postprocessingscript.md

@@ -26,6 +26,10 @@ By typing the function name you are calling the diagnostics script. This functio
 Figures will be generated and you can browse them as you would like. The figures contain some visual representations of the calculations that are being preformed based on the quality of the data file. Follow along below for a more in depth view of some of the figures.
 
 # Data Quality  
+**Single**
+
+ ***
+
 The first plot is a general overview of how much good data has been found in your data set. If the % of time and the % of channels removed from your data is very high you might want to reconsider your experiment modality. From the figure below:
 
 ![diag4](/uploads/0264d39ab0337a36484d1867fd9bb4bf/diag4.png)
@@ -38,7 +42,16 @@ The designation of *Bad* is based off a few simple calculations based on paramet
 
 ![diag7](/uploads/dc996102b39d4da75a173af582a53db6/diag7.png)
 
-*As a side Note is is possible but not likely for a channel to be flagged for more than one of these classifications. This would mean diagnostics will find high numbers of bad channels but a lower number of removals. Make sure you look at the printout in the editor.*  
+*As a side Note is is possible but not likely for a channel to be flagged for more than one of these classifications. This would mean diagnostics will find high numbers of bad channels but a lower number of removals. Make sure you look at the printout in the editor.*    
+
+
+**Study**
+
+ ***
+
+The study option will present something very similar to these pie graphs but it will be in a stack bar graph, and be done for all of the data sets loaded. Going up the y axis is the subject number, and the x axis is the percentage of channels or time. 
+
+*Image*
 
 
 # Channel Flags    
@@ -107,6 +120,11 @@ time segment that was flagged between all of the channels combined. The percenta
 For more information on these calculations see the [Pipeline Scripts Page](https://git.sharcnet.ca/bucanl_pipelines/eeg_pipe_asr_amica/wikis/pipeline-scripts#scalpart).
 
 # Component Classification 
+
+**Single**
+
+ ***
+
 Component classification can only be done on files that have successfully run through  the complete dipfit script. It this script we make use of a plugin  tool box developed by Laura Frolich called IC_MARC (classification of Independent Components of EEG into Multiple ARtifact Classes). This plugin will look at the components generated by AMICA, and attempt to classify them by their characteristics. It then saves the probability of the component belonging to one of the following categories.
  * Neural
  * Eye Blink
@@ -123,6 +141,12 @@ The pie graph in diagnostics shows the fraction of components that were placed i
 
 These figures can give you a quick idea on how well you are isolating components. Ideally you are looking for components that have have a high % in one category, indicating a clear isolation.
 
+**Study**
+
+ ***
+
+The study option will present something very similar to the pie graph but is represented....
+
 # Creating your own plotting functions
 In order to create your own diagnostics tool you should make sure you are familiar with:
  * The above content