Tony Fast tonyfast

Proposal

Abstract

The IPython tab completion machinery is messy, complicated, and difficult to extend. The IPython pull request 2701, for instance, envisioned a feature -- tab completion based on function-specific annotations -- which, because of the current sub-optimal completion API, required a quite a bit of hacking into the core completer logic to execute. This proposal envisions a refactoring of the IPython tab completion machinery -- largely IPython/core/completer.py, IPython/core/completerlib.py, and a little bit of IPython/core/interactiveshell.py -- in a way that simplifies and unifies the codebase while providing a powerful and flexible public-facing API, enabling its extensibility in 3rd party projects.

History of the Tab Completion Machinery

Started closely tied to GNU readline, and to the stdlib's rlcompleter.py module
Hacked to be able to work with non-readline frontends, like the notebook and qtconsole.

Indented tree layout demo.

James Sugrue [asked][1], "@GitHubAPI is there a way to find the number of stars for a given repository?"

Example

$ curl -ni "https://api.github.com/search/repositories?q=more+useful+keyboard" -H 'Accept: application/vnd.github.preview'

5 entertaining things you can find with the GitHub Search API

Let's have some command-line fun with curl, [jq][1], and the [new GitHub Search API][2].

Today we're looking for:

The hottest repositories created in the last week.
The oldest user accounts with zero followers. (So sad.)
Issue trends by language.
Clojure projects defying convention.

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

	{
	"metadata": {
	"name": "MLENS_1MILLION"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{

	1. pip install -r reqs.pip
	2. server.py
	3. open client.html in browser
	4. redis-cli publish push '123456'
	5. check browser console

	#!/usr/bin/env python
	"""strip outputs from an IPython Notebook

	Opens a notebook, strips its output, and writes the outputless version to the original file.

	Useful mainly as a git filter or pre-commit hook for users who don't want to track output in VCS.

	This does mostly the same thing as the `Clear All Output` command in the notebook UI.

	LICENSE: Public Domain