leelasd

; RUN CONTROL PARAMETERS = 
integrator               = sd
; start time and timestep in ps = 
tinit                    = 0
dt                       = 0.002

; 6 ns.  This turns out to be long enough for systems without slow intramolecular degrees of freedom
nsteps               = 1000000
; mode for center of mass motion removal = 

leelasd

; RUN CONTROL PARAMETERS = 
integrator               = sd
; start time and timestep in ps = 
tinit                    = 0
dt                       = 0.002

; 6 ns.  This turns out to be long enough for systems without slow intramolecular degrees of freedom
nsteps               =  500000
; mode for center of mass motion removal = 

leelasd

#!/bin/bash
#BSUB -o .                       ## requests outfile be generated
#BSUB -a openmpi
#BSUB -q shared                  ## specify queue to run on, shared = default
#BSUB -J ACH           ## job-name
#BSUB -W 24:00                   ## walltime <HH:MM>= 24 hrs
#BSUB -n 8  
#BSUB -R "rusage[mem=50000]"     ## memory limit= MB count only (1024MB = 1GB)
module load Apps/Gromacs

leelasd

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
dat=pd.read_csv("Gban_data.csv")
def randrange(n, vmin, vmax):
    return (vmax-vmin)*np.random.rand(n) + vmin

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

leelasd

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
###############
def get_RDF(filename='1'):
    rdf=pd.read_csv(filename+'/rdf.xvg', header=None, delim_whitespace=True,skiprows=14)
    cn=pd.read_csv(filename+'/rdf_cn.xvg', header=None, delim_whitespace=True,skiprows=14)
    rdf.columns=['R','RDF']
    cn.columns=['R','CN']
    fig, ax1 = plt.subplots()

leelasd

import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
%matplotlib inline
cm = plt.cm.get_cmap('rainbow')
df1=pd.read_csv('Dat.csv')
df2=pd.read_csv('Dat2.csv')
df=df1.append(df2)
df=df.sort(['RDF','SIG'])

leelasd

import string
import pandas as pd
df=pd.read_csv('Best_LJS_Zn.csv')
df.SIG=df.SIG*0.1
df.EPS=df.EPS*4.184
with open("top.top", "r") as f:
    lines = f.readlines()
for i in range(len(df.SIG)):
	SIGMA='%.5e'%df.SIG[i]
	EPSILON='%.5e'%df.EPS[i]

leelasd

4
 0.241   0.917073  -0.051843   0.000000
 0.241  -0.280127   0.874757   0.000000
-0.241  -0.287527  -0.371443   0.571500
-0.241  -0.287527  -0.371443  -0.571500

leelasd

#!/usr/bin/env ruby
# 
# This script is a wrapper around pandoc that uses pandoc's
# builtin citeproc support to generate a markdown bibliography
# from bibtex.
#
# Inspired by Jacob Barney's [bib2mkd][] script.
#
# [bib2mkd]: http://jmbarney.dyndns.org/?/linux/bib2mkd/
#

leelasd

def Sim_Anneal(ps,const):
        T=8.00
        delta = 0.05
        nmoves=10
        while (T > 5e-2):
                accept=0
                reject=0
                Beta=(1.0/T)
                for i in range(0,nmoves):
                        old_ene = Goodness_of_Fit(ps)