vidavidorra

Auto-deploying Doxygen documentation to gh-pages with Travis CI

This explains how to setup for GitHub projects which automatically generates Doxygen code documentation and publishes the documentation to the gh-pages branch using Travis CI. This way only the source files need to be pushed to GitHub and the gh-pages branch is automatically updated with the generated Doxygen documentation.

Sign up for Travis CI and add your project

Get an account at Travis CI. Turn on Travis for your repository in question, using the Travis control panel.

Create a clean gh-pages branch

To create a clean gh-pages branch, with no commit history, from the master branch enter the code below in the Git Shell. This will create a gh-pages branch with one file, the README.md in it. It doesn't really matter what file is uploaded in it since it will be overwritten when the automatically generated documentation is published to th

kblomqvist

#!/bin/bash
# Installation:
#   cd my_gitproject
#   wget -O pre-commit.sh http://tinyurl.com/mkovs45
#   ln -s ../../pre-commit.sh .git/hooks/pre-commit
#   chmod +x pre-commit.sh

OPTIONS="-A8 -t8 --lineend=linux"

RETURN=0

evansd

function substitute {
    if [ -z "$1" -o -z "$2" ]; then
        echo "Usage: substitue FROM_STRING TO_STRING [OPTION]..."
        echo
        echo "Replace all occurances of FROM_STRING (a sed-compatible regular"
        echo "expression) with TO_STRING in all files for which ack-grep matches"
        echo "FROM_STRING."
        echo
        echo "Any additional options are passed directly to ack-grep (e.g.,"
        echo " --type=html would only run the substitution on html files)." 

GaelVaroquaux

Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy