Merge pull request #33 from sappelhoff/minorclean

mixed formatting and doc improvements

Makefile

@@ -1,7 +1,7 @@
 PYTHON = python
 
 help:
-	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "Please use \`make <target>\` where <target> is one of"
 	@echo "  fetch         to fetch the data"
 	@echo "  sensor        run sensor space processing"
 	@echo "  source        run source space processing"

README.md

@@ -1,6 +1,6 @@
 [![CircleCI](https://circleci.com/gh/brainthemind/CogBrainDyn_MEG_Pipeline.svg?style=svg)](https://circleci.com/gh/brainthemind/CogBrainDyn_MEG_Pipeline)
 
-# 0 Credits 
+# 0 Credits
 
 This example pipeline for MEG/EEG data processing with MNE python was build jointly by the [Cognition and Brain Dynamics Team](https://brainthemind.com/) and the [MNE Python Team](https://martinos.org/mne/stable/index.html),
 based on scripts originally developed for this publication:
@@ -18,18 +18,16 @@ First, you need to make sure you have mne-python installed and working on your s
 Get the scripts through git:
 
 	$ git clone https://github.com/mne-tools/mne-study-template.git
-	
-If you do not know how to use git, download the scripts [here](https://github.com/mne-tools/mne-study-template/archive/master.zip). 
+
+If you do not know how to use git, download the scripts [here](https://github.com/mne-tools/mne-study-template/archive/master.zip).
 
 For source analysis you'll also need freesurfer, follow the instructions on [their website](https://surfer.nmr.mgh.harvard.edu/).
 
 
 # 2 Set your data to a proper place
 
-In our example, we will use .fif raw data you can find here:
-https://osf.io/m9nwz/
-
-The name of the study will be "Localizer".
+In our example, we will use `.fif` raw data from the "Localizer" study. You can
+find the data on the Open Science Framework: https://osf.io/m9nwz/
 
 Let's create a folder called "ExampleData" wherever you want on your computer.
 
@@ -38,27 +36,26 @@ In the ExampleData folder, you need to create three subfolders:  "MEG", "system_
 ![xx](https://image.noelshack.com/fichiers/2019/15/4/1554998135-path.png)
 
 
-The "MEG" folder will contain a folder for each participant
-The "system_calibration_files" folder will contain the calibration files (download them from OSF)
-The "subjects" folder will contain participant MRI files.
+- The "MEG" folder will contain a folder for each participant
+- The "system_calibration_files" folder will contain the calibration files
+  (download them from OSF)
+- The "subjects" folder will contain participant MRI files.
 
 Here is an example of what the MEG folder should contain:
 
 ![xx](https://image.noelshack.com/fichiers/2019/15/4/1554998137-path1.png)
 
-Then you put the raw data for each subject in their own folder. The raw data file name should respect this format:
-subjectID_StudyName_raw.fif
+Then you put the raw data for each subject in their own folder. The raw data file name should respect this format: `subjectID_StudyName_raw.fif`
 
-or, if your data has multiple runs:
-subjectID_StudyNamerun01_raw.fif
+or, if your data has multiple runs: `subjectID_StudyNamerun01_raw.fif`
 
 
 ![xx](https://image.noelshack.com/fichiers/2019/15/4/1554998137-path2.png)
 
 # 3 Adapt config.py
 
 All specific settings to be used in your analysis are defined in [config.py](config.py).
-See the comments for explanations and recommendations. 
+See the comments for explanations and recommendations.
 
 
 # 4 Processing steps

config.py

@@ -498,15 +498,18 @@
 ica_decim = 11
 
 
-# ``default_reject_comps`` : dict
+# ``default_reject_comps_factory`` : callable
+#    A factory function that returns a default rejection component dictionary:
 #    A dictionary that specifies the indices of the ICA components to reject
 #    for each subject. For example you can use:
 #    rejcomps_man['subject01'] = dict(eeg=[12], meg=[7])
 
-def default_reject_comps():
+def default_reject_comps_factory():
+    """Return the default rejection component dictionary."""
     return dict(meg=[], eeg=[])
 
-rejcomps_man = defaultdict(default_reject_comps)
+
+rejcomps_man = defaultdict(default_reject_comps_factory)
 
 # ``ica_ctps_ecg_threshold``: float
 #    The threshold parameter passed to `find_bads_ecg` method.