woemler

Elasticsearch Setup

1. Create new Elasticsearch domain with the desired configuration. Be sure to enable SSL and user-authentication.
2. Create a user for your Study Tracker integration (eg. studytracker).
3. Create a role for the user, grant it unlimited privileges on the studies index and the indics:data/write/bulk permission.

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### Data Set Manifest Example
displayName: Cancer Cell Line Encyclopedia  # The data set display name.  Short, but descriptive
shortName: ccle  # Data set short name.  No whitespace or file system-forbidden characters.  Maybe this value could just come from the manifest file name? eg ccle.yaml
source: Borad Institute  # Source organization of the data
version: v1  # Version number, if available.
description: Cancer cell line genomic data from the Broad Institute.  # General notes or data set description.

attributes: # Dynamic key value attributes.  Allows capturing of custom attributes for historical record
  source-species: "Homo sapiens"

woemler

import com.fasterxml.jackson.databind.ObjectMapper;
import org.springframework.http.*;
import org.springframework.web.client.RestClientException;
import org.springframework.web.client.RestTemplate;

import java.io.IOException;
import java.util.List;

/**
 * @author woemler

woemler

package me.woemler.sqlbuilder;

import org.springframework.util.Assert;

import java.util.Arrays;
import java.util.List;

/**
 * Based on com.nurkiewicz.jdbcrepository.TableDescription, with a number of modifications.
 * 

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package me.woemler.myapp.config;

import org.springframework.context.annotation.ComponentScan;
import org.springframework.context.annotation.Configuration;

@Configuration
@ComponentScan(basePackages = { "me.woemler.myapp" })
public class ApplicationConfig {
	
}

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from BeautifulSoup import BeautifulSoup
import urllib2
import re


def fetch_page_soup(url):
    """ Fetches page data from a URL and returns a parsed BeautifulSoup object """

    try:
        response = urllib2.urlopen(url)

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import pandas as pd

# Read a TSV file into a data frame
# This will automatically handle the data types
df = pd.read_csv("my_data.txt", sep="\t")

# Write the data frame to a storer HDF5 file in the table "data", overwriting existing files
df.to_hdf("my_data.store.h5", 'data', mode="w") 

# Writing to ta table formatted HDF5 file is a little trickier

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import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Controller;
import org.springframework.ui.ModelMap;
import org.springframework.web.bind.annotation.RequestMapping;

@Controller
public class DefaultController {
    
    @Value("${app.var2}")
    private String var2;