# Optimization Studies in Python#

The optimization study introduced in the preceding lesson used AnyBody’s builtin facilities for optimizing. Sometimes that is not enough, either because the objective functions depends on data that is not easily included in AnyBody, or because a different solver is needed.

In this tutorial we use an external optimizer together with AnyBody. The example is based on the model from the previous lesson but uses an optimizer from the Scipy python library. The same could also have been achived with other optimization frameworks (like NLopt, or languages (like MatLab).

## Requirements#

Before we begin you need to install the Python and some libraries. Using the `conda`

package manager, makes this much easier.

We also need one additional Python library (AnyPyTools) which will make it easier to work with AnyBody from Python. AnyPyTools can be easily installed from the command prompt. Open the Anaconda command prompt and type:

```
conda install anypytools
```

## Example Python script#

First we show the full Python program used in this tutorial. Secondly, we will go through and explain the different sections in the file.

```
1 import math
2 import scipy
3
4 from anypytools import AnyPyProcess
5 from anypytools.macro_commands import Load, OperationRun, Dump, SetValue
6
7
8 def run_model(saddle_height, saddle_pos, silent=False):
9 """Run the AnyBody model and return the metabolism results"""
10 macro = [
11 Load("BikeModel2D.main.any"),
12 SetValue("Main.BikeParameters.SaddleHeight", saddle_height),
13 SetValue("Main.BikeParameters.SaddlePos", saddle_pos),
14 OperationRun("Main.Study.InverseDynamics"),
15 Dump("Main.Study.Output.Pmet_total"),
16 Dump("Main.Study.Output.Abscissa.t"),
17 ]
18 app = AnyPyProcess(silent=silent)
19 results = app.start_macro(macro)
20 return results[0]
21
22
23 def objfun(designvars):
24 """Calculate the objective function value"""
25 saddle_height = designvars[0]
26 saddle_pos = designvars[1]
27 result = run_model(saddle_height, saddle_pos, silent=True)
28
29 if "ERROR" in result:
30 raise ValueError("Failed to run model")
31
32 pmet = scipy.integrate.trapz(result["Pmet_total"], result["Abscissa.t"])
33
34 return float(pmet)
35
36
37 def seat_distance_constraint(designvars):
38 """Compute contraint value which must be larger than zero"""
39 return math.sqrt(designvars[0] ** 2 + designvars[1] ** 2) - 0.66
40
41
42 constaints = {"type": "ineq", "fun": seat_distance_constraint}
43 bounds = [(0.61, 0.69), (-0.22, -0.05)]
44 initial_guess = (0.68, -0.15)
45
46 solution = scipy.optimize.minimize(
47 objfun, initial_guess, constraints=constaints, bounds=bounds, method="SLSQP"
48 )
49
50 print(solution)
```

A copy of the file can be `downloaded here.`

For now you can place the `optimize.py`

next to your main file `BikeModel2D.main.any`

.
If you didn’t complete the model from lesson 2, you can download the
`finshed model here`

.

## Importing the necessary libraries#

The first part of the code is the `import`

statements. They include the
libraries which is used by the code:

```
1 import math
2 import scipy
3
4 from anypytools import AnyPyProcess
5 from anypytools.macro_commands import Load, OperationRun, Dump, SetValue
```

## Running a model from Python#

For the external optimizers to work, we need a way to run AnyBody models from Python and record the results of the simulations, so we need to create a function to do this. There are more information on how to do this in the documentaion for AnyPyTools. So here we will just show how the code looks and not discuss the details.

```
8 def run_model(saddle_height, saddle_pos, silent=False):
9 """Run the AnyBody model and return the metabolism results"""
10 macro = [
11 Load("BikeModel2D.main.any"),
12 SetValue("Main.BikeParameters.SaddleHeight", saddle_height),
13 SetValue("Main.BikeParameters.SaddlePos", saddle_pos),
14 OperationRun("Main.Study.InverseDynamics"),
15 Dump("Main.Study.Output.Pmet_total"),
16 Dump("Main.Study.Output.Abscissa.t"),
17 ]
18 app = AnyPyProcess(silent=silent)
19 results = app.start_macro(macro)
20 return results[0]
21
22 result = run_model(0.66, -0.16)
23 print(result["Main.Study.Output.Pmet_total"])
```

The function `run_model()`

takes `saddle_height`

and `saddle_pos`

as input
and return the `Pmet`

metabolism output from AnyBody.

If you use an interactive Python environment (like IPython) you could try calling the function directly to to test it and investigate the results:

```
In [4]: result = run_model(0.66, -0.16)
[****************100%******************] 1 of 1 completeTotal time: 0.8 seconds
In [5]: print(result.keys())
odict_keys(['Main.Study.Output.Pmet_total', 'Main.Study.Output.Abscissa.t'])
In [6]: print(result["Main.Study.Output.Pmet_total"])
[ 17.20903341 73.49291834 209.58490241 379.67002659 559.57715608
736.92126247 901.88875426 1045.75303378 1162.65470516 1248.32088806
1299.79539032 1315.38241529 1294.6947524 1238.68684947 1149.59584772
1030.78784505 886.60667952 722.43408728 547.1840971 368.64175002
198.07134668 53.41928909 25.84379129 30.60376508 23.17442367
24.30809055 139.3209062 292.35610808 469.73382854 649.02576552
821.74094457 977.02863522 1108.05435136 1209.79739513 1278.65973442
1312.31195028 1309.70451022 1271.1212895 1198.17227557 1093.68215448
961.51890402 806.51623776 634.74029158 458.00117565 280.40563034
121.30841553 21.54859903 28.97200722 26.82989147 17.2090334 ]
```

As we expected the output contains the `Main.Study.Output.Pmet_total`

value for each timestep in our model.

## Defining the objective function#

The next step is to define the objective function. The objective function should
take a list of design values as input and return the objective function value.
In Lesson 2 the objective function was the time integral of the
metabolism variable. So we will do the same here with Scipy’s
`numpy.trapz()`

: function.

```
23 def objfun(x):
24 saddle_height = x[0]
25 saddle_pos = x[1]
26 result = run_model(saddle_height, saddle_pos, silent=True)
27
28 if "ERROR" in result:
29 raise ValueError("Failed to run model")
30 # Integrate Pmet_total
31 pmet = scipy.integrate.trapz(result["Pmet_total"], result["Abscissa.t"])
32
33 return float(pmet)
```

Note

The function also checks the results for errors reported
by AnyBody and raises a `ValueError`

exception if that happens.
There could be ways of handle error without failing but it is important to
handle model failures, otherwise they may go unnoticed or mess with the
optimization results.

Again, we can run this function interactively to test it:

```
In [9]: pmet = objfun([0.66, -0.16])
In [10]: print(pmet)
505.329399532772
```

Now we get the time integral of the `Pmet_total`

variable as as single value,
and we are now ready to define the optimization process.

## Setting up the optimization study#

We wrap things up by creating a function, similar to what we did in AnyBody, as well as defining the bounds and initial guess for the design variables.

```
37 def seat_distance_constraint(x):
38 """ Compute contraint value which must be larger than zero"""
39 return (math.sqrt(x[0] ** 2 + x[1] ** 2) - 0.66)
40
41
42 constaints = {"type": "ineq", "fun": seat_distance_constraint}
43 bounds = [(0.61, 0.69), (-0.22, -0.05)]
44 initial_guess = (0.68, -0.15)
```

The documentation `scipy.optimize.minimize()`

has more information on how to define bounds, contraints, tolerances, etc.

Finally, we call the `scipy.optimize.minimize`

function run the optimizer. In this case we used the
SLSQP algorithm.

```
46 solution = scipy.optimize.minimize(
47 objfun, initial_guess, constraints=constaints, bounds=bounds, method="SLSQP"
48 )
```

Let us try to do this interactively and look at the results.

```
In [11]: solution = scipy.optimize.minimize(
...: objfun, initial_guess, constraints=constaints, bounds=bounds, method="SLSQP"
...: )
In [12]: print(solution)
fun: 503.57634385063113
jac: array([39.43954086, -6.95677948])
message: 'Optimization terminated successfully.'
nfev: 56
nit: 12
njev: 12
status: 0
success: True
x: array([ 0.65010304, -0.11386853])
```

And there we have it!
We can now take advantage of the many different algorithms and settings available for `scipy.optimize.minimize()`

.
We could also use a different package or customize our own algorithm, constraints etc.

The possibilities are practically endless. The full example from this tutorial can be
`downloaded here`

.

For more information regarding the `AnyPyTools`

python package follow this link.
You can also check out this webcast.