@ -9,6 +9,8 @@ import seaborn as sns
import matplotlib . pyplot as plt
import shutil
import copy
from inspect import signature
import os
sns . set ( )
@ -16,6 +18,7 @@ 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 }
@ -27,8 +30,34 @@ class REEPS:
: 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 extract ant ( g / L )
: param diluant_rho : ( float ) density of dilu ant ( 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 ,
@ -44,7 +73,12 @@ class REEPS:
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
@ -57,9 +91,14 @@ class REEPS:
self . _aq_solvent_rho = aq_solvent_rho
self . _extractant_rho = extractant_rho
self . _diluant_rho = diluant_rho
self . _temp_xml_filename = " temp.xml "
shutil . copyfile ( phases_xml_filename , self . _temp_xml_filename )
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 )
@ -69,6 +108,27 @@ class REEPS:
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 [ ' D(m) ' ] . values
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
@ -76,6 +136,7 @@ class REEPS:
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 :
@ -86,8 +147,10 @@ class REEPS:
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 :
@ -232,35 +295,72 @@ class REEPS:
]
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 objective ( self , x ) :
""" Log mean squared error between measured and predicted data
: param x : ( list ) thermo properties varied to minimize LMSE """
temp_xml_filename = self . _temp_xml_filename
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
exp_df = self . _exp_df
x = np . array ( x )
opt_dict = copy . deepcopy ( self . _opt_dict )
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_filename )
phases_copy = ct . import_phases ( temp_xml_filename , phase_names )
phases_copy = ct . import_phases ( phases_xml_filename , phase_names )
mix = ct . Mixture ( phases_copy )
pred = [ ]
predicted_dict = { " re_aq " : [ ] ,
" re_org " : [ ] ,
" h " : [ ] ,
" z " : [ ]
}
for row in in_moles . values :
mix . species_moles = row
mix . equilibrate ( ' TP ' , log_level = 0 )
@ -268,17 +368,86 @@ class REEPS:
org_ind , complex_name ) ]
re_aq = mix . species_moles [ mix . species_index (
aq_ind , rare_earth_ion_name ) ]
pred . append ( np . log10 ( re_org / re_aq ) )
pred = np . array ( pred )
meas = np . log10 ( exp_df [ ' D(m) ' ] . values )
obj = np . sum ( ( pred - meas ) * * 2 )
return obj
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 i == len ( x . shape ) :
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 , kwargs ) - > float :
""" Fits experimental to modeled data by estimating complex reference enthalpy
def fit ( self , objective_function= None , optimizer = None , objective_kwargs = None , optimizer_kwargs = None ) - > dic t:
""" Fits experimental to modeled data by minimizing objective function
Returns estimated complex enthalpy in J / mol
: param kwargs : ( dict ) parameters and options for scipy . minimize
: 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 )
# x_guess = []
i = 0
@ -287,12 +456,20 @@ class REEPS:
# x_guess.append(opt_dict[species_name][thermo_prop])
i + = 1
x_guess = np . ones ( i )
res = minimize ( self . objective , x_guess , * * kwargs )
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 ] * = res . x [ i ]
opt_dict [ species_name ] [ thermo_prop ] * = est_pa ram eter s[ i ]
i + = 1
self . update_predicted_dict ( )
return opt_dict
def update_xml ( self ,
@ -323,8 +500,11 @@ class REEPS:
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
def parity_plot ( self ) :
def parity_plot ( self , species = ' re_aq ' ):
""" Parity plot between measured and predicted rare earth composition """
phases_copy = self . _phases . copy ( )
mix = ct . Mixture ( phases_copy )
@ -345,8 +525,31 @@ class REEPS:
max_data = np . max ( [ pred , meas ] )
min_max_data = np . array ( [ min_data , max_data ] )
fig , ax = plt . subplots ( )
sns . scatterplot ( meas , pred , color = " r " )
sns . lineplot ( min_max_data , min_max_data , color = " b " )
ax . set ( xlabel = ' Measured X equilibrium ' , ylabel = ' Predicted X equilibrium ' )
sns . scatterplot ( meas , pred , color = " r " ,
label = " Rare earth equilibrium concentration (mol/L) " )
sns . lineplot ( min_max_data , min_max_data , color = " b " , label = " " )
ax . set ( xlabel = ' Measured ' , ylabel = ' Predicted ' )
plt . show ( )
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 [ ' REeq(m) ' ] . values
num = sum ( ( actual_y - predicted_y ) * * 2 )
den = sum ( ( actual_y - np . mean ( actual_y ) ) * * 2 )
r_2 = ( 1 - num / den )
return r_2
titusquah
5 years ago