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test2d_seperate.py
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#!/public/home/users/bio001/tools/python-2.7.11/bin/python
import sdf
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import numpy as np
import os
from numpy import ma
from matplotlib import colors, ticker, cm
from matplotlib.mlab import bivariate_normal
import matplotlib.colors as mcolors
import scipy.ndimage as ndimage
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import matplotlib.gridspec as gridspec
import multiprocessing as mp
if __name__ == "__main__":
print ('This is main of module "test2d.py"')
######## 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*np.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
jalf = 4*np.pi*epsilon0*m0*v0**3/q0/wavelength**2
print('electric field unit: '+str(exunit))
print('magnetic field unit: '+str(bxunit))
print('density unit nc: '+str(denunit))
font = {'family' : 'monospace',
'color' : 'black',
'weight' : 'normal',
'size' : 20,
}
##below is for norm colorbar
class MidpointNormalize(colors.Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
##end for norm colorbar####
def processplot(n):
from_path_list=['./a0_200_n_02_l_10_qe/','./a0_200_n_02_l_10_no/','./a0_100_n_01_l_10_qe/','./a0_100_n_01_l_10_no/']
to_path_list=['./jpg_a0_200_n_02_l_10_qe/','./jpg_a0_200_n_02_l_10_no/','./jpg_a0_100_n_01_l_10_qe/','./jpg_a0_100_n_01_l_10_no/']
######### Parameter you should set ###########
# youwant = ['electron_x_px','electron_density','electron_en','electron_theta_en','ey'] #,'electron_ekbar']
youwant = ['jx_averaged','jy_averaged','ey','bz','ex','ey_averaged','bz_averaged','e_0_density','e_0_ekbar','e_1_density','e_1_ekbar','c_0_density','c_0_ekbar','e_0_en','e_1_en','c_0_en']#,'e_0_theta_en','e_1_theta_en','c_0_theta_en','c_1_theta_en','e_0_x_px','e_1_x_px','c_0_x_px','c_1_x_px']
# youwant = ['e_0_en','e_1_en','c_0_en']#,'e_0_theta_en','e_1_theta_en','c_0_theta_en','c_1_theta_en','e_0_x_px','e_1_x_px','c_0_x_px','c_1_x_px']
#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...
######### Script code drawing figure ################
for name in youwant:
for i in range(np.size(from_path_list)):
from_path = from_path_list[i]
to_path = to_path_list[i]
if (name[0:2] == 'jx') or (name[0:2] == 'jy') or (name[0:2] == 'jz'):
data = sdf.read(from_path+"current"+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)
ex = data['Current/'+str.capitalize(name)].data/jalf
if np.min(ex.T) == np.max(ex.T):
continue
eee=np.max([-np.min(ex.T),np.max(ex.T)])
levels = np.linspace(-eee, eee, 40)
plt.contourf(X, Y, ex.T, norm=MidpointNormalize(midpoint=0.), cmap=cm.PiYG)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar()
cbar.set_label('Normalized current',fontdict=font)
plt.xlabel('X [$\mu m$]',fontdict=font)
plt.ylabel('Y [$\mu m$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
#plt1 = plt.twinx()
#plt1.plot(x,ex[:,y.size/2.0],'-k',linewidth=2.5)
#plt1.set_ylabel('Normalized '+name)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
if (name[0:2] == 'ex') or (name[0:2] == 'ey') or (name[0:2] == 'ez'):
data = sdf.read(from_path+"e_fields"+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)
ex = data['Electric Field/'+str.capitalize(name)].data/exunit
if np.min(ex.T) == np.max(ex.T):
continue
eee=np.max([-np.min(ex.T),np.max(ex.T)])
levels = np.linspace(-eee, eee, 40)
plt.contourf(X, Y, ex.T, levels=levels, cmap=cm.seismic)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=[-eee, -eee/2, 0, eee/2, eee])
cbar.set_label('Normalized electric field',fontdict=font)
plt.xlabel('X [$\mu m$]',fontdict=font)
plt.ylabel('Y [$\mu m$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
#plt1 = plt.twinx()
#plt1.plot(x,ex[:,y.size/2.0],'-k',linewidth=2.5)
#plt1.set_ylabel('Normalized '+name)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[0:2] == 'bx') or (name[0:2] == 'by') or (name[0:2] == 'bz'):
data = sdf.read(from_path+"b_fields"+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)
ex = data['Magnetic Field/'+str.capitalize(name)].data/bxunit
if np.min(ex.T) == np.max(ex.T):
continue
eee=np.max([-np.min(ex.T),np.max(ex.T)])
levels = np.linspace(-eee, eee, 40)
plt.contourf(X, Y, ex.T, levels=levels, cmap=cm.seismic)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=[-eee, -eee/2, 0, eee/2, eee])
cbar.set_label('Normalized magnetic field',fontdict=font)
plt.xlabel('X [$\mu m$]',fontdict=font)
plt.ylabel('Y [$\mu m$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
#plt1 = plt.twinx()
#plt1.plot(x,ex[:,y.size/2.0],'-k',linewidth=2.5)
#plt1.set_ylabel('Normalized '+name)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-7:] == 'density'):
data = sdf.read(from_path+"density"+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)
den = data['Derived/Number_Density/'+name[0:-8]].data/denunit
if np.min(den.T) == np.max(den.T):
continue
levels = np.linspace(np.min(den.T), np.max(den.T), 40)
plt.contourf(X, Y, den.T, levels=levels, cmap=cm.nipy_spectral)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=np.linspace(np.min(den.T), np.max(den.T), 5))
cbar.set_label(name+'[$n_c$]', fontdict=font)
plt.xlabel('X [$\mu m$]',fontdict=font)
plt.ylabel('Y [$\mu m$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-5:] == 'ekbar'):
data = sdf.read(from_path+"ekbar"+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)
den = data['Derived/EkBar_averaged/'+name[0:-6]].data/(q0*1.0e6)
if np.min(den.T) == np.max(den.T):
continue
levels = np.linspace(np.min(den.T), np.max(den.T), 40)
plt.contourf(X, Y, den.T, levels=levels, cmap=cm.jet)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=np.linspace(np.min(den.T), np.max(den.T), 5))
cbar.set_label(name+'[MeV]', fontdict=font)
plt.xlabel('X [$\mu m$]',fontdict=font)
plt.ylabel('Y [$\mu m$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-4:] == 'x_px'):
data = sdf.read(from_path+"dist"+str(n).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
den = data['dist_fn/x_px/'+name[0:-5]].data[:,:,0]
den = np.log(den+1.0)
if np.min(den.T) == np.max(den.T):
continue
levels = np.linspace(np.min(den.T), np.max(den.T), 40)
dist_x = data['Grid/x_px/'+name[0:-5]].data[0]/1.0e-6
dist_y = data['Grid/x_px/'+name[0:-5]].data[1]/(m0*v0)
dist_X, dist_Y = np.meshgrid(dist_x, dist_y)
plt.contourf(dist_X, dist_Y, den.T, levels=levels, cmap=cm.nipy_spectral)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=np.linspace(np.min(den.T), np.max(den.T), 5))
cbar.set_label(name+'[$log_{10}(A.U.)$]', fontdict=font)
plt.xlabel('X [$\mu m$]',fontdict=font)
plt.ylabel('$P_x$ [$m_ec$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-4:] == 'y_py'):
data = sdf.read(from_path+"dist"+str(n).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
den = data['dist_fn/y_py/'+name[0:-5]].data[:,:,0]
den = np.log(den+1.0)
if np.min(den.T) == np.max(den.T):
continue
levels = np.linspace(np.min(den.T), np.max(den.T), 40)
dist_x = data['Grid/y_py/'+name[0:-5]].data[0]/1.0e-6
dist_y = data['Grid/y_py/'+name[0:-5]].data[1]/(m0*v0)
dist_X, dist_Y = np.meshgrid(dist_x, dist_y)
plt.contourf(dist_X, dist_Y, den.T, levels=levels, cmap=cm.nipy_spectral)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=np.linspace(np.min(den.T), np.max(den.T), 5))
cbar.set_label(name+'[$log_{10}(A.U.)$]', fontdict=font)
plt.xlabel('Y [$\mu m$]',fontdict=font)
plt.ylabel('$P_y$ [$m_ec$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-5:] == 'py_pz'):
data = sdf.read(from_path+"dist"+str(n).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
den = data['dist_fn/py_pz/'+name[0:-6]].data[:,:,0]
den = np.log(den+1.0)
if np.min(den.T) == np.max(den.T):
continue
levels = np.linspace(np.min(den.T), np.max(den.T), 40)
dist_x = data['Grid/py_pz/'+name[0:-6]].data[0]/(m0*v0)
dist_y = data['Grid/py_pz/'+name[0:-6]].data[1]/(m0*v0)
dist_X, dist_Y = np.meshgrid(dist_x, dist_y)
plt.contourf(dist_X, dist_Y, den.T, levels=levels, cmap=cm.nipy_spectral)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=np.linspace(np.min(den.T), np.max(den.T), 5))
cbar.set_label(name+'[$log_{10}(A.U.)$]', fontdict=font)
plt.xlabel('$P_y$ [$m_ec$]',fontdict=font)
plt.ylabel('$P_z$ [$m_ec$]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-8:] == 'theta_en'):
data = sdf.read(from_path+"dist"+str(n).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
denden = data['dist_fn/theta_en/'+name[0:-9]].data[:,:,0]
den = np.log(denden+1.0)
if np.min(den.T) == np.max(den.T):
continue
levels = np.linspace(np.min(den.T), np.max(den.T), 40)
dist_x = data['Grid/theta_en/'+name[0:-9]].data[0]
dist_y = data['Grid/theta_en/'+name[0:-9]].data[1]/(q0*1.0e6)
dist_X, dist_Y = np.meshgrid(dist_x, dist_y)
plt.contourf(dist_X, dist_Y, den.T, levels=levels, cmap=cm.nipy_spectral)
#### manifesting colorbar, changing label and axis properties ####
cbar=plt.colorbar(ticks=np.linspace(np.min(den.T), np.max(den.T), 5))
cbar.set_label(name+'[$log_{10}(A.U.)$]', fontdict=font)
plt.xlabel('$\Psi$ [rad]',fontdict=font)
plt.ylabel('$Energy$ [MeV]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
plt1 = plt.twinx()
plt1.plot(dist_x,np.sum(denden,axis=1),'-y',linewidth=2.5)
#plt1.set_ylabel('Normalized '+name)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
elif (name[-2:] == 'en'):
data = sdf.read(from_path+"dist"+str(n).zfill(4)+".sdf",dict=True)
header=data['Header']
time=header['time']
den = data['dist_fn/en/'+name[0:-3]].data[:,0,0]
dist_x = data['Grid/en/'+name[0:-3]].data[0]/(q0*1.0e6)
plt.plot(dist_x,den,'-r',linewidth=3)
#### manifesting colorbar, changing label and axis properties ####
plt.xlabel('Energy [MeV]',fontdict=font)
plt.ylabel('dN/dE [A.U.]',fontdict=font)
plt.xticks(fontsize=20); plt.yticks(fontsize=20);
plt.yscale('log')
plt.title(name+' at '+str(round(time/1.0e-15,6))+' fs',fontdict=font)
fig = plt.gcf()
fig.set_size_inches(12, 7)
fig.savefig(to_path+name+str(n).zfill(4)+'.png',format='png',dpi=100)
plt.close("all")
print('finised '+str(round(100.0*(n-start+step)/(stop-start+step),4))+'%')
if __name__ == '__main__':
start = 0 # start time
stop = 12 # end time
step = 1
inputs = range(start,stop+step,step)
pool = mp.Pool(processes=1)
results = pool.map(processplot,inputs)
print(results)