| #!/usr/bin/env python | |
| from __future__ import print_function | |
| import sys | |
| import numpy as np | |
| from scipy.ndimage.measurements import center_of_mass | |
| dxy = 0.1 |
| #include<iostream> | |
| int main() { | |
| double total = 0; | |
| double tmp; | |
| while (std::cin >> tmp) { | |
| total += tmp; | |
| } | |
| std::cout << total; | |
| return 0; |
| #include<iostream> | |
| #include<stdexcept> | |
| #include<string> | |
| void print_usage() { | |
| std::cout << "usage: test [args...]\n" | |
| << '\n' | |
| << " help display this message and exit\n" | |
| << " x <int> set x\n"; | |
| } |
| #!/usr/bin/env python3 | |
| """ | |
| Reduce event-by-event observables (multiplicity, Qn vectors) to batch | |
| quantitities (<mult>, flow cumulants) for each design point and centrality. | |
| Creates files <IC name>_<observable>.dat. | |
| """ | |
| import glob |
| #!/usr/bin/bash | |
| # Provides a command-line interface to generate a bibtex file using Inpire's web tool | |
| # https://inspirehep.net/submit?doctype=bibtex&act=SBI | |
| # Usage: ./generate-bibtex.sh file.tex | |
| curl --silent \ | |
| --form "step=1" \ | |
| --form "access=`date +%s_%N`" \ | |
| --form "act=SBI" \ |
| #!/usr/bin/env python3 | |
| """ Read an array from stdin and write to a numpy file. """ | |
| import sys | |
| import numpy as np | |
| def main(): |
| import subprocess | |
| # Read a gzipped file from within Python. | |
| # Faster than the standard library gzip module. | |
| # Require Python 3.2+ for context manager support. | |
| with subprocess.Popen(('zcat', filename), stdout=subprocess.PIPE) as proc: | |
| for l in proc.stdout: | |
| pass |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| from __future__ import division, print_function | |
| import sys | |
| import numpy as np | |
| import h5py |
This document specifies new formats for
These formats aim to be as simple and flexible as possible while still containing all necessary information. Ideally, existing files should be convertible by simply prepending several comment lines.
| #!/usr/bin/env python3 | |
| """ | |
| Determine how finite nucleon size affects the Woods-Saxon distribution. | |
| Specifically, finite nucleons increase the effective diffusivity $a$ (surface | |
| thickness). This script computes the change by convolving the radial | |
| Woods-Saxon function with a Gaussian of width $w$, then fitting the resulting | |
| convolution to a Woods-Saxon with corrected diffusivity $a'$. |