mirror of
https://github.com/ANL-CEEESA/LLEPE.git
synced 2025-12-06 09:58:52 -06:00
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.
This commit is contained in:
titusquah
5
.idea/dictionaries/Titus.xml
generated
5
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@@ -1,6 +1,7 @@
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@@ -12,11 +13,15 @@
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|
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
<w>quah</w>
|
||||
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|
||||
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|
||||
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||||
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140
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
</component>
|
||||
<component name="XDebuggerManager">
|
||||
<breakpoint-manager>
|
||||
|
||||
@@ -50,7 +50,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<t0 units="K">298.14999999999998</t0>
|
||||
|
||||
<h0 units="J/mol" updated="Updated at 14:33 6-5-2020">-4704699.156668724</h0>
|
||||
<h0 units="J/mol" updated="Updated at 16:49 6-11-2020">-4704703.645715787</h0>
|
||||
<s0 units="J/mol/K"> 1117.965 </s0>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
|
||||
File diff suppressed because one or more lines are too long
572
reeps.py
572
reeps.py
@@ -1,572 +0,0 @@
|
||||
from datetime import datetime
|
||||
import cantera as ct
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from scipy.optimize import minimize
|
||||
# noinspection PyPep8Naming
|
||||
import xml.etree.ElementTree as ET
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
import shutil
|
||||
import copy
|
||||
from inspect import signature
|
||||
import os
|
||||
|
||||
sns.set()
|
||||
sns.set(font_scale=1.6)
|
||||
|
||||
class REEPS:
|
||||
"""REEPS (Rare earth extraction parameter searcher)
|
||||
Takes in experimental data
|
||||
Returns parameters for GEM
|
||||
Only good for 1 rare earth and 1 extractant
|
||||
:param exp_csv_filename: (str) csv file name with experimental data
|
||||
:param phases_xml_filename: (str) xml file with parameters for equilibrium calc
|
||||
:param opt_dict: (dict) optimize info {species:{thermo_prop:guess}
|
||||
:param phase_names: (list) names of phases in xml file
|
||||
:param aq_solvent_name: (str) name of aqueous solvent in xml file
|
||||
:param extractant_name: (str) name of extractant in xml file
|
||||
:param diluant_name: (str) name of diluant in xml file
|
||||
:param complex_name: (str) name of complex in xml file
|
||||
:param rare_earth_ion_name: (str) name of rare earth ion in xml file
|
||||
:param aq_solvent_rho: (float) density of solvent (g/L)
|
||||
:param extractant_rho: (float) density of extractant (g/L)
|
||||
:param diluant_rho: (float) density of diluant (g/L)
|
||||
If no density is given, molar volume/molecular weight is used from xml
|
||||
:param objective_function: (function) function to compute objective
|
||||
By default, the objective function is log mean squared error
|
||||
of distribution ratio np.log10(re_org/re_aq)
|
||||
Function needs to take inputs:
|
||||
objective_function(predicted_dict, measured_df, **kwargs)
|
||||
**kwargs is optional
|
||||
Below is the guide for referencing predicted values
|
||||
| To access | Use |
|
||||
|------------------------------------- |--------------------------|
|
||||
| predicted rare earth eq conc in aq | predicted_dict['re_aq'] |
|
||||
| predicted rare earth eq conc in org | predicted_dict['re_org'] |
|
||||
| predicted hydrogen ion conc in aq | predicted_dict['h'] |
|
||||
| predicted extractant conc in org | predicted_dict['z'] |
|
||||
| predicted rare earth distribution ratio | predicted_dict['re_d'] |
|
||||
For measured values, use the column names in the experimental data file
|
||||
:param optimizer: (function) function to perform optimization
|
||||
By default, the optimizer is scipy's optimize function with
|
||||
default_kwargs= {"method": 'SLSQP',
|
||||
"bounds": [(1e-1, 1e1)*len(x_guess)],
|
||||
"constraints": (),
|
||||
"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
|
||||
Function needs to take inputs:
|
||||
optimizer(objective_function, x_guess, **kwargs)
|
||||
**kwargs is optional
|
||||
:param temp_xml_file_path: (str) path to temporary xml file
|
||||
default is local temp folder
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
exp_csv_filename,
|
||||
phases_xml_filename,
|
||||
opt_dict,
|
||||
phase_names,
|
||||
aq_solvent_name,
|
||||
extractant_name,
|
||||
diluant_name,
|
||||
complex_name,
|
||||
rare_earth_ion_name,
|
||||
aq_solvent_rho=None,
|
||||
extractant_rho=None,
|
||||
diluant_rho=None,
|
||||
objective_function='Log-MSE',
|
||||
optimizer='SLSQP',
|
||||
temp_xml_file_path=None
|
||||
):
|
||||
self._built_in_obj_list = ['Log-MSE']
|
||||
self._built_in_opt_list = ['SLSQP']
|
||||
self._exp_csv_filename = exp_csv_filename
|
||||
self._phases_xml_filename = phases_xml_filename
|
||||
self._opt_dict = opt_dict
|
||||
self._phase_names = phase_names
|
||||
self._aq_solvent_name = aq_solvent_name
|
||||
self._extractant_name = extractant_name
|
||||
self._diluant_name = diluant_name
|
||||
self._complex_name = complex_name
|
||||
self._rare_earth_ion_name = rare_earth_ion_name
|
||||
self._aq_solvent_rho = aq_solvent_rho
|
||||
self._extractant_rho = extractant_rho
|
||||
self._diluant_rho = diluant_rho
|
||||
self._objective_function = None
|
||||
self.set_objective_function(objective_function)
|
||||
self._optimizer = None
|
||||
self.set_optimizer(optimizer)
|
||||
if temp_xml_file_path is None:
|
||||
temp_xml_file_path = '{0}\\temp.xml'.format(os.getenv('TEMP'))
|
||||
self._temp_xml_file_path = temp_xml_file_path
|
||||
shutil.copyfile(phases_xml_filename, self._temp_xml_file_path)
|
||||
self._phases = ct.import_phases(phases_xml_filename, phase_names)
|
||||
self._exp_df = pd.read_csv(self._exp_csv_filename)
|
||||
|
||||
self._in_moles = None
|
||||
|
||||
self._aq_ind = None
|
||||
self._org_ind = None
|
||||
|
||||
self.set_in_moles(feed_vol=1)
|
||||
self._predicted_dict = None
|
||||
self.update_predicted_dict()
|
||||
|
||||
@staticmethod
|
||||
def log_mean_squared_error(predicted_dict, meas_df):
|
||||
meas = meas_df.values[:, 2]
|
||||
pred = predicted_dict['re_org'] / predicted_dict['re_aq']
|
||||
log_pred = np.log10(pred)
|
||||
log_meas = np.log10(meas)
|
||||
obj = np.sum((log_pred - log_meas) ** 2)
|
||||
return obj
|
||||
|
||||
@staticmethod
|
||||
def slsqp_optimizer(objective, x_guess):
|
||||
optimizer_kwargs = {"method": 'SLSQP',
|
||||
"bounds": [(1e-1, 1e1)] * len(x_guess),
|
||||
"constraints": (),
|
||||
"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
|
||||
res = minimize(objective, x_guess, **optimizer_kwargs)
|
||||
est_parameters = res.x
|
||||
return est_parameters
|
||||
|
||||
def get_exp_csv_filename(self) -> str:
|
||||
return self._exp_csv_filename
|
||||
|
||||
def set_exp_csv_filename(self, exp_csv_filename):
|
||||
self._exp_csv_filename = exp_csv_filename
|
||||
self._exp_df = pd.read_csv(self._exp_csv_filename)
|
||||
self.update_predicted_dict()
|
||||
return None
|
||||
|
||||
def get_phases(self) -> list:
|
||||
return self._phases
|
||||
|
||||
def set_phases(self, phases_xml_filename, phase_names):
|
||||
"""Change xml and phase names
|
||||
Also runs set_in_mole to set initial moles to 1 g/L"""
|
||||
self._phases_xml_filename = phases_xml_filename
|
||||
self._phase_names = phase_names
|
||||
shutil.copyfile(phases_xml_filename, self._temp_xml_file_path)
|
||||
self._phases = ct.import_phases(phases_xml_filename, phase_names)
|
||||
self.set_in_moles(feed_vol=1)
|
||||
self.update_predicted_dict()
|
||||
return None
|
||||
|
||||
def get_opt_dict(self) -> dict:
|
||||
return self._opt_dict
|
||||
|
||||
def set_opt_dict(self, opt_dict):
|
||||
self._opt_dict = opt_dict
|
||||
return None
|
||||
|
||||
def get_aq_solvent_name(self) -> str:
|
||||
return self._aq_solvent_name
|
||||
|
||||
def set_aq_solvent_name(self, aq_solvent_name):
|
||||
self._aq_solvent_name = aq_solvent_name
|
||||
return None
|
||||
|
||||
def get_extractant_name(self) -> str:
|
||||
return self._extractant_name
|
||||
|
||||
def set_extractant_name(self, extractant_name):
|
||||
self._extractant_name = extractant_name
|
||||
return None
|
||||
|
||||
def get_diluant_name(self) -> str:
|
||||
return self._diluant_name
|
||||
|
||||
def set_diluant_name(self, diluant_name):
|
||||
self._diluant_name = diluant_name
|
||||
return None
|
||||
|
||||
def get_complex_name(self) -> str:
|
||||
return self._complex_name
|
||||
|
||||
def set_complex_name(self, complex_name):
|
||||
self._complex_name = complex_name
|
||||
return None
|
||||
|
||||
def get_rare_earth_ion_name(self) -> str:
|
||||
return self._rare_earth_ion_name
|
||||
|
||||
def set_rare_earth_ion_name(self, rare_earth_ion_name):
|
||||
self._rare_earth_ion_name = rare_earth_ion_name
|
||||
return None
|
||||
|
||||
def get_aq_solvent_rho(self) -> str:
|
||||
return self._aq_solvent_rho
|
||||
|
||||
def set_aq_solvent_rho(self, aq_solvent_rho):
|
||||
self._aq_solvent_rho = aq_solvent_rho
|
||||
return None
|
||||
|
||||
def get_extractant_rho(self) -> str:
|
||||
return self._extractant_rho
|
||||
|
||||
def set_extractant_rho(self, extractant_rho):
|
||||
self._extractant_rho = extractant_rho
|
||||
return None
|
||||
|
||||
def get_diluant_rho(self) -> str:
|
||||
return self._diluant_rho
|
||||
|
||||
def set_diluant_rho(self, diluant_rho):
|
||||
self._diluant_rho = diluant_rho
|
||||
return None
|
||||
|
||||
def set_in_moles(self, feed_vol):
|
||||
"""Function that initializes mole fractions
|
||||
:param feed_vol: (float) feed volume of mixture (g/L)"""
|
||||
phases_copy = self._phases.copy()
|
||||
exp_df = self._exp_df.copy()
|
||||
solvent_name = self._aq_solvent_name
|
||||
extractant_name = self._extractant_name
|
||||
diluant_name = self._diluant_name
|
||||
solvent_rho = self._aq_solvent_rho
|
||||
extractant_rho = self._extractant_rho
|
||||
diluant_rho = self._diluant_rho
|
||||
re_name = self._rare_earth_ion_name
|
||||
|
||||
mixed = ct.Mixture(phases_copy)
|
||||
aq_ind = None
|
||||
solvent_ind = None
|
||||
for ind, phase in enumerate(phases_copy):
|
||||
if solvent_name in phase.species_names:
|
||||
aq_ind = ind
|
||||
solvent_ind = phase.species_names.index(solvent_name)
|
||||
if aq_ind is None:
|
||||
raise Exception('Solvent "{0}" not found \
|
||||
in xml file'.format(solvent_name))
|
||||
|
||||
if aq_ind == 0:
|
||||
org_ind = 1
|
||||
else:
|
||||
org_ind = 0
|
||||
self._aq_ind = aq_ind
|
||||
self._org_ind = org_ind
|
||||
extractant_ind = phases_copy[org_ind].species_names.index(
|
||||
extractant_name)
|
||||
diluant_ind = phases_copy[org_ind].species_names.index(diluant_name)
|
||||
|
||||
re_ind = phases_copy[aq_ind].species_names.index(re_name)
|
||||
re_charge = phases_copy[aq_ind].species(re_ind).charge
|
||||
|
||||
mix_aq = mixed.phase(aq_ind)
|
||||
mix_org = mixed.phase(org_ind)
|
||||
solvent_mw = mix_aq.molecular_weights[solvent_ind] # g/mol
|
||||
extractant_mw = mix_org.molecular_weights[extractant_ind]
|
||||
diluant_mw = mix_org.molecular_weights[diluant_ind]
|
||||
if solvent_rho is None:
|
||||
solvent_rho = mix_aq(aq_ind).partial_molar_volumes[
|
||||
solvent_ind] / solvent_mw * 1e6 # g/L
|
||||
self._aq_solvent_rho = solvent_rho
|
||||
if extractant_rho is None:
|
||||
extractant_rho = mix_org(org_ind).partial_molar_volumes[
|
||||
extractant_ind] / extractant_mw * 1e6
|
||||
self._extractant_rho = extractant_rho
|
||||
if diluant_rho is None:
|
||||
diluant_rho = mix_org(org_ind).partial_molar_volumes[
|
||||
extractant_ind] / extractant_mw * 1e6
|
||||
self._diluant_rho = diluant_rho
|
||||
|
||||
in_moles_data = []
|
||||
aq_phase_solvent_moles = feed_vol * solvent_rho / solvent_mw
|
||||
for row in exp_df.values:
|
||||
h_plus_moles = feed_vol * row[0]
|
||||
hydroxide_ions = 0
|
||||
rare_earth_moles = feed_vol * row[6]
|
||||
chlorine_moles = re_charge * rare_earth_moles + h_plus_moles
|
||||
extractant_moles = feed_vol * row[3]
|
||||
extractant_vol = extractant_moles * extractant_mw / extractant_rho
|
||||
diluant_vol = feed_vol - extractant_vol
|
||||
diluant_moles = diluant_vol * diluant_rho / diluant_mw
|
||||
complex_moles = 0
|
||||
|
||||
species_moles = [aq_phase_solvent_moles,
|
||||
h_plus_moles,
|
||||
hydroxide_ions,
|
||||
chlorine_moles,
|
||||
rare_earth_moles,
|
||||
extractant_moles,
|
||||
diluant_moles,
|
||||
complex_moles,
|
||||
]
|
||||
in_moles_data.append(species_moles)
|
||||
self._in_moles = pd.DataFrame(in_moles_data, columns=mixed.species_names)
|
||||
self.update_predicted_dict()
|
||||
return None
|
||||
|
||||
def get_in_moles(self) -> pd.DataFrame:
|
||||
return self._in_moles
|
||||
|
||||
def set_objective_function(self, objective_function):
|
||||
"""Set objective function. see class docstring for instructions"""
|
||||
if not callable(objective_function) \
|
||||
and objective_function not in self._built_in_obj_list:
|
||||
raise Exception(
|
||||
"objective_function must be a function "
|
||||
"or in this strings list: {0}".format(self._built_in_obj_list))
|
||||
if callable(objective_function):
|
||||
if len(signature(objective_function).parameters) < 2:
|
||||
raise Exception(
|
||||
"objective_function must be a function "
|
||||
"with at least 3 arguments:"
|
||||
" f(predicted_dict, experimental_df,**kwargs)")
|
||||
if objective_function == 'Log-MSE':
|
||||
objective_function = self.log_mean_squared_error
|
||||
self._objective_function = objective_function
|
||||
return None
|
||||
|
||||
def get_objective_function(self):
|
||||
return self._objective_function
|
||||
|
||||
def set_optimizer(self, optimizer):
|
||||
if not callable(optimizer) \
|
||||
and optimizer not in self._built_in_opt_list:
|
||||
raise Exception(
|
||||
"optimizer must be a function "
|
||||
"or in this strings list: {0}".format(self._built_in_opt_list))
|
||||
if callable(optimizer):
|
||||
if len(signature(optimizer).parameters) < 2:
|
||||
raise Exception(
|
||||
"optimizer must be a function "
|
||||
"with at least 2 arguments: "
|
||||
"f(objective_func,x_guess,**kwargs)")
|
||||
if optimizer == 'SLSQP':
|
||||
optimizer = self.slsqp_optimizer
|
||||
self._optimizer = optimizer
|
||||
return None
|
||||
|
||||
def get_optimizer(self):
|
||||
return self._optimizer
|
||||
|
||||
def update_predicted_dict(self, phases_xml_filename=None):
|
||||
if phases_xml_filename is None:
|
||||
phases_xml_filename = self._phases_xml_filename
|
||||
phase_names = self._phase_names
|
||||
aq_ind = self._aq_ind
|
||||
org_ind = self._org_ind
|
||||
complex_name = self._complex_name
|
||||
extractant_name = self._extractant_name
|
||||
rare_earth_ion_name = self._rare_earth_ion_name
|
||||
in_moles = self._in_moles
|
||||
|
||||
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
|
||||
18
setup.py
18
setup.py
@@ -2,11 +2,23 @@ from setuptools import setup
|
||||
|
||||
setup(
|
||||
name='reeps',
|
||||
version='1.0.0',
|
||||
packages=['tests'],
|
||||
version='0.0.0',
|
||||
packages=['reeps'],
|
||||
package_data={
|
||||
'csvs': ['data/csvs/*.csv'],
|
||||
'xmls': ['data/xmls/*.xml']
|
||||
},
|
||||
include_package_data=True,
|
||||
zip_safe=False,
|
||||
url='',
|
||||
license='',
|
||||
author='Titus Quah',
|
||||
author_email='',
|
||||
description='Rare earth element parameter searcher'
|
||||
description='Rare earth element parameter searcher',
|
||||
install_requires=['cantera==2.4.0',
|
||||
'pandas==1.0.3',
|
||||
'numpy==1.15.4',
|
||||
'scipy==1.4.1',
|
||||
'seaborn==0.10.1',
|
||||
'matplotlib==3.1.3']
|
||||
)
|
||||
|
||||
@@ -1,33 +1,15 @@
|
||||
import json
|
||||
import numpy as np
|
||||
import pyswarms as ps
|
||||
import sys
|
||||
sys.path.append('../')
|
||||
from reeps import REEPS1
|
||||
from reeps import REEPS
|
||||
|
||||
with open('multi_ree_settings.txt') as file:
|
||||
testing_params = json.load(file)
|
||||
|
||||
beaker = REEPS1(**testing_params)
|
||||
beaker = REEPS(**testing_params)
|
||||
|
||||
|
||||
# def new_obj(predicted_dict, meas_df, epsilon):
|
||||
# meas_cols = list(meas_df)
|
||||
# pred_keys = list(predicted_dict.keys())
|
||||
# meas = meas_df[meas_cols[2]]
|
||||
# pred = (predicted_dict['re_org'] + epsilon) / (predicted_dict['re_aq'] + epsilon)
|
||||
# log_pred = np.log10(pred)
|
||||
# log_meas = np.log10(meas)
|
||||
# obj = np.sum((log_pred - log_meas) ** 2)
|
||||
# return obj
|
||||
# #
|
||||
# #
|
||||
# # def new_obj(ping):
|
||||
# # print(ping)
|
||||
# beaker.set_objective_function(new_obj)
|
||||
# objective_kwargs = {"epsilon": 1e-14}
|
||||
# beaker.set
|
||||
# noinspection PyUnusedLocal
|
||||
def optimizer(func, x_guess):
|
||||
lb = np.array([1e-1])
|
||||
ub = np.array([1e1])
|
||||
@@ -48,6 +30,6 @@ minimizer_kwargs = {"method": 'SLSQP',
|
||||
est_enthalpy = beaker.fit()
|
||||
print(est_enthalpy)
|
||||
|
||||
# beaker.update_xml(est_enthalpy)
|
||||
# beaker.parity_plot()
|
||||
# print(beaker.r_squared())
|
||||
beaker.update_xml(est_enthalpy)
|
||||
# beaker.parity_plot('Nd_d_eq', print_r_squared=True)
|
||||
print(beaker.r_squared())
|
||||
|
||||
@@ -1,14 +1,14 @@
|
||||
import json
|
||||
from unittest import TestCase
|
||||
|
||||
import numpy as np
|
||||
import pyswarms as ps
|
||||
import sys
|
||||
sys.path.append('../')
|
||||
from reeps import REEPS
|
||||
from reeps import REEPS1
|
||||
|
||||
with open('one_ree_settings.txt') as file:
|
||||
testing_params = json.load(file)
|
||||
|
||||
beaker = REEPS(**testing_params)
|
||||
beaker = REEPS1(**testing_params)
|
||||
|
||||
|
||||
# def new_obj(predicted_dict, meas_df, epsilon):
|
||||
@@ -48,6 +48,118 @@ minimizer_kwargs = {"method": 'SLSQP',
|
||||
est_enthalpy = beaker.fit()
|
||||
print(est_enthalpy)
|
||||
|
||||
# beaker.update_xml(est_enthalpy)
|
||||
|
||||
beaker.update_xml(est_enthalpy)
|
||||
# beaker.parity_plot()
|
||||
# print(beaker.r_squared())
|
||||
print(beaker.r_squared())
|
||||
# class TestREEPS1(TestCase):
|
||||
# def test_slsqp_optimizer(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_log_mean_squared_error(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_exp_df(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_exp_df(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_phases(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_phases(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_opt_dict(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_opt_dict(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_aq_solvent_name(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_aq_solvent_name(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_extractant_name(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_extractant_name(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_diluant_name(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_diluant_name(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_complex_names(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_complex_names(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_rare_earth_ion_names(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_rare_earth_ion_names(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_aq_solvent_rho(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_aq_solvent_rho(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_extractant_rho(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_extractant_rho(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_diluant_rho(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_diluant_rho(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_in_moles(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_in_moles(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_objective_function(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_objective_function(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_set_optimizer(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_optimizer(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_update_predicted_dict(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_get_predicted_dict(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test__internal_objective(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_fit(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_update_xml(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_parity_plot(self):
|
||||
# self.fail()
|
||||
#
|
||||
# def test_r_squared(self):
|
||||
# self.fail()
|
||||
|
||||
Reference in New Issue
Block a user