@ -9,6 +9,8 @@ import seaborn as sns
import matplotlib . pyplot as plt import matplotlib . pyplot as plt
import shutil import shutil
import copy import copy
from inspect import signature
import os
sns . set ( ) sns . set ( )
@ -16,6 +18,7 @@ class REEPS:
""" REEPS (Rare earth extraction parameter searcher)
""" REEPS (Rare earth extraction parameter searcher)
Takes in experimental data
Takes in experimental data
Returns parameters for GEM
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 exp_csv_filename : ( str ) csv file name with experimental data
: param phases_xml_filename : ( str ) xml file with parameters for equilibrium calc
: param phases_xml_filename : ( str ) xml file with parameters for equilibrium calc
: param opt_dict : ( dict ) optimize info { species : { thermo_prop : guess }
: 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 rare_earth_ion_name : ( str ) name of rare earth ion in xml file
: param aq_solvent_rho : ( float ) density of solvent ( g / L )
: param aq_solvent_rho : ( float ) density of solvent ( g / L )
: param extractant_rho : ( float ) density of extractant ( 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
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 ,
def __init__ ( self ,
@ -44,7 +73,12 @@ class REEPS:
aq_solvent_rho = None ,
aq_solvent_rho = None ,
extractant_rho = None ,
extractant_rho = None ,
diluant_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 . _exp_csv_filename = exp_csv_filename
self . _phases_xml_filename = phases_xml_filename
self . _phases_xml_filename = phases_xml_filename
self . _opt_dict = opt_dict
self . _opt_dict = opt_dict
@ -57,9 +91,14 @@ class REEPS:
self . _aq_solvent_rho = aq_solvent_rho
self . _aq_solvent_rho = aq_solvent_rho
self . _extractant_rho = extractant_rho
self . _extractant_rho = extractant_rho
self . _diluant_rho = diluant_rho
self . _diluant_rho = diluant_rho
self . _objective_function = None
self . _temp_xml_filename = " temp.xml "
self . set_objective_function ( objective_function )
shutil . copyfile ( phases_xml_filename , self . _temp_xml_filename )
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 . _phases = ct . import_phases ( phases_xml_filename , phase_names )
self . _exp_df = pd . read_csv ( self . _exp_csv_filename )
self . _exp_df = pd . read_csv ( self . _exp_csv_filename )
@ -69,6 +108,27 @@ class REEPS:
self . _org_ind = None
self . _org_ind = None
self . set_in_moles ( feed_vol = 1 )
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 :
def get_exp_csv_filename ( self ) - > str :
return self . _exp_csv_filename
return self . _exp_csv_filename
@ -76,6 +136,7 @@ class REEPS:
def set_exp_csv_filename ( self , exp_csv_filename ) :
def set_exp_csv_filename ( self , exp_csv_filename ) :
self . _exp_csv_filename = exp_csv_filename
self . _exp_csv_filename = exp_csv_filename
self . _exp_df = pd . read_csv ( self . _exp_csv_filename )
self . _exp_df = pd . read_csv ( self . _exp_csv_filename )
self . update_predicted_dict ( )
return None
return None
def get_phases ( self ) - > list :
def get_phases ( self ) - > list :
@ -86,8 +147,10 @@ class REEPS:
Also runs set_in_mole to set initial moles to 1 g / L """
Also runs set_in_mole to set initial moles to 1 g / L """
self . _phases_xml_filename = phases_xml_filename
self . _phases_xml_filename = phases_xml_filename
self . _phase_names = phase_names
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 . _phases = ct . import_phases ( phases_xml_filename , phase_names )
self . set_in_moles ( feed_vol = 1 )
self . set_in_moles ( feed_vol = 1 )
self . update_predicted_dict ( )
return None
return None
def get_opt_dict ( self ) - > dict :
def get_opt_dict ( self ) - > dict :
@ -232,35 +295,72 @@ class REEPS:
]
]
in_moles_data . append ( species_moles )
in_moles_data . append ( species_moles )
self . _in_moles = pd . DataFrame ( in_moles_data , columns = mixed . species_names )
self . _in_moles = pd . DataFrame ( in_moles_data , columns = mixed . species_names )
self . update_predicted_dict ( )
return None
return None
def get_in_moles ( self ) - > pd . DataFrame :
def get_in_moles ( self ) - > pd . DataFrame :
return self . _in_moles
return self . _in_moles
def objective ( self , x ) :
def set_objective_function ( self , objective_function ) :
""" Log mean squared error between measured and predicted data
""" Set objective function. see class docstring for instructions """
: param x : ( list ) thermo properties varied to minimize LMSE """
if not callable ( objective_function ) \
temp_xml_filename = self . _temp_xml_filename
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
phase_names = self . _phase_names
aq_ind = self . _aq_ind
aq_ind = self . _aq_ind
org_ind = self . _org_ind
org_ind = self . _org_ind
complex_name = self . _complex_name
complex_name = self . _complex_name
extractant_name = self . _extractant_name
rare_earth_ion_name = self . _rare_earth_ion_name
rare_earth_ion_name = self . _rare_earth_ion_name
in_moles = self . _in_moles
in_moles = self . _in_moles
exp_df = self . _exp_df
x = np . array ( x )
phases_copy = ct . import_phases ( phases_xml_filename , phase_names )
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 )
mix = ct . Mixture ( phases_copy )
mix = ct . Mixture ( phases_copy )
pred = [ ]
predicted_dict = { " re_aq " : [ ] ,
" re_org " : [ ] ,
" h " : [ ] ,
" z " : [ ]
}
for row in in_moles . values :
for row in in_moles . values :
mix . species_moles = row
mix . species_moles = row
mix . equilibrate ( ' TP ' , log_level = 0 )
mix . equilibrate ( ' TP ' , log_level = 0 )
@ -268,17 +368,86 @@ class REEPS:
org_ind , complex_name ) ]
org_ind , complex_name ) ]
re_aq = mix . species_moles [ mix . species_index (
re_aq = mix . species_moles [ mix . species_index (
aq_ind , rare_earth_ion_name ) ]
aq_ind , rare_earth_ion_name ) ]
pred . append ( np . log10 ( re_org / re_aq ) )
hydrogen_ions = mix . species_moles [ mix . species_index ( aq_ind , ' H+ ' ) ]
pred = np . array ( pred )
extractant = mix . species_moles [ mix . species_index (
meas = np . log10 ( exp_df [ ' D(m) ' ] . values )
org_ind , extractant_name ) ]
obj = np . sum ( ( pred - meas ) * * 2 )
predicted_dict [ ' re_aq ' ] . append ( re_aq )
return obj
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 fit ( self , kwargs ) - > float :
def get_predicted_dict ( self ) :
""" Fits experimental to modeled data by estimating complex reference enthalpy
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 , 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
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 )
opt_dict = copy . deepcopy ( self . _opt_dict )
# x_guess = []
# x_guess = []
i = 0
i = 0
@ -287,12 +456,20 @@ class REEPS:
# x_guess.append(opt_dict[species_name][thermo_prop])
# x_guess.append(opt_dict[species_name][thermo_prop])
i + = 1
i + = 1
x_guess = np . ones ( i )
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
i = 0
for species_name in opt_dict . keys ( ) :
for species_name in opt_dict . keys ( ) :
for thermo_prop in opt_dict [ species_name ] . 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
i + = 1
self . update_predicted_dict ( )
return opt_dict
return opt_dict
def update_xml ( self ,
def update_xml ( self ,
@ -323,8 +500,11 @@ class REEPS:
now . year ) )
now . year ) )
tree . write ( phases_xml_filename )
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 """
""" Parity plot between measured and predicted rare earth composition """
phases_copy = self . _phases . copy ( )
phases_copy = self . _phases . copy ( )
mix = ct . Mixture ( phases_copy )
mix = ct . Mixture ( phases_copy )
@ -345,8 +525,31 @@ class REEPS:
max_data = np . max ( [ pred , meas ] )
max_data = np . max ( [ pred , meas ] )
min_max_data = np . array ( [ min_data , max_data ] )
min_max_data = np . array ( [ min_data , max_data ] )
fig , ax = plt . subplots ( )
fig , ax = plt . subplots ( )
sns . scatterplot ( meas , pred , color = " r " )
sns . scatterplot ( meas , pred , color = " r " ,
sns . lineplot ( min_max_data , min_max_data , color = " b " )
label = " Rare earth equilibrium concentration (mol/L) " )
ax . set ( xlabel = ' Measured X equilibrium ' , ylabel = ' Predicted X equilibrium ' )
sns . lineplot ( min_max_data , min_max_data , color = " b " , label = " " )
ax . set ( xlabel = ' Measured ' , ylabel = ' Predicted ' )
plt . show ( )
plt . show ( )
return None
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