def __init__(self, DMC_rcfile, NUC_csvfile):
# read arguments from a file, see more in 'loadfile.py', default DMC_rcfile='DMC.rc'
# NUC_csvfile is the file of nuclei coordinares ( recommend *.csv format)
if( os.path.isfile(DMC_rcfile) ):
self.myload = lf.Load(DMC_rcfile)
# mass should move in array[0], if for different particles
self.mass = self.myload.args['mass']
self.dt = self.myload.args['dt']
self.calctimes = int( self.myload.args['calctimes'] )
self.alpha = self.myload.args['alpha']
self.nelectrons = int( self.myload.args['nelectrons'] )
self.nnuclei = int( self.myload.args['nnuclei'] )
self.nwalkers_max = int( self.myload.args['nwalkers_max'] )
self.nwalkers_initial = int( self.myload.args['nwalkers_initial'] )
self.sigma = np.sqrt( self.dt/self.mass )
self.nwalkers_current = self.nwalkers_initial
else:
print("error, %s doesn't exist"%DMC_rcfile)
exit()
# read the configuration of nuclei
# 4-d array note as [mass, x, y, z]
self.nuclei = np.zeros((self.nnuclei, 4))
# simply initialze as:
# > self.nuclei[0,:] = [1836, -0.699, 0.000, 0.000]
# > self.nuclei[1,:] = [1836, 0.699, 0.000, 0.000]
# or may read nuclei coordinates from (csv-like) data-file (for example, named as 'NUC.csv'):
if( os.path.isfile(NUC_csvfile) ):
self.nuclei = (pd.read_csv(NUC_csvfile,header=None)).values
else:
print("error, %s doesn't exist"%NUC_csvfile)
exit()
# creat walkers array
# with array_like_structure: [ number_of_walker, number_of_e, 4-d coordinates ]
self.walkers = np.zeros(( self.nwalkers_max, self.nelectrons, 4))
# avoid electron too closed for each walker, so we give different electron different positioin
# > alternative to read from an ELE_csvfile
for i in range(self.nelectrons):
self.walkers[:,i,0] = 1.000 # electron mass, you can revise for the miu meson-eletron mixture
self.walkers[:,i,1] = 1.000 * np.cos( np.pi*i/(self.nelectrons) )
self.walkers[:,i,2] = 1.000 * np.sin( np.pi*i/(self.nelectrons) )
self.walkers[:,i,3] = 0.000
# creat matrix recording potential
self.potential = np.zeros((self.nwalkers_max))
# creat time-potential series
self.ERseries = np.zeros((self.calctimes))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import os
import loadfile as lf
print('''
### < by Shin He > this procedure is mainly do with IR calculation of water
### each line should give a/or some statistic quantity at each step
### only one argument need
### make sure _put.in _plot.in here\n
''')
fflg = str(sys.argv[1]) #the filename to plot
myinfo = lf.Load('info.now')
myinfo.add('put.rc')
os.system('\nhead -n 1 %s' % fflg)
a = pd.read_csv(fflg, sep='\s+', header=None, low_memory=False)
if not setinfo.is_number(a.values[0, 0]):
a = pd.read_csv(fflg, sep='\s+', low_memory=False)
fflg = (fflg.spilt('.', 1))[0]
mtx = a.values.T
x = mtx[0, ]
y = mtx[1:, ]
rangcct = 500000 # the max length for stat
u0ui = np.zeros((rangcct, len(y)))
#!/usr/bin/env python
# coding=utf-8
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import loadfile as lf
fflg = str(sys.argv[1]) # the filename to plot
clmn = int(sys.argv[2]) # the column to plot
myinfo = lf.Load('map.rc')
a = pd.read_csv(fflg, sep='\s+', header=None)
statname = 'null'
if not lf.is_number(a.values[0, 0]):
a = pd.read_csv(fflg, sep='\s+')
statname = str(a.columns.values[clmn])
xs = a.values[:, 0] * myinfo.args['mapdtime']
if (clmn == 0):
ys = a.values[:, 1:]
else:
ys = a.values[:, clmn]
if (clmn == 0):
nclmn = len(ys[0, :])
for i in range(nclmn):
plt.plot(xs,
ys[:, i],
#!/usr/bin/python3
# Filename: fft.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import loadfile as lf
myinfo = lf.Load('_plot.in')
myinfo.add('_put.in')
val=pd.read_csv('.irdat.tmp')
t=val.values.T[0]
cct=val.values.T[1]
t=np.arange(0,len(cct))
print(t,cct)
N=len(cct)
T=len(t)
cw = np.fft.fft(cct)
freq = np.fft.fftfreq(N,d=myinfo.args['dtime'])
print(len(freq),len(cw))
plt.plot(t,cct,'b--',label='cct plot')
#plt.plot(freq,np.abs(cw),'r-',label='fft plot')
plt.legend()
plt.show()
plt.savefig('IR.png')
#f=np.arange(N)/T
#tmp=(1-np.exp(-8*f[0:cut]))/(8*f[0:cut])*cw[0:cut]
#tmp[0]=cw[0]
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import loadfile as lf
# load argument file
myload = lf.Load('map.rc')
# optimized arguments
timemax = int(1600 / myload.args['mapdtime'])
Ndivide = 1000
sigma = 600
a = pd.read_csv('pj2.dat', sep='\s+', header=None)
a = a.values.T
if (timemax > len(a[0])):
timemax = len(a[0])
time = a[0, :timemax] * myload.args['mapdtime']
# and add Gauss smooth
real = a[1, :timemax] * np.exp(-time * time /
(2 * sigma**2)) # real part of <phi|U|Psi>
imag = a[2, :timemax] * np.exp(-time * time /
(2 * sigma**2)) # imaginary part of <phi|U|Psi>
E = np.linspace(-0.03, -0.01, Ndivide + 1)
Et = np.outer(E, time)
expiEt_r = np.cos(Et)
expiEt_i = np.sin(Et)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import loadfile as lf
import os
# get arguments from terminal
narg = len(sys.argv) - 1
fflg = str(sys.argv[1]) # the filename to plot
clmn = int(sys.argv[2]) # the column to plot
# load the configuration file
if os.path.isfile('./put.rc'):
myinfo = lf.Load('put.rc')
else:
print('Warning: put.rc is need in current dir!')
exit()
if os.path.isfile('./plot.rc'):
myinfo.Add('plot.rc')
else:
print('Warning: plot.rc is need in current dir!')
exit()
# read the data file
a = pd.read_csv(fflg, sep='\s+', header=None)
name = 'Nan'
if not lf.is_number(a.values[0, 0]):
a = pd.read_csv(fflg, sep='\s+')
name = str(a.columns.values[clmn])