acjackman

#!/usr/bin/python

# A script to automate changelog generation from Git commit messages
#
# For use with a git-flow workflow, it will take changes from the last tagged release
# where commit messages contain NEW, FIXED, and IMPROVED keywords and sort and fromat
# them into a Markdown release note list.
#
# Forked from https://gist.github.com/ttscoff/17fbce4f229609082b45681bba7a9967

acjackman

from datetime import datetime
from os import path
from time import sleep
from urllib import parse

import redis
from celery.task.control import inspect
from django.conf import settings

redis_urlparts = parse.urlparse(settings.CELERY_BROKER_URL)

acjackman

from datetime import datetime
from os import path
from time import sleep
from urllib import parse

import redis
from celery.task.control import inspect
from django.conf import settings

redis_urlparts = parse.urlparse(settings.CELERY_BROKER_URL)

acjackman

UPDATE RFID.dbo.rfid_active_assignment2
	SET LS_Size = Size
FROM 
	Routeman.dbo.ContractLine AS cl 
	JOIN RFID.dbo.rfid_active_assignment2 as asgn 
		ON asgn.LS_ContractNumber = cl.ContractNumber
		AND asgn.LS_ContractLineNumber_smInt = cl.LineNumber

acjackman

calc_indexes <- function(n_rows, n_cols){
    index <- 0:(n_rows * n_cols - 1)
    row_0 <- floor(index / n_cols)
    list(
        row = row_0 + 1,
        col = index - (row_0 * n_cols) + 1
    )
}

acjackman

library(combinat)
library(plyr)

run_simulation <- function(c_size, t_size, c_mean, t_mean){
	### Generate and Combine data
	control <- rnorm(c_size, c_mean, 1)
	treatment <- rnorm(t_size,t_mean, 1)

	group_labels <- c(rep("control", length(control)),
	                  rep("treatment", length(treatment)))

acjackman

library(combinat)
library(plyr)
library(doMC)
doMC::registerDoMC(cores=8)
grads <- read.csv("GradCleanup.csv")

# Subset data to look at only three years (5!)^3 permutations
grads <- subset(grads, Cohort > 2003)

# Block by Cohort, group by Borough

acjackman

library(combinat)
library(plyr)
# Generate data
borough <- c(rep("Bronx",6), rep("Manhattan",6))
year <- rep(2001:2006,2)
gradrate <- runif(12,.5,1)
data <- data.frame(borough,year,gradrate)


grads <- read.csv("GradCleanup.csv")

acjackman

DROP TABLE IF EXISTS child;
DROP TABLE IF EXISTS parent;
CREATE TABLE parent (id INT NULL, -- NOTE THE ALLOW NULL
	                 name text,
                     PRIMARY KEY (id)
) ENGINE=INNODB;
insert into parent(id, name) values (NULL, "UNKOWN"); -- FAILS because of primary key
insert into parent(id, name) values (1,"BOB"); 

CREATE TABLE child (id INT NULL, 

acjackman

# Package to connect to MySQL from 
# install.packages("RMySQL")
library("RMySQL")

# MySQL authentication credentials
# Get these from Jacob Moody or CSR
dbuser <- ""
dbpassword <- ""
dbhost <- "statmm.byu.edu"
dbport <- 3306