def read(self, navg=None):
g3d = Grid3d(self.__h5file)
self.__fxy = g3d.read(x0=self.__zeta_pos, navg=navg)
self.__x_array = g3d.get_x_arr(1)
self.__y_array = g3d.get_x_arr(2)
return self.__x_array, self.__y_array, self.__fxy
def plot_hipace_Ex(zeta_pos):
ExmBy_path = './DATA/field_ExmBy_000000.h5'
By_path = './DATA/field_By_000000.h5'
ExmBy_g3d2 = Grid3d(ExmBy_path)
By_g3d2 = Grid3d(By_path)
ExmBy = np.transpose(ExmBy_g3d2.read(x2=0.0))
By = np.transpose(By_g3d2.read(x2=0.0))
Ex = ExmBy + By
# fig = plt.figure()
# ax = fig.add_subplot(111)
idx = np.abs(By_g3d2.get_zeta_arr() - zeta_pos).argmin()
return By_g3d2.get_x_arr(2), Ex[:, idx]
def lvst_Wr(args):
beam = set_beam(args)
plasma = set_plasma(args)
h5flist = H5FList(args.path, 'g3d')
flist = h5flist.get()
for file in flist:
g3d = Grid3d(file)
if (g3d.name == 'ExmBy' or g3d.name == 'EypBx'):
cmp_plot_Wr(args, g3d, plasma, beam)
else:
print('ERROR: Dataset is no ExmBy or EypBx dataset!')
sys.exit(1)
def main():
parser = blowout_geo_parser()
args = parser.parse_args()
h5flist = H5FList(args.path, 'g3d')
flist = h5flist.get()
for file in flist:
g3d = Grid3d(file)
if (g3d.name == 'plasma_charge'):
blowout = Blowout(g3d, args)
blowout.plot_r_z_plane()
blowout.plot_r_lines()
blowout.plot_rb()
blowout.calc_deltarho_rhomax_sheathcharge()
blowout.plot_deltarho_rhomax_sheathcharge()
blowout.plot_model_sheath()
else:
print('ERROR: HDF5 file must contain a plasma_charge dataset!')
sys.exit(1)
def main():
#### Defining constants as in HiPACE
#define M_ELEC_PER_M_NUCL 5.4857990946e-4
#define E_MUON_MASS_RATIO 4.83633170e-3 /* from NIST database */
ELECTRON_CHARGE_IN_COUL = 1.60217657e-19
ELECTRON_MASS_IN_KG = 9.10938291e-31
SPEED_OF_LIGHT_IN_M_PER_S = 299792458
VAC_PERMIT_FARAD_PER_M = 8.854187817e-12
HYDROGEN_IONIZATION_ENERGIE_EV = 13.659843449
omega_alpha = 4.13e16
# [s^-1]
E_alpha = 5.1e11
# [V/m]
elec_density = 5e+22 # [1/m^3]
omega_p = np.sqrt(elec_density * (ELECTRON_CHARGE_IN_COUL**2) /
(VAC_PERMIT_FARAD_PER_M * ELECTRON_MASS_IN_KG))
# calculation of the plasma frequency
#print('omega_p is %0.3e' %omega_p)
E_0 = omega_p * ELECTRON_MASS_IN_KG * SPEED_OF_LIGHT_IN_M_PER_S / ELECTRON_CHARGE_IN_COUL
#calculation of the denormalization factor for the electric field
#print('E_0 is %0.3e' %E_0)
#home path Path definitions
Ez_path1 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs_new4/DATA/field_Ez_000000.h5'
ExmBy_path1 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs_new4/DATA/field_ExmBy_000000.h5'
EypBx_path1 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs_new4/DATA/field_EypBx_000000.h5'
Bx_path1 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs_new4/DATA/field_Bx_000000.h5'
By_path1 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs_new4/DATA/field_By_000000.h5'
#neutral_density_path = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs/DATA/density_plasma_neutral_000000.h5'
neutral_density_path1 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs_new4/DATA/density_ionized_electrons_plasma_Cs_000000.h5'
#home path Path definitions
# Ez_path2 = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/cs5/DATA/field_Ez_000000.h5'
# ExmBy_path2 = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/cs5/DATA/field_ExmBy_000000.h5'
# EypBx_path2 = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/cs5/DATA/field_EypBx_000000.h5'
# Bx_path2 = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/cs5/DATA/field_Bx_000000.h5'
# By_path2 = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/cs5/DATA/field_By_000000.h5'
#
# #neutral_density_path = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs/DATA/density_plasma_neutral_000000.h5'
# neutral_density_path2 = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/cs5/DATA/density_ionized_electrons_plasma_Cs_000000.h5'
#home path Path definitions
Ez_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test6/DATA/field_Ez_000000.h5'
ExmBy_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test6/DATA/field_ExmBy_000000.h5'
EypBx_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test6/DATA/field_EypBx_000000.h5'
Bx_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test6/DATA/field_Bx_000000.h5'
By_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test6/DATA/field_By_000000.h5'
#neutral_density_path = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs/DATA/density_plasma_neutral_000000.h5'
neutral_density_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test6/DATA/density_ionized_electrons_plasma_H_000000.h5'
#home path Path definitions
Ez_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/return_of_e2/rp_1_rb_1_2/DATA/field_Ez_000000.h5'
ExmBy_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/return_of_e2/rp_1_rb_1_2/DATA/field_ExmBy_000000.h5'
EypBx_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/return_of_e2/rp_1_rb_1_2/DATA/field_EypBx_000000.h5'
Bx_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/return_of_e2/rp_1_rb_1_2/DATA/field_Bx_000000.h5'
By_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/return_of_e2/rp_1_rb_1_2/DATA/field_By_000000.h5'
#neutral_density_path = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs/DATA/density_plasma_neutral_000000.h5'
neutral_density_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/return_of_e2/rp_1_rb_1_2/DATA/density_ionized_electrons_plasma_H_000000.h5'
if not os.path.exists('plots'):
os.makedirs('plots')
if not os.path.exists('plots/pp-ionization/'):
os.makedirs('plots/pp-ionization/')
#vektorized function for calc ion rate
vectorfunc = np.vectorize(calc_ion_rate)
#Ez_g3d = Grid3d(Ez_path)
#ExmBy_g3d = Grid3d(ExmBy_path)
#EypBx_g3d = Grid3d(EypBx_path)
#Bx_g3d = Grid3d(Bx_path)
By_g3d1 = Grid3d(By_path1)
neutral_density_g3d1 = Grid3d(neutral_density_path1)
By_g3d2 = Grid3d(By_path2)
Ez_g3d2 = Grid3d(Ez_path2)
ExmBy_g3d2 = Grid3d(ExmBy_path2)
EypBx_g3d2 = Grid3d(EypBx_path2)
Bx_g3d2 = Grid3d(Bx_path2)
neutral_density_g3d2 = Grid3d(neutral_density_path2)
#Reading in the data
Ez = np.transpose(Ez_g3d2.read(x2=0.0))
ExmBy = np.transpose(ExmBy_g3d2.read(x2=0.0))
EypBx = np.transpose(EypBx_g3d2.read(x2=0.0))
Bx = np.transpose(Bx_g3d2.read(x2=0.0))
By = np.transpose(By_g3d2.read(x2=0.0))
# neutral_density1 = np.transpose(neutral_density_g3d1.read(x1=0.0))
# plt.pcolormesh(By_g3d1.get_zeta_arr(), By_g3d1.get_x_arr(2), neutral_density1, cmap=cm.Blues_r) #
# cb = plt.colorbar()
# plt.clim(0,-3)
# #plt.imshow(neutral_density)
# plt.show()
neutral_density2 = np.transpose(neutral_density_g3d2.read(x1=0.0))
plt.pcolormesh(By_g3d2.get_zeta_arr(),
By_g3d2.get_x_arr(2),
np.log(np.abs(neutral_density2)),
cmap=cm.Blues) #
cb = plt.colorbar()
#plt.clim(-2,-25)
#plt.imshow(neutral_density)
plt.show()
# difference_parallel = neutral_density1 - neutral_density2
# plt.pcolormesh(By_g3d1.get_zeta_arr(), By_g3d1.get_x_arr(2), difference_parallel, cmap=cm.seismic) #
# cb = plt.colorbar()
# #plt.clim(3,-3)
# #plt.imshow(neutral_density)
# plt.show()
E_magnitude = np.sqrt((ExmBy + By)**2 + (EypBx - Bx)**2 + Ez**2) * E_0
print('The maximum magnitude of the electric field is %0.3e' %
np.max(E_magnitude))
### Plotting the magnitude
plt.contourf(By_g3d2.get_zeta_arr(),
By_g3d2.get_x_arr(2),
E_magnitude,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ |E|$', fontsize=14)
plt.savefig('plots/pp-ionization/E_magnitude.png')
plt.show()
vmax1 = np.max(ExmBy + By)
vmin1 = np.min(ExmBy + By)
midpoint1 = 1 - vmax1 / (vmax1 + np.abs(vmin1))
shifted_cmap1 = shiftedColorMap(cm.seismic,
midpoint=midpoint1,
name='shifted')
### Plotting the magnitude
plt.contourf(
By_g3d2.get_zeta_arr(),
By_g3d2.get_x_arr(2),
ExmBy + By,
200,
cmap=shifted_cmap1) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ Ex $', fontsize=14)
plt.savefig('plots/pp-ionization/E_x.png')
plt.show()
vmax = np.max(EypBx - Bx)
vmin = np.min(EypBx - Bx)
midpoint = 1 - vmax / (vmax + np.abs(vmin))
shifted_cmap = shiftedColorMap(cm.seismic,
midpoint=midpoint,
name='shifted')
### Plotting the magnitude
plt.contourf(By_g3d2.get_zeta_arr(),
By_g3d2.get_x_arr(2),
EypBx - Bx,
200,
cmap=shifted_cmap) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ Ey$', fontsize=14)
plt.savefig('plots/pp-ionization/E_y.png')
plt.show()
def main():
#### Defining constants as in HiPACE
#define M_ELEC_PER_M_NUCL 5.4857990946e-4
#define E_MUON_MASS_RATIO 4.83633170e-3 /* from NIST database */
ELECTRON_CHARGE_IN_COUL = 1.60217657e-19
ELECTRON_MASS_IN_KG = 9.10938291e-31
SPEED_OF_LIGHT_IN_M_PER_S = 299792458
VAC_PERMIT_FARAD_PER_M = 8.854187817e-12
HYDROGEN_IONIZATION_ENERGIE_EV = 13.659843449
omega_alpha = 4.13e16
# [s^-1]
E_alpha = 5.1e11
# [V/m]
elec_density = 1e+23 # [1/m^3]
omega_p = np.sqrt(elec_density * (ELECTRON_CHARGE_IN_COUL**2) /
(VAC_PERMIT_FARAD_PER_M * ELECTRON_MASS_IN_KG))
# calculation of the plasma frequency
#print('omega_p is %0.3e' %omega_p)
E_0 = omega_p * ELECTRON_MASS_IN_KG * SPEED_OF_LIGHT_IN_M_PER_S / ELECTRON_CHARGE_IN_COUL
#calculation of the denormalization factor for the electric field
#print('E_0 is %0.3e' %E_0)
kp = SPEED_OF_LIGHT_IN_M_PER_S / omega_p
vectorfunc = np.vectorize(calc_ion_rate)
ionization_rate_crit = vectorfunc(3.2e10, 1, 13.659843449, 13.659843449, 0,
0)
print('ionization rate at crit field: %f' % ionization_rate_crit)
#home path Path definitions
Ez_path = './DATA/field_Ez_000000.0.h5'
ExmBy_path = './DATA/field_ExmBy_000000.0.h5'
EypBx_path = './DATA/field_EypBx_000000.0.h5'
Bx_path = './DATA/field_Bx_000000.0.h5'
By_path = './DATA/field_By_000000.0.h5'
#neutral_density_path = '/Users/diederse/desy/PIC-sim/HiPACE/tests/interaction_tests2/Cs/DATA/density_plasma_neutral_000000.h5'
neutral_density_path = './DATA/density_plasma_H_000000.0.h5'
### nersc path HAS TO BE MOUNTED
#Ez_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_Ez_000000.h5'
#ExmBy_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_ExmBy_000000.h5'
#EypBx_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_EypBx_000000.h5'
#Bx_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_Bx_000000.h5'
#By_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_By_000000.h5'
#neutral_density_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/density_plasma_neutral_000000.h5'
#vektorized function for calc ion rate
Ez_g3d = Grid3d(Ez_path)
ExmBy_g3d = Grid3d(ExmBy_path)
EypBx_g3d = Grid3d(EypBx_path)
Bx_g3d = Grid3d(Bx_path)
By_g3d = Grid3d(By_path)
neutral_density_g3d = Grid3d(neutral_density_path)
if not os.path.exists('./plots'):
os.makedirs('./plots')
if not os.path.exists('./plots/pp-ionization/'):
os.makedirs('./plots/pp-ionization/')
#Reading in the data
Ez = np.transpose(Ez_g3d.read(x2=0.0))
ExmBy = np.transpose(ExmBy_g3d.read(x2=0.0))
EypBx = np.transpose(EypBx_g3d.read(x2=0.0))
Bx = np.transpose(Bx_g3d.read(x2=0.0))
By = np.transpose(By_g3d.read(x2=0.0))
neutral_density = np.transpose(neutral_density_g3d.read(x2=0.0))
fig = plt.figure()
ax = fig.add_subplot(111)
plt.pcolormesh(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
neutral_density,
cmap=cm.Blues) #
cb = plt.colorbar()
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar()
cb.set_label(label=r'$n_p/n_0$', fontsize=16)
plt.savefig('./plots/pp-ionization/charge_density.png')
#plt.clim(0,3)
#plt.imshow(neutral_density)
plt.show()
plt.close(fig)
'''
#### Test to plot the By field
print(By_g3d.get_x_arr(0)) #'zeta array limits are %f' %
plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), By, 200, cmap=cm.seismic)
plt.ylabel(r'$k_p x$', fontsize =14)
#plt.xlim(By_g3d.get_zeta_arr)
#plt.ylim(By_g3d.get_x_arr)
plt.xlabel(r'$k_p \zeta$', fontsize =14)
cb = plt.colorbar()
cb.set_label(label = r'$B_y/E_0$', fontsize = 14)
plt.show()
'''
#check for directory
## computing the Magnitude of E:
E_magnitude = np.sqrt((ExmBy + By)**2 + (EypBx - Bx)**2 + Ez**2) * E_0
number_above_5_6_e10 = np.where(E_magnitude > 5.7e10)
number = E_magnitude[number_above_5_6_e10]
print(number)
print(
'the number of grid points with an electric field above 5.6e10 is %0.3e'
% len(number))
#print(number_above_5_6_e10)
print('The maximum magnitude of the electric field is %0.3e' %
np.max(E_magnitude))
### Plotting the magnitude
fig = plt.figure()
ax = fig.add_subplot(111)
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
E_magnitude / E_0,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar()
cb.set_label(label=r'$ |E|/E_0$', fontsize=16)
plt.savefig('./plots/pp-ionization/E_magnitude.png')
plt.show()
plt.close(fig)
## Computing the ionization rate:
#ionization_rate = np.nan_to_num(omega_alpha / (2.0 * np.pi) *4.0 * E_alpha/ E_magnitude * np.exp (-2.0/3.0 * E_alpha/ E_magnitude ) )# this is just correct for hydrogen!
ionization_rate = vectorfunc(E_magnitude, 1, 13.659843449, 13.659843449, 0,
0) # complete formulae for hydrogen
#ionization_rate = vectorfunc(E_magnitude, 2, 54.4177650, 24.58738880, 0,0 )
#print('The maximum ionization rate is %0.3e' % np.max(ionization_rate))
### Plotting the ionization rate:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
ionization_rate,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar()
cb.set_label(label='Ionization rate [1/s]', fontsize=16)
plt.savefig('./plots/pp-ionization/ion_rate.png')
plt.show()
plt.close(fig)
#plotting the rate vs electric field in general
#E_magnitude2 = 10**np.array(list(range(10, 15)))
#ionization_rate_plot = np.nan_to_num(omega_alpha / (2.0 * np.pi) *4.0 * E_alpha/ E_magnitude2 * np.exp (-2.0/3.0 * E_alpha/ E_magnitude2 ) )
#plt.plot(E_magnitude2, ionization_rate_plot)
#plt.show()
cbarvektor = np.linspace(0, 1, 11, endpoint=True)
#print(By_g3d.get_zeta_arr())
#print(np.abs(By_g3d.get_zeta_arr()[1] -By_g3d.get_zeta_arr()[0]))
deltat = np.abs(By_g3d.get_zeta_arr()[1] -
By_g3d.get_zeta_arr()[0]) / omega_p
print('Deltat is %0.3e' % deltat)
#computing the ionization probability
ion_probability = 1.0 - np.exp(-ionization_rate * deltat)
ionization_prob_crit = 1.0 - np.exp(-ionization_rate_crit * deltat)
print('ionization prob at crit field: %f' % ionization_prob_crit)
crit_ionization = ion_probability
crit_ionization[:, :] = ionization_prob_crit
#print('The maximum ionization probability is %0.3e' % np.max(ion_probability))
### Plotting the ionization probability:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
ion_probability,
200,
cmap=cm.Reds
) # here cmap reds, since it is not divering vmin=0, vmax=1,
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar(ticks=cbarvektor)
#cb.set_clim(vmin=0,vmax=1)
#cb.set_ticks(cbarvektor)
#plt.clim(0,1)
cb.set_label(label='Ionization probability', fontsize=16)
plt.show()
plt.close(fig)
#calculate the cumulative probability:
cum_prob = (1 - np.cumprod(1 - ion_probability[:, ::-1], axis=1)
)[:, ::-1] #np.flip(np.flip(ion_probability, 1).cumsum(), 1) #
### Plotting the ionization probability:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
cum_prob,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar(ticks=cbarvektor)
#plt.clim(0,1.01)
cb.set_label(label=r'$P_{ADK}$', fontsize=16)
plt.savefig('./plots/pp-ionization/cum_ion_prob.png')
plt.show()
plt.close(fig)
'''START ONLY FOR CRITICAL IONIZATION PROBABILITY CHECK '''
#calculate the cumulative probability:
cum_prob_crit = (
1 - np.cumprod(1 - crit_ionization[:, ::-1], axis=1)
)[:, ::-1] #np.flip(np.flip(ion_probability, 1).cumsum(), 1) #
### Plotting the ionization probability:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
cum_prob_crit,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar(ticks=cbarvektor)
#plt.clim(0,1.01)
cb.set_label(label=r'$P_{ADK}$', fontsize=16)
plt.savefig('./plots/pp-ionization/cum_ion_prob_crit.png')
plt.show()
plt.close(fig)
''' END ONLY FOR CRITICAL IONIZATION PROBABILITY CHECK '''
### Plotting the ionization probability:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.pcolormesh(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
ion_probability,
cmap=cm.Reds) #here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar(ticks=cbarvektor)
plt.clim(0, 1)
cb.set_label(label='Ionization probability', fontsize=16)
plt.savefig('./plots/pp-ionization/ion_prob.png')
plt.show()
plt.close(fig)
#Plotting the differences between HiPACE and analytical model
differenz = (
cum_prob
) - neutral_density # cum prob + 1 because it is preionized to level 1
#producing a asymmetric Colorbar
vmax = np.max(differenz)
vmin = np.min(differenz)
midpoint = 1 - vmax / (vmax + np.abs(vmin))
shifted_cmap = shiftedColorMap(cm.seismic,
midpoint=midpoint,
name='shifted')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
differenz,
200,
cmap=shifted_cmap
) # cm.seismic) # here cmap reds, since it is not diverging
plt.ylabel(r'$k_p x$', fontsize=16)
plt.xlabel(r'$k_p \zeta$', fontsize=16)
cb = plt.colorbar()
plt.clim(vmin, vmax)
cb.set_label(label=r'$P_{ADK} - n_p/n_0$', fontsize=16)
plt.savefig('./plots/pp-ionization/diff_ion_prob.png')
plt.show()
plt.close(fig)
print('summed absolut difference: %f' % (np.sum(abs(differenz))))
'''
def main():
basic_cols = ['#75b765', '#808080', '#ffd700']
basic_cols = ['#2020ff', '#808080', '#ff2020']
basic_cols = ['#0000ff', '#00ffff', '#808080', '#ffff00', '#ff0000']
my_cmap = LinearSegmentedColormap.from_list('mycmap', basic_cols)
parser = binSlab_parser()
args = parser.parse_args()
if args.latexfont:
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
modnum = args.modlines
density_path = args.dens_data
if not args.tracksoff and not args.track_range:
print(
'ERROR: tracks not turned off, but no tracking range given. Provide tracking range with --track_range to get correct colormap.'
)
# NHC=2
proc_suffix_str = '_proc_'
ppart_track_str = 'ppart_track'
bin_fending = '.bin'
for density_files in args.dens_data:
ionized_density_g3d1 = Grid3d(density_files)
ionized_density = np.transpose(ionized_density_g3d1.read(x2=0.0))
print(density_files)
timestamp = density_files.split("_")[-1].split(".h5")[0]
if args.old_timestamp:
timestamp = timestamp.split(".")[0]
if args.beam_data:
for beam_density_files in args.beam_data:
if timestamp in beam_density_files:
beam_density_g3d1 = Grid3d(beam_density_files)
beam_density = np.transpose(beam_density_g3d1.read(x2=0.0))
for filepath in args.path:
filename = ''
slash = '/'
if slash in filepath:
dirname, filename = filepath.split('/')
else:
dirname = filepath
array = np.empty(0)
for files in os.listdir(dirname):
if filename != '':
if filename in files:
if (timestamp in files) or args.manual:
print('Reading: %s/%s' % (dirname, files))
filepath = dirname + slash + files
array = np.append(
array, np.fromfile(filepath, dtype=np.float32))
else:
if (timestamp in files) or args.manual:
print('Reading: %s/%s' % (dirname, files))
filepath = dirname + slash + files
array = np.append(
array, np.fromfile(filepath, dtype=np.float32))
''' reshape the binary data to a matrix with particle
tag information in each row'''
array = np.reshape(array, (int(len(array) / 11), 11))
print(np.shape(array))
''' set up figure for 3D plotting '''
''' In order to simplify the splitting and plotting, the particle
information matrix is sorted by the plasma species, the proc tag, then by the particle tag
and finally by zeta '''
indizes = np.lexsort(
(array[:, 5], array[:, 6], array[:, 7], array[:, 9]))
array = array[indizes]
''' split by different plasma species types '''
w = np.split(array, np.where(np.diff(array[:, 9]) != 0)[0] + 1)
for k in range(len(w)):
# if not args.tracksoff and args.dens_data and args.beam_data:
# fig = plt.figure(figsize=(9,5))
# else:
fig = plt.figure()
ax = fig.add_subplot(111)
''' get min and max value for universal colorbar later '''
max_density = np.max(np.abs(ionized_density))
min_density = 0
if args.clim:
levels = MaxNLocator(nbins=512).tick_values(
args.clim[0], args.clim[1])
max_level = max_density
vmin = args.clim[0]
vmax = args.clim[1]
#selecting correct extend method
if min_density < args.clim[0] and max_density > args.clim[
1]:
extend = 'both'
elif min_density < args.clim[
0] and max_density <= args.clim[1]:
extend = 'min'
elif min_density >= args.clim[
0] and max_density > args.clim[1]:
extend = 'max'
elif min_density >= args.clim[
0] and max_density <= args.clim[1]:
extend = 'neither'
else:
print(
'Error: unexpected case, couldn\'t extend in the correct way!'
)
extend = 'neither'
else:
levels = MaxNLocator(nbins=512).tick_values(0, max_density)
max_level = max_density
vmin = min_density
vmax = max_density
extend = 'neither'
if args.ptype == 'pcolormesh':
plot1 = plt.pcolormesh(ionized_density_g3d1.get_zeta_arr(),
ionized_density_g3d1.get_x_arr(2),
np.abs(ionized_density),
cmap=cm.PuBu,
alpha=1) #
elif args.ptype == 'contourf':
plot1 = plt.contourf(ionized_density_g3d1.get_zeta_arr(),
ionized_density_g3d1.get_x_arr(2),
np.abs(ionized_density),
cmap=cm.PuBu,
levels=levels,
vmin=vmin,
vmax=vmax,
extend=extend)
else:
print('This type is not implemented yet')
cbarmap = plt.cm.ScalarMappable(cmap=cm.PuBu)
cbarmap.set_array(np.abs(ionized_density))
if args.clim:
cbarmap.set_clim(args.clim[0], args.clim[1])
cbar1 = plt.colorbar(cbarmap,
boundaries=np.arange(
args.clim[0],
args.clim[1] + 0.0002, 0.0001),
extend=extend,
fraction=0.046,
pad=0.1)
ticks = MaxNLocator(5).tick_values(vmin, vmax)
cbar1.set_ticks(ticks)
plot1.set_clim([args.clim[0], args.clim[1]])
else:
cbarmap.set_clim([0, max_density])
cbar1 = plt.colorbar(cbarmap,
boundaries=np.arange(
0, max_density + 0.0002, 0.0001),
extend=extend,
fraction=0.046,
pad=0.1)
ticks = MaxNLocator(5).tick_values(vmin, vmax)
cbar1.set_ticks(ticks)
plot1.set_clim([0, max_density])
if args.xlim:
plt.xlim(args.xlim[0], args.xlim[1])
if args.ylim:
plt.ylim(args.ylim[0], args.ylim[1])
cbar1.ax.set_title(r'$n_p/n_0$', fontsize=16, pad=args.cbarpad)
cbar1.ax.tick_params(labelsize=args.axticklabelsize)
if args.beam_data:
max_density = np.max(np.abs(beam_density))
cmap = plt.cm.Reds
# Get the colormap colors
my_cmap = cmap(np.arange(cmap.N))
# Set alpha
my_cmap[:, -1] = np.linspace(0, 1, cmap.N)
# Create new colormap
my_cmap = ListedColormap(my_cmap)
if args.cblim:
levels = MaxNLocator(nbins=512).tick_values(
args.cblim[0], args.cblim[1])
max_level = max_density
vmin = args.cblim[0]
vmax = args.cblim[1]
#selecting correct extend method
if min_density < args.cblim[
0] and max_density > args.cblim[1]:
extend = 'both'
elif min_density < args.cblim[
0] and max_density <= args.cblim[1]:
extend = 'min'
elif min_density >= args.cblim[
0] and max_density > args.cblim[1]:
extend = 'max'
elif min_density >= args.cblim[
0] and max_density <= args.cblim[1]:
extend = 'neither'
else:
print(
'Error: unexpected case, couldn\'t extend in the correct way!'
)
extend = 'neither'
else:
levels = MaxNLocator(nbins=512).tick_values(
0, max_density)
max_level = max_density
vmin = 0
vmax = max_density
extend = 'neither'
if args.ptype == 'pcolormesh':
plt.pcolormesh(beam_density_g3d1.get_zeta_arr(),
beam_density_g3d1.get_x_arr(2),
np.abs(beam_density),
cmap=my_cmap,
vmin=vmin,
vmax=vmax)
elif args.ptype == 'contourf':
plt.contourf(beam_density_g3d1.get_zeta_arr(),
beam_density_g3d1.get_x_arr(2),
np.abs(beam_density),
cmap=my_cmap,
levels=levels,
vmin=vmin,
vmax=vmax,
extend=extend)
else:
print('This type is not implemented yet')
cbarmap = plt.cm.ScalarMappable(cmap=my_cmap)
cbarmap.set_array(np.abs(beam_density))
if args.cblim:
# Note on colorbar: boundaries have to be set manually, because otherwise there will be ugly stripes
# afterwards the ticks have to set manually as well, set them at the correct place
cbarmap.set_clim(args.cblim[0], args.cblim[1])
cbar2 = plt.colorbar(cbarmap,
boundaries=np.arange(
args.cblim[0],
args.cblim[1] + 0.0002,
0.0001),
extend=extend,
fraction=0.046,
pad=0.1)
ticks = MaxNLocator(5).tick_values(vmin, vmax)
cbar2.set_ticks(ticks)
cbar2.set_clim([args.cblim[0], args.cblim[1]])
else:
#cbarmap.set_clim(0, max_density)
cbar2 = plt.colorbar(cbarmap,
boundaries=np.arange(
0, max_density + 0.0001,
0.0001),
fraction=0.046,
pad=0.1)
ticks = MaxNLocator().tick_values(vmin, vmax)
cbar2.set_ticks(ticks)
cbar2.set_clim([0, max_density])
cbar2.ax.set_title(r'$n_b/n_0$', pad=args.cbarpad)
cbar2.ax.tick_params(labelsize=args.axticklabelsize)
if not args.tracksoff:
if args.track_color == "u_tot":
cmin = min(w[k][:, 8])
cmax = max(w[k][:, 8])
chosencmap = cm.jet
elif args.track_color == "beta_z":
cmin = -1
cmax = 1
chosencmap = my_cmap
elif args.track_color == "beta_y":
cmin = -1
cmax = 1
chosencmap = my_cmap
elif args.track_color == "none":
print('no coloring option selected.')
else:
print(
"This attribute doesn't exist or is not implemented yet"
)
break
''' Splitting the array into each particle trajectory by proc tag'''
d = np.split(w[k],
np.where(np.diff(w[k][:, 7]) != 0)[0] + 1)
for i in range(len(d)):
''' Splitting the array into each particle trajectory by part tag'''
e = np.split(d[i],
np.where(np.diff(d[i][:, 6]) != 0)[0] + 1)
number = int(np.floor(len(e) / modnum))
cmap = plt.get_cmap('jet')
colors = [cmap(i) for i in np.linspace(0, 1, number)]
colors2 = [cmap(i) for i in np.linspace(0, 1, 10000)]
start_segments = np.linspace(args.track_range[0],
args.track_range[1],
10000)
for j in range(int(np.floor(len(e) / modnum))):
x = e[modnum * j][:, 0]
y = e[modnum * j][:, 1]
z = e[modnum * j][:, 5]
if args.track_color == "u_tot":
c = e[modnum * j][:, 8]
elif args.track_color == "beta_z":
c = e[modnum * j][:, 4] / np.sqrt(
1 + e[modnum * j][:, 8]**2)
elif args.track_color == "beta_y":
c = e[modnum * j][:, 2] / np.sqrt(
1 + e[modnum * j][:, 8]**2)
print(
"laenge track proc tag %i part tag %i ist %i" %
(i, modnum * j, len(z)))
if args.track_color == "u_tot":
plot_2D_colourline(z, x, c, cmin, cmax,
args.linewidth)
elif args.track_color == "beta_z":
plot_2D_colourline_beta(
z, x, c, args.linewidth)
elif args.track_color == "beta_y":
plot_2D_colourline_beta(
z, x, c, args.linewidth)
elif args.track_color == 'none':
if len(x) > 0:
index = np.argmin(
abs(start_segments - x[-1]))
# print('index : %i' %index)
# print('x0 ist %f' %x[-1])
ax.plot(z,
x,
color=colors2[index],
linewidth=0.3)
''' Set colorbar '''
if args.track_color != "none":
norm = matplotlib.colors.Normalize(vmin=np.min(cmin),
vmax=np.max(cmax))
# choose a colormap
c_m = chosencmap
# create a ScalarMappable and initialize a data structure
s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm)
s_m.set_array([])
cbar = plt.colorbar(s_m, fraction=0.052, pad=0.06)
else:
# norm = matplotlib.colors.Normalize( vmin= 0, vmax=6)
# choose a colormap
c_m = cm.jet
# create a ScalarMappable and initialize a data structure
s_m = matplotlib.cm.ScalarMappable(cmap=c_m)
s_m.set_array(
[args.track_range[0], args.track_range[1]])
cbar = plt.colorbar(s_m, fraction=0.052, pad=0.06)
if args.track_color == "u_tot":
cbar.ax.set_title(r'$|u|$', pad=args.cbarpad)
elif args.track_color == "beta_z":
cbar.ax.set_title(r'$\beta_z$', pad=args.cbarpad)
elif args.track_color == "beta_y":
cbar.ax.set_title(r'$\beta_y$', pad=args.cbarpad)
else:
cbar.ax.set_title(r'$k_p\,X_0$',
fontsize=16,
pad=args.cbarpad)
ax.set_xlabel(r'$k_p\,\zeta$', fontsize=16)
ax.set_ylabel(r'$k_p\,x$', fontsize=16, labelpad=-5)
ax.tick_params(labelsize=args.axticklabelsize)
cbar.ax.tick_params(labelsize=args.axticklabelsize)
if args.adjust_bottom:
fig.subplots_adjust(bottom=args.adjust_bottom)
savepath = 'plots/g3d-slice'
mkdirs_if_nexist(savepath)
if not args.tracksoff:
tracked = 'tracked_'
else:
tracked = ''
if args.savename:
savename = '/' + args.savename + '_'
else:
savename = '/ionized_electron_density_'
if args.file_format == 'eps':
save_path_name = savepath + savename + tracked + timestamp + '.eps'
print('Saving figure...')
fig.savefig(save_path_name, format='eps', dpi=args.dpi)
print('Writing file...')
elif args.file_format == 'pdf':
save_path_name = savepath + savename + tracked + timestamp + '.pdf'
print('Saving figure...')
fig.savefig(save_path_name, format='pdf', dpi=args.dpi)
print('Writing file...')
elif args.file_format == 'png':
save_path_name = savepath + savename + tracked + timestamp + '.png'
print('Saving figure...')
fig.savefig(save_path_name, format='png', dpi=args.dpi)
print('Writing file...')
else:
print(
'ERROR: unknown file output format. Please choose from eps, pdf or png'
)
if args.verbose:
sys.stdout.write('Saved: %s\n' % save_path_name)
sys.stdout.flush()
plt.close(fig)
print('Done!')
def main():
#### Defining constants as in HiPACE
#define M_ELEC_PER_M_NUCL 5.4857990946e-4
#define E_MUON_MASS_RATIO 4.83633170e-3 /* from NIST database */
ELECTRON_CHARGE_IN_COUL = 1.60217657e-19
ELECTRON_MASS_IN_KG = 9.10938291e-31
SPEED_OF_LIGHT_IN_M_PER_S = 299792458
VAC_PERMIT_FARAD_PER_M = 8.854187817e-12
HYDROGEN_IONIZATION_ENERGIE_EV = 13.659843449
omega_alpha = 4.13e16
# [s^-1]
E_alpha = 5.1e11
# [V/m]
elec_density = 1e+23 # [1/m^3]
omega_p = np.sqrt(4 * np.pi * elec_density * (ELECTRON_CHARGE_IN_COUL**2) /
(VAC_PERMIT_FARAD_PER_M * ELECTRON_MASS_IN_KG))
# calculation of the plasma frequency
#print('omega_p is %0.3e' %omega_p)
E_0 = omega_p * ELECTRON_MASS_IN_KG * SPEED_OF_LIGHT_IN_M_PER_S / ELECTRON_CHARGE_IN_COUL
#calculation of the denormalization factor for the electric field
#print('E_0 is %0.3e' %E_0)
kp = SPEED_OF_LIGHT_IN_M_PER_S / omega_p
print('kp = ' + str(kp))
#home path Path definitions
Ez_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test/DATA/field_Ez_000000.h5'
ExmBy_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test/DATA/field_ExmBy_000000.h5'
EypBx_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test/DATA/field_EypBx_000000.h5'
Bx_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test/DATA/field_Bx_000000.h5'
By_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test/DATA/field_By_000000.h5'
# neutral_density_path2 = '/Users/diederse/desy/PIC-sim/HiPACE/tests/the_return_of_the_local_electron/theo_test/DATA/density_ionized_electrons_plasma_H_000000.h5'
#Ez_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_Ez_000000.h5'
#ExmBy_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_ExmBy_000000.h5'
#EypBx_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_EypBx_000000.h5'
#Bx_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_Bx_000000.h5'
#By_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/field_By_000000.h5'
#neutral_density_path = '/Users/diederse/mountpoints/ed_scratch/simulations/hipace/tests/neutral1/DATA/density_plasma_neutral_000000.h5'
#vektorized function for calc ion rate
vectorfunc = np.vectorize(calc_ion_rate)
Ez_g3d = Grid3d(Ez_path2)
ExmBy_g3d = Grid3d(ExmBy_path2)
EypBx_g3d = Grid3d(EypBx_path2)
Bx_g3d = Grid3d(Bx_path2)
By_g3d = Grid3d(By_path2)
# neutral_density_g3d = Grid3d(neutral_density_path2)
#Reading in the data
Ez = np.transpose(Ez_g3d.read(x2=0.0))
ExmBy = np.transpose(ExmBy_g3d.read(x2=0.0))
EypBx = np.transpose(EypBx_g3d.read(x2=0.0))
Bx = np.transpose(Bx_g3d.read(x2=0.0))
By = np.transpose(By_g3d.read(x2=0.0))
# neutral_density = np.transpose(neutral_density_g3d.read(x2=0.0))
# plt.pcolormesh(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), neutral_density, cmap=cm.Blues_r) #
# cb = plt.colorbar()
# plt.clim(0,-3)
# #plt.imshow(neutral_density)
# plt.show()
'''
#### Test to plot the By field
print(By_g3d.get_x_arr(0)) #'zeta array limits are %f' %
plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), By, 200, cmap=cm.seismic)
plt.ylabel(r'$k_p x$', fontsize =14)
#plt.xlim(By_g3d.get_zeta_arr)
#plt.ylim(By_g3d.get_x_arr)
plt.xlabel(r'$k_p \zeta$', fontsize =14)
cb = plt.colorbar()
cb.set_label(label = r'$B_y/E_0$', fontsize = 14)
plt.show()
'''
#check for directory
if not os.path.exists('plots'):
os.makedirs('plots')
if not os.path.exists('plots/pp-ionization/'):
os.makedirs('plots/pp-ionization/')
## computing the Magnitude of E:
E_magnitude = np.sqrt((ExmBy + By)**2 + (EypBx - Bx)**2 + Ez**2) * E_0
# vmax1 = np.max(ExmBy + By)
# vmin1 = np.min(ExmBy + By)
# midpoint1 = 1 - vmax1/(vmax1 + np.abs(vmin1))
# shifted_cmap1 = shiftedColorMap(cm.seismic, midpoint=midpoint1, name='shifted')
#
# ### Plotting Ex from HiPACE
# plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), ExmBy + By, 200, cmap=shifted_cmap1) # here cmap reds, since it is not divering
# plt.ylabel(r'$k_p x$', fontsize =14)
# plt.xlabel(r'$k_p \zeta$', fontsize =14)
# cb = plt.colorbar()
# cb.set_label(label = r'$ Ex$ HiPACE', fontsize = 14)
# plt.savefig('plots/pp-ionization/E_x.png')
# plt.show()
# Plotting Ey from HiPACE
# vmax = np.max(EypBx - Bx)
# vmin = np.min(EypBx - Bx)
# midpoint = 1 - vmax/(vmax + np.abs(vmin))
# shifted_cmap = shiftedColorMap(cm.seismic, midpoint=midpoint, name='shifted')
#
# ### Plotting the magnitude
# print('length By_g3d.get_x_arr(2) = ' + str(len(By_g3d.get_x_arr(2))))
# plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), EypBx - Bx, 200, cmap=shifted_cmap) # here cmap reds, since it is not divering
# plt.ylabel(r'$k_p x$', fontsize =14)
# plt.xlabel(r'$k_p \zeta$', fontsize =14)
# cb = plt.colorbar()
# cb.set_label(label = r'$ Ey$', fontsize = 14)
# plt.savefig('plots/pp-ionization/E_y.png')
# plt.show()
nb = 1.2
ni = 1
can_field = np.zeros(shape=(256, 300))
can_field2 = np.zeros(shape=(256, 300))
length_x_step = 24 / 256
for i in range(0, 128):
can_field[127 - i,
150:200] = (63 / 64)**2 * nb * 0.5 / (length_x_step *
(i + 0.5))
can_field[128 + i,
150:200] = -(63 / 64)**2 * nb * 0.5 / (length_x_step *
(i + 0.5))
# Rp needs to be adjusted here as well:
can_field2[127 - i,
0:200] = -(63 / 64)**2 * ni * 0.5 / (length_x_step *
(i + 0.5))
can_field2[128 + i,
0:200] = +(63 / 64)**2 * ni * 0.5 / (length_x_step *
(i + 0.5))
#can_field2[127-i, 0:200] = -(255/64)**2 * ni * 0.5 / (0.09375 * (i + 0.5))
#can_field2[128+i, 0:200] = +(255/64)**2 * ni * 0.5 / (0.09375 * (i + 0.5))
### FOR Rp = 2
can_field[127 - i,
150:200] = (129 / 64)**2 * nb * 0.5 / (length_x_step *
(i + 0.5))
can_field[128 + i,
150:200] = -(129 / 64)**2 * nb * 0.5 / (length_x_step *
(i + 0.5))
# Rp needs to be adjusted here as well:
can_field2[127 - i,
0:200] = -(129 / 64)**2 * ni * 0.5 / (length_x_step *
(i + 0.5))
can_field2[128 + i,
0:200] = +(129 / 64)**2 * ni * 0.5 / (length_x_step *
(i + 0.5))
### for rp=rb=2
for i in range(0, 22):
can_field[127 - i, 150:200] = (length_x_step * (i + 0.5)) * nb * 0.5
can_field[128 + i, 150:200] = -length_x_step * (i + 0.5) * nb * 0.5
can_field2[127 - i, 0:200] = -(length_x_step * (i + 0.5)) * ni * 0.5
can_field2[128 + i, 0:200] = +length_x_step * (i + 0.5) * ni * 0.5
# for i in range(0,11):
# can_field[127-i, 150:200] = (length_x_step * (i + 0.5)) * nb * 0.5
# can_field[128+i, 150:200] = -length_x_step * (i + 0.5) * nb * 0.5
#for Rp = 255/64
# for i in range(0, 43):
# can_field2[127-i, 0:200] = -(0.09375 * (i + 0.5)) * ni * 0.5
# can_field2[128+i, 0:200] = +0.09375 * (i + 0.5) * ni * 0.5
#for Rp = 63/64
# for i in range(0, 11):
# can_field2[127-i, 0:200] = -(length_x_step * (i + 0.5)) * ni * 0.5
# can_field2[128+i, 0:200] = +length_x_step * (i + 0.5) * ni * 0.5
#for Rp = 33/64
# for i in range(0, 6):
# can_field2[127-i, 0:200] = -(0.09375 * (i + 0.5)) * ni * 0.5
# can_field2[128+i, 0:200] = +0.09375 * (i + 0.5) * ni * 0.5
can_field += can_field2
plt.plot(np.arange(-12, 12, length_x_step), can_field[:, 175])
plt.show()
vmax = np.max(can_field)
vmin = np.min(can_field)
midpoint = 1 - vmax / (vmax + np.abs(vmin))
shifted_cmap = shiftedColorMap(cm.seismic,
midpoint=midpoint,
name='shifted')
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
can_field,
200,
cmap=shifted_cmap) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ E_r/E_0$ analytical', fontsize=14)
plt.axvline(x=-2, color='black')
plt.axhline(y=129 / 64, color='black')
plt.text(-7.5, 11, 'IV', fontsize=16)
plt.text(-1, 11, 'III', fontsize=16)
plt.text(-1, 0.5, 'I', fontsize=16)
plt.text(-7.5, 0.5, 'II', fontsize=16)
plt.xlim(-8, 0)
plt.ylim(0, 12)
plt.show()
## Computing the ionization rate:
#ionization_rate = np.nan_to_num(omega_alpha / (2.0 * np.pi) *4.0 * E_alpha/ E_magnitude * np.exp (-2.0/3.0 * E_alpha/ E_magnitude ) )# this is just correct for hydrogen!
ionization_rate = vectorfunc(
abs(can_field) * E_0, 1, 13.659843449, 13.659843449, 0,
0) # complete formulae for hydrogen
#ionization_rate = vectorfunc(E_magnitude, 2, 54.4177650, 24.58738880, 0,0 )
potential = np.zeros(shape=(256, 300))
#potential[0:128, :] = -np.cumsum(can_field[0:128, :], axis=0)
potential[128:, :] = -np.cumsum(can_field[128:, :] * 0.09375, axis=0)
# for i in range(128):
# potential[128+i] = potential[127-i,:]
startradius = 63 / 63
endradius = 1.57
def calc_pot_at_x_within_bunch(x):
return potential[int(128 + np.floor(x / 0.09375)), 190]
def calc_pot_at_x_behind_bunch(x):
return -potential[int(128 + np.floor(x / 0.09375)), 10]
print('potential at 1.567:')
print(calc_pot_at_x_within_bunch(endradius))
print('potential at 63/64:')
print(calc_pot_at_x_within_bunch(startradius))
print('therefore the total kinetic energy the electron gained is: ')
print(
calc_pot_at_x_within_bunch(endradius) -
calc_pot_at_x_within_bunch(startradius) +
calc_pot_at_x_behind_bunch(endradius))
plt.plot(By_g3d.get_x_arr(2)[128:],
-potential[128:, 10],
label=r'$E_{pot}$ behind beam analytical')
plt.plot(By_g3d.get_x_arr(2)[128:],
potential[128:, 190],
label=r'$-E_{pot}$ within beam analytical')
#plt.plot(By_g3d.get_x_arr(2)[128:], can_field[128:,10], label=r'$E_r/E_0$ analytical')
plt.legend()
plt.grid()
plt.show()
vmax = np.max(potential)
vmin = np.min(potential)
midpoint = 1 - vmax / (vmax + np.abs(vmin))
shifted_cmap = shiftedColorMap(cm.seismic,
midpoint=midpoint,
name='shifted')
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
potential,
200,
cmap=shifted_cmap) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ V$ analytical', fontsize=14)
plt.show()
ekin_at_r1 = -(potential - potential[133, 190])
vmax = np.max(ekin_at_r1)
vmin = np.min(ekin_at_r1)
midpoint = 1 - vmax / (vmax + np.abs(vmin))
shifted_cmap = shiftedColorMap(cm.seismic,
midpoint=midpoint,
name='shifted')
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
ekin_at_r1,
200,
cmap=shifted_cmap) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ epot @ r = 1 $ analytical', fontsize=14)
plt.show()
### Plotting the magnitude
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
E_magnitude,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ |E|$ HiPACE', fontsize=14)
plt.savefig('plots/pp-ionization/E_magnitude.png')
plt.show()
#print('The maximum ionization rate is %0.3e' % np.max(ionization_rate))
### Plotting the ionization rate:
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
can_field,
200,
cmap=cm.seismic) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label=r'$ |E| analytical$', fontsize=14)
plt.savefig('plots/pp-ionization/can_field.png')
plt.show()
plt.plot(By_g3d.get_x_arr(2), (ExmBy + By)[:, 190],
label=r'$Ex/E_0$ HiPACE')
plt.plot(By_g3d.get_x_arr(2),
can_field[:, 190],
label=r'$Ex/E_0$ analytical')
plt.legend()
plt.show()
print('E at roughly 0.5 within beam: %f ' % can_field[122, 190])
print('E at roughly 0.5 behind beam : %f ' % can_field[122, 1])
print('E at roughly 4 within beam: %f ' % can_field[85, 190])
print('E at roughly 4 behind beam : %f ' % can_field[85, 1])
print('E at roughly 4 within beam: %f ' % can_field[85, 190])
print('E at roughly 4 behind beam : %f ' % can_field[85, 1])
#print('The maximum ionization rate is %0.3e' % np.max(ionization_rate))
### Plotting the ionization rate:
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
ionization_rate,
200,
cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb = plt.colorbar()
cb.set_label(label='Ionization rate [1/s]', fontsize=14)
plt.savefig('plots/pp-ionization/ion_rate.png')
plt.show()
cbarvektor = np.linspace(0, 1, 11, endpoint=True)
#print(By_g3d.get_zeta_arr())
#print(np.abs(By_g3d.get_zeta_arr()[1] -By_g3d.get_zeta_arr()[0]))
deltat = np.abs(By_g3d.get_zeta_arr()[1] -
By_g3d.get_zeta_arr()[0]) / omega_p
print('Deltat is %0.3e' % deltat)
#computing the ionization probability
ion_probability = 1.0 - np.exp(-ionization_rate * deltat)
#print('The maximum ionization probability is %0.3e' % np.max(ion_probability))
## Plotting the ionization probability:
plt.contourf(By_g3d.get_zeta_arr(),
By_g3d.get_x_arr(2),
ion_probability,
200,
cmap=cm.Reds
) # here cmap reds, since it is not divering vmin=0, vmax=1,
plt.ylabel(r'$k_p x$', fontsize=14)
plt.xlabel(r'$k_p \zeta$', fontsize=14)
cb.set_label(label='Ionization probability', fontsize=14)
plt.show()
# cbarvektor = np.linspace(0,1,11, endpoint = True)
# #print(By_g3d.get_zeta_arr())
# #print(np.abs(By_g3d.get_zeta_arr()[1] -By_g3d.get_zeta_arr()[0]))
# deltat = np.abs(By_g3d.get_zeta_arr()[1] -By_g3d.get_zeta_arr()[0]) / omega_p
# print('Deltat is %0.3e' %deltat)
# #computing the ionization probability
# ion_probability = 1.0 - np.exp(-ionization_rate * deltat )
# #print('The maximum ionization probability is %0.3e' % np.max(ion_probability))
# ### Plotting the ionization probability:
# plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), ion_probability, 200,cmap=cm.Reds) # here cmap reds, since it is not divering vmin=0, vmax=1,
# plt.ylabel(r'$k_p x$', fontsize =14)
# plt.xlabel(r'$k_p \zeta$', fontsize =14)
#
# cb = plt.colorbar(ticks = cbarvektor)
# #cb.set_clim(vmin=0,vmax=1)
# #cb.set_ticks(cbarvektor)
# #plt.clim(0,1)
# cb.set_label(label = 'Ionization probability', fontsize = 14)
# plt.show()
'''
#calculate the cumulative probability:
cum_prob = (1 - np.cumprod(1-ion_probability[:,::-1], axis=1))[:,::-1] #np.flip(np.flip(ion_probability, 1).cumsum(), 1) #
### Plotting the ionization probability:
plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), cum_prob, 200, cmap=cm.Reds) # here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize =14)
plt.xlabel(r'$k_p \zeta$', fontsize =14)
cb = plt.colorbar(ticks = cbarvektor)
#plt.clim(0,1.01)
cb.set_label(label = 'Ionization probability', fontsize = 14)
plt.savefig('plots/pp-ionization/cum_ion_prob.png')
plt.show()
### Plotting the ionization probability:
plt.pcolormesh(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), ion_probability, cmap=cm.Reds) #here cmap reds, since it is not divering
plt.ylabel(r'$k_p x$', fontsize =14)
plt.xlabel(r'$k_p \zeta$', fontsize =14)
cb = plt.colorbar(ticks = cbarvektor)
plt.clim(0,1)
cb.set_label(label = 'Ionization probability', fontsize = 14)
plt.savefig('plots/pp-ionization/ion_prob.png')
plt.show()
#Plotting the differences between HiPACE and analytical model
differenz = ( cum_prob + 1 ) - neutral_density # cum prob + 1 because it is preionized to level 1
#producing a asymmetric Colorbar
vmax = np.max(differenz)
vmin = np.min(differenz)
midpoint = 1 - vmax/(vmax + np.abs(vmin))
shifted_cmap = shiftedColorMap(cm.seismic, midpoint=midpoint, name='shifted')
plt.contourf(By_g3d.get_zeta_arr(), By_g3d.get_x_arr(2), differenz, 200, cmap=shifted_cmap ) # cm.seismic) # here cmap reds, since it is not diverging
plt.ylabel(r'$k_p x$', fontsize =14)
plt.xlabel(r'$k_p \zeta$', fontsize =14)
cb = plt.colorbar()
plt.clim(vmin,vmax)
cb.set_label(label = r'$N_{H^+ analyt} - N_{H^+ HiPace}$', fontsize = 14)
plt.savefig('plots/pp-ionization/diff_ion_prob.png')
plt.show()
'''
'''