One might be interested in estimating the probability of observing certain units X of chemical Y all along the model execution.

From the point of view of stochastic models, this is equivalent to assess, for the chemical Y, the solution of its master equation which rules the change, in time, of such a probability. Since for most models this equation is not solvable, its solution is often estimated by numerical ensembles, as in pyTSA.

Command meq allows to easily estimate this quantity, and plots it either as a heatmap or a 3D surface.

**Example: **For time in [0,100] estimate the time-varying probability of the columns Preys and Predators and visualize it as a heatmap (meq2d)

obj.meq2d(start=0, stop=100, ...)