OPV light-intensity dependant JV fits with SIMsalabim (fake data)
This notebook is a demonstration of how to fit light-intensity dependent JV curves with drift-diffusion models using the SIMsalabim package.
[1]:
# Import necessary libraries
import warnings, os, sys, shutil
# remove warnings from the output
os.environ["PYTHONWARNINGS"] = "ignore"
warnings.filterwarnings(action='ignore', category=FutureWarning)
warnings.filterwarnings(action='ignore', category=UserWarning)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import default_rng
import torch, copy, uuid
import pySIMsalabim as sim
from pySIMsalabim.experiments.JV_steady_state import *
import ax, logging
from ax.utils.notebook.plotting import init_notebook_plotting, render
init_notebook_plotting() # for Jupyter notebooks
try:
from optimpv import *
from optimpv.axBOtorch.axUtils import *
except Exception as e:
sys.path.append('../') # add the path to the optimpv module
from optimpv import *
from optimpv.axBOtorch.axUtils import *
[INFO 08-13 17:55:14] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.
[INFO 08-13 17:55:14] ax.utils.notebook.plotting: Please see
(https://ax.dev/tutorials/visualizations.html#Fix-for-plots-that-are-not-rendering)
if visualizations are not rendering.
Define the parameters for the simulation
[2]:
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)
bulk_tr = FitParam(name = 'l2.N_t_bulk', value = 1e20, bounds = [1e19,1e22], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$N_{T}$', unit=r'm$^{-3}$', axis_type = 'log', force_log = False)
params.append(bulk_tr)
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 = False)
params.append(preLangevin)
R_series = FitParam(name = 'R_series', value = 1e-4, bounds = [1e-5,1e-3], log_scale = True, value_type = 'float', fscale = None, rescale = False, display_name=r'$R_{series}$', unit=r'$\Omega$ m$^2$', axis_type = 'log', force_log = False)
params.append(R_series)
# save the original parameters for later
params_orig = copy.deepcopy(params)
Generate some fake data
Here we generate some fake data to fit. The data is generated using the same model as the one used for the fitting, so it is a good test of the fitting procedure. For more information on how to run SIMsalabim from python see the pySIMsalabim package.
[3]:
# Set the session path for the simulation and the input files
session_path = os.path.join(os.path.join(os.path.abspath('../'),'SIMsalabim','SimSS'))
input_path = os.path.join(os.path.join(os.path.join(os.path.abspath('../'),'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)
# Show the device structure
fig = sim.plot_band_diagram(simulation_setup, session_path)
# reset simss
# Set the JV parameters
Gfracs = [0.1,0.5,1.0] # 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)})
# Add the layer files to the command line arguments
# cmd_pars.append({'par':'l1', 'val':l1})
# cmd_pars.append({'par':'l2', 'val':l2})
# cmd_pars.append({'par':'l3', 'val':l3})
# 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]
plt.figure()
for Gfrac in Gfracs:
plt.plot(X[X[:,1]==Gfrac,0],y[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac))
plt.xlabel('Voltage [V]')
plt.ylabel('Current density [A/m$^2$]')
plt.legend()
plt.show()
Run the optimization
[4]:
# Define the Agent and the target metric/loss function
from optimpv.DDfits.JVAgent import JVAgent
metric = 'nrmse' # can be 'nrmse', 'mse', 'mae'
loss = 'linear' # can be 'linear', 'huber', 'soft_l1'
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)
print('Best fit: ',best_fit_possible)
Best fit: 0.002155272360822385
[5]:
from optimpv.axBOtorch.axBOtorchOptimizer import axBOtorchOptimizer
from botorch.acquisition.logei import qLogNoisyExpectedImprovement
from ax.adapter.transforms.standardize_y import StandardizeY
from ax.adapter.transforms.unit_x import UnitX
from ax.adapter.transforms.remove_fixed import RemoveFixed
from ax.adapter.transforms.log import Log
from ax.generators.torch.botorch_modular.utils import ModelConfig
from ax.generators.torch.botorch_modular.surrogate import SurrogateSpec
from gpytorch.kernels import MaternKernel
from gpytorch.kernels import ScaleKernel
from botorch.models import SingleTaskGP
model_gen_kwargs_list = None
parameter_constraints = None
model_kwargs_list = [{},{"torch_device":torch.device("cuda" if torch.cuda.is_available() else "cpu"),'botorch_acqf_class':qLogNoisyExpectedImprovement,'transforms':[RemoveFixed, Log,UnitX, StandardizeY],'surrogate_spec':SurrogateSpec(model_configs=[ModelConfig(botorch_model_class=SingleTaskGP,covar_module_class=ScaleKernel, covar_module_options={'base_kernel':MaternKernel(nu=2.5, ard_num_dims=len(params))})])}]
# Define the optimizer
optimizer = axBOtorchOptimizer(params = params, agents = jv, models = ['SOBOL','BOTORCH_MODULAR'],n_batches = [1,45], batch_size = [10,2], ax_client = None, max_parallelism = -1, model_kwargs_list = model_kwargs_list, model_gen_kwargs_list = None, name = 'ax_opti',parallel=True)
[6]:
# optimizer.optimize() # run the optimization with ax
optimizer.optimize_turbo() # run the optimization with turbo
[INFO 08-13 17:55:15] optimpv.axBOtorchOptimizer: Starting optimization with 46 batches and a total of 100 trials
[INFO 08-13 17:55:16] optimpv.axBOtorchOptimizer: Starting Sobol batch 1 with 10 trials
[INFO 08-13 17:55:17] optimpv.axBOtorchOptimizer: Finished Sobol
[INFO 08-13 17:55:19] optimpv.axBOtorchOptimizer: Finished Turbo batch 2 with 2 trials with current best value: 1.48e-01, TR length: 8.00e-01
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[INFO 08-13 17:55:39] ax.api.client: Trial 90 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 91 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 92 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 93 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 94 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 95 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 96 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 97 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 98 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 99 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 100 marked COMPLETED.
[INFO 08-13 17:55:39] ax.api.client: Trial 101 marked COMPLETED.
[INFO 08-13 17:55:39] optimpv.axBOtorchOptimizer: Turbo is terminated as the max number (100) of trials is reached
[7]:
# get the best parameters and update the params list in the optimizer and the agent
ax_client = optimizer.ax_client # get the ax client
optimizer.update_params_with_best_balance() # update the params list in the optimizer with the best parameters
jv.params = optimizer.params # update the params list in the agent with the best parameters
# print the best parameters
print('Best parameters:')
for p,po in zip(optimizer.params, params_orig):
print(p.name, 'fitted value:', p.value, 'original value:', po.value)
print('\nSimSS command line:')
print(jv.get_SIMsalabim_clean_cmd(jv.params)) # print the simss command line with the best parameters
Best parameters:
l2.mu_n fitted value: 5.233264447505195e-08 original value: 7e-08
l2.mu_p fitted value: 1.3180291704862906e-07 original value: 5e-08
l2.N_t_bulk fitted value: 7.994944863168142e+19 original value: 1e+20
l2.preLangevin fitted value: 0.007330158833088105 original value: 0.01
R_series fitted value: 0.00017976242661476134 original value: 0.0001
SimSS command line:
./simss -l2.mu_n 5.233264447505195e-08 -l2.mu_p 1.3180291704862906e-07 -l2.N_t_bulk 7.994944863168142e+19 -l2.preLangevin 0.007330158833088105 -R_series 0.00017976242661476134
[8]:
# Plot optimization results
data = ax_client.summarize()
all_metrics = optimizer.all_metrics
plt.figure()
plt.plot(np.minimum.accumulate(data[all_metrics]), label="Best value seen so far")
plt.yscale("log")
plt.xlabel("Iteration")
plt.ylabel("Target metric: " + metric + " with " + loss + " loss")
plt.legend()
plt.title("Best value seen so far")
print("Best value seen so far is ", min(data[all_metrics[0]]), "at iteration ", int(data[all_metrics[0]].idxmin()))
print("Best value possible is ", best_fit_possible)
plt.show()
Best value seen so far is 0.008700776111393282 at iteration 67
Best value possible is 0.002155272360822385
[10]:
# Plot the density of the exploration of the parameters
# this gives a nice visualization of where the optimizer focused its exploration and may show some correlation between the parameters
plot_dens = True
if plot_dens:
from optimpv.posterior.posterior import *
params_orig_dict, best_parameters = {}, {}
for p in params_orig:
params_orig_dict[p.name] = p.value
for p in optimizer.params:
best_parameters[p.name] = p.value
fig_dens, ax_dens = plot_density_exploration(params, optimizer = optimizer, best_parameters = best_parameters, params_orig = params_orig_dict, optimizer_type = 'ax')
[11]:
# rerun the simulation with the best parameters
yfit = jv.run(parameters={}) # run the simulation with the best parameters
viridis = plt.get_cmap('viridis', len(Gfracs))
plt.figure(figsize=(10,10))
linewidth = 2
for idx, Gfrac in enumerate(Gfracs[::-1]):
plt.plot(X[X[:,1]==Gfrac,0],y[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac),color=viridis(idx),alpha=0.5,linewidth=linewidth)
plt.plot(X[X[:,1]==Gfrac,0],yfit[X[:,1]==Gfrac],label='Gfrac = '+str(Gfrac)+' fit',linestyle='--',color=viridis(idx),linewidth=linewidth)
plt.xlabel('Voltage [V]')
plt.ylabel('Current density [A m$^{-2}$]')
plt.legend()
plt.show()
[12]:
# 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('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)