gone_brainhackin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import nibabel as nib\n",
    "from extract_from_masks import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-cf7a06945afb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mmasks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matlas_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "#extracting from masks\n",
    "\n",
    "parentdir = op.expanduser('~/Dropbox/data_analysis/brainhack_2017_data/jason')\n",
    "img_name = op.join(parentdir,'cope10.nii')\n",
    "atlas_file = 'HarvardOxford-cort-maxprob-thr25-2mm.nii.gz'\n",
    "\n",
    "\n",
    "masks = generate_masks(atlas_file)\n",
    "\n",
    "masks[0].get_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "check = extract_from_masks(masks[:2],img_name,average = False,dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 771555 columns</p>\n",
       "</div>"
      ],
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       "    0       1       2       3       4       5       6       7       8       \\\n",
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       "11     0.0     0.0     0.0  \n",
       "12     0.0     0.0     0.0  \n",
       "13     0.0     0.0     0.0  \n",
       "14     0.0     0.0     0.0  \n",
       "15     0.0     0.0     0.0  \n",
       "16     0.0     0.0     0.0  \n",
       "17     0.0     0.0     0.0  \n",
       "18     0.0     0.0     0.0  \n",
       "19     0.0     0.0     0.0  \n",
       "20     0.0     0.0     0.0  \n",
       "21     0.0     0.0     0.0  \n",
       "22     0.0     0.0     0.0  \n",
       "23     0.0     0.0     0.0  \n",
       "24     0.0     0.0     0.0  \n",
       "25     0.0     0.0     0.0  \n",
       "26     0.0     0.0     0.0  \n",
       "27     0.0     0.0     0.0  \n",
       "28     0.0     0.0     0.0  \n",
       "29     0.0     0.0     0.0  \n",
       "30     0.0     0.0     0.0  \n",
       "\n",
       "[31 rows x 771555 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os.path as op\n",
    "import numpy as np\n",
    "\n",
    "def process_design(dfile):\n",
    "\n",
    "    df = pd.DataFrame.from_csv(dfile,index_col=None)\n",
    "    df = df.sort_values('onsets')\n",
    "\n",
    "    if 'amplitudes' not in df:\n",
    "        df.loc[:,'amplitudes'] = np.ones([len(df.onsets),1])\n",
    "\n",
    "        \n",
    "\n",
    "    conds = df['conditions'].unique()\n",
    "\n",
    "\n",
    "    df.loc[:,'trial'] = np.arange(1,len(df.onsets)+1)\n",
    "    fnames = ['%03d_%s' % (row['trial'],row['conditions']) for (i,row) in df.loc[:,['trial','conditions']].iterrows()]\n",
    "    df.loc[:,'filename'] = fnames\n",
    "    df = df[['conditions','onsets','durations','amplitudes','filename']]\n",
    "    \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfile = op.expanduser('/Users/thrillhouse/Dropbox/data_analysis/brainhack_2017_data/jason/onsets_first10.csv')\n",
    "process_design(dfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pybetaseries_general import *\n",
    "\n",
    "mask = '/Users/thrillhouse/Dropbox/data_analysis/brainhack_2017_data/jason/preproc/functional_mask.nii.gz'\n",
    "epi = '/Users/thrillhouse/Dropbox/data_analysis/brainhack_2017_data/jason/preproc/unsmoothed_timeseries.nii.gz'\n",
    "design = '/Users/thrillhouse/Dropbox/data_analysis/brainhack_2017_data/jason/onsets_first10.csv'\n",
    "outdir = '/Users/thrillhouse/Dropbox/data_analysis/brainhack_2017_data/'\n",
    "\n",
    "test = pybetaseries(img=epi,mask=mask,fsl=False,design=design,outdir=outdir,TR=2.0,HPF=100.,time_res=.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.LSS(None,None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import nilearn.plotting as plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot.plot_img(template)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "\n",
    "TR = 2.0\n",
    "p=[6,16,1,1,6,0,32]\n",
    "\n",
    "# def spm_hrf(TR,p=[6,16,1,1,6,0,32]):\n",
    "\"\"\" An implementation of spm_hrf.m from the SPM distribution\n",
    "\n",
    "Arguments:\n",
    "\n",
    "Required:\n",
    "TR: repetition time at which to generate the HRF (in seconds)\n",
    "\n",
    "Optional:\n",
    "p: list with parameters of the two gamma functions:\n",
    "                                                     defaults\n",
    "                                                    (seconds)\n",
    "   p[0] - delay of response (relative to onset)         6\n",
    "   p[1] - delay of undershoot (relative to onset)      16\n",
    "   p[2] - dispersion of response                        1\n",
    "   p[3] - dispersion of undershoot                      1\n",
    "   p[4] - ratio of response to undershoot               6\n",
    "   p[5] - onset (seconds)                               0\n",
    "   p[6] - length of kernel (seconds)                   32\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "p=[float(x) for x in p]\n",
    "\n",
    "fMRI_T = 16.0\n",
    "\n",
    "TR=float(TR)\n",
    "dt  = TR/fMRI_T\n",
    "u   = np.arange(p[6]/dt + 1) - p[5]/dt\n",
    "hrf=scipy.stats.gamma.pdf(u,p[0]/p[2],scale=1.0/(dt/p[2])) - scipy.stats.gamma.pdf(u,p[1]/p[3],scale=1.0/(dt/p[3]))/p[4]\n",
    "good_pts=np.array(range(np.int(p[6]/TR)))*fMRI_T\n",
    "hrf=hrf[good_pts.astype(int)]\n",
    "# hrf = hrf([0:(p(7)/RT)]*fMRI_T + 1);\n",
    "hrf = hrf/np.sum(hrf);\n",
    "# return hrf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c791650>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i2eJmcA0HlgMbgZXAqDbOOw9YD1QB/w30TnjvNOBt5xp/AY5p64v9+Mc2m0yr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a5043d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hrf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}