refactor!: generalize data generation interface

[redeboer]

Large refactoring of the data module with the aim to generalize its hit-and-miss mechanisms to general domain DataSample types.

Interface changes

• Removed generate_data() and generate_phsp() façade functions. The new way to generate phase space and hit-and-miss data samples is:

  from tensorwaves.data import IntensityDistributionGenerator
  from tensorwaves.data.phasespace import TFPhaseSpaceGenerator
  from tensorwaves.data.rng import TFUniformRealNumberGenerator
  
  # intensity: Function
  # helicity_transformer: SympyDataTransformer
  rng = TFUniformRealNumberGenerator(seed=0)
  phsp_generator = TFPhaseSpaceGenerator(
      initial_state_mass=3.0969,
      final_state_masses={0: 0.0, 1: 0.135, 2: 0.135},
  )
  data_generator = IntensityDistributionGenerator(
      domain_generator=phsp_generator,
      domain_transformer=helicity_transformer,
      function=intensity,
  )
  phsp = phsp_generator.generate(100_000, rng)
  data = data_generator.generate(10_000, rng)

• UniformRealNumberGenerator -> RealNumberGenerator
• PhaseSpaceGenerator -> DataGenerator
• PhaseSpaceGenerator.setup() has been merged into the constructor (DataGenerator.__init__()) and removed from the interface.
• Split old TFPhaseSpaceGenerator into an unweighted TFPhaseSpaceGenerator (implements DataGenerator) and a TFWeightedPhaseSpaceGenerator (implements WeightedDataGenerator). TFPhaseSpaceGenerator can be used instead of the old generate_phsp() function.
• Collected RNGs under a new sub-module data.rng

New features

• New interface: DataGenerator, which does not generate weights as opposed to FourMomentumGenerator.
• IntensityDistributionGenerator, which should be used instead of generate_data()
• IdentityTransformer, which is just the identity implementation of a DataTransformer.
• NumpyUniformRNG

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