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Erik Henning Thiede
committed
First commit, adding the alpha code for the EMUS
package.
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‎LICENSE.txt‎

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This code is released under the MIT License
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Copyright (c) 2016 Erik Henning Thiede
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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‎README.txt‎

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-----------
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Description
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-----------
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This is an implementation of the EMUS algorithm for recombining data
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from umbrella sampling calculations and other data sources coming from
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biased probability densities.
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-------------------
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Usage and Arguments
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-------------------
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To be continued....
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‎argparsetest.py‎

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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("x", type=float, help="the base")
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parser.add_argument("y", type=float, help="the exponent")
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parser.add_argument("-v", "--verbosity", action="count", default=0)
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args = parser.parse_args()
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answer = args.x**args.y
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if args.verbosity >= 2:
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print "Running '{}'".format(__file__)
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if args.verbosity >= 1:
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print "{}^{} ==".format(args.x, args.y),
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print answer

‎asciiemus.txt‎

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#########################################################################
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# #
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# /-/-=/-= #
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# // ZX " #
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# / /XZX (O) ---, #
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# // ZXZX _____`\ #
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# / ZXZXZX #
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# / ;,ZXZ #
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# / :ZX%\ #
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# /% : X\ #
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# :/: %:X: #
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# /%:% %\\ #
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# /:%: % \ #
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# / :% : \ #
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# / :% :%\ #
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# /% % \ #
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# /%: %: %\ #
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# //% : %\ #
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# %%%%%%%%%% %% %% / / : : #
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# %%%%%%%% %% % %%%% \: #
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# %%%%%% : \ #
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# %%%% :%:\ #
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# % % % \ #
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# % %% \ #
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# % %%% %%%%%% \ #
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# % %%%%%%%% %%%\ #
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# % %% %%%%%% %\ #
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# % %%%%%%%%%%%%% %%%%\\ #
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# %%%%/%%%%%%%%%%%%%%%%%%%%%%% %%%%%%% %%% %%\ #
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# / %%%%%%%%% % %% % %%% % %%%%% %%%\ #
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# (_____)%%(_____)%%%%%%\ #
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# ((_____) ((____) #
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# (___) (___) #
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# (__) (__) #
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# (__) (__) #
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# (__) (__) #
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# (__) (__) #
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# ( ) ( ) V@V #
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# ( ) ( ) | #
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# ( ) ( ) \ | / / #
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# (__ | (___ | \ | / #
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# ___( / \ \____ \ |--- \\|// #
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# <(____/ \_____(>\_____(> -===-= #
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# #
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###################### EMUS FINISHED SUCCESSFULLY #######################

‎autocorrelation.py‎

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# -*- coding: utf-8 -*-
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"""
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Tools for analyzing the autocorrelation of a time series
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"""
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import numpy as np
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def autocorrfxn(timeseries,lagmax):
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ts = np.asarray(timeseries)
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ts -= np.average(ts) # Set to mean 0
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N = len(timeseries)
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corrfxn = np.zeros(lagmax)
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for dt in xrange(lagmax):
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corrfxn[dt] = (np.dot(timeseries[0:N-dt],timeseries[dt:N])) # sum of ts[t+dt]*ts[t]
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corrfxn /= corrfxn[0] # Normalize
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return corrfxn
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def ipce(timeseries,lagmax=None):
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"""
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Initial positive correlation time estimator
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"""
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if lagmax == None:
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lagmax = len(timeseries)/2
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corrfxn = autocorrfxn(timeseries,lagmax)
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i = 0
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t = 0
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while i < 0.5*lagmax:
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gamma = corrfxn[2*i] + corrfxn[2*i+1]
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if gamma < 0.0:
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print 'stop at ',2*i
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break
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else:
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t += gamma
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i += 1
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tau = 2*t - 1
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std = np.std(timeseries)
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mean = np.average(timeseries)
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sigma = std * tau / len(timeseries)
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return tau, mean, sigma
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def icce(timeseries,lagmax=None):
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"""
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Initial convex correlation time estimator
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REWRITE TO BE MORE EFFICIENT!
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"""
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if lagmax == None:
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lagmax = len(timeseries)/2
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corrfxn = autocorrfxn(timeseries,lagmax)
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t = corrfxn[0] + corrfxn[1]
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i = 1
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gammapast = t
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gamma = corrfxn[2*i] = corrfxn[2*i+1]
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while i < 0.5*lagmax:
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gammafuture = corrfxn[2*i+2] + corrfxn[2*i+3]
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if gamma > 0.5*(gammapast+gammafuture) :
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print 'stop at ',2*i
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break
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else:
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t += gamma
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gammapast = gamma
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gamma = gammafuture
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i += 1
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tau = 2*t - 1
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std = np.std(timeseries)
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mean = np.average(timeseries)
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sigma = std * tau / len(timeseries)
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return tau, mean, sigma
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