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uniform_spectrum.py
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uniform_spectrum.py
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import sdf
import matplotlib
matplotlib.use('agg')
#%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
#from numpy import ma
from matplotlib import colors, ticker, cm
from matplotlib.mlab import bivariate_normal
from optparse import OptionParser
import os
######## Constant defined here ########
pi = 3.1415926535897932384626
q0 = 1.602176565e-19 # C
m0 = 9.10938291e-31 # kg
v0 = 2.99792458e8 # m/s^2
kb = 1.3806488e-23 # J/K
mu0 = 4.0e-7*pi # N/A^2
epsilon0 = 8.8541878176203899e-12 # F/m
h_planck = 6.62606957e-34 # J s
wavelength= 1.0e-6
frequency = v0*2*pi/wavelength
exunit = m0*v0*frequency/q0
bxunit = m0*frequency/q0
denunit = frequency**2*epsilon0*m0/q0**2
print('electric field unit: '+str(exunit))
print('magnetic field unit: '+str(bxunit))
print('density unit nc: '+str(denunit))
font = {'family' : 'monospace',
'style' : 'normal',
'color' : 'black',
'weight' : 'normal',
'size' : 20,
}
data = sdf.read('./Data/'+str(12).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
px = data['Particles/Px/subset_high_e/electron'].data/(m0*v0)
py = data['Particles/Py/subset_high_e/electron'].data/(m0*v0)
grid_x = data['Grid/Particles/subset_high_e/electron'].data[0]/wavelength
grid_y = data['Grid/Particles/subset_high_e/electron'].data[1]/wavelength
work_x = data['Particles/Time_Integrated_Work_x/subset_high_e/electron'].data
work_y = data['Particles/Time_Integrated_Work_y/subset_high_e/electron'].data
#field_ex = data['Particles/field_ex/subset_high_e/electron'].data/exunit
#field_ey = data['Particles/field_ey/subset_high_e/electron'].data/exunit
#field_bz = data['Particles/field_bz/subset_high_e/electron'].data/bxunit
gg = (px**2+py**2+1)**0.5
px = px [(abs(grid_y) < 3.2) & (gg > 0.0)]
py = py [(abs(grid_y) < 3.2) & (gg > 0.0)]
work_x = work_x [(abs(grid_y) < 3.2) & (gg > 0.0)]
work_y = work_y [(abs(grid_y) < 3.2) & (gg > 0.0)]
gg = (px**2+py**2+1)**0.5
theta = np.arctan2(py,px)*180.0/np.pi
x_1 = np.linspace(-12.,12.,200)
y_1,thrush = np.histogram(theta[work_x > work_y],bins=200,range=(-12.5,12.5))
y_2,thrush = np.histogram(theta[work_x < work_y],bins=200,range=(-12.5,12.5))
plt.subplot(2,1,1)
# plt.subplot()
plt.scatter(theta[work_x > work_y], gg[work_x > work_y], c='magenta', s=3, edgecolors='None', alpha=0.4, label='Work$_x$ > Work$_y$')
plt.scatter(theta[work_x < work_y], gg[work_x < work_y], c='cyan', s=3, edgecolors='None', alpha=0.6, label='Work$_x$ < Work$_y$')
#plt.legend(loc='upper left',fontsize=20,framealpha=0.5)
plt.xlim(-12,12)
# plt.ylim(0,400)
plt.xlabel(r'$\theta$'+' [degree]',fontdict=font)
plt.ylabel('$\gamma$',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.ylim(25,800.0)
plt.twinx()
plt.plot(x_1,(y_1+y_2)/1000,'-k',linewidth=3,label='Total')
plt.plot(x_1,y_1/1000,linestyle='--',color='red',linewidth=3, label='Work$_x$ > Work$_y$')
plt.plot(x_1,y_2/1000,linestyle='--',color='blue',linewidth=3, label='Work$_x$ > Work$_y$')
plt.ylabel('dN/d'+r'$\theta$'+'[A.U.]', color='blue',fontdict=font)
plt.yticks(fontsize=20,color='blue')
plt.ylim(0,0.9)
plt.xlim(-12,12)
plt.subplot(2,1,2)
def pxpy_to_energy(gamma, weight):
binsize = 200
en_grid = np.linspace(0.5,799.5,200)
en_bin = np.linspace(0,800.0,201)
en_value = np.zeros_like(en_grid)
for i in range(binsize):
en_value[i] = sum(weight[ (en_bin[i]<=gamma) & (gamma<en_bin[i+1]) ])
return (en_grid, en_value)
to_path='./figure_wrap_up/'
######### Parameter you should set ###########
start = 12 # start time
stop = 12 # end time
step = 1 # the interval or step
# youwant = ['electron_x_px','electron_density','electron_en','electron_theta_en','ey'] #,'electron_ekbar']
youwant = ['electron_en']#,'electron_no_en']#,'ey','ex','ey_averaged','bz','bz_averaged','Subset_high_e_density','Subset_high_e_ekbar']
#youwant field ex,ey,ez,bx,by,bz,ex_averaged,bx_averaged...
#youwant Derived electron_density,electron_ekbar...
#youwant dist_fn electron_x_px, electron_py_pz, electron_theta_en...
if (os.path.isdir('jpg') == False):
os.mkdir('jpg')
######### Script code drawing figure ################
for n in range(start,stop+step,step):
#### header data ####
data = sdf.read("./Data/"+str(n).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
x = data['Grid/Grid_mid'].data[0]/1.0e-6
print('ok')
y = data['Grid/Grid_mid'].data[1]/1.0e-6
X, Y = np.meshgrid(x, y)
for name in youwant:
px = data['Particles/Px/subset_high_e/electron'].data/(m0*v0)
py = data['Particles/Py/subset_high_e/electron'].data/(m0*v0)
grid_x = data['Grid/Particles/subset_high_e/electron'].data[0]/wavelength
grid_y = data['Grid/Particles/subset_high_e/electron'].data[1]/wavelength
work_x = data['Particles/Time_Integrated_Work_x/subset_high_e/electron'].data
work_y = data['Particles/Time_Integrated_Work_y/subset_high_e/electron'].data
gg = (px**2+py**2+1.0)**0.5
ww = data['Particles/Weight/subset_high_e/electron'].data*6.4e-6
theta = np.arctan2(py,px)*180.0/np.pi
px_x_d = px[ (grid_x>5) & (abs(grid_y)<3.) & (work_x>work_y)]
py_x_d = py[ (grid_x>5) & (abs(grid_y)<3.) & (work_x>work_y)]
gg_x_d = gg[ (grid_x>5) & (abs(grid_y)<3.) & (work_x>work_y)]
ww_x_d = ww[ (grid_x>5) & (abs(grid_y)<3.) & (work_x>work_y)]
dist_x1, den1 = pxpy_to_energy(gg_x_d,ww_x_d)
px_y_d = px[ (grid_x>5) & (abs(grid_y)<3.) & (work_x<=work_y)]
py_y_d = py[ (grid_x>5) & (abs(grid_y)<3.) & (work_x<=work_y)]
gg_y_d = gg[ (grid_x>5) & (abs(grid_y)<3.) & (work_x<=work_y)]
ww_y_d = ww[ (grid_x>5) & (abs(grid_y)<3.) & (work_x<=work_y)]
dist_x2, den2 = pxpy_to_energy(gg_y_d,ww_y_d)
px_d = px[ (grid_x>5) & (abs(grid_y)<3.) ]
py_d = py[ (grid_x>5) & (abs(grid_y)<3.) ]
gg_d = gg[ (grid_x>5) & (abs(grid_y)<3.) ]
ww_d = ww[ (grid_x>5) & (abs(grid_y)<3.) ]
dist_x, den = pxpy_to_energy(gg_d,ww_d)
#plt.plot(dist_x*0.51,den,'-k',marker='o',markersize=10,linewidth=3,markevery=8)
#plt.plot(dist_x1*0.51,den1,'-r',marker='^',markersize=10,linewidth=3,markevery=8)
#plt.plot(dist_x2*0.51,den2,'-b',marker='s',markersize=10,linewidth=3,markevery=8)
plt.plot(dist_x*0.51,den/2.0,'-k',linewidth=4,label='Total')
plt.plot(dist_x1*0.51,den1/2.0,':r',linewidth=4,label='W$_x$ > W$_y$')
plt.plot(dist_x2*0.51,den2/2.0,':b',linewidth=4,label='W$_x$ < W$_y$')
print('g>200, LDA: ', np.sum(den1[dist_x1>200])/np.sum(den[dist_x>200]), ' TDA: ', np.sum(den2[dist_x2>200])/np.sum(den[dist_x>200]), 'charge:', np.sum(den[dist_x>200]))
print('g>400, LDA: ', np.sum(den1[dist_x1>400])/np.sum(den[dist_x>400]), ' TDA: ', np.sum(den2[dist_x2>400])/np.sum(den[dist_x>400]), 'charge:', np.sum(den[dist_x>400]))
print('g>600, LDA: ', np.sum(den1[dist_x1>600])/np.sum(den[dist_x>600]), ' TDA: ', np.sum(den2[dist_x2>600])/np.sum(den[dist_x>600]), 'charge:', np.sum(den[dist_x>600]))
#### manifesting colorbar, changing label and axis properties ####
plt.grid(which='major',color='k', linestyle='--', linewidth=0.3)
plt.xlabel('Energy [MeV]',fontdict=font)
plt.ylabel('dN/dE [MeV$^{-1}$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.yscale('log')
plt.xlim(0,400)
plt.legend(loc='upper right',fontsize=16,framealpha=1.0)
plt.text(285,4e8,'t='+str(round(time/1.0e-15,0))+' fs',fontdict=font)
plt.subplots_adjust(left=0.2, bottom=None, right=0.88, top=None,
wspace=None, hspace=None)
fig = plt.gcf()
fig.set_size_inches(9.2, 14)
fig.savefig('./figure_wrap_up/'+'comb_2_2_njp.png',format='png',dpi=160)
plt.close("all")