-
-
Welcome to reeps's docs! - the Rare earth element parameter searcher¶
-REEPS is a package for thermodynamic parameter estimation specifically -for rare earth element extraction modeling.
-REEPS takes experimental data in a csv and system data in a xml.
+
+REEPS — reeps 1.0.0 documentation
+ LLEPE — LLEPE 1.0.0 documentation
@@ -49,7 +49,7 @@
- reeps
+ LLEPE
@@ -89,7 +89,7 @@
Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator¶
+LLEPE is a package for thermodynamic parameter estimation for liquid-liquid extraction modeling
+LLEPE takes experimental data in a csv and system data in a xml.
The package then uses Cantera, another python package, to simulate equilibrium.
Error between predicted and experimental data is then minimized.
diff --git a/docs/build/html/modules/reeps.html b/docs/build/html/modules/reeps.html index 397f692..7e84d0e 100644 --- a/docs/build/html/modules/reeps.html +++ b/docs/build/html/modules/reeps.html @@ -8,7 +8,7 @@ -Searchers
-
-
- REEPS
-
+
- LLEPE @@ -107,7 +107,7 @@ @@ -138,7 +138,7 @@
- Docs » -
- REEPS +
- LLEPE
-
@@ -157,21 +157,20 @@
--
REEPS¶
++LLEPE¶
Parameters¶
-
-
-
-class
reeps.REEPS(exp_csv_filename, phases_xml_filename, phase_names, aq_solvent_name, extractant_name, diluant_name, complex_names, rare_earth_ion_names, re_species_list=None, aq_solvent_rho=None, extractant_rho=None, diluant_rho=None, opt_dict=None, objective_function='Log-MSE', optimizer='SLSQP', temp_xml_file_path=None)[source]¶
- Rare earth elements (REE or RE) Takes in experimental data -Returns parameters for GEM
+-
+class
llepe.LLEPE(exp_data, phases_xml_filename, phase_names, aq_solvent_name, extractant_name, diluant_name, complex_names, extracted_species_ion_names, extracted_species_list=None, aq_solvent_rho=None, extractant_rho=None, diluant_rho=None, opt_dict=None, objective_function='Log-MSE', optimizer='SLSQP', temp_xml_file_path=None, dependant_params_dict=None)[source]¶
+ Liquid-Liquid Extraction Parameter estimator
Note
-The order in which the REEs appear in the csv file must be the same -order as they appear in the xml, complex_names and -rare_earth_ion_names.
-For example, say in exp_csv_filename's csv, RE_1 is Nd RE_2 is Pr, +
The order in which the extracted species (ES) appear in the csv file +must be the same order as they appear in the xml, complex_names and +extracted_species_ion_names.
+For example, say in exp_csv_filename's csv, ES_1 is Nd ES_2 is Pr, and
aq_solvent_name = 'H2O(L)' extractant_name = '(HA)2(org)' @@ -187,22 +186,24 @@ Pr_aq_i, Pr_aq_eq, Pr_d_eq]
The organic speciesArray must be "(HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org)"
complex_names = ['Nd(H(A)2)3(org)', 'Pr(H(A)2)3(org)'] -rare_earth_ion_names = ['Nd+++', 'Pr+++'] +extracted_species_ion_names = ['Nd+++', 'Pr+++']
- Parameters
-
-
exp_csv_filename --
(str) csv file name with experimental data
+exp_data --
(str or pd.DataFrame) csv file name +or DataFrame with experimental data
In the .csv file, the rows are different experiments and columns are the measured quantities.
The ordering of the columns needs to be:
-[h_i, h_eq, z_i, z_eq, {RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq, -{RE_2}_aq_i, {RE_2}_aq_eq, {RE_2}_d_eq,... -{RE_N}_aq_i, {RE_N}_aq_eq, {RE_N}_d_eq]
+[h_i, h_eq, z_i, z_eq, +{ES_1}_aq_i, {ES_1}_aq_eq, {ES_1}_d_eq, +{ES_2}_aq_i, {ES_2}_aq_eq, {ES_2}_d_eq,... +{ES_N}_aq_i, {ES_N}_aq_eq, {ES_N}_d_eq]
Naming does not matter, just the order.
-Where {RE_1}-{RE_N} are the rare earth element names of interest +
Where {ES_1}-{ES_N} are the extracted species names of interest i.e. Nd, Pr, La, etc.
Below is an explanation of the columns.
@@ -239,18 +240,18 @@ extractant (mol/L) extractant (mol/L)
@@ -260,10 +261,10 @@ for equilibrium calc4
{RE}_aq_i
Initial concentration of RE ions (mol/L)
{ES}_aq_i
Initial concentration of ES ions (mol/L)
5
{RE}_aq_eq
Equilibrium concentration of RE ions +
{ES}_aq_eq
Equilibrium concentration of ES ions in aqueous phase (mol/L)
6
{RE}_d_eq
{ES}_d_eq
Equilibrium Ratio between amount of -RE atoms in organic to aqueous
Would recommend copying and modifying xmls located in data/xmls or in Cantera's "data" folder
speciesArray fields need specific ordering.
-In aqueous phase: aq_solvent_name, H+, OH-, Cl-, RE_1, RE_2, ..., RE_N
-For aqueous phase, RE_1-RE_N represent RE ion names i.e. Nd+++, Pr+++
-In organic phase : extractant_name, diluant_name, RE_1, RE_2, ..., RE_N
-For organic phase, RE_1-RE_N represent RE complex names +
In aqueous phase: aq_solvent_name, H+, OH-, Cl-, ES_1, ES_2, ..., ES_N
+(ES_1-ES_N) represent ES ion names i.e. Nd+++, Pr+++
+In organic phase : extractant_name, diluant_name, ES_1, ES_2, ..., ES_N
+(ES_1-ES_N) represent ES complex names i.e. Nd(H(A)2)3(org), Pr(H(A)2)3(org)
phase_names --
(list) names of phases in xml file
@@ -272,18 +273,14 @@ i.e. Nd(H(A)2)3(org), Pr(H(A)2)3(org)aq_solvent_name -- (str) name of aqueous solvent in xml file
extractant_name -- (str) name of extractant in xml file
diluant_name -- (str) name of diluant in xml file
-complex_names --
(list) names of complexes in xml file.
-Ensure the ordering is correct
-
-rare_earth_ion_names --
(list) names of rare earth ions in xml file
-Ensure the ordering is correct
-
-re_species_list --
(list) names of rare earth elements.
+complex_names -- (list) names of complexes in xml file.
+extracted_species_ion_names -- (list) names of extracted species ions +in xml file
+extracted_species_list --
(list) names of extracted species elements.
-
-
- If
None, re_species_list will be rare_earth_ion_names without '+' i.e. 'Nd+++'->'Nd'
+- If
None, extracted_species_list will be extracted_species_ion_names without '+' i.e. 'Nd+++'->'Nd'
Ensure the ordering is correct
- If
aq_solvent_rho --
(float) density of solvent (g/L)
If
@@ -296,26 +293,26 @@ i.e. Nd(H(A)2)3(org), Pr(H(A)2)3(org)None, molar volume/molecular weight is used from xmlopt_dict --
(dict) dictionary containing info about which species parameters are updated to fit model to experimental data
-Should have the format as below
-opt_dict = {"species1": - {"parameter1": "guess1", - "parameter2": "guess2", - ... - "parameterN": "guessN"} - "species2": - {"parameter1": "guess1", - "parameter2": "guess2", - ... - "parameterN": "guessN"} - ... - "speciesN": - {"parameter1": "guess1", - "parameter2": "guess2", - ... - "parameterN": "guessN"} - } +
Should have the format as below. Dictionary keys under user defined +parameter name must be named as shown below ('upper_element_name', +'upper_attrib_name', etc.). 'attrib_name's and 'attrib_value's can +be None. {} denotes areas for user to fill in.
++opt_dict = {"{user_defined_name_for_parameter_1}": + {'upper_element_name': {param_upper_element}, + 'upper_attrib_name': {param_upper_attrib_name}, + 'upper_attrib_value': {param_upper_attrib_value}, + 'lower_element_name': {param_lower_element}, + 'lower_attrib_name': {param_lower_attrib_name}, + 'lower_attrib_value': {param_lower_attrib_value}, + 'input_format': {str format to input input_value} + 'input_value': {guess_value}}, + "{user_defined_name_for_parameter_2}": + ... + ... + }
See example files for more examples.
objective_function --
(function or str) function to compute objective
By default, the objective function is log mean squared error @@ -347,18 +344,18 @@ of distribution ratio
extractant conc in org
predicted_dict['z_eq']
RE ion eq conc in aq
predicted_dict['{RE}_aq_eq']
ES ion eq conc in aq
predicted_dict['{ES}_aq_eq']
RE complex eq conc in org
predicted_dict['{RE}_org_eq']
ES complex eq conc in org
predicted_dict['{ES}_org_eq']
RE distribution ratio
predicted_dict['{RE}_d_eq']
ES distribution ratio
predicted_dict['{ES}_d_eq']
Replace "{RE}" with rare earth element i.e. Nd, La, etc.
+Replace "{ES}" with extracted species element i.e. Nd, La, etc.
For measured values, use the same names, but replace
predicted_dictwithmeasured_df-"bounds": [(1e-1, 1e1)]
Though fit() returns a structure identical to opt_dict with correct -scaled values, in case bounds and constraints are used, you must -note the optimized x's are first multiplied by the initial guess -before written to the xml.
By default, the optimizer is scipy's optimize function with
default_kwargs= {"method": 'SLSQP', @@ -394,7 +387,10 @@ before written to the xml.
Function needs to take inputs:
optimizer(objective_function, x_guess, kwargs)
-kwargsis optionalFunction needs to return: (np.ndarray) Optimized parameters
+-
+
- Function needs to return: ((np.ndarray, float)) Optimized parameters,
objective_function value
+
+
temp_xml_file_path --
(str) path to temporary xml file.
This xml file is a duplicate of the phases_xml_file name and is @@ -402,33 +398,47 @@ modified during the optimization process to avoid changing the original xml file
default is local temp folder
dependant_params_dict --
(dict) dictionary containing information +about parameters dependant on opt_dict. Has a similar structure to +opt_dict except instead of input values, it has 3 other fields: +'function', 'kwargs', and 'independent_params.
+'function' is a function of the form +
+function(independent_param__value_list, **kwargs)+'kwargs' are the extra arguments to pass to function +'independent_params' is a list of parameter names in opt_dict that the +dependent_param is a function of.See example code for usage.
+-
-
-
-
fit(objective_function=None, optimizer=None, objective_kwargs=None, optimizer_kwargs=None) → dict[source]¶
+ -
+
fit(objective_function=None, optimizer=None, objective_kwargs=None, optimizer_kwargs=None) → tuple[source]¶ Fits experimental to modeled data by minimizing objective function with optimizer. Returns dictionary with opt_dict structure
- Parameters
-
-
objective_function -- (function) function to compute objective
-optimizer -- (function) function to perform optimization
-optimizer_kwargs -- (dict) arguments for optimizer
-objective_kwargs -- (dict) arguments for objective function
+objective_function -- (function) function to compute objective +If 'None', last set objective or default function is used
+optimizer -- (function) function to perform optimization +If 'None', last set optimizer or default is used
+optimizer_kwargs -- (dict) optional arguments for optimizer
+objective_kwargs -- (dict) optional arguments +for objective function
-- Returns opt_dict -
(dict) optimized opt_dict. Has identical structure -as opt_dict
+- Returns tuple +
(opt_dict (dict), opt_value (float)) +optimized opt_dict: Has identical structure as opt_dict
-
-
-
-
get_aq_solvent_name() → str[source]¶
+ -
+
get_aq_solvent_name() → str[source]¶ Returns aq_solvent_name
- Returns @@ -438,8 +448,8 @@ as opt_dict
-
-
-
-
get_aq_solvent_rho() → str[source]¶
+ -
+
get_aq_solvent_rho() → str[source]¶ Returns aqueous solvent density (g/L)
- Returns @@ -449,8 +459,8 @@ as opt_dict
-
-
-
-
get_complex_names() → list[source]¶
+ -
+
get_complex_names() → list[source]¶ Returns list of complex names
- Returns @@ -460,15 +470,25 @@ as opt_dict
-
-
-
-
get_diluant_name() → str[source]¶
+ -
+
get_dependant_params_dict()[source]¶
+ Returns the dependant_params_dict +:return: dependant_params_dict: (dict) dictionary containing
++
+information about parameters dependant on opt_dict
+
-
+
-
+
get_diluant_name() → str[source]¶ Returns diluant name :return: diluant_name: (str) name of diluant in xml file
-
-
-
-
get_diluant_rho() → str[source]¶
+ -
+
get_diluant_rho() → str[source]¶ Returns diluant density (g/L)
- Returns @@ -478,8 +498,8 @@ as opt_dict
-
-
-
-
get_exp_df() → pandas.core.frame.DataFrame[source]¶
+ -
+
get_exp_df() → pandas.core.frame.DataFrame[source]¶ Returns the experimental DataFrame
- Returns @@ -489,8 +509,8 @@ as opt_dict
-
-
-
-
get_extractant_name() → str[source]¶
+ -
+
get_extractant_name() → str[source]¶ Returns extractant name
- Returns @@ -500,8 +520,8 @@ as opt_dict
-
-
-
-
get_extractant_rho() → str[source]¶
+ -
+
get_extractant_rho() → str[source]¶ Returns extractant density (g/L)
- Returns @@ -511,8 +531,32 @@ as opt_dict
-
-
-
-
get_in_moles() → pandas.core.frame.DataFrame[source]¶
+ -
+
get_extracted_species_ion_names() → list[source]¶
+ Returns list of extracted species ion names
+-
+
- Returns +
extracted_species_ion_names: (list) names of +extracted species ions in xml file
+
+
-
+
-
+
get_extracted_species_list() → list[source]¶
+ Returns list of extracted species names
+-
+
- Returns +
extracted_species_list: (list) names of extracted species in +xml file
+
+
-
+
-
+
get_in_moles() → pandas.core.frame.DataFrame[source]¶ Returns the in_moles DataFrame which contains the initial mole fractions of each species for each experiment
-
@@ -523,8 +567,8 @@ fractions of each species for each experiment
-
-
-
-
get_objective_function()[source]¶
+ -
+
get_objective_function()[source]¶ Returns objective function
- Returns @@ -535,8 +579,8 @@ error between model and experimental data
-
-
-
-
get_opt_dict() → dict[source]¶
+ -
+
get_opt_dict() → dict[source]¶ Returns the dictionary containing optimization information
- Returns @@ -547,8 +591,8 @@ species parameters are updated to fit model to experimental data
-
-
-
-
get_optimizer()[source]¶
+ -
+
get_optimizer()[source]¶ Returns objective function
- Returns @@ -559,8 +603,8 @@ function
-
-
-
-
get_phases() → list[source]¶
+ -
+
get_phases() → list[source]¶ Returns the list of Cantera solutions
- Returns @@ -570,8 +614,8 @@ function
-
-
-
-
get_predicted_dict()[source]¶
+ -
+
get_predicted_dict()[source]¶ Returns predicted dictionary of species concentrations that xml parameters predicts given current in_moles
-
@@ -582,32 +626,8 @@ that xml parameters predicts given current in_moles
-
-
-
-
get_rare_earth_ion_names() → list[source]¶
- Returns list of rare earth ion names
--
-
- Returns -
rare_earth_ion_names: (list) names of rare earth ions in -xml file
-
-
-
-
-
-
get_re_species_list() → list[source]¶
- Returns list of rare earth element names
--
-
- Returns -
re_species_list: (list) names of rare earth elements in -xml file
-
-
-
-
-
-
get_temp_xml_file_path()[source]¶
+ -
+
get_temp_xml_file_path()[source]¶ Returns path to temporary xml file.
This xml file is a duplicate of the phases_xml_file name and is modified during the optimization process to avoid changing the original @@ -620,9 +640,9 @@ xml file.
-
-
-
-
log_mean_squared_error(predicted_dict, meas_df)[source]¶
- Default objective function for REEPS
+-
+
log_mean_squared_error(predicted_dict, meas_df)[source]¶
+ Default objective function for LLEPE
Returns the log mean squared error of predicted distribution ratios (d=n_org/n_aq) to measured d.
@@ -641,41 +661,110 @@ to measured d.
-
-
-
-
parity_plot(compared_value=None, save_path=None, print_r_squared=False)[source]¶
+ -
+
parity_plot(compared_value=None, c_data=None, c_label=None, plot_title=None, save_path=None, print_r_squared=False, data_labels=None, legend=True)[source]¶ Parity plot between measured and predicted compared_value. -Default compared value is {RE_1}_aq_eq
+Default compared value is {ES_1}_aq_eq- Parameters
compared_value -- (str) Quantity to compare predicted and experimental data. Can be any column containing "eq" in exp_df i.e. -h_eq, z_eq, {RE}_d_eq, etc.
+h_eq, z_eq, {ES}_d_eq, etc.
+plot_title --
(str or boolean)
+-
+
- If None (default): Plot title will be generated from compared_value
Recommend to just explore. If h_eq, plot_title is +"H^+ eq conc".
+
+
If str: Plot title will be plot_title string
+If "False": No plot title
+
+c_data -- (list or np.ndarray) data for color axis
+c_label -- (str) label for color axis
save_path -- (str) save path for parity plot
print_r_squared -- (boolean) To plot or not to plot r-squared value. Prints 2 places past decimal
+data_labels -- labels for the data such as paper's name where +experiment is pulled from.
+legend -- whether to display legend for data_labels. Has no +effect if data_labels is None
+
+- Return fig, ax +
returns the figure and axes objects
+
+
-
+
-
+static
plot_3d_data(x_data, y_data, z_data, c_data=None, x_label=None, y_label=None, z_label=None, c_label=None)[source]¶
+ -
+
- Parameters +
-
+
x_data -- (list) list of data for x axis
+y_data -- (list) list of data for y axis
+z_data -- (list) list of data for z axis
+c_data -- (list) list of data for color axis
+x_label -- (str) label for x axis
+y_label -- (str) label for y axis
+z_label -- (str) label for z axis
+c_label -- (str) label for color axis
+- Returns +
- +
-
-
-
-
r_squared(compared_value=None)[source]¶
+ -
+
r_squared(compared_value=None)[source]¶ r-squared value comparing measured and predicted compared value
Closer to 1, the better the model's predictions.
- Parameters
compared_value -- (str) Quantity to compare predicted and experimental data. Can be any column containing "eq" in exp_df i.e. -h_eq, z_eq, {RE}_d_eq, etc.
+h_eq, z_eq, {ES}_d_eq, etc. default is {ES}_aq_eq
-
-
-
-
set_aq_solvent_name(aq_solvent_name)[source]¶
+ -
+static
scipy_minimize(objective, x_guess, optimizer_kwargs=None)[source]¶
+ The default optimizer for LLEPE
+Uses scipy.minimize
+By default, options are
+++default_kwargs= {"method": 'SLSQP', + "bounds": [(1e-1, 1e1)]*len(x_guess), + "constraints": (), + "options": {'disp': True, + 'maxiter': 1000, + 'ftol': 1e-6}} +
-
+
- Parameters +
-
+
objective -- (func) the objective function
+x_guess -- (np.ndarray) the initial guess (always 1)
+optimizer_kwargs -- (dict) dictionary of options for minimize
+
+- Returns +
((np.ndarray, float)) Optimized parameters, +objective_function value
+
+
-
+
-
+
set_aq_solvent_name(aq_solvent_name)[source]¶ Change aq_solvent_name to input aq_solvent_name
- Parameters @@ -685,8 +774,8 @@ h_eq, z_eq, {RE}_d_eq, etc.
-
-
-
-
set_aq_solvent_rho(aq_solvent_rho)[source]¶
+ -
+
set_aq_solvent_rho(aq_solvent_rho)[source]¶ Changes aqueous solvent density (g/L) to input aq_solvent_rho
- Parameters @@ -696,8 +785,8 @@ h_eq, z_eq, {RE}_d_eq, etc.
-
-
-
-
set_complex_names(complex_names)[source]¶
+ -
+
set_complex_names(complex_names)[source]¶ Change complex names list to input complex_names
- Parameters @@ -707,8 +796,16 @@ h_eq, z_eq, {RE}_d_eq, etc.
-
-
-
-
set_diluant_name(diluant_name)[source]¶
+ -
+
set_dependant_params_dict(dependant_params_dict)[source]¶
+ Sets the dependant_params_dict +:param dependant_params_dict: (dict) dictionary containing information +about parameters dependant on opt_dict
+
-
+
-
+
set_diluant_name(diluant_name)[source]¶ Change diluant_name to input diluant_name
- Parameters @@ -718,8 +815,8 @@ h_eq, z_eq, {RE}_d_eq, etc.
-
-
-
-
set_diluant_rho(diluant_rho)[source]¶
+ -
+
set_diluant_rho(diluant_rho)[source]¶ Changes diluant density (g/L) to input diluant_rho
- Parameters @@ -729,29 +826,30 @@ h_eq, z_eq, {RE}_d_eq, etc.
-
-
-
-
set_exp_df(exp_csv_filename)[source]¶
+ -
+
set_exp_df(exp_data)[source]¶ Changes the experimental DataFrame to input exp_csv_filename data -and renames columns to internal REEPS names
-h_i, h_eq, z_i, z_eq, {RE}_aq_i, {RE}_aq_eq, {RE}_d
+and renames columns to internal LLEPE names +h_i, h_eq, z_i, z_eq, {ES}_aq_i, {ES}_aq_eq, {ES}_d
See class docstring on "exp_csv_filename" for further explanations.
- Parameters -
exp_csv_filename -- (str) file name/path for experimental data csv
+exp_data -- (str or pd.DataFrame) +file name/path or DataFrame for experimental data csv
-
-
-
-
set_extractant_name(extractant_name)[source]¶
+ -
+
set_extractant_name(extractant_name)[source]¶ Change extractant_name to input extractant_name :param extractant_name: (str) name of extractant in xml file
-
-
-
-
set_extractant_rho(extractant_rho)[source]¶
+ -
+
set_extractant_rho(extractant_rho)[source]¶ Changes extractant density (g/L) to input extractant_rho
- Parameters @@ -761,8 +859,38 @@ and renames columns to internal REEPS names
-
-
-
-
set_in_moles(feed_vol)[source]¶
+ -
+
set_extracted_species_ion_names(extracted_species_ion_names)[source]¶
+ -
+
- Change list of extracted species ion names to input
extracted_species_ion_names
+
+
-
+
- Parameters +
extracted_species_ion_names -- (list) names of extracted species +ions in xml file
+
+
-
+
-
+
set_extracted_species_list(extracted_species_list)[source]¶
+ -
+
- Change list of extracted species ion names to input
extracted_species_ion_names
+
+
-
+
- Parameters +
extracted_species_list -- (list) names of extracted species in +xml file
+
+
-
+
-
+
set_in_moles(feed_vol)[source]¶ Function that initializes mole fractions to input feed_vol
This function is called at initialization
Sets in_moles to a pd.DataFrame containing initial mole fractions
@@ -770,14 +898,14 @@ and renames columns to internal REEPS namesThis function also calls update_predicted_dict
- Parameters -
feed_vol -- (float) feed volume of mixture (g/L)
+feed_vol -- (float) feed volume of mixture (L)
-
-
-
-
set_objective_function(objective_function)[source]¶
+ -
+
set_objective_function(objective_function)[source]¶ Change objective function to input objective_function.
See class docstring on "objective_function" for instructions
@@ -791,8 +919,8 @@ error between model and experimental data-
-
-
-
set_opt_dict(opt_dict)[source]¶
+ -
+
set_opt_dict(opt_dict)[source]¶ Change the dictionary to input opt_dict.
opt_dict specifies species parameters to be updated to fit model to data
@@ -806,8 +934,8 @@ species parameters are updated to fit model to experimental data
-
-
-
-
set_optimizer(optimizer)[source]¶
+ -
+
set_optimizer(optimizer)[source]¶ Change optimizer function to input optimizer.
See class docstring on "optimizer" for instructions
@@ -821,11 +949,11 @@ function-
-
-
-
set_phases(phases_xml_filename, phase_names)[source]¶
+ -
+
set_phases(phases_xml_filename, phase_names)[source]¶ Change list of Cantera solutions by inputting new xml file name and phase names
-Also runs set_in_moles to set initial molality to 1 g/L
+Also runs set_in_moles to set feed volume to 1 L
- Parameters
-
@@ -838,38 +966,8 @@ for equilibrium calc
-
-
-
-
set_rare_earth_ion_names(rare_earth_ion_names)[source]¶
- -
-
- Change list of rare earth ion names to input
rare_earth_ion_names
-
-
-
-
- Parameters -
rare_earth_ion_names -- (list) names of rare earth ions in -xml file
-
-
-
-
-
-
set_re_species_list(re_species_list)[source]¶
- -
-
- Change list of rare earth ion names to input
rare_earth_ion_names
-
-
-
-
- Parameters -
re_species_list -- (list) names of rare earth elements in -xml file
-
-
-
-
-
-
set_temp_xml_file_path(temp_xml_file_path)[source]¶
+ -
+
set_temp_xml_file_path(temp_xml_file_path)[source]¶ Changes temporary xml file path to input temp_xml_file_path.
This xml file is a duplicate of the phases_xml_file name and is modified during the optimization process to avoid changing the original @@ -882,34 +980,8 @@ xml file.
-
-
-
-static
slsqp_optimizer(objective, x_guess)[source]¶
- The default optimizer for REEPS
-Uses scipy.minimize with options
---default_kwargs= {"method": 'SLSQP', - "bounds": [(1e-1, 1e1)*len(x_guess)], - "constraints": (), - "options": {'disp': True, - 'maxiter': 1000, - 'ftol': 1e-6}} -
-
-
- Parameters -
-
-
objective -- (func) the objective function
-x_guess -- (np.ndarray) the initial guess (always 1)
-
-- Returns -
(np.ndarray) Optimized parameters
-
-
-
-
-
-
update_predicted_dict(phases_xml_filename=None, phase_names=None)[source]¶
+ -
+
update_predicted_dict(phases_xml_filename=None, phase_names=None)[source]¶ Function that computes the predicted equilibrium concentrations the fed phases_xml_filename parameters predicts given the initial mole fractions set by in_moles()
@@ -927,8 +999,8 @@ IfNone
-
-
-
-
update_xml(info_dict, phases_xml_filename=None)[source]¶
+ -
+
update_xml(info_dict, phases_xml_filename=None, dependant_params_dict=None)[source]¶ updates xml file with info_dict
- Parameters @@ -938,6 +1010,8 @@ Requires an identical structure to opt_dict
phases_xml_filename -- (str) xml filename if editing other xml If
None, the current xml will be modified and the internal Cantera phases will be refreshed to the new values.dependant_params_dict -- (dict) dictionary containing information +about parameters dependant on info_dict
Python Module Index — reeps 1.0.0 documentation +Python Module Index — LLEPE 1.0.0 documentation @@ -55,7 +55,7 @@ - reeps + LLEPE @@ -95,7 +95,7 @@Searchers
@@ -110,7 +110,7 @@ @@ -160,17 +160,17 @@Python Module Index
diff --git a/docs/build/html/search.html b/docs/build/html/search.html index ea67610..5235d6c 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -8,7 +8,7 @@ -- r + l - reeps+ llepeSearch — reeps 1.0.0 documentation +Search — LLEPE 1.0.0 documentation @@ -49,7 +49,7 @@ - reeps + LLEPE @@ -89,7 +89,7 @@Searchers
@@ -104,7 +104,7 @@ diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index b4940a8..b0d14de 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ 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Started","Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator","LLEPE"],titleterms:{Using:0,bleed:0,develop:0,doc:2,docker:0,earth:[],edg:0,element:[],estim:2,extract:2,get:1,imag:0,indic:2,instal:0,liquid:2,llepe:[2,3],paramet:[2,3],prerequisit:0,rare:[],reep:[],releas:0,searcher:[],stabl:0,start:1,tabl:2,version:0,welcom:2}}) \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 39b252a..8ad23e1 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,7 +17,7 @@ sys.path.insert(0, os.path.abspath('../../')) # -- Project information ----------------------------------------------------- -project = 'reeps' +project = 'LLEPE' copyright = '2020, Titus Quah, Nwike Iloeje' author = 'Titus Quah, Nwike Iloeje' diff --git a/docs/source/index.rst b/docs/source/index.rst index 0cef716..9a973a9 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,12 +3,11 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Welcome to reeps's docs! - the Rare earth element parameter searcher -==================================================================== -REEPS is a package for thermodynamic parameter estimation specifically -for rare earth element extraction modeling. +Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator +=========================================================================== +LLEPE is a package for thermodynamic parameter estimation for liquid-liquid extraction modeling -REEPS takes experimental data in a csv and system data in a xml. +LLEPE takes experimental data in a csv and system data in a xml. The package then uses Cantera, another python package, to simulate equilibrium. diff --git a/docs/source/modules/reeps.rst b/docs/source/modules/reeps.rst index 9be4450..bba5c13 100644 --- a/docs/source/modules/reeps.rst +++ b/docs/source/modules/reeps.rst @@ -1,8 +1,8 @@ .. _reeps: -.. automodule:: reeps +.. automodule:: llepe -REEPS +LLEPE ===== @@ -10,6 +10,6 @@ REEPS Parameters ---------- -.. autoclass:: REEPS +.. autoclass:: LLEPE :members: :inherited-members: \ No newline at end of file diff --git a/reeps/__init__.py b/reeps/__init__.py deleted file mode 100644 index f970a60..0000000 --- a/reeps/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .reeps import REEPS \ No newline at end of file diff --git a/reeps/reeps.py b/reeps/reeps.py deleted file mode 100644 index 8c43006..0000000 --- a/reeps/reeps.py +++ /dev/null @@ -1,1172 +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 -import re -import pkg_resources - -sns.set() -sns.set(font_scale=1.6) - - -class REEPS: - r""" - Rare earth elements (REE or RE) Takes in experimental data - Returns parameters for GEM - - .. note:: - - The order in which the REEs appear in the csv file must be the same - order as they appear in the xml, complex_names and - rare_earth_ion_names. - - For example, say in exp_csv_filename's csv, RE_1 is Nd RE_2 is Pr, - and - - .. code-block:: python - - aq_solvent_name = 'H2O(L)' - extractant_name = '(HA)2(org)' - diluent_name = 'dodecane' - - Then: - - The csvs column ordering must be: - - [h_i, h_eq, z_i, z_eq, Nd_aq_i, Nd_aq_eq, Nd_d_eq, - Pr_aq_i, Pr_aq_eq, Pr_d_eq] - - The aqueous speciesArray must be - "H2O(L) H+ OH- Cl- Nd+++ Pr+++" - - The organic speciesArray must be - "(HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org)" - - .. code-block:: python - - complex_names = ['Nd(H(A)2)3(org)', 'Pr(H(A)2)3(org)'] - rare_earth_ion_names = ['Nd+++', 'Pr+++'] - - - :param exp_csv_filename: (str) csv file name with experimental data - - In the .csv file, the rows are different experiments and - columns are the measured quantities. - - The ordering of the columns needs to be: - - [h_i, h_eq, z_i, z_eq, - {RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq, - {RE_2}_aq_i, {RE_2}_aq_eq, {RE_2}_d_eq,... - {RE_N}_aq_i, {RE_N}_aq_eq, {RE_N}_d_eq] - - Naming does not matter, just the order. - - Where {RE_1}-{RE_N} are the rare earth element names of interest - i.e. Nd, Pr, La, etc. - - Below is an explanation of the columns. - - +-------+------------+------------------------------------------+ - | Index | Column | Meaning | - +=======+============+==========================================+ - | 0 | h_i | Initial Concentration of | - | | | H+ ions (mol/L) | - +-------+------------+------------------------------------------+ - | 1 | h_eq | Equilibrium concentration of | - | | | H+ ions (mol/L) | - +-------+------------+------------------------------------------+ - | 2 | z_i | Initial concentration of | - | | | extractant (mol/L) | - +-------+------------+------------------------------------------+ - | 3 | z_eq | Equilibrium concentration of | - | | | extractant (mol/L) | - +-------+------------+------------------------------------------+ - | 4 | {RE}_aq_i | Initial concentration of RE ions (mol/L) | - +-------+------------+------------------------------------------+ - | 5 | {RE}_aq_eq | Equilibrium concentration of RE ions | - | | | in aqueous phase (mol/L) | - +-------+------------+------------------------------------------+ - | 6 | {RE}_d_eq | Equilibrium Ratio between amount of | - | | | RE atoms in organic to aqueous | - +-------+------------+------------------------------------------+ - :param phases_xml_filename: (str) xml file with parameters - for equilibrium calc - - Would recommend copying and modifying xmls located in data/xmls - or in Cantera's "data" folder - - speciesArray fields need specific ordering. - - In aqueous phase: aq_solvent_name, H+, OH-, Cl-, RE_1, RE_2, ..., RE_N - - For aqueous phase, RE_1-RE_N represent RE ion names i.e. Nd+++, Pr+++ - - In organic phase : extractant_name, diluant_name, RE_1, RE_2, ..., RE_N - - For organic phase, RE_1-RE_N represent RE complex names - i.e. Nd(H(A)2)3(org), Pr(H(A)2)3(org) - - :param phase_names: (list) names of phases in xml file - - Found in the xml file under- - :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_names: (list) names of complexes in xml file. - - Ensure the ordering is correct - :param rare_earth_ion_names: (list) names of rare earth ions in xml file - - Ensure the ordering is correct - :param re_species_list: (list) names of rare earth elements. - - If ``None``, re_species_list will be rare_earth_ion_names without '+' - i.e. 'Nd+++'->'Nd' - - Ensure the ordering is correct - :param aq_solvent_rho: (float) density of solvent (g/L) - - If ``None``, molar volume/molecular weight is used from xml - :param extractant_rho: (float) density of extractant (g/L) - - If ``None``, molar volume/molecular weight is used from xml - :param diluant_rho: (float) density of diluant (g/L) - - If ``None``, molar volume/molecular weight is used from xml - :param opt_dict: (dict) dictionary containing info about which - species parameters are updated to fit model to experimental data - - Should have the format as below - - .. code-block:: python - - opt_dict = {"species1": - {"parameter1": "guess1", - "parameter2": "guess2", - ... - "parameterN": "guessN"} - "species2": - {"parameter1": "guess1", - "parameter2": "guess2", - ... - "parameterN": "guessN"} - ... - "speciesN": - {"parameter1": "guess1", - "parameter2": "guess2", - ... - "parameterN": "guessN"} - } - :param objective_function: (function or str) function to compute objective - - By default, the objective function is log mean squared error - of distribution ratio - - .. code-block:: python - - np.sum((np.log10(d_pred)-np.log10(d_meas))^2) - - Function needs to take inputs: - - .. code-block:: python - - objective_function(predicted_dict, measured_df, kwargs) - - ``kwargs`` is optional - - Function needs to return: (float) value computed by objective function - - Below is the guide for referencing predicted values - - +---------------------------+--------------------------------+ - | To access | Use | - +===========================+================================+ - | hydrogen ion conc in aq | predicted_dict['h_eq'] | - +---------------------------+--------------------------------+ - | extractant conc in org | predicted_dict['z_eq'] | - +---------------------------+--------------------------------+ - | RE ion eq conc in aq | predicted_dict['{RE}_aq_eq'] | - +---------------------------+--------------------------------+ - | RE complex eq conc in org | predicted_dict['{RE}_org_eq'] | - +---------------------------+--------------------------------+ - | RE distribution ratio | predicted_dict['{RE}_d_eq'] | - +---------------------------+--------------------------------+ - - Replace "{RE}" with rare earth element i.e. Nd, La, etc. - - For measured values, use the same names, but - replace ``predicted_dict`` with ``measured_df`` - :param optimizer: (function or str) function to perform optimization - - .. note:: - - The optimized variables are not directly the species parameters, - but instead are first multiplied by the initial guess before - sending becoming the species parameters. - - For example, say - - .. code-block:: python - - opt_dict = {'Nd(H(A)2)3(org):'h0':-4.7e6} - - If the bounds on h0 need to be [-4.7e7,-4.7e5], then - divide the bounds by the guess and get - - .. code-block:: python - - "bounds": [(1e-1, 1e1)] - - Though fit() returns a structure identical to opt_dict with correct - scaled values, in case bounds and constraints are used, you must - note the optimized x's are first multiplied by the initial guess - before written to the xml. - - - By default, the optimizer is scipy's optimize function with - - .. code-block:: python - - 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 - - Function needs to return: (np.ndarray) Optimized parameters - - :param temp_xml_file_path: (str) path to temporary xml file. - - This xml file is a duplicate of the phases_xml_file name and is - modified during the optimization process to avoid changing the original - xml file - - default is local temp folder - """ - - def __init__(self, - exp_csv_filename, - phases_xml_filename, - phase_names, - aq_solvent_name, - extractant_name, - diluant_name, - complex_names, - rare_earth_ion_names, - re_species_list=None, - aq_solvent_rho=None, - extractant_rho=None, - diluant_rho=None, - opt_dict=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_names = complex_names - self._rare_earth_ion_names = rare_earth_ion_names - 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._re_species_list = re_species_list - self.set_optimizer(optimizer) - if temp_xml_file_path is None: - temp_xml_file_path = r'{0}/temp.xml'.format(os.getenv('TEMP')) - self._temp_xml_file_path = temp_xml_file_path - # Try and except for adding package data to path. - # This only works for sdist, not bdist - # If bdist is needed, research "manifest.in" python setup files - try: - shutil.copyfile(self._phases_xml_filename, - self._temp_xml_file_path) - self._phases = ct.import_phases(self._phases_xml_filename, - phase_names) - except FileNotFoundError: - self._phases_xml_filename = \ - pkg_resources.resource_filename('reeps', - r'..\data\xmls\{0}'.format( - phases_xml_filename)) - shutil.copyfile(self._phases_xml_filename, - self._temp_xml_file_path) - self._phases = ct.import_phases(self._phases_xml_filename, - phase_names) - try: - self._exp_df = pd.read_csv(self._exp_csv_filename) - except FileNotFoundError: - self._exp_csv_filename = pkg_resources.resource_filename( - 'reeps', r'..\data\csvs\{0}'.format(self._exp_csv_filename)) - self._exp_df = pd.read_csv(self._exp_csv_filename) - - self._exp_df_columns = ['h_i', 'h_eq', 'z_i', 'z_eq'] - if self._re_species_list is None: - self._re_species_list = [] - for name in self._rare_earth_ion_names: - species = name.replace('+', '') - self._re_species_list.append(species) - for species in self._re_species_list: - self._exp_df_columns.append('{0}_aq_i'.format(species)) - self._exp_df_columns.append('{0}_aq_eq'.format(species)) - self._exp_df_columns.append('{0}_d_eq'.format(species)) - self._exp_df.columns = self._exp_df_columns - - self._in_moles = None - - self._aq_ind = None - self._org_ind = None - self._re_charges = None - - self.set_in_moles(feed_vol=1) - self._predicted_dict = None - self.update_predicted_dict() - - @staticmethod - def scipy_minimize(objective, x_guess, optimizer_kwargs=None): - """ The default optimizer for REEPS - - Uses scipy.minimize - - By default, options are - - .. code-block:: python - - default_kwargs= {"method": 'SLSQP', - "bounds": [(1e-1, 1e1)]*len(x_guess), - "constraints": (), - "options": {'disp': True, - 'maxiter': 1000, - 'ftol': 1e-6}} - - :param objective: (func) the objective function - :param x_guess: (np.ndarray) the initial guess (always 1) - :param optimizer_kwargs: (dict) dictionary of options for minimize - :returns: (np.ndarray) Optimized parameters - """ - if optimizer_kwargs is None: - 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 log_mean_squared_error(self, predicted_dict, meas_df): - """Default objective function for REEPS - - Returns the log mean squared error of - predicted distribution ratios (d=n_org/n_aq) - to measured d. - - np.sum((np.log10(d_pred)-np.log10(d_meas))\**2) - - :param predicted_dict: (dict) contains predicted data - :param meas_df: (pd.DataFrame) contains experimental data - :return: (float) log mean squared error between predicted and measured - """ - meas = np.concatenate([meas_df['{0}_d_eq'.format(species)].values - for species in self._re_species_list]) - pred = np.concatenate([ - predicted_dict['{0}_d_eq'.format(species)] - for species in self._re_species_list]) - log_pred = np.log10(pred) - log_meas = np.log10(meas) - obj = np.sum((log_pred - log_meas) ** 2) - return obj - - def get_exp_df(self) -> pd.DataFrame: - """Returns the experimental DataFrame - - :return: (pd.DataFrame) Experimental data - """ - return self._exp_df - - def set_exp_df(self, exp_csv_filename): - """Changes the experimental DataFrame to input exp_csv_filename data - and renames columns to internal REEPS names - - - h_i, h_eq, z_i, z_eq, {RE}_aq_i, {RE}_aq_eq, {RE}_d - - See class docstring on "exp_csv_filename" for further explanations. - - :param exp_csv_filename: (str) file name/path for experimental data csv - """ - self._exp_csv_filename = exp_csv_filename - try: - self._exp_df = pd.read_csv(self._exp_csv_filename) - except FileNotFoundError: - self._exp_csv_filename = pkg_resources.resource_filename( - 'reeps', r'..\data\csvs\{0}'.format(self._exp_csv_filename)) - self._exp_df = pd.read_csv(self._exp_csv_filename) - self._exp_df_columns = ['h_i', 'h_eq', 'z_i', 'z_eq'] - if self._re_species_list is None: - self._re_species_list = [] - for name in self._rare_earth_ion_names: - species = name.replace('+', '') - self._re_species_list.append(species) - for species in self._re_species_list: - self._exp_df_columns.append('{0}_aq_i'.format(species)) - self._exp_df_columns.append('{0}_aq_eq'.format(species)) - self._exp_df_columns.append('{0}_d_eq'.format(species)) - self._exp_df.columns = self._exp_df_columns - self.set_in_moles(feed_vol=1) - self.update_predicted_dict() - return None - - def get_phases(self) -> list: - """ - Returns the list of Cantera solutions - - :return: (list) list of Cantera solutions/phases - """ - return self._phases - - def set_phases(self, phases_xml_filename, phase_names): - """Change list of Cantera solutions by inputting - new xml file name and phase names - - Also runs set_in_moles to set feed volume to 1 L - - :param phases_xml_filename: (str) xml file with parameters - for equilibrium calc - :param phase_names: (list) names of phases in xml file - """ - self._phases_xml_filename = phases_xml_filename - self._phase_names = phase_names - # Try and except for adding package data to path. - # This only works for sdist, not bdist - # If bdist is needed, research "manifest.in" python setup files - try: - shutil.copyfile(self._phases_xml_filename, - self._temp_xml_file_path) - self._phases = ct.import_phases(self._phases_xml_filename, - phase_names) - except FileNotFoundError: - self._phases_xml_filename = \ - pkg_resources.resource_filename('reeps', - r'..\data\xmls\{0}'.format( - phases_xml_filename)) - shutil.copyfile(self._phases_xml_filename, - self._temp_xml_file_path) - self._phases = ct.import_phases(self._phases_xml_filename, - phase_names) - self.set_in_moles(feed_vol=1) - self.update_predicted_dict() - return None - - def get_opt_dict(self) -> dict: - """ - Returns the dictionary containing optimization information - - :return: (dict) dictionary containing info about which - species parameters are updated to fit model to experimental data - """ - return self._opt_dict - - def set_opt_dict(self, opt_dict): - """ - Change the dictionary to input opt_dict. - - opt_dict specifies species parameters to be updated to - fit model to data - - See class docstring on "opt_dict" for more information. - - :param opt_dict: (dict) dictionary containing info about which - species parameters are updated to fit model to experimental data - """ - - self._opt_dict = opt_dict - return None - - def get_aq_solvent_name(self) -> str: - """Returns aq_solvent_name - - :return: aq_solvent_name: (str) name of aqueous solvent in xml file - """ - return self._aq_solvent_name - - def set_aq_solvent_name(self, aq_solvent_name): - """ Change aq_solvent_name to input aq_solvent_name - - :param aq_solvent_name: (str) name of aqueous solvent in xml file - """ - self._aq_solvent_name = aq_solvent_name - return None - - def get_extractant_name(self) -> str: - """Returns extractant name - - :return: extractant_name: (str) name of extractant in xml file - """ - return self._extractant_name - - def set_extractant_name(self, extractant_name): - """ - Change extractant_name to input extractant_name - :param extractant_name: (str) name of extractant in xml file - """ - self._extractant_name = extractant_name - return None - - def get_diluant_name(self) -> str: - """ Returns diluant name - :return: diluant_name: (str) name of diluant in xml file - """ - return self._diluant_name - - def set_diluant_name(self, diluant_name): - """ - Change diluant_name to input diluant_name - - :param diluant_name: (str) name of diluant in xml file - """ - self._diluant_name = diluant_name - return None - - def get_complex_names(self) -> list: - """Returns list of complex names - - :return: complex_names: (list) names of complexes in xml file. - """ - return self._complex_names - - def set_complex_names(self, complex_names): - """Change complex names list to input complex_names - - :param complex_names: (list) names of complexes in xml file. - """ - self._complex_names = complex_names - return None - - def get_rare_earth_ion_names(self) -> list: - """Returns list of rare earth ion names - - :return: rare_earth_ion_names: (list) names of rare earth ions in - xml file - """ - return self._rare_earth_ion_names - - def set_rare_earth_ion_names(self, rare_earth_ion_names): - """Change list of rare earth ion names to input - rare_earth_ion_names - - :param rare_earth_ion_names: (list) names of rare earth ions in - xml file - """ - self._rare_earth_ion_names = rare_earth_ion_names - return None - - def get_re_species_list(self) -> list: - """Returns list of rare earth element names - - :return: re_species_list: (list) names of rare earth elements in - xml file - """ - return self._re_species_list - - def set_re_species_list(self, re_species_list): - """Change list of rare earth ion names to input - rare_earth_ion_names - - :param re_species_list: (list) names of rare earth elements in - xml file - """ - self._re_species_list = re_species_list - return None - - def get_aq_solvent_rho(self) -> str: - """Returns aqueous solvent density (g/L) - - :return: aq_solvent_rho: (float) density of aqueous solvent - """ - return self._aq_solvent_rho - - def set_aq_solvent_rho(self, aq_solvent_rho): - """Changes aqueous solvent density (g/L) to input aq_solvent_rho - - :param aq_solvent_rho: (float) density of aqueous solvent - """ - self._aq_solvent_rho = aq_solvent_rho - return None - - def get_extractant_rho(self) -> str: - """Returns extractant density (g/L) - - :return: extractant_rho: (float) density of extractant - """ - return self._extractant_rho - - def set_extractant_rho(self, extractant_rho): - """Changes extractant density (g/L) to input extractant_rho - - :param extractant_rho: (float) density of extractant - """ - self._extractant_rho = extractant_rho - return None - - def get_diluant_rho(self) -> str: - """Returns diluant density (g/L) - - :return: diluant_rho: (float) density of diluant - """ - return self._diluant_rho - - def set_diluant_rho(self, diluant_rho): - """Changes diluant density (g/L) to input diluant_rho - - :param diluant_rho: (float) density of diluant - """ - self._diluant_rho = diluant_rho - return None - - def set_in_moles(self, feed_vol): - """Function that initializes mole fractions to input feed_vol - - This function is called at initialization - - Sets in_moles to a pd.DataFrame containing initial mole fractions - - Columns for species and rows for different experiments - - This function also calls update_predicted_dict - - :param feed_vol: (float) feed volume of mixture (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_names = self._rare_earth_ion_names - re_species_list = self._re_species_list - - 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_list = [phases_copy[aq_ind].species_names.index(re_name) - for re_name in re_names] - re_charges = np.array([phases_copy[aq_ind].species(re_ind).charge - for re_ind in re_ind_list]) - self._re_charges = re_charges - - 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 index, row in exp_df.iterrows(): - h_plus_moles = feed_vol * row['h_i'] - hydroxide_ions = 0 - rare_earth_moles = np.array([feed_vol * row[ - '{0}_aq_i'.format(re_species)] - for re_species in re_species_list]) - re_charge_sum = np.sum(re_charges * rare_earth_moles) - chlorine_moles = re_charge_sum + h_plus_moles - extractant_moles = feed_vol * row['z_i'] - extractant_vol = extractant_moles * extractant_mw / extractant_rho - diluant_vol = feed_vol - extractant_vol - diluant_moles = diluant_vol * diluant_rho / diluant_mw - complex_moles = np.zeros(len(re_species_list)) - - species_moles_aq = [aq_phase_solvent_moles, - h_plus_moles, - hydroxide_ions, - chlorine_moles] - species_moles_aq.extend(list(rare_earth_moles)) - species_moles_org = [extractant_moles, diluant_moles] - species_moles_org.extend(list(complex_moles)) - if aq_ind == 0: - species_moles = species_moles_aq + species_moles_org - else: - species_moles = species_moles_org + species_moles_aq - 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: - """Returns the in_moles DataFrame which contains the initial mole - fractions of each species for each experiment - - :return: in_moles: (pd.DataFrame) DataFrame with initial mole fractions - """ - return self._in_moles - - def set_objective_function(self, objective_function): - """Change objective function to input objective_function. - - See class docstring on "objective_function" for instructions - - :param objective_function: (func) Objective function to quantify - error between model and experimental data - """ - 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): - """Returns objective function - - :return: objective_function: (func) Objective function to quantify - error between model and experimental data - """ - return self._objective_function - - def set_optimizer(self, optimizer): - """Change optimizer function to input optimizer. - - See class docstring on "optimizer" for instructions - - :param optimizer: (func) Optimizer function to minimize objective - function - """ - 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.scipy_minimize - self._optimizer = optimizer - return None - - def get_optimizer(self): - """Returns objective function - - :return: optimizer: (func) Optimizer function to minimize objective - function - """ - return self._optimizer - - def get_temp_xml_file_path(self): - """Returns path to temporary xml file. - - This xml file is a duplicate of the phases_xml_file name and is - modified during the optimization process to avoid changing the original - xml file. - - :return: temp_xml_file_path: (str) path to temporary xml file. - """ - return self._temp_xml_file_path - - def set_temp_xml_file_path(self, temp_xml_file_path): - """Changes temporary xml file path to input temp_xml_file_path. - - This xml file is a duplicate of the phases_xml_file name and is - modified during the optimization process to avoid changing the original - xml file. - - :param temp_xml_file_path: (str) path to temporary xml file. - """ - self._temp_xml_file_path = temp_xml_file_path - return None - - def update_predicted_dict(self, - phases_xml_filename=None, - phase_names=None): - """Function that computes the predicted equilibrium concentrations - the fed phases_xml_filename parameters predicts given the initial - mole fractions set by in_moles() - - :param phases_xml_filename: (str)xml file with parameters - for equilibrium calc. If ``None``, the - current phases_xml_filename is used. - :param phase_names: (list) names of phases in xml file. - If ``None``, the current phases_names is used. - """ - if phases_xml_filename is None: - phases_xml_filename = self._phases_xml_filename - if phase_names is None: - phase_names = self._phase_names - aq_ind = self._aq_ind - org_ind = self._org_ind - complex_names = self._complex_names - extractant_name = self._extractant_name - rare_earth_ion_names = self._rare_earth_ion_names - in_moles = self._in_moles - re_species_list = self._re_species_list - - phases_copy = ct.import_phases(phases_xml_filename, phase_names) - mix = ct.Mixture(phases_copy) - key_names = ['h_eq', 'z_eq'] - for re_species in re_species_list: - key_names.append('{0}_aq_eq'.format(re_species)) - key_names.append('{0}_org_eq'.format(re_species)) - key_names.append('{0}_d_eq'.format(re_species)) - - predicted_dict = {'{0}'.format(key_name): [] - for key_name in key_names} - - for row in in_moles.values: - mix.species_moles = row - mix.equilibrate('TP', log_level=0) - re_org_array = np.array([mix.species_moles[mix.species_index( - org_ind, complex_name)] for complex_name in complex_names]) - re_aq_array = np.array([mix.species_moles[mix.species_index( - aq_ind, re_ion_name)] for re_ion_name in rare_earth_ion_names]) - d_array = re_org_array / re_aq_array - hydrogen_ions = mix.species_moles[mix.species_index(aq_ind, 'H+')] - extractant = mix.species_moles[mix.species_index( - org_ind, extractant_name)] - for index, re_species in enumerate(re_species_list): - predicted_dict['{0}_aq_eq'.format( - re_species)].append(re_aq_array[index]) - predicted_dict['{0}_org_eq'.format( - re_species)].append(re_org_array[index]) - predicted_dict['{0}_d_eq'.format( - re_species)].append(d_array[index]) - predicted_dict['h_eq'].append(hydrogen_ions) - predicted_dict['z_eq'].append(extractant) - for key, value in predicted_dict.items(): - predicted_dict[key] = np.array(value) - self._predicted_dict = predicted_dict - return None - - def get_predicted_dict(self): - """Returns predicted dictionary of species concentrations - that xml parameters predicts given current in_moles - - :return: predicted_dict: (dict) dictionary of species concentrations - """ - return self._predicted_dict - - def _internal_objective(self, x, kwargs=None): - """ - Internal objective function. Uses objective function to compute value - If the optimizer requires vectorized variables ie pso, this function - takes care of it - - :param x: (list) thermo properties varied to minimize objective func - :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) - 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(): - if not np.isnan( - x[i]): # if nan, do not update xml with nan - 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() - self.update_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 - with optimizer. Returns dictionary with opt_dict structure - - :param objective_function: (function) function to compute objective - If 'None', last set objective or default function is used - :param optimizer: (function) function to perform optimization - If 'None', last set optimizer or default is used - :param optimizer_kwargs: (dict) optional arguments for optimizer - :param objective_kwargs: (dict) optional arguments - for objective function - :returns opt_dict: (dict) optimized opt_dict. Has identical structure - as opt_dict - """ - 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 - 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}} - Requires an identical structure to opt_dict - :param phases_xml_filename: (str) xml filename if editing other xml - If ``None``, the current xml will be modified and the internal - Cantera phases will be refreshed to the new values. - """ - 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 - - def parity_plot(self, - compared_value=None, - color_axis=None, - plot_title=None, - save_path=None, - print_r_squared=False): - """ - Parity plot between measured and predicted compared_value. - Default compared value is {RE_1}_aq_eq - - :param compared_value: (str) Quantity to compare predicted and - experimental data. Can be any column containing "eq" in exp_df i.e. - h_eq, z_eq, {RE}_d_eq, etc. - :param plot_title: (str or boolean) - - If None (default): Plot title will be generated from compared_value - Recommend to just explore. If h_eq, plot_title is - "H^+ eq conc". - - If str: Plot title will be plot_title string - - If "False": No plot title - :param color_axis: (dict) - :param save_path: (str) save path for parity plot - :param print_r_squared: (boolean) To plot or not to plot r-squared - value. Prints 2 places past decimal - """ - exp_df = self.get_exp_df() - predicted_dict = self.get_predicted_dict() - re_species_list = self._re_species_list - extractant_name = self.get_extractant_name() - re_charges = self._re_charges - if compared_value is None: - compared_value = '{0}_aq_eq'.format(re_species_list[0]) - pred = predicted_dict[compared_value] - meas = exp_df[compared_value].values - min_data = np.min([pred, meas]) - max_data = np.max([pred, meas]) - min_max_data = np.array([min_data, max_data]) - name_breakdown = re.findall('[^_\W]+', compared_value) - compared_species = name_breakdown[0] - if compared_species == 'h': - default_title = '$H^+$ eq. conc. (mol/L)' - elif compared_species == 'z': - default_title = '{0} eq. conc. (mol/L)'.format(extractant_name) - else: - phase = name_breakdown[1] - if phase == 'aq': - re_charge = re_charges[re_species_list.index(compared_species)] - default_title = '$%s^{%d+}$ eq. conc. (mol/L)' \ - % (compared_species, re_charge) - elif phase == 'd': - default_title = '{0} distribution ratio'.format( - compared_species) - else: - default_title = '{0} complex eq. conc. (mol/L)'.format( - compared_species) - fig, ax = plt.subplots() - if color_axis is None: - sns.scatterplot(meas, pred, color="r", - legend=False) - else: - key = list(color_axis.keys())[0] - value = list(color_axis.values())[0] - if key == 'predicted': - y = self.get_predicted_dict()[value] - elif key == 'measured': - y = self.get_exp_df()[value].values - else: - raise Exception('color_axis must be a dictionary with key' - '"predicted" or "measured"') - y = np.array(y) - meas = np.array(meas) - pred = np.array(pred) - - p1 = ax.scatter(meas, pred, c=y, alpha=1, cmap='viridis') - c_bar = fig.colorbar(p1, format='%.2f') - # Fix next line. value is just the dictionary value. - c_bar.set_label(value, rotation=270, labelpad=20) - sns.lineplot(min_max_data, min_max_data, color="b", label="") - - if print_r_squared: - ax.text(min_max_data[0], - min_max_data[1] * 0.9, - '$R^2$={0:.2f}'.format(self.r_squared(compared_value))) - # plt.legend(loc='lower right') - # else: - # plt.legend() - ax.set(xlabel='Measured', ylabel='Predicted') - if plot_title is None: - ax.set_title(default_title) - elif isinstance(plot_title, str): - ax.set_title(plot_title) - plt.show() - if save_path is not None: - plt.savefig(save_path, bbox_inches='tight') - return None - - def r_squared(self, compared_value=None): - """r-squared value comparing measured and predicted compared value - - Closer to 1, the better the model's predictions. - - :param compared_value: (str) Quantity to compare predicted and - experimental data. Can be any column containing "eq" in exp_df i.e. - h_eq, z_eq, {RE}_d_eq, etc. default is {RE}_aq_eq - """ - exp_df = self.get_exp_df() - predicted_dict = self.get_predicted_dict() - re_species_list = self._re_species_list - if compared_value is None: - compared_value = '{0}_aq_eq'.format(re_species_list[0]) - pred = predicted_dict[compared_value] - predicted_y = np.array(pred) - actual_y = exp_df[compared_value].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 diff --git a/setup.py b/setup.py index 4de9d9d..e7bd2fa 100644 --- a/setup.py +++ b/setup.py @@ -1,12 +1,13 @@ from setuptools import setup setup( - name='reeps', + name='llepe', version='0.0.0', - packages=['reeps'], + packages=['LLEPE'], package_data={ 'csvs': ['data/csvs/*.csv'], - 'xmls': ['data/xmls/*.xml'] + 'xmls': ['data/xmls/*.xml'], + 'tests': ['tests/*.txt'], }, include_package_data=True, zip_safe=False, @@ -14,11 +15,10 @@ setup( license='', author='Titus Quah', author_email='', - description='Rare earth element parameter searcher', + description='Liquid-liquid extraction 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'] ) diff --git a/tests/multi_ree_settings.txt b/tests/multi_ree_settings.txt deleted file mode 100644 index 0a61bb9..0000000 --- a/tests/multi_ree_settings.txt +++ /dev/null @@ -1 +0,0 @@ -{"exp_csv_filename": "../data/csvs/Nd_exp_data.csv", "phases_xml_filename": "../data/xmls/twophase.xml", "opt_dict": {"Nd(H(A)2)3(org)": {"h0": -4662344.6400}}, "phase_names": ["HCl_electrolyte", "PC88A_liquid"], "aq_solvent_name": "H2O(L)", "extractant_name": "(HA)2(org)", "diluant_name": "dodecane", "complex_names": ["Nd(H(A)2)3(org)"], "rare_earth_ion_names": ["Nd+++"], "aq_solvent_rho": 1000.0, "extractant_rho": 960.0, "diluant_rho": 750.0} \ No newline at end of file diff --git a/tests/one_ree_settings.txt b/tests/one_ree_settings.txt deleted file mode 100644 index f452627..0000000 --- a/tests/one_ree_settings.txt +++ /dev/null @@ -1 +0,0 @@ -{"exp_csv_filename": "../data/csvs/exp_data.csv", "phases_xml_filename": "../data/xmls/twophase.xml", "opt_dict": {"Nd(H(A)2)3(org)": {"h0": -4662344.6400}}, "phase_names": ["HCl_electrolyte", "PC88A_liquid"], "aq_solvent_name": "H2O(L)", "extractant_name": "(HA)2(org)", "diluant_name": "dodecane", "complex_name": "Nd(H(A)2)3(org)", "rare_earth_ion_name": "Nd+++", "aq_solvent_rho": 1000.0, "extractant_rho": 960.0, "diluant_rho": 750.0} \ No newline at end of file diff --git a/tests/test_multi_reeps.py b/tests/test_multi_reeps.py deleted file mode 100644 index e2017bc..0000000 --- a/tests/test_multi_reeps.py +++ /dev/null @@ -1,37 +0,0 @@ -import json -import numpy as np -import sys -sys.path.append('../') -from reeps import REEPS - -with open('multi_ree_settings.txt') as file: - testing_params = json.load(file) - -beaker = REEPS(**testing_params) - - -def optimizer(func, x_guess): - lb = np.array([1e-1]) - ub = np.array([1e1]) - bounds = (lb, ub) - options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9} - mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, - dimensions=1, - options=options, - bounds=bounds) - f_opt, x_opt = mini_optimizer.optimize(func, iters=100) - - return x_opt - - -minimizer_kwargs = {"method": 'SLSQP', - "bounds": [(1e-1, 1e1)], - "constraints": (), - "options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}} -# est_enthalpy = beaker.fit(optimizer=optimizer) -est_enthalpy = beaker.fit() -print(est_enthalpy) - -beaker.update_xml(est_enthalpy) -# beaker.parity_plot('Nd_d_eq', print_r_squared=True) -print(beaker.r_squared()) diff --git a/tests/test_reeps.py b/tests/test_reeps.py index 93ae2c0..10e5ece 100644 --- a/tests/test_reeps.py +++ b/tests/test_reeps.py @@ -1,165 +1,51 @@ import json -from unittest import TestCase - -import numpy as np -import pyswarms as ps -from reeps import REEPS1 - -with open('one_ree_settings.txt') as file: - testing_params = json.load(file) - -beaker = REEPS1(**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]) - bounds = (lb, ub) - options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9} - mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, dimensions=1, - options=options, bounds=bounds) - f_opt, x_opt = mini_optimizer.optimize(func, iters=100) - - return x_opt - - -minimizer_kwargs = {"method": 'SLSQP', - "bounds": [(1e-1, 1e1)], - "constraints": (), - "options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}} -# est_enthalpy = beaker.fit(optimizer=optimizer) -est_enthalpy = beaker.fit() -print(est_enthalpy) - - -beaker.update_xml(est_enthalpy) -# beaker.parity_plot() -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() +from llepe import LLEPE +import pkg_resources + +validation_parameters_filename = pkg_resources.resource_filename( + 'llepe', + r'..\tests\validation_parameters.txt') +validation_values_filename = pkg_resources.resource_filename( + 'llepe', + r'..\tests\validation_values.txt') + +with open(validation_parameters_filename) as file: + validation_params = json.load(file) +with open(validation_values_filename) as file: + validation_values = json.load(file) +searcher = LLEPE(**validation_params) + + +def array_to_list_in_dict(dictionary): + new_dictionary = {} + for key, value in dictionary.items(): + new_dictionary[key] = list(value) + return new_dictionary + + +def test_init(): + searcher.update_xml(validation_params['opt_dict']) + predicted_dict1 = searcher.get_predicted_dict() + predicted_dict1 = array_to_list_in_dict(predicted_dict1) + in_moles = searcher.get_in_moles().to_dict('list') + assert predicted_dict1 == validation_values['predicted_dict1'], \ + "Prediction dicts are not equal. Error with set_in_moles, " + "update_predicted_dict, or changed xmls, or changed data" + assert in_moles == validation_values['in_moles'], \ + "In_moles are different. Error with set_in_moles or data" + return None + + +def test_fit(): + est_enthalpy, obj_value = searcher.fit() + searcher.update_xml(est_enthalpy) + predicted_dict2 = searcher.get_predicted_dict() + predicted_dict2 = array_to_list_in_dict(predicted_dict2) + r2 = searcher.r_squared() + assert est_enthalpy == validation_values['est_enthalpy'], \ + "estimated enthalpy is not equal. Check fit method" + assert predicted_dict2 == validation_values['predicted_dict2'], \ + "Prediction dicts are not equal. Error with set_in_moles, " + "update_predicted_dict, or changed xmls, or changed data" + assert r2 == validation_values['r2'], "r-squared value is off, check " \ + "r_squared method"
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