adelavega

from bids.grabbids import BIDSLayout
import pandas as pd
import os.path as op

layout = BIDSLayout('../')
event_files = layout.get(task='objectcategories', type='events', return_type='file')

for f in event_files:
    events = pd.read_csv(f, delimiter='\t')
    events['stim_file'] = events.stim_file.str.replace('stimuli/', '')

adelavega

import numpy as np
from tools import ProgressBar
from joblib import Parallel, delayed
import pandas as pd

def permutation_parallel(X, y, cla, feat_names, region, i):   
    newY = np.random.permutation(y)
    cla_fits = cla.fit(X, newY)
    fit_w = np.log(cla_fits.theta_[1] / cla_fits.theta_[0])
    

adelavega

from nilearn import plotting as niplt
import nibabel as nib
import seaborn as sns
import numpy as np

def plot_subset(nifti, layers, colors = None, **kwargs):
    if not isinstance(nifti, nib.Nifti1Image):    
        nifti = nib.load(nifti)
    data = nifti.get_data()

adelavega

"""" This script generates stimuli for the keep track task. 

Counterbalancing rules it tries to implement:
	- Each category is used as a target equal number of times (only possible when number of targets is divisible by number of categories)
	- Each word used equally often as a target, distractor, and final word
	- Last word in the trial is always a distractor
	- Target words and final words do not repeat across adjacent trials
	- Distractors can repeat across trials

Number of targets per category:

adelavega

##############################################################
##### Bootstrap of group difference                      #####
##############################################################

### First, we generate two samples (N = 25) with a difference of 0.5
### Second, test using parametric t.test
### Then we define a function to test the difference between the two samples
### using indices that boot will provide

## Then we run boot. Importantly, define the strata of your group labels

adelavega

loadAllCSVs <- function (INPUT_PATH) {
  # Get list of files
  file_list <- list.files(INPUT_PATH)
  
  # Iterate through files and add them to "dataset"
  for (file in file_list){
    file <- paste(INPUT_PATH,file,sep="")
    # if the merged dataset doesn't exist, create it
    if (!exists("dataset")){
      dataset <- read.table(file, header=TRUE, sep=",")

adelavega

# Loads all CSV files into a data set in given INPUT_PATH
loadAllCSVs <- function (INPUT_PATH) {
  # Get list of files
  file_list <- list.files(INPUT_PATH)
  
  # Iterate through files and add them to "dataset"
  for (file in file_list){
    file <- paste(INPUT_PATH,file,sep="")
    # if the merged dataset doesn't exist, create it
    if (!exists("dataset")){

adelavega

def dcor_matrix(data):	
	"""Creates a correlation matrix using the dcor (distance correlation) function based on the R (energy) implementation
	Assumes that variables are columns
	Input:
		data - x by y np array where y is the number of variables
	Output:
		Returns a correlation matrix as a np array of shape y by y 

	"""
	import numpy as np

adelavega

# Contains basic temporal discounting analysis functions.
# Does not implement multilevel analysis (will be implemented later)
# Typical workflow:
# Input: TD Trials in trialData with the following columns: 
#   later value
#   delay
#   later_choice - 1 if subject chose "later" option, 0 if not
#   condition - factor indication condition
#   sub - subject number (factor)
#   1. Get Discounted Value at each delay for each subject