Python Plant Model

class ExternalPlant(central_param: ndarray = array([0.5, 0.5]), param_limits: ndarray = array([[0, 1], [0, 1]]), name: str | None = None)

Bases: Model, ArtificialModelInterface

A plant model which uses functions defined outside the class to evaluate the forward pass and the jacobian Param0: Water [0,1] Param1: Sun [0,1] Data0: Size [0,2] # the more water and sun the better Data1: Health [0,1], to much water is not good, too much sun is not good Data2: Trauerfliegen :P

forward(param)

Executed the forward pass of the model to obtain data from a parameter.

Parameters:

param (np.ndarray) – The parameter for which the data should be generated.

Returns:

The data generated from the parameter.

Return type:

np.ndarray

Examples:

import numpy as np
from eulerpi.examples.heat import Heat
from eulerpi.core.model import JaxModel
from jax import vmap

# instantiate the heat model
model = Heat()

# define a 3D example parameter for the heat model
example_param = np.array([1.4, 1.6, 0.5])

# the forward simulation is achieved by using the forward method of the model
sim_result = model.forward(example_param)

# in a more realistic scenario, we would like to perform the forward pass on multiple parameters at once
multiple_params = np.array([[1.5, 1.5, 0.5],
                            [1.4, 1.4, 0.6],
                            [1.6, 1.6, 0.4],
                            model.central_param,
                            [1.5, 1.4, 0.4]])

# try to use jax vmap to perform the forward pass on multiple parameters at once
if isinstance(model, JaxModel):
    multiple_sim_results = vmap(model.forward, in_axes=0)(multiple_params)

# if the model is not a jax model, we can use numpy vectorize to perform the forward pass
else:
    multiple_sim_results = np.vectorize(model.forward, signature="(n)->(m)")(multiple_params)

generate_artificial_params(num_samples: int)

This method must be overwritten an return an numpy array of num_samples parameters.

Parameters:

num_samples (int) – The number of parameters to generate.

Returns:

The generated parameters.

Return type:

np.ndarray

Raises:

NotImplementedError – If the method is not overwritten in a subclass.

jacobian(param)

Evaluates the jacobian of the forward() method.

Parameters:

param (np.ndarray) – The parameter for which the jacobian should be evaluated.

Returns:

The jacobian for the variables returned by the forward() method with respect to the parameters.

Return type:

np.ndarray

Examples:

import numpy as np
from eulerpi.examples.heat import Heat
from eulerpi.core.model import JaxModel
from jax import vmap

# instantiate the heat model
model = Heat()

# define a 3D example parameter for the heat model
example_param = np.array([1.4, 1.6, 0.5])

sim_jacobian = model.jacobian(example_param)

# Similar to the forward pass, also the evaluation of the jacobian can be vectorized.
# This yields a 3D array of shape (num_params, data_dim, param_dim) = (4,5,3) in this example.

multiple_params = np.array([[1.5, 1.5, 0.5],
                            [1.4, 1.4, 0.6],
                            model.central_param,
                            [1.5, 1.4, 0.4]])

# try to use jax vmap for vectorization if possible
if isinstance(model, JaxModel):
    multiple_sim_jacobians = vmap(model.jacobian, in_axes=0)(multiple_params)

# if the model is not a jax model, we can use numpy vectorize to vectorize
else:
    multiple_sim_jacobians = np.vectorize(model.jacobian, signature="(n)->(m)")(multiple_params)

CENTRAL_PARAM = array([0.5, 0.5])

PARAM_LIMITS = array([[0, 1],        [0, 1]])

data_dim: int | None = 3

param_dim: int | None = 2

class JaxPlant(central_param: ndarray = array([0.5, 0.5]), param_limits: ndarray = array([[0, 1], [0, 1]]), name: str | None = None, **kwargs)

Bases: JaxModel, ArtificialModelInterface

A plant model which inherits from the JaxModel to define the jacobian Param0: Water [0,1] Param1: Sun [0,1] Data0: Size [0,2] # the more water and sun the better Data1: Health [0,1], to much water is not good, too much sun is not good Data2: Sciarid :P

classmethod forward(param)

Executed the forward pass of the model to obtain data from a parameter.

Parameters:

param (np.ndarray) – The parameter for which the data should be generated.

Returns:

The data generated from the parameter.

Return type:

np.ndarray

Examples:

import numpy as np
from eulerpi.examples.heat import Heat
from eulerpi.core.model import JaxModel
from jax import vmap

# instantiate the heat model
model = Heat()

# define a 3D example parameter for the heat model
example_param = np.array([1.4, 1.6, 0.5])

# the forward simulation is achieved by using the forward method of the model
sim_result = model.forward(example_param)

# in a more realistic scenario, we would like to perform the forward pass on multiple parameters at once
multiple_params = np.array([[1.5, 1.5, 0.5],
                            [1.4, 1.4, 0.6],
                            [1.6, 1.6, 0.4],
                            model.central_param,
                            [1.5, 1.4, 0.4]])

# try to use jax vmap to perform the forward pass on multiple parameters at once
if isinstance(model, JaxModel):
    multiple_sim_results = vmap(model.forward, in_axes=0)(multiple_params)

# if the model is not a jax model, we can use numpy vectorize to perform the forward pass
else:
    multiple_sim_results = np.vectorize(model.forward, signature="(n)->(m)")(multiple_params)

generate_artificial_params(num_samples: int)

This method must be overwritten an return an numpy array of num_samples parameters.

Parameters:

num_samples (int) – The number of parameters to generate.

Returns:

The generated parameters.

Return type:

np.ndarray

Raises:

NotImplementedError – If the method is not overwritten in a subclass.

CENTRAL_PARAM = array([0.5, 0.5])

PARAM_LIMITS = array([[0, 1],        [0, 1]])

data_dim: int | None = 3

param_dim: int | None = 2