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Copy pathinteraktivabsturz.py
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interaktivabsturz.py
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#!/usr/bin/python
import glob
import re
import numpy as np
import scipy as sp
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, CheckButtons
#text.usetex: True
###
### variablen zuweisen ordner durchfilzen
###
br=[]
rf=[]
ra1=[]
ch=[]
beta=0.9
omega=np.logspace(4.0,7.301,20,10)
tau_c=1.0e-6
K_DD=1.0e9
delta_sigma_CSA=0.226
sef=glob.glob('*K.sef')
sef.sort()
sdf=glob.glob('*K.sdf')
sdf.sort()
verschiebefaktoren=[]
verschiebetemperaturen=[]
for i in range(0,sef.__len__()):
verschiebetemperaturen.append(0)
verschiebefaktoren.append(0)
wurzelomega=[]
for om in omega: wurzelomega.append(om**0.5)
###
### funktionen definieren
###
##die spektraldichte J und die Suszibilitaet Chi
def J(omega,tau_c,beta):
return tau_c/(1. + omega **2 * tau_c **2)**beta
def Chi(omega,tau_c,beta,K_DD):
return K_DD*omega*J(omega,tau_c,beta)
##die auswertefunktion fuer die Diffusion und die rate. mal sehen...
def R_1(omega,R1_0,D):
mu_0 =1.2566e-6
h_quer = 6.626e-34/(2.*np.pi)
gamma_H=2.675e8*2*np.pi
N_a=6.022e23
n_H=21.0
rho=rho_mTCP=1.15*1e6
M=M_mTCP=368.4
N=n_H*N_a*rho/M
B=np.pi/30.*(1.+4.*(2.**0.5))*(mu_0/4./np.pi * h_quer * gamma_H **2)**2 * N
print omega,R1_0,D,B,R1_0-B/(D**1.5) *omega**0.5
return R1_0-B/(D**1.5) *omega**0.5
##die verschiebefunktion fuer die suszibilitaet
def update(val):
fin=open(sef[int(picker.val)],'r')
sefdata=fin.readlines()
for i in range(0,4):sefdata.pop(0)
ch=[]
br=[]
ra1=[]
zone=[]
rf=[]
for data in sefdata:
liste=data.split()
# liste = re.findall(r"[\w.][\f]+",data)
br.append(liste[0])
br=map(float,br)
ra1.append(liste[2])
ra1=map(float,ra1)
zone.append(liste[5])
zone=map(int,zone)
rf.append(liste[6])
slide=10.0**val
for i,b in enumerate(br):
br[i]=br[i]*1e6
ch.append(ra1[i]*br[i])
br[i]=br[i]*slide
#for line in ax.lines: print line
ax.lines[int(picker.val)*2+1].set_xdata(br)
plt.draw()
verschiebefaktoren[int(picker.val)]=slide
verschiebetemperaturen[int(picker.val)]=temps[int(picker.val)]
plt.figure(4)
plt.cla()
plt.plot(verschiebetemperaturen,verschiebefaktoren)
plt.draw()
return slide
# fin=open(sef[int(picker.val)],'r')
# sefdata=fin.readlines()
# for i in range(0,4):sefdata.pop(0)
# ch=[]
# br=[]
# ra1=[]
# rf=[]
# for data in sefdata:
# liste=data.split()
# # liste = re.findall(r"[\w.][\f]+",data)
# br.append(liste[0])
# br=map(float,br)
# ra1.append(liste[2])
# ra1=map(float,ra1)
# rf.append(liste[6])
# slide=10.0**val
# for i,b in enumerate(br):
# br[i]=br[i]*10e6
# ch.append(ra1[i]*br[i])
# br[i]=br[i]*slide
# ax.lines[int(picker.val)].set_xdata(br)
# plt.draw()
## den pick gibts nur anstandshalber
def pick(val):
return val
def r_ref(r):
br=[]
ra1=[]
rf=[]
d=10**float(sd0.val)
for i,om in enumerate(omega):
br.append(omega[i]**0.5)
ra1.append(R_1(omega[i],r,d))
wurzelax.lines[wurzelax.lines.__len__()-1].set_ydata(ra1)
plt.draw()
def d0(d):
br=[]
ra1=[]
rf=[]
r=float(sr0.val)
d=10**d
for i,om in enumerate(omega):
br.append(omega[i]**0.5)
ra1.append(R_1(omega[i],r,d))
wurzelax.lines[wurzelax.lines.__len__()-1].set_ydata(ra1)
plt.draw()
return d
def reset(event):
stau_c.reset()
plt.figure(1)
ax = plt.axes([0.1,0.2,0.55,0.7])
plt.xscale('log')
plt.yscale('log')
plt.xlabel('omega')
plt.ylabel('Chi')
axcolor = 'lightgoldenrodyellow'
axtau_c=plt.axes([0.6,0.1,0.3,0.02],axisbg=axcolor)
stau_c=Slider(axtau_c,'log (tau_c)',-7,2,valinit=np.log10(tau_c))
axpicker=plt.axes([0.1,0.1,0.25,0.02],axisbg=axcolor)
picker=Slider(axpicker,'pick set',0,sef.__len__()-0.01,valinit=0)
resetax =plt.axes([0.8,0.025,0.1,0.04])
button = Button(resetax,'reset',color=axcolor,hovercolor='0.975')
plt.figure(2)
wurzelax=plt.axes([0.1,0.1,0.8,0.8])
axr0=plt.axes([0.05,0.02,0.6,0.02],axisbg=axcolor)
sr0=Slider(axr0,'r0',500,2000,valinit=1.0)
axD=plt.axes([0.7,0.02,0.2,0.02],axisbg=axcolor)
sd0=Slider(axD,'D',-15,-7,valinit=-11)
plt.figure(3)
##wird spaeter bemalt
plt.figure(4)
fakchiax=plt.axes([0.15,0.15,0.8,0.8])
plt.title('verschiebefaktoren in der suszeptiblitaet')
plt.xlabel('T')
plt.ylabel('schiebefaktoren a.u.')
##interaktion mit der gui
picker.on_changed(pick)
button.on_clicked(reset)
stau_c.on_changed(update)
sr0.on_changed(r_ref)
sd0.on_changed(d0)
sefdata=[]
temps=[]
for filename in sef:
fin=open(filename,'r')
sefdata=fin.readlines()
for i in range(0,4): sefdata.pop(0)
chi=[]
brlx=[]
t1=[]
r1=[]
percerr=[]
abserr=[]
zone=[]
relativefile=[]
for data in sefdata:
liste=data.split()
# liste = re.findall(r"[\w.][\f]+",data)
brlx.append(liste[0])
brlx=map(float,brlx)
t1.append(liste[1])
t1=map(float,t1)
r1.append(liste[2])
r1=map(float,r1)
percerr.append(liste[3])
percerr=map(float,percerr)
abserr.append(liste[4])
abserr=map(float,abserr)
zone.append(liste[5])
zone=map(int,zone)
relativefile.append(liste[6])
fin2=open(relativefile[0],'r')
sdfdata=fin2.readlines()
temp=sdfdata[sdfdata.index('ZONE=\t'+str(zone[zone.__len__()-2])+'\r\n')+7]
temp=temp[6:]
temp=temp.rstrip()
temps.append(float(temp))
#print repr(temp)
for i,b in enumerate(brlx):
brlx[i]=brlx[i]*1e6
chi.append(r1[i]*brlx[i])
plt.figure(1)
plt.title(relativefile[0][0:-9])
plt.axes([0.1,0.2,0.55,0.7])
#plt.plot(brlx,map(lambda x:Chi(x,1e-6,0.7,1e8),brlx))
brlx=np.array(brlx)
chi=np.array(chi)
fitpars, covmat = curve_fit(
Chi,
brlx,
chi,
p0=[1e-6,0.7,1e10],
maxfev=10000)
print 'fitparamer temperatur (tau,beta,kopplungskonstante)'+temp+str(fitpars)
plt.plot(brlx,
map(lambda x:Chi(x,fitpars[0],fitpars[1],fitpars[2]),brlx),
#label='Chi '+temp+' K'
)
plt.plot(brlx,chi,
label=temp+' K',
marker='o',linestyle='None')
plt.figure(2)
plt.title('Wurzel')
wurzelax=plt.axes([0.1,0.1,0.8,0.8])
for i, b in enumerate(brlx):
brlx[i]=brlx[i]**0.5
plt.plot(brlx,r1,label=relativefile[0])
plt.figure(3)
plt.title('Rate')
plt.xscale('log')
plt.yscale('log')
for i,b in enumerate(brlx):
brlx[i]=brlx[i]**2 #wir hatten die wurzel gezogen
plt.plot(brlx,r1,label=relativefile[0])
plt.figure(4)
print (map(lambda x: x**0.5,omega),map(lambda y:R_1(y,2,1e-10),wurzelomega))
plt.figure(2)
plt.plot(wurzelomega,map(lambda x: R_1(x,20,10e-9),omega))
plt.figure(1)
plt.plot(omega, Chi(omega,1e-6,0.7,1e8),label='chi mit tau_c =1e-6')
plt.plot(omega, Chi(omega,1e-8,0.7,1e8),label='chi mit tau_c =1e-8')
plt.plot(omega, Chi(omega,1e-4,0.7,1e8),label='chi mit tau_c =1e-4')
plt.legend(loc='center left',bbox_to_anchor=(1,0.5))
plt.savefig('suscibility',dpi=300,orientation='landscape')
plt.figure(2)
plt.savefig('wurzel',dpi=300,orientation='landscape')
plt.figure(3)
plt.savefig('rate',dpi=300,orientation='landscape')
plt.figure(4)
plt.savefig('verschiebeparameter',dpi=300,orientation='landscape')
plt.show()
#set1ax=plt.axes([0.7,0.025,0.1,0.04])
#set1 = Button(set1ax,'set1 relativefile?? somehow',color=axcolor)
#for i in range(0,ax.lines.__len__()-1): print ax.lines[i]
#vor dem plt.show kann man noch diese 5 zeilen pasten
#rax = plt.axes([0.025,0.5,0.15,0.15],axisbg=axcolor)
#def colorfunc(label):
# l.set_color(label)
# plt.draw()
#radio.on_clicked(colorfunc)
#def J(x,tau_c,beta):
# return tau_c /((1.0 + ( x * tau_c )**2 )** beta)
#plt.figure(4)
#plt.plot(omega,J(omega,1e-8,0.5))
#def Chi(x,tau_c,K_DD,beta):
# return 1.08 * 1.08 * J(x,tau_c,beta)