Kevin Mader kmader

Polyglot Data Science with IPython & friends

A demonstration of how to use Python, Julia, Fortran and R cooperatively to analyze data, in the same process.

This is supported by the IPython kernel and a few extensions that take advantage of IPython's magic system to provide low-level integration between Python and other languages.

See the companion notebook for data preparation and setup.

AOE2HD - For Mac OSX

Estimated time: 10 minutes

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Installing PortingKit

Code for Keras plays catch blog post

Train

python qlearn.py

Test

Generate figures

Serving Flask under a subpath

Your Flask app object implements the __call__ method, which means it can be called like a regular function. When your WSGI container receives a HTTP request it calls your app with the environ dict and the start_response callable. WSGI is specified in PEP 0333. The two relevant environ variables are:

SCRIPT_NAME
The initial portion of the request URL's "path" that corresponds to the application object, so that the application knows its virtual "location". This may be an empty string, if the application corresponds to the "root" of the server.

	from functools import wraps
	from itertools import islice, tee, zip_longest, chain, product
	from collections import deque
	from pandas import DataFrame

	nwise = lambda g, , n=2: zip((islice(g, i, None) for i, g in enumerate(tee(g, n))))
	nwise_longest = lambda g, , n=2, fv=object(): zip_longest((islice(g, i, None) for i, g in enumerate(tee(g, n))), fillvalue=fv)
	first = lambda g, *, n=1: zip(chain(repeat(True, n), repeat(False)), g)
	last = lambda g, , m=1, s=object(): ((y[-1] is s, x) for x, y in nwise_longest(g, n=m+1, fv=s))

	"""Performs automatic speed edits to audio books.

	Example usage:

	Assuming you have an audiobook book.aax on your Desktop:

	1. Convert it to wav:
	ffmpeg -i ~/Desktop/book.aax ~/Desktop/book.wav

	2. Adjust the speed:

	from keras.layers.core import Layer
	import keras.backend as K

	if K.backend() == 'tensorflow':
	import tensorflow as tf
	def K_arange(start, stop=None, step=1, dtype='int32'):
	result = tf.range(start, limit=stop, delta=step, name='arange')
	if dtype != 'int32':
	result = K.cast(result, dtype)
	return result

	"""
	AWS Batch wrapper for Luigi

	From the AWS website:

	AWS Batch enables you to run batch computing workloads on the AWS Cloud.

	Batch computing is a common way for developers, scientists, and engineers
	to access large amounts of compute resources, and AWS Batch removes the
	undifferentiated heavy lifting of configuring and managing the required