#!/usr/bin/env python
# coding: utf-8

# # Interacting with PyBOP optimisers
# 
# This notebook introduces two interfaces to interact with PyBOP's optimiser classes.
# 
# ### Set the Environment

# In[ ]:


get_ipython().run_line_magic('pip', 'install --upgrade pip ipywidgets -q')
get_ipython().run_line_magic('pip', 'install pybop -q')

# Import the necessary libraries
import numpy as np

import pybop

pybop.plot.PlotlyManager().pio.renderers.default = "notebook_connected"


# Let's fix the random seed in order to generate consistent output during development, although this does not need to be done in practice.

# In[ ]:


np.random.seed(8)


# ## Setup the model, problem, and cost
# 
# The code block below sets up the model, problem, and cost objects. For more information on this process, take a look at other notebooks in the examples directory.

# In[ ]:


# Load the parameters
parameter_set = pybop.ParameterSet(
    json_path="../scripts/parameters/initial_ecm_parameters.json"
)
parameter_set.import_parameters()
# Define the model
model = pybop.empirical.Thevenin(
    parameter_set=parameter_set, options={"number of rc elements": 1}
)

# Define the parameters
parameters = pybop.Parameter(
    "R0 [Ohm]",
    prior=pybop.Gaussian(0.0002, 0.0001),
    bounds=[1e-4, 1e-2],
)

# Generate synthetic data
t_eval = np.arange(0, 900, 2)
values = model.predict(t_eval=t_eval)

# Form dataset
dataset = pybop.Dataset(
    {
        "Time [s]": t_eval,
        "Current function [A]": values["Current [A]"].data,
        "Voltage [V]": values["Voltage [V]"].data,
    }
)

# Construct problem and cost
problem = pybop.FittingProblem(model, parameters, dataset)
cost = pybop.SumSquaredError(problem)


# ## Interacting with the Optimisers
# 
# Now that we have set up the required objects, we can introduce the two interfaces for interacting with PyBOP optimisers. These are:
#  
# 1. The direct optimiser (e.g. `pybop.XNES`)
# 2. The optimisation class (i.e. `pybop.Optimisation`)
#  
# These two methods provide two equivalent ways of interacting with PyBOP's optimisers. The first method provides a direct way to select the Optimiser, with the second method being a more general method with a default optimiser (`pybop.XNES`) set if you don't provide an optimiser. 
# 
# First, the direct interface is presented. With this interface the user can select from the [list of optimisers](https://github.com/pybop-team/PyBOP?tab=readme-ov-file#supported-methods) supported in PyBOP and construct them directly. Options can be passed as kwargs, or through get() / set() methods in the case of PINTS-based optimisers.

# In[ ]:


optim_one = pybop.XNES(
    cost, max_iterations=50
)  # Direct optimiser class with options as kwargs
optim_one.set_max_iterations(
    50
)  # Alternative set() / get() methods for PINTS optimisers
results = optim_one.run()


# Next, the `Optimisation` interface is less direct than the previous one, but provides a single class to work with across PyBOP workflows. The options are passed the same way as the above method, through kwargs or get() / set() methods.

# In[ ]:


optim_two = pybop.Optimisation(
    cost, optimiser=pybop.XNES, max_iterations=50
)  # Optimisation class with options as kwargs
optim_two.set_max_iterations(
    50
)  # Alternative set() / get() methods for PINTS optimisers
results2 = optim_two.run()


# We can show the equivalence of these two methods by comparing the optimiser objects:

# In[ ]:


isinstance(optim_one, type(optim_two.optimiser))


# For completeness, we can show the optimiser solutions:

# In[ ]:


print("Estimated parameters x1:", results.x)
print("Estimated parameters x2:", results2.x)


# ## Closing Comments
# 
# As both of these API's provide access to the same optimisers, please use either as you prefer. A couple things to note:
# 
# - If you are using a SciPy-based optimiser (`pybop.SciPyMinimize`, `pybop.SciPyDifferentialEvolution`), the `set()` / `get()` methods for the optimiser options are not currently supported. These optimisers require options to be passed as kwargs.
# - The optimiser passed to `pybop.Optimisation` must not be a constructed object.