The solution is to use FunctionWrappers.jl which provides a high
interface for C function pointers.

All tests passed.

update_state functions but performance gets worst.
With bench/pkg/abstract_arrays.jl i've highligthed each time a function
is in a collection performance gets worst.

implemetend in the package.
A lot of files has been modified because some of the internal syntax has
changed. But the top level methods should still work.

Two tests about the plots of synchronized oscillatory simulations have
been added.

All tests passed.

Julia shouldn't crash but rather raise an error about the existence
of a function generated by metaprogramming. I didn't manage to isolate
the segfault withtout the package.
To overcome the issue, I add another level of multiple dispatch/abstract
type for synchronized models.
Test of the euclidean distance automaton works.

Meta code generation for next_state! and simulation of synchronized trajectories. bench/pkg/euclidean_distance*.jl works but segfault with test/automata/euclidean*

This commit groups the change operated to the creation of models and
simulate function of a ContinuousTimeModel.
The general idea is to create a concrete type and a simulate function
 per model creation by metaprogramming.
- Now, ContinuousTimeModel is an abstract type. Each creation of a model
defines a concrete type T <: ContinuousTimeModel by meta programming.
- f! and isabsorbing ContinuousTimeModel fields are Symbols.
- simulate(::ContinuousTimeModel) is run by multiple dispatch, according
to the type of the model.

Can't run the whole tests for now but unit/simulate_available_models.jl
runs properly (i've updated the list of models in this commit), and I've
manually checked in the repl that simulations run correctly (distributed
/ plots).

edge_candidates on top-level of next_state! function.

allocation per next_state!. Now update_state! functions returns a Symbol
as location instead of modifying a vector.

check_constraints and update_state! functions of an Edge in order to
improve performance.
Acutally the gain is low, but it improved the readabilty of the code.
All tests passed.

check_constraint and update_state functions are created inside
create_automaton_*(), by the evaluation of meta_elementary_functions <:
Expr. check_constraint and update_state don't depend on constants::Dict
anymore: constants are interpolated inside the expression.

Add of tests for euclidean distance automaton.
Add a new function euclidean_distance that computes the euclidean
distance of a Trajectory compared to observations along a discrete
timeline.

1) LHA now have names. 2) Period automaton executes well and was runned on the doping 3way oscillator model. Not stattistically tested yet

Simulation of synchronized models is easily distributable.
Change of the signature of check_constraints and update_state in order
to access the model parameters.
Test about distributed simulations was renamed and completed.

Change of signatures of unit functions update_state!, check_constraints
of the automata edges. Use getfield instead of getproperty.

Now the execution of statistical estimation of the distance G is of the
order of Cosmos !

Reimplementation of easy access / modifications of StateLHA fields for better performance. Tests passed

models. It's a better semantic and improve performance as well as
readability of the code. All the tests passes (except the remark in the
last commit).

variableautomaton. It improves performance.

Tests of Cosmos doesn't pass on the bounds (with a parameter equal to
zero). It's a problem of design (there are issues for cosmos too).

of automata: it wasn't an issue about the simulation code of LHA but an
issue of LHA designs.

Improvement of a Cosmos utils method.

Now A.constants is a NamedTuple.

distributed_mean_value_lha now takes several variables.

Fix about behavior of automata when the initial state is absorbing or
the parameters are equal to zero.

Add of supplementary unit tests.

The two Cosmos tests checks the statistical correctness of estimated
distance value for R1,R2,R3,R5 experiments. Also, they test that all
trajectories are accepted.

A bunch of errors were fixed in automata files (Cosmos .lha and julia
./automata files) and simulation function that introduced statistical
bias, i.e. approximated values that were closed but not in the
confidence interval. The general structure of simulation and was
reworked in order to suits well te behavior of Cosmos.

copyto! is implemented for LHA states.

Automaton G works almost well, a bias in the distance is identified
compared to Cosmos when one of the model parameters is zero.

Implementation of volatile_simulate, which simulates SynchronizedModel
without saving values: it only returns the last LHA state of the
simulation.

Implementation of distributed computations of variable automata with
volatile_simulate.

Tests passed.

Fix about methods of SynchronizedTrajectory + tests.

Simulation for ParametricModel works.

order to replace vcat by push!.

The benchmarks are at least equal, and better when the buffer size is
small. However, no significative difference of performance with adequate
buffer size.

My opinion the gain can be seen when simulations are going to be very
long with more complicated models than we have implemented for now.

All tests passed.

Automaton G is implemented and runs OK. Not tested with Cosmos call.

The next step of development is the restruction of data collection for
trajectories.

From now on: Trajectory.values::Matrix{Float64}, it will be
Trajectory.values::Vector{Vector{Float64}}.

According to small performance tests I've made, it will increase the
performance of the package.

Add of a new type of Trajectory: SynchronizedTrajectory which is created
by the simulation of a synchronized model with an LHA.

Simulate function for SynchronizedModel executes and is tested by two
simple models.

Next step : Cosmos based tests.

Add of several useful functions for access values of model, lha and
trajectory objects.

an object.

Type stability of simulate, dist, read_trajectory seems to be preserved.