Chirped LGS

Simulate a chirped sodium LGS beacon

from pylgs.lgssystem import LGSSystem
import numpy as np

lgs = LGSSystem(
    'NaD2_Repump', 
    dict(
        # Repump beam parameters
        EllipticityDegrees1=45.,
        PolarizationAngleDegrees1=0,
        # 'DetuningHz1=1.0832e9,
        LaserWidthHz1=10.0e6,
        IntensitySI1=.2 * 75.,

        # Pump beam parameters
        EllipticityDegrees2=45.,
        PolarizationAngleDegrees2=0,
        # 'DetuningHz2=-6.268e8,
        LaserWidthHz2=10.0e6,
        IntensitySI2=.8 * 75.,

        # Environmental parameters
        BFieldG=.5,
        MagneticZenithDegrees=45.,
        MagneticAzimuthDegrees=45.,
        SDampingCollisionRatePerS=4081.63,
        BeamTransitRatePerS=131.944,
        VccRatePerS=28571.,
        TemperatureK=185.,
        RecoilParameter=1
    )
)

mu = dict(nu=2*np.pi*4e3)
model, sol = lgs.adaptive_stationary_floquet_model(
    dict(
        DetuningHz1="1.0832e9 + 1200e3 * t", 
        DetuningHz2="-6.268e8 + 1200e3 * t"
    ),
    mu,
    np.linspace(0, 1e-3, 500),
    n_vector=10,
    max_weight=.1
)

Creating model with 6 velocity groups
Solving model
Creating model with 12 velocity groups
Solving model
Creating model with 18 velocity groups
Solving model
Creating model with 24 velocity groups
Solving model
Creating model with 36 velocity groups
Solving model
CPU times: user 5min 11s, sys: 9.72 s, total: 5min 21s
Wall time: 5min 18s

model.reconstructed_total_flux(sol, mu).real.visualize()

model.reconstructed_flux_distribution(sol, mu).real.visualize(xaxis_range=(-.5, .5))

model.level_population_distribution(model.reconstruct(sol, mu)).real.visualize(xaxis_range=(-.2, .2))