| // Simple IPython Notebook test | |
| // Requires PhantomJS and CasperJS. | |
| // To run: | |
| // 1) Start a notebook server in an empty directory. | |
| // 2) casperjs test_basic.js | |
| var casper = require('casper').create({ | |
| // verbose: true, | |
| // logLevel: "debug" | |
| }); |
| # Download Spark 1.4 from http://spark.apache.org/downloads.html | |
| # | |
| # Download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv | |
| # Launch SparkR using | |
| # ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3 | |
| # The SparkSQL context should already be created for you as sqlContext | |
| sqlContext | |
| # Java ref type org.apache.spark.sql.SQLContext id 1 |
| """ | |
| 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 |
| 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 |
Author: Fernando Pérez.
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.
| 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)) |
Estimated time: 10 minutes
| """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: |
