cmpute

# requirements: pandas, lxml

import pandas as pd
import sqlite3, time
from datetime import datetime

def fetch():
    df = pd.read_html('https://www.mersenne.org/report_recent_results/')[1] # first table is the login form
    print(datetime.now().strftime('%c'), ">> Table fetched, data summary:")
    print(df)

cmpute

import enum
from io import RawIOBase
import os.path as osp
import xml.etree.ElementTree as xml
from collections import namedtuple
from pathlib import Path
from matplotlib import pyplot as plt
from matplotlib import cm, colors as mcolors
from itertools import islice, filterfalse

cmpute

// This code snippet test rtree with spherical coordinate system.
// It will generate grid points of a cubic grid and store them in an rtree.
// And then query is done by find points within some (approximate) distance
// from query point. The query point can be specified by command line args.
//
// Commandline options:
//     no args: query point (0, 0, 0) with radius 2
//     3 args: query point (x, y, z) with radius 2
//     4 args: query point (x, y, z) with radius r

cmpute

# cython: embedsignature=True, profile=True

from libc.math cimport log, sqrt
from libc.stdlib cimport rand, srand
from libc.time cimport time, time_t
import numpy as np
cimport numpy as np
cimport cython
from cython.view cimport array

cmpute

# See https://github.com/tcardonne/docker-github-runner for instructions
FROM tcardonne/github-runner:ubuntu-20.04

RUN curl -L -o miniconda.sh https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-x86_64.sh
RUN bash miniconda.sh -b -p /miniconda
ENV CONDA="/miniconda"

ENTRYPOINT ["/entrypoint.sh"]
CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"]

cmpute

"""
This moduled provide patched version of builtin Zipfile class as in https://github.com/ThomasPinna/python_zipfile_improvement
You can have better speed when read several files from a zip file containing a large number of files
Modifications are marked with '===== PATCH ====='
"""
import io
import struct
from zipfile import *
from zipfile import (_CD_COMMENT_LENGTH, _CD_EXTRA_FIELD_LENGTH,
                     _CD_FILENAME_LENGTH, _CD_LOCAL_HEADER_OFFSET,

cmpute

# git: remove all remote branches except for master/main
git branch -r | grep -Eo 'cmpute/.*' | cut -c 8- - | xargs -n 10 git push cmpute --delete

# tar: unzip multiple tarballs in a row
find <INPUT_DIR> -name "*.tgz" | xargs tar -xz -C <OUTPUT_DIR> --checkpoint=10000 -f

# wavpack: compress DSD files
wavpack <INPUT_FILES> <OUTPUT_FILES> -h -m -v –-import-id3

# slurm: Run a slurm interactive session with gpu on Greatlakes

cmpute

import numpy as np
import msgpack
import msgpack_numpy
msgpack_numpy.patch()

import socket, time, os
from socket_helpers import recv_msg, send_msg

sout = socket.socket(family=socket.AF_UNIX, type=socket.SOCK_DGRAM)
sout.connect("/tmp/ros-ipc-data.sock")

cmpute

# This scripts calculates the Chebyshev approximation of Bessel functions
# 
# Chebyshev approximation intro:
# - https://en.wikipedia.org/wiki/Approximation_theory#Chebyshev_approximation
# - https://mathworld.wolfram.com/ChebyshevApproximationFormula.html
# - http://www.chebfun.org/examples/cheb/ExactChebCoeffs.html
# - https://www.eeeguide.com/chebyshev-approximation/
#
# Online modified Bessel function calculator
# - https://www.wolframalpha.com/input/?i=BesselI%5B0%2Cx%5D

cmpute

from time import sleep
from tqdm import trange, tqdm
from random import random
from multiprocessing import Pool, Manager
from itertools import count

def _wrap_func(func, args, pool, nlock):
    n = -1
    with nlock:
        n = next(i for i,v in enumerate(pool) if v == 0)