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h<-c(1,2,10)
j<-c(1,2,9)
foo <- cbind(h,j)

h<-c(1,2,10,9)
j<-c(1,2,9,10)
foo2 <- cbind(h,j)

foo3 <-           

alexstorer

SQL Dump

Goal: Convert .sql file ("sql dump") into CSV files.

Get the data

The data lives in a zip file here:

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# Import statements - what libraries will we use?
from lxml import etree
from lxml import html
import urllib2, signal, time, random

# These are helper functions for handling network errors
def handler(signum, frame):
    raise Exception('Last Resort!')

signal.signal(signal.SIGALRM, handler)

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from selenium import webdriver  
import time
import re
import csv

def isReady(browser):
    return browser.execute_script("return document.readyState")=="complete"


browser = webdriver.Firefox()

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install.packages('rjson')
library(rjson)
help(package=rjson)

orig.data <- read.csv('/Users/astorer/Work/lgermine/gradCPT_results_8-13-13.csv',stringsAsFactors=FALSE)

#install.packages('RJSONIO')
library(RJSONIO)

getDataFromJson <- function(charstr) {

alexstorer

ggplot2 Qustions

I'm not sure why there are differences between these two seemingly similar ways of plotting using ggplot2 and its geom_point options.

library(ggplot2)
data(mtcars)

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from setuptools.command import easy_install
easy_install.main( ["-U","https://pylucene-extra.apache-extras.org.codespot.com/files/JCC-2.15-py2.7-win32.egg"] )
easy_install.main( ["-U","https://pylucene-extra.apache-extras.org.codespot.com/files/lucene-3.6.2-py2.7-win32.egg"] )

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#!/usr/env python

from subprocess import call
import datetime
import os

import xlrd
import csv

def csv_from_excel():

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VARSTOCASES
/MAKE stimulus1 from Task_1#1_stimulus Task_1#2_stimulus Task_1#3_stimulus Task_1#4_stimulus Task_1#5_stimulus Task_1#6_stimulus Task_1#7_stimulus Task_1#8_stimulus Task_1#9_stimulus Task_1#10_stimulus Task_1#11_stimulus Task_1#12_stimulus Task_1#13_stimulus Task_1#14_stimulus Task_1#15_stimulus Task_1#16_stimulus Task_1#17_stimulus Task_1#18_stimulus Task_1#19_stimulus Task_1#20_stimulus
/MAKE latency1 from Task_1#1_latency Task_1#2_latency Task_1#3_latency Task_1#4_latency Task_1#5_latency Task_1#6_latency Task_1#7_latency Task_1#8_latency Task_1#9_latency Task_1#10_latency Task_1#11_latency Task_1#12_latency Task_1#13_latency Task_1#14_latency Task_1#15_latency Task_1#16_latency Task_1#17_latency Task_1#18_latency Task_1#19_latency Task_1#20_latency
/MAKE CORRECT_latency1 from Task_1#1_CORRECT_latency Task_1#2_CORRECT_latency Task_1#3_CORRECT_latency Task_1#4_CORRECT_latency Task_1#5_CORRECT_latency Task_1#6_CORRECT_latency Task_1#7_CORRECT_latency Task_1#8_CORRECT_latency Task_1#9_CORRECT_late

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def handler(signum, frame):
    raise Exception('Last Resort!')

signal.signal(signal.SIGALRM, handler)

def tryOpenParse(thisurl):
    global parser
    #print "trying to parse " + thisurl
    print "trying to parse..."
    loaded = False