ebbflow 0.0.3

A template class for developing mechanistic models

ebbflow

ebbflow is a Python package designed for running mechanistic models.

Features

• Compatible with SciPy's solve_ivp solver.
• Captures model intermediates at specified time points.
• Exports results to a pandas DataFrame for analysis.

Installation

You can install ebbflow directly from PyPI:

pip install ebbflow

Quickstart

To use ebbflow you start by defining a new class that inherits BaseMechanisticModel:

In the __init__ of this class you pass all the constants as arguments. You can also provide a list of variables to include in the output. These are values that you can set each time you initalize a new model.

The model method is where you define the model calculations. This must take time (t) and state_vars as the arguments. Once you have defined all the calculation steps it is important to call self.save(). This allows the class to capture all the intermediate values in your model during the integration. Finally, the model method should return a list of differentials. Make sure the order of the differentials matches the order of the state_vars.

from ebbflow import BaseMechanisticModel

class DemoModel(BaseMechanisticModel):
    def __init__(self, kAB, kBO, YBAB, vol, outputs):
        self.kAB = kAB        
        self.kBO = kBO
        self.YBAB = YBAB
        self.vol = vol        
        self.outputs = outputs
    
    def model(self, t, state_vars):
        kAB = self.kAB
        kBO = self.kBO
        YBAB = self.YBAB
        vol = self.vol

        # Variables with Differential Equation #
        A = state_vars[0]
        B = state_vars[1]

        # Model Equations # 
        concA = A/vol
        concB = B/vol
        UAAB = kAB*concA
        PBAB = UAAB*YBAB
        UBBO = kBO*concB

        # Differential Equations # 
        dAdt = -UAAB
        dBdt = PBAB - UBBO

        self.save()
        return [dAdt, dBdt]

With the model defined we can now set the parameters and run an integration. First, we create an instance of our class. In this example we call it demo. We set the value of our parameters using and specify the variable to include in the output.

demo = DemoModel(
    kAB=0.42, kBO=0.03, YBAB=1.0, vol=1.0, 
    outputs=['t', 'A', 'B', 'concA', 'concB', 'dAdt']
    )

We can now call the run_model method to perform an integration. We select the solver method to use (RK4), the time span to integrate (t_span), the initial state variables (y0), the evaluation times (t_eval) and the integration interval for RK4.

demo.run_model(
    "RK4", t_span=(0, 120), y0=[3.811, 4.473], t_eval=np.arange(0,121,10),
    integ_interval=0.001
    )

After the model finishes running we can export the results to a dataframe for analysis.

df = demo.to_dataframe()
print(df)

This will print the results at the times based on t_eval.

          t             A         B         concA     concB          dAdt
0     0.000  3.809400e+00  4.474466  3.809400e+00  4.474466 -1.599948e+00
1     9.999  5.714814e-02  6.292568  5.714814e-02  6.292568 -2.400222e-02
2    19.999  8.569694e-04  4.706319  8.569694e-04  4.706319 -3.599271e-04
3    29.999  1.285075e-05  3.487197  1.285075e-05  3.487197 -5.397315e-06
4    39.999  1.927044e-07  2.583389  1.927044e-07  2.583389 -8.093585e-08
5    49.999  2.889714e-09  1.913822  2.889714e-09  1.913822 -1.213680e-09
6    59.999  4.333292e-11  1.417794  4.333292e-11  1.417794 -1.819983e-11
7    69.999  6.498022e-13  1.050328  6.498022e-13  1.050328 -2.729169e-13
8    79.999  9.744159e-15  0.778102  9.744159e-15  0.778102 -4.092547e-15
9    89.999  1.461193e-16  0.576432  1.461193e-16  0.576432 -6.137010e-17
10   99.999  2.191143e-18  0.427031  2.191143e-18  0.427031 -9.202800e-19
11  109.999  3.285745e-20  0.316353  3.285745e-20  0.316353 -1.380013e-20
12  119.999  4.927164e-22  0.234360  4.927164e-22  0.234360 -2.069409e-22

When using the "RK4" equation we can continue running our model from a previous time point. This allows us to start a model with a set of constants then moddify these constants at a chosen timepoint.

# This will create a list with our 2 state variables, A and B
new_stateVars =  df.iloc[-1, df.columns.isin(['A', 'B'])].tolist()

# We change the value of kAB
model.change_constants({"kAB": 0.5})

# Next we run the model with the new initial values and time span
model.run_model(
    "RK4", t_span=(120, 220), y0=new_stateVars, t_eval=np.arange(120,221,10), 
    integ_interval=0.01, prev_output=result
    )

new_result = model.to_dataframe()
display(new_result)

As you can see this model run starts at t=120 and goes to the new stop time of 220.

kAB updated to 0.5
Running Model...
t	A	B	concA	concB	dAdt
0	129.98	3.319897e-24	0.173618	3.319897e-24	0.173618	-1.659949e-24
1	139.98	2.236929e-26	0.128619	2.236929e-26	0.128619	-1.118465e-26
2	149.98	1.507231e-28	0.095284	1.507231e-28	0.095284	-7.536155e-29
3	159.98	1.015564e-30	0.070588	1.015564e-30	0.070588	-5.077821e-31
4	169.98	6.842818e-33	0.052293	6.842818e-33	0.052293	-3.421409e-33
5	179.98	4.610655e-35	0.038739	4.610655e-35	0.038739	-2.305327e-35
6	189.98	3.106635e-37	0.028699	3.106635e-37	0.028699	-1.553317e-37
7	199.98	2.093234e-39	0.021261	2.093234e-39	0.021261	-1.046617e-39
8	209.98	1.410410e-41	0.015750	1.410410e-41	0.015750	-7.052050e-42
9	219.98	9.503268e-44	0.011668	9.503268e-44	0.011668	-4.751634e-44