trajectory_transform_comparison_ibl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "78fc5323-ddf4-4d17-8fd5-209abed86d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bf21c52f-80fb-4bab-a9bc-6cb9e79f0785",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeDistance(data,ot,ct):\n",
    "    return np.sqrt(np.sum(np.power(data[ot].values-data[ct].values,2),axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27b8d1f-2746-456c-bcee-dcb1f78d8b08",
   "metadata": {},
   "source": [
    "# build trajectory data csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f037852b-a5d4-4698-b1c3-c75602816946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load all EIDs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af220ac9-23a6-4d41-9ac4-02f7d541a04e",
   "metadata": {},
   "source": [
    "# analyze insertions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "38793e8f-5620-48bc-928f-00c041d940ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./data/trajectory_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c88c4709-4eb5-4173-afda-96afda1486b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove data rows we are concerned about\n",
    "# 1: remove rows where px/py/pz are all equal to mx/my/mz\n",
    "data['pm_id'] = np.all(data[['px','py','pz']].values==data[['mx','my','mz']].values,\n",
    "                               axis=1)\n",
    "# 2: remove rows where mx/my/mz are too far from ex/ey/ez\n",
    "data['me_dist'] = computeDistance(data,['mx','my','mz'],['ex','ey','ez'])\n",
    "# 3: remove rows where ez > 0\n",
    "data['dir_error'] = data['ez']>0\n",
    "# 4: remove rows where mz > 0\n",
    "data['dir_error2'] = data['mz']>0\n",
    "\n",
    "data = data.loc[(data['pm_id']==False) & \n",
    "                (data['me_dist']<1000) & \n",
    "                (data['dir_error']==False) & \n",
    "                (data['dir_error2']==False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b88d28a8-d31f-4937-9695-e4ad847bed5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'{0} sessions with matched planned/micro-manip coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c34e9e23-36d7-4d47-a23c-62dbf98c0ffb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>pid</th>\n",
       "      <th>px</th>\n",
       "      <th>py</th>\n",
       "      <th>pz</th>\n",
       "      <th>mx</th>\n",
       "      <th>my</th>\n",
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       "      <th>ey</th>\n",
       "      <th>ez</th>\n",
       "      <th>pm_id</th>\n",
       "      <th>me_dist</th>\n",
       "      <th>dir_error</th>\n",
       "      <th>dir_error2</th>\n",
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       "  </thead>\n",
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       "<p>422 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "     Unnamed: 0                                   pid      px    py    pz  \\\n",
       "0             0  ef03e569-2b50-4534-89f4-fb1e56e81461 -2243.0 -2000  -169   \n",
       "2             2  f4f45cff-c45c-413a-92ea-b6533e3e0019 -1829.0   250  -551   \n",
       "4             4  7bc16ab1-1b0d-4f52-9e4b-210106ea344c  1245.0 -6250  -448   \n",
       "5             5  39883ded-f5a2-4f4f-a98e-fb138eb8433e  2236.0 -6750 -1032   \n",
       "6             6  55423355-281c-41f2-8ef7-5aa098ffbbed -3430.0 -1750  -694   \n",
       "..          ...                                   ...     ...   ...   ...   \n",
       "671         671  70da415f-444d-4148-ade7-a1f58a16fcf8 -2243.0 -2000  -169   \n",
       "673         673  72274871-80e5-4fb9-be1a-bb04acebb1de -3577.0 -4000  -884   \n",
       "676         676  e018f54c-e404-4232-8cb3-119ac1b2c7f1   800.0  1000  -622   \n",
       "679         679  3017e4dc-2150-43ad-9b08-b95b48ba8dfa -1821.0 -1750   -74   \n",
       "680         680  625a7bc9-00cf-44f6-a508-3ed89d23ba3a -4364.0 -4000 -1678   \n",
       "\n",
       "         mx      my      mz           ex           ey    ez  pm_id  \\\n",
       "0   -1887.0 -2105.4  -190.7 -2089.000000 -2050.000000   -68  False   \n",
       "2   -1818.0   250.0  -581.0 -2094.813917  -132.118553  -468  False   \n",
       "4    1232.5 -6242.5 -1178.2  1461.000000 -6175.000000  -843  False   \n",
       "5    2217.0 -6753.0 -1488.0  1886.000000 -6275.000000  -943  False   \n",
       "6   -3380.0 -1756.0  -920.0 -3614.000000 -1600.000000  -793  False   \n",
       "..      ...     ...     ...          ...          ...   ...    ...   \n",
       "671 -2284.6 -2079.2  -451.0 -2664.000000 -2000.000000  -243  False   \n",
       "673 -3973.8 -3835.2 -1445.6 -3989.000000 -3625.000000 -1218  False   \n",
       "676   544.0  1285.0     0.0   595.025390  1616.484692  -868  False   \n",
       "679 -1828.9 -1749.1  -384.0 -2014.000000 -1700.000000   -93  False   \n",
       "680 -4123.6 -4029.8 -1778.4 -4114.000000 -3750.000000 -1418  False   \n",
       "\n",
       "        me_dist  dir_error  dir_error2  \n",
       "0    242.751828      False       False  \n",
       "2    485.190203      False       False  \n",
       "4    411.251188      False       False  \n",
       "5    796.912793      False       False  \n",
       "6    308.579001      False       False  \n",
       "..          ...        ...         ...  \n",
       "671  439.864752      False       False  \n",
       "673  310.188394      False       False  \n",
       "676  930.542687      False       False  \n",
       "679  348.358752      False       False  \n",
       "680  456.364284      False       False  \n",
       "\n",
       "[422 rows x 15 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "0b308c2b-b8e0-4fb4-b80b-77ea951fb20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "needlesInverse = (1 / 1.087, 1, 1 / 0.952)\n",
    "mriInverse = (0.97, 1.05, 1.13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "a5843d97-bc47-4d75-bc96-ba75bf9be9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# run mx/my/z through the two inverses, then compare distances\n",
    "data['mx_ni'] = data['mx']*needlesInverse[0]\n",
    "data['my_ni'] = data['my']*needlesInverse[1]\n",
    "data['mz_ni'] = data['mz']*needlesInverse[2]\n",
    "\n",
    "data['mx_mi'] = data['mx']*mriInverse[0]\n",
    "data['my_mi'] = data['my']*mriInverse[0]\n",
    "data['mz_mi'] = data['mz']*mriInverse[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "b1586621-b421-480b-bbc5-b6f238f31456",
   "metadata": {},
   "outputs": [],
   "source": [
    "needlesDist = computeDistance(data,['mx_ni','my_ni','mz_ni'],['ex','ey','ez'])\n",
    "mriDist = computeDistance(data,['mx_mi','my_mi','mz_mi'],['ex','ey','ez'])\n",
    "rawDist = computeDistance(data,['mx','my','mz'],['ex','ey','ez'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "ec48cc95-b93b-48f6-bbf8-e5731e979427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "611.0911193032834"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(needlesDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "12271ca8-9f97-40c0-8ae2-ea0c8a227a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "538.2979324556243"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(mriDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1e08e917-02a4-4c5a-a3b9-157f28feba0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "611.8289109049389"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(needlesDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "fa17520a-29f9-4744-83b6-c3432d13ebc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "562.6694004041028"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(mriDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "64d52560-c622-4ae4-909f-5b4fa2d7a134",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(528.9175928251963, 539.1710188008358)\n"
     ]
    }
   ],
   "source": [
    "print((np.median(rawDist),np.mean(rawDist)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7c11ab94-498a-40fc-ba39-161db45d677e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0,1600,50)\n",
    "plt.figure(figsize=(10, 6), dpi=80)\n",
    "plt.hist([needlesDist,mriDist,rawDist], bins, alpha=0.5, label=['NeedlesTransform','MRIAvgTransform','raw'])\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Distance between micro-manip and histology tip positions, in CCF (um)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f68be8fc-aba2-4f32-bb78-878563989530",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b8f6d5d-59f0-4749-9363-7b9acb389c8c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:iblenv] *",
   "language": "python",
   "name": "conda-env-iblenv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}