measurements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Axon growth quantification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from src.utils import *\n",
    "import re\n",
    "\n",
    "# add auto update of libraries\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# viewer = napari.Viewer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, specify the name of a measurement and folder name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what is the name of a measurement?\n",
      "where is it located?\n"
     ]
    }
   ],
   "source": [
    "print('what is the name of a measurement?')\n",
    "name = input()\n",
    "print('where is it located?')\n",
    "folder_name = input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next cell will open the swc files from the folder and save them in the readable for Python format. Be sure to correctly specify the folder name! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of time points is 4\n"
     ]
    }
   ],
   "source": [
    "folder_name = r'data\\raw\\Control\\X5773\\Control_X5773_SNT-c-a-1-08112022'\n",
    "axons = read_axon(folder_name)\n",
    "print('Number of time points is', len(axons))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next cell would shift origins of axons from different time points to the one point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Axons from different time points were shifted to the same origin\n"
     ]
    }
   ],
   "source": [
    "shifted_axons = shift_axon(axons)\n",
    "print('Axons from different time points were shifted to the same origin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next cell will visualize all axons from different time points, each with unique color in the open Viewer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: DirectWrite: CreateFontFaceFromHDC() failed (Indicates an error in an input file such as a font file.) for QFontDef(Family=\"MS Sans Serif\", pointsize=12, pixelsize=16, styleHint=5, weight=400, stretch=100, hintingPreference=0) LOGFONT(\"MS Sans Serif\", lfWidth=0, lfHeight=-16) dpi=96\n",
      "WARNING: QWindowsWindow::setGeometry: Unable to set geometry 1629x986+1141-1143 (frame: 1651x1042+1130-1188) on QWidgetWindow/\"_QtMainWindowClassWindow\" on \"\\\\.\\DISPLAY2\". Resulting geometry: 1623x969+1144-1129 (frame: 1645x1025+1133-1174) margins: 11, 45, 11, 11 minimum size: 385x489 MINMAXINFO(maxSize=POINT(x=0, y=0), maxpos=POINT(x=0, y=0), maxtrack=POINT(x=0, y=0), mintrack=POINT(x=600, y=790)))\n"
     ]
    }
   ],
   "source": [
    "viewer = napari.Viewer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vizualize axons\n",
    "for i, axon in enumerate(shifted_axons):\n",
    "    color = np.random.rand(3) # generate new color\n",
    "    viewer.add_shapes(axon, name=f'axon t ={i+1}', shape_type='path', ndim = 2, edge_width=1, edge_color=color, face_color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vizualize the growth\n",
    "growth = np.array( [axon[-1] for axon in shifted_axons])\n",
    "viewer.add_shapes(growth, name='growth path', shape_type='path', ndim = 2, edge_width=1, edge_color='green', face_color='green')\n",
    "viewer.add_points(growth, size=1, face_color='green')\n",
    "\n",
    "# save growth to csv\n",
    "growth.tofile(f'growth_{name}.csv',sep=',',format='%10.5f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use matplotlib to plot the axons\n",
    "fig, ax = plt.figure(figsize=(10,10)), plt.axes()\n",
    "for i, axon in enumerate(shifted_axons):\n",
    "    ax.plot(axon[:,0], axon[:,1], label = 'time ' + str(i))\n",
    "    \n",
    "ax.plot(growth[:,0], growth[:,1], 'o-', label='growth')\n",
    "ax.legend()\n",
    "ax.set_aspect('equal')\n",
    "# set x and y titles\n",
    "ax.set_xlabel('x (in $\\mu m$)')\n",
    "ax.set_ylabel('y (in $\\mu m$)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below cell would compute the required parameters for each time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\aagafono\\Desktop\\small_stuff\\gabriela\\Tavakoli2022\\src\\utils.py:85: RuntimeWarning: invalid value encountered in divide\n",
      "  x = x / np.linalg.norm(x)\n"
     ]
    }
   ],
   "source": [
    "# check if there is already a file with measurements\n",
    "if os.path.isfile(f'measurements.csv'):\n",
    "    measurements = pd.read_csv('measurements.csv')\n",
    "    # create dictionary from the dataframe\n",
    "    measurements = measurements.to_dict('list')\n",
    "else:\n",
    "    measurements = defaultdict(list) # set measurements dictionary\n",
    "\n",
    "# set columns names \n",
    "columns = ['Genotype', 'Name of a measurement', 'Time', 'Coordinate of the tip node', 'Axon length ($\\mu m$)', 'Speed from $t_{i-1}$ to $t_{i}$ ($\\mu m / \\text{sec}$)',\n",
    "        'Axon growth distance from $t_{i-1}$ to $t_{i}$ ($\\mu m$)',  'Angle change from $t_{i-1}$ to $t_{i}$ (%)', 'Total growth during all time ($\\mu m$)', \n",
    "        'Total speed during all time ($\\mu m / \\text{sec}$)', 'Total angle change (%)']\n",
    "\n",
    "print(len(columns))\n",
    "measurements = measure(growth, measurements, columns, genotype='control', name_of_measurement='test')\n",
    "measurements = pd.DataFrame(measurements)\n",
    "measurements.to_csv('measurements.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next cell is not compulsory and can be used to visualize the computed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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PUHZavGKjaAkGAAAQaibD6OtbtRO3YcMGLVy4ULGxsZo1a5Yk6cMPP1Rzc7PeeOMNTZs2LVRTnxCPxyObzSa32y2r1RrucgAAAAAAw5Tfb2ivuznQBqw7YKk80Kg6b1uf11ljIgKrVQ7fc2V0cpwstAQDAADod0ebG4Q0dJk/f75yc3P1xBNPKCKic1FNR0eHbrjhBm3fvl3FxcWhmvqEELoAAAAAAPpTW4dfO+u8gQ3sK7pWrmx3etXc7uvzupG2mOBVK12fpyVEyWQiXAEAABgogyJ0iY2N1caNGzVhwoSg41u3btWMGTPU1NQUqqlPCKELAAAAAOB4eFraO0OVwF4rXlU6G7X7YJN8/t7ffkeYTRqbFq/crmAlJz1eufZEjbPHKz6arVgBAAAGg6PNDUL63ZvVatXu3bt7hC5VVVVKTEwM5dQAAAAAAISEYRja72k9tM/KYXuuHGho7fO6hOgI5dg791s5vCVYVkqcIi3mAXwGAAAACJWQhi6XX365rr/+ej300EOaO3euJGndunX6wQ9+oCuuuCKUUwMAAAAAcEI6fH7tOtgUFKpUHmhUpdOrxtaOPq9LT4wOClW6P8+wRtMSDAAAYJgLaejy0EMPyWQy6aqrrlJHR+c3pJGRkbrpppv0s5/9LJRTAwAAAABwVLytHdru9KrC2dAVrHhV4WzUrjqv2n29twQzm6QxqfGBYCXHHq/c9ASNsyfIFhs5wM8AAAAAg0VI93Tp1tTUpMrKSklSTk6O4uLiQj3lCWFPFwAAAAAYXgzDUG1jW/CqFWfnypW97pY+r4uNtCgnvStcCey5kqAxqXGKjrAM4DMAAABAOA2KPV12794th8OhuLg4TZ48ucdYVlZWKKcHAAAAAJxkfH5D1fVNPfZaqXR65W5u7/O61PioHnut5NjjNdIWK7OZlmAAAAA4OiENXbKzs1VTU6P09PSg43V1dcrOzpbP5wvl9AAAAACAYaql3dfVEix41cr2Wq/aOvy9XmMySaOTYw+tWDnsY3J81AA/AwAAAAxHIQ1dDMPodZPAxsZGxcTEhHJqAAAAAMAwUO9tU0VXoFJxoLHzc2ejquub1Vez7KgIs8alxXeuXDksWBlnj1dMJC3BAAAAEDohCV2WLl0qSTKZTLrjjjuC9nDx+Xz64IMPVFhYGIqpAQAAAABDjN9vaK+7+bCWYF5Vdq1eqfO29XmdLTZSuV3BSk56fCBcGZ0cJwstwQAAABAGIQldNm7cKKlzpcvmzZsVFXVomXZUVJQKCgp06623hmJqAAAAAMAg1drh087apqCN7CsONGq706vm9r7bT4+0xfTYbyU3PUGp8VG9dlcAAAAAwiUkocs777wjSbr22mv16KOPymq1hmIaAAAAAMAg5Glp7wxVutuBda1e2X2wST5/7z3BIi0mjU2NDwpVuluCxUeHtDM2AAAA0G9C+p3rU089FcrbAwAAAADCxDAM7fe0drUEa1Cl0xtYvXKgobXP6xKiIwJ7reSkx3d9TFBWSpwiLeYBfAYAAABA/+PXhQAAAAAAfWr3+bWrLrglWPfKlcbWjj6vy7BGB1atHL56JT0xmpZgAAAAGLYIXQAAAAAA8rZ2dAYqXeFK94b2u+q8avf13hLMYjZpTEqcxgW1BItXTnqCrDGRA/wMAAAAgPAjdAEAAACAk4RhGKptbOsMVQIrVjoDlhp3S5/XxUZaDrUC6169kp6gMalxio6wDOAzAAAAAAY3QhcAAAAAGGZ8fkNVB4NbgnWvXvG09N0SLC0hKrBq5fCWYCOsMTKbaQkGAAAAfJGwhy4ffPCBZs+eHe4yAAAAAGDIaWn3dbUE69rEvitg2V7rVVuHv9drTCbJkRynHHv8YS3BOv8kx0cN8DMAAAAAhpewhy5f+9rXtHv37nCXAQAAAACDVr23TRXdq1a6WoNVHGjUHlezjN63W1FUhFnj0uJ7rFrJTotXTCQtwQAAAIBQGJDQ5etf/3qvxw3D0MGDB4/pXsXFxXrwwQf10UcfqaamRi+++KIuuuiiwPg111yjZ555JuiahQsXatWqVcdcNwAAAAAMFL/f0B5X82EtwbyBgOWgt63P62yxkZ2Bij2hc9+V9ATl2hM1KjlWFlqCAQAAAANqQEKXN998U3/605+UkJAQdNwwDBUXFx/TvbxerwoKCnTdddfpkksu6fWc8847T0899VTgcXR09LEXDQAAAAAh0Nrh087apqB9Viqdjdru9Kq53dfndaOSYjXuiJZguekJSo2PkslEuAIAAAAMBgMSupx55plKTExUUVFRj7EpU6Yc070WLVqkRYsWfe450dHRyszMPKb7AgAAAEB/cje3B21kX9kVsOw+2CR/Hy3BIi0mjU3tvSVYfHTYu0MDAAAA+AID8l37Cy+80OfYv/71r36fb/Xq1UpPT1dycrLOOuss3XfffUpNTe33eQAAAACc3AzD0D5PiyoPeFVxoEEVzsbOz52Ncja09nldQnSEcrpagnUGLJ1BiyMlTpEW8wA+AwAAAAD9adj9qtR5552nSy65RNnZ2aqsrNRtt92mRYsWaf369bJYet8ssrW1Va2th94QeTyegSoXAAAAwBDQ7vNrV92hlmCV3atXnF41tnb0eV2GNTpoxUr35+mJ0bQEAwAAAIahYRe6fOMb3wh8PnnyZE2ZMkU5OTlavXq1zj777F6vWbFihZYvXz5QJQIAAAAYpLytHUEtwbr3XNlV16SOPnqCWcwmjUmJU84RLcHG2eNljYkc4GcAAAAAIJzCErrs27dvwPZcGTdunNLS0lRRUdFn6LJs2TItXbo08Njj8cjhcAxIfQAAAAAGlmEYcja2BtqAVR4WsNS4W/q8LjbSopz0+MNagnV+HJMar6gIWoIBAAAACFPosmDBAm3atGlA5qqurlZdXZ1GjBjR5znR0dGKjo4ekHoAAAAADAyf31DVwabgVStdIYunpe+WYGkJUcqxJwTvuZKeoBHWGJnNtAQDAAAA0LewhC6G0fuy/KPR2NioioqKwOMdO3aopKREKSkpSklJ0fLly3XppZcqMzNTlZWV+uEPf6jc3FwtXLiwP0oHAAAAMMg0t/m0vfbwvVa8qjjQqB21XrX5/L1eYzJJjuS4w/ZaiQ+sXkmKixrgZwAAAABguAhL6HIiG0Zu2LBBX/rSlwKPu9uCXX311Xrssce0adMmPfPMM3K5XBo5cqQWLFige++9l5UsAAAAwBB30NsWtM9K9+d7XM3q6/e6oiLMGpcW32Mj++y0eMVEWgb2CQAAAAAY9sISupyIM88883NXyrz++usDWA0AAACA/uT3G9rjau6x10ql06uD3rY+r0uKi1SuPXgj+xx7gkYlx8pCSzAAAAAAA2TIhS4AAAAAhr7WDp921jb1WLWyvbZRLe29twSTpFFJsYG9Vro3tc9JT1BqfNQJragHAAAAgP4QltDFYmEZPwAAAHAycDe3B0KVw1eu7D7YJH8fC9gjLSZlp8UHVq10fxxnj1dcFL83BgAAAGDwCss7lo0bN4ZjWgAAAAAhYBiG9nlaAhvZVxzWEszZ0NrndYnREcpJP7IlWLyyUuIUYTEP4DMAAAAAgP7Br4kBAAAAOCrtPr921TUFrVzp3nvF2+br87oMa3RnqNLVCqz7Y3piNC3BAAAAAAwrAxK6/OIXv9D3vvc9ffLJJ5owYQLtxQAAAIBBrLG144hN7Ds/7qprUkcfPcEsZpPGpMb1aAmWY49XYkzkAD8DAAAAAAiPAQldCgsLJUm33XabPvvsM8XGxmrSpEmaPHmyTj31VJ1//vkDUQYAAACALoZhyNnYGmgDVnnYhvY17pY+r4uLsijH3hmmHGoJlqAxqfGKiqAlGAAAAICTm8kwjD62rwydxsZGffLJJ9q8ebO2bNmiRx55ZKBL+Fwej0c2m01ut1tWqzXc5QAAAADHzec3VHWwsyVYxREtwTwtHX1el5YQ1WPVSm56gjKtMTKbaQkGAAAA4ORytLlBSEOXuXPnatWqVUMuuCB0AQAAwFDT3Obr3GelK1CpdHpVcaBRO2q9avP5e73GZJKyUuKCWoF1hyxJcVED/AwAAAAAYPA62twgpO3F3n//fbW0tPQowOPx6Kc//alWrlwZyukBAACAYeegty1on5Xuz/e4mtXXr1NFR5g1rpeWYNlp8YqJZL9FAAAAAOgvIQldLrvsMs2YMUMmk0kHDhxQenp60LjX69VDDz1E6AIAAAD0wu83tMfVHGgDdnjAUt/U3ud1SXGRyu2lJdjIpFhZaAkGAAAAACEXktAlKytLL7/8sgzDUEFBgVJTU1VQUKCCggIVFhZq27ZtGjFiRCimBgAAAIaM1g6fdtR6VXnAG7TnyvbaRrW0994STJJGJcUqJz1BufYE5aTHB4KWlPgomUyEKwAAAAAQLiEJXR5++GFJUlRUlNatW6e9e/dq48aNKikp0Ysvvii/368HHnggFFMDAAAAg467uT3QBqzysJZguw82yd9HS7BIi0nZafFBK1Zy7AkaZ49XXFRIuwQDAAAAAI5TSN+teb1eRUZGSpIuvPDCUE4FAAAAhJVhGNrnaQnaZ6Xzc69qG1v7vC4xOqJz1coRLcEcybGKsJgH8BkAAAAAAE5USEOX7sAFAAAAGC7afX7tqvOq4oA3EKx0r2Dxtvn6vC7TGhPUCqw7YLEnRtMSDAAAAACGCfoSAAAAAL1obO0IagXW/XFXXZM6+ugJZjGbNCY17tCKFXuCctITlGOPV2IMv5AEAAAAAMMdoQsAAABOWoZhyNnY2hmoHGhUpdMbaA+2z9PS53VxUZZAsJJjjw+0BMtKiVdUBC3BAAAAAOBkRegCAACAYa/D51dVfXPnyhVn8OqVhpaOPq9LS4gOClW6g5ZMa4zMZlqCAQAAAACCEboAAABg2Ghu83Xur9K1x0qFs1GVB7zaUetVm8/f6zVmk+RIiQu0Auv8GK8ce4KS4qIG+BkAAAAAAIaykIcuLS0t2rRpkw4cOCC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      "text/plain": [
       "<Figure size 2000x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns_to_show = ['Axon length ($\\mu m$)', 'Speed from $t_{i-1}$ to $t_{i}$ ($\\mu m / \\text{sec}$)',\n",
    "        'Axon growth distance from $t_{i-1}$ to $t_{i}$ ($\\mu m$)',  'Angle change from $t_{i-1}$ to $t_{i}$ (%)']\n",
    "\n",
    "# visualize all columns in a single plot\n",
    "fig, ax = plt.subplots(4, 1, figsize=(20, 20))\n",
    "for i, column in enumerate(columns_to_show):\n",
    "    sns.lineplot(x=columns[2], y=column, data=measurements, ax=ax[i], hue='Name of a measurement')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Genotype</th>\n",
       "      <th>Name of a measurement</th>\n",
       "      <th>Time</th>\n",
       "      <th>Coordinate of the tip node</th>\n",
       "      <th>Axon length ($\\mu m$)</th>\n",
       "      <th>Speed from $t_{i-1}$ to $t_{i}$ ($\\mu m / \\text{sec}$)</th>\n",
       "      <th>Axon growth distance from $t_{i-1}$ to $t_{i}$ ($\\mu m$)</th>\n",
       "      <th>Angle change from $t_{i-1}$ to $t_{i}$ (%)</th>\n",
       "      <th>Total growth during all time ($\\mu m$)</th>\n",
       "      <th>Total speed during all time ($\\mu m / \\text{sec}$)</th>\n",
       "      <th>Total angle change (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>test</td>\n",
       "      <td>0</td>\n",
       "      <td>[-24.471807999999996, 45.126912000000004]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>control</td>\n",
       "      <td>test</td>\n",
       "      <td>1</td>\n",
       "      <td>[-32.217472, 52.648064000000005]</td>\n",
       "      <td>10.796436</td>\n",
       "      <td>10.796436</td>\n",
       "      <td>10.796436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>control</td>\n",
       "      <td>test</td>\n",
       "      <td>2</td>\n",
       "      <td>[-45.575936, 65.894272]</td>\n",
       "      <td>29.608571</td>\n",
       "      <td>18.812511</td>\n",
       "      <td>18.812511</td>\n",
       "      <td>0.600771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>control</td>\n",
       "      <td>test</td>\n",
       "      <td>3</td>\n",
       "      <td>[-67.802624, 89.917056]</td>\n",
       "      <td>62.319472</td>\n",
       "      <td>32.727967</td>\n",
       "      <td>32.727967</td>\n",
       "      <td>2.465714</td>\n",
       "      <td>62.319472</td>\n",
       "      <td>15.579868</td>\n",
       "      <td>8.547921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Genotype Name of a measurement  Time  \\\n",
       "0  control                  test     0   \n",
       "1  control                  test     1   \n",
       "2  control                  test     2   \n",
       "3  control                  test     3   \n",
       "\n",
       "                  Coordinate of the tip node  Axon length ($\\mu m$)  \\\n",
       "0  [-24.471807999999996, 45.126912000000004]               0.000000   \n",
       "1           [-32.217472, 52.648064000000005]              10.796436   \n",
       "2                    [-45.575936, 65.894272]              29.608571   \n",
       "3                    [-67.802624, 89.917056]              62.319472   \n",
       "\n",
       "   Speed from $t_{i-1}$ to $t_{i}$ ($\\mu m / \\text{sec}$)  \\\n",
       "0                                           0.000000        \n",
       "1                                          10.796436        \n",
       "2                                          18.812511        \n",
       "3                                          32.727967        \n",
       "\n",
       "   Axon growth distance from $t_{i-1}$ to $t_{i}$ ($\\mu m$)  \\\n",
       "0                                           0.000000          \n",
       "1                                          10.796436          \n",
       "2                                          18.812511          \n",
       "3                                          32.727967          \n",
       "\n",
       "   Angle change from $t_{i-1}$ to $t_{i}$ (%)  \\\n",
       "0                                  180.000000   \n",
       "1                                         NaN   \n",
       "2                                    0.600771   \n",
       "3                                    2.465714   \n",
       "\n",
       "   Total growth during all time ($\\mu m$)  \\\n",
       "0                                     NaN   \n",
       "1                                     NaN   \n",
       "2                                     NaN   \n",
       "3                               62.319472   \n",
       "\n",
       "   Total speed during all time ($\\mu m / \\text{sec}$)  Total angle change (%)  \n",
       "0                                                NaN                      NaN  \n",
       "1                                                NaN                      NaN  \n",
       "2                                                NaN                      NaN  \n",
       "3                                          15.579868                 8.547921  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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