kchawla-pi

import glob, hashlib, csv

# replace with file location
path = r'/mnt/chromeos/MyFiles/Downloads/CSVs/'
files = glob.glob(path + r"/*.csv")
output = r"\output.csv"

# this is where we'll store the values
hits = []

kchawla-pi

"""
Gist made by Kshitij Chawla (Github name: kchawla-pi) for the Nilearn library in Feb/March 2019.
GPLv3
"""

def replace_parameters(replacement_params,
                       end_version='future',
                       lib_name='Nilearn',
                       ):
    """

kchawla-pi

import os
from tempfile import NamedTemporaryFile

import pandas as pd
from nose.tools import assert_true

from nistats import datasets


def test_make_events_file_localizer_first_level():

kchawla-pi

def _download_spm_auditory_data(data_dir, subject_dir, subject_id):
    print("Data absent, downloading...")
    url = ("http://www.fil.ion.ucl.ac.uk/spm/download/data/MoAEpilot/"
           "MoAEpilot.zip")
    archive_path = os.path.join(subject_dir, os.path.basename(url))
    _fetch_file(url, subject_dir)
    try:
        _uncompress_file(archive_path)
    except:

kchawla-pi

def fetch_spm_auditory(data_dir=None, data_name='spm_auditory',
                       subject_id="sub001", verbose=1):
    """Function to fetch SPM auditory single-subject data.
    Parameters
    ----------
    data_dir: string
        Path of the data directory. Used to force data storage in a specified
        location. If the data is already present there, then will simply
        glob it.
    Returns

kchawla-pi

import nilearn
import os
import pandas
from nistats.second_level_model import SecondLevelModel

# 4D images paths
img1 = nilearn.image.load_img(os.path.expanduser('~/nilearn_data/adhd/data/0010128/0010128_rest_tshift_RPI_voreg_mni.nii.gz'))
img2 = nilearn.image.load_img(os.path.expanduser('~/nilearn_data/adhd/data/2497695/2497695_rest_tshift_RPI_voreg_mni.nii.gz'))

# Single 4D image

kchawla-pi

"""
From 
nilearn/examples/02_decoding/plot_haxby_searchlight.py
Erring code section is after Line # 75.
"""
"""
Searchlight analysis of face vs house recognition
==================================================

Searchlight analysis requires fitting a classifier a large amount of

kchawla-pi

# -*- encoding: utf-8 -*-
# !/usr/bin/env python3
"""
Accepts the path to a text file and extracts the words from it, without repetition,
and writs them to a text file 'unique_words_list.txt' in the samedirectory/folder.
"""
from pathlib import Path


def extract_words(filepath):