lindenb

converting drugbank to TSV using XSLT and xsltstream http://lindenb.github.io/jvarkit/XsltStream.html

e.g:

java -jar dist/xsltstream.jar \
    -n '{http://www.drugbank.ca}drug' \
    -t drugbank2tsv.xsl \
    /path/to/full_database.xml
 

lindenb

/*
Author: Pierre Lindenbaum @yokofakun

related: 
http://bionics.it/posts/parsing-drugbank-xml-or-any-large-xml-file-in-streaming-mode-in-go

*/
import ca.drugbank.*;
import java.util.*;
import java.util.stream.*;

markjay4k

# -*- coding: utf-8 -*-
"""
    Animated 3D sinc function
"""

from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph.opengl as gl
import pyqtgraph as pg
import numpy as np
import sys

samuell

SELECT node.title,timestamp,node_revisions.body 
  FROM node 
  LEFT JOIN node_revisions  ON node.vid=node_revisions.vid
  INTO OUTFILE '/tmp/nodes.csv' 
    FIELDS TERMINATED BY ',' 
    ENCLOSED BY '"' 
    LINES TERMINATED BY '\n';

sgsfak

We couldn’t find that file to show.

behe

defmodule ATGCCount do
  def count(sequence), do: cnt(String.to_char_list(sequence),0,0)
  def cnt([65|t],at,gc), do: cnt(t,at+1,gc)
  def cnt([84|t],at,gc), do: cnt(t,at+1,gc)
  def cnt([71|t],at,gc), do: cnt(t,at,gc+1)
  def cnt([67|t],at,gc), do: cnt(t,at,gc+1)
  def cnt([62|_],at,gc), do: {at,gc}
  def cnt([],at,gc), do: {at,gc}
  # def cnt(_,0,0), do: {0,0}
  def cnt([_|t], at, gc), do: cnt(t,at,gc)

knewter

---
- name: Create directory to put elixir in
  sudo: 'yes'
  shell: "mkdir -p /opt/elixir/v0.15.0"

- name: Install unzip
  apt: name=unzip state=present update_cache=true

- name: Unzip elixir release
  sudo: 'yes'

Ismael-VC

#!/usr/bin/env julia


function gc_count(fasta::String)
    file = open(fasta, "r")
    lines = readall(file)
    gc = at = 0
    for c in lines.data
        if (c == 'G') || (c == 'C')
            gc += 1

alco

use std::io::fs::File;

enum ReadState {
    Normal,
    Newline,
    Skipline,
}

fn main() {
    let mut at = 0i;

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t