msultan

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

rmcgibbo

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)

calstad

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.

msultan

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