SUND Toolbox

https://isbgroup.eu/sund-toolbox/2.4.1/
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SUND is a Python package for high-level, object-oriented modeling and fast simulation of ODE/DAE...
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### Install required packages

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/usecases/parameter-estimation.html

Commands to install the necessary dependencies for running the parameter estimation example.

```bash
uv add sund numpy cma scipy
```

```bash
pip install sund numpy cma scipy
```

--------------------------------

### Install Required Packages

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/usecases/parameter-estimation.html

Commands to install the necessary dependencies for running the parameter estimation example.

```bash
uv add sund numpy pypesto
```

```bash
pip install sund numpy pypesto
```

--------------------------------

### Install SUND and dependencies

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/usecases/parameter-estimation.html

Commands to install the required packages for running the parameter estimation example.

```bash
uv add sund numpy scipy
```

```bash
pip install sund numpy scipy
```

--------------------------------

### Install SUND from PyPI

Source: https://isbgroup.eu/sund-toolbox/2.4.1/index.html

Standard installation commands using uv or pip.

```bash
uv add sund
```

```bash
pip install sund
```

--------------------------------

### Install SUND from URL

Source: https://isbgroup.eu/sund-toolbox/2.4.1/index.html

Installation commands for specific versions or the latest release hosted on the project homepage.

```bash
uv add https://isbgroup.eu/sund-toolbox/releases/sund-latest.tar.gz # Latest version
uv add https://isbgroup.eu/sund-toolbox/releases/sund-<version>.tar.gz # Specific version
```

```bash
pip install https://isbgroup.eu/sund-toolbox/releases/sund-latest.tar.gz # Latest version
pip install https://isbgroup.eu/sund-toolbox/releases/sund-<version>.tar.gz # Specific version
```

--------------------------------

### Compiler Benchmark Example

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/compiler-benchmark.ipynb

A basic example demonstrating compiler benchmarking.

```text
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
```

--------------------------------

### Install required packages

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/models/minimal-ode-model.html

Install the necessary dependencies using either uv or pip.

```bash
uv add sund numpy matplotlib
```

```bash
pip install sund numpy matplotlib
```

--------------------------------

### Install latest version from URL

Source: https://isbgroup.eu/sund-toolbox/2.4.1/versions.html

Use these commands to install the latest version of the toolbox directly from the project's release URL.

```shell
uv add https://isbgroup.eu/sund-toolbox/releases/sund-latest.tar.gz
```

```shell
pip install https://isbgroup.eu/sund-toolbox/releases/sund-latest.tar.gz
```

--------------------------------

### Full parameter estimation workflow

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/usecases/parameter-estimation.html

A complete Python script demonstrating data loading, model installation, simulation setup, objective function definition, and optimization using differential evolution.

```python
# %% Import packages
import json
from pathlib import Path

import numpy as np
from scipy.optimize import Bounds, differential_evolution

import sund


# Create a numpy serializer for json
class NumpyEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, np.ndarray):
            return o.tolist()
        return super().default(o)


# %% Load the data and get the total time vector for all observables in the experiments
data = json.loads(Path("data_m1.json").read_text())


for experiment in data:
    all_times = [
        t
        for observable in data[experiment]["observables"]
        for t in data[experiment]["observables"][observable]["time"]
    ]
    # Store the time vector for each experiment
    data[experiment]["all_times"] = sorted(np.unique(all_times))


# %% Install and load the model
sund.install_model("m1.txt")
m1 = sund.load_model("m1")

# %% Create a activity objects
activity1 = sund.Activity(time_unit="m")
activity1.add_output(
    type="piecewise_constant",
    name="activation",
    t=data["dose1"]["inputs"]["activation"]["t"],
    f=data["dose1"]["inputs"]["activation"]["f"],
)

activity2 = sund.Activity(time_unit="m")
activity2.add_output(
    type="piecewise_constant",
    name="activation",
    t=data["dose2"]["inputs"]["activation"]["t"],
    f=data["dose2"]["inputs"]["activation"]["f"],
)

# %% Create simulation objects
m1_sims = {}
m1_sims["dose1"] = sund.Simulation(
    models=m1,
    activities=activity1,
    time_unit="m",
    time_vector=data["dose1"]["all_times"],
)
m1_sims["dose2"] = sund.Simulation(
    models=m1,
    activities=activity2,
    time_unit="m",
    time_vector=data["dose2"]["all_times"],
)


# %%  Define a objective function
def obj_fun(params, sims, data):
    cost = 0
    for experiment in data:
        try:
            sims[experiment].simulate(parameter_values=params)

            for observable_key, observable_data in data[experiment]["observables"].items():
                feature_idx = sims[experiment].feature_names.index(observable_key)
                t_idx = np.where(np.isin(sims[experiment].time_vector, observable_data["time"]))
                y_sim = sims[experiment].feature_values[t_idx, feature_idx]
                y = observable_data["mean"]
                sem = observable_data["SEM"]

                cost += np.sum(np.square((y_sim - y) / sem))

        except Exception as e:
            if "CVODE" not in str(e):
                print(f"Error in {experiment}: {e}")
                raise
            cost += 1e29  # in-case of CVODE error exception add a "high cost"

    return cost


# Define a function to use parameters in log-space to calculate the cost
def obj_fun_log(params_log, sim, data):
    params = np.exp(params_log.copy())
    return obj_fun(params, sim, data)


# %% Prepare input for the optimization
# Define a initial parameter set
params0_m1 = [1, 0.0001, 1000000]  # = [k1, k2, kfeed]
x0_log_m1 = np.log(params0_m1)

# parameter bounds
bounds_m1_log = Bounds(
    [np.log(1e-6)] * len(x0_log_m1),
    [np.log(1e6)] * len(x0_log_m1),
)  # Create a bounds object


# callback function to save the current parameter values and cost
def callback_m1(x, _convergence):
    out = {"x": list(x)}
    Path("m1-temp.json").write_text(json.dumps(out))


def callback_m1_log(x, convergence):
    callback_m1(np.exp(x.copy()), convergence)


# arguments for the objective function
args_m1 = (m1_sims, data)

# %% call the optimization function
res = differential_evolution(
    func=obj_fun_log,
    bounds=bounds_m1_log,
    args=args_m1,
    x0=x0_log_m1,
    callback=callback_m1_log,
    disp=True,
)  # This is the optimization

# %% Print and extract the results
res["x"] = np.exp(res["x"])  # convert back from log scale

print(f"\nOptimized parameter values: {res['x']}")
print(f"Optimized cost: {res['fun']}")

# save the results to a json file
file_name = f"m1 ({res['fun']:.3f}).json"
res_out = {"f": res["fun"], "x": res["x"]}
Path(file_name).write_text(json.dumps(res_out, cls=NumpyEncoder))
```

--------------------------------

### Install and Load Models

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/published/herrgardh_2023/herrgardh_2023.ipynb

Demonstrates how to install and load various physiological models into the sund environment.

```python
# %% Import the model
sund.install_model(herrgardh_2023_str)
Herrgardh_2023 = sund.load_model("Herrgardh_2023")

# %% Import the segmented model parts
sund.install_model(brannmark_2013_str)
Brannmark = sund.load_model("Brannmark_2013")

sund.install_model(dallaman2007_str)
DallaMan = sund.load_model("DallaMan_2007")

sund.install_model(hall_2011_str)
Hall = sund.load_model("Hall_2011")

sund.install_model(herrgardh_2021_str)
Herrgardh_2021 = sund.load_model("Herrgardh_2021")

sund.install_model(herrgardh_2023_top_str)
```

--------------------------------

### Install a model

Source: https://isbgroup.eu/sund-toolbox/2.4.1/functionality/install-import-load.html

Use install_model to register a model file for use.

```python
sund.install_model('model.txt')
```

--------------------------------

### Install required packages

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/usecases/training-validation-and-uncertainty.html

Commands to install the necessary dependencies for the project.

```bash
uv add sund numpy scipy matplotlib
```

```bash
pip install sund numpy scipy matplotlib
```

--------------------------------

### Import Model

Source: https://isbgroup.eu/sund-toolbox/2.4.1/model-format/model-format.html

Load a defined model after installation.

```python
sund.import_model('my_model')
```

--------------------------------

### SimulationObject.simulate usage example

Source: https://isbgroup.eu/sund-toolbox/2.4.1/modules/simulation/simulate.html

Demonstrates the required keyword-only argument syntax for calling the simulate method.

```python
sim.simulate(time_vector=..., parameter_values=..., reset=True)
```

--------------------------------

### Retrieve installed models list

Source: https://isbgroup.eu/sund-toolbox/2.4.1/modules/public_functions/installed_models.html

Returns a list of strings representing the names of all models currently installed in the package.

```python
def installed_models() -> list[str]:
    """Produces a list of the names of the installed models.

    Returns:
        A list of installed models
    """
    pkgpath = Path(__file__).parent / "Models"
    return [name for _, name, _ in pkgutil.iter_modules([str(pkgpath)])]
```

--------------------------------

### Benchmark Model Compilation with Different Compilers

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/compiler-benchmark.ipynb

Demonstrates how to switch between Zig and system compilers to install performance models, including error handling for missing compiler availability.

```python
from sund.tools import SundCompilerError

zig_name = "bench_compiler_zig"
sys_name = "bench_compiler_system"

for name in [zig_name, sys_name]:
    if name in sund.installed_models():
        sund.uninstall_model(name)

# Install with zig
print("Compiling model with zig...")
sund.set_compiler("zig")
try:
    sund.install_model(_make_perf_model(zig_name))
    print(f"  Done. Installed '{zig_name}' with zig compiler.")
except SundCompilerError as e:
    print(f"  zig compiler not available â€” cannot run benchmark.\n  {e}")
    raise SystemExit from e

# Install with system compiler
print("Compiling model with system compiler...")
sund.set_compiler("system")
try:
    sund.install_model(_make_perf_model(sys_name))
    print(f"  Done. Installed '{sys_name}' with system compiler.")
except SundCompilerError as e:
    print(f"  System compiler not available â€” cannot run benchmark.\n  {e}")
```

--------------------------------

### sund.install_model

Source: https://isbgroup.eu/sund-toolbox/2.4.1/functionality/install-import-load.html

Installs a model from a file path. Supports specifying the compiler optimization level.

```APIDOC
## sund.install_model(path, compile_optimization_level)

### Description
Installs a model from the specified file path so it can be loaded or imported later.

### Parameters
- **path** (string) - Required - The file path to the model definition (e.g., 'model.txt').
- **compile_optimization_level** (int) - Optional - Optimization level for compilation (0, 1, 2, or 3). Defaults to 2.
```

--------------------------------

### Initial State Conditions

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/published/herrgardh_2023/herrgardh_2023.ipynb

Starting values for the model variables at time zero.

```text
betamass(0) = 1
diabetes(0) = 0
G_p(0)      = 159.2072                                                           
G_t(0)      = 130                                                        
I_l(0)      = 4.5                                                    
I_p(0)      = 1.4950                                                       
Q_sto1(0)   = 0                                                
Q_sto2(0)   = 0                                                     
Q_gut(0)    = 0                                                      
I_1(0)      = 25                                                        
I_d(0)      = 25                                                        
I_po(0)     = 3.6                                     
Y(0)        = 0                                
Y2(0)       = 0
INS_f(0)    = 0
INS(0)      = 0
RE(0)       = 0
Glu_in(0)   = 2.470962100570481
G6P(0)      = 0.004273753196181
```

--------------------------------

### Load a model object

Source: https://isbgroup.eu/sund-toolbox/2.4.1/functionality/install-import-load.html

Create a model object from an installed model using load_model.

```python
model = sund.load_model('model')
```

--------------------------------

### Implementation of sund.install_model

Source: https://isbgroup.eu/sund-toolbox/2.4.1/modules/public_functions/install_model.html

This function checks for existing installations, manages module unloading, handles file system cleanup, and performs model compilation.

```python
# Check if already installed and up to date
if not force and model["NAME"] in installed_models() and modulefile.exists():
    existing_hash = _read_model_hash(hashfile) if hashfile.exists() else None

    if existing_hash == current_hash:
        logger.info("Model '%s' is already installed and up to date.", model["NAME"])
        continue

    if existing_hash is None:
        logger.info(
            "Model '%s' is missing content hash. Reinstalling model and creating hash sidecar.",  # noqa: E501
            model["NAME"],
        )
    else:
        logger.info(
            "Model '%s' content differs from installed version. Reinstalling.",
            model["NAME"],
        )

# Check if imported
if ("sund.Models." + model["NAME"]) in sys.modules:
    logger.warning(
        "Model '%s' has already been imported into memory. Trying to unload the module, but you might need to restart Python before re-installing the model.",  # noqa: E501
        model["NAME"],
    )

    sys.modules.pop("sund.Models." + model["NAME"])
# Check if modulefile exist on file system and remove
if modulefile.exists():
    try:
        modulefile.unlink()
    except (OSError, PermissionError) as err:
        msg = f"Model '{model['NAME']}' cannot be re-installed, probably because it is being used by another process. Try terminating any process that might use the model and try again."  # noqa: E501
        raise ValueError(msg) from err
if hashfile.exists():
    with contextlib.suppress(builtins.BaseException):
        hashfile.unlink()

# create C-file and module file
logger.info("Compiling model '%s'...", model["NAME"])
cfile = tools._model_cfile(model)
tools._model_module_file(cfile, compile_optimization_level=compile_optimization_level)
if not save_compiled_model:
    Path(cfile).unlink()
logger.info("Model '%s' successfully installed.", model["NAME"])

try:
    _write_model_hash(hashfile, current_hash)
except Exception as e:  # noqa: BLE001
    warnings.warn(
        f"Could not write hash file for model '{model['NAME']}': {e}",
        stacklevel=2,
    )
```

--------------------------------

### Execute ODE model simulation

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/models/minimal-ode-model.html

Full workflow for installing, loading, configuring, and simulating a model with SUND.

```python
# %% Import packages
import matplotlib.pyplot as plt
import numpy as np

import sund

# %% Define the model name
model_name = "minimal_ode"

# %% Install the model by using the SUND function 'install_model'
sund.install_model(f"{model_name}.txt")

# %% Import the model object by using the SUND function 'load_model'
model = sund.load_model(f"{model_name}")

# %% The built in model objects functions can now be used
model.state_values = [2, 2]
print(model.state_values)
model.reset_states()
print(model.state_values)

# %% A simulation object can be constructed from the model template
sim1 = sund.Simulation(models=model, time_unit=model.time_unit)
print(sim1.state_values)

# Note that the simulation object will be crated from the current instance of the model object
model.state_values = [2, 2]
sim2 = sund.Simulation(models=model, time_unit=model.time_unit)
print(sim2.state_values)

# %% Now we can simulate the model using the simulation object
sim1.simulate(time_vector=np.linspace(0, 10, 100))

fig = plt.figure()
feature_index = sim1.feature_names.index("yA")
plt.plot(sim1.time_vector, sim1.feature_values[:, feature_index])
plt.xlabel(sim1.time_unit)
plt.ylabel("A.U.")
plt.title("Simulation result")

# %% Simulating the object again will start the simulation from the last result
sim1.simulate(time_vector=np.linspace(0, 10, 100))

fig = plt.figure()
feature_index = sim1.feature_names.index("yA")
plt.plot(sim1.time_vector, sim1.feature_values[:, feature_index])
plt.xlabel(sim1.time_unit)
plt.ylabel("A.U.")
plt.title("Simulation result")

# %% to recreate the original results we need to reset the simulation object
sim1.reset_states()
sim1.simulate(time_vector=np.linspace(0, 10, 100))

fig = plt.figure()
feature_index = sim1.feature_names.index("yA")
plt.plot(sim1.time_vector, sim1.feature_values[:, feature_index])
plt.xlabel(sim1.time_unit)
plt.ylabel("A.U.")
plt.title("Simulation result")

plt.show()
```

--------------------------------

### Full parameter estimation workflow

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/usecases/parameter-estimation.html

A comprehensive script covering data preparation, model setup, objective function definition, and optimization using CMA-ES and Nelder-Mead.

```python
# %% Import packages
import json
from pathlib import Path

import cma
import numpy as np
from scipy.optimize import minimize as scipy_minimize

import sund


# Create a numpy serializer for json
class NumpyEncoder(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, np.ndarray):
            return o.tolist()
        return super().default(o)


# %% Load the data and get the total time vector for all observables in the experiments
data = json.loads(Path("data_m1.json").read_text())

for experiment in data:
    all_times = [
        t
        for observable in data[experiment]["observables"]
        for t in data[experiment]["observables"][observable]["time"]
    ]
    data[experiment]["all_times"] = sorted(np.unique(all_times))


# %% Install and load the model
sund.install_model("m1.txt")
m1 = sund.load_model("m1")

# %% Create activity objects
activity1 = sund.Activity(time_unit="m")
activity1.add_output(
    type="piecewise_constant",
    name="activation",
    t=data["dose1"]["inputs"]["activation"]["t"],
    f=data["dose1"]["inputs"]["activation"]["f"],
)

activity2 = sund.Activity(time_unit="m")
activity2.add_output(
    type="piecewise_constant",
    name="activation",
    t=data["dose2"]["inputs"]["activation"]["t"],
    f=data["dose2"]["inputs"]["activation"]["f"],
)

# %% Create simulation objects
m1_sims = {}
m1_sims["dose1"] = sund.Simulation(
    models=m1,
    activities=activity1,
    time_unit="m",
    time_vector=data["dose1"]["all_times"],
)
m1_sims["dose2"] = sund.Simulation(
    models=m1,
    activities=activity2,
    time_unit="m",
    time_vector=data["dose2"]["all_times"],
)


# %% Define an objective function
def obj_fun(params, sims, data):
    cost = 0
    for experiment in data:
        try:
            sims[experiment].simulate(parameter_values=params)

            for observable_key, observable_data in data[experiment]["observables"].items():
                feature_idx = sims[experiment].feature_names.index(observable_key)
                t_idx = np.where(np.isin(sims[experiment].time_vector, observable_data["time"]))
                y_sim = sims[experiment].feature_values[t_idx, feature_idx]
                y = observable_data["mean"]
                sem = observable_data["SEM"]

                cost += np.sum(np.square((y_sim - y) / sem))

        except Exception as e:
            if "CVODE" not in str(e):
                print(f"Error in {experiment}: {e}")
                raise
            cost += 1e29

    return cost


def obj_fun_log(params_log, sims, data):
    params = np.exp(params_log)
    return obj_fun(params, sims, data)


# %% Prepare input for the optimization
params0 = [1, 0.0001, 1000000]  # = [k1, k2, kfeed]
x0_log = np.log(params0)

# Parameter bounds (log-space)
lb = [np.log(1e-6)] * len(x0_log)
ub = [np.log(1e6)] * len(x0_log)

# %% Run CMA-ES optimization
# Scale population size and iterations with number of parameters
n_params = len(x0_log)
popsize = max(50, int(10 * np.sqrt(n_params)))
maxiter = max(100, (1000 * n_params) // popsize)

cma_options = {
    "bounds": [lb, ub],
    "maxiter": maxiter,
    "popsize": popsize,
    "verbose": 1,
    "tolfun": 1e-9,
    "tolx": 1e-9,
}


# Wrap the objective so CMA-ES only passes the parameter vector
def objective(x):
    return obj_fun_log(x, m1_sims, data)


# Callback to save progress
def cma_callback(es):
    out = {"x": np.exp(es.result.xbest).tolist(), "f": es.result.fbest}
    Path("m1-temp.json").write_text(json.dumps(out))


sigma0 = 0.3  # initial step size
res = cma.CMAEvolutionStrategy(x0_log, sigma0, inopts=cma_options).optimize(
    objective, callback=cma_callback
).result

# %% Local polish with Nelder-Mead
# CMA-ES is good at finding the right region, but a local solver can
# refine the solution further.
polish_res = scipy_minimize(
    objective,
    x0=res.xbest,
    method="Nelder-Mead",
    bounds=list(zip(lb, ub)),
    options={"maxfev": max(200, 200 * n_params), "xatol": 1e-9, "fatol": 1e-9, "adaptive": True},
)

if polish_res.fun < res.fbest:
    print(f"Nelder-Mead improved: {res.fbest:.6g} -> {polish_res.fun:.6g}")
    best_params = np.exp(polish_res.x)
    best_cost = polish_res.fun
else:
    best_params = np.exp(res.xbest)
    best_cost = res.fbest

# %% Print and extract the results

print(f"\nOptimized parameter values: {best_params}")
print(f"Optimized cost: {best_cost}")

# Save the results
file_name = f"m1 ({best_cost:.3f}).json"
res_out = {"f": best_cost, "x": best_params}
Path(file_name).write_text(json.dumps(res_out, cls=NumpyEncoder))
```

--------------------------------

### Simulate and Plot SUND Model Scenarios

Source: https://isbgroup.eu/sund-toolbox/2.4.1/examples/published/podeus-2024.html

This script installs a model, defines multiple activity scenarios, runs simulations for each, and plots the results in a 2x2 grid.

```python
import matplotlib.pyplot as plt
import numpy as np

import sund

# %% Install the model by using the SUND function 'install_model'
model_name = "alcoholmodel"
sund.install_model(f"{model_name}.txt")

# %% Load the model by using the sund function 'load_model'
model = sund.load_model(f"{model_name}")

# %% Create an activity and simulate an alcoholic drink
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(type="piecewise_constant", name="EtOH_conc", t=[0, 20], f=[0, 5.1, 0])
activity.add_output(
    type="piecewise_constant",
    name="vol_drink_per_time",
    t=[0, 20],
    f=[0, 0.05135, 0],
)
activity.add_output(
    type="piecewise_constant",
    name="kcal_liquid_per_vol",
    t=[0, 20],
    f=[0, 129.82, 0],
)
activity.add_output(type="constant", name="sex", f=1)
activity.add_output(type="constant", name="weight", f=82.66)
activity.add_output(type="constant", name="height", f=1.77)

# Create a simulation object
sim = sund.Simulation(
    models=model,
    activities=activity,
    time_unit=model.time_unit,
    time_vector=np.arange(0, 250, 0.1),
)

sim.simulate()

# Plot the results
fig = plt.figure(figsize=(18 * (1 / 2.54), 18 * (1 / 2.54)))
ax = fig.add_subplot(2, 2, 1)
ax.plot(
    sim.time_vector,
    sim.feature_values[:, sim.feature_names.index("Blood alcohol concentration")],
    label="Blood alcohol concentration",
)
ax.set_xlabel("Time (min)")
ax.set_ylabel("BAC (mg/dL)")
ax.legend(edgecolor="None", loc="upper right")

# %% Create an activity and simulate an alcoholic drink with food
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(type="piecewise_constant", name="EtOH_conc", t=[20, 35], f=[0, 20, 0])
activity.add_output(
    type="piecewise_constant",
    name="vol_drink_per_time",
    t=[20, 35],
    f=[0, 0.009606, 0],
)
activity.add_output(
    type="piecewise_constant",
    name="kcal_liquid_per_vol",
    t=[20, 35],
    f=[0, 360, 0],
)
activity.add_output(type="piecewise_constant", name="kcal_solid", t=[1e-8, 20], f=[0, 700, 0])
activity.add_output(type="constant", name="sex", f=1)
activity.add_output(type="constant", name="weight", f=75.80)
activity.add_output(type="constant", name="height", f=1.83)

# Create a simulation object
sim = sund.Simulation(
    models=model,
    activities=activity,
    time_unit=model.time_unit,
    time_vector=np.arange(0, 400, 0.1),
)

sim.simulate()

# Plot the results
ax = fig.add_subplot(2, 2, 2)
ax.plot(
    sim.time_vector,
    sim.feature_values[:, sim.feature_names.index("Blood alcohol concentration")],
    label="Blood alcohol concentration",
)
ax.set_xlabel("Time (min)")
ax.set_ylabel("BAC (mg/dL)")
ax.legend(edgecolor="None", loc="upper right")

# %% Create an activity and simulate a non-alcoholic drink
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(
    type="piecewise_constant",
    name="vol_drink_per_time",
    t=[0, 3],
    f=[0, 0.5 / 3, 0],
)
activity.add_output(type="piecewise_constant", name="kcal_liquid_per_vol", t=[0, 3], f=[0, 664, 0])
activity.add_output(type="constant", name="sex", f=1)
activity.add_output(type="constant", name="weight", f=65.00)
activity.add_output(type="constant", name="height", f=1.71)

# Create a simulation object
sim = sund.Simulation(
    models=model,
    activities=activity,
    time_unit=model.time_unit,
    time_vector=np.arange(0, 125, 0.1),
)

sim.simulate()

# Plot the results
ax = fig.add_subplot(2, 2, 3)
ax.plot(
    sim.time_vector,
    sim.feature_values[:, sim.feature_names.index("Gastric volume")],
    label="Gastric volume",
)
ax.set_xlabel("Time (min)")
ax.set_ylabel("Volume (mL)")
ax.legend(edgecolor="None", loc="upper right")

# %% Create an activity and simulate the PEth profile from an alcoholic drink
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(type="piecewise_constant", name="EtOH_conc", t=[0, 15], f=[0, 6.41, 0])
activity.add_output(
    type="piecewise_constant",
    name="vol_drink_per_time",
    t=[0, 15],
    f=[0, 0.0473173, 0],
)
activity.add_output(
    type="piecewise_constant",
    name="kcal_liquid_per_vol",
    t=[0, 15],
    f=[0, 322.3, 0],
)
activity.add_output(type="constant", name="sex", f=1)
activity.add_output(type="constant", name="weight", f=59.23)
activity.add_output(type="constant", name="height", f=1.78)

# Create a simulation object
sim = sund.Simulation(
    models=model,
    activities=activity,
    time_unit=model.time_unit,
    time_vector=np.arange(0, 400, 0.1),
)

sim.simulate()

# Plot the results
ax = fig.add_subplot(2, 2, 4)
ax.plot(sim.time_vector, sim.feature_values[:, sim.feature_names.index("PEth")], label="PEth")
ax.set_xlabel("Time (min)")
ax.set_ylabel("PEth (ng/mL)")
ax.legend(edgecolor="None", loc="upper right")


plt.show()
```
Tokens:
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