demo2_child_structures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo 2: Generation of 2pc-equivalent child structures\n",
    "\n",
    "* Author: Mauricio Fernández\n",
    "* Last update: 2020-06-17\n",
    "\n",
    "Table of contents\n",
    "\n",
    "* [1. Example in 2D](#1.-Example-in-2D)\n",
    "    * [1.1 Load roots](#1.1-Load-roots)\n",
    "    * [1.2 Phase extension](#1.2-Phase-extension)\n",
    "    * [1.3 Kernel application](#1.3-Kernel-application)\n",
    "    * [1.4 Kernel-based extension](#1.4-Kernel-based-extension)\n",
    "    * [1.5 Phase coalescence](#1.5-Phase-coalescence)\n",
    "* [2. Example from 1D to 3D](#2.-Example-from-1D-to-3D)\n",
    "* [3. Generation of expandable hdf5 database](#3.-Generation-of-expandable-hdf5-database)\n",
    "    * [3.1 Initialization of database](#3.1-Initialization-of-database)\n",
    "    * [3.2 Branching in database](#3.2-Branching-in-database)\n",
    "    * [3.3 Further examples](#3.3-Further-examples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description\n",
    "The present notebook demonstrates how 2pc-equivalent child structures are generated from given 2pc-equivalent root structures. At the end of the notebook, the generation of an expandable hdf5 database is demonstrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import src.eq2pc as e2\n",
    "import src.database as db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Example in 2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Load roots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of currently available roots:\n",
      "\tdata/roots/1d_12_nev_5\n",
      "\tdata/roots/2d_4_3_nev_3_2\n",
      "\tdata/roots/2d_4_3_nev_5\n",
      "\tdata/roots/3d_2_3_4_nev_5\n"
     ]
    }
   ],
   "source": [
    "roots = e2.roots_saved()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are the shown structures related?\n",
      "\tFalse\n",
      "Are the shown structures 2PC-equivalent?\n",
      "\tTrue\n"
     ]
    }
   ],
   "source": [
    "Ss = e2.root_load(roots[2], check=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Phase extension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate 2pc-equivalent child structures through phase extension based on trivial embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ssp = [e2.phe(S, [2,3]) for S in Ss]\n",
    "for S in Ssp: e2.plot_S(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check inherited 2pc-equivalence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e2.Ceq(Ssp[0],Ssp[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Kernel application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1]\n",
      " [0 1 0]]\n",
      "[[1 0 0]\n",
      " [1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "Ks = [\n",
    "      np.array([[1,1,1],[0,1,0]])\n",
    "      ,np.array([[1,0,0],[1,1,0]])\n",
    "      ]\n",
    "for K in Ks: print(K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply kernels to previously phase extended structures to generate new child structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sspp = [e2.ka(S, Ks) for S in Ssp]\n",
    "for S in Sspp: e2.plot_S(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Kernel-based extension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original root structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 2],\n",
       "        [1, 2, 2],\n",
       "        [2, 2, 1],\n",
       "        [1, 2, 2]],\n",
       "\n",
       "       [[1, 1, 2],\n",
       "        [2, 2, 1],\n",
       "        [2, 2, 1],\n",
       "        [2, 2, 1]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1]\n",
      " [0 1 0]]\n",
      "[[1 0 0]\n",
      " [1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "Ks = [\n",
    "      np.array([[1,1,1],[0,1,0]])\n",
    "      ,np.array([[1,0,0],[1,1,0]])\n",
    "      ]\n",
    "for K in Ks: print(K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generation of child structures through kernel-based extension directly applied to original root structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fq1at0n//+18NDg6qv7/f674A+ETakLhy5YpOnjypTz75RJI0efJkTZ482eu+APhE2tONH3/8UcFgUC+99JIqKyu1fft29fX1/WNdY2Oj4vG44vG42EkP3D/ShsTw8LA6Ojq0Y8cOJZNJTZ06Ve+8884/1t0+Ldubx3gAZELakCguLlZxcbGWLFkiSVq3bp06Ojo8bwyAP6QNiVmzZmnOnDnq7OyUJJ04cULl5eWeNwbAHxzd3Xj//fdVX1+vwcFBlZaWat++fV73BcAnHIVENBpVe3u7170A8CF2XAKwIiQAWBESAKwICQBWhAQAK09G6lfGYp7cDZka8G4vp2dj2T0aI+9ZXSn7xvXzs/AURxIArAgJAFaEBAArQgKYYLZu3apQKKQFCxY4Wk9IABPMli1b1Nzc7Hg9IQFMMMuWLdOMGTMcryckAFilDYnOzk5Fo9FbX/n5+WpoaBiP3gDcg1QqdWuUZDweV2Nj45jqpd1MNX/+fJ05c0aSNDIyotmzZ2vt2rVj+lAA3gkGg65uZhzV6caJEyc0b948zZ0717UGAPjbqEKiqalJdXV1d/y926dlp1IpV5oD4L66ujo9+eST6uzsVHFxsfbu3WtdHzDG2UMLg4ODKioq0tmzZzVz5kzr2ng8zrMbN3n5XIFXvHpegZ+FpHv795ETCOh/HK4tc/nZKcdHEkePHlVVVVXagABwf3EcEvv377/rqQaA+5ejkOjv79cXX3yhmpoar/sB4DOO5kk8+OCD+v33373uBYAPseMSgBUhAcCKkABgRUgAsCIkAFg53nE5GgUFBSopKXG0NpVKKRgMut1C1tX1sna21fWyth/qdnV16fLly6Oqn8kdlzIZFovFqOtx7Wyr62XtbKt70yTJTHX45XYvnG4AsCIkAFjlvPnmm29muolYLEZdj2tnW10va2dbXUn637fe0gMO1xYUFSmRSLj22Z5cuATgrqx4VBzAxERIALAiJABYERIArAgJAFaEBAArQgKAFSEBwIqQAGBFSACwIiQAWBESAKwICQBWhAQAK0ICgBUhAcCKkABg5eiFwQAyq/rppx2P4S8oKHD1sxlfB8CK0w0AVoQEACtCAoAVIQHAipAAYPV/bWwzb68jljwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = [e2.kbe(S, Ks) for S in Ss]\n",
    "for S in temp: e2.plot_S(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat with new kernels in order to generate more complex structures with increasing number of phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ks = [\n",
    "    np.array([[1,1,1],[1,0,1],[1,0,1]]) \n",
    "    for _ in range(np.max(temp[0]))\n",
    "]\n",
    "temp = [e2.kbe(S, Ks) for S in temp]\n",
    "for S in temp: e2.plot_S(S) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Phase coalescence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping between new and old phase indices [new, [old1,...]]\n",
      "[[1, [2, 4, 3]], [2, [1, 8, 5]], [3, [6]], [4, [7]]]\n",
      "[2 1 1 ... 3 4 2]\n"
     ]
    }
   ],
   "source": [
    "S = np.array([1, 2, 4, 3, 1, 8, 5, 2, 6, 7, 8])\n",
    "Sout = e2.phc(S, [[2, 4, 3], [1, 8, 5]], info=True)\n",
    "print(Sout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fq1at0n//+18NDg6qv7/f674A+ETakLhy5YpOnjypTz75RJI0efJkTZ482eu+APhE2tONH3/8UcFgUC+99JIqKyu1fft29fX1/WNdY2Oj4vG44vG42EkP3D/ShsTw8LA6Ojq0Y8cOJZNJTZ06Ve+8884/1t0+Ldubx3gAZELakCguLlZxcbGWLFkiSVq3bp06Ojo8bwyAP6QNiVmzZmnOnDnq7OyUJJ04cULl5eWeNwbAHxzd3Xj//fdVX1+vwcFBlZaWat++fV73BcAnHIVENBpVe3u7170A8CF2XAKwIiQAWBESAKwICQBWhAQAK09G6lfGYp7cDZka8G4vp2dj2T0aI+9ZXSn7xvXzs/AURxIArAgJAFaEBAArQgKYYLZu3apQKKQFCxY4Wk9IABPMli1b1Nzc7Hg9IQFMMMuWLdOMGTMcryckAFilDYnOzk5Fo9FbX/n5+WpoaBiP3gDcg1QqdWuUZDweV2Nj45jqpd1MNX/+fJ05c0aSNDIyotmzZ2vt2rVj+lAA3gkGg65uZhzV6caJEyc0b948zZ0717UGAPjbqEKiqalJdXV1d/y926dlp1IpV5oD4L66ujo9+eST6uzsVHFxsfbu3WtdHzDG2UMLg4ODKioq0tmzZzVz5kzr2ng8zrMbN3n5XIFXvHpegZ+FpHv795ETCOh/HK4tc/nZKcdHEkePHlVVVVXagABwf3EcEvv377/rqQaA+5ejkOjv79cXX3yhmpoar/sB4DOO5kk8+OCD+v33373uBYAPseMSgBUhAcCKkABgRUgAsCIkAFg53nE5GgUFBSopKXG0NpVKKRgMut1C1tX1sna21fWyth/qdnV16fLly6Oqn8kdlzIZFovFqOtx7Wyr62XtbKt70yTJTHX45XYvnG4AsCIkAFjlvPnmm29muolYLEZdj2tnW10va2dbXUn637fe0gMO1xYUFSmRSLj22Z5cuATgrqx4VBzAxERIALAiJABYERIArAgJAFaEBAArQgKAFSEBwIqQAGBFSACwIiQAWBESAKwICQBWhAQAK0ICgBUhAcCKkABg5eiFwQAyq/rppx2P4S8oKHD1sxlfB8CK0w0AVoQEACtCAoAVIQHAipAAYPV/bWwzb68jljwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for S in Sspp: e2.plot_S(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ssppp = [e2.phc(S, [[1,2]]) for S in Sspp]\n",
    "for S in Ssppp: e2.plot_S(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Example from 1D to 3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load 1D root structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of currently available roots:\n",
      "\tdata/roots/1d_12_nev_5\n",
      "\tdata/roots/2d_4_3_nev_3_2\n",
      "\tdata/roots/2d_4_3_nev_5\n",
      "\tdata/roots/3d_2_3_4_nev_5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB+CAYAAABMDwy9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAJwUlEQVR4nO3dX2jW9R7A8c86S86ho3U8brV6HCZGPW7B/sVuQjBZSoTBNsKxQNtkEF0U0kW33pQEkuvPTRAhBg66adGfXaRIsJJYjiCsEHKwLak9ZJRa6MZzLg7I8ZS17Pfd7+nZ6wVe7MeP5/eBz4P63u/3bDXlcrkcAAAAGbsu7wEAAIDqJDYAAIAkxAYAAJCE2AAAAJIQGwAAQBJiAwAASKI27wEAAIClt23btiiVSlccW7NmTYyNjWV2DbEBAADLUKlUiomJiSuOdXR0ZHoNj1EBAABJiA0AACAJsQEAACQhNgAAgCTEBgAAkITYAAAAkhAbAABAEhUdG2NjY3HnnXfGhg0bYt++fXmPQ0amp6dj8+bNUSwWo6mpKYaHh/MeiYwtLCxEa2trPPjgg3mPQoa+//776O3tjbvuuiuKxWJ89NFHeY9ERp5//vloamqK5ubm6Ovri59//jnvkbhGAwMDUV9fH83NzZePfffdd9HV1RV33HFHdHV1xdmzZ3OckOWmYmNjYWEhHn/88Xjvvffi5MmTcfjw4Th58mTeY5GB2tra2L9/f3z++edx/PjxePnll+22ygwPD0exWMx7DDL2xBNPxLZt2+KLL76ITz/91I6rxOzsbLzwwgsxMTERn332WSwsLMTIyEjeY3GNdu3a9Yvf/rxv377YsmVLnDp1KrZs2eIbuCypio2Njz/+ODZs2BDr16+PFStWxI4dO2J0dDTvschAQ0NDtLW1RUTEypUro1gsxuzsbM5TkZWZmZl45513Yvfu3XmPQoZ++OGH+OCDD2JwcDAiIlasWBE33XRTzlORlfn5+fjpp59ifn4+Lly4ELfeemveI3GNNm3aFKtXr77i2OjoaOzcuTMiInbu3BlvvvlmHqOxTFVsbMzOzsbatWsvf10oFPyHtApNTU3F5ORkdHZ25j0KGXnyySfjueeei+uuq9i/XrgGX331VdTV1cWjjz4ara2tsXv37jh//nzeY5GB2267LZ566qlobGyMhoaGuPHGG+P+++/Peywy9M0330RDQ0NE/Pcbft9++23OE7GcVOz/Bsrl8i+O1dTU5DAJqZw7dy56enriwIEDsWrVqrzHIQNvv/121NfXR3t7e96jkLH5+fk4ceJEPPbYYzE5ORk33HCDRzGqxNmzZ2N0dDROnz4dX3/9dZw/fz5ef/31vMcCqkTFxkahUIjp6enLX8/MzLitW0UuXboUPT090d/fH93d3XmPQ0bGx8fjrbfeinXr1sWOHTvi6NGj8cgjj+Q9FhkoFApRKBQu34Xs7e2NEydO5DwVWXj//ffj9ttvj7q6urj++uuju7s7Pvzww7zHIkM333xznDlzJiIizpw5E/X19TlPxHJSsbFxzz33xKlTp+L06dNx8eLFGBkZie3bt+c9Fhkol8sxODgYxWIx9uzZk/c4ZOjZZ5+NmZmZmJqaipGRkbjvvvt8h7RK3HLLLbF27dr48ssvIyLiyJEjsXHjxpynIguNjY1x/PjxuHDhQpTL5Thy5IgP/1eZ7du3x8GDByMi4uDBg/HQQw/lPBHLSW3eA1xNbW1tvPTSS7F169ZYWFiIgYGBaGpqynssMjA+Ph6HDh2Ku+++O1paWiIi4plnnokHHngg58mA3/Liiy9Gf39/XLx4MdavXx+vvfZa3iORgc7Ozujt7Y22traora2N1tbWGBoaynssrlFfX18cO3YsSqVSFAqF2Lt3bzz99NPx8MMPx6uvvhqNjY3xxhtv5D0my0hN+dc+HAEAAFS1jo6OmJiY+N1jf0bFPkYFAAD8tYkNAAAgCbEBAAAkITYAAIAkxAYAAJBExcfGK6+8kvcIJGS/1ctuq5fdVi+7rV52S17EBrmy3+plt9XLbquX3VYvuyUr09PTsXnz5igWi9HU1BTDw8O/eX7F/lI/AACgstTW1sb+/fujra0tfvzxx2hvb4+urq7YuHHjr5//R168pqZmyW+FlCPibzU1S3xVlkpe+21tb1/ya+Zl8pNPcrmu3Vavubm56OjoWPLr5vVezkse7+W8dkt6dpve1NRUlEqlvMdIrqGhIRoaGiIiYuXKlVEsFmN2djab2LguIv7+p0eE/GX5mzEr3Q3LLNaX026XG+9loJItx5ibmpqKycnJ6OzsvOo5HqMCAAAi4pd3wYaGhmJoaOgX5507dy56enriwIEDsWrVqqu+ntgAAAAiIqKuru5376xeunQpenp6or+/P7q7u3/z3Ir/aVQAAEBlKJfLMTg4GMViMfbs2fO754sNAABgUcbHx+PQoUNx9OjRaGlpiZaWlnj33Xever7HqAAAgEW59957o1wuL/p8dzYAAIAkxAYAAJCE2AAAAJIQGwAAQBJiAwAASEJsAAAASYgNAAAgCbEBAAAkITYAAIAkxAYAAJCE2AAAAJIQGwAAQBJiAwAASEJsAAAASYgNAAAgCbEBAAAkITYAAIAkxAYAAJCE2AAAAJIQGwAAQBJiAwAASEJsAAAASYgNAAAgCbEBAADL0OQnn8Q/a2qu+JM1sQEAACQhNgAAgCTEBgAAkITYAAAAkhAbAABAEmIDAABIQmwAAABJiA0AACAJsQEAACQhNgAAgCTEBgAAkITYAAAAkhAbAABAEmIDAABIQmwAAABJiA0AACAJsQEAACQhNgAAgCTEBgAAkITYAAAAkhAbAABAEmIDAABIQmwAAABJiA0AAGBRBgYGor6+Ppqbmxd1vtgAAAAWZdeuXTE2Nrbo88UGAACwKJs2bYrVq1cv+nyxAQAAJFGb9wAAAEBlmJubi46OjstfDw0NxdDQ0DW/ntgAAAAiIqKuri4mJiYye70/FBv/+ve/Y926dZldfDHm5uairq5uSa/J0slrv/9b7NWu2N6ey3Xttnrltdu83st5yeO97N/c6mW36U1NTeU9QkX6Q7FRKpVSzXFVHR0dmdYVlcV+q5fdVi+7rV52W73slqz09fXFsWPHolQqRaFQiL1798bg4OBVz/cYFQAAsCiHDx/+Q+f7aVQAAEASFR8bf+bT71Q++61edlu97LZ62W31slvyUlMul8t5DwEAACytv9XUxD/+79hd7e2Zfr6n4u9sAAAAf01iAwAASEJsAAAASYgNAAAgCbEBAAAkITYAAIAkxAYAAJCE2AAAAJIQGwAAQBJiAwAASEJsAAAASYgNAAAgCbEBAAAkITYAAIAkxAYAAJCE2AAAAJIQGwAAQBJiAwAASEJsAAAASYgNAAAgidq8BwAAAJZe19atUSqVrji2Zs2aTK9RUy6Xy5m+IgAAQHiMCgAASERsAAAASYgNAAAgCbEBAAAkITYAAIAk/gN51EQV+cn1hAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x144 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are the shown structures related?\n",
      "\tFalse\n",
      "Are the shown structures 2PC-equivalent?\n",
      "\tTrue\n"
     ]
    }
   ],
   "source": [
    "roots = e2.roots_saved()\n",
    "Ss = e2.root_load(roots[0], check=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define 3D kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "K1 = np.zeros([2, 2, 3], dtype=np.int)\n",
    "K1[0, 0, 0] = 1\n",
    "K1[0, 0, 1] = 1\n",
    "K1[0, 0, 2] = 1\n",
    "K1[0, 1, 0] = 1\n",
    "K1[1, 0, 0] = 1\n",
    "K1[1, 1, 0] = 1\n",
    "K2 = np.ones([2, 2, 3], dtype=np.int)\n",
    "K2[0, 0, 2] = 0\n",
    "K2[0, 1, 1] = 0\n",
    "K2[1, 1, 0] = 0\n",
    "K2[1, 1, 1] = 0\n",
    "Ks = [K1,K2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Child structures through kernel-based extension from 1D to 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ssp = [e2.kbe(S, Ks) for S in Ss]\n",
    "for S in Ssp: e2.plot_S(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check inherited 2pc-equivalence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e2.Ceq(Ssp[0],Ssp[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Generation of expandable hdf5 database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Initialization of database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize database from saved roots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of currently available roots:\n",
      "\tdata/roots/1d_12_nev_5\n",
      "\tdata/roots/2d_4_3_nev_3_2\n",
      "\tdata/roots/2d_4_3_nev_5\n",
      "\tdata/roots/3d_2_3_4_nev_5\n"
     ]
    }
   ],
   "source": [
    "db.roots_to_db()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print database current tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root:NXroot\n",
      "  1d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(12)\n",
      "      root2 = int32(12)\n",
      "  2d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "    3ph1:NXgroup\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "  3d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(2x3x4)\n",
      "      root2 = int32(2x3x4)\n"
     ]
    }
   ],
   "source": [
    "db.print_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Branching in database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a branch for child generation with provided kernels and possible phase coalescence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...Branch 2d/2ph1/b1 does not exist, filling.\n",
      "...New content:\n",
      "\tNumber of elements in branch: 3\n",
      "\tk1\n",
      "\tk2\n",
      "\tphc\n"
     ]
    }
   ],
   "source": [
    "Ks = [\n",
    "      np.array([[1,1,1],[0,1,0]])\n",
    "      ,np.array([[1,0,0],[1,1,0]])\n",
    "      ]\n",
    "phc = [[2,1]]\n",
    "db.branch('2d/2ph1',name='b1',Ks=Ks,phc=phc,info=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print current tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root:NXroot\n",
      "  1d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(12)\n",
      "      root2 = int32(12)\n",
      "  2d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      b1:NXgroup\n",
      "        k1 = int32(2x3)\n",
      "        k2 = int32(2x3)\n",
      "        phc = int32(2x2)\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "    3ph1:NXgroup\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "  3d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(2x3x4)\n",
      "      root2 = int32(2x3x4)\n"
     ]
    }
   ],
   "source": [
    "db.print_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate branch with children saving in database and plotting along evaluated branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corresponding root folder\n",
      "/2d/2ph1\n",
      "...Plotting root structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...Plotting root structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********************************\n",
      "Evaluating child level 1\n",
      "/2d/2ph1/b1\n",
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = db.branch_ev('2d/2ph1/b1',save_children=True,plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[1, 1, 1, ..., 1, 2, 2],\n",
      "       [2, 1, 2, ..., 1, 1, 2],\n",
      "       [1, 1, 1, ..., 1, 2, 2],\n",
      "       ...,\n",
      "       [1, 1, 2, ..., 2, 1, 2],\n",
      "       [1, 1, 1, ..., 1, 2, 2],\n",
      "       [2, 1, 2, ..., 1, 1, 2]]), array([[1, 1, 1, ..., 1, 2, 2],\n",
      "       [2, 1, 2, ..., 1, 1, 2],\n",
      "       [1, 2, 2, ..., 1, 1, 1],\n",
      "       ...,\n",
      "       [1, 1, 2, ..., 2, 1, 2],\n",
      "       [1, 2, 2, ..., 1, 1, 1],\n",
      "       [1, 1, 2, ..., 2, 1, 2]])]\n"
     ]
    }
   ],
   "source": [
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root:NXroot\n",
      "  1d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(12)\n",
      "      root2 = int32(12)\n",
      "  2d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      b1:NXgroup\n",
      "        child1 = int32(8x9)\n",
      "        child2 = int32(8x9)\n",
      "        k1 = int32(2x3)\n",
      "        k2 = int32(2x3)\n",
      "        phc = int32(2x2)\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "    3ph1:NXgroup\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "  3d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(2x3x4)\n",
      "      root2 = int32(2x3x4)\n"
     ]
    }
   ],
   "source": [
    "db.print_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Further examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From same root, create alternative branches and evaluate as desired with or without phase coalescence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corresponding root folder\n",
      "/2d/2ph1\n",
      "...Plotting root structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...Plotting root structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAE3CAYAAABy5gduAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAK8klEQVR4nO3dz0sb+R/H8Ve2ofv98lVZpAqW9FKEdhrZzZrpuoLIehB3y9KDgVLxsKKQS2/+A3vtRVDoXgKePNhrL9bD4k1YJIt7WFykhwY07CEeFipsacjme1i+5fv99kdiOsnEl8/HLTiZvAd5OuMY80nU6/W6ANj4KO4BAESLqAEzRA2YIWrADFEDZpJxDwA4+Prrr3VyctLSc69cuaLt7e3IZiFqIAInJycqFostPTcMw0hn4fIbMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWDGIurt7W3duHFDw8PDevjwYdzjtNXi4qIGBwc1MjIS9yhtd3R0pKmpKQVBoHQ6rbW1tbhHOhfOfdS1Wk0PHjzQ06dPdXBwoM3NTR0cHMQ9VtssLCxE+m963SyZTGplZUW//fabfvrpJ/3www/W39uonPuo9/b2NDw8rOvXr+vy5cu6f/++njx5EvdYbTM5Oan+/v64x+iIoaEhjY6OSpJ6e3sVBIHK5XLMU3W/cx91uVzWtWvXXj9OpVJ84w2VSiXt7+9rbGws7lG63rmP+m0fW55IJGKYBO1yenqqXC6n1dVV9fX1xT1O1zv3UadSKR0dHb1+fHx8rKtXr8Y4EaJUrVaVy+U0Pz+v2dnZuMc5F8591Ldv39azZ8/0/PlzvXr1So8fP9bdu3fjHgsRqNfrWlpaUhAEWl5ejnucc+PcR51MJvXo0SPNzMwoCALdu3dP6XQ67rHaZm5uTuPj4zo8PFQqldL6+nrcI7XN7u6uNjY2tLOzo0wmo0wmo62trbjH6noJ1tICPlwYhh/0wYOtPvdtzv2ZGsD/ImrADFEDZogaMGMTdaFQiHuEjrpIx3uRjjUKRH1OXaTjvUjHGgWbqAH8rS0L5CUSiY7/tKhLunSB3vMd1/F+ns12/DUrlUrki8g1o1QqtbySZZzaEvVHkv7Rjh0jdlG+SaLbxfGDJApcfgNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmmop6e3tbN27c0PDwsB4+fNjumQB8gIZR12o1PXjwQE+fPtXBwYE2Nzd1cHDQidkAtKBh1Ht7exoeHtb169d1+fJl3b9/X0+ePOnEbABa0DDqcrmsa9euvX6cSqVULpff2K5QKCgMQ4VhqHq0MwI4g4arXtbrbyaaeMsSqvl8Xvl8XtLFWlIW6DYNz9SpVEpHR0evHx8fH+vq1attHQpA6xpGffv2bT179kzPnz/Xq1ev9PjxY929e7cTswFoQcPL72QyqUePHmlmZka1Wk2Li4tKp9OdmA1ACxpGLUl37tzRnTt32j0LgAjwjjLADFEDZogaMEPUgBmiBswQNWCGqIEud3R0pKmpKQVBoHQ6rbW1tfdu39TfqQHEJ5lMamVlRaOjo3rx4oWy2aymp6d169att27PmRrockNDQxodHZUk9fb2KgiCt/6n5H9wpgZiVqlUFIbh68f//R+P/69UKml/f19jY2Pv3B9RAzEbGBhQsVhsuN3p6alyuZxWV1fV19f3zu24/AbOgWq1qlwup/n5ec3Ozr53W6IGuly9XtfS0pKCINDy8nLD7Yka6HK7u7va2NjQzs6OMpmMMpmMtra23rk9v1MDXW5iYuKtHyv2LpypATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YKYtbz75PJtt6g3qOH/+dYHWSXsZ9wAt4kwNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwEzDqBcXFzU4OKiRkZFOzAPgAzWMemFhQdvb252YBUAEGkY9OTmp/v7+TswCIAKRrXpZKBRUKBQkSZVKJardAjijyG6U5fN5FYtFFYtFDQwMRLVbAGfE3W/ADFEDZhpGPTc3p/HxcR0eHiqVSml9fb0TcwFoUcMbZZubm52YA0BEuPwGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAmchWvQQusv2ff1ZPItHSc29ms5HOwpkaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgJmGUR8dHWlqakpBECidTmttba0TcwFoUcO1tJLJpFZWVjQ6OqoXL14om81qenpat27d6sR8AM6o4Zl6aGhIo6OjkqTe3l4FQaByudz2wQC05kyrXpZKJe3v72tsbOyNrxUKBRUKBUlSpVKJZjoAZ9b0jbLT01Plcjmtrq6qr6/vja/n83kVi0UVi0UNDAxEOiSA5jUVdbVaVS6X0/z8vGZnZ9s9E4AP0DDqer2upaUlBUGg5eXlTswE4AM0jHp3d1cbGxva2dlRJpNRJpPR1tZWJ2YD0IKGN8omJiZUr9c7MQuACPCOMsAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9RAl1tcXNTg4KBGRkaa2p6ogS63sLCg7e3tprcnaqDLTU5Oqr+/v+ntk22cBUATKpWKwjB8/Tifzyufz7e8P6IGYjYwMKBisRjZ/rj8BswQNWCGqIEuNzc3p/HxcR0eHiqVSml9ff292/M7NdDlNjc3z7Q9Z2rADFEDZogaMEPUgBmiBswQNWCmYdQvX77UF198oc8++0zpdFrff/99J+YC0KKGf6f++OOPtbOzo56eHlWrVU1MTOibb77Rl19+2Yn5AJxRwzN1IpFQT0+PJKlaraparSqRSLR9MACtaep36lqtpkwmo8HBQU1PT2tsbOyNbQqFgsIwVBiGqlQqkQ8KoDlNRX3p0iX98ssvOj4+1t7enn799dc3tsnn8yoWiyoWixoYGIh8UADNOdPd708++URfffXVmT5aBUBnNYy6Uqnojz/+kCT9+eef+vHHH3Xz5s22DwagNQ3vfv/+++/67rvvVKvV9Ndff+nevXv69ttvOzEbgBY0jPrTTz/V/v5+J2YBEAHeUQaYIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzDT/MvxWlUklhGLZj1+9UqVQu1MJ8cR1vkM12/DXjOtZSqdTx14xCW6I+OTlpx27fKwxDFYvFjr9uXC7S8V6kY40Cl9+AGaIGzNhEnc/n4x6hoy7S8V6kY41Col6v1+MeAjjvLiUS+meLz72ZzUZ6z8DmTA3gb0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVgpi0L5AEXzfTMTMsLQ165ciXSWVihAzDD5TdghqgBM0QNmCFqwAxRA2b+DSPmJbYnNgV3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********************************\n",
      "Evaluating child level 1\n",
      "/2d/2ph1/fibers\n",
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DhqT1Xnr2Qw/M2l50a+ebKlzhAUMIHjCE4AFDCB4whOABQwgeMITgAUMIHjCE4AFDCB4whOABQ9J6Lz3csbJXvI+1871fXOEBQwgeMITgAUMIHjCE4AFDCB4whOABQwgeMITgAUMIHjCE4AFDhnUvfSwWUzgcvuPxeDwun8+XsuNm8vxMXvu/eX4sFkvZMVPKSwOhUIj5IzCb+cMzf5TkjU3yP9fnx1t6wBCCBwxJi+AjkQjzR2A280d+/nDL8jzuFACk0uisLP0nydcWhkKKRqPO1pIWV3gAw4PgAUMIHjCE4AFDCB4whOABQwgeMITgAUMIHjCE4AFDCB4whOABQwgeMITgAUMIHjCE4AFDCB4whOABQwgeMITgAUMIHjCE4AFDCB4whOABQ4b1t8cCFlU+/bS6urqSem1OTo7TtfCbZwBDeEsPGELwgCEEDxhC8IAhBA8Y8v88kFwJBv91HgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ks = [\n",
    "      np.array([[0,0,0,0,0],[0,0,0,0,0],[0,1,1,1,0],[0,0,0,0,0],[0,0,0,0,0]])\n",
    "      ,np.array([[0,0,0,0,0],[0,1,0,0,0],[0,0,1,0,0],[0,0,0,1,0],[0,0,0,0,0]])\n",
    "      ]\n",
    "phc = [[1,2]]\n",
    "db.branch('2d/2ph1',name='fibers',Ks=Ks,phc=phc)\n",
    "out = db.branch_ev('2d/2ph1/fibers',save_children=True,plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create branches of arbitrary depth (default kernels and no phase coalescence are applied)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corresponding root folder\n",
      "/2d/2ph1\n",
      "...Plotting root structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...Plotting root structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAE3CAYAAABy5gduAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAK8klEQVR4nO3dz0sb+R/H8Ve2ofv98lVZpAqW9FKEdhrZzZrpuoLIehB3y9KDgVLxsKKQS2/+A3vtRVDoXgKePNhrL9bD4k1YJIt7WFykhwY07CEeFipsacjme1i+5fv99kdiOsnEl8/HLTiZvAd5OuMY80nU6/W6ANj4KO4BAESLqAEzRA2YIWrADFEDZpJxDwA4+Prrr3VyctLSc69cuaLt7e3IZiFqIAInJycqFostPTcMw0hn4fIbMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWDGIurt7W3duHFDw8PDevjwYdzjtNXi4qIGBwc1MjIS9yhtd3R0pKmpKQVBoHQ6rbW1tbhHOhfOfdS1Wk0PHjzQ06dPdXBwoM3NTR0cHMQ9VtssLCxE+m963SyZTGplZUW//fabfvrpJ/3www/W39uonPuo9/b2NDw8rOvXr+vy5cu6f/++njx5EvdYbTM5Oan+/v64x+iIoaEhjY6OSpJ6e3sVBIHK5XLMU3W/cx91uVzWtWvXXj9OpVJ84w2VSiXt7+9rbGws7lG63rmP+m0fW55IJGKYBO1yenqqXC6n1dVV9fX1xT1O1zv3UadSKR0dHb1+fHx8rKtXr8Y4EaJUrVaVy+U0Pz+v2dnZuMc5F8591Ldv39azZ8/0/PlzvXr1So8fP9bdu3fjHgsRqNfrWlpaUhAEWl5ejnucc+PcR51MJvXo0SPNzMwoCALdu3dP6XQ67rHaZm5uTuPj4zo8PFQqldL6+nrcI7XN7u6uNjY2tLOzo0wmo0wmo62trbjH6noJ1tICPlwYhh/0wYOtPvdtzv2ZGsD/ImrADFEDZogaMGMTdaFQiHuEjrpIx3uRjjUKRH1OXaTjvUjHGgWbqAH8rS0L5CUSiY7/tKhLunSB3vMd1/F+ns12/DUrlUrki8g1o1QqtbySZZzaEvVHkv7Rjh0jdlG+SaLbxfGDJApcfgNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmmop6e3tbN27c0PDwsB4+fNjumQB8gIZR12o1PXjwQE+fPtXBwYE2Nzd1cHDQidkAtKBh1Ht7exoeHtb169d1+fJl3b9/X0+ePOnEbABa0DDqcrmsa9euvX6cSqVULpff2K5QKCgMQ4VhqHq0MwI4g4arXtbrbyaaeMsSqvl8Xvl8XtLFWlIW6DYNz9SpVEpHR0evHx8fH+vq1attHQpA6xpGffv2bT179kzPnz/Xq1ev9PjxY929e7cTswFoQcPL72QyqUePHmlmZka1Wk2Li4tKp9OdmA1ACxpGLUl37tzRnTt32j0LgAjwjjLADFEDZogaMEPUgBmiBswQNWCGqIEud3R0pKmpKQVBoHQ6rbW1tfdu39TfqQHEJ5lMamVlRaOjo3rx4oWy2aymp6d169att27PmRrockNDQxodHZUk9fb2KgiCt/6n5H9wpgZiVqlUFIbh68f//R+P/69UKml/f19jY2Pv3B9RAzEbGBhQsVhsuN3p6alyuZxWV1fV19f3zu24/AbOgWq1qlwup/n5ec3Ozr53W6IGuly9XtfS0pKCINDy8nLD7Yka6HK7u7va2NjQzs6OMpmMMpmMtra23rk9v1MDXW5iYuKtHyv2LpypATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YKYtbz75PJtt6g3qOH/+dYHWSXsZ9wAt4kwNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwEzDqBcXFzU4OKiRkZFOzAPgAzWMemFhQdvb252YBUAEGkY9OTmp/v7+TswCIAKRrXpZKBRUKBQkSZVKJardAjijyG6U5fN5FYtFFYtFDQwMRLVbAGfE3W/ADFEDZhpGPTc3p/HxcR0eHiqVSml9fb0TcwFoUcMbZZubm52YA0BEuPwGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAmchWvQQusv2ff1ZPItHSc29ms5HOwpkaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgJmGUR8dHWlqakpBECidTmttba0TcwFoUcO1tJLJpFZWVjQ6OqoXL14om81qenpat27d6sR8AM6o4Zl6aGhIo6OjkqTe3l4FQaByudz2wQC05kyrXpZKJe3v72tsbOyNrxUKBRUKBUlSpVKJZjoAZ9b0jbLT01Plcjmtrq6qr6/vja/n83kVi0UVi0UNDAxEOiSA5jUVdbVaVS6X0/z8vGZnZ9s9E4AP0DDqer2upaUlBUGg5eXlTswE4AM0jHp3d1cbGxva2dlRJpNRJpPR1tZWJ2YD0IKGN8omJiZUr9c7MQuACPCOMsAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9SAGaIGzBA1YIaoATNEDZghasAMUQNmiBowQ9RAl1tcXNTg4KBGRkaa2p6ogS63sLCg7e3tprcnaqDLTU5Oqr+/v+ntk22cBUATKpWKwjB8/Tifzyufz7e8P6IGYjYwMKBisRjZ/rj8BswQNWCGqIEuNzc3p/HxcR0eHiqVSml9ff292/M7NdDlNjc3z7Q9Z2rADFEDZogaMEPUgBmiBswQNWCmYdQvX77UF198oc8++0zpdFrff/99J+YC0KKGf6f++OOPtbOzo56eHlWrVU1MTOibb77Rl19+2Yn5AJxRwzN1IpFQT0+PJKlaraparSqRSLR9MACtaep36lqtpkwmo8HBQU1PT2tsbOyNbQqFgsIwVBiGqlQqkQ8KoDlNRX3p0iX98ssvOj4+1t7enn799dc3tsnn8yoWiyoWixoYGIh8UADNOdPd708++URfffXVmT5aBUBnNYy6Uqnojz/+kCT9+eef+vHHH3Xz5s22DwagNQ3vfv/+++/67rvvVKvV9Ndff+nevXv69ttvOzEbgBY0jPrTTz/V/v5+J2YBEAHeUQaYIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzRA2YIWrADFEDZogaMEPUgBmiBswQNWCGqAEzDT/MvxWlUklhGLZj1+9UqVQu1MJ8cR1vkM12/DXjOtZSqdTx14xCW6I+OTlpx27fKwxDFYvFjr9uXC7S8V6kY40Cl9+AGaIGzNhEnc/n4x6hoy7S8V6kY41Col6v1+MeAjjvLiUS+meLz72ZzUZ6z8DmTA3gb0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVghqgBM0QNmCFqwAxRA2aIGjBD1IAZogbMEDVgpi0L5AEXzfTMTMsLQ165ciXSWVihAzDD5TdghqgBM0QNmCFqwAxRA2b+DSPmJbYnNgV3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********************************\n",
      "Evaluating child level 1\n",
      "/2d/2ph1/b2\n",
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pq6srbQMASK+EZ+q1a9dqZGREkhSPx/X666+rpaXF9cEApGZeb5RdvHhR//nPf7R69Wq35gHwD+3t7dq0aZNGR0dVWlqq48ePP/P4ef1M3dfXp/b29qf+XU9Pj3p6eiSJXS+BNOrt7Z3X8UmfqWdnZ3Xq1Cm9//77T/17dr0EckPSUZ87d07V1dV69dVX3ZwHwAIlHXVvb++cL70B5I6kor5//76+/vprtba2uj0PgAVK6o2yl19+Wb///rvbswBIAz77DRhD1IAxRA0YQ9SAMUQNGEPUgDFEDRhD1IAxRA0Y48rtjMY8ryg0Wjbvx0WjURUXF6d/INZN37pP3EsrY2suwELWHRsbS+8wGeJK1Ldv307pcaFQKK33amLd3Fn3RXqu2cbLb8AYogaMyamoF3KvY9bN7XVfpOeabR7HcZxsDwE87/I8Hv1fio+tCAYze99vAM8XogaMIWrAGKIGjCFqwBiiBowhasAYogaMIWrAGKIGjCFqwBiiBowhasAYogaMIWrAGKIGjCFqwBiiBowhasAYogaMIWrAGKIGjCFqwBiiBoxxZYM84EXT9PbbKW8MuWLFirTOwg4dgDG8/AaMIWrAGKIGjCFqwBiiBoz5f8DrsEUbX0CVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********************************\n",
      "Evaluating child level 2\n",
      "/2d/2ph1/b2/b1\n",
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W1iorK0tZWVk6evTokOcM+zf8F198cd/f19TU2OsSgCMWLlwoa4R7+vGkHWAQAg8YhMADBiHwgEEIPGAQAg8YhMADBiHwgEEIPGAQAg8YhMADBiHwgEEIPGAQAg8YhMADBiHwgEE83T32fyXNsXlu5wgn+t8lRjubtgy9gOgDxaRrlSQFYt2APYzwgEEIPGAQAg8YhMADBrG1e+wbb7yh1NRUPfXUU1q7dq06OjpcbRKAM2ztHhsKhXT27Fn9+uuveuKJJ7R7927XGgTgHFu7xy5btkzx8X9/o/fss8+qsbHRne4AOCrqv+E/+eQTrVixYtDXw+GwgsGggsGgovgmHYADonrwZteuXYqPj1dRUdGgxwzcPfYhdo8FYsp24CsqKnT48GHV1NQojiADY4KtwFdXV+udd97Rd999p0ceecTpngC4xNbusa+99pquX7+uUCikrKwsvfLKK170CiBKtnaPLSkpcaUZAO7iSTvAIJ5Oj80OBBSJRLwsCWAATwMPmCiadSBSnWxE3NIDRiHwgEEIPGAQAg8YhMADBiHwgEEIPGAQAg8YhMADBiHwgEEIPGAQAg8YhMADBvF2tlznTzHbyRUAIzxgFAIPGITAAwYh8IBBbO0ee8e7776ruLg4tbW1udIcAGfZ2j1WkhoaGnTs2DElJye70hgA59naPVaSXn/9de3Zs4dtpoAxxNb38IcOHdKsWbOUmZk57LHhcFjhcFiS1NphpxoAp4w48F1dXdq1a5e+/vrrf3X8wN1jg37uBoBYGvGn9L///rsuX76szMxMpaSkqLGxUTk5Ofrjjz/c6A+Ag0Y8wmdkZKilpaX/55SUFEUiEU2fPt3RxgA4z9busQDGJlu7xw5UX1/vVC8AXMaTdoBBCDxgEAIPGITAAwYh8IBBCDxgEAIPjGFDTV+/HwIPjGGDTV8fDIEHxrDBpq8PxttlqgGMSGtrq4LBYP/PA2ef2kHggVHM5/MpEok49n7c0gMGIfCAQQg8MIaNdPo6f8MDY9hw09f/iREeMIinI3x962MK/iflvq+1trbK5/N52Y77dQMxqjuEWNR9EK91rC784mngh9qhJhgMOvr1w79F3QezZizrjmbc0gMGIfCAQUZN4KN5XJC6o7uuSdc62sVZlmXFugngQfZQXJz+2+a5qYEAj9YCsIfAAwYh8IBBCDxgEAIPGITAAwYh8IBBCDxgEAIPGITAAwYh8IBBCDxgEAIPGITAAwYh8IBBCDxgEAIPGITAAwYh8IBBCDxgEAIPGITAAwYh8IBBCDxgEPaHB1wWev75ITdSHcr06dMd7YWdZwCDcEsPGITAAwYh8IBBCDxgEAIPGOT/ABY5mJ6ETxW1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********************************\n",
      "Evaluating child level 3\n",
      "/2d/2ph1/b2/b1/b1\n",
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Dhw9nXYeTSQKL1GengVuzZo1u3bqlYDCocDisxx9/fO51SjifrkraMcdtt+f4osz/KGDUMp3Z9GS+JxBchCw9Vkmun+QxH36/f/rsT58/DdyCCR7A/KTTaYVCoenL0WhU0Wj0vuU+fxq4TAgeWMC8Xq8SiUTGZeZzGjg+tAMWsdlOA5cJwQOL1FyngcuE4IFF6rPTwJ09e1aBQECBQECnT5/OuA7v4YFFKtNp4ObCKzxgSEmDXx0M6rbjzPoHQPFlDX6u3fcOHjyolStX5vzeAUD5ZX0PP9fue5L00ksv6Yc//GHRhwTgjqzBz7X7HoDFZ17v4b+4+96RI0fU2tqq7u5uTUxMzLpOLBZTKBRSKBRSOp0ufGIAecs5+C/uvrd3714NDQ0pmUzK7/dr3759s64XjUaVSCSUSCTk9XpdGxzA/OUU/Gy771VXV6uyslIVFRXas2ePBgYGijoogMJlDX6u3fdSqdT0348fP67m5uYc7u6C7n1skM8fAIXKWtJnu++1tLQoEAhIkl555RUdO3ZMyWRSHo9HtbW1ev3114s+LIDCZA1+rt33Nm3aVJSBABQPu9YChhA8YAjBA4YQPGAIwQOGEDxgCMEDhhA8YAjBA4YQPGAIwQOGEDxgSGmPO72qsp3JFQAHmgNF9z+a+zTp2TS4OYjYpAdMIXjAEIIHDCF4wBCCBwwheMAQggcMIXjAEIIHDCF4wBCCBwwheMAQggcMIXjAkKzB37lzR0888YTa2trU1NSkAwcOSJLGx8cVDodVX1+vcDisiYmJog8LoDBZg3/ooYd09uxZXbx4UclkUvF4XOfPn1dPT486Ojp05coVdXR0qKenpxTzAihA1uA9Ho8efvhhSdLk5KQmJyfl8XjU19enSCQiSYpEIjpx4kRxJwVQsJzew09NTSkQCMjn8ykcDmvt2rUaGRmR3++XJPn9fo2Ojs66biwWUygUUigUUvqOe4MDmL+cgq+srFQymdT169c1MDCg999/P+c7iEajSiQSSiQS8n457zkBuGBen9I/+uijWr9+veLxuKqrq5VKpSRJqVRKPp+vKAMCcE/W4NPptG7evClJ+uSTT/Tuu++qoaFBnZ2d6u3tlST19vZqy5YtxZ0UQMGyfmttKpVSJBLR1NSUPv30U23btk2bN2/WunXrtG3bNh09elSPPfaY3nzzzVLMC6AAWYNvbW3V4ODgfdcvW7ZMZ86cKcpQAIqDPe0AQwgeMITggUWsu7tbPp9Pzc3NOS1P8MAitmvXLsXj8ZyXJ3hgEWtvb9fSpUtzXr6kJ5Mc9ixT6HLt9OV0Oi2v11vKEbJydaagOz/mgX+eXFLKmYaHh0tyP+l0WqFQaPpyNBpVNBrN++eVNPixsbEZl0OhkBKJRClHyIqZcsNMpeH1el19TGzSA4YQPGBIWYMv5L1IsTBTbphpYdixY4fWrVuny5cvq6amRkePHs24vMdxHKdEswEmVXo8+u88120IBnkPDyA/BA8YQvCAIQQPGELwgCEEDxhC8IAhBA8YQvCAIQQPGELwgCEEDxhC8IAhBA8YQvCAIQQPGELwgCEEDxhC8IAhBA8YQvCAIQQPGELwgCEEDxhS0pNJAhaFn376vhOp5mr58uWuzsKZZwBD2KQHDCF4wBCCBwwheMAQggcM+T+uVJ5xLIzK8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...plotting generated child structures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3bsjn8ykQCOihhx6af50izod72LfTHCzx5jxfhHpHDn+Gho9a6/F4Zo7+9PXDwBE8UKYSiYT8fv/M6VAopFAodNdyXz8MXCoEDyxi1dXVikajKZdZyGHgeNEOKGNzHQYuFYIHytR8h4FLheCBMvXVYeDOnDkjr9crr9erU6dOpVyH5/BAmUp1GLj58AgPGMIjPPIi9fvsWCzSPsLPt/vewYMHtWrVqoyfOwAovbSP8PPtvidJL774on76058WfEgA+ZE2+Pl23wNQfhb0ot03d987cuSI2tra1NPTo8nJyTnXCYfD8vv98vv9up77vABykHHw39x9b+/evRoeHlYsFpPH49G+ffvmXC8UCikajSoajSr1Tn8ACi2j4Ofafa+mpkaVlZWqqKjQnj17NDg4WNBBAeQubfDz7b4Xj8dnfj5+/LhaWloKMyHKRFUO/1Asae/tr3bfa21tldfrlSS9+uqrOnbsmGKxmFwul+rq6vTGG28UfFgAuUkb/Hy7723atKkgAwEoHHatBQwheMAQggcMIXjAEIIHDCF4wBCCBwwheMAQggcMIXjAEIIHDCF4wBA+m4j8MHwU13JC8ECB/Y+kHVmu25jPQcQmPWAKwQOGEDxgCMEDhhA8YAjBA4YQPGAIwQOGEDxgCMEDhhA8YAjBA4YQPGAIwQOGpA3+1q1bevTRR9Xe3q7m5mYdOHBAkjQxMaFAIKCGhgYFAgFNTk4WfFgAuUkb/H333aczZ87owoULisViikQiOnfunHp7e9XZ2anLly+rs7NTvb29xZgXQA7SBu9yuXT//fdLkqanpzU9PS2Xy6X+/n4Fg0FJUjAY1IkTJwo7KYCcZfQcPplMyuv1yu12KxAIaO3atRodHZXH45EkeTwejY2NzbluOByW3++X3+/X9fzNDSALGQVfWVmpWCymq1evanBwUB999FHGVxAKhRSNRhWNRrU06zEB5MOCXqV/4IEHtH79ekUiEdXU1Cgej0uS4vG43G53QQYEkD9pg08kEpqampIkffHFF3r//ffV2Niorq4u9fX1SZL6+vq0ZcuWwk4KIGdpv7U2Ho8rGAwqmUzqyy+/1LZt27R582atW7dO27Zt09GjR/Xggw/qrbfeKsa8AHKQNvi2tjYNDQ3ddf7y5ct1+vTpggwFoDDY0w4whOABQwgeKGM9PT1yu91qaWnJaHmCB8rYrl27FIlEMl6e4IEy1tHRoWXLlmW8fFEPJjm1fLleqaubOZ1IJFRdXV3MEdJipsx8c6ZXLpVwmH8r5v00MjJSlOtJJBLy+/0zp0OhkEKhUNa/r6jBj4+Pzzrt9/sVjUaLOUJazJQZZiqO6urqvN4mNukBQwgeMKSkwefyXKRQmCkzzLQ47NixQ+vWrdOlS5dUW1uro0ePplze5TiOU6TZAJMqXS79V5brNvp8PIcHkB2CBwwheMAQggcMIXjAEIIHDCF4wBCCBwwheMAQggcMIXjAEIIHDCF4wBCCBwwheMAQggcMIXjAEIIHDCF4wBCCBwwheMAQggcMIXjAEIIHDCnqwSQBiwJPPHHXgVQztWLFirzOwpFnAEPYpAcMIXjAEIIHDCF4wBCCBwz5Pzi+cdXvQkfBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "branch_path = '2d/2ph1/b2/b1/b1'\n",
    "db.branch(branch_path)\n",
    "out = db.branch_ev(branch_path,save_children=True,plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root:NXroot\n",
      "  1d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(12)\n",
      "      root2 = int32(12)\n",
      "  2d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      b1:NXgroup\n",
      "        child1 = int32(8x9)\n",
      "        child2 = int32(8x9)\n",
      "        k1 = int32(2x3)\n",
      "        k2 = int32(2x3)\n",
      "        phc = int32(2x2)\n",
      "      b2:NXgroup\n",
      "        b1:NXgroup\n",
      "          b1:NXgroup\n",
      "            b1:NXgroup\n",
      "            child1 = int32(32x24)\n",
      "            child2 = int32(32x24)\n",
      "          child1 = int32(16x12)\n",
      "          child2 = int32(16x12)\n",
      "        child1 = int32(8x6)\n",
      "        child2 = int32(8x6)\n",
      "      fibers:NXgroup\n",
      "        child1 = int32(20x15)\n",
      "        child2 = int32(20x15)\n",
      "        k1 = int32(5x5)\n",
      "        k2 = int32(5x5)\n",
      "        phc = int32(2x2)\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "    3ph1:NXgroup\n",
      "      root1 = int32(4x3)\n",
      "      root2 = int32(4x3)\n",
      "  3d:NXgroup\n",
      "    2ph1:NXgroup\n",
      "      root1 = int32(2x3x4)\n",
      "      root2 = int32(2x3x4)\n"
     ]
    }
   ],
   "source": [
    "db.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}