src.infer.motif_production_rates module

motif_production_rates_array_from_motif_production_rate_constants_array_and_motif_concentrations_array(motif_production_rate_constants: Array, motif_logconcentrations_trajectory: Array, motif_concentration_trajectory_times_array: Array = None)

calculates motif production rates from motif production rate constants and motif concentrations Parameters: ———– motif_production_rate_constants : jax.Array motif_concentration_trajectories : jax.Array

Returns:

motif_production_rates : jax.Array

motif_production_rates_array_from_motif_production_counts(motif_production_rates_model: Model, motif_production_rates_estimate: Vector, motif_production_counts: list[Array], sample_key: Array, minimization_function: Callable)

Parameters :

motif_production_rate : jax.Array motif_production_counts : list[jax.Array] sample_key : jax.Array minimization_function : Callable

minimizes nifty8.re.likelihood_impl.Poissonian

Returns:

motif_production_rates_samples : list[jax.Array]

left_reactant_logconcentration(motif_logconcentrations_array: Array, motiflength: int, number_of_letters: int)

if not del_t c_l for motiflength = 4: collision_exponent[l1,l2,l3,r2,r3,r4][p1,p2,p3,p4] += c[l1,l2,l3,0] For jit, motiflength and number_of_letters are stated explicitly, the motif_logconcentrations_array must fulfill motiflength = len(motif_logconcentrations_array.shape) nol = motif_logconcentrations_array.shape[1]

Parameters:

motif_logconcentrations_array : jax.Array motiflength : int number_of_letters : int

Returns:

collision_exponent : jax.Array