4_DataMunging_ResearchNote.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import re\n",
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Loading the outcomes\n",
      "\n",
      "- dfs\n",
      "    - meanDF\n",
      "    - axialDf\n",
      "    - radialDf\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjects= [x for x in os.listdir('/Volumes/CCNC_3T/KangIk/2014_05_DKI_project') if x.startswith('CHR') or x.startswith('NOR') or x.startswith('FEP')]\n",
      "meanDf = {}\n",
      "axialDf = {}\n",
      "radialDf = {}\n",
      "for subject in subjects:\n",
      "    try:\n",
      "        df = pd.read_csv(os.path.join(subject,'DKI','mean.txt'),sep='\\s+',names=['ROIs','mean']).reset_index()\n",
      "        df.columns=['modality','ROIs','mean']\n",
      "        df['subject'] = subject\n",
      "        df['group'] = df.subject.map(lambda x:x[:3])\n",
      "\n",
      "        meanDf[subject] = df[df.modality=='kmean.nii'][['subject','group','ROIs','mean']]\n",
      "        axialDf[subject] = df[df.modality=='kax.nii'][['subject','group','ROIs','mean']]\n",
      "        radialDf[subject] = df[df.modality=='krad.nii'][['subject','group','ROIs','mean']]\n",
      "        \n",
      "    except:\n",
      "        pass\n",
      "\n",
      "meanDf = pd.concat([x for x in meanDf.values()],axis=0)\n",
      "axialDf = pd.concat([x for x in axialDf.values()],axis=0)\n",
      "radialDf = pd.concat([x for x in radialDf.values()],axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 287
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "meanDf.ROIs.drop_duplicates()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 276,
       "text": [
        "18        dki_lh_LPFC.nii.gz\n",
        "19         dki_lh_LTC.nii.gz\n",
        "20        dki_lh_MPFC.nii.gz\n",
        "21         dki_lh_MTC.nii.gz\n",
        "22         dki_lh_OCC.nii.gz\n",
        "23         dki_lh_OFC.nii.gz\n",
        "24          dki_lh_PC.nii.gz\n",
        "25         dki_lh_SMC.nii.gz\n",
        "26    dki_lh_thalamus.nii.gz\n",
        "27        dki_rh_LPFC.nii.gz\n",
        "28         dki_rh_LTC.nii.gz\n",
        "29        dki_rh_MPFC.nii.gz\n",
        "30         dki_rh_MTC.nii.gz\n",
        "31         dki_rh_OCC.nii.gz\n",
        "32         dki_rh_OFC.nii.gz\n",
        "33          dki_rh_PC.nii.gz\n",
        "34         dki_rh_SMC.nii.gz\n",
        "35    dki_rh_thalamus.nii.gz\n",
        "Name: ROIs, dtype: object"
       ]
      }
     ],
     "prompt_number": 276
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for roi in meanDf.ROIs.drop_duplicates():\n",
      "    a = meanDf[meanDf.ROIs==roi]\n",
      "    display(a.groupby('group').mean().plot(kind='line'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x152ba6890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1528040d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1537dc790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137a4e090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x13671dc50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x152bc6590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1376066d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x13762ea50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x152b01410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x13670b890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137ac91d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137af6f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137a98a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x152824690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x152803210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x15281f090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137a60f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x153775710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB5utqaz7WjeJAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152ba6e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152ba6b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1KDiICIiNag4iIhIDSoOIiJSw/8D4uhSUB98ACkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152ba6f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD8R3TfVMR0o3AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152ba64d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGERExIUSg4iIuFBiEBERF0oMIiLi4v8BtynHcu+UytwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152804250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbrffAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1537dc650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137a4e6d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBGRWlQcRESkFhUHERGpRcVBRERq+f8tqfsAPu+AQAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13671d150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njSwXMXT69FgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152bc6210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSA0qDiIiUsP/BwoXg7aetwfGAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152ba6c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiNMDEREZISJgYiIjDAxEBGRkf8HHmhbU9AXhYYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13762e6d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx+z9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13762dd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkTL+P3Rt1nR1auxUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136737ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137af7750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137af6c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136725d10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUXEQEZFGVBxERKSR/w+VQOP5FhfknAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137ac2650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBB2pDAIIYSwI4VBCCGEHSkMQggh7Px/DP25OOaJbsEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e7b450>"
       ]
      }
     ],
     "prompt_number": 279
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Standard diviation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjects= [x for x in os.listdir('/Volumes/CCNC_3T/KangIk/2014_05_DKI_project') if x.startswith('CHR') or x.startswith('NOR') or x.startswith('FEP')]\n",
      "meanDf = {}\n",
      "axialDf = {}\n",
      "radialDf = {}\n",
      "for subject in subjects:\n",
      "    try:\n",
      "        df = pd.read_csv(os.path.join(subject,'DKI','std.txt'),sep='\\s+',names=['ROIs','std']).reset_index()\n",
      "        df.columns=['modality','ROIs','std']\n",
      "        df['subject'] = subject\n",
      "        df['group'] = df.subject.map(lambda x:x[:3])\n",
      "\n",
      "        meanDf[subject] = df[df.modality=='kmean.nii'][['subject','group','ROIs','std']]\n",
      "        axialDf[subject] = df[df.modality=='kax.nii'][['subject','group','ROIs','std']]\n",
      "        radialDf[subject] = df[df.modality=='krad.nii'][['subject','group','ROIs','std']]\n",
      "        \n",
      "    except:\n",
      "        pass\n",
      "\n",
      "meanDf = pd.concat([x for x in meanDf.values()],axis=0)\n",
      "axialDf = pd.concat([x for x in axialDf.values()],axis=0)\n",
      "radialDf = pd.concat([x for x in radialDf.values()],axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 280
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for roi in meanDf.ROIs.drop_duplicates():\n",
      "    a = meanDf[meanDf.ROIs==roi]\n",
      "    display(a.groupby('group').mean().plot(kind='line'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x114e83e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1376d2910>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x15343e290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x114e70850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x136701a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1145d9bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1367008d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1376e0210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x152bb4350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137a5b690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1534077d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1367288d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137660310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x15376af90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137ac3b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137ad38d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x137ab3bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x15282a050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niDTx/wEeawBzh96siwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e83810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFKHiICIiRag4iIhIEf8Pkb6zf2cn5o8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e83750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNuxjl/xoIwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e83710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEPCAYAAAC+35gCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRJNJ0hBCCNFkkjSEEEI0mSQNIYQQTfb/cBNss+lOB7oAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e83310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUoOKg4iI1PD/AaqNm3A4wqByAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1376d2250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x15343e810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRETK+f+VzPXQuZwCTAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e70750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114e83290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIi5UHERExIWKg4iIuPh/2he5z4XRVzgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1145d91d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136701650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtYVNX+P/D3oIOYg9dIDCgSUEYQB4WISgRT8a4lGZIX\nTlNy6nhOFJbZTe0iUXL8glqhp7Kr0fmVUpqcJEHNRLQgL6ipgSJEeQ3RlNv+/bFicBwcQDbsubxf\nz8OTe89m7898nmkWe332WkslSZIEIiKiKzgoHQAREVkeNg5ERGSCjQMREZlg40BERCbYOBARkQk2\nDkREZKLJxiEzMxO+vr7w8fFBUlKSyesHDx5EaGgonJyckJycbNhfUlKCiIgI+Pn5wd/fH6mpqYbX\n8vLycPvttyMwMBDBwcHYtWuXTG+HiIjkoDI3zqG2thb9+/dHVlYW3NzcEBwcjDVr1kCr1RqOOXny\nJI4dO4Z169ahR48eSEhIAACUl5ejvLwcOp0OlZWVGDJkCDIyMuDr64vw8HDMnz8fkZGR2LhxI15/\n/XVkZ2e3/bslIqJmMXvnkJeXB29vb3h6ekKtViM6OhoZGRlGx7i4uCAoKAhqtdpov6urK3Q6HQBA\no9FAq9WitLQUANCnTx/88ccfAIBz587Bzc1NtjdERESt19Hci6WlpfDw8DBsu7u7Y+fOnS2+SHFx\nMfLz8xESEgIAeO2113D33Xdj7ty5qKurw44dO1p8TiIiajtm7xxUKlWrL1BZWYmoqCikpKRAo9EA\nAPR6PVJTU3H8+HEsXboUDz30UKuvQ0RE8jF75+Dm5oaSkhLDdklJCdzd3Zt98urqakyZMgXTp0/H\n5MmTDfvz8vKQlZUFAIiKisLDDz98zeuXlZU1+3pERAR4eXnhyJEjrTqH2TuHoKAgHD58GMXFxaiq\nqkJ6ejomTpzY6LFX17UlSYJer8eAAQMQHx9v9Jq3tze2bNkCANi8eTP69evX6DnLysogSRJ/ZPhZ\nsGCB4jHY0g/zyXxa8s/Ro0eb3Qhci9k7h44dO2L58uWIjIxEbW0t9Ho9tFot0tLSAABxcXEoLy9H\ncHAwKioq4ODggJSUFBQWFqKgoAAfffQRAgICEBgYCABITEzE6NGjsXLlSvzjH//A5cuX0blzZ6xc\nubLVb4TMKy4uVjoEm8J8yov5tDxmGwcAGDNmDMaMGWO0Ly4uzvBvV1dXo66nenfffTfq6uoaPWdQ\nUNB1FbaJiKh9cIS0nYiNjVU6BJvCfMqL+bQ8ZgfBKU2lUsGCwyMiskhyfHfyzsFO5OTkKB2CTWE+\n5SVHPnv27AmVSmVXPz179mx98q+hyZoDEZE1OHv2rN31NMgxFu2a52a3EhHZAnv8vrjWe2a3EhER\ntQk2DnaCfeTyYj7lxXxaHjYOREQWbOHChZgxY0a7X5eNg50IDw9XOgSbwnzKy17z2Zwv/rYsOpvD\nxoGIiEywcbAT7NOVF/MpL3vIZ1JSEtzd3dG1a1f4+vri66+/RmJiItLT0+Hs7GyYg66oqAjDhg1D\n165dMWrUKJw6dUqReDnOgYiojR06dAgrVqzA7t274erqiuPHj6OmpgbPPvssjh49ig8++MBwbExM\nDO666y5kZWUhNzcX48aNM1ryoL2wcbAT9tqn21aYT3m1Vz7l6r5v6RCCDh064PLly9i/fz969eqF\nW2655a/zSEbjEY4fP47du3dj8+bNUKvVGDp0KCZMmKDI+A12KxGR3ZAkeX5aytvbG//3f/+HhQsX\nonfv3pg2bRp+/fVXk+PKysrQo0cPdO7c2bDv1ltvbc1bvm5sHOyEPfTptifmU172kM9p06Zh27Zt\nOHbsGFQqFebNmwcHB+Ov4D59+uDs2bO4ePGiYV/98e2NjQMRURv7+eefsXnzZly+fBmdOnWCk5MT\nOnTogN69e6O4uNjQbXTrrbciKCgICxYsQHV1Nb777jusX79ekZg5txIR2QRL/r7Yu3cvHn74YRw4\ncABqtRp33XUXVq5cCUdHR0yaNAn79+9H3759sXv3bhQVFWHWrFnIz89HaGgofH19ce7cOaOidb22\nnFuJjQMR2QR7/L7gxHvUavbQp9uemE95MZ+Wh40DERGZYLcSEdkEe/y+YLcSERG1KzYOdoJ9uvJi\nPuXFfFoeNg5ERGSCNQcisgk9e/bE2bNnlQ6jXfXo0QNnzpwx2c9xDkREZIIFaWo29unKo6YG2LYN\neO+9HKVDsSn8fFoeTtlN1IRTp4DMTGDDBuCbb4BbbgGKioABA4CQEKWjI2obTd45ZGZmwtfXFz4+\nPkhKSjJ5/eDBgwgNDYWTkxOSk5MN+0tKShAREQE/Pz/4+/sjNTXV8Fp0dDQCAwMRGBiI2267zbAC\nErUdrj/QfJIE/PQTsHgxcOedgJcX8PnnwD33AHv3Avn5wMcfh2PiRGDPHqWjtQ38fFoeszWH2tpa\n9O/fH1lZWXBzc0NwcDDWrFkDrVZrOObkyZM4duwY1q1bhx49eiAhIQEAUF5ejvLycuh0OlRWVmLI\nkCFYt26d0e8CwNy5c9G9e3c8//zzpsGx5kDt5MIF4Ntvxd3B118DnToB48aJn2HDxPbVPvsMiI8H\ncnKAfv3aPWSia2rzmkNeXh68vb3h6ekJtVqN6OhoZGRkGB3j4uKCoKAgqNVqo/2urq7Q6XQAAI1G\nA61Wi7KyMqNjJEnCZ599hmnTprXqTVDT2KdrqqgIWL4cGDMG6NMHSEkB+vcHsrKAw4fF9qhRjTcM\nOTk5mDoVeOUVYORI4Nix9o/flvDzaXnM1hxKS0vh4eFh2HZ3d8fOnTtbfJHi4mLk5+cj5KoO2m3b\ntqF3797w8vJq8TmJWqq6Gvj+e3F3sGGDqCWMHQvo9cCnnwLdurX8nA89BFRWAiNGAFu3ikaGyBaY\nbRzkWH2osrISUVFRSElJgUajMXptzZo1iImJafU1qGn22qd78qQoJq9fD2zaBPTtK7qKVq8GhgwB\nHK7zeb0r8/mvfwHnz4s7iC1bgF69ZAndrtjr59OSmW0c3NzcUFJSYtguKSmBu7t7s09eXV2NKVOm\nYPr06Zg8ebLRazU1NVi7di1+/PFHs+eIjY2Fp6cnAKB79+7Q6XSGD1L9rSi3uV2/LUlA9+7h2LAB\n+OSTHBw/DkRGhmPcOGDq1Bz06tU213/2WWDfvhzceSewa1c4una1jHxw2z62c3JysHr1agAwfF+2\nmmRGdXW11LdvX6moqEi6fPmyNGjQIKmwsLDRYxcsWCAtWbLEsF1XVyfNmDFDio+Pb/T4jRs3SuHh\n4eYuLzURHrVAdna20iG0mfPnJWndOkl65BFJuvlmSfLxkaT4eEnatEmSLl1qm2s2ls+6Okl69FFJ\nCguTpAsX2ua6tsqWP59KkOO70+ydQ8eOHbF8+XJERkaitrYWer0eWq0WaWlpAIC4uDiUl5cjODgY\nFRUVcHBwQEpKCgoLC1FQUICPPvoIAQEBhkdVFy9ejDFjxgAA0tPTWYim63b0aEPtYMcO4PbbRXfR\n3LnKPTmkUokCd2wsMGUKsG5d48VsImvA6TPIKlRXA99919AgnD3b8KjpiBFA165KR9igpgaYOlXU\nMz79FOjIoabUzji3Etm0338HNm4UjcGmTYCPT0ODMHjw9ReT28Ply8DEiYCrK/Dee5YdK9kezq1E\nzVZfvLJkdXXADz8AL70kpqXo1w/46isxDuHAASAvD1iwAAgKUv7Ltql8duoEfPEF8Msv4mkm/o1j\nnjV8Pu0Nb3hJUefPi0Fn9SOTnZ2B8eOBxETg7rsBR0elI7x+XbqIR2jvuQd47jkxHQeRtWC3ErW7\nI0dEY7B+PZCbC4SGNnQXeXsrHZ38Tp0SU3BMnw7Mn690NGQPWHMgq1BVJaa5ri8mnz8vRibXF5Od\nnZWOsO2VlQFhYWIupjlzlI6GbB1rDtRs7d2n+9tvohAbFQXcdBPw7LNAjx7AmjXAiRPAf/4D3Huv\n9TYMLc3nzTeL7rPXXxejs8kYaw6WhzUHkkVdHfDjjw3dRUeOiOkkJkwA3nxTNBD2ztNTrAcREQFo\nNKLhJLJU7Fai61ZRIR4xrS8m9+jRUDu4+27gqol66S8FBUBkpLiD+GtMKJGsWHOgdvfzzw21g507\nxWI448eLBqFvX6Wjsx47dgCTJgH//a8oVhPJiTUHarbr7dOtqhJ95fHxYhBaRIQYc/DPfwK//gr8\n73/i3/bWMLS2jzw0VIyevv9+MX7D3rHmYHlYcyATv/4quok2bAA2bwa0WnFn8N//AoMGiTmEqPWG\nDwfeeUeMpN60CRg4UOmIiBqwW4lQVwfs3t3QXXT0qOgTHzcOGD0acHFROkLb9umnQEKCWG7Ux0fp\naMgWyPHdyTsHO/XHH+LJmQ0bxPxFN94oGoPkZFFHYDG5/URHi9XkRo4Uq8ndcovSERGx5mA3srNz\ncPCg+PIfPhzw8ADefVfMU5SbC+zfL57BHzaMDUNzyN1H/vDDoq4zYgRQXi7rqa0Caw6Wh3cONuzy\nZbFs5YYNol7g4CCeLHriCdFAdOmidIR0pfh48XjwqFGii6lnT6UjInvGmoONKStrqB1kZwP+/g1j\nDwICWEy2dJIEPP206F7KyrLeEeSkLI5zINTWArt2NTQIxcXGxeQbb1Q6QmopSQL+/nfg0CFRD+rc\nWemIyNpwnIOdOncO+OwzYNYsoE8f0V9dXQ2kpIgFctasETOAXtkwsE9XXm2ZT5VKTDni5iam2Kiq\narNLWQx+Pi0PGwcrIEli4NkbbwDh4eJplvffFwvi7NoF7NsHvPYaMHQol6S0FR06iOk11GrR0NfU\nKB0R2Rt2K1moS5dEUbK+u6impqF2MHw4cMMNSkdI7eHSJTF5obu7GDCn9Ap4ZB1Yc7AxJ06Ikcnr\n14uGISCgYd4if38Wk+3VhQviCaYhQ0TXIT8H1BTWHKxcbS3w/fdiCUmdTkxNsWWLGBRVVAR89x3w\nzDNiWoXWfiGwT1de7ZnPLl3E3eN33wEvvNBul21X/HxaHvZQt7OzZ8VkdRs2AJmZoqA8bhywfDlw\nxx2sGVDjuncXn5thw8TjrfPmKR0R2Tp2K7UxSQIKCxsWwSkoEMtFjh8vlsrkVAnUEqWl4vOTkAA8\n9pjS0ZCl4txKFurPP8UAtPpiMiDuDp55Rkx5zefW6Xq5uYnBcWFhYjW5mTOVjohsFWsOMikpAd5+\nWzxZ0ru3eLT0llvE3UJREbBihbhTUKphYJ+uvJTM5223iUkT580DvvhCsTBkxc+n5eGdw3WqrRUT\n1tV3F5WViSUfH3wQ+OADsWQmUVvRasWTbZGRomAdGal0RGRrWHNogTNnRBF5wwZRHHR3bxh7EBIi\nBi4RtafvvwcmTwY+/1wMgiQCOM6hzUmSGH1cXzv46ScxQnncONFF5OGhWGhEBllZQEyMuJMIClI6\nGrIE7TLOITMzE76+vvDx8UFSUpLJ6wcPHkRoaCicnJyQnJxs2F9SUoKIiAj4+fnB398fqampRr+3\nbNkyaLVa+Pv7Y54FPZd38aJoCB59FPD0FEs4lpaKsQi//w58+SUQF2d9DQP7dOVlSfkcMQJYtUo8\nAbdvn9LRXB9LyicJZmsOtbW1mDNnDrKysuDm5obg4GBMnDgRWq3WcEyvXr2wbNkyrFu3zuh31Wo1\nli5dCp1Oh8rKSgwZMgQjR46EVqtFdnY2vvzyS+zZswdqtRonT55sm3fXTMeONdwdbNsGDB4s7g42\nbhR9uxyRSpZu0iQxkjoyUgyk9PZWOiKydmYbh7y8PHh7e8PT0xMAEB0djYyMDKPGwcXFBS4uLthQ\n/8zmX1xdXeHq6goA0Gg00Gq1KCsrg1arxVtvvYX58+dD/deSYy7tvEhxTQ2wY0dDg1BeLorJM2cC\nH38sBhzZmvDwcKVDsCmWmM+YGOPlRq3p7tYS82nvzHYrlZaWwuOKT5i7uztKS0tbfJHi4mLk5+cj\nJCQEAHD48GFs3boVd9xxB8LDw7F79+4Wn7OlTp8WX/zTpolHTf/1LzHj5apVonH44APggQdss2Eg\n+zF7NjBnjmggfv9d6WjImpm9c1DJ0J9SWVmJqKgopKSkQKPRAABqampw9uxZ5ObmYteuXZg6dSp+\n+eWXRn8/NjbWcOfSvXt36HQ6w18Z9f2UjW1LEvDOOznIzQUOHAjHvn3AwIE5uOMOYM+ecLi5ieMv\nXQI6dGj6fNa+fWWfriXEY+3blpzPhIRwnD8P3HlnDpYuBSZMsKz4rC2f1rCdk5OD1atXA4Dh+7LV\nJDN27NghRUZGGrYXL14svfbaa40eu3DhQmnJkiVG+6qqqqRRo0ZJS5cuNdo/evRoKScnx7Dt5eUl\nnTp1yuScTYRnorJSkr78UpLi4iTJ3V2S+vaVpH/+U5IyMyXpzz9bdCqbk52drXQINsXS81lXJ0lP\nPCFJISGSVFGhdDRNs/R8WpuWfnc2xmy3UlBQEA4fPozi4mJUVVUhPT0dEydOvFYjY7Kt1+sxYMAA\nxMfHG702efJkbN68GQDw888/o6qqCr169bquxq2oSExaN2YM4OoK/PvfgI8PsGkTcOQIkJoqinRO\nTtd1eptR/9cGycPS86lSAcnJYkbfSZPEuhCWzNLzaY+aHOewceNGxMfHo7a2Fnq9HvPnz0daWhoA\nIC4uDuXl5QgODkZFRQUcHBzg7OyMwsJCFBQUICwsDAEBAYbuqcWLF2PMmDGorq7GQw89hIKCAjg6\nOiI5ObnRD0djz+pWV4uBP/XF5FOnRMMwbpyY875bN5kyQ2QDamvFSnKVlWKqjb+eASEbZzeD4E6d\nEo+Vbtgg5pS57baGkcnBwVwdqzlycnL415mMrCmf1dXAlCli9cCPP7bMkfzWlE9rYBeL/YSGAl5e\n4q+ekSPFIJ8ffgBeeklMWcGGgcg8tRr47DPg5EkxgLOuTumIyBpY/J3DN99ICAsDOnVSOhoi61Y/\nBiIkBFi6lIM7bZnddCsRkTzOngWGDxfTwixapHQ01FbsoluJ5HHlc+TUetaazx49xIzC6enAkiVK\nR9PAWvNpy7ieA5GduekmMZPr0KFiPeq4OKUjIkvEbiUiO3X0KDBsmFi1cPp0paMhOXENaSK6bl5e\noovpnnvEetSTJysdEVkS1hzsBPt05WUr+fTzE+OHZs8WswooxVbyaUvYOBDZuSFDxDiimBjgu++U\njoYsBWsORARAzD4wfbpYJ33wYKWjodbgo6xEJJtRo4C0NDEtTWGh0tGQ0tg42An26crLVvN5773A\nG2+IhuIaS6y0CVvNpzXj00pEZGT6dOD8eWDECLGmupub0hGRElhzIKJGvfEG8O67wJYtYuAcWQ+O\ncyCiNvPUU0BFhVgsKzub66vbG9Yc7AT7dOVlL/l86SUxinrsWDGra1uxl3xaEzYORHRNKpVYeler\nFSOoLX25UZIPaw5E1KTaWjFI7tIl4P/9Py43auk4zoGI2kWHDsCHH4pGYtYs8V+ybWwc7AT7dOVl\nj/l0dAT++1+gvBx49FFAzpt6e8ynpWPjQETN1rkzkJEB7NkDzJ0rbwNBloU1ByJqsTNngIgI4L77\ngAULlI6GrsZxDkSkiJ49xUR9YWFiNbknn1Q6IpIbu5XsBPt05cV8Ar17i+VGU1OBVatady7m0/Lw\nzoGIrpuHh2gghg0Tq8lNm6Z0RCQX1hyIqNX27RMT9a1cCUycqHQ0xHEORGQR/P2Br74CHn5Y3EmQ\n9WPjYCfYpysv5tNUcLAYPT1tGvD99y37XebT8jTZOGRmZsLX1xc+Pj5ISkoyef3gwYMIDQ2Fk5MT\nkpOTDftLSkoQEREBPz8/+Pv7IzU11fDawoUL4e7ujsDAQAQGBiIzM1Omt0NESgoLEyOp770XyM9X\nOhpqDbM1h9raWvTv3x9ZWVlwc3NDcHAw1qxZA61Wazjm5MmTOHbsGNatW4cePXogISEBAFBeXo7y\n8nLodDpUVlZiyJAhyMjIgK+vLxYtWgRnZ2c82cTzb6w5EFmnzz8H5swBNm8Wk/ZR+2rzmkNeXh68\nvb3h6ekJtVqN6OhoZGRkGB3j4uKCoKAgqK+aicvV1RU6nQ4AoNFooNVqUVpaanidX/pEtmvKFCAp\nSSw3WlSkdDR0Pcw2DqWlpfDw8DBsu7u7G33BN1dxcTHy8/MREhJi2Lds2TIMGjQIer0e586da/E5\nqWXYpysv5rNpM2cCzzwjnmJq6muD+bQ8Zsc5qFSqVl+gsrISUVFRSElJgUajAQA8+uijePHFFwEA\nL7zwAhISEvDOO+80+vuxsbHw9PQEAHTv3h06nQ7h4eEAGj5Q3OY2ty1z288PeOSRcIwcCSQm5qBb\nN8uKz1a2c3JysHr1agAwfF+2ltmaQ25uLhYuXGgoGCcmJsLBwQHz5s0zOXbRokXQaDSGmgMAVFdX\nY/z48RgzZgzi4+MbvUZxcTEmTJiAvXv3mgbHmgORTXjuOSAzU9QgunVTOhrb1+Y1h6CgIBw+fBjF\nxcWoqqpCeno6Jl5jhMvVgUiSBL1ejwEDBpg0DL/++qvh32vXrsXAgQOvN34isgKvvALcdRcwbhxw\n4YLS0VBzNDlCeuPGjYiPj0dtbS30ej3mz5+PtLQ0AEBcXBzKy8sRHByMiooKODg4wNnZGYWFhSgo\nKEBYWBgCAgIM3VOJiYkYPXo0Zs6ciYKCAqhUKtx2221IS0tD7969TYPjnYNscnJyDLej1HrMZ8vV\n1QF6vag/fPkl4OTU8BrzKS85vjs5fYad4P988mI+r09NjRgkV10tFg6qf8iR+ZQXGwcisjpVVcDk\nyWLa7w8+ABw4T4PsOLcSEVkdR0cxSO7ECeAf/+BqcpaKjYOdqH/sjeTBfLZO586i7vDDD8DTTwPZ\n2TlKh0RX4XoORKSIrl2BjRuB8HDg9Gmx7ChZDtYciEhR5eXA0KGii+kaw6GohbiGNBFZPVdXsQZE\n/XrUer3SERHAmoPdYB+5vJhPeRUV5SArC3jxRSA9XeloCOCdAxFZCB8fUYMYORLo0gUYP17piOwb\naw5EZFHy8kTD8OmnwPDhSkdjnTjOgYhszu23A599BjzwAJCbq3Q09ouNg51gH7m8mE95XZ3P8HDg\n/feBSZOAn35SJCS7x8aBiCzS2LHA8uXAmDHAoUNKR2N/WHMgIov23nvAggXA1q2ATOvY2DyOcyAi\nm/e3vwGVlWK50W3bgD59lI7IPrBbyU6wj1xezKe8msrnP/8JPPSQeMz11Kn2icnesXEgIqswf754\nxHX0aKCiQulobB9rDkRkNSQJmDMH2LtXrEl9ww1KR2SZuNgPEdmdujogNhb47Tcx7XenTkpHZHk4\nCI6ajX3k8mI+5dWSfDo4AO++C2g0QEyMWHqU5MfGgYisTseOwCefABcuiEJ1XZ3SEdkedisRkdW6\neFEUqAcOFAPmVCqlI7IM7FYiIrt2ww3A+vVisr7587ketZzYONgJ9pHLi/mUV2vy2bWreHJp/Xog\nMVG+mOwdR0gTkdXr1QvYtEksN+rsLAbNUeuw5kBENqO4WCw3umiRmHbDXnFuJSKiK3h6ijuIiAjx\nqOv99ysdkfVizcFOsI9cXsynvOTMZ//+YrnROXOAr7+W7bR2h40DEdmcQYOAjAxg1iyA7fj1abJx\nyMzMhK+vL3x8fJCUlGTy+sGDBxEaGgonJyckJycb9peUlCAiIgJ+fn7w9/dHamqqye8mJyfDwcEB\nZ86caeXboKaEh4crHYJNYT7l1Rb5vOMOID0dmDpVPOpKLWO2caitrcWcOXOQmZmJwsJCrFmzBgcO\nHDA6plevXli2bBnmzp1rtF+tVmPp0qXYv38/cnNzsWLFCqPfLSkpwaZNm3DrrbfK+HaIiBoMHy6m\n2pgwAdizR+lorIvZxiEvLw/e3t7w9PSEWq1GdHQ0MjIyjI5xcXFBUFAQ1Gq10X5XV1fodDoAgEaj\ngVarRVlZmeH1J598Eq+//rpc74OawD5yeTGf8mrLfI4fD6SmiuVGf/65zS5jc8w+rVRaWgoPDw/D\ntru7O3bu3NniixQXFyM/Px8hISEAgIyMDLi7uyMgIKDF5yIiaqkHHhDzMI0cKZYbZYdF08w2DioZ\nJiqprKxEVFQUUlJSoNFocPHiRSxevBibNm0yHGPuedzY2Fh4/rVwbPfu3aHT6Qz9k/V/bXC76e3w\n8HCLisfat5lP68tn3745mDABGDEiHNu2AQcPWs77b+12Tk4OVq9eDQCG78vWMjsILjc3FwsXLkRm\nZiYAIDExEQ4ODpg3b57JsYsWLYJGo0FCQoJhX3V1NcaPH48xY8YgPj4eALB3716MGDECN/y1SseJ\nEyfg5uaGvLw83HTTTcbBcRAcEcns5ZeBzz4DtmwBevZUOpq20eYT7wUFBeHw4cMoLi5GVVUV0tPT\nMXHixEaPvToQSZKg1+sxYMAAQ8MAAAMHDsRvv/2GoqIiFBUVwd3dHT/++KNJw0Dyqv8rg+TBfMqr\nPfP5/POi/sDlRs0z2zh07NgRy5cvR2RkJAYMGIAHHngAWq0WaWlpSEtLAwCUl5fDw8MDS5cuxSuv\nvIJbbrkFlZWV2L59Oz766CNkZ2cjMDAQgYGBhjuQK8nRdUVE1FwqFZCUBAwZIp5iunhR6YgsE+dW\nIiK7VFcnBsmdPg2sWwc4OiodkXy4hjQRUSvU1Ij5lzp0AD79VKwwZwu42A81G/vI5cV8ykupfHbs\nKBqFigrgkUe43OiV2DgQkV3r1AlYuxY4fBh4/HGuJleP3UpERAD++ENMtzF6NPDqq0pH0zpcz4GI\nSCbdugH/+x8wbJhYTe6ZZ5SOSFnsVrIT7COXF/MpL0vJ5403isWCVq0CVqxQOhpl8c6BiOgKN98M\nZGWJOwiNRjzuao9YcyAiasTBg2K50eXLgSlTlI6mZVhzICJqI76+YpnR0aOBLl3Ef+0Jaw52wlL6\ndG0F8ykvS81nYKAYPT1zppjq256wcSAiMiM0FFizBoiKAnbtUjqa9sOaAxFRM3z5JTB7tihW+/sr\nHY15rDkQEbWTiRPFanKRkUBODuDjo3REbYvdSnbCUvt0rRXzKS9ryee0acDChWK50ZISpaNpW7xz\nICJqgUcE0DXiAAAOwUlEQVQeAc6fB0aMEEXq3r2VjqhtsOZARHQdFi0CPv9cdDFZ2nKjXM+BiEgh\nkgTMnQts3y6m3HB2VjqiBlzPgZrNWvp0rQXzKS9rzKdKBSxZAgQEiGL1n38qHZG82DgQEV0nlQp4\n6y2gTx+xolxVldIRyYfdSkRErVRdLQbJOTkBn3wilh1VEruViIgsgFoNpKcDp0/bznKjbBzshDX2\n6Voy5lNetpBPJycxD9OhQ8ATT1j/cqNsHIiIZKLRABs2iPEPL76odDStw5oDEZHMTp4EwsKAv/0N\nePrp9r8+51YiIrJALi5i7ENYmBj/8OijSkfUcuxWshO20KdrSZhPedliPt3dxQyur74KfPih0tG0\nHO8ciIjaSN++wDffAPfcI+oR996rdETNx5oDEVEb+/FHsczoRx8Bo0a1/fXaZZxDZmYmfH194ePj\ng6SkJJPXDx48iNDQUDg5OSE5Odmwv6SkBBEREfDz84O/vz9SU1MNr73wwgsYNGgQdDod7rnnHpTY\n+ty3RGTXBg8G1q4FHnwQ+O47paNpJsmMmpoaycvLSyoqKpKqqqqkQYMGSYWFhUbH/P7779KuXbuk\n5557TlqyZIlh/6+//irl5+dLkiRJ58+fl/r162f43YqKCsNxqampkl6vb/T6TYRHLZCdna10CDaF\n+ZSXveTzm28kycVFknbvbtvryPHdafbOIS8vD97e3vD09IRarUZ0dDQyMjKMjnFxcUFQUBDUarXR\nfldXV+h0OgCARqOBVqtFWVkZAMD5iukLKysrceONN8rQzBERWbaRI4GVK4Fx44D9+5WOxjyzBenS\n0lJ4eHgYtt3d3bFz584WX6S4uBj5+fkICQkx7Hvuuefw4Ycf4oYbbkBubm6Lz0ktEx4ernQINoX5\nlJc95XPy5IblRrdsAby8lI6ocWbvHFQqVasvUFlZiaioKKSkpECj0Rj2v/rqqzh+/DhiY2PxxBNP\ntPo6RETW4sEHgRdeEKvJnTihdDSNM3vn4ObmZlQsLikpgbu7e7NPXl1djSlTpmD69OmYPHlyo8fE\nxMRg7Nix1zxHbGwsPD09AQDdu3eHTqcz/JVR/2w0t5vevvI5ckuIx9q3mU/ms7Xb/fvnYPRoYMSI\ncGzdChQWXv/5cnJysHr1agAwfF+2mrmCRHV1tdS3b1+pqKhIunz5cqMF6XoLFiwwKkjX1dVJM2bM\nkOLj402O/fnnnw3/Tk1NlaZPn97oOZsIj1rAXgp+7YX5lJc95/OFFyRp0CBJOnNGvnPK8d3Z5DiH\njRs3Ij4+HrW1tdDr9Zg/fz7S0tIAAHFxcSgvL0dwcDAqKirg4OAAZ2dnFBYWoqCgAGFhYQgICDB0\nTyUmJmL06NGIiorCoUOH0KFDB3h5eeGtt97CTTfdZHJtjnMgIlsnScCTTwK5uWLKjSt6368b15Am\nIrIBkiTWgSgqErO6Ojm17nxc7Iea7co+XWo95lNe9p5PlQpISxMT9k2dKlaWUxobByIiC9Chg5ig\nT5KAmTOB2lpl42G3EhGRBfnzTzFIzstLDJi7nhEF7FYiIrIxnTsDX34J7NsHJCQot9woGwc7Ye99\nunJjPuXFfBrTaICvvwY2bwYWLVImBq7nQERkgXr0EGtB1K8ml5DQvtdnzYGIyIKVlIgGYv58YPbs\n5v0O15AmIrJxHh5icFx4uOhuiolpn+uy5mAn2KcrL+ZTXsyned7ewP/+J0ZSX7VqQpvhnQMRkRXw\n8wPWrwfGjgW6dBEzurYl1hyIiKzItm3AlCli2dG77mr8GI5zICKyM0OHipHU994L/Phj212HjYOd\nYJ+uvJhPeTGfLRMZCbz9thhJfeBA21yDNQciIit0331iudFRo8Ryo337ynt+1hyIiKzYm28CS5aI\nWoSbm9jHcQ5ERHbusceAykrx9NLWrWLabzmw5mAn2KcrL+ZTXsxn6zz9tHiCKTISOHdOnnPyzoGI\nyAa8/DJw/rwoUsuBNQciIhtRVwd88gkwYwbXkCYioqtwEBw1G/t05cV8yov5tDxsHIiIyAS7lYiI\nbAy7lYiIqE2wcbAT7NOVF/MpL+bT8rBxICIiE6w5EBHZGNYciIioTTSrccjMzISvry98fHyQlJRk\n8vrBgwcRGhoKJycnJCcnG/aXlJQgIiICfn5+8Pf3R2pqquG1p556ClqtFoMGDcJ9992HP/74Q4a3\nQ9fCPl15MZ/yYj4tT5ONQ21tLebMmYPMzEwUFhZizZo1OHDV6hK9evXCsmXLMHfuXKP9arUaS5cu\nxf79+5Gbm4sVK1YYfnfUqFHYv38/fvrpJ/Tr1w+JiYkyvi26WkFBgdIh2BTmU17Mp+VpsnHIy8uD\nt7c3PD09oVarER0djYyMDKNjXFxcEBQUBLVabbTf1dUVOp0OAKDRaKDValFWVgYAGDlyJBwcxOVD\nQkJw4sQJWd4QNe6cXFM1EgDmU27Mp+VpsnEoLS2Fh4eHYdvd3R2lpaUtvlBxcTHy8/MREhJi8tq7\n776LsWPHtvicRETUNppsHFQqVasvUllZiaioKKSkpECj0Ri99uqrr8LR0RExMTGtvg5dW3FxsdIh\n2BTmU17Mp+Vpcj0HNzc3lJSUGLZLSkrg7u7e7AtUV1djypQpmD59OiZPnmz02urVq/H111/j22+/\nbfR3vby8ZGmcSHj//feVDsGmMJ/yYj7l4+Xl1epzNNk4BAUF4fDhwyguLsbNN9+M9PR0rFmzptFj\nr36uVpIk6PV6DBgwAPHx8UavZWZm4o033sCWLVvg5OTU6PmOHDnS3PdBREQyatYguI0bNyI+Ph61\ntbXQ6/WYP38+0tLSAABxcXEoLy9HcHAwKioq4ODgAGdnZxQWFqKgoABhYWEICAgw3AEkJiZi9OjR\n8PHxQVVVFXr27AkACA0NxZtvvtmGb5WIiJrLokdIExGRMhQZIV1eXo7o6Gh4e3sjKCgI48aNw+HD\nhzFw4ECj4xYuXGgYVBcbG4u+ffsiMDAQgwcPxrZt25QInexEhw4dEBgYaPg5duwYcnJy0K1bN6P9\nmzdvNjp+4MCBmDp1Kv7880+F3wHZGgcHB6OxZEuWLMGiRYsM2ytXroRWq4VWq0VISAi2b99ueC08\nPBy+vr7Q6XQIDQ1FYWFh09eTN/ymSZKEe++9F8OHD8eRI0ewe/duvPbaa/jtt99MjlWpVIbuKJVK\nhSVLliA/Px///ve/8dhjj7V36GRHbrjhBuTn5xt+br31VgDAsGHDjPYPHz7c6Pi9e/fC0dERb7/9\ntpLhkw1ydHTE2rVrcfr0aQDGT5KuX78eK1euxPbt23HgwAG8/fbbiImJMXyvqlQqfPLJJygoKEBc\nXBzmzZvX5PXavXHIzs6Go6MjZs+ebdg3cODARp+AkiTJqMhd/+877rgDR48ebftgia7SnF7Yu+++\nmw9TkOzUajVmz56NpUuXmryWlJSEJUuWGGq4gYGBmDVrFlasWGFybHO/P9u9cdi3bx+GDBnS6GtH\njx41umVPS0tr9FHWzMxM+Pv7t3WoZMf+/PNPw+dwypQphv3btm0z+owWFRUZ/V5NTQ02btyIgICA\n9g6Z7MBjjz2Gjz/+GBUVFQAa7h4KCwtNvleDgoKwf/9+w3b9HzbN/f5s8lFWuZkbt+Dl5YX8/HzD\n9qJFiwxvSJIkPPXUU3j22Wdx7Ngxo/40Irl17tzZ6LNYb+jQofjqq69M9tc3JgAQFhYGvV7f5jGS\n/XF2dsbMmTORmpqKzp07m72TvbrX5cEHH0RVVRXOnj2LvXv3Nnmtdr9z8PPzww8//NDs46+uORw6\ndAhLlizBSy+91FYhErVYfWOSn5+PlJQUdOzY7n93kZ2Ij4/HO++8gwsXLhj2DRgwALt37zY67ocf\nfjDcIdTXHH755Rc8/PDDeOONN5q8Trs3DsOHD8fly5exatUqw749e/YYjcK+UmM1hzlz5qCkpAQ7\nduxo22CJiCxMjx49MHXqVLzzzjuGP56ffvppzJs3D2fOnAEgZrl9//33jR7cqf/+fPnll7Fu3Toc\nP37c7HUUeZR17dq1yMrKgre3N/z9/fHcc8+hT58+jXY5Xbnvyn8///zzRo9xEcnpWp/Fq2sOX3zx\nxTWPJ5LTlZ+xhIQEnDp1yrA9YcIEPPTQQ7jzzjuh1WoRFxeHjz/+GL179zb5fScnJzz++ONYvHix\n+etxEBwREV2Ny4QSEZEJNg5ERGSCjQMREZlg40BERCbYOBARkQk2DkREZIKNAxERmWDjQNSE2tpa\npUMgandsHMjuvfzyy/D19cXQoUMRExOD5ORkRERE4IknnkBwcDBSUlLw7bffYvDgwQgICIBer0dV\nVRUAwNPT0zBlwe7duxEREQFALFQ1Y8YM3HnnnejXrx/+85//KPb+iK4HZwcju7Zr1y588cUX2LNn\nD6qqqjB48GDD1MfV1dXYtWsXLl26hH79+mHz5s3w9vbGrFmz8NZbb+Hxxx83O23Gvn37kJubi8rK\nSgQGBmLcuHHo06dPe701olbhnQPZte3bt2Py5MlwdHSERqPBhAkTDK898MADAIBDhw7htttug7e3\nNwBg1qxZ2Lp1q9nzqlQqTJo0CZ06dUKvXr0QERGBvLy8tnsjRDJj40B2TaVSXXNO/C5dujS6X5Ik\nwx1Dx44dUVdXBwC4dOmS2Ws5OPB/N7Ie/LSSXbvrrrvw1Vdf4fLly6isrMT69esNr9U3Gv3790dx\ncbFhacUPP/wQw4YNAyBqDvXz6H/++edGv5uRkYHLly/j9OnTyMnJQXBwcHu9LaJWY82B7FpQUBAm\nTpyIgIAA9O7dGwMHDkS3bt2gUqmMpjh+7733cP/996Ompga33347/v73vwMAFixYAL1ej65duyI8\nPNxocaqAgABERETg1KlTePHFF+Hq6qrY+yRqKU7ZTXbvwoUL6NKlCy5evIhhw4Zh1apV0Ol0rTrn\nokWLoNFokJCQIFOURO2Ldw5k92bPno3CwkJcunQJsbGxrW4Y6nEBILJmvHMgIiITLEgTEZEJNg5E\nRGSCjQMREZlg40BERCbYOBARkQk2DkREZOL/A6DDG41TVDN2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1376e06d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x152bb4110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137a5be50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x153407110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBhER8aDCICIiHlQYRETEw/8D5IJL1E58FxkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136728550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA/8f2I0JASYtqmgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1376dad90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEKCAYAAAD5MJl4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136720850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEPCAYAAAC+35gCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137660e90>"
       ]
      }
     ],
     "prompt_number": 284
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Statistics"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Mean kurtosis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for roi,df in meanDf.groupby('ROIs'):\n",
      "    print roi\n",
      "    display(df.groupby('group').mean())\n",
      "    display(df.groupby('group').std())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_LPFC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.713945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.721974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.727688</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.713945\n",
        "FEP    0.721974\n",
        "NOR    0.727688"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.038738</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.026094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.015223</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.038738\n",
        "FEP    0.026094\n",
        "NOR    0.015223"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_LTC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.700370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.723272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.726821</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.700370\n",
        "FEP    0.723272\n",
        "NOR    0.726821"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.031542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.023447</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.020200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.031542\n",
        "FEP    0.023447\n",
        "NOR    0.020200"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_MPFC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.690009</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.703183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.703865</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.690009\n",
        "FEP    0.703183\n",
        "NOR    0.703865"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.033182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.041003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.021591</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.033182\n",
        "FEP    0.041003\n",
        "NOR    0.021591"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_MTC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.722662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.733739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.748711</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.722662\n",
        "FEP    0.733739\n",
        "NOR    0.748711"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.041779</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.035192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.031638</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.041779\n",
        "FEP    0.035192\n",
        "NOR    0.031638"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_OCC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.746452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.755918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.765200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.746452\n",
        "FEP    0.755918\n",
        "NOR    0.765200"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.028194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.022020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.026259</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.028194\n",
        "FEP    0.022020\n",
        "NOR    0.026259"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_OFC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.722706</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.728249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.739027</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.722706\n",
        "FEP    0.728249\n",
        "NOR    0.739027"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.052861</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.037416</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.035179</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.052861\n",
        "FEP    0.037416\n",
        "NOR    0.035179"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_PC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.702682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.709098</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.710664</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.702682\n",
        "FEP    0.709098\n",
        "NOR    0.710664"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.031190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.019247</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.019590</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.031190\n",
        "FEP    0.019247\n",
        "NOR    0.019590"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_SMC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.724001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.735326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.734699</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.724001\n",
        "FEP    0.735326\n",
        "NOR    0.734699"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.029863</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.027751</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.021092</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.029863\n",
        "FEP    0.027751\n",
        "NOR    0.021092"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_thalamus.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.903498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.918842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.935522</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.903498\n",
        "FEP    0.918842\n",
        "NOR    0.935522"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.046200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.051445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.052311</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.046200\n",
        "FEP    0.051445\n",
        "NOR    0.052311"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_LPFC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.707430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.714317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.720157</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.707430\n",
        "FEP    0.714317\n",
        "NOR    0.720157"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.034513</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.024645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.015886</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.034513\n",
        "FEP    0.024645\n",
        "NOR    0.015886"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_LTC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.708185</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.726197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.731181</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.708185\n",
        "FEP    0.726197\n",
        "NOR    0.731181"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.025989</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.023266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.022209</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.025989\n",
        "FEP    0.023266\n",
        "NOR    0.022209"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_MPFC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.689092</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.707175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.702684</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.689092\n",
        "FEP    0.707175\n",
        "NOR    0.702684"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.032956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.035585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.016643</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.032956\n",
        "FEP    0.035585\n",
        "NOR    0.016643"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_MTC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.745574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.755630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.768450</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.745574\n",
        "FEP    0.755630\n",
        "NOR    0.768450"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.036814</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.038591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.037545</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.036814\n",
        "FEP    0.038591\n",
        "NOR    0.037545"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_OCC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.759080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.777795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.787226</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.759080\n",
        "FEP    0.777795\n",
        "NOR    0.787226"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.028269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.022429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.025136</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.028269\n",
        "FEP    0.022429\n",
        "NOR    0.025136"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_OFC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.718458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.723719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.733383</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.718458\n",
        "FEP    0.723719\n",
        "NOR    0.733383"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.052354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.033136</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.032022</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.052354\n",
        "FEP    0.033136\n",
        "NOR    0.032022"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_PC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.716437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.723522</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.727780</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.716437\n",
        "FEP    0.723522\n",
        "NOR    0.727780"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.029424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.018484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.022702</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.029424\n",
        "FEP    0.018484\n",
        "NOR    0.022702"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_SMC.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.724368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.731187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.731406</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.724368\n",
        "FEP    0.731187\n",
        "NOR    0.731406"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.027736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.023546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.021289</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.027736\n",
        "FEP    0.023546\n",
        "NOR    0.021289"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_rh_thalamus.nii.gz\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.869149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.888295</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.900780</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.869149\n",
        "FEP    0.888295\n",
        "NOR    0.900780"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>group</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CHR</th>\n",
        "      <td> 0.047826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FEP</th>\n",
        "      <td> 0.055237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOR</th>\n",
        "      <td> 0.053638</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "           mean\n",
        "group          \n",
        "CHR    0.047826\n",
        "FEP    0.055237\n",
        "NOR    0.053638"
       ]
      }
     ],
     "prompt_number": 321
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "meanDf.ROIs.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 332,
       "text": [
        "array(['dki_lh_LPFC.nii.gz', 'dki_lh_LTC.nii.gz', 'dki_lh_MPFC.nii.gz',\n",
        "       'dki_lh_MTC.nii.gz', 'dki_lh_OCC.nii.gz', 'dki_lh_OFC.nii.gz',\n",
        "       'dki_lh_PC.nii.gz', 'dki_lh_SMC.nii.gz', 'dki_lh_thalamus.nii.gz',\n",
        "       'dki_rh_LPFC.nii.gz', 'dki_rh_LTC.nii.gz', 'dki_rh_MPFC.nii.gz',\n",
        "       'dki_rh_MTC.nii.gz', 'dki_rh_OCC.nii.gz', 'dki_rh_OFC.nii.gz',\n",
        "       'dki_rh_PC.nii.gz', 'dki_rh_SMC.nii.gz', 'dki_rh_thalamus.nii.gz'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 332
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ROI in meanDf.ROIs.unique():\n",
      "    toCompare = 'mean'\n",
      "    mean = meanDf[meanDf.ROIs==ROI][toCompare]\n",
      "    GROUP = meanDf[meanDf.ROIs==ROI].group\n",
      "\n",
      "    formula = 'mean ~ GROUP'\n",
      "#    formula = 'RC ~ GROUP'\n",
      "\n",
      "    lm = ols(formula, meanDf[meanDf.ROIs==ROI]).fit()\n",
      "\n",
      "    output=anova_lm(lm)\n",
      "    if output.ix['GROUP','PR(>F)'] > 0.05:\n",
      "        print '{0} \\t\\t {1}~GROUP'.format(ROI,toCompare)\n",
      "        print '========='\n",
      "        print output\n",
      "        print '\\n\\n\\n'\n",
      "    #print lm.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ROI in radialDf.ROIs.unique():\n",
      "    toCompare = 'mean'\n",
      "    mean = meanDf[meanDf.ROIs==ROI][toCompare]\n",
      "    GROUP = meanDf[meanDf.ROIs==ROI].group\n",
      "\n",
      "    formula = 'mean ~ GROUP'\n",
      "#    formula = 'RC ~ GROUP'\n",
      "\n",
      "    lm = ols(formula, meanDf[meanDf.ROIs==ROI]).fit()\n",
      "\n",
      "    output=anova_lm(lm)\n",
      "    if output.ix['GROUP','PR(>F)'] < 0.05:\n",
      "        print '{0} \\t\\t {1}~GROUP'.format(ROI,toCompare)\n",
      "        print '========='\n",
      "        print output\n",
      "        print '\\n\\n\\n'\n",
      "    #print lm.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_LTC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq          F    PR(>F)\n",
        "GROUP       2  0.016242  0.008121  12.378656  0.000014\n",
        "Residual  110  0.072164  0.000656        NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_lh_MTC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F    PR(>F)\n",
        "GROUP       2  0.013841  0.006920  5.195958  0.006978\n",
        "Residual  110  0.146505  0.001332       NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_lh_OCC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F    PR(>F)\n",
        "GROUP       2  0.007117  0.003558  5.317194  0.006247\n",
        "Residual  110  0.073615  0.000669       NaN       NaN"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_lh_thalamus.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F   PR(>F)\n",
        "GROUP       2  0.020781  0.010390  4.160005  0.01813\n",
        "Residual  110  0.274744  0.002498       NaN      NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_LTC.nii.gz \t\t mean~GROUP"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "=========\n",
        "           df    sum_sq   mean_sq          F    PR(>F)\n",
        "GROUP       2  0.011638  0.005819  10.184267  0.000088\n",
        "Residual  110  0.062853  0.000571        NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_MPFC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F    PR(>F)\n",
        "GROUP       2  0.006619  0.003310  3.927653  0.022511\n",
        "Residual  110  0.092693  0.000843       NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_MTC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq        F    PR(>F)\n",
        "GROUP       2  0.010649  0.005324  3.76868  0.026117\n",
        "Residual  110  0.155410  0.001413      NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_OCC.nii.gz \t\t mean~GROUP"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "=========\n",
        "           df    sum_sq   mean_sq          F    PR(>F)\n",
        "GROUP       2  0.016498  0.008249  12.597135  0.000012\n",
        "Residual  110  0.072033  0.000655        NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_thalamus.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq        F  PR(>F)\n",
        "GROUP       2  0.020483  0.010242  3.76937  0.0261\n",
        "Residual  110  0.298876  0.002717      NaN     NaN"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 345
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ROI in axialDf.ROIs.unique():\n",
      "    toCompare = 'mean'\n",
      "    mean = meanDf[meanDf.ROIs==ROI][toCompare]\n",
      "    GROUP = meanDf[meanDf.ROIs==ROI].group\n",
      "\n",
      "    formula = 'mean ~ GROUP'\n",
      "#    formula = 'RC ~ GROUP'\n",
      "\n",
      "    lm = ols(formula, meanDf[meanDf.ROIs==ROI]).fit()\n",
      "\n",
      "    output=anova_lm(lm)\n",
      "    if output.ix['GROUP','PR(>F)'] < 0.05:\n",
      "        print '{0} \\t\\t {1}~GROUP'.format(ROI,toCompare)\n",
      "        print '========='\n",
      "        print output\n",
      "        print '\\n\\n\\n'\n",
      "    #print lm.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dki_lh_LTC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq          F    PR(>F)\n",
        "GROUP       2  0.016242  0.008121  12.378656  0.000014\n",
        "Residual  110  0.072164  0.000656        NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_lh_MTC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F    PR(>F)\n",
        "GROUP       2  0.013841  0.006920  5.195958  0.006978\n",
        "Residual  110  0.146505  0.001332       NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_lh_OCC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F    PR(>F)\n",
        "GROUP       2  0.007117  0.003558  5.317194  0.006247\n",
        "Residual  110  0.073615  0.000669       NaN       NaN"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_lh_thalamus.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F   PR(>F)\n",
        "GROUP       2  0.020781  0.010390  4.160005  0.01813\n",
        "Residual  110  0.274744  0.002498       NaN      NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_LTC.nii.gz \t\t mean~GROUP"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "=========\n",
        "           df    sum_sq   mean_sq          F    PR(>F)\n",
        "GROUP       2  0.011638  0.005819  10.184267  0.000088\n",
        "Residual  110  0.062853  0.000571        NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_MPFC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq         F    PR(>F)\n",
        "GROUP       2  0.006619  0.003310  3.927653  0.022511\n",
        "Residual  110  0.092693  0.000843       NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_MTC.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq        F    PR(>F)\n",
        "GROUP       2  0.010649  0.005324  3.76868  0.026117\n",
        "Residual  110  0.155410  0.001413      NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_OCC.nii.gz \t\t mean~GROUP"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "=========\n",
        "           df    sum_sq   mean_sq          F    PR(>F)\n",
        "GROUP       2  0.016498  0.008249  12.597135  0.000012\n",
        "Residual  110  0.072033  0.000655        NaN       NaN\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dki_rh_thalamus.nii.gz \t\t mean~GROUP\n",
        "=========\n",
        "           df    sum_sq   mean_sq        F  PR(>F)\n",
        "GROUP       2  0.020483  0.010242  3.76937  0.0261\n",
        "Residual  110  0.298876  0.002717      NaN     NaN"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 344
    }
   ],
   "metadata": {}
  }
 ]
}