[TST] Patch permissions error in testing

README.md

@@ -2,10 +2,17 @@
 
 TE-Dependent Analysis (_tedana_) is a Python module for denoising multi-echo fMRI data.
 
-tedana is part of the ME-ICA pipeline, and therefore assumes that you're working with already preprocessed data. If you're in need of a preprocessing pipeline, we recommend [FMRIPREP](https://github.com/poldracklab/fmriprep/), which has been tested for compatibility with multi-echo fMRI data.
+[![CircleCI](https://circleci.com/gh/ME-ICA/tedana.svg?style=shield)](https://circleci.com/gh/ME-ICA/tedana)
+[![Documentation Status](https://readthedocs.org/projects/tedana/badge/?version=latest)](http://tedana.readthedocs.io/en/latest/?badge=latest)
+[![License](https://img.shields.io/badge/License-LGPL%202.0-blue.svg)](https://opensource.org/licenses/LGPL-2.1)
+
 
 ## About
 
+tedana is part of the ME-ICA pipeline, and therefore assumes that you're working with already preprocessed data. If you're in need of a preprocessing pipeline, we recommend [FMRIPREP](https://github.com/poldracklab/fmriprep/), which has been tested for compatibility with multi-echo fMRI data.
+
+### Why Multi-Echo?
+
 Multi-echo fMRI data collection entails acquires multiple TEs (commonly called [echo times](http://mriquestions.com/tr-and-te.html)) for each collected fMRI volume.
 Our signal of interest, Blood Oxygen-Level Dependent or [BOLD signal](http://www.fil.ion.ucl.ac.uk/spm/course/slides10-zurich/Kerstin_BOLD.pdf), is known to decay at a set rate within each fMRI volume.
 Collecting multiple echos therefore allows us to infer if components of fMRI signal are BOLD-related or driven by acquisition artifacts, like participant motion.
@@ -44,7 +51,7 @@ Want to learn more about our plans for developing tedana ? Check out [our roadma
 
 We ask that all contributions to tedana respect our [code of conduct](https://github.com/emdupre/tedana/blob/master/Code_of_Conduct.md).
 
-### Mozilla Global Sprint (10-11 May, 2018)
+### :earth_americas: Mozilla Global Sprint (10-11 May, 2018) :earth_africa:
 
 This year, tedana will be participating in the [Mozilla Global Sprint](https://foundation.mozilla.org/opportunity/global-sprint/) !
-Look out for issues tagged `global-sprint` for good places to get started during the sprint.
+Check out issues tagged [![mozsprint](https://img.shields.io/badge/-mozsprint-0052cc.svg)](https://github.com/ME-ICA/tedana/labels/mozsprint) for good places to get started during the sprint.

circle.yml

@@ -21,7 +21,7 @@ jobs:
           name: download and unzip processed data
           command: |
             curl -L -o /home/neuro/data/test_res.tar.gz https://www.dropbox.com/s/yzswu6ljnfxlhyo/test_res.tar.gz?dl=0
-            tar -xvzf /home/neuro/data/test_res.tar.gz -C /home/neuro/data
+            tar -xvzf /home/neuro/data/test_res.tar.gz --no-same-owner -C /home/neuro/data/
 
 
       - run:

tedana/interfaces/tedana.py

@@ -2,6 +2,7 @@
 import sys
 import gzip
 import pickle
+import textwrap
 import numpy as np
 import nibabel as nib
 from sklearn import svm
@@ -1257,8 +1258,7 @@ def gscontrol_mmix(mmix, acc, rej, midk, empty, head):
     """
     Make medn version of T1 corrected time series
     """
-    niwrite(Gmu[:, :, :, np.newaxis] + unmask((bold_noT1gs+resid)*Gstd[mask][:, np.newaxis]
-                                              mask),
+    niwrite(Gmu[:, :, :, np.newaxis] + unmask((bold_noT1gs+resid)*Gstd[mask][:, np.newaxis], mask),
             aff, 'dn_ts_OC_T1c.nii', head)
 
     """

tedana/tests/test_tedana.py

@@ -1,4 +1,6 @@
-"""Tests for tedana."""
+"""
+Tests for tedana.
+"""
 
 import os.path
 from tedana.interfaces import tedana
@@ -7,6 +9,7 @@
 import numpy as np
 from pathlib import Path
 
+
 def test_basic_tedana():
     """
     A very simple test, to confirm that tedana creates output
@@ -63,6 +66,6 @@ def test_outputs():
      'veins_l0.nii',
      'veins_l1.nii']
     test_dir = Path('/home/neuro/data/test_res/')
-    res_dir =  Path('/home/neuro/code/TED/')
+    res_dir = Path('/home/neuro/code/TED/')
     for fn in nifti_test_list:
-        compare_nifti(fn, test_dir, res_dir)
+        compare_nifti(fn, test_dir, res_dir)