Hide domain generator progress bar when generating hit-and-miss sample

[redeboer]

When using a TFPhaseSpaceGenerator as domain_generator in IntensityDistributionGenerator, calling generate() results in several tqdm progress bars if the generated sample is generated in bunches. This is because TFPhaseSpaceGenerator uses a hit-and-miss strategy as well. It's therefore actually better to use TFWeightedPhaseSpaceGenerator, but even so, it would be nicer if the progress bar for the domain can be deactivated.

How to reproduce?

pip install -q ampform[viz]==0.12.1 tensorwaves[jax,pwa]==0.4.0

import ampform
import qrules
from ampform.dynamics.builder import create_relativistic_breit_wigner_with_ff
from tensorwaves.data import (
    IntensityDistributionGenerator,
    TFPhaseSpaceGenerator,
    TFUniformRealNumberGenerator,
)
from tensorwaves.data.transform import SympyDataTransformer
from tensorwaves.function.sympy import create_parametrized_function

reaction = qrules.generate_transitions(
    initial_state="J/psi(1S)",
    final_state=["gamma", "pi0", "pi0"],
    allowed_intermediate_particles=["f(0)"],
    allowed_interaction_types=["strong", "EM"],
)

builder = ampform.get_builder(reaction)
resonances = reaction.get_intermediate_particles()
for p in resonances:
    builder.set_dynamics(p.name, create_relativistic_breit_wigner_with_ff)
model = builder.formulate()

intensity = create_parametrized_function(
    model.expression.doit(),
    parameters=model.parameter_defaults,
    backend="jax",
)
helicity_transformer = SympyDataTransformer.from_sympy(
    model.kinematic_variables, backend="jax"
)

phsp_generator = TFPhaseSpaceGenerator(
    initial_state_mass=reaction.initial_state[-1].mass,
    final_state_masses={i: p.mass for i, p in reaction.final_state.items()},
)
data_generator = IntensityDistributionGenerator(
    function=intensity,
    domain_generator=phsp_generator,
    domain_transformer=helicity_transformer,
)
rng = TFUniformRealNumberGenerator(seed=0)
phsp_momenta = phsp_generator.generate(1_000_000, rng)
data_momenta = data_generator.generate(100_000, rng)