=0.65],((()Node192[a=-1.0,b=0.0,x=0.0,y=-14.0,r=0.88])((()Node527[a=1.0,b=-1.0,x=1.0,y=-14.0,r=0.85])((()Node591[a=1.0,b=-1.0,x=0.0,y=-13.0,r=1.04])()Node169[a=0.0,b=-1.0,x=-1.0,y=-12.0,r=1.0099492850296317]))Node421[a=1.0,b=-1.0,x=-1.0,y=-11.0,r=1.29])Node165[a=0.0,b=1.0,x=-2.0,y=-10.0,r=1.23])Node130[a=-1.0,b=-1.0,x=-2.0,y=-11.0,r=1.45])Node106[a=0.0,b=-1.0,x=-1.0,y=-10.0,r=1.2592804255064793],Node105[a=1.0,b=0.0,x=0.0,y=-9.0,r=1.23])Node85[a=0.0,b=-1.0,x=-1.0,y=-9.0,r=1.49])Node66[a=-1.0,b=-1.0,x=-1.0,y=-8.0,r=1.38])Node39[a=0.0,b=-1.0,x=0.0,y=-7.0,r=1.13])Node31[a=-1.0,b=-1.0,x=0.0,y=-6.0,r=1.07])Node23[a=-1.0,b=0.0,x=1.0,y=-5.0,r=0.73],(((()Node952[a=1.0,b=-1.0,x=2.0,y=-10.0,r=1.18])(()Node84[a=-1.0,b=-1.0,x=0.0,y=-8.0,r=0.91])Node38[a=-1.0,b=0.0,x=1.0,y=-7.0,r=1.38],(((Node129[a=-1.0,b=-1.0,x=3.0,y=-8.0,r=1.27],()Node128[a=0.0,b=-1.0,x=4.0,y=-8.0,r=1.35])Node65[a=0.0,b=-1.0,x=4.0,y=-7.0,r=0.98],((((Node162[a=1.0,b=0.0,x=8.0,y=-7.0,r=1.2685669416870335],()Node283[a=1.0,b=-1.0,x=8.0,y=-8.0,r=0.9586343616711437])Node127[a=1.0,b=-1.0,x=7.0,y=-7.0,r=0.81],(()Node189[a=0.0,b=-1.0,x=6.0,y=-12.0,r=0.94])((()Node78[a=-1.0,b=-1.0,x=6.0,y=-11.0,r=0.66])((()Node537[a=0.0,b=-1.0,x=7.0,y=-11.0,r=1.22])()Node887[a=0.0,b=-1.0,x=7.0,y=-10.0,r=0.94])()Node473[a=0.0,b=-1.0,x=7.0,y=-9.0,r=0.81])()Node126[a=0.0,b=-1.0,x=6.0,y=-7.0,r=1.19],()Node93[a=1.0,b=0.0,x=7.0,y=-6.0,r=0.74])Node104[a=1.0,b=0.0,x=6.0,y=-6.0,r=1.4108167024543916])Node83[a=0.0,b=1.0,x=5.0,y=-6.0,r=1.32],((((()Node745[a=-1.0,b=-1.0,x=3.0,y=-14.0,r=0.62]))Node919[a=-1.0,b=-1.0,x=4.0,y=-13.0,r=1.15])()Node772[a=0.0,b=-1.0,x=5.0,y=-12.0,r=1.28])(()Node798[a=1.0,b=0.0,x=5.0,y=-11.0,r=0.8])(((()Node151[a=-1.0,b=-1.0,x=4.0,y=-11.0,r=0.96]))Node15[a=0.0,b=-1.0,x=5.0,y=-10.0,r=1.03])()Node82[a=1.0,b=-1.0,x=6.0,y=-8.0,r=0.96])Node64[a=1.0,b=-1.0,x=5.0,y=-7.0,r=1.37])Node53[a=1.0,b=1.0,x=4.0,y=-6.0,r=0.5])Node37[a=1.0,b=0.0,x=3.0,y=-7.0,r=1.28])Node30[a=0.0,b=-1.0,x=2.0,y=-7.0,r=0.75])Node22[a=0.0,b=-1.0,x=2.0,y=-6.0,r=0.83])Node15[a=1.0,b=-1.0,x=2.0,y=-5.0,r=1.41])Node11[a=1.0,b=-1.0,x=1.0,y=-4.0,r=0.68])Node9[a=-1.0,b=0.0,x=0.0,y=-3.0,r=1.224188604200087])Node6[a=1.0,b=-1.0,x=1.0,y=-3.0,r=0.95])Node4[a=1.0,b=-1.0,x=0.0,y=-2.0,r=0.82])Node2[a=-1.0,b=0.0,x=-1.0,y=-1.0,r=0.53])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.62])Node_0[a=0,b=0,x=0,y=0,r=1]\"\n", " # Draw the tree and get the illuminated coordinates\n", " newick_string = complete_tree_generator15(max_depth = 40, max_numb_children = 2, max_node_radius = 1.2)\n", " number_of_groups = count_groups15(newick_string)\n", " illuminated_coords = draw_tree_newick15(newick_string, title=f\"Globular morpho-tree with {number_of_groups} nodes\")\n", " #print(f\"Illuminated coordinates: {illuminated_coords}\")\n", " # The globular tree is trimmed here:\n", " number_of_branch_deletions = 30\n", " for i in range(number_of_branch_deletions):\n", " newick_string = deletion_of_branch15(newick_string )\n", " number_of_groups = count_groups15(newick_string)\n", " illuminated_coords = draw_tree_newick15(newick_string, title=f\"Trimmed morpho-tree with {number_of_groups} nodes\")\n", " \n", " " ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise.** Run the previous program to assess what rich structure have the trimmed trees. \n", ">\n", "> *Answer.* The trimmed trees look more complex than the parental golobular ones.\n", "\n", "**Exercise.** Test how powerful is Evolution to solve trimmed trees.\n", ">\n", "> *Answer.* In the following program we depict the fitness function over generations for various values of population sizes that try to reproduce a trimmed globular tree that pressumibly has a rich structure.\n" ] }, { "metadata": {}, "cell_type": "code", "source": [ "# Program evolution_of_newick_morphological_trimmed_trees16.py\n", "\n", "# Purpose:\n", "# To explore the battle of Evolution against the complexity of trimmed morpho-trees.\n", "# More specifically:\n", "# An ordinary target morpho-tree is created. It is globular.\n", "# Some arms are trimmed in order to generate structure.\n", "# The resultant tree is given as a task to Evolution\n", "# that must reproduce an area associated to the target tree.\n", "\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\n", "#\n", "# In this version:\n", "# 1. Mutation has been inbuilt with the power\n", "# to have children in the immediate neighborhood of the father and\n", "# to change radius and the topology of trees\n", "# by means of insertion and deletion of leaves.\n", "# 2. Recombination has been enabled with the power\n", "# to exchange leaves between trees.\n", "# We expect a good proportion of the population to\n", "# be able to successfully recombine, mutate and reproduce.\n", "# 3. A cut-off for the length of the Newick string has been introduced\n", "# to avoid overgrowth of trees.\n", "# 4. The seed for the random number generator has been canceled\n", "# to allow different histories for different runs.\n", "#\n", "# A bug was corrected in the function that inserts a node.\n", "# The criterion for insertion was changed from \"a leaf is just after a left parenthesis\"\n", "# to \"a leaf begins with `N` that is preceded by a left parenthesis\".\n", "#\n", "\n", "\n", "\n", "\n", "# We reuse pieces of code made with the help of Colab and Gemini 2.5 Flash.\n", "# With the assistance of the AI of Visual Studio Code.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 10,\n", " 'max_number_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': 100,\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001,\n", " 'crossover_rate': 0.001\n", "}\n", "\n", "\n", "def print_parameters16(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"INITIAL TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"INITIAL GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,r=16.3]\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 grammmar 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", "\n", "def replace_str_subsequence16(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_length16(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_groups16(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_groups16(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups16(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups16(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info16(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", "def extract_all_infos16(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_groups16(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info16(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates16(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_center16(newick_string, j):\n", " \"\"\" Finds the address of the father of a node at j\"\"\"\n", " #print(\"find_father_center16(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_groups16(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info16(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator16(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_generator16(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates16(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children16(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_generator16(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_generator16(max_depth, max_numb_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_generator16(...)\")\n", " occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n", " actual_depth = max_depth + random.randint(-5,5) # Number of allowed divisions along each branch.\n", " #print(f\"Actual depth: {actual_depth}\")\n", " # () is used as a marker for insertion.\n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center16(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children16(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence16(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# Connectivity issues are not checked.\n", "\n", "# A bug in the drawing procedure has been corrected, and the drawing of trees is now correct.\n", "# The bug was related to the function that finds the father of a node.\n", "\n", "# With the help of Gemini, Google and the AI of Visual Studio Code.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick16(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence16(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " #print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " # 7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string at examine_subsequence16(tokens, j).\"\n", " else:\n", " return False, \"Invalid Newick sub-string at examine_subsequence16(tokens, j).\"\n", "\n", "\n", "def simplify16(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length16(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " print(\"Valid string at simplify16(), line 562\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " print(\"Invalid string at simplify16(), line 562\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax16(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick16(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " print(f\"Length of tokens at line 587: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string at validate_newick_syntax16_1.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string at validate_newick_syntax16_2.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string at validate_newick_syntax16_3.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid16, message16 = examine_subsequence16(tokens, j)\n", " if is_valid16:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string inside validate_newick_syntax16_4.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid16, message16 = simplify16(newick_str)\n", " if is_valid16:\n", " return True, \"Valid morpho-tree at validate_newick_syntax16(newick_str).\"\n", " else:\n", " return False, \"Invalid morpho-tree at validate_newick_syntax16_5.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "#def test_validator16(max_depth, max_numb_children, max_node_radius):\n", "def test_validator16( ):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax16(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax16(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 2\n", " max_node_radius = 1.2\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " s = complete_tree_generator16(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax16(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area16(x,y,r):\n", " \"\"\"\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " \"\"\"\n", " #print(\"get_illuminated_area16\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick16(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles16(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves(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", " # Dectect 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", " # Descipher 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_groups16(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father16(newick_string, ax, tree_color='lightblue', diff_color='red'):\n", " \"\"\"Draw lines connecting leaves to fathers\"\"\"\n", "\n", " #print(\"\\n\\nconnect_leaves_to_father16\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " all_leaves = find_leaves(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info16(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=16],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_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_groups16(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info16(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_syntax16(newick_string)\n", " #print(f\"is valid at connect_leaves_to_father16(...) : {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_groups16(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "\n", "def draw_tree_links16(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_links16\")\n", " #print(f\"Newick String: {newick_string}\")\n", " length_string = len(newick_string)\n", " #print(f\"length_string: {length_string}\")\n", " groups = detect_all_groups16(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_links16\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " newick_string, number_of_groups = connect_leaves_to_father16(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_groups16(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", " [a0,b0,x0,y0,r0] = extract_info16(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_newick16(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos16(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 16))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles16(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area16(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick16(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links16(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "def generate_and_test_target_trimmed__morpho_tree16(target_tree_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " target_tree = complete_tree_generator16(target_tree_params['max_depth'], target_tree_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #target_tree = complete_tree_generator16(max_depth, max_numb_children, max_node_radius) #line 264\n", " # Trim tree\n", " for i in range(2):\n", " target_tree = deletion_of_branch16(target_tree)\n", " print(f\"target_tree at generate_and_test_target_trimmed__morpho_tree16: {target_tree}\")\n", " # Validate tree\n", " is_valid, message = validate_newick_syntax16(target_tree)\n", " illuminated_coords = set()\n", "\n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos16(target_tree)\n", " illuminated_coords = get_illuminated_area16(x,y,r)\n", " else:\n", " print(\"Newick string is not valid at generate_and_test_target_trimmed__morpho_tree16(target_tree_params)\")\n", "\n", " return target_tree, illuminated_coords\n", "\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree16(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " newick_tree = complete_tree_generator16(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator16(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax16(newick_tree)\n", " illuminated_coords = set()\n", "\n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos16(newick_tree)\n", " illuminated_coords = get_illuminated_area16(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " \"\"\"\n", " is_valid, message = validate_newick_syntax16(newick_tree)\n", " print(f\"is the Newick String valid?: {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", " \"\"\"\n", " return newick_tree, illuminated_coords\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick16(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator16(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "def get_illuminated_coordinates16(newick_string):\n", " \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n", " a,b,x,y,r = extract_all_infos16(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area16(x,y,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_illumination16(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_coordinates16(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", "\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_branch16(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_recombination16(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_position_branch_recombination16\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch16(newick_string1)\n", " branch2 = find_recombinant_branch16(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_recombination16(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_leaves_recombination16\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " leaves1 = find_leaves(newick_string1)\n", " leaves2 = find_leaves(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_squares16(newick_string):\n", " \"\"\"Detects all the occupied squares in a tree\"\"\"\n", " occupied_squares_set = set()\n", " groups = detect_all_groups16(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info16(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def newick_mutation_of_radius16(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_subsequence16(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_patterns16(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_branch16(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_patterns16(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node16[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node16[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_generator16(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_16(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\"Group160: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center16(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares16(newick_string)\n", " info, occupied_squares_set = node_info_generator16(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_16(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\"Group160: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center16(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares16(newick_string)\n", " info, occupied_squares_set = node_info_generator16(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_16(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\"Group160: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center16(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares16(newick_string)\n", " info, occupied_squares_set = node_info_generator16(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_leaf16(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_16(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_16(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_16(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_leaf16(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_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", "def find_branch16(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_groups16(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", "\n", "def deletion_of_branch16(newick_string ):\n", " group, start = newick_find_all_groups16(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_branch16(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", "def newick_mutation_branches16(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_radius16(newick_string)\n", " newick_string = insertion_of_branch16(newick_string)\n", " newick_string = deletion_of_branch16(newick_string)\n", " return newick_string\n", "\n", "def newick_mutation_leaves16(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_radius16(newick_string)\n", " newick_string = insertion_of_leaf16(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n", " newick_string = deletion_of_leaf16(newick_string)\n", " return newick_string\n", "\n", "\n", "def plot_illuminated_area16(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", "# ======================\n", "\n", "def run_evolution_newick_morpho_trees16(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_tree, target_illuminated_area = generate_and_test_target_trimmed__morpho_tree16(target_tree_params)#926\n", " print(f\"target_tree: {target_tree} \\n\\n\")\n", " number_of_groups = count_groups16(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " length_of_target_tree = len(target_tree)\n", " #draw_newick_string16(target_tree, \"Target Tree\")\n", " #target_area = get_illuminated_coordinates16(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick16(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\n", " #print(f\"Population initialized with {len(population)} individuals.\")\n", " #print(f\"Population: {population}\")\n", "\n", "\n", "\n", " best_tree_overall = None\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", " generation = 0\n", " for generation in range(ga_params['generations']):\n", " if generation % 10 == 0:\n", " print(f\"\\n--- Generation {generation+1}/{ga_params['generations']}, Best fitness {round(best_fitness_overall, 5)} ---\")\n", " fitness_scores = [calculate_fitness_illumination16(tree, target_illuminated_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_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, generation\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_generator16(target_tree_params['max_depth'], target_tree_params['max_number_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_recombination12\n", " # child1, child2 = newick_position_branch_recombination12(p1, p2)\n", " # Crossover newick_leaves_recombination12\n", " child1, child2 =newick_leaves_recombination16(p1,p2)\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation_leaves16(child1, ga_params)\n", " mutated_child2 = newick_mutation_leaves16(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_generator16(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'])\n", " if new_tree:\n", " population.append(new_tree)\n", " generation += 1 # Increment generation count after the loop\n", " #print(\"\\n--- Evolution Finished ---\")\n", " #print(f\"Final Best Fitness: {best_fitness_overall:.4f}\")\n", "\n", " return target_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, generation\n", "\n", "\n", "\n", "# Generate the Newick string of a morpho-tree and draw it\n", "if __name__ == \"__main__\":\n", " print_parameters16(target_tree_params, ga_params)\n", " # Run the evolution loop\n", " target_tree, target_illuminated_area, evolved_best_tree, evolved_best_fitness, history, generation = run_evolution_newick_morpho_trees16(target_tree_params, ga_params)\n", " number_of_groups = count_groups16(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups}\")\n", " title = \"Target Tree. Number of groups or nodes: \" + str(number_of_groups)\n", " draw_tree_newick16(target_tree, title)\n", " title = \"Evolved Best Tree\"\n", " draw_tree_newick16(evolved_best_tree, title)\n", "\n", " # Plot Symmetric Difference Area\n", " evolved_best_area = get_illuminated_coordinates16(evolved_best_tree)\n", " symmetric_diff_area = target_illuminated_area.symmetric_difference(evolved_best_area )\n", " ##print(f\"Symmetric Difference Area: {symmetric_diff_area}\")\n", " ##print(f\"Number of cells in symmetric difference area: {len(symmetric_diff_area)}\")\n", "\n", "\n", " fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", "\n", "\n", " plot_illuminated_area16(axes[0], target_illuminated_area, title=f\"Target Area (number of cells: {len(target_illuminated_area)})\")\n", " plot_illuminated_area16(axes[1], evolved_best_area, title=f\"Evolved Best Area (number of cells: {len(evolved_best_area)})\")\n", " plot_illuminated_area16(axes[2], symmetric_diff_area, title=f\"Symmetric Difference (Error Area: {len(symmetric_diff_area)})\")\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", " # Plot Fitness History\n", " plt.figure(figsize=(10, 6))\n", " plt.plot(history, marker='o', linestyle='-', color='blue')\n", " plt.title('Best Fitness Progression Over Generations')\n", " plt.xlabel('Generation')\n", " plt.ylabel('Fitness')\n", " plt.grid(True)\n", " plt.show()\n", "\n", "\n", " print(f\"Evolved best tree: {evolved_best_tree}\")\n", " print(f\"Evolved best fitness: {evolved_best_fitness:.2}\")\n", " print(f\"Generation: {generation}\")\n", " print(f\"Number of nodes in target tree: {number_of_groups}\")\n", " #test_validator16()\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()" ], "outputs": [], "execution_count": null }, { "metadata": {}, "cell_type": "markdown", "source": [ "**Observation.** Trimmed trees do not look more difficult to Evolution than ordinary ones.\n", "\n", "**Exercise.** Make a comparison of the behavior of Evolution for diverse values of population size. This measures the behavior of Evolution when resources diminish.\n", ">\n", "> *Answer.* The following program runs Evolution for diverse values of population size." ] }, { "metadata": { "ExecuteTime": { "end_time": "2026-10-06T17:44:45.244380Z", "start_time": "2026-10-06T13:03:05.933823Z" } }, "cell_type": "code", "source": [ "# Program evolution_of_newick_morphological_trimmed_trees17.py\n", "# Morphological trees considered here are like lighting trees:\n", "# an electrical thread in the form of tree covers an area\n", "# and there is lamp at each node.\n", "# Each lamp has a given power and a circle of illumination,\n", "# whose radius is determined by a threshold intensity.\n", "# In morphology, this corresponds with a threshold concentration of a messenger.\n", "# Evolution is given the task of illuminating\n", "# the area determined by a random target tree.\n", "# Trees are encoded in Newick Notation and\n", "# everything is done over the correspond strings\n", "# without resorting to the Node class.\n", "# Thus, it is possible to use the Newick strings as a genotype and\n", "# the corresponding trees as a phenotype.\n", "#\n", "# In this version:\n", "# 1. Mutation has been inbuilt with the power\n", "# to have children in the immediate neighborhood of the father and\n", "# to change radius and the topology of trees\n", "# by means of insertion and deletion of leaves.\n", "# 2. Recombination has been enabled with the power\n", "# to exchange leaves between trees.\n", "# We expect a good proportion of the population to\n", "# be able to successfully recombine, mutate and reproduce.\n", "# 3. A cut-off for the length of the Newick string has been introduced\n", "# to avoid overgrowth of trees.\n", "# 4. The seed for the random number generator has been canceled\n", "# to allow different histories for different runs.\n", "#\n", "# A bug was corrected in the function that inserts a node.\n", "# The criterion for insertion was changed from \"a leaf is just after a left parenthesis\"\n", "# to \"a leaf begins with `N` that is preceded by a left parenthesis\".\n", "#\n", "# Purpose:\n", "# To study the effect of the decreasing of resources over Evolution of morpho-trees.\n", "# To that aim, we depict various fitness functions for diverse\n", "# values of population sizes.\n", "# Target trees are gven a rich structure by deleting some arms.\n", "\n", "\n", "\n", "\n", "# We reuse pieces of code made with the help of Colab and Gemini 2.5 Flash.\n", "# With the assistance of the AI of Visual Studio Code.\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", "# 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", "# Parameters for the target tree generation\n", "target_tree_params = {\n", " 'max_depth': 15,\n", " 'max_number_children': 2,\n", " 'max_node_radius': 1.5,\n", " 'number_of_branch_deletions': 10\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': -1, # fake initialization\n", " 'max_depth': target_tree_params['max_depth'],\n", " 'max_number_children': target_tree_params['max_number_children'],\n", " 'max_node_radius': 1.5,\n", " 'mutation_rate': 0.001,\n", " 'crossover_rate': 0.001\n", "}\n", "\n", "\n", "def print_parameters17(target_tree_params, ga_params):\n", " print(\"LAB: EVOLUTION OF NEWICK MORPHO-TREES\")\n", " print(\"PURPOSE: \"\n", " + \"\\n A random morpho-tree T is generated.\"\n", " + \"\\n Evolution tries to restore the tree T\"\n", " + \"\\n beginning from a population of random trees.\")\n", " print(f\"EXPERIMENT {start_time}\")\n", " print(f\"RECOMBINATION OF LEAVES: {recombination_of_leaves}\")\n", " #print(f\"SEED FOR THE GENETIC ALGORITHM: {ga_params['seed']}\")\n", " print(f\"INITIAL TARGET TREE PARAMETERS: {target_tree_params}\")\n", " print(f\"INITIAL GENETIC ALGORITHM PARAMETERS: {ga_params}\\n\")\n", "\n", "\n", "# GENERATION OF A MORPHO-TREE\n", "\n", "# Program morpho_tree_drawer.py\n", "\n", "# The following program generates a Newick strings\n", "# that encodes for a morpho-tree and draws it.\n", "# Children are located in the immediate neighborhood of the father.\n", "# If postions are occupied, no children.\n", "# Then, children are generated only when there is place for them.\n", "\n", "# ==== Grammar_generator =======\n", "\n", "# This program uses a hidden grammar to generate Newick expressions that encode for morphological trees.\n", "# The information of each node is generated on the image of\n", "# Node25[a=18,b=18,x=19,y=0,r=17.3]\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 grammmar 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", "\n", "def replace_str_subsequence17(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_length17(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_groups17(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_groups17(newick_string)\")\n", " #print(f\"Newick String: {newick_string}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=0,b=1,x=2,y=2,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_]*\\[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", " #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 gropus: {starts}\")\n", " #print(f\"groups: {groups}\")\n", "\n", "\n", " \"\"\"\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_at_position2(new_s, \"n\", starts[j], len(groups[j]))\n", " print(f\"new string = '{new_s}'\")\n", " \"\"\"\n", " return groups\n", "\n", "def count_groups17(newick_string):\n", " \"\"\"The number of groups is counted.\"\"\"\n", " groups = detect_all_groups17(newick_string)\n", " number_of_groups = len(groups)\n", " return number_of_groups\n", "\n", "def extract_info17(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", "def extract_all_infos17(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_groups17(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", " a = []\n", " b = []\n", " x = []\n", " y = []\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", " [ai,bi,xi,yi,ri] = extract_info17(groups[i])\n", " #print(f\"a: {ai}, b: {bi}, x: {xi}, y: {yi}, r: {ri}\")\n", " a.append(ai)\n", " b.append(bi)\n", " x.append(xi)\n", " y.append(yi)\n", " r.append(ri)\n", " return a,b,x,y,r\n", "\n", "\n", "def get_neighboring_coordinates17(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_center17(newick_string, j):\n", " \"\"\" Finds the address of the father of a node at j\"\"\"\n", " #print(\"find_father_center17(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_groups17(tail)\n", " if father_group:\n", " #print(f\"Father_group 1: {father_group}\")\n", " [af,bf,xf,yf,rf] = extract_info17(father_group[0])\n", " #print(f\"Info: a: {af}, b: {bf}, x: {xf}, y: {yf}, r: {rf}\")\n", " father_center = [xf,yf]\n", " return father_center\n", " else:\n", " return \"\"\n", "\n", "\n", "\n", "def node_info_generator17(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_generator17(father_center, counter, occupied_squares_set)\")\n", " coordinates_neighbors = get_neighboring_coordinates17(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", " na = nx - father_center[0]\n", " nb = ny - father_center[1]\n", " nr = random.uniform(0.5, max_node_radius)\n", " nr = round(nr, 2)\n", " info = \"Node\" + str(counter) + \"[\" + \"a=\" + str(na) + \",\" + \"b=\" + str(nb) + \",\" + \"x=\" + str(nx) + \",\" + \"y=\" + str(ny) + \",\" + \"r=\" + str(nr)+ \"]\"\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_children17(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_generator17(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_generator17(max_depth, max_numb_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_generator17(...)\")\n", " occupied_squares_set = set() # Initialize a new set for this tree's square coordinates\n", " actual_depth = max_depth + random.randint(-5,5) # Number of allowed divisions along each branch.\n", " #print(f\"Actual depth: {actual_depth}\")\n", " # () is used as a marker for insertion.\n", " tree = \"()Node_0[a=0,b=0,x=0,y=0,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_center17(tree, j)\n", " #print(f\"Father center: {father_center}\")\n", " if not father_center:\n", " continue\n", " numb_children = random.randint(0, max_numb_children)\n", " if numb_children == 0:\n", " continue\n", " else:\n", " # Replacement\n", " generated_children, counter, occupied_squares_set = generate_children17(father_center, numb_children, counter, occupied_squares_set, max_node_radius)\n", " tree = replace_str_subsequence17(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 tree: {tree}\")\n", " return tree\n", "\n", "# === VALIDATOR ====\n", "\n", "\n", "# Program validator_for_newick_morpho_trees.py\n", "# The task is to decide whether a Newick string encodes for a morpho tree.\n", "# To solve the task,\n", "# we simply go backwards over the procedure of creation of Newick strings.\n", "# The program has two sections.\n", "# In the first,\n", "# the string in divided in tokens, substrings of predefined content,\n", "# and all groups that fit into the pattern\n", "# info = NodeB_3_x_2[a=2,b=2,r=3] are detected.\n", "# Each found group undergoes a detailed test for correctness.\n", "# If some group is wrong, the whole string is rejected.\n", "# If all are correct, the test continues further on.\n", "# In the second section,\n", "# each info is replaced by \"n\",\n", "# then, each \"n\" is replaced by\"\"\n", "# then each \"()\" is replaced by \"\".\n", "# If a formula is correct,\n", "# the final product of this algorithm is the void sting \"\",\n", "# and its length is zero.\n", "# Connectivity issues are not checked.\n", "\n", "# A bug in the drawing procedure has been corrected, and the drawing of trees is now correct.\n", "# The bug was related to the function that finds the father of a node.\n", "\n", "# With the help of Gemini, Google and the AI of Visual Studio Code.\n", "\n", "\n", "\n", "# --- TOKENIZER FOR NEWICK STRINGS ---\n", "# --- Elements to create well-formed Newick info-s of Morpho_trees.---\n", "# --- Every info is modeled on the pattern\n", "# --- NodeB_3_x_2[a=1,b=0,x=4,y=5,r=1.2]\n", "# --- where a nad b are -1,0,1 except (0,0)\n", "# --- because they represent a step awya from the father.\n", "# --- If a substring fits into this pattern,\n", "# --- it must produce the sequence 73756275627564.\n", "# --- Say, each info begins with a noun = letters, number 7,\n", "# --- and ends with a right bracket number 4.\n", "# --- Assigned numbers are at the right of comments.\n", "# --- By Gemini\n", "TOKEN_SPECIFICATION_NEWICK = [\n", " ('LPAREN', r'\\('), # Left Parenthesis, 0\n", " ('RPAREN', r'\\)'), # Right Parenthesis, 1\n", " ('COMMA', r','), # Comma separator for children, 2\n", " ('LBRACKET', r'\\['), # Left Bracket for metadata, 3\n", " ('RBRACKET', r'\\]'), # Right Bracket for metadata, 4\n", " ('EQUALS', r'='), # Equals sign in metadata, 5\n", " ('NUMBER', r'-?\\d+(?:\\.\\d+)?'), # Numbers (integers or floats, possibly negative), 6\n", " ('LETTERS', r'[a-zA-Z_0-9]+'), # Node labels (e.g., 'Node0', 'Target_Root_26'), or variables (x, y, r), 7\n", " ('SEMICOLON', r';'), # Optional semicolon at end of Newick string, 8\n", " ('SKIP', r'[ \\t\\n]+'), # Skip spaces, tabs, newlines, 9\n", " ('MISMATCH', r'.'), # Any other illegal character, *\n", "]\n", "\n", "def tokenize_newick17(newick_str):\n", " \"\"\"Converts a Newick string into a list of (token_type, value) tuples.\"\"\"\n", " #print(\"\\ntokenize_newick\")\n", " tokens = []\n", " tok_regex = '|'.join(f'(?P<{name}>{pattern})' for name, pattern in TOKEN_SPECIFICATION_NEWICK)\n", " tokens = []\n", " for mo in re.finditer(tok_regex, newick_str):\n", " kind = mo.lastgroup\n", " value = mo.group()\n", " if kind == 'SKIP':\n", " continue\n", " elif kind == 'MISMATCH':\n", " raise SyntaxError(f\"Newick Grammar Violation: Illegal character '{value}' in '{newick_str}'\")\n", " tokens.append((kind, value))\n", " return tokens\n", "\n", "# --- VALIDATOR FOR INFO SUBSTRINGS THAT MUST FIT INTO THE PATTERN ---\n", "# --- info = NodeB_3_x_2[a=1,b=-1,x=2,y=4,r=3.2], 22 tokens\n", "def examine_subsequence17(tokens, j):\n", " #print(\"\\nexamine_subsequence\")\n", " # Every info must begin with a letter, whose index is 7.\n", " tracker = [\"LETTERS\"]\n", " tracker2 = \"7\"\n", " if tokens[j + 1][0] == 'LBRACKET':\n", " tracker.append('LBRACKET')\n", " tracker2 += \"3\"\n", " if tokens[j + 2][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 3][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 4][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 5][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 6][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 7][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 8][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 9][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 10][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 11][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 12][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 13][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 14][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 15][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 16][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 17][0] == 'COMMA':\n", " tracker.append('COMMA')\n", " tracker2 += \"2\"\n", " if tokens[j + 18][0] == 'LETTERS':\n", " tracker.append('LETTERS')\n", " tracker2 += \"7\"\n", " if tokens[j + 19][0] == 'EQUALS':\n", " tracker.append('EQUALS')\n", " tracker2 += \"5\"\n", " if tokens[j + 20][0] == 'NUMBER':\n", " tracker.append('NUMBER')\n", " tracker2 += \"6\"\n", " if tokens[j + 21][0] == 'RBRACKET':\n", " tracker.append('RBRACKET')\n", " tracker2 += \"4\"\n", " #print(f\"Tokens: {tracker}\")\n", " #print(f\"tracker2: {tracker2}\")\n", " master_pattern = \"7375627562756275627564\"\n", " # 7375627562756275627564\n", " #print(f\"master pattern: {master_pattern}\")\n", " if tracker2 == master_pattern:\n", " return True, \"Valid Newick sub-string.\"\n", " else:\n", " return False, \"Invalid Newick sub-string.\"\n", "\n", "\n", "def simplify17(text):\n", " # Assuming that all infos of a string have been declared correct,\n", " # the string is further tested for the balance of parentheses.\n", " # print(\"\\nsimplify(text)\")\n", " # print(f\"text: {text}\")\n", " # Detect a pattern on the model info = NodeB_3_x_2[a=2,b=2,x=3,y=7,r=2.3]\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_]*\\[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, text):\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_infos = len(starts)\n", " #print(f\"number_of_infos = {number_of_infos}\")\n", " #print(f\"starts of infos: {starts}\")\n", " #print(f\"groups or infos: {groups}\")\n", " new_s = text\n", " # All infos are replaced by the dumb variable n\n", " for j in range(number_of_infos - 1, -1, -1):\n", " #print(f\"start_of_info[{j}]: {starts[j]}, len(groups[{j}]): {len(groups[j])}\")\n", " new_s = replace_strig_of_length17(new_s, \"n\", starts[j], len(groups[j]))\n", " #print(f\"new string = '{new_s}'\")\n", " # Balance of parentheses is inquired.\n", " new_s = new_s.replace(\"n,\", \"\")\n", " new_s = new_s.replace(\"n\", \"\")\n", " #print(f\"Simplified string = {new_s} \")\n", " l = len(new_s)\n", " #print(f\"Length of Simplified string = {l}\")\n", " for i in range(l):\n", " new_s = new_s.replace(\"()\", \"\")\n", " final_length = len(new_s)\n", " # print(f\"Final string = {new_s}, final_length = {final_length}\")\n", " if final_length == 0:\n", " #print(\"Valid string at simplify\")\n", " return True, \"Valid Newick string.\"\n", " else:\n", " #print(\"Invalid string at simplify\")\n", " return False, \"Invalid Newick string.\"\n", "\n", "\n", "\n", "def validate_newick_syntax17(newick_str):\n", " \"\"\"\n", " Validates if a Newick string is syntactically correct.\n", " This function checks for balanced parentheses, proper comma usage for children,\n", " and correct metadata format, without building a tree object.\n", " \"\"\"\n", " #print(\"Validating Newick syntax\")\n", " tokens = []\n", " try:\n", " tokens = tokenize_newick17(newick_str)\n", " #print(f\"newick_str: {newick_str}\")\n", " #print(f\"Tokens: {tokens}\")\n", " except SyntaxError as e:\n", " return False, str(e)\n", " if not tokens:\n", " return False, \"Empty Newick string.\"\n", " l = len(tokens)\n", " #print(f\"Length of tokens: {l}\")\n", " j = 0\n", " indexes_of_tokens = []\n", " if l == 1:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS': # NodeA\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif l == 2:\n", " kind, value = tokens[j]\n", " if kind == 'LETTERS' and tokens[1][0] == 'SEMICOLON': #NodeC;\n", " return True, \"Valid Newick string.\"\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " elif 3 < l < 22: # No redundancy is assumed\n", " return False, \"Invalid Newick string.\"\n", " elif l >= 22:\n", " #print(f\"Length of string: {l}\")\n", " for j in range(l-20):\n", " #print(f\"tokens[{j}]: {tokens[j]}\")\n", " #print(f\"tokens[{j+1}]: {tokens[j+1]}\")\n", " if tokens[j][0] == 'LETTERS' and tokens[j+1][0] == 'LBRACKET':\n", " #print(f\"Starting index : {j}\".format(j=j))\n", " indexes_of_tokens.append(j)\n", " # info that starts at j is examined for correctness\n", " is_valid17, message17 = examine_subsequence17(tokens, j)\n", " if is_valid17:\n", " pass\n", " else:\n", " return False, \"Invalid Newick string.\"\n", " #print(f\"Starting indexes of tokens: {indexes_of_tokens }\")\n", " is_valid17, message17 = simplify17(newick_str)\n", " if is_valid17:\n", " return True, \"Valid morpho-tree.\"\n", " else:\n", " return False, \"Invalid morpho-tree.\"\n", "\n", "\n", "\n", "\n", "# Generate a sample of Newick strings that are immediately tested by a validator\n", "def test_validator17(max_depth, max_numb_children, max_node_radius):\n", " # Specific test\n", " model = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]\"\n", " #print(\"Simple test\")\n", " #print(f\"Model of Newick String: {model}\")\n", " is_valid, message = validate_newick_syntax17(model)\n", " #print(f\"message: {message}\")\n", " invalid_string = \"NodeB_3_x_2[a=-1,b=0,x=-4,y=5,r=1.2]]\"\n", " #print(f\"\\nInvalid_string: {invalid_string}\")\n", " is_valid, message = validate_newick_syntax17(invalid_string)\n", " #print(f\"message: {message}\")\n", " #print(\"\\n\")\n", "\n", "\n", " #print(\"\\nMassive test of Newick formulas encoding for morpho-trees:\\n\" + \"=\"*47)\n", " max_depth = 7\n", " max_numb_children = 3\n", " #print(f\"max_depth: {max_depth}\")\n", " #print(f\"max_numb_children: {max_numb_children} \\n\"+ \"=\"*20)\n", " for i in range(1, 100):\n", " p, s = complete_tree_generator17(max_depth, max_numb_children, max_node_radius)\n", " #print(f\"pattern {p}\")\n", " #print(f\"Morpho-tree {i}: {s}\")\n", " is_valid, message = validate_newick_syntax17(s)\n", " #print(f\"is valid at main : {is_valid}, message: {message}\")\n", " #status = \"✅ VALID\" if is_valid else \"❌ INVALID\"\n", " #print(f\"Morpho-tree {i}:: {status} - {message}\")\n", "\n", "\n", "\n", "\n", "# ======== DRAWING ================\n", "\n", "def get_illuminated_area17(x,y,r):\n", " \"\"\"\n", " The illuminated area enclosess all unitary squares that intersetcs the circles of illumination.\n", " Each square is denoted by its left botton corner. These will be found in the set illuminated_coords.\n", " \"\"\"\n", " #print(\"get_illuminated_area17\")\n", " #print(f\"x: {x}, y: {y}, r: {r}\")\n", " illuminated_coords = set()\n", "\n", "\n", " for xi,yi,ri in zip(x,y,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\n", " # This covers the case of small circles not intersecting grid lines.\n", " illuminated_coords.add((math.floor(circle_center_x), math.floor(circle_center_y)))\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", " illuminated_coords.add((ix, y_line))\n", " illuminated_coords.add((ix, y_line - 1))\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", " illuminated_coords.add((x_line, iy))\n", " # Cell (x_line - 1, iy) - its right edge is at x_line\n", " illuminated_coords.add((x_line - 1, iy))\n", " # print(f\"illuminated_coords {illuminated_coords}\")\n", "\n", " return illuminated_coords\n", "\n", "\n", "def plot_illuminated_area_newick17(ax, illuminated_coords, title, color='blue', alpha=0.1):\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", " #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_circles17(x,y,r, ax, tree_color='lightblue', diff_color='red'):\n", " for xi,yi,ri in zip(x,y,r):\n", " # Plot the square representing the cell\n", " rect = patches.Rectangle((xi, yi), 1, 1, facecolor=tree_color, 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='red', alpha=0.1, zorder=2)\n", " ax.add_patch(circle)\n", "\n", "def find_leaves(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", " # Dectect 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", " # Descipher 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_groups17(bouquet_of_leaves)\n", " for loaf in leaves:\n", " loaf = loaf.replace(\" \", \"\")\n", " all_leaves.append(loaf)\n", " return all_leaves\n", "\n", "\n", "\n", "def connect_leaves_to_father17(newick_string, ax, tree_color='lightblue', diff_color='red'):\n", " \"\"\"Draw lines connecting leaves to fathers\"\"\"\n", "\n", " #print(\"\\n\\nconnect_leaves_to_father17\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " all_leaves = find_leaves(newick_string)\n", " for leaf in all_leaves:\n", " [al,bl,xl,yl,rl] = extract_info17(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=17],Node7[x=6,y=17,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_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_groups17(father_with_tail)\n", " if father_group:\n", " [af,bf,xf,yf,rf] = extract_info17(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_syntax17(newick_string)\n", " #print(f\"is valid at connect_leaves_to_father17(...) : {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_groups17(newick_string)\n", " number_of_groups = len(groups)\n", " return newick_string, number_of_groups\n", "\n", "\n", "\n", "\n", "def draw_tree_links17(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_links17\")\n", " #print(f\"Newick String: {newick_string}\")\n", " length_string = len(newick_string)\n", " #print(f\"length_string: {length_string}\")\n", " groups = detect_all_groups17(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_links17\")\n", " #print(f\"Newick String: {newick_string}\")\n", "\n", " newick_string, number_of_groups = connect_leaves_to_father17(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_groups17(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", " [a0,b0,x0,y0,r0] = extract_info17(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_newick17(newick_string, title):\n", " #print(\"\\n=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " #print(\"=========== DRAWING=======\\n\")\n", " a,b,x,y,r = extract_all_infos17(newick_string)\n", " fig, ax = plt.subplots(figsize=(7, 17))\n", " ax.set_aspect('equal')\n", " plot_tree_nodes_and_their_diffusion_circles17(x,y,r, ax, tree_color='lightgreen', diff_color='red')\n", "\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area17(x,y,r)\n", " #print(f\"Illuminated cells: {illuminated_coords}\")\n", " #print(f\"Number of illuminated cells: {len(illuminated_coords)}\")\n", " # Tree is drawn\n", " #title = \"morpho-tree: lightgreen, illuminated circles: red, illuminated Area: lightblue\"\n", " plot_illuminated_area_newick17(ax, illuminated_coords, title, color='blue', alpha=0.1)\n", " # Lines that define the tree are added\n", " draw_tree_links17(newick_string, ax, diff_color='red' )\n", "\n", " plt.tight_layout()\n", " plt.show()\n", " return illuminated_coords\n", "\n", "def generate_and_test_trimmed_target_morpho_tree17(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " target_tree = complete_tree_generator17(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator17(max_depth, max_numb_children, max_node_radius) #line 264\n", " # Trim tree\n", " number_of_branch_deletions = 5\n", " for i in range(number_of_branch_deletions):\n", " target_tree = deletion_of_branch17(target_tree)\n", " # Validate tree\n", " is_valid, message = validate_newick_syntax17(target_tree)\n", " illuminated_coords = set()\n", "\n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos17(target_tree)\n", " illuminated_coords = get_illuminated_area17(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", "\n", " return target_tree, illuminated_coords\n", "\n", "\n", "# Generate the Newick string of a morpho-tree, test and draw it\n", "# def generate_test_and_draw_morpho_tree(target_tree_params['max_depth'], target_tree_params['max_number_children'], target_tree_params['max_node_radius'] ):\n", "def generate_and_test_morpho_tree17(ga_params):\n", " #print(\"generate_and_test_morpho_tree( ga_params)\"\"\n", "\n", " newick_tree = complete_tree_generator17(ga_params['max_depth'], ga_params['max_number_children'], ga_params['max_node_radius']) #line 264\n", " #newick_tree = complete_tree_generator17(max_depth, max_numb_children, max_node_radius) #line 264\n", " is_valid, message = validate_newick_syntax17(newick_tree)\n", " illuminated_coords = set()\n", "\n", " if is_valid:\n", " a,b,x,y,r = extract_all_infos17(newick_tree)\n", " illuminated_coords = get_illuminated_area17(x,y,r)\n", " else:\n", " print(\"Newick string is not valid\")\n", " \"\"\"\n", " is_valid, message = validate_newick_syntax17(newick_tree)\n", " print(f\"is the Newick String valid?: {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", " \"\"\"\n", " return newick_tree, illuminated_coords\n", "\n", "\n", "\n", "\n", "\n", "# --- Evolutionary functions ---\n", "\n", "def create_initial_population_newick17(pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'], max_node_radius=ga_params['max_node_radius'] ):\n", " population = []\n", " for _ in range(pop_size):\n", " new_tree = complete_tree_generator17(max_depth, max_children, max_node_radius)\n", " population.append(new_tree)\n", " return population\n", "\n", "def get_illuminated_coordinates17(newick_string):\n", " \"\"\"Coordinates of illuminated cells are extracted from the Newick string\"\"\"\n", " a,b,x,y,r = extract_all_infos17(newick_string)\n", " # Illuminated area is calculated\n", " illuminated_coords = get_illuminated_area17(x,y,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_illumination17(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_coordinates17(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", "\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_branch17(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_recombination17(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_position_branch_recombination17\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " branch1 = find_recombinant_branch17(newick_string1)\n", " branch2 = find_recombinant_branch17(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_recombination17(newick_string1, newick_string2):\n", " \"\"\" Recombination of full functional branches\"\"\"\n", " #print(\"\\nnewick_leaves_recombination17\")\n", " #print(f\"newick_string1: {newick_string1}\")\n", " #print(f\"newick_string2: {newick_string2}\")\n", " leaves1 = find_leaves(newick_string1)\n", " leaves2 = find_leaves(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_squares17(newick_string):\n", " \"\"\"Detects all the occupied squares in a tree\"\"\"\n", " occupied_squares_set = set()\n", " groups = detect_all_groups17(newick_string)\n", " for group in groups:\n", " [a,b,x,y,r] = extract_info17(group)\n", " occupied_squares_set.add((x, y))\n", " return occupied_squares_set\n", "\n", "def newick_mutation_of_radius17(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_subsequence17(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_patterns17(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_branch17(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_patterns17(newick_string)\n", " # String to be inserted\n", " # child_string = \"(Node17[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.83],Node17[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_generator17(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_17(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\"Group170: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center17(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares17(newick_string)\n", " info, occupied_squares_set = node_info_generator17(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_17(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\"Group170: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center17(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares17(newick_string)\n", " info, occupied_squares_set = node_info_generator17(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_17(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\"Group170: {group}\")\n", " # String to be inserted\n", " father_center = find_father_center17(newick_string, start)\n", " #print(f\"Father center: {father_center}\")\n", " occupied_squares_set = find_occupied_squares17(newick_string)\n", " info, occupied_squares_set = node_info_generator17(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_leaf17(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_17(newick_string, counter, max_illumination_radius)\n", " case 2:\n", " newick_string = select_one_insertion_pattern_parentheses_17(newick_string, counter, max_illumination_radius)\n", " case 3:\n", " newick_string = select_one_insertion_pattern_left_parenthesis_17(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_leaf17(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_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", "def find_branch17(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_groups17(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", "\n", "def deletion_of_branch17(newick_string ):\n", " group, start = newick_find_all_groups17(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_branch17(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", "def newick_mutation_branches17(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_radius17(newick_string)\n", " newick_string = insertion_of_branch17(newick_string)\n", " newick_string = deletion_of_branch17(newick_string)\n", " return newick_string\n", "\n", "def newick_mutation_leaves17(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_radius17(newick_string)\n", " newick_string = insertion_of_leaf17(newick_string, counter=random.randint(0, 1000), max_illumination_radius=ga_params['max_node_radius'])\n", " newick_string = deletion_of_leaf17(newick_string)\n", " return newick_string\n", "\n", "\n", "def plot_illuminated_area17(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 myEvolutionaryProcess17(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_tree, target_illuminated_area = generate_and_test_trimmed_target_morpho_tree17(ga_params)#891\n", " for i in range(2):\n", " target_tree = deletion_of_branch17(target_tree)\n", " print(f\"target_tree: {target_tree} \\n\\n\")\n", " number_of_groups17 = count_groups17(target_tree)\n", " print(f\"Number of groups or nodes in target tree: {number_of_groups17}\")\n", " length_of_target_tree = len(target_tree)\n", " #draw_newick_string17(target_tree, \"Target Tree\")\n", " # target_area = get_illuminated_coordinates17(target_tree)\n", " #print(f\"Target illuminated area: {target_illuminated_area}\")\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_newick17(\n", " pop_size=ga_params['pop_size'], max_depth=ga_params['max_depth'], max_children=ga_params['max_number_children'],\n", " max_node_radius=ga_params['max_node_radius'] )\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", " history17 = []\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_illumination17(tree, target_illuminated_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", " history17.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 history17, number_of_groups17\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_generator17(target_tree_params['max_depth'], target_tree_params['max_number_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_recombination17\n", " # child1, child2 = newick_position_branch_recombination17(p1, p2)\n", " # Crossover newick_leaves_recombination17\n", " child1, child2 =newick_leaves_recombination17(p1,p2)\n", " # Mutation: Apply mutation to both children\n", " mutated_child1 = newick_mutation_leaves17(child1, ga_params)\n", " mutated_child2 = newick_mutation_leaves17(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_generator17(target_tree_params['max_depth'], target_tree_params['max_number_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_tree, target_illuminated_area, best_tree_overall, best_fitness_overall, history, number_of_groups\n", "\n", " return history17, number_of_groups17\n", "\n", "\n", "def draw_all_histories17(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", " # By the AI of VSCode.\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", "def main17(target_tree_params, ga_params):\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 = [20000, 20001, 20002, 20003, 20004 ] # Example numbers of generations for different lengths of target sequence\n", " #numbers_of_generations = [11, 12, 13, 14, 15 ]\n", " numbers_of_generations = [7001, 7002, 7003, 7004, 7005 ]\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", " target_tree_params['pop_size'] = population_sizes[i]\n", " ga_params['generations'] = numbers_of_generations[i]\n", " print(f\"\\nRunning evolutionary process for max_depth = {target_tree_params['max_depth']} and generations = {ga_params['generations']}\")\n", " history17, number_of_groups17 = myEvolutionaryProcess17(target_tree_params, ga_params)\n", " fitness_histories.append(history17)\n", " numbers_of_nodes.append(number_of_groups17)\n", " draw_all_histories17(fitness_histories, numbers_of_nodes, population_sizes)\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_parameters17(target_tree_params, ga_params)\n", " main17(target_tree_params, ga_params)\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_newick_morphological_trees17.py" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LAB: EVOLUTION OF NEWICK MORPHO-TREES\n", "PURPOSE: \n", " A random morpho-tree T is generated.\n", " Evolution tries to restore the tree T\n", " beginning from a population of random trees.\n", "EXPERIMENT 1791291786.015035\n", "RECOMBINATION OF LEAVES: False\n", "INITIAL TARGET TREE PARAMETERS: {'max_depth': 15, 'max_number_children': 2, 'max_node_radius': 1.5, 'number_of_branch_deletions': 10}\n", "INITIAL GENETIC ALGORITHM PARAMETERS: {'pop_size': 70, 'generations': -1, 'max_depth': 15, 'max_number_children': 2, 'max_node_radius': 1.5, 'mutation_rate': 0.001, 'crossover_rate': 0.001}\n", "\n", "\n", "Running evolutionary process for max_depth = 15 and generations = 7001\n", "target_tree: Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 1\n", "\n", "--- Generation 1/7001, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7001, Best fitness 0.84211 ---\n", "\n", "--- Generation 2001/7001, Best fitness 0.84211 ---\n", "\n", "--- Generation 3001/7001, Best fitness 0.84211 ---\n", "\n", "--- Generation 4001/7001, Best fitness 0.84211 ---\n", "\n", "--- Generation 5001/7001, Best fitness 0.84211 ---\n", "\n", "--- Generation 6001/7001, Best fitness 0.84211 ---\n", "\n", "--- Generation 7001/7001, Best fitness 0.84211 ---\n", "\n", "Running evolutionary process for max_depth = 15 and generations = 7002\n", "target_tree: ((((((((Node25[a=1.0,b=0.0,x=5.0,y=-5.0,r=1.05])Node21[a=1.0,b=-1.0,x=4.0,y=-5.0,r=0.87])Node13[a=1.0,b=-1.0,x=3.0,y=-4.0,r=1.35])Node9[a=1.0,b=-1.0,x=2.0,y=-3.0,r=0.74])Node7[a=1.0,b=-1.0,x=1.0,y=-2.0,r=0.54])Node4[a=-1.0,b=-1.0,x=0.0,y=-1.0,r=1.12],(()Node6[a=1.0,b=0.0,x=2.0,y=1.0,r=0.64],(()Node11[a=-1.0,b=0.0,x=-1.0,y=2.0,r=0.86],(((()Node29[a=0.0,b=1.0,x=1.0,y=4.0,r=0.95],(((((((Node211[a=0.0,b=1.0,x=7.0,y=6.0,r=0.6],Node210[a=1.0,b=1.0,x=8.0,y=6.0,r=1.07])Node168[a=1.0,b=-1.0,x=7.0,y=5.0,r=0.92])Node138[a=0.0,b=1.0,x=6.0,y=6.0,r=0.67])Node112[a=1.0,b=1.0,x=6.0,y=5.0,r=0.84])Node87[a=1.0,b=1.0,x=5.0,y=4.0,r=0.9])Node67[a=1.0,b=0.0,x=4.0,y=3.0,r=0.89],((((Node207[a=1.0,b=1.0,x=6.0,y=7.0,r=0.96])Node166[a=1.0,b=1.0,x=5.0,y=6.0,r=1.09])Node111[a=-1.0,b=0.0,x=4.0,y=5.0,r=0.68])Node86[a=1.0,b=1.0,x=5.0,y=5.0,r=1.4],(((Node206[a=1.0,b=1.0,x=8.0,y=5.0,r=0.83])Node137[a=1.0,b=1.0,x=7.0,y=4.0,r=0.67],(Node165[a=1.0,b=0.0,x=8.0,y=2.0,r=0.62],(Node205[a=1.0,b=1.0,x=9.0,y=2.0,r=0.92],Node204[a=1.0,b=-1.0,x=9.0,y=0.0,r=0.74])Node164[a=1.0,b=-1.0,x=8.0,y=1.0,r=1.14])Node136[a=1.0,b=-1.0,x=7.0,y=2.0,r=1.2])Node110[a=1.0,b=0.0,x=6.0,y=3.0,r=0.73],((Node163[a=1.0,b=1.0,x=8.0,y=4.0,r=1.2])Node135[a=1.0,b=-1.0,x=7.0,y=3.0,r=1.21])Node109[a=1.0,b=1.0,x=6.0,y=4.0,r=1.35])Node85[a=1.0,b=-1.0,x=5.0,y=3.0,r=1.33])Node66[a=1.0,b=1.0,x=4.0,y=4.0,r=0.9])Node52[a=1.0,b=-1.0,x=3.0,y=3.0,r=1.15],(((((Node203[a=1.0,b=1.0,x=3.0,y=10.0,r=1.3],Node202[a=0.0,b=1.0,x=2.0,y=10.0,r=1.4])Node134[a=0.0,b=1.0,x=2.0,y=9.0,r=0.83])Node108[a=1.0,b=1.0,x=2.0,y=8.0,r=0.85],((Node162[a=1.0,b=1.0,x=0.0,y=10.0,r=1.06])Node133[a=-1.0,b=1.0,x=-1.0,y=9.0,r=0.61],((Node201[a=1.0,b=1.0,x=0.0,y=9.0,r=1.1])Node161[a=0.0,b=1.0,x=-1.0,y=8.0,r=0.6],(Node200[a=-1.0,b=1.0,x=-3.0,y=9.0,r=0.84],Node199[a=0.0,b=1.0,x=-2.0,y=9.0,r=0.61])Node160[a=-1.0,b=1.0,x=-2.0,y=8.0,r=1.09])Node132[a=-1.0,b=-1.0,x=-1.0,y=7.0,r=1.46])Node107[a=-1.0,b=1.0,x=0.0,y=8.0,r=1.19])Node84[a=-1.0,b=1.0,x=1.0,y=7.0,r=1.14])Node65[a=1.0,b=1.0,x=2.0,y=6.0,r=0.77],((((Node198[a=-1.0,b=1.0,x=-3.0,y=8.0,r=0.78])Node159[a=-1.0,b=1.0,x=-2.0,y=7.0,r=0.85])Node106[a=-1.0,b=0.0,x=-1.0,y=6.0,r=1.25])Node83[a=0.0,b=1.0,x=0.0,y=6.0,r=1.07])Node64[a=-1.0,b=0.0,x=0.0,y=5.0,r=1.17])Node51[a=-1.0,b=1.0,x=1.0,y=5.0,r=1.17])Node28[a=1.0,b=1.0,x=2.0,y=4.0,r=0.89])Node19[a=-1.0,b=0.0,x=1.0,y=3.0,r=0.76])Node14[a=1.0,b=1.0,x=2.0,y=3.0,r=0.78])Node10[a=1.0,b=0.0,x=1.0,y=2.0,r=0.83])Node5[a=-1.0,b=1.0,x=0.0,y=2.0,r=1.17])Node3[a=0.0,b=1.0,x=1.0,y=1.0,r=0.92])Node2[a=1.0,b=-1.0,x=1.0,y=0.0,r=1.19])Node1[a=0.0,b=1.0,x=0.0,y=1.0,r=0.85])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 64\n", "\n", "--- Generation 1/7002, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7002, Best fitness 0.77005 ---\n", "\n", "--- Generation 2001/7002, Best fitness 0.78723 ---\n", "\n", "--- Generation 3001/7002, Best fitness 0.86316 ---\n", "\n", "--- Generation 4001/7002, Best fitness 0.92021 ---\n", "\n", "--- Generation 5001/7002, Best fitness 0.92513 ---\n", "\n", "--- Generation 6001/7002, Best fitness 0.92553 ---\n", "\n", "--- Generation 7001/7002, Best fitness 0.94118 ---\n", "\n", "Running evolutionary process for max_depth = 15 and generations = 7003\n", "target_tree: ((((((((((((((Node264[a=-1.0,b=0.0,x=-7.0,y=6.0,r=0.93])Node213[a=-1.0,b=0.0,x=-6.0,y=6.0,r=1.22],Node212[a=-1.0,b=1.0,x=-6.0,y=7.0,r=0.55])Node170[a=-1.0,b=1.0,x=-5.0,y=6.0,r=1.17])Node132[a=-1.0,b=0.0,x=-4.0,y=5.0,r=0.71],((Node211[a=-1.0,b=1.0,x=-5.0,y=7.0,r=0.62])Node169[a=-1.0,b=0.0,x=-4.0,y=6.0,r=0.64])Node131[a=0.0,b=1.0,x=-3.0,y=6.0,r=1.18])Node99[a=-1.0,b=0.0,x=-3.0,y=5.0,r=0.96],(Node130[a=1.0,b=-1.0,x=0.0,y=5.0,r=1.19])Node98[a=1.0,b=1.0,x=-1.0,y=6.0,r=1.14])Node75[a=1.0,b=1.0,x=-2.0,y=5.0,r=0.85])Node55[a=-1.0,b=0.0,x=-3.0,y=4.0,r=1.42],((((Node210[a=-1.0,b=0.0,x=-6.0,y=3.0,r=0.86],()Node209[a=-1.0,b=-1.0,x=-6.0,y=2.0,r=0.6])Node168[a=0.0,b=1.0,x=-5.0,y=3.0,r=1.18])Node129[a=-1.0,b=0.0,x=-5.0,y=2.0,r=1.41])Node74[a=-1.0,b=-1.0,x=-4.0,y=2.0,r=0.6])Node54[a=-1.0,b=-1.0,x=-3.0,y=3.0,r=0.68])Node34[a=-1.0,b=1.0,x=-2.0,y=4.0,r=1.34],(((Node97[a=0.0,b=1.0,x=-4.0,y=4.0,r=1.02],(((Node262[a=-1.0,b=-1.0,x=-7.0,y=3.0,r=0.93],Node261[a=-1.0,b=1.0,x=-7.0,y=5.0,r=1.43])Node208[a=-1.0,b=-1.0,x=-6.0,y=4.0,r=0.67],(Node260[a=-1.0,b=-1.0,x=-7.0,y=4.0,r=0.91])Node207[a=-1.0,b=0.0,x=-6.0,y=5.0,r=1.11])Node128[a=0.0,b=1.0,x=-5.0,y=5.0,r=0.83])Node96[a=-1.0,b=1.0,x=-5.0,y=4.0,r=0.72])Node73[a=-1.0,b=1.0,x=-4.0,y=3.0,r=0.65],(((Node206[a=-1.0,b=-1.0,x=-7.0,y=0.0,r=0.75],(Node259[a=-1.0,b=0.0,x=-8.0,y=1.0,r=1.45])Node205[a=-1.0,b=0.0,x=-7.0,y=1.0,r=1.12])Node127[a=-1.0,b=1.0,x=-6.0,y=1.0,r=1.3])Node95[a=-1.0,b=-1.0,x=-5.0,y=0.0,r=0.95])Node72[a=-1.0,b=-1.0,x=-4.0,y=1.0,r=1.43])Node53[a=-1.0,b=-1.0,x=-3.0,y=2.0,r=0.57])Node33[a=-1.0,b=0.0,x=-2.0,y=3.0,r=0.63])Node26[a=-1.0,b=-1.0,x=-1.0,y=3.0,r=1.28])Node11[a=-1.0,b=1.0,x=0.0,y=4.0,r=1.12],((((((((Node204[a=1.0,b=-1.0,x=6.0,y=2.0,r=1.03])Node126[a=-1.0,b=0.0,x=5.0,y=3.0,r=1.24])Node52[a=1.0,b=-1.0,x=6.0,y=3.0,r=0.68],((((Node258[a=1.0,b=-1.0,x=8.0,y=5.0,r=1.32])Node125[a=1.0,b=0.0,x=7.0,y=6.0,r=1.4],Node124[a=1.0,b=-1.0,x=7.0,y=5.0,r=1.06])Node94[a=1.0,b=1.0,x=6.0,y=6.0,r=1.24],(((Node203[a=1.0,b=1.0,x=8.0,y=4.0,r=1.25],(Node257[a=-1.0,b=-1.0,x=6.0,y=1.0,r=1.43],Node256[a=1.0,b=-1.0,x=8.0,y=1.0,r=0.55])Node202[a=0.0,b=-1.0,x=7.0,y=2.0,r=1.43])Node167[a=0.0,b=-1.0,x=7.0,y=3.0,r=0.56],(Node255[a=1.0,b=0.0,x=9.0,y=3.0,r=1.18],Node254[a=1.0,b=-1.0,x=9.0,y=2.0,r=1.38])Node166[a=1.0,b=-1.0,x=8.0,y=3.0,r=0.88])Node123[a=1.0,b=-1.0,x=7.0,y=4.0,r=1.16])Node93[a=1.0,b=0.0,x=6.0,y=5.0,r=1.34])Node71[a=-1.0,b=1.0,x=5.0,y=5.0,r=1.25])Node51[a=1.0,b=0.0,x=6.0,y=4.0,r=1.41])Node45[a=1.0,b=0.0,x=5.0,y=4.0,r=1.3],((((Node165[a=0.0,b=1.0,x=5.0,y=8.0,r=0.6],(Node253[a=1.0,b=1.0,x=7.0,y=9.0,r=1.36])Node164[a=1.0,b=1.0,x=6.0,y=8.0,r=0.98])Node122[a=1.0,b=1.0,x=5.0,y=7.0,r=0.6])Node92[a=0.0,b=1.0,x=4.0,y=6.0,r=1.05],(()Node163[a=1.0,b=1.0,x=6.0,y=7.0,r=1.06])Node91[a=1.0,b=1.0,x=5.0,y=6.0,r=0.68])Node70[a=1.0,b=1.0,x=4.0,y=5.0,r=0.79],((Node200[a=-1.0,b=0.0,x=4.0,y=2.0,r=1.24])Node121[a=1.0,b=-1.0,x=5.0,y=2.0,r=1.43])Node69[a=1.0,b=-1.0,x=4.0,y=3.0,r=0.73])Node44[a=-1.0,b=0.0,x=3.0,y=4.0,r=0.77])Node32[a=1.0,b=-1.0,x=4.0,y=4.0,r=1.12])Node25[a=0.0,b=-1.0,x=3.0,y=5.0,r=1.27])Node19[a=1.0,b=1.0,x=3.0,y=6.0,r=1.11],(((((Node162[a=1.0,b=1.0,x=3.0,y=11.0,r=0.72])Node90[a=1.0,b=1.0,x=2.0,y=10.0,r=0.58],((Node161[a=0.0,b=1.0,x=0.0,y=12.0,r=0.5])Node120[a=-1.0,b=1.0,x=0.0,y=11.0,r=0.71],((Node251[a=-1.0,b=1.0,x=0.0,y=13.0,r=0.96])Node160[a=0.0,b=1.0,x=1.0,y=12.0,r=1.02],((Node250[a=1.0,b=1.0,x=4.0,y=14.0,r=0.7],Node249[a=0.0,b=1.0,x=3.0,y=14.0,r=0.97])Node199[a=1.0,b=1.0,x=3.0,y=13.0,r=0.58],(Node248[a=-1.0,b=0.0,x=1.0,y=13.0,r=0.88],Node247[a=0.0,b=1.0,x=2.0,y=14.0,r=1.26])Node198[a=0.0,b=1.0,x=2.0,y=13.0,r=0.58])Node159[a=1.0,b=1.0,x=2.0,y=12.0,r=1.47])Node119[a=0.0,b=1.0,x=1.0,y=11.0,r=1.37])Node89[a=0.0,b=1.0,x=1.0,y=10.0,r=1.02])Node68[a=0.0,b=1.0,x=1.0,y=9.0,r=0.93],(((((Node246[a=0.0,b=1.0,x=4.0,y=13.0,r=0.8])Node197[a=-1.0,b=1.0,x=4.0,y=12.0,r=1.07],(Node245[a=0.0,b=1.0,x=5.0,y=13.0,r=1.18])Node196[a=0.0,b=1.0,x=5.0,y=12.0,r=1.09])Node157[a=1.0,b=1.0,x=5.0,y=11.0,r=0.57],((Node244[a=1.0,b=-1.0,x=7.0,y=8.0,r=0.6])Node194[a=1.0,b=0.0,x=6.0,y=9.0,r=0.58])Node156[a=1.0,b=-1.0,x=5.0,y=9.0,r=0.77])Node116[a=1.0,b=1.0,x=4.0,y=10.0,r=1.27],((Node193[a=1.0,b=1.0,x=6.0,y=11.0,r=0.62])Node155[a=1.0,b=1.0,x=5.0,y=10.0,r=0.51])Node115[a=1.0,b=0.0,x=4.0,y=9.0,r=0.61])Node87[a=1.0,b=0.0,x=3.0,y=9.0,r=0.95])Node67[a=1.0,b=1.0,x=2.0,y=9.0,r=0.88])Node50[a=-1.0,b=1.0,x=1.0,y=8.0,r=1.4])Node24[a=1.0,b=1.0,x=2.0,y=7.0,r=0.73])Node18[a=-1.0,b=1.0,x=1.0,y=6.0,r=1.15])Node14[a=0.0,b=1.0,x=2.0,y=5.0,r=1.04])Node10[a=1.0,b=1.0,x=2.0,y=4.0,r=0.66])Node6[a=0.0,b=1.0,x=1.0,y=3.0,r=0.7],((((((((Node154[a=1.0,b=0.0,x=4.0,y=8.0,r=0.65])Node114[a=0.0,b=1.0,x=3.0,y=8.0,r=1.33],Node113[a=1.0,b=0.0,x=4.0,y=7.0,r=0.65])Node66[a=1.0,b=-1.0,x=3.0,y=7.0,r=0.88])Node43[a=1.0,b=1.0,x=2.0,y=8.0,r=0.64])Node31[a=-1.0,b=1.0,x=1.0,y=7.0,r=0.58])Node23[a=1.0,b=1.0,x=2.0,y=6.0,r=0.96],((Node42[a=1.0,b=0.0,x=0.0,y=7.0,r=0.89],(((((Node243[a=0.0,b=1.0,x=-1.0,y=13.0,r=0.88],Node242[a=-1.0,b=1.0,x=-2.0,y=13.0,r=0.71])Node192[a=1.0,b=1.0,x=-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e134[a=-1.0,b=0.0,x=-5.0,y=9.0,r=0.95])Node76[a=-1.0,b=1.0,x=-4.0,y=9.0,r=0.65])Node57[a=-1.0,b=1.0,x=-3.0,y=8.0,r=0.7],(((Node215[a=-1.0,b=0.0,x=-4.0,y=7.0,r=0.78],Node214[a=-1.0,b=1.0,x=-4.0,y=8.0,r=0.99])Node171[a=-1.0,b=-1.0,x=-3.0,y=7.0,r=1.25])Node133[a=-1.0,b=0.0,x=-2.0,y=8.0,r=1.24])Node56[a=1.0,b=1.0,x=-1.0,y=8.0,r=0.71])Node35[a=0.0,b=1.0,x=-2.0,y=7.0,r=0.59])Node27[a=-1.0,b=1.0,x=-2.0,y=6.0,r=0.74])Node12[a=0.0,b=1.0,x=-1.0,y=5.0,r=1.19])Node7[a=-1.0,b=1.0,x=-1.0,y=4.0,r=0.59])Node3[a=0.0,b=1.0,x=0.0,y=3.0,r=0.99])Node2[a=-1.0,b=1.0,x=0.0,y=2.0,r=1.29])Node1[a=1.0,b=1.0,x=1.0,y=1.0,r=0.75])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 254\n", "\n", "--- Generation 1/7003, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7003, Best fitness 0.84022 ---\n", "\n", "--- Generation 2001/7003, Best fitness 0.88827 ---\n", "\n", "--- Generation 3001/7003, Best fitness 0.91243 ---\n", "\n", "--- Generation 4001/7003, Best fitness 0.94101 ---\n", "\n", "--- Generation 5001/7003, Best fitness 0.94413 ---\n", "\n", "--- Generation 6001/7003, Best fitness 0.95238 ---\n", "\n", "--- Generation 7001/7003, Best fitness 0.95518 ---\n", "\n", "Running evolutionary process for max_depth = 15 and generations = 7004\n", "target_tree: ((((((((Node88[a=0.0,b=1.0,x=-3.0,y=5.0,r=1.11])Node71[a=-1.0,b=1.0,x=-3.0,y=4.0,r=1.17],(Node111[a=-1.0,b=-1.0,x=-3.0,y=3.0,r=1.47],(Node133[a=1.0,b=1.0,x=0.0,y=6.0,r=0.6],(Node156[a=1.0,b=1.0,x=0.0,y=7.0,r=0.69])Node132[a=0.0,b=1.0,x=-1.0,y=6.0,r=1.23])Node110[a=1.0,b=1.0,x=-1.0,y=5.0,r=0.51])Node70[a=0.0,b=1.0,x=-2.0,y=4.0,r=1.5])Node55[a=0.0,b=1.0,x=-2.0,y=3.0,r=0.55],(((Node131[a=1.0,b=1.0,x=2.0,y=5.0,r=0.65],((Node188[a=1.0,b=1.0,x=2.0,y=7.0,r=1.17])Node155[a=1.0,b=1.0,x=1.0,y=6.0,r=1.23])Node130[a=-1.0,b=1.0,x=0.0,y=5.0,r=1.44])Node87[a=1.0,b=1.0,x=1.0,y=4.0,r=1.3])Node69[a=1.0,b=0.0,x=0.0,y=3.0,r=1.08])Node54[a=1.0,b=1.0,x=-1.0,y=3.0,r=1.09])Node31[a=1.0,b=1.0,x=-2.0,y=2.0,r=0.84],(((((Node187[a=-1.0,b=0.0,x=-7.0,y=3.0,r=0.62])Node154[a=-1.0,b=1.0,x=-6.0,y=3.0,r=0.77],(Node186[a=-1.0,b=1.0,x=-7.0,y=2.0,r=0.84])Node153[a=-1.0,b=-1.0,x=-6.0,y=1.0,r=1.29])Node109[a=-1.0,b=1.0,x=-5.0,y=2.0,r=1.29])Node68[a=-1.0,b=-1.0,x=-4.0,y=1.0,r=0.91],((((Node185[a=1.0,b=-1.0,x=-5.0,y=1.0,r=0.95])Node152[a=-1.0,b=-1.0,x=-6.0,y=2.0,r=0.75],(Node184[a=-1.0,b=0.0,x=-6.0,y=4.0,r=0.89],Node183[a=-1.0,b=1.0,x=-6.0,y=5.0,r=1.14])Node151[a=0.0,b=1.0,x=-5.0,y=4.0,r=0.63])Node129[a=-1.0,b=-1.0,x=-5.0,y=3.0,r=0.61],(Node182[a=-1.0,b=1.0,x=-5.0,y=6.0,r=0.85],Node181[a=-1.0,b=0.0,x=-5.0,y=5.0,r=0.64])Node128[a=0.0,b=1.0,x=-4.0,y=5.0,r=1.19])Node108[a=0.0,b=1.0,x=-4.0,y=4.0,r=0.58])Node67[a=-1.0,b=1.0,x=-4.0,y=3.0,r=0.97])Node42[a=1.0,b=0.0,x=-3.0,y=2.0,r=1.39])Node30[a=-1.0,b=1.0,x=-4.0,y=2.0,r=0.81])Node12[a=-1.0,b=0.0,x=-3.0,y=1.0,r=0.53])Node9[a=0.0,b=1.0,x=-2.0,y=1.0,r=1.0],((((((((()Node107[a=1.0,b=1.0,x=3.0,y=4.0,r=1.24],((Node180[a=1.0,b=1.0,x=4.0,y=6.0,r=1.1],Node179[a=0.0,b=1.0,x=3.0,y=6.0,r=0.77])Node149[a=1.0,b=1.0,x=3.0,y=5.0,r=1.41],(Node178[a=1.0,b=1.0,x=2.0,y=6.0,r=1.3])Node148[a=-1.0,b=1.0,x=1.0,y=5.0,r=0.69])Node106[a=0.0,b=1.0,x=2.0,y=4.0,r=1.46])Node66[a=1.0,b=0.0,x=2.0,y=3.0,r=1.18],((((Node146[a=0.0,b=1.0,x=-2.0,y=7.0,r=1.27])Node127[a=0.0,b=1.0,x=-2.0,y=6.0,r=1.28],(Node176[a=-1.0,b=0.0,x=-4.0,y=6.0,r=1.1],Node175[a=0.0,b=1.0,x=-3.0,y=7.0,r=1.3])Node126[a=-1.0,b=1.0,x=-3.0,y=6.0,r=1.0])Node105[a=-1.0,b=1.0,x=-2.0,y=5.0,r=1.43])Node86[a=-1.0,b=0.0,x=-1.0,y=4.0,r=1.38])Node65[a=-1.0,b=1.0,x=0.0,y=4.0,r=1.48])Node53[a=0.0,b=1.0,x=1.0,y=3.0,r=0.68])Node41[a=1.0,b=0.0,x=1.0,y=2.0,r=1.29])Node29[a=-1.0,b=1.0,x=0.0,y=2.0,r=0.7])Node23[a=1.0,b=0.0,x=1.0,y=1.0,r=1.19],((Node62[a=1.0,b=-1.0,x=3.0,y=0.0,r=1.41])Node39[a=1.0,b=1.0,x=2.0,y=1.0,r=1.07])Node22[a=1.0,b=-1.0,x=1.0,y=0.0,r=1.23])Node16[a=1.0,b=-1.0,x=0.0,y=1.0,r=1.31])Node11[a=0.0,b=1.0,x=-1.0,y=2.0,r=0.55])Node8[a=1.0,b=1.0,x=-1.0,y=1.0,r=1.39])Node5[a=-1.0,b=1.0,x=-2.0,y=0.0,r=1.08],((()Node10[a=-1.0,b=-1.0,x=-4.0,y=-1.0,r=1.21])Node7[a=-1.0,b=1.0,x=-3.0,y=0.0,r=1.22],Node6[a=1.0,b=1.0,x=-1.0,y=0.0,r=1.09])Node4[a=-1.0,b=0.0,x=-2.0,y=-1.0,r=0.93])Node2[a=-1.0,b=-1.0,x=-1.0,y=-1.0,r=1.07],((((((((((Node142[a=1.0,b=0.0,x=1.0,y=-5.0,r=1.3])Node99[a=1.0,b=-1.0,x=0.0,y=-5.0,r=0.72])Node80[a=1.0,b=0.0,x=-1.0,y=-4.0,r=1.36])Node59[a=-1.0,b=-1.0,x=-2.0,y=-4.0,r=0.7])Node50[a=-1.0,b=1.0,x=-1.0,y=-3.0,r=1.22])Node36[a=-1.0,b=0.0,x=0.0,y=-4.0,r=1.33])Node27[a=-1.0,b=-1.0,x=1.0,y=-4.0,r=0.91])Node19[a=0.0,b=-1.0,x=2.0,y=-3.0,r=1.19],(((((()Node98[a=-1.0,b=1.0,x=3.0,y=3.0,r=0.61],((Node167[a=1.0,b=0.0,x=7.0,y=1.0,r=1.19],Node166[a=0.0,b=1.0,x=6.0,y=2.0,r=1.35])Node141[a=1.0,b=-1.0,x=6.0,y=1.0,r=0.62])Node97[a=1.0,b=0.0,x=5.0,y=2.0,r=0.58])Node79[a=-1.0,b=1.0,x=4.0,y=2.0,r=1.21])Node49[a=1.0,b=1.0,x=5.0,y=1.0,r=0.63],((Node117[a=0.0,b=-1.0,x=4.0,y=-3.0,r=1.21])Node96[a=1.0,b=-1.0,x=4.0,y=-2.0,r=1.02])Node48[a=-1.0,b=-1.0,x=3.0,y=-1.0,r=1.46])Node35[a=0.0,b=1.0,x=4.0,y=0.0,r=0.9],(((Node140[a=-1.0,b=0.0,x=4.0,y=-4.0,r=1.19])Node78[a=-1.0,b=-1.0,x=5.0,y=-4.0,r=1.41],(((Node165[a=0.0,b=1.0,x=6.0,y=0.0,r=1.33])Node116[a=0.0,b=1.0,x=6.0,y=-1.0,r=1.07],(Node164[a=1.0,b=0.0,x=8.0,y=-1.0,r=1.35],Node163[a=0.0,b=1.0,x=7.0,y=0.0,r=1.41])Node115[a=1.0,b=1.0,x=7.0,y=-1.0,r=1.08])Node95[a=1.0,b=1.0,x=6.0,y=-2.0,r=1.14])Node77[a=-1.0,b=0.0,x=5.0,y=-3.0,r=0.95])Node47[a=1.0,b=-1.0,x=6.0,y=-3.0,r=1.31],(Node58[a=0.0,b=1.0,x=5.0,y=0.0,r=1.38])Node46[a=0.0,b=1.0,x=5.0,y=-1.0,r=0.84])Node34[a=1.0,b=-1.0,x=5.0,y=-2.0,r=1.1])Node26[a=1.0,b=1.0,x=4.0,y=-1.0,r=1.1],(((((Node139[a=1.0,b=-1.0,x=4.0,y=-7.0,r=0.9])Node94[a=1.0,b=0.0,x=3.0,y=-6.0,r=0.82])Node76[a=-1.0,b=-1.0,x=2.0,y=-6.0,r=0.62],((Node138[a=-1.0,b=0.0,x=2.0,y=-7.0,r=0.58])Node93[a=-1.0,b=-1.0,x=3.0,y=-7.0,r=0.89])Node75[a=1.0,b=-1.0,x=4.0,y=-6.0,r=0.65])Node45[a=0.0,b=-1.0,x=3.0,y=-5.0,r=1.24],((((Node162[a=0.0,b=1.0,x=7.0,y=-2.0,r=1.08])Node92[a=1.0,b=1.0,x=7.0,y=-3.0,r=1.32])Node74[a=1.0,b=1.0,x=6.0,y=-4.0,r=0.83],((()Node114[a=1.0,b=0.0,x=8.0,y=-7.0,r=0.55])Node91[a=1.0,b=-1.0,x=7.0,y=-7.0,r=1.16])Node73[a=1.0,b=-1.0,x=6.0,y=-6.0,r=1.29])Node57[a=1.0,b=0.0,x=5.0,y=-5.0,r=1.14],((((Node136[a=0.0,b=1.0,x=8.0,y=-2.0,r=1.38],(Node157[a=1.0,b=1.0,x=10.0,y=-3.0,r=1.09])Node135[a=1.0,b=-1.0,x=9.0,y=-4.0,r=0.71])Node113[a=1.0,b=1.0,x=8.0,y=-3.0,r=1.1],(Node134[a=0.0,b=1.0,x=8.0,y=-4.0,r=1.31])Node112[a=1.0,b=-1.0,x=8.0,y=-5.0,r=0.55])Node89[a=1.0,b=1.0,x=7.0,y=-4.0,r=1.15])Node72[a=1.0,b=1.0,x=6.0,y=-5.0,r=1.44])Node56[a=1.0,b=-1.0,x=5.0,y=-6.0,r=0.69])Node44[a=1.0,b=-1.0,x=4.0,y=-5.0,r=0.98])Node33[a=0.0,b=-1.0,x=3.0,y=-4.0,r=0.75])Node25[a=0.0,b=-1.0,x=3.0,y=-3.0,r=0.81])Node18[a=1.0,b=0.0,x=3.0,y=-2.0,r=0.54])Node14[a=1.0,b=-1.0,x=2.0,y=-2.0,r=1.29],((((Node43[a=-1.0,b=1.0,x=-1.0,y=-2.0,r=1.26])Node32[a=-1.0,b=-1.0,x=0.0,y=-3.0,r=0.89])Node24[a=0.0,b=1.0,x=1.0,y=-2.0,r=0.72])Node17[a=1.0,b=-1.0,x=1.0,y=-3.0,r=1.17])Node13[a=-1.0,b=-1.0,x=0.0,y=-2.0,r=1.22])Node3[a=1.0,b=0.0,x=1.0,y=-1.0,r=0.57])Node1[a=0.0,b=-1.0,x=0.0,y=-1.0,r=0.62])Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 139\n", "\n", "--- Generation 1/7004, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7004, Best fitness 0.79845 ---\n", "\n", "--- Generation 2001/7004, Best fitness 0.83922 ---\n", "\n", "--- Generation 3001/7004, Best fitness 0.85317 ---\n", "\n", "--- Generation 4001/7004, Best fitness 0.876 ---\n", "\n", "--- Generation 5001/7004, Best fitness 0.896 ---\n", "\n", "--- Generation 6001/7004, Best fitness 0.90323 ---\n", "\n", "--- Generation 7001/7004, Best fitness 0.90726 ---\n", "\n", "Running evolutionary process for max_depth = 15 and generations = 7005\n", "target_tree: Node_0[a=0,b=0,x=0,y=0,r=1] \n", "\n", "\n", "Number of groups or nodes in target tree: 1\n", "\n", "--- Generation 1/7005, Best fitness -1.0 ---\n", "\n", "--- Generation 1001/7005, Best fitness 0.64286 ---\n", "\n", "--- Generation 2001/7005, Best fitness 0.9 ---\n", "\n", "--- Generation 3001/7005, Best fitness 0.9 ---\n", "\n", "--- Generation 4001/7005, Best fitness 0.9 ---\n", "\n", "--- Generation 5001/7005, Best fitness 0.9 ---\n", "\n", "--- Generation 6001/7005, Best fitness 0.9 ---\n", "\n", "--- Generation 7001/7005, Best fitness 0.9 ---\n", "draw_all_historiesk4(fitness_histories, lengths_of_target_sequence)\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Execution time: 4 hours, 41 minutes, 39 seconds\n" ] } ], "execution_count": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations.** \n", "1. The task for one node induces a chaotic behavior of the fitness function. \n", "2. The differences among the fitness functions for 64, 139 and 254 nodes of trimmed trees were insginificant. And after almost five hours of CPU work, a final fintess of 0.9 was achieved. This is very much like ordinary trees. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GRADUATION\n", "\n", "Choose one theme among the following ones to work on it:\n", "\n", "1. Make up your mind to decide what are the important questions. Compare your choice with the opinion of the Author: In our opinion, the most important question is the following: How must we define complexity of morpho-trees in order that the dictum that complexity must smash Evolution becomes obviously true? Maybe, one must invent abstract tasks to challenge Evolution, for instance: to reproduce a collar of holes that reminds us of the vertebral spine.\n", "1. By means of the symmetric difference o sets, we have defined fitness as a measure of the coincidence of illumination areas, one of the target tree, and the other that of the test tree. Our measure of fitness does not seem to be perfect because in large globules, there could be too many cells in the symmetric difference of target and test trees (that measures the error), but nevertheless, the fitness is high.\n", "1. Our definition of fitness allows that many trees could resolve the same problem. This eases the task and so complexity is evaded. But, Nature does not conceive form as just the exterior look. In nature, every structure is replenished with substructures of the most varied nature. Model and study this new situation.\n", "1. The number of nodes in the tree was our measure of complexity. That measure does not fulfill our expectation that complexity smashes Evolution. This is possibly due to a group of hidden variables. Our proposal of trimmed trees was, in our opinion, a failure. The possible reason is that our trees somehow resembles a tree in the form of a pie that are expected to be easily evolvable. We know this, because trimmed trees have voids with a radial form. Discover other new hidden variables and study their impact over Evolution. =\n", "1. Optimize the code for speed. Two ideas are:\n", ">\n", "> i. Use a binary matrix to register occupancy of squares instead of a set, which is a heavy-load structure.\n", ">\n", "> ii. In used programs, to gather the coordinates of illuminated squares, a grid was formed and scanned twice, vertically and horizontally. If one does this only in one sense, the error is small, so, it can be dispensed.\n", "\n", "6. Our procedure to validate a morpho-tree is imperfect. For example, it does not test connectivity of the graphs formed by linking children to fathers, and fathers and children are not neighbors after recombination but no error is declared. Do something better.\n", "1. We have encountered troubles with multiple occupancy of squares. We tolerated it in early programs because Nature has a remedy: apoptosis or self-purge. In last programs, we preferred to accept only those mutations that produced no overcrodwing. This creates a distance with Nature, where there is no beforehand remedy for future problems. Restore naturalness. For instance, prove that Nature chastice double and much more multiple occupancy of the same space. Insert the corresponding term in the fitness function. To that aim, use an occupancy matrix with integer values.\n", "1. We have encountered troubles with multiple occupancy of squares. We tolerated it in early programs because Nature has a remedy: apoptosis or self-purge. In last programs, we preferred to accept only those mutations that produced no overcrowding. This creates a distance with Nature, where there is no beforehand remedy for future problems. Restore naturalness. For instance, prove that Nature chastises double and much more multiple occupancy of the same space and insert the corresponding term in the fitness function. To that aim, use an occupancy matrix with integer values.\n", "1. We have twisted Evolution to make it computable by ordinary desktop computers. For instance, we have programmed mutation to produce children in the immediate vicinity of the father. If a proposed mutation tears the tree, it is not accepted. Instead of this, add to the selection function a term to chastise torn trees and justify its naturalness.\n", "1. The complexity of results has overcome our intuition. In this cases, a good tool is multivariate statistical analysis. One might try, for instance, [multiple regression](https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=CAESegHrOzAVdHU3nLFF8liVjLWJV84A32_-TIowlCRoPITzW35H9v_qq8m0U8EKAge_64QYBExz-x7TZrlh0to2JgMqdYTMys4Uswyis822EQnjTD7KDeIuef0bypIbvq9wFrLl0BOaK14qermxw0bKbljpc002YfIzaXx5&ved=2ahUKEwjJ5-Ow7ayXAxX7SjABHQ4RDpcQFnoECB8QAQ&uoh=2&usg=AOvVaw3Tz5ClVBQO9sll1iFC6Fm3) to quantify the impact of some input variables over the output one. Use this for our case in which the input variables are the number of nodes, population size, the number of children, the max-depth, globularity, a measure of rich structure, and other hidden variables of your invention. The output variable could be the final best fitness. Also, if you decide to use diverse metrics or fitness functions, you would need [multivariate regression analysis](https://www.tqmp.org/RegularArticles/vol20-2/p186/p186.pdf).\n", "1. If you feel overcome with so much work to disentangle simple questions, create a cybercommunity ofexperimentalists and give them the task of figuring out a robust answer to the question: Does the complexity of morpho-trees smash Evolution? Of course, they must invent a definition of complexity that causes Evolution to behave as expected, else prove that no definiton of complexity exists that does that.\n", "1. Is the Evolution of 2D morpho-trees governed by pleiotropy? We consider that observed evolution by jumps is a sign of pleiotropy. But the arising of monomorphism must be also investigated. Research it.\n", "1. The Evolution of fitness advances by jumps. Nevertheless, the enveloping curve looks, in general, as the old pattern of a convex function that tends to a maximal value, which is its state of equilibrium. For this reason, the MAX-theory seems a good candidate to explain the form of that envelope. If a good indicator of complexity is found, the behavior of Evolution in its battle against complexity is already predicted by this theory. Test this expectancy." ] }, { "metadata": {}, "cell_type": "markdown", "source": [ "# CONCLUSIONS\n", "\n", "[A previous work](https://drive.google.com/file/d/1P2b0SPQbbW8alRUJKkbviHMrRwj8BlNJ/view?usp=sharing) has shown us that a good strategy to create evolutionary stability is pleiotropy, that happens when one change causes many effects. Imagine a bicycle pignone that connects to the chain. Changing the width of a tooth in the pignone causes an attachment defect on all links in the chain, that are more than 100. The result is that no user is interested in making such a change. The final effect is design stability. We saw this in phylogenetic trees and that is why Evolution in the space of those trees employs too many generations to get something similar to a target tree.\n", "\n", "Since cellular reproduction is also described by a tree, we imagined that morphogenesis is a modulation of reproduction that might explain two contradictory facts. First: why the flies are the same all over the world. Second: why all species have too many highly variable small details. Trees have the potentiality of explaining both facts. In first place, leaves of trees can mutate without distorting the tree. Therefore, trees are a source of tremendous variability. In second place, deeper nodes can mutate to the price of causing destroying changes that might amount to death. Whence, trees are reticent to changes, they can facilitate stability. Thus, we formulated a model for morphogenesis that seemed to be simple and prospective: the lighting network Model. This model, which says that inside the reproductive tree there is a subtree, whose nodes expells messengers that can regulate the reproduction or cause differentiation. So, its nodes are like lamps in the illuminating network of a house. In [the previous contribution](https://drive.google.com/file/d/1RxHqtlZGUFg7FAWQ0HLso5ZFZRi2O7nm/view?usp=sharing) we used the node class, an element of the graph theory (nodes and edges), to encode these trees, the morpho-trees. This encoding is natural but extremely expensive to work with. Therefore, it is a must to study other options.\n", "\n", "In this contribution, we researched the possibility of using strings or sequences of letters to encode trees. We engineered the Evolution of trees thanks to the [Newick format or Newick Notation of trees](https://en.wikipedia.org/w/index.php?title=Newick_format&oldid=1375769084) as strings. This possibility enabled us to challenge Evolution with the task of reproducing the shape of an area that was illuminated by a target morpho-tree. Our two main results are:\n", "1. The number of nodes is poorly related to complexity. In fact, it has been observed in many cases that a task with a higher number of nodes is resolved earlier and to a best degree than with a lower one. Thus, the most important open question is how to define the complexity of morpho-trees in order to show that complexity smashes Evolution, such as we have found it in previous works. Maybe, one needs to challenge Evolution with abstract tasks, like reproducing the structure of a collar of holes that models a vertebral spine. If in this task, complexity, given by the number of holes, smashes Evolution, this would explain the evolutionary stability of the shape of vertebrates.\n", "2. According to our simulations, Evolution destroys morpho-trees. To make this result compatible with Nature, a prediction is mandatory. What we see in Nature is that human beings have two eyes, and, by the same token, the form and shape of all species are dominated by monomorphic characters. Thus, the falsifying prediction is that the fossil record of all species must be filled in every kind of monstrosities (forms that are very different from the present ones, and with a very low fitness). One can test this prediction by going into the museum and observing fossils.\n", "\n", "\n", "\n", "\n" ] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyPMZ4q5qDW6pu28cQEFFU0L", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.5" } }, "nbformat": 4, "nbformat_minor": 0 }