hliang

filepath_zi     = "grd3_b_cn_zi"
filepath_pinyin = "grd3_b_cn_pinyin"
filepath_ciyu   = "grd3_b_cn_ciyu"

lst_zi = []
lst_pinyin = []
lst_ciyu = []

with open(filepath_zi) as f:
    lst_zi = f.readlines()

hliang

# API URL: https://gnomad.broadinstitute.org/api/
# Document: https://github.com/macarthur-lab/gnomadjs/blob/master/projects/gnomad-api/README.md

## gene ######################
{
  gene(gene_name: "NLRP3") {
    symbol
    name
    full_gene_name
    hgnc_id

hliang

v2ex.com##img[class="avatar"]

hliang

import { connect } form 'react-redux';

const SAVE_WEATHER = "SAVE_WEATHER";

export default function saveWeather(response) {
  return {
    type: SAVE_WEATHER,
    data: response
  }
}

hliang

library(zoo)
# library("lubridate")
collectl_fn = list.files("D:/tmp/abruijn/", patter=".*.p.txt", full.names = TRUE); collectl_fn


zcolall = list()
slurmout_all = list()
for (i in seq_along(collectl_fn)) {
  jobid = gsub(".*mon_(\\d+)-(cn\\d+).*.p.txt", "\\1", collectl_fn[i])
  nodeid = gsub(".*mon_(\\d+)-(cn\\d+).*.p.txt", "\\2", collectl_fn[i])

hliang

---
title: "NLP toys"
author: "hliang"
date: "8/24/2016"
output: html_document
---
  
  Use NLP tools to analyze song lyrics.

load packages required:

hliang

# king death
kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
kings
# store the data in a time series object
kingstimeseries <- ts(kings)
kingstimeseries

# the number of births per month in New York city, from January 1946 to December 1959 (originally collected by Newton)
births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")
birthstimeseries <- ts(births, frequency=12, start=c(1946,1))

hliang

#! /bin/bash

GCC_VERSION="6.4.0"
WORKDIR="$HOME/gcc"
INSTALLDIR="/BIGDATA/app/gcc/6.4.0"

# get the source code
cd $WORKDIR
wget http://www.netgull.com/gcc/releases/gcc-${GCC_VERSION}/gcc-${GCC_VERSION}.tar.xz
tar -xf gcc-${GCC_VERSION}.tar.xz

hliang

#! /usr/bin/env python

'''
==================
  ucsc_snapshots
==================

retrieve pictures from the UCSC Genome Browser based on coordinates
specified from BED3+ file and a session ID (hgsid).

hliang

# Assumes you've already run coverageBed -hist, and grep'd '^all'. E.g. something like:
# find *.bam | parallel 'bedtools -abam {} -b capture.bed -hist | grep ^all > {}.all.txt'

setwd("./qc/")
# Get a list of the bedtools output files you'd like to read in
bedcov_dir = "./bedcov_cds_canol/"
print(files <- list.files(path=bedcov_dir, recursive=TRUE, pattern="samp.*all.txt$", full.names=TRUE))


# This regular expression leaves me with "samp01", "samp02", and "samp03" in the legend.