This collection contains a recipe to start a girder instance with a public IP by combining Vagrant with docker.
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{ | |
"name": "grep", | |
"tool-version": "2.21", | |
"description": "Linux grep tool.", | |
"command-line": "grep [IGNORE_CASE] [MAX_COUNT] [PATTERN] [FILE] > [STDOUT]", | |
"docker-image": "ubuntu", | |
"docker-index": "http://index.docker.io", | |
"schema-version": "0.2", | |
"inputs": [ | |
{ |
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from glob import glob | |
import os | |
import numpy as np | |
fl = glob('model01/task001/SAD_???/copes/mni/model001/task001_cope01.nii.gz') | |
os.system('fslmerge -t output.nii.gz %s' % ' '.join(fl)) | |
os.system('fslmaths output.nii.gz -abs -thr 1 -bin output2') | |
os.system('fslstats -t output2 -V > output2.txt') | |
foo = np.genfromtxt('output2.txt') | |
idx = np.argsort(foo[:, 1]) | |
zip(np.array(fl)[idx], foo[idx, 1]) |
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#!/bin/bash | |
for sub in '315' '318' '332' ; do | |
for run in 1 2 3 4 ; do | |
mkdir /om/project/gates/wmfilt/openfmri/ds001/${sub} | |
mkdir /om/project/gates/wmfilt/openfmri/ds001/${sub}/model | |
mkdir /om/project/gates/wmfilt/openfmri/ds001/${sub}/model/model001 | |
mkdir /om/project/gates/wmfilt/openfmri/ds001/${sub}/model/model001/onsets | |
mkdir /om/project/gates/wmfilt/openfmri/ds001/${sub}/model/model001/onsets/task001_run00${run} | |
mkdir /om/project/gates/wmfilt/openfmri/ds001/${sub}/BOLD |
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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"> | |
<title>Photo Viewer for Local Files and Directories</title> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<link rel="stylesheet" href="http://static.jstree.com/3.0.4/assets/dist/themes/default/style.min.css" /> | |
<link rel="stylesheet" type="text/css" href="http://rii.uthscsa.edu/mango/papaya/papaya.css?version=0.6.5&build=692" /> | |
<script type="text/javascript" src="http://rii.uthscsa.edu/mango/papaya/papaya.js?version=0.6.5&build=692"></script> |
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from scipy.spatial.distance import pdist, squareform | |
import numpy as np | |
from numbapro import jit, float32 | |
def distcorr(X, Y): | |
""" Compute the distance correlation function | |
>>> a = [1,2,3,4,5] | |
>>> b = np.array([1,2,9,4,4]) |
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from nipype import Workflow, Node, Function | |
def myrandomfunc(N): | |
import numpy as np | |
return np.random.random_integers(1, 10, N) | |
def mysumfunc(X): | |
return X.sum() | |
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$ find SUB_1024011/resting -type f | |
SUB_1024011/resting/mask_files/median_brain_mask.nii | |
SUB_1024011/resting/mask_files/T1_out_brain_pve_0_maths_warped.nii | |
SUB_1024011/resting/mask_files/T1_out_brain_pve_1_maths_warped.nii | |
SUB_1024011/resting/mask_files/T1_out_brain_pve_2_maths_warped.nii | |
SUB_1024011/resting/mask_files/target/output_warped_image_maths.nii | |
SUB_1024011/resting/parcellations/aparc/smooth/rest_01_avgwf.txt | |
SUB_1024011/resting/parcellations/aparc/smooth/rest_01_summary.stats | |
SUB_1024011/resting/parcellations/aparc/smooth/rest_02_avgwf.txt | |
SUB_1024011/resting/parcellations/aparc/smooth/rest_02_summary.stats |
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import os | |
from glob import glob | |
from nipype import config | |
config.enable_provenance() | |
from nipype import Node, Function, Workflow, IdentityInterface | |
from nipype.interfaces.io import SelectFiles, DataSink | |
import os |