metricas_ic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from logging import raiseExceptions\n",
    "from tokenize import Double\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "torch.manual_seed(125)\n",
    "import random\n",
    "random.seed(125)\n",
    "import torch_f as torch_f\n",
    "from modelovae import Node, GRASSEncoder, GRASSDecoder, deserialize\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import statistics\n",
    "from scipy.stats import sem, t, wasserstein_distance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_gpu = True\n",
    "device = torch.device(\"cuda:0\" if use_gpu and torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "def count_fn(f):\n",
    "    def wrapper(*args, **kwargs):\n",
    "        wrapper.count += 1\n",
    "        return f(*args, **kwargs)\n",
    "    wrapper.count = 0\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def traverseleaves(root, leaves):\n",
    "    if root is not None:\n",
    "        traverseleaves(root.left, leaves)\n",
    "        if root.is_leaf():\n",
    "            leaves.append(root)\n",
    "        traverseleaves(root.right, leaves)\n",
    "        return\n",
    "\n",
    "def traverse(root):\n",
    "        if root is not None:\n",
    "            traverse(root.left)\n",
    "            root.radius=root.radius.reshape(4)\n",
    "            traverse(root.right)\n",
    "            return\n",
    "\n",
    "def traverseradius(root, rlist):\n",
    "    if root is not None:\n",
    "        traverseradius(root.left, rlist)\n",
    "        rlist.append(root.radius[3])\n",
    "        print(\"radius\", root.radius[3])\n",
    "        traverseradius(root.right, rlist)\n",
    "        return \n",
    "\n",
    "def traversefeatures(root, features):\n",
    "    if root is not None:\n",
    "        traversefeatures(root.left, features)\n",
    "        features.append(root.radius[3].cpu().item())\n",
    "        traversefeatures(root.right, features)\n",
    "        return features\n",
    "\n",
    "def traversefeatures2(root, features):\n",
    "    if root is not None:\n",
    "        traversefeatures2(root.left, features)\n",
    "        features.append(root.radius[0][3].cpu().item())\n",
    "        traversefeatures2(root.right, features)\n",
    "        return features          \n",
    "\n",
    "def traverse_conexiones(root, tree):\n",
    "        \"\"\"\n",
    "        traverse function will print all the node in the tree.\n",
    "        \"\"\"\n",
    "        if root is not None:\n",
    "            traverse_conexiones(root.left, tree)\n",
    "            if root.right is not None:\n",
    "                tree.append((root.data, root.right.data))\n",
    "            if root.left is not None:\n",
    "                tree.append((root.data, root.left.data))\n",
    "            traverse_conexiones(root.right, tree)\n",
    "            return tree\n",
    "\n",
    "@count_fn\n",
    "def createNode(data, radius,left = None, right = None):\n",
    "        \"\"\"\n",
    "        Utility function to create a node.\n",
    "        \"\"\"\n",
    "        return Node(data, radius, left, right)\n",
    " \n",
    "\n",
    "\n",
    "def read_tree(filename, dir):\n",
    "    with open('./' +dir +'/' +filename, \"r\") as f:\n",
    "        byte = f.read() \n",
    "        return byte\n",
    "\n",
    "def numerar_nodos(root, count):\n",
    "    if root is not None:\n",
    "        numerar_nodos(root.left, count)\n",
    "        root.data = len(count)\n",
    "        count.append(1)\n",
    "        numerar_nodos(root.right, count)\n",
    "        return \n",
    "\n",
    "def number_nodes(data_loader, batch_size):\n",
    "    n_no = []\n",
    "    qzero = 0\n",
    "    qOne = 0\n",
    "    qtwo = 0\n",
    "    for batch in data_loader:\n",
    "        for tree in batch:\n",
    "            tree = tree[0]\n",
    "            count = []\n",
    "            n = tree.count_nodes(tree, count)\n",
    "            n_no.append(len(n))\n",
    "            li = []\n",
    "            tree.traverseInorderChilds(tree, li)\n",
    "            zero = [a for a in li if a == 0]\n",
    "            one = [a for a in li if a == 1]\n",
    "            two = [a for a in li if a == 2]\n",
    "            qzero += len(zero)\n",
    "            qOne += len(one)\n",
    "            qtwo += len(two)\n",
    "\n",
    "    nprom = np.mean(n_no)\n",
    "    qzero /= len(data_loader)*batch_size\n",
    "    qOne /= len(data_loader)*batch_size\n",
    "    qtwo /= len(data_loader)*batch_size\n",
    "    if round(qzero) == 0:\n",
    "        qzero = 1\n",
    "    if round(qOne) == 0:\n",
    "        qOne = 1\n",
    "    if round(qtwo) == 0:\n",
    "        qtwo = 1\n",
    "    mult = torch.tensor([1/round(qzero),1/round(qOne),1/round(qtwo)], device = device)\n",
    "    return mult\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_testing(v, max, decoder, mult, min):\n",
    "    def decode_node(v, max, decoder, mult, min):\n",
    "\n",
    "        cl = decoder.nodeClassifier(v)\n",
    "        _, label = torch.max(cl, 1)\n",
    "        label = label.data\n",
    "        \n",
    "        \n",
    "        if label == 1 and createNode.count <= max:\n",
    "            right, radius = decoder.internalDecoder(v)\n",
    "                \n",
    "            d = createNode(1, radius) \n",
    "            \n",
    "            d.right = decode_node(right, max, decoder, mult, min)\n",
    "            return d\n",
    "\n",
    "        elif label == 2 and createNode.count <= max:\n",
    "            left, right, radius = decoder.bifurcationDecoder(v)\n",
    "                \n",
    "            d = createNode(1, radius)\n",
    "            \n",
    "            d.right = decode_node(right, max, decoder, mult, min)\n",
    "            d.left = decode_node(left, max, decoder, mult, min)\n",
    "        \n",
    "            return d\n",
    "\n",
    "       \n",
    "        elif label == 0 : ##output del classifier\n",
    "            radio = decoder.featureDecoder(v)\n",
    "            return createNode(1,radio)  \n",
    "        \n",
    "\n",
    "    createNode.count = 0\n",
    "    v = decoder.sample_decoder(v)\n",
    "    dec = decode_node (v, max, decoder, mult, min)\n",
    "\n",
    "    return dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_collate(batch):\n",
    "    return batch\n",
    "\n",
    "\n",
    "class tDataset(Dataset):\n",
    "    def __init__(self, l, dir, transform=None):\n",
    "        self.names = l\n",
    "        self.transform = transform\n",
    "        self.data = [] #lista con las strings de todos los arboles\n",
    "        for file in self.names:\n",
    "            self.data.append(read_tree(file, dir))\n",
    "        self.trees = []\n",
    "\n",
    "        for tree in self.data:\n",
    "            deserial = deserialize(tree)\n",
    "            self.trees.append(deserial)\n",
    "            \n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.names)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        #file = self.names[idx]\n",
    "        #string = read_tree(file)\n",
    "        tree = self.trees[idx]\n",
    "        name = self.names[idx]\n",
    "        return tree\n",
    "\n",
    "batch_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d data/paper/VascusynthP5eps01\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "dataset_name = \"Vascusynth\"\n",
    "p = str(5)\n",
    "eps = str(0)+str(1)\n",
    "d = \"data/paper/\" + dataset_name + \"P\" +p + \"eps\" + eps\n",
    "print(\"d\", d)\n",
    "t_list = os.listdir(d)[:100]\n",
    "print(len(t_list))\n",
    "dataset = tDataset(t_list, d )\n",
    "data_loader = DataLoader(dataset, batch_size = batch_size, shuffle=True, collate_fn=my_collate)\n",
    "    \n",
    "i = 0\n",
    "d = None\n",
    "d_nodos = []\n",
    "n_bifo = []\n",
    "for batch in data_loader:\n",
    "    for input in batch:\n",
    "        count = []\n",
    "        numerar_nodos(input, count)\n",
    "        d_nodos.append(len(count))\n",
    "        li = []\n",
    "        input.traverseInorderChilds(input, li)\n",
    "        two = [a for a in li if a == 2]\n",
    "        n_bifo.append(len(two))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(node1, node2):\n",
    "    \"\"\"\n",
    "    Calculates the Euclidean distance between two nodes\n",
    "    \"\"\"\n",
    "    if node1 is not None and node2 is not None:\n",
    "        dist = ((node1.radius[0]-node2.radius[0])**2 + \n",
    "                (node1.radius[1]-node2.radius[1])**2 + \n",
    "                (node1.radius[2]-node2.radius[2])**2)**0.5\n",
    "    else:\n",
    "        dist = 0\n",
    "    return dist\n",
    "\n",
    "def tree_length(node):\n",
    "    '''calcula el largo total del arbol'''\n",
    "    if node is None:\n",
    "        return 0\n",
    "\n",
    "    stack = [(node, None)]\n",
    "    length = 0\n",
    "\n",
    "    while stack:\n",
    "        curr, prev = stack.pop()\n",
    "\n",
    "        if curr.left:\n",
    "            stack.append((curr.left, curr))\n",
    "\n",
    "        if curr.right:\n",
    "            stack.append((curr.right, curr))\n",
    "\n",
    "        if prev:\n",
    "            length += distance(prev, curr)\n",
    "\n",
    "    return length\n",
    "\n",
    "def total_length(root, node, d):\n",
    "    '''\n",
    "    calcula la distancia del primer a ultimo nodo, arbol sin bifurcaciones\n",
    "    '''\n",
    "    if node.left:\n",
    "        total_length(root, node.left, d)\n",
    "    if node.isLeaf():\n",
    "        #d = distance(root, node)\n",
    "        d.append(distance(root, node))\n",
    "        return \n",
    "    if node.right:\n",
    "        total_length(root, node.right, d)\n",
    "\n",
    "def total_path_length(node):\n",
    "    \"\"\"\n",
    "    Calculates the total path length of the tree\n",
    "    \"\"\"\n",
    "    if node is None:\n",
    "        return 0\n",
    "    else:\n",
    "        return distance(node, node.left) + total_path_length(node.left) + distance(node, node.right) + total_path_length(node.right)\n",
    "\n",
    "def tortuosity(root):\n",
    "    \"\"\"\n",
    "    Calculates the tortuosity of the tree\n",
    "    \"\"\"\n",
    "    total_path_length_value = total_path_length(root)\n",
    "    d = []\n",
    "    max_path_length_value = total_length(root,root, d)\n",
    "    return total_path_length_value / d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tr(root):\n",
    "        if root is not None:\n",
    "            tr(root.left)\n",
    "            root.radius[0][3]=root.radius[0][3]/10\n",
    "            if root.radius[0][3]<0:\n",
    "                root.radius[0][3]=0\n",
    "            tr(root.right)\n",
    "            return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "enmax_palette = [\"#FE7871\", \"#B50603\"]\n",
    "\n",
    "color_codes_wanted = ['pink', 'blue']\n",
    "col = lambda x: enmax_palette[color_codes_wanted.index(x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tortuosidad = []\n",
    "n_bif = []\n",
    "largo = []\n",
    "n_nodes = []\n",
    "ratio_radios = []\n",
    "for batch in data_loader:\n",
    "    for input in batch:\n",
    "\n",
    "        graph = nx.Graph()\n",
    "        input.toGraph(graph, 0 , False, flag = 0)\n",
    "        degree1 = []\n",
    "\n",
    "        for node in graph.nodes():\n",
    "            if graph.degree(node) == 1:\n",
    "                degree1.append(node)\n",
    "       \n",
    "        caminos = [ nx.shortest_path( graph, degree1[0], t ) for t in degree1[1:] ]\n",
    "    \n",
    "        subarboles = []\n",
    "        for g in caminos:\n",
    "            grafo = nx.Graph()\n",
    "            grafo.add_nodes_from(g)\n",
    "            et = []\n",
    "            for node in grafo.nodes:\n",
    "                edge = graph[node]\n",
    "                edges = [a for a in list(edge.keys()) if a in g]\n",
    "                edge_tuple = [(a,b) for a,b in zip([node]*len(edges), edges) ]\n",
    "                for tuple in edge_tuple:\n",
    "                    et.append(tuple)\n",
    "            grafo.add_edges_from(et)\n",
    "    \n",
    "            aRecorrer = []\n",
    "            numeroNodoInicial = degree1[0]\n",
    "            r = graph.nodes[numeroNodoInicial]['radio']\n",
    "            rad = list(graph.nodes[numeroNodoInicial]['posicion'])\n",
    "            rad.append(r)\n",
    "\n",
    "            nodoRaiz = Node( numeroNodoInicial, radius =  rad )\n",
    "\n",
    "            for vecino in grafo.neighbors( numeroNodoInicial ):\n",
    "                if vecino != numeroNodoInicial:\n",
    "                    aRecorrer.append( (vecino, numeroNodoInicial,nodoRaiz ) )\n",
    "            \n",
    "            while len(aRecorrer) != 0:\n",
    "                nodoAAgregar, numeroNodoPadre,nodoPadre = aRecorrer.pop(0)\n",
    "                r = graph.nodes[nodoAAgregar]['radio']\n",
    "                rad = list(graph.nodes[nodoAAgregar]['posicion'])\n",
    "                rad.append(r)\n",
    "                nodoActual = Node( nodoAAgregar, radius =  rad)\n",
    "                nodoPadre.agregarHijo( nodoActual )\n",
    "                for vecino in grafo.neighbors( nodoAAgregar ):\n",
    "                    if vecino != numeroNodoPadre:\n",
    "                        aRecorrer.append( (vecino, nodoAAgregar,nodoActual) )\n",
    "            subarboles.append(nodoRaiz)\n",
    "            tortuosidad.append(tortuosity(nodoRaiz))\n",
    "        li = []\n",
    "        input.traverseInorderChilds(input, li)\n",
    "        two = [a for a in li if a == 2]\n",
    "        n_bif.append(len(two))\n",
    "        total_path_length_value = tree_length(input)\n",
    "        largo.append(total_path_length_value.cpu().item())\n",
    "        count = []\n",
    "        n = input.countNodes(input, count)\n",
    "        n_nodes.append(len(n))\n",
    "        features = []\n",
    "        traversefeatures(input, features)\n",
    "        ratio_radios.append(sum(features)/len(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "def calculo_generados(latent_size, Grassdecoder, mult, n=100):\n",
    "    tortuosidad_generados = []\n",
    "    n_bif_generados = []\n",
    "    n_nodes_generados = []\n",
    "    largo_generados = []\n",
    "    ratio_radios_generados = []\n",
    "    with torch.no_grad():\n",
    "        for i in range(n):\n",
    "            noise = torch.randn(batch_size, latent_size).to(device)\n",
    "            generated_images = decode_testing(noise, 100, Grassdecoder, mult, 1)\n",
    "            count = []\n",
    "            numerar_nodos(generated_images, count)\n",
    "            graph = nx.Graph()\n",
    "            generated_images.toGraph(graph, 0 , True, flag = 0)\n",
    "            degree1 = []\n",
    "\n",
    "            for node in graph.nodes():\n",
    "                if graph.degree(node) == 1:\n",
    "                    degree1.append(node)\n",
    "            caminos = [ nx.shortest_path( graph, degree1[0], t ) for t in degree1[1:] ]\n",
    "            subarboles = []\n",
    "            for g in caminos:\n",
    "                grafo = nx.Graph()\n",
    "                grafo.add_nodes_from(g)\n",
    "                et = []\n",
    "                for node in grafo.nodes:\n",
    "                    edge = graph[node]\n",
    "                    edges = [a for a in list(edge.keys()) if a in g]\n",
    "                    edge_tuple = [(a,b) for a,b in zip([node]*len(edges), edges) ]\n",
    "                    for tuple in edge_tuple:\n",
    "                        et.append(tuple)\n",
    "                grafo.add_edges_from(et)\n",
    "        \n",
    "                aRecorrer = []\n",
    "                numeroNodoInicial = degree1[0]\n",
    "                r = graph.nodes[numeroNodoInicial]['radio']\n",
    "                rad = list(graph.nodes[numeroNodoInicial]['posicion'])\n",
    "                rad.append(r)\n",
    "\n",
    "                nodoRaiz = Node( numeroNodoInicial, radius =  rad )\n",
    "\n",
    "                for vecino in grafo.neighbors( numeroNodoInicial ):\n",
    "                    if vecino != numeroNodoInicial:\n",
    "                        aRecorrer.append( (vecino, numeroNodoInicial,nodoRaiz ) )\n",
    "                \n",
    "                while len(aRecorrer) != 0:\n",
    "                    nodoAAgregar, numeroNodoPadre,nodoPadre = aRecorrer.pop(0)\n",
    "                    r = graph.nodes[nodoAAgregar]['radio']\n",
    "                    rad = list(graph.nodes[nodoAAgregar]['posicion'])\n",
    "                    rad.append(r)\n",
    "                    nodoActual = Node( nodoAAgregar, radius =  rad)\n",
    "                    nodoPadre.agregarHijo( nodoActual )\n",
    "                    for vecino in grafo.neighbors( nodoAAgregar ):\n",
    "                        if vecino != numeroNodoPadre:\n",
    "                            aRecorrer.append( (vecino, nodoAAgregar,nodoActual) )\n",
    "                subarboles.append(nodoRaiz)\n",
    "                tortuosidad_generados.append(tortuosity(nodoRaiz))\n",
    "            li = []\n",
    "            generated_images.traverseInorderChilds(generated_images, li)\n",
    "            two = [a for a in li if a == 2]\n",
    "            n_bif_generados.append(len(two))\n",
    "            count = []\n",
    "            n = generated_images.countNodes(generated_images, count)\n",
    "            n_nodes_generados.append(len(n))\n",
    "            traverse(generated_images)\n",
    "            \n",
    "            total_path_length_value = tree_length(generated_images)\n",
    "            #print(\"total_path_length_value\", total_path_length_value)\n",
    "            try:\n",
    "                largo_generados.append(total_path_length_value.cpu().item())\n",
    "            except:\n",
    "                largo_generados.append(total_path_length_value)\n",
    "            features = []\n",
    "            \n",
    "            traversefeatures(generated_images, features)\n",
    "            ratio_radios_generados.append(sum(features)/len(features))\n",
    "    return ratio_radios_generados, tortuosidad_generados, largo_generados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity(vec1, vec2):\n",
    "    #modificar con como la calcule yo\n",
    "    dot_product = np.dot(vec1, vec2)\n",
    "    norm_vec1 = np.linalg.norm(vec1)\n",
    "    norm_vec2 = np.linalg.norm(vec2)\n",
    "    return dot_product / (norm_vec1 * norm_vec2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confidence_interval(data, confidence=0.95):\n",
    "    n = len(data)\n",
    "    m = np.mean(data)\n",
    "    std_err = sem(data)\n",
    "    h = std_err * t.ppf((1 + confidence) / 2, n - 1)\n",
    "    ci = t.interval(confidence, data)\n",
    "    return m, m - h, m + h\n",
    "\n",
    "def calculate_cosine_similarity_confidence_interval(n_iterations, latent_size, Grassdecoder, mult, confidence=0.95):\n",
    "    cosine_similarity_valuesR = []\n",
    "    cosine_similarity_valuesL = []\n",
    "    cosine_similarity_valuesT = []\n",
    "    emd_values_radios =[]\n",
    "    emd_values_tortuosidad =[]\n",
    "    emd_values_largo =[]\n",
    "\n",
    "    for _ in range(n_iterations):\n",
    "       \n",
    "        ratio_radios_generados, tortuosidad_generados, largo_generados = calculo_generados(latent_size, Grassdecoder, mult)\n",
    "        \n",
    "        ###Histograma radio\n",
    "        sns.displot([ratio_radios, ratio_radios_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette = enmax_palette)\n",
    "        ax = plt.gca()\n",
    "        patches = ax.patches\n",
    "        l = int(len(patches)/2)\n",
    "        patches_radius = patches[:l]\n",
    "        patches_radius_generados = patches[l:]\n",
    "        counts_radius = [patch.get_height() for patch in patches_radius]\n",
    "        counts_radius_generados = [patch.get_height() for patch in patches_radius_generados]\n",
    "\n",
    "        ##Histograma tortuosidad\n",
    "        #tortuosidad_generados = [t for t in tortuosidad_generados if t<10]\n",
    "        sns.displot([tortuosidad, tortuosidad_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette = enmax_palette, bins=50)\n",
    "        ax = plt.gca()\n",
    "        patches = ax.patches\n",
    "        l = int(len(patches)/2)\n",
    "        patches_tortuosidad = patches[:l]\n",
    "        patches_tortuosidad_generados = patches[l:]\n",
    "        counts_tortuosidad = [patch.get_height() for patch in patches_tortuosidad]\n",
    "        counts_tortuosidad_generados = [patch.get_height() for patch in patches_tortuosidad_generados]\n",
    "\n",
    "        ##Histograma largo\n",
    "        sns.displot([largo, largo_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=enmax_palette)\n",
    "        ax = plt.gca()\n",
    "        patches = ax.patches\n",
    "        l = int(len(patches)/2)\n",
    "        patches_length = patches[:l]\n",
    "        patches_length_generados = patches[l:]\n",
    "        counts = [patch.get_height() for patch in patches]\n",
    "        counts_length = [patch.get_height() for patch in patches_length]\n",
    "        counts_length_generados = [patch.get_height() for patch in patches_length_generados]\n",
    "\n",
    "        \n",
    "        cosine_sim_radios = cosine_similarity(counts_radius, counts_radius_generados)\n",
    "        cosine_sim_tortuosidad = cosine_similarity(counts_tortuosidad,counts_tortuosidad_generados)\n",
    "        cosine_sim_largo = cosine_similarity(counts_length, counts_length_generados)\n",
    "        cosine_similarity_valuesR.append(cosine_sim_radios)\n",
    "        cosine_similarity_valuesT.append(cosine_sim_tortuosidad)\n",
    "        cosine_similarity_valuesL.append(cosine_sim_largo)\n",
    "\n",
    "        # Calculate EMD (Wasserstein Distance)\n",
    "        emd_radios = wasserstein_distance(range(len(counts_radius)), range(len(counts_radius_generados)), counts_radius, counts_radius_generados)\n",
    "        emd_tortuosidad = wasserstein_distance(range(len(counts_tortuosidad)), range(len(counts_tortuosidad_generados)), counts_tortuosidad, counts_tortuosidad_generados)\n",
    "        emd_largo = wasserstein_distance(range(len(counts_length)), range(len(counts_length_generados)), counts_length, counts_length_generados)\n",
    "        \n",
    "        # Append EMD values\n",
    "        emd_values_radios.append(emd_radios)\n",
    "        emd_values_tortuosidad.append(emd_tortuosidad)\n",
    "        emd_values_largo.append(emd_largo)\n",
    "    \n",
    "    # Calculate confidence intervals\n",
    "    resultR_e = confidence_interval(emd_values_radios, confidence)\n",
    "    resultT_e = confidence_interval(emd_values_tortuosidad, confidence)\n",
    "    resultL_e = confidence_interval(emd_values_largo, confidence)\n",
    "    \n",
    "    resultR = confidence_interval(cosine_similarity_valuesR, confidence)\n",
    "    resultT = confidence_interval(cosine_similarity_valuesT, confidence)\n",
    "    resultL = confidence_interval(cosine_similarity_valuesL, confidence)\n",
    "    return resultR, resultL, resultT, resultR_e, resultT_e, resultL_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12337\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eiarussi/.local/lib/python3.10/site-packages/seaborn/axisgrid.py:453: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
      "  fig = plt.figure(figsize=figsize)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJNXlXgBAOTU3N0epVEqdo66uLhobG1PnAACgfxLdwCGrubk5pk+dGlu3b0+d5/ChQ6Np9WrhDQBwCBLdwCGrVCrF1u3b4/rj3hCThg9PmWN9W1tcv/LpKJVKohsA4BAkuoFD3qThw2PqyBHlXgYAAAOQN1IDAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACBJWaP7pz/9abzzne+MhoaGqKqqivvuu6/L4x/84Aejqqqqy+3cc88tz2IBAACgm8oa3W1tbXHiiSfGokWL9nnMueeeGy+88ELn7Vvf+lYfrhAAAAB6rrqck5933nlx3nnn7feYmpqaqK+vP+Ax29vbo729vfPj1tbWHq8PAAAADka/f033I488EmPHjo2pU6fGRz7ykdi4ceN+j1+4cGHU1tZ23iZMmNBHKwUAAICu+nV0n3vuufH1r389Fi9eHJ/73Ofi0UcfjfPOOy927969z89ZsGBBbN68ufP27LPP9uGKAQAA4PfK+vTyV3PRRRd1/vn444+PE044IaZMmRKPPPJInH322Xv9nJqamqipqemrJQIAAMA+9eufdP+xo446Kurq6mLNmjXlXgoAAAC8qoqK7ueeey42btwY48ePL/dSAAAA4FWV9enlW7Zs6fJT63Xr1sXy5ctj9OjRMXr06LjhhhviwgsvjPr6+li7dm186lOfiqOPPjpmz55dxlUDAADAgSlrdC9dujTOOuuszo/nz58fERFz586NW2+9NVasWBFf+9rXYtOmTdHQ0BDnnHNO/P3f/73XbAMAAFARyhrdZ555ZhRFsc/Hf/zjH/fhagAAAKB3VdRrugEAAKCSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAklSXewEMLM3NzVEqlVLnqKuri8bGxtQ5AAAAeoPoptc0NzfH9GnTYuu2banzHD5sWDStWiW8AQCAfk9002tKpVJs3bYtvnHp3JheX58yR1NLS1xy+9eiVCqJbgAAoN8T3fS66fX1cfLECeVeBgAAQNl5IzUAAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJL0KLqPOuqo2Lhx4x73b9q0KY466qiDXhQAAAAMBD2K7vXr18fu3bv3uL+9vT2ef/75g14UAAAADATV3Tn4P/7jPzr//OMf/zhqa2s7P969e3csXrw4Jk2a1GuLAwAAgErWreg+//zzIyKiqqoq5s6d2+WxwYMHx6RJk+Kf//mfe21xAAAAUMm6Fd0dHR0RETF58uT45S9/GXV1dSmLAgAAgIGgW9H9inXr1vX2OgAAAGDA6VF0R0QsXrw4Fi9eHC+++GLnT8Bfcccddxz0wgAAAKDS9Si6b7jhhrjxxhvjTW96U4wfPz6qqqp6e10AAABQ8XoU3V/5ylfirrvuiksuuaS31wMAAAADRo9+T/eOHTvi9NNP7+21AAAAwIDSo+j+q7/6q7j77rt7ey0AAAAwoPTo6eXbt2+P2267LR566KE44YQTYvDgwV0ev+mmm3plcQAAAFDJehTdK1asiDe+8Y0REbFy5couj3lTNQAAAPhfPYrun/zkJ729DgAAABhwevSabgAAAODV9egn3WedddZ+n0b+8MMP93hBAAAAMFD0KLpfeT33K3bu3BnLly+PlStXxty5c3tjXQAAAFDxehTdN998817vv/7662PLli0HtSAAAAAYKHr1Nd1z5syJO+64ozeHBAAAgIrVq9G9ZMmSGDp0aG8OCQAAABWrR08vf8973tPl46Io4oUXXoilS5fGtdde2ysLAwAAgErXo+iura3t8vGgQYNi6tSpceONN8Y555zTKwsDAACAStej6L7zzjt7ex0AAAAw4PQoul+xbNmyaGpqioiIY489Nk466aReWRQAAAAMBD2K7hdffDEuuuiieOSRR2LUqFEREbFp06Y466yz4p577okjjzyyN9cIAAAAFalH715+1VVXxcsvvxy/+c1v4qWXXoqXXnopVq5cGa2trfHRj360t9cIAAAAFalHP+l+4IEH4qGHHorp06d33veGN7whFi1a5I3UAAAA4P/06CfdHR0dMXjw4D3uHzx4cHR0dBz0ogAAAGAg6FF0v/3tb4+Pfexj8bvf/a7zvueffz4+8YlPxNlnn91riwMAAIBK1qPo/vKXvxytra0xadKkmDJlSkyZMiUmT54cra2t8aUvfam31wgAAAAVqUev6Z4wYUL86le/ioceeihWrVoVERHTp0+PWbNm9eriAAAAoJJ16yfdDz/8cLzhDW+I1tbWqKqqij/90z+Nq666Kq666qo49dRT49hjj42f/exnWWsFAACAitKt6L7lllvisssui5EjR+7xWG1tbXz4wx+Om266qdcWBwAAAJWsW9H961//Os4999x9Pn7OOefEsmXLDnpRAAAAMBB0K7o3bNiw118V9orq6ur47//+74NeFAAAAAwE3Yru1772tbFy5cp9Pr5ixYoYP378QS8KAAAABoJuRfc73vGOuPbaa2P79u17PLZt27a47rrr4s///M97bXEAAABQybr1K8OuueaauPfee+OYY46JK6+8MqZOnRoREatWrYpFixbF7t274+/+7u9SFgoAAACVplvRPW7cuHjsscfiIx/5SCxYsCCKooiIiKqqqpg9e3YsWrQoxo0bl7JQAAAAqDTdiu6IiIkTJ8YPf/jD+J//+Z9Ys2ZNFEURr3/96+OII47IWB9wEJqamipy7IEo6/8v+wAA0L91O7pfccQRR8Spp57am2sBesnG9vYYFBFz5sxJnWfQ/80VMSJ1nkrWF3thHwAA+q8eRzfQf728a1d0RMRX3/eXcdLkSSlzPLVufVx297fj5V27UsYfKLL3wj4AAPRvohsGsGPGjY2TJ05IGbtt69aUcQeqrL2wDwAA/Vu3fmUYAAAAcOBENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASarLvQDoiaampoocGwAAOLSIbipK+44dERExZ86cPpsLAACgp0Q3FWXXrl0REXH5lMlxel1dyhyPlUpx29p1nXMBAAD0lOimIjUMGxZTR45IGXt9W1vKuAAAwKHHG6kBAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQpa3T/9Kc/jXe+853R0NAQVVVVcd9993V5vCiK+MxnPhPjx4+PYcOGxaxZs+KZZ54pz2IBAACgm8oa3W1tbXHiiSfGokWL9vr45z//+fjiF78YX/nKV+KJJ56I4cOHx+zZs2P79u19vFIAAADovupyTn7eeefFeeedt9fHiqKIW265Ja655pp497vfHRERX//612PcuHFx3333xUUXXdSXSwUAAIBu67ev6V63bl20tLTErFmzOu+rra2NGTNmxJIlS/b5ee3t7dHa2trlBgAAAOXQb6O7paUlIiLGjRvX5f5x48Z1PrY3CxcujNra2s7bhAkTUtcJAAAA+9Jvo7unFixYEJs3b+68Pfvss+VeEgAAAIeofhvd9fX1ERGxYcOGLvdv2LCh87G9qampiZEjR3a5AQAAQDn02+iePHly1NfXx+LFizvva21tjSeeeCJmzpxZxpUBAADAgSnru5dv2bIl1qxZ0/nxunXrYvny5TF69OhobGyMj3/84/EP//AP8frXvz4mT54c1157bTQ0NMT5559fvkUDAADAASprdC9dujTOOuuszo/nz58fERFz586Nu+66Kz71qU9FW1tbXH755bFp06Z461vfGg888EAMHTq0XEsGAACAA1bW6D7zzDOjKIp9Pl5VVRU33nhj3HjjjX24KgAAAOgd/fY13QAAAFDpRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkKS63AsA2J+mpqaKHJv+p7m5OUqlUuocdXV10djYmDoHAFBZRDfQL21sb49BETFnzpzUeQb931wRI1Lnobyam5tj+rRpsXXbttR5Dh82LJpWrRLeAEAn0Q30Sy/v2hUdEfHV9/1lnDR5UsocT61bH5fd/e14edeulPHpP0qlUmzdti2+cencmF5fnzJHU0tLXHL716JUKoluAKCT6Ab6tWPGjY2TJ05IGbtt69aUcem/ptfXp309AQDsjTdSAwAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJNXlXgAAlaGpqSl1/Pb29qipqUkZO3vtAAD7IroB2K+N7e0xKCLmzJmTOs+gqqroKIrUOdp37EgdHwDgj4luAPbr5V27oiMivvq+v4yTJk9KmeOpdevjsru/HZdPmRyn19X1+viPlUpx29p1sWvXrl4fGwBgf0Q3AAfkmHFj4+SJE1LGbtu6NSIiGoYNi6kjR/T6+Ovb2np9TACAA+GN1AAAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCTV5V4Av9fc3BylUil1jrq6umhsbEydAwAAgP8luvuJ5ubmmD5tWmzdti11nsOHDYumVauENwAAQB8Q3f1EqVSKrdu2xTcunRvT6+tT5mhqaYlLbv9alEol0Q0AANAHRHc/M72+Pk6eOKHcywAAAKAXeCM1AAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAk/Tq6r7/++qiqqupymzZtWrmXBQAAAAekutwLeDXHHntsPPTQQ50fV1f3+yUDAABARFRAdFdXV0d9fX25lwEAAADd1q+fXh4R8cwzz0RDQ0McddRR8f73vz+am5v3e3x7e3u0trZ2uQEAAEA59OvonjFjRtx1113xwAMPxK233hrr1q2LP/mTP4mXX355n5+zcOHCqK2t7bxNmDChD1cMAAAAv9evo/u8886Lv/iLv4gTTjghZs+eHT/84Q9j06ZN8Z3vfGefn7NgwYLYvHlz5+3ZZ5/twxUDAADA7/X713T/oVGjRsUxxxwTa9as2ecxNTU1UVNT04erAgAAgL3r1z/p/mNbtmyJtWvXxvjx48u9FAAAAHhV/Tq6//Zv/zYeffTRWL9+fTz22GNxwQUXxGGHHRYXX3xxuZcGAAAAr6pfP738ueeei4svvjg2btwYRx55ZLz1rW+Nxx9/PI488shyLw0AAABeVb+O7nvuuafcSwAAAIAe69dPLwcAAIBKJroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCTV5V4Afa+pqamixgXg95qbm6NUKqWNX1dXF42NjWnjA8ChRnQfQtp37IiIiDlz5vTJPAD0rubm5pg+bVps3bYtbY7Dhw2LplWrhDcA9BLRfQjZtWtXRERcPmVynF5X1+vjP1YqxW1r13XOA0DvKpVKsXXbtvjGpXNjen19r4/f1NISl9z+tSiVSqIbAHqJ6D4ENQwbFlNHjuj1cde3tfX6mADsaXp9fZw8cUK5lwEAHABvpAYAAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAEmqy70AABhImpqaKnJsACCH6AaAXtC+Y0dERMyZM6fP5gIA+j/RDQC9YNeuXRERcfmUyXF6XV3KHI+VSnHb2nWdcwEA/Z/oBoBe1DBsWEwdOSJl7PVtbSnjAgB5vJEaAAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQpLrcCwAA+pempqbU8dvb26OmpiZ1jrq6umhsbEydYyBobm6OUqmUOkf2XvTFOfTF12z2HH1xTQyEr6eBInsv7EP3iG4AICIi2nfsiIiIOXPmpM4zqKoqOooidY7Dhw2LplWr/KNwP5qbm2P6tGmxddu21Hky96KvzqEvvmaz58i+JgbC19NA0Rd7YR+6R3QDABERsWvXroiIuHzK5Di9ri5ljsdKpbht7br46vv+Mk6aPClljqaWlrjk9q9FqVTyD8L9KJVKsXXbtvjGpXNjen19yhzZe9EX5/DUuvVx2d3frujroi+uiYHw9TRQZO+Ffeg+0Q0AdNEwbFhMHTkiZez1bW0REXHMuLFx8sQJKXPQPdPr6yt+LzLPoW3r1ohwXRyogfD1NFDYi/7DG6kBAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACSpiOhetGhRTJo0KYYOHRozZsyIJ598stxLAgAAgFfV76P729/+dsyfPz+uu+66+NWvfhUnnnhizJ49O1588cVyLw0AAAD2q7rcC3g1N910U1x22WXxoQ99KCIivvKVr8QPfvCDuOOOO+LTn/70Hse3t7dHe3t758ebN2+OiIjW1ta+WXAPbdmyJSIiftXcHFv+YP29acVzz0dExPq2tlj+0qZeH399W1tERPz6ueejOOywXh8/Iv8cXhk7Iu88BsI5RPh6OlC+ng6Mr6cD4+vpwPy/DRsiImLZsmWd319726BBg6KjoyNl7L6aY/Xq1RGR+2+P7L3oi3MYCNdFX1wTA+HrKcK1fSD6Yh8iIurr66O+vj5t/N40YsSIqKqq2ufjVUVRFH24nm7ZsWNHHH744fHv//7vcf7553feP3fu3Ni0aVPcf//9e3zO9ddfHzfccEMfrhIAAIBD1ebNm2PkyJH7fLxf/6S7VCrF7t27Y9y4cV3uHzduXKxatWqvn7NgwYKYP39+58cdHR3x0ksvxZgxY/b7Xx8ONa2trTFhwoR49tln9/sFQvnZq8phryqDfaoc9qpy2KvKYa8qg32qLCNGjNjv4/06unuipqYmampqutw3atSo8iymAowcOdKFXCHsVeWwV5XBPlUOe1U57FXlsFeVwT4NDP36jdTq6urisMMOiw3/97qBV2zYsKFint8PAADAoatfR/eQIUPilFNOicWLF3fe19HREYsXL46ZM2eWcWUAAADw6vr908vnz58fc+fOjTe96U1x2mmnxS233BJtbW2d72ZOz9TU1MR11123x1Px6X/sVeWwV5XBPlUOe1U57FXlsFeVwT4NLP363ctf8eUvfzm+8IUvREtLS7zxjW+ML37xizFjxoxyLwsAAAD2qyKiGwAAACpRv35NNwAAAFQy0Q0AAABJRDcAAAAkEd0AAACQRHRXsEWLFsWkSZNi6NChMWPGjHjyySf3e/x3v/vdmDZtWgwdOjSOP/74+OEPf9jl8aIo4jOf+UyMHz8+hg0bFrNmzYpnnnmmyzGTJk2KqqqqLrfPfvazvX5uA0lv79O9994b55xzTowZMyaqqqpi+fLle4yxffv2mDdvXowZMyZe85rXxIUXXhgbNmzozdMakMqxV2eeeeYe19QVV1zRm6c1IPXmXu3cuTOuvvrqOP7442P48OHR0NAQH/jAB+J3v/tdlzFeeumleP/73x8jR46MUaNGxaWXXhpbtmxJOb+Bohz75PtUz/T233/XX399TJs2LYYPHx5HHHFEzJo1K5544okux7imeqYce+W66r7e3qc/dMUVV0RVVVXccsstXe53TfVjBRXpnnvuKYYMGVLccccdxW9+85visssuK0aNGlVs2LBhr8f/4he/KA477LDi85//fPH0008X11xzTTF48ODiv/7rvzqP+exnP1vU1tYW9913X/HrX/+6eNe73lVMnjy52LZtW+cxEydOLG688cbihRde6Lxt2bIl/XwrVcY+ff3rXy9uuOGG4qtf/WoREcVTTz21xzhXXHFFMWHChGLx4sXF0qVLize/+c3F6aefnnWaA0K59uptb3tbcdlll3W5pjZv3px1mgNCb+/Vpk2bilmzZhXf/va3i1WrVhVLliwpTjvttOKUU07pMs65555bnHjiicXjjz9e/OxnPyuOPvro4uKLL04/30pVrn3yfar7Mv7+++Y3v1k8+OCDxdq1a4uVK1cWl156aTFy5MjixRdf7DzGNdV95dor11X3ZOzTK+69997ixBNPLBoaGoqbb765y2Ouqf5LdFeo0047rZg3b17nx7t37y4aGhqKhQsX7vX49773vcWf/dmfdblvxowZxYc//OGiKIqio6OjqK+vL77whS90Pr5p06aipqam+Na3vtV538SJE/e4wNm33t6nP7Ru3bq9htymTZuKwYMHF9/97nc772tqaioioliyZMlBnM3AVo69Kor/je6PfexjB7X2Q03mXr3iySefLCKi+O1vf1sURVE8/fTTRUQUv/zlLzuP+dGPflRUVVUVzz///MGczoBVjn0qCt+neqIv9mrz5s1FRBQPPfRQURSuqZ4qx14Vheuqu7L26bnnnite+9rXFitXrtxjT1xT/Zunl1egHTt2xLJly2LWrFmd9w0aNChmzZoVS5Ys2evnLFmypMvxERGzZ8/uPH7dunXR0tLS5Zja2tqYMWPGHmN+9rOfjTFjxsRJJ50UX/jCF2LXrl29dWoDSsY+HYhly5bFzp07u4wzbdq0aGxs7NY4h5Jy7dUrvvnNb0ZdXV0cd9xxsWDBgti6dWu3xzhU9NVebd68OaqqqmLUqFGdY4waNSre9KY3dR4za9asGDRo0B5Pw6R8+/QK36cOXF/s1Y4dO+K2226L2traOPHEEzvHcE11T7n26hWuqwOTtU8dHR1xySWXxCc/+ck49thj9zqGa6r/qi73Aui+UqkUu3fvjnHjxnW5f9y4cbFq1aq9fk5LS8tej29pael8/JX79nVMRMRHP/rROPnkk2P06NHx2GOPxYIFC+KFF16Im2666aDPa6DJ2KcD0dLSEkOGDNnjH6HdHedQUq69ioh43/veFxMnToyGhoZYsWJFXH311bF69eq49957u3cSh4i+2Kvt27fH1VdfHRdffHGMHDmyc4yxY8d2Oa66ujpGjx7tutqLcu1ThO9T3ZW5V9///vfjoosuiq1bt8b48ePjwQcfjLq6us4xXFPdU669inBddUfWPn3uc5+L6urq+OhHP7rPMVxT/Zfoplvmz5/f+ecTTjghhgwZEh/+8Idj4cKFUVNTU8aVQWW6/PLLO/98/PHHx/jx4+Pss8+OtWvXxpQpU8q4skPTzp07473vfW8URRG33npruZfDPuxvn3yf6j/OOuusWL58eZRKpfjqV78a733ve+OJJ57YIwwov1fbK9dVeS1btiz+5V/+JX71q19FVVVVuZdDD3h6eQWqq6uLww47bI93o96wYUPU19fv9XPq6+v3e/wr/9udMSMiZsyYEbt27Yr169d39zQGvIx9OhD19fWxY8eO2LRp00GNcygp117tzYwZMyIiYs2aNQc1zkCVuVevhNxvf/vbePDBB7v89LS+vj5efPHFLsfv2rUrXnrpJdfVXpRrn/bG96n9y9yr4cOHx9FHHx1vfvOb4/bbb4/q6uq4/fbbO8dwTXVPufZqb1xX+5axTz/72c/ixRdfjMbGxqiuro7q6ur47W9/G3/zN38TkyZN6hzDNdV/ie4KNGTIkDjllFNi8eLFnfd1dHTE4sWLY+bMmXv9nJkzZ3Y5PiLiwQcf7Dx+8uTJUV9f3+WY1tbWeOKJJ/Y5ZkTE8uXLY9CgQf6r9V5k7NOBOOWUU2Lw4MFdxlm9enU0Nzd3a5xDSbn2am9e+bVi48ePP6hxBqqsvXol5J555pl46KGHYsyYMXuMsWnTpli2bFnnfQ8//HB0dHR0/ocSfq9c+7Q3vk/tX1/+/dfR0RHt7e2dY7imuqdce7U3rqt9y9inSy65JFasWBHLly/vvDU0NMQnP/nJ+PGPf9w5hmuqHyv3O7nRM/fcc09RU1NT3HXXXcXTTz9dXH755cWoUaOKlpaWoiiK4pJLLik+/elPdx7/i1/8oqiuri7+6Z/+qWhqaiquu+66vf7KsFGjRhX3339/sWLFiuLd7353l18Z9thjjxU333xzsXz58mLt2rXFv/3bvxVHHnlk8YEPfKBvT76CZOzTxo0bi6eeeqr4wQ9+UEREcc899xRPPfVU8cILL3Qec8UVVxSNjY3Fww8/XCxdurSYOXNmMXPmzL478QpUjr1as2ZNceONNxZLly4t1q1bV9x///3FUUcdVZxxxhl9e/IVprf3aseOHcW73vWu4nWve12xfPnyLr8Sp729vXOcc889tzjppJOKJ554ovj5z39evP71r/erWPajHPvk+1TP9PZebdmypViwYEGxZMmSYv369cXSpUuLD33oQ0VNTU2xcuXKznFcU91Xjr1yXXVfxr8p/tje3lHeNdV/ie4K9qUvfalobGwshgwZUpx22mnF448/3vnY2972tmLu3Lldjv/Od75THHPMMcWQIUOKY489tvjBD37Q5fGOjo7i2muvLcaNG1fU1NQUZ599drF69erOx5ctW1bMmDGjqK2tLYYOHVpMnz69+Md//Mdi+/btqedZ6Xp7n+68884iIva4XXfddZ3HbNu2rfjrv/7r4ogjjigOP/zw4oILLugS5exdX+9Vc3NzccYZZxSjR48uampqiqOPPrr45Cc/6fd0H4De3KtXfqXb3m4/+clPOo/buHFjcfHFFxevec1ripEjRxYf+tCHipdffjn7VCtaX++T71M915t7tW3btuKCCy4oGhoaiiFDhhTjx48v3vWudxVPPvlklzFcUz3T13vluuqZ3v43xR/bW3S7pvqvqqIoir77uToAAAAcOrymGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIMn/B4IPb9aQ2BogAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = [1.,1.,1.]\n",
    "mult = torch.Tensor(a)\n",
    "latent_size = 64\n",
    "Grassdecoder = GRASSDecoder(latent_size=latent_size, hidden_size=256, mult = mult.to(device))\n",
    "Grassdecoder = Grassdecoder.to(device)\n",
    "Grassdecoder.eval()\n",
    "checkpoint = torch.load(\"output/\" + dataset_name + \"P\" +p + \"eps\" + eps+ \"-best-nuevoweight.pth\")\n",
    "print(checkpoint['epoch'])\n",
    "\n",
    "Grassdecoder.load_state_dict(checkpoint['decoder_state_dict'])\n",
    "epoch = checkpoint['epoch']\n",
    "\n",
    "#mean_cosine_similarity, lower_bound, upper_bounds, ratio_radios_generados = calculate_cosine_similarity_confidence_interval(10)\n",
    "resultR, resultL, resultT, resultR_e, resultT_e, resultL_e = calculate_cosine_similarity_confidence_interval(10, latent_size, Grassdecoder, mult)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COsine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radius\n",
      "mean 0.9\n",
      "bounds 0.88 0.92\n",
      "Tortuosity\n",
      "mean 0.6\n",
      "bounds 0.55 0.64\n",
      "Length\n",
      "mean 0.85\n",
      "bounds 0.82 0.87\n"
     ]
    }
   ],
   "source": [
    "mean, lower, upper = resultR\n",
    "print(\"Radius\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))\n",
    "mean, lower, upper = resultT\n",
    "print(\"Tortuosity\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))\n",
    "mean, lower, upper = resultL\n",
    "print(\"Length\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radius\n",
      "mean 0.69\n",
      "bounds 0.52 0.85\n",
      "Tortuosity\n",
      "mean 5.4\n",
      "bounds 4.33 6.47\n",
      "Length\n",
      "mean 1.77\n",
      "bounds 1.59 1.94\n"
     ]
    }
   ],
   "source": [
    "mean, lower, upper = resultR_e\n",
    "print(\"Radius\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))\n",
    "mean, lower, upper = resultT_e\n",
    "print(\"Tortuosity\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))\n",
    "mean, lower, upper = resultL_e\n",
    "print(\"Length\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from scipy.stats import wasserstein_distance\\nimport numpy as np\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\ndef calculate_emd_confidence_interval(n_iterations, latent_size, Grassdecoder, mult, confidence=0.95):\\n    emd_values_radios = []\\n    emd_values_tortuosidad = []\\n    emd_values_largo = []\\n\\n    for _ in range(n_iterations):\\n        # Assuming calculo_generados generates the synthetic data\\n        ratio_radios_generados, tortuosidad_generados, largo_generados = calculo_generados(latent_size, Grassdecoder, mult)\\n        \\n        ### Histogram for Radios\\n        sns.displot([ratio_radios, ratio_radios_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=\"coolwarm\", bins = 25)\\n        ax = plt.gca()\\n        patches = ax.patches\\n        l = int(len(patches)/2)\\n        patches_radius = patches[:l]\\n        patches_radius_generados = patches[l:]\\n        counts_radius = [patch.get_height() for patch in patches_radius]\\n        counts_radius_generados = [patch.get_height() for patch in patches_radius_generados]\\n\\n        ### Histogram for Tortuosidad\\n        sns.displot([tortuosidad, tortuosidad_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=\"coolwarm\", bins = 25)\\n        ax = plt.gca()\\n        patches = ax.patches\\n        l = int(len(patches)/2)\\n        patches_tortuosidad = patches[:l]\\n        patches_tortuosidad_generados = patches[l:]\\n        counts_tortuosidad = [patch.get_height() for patch in patches_tortuosidad]\\n        counts_tortuosidad_generados = [patch.get_height() for patch in patches_tortuosidad_generados]\\n\\n        ### Histogram for Largo\\n        sns.displot([largo, largo_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=\"coolwarm\", bins = 25)\\n        ax = plt.gca()\\n        patches = ax.patches\\n        l = int(len(patches)/2)\\n        patches_length = patches[:l]\\n        patches_length_generados = patches[l:]\\n        counts_length = [patch.get_height() for patch in patches_length]\\n        counts_length_generados = [patch.get_height() for patch in patches_length_generados]\\n\\n        # Calculate EMD (Wasserstein Distance)\\n        emd_radios = wasserstein_distance(range(len(counts_radius)), range(len(counts_radius_generados)), counts_radius, counts_radius_generados)\\n        emd_tortuosidad = wasserstein_distance(range(len(counts_tortuosidad)), range(len(counts_tortuosidad_generados)), counts_tortuosidad, counts_tortuosidad_generados)\\n        emd_largo = wasserstein_distance(range(len(counts_length)), range(len(counts_length_generados)), counts_length, counts_length_generados)\\n        \\n        # Append EMD values\\n        emd_values_radios.append(emd_radios)\\n        emd_values_tortuosidad.append(emd_tortuosidad)\\n        emd_values_largo.append(emd_largo)\\n    \\n    # Calculate confidence intervals\\n    resultR = confidence_interval(emd_values_radios, confidence)\\n    resultT = confidence_interval(emd_values_tortuosidad, confidence)\\n    resultL = confidence_interval(emd_values_largo, confidence)\\n\\n    return resultR, resultL, resultT\\n\\nresultR, resultL, resultT = calculate_emd_confidence_interval(10, latent_size, Grassdecoder, mult)'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from scipy.stats import wasserstein_distance\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def calculate_emd_confidence_interval(n_iterations, latent_size, Grassdecoder, mult, confidence=0.95):\n",
    "    emd_values_radios = []\n",
    "    emd_values_tortuosidad = []\n",
    "    emd_values_largo = []\n",
    "\n",
    "    for _ in range(n_iterations):\n",
    "        # Assuming calculo_generados generates the synthetic data\n",
    "        ratio_radios_generados, tortuosidad_generados, largo_generados = calculo_generados(latent_size, Grassdecoder, mult)\n",
    "        \n",
    "        ### Histogram for Radios\n",
    "        sns.displot([ratio_radios, ratio_radios_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=\"coolwarm\", bins = 25)\n",
    "        ax = plt.gca()\n",
    "        patches = ax.patches\n",
    "        l = int(len(patches)/2)\n",
    "        patches_radius = patches[:l]\n",
    "        patches_radius_generados = patches[l:]\n",
    "        counts_radius = [patch.get_height() for patch in patches_radius]\n",
    "        counts_radius_generados = [patch.get_height() for patch in patches_radius_generados]\n",
    "\n",
    "        ### Histogram for Tortuosidad\n",
    "        sns.displot([tortuosidad, tortuosidad_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=\"coolwarm\", bins = 25)\n",
    "        ax = plt.gca()\n",
    "        patches = ax.patches\n",
    "        l = int(len(patches)/2)\n",
    "        patches_tortuosidad = patches[:l]\n",
    "        patches_tortuosidad_generados = patches[l:]\n",
    "        counts_tortuosidad = [patch.get_height() for patch in patches_tortuosidad]\n",
    "        counts_tortuosidad_generados = [patch.get_height() for patch in patches_tortuosidad_generados]\n",
    "\n",
    "        ### Histogram for Largo\n",
    "        sns.displot([largo, largo_generados], multiple=\"dodge\", height=10, aspect=1, legend=False, palette=\"coolwarm\", bins = 25)\n",
    "        ax = plt.gca()\n",
    "        patches = ax.patches\n",
    "        l = int(len(patches)/2)\n",
    "        patches_length = patches[:l]\n",
    "        patches_length_generados = patches[l:]\n",
    "        counts_length = [patch.get_height() for patch in patches_length]\n",
    "        counts_length_generados = [patch.get_height() for patch in patches_length_generados]\n",
    "\n",
    "        # Calculate EMD (Wasserstein Distance)\n",
    "        emd_radios = wasserstein_distance(range(len(counts_radius)), range(len(counts_radius_generados)), counts_radius, counts_radius_generados)\n",
    "        emd_tortuosidad = wasserstein_distance(range(len(counts_tortuosidad)), range(len(counts_tortuosidad_generados)), counts_tortuosidad, counts_tortuosidad_generados)\n",
    "        emd_largo = wasserstein_distance(range(len(counts_length)), range(len(counts_length_generados)), counts_length, counts_length_generados)\n",
    "        \n",
    "        # Append EMD values\n",
    "        emd_values_radios.append(emd_radios)\n",
    "        emd_values_tortuosidad.append(emd_tortuosidad)\n",
    "        emd_values_largo.append(emd_largo)\n",
    "    \n",
    "    # Calculate confidence intervals\n",
    "    resultR = confidence_interval(emd_values_radios, confidence)\n",
    "    resultT = confidence_interval(emd_values_tortuosidad, confidence)\n",
    "    resultL = confidence_interval(emd_values_largo, confidence)\n",
    "\n",
    "    return resultR, resultL, resultT\n",
    "\n",
    "resultR, resultL, resultT = calculate_emd_confidence_interval(10, latent_size, Grassdecoder, mult)'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radius\n",
      "mean 0.9\n",
      "bounds 0.88 0.92\n",
      "Tortuosity\n",
      "mean 0.6\n",
      "bounds 0.55 0.64\n",
      "Length\n",
      "mean 0.85\n",
      "bounds 0.82 0.87\n"
     ]
    }
   ],
   "source": [
    "mean, lower, upper = resultR\n",
    "print(\"Radius\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))\n",
    "mean, lower, upper = resultT\n",
    "print(\"Tortuosity\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))\n",
    "mean, lower, upper = resultL\n",
    "print(\"Length\")\n",
    "print(\"mean\", np.round(mean,2))\n",
    "print(\"bounds\", np.round(lower,2), np.round(upper,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per branch statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0085\n",
      "  Left child average radius: 0.0089\n",
      "  Right child average radius: 0.0083\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0089\n",
      "  Left child average radius: 0.0083\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0110\n",
      "  Left child average radius: 0.0115\n",
      "  Right child average radius: 0.0124\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0115\n",
      "  Left child average radius: 0.0124\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0175\n",
      "  Left child average radius: 0.0175\n",
      "  Right child average radius: 0.0187\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0175\n",
      "  Left child average radius: 0.0187\n",
      "  Right child average radius: 0.0040\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0187\n",
      "  Left child average radius: 0.0040\n",
      "  Right child average radius: 0.0149\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0040\n",
      "  Left child average radius: 0.0149\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0075\n",
      "  Left child average radius: 0.0053\n",
      "  Right child average radius: 0.0086\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0053\n",
      "  Left child average radius: 0.0086\n",
      "  Right child average radius: 0.0099\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0086\n",
      "  Left child average radius: 0.0099\n",
      "  Right child average radius: 0.0107\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0099\n",
      "  Left child average radius: 0.0107\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0076\n",
      "  Left child average radius: 0.0089\n",
      "  Right child average radius: 0.0085\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0089\n",
      "  Left child average radius: 0.0085\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0073\n",
      "  Left child average radius: 0.0097\n",
      "  Right child average radius: 0.0096\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0097\n",
      "  Left child average radius: 0.0096\n",
      "  Right child average radius: 0.0073\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0096\n",
      "  Left child average radius: 0.0073\n",
      "  Right child average radius: 0.0076\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0073\n",
      "  Left child average radius: 0.0076\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0077\n",
      "  Left child average radius: 0.0087\n",
      "  Right child average radius: 0.0234\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0087\n",
      "  Left child average radius: 0.0234\n",
      "  Right child average radius: 0.0147\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0234\n",
      "  Left child average radius: 0.0147\n",
      "  Right child average radius: 0.0133\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0147\n",
      "  Left child average radius: 0.0133\n",
      "  Right child average radius: 0.0282\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0133\n",
      "  Left child average radius: 0.0282\n",
      "  Right child average radius: 0.0181\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0282\n",
      "  Left child average radius: 0.0181\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0085\n",
      "  Left child average radius: 0.0104\n",
      "  Right child average radius: 0.0060\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0104\n",
      "  Left child average radius: 0.0060\n",
      "  Right child average radius: 0.0058\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0060\n",
      "  Left child average radius: 0.0058\n",
      "  Right child average radius: 0.0118\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0058\n",
      "  Left child average radius: 0.0118\n",
      "  Right child average radius: 0.0218\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0118\n",
      "  Left child average radius: 0.0218\n",
      "  Right child average radius: 0.0221\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0218\n",
      "  Left child average radius: 0.0221\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0067\n",
      "  Left child average radius: 0.0116\n",
      "  Right child average radius: 0.0197\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0116\n",
      "  Left child average radius: 0.0197\n",
      "  Right child average radius: 0.0205\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0197\n",
      "  Left child average radius: 0.0205\n",
      "  Right child average radius: 0.0070\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0205\n",
      "  Left child average radius: 0.0070\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0073\n",
      "  Left child average radius: 0.0093\n",
      "  Right child average radius: 0.0045\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0093\n",
      "  Left child average radius: 0.0045\n",
      "  Right child average radius: 0.0229\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0045\n",
      "  Left child average radius: 0.0229\n",
      "  Right child average radius: 0.0074\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0229\n",
      "  Left child average radius: 0.0074\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0064\n",
      "  Left child average radius: 0.0067\n",
      "  Right child average radius: 0.0064\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0067\n",
      "  Left child average radius: 0.0064\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0094\n",
      "  Left child average radius: 0.0118\n",
      "  Right child average radius: 0.0138\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0118\n",
      "  Left child average radius: 0.0138\n",
      "  Right child average radius: 0.0111\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0138\n",
      "  Left child average radius: 0.0111\n",
      "  Right child average radius: 0.0097\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0111\n",
      "  Left child average radius: 0.0097\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0093\n",
      "  Left child average radius: 0.0095\n",
      "  Right child average radius: 0.0098\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0095\n",
      "  Left child average radius: 0.0098\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0065\n",
      "  Left child average radius: 0.0039\n",
      "  Right child average radius: 0.0153\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0039\n",
      "  Left child average radius: 0.0153\n",
      "  Right child average radius: 0.0212\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0153\n",
      "  Left child average radius: 0.0212\n",
      "  Right child average radius: 0.0228\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0212\n",
      "  Left child average radius: 0.0228\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0461\n",
      "  Left child average radius: 0.0390\n",
      "  Right child average radius: 0.0462\n",
      "  children sum: 0.0002\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0390\n",
      "  Left child average radius: 0.0462\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0078\n",
      "  Left child average radius: 0.0232\n",
      "  Right child average radius: 0.0224\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0232\n",
      "  Left child average radius: 0.0224\n",
      "  Right child average radius: 0.0220\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0224\n",
      "  Left child average radius: 0.0220\n",
      "  Right child average radius: 0.0074\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0220\n",
      "  Left child average radius: 0.0074\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0111\n",
      "  Left child average radius: 0.0145\n",
      "  Right child average radius: 0.0088\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0145\n",
      "  Left child average radius: 0.0088\n",
      "  Right child average radius: 0.0126\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0088\n",
      "  Left child average radius: 0.0126\n",
      "  Right child average radius: 0.0102\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0126\n",
      "  Left child average radius: 0.0102\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0206\n",
      "  Left child average radius: 0.0224\n",
      "  Right child average radius: 0.0189\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0224\n",
      "  Left child average radius: 0.0189\n",
      "  Right child average radius: 0.0186\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0189\n",
      "  Left child average radius: 0.0186\n",
      "  Right child average radius: 0.0066\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0186\n",
      "  Left child average radius: 0.0066\n",
      "  Right child average radius: 0.0141\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0066\n",
      "  Left child average radius: 0.0141\n",
      "  Right child average radius: 0.0222\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0141\n",
      "  Left child average radius: 0.0222\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0525\n",
      "  Left child average radius: 0.0423\n",
      "  Right child average radius: 0.0327\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0423\n",
      "  Left child average radius: 0.0327\n",
      "  Right child average radius: 0.0419\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0327\n",
      "  Left child average radius: 0.0419\n",
      "  Right child average radius: 0.0407\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0419\n",
      "  Left child average radius: 0.0407\n",
      "  Right child average radius: 0.0301\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0407\n",
      "  Left child average radius: 0.0301\n",
      "  Right child average radius: 0.0241\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0301\n",
      "  Left child average radius: 0.0241\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0091\n",
      "  Left child average radius: 0.0094\n",
      "  Right child average radius: 0.0095\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0094\n",
      "  Left child average radius: 0.0095\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0215\n",
      "  Left child average radius: 0.0221\n",
      "  Right child average radius: 0.0057\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0221\n",
      "  Left child average radius: 0.0057\n",
      "  Right child average radius: 0.0214\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0057\n",
      "  Left child average radius: 0.0214\n",
      "  Right child average radius: 0.0217\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0214\n",
      "  Left child average radius: 0.0217\n",
      "  Right child average radius: 0.0050\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0217\n",
      "  Left child average radius: 0.0050\n",
      "  Right child average radius: 0.0226\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0050\n",
      "  Left child average radius: 0.0226\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0285\n",
      "  Left child average radius: 0.0290\n",
      "  Right child average radius: 0.0066\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0290\n",
      "  Left child average radius: 0.0066\n",
      "  Right child average radius: 0.0289\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0066\n",
      "  Left child average radius: 0.0289\n",
      "  Right child average radius: 0.0296\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0289\n",
      "  Left child average radius: 0.0296\n",
      "  Right child average radius: 0.0118\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0296\n",
      "  Left child average radius: 0.0118\n",
      "  Right child average radius: 0.0272\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0118\n",
      "  Left child average radius: 0.0272\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0106\n",
      "  Left child average radius: 0.0130\n",
      "  Right child average radius: 0.0073\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0130\n",
      "  Left child average radius: 0.0073\n",
      "  Right child average radius: 0.0097\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0073\n",
      "  Left child average radius: 0.0097\n",
      "  Right child average radius: 0.0111\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0097\n",
      "  Left child average radius: 0.0111\n",
      "  Right child average radius: 0.0058\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0111\n",
      "  Left child average radius: 0.0058\n",
      "  Right child average radius: 0.0108\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0058\n",
      "  Left child average radius: 0.0108\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0212\n",
      "  Left child average radius: 0.0212\n",
      "  Right child average radius: 0.0064\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0212\n",
      "  Left child average radius: 0.0064\n",
      "  Right child average radius: 0.0216\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0064\n",
      "  Left child average radius: 0.0216\n",
      "  Right child average radius: 0.0136\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0216\n",
      "  Left child average radius: 0.0136\n",
      "  Right child average radius: 0.0190\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0136\n",
      "  Left child average radius: 0.0190\n",
      "  Right child average radius: 0.0219\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0190\n",
      "  Left child average radius: 0.0219\n",
      "  Right child average radius: 0.0070\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0219\n",
      "  Left child average radius: 0.0070\n",
      "  Right child average radius: 0.0228\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0070\n",
      "  Left child average radius: 0.0228\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0096\n",
      "  Left child average radius: 0.0121\n",
      "  Right child average radius: 0.0112\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0121\n",
      "  Left child average radius: 0.0112\n",
      "  Right child average radius: 0.0077\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0112\n",
      "  Left child average radius: 0.0077\n",
      "  Right child average radius: 0.0093\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0077\n",
      "  Left child average radius: 0.0093\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0086\n",
      "  Left child average radius: 0.0113\n",
      "  Right child average radius: 0.0101\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0113\n",
      "  Left child average radius: 0.0101\n",
      "  Right child average radius: 0.0120\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0101\n",
      "  Left child average radius: 0.0120\n",
      "  Right child average radius: 0.0095\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0120\n",
      "  Left child average radius: 0.0095\n",
      "  Right child average radius: 0.0060\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0095\n",
      "  Left child average radius: 0.0060\n",
      "  Right child average radius: 0.0078\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0060\n",
      "  Left child average radius: 0.0078\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0185\n",
      "  Left child average radius: 0.0182\n",
      "  Right child average radius: 0.0207\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0182\n",
      "  Left child average radius: 0.0207\n",
      "  Right child average radius: 0.0222\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0207\n",
      "  Left child average radius: 0.0222\n",
      "  Right child average radius: 0.0147\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0222\n",
      "  Left child average radius: 0.0147\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0140\n",
      "  Left child average radius: 0.0310\n",
      "  Right child average radius: 0.0305\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0310\n",
      "  Left child average radius: 0.0305\n",
      "  Right child average radius: 0.0102\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0305\n",
      "  Left child average radius: 0.0102\n",
      "  Right child average radius: 0.0288\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0102\n",
      "  Left child average radius: 0.0288\n",
      "  Right child average radius: 0.0283\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0288\n",
      "  Left child average radius: 0.0283\n",
      "  Right child average radius: 0.0189\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0283\n",
      "  Left child average radius: 0.0189\n",
      "  Right child average radius: 0.0273\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0189\n",
      "  Left child average radius: 0.0273\n",
      "  Right child average radius: 0.0149\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0273\n",
      "  Left child average radius: 0.0149\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0236\n",
      "  Left child average radius: 0.0249\n",
      "  Right child average radius: 0.0203\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0249\n",
      "  Left child average radius: 0.0203\n",
      "  Right child average radius: 0.0178\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0203\n",
      "  Left child average radius: 0.0178\n",
      "  Right child average radius: 0.0309\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0178\n",
      "  Left child average radius: 0.0309\n",
      "  Right child average radius: 0.0344\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0309\n",
      "  Left child average radius: 0.0344\n",
      "  Right child average radius: 0.0161\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0344\n",
      "  Left child average radius: 0.0161\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0357\n",
      "  Left child average radius: 0.0381\n",
      "  Right child average radius: 0.0375\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0381\n",
      "  Left child average radius: 0.0375\n",
      "  Right child average radius: 0.0161\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0375\n",
      "  Left child average radius: 0.0161\n",
      "  Right child average radius: 0.0387\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0161\n",
      "  Left child average radius: 0.0387\n",
      "  Right child average radius: 0.0273\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0387\n",
      "  Left child average radius: 0.0273\n",
      "  Right child average radius: 0.0154\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0273\n",
      "  Left child average radius: 0.0154\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0095\n",
      "  Left child average radius: 0.0121\n",
      "  Right child average radius: 0.0141\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0121\n",
      "  Left child average radius: 0.0141\n",
      "  Right child average radius: 0.0061\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0141\n",
      "  Left child average radius: 0.0061\n",
      "  Right child average radius: 0.0116\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0061\n",
      "  Left child average radius: 0.0116\n",
      "  Right child average radius: 0.0109\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0116\n",
      "  Left child average radius: 0.0109\n",
      "  Right child average radius: 0.0051\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0109\n",
      "  Left child average radius: 0.0051\n",
      "  Right child average radius: 0.0079\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0051\n",
      "  Left child average radius: 0.0079\n",
      "  Right child average radius: 0.0070\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0079\n",
      "  Left child average radius: 0.0070\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0069\n",
      "  Left child average radius: 0.0219\n",
      "  Right child average radius: 0.0209\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0219\n",
      "  Left child average radius: 0.0209\n",
      "  Right child average radius: 0.0209\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0209\n",
      "  Left child average radius: 0.0209\n",
      "  Right child average radius: 0.0069\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0209\n",
      "  Left child average radius: 0.0069\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0024\n",
      "  Left child average radius: 0.0000\n",
      "  Right child average radius: 0.0055\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0000\n",
      "  Left child average radius: 0.0055\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0216\n",
      "  Left child average radius: 0.0266\n",
      "  Right child average radius: 0.0240\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0266\n",
      "  Left child average radius: 0.0240\n",
      "  Right child average radius: 0.0144\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0240\n",
      "  Left child average radius: 0.0144\n",
      "  Right child average radius: 0.0226\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0144\n",
      "  Left child average radius: 0.0226\n",
      "  Right child average radius: 0.0104\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0226\n",
      "  Left child average radius: 0.0104\n",
      "  Right child average radius: 0.0194\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0104\n",
      "  Left child average radius: 0.0194\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0208\n",
      "  Left child average radius: 0.0208\n",
      "  Right child average radius: 0.0206\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0208\n",
      "  Left child average radius: 0.0206\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0281\n",
      "  Left child average radius: 0.0295\n",
      "  Right child average radius: 0.0124\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0295\n",
      "  Left child average radius: 0.0124\n",
      "  Right child average radius: 0.0253\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0124\n",
      "  Left child average radius: 0.0253\n",
      "  Right child average radius: 0.0286\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0253\n",
      "  Left child average radius: 0.0286\n",
      "  Right child average radius: 0.0055\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0286\n",
      "  Left child average radius: 0.0055\n",
      "  Right child average radius: 0.0289\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0055\n",
      "  Left child average radius: 0.0289\n",
      "  Right child average radius: 0.0225\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0289\n",
      "  Left child average radius: 0.0225\n",
      "  Right child average radius: 0.0308\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0225\n",
      "  Left child average radius: 0.0308\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0168\n",
      "  Left child average radius: 0.0001\n",
      "  Right child average radius: 0.0233\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0001\n",
      "  Left child average radius: 0.0233\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0265\n",
      "  Left child average radius: 0.0288\n",
      "  Right child average radius: 0.0104\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0288\n",
      "  Left child average radius: 0.0104\n",
      "  Right child average radius: 0.0278\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0104\n",
      "  Left child average radius: 0.0278\n",
      "  Right child average radius: 0.0276\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0278\n",
      "  Left child average radius: 0.0276\n",
      "  Right child average radius: 0.0189\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0276\n",
      "  Left child average radius: 0.0189\n",
      "  Right child average radius: 0.0242\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0189\n",
      "  Left child average radius: 0.0242\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0040\n",
      "  Left child average radius: 0.0176\n",
      "  Right child average radius: 0.0172\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0176\n",
      "  Left child average radius: 0.0172\n",
      "  Right child average radius: 0.0170\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0172\n",
      "  Left child average radius: 0.0170\n",
      "  Right child average radius: 0.0026\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0170\n",
      "  Left child average radius: 0.0026\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0078\n",
      "  Left child average radius: 0.0075\n",
      "  Right child average radius: 0.0088\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0075\n",
      "  Left child average radius: 0.0088\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0132\n",
      "  Left child average radius: 0.0132\n",
      "  Right child average radius: 0.0162\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0132\n",
      "  Left child average radius: 0.0162\n",
      "  Right child average radius: 0.0211\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0162\n",
      "  Left child average radius: 0.0211\n",
      "  Right child average radius: 0.0186\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0211\n",
      "  Left child average radius: 0.0186\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0260\n",
      "  Left child average radius: 0.0281\n",
      "  Right child average radius: 0.0136\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0281\n",
      "  Left child average radius: 0.0136\n",
      "  Right child average radius: 0.0084\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0136\n",
      "  Left child average radius: 0.0084\n",
      "  Right child average radius: 0.0108\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0084\n",
      "  Left child average radius: 0.0108\n",
      "  Right child average radius: 0.0252\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0108\n",
      "  Left child average radius: 0.0252\n",
      "  Right child average radius: 0.0298\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0252\n",
      "  Left child average radius: 0.0298\n",
      "  Right child average radius: 0.0307\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0298\n",
      "  Left child average radius: 0.0307\n",
      "  Right child average radius: 0.0215\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0307\n",
      "  Left child average radius: 0.0215\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0064\n",
      "  Left child average radius: 0.0020\n",
      "  Right child average radius: 0.0076\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0020\n",
      "  Left child average radius: 0.0076\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0376\n",
      "  Left child average radius: 0.0424\n",
      "  Right child average radius: 0.0139\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0424\n",
      "  Left child average radius: 0.0139\n",
      "  Right child average radius: 0.0465\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0139\n",
      "  Left child average radius: 0.0465\n",
      "  Right child average radius: 0.0471\n",
      "  children sum: 0.0002\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0465\n",
      "  Left child average radius: 0.0471\n",
      "  Right child average radius: 0.0351\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0471\n",
      "  Left child average radius: 0.0351\n",
      "  Right child average radius: 0.0377\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0351\n",
      "  Left child average radius: 0.0377\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0210\n",
      "  Left child average radius: 0.0210\n",
      "  Right child average radius: 0.0207\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0210\n",
      "  Left child average radius: 0.0207\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0105\n",
      "  Left child average radius: 0.0111\n",
      "  Right child average radius: 0.0042\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0111\n",
      "  Left child average radius: 0.0042\n",
      "  Right child average radius: 0.0093\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0042\n",
      "  Left child average radius: 0.0093\n",
      "  Right child average radius: 0.0105\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0093\n",
      "  Left child average radius: 0.0105\n",
      "  Right child average radius: 0.0105\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0105\n",
      "  Left child average radius: 0.0105\n",
      "  Right child average radius: 0.0040\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0105\n",
      "  Left child average radius: 0.0040\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0207\n",
      "  Left child average radius: 0.0209\n",
      "  Right child average radius: 0.0055\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0209\n",
      "  Left child average radius: 0.0055\n",
      "  Right child average radius: 0.0206\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0055\n",
      "  Left child average radius: 0.0206\n",
      "  Right child average radius: 0.0214\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0206\n",
      "  Left child average radius: 0.0214\n",
      "  Right child average radius: 0.0193\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0214\n",
      "  Left child average radius: 0.0193\n",
      "  Right child average radius: 0.0120\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0193\n",
      "  Left child average radius: 0.0120\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0145\n",
      "  Left child average radius: 0.0161\n",
      "  Right child average radius: 0.0165\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0161\n",
      "  Left child average radius: 0.0165\n",
      "  Right child average radius: 0.0077\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0165\n",
      "  Left child average radius: 0.0077\n",
      "  Right child average radius: 0.0150\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0077\n",
      "  Left child average radius: 0.0150\n",
      "  Right child average radius: 0.0071\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0150\n",
      "  Left child average radius: 0.0071\n",
      "  Right child average radius: 0.0138\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0071\n",
      "  Left child average radius: 0.0138\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0062\n",
      "  Left child average radius: 0.0112\n",
      "  Right child average radius: 0.0223\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0112\n",
      "  Left child average radius: 0.0223\n",
      "  Right child average radius: 0.0212\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0223\n",
      "  Left child average radius: 0.0212\n",
      "  Right child average radius: 0.0045\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0212\n",
      "  Left child average radius: 0.0045\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0175\n",
      "  Left child average radius: 0.0076\n",
      "  Right child average radius: 0.0148\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0076\n",
      "  Left child average radius: 0.0148\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0143\n",
      "  Left child average radius: 0.0143\n",
      "  Right child average radius: 0.0139\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0143\n",
      "  Left child average radius: 0.0139\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0228\n",
      "  Left child average radius: 0.0240\n",
      "  Right child average radius: 0.0232\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0240\n",
      "  Left child average radius: 0.0232\n",
      "  Right child average radius: 0.0075\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0232\n",
      "  Left child average radius: 0.0075\n",
      "  Right child average radius: 0.0229\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0075\n",
      "  Left child average radius: 0.0229\n",
      "  Right child average radius: 0.0220\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0229\n",
      "  Left child average radius: 0.0220\n",
      "  Right child average radius: 0.0209\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0220\n",
      "  Left child average radius: 0.0209\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0211\n",
      "  Left child average radius: 0.0229\n",
      "  Right child average radius: 0.0206\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0229\n",
      "  Left child average radius: 0.0206\n",
      "  Right child average radius: 0.0190\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0206\n",
      "  Left child average radius: 0.0190\n",
      "  Right child average radius: 0.0099\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0190\n",
      "  Left child average radius: 0.0099\n",
      "  Right child average radius: 0.0116\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0099\n",
      "  Left child average radius: 0.0116\n",
      "  Right child average radius: 0.0070\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0116\n",
      "  Left child average radius: 0.0070\n",
      "  Right child average radius: 0.0100\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0070\n",
      "  Left child average radius: 0.0100\n",
      "  Right child average radius: 0.0219\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0100\n",
      "  Left child average radius: 0.0219\n",
      "  Right child average radius: 0.0076\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 9:\n",
      "  Parent average radius: 0.0219\n",
      "  Left child average radius: 0.0076\n",
      "  Right child average radius: 0.0211\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 10:\n",
      "  Parent average radius: 0.0076\n",
      "  Left child average radius: 0.0211\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0425\n",
      "  Left child average radius: 0.0434\n",
      "  Right child average radius: 0.0111\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0434\n",
      "  Left child average radius: 0.0111\n",
      "  Right child average radius: 0.0419\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0111\n",
      "  Left child average radius: 0.0419\n",
      "  Right child average radius: 0.0433\n",
      "  children sum: 0.0002\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0419\n",
      "  Left child average radius: 0.0433\n",
      "  Right child average radius: 0.0126\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0433\n",
      "  Left child average radius: 0.0126\n",
      "  Right child average radius: 0.0437\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0001\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0126\n",
      "  Left child average radius: 0.0437\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0001\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0099\n",
      "  Left child average radius: 0.0104\n",
      "  Right child average radius: 0.0086\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0104\n",
      "  Left child average radius: 0.0086\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0218\n",
      "  Left child average radius: 0.0224\n",
      "  Right child average radius: 0.0159\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0224\n",
      "  Left child average radius: 0.0159\n",
      "  Right child average radius: 0.0255\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0159\n",
      "  Left child average radius: 0.0255\n",
      "  Right child average radius: 0.0210\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0255\n",
      "  Left child average radius: 0.0210\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0076\n",
      "  Left child average radius: 0.0051\n",
      "  Right child average radius: 0.0119\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0051\n",
      "  Left child average radius: 0.0119\n",
      "  Right child average radius: 0.0156\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0119\n",
      "  Left child average radius: 0.0156\n",
      "  Right child average radius: 0.0160\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0156\n",
      "  Left child average radius: 0.0160\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0094\n",
      "  Left child average radius: 0.0102\n",
      "  Right child average radius: 0.0090\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0102\n",
      "  Left child average radius: 0.0090\n",
      "  Right child average radius: 0.0054\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0090\n",
      "  Left child average radius: 0.0054\n",
      "  Right child average radius: 0.0118\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0054\n",
      "  Left child average radius: 0.0118\n",
      "  Right child average radius: 0.0068\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0118\n",
      "  Left child average radius: 0.0068\n",
      "  Right child average radius: 0.0146\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0068\n",
      "  Left child average radius: 0.0146\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0246\n",
      "  Left child average radius: 0.0287\n",
      "  Right child average radius: 0.0168\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0287\n",
      "  Left child average radius: 0.0168\n",
      "  Right child average radius: 0.0264\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0168\n",
      "  Left child average radius: 0.0264\n",
      "  Right child average radius: 0.0249\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0264\n",
      "  Left child average radius: 0.0249\n",
      "  Right child average radius: 0.0069\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0249\n",
      "  Left child average radius: 0.0069\n",
      "  Right child average radius: 0.0244\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0069\n",
      "  Left child average radius: 0.0244\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0068\n",
      "  Left child average radius: 0.0040\n",
      "  Right child average radius: 0.0070\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0040\n",
      "  Left child average radius: 0.0070\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0119\n",
      "  Left child average radius: 0.0242\n",
      "  Right child average radius: 0.0221\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0242\n",
      "  Left child average radius: 0.0221\n",
      "  Right child average radius: 0.0223\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0221\n",
      "  Left child average radius: 0.0223\n",
      "  Right child average radius: 0.0206\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0223\n",
      "  Left child average radius: 0.0206\n",
      "  Right child average radius: 0.0106\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0206\n",
      "  Left child average radius: 0.0106\n",
      "  Right child average radius: 0.0126\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0106\n",
      "  Left child average radius: 0.0126\n",
      "  Right child average radius: 0.0102\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 7:\n",
      "  Parent average radius: 0.0126\n",
      "  Left child average radius: 0.0102\n",
      "  Right child average radius: 0.0077\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 8:\n",
      "  Parent average radius: 0.0102\n",
      "  Left child average radius: 0.0077\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0125\n",
      "  Left child average radius: 0.0107\n",
      "  Right child average radius: 0.0109\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0107\n",
      "  Left child average radius: 0.0109\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0067\n",
      "  Left child average radius: 0.0065\n",
      "  Right child average radius: 0.0068\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0065\n",
      "  Left child average radius: 0.0068\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0084\n",
      "  Left child average radius: 0.0094\n",
      "  Right child average radius: 0.0052\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0094\n",
      "  Left child average radius: 0.0052\n",
      "  Right child average radius: 0.0118\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0052\n",
      "  Left child average radius: 0.0118\n",
      "  Right child average radius: 0.0087\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0118\n",
      "  Left child average radius: 0.0087\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0187\n",
      "  Left child average radius: 0.0192\n",
      "  Right child average radius: 0.0085\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0192\n",
      "  Left child average radius: 0.0085\n",
      "  Right child average radius: 0.0193\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0085\n",
      "  Left child average radius: 0.0193\n",
      "  Right child average radius: 0.0199\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0193\n",
      "  Left child average radius: 0.0199\n",
      "  Right child average radius: 0.0172\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0199\n",
      "  Left child average radius: 0.0172\n",
      "  Right child average radius: 0.0203\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0172\n",
      "  Left child average radius: 0.0203\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0195\n",
      "  Left child average radius: 0.0236\n",
      "  Right child average radius: 0.0218\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0236\n",
      "  Left child average radius: 0.0218\n",
      "  Right child average radius: 0.0206\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0218\n",
      "  Left child average radius: 0.0206\n",
      "  Right child average radius: 0.0114\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0206\n",
      "  Left child average radius: 0.0114\n",
      "  Right child average radius: 0.0078\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0114\n",
      "  Left child average radius: 0.0078\n",
      "  Right child average radius: 0.0092\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0078\n",
      "  Left child average radius: 0.0092\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0082\n",
      "  Left child average radius: 0.0074\n",
      "  Right child average radius: 0.0100\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0074\n",
      "  Left child average radius: 0.0100\n",
      "  Right child average radius: 0.0062\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0100\n",
      "  Left child average radius: 0.0062\n",
      "  Right child average radius: 0.0100\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0062\n",
      "  Left child average radius: 0.0100\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0097\n",
      "  Left child average radius: 0.0124\n",
      "  Right child average radius: 0.0106\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0124\n",
      "  Left child average radius: 0.0106\n",
      "  Right child average radius: 0.0060\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0106\n",
      "  Left child average radius: 0.0060\n",
      "  Right child average radius: 0.0100\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0060\n",
      "  Left child average radius: 0.0100\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0099\n",
      "  Left child average radius: 0.0143\n",
      "  Right child average radius: 0.0169\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0143\n",
      "  Left child average radius: 0.0169\n",
      "  Right child average radius: 0.0083\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0169\n",
      "  Left child average radius: 0.0083\n",
      "  Right child average radius: 0.0100\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0083\n",
      "  Left child average radius: 0.0100\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0219\n",
      "  Left child average radius: 0.0225\n",
      "  Right child average radius: 0.0213\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0225\n",
      "  Left child average radius: 0.0213\n",
      "  Right child average radius: 0.0074\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0213\n",
      "  Left child average radius: 0.0074\n",
      "  Right child average radius: 0.0220\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0074\n",
      "  Left child average radius: 0.0220\n",
      "  Right child average radius: 0.0223\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0220\n",
      "  Left child average radius: 0.0223\n",
      "  Right child average radius: 0.0264\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0223\n",
      "  Left child average radius: 0.0264\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0053\n",
      "  Left child average radius: 0.0030\n",
      "  Right child average radius: 0.0052\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0030\n",
      "  Left child average radius: 0.0052\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0137\n",
      "  Left child average radius: 0.0143\n",
      "  Right child average radius: 0.0186\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0143\n",
      "  Left child average radius: 0.0186\n",
      "  Right child average radius: 0.0068\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0186\n",
      "  Left child average radius: 0.0068\n",
      "  Right child average radius: 0.0143\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0068\n",
      "  Left child average radius: 0.0143\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0076\n",
      "  Left child average radius: 0.0080\n",
      "  Right child average radius: 0.0080\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0080\n",
      "  Left child average radius: 0.0080\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0095\n",
      "  Left child average radius: 0.0096\n",
      "  Right child average radius: 0.0095\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0096\n",
      "  Left child average radius: 0.0095\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0102\n",
      "  Left child average radius: 0.0101\n",
      "  Right child average radius: 0.0098\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0101\n",
      "  Left child average radius: 0.0098\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0069\n",
      "  Left child average radius: 0.0064\n",
      "  Right child average radius: 0.0068\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0064\n",
      "  Left child average radius: 0.0068\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0162\n",
      "  Left child average radius: 0.0164\n",
      "  Right child average radius: 0.0180\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0164\n",
      "  Left child average radius: 0.0180\n",
      "  Right child average radius: 0.0152\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0180\n",
      "  Left child average radius: 0.0152\n",
      "  Right child average radius: 0.0140\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0152\n",
      "  Left child average radius: 0.0140\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0086\n",
      "  Left child average radius: 0.0088\n",
      "  Right child average radius: 0.0085\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0088\n",
      "  Left child average radius: 0.0085\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0212\n",
      "  Left child average radius: 0.0237\n",
      "  Right child average radius: 0.0204\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0237\n",
      "  Left child average radius: 0.0204\n",
      "  Right child average radius: 0.0137\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0204\n",
      "  Left child average radius: 0.0137\n",
      "  Right child average radius: 0.0211\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0137\n",
      "  Left child average radius: 0.0211\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0097\n",
      "  Left child average radius: 0.0103\n",
      "  Right child average radius: 0.0099\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0103\n",
      "  Left child average radius: 0.0099\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0203\n",
      "  Left child average radius: 0.0208\n",
      "  Right child average radius: 0.0208\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0208\n",
      "  Left child average radius: 0.0208\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0141\n",
      "  Left child average radius: 0.0114\n",
      "  Right child average radius: 0.0148\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0114\n",
      "  Left child average radius: 0.0148\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0088\n",
      "  Left child average radius: 0.0185\n",
      "  Right child average radius: 0.0185\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0185\n",
      "  Left child average radius: 0.0185\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0214\n",
      "  Left child average radius: 0.0213\n",
      "  Right child average radius: 0.0199\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0213\n",
      "  Left child average radius: 0.0199\n",
      "  Right child average radius: 0.0057\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0199\n",
      "  Left child average radius: 0.0057\n",
      "  Right child average radius: 0.0209\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0057\n",
      "  Left child average radius: 0.0209\n",
      "  Right child average radius: 0.0062\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0209\n",
      "  Left child average radius: 0.0062\n",
      "  Right child average radius: 0.0198\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0062\n",
      "  Left child average radius: 0.0198\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0105\n",
      "  Left child average radius: 0.0113\n",
      "  Right child average radius: 0.0092\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0113\n",
      "  Left child average radius: 0.0092\n",
      "  Right child average radius: 0.0079\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0092\n",
      "  Left child average radius: 0.0079\n",
      "  Right child average radius: 0.0109\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0079\n",
      "  Left child average radius: 0.0109\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0123\n",
      "  Left child average radius: 0.0137\n",
      "  Right child average radius: 0.0080\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0137\n",
      "  Left child average radius: 0.0080\n",
      "  Right child average radius: 0.0191\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0080\n",
      "  Left child average radius: 0.0191\n",
      "  Right child average radius: 0.0125\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0191\n",
      "  Left child average radius: 0.0125\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0138\n",
      "  Left child average radius: 0.0182\n",
      "  Right child average radius: 0.0180\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0182\n",
      "  Left child average radius: 0.0180\n",
      "  Right child average radius: 0.0178\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0180\n",
      "  Left child average radius: 0.0178\n",
      "  Right child average radius: 0.0120\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0178\n",
      "  Left child average radius: 0.0120\n",
      "  Right child average radius: 0.0109\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0120\n",
      "  Left child average radius: 0.0109\n",
      "  Right child average radius: 0.0126\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0109\n",
      "  Left child average radius: 0.0126\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0126\n",
      "  Left child average radius: 0.0100\n",
      "  Right child average radius: 0.0118\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0100\n",
      "  Left child average radius: 0.0118\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0080\n",
      "  Left child average radius: 0.0083\n",
      "  Right child average radius: 0.0076\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0083\n",
      "  Left child average radius: 0.0076\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n",
      "Bifurcation 1:\n",
      "  Parent average radius: 0.0114\n",
      "  Left child average radius: 0.0273\n",
      "  Right child average radius: 0.0240\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 2:\n",
      "  Parent average radius: 0.0273\n",
      "  Left child average radius: 0.0240\n",
      "  Right child average radius: 0.0248\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 3:\n",
      "  Parent average radius: 0.0240\n",
      "  Left child average radius: 0.0248\n",
      "  Right child average radius: 0.0124\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 4:\n",
      "  Parent average radius: 0.0248\n",
      "  Left child average radius: 0.0124\n",
      "  Right child average radius: 0.0092\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 5:\n",
      "  Parent average radius: 0.0124\n",
      "  Left child average radius: 0.0092\n",
      "  Right child average radius: 0.0080\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "Bifurcation 6:\n",
      "  Parent average radius: 0.0092\n",
      "  Left child average radius: 0.0080\n",
      "  Right child average radius: 0.0000\n",
      "  children sum: 0.0000\n",
      "  parent: 0.0000\n",
      "  Respects Murray's Law: No\n",
      "\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "def traverse_and_collect_branches(node, current_branch, branches):\n",
    "    if not node:\n",
    "        return\n",
    "    \n",
    "    # Extract the radius from the fourth position of the tensor\n",
    "    current_branch.append(node.radius[3].item())\n",
    "    \n",
    "    # If it's a leaf or a bifurcation, store the branch and start new branches\n",
    "    if (node.left and node.right) or (not node.left and not node.right):\n",
    "        if current_branch:\n",
    "            branches.append(current_branch[:])\n",
    "        current_branch.clear()\n",
    "\n",
    "    if node.left:\n",
    "        traverse_and_collect_branches(node.left, current_branch[:], branches)\n",
    "    if node.right:\n",
    "        traverse_and_collect_branches(node.right, current_branch[:], branches)\n",
    "\n",
    "def calculate_branch_average(branch):\n",
    "    return sum(branch) / len(branch) if branch else 0\n",
    "\n",
    "def check_murrays_law(node):\n",
    "    branches = []\n",
    "    traverse_and_collect_branches(node, [], branches)\n",
    "    \n",
    "    for i in range(1, len(branches)):\n",
    "        parent_avg = calculate_branch_average(branches[i - 1])\n",
    "        left_child_avg = calculate_branch_average(branches[i]) if i < len(branches) else 0\n",
    "        right_child_avg = calculate_branch_average(branches[i + 1]) if i + 1 < len(branches) else 0\n",
    "        \n",
    "        # Check Murray's Law\n",
    "        murray_respect = parent_avg**3 == left_child_avg**3 + right_child_avg**3\n",
    "        \n",
    "        print(f\"Bifurcation {i}:\")\n",
    "        print(f\"  Parent average radius: {parent_avg:.4f}\")\n",
    "        print(f\"  Left child average radius: {left_child_avg:.4f}\")\n",
    "        print(f\"  Right child average radius: {right_child_avg:.4f}\")\n",
    "        print(f\"  children sum: {left_child_avg**3 + right_child_avg**3:.4f}\")\n",
    "        print(f\"  parent: {parent_avg**3:.4f}\")\n",
    "        print(f\"  Respects Murray's Law: {'Yes' if murray_respect else 'No'}\\n\")\n",
    "\n",
    "\n",
    "\n",
    "# Example usage\n",
    "\n",
    "for batch in data_loader:\n",
    "    for t_l, input in enumerate(batch):\n",
    "        if t_l > 1:\n",
    "            break\n",
    "        check_murrays_law(input)\n",
    "        print(t_l)\n",
    "        "
   ]
  },
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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