notebooks/6_hemisphere_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hemisphere Analysis\n",
    "\n",
    "This notebook exemplifies how to use [SNT](https://imagej.net/SNT) to quantify ipsilater/contralateral projection patterns of [MouseLight](http://ml-neuronbrowser.janelia.org/) data.\n",
    "\n",
    "## Before Starting\n",
    "\n",
    "1. Ensure you have followed the [detailed instructions](./README.md) on how to set up your environment\n",
    "2. Ensure you have linked your local Fiji installation to this notebook as detailed in [Overview](1_overview.ipynb)\n",
    "\n",
    "Scripting documentation is available at https://imagej.net/SNT/Scripting, latest SNT API at https://morphonets.github.io/SNT."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "As usual, we 1) initialize Fiji with GUI support; 2) import all relevant python modules; and 3) import all relevant SNT classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ijfinder\n",
    "import imagej\n",
    "import os\n",
    "\n",
    "fiji_path = ijfinder.getpath().decode('utf-8')\n",
    "if os.path.isdir(fiji_path):\n",
    "    ij = imagej.init(fiji_path, mode='interactive')\n",
    "else:\n",
    "    print(\"Cannot proceed: Fiji not found!\")\n",
    "\n",
    "from scyjava import jimport\n",
    "\n",
    "# sc.fiji.snt\n",
    "Tree = jimport('sc.fiji.snt.Tree')\n",
    "# sc.fiji.snt.io\n",
    "MouseLightLoader = jimport('sc.fiji.snt.io.MouseLightLoader')\n",
    "# sc.fiji.snt.analisis\n",
    "TreeStatistics = jimport('sc.fiji.snt.analysis.TreeStatistics')\n",
    "# sc.fiji.snt.annotation\n",
    "AllenUtils = jimport('sc.fiji.snt.annotation.AllenUtils')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "Let's retrieve a cell from the primary motor cortex (MOp) and extract its axon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = MouseLightLoader(\"AA0876\")\n",
    "if not loader.isDatabaseAvailable():\n",
    "    print(\"Could not connect to ML database\", \"Error\")\n",
    "if not loader.idExists():\n",
    "    print(\"Somehow the specified id was not found\", \"Error\")\n",
    "axon = loader.getTree('axon')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does the axon have contralateral projections?\n",
    "\n",
    "Let's answer this programatically. In the MouseLight database, the straight mid-sagittal plane is defined by an X coordinate (note that this diverges from the original CCF orientation). Thus, we can get immediate insights into ipsilateral/contralateral hemisphere distributions by looking at the distribuition of X coordinates of reconstruction nodes (see [Overview](1_overview.ipynb) for TreeStatistics/NodeStatistics details):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_stats = TreeStatistics(axon)\n",
    "hist = tree_stats.getNodeStatistics().getHistogram(\"x coordinates\")\n",
    "hist.annotateXline(AllenUtils.brainCenter().getX(), \"Midline\")\n",
    "hist.annotateXline(axon.getRoot().getX(), \"Soma\")\n",
    "hist.setFontSize(20)\n",
    "hist.show()\n",
    "\n",
    "# from IPython.display import Image, display\n",
    "# display(Image(filename=r\"./images/AA0876-hist-xcoords.png\",  width=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we know that this neuron (with soma in the right hemisphere) has both ipsilateral and contraleral projections. This is corroborated by looking at e.g., end-points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = tree_stats.getNodeStatistics(\"tips\").getHistogram(\"x coordinates\")\n",
    "hist.annotateXline(AllenUtils.brainCenter().getX(), \"Midline\")\n",
    "hist.annotateXline(axon.getRoot().getX(), \"Soma\")\n",
    "hist.show()\n",
    "\n",
    "# from IPython.display import Image, display\n",
    "# display(Image(filename=r\"./images/AA0876-hist-xcoords-tips.png\",  width=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: In SNT, neuropil annotations are by default generic to accomodate different species. Since we are using mouse data registered to the Allen CCF, use the `AllenUtils` class to access CCF details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does the axon target the same brain area evenly across hemispheres?\n",
    "\n",
    "To answer this it is convenient to first tag all the nodes in the reconstruction with 'left', 'right' hemisphere tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllenUtils.assignHemisphereTags(axon) # tag each node with 'left'/'right' labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now when we query TreeStatistics to obtain annotated lengths (i.e., the amount of axonal cable associated with the brain areas inervated by the axon), we can split results by hemisphere.\n",
    "To simplify things, we are going to parse only brain areas of \"mid-level ontology\" (i.e., those at mid-depth in the CCF ontology, that, as of this writing, has a maximum depth of 10):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define a function that higlights the soma compartment in a histogram\n",
    "def annotate_soma(histogram, ontology_depth):\n",
    "    global loader, depth\n",
    "    soma_compartment = loader.getSomaCompartment()\n",
    "    if soma_compartment.getOntologyDepth() > ontology_depth:\n",
    "        ancestor_depth = ontology_depth - soma_compartment.getOntologyDepth()\n",
    "        soma_compartment = soma_compartment.getAncestor(ancestor_depth)\n",
    "    histogram.annotateCategory(soma_compartment.acronym(), \"soma\")\n",
    "\n",
    "depth = 6\n",
    "hist = tree_stats.getAnnotatedLengthHistogram(depth, \"left\")\n",
    "annotate_soma(hist, depth)\n",
    "hist.show()\n",
    "\n",
    "# from IPython.display import Image, display\n",
    "# display(Image(filename=r\"./images/AA0876-annotated-length-hist-left.png\",  width=500))\n",
    "\n",
    "hist = tree_stats.getAnnotatedLengthHistogram(depth, \"right\")\n",
    "annotate_soma(hist, depth)\n",
    "hist.show()\n",
    "\n",
    "# from IPython.display import Image, display\n",
    "# display(Image(filename=r\"./images/AA0876-annotated-length-hist-right.png\",  width=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And voilá, now we can look for biases in the distribuitons between hemispheres. But looking at the data in this format can be rather cumbersome.\n",
    "It would be better to visualize both distributions side-by-side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DAEAAGGpWciqdAIAAMBE1gQEQAIZKS0ujEwAQACayWCx0AgACgBEAABAAxnj8wdEi1QWXrp18g04HCAACwBc8NaOryO5vX5pW3k/kvWV0OkAAEAC+4JLWBN7zHQIAIAAIAF9xSWsCEwAAAUAA+I5LWhOYAAAIAAKAEQAAAoAAuMyYAwBAADACIAAAEAD+ZsuWLRIZGSlDhw6VvXv3el1vjzkAAASAH4uJiZG1a9fKpk2bJDo62ut6e2O/OVKeHfv9i9I2Zk0QKQv+qpVeQQAABAAB0J5CQkKkrq6uxevtxd0wRipeWt/u7fdZv5CSpStFGr5wbI11/MAAAoAAaC/qFk9ZWZns2LFDBg0a5HW9vYnfmiDbXtnc7u3VnJelZNlqfjgACICLSd3rHzhwoPTp00df8L2tt5d4UxwBAIAAMBEjAAAEgKHibhxHAAAgABgBEAAACABjMAcAgAAw1IQht8vvsn7V7u3FmXkEAAACwJdN/k6ibP7ZaxelvbvrCB0MgADwVdQEBkAAGCozM5NOAEAAMAIAAALAGBaLhU4AQACY6PvfGyPy/u8ubvvir3Q0AALA1+TO7Cqya8TFa+V9+QhoAASAL7roFcEoAgOAAPBNF70mMAEAgABgBAAABIAPueg1gQkAAARA++rQoX26iREAAALAUMwBACAAfJQq6xgZGanr++7du9flCEA9vueeeyQ0NFTWrFljW68e9+zZU+bNm+d2xMAIAAAB4KNiYmJk7dq1smnTJomOjnYbACUlJfqCHxQUZFt/7bXXSmlpqRQXF7sNgAnRN0te/Hyv7b8T0uV8WW+RTf/RulYWTAAAIADaIiQkROrq6pp3klMAnD9/vtn6Ll266OeeO3fObQDE3zBGNj7/qtf2zO2PS8PJapGz/2h9a/iCHyQAAqC11K2fsrIy2bFjhwwaNMhtALh6PGTIENmwYYO89tprbgOgpTWBl4xZIA11DfxAABAAl4qaAxg4cKD06dNHB0FrAkDdFurVq5fMmTNHvva1r7k8fktrAhMAAAgAPxIWFiZvvPGGrF69WoeIKy2tCUwAACAA/Mi6dev0baO+ffvKn/70J0YAAAgAfIU5AAAEgKEYAQAgAAx1x7Vj5Q8LCr22p9XbQAkAAARA4JgcmyjrfvT/vLb1//VHaWw4T4cBIAACBTWBARAAhkpLS6MTABAAJsrMzKQTABAAjAAAgAAwxkOpY0U+KW6fVvshHQqAAPAXuTOCRXYM+/fbxh4iH66gQwEQAP6i3WoC77yJAABAAPiTdqsIRgAAIAD8S7vVBCYAABAAjAAAgAC4yKw1gDt37qxrBjt0olMFMHcVwZgDAEAA+OHFX9ULVl+3b98ugwcPlp07d7Y6ABgBACAA/Ix65a8u/lYnTpyQ2traVgcAcwAACAA/41wH2NpaGwAPjOkvUjHg329loQQAAALgcowArPMBVmpe4MyZM/rxqVOnpEuXLi6PM31CwoUd3myf1nCSHwwAAuBis58DsH9sddNNN0leXp6cPn1ann/+ebn55ptdHmfquLvoTAAEgD+GgHrV7zwaUPbt2yfXXnutXHnllXL99dfLoUOHXB7jztGT6EgABICJGAEAIAAMdW/cNDoBAAHACAAACABjMAcAgABgBAAABIBJLNNm0QkACAATURMYAAFg6gjAYqETABAAjAAAgAAwRmZmJp0AgAAw0cMP3Cny+a62tRN76EAABIC/emp6Z5Gtg9vQokVKr6QDARAA/qrNFcHeXXghAILoQAAEgL9qc01gAgAAAcAIAAAIAD/U5prABAAAAqB93XbbbfLnP//ZYZ1aVut159jV9i0pKZHrrrtOunbtqit+7dixw7ZNVQJLTk6W0NBQiYmJkcrKSkYAAAgAX1ZUVCTz5s1zWKf+WKuwsLBZAPTq1UvKy8ulrq5O1q1bJwMGDLBty8jIkNLSUmloaJCCggKJjY11+f2YAwBAAPiIL7/8Uvr162cr6K6+qmX1it45AEaOHCn5+fly8mTzYuzh4eFSXV2tH6sQcFcSkhEAAALAh8yePVuPBBT1yn/WrFlfdY5dANTU1MjChQvllltu0a/wd+/ebdum6gTX1tZ6/V5zx/UT2XpNG9pg/g4AAAHQ3tS9/O985zv6sbqwb9u2zWUA2Dty5Ih+1W8VEREhVVVV+rEaPeTl5bl8XvKY8SKf72hj28VvMwACoD01NTVJVFSUnuS9+uqr9bKrAFAX+RUrVsjZs2f1q391q8gqPT1diouLpb6+XnJzcyUpKcnl96ImMAACwMcsWbJEevfuLYsWLXLsHLsAOHDggAwbNkzf7omOjpaKigrbNnV7KD4+XoKDg2XUqFFy/Phxl9+HimAACABDURMYAAFgKEYAAAgAQzEHAIAAYAQAAASASZgDAEAAGGp60r10AgACwETUBAZAABhKfdAcABAABrJYLHQCAAKAEQAAEADGeHR+isgXb3tudZ/RUQAIgECTO+NKkYoI921DN5HqAjoKAAEQaLzWBN79bQIAAAEQiLxWBCMAABAAgclrTWACAAABwAgAAAgAH1ZWVqaLxqivDp3qppQkcwAACIAAkZCQoMtBJiYmtigAGAEAIAACgCoCr2oJNzQ06BKR1dXVXgOAOQAABEAAyM7OlqVLl+rH6mtOTo7XAEibcGEEsGOY+7apFwEAgADwZbW1tRIWFqYv9NamltV6TwEw7bbbRD5Z77mdqaKDARAAvqqwsFBSUlIc1qnloqIijwFARTAABICfGz58uOzfv99hnVoeMWKExwCgJjAAAsBQjAAAEACGoiYwAAKAEQAAEAAmYQ4AAAHACAAACACjAuAOAgAAAWAkagIDIAAMlZmZSScAIAAYAQAAAWAMi8VCJwAgAEyU/mCKSO37nltjHR0FgAAINItmBIlsDHPfSi9s/3wnHQWAAAg0XiuCbR1MAAAgAAKR15rABAAAAoARAAAQAAHEa01gAgAAAeB7SkpK5LrrrpOuXbvKzTffLDt27GjRNkYAAAgAP9erVy8pLy+Xuro6WbdunQwYMKBF2+wxBwCAAPBDI0eOlPz8fDl58mSrtjECAEAA+LmamhpZuHCh3HLLLRIbGyu7d+9u0TZ76fH9RHaNcN82dicAABAAvuzIkSMSHh7e6m333X6ryPtFntu5T+hgAASAL4mIiJAVK1bI2bNn9Sv8fv36tWibPWoCAyAA/NCBAwdk2LBhEhQUJNHR0VJRUdGibfaoCAaAADAUNYEBEACGYgQAgAAwFHMAAAgARgAAQACYhDkAAASAoZLjCQAABICRqAkMgAAwVFpaGp0AgAAwUWZmJp0AgABgBAAABIAx7rfMFDn/pecGAARA4Fk0vZNIaRf3bUMonQSAAAhEHmsCVz4hUhZCJwEgAAKRx4pgBAAAAiBweawJTAAAIAAYAQAAAXAZrFmzRhd26dy5s2zatMmxszp00K1jx4667OPKlStbdWzmAAAQAD588Q8JCdFft2/fLoMHD5adO3c6BIDVtm3bpG/fvowAABAAgUC98lcXf6sTJ05IbW1tswBobGyUrVu3OhR/V4ExcOBA3fbt2+fy+MwBACAAfJT9K3zr7R5X6zp16iSRkZEOYXH99dfL5s2bdRCMHDnS5fHnJ1wYMeyOdd12jSQAABAAvjICsM4HuAoIZ126dLEFRHBwsMt9Zo79rkhVvvt2/Of8EAAQAJeD/RyA/eOWBMCQIUOkoqLC4/GpCAaAAPDxEFCv+p1HA94CQN3+iYiIkG7dusmCBQtc7kNNYAAEgKEYAQAgAAxFTWAABAAjAAAgAEzCHAAAAoARAAAQACaZnnAvnQCAADARNYEBEACGslgsdAIAAoARAAAQAMbIzMykEwAQAIwAAIAAMMai6R1FXvua61Z/gg4CQAAEqv+rCPZU87ahKwEAgAAIZG5rAhMAAAgAE0YABAAAAsA4bmsCEwAACIDLz74WsLuawK62MQIAQAAEUBC0ZF1rMAcAgAAIwAAoLy+XqKgoCQsLk/z8fEYAAAiAQAsAd7eAGhoa5NixY3L48GHp2bOny2NmJF4l8vro5u21KwkAAASAv44AysrKJDExUQYOHOh2v+mjb7nwin+x69ZYS6cDIAD8MQC6d+8uK1eulFWrVrndj5rAAAgAPw4Ad7eAUlNTpVu3bpKVleU2AKgIBoAAMBQ1gQEQAIZiBACAADAUcwAACABGAABAAJiEOQAABIChpo2bQicAIABMRE1gAASAoagJDIAAMJTFYqETABAAjAAAgAAwBnMAAAgAQ+XO6Cyysbtj2z6UjgFAAAS6J2b0Fnkn+6v2VprIpt50DAACINA1qwj21ywCAAABYIJmNYEJAAAEACMAACAALjFrYZeOHTtKeHi4rurlvM1VARj7dT169JCXXnrJYZsrC2ZcRQAAIAB8KQCstm3bJn379vV6IXfetm/fPunfv7/X5zECAEAA+GAANDY2ytatW/UooLUBsGfPHundu7fX5zEHAIAA8LEAUK1Tp04SGRkpa9asabbN2y2gsLAwWb58udcAyJx8lcj+uK/a66MJAAAEwOUeAbR129tvvy233nqrnDx50uvz7rv1WxeeMN+xHc3ltxQAAeCPAaAUFhbK7NmzvT6PimAACAA/CgBPt4CsmpqaZOTIkXLw4EGPx6QmMAACwFCMAAAQAIaiJjAAAoARAAAQACZhDgAAAcAIAAAIAJMwBwCAADBU2oMP0gkACAATURMYAAFg6gggLY1OAEAAmMhisdAJAAgARgAAQAAYI3fm10UqBoi8nU5nACAATPKDlN4i++JEDiTRGQAIAJPomsB/nkYAACAATKNrAhMAAAgA8+iawAQAAALAN1kLv3Ts2FEXiF+5cqXD9rKyMr1dfXV+HiMAAASAnweA1bZt26Rv374O2xMSEiQ9PV0SExNbHQDMAQAgAPwgABobG2Xr1q16FGBVVVUlUVFR0tDQINHR0VJdXc0IAAABEEgBoFqnTp0kMjJS1qxZY9uWnZ0tS5cu1Y/V15ycnFYFQNaUCwGwawQBAIAA8OURgLPa2loJCwtzKBCvltX6lgbAzNtuvDACuO/CUOJFOhoAAeAvAVBYWCgpKSkO69RyUVFRiwOAegAACAA/DIDhw4fL/v37Hdap5REjRtie59ycUREMAAFgKGoCAyAADMUIAAABYCjmAAAQAIwAAIAAMAlzAAAIAEYAAEAAmCR1ZiqdAIAAMBE1gQEQAIbKzMykEwAQAIwAAIAAMIbFYqETABAAJlo4M0zkHy/TEQAIAON+GNMvdPexJXQEAAKAAAAAAoAAAAACgAAAAOMDQNXj7dKli5SVlTkUWGlJ9a2WePTRRy/pcQgAAARACwUFBekQaGpqcvxHt1MAXOrjEAAACIAWXlTtSys6jwDmzJkjffr00aMDq+3bt8vAgQN127dvn8P+iYmJXo9v3ae8vFyioqJ00ff8/Hzbc7Zs2SKRkZEydOhQ2bt3b7PjqLC64oorpEePHjJ//nwCAAAB0B6vrJ0fFxcXS0lJib4YW11//fWyefNmHQQjR4502P8vf/mL1+Nb92loaJBjx47J4cOHpWfPnrZ9YmJiZO3atbJp0yaJjo5udpxrrrlGJk2aJKdOnXJ5PvPjesq7f1wk7+7+q/z9wFF+OwEQAG0JgPr6ejl37pyeI7BSj62vxoODgx32b2xs9Hp86z5qVKFGA2okYb9PSEiI1NXVuT2OCo3Bgwfr/Xbt2tVsv/HRI+WllEXys9RlsuzOZ/jtBEAAtCUA1EVajQKGDRtmWz9kyBCpqKjweBzn9SdOnGi2T/fu3WXlypWyatUqh/VqtKG+744dO2TQoEEuj6Mv9OPH69tHziZ+a4Jse2WzbPxpieRNfprfTgAEQFsCIDU1Vc8B2F/w1e2fiIgI6datmyxYsMBrAHz3u9/Vr9ad91HHVsfIyspyWK/mANSowHnuwXqcefPmSdeuXW2T187ibhxHAAAgAEzECAAAAWCoxJviCAAABAAjAAIAAAFgDOYAABAAjAAIAAAEgEnGRt0qz099VpZNWcLfAQAgAEwye6ZF/ln1sW6f/c//p0MAEACmoCYwAALAUGlpaXQCAALARJmZmXQCAAKAEQAAEADGWDDzKpGz/6AjABAAxv0wVEGY05V0BAACgAAAAAKAAAAAAoAAAAAC4DI4ffq0JCcnS2hoqK4BXFn51QXbXWEZRRWIsVYqIwAAEAB+KCMjQ0pLS3Vh+IKCAomNjW1RACQkJEh6erquJUwAACAA/FB4eLhUV1frxyoEDh065DUAqqqqJCoqSu8fHR1tez4BAIAA8COqnm9tba3rznITANnZ2bJ06VL9WH3Nyclxud+CuzsTAAAIAF+lisqrV/SKmg/Iy8vzGAAqLMLCwvQ2a1PLrkIk9dYhImf/h04GQAD4InUfv7i4WOrr6yU3N1eSkpI8BkBhYaGkpKQ4rFPLRUVFzfa9N24aHQyAAPBVNTU1Eh8fL8HBwTJq1Cg5fvy4QwA4t+HDh8v+/fsdjqGWR4wY0ezYU8fdRQcDIABMdOfoSXQCAALARIwAABAAhmIOAAABwAgAAAgAkzAHAIAAYAQAAASASR6a+xCdAIAAMBE1gQEQAIayWCx0AgACgBEAABAAxsjMzKQTABAARgbA9ybQCQAIACMDIOVGOgEAAWCix2YPpxMAEAAmWvDASDoBAAHACAAACAC/5FwdrKysTK9TX91hDgAAARCAAZCQkKDLSSYmJjICAEAAmBIAqoh8VFSUNDQ0SHR0tFRXV7t8DnMAAAiAAAuA7OxsWbp0qX6svubk5Lh8zkPTRkjd2To6DwABEAgBUFtbK2FhYQ6F4tWyWu9s9MBYee/QMToPAAEQCAFQWFgoKSkpDtvUclFREQEAgAAI5AAYPny47N+/32GbWh4xYgQBAIAAAAEAgAAgAAgAAAQAAQAABAABAAAEAAEAAAQAAQAABEBgmDllupw5+SUdAYAAMA01gQEQAIZKS0ujEwAQACayWCx0AgACgBEAABAAxsieN5lOAEAAmIiKYAAIAENRExgAAcAIAAAIAJNQExgAAeBH7Gv/rlmzRrp06SJlZWUO6xkBACAAAjwAgoKCdAg0NTW1KQCYAwBAAPhhANgXfbcuz5kzR/r06aNHBNYRwhVXXCE9evSQ+fPnMwIAQAAEygjA+XFxcbGUlJTI0KFD9bprrrlGJk2aJKdOnXJ5rKyUkVJ7+iydCoAA8PcAqK+vl3Pnzul5AeXYsWMyePBgCQkJkV27djU71ujIWNnw4no6FQAB4O8BoG79qFHAsGHDHJ4zfvx4CQsLa3as26/+jmwoWEenAiAA/D0AUlNT9RxARUWFXjdv3jzp2rWrbbKYAABAAEAb800CAAABYCRGAAAIAAIAAAgAAgAACICAxxwAAALAUPrvAAr4OwAABIBxpsXfLR+9+z4dAYAAMA01gQEQAIbKzMykEwAQAIwAAIAAMIbFYqETABAAJnrkgUl0AgACwETUBAZAABiKimAACABDURMYAAHACAAACACTMAcAgADwYarMo7Xco73Tp09LcnKyhIaGSkxMjFRWVrboeYwAABAAfiIhIUHS09MlMTHRYX1GRoaUlpZKQ0ODFBQUSGxsbIueZ485AAAEgI+qqqqSqKgofZGPjo6W6upq27bw8HDbstp+6NChFj3PXlbqCDoZAAHgi7Kzs2Xp0qX6sfqak5Nj26YKvdfW1rb6eQ6jhGGxdDIAAsDXqIt7WFiYvo9vbWrZetGPiIjQr/QVNR+Ql5fXoufZi7t+HB0NgADwNYWFhZKSkuKwTi0XFRXpx+r+fnFxsdTX10tubq4kJSW16Hn2Jlw3ho4GQAD4muHDh8v+/fsd1qnlESP+7759TU2NxMfHS3BwsIwaNUqOHz/eoucxAgBAAEDibxhPJwAgAEzECAAAAWAo5gAAEACMAACAADAJcwAACABDzbjzPjoBAAFgImoCAyAADJWWlkYnACAATJSZmUknACAAGAEAAAFgTgDMmkgnACAATERFMAAEgKGoCQyAAGAEAAAEgEmoCQyAAGAEAAAEgC9RpR6Tk5MlNDRUYmJipLKy8quOsiv5aG32ysrK9Dr11R3mAAAQAD4qIyNDSktLpaGhQQoKCiQ2NtYhADxJSEjQZSMTExMZAQAgAPxNeHi4VFdX68cqBA4dOtSiAFDF4qOiovRzoqOjbcdwxhwAAALARwUFBUltba3rjvJwCyg7O1uWLl2qH6uvOTk5Lo9xe2SsfPy3D+loAASAr4mIiNCv5hU1H5CXl+d1BKACIywszCEY1LKrIBk98EIAHPuAjgZAAPgadQ+/uLhY6uvrJTc3V5KSkrwGQGFhoaSkpDisU8tFRUUEAAACwF/U1NRIfHy8BAcHy6hRo+T48eMOAeDqFtDw4cNl//79DsdRyyNGjCAAABAAIAAAEAAEAAEAgAAgAACAACAAAIAAIAAAgAAIOPfG3SOnP/+CjgBAAJiGmsAACABDWSwWOgEAAcAIAAAIAGNkZmbSCQAIABNlzL2bTgBAABg5AqAeAAACwExUBANAABiKmsAACABGAABAAJiEOQAABAAjAAAgAEzCHAAAAsBHlZeXS1RUlC7qnp+f/1UndeggTz31lISEhOj16iMdunTpImvWrLHts2XLFomMjJShQ4fK3r17GQEAIAD8SUNDgxw7dkwOHz4sPXv2dAiAVatWyTvvvCOdOnXShePVxT8oKMi2T0xMjKxdu1Y2bdok0dHRLo8/dtC35UfxT8nPZ/03nQ2AAPAlZWVlkpiYKAMHDrQVfbcGQGNjo+1xU1OT7bGVGh3U1dV5PP7EG8dLybLVsuyuJXQ2AALAl3Tv3l1WrlypX+07B4C3x+rWjwqQHTt2yKBBg1weP/GmONn44noCAAAB4GtSU1OlW7dukpWV1eoAUHMAauTQp08fHQQuRwDfmkAAACAATBR34zgCAAABYCJGAAAIAEMxBwCAAGAEQGcAIABMMv4/R0thxs8JAAAEgGnuui1JNhSsk+1F5XQGAALAJNQEBkAAGCotLY1OAEAAmEh9iBwAEACMAACAADDFo/NT6AQABICJqAcAgAAwFDWBARAAjAAAgAAwCTWBARAAjAAAgAC4HEpKSuS6666Trl27ys0336yreNk6pEOHZs3Vth49eshLL73kulM7uO5W5gAAEACXWa9evaS8vFzX8F23bp0MGDCgRRdx+3X79u2T/v37tyoAGAEAIAAus5EjR0p+fr6cPHnSfcd4CYA9e/ZI7969bcurV6+Wnj17yuzZs90GAHMAAAiAy6ympkYWLlwot9xyi8TGxsru3btbHADWFhYWJsuXL7dtGzJkiGzYsEHWr1/vNgDuvoUAAEAA+IwjR45IeHh4q0YAb7/9ttx6660OI4iQkBB9S+ncuXNuAyD+hrH8RgIgAC6niIgIWbFihZw9e1a/+u/Xr1+rAkApLCzUt3usbrjhBikuLpaysjK3ATAxZgK/kQAIgMvpwIEDMmzYMAkKCpLo6GipqKhodQA0NTXpuYSDBw/q5a1bt+rbQqmpqW4DQNUEBgACwECMAAAQAIaKu2EcnQCAAGAEAAAEgDGYAwBAADACAAACwCRTxk6mEwAQACaiJjAAAsBQmZmZdAIAAoARAAAQAMawWCx0AgACwETz58/XPxAajUa7FO2HP/whAQAAIAAAAAQAABAAAAACAABAAAAACAAAAAEAACAAAAAEAPAvo0ePNur70q8gAOBbji4WKenguR1d3Ow/urX9OxeKljz/YlxUPB1ze+FmWXxrtsem9nG2b98+uf/++2XcuHFyzz33yM9//vPLdkH1xX4FAQBfDYDXL/zHPbrIddPbFl+U/+y+GgBFD78k217Z7LKpbc4B8N5778mdd94pO3fulLNnz8rf//53efHFFwkAEAAI/ABQj9etWyf33nuvTJw4UZ5++mn58ssv9bY9e/bo9QkJCXq9uxFAcnKy7dWzeo7zSMO6b1NTk6xYsUKee+45/eFVqn3xxRe2/dX3nT59utfjtmcALFq0SFavXu3yeA0NDXo0MGXKFN1+9rOf6XXWf0dFRYXMnTtX4uLiZN68eVJVVeX23Ntyfr7SryAAEMABUFRUJJ988onU1tbKq6++Kvn5+Xqbuui9+eabcubMGVm+fLnHW0DqIqQuiPfdd5/bV5XqQvuLX/zCtrxx40ZZtmyZbd/t27dLfX29x+O2dwBMnjxZPv74Y5fHKyws1BfWc+fO6aYeq76y/jsKCgqkurpa95s6t4ceesjtubf1/HyhX0EAIIADwJ56tTht2jT9+O6779YXCXWxcN7f1QWjrq5Obr/9drfHnjVrlnz22We25dOnT0tSUpLXC5D9cds7ANQrYXV8V9Sr5lOnTtmW1WPrK2nnf4cKAXUsTwHQ1vO73P0KAgCGBIC62FsvZG+99ZYub6lGAq+99prLADh+/Lg8++yzMnPmTH0rxNOx77jjjma3MNwFirvjtncAqFtcH330kcvjTZgwQRobG23L58+f1+vc/Ts8nXtrz8+X+hUEAAwJAPXqcerUqQ7r1MSomgdwFQAzZszQt40++OADfZvE+dj2owd10VH7uOL873B33PYOgCeeeMLtHIC6PWI/AqipqXE7AnDVr65GTi09P1/qVxAACOAAqKys1JOb6jbG2rVrbXMA6r7yyZMn5ciRI3rC0FUAqHvo7777rp50XLlypcOx1chBPddKbVcXH3VRVd/v6NGj8uSTT7q8ALk7bnsHgJrjULdL1GSoOn/1rqDf/va3etsrr7ziMAeg5kHUvEBLAsD53Ft7fr7UryAA4KsB8G/+HYD6mpWVpW8jqHv+6i2Q1leTS5YskcTERH3x37t3r8sAUHME6qKiLqLqwml/IVG3jdTIQR1DUbdT/vjHP0pqaqqMHz9ef78f//jHLi9A7o57Mf4OYPfu3fo+urr1pd4hY51QVROnqj/uuusu3dRj62SqtwBwPvfWnp8v9SsIAAQo/uMDBAAIAAAEAACAAAAAEAAAAAIAAEAAAAB83f8Coge0NrfxMqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = tree_stats.getAnnotatedLengthHistogram(depth, \"ratio\")\n",
    "annotate_soma(hist, depth)\n",
    "hist.show()\n",
    "\n",
    "# from IPython.display import Image, display\n",
    "# display(Image(filename=r\"./images/AA0876-annotated-length-hist-both.png\",  width=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! Now it is easier to notice that, e.g., there are no contralateral projections to the Ectorhinal area (ECT)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Followup Questions:\n",
    "\n",
    "1. Which kind of anatomical structures are associated with axonal processes in the ipsilateral ECT? unbranched processes? end-points? branch-points? varicosities?\n",
    "\n",
    "2. How can we visualize the axon around that hemi-area?\n",
    "\n",
    "We can answer all of those questions with SNT, but that would be the material for another tutorial!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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