Vanessasaurus vsoch

## vcademy_mutual_information.m
% Mutual Information
% This shows why mutual information is important for registration, more-so
% than another metric like the least sum of squares.
% VSoch July 2013

% First, let's read in two pickle images that we want to register
% Note - this is a picture I found on google search, and I transformed it in
% photoshop by moving it down and right, and then CONTROL+I to get the inverse
pickle1 = imread('pickle1.png');
pickle2 = imread('pickle2.png');

## vcademy_mds.m
% Multidimensional scaling (MDS) Example

% Load matlab cities data
load cities

% This data has cities in rows, and different categories for ratings in
% columns.  We will implement MDS to assess city similarity based on
% ratings.

% Step 1: Set up our proximity matrix

## print_dr_report.m
function print_dr_report(imgfolder)
% This function takes a folder of images produced by visual_dr
% and prints and html report page

filey = fopen([imgfolder 'index.html' ],'w');

% Print top of html file
fprintf(filey,'%s\n','<html><body><h2>Dual Regression Report</h2>');

% Get all images in folder

## cytoscapeExport.m
function cytoscapeExport(links,weights,threshold,exptype)

% This function will take a list of links and weights, and create a file to
% import into Cytoscape for further graph analysis

% exptype is the export type - 'all' means voxelwise above a threshold,
% 'region' means creating graph of average regional values

% links is an n by 2 list of connections, each a voxel id
% weights is a correlation value corresponding to each link set

## flattenSphere.m
function flat = flattenSphere(mr)

% First read in sphere files and vox mapping
%vox = spm_read_vols(spm_vol('NDARAK333GZA.lh.vtxvol.nii'));
mr = spm_read_vols(spm_vol(mr));

% First create a random maximum length - we will crop at the end
maxlength = floor(pi*size(mr,1));

% Now go through vox image and save vector of x,y,z coordinates

## nc_spatial_kurtosis.m
function feature = nc_kurtosis_spatial(spatialMap)

    % FEATURE NAME: Kurtosis
    % FEATURE TYPE: spatial.gist

    % We take a component image, sum the voxel values quadrupled, and divide by the
    % number of voxels, subtract 3.  We then normalize the value by linear scaling transform of abs(ln(kurtosis))
    % This does the same thing as doing kurtosis(curr_network(:))

    ICkurt = sum(power(spatialMap,4)) / length(spatialMap(:));

## plotly.R
# This function will use plotly to create an online boxplot!

plotlyBox = function(username,key,datastore) {

  # Username is your plotly username
  # API key is found via plot.ly, login --> Access plotly --> settings
  # datalist is a list of data
  # The number of plots, N, is determined by length(N)

  # Eg, put your data into a list

## searchNdarLocal.py
#!/usr/bin/python

"""
searchNdar: reads in a set of strings from file, and search behavioral metrics
to find questions / subscales that might be of interest

python searchNdarLocal.py --o outfile --i /home
/vanessa/Documents/Work/NDAR/behavioral -w input.txt

--o is the outfile name, without extension

## exportRWeka.R
exportRWeka = function(data,relation_name,outname) {

  # Data is a data matrix, with features in columns, data in rows
  # Edit the script to match whatever is the missing value for your data
  # Rownames will be exported as well, and should be data labels
  # relation_name is the name of the relation in Weka
  # outname is the output file
  # If you have a nominal outcome variable (eg, you want to color
  # your data by a label in Weka) change the variable type as follows:
  # @attribute groupVar {1,2}

## clinPheno.py
#!/usr/bin/python

"""
clinPheno: Methods for querying a SQL database of clinical
variables to establish a clinical phenotype.  The script will
search a data dictionary (database) for a set of user specified
terms (in some input.txt file), and then extract the matches
from the clinical database, for import into R (see clinPhenoR.R)

# SAMPLE INPUT.TXT file:
	% Mutual Information
	% This shows why mutual information is important for registration, more-so
	% than another metric like the least sum of squares.
	% VSoch July 2013

	% First, let's read in two pickle images that we want to register
	% Note - this is a picture I found on google search, and I transformed it in
	% photoshop by moving it down and right, and then CONTROL+I to get the inverse
	pickle1 = imread('pickle1.png');
	pickle2 = imread('pickle2.png');
	% Multidimensional scaling (MDS) Example

	% Load matlab cities data
	load cities

	% This data has cities in rows, and different categories for ratings in
	% columns. We will implement MDS to assess city similarity based on
	% ratings.

	% Step 1: Set up our proximity matrix
	function print_dr_report(imgfolder)
	% This function takes a folder of images produced by visual_dr
	% and prints and html report page

	filey = fopen([imgfolder 'index.html' ],'w');

	% Print top of html file
	fprintf(filey,'%s\n','<html><body><h2>Dual Regression Report</h2>');

	% Get all images in folder
	function cytoscapeExport(links,weights,threshold,exptype)

	% This function will take a list of links and weights, and create a file to
	% import into Cytoscape for further graph analysis

	% exptype is the export type - 'all' means voxelwise above a threshold,
	% 'region' means creating graph of average regional values

	% links is an n by 2 list of connections, each a voxel id
	% weights is a correlation value corresponding to each link set
	function flat = flattenSphere(mr)

	% First read in sphere files and vox mapping
	%vox = spm_read_vols(spm_vol('NDARAK333GZA.lh.vtxvol.nii'));
	mr = spm_read_vols(spm_vol(mr));

	% First create a random maximum length - we will crop at the end
	maxlength = floor(pi*size(mr,1));

	% Now go through vox image and save vector of x,y,z coordinates
	function feature = nc_kurtosis_spatial(spatialMap)

	% FEATURE NAME: Kurtosis
	% FEATURE TYPE: spatial.gist

	% We take a component image, sum the voxel values quadrupled, and divide by the
	% number of voxels, subtract 3. We then normalize the value by linear scaling transform of abs(ln(kurtosis))
	% This does the same thing as doing kurtosis(curr_network(:))

	ICkurt = sum(power(spatialMap,4)) / length(spatialMap(:));
	# This function will use plotly to create an online boxplot!

	plotlyBox = function(username,key,datastore) {

	# Username is your plotly username
	# API key is found via plot.ly, login --> Access plotly --> settings
	# datalist is a list of data
	# The number of plots, N, is determined by length(N)

	# Eg, put your data into a list
	#!/usr/bin/python

	"""
	searchNdar: reads in a set of strings from file, and search behavioral metrics
	to find questions / subscales that might be of interest

	python searchNdarLocal.py --o outfile --i /home
	/vanessa/Documents/Work/NDAR/behavioral -w input.txt

	--o is the outfile name, without extension
	exportRWeka = function(data,relation_name,outname) {

	# Data is a data matrix, with features in columns, data in rows
	# Edit the script to match whatever is the missing value for your data
	# Rownames will be exported as well, and should be data labels
	# relation_name is the name of the relation in Weka
	# outname is the output file
	# If you have a nominal outcome variable (eg, you want to color
	# your data by a label in Weka) change the variable type as follows:
	# @attribute groupVar {1,2}
	#!/usr/bin/python

	"""
	clinPheno: Methods for querying a SQL database of clinical
	variables to establish a clinical phenotype. The script will
	search a data dictionary (database) for a set of user specified
	terms (in some input.txt file), and then extract the matches
	from the clinical database, for import into R (see clinPhenoR.R)

	# SAMPLE INPUT.TXT file: