Jablonski release

[olistarosta]

Jablonski release!

At dyscolab we released jablonski, a new package which extends poincare with functionality for photochemical systems. It brings poincare's philosophy of composable systems, modular design and utility to a new class of systems, including bindings for defining energy levels, common transitions and simulation tools specific to spectroscopy. It provides a centralized place to define, simulate and graph spectroscpic system's, making it easy to define systems and analyze different facets of their behaviour.
Let's implement a three level system as a quick example.

import pint
from jablonski import *
from jablonski.transitions import *
u = pint.get_application_registry()

class Model(SpectroscopicSystem):
    # Define states with energies, mid is a triplet state
    low: SingletState = initial(0 * u.eV, "singlet", default=100)
    mid: TripletState = initial(2 * u.eV, "triplet", default=0) 
    high: SingletState = initial(3 * u.eV, "singlet", default=0)

    # Define absorptions and emissions 
    absorption_1 = Absorption(ground=low, excited=high, rate=1e-15 * u.cm**2) 
    emission_1 = Phosphorescence(ground=low, excited=mid, rate=2e8 / u.s)
    emission_2 = IntersystemCrossing(source=high, target=mid, rate=0.5e8 / u.s)
    emission_3 = transitions.Fluorescence(ground=low, excited=high, rate=1e8 / u.s)

Describing spectroscopic systems can be a pain, so we can use jablonski's inbuilt jablonski diagram function to visualize it instead

from jablonski.plots import *

jablonski_diagram(Model, figsize=(4.5,2.5))

We can simulate the response to a pulse and get level population as a function of time

from jablonski.simulation import *
sim = Simulator(Model)

# Picewise simulation with a delta excitation at t = 0 and a pulse between t = 1e-8 and t = 6e-8
pulse = pulse_excitation(
    Model.absorption_1, start=1e-8, width=4e-8, height=1e23 / (u.cm**2 * u.s)
)  # delta excitiation
result_1 = piecewise(sim, events=pulse, save_at=np.linspace(0, 10e-8, 100))
result_1.to_dataframe().plot(figsize=(4.5,2.5))

Or instead get the spectral time resolved emission

result_2 = spectral_time_resolved_emission(
    Model,
    excitation=pulse,
    save_at=np.linspace(0, 10e-8, 100),
    join_by_energy=True,
)
result_2.pint.dequantify().to_dataframe().plot(figsize=(4.5,2.5))

Jablonski also has tools for analyzing long term behavior. We can instead get the steady state spectra, and plot it with an arbitrary width

graph_spectra(Model, excitation_transition= Model.absorption_1, height=1e23 / (u.cm**2 * u.s))

Jablonski also includes a wide range other pre-made transitions, including non-linear ones such as energy transfer upconversion, more analysis tools such as parameter sweeps for emission spectra at different excitation levels and more output tools such as the ability to generate Latex source with all of a model's equations. To learn more see the documentation or start simulating with:

pip install jablonski

Development is still ongoing, and the roadmap includes:

• Stochastic simulations: simulate the same model stochastically using gillespie's algorithm with a single line of code, similar to what's already implemented in simbio.
• A more comprehensive analysis sweep, including automatic lifetime inference and multi-dimensional parameter sweeps.