rob-p

import requests
import argparse
import pprint
import os

def do_upload(args):
    ACCESS_TOKEN = args.token
    depid = args.deposition_id
    url = 'https://zenodo.org/api/deposit/depositions/{}'.format(depid)
    r = requests.get(url, params={'access_token': ACCESS_TOKEN})

rob-p

rob-p

from math import *

# function [alpha, xmin, L]=plfit(x, varargin)
# PLFIT fits a power-law distributional model to data.
#    Source: http://www.santafe.edu/~aaronc/powerlaws/
# 
#    PLFIT(x) estimates x_min and alpha according to the goodness-of-fit
#    based method described in Clauset, Shalizi, Newman (2007). x is a 
#    vector of observations of some quantity to which we wish to fit the 
#    power-law distribution p(x) ~  x^-alpha for x >= xmin.

rob-p

#include "concurrentqueue.h"
//#include "blockingconcurrentqueue.h"
#include <thread>
#include <atomic>
#include <vector>
#include <iostream>

using namespace moodycamel ;
using namespace std ;

rob-p

/**
MWE for salmon segfault
**/

#include <boost/thread/thread.hpp>

#include <cstdint>
#include <cstdio>
#include <cstring>
#include <fstream>

rob-p

# image: COMBINE-lab/salmon
# This dockerfile is based on the one created by
# Titus Brown (available at https://github.com/ctb/2015-docker-building/blob/master/salmon/Dockerfile)
FROM centos:latest

ENV PACKAGES git gcc make g++ liblzma-dev libbz2-dev \
    ca-certificates zlib1g-dev libcurl4-openssl-dev curl unzip autoconf apt-transport-https ca-certificates gnupg software-properties-common wget
ENV SALMON_VERSION 1.2.1

# salmon binary will be installed in /home/salmon/bin/salmon

rob-p

#!/usr/bin/env bash
pn=$1
convert $1 $1.ps
gs -dPDFA -dBATCH -dNOPAUSE -dNOOUTERSAVE -dUseCIEColor -sProcessColorModel=DeviceCMYK -sDEVICE=pdfwrite -sPDFACompatibilityPolicy=1 -sOutputFile=$1.a.pdf $1.ps

rob-p

This is an interesting question, and I think the answer depends on what your primary goal is. Istvan makes good points in favor of straight-forward C (integrated with Python) for building tools that are easy for others, without a ton of experience in programming languages, to modify etc. However, if your primary goal is to make efficient and robust tools for others to use, then let me offer a somewhat different perspective.

The language in which one develops a project has important implications beyond just the speed and memory profiles that will result. Both C++ and Rust will allow you to write very fast tools, making use of zero-cost abstractions where feasible, with predictable and frugal memory profiles (assuming you choose the right algorithms and are careful in designing the programs). However, two important areas where I see these languages diverging are safety and maintainability. By safety, I mean minimizing the types of nefarious memory and correctness bugs that can easily slip into "bare-met

rob-p

from math import *

# function [alpha, xmin, L]=plfit(x, varargin)
# PLFIT fits a power-law distributional model to data.
#    Source: http://www.santafe.edu/~aaronc/powerlaws/
# 
#    PLFIT(x) estimates x_min and alpha according to the goodness-of-fit
#    based method described in Clauset, Shalizi, Newman (2007). x is a 
#    vector of observations of some quantity to which we wish to fit the 
#    power-law distribution p(x) ~  x^-alpha for x >= xmin.

rob-p

nextflow.enable.dsl=2

process foo {
    publishDir "output"

    input:
    val(prefix)

    output:
    path "${prefix}_foo"