alexstorer

%Generate neighbors and relations matrix for each MSA
%system(rename msa*.mat msa.mat);
%filename=strcat('\nsf\usr\S\sjaffe\Work\Segregation\Matlab\msa', str2int(job_instance))
missing=importdata('/nfs/home/S/sjaffe/Work/Segregation/Matlab/missing.txt');
%16 34
path(path, '/nfs/projects_nobackup/s/sjaffe')
path(path, '/nfs/home/S/sjaffe/Work/Segregation/Matlab')
cd '/nfs/projects_nobackup/s/sjaffe'

for file_num=10:52

alexstorer

%d=6378.1*acos(sin(2*pi*lat(i)/360)*sin(2*pi*lat(j)/360)+cos(2*pi*(lng(i)-lng(j))/360)*cos(2*pi*lat(i)/360)*cos(2*pi*lat(j)/360));
tic
N = 4000;

% test some distances
lat = rand(N,1)*180;
lng = rand(N,1)*180;

% the loopy way
d = zeros(N);

alexstorer

hsb2 <- read.table('http://www.ats.ucla.edu/stat/R/faq/hsb2.csv', header=T, sep=",")
attach(hsb2)
sesmeans  <- tapply(math, ses, mean)
femaleses = tapply(math, list(as.factor(ses), as.factor(female)), mean)

bp <- barplot(femaleses, beside = TRUE, main = "Math by SES and gender", 
              col = c("lightblue", "mistyrose", "lavender"),
              xlab = "Gender", names = c("Male", "Female"), 
              ylab = "Mean Math Score", legend = c("Low", "Medium", "High"), 
              args.legend = list(title = "SES", x = "topright", cex = .7), ylim = c(0, 90))

alexstorer

function h = plotDecision(dprime,sdnoise,criterion)
% h = plotDecision(dprime, sdnoise, criterion)
%
% plotDecision makes a plot of two Gaussian functions, inspired by signal
% detection theory.  The inputs are given in terms of standard deviates of
% the signal distribution:
%
% dprime    - d', the distance between the means of the Gaussians
% sdnoise   - $\sigma$, the ratio of the standard deviation of the noise to
%             the standard deviation of the signal.

alexstorer

import fnmatch
import os
import shutil
import re

for file in os.listdir('.'):
    flist = os.path.basename(file).split('.')
    fname = flist[0]    
    fext = '.blah'

alexstorer

% labelImages(impath)
% ---------------------------------------
% Places labels on an image, and saves the marker locations and a 3x3
% square pixel neighborhood of RGB values.
%
% Usage:
% labelImages('/Users/astorer/Desktop/test.jpg')
% LEFT CLICK  - Add a point
% RIGHT CLICK - Delete a point
% 's'         - Save progress

alexstorer

from lxml import etree
import csv   
import urllib2
import urllib
import re

def downloadallarticles(tree,year):
    articles = tree.xpath('.//*[@id="middle2level"]/table[2]//a')
    destpath = '/Users/astorer/Work/sgrossman/output/'
    for a in articles:

alexstorer

num_statements = 5;
num_blocks = 9;

for run = 1:8
    
    % 150 statements, 50 of each type
    physical_order = Shuffle(1:50);
    mental_order = Shuffle(1:50);
    emotional_order = Shuffle(1:50);
    physical_num = 1

alexstorer

Universe = vanilla
Executable = /usr/local/bin/matlab
Arguments = -nodisplay -r \"addpath('/nfs/home/A/astorer/Work/dchan');
test($(Process))\"
when_to_transfer_output = ON_EXIT_OR_EVICT
transfer_output_files = out.$(PROCESS)

input =
output = out.$(Process)
error = error.$(Process)

alexstorer

# open the files in the directory, parse them and get the text.


#eg.text_content()

import glob
from lxml import etree
import lxml.html
import csv   
import urllib2