""" Test EQE module with pySIMsalabim"""
######### Package Imports #########################################################################
import os, sys, copy
import numpy as np
try:
from optimpv import *
from optimpv.optimizers.scipyOpti.scipyOptimizer import ScipyOptimizer
from optimpv.models.Diodefits.DiodeAgent import DiodeAgent
from optimpv.models.Diodefits.DiodeModel import *
except Exception as e:
# Add the parent directory to the system path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
from optimpv import *
from optimpv.optimizers.scipyOpti.scipyOptimizer import ScipyOptimizer
from optimpv.models.Diodefits.DiodeAgent import DiodeAgent
from optimpv.models.Diodefits.DiodeModel import *
######### Test Functions #########################################################################
def test_SOO_diode_fit():
"""Test the single-objective optimization of a diode model using scipyOptimizer with scipy.optimize.minimize."""
try:
params = []
J0 = FitParam(name = 'J0', value = 1e-5, bounds = [1e-6,1e-3], log_scale = True, rescale = False, value_type = 'float', type='range', display_name=r'$J_0$', unit='A m$^{-2}$', axis_type = 'log',force_log=True)
params.append(J0)
n = FitParam(name = 'n', value = 1.5, bounds = [1,2], log_scale = False, value_type = 'float', type='range', display_name=r'$n$', unit='', axis_type = 'linear')
params.append(n)
R_series = FitParam(name = 'R_series', value = 1e-4, bounds = [1e-5,1e-3], log_scale = True, rescale = False, value_type = 'float', type='range', display_name=r'$R_{\text{series}}$', unit=r'$\Omega$ m$^2$', axis_type = 'log',force_log=True)
params.append(R_series)
R_shunt = FitParam(name = 'R_shunt', value = 1e-1, bounds = [1e-2,1e2], log_scale = True, rescale = False, value_type = 'float', type='fixed', display_name=r'$R_{\text{shunt}}$', unit=r'$\Omega$ m$^2$', axis_type = 'log',force_log=True)
params.append(R_shunt)
# original values
params_orig = copy.deepcopy(params)
# Create JV to fit
X = np.linspace(0.001,1,100)
y = NonIdealDiode_dark(X, J0.value, n.value, R_series.value, R_shunt.value)
# Define the Agent and the target metric/loss function
metric = 'mse' # can be 'nrmse', 'mse', 'mae'
loss = 'soft_l1' # can be 'linear', 'huber', 'soft_l1'
exp_format = 'dark' # can be 'dark', 'light' depending on the type of data you have
use_pvlib = False # if True, use pvlib to calculate the diode model if not use the implementation in DiodeModel.py
diode = DiodeAgent(params, X, y, metric = metric, loss = loss, minimize=True,exp_format=exp_format,use_pvlib=use_pvlib,transforms='log')
optimizer = ScipyOptimizer(params=params, agents=diode, method='L-BFGS-B', options={}, name='scipy_opti', parallel_agents=True, max_parallelism=os.cpu_count()-1, verbose_logging=True)
optimizer.optimize() # run the optimization with ax
assert True
except Exception as e:
assert False, "Error occurred during diode fitting: {}".format(e)
[docs]
def test_SOO_diode_fit():
"""Test the single-objective optimization of a diode model using scipyOptimizer with scipy.optimize.least_squares."""
try:
params = []
J0 = FitParam(name = 'J0', value = 1e-5, bounds = [1e-6,1e-3], log_scale = True, rescale = False, value_type = 'float', type='range', display_name=r'$J_0$', unit='A m$^{-2}$', axis_type = 'log',force_log=True)
params.append(J0)
n = FitParam(name = 'n', value = 1.5, bounds = [1,2], log_scale = False, value_type = 'float', type='range', display_name=r'$n$', unit='', axis_type = 'linear')
params.append(n)
R_series = FitParam(name = 'R_series', value = 1e-4, bounds = [1e-5,1e-3], log_scale = True, rescale = False, value_type = 'float', type='range', display_name=r'$R_{\text{series}}$', unit=r'$\Omega$ m$^2$', axis_type = 'log',force_log=True)
params.append(R_series)
R_shunt = FitParam(name = 'R_shunt', value = 1e-1, bounds = [1e-2,1e2], log_scale = True, rescale = False, value_type = 'float', type='fixed', display_name=r'$R_{\text{shunt}}$', unit=r'$\Omega$ m$^2$', axis_type = 'log',force_log=True)
params.append(R_shunt)
# original values
params_orig = copy.deepcopy(params)
# Create JV to fit
X = np.linspace(0.001,1,100)
y = NonIdealDiode_dark(X, J0.value, n.value, R_series.value, R_shunt.value)
# Define the Agent and the target metric/loss function
metric = 'mse' # can be 'nrmse', 'mse', 'mae'
loss = 'soft_l1' # can be 'linear', 'huber', 'soft_l1'
exp_format = 'dark' # can be 'dark', 'light' depending on the type of data you have
use_pvlib = False # if True, use pvlib to calculate the diode model if not use the implementation in DiodeModel.py
diode = DiodeAgent(params, X, y, metric = metric, loss = loss, minimize=True,exp_format=exp_format,use_pvlib=use_pvlib,transforms='log')
optimizer = ScipyOptimizer(params=params, agents=diode, method='dogbox', options={}, name='scipy_opti', parallel_agents=True, max_parallelism=os.cpu_count()-1, verbose_logging=True)
optimizer.optimize_least_squares() # run the optimization with ax
assert True
except Exception as e:
assert False, "Error occurred during diode fitting: {}".format(e)