massie’s gists

massie / gist:3207745

Created July 30, 2012 15:21

CXF Test Case

	import org.apache.cxf.feature.AbstractFeature;
	import org.apache.cxf.feature.LoggingFeature;
	import org.apache.cxf.jaxrs.JAXRSServerFactoryBean;
	import org.apache.cxf.jaxrs.client.JAXRSClientFactory;
	import org.apache.cxf.jaxrs.client.WebClient;
	import org.apache.cxf.jaxrs.lifecycle.SingletonResourceProvider;
	import org.junit.Test;

	import javax.ws.rs.Consumes;
	import javax.ws.rs.GET;

massie / CalculateCosts.scala

Created December 13, 2012 00:55

Calculating DynamoDB costs

	package edu.berkeley.cs.amplab.carat.dynamodb

	import com.amazonaws.services.dynamodb.model.{TableDescription, DescribeTableRequest}
	import collection.JavaConversions._

	class DynamoDBCostStructure(cp10wu: Double, cp50ru: Double, cpgb: Double) {
	private val costPer10WriteUnits: Double = cp10wu
	private val costPer50ReadUnits: Double = cp50ru
	private val costPerGB: Double = cpgb
	private val hoursPerMonth = 24 * 31

massie / gist:6302534

Created August 22, 2013 02:26

Spark, Parquet and Avro dependencies

	<dependency>
	<groupId>org.spark-project</groupId>
	<artifactId>spark-core_${scala.version}</artifactId>
	<version>0.7.3</version>
	</dependency>
	<dependency>
	<groupId>com.twitter</groupId>
	<artifactId>parquet-avro</artifactId>
	<version>1.0.0</version>
	</dependency>

massie / AvroSerializable.scala

Last active May 23, 2016 19:10

A simple Scala class that wraps Avro Generic and Specific objects to make them "Serializable".

	package com.zenfractal

	import org.apache.avro.generic.{GenericDatumWriter, GenericDatumReader, IndexedRecord}
	import java.io.{ObjectOutputStream, IOException, ObjectInputStream}
	import org.apache.avro.specific.{SpecificDatumWriter, SpecificRecordBase, SpecificDatumReader}
	import org.apache.avro.Schema
	import org.apache.avro.io._

	/**
	* Makes Avro objects 'Serializable', e.g.

massie / KryoRegistrator.scala

Created October 29, 2013 23:59

Here's an example of how to embed Avro objects into a Kryo stream. You only need to register each Avro Specific class in the KryoRegistrator using the AvroSerializer class below and you're ready to go.

	/*
	* Copyright (c) 2013. Regents of the University of California
	*
	* Licensed under the Apache License, Version 2.0 (the "License");
	* you may not use this file except in compliance with the License.
	* You may obtain a copy of the License at
	*
	* http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing, software

massie / gist:72209a3c15166eb4511f

Created September 12, 2014 23:00

Scala SHA-1 Example

	package example
	import java.io.FileInputStream
	import java.security.MessageDigest

	object Sha1Example {

	def main(args: Array[String]): Unit = {
	val start = System.currentTimeMillis()
	val sha1 = MessageDigest.getInstance("SHA1")
	val bytes = new Array[Byte](1024 * 1024)

massie / gist:aa1ffab5d2712a466d04

Created May 16, 2015 00:09

Verify Onename

Verifying I am +massie on my passcard. https://onename.com/massie

massie / commiters.txt

Created February 24, 2016 00:28

Committers to ADAM over the last year

	$ git log --since=2015-01-01 --pretty=format:"%an" \| sort \| uniq
	Allen Day
	Andrew Chen
	Anton Stamov
	Arun Ahuja
	Audrey Musselman-Brown
	Beau Norgeot
	Bryan Jacobs
	Eric
	Eric Tu

massie / gist:6b01965dc35e31321164

Created March 23, 2016 20:55

Hormone DOT file

	// Hormone graph
	digraph G {
	//rankdir=LR;
	node[color="green"];
	GH; FSH; LH; TSH;Melatonin;ACTH;OX;ADH;ENDOR;PRO;CALCI;PTH;ANP;THYM;INS;SOM;GLU;
	node[shape="plaintext"];
	H;AP;PP;G;T;KIDNEY;AC;BM;PG;AM;BMa;PT;B;HEART;URINE;Thymus;TCELL;PAN;UTERUS;MAMMARY;IMMUNE;
	node[shape="oval",color="black"];
	BMa[label="Bone Marrow"];
	H [label="Hypothalamus"];

massie / gist:378eb305ab68602c6ac3907f38f02418

Created April 26, 2020 14:49

Code for pulling in CA Data from CHHS Open Data

	url = 'https://data.chhs.ca.gov/dataset/6882c390-b2d7-4b9a-aefa-2068cee63e47/resource/6cd8d424-dfaa-4bdd-9410-a3d656e1176e/download/covid19data.csv'
	states = pd.read_csv(url,
	header=0,
	names=["state", "date","positive"],
	usecols=['date', 'state', 'positive'],
	parse_dates=['date'],
	index_col=['state', 'date'],
	squeeze=True).sort_index()

	# You'll also need to set cutoff = 1 for calls to prepare_cases and catch empty sequences

Matt Massie massie