matthewd

#!/bin/bash

# This script will make a best-effort attempt at showing modifications
# to package-provided config files on a Debian system.
#
# It's subject to some pretty significant limitations: most notably,
# there's no way to identify all such config files. We approximate the
# answer by looking first at dpkg-managed conffiles, and then hoping
# that most of the time, if maintainer scripts are managing files
# themselves, they're using ucf. So, DO NOT TRUST THIS SCRIPT to find

hrldcpr

One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!

alexnederlof

#!/bin/sh

WEBDIR=yourwebdir
WORKSPACE=your/workspace
TEX_FILE_NAME=your_file

echo
echo "**** Pulling changes into Live [Hub's post-update hook]"
echo

abhinav-upadhyay

#!/usr/bin/env python

# An example of decoding/encoding datetime values in JSON data in Python.
# Code adapted from: http://broadcast.oreilly.com/2009/05/pymotw-json.html

# Copyright (c) 2023, Abhinav Upadhyay
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:

killercup

/*
 * I add this to html files generated with pandoc.
 */

html {
  font-size: 100%;
  overflow-y: scroll;
  -webkit-text-size-adjust: 100%;
  -ms-text-size-adjust: 100%;
}

kalebo

Setting up a FlexLM service for SystemD

Create directory structure

1. Create the directory for the flexlm user to use, e.g., /opt/flexlm
2. Place all the binaries for the licence manager and the licence in a vendor specific subdirectory

At this point you should have something similar to following directory structure:

/opt/flexlm/
└── VENDOR

juanAFernandez

from pelican import signals
from pelican.readers import BaseReader
import json

class JsonReader(BaseReader):

    enabled = True

    def __init__(self, settings):
        super(JsonReader, self).__init__(settings)

f-huang

import math
import pandas as pd

def read_csv_in_chunks(path, n_lines, **read_params):
    if 'chunksize' not in read_params or read_params['chunksize'] < 1:
        read_params['chunksize'] = 80000
    chunks = [0] * math.ceil(n_lines / read_params['chunksize'])
    for i, chunk in enumerate(pd.read_csv(path, **read_params)):
        percent = min(((i + 1) * read_params['chunksize'] / n_lines) * 100, 100.0)
        print("#" * int(percent), f"{percent:.2f}%", end='\r', flush=True)