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# foursquareASD will create a map of ASD locations across the United States | |
library(RJSONIO) | |
library(RCurl) | |
options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl"))) | |
# First, read in file with latitudes and longitudes of major cities | |
# Obtained from http://notebook.gaslampmedia.com/download-zip-code-latitude-longitude-city-state-county-csv/ | |
ll = read.csv('zip_codes_states.csv',sep=",",head=TRUE) |
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#!/bin/bash | |
# Key off existence of the "data" mount point to determine whether | |
# this is the first boot. | |
if [[ ! -e /mnt/data ]]; then | |
# Basic setup - get the JRE installed | |
apt-get update | |
apt-get install --fix-broken | |
mkdir -p /mnt/data | |
fi |
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domain total_points earned_points | |
domain expertise, source 4 2 | |
domain expertise,value 4 2 | |
domain expertise, impact 4 1 | |
programming, breadth 5 4 | |
programming, depth 5 4 | |
programming, software 7 5 | |
methods, implementation 5 3 | |
methods,understanding 7 4 | |
communication, verbal 6 4 |
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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 |
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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 |
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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 |
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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(:)); |
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# 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 |
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#!/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 |
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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} |
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