msultan

2016 PyData SF

Mental Models to Use and Avoid as a Data Scientist

Jonathan Whitmore, PhD
Data Scientist, SVDS
@jbwhitmore

Watch the talk on YouTube here:

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

Topology and Data by Gunnar Carlsson
Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

Created by Christopher Manning

Gallery

[](http://bl.ocks.org/d/1703449/#/[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, 24157817, 39088169]43/0/1) [![Star](https://raw.github.co

Created by Christopher Manning

Gallery

[](http://bl.ocks.org/d/1703449/#/[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, 24157817, 39088169]43/0/1) [![Star](https://raw.github.co

	from __future__ import print_function

	"""
	simulatedtempering.py: Implements simulated tempering

	This is part of the OpenMM molecular simulation toolkit originating from
	Simbios, the NIH National Center for Physics-Based Simulation of
	Biological Structures at Stanford, funded under the NIH Roadmap for
	Medical Research, grant U54 GM072970. See https://simtk.org.

	ffmpeg -i molecule.%5d.ppm -c:v libx264 -preset slow -crf 18 molecule.mkv

	# -i input filenames
	# -c:v codec (libx264 --> H.264)
	# -preset duh
	# -crf quality factor, lower = better, 18 = practically lossless
	# output filename

	# For colors, make sure you're using yuv444 which should be the default in newer ffmpeg

	HEADER HEADER
	ATOM 1 N LYS 1 38.807 20.601 18.408 1.00 .00
	ATOM 2 H1 LYS 1 39.388 20.210 17.695 1.00 .00
	ATOM 3 H2 LYS 1 37.905 20.169 18.376 1.00 .00
	ATOM 4 H3 LYS 1 39.223 20.439 19.303 1.00 .00
	ATOM 5 CA LYS 1 38.666 22.049 18.184 1.00 .00
	ATOM 6 HA LYS 1 39.651 22.511 18.241 .00 .00
	ATOM 7 CB LYS 1 38.086 22.288 16.784 1.00 .00
	ATOM 8 HB1 LYS X 1 37.069 21.897 16.759 1.00 .00
	ATOM 9 HB2 LYS 1 38.692 21.737 16.068 1.00 .00

	import mdtraj as md
	import numpy as np

	n_frames = 100
	coords = np.random.multivariate_normal(mean=[0, 0, 0], cov=[[2, 1, 0], [0, 1, 0], [0 ,0, 1]], size=n_frames)
	coords = np.reshape(coords, (n_frames, 1, 3))

	with md.XTCTrajectoryFile('output.xtc', 'w') as f:
	f.write(coords)

	import time
	import random

	from IPython import parallel

	# create client & view
	rc = parallel.Client()
	dv = rc[:]

	# scatter 'id', so id=0,1,2 on engines 0,1,2