View 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'); |
View 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 |
View 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 |
View 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 |
View 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 |
View 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(:)); |
View 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 |
View 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 |
View 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} |
View 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: |
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