JohannesBuchner

import sys, os
import joblib
import subprocess

mem = joblib.Memory('.', verbose=False)
@mem.cache
def run_cmd(args, env):
	process = subprocess.run(args, capture_output=True, text=True)
	return process.stdout, process.stderr, process.returncode

JohannesBuchner

#!/bin/bash

# you need to install:
# pip install openbases
# sudo apt install texlive-xetex pandoc pandoc-citeproc

PDF_INFILE=paper.md
PDF_LOGO=logo.png
PDF_OUTFILE=paper.pdf
TEX_OUTFILE=paper.tex

JohannesBuchner

#!/bin/bash
# example usage:
# bash xray_opt_gif.sh  155.87737 +19.86508

RA=$1
DEC=$2

wget -nc "https://alasky.unistra.fr/hips-thumbnails/thumbnail?ra=${RA}&dec=${DEC}&fov=0.21750486127986932&width=500&height=500&hips_kw=CDS%2FP%2FSDSS9%2Fcolor" -O opt.jpg
wget -nc "https://alasky.unistra.fr/hips-thumbnails/thumbnail?ra=${RA}&dec=${DEC}&fov=0.21750486127986932&width=500&height=500&hips_kw=xcatdb%2FP%2FXMM%2FPN%2Fcolor" -O xmm.jpg

JohannesBuchner

# columns: id1,id2,id3,id4,id5,id6,v1,v2,v3

f=G1_1e7_1e2_0_0.csv
awk="time mawk"

# groupby simple
$awk -F, 'NR>1 { a[$1] += $7 } END {for (i in a) print i, a[i]}' $f >/dev/null
$awk -F, 'NR>1 { a[$1,$2] += $7 } END { for (comb in a) { split(comb,sep,SUBSEP); print sep[1], sep[2], a[sep[1],sep[2]]; }}' $f >/dev/null
$awk -F, 'NR>1 { a[$3] += $7; n[$3]++; b[$3] += $9; } END {for (i in a) print i, a[i], b[i]/n[i];}' $f >/dev/null
$awk -F, 'NR>1 { a[$4] += $7; n[$4]++; b[$4] += $8; } END {for (i in a) print i, a[i]/n[i], b[i]/n[i];}' $f >/dev/null

JohannesBuchner

import re
import pystan
import hashlib
import pickle
import os

def build_model(code):
    lines = code.split("\n")
    lines = [re.sub('//.*$', '', line).strip() for line in lines]
    lines = [line.replace('    ', ' ').replace('  ', ' ').replace('  ', ' ')

JohannesBuchner

#!/bin/bash

# how to identify the processes to STOP/CONT
# this is any part of process command line
PROCESS="myscript.py"

# run this in one terminal:
# it tries to resume the processes every so often
while sleep 10m; do
pgrep -f "${PROCESS}"|xargs -rt kill -CONT

JohannesBuchner

"""
If you ever made a plot of "fractions" or "rates" with symmetric error 
bars, like in the plot shown, this mini-tutorial is for you. Here is how
 to compute correct error bars, so that uncertainties in the fractions 
do not go below 0% or above 100%.

 If you have a histogram for 
instance, and you detected k objects in a given bin. What is the rate 
underlying at quantiles q=0.1, 0.5(median), 0.9?
"""

JohannesBuchner

import numpy as np

def mcmc(logfunction, x0, nsteps, sigma_p):
    samples = np.empty((nsteps, len(x0)))
    logL0 = logfunction(x0)
    naccepts = 0
    for i in range(nsteps):
        x1 = np.random.normal(x0, sigma_p)
        logL1 = logfunction(x1)
        if logL1 - logL0 > np.log(np.random.uniform()):

JohannesBuchner

import sys
import numpy
import h5py

filename = sys.argv[1]
outfilename = filename.replace('.npy', '').replace('.gz', '').replace('.csv', '') + '.h5'
if filename.endswith('.npy'):
    print('loading NPY...')
    data = numpy.load(filename)
else:

JohannesBuchner

"""
Download custom spectrum from http://www.swift.ac.uk/, programmatically

How to use:

$ python fetchswiftspectrum.py grbid timeslicespec
$ wget -nc --continue $(cat customspec.url)/a.tar.gz

"""