rxaviers

People

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jimbojsb

Step 0:

Get Homebrew installed on your mac if you don't already have it

Step 1:

Install highlight. "brew install highlight". (This brings down Lua and Boost as well)

Step 2:

elowy01

*bcftools filter
*Filter variants per region (in this example, print out only variants mapped to chr1 and chr2)
qbcftools filter -r1,2 ALL.chip.omni_broad_sanger_combined.20140818.snps.genotypes.hg38.vcf.gz

*printing out info for only 2 samples:
bcftools view -s NA20818,NA20819 filename.vcf.gz

*printing stats only for variants passing the filter:
bcftools view -f PASS filename.vcf.gz

oakes

Contents

• Why care about Rust?
• The Toolchain
• Creating a New Project
• Modules
• Crates
• Types
• References
• Mutability

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

alimanfoo

import numpy as np


def find_runs(x):
    """Find runs of consecutive items in an array."""

    # ensure array
    x = np.asanyarray(x)
    if x.ndim != 1:
        raise ValueError('only 1D array supported')

PoisonAlien

#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <htslib/sam.h>

int main(int argc, char *argv[]){
	
	samFile *fp_in = hts_open(argv[1],"r"); //open bam file
	bam_hdr_t *bamHdr = sam_hdr_read(fp_in); //read header
	bam1_t *aln = bam_init1(); //initialize an alignment

GaelVaroquaux

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy

levand

Since it has come up a few times, I thought I’d write up some of the basic ideas around domain modeling in Clojure, and how they relate to keyword names and Specs. Firmly grasping these concepts will help us all write code that is simpler, cleaner, and easier to understand.

Clojure is a data-oriented language: we’re all familiar with maps, vectors, sets, keywords, etc. However, while data is good, not all data is equally good. It’s still possible to write “bad” data in Clojure.

“Good” data is well defined and easy to read; there is never any ambiguity about what a given data structure represents. Messy data has inconsistent structure, and overloaded keys that can mean different things in different contexts. Good data represents domain entities and a logical model; bad data represents whatever was convenient for the programmer at a given moment. Good data stands on its own, and can be reasoned about without any other knowledge of the codebase; bad data is deeply and tightly coupled to specific generating and

Integralist

In Clojure you can apply destructuring within either a let binding list; function parameter list or even a macro.

A simple example would be:

(def coords [5 7]) ; define a symbol "coords" that points to a vector [5 7]
(let [[x y] coords] (println "x:" x "y:" y))
; => x: 5 y: 7