08_Cytoarchitectonic_Type.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import nibabel as nb\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "from nipype.interfaces import fsl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/home/julia/workspace/mouse_gradients/regions.csv\")\n",
    "df = df.drop(columns=['Unnamed: 0', 'comment'])\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = '/home/julia/data/gradients/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare region masks for sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for region in df['acronym']:\n",
    "    resamp = fsl.FLIRT(in_file=data_dir+'allen_atlas/regions/{}.nii.gz'.format(region),\n",
    "                       reference=data_dir+'allen_atlas/template_200um.nii.gz',\n",
    "                       apply_isoxfm=0.2,\n",
    "                       interp='nearestneighbour',\n",
    "                       out_file=data_dir+'allen_atlas/regions/{}_200um.nii.gz'.format(region))\n",
    "    resamp.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample gradients in regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gradients = np.array(nb.load(data_dir+'results/embedding/embed.nii.gz').dataobj)[:,:,:,:6]\n",
    "cortex = np.array(nb.load(data_dir+'allen_atlas/cortex_mask_tight_200um.nii.gz').dataobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for g in range(6):\n",
    "    g_regions = []\n",
    "    for region in df['acronym']:\n",
    "        mask = np.array(nb.load(data_dir+'allen_atlas/regions/{}_200um.nii.gz'.format(region)).dataobj)\n",
    "        g_regions.append(np.mean(gradients[:,:,:,g][mask==1]))\n",
    "    df['gradient {}'.format(g)] = g_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['acronym'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlate cytoarchitectonic type with gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient 1: Kendall's tau=-0.01, p=5.8386\n",
      "Gradient 2: Kendall's tau=-0.39, p=0.3054\n",
      "Gradient 3: Kendall's tau=0.13, p=3.8186\n",
      "Gradient 4: Kendall's tau=0.21, p=2.4880\n",
      "Gradient 5: Kendall's tau=-0.36, p=0.5423\n",
      "Gradient 6: Kendall's tau=0.25, p=1.4879\n"
     ]
    },
    {
     "data": {
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7P14Ycf6qOPPtLUqVP1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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "\n",
    "for g in range(6):\n",
    "    taus = []\n",
    "    surrogates = np.load(data_dir + 'results/null_models/volume/surrogates/vol{}_surrogates.npy'.format(g))\n",
    "    for s in range(10000):\n",
    "        s_regions = []\n",
    "        for region in df['acronym']:\n",
    "            # create a full brain map from the surrogate\n",
    "            brain_s = np.zeros_like(cortex)\n",
    "            brain_s[cortex==1] = surrogates[s,:]\n",
    "            # load the region map \n",
    "            mask = np.array(nb.load(data_dir+'allen_atlas/regions/{}_200um.nii.gz'.format(region)).dataobj)\n",
    "            # sample mean of surrogate in that region\n",
    "            s_regions.append(np.mean(brain_s[mask==1]))\n",
    "        # calculate Kendall's tau\n",
    "        tau, _ = stats.kendalltau(df['type'], s_regions)\n",
    "        taus.append(tau)\n",
    "    tau, _ = stats.kendalltau(df['type'], df['gradient {}'.format(g)])\n",
    "    p_val = np.where(np.abs(taus)>=np.abs(tau))[0].shape[0] / (len(taus)+1) * 6\n",
    "    print('Gradient {}: Kendall\\'s tau={:.2f}, p={:.4f}'.format(g+1, tau, p_val))\n",
    "    \n",
    "    \n",
    "    fig.add_subplot(3,2,g+1)\n",
    "    plt.hist(taus, color='grey', label='Surrogate maps')\n",
    "    plt.vlines(tau, 0,2000, color='darkred', label='Gradient {}'.format(g+1))\n",
    "    plt.xlabel('Kendall\\'s tau')\n",
    "    plt.title('Gradient {}'.format(g+1))\n",
    "    sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qk2WZB//7vym6cAF9VBQ9U1PJTE+nd9eutG7ZMmQrpU1bt/J/u3ahUalwulyo\nJAmnLDN6yBDG3Xprk1yhXYlc0tFSV65ER8u6devQarWMHz9eGYuKiuLuu+/m9ddf59y5c7Rs2TIo\nzwpVLJpKpVJWOx5kWa4USvePw27H6nDgcjrBI5aX/Y5qj0atplvnznTr3Jl7R42ivKICZ4CqPyfP\nnaPowgUALFYrO/btY8e+fQC0bN5cEciMtLSgreJW//gjqzZtwqDXo9VolK21VqNh9Y8/IkkSo4YO\nDcmqVdCwVPsbrOpYkWWZzz77jCNHjjB69Gil+MPhw4f55ptvaN++Pffdd19oZxsmcnNz6dChAzFV\nijBkZGQgyzK5ubl+ouhZCVZHeXl5wKpDDRmLJkkSOne6nDd2hwOHw1Fpo7TbK3OuHQ5FJBtqVVld\n0YuTZ84QpdNhDVDC7tz586zfvJn1mzejUqlIbd+ezPR0hvbvT6sWLeo1jwqLhdU//IBBr0etUuFy\nuTBbLD5b/UVff0331FTiDAaitFp0Oh1R7uLAgsZFtaI4btw4n9effPIJJSUlrFu3jlatWvkce+SR\nR5g4cSIV1ZSzauwUFRX5vWeA5ORkgKDWkYyEWDStRoNWo8F7c+5wOnF47JQOBza7PWye7/69erFo\n9mxyCwoUe2RBYaHfeS6Xi7yCAvIKCujepUu9RXHvwYM4XS70NQSAG00mfv3tNwb06oXNZkMymSor\np6vV6DQaNFptpVhqtUEJARJVckJHrdf6ixYtYuLEiQHF4aqrrmLixIl89tlnTJkyJagTjAQsFkvA\nv/hR7pQ7a4AahYEcKN5Ut5KM1Fg0jVqNxp1K58HH8+3l0HE1gEhqtVoy0tLISEvjfuBiWRl78vIq\nRTI312clrdfpSO/Y0e8eP+3cSV5BgdINMboak0V5RQXOS6zenU6nT41L71REu92O5M7XliQJrXsV\nqdFolC14bU1VokpO6Km1KJ4+fZroGgz7BoOB06dPB2VSkYZerw/Y18QjhlFBzEduTLFoavc/Rrzs\nlA6nE4vVisVqxWq343I6G2QlmRgfr6QhyrLM0ZMnFZGMiY4OaOv7accOduzfz+off0SjVpPesaMS\nG9mxTRslNTAuJuaSqzu1Wl1jjUvF7CDLOC0WLO5AckUo3atIj72yOtukp1SWPipKMbPI7ufXt1SW\nwJdai2KbNm346quvuOeee/xEwGq1smrVKq655pqgTzASSE5ODrhFLioqAgiak8VDY+7tq1GriTUY\niDUYKh057lWk1W7HarPhdIskhM42KUkSHdq0oUObNowbNizgc+wOB/sOHVJeO5xO9v/+O/t//52F\nX31FfGxsZcWfrl1J7dgRtdGa678AACAASURBVNvjHKg0Wn1rXHr/sXA6nYpQenK3vWMlNWo1RpOJ\n5Rs24HA4KCwpqXSCua+XJInEuLgmVSUnVNRaFKdPn87//M//cPfddzN58mQ6dOgAQEFBAYsXLyY/\nP59//vOfIZtoOElPT2fhwoVUVFT4OFtycnKU48HkSuntK0kSOp0OnU6Hp+uLJyTI4bZL2uz2yq1p\nCJ04gbaTFSYT1/XsyZ68vICtHcqMRv5v1y7+z93GNyk+HkdFBc0TE32E0elyYbJaGXvLLUGpYFQ1\nd9vTwEyj1bLv4EG0Wi0x0dFE6XSVq3KbDavNhsPh4EJZGTaHo8lUyQkVtRbFCRMmYDKZePPNN/nn\nP/+pfNFkWUav1/P0008zYcKEkE00nGRlZfHRRx+xbNkyJU7RZrOxYsUK+vTpE9DOGgyC2YwnUtAG\n8Mh6vN1Vw4NC6cRJjI/nqQcewOlyUVBYqNgi8woKAoYBXSgrY2BmJsdOnsTiclWuDtVqVJLEmJtv\nDkmNS3DnbrtF8vdjxzhaWIjBLYrRUVEY3H8kZZcLq81GhdmshCsJ6kedgqqmTZvGXXfdxc8//0yh\n29vXtm1bBg0aRLy7odKVSK9evcjKymLOnDkUFRXRrl07Vq5cyalTp0ROeBAI5O323np7vN0Ou73S\neRFEkVSrVHRJSaFLSgoTsrIwWSzs80pDPO02kQDcN3o0iQkJ7D14kPKKCuJiYtBqNLyzaBGHjx8n\ns2tXeqWnhywyoKikBIvNhsPpVPrbaNw2SE+zs4S4OErLy5WePoK6U+dI07i4OIZHsE0rVMyePZs3\n3niD7OxsSktLSUtL44MPPqBv374he2ZTDrvw3np7cLqzeax2O3a3jTLYGTkGvZ7+GRn0z8gA4Gxx\nMbtzczl8/LiSLTPAy/P/aXY2F0pL+X7bNr7ftg2Ajm3bKn1sunbsGLSA7hZJSciyjMPT3ZE/HFul\n5eWo3J0RkWXOFBdXhgO5HTgeJ05jqQgUTkTuc5iob0EIEXbxB7IsKw4cq82GzWoN+kryUjz50kvk\nB4iR9KDX6eiRmqqI5DWXkYa4JSeH1z/+GGuASAgPUVotT0yf7iPckiQhuf+rdTtvPEUvroTiu8Gm\nTn/Cjh8/zoIFC8jJyaGsrMyv8bokSWzcuDGoE2yKhKJD2ZWI0i/avZp0Op1Y7XYsVquy3Q51zOT4\nrCx+PXCAPbm5nCsp8TtusdnYuX8/O/fvB6Bbp068+OST9XpWl3btcDidqCQpoPCrJAmHy0Vq+/Y+\n40q6plc4kMo7ZtIrZVGkKdZBFA8ePMjkyZOx2Wx06NCBwsJCunTpwoULFyguLqZdu3Yhczg0JURz\novqjVqsxqNUY3EHYDqdTWUV6PLTBdtwM6N2bAb17I8syp86dU6qP7z90CEuANMQ2V10V8D4FhYWk\ntG5d4/b29+PHMURHY7ZYUKtUSs9sAMn9Olqv59DRoz4rxUAoDpwqMZMqdwaOVqOpDC7XaNBGWPm0\nUFNrUXzrrbfQarUsW7aMxMREBg4cyHPPPceAAQNYunQpr732GnPnzg3lXJsEojlR8NCo1WjcbWIB\nxQ5ZNWYyGCIpSRLXtGrFNa1aMWrIEOx2O7lHjpDjFknPFjtQNfLS8nKefPlloqOiyEhLU1rGXlUl\nLbG8ogK9e2VXWl6uCBuShFqWSYyPR3KfV1e8M3Ac7u25d3C5zite8koXylqL4q5du5g4cSIdO3bk\nQhWX/4QJE9i5cydz5szh/fffD/okmxKiOVHo8IQDKTGTDkelLdJmw2yz4fJyYATjWRmpqWSkpjJl\nzBhKy8vJOXgwYIB3zsGDyLKMyWJha04OW93xr1cnJ1cKZHo6PVNTlcwaz/dDojKbxVuaLpVZUxe8\n/2CYzWbMXqmKHqH0bLkDFRZprNRaFCsqKmjbti2A4uo3mUzK8T59+vDaa68FeXpNj0goCNFUUGxo\n7uwbq82G2Z2i6HA4giaQAAlxcdxYTb57TjUtGk4XFXG6qIi1P/1UGTrUvj0Xysqw2+2oPCmWbmTg\nQmlpZe/tICcTeOMnlOCz9fZ8plq351uj0TQ6Z06tRbFFixYUu0vHx8bGEh0dzdGjR5XjZWVll0ya\nF1yaSC0IcaUjSZIS6wdUCqTFgsVmw+6OjwyVw2bGhAnc0LevYo88duqU3zlOd8UfD4FkJlyb2apb\nb2+hlACN24mj8RLMSI6hrLUopqens9/tQQPo168fn376KRkZGbhcLj777LOgp7s1RRpTQYgrGe+C\nvHa3R9tktfpUKw/ms3q7C1FMB85fvEiOu5hFTl4epUajz/katRqJytJonu2zJElER0cjSRI79u3j\npkvU8ww1PnndXo3CAGVVGaXRKBWDIimGstZximvWrGHRokXMnz8fvV7PgQMHuO+++zCbzUBlJZl5\n8+ZdsriqoJJaxymK5kQRhd1ux2y1YrJYQr6ChErhO3LiBLvz8vhu61ZOnj1LUnw8ifHxmMxmXC4X\nKpWKaL2ewjNncDqdqFQqenTpQqbbYdP+mmsi9vviEXRPfrdOq1XqT6pVqrCECF1W8Pbp06f59ttv\nUavV3HjjjYrNUXBpahJFD6I5UWRjs9sxuStwOxwOZPfKLVRsycnhoy+/xBAV5beqstpsnDh7NuB1\niXFxike7d3o6iRGekusRSc//e7bdnkrmoe6wWCtRtNls5OTkkJycTPsqgaGC+lEbURQ0Hmw2Gyar\nNaQCWWGx8PeXX0brXkX5HDObOXf+fK2yeTpccw2Z3bpx28CBXB3ksnehxC9EyG2n1Gg0ShHkYFCr\ntalKpWLatGnMmjVLiKKgSVNdPronTzsxLk7xYgdbIGP0ekYOGaI00PIWRn1UFM2TkhiUmUm0Xs+e\n3FwOHj0a0IN+5ORJjpw8Sb+ePRuVKNbk+QZQqdU0i4+vtoJ6bamVKGo0Glq0aBERbTCbEk25IESk\nUZd8dI+TJpBAXm6Yj6dE2eoffvApYaZWqRg3bBgjbrwRSZKYNGIERpNJqfjza24u586fV+5j0Ovp\nEmCBs3HLFnLz88l0d0OMj431OyeS8BbKYOW919qKmZWVxdq1a5kyZUqjiztqbHj+AS76+msqLBYc\ndjsajYaY6GjuHT1aOFrCQH3z0b0F0mKzYTKbMVutuByOev0DliSJkTfdRP+MDL754QculJeTFBfH\n6KFDaZ6Y6HNurMHgk4Z4pqiIX90e7Wi9PuB28+ddu9idm8vGLVuQJInO7dopLRrSOnQI2hY1kqm1\no+Xw4cM89dRTJCQkcP/995OSkhKwZ0vr1q2DPskrkZpsil+sXs17X3yBye3Z985aMERH8/9NmsSk\nkSMbaKYCo8nEfX//u5LmVhWHu6TZojlzauUIk2UZi9VauYK0WuuUSSPLMmt++onVP/xQWaHbHbeq\nVqkYOWSIslKsD1abjfuefhpbNVV4ovV6enbpQma3bmSmp0fc1luSJJonJV22M7LWK8VRo0YhSRKy\nLLN9+/Zqz8vNzb2sCTV1jCYTHyxdislkQqVW+3zBZVnG5D4++uabhSe6gQh2ProkSUTr9UTr9Uom\njclsxuQWyJrWKWt++olVmzZVtiNwi5dMpT1t1aZNAIy86aa6v0nAbLVyQ9++7MnL4/zFi/7HLRa2\n79vH9n37ALiqRQvuvPVWbh88uF7PCyYVFgsH8vPR63TEREdzXc+e9TY11VoUH330UbFlawB+2rGD\nMqMRdRVBhD+CXsuMRn7cvp0R9fzyC+pGKPPRvTNpEl2uytWj2YzFavUre1ZhsfDNDz9U9mMpLfVr\nXBUXE8PqH35g6PXX16tfTGJcHH+ZMgVZlik8fVrphrj/998Drh7PFBcHNAF42jkEavIVbDwr56+/\n/15p3lV88SJ6na7epqZai+Ljjz9e5wkL6s6evDxkWa72F+lZre/Jy2tyohgux1ND5aOrVCpi3FV9\nHO7ufp52sS5ZVtogmC0WJJWqMtXP0yuJymZbdoeDnLy8S5YOqwlJkmjXujXtWrfmjptvxma3cyA/\nvzLL5sABjpw8qZzbp2tXv+v3HTzI7Pnz6ZWersRGtmzevN7zqYk1P/7I4jVrMJvNGPR6tFotpWVl\nlLhczP38c2RZrrOpSVSUFEQ84a5E7p2PLkkSFWaz4vWNiY5GdrmCno+uUauJjYkhNiZGEUhZlitD\ncdRqTGYzDodDOV+tViOpVFSYTJwPcuMqnVZL7/R0eqenc//YsVwoLWVPXh75hYW0qlLeDGB3bi4V\nZjO/7N7NL7t3A3BNq1ZKhk2PLl2UHPPLocJi4Ys1a6ioqEAGZWXtdHvly41G/rNkSZ1NTZcUxXPn\nziFJEsnJyUBlj+fFixf7nXfVVVc1yd4twaZXejorNmyodrXoGe8d4C/0lUq4K5F78tHnfv55pfPL\n3QfFQ0x0NP/fPfeEzMbrEciy8nI0ajVRUVHEx8RgslioMJsxWyy43AIpAcUh7uaXlJDA0P79Gdq/\nf8DjuwP4FU6ePcvJs2f55ocf0KjVdO3USSmL1qFNm3pFtOzct4+yALUjJUlSOkFeLC+vs6mpRlEs\nKChg9OjRzJw5kxkzZgCV5cJefvllZRun3EijoWvXriK4+zK56brriI+NDWhXlGUZp9NJfGwsN113\nXRhn2XBESiVyH+eHu7y/5zcTqn7VVYmPi+PkuXNE6XQY3Ntsg3ularJYqDCZsNhstEhKCvlcamLa\n2LH86q74c+LMGb/jDqeTfYcOse/QIRZmZ5PWoQOzn3qqzs/Ze/Bgtcc8+iTLMjv27w+eKK5YsYKE\nhASl17E3s2bNolu3bkBl0vrMmTP58ssv+dvf/lbrhwv8iTUYeGjixD9WJdUcbyqe50ioRG40mfhi\nzRpaNW9euX02mXC6XKhVKmLctRi/WLMm5BEBUe5yW572ChdKS9G7BdIQHU2MwYDD4Qh7wHWf7t3p\n0707AEUXLlS2iz1wgJyDBzF61WD10LGamgm5+fl0ateu2uK1gTzk3niE8ay75GFtqVEUt27dys03\n3+zTZtJDWloa/fr1U14PHz6cLVu21OnhgsBMcJshFn39NSazGbvDgVajweAVvN1UiIRK5FWFOZDo\nNESLiMMBugZabDYsNhslpaUY9HpiY2KoMJuJjYmp3Fa7XGHNREtOSuLWgQO5deBAnC4X+cePKz21\nDx45gtPlIjNAycGS0lKeee01dFot3Tt3VgLI2119tbJ7qhqsXh2B7J41UaMoHjt2jHHjxtXqRh07\ndmTNmjV1erggMJIkMXHECEYOGdLkq+REQiVyb2F2ulx+K0W1StUgLSLKjEalr7PHrqls4yUJs8WC\n2Wrl1LlzNE9MxHWJEJ+GRq1Skdq+Pant2zNh+HBMZjP7Dh2iZ1qa37l73NXIbXa7UnwXoFlCglLx\np1NKCpu2bKk2r1yWZVSSxHU9e9ZpnjWKotlsxlAl5CEhIYGvv/6aNm3a+IzHxsb6tCcQXD6xBkOT\nb04VCZXIE+PiUKtUlJSWUlJaqrQMlQCppIRmCQkN0iKiRWKi0vDe6dWnxYNao0GWZZolJAC+IT52\nhwOzxaLUgQxmq4X6YoiOrvb3tv/QoYDjJaWlfLd1K99t3VoZt6tSKXGRiuC7G3qpJInE+Pg6299r\ndPnExcVRVFTke4FKRZcuXfxS/IqLi4kTfUMEQcbj+a0wm/220Z5K5PeMHBnSFXS/jAxMFgvnL1xA\nkiTU7jJVHkfY+QsXMFksIW8Rcc/IkZVCIElK/xPPj06rVXo5Tx492u9arUZDfGwsV7VoQXLz5sTF\nxqLWaCI2IeORyZN58YknmDB8OF1SUlBVE4nh9BJ3tUqFyp3goHG/3/rY32tcKaampvLzzz8rnuea\n+Pnnn0lNTa3TwwWC2uCxoX6+enVltRmvSuRTx4xpMBtrtdu0Bnl6pW3s5v792bRlS2W+s9fK2eWO\nzbtlwABaNmtW4330Oh16d5EKk3v1aLVaQ14kty5o1Gq6de5Mt86duXfUKMorKth78CB78vL49cAB\nv7AjnVZLfEwMBr2exLg4orwyWur87JoO3nbbbfz73/9m06ZN3HLLLdWet3HjRnbs2MF///d/13kC\nAsGlCLeNdfvevcRER6PTaikpLfXZekqSRIukJLQaTYP04n7pb3/jmVdf5btt23A6HErcqiRJ3DJg\nAC/VIfpDpVIRazAQazBUVhE3mzFZLDjrWcEnlMTFxDCoTx8G9emDLMucPHtWsTXu//137rj5ZtI6\ndlRyn/tlZNT7u1FjlRybzcbYsWMpLCzkz3/+M3fffTfXXHONcvzkyZMsW7aM+fPnk5KSwooVKwJ6\nqgX+iMrbjYcV337LvKVLSYqPr3S0VMloUatUXCgr48EJE7jz1lsbZE5ni4v5fPVqii9epEViIpNH\nj77kCrE2yLKs1H+MBO91bbDb7dgdDmIMhtBXydHpdPznP/9hxowZvPfee7z//vvExsYSGxuL0WjE\naDQiyzIdO3bk/fffF4IouCLx9oCrVSriAzSbb+he3K1atOCv998f9PtKkoRBr8eg1+N0OiudM+5u\nfKFu0lVftO7eLcHikml+bdu2ZdWqVSxdupT169fz+++/U1RURExMDNdeey23334748ePJyoIuYwC\nQSQSCR7wcKD2yr/2NOkyuVfJkeC9DhWX1c1PUH/E9rlxsWTNmup7cZvNTB0zJqT515GCp0CuKQK3\n1w1eZFYgaMpEigc83HgXyPVsryssFmw22xWzeozIlWJZWRmvvPIK3377LRaLhYyMDJ599lm61rIy\nTH5+Pi+88AK//vorWq2WoUOHMmvWLJp5GaLz8/P58ssv2bx5M8ePHycmJobu3bvzl7/8he7uvE0P\nzzzzDCtXrvR7Tq9evVi6dGm93qNYKTZORC/uwNhsNiW8J1ze6yt2pehyuZgxYwaHDh3igQceICkp\nicWLFzNlyhRWrFhBu3btarz+zJkz3HvvvcTHx/PEE09gMpn46KOPOHToEEuXLlUMssuXL2f58uXc\ndtttTJ48mfLycpYsWcKECROYP38+11cJrYiOjuaf//ynz1izIHj7BI0LkWUUGE+L14S4OMxWKyaz\nGYvFEvbUwvoQcaK4bt06du/ezbvvvsuwYcOAymITt99+O++88w6zZ8+u8fr3338fq9XKwoULadWq\nFQAZGRlMnz6d7Oxs7r77bgBGjhzJY489RoyXJ/Guu+5ixIgRvPvuu36iqNFoGDNmTDDfqkBwxeHt\nvXY4nZjM5spspCC0d20oIq5X6fr162nZsqVPsHizZs0YPnw4GzduxF5NpzEPGzZs4Oabb1YEEWDg\nwIG0b9+etWvXKmM9evTwEUSApKQkrr32WvLz8wPe2+l0YjQa6/O2BIImh0atJj42lquTk2nh3taq\n3bnbkUzErRRzc3Pp3r273wfXs2dPlixZwvHjx+nUqVPAa8+ePcv58+fp0aOH37GMjAw2b958yecX\nFRWRFKBIZ0VFBX379sVsNpOYmMjYsWN58sknqw1F8tgMq6O8vFzkiguaDB7nTNXCFJEY+xhxolhU\nVOS3dQVo6e4xe+7cuWpF8dy5cwBK6wRvkpOTOX/+vJKJEIidO3eyZ88eHnvsMb9r//znP9O1a1dc\nLhfff/89CxYsID8/nw8//LBO708gaMpoNRq0sbHEx8Zit9sxWa2VpdgiKLUw4kTRYrEEzIzxjFks\nlmqvtVqtPud641nRWSwWv20zwPnz5/nb3/5Gu3bteOCBB3yOVa0mPmrUKFq1asX8+fPZvHkzgwYN\n8rvfpbzKl1pJCiKTcHUUvBLRarUkuAs5mCwWjCZTRIT2hE0UbTYbpaWlPmPNmjVDr9djs9kCng+g\nr6YsPfwhfIGu9whmoOtNJhMPPfQQZrOZ+fPn+9WQDMQDDzzA/Pnz2bJlS0BRFFxZhLuj4JWMJElK\n3UerzUaFyYQpjIHhYRPF3bt3M3XqVJ+xTZs2kZycrGyDvfGMebbRgfAcq1oD0jPWvHlzv62zzWbj\n8ccf59ChQ3z00Ud07ty5VvNv0aJFZY/ZKsIuuDIJd0fBpkKUTkeUTkecw0GFyYTRbMbldDaoOIZN\nFNPT0/n44499xpKTk0lPT2f37t1+LT737t2LwWCoMU6xVatWNGvWjP379/sd27t3r1/wt8vlYtas\nWWzZsoW33nqrTlvaM2fOYLfbRaxiEyBSOgo2JbQaDYnx8cTFxGAymzE2YFhP2EJyEhISGDhwoM9P\nVFQUWVlZnDt3jk2bNinnlpSUsG7dOm655RafahjHjx/n+PHjPve97bbb+O677zh79qwytmXLFo4e\nPUpWVpbPuf/6179Ys2YN//u//6vERFbFarUGDMOZO3cuAIMHD677mxc0KpTGVdU46DRqNU6nk205\nOQ08sysftVpNnHdYj7snTihNFRHnaLn99tvp3bs3Tz/9tJLR8vnnn+NyuXj88cd9zvW0Xv3uu++U\nsYcffph169YxdepU7rvvPkwmE/Pnzyc9Pd0n+HrBggUsXryYzMxM9Ho92e4tkAfPuUVFRYwbN45R\no0bRsWNHxfu8ZcsWRowYwXVNpP9yUyYSOgoK/gjrCXVQeMSJolqt5oMPPmD27NksXLgQq9VKz549\nefnll0lJSbnk9VdffTWfffYZL730Eq+++iparZYhQ4bw7LPP+nil89zdwnbv3s3u3bv97uMRxfj4\neIYMGcLmzZtZuXIlLpeL9u3b88wzz/jZRAVXJpHQUVDwB56g8PjYWCxWKxVmM2aLJWh2x4gsCNEU\nEAUhGg9Gk4n7/v73ymZR1dRTtDkcLJozR9gUw4SnYo9Gq0V/mcWuIy7NTyCINCKho6CgZjwFcS9X\nECECt88CQSQi6ik2HcT2OUyI7XPjJFLqKYrMmtAhVooCQR0Idz1FkVkTeoQoCgR1INwrNE9mjT4q\nSrFvylTa1ERmTXAQoigQ1IJIWKEZTSYWr16N3W6nqKQEuXJiQGX+cGJcnMisCQLC+ywQ1ALPCk2n\n1RJnMJDkTkHTabV8mp3NUq8CxqFi+969lJaXc6GsDEmSUKtUqN3CLEkSF8rKuFheLjJrLhMhigLB\nJaht7nOF2RzSeZw9f55yoxFVgDQ3SZJQqVSUG42cPX8+pPO40hGiKBBcgkjJfT5bXAySVO02XZIk\nkKTK8wT1RoiiQHAJIiX3uVWLFgDVprN5xj3nCeqHEEWB4BJESu5zq+bNiTMYAhZflWUZl8tFnMFA\nq+bNQzqPKx3hfRYILkG/jAzUarWSxVIVh8OBWq2mf69eIZ9HQlxcZXHj8vJKcQQk/vA+G6KjQz6P\nKx2xUhQILkGk5D7HGgxMHjUKnVZL26uvplXz5iQnJdGqeXPaXX01Op2OyaNGiXCcy0SsFAWCWhAp\nuc/e8/A4XCRJwuF0ihzsICFyn8OEyH1unERq7nO45nElIkQxTKSnpyPLMnGiMKlA0ODExcXx/fff\nBzwmbIphIlAAbiDKy8spF2XuFcTn8Qfis/AlWJ+HWClGOGKb7Yv4PP5AfBa+BOvzECtFgUAg8EKI\nokAgEHghRFEgEAi8EKIoEAgEXghRFAgEAi+EKAoEAoEXQhQFAoHACxGnKBAIBF6IlaJAIBB4IURR\nIBAIvBCiKBAIBF4IURQIBAIvhCgKBAKBF6LydoRy7tw5Pv30U3Jycti/fz8mk4lPP/2U/v37h3tq\nDc7evXtZuXIl27Zt49SpUyQmJpKZmclf//pXUlJSwj29BmXfvn28//77HDhwgPPnzxMXF0d6ejqP\nPvooffr0Cff0ws68efOYM2cO6enpZGdn1+seQhQjlCNHjjBv3jxSUlJIS0tj9+7d4Z5S2Pjwww/5\n9ddfycrKIi0tjaKiIhYtWsTYsWNZvnw5nTp1CvcUG4zCwkKcTifjx48nOTmZ8vJyvv76a+677z7m\nzZvHoEGDwj3FsFFUVMR7772HwWC4rPuIOMUIxWg0YrfbSUpKYuPGjTz66KNNdqX466+/0qNHD3Q6\nnTJ29OhRRo8ezciRI3nppZfCOLvwYzabGTZsGD169OA///lPuKcTNp555hlOnTqFLMuUlZXVe6Uo\nbIoRSmxsLElJSeGeRkTQp08fH0EEaN++PV26dCE/Pz9Ms4ocoqOjadasGWVlZeGeStjYu3cvX331\nFc8+++xl30uIoqBRIssyxcXFTfYPh9FopKSkhIKCAl577TUOHTrEgAEDwj2tsCDLMv/6178YO3Ys\nXbt2vez7CZuioFHy1VdfcfbsWZ544olwTyUsPPfcc6xfvx4ArVbLpEmTePjhh8M8q/CwatUqDh8+\nzLvvvhuU+wlRFDQ68vPzef755+nbty9jxowJ93TCwqOPPsrEiRM5c+YM2dnZ2Gw27Ha7n5nhSsdo\nNPLqq68yY8YMWrZsGZR7iu2zoFFRVFTEQw89REJCAm+++SYqVdP8CqelpTFo0CDuuusu5s+fz2+/\n/RYUe1pj47333kOr1TJ9+vSg3bNpfqMEjZLy8nIefPBBysvL+fDDD0lOTg73lCICrVbLLbfcwoYN\nG7BYLOGeToNx7tw5PvnkEyZPnkxxcTEnTpzgxIkTWK1W7HY7J06coLS0tM73FdtnQaPAarXy8MMP\nc/ToURYsWEDHjh3DPaWIwmKxIMsyFRUV6PX6cE+nQTh//jx2u505c+YwZ84cv+O33HILDz74IE89\n9VSd7itEURDxOJ1O/vrXv7Jnzx7mzp1L7969wz2lsFFSUkKzZs18xoxGI+vXr+fqq6+mefPmYZpZ\nw9OmTZuAzpU33ngDk8nEc889R/v27et8XyGKEczcuXMBlFi87Oxsdu3aRXx8PPfdd184p9agvPTS\nS3z33XcMHTqUixcv+gTlxsTEMGzYsDDOrmH561//SlRUFJmZmSQnJ3P69GlWrFjBmTNneO2118I9\nvQYlLi4u4O/+k08+Qa1W1/t7ITJaIpi0tLSA49dccw3fffddA88mfEyZMoXt27cHPNbUPovly5eT\nnZ3N4cOHKSsrIy4u2K5r6QAACWlJREFUjt69e/PAAw/Qr1+/cE8vIpgyZcplZbQIURQIBAIvhPdZ\nIBAIvBCiKBAIBF4IURQIBAIvhCgKBAKBF0IUBQKBwAshigKBQOCFEEWBQCDwQoiioMmwbds20tLS\nWLFiRUifc+LECdLS0nj77bdD+hxBaBBpfoKQYTabWbJkCRs2bODw4cNUVFSQkJBA9+7dGT58OHfc\ncQcaTXC/grm5uWzcuJFx48bRpk2boN472FSXsRSITZs2Rfz7uVIQoigICceOHWPGjBkcPXqUgQMH\nMmPGDJKSkjh//jxbtmzh2Wef5fDhwzz99NNBfW5ubi7vvPMO/fr18xOR6667jr179wZdiOvL7Nmz\nfV7v2rWLJUuWMHHiRPr27etzrGoRCEHoiIxvh+CKwmKx8NBDD3HixAnefvttbrvtNp/jM2bMYO/e\nvezbty9ozzQajcTGxtZ4jkqlIioqKmjPvFyqVg13Op0sWbKE3r17N9mK4pGAsCkKgs6yZcs4cuQI\n06dP9xNEDxkZGdx7770+Yxs3bmTSpEn07t2bzMxMJk2axMaNG/2uvfnmm5kyZQoHDhzgT3/6E337\n9uWOO+7g7bffVqpPT506lbS0NNLS0njmmWeA6m2KsiyzdOlSxo8fT2ZmJpmZmYwePZo333xTOcdo\nNPL6668zfvx4+vfvT48ePbj11luZM2cOZrP5sj6v6rDZbFx//fVMmjQp4PEPP/yQtLQ0duzYAcCK\nFStIS0vjl19+4e2332bo0KH06NGD0aNHs3r16oD32LdvH48++qjynm6//Xbee+89HA5HSN5TY0Cs\nFAVBx9NQaeLEibW+ZtGiRTz//PN07NiRRx55BICVK1fy6KOP8vzzz/vd69SpU9x///1kZWVx2223\nYTKZGDBgAEVFRSxZsoSHH35YKUTbrl27Gp/997//na+//ppevXrx8MMPExcXR0FBAevXr2fmzJkA\nnD17luXLl3PbbbcxatQoNBoN27dv58MPPyQ3N5f58+fX+r3WFp1Ox7hx4/joo48oKCjwK6z75Zdf\n0r59e6677jqf8Tlz5mAymbjnnnuASrF88sknsVqt3Hnnncp5P/zwA4899hgpKSk88MADJCQksGfP\nHt566y1yc3N56623gv6eGgWyQBBk+vXrJ/fp06fW51+8eFHu3bu3PGzYMLm8vFwZLy8vl2+55Ra5\nd+/ecmlpqTI+dOhQOTU1VV66dKnfvb788ks5NTVV3rp1q9+xrVu3yqmpqfKXX36pjK1evVpOTU2V\nn3rqKdnpdPqc7/3aarXKNpvN756vv/66nJqaKufk5ChjhYWFcmpqqvzWW2/V8hPwnbv3/AoKCuTU\n1FT55Zdf9jl3586dcmpqqvzBBx/4XT9kyBC5rKxMGS8rK5OHDBkiX3fddbLZbJZlWZYtFos8cOBA\nefLkybLdbve598cff1ztZ9gUENtnQdAxGo3ExMTU+vzNmzdjMpmYMmWKj10wNjaWKVOmYDKZ+OWX\nX3yuSUxM9Fn11Jevv/4agFmzZvk1wfJ+rdPp0Gq1ADgcDkpLSykpKWHgwIEA5OTkXPZcAtGhQwf6\n9etHdna2z5Z2+fLlaDQaxo0b53fNPffcQ1xcnPI6Li6OSZMmUVpayrZt24DKz7y4uJg777yTsrIy\nSkpKlJ8bb7xROacpIrbPgqATGxtLRUVFrc8/ceIEAF26dPE75hkrLCz0GW/bti1qtfoyZlnJsWPH\nSE5OpkWLFpc8d9GiRXzxxRccPnwYl8vlc6w+DZJqy4QJE3jqqaf44YcfGDZsGEajkXXr1jFkyJCA\n8w7Uv6ZTp07AH5+1p5r7c889V+1zi4uLgzH9RocQRUHQ6dKlCzt27KCwsJC2bduG5BnR0dEhuW91\nfPzxx7z00ksMHjyYqVOn0rJlS7RaLWfPnuWZZ55BDmGt5ttvv51///vfLF++nGHDhrF27VpMJhPj\nx4+v9z0983366afp2rVrwHOC1Ue5sSFEURB0brvtNnbs2MGyZct48sknL3m+Rzh///13BgwY4HPs\n8OHDPudcCkmS6jTX9u3bs2nTJoqLi2tcLWZnZ3PNNdcwb948n231Tz/9VKfn1QedTsfYsWNZuHCh\n4vBp1aoVN9xwQ8DzCwoK/MY8K0NP7KanoVN0dLRiAhBUImyKgqAzfvx4OnTowEcffRQwpAZg//79\nLFq0CIBBgwZhMBj47LPPMBqNyjlGo5HPPvsMg8HAoEGDavVsg8EA1H47O3r0aABeeeUVvy2x9+pP\npVIhSZLPmMPhYN68ebV6zuUyYcIEnE4nc+bMYc+ePYwbN65a88Hnn39OeXm58rq8vJwvvviC+Ph4\npY/L4MGDad68OfPmzePixYt+97BYLD6/i6aEWCkKgk50dDT/+c9/mDFjBo8++iiDBw9m4MCBJCYm\nUlJSwrZt2/j555/585//DEB8fDxPPfUUzz//PBMmTFCcBytXruTYsWM8//zzPo6DmujZsycqlYr3\n33+f0tJSDAYDbdq0oVevXgHPHz58OBs2bGDVqlUcO3aMm2++mfj4eI4ePcrPP//MN998A0BWVhav\nvvoqDz74ILfeeitGo5FvvvmmwbJjOnXqRN++ffnqq6+QJIm777672nOTkpIYP3684ohasWIFp06d\n4t///rdidjAYDLz88ss8+uijZGVlcdddd5GSkkJZWRkFBQV8++23vPPOO/Tv379B3l8kIURREBJS\nUlJYtWoVS5YsYf369bz//vuYTCYSEhLo0aMHL730krJKA7j33ntp2bIl8+fPV3r5pqen8+6779ap\nVWXr1q154YUXmDdvHv/85z+x2+2MGzeuWlEEePXVV7n22mtZvnw57777LiqVijZt2pCVlaWc86c/\n/QlZllm+fDn/7//9P5KTkxk+fDh33XUXI0aMqMcnVHcmTJjArl276N+/f43mhKeeeoqdO3eyePFi\niouL6dChA3PmzPH5vAFuuOEG/v/27tgEQhgK4/hXWQZcwIkcwDKOIKkcwgUsLayChYVLmBGygytc\ndRA4rjgQ1Nz/16Z51Ufy4L147zWOo9Z11XEcMsaoqipZa3+azc4Jv/kBD7Ftm7qu0zAMquv643xZ\nFvV9r2ma/vKGdxZ6isBDzPOssiy/jk7iHDyfgRt7bxUKIWjfdznnVBTF1WVljVAEbizGKOecjDFq\nmkZt215dUvboKQJAgp4iACQIRQBIEIoAkCAUASBBKAJA4gXiIIoD8SXt3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('ticks')\n",
    "sns.set_context('talk')\n",
    "c = 'darkslategray'\n",
    "\n",
    "fig = plt.figure(figsize=(4, 3))\n",
    "sns.regplot(df['type'], df['gradient 1'], \n",
    "            line_kws={'color':c, \n",
    "                      'linestyle':'--', },\n",
    "            scatter_kws={'color':c}, \n",
    "            truncate=True,\n",
    "            fit_reg=True)\n",
    "plt.yticks([-0.025, 0, 0.025, 0.050], [-0.025, 0, 0.025, 0.050])\n",
    "plt.xlabel('Cortical Type')\n",
    "plt.ylabel('Gradient 2')\n",
    "sns.despine()\n",
    "plt.savefig('/home/julia/Documents/projects/mouse_gradients/figures/pics/cortical_type.svg')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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