def kspec(rc):
import AnalysisFunctions as af
import numpy as np
from subs import ask_for_steps
rc.vars2load(['bx', 'by', 'bz'])
bs, fs, step = ask_for_steps(rc.numslices)
ebx = np.zeros(rc.nx / 2 + 1)
eby = np.zeros(rc.nx / 2 + 1)
ebz = np.zeros(rc.nx / 2 + 1)
for it in range(bs, fs, step):
print('time slice', it)
rc.loadslice(it)
kk, ekx, eky, ekz, ekb = af.fperpspecvec(rc.bx, rc.by, rc.bz)
ebx = ebx + ekx
eby = eby + eky
ebz = ebz + ekz
kk = kk * 2 * np.pi / rc.lx
dk = kk[2] - kk[1]
ebz = ebz * float(step) / (float(fs - bs) * dk)
eby = eby * float(step) / (float(fs - bs) * dk)
ebx = ebx * float(step) / (float(fs - bs) * dk)
outf = open('kspec.' + rc.dirname + '.dat', 'w')
for i in range(rc.nx / 2):
print(kk[i], ebx[i], eby[i], ebz[i], file=outf)
outf.close()
#!/usr/bin/env python
import numpy as np
from subs import create_object,ask_for_steps
from AnalysisFunctions import fcorr
rc=create_object()
rc.vars2load(['bx','by'])
ofile=open('intscale.'+rc.dirname+'.dat','w')
ie=1/np.e
bs,fs,step=ask_for_steps(rc.numslices)
for i in range(bs,fs,step):
print i
rc.loadslice(i)
rx,bxcor=fcorr(rc.bx,rc.bx,ax=0,dx=rc.dx)
# lxc=rx[abs(bxcor-ie).argmin()]
lxint=np.sum(bxcor)*rc.dx
ry,bycor=fcorr(rc.by,rc.by,ax=1,dx=rc.dy)
# lyc=ry[abs(bycor-ie).argmin()]
lyint=np.sum(bycor)*rc.dy
print >> ofile,rc.time,lxint,lyint,0.5*(lxint+lyint)
ofile.close()
def zpzmlclm(rc):
import AnalysisFunctions as af
import numpy as np
from subs import ask_for_steps
rc.vars2load(['bx','by','bz','jix','jiy','j*z','ni','jex','jey','jez','ne'])
bs,fs,step=ask_for_steps(rc.numslices)
#bs=0; fs=rc.numslices; step=1
nt=(fs-bs)/step+1
ie=1/np.e
ezp=np.zeros(nt); ezm=np.zeros(nt)
tt=np.zeros(nt); tnldi=np.zeros(nt)
lcp=np.zeros(nt); lcm=np.zeros(nt)
#time_series_output=raw_input("Output zp, zm etc as a time series? ")
time_series_output = ''
if time_series_output == 'y':
zpkxt = np.zeros((rc.nx/2,nt))
zpkyt = np.zeros((rc.nx/2,nt))
zpkzt = np.zeros((rc.nx/2,nt))
zmkxt = np.zeros((rc.nx/2,nt))
zmkyt = np.zeros((rc.nx/2,nt))
zmkzt = np.zeros((rc.nx/2,nt))
for it in range(bs,fs,step):
print('time slice',it)
rc.loadslice(it)
idx=(it-bs)/step
rho=rc.ni+rc.ne*rc.m_e
rc.jix=rc.jix/rc.ni; rc.jiy=rc.jiy/rc.ni;rc.j*z=rc.j*z/rc.ni
rc.jex=rc.jex/rc.ne; rc.jey=rc.jey/rc.ne;rc.jez=rc.jez/rc.ne
#
vcx = (rc.jix+rc.jex*rc.m_e)/(1+rc.m_e)
vcy = (rc.jiy+rc.jey*rc.m_e)/(1+rc.m_e)
vcz = (rc.j*z+rc.jez*rc.m_e)/(1+rc.m_e)
rc.bx=(rc.bx-np.mean(rc.bx))/np.sqrt(rho)
rc.by=(rc.by-np.mean(rc.by))/np.sqrt(rho)
rc.bz=(rc.bz-np.mean(rc.bz))/np.sqrt(rho)
#
vcx=vcx-np.mean(vcx);vcy=vcy-np.mean(vcy);vcz=vcz-np.mean(vcz)
#
zpx=vcx+rc.bx; zpy=vcy+rc.by; zpz=vcz+rc.bz
#
ezp[idx]=0.5*np.mean(rho*(np.abs(zpx)**2+np.abs(zpy)**2+np.abs(zpz)**2))
zmx=vcx-rc.bx; zmy=vcy-rc.by; zmz=vcz-rc.bz
#
ezm[idx]=0.5*np.mean(rho*(np.abs(zmx)**2+np.abs(zmy)**2+np.abs(zmz)**2))
## #Correlation for z+
#kwave,zkx,zky,zkz,zk = af.PerpSpecVec(zpx,zpy,zpz)
kwave,zkx,zky,zkz,zk = af.fperpspecvec(zpx,zpy,zpz)
if time_series_output == 'y':
zpkt[:,idx]=zk
lcp[idx] = (2*np.pi/sum(zk[1:]))*sum(zk[1:]/kwave[1:])
## #Correlation for z-
#kwave,zkx,zky,zkz,zk = af.PerpSpecVec(zmx,zmy,zmz)
kwave,zkx,zky,zkz,zk = af.fperpspecvec(zmx,zmy,zmz)
if time_series_output == 'y':
zmkt[:,idx]=zk
lcm[idx] = (2*np.pi/sum(zk[1:]))*sum(zk[1:]/kwave[1:])
#
tt[idx]=round(it*rc.dtmovie,4)
# wci*tnl(di) = Va/Z (L/di)^(1/3)
# Matthaeus EA, ApJ 2014
tnldi[idx]=(rc.B0*((lcp[idx]+lcm[idx])/2.)**(1./3.))/(2*np.sqrt(ezp[idx]+ezm[idx]))
outf=open('zpzmlc.'+rc.dirname+'.dat','w')
print('#','t','tt','ezp','ezm','lcp','lcm','tnldi', file=outf)
for i in range(nt):
print(tt[i],tt[i]*2*np.pi/rc.lx,ezp[i],ezm[i],lcp[i],lcm[i],tnldi[i], file=outf)
outf.close()
rc.fin()
if time_series_output == 'y':
zpkxt.tofile('zpkxt.'+rc.dirname+'.dat')
zpkyt.tofile('zpkyt.'+rc.dirname+'.dat')
zpkzt.tofile('zpkzt.'+rc.dirname+'.dat')
zmkxt.tofile('zmkxt.'+rc.dirname+'.dat')
zmkyt.tofile('zmkyt.'+rc.dirname+'.dat')
zmkzt.tofile('zmkzt.'+rc.dirname+'.dat')
