Rosetta
Functions | Variables
autocorrelation Namespace Reference

Functions

def find_nonmodulo_autocorrelation (x)
 
def find_autocorrelation (x)
 

Variables

 parser = helpers.define_args('torsion', 'plot')
 
 type
 
 float
 
 default
 
 arguments = parser.parse_args()
 
 iterations = helpers.load_array(arguments.job, 'iterations')
 
 torsions = helpers.load_arrays(arguments.job, 'torsions')
 
def autocorrelation = find_autocorrelation(torsions[key])
 
 color = pylab.cm.Dark2(numpy.median(autocorrelation))
 
 label
 
 key
 

Function Documentation

◆ find_autocorrelation()

def autocorrelation.find_autocorrelation (   x)
 This homebrew autocorrelation function is specifically written to 
handle modulo variable like dihedral angles.  Think of the cosine function 
as an alternative, and more appropriate, metric for overlap. 

References helpers.connect_to_database(), ObjexxFCL.len(), numeric::conversions.radians(), range, and sum().

◆ find_nonmodulo_autocorrelation()

def autocorrelation.find_nonmodulo_autocorrelation (   x)
 The standard correlation function isn't meant for modulo variables, 
like dihedral angles.  I'm sure this version is much faster, but it will 
choke on angles constantly jumping between 180 and -180. 

References ObjexxFCL.len().

Variable Documentation

◆ arguments

autocorrelation.arguments = parser.parse_args()

◆ autocorrelation

autocorrelation.autocorrelation = find_autocorrelation(torsions[key])

◆ color

autocorrelation.color = pylab.cm.Dark2(numpy.median(autocorrelation))

◆ default

autocorrelation.default

◆ float

autocorrelation.float

◆ iterations

autocorrelation.iterations = helpers.load_array(arguments.job, 'iterations')

◆ key

autocorrelation.key

◆ label

autocorrelation.label

◆ parser

autocorrelation.parser = helpers.define_args('torsion', 'plot')

◆ torsions

autocorrelation.torsions = helpers.load_arrays(arguments.job, 'torsions')

◆ type

autocorrelation.type