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improve script
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Francesco Massimo committed Nov 21, 2023
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232 changes: 115 additions & 117 deletions InputNamelist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,28 +78,28 @@
random_seed = smilei_mpi_rank
)

# ######################### Define the laser pulse
#
# #### laser parameters
# laser_fwhm = 25.5*math.sqrt(2)*fs # laser FWHM duration in field, i.e. FWHM duration in intensity*sqrt(2)
# laser_waist = 12*um # laser waist, conversion from um
# center_laser = Lx-1.7*laser_fwhm # laser position at the start of the simulation
# x_focus = (center_laser+0.1*laser_fwhm) # laser focal plane position
# a0 = 1.8 # laser peak field, normalized by E0 defined above
#
# #### Define a Gaussian bunch with Gaussian temporal envelope
# LaserEnvelopeGaussianAM(
# a0 = a0,
# omega = (2.*math.pi/lambda0*c)/reference_frequency, # laser frequency, normalized
# focus = [x_focus,0.], # laser focus, [x,r] position
# waist = laser_waist, # laser waist
# time_envelope = tgaussian(center=center_laser, fwhm=laser_fwhm), # time profile of the laser pulse
# envelope_solver = 'explicit_reduced_dispersion',
# Envelope_boundary_conditions = [ ["reflective"],["PML"] ],
# Env_pml_sigma_parameters = [[0.9 ,2 ],[80.0,2] ,[80.0,2 ]],
# Env_pml_kappa_parameters = [[1.00,1.00,2],[1.00,1.00,2],[1.00,1.00,2]],
# Env_pml_alpha_parameters = [[0.90,0.90,1],[0.65,0.65,1],[0.65,0.65,1]]
# )
######################### Define the laser pulse

#### laser parameters
#laser_fwhm = 25.5*math.sqrt(2)*fs # laser FWHM duration in field, i.e. FWHM duration in intensity*sqrt(2)
#laser_waist = 12*um # laser waist, conversion from um
#center_laser = Lx-1.7*laser_fwhm # laser position at the start of the simulation
#x_focus = (center_laser+0.1*laser_fwhm) # laser focal plane position
#a0 = 1.8 # laser peak field, normalized by E0 defined above

#### Define a Gaussian bunch with Gaussian temporal envelope
#LaserEnvelopeGaussianAM(
# a0 = a0,
# omega = (2.*math.pi/lambda0*c)/reference_frequency, # laser frequency, normalized
# focus = [x_focus,0.], # laser focus, [x,r] position
# waist = laser_waist, # laser waist
# time_envelope = tgaussian(center=center_laser, fwhm=laser_fwhm), # time profile of the laser pulse
# envelope_solver = 'explicit_reduced_dispersion',
# Envelope_boundary_conditions = [ ["reflective"],["PML"] ],
# Env_pml_sigma_parameters = [[0.9 ,2 ],[80.0,2] ,[80.0,2 ]],
# Env_pml_kappa_parameters = [[1.00,1.00,2],[1.00,1.00,2],[1.00,1.00,2]],
# Env_pml_alpha_parameters = [[0.90,0.90,1],[0.65,0.65,1],[0.65,0.65,1]]
#)


######################### Define a moving window
Expand All @@ -109,97 +109,95 @@
velocity_x = c_normalized,
)

# ######################### Define the plasma
#
# ##### plasma parameters
# plasma_plateau_density_1_ov_cm3 = 1.3e18
# n0 = plasma_plateau_density_1_ov_cm3*1e6/ncrit # plasma plateau density in units of critical density defined above
# Radius_plasma = 30.*um # Radius of plasma
# Lramp = 15*um # Plasma density upramp length
# Lplateau = 1*mm # Length of density plateau
# Ldownramp = 15*um # Length of density downramp
# x_begin_upramp = Lx # x coordinate of the start of the density upramp
# x_begin_plateau = x_begin_upramp+Lramp # x coordinate of the end of the density upramp / start of density plateau
# x_end_plateau = x_begin_plateau+Lplateau # x coordinate of the end of the density plateau start of the density downramp
# x_end_downramp = x_end_plateau+Ldownramp # x coordinate of the end of the density downramp
#
# #### plasma density profile
# longitudinal_profile = polygonal(xpoints=[x_begin_upramp,x_begin_plateau,x_end_plateau,x_end_downramp],xvalues=[0.,n0,n0,0.])
# def plasma_density(x,r):
# profile_r = 0.
# if ((r)**2<Radius_plasma**2):
# profile_r = 1.
# return profile_r*longitudinal_profile(x,r)
#
# ###### define the plasma electrons
# Species(
# name = "plasmaelectrons",
# position_initialization = "regular",
# momentum_initialization = "cold",
# particles_per_cell = 4,
# regular_number = [2,2,1],
# c_part_max = 1.0,
# mass = 1.0,
# charge = -1.0,
# number_density = plasma_density,
# mean_velocity = [0.0, 0.0, 0.0],
# temperature = [0.,0.,0.],
# pusher = "ponderomotive_borisBTIS3",
# time_frozen = 0.0,
# boundary_conditions = [
# ["remove", "remove"],
# ["remove", "remove"],
# ],
# )
########################## Define the plasma
#
###### plasma parameters
#plasma_plateau_density_1_ov_cm3 = 1.3e18
#n0 = plasma_plateau_density_1_ov_cm3*1e6/ncrit # plasma plateau density in units of critical density defined above
#Radius_plasma = 30.*um # Radius of plasma
#Lramp = 15*um # Plasma density upramp length
#Lplateau = 1*mm # Length of density plateau
#Ldownramp = 15*um # Length of density downramp
#x_begin_upramp = Lx # x coordinate of the start of the density upramp
#x_begin_plateau = x_begin_upramp+Lramp # x coordinate of the end of the density upramp / start of density plateau
#x_end_plateau = x_begin_plateau+Lplateau # x coordinate of the end of the density plateau start of the density downramp
#x_end_downramp = x_end_plateau+Ldownramp # x coordinate of the end of the density downramp

##### plasma density profile
#longitudinal_profile = polygonal(xpoints=[x_begin_upramp,x_begin_plateau,x_end_plateau,x_end_downramp],xvalues=[0.,n0,n0,0.])
#def plasma_density(x,r):
# profile_r = 0.
# if ((r)**2<Radius_plasma**2):
# profile_r = 1.
# return profile_r*longitudinal_profile(x,r)

####### define the plasma electrons
#Species(
# name = "plasmaelectrons",
# position_initialization = "regular",
# momentum_initialization = "cold",
# particles_per_cell = 4,
# regular_number = [2,2,1],
# mass = 1.0,
# charge = -1.0,
# number_density = plasma_density,
# mean_velocity = [0.0, 0.0, 0.0],
# temperature = [0.,0.,0.],
# pusher = "ponderomotive_borisBTIS3",
# time_frozen = 0.0,
# boundary_conditions = [
# ["remove", "remove"],
# ["remove", "remove"],
# ],
#)


# ######################## Define the electron bunch
#
# ##### electron bunch parameters
# Q_bunch = -15*pC # Total charge of the electron bunch
# sigma_x = 1.5*um # initial longitudinal rms size
# sigma_r = 2*um # initial transverse/radial rms size (cylindrical symmetry)
# bunch_energy_spread = 0.01 # initial rms energy spread / average energy (not in percent)
# bunch_normalized_emittance = 3.*mm_mrad # initial rms emittance, same emittance for both transverse planes
# delay_behind_laser = 18.5*um # distance between center_laser and center_bunch
# center_bunch = center_laser-delay_behind_laser # initial position of the electron bunch in the window
# gamma_bunch = 200. # initial relativistic Lorentz factor of the bunch
#
# npart = 50000 # number of computational macro-particles to model the electron bunch
# normalized_species_charge = -1 # For electrons
# Q_part = Q_bunch/npart # charge for every macroparticle in the electron bunch
# weight = Q_part/((c/omega0)**3*ncrit*normalized_species_charge)
#
# #### initialize the bunch using numpy arrays
# #### the bunch will have npart particles, so an array of npart elements is used to define the x coordinate of each particle and so on ...
# array_position = np.zeros((4,npart)) # positions x,y,z, weight
# array_momentum = np.zeros((3,npart)) # momenta x,y,z
#
# #### The electron bunch is supposed at waist. To make it convergent/divergent, transport matrices can be used
# array_position[0,:] = np.random.normal(loc=center_bunch, scale=sigma_x, size=npart) # generate random number from gaussian distribution for x position
# array_position[1,:] = np.random.normal(loc=0., scale=sigma_r, size=npart) # generate random number from gaussian distribution for y position
# array_position[2,:] = np.random.normal(loc=0., scale=sigma_r, size=npart) # generate random number from gaussian distribution for z position
# array_momentum[0,:] = np.random.normal(loc=gamma_bunch, scale=bunch_energy_spread*gamma_bunch, size=npart) # generate random number from gaussian distribution for px position
# array_momentum[1,:] = np.random.normal(loc=0., scale=bunch_normalized_emittance/sigma_r, size=npart) # generate random number from gaussian distribution for py position
# array_momentum[2,:] = np.random.normal(loc=0., scale=bunch_normalized_emittance/sigma_r, size=npart) # generate random number from gaussian distribution for pz position
#
# array_position[3,:] = np.multiply(np.ones(npart),weight)

# #### define the electron bunch
# Species(
# name = "electronbunch",
# position_initialization = array_position,
# momentum_initialization = array_momentum,
# c_part_max = 1.0,
# mass = 1.0,
# charge = -1.0,
# relativistic_field_initialization = True,
# pusher = "ponderomotive_borisBTIS3",
# boundary_conditions = [
# ["remove", "remove"],
# ["remove", "remove"],
# ],
# )
######################## Define the electron bunch

###### electron bunch parameters
#Q_bunch = -15*pC # Total charge of the electron bunch
#sigma_x = 1.5*um # initial longitudinal rms size
#sigma_r = 2*um # initial transverse/radial rms size (cylindrical symmetry)
#bunch_energy_spread = 0.01 # initial rms energy spread / average energy (not in percent)
#bunch_normalized_emittance = 3.*mm_mrad # initial rms emittance, same emittance for both transverse planes
#delay_behind_laser = 18.5*um # distance between center_laser and center_bunch
#center_bunch = center_laser-delay_behind_laser # initial position of the electron bunch in the window
#gamma_bunch = 200. # initial relativistic Lorentz factor of the bunch

#npart = 50000 # number of computational macro-particles to model the electron bunch
#normalized_species_charge = -1 # For electrons
#Q_part = Q_bunch/npart # charge for every macroparticle in the electron bunch
#weight = Q_part/((c/omega0)**3*ncrit*normalized_species_charge)

##### initialize the bunch using numpy arrays
##### the bunch will have npart particles, so an array of npart elements is used to define the x coordinate of each particle and so on ...
#array_position = np.zeros((4,npart)) # positions x,y,z, weight
#array_momentum = np.zeros((3,npart)) # momenta x,y,z

##### The electron bunch is supposed at waist. To make it convergent/divergent, transport matrices can be used
#array_position[0,:] = np.random.normal(loc=center_bunch, scale=sigma_x, size=npart) # generate random number from gaussian distribution for x position
#array_position[1,:] = np.random.normal(loc=0., scale=sigma_r, size=npart) # generate random number from gaussian distribution for y position
#array_position[2,:] = np.random.normal(loc=0., scale=sigma_r, size=npart) # generate random number from gaussian distribution for z position
#array_momentum[0,:] = np.random.normal(loc=gamma_bunch, scale=bunch_energy_spread*gamma_bunch, size=npart) # generate random number from gaussian distribution for px position
#array_momentum[1,:] = np.random.normal(loc=0., scale=bunch_normalized_emittance/sigma_r, size=npart) # generate random number from gaussian distribution for py position
#array_momentum[2,:] = np.random.normal(loc=0., scale=bunch_normalized_emittance/sigma_r, size=npart) # generate random number from gaussian distribution for pz position

#array_position[3,:] = np.multiply(np.ones(npart),weight)

##### define the electron bunch
#Species(
# name = "electronbunch",
# position_initialization = array_position,
# momentum_initialization = array_momentum,
# mass = 1.0,
# charge = -1.0,
# relativistic_field_initialization = True,
# pusher = "ponderomotive_borisBTIS3",
# boundary_conditions = [
# ["remove", "remove"],
# ["remove", "remove"],
# ],
#)


######################### Current filter
Expand Down Expand Up @@ -230,12 +228,12 @@
fields = ['Ex','Ey','Rho','Env_A_abs','Env_E_abs','Bz','BzBTIS3']
)

# ##### Diagnostic for the electron bunch macro-particles
# DiagTrackParticles(
# species = "electronbunch",
# every = 200,
# attributes = ["x", "y", "z", "px", "py", "pz", "w"]
# )
##### Diagnostic for the electron bunch macro-particles
#DiagTrackParticles(
# species = "electronbunch",
# every = 200,
# attributes = ["x", "y", "z", "px", "py", "pz", "w"]
#)


######################### Load balancing (for parallelization)
Expand Down
6 changes: 1 addition & 5 deletions Postprocessing_Scripts/Laser_waist_theory_vs_Smilei.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,6 @@
lambda0 = S.namelist.lambda0*1e6 # laser wavelength, um
conversion_factor = lambda0/(2.*math.pi)


####### load simulation
S = happi.Open(".")

####### create grid
dx = S.namelist.Main.cell_length[0] # resolution on the x direction
nx = S.namelist.Main.number_of_cells[0] # number of grid points in the x direction
Expand Down Expand Up @@ -62,7 +58,7 @@
half_waist_simulated.append( half_waist )

# analytical waist, Rayleigh formula
waist_analytical.append(waist0*math.sqrt(1.+((iter*dt-S.namelist.x_focus)/Zr)**2))
waist_analytical.append(waist0*math.sqrt(1.+((S.namelist.Lx+iter*dt-S.namelist.x_focus)/Zr)**2))

waist_analytical= np.asarray(waist_analytical)
waist_simulated = 2.*np.asarray(half_waist_simulated)
Expand Down

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