"""CVAgent class for steady-state CV simulations"""
######### Package Imports #########################################################################
import numpy as np
import pandas as pd
import os, uuid, sys, copy
from scipy import interpolate
from optimpv import *
from optimpv.general.general import calc_metric, loss_function, transform_data
from optimpv.models.DDfits.SIMsalabimAgent import SIMsalabimAgent
from pySIMsalabim import *
from pySIMsalabim.experiments.CV import *
######### Agent Definition #######################################################################
[docs]
class CVAgent(SIMsalabimAgent):
"""CVAgent class for Capacitance-Voltage simulations with SIMsalabim
Parameters
----------
params : list of Fitparam() objects
List of Fitparam() objects.
X : array-like
1-D or 2-D array containing the voltage values.
y : array-like
1-D array containing the current values.
session_path : str
Path to the session directory.
Vmin : float, optional
minimum voltage, by default 0.
Vmax : float, optional
maximum voltage, by default 1.2.
V_step : float, optional
Voltage difference, determines at which voltages the capacitance is determined, by default 0.1.
freq : float, optional
Frequency of the CV measurement [Hz], by default 1e4.
G_frac : float, optional
Fractional light intensity, by default 1.
del_V : float, optional
Voltage step for the impedance simulation, by default 0.01.
simulation_setup : str, optional
Path to the simulation setup file, if None then use the default file 'simulation_setup.txt'in the session_path directory, by default None.
exp_format : str or list of str, optional
Format of the CV data, possible values are: 'CV', 'MottSchottky', by default 'CV'.
metric : str or list of str, optional
Metric to evaluate the model, see optimpv.general.calc_metric for options, by default 'mse'.
loss : str or list of str, optional
Loss function to use, see optimpv.general.loss_function for options, by default 'linear'.
threshold : int or list of int, optional
Threshold value for the loss function used when doing multi-objective optimization, by default 100.
minimize : bool or list of bool, optional
If True then minimize the loss function, if False then maximize the loss function (note that if running a fit minize should be True), by default True.
yerr : array-like or list of array-like, optional
Errors in the current values, by default None.
weight : array-like or list of array-like, optional
Weights used for fitting if weight is None and yerr is not None, then weight = 1/yerr**2, by default None.
tracking_metric : str or list of str, optional
Additional metrics to track and report in run_Ax output, by default None.
tracking_loss : str or list of str, optional
Loss functions to apply to tracking metrics, by default None.
tracking_exp_format : str or list of str, optional
Experimental formats for tracking metrics, by default None.
tracking_X : array-like or list of array-like, optional
X values for tracking metrics, by default None.
tracking_y : array-like or list of array-like, optional
y values for tracking metrics, by default None.
tracking_weight : array-like or list of array-like, optional
Weights for tracking metrics, by default None.
transforms : str or list of str, optional
Type of transformation to apply to data before metric calculation, if a list is provided, transformations are applied sequentially, see optimpv.general.transform_data for options, by default 'linear'.
name : str, optional
Name of the agent, by default 'CV'.
**kwargs : dict
Additional keyword arguments.
"""
def __init__(self, params, X, y, session_path, Vmin=0.5, Vmax=1.0, V_step=0.1,freq=1e4, G_frac=1, del_V=0.01,
simulation_setup=None, exp_format='CV', metric='mse', loss='linear', threshold=100, minimize=True,
yerr=None, weight=None, tracking_metric=None, tracking_loss=None, tracking_exp_format=None,
tracking_X=None, tracking_y=None, tracking_weight=None, transforms='linear', tracking_transforms = 'linear', name='CV', **kwargs):
super().__init__(params, X, y, session_path, simulation_setup, exp_format, metric, loss, threshold, minimize, yerr, weight, tracking_metric, tracking_loss, tracking_exp_format, tracking_X, tracking_y, tracking_weight, transforms, tracking_transforms, name, **kwargs)
# CVAgent specific parameters
self.Vmin = Vmin
self.Vmax = Vmax
self.V_step = V_step
self.freq = freq
self.G_frac = G_frac
self.del_V = del_V
[docs]
def target_metric(self, y, yfit, metric_name, X=None, Xfit=None,weight=None):
"""Calculate the target metric depending on self.metric
Parameters
----------
y : array-like
1-D array containing the target values.
yfit : array-like
1-D array containing the fitted values.
metric_name : str
Metric to evaluate the model, see optimpv.general.calc_metric for options.
X : array-like, optional
1-D array containing the x axis values, by default None.
Xfit : array-like, optional
1-D array containing the x axis values, by default None.
weight : array-like, optional
1-D array containing the weights, by default None.
Returns
-------
float
Target metric value.
"""
return calc_metric(y,yfit,sample_weight=weight,metric_name=metric_name)
[docs]
def run_Ax(self, parameters):
"""Function to run the simulation with the parameters and return the target metric value for Ax optimization
Parameters
----------
parameters : dict
Dictionary with the parameter names and values.
Returns
-------
dict
Dictionary with the target metric value.
"""
df = self.run_CV_simulation(parameters)
return self._run_Ax(df,self.reformat_CV_data)
[docs]
def run_CV_simulation(self, parameters):
"""Run the simulation with the parameters and return the simulated values
Parameters
----------
parameters : dict
Dictionary with the parameter names and values.
Returns
-------
dataframe
Dataframe with the simulated CV values.
"""
parallel = self.kwargs.get('parallel', False)
max_jobs = self.kwargs.get('max_jobs', 1)
# output_file = self.kwargs.get('output_file', 'CapVol.dat')
VarNames,custom_pars,clean_pars = [],[],[]
# check if cmd_pars is in kwargs
if 'cmd_pars' in self.kwargs:
cmd_pars = self.kwargs['cmd_pars']
for cmd_par in cmd_pars:
if (cmd_par['par'] not in self.SIMsalabim_params['l1'].keys()) and (cmd_par['par'] not in self.SIMsalabim_params['setup'].keys()):
custom_pars.append(cmd_par)
else:
clean_pars.append(cmd_par)
VarNames.append(cmd_par['par'])
else:
cmd_pars = []
# prepare the cmd_pars for the simulation
clean_pars = self.get_SIMsalabim_clean_cmd_pars(parameters)
# Run the CV simulation
UUID = self.kwargs.get('UUID',str(uuid.uuid4()))
# remove UUID and output_file and cmd_pars from kwargs
dummy_kwargs = copy.deepcopy(self.kwargs)
if 'UUID' in dummy_kwargs:
dummy_kwargs.pop('UUID')
if 'output_file' in dummy_kwargs:
dummy_kwargs.pop('output_file')
if 'cmd_pars' in dummy_kwargs:
dummy_kwargs.pop('cmd_pars')
ret, mess = run_CV_simu(self.simulation_setup, self.session_path, self.freq, self.Vmin, self.Vmax, self.V_step, G_frac=self.G_frac, del_V=self.del_V, run_mode=False, output_file = 'CapVol.dat', UUID=UUID, cmd_pars=clean_pars, **dummy_kwargs)
if type(ret) == int:
if not (ret == 0 or ret == 95):
print('Error in running SIMsalabim: '+mess)
return np.nan
elif isinstance(ret, subprocess.CompletedProcess):
if not(ret.returncode == 0 or ret.returncode == 95):
# print('Error in running SIMsalabim: '+mess)
return np.nan
else:
if not all([(res == 0 or res == 95) for res in ret]):
# print('Error in running SIMsalabim: '+mess)
return np.nan
try:
df = pd.read_csv(os.path.join(self.session_path, 'CapVol_'+UUID+'.dat'), sep=r'\s+')
except:
print('No CV data found for UUID '+UUID + ' and cmd_pars '+str(cmd_pars))
return np.nan
return df
[docs]
def run(self, parameters,X=None,exp_format='CV'):
"""Run the simulation with the parameters and return an array with the simulated values in the format specified by exp_format (default is 'Cf')
Parameters
----------
parameters : dict
Dictionary with the parameter names and values.
X : array-like, optional
1-D array containing the x axis values, by default None.
exp_format : str, optional
Format of the experimental data, by default 'Cf'.
Returns
-------
array-like
1-D array with the simulated current values.
"""
df = self.run_CV_simulation(parameters)
if df is np.nan or len(df) == 0:
return np.nan
if X is None:
X = self.X[0]
Xfit, yfit = self.reformat_CV_data(df, X, exp_format)
return yfit