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config_generator.py
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config_generator.py
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import random
import pybamm
from experiment.experiment_generator import experiment_generator
from utils.degradation_parameter_generator import degradation_parameter_generator
from utils.parameter_value_generator import parameter_value_generator
# possible chemistries for the bot
chemistries = [
"Ai2020",
"Chen2020",
"Marquis2019",
"OKane2022"
]
# possible "particle mechanics" for the bot, to be used with Ai2020 parameters
particle_mechanics_list = [
"swelling only",
"swelling and cracking",
]
# possible "SEI" for the bot
sei_list = [
"ec reaction limited",
"reaction limited",
"solvent-diffusion limited",
"electron-migration limited",
"interstitial-diffusion limited",
]
# parameters that can be varied in comparisons, of the form -
# parameter: {
# "print_name": str
# "bounds": (lower_bound, upper_bound)
# }
# if the bounds are given as None, the default bounds will be used -
# (parameter_values[parameter] / 2, parameter_values[parameter] * 2)
# the varied value will always be in these bounds
param_to_vary_dict = {
"Electrode height [m]": {"print_name": None, "bounds": (0.1, None)},
"Electrode width [m]": {"print_name": None, "bounds": (0.1, None)},
"Negative electrode conductivity [S.m-1]": {
"print_name": None,
"bounds": (None, None),
},
"Negative electrode porosity": {"print_name": None, "bounds": (None, None)},
"Negative electrode active material volume fraction": {
"print_name": None,
"bounds": (None, None),
},
"Negative electrode Bruggeman coefficient (electrolyte)": {
"print_name": None,
"bounds": (None, None),
},
"Negative electrode exchange-current density [A.m-2]": {
"print_name": r"$j_{0,n}$",
"bounds": (None, None),
},
"Positive electrode porosity": {"print_name": None, "bounds": (None, None)},
"Positive electrode exchange-current density [A.m-2]": {
"print_name": r"$j_{0,p}$",
"bounds": (None, None),
},
"Positive electrode Bruggeman coefficient (electrolyte)": {
"print_name": None,
"bounds": (None, None),
},
}
def config_generator(
choice,
test_config={
"chemistry": None,
"is_experiment": None,
"number_of_comp": None,
"degradation_mode": None,
},
):
"""
Generates a random configuration to plot.
Parameters
----------
choice : str
Can be "model comparison", "parameter comparison" or
"degradation comparison (summary variables)".
test_config : dict
Should be used while testing to deterministically test this
function.
Returns
-------
config: dict
"""
config = {}
model_options = {}
# choose a random chemistry
# don't select randomly if testing
if test_config["chemistry"] is not None:
chemistry = test_config["chemistry"]
# use only Mohtat2020 and SPM till others are fixed
elif choice == "degradation comparison":
chemistry = "Mohtat2020"
else:
chemistry = random.choice(chemistries)
parameter_values = pybamm.ParameterValues(chemistry)
# choose random degradation for a degradation comparison
if choice == "degradation comparison":
# add degradation / update model options
if chemistry == "Ai2020":
degradation_value = particle_mechanics_list[0]
degradation_mode = "particle mechanics"
model_options.update(
{
degradation_mode: degradation_value,
}
)
elif chemistry == "Mohtat2020":
if test_config["degradation_mode"] is None:
degradation_mode = random.choice(["SEI", "particle mechanics"])
else:
degradation_mode = test_config["degradation_mode"]
if degradation_mode == "particle mechanics":
degradation_value = particle_mechanics_list[0]
elif degradation_mode == "SEI":
degradation_value = random.choice(sei_list)
model_options.update(
{
degradation_mode: degradation_value,
"loss of active material": "stress-driven"
if degradation_mode == "particle mechanics"
else "none",
"SEI porosity change": random.choice(["true", "false"])
if degradation_mode == "SEI"
else "false",
}
)
else:
degradation_value = random.choice(sei_list)
degradation_mode = "SEI"
model_options.update(
{
degradation_mode: degradation_value,
}
)
# no degradation
else:
model_options = None
# list of all the possible models
models = [
pybamm.lithium_ion.DFN(options=model_options),
pybamm.lithium_ion.SPM(options=model_options),
pybamm.lithium_ion.SPMe(options=model_options),
]
# choose random configuration for no degradation
if choice == "model comparison" or choice == "parameter comparison":
# generating number of models to be compared
# don't select randomly if testing
if test_config["number_of_comp"] is not None:
number_of_comp = test_config["number_of_comp"]
elif choice == "model comparison":
number_of_comp = random.randint(2, 3)
elif choice == "parameter comparison":
number_of_comp = 1
# selecting the models for comparison
random.shuffle(models)
models_for_comp = models[:number_of_comp]
models_for_comp = dict(list(enumerate(models_for_comp)))
# if the comparison should be made with an experiment
# don't select randomly when testing
if test_config["is_experiment"] is not None:
is_experiment = test_config["is_experiment"]
else:
is_experiment = random.choice([True, False])
if is_experiment:
# generating a random experiment
cycle = experiment_generator()
number = random.randint(1, 3)
# generating parameter values with varied "Ambient temperature [K]"
params = parameter_value_generator(
parameter_values.copy(),
{
"Ambient temperature [K]": (265, 355),
},
)
else:
cycle = None
number = None
# generating parameter values with varied "Ambient temperature [K]" and
# "Current function [A]"
params = parameter_value_generator(
parameter_values.copy(),
{
"Current function [A]": (None, None),
"Ambient temperature [K]": (265, 355),
},
)
# choosing a parameter to be varied
if choice == "parameter comparison":
param_to_vary = random.choice(list(param_to_vary_dict.keys()))
elif choice == "model comparison":
param_to_vary = None
# updating the config dictionary
config.update(
{
"chemistry": chemistry,
"models_for_comp": models_for_comp,
"is_experiment": is_experiment,
"cycle": cycle,
"number": number,
"param_to_vary_info": {
param_to_vary: {
"print_name": param_to_vary_dict[param_to_vary]["print_name"],
"bounds": param_to_vary_dict[param_to_vary]["bounds"],
}
}
if param_to_vary is not None
else None,
"params": params,
"varied_values_override": None,
}
)
elif choice == "degradation comparison":
# choosing a random model
model = models[1]
# choosing a random experiment
cycle = experiment_generator()
number = 500
number_of_comp = random.randint(2, 3)
# generating a random parameter to vary and the parameter values after
# varying it
param_values, degradation_parameter = degradation_parameter_generator(
chemistry,
number_of_comp,
degradation_mode=degradation_mode,
degradation_value=degradation_value,
)
varied_values = [x[degradation_parameter] for x in param_values]
# updating the config dictionary
config.update(
{
"model": model,
"chemistry": chemistry,
"cycle": cycle,
"number": number,
"degradation_mode": degradation_mode,
"degradation_value": degradation_value,
"param_values": param_values,
"degradation_parameter": degradation_parameter,
"varied_values": varied_values,
}
)
return config