{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21e032c6",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "#   RUNME.setup\n",
    "#  This helps set up your kernal's environment in order to avoid errors\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from canopyhydro.Cylinder import Cylinder\n",
    "from canopyhydro.CylinderCollection import CylinderCollection\n",
    "from canopyhydro.Forester import Forester\n",
    "\n",
    "# Determines where configuration file is located\n",
    "# file contains directory info and model input settings\n",
    "config_file = os.environ[\n",
    "    \"CANOPYHYDRO_CONFIG\"\n",
    "] = f\"{os.getcwd()}/canopyhydro_config.toml\"\n",
    "log_config = os.environ[\"CANOPYHYDRO_LOG_CONFIG\"] = f\"{os.getcwd()}/logging_config.yml\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d2a621",
   "metadata": {},
   "source": [
    "### Cylinder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc97e998",
   "metadata": {},
   "source": [
    "The Cylinder class is used to represent the 3-D cylinders that make up a QSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca2e3ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cylinder( cyl_id=1.0, x=[3, 6], y=[2, 4], z=[6, 12], radius=2.0, length=0.064433, branch_order=0.0, branch_id=0.0, volume=0.010021, parent_id=0.0, reverse_branch_order=32.0, segment_id=0.0\n"
     ]
    }
   ],
   "source": [
    "# A trivial example of a Cylinder object\n",
    "\n",
    "\n",
    "myCyl = Cylinder(\n",
    "    cyl_id=1.0,\n",
    "    x=[3, 6],\n",
    "    y=[2, 4],\n",
    "    z=[6, 12],\n",
    "    radius=2.0,\n",
    "    length=0.064433,\n",
    "    branch_order=0.0,\n",
    "    branch_id=0.0,\n",
    "    volume=0.010021,\n",
    "    parent_id=0.0,\n",
    "    reverse_branch_order=32.0,\n",
    "    segment_id=0.0,\n",
    ")\n",
    "print(myCyl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694d11bf",
   "metadata": {},
   "source": [
    "The most important function of Cylinder objects is their ability to return data regarding the projections onto planes. Cylinder objects utilize our custom 'geometry' module to calculate their projections onto the XY, XZ and YZ planes. \\\n",
    "For more on this, see [Projecting Cylinders](./projecting_cylinders.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67714844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.4758759656487898, 1.799974739237794 0.4584841440751037, 1.795000925287278 0.4413159569448355, 1.7893164799034582 0.4244146451245806, 1.7829100222420806 0.4078239814963779, 1.7757736613440929 0.3915878424977104, 1.7679032717111878 0.3757497499756175, 1.7592987218199836 0.3603523923999496, 1.7499640466822795 0.3454371359115821, 1.7399075568092086 0.3310435367705419, 1.729141877626351 0.3172088674237017, 1.71768391545405 0.3039676685611681, 1.7070712498977356 0.2929287501022643, 1.7071067811865475 0.2928932188134524, 0.7071067811865476 -0.7071067811865476, 0.7070712498977356 -0.7070712498977356, 0.7055547485197222 -0.7086486608663443))\n",
      "[0.57735027 0.57735027 0.57735027]\n",
      "[-0.57735027 -0.57735027 -0.57735027]\n",
      "0.6154797086703873\n",
      "4.642087600836025\n"
     ]
    }
   ],
   "source": [
    "# Projecting a cylinder onto the XY plane\n",
    "import numpy as np\n",
    "\n",
    "from canopyhydro.Cylinder import Cylinder\n",
    "from canopyhydro.CylinderCollection import CylinderCollection\n",
    "\n",
    "cyl = Cylinder(\n",
    "    1, np.array([0, 1]), np.array([0, 1]), np.array([0, 1]), 1, 1, 0, 0, 1, 0, 0, 0\n",
    ")\n",
    "cyl.get_projection(\"XY\")\n",
    "print(cyl.projected_data[\"XY\"][\"polygon\"])\n",
    "print(cyl.projected_data[\"XY\"][\"base_vector\"])\n",
    "print(cyl.projected_data[\"XY\"][\"anti_vector\"])\n",
    "print(cyl.projected_data[\"XY\"][\"angle\"])\n",
    "print(cyl.projected_data[\"XY\"][\"area\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9cd7c6",
   "metadata": {},
   "source": [
    "The main use of this functionality is shown below. Namely, projecting cylinders allows us to more readily provide visualizations of the tree canopy they represent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a3b2b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:21:30.286 |MainThread   | INFO    | geometry.py:617 -    draw_cylinders_3D() | model - Plotting cylinder in 3D\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:21:30.286 |MainThread   | INFO    | geometry.py:617 -    draw_cylinders_3D() | model - Plotting cylinder in 3D\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we show the 3D view and the 3 possible 2D projections of a Cylinder object\n",
    "myCyl = Cylinder(\n",
    "    cyl_id=1.0,\n",
    "    x=[0, 3],\n",
    "    y=[0, 2],\n",
    "    z=[0, 6],\n",
    "    radius=2.0,\n",
    "    length=0.064433,\n",
    "    branch_order=0.0,\n",
    "    branch_id=0.0,\n",
    "    volume=0.010021,\n",
    "    parent_id=0.0,\n",
    "    reverse_branch_order=32.0,\n",
    "    segment_id=0.0,\n",
    ")\n",
    "\n",
    "\n",
    "fig = myCyl.draw_3D(show=False, draw_projections=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e434f057",
   "metadata": {},
   "source": [
    "In the above 3D representations, the entire surface of cylinder must be calculated, s this may be computationally intensive. \\\n",
    "As such, the 'get_projection' function calculates statistics regarding 2D projections directly instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa5ac7c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'myCyl' as seen from above\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:21:35.784 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:21:35.784 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JzZrFd13uuOMhHHsEEGTbt8d/zToProsXL3YgBqrD9Agg6L7/Pv5rMkkmOKZHALCZuDUdTwFJYEyPAPCT3bvjvyaTZAJiegSAI5m4YRuTZIJhegSAoystjf+aTJIJgukRAI6tvDz+azJJJgCmRwCo3t698V+TSdIgpkcAqLkDB+K/JpOkIUyPABAbEyXJJBlnTI8A4B1MknHE9AgA3sIkGQdMjwBQd/F+AojEJOk6pkcAcMZxx8V/TSZJl+zdK/3610yPAOCUBg3ivyaTpAs++US6+mrp889NJwEA/zBRkkySDrIs6bHHpG7dKEgAcFpaWvzXZJJ0SEmJdP310pw5ppMAgD9lZMR/TUrSAWvXSldcIRUXm04CAP7VqFH812R3ax3NnSt1705BAoDbGjeO/5qUZC1ZljRxoj1B7tljOg0A+F+TJvFfk92ttVBZKd15p/T446aTAEBwNGsW/zUpyRjt2yddd500a5bpJAAQHCkpUlZW/NelJGOwd6905ZXS/PmmkwBAsOTmSqFQ/NelJGuovFzq319atMh0EgAInrw8M+ty4k4N7N1LQQKASa1amVmXkqzGvn3SL35BQQKASa1bm1mXkjyGykpp+HBp3jzTSQAg2Nq0MbMuJRmFZdnPgJw503QSAEB+vpl1Kcko/ud/pCeeMJ0CAJCSIp12mpm1KcmjmDtXGjvWdAoAgCSdcYaZBy5LlOQR1qyxnwVpWaaTAAAkqXNnc2tTkofZtcu+F2tZmekkAICDzj7b3NqU5L9Zlv08yC++MJ0EAHC4bt3MrU1J/tsjj9jHIgEAiSM1VerY0dz6lKSk5culwkLTKQAA/6lrV7soTQl8SZaV2SfqHDhgOgkA4D9deKHZ9QNfkmPHSsXFplMAAI6GkjRo4ULpmWdMpwAAHE1qqnTeeWYzBLYky8qkG280nQIAEM1550kNGpjNENiSvPdeafNm0ykAANFcfrnpBAEtydWr7XuzAgASV79+phMEsCQtS7rtNqmiwnQSAEA0+fnmHo91uMCV5KuvSkuXmk4BADiWgQNNJ7AFqiR//FH6zW9MpwAAVGfwYNMJbIEqyaeekr780nQKAMCxnH661KmT6RS2wJRkSYn04IOmUwAAqjNsmBQKmU5hC0xJPvqo9MMPplMAAKpzzTWmE/wkppKcNGmSOnTooPT0dKWnp6tHjx6aP3++W9kcs3u3NHGi6RQAgOpccIHUqpXpFD+JqSSbN2+uCRMmaOXKlVqxYoUuvvhiDRgwQGvXrnUrnyOefFIKh02nAABU56abTCeoKmRZllWXN8jMzNTDDz+sG264oUZfHw6HlZGRoZKSEqWnp9dl6RopL5datJB27nR9KQBAHWRmSl9/LdWv7/5aNe2ierVdoKKiQq+++qrKysrUo0ePqF8XiUQUiUSqBIunqVMpSADwghtvjE9BxiLmE3dWr16tE044Qampqbrllls0e/ZstW3bNurXFxUVKSMj49CWm5tbp8CxqKyUHnssbssBAGopKUn61a9MpzhSzLtb9+3bpy1btqikpESvvfaapkyZoiVLlkQtyqNNkrm5uXHZ3TpvXmLc+w8AcGxXXSXNmhW/9Wq6u7XOxyT79Omj1q1b69lnn3U0mBP69bOLEgCQ2P7xD6lr1/itV9MuqvN1kpWVlVUmxUSxebPkgatTACDwLrkkvgUZi5hO3CksLFTfvn2Vl5en0tJSzZgxQ4sXL9aCBQvcyldrU6faT/wAACS2e+4xnSC6mEry22+/1XXXXadvvvlGGRkZ6tChgxYsWKBLLrnErXy1UlkpTZ9uOgUAoDq9etk3EEhUMZXk888/71YOR73/PjcyBwAveOAB0wmOzZf3bn35ZdMJAADV6d9fOsZl9gnBdyW5f7/0+uumUwAAjiU5WXroIdMpque7kly8mKd9AECiu+UWKT/fdIrq+a4k//xn0wkAAMeSmSn94Q+mU9SMr0rSsqS5c02nAAAcy4QJdlF6ga9KctUqads20ykAANF07y7V8KFRCcFXJfnWW6YTAACiOe44acoU+2bmXuGhqNVLwBv/AAD+7Z57pHbtTKeIjW9Ksrxc+ugj0ykAAEdz1llSYaHpFLHzTUl++KF9jSQAILHUry+9+KK9u9VrfFOS779vOgEA4Ggefth7u1kP8k1J/u1vphMAAP7TwIFSQYHpFLXni5KsqLAf2AkASBwtW0rPPy+FQqaT1J4vSnLdOqm01HQKAMBB9evbd0Br1Mh0krrxRUmuWGE6AQDgcC+8IHXqZDpF3fmiJD/5xHQCAMBBhYXS0KGmUzjDFyX52WemEwAAJGnwYOmPfzSdwjm+KMm1a00nAACcf759PaSXbjtXHc9/lF27pO3bTacAgGBr31564w37hB0/8XxJrl9vOgEABFvr1tLbb3v/TNaj8XxJFhebTgAAwdWypbRokdSsmekk7vB8SW7aZDoBAATTwYLMyzOdxD2eL8kvvzSdAACC5/TTpaVLpVNOMZ3EXZ4vya++Mp0AAIKlc2f7oRLNm5tO4j7Pl+TXX5tOAADB0bu39N57Una26STx4fmS5PIPAIiP4cOlefOkjAzTSeLH0yVZUSHt3Gk6BQD4WygkFRVJU6dKKSmm08RXPdMB6mLXLsmyTKcAAP/KyJBmzJAuv9x0EjM8XZK7d5tOAAD+1amT9Oqr0qmnmk5ijqd3t4bDphMAgD/deqv04YfBLkjJ45MkD1oGAGc1bixNmSINGGA6SWLw9CRZXm46AQD4x4AB0po1FOThPF2SP/5oOgEAeF+jRtJLL0mzZ0snnWQ6TWLx9O7WfftMJwAAbxswQHrmGalpU9NJEpOnJ8nKStMJAMCbDp8eKcjoPD1JUpIAEDumx5rz9CSZ5On0ABBfTI+x8/QkmZxsOgEAeAPTY+14ehYL2j0EASBWTI914+lJsn590wkAIHExPdadpyfJ4483nQAAEg/To3M8PUmecILpBACQWJgeneXpSTI93XQCAEgMTI/u8PQk2aiR6QQAYB7To3s8PUmeeKL9xGwACCKmR/d5uiSTk6WsLNMpACD+BgyQ/vlPadgwhgU3ebokJSk723QCAIgfpsf4iqkki4qK1LVrV6WlpSk7O1tXXHGF1q1b51a2GsnJMbo8AMQN02P8xVSSS5YsUUFBgZYtW6Z33nlH+/fv16WXXqqysjK38lXr5JONLQ0AccH0aE5MZ7e+9dZbVX49bdo0ZWdna+XKlbrgggscDVZTeXlGlgWAuODMVbPqdAlISUmJJCkzMzPq10QiEUUikUO/DofDdVnyCKec4ujbAUBCaNRIeuIJ6eqr2bVqUq1P3KmsrNTo0aPVs2dPtW/fPurXFRUVKSMj49CWm5tb2yWPqlUrR98OAIzj2GPiCFmWZdXmG2+99VbNnz9fH3zwgZo3bx716442Sebm5qqkpETpDtwy56uvJId7FwCMYHqMn3A4rIyMjGq7qFa7W2+77Ta9+eabWrp06TELUpJSU1OVmppam2VqJCfHvtF5eblrSwCA6zj2mJhi2t1qWZZuu+02zZ49W4sWLVLLli3dylVjSUlSfr7pFABQO5y5mthiKsmCggK99NJLmjFjhtLS0rR9+3Zt375de/fudStfjbRta3R5AKgVjj0mvphKctKkSSopKdFFF12kZs2aHdpmzZrlVr4aOfNMo8sDQEyYHr0jpmOStTzHx3UdO5pOAAA1w7FHb/H8vVslqXNn0wkA4NiYHr3JFyWZnS1Vc5ItABjDsUfv8kVJSlK3bqYTAEBVTI/e55uS7N7ddAIA+AnToz/4piR79DCdAACYHv3GNyXZpYuUkmI6BYAgY3r0H9+UZP360jnnmE4BIIiYHv3LNyUpSb16mU4AIGiYHv3NVyXZu7fpBACCgukxGHxVkt27Sw0bmk4BwO+YHoPDVyWZkiJdfLHpFAD8iukxeHxVkpLUt6/pBAD8iOkxmHxXkv36mU4AwE+YHoPNdyWZlyd16mQ6BQA/YHqE70pSkgYONJ0AgJcxPeIgX5bklVeaTgDAq5gecThflmTbtlJ+vukUALyE6RFH48uSDIWkIUNMpwDgFUyPiMaXJSlJQ4eaTgAg0TE9ojq+Lck2baSuXU2nAJComB5RE74tSUm67jrTCQAkGqZHxMLXJTl0KM+YBPATpkfEytclmZXFNZMAmB5Re74uSUm66SbTCQCYxPSIuvB9SfbqJZ16qukUAOKN6RFO8H1JJiVJv/qV6RQA4onpEU7xfUlK0ogR0vHHm04BwG1Mj3BaIEryxBOl6683nQKAm5ge4YZAlKQkjRrF/ziAHzE9wk2BKcnTT7d/0gTgH0yPcFtgSlKSxo0znQCAE5geES+BKsnu3aWLLjKdAkBdMD0ingJVkpJ0zz2mEwCoDaZHmBC4krz4Yum880ynABCLn/9cWruW6RHxF7iSDIWk++4znQJATRycHufMkZo1M50GQRS4kpTsabJXL9MpABwL0yMSQSBLUpIefNB0AgBHw/SIRBLYkuzeXRo0yHQKAIdjekSiCWxJSlJRkVSvnukUAJgekagCXZKnn84TQgDTmB6RyAJdkpI0fryUmWk6BRA8TI/wgsCXZGam9Mc/mk4BBAvTI7wi8CUpSTffLHXubDoF4H9Mj/AaSlJScrL09NP8RAu4iekRXkRJ/lv37vZECcBZTI/wMkryMBMm8D8x4CSmR3gdJXmYE0+UnnzSdArA+5ge4Rcxl+TSpUvVv39/5eTkKBQKac6cOS7EMmfQIGnwYNMpAO9ieoSfxFySZWVl6tixo5566ik38iSEp56SmjQxnQLwFqZH+FHMN2Xr27ev+vbt60aWhNGkiTR5sjRwoOkkgDf8/OfSM89QjvAf149JRiIRhcPhKpsXXHGFNGKE6RRAYmN6hN+5XpJFRUXKyMg4tOXm5rq9pGMee0xq3dp0CiAxcewRQeB6SRYWFqqkpOTQtnXrVreXdExamjRjBk8KAQ7H9Iggcb0kU1NTlZ6eXmXzkm7d7EdqAWB6RPBwnWQNjBkj9e9vOgVgDtMjgirmHYl79uxRcXHxoV9v2rRJn3zyiTIzM5WXl+douESRlCRNny6ddZa0ebPpNEB8ceYqgizmSXLFihXq3LmzOv/7sRljxoxR586d9fvf/97xcImkUSPpz3+W6tc3nQSID6ZHoBaT5EUXXSTLstzIkvA6d5amTJGuucZ0EsBdTI+AjWOSMRo2TBo71nQKwB1Mj0BVlGQtTJgg9etnOgXgLM5cBY5ESdZCcrL0yivSmWeaTgLUHdMjEB0lWUtpadKbb/KXCryN6RE4NkqyDvLypHnz7MIEvITpEagZSrKOOnWy/6I57jjTSYCaYXoEao6SdMDFF9v3eE3i3yYSGNMjEDv+WnfIL34hPfec6RTA0TE9ArVDSTpo5Ejp8cdNpwB+wvQI1A0l6bDbb5cefth0CoDpEXACJemCsWOlhx4ynQJBxfQIOIfHCbvk//0/+2HNY8aYToIg4Z6rgLOYJF10553SpEns6oL7mB4Bd1CSLrvlFul//9eeKgE3cOwRcA8lGQfDhklvvCE1aGA6CfyE6RFwHyUZJ5dfLr33npSVZToJ/IDpEYgPSjKOzjlHWrZMOvVU00ngVUyPQHxRknF26ql2UV5wgekk8BqmRyD+KEkDsrKkt9+279ADVIfpETCHkjQkNVWaMsW+jV1ysuk0SFRMj4BZlKRBoZB9G7tFi6TsbNNpkEiYHoHEQEkmgAsukFatks47z3QSJAKmRyBxUJIJIifHvkSksJC/GIOK6RFIPJRkAqlXT3rwQWnBAqlpU9NpEE9Mj0BioiQT0CWXSJ99Jg0YYDoJ3Mb0CCQ2SjJBNWkizZ4tPf+8lJZmOg3cwPQIJD5KMoGFQva1lGvWSJdeajoNnML0CHgHJekBeXnSW29J06ZJmZmm06AumB4Bb6EkPSIUkoYPlz7/XLr2WtNpECumR8CbKEmPyc6WXnxRWrJEat/edBrUBNMj4F2UpEcdvAHBE0+wCzZRMT0C3kdJeli9etJtt0nFxdKYMVJKiulEOOjKK+1d40yPgLdRkj7QqJH0yCPSunXSddfxl7JJubnSG29Ir70mnXSS6TQA6oqS9JFTTpGmT7cvGbnqKtNpgiU1VbrnHnt6/PnPTacB4BRK0ofatpVmzbLLcuhQKYk/ZVf98pfSv/4l3X+/1LCh6TQAnMRfnz7Wrp00Y4a0YYN0661SgwamE/nLZZdJK1ZIr7xiT/EA/IeSDIBWraSnn5a2bJEeeEA6+WTTibztssukDz+0b/Bw9tmm0wBwEyUZII0bS3ffLW3aJL3+utSnj+lE3pGcLA0ZIq1caZdjjx6mEwGIB0oygI47Tho0SHrnHWnjRvuEk7w806kSU+PG0rhx9g8WM2dKZ51lOhGAeApZlmXFc8FwOKyMjAyVlJQoPT09nkvjGCorpfffl15+2Z4yf/jBdCJzQiF7yh45Uho40D5zFYC/1LSLKEkcYf9+adEi+1Fdb7whbd9uOlF8nH22vUv16qs5bgv4HSUJR1RWSh9/LP31r9KCBdLf/26/5gf16kk9e9oPtx4wwD7BCUAwUJJwRUmJfXP1pUvtbdUq6cAB06lq7owzpIsvlnr3tv+ZkWE6EQATatpF9eKYCT6QkWHfUebgXWXKy+0zPlessLdPP7UvrK+oMJtTkpo0kTp1krp1s7dzz7VPxAGAmqIkUSfHHy+df769HRSJ2Dcw+Pxz+5/FxdKXX9rb119LP/7o3Prp6faZuXl50qmn2lvbtvbWtCn3sQVQN5QkHJeaaj/r8mjPu7Qsafdu6dtvpe+/l3btksJhqbTULs9IxJ5CKyvtaxOTk+33a9BASkuzSzEry96aNuU2cADcVauSfOqpp/Twww9r+/bt6tixo5544gl169bN6WzwoVDIfmpJo0amkwBA9WK+mcCsWbM0ZswYjR8/Xh9//LE6duyoyy67TN9++60b+QAAMCbmkpw4caJuuukmjRgxQm3bttUzzzyj448/Xi+88IIb+QAAMCamkty3b59WrlypPofd9DMpKUl9+vTRRx99dNTviUQiCofDVTYAALwgppLcuXOnKioqdNJ/PHL9pJNO0vYot2UpKipSRkbGoS03N7f2aQEAiCPXb3BeWFiokpKSQ9vWrVvdXhIAAEfEdHZr48aNlZycrB07dlR5fceOHWratOlRvyc1NVWp3CEaAOBBMU2SKSkpOvvss7Vw4cJDr1VWVmrhwoXqwQP2AAA+E/N1kmPGjNHw4cPVpUsXdevWTY8++qjKyso0YsQIN/IBAGBMzCU5ZMgQfffdd/r973+v7du3q1OnTnrrrbeOOJkHAACv4ykgAIDAqWkXuX52KwAAXkVJAgAQBSUJAEAUlCQAAFHE/XmSB88T4h6uAABTDnZQdeeuxr0kS0tLJYl7uAIAjCstLVVGRkbU34/7JSCVlZXatm2b0tLSFAqFjvj9cDis3Nxcbd261beXiAThM0rB+JxB+IwSn9NPgvAZpeo/p2VZKi0tVU5OjpKSoh95jPskmZSUpObNm1f7denp6b7+A5SC8RmlYHzOIHxGic/pJ0H4jNKxP+exJsiDOHEHAIAoKEkAAKJIuJJMTU3V+PHjff14rSB8RikYnzMIn1Hic/pJED6j5NznjPuJOwAAeEXCTZIAACQKShIAgCgoSQAAoqAkAQCIImFKcunSperfv79ycnIUCoU0Z84c05EcV1RUpK5duyotLU3Z2dm64oortG7dOtOxHDVp0iR16NDh0AW8PXr00Pz5803Hct2ECRMUCoU0evRo01Ecde+99yoUClXZ8vPzTcdy3Ndff61rrrlGWVlZatCggc4880ytWLHCdCxHnXLKKUf8WYZCIRUUFJiO5piKigr97ne/U8uWLdWgQQO1bt1a999/f7X3Zz2WuN9xJ5qysjJ17NhRI0eO1KBBg0zHccWSJUtUUFCgrl276sCBA7r77rt16aWX6p///KcaNmxoOp4jmjdvrgkTJui0006TZVmaPn26BgwYoFWrVqldu3am47li+fLlevbZZ9WhQwfTUVzRrl07vfvuu4d+Xa9ewvy14Yhdu3apZ8+e6tWrl+bPn68mTZpow4YNatSokelojlq+fLkqKioO/XrNmjW65JJLNHjwYIOpnPWnP/1JkyZN0vTp09WuXTutWLFCI0aMUEZGhu64447avamVgCRZs2fPNh3Ddd9++60lyVqyZInpKK5q1KiRNWXKFNMxXFFaWmqddtpp1jvvvGNdeOGF1qhRo0xHctT48eOtjh07mo7hqnHjxlnnnXee6RhxN2rUKKt169ZWZWWl6SiO6devnzVy5Mgqrw0aNMgaNmxYrd8zYXa3BlFJSYkkKTMz03ASd1RUVGjmzJkqKytTjx49TMdxRUFBgfr166c+ffqYjuKaDRs2KCcnR61atdKwYcO0ZcsW05EcNXfuXHXp0kWDBw9Wdna2OnfurOeee850LFft27dPL730kkaOHHnUB0141bnnnquFCxdq/fr1kqRPP/1UH3zwgfr27Vvr9/TXfhMPqays1OjRo9WzZ0+1b9/edBxHrV69Wj169NCPP/6oE044QbNnz1bbtm1Nx3LczJkz9fHHH2v58uWmo7jmnHPO0bRp09SmTRt98803uu+++3T++edrzZo1SktLMx3PERs3btSkSZM0ZswY3X333Vq+fLnuuOMOpaSkaPjw4abjuWLOnDnavXu3rr/+etNRHHXXXXcpHA4rPz9fycnJqqio0AMPPKBhw4bV/k3rOt66QQHY3XrLLbdYLVq0sLZu3Wo6iuMikYi1YcMGa8WKFdZdd91lNW7c2Fq7dq3pWI7asmWLlZ2dbX366aeHXvPj7tb/tGvXLis9Pd1Xu8+PO+44q0ePHlVeu/32263u3bsbSuS+Sy+91PrZz35mOobjXnnlFat58+bWK6+8Yn322WfWiy++aGVmZlrTpk2r9XtSkgYUFBRYzZs3tzZu3Gg6Slz07t3buvnmm03HcNTs2bMtSVZycvKhTZIVCoWs5ORk68CBA6YjuqZLly7WXXfdZTqGY/Ly8qwbbrihymtPP/20lZOTYyiRuzZv3mwlJSVZc+bMMR3Fcc2bN7eefPLJKq/df//9Vps2bWr9nuxujSPLsnT77bdr9uzZWrx4sVq2bGk6UlxUVlYqEomYjuGo3r17a/Xq1VVeGzFihPLz8zVu3DglJycbSuauPXv26IsvvtC1115rOopjevbsecSlWOvXr1eLFi0MJXLX1KlTlZ2drX79+pmO4rjy8vIjHqCcnJysysrKWr9nwpTknj17VFxcfOjXmzZt0ieffKLMzEzl5eUZTOacgoICzZgxQ2+88YbS0tK0fft2SfaDPxs0aGA4nTMKCwvVt29f5eXlqbS0VDNmzNDixYu1YMEC09EclZaWdsSx5IYNGyorK8tXx5jHjh2r/v37q0WLFtq2bZvGjx+v5ORkDR061HQ0x9x5550699xz9eCDD+qqq67SP/7xD02ePFmTJ082Hc1xlZWVmjp1qoYPH+67S3kkqX///nrggQeUl5endu3aadWqVZo4caJGjhxZ+zet43TrmPfee8+SdMQ2fPhw09Ecc7TPJ8maOnWq6WiOGTlypNWiRQsrJSXFatKkidW7d2/r7bffNh0rLvx4THLIkCFWs2bNrJSUFOvkk0+2hgwZYhUXF5uO5bi//OUvVvv27a3U1FQrPz/fmjx5sulIrliwYIElyVq3bp3pKK4Ih8PWqFGjrLy8PKt+/fpWq1atrN/+9rdWJBKp9XvyqCwAAKLgOkkAAKKgJAEAiIKSBAAgCkoSAIAoKEkAAKKgJAEAiIKSBAAgCkoSAIAoKEkAAKKgJAEAiIKSBAAgCkoSAIAo/j/hcunGy2+FOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'myCyl' as seen from above\n",
      "The 'XY' projection of myCyl as an area of 25.19 cm^3\n",
      "       and the cylinder makes an angle of 1.03 radians with the XY plane\n",
      "'myCyl' as seen from the 'fromt' of the tree\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:21:36.093 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:21:36.093 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 'XZ' projection of myCyl as an area of 30.42 cm^3\n",
      "       and the cylinder makes an angle of 0.29 radians with the XZ plane\n",
      "'myCyl' as seen from one 'side' of the tree\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:21:36.325 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:21:36.325 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 'YZ' projection of myCyl as an area of 30.68 cm^3\n",
      "       and the cylinder makes an angle of 0.44 radians with the YZ plane\n"
     ]
    }
   ],
   "source": [
    "# The get_projection function allows for the retrieval of\n",
    "# projection data without the need to define the entire surface of the cylinder\n",
    "\n",
    "\n",
    "myCyl = Cylinder(\n",
    "    cyl_id=1.0,\n",
    "    x=[3, 6],\n",
    "    y=[2, 4],\n",
    "    z=[6, 12],\n",
    "    radius=2.0,\n",
    "    length=0.064433,\n",
    "    branch_order=0.0,\n",
    "    branch_id=0.0,\n",
    "    volume=0.010021,\n",
    "    parent_id=0.0,\n",
    "    reverse_branch_order=32.0,\n",
    "    segment_id=0.0,\n",
    ")\n",
    "\n",
    "print(\"'myCyl' as seen from above\")\n",
    "myCyl.get_projection(\"XY\")\n",
    "myCyl.draw(plane=\"XY\")\n",
    "plt.show()\n",
    "print(\"'myCyl' as seen from above\")\n",
    "print(\n",
    "    f\"The 'XY' projection of myCyl as an area of {round(myCyl.projected_data['XY']['area'],2)} cm^3\"\n",
    ")\n",
    "print(\n",
    "    f\"       and the cylinder makes an angle of {round(myCyl.projected_data['XY']['angle'],2)} radians with the XY plane\"\n",
    ")\n",
    "\n",
    "\n",
    "print(\"'myCyl' as seen from the 'fromt' of the tree\")\n",
    "myCyl.get_projection(\"XZ\")\n",
    "myCyl.draw(plane=\"XZ\")\n",
    "plt.show()\n",
    "print(\n",
    "    f\"The 'XZ' projection of myCyl as an area of {round(myCyl.projected_data['XZ']['area'],2)} cm^3\"\n",
    ")\n",
    "print(\n",
    "    f\"       and the cylinder makes an angle of {round(myCyl.projected_data['XZ']['angle'],2)} radians with the XZ plane\"\n",
    ")\n",
    "\n",
    "print(\"'myCyl' as seen from one 'side' of the tree\")\n",
    "myCyl.get_projection(\"YZ\")\n",
    "myCyl.draw(plane=\"YZ\")\n",
    "plt.show()\n",
    "print(\n",
    "    f\"The 'YZ' projection of myCyl as an area of {round(myCyl.projected_data['YZ']['area'],2)} cm^3\"\n",
    ")\n",
    "print(\n",
    "    f\"       and the cylinder makes an angle of {round(myCyl.projected_data['YZ']['angle'],2)} radians with the YZ plane\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54814c6",
   "metadata": {},
   "source": [
    "For more information regarding these 2D projections, see [Projecting Cylinders](projecting_cylinders.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdc5c70",
   "metadata": {},
   "source": [
    "### Cylinder Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ea874e",
   "metadata": {},
   "source": [
    "Cylinder Collections are just as they sound and, at the most basic level, a Cylinder Collection is defined as a list of 1 or more Cylinder objects. \\\n",
    "Cylinder Collections almost always represent QSM's (or parts of a QSM), and are meant to help users explore their QSMs. \\\n",
    "Below, we demonstrate how one might initialize a cylinder collection using cylinder data (e.g. QSM data) stored in a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65608029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:11.877 |MainThread   | INFO    | CylinderCollection.py:295 -             from_csv() | model - Processing &lt;_io.TextIOWrapper name='./data/input/5_SmallTree.csv' mode='r' encoding='UTF-8'&gt;\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:11.877 |MainThread   | INFO    | CylinderCollection.py:295 -             from_csv() | model - Processing <_io.TextIOWrapper name='./data/input/5_SmallTree.csv' mode='r' encoding='UTF-8'>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}\n",
      "{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}\n",
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       "2024.10.14 18:34:11.958 |MainThread   | INFO    | CylinderCollection.py:323 -             from_csv() | model - ./data/input/5_SmallTree.csv initialized with 517 cylinders\n"
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       "2024.10.14 18:34:11.966 |MainThread   | INFO    | CylinderCollection.py:338 -    project_cylinders() | model - Projection into XY axis begun for file 5_SmallTree.csv\n"
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       "2024.10.14 18:34:14.668 |MainThread   | INFO    | CylinderCollection.py:347 -    project_cylinders() | model - Projection into XY axis complete for file 5_SmallTree.csv\n"
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       "2024.10.14 18:34:14.671 |MainThread   | WARNING | CylinderCollection.py:497 -        get_end_nodes() | model - Graph not initialized, running initialize_graph_from with default values\n"
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       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:15.260 |MainThread   | INFO    | CylinderCollection.py:774 - find_flow_components() | model - 5_SmallTree.csv found to have 70 drip components\n"
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      "reached_End of find flows\n"
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       "</pre>\n"
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       "2024.10.14 18:34:15.269 |MainThread   | INFO    | CylinderCollection.py:845 -      calculate_flows() | model - np flow chars [Flow(num_cylinders=216.0, projected_area=16.51706021532694, surface_area=19.495074988550606, angle_sum=180.03288665020426, volume=3.9062960000000007, sa_to_vol=83646.49652248439, drip_node_id=0.0, drip_node_loc=(-0.299115, 2.537844, -0.598273)), None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]\n"
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       "</pre>\n"
      ],
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       "2024.10.14 18:34:15.274 |MainThread   | INFO    | CylinderCollection.py:846 -      calculate_flows() | model -  flow chars []\n"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:15.277 |MainThread   | INFO    | CylinderCollection.py:848 -      calculate_flows() | model -  togetha [Flow(num_cylinders=216.0, projected_area=16.51706021532694, surface_area=19.495074988550606, angle_sum=180.03288665020426, volume=3.9062960000000007, sa_to_vol=83646.49652248439, drip_node_id=0.0, drip_node_loc=(-0.299115, 2.537844, -0.598273))]\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:15.277 |MainThread   | INFO    | CylinderCollection.py:848 -      calculate_flows() | model -  togetha [Flow(num_cylinders=216.0, projected_area=16.51706021532694, surface_area=19.495074988550606, angle_sum=180.03288665020426, volume=3.9062960000000007, sa_to_vol=83646.49652248439, drip_node_id=0.0, drip_node_loc=(-0.299115, 2.537844, -0.598273))]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:15.454 |MainThread   | INFO    | CylinderCollection.py:1003 -           statistics() | model - Found hull alpha shape stats\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:15.454 |MainThread   | INFO    | CylinderCollection.py:1003 -           statistics() | model - Found hull alpha shape stats\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:15.914 |MainThread   | INFO    | CylinderCollection.py:1023 -           statistics() | model - found projected areas\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:15.914 |MainThread   | INFO    | CylinderCollection.py:1023 -           statistics() | model - found projected areas\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:16.689 |MainThread   | INFO    | utils.py:150 -            save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:16.689 |MainThread   | INFO    | utils.py:150 -            save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:16.696 |MainThread   | INFO    | utils.py:177 -            save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:16.696 |MainThread   | INFO    | utils.py:177 -            save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:16.703 |MainThread   | INFO    | CylinderCollection.py:1003 -           statistics() | model - Found hull alpha shape stats\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:16.703 |MainThread   | INFO    | CylinderCollection.py:1003 -           statistics() | model - Found hull alpha shape stats\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:17.101 |MainThread   | INFO    | CylinderCollection.py:1023 -           statistics() | model - found projected areas\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:17.101 |MainThread   | INFO    | CylinderCollection.py:1023 -           statistics() | model - found projected areas\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:17.737 |MainThread   | INFO    | utils.py:150 -            save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:17.737 |MainThread   | INFO    | utils.py:150 -            save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 18:34:17.740 |MainThread   | INFO    | utils.py:177 -            save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 18:34:17.740 |MainThread   | INFO    | utils.py:177 -            save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'./data/output//statistics/5_SmallTree__statistics.csv'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example showing the most basic possible cylinder Collection4\n",
    "myCollection = CylinderCollection()\n",
    "# The below file is one of our several testing files, featuring only\n",
    "# the trunk of a tree and one of its branches\n",
    "myCollection.from_csv(\"5_SmallTree.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11ba84a",
   "metadata": {},
   "source": [
    "\n",
    "The below demonstrates how these projection and drawing functionality of the cylinder class can be extended to allow for drawing, highlighting and filtering entire tree canopies. \\\n",
    "We recommend sticking to 2D drawings when exploring your data as this is a lot less computationally intensive than the alternative. However the same '.draw' function can generate 3D plots of cylinder collections!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4383de18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:00.333 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:00.333 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:00.339 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:00.339 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZVcU1PkVx1OcfPBh4+mm+f/99vj90CPj+e3qoyZNdC7pu3cqF9Ycecj7evz+XEX74gXM+tZEG9kLA8Oij9DJhYYwyJiVxzuYpX7Gigh4rO9vz4vZXX3EB/Oab+Xn7kNFgYPZJVBQzSubMcfRu8wZpYC/0OGw2RiO9aXyYnMziPe6EpigMmtizUOLjXcVkz5ipr6cHVRspiyAEBLt2da8keHy8I5+xpoZDy7w84O9/5760N9+kZ/RUectkYtDEbOa6opqI2ATdKS9nGtWNN3r/3SuuYNCkuprrbIMGMfBx771cizMYWGXrgw8cuZJ2Tp9my6r/+z/O89SOSorYBF05dQp47jngiSe6t5fMaOROhc8/53JAUpJrXZTwcKZ/daye3NhIUYaHc1vPyZPqNrgXsQm6sWUL07B+9zvvN4rm59Mbrl1LEYWHAzfd5HmvXFwcexhUVDiOpaYyV9LOjBlcOsjP9/IP6SISjRQ0p7KSycDjx3Po6Gn41twMvPMOBdCnD9fJbDZ6n4MHOVwsKmK61sKFzt8tKWF2SMedCrm5juKte/YwP7PjXPHkSQ5JO3ZKdYc396uITdAMm427o8+cYc80ewFWReH+t61bOY8yGjkUtFhYSbl/f+ZR1tRQZEOG8H2vXlyLq6hwrbhcU8N1u44VtsrKuC5n92hffMHlhREjnD9nsXBxvbqaovVUcUtC/0LAsWcP8PjjvKkzMhiWt9cBMRg4pJs+3XOuYp8+LD3XLscdL77I3ddDhzLI0n6zSP/+3LHQkfR0ervVq7kkMGkS53uK4uzhjEYKOyeHlcL8Ud5OxCaoisUCLFpE7/OXv3SvJLk7du/mroSaGmb6r1pF4QwdynmbweC5+01iIhe58/I4TDQYuKP86FHal5bG4eatt7oGW3xBxCaoxtGj3CX961/7f1f0unUM7aenU2x33cU9bAMHcs9cSkrn8y2LhXM+d7vPd+/mMNMuNH8tCYjYBFX49785VHz5Zefhnb+4+GLnasgAM0o2bGDUMS7OvWfbuZNBlaQkz/8AWlo6L9fXXST0L/iVmhrgzjsZDXzvPf8Lrb6eIX93tfnDw1nnZNgwCr221vn8jh30ZBdfTKF5qnpssTi6nNorNPsD8WyC3zhzhovETzzRvaTezrDZWEvSaORygacFcIuFu8BTUpj3+OabDLykpHC4OWoUh5nth4/2Rht2Wlo4hGxqYhJz+205viBiE/zGwoXAY495J7SSEkYn22OzuQ7lysqYUuWuAJHNxnlWTQ2Hjldd5fhuSgpTsr7/nt7OYmEV5O3b+dlz59jLLSkJ+J//4dJDVRU/99FHFJrM2YSA4qmnKBpvu3oePszv2Td3NjZSDNXVDHA88ABrhlx4IT2VHZuNe9/q6iiGyy931Dhpj8HAIaPBADz/PL3V3LmuOwV27QKWL2fQJTER+OtfgQkT/JsvKWITfMJmA/7f/+Ow7sknu/87cXFcgysuBh5+mAvXra3An/7EeZrBwMz8774Djhzh+yuu6FpRVUVhNsjw4Z4/P3as8wL4jBn0cJ9+yvU3e3ccXxCxCd2moQG45x4u/N5xh/ffb5+7dOQIh3U33eQ4Fh7uqO9os7EFcHj4+RsmtsfumXr3dtT57yqJic7tg31FxCZ4RUUFAxUbNzJr4447uic0q5WBin79uIA8bpz75oV2jEbvb3xFcQj6ssu8t9HfiNiELtHSwpZL337LMnSLF3NYVlnJlr+RkUwsbl/78fvvHY3uAUfBnk8/ZbTvoovoHWfPVmcvWWUl197OnvX/b3cHEZtwXr7+muW7b78dWLLEOeyenMyhn8XCxWKzmceNRgYd7N7K7mEuuIB9A86dYznxvn3Vs/vIEZY+V/Ma3iBiEzxitbIQ6ubNzD3sGKJvj8nESF57evVitauOBa9HjXLsG7N3wbFYGP3zVL6gOwwfHjhCA0RsggdOnGBdxmnTOLfqSveZjsTEMNTurrp8r17O6VaKwkpYO3dyfuUuQ8Rb+vf3/Tf8iYhNcGHDBobzV6zwranGuXNdF6nB4LjWe+9xaKpWb2u9ELEJTuzYwXSrTz7xvXBpa6uz2Pbt48bNPn3oydLTubbWMThy443M3rjlFt+uH2iI2IQ2vvkG+NWvGF30R4Vgg4GCs+chlpU5+rMpCte9vv2WicHtiYjg/G3LFgZRggXJ+hcAcEvM9ddzKLdyJXMcf/Mbhua7y4QJ3GMGsK5H+/bqBgM9m6fmtIMGcb7X2ur+fMeM/p6AeLYQZ/9+4Gc/o6fJy3OuxVFZyfW0Z5/t3vwpIsLRdLCoyNENtKtccw29W/sKWHaOHWM6VU9qjiSeLYQpKOAw7Re/YBumjkVvkpKY9b5gAbNFusOIEVzI7s5mzN69WVeyqcn13CWXcP2vpaV7dumBiC1EWbeONTbee4+Z9Z6YMoVFVH/72+7d2BkZHKIOGuRakbgrxMTwn8L69a6teYcOdZQe7wnIMDLIsdlY937LFt7wSUnMon/pJeDjj7sW2u/ThzU+3noL6E4D2ZkzOQz97DO+twdAurLgbDSymvHZs4yQXnQRd2IDXL/rSm/skhJ6SX+s3fmCiC2IKS2lOKxWDgctFgYqwsKYSOxNebYpU7jn7K67vMtjtFgorvBwtnYCWHx1xw6eAxgoaZ9DWVnJMnITJjiORUZy7a2wkMPKzEz+Zmsr/053W2AUhRtH4+I6T3LWChFbEGK1Mml4xQoGOH796+7V0W+P0chAxQcfuK5/1dYCjzxCMdlD+3Y++4yiKizkDa8ofB8dzfmiu1JxSUk8X1jI/MrmZmDMGNYzmTiRi+55edwu07cvlxQ6iq2+nkPl6dMDJ5NExBZkfP45xRUWxhvd253TnXHTTexzfcMNjoDHoUPAq6+yJuSHH1KMN9/MHdf2cnODBlEc7atZ1dTQ1unT+b66mpFRe580q5UeNDycmzffe4+JxS0trAsZF8d1wTNnmPw8ahTFaLWyIOyJE/4taeAPRGxBgqJwbey997hjWo3G7EYjcNttvM7vf8/w+yuv0IuaTNwVsHQpy4g3NNADGo2OyscWC+eOl17KHMht2xi+DwvjIvro0a6L6Xv2MJr50EP83KZNDPcfO0bRXXABt+ycOMEF8l69KO72m1ADBRFbENDczKTho0fpLdTsD33JJZzv/exnHALecQd7UpeVUWAXXUSPtny54zuVlRTEpk30XFu3cngbEcFkZE+ViwEmJdfVOdK+pk9nKteUKfy9W25hkOfQIe+6luqBiK2Hc+AASxMMGsSbT43iou2Ji+OetjVreKPbbKwbkpnJeRbAcHxrK+dW1dWcMy1b5sj+/9OfKD6AO7/PFyWcOtX5fU4Oa/WnpzvmkBs2sCjQwIH++1v9jYith3LgAIeLGzcyOLFoke9BkK7Sq5dDWEaj67xw1y4+z5rlfn/aHXewr9o993Tv+gYDBbZxIz15aSmHoCtWsN4/wCFmx0V6vRGx9SBqarg29tZbzKD/7/9meW/7jR8oZGfTy3jaCFpRweFg+53c3tKrF6OVV13Filvp6YxMTprENbmKCg4/A6nrmIgtgKmp4Q25axezKHbuZERv/nwmDbev96E19fWez914I3Mhv/ySJRDaY7UyuHHZZZxjti/17Q2HDnHoOnkyh5Uvv8yoZH4+1+SmTmU619ix+i9m25FmiAFGRQUbqm/ZwrD2mDG8Ya66iom57rqu6EFFBRsJ3nCDZ5tKSvi47jrHsQ8/5Hpdnz4Uw8CBXFfzhqIiRjJHj2bJhiFDKF57BS2LhRWST53i/HHcOAZu1ECaIfZgXn2VN9NnnzEtSat5mLckJ7Pw6SefcLg4frxjqaG0lBHIiRM5jPvgA37WYOCWmk8/pQBjYymIropt715H/ciaGkZd+/blGts33ziyY8LCHOt3X3/N650545jP6YV4tgBj4kSWdnv0Ub0t6ToVFfwHAVBcLS2Omx3g8O7TT7lGBzB0n5DAYXB8vOduox354gvOw4YNYxKywcDF8x076PXt7NnD46dPc0hrsTA/tHdv4Oc/98uf3IY396vX/zcLCgqQk5ODlJQUGAwGrFmzxun8448/jhEjRiAqKgrx8fHIysrCjh07vL1MSFJdzRsnJ0dvSzrn3Dng/feZFZKXR+9hszkShtu3iVIUBjIGD+b7I0foFWtr6f28GUJOmsTgS0aGw4tGRdFrtd9keuml3Glgr5xsMnGb0NSp9LJ6uRevxdbY2IjMzEysXLnS7fkf/ehHWLFiBfbt24dt27YhPT0dM2bMwA8//OCzscHOJ59w/tG+t3OgUV/PNa5Zs7iIbLNRfEYj8K9/MYBz4ABFd/QoRTl6NHdp79nDIeC4cZyrHTnCfy6+MmsWRW+nro5C67hYnpFB++y1LbXGp2GkwWBAbm4uZs+e7fEzdje7ceNGTG8/tjjP50NxGHnDDfQAL76otyWunDnjaAhv7woD0EPt2cNM/O++Y4aJ1cr1r4ED+c9jwAAOFxMSGIk8epR9q59/Hvjb3/wT9Nm1i9dJTaXo58zRpjpXwARIWlpa8Ne//hWxsbHIzMxU81I9nupqbpDctElvS9wTH+8+HSoujrUlAeeGF+npDFokJzvvmUtMpGf7/HMK9IUXWOvEV8aOpRfdvTtwy+CpIra1a9fitttuQ1NTE5KTk7Fhwwb097DPwWKxwGLf2AT+pwhF3niDCbZTpuhtiX8YOZKPigo2zujbl39b794M24eFcXg5eTJTvy65hAvhvjBpEvNEA6kKshOKDwBQcnNzXY43NDQohw4dUgoLC5X58+cr6enpSlVVldvfWLJkiQLA5WE2m30xrUdhtSrKsGGK8uc/622JejQ3K8rGjYryhz8oSl6e6/miIkU5elR7u3zFbDZ3+X5Vfc4GAMOGDcP8+fOxePFil3PuPFtqampIzdl27eJ/5dJS53Jvwcj69dyVvX8/h3pRUY7uoePHs4VUTyJg5mx2bDabk6DaYzKZYOpOvk4Q8c47jibroUB6Oh8tLdyW402Dwp6M12JraGhASUlJ2/vS0lIUFxcjISEB/fr1w9KlS3HjjTciOTkZNTU1WLlyJU6cOIFbb73Vr4YHCzYba3ssXaq3JdoTERE6QgO6IbZdu3Zhmj38BGDRokUAgHnz5uHll1/GgQMH8Prrr6Ompgb9+vXDuHHj8Pnnn+NiT6VvQ5xVqxiJPM9IXAgCJF1LR8rKuBdszhxmrYcCihJYdUF8RdV0LcE/NDezTsiwYSyWEyoEk9C8RbL+deKhh5hJUVTUvf1cQs9DxKYD33/PoMjq1c5Ju0JwI8NIHTAYmNAbKDuIBW0QsenA0KFczC0u1tsSQUtEbDpgNDIvcO9evS0RtETEphOXXiqeLdQQselAbS2FFhurtyWClojYNGb/fu7n2r7dv00vhMBHQv8acuQIsHAhcPw496/97Gd6WyRoiYhNQxYt4k5se2k3IbSQYaRGrF/PAqU//7kILVQRsWlAaysbFMbEAH/4g97WCHohw0gNWLGC5d2efTawWxoJ6iKeTWVqath1JjWVLXKF0EXEpjLffsvyapddFpjl1QTtELGpTEUFnyXpWBCxqczJk3xOTtbXDkF/RGwqc+QIC9t4KC4mhBAiNpUZOpQl28Ik7hvyiNhUZuhQPh8+rK8dgv6I2FRGxCbYEbGpjL3GSFmZrmYIAYCITWUKCvhs77wphC4iNpV57TU+33OPrmYIAYCITUXq6tisvU8fQFodCBKQVpFt29iZMysLiI7W2xpBb8Szqci337JpxogRelsiBAIiNhX5/ns+Dx+urx1CYCBiU5GDB/n8ox/pa4cQGIjYVKSqipWPhw3T2xIhEBCxqYTVChw6xCTkqCi9rRECARGbSpw5wxa+AwbobYkQKIjYVMLedry+Xl87hMBBxKYSiYl8DqUG7ULniNhUwi6206f1tUMIHERsKlFYyGcZRgp2RGwq8Y9/8HnuXH3tEAIHEZsKnDnDev4Ay40LAiBiU4XnnmOBn8xMaQslOBCx+ZnKSpYbDwsDlixhs3pBAERsfkVRgHvvZQTyjjukW43gjIjNj7z6KrB2LeuO/OUvelsjBBoiNj/y4otAZCRFJ/2yhY54LbaCggLk5OQgJSUFBoMBa9asaTvX2tqKRx99FKNHj0ZUVBRSUlJw55134qS9BncQ09QEFBVxR3ZWlt7WCIGI12JrbGxEZmYmVq5c6XKuqakJRUVFeOyxx1BUVIQPPvgABw8exI033ugXYwMZe6k6e51IQeiI1zVIsrOzkZ2d7fZcbGwsNmzY4HRsxYoVGD9+PI4dO4a0tLTuWdkDKCri84UX6muHELioXvDHbDbDYDAgLi7O7XmLxQJLu64TdXV1apukCh9/zGdpDSV4QtUASXNzMx599FHcfvvtiImJcfuZZcuWITY2tu2Rai8h3IOoqADWrAF69wYeflhva4RARTWxtba2Ys6cOVAUBS+99JLHzy1evBhms7ntUV5erpZJqvHCC+xUc9ddsllU8Iwqw0i70I4ePYrNmzd79GoAYDKZYDKZ1DBDM959l17tV7/S2xIhkPG7Z7ML7dChQ9i4cSP69evn70sEFNXVbHg4cCAwcqTe1giBjNeeraGhASX2Pf8ASktLUVxcjISEBCQnJ+PHP/4xioqKsHbtWlitVlRWVgIAEhISEBER4T/LA4SvvuLzuHH62iH0ABQvyc/PVwC4PObNm6eUlpa6PQdAyc/P79Lvm81mBYBiNpu9NU0XfvtbRQEU5amn9LZE0ANv7levPdvVV18NRVE6E6+3P9mjycvjQvaVV+ptiRDoSG6kD+zZA+zYAbS2itiE8yNi8wH7isZ99wG9eulrixD4iNi6idkMvPUWEB4upQ+EriFi6yZPP83hY3Y2kJSktzVCT0DE1k1WraLYBg7U2xKhpyBi6waVlcyHBIApU/S1Reg5iNi6wZ49jteXXaafHULPQnpqd4OCAlbN6tVLWvgKXUc8Wzc4dYqVtIYMYTRSELqCiK0b2EsgSEdRwRtEbN3g6FE+Dx6srx1Cz0LE5iU2m0Ns6em6miL0MCRA4iVHjwJ9+nBHdhDXLxJUQMTmJXv3skvNmTMS9he8Q4aRXmJfY4uKkgCJ4B3i2bxk927Whhw1CjDKvyrBC+R28YLWVmDLFtYckXojgreI2Lxg1y6gsZGvp03T1xah5yFi84L8fD6HhQGTJ+tri9DzELF5webNfJ4wgQESQfAGEVsXqa/nfA0ArrpKV1OEHoqIrYu8/DJgtfL1FVfoa4vQM5HQ/3lobmYn0cWL+X7qVCAnR1+bhJ6JiM0DjY3AK68Azzzj2JUdHg4sXcq9bILgLTKM9MDq1Wz/VFEB9O0LPPooUF4OTJqkt2VCT0U8mweeeYbPN93EYWRCgr72CD0f8WwesO9VGzNGhCb4BxGbBwwGZvVL/qPgL+RW8sB33zHD/z8drwTBZ0RsbrDZgOPH+boHtvgWAhQRmxt++IE9sgERm+A/RGxusHs1QEofCP5DxOaGU6f4HBEBxMfra4sQPIjY3GDfs9bSAsTE6GuLEDzIorYbzGYgOpqJx5GRelsjBAsiNjfU1nJLjdEIxMbqbY0QLMgw0g32xOPERO7KFgR/IGJzw8mTfE5O1tcOIbgQsbnB7tlSUvS1QwguRGweMBqBs2f1tkIIJkRsbrjuOqZs7dgB1NTobY0QLIjY3DB1Kp8bGoCnntLXFiF4ELG5Ydw44Mc/5usVK7hDWxB8RcTmgSef5LzNYgF+9zu9rRGCAa/FVlBQgJycHKSkpMBgMGDNmjVO5z/44APMmDED/fr1g8FgQHFxsZ9M1ZYRI4ChQ9mLrbQU2L9fb4uEno7XYmtsbERmZiZWrlzp8fzkyZOxfPlyn43Tm+pqoKkJ2LQJKCrS2xqhp+N1fkR2djays7M9np87dy4AoMze5b2HUlvLHEk7Q4boZooQJOiejGSxWGCxWNre19XV6WiNg9JS5/ciNsFXdA+QLFu2DLGxsW2P1ADZGt1ebBERkk0i+I7uYlu8eDHMZnPbozxA4uxHjjhep6dLlS3Bd3QfRppMJphMJr3NcGH7dgpMUbi3TRB8RXexBSp1dUzZAtiPTRB8xWuxNTQ0oKSkpO19aWkpiouLkZCQgLS0NJw+fRrHjh3Dyf/sUzl48CAAICkpCUlJSX4yW33a/YnIyNDPDiGIULwkPz9fAeDymDdvnqIoirJq1Sq355csWdKl3zebzQoAxWw2e2ua37BYFMVoVBQOIhXlo490M0UIcLy5Xw2Koij6yNw9dXV1iI2NhdlsRoxO1XYOHmQGiZ1vvwVGjtTFFCHA8eZ+lRibG374gcVZjUaWRUhP19siIRiQAIkbWlocmf42m4T9Bf8gt5EbOq5E2EuRC4IviNjcEBHh/L5dNpkgdBsRmxs6ejYRm+APZM7mhlOnWMYuKYnBkd699bZICAZEbG7Ys4fl7CoqgMOHpSqy4B9kGOmG9esdr6dNk6rIgn8QsXWguRkoKHC8v/Za/WwRggsRWwe2bHEuzjpjhm6mCEGGDJA6kJ8PGAzAgAHcMCpJyIK/EM/WgYICph9XVwOXXUbhCYI/ELG1o74e+Oorx/tp0/SzRQg+RGzt2LaN3UbtiNgEfyJia8dnnzleZ2QAgwbpZ4sQfIjY/kNDA/D6644e2uLVBH8j0cj/sHy5oyhrWBjwwAP62iMEH+LZAJSVAc8843iflcVIpCD4ExEbgI8+YrgfoFd79ll97RGCk5AfRioKsHQpt9HExQEzZ0q9EUEdQt6zffstF7ABNtN46CFdzRGCmJAX2+bNjtd9+rDrqCCoQciLbds2x+vLL3ctiSAI/iLkxda+FZRU0RLUJORvr169HK+//loqaQnqEfJiu/VWx+vaWuc5nCD4k5AXW2Ym964BHEYeP66vPULwEvJiMxgcpetsNuDYMX3tEYKXkBdbczOraNkZPVo/W4TgJuTFduCA8x62UaP0s0UIbkJebPv3O15HRADDhulnixDciNjaiW3kSKkRKahHyItt3z7Ha5mvCWoS8mJr79lkviaoSUiL7bXXnEP9aWm6mSKEACEttj//2fn9hg362CGEBiErttZWhv3bI9W0BDUJWbEdOOCadJyZqY8tQmgQsmIrLnY9dumlWlshhBIhK7a9e53f9+3rvLdNEPxNyIqto2fLzJTNo4K6hOTtpSiuYpMhpKA2ISm2kyfZpL49EhwR1MZrsRUUFCAnJwcpKSkwGAxYs2aN03lFUfDb3/4WycnJiIyMRFZWFg4dOuQve/2CBEcEPfBabI2NjcjMzMTKlSvdnn/66afx/PPP4+WXX8aOHTsQFRWFmTNnorm52Wdj/UVHsRmNkqolaIDiAwCU3Nzctvc2m01JSkpS/vjHP7Ydq62tVUwmk/Kvf/2rS79pNpsVAIrZbPbFtE6ZNUtROHPjIy5OtUsJQY4396tf52ylpaWorKxEVlZW27HY2FhMmDABhYWF/rxUtzl1CsjLcz5WWwt8950u5gghhF93b1VWVgIAEhMTnY4nJia2neuIxWKBxWJpe19XV+dPk1z4/numanUkN1dq/Avqons0ctmyZYiNjW17pKamqnq9ceOAfv1cj+fmqnpZQfCv2JKSkgAAVVVVTserqqraznVk8eLFMJvNbY/y8nJ/muRCWBhwxRWux3fvZicbQVALv4ptyJAhSEpKwqZNm9qO1dXVYceOHZg4caLb75hMJsTExDg91KZvX9djUVEsaycIauH1nK2hoQElJSVt70tLS1FcXIyEhASkpaVh4cKF+P3vf49hw4ZhyJAheOyxx5CSkoLZs2f7026fcDd97N9fmmoIKuNtqDM/P18B4PKYN2+eoigM/z/22GNKYmKiYjKZlOnTpysHDx5UJZTaHSwWRend2zn0DyjK3LmqXE4Icry5Xw2KYm9wGxjU1dUhNjYWZrNZlSFlYSFw5ZWux195BbjvPr9fTghyvLlfdY9Gak37fmztmTJFWzuE0CPkxFZV5To369sXGDFCH3uE0CHkxHbkiGs5hIQEiUQK6hNyYrPZXI8lJ2tvhxB6iNggO7QFbQi520zEJuhFyN1mIjZBL0LuNhOxCXoRcreZu1QtEZugBSF1mykKUFbmfKxPH+Dii3UxRwgxQkpse/cCZrPzsaYmIDpaH3uE0CKkxOapyNfll2trhxCahJTYbr0VWL7cNVvkmWdcs0oEwd+ElNiam4Hf/Y5zt/bs2OG+lqQg+JOQElvv3sBdd7ke//WvgfHjNTdHCDFCSmwA8Kc/AfHxzsdeew1Yv14Xc4QQIuTEZjS6lrIzmwGVi3oJQuiJbckSoKHB+VhqquxnE9QnpMSmKMA77zgfMxrZwUb2swlqE1Ji274dKC11PmazyRBS0IaQEtvbb7s//tOfamuHEJqEjNjOnQPefdf1eFqa+2pbguBvQkZsmzYBP/zgevz22yXrX9CGkLnNZAgp6E3IiM3donW/fsDo0drbIoQmISO2d94BBg92PhYZ6ZonKQhqETJiO3UKOHnS8T4+Hrj+emDzZv1sEkILv3YeDWT273dO0zpzBvjrX4GzZ4F2XYkFQTVCxrPdf7/746dOaWuHELqEjNgSEtwfP31aWzuE0CVkxBYWxv1sHRHPJmhFyMzZjhzhTm2ASceJidwwetNN+tolhA4h49n+9jfHa0Vh/ciPPgIqKvSzSQgtQkJsLS3AP/7h/tyll2pqihDChITYVq92nxeZlgZcd5329gihSUiI7cUX3R+/7z6gVy9tbRFCl6AXW1ER8OWXrsfDwoD587W3RwhdglpsigI8/LD7c/ffLx1HBW0JarGtXQts2eJ6vF8/4MknNTdHCHGCVmytrcAjj7g/t2QJEBenqTmCELxiW7MGOHjQ9fiwYcAvfqG5OYIQvGLbtQtISXE9/vTTQESE9vYIQlCmaykK8Pe/uyYZp6ZKepagH0Hp2U6ccJ/NP2eOFGMV9EMVsdXX12PhwoUYPHgwIiMjceWVV+Krr75S41Ju2bvX/fGpUzUzQRBcUEVs99xzDzZs2IA33ngD+/btw4wZM5CVlYUTJ06ocTkXPPVakzxIQU/8LrazZ8/i/fffx9NPP42rrroKGRkZePzxx5GRkYGXXnrJ35dzizvPFh8PDBqkyeUFwS1+D5CcO3cOVqsVvTvs1IyMjMS2bdtcPm+xWGCxWNre19XV+WyD0QiEhzvXHJHmGYLe+N2zRUdHY+LEiXjyySdx8uRJWK1WvPnmmygsLESFm81jy5YtQ2xsbNsj1Q9dLtavdwjNYOAObU9lEQRBK1SZs73xxhtQFAUXXHABTCYTnn/+edx+++0wuqnzvXjxYpjN5rZHeXm5z9dvbHS8VhTu0M7I8PlnBcEnVFlnGzp0KLZu3YrGxkbU1dUhOTkZP/nJT3DhhRe6fNZkMsFkMvnt2jYbN4t2xF39EUHQElXX2aKiopCcnIwzZ84gLy8PN2mwotxu+udEZKTqlxaETlHFs+Xl5UFRFAwfPhwlJSV45JFHMGLECNx9991qXM6Js2fdHxfPJuiNKmIzm81YvHgxjh8/joSEBNxyyy1YunQpwsPD1bicE3V1wMCBgNXKqGREBBAVBURHq35pQegUVcQ2Z84czJkzR42fPi8NDUB1tetxGUYKehN0uZFms/vjsbHa2iEIHQk6sdXUuD8uYhP0JujEtnOn++NRUdraIQgdCTqxeQr9X3CBtnYIQkeCTmy7drkeS0tjhFIQ9CSoxGazsU5kR8aM0d4WQehIUIitvh547z1g9my+7sjYsZqbJAgu9NgaJCdPAh9/DHz4IbBpk/t8SDvi2YRAoEeK7eGHgWef7frnRWxCINAjh5FHjnT9s3feCfTvr54tgtBVeqTYRo3q2udeeAF4/XV1bRGErtIjxZaT4/ncyJHAo4+yc80vf6mdTYJwPnrknG3cOGDSJOCLL5jZf+WVLL56000sLy4IgUiPFJvBALz1FjvUXH89MGCA3hYJwvnpkWIDgMGDgXnz9LZCELpOj5yzCUJPRMQmCBohYhMEjRCxCYJGiNgEQSNEbIKgESI2QdAIEZsgaISITRA0QsQmCBohYhMEjRCxCYJGiNgEQSNEbIKgEQG3xUZRFAD+aWQvCGpjv0/t921nBJzY6v9T+NEfjewFQSvq6+sRe57uLQalK5LUEJvNhpMnTyI6OhoGg0FvcwDwv1dqairKy8sRExOjtzl+I1j/LkC7v01RFNTX1yMlJQVGY+ezsoDzbEajEYMGDdLbDLfExMQE3U0JBO/fBWjzt53Po9mRAIkgaISITRA0QsTWBUwmE5YsWQKTyaS3KX4lWP8uIDD/toALkAhCsCKeTRA0QsQmCBohYhMEjRCxCYJGiNjOw8qVK5Geno7evXtjwoQJ2Llzp94m+UxBQQFycnKQkpICg8GANWvW6G2S31i2bBnGjRuH6OhoDBw4ELNnz8bBgwf1NguAiK1T3n33XSxatAhLlixBUVERMjMzMXPmTFRXV+ttmk80NjYiMzMTK1eu1NsUv7N161YsWLAA27dvx4YNG9Da2ooZM2agsbFRb9MARfDI+PHjlQULFrS9t1qtSkpKirJs2TIdrfIvAJTc3Fy9zVCN6upqBYCydetWvU1RxLN5oKWlBbt370ZWVlbbMaPRiKysLBQWFupomeANZrMZAJCQkKCzJTKM9EhNTQ2sVisSExOdjicmJqKyslInqwRvsNlsWLhwISZNmoRRXe0NrSIBl/UvCP5iwYIF2L9/P7Zt26a3KQBEbB7p378/evXqhaqqKqfjVVVVSEpK0skqoas8+OCDWLt2LQoKCgJmy5YMIz0QERGBMWPGYNOmTW3HbDYbNm3ahIkTJ+pomdAZiqLgwQcfRG5uLjZv3owhQ4bobVIb4tk6YdGiRZg3bx7Gjh2L8ePH47nnnkNjYyPuvvtuvU3ziYaGBpSUlLS9Ly0tRXFxMRISEpCWlqajZb6zYMECvP322/jwww8RHR3dNr+OjY1FZGSkvsbpHQ4NdF544QUlLS1NiYiIUMaPH69s375db5N8Jj8/XwHg8pg3b57epvmMu78LgLJq1Sq9TVNki40gaITM2QRBI0RsgqARIjZB0AgRmyBohIhNEDRCxCYIGiFiEwSNELEJgkaI2ARBI0RsgqARIjZB0AgRmyBoxP8HTypkooseiNkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:00.678 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:00.678 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:00.682 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:00.682 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AxYuNP/fVK45oBQcDhw+zd+nmxmCp1cZ+jfvnDBmACRPS1mZbIaXmUuHKFU5wb9jAfzTm1rQpMHMmC5wLIRzDtm3Ali3A1Klp+4B8/TqwcCEDblpERTFx0dmZw8EaDb/G/bNGw5/nyZO2e9kKCZBpFBgI/PMPg+WuXeZLssmQAfjwQ6BPH6BmTU6oCyHSt5kzmZvQv3/qr7F1K/DkCbNUhXGkFmsa5csHdO/OT3pPngArV3JI1NSxOToa2LSJVTCyZ+f1S5XiXMXz56a9lxDCNvTsCdy/n7aSl9euAcWLm65NQj8JkClwdwfatAGWLWOw3LbN9IFSJzSUQ70zZnA4Y9w489xHCGFdEyZwLvLs2dSdLwHSMiRAGiFjRvb2PvzQ/PfSaoFhw4Dvvzf/vYQQluXsDEyfDowfn7rznz4FcuUybZtEYhIgU+Gnnyx3r4kTzbPIWAhhXblzA8WKpa7IuEZj3Sx4289cMQ0JkKlQtizQvr3l7tejh+XuJYSwnO++A6ZMMe6ciAjAxcUszUnRy5fA4MHGt9leSYBMpQULgIYNLXOvU6e4AbMQIn3JkYMFRIxZWnbzJtcuWlJ0NIeEu3RhTsaAAZa9v7VIgEwlFxdg+3auQ3J2Nv/9ZC5SiPRpwADjemTXrrF8pSUoxWz+Vq0ALy9g7VqgWjXL3NsWSIBMA2dn7uodFgYsWgQ0aAAULJj2Khn6zJghO4MIkR5lz85pm4MHDTv+xAnOXZpbQADQti2rjK1ZA3zyieNV/5JCAWYQEcHstHHjWKnCVLJl4xyAJXqsQgjLCQ4Gvv4aWL066WO0Wm5vdeoUa0pnymSetgQFAaNHM2t/2DAmE6UnUijAylxdgREjuGFqrVqmu25YGNCvn+muJ4SwDR4eQMWKSY8SPXjAjZA9PTlaZY7g+Po14O/PId8+fTjsm96Co7EkQJpRmTKsmD94sOmuOWMGdw0XQsR36ZJ9bw7cty97iAmtWsUPxpMns6KXqSkFLF/O5Jtq1fjnhHtMOioJkGbm5MSqGWPGmOZ6SjlOBpkQhjp3DvjySxbxKF8eaN2aw4R//81tnmx/IokVuqpWBfbu5ffBwUC3btzPcdUqwMfHtPdTih/gW7Xi6NSGDdwGS8SSOUgL6t2bqdJplS0by9IJIWjxYu5K8dNPQEwMMz3PnuU0x9mz3JM1b14OYxYsyDJtXl58eHqabz7PWKGh3Du2b1/2GMeNAypUMOxcpTg/uW0bh0tDQ7n/ZGQkk2vivtNrNJzTLFgQGD6c2105CmPiiRnyLUVSpk0DXrxgXde0CAsDjh7lDiBCCGZc1q7NPzs7s+h/qVLMwtR5/Bg4eZLB88wZJqPoHhERPCZ7dgbNfPliA6iXF4OUp6f5X4ebG5dwjBnDjQxSCtxRUZy33LgRePiQGyLXqwcUKMBrubkxJ0KkjvQgraBvX+CPP9J2jXLl+KYghODQ6vTpQNGiqb+GUhzWDAyMHzwDA/mBtFo1YOhQ8yeuKMUs+LAwBkonPRNh9+9zqsXJiQGxRQv2BkXKZD9IG6cU10zu25e269y6BRQubJImCWHXSpZkko6+YGIKWi0XyU+cyGA0YACnOsxp9WruSzt7NpA5M5+LjGR26blznGO1dEWd9ECWedg4jQbYvRvIkiVt11m+3DTtEcKePXvGkm2pCY7nzwM3bjAAJsfJCfjsM+DQIQ651q3LUSBzbaYO8H69enF5x6NHwI4dXKxfrhywZIkER0uQOUgrcXICrl/nXEFq+/CSqCMEpxrKlzf+vPnz2UMrVYqZrlotS0iWKMFlDqVLs2JNxoyx57i4MLO0QwfmFNSuDfTvz+UX5ijg4e3NzNb33gM6dWKv0lYSihyBBEgrypcv9j93aoJkjRqmb5MQ9ubcOcMzPQHg1SvuolGmDLBuXfzyaZGRTOLRlVe7fp2JME5OPKdqVR6XOTMwaBCD5aRJQJ06LDvZrFnay7Hdvs12bdoEZM3KZRiHDsmifWuQOUgbcPQoP4mmNMwTl7Mz8Pw5104J4ci6deP2c/Xrp3zstWsMdD/+aFyVq5gYPuL2JuMKDOTGBRcvMrGmTh3Drw0AV65wjnPbNi5Had2awdbDw7jriJRJko4dOnQIePddw4/v2hWYN8987RHCXtSqBWzeDOTKlfKxTZoAS5earzd24wbXYoaEJL+GUSkuNdmwgXOLhQtzfvHDD9lrFOYj6yDtUO3anPu4ejXlY52dmU0nhD1RyvS7QWi1XFtsSHC8dYvTGSkFx8ePGXCrVjVu6BbgMpOlSxn8fvyRPcDRo5lEdOwYcOQIHy9esGhB8+bADz/IWkVbJQHShvj5GRb4YmJiFzYLYS8OHgSmTmWQatECqFQp7QHz1i3gnXcMO3bzZgakpJw4wSUVMTH8v7h2LTByJODryyQcY9YZVqrEBKD9+1lHNSqKhT1q1WJFLUMCurA+CZA2xJikm+PH+SYjhL147z3OzQUEMHhMnMjkF42GPa2cOdnTypkz9s+VKiVfBs2YBJ2DB4Fvvon/XGQkk3HWrOF1Ro9m8hzA/19KAYcPc+H+48dA06YcCjV0pqduXT6EfZIAaUOMCZDHjkmAFPZHo2EgihvUtFrO2T1/zseLF/x69y7n8ZYuTbrHZegSj9BQLo9wceH3gYHArFk8/9NPgRUr9CfgaDSc/qhdm8F061Z+OG3Y0PjXLuyPJOnYEKW4LjIwMOVjGzUC/v3X8Xb4Fo7l+nVmnS5erD+j09eXAbJAAQZarZb/j3R/1j3OnuUH0GbNgLlzGSi7d2f5OOFYJEnHTmk0/E+8cWPKx+7cyeGpfPm4gLhJE85x6EpSCZEeFCvG4c1OnVg9JmF5t6goFiT38YkdrnVyiv8A+IFy7lz+fOJEIE8ey78WYX+k1JyNMXbxf2Ag30AaNGDpuhw5uH2N7Y8LCGGYMmWAIUOAypWBy5fj/yw6mj8vXpwZpEWKcMmEjw+Tah4+BHr25PDp3r1cB5kzpzVehbBHEiBtTFqr47x8yQXLdesyw2/CBFbiGDuWbyZC2Jtr14Cff+ZQ68iR8eufvn6tf9Tk9m0GxpUreV6bNgykn3/OZRVCGEKGWG2MqeZEDh6MX8x4wwZgxAgWOG/TxjT3EMLcli1jxuv8+Vy/WKYMi2QsWABkyJB0gBw/HvjoIyayjRvHuUelgAsX+FUp4MEDLu04cYJznTlysL6qOWqqCvskPUgbkz07iyebg1bLT9CPH5vn+kKYSng4e4CBgfEr39Srx7JyPXrw33NkZOLs0+fPuY+jbsTkzBkGvyZNgFOnuJdiu3bcNurNG6BzZ35wbNiQAVIIHelB2qAaNRLPtZiKUkCXLiyELIQtOn+ec47DhzPxLKEPP2Rg69MnfnUepbgt1IEDQNmyXKS/ciWHaN9/n0GxQwdWrNK3NdYnnzD43r3LOUwhJEDaoBo1gEWLzHf9vXv5NTKS6782bgQ+/pgZgg0bShKDsK4+fVh9Jrn5eD8/IDgYmDOHH/hev+bzXl4cgWncmF+fPgW+/x74/feU90/UaDhnP3gwh3ZlCZWQAGmDzL2NVXg4Py0fPhy75nLdOn4tXpw7EmSQfxnCSv75B5g5k/9Ge/bkEg19wer1a9YznTZN/+bjO3YwgC5ZwqkLQ3h7c23lmjXcsFg4NikUYIMiIljKypjdyjUa0y3tWLGCa8uEsKbwcAbK//7jnGPjxrGBMiaGPcQ//uCQa0IzZ/KD3q+/xlbPMVRMDLebWriQc5cifTEmnkiSjg1ydWUNSkM5OXHY1FSl5yZPlnWUwvqyZgX+9z8m6Vy4wKC1dSv/bW7YwNJxtWtzOdPx4/zZ4sXsdUZEcFjV2OAIMIt19GgO8wrHJj1IG9W/P3c+SEnOnNylQLf567RpwMCBxvU+9dm3T4osC9vy6hUwfTqHYC9eZLJNhQrMcI37KFzYNJngU6dyXfGIEWm/lrAdZutBzpw5ExUqVIC7uzvc3d3h6+uLrVu3JnvO6tWrUapUKWTKlAnly5fHli1bjLmlw+rWLeU94urUYaWQuDujv3mT9uAIAL16SWEBkXYxMaa7VpYsLK347rucgti3jwXHx47lB8oOHTjcaqplUv36sSTdyJEyouKojAqQBQsWxPjx43Hy5EmcOHEC77//Plq2bIkLFy7oPf7QoUNo164dunbtitOnT8PPzw9+fn44f/68SRqfnpUtyyEjfXvdZcrEYdADBxIH0VmzjLtPrVr6U97Pn2fWX+3aLAbdsyfrXgphqOhoJro8emS6a27cCBw9CgwYkLrhU2N9+y13GtElsQnHkuYh1pw5c2LSpEno2rVrop+1bdsW4eHh2BRn0V2tWrVQqVIlzDLindwRh1h1YmKAkyeBQ4f4RlO9OlPc9QU1gFmo168bfv3PPmOP05AC6aVKAZcuGX5tIW7f5gbBCxakvUD45cvsLe7cyXlHSxTmj4ri3Ofatfq3wxL2xyJJOjExMVixYgXCw8Ph6+ur95jDhw+jUaNG8Z5r0qQJDh8+nOy1IyIiEBISEu/hqJydueyjf3/A35//WZMKjgDXkBmjaVPDz7l8Gdi1y7jrC8dWuDCTZbp0YYWbtJg3j1VwBg603K41s2ez7RIcHZPRATIgIADZsmWDq6srevTogfXr16NMmTJ6jw0KCoKnp2e85zw9PREUFJTsPfz9/eHh4fH24e3tbWwzHVbfvsDQoYa9gTRpAnz5JYsDxJ3HTM7w4Wlrn3A8RYpwuUXnzkx6SY2ICO7peP48l3xYwsuXwJ49HLERjsnoAFmyZEmcOXMGR48eRc+ePdGpUydcvHjRpI0aOnQogoOD3z7u3btn0uundz//zIw/XVFmpYCrV7mnXunSDIa//AL8/XdsQYCDB5kBm1IVncOHgW3bzP8aRPpSvDj3YfzmG84hGkopBsaBA4HQUCbOuLmZr51x+fvzw6ZU1HFcRtdLyZgxI4oVKwYAqFq1Ko4fP46pU6di9uzZiY718vLCowQz9I8ePYKXl1ey93B1dYVrSimcwijFi3Phc1KcnZm5+sUXnDNatizpY/v2ZcCNiQHOneO+e7IBrUhJqVLctHjsWP5bHD1a/7+bkBDOM27fzmHZChW45GjpUst9OLt5E3j2zHS76wj7lOZCAVqtFhEREXp/5uvri10JJq127NiR5JylsL4cOfhGtGdP0iXvrl3jm52rK1ClCpA3r6wVE4Zxd2dPsk8fZohOn85EmAsXgEmTuBVb//4cUv35Z2D1ag7rz5/PZRyGloxLq9Gj5d+0MLIHOXToUDRt2hQ+Pj4IDQ3FsmXLsHfvXmzfvh0A0LFjRxQoUAD+/v4AgH79+qFevXr45Zdf0KxZM6xYsQInTpzAnDlzTP9KhEnVr8+hsPz5Y+u1xnXlSvzvR4/mzgsffWSR5gk7V6YMsGoVa5527swRjqZNublxwv0Yt23jSMXKlZZp26FDrMkqqQ8CyghdunRRhQoVUhkzZlR58uRRDRs2VP/+++/bn9erV0916tQp3jmrVq1SJUqUUBkzZlRly5ZVmzdvNuaWSimlgoODFQAVHBxs9Lkibc6dU8rZOe5sZtIPJyelFi+2dotFehIWplThwkqtWmWZ+2m1SrVsqVRIiGXuJyzPmHgipeZEin74gQkLhnB2Bk6fZnEBIdJq8GCWlfvnH/Mmyxw8yDnP8+eBBg04zy7SJ2PiiQRIkaKwMKBcOeDOndjncuTgtkA7diSusJM3LzerLVnSsu0U6cuZMxzqP3sWKFTIfPeJjubUwB9/AMWKJR7iFemL7OYhTCpbNuDYMfYkW7UC/vyTG9Fu3szkiYQeP2bW4dWrlm+rSB9iYrgk5KefzBscASalde7MD3QSHEVcsi2uMEjevMC4cYmf79CBqfgDBgBabezzjx9zmPXmTaBAAcu1U6QPM2eyZ2fuoc6oKJaRW7/evPcR9kl6kCLN+vYFli9PPEcUGcnUfSGMceUKMGwYMGdObCELc1mwgAU0pOco9JEAKUyiTRsuAk/ozBmLN0XYuV9/BRo1AqpWNe993rzhNEGrVua9j7BfEiCFyXTtClSuHP+5/fs5NLt5M3cZkT0mRXIePGAVp8mTU3f+xYvMRjXE3LncdzW54v/Csck/DWFS/frF/14pDpd9/DEXg7u48A3Jz4/ZsULENWkSt2ArXDh155cowV0/rl1L/rhXr4Ddu4FmzVJ3H+EYJEAKk2rePOX1akpx/8kqVbjXpRAA9zv9809mS6dWhgzcNHzAABY3T8qMGSx1J4XIRXIkQAqTypkz8TBrUq5d41rKyZPjZ8AKx/Trr0DLllyLmBa5cnFYv2dP/f+uQkO5K02CrWqFSEQCpDC5XbuA997jG1VKm7JERQGDBklhaEf37Bl7fmnpPcZVsSLQooX+pUm//86pAOk9ipRIgBQmlz07k3OePuVcz+3bTJzo3RvIkkX/OTNnWrKFwtZMnQo0bswi5qbSpg3w+jX3PdV5+ZKVeerWNfw60dFMHpIEM8cjpeaERT1+DHTpwqzWuLJnB168sEqThJVFRQHVqzO5JuH+izExwJdfsiZrpUrGXzsmhnucjhrFLdp++oll5WrVMuz8bdu4JVfBgkB4ePwgmTs3ny9YkMHY3Gs2hWkYE0/kr1RYVN68LDxduDBw927s86bsOQj7cuwYEBysf92jszNHF374gUP2P/6Y8rB9wvNnzQLatYvNbjUkOAYEcGPnqlW5J2WmTPF/rhSHhe/f50OGa9MnGWIVFqfRJH6Ty5/fOm0R1rdjBxNmkgoyHh7sxdWtyyzpGTPYM4zr2TNgyBD957u7c2nR3r0sfp6cwEAm98ybBwwcyFGNx48TH6fRMCGtcGEuYZJKPOmTBEhhFcHB8b/38LBOO4T17dxpWEZpo0YMRtOnA2XLcuh12TLOcX/1FQtV6DN3LsvJFS/O6jn6hIdzvW67dsxyffiQmzkHBgJTpug/5/x57gAi0i8JkMIqJEAKAAgJ4RDr++8bdvzDh8CmTQx6T55wyPT997mG8q+/Eh///Dl7qG3asGj+gwfxf377NtC2LeDtzXZ06gRMnMhh1YkTWU/46FHg3r345z19CgwfDrz7bqpetrATMgcpLC4igo+4JEDaty1bGKgSztWlZP9+7jWaJ49hx9+5w+2v3nkHWLmS62izZwc+/BBo3z7x8aNGMTFHowE8PdkjPHcO2LCB2ayFC3Orq/nzgaxZ9d+zRw/2Fr29Y5/LlYvzlDVqGPd6UysmRoZxrUECpLC4hL1HQAKkPbt7F1iyBGja1PhzDR1ejcvJics1evfmEGfDhvqPO3eOyTQVK3IucexYzkMWLcoC5cOHp5xco9FwKFff89HRSQdVU+vRg8O/hva0hWlIgBQW9/Jl4ucs9UYjTEspYOhQYPz41GVy7tzJCjpJCQ9nAfILF7gzzNGjDBSursClS0lnPz9/DowcyaHYy5eZcDNiBHuQI0ca3j53d36gS/gB7tEj9nrv3QN8fAy/XmpNnw706cOM2Y4dzX8/QRIghcVduJD4uYRzPMI+rFkD1KyZuiDx8CF3eHnvvfjP37/PxJjLlzksWqYMk3IaN2a28+DBscd168aMUy8vPnfxIoNJaCiTbo4d4/Dp4sXMOjWWtzd7yOXLx3/+8GEuF1m+HPj+e+Ova6yMGblcZdw43m/CBPPfU0iAFFaQ8M0GkFqs9ujFCw6trluXuvO3b2eSS+bM/P7iRQZGjYabcJctG//4xYvjP1ewIJd8dO3KYgB//835yUGD+HXyZPb0li+PXcRfrRoLE7i4GNZGHx9+eEv4b/bQIfboFi8GevUCfv7Z/NMEGg2Dfo8e/F0ZsnZYq+WuOVJfJZWUHQgODlYAVHBwsLWbIkxAq1UqWzalOEDHh5ubUq1aKVWtmlKVKim1bp21WylS8u23Sp05k7pz79xRKlcupdq0UergQaW++EKp//1PqXv3kj7n+++VunUr9vvQUKWmTVOqUSOlypVT6uZNPv/qlVJduyo1f37q2hbXnj1KjR2b+Pk2bfjvWCmlTp1SqnlzpbZsSfv9DBEUpFT79kn/PCZGqQMHlOrXT6nWrS3XLnthTDyRHqSwOI2GmYdx94MMDQXWr4/9vnVrzjdZKktQGGf/fvaYKlY0/tzr15lYU7kyh1H37gWmTQNy5Ej+vLt32aO7fZvDqPfucVnG9u3Mbu3blz25YcNYNMDXNzWvjP8W9+9nZm7Romzf0KGxGytHRHDIUzfnWrkysHYt52HXr+fwZ0qvJS08PbnjycGDQJ06fE6rBf77j0Pe9+8DtWtzy69ChczXDkcgAVJYRcGC/I+cnBkzJEDaqilTgKVLjT/vwgVmrTZsCCxcyCCT0vKFyEgO5wYGMiB6eHBYs3Tp2GPeeYcFz1u1YpAoXtzwNkVHA//+y3WUWi2QLRsr7gwezKC8aRMD0ccf8/rBwdzLNC4XF2bFBgRw2ci33wJNmhjeBmMNHMjs2pgYBueHDxkUBw60TNKQo5AAKazizz8Z/MLDkz5GgqPtcnY2fs3jyZMMGp98wvqqTgnKlERGcr5w3TrAzY2D7wCDT44cDIhjxyadbFOkCD9UTZnCHmZSlAKuXGEG7ebNLDaQMyfnFDt0iJ+NW6gQawd37sy1lkuXsohAly76r12+PAN027bmDZBubuzd9u3LDxzLlxs+ryoMJwFSWEWZMuwVTJgAjBnDN8eEWre2fLuEYapWBU6d0l9gXJ9du1hHtWdPJs/EDUIhIcCcORwy/OIL9ohSuzPGu+9yaH7RIv3LIRYuZI9Qq2X91jp1+GEtuVrAWbOyR+rkxABdvz6HXKtX178u0cWFAezNG+M/RBjjk09YwGD/fqBkSQbmuD3b1695fymknnqy3ZWwCXXrAgcOxH5fpAhw44b12iOSd/EiK9mMGmXY8a1asTzb/v2xb9gPH3Lz4tu3gW++YeAxxZu5UgyOgwfHzz4NDmbv8swZBpfPPjN8Z5CHD7mOcu5czkF27swg3rEj8MEHiY+fOZM93pSKo6dGYCCrA+XJw11OsmVjZaAdO1hO7+JFDr16egJBQTynQAHOF1eqxG2/MmY0fbvshWx3JexKZCRw5Ej856TGpW0rXZpvxIa4fJlLME6fjg2AV64wmWb4cKBCBdO2TaNh0k/79hx6dHPj8z/9xLnPESOMv2b+/Ay8gYFAvnzsgc6dy8AeE8Ph17jq1+cHCFMGyMhIzrMeOMCqRY8eAd27s11583JKokoVLnOJO9yqFGvQnj3L3vPkyXxu0SLpXaZEAqSwuk2buDYtrurVrdMWYRiNJnYRfUpJIRMmcJNiXSBUivs6zpjBN3Zz8PDgcGi/fpynq1iRbW3RIvXX7NWLc5tjxzIQBQRweLZrV34AaNqUH+xy5mQv7dIl072emBgOo3p7MxBmz87h3WLFUk5y0mhiN3Zu1sx0bXIEEiCF1SXsPQIc0hKmN2wYlyXUqcMhuLRo0YIJLL16JX2Mrk7rvn2xzy1ZwgQWcwVHnUqVuPRi8mT2HvPlS9v1KldmJZtXr9gT3bmTH+QWLuQykwMHOOT54gWQOzd7m9evM4illbMzKxatWJH2awnDyRyksLpr14ASJRI//957/FTetCl7ADIclHYPHnC93MGDHKJzc+PygDp1mIhizO84KopzcMuXJ31M27b8+z11it8/f84e19q1ibNYzWnoUL52jYZDkOXKpe46//zD63TrxoSilSv1H/fkCT+MPH/O4disWZnQVLMmA62hc59xtWvHLFpL/t7SI5mDFHaleHG+kSZ8szlwIPZTuZcXh4jKluUWRdHRwM2bTFDInp1zQUWLWqP19qVAAe6N2KYNvw8OZg9+8WIGsgwZmNhSowbfyLNnT/paLi4sExcSknQps+vXmRCjM2wYMHq05d/kr15lluejR8CkSQxcgwfHX0tpiOrVOZzavTt7kknJkwfo35//pkeOZEbpqVOs4TpjBucSjS0mUKwYf5/6PkwK85AepLAJp0/zE3Zq/zVmy8ZP7eZMq3cEkZHsYV68yCUEup1XfHwYOCtUiJ8FuXIlg91nnyW+VlQUe6i7dnFubutWDrWOH2+xlwOAFXcmTWLGrM7Dh3zu4UPuJlKggGHXmjaNS5QOH+ac4KefJn2sUsDnnyfdyzTW+vX8+2nb1jTXc1TSgxR2p3JllvYaN45zOA8f8quhwsKYvl+rltma6BAyZgQaNOBDRynOJQYEcGH95MmxBb8LF2awaNQocY/o4kW+oevK0a1axUzKjh0NK7RtKhs3An5+8Z/Lnx/47Tdg2TIuMbp2zbBe7d69DIzPniUfHAEO52bKZLr1kJUrc/mIBEjLkR6ksEkxMcDx4+x1bN3KP6ekSBFubVSvnvnbJ7ge8MoVDoOfO8cPNF5e/P2//z7X5k2aFLscRCkuvYiIYJbpL7+Yt2apTps2DIRJFR/o3p3rBf/+O/nr3LzJjNygIFbTMWQt4cqVTODRbdGVFhs38jF/ftqv5cikBynsnrMze4O1anEx+uPHrJVZqBDfmLRavlHNmBG7v+TNm1x31qsXh/GyZbPqS0j3XF055Bp3HWNgIIsBfPst/850lV3++Yf1TnVrXhs2ZC/M3AHy+XMO8yZXmWfOHL6G33/nkpCkLFvGucyFCw1faN+2LRN6Tp1KXL/VUG/esPh6jhxsq7AgM+4qYjKy3ZVISkyMUjNnJt4+K0cOpf7+29qtc1xarVJFiypVoQK3obpwQanHj/mzq1e5XVRUlPnbsWiRYVunBQcr5eXFrav00WqVKlFCqX/+Mb4NISFKNWvG7bmMdfGiUh9/rNS+fcafK/QzJp5IwrCwa05O3EA2IIC9Ep0XLzicFxhovbY5stu3WVpu+HBmru7YEVtkvHhx7r6xfbv527F9O9C4ccrHubszCaZZM/3ZqRs3cl3lxx8b3wY3N1bvGTTI8HOUYhGCn3/myEndusbfN7VOn+YwuAAkQIp0oXBhvgnPnh2bbBEayjWUwcFWbZrDCQ1lEYH332fB+SVLuEj/k09i5yO9vc0/vPr6NQNN1qyGHV+rFnfpSFjm8MEDBqnUbO+lU70658gNyWh9+RL46isORy9alPTuJeby5Annh4XMQYp0RKNhwkW1avykHxjIrMmGDbm8wNA3SpF6MTHsuTs7881d92GlTRv+PQwZwiUjrq6pWyyfkFIMKI8f84097uPECeNKFoaHM5vVx4fzpLVqcb1t376cn0ztDiM6Awdy2UfNmvxAp8+hQ+w1+vvHL7RuSY0bs6g7APTuzaUtjkoCpEh3qlRhb7JOHb55njzJHsuvv/LNW/bNM59hw7jd1PHjiZOkcuVige+tW4HvvmPiStasTEIB+NXVNelqPhoNhyt1IwK647JnZ9m6PHn4KFGCvcAvv+Qb/P37LDKRnOfP2Xv86Scu42jThstSJk7kzh3e3qn9jcRycmKBgJ49WbQgYcD96Se2Y9UqIEuWtN8vLfLk4TDr06f80JNSvdd0ywJzomkmSToiNQ4eVEqjiZ+8kyWLUhMnWrtl6dPixUq5uip16FDKx4aGKvX0qVKvXjEBxlzu3VOqeXOlXr9O+pi7d5lEc/Vq7HOHDyvl56fU//5n+jZt3qzUsGGJnx88WKnz501/v9T48Uelrl9XauxYpc6csXZrTEuSdIQAexFz5sQvQffqFYf54hbPFmkXGgoMGMChQV/flI/Plo09ysyZzVtjt2BB/n337Zt0labff2cJw7j/TgoXZuKXvk2X06pwYf17nXboYLqqO2lVqBDXb777LisrOSoJkCJd+/przivt2cNF7ADXUDZvzjmqtFCKQ1ABAVzjt3Ahsx1fv05zs+3OypUMdsmtI7SW2rU5Fxm31FxckyaxSs2XX3L4NiYG6NOHW1slV4g9NZ4+ZdGAGTMS/6x8eZb3s4XSLT4+rJ5UowZw7Ji1W2M9Mgcp0j2NhgUE7twBWrYEtm1jj+fDD1kFpnRpvimFh3N5iL7Hs2csFB0VxTJ4gYEsVBAZGf9eTk7stfz8M3dfcJSdF/78k3N1tjpX1a0bg/euXfGXA+l8/jl7ke3bM9v0iy+4Jdfs2fxAZYq/x8hIJpFNmZJ0EfgqVbjMIrVFBUylUCEmKmXJknxR9nTPAkO+aSZzkMJUwsOVevfd2DnJDBmUyp2bX+POVabm4eQU//sqVZTavdvar9j8zp/nXO+tW9ZuSfIiIjivePMmv3/yhAUM4vrnHxYEWLGC3//+u1I7d6b93lqtUj16KLVnT/LHXb1qnnlPY4WHK9WlC//coYNSDx5Ytz2mZEw8kR6kcChZsgCbNrEY95kzTON/+jTp411cuF7Pw4PzZkWKcE2f7pE/f+yfc+Vi9ub//sf9Fk+d4lrA5s1Zw9PYrZXsxZ9/sleW1NIFW5ExIzBrFofdp05lhmtEROw+kR4eHBmoVYvDq23acBRgyBD9vU5j/PYbd6upXz/544oX5/ykqXqtqZUlC6cKtmzhOsy0bjZtr6RYuXBIjx7xDfL5c66ZzJFD/yNLFuOTSJTiXOTgwZz/BDj02K2baXa2tyUREdwqavp0+9ll4uRJzjEuX86h9mzZGJDWr+delXnzcoj1k084L/jll9xFw80tdffbtIm7gEyerP/nSgG3bvGYgweBS5f4byRbNm7sXKkS50jz5EnlC06lSpW4tdmSJWlfA2pLjIknEiCFMJOoKM5hjRzJOUyd/Pk5T1mgAL/qHuXKxS/8bQ9WreK6vocPTbPw39o2bODOJAcPcnTBw4NBtGLF1FWXOX+eW7gtWRI7P5swIIaFcWSifn1mjeoCcUQEC/GfPs3RjidP+GGtWDH+O2nSJOmNqtNCKba3Vy9Wooo7r5wrF//9FirEPUDtcU2xBEghbEhwMJc/TJ7MDMmkODlxF/rx423/jUerZcLL4MHc3kpXecXehYcz4C9axASuKVMYHJcvZ0AYNcrwEYXHj1l8YOlSfkBKKSAaQqvlEOzu3Vx+oRRHQD7+2DSVop48YRGHIkVYuGHChNifKcURl4cPee9//2UPu1u3lAsx2BIJkELYoKtXOUcZGMjqLvfvc/5L92cdX18umzBF9RZTe/QIWLCAFXEePmT255Qp7GmlF+3asfZqxowMClu3Mvu0alUgd24Ot6a03dXt28yYLlGCATW1ATEpPXqwalH27NzEetMmfsBq0QL46CMuuTHW339zr8mJE7k9ma8vl8gk59QprjUODweGDrXsRtipJQFSCDsTEgL068e1lACHspYs4VIUa9P1FufM4RBkiRLAN99wbs4SGx5b2syZTJZp1Ijfx8SwhxR3TnrHjvhLNV6/Zo9z2zbg3j1+uCldmh8g3Ny4xGPKFH44+u23tLXv9m2OSMyeHf/5kBAGti1bONz90Ucchk0pIIeEcKcRHx/g++8539imDXvNhi7befGCr9HTM1UvyaIkQAphpxYu5GbDumIDP/7IOUxrJEkk7C22acOeVO3a5q1+Y2337nG+UTds3KsXfxe//cYPBl5eLDyxfTt7bseO8e+nXj0GJB+f2GtdvgzMmxcbUCMiOCeZlqzY3r35Yap48aSPefmSGzyfPs0AmC0b11ZWr84hY918sS55aMwYJgIBDHaDBrHd6ZFR8cSY9SM///yzqlatmsqWLZvKkyePatmypbp8+XKy5yxYsEABiPdwdXU15rayDlI4lHPnlCpZMnY9Zf78Ss2fn7oNd1MjOpq1OLNnV6pMGaWmTlXq+XPL3NtWdO+u1NGjrEP6zjtKvXzJ5x88YN3W339XKnNmpXr2VCoykr+n5s2Vat9eqXbtlPrgA6WKFVPqo4/45+XLef6tWzz22bPUtev+faW++sr480JDuenypElc19imjVL16ik1aFDiOrVLlyq1dm3q2mcPjIknRgXIJk2aqAULFqjz58+rM2fOqI8++kj5+PiosLCwJM9ZsGCBcnd3V4GBgW8fQUFBxtxWAqRwOCEhSn3+efzCA1mz8s3xwAHzFfgODuYO9nnyKLVunXkLiduysDAGSW9vpUaNiv+zW7f4oWX0aKUKFFBqwgSlgoKU+vBDpd57j8Fn/nylRo5UqmJFpZo2VapfPxZA12qVWrRIqc8+S93vtn//xMUNUmv8eKV27Ur8fNu2lvswZg1mK1a+bds2dO7cGWXLlkXFihWxcOFC3L17FydPnkz2PI1GAy8vr7cPT3sYqBbCitzcOET255/ABx9wSDM8nEOe773HLZnGj+fQp6ncvMnh0zt3OGzYqlX6HkpNTtasTMjJlIl7igYF8fmXL1mUfe5c/o42b+Zc8SefsMCAnx/n4nbu5LDmqVOcE+zfn8OZuk2jnz5lpqwxHj5kFqmpEmG6d+e/p4R0hdoF0lZq7tq1awqACggISPKYBQsWKGdnZ+Xj46MKFiyoWrRooc6nsKfLmzdvVHBw8NvHvXv3pAcpHNrt2+zJFC6cuMSdi4tS2bIplSOHUnnzKlWwIIcFS5ZUqmhRpSpUUKpvX6WWLWOZNX09l717lcqVS6mWLdN378FQhw8rVa2aUjNm8HfWogWHHps1Yw+wTRv2AsuW5e81Kir23AsXlNqxI+lrX7jAvxt3d/6+p07lUG5MjP7jb95UauhQ9uwvXTLpy1R163K0Iq7gYKVatUq/owfG9CBTnaSj1WrRokULvHz5EgcPHkzyuMOHD+PatWuoUKECgoODMXnyZOzfvx8XLlxAwSQWz4wcORKjRo1K9Lwk6QhHp9Vyq67587npblRU8msr9cmTh72dmjX59coV9nAGDWKyhqMUWE/KmzfAp5+ycPnTp6y6c+IEl380bMhdQTJmZMLNvn3AunVcptOgAdC1a9KFyONq357H/vQTMHYs10cGBDBrtFo1Lil5+JBZw15eLI9XvrxpX+fWrVx29NNPiX82cyb/nXz6qWnvaQssksXas2dPbN26FQcPHkwy0OkTFRWF0qVLo127dhgzZozeYyIiIhAREfH2+5CQEHh7e0uAFCKO4GAuGH/9msN6UVF8xP3zw4dc/H3lCt+AtdrE19FoWKO0e3fLvwZb9MMPHNauX59rGX19WRFnyhRW2fn5Zx7z/vuxQ9BKcQj1zz9Zu7RXLw6DJ6V9exYQGDSIAfXHH/n8lSu8/smTXFbi5cWM0xIlGCDLl+eaSlN8iNm/n69NX1GKqCigdWtg7dqU13zaG2MCZKqSx3v37o1NmzZh//79RgVHAHBxcUHlypVx/fr1JI9xdXWFa3qoWyWEGXl4cJ7QUGFhfOM9cgQ4ehQ4fJhza0oBI0awx2DM9eyNVsue3vXrrHLTtKn+AgfFirE3eOoUj3/8mMHs4kV+LVKEyzymTePC/w4dGMgaNODj5k3Wpn3wgFuANW6sP6Apxd5j7dosa3fpEq8zaBDLDupERbGmb0AAlwHdusXlJnXrpu738OIFfxfJne/iwg9MY8bw4aiM6kEqpdCnTx+sX78ee/fuRfHkFuIkISYmBmXLlsVHH32EX3/91aBzZB2kEKanFDfFnTWLmwbHxHBI7Y8/YjeXtgWRkQzqu3YxSeXFi/g/d3JiLyfhIyyMZdlu3GCQcXPjsHKVKgwQR4/ymBo1uKi+dGn2CF+94trTN2/4tXVr1h7NnRsYOJC1SAH+/g4eZJJOaCh/dx9/HNvjCg1lRZ4dO7g+smNHJu4AHF6dPp1JQN9+y2Qff38WSTe3GzfYA16+PPmeqK6M3caNUqzcIN9++y2WLVuGjRs3omSc8QMPDw9k/v/aRh07dkSBAgXg7+8PABg9ejRq1aqFYsWK4eXLl5g0aRI2bNiAkydPooyB6VgSIIUwr9OnWTf0zBkO7Y0dy3Jm1piP1GqZObpzJ3u8zs6cK23UiLtLJMysjYnhfOC5c8ChQ8wuDQtjIe+aNbndmG4D4JUrOezcoAEDWu7cnF/csoWFwSMj2av29+fv4ddf2YNs3x7o1CnpNoeFcS5y0yYG0E6dYhfea7WssLNoESvs9OrF7baGDuXvt04dtmHWLA7lTppk/p07Vq1ir1Q3tJuUv/5iUP/kE/O2x5LMFiA1SeR8L1iwAJ07dwYA1K9fH4ULF8bC/6+ZNWDAAKxbtw5BQUHIkSMHqlatirFjx6Ky7l+PASRACmF+UVGsqjJyJANF6dIsPdaunenmoZRiMFi6lCXTdD0q3buQRsPnfHwYEPPlY3vipCRA3zuWkxPn6WrXZo8wufJqMTFMrlm9mj1S3XzjxIkMqiVLcj7Rx4eFu93c2MubP9+w13jrFgPL2bO87hdfxAa8ixf5ev79lz3Is2d5n969+fMrV/g7b9+exdHN5dgxBuxp05I/7s0blhRcvdp8bbE0s1XSsRYpFCCE5Zw+rVT16vEr+Ywfr9SLF6m/5vXrXKbi56fU2LFcupCSoCBWorl7N/X3TUlEhFKbNytVvjyXckRGmu7aMTFK7d6tVNeurLCzcSOvv20bf6dff83lObNmxa9mEx2t1K+/KvXll/wdmFp4OJerGLqc5/vvlUpmJZ/dscgyD0uSHqQQlnflCocY//qLPbhs2dib/OwzDlGmNC/15AmH8nbv5uLz9u057GhI8QHdVlHTp3N41Nyio4FmzdijnDmTSy1MKSSEvbCtW5l9fOcOe6rNmrEAxIwZnM+Mu5n2tWvcTqxtWz5MVbRhwAD+Haa0U4fOnTtsx8qVprm/tUmxciGEyTx+zEA1fXrsxs9ZsnCtXtz1lAUKxJ5z9izn2L77jsHU0F0hAK497NyZyULvvGPSl5KsmBhmbgYGMot05Ei+TlP75hvOl54+zd0vsmXj+krdEo533uFcpYsL5y+nTWPG8YQJ8Quhp8aOHRw+HjfOuPO6dwf+9z8OY9s7CZBCCJPTZXfeucMlEAlXarm5cd3gxx+z17lihfE73j9/zgSXKVOAokVN1XLDabUsmpA9O1/jd99xvaMptWzJ4Fi6NDNJ3dy4nOTWLS4RuXWLO4pER/P4zJkZLI8cYVJN/fqpS+J58YK/2zVrjJ9TfviQc6OLFxt/X1sjAVIIYXZPnzLZQ7eucu9eJvcADIytWzP78YMPYrdX0kcpXuvlSy6j+OWX5LdyMjelOKRYrBiXRBQowO2lTGHoUNY/zZGD6xoNWT7x6hUTmk6fZv3dnDnZm4+7VtIQXbtyeNXY83RGjWJwrlcvdefbCgmQQgiLi4zk0onoaAZO3XCsuzt7lS1bMtjcvQtcvco5Nt3Xly/ZS+rYkW/+1q4TohQwfDg3Sn78mMtBvv02/jEREexNx+35PXjAc1+9AipUYKDVZeoCXIP5/Dl7qf37G9+u0FBmlT56xLWTOXMadt6qVeyVDhxo/D11wsM5j7xunX2XI5QAKYSwCKX4Zn3lCheUFynCJQvR0SxltmYN31AfPdJ/vpMTk3CKFGEgCghgYFmyxPS1R42lq3STLRuDWqFCrIkKMBiOGhXb9nfe4df8+WODx4EDXDbj58ehzZgYDqs+e8YPBblzp65dkZFMlnr4kPdIrhfauzeHZXv3ZpA0Zi5YnwUL+EGmQ4e0XceaJEAKIVLtyhX26BLuG/LqFQPF1asceoyM5POenszILFo0dmuuuGJiuIB/+XKeX60ah1BLlGBQ0fUWleIu9gMGcE3m2LGcA0zrm3paDRnC+dbISA4ZJ1cwICGtlms+16xhstIff/B13rzJnyvFrNZXr2Ir9Bh63c8+Y3bsjh36j3n2DBg2jHOP48czkzitYmKAFi2YkWuOBCZLkAAphEi1tWs5VKjRxH+8esU32dKlGQwzZTLP/W/cYBD67z/uffnXX5bNZtUnIoLzrBs3MnvX2JJw4eGsyXr6NJdy+PrGFjzw8GDwbdCAQ5iGUopD0g0bMus3od27WTy9USPgq6+Ma29S7tzhPZcu5fCzPTJ7sXIhRPpl7bJiRYuy0s3kyZwHrFCBpdm++sp6Gzi7urK493vvMWHHxcW4SjdZszIw3r3LtaUtW8b/uVIcvi1cmAXQDaHRsBrO11+zBF+tWvF//u+/7EXqC56pce4ch2tXrrStWr3mZMdTrUKI9MrZmcsKjh9n77FrV/bcQkOt2y6NhktQ9uzhXo2GunWLCTWvX7P2qr7rTp/OOqy3bhnXnmnTuEXWw4exz0dHA8uWAbNnm+ZDxd69HPJetsxxgiMgAVIIYcMqVmSQ/OgjDk/6+sbO31mLkxOD0v79HMI0xObNHKIuVQrIlUv/MZkyAXPmcIPm4GDD25M5Myvx9OzJ2qkA68oWKmSaKkSrVnH945Ilyde4TY8kQAohbJqrKwPMkiWcn6xenfNr1uTkxPWaoaEMaLr1n0k5cYLbdY0alfxxefMyoaZ799hCAYYoWJBLSvr0YYGDmzfTXmhBq+U+oSdPAnPnpr+Nkw0hAVIIYRfat+eyhsyZmS37+ees92otGg3XMrZuDbRpE3+IM6EMGdiDbNgw5euWK8f51rNnjWvPu+9yJ5N+/ZgQVKFC/J/fvg2MHs3kJ602+WudPMnXlT8/S9zZ87rHtJAsViGEXQkMZDWXa9fYu/ziCwaFihWt16a7d7nW8PvvEyfZKMUEnxcvuN+jub15w3WKCxdyDefr1wxyt28D3bqx8tGRIyxX5+fH6ji63uGLF+w1Ojvza/bs5m+vpckyDyFEuqYU11ZOncpCBDExDJr9+nGdnjXWTr5+zfsXLw6UKcNh1WPHuAfky5fMMl2/3nzLY3T27ePvZsgQ3m/Bgtii8XEFBXHZyp49XNNYuDDneUeOtO6HDXOTACmEcBh37zJJZc4c9oDy5GFRAkOGM42h1eofavz3X2Z3vnoFnDkDhIWxR1axInu3ISHsicXE8BrGrqE0Vps2wKBBzIitU4cl8lKq+friBZOhGjVK/8OpEiCFEA7n1SsGyrFjmQU6cCC3dTK2rqtWy6UW587xcfYsv+p22HB1ZQDMmJE92ZcvWTwhTx5uU1W/Ph+hoSy6MGcO932sVw/4+29mhZqSUvyQcP48E4H++YeBbtQoJv2I+CRACiEc1rNn3HNx7VrWc126NPm6rtHRHGacO5fbTgUEcB6vdGkmulSsyK+FCnF4NDKSlXUiIxmI3dyYWQuwt3jwILNsz51j2b5vvuGw77hx7MkNH576fRV1Zeo2bQIuXIhdF+rjw9qu9+7xXo0ape76jkAq6QghHFauXKwVumgRE2eqVwf8/Tk/qBs+jI7mXN2qVQykLi5A7dpcS1ihAoNjapY1uLsDTZsyIeb5c/Yez5xh8H3zhnOSAQFcjtGhg2G92zt3GMAPHGBALFKEST/t28cveD5kCI+19+2obIn0IIUQ6dbNm6wd+t9/3Pj4m28YbNau5TKNTz/lnF2dOqZJ7Hn5Eujbl0GqS5fEVWyWL+e9X77kMG6GDKwUFLd4gO6csDAG2Ro1mGDz3nvJb0Ddti2vt3Rp2l9HeiY9SCGEAHtbe/dymcPw4QyUX33FeqJ165o22/XIEc5/TpgAlC2r/5h27Tj8Ono0M13v3GFP9+RJBsAOHbg7ChA7jzp5smH3f/LEuPqwImXpPF9JCOHoMmRgke2zZ1lxpkMH9shMGRwvXWL5uVWrkg6OOuPGAb16cYePQoUYuNevZ09x9uzY4zw8OKdpiKgo7qfZpEnqX4NITAKkEMIhlC/P3lqnThziNKXVq7m0wpA9Ert0YdD+9FPORwIcVn3vPeCnn+IfW7gw5zNTcuMGE3iKFDG25SI5EiCFEA6jdm0OsXbvHrsfoymcPZu4tFtSVqxgNuzvv7PXOXYse4D61K/PIeKUHDliv/sz2jIJkEIIhzJkCOfr5s83/tyoKPbo9u9n8fSff+ZWXAUKGL6tVLZs3DJryBAGyMqVuQfnuXOJj61Th9mrKdm6ldmzwrQkSUcI4VCcnRncfH1ZN7VECWaLPnnCebwnTxL/2dmZPU4XF/bUfHxYFKBiRf7Zw8O4NhQtyl7sr7+yfmvt2gyYBQvyq4sLj3N359IOpRIHYKW4hGTtWhYK+P13k/x6RByyzEMIka5dvMienm4HC40mdsH91avAhx9yPWGePKw8E/drnjwsE2eu8msJA9+WLdw4uXRpJvD4+DCBp3//2Pqox48Da9Zw3lHX+yxd2jztS4+kko4QQvw/rZaBSF/Wau/erJE6Y4bhQ6TmFhPDQuJ37vCxYweHdb28+LVFCyb4FCtm7ZbaJ2PiicxBCiHSNSenpJd0TJrEGq4NGrA3aQucnTmnWbs2101Oncp9GZct4/ZU1atLcLQUCZBCCIeVOTP3TezRg5swjxvHGqu2xM0NyJqVFXK+/Za9Xdsf90sfJEAKIRyaRgN8/jn3Qrx1C6hWDTh82Nqtim/WLO7W8ddfLCiwZ4+1W+QYJEAKIQSAnDmBefOYDfrVV5yfNLSSjblpNCy4Hh7OvRunTZNepCVIgBRCiDjq1+fyiRw5WMh8+HDDqtlYwuDBnJ+8fBn44w9rt4brQdMzCZBCCJFApkzAmDHAggUsH9eoERfir1+fdNUbS+nThxsjT5kCfPkll6tYS9zasemRBEghhEhCvnzA0KHMcP3uO2aSFisG/PCDdQNTvnysuxoayp1Bhgzh7h+WFB1t2oLvtkgCpBBCpMDJiVmuq1dz02MPDxYYaNyYZd6swcuLgTokhG376ism80RHW+b+z5/H38cyPZIAKYQQRvD0ZHm4y5fZcxs71npBskYNVgmaMoWJO7lycd50xw7z3/vJE1YgSs8kQAohRCo4OQHvv895uN69LT/EqVOqFDBzJmu7VqrEYeCTJ4EvvgCuXDHffZ8+ZSm+9EwCpBBCpEG5ctzj8bvvrNeGggW5RnLQINaeHTKEhdCnTAEGDuTSEFOTHqQQQogUDR7MpSHbtlnungnXQebKxWo7EyYA//7LOcqZM4EOHYCvv+ZQbEyM6e7/8KH0IIUQQqTAxYVLQnr1stxQa3h44ueyZuVWXmvXcqgV4I4fa9Zwh5JWrYATJ9J+b6X4YaBkybRfy5ZJgBRCCBOoUAHo3Bn43//Mf69z57gVlj4uLsxmvXwZGD8+dkutr79m8Fy2jNtnpSWQb9rEDN68eVN/DXsgAVIIIUxkyBCuj9y0ybz3mTUr+UCs0XB9ZK5cQL9+sUs/3N05N/nll0DHjsD8+cbfOzoamDOHhdPTOwmQQghhIi4uLAHXpw/w+LF57vHiBQsEJNWDjKtbN67X7Ngx/pBs1arAunXAkSPA338bd/8//+T1MmY07jx7JAFSCCFMqEwZZpN27WqeguILF7IogKE++ogZtp9/zsX9Os7OTOJZvBi4ds2wa4WGcu7x00+NarLdkgAphBAm1rMng6Opa5VqtcDevdzg2RjVqgEjRrDyTly6IDlggP6kn4QmT+bQrkZj3P3tlQRIIYQwMY2G83sTJgCXLpnuutu3A02apC5AVavGLb0OHoz/fO7cDJ59+jAAx8Rw0+hXr9hjfPmSRQEuXQLu3eOaS3MNH9sajVK2v6tYSEgIPDw8EBwcDHd3d2s3RwghDLJlCzBsGDdgdnVN+/XatWOCjJtb6s5/9Qr47DNg1SouCdGJjgZq1gSePQN8fbmbSYYM7GHqHqdOAd7eQFgY10B+9RXQowePsyfGxBPpQQohhJl89BHw7rvAjz+m/VqhoVx3mNrgCHDrrm+/BTp1YnDbsYPbd/Xpw0IC69bxPj17AnPnMlt2+nTgl1/Y0/zjDwbEY8e4o0irVsCePWl/bbZKepBCCGFGr19zL8lhw7ivpK149IgFBf74g8OmP/4IvPce8OYNk4y8vVkhyNkZWLGCvcyDBxlMy5blNV69AiZN4tKWMWMMy6y1NulBCiGEjcicmTttfPMN65faCk9PzjX26ME6rmfPAm3bMpGnfXvue9mmDecdV65kDzIiIjY4AuyRjhjBNZfDh/Pr69dJ33PDBhZTsBcSIIUQwszKlWOvrHNn8yz9SI2lS5l8068fkD8/v65Zw+LmR44AO3ey2HnHjpyTrF+fPcZjxxJfq1AhBtk6dRhU162L/zqDgjise/06MG+epV5h2skQqxBCWIBSwCefAPXqMRhZ09atLBAwY0bKGbGdOwMFCjCbddQo9oRHjGBpPX2iozl3uXMnt9wKDmbx9PHjgaJFTf1KjGe2IVZ/f39Ur14dbm5uyJs3L/z8/HDFgA3HVq9ejVKlSiFTpkwoX748tmzZYsxthRDC7mk07D398Qdw+rT12hEQACxaxHakFBxv3WLFnHHj2APs2JFzjcOHA1ev6j8nQwbujzluHOcnZ85kwXQPD9O/FnMzKkDu27cPvXr1wpEjR7Bjxw5ERUWhcePGCE9mhemhQ4fQrl07dO3aFadPn4afnx/8/Pxw/vz5NDdeCCHsSc6cXB/Zq5f1guS4ccDUqSkvz4iJYVGAPn34fY0awPffM/N1wQIOxd65o//cyEgm+Cxdys2b69fnEPM331j3w4Gx0jTE+uTJE+TNmxf79u1D3bp19R7Ttm1bhIeHY1Oc6r21atVCpUqVMGvWLIPuI0OsQoj0ZPduoG9fLgEZOzb5fRUjI1kqLnNmLvavXh14553ke39v3jD71MUl/vPbtnEnkMGDU27j4MFA3brAxx/HPnfuHK8xeDCzYLt25XKQfPninztoEGvANmwY//kHDzj8evYsE5eskfVqTDxJ0xLP4P/fLyVnzpxJHnP48GF8l2Cr7SZNmmDDhg1JnhMREYGIiIi334eEhKSlmUIIYVPef589qenTGfC++45rDxMGNK2WWaYdOgDFiwPHjzMg3brFAFmsWGzQzJ+f50RHc32iVsus0po1Y5+fPh1YvTrl9i1cyKzVjz/mUOqaNcDRowzW9++zWEDFihxu7d4d+Oef2HM3bWJ2a8LgCHAuc8wYIDCQhdQXLwZy5EjVr9AiUh0gtVot+vfvj3fffRflypVL8rigoCB4enrGe87T0xNBQUFJnuPv749Ro0altmlCCGHzXFy4L+MXX3ANYtWq3IpKt1ZSKQ5jNm/OgApwbWLr1vyzVgvcuMGgOWkSg46zM3D3Lvdq7NiR84zLljFQLl8em5GanIMHGeTKlWNSUYkS/Dp0KJd8TJ/OgH32LNsYtyLP/fscQl61Kvl75MvHuq5du7JdpqgyZA6pDpC9evXC+fPncTBhYT8TGDp0aLxeZ0hICLy9vU1+HyGEsLa8edkrPHmS2a3LljGQ7d3LINWqlf7znJzYqyxenEEWYI/s+HH21CZM4O4doaFArVrs1R0/rv9aSjHg/fkniwd07sxAPGJE/KHczJnZiyxRgo/oaAZlgH/u2xf4/XcG2aJFGdCTUqoU9898/jzxEK2tSFWA7N27NzZt2oT9+/ejYMGCyR7r5eWFR48exXvu0aNH8PLySvIcV1dXuNrqRwohhDCDqlWBAwe4BOPXXxkwe/Rgj9CQubrTp5lA89dfDGpduvD5iAjWUd2xgz2/8eN5PaW4pnHBAgZFXTbKmDEsFKBvei5TJs5v6ly5wkAHsJfapQur8vz2G3u/KalRI+VjrMmoLFalFHr37o3169dj9+7deOedd1I8x9fXF7t27Yr33I4dO+Dr62tcS4UQIp3TaFi/9e+/gRMnOPTYtCl38Fi5Mn5wiuvJE+Cnn/Sva3R1ZQHyn34C/P2Br7/mnpU+Ppw/vHePWaYHDnBfyNy52ZPVFUaP279JGCDPnuVc5K5dLCKgS+gJDLTdXqExjOpB9urVC8uWLcPGjRvh5ub2dh7Rw8MDmTNnBgB07NgRBQoUgL+/PwCgX79+qFevHn755Rc0a9YMK1aswIkTJzBnzhwTvxQhhEg/8uVjtuigQaxsM38+v2/enD21ypUZDKOjmeAzZQqQLZv+a50/z+HS6GjOT77/PlClCn8WFcUlHzExHFZ98waoVInHHzzI4gCPH/N+33wTf9/I//5jW0aNYiJPXOlhz0ijepAzZ85EcHAw6tevj3z58r19rFy58u0xd+/eRWBg4Nvva9eujWXLlmHOnDmoWLEi1qxZgw0bNiSb2COEEII0GvYA584FLl5kxuqAAQxwU6fyz926Ja5SoxT3j2zbloUBWrbkHOKjR0CuXLHHubjwHk5O7DXmyQNs3sydQwYNYuBs0oTBd8AAbuG1cyeD7bVrnOv87TcWFAA4R5kwG9deSak5IYSwQ9eusUrNsmWcq+zenUs93rzhAv2NG5kR+9VX8bfIuniRaxDDw3lewtmu3bu5jKRLF+DQISbdhIUBpUuz3NzKlRyeffqUZeSGDeND59w5JvtMnWqRX4PRZDcPIYRI54oXZzJP1apAs2bMCK1UiUOoHh7A+vXMKk24f2SZMpyrnDyZc4etWnE7q6go/lw3z6jRsJDBypXA7NkMqEeOcIi3cmUG4Fat2Ju8dCn2+nfvxq69tHcSIIUQwo7lycNA6eTE4c7mzbncY84cLvFI7rxhwxgcIyOBTz8FJk5k1quuTsvTp1z32K8f1zsuWMBEnJAQrrHMm5eJP2PH8mdKcceOYsUs89rNTYZYhRDCzijFodB58xjcKlRgUo2OVsuScIsXMxD27Mkh0pSuuW9fbNGBUqW4drJtWyb16NY7XrrEQgDffAP4+XGt5ty5HLY9e5aBevx41p1Nq5Mned1Jk5hdawoWKzUnhBDCciIi2ONbt47bZs2axQzUhJsQOzlxuchHH7HazuzZXI7Rvn3S19ZouNzD25s90okT41fJ0SlRguXnvLzY66xTh+f26cP1lh9/zICc2gCpC9QzZ3IYeeJE0wVHY0mAFEIIG/fsGYPc8ePMNF27Nv5uHFFR7EnqMknjKlqUQcZQWbJwWYe+4AiwJ6nV8s8DB8ZfzlGlClC7Nnt9lSpx2ytDl3totcyenT+flX/mztVfrMCSZA5SCCFs3IYN7DGuWwe0aZN4q6patZhAYwr58rEXl9zkm7s7M1oTBr/ISAbYefO4LOSrr+Kvm0xKUBB3Drl3j1m5339v/eAISIAUQgib17UrM0qT6o01bgz8+6/p7leuHHDhQtI/L1+ew6kJ3bkDFCrEdn71FZeR9OwZ2+NMytWrLIP37bdcq2krJEAKIYSdq1QJOHPGdNdr1ozDnUnx9eWw7a1biX8WN7DWqsVSeePGJX+/c+eSrzd77x6Xj1iaBEghhLBzTk7A69fAzZumuV7VqqwFm9zP//qL23XF3dq3eHH2dNeujX2uXTuuq1y/Xv+19u1jME6ucPmVK1xvaWkSIIUQws6Fh3Pu7/vvUx7ONISTE9c4vnqV9DGenkyo2bgx/vP9+3N5ybNnsc+NHs2lIQEB8Y9dsoSPjRuTz1T18uI8paVJgBRCCDu3fDl36WjdmqXhTGH6dAbd5OTKxSCqq8IDMMt13DhW9on73KxZfO7pUyYAjRnDHu+cOfqzb+PKl49rMy1NlnkIIYQdUwrYtInDms7OrKF6/jwTbSyhbl3u+tGgQexzZcsyWWfLFq7FBFj+bsoUFlbPkoXFDRo04NDpy5es63rxIo8LC4t96DJqk9rqy5wkQAohhB07coTzd7pKN7/8AnTsyO2nLLHvfIsWvGfcAAlwuLd1axYS0C3ZKF6cGypfvcqAd/gwA6KHB9drrl7NLb1Kl+YykaxZY1+XNUiAFEIIOzZvHvDzz7Hf58jBecBRo+I/by7e3sD9++zlxV2G4uLCNvzwAwsH6AQFAVu3cp4zodKlgV69GCj1/dzSbKAJQgghUuPJEwYmT8/4zzdsyEX7+/dbph2VKrEOa0JVqrB3uGcPv3/0iMk4SQU/Ly/gs8/iB1RrkgAphBB26s8/WURAn7FjWeQ7JMT87WjZMv5yj7iGD2cWa0QEs1Vbtkz+Wm3bconJtWsmb6bRJEAKIYQdionh/GPt2vp/nikTM0X/9z/zt6VcOSYGJXT/Pivp1K7NTNU9e7gzSHI0Gu5VOXgwX6M1SYAUQgg7pMsQTa4YeKVKwDvvxF+4bw4aDbNTdWswIyNZaWfIEOCnn7jsIySEQTulJR0A12B+8QUwdap5250SCZBCCGGHli5lEEnJ4ME81tzrCJs04dzizp3AJ5+wV7lkCQM0EH/JhyE++4wl6K5cMU97DSEBUggh7MyNG0xoyZYt5WOdnbm/Y//+ye/QkVbnzwPVqwP//ccs1ITBcNs21mU1xuTJXC5iraFWWeYhhBB2ZtEiVs4xVOHCDFgzZ3LHDFNRipmys2cD+fOzBzlnDrB7N1CsGNc2Fi3KogHR0YYF9Lhy5wY6dWKAHzTIdO02lPQghRDCjsTEsKapsZVyOnZkUo8phiyjo1lbtVUrXnPGDPb2jh7lDh+tWrEWa9GiLFbw11/Ahx+m7l6tWgGXLrHKjqVplDJnp9s0QkJC4OHhgeDgYLjbwi6aQghhJTt3ApcvA717G3/u06dcFrJmDRfyGys8HFiwANixg4Hriy+STrp5/pyFCl6+5HZVGzcySSc1nj9n4o+XV+rOj8uYeCI9SCGEsCPLlwOff566c3Pn5ibGY8cad96jR1zP2Lkze4UbNvDPCYPjpUvsUQJAzpzsVfr6sjc5bhxrq6ZGzpymCY7GkjlIIYSwE2FhrGGa3NZQKWnaFPjnH+Cbb2K3tAoP59e4W2XFHVvMkwfo0IHrKpMSGgoMGMCkoBEjWB9WV0h9/37g5EnOg/72G/eTtAcSIIUQwk6sW8clFGk1YQLQrBnrnubNy6LgWbKkvjB4TAzQvTswfjxQsiSXaBQvzl5fzZq8dokSrBNrqV1GTEHmIIUQwk60acPNiE2xS8eJE9zweMaMtF9r8GDu2tGiRexz16+zvSVLsid57BiHW4sV48+VYo817te4fx4+nHVcTc2YeCI9SCGEsAN377Iouam2sKpWDdi+3bD6qAEBDFiVK3OYtHr12J/Nnct2xQ2Ouva2a8flGTExwN9/c6j1+HFuefX996zA4+TErwn/bAt9IQmQQghhBw4cAOrXN+01hwwBPv2Uw6AJk2C0Wm5L9ddfTMz54w9mk06YwKHY774DXrwATp8Gpk9PfO0LF1iHFeDxrVrxATDJqGhR074Wc5AAKYQQdiAkBPDxMe0141bZWb6cPbfwcBYi2LaNaxcXLOAcJcC9H5ctAx4/ZrLN+vXsVfbuzUSeuI8GDXitTJmADAkiTZ48vEbevKZ9PaYmc5BCCGEHJk5kvdOKFU1/7YULuVbx1SuWsevUidmuhmxarBTXOj55ov8REhI7pJolC8+ZN4/VfRo1Mv1rSYnMQQohRDoTEgK4uZnn2p06sRzcgAGAv79x52o0zE7NkYOZqvrs3ct9Hrt0Afz8gAoVgIMHrRMgjSGFAoQQwg6EhpovcUWjYY3WQ4c41Gpq9etzicrdu5x/zJhR//6RtkZ6kEIIYQeCg83XgwSAxo2ZmHPyJPD6NXt7puTiAvTrxwA5ahQTfGyd9CCFEMLG6bJFt2833z0qVQLOngUmTQJu3wZ+/9089/H05NrLbNmAqCjz3MNUJEAKIYQN02pZ8WbpUu6gceKEee7j5MShzzdvgNGj2Ys0dj7SGCVLWnczZENIgBRCCBvm78+F/OXKca/FMWOAO3fMc6933+U8JMCsUzc3YNgw82y0XLw4cOqU6a9rShIghRDCRu3YwcX5bdvy+yxZGCR79+bSClNr2JC9VJ3evbn28oMPWFTgr79YMi44OPX30Gq5rGTpUpans2USIIUQwgbdu8e5uvHj4z/v6ck1kV9/zT0STalYMS7i/9//gIgIPte9O/Dvv6yK4+nJ5RlffMHdPZYt4/ITQx05wmo6Gg23zCpSxLTtNzUpFCCEEDYmIoKFvmfMAAoU0H/M3r0MULNnM+CY0r59wC+/sKxc6dL6jwkPZ7Wdf/5hoG7ShEFT344gDx5wC6y8eYGhQ82bjZsSY+KJBEghhLAxYWHcDaNSpeSP274dqFsXyJzZ9G14/pwl6N57j73V5ILw69csWn73LjdJ1rl5E1i9msXOR46M3cnDmiRACiGESDOl2EM9fhz488+Uj580icEdYCGAIkW4N2SNGomPvX+fJegsTUrNCSGESDONBujRg0Ooc+cC3bolPiY6mvOSGzawB3nvHtCnD3uMSfU6//uP+1rOmmXO1qedJOkIIYRIVp8+TLA5diz+85GRQMeOHEL97jvOQf77L3f5SGopyv37TDKaMsXszU4zCZBCCCGSpdGwss7o0dymSidjRiYK9enD5SAFCgBffgn07cvndMOtOm/esObrjBncBsvWSYAUQgiRoqxZgalTudwjOlr/MTVrcn3jokUsbNCjB9c9ApzP7NuX6ymTysy1NRIghRBCGKRoURYxHzYs6WM8PID585mxevcuK/IADK7VqwO1a1umraYgSTpCCCEM1qwZ68GuXs0MVX00GqBrV6BaNR7/4AGQO7f5CqCbiwRIIYQQRhk+nNV0ypYFypSJ/7MXL4DNm5msExMD/PAD5x5797ZOW9NCAqQQQgijODkBM2cya3X5cgbFjRu5fMPNjb3GmTM5b2nPJEAKIYQwWo4cnIv09QU+/BBo0YIZqhnSUVRJRy9FCCGEJR05wvWMH3xg7ZaYh2SxCiGEMFpUFAumN2pk7ZaYjwRIIYQQRluzhlmspt5JxJYYHSD379+P5s2bI3/+/NBoNNiwYUOyx+/duxcajSbRIygoKLVtFkIIYUVKAStXxm7knF4ZHSDDw8NRsWJFTJ8+3ajzrly5gsDAwLePvHnzGntrIYQQNmDNGm7F5eJi7ZaYl9FJOk2bNkXTpk2NvlHevHmRPXt2o88TQghhO16/Zim5tWut3RLzs9gcZKVKlZAvXz588MEH+O+//5I9NiIiAiEhIfEeQgghrG/8eO7ckTGjtVtifmYPkPny5cOsWbOwdu1arF27Ft7e3qhfvz5OnTqV5Dn+/v7w8PB4+/D29jZ3M4UQQqTg2jVuV9WggbVbYhkapZRK9ckaDdavXw8/Pz+jzqtXrx58fHywePFivT+PiIhARETE2+9DQkLg7e1t0A7QQgghTE8pJuX8/jvg5WXt1qReSEgIPDw8DIonVikUUKNGDRw8eDDJn7u6usLV1dWCLRJCCJGcNWuAunXtOzgayyoB8syZM8iXL581bi2EEMJIISHAkiXAunXWbollGR0gw8LCcP369bff37p1C2fOnEHOnDnh4+ODoUOH4sGDB1i0aBEAYMqUKXjnnXdQtmxZvHnzBvPmzcPu3bvx77//mu5VCCGEMJvRo4EffwScna3dEssyOkCeOHECDeLM0H733XcAgE6dOmHhwoUIDAzE3bt33/48MjISAwcOxIMHD5AlSxZUqFABO3fujHcNIYQQtunsWSAiAqhRw9otsbw0JelYijGTqkIIIUxDqwU++QSYP5+7d6QHxsQTqcUqhBBCr4ULAT+/9BMcjSXbXQkhhNCrQAGgcWNrt8J6JEAKIYTQq0kTa7fAumSIVQghhNBDAqQQQgihhwRIIYQQQg8JkEIIIYQeEiCFEEIIPSRACiGEEHpIgBRCCCH0kAAphBBC6CEBUgghhNBDAqQQQgihhwRIIYQQQg8JkEIIIYQeEiCFEEIIPSRACiGEEHrYxXZXSikA3AlaCCGESC1dHNHFleTYRYAMDQ0FAHh7e1u5JUIIIdKD0NBQeHh4JHuMRhkSRq1Mq9Xi4cOHcHNzg0ajSfP1QkJC4O3tjXv37sHd3d0ELbQ/jv47kNfv2K8fkN+Bo75+pRRCQ0ORP39+ODklP8toFz1IJycnFCxY0OTXdXd3d6h/GPo4+u9AXr9jv35AfgeO+PpT6jnqSJKOEEIIoYcESCGEEEIPhwyQrq6uGDFiBFxdXa3dFKtx9N+BvH7Hfv2A/A4c/fUbwi6SdIQQQghLc8gepBBCCJESCZBCCCGEHhIghRBCCD0kQAohhBB6OEyAfP78Odq3bw93d3dkz54dXbt2RVhYWLLn1K9fHxqNJt6jR48eFmpx2k2fPh2FCxdGpkyZULNmTRw7dizZ41evXo1SpUohU6ZMKF++PLZs2WKhlpqHMa9/4cKFif6uM2XKZMHWmtb+/fvRvHlz5M+fHxqNBhs2bEjxnL1796JKlSpwdXVFsWLFsHDhQrO301yMff179+5N9Pev0WgQFBRkmQabmL+/P6pXrw43NzfkzZsXfn5+uHLlSornpbf3gLRymADZvn17XLhwATt27MCmTZuwf/9+dO/ePcXzunXrhsDAwLePiRMnWqC1abdy5Up89913GDFiBE6dOoWKFSuiSZMmePz4sd7jDx06hHbt2qFr1644ffo0/Pz84Ofnh/Pnz1u45aZh7OsHWFEk7t/1nTt3LNhi0woPD0fFihUxffp0g46/desWmjVrhgYNGuDMmTPo378/vv76a2zfvt3MLTUPY1+/zpUrV+L9G8ibN6+ZWmhe+/btQ69evXDkyBHs2LEDUVFRaNy4McLDw5M8J729B5iEcgAXL15UANTx48ffPrd161al0WjUgwcPkjyvXr16ql+/fhZooenVqFFD9erV6+33MTExKn/+/Mrf31/v8W3atFHNmjWL91zNmjXVN998Y9Z2mouxr3/BggXKw8PDQq2zLABq/fr1yR4zePBgVbZs2XjPtW3bVjVp0sSMLbMMQ17/nj17FAD14sULi7TJ0h4/fqwAqH379iV5THp7DzAFh+hBHj58GNmzZ0e1atXePteoUSM4OTnh6NGjyZ67dOlS5M6dG+XKlcPQoUPx6tUrczc3zSIjI3Hy5Ek0atTo7XNOTk5o1KgRDh8+rPecw4cPxzseAJo0aZLk8bYsNa8fAMLCwlCoUCF4e3ujZcuWuHDhgiWaaxPS099/WlSqVAn58uXDBx98gP/++8/azTGZ4OBgAEDOnDmTPEb+DSRmF8XK0yooKCjRUEmGDBmQM2fOZOcYvvjiCxQqVAj58+fHuXPn8P333+PKlStYt26duZucJk+fPkVMTAw8PT3jPe/p6YnLly/rPScoKEjv8fY4B5Oa11+yZEnMnz8fFSpUQHBwMCZPnozatWvjwoULZimUb2uS+vsPCQnB69evkTlzZiu1zDLy5cuHWbNmoVq1aoiIiMC8efNQv359HD16FFWqVLF289JEq9Wif//+ePfdd1GuXLkkj0tP7wGmYtcBcsiQIZgwYUKyx1y6dCnV1487R1m+fHnky5cPDRs2xI0bN1C0aNFUX1fYHl9fX/j6+r79vnbt2ihdujRmz56NMWPGWLFlwhJKliyJkiVLvv2+du3auHHjBn777TcsXrzYii1Lu169euH8+fM4ePCgtZtid+w6QA4cOBCdO3dO9pgiRYrAy8srUXJGdHQ0nj9/Di8vL4PvV7NmTQDA9evXbTpA5s6dG87Oznj06FG85x89epTk6/Xy8jLqeFuWmtefkIuLCypXrozr16+bo4k2J6m/f3d393Tfe0xKjRo17D6o9O7d+21SYkojIenpPcBU7HoOMk+ePChVqlSyj4wZM8LX1xcvX77EyZMn3567e/duaLXat0HPEGfOnAHA4RhbljFjRlStWhW7du16+5xWq8WuXbvi9ZLi8vX1jXc8AOzYsSPJ421Zal5/QjExMQgICLD5v2tTSU9//6Zy5swZu/37V0qhd+/eWL9+PXbv3o133nknxXPk34Ae1s4SspQPP/xQVa5cWR09elQdPHhQFS9eXLVr1+7tz+/fv69Kliypjh49qpRS6vr162r06NHqxIkT6tatW2rjxo2qSJEiqm7dutZ6CUZZsWKFcnV1VQsXLlQXL15U3bt3V9mzZ1dBQUFKKaW+/PJLNWTIkLfH//fffypDhgxq8uTJ6tKlS2rEiBHKxcVFBQQEWOslpImxr3/UqFFq+/bt6saNG+rkyZPq888/V5kyZVIXLlyw1ktIk9DQUHX69Gl1+vRpBUD9+uuv6vTp0+rOnTtKKaWGDBmivvzyy7fH37x5U2XJkkUNGjRIXbp0SU2fPl05Ozurbdu2WeslpImxr/+3335TGzZsUNeuXVMBAQGqX79+ysnJSe3cudNaLyFNevbsqTw8PNTevXtVYGDg28erV6/eHpPe3wNMwWEC5LNnz1S7du1UtmzZlLu7u/rqq69UaGjo25/funVLAVB79uxRSil19+5dVbduXZUzZ07l6uqqihUrpgYNGqSCg4Ot9AqM98cffygfHx+VMWNGVaNGDXXkyJG3P6tXr57q1KlTvONXrVqlSpQooTJmzKjKli2rNm/ebOEWm5Yxr79///5vj/X09FQfffSROnXqlBVabRq6ZQsJH7rX3KlTJ1WvXr1E51SqVEllzJhRFSlSRC1YsMDi7TYVY1//hAkTVNGiRVWmTJlUzpw5Vf369dXu3but03gT0PfaAcT7O3WE94C0ku2uhBBCCD3seg5SCCGEMBcJkEIIIYQeEiCFEEIIPSRACiGEEHpIgBRCCCH0kAAphBBC6CEBUgghhNBDAqQQQgihhwRIIYQQQg8JkEIIIYQeEiCFEEIIPSRACiGEEHr8Hx1DLAmMNwhtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:01.040 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:01.040 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:01.043 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:01.043 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XZ Projection of a collection of cylinders\n"
     ]
    }
   ],
   "source": [
    "myCollection = CylinderCollection()\n",
    "# The below file is one of our several testing files, featuring only\n",
    "# the trunk of a tree and one of its branches\n",
    "myCollection.from_csv(\"charlie_brown.csv\")\n",
    "\n",
    "# by filtering for cyl_id>100, we are only plotting the\n",
    "# cylinders that are part of the branch\n",
    "myCollection.draw(\"XZ\", show=True, save=False)\n",
    "myCollection.draw(\"XY\", show=True, save=False)\n",
    "myCollection.draw(\"YZ\", show=True, save=False)\n",
    "# myCollection.draw(\"3D\", show=False, save=False, file_name_ext=\"2d_3d_comparison\")\n",
    "\n",
    "\n",
    "print(\"XZ Projection of a collection of cylinders\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd86c92f",
   "metadata": {},
   "source": [
    "This same 'draw' function can also be used to highlight features (that is, groups of cylinders) of interest in a collection, and to filter a collection to only display smaller sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "615abe1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:21.455 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 462 cylinders matched criteria\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:21.455 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 462 cylinders matched criteria\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:21.460 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:21.460 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:21.749 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 462 cylinders matched criteria\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:21.749 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 462 cylinders matched criteria\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">2024.10.14 22:24:21.751 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n",
       "</pre>\n"
      ],
      "text/plain": [
       "2024.10.14 22:24:21.751 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myCollection = CylinderCollection()\n",
    "# The below file is one of our several testing files, featuring only\n",
    "# the trunk of a tree and one of its branches\n",
    "# myCollection.from_csv(\"10_MediumCollection.csv\")\n",
    "\n",
    "myCollection.draw(\n",
    "    \"XZ\",\n",
    "    filter_lambda=lambda: cyl_id > 50,\n",
    "    highlight_lambda=lambda: branch_order > 0\n",
    ")  # noqa\n",
    "\n",
    "\n",
    "myCollection.draw(\n",
    "    \"XZ\",\n",
    "    filter_lambda=lambda: cyl_id > 50,\n",
    "    highlight_lambda=lambda: cyl_id > 100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4cb1f2",
   "metadata": {},
   "source": [
    "To understand how these features support canoPyHydro's titular 'flow identification' functionality, check out [Flow Identification and Drawing](flow_identification_drawing.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f23faa",
   "metadata": {},
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525152f2",
   "metadata": {},
   "source": [
    "### Forester"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ada9f74",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f7f38d25",
   "metadata": {},
   "source": [
    "Forester objects allow users to conveniently create and manage Cylinder Collections. In particular, Foresters are useful for reading in and processing QSM files.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1be0566",
   "metadata": {},
   "source": [
    "When a Forester object is created, available file names are read from the default directory, './data/input/'. \\\n",
    "This list of available files can be accessed through the Forester.file_names attribute, as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3661ce99",
   "metadata": {
    "tags": [
     "myTag"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files available in ./data/input/: ['data/input/Secrest10-08_000000.csv', 'data/input/10_MediumCollection.csv', 'data/input/1_TenCyls.csv', 'data/input/6_LargeTree.csv', 'data/input/charlie_brown.csv', 'data/input/example_tree.csv', 'data/input/minimal_tree.ods', 'data/input/minimal_tree.csv', 'data/input/5_SmallTree.csv']\n"
     ]
    }
   ],
   "source": [
    "# Creating a new Forester object\n",
    "myForester = Forester()\n",
    "print(\n",
    "    f\"Files available in {myForester.directory}: {list(map(str,myForester.file_names))}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece770db",
   "metadata": {},
   "source": [
    "Optionally, a custom path may be passed to the Forester object, In which case, the Forester will look for files in the passed directory instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3d3873f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files available in data/test/: ['data/test/10_MediumCollection.csv', 'data/test/1_TenCyls.csv', 'data/test/3_HappyPathWTrunk.csv', 'data/test/2_EZ_projection.csv', 'data/test/4_LargeCollection.csv', 'data/test/5_SmallTree.csv', 'data/test/6_LargeTree.csv', 'data/test/8_DripPathMidBranch.csv', 'data/test/7_DripPathAdjToTrunk.csv', 'data/test/9_DripOnTrunk.csv']\n"
     ]
    }
   ],
   "source": [
    "# Passing a custom directory to the Forester object will change the directory attribute\n",
    "directory = \"/data/test/\"\n",
    "myForester = Forester(\"data/test/\")\n",
    "print(\n",
    "    f\"Files available in {myForester.directory}: {list(map(str,myForester.file_names))}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ae18fe",
   "metadata": {},
   "source": [
    "The 'qsm_to_collection' function can be used create CylinderCollections from a specified file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27e6af2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024.10.14 21:09:45.801 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/input/example_tree.csv\n",
      "2024.10.14 21:09:46.438 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - example_tree.csv initialized with 9998 cylinders\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forester has 1 CylinderCollection, imported from example_tree.csv\n"
     ]
    }
   ],
   "source": [
    "from src.canopyhydro.Forester import Forester\n",
    "# Importing a QSM file as a CylinderCollection\n",
    "myForester = Forester(\"./data/input/\")\n",
    "myForester.qsm_to_collection(\"example_tree.csv\")\n",
    "\n",
    "cylCollections = myForester.cylinder_collections\n",
    "firstCollection = cylCollections[0]\n",
    "\n",
    "print(\n",
    "    f\"Forester has {len(cylCollections)} CylinderCollection, imported from {cylCollections[0].file_name}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd8c24b",
   "metadata": {},
   "source": [
    "If 'All' is provided as the file name, all of the files in the given directory will be read in as CylinderCollections. \\\n",
    "(Note that this may require a significant amount of memory.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "984272e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024.10.14 21:13:20.160 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/10_MediumCollection.csv\n",
      "2024.10.14 21:13:21.528 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 10_MediumCollection.csv initialized with 10501 cylinders\n",
      "2024.10.14 21:13:21.530 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/1_TenCyls.csv\n",
      "2024.10.14 21:13:21.538 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 1_TenCyls.csv initialized with 10 cylinders\n",
      "2024.10.14 21:13:21.540 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/3_HappyPathWTrunk.csv\n",
      "2024.10.14 21:13:21.556 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 3_HappyPathWTrunk.csv initialized with 120 cylinders\n",
      "2024.10.14 21:13:21.558 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/2_EZ_projection.csv\n",
      "2024.10.14 21:13:21.562 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 2_EZ_projection.csv initialized with 5 cylinders\n",
      "2024.10.14 21:13:21.564 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/4_LargeCollection.csv\n",
      "2024.10.14 21:13:24.783 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 4_LargeCollection.csv initialized with 51524 cylinders\n",
      "2024.10.14 21:13:24.787 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/5_SmallTree.csv\n",
      "2024.10.14 21:13:24.833 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 5_SmallTree.csv initialized with 517 cylinders\n",
      "2024.10.14 21:13:24.837 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/6_LargeTree.csv\n",
      "2024.10.14 21:13:30.074 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 6_LargeTree.csv initialized with 83474 cylinders\n",
      "2024.10.14 21:13:30.076 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/8_DripPathMidBranch.csv\n",
      "2024.10.14 21:13:30.095 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 8_DripPathMidBranch.csv initialized with 131 cylinders\n",
      "2024.10.14 21:13:30.097 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/7_DripPathAdjToTrunk.csv\n",
      "2024.10.14 21:13:30.110 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 7_DripPathAdjToTrunk.csv initialized with 117 cylinders\n",
      "2024.10.14 21:13:30.112 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/9_DripOnTrunk.csv\n",
      "2024.10.14 21:13:30.127 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 9_DripOnTrunk.csv initialized with 198 cylinders\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forester created 10 CylinderCollections, imported from the following files\n",
      "      ['10_MediumCollection.csv', '1_TenCyls.csv', '3_HappyPathWTrunk.csv', '2_EZ_projection.csv', '4_LargeCollection.csv', '5_SmallTree.csv', '6_LargeTree.csv', '8_DripPathMidBranch.csv', '7_DripPathAdjToTrunk.csv', '9_DripOnTrunk.csv']\n"
     ]
    }
   ],
   "source": [
    "# Reading in all files in the directory as collections\n",
    "myForester = Forester(\"./data/test/\")\n",
    "myForester.qsm_to_collection(\"All\")\n",
    "cylCollections = myForester.cylinder_collections\n",
    "firstCollection = cylCollections[0]\n",
    "print(\n",
    "    f\"\"\"Forester created {len(cylCollections)} CylinderCollections, imported from the following files\n",
    "      {list(map(lambda x: x.file_name,cylCollections))}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d283d9cd",
   "metadata": {},
   "source": [
    "Putting all that we have learned together, you can see that all of the statistics available through canoPyHydro can be generated with the below 10 lines of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a310a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = Forester(\"data/input/\")\n",
    "forest.get_file_names()\n",
    "forest.qsm_to_collection(file_name=\"5_SmallTree.csv\")\n",
    "collection = forest.cylinder_collections[0]\n",
    "collection.project_cylinders(\"XY\")\n",
    "collection.initialize_digraph_from(in_flow_grade_lim=-0.16)\n",
    "collection.find_flow_components()\n",
    "print(\"finished_find_flow_components\")\n",
    "collection.calculate_flows()\n",
    "pickle_collection(collection)"
   ]
  }
 ],
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