API reference

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`jaxley.Compartment`	A single compartment.
`jaxley.Branch`	A branch made up of one or multiple compartments (without branchpoints).
`jaxley.Cell`	A cell made up of one or multiple branches (with branchpoints).
`jaxley.Network`	A network made up of multiple cells, connected by synapses.

`jaxley.integrate`	Solves ODE and simulates neuron model.
`jaxley.integrate.build_init_and_step_fn`	Return `init_fn` and `step_fn` which initialize modules and run update steps.
`jaxley.utils.dynamics.build_dynamic_state_utils`	Return functions which extract the dynamic (ODE) states of a `jx.Module`.

`jaxley.read_swc`	Reads SWC file into a Cell.
`jaxley.morphology.distance_direct`	Returns the direct distance between a root and other compartments.
`jaxley.morphology.distance_pathwise`	Returns the pathwise distance between a root and other compartments.
`jaxley.morphology.morph_delete`	Deletes part of a morphology.
`jaxley.morphology.morph_connect`	Combines two morphologies into a single cell.

`jaxley.channels.Channel`	Channel base class.
`jaxley.channels.HH`	Hodgkin-Huxley channel based on Sterratt, Graham, Gillies & Einevoll.
`jaxley.channels.Na`	Sodium channel based on Sterratt, Graham, Gillies & Einevoll.
`jaxley.channels.K`	Potassium channel based on Sterratt, Graham, Gillies & Einevoll.
`jaxley.channels.Leak`	Leak channel.
`jaxley.channels.Izhikevich`	Izhikevich neuron model.
`jaxley.channels.Rate`	Rate-based, unit-less, neuron model.
`jaxley.channels.Fire`	Mechanism to reset the voltage when it crosses a threshold.

`jaxley.pumps.Pump`	Pump base class.
`jaxley.pumps.CaFaradayConcentrationChange`	Update the intracellular calcium ion concentration depending on calcium current.
`jaxley.pumps.CaNernstReversal`	Compute Calcium reversal from inner and outer concentration of calcium.
`jaxley.pumps.CaPump`	Calcium dynamics based on Destexhe et al. 1994.

`jaxley.synapses.Synapse`	Base class for a synapse.
`jaxley.synapses.CurrentSynapse`	A current-based synapse.
`jaxley.synapses.ConductanceSynapse`	A conductance based synapse.
`jaxley.synapses.DynamicSynapse`	A state-based synapse with fixed time constant.
`jaxley.synapses.IonotropicSynapse`	A state-based synapse with voltage dependent time constant.
`jaxley.synapses.SpikeSynapse`	Synapse to be used in networks of LIF neurons.
`jaxley.synapses.AlphaSynapse`	Alpha synapse which responds to binary pre-synaptic spike trains.

`jaxley.connect.connect`	Connect specific compartments of a network with a synapse.
`jaxley.connect.connectivity_matrix_connect`	Connect cells of a network with synapses via a boolean connectivity matrix.
`jaxley.connect.fully_connect`	Fully (densely) connect cells of a network with synapses.
`jaxley.connect.sparse_connect`	Sparsely connect cells of a network with synapses.

`jaxley.optimize.transforms.SigmoidTransform`	Sigmoid transformation.
`jaxley.optimize.transforms.SoftplusTransform`	Softplus transformation.
`jaxley.optimize.transforms.NegSoftplusTransform`	Negative softplus transformation.
`jaxley.optimize.transforms.AffineTransform`
`jaxley.optimize.transforms.ChainTransform`	Chaining together multiple transformations
`jaxley.optimize.transforms.MaskedTransform`
`jaxley.optimize.transforms.CustomTransform`	Custom transformation.
`jaxley.optimize.optimizer.TypeOptimizer`	optax wrapper which allows different argument values for different params.

`jaxley.io.graph.to_swc_graph`	Read a SWC file and return a SWC graph via networkX.
`jaxley.io.graph.build_compartment_graph`	Return a networkX graph that indicates the compartment structure.
`jaxley.io.graph.vis_compartment_graph`	Visualize a compartment graph.
`jaxley.io.graph.from_graph`	Return a Jaxley module from a compartmentalized networkX graph.
`jaxley.modules.base.to_graph`	Export a jx.Module as a networkX compartment graph.

`jaxley.solver_gate.save_exp`	Clip the input to a maximum value and return its exponential.
`jaxley.solver_gate.solve_gate_implicit`
`jaxley.solver_gate.solve_gate_exponential`
`jaxley.solver_gate.exponential_euler`	An exact solver for the linear dynamical system dx = -(x - x_inf) / x_tau.
`jaxley.solver_gate.heaviside`	Compute the heaviside step function with a custom derivative.