FS_noted.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "__author__ = 'kcho'\n",
    "import re\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "#import matplotlib\n",
    "#matplotlib.use('GTK')\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "import valueSwap\n",
    "import textwrap\n",
    "import ccncpy.ccncpy as ccncpy\n",
    "from mpltools import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main(subject_loc, backgrounds, roi_list, meanDfLoc,verbose, brain):\n",
    "    ##########################################################\n",
    "    # Find freesurfer dir\n",
    "    ##########################################################\n",
    "    FS_description= ['bem','mri','scripts',\n",
    "                     'src','stats','surf','tmp']\n",
    "    freesurfer_dir = ccncpy.subDirSearch(FS_description, \n",
    "                                         subject_loc)\n",
    "\n",
    "    if len(freesurfer_dir) > 1:\n",
    "        print freesurfer_dir\n",
    "        sys.exit(re.sub('\\s+',' ',\n",
    "        'There are more than 1 freesurfer directory \\\n",
    "                under {0}'.format(subject_loc)))\n",
    "    else:\n",
    "        freesurfer_dir = ''.join(freesurfer_dir)\n",
    "\n",
    "\n",
    "    ##########################################################\n",
    "    # Freesurfer setting\n",
    "    ##########################################################\n",
    "    os.environ[\"FREESURFER_HOME\"] = '/Applications/freesurfer'\n",
    "    # where is the freesurfer directory\n",
    "    os.environ[\"SUBJECTS_DIR\"] = '{0}'.format(os.path.dirname(freesurfer_dir))\n",
    "\n",
    "    ##########################################################\n",
    "    # Summarize cortical thickness\n",
    "    ##########################################################\n",
    "    # if no mean table is given but background list is given\n",
    "    if meanDfLoc == False and backgrounds != None: ##None:비었음. ##False:non-exist\n",
    "        print 'Calculating cortical thickness in {0}'.format(backgrounds)\n",
    "        # make mean table\n",
    "        meanDfName = raw_input('Name of the background subject : ')\n",
    "\n",
    "        if verbose:\n",
    "            meanDf = collectStats_v2(backgrounds)\n",
    "        else:\n",
    "            meanDf = collectStats(backgrounds)\n",
    "\n",
    "    # if no mean table is given, and backround list is empty\n",
    "    # use subject_loc as the background\n",
    "    elif meanDfLoc == False and backgrounds == None:\n",
    "        print 'No background subjects are given, now running only {0}'.format(subject_loc)\n",
    "        meanDfName = ''\n",
    "\n",
    "        if verbose:\n",
    "            meanDf = collectStats_v2([subject_loc])\n",
    "        else:\n",
    "            meanDf = collectStats([subject_loc])\n",
    "\n",
    "    # if meanDfLoc is given\n",
    "    else:\n",
    "        print 'Now comparing with mean_thickness.csv in /ccnc_bin/meanThickness/'\n",
    "        meanDfName = 'NOR'\n",
    "        #meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv')\n",
    "        if verbose:\n",
    "            meanDf = pd.read_csv('/ccnc_bin/meanThickness/detailed_mean_2015_12_28.csv', index_col=0)\n",
    "        else:\n",
    "            meanDf = pd.read_csv('/ccnc_bin/meanThickness/new_mean_OCT15.csv', index_col=0)\n",
    "\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "    ##########################################################\n",
    "    # Get roi dict : 8 cortex each hemisphere\n",
    "    ##########################################################\n",
    "    roiDict = get_cortical_rois()\n",
    "    print roiDict, '<--roiDict'\n",
    "\n",
    "    ##########################################################\n",
    "    # annotation2label --> merge labels --> freesurfer/tmp\n",
    "    ##########################################################\n",
    "    \n",
    "    if verbose:\n",
    "        thicknessDf = collectStats_v2([os.path.dirname(freesurfer_dir)])#backgrounds)\n",
    "        draw_thickness_detailed(thicknessDf,meanDf,os.path.basename(subject_loc), meanDfName, subject_loc)\n",
    "    else:\n",
    "        thicknessDf = collectStats([os.path.dirname(freesurfer_dir)])#backgrounds)\n",
    "        draw_thickness(thicknessDf,meanDf,os.path.basename(subject_loc), meanDfName, subject_loc)\n",
    "\n",
    "\n",
    "    if brain:\n",
    "        if verbose:\n",
    "            print freesurfer_dir,\n",
    "            print os.path.join(freesurfer_dir,'tmp/thick_kev_detailed.csv')\n",
    "            valueSwap.main(freesurfer_dir, \n",
    "                    os.path.join(freesurfer_dir,'tmp/thick_kev_detailed.csv'))\n",
    "        else:\n",
    "            print 'Please select verbose mode'\n",
    "            pass\n",
    "\n",
    "    print \"*\"*80\n",
    "    print 'Outputs are saved in /ccnc/mri_team'\n",
    "    print \"*\"*80\n",
    "\n",
    "    #volumeDf = openStatsTable(freesurfer_dir)\n",
    "    #volumeDf['name'] = volumeDf.side + '_' + volumeDf.ROI\n",
    "\n",
    "    #volumeDf = getSummary(volumeDf,roiDict)\n",
    "    #print volumeDf\n",
    "    # graph\n",
    "    #draw_graph(volumeDf)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    #freesurfer_table = openTable('/Applications/freesurfer/FreeSurferColorLUT.txt')\n",
    "    #print freesurfer_table\n",
    "\n",
    "    #for roi in roi_list:\n",
    "    #    print freesurfer_table[roi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/ccnc_bin/mini_env/bin/python\n",
    "\n",
    "#from mpltools import layout\n",
    "\n",
    "style.use('ggplot')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def draw_thickness_detailed(thicknessDf, meanDf, subjName, meanDfName,subject_loc):\n",
    "    roiOrder = ['LPFC', 'OFC', 'MPFC', 'LTC', 'MTC', 'SMC', 'PC', 'OCC']\n",
    "\n",
    "    def getRegion(roi):\n",
    "        roiDict = get_cortical_rois()\n",
    "        for region, roiList in roiDict.iteritems():\n",
    "            if roi in roiList:\n",
    "                return region\n",
    "\n",
    "    thicknessDf['roi'] = thicknessDf.subroi.str[3:]\n",
    "    thicknessDf['side'] = thicknessDf.subroi.str[:2]\n",
    "    thicknessDf['region'] = thicknessDf.roi.apply(getRegion)\n",
    "    meanDf['roi'] = meanDf.subroi.str[3:]\n",
    "    meanDf['side'] = meanDf.subroi.str[:2]\n",
    "    meanDf = meanDf.groupby(['roi','side']).mean().reset_index()\n",
    "    meanDf['region'] = meanDf.roi.apply(getRegion)\n",
    "\n",
    "    gb = thicknessDf.groupby('region')\n",
    "    thicknessDf = pd.concat([gb.get_group('LPFC'),\n",
    "                        gb.get_group('OFC'),\n",
    "                        gb.get_group('MPFC'),\n",
    "                        gb.get_group('LTC'),\n",
    "                        gb.get_group('MTC'),\n",
    "                        gb.get_group('SMC'),\n",
    "                        gb.get_group('PC'),\n",
    "                        gb.get_group('OCC')])\n",
    "\n",
    "    gbmean = meanDf.groupby('region')\n",
    "    meanDf = pd.concat([gbmean.get_group('LPFC'),\n",
    "                        gbmean.get_group('OFC'),\n",
    "                        gbmean.get_group('MPFC'),\n",
    "                        gbmean.get_group('LTC'),\n",
    "                        gbmean.get_group('MTC'),\n",
    "                        gbmean.get_group('SMC'),\n",
    "                        gbmean.get_group('PC'),\n",
    "                        gbmean.get_group('OCC')])\n",
    "                        \n",
    "    gb = thicknessDf.groupby('side')\n",
    "    label = thicknessDf.subroi.str[3:].unique()\n",
    "\n",
    "    fig = plt.figure(figsize=(22,12))\n",
    "    fig.suptitle(\"Cortical thickness in all regions\", fontsize=20)\n",
    "    #fig.set_ylabel('Cortical thickness in mm', fontsize=16)\n",
    "\n",
    "    #plt.ylabel('Cortical thickness', fontsize=16)\n",
    "    #plt.xticks(range(len(label)), label)\n",
    "\n",
    "    lh_g = plt.subplot2grid((2,2),(0, 0), colspan=2)\n",
    "    rh_g = plt.subplot2grid((2,2),(1, 0), colspan=2)\n",
    "    #ax1 = fig.add_subplot(211)\n",
    "    #ax2 = fig.add_subplot(212)\n",
    "    lh_g.plot(gb.get_group('lh')['thickness'],'r',label=subjName)\n",
    "\n",
    "    lh_g.plot(meanDf.groupby('side').get_group('lh')['thickness'],'r--',label=meanDfName)\n",
    "\n",
    "    # error bar\n",
    "\n",
    "    eb1 = lh_g.errorbar(range(len(meanDf.roi.unique())),\n",
    "                        meanDf.groupby('side').get_group('lh')['thickness'],\n",
    "                        meanDf.groupby('side').get_group('lh')['std'],\n",
    "                        linestyle='None',\n",
    "                        marker='^')\n",
    "    eb1[-1][0].set_linestyle('--')\n",
    "\n",
    "    lh_g.set_xlabel('Left', fontsize=16)\n",
    "    #lh_g.set_ylabel('Cortical thickness in mm', fontsize=16)\n",
    "    lh_g.set_ylim(1.0, 4.7)\n",
    "\n",
    "    lh_g.set_xticks(range(len(label)))\n",
    "    lh_g.set_xticklabels(['' for x in label])\n",
    "    lh_g.set_xlim(-.5, 32.5)\n",
    "    lh_g.legend()\n",
    "    legend = lh_g.legend(frameon = 1)\n",
    "    frame = legend.get_frame()\n",
    "    frame.set_facecolor('white')\n",
    "\n",
    "    ######fill\n",
    "    roiDict = get_cortical_rois()\n",
    "    startNum = 0\n",
    "    switch = 0\n",
    "    x_starting_point = 0\n",
    "    for region in roiOrder:\n",
    "        if switch == 0:\n",
    "            switch = 1 \n",
    "            pass\n",
    "            x_starting_point = startNum\n",
    "            startNum = startNum + len(roiDict[region])\n",
    "\n",
    "            lh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,\n",
    "                    region,\n",
    "                    horizontalalignment='center',\n",
    "                    alpha=.4,\n",
    "                    fontsize=15)\n",
    "        else:\n",
    "            alpha = 0.2\n",
    "            col='green'\n",
    "            p = lh_g.axvspan(x_starting_point-.5, startNum-.5, facecolor=col, alpha=alpha)\n",
    "            x_starting_point = startNum\n",
    "            startNum = startNum + len(roiDict[region])\n",
    "            switch = 0 \n",
    "\n",
    "            lh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,\n",
    "                    region,\n",
    "                    horizontalalignment='center',\n",
    "                    alpha=.4,\n",
    "                    fontsize=15)\n",
    "\n",
    "    ## annotation\n",
    "    mergedDf = pd.merge(meanDf.groupby('side').get_group('lh'),\n",
    "                        gb.get_group('lh'),\n",
    "                        on=['roi','side','region'],\n",
    "                        how='inner')\n",
    "\n",
    "    mergedDf['mean_sub_indv'] = mergedDf.thickness_x - mergedDf.thickness_y\n",
    "    for row in mergedDf[abs(mergedDf['mean_sub_indv']) > 0.5].iterrows():\n",
    "        #lh_g.text(row[0], row[1].thickness_y - row[1].mean_sub_indv/2,\n",
    "                #row[1].roi,\n",
    "                #horizontalalignment='center',\n",
    "                #fontsize=20)\n",
    "        lh_g.annotate(row[1].roi,\n",
    "                xy=(row[0], row[1].thickness_y),\n",
    "                xytext=(row[0], 1.5-row[1].mean_sub_indv/3),\n",
    "                arrowprops=dict(facecolor='green', shrink=0.05),\n",
    "                horizontalalignment='left',\n",
    "                fontsize=20)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    rh_g.plot(gb.get_group('rh')['thickness'],'b',label=subjName)\n",
    "    rh_g.plot(meanDf.groupby('side').get_group('rh')['thickness'],'b--',label=meanDfName)\n",
    "\n",
    "    #error bar\n",
    "#     eb2 = rh_g.errorbar(range(len(meanDf.roi.unique())),\n",
    "#                         meanDf.groupby('side').get_group('rh')['thickness'],\n",
    "#                         meanDf.groupby('side').get_group('rh')['std'],\n",
    "#                         linestyle='None',\n",
    "#                         marker='^',\n",
    "#                         color='b')\n",
    "#     eb2[-1][0].set_linestyle('--')\n",
    "\n",
    "    #label = ['LPFC' 'OFC' 'MPFC' 'LTC' 'MTC' 'SMC' 'PC' 'OCC','b']\n",
    "    xticksNum = range(8)\n",
    "\n",
    "    #rh_g.set_xticklabels(label)\n",
    "    rh_g.set_xlabel('Right', fontsize=16)\n",
    "    rh_g.set_ylim(1, 4.7)\n",
    "    rh_g.set_xlim(-.5, 32.5)\n",
    "    rh_g.set_xticks(range(len(label)))\n",
    "    rh_g.set_xticklabels(label)\n",
    "\n",
    "\n",
    "    rh_g.legend()\n",
    "    legend = rh_g.legend(frameon = 1)\n",
    "    frame = legend.get_frame()\n",
    "    frame.set_facecolor('white')\n",
    "\n",
    "    ######fill\n",
    "    startNum = 0\n",
    "    switch = 0\n",
    "    x_starting_point = 0\n",
    "    for region in roiOrder:\n",
    "        if switch == 0:\n",
    "            switch = 1 \n",
    "            pass\n",
    "            x_starting_point = startNum\n",
    "            startNum = startNum + len(roiDict[region])\n",
    "\n",
    "            rh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,\n",
    "                    region,\n",
    "                    horizontalalignment='center',\n",
    "                    alpha=.4,\n",
    "                    fontsize=15)\n",
    "        else:\n",
    "            alpha = 0.2\n",
    "            col='green'\n",
    "            p = rh_g.axvspan(x_starting_point-.5, startNum-.5, facecolor=col, alpha=alpha)\n",
    "            x_starting_point = startNum\n",
    "            startNum = startNum + len(roiDict[region])\n",
    "            switch = 0 \n",
    "\n",
    "            rh_g.text((x_starting_point-.5 + startNum-.5)/2, 1.2,\n",
    "                    region,\n",
    "                    horizontalalignment='center',\n",
    "                    alpha=.4,\n",
    "                    fontsize=15)\n",
    "\n",
    "    ## annotation\n",
    "    mergedDf = pd.merge(meanDf.groupby('side').get_group('rh'),\n",
    "                        gb.get_group('rh'),\n",
    "                        on=['roi','side','region'],\n",
    "                        how='inner')\n",
    "\n",
    "    mergedDf['mean_sub_indv'] = mergedDf.thickness_x - mergedDf.thickness_y\n",
    "    for row in mergedDf[abs(mergedDf['mean_sub_indv']) > 0.5].iterrows():\n",
    "        #rh_g.text(row[0], row[1].thickness_y - row[1].mean_sub_indv/2,\n",
    "                #row[1].roi,\n",
    "                #horizontalalignment='center',\n",
    "                #fontsize=20)\n",
    "        rh_g.annotate(row[1].roi,\n",
    "                xy=(row[0], row[1].thickness_y),\n",
    "                xytext=(row[0], 1.5-row[1].mean_sub_indv/3),\n",
    "                arrowprops=dict(facecolor='green', shrink=0.05),\n",
    "                horizontalalignment='left',\n",
    "                fontsize=20)\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    plt.tight_layout(pad=7, w_pad=3, h_pad=0.2)\n",
    "\n",
    "\n",
    "    #legend = plt.legend(frameon = 1)\n",
    "    #frame = legend.get_frame()\n",
    "    ##frame.set_color('white')\n",
    "    #frame.set_facecolor('white')\n",
    "    ##frame.set_edgecolor('red')\n",
    "    labels = rh_g.get_xticklabels()\n",
    "    plt.setp(labels, rotation=30)\n",
    "    labels = lh_g.get_xticklabels()\n",
    "    plt.setp(labels, rotation=30)\n",
    "    #plt.show()\n",
    "    fig.savefig(subject_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_thickness(thicknessDf,meanDf, subjName, meanDfName, subject_loc):\n",
    "    thicknessDf['roi'] = thicknessDf.subroi.str[3:]\n",
    "    thicknessDf['side'] = thicknessDf.subroi.str[:2]\n",
    "    meanDf['roi'] = meanDf.subroi.str[3:]\n",
    "    meanDf['side'] = meanDf.subroi.str[:2]\n",
    "\n",
    "    gb = thicknessDf.groupby('roi')\n",
    "    thicknessDf = pd.concat([gb.get_group('LPFC'),\n",
    "                        gb.get_group('OFC'),\n",
    "                        gb.get_group('MPFC'),\n",
    "                        gb.get_group('LTC'),\n",
    "                        gb.get_group('MTC'),\n",
    "                        gb.get_group('SMC'),\n",
    "                        gb.get_group('PC'),\n",
    "                        gb.get_group('OCC')])\n",
    "\n",
    "\n",
    "    gbmean = meanDf.groupby('roi')\n",
    "    meanDf = pd.concat([gbmean.get_group('LPFC'),\n",
    "                        gbmean.get_group('OFC'),\n",
    "                        gbmean.get_group('MPFC'),\n",
    "                        gbmean.get_group('LTC'),\n",
    "                        gbmean.get_group('MTC'),\n",
    "                        gbmean.get_group('SMC'),\n",
    "                        gbmean.get_group('PC'),\n",
    "                        gbmean.get_group('OCC')])\n",
    "\n",
    "    gb = thicknessDf.groupby('side')\n",
    "    label = thicknessDf.subroi.str[3:].unique()\n",
    "\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    fig.suptitle(\"Cortical thickness in eight regions\", fontsize=20)\n",
    "    #plt.ylabel('Cortical thickness', fontsize=16)\n",
    "    #plt.xticks(range(len(label)), label)\n",
    "\n",
    "    lh_g = plt.subplot2grid((2,2),(0, 0), rowspan=2)\n",
    "    rh_g = plt.subplot2grid((2,2),(0, 1), rowspan=2)\n",
    "    #ax1 = fig.add_subplot(211)\n",
    "    #ax2 = fig.add_subplot(212)\n",
    "    lh_g.plot(gb.get_group('lh')['thickness'],'r',label=subjName)\n",
    "\n",
    "    lh_g.plot(meanDf.groupby('side').get_group('lh')['thickness'],'r--',label=meanDfName)\n",
    "\n",
    "    # error bar\n",
    "\n",
    "    eb1 = lh_g.errorbar(range(len(meanDf.roi.unique())),\n",
    "                        meanDf.groupby('side').get_group('lh')['thickness'],\n",
    "                        meanDf.groupby('side').get_group('lh')['std'],\n",
    "                        linestyle='None',\n",
    "                        marker='^')\n",
    "    eb1[-1][0].set_linestyle('--')\n",
    "\n",
    "    lh_g.set_xlabel('Left', fontsize=16)\n",
    "    lh_g.set_ylabel('Cortical thickness in mm', fontsize=16)\n",
    "    lh_g.set_ylim(1.0, 4)\n",
    "\n",
    "    lh_g.set_xticks(range(8))\n",
    "    lh_g.set_xticklabels(label)\n",
    "    lh_g.set_xlim(-.5, 7.5)\n",
    "    lh_g.legend()\n",
    "    legend = lh_g.legend(frameon = 1)\n",
    "    frame = legend.get_frame()\n",
    "    frame.set_facecolor('white')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    rh_g.plot(gb.get_group('rh')['thickness'],'b',label=subjName)\n",
    "    rh_g.plot(meanDf.groupby('side').get_group('rh')['thickness'],'b--',label=meanDfName)\n",
    "\n",
    "    # error bar\n",
    "    eb2 = rh_g.errorbar(range(len(meanDf.roi.unique())),\n",
    "                        meanDf.groupby('side').get_group('rh')['thickness'],\n",
    "                        meanDf.groupby('side').get_group('rh')['std'],\n",
    "                        linestyle='None',\n",
    "                        marker='^',\n",
    "                        color='b')\n",
    "    eb2[-1][0].set_linestyle('--')\n",
    "\n",
    "    #label = ['LPFC' 'OFC' 'MPFC' 'LTC' 'MTC' 'SMC' 'PC' 'OCC','b']\n",
    "    xticksNum = range(8)\n",
    "\n",
    "    #rh_g.set_xticklabels(label)\n",
    "    rh_g.set_xlabel('Right', fontsize=16)\n",
    "    rh_g.set_ylim(1, 4)\n",
    "    rh_g.set_xlim(-.5, 7.5)\n",
    "    rh_g.set_xticks(range(8))\n",
    "    rh_g.set_xticklabels(label)\n",
    "\n",
    "\n",
    "    rh_g.legend()\n",
    "    legend = rh_g.legend(frameon = 1)\n",
    "    frame = legend.get_frame()\n",
    "    frame.set_facecolor('white')\n",
    "\n",
    "    #plt.tight_layout()\n",
    "    #plt.tight_layout(pad=2, w_pad=2, h_pad=20)\n",
    "\n",
    "\n",
    "    #legend = plt.legend(frameon = 1)\n",
    "    #frame = legend.get_frame()\n",
    "    ##frame.set_color('white')\n",
    "    #frame.set_facecolor('white')\n",
    "    ##frame.set_edgecolor('red')\n",
    "    #plt.show()\n",
    "    plt.savefig('/ccnc/mri_team/'+subject_loc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dictWithTuple2df(thicknessDict):\n",
    "    df = pd.DataFrame.from_dict(thicknessDict)\n",
    "    df = df.stack().reset_index()\n",
    "    df = df[df.level_0==0].merge(df[df.level_0==1], on='level_1', how='inner')\n",
    "    df.columns = ['__','subroi','thickness','_','std']\n",
    "    df['side'] = df['subroi'].str[:2]\n",
    "    return df[['subroi','thickness','side','std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getThickness(freesurfer_dir,roiDict):\n",
    "    thicknessDict={}\n",
    "    for side in ['lh','rh']:\n",
    "        for cortex, rois in roiDict.iteritems():\n",
    "            print cortex, rois, 'hahahiehofihi'\n",
    "            if len(rois) > 1:\n",
    "#                 command = 'mris_anatomical_stats \\\n",
    "#                 -l {loc}/{side}_{cortex} FREESURFER {side} 2>/dev/null'.format(\n",
    "#                     loc=os.path.join(freesurfer_dir,'tmp'),\n",
    "#                     side=side,\n",
    "#                     cortex=cortex\n",
    "#                 )\n",
    "                command = 'mris_anatomical_stats \\\n",
    "                -l {loc}/{side}_{cortex} FREESURFER {side}'.format(\n",
    "                    loc=os.path.join(freesurfer_dir,'tmp'),\n",
    "                    side=side,\n",
    "                    cortex=cortex\n",
    "                )\n",
    "            else:\n",
    "#                 command = 'mris_anatomical_stats \\\n",
    "#                 -l {loc}/{side}.{cortex}.label FREESURFER {side} 2>/dev/null'.format(\n",
    "#                     loc=os.path.join(freesurfer_dir,'tmp'),\n",
    "#                     side=side,\n",
    "#                     cortex=cortex\n",
    "#                 )\n",
    "                command = 'mris_anatomical_stats \\\n",
    "                -l {loc}/{side}.{cortex}.label FREESURFER {side}'.format(\n",
    "                    loc=os.path.join(freesurfer_dir,'tmp'),\n",
    "                    side=side,\n",
    "                    cortex=cortex\n",
    "                )\n",
    "            print command\n",
    "            #output=os.popen(re.sub('\\s+',' ',command)).read()\n",
    "            output = os.popen(command).read()\n",
    "            print output\n",
    "            thickness = re.search('thickness\\s+=\\s+(\\S+)\\s+mm\\s+\\S+\\s+(\\S+)', output).group(1,2)\n",
    "            thickness = tuple([float(x) for x in thickness])\n",
    "            thicknessDict[side+'_'+cortex] = thickness\n",
    "            print cortex, thickness\n",
    "            #try:\n",
    "                #os.remove('{loc}/{side}_{cortex}'.format(\n",
    "                    #loc=os.path.join(freesurfer_dir,'tmp'),\n",
    "                    #side=side,\n",
    "                    #cortex=cortex\n",
    "                #))\n",
    "            #except:\n",
    "                #pass\n",
    "    return thicknessDict\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mergeLabel(freesurfer_dir, roiDict):\n",
    "    for side in ['lh','rh']:\n",
    "        for cortex, rois in roiDict.iteritems():\n",
    "            command = 'mri_mergelabels {inLabel} -o {outLabel} 2>/dev/null'.format(\n",
    "                inLabel = ' '.join(['-i '+os.path.join(freesurfer_dir,'tmp',side+'.'+x+'.label') for x in rois]),\n",
    "                outLabel = os.path.join(freesurfer_dir,'tmp',side+'_'+cortex))\n",
    "            os.popen(command).read()\n",
    "\n",
    "            #for roi in [os.path.join(freesurfer_dir,'tmp',side+'.'+x+'.label') for x in rois]:\n",
    "                #print roi\n",
    "                #try:\n",
    "                    #os.remove(roi)\n",
    "                #except:\n",
    "                    #pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def makeLabel(freesurfer_dir):\n",
    "    '''\n",
    "    Run mri_annotation2label for lh and rh hemisphere.\n",
    "    Creates labels in $freesurfer_dir/tmp?\n",
    "    '''\n",
    "    for side in ['lh','rh']:\n",
    "        command = 'mri_annotation2label \\\n",
    "            --subject {basename} \\\n",
    "            --hemi {side} --outdir {outDir} 2>/dev/null'.format(basename=os.path.basename(freesurfer_dir),\n",
    "                                                    side=side,\n",
    "                                                    outDir=os.path.join(freesurfer_dir,'tmp'))\n",
    "        os.popen(re.sub('\\s+',' ',command)).read()\n",
    "\n",
    "        command = 'mri_annotation2label \\\n",
    "            --subject {basename} \\\n",
    "            --hemi {side} --outdir {outDir} --ctab {outDir}/{side}_ctab.txt 2>/dev/null'.format(basename=os.path.basename(freesurfer_dir),\n",
    "                                                    side=side,\n",
    "                                                    outDir=os.path.join(freesurfer_dir,'tmp'))\n",
    "        os.popen(re.sub('\\s+',' ',command)).read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def collectStats_v2(backgrounds):\n",
    "    '''\n",
    "    CollectStats version 2\n",
    "    Summarise cortical thickness in more than one subjects.\n",
    "    'backgrounds' should given in python list format.\n",
    "    For each background,\n",
    "    1. Creates labels using makeLabel\n",
    "    2. Merges labels according to the roiDict using mergeLabel\n",
    "    3. Estimates cortical thickness in each merged labels (dict)\n",
    "    4. Converts dict to pandas Dataframe using dictWithTuple2df\n",
    "    5. save df to freesurfer_dir/tmp/thick_kev_detailed.csv\n",
    "    Than mean df is returned.\n",
    "    '''\n",
    "    #collectStats('ha','ho',ha')\n",
    "\n",
    "    # cortical regions as a dictionary\n",
    "    roiDict = get_cortical_rois_detailed()\n",
    "\n",
    "    subjectDict = {}\n",
    "    for background in backgrounds:\n",
    "        # freesurfer sub-directory description\n",
    "        FS_description= ['bem','mri','scripts','src','stats','surf','tmp']\n",
    "        freesurfer_dir = ccncpy.subDirSearch(FS_description, background)\n",
    "\n",
    "        if len(freesurfer_dir) > 1:\n",
    "            sys.exit(re.sub('\\s+',' ',\n",
    "            'There are more than 1 freesurfer directory \\\n",
    "                    under {0}'.format(subject_loc)))\n",
    "        else:\n",
    "            freesurfer_dir = ''.join(freesurfer_dir)\n",
    "\n",
    "        if not os.path.isfile(os.path.join(freesurfer_dir,'tmp','thick_kev_detailed.csv')):\n",
    "            makeLabel(freesurfer_dir)\n",
    "            mergeLabel(freesurfer_dir, roiDict)\n",
    "            thicknessDict = getThickness(freesurfer_dir, roiDict)\n",
    "            thicknessDf = dictWithTuple2df(thicknessDict)\n",
    "            thicknessDf.to_csv(os.path.join(freesurfer_dir,'tmp','thick_kev_detailed.csv'))\n",
    "            \n",
    "        else:\n",
    "            thicknessDf = pd.read_csv(os.path.join(freesurfer_dir,'tmp','thick_kev_detailed.csv'))\n",
    "\n",
    "        subjectDict[background] = thicknessDf\n",
    "\n",
    "\n",
    "    # sum up dataframes in a subjectDict dictionary\n",
    "    finalDf = pd.concat([x for x in subjectDict.values()])\n",
    "\n",
    "    # return mean\n",
    "    return finalDf.groupby('subroi').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def collectStats(backgrounds):\n",
    "    '''\n",
    "    Summarise cortical thickness in more than one subjects.\n",
    "    'backgrounds' should given in python list format.\n",
    "    For each background,\n",
    "    1. Creates labels using makeLabel\n",
    "    2. Merges labels according to the roiDcit using mergeLabel\n",
    "    3. Estimates cortical thickness in each merged labels (dict)\n",
    "    4. Converts dict to pandas Dataframe using dictWithTuple2df\n",
    "    5. save df to freesurfer_dir/tmp/thick_kev.csv\n",
    "    Than mean df is returned.\n",
    "    '''\n",
    "    #collectStats('ha','ho',ha')\n",
    "\n",
    "    # 8 cortex dictionary\n",
    "    roiDict = get_cortical_rois()\n",
    "\n",
    "    subjectDict = {}\n",
    "    for background in backgrounds:\n",
    "\n",
    "        # freesurfer sub-directory description\n",
    "        FS_description= ['bem','mri','scripts','src','stats','surf','tmp']\n",
    "        freesurfer_dir = ccncpy.subDirSearch(FS_description, background)\n",
    "\n",
    "        if len(freesurfer_dir) > 1:\n",
    "            sys.exit(re.sub('\\s+',' ',\n",
    "            'There are more than 1 freesurfer directory \\\n",
    "                    under {0}'.format(subject_loc)))\n",
    "        else:\n",
    "            freesurfer_dir = ''.join(freesurfer_dir)\n",
    "\n",
    "        if not os.path.isfile(os.path.join(freesurfer_dir,'tmp','thick_kev.csv')):\n",
    "            makeLabel(freesurfer_dir)\n",
    "            mergeLabel(freesurfer_dir, roiDict)\n",
    "            thicknessDict = getThickness(freesurfer_dir, roiDict)\n",
    "            thicknessDf = dictWithTuple2df(thicknessDict)\n",
    "            thicknessDf.to_csv(os.path.join(freesurfer_dir,'tmp','thick_kev.csv'))\n",
    "            \n",
    "        else:\n",
    "            thicknessDf = pd.read_csv(os.path.join(freesurfer_dir,'tmp','thick_kev.csv'))\n",
    "\n",
    "        subjectDict[background] = thicknessDf\n",
    "\n",
    "\n",
    "    # sum up dataframes in a subjectDict dictionary\n",
    "    finalDf = pd.concat([x for x in subjectDict.values()])\n",
    "\n",
    "    # return mean\n",
    "    return finalDf.groupby('subroi').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_graph(volumeDf):\n",
    "    gb = volumeDf.groupby(['side','cortex'])\n",
    "    lh_volume_sums={}\n",
    "    rh_volume_sums={}\n",
    "    cortexList = []\n",
    "    for (side,cortex), grp in gb:\n",
    "        print cortex\n",
    "        if side == 'lh':\n",
    "            lh_volume_sums[side+'_'+cortex] = grp['Volume'].sum()\n",
    "            cortexList.append(cortex)\n",
    "        else:\n",
    "            rh_volume_sums[side+'_'+cortex] = grp['Volume'].sum()\n",
    "\n",
    "    plt.plot(lh_volume_sums.values(),'r')\n",
    "    plt.plot(rh_volume_sums.values(),'b')\n",
    "    plt.xticks(range(len(cortexList)), cortexList)\n",
    "    plt.show()\n",
    "    # plt.plot(gb.get_group('lh')['Volume'],'r-')\n",
    "    # plt.plot(gb.get_group('rh')['Volume'],'b-')\n",
    "    # plt.xticks(range(len(gb.get_group('lh')['ROI'])),gb.get_group('lh')['ROI'])\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getSummary(volumeDf,roiDict):\n",
    "    # thalamus : lh, 10, rh, 49\n",
    "    # lh_OFC : 1019 1014 1012\n",
    "\n",
    "\n",
    "    columnMake = pd.DataFrame.from_dict(roiDict,orient='index').T.stack().reset_index()\n",
    "    columnMake.columns = ['order','cortex','subroi']\n",
    "\n",
    "    volumeDf =  pd.merge(volumeDf,\n",
    "             columnMake[['cortex','subroi']],\n",
    "             left_on='ROI',\n",
    "             right_on='subroi',\n",
    "             how='inner').drop('subroi',axis=1)\n",
    "\n",
    "    return volumeDf\n",
    "\n",
    "\n",
    "\n",
    "#    ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "#    ts = ts.cumsum()\n",
    "#    plt.figure(); ts.plot();plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def openStatsTable(freesurfer_dir):\n",
    "    statsROI = os.path.join(freesurfer_dir,'stats')\n",
    "    filesToRead = 'aparc.stats'\n",
    "\n",
    "    dfList = []\n",
    "    for side in ['lh','rh']:\n",
    "        statsFile = os.path.join(statsROI,side+'.'+filesToRead)\n",
    "        with open(statsFile,'r') as f:\n",
    "            lines = f.readlines()\n",
    "\n",
    "        linesEdited = [re.search('(\\w+)\\s+(\\d+)\\s+(\\d+)\\s+(\\d+)',x).group(1,4) for x in lines if re.search('^\\w',x)]\n",
    "        df = pd.DataFrame(linesEdited)\n",
    "        df.columns = ['ROI','Volume']\n",
    "        df['Volume'] = df['Volume'].astype(int)\n",
    "        df['side']=side\n",
    "        dfList.append(df)\n",
    "\n",
    "    return pd.concat(dfList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def openTable(f_file):\n",
    "\n",
    "\n",
    "\n",
    "    with open(f_file,'r') as f:\n",
    "        lines = f.readlines()\n",
    "    lines_edited = [re.search('^(\\d+)\\s+(\\S+)',x).group(2,1) for x in lines if re.search('^\\d',x)]\n",
    "    lines_edited_dict = dict(lines_edited)\n",
    "\n",
    "    return lines_edited_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def roi_extraction(subjectDir, roiName, roiNumber=False, outputDir=False):\n",
    "    '''\n",
    "    Extracts ROI from freesurfer output\n",
    "    roiName : string\n",
    "    roiNumber : list of number\n",
    "    '''\n",
    "\n",
    "    # FREESURFER vs freesurfer\n",
    "    inputName =  '{subjectDir}/{freesurferDir}/mri/aparc+aseg.mgz'.format(\n",
    "                subjectDir=subjectDir,\n",
    "                freesurferDir=freesurferDIR)\n",
    "\n",
    "    # If the output directory is specified\n",
    "    if outputDir:\n",
    "        outputName = '{outputDir}/{roiName}.nii.gz'.format(\n",
    "                outputDir = outputDir,\n",
    "                roiName = roiName)\n",
    "    else:\n",
    "        outputName = '{subjectDir}/ROI/{roiName}.nii.gz'.format(\n",
    "                subjectDir=subjectDir,\n",
    "                roiName=roiName)\n",
    "\n",
    "\n",
    "    # If the number of the ROI is secificed\n",
    "    if roiNumber:\n",
    "        command = 'mri_binarize --i {inputName} \\\n",
    "                                --match {roiNumber} \\\n",
    "                                --o {outputName}'.format(\n",
    "                                    inputName=inputName,\n",
    "                                    roiNumber=roiNumber,\n",
    "                                    outputName=outputName)\n",
    "\n",
    "    else:\n",
    "\n",
    "\n",
    "        command = 'mri_binarize --i {inputName} \\\n",
    "                                --match {roiNumber} \\\n",
    "                                --o {outputName}'.format(\n",
    "                                    inputName=inputName,\n",
    "                                    roiNumber=roiNumber,\n",
    "                                    outputName=outputName)\n",
    "\n",
    "    command = re.sub('\\s+',' ',command)\n",
    "    output = os.popen(command).read()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_cortical_rois():\n",
    "    '''\n",
    "    returns 8 cortical divisions in dictionary\n",
    "    '''\n",
    "    roiDict = {'OFC' : ['parsorbitalis', 'medialorbitofrontal', 'lateralorbitofrontal'],\n",
    "           'MPFC' : ['caudalanteriorcingulate', 'rostralanteriorcingulate', 'superiorfrontal'],\n",
    "           'LPFC' : [ 'parstriangularis', 'rostralmiddlefrontal', 'frontalpole', 'parsopercularis'],\n",
    "           'SMC' : [ 'precentral', 'caudalmiddlefrontal', 'postcentral', 'paracentral'],\n",
    "           'PC' : ['inferiorparietal', 'supramarginal', 'precuneus', 'posteriorcingulate', 'isthmuscingulate', 'superiorparietal'],\n",
    "           'MTC' : ['entorhinal', 'parahippocampal', 'fusiform'],\n",
    "           'LTC' : ['transversetemporal', 'superiortemporal', 'bankssts', 'inferiortemporal', 'middletemporal', 'temporalpole'],\n",
    "           'OCC' : ['pericalcarine', 'lingual', 'lateraloccipital', 'cuneus']}\n",
    "    return roiDict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_cortical_rois_detailed():\n",
    "    '''\n",
    "    returns more detailed cortical divisions\n",
    "    '''\n",
    "    roiDict = {'OFC' : ['parsorbitalis', 'medialorbitofrontal', 'lateralorbitofrontal'],\n",
    "           'MPFC' : ['caudalanteriorcingulate', 'rostralanteriorcingulate', 'superiorfrontal'],\n",
    "           'LPFC' : [ 'parstriangularis', 'rostralmiddlefrontal', 'frontalpole', 'parsopercularis'],\n",
    "           'SMC' : [ 'precentral', 'caudalmiddlefrontal', 'postcentral', 'paracentral'],\n",
    "           'PC' : ['inferiorparietal', 'supramarginal', 'precuneus', 'posteriorcingulate', 'isthmuscingulate', 'superiorparietal'],\n",
    "           'MTC' : ['entorhinal', 'parahippocampal', 'fusiform'],\n",
    "           'LTC' : ['transversetemporal', 'superiortemporal', 'bankssts', 'inferiortemporal', 'middletemporal', 'temporalpole'],\n",
    "           'OCC' : ['pericalcarine', 'lingual', 'lateraloccipital', 'cuneus']}\n",
    "\n",
    "    detailed = []\n",
    "    for key, roi_list in roiDict.iteritems():\n",
    "        detailed = detailed + roi_list\n",
    "\n",
    "\n",
    "    detailed_ROIs = {}\n",
    "    for roi in detailed:\n",
    "        detailed_ROIs[roi] = [roi]\n",
    "\n",
    "    return detailed_ROIs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now comparing with mean_thickness.csv in /ccnc_bin/meanThickness/\n",
      "{'LTC': ['transversetemporal', 'superiortemporal', 'bankssts', 'inferiortemporal', 'middletemporal', 'temporalpole'], 'OFC': ['parsorbitalis', 'medialorbitofrontal', 'lateralorbitofrontal'], 'MTC': ['entorhinal', 'parahippocampal', 'fusiform'], 'PC': ['inferiorparietal', 'supramarginal', 'precuneus', 'posteriorcingulate', 'isthmuscingulate', 'superiorparietal'], 'LPFC': ['parstriangularis', 'rostralmiddlefrontal', 'frontalpole', 'parsopercularis'], 'OCC': ['pericalcarine', 'lingual', 'lateraloccipital', 'cuneus'], 'SMC': ['precentral', 'caudalmiddlefrontal', 'postcentral', 'paracentral'], 'MPFC': ['caudalanteriorcingulate', 'rostralanteriorcingulate', 'superiorfrontal']} <--roiDict\n",
      "/Users/dahye_bae/DNO43_SCS/T1/FREESURFER /Users/dahye_bae/DNO43_SCS/T1/FREESURFER/tmp/thick_kev_detailed.csv\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'inferior.tif'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-a1c6c291e6d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/dahye_bae/DNO43_SCS/T1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ctx_lh_G_cuneus'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-35-76ad05d32ae7>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(subject_loc, backgrounds, roi_list, meanDfLoc, verbose, brain)\u001b[0m\n\u001b[1;32m    101\u001b[0m             \u001b[0;32mprint\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreesurfer_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'tmp/thick_kev_detailed.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m             valueSwap.main(freesurfer_dir, \n\u001b[0;32m--> 103\u001b[0;31m                     os.path.join(freesurfer_dir,'tmp/thick_kev_detailed.csv'))\n\u001b[0m\u001b[1;32m    104\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0;32mprint\u001b[0m \u001b[0;34m'Please select verbose mode'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/ccnc_bin/freesurferSummary/valueSwap.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m(freesurferLoc, indcsv)\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m     \u001b[0;31m#mergeLabel(freesurferLoc, mergedDf.roi.unique())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m     \u001b[0mmakeBrainPic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreesurferLoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/ccnc_bin/freesurferSummary/valueSwap.py\u001b[0m in \u001b[0;36mmakeBrainPic\u001b[0;34m(freesurfer_dir)\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mprint\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0;31m#shutil.move(img.split('_')[1],\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m                     \u001b[0;31m#os.path.join('/ccnc/mri_team/',\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m                         \u001b[0;31m#folderName + '_' + os.path.basename(img)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.pyc\u001b[0m in \u001b[0;36mmove\u001b[0;34m(src, dst)\u001b[0m\n\u001b[1;32m    299\u001b[0m             \u001b[0mrmtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m             \u001b[0mcopy2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_dst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    302\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.pyc\u001b[0m in \u001b[0;36mcopy2\u001b[0;34m(src, dst)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0mdst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m     \u001b[0mcopyfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m     \u001b[0mcopystat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.pyc\u001b[0m in \u001b[0;36mcopyfile\u001b[0;34m(src, dst)\u001b[0m\n\u001b[1;32m     80\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mSpecialFileError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`%s` is a named pipe\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfsrc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m             \u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'inferior.tif'"
     ]
    }
   ],
   "source": [
    "main('/Users/dahye_bae/DNO43_SCS/T1', 'ctx_lh_G_cuneus', True, True, True, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
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