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LN_tools.py
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LN_tools.py
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import os
import sys
import numpy as np
import optparse as op
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
import imageio #for making animation
import csv
from tqdm import tqdm
from nrgWrapper import read_from_nrg_files
from parIOWrapper import read_species_gradients
from parIOWrapper import read_species_tempdens
from ParIO import Parameters
from fieldlib import fieldfile
from geomWrapper import ky
from geomWrapper import init_read_geometry_file
from read_write_geometry import read_geometry_local
from read_iterdb_file import read_iterdb_file
from FFT_general import FFT_function_time
from FFT_general import spectral_density
from FFT_general import sort_x_f
from momentsWrapper_max import LILO_moments_from_mom_file
from momlib import momfile
#input the suffix , plot nrg_es, em, return time_start,time_end
#from LN_tools import start_end_time
#time_start,time_end=start_end_time(suffix,pars)
def get_suffix():
parser=op.OptionParser(description='Some infrastructure for reading in, manipulating, and plotting nonlinear field data.')
#parser.add_option('--plot_theta','-g',action='store_const',const=False,help = 'Plot global mode structures decomposed in poloidal m number.',default=True)
options,args=parser.parse_args()
print("options",options)
print("args",args)
if len(args)!=1:
exit("""
Please include run number as argument (e.g., 0001)."
\n""")
suffix = args[0]
if suffix in ['dat','.dat']:
suffix = '.dat'
else:
suffix = '_'+suffix
return suffix
def start_end_time(suffix,pars): #suffix in the format of "_1" or ".dat"
if pars['n_spec'] == 1:
time, nrge = read_from_nrg_files(pars,suffix,False)
elif pars['n_spec'] == 2:
time, nrgi, nrge = read_from_nrg_files(pars,suffix,False)
elif pars['n_spec'] == 3:
time, nrgi, nrge, nrgz = read_from_nrg_files(pars,suffix,False)
plt.clf()
plt.plot(time,nrge[:,6],label=r"$Q_{es}$ of electron")
plt.plot(time,nrge[:,7],label=r"$Q_{em}$ of electron")
plt.title('nrg of electron')
plt.xlabel('time')
plt.legend()
plt.show()
scan_all = str(input("Scan all(Y/N):\n"))
if scan_all=='n' or scan_all=='N':
time_start = float(input("Start time:\n"))
time_end = float(input("End time:\n"))
time_start_index=np.argmin(abs(time-float(time_start)))
time_end_index=np.argmin(abs(time-float(time_end)))
time_start = time[time_start_index]
time_end = time[time_end_index]
elif scan_all=='y' or scan_all=='Y':
time_start = time[0]
time_end = time[-1]
time_start_index=0
time_end_index=len(time)-1
else:
print("Please respond with y, Y , n, N")
sys.exit()
plt.clf()
plt.plot(time,nrge[:,6],label=r"$Q_{es}$ of electron")
plt.plot(time,nrge[:,7],label=r"$Q_{em}$ of electron")
plt.title('nrg of electron')
plt.axvline(time_start,color='red',label="time start",alpha=1)
plt.axvline(time_end,color='blue',label="time end",alpha=1)
plt.xlabel('time')
plt.legend()
plt.show()
return time_start,time_end
# n_list, ky_list=ky_list_calc(suffix)
def ky_list_calc(suffix):
#Import the parameters from parameter file using ParIO
par = Parameters()
par.Read_Pars('parameters'+suffix)
pars = par.pardict
B_gauss=10.**4 #1T=10^4Gauss
qref = 1.6E-19 #in C
c = 1. #in 3*10^8m/s
m_kg = 1.673E-27 #in kg
Bref = pars['Bref'] #in Tesla
Tref = pars['Tref'] #in keV
nref = pars['nref'] #in 10^(19) /m^3
Lref = pars['Lref'] #in m
mref = pars['mref'] #in proton mass(kg)
q0 = pars['q0'] #unitless, safety factor/q95
x0 = pars['x0'] #x/a, location
kymin = pars['kymin'] #in rhoi
nky0 = pars['nky0'] #in total number of ky
n_step = pars['n0_global'] #in rhoi
nref = nref * 1.E19 #in the unit of /m^3
Tref = Tref * qref * 1.E03 #in the unit of J
mref = mref * m_kg #in the unit of kg
pref = nref * Tref #in Pa*k_{B}
cref = np.sqrt(Tref / mref) #in the unit of m/s
Omegaref = qref * Bref / mref / c #in rad/s
rhoref = cref / Omegaref #in m rho_i(ion gyroradii)
rhorefStar = rhoref / Lref #Unitless
gyroFreq= cref/Lref #the facor convert frequency from cs/a to rad/s
#Determine the n for kymin
ky_n1=1.*q0*rhoref/(Lref*x0) #ky for n=1
n_min=round(kymin/ky_n1) #n for ky_min
if n_min==0:
n_min=1
n_min=0
ky_list = np.linspace(0,(pars['nky0']-1)*pars['kymin'],num=pars['nky0'])
n_list= []
for i in range(len(ky_list)):
if i==0:
n_list.append(0)
elif i!=0:
n_list.append(round(ky_list[i]/ky_n1))
return n_list, ky_list
#************Sample function line
#omegaDoppler=Doppler_calc(suffix,iky,iterdb_file_name)
def Doppler_calc(suffix,iky,iterdb_file_name):
#Import the parameters from parameter file using ParIO
par = Parameters()
par.Read_Pars('parameters'+suffix)
pars = par.pardict
x0 = pars['x0'] #x/a, location
#Import the parameters from parameter file using ParIO
n_list, ky_list = ky_list_calc(suffix)
#**********************Doppler shift**********************************************
rhot0, te0, ti0, ne0, ni0, nz0, vrot0 = read_iterdb_file(iterdb_file_name)
uni_rhot = np.linspace(min(rhot0),max(rhot0),int(len(rhot0)*10.))
x0_index=np.argmin(abs(uni_rhot-x0))
vrot_u = np.interp(uni_rhot,rhot0,vrot0)
omegaDoppler_kHZ = vrot_u[x0_index]*n_list[iky]/(2.*np.pi*1000.)
#**********************Doppler shift**********************************************
return omegaDoppler_kHZ
#from LN_tools import frequency_Doppler
#new_frequency_kHZ, new_amplitude_frequency, new_amplitude_growth=frequency_Doppler(frequency_kHZ,amplitude_frequency,amplitude_growth,omegaDoppler_kHz)
def frequency_Doppler(frequency_kHZ,amplitude_frequency,amplitude_growth,omegaDoppler_kHZ):
new_amplitude_frequency=amplitude_frequency
new_amplitude_growth=amplitude_growth
new_frequency_kHZ=frequency_kHZ+omegaDoppler_kHZ
return new_frequency_kHZ, new_amplitude_frequency, new_amplitude_growth
def k_f_plot(f_ky_f,amplitude_ky_f,ky_list,n_list,pic_path,csv_path,name='0'):
f_ky_f=np.array(f_ky_f)
amplitude_ky_f=np.array(amplitude_ky_f)
(nky0,len_f)=np.shape(f_ky_f)
with open(csv_path+'/0ky_f_'+name+'.csv', 'w') as csvfile: #clear all and then write a row
csv_data = csv.writer(csvfile, delimiter=',')
csv_data.writerow(['ky','f',name])
csvfile.close()
for i in range(nky0):
for j in range(len_f):
with open(csv_path+'/0ky_f_'+name+'.csv', 'a+') as csvfile: #clear all and then write a row
csv_data = csv.writer(csvfile, delimiter=',')
csv_data.writerow([ky_list[i],f_ky_f[i,j],amplitude_ky_f[i,j]])
csvfile.close()
uni_freq = np.linspace(np.min(f_ky_f), np.max(f_ky_f),num=len(f_ky_f[0,:])*10)
#print('len of uni_freq='+str(int(len(f_ky_f[0,:])*10)))
len_uni_freq=len(uni_freq)
frequency_kHZ_uni=np.zeros((nky0,len_uni_freq))
amplitude_frequency_uni=np.zeros((nky0,len_uni_freq))
ky_plot=np.zeros((nky0,len_uni_freq))
for i_ky in range(nky0):
frequency_kHZ_uni[i_ky,:]=uni_freq
amplitude_frequency_uni[i_ky,:]=0.1*np.interp(uni_freq,f_ky_f[i_ky,:],amplitude_ky_f[i_ky,:])
plt.clf()
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.ylabel(str(name),fontsize=10)
plt.plot(frequency_kHZ_uni[i_ky,:],amplitude_frequency_uni[i_ky,:],label='intper')
plt.plot(f_ky_f[i_ky,:],amplitude_ky_f[i_ky,:],label='original')
plt.legend()
plt.savefig(pic_path+'/0n='+str(n_list[i_ky])+'.png')
ky_plot2=np.zeros((nky0,len_f))
for i_ky in range(nky0):
ky_plot[i_ky,:]=[ky_list[i_ky]]*len_uni_freq
ky_plot2[i_ky,:]=[ky_list[i_ky]]*len_f
plt.clf()
plt.ylabel(r'$k_y \rho_s$',fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.contourf(f_ky_f,ky_plot2,np.log(amplitude_ky_f))#,level=[50,50,50])#,cmap='RdGy')
for ky in ky_list:
plt.axhline(ky,color='red',alpha=0.5)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.axhline(ky_list[0],color='red',alpha=0.5,label='n starts from'+str(n_list[0]) )#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.legend()
plt.colorbar()
plt.title('log('+str(name)+') contour plot',fontsize=10)
plt.savefig(pic_path+'/'+str(name)+'_log_contour_plot.png')
plt.clf()
plt.ylabel(r'$k_y \rho_s$',fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.contourf(f_ky_f,ky_plot2,amplitude_ky_f)#,level=[50,50,50])#,cmap='RdGy')
for ky in ky_list:
plt.axhline(ky,color='red',alpha=0.5)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.axhline(ky_list[0],color='red',alpha=0.5,label='n starts from'+str(n_list[0]) )#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.legend()
plt.colorbar()
plt.title(str(name)+' contour plot',fontsize=10)
plt.savefig(pic_path+'/'+str(name)+'_contour_plot.png')
#plot the y(freq)
f=frequency_kHZ_uni[0,:]
amplitude_f=np.sum(amplitude_frequency_uni,axis=0)
d = {'f(kHz)':f}
df=pd.DataFrame(d, columns=['f(kHz)'])
df.to_csv(csv_path+'/0f_list.csv',index=False)
d = {'ky':ky_list}
df=pd.DataFrame(d, columns=['ky'])
df.to_csv(csv_path+'/0ky_list.csv',index=False)
with open(csv_path+'/0'+name+'_matrix_f_ky.csv',"w+") as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
csvWriter.writerows(amplitude_frequency_uni)
plt.clf()
plt.plot(f,amplitude_f)
plt.ylabel(str(name),fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.savefig(pic_path+'/0'+str(name)+'_spectrum.png')
d = {'f(kHz)':f,'B_R(Gauss)':amplitude_f}
df=pd.DataFrame(d, columns=['f(kHz)','B_R(Gauss)'])
df.to_csv(csv_path+'/0'+name+'_spectrum_freq.csv',index=False)
return f,amplitude_f
def k_f_density_plot(f_ky_f,amplitude_ky_f,ky_list,n_list,pic_path,csv_path,name='0'):
f_ky_f=np.array(f_ky_f)
amplitude_ky_f=np.array(amplitude_ky_f)
(nky0,len_f)=np.shape(f_ky_f)
with open(csv_path+'/0ky_f_'+name+'.csv', 'w') as csvfile: #clear all and then write a row
csv_data = csv.writer(csvfile, delimiter=',')
csv_data.writerow(['ky','f',name])
csvfile.close()
for i in range(nky0):
for j in range(len_f):
with open(csv_path+'/0ky_f_'+name+'.csv', 'a+') as csvfile: #clear all and then write a row
csv_data = csv.writer(csvfile, delimiter=',')
csv_data.writerow([ky_list[i],f_ky_f[i,j],amplitude_ky_f[i,j]])
csvfile.close()
uni_freq = np.linspace(np.min(f_ky_f), np.max(f_ky_f),num=len(f_ky_f[0,:])*3)
#print('len of uni_freq='+str(int(len(f_ky_f[0,:])*3)))
len_uni_freq=len(uni_freq)
frequency_kHZ_uni=np.zeros((nky0,len_uni_freq))
amplitude_frequency_uni=np.zeros((nky0,len_uni_freq))
#for testing, no interprolation
#len_uni_freq=len(f_ky_f[0,:])
#frequency_kHZ_uni=np.zeros((nky0,len_uni_freq))
#amplitude_frequency_uni=np.zeros((nky0,len_uni_freq))
ky_plot=np.zeros((nky0,len_uni_freq))
print('Interpolate the with all ky')
for i_ky in tqdm(range(nky0)):
frequency_kHZ_uni[i_ky,:]=uni_freq
amplitude_frequency_uni[i_ky,:]=np.interp(uni_freq,f_ky_f[i_ky,:],amplitude_ky_f[i_ky,:])
#for testing, not interprolation
#frequency_kHZ_uni[i_ky,:]=f_ky_f[i_ky,:]
#amplitude_frequency_uni[i_ky,:]=amplitude_ky_f[i_ky,:]
plt.clf()
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.ylabel(str(name),fontsize=10)
plt.plot(frequency_kHZ_uni[i_ky,:],amplitude_frequency_uni[i_ky,:],label='intper')
plt.plot(f_ky_f[i_ky,:],amplitude_ky_f[i_ky,:],label='original')
plt.legend()
plt.savefig(pic_path+'/0n='+str(n_list[i_ky])+'.png')
ky_plot2=np.zeros((nky0,len_f))
for i_ky in range(nky0):
ky_plot[i_ky,:]=[ky_list[i_ky]]*len_uni_freq
ky_plot2[i_ky,:]=[ky_list[i_ky]]*len_f
plt.clf()
plt.ylabel(r'$k_y \rho_s$',fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.contourf(f_ky_f,ky_plot2,np.log(amplitude_ky_f))#,level=[50,50,50])#,cmap='RdGy')
for ky in ky_list:
plt.axhline(ky,color='red',alpha=0.5)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.axhline(ky_list[0],color='red',alpha=0.5,label='n starts from'+str(n_list[0]) )#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.legend()
plt.colorbar()
plt.title('log('+str(name)+') contour plot',fontsize=10)
plt.savefig(pic_path+'/'+str(name)+'_log_contour_plot.png')
plt.clf()
plt.ylabel(r'$k_y \rho_s$',fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.contourf(f_ky_f,ky_plot2,amplitude_ky_f)#,level=[50,50,50])#,cmap='RdGy')
for ky in ky_list:
plt.axhline(ky,color='red',alpha=0.5)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.axhline(ky_list[0],color='red',alpha=0.5,label='n starts from'+str(n_list[0]) )#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.legend()
plt.colorbar()
plt.title(str(name)+' contour plot',fontsize=10)
plt.savefig(pic_path+'/'+str(name)+'_contour_plot.png')
#plot the y(freq)
f=frequency_kHZ_uni[0,:]
amplitude_f=np.sum(amplitude_frequency_uni,axis=0)
d = {'f(kHz)':f}
df=pd.DataFrame(d, columns=['f(kHz)'])
df.to_csv(csv_path+'/0f_list.csv',index=False)
d = {'ky':ky_list}
df=pd.DataFrame(d, columns=['ky'])
df.to_csv(csv_path+'/0ky_list.csv',index=False)
with open(csv_path+'/0'+name+'_matrix_f_ky.csv',"w+") as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
csvWriter.writerows(amplitude_frequency_uni)
plt.clf()
plt.plot(f,amplitude_f)
plt.ylabel(str(name),fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
plt.savefig(pic_path+'/0'+str(name)+'_spectrum.png')
d = {'f(kHz)':f,'B_R(Gauss)':amplitude_f}
df=pd.DataFrame(d, columns=['f(kHz)','B_R(Gauss)'])
df.to_csv(csv_path+'/0'+name+'_spectrum_freq.csv',index=False)
return f,amplitude_f
def LILO_moments_from_mom_file(pars,suffix,plot,setTime=-1):
momen = momfile('mom_e'+suffix,pars)
if (setTime == -1):
momen.set_time(momen.tmom[setTime])
print('Reading momentss are at t = ', momen.tmom[setTime])
else:
isetTime = np.argmin(abs(np.array(momen.tmom)-setTime))
momen.set_time(momen.tmom[isetTime])
print('Reading momentss are at t = ', momen.tmom[isetTime])
deln_global = momen.dens()[:,:,:]
return deln_global
def BES_f_spectrum_FFT(suffix,iterdb_file_name,manual_Doppler,min_Z0,max_Z0,\
Outboard_mid_plane,time_step,time_start,time_end,\
plot,show,csv_output,pic_path,csv_path):
#Where inz is the number of the element in nz
#Import the parameters from parameter file using ParIO
par = Parameters()
par.Read_Pars('parameters'+suffix)
pars = par.pardict
#getting B field using read_write_geometry.py
gpars,geometry = read_geometry_local(pars['magn_geometry'][1:-1]+suffix)
#Get geom_coeff from ParIO Wrapper
geom_type, geom_pars, geom_coeff = init_read_geometry_file(suffix, pars)
real_Z=geometry['gl_z']
real_R=geometry['gl_R']
J=geometry['gjacobian']
#Import the field file using fieldlib
momen = momfile('mom_e'+suffix,pars)
time = np.array(momen.tmom) #time stampes
B_gauss=10.**4. #1T=10^4Gauss
qref = 1.6E-19 #in C
c = 1. #in 3*10^8m/s
m_kg = 1.673E-27 #in kg
Bref = pars['Bref'] #in Tesla
Tref = pars['Tref'] #in keV
nref = pars['nref'] #in 10^(19) /m^3
Lref = pars['Lref'] #in m
mref = pars['mref'] #in proton mass(kg)
q0 = pars['q0'] #unitless, safety factor/q95
x0 = pars['x0'] #x/a, location
kymin = pars['kymin'] #in rhoi
nky0 = pars['nky0'] #in total number of ky
nkx0 = pars['nx0'] #in total number of ky
n_step = pars['n0_global'] #in rhoi
nref = nref * 1.E19 #in the unit of /m^3
Tref = Tref * qref * 1.E03 #in the unit of J
mref = mref * m_kg #in the unit of kg
pref = nref * Tref #in Pa*k_{B}
cref = np.sqrt(Tref / mref) #in the unit of m/s
Omegaref = qref * Bref / mref / c #in rad/s
rhoref = cref / Omegaref #in m rho_i(ion gyroradii)
print('rhoref='+str(rhoref))
rhorefStar = rhoref / Lref #Unitless
gyroFreq= cref/Lref #the facor convert frequency from cs/a to rad/s
#ky comes from geomWrapper.py
ky_GENE_temp=ky(pars, geom_coeff,plot=False) #ky for ky min for differen z
#print('ky shape: '+str(np.shape(ky_GENE_temp)))
#Determine the n for kymin
ky_n1=1.*q0*rhoref/(Lref*x0) #ky for n=1
n_list,ky_list=ky_list_calc(suffix)
#print('n0 list length: '+str(len(n_list)))
#print('n0 list: '+str(n_list))
n_min=np.min(n_list)
#ky_GENE_n1=ky_GENE_temp/float(n_min)
nz=len(real_Z)
ky_GENE_grid=np.zeros((nz,nky0,nkx0))#outer product of the two vectors
for i in range(nz):
for j in range(nky0):
for k in range(nkx0):
ky_GENE_grid[i,j,k]=ky_list[j]
print("kygrid"+str(np.shape(ky_GENE_grid)))
print('n0 list length: '+str(len(n_list)))
print('n0 list: '+str(n_list))
time_start_index=np.argmin(abs(time - time_start))
time_end_index=np.argmin(abs(time - time_end))
time_list_temp = time[time_start_index:time_end_index+1]
time_list=[]
for i in range(0,len(time_list_temp),time_step):
time_list.append(time_list_temp[i])
time_list=np.array(time_list)
nky0=len(n_list)
ntime=len(time_list)
n1_ky_kx_t=np.zeros((nky0,nkx0,ntime),dtype=complex)
if os.path.isdir(csv_path): #if path does not exist, then create 'csv'
pass
else:
os.mkdir(csv_path)
if os.path.isdir(pic_path): #if path does not exist, then create 'pic'
pass
else:
os.mkdir(pic_path)
print("**********Scan starts, output in csv and pic***************")
for i in range(len(time_list)):
time0=time_list[i]
itime = np.argmin(abs(time - time0))
print("Looking at the spectra at time:"+str(time[itime]))
#This sets the time step you want to read in
#field.set_time(time[itime])
if 'x_local' in pars:
if pars['x_local']:
x_local = True
else:
x_local = False
else:
x_local = True
if x_local:
kxmin = 2.0*np.pi/pars['lx']
kxgrid = np.linspace(-(pars['nx0']/2-1)*kxmin,pars['nx0']/2*kxmin,num=pars['nx0'])
kxgrid = np.roll(kxgrid,int(pars['nx0']/2+1))
#print("kxgrid"+str(kxgrid))
#print("kygrid"+str(kygrid))
zgrid = np.linspace(-np.pi,np.pi,pars['nz0'],endpoint=False)
deln_global= LILO_moments_from_mom_file(pars,suffix,False,setTime=time[itime])
n1=deln_global[:,:,:]+0.j
n1_z_ky_kx=n1
n1_GENE_ky0=n1_z_ky_kx*rhorefStar*nref
n1_GENE_ky= np.zeros(np.shape(n1_GENE_ky0[0,:,:]),dtype=complex)
#*****Sum over Z************
sum_length_TEMP=0
if Outboard_mid_plane==True:
#nZ_list=[0,int(len(real_Z)/2)]
nZ_list=[int(len(real_Z)/2)]
for nZ in nZ_list:
length=J[nZ]
sum_length_TEMP=sum_length_TEMP+length
n1_GENE_ky=n1_GENE_ky+n1_GENE_ky0[nZ,:,:]*length
else:
for nZ in range(len(real_Z)):
if min_Z0<=real_Z[nZ] and real_Z[nZ]<=max_Z0:
length=J[nZ]
sum_length_TEMP=sum_length_TEMP+length
n1_GENE_ky=n1_GENE_ky+n1_GENE_ky0[nZ,:,:]*length
n1_GENE_ky=n1_GENE_ky/sum_length_TEMP
#*****Sum over Z************
n1_ky_kx=n1_GENE_ky
print('*****************')
print('*****n1_ky*******')
print('*****************')
print(n1_ky_kx)
else: #x_local = False
print("Sorry, cannot handle Global Nonlinear yet...")
pass
#**Finished reading the Br
#Recall B1_ky_t_inz=np.zeros((nky0,ntime))
n1_ky_kx_t[:,:,i]=n1_ky_kx
ky_GENE_inz = ky_GENE_grid[int(len(real_Z)/2),:,:]
amplitude_frequency_sum=0
amplitude_growth_sum=0
#print(str(B1_ky_t_inz))
if plot==True:
ims_n1=[]
n1_ky_f=[]
growth_ky_f=[]
f_ky_f=[]
time_list=time_list/gyroFreq*(1000.)
for iky in range(nky0):
count_TEMP=0
for ikx in range(nkx0):
n1_inz_t=(n1_ky_kx_t[iky,ikx,:])
#frequency,amplitude_frequency,amplitude_growth=window_FFT_function_time(B1_inz_t,time_list,plot=False)
frequency,amplitude_frequency,amplitude_growth=FFT_function_time(n1_inz_t,time_list,plot=False)
if manual_Doppler==999:
omegaDoppler_kHZ=Doppler_calc(suffix,iky,iterdb_file_name)
else:
omegaDoppler_kHZ=manual_Doppler*n_list[iky]
count_TEMP=count_TEMP+1
if count_TEMP ==1:
new_frequency_kHZ_ky=frequency
new_amplitude_frequency=amplitude_frequency**2.
else:
new_amplitude_frequency=new_amplitude_frequency+amplitude_frequency**2.
f_ky_f.append(new_frequency_kHZ_ky)
n1_ky_f.append(new_amplitude_frequency**0.5)
f_ky_f=np.array(f_ky_f)
n1_ky_f=abs(np.array(n1_ky_f))
f,amplitude_f=k_f_plot(f_ky_f,n1_ky_f,ky_list,n_list,pic_path,csv_path,name='n1')
return f,amplitude_f
def BES_f_spectrum_density(suffix,iterdb_file_name,manual_Doppler,min_Z0,max_Z0,\
Outboard_mid_plane,time_step,time_start,time_end,percent_window,window_for_FFT,\
plot,show,csv_output,pic_path,csv_path):
#Where inz is the number of the element in nz
#Import the parameters from parameter file using ParIO
par = Parameters()
par.Read_Pars('parameters'+suffix)
pars = par.pardict
#getting B field using read_write_geometry.py
gpars,geometry = read_geometry_local(pars['magn_geometry'][1:-1]+suffix)
#Get geom_coeff from ParIO Wrapper
geom_type, geom_pars, geom_coeff = init_read_geometry_file(suffix, pars)
real_Z=geometry['gl_z']
real_R=geometry['gl_R']
J=geometry['gjacobian']
#Import the field file using fieldlib
momen = momfile('mom_e'+suffix,pars)
time = np.array(momen.tmom) #time stampes
B_gauss=10.**4. #1T=10^4Gauss
qref = 1.6E-19 #in C
c = 1. #in 3*10^8m/s
m_kg = 1.673E-27 #in kg
Bref = pars['Bref'] #in Tesla
Tref = pars['Tref'] #in keV
nref = pars['nref'] #in 10^(19) /m^3
Lref = pars['Lref'] #in m
mref = pars['mref'] #in proton mass(kg)
q0 = pars['q0'] #unitless, safety factor/q95
x0 = pars['x0'] #x/a, location
kymin = pars['kymin'] #in rhoi
nky0 = pars['nky0'] #in total number of ky
nkx0 = pars['nx0'] #in total number of ky
n_step = pars['n0_global'] #in rhoi
nref = nref * 1.E19 #in the unit of /m^3
Tref = Tref * qref * 1.E03 #in the unit of J
mref = mref * m_kg #in the unit of kg
pref = nref * Tref #in Pa*k_{B}
cref = np.sqrt(Tref / mref) #in the unit of m/s
Omegaref = qref * Bref / mref / c #in rad/s
rhoref = cref / Omegaref #in m rho_i(ion gyroradii)
print('rhoref='+str(rhoref))
rhorefStar = rhoref / Lref #Unitless
gyroFreq= cref/Lref #the facor convert frequency from cs/a to rad/s
#ky comes from geomWrapper.py
ky_GENE_temp=ky(pars, geom_coeff,plot=False) #ky for ky min for differen z
#print('ky shape: '+str(np.shape(ky_GENE_temp)))
#Determine the n for kymin
ky_n1=1.*q0*rhoref/(Lref*x0) #ky for n=1
n_list,ky_list=ky_list_calc(suffix)
print('n0 list length: '+str(len(n_list)))
print('n0 list: '+str(n_list))
n_min=np.min(n_list)
#ky_GENE_n1=ky_GENE_temp/float(n_min)
nz=len(real_Z)
ky_GENE_grid=np.zeros((nz,nky0,nkx0))#outer product of the two vectors
for i in range(nz):
for j in range(nky0):
for k in range(nkx0):
ky_GENE_grid[i,j,k]=ky_list[j]
print("kygrid"+str(np.shape(ky_GENE_grid)))
print('n0 list length: '+str(len(n_list)))
print('n0 list: '+str(n_list))
time_start_index=np.argmin(abs(time - time_start))
time_end_index=np.argmin(abs(time - time_end))
time_list_temp = time[time_start_index:time_end_index+1]
time_list=[]
for i in range(0,len(time_list_temp),time_step):
time_list.append(time_list_temp[i])
time_list=np.array(time_list)
nky0=len(n_list)
ntime=len(time_list)
n1_ky_kx_t=np.zeros((nky0,nkx0,ntime),dtype=complex)
if os.path.isdir(csv_path): #if path does not exist, then create 'csv'
pass
else:
os.mkdir(csv_path)
if os.path.isdir(pic_path): #if path does not exist, then create 'pic'
pass
else:
os.mkdir(pic_path)
print("**********Scan starts, output in csv and pic***************")
for i in range(len(time_list)):
time0=time_list[i]
itime = np.argmin(abs(time - time0))
print("Looking at the spectra at time:"+str(time[itime]))
#This sets the time step you want to read in
#field.set_time(time[itime])
if 'x_local' in pars:
if pars['x_local']:
x_local = True
else:
x_local = False
else:
x_local = True
if x_local:
kxmin = 2.0*np.pi/pars['lx']
kxgrid = np.linspace(-(pars['nx0']/2-1)*kxmin,pars['nx0']/2*kxmin,num=pars['nx0'])
kxgrid = np.roll(kxgrid,int(pars['nx0']/2+1))
#print("kxgrid"+str(kxgrid))
#print("kygrid"+str(kygrid))
zgrid = np.linspace(-np.pi,np.pi,pars['nz0'],endpoint=False)
deln_global= LILO_moments_from_mom_file(pars,suffix,False,setTime=time[itime])
n1=deln_global[:,:,:]+0.j
n1_z_ky_kx=n1
n1_GENE_ky0=n1_z_ky_kx*rhorefStar*nref
n1_GENE_ky= np.zeros(np.shape(n1_GENE_ky0[0,:,:]),dtype=complex)
#*****Sum over Z************
sum_length_TEMP=0
if Outboard_mid_plane==True:
#nZ_list=[0,int(len(real_Z)/2)]
nZ_list=[int(len(real_Z)/2)]
for nZ in nZ_list:
length=J[nZ]
sum_length_TEMP=sum_length_TEMP+length
n1_GENE_ky=n1_GENE_ky+n1_GENE_ky0[nZ,:,:]*length
else:
for nZ in range(len(real_Z)):
if min_Z0<=real_Z[nZ] and real_Z[nZ]<=max_Z0:
length=J[nZ]
sum_length_TEMP=sum_length_TEMP+length
n1_GENE_ky=n1_GENE_ky+n1_GENE_ky0[nZ,:,:]*length
n1_GENE_ky=n1_GENE_ky/sum_length_TEMP
#*****Sum over Z************
n1_ky_kx=n1_GENE_ky
print('*****************')
print('*****n1_ky*******')
print('*****************')
print(n1_ky_kx)
else: #x_local = False
print("Sorry, cannot handle Global Nonlinear yet...")
pass
#**Finished reading the Br
#Recall B1_ky_t_inz=np.zeros((nky0,ntime))
n1_ky_kx_t[:,:,i]=n1_ky_kx
ky_GENE_inz = ky_GENE_grid[int(len(real_Z)/2),:,:]
amplitude_frequency_sum=0
amplitude_growth_sum=0
#print(str(B1_ky_t_inz))
if plot==True:
ims_n1=[]
n1_ky_f=[]
growth_ky_f=[]
f_ky_f=[]
time_list=time_list/gyroFreq*(1000.)
for iky in range(nky0):
count_TEMP=0
for ikx in range(nkx0):
n1_inz_t=(n1_ky_kx_t[iky,ikx,:])
frequency,amplitude_frequency_sq=spectral_density(n1_inz_t,time_list,window_for_FFT=window_for_FFT,plot=False)
frequency_kHZ=frequency
amplitude_frequency=abs(np.sqrt(amplitude_frequency_sq))
if manual_Doppler==999:
omegaDoppler_kHZ=Doppler_calc(suffix,iky,iterdb_file_name)
else:
omegaDoppler_kHZ=manual_Doppler*n_list[iky]
count_TEMP=count_TEMP+1
if count_TEMP ==1:
new_frequency_kHZ_ky=frequency_kHZ
new_amplitude_frequency=(2.*amplitude_frequency)**2.
else:
new_amplitude_frequency=new_amplitude_frequency+(2.*amplitude_frequency)**2.
f_ky_f.append(new_frequency_kHZ_ky)
n1_ky_f.append(new_amplitude_frequency**0.5)
f_ky_f=np.array(f_ky_f)
n1_ky_f=abs(np.array(n1_ky_f))
f,amplitude_f=k_f_plot(f_ky_f,n1_ky_f,ky_list,n_list,pic_path,csv_path,name='n1')
return f,amplitude_f
def RIP_f_spectrum_FFT(suffix,iterdb_file_name,manual_Doppler,min_Z0,max_Z0,\
Outboard_mid_plane,time_step,time_start,time_end,\
plot,show,csv_output,pic_path,csv_path):
#Where inz is the number of the element in nz
#Import the parameters from parameter file using ParIO
par = Parameters()
par.Read_Pars('parameters'+suffix)
pars = par.pardict
#getting B field using read_write_geometry.py
gpars,geometry = read_geometry_local(pars['magn_geometry'][1:-1]+suffix)
#Get geom_coeff from ParIO Wrapper
geom_type, geom_pars, geom_coeff = init_read_geometry_file(suffix, pars)
real_Z=geometry['gl_z']
real_R=geometry['gl_R']
J=geometry['gjacobian']
#Import the field file using fieldlib
field = fieldfile('field'+suffix,pars)
time = np.array(field.tfld) #time stampes
B_gauss=10.**4. #1T=10^4Gauss
qref = 1.6E-19 #in C
c = 1. #in 3*10^8m/s
m_kg = 1.673E-27 #in kg
Bref = pars['Bref'] #in Tesla
Tref = pars['Tref'] #in keV
nref = pars['nref'] #in 10^(19) /m^3
Lref = pars['Lref'] #in m
mref = pars['mref'] #in proton mass(kg)
q0 = pars['q0'] #unitless, safety factor/q95
x0 = pars['x0'] #x/a, location
kymin = pars['kymin'] #in rhoi
nky0 = pars['nky0'] #in total number of ky
nkx0 = pars['nx0'] #in total number of ky
n_step = pars['n0_global'] #in rhoi
nref = nref * 1.E19 #in the unit of /m^3
Tref = Tref * qref * 1.E03 #in the unit of J
mref = mref * m_kg #in the unit of kg
pref = nref * Tref #in Pa*k_{B}
cref = np.sqrt(Tref / mref) #in the unit of m/s
Omegaref = qref * Bref / mref / c #in rad/s
rhoref = cref / Omegaref #in m rho_i(ion gyroradii)
print('rhoref='+str(rhoref))
rhorefStar = rhoref / Lref #Unitless
gyroFreq= cref/Lref #the facor convert frequency from cs/a to rad/s
#ky comes from geomWrapper.py
ky_GENE_temp=ky(pars, geom_coeff,plot=False) #ky for ky min for differen z
#print('ky shape: '+str(np.shape(ky_GENE_temp)))
#Determine the n for kymin
ky_n1=1.*q0*rhoref/(Lref*x0) #ky for n=1
n_list,ky_list=ky_list_calc(suffix)
print('n0 list length: '+str(len(n_list)))
print('n0 list: '+str(n_list))
n_min=np.min(n_list)
#ky_GENE_n1=ky_GENE_temp/float(n_min)
nz=len(real_Z)
ky_GENE_grid=np.zeros((nz,nky0,nkx0))#outer product of the two vectors
for i in range(nz):
for j in range(nky0):
for k in range(nkx0):
ky_GENE_grid[i,j,k]=ky_list[j]
print("kygrid"+str(np.shape(ky_GENE_grid)))
print('n0 list length: '+str(len(n_list)))
print('n0 list: '+str(n_list))
#B1=abs(np.mean(Apar_GENE[z,:])*len(Apar_GENE[z,:])*(ky_GENE_temp[z]/rhoref)*Bref*B_gauss*rhorefStar*rhoref)
Apar_to_B1=abs((1./rhoref)*Bref*B_gauss*rhorefStar*rhoref) #B1=Apar*ky_GENE_temp*Apar_to_B1
time_start_index=np.argmin(abs(time - time_start))
time_end_index=np.argmin(abs(time - time_end))
time_list_temp = time[time_start_index:time_end_index+1]
time_list=[]
for i in range(0,len(time_list_temp),time_step):
time_list.append(time_list_temp[i])
time_list=np.array(time_list)
nky0=len(n_list)
ntime=len(time_list)
B1_ky_kx_t=np.zeros((nky0,nkx0,ntime),dtype=complex)
if os.path.isdir(csv_path): #if path does not exist, then create 'csv'
pass
else:
os.mkdir(csv_path)
if os.path.isdir(pic_path): #if path does not exist, then create 'pic'
pass
else:
os.mkdir(pic_path)
print("**********Scan starts, output in csv and pic***************")
for i in range(len(time_list)):
time0=time_list[i]
itime = np.argmin(abs(time - time0))
field.set_time(time_list[i])
print("Looking at the spectra at time:"+str(time[itime]))
#This sets the time step you want to read in
#field.set_time(time[itime])
if 'x_local' in pars:
if pars['x_local']:
x_local = True
else:
x_local = False
else:
x_local = True
if x_local:
kxmin = 2.0*np.pi/pars['lx']
kxgrid = np.linspace(-(pars['nx0']/2-1)*kxmin,pars['nx0']/2*kxmin,num=pars['nx0'])
kxgrid = np.roll(kxgrid,int(pars['nx0']/2+1))
#print("kxgrid"+str(kxgrid))
#print("kygrid"+str(kygrid))
zgrid = np.linspace(-np.pi,np.pi,pars['nz0'],endpoint=False)
apar=field.apar()[:,:,:]+0.j
apar_z_ky_kx=apar
B1_GENE_ky0=apar_z_ky_kx*(ky_GENE_grid*Apar_to_B1)
B1_GENE_ky= np.zeros(np.shape(B1_GENE_ky0[0,:,:]),dtype=complex)
#*****Sum over Z************
sum_length_TEMP=0
if Outboard_mid_plane==True:
B1_GENE_ky=B1_GENE_ky0[int(len(real_Z)/2),:,:]
else:
for nZ in range(len(real_Z)):
if min_Z0<=real_Z[nZ] and real_Z[nZ]<=max_Z0:
length=J[nZ]
sum_length_TEMP=sum_length_TEMP+length
B1_GENE_ky=B1_GENE_ky+B1_GENE_ky0[nZ,:,:]*length
B1_GENE_ky=B1_GENE_ky/sum_length_TEMP
#*****Sum over Z************
B1_ky_kx=B1_GENE_ky
print('*****************')
print('*****B1_ky*******')
print('*****************')
print(B1_ky_kx)
else: #x_local = False
print("Sorry, cannot handle Global Nonlinear yet...")
pass
#**Finished reading the Br
#Recall B1_ky_t_inz=np.zeros((nky0,ntime))
B1_ky_kx_t[:,:,i]=B1_ky_kx
ky_GENE_inz = ky_GENE_grid[int(len(real_Z)/2),:,:]
amplitude_frequency_sum=0
amplitude_growth_sum=0
#print(str(B1_ky_t_inz))
if plot==True:
ims_n1=[]
B1_ky_f=[]
growth_ky_f=[]
f_ky_f=[]
for iky in range(nky0):
count_TEMP=0
for ikx in range(nkx0):
B1_inz_t=(B1_ky_kx_t[iky,ikx,:])
#frequency,amplitude_frequency,amplitude_growth=window_FFT_function_time(B1_inz_t,time_list,plot=False)
frequency,amplitude_frequency,amplitude_growth=FFT_function_time(B1_inz_t,time_list,plot=False)
if manual_Doppler==999:
omegaDoppler_kHZ=Doppler_calc(suffix,iky,iterdb_file_name)
else:
omegaDoppler_kHZ=manual_Doppler*n_list[iky]
frequency_kHZ=frequency*gyroFreq/(1000.)+omegaDoppler_kHZ
count_TEMP=count_TEMP+1
if count_TEMP ==1:
new_frequency_kHZ_ky=frequency_kHZ
new_amplitude_frequency=(2.*amplitude_frequency)**2.
else:
new_amplitude_frequency=new_amplitude_frequency+(2.*amplitude_frequency)**2.
f_ky_f.append(new_frequency_kHZ_ky)
B1_ky_f.append(new_amplitude_frequency**0.5)
f_ky_f=np.array(f_ky_f)
B1_ky_f=abs(np.array(B1_ky_f))
f,amplitude_f=k_f_plot(f_ky_f,B1_ky_f,ky_list,n_list,pic_path,csv_path,name='B1')
return f,amplitude_f