examples/Python/2_ALLN_typing_morphology.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ALLN - Antenna lobe local neuron\n",
    "This notebook contains basic analysis about local neurons in the antenna lobe present in the `hemibrain` dataset. Different analysis include basic summary of the neuron types, plotting etc.\n",
    "\n",
    "### Dependencies\n",
    "You will need the following dependencies which can be installed using `pip`:\n",
    "1. [neuprint-python](https://github.com/connectome-neuprint/neuprint-python): `pip3 install neuprint-python`\n",
    "2. [navis](https://navis.readthedocs.io/en/latest/): `pip3 install navis` \n",
    "\n",
    "In addition, you will need to have your neuPrint authorization token set as environmental variable. See neuprint-python's [docs](https://connectome-neuprint.github.io/neuprint-python/docs/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection\n",
    "sns.set_color_codes('muted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_fontsize = 16 \n",
    "title_fontsize = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "navis version: 0.3.2\n"
     ]
    }
   ],
   "source": [
    "import navis\n",
    "print('navis version:', navis.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import navis.interfaces.neuprint as neu\n",
    "client = neu.Client('https://neuprint.janelia.org/', dataset='hemibrain:v1.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#navis display options for frontal view..\n",
    "elev = -180\n",
    "azim = -90\n",
    "dist = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load the datasheet from the google drive, this will contains bodyIds, types and morphological details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://drive.google.com/file/d/1ACJ0u7WxjkUil0jJFd5Bkwl3w1MgwUhZ/view?usp=sharing'\n",
    "path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "al_datasheet = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>voxels</th>\n",
       "      <th>soma</th>\n",
       "      <th>side</th>\n",
       "      <th>...</th>\n",
       "      <th>axon.outputs</th>\n",
       "      <th>dend.outputs</th>\n",
       "      <th>axon.inputs</th>\n",
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       "      <th>axon.length</th>\n",
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       "      <th>pd.length</th>\n",
       "      <th>segregation_index</th>\n",
       "      <th>notes</th>\n",
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       "      <td>66.080358</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>Type: broad LN belonging to the LN1 type (Tana...</td>\n",
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       "      <td>1038</td>\n",
       "      <td>5</td>\n",
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       "      <td>1372.668174</td>\n",
       "      <td>717.893513</td>\n",
       "      <td>440.538674</td>\n",
       "      <td>39.312691</td>\n",
       "      <td>0.423012</td>\n",
       "      <td>Type: weak regional or sparse LNs with a simil...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>333.355105</td>\n",
       "      <td>118.997320</td>\n",
       "      <td>0.048766</td>\n",
       "      <td>Type: weak regional or sparse LNs with a simil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2040301572</td>\n",
       "      <td>198</td>\n",
       "      <td>372</td>\n",
       "      <td>372</td>\n",
       "      <td>1116</td>\n",
       "      <td>Traced</td>\n",
       "      <td>lLN11_R</td>\n",
       "      <td>367013585</td>\n",
       "      <td>True</td>\n",
       "      <td>right</td>\n",
       "      <td>...</td>\n",
       "      <td>216</td>\n",
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       "      <td>29</td>\n",
       "      <td>324</td>\n",
       "      <td>1437.415433</td>\n",
       "      <td>160.300071</td>\n",
       "      <td>918.722654</td>\n",
       "      <td>107.007237</td>\n",
       "      <td>0.021254</td>\n",
       "      <td>Type: weak regional or sparse LNs with a simil...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1826445251</td>\n",
       "      <td>207</td>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>1233</td>\n",
       "      <td>Traced</td>\n",
       "      <td>lLN12a_R</td>\n",
       "      <td>390802154</td>\n",
       "      <td>True</td>\n",
       "      <td>right</td>\n",
       "      <td>...</td>\n",
       "      <td>1221</td>\n",
       "      <td>9</td>\n",
       "      <td>166</td>\n",
       "      <td>211</td>\n",
       "      <td>1667.833740</td>\n",
       "      <td>788.073478</td>\n",
       "      <td>597.583386</td>\n",
       "      <td>32.278918</td>\n",
       "      <td>0.376746</td>\n",
       "      <td>Type: regional LNs that innervate the same are...</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bodyid   pre  post  upstream  downstream  status      name      voxels  \\\n",
       "0  1578826464  1900  7210      7210        7501  Traced  lLN1_c_R  2691885349   \n",
       "1  1670278227   180   271       271        1044  Traced   lLN11_R   373927384   \n",
       "2  1979569670   107   185       185         535  Traced   lLN11_R   271448716   \n",
       "3  2040301572   198   372       372        1116  Traced   lLN11_R   367013585   \n",
       "4  1826445251   207   381       381        1233  Traced  lLN12a_R   390802154   \n",
       "\n",
       "   soma   side  ... axon.outputs dend.outputs axon.inputs dend.inputs  \\\n",
       "0  True  right  ...         2207         5221        2092        5098   \n",
       "1  True  right  ...         1038            5         110         160   \n",
       "2  True  right  ...          431           89         103          71   \n",
       "3  True  right  ...          216          814          29         324   \n",
       "4  True  right  ...         1221            9         166         211   \n",
       "\n",
       "  total.length  axon.length  dend.length   pd.length  segregation_index  \\\n",
       "0  9754.820208  2399.378774  5678.107814   66.080358           0.000033   \n",
       "1  1372.668174   717.893513   440.538674   39.312691           0.423012   \n",
       "2   886.728404   328.465711   333.355105  118.997320           0.048766   \n",
       "3  1437.415433   160.300071   918.722654  107.007237           0.021254   \n",
       "4  1667.833740   788.073478   597.583386   32.278918           0.376746   \n",
       "\n",
       "                                               notes  \n",
       "0  Type: broad LN belonging to the LN1 type (Tana...  \n",
       "1  Type: weak regional or sparse LNs with a simil...  \n",
       "2  Type: weak regional or sparse LNs with a simil...  \n",
       "3  Type: weak regional or sparse LNs with a simil...  \n",
       "4  Type: regional LNs that innervate the same are...  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al_datasheet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter annotations to usable details now..\n",
    "al_annotations = al_datasheet[['bodyid','name','soma','anatomy.group','segregation_index']]\n",
    "#rename then to neuprint types..\n",
    "al_annotations = al_annotations.rename(columns={'bodyid': 'bodyId', 'name': 'type','anatomy.group': 'morpho_group'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>v2contra_LN_sparse_bil</td>\n",
       "      <td>0.005862</td>\n",
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       "      <td>v_LN_sparse_bil</td>\n",
       "      <td>0.209587</td>\n",
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       "      <th>194</th>\n",
       "      <td>2101391269</td>\n",
       "      <td>vLN28_R</td>\n",
       "      <td>False</td>\n",
       "      <td>v_LN_sparse_</td>\n",
       "      <td>0.508483</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>5813055448</td>\n",
       "      <td>vLN29_R</td>\n",
       "      <td>True</td>\n",
       "      <td>v_LN_sparse_</td>\n",
       "      <td>0.472716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1702323388</td>\n",
       "      <td>lLN14_R</td>\n",
       "      <td>False</td>\n",
       "      <td>l_LN_sparse_</td>\n",
       "      <td>0.376630</td>\n",
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      ],
      "text/plain": [
       "         bodyId      type   soma            morpho_group  segregation_index\n",
       "0    1578826464  lLN1_c_R   True             l_LN_broad_           0.000033\n",
       "1    1670278227   lLN11_R   True          l_LN_regional_           0.423012\n",
       "2    1979569670   lLN11_R   True          l_LN_regional_           0.048766\n",
       "3    2040301572   lLN11_R   True          l_LN_regional_           0.021254\n",
       "4    1826445251  lLN12a_R   True          l_LN_regional_           0.376746\n",
       "..          ...       ...    ...                     ...                ...\n",
       "192  5813040492  v2LN38_L  False  v2contra_LN_sparse_bil           0.005862\n",
       "193  5813071384   vLN26_R   True         v_LN_sparse_bil           0.209587\n",
       "194  2101391269   vLN28_R  False            v_LN_sparse_           0.508483\n",
       "195  5813055448   vLN29_R   True            v_LN_sparse_           0.472716\n",
       "196  1702323388   lLN14_R  False            l_LN_sparse_           0.376630\n",
       "\n",
       "[197 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al_annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>5901197989</td>\n",
       "      <td>l_LN_sparse_bil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>5901198734</td>\n",
       "      <td>v2_LN_regional_bil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>5901199598</td>\n",
       "      <td>v2_LN_sparse_bil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>5901206553</td>\n",
       "      <td>il3_LN_broad_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>5901218894</td>\n",
       "      <td>l_LN_broad_</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         BodyId          morphology\n",
       "0    1547454812         l_LN_broad_\n",
       "1    1578826464         l_LN_broad_\n",
       "2    1610530558         l_LN_broad_\n",
       "3    1639886198        l_LN_patchy_\n",
       "4    1640555311        l_LN_patchy_\n",
       "..          ...                 ...\n",
       "192  5901197989     l_LN_sparse_bil\n",
       "193  5901198734  v2_LN_regional_bil\n",
       "194  5901199598    v2_LN_sparse_bil\n",
       "195  5901206553       il3_LN_broad_\n",
       "196  5901218894         l_LN_broad_\n",
       "\n",
       "[197 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al_comp = al_annotations[['bodyId','type', 'morpho_group']]\n",
    "al_comp = al_comp.rename(columns={'bodyId': 'BodyId', 'type': 'Type','morpho_group': 'morphology'})\n",
    "al_comp = al_comp.sort_values('BodyId')\n",
    "al_comp = al_comp.reset_index(drop = True)\n",
    "al_comp = al_comp[['BodyId','morphology']]\n",
    "al_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove the contralateral LN, though it is typed, it is coming from the other side so we do not use it..\n",
    "al_lns = al_annotations[~al_annotations['morpho_group'].str.startswith('v2contra_')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Summary of local neuron types in antenna lobe ---\n",
      "\n",
      "--- Total ---\n",
      "Typed Antenna lobe neurons: 197\n",
      "Antenna lobe neurons (used in analysis): 196\n",
      "\n",
      "--- By cross over ---\n",
      "Bilateral: 78\n",
      "Unilateral: 118\n",
      "Total: 196\n",
      "\n",
      "--- By lineage ---\n",
      "v: 8\n",
      "v2: 81\n",
      "il3: 2\n",
      "l: 95\n",
      "l2: 10\n",
      "Contra: 1\n",
      "unknown: 0\n",
      "Total: 197\n",
      "\n",
      "--- By morphology ---\n",
      "sparse: 98\n",
      "regional: 42\n",
      "broad: 42\n",
      "patchy: 14\n",
      "unknown: 0\n",
      "Total: 196\n"
     ]
    }
   ],
   "source": [
    "print('--- Summary of local neuron types in antenna lobe ---\\n')\n",
    "\n",
    "print('--- Total ---')\n",
    "print('Typed Antenna lobe neurons:', len(al_annotations))\n",
    "print('Antenna lobe neurons (used in analysis):', len(al_lns))\n",
    "\n",
    "print('\\n--- By cross over ---')\n",
    "# Bilateral neurons..\n",
    "bilateral_lns = al_lns[al_lns['morpho_group'].str.contains('_bil')]\n",
    "unilateral_lns = al_lns[al_lns['morpho_group'].str.endswith('_')]\n",
    "print('Bilateral:', len(bilateral_lns))\n",
    "print('Unilateral:', len(unilateral_lns))\n",
    "print('Total:', len(bilateral_lns)+ len(unilateral_lns))\n",
    "\n",
    "print('\\n--- By lineage ---')\n",
    "# v neurons..\n",
    "v_lns = al_lns[al_lns['morpho_group'].str.startswith('v_')]\n",
    "# v2 neurons..\n",
    "v2_lns = al_lns[al_lns['morpho_group'].str.startswith('v2_')] #there is v2, and also v2contra\n",
    "\n",
    "# il3 neurons..\n",
    "il3_lns = al_lns[al_lns['morpho_group'].str.startswith('il3_')]\n",
    "# l neurons..\n",
    "l_lns = al_lns[al_lns['morpho_group'].str.startswith('l_')]\n",
    "# l2 neurons..\n",
    "l2_lns = al_lns[al_lns['morpho_group'].str.startswith('l2_')]\n",
    "# contra neurons..\n",
    "contra_lns = al_annotations[al_annotations['morpho_group'].str.contains('contra_')]\n",
    "# unknownlineage neurons..\n",
    "unknownlineage_lns = al_annotations[al_annotations['morpho_group'].str.startswith('_')]\n",
    "\n",
    "print('v:', len(v_lns))\n",
    "print('v2:', len(v2_lns))\n",
    "print('il3:', len(il3_lns))\n",
    "print('l:', len(l_lns))\n",
    "print('l2:', len(l2_lns))\n",
    "print('Contra:', len(contra_lns))\n",
    "print('unknown:', len(unknownlineage_lns))\n",
    "print('Total:', len(v_lns)+ len(v2_lns) + len(il3_lns)+ len(l_lns)+len(l2_lns)+\n",
    "      len(contra_lns)+len(unknownlineage_lns))\n",
    "\n",
    "\n",
    "print('\\n--- By morphology ---')\n",
    "# sparse neurons..\n",
    "sparse_lns = al_lns[al_lns['morpho_group'].str.contains('_sparse_')]\n",
    "# regional neurons..\n",
    "regional_lns = al_lns[al_lns['morpho_group'].str.contains('_regional_')]\n",
    "# broad neurons..\n",
    "broad_lns = al_lns[al_lns['morpho_group'].str.contains('_broad_')]\n",
    "# patchy neurons..\n",
    "patchy_lns = al_lns[al_lns['morpho_group'].str.contains('_patchy_')]\n",
    "# unknown neurons..\n",
    "unknownmorpho_lns = al_lns[al_lns['morpho_group'].str.contains('LN__')]\n",
    "\n",
    "print('sparse:', len(sparse_lns))\n",
    "print('regional:', len(regional_lns))\n",
    "print('broad:', len(broad_lns))\n",
    "print('patchy:', len(patchy_lns))\n",
    "print('unknown:', len(unknownmorpho_lns))\n",
    "print('Total:', len(sparse_lns)+ len(regional_lns)+ len(broad_lns)+len(patchy_lns)+len(unknownmorpho_lns))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## convert the above information to a table format for display.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage</th>\n",
       "      <th>morpho_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>l</td>\n",
       "      <td>broad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l</td>\n",
       "      <td>regional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>l</td>\n",
       "      <td>regional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l</td>\n",
       "      <td>regional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>l</td>\n",
       "      <td>regional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lineage morpho_type\n",
       "0       l       broad\n",
       "1       l    regional\n",
       "2       l    regional\n",
       "3       l    regional\n",
       "4       l    regional"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_lns = al_lns.copy()\n",
    "table_lns['lineage'] = table_lns.morpho_group.map(\n",
    "                                lambda x: x.split('_')[0].lower())\n",
    "table_lns['morpho_type'] = table_lns.morpho_group.map(\n",
    "                                lambda x: x.split('_')[2].lower())\n",
    "columns = ['bodyId','type', 'soma', 'morpho_group','segregation_index']\n",
    "table_lns.drop(columns, inplace=True, axis=1)\n",
    "table_lns = table_lns[table_lns.morpho_type != '']\n",
    "table_lns = table_lns[(table_lns.lineage != '') & (table_lns.lineage != 'contra')]\n",
    "\n",
    "table_lns.loc[table_lns.lineage == '','lineage'] = 'unknown'\n",
    "table_lns.loc[table_lns.morpho_type == '','morpho_type'] = 'unknown'\n",
    "\n",
    "#change contralateral stuff for which lineages are known to simple lineages\n",
    "table_lns.loc[table_lns.lineage == 'v2contra','lineage'] = 'v2'\n",
    "\n",
    "table_lns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['l', 'l2', 'v', 'v2', 'il3'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_lns.lineage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>lineage</th>\n",
       "      <th>il3</th>\n",
       "      <th>l</th>\n",
       "      <th>l2</th>\n",
       "      <th>v</th>\n",
       "      <th>v2</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>morpho_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>broad</th>\n",
       "      <td>2.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patchy</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regional</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sparse</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>2.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lineage      il3     l    l2    v    v2  Total\n",
       "morpho_type                                   \n",
       "broad        2.0  39.0   1.0  0.0   0.0   42.0\n",
       "patchy       0.0  14.0   0.0  0.0   0.0   14.0\n",
       "regional     0.0  19.0   0.0  4.0  19.0   42.0\n",
       "sparse       0.0  23.0   9.0  4.0  62.0   98.0\n",
       "Total        2.0  95.0  10.0  8.0  81.0  196.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_lns_print = table_lns.pivot_table(index='morpho_type', columns='lineage', \n",
    "                        aggfunc=len, fill_value=0)\n",
    "table_lns_print.loc['Total']= table_lns_print.sum(numeric_only=True, axis=0)\n",
    "table_lns_print.loc[:,'Total'] = table_lns_print.sum(numeric_only=True, axis=1)\n",
    "table_lns_print = table_lns_print.astype('float')\n",
    "table_lns_print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_lns_mask= table_lns_print*0\n",
    "table_lns_mask.loc['Total'] = 1\n",
    "table_lns_mask.loc[:,'Total'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "\n",
    "ax = sns.heatmap(table_lns_print, cmap='Greys', annot=True, fmt='.0f', ax=ax, mask=table_lns_mask == 1,\n",
    "                 cbar=False, annot_kws={\"size\": 14},linewidths=2.5)\n",
    "ax = sns.heatmap(table_lns_print, cmap=ListedColormap(['whitesmoke']), annot=True, fmt='.0f', ax=ax, \n",
    "                 mask=table_lns_mask != 1,cbar=False, annot_kws={\"size\": 14},linewidths=2.5)\n",
    "\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "\n",
    "ax.tick_params(axis=u'both', which=u'both', length=0)\n",
    "ax.set_yticklabels(ax.get_yticklabels(), rotation=0, size=label_fontsize-2)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0, size=label_fontsize-2)\n",
    "plt.tick_params(axis='both', which='major', labelsize=label_fontsize-2, labelbottom = False, \n",
    "                bottom=False, top = False, labeltop=True)\n",
    "\n",
    "plt.xlabel('lineage\\n', fontsize = label_fontsize,weight = 'bold')\n",
    "plt.ylabel('morphology groups', fontsize = 14,weight = 'bold')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.box(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Fetch skeletons from neuprint for display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bodyidList = al_lns.bodyId.tolist()\n",
    "len(bodyidList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Fetching'), FloatProgress(value=0.0, max=196.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fetch skeletons and heal fragments within a specific threshold..\n",
    "alln_skel = neu.fetch_skeletons(bodyidList, heal=1000, max_threads=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>n_nodes</th>\n",
       "      <th>n_connectors</th>\n",
       "      <th>n_branches</th>\n",
       "      <th>n_leafs</th>\n",
       "      <th>cable_length</th>\n",
       "      <th>soma</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>navis.TreeNeuron</td>\n",
       "      <td>lLN16b_R</td>\n",
       "      <td>1701965055</td>\n",
       "      <td>3904</td>\n",
       "      <td>241</td>\n",
       "      <td>151</td>\n",
       "      <td>None</td>\n",
       "      <td>108657.062500</td>\n",
       "      <td>23</td>\n",
       "      <td>8 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>navis.TreeNeuron</td>\n",
       "      <td>v2LN34e_R</td>\n",
       "      <td>1670636531</td>\n",
       "      <td>3706</td>\n",
       "      <td>382</td>\n",
       "      <td>180</td>\n",
       "      <td>None</td>\n",
       "      <td>159913.359375</td>\n",
       "      <td>3677</td>\n",
       "      <td>8 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>navis.TreeNeuron</td>\n",
       "      <td>lLN11_R</td>\n",
       "      <td>1670278227</td>\n",
       "      <td>4295</td>\n",
       "      <td>451</td>\n",
       "      <td>142</td>\n",
       "      <td>None</td>\n",
       "      <td>171583.515625</td>\n",
       "      <td>864</td>\n",
       "      <td>8 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>navis.TreeNeuron</td>\n",
       "      <td>v2LN34f_R</td>\n",
       "      <td>1668275074</td>\n",
       "      <td>6885</td>\n",
       "      <td>713</td>\n",
       "      <td>331</td>\n",
       "      <td>None</td>\n",
       "      <td>249866.812500</td>\n",
       "      <td>6826</td>\n",
       "      <td>8 nanometer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>navis.TreeNeuron</td>\n",
       "      <td>lLN12b_R</td>\n",
       "      <td>1671270875</td>\n",
       "      <td>5003</td>\n",
       "      <td>913</td>\n",
       "      <td>288</td>\n",
       "      <td>None</td>\n",
       "      <td>195711.937500</td>\n",
       "      <td>7</td>\n",
       "      <td>8 nanometer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               type       name          id  n_nodes  n_connectors  n_branches  \\\n",
       "0  navis.TreeNeuron   lLN16b_R  1701965055     3904           241         151   \n",
       "1  navis.TreeNeuron  v2LN34e_R  1670636531     3706           382         180   \n",
       "2  navis.TreeNeuron    lLN11_R  1670278227     4295           451         142   \n",
       "3  navis.TreeNeuron  v2LN34f_R  1668275074     6885           713         331   \n",
       "4  navis.TreeNeuron   lLN12b_R  1671270875     5003           913         288   \n",
       "\n",
       "  n_leafs   cable_length  soma        units  \n",
       "0    None  108657.062500    23  8 nanometer  \n",
       "1    None  159913.359375  3677  8 nanometer  \n",
       "2    None  171583.515625   864  8 nanometer  \n",
       "3    None  249866.812500  6826  8 nanometer  \n",
       "4    None  195711.937500     7  8 nanometer  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alln_skel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotskeletons(plotting_skel, color, scaling_skel, axes, alpha = 0.4, outline = False):\n",
    "    if outline == True:\n",
    "        p_al = Poly3DCollection([al_outlines[:, [0, 2, 1]]], linewidth=1.5, \n",
    "                        facecolor=(0, 0, 0, 0), edgecolor=(0, 0, 0, 1), linestyle='-')\n",
    "        axes.scatter(al_outlines[:,0],al_outlines[:,2],al_outlines[:,1], alpha = 0)\n",
    "        axes.add_collection3d(p_al)\n",
    "    else:\n",
    "        navis.plot2d(al,ax=ax,method='3d', lw=.8)\n",
    "    navis.plot2d(scaling_skel, method='3d', ax = ax, connectors=False, color = 'black', linewidth=0, alpha = 0)\n",
    "    navis.plot2d(plotting_skel, method='3d', ax = axes, connectors=False, color = color, linewidth=1, alpha = alpha)\n",
    "    # Rotate to frontal view\n",
    "    axes.elev = elev\n",
    "    axes.azim = azim\n",
    "    axes.dist = dist\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<navis.Volume(name=AL(R), color=(0.9, 0.9, 0.9, 0.05), vertices.shape=(38829, 3), faces.shape=(77672, 3))>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fetch mesh for Right Antenna lobe..\n",
    "al = neu.fetch_roi('AL(R)')\n",
    "al.color = (.9, .9, .9, .05)\n",
    "al"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate 2D outlines for the al (not required, but useful for other kinds of display)\n",
    "al2d = np.array(al.to_2d(alpha = 2,view = 'xz'))\n",
    "al_outlines = np.append(al2d, np.repeat(al.center[2], al2d.shape[0]).reshape(al2d.shape[0], 1), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "figwidth_size = 5\n",
    "figheigth_size = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bilateral/Unilateral neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get bilateral neurons\n",
    "billn_skel = alln_skel[np.in1d(alln_skel.id, bilateral_lns.bodyId)]\n",
    "#get uni neurons\n",
    "uniln_skel = alln_skel[np.in1d(alln_skel.id, unilateral_lns.bodyId)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is just for consistent display distance..\n",
    "scaling_skel = billn_skel[np.in1d(billn_skel.id, [1698679708,2041993364])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "skelbg_color = 'grey'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pf/jDzxn2x0E8vHF0q7jyk1xICoxE+El//J05KuouqeviR5nUmtLsYr7Oz/v5LH7Ojh/JUReMx5rntujY9P2+9eJbNACwt7eHi4sLFItFPH36NLDC6dipHYXDYZTLZTSbTTx69Mg9gPo9k4AGgwGKxSI2NjbcAkDdpl6vo16vYzAYIJfLoVAoIJPJBKIbalny+9DUAb/HEQMF2pyMLsbS0hLS6TTi8TgKhcIvfdR+Nej1emi1Wm6hpr7GPLBkMum2bmIUk4I99SS6t/w8n9V+v49UKoWdnR3s7+/j1atX+P3vf49SqYRkMvlThvfhiYeKv9Yd0TJQTYTb2KhQqRMbALLZ7DULRkPjvmukJRXqThFqjSjJ+OTju0N+Dgv/ptem1pQf4tawtH/+2WyGg4MDTCYTPHz4EG/evHGNu0qlUiClQO/NcDjE4eEhVlZWUCwWAzqSH+UbDoc4OTlBOBzG9vY2ms0mqtUqBoMBSqUScrmcy7OZp8X5xOITNt1fJWJas51OB7FYDOfn5wCAVCqFra0tRCIRLC0t/ZxHy/B3gCkJTDUA4BYNfn+a6lCpVHB8fIxSqYRarYalpSU8evTob0UKPyzxTCYTdDqdgJXA/+vEZUo+cOWCaTSI70+n084Noovl6x16LjUrffNfk//4d1/0nTfBfXLzrR3fbfItp3maDscyHo/x+vVrxGKxQE8cujv1eh2xWAzPnj0L3CeO6ccff8Tm5qYLU/v3m/eOhP327Vs0m01ks1kUi0UUCoVroXz/HqgL61+vf/91EaGeNhwO0Ww2ncgKAI8fP0YymTTi+Qjx9u1bvHr1Cv/yL/+CyWSCg4MDlEqlv/VdfdhwOrOR9WHmxOfDygeQJMFJEY1GXVIhH+JOp+P0AWYvA/M7DNLCUevEd5O0ApyTX3NVNIFNXSa+rq6TWkeE9orWMSghTyYT5PN551cPBgM8e/YsQFb5fB5LS0vY3NzEmzdv8N1337kM41Qq5SwaRrN0nDy3T7yhUAhPnjxxhKDWp+/2+YuUWjI+cSsx8W9qocZiMZRKJYxGI4xGIwwGA7RarY8qk9lwicFggJcvX6LT6bh5/PTp0190zFsnHvqTGsL1o0b0T+PxOIbDoRMmNYFPoy1+gp3mwGjESldgX1hWAvI1Jj+9X0mG7yNJ6aRTstJVPhKJOJG23++7rFtGelKpFBKJBP785z8jm82iUCjg8ePH19w7bROyu7sL4DLq8Pr1a2xtbaFYLDqLkdqYT5RKPrz/wFWio5/9rQSm91zdWxXedZzzrCA/pSEejyORSDgRfWVlxcTljwz/9V//hVarhYcPH14rNfp7cevE0+v1rom3Otm54lP8ItkQ4fBlurrfE7ler7ucEE4w3w1SoqDI+j4C1AnuuxF8XY+tFoMWRM77YqrVKk5PT5HP5117iFgshgcPHrjrGw6H6Pf72N3dRTabvdb8DJjfMrVUKiEej+P169duHFrHNi9j2hfR51k1/nvVAgqFQgFC9u+3f/+0xk4/w/enUimMx2M0m02cn5+jWCz+1MfLcMt49+6da2USiUQwGAxuJNfqVolHV0VCV71Wq+UeSLpZtHxms6v6LH1o+ZPNymkZqaXjZwerm6WTwl+x1W1SqFWgBOdPQj/TmddzcHCATz75xPUOUmuC/yKRCL766is3Rr8mzLdCtDYqnU7jyZMnrmwimUy6CKBaeprHRJL3vy9f11Ki4HXpePgd+WkP/Mw8l0vJk68nk0kXcfEtJcOHwdu3b/Hu3Ts8e/YMqVQK5+fnaLfbN+IO32rZ7WAwuGYhEExso5Wgq66/15S+piIzJ7vWD7HAVCe0RlwItcL8qItOFACBc+vfdUM/Wm1+BK1SqThy8AVpQicnzzEcDq9ZLOrWUY8h2WUyGXz66ad48uQJgMuHRj+nFifJ2rcANYHQ7wuk6QvqKoXD4UCBLe+nvi8cDgfuFRckfpbHZY5Wu93+ZQ+e4UbAjpMsZ4nFYi4Q8Etxq8RDQiAZkGBYUMeHmS0ygasCQW67wtC6rpacEMPhEOFwGN1ud26zd7UY3qc5qEvDyaO5DDomJVGSnrpmKqDy90qlgufPn7vzKQGqm6MTW89JYvEzu3VSc/wAkMlk8ODBA4zHY/zv//4vjo6OcHZ25u7ndHqZFc6eR/wsXT4eX0Phfr6OH0njeDQPxHeplOhp0fnXwvPf1MNt+GXI5/OBTOWlpSXU6/W5i+fPxa0SD1c6XeFms1kgiVAnIl2fSCTiCMbf44mEEwpdpuvr6q2iqU4WX6vg2Pgaz0/y8t/ni9Hac0aP65PIwcEBstmsy7TWY/KzqhXpdfodDn23RcfG6+h0Otjf30c6ncb29ja63S6WlpbQ6/Xw6tUrvHr1Ct9++y3evXvnEvfUClWi1r+pHsPrV6uT91vbWZBsJpOJs0opqlPP4v1m3RsTCPmMGD4sstksGo2G+12z0X8pblXj0Qxh4CrtG7jekU/FWZ3cDG1zFQauh4a5R7nffIrQVVatHNVuNNyvmpK6BByjrtI6STW5LhwOo91u48WLF3MztX13hfeH1+dnHKs1xN99LYYN2JlhnM1mEY/H8fjxYwwGAwwGA1dxTILRYlffxVIrThMW9RpUkCf0WvW42tOI18oFZjgcupqvyWTiyjMMHw7FYhFHR0fXdNFOp/Nza7au4VYtHj58qlVw9VNrh534KBbrZNdolkZOtAkUH2paQqozuAsVUtOV2o/MtNtt1Go1vH37Fufn567/MyepCsqcfLo7qD9OtZR0TKqH6P3wXTr+znuhrV8BuApxWjCbm5uugDOfz6NWqznxlmUJ+iD5GhgJhkRH8mcaPl0yP5XAj3gBV3qbLjBq6fHYtI6KxWIgj0ubvxkWj2g06p5/AK6SvlKp/PJj/+Ij/LWDe53lmICUSCSuRZJ0xebDrCuragKqB6lVMp1e1qnwOKr3aHRNSYef6/f7ODw8dNXTyWQSo9EIzWYTx8fHrvVDqVTC48eP3QRS7cqPeuXzeVd5zfP5FoJOVLVe1JrhKnN0dOTumU58jjUejyOZTKJer6NQKGBjYwP7+/vY29tzdVs8nxIj4YvXeo7hcIhCoRDonaR9oXkv/WsgYZJkVKDXxYe9nPUz7Xbbspg/IMLhMJaWlpxVPBwOUSwWXQDjl+BWiUfDuM1m05GG7vdEwVhXfgAuk1YfaiUK3WUUuNJ0uBuF6jAqfurKPBwOXW3ScDhENpvF1tZWwNqYTqeo1WquNUA4HMZ3332HBw8eIJ1Ouy2DZ7OZszyI8/NzZDIZNJtNV4rgt3X1rS9eb7PZdG1Jed82NzfdRKRmkkql3LG4FXEoFMLr168BAM+fP0er1cLLly+Ry+WwvLzs8jJ8q8X/DnRhUNcWuNLDlCA1VYCfY3qEv8goAQ+HQ9f9UPsVzXNnDYtDu912bj/w/u28/x7cKvFw9WV3Oo0UKfSBA65yYNQM93NRNMrlayHUMvScmjuiwvLJyQkqlQoSiQQikQjW19edrtTv9/Hq1SuUy2Wsr68jn8+78o/9/X0X/mW/53Q67YorOaZPPvkEw+EQtVoNp6enmM2udlhIpVKu9INuXqPRQL1eRyqVclaLivQ6dr+nDrOep9MpWq0WDg4O8PLlS6yuriKfz6NarTqXLB6Pu9osXxNTwV3JyHcjeb99gZ33W91bX+/j99xut123Af2OuYjcVN6I4eejWq26diUAXK7dTeDWi0SbzabTHFQb0NXS/+lHp9TsB65647J1Bt2oyWTidllgBTtw1cRbw9PUnUajEf74xz8il8uhVqthd3cXsVgM9Xod5XIZmUwGsVgMS0tL2Nracu1CGcIfDAZu9WfdEbfhicVi+PzzzwNh6slkgna7jW636xqiEex1Q61Dr8ePNAHXCzfnvefg4ADVahWlUgmrq6vIZDLOMksmk07AVZ2H90pdWL7fF5X1+/SbsKmg7H+vbFJWr9exsrLiFgr9x3GxA5/h9lCr1VzezurqKgaDAX788Uc8evTIWdnHx8cIh8PY3Nz8qYf9sEWiKpxqNTndKeBqtZxMJkgmk3M30SOB0CxPJpOu5SZBEtBCUk2c8y2Hk5MTJJNJPHnyBLlcDo1GA/1+H9lsFn/4wx+wtLSEyeRyq+TXr1+7Is15CYEUoDkpX7165XQKFXLZBVA/ByBAoqpNzdNNfDeE95P6C1/b2dnBw4cP8e7dOxwfH2NpaQn5fN71+PVbZfiCt0bTlPB8wlFXlq+pLqekqGKltl7g90JNgdZSo9FAJBL5VbRF/VA4PT1FsVhErVbDq1evEI1GsbGxEeiNFIvFAlLCL8GtEw9XfkaEuIIzOhIOh9Hr9VxfYn6G76Fmw/cw2kE3gySmSXmcOHS5dALpitpqtTAcDvHkyRPUajVUKhXE43F8+eWXrqUELa50Oo3PP/8c5XIZwNXk0859nOzj8Rjlchmz2SxgeSnhacW6Rpnmib2qb83TgwiSDjUt1Uk2NzcxGo1Qq9Wwv7+Ps7MzrK6uOvLp9/tIJBLY3d1FOp2+RnbD4RCZTMbde46PBKHulkb59D1KXMPhEOPxOOBGUXwmARLT6dR1KbwpjcEQBBf0YrHoym6Yc8Xmb9ls1vVP+sXnu01XazKZoNFoIB6Puwp0Pohq+XCy+HU64fBViwfmeDDEyn27KcTSKvKvJ5fLzY1qhcOXe473+31sbm4iHA6jXq+jWq1id3f3vdEeft4XPPl6v9/H/v4+lpeXXcMuHkMFVt/l5Ht0fGrt+NaakotaTPNEXP/eTKdTnJ+fY29vz5F/IpHAxcUFlpaWUCgUnDnNc9A6JYkACJCOT5oqXKs2R7f59PT0miXo5xPxOPrc5PN5s3xuAQcHBwiFQlhbW0Or1UKpVMLp6SkqlQpWVlYQiURwdnaGlZUVbG1t/dTDvtfVuvU8HrpK9NFJAvpAklz4kPMB5AOXyWSQTqdd1/1kMukiOI1GIxB1Yd4IcBX5UXdLCSAUutwWmf/PZrMYDAao1+uBRmPzdAf//5x07ClDkVqTAucJsSp469iUVHz9RCcjj6W/+y6gRhA5oVdXV/Hll18iGo1ibW0NL168wJMnTxyRHx8fO8LS+8f74YfklUA5Ht8N5cLRarVcs3d1K/3ggW/dDYdD1Ot1dDqdX/ZgGq5hdXUV1WoVyWQSq6uriEQi2Nrawmeffeb6JX3yySc/h3T+Km6VeJRUIpEIstlsQFcAgtXqXKk1iVBFZL6fkSFOJGoC2uVO3bn3hQFZTlCr1QBcmvo7Ozs4OTkJ5J4o1CWaZ6lks1nXWa/X6+Hi4gLn5+d49+4d3rx5g8PDQxwdHaFSqbh0AL1+X+PR88zTT/xJrlEnTnTeI32NRPv8+XM0Gg0Mh0Osr69jdXXVkcPe3h76/X6A0PhPa79YCtHv9wPkRvjW4mQycYmM+qzos6D3lZYVUzFarRaazeZPfQwNPwHcaJB7sxHUP3d2dm5U4L9V4qF5rpPX7+ELXOklTCykkMiVVfdv4gNIi4RbfPg7V/juBs/D97TbbVQqFcxmM7cPFQnt0aNH2N/fD1g9wJXZT/jEAFxlEh8cHOD4+NhNklDosqF5Npt1JQt7e3t49eqVizyxdxELY3X1Vw2JOhcnsVoIajmoW6auG8mceUBLS0uuaHQymaBWq7ncpm+//dYV4+pe75odTleJ+TpquVGzoeXZ7Xbd7hRq3fH+6+96Hi4c1Pb8oILhl2Ntbe1GspJ/Cm5VXNYeOxotKRQK6HQ6buKw+ExdET7gWnXOVhqceMAVgZHc+KBrEiGPB1y5IVToORm433goFHI6QrfbRS6XC2hR8yI74/HY5emEw2E8e/YMhUIhoFX4yXXRaBQPHjxw24+0Wi1cXFw464FjTaVSSKVSgdwWWhWxWAyZTCZA5mpJ+hYU77fqapPJBCsrK/juu++wsrKC5eVllMtltFotVCoVt6/67373O9eCNplMBoRxnlfPrWPg9Wpxr35XAAJuqX5ev2MuRLxHg8HAJUMafjlKpRKOj4/dnne3iVsjHk5uX+Pg6pjJZFzfFbVWgKuwtOo9o9EIwFWZhEazNEzP93Il1iRDFUZTqRR2d3dxenqK09NT10qU5KirNKHWx2AwwNnZmdv/O5PJ4OnTpy7UTyLl5weDQcCd4j0Jh8MoFotYXl4OFK8yosAdVzUHKRaLIRK5bKdaLpfx8uVLR5gsPUilUohEIm6SkwB8YZ9WCR+0eDyOr776yl3Dn/70J1cysr6+7q6Fbg8zyHnubDYb2AKHZE1XjGF8FbzVUtOfaukpeTL/6rY3s/u1gXu9dzqdu0s8vV7PRWL8MDBw+UDm83k0Go1AxIY9lrk6042aV1nuR354XBIWk9SYz6NuAk369fV1VKtVl/dD3YUhfEIth3K5jHK5jOXlZTx58sRFe3h9Gqmh/jQYDNwxmCLASd9sNt0mbLS6uOdRJpMJpAjwXqoFxZSE8/Nz1Ot19/BQFIzH41haWsLKyoq7ZyTyUOiy5zWbmPF4JJRHjx7h4uICtVoN8XgcmUwGxWIRo9HItWit1WqYTqe4uLhAuVx2eVCsmWN3wUql4shrNptha2vLRdVIwiqU+yI+cJVQmk6nLaP5FsD58+///u9YXV3FixcvbuU8t0I80+nU9dwBENAWVEQGLgspe72e672sjbAAONO83+87QtGJqKugHlcT+VqtltOMmH2rK2sul0Ov1wu4W5lMBvV6PaAV0cKZTCZ4+vRpwAUioTFvSV2yUCgUuB+apau1ZBTiuU+2kh2vS3+qCxuNRrG9vY1Op4Nut+uyn5l1XKlU8OrVK2SzWZdhvbS0hOXlZddknVYarTmSMR++aDSKRqOBcrmMtbU1NzbeOwYTIpEINjc3XfSpXq+jUqm4BSSXy7mxq1nPe8bjqkhNImR2t+FmQVf/8PAQZ2dn6Ha7yOfzt3a+WyEebtamuoiuWJpTEg6Hndiq0SxOLOAqKY35JBrKBRDYAge4qmOiS8BIl4qyAFx91uHhIVKplGvaxfD/u3fv3DlqtRpqtRoePHiAbDZ7rYcPr023xuF1qGbDsLCmDFCz0NapPJbvDvnaF49BwZ2kwmul2/X48WOcnZ3h6OgIjx8/dukJodBly4xyuYxUKoWzszO0Wi0Ui0V8+umnCIVCOD4+RjQaxfr6Oh4/foxKpYJMJuMIkvVk/P54bYw8FgoFbG1tBfoAkfT5OV4v4Ze58D1GOj8f9CQo/vuvnZyc4Pz83KVVhEIhLC8v49WrV7em99w48fgruT6IANzE9F0Tdbt0N1CfbFQ3Aq40H42C8SFl8p66ZDwfLQEArm/NX/7yFywvL2NnZ8clLHI1r1arWFtbcxoKJ4rfA5qgpcNyC43gqKtHQuU2s34OC49FsVxzWti/iOehe8qx+7k9jN61Wi2sra25z21vb+Po6MgRTKPRQDKZxPHxMbrdLhKJBLa2ttw1rq2tXdNmeF36YOt4SXL6vfP66d6q9Qgg8D3PZjPbfeLvQKfTwffff496vY6lpSUXZaabenFxgUwmgy+++AKxWAxHR0cuaJHP53FxcYEHDx7c+LhuhXj8FX7eZNJIBVfwfD6PcrnsVj5ONj6UFIwpLJLYSDjAlQWhURI/zR+Am7gsmKQLU6vVkM/nsbq66sgMuOzG1ul0UCwWA5MdQODcvG6eV10mP1dFLTSuLNSmdAKqi8pWBRp2VhIk6egYqC+RAE5PT9FsNl10CriMaFQqFTx+/BiZTMaNeW1tDYlEIlDOoNYKXSZ+P5pyQMLn98FIHJ8Jjk+JmN+3LiA8jhWK/nyk02n89re/DQRphsOhC+x89tlngU6PvV7PuVjzLKSbwo0TDyeJn7VL+OFoTezjBNJQL3BVLsCcHroVvqnOSattOimU8qGORCIol8toNBrOvXr27Bmi0agLsb9+/do1tT48PMRnn32Gbrfret/4Yrau1GpVsYMbf9dkRr02EiyFaN9943WzWFKJRSOFjLZxglJropDNsD1JuN/vu++K7zk7O8P6+rpLoNQER1qffp8kTU6ct9jwOH6USjUcdcnVXeU9uK0JcN+hViYA1+jufZsmqjSQSqVuLVfqxhMIfbdAE834kxPP70inlouSC4+rES/gqhSAPijfq5oAEBQt37x5g8FggN3dXWxubqJQKLgJqgRZrVbRaDTQ6/VcdEgJTQlU81d0vBoh4hh5H/g7c5ioQ/kJigBc+wySLx+MUOgqr0evlT2DOGFJXuzFrASoofsHDx7g9PTUifE8Hq0mJTmeb16WspKOupf+IjPvnvH6tLCU2yQZbhfsuEk3rFQqYXt7+1bOdSvfJotCfb0HuMrZ4YTgxOODygmjkSuusLqCcgKrC6DhZua7zGYzJ7aWy2VMp1Nsbm6682j0i5ORqzq79XU6HQwGA+RyuWuJjj5hqdYTDoddvx6/Y59OLlpb6rbw2J1Ox2UMc/LSYqF1oBE1jl9dMd53npfCMsshaG2NRiMsLy/j+PjYuUWa0KkEp8EAtjDh6zwvc3fUbfLfo5YTj+ETFtMPDLeLedHE28KtEY9GX7QlAoDASqtVyJxA2lmPq7r6+/zJ/Zd0RwaGzKnkU3imlaHERWuEbhDP9/z5c0eYrVYL7969Q6FQwGAwCITXgfnb9eok4hh5vZxISpS8PkJdq3mTUMeubUV53eHwZaV9vV6/ppkkk0nUajUnGmvIHrjclysajeLw8DBADppjpNaORt9YL0c3m/lL+kArcRFq8WhOFJ8DRj4Nt4uLiwvndt82biWcrpm5qonwQaZFwZVb9Y1EIoFutxvoVsgHkS1GSTqaAwNc6S0aRldrJpfLudXcT0gErvSUH3/8EbPZzKXjD4dD18+GFhR9ZY3qKPr9vgvxqwWjGg9X/3Q6HSgNabfbLvFPS0NoGTKniedVt8Uvlq1Wq66erdfr4c2bN1hdXQ2UlJDctff027dv3R7vPKbuU89r5H7wJDhdICh2a9SL4+b3o6/xOnkf+E81CsPtodls4sGDBwtxa2+NeJizoeKrro5qoWg4OpFIoNlsBkRR4LoYre4XiUBdM81wJtGFw2Gk02m8evUKq6uryOVyzjpTwTOXy2F/f9+N7ZNPPrmWVcuER64OuqJrdEZFYrXMaD3E43HnGvX7fbTbbUSjUZdPpNvR0H3VbHBaib6uxTHR8huPx9jb28Pa2ppLImT91Gg0QqfTcQTDfek1SgZclaIwDymTybh7xu9Hrxe4Wgw0aVOtW76mFqTvrs4jdsPNo1QquYXutnErxKPaAyckV2Oa9up+KTkxbErtgJMBgOuGppXZXJH1HECwQFHLF54+fYput+vaVajIywnWaDQcyTGhCrjac5yTPJlMuuSrzz//3F0TCVbdOiDYHEsT7jTkTgtiXj1Sr9dz4qvuL89SBs2domVGS4plDfl83h2bnR6bzaa7RySUra0tZyXyO+W4uSAoQZBoNZqoZKvfg+p5/L8uHuq+6oJluF0sciuhWyMe4PJh5IpOt8EPsao7Rh+f28YQmi+j7gFwJXLydXXtNJKieSbxeNx12Ot0Ojg8PEQodNn68ejoCOl0Gg8fPkS/38fr169dI7LT01MUCgVXEX52doZarYanT58GXAmSgq4eqmOoRcSxU0PpdDqu9aiWgUynl1m7TLIE4Jqi+aDFx6LMZrOJi4sLl4PUarWchdXr9RCLxVxxZzKZdCTcbrdd+oJqa7QqeU81IdTXcPQzasJrZjIXKkItZN4bc7fuF26NeHSCMfKkIqYKl74WE4lEEIvF0Gw2Xdq9PqTAVX4O/w8gEKXRyA/fQ3KgJRQOh10zrB9++AF7e3t4/vy5S5oLh8Po9/v48ccf3RbAsVgMjUbDlTCwTSrPq9aGWl/qQqhlwOviz3g8jl6vh8Fg4GrH5pVmFAoFlw2sbg3vMd3BH374AaVSCZ9++qnTrLhbaiwWw8XFhSsd0UUgEom4lrJ+y9V5EUXgKhlQ3Sltn6GuskYpfYGe71Grj68Z7gduhXgowurKDlxZHUBwZwUguKsnrQ/uPKpRDU42QgVi7uesBMSueP5ngCt3gNGhJ0+eOKtnaWnJRXhevHiBdruNk5MTxONxJxqvrKyg3++7hD1GmzTvRF0ireHSTF4m69Ey5PiYT0PXczAYoN1uu21paEFyYpLcmSnMzOSnT58GiJvp8JVKxe2rRd2o3++73R+U9OmGqQWj95JuMfON+HeSES0kjdApiQFXVhChpGbEc79wK83e2XFPLR/mmMyrPtfJqZYQi02p46jOoBqDrqZ8UH33bDgcOpFVo2W0UN68eYPHjx874mi1Wuh0Oq4+S0VbtgZlwtVkMnG7SVCo5bVyEieTSefKpNPpwCTV69H93zXXB7giVL92xtdDiO+//x4bGxuBKmMlCpKMb32xfKXZbOLk5AQ7OzuO1H3xV91nWm5ao6VEo2PwXWISm75HkyNvo17IcOt472pxK8RTr9cDuSOc6HzIKeiqJUQi4GTjBNKGYOqmaXSFf1My0mMAQZeOm/Fxgrx7987lwTQaDeTzeaysrLiJqBOKq30oFHL6B1uW8p+WLAyHQxwdHaHRaLioQbvdRiQScZv9cRJqkh9FWYbVSbCRSCRQHa8hao10TadTHB8fI5VKYWVlJfB5uoLME9LCXd6zVCqFw8NDl7m9u7vrhGm/vkqjiQACmo+6nRyXpkFw3Np3SYuCB4MBkskkVlZW/p5H0fBhsbgN/fwcGd//53vUgvHdrHl5QL42pIWgnFA68XQiqjsXiUScC9JqtdBut/H27VtnVW1ubmJ9fd25S5qjwmPoBOI49H1qeUQiETx8+BBnZ2cYj8fY3t7G3t4eUqnUtagQr5PZ3BwrrcVwOByw/qifaN5MKBRCpVJBOBxGo9FArVZDq9VCOHzZHiMWiyEejyOZTAY0GC4KOu5nz545giqXy6hUKigUCm4zQnXD/PottWqYy0O9RoVlRig1qVKt33g87vQ6w/3BrVSn60oMIGBuq1sABNtH8P90UWgZaUGorzfw/ZyA1GxUQHYXG73q/zyZTFyhXL/fd5GddDp9rV8OQ/EcLycLz0lXkpm6qnGw09/Tp09RrVZRq9UQiUSwvb19baKxrxCjgcx6VvGZY9DrVTG2Uqlgb28PmUwGGxsbTisbj8fo9XouOfHx48coFosuCpfL5ZzrxWr2WCzmolzFYhHNZhPffvststkstre3XV9pjlEje/p9+nlSeh95j/n9KBGrTmi4X7gVi4ekEI1GXZmBRpn8CaQiq1otJBzmjah1RAtKNSKfmHg+PvCq+/DzL1++xObmJv7hH/4Bw+HQJQZqqJjHVfeM7h/Jsd1uBywIWnCJRMJ1FiwUCmi3265QU60WdVc0ZK2WhNaX8dqAYEpBq9VCJBLB7u6uC0OTYPmd0LrR6xwOh45kNA1A3abl5WV89dVXrkVsp9NBNpsNZGcr6fA+aO6WvxDwvqrFy+NolMxwv3ArUS1qFBQH1XQmQWiEgxYLs4i5ivMznAjz9BxOHu1toxGeeSKmWkXJZBKbm5uuXCMSiaDRaASsJr6f7pGf18Jm58y45lgnk4lriwHAlSBUKhV8++23KJVKbh8r1XFIWCRLPZfu2a5CPP/Plq0U5YGghqbhcSVxdX2XlpZcng/bjMTjcUwmlzuEzGYzVzjLAk7qP75rTPdK/646np8DxFIKfY9FtO4fbpx4VDzUJu06OfSnrpZctbWwkaY7CUAT1jgZ+cCri+drQuoS0GqZTCaONNT1UyuFFgj3Uvcjb5yYLLXgfuzT6eV2PGpdAJer/IMHD9But1Eul3F0dITf/va3rqA1kUi4MgxmMNPy4fj9cRC8J8ViMdDqQ8VcJTHeB76uZK5uUa/Xc3VZJFPtHTQej1GtVhGLxZDL5QI5R7w3BO+pRiFpydEtVTfdj4gZ7gdunHiUJDTioi6Lajl+bYjmlAAITAZfF6J7otEYWkBcKZX0VBsJh8OuuRdwJe5Go1Fks1mn48TjcZdJPO+Y6lpEIhHXh7jb7TrSJWHSOgqFQiiVStja2nJJgtregu9Xa03vj+bR8P3dbhfn5+c4Pz/HJ598cvnlitWmOhiP499TtYgmk4lzCdlAPplMukp6kiTvHcfRaDSQSCTcNjY8p1o1PrHo4nNxceHEaxWhDfcLN0482rxLw+bqVvmJZEoMvtmv0NWaZro259KsXT0mz8X30PQvl8s4Pj52zcqr1SpmsxlSqZSbVIz+aOhYw/QcC1uoAnC7ITDkrmUNrPL2IzVKAH4ETSN0aulxLC9fvnQ5RYPBwFlwSk7+veR51BXyNTISCt27TqfjiJX3R3UmAC5najabOcLWCKd+h3pd4fBl1nYul0OtVkOhUHDpCuZq3T/cOPEwH0PbUgBXK51GqHQ1pOXiZ+ECQXNb3Qe1dHSl1kQ0dS+YfTydTpHJZNDtdtHr9fBv//Zv2NnZwcrKiptQ1HuUBFWr0InMDGnVS+gC0mqgRUGXaTqdzu3875OaXrtaV+HwZbLhyckJ3r5963aA0EI/vfe+UK7voZDtE7VaYMyeVt1FXWa+R3Uj1XB0IfCDC/p8MHx+fn7utszR79NwP3CrtVqa68KHVR98vodakFo7qhMAV5NHJ4N/HkIjXqrHVCoVDIdDPHv2zJUjPHnyJDCBstms20xvOp26mia2kojFYm7TPC1l4ORjhjI1FiVCnfCz2Qz/93//h+FwiN///vcBq4NEx/ugFgMjUJVKBY1GA8ClVfbixYuAewpcWWNcBPydXUmG/F0XCv5UHUgrzSk2UwinJURw8eB1+GRNEvWtWF4rP3/bO1oaPgxuJY+HJMIolVoOfA9XWT74WkzKCeI3/QaCOSwqTqqr4JPWZDJBtVpFt9vF9vY2UqkUer0eVldX0Ww20el0cHp6irW1NVeRzezi8/NztNttt+UNW008f/7cuVPcg71UKrnr8y0G4Epo5Ti5mZ5Oet+90uM1m010u13UajWkUins7OwgHA7j66+/xn/8x3/gd7/7nSttUOuI9wcIamZ+2w0VlEmYHAMjbGzApttEU6fi+9Td5vg5Jo5Hx8bvi/eKuU7qkhnuF26ceNrtNlKpVCAZzA+JqqDok4SWTWjIV/N41O3R9+hx9GGfTCau5wxdk3Q67TrzkfAonobDl3tQ8dj//M//jHq97sofeBz+bWVlBUtLS07T0aiNhrLVfQyFQnj48CE6nQ4ajYYrayA4SQeDAY6OjtDv912ezaNHj1ylOQBsbW3h5cuXaDabrmaMBKIWjh+61kRHFc5ns6sOgeraqYvE74dZyZpbxOPwvs6LBs6LVoVCITSbTffdMjpo4vL9w40Sj5Y66MOi0Rk+dH6USYnEdy20PEKjViq4+uUKSlbUHE5PT7GysoJnz545LWc8HuPt27coFosYDofIZDIB92t/fx8XFxdYX193oepWq+VC7s1mE61WC8vLy9cEWuB6PRojP+fn5wAum449ffrU3RuNnpXLZZycnGBjY8O131CrkfeKpPTgwYPAxFeXk+ThZ5WzIbvfZ1cJZzabua1xtKcyCYjfrS4AJK15C8L7IlWaHc7vXnUxw/3BjS4lmnkLXJn1mn0LBMPh2mtYXQ2+D7jKTyFIUppWrxNAI09ccfv9Ph4/fozPP//cRZkGgwGOj48xm83wj//4jwGdhCv6zs4OBoMB9vb23I4JbJpVKBSwu7uLVqvldCy6Ieom6TXznOvr69je3sbq6irOzs4CxMTPxmIxpNPpgPXl3xv9/8XFReDeqz7EsSgh8x6MRiO0Wq3A+1R/a7VabjdUPadaPrqg8P754/P/7x+LXRJVX9LUAcP9wY0SDxPAVPhVM5yWBxAULVVk5MOsIitwtQrqg83VXFdZX9PgBPjNb36DnZ2dgKvz7t07pFIpPHz40B1vc3MzcKxIJILnz58jnU67XRZXV1dRKpVQKpWQy+WQSqXwl7/8xV0/hVfeCx3P+fk5Wq0WstkskskkNjY2XGsNfwLX63VXD6WRIU22o8v22Wef4fT01L3G0o9QKHRt3zC1eLS/ke7mwRSBVqvlXFu1UnhcHut935sPJT1dbOr1eiAdgu8zV+t+4sa/US2J0OJHWiiqqwDBNqnAVR8WnYRq0tNK4uTQGiytxVJRV10BTizqPiyU1ARC/RzP/+jRI1Sr1YArRSF0NBphZWUFb9++defgBNTK9fF47Hr10IJIJpMoFAp49+6dO2e73caf/vQn16gLCO5HFolEAj2i4/E4lpaWEA6H8f333+PNmzeo1WrumoBg5jbvBY9HouS4e70eLi4u3H5Z+ll+l8x18kV8XxBWa0XJXL9vNppXl1Dzggz3DzdKPL6OwMlGEuKuBnzINeyrq5r2qNEHWK2fwEWErzfN8vUGnkMF0kwmg7Ozs4CupBXTmlUdiUSQTqdxfn7uJtDp6SnOzs7w9OlTbG1tORLSkgx1F9l+o1QquXsUjUaRy+Uc2XY6Hfz5z39GPB53+1pr4h3vmwq+Ovnj8TiGw6FrNsZ7rW6WkhGJJ5vNur28/M5/+l1MJhOXUKjfmRLOvDwfPZ9qYRyflpZwgVJr2XC/cKOqHSe/9lzxW15qzZGf28PP8CGkK8aHmlEZ/k0nFpvJc+X0ExV9C4ouyjfffINWq4V0Oo2NjY2ANeNPns3NTfz444+uZuns7AwvXrxwESDugKF7rKvb0Gw28cknn+Di4iLQHpbHf/fundtCNplMus6HOlH9e6fa13Q6xc7OjotKaTW4WhE8piYDxmIxFAoF9Hq9QNIfz6HbRDMhcl4Gskbv5qUIKAEytyidTrvuirS42JqD5zTcL9wo8dDy0ExdNb9DoauufXwANQ8HCOaZaHRMJ5hGqvhe3fKGD6qfdOhPgNnsctfRnZ0dd2xNqFMhlxbSeDzG+fm5s1RUOC8Wizg/P3eT3xd2W60W6vU61tbWAq5mvV7HxcUFQqHLXUwHg4Hb+YL3VfNdlEhJACSHVquFYrF4LTyu/ZhV+FUSCIfDrnaNkUZdNPx7p+fgfddz6uLB19Vq5T3gveRxEokE2u22azViGs/9w40Sz2w2C/TO8duWcrVUMZlgRIzvI+heaNsLP3SuERbVZ3x3QcHPsiufn8gGXN/vKxQKYWdnx9VlsSaKltxsNsPbt2+RyWRcoWM4fNnDeX9/H5lMxmVGc5z9fh/1eh0bGxt4+PAhut0u/vznPwOA63fjjwEIRo3G4zH++7//2/V0ZkKf6jlqoeh95L1TbYtkwSRBtWApamsUUUmF34N/n5UofTea59TvldfNv1vZxP3CjRIPLQ99uAG4VVNXOH3o/dCvEo8+rH6Egwlq/DwnqJr6Sh662s5mM7eL5mAwcL12lAz5XnUJ8/m8E5HpmvEac7kcNjY2UKlU8N1332FlZcUdc3193f2u1t3R0REePXqEYrHorKp//Md/RL1ex9nZmSu01PsJBMP0g8EAhUIBn332GarVKl6+fIlsNotHjx5ds4z0p1pO/I40PK49lAl+T/PuqX//fLL3rSi1QvW75TPAXS7M4rl/uFHi4b7nAK49ROpSaSo+f9fwt5KXahx004CrCedXp7NinZ8lVFvghItGo+h0Oq5TH8/rTwi+Ru2EFdTVatVtNRyJRFAoFJwVxB1BS6WSKw3hMUiK/+///T+EQiF89tln7vrZ8lXbZLxPIzk8PHS7on755ZdIJBJYX1/H6uoqDg8P8fbtW0eO6vIRakn5uVZq4TDqpQTG74C/670jfL3M14P4nOhiw++FojJ33TDcL9zoUsLiSn2wtBrbt26AYG3PZDIJlBz4GgHFW76XWhGPo66Caku+NcVjM6SttWEcq05yrT2azWZYW1tDPB7H+fm5E0UJugQbGxtYX18P5LZoLszLly8xHo/x5ZdfBu4hOw/6ESu1TgA4Pebp06dYW1tz+4fxPA8ePEC9Xnc7svrWktZiqSusSZm8J3rfOAY/QuhXoHOMPL4K6fy+9B7Ps2zD4cv91HzLyXD3ceM2LKNL/uTlysbVTM3rUCjkmqWr8KyhbZ0UmpTH4/jtNXksJSTgapVmW0+tefIFZQABEuBEy2az6HQ6ODs7cxMLuFq9lVzVUplOL1thvHnzBqPRCL/73e+Qy+UCgi2jXeVy2Y1/nuY1m82wvLyMWCyGJ0+euL2zaA0xMfLw8DCw95XeB16fWkT+4uD/U6LgoqFWmRI0+20ryXF8JGH//ujfOR7Td+4fbpx4tB8xH2atweED6pMPRWlOXCUsPohMxvMjXVr46IeMdaXVyadJiMBVWwi+j39Ti4rEWavVsLa2hgcPHuD4+PiaCKqRMR1rOHzZbCyRSODzzz8P7NIAXBHXwcEB4vE4NjY2AkI030uLkPcjEok4y0Dd2lwuh1gshlqt5q5PJ7neO95jtVDUsiGpqJWj79X3qOurLhbvEe+nH53j34l52pbhfuBWqu+y2azbTRS42k0AQODB5UTW7GNdFXXiqxVE8ul2uy6CM2/fLrpt1FTUotGJ55/Hj87ohJpOLwtFl5aWMJtd9tTpdrvOclJB23c7ALgdMX23ZDq9zIJ++fKli3DpPdIe0H6JiT9B6T7OZjMUCgV0u93AuXjdJCjN91HC4TX4oXmfyNXy5KLCa1arV3UdQs+thK9hfMP9w62FC9LptMvi1YebDzAJRPUDv6hRJxL/zwlFQZN7T/muAN+rE4LH5mrLtqS6eqsWpVmzJBRtbqX6COGv4vy/EqtPFrPZDF9//TVevnyJ9fX1QCIjz61lJr5exffocTUNgd0RfVGfnyGRzxOyfStGx89x+fdAgwWaDKrfjY7ZX1x8Adxw/3BrxBONRt1+TuryaEQJCBZ7auGjRsN8ayQcDrvexb1eD7VazXUB9N0w330DEEjP94svNZfIn4BnZ2eBFg06SeeRpGoYfo9lX+heXV3FF198gZ2dnUCWsI5BicAnAH2vklyxWESn00Gn0wkc05/w3DNM3Va1QPT+63fsh9rVGgIQiDDydV1Y9FrmRbgM9xO32uiEuo26Hao1MFqlWoCWWvgmvbpmKlYy1M4dQXlu3e5Xk9zolkWjUXS73UBECwiuvBzHaDTC0dERDg4O3HWl02k0m83Aqg5caU08N3+fRxoEs519nQm4aqDP4zE7fB7x+S7V+fm52ynDd3VUt/IJ2A+Lz3Oz9Fh6/fp33xXzkwtpCfvWjv/TcL9w6x2WUqkUOp1OQEzUxD8/IqJlFkCwkZa/txSbV2n/G+4zTqKJx+MuT0ddC241zLalzAkCgvoTPxOPx/HgwQOXr3N8fIxSqYR8Po+TkxO3H7lOIt+6UN1Eoa/5vYcAOIFZ82H8kLsSkFpB9XrdRc70NRK/EpqOy7fifEtEXTrf2uM5+J341hmh378SFO+HWlOG+4XQ31hRbmy56ff7GAwGcycm4U88ko3fgVAtC19H4cPNydXv95HJZAIWDcXW8XiM/f19PH78GJHIZTNztnrQCa4uhLp7HOfFxQXOz8/x4sULAFeCqW8F6HX7IX4/yY7H8YsttZRhnoun4Wt1azTSxvvMnsuhUHBXD002VLJRYVvJYl4Teb0mPScQjHKqVQgEd3vlds8U5A13Du8NRy4sFz2ZTAbqbzjx/d46OlH5UHKS6GTl398XcmceD7d7YVSIE6Hf7yMej7vCT4rNtVoN3W43kIDo6x1KDpw82vzLt3b0vL7lwmOpG6UukZ9xrGPRhMfpNNgcTKvzdWNBJRP9vKZAzNNe9Jp1DJq75FtU73OhNCXC163ULWbHR8P9w0KLYOimMAPZD537Vcx+foz/kOsq67sb1HeY4Uw3imTHOqBYLOaiPvwcH/pWqxWItqlrAFxZGbFYDK1WyyXM6eTj+EiKmhwHBJuxsyDTDz2rdaHakbquzB/ydRO9T75bpSTPManY70fwOB619pSASIC+PsTXfVfNvyYuPJPJBPV6HQAClqrh/uCDVN8lk0kUi0Xn1jBb1/fpfTFTV15/UutqPi9nREkomUy6yRmJRNBqtVzio66+kUgEnU7HbWmj4XMNIafTaYzHY+zt7aHdbl9zURRKOvq770Kqq+Yfi2PX8hES3mQycZqWkoxmL/thdR6Tv/NYek/0/mpKgn9Neh2+ZsPvw3cPgUudjo3IOp2OaxhnFs/9xAddTij8AnCV4gzr+oIrV18/38Y31f3QMhDc+piaBLfZzWQyga6CKtzyOL1eD9lsNuAGqJbCBMKNjQ1HUH7uEBCsV/NdMCUTdXvUegCuh+t18vtujSZV+sW3+nlfLFaXx7dUaOkwlUETG5XYhsNhYOcKPQZfZ8kFNTwdfygUcjlWhvuHj8aOVRLipCM5dLtdZx2pOa+TxneF+DDrxAeuXAWG4EejUWBvqGg06iqydUL4bgknUCgUcq1Gi8ViwBJQC4zn5m6kKqADVztp0BqZToO7dQII1J5NJhM0Gg3XtlTH5idgKgH790nJTsej98q3uvr9vnNP9VoBuPvH+wtcpQP4OhkJmIWxqnPlcrlA+YnhfuGjbHQSDocRi8Vc46zl5WWXpawP+jw3hpPKD11zYjGios2y/GRFEgMnAoVt1SXUjUin0y5SppMUuL6rghaz+hPez69Rl4tjD4VCaLVa2N/fR7/fd9emor1vOaj1BwQjXFp5rzlVej+1vqrT6QQ6P6q+RLLx3WPqVtrqQ0tfCBJWsVh07WMtc/l+4qOxeP4atAhSQ7H+6q0ahL6ugqtaBNz7XKNBfo4LkxK5Q2oymbx2TjYmPzg4wMrKiptQuor7VgNF4XkajloHHDcn5fHxsdt+uVQqBQjFJ0QNuSuxadRJ75OGtn0dptvtOi0OuHLTVMhWoVyb7/upBbQKtT9RIpFwfYi0VMXP+DbcDywsj+eXgnkdquX4E5VJf/oZnWy+8BwKXW6t8vLlS6yurjqXRYlFI20M/dM1U4thOr3cIbRer7vdSP3iWD9CxP/7Wo6ft8NzdDodnJycYGdnJ9A+hFBLZzKZoNvtotFoYGtrK0BwapH5+6eTLGlxdbvdwJbSmm1OMlSiYqSQ30csFnNEolE33c9LK+R5j2j1FQoF1wfacOfwXoHuTlg8wOVDyb3NVcNQd4IPOQmGr6vL4icnplIpFAoFNBoN5HI5t193NBp1Og9Jh1qE7mZBdy0ejyMcDmN5eRkXFxc4OzvDo0eProWd1cIAENCRVCDWMXPSD4fDQHdCILgPPXBJJOfn57i4uEAkEkGpVHLn4Pv1pxbSkiCYRkBC0QgZ3SQNgU+nU7evu1p7arn5UE3IX0T4OgMJhvuHO0M8wKUAPS+pjKuvwk/+I3lohTczgLe3t/Hdd9+5nTtJPjp5GIkJhUJu7yo/G5jEkcvlcH5+7iyUlZUVLC0tBawCjWqpleHnywBXrgkzefleuiXcYjmZTKJer7t+y9oo3icAHqPX6znS8cPXzHHSnUhZl6bETXdILTDtDqnXSMyLxvE7Aa5EanO17ifuFPEAQD6fR71eDzzsvsul1oXqD35ujFoWu7u72N/fx2AwwMrKCoBgNIZE4IfLeR6SVCKRQLPZxGAwwMnJCVKplNvlEwgWkDLbWMsgOB5adRcXF2g0Gnj16pVrIMaJOhqNUC6XUSwW0Wq1EAqF8PnnnyMejwcsLRWYSRpq1fDvtFZ4jYPBAPF4POBm0uKideML+HpP1L3TLGm6YHSpNM1ALdO/ZjEZ7jbuHPEAV61HtfbHb8mgmbTzLBOChJDJZLC9vY2joyM0Gg1kMpmARqTuHDUKtQK4QieTSQDA8vIyOp0OlpeX3QRT148T0e8ZxHMxshaLxXB0dIQvv/wS4XDYHZPuVrlcRqVSwe7uLkqlkrtmXi9/UjMZjUbunzbK18gZP0MSisVirtpf3Vjen/F47AR2XQjU5QKCqQN6fnUV+T5fvzLcL9xJ4olGoy6ErasscBWZAq4XO9KV4YPt5+hkMhns7OygUqmg2WxiY2PDFUZSWB0MBs7NortDLWI2m7mtcs7OzgKuHScgs4H9ML8KrDpZGV5eW1tzUaXp9LJ3c7lcRi6Xw/b2tmvVQTGabhizr7UcQXUYnfC0bEieJJv3QUPqej0aop/3008C5ec0aul/r4b7hTtJPABcng+301Frxo+yAMGeMvyprSWoQaTTaTx8+BB7e3vuvRqWzufzyGazLofG1yoAoN1uo1arYXt7OxANCoVCrh2Hisw6NuAq0fDNmzcol8uu6p37ro9GI5ycnCAUCmF3dzcwQRmibrfbLsmP18fjc493pimQiJLJpKsXozvENiN6jfypUTS+rjqVb8Uo6dNKVLdT7/dwOEShULjpx8bwkeDOEg9wafnQ7fK3xdGIDHBlSegkAoJJc/wcCahWq2F9fT0Q9eGETKVSSKVSGA6HTqDlaj8ajbC8vIyNjQ0ACBCXFoFqywolyna7jePjY1xcXGBtbQ3Ly8uBsPfp6SlisRgePnzo3Cb2GNJcHv+fRsk0KVDvAS3BZDLpehWp5cZ7yZII7avk6zt8r5KOakjzxsDXE4lEoOTCcL9wZ/J4/hbG4zH6/b7LnGVGLa9PV1YAAZeEf1Ny6vV6ePXqFZ4+feqSFyORiIvykIBU62i32+4Yp6enGA6HeP78eSBcrsTnly7wb69fv8bS0hKWl5cDiX3AZV+j/f19d1xem7pn89IINM2g0Wig3W67qBQALC0tuSgYa+ZYUjKPvFS019c0X4pQwVwzqjUSyPcBuFYGYrizeK+vfG+Ih6DOwpWYrof2ByaJkHy40qoYO5vNUKvVcHJyggcPHiCfz7tWG1qeQCuD52Kf6Xg8jq+//hqRSASff/65m2iz2VVJBnUYJZ9wOIwff/wRjx8/dkl2nLiVSgWVSsVth6yiukbWeA1AsPc0cEmuzWbT9SsC4FqVvA9azKn9glgJTzdONTPVxkie2nhMLVMALlJGfclwL/DrIZ6/Bl4rJ49GcDRHRTOKe70eqtUqer0exuMxSqUSisViIEcGuJzYdPloCTWbTbf/ebFYRLFYdOfXCBERCl2WaLx69QqffvqpS4is1Wp48+YNlpeXsbW15awU1UV819Hf/ZTRLtV7SA4rKys/W8glGZFwaXX5OVZKMNqOIxQKudC8Ra/uLYx4/h5wwtJS6nQ6qNVqKJfLSCQSKJVKyOVySCQSrj0EExx165x6vY5Go+FW83A4jGw2i2w2G2jaPp1OcXR0hEQigZWVFRwfH6PRaCCfz2M0GmF7exvpdDoQndP6M78WjefSfCSSjpLr6urqjd0zRso4NnXNDL86GPHcJMbjMS4uLnBxcYHxeIyNjQ2USiVnLTUaDTQaDSwvL7u/vXv3Dl9//TV+85vfYDabIZ/PI5PJBNy3b775BqFQCKVSCc1mE7lcDktLS658Y57LCFxvdapuHcer4rkeIxKJOEvMYLhh3P1arY8J0WgUa2trWFtbQ7PZxNu3b3FxcYFHjx4hkUgglUphMBig3W6jUCggFAqhUCjg8ePHgbYRWhIRCoWwtLSEer2OXq+H3d1dlzXM6JIv0Crx0MXRVAFNJNSQvobErRbK8CFgFs8NgBbN2dmZc73oIkWjUeRyuUA0p16v49tvv8X6+jpSqRSKxSLS6TR+/PFHtNtt/OY3v3FlD3SpeB7gKkKniY3qOrF/kGZsA1e5TNrRMR6PI5/Pf5gbZ7jvMFdrERgOh2i32xgOhzg7O3O6zGx2WTiqJRiNRsPVTB0dHWF1dRWJRALVahWrq6vY3NwMhK1VFNZyEA1hq4jsi8UaQVItyIjHcIswV2sRiMfjKJVKAC7zYn744QeMRiOk02lUq1UsLS25yZ/P5zGZTJDP5zEcDpHJZDCZTFAulx35sOmYWjQaxdJQOt0pJuNpvxtNptSkPRKVwbBoGPHcEmhhMDcllUqh1+sFCi9JDBsbG25/r0QigVarhYODAzx79gzRaNSF6DVhT5MOAQTas3KXCeB6g3mObV6tlsGwKJirdYtglKpQKLgKb530vrCr5HJ4eIhYLIatrS0AV3VQbETPv2kCot/kHQjqQarv0CXL5XLW4c9wWzCN50OAvXS63S76/T7G4zFWVlawurrqSKPb7SKTyQQyqVmycXBw4CrTAQSS7+haMWyuIXNaVFrWoNAwe7FYtGZbhtuCEc+HQK/Xcy00ptMpKpUKut0unjx54lwlbgDIUHcikXAuVL/fx9HREUKhEDY3N11ZgZKJisbqhmkNlF+NrxaR5fAYbhFGPB8Cs9kMzWbT1ScdHh4iEolgY2MjIBSzNKPf7yMajbrEQhJMuVzG4eEhhsMhvvjiC6TTaZdQSAuH5wOuLCO/oZafcFgoFJzbZjDcAox4PhSazabTdfb29rC6uur6zPi5NtwojxnK7K8cCoVwfn6OarWKSCSCdruNL774whGItvUArm+HrH+jW5ZOp60C3HDbsHD6hwIrtqvVKobDIfL5fKDiXPWXRCLh+uCwz088HkcikcDy8rIrn2g2m4FarXm9cHw3S/+fTCaNdAwfFFa5d8tghTkJp9PpBFwsINg/hyF1NoifTqeoVqtot9uuLUUymXT5OqrpaPtQv16LUaxEImEJg4YPDrN4bhkkHtZdaSV5LBZzOzn49Ve0TMLhMNLptOu3rAIxSYeNybT9hF+nxVA6+wUZDB8SRjwLABt/aSMs4GrXTW0sRkLhLp3AVbN6Cs7ce0obmKlLBQS3QWbUzCwdw8cCc7UWAO7Rns/ncXFx4YiG4W+/l47fAN3Pw1ExWfcZ960c/i2ZTBrpGD4qWFRrQeh2u+j1ejg8PEQikXBNwJLJJBKJhNtnSgkJQICUtF7rfVErX9cpFovW+sLwoWDh9A8N5vRMp1NcXFw4kZnbx6yvr7vePf7ndGcGddO02l3dNQuXGz4SGPF8LOBuGNzXajaboV6vo1wuY319HcVi8VpRJzfhI9lwdwndp4oEFolEkM1mzcoxfAww4vlYwYTB8/NznJ2dYXd3F0Aw+9hvgaFdCBlWZwW8EY7hI4IlEH6sYAP4zc1NVKtVZLNZtzkfhebhcAgAAY2HmlA6nbaN7wx3DkY8HwkikQgKhQJqtZrrRqjQrWxsxwbDXYcRz0eEZDLp9oL3YWRjuE+wp/kjwurqKur1Ovr9/oceisFwqzDi+YgQjUaxsbGBvb09p+sYDPcRFtX6CFEul3F6eoqdnR3XQsNguIOwcPpdQ6vVwv7+PkqlEjY2NixMbriLMOK5ixiPxzg6OkK73cbu7i7S6fSHHpLB8HNgxHOXUavVcHBwgPX1dWxsbHzo4RgMPxVGPHcdw+EQP/zwA4rFIra3tz/0cAyGnwIjnvuAXq+HP/7xj3j69CmWlpZcP+elpSUsLS196OEZDD6sZOKuo9/vo9frYTqd4j//8z/dxn6TyQSDwQA7Ozv413/912vV7QbDxwgjnjuCd+/e4eTkBI8fP0Y+n0e1WsWzZ89wdHSEbreL/f19fPPNN/jqq6+MfAwfPczVuiMYj8eoVCqoVCqYzWbo9Xo4OTlBOBzGaDTCeDxGtVrFb37zG/zTP/2TKz41GD4g3rsCWubyHcBkMsHBwQGi0ShWVlZQrVbx/fffux7O8XjcbdB3cnKCP/7xj64vs8HwMcIsnjuCSqWCw8ND1Go1tNttAEC73XZFpZPJBMPhEIVCASsrK3j69Ck+//zzDzlkg8HE5buMRqOBer2OVCqFt2/f4vT01LXNGI1GaLfbCIfDWFtbQygUwnA4RKfT+cCjNhjeDyOeO4BUKoXV1VX0+31EIhH0+30XzUokEsjlcjg/P8d4PMbq6irC4bDVeBk+ahjx3AHE43HE43G0Wi00Gg0Mh0OEQiGkUikAQLFYRCaTQa/XQygUQi6Xw7Nnzz7wqA2G98M0njuG/f19FItFXFxc4OXLl+h2u25/9lgshq2tLXzxxRdWVGr4GGCZy/cV3W4XzWYTiUQCxWLxQw/HYFAY8RgMhoXD8ngMBsPHAyMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhcOIx2AwLBxGPAaDYeEw4jEYDAuHEY/BYFg4jHgMBsPCYcRjMBgWDiMeg8GwcBjxGAyGhSP6N14PLWQUBoPhVwWzeAwGw8JhxGMwGBYOIx6DwbBwGPEYDIaFw4jHYDAsHEY8BoNh4fj/Tf1v277pm90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2d plot\n",
    "fig = plt.figure(figsize=(figwidth_size, figheigth_size))\n",
    "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n",
    "plotbilln_skel = billn_skel\n",
    "ax = plotskeletons(plotting_skel = plotbilln_skel[0], color = skelbg_color, scaling_skel = scaling_skel, \n",
    "             axes = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2d plot\n",
    "fig = plt.figure(figsize=(figwidth_size, figheigth_size))\n",
    "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n",
    "plotuniln_skel = uniln_skel\n",
    "ax = plotskeletons(plotting_skel = plotuniln_skel[0], color = skelbg_color, scaling_skel = scaling_skel, \n",
    "             axes = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Morphological types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotmorphotypeskeletons(plotting_skel, scaling_skel, axes, color,alpha = 1):\n",
    "    navis.plot2d(al,ax=ax,method='3d', lw=.8)\n",
    "    navis.plot2d(scaling_skel, method='3d', ax = ax, connectors=False, color = 'black', linewidth=0, alpha = 0)\n",
    "    navis.plot2d(plotting_skel, method='3d', ax = axes, connectors=False, color = color, linewidth=1, alpha = alpha)\n",
    "    # Rotate to frontal view\n",
    "    axes.elev = elev\n",
    "    axes.azim = azim\n",
    "    axes.dist = dist\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparseln_skel = alln_skel[np.in1d(alln_skel.id, sparse_lns.bodyId)]\n",
    "regionalln_skel = alln_skel[np.in1d(alln_skel.id, regional_lns.bodyId)]\n",
    "broadln_skel = alln_skel[np.in1d(alln_skel.id, broad_lns.bodyId)]\n",
    "patchyln_skel = alln_skel[np.in1d(alln_skel.id, patchy_lns.bodyId)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2d plot\n",
    "fig = plt.figure(figsize=(figwidth_size, figheigth_size))\n",
    "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n",
    "sparseln_skel_plot = sparseln_skel[np.in1d(sparseln_skel.id, [1976565858])]\n",
    "sparse_col = [0.905, 0.152, 0.243]\n",
    "ax = plotmorphotypeskeletons(plotting_skel = sparseln_skel_plot, color = sparse_col, \n",
    "                             scaling_skel = scaling_skel,axes = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### regional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2d plot\n",
    "fig = plt.figure(figsize=(figwidth_size, figheigth_size))\n",
    "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n",
    "regionalln_skel_plot = regionalln_skel[np.in1d(regionalln_skel.id, [5813018729])]\n",
    "regional_col = [0.721, 0.0, 0.407]\n",
    "ax = plotmorphotypeskeletons(plotting_skel = regionalln_skel_plot, color = regional_col, \n",
    "                             scaling_skel = scaling_skel, axes = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### broad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2d plot\n",
    "fig = plt.figure(figsize=(figwidth_size, figheigth_size))\n",
    "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n",
    "broadln_skel_plot = broadln_skel[np.in1d(broadln_skel.id, [5813130064])]\n",
    "broad_col = [0.047, 0.376, 0.698]\n",
    "ax = plotmorphotypeskeletons(plotting_skel = broadln_skel_plot, color = broad_col, \n",
    "                             scaling_skel = scaling_skel, axes = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### patchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a 2d plot\n",
    "fig = plt.figure(figsize=(figwidth_size, figheigth_size))\n",
    "ax = fig.add_subplot(1, 1, 1, projection = '3d')\n",
    "patchyln_skel_plot = patchyln_skel[np.in1d(patchyln_skel.id, [1670916819])]\n",
    "patchy_col = [0.631, 0.847, 0.921]\n",
    "ax = plotmorphotypeskeletons(plotting_skel = patchyln_skel_plot, color = patchy_col, \n",
    "                             scaling_skel = scaling_skel, axes = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}