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main.py
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main.py
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# -*- coding: utf-8 -*-
__author__ = 'janekg89'
import numpy as np # module for scientific computing
import matplotlib.pyplot as plt # module for plotting "a la" matlab
import simulation
import timeit
import analyse_tool
import matplotlib.cm as cm
import test_cython.genereatefracincrements as ginc
import lown_cython.genereatefracincrements as ginc1
import cProfile
import pstats
#print timeit.timeit(" import Spectral_method; Spectral_method.Felix_Method(D=2,particles=1,length=int(1e6),alpha=0.5).compute_trajectory()", number=1)
#Spectral_method.Felix_Method(D=2,particles=1,length=1000,alpha=0.5).compute_trajectory()
#analyse_tool.Analyse(D=2,particles=1000,length=1000,alpha=0.6).plotting()
#a.plotting(error=2)
#for j in range(10):
# a.plotting(msdtype="time",particlemsdtime=j)
#a.plotting(error=2)
#plt.show()
#for j in range(10):
#a,b=analyse_tool.Analyse(D=2,particles=5000,length=200,alpha=0.5).distribution(t=199)
#print analyse_tool.Analyse(D=2,particles=50,length=20,alpha=0).D
def show_gaussian():
length=1000
steps=30
gaussianparamter=[]
for i in range(steps):
print i
c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=0.5,dt=1)
shades=np.linspace(0,1,steps+1)
b=c.nongaussian_parameter()
gaussianparamter.append(b)
gaussianparamter=np.array(gaussianparamter)
plt.errorbar(range(length+1), gaussianparamter.mean(axis=0), yerr=gaussianparamter.std(axis=0),errorevery=10,label="non-Gaussianparamter")
plt.xlabel('t', fontsize=10)
plt.ylabel('$\\alpha_2(t)$ non-Gaussian parameter', fontsize=10)
plt.legend(loc=3,fontsize='small')
plt.savefig('midtermreport/data/nongaussian.png',dpi=300)
plt.show()
def show_rescaled():
colornum=-1
length=1100
steps=5
shades=np.linspace(0,1,steps+1)
colors = iter(cm.rainbow(np.linspace(0, 1, 6)))
plt.figure(figsize=(6,3))
c=analyse_tool.Analyse(D=2,particles=10000,length=length+1,alpha=0.5,dt=1,version="cpp")
analt=c.rescaled_analytical_distribution(1,5)
for j in range(100,1100,1000/steps):
colornum=1+colornum
shade=shades[colornum]
#d=c.rescaled_analytical_distribution(t=j,r_dis=50)
h=c.rescaled_function(t=j,histpoints=35)
plt.loglog(h[0],h[1],color=next(colors), label="rescaled function at $t=%d$" %(j))
#plt.plot(d[0],d[1],color='%f' %(shade))
plt.plot(analt[0],analt[1],"--", label="analytical rescaled function")
plt.xlabel('rescaled distance $ r_{res} $ ', fontsize=10)
plt.ylabel('rescaled distribution', fontsize=10)
plt.legend(loc=3, fontsize='small')
plt.xlim([0.06,5])
plt.ylim([0.0003,1])
plt.savefig('midtermreport/data/rescaled.png',dpi=300)
plt.show()
def show_distrib():
colornum=-1
length=1000
steps=50
shades=np.linspace(0,1,steps+1)
c=analyse_tool.Analyse(D=2,particles=6000,length=length+1,alpha=0.5,dt=1,version="cpp")
for j in range(100,1000,900/steps):
colornum=1+colornum
shade=shades[colornum]
f=c.analytical_distribution_of_particles(t=j,r_dis=50)
g=c.distribution(t=j)
plt.semilogy(g[0],g[1],color='%f' %(shade))
plt.plot(f[0],f[2],color='%f' %(shade))
plt.show()
def plot_msd_invert():
c=analyse_tool.Analyse(D=2,particles=500,length=5000,alpha=0.5,dt=1)
c.invert_time()
plt.show()
#c=analyse_tool.Analyse(D=2,particles=6000,length=1000,alpha=0.5,dt=1)
#msddopp=[]
#for i in range(5999):
# msdd=c.msd_time(i=i)
# msddopp.append(msdd)
#msddopp=np.array(msddopp)
#c.trajectory=msddopp
#c.plotting(showlegend="yes")
#c.plotting(showlegend="yes")
#plt.show()
#plot_msd_invert()
#show_gaussian()
#show_rescaled()
#show_distrib()
#show_rescaled()
#def plot_different_dt():
# gaussianparamter=[]
# for i in range(steps):
# c=analyse_tool.Analyse(D=2,particles=5000,length=length+1,alpha=0.5,dt=j)
# shades=np.linspace(0,1,steps+1)
# b=c.nongaussian_parameter()
# gaussianparamter.append(b)
# gaussianparamter=np.array(gaussianparamter)
# plt.errorbar(range(length+1), gaussianparamter.mean(axis=0), yerr=gaussianparamter.std(axis=0))
#plt.show()
#plot_different_dt()
def plot_ensemble_mean_of_time_msd():
c=analyse_tool.Analyse(D=2,particles=10,length=3000,alpha=0.5,dt=1,version="cpp")
msd_all=[]
for ii in range(c.particles):
print ii
msd,std=c.msd_time(i=ii)
msd_all.append(msd)
colors=['r','b','g','k','c','w','b','r','g','b','k','c','w','b','r','g','b','k','c','w','bo','ro','go','bo','ko','co','wo','bo']
msd_time=np.array(msd_all)
msd_time_mean=msd_time.mean(axis=0)
#_edplt.plot(c.t*c.dt,c.msdanalyt(),":",color=colors[1], label="analytisch D=%f,particles=%d,length=%d,alpha=%f" %(c.D,c.particles,c.n,c.alpha))
plt.errorbar(c.t*c.dt, msd_time_mean, yerr=0,label="time +ensemble average")
#plt.show()
return msd_time_mean
#plot_msd_invert()
#plt.plot(plot_ensemble_mean_of_time_msd())
#plt.show()
#show_gaussian()
#show_rescaled()
#show_distrib()
#print timeit.timeit("import analyse_tool; analyse_tool.Analyse(D=2,particles=1024,length=1024,alpha=0.5,dt=1,version='cpp').compute_trajectory()", number=1)
#print timeit.timeit("import analyse_tool; analyse_tool.Analyse(D=2,particles=1024,length=1024,alpha=0.5,dt=1).compute_trajectory()", number=1)
#e=analyse_tool.Analyse(D=2,particles=1000,length=1024*2,alpha=0.5,dt=1.,version='cpp')
#plt.plot(e.compute_trajectory()[0],"r")
'''
a = ginc.generateIncrements(N=e.n, D=np.array(2.0), tau=np.array(1.0), alpha=np.array(0.5))
v_t=(np.random.normal(0,np.sqrt(1.),size=e.n))
v_t=np.array(v_t)
v_frq=np.fft.fft(v_t)
v_ano_frq= np.sqrt(e.z().real[:2048]*2.)*v_frq
plt.plot(v_ano_frq.real)
plt.plot(a,"r")
print v_ano_frq.std()
print np.array(a).std()
d=analyse_tool.Analyse(D=2.0,particles=2000,length=1000,alpha=0.5,dt=0.5,x=4)
#plt.plot(d.compute_trajectory()[0])
e=analyse_tool.Analyse(D=2,particles=2000,length=1000,alpha=0.5,dt=1.,version='cpp')
print d.compute_trajectory().shape
print e.compute_trajectory().shape
d.plotting(showlegend="Yes")
#e.plotting(msdtype="time",scale="lin",showlegend="Yes")
e.plotting(showlegend="Yes",scale="lin")
#d.plotting(scale="lin",showlegend="Yes")
plt.show()
import revreaddy.build.Debug.revreaddy as rdy
s =rdy.Sim()
s.run(steps=10000, timestep=0.1)
'''
'''
d=analyse_tool.Analyse(D=2.0,particles=2000,length=1000,alpha=0.5,dt=0.5,x=4)
a=d.z()
b=d.z_alternativ()
plt.plot(np.real(a))
plt.plot(np.real(b))
plt.show()
'''
d=analyse_tool.Analyse(D=2.0,particles=20,length=500,alpha=0.7,dt=0.5,x=2,version="lowen")
d.plotting()
plt.show()
'''
inc1 = ginc1.pyIncrements(d.n,d.particles)
inc1.generateIncrements1(d.D, d.dt, d.alpha)
a = inc1.returnIncrements()
b= a[0][0][:]
plt.plot(b)
plt.show()
from scipy.stats import norm
x=np.linspace(-5,5)
a=norm.pdf(x)
plt.plot(x,a)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge are off
plt.ylabel('$\\rho (y)$', fontsize=15)
plt.xlabel('$y$', fontsize=15)
plt.show()
x=np.linspace(0,5)
msd=x**0.5*2
msd1=x*2
plt.plot(x,msd1)
plt.plot(x,msd)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge are off
plt.ylabel('$MSD$', fontsize=15)
plt.xlabel('$t$', fontsize=15)
plt.show()
'''