# ================
# SETUP SIMULATION
# ================
# Constants
c0 = 1 # um/ps
di = 0.3 # 0.3 um
dn = di / c0 # (0.3 um) / (300 um/ps) = 0.001 ps = 1 fs
epsilon0 = 1
mu0 = 1
# Define bounds
i0 = -100 # -100 um
i1 = 1100 # 1100 um
n0 = -300 # (1 fs) * (-300 um) / (0.3 um/step) = (1 fs) * (-1,000 steps) = -1,000 fs = -1 ps
n1 = 5100 # (1 fs) * (5100 um) / (0.3 um/step) = (1 fs) * (17,000 steps) = 17,000 fs = 17 ps
# Calculate dimensions
nlen, ilen = Sim.calc_dims(n0, n1, dn, i0, i1, di)
print('nlen=%i, ilen=%i' % (nlen, ilen))
# Create a arrays that hold the value of the center of each cell
t = np.linspace(n0 + dn / 2, n1 + dn / 2, nlen, endpoint=False) * (
10 / 3) # Multiply by 10/3 to get from um -> fs
z = np.linspace(i0 + di / 2, i1 + di / 2, ilen, endpoint=False)
# =============
# SETUP CURRENT
# =============
# Set current location
cp_loc_val = -50 # -250 um
cp_time_val = 0 # 0 fs
# Find current indicies
cp_loc_ind = np.argmin(np.abs(np.subtract(z, cp_loc_val)))
cp_time_ind = np.argmin(np.abs(np.subtract(t, cp_time_val)))
from matplotlib.ticker import MaxNLocator
import matplotlib.gridspec as gridspec
# Constants
c0 = 3e8 # um/ps
di = 0.03e-6 # 0.03 um
dn = di / c0 # (0.03 um) / (3e8 m/s) = 0.1 fs
epsilon0 = 8.854187e-12
mu0 = np.divide(1, np.multiply(epsilon0, np.square(c0)))
# Define bounds
i0 = -1e-6 # -1 um
i1 = 2e-6 # 2 um
n0 = -0.5e-12 # -0.5 ps
n1 = 2.5e-12 # 2.5 ps
# Calculate dimensions
nlen, ilen = Sim.calc_dims(n0, n1, dn, i0, i1, di)
# Create a arrays that hold the value of the center of each cell
t = np.linspace(n0 + dn / 2, n1 + dn / 2, nlen, endpoint=False)
z = np.linspace(i0 + di / 2, i1 + di / 2, ilen, endpoint=False)
# Print simulation bounds
print('nlen=%i, ilen=%i' % (nlen, ilen))
cp_loc_val = -0.5e-6 # -0.5 um
cp_time_val = 0 # 0 fs
# Find indicies
cp_loc_ind = np.argmin(np.abs(np.subtract(z, cp_loc_val)))
cp_time_ind = np.argmin(np.abs(np.subtract(t, cp_time_val)))
# Find start and end indicies in time
spread = 3500
cp_time_s = cp_time_ind - spread
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator
import matplotlib.gridspec as gridspec
# Constants
c0 = 3e8 # um/ps
di = 0.03e-6 # 0.03 um
dn = di/c0 # (0.03 um) / (3e8 m/s) = 0.1 fs
epsilon0 = 8.854187e-12
mu0 = np.divide(1, np.multiply(epsilon0, np.square(c0)))
# Define bounds
i0 = -1e-6 # -1 um
i1 = 2e-6 # 2 um
n0 = -0.5e-12 # -0.5 ps
n1 = 2.5e-12 # 2.5 ps
# Calculate dimensions
nlen, ilen = Sim.calc_dims(n0, n1, dn, i0, i1, di)
# Create a arrays that hold the value of the center of each cell
t = np.linspace(n0+dn/2, n1+dn/2, nlen, endpoint=False)
z = np.linspace(i0+di/2, i1+di/2, ilen, endpoint=False)
# Print simulation bounds
print('nlen=%i, ilen=%i' % (nlen, ilen))
# Specify the location of our current pulse in time and space
# In[2]:
cp_loc_val = -0.5e-6 # -0.5 um
cp_time_val = 0 # 0 fs
# ## Running the Simulation
#
# Create and run our simulation (or load simulation if one already exists)
# In[10]:
# Create Sim object
tqdmarg = {'desc': 'Executing simulation', 'leave': True}
s = Sim(i0,
i1,
di,
n0,
n1,
dn,
epsilon0,
mu0,
'absorbing',
current,
material,
nstore=np.arange(0, nlen, 50),
istore=[5, ilen - 6])
# Run simulation if simulation save doesn't exist
sim_file = Path(fsave)
if sim_file.is_file():
# Load results
dat = np.load(fsave)
t = dat['t']
els = dat['els']
erls = dat['erls']
hls = dat['hls']
ilen,
nlen,
material_ind_start,
material_ind_end,
chi,
inf_perm,
tqdmarg={'desc': 'Calculating chi^m'})
# Create Sim object
tqdmarg = {'desc': 'Executing simulation'}
s = Sim(i0,
i1,
di,
n0,
n1,
dn,
epsilon0,
mu0,
'absorbing',
thzpulse,
drude_material,
nstore=np.arange(0, nlen, 50),
istore=[5, ilen - 6])
# Run simulation
s.simulate(tqdmarg)
# Export visualization
vis.timeseries(s, z, iunit='um')
# Export and save arrays
hls, els, hrls, erls = s.export_ifields()
chi = drude_material.export_chi()
np.savez('drude_model_numeric.npz',
t=t,
# Run pre-simulation analysis
# ---------------------------
# Run simulation if simulation save doesn't exist
sim_file = 'dynamic_material_pre_analysis.npz'
if Path(sim_file).is_file():
# Load results
dat = np.load(sim_file)
els = dat['els']
else:
# Create Sim object, observe the field at the material start index
s = Sim(i0,
i1,
di,
n0,
n1,
dn,
epsilon0,
mu0,
'absorbing',
thzpulse,
istore=[m_z_start_ind])
# Run simulation
s.simulate(tqdmarg={
'desc': 'Executing pre-simulation analysis',
'leave': True
})
hls, els, hrls, erls = s.export_ifields()
np.savez(sim_file, els=els)
# Determine the temporal index at which the thz pulse is incident on the material
thz_incident_n_ind = np.argmax(np.real(els))