swayson

Map	Action
<F1>	Causes Netrw to issue help
<cr>	Netrw will enter the directory or read the file
<del>	Netrw will attempt to remove the file/directory
-	Makes Netrw go up one directory
a	Toggles between normal display, hiding (suppress display of files matching g:netrw_list_hide) showing (display only files which match g:netrw_list_hide)
c	Make browsing directory the current directory
C	Setting the editing window
d	Make a directory

SA Online 'Maker' Resources

Johan Beyers has a great list of resources as well. http://johan.beyers.co.za/pages/resources.html

Links to various South African online stores that may be useful to anyone 'making' things.

Name	URL	Notes
Netram	http://www.netram.co.za	Electronics, Quad Copters, Robotics, CNC, 3D Printing
Hobby Tronics	http://www.hobbytronics.co.za	CNC Parts, Aluminium Extrusion, Electronics

Module autoreload

To auto-reload modules in jupyter notebook (so that changes in files *.py doesn't require manual reloading):

# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2

Build docker image

$ cd /path/to/Dockerfile
$ sudo docker build .

View running processes

Map	Action
<F1>	Causes Netrw to issue help
<cr>	Netrw will enter the directory or read the file
<del>	Netrw will attempt to remove the file/directory
-	Makes Netrw go up one directory
a	Toggles between normal display, hiding (suppress display of files matching g:netrw_list_hide) showing (display only files which match g:netrw_list_hide)
c	Make browsing directory the current directory
C	Setting the editing window
d	Make a directory

	# Author: Kyle Kastner
	# License: BSD 3-Clause
	# See core implementations here http://geekyisawesome.blogspot.ca/2016/10/using-beam-search-to-generate-most.html
	# Also includes a reduction of the post by Yoav Goldberg to a script
	# markov_lm.py
	# https://gist.github.com/yoavg/d76121dfde2618422139

	# These datasets can be a lot of fun...
	#
	# https://github.com/frnsys/texts

	def from_sklearn(docs,vect,lda,**kwargs):

	"""Create Prepared Data from sklearn's vectorizer and Latent Dirichlet
	Application

	Parameters
	----------
	docs : Pandas Series.
	Documents to be passed as an input.
	vect : Scikit-Learn Vectorizer (CountVectorizer,TfIdfVectorizer).