basic simulators

`boundary_functions`

Define a collection of boundary functions for the simulators in the package.

`angle(t=1, theta=1)`

Angle boundary function.

Arguments

t (float or np.ndarray, optional): Time point(s). Defaults to 1.
theta (float, optional): Angle in radians. Defaults to 1.

Returns

np.ndarray: Array of boundary values, same shape as t

conflict_gamma(t=array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1,2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2,3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3,4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4,5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6,7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,19.8, 19.9]), theta=0.5, scale=1, alpha_gamma=1.01, scale_gamma=0.3)

Conflict bound that allows initial divergence then collapse.

Arguments

!!! t "(float, np.ndarray)"
    Time points (with arbitrary measure, but in HDDM it is used as seconds),
    at which to evaluate the bound. Defaults to np.arange(0, 20, 0.1).
!!! theta "float"
    Collapse angle. Defaults to 0.5.
!!! scale "float"
    Scaling the gamma distribution of the boundary
    (since bound does not have to integrate to one). Defaults to 1.0.
!!! alpha_gamma "float"
    alpha parameter for a gamma in scale shape parameterization. Defaults to

`constant(t=0)`

Constant boundary function.

Arguments

t (float or np.ndarray, optional): Time point(s). Defaults to 0.

Returns

float or np.ndarray: Constant boundary value(s), same shape as t

`generalized_logistic(t=1, B=2.0, M=3.0, v=0.5)`

Generalized logistic bound.

Arguments

t (float or np.ndarray, optional): Time point(s). Defaults to 1.
B (float, optional): Growth rate. Defaults to 2.0.
M (float, optional): Time of maximum growth. Defaults to 3.0.
v (float, optional): Affects near which asymptote maximum growth occurs.
Defaults to 0.5.

Returns

np.ndarray: Array of boundary values, same shape as t

`weibull_cdf(t=1, alpha=1, beta=1)`

Boundary based on weibull survival function.

Arguments

t (float or np.ndarray, optional): Time point(s). Defaults to 1.
alpha (float, optional): Shape parameter. Defaults to 1.
beta (float, optional): Scale parameter. Defaults to 1.

Returns

np.ndarray: Array of boundary values, same shape as t

`drift_functions`

Define a collection of drift functions for the simulators in the package.

attend_drift(t=array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1,2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2,3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3,4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4,5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6,7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,19.8, 19.9]), ptarget=-0.3, pouter=-0.3, pinner=0.3, r=0.5, sda=2)

Shrink spotlight model, which involves a time varying function dependent on a linearly decreasing standard deviation of attention.

Arguments

!!! t "np.ndarray"
    Timepoints at which to evaluate the drift.
    Usually np.arange() of some sort.
!!! pouter "float"
    perceptual input for outer flankers
!!! pinner "float"
    perceptual input for inner flankers
!!! ptarget "float"
    perceptual input for target flanker
!!! r "float"
    rate parameter for sda decrease
!!! sda "float"
    width of attentional spotlight

Return

np.ndarray Drift evaluated at timepoints t

attend_drift_simple(t=array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1,2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2,3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3,4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4,5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6,7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,19.8, 19.9]), ptarget=-0.3, pouter=-0.3, r=0.5, sda=2)

Drift function for shrinking spotlight model, which involves a time varying function dependent on a linearly decreasing standard deviation of attention.

Arguments

!!! t "np.ndarray"
    Timepoints at which to evaluate the drift.
    Usually np.arange() of some sort.
!!! pouter "float"
    perceptual input for outer flankers
!!! ptarget "float"
    perceptual input for target flanker
!!! r "float"
    rate parameter for sda decrease
!!! sda "float"
    width of attentional spotlight

Return

np.ndarray Drift evaluated at timepoints t

constant(t=array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1,2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2,3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3,4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4,5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6,7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,19.8, 19.9]))

Constant drift function.

Arguments

!!! t "np.ndarray, optional"
    Timepoints at which to evaluate the drift. Defaults to
    np.arange(0, 20, 0.1).

Returns

np.ndarray: Array of drift values, same length as t

`ds_conflict_drift(t=array([0.000e+00, 1.000e-03, 2.000e-03, ..., 9.997e+00, 9.998e+00,9.999e+00]), tinit=0, dinit=0, tslope=1, dslope=1, tfixedp=1, tcoh=1.5, dcoh=1.5)`

This drift is inspired by a conflict task which involves a target and a distractor stimuli both presented simultaneously.

Two drift timecourses are linearly combined weighted by the coherence in the respective target and distractor stimuli. Each timecourse follows a dynamical system as described in the ds_support_analytic() function.

Arguments

!!! t "np.ndarray"
    Timepoints at which to evaluate the drift.
    Usually np.arange() of some sort.
!!! tinit "float"
    Initial condition of target drift timecourse
!!! dinit "float"
    Initial condition of distractor drift timecourse
!!! tslope "float"
    Slope parameter for target drift timecourse
!!! dslope "float"
    Slope parameter for distractor drift timecourse
!!! tfixedp "float"
    Fixed point for target drift timecourse
!!! tcoh "float"
    Coefficient for the target drift timecourse
!!! dcoh "float"
    Coefficient for the distractor drift timecourse

Return

np.ndarray The full drift timecourse evaluated at the supplied timepoints t.

`ds_support_analytic(t=array([0.000e+00, 1.000e-03, 2.000e-03, ..., 9.997e+00, 9.998e+00,9.999e+00]), init_p=0, fix_point=1, slope=2)`

Solve DE.

DE is of the form: x' = slope*(fix_point - x), with initial condition init_p. The solution takes the form: (init_p - fix_point) * exp(-slope * t) + fix_point

Arguments

!!! t "np.ndarray"
    Timepoints at which to evaluate the drift. Usually np.arange() of some sort.
!!! init_p "float"
    Initial condition of dynamical system
!!! fix_point "float"
    Fixed point of dynamical system
!!! slope "float"
    Coefficient in exponent of the solution.

Return

np.ndarray The gamma drift evaluated at the supplied timepoints t.

gamma_drift(t=array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1,2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2,3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3,4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4,5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6,7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9,11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. ,12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1,13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2,14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3,15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4,16.5, 16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5,17.6, 17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6,18.7, 18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7,19.8, 19.9]), shape=2, scale=0.01, c=1.5)

Drift function that follows a scaled gamma distribution.

Arguments

!!! t "np.ndarray"
    Timepoints at which to evaluate the drift.
    Usually np.arange() of some sort.
!!! shape "float"
    Shape parameter of the gamma distribution
!!! scale "float"
    Scale parameter of the gamma distribution
!!! c "float"
    Scalar parameter that scales the peak of
    the gamma distribution.
    (Note this function follows a gamma distribution
    but does not integrate to 1)

Return

np.ndarray
    The gamma drift evaluated at the supplied timepoints t.

`simulator`

This module defines the basic simulator function which is the main workshorse of the package. In addition some utility functions are provided that help with preprocessing the output of the simulator function.

`bin_arbitrary_fptd(out=None, bin_dt=0.04, nbins=256, nchoices=2, choice_codes=[-1.0, 1.0], max_t=10.0)`

Takes in simulator output and returns a histogram of bin counts Arguments

!!! out "np.ndarray"
    Output of the 'simulator' function
bin_dt : float
    If nbins is 0, this determines the desired bin size
    which in turn automatically determines the resulting number of bins.
nbins : int
    Number of bins to bin reaction time data into.
    If supplied as 0, bin_dt instead determines the number of
    bins automatically.
!!! nchoices "int <default=2>"
    Number of choices allowed by the simulator.
choice_codes = list[float] <default=[-1.0, 1.0]>
    Choice labels to be used.
!!! max_t "float"
    Maximum RT to consider.

Returns

2d array (nbins, nchoices): A histogram of bin counts

`bin_simulator_output(out=None, bin_dt=0.04, nbins=0, max_t=-1, freq_cnt=False)`

Turns RT part of simulator output into bin-identifier by trial

Arguments

out : dict
    Output of the 'simulator' function
bin_dt : float
    If nbins is 0, this determines the desired
    bin size which in turn automatically
    determines the resulting number of bins.
nbins : int
    Number of bins to bin reaction time data into.
    If supplied as 0, bin_dt instead determines the number of
    bins automatically.
max_t : float <default=-1>
    Override the 'max_t' metadata as part of the simulator output.
    Sometimes useful, but usually default will do the job.
freq_cnt : bool <default=False>
    Decide whether to return proportions (default) or counts in bins.

Returns

A histogram of counts or proportions.

`bin_simulator_output_pointwise(out=(array([0]), array([0])), bin_dt=0.04, nbins=0)`

Turns RT part of simulator output into bin-identifier by trial

Arguments

!!! out "tuple"
    Output of the 'simulator' function
!!! bin_dt "float"
    If nbins is 0, this determines the desired
    bin size which in turn automatically
    determines the resulting number of bins.
!!! nbins "int"
    Number of bins to bin reaction time data into.
    If supplied as 0, bin_dt instead determines the
    number of bins automatically.

Returns

2d array. The first columns collects bin-identifiers
by trial, the second column lists the corresponding choices.

`make_boundary_dict(config, theta)`

Create a dictionary containing boundary-related parameters and functions.

This function extracts boundary-related parameters from the input theta dictionary, based on the boundary configuration specified in the config. It also retrieves the appropriate boundary function and multiplicative flag from the boundary_config.

Parameters:

Name	Type	Description	Default
`config`	`dict`	A dictionary containing model configuration, including the boundary name.	required
`theta`	`dict`	A dictionary of parameter values, potentially including boundary-related parameters.	required

Returns:

Type	Description
`dict`	A dictionary containing: - boundary_params (dict): Extracted boundary-related parameters. - boundary_fun (callable): The boundary function corresponding to the specified boundary name. - boundary_multiplicative (bool): Flag indicating if the boundary is multiplicative.

`make_drift_dict(config, theta)`

Create a dictionary containing drift-related parameters and functions.

This function extracts drift-related parameters from the input theta dictionary, based on the drift configuration specified in the config. It also retrieves the appropriate drift function from the drift_config.

Parameters:

Name	Type	Description	Default
`config`	`dict`	A dictionary containing model configuration, including the drift name.	required
`theta`	`dict`	A dictionary of parameter values, potentially including drift-related parameters.	required

Returns:

Type	Description
`dict`	A dictionary containing: - drift_fun (callable): The drift function corresponding to the specified drift name. - drift_params (dict): Extracted drift-related parameters. If no drift name is specified in config, returns an empty dictionary.

`simulator(theta, model='angle', n_samples=1000, delta_t=0.001, max_t=20, no_noise=False, bin_dim=None, bin_pointwise=False, sigma_noise=None, smooth_unif=True, return_option='full', random_state=None)`

Basic data simulator for the models included in HDDM.

Arguments

theta : list, numpy.array, dict or pd.DataFrame
    Parameters of the simulator. If 2d array, each row is treated as a 'trial'
    and the function runs n_sample * n_trials simulations.
deadline : numpy.array <default=None>
    If supplied, the simulator will run a deadline model. RTs will be returned
!!! model "str <default='angle'>"
    Determines the model that will be simulated.
!!! n_samples "int <default=1000>"
    Number of simulation runs for each row in the theta argument.
!!! delta_t "float"
    Size fo timesteps in simulator (conceptually measured in seconds)
!!! max_t "float"
    Maximum reaction the simulator can reach
!!! no_noise "bool <default=False>"
    Turn noise of (useful for plotting purposes mostly)
!!! bin_dim "int | None <default=None>"
    Number of bins to use (in case the simulator output is
    supposed to come out as a count histogram)
!!! bin_pointwise "bool <default=False>"
    Wheter or not to bin the output data pointwise.
    If true the 'RT' part of the data is now specifies the
    'bin-number' of a given trial instead of the 'RT' directly.
    You need to specify bin_dim as some number for this to work.
!!! sigma_noise "float | None <default=None>"
    Standard deviation of noise in the diffusion process. If None, defaults to 1.0 for most models
    and 0.1 for LBA models. If no_noise is True, sigma_noise will be set to 0.0.
    If 'sd' or 's' is passed via theta dictionary, sigma_noise must be None.
!!! smooth_unif "bool <default=True>"
    Whether to add uniform random noise to RTs to smooth the distributions.
!!! return_option "str <default='full'>"
    Determines what the function returns. Can be either
    'full' or 'minimal'. If 'full' the function returns
    a dictionary with keys 'rts', 'responses' and 'metadata', and
    metadata contains the model parameters and some additional
    information. 'metadata' is a simpler dictionary with less information
    if 'minimal' is chosen.
!!! random_state "int | None <default=None>"
    Integer passed to random_seed function in the simulator.
    Can be used for reproducibility.

Return

dictionary where keys can be (rts, responses, metadata) or (rt-response histogram, metadata) or (rts binned pointwise, responses, metadata)

`validate_ssm_parameters(model, theta)`

Validate the parameters for Sequential Sampling Models (SSM).

This function checks the validity of parameters for different SSM models. It performs specific checks based on the model type.

Parameters:

Name	Type	Description	Default
`model`	`str`	The name of the SSM model.	required
`theta`	`dict`	A dictionary containing the model parameters.	required

Exceptions:

Type	Description
`ValueError`	If any of the parameter validations fail.

`theta_processor`

Define the AbstractThetaProcessor and its concrete implementation.

SimpleThetaProcessor for processing theta parameters based on model configurations.

Classes

AbstractThetaProcessor: An abstract base class that defines the interface for processing theta parameters.
SimpleThetaProcessor: A concrete implementation of AbstractThetaProcessor that processes theta parameters based on various model configurations.

The SimpleThetaProcessor class includes methods to handle different models such as single particle models, multi-particle models, LBA-based models, and various choice models. It modifies the theta parameters according to the specified model configuration and number of trials.

`AbstractThetaProcessor (ABC)`

Abstract base class for theta processors.

`process_theta(self, theta, model_config, n_trials)`

Abstract method to process theta parameters.

Parameters:

Name	Type	Description	Default
`theta`	`Dict[str, Any]`	Dictionary of theta parameters.	required
`model_config`	`Dict[str, Any]`	Dictionary of model configuration.	required
`n_trials`	`int`	Number of trials.	required

Returns

Dict[str, Any]: Processed theta parameters.

`SimpleThetaProcessor (AbstractThetaProcessor)`

Simple implementation of the AbstractThetaProcessor.

This class collects functions (for now very simple) that build the bridge between the model_config level specification of the model and the theta parameters that are used in the simulator.

`process_theta(self, theta, model_config, n_trials)`

Process theta parameters based on the model configuration.

Parameters:

Name	Type	Description	Default
`theta`	`Dict[str, Any]`	Dictionary of theta parameters.	required
`model_config`	`Dict[str, Any]`	Dictionary of model configuration.	required
`n_trials`	`int`	Number of trials.	required

Returns

Dict[str, Any]: Processed theta parameters.