Validate("Temporal profile " + name, thisJz, 0.01)
# Verify that "time_fields_frozen" works
Late_Ez = S.Field.Field0.Ez(timesteps=190).getData()[0]
Validate("Ez field at late time", Late_Ez, 0.01)
# Verify that "time_frozen" works
Late_density = S.Field.Field0.Rho_trapezoidal(timesteps=190).getData()[0]
Validate("Density of a species at a late time", Late_density, 0.01)
# Maxwell-Juttner initialization
i = -1
drift = ["no", "px", "py", "pz"]
for eon in S.namelist.mj_species:
i += 1
p = S.ParticleDiagnostic(i).get()
p_distr = p["data"][0]
p = p[drift[i]] if i > 0 else p["px"]
# # theory
# g0 = S.namelist.g0 if i>0 else 1.
# Te = S.namelist.Te
# fth = (g0 * np.sqrt(1.+p**2)+Te) * np.exp( -g0/Te* (np.sqrt(1.+p**2) - np.sqrt(1.-g0**-2)*p) )
# itg = (p[1]-p[0])*np.sum(fth)
# fth = fth/itg
# plt.plot(px, p_distr, 'o', markeredgecolor=c[i], markerfacecolor="none")
# plt.plot(px, fth, '-', color=c[i])
b, a = butter(8, 0.15, btype='low', analog=False)
p_filt = filtfilt(b, a, p_distr)
Validate("Maxwell-Juttner Momentum distribution (" + drift[i] + " drift)",
p_filt,
p_filt.max() * 1e-2)
import matplotlib.pyplot as plt
from scipy.special import erf as erf
plt.ion()
path = "Maxwellianization1"
sim = Smilei(path)
coulomb_log = np.double(sim.namelist.Collisions[0].coulomb_log)
dt = np.double(sim.namelist.Main.timestep) / (2 * np.pi)
re_ = 2.8179403267e-15 # meters
wavelength = 1e-6 # meters
c = 3e8
times = sim.ParticleDiagnostic(0).getAvailableTimesteps()
electrons = sim.ParticleDiagnostic(0, slice={"x": "all"}).get()
vx = electrons["vx"]
fig = None
fig = plt.figure(1)
if fig: fig.clf()
if fig: ax = fig.add_subplot(1, 1, 1)
for i, t in enumerate(times):
if i % 3 > 0: continue
A = electrons["data"][i]
if fig:
#ax.cla()
ax.plot(vx, A, 'b')
plt.draw()
plt.pause(0.0001)
sim = Smilei(path)
mass_ion = np.double(sim.namelist.Species["ion1"].mass)
charge_ion = np.double(sim.namelist.Species["ion1"].charge)
density_ion = np.double(sim.namelist.Species["ion1"].charge_density)
temperature_ion = np.double(sim.namelist.Species["ion1"].temperature)
velocity_electron = np.double(sim.namelist.Species["electron1"].mean_velocity)[0]
temperature_electron = np.double(sim.namelist.Species["electron1"].temperature)
coulomb_log = np.double(sim.namelist.Collisions[0].coulomb_log)
dt = np.double(sim.namelist.Main.timestep)/(2*np.pi)
re = 2.8179403267e-15 # meters
wavelength = 1e-6 # meters
c = 3e8
coeff = (2.*np.pi/wavelength)**2*re*c
times = sim.ParticleDiagnostic(diagNumber=0).getAvailableTimesteps()
e_vx_mean = np.zeros(len(times))
e_vperp2 = np.zeros(len(times))
i_vx_mean = np.zeros(len(times))
Ti = np.zeros(len(times))
electrons0 = sim.ParticleDiagnostic(0, slice={"x":"all"}).get()
electrons1 = sim.ParticleDiagnostic(1, slice={"x":"all"}).get()
ions = sim.ParticleDiagnostic(2, slice={"x":"all"}).get()
fig = None
#fig = plt.figure(1)
if fig: fig.clf()
if fig: ax = fig.add_subplot(1,1,1)
for i,t in enumerate(times):
return y for path in ["Stopping_power1","Stopping_power2","Stopping_power3"]: sim = Smilei(path) temperature_electron = np.double(sim.namelist.Species["backgroundelectron"].temperature) density_electron = np.double(sim.namelist.Species["backgroundelectron"].charge_density) coulomb_log = np.double(sim.namelist.Collisions[0].coulomb_log) dt = np.double(sim.namelist.Main.timestep)/(2*np.pi) re = 2.8179403267e-15 # meters wavelength = 1e-6 # meters c = 3e8 times = sim.ParticleDiagnostic(diagNumber=0).getAvailableTimesteps() nx = sim.ParticleDiagnostic(diagNumber=0,timesteps=0).get()["x"].size Ekin = np.zeros((nx,len(times))) electrons = sim.ParticleDiagnostic(0).get() ekin = electrons["ekin"]*0.511 dekin = np.diff(ekin) dekin = np.hstack((dekin, dekin[-1])) fig = None #fig = plt.figure(1) if fig: fig.clf() if fig: ax = fig.add_subplot(1,1,1) for i,t in enumerate(times): A = electrons["data"][i] A = A*dekin
import numpy as np
import matplotlib.pyplot as plt
ln = np.log
plt.ion()
D = []
colors = ["k", "r", "g", "b", "m"]
for elm in ["C", "Al", "Zn", "Sn", "Au"]:
S1=Smilei("ionization_multiple"+elm+"1")
S2=Smilei("ionization_multiple"+elm+"2")
color = colors.pop()
timestep1 = np.round(np.double(S1.namelist.Main.timestep), decimals=1)
D.append( S1.ParticleDiagnostic(0,slice={"ekin":[0,1]},
linestyle="-", color=color,
label=elm) )
timestep2 = int(np.double(S2.namelist.Main.timestep))
D.append( S2.ParticleDiagnostic(0,slice={"ekin":[0,1]},
linestyle="", marker=".", color=color ) )
# Plot simulation result
plt.figure(1, figsize=(6,3.5))
multiPlot(*D)
fig =plt.gcf()
ax = plt.gca()
# Make nicer plot
legend = plt.legend( loc=2, prop={'size':10})
ax.add_artist(legend)
import os, re, numpy as np
from scipy.special import erf
from Smilei import *
S = Smilei(".", verbose=False)
for i in range(3):
ion = "ion" + str(i)
eon = "eon" + str(i)
ion_mass = S.namelist.Species[ion].mass
times = np.double(S.ParticleDiagnostic(0).getAvailableTimesteps())
ones = np.ones_like(times)
eon_vx = S.ParticleDiagnostic(i * 3 + 0, slice={"x": "all"}).get()
eon_mean_vx = np.array(eon_vx["data"])
eon_mean_vx = (np.outer(ones, eon_vx["vx"]) *
eon_mean_vx).sum(axis=1) / eon_mean_vx.sum(axis=1)
eon_vp2 = S.ParticleDiagnostic(i * 3 + 1, slice={"x": "all"}).get()
eon_mean_vp2 = np.array(eon_vp2["data"])
eon_mean_vp2 = (np.outer(ones, eon_vp2["vperp2"]) *
eon_mean_vp2).sum(axis=1) / eon_mean_vp2.sum(axis=1)
ion_vx = S.ParticleDiagnostic(i * 3 + 2, slice={"x": "all"}).get()
ion_vxd = np.array(ion_vx["data"])
ion_mean_vx = (np.outer(ones, ion_vx["vx"]) *
ion_vxd).sum(axis=1) / ion_vxd.sum(axis=1)
ion_T = (np.outer(ones, ion_vx["vx"]) -
np.outer(ion_mean_vx, np.ones_like(ion_vx["vx"])))**2 * ion_vxd
ion_T = ion_T.sum(axis=1) / ion_vxd.sum(axis=1) * ion_mass
'font.size': 20
})
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['ytick.minor.size'] = 5
S = Smilei('/Users/mica/RESULTS/SMILEI/thermalPlasmaPyDrift/')
T = S.namelist.Te
mu = 1. / T
print mu
v0 = S.namelist.v0
g0 = 1. / m.sqrt(1. - v0**2)
# read p-distribution fct
fp = np.array(S.ParticleDiagnostic(1).getData())[0]
p = np.array(S.ParticleDiagnostic(1).get()['py'])
print 'int over all py:', (p[1] - p[0]) * np.sum(fp)
# compute theoretical distribution fct
fth = np.zeros(p.shape)
fth = (g0 * np.sqrt(1. + p**2) + T) * np.exp(-g0 * mu *
(np.sqrt(1. + p**2) - v0 * p))
itg = (p[1] - p[0]) * np.sum(fth)
fth = fth / itg
# plot
plt.figure(1)
plt.semilogy(p, fp, 'b')
plt.hold('on')
plt.semilogy(p, fth, 'r')
'font.serif': 'Times',
'font.size': 20
})
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['ytick.minor.size'] = 5
S = Smilei('/Users/mica/RESULTS/SMILEI/thermalPlasmaNoDrift/')
T = S.namelist.Te
mu = 1. / T
v0 = S.namelist.v0
g0 = 1. / m.sqrt(1. - v0**2)
# read gamma-distribution fct
fg = np.array(S.ParticleDiagnostic(0).getData())[0]
g = np.array(S.ParticleDiagnostic(0).get()['gamma'])
print 'int over all g:', (g[1] - g[0]) * np.sum(fg)
# compute theoretical distribution fct
fth = np.zeros(g.shape)
fth = g * np.sqrt(g**2 - 1.) * np.exp(-mu * g)
itg = (g[1] - g[0]) * np.sum(fth)
fth = fth / itg
# read p-distribution fct
fp = np.array(S.ParticleDiagnostic(1).getData())[0]
p = np.array(S.ParticleDiagnostic(1).get()['px'])
print 'int over all px:', (p[1] - p[0]) * np.sum(fp)
# compute theoretical distribution fct
# get electron velocity
ve = np.double(S1.namelist.Species["electron1"].mean_velocity[0])
Ee = (1. / np.sqrt(1. - ve**2) - 1.) * 511. # energy
cse = np.interp(np.log(Ee), np.log(cs[:, 0]),
cs[:, 1]) # interpolate cross section
# get density
ne = np.double(S1.namelist.Species["electron1"].charge_density)
# get ion charge
q0 = np.double(S1.namelist.Species["ion1"].charge)
# Plot simulation result
D1 = S1.ParticleDiagnostic("#0/#1",
slice={"x": "all"},
linestyle="None",
marker=".",
label="We=Wi")
D2 = S2.ParticleDiagnostic("#0/#1",
slice={"x": "all"},
linestyle="None",
marker=".",
label="We=10. Wi")
D3 = S3.ParticleDiagnostic("#0/#1",
slice={"x": "all"},
linestyle="None",
marker=".",
label="We=0.1 Wi")
multiPlot(D1, D2, D3, vmin=q0, skipAnimation=True)
print "Analyzing " + element
S = Smilei("ionization_equilibrium" + element)
npoints = S.namelist.npoints
every = S.namelist.DiagParticles[0].every
ts = int(t0 * S.namelist.Main.referenceAngularFrequency_SI /
S.namelist.Main.timestep / every) # timestep at 1ps
Z = []
Zfinal = []
T = []
Tfinal = []
for i in range(npoints):
D = S.ParticleDiagnostic("#" + str(4 * i + 2) + "/#" + str(4 * i + 3),
slice={"x": "all"},
units=["ps"],
marker=".")
Z.append(D)
Zfinal.append(np.array(D.getData())[ts])
D = S.ParticleDiagnostic("#" + str(4 * i + 0) + "/#" + str(4 * i + 1),
slice={"x": "all"},
units=["ps"],
marker=".")
T.append(D)
Tfinal.append(np.array(D.getData())[ts] * (511. * 2. / 3.))
#multiPlot(*Z, figure=1, skipAnimation=True)
#multiPlot(*T, figure=2, skipAnimation=True)
plt.figure(1, figsize=(8, 3.5))
# First, slowing down for a few examples
########################################
D = []
sims = ["2", "3", "4"]
for sim in sims:
S = Smilei("ionization_stopping_power" + sim)
D.append(
S.ParticleDiagnostic("#0/#1",
slice={"x": "all"},
units=["fs"],
linestyle="None",
marker='o',
markersize=4,
markeredgewidth=0.,
skipAnimation=True))
multiPlot(*D)
fig = plt.gcf()
ax = plt.gca()
ax.xaxis.labelpad = 0
ax.yaxis.labelpad = 0
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim(10., 1e6)
ax.set_ylim(1e-2, 3.)
ax.set_position([0.1, 0.19, 0.35, 0.77])
# Theory
temperature = []
density = []
for path in ["conductivity1", "conductivity2", "conductivity3"]:
sim = Smilei(path)
ncases = 0
while sim.namelist.DiagParticles[ncases].output == "jx_density":
ncases += 1
if ncases == 0: continue
coulomb_log = np.double(sim.namelist.Collisions[0].coulomb_log)
dt = np.double(sim.namelist.Main.timestep) / (2 * np.pi)
times = sim.ParticleDiagnostic(diagNumber=0).getAvailableTimesteps()
vx_mean = np.zeros((ncases, len(times)))
fig = None
#fig = plt.figure(1)
if fig: fig.clf()
if fig: ax = fig.add_subplot(1, 1, 1)
for k in range(ncases):
evx_density = -np.array(sim.ParticleDiagnostic(k).getData())
edensity = np.array(sim.ParticleDiagnostic(k + ncases).getData())
vx_mean[k, :] = evx_density / edensity
times *= 3.33 * dt # fs
fig = plt.figure(2)
# [0.132194,0.008498],[0.155568,0.024568],[0.183074,0.042322],[0.215444,0.059764],[0.253537,0.074867],[0.298365,0.087020],[0.351120,0.096034],[0.413202,0.101988],[0.486261,0.105125],[0.572238,0.105786],
# [0.673416,0.104358],[0.792484,0.101245],[0.932605,0.096836],[1.097501,0.091491],[1.291552,0.085529],[1.519914,0.079223],[1.788653,0.072813],[2.104908,0.066472],[2.477081,0.060321],[2.915059,0.054454],
# [3.430476,0.048935],[4.037025,0.043802],[4.750819,0.039074],[5.590821,0.034755],[6.579345,0.030837],[7.742651,0.027303],[9.111645,0.024132],[10.72269,0.021300],[12.61859,0.018779],[14.84971,0.016545],
# [17.47531,0.014570],[20.56516,0.012829],[24.20132,0.011297],[28.48041,0.009954],[33.51608,0.008778],[39.44213,0.007751],[46.41597,0.006855],[54.62287,0.006075],[64.28084,0.005398],[75.64647,0.004811],
# [89.02167,0.004303],[104.7617,0.003865],[123.2848,0.003488],[145.0831,0.003164],[170.7355,0.002888],[200.9236,0.002653],[236.4493,0.002454],[278.2564,0.002288],[327.4554,0.002149],[385.3535,0.002035],
# [453.4886,0.001944],[533.6708,0.001871],[628.0302,0.001815],[739.0734,0.001774],[869.7505,0.001746],[1023.532,0.001730],[1204.505,0.001723],[1417.476,0.001725],[1668.103,0.001735],[1963.044,0.001751],
# [2310.133,0.001772],[2718.592,0.001798],[3199.272,0.001827],[3764.942,0.001860],[4430.628,0.001895],[5214.017,0.001932],[6135.917,0.001971],[7220.821,0.002012],[8497.548,0.002053],[10000.01,0.002096]
#])
for i in range(3):
ion = "ion" + str(i)
eon = "eon" + str(i)
ion_mean_charge = S.ParticleDiagnostic("#" + str(2 * i) + "/#" +
str(2 * i + 1),
slice={
"x": "all"
}).get()
times = ion_mean_charge["times"] * S.namelist.Main.timestep
ion_mean_charge = np.array(ion_mean_charge["data"])
# # theory
# ion_charge = S.namelist.Species[ion].charge
# eon_density = S.namelist.Species[eon].charge_density
# eon_velocity = S.namelist.Species[eon].mean_velocity[0]
# eon_energy = ((1.-eon_velocity**2)**-.5-1.)*511. # energy in keV
# eon_cs = np.interp(np.log(eon_energy), np.log(cs[:,0]), cs[:,1]) # interpolate cross section
# t = np.linspace(0., S.namelist.Main.sim_time, 1000)
# q = ion_charge + 1. - np.exp(-eon_velocity*eon_density*eon_cs*t)
# plt.plot(t,q,'--k')
# plt.plot(times, ion_mean_charge)