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QUESTION
I have been looking for a while, but could not find the answer to this specific question anywhere, sorry if it is a duplicate!
I have started to build a python package based on the xarray-simlab framework with the goal to provide a modular toolbox for building reproducible and flexible marine ecosystem models. Xarray-simlab at the moment only supports explicit step-sizes to solve the model functions. In order to solve complex models more safely & efficiently, I have instead started using GEKKO as a solver backend, as the model syntax seems well suited. (Note: At the moment I will only need functionality to solve the model equations over time, but I would like to make use of GEKKO's optimization functionality to fit model parameters to field or lab data at later stages.)
The current prototype of the package creates a xsimlab process class that passes the GEKKO model instance
m
to all sub-processes. Process classes that inherit the model instance initialize m.SV
, m.Param
or define m.Intermediates
based on the processes added to the model & parameters (incl. SV dimensions) supplied at runtime. In the next step all initialized intermediates are accumulated to the affected state variables in m.Equations
. Once successfully solved, GEKKO variables are repackaged into a xarray data structure, that includes relevant metadata and can be analysed further. The package prototype can solve basic models using IMODE=7, but I have come across one issue related to the time steps of that solver:
I was expecting functionality similar to scipy's odeint, with adaptive time step evaluation, but obviously this does not seem to be the case and instead it evaluates the model at the discrete time-steps supplied.
The package is still under heavy development, and there are plenty of features that I am still trying to improve, so below is a minimal code example of a simple chemostat model. The model describes a phytoplankton state variable growing on a nutrient in a simplified flow-through system. The nutrient flows in at a constant rate, and phytoplankton dies and is lost from the system at a constant rate:
...ANSWER
Answered 2020-Jul-06 at 22:54Try to increase the number of nodes per segment with:
QUESTION
So the problem that is being faced here is the curve fitting of the Monod equations to the experimental data. The model of bacteria growth and degradation of the organic carbon looks like this:
dX/dt = (u * S * X )/(K + S)
dS/dt = ((-1/Y) * u * S * X )/(K + S)
These equations are solved using the scipy odeint function. Results after integration are stored into two vectors, one for growth, and the another one for degradation. The next step is to curve fit this model to the experimentally observed data and estimate the model parameters: u, K and Y. Once the code is run, the following error is produced:
...ANSWER
Answered 2020-Jun-16 at 12:26The result of f()
needs to have the same shape as the experimental data you feed into curve_fit
as third parameter. In the last line of f()
you just take the t = 0s value of the solution for both ODEs and return that, but you should return the complete solution. When fitting several sets of data at once using curve_fit
, just concat them (stack horizontally), i.e.
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