mirror of https://github.com/ANL-CEEESA/LLEPE
Included package data in data/csvs and data/xmls. Note this only works for sdists. If bdist is needed, research "manifest.in" python setup files.
titusquah
5 years ago
parent
f07fb03b93
commit
77e4cefd2c
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from datetime import datetime
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import cantera as ct
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import pandas as pd
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import numpy as np
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from scipy.optimize import minimize
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# noinspection PyPep8Naming
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import xml.etree.ElementTree as ET
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import seaborn as sns
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import matplotlib.pyplot as plt
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import shutil
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import copy
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from inspect import signature
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import os
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sns.set()
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sns.set(font_scale=1.6)
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class REEPS:
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"""REEPS (Rare earth extraction parameter searcher)
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Takes in experimental data
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Returns parameters for GEM
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Only good for 1 rare earth and 1 extractant
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:param exp_csv_filename: (str) csv file name with experimental data
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:param phases_xml_filename: (str) xml file with parameters for equilibrium calc
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:param opt_dict: (dict) optimize info {species:{thermo_prop:guess}
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:param phase_names: (list) names of phases in xml file
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:param aq_solvent_name: (str) name of aqueous solvent in xml file
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:param extractant_name: (str) name of extractant in xml file
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:param diluant_name: (str) name of diluant in xml file
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:param complex_name: (str) name of complex in xml file
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:param rare_earth_ion_name: (str) name of rare earth ion in xml file
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:param aq_solvent_rho: (float) density of solvent (g/L)
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:param extractant_rho: (float) density of extractant (g/L)
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:param diluant_rho: (float) density of diluant (g/L)
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If no density is given, molar volume/molecular weight is used from xml
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:param objective_function: (function) function to compute objective
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By default, the objective function is log mean squared error
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of distribution ratio np.log10(re_org/re_aq)
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Function needs to take inputs:
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objective_function(predicted_dict, measured_df, **kwargs)
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**kwargs is optional
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Below is the guide for referencing predicted values
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| To access | Use |
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|------------------------------------- |--------------------------|
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| predicted rare earth eq conc in aq | predicted_dict['re_aq'] |
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| predicted rare earth eq conc in org | predicted_dict['re_org'] |
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| predicted hydrogen ion conc in aq | predicted_dict['h'] |
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| predicted extractant conc in org | predicted_dict['z'] |
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| predicted rare earth distribution ratio | predicted_dict['re_d'] |
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For measured values, use the column names in the experimental data file
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:param optimizer: (function) function to perform optimization
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By default, the optimizer is scipy's optimize function with
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default_kwargs= {"method": 'SLSQP',
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"bounds": [(1e-1, 1e1)*len(x_guess)],
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"constraints": (),
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"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
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Function needs to take inputs:
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optimizer(objective_function, x_guess, **kwargs)
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**kwargs is optional
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:param temp_xml_file_path: (str) path to temporary xml file
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default is local temp folder
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"""
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def __init__(self,
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exp_csv_filename,
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phases_xml_filename,
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opt_dict,
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phase_names,
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aq_solvent_name,
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extractant_name,
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diluant_name,
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complex_name,
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rare_earth_ion_name,
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aq_solvent_rho=None,
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extractant_rho=None,
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diluant_rho=None,
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objective_function='Log-MSE',
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optimizer='SLSQP',
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temp_xml_file_path=None
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):
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self._built_in_obj_list = ['Log-MSE']
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self._built_in_opt_list = ['SLSQP']
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self._exp_csv_filename = exp_csv_filename
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self._phases_xml_filename = phases_xml_filename
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self._opt_dict = opt_dict
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self._phase_names = phase_names
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self._aq_solvent_name = aq_solvent_name
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self._extractant_name = extractant_name
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self._diluant_name = diluant_name
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self._complex_name = complex_name
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self._rare_earth_ion_name = rare_earth_ion_name
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self._aq_solvent_rho = aq_solvent_rho
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self._extractant_rho = extractant_rho
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self._diluant_rho = diluant_rho
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self._objective_function = None
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self.set_objective_function(objective_function)
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self._optimizer = None
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self.set_optimizer(optimizer)
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if temp_xml_file_path is None:
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temp_xml_file_path = '{0}\\temp.xml'.format(os.getenv('TEMP'))
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self._temp_xml_file_path = temp_xml_file_path
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shutil.copyfile(phases_xml_filename, self._temp_xml_file_path)
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self._phases = ct.import_phases(phases_xml_filename, phase_names)
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self._exp_df = pd.read_csv(self._exp_csv_filename)
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self._in_moles = None
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self._aq_ind = None
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self._org_ind = None
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self.set_in_moles(feed_vol=1)
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self._predicted_dict = None
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self.update_predicted_dict()
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@staticmethod
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def log_mean_squared_error(predicted_dict, meas_df):
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meas = meas_df.values[:, 2]
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pred = predicted_dict['re_org'] / predicted_dict['re_aq']
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log_pred = np.log10(pred)
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log_meas = np.log10(meas)
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obj = np.sum((log_pred - log_meas) ** 2)
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return obj
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@staticmethod
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def slsqp_optimizer(objective, x_guess):
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optimizer_kwargs = {"method": 'SLSQP',
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"bounds": [(1e-1, 1e1)] * len(x_guess),
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"constraints": (),
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"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
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res = minimize(objective, x_guess, **optimizer_kwargs)
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est_parameters = res.x
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return est_parameters
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def get_exp_csv_filename(self) -> str:
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return self._exp_csv_filename
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def set_exp_csv_filename(self, exp_csv_filename):
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self._exp_csv_filename = exp_csv_filename
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self._exp_df = pd.read_csv(self._exp_csv_filename)
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self.update_predicted_dict()
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return None
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def get_phases(self) -> list:
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return self._phases
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def set_phases(self, phases_xml_filename, phase_names):
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"""Change xml and phase names
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Also runs set_in_mole to set initial moles to 1 g/L"""
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self._phases_xml_filename = phases_xml_filename
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self._phase_names = phase_names
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shutil.copyfile(phases_xml_filename, self._temp_xml_file_path)
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self._phases = ct.import_phases(phases_xml_filename, phase_names)
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self.set_in_moles(feed_vol=1)
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self.update_predicted_dict()
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return None
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def get_opt_dict(self) -> dict:
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return self._opt_dict
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def set_opt_dict(self, opt_dict):
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self._opt_dict = opt_dict
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return None
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def get_aq_solvent_name(self) -> str:
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return self._aq_solvent_name
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def set_aq_solvent_name(self, aq_solvent_name):
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self._aq_solvent_name = aq_solvent_name
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return None
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def get_extractant_name(self) -> str:
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return self._extractant_name
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def set_extractant_name(self, extractant_name):
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self._extractant_name = extractant_name
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return None
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def get_diluant_name(self) -> str:
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return self._diluant_name
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def set_diluant_name(self, diluant_name):
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self._diluant_name = diluant_name
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return None
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def get_complex_name(self) -> str:
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return self._complex_name
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def set_complex_name(self, complex_name):
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self._complex_name = complex_name
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return None
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def get_rare_earth_ion_name(self) -> str:
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return self._rare_earth_ion_name
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def set_rare_earth_ion_name(self, rare_earth_ion_name):
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self._rare_earth_ion_name = rare_earth_ion_name
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return None
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def get_aq_solvent_rho(self) -> str:
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return self._aq_solvent_rho
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def set_aq_solvent_rho(self, aq_solvent_rho):
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self._aq_solvent_rho = aq_solvent_rho
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return None
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def get_extractant_rho(self) -> str:
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return self._extractant_rho
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def set_extractant_rho(self, extractant_rho):
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self._extractant_rho = extractant_rho
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return None
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def get_diluant_rho(self) -> str:
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return self._diluant_rho
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def set_diluant_rho(self, diluant_rho):
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self._diluant_rho = diluant_rho
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return None
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def set_in_moles(self, feed_vol):
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"""Function that initializes mole fractions
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:param feed_vol: (float) feed volume of mixture (g/L)"""
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phases_copy = self._phases.copy()
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exp_df = self._exp_df.copy()
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solvent_name = self._aq_solvent_name
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extractant_name = self._extractant_name
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diluant_name = self._diluant_name
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solvent_rho = self._aq_solvent_rho
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extractant_rho = self._extractant_rho
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diluant_rho = self._diluant_rho
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re_name = self._rare_earth_ion_name
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mixed = ct.Mixture(phases_copy)
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aq_ind = None
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solvent_ind = None
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for ind, phase in enumerate(phases_copy):
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if solvent_name in phase.species_names:
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aq_ind = ind
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solvent_ind = phase.species_names.index(solvent_name)
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if aq_ind is None:
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raise Exception('Solvent "{0}" not found \
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in xml file'.format(solvent_name))
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if aq_ind == 0:
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org_ind = 1
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else:
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org_ind = 0
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self._aq_ind = aq_ind
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self._org_ind = org_ind
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extractant_ind = phases_copy[org_ind].species_names.index(
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extractant_name)
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diluant_ind = phases_copy[org_ind].species_names.index(diluant_name)
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re_ind = phases_copy[aq_ind].species_names.index(re_name)
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re_charge = phases_copy[aq_ind].species(re_ind).charge
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mix_aq = mixed.phase(aq_ind)
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mix_org = mixed.phase(org_ind)
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solvent_mw = mix_aq.molecular_weights[solvent_ind] # g/mol
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extractant_mw = mix_org.molecular_weights[extractant_ind]
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diluant_mw = mix_org.molecular_weights[diluant_ind]
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if solvent_rho is None:
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solvent_rho = mix_aq(aq_ind).partial_molar_volumes[
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solvent_ind] / solvent_mw * 1e6 # g/L
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self._aq_solvent_rho = solvent_rho
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if extractant_rho is None:
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extractant_rho = mix_org(org_ind).partial_molar_volumes[
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extractant_ind] / extractant_mw * 1e6
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self._extractant_rho = extractant_rho
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if diluant_rho is None:
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diluant_rho = mix_org(org_ind).partial_molar_volumes[
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extractant_ind] / extractant_mw * 1e6
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self._diluant_rho = diluant_rho
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in_moles_data = []
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aq_phase_solvent_moles = feed_vol * solvent_rho / solvent_mw
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for row in exp_df.values:
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h_plus_moles = feed_vol * row[0]
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hydroxide_ions = 0
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rare_earth_moles = feed_vol * row[6]
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chlorine_moles = re_charge * rare_earth_moles + h_plus_moles
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extractant_moles = feed_vol * row[3]
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extractant_vol = extractant_moles * extractant_mw / extractant_rho
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diluant_vol = feed_vol - extractant_vol
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diluant_moles = diluant_vol * diluant_rho / diluant_mw
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complex_moles = 0
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species_moles = [aq_phase_solvent_moles,
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h_plus_moles,
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hydroxide_ions,
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chlorine_moles,
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rare_earth_moles,
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extractant_moles,
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diluant_moles,
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complex_moles,
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]
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in_moles_data.append(species_moles)
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self._in_moles = pd.DataFrame(in_moles_data, columns=mixed.species_names)
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self.update_predicted_dict()
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return None
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def get_in_moles(self) -> pd.DataFrame:
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return self._in_moles
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def set_objective_function(self, objective_function):
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"""Set objective function. see class docstring for instructions"""
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if not callable(objective_function) \
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and objective_function not in self._built_in_obj_list:
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raise Exception(
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"objective_function must be a function "
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"or in this strings list: {0}".format(self._built_in_obj_list))
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if callable(objective_function):
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if len(signature(objective_function).parameters) < 2:
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raise Exception(
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"objective_function must be a function "
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"with at least 3 arguments:"
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" f(predicted_dict, experimental_df,**kwargs)")
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if objective_function == 'Log-MSE':
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objective_function = self.log_mean_squared_error
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self._objective_function = objective_function
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return None
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def get_objective_function(self):
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return self._objective_function
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def set_optimizer(self, optimizer):
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if not callable(optimizer) \
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and optimizer not in self._built_in_opt_list:
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raise Exception(
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"optimizer must be a function "
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"or in this strings list: {0}".format(self._built_in_opt_list))
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if callable(optimizer):
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if len(signature(optimizer).parameters) < 2:
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raise Exception(
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"optimizer must be a function "
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"with at least 2 arguments: "
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"f(objective_func,x_guess,**kwargs)")
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if optimizer == 'SLSQP':
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optimizer = self.slsqp_optimizer
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self._optimizer = optimizer
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return None
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def get_optimizer(self):
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return self._optimizer
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def update_predicted_dict(self, phases_xml_filename=None):
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if phases_xml_filename is None:
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phases_xml_filename = self._phases_xml_filename
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phase_names = self._phase_names
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aq_ind = self._aq_ind
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org_ind = self._org_ind
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complex_name = self._complex_name
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extractant_name = self._extractant_name
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rare_earth_ion_name = self._rare_earth_ion_name
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in_moles = self._in_moles
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phases_copy = ct.import_phases(phases_xml_filename, phase_names)
|
|
||||||
mix = ct.Mixture(phases_copy)
|
|
||||||
predicted_dict = {"re_aq": [],
|
|
||||||
"re_org": [],
|
|
||||||
"h": [],
|
|
||||||
"z": []
|
|
||||||
}
|
|
||||||
|
|
||||||
for row in in_moles.values:
|
|
||||||
mix.species_moles = row
|
|
||||||
mix.equilibrate('TP', log_level=0)
|
|
||||||
re_org = mix.species_moles[mix.species_index(
|
|
||||||
org_ind, complex_name)]
|
|
||||||
re_aq = mix.species_moles[mix.species_index(
|
|
||||||
aq_ind, rare_earth_ion_name)]
|
|
||||||
hydrogen_ions = mix.species_moles[mix.species_index(aq_ind, 'H+')]
|
|
||||||
extractant = mix.species_moles[mix.species_index(
|
|
||||||
org_ind, extractant_name)]
|
|
||||||
predicted_dict['re_aq'].append(re_aq)
|
|
||||||
predicted_dict['re_org'].append(re_org)
|
|
||||||
predicted_dict['h'].append(hydrogen_ions)
|
|
||||||
predicted_dict['z'].append(extractant)
|
|
||||||
predicted_dict['re_aq'] = np.array(predicted_dict['re_aq'])
|
|
||||||
predicted_dict['re_org'] = np.array(predicted_dict['re_org'])
|
|
||||||
predicted_dict['h'] = np.array(predicted_dict['h'])
|
|
||||||
predicted_dict['z'] = np.array(predicted_dict['z'])
|
|
||||||
|
|
||||||
self._predicted_dict = predicted_dict
|
|
||||||
return None
|
|
||||||
|
|
||||||
def get_predicted_dict(self):
|
|
||||||
return self._predicted_dict
|
|
||||||
|
|
||||||
def _internal_objective(self, x, kwargs=None):
|
|
||||||
"""default Log mean squared error between measured and predicted data
|
|
||||||
:param x: (list) thermo properties varied to minimize LMSE
|
|
||||||
:param kwargs: (list) arguments for objective_function
|
|
||||||
"""
|
|
||||||
temp_xml_file_path = self._temp_xml_file_path
|
|
||||||
exp_df = self._exp_df
|
|
||||||
objective_function = self._objective_function
|
|
||||||
opt_dict = copy.deepcopy(self._opt_dict)
|
|
||||||
i = 0
|
|
||||||
for species_name in opt_dict.keys():
|
|
||||||
for _ in opt_dict[species_name].keys():
|
|
||||||
i += 1
|
|
||||||
x = np.array(x)
|
|
||||||
|
|
||||||
if len(x.shape) == 1:
|
|
||||||
xs = np.array([x])
|
|
||||||
vectorized_x = False
|
|
||||||
else:
|
|
||||||
vectorized_x = True
|
|
||||||
xs = x
|
|
||||||
objective_values = []
|
|
||||||
for x in xs:
|
|
||||||
i = 0
|
|
||||||
for species_name in opt_dict.keys():
|
|
||||||
for thermo_prop in opt_dict[species_name].keys():
|
|
||||||
opt_dict[species_name][thermo_prop] *= x[i]
|
|
||||||
i += 1
|
|
||||||
self.update_xml(opt_dict, temp_xml_file_path)
|
|
||||||
|
|
||||||
self.update_predicted_dict(temp_xml_file_path)
|
|
||||||
predicted_dict = self.get_predicted_dict()
|
|
||||||
|
|
||||||
if kwargs is None:
|
|
||||||
# noinspection PyCallingNonCallable
|
|
||||||
obj = objective_function(predicted_dict, exp_df)
|
|
||||||
else:
|
|
||||||
# noinspection PyCallingNonCallable
|
|
||||||
obj = objective_function(predicted_dict, exp_df, **kwargs)
|
|
||||||
objective_values.append(obj)
|
|
||||||
if vectorized_x:
|
|
||||||
objective_values = np.array(objective_values)
|
|
||||||
else:
|
|
||||||
objective_values = objective_values[0]
|
|
||||||
return objective_values
|
|
||||||
|
|
||||||
def fit(self, objective_function=None, optimizer=None, objective_kwargs=None, optimizer_kwargs=None) -> dict:
|
|
||||||
"""Fits experimental to modeled data by minimizing objective function
|
|
||||||
Returns estimated complex enthalpy in J/mol
|
|
||||||
:param objective_function: (function) function to compute objective
|
|
||||||
:param optimizer: (function) function to perform optimization
|
|
||||||
:param optimizer_kwargs: (dict) arguments for optimizer
|
|
||||||
:param objective_kwargs: (dict) arguments for objective function
|
|
||||||
"""
|
|
||||||
if objective_function is not None:
|
|
||||||
self.set_objective_function(objective_function)
|
|
||||||
if optimizer is not None:
|
|
||||||
self.set_optimizer(optimizer)
|
|
||||||
|
|
||||||
def objective(x):
|
|
||||||
return self._internal_objective(x, objective_kwargs)
|
|
||||||
|
|
||||||
optimizer = self._optimizer
|
|
||||||
opt_dict = copy.deepcopy(self._opt_dict)
|
|
||||||
i = 0
|
|
||||||
for species_name in opt_dict.keys():
|
|
||||||
for _ in opt_dict[species_name].keys():
|
|
||||||
i += 1
|
|
||||||
x_guess = np.ones(i)
|
|
||||||
|
|
||||||
if optimizer_kwargs is None:
|
|
||||||
# noinspection PyCallingNonCallable
|
|
||||||
est_parameters = optimizer(objective, x_guess)
|
|
||||||
else:
|
|
||||||
# noinspection PyCallingNonCallable
|
|
||||||
est_parameters = optimizer(objective, x_guess, **optimizer_kwargs)
|
|
||||||
|
|
||||||
i = 0
|
|
||||||
for species_name in opt_dict.keys():
|
|
||||||
for thermo_prop in opt_dict[species_name].keys():
|
|
||||||
opt_dict[species_name][thermo_prop] *= est_parameters[i]
|
|
||||||
i += 1
|
|
||||||
self.update_predicted_dict()
|
|
||||||
return opt_dict
|
|
||||||
|
|
||||||
def update_xml(self,
|
|
||||||
info_dict,
|
|
||||||
phases_xml_filename=None):
|
|
||||||
"""updates xml file with info_dict
|
|
||||||
:param info_dict: (dict) info in {species_names:{thermo_prop:val}}
|
|
||||||
:param phases_xml_filename: (str) xml filename if editing other xml
|
|
||||||
"""
|
|
||||||
if phases_xml_filename is None:
|
|
||||||
phases_xml_filename = self._phases_xml_filename
|
|
||||||
|
|
||||||
tree = ET.parse(phases_xml_filename)
|
|
||||||
root = tree.getroot()
|
|
||||||
# Update xml file
|
|
||||||
for species_name in info_dict.keys():
|
|
||||||
for thermo_prop in info_dict[species_name].keys():
|
|
||||||
for species in root.iter('species'):
|
|
||||||
if species.attrib['name'] == species_name:
|
|
||||||
for changed_prop in species.iter(thermo_prop):
|
|
||||||
changed_prop.text = str(
|
|
||||||
info_dict[species_name][thermo_prop])
|
|
||||||
now = datetime.now()
|
|
||||||
changed_prop.set('updated',
|
|
||||||
'Updated at {0}:{1} {2}-{3}-{4}'
|
|
||||||
.format(now.hour, now.minute,
|
|
||||||
now.month, now.day,
|
|
||||||
now.year))
|
|
||||||
|
|
||||||
tree.write(phases_xml_filename)
|
|
||||||
if phases_xml_filename == self._phases_xml_filename:
|
|
||||||
self.set_phases(self._phases_xml_filename, self._phase_names)
|
|
||||||
return None
|
|
||||||
|
|
||||||
# noinspection PyUnusedLocal
|
|
||||||
def parity_plot(self, species='re_aq', save_path=None, print_r_squared=False):
|
|
||||||
"""Parity plot between measured and predicted rare earth composition"""
|
|
||||||
phases_copy = self._phases.copy()
|
|
||||||
mix = ct.Mixture(phases_copy)
|
|
||||||
aq_ind = self._aq_ind
|
|
||||||
exp_df = self._exp_df
|
|
||||||
in_moles = self._in_moles
|
|
||||||
rare_earth_ion_name = self._rare_earth_ion_name
|
|
||||||
pred = []
|
|
||||||
for row in in_moles.values:
|
|
||||||
mix.species_moles = row
|
|
||||||
mix.equilibrate('TP', log_level=0)
|
|
||||||
re_aq = mix.species_moles[mix.species_index(
|
|
||||||
aq_ind, rare_earth_ion_name)]
|
|
||||||
pred.append(re_aq)
|
|
||||||
pred = np.array(pred)
|
|
||||||
meas = exp_df.values[:, 1]
|
|
||||||
min_data = np.min([pred, meas])
|
|
||||||
max_data = np.max([pred, meas])
|
|
||||||
min_max_data = np.array([min_data, max_data])
|
|
||||||
fig, ax = plt.subplots()
|
|
||||||
re_element = ''
|
|
||||||
n_plus = 0
|
|
||||||
for char in self._rare_earth_ion_name:
|
|
||||||
if char.isalpha():
|
|
||||||
re_element = '{0}{1}'.format(re_element, char)
|
|
||||||
else:
|
|
||||||
n_plus += 1
|
|
||||||
re_ion_name = '$%s^{%d+}$' % (re_element, n_plus)
|
|
||||||
p1 = sns.scatterplot(meas, pred, color="r",
|
|
||||||
label="{0} eq. conc. (mol/L)".format(re_ion_name),
|
|
||||||
legend=False)
|
|
||||||
p2 = sns.lineplot(min_max_data, min_max_data, color="b", label="")
|
|
||||||
if print_r_squared:
|
|
||||||
p1.text(min_max_data[0], min_max_data[1]*0.9, '$R^2$={0:.2f}'.format(self.r_squared()))
|
|
||||||
plt.legend(loc='lower right')
|
|
||||||
else:
|
|
||||||
plt.legend()
|
|
||||||
ax.set(xlabel='Measured', ylabel='Predicted')
|
|
||||||
plt.show()
|
|
||||||
if save_path is not None:
|
|
||||||
plt.savefig(save_path, bbox_inches='tight')
|
|
||||||
return None
|
|
||||||
|
|
||||||
def r_squared(self):
|
|
||||||
"""r-squared value comparing measured and predicted rare earth composition"""
|
|
||||||
phases_copy = self._phases.copy()
|
|
||||||
mix = ct.Mixture(phases_copy)
|
|
||||||
aq_ind = self._aq_ind
|
|
||||||
exp_df = self._exp_df
|
|
||||||
in_moles = self._in_moles
|
|
||||||
rare_earth_ion_name = self._rare_earth_ion_name
|
|
||||||
pred = []
|
|
||||||
for row in in_moles.values:
|
|
||||||
mix.species_moles = row
|
|
||||||
mix.equilibrate('TP', log_level=0)
|
|
||||||
re_aq = mix.species_moles[mix.species_index(
|
|
||||||
aq_ind, rare_earth_ion_name)]
|
|
||||||
pred.append(re_aq)
|
|
||||||
predicted_y = np.array(pred)
|
|
||||||
actual_y = exp_df.values[:, 1]
|
|
||||||
num = sum((actual_y - predicted_y) ** 2)
|
|
||||||
den = sum((actual_y - np.mean(actual_y)) ** 2)
|
|
||||||
r_2 = (1 - num / den)
|
|
||||||
return r_2
|
|
||||||
Loading…
Reference in new issue