Difference between revisions of "MEG Software and Analysis"

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parsemarks2.py
 
parsemarks2.py
usage: parsemarks2.py [-h] [-l] [-s] [-m marker...] dataset
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usage: parsemarks2.py [-h] [-l] [-m marker...] [-s] dataset
   
 
Extract the marks from the marker file associated with dataset and print them
 
Extract the marks from the marker file associated with dataset and print them

Revision as of 06:45, 17 September 2018

MEG Core pyctf tools ported to Python 3

pyctf tools are a collection of Python scripts useful in the analysis of data sets collected from the CTF scanner. These tools have been rewritten using modern Python 3 syntax following standard coding conventions. Most of these programs will run unmodified under MacOS, Windows, and the various versions of Linux with a Python 3.4 distribution or later installed. Python programs requiring modules not included in the standard Python library are indicated.

parsemarks.py
usage: parsemarks.py [-h] [-l] dataset

Extract the marks from the marker file associated with dataset 
and print themin a useful format.

positional arguments:
  dataset     path to CTF dataset or MarkerFile.mrk (required)

optional arguments:
  -h, --help  show this help message and exit
  -l          the marks are labeled in the output. Useful for debugging.

parsemarks2.py provides provides more control over the output.

parsemarks2.py
usage: parsemarks2.py [-h] [-l] [-m marker...] [-s] dataset

Extract the marks from the marker file associated with dataset and print them
in a useful format.

positional arguments:
  dataset     path to CTF dataset (required)

optional arguments:
  -h, --help  show this help message and exit
  -l          the marks are labeled in the output. Useful for debugging.
  -m marker   the specified mark(s) is reported (default is all markers)
  -s          shows a list of markers in dataset and exits

Requires the xlsxwriter module for writing Excel files.

parsemarks_report.py
usage: parsemarks_report.py [-h] [-v] studydir

Reports on the marker set from every dataset directory under a study
directory. MEG studies consists of a collection of datasets, each with its own
MarkerFile.mrk, organized under a top level (studydir) directory. Output is an
excel file stored in your ~/excel folder.

positional arguments:
  studydir    path to a toplevel directory holding a set of dataset
              directories (required)

optional arguments:
  -h, --help  show this help message and exit

Stimulus Presentation Software

PsychoPy: Psychology software in Python PsychoPy is an open-source application that allows you to run a wide range of neuroscience, psychology and psychophysics experiments. It’s a free, powerful alternative to Presentation™ or to e-Prime™, written in Python (a free alternative to Matlab™ ).

Presentation: NeuroBehavioral Systems (NBS), Inc. Presentation® is a stimulus delivery and experiment control program for neuroscience written for Microsoft Windows.

E-prime 3: Psychology Software Tools E-Prime® 3.0 software for behavioral research. Build your own experiments using E-Prime’s easy-to-use graphical interface. Design, collect, and analyze data – all within a few hours!

MEG Data Analysis

This section covers all aspects of MEG data analysis. The following pages assume that you have AFNI installed and have a reasonably good idea of how to use it.

Miscellaneous Documentation

  • Adobe-ps.png SensLayout-275 — a color picture showing the sensor names and relative locations (ps).
  • Pdf.png SensLayout-275 - a color picture showing the sensor names and relative locations (pdf).