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ps aux | grep name | grep -v grep

ps aux | grep name | grep -v grep | awk '{print $2}'

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from scipy import sparse, io

m = sparse.csr_matrix([[0,0,0],[1,0,0],[0,1,0]])
m              # <3x3 sparse matrix of type '<type 'numpy.int64'>' with 2 stored elements in Compressed Sparse Row format>

io.mmwrite("test.mtx", m)
del m

newm = io.mmread("test.mtx")
newm           # <3x3 sparse matrix of type '<type 'numpy.int32'>' with 2 stored elements in COOrdinate format>

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# Since there is no real autocorrelation compute function in python and numpy
# I write my own one

import numpy as np

# Suppose data array represent a time series data
# each element represent data at given time
# Assume time are equally spaced
def acf(data):
	mean = np.mean(data)

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Notes about LAMMPS source codes, compute_gyration.h and compute_gyration.cpp.

compute_gyration.h

#ifdef COMPUTE_CLASS

ComputeStyle(gyration,ComputeGyration)

#else

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console