""" Test EQE module with pySIMsalabim"""
######### Package Imports #########################################################################
import warnings, os, sys, shutil
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import default_rng
from copy import deepcopy
import torch, copy, uuid
import ax, logging
try:
from optimpv 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.BayesInfEmcee.EmceeOptimizer import EmceeOptimizer
import pySIMsalabim as sim
from pySIMsalabim.experiments.JV_steady_state import *
from optimpv.DDfits.JVAgent import JVAgent
######### Test Functions #########################################################################
[docs]
def test_SOO_JV_fit_emcee():
"""Test the single-objective optimization of a diode model using axBOtorchOptimizer."""
try:
params = [] # list of parameters to be optimized
mun = FitParam(name = 'l2.mu_n', value = 7e-8, bounds = [1e-9,1e-6], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\mu_n$', unit='m$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', force_log = True)
params.append(mun)
mup = FitParam(name = 'l2.mu_p', value = 5e-8, bounds = [1e-9,1e-6], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\mu_p$', unit=r'm$^2$ V$^{-1}$s$^{-1}$', axis_type = 'log', force_log = True)
params.append(mup)
preLangevin = FitParam(name = 'l2.preLangevin', value = 1e-2, bounds = [0.005,1], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$\gamma_{pre}$', unit=r'', axis_type = 'log', force_log = True)
params.append(preLangevin)
# Set the session path for the simulation and the input files
session_path = os.path.join(os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')),'SIMsalabim','SimSS'))
input_path = os.path.join(os.path.join(os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')),'Data','simsalabim_test_inputs','fakeOPV')))
simulation_setup_filename = 'simulation_setup_fakeOPV.txt'
simulation_setup = os.path.join(session_path, simulation_setup_filename)
# path to the layer files defined in the simulation_setup file
l1 = 'ZnO.txt'
l2 = 'ActiveLayer.txt'
l3 = 'BM_HTL.txt'
l1 = os.path.join(input_path, l1)
l2 = os.path.join(input_path, l2)
l3 = os.path.join(input_path, l3)
# copy this files to session_path
force_copy = True
if not os.path.exists(session_path):
os.makedirs(session_path)
for file in [l1,l2,l3,simulation_setup_filename]:
file = os.path.join(input_path, os.path.basename(file))
if force_copy or not os.path.exists(os.path.join(session_path, os.path.basename(file))):
shutil.copyfile(file, os.path.join(session_path, os.path.basename(file)))
else:
print('File already exists: ',file)
# reset simss
# Set the JV parameters
Gfracs = [0.1,0.5,1] # Fractions of the generation rate to simulate (None if you want only one light intensity as define in the simulation_setup file)
UUID = str(uuid.uuid4()) # random UUID to avoid overwriting files
cmd_pars = [] # see pySIMsalabim documentation for the command line parameters
# Add the parameters to the command line arguments
for param in params:
cmd_pars.append({'par':param.name, 'val':str(param.value)})
# Run the JV simulation
ret, mess = run_SS_JV(simulation_setup, session_path, JV_file_name = 'JV.dat', G_fracs = Gfracs, parallel = True, max_jobs = 3, UUID=UUID, cmd_pars=cmd_pars)
# save data for fitting
X,y = [],[]
X_orig,y_orig = [],[]
if Gfracs is None:
data = pd.read_csv(os.path.join(session_path, 'JV_'+UUID+'.dat'), sep=r'\s+') # Load the data
Vext = np.asarray(data['Vext'].values)
Jext = np.asarray(data['Jext'].values)
G = np.ones_like(Vext)
rng = default_rng()#
noise = rng.standard_normal(Jext.shape) * 0.01 * Jext
Jext = Jext + noise
X = Vext
y = Jext
plt.figure()
plt.plot(X,y)
plt.show()
else:
for Gfrac in Gfracs:
data = pd.read_csv(os.path.join(session_path, 'JV_Gfrac_'+str(Gfrac)+'_'+UUID+'.dat'), sep=r'\s+') # Load the data
Vext = np.asarray(data['Vext'].values)
Jext = np.asarray(data['Jext'].values)
G = np.ones_like(Vext)*Gfrac
rng = default_rng()#
noise = rng.standard_normal(Jext.shape) * 0.005 * Jext
if len(X) == 0:
X = np.vstack((Vext,G)).T
y = Jext + noise
y_orig = Jext
else:
X = np.vstack((X,np.vstack((Vext,G)).T))
y = np.hstack((y,Jext+ noise))
y_orig = np.hstack((y_orig,Jext))
# remove all the current where Jext is higher than a given value
X = X[y<200]
X_orig = copy.deepcopy(X)
y_orig = y_orig[y<200]
y = y[y<200]
# Define the Agent and the target metric/loss function
metric = 'mse' # can be 'nrmse', 'mse', 'mae'
loss = 'linear' # can be 'linear', 'huber', 'soft_l1'
# create a different params list for the agent
params_agent = copy.deepcopy(params)
#select a random value between the bounds, we do this because the walkers will be randomly initialized from the param.value
for param in params_agent:
if param.force_log:
param.value =10**np.random.uniform(np.log10(param.bounds[0]),np.log10(param.bounds[1]))
else:
param.value = np.random.uniform(param.bounds[0],param.bounds[1])
jv = JVAgent(params, X, y, session_path, simulation_setup, parallel = True, max_jobs = 3, metric = metric, loss = loss)
# Calulate the target metric for the original parameters
best_fit_possible = loss_function(calc_metric(y,y_orig, metric_name = metric),loss)
# Define the Bayesian Inference object
optimizer = EmceeOptimizer(params = params, agents = jv, nwalkers=20, nsteps=20, burn_in=10, progress=True, name='emcee_opti')
optimizer.optimize()
# Clean up the output files (comment out if you want to keep the output files)
sim.clean_all_output(session_path)
sim.delete_folders('tmp',session_path)
# uncomment the following lines to delete specific files
sim.clean_up_output('nk_',session_path)
sim.clean_up_output('ZnO',session_path)
sim.clean_up_output('ActiveLayer',session_path)
sim.clean_up_output('BM_HTL',session_path)
sim.clean_up_output('simulation_setup_fakeOPV',session_path)
assert True
except Exception as e:
assert False, "Error occurred during JV fitting: {}".format(e)