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LN_plot_k_f_B_contour.py
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LN_plot_k_f_B_contour.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#instruction: on can put local linear simulations
bin=10
quant_name='B1' #'B1' or 'n1'
n1_doppler=-8.
contour_plot_ky_max=0.25
f_min=100
f_max=600
n_min=0
kymin=0.03
#n_step=3.77
ky_1=0.00775536103953903
n_step=kymin/ky_1
print(n_step)
path='./33rd_3spec_nky24/RIP_csv'
Frequency_flip=True #change to True if one wants to look at electron direction
if Frequency_flip==True:
label0='electron direction f(kHz)'
else:
label0='electron direction f(kHz)'
#path='2d_list/36_midplan_hann_V2'
#ky rho_s for n=1
#ky_1=0.008
plot_scale=1.2 #How much blank space one wants in y axis
sim_scale=1. #the y_plot_sim=y_sim*sim_scale
norm_to_max=True #Ture if one wants to noralized sim and exp to their respected maximum
plot_linear=False #plot linear contour
plot_log=False #plot log contour
plot_subplot=True #Plot subplot for the different ky
total_row=3 #Total rows for the subplot
eta=2.16596001839912
omn=2.77105293432639
omegastar_nor=omn*(1.+eta)
omstar=(omegastar_nor+abs(n1_doppler))
f_csv=pd.read_csv(path+'/0f_list.csv')
f=f_csv['f(kHz)']
print('len(f)'+str(len(f)))
LL_csv=pd.read_csv('./LL_MTM.csv')
LL_f_plasma=LL_csv['omega(kHz)']
LL_gamma=LL_csv['gamma(cs/a)']
LL_ky=LL_csv['kymin']
LL_f=LL_f_plasma+LL_ky/ky_1*n1_doppler
ky_csv=pd.read_csv(path+'/0ky_list.csv')
ky=ky_csv['ky']
f1=open(path+"/0"+quant_name+"_matrix_f_ky.csv","r")
lines=f1.readlines()
if Frequency_flip==True:
Frequency_flip_constant=-1.
else:
Frequency_flip_constant=1.
def smooth(avg_list,bin_size):
if bin_size==1:
list_avg=avg_list
dev=np.mean(abs(list_avg[1:]-list_avg[:-1]))*0.5
list_dev=np.array([dev]*len(list_avg))
else:
l_list=len(avg_list)
list_avg=np.zeros(l_list-bin_size+1)
list_dev=np.zeros(l_list-bin_size+1)
avg=0
for i in range(0,len(list_avg)):
list_avg[i]=np.mean(avg_list[i:i+bin_size])
list_dev[i]=np.std(avg_list[i:i+bin_size])
return list_avg, list_dev
def smooth_f_x(f,x,dx_size):
x_min=np.min(x)
f_avg_list=[]
f_std_list=[]
x_avg_list=[]
x_std_list=[]
while(x_min<np.max(x)):
f_temp=[]
for i in range(len(x)):
if x_min<=x[i] and x[i]<x_min+dx_size:
f_temp.append(f[i])
f_avg_list.append(np.mean(f_temp))
f_std_list.append(np.std(f_temp))
x_avg_list.append(x_min+dx_size/2.)
x_std_list.append(dx_size/2.)
x_min=x_min+dx_size
print('x_min='+str(x_min))
return f_avg_list,f_std_list,x_avg_list,x_std_list
n_list=np.arange(n_min,n_min+len(ky)*n_step,n_step)
print('n_list'+str(n_list))
#print(np.min(f))
#print(np.max(f))
uni_freq=np.linspace(np.min(f), np.max(f)+np.max(n_list)*n1_doppler,num=int(len(f)*1.2))
frequency_kHZ_uni=np.zeros( (len(ky), len(uni_freq)) )
amplitude_frequency_uni=np.zeros( (len(ky), len(uni_freq)) )
ky_plot=np.zeros( (len(ky), len(uni_freq)) )
i_ky=0
for line in lines:
line=line[:-1]
n_temp=n_list[i_ky]
print('n_temp='+str(n_temp))
f_temp=f+n_temp*n1_doppler
#print('**********')
#print(f_temp)
#print(line.split(','))
B_f_temp=[]
for ele in line.split(','):
B_f_temp.append(float(ele))
#print(len(f_temp))
#print(len(B_f_temp))
frequency_kHZ_uni[i_ky,:]=uni_freq
amplitude_frequency_uni[i_ky,:]=np.interp(uni_freq,f_temp,B_f_temp)
ky_plot[i_ky,:]=[ky[i_ky]]*len(uni_freq)
i_ky=i_ky+1
uni_freq=Frequency_flip_constant*uni_freq
frequency_kHZ_uni=Frequency_flip_constant*frequency_kHZ_uni
ky_list=np.arange(np.min(ky_plot),np.max(ky_plot),0.0000001)
print('ky_list'+str(ky_list))
if plot_linear==True:
plt.clf()
plt.ylabel(r'$k_y \rho_s$',fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
#plt.plot(omstar120*ky_list/ky_1,ky_list,label=r'line of $\omega_{*e}$ with 1.2 $\omega_{*Te}$')
plt.plot(omstar*ky_list/ky_1,ky_list,label=r'line of $\omega_{*e}$')
for i in range(len(LL_f)):
if i == 1:
plt.plot(-LL_f[i],LL_ky[i],'o',color='orange',markersize=10.*LL_gamma[i],alpha=0.5,label=r'Local linear simulations')
else:
plt.plot(-LL_f[i],LL_ky[i],'o',color='orange',markersize=10.*LL_gamma[i],alpha=0.5)
plt.contourf(frequency_kHZ_uni,ky_plot,amplitude_frequency_uni,levels=1000,extend='both')#,cmap='RdGy')
#plt.contourf(-frequency_kHZ_uni,ky_plot,amplitude_frequency_uni)#,level=[50,50,50])#,cmap='RdGy')
for i_n in range(int(np.max(n_list))):
if i_n%5==0:
plt.axhline(i_n*ky_1,color='red',alpha=0.5)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
else:
plt.axhline(i_n*ky_1,color='red',alpha=0.1)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.axhline(ky[0],color='red',alpha=0.5,label='n starts from '+str(int(n_list[0])) )#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.xlim(0,1000)
plt.ylim(0,contour_plot_ky_max*2)
plt.colorbar()
plt.legend(loc='upper left')
plt.title(r'log('+quant_name+') contour plot',fontsize=10)
#plt.title(r'$B_r$ contour plot',fontsize=10)
plt.savefig('contour_log.png')
plt.show()
if plot_log==True:
plt.clf()
plt.ylabel(r'$k_y \rho_s$',fontsize=10)
plt.xlabel(r'$f(kHz)$',fontsize=10)
#plt.plot(omstar120*ky_list/ky_1,ky_list,label=r'line of $\omega_{*e}$ with 1.2 $\omega_{*Te}$')
plt.plot(omstar*ky_list/ky_1,ky_list,label=r'line of $\omega_{*e}$')
for i in range(len(LL_f)):
if i == 1:
plt.plot(-LL_f[i],LL_ky[i],'o',color='orange',markersize=10.*LL_gamma[i],alpha=0.9,label=r'Local linear simulations')
else:
plt.plot(-LL_f[i],LL_ky[i],'o',color='orange',markersize=10.*LL_gamma[i],alpha=0.5)
plt.contourf(frequency_kHZ_uni,ky_plot,np.log10(amplitude_frequency_uni),levels=1000,extend='both')#,cmap='RdGy')
#plt.contourf(-frequency_kHZ_uni,ky_plot,amplitude_frequency_uni)#,level=[50,50,50])#,cmap='RdGy')
for i_n in range(int(np.max(n_list))):
if i_n%5==0:
plt.axhline(i_n*ky_1,color='red',alpha=0.5)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
else:
plt.axhline(i_n*ky_1,color='red',alpha=0.1)#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.axhline(ky[0],color='red',alpha=0.5,label='n starts from '+str(int(n_list[0])) )#alpha control the transparency, alpha=0 transparency, alpha=1 solid
plt.xlim(0,1000)
plt.ylim(0,contour_plot_ky_max*2)
plt.colorbar()
plt.legend(loc='upper left')
plt.title(r'log('+quant_name+') contour plot',fontsize=10)
#plt.title(r'$B_r$ contour plot',fontsize=10)
plt.savefig('contour_log.png')
plt.show()
if plot_subplot==True:
for i in range(total_row):
for j in range(int(np.ceil(len(n_list)/float(total_row)))):
plt.clf()
x_TEMP=frequency_kHZ_uni[3*i+j,:]
y_TEMP=amplitude_frequency_uni[3*i+j,:]
print(np.shape(frequency_kHZ_uni))
print(np.shape(amplitude_frequency_uni))
ky_TEMP=ky[3*i+j]
print(ky_TEMP)
print(round(ky_TEMP,4))
plt.plot(x_TEMP,y_TEMP)
plt.grid()
plt.title('ky='+str(round(ky_TEMP,4)))
plt.xlim([0, abs(f_min)])
plt.savefig('ky='+str(round(ky_TEMP,4))+'.png')
fig, ax=plt.subplots(nrows=3,ncols=int(np.ceil(len(n_list)/3.)),sharex=True,sharey=True)
#nrows is the total rows
#ncols is the total columns
#sharex true means the xaxies will be shared
i_TEMP=0
for i in range(total_row):
for j in range(int(np.ceil(len(n_list)/float(total_row)))):
x_TEMP=frequency_kHZ_uni[i_TEMP,:]
y_TEMP=amplitude_frequency_uni[i_TEMP,:]
ky_TEMP=ky[i_TEMP]
print(ky_TEMP)
print(round(ky_TEMP,4))
ax[i,j].plot(x_TEMP,y_TEMP)
ax[i,j].set_title('ky='+str(round(ky_TEMP,4)))
ax[i,j].set_xlim([abs(f_max), abs(f_min)])
ax[i,j].grid()
i_TEMP=i_TEMP+1
#i_TEMP=3*i+j
if i==total_row-1:
ax[i,j].set_xlabel('frequency(kHz)')
#ax[i,j].set_ylabel(r'$B_r(Gauss/\sqrt{kHz})$')
#ax[0,0].plot(x,np.sin(x),label='sin(x)')
#ax[0,0].set_xlabel('x')
#ax[0,0].set_ylabel('sin(x)',fontsize=fontsize0)
#ax1.set_title() #for the set the title name
#ax[1,1].set_xlim([abs(f_max), abs(f_min)])
plt.tight_layout()
plt.show()
Exp_data=pd.read_csv('Experiment/delta_br_vs_f.csv')
Exp_data['delta_br_vs_f']=Exp_data['delta_br_vs_f']*10000.
row1=uni_freq
row2=(np.sum(amplitude_frequency_uni**2.,axis=0)*2.)**0.5
plt.clf()
plt.plot(row1,row2)
plt.title('plot_all_range '+label0)
plt.xlabel('frequency(kHz)')
plt.show()
def sort_x_f(x_unsort,f_unsort):
arr_unsort=[x_unsort,f_unsort]
f_x_unsort=tuple(map(tuple, np.transpose(arr_unsort)))
f_x_sort=sorted(f_x_unsort, key=lambda f_x_unsort: f_x_unsort[0])
f_x_sort=np.array(f_x_sort)
f_x_sort=np.transpose(f_x_sort)
x_sort=f_x_sort[0,:]
f_sort=f_x_sort[1,:]
return x_sort,f_sort
row1,row2=sort_x_f(row1,row2)
index_min=np.argmin(abs(row1-f_min))
index_max=np.argmin(abs(row1-f_max))
row1=row1[index_min-bin:index_max+bin]
row2=row2[index_min-bin:index_max+bin]
plt.clf()
plt.plot(row1,row2)
plt.title('plot_zoom '+label0)
plt.xlabel('frequency(kHz)')
plt.show()
f_avg0, f_dev0=smooth(row1,bin)
B1_avg0, B1_dev0=smooth(row2,bin)
f_avg=[]
f_dev=[]
B1_avg=[]
B1_dev=[]
if bin==1:
f_avg=f_avg0
f_dev=f_dev0
B1_avg=B1_avg0
B1_dev=B1_dev0
else:
for i in range(len(f_avg0)):
if i%(bin/2)==0:
f_avg.append(f_avg0[i])
f_dev.append(f_dev0[i])
B1_avg.append(B1_avg0[i])
B1_dev.append(B1_dev0[i])
#print(B1_avg)
#print("sim_max="+str(sim_max))
print(f_dev)
f_avg_list,f_std_list,x_avg_list,x_std_list=smooth_f_x(Exp_data['delta_br_vs_f'],Exp_data['x'],np.mean(f_dev))
d = {'f(kHz)':x_avg_list,'f_err(kHz)':x_std_list,'B_R(Gauss)':f_avg_list,'B_R_err(Gauss)':f_std_list}
df_exp=pd.DataFrame(d, columns=['f(kHz)','f_err(kHz)','B_R(Gauss)','B_R_err(Gauss)'])
df_exp.to_csv('0_exp_B_r_f_smooth.csv',index=False)
d = {'f(kHz)':np.array(f_avg),'f_err(kHz)':f_dev,'B_R(Gauss)':B1_avg,'B_R_err(Gauss)':B1_dev}
df=pd.DataFrame(d, columns=['f(kHz)','f_err(kHz)','B_R(Gauss)','B_R_err(Gauss)'])
df.to_csv('0B_r_f_smooth.csv',index=False)
print('-1.*np.array(f_avg)'+str(-1.*np.array(f_avg)))
if norm_to_max==True:
print('norm_to_max==True')
#**********for normalized to max = 1***********
df_exp['B_R(Gauss)']=df_exp['B_R(Gauss)']/np.max(df_exp['B_R(Gauss)'])
df['B_R(Gauss)']=df['B_R(Gauss)']/np.max(df['B_R(Gauss)'])*sim_scale
print('sim_scale'+str(sim_scale))
print('df[B_R(Gauss)]'+str(df['B_R(Gauss)']))
#25% uncertainty.
df_exp['B_R_err(Gauss)']=df_exp['B_R(Gauss)']*0.25/np.max(df_exp['B_R(Gauss)'])
df['B_R_err(Gauss)']=df['B_R_err(Gauss)']/np.max(df['B_R(Gauss)'])*sim_scale
#********for normalized to max = 1************
else:
#*******the y_plot_sim=y_sim*sim_scale*********
df['B_R(Gauss)']=df['B_R(Gauss)']*sim_scale
df['B_R_err(Gauss)']=df['B_R_err(Gauss)']*sim_scale
#*******the y_plot_sim=y_sim*sim_scale*********
sim_max=np.max(df_exp['B_R(Gauss)'])*plot_scale
plt.clf()
ax=df_exp.plot(kind='scatter',x='f(kHz)',xerr='f_err(kHz)',y='B_R(Gauss)',yerr='B_R_err(Gauss)',\
ylim=(0.00,sim_max),xlim=(f_min,f_max),\
color='red',label='Experiment',alpha=0.5)
#df.plot(kind='scatter',x='f(kHz)',y='B_R(Gauss)',\
# ylim=(0.00,sim_max),xlim=(f_min,f_max),\
# secondary_y=True,color='green',label='GENE simulation',ax=ax)
df.plot(kind='scatter',x='f(kHz)',xerr='f_err(kHz)',y='B_R(Gauss)',yerr='B_R_err(Gauss)',\
ylim=(0.00,sim_max),xlim=(f_min,f_max),\
secondary_y=True,color='green',label='GENE simulation',ax=ax)
ax.set_xlabel(r'$frequency(kHz)$',fontsize=15)
ax.set_ylabel(r'$\bar{B}_r(Gauss/\sqrt{Hz})$',fontsize=15)
plt.title(r'$\bar{B}_r$ spectrogram',fontsize=20)
plt.savefig('B1_f.png')
plt.show()
#********RIP***********
print('len(f)'+str(len(f)))