/
wrap_px_py_insert.py
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wrap_px_py_insert.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
import matplotlib.colors as mcolors
######## 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' : 25,
}
font_size = 25
######### Parameter you should set ###########
upper = matplotlib.cm.jet(np.arange(256))
lower = np.ones((int(256/4),4))
for i in range(3):
lower[:,i] = np.linspace(1, upper[0,i], lower.shape[0])
cmap = np.vstack(( lower, upper ))
mycolor_jet = matplotlib.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
if __name__ == "__main__":
part_number = 2
nsteps = 79580 #sum(1 for line in open(from_path+'x_0000.txt'))/part_number
from_path = './Data_no_part01/'
to_path = './jpg_no_part01/'
t0 = np.loadtxt(from_path+'t_0000.txt')/2/np.pi
x0 = np.loadtxt(from_path+'x_0000.txt')/2/np.pi
y0 = np.loadtxt(from_path+'y_0000.txt')/2/np.pi
px0 = np.loadtxt(from_path+'px_0000.txt')
py0 = np.loadtxt(from_path+'py_0000.txt')
t0 = np.reshape(t0,(part_number,nsteps))
x0 = np.reshape(x0,(part_number,nsteps))
y0 = np.reshape(y0,(part_number,nsteps))
px0 = np.reshape(px0,(part_number,nsteps))
py0 = np.reshape(py0,(part_number,nsteps))
gg0 = (px0**2+py0**2+1)**0.5
ww0 = np.zeros_like(gg0)+1
R0 = gg0-px0
n=1
plt.subplot(1,2,1)
print('no max gg:',np.max(gg0[n,:]))
print('t0 max:',np.max(t0[n,:]*10./3.),'fs')
plt.scatter(px0[n,t0[n,:]*10./3.<1000], py0[n,t0[n,:]*10./3.<1000], c=t0[n,t0[n,:]*10./3.<1000]*10./3., norm=colors.Normalize(vmin=0,vmax=1000), s=3, cmap='rainbow', edgecolors='None', alpha=1,zorder=3)
#cbar=plt.colorbar( ticks=np.linspace(0, 500, 3) ,pad=0.005)
#cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=font_size)
#cbar.set_label('$\gamma$',fontdict=font)
px = np.linspace(-400,15400,501)
py = np.linspace(-800,800,401)
px, py = np.meshgrid(px, py)
R = (1+px**2+py**2)**0.5-px
R[R<0.1]=0.1
levels = np.logspace(1,3,5)
print(np.min(R),np.max(R))
plt.contour(px, py, R, levels=levels, norm=mcolors.LogNorm(vmin=levels.min(), vmax=levels.max()), linestyles='dashed', cmap='gist_gray',zorder=0,linewidths=2.5)
#plt.contourf(px, py, R, levels=levels, norm=mcolors.LogNorm(vmin=levels.min(), vmax=levels.max()), cmap=mycolor_jet)
#cbar=plt.colorbar(pad=0.1, ticks=np.logspace(0,3,7))#,orientation="horizontal")
#cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=font_size)
#cbar.set_label('R', fontdict=font)
# plt.scatter(px0[condition], py0[condition], c=t0[condition], norm=colors.Normalize(vmin=0,vmax=400), s=20, cmap='nipy_spectral', marker=(5, 1), edgecolors='None', alpha=1)
# plt.scatter(px1[condition], py1[condition], c=t1[condition], norm=colors.Normalize(vmin=0,vmax=400), s=20, cmap='winter', edgecolors='None', alpha=1)
# cbar=plt.colorbar( ticks=np.linspace(0, 2000, 5) ,pad=0.005)
# cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=20)
# cbar.set_label('$\gamma$',fontdict=font)
plt.xlabel('$p_x$ [$m_ec$]',fontdict=font)
plt.ylabel('$p_y$ [$m_ec$]',fontdict=font)
plt.xticks([0,500,1000,1500],fontsize=font_size); plt.yticks([-200,0,200],fontsize=font_size);
#plt.xscale('log')
plt.xlim(-200,1800)
plt.ylim(-320,320)
from_path = './Data_qe_part01/'
to_path = './jpg_qe_part01/'
t1 = np.loadtxt(from_path+'t_0000.txt')/2/np.pi
x1 = np.loadtxt(from_path+'x_0000.txt')/2/np.pi
y1 = np.loadtxt(from_path+'y_0000.txt')/2/np.pi
px1 = np.loadtxt(from_path+'px_0000.txt')
py1 = np.loadtxt(from_path+'py_0000.txt')
radt1 = np.loadtxt(from_path+'radt_0000.txt')
radn1 = np.loadtxt(from_path+'radn_0000.txt')
t1 = np.reshape(t1,(part_number,nsteps))
x1 = np.reshape(x1,(part_number,nsteps))
y1 = np.reshape(y1,(part_number,nsteps))
px1 = np.reshape(px1,(part_number,nsteps))
py1 = np.reshape(py1,(part_number,nsteps))
radt1 = np.reshape(radt1,(part_number,nsteps))
radn1 = np.reshape(radn1,(part_number,nsteps))
gg1 = (px1**2+py1**2+1)**0.5
ww1 = np.zeros_like(gg1)+1
R1 = gg1-px1
print('qe max gg:',np.max(gg1[n,:]))
plt.subplot(1,2,2)
plt.scatter(px1[n,t1[n,:]*10./3.<1000], py1[n,t1[n,:]*10./3.<1000], c=t1[n,t1[n,:]*10./3.<1000]*10./3., norm=colors.Normalize(vmin=0,vmax=1000), s=8, cmap='rainbow', edgecolors='None', alpha=1,zorder=1)
#cbar=plt.colorbar( ticks=np.linspace(0, 12000, 3) ,pad=0.005)
#cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=font_size)
#cbar.set_label('$\gamma$',fontdict=font)
condition = ((radn1[n,1:]-radn1[n,:-1]) > 0) & ((radt1[n,1:]-radt1[n,:-1]) > 5.0 )
plt.scatter(px1[n,:-1][condition], py1[n,:-1][condition], c=(radt1[n,1:]-radt1[n,:-1])[condition]*0.51, norm=colors.Normalize(vmin=50,vmax=500), s=150, cmap='hot', marker='*', edgecolors='k', alpha=1,zorder=2)
#cbar=plt.colorbar( ticks=np.linspace(0, 500, 6) ,pad=0.005)
#cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=font_size)
#cbar.set_label('$E_{ph}$ [MeV]',fontdict=font)
px = np.linspace(-400,15400,501)
py = np.linspace(-800,800,401)
px, py = np.meshgrid(px, py)
R = (1+px**2+py**2)**0.5-px
R[R<0.1]=0.1
levels = np.logspace(1,3,5)
print(np.min(R),np.max(R))
plt.contour(px, py, R, levels=levels, norm=mcolors.LogNorm(vmin=levels.min(), vmax=levels.max()), linestyles='dashed', cmap='gist_gray',zorder=0,linewidths=2.5)
#plt.contourf(px, py, R, levels=levels, norm=mcolors.LogNorm(vmin=levels.min(), vmax=levels.max()), cmap=mycolor_jet)
#cbar=plt.colorbar(pad=0.1, ticks=np.logspace(0,3,7))#,orientation="horizontal")
#cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=font_size)
#cbar.set_label('R', fontdict=font)
# plt.scatter(px0[condition], py0[condition], c=t0[condition], norm=colors.Normalize(vmin=0,vmax=400), s=20, cmap='nipy_spectral', marker=(5, 1), edgecolors='None', alpha=1)
# plt.scatter(px1[condition], py1[condition], c=t1[condition], norm=colors.Normalize(vmin=0,vmax=400), s=20, cmap='winter', edgecolors='None', alpha=1)
# cbar=plt.colorbar( ticks=np.linspace(0, 2000, 5) ,pad=0.005)
# cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=20)
# cbar.set_label('$\gamma$',fontdict=font)
plt.xlabel('$p_x$ [$m_ec$]',fontdict=font)
plt.ylabel('$p_y$ [$m_ec$]',fontdict=font)
plt.xticks([0,500,1000,1500],fontsize=font_size);
plt.yticks([-200,0,200],fontsize=font_size);
#plt.xscale('log')
plt.xlim(-200,1800)
plt.ylim(-320,320)
plt.subplots_adjust(left=0.12, bottom=0.16, right=0.99, top=0.99,
wspace=0.3, hspace=0.1)
#plt.show()
#lt.figure(figsize=(100,100))
fig = plt.gcf()
fig.set_size_inches(14., 6.5)
fig.savefig('./wrap_px_py_insert_no.png',format='png',dpi=160)
plt.close("all")