API Reference

replay_trajectory_classification.classifier	State space models that classify trajectories as well as decode the trajectory from population spiking
replay_trajectory_classification.clusterless_simulation	Simulate clusterless spikes and associated spike waveform features.
replay_trajectory_classification.continuous_state_transitions	Classes for constructing different types of movement models.
replay_trajectory_classification.core	Core algorithms for decoding.
replay_trajectory_classification.decoder	Main classes for decoding trajectories from population spiking
replay_trajectory_classification.discrete_state_transitions	Classes to generate transitions between categories.
replay_trajectory_classification.environments	Classes for constructing discrete grids representations of spatial environments in 1D and 2D
replay_trajectory_classification.initial_conditions	Classes for constructing the initial conditions for the state space models.
replay_trajectory_classification.observation_model	Class for representing an environment and a condition (trial type, etc.)
replay_trajectory_classification.simulate	Main code for simulating position and sorted spikes or clusterless spikes and waveforms.
replay_trajectory_classification.sorted_spikes_simulation	Functions for generating clustered spikes data.
replay_trajectory_classification.standard_decoder	Functions for common Bayesian decoding algorithms.
replay_trajectory_classification.likelihoods.calcium_likelihood	Calculate a Gamma likelihood for calcium imaging activity traces.
replay_trajectory_classification.likelihoods.diffusion	Calculate diffusion distances by simulating diffusion at each position bin.
replay_trajectory_classification.likelihoods.multiunit_likelihood	Estimates a marked point process likelihood where the marks are features of the spike waveform.
replay_trajectory_classification.likelihoods.multiunit_likelihood_gpu	Estimates a marked point process likelihood where the marks are features of the spike waveform using GPUs.
replay_trajectory_classification.likelihoods.multiunit_likelihood_integer	Estimates a marked point process likelihood where the marks are features of the spike waveform.
replay_trajectory_classification.likelihoods.multiunit_likelihood_integer_gpu	Estimates a marked point process likelihood where the marks are features of the spike waveform using GPUs.
replay_trajectory_classification.likelihoods.spiking_likelihood_glm	Estimates a Poisson likelihood using place fields estimated with a GLM with a spline basis
replay_trajectory_classification.likelihoods.spiking_likelihood_kde	Estimates a Poisson likelihood using place fields estimated with a kernel density estimate.
replay_trajectory_classification.likelihoods.spiking_likelihood_kde_gpu	Estimates a Poisson likelihood using place fields estimated with a KDE using GPUs