rename for rtd

docs/_Examples/.ipynb_checkpoints/1_getting_started-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/_Examples/iterative_fitter.py

@@ -0,0 +1,243 @@
+import llepe
+import pandas as pd
+import numpy as np
+import json
+
+
+def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
+    new_lp_df = lp_df.copy()
+    index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
+    new_lp_df.at[index, mini_lin_param] = input_val
+    return new_lp_df
+
+
+species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
+pitzer_param_list = ['beta0', 'beta1']
+lin_param_list = ['intercept']
+meas_pitzer_param_df = pd.read_csv("../../data/csvs/may_pitzer_params.csv")
+pitzer_params_filename = "../../data/jsons/min_h0_pitzer_params.txt"
+with open(pitzer_params_filename) as file:
+    pitzer_params_dict = json.load(file)
+ext_h0_filename = "../../data/jsons/min_h0_guess_ext_h0.txt"
+with open(ext_h0_filename) as file:
+    ext_h0_dict = json.load(file)
+labeled_data = pd.read_csv("../../data/csvs/"
+                           "multicomponent_PC88A_HCL_NdPrCeLaDySmY.csv")
+exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
+xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
+lin_param_df = pd.read_csv("../../data/csvs"
+                           "/zeroes_removed_min_h0_pitzer_lin_params.csv")
+new_lin_param_df = lin_param_df.copy()
+for ind, row in lin_param_df.iterrows():
+    new_lin_param_df.at[ind, 'slope'] = 3
+estimator_params = {'exp_data': exp_data,
+                    'phases_xml_filename': xml_file,
+                    'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
+                    'aq_solvent_name': 'H2O(L)',
+                    'extractant_name': '(HA)2(org)',
+                    'diluant_name': 'dodecane',
+                    'complex_names': ['{0}(H(A)2)3(org)'.format(species)
+                                      for species in species_list],
+                    'extracted_species_ion_names': ['{0}+++'.format(species)
+                                                    for species in
+                                                    species_list],
+                    'aq_solvent_rho': 1000.0,
+                    'extractant_rho': 960.0,
+                    'diluant_rho': 750.0,
+                    'temp_xml_file_path': 'outputs/temp.xml',
+                    'objective_function': llepe.lmse_perturbed_obj
+                    }
+estimator = llepe.LLEPE(**estimator_params)
+
+
+def ext_to_complex(h0, custom_obj_dict, mini_species):
+    linear_params = custom_obj_dict['lin_param_df']
+    row = linear_params[linear_params['species'] == mini_species]
+    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]
+
+
+dependant_params_dict = {}
+for species, complex_name in zip(species_list,
+                                 estimator_params['complex_names']):
+    inner_dict = {'upper_element_name': 'species',
+                  'upper_attrib_name': 'name',
+                  'upper_attrib_value': complex_name,
+                  'lower_element_name': 'h0',
+                  'lower_attrib_name': None,
+                  'lower_attrib_value': None,
+                  'input_format': '{0}',
+                  'function': ext_to_complex,
+                  'kwargs': {"mini_species": species},
+                  'independent_params': '(HA)2(org)_h0'}
+    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
+estimator.update_xml(pitzer_params_dict)
+estimator.set_custom_objects_dict({'lin_param_df': new_lin_param_df})
+estimator.set_dependant_params_dict(dependant_params_dict)
+estimator.update_xml(ext_h0_dict,
+                     dependant_params_dict=dependant_params_dict)
+eps = 1e-20
+mini_eps = 1e-4
+pitzer_guess_dict = {'species': [],
+                     'beta0': [],
+                     'beta1': []}
+for species in species_list:
+    pitzer_guess_dict['species'].append(species)
+    for param in pitzer_param_list:
+        mini_dict = pitzer_params_dict['{0}_{1}'.format(species, param)]
+        value = mini_dict['input_value']
+        pitzer_guess_dict[param].append(value)
+pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
+ext_h0_guess = ext_h0_dict['(HA)2(org)_h0']['input_value']
+lin_guess_df = new_lin_param_df.copy()
+
+ignore_list = []
+optimizer = 'scipy_minimize'
+output_dict = {'iter': [0],
+               'best_obj': [1e20],
+               'rel_diff': [1e20],
+               'best_ext_h0': [1e20]}
+for species in species_list:
+    for lin_param in lin_param_list:
+        output_dict['{0}_{1}'.format(species, lin_param)] = [1e20]
+    for pitzer_param in pitzer_param_list:
+        output_dict['{0}_{1}'.format(species, pitzer_param)] = [1e20]
+i = 0
+rel_diff = 1000
+obj_diff1 = 1000
+obj_diff2 = 1000
+while obj_diff1 > eps or obj_diff2 > eps:
+    i += 1
+    print(i)
+    best_obj = 1e20
+    best_ext_h0 = 0
+    output_dict['iter'].append(i)
+    for species in species_list:
+        print(species)
+        lower_species = species.lower()
+        info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
+                                       'upper_attrib_name': 'name',
+                                       'upper_attrib_value': '(HA)2(org)',
+                                       'lower_element_name': 'h0',
+                                       'lower_attrib_name': None,
+                                       'lower_attrib_value': None,
+                                       'input_format': '{0}',
+                                       'input_value': ext_h0_guess}}
+
+        for pitzer_param in pitzer_param_list:
+            if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
+                pitzer_row = pitzer_guess_df[
+                    pitzer_guess_df['species'] == species]
+                inner_dict = {'upper_element_name': 'binarySaltParameters',
+                              'upper_attrib_name': 'cation',
+                              'upper_attrib_value':
+                                  '{0}+++'.format(species),
+                              'lower_element_name': pitzer_param,
+                              'lower_attrib_name': None,
+                              'lower_attrib_value': None,
+                              'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
+                              'input_value':
+                                  pitzer_row[pitzer_param].values[0]
+                              }
+                info_dict['{0}_{1}'.format(
+                    species, pitzer_param)] = inner_dict
+        for lin_param in lin_param_list:
+            if '{0}_{1}'.format(species, lin_param) not in ignore_list:
+                lin_row = lin_guess_df[lin_guess_df['species'] == species]
+                inner_dict = {'custom_object_name': 'lin_param_df',
+                              'function': mod_lin_param_df,
+                              'kwargs': {'mini_species': species,
+                                         'mini_lin_param': lin_param},
+                              'input_value': lin_row[lin_param].values[0]
+                              }
+                info_dict['{0}_{1}'.format(
+                    species, lin_param)] = inner_dict
+        estimator.set_opt_dict(info_dict)
+        estimator.update_custom_objects_dict(info_dict)
+        estimator.update_xml(info_dict)
+        obj_kwargs = {'species_list': species_list, 'epsilon': 1e-100}
+        bounds = [(1e-1, 1e1)] * len(info_dict)
+        optimizer_kwargs = {"method": 'l-bfgs-b',
+                            "bounds": bounds}
+        opt_dict, obj_value = estimator.fit(
+            objective_kwargs=obj_kwargs,
+            optimizer_kwargs=optimizer_kwargs)
+        if obj_value < best_obj:
+            best_obj = obj_value
+            best_ext_h0 = opt_dict['(HA)2(org)_h0']['input_value']
+
+        for lin_param in lin_param_list:
+            if '{0}_{1}'.format(species, lin_param) not in ignore_list:
+                mini_dict = opt_dict['{0}_{1}'.format(species, lin_param)]
+                value = mini_dict['input_value']
+                output_dict['{0}_{1}'.format(species, lin_param)].append(value)
+            else:
+                value = output_dict['{0}_{1}'.format(species, lin_param)][-1]
+                output_dict['{0}_{1}'.format(species, lin_param)].append(value)
+        for pitzer_param in pitzer_param_list:
+            if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
+                mini_dict = opt_dict['{0}_{1}'.format(species, pitzer_param)]
+                value = mini_dict['input_value']
+                output_dict['{0}_{1}'.format(
+                    species, pitzer_param)].append(value)
+            else:
+                value = output_dict['{0}_{1}'.format(
+                    species, pitzer_param)][-1]
+                output_dict['{0}_{1}'.format(
+                    species, pitzer_param)].append(value)
+        estimator.update_custom_objects_dict(info_dict)
+        estimator.update_xml(opt_dict)
+    pitzer_guess_dict = {'species': []}
+    for pitzer_param in pitzer_param_list:
+        pitzer_guess_dict[pitzer_param] = []
+    lin_guess_dict = {'species': []}
+    for lin_param in lin_param_list:
+        lin_guess_dict[lin_param] = []
+    for species in species_list:
+        pitzer_guess_dict['species'].append(species)
+        lin_guess_dict['species'].append(species)
+        for pitzer_param in pitzer_param_list:
+            pitzer_str = '{0}_{1}'.format(species, pitzer_param)
+            value_list = output_dict['{0}_{1}'.format(species, pitzer_param)]
+            value = value_list[-1]
+            pitzer_guess_dict[pitzer_param].append(value)
+            if i > 2:
+                mini_rel_diff1 = np.abs(value_list[-1]
+                                        - value_list[-2]) / (
+                                     np.abs(value_list[-2]))
+                mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
+                    np.abs(value_list[-3]))
+                if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
+                    if pitzer_str not in ignore_list:
+                        ignore_list.append(pitzer_str)
+        for lin_param in lin_param_list:
+            lin_str = '{0}_{1}'.format(species, lin_param)
+            value_list = output_dict['{0}_{1}'.format(species, lin_param)]
+            value = value_list[-1]
+            lin_guess_dict[lin_param].append(value)
+            if i > 2:
+                mini_rel_diff1 = np.abs(value_list[-1]
+                                        - value_list[-2]) / (
+                                     np.abs(value_list[-2]))
+                mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
+                    np.abs(value_list[-3]))
+                if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
+                    if lin_str not in ignore_list:
+                        ignore_list.append(lin_str)
+    pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
+    lin_guess_df = pd.DataFrame(lin_guess_dict)
+    ext_h0_guess = best_ext_h0
+
+    output_dict['best_ext_h0'].append(best_ext_h0)
+    output_dict['best_obj'].append(best_obj)
+    output_dict['rel_diff'].append(100)
+    output_df = pd.DataFrame(output_dict)
+    old_row = output_df.iloc[-2, :].values[4:]
+    new_row = output_df.iloc[-1, :].values[4:]
+    rel_diff = np.sum(np.abs(new_row - old_row) / np.abs(old_row))
+    del(output_dict['rel_diff'][-1])
+    output_dict['rel_diff'].append(rel_diff)
+    output_df = pd.DataFrame(output_dict)
+    output_df.to_csv('outputs/iterative_fitter_output_multicomponent.csv')
+    obj_diff1 = output_dict['best_obj'][-2]-output_dict['best_obj'][-1]
+    if i > 2:
+        obj_diff2 = output_dict['best_obj'][-3] - output_dict['best_obj'][-1]

docs/_Examples/iterative_fitter_eval_grapher.py

@@ -0,0 +1,275 @@
+import llepe
+import pandas as pd
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+import matplotlib
+import re
+
+
+def set_size(w, h, ax=None):
+    """ w, h: width, height in inches """
+    if not ax:
+        ax = plt.gca()
+    left = ax.figure.subplotpars.left
+    right = ax.figure.subplotpars.right
+    top = ax.figure.subplotpars.top
+    bottom = ax.figure.subplotpars.bottom
+    fig_width = float(w) / (right - left)
+    fig_height = float(h) / (top - bottom)
+    ax.figure.set_size_inches(fig_width, fig_height)
+
+
+font = {'family': 'sans serif',
+        'size': 24}
+matplotlib.rc('font', **font)
+matplotlib.rc('xtick', labelsize=18)
+matplotlib.rc('ytick', labelsize=18)
+matplotlib.rcParams['lines.linewidth'] = 4
+matplotlib.rcParams['lines.markersize'] = 10
+
+
+def ext_to_complex(h0, custom_obj_dict, mini_species):
+    linear_params = custom_obj_dict['lin_param_df']
+    row = linear_params[linear_params['species'] == mini_species]
+    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]
+
+
+def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
+    new_lp_df = lp_df.copy()
+    index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
+    new_lp_df.at[index, mini_lin_param] = input_val
+    return new_lp_df
+
+
+info_df = pd.read_csv('outputs/iterative_fitter_output4.csv')
+test_row = -1
+pitzer_params_filename = "../../data/jsons/min_h0_pitzer_params.txt"
+with open(pitzer_params_filename) as file:
+    pitzer_params_dict = json.load(file)
+pitzer_params_df = pd.DataFrame(pitzer_params_dict)
+species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
+pitzer_param_list = ['beta0', 'beta1']
+labeled_data = pd.read_csv("../../data/csvs/"
+                           "PC88A_HCL_NdPrCeLaDySmY.csv")
+labeled_data = labeled_data.sort_values(['Feed Pr[M]', 'Feed Ce[M]'],
+                                        ascending=True)
+exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
+xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
+lin_param_df = pd.read_csv("../../data/csvs"
+                           "/zeroes_removed_min_h0_pitzer_lin_params.csv")
+estimator_params = {'exp_data': exp_data,
+                    'phases_xml_filename': xml_file,
+                    'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
+                    'aq_solvent_name': 'H2O(L)',
+                    'extractant_name': '(HA)2(org)',
+                    'diluant_name': 'dodecane',
+                    'complex_names': ['{0}(H(A)2)3(org)'.format(species)
+                                      for species in species_list],
+                    'extracted_species_ion_names': ['{0}+++'.format(species)
+                                                    for species in
+                                                    species_list],
+                    'aq_solvent_rho': 1000.0,
+                    'extractant_rho': 960.0,
+                    'diluant_rho': 750.0,
+                    'temp_xml_file_path': 'outputs/temp.xml',
+                    'objective_function': llepe.lmse_perturbed_obj
+                    }
+dependant_params_dict = {}
+for species, complex_name in zip(species_list,
+                                 estimator_params['complex_names']):
+    inner_dict = {'upper_element_name': 'species',
+                  'upper_attrib_name': 'name',
+                  'upper_attrib_value': complex_name,
+                  'lower_element_name': 'h0',
+                  'lower_attrib_name': None,
+                  'lower_attrib_value': None,
+                  'input_format': '{0}',
+                  'function': ext_to_complex,
+                  'kwargs': {"mini_species": species},
+                  'independent_params': '(HA)2(org)_h0'}
+    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
+info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
+                               'upper_attrib_name': 'name',
+                               'upper_attrib_value': '(HA)2(org)',
+                               'lower_element_name': 'h0',
+                               'lower_attrib_name': None,
+                               'lower_attrib_value': None,
+                               'input_format': '{0}',
+                               'input_value':
+                                   info_df.iloc[test_row, :]['best_ext_h0']}}
+for species in species_list:
+    for pitzer_param in pitzer_param_list:
+        pitzer_str = "{0}_{1}".format(species, pitzer_param)
+        value = info_df.iloc[test_row, :][pitzer_str]
+        pitzer_params_dict[pitzer_str]['input_value'] = value
+    lin_str = "{0}_slope".format(species)
+    inner_dict = {'custom_object_name': 'lin_param_df',
+                  'function': mod_lin_param_df,
+                  'kwargs': {'mini_species': species,
+                             'mini_lin_param': 'slope'},
+                  'input_value': 3
+                  }
+    info_dict[lin_str] = inner_dict
+    lin_str = "{0}_intercept".format(species)
+    value = info_df.iloc[test_row, :][lin_str]
+    inner_dict = {'custom_object_name': 'lin_param_df',
+                  'function': mod_lin_param_df,
+                  'kwargs': {'mini_species': species,
+                             'mini_lin_param': 'intercept'},
+                  'input_value': value
+                  }
+    info_dict[lin_str] = inner_dict
+
+info_dict.update(pitzer_params_dict)
+estimator = llepe.LLEPE(**estimator_params)
+estimator.set_custom_objects_dict({'lin_param_df': lin_param_df})
+estimator.update_custom_objects_dict(info_dict)
+estimator.update_xml(info_dict,
+                     dependant_params_dict=dependant_params_dict)
+compared_value = 'La_org_eq'
+plot_title = None
+legend = True
+predicted_dict = estimator.get_predicted_dict()
+exp_df = estimator.get_exp_df()
+pred = pd.DataFrame(predicted_dict)[compared_value].fillna(0).values
+meas = exp_df[compared_value].fillna(0).values
+name_breakdown = re.findall('[^_\W]+', compared_value)
+compared_species = name_breakdown[0]
+data_labels = list(labeled_data['label'])
+if compared_species == 'h':
+    feed_molarity = exp_df['h_i'].fillna(0).values
+elif compared_species == 'z':
+    feed_molarity = exp_df['z_i'].fillna(0).values
+else:
+    feed_molarity = exp_df[
+        '{0}_aq_i'.format(compared_species)].fillna(0).values
+combined_df = pd.DataFrame({'pred': pred,
+                            'meas': meas,
+                            'label': data_labels,
+                            'feed_molarity': feed_molarity})
+combined_df = combined_df[(combined_df['feed_molarity'] != 0)]
+meas = combined_df['meas'].values
+pred = combined_df['pred'].values
+
+min_data = np.min([pred, meas])
+max_data = np.max([pred, meas])
+min_max_data = np.array([min_data, max_data])
+
+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':
+        extracted_species_charge = extracted_species_charges[
+            extracted_species_list.index(
+                compared_species)]
+        default_title = '$%s^{%d+}$ eq. conc. (mol/L)' \
+                        % (compared_species, extracted_species_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(figsize=(8, 6))
+
+if isinstance(data_labels, list):
+    # unique_labels = list(set(data_labels))
+    unique_labels = ['Li (1987)',
+                     'Kim (2012)',
+                     'Formiga (2016)',
+                     'Banda (2014)',
+                     ]
+    color_list = ['r', 'g', 'b', 'm']
+    marker_list = ['o', 's', 'P', 'X', ]
+    for ind, label in enumerate(unique_labels):
+        filtered_data = combined_df[combined_df['label'] == label]
+        filtered_meas = filtered_data['meas']
+        filtered_pred = filtered_data['pred']
+        if len(filtered_pred) != 0:
+            ax.scatter(filtered_meas,
+                       filtered_pred,
+                       label=label,
+                       color=color_list[ind],
+                       marker=marker_list[ind])
+    if legend:
+        ax.legend(loc=4)
+ax.plot(min_max_data, min_max_data, color="b", label="")
+
+ax.text(min_max_data[0],
+        min_max_data[1] * 0.9,
+        '$R^2$={0:.2f}'.format(estimator.r_squared(compared_value)))
+
+ax.set(xlabel='Measured', ylabel='Predicted')
+if plot_title is None:
+    ax.set_title(default_title)
+set_size(8, 6)
+plt.tight_layout()
+plt.show()
+# exp_data = estimator.get_exp_df()
+# feed_cols = []
+# for col in exp_data.columns:
+#     if 'aq_i' in col:
+#         feed_cols.append(col)
+# exp_data['total_re'] = exp_data[feed_cols].sum(axis=1)
+# label_list = []
+# for index, row in exp_data[feed_cols].iterrows():
+#     bool_list = list((row > 0).values)
+#     label = ''
+#     for species, el in zip(species_list, bool_list):
+#         if el:
+#             label = '{0}-{1}'.format(label, species)
+#     label = label[1:]
+#     label_list.append(label)
+# r2s = ""
+# for species in species_list:
+#     # if species=='La':
+#     # save_name = 'outputs' \
+#     #             '/parity_iterative_fitter_{0}_org_eq'.format(species)
+#     save_name = None
+#     fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),
+#                                     c_data=
+#                                     exp_data['total_re'].values,
+#                                     c_label='Feed total RE '
+#                                             'molarity (mol/L)',
+#                                     print_r_squared=False,
+#                                     plot_title='')
+#     ax.plot([0, 0.05], [0, 0.05], c='b')
+#     ax.text(0.01, 0.04,
+#             '$R^2$={0:.2f}'.format(estimator.r_squared(
+#                 '{0}_org_eq'.format(species))))
+#     ax.set_xlim((0, 0.05))
+#     ax.set_ylim((0, 0.05))
+#     r2s += str(estimator.r_squared('{0}_org_eq'.format(species))) + ','
+#
+#     # fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),
+#     #                                 data_labels=list(labeled_data['label']),
+#     #                                 print_r_squared=True,
+#     #                                 save_path=save_name)
+#     # ax.legend(loc=4)
+# pred_df = pd.DataFrame(estimator.get_predicted_dict())
+# new_cols = []
+# for col in pred_df.columns:
+#     new_cols.append("pred_{0}".format(col))
+# pred_df.columns = new_cols
+# new_cols = ['label',
+#             'h_i',
+#             'h_eq',
+#             'z_i',
+#             'z_eq'
+#             ]
+# for species in species_list:
+#     new_cols.append("{0}_aq_i".format(species))
+#     new_cols.append("{0}_aq_eq".format(species))
+#     new_cols.append("{0}_d_eq".format(species))
+# labeled_data.columns = new_cols
+# total_df = labeled_data.join(pred_df)
+# total_df.to_csv('if_mse_total_df.csv')
+# short_info_dict = {}
+# for key, value in info_dict.items():
+#     short_info_dict[key] = value['input_value']
+# with open("outputs/iterative_fitter_short_info_dict.txt", 'w') as file:
+#     json.dump(short_info_dict, file)

docs/_Examples/iterative_fitter_monitor.py

@@ -0,0 +1,51 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+
+go = 'y'
+parameters = 'slope,intercept,beta0,beta1'.split(',')
+# while go == 'y':
+#     go = input('continue? ')
+#     if go != 'y':
+#         break
+#     plt.close('all')
+df = pd.read_csv('outputs/iterative_fitter_output4.csv')
+info_cols = {parameter: [] for parameter in parameters}
+for col in df.columns:
+    for parameter in parameters:
+        if parameter in col:
+            info_cols[parameter].append(col)
+for parameter in parameters:
+    mini_df = df[info_cols[parameter]]
+    fig, ax = plt.subplots()
+    ax.set_title(parameter)
+    for col in info_cols[parameter]:
+        ax.plot(df['iter'].values[1:],
+                df[col].values[1:],
+                label=col,
+                linestyle='-',
+                marker='o')
+    ax.set_xlabel('iteration')
+    ax.set_ylabel('Value')
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig('outputs/{0}.png'.format(parameter))
+fig, ax = plt.subplots()
+ax.set_title('best_obj_value')
+ax.plot(df['iter'].values[1:],
+        df['best_obj'].values[1:],
+        linestyle='-',
+        marker='o')
+ax.set_xlabel('iteration')
+ax.set_ylabel('Value')
+plt.tight_layout()
+plt.savefig('outputs/best_obj.png')
+fig, ax = plt.subplots()
+ax.set_title('rel_diff')
+ax.plot(df['iter'].values[1:],
+        df['rel_diff'].values[1:],
+        linestyle='-',
+        marker='o')
+ax.set_xlabel('iteration')
+ax.set_ylabel('Value')
+plt.tight_layout()
+# plt.savefig('outputs/rel_diff.png')

docs/_Examples/iterative_fitter_w_mse.py

@@ -0,0 +1,274 @@
+import llepe
+import pandas as pd
+import numpy as np
+import json
+
+
+def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
+    new_lp_df = lp_df.copy()
+    index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
+    new_lp_df.at[index, mini_lin_param] = input_val
+    return new_lp_df
+
+
+def ext_to_complex(h0, custom_obj_dict, mini_species):
+    linear_params = custom_obj_dict['lin_param_df']
+    mini_row = linear_params[linear_params['species'] == mini_species]
+    val = mini_row['slope'].values[0] * h0[0] + mini_row['intercept'].values[0]
+    return val
+
+
+species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
+pitzer_param_list = ['beta0', 'beta1']
+lin_param_list = ['intercept']
+short_info_filename = 'outputs/iterative_fitter_short_info_dict.txt'
+with open(short_info_filename) as file:
+    short_info_dict = json.load(file)
+labeled_data = pd.read_csv("../../data/csvs/"
+                           "zeroes_removed_PC88A_HCL_NdPrCeLaDySmY.csv")
+exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
+xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
+lin_param_df = pd.read_csv("../../data/csvs"
+                           "/zeroes_removed_min_h0_pitzer_lin_params.csv")
+new_lin_param_df = lin_param_df.copy()
+for ind, row in lin_param_df.iterrows():
+    new_lin_param_df.at[ind, 'slope'] = 3
+    species = row['species']
+    val = short_info_dict['{0}_intercept'.format(species)]
+    new_lin_param_df.at[ind, 'intercept'] = val
+estimator_params = {'exp_data': exp_data,
+                    'phases_xml_filename': xml_file,
+                    'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
+                    'aq_solvent_name': 'H2O(L)',
+                    'extractant_name': '(HA)2(org)',
+                    'diluant_name': 'dodecane',
+                    'complex_names': ['{0}(H(A)2)3(org)'.format(species)
+                                      for species in species_list],
+                    'extracted_species_ion_names': ['{0}+++'.format(species)
+                                                    for species in
+                                                    species_list],
+                    'aq_solvent_rho': 1000.0,
+                    'extractant_rho': 960.0,
+                    'diluant_rho': 750.0,
+                    'temp_xml_file_path': 'outputs/temp1.xml',
+                    'objective_function': llepe.mean_squared_error,
+                    'custom_objects_dict': {'lin_param_df': new_lin_param_df}
+                    }
+dependant_params_dict = {}
+for species, complex_name in zip(species_list,
+                                 estimator_params['complex_names']):
+    inner_dict = {'upper_element_name': 'species',
+                  'upper_attrib_name': 'name',
+                  'upper_attrib_value': complex_name,
+                  'lower_element_name': 'h0',
+                  'lower_attrib_name': None,
+                  'lower_attrib_value': None,
+                  'input_format': '{0}',
+                  'function': ext_to_complex,
+                  'kwargs': {"mini_species": species},
+                  'independent_params': '(HA)2(org)_h0',
+                  }
+    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
+info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
+                               'upper_attrib_name': 'name',
+                               'upper_attrib_value': '(HA)2(org)',
+                               'lower_element_name': 'h0',
+                               'lower_attrib_name': None,
+                               'lower_attrib_value': None,
+                               'input_format': '{0}',
+                               'input_value':
+                                   short_info_dict['(HA)2(org)_h0']}}
+for species in species_list:
+    for param in pitzer_param_list:
+        name = "{0}_{1}".format(species, param)
+        inner_dict = {'upper_element_name': 'binarySaltParameters',
+                      'upper_attrib_name': 'cation',
+                      'upper_attrib_value': '{0}+++'.format(species),
+                      'lower_element_name': param,
+                      'lower_attrib_name': None,
+                      'lower_attrib_value': None,
+                      'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
+                      'input_value':
+                          short_info_dict[name]}
+        info_dict[name] = inner_dict
+    for param in lin_param_list:
+        name = "{0}_{1}".format(species, param)
+        inner_dict = {'custom_object_name': 'lin_param_df',
+                      'function': mod_lin_param_df,
+                      'kwargs': {'mini_species': species,
+                                 'mini_lin_param': param},
+                      'input_value': short_info_dict[name]
+                      }
+
+estimator = llepe.LLEPE(**estimator_params)
+estimator.update_xml(info_dict,
+                     dependant_params_dict=dependant_params_dict)
+estimator.set_dependant_params_dict(dependant_params_dict)
+eps = 1e-20
+mini_eps = 1e-4
+pitzer_guess_dict = {'species': [],
+                     'beta0': [],
+                     'beta1': []}
+for species in species_list:
+    pitzer_guess_dict['species'].append(species)
+    for param in pitzer_param_list:
+        mini_dict = info_dict['{0}_{1}'.format(species, param)]
+        value = mini_dict['input_value']
+        pitzer_guess_dict[param].append(value)
+pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
+ext_h0_guess = info_dict['(HA)2(org)_h0']['input_value']
+lin_guess_df = new_lin_param_df.copy()
+
+ignore_list = []
+optimizer = 'scipy_minimize'
+output_dict = {'iter': [0],
+               'best_obj': [1e20],
+               'rel_diff': [1e20],
+               'best_ext_h0': [1e20]}
+for species in species_list:
+    for lin_param in lin_param_list:
+        output_dict['{0}_{1}'.format(species, lin_param)] = [1e20]
+    for pitzer_param in pitzer_param_list:
+        output_dict['{0}_{1}'.format(species, pitzer_param)] = [1e20]
+i = 0
+rel_diff = 1000
+obj_diff1 = 1000
+obj_diff2 = 1000
+while obj_diff1 > eps or obj_diff2 > eps:
+    i += 1
+    print(i)
+    best_obj = 1e20
+    best_ext_h0 = 0
+    output_dict['iter'].append(i)
+    for species in species_list:
+        print(species)
+        lower_species = species.lower()
+        info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
+                                       'upper_attrib_name': 'name',
+                                       'upper_attrib_value': '(HA)2(org)',
+                                       'lower_element_name': 'h0',
+                                       'lower_attrib_name': None,
+                                       'lower_attrib_value': None,
+                                       'input_format': '{0}',
+                                       'input_value': ext_h0_guess}}
+
+        for pitzer_param in pitzer_param_list:
+            if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
+                pitzer_row = pitzer_guess_df[
+                    pitzer_guess_df['species'] == species]
+                inner_dict = {'upper_element_name': 'binarySaltParameters',
+                              'upper_attrib_name': 'cation',
+                              'upper_attrib_value':
+                                  '{0}+++'.format(species),
+                              'lower_element_name': pitzer_param,
+                              'lower_attrib_name': None,
+                              'lower_attrib_value': None,
+                              'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
+                              'input_value':
+                                  pitzer_row[pitzer_param].values[0]
+                              }
+                info_dict['{0}_{1}'.format(
+                    species, pitzer_param)] = inner_dict
+        for lin_param in lin_param_list:
+            if '{0}_{1}'.format(species, lin_param) not in ignore_list:
+                lin_row = lin_guess_df[lin_guess_df['species'] == species]
+                inner_dict = {'custom_object_name': 'lin_param_df',
+                              'function': mod_lin_param_df,
+                              'kwargs': {'mini_species': species,
+                                         'mini_lin_param': lin_param},
+                              'input_value': lin_row[lin_param].values[0]
+                              }
+                info_dict['{0}_{1}'.format(
+                    species, lin_param)] = inner_dict
+        estimator.set_opt_dict(info_dict)
+        estimator.update_custom_objects_dict(info_dict)
+        estimator.update_xml(info_dict)
+        obj_kwargs = {'species_list': species_list}
+        bounds = [(1e-1, 1e1)] * len(info_dict)
+        optimizer_kwargs = {"method": 'l-bfgs-b',
+                            "bounds": bounds}
+        opt_dict, obj_value = estimator.fit(
+            objective_kwargs=obj_kwargs,
+            optimizer_kwargs=optimizer_kwargs)
+        if obj_value < best_obj:
+            best_obj = obj_value
+            best_ext_h0 = opt_dict['(HA)2(org)_h0']['input_value']
+
+        for lin_param in lin_param_list:
+            if '{0}_{1}'.format(species, lin_param) not in ignore_list:
+                mini_dict = opt_dict['{0}_{1}'.format(species, lin_param)]
+                value = mini_dict['input_value']
+                output_dict['{0}_{1}'.format(species, lin_param)].append(value)
+            else:
+                value = output_dict['{0}_{1}'.format(species, lin_param)][-1]
+                output_dict['{0}_{1}'.format(species, lin_param)].append(value)
+        for pitzer_param in pitzer_param_list:
+            if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
+                mini_dict = opt_dict['{0}_{1}'.format(species, pitzer_param)]
+                value = mini_dict['input_value']
+                output_dict['{0}_{1}'.format(
+                    species, pitzer_param)].append(value)
+            else:
+                value = output_dict['{0}_{1}'.format(
+                    species, pitzer_param)][-1]
+                output_dict['{0}_{1}'.format(
+                    species, pitzer_param)].append(value)
+        estimator.update_custom_objects_dict(info_dict)
+        estimator.update_xml(opt_dict)
+    pitzer_guess_dict = {'species': []}
+    for pitzer_param in pitzer_param_list:
+        pitzer_guess_dict[pitzer_param] = []
+    lin_guess_dict = {'species': []}
+    for lin_param in lin_param_list:
+        lin_guess_dict[lin_param] = []
+    for species in species_list:
+        pitzer_guess_dict['species'].append(species)
+        lin_guess_dict['species'].append(species)
+        for pitzer_param in pitzer_param_list:
+            pitzer_str = '{0}_{1}'.format(species, pitzer_param)
+            value_list = output_dict['{0}_{1}'.format(species, pitzer_param)]
+            value = value_list[-1]
+            pitzer_guess_dict[pitzer_param].append(value)
+            if i > 2:
+                mini_rel_diff1 = np.abs(value_list[-1]
+                                        - value_list[-2]) / (
+                                     np.abs(value_list[-2]))
+                mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
+                    np.abs(value_list[-3]))
+                if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
+                    if pitzer_str not in ignore_list:
+                        ignore_list.append(pitzer_str)
+        for lin_param in lin_param_list:
+            lin_str = '{0}_{1}'.format(species, lin_param)
+            value_list = output_dict['{0}_{1}'.format(species, lin_param)]
+            value = value_list[-1]
+            lin_guess_dict[lin_param].append(value)
+            if i > 2:
+                mini_rel_diff1 = np.abs(value_list[-1]
+                                        - value_list[-2]) / (
+                                     np.abs(value_list[-2]))
+                mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
+                    np.abs(value_list[-3]))
+                if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
+                    if lin_str not in ignore_list:
+                        ignore_list.append(lin_str)
+    pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
+    lin_guess_df = pd.DataFrame(lin_guess_dict)
+    ext_h0_guess = best_ext_h0
+
+    output_dict['best_ext_h0'].append(best_ext_h0)
+    output_dict['best_obj'].append(best_obj)
+    output_dict['rel_diff'].append(100)
+    output_df = pd.DataFrame(output_dict)
+    old_row = output_df.iloc[-2, :].values[4:]
+    new_row = output_df.iloc[-1, :].values[4:]
+    rel_diff = np.sum(np.abs(new_row - old_row) / np.abs(old_row))
+    del (output_dict['rel_diff'][-1])
+    output_dict['rel_diff'].append(rel_diff)
+    output_df = pd.DataFrame(output_dict)
+    output_df.to_csv('outputs/iterative_fitter_w_mse_output.csv')
+    obj_diff1 = np.abs(
+        output_dict['best_obj'][-1] - output_dict['best_obj'][-2])
+    if i > 2:
+        obj_diff2 = np.abs(
+            output_dict['best_obj'][-1] - output_dict['best_obj'][-3])

docs/_Examples/outputs/iterative_fitter_info_dict.txt

@@ -0,0 +1 @@
+{"(HA)2(org)_h0": {"upper_element_name": "species", "upper_attrib_name": "name", "upper_attrib_value": "(HA)2(org)", "lower_element_name": "h0", "lower_attrib_name": null, "lower_attrib_value": null, "input_format": "{0}", "input_value": -1376882.3191117246}, "Nd_slope": {"custom_object_name": "lin_param_df", "function": 

docs/_Examples/outputs/iterative_fitter_output.csv

@@ -0,0 +1,24 @@
+,iter,best_obj,rel_diff,best_ext_h0,Nd_slope,Nd_intercept,Nd_beta0,Nd_beta1,Pr_slope,Pr_intercept,Pr_beta0,Pr_beta1,Ce_slope,Ce_intercept,Ce_beta0,Ce_beta1,La_slope,La_intercept,La_beta0,La_beta1,Dy_slope,Dy_intercept,Dy_beta0,Dy_beta1,Sm_slope,Sm_intercept,Sm_beta0,Sm_beta1,Y_slope,Y_intercept,Y_beta0,Y_beta1
+0,0,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20
+1,1,0.00016134227249800634,28.000000000000057,-1376877.7655517121,2.997016104344558,-798273.2701355412,0.8974067641732357,9.428749928658126,2.9952636557673946,-812187.4113891785,0.5879108865959323,5.448324456659969,3.000763765252656,-797785.6715641882,0.6005000281672793,4.910000049022438,3.0029250992523053,-797753.1250426786,0.5930012908436624,5.277035454314173,3.005188887840825,-797963.3947393347,0.6129503793970729,5.359935242349485,3.0049141894588356,-788597.673243324,0.5977991609874977,5.278978873136207,3.004211003525449,-808858.5407597149,0.9656358640797775,8.51418935794808
+2,2,8.491196928368016e-05,3.753743510814017,-1375785.2564991405,2.9970374674100313,-798274.3744081341,0.8974068120162436,9.42874961145886,2.994978971888568,-812110.2180829814,0.5879107200993189,5.448316407663497,2.9947405016045665,-813706.4008379887,0.45247610268275573,0.4910000049022438,2.9955784444500653,-811485.5788168659,0.9744013695120132,11.536623868407073,3.0051886646689425,-797963.3849246526,0.6129504080166187,5.359935711747232,2.998286291636726,-796410.7377837397,0.6989623741537213,6.874571631031283,2.9980686905189895,-816110.2339357884,0.8864580596701681,9.871827648349246
+3,3,8.491196925751041e-05,1.3872557586710772,-1376877.1540548366,2.981636126077384,-815701.8403939944,0.9757586265135513,7.972840020817166,2.9919947944629675,-814690.7699676664,0.6543020425532663,6.401935572967909,2.9912895029379314,-812768.7207378447,0.4524742361735793,0.4909988885665818,2.8816137399778095,-1165713.786906667,0.6673287483674718,11.13954713731451,3.0051888954051678,-797963.3977338726,0.6129504256882,5.359936001703055,2.998286456943957,-796410.7468898736,0.6989623710221783,6.8745716509720465,2.9980701639611307,-816110.3144777927,0.8864580604329536,9.87182766313202
+4,4,8.491196864453183e-05,3.124350706237914,-1376877.152537996,3.0030651668677755,-788714.7627699226,0.8876477500590181,7.1795656837403845,2.9492981846969997,-866396.0136278999,0.06543020425532663,14.481702424339913,2.9915899054971504,-812784.8309966518,0.4524742264332296,0.49099888634665106,2.802400719850797,-1217568.8940603866,0.734765523062068,16.501184782487442,3.005188678309654,-797963.3880226224,0.6129504505231561,5.359936408928357,2.9982864229571073,-796410.7471635938,0.6989623610327844,6.874571712665922,2.998069983411216,-816110.5901289525,0.8864580599623918,9.871827432740531
+5,5,8.491196857946013e-05,1.1315146871019859,-1376877.1544992805,3.009261826624499,-790493.4807183052,0.8876820541130862,7.180366876068199,2.95493306423167,-941679.8410781198,0.06542242940021062,14.596387046646438,2.9939929242661925,-813370.2300667212,0.4524750816922801,0.49099914533270717,2.7412268066767798,-1181700.5928732348,0.0734765523062068,17.778208795501687,3.0051889032028973,-797963.4006334972,0.6129504699710735,5.359936728023126,2.9982864523758734,-796410.7494542947,0.6989623571542753,6.874571712665922,2.9980699791830183,-816110.5900394547,0.8864580599623918,9.871827432586276
+6,6,8.491196857868175e-05,0.8212092953360018,-1376877.1544988493,3.018448578935926,-756591.4774984489,0.8847071856293566,7.127732469249149,2.946746698619443,-914521.2680772808,0.0655303675034554,17.87160024357031,2.9951696006679125,-813508.1242683842,0.45247514275357914,0.4909991719996952,2.8979917114047042,-880424.1009942711,0.07397534024322364,14.445668342341632,3.005188689731846,-797963.3909449822,0.6129504919765936,5.359937088895021,2.998286417872815,-796410.7494542947,0.6989623397580044,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+7,7,8.491196857967895e-05,2.8535135475763593,-1376877.1544988335,2.9803570928818193,-831169.3940393181,2.3855150593783625,0.9943792524787087,2.927591618356134,-906967.4787334907,0.06534292291155007,16.133908724056262,3.0029738570325497,-795932.5448638537,0.4524823795863963,0.4909954629866391,2.9609895390962344,-881255.3332121583,0.07326400481624591,14.648731908806054,3.005188911656549,-797963.4035243823,0.6129505135552498,5.3599374429477535,2.998286452518918,-796410.7494542947,0.6989623360131538,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+8,8,8.491196857967895e-05,0.5074295633771292,-1376877.1544988335,3.046988319365659,-695733.7822299139,2.118879762861775,0.9832916930997248,2.9452585522682813,-902617.5883506279,0.06531634264974066,14.535540289940167,3.001724622774424,-796341.6549504208,0.45078352007925804,0.49034083082370367,2.9722238052604713,-851355.1697072648,0.07317245680858252,14.005551062985573,3.0051886986863527,-797963.3937399577,0.6129505334265856,5.359937768806609,2.998286418340077,-796410.7494542947,0.6989623190362244,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+9,9,8.491196857967895e-05,0.8233569178526139,-1376877.1544988335,2.9825936071777024,-834718.8445277695,2.265417073982851,0.994361984624478,2.960695251829361,-883765.0750479599,0.06535572997952777,14.395940812001363,3.0011755877522677,-797620.9536781705,0.451768694057885,0.4905988771385775,3.01363818781501,-765715.3192727776,0.07374063593564315,19.03231059683384,3.0051889209677576,-797963.4064770396,0.6129505574921508,5.359938163641345,2.998286452651492,-796410.7494542947,0.6989623152735008,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+10,10,8.491196857967895e-05,0.4773109299773383,-1376877.1544988335,3.0188840683355007,-758130.1305298645,2.1690523176501006,0.9984406194127493,2.982890660585592,-840275.2594169229,0.0654841686928967,14.704161508382992,2.997539444125265,-805146.4436967466,0.450156356171543,0.4900762819026168,2.9884659287162063,-823730.0588083962,0.07331458114972399,16.339393531863305,3.0051887064580316,-797963.3965185066,0.6129505756465488,5.359938461354856,2.998286418810594,-796410.7494542947,0.6989622987206754,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+11,11,8.491196857967895e-05,1.3118334901996995,-1376877.1544988335,3.0139001564181234,-777482.0137288059,2.4891695692571334,1.0067452500133052,2.7082813102477377,-1340320.351525344,0.06570193477294409,15.318347397454074,2.9937062166470865,-811848.1861685036,0.3807242781267447,0.4571366236203172,3.0321484162541092,-734038.497344836,0.07278743339509186,15.771531203484976,3.0051889316605727,-797963.4095808832,0.6129506027939315,5.359938906721338,2.9982864527868296,-796410.7494542947,0.6989622949357988,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+12,12,8.491196857967895e-05,0.6176079935219678,-1376877.1544988335,2.9991637169323253,-799348.0533050542,2.0606574395422745,0.9999146041973166,2.645965929040643,-1371545.8507318976,0.06530562967326357,14.087654782739659,2.994388651142047,-812440.2720371439,0.3797153647239341,0.45689994044316656,2.976360040153504,-826947.2829702377,0.0721742508966242,13.950564590026154,3.005188713573002,-797963.3993561977,0.6129506195016742,5.3599391806962355,2.9982864192709067,-796410.7494542947,0.6989622787849055,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+13,13,8.491196857967895e-05,0.447458797489282,-1376877.1544988335,2.9980180983111584,-800782.7580580596,2.384966567099209,1.0089359468087284,2.624593032594632,-1360513.8600727562,0.0652016206426237,13.974111290564698,2.9921196188008023,-811824.6368088808,0.37971475358406565,0.4568995049435289,2.9835754249798847,-836109.188415324,0.07001616810133855,16.854824585869906,3.0051889446809152,-797963.4129476986,0.6129506507011362,5.359939692544431,2.998286452925373,-796410.7494542947,0.6989622749758465,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+14,14,8.491196857967895e-05,0.348360677031186,-1376877.1544988335,2.9982081321172895,-800795.2294772209,2.384966526824247,1.0089359542883563,2.666975566695788,-1499994.1414862953,0.06545352162164432,15.242130244485235,2.9921792830474856,-811827.830457512,0.3797147346864944,0.45689949537118524,3.0165968899178837,-760698.0568963296,0.06972550438458386,17.353271986524845,3.005188720514459,-797963.4023398461,0.6129506661375982,5.359939945648347,2.9982864196902987,-796410.7494542947,0.6989622591872565,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+15,15,8.491196857967895e-05,0.8542573291619304,-1376877.1544988335,2.998712505758874,-802387.4877053623,1.5206330649921567,0.9981622444785841,2.713156877040639,-1478192.0473444732,0.06274057809944926,13.092934019657042,2.994891728361129,-812682.5967323158,0.38018937518269785,0.4567780326119338,3.03009818160027,-757243.0989114699,0.0690109050871497,13.154321394861364,3.005188961745495,-797963.4167757028,0.6129507031227088,5.359940552366342,2.9982864530536806,-796410.7494542947,0.6989622553597238,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+16,16,8.491196857967895e-05,0.9448120605121391,-1376877.1544988335,2.997515876288025,-800762.0931049074,2.371926193409047,1.0392028818516033,2.529014996778218,-1394593.2488741723,0.06362240882017806,15.72054555069271,2.992321826257504,-811985.236014034,0.380188573336472,0.45677745562445643,3.0311603879490625,-757395.5635541482,0.06901090642373163,13.15434949309279,3.0051887279351726,-797963.4056092768,0.6129507173831404,5.359940786171395,2.9982864200995016,-796410.7494542947,0.6989622399197148,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+17,17,8.491196857967895e-05,0.06986866489189483,-1376877.1544988335,2.998185517350526,-800940.9851411111,2.3719292648415435,1.0392030732451656,2.572480079015409,-1398076.7310485386,0.06363412395715484,14.965559296480045,2.9916507242807455,-811803.1343536598,0.3801884696593834,0.4567773926702631,3.029587351548093,-757002.5991619993,0.06901085836588974,13.153841661748762,3.00518898709267,-797963.4214979863,0.6129507639240588,5.359941549628348,2.9982864531810547,-796410.7494542947,0.6989622360690536,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+18,18,8.491196857967895e-05,7.537649140057278,-1376877.1544988335,2.9139186135307895,-903717.07595184,1.1713250218349551,7.8961519078936995,2.551409491193631,-1396607.749248508,0.06364635989520172,15.028103657774764,2.992358699183809,-811883.3497541018,0.38014823465953007,0.45676064010613937,3.0225440094645197,-757527.3282072913,0.06907140877109968,16.557914647062493,3.0051887374402306,-797963.4094522469,0.6129507769959467,5.3599417639476465,2.9982864204802095,-796410.7494542947,0.6989622209402793,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+19,19,8.491196857967895e-05,0.3395564898628593,-1376877.1544988335,2.9206876794423295,-906067.0736166579,1.171453185504549,7.897253707676486,2.542473620212442,-1395291.4660299404,0.06365232727832787,15.064625151652315,2.9913152280569513,-811600.2380135104,0.3801480833041433,0.45676053922619353,3.00948706713011,-770684.9871133575,0.06894408512873036,21.578016654838063,3.0051890362364753,-797963.4285937521,0.6129508452597554,5.359942883786607,2.998286453304157,-796410.7494542947,0.6989622170705244,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+20,20,8.491196857967895e-05,0.27887232716945426,-1376877.1544988335,2.932945093248787,-893472.3867422906,1.2372118357462467,8.367708345062676,2.5681008604924935,-1424501.3718411059,0.06389293355566235,16.006743784511794,2.9915688192964494,-811676.8964187511,0.38001482584768576,0.4566967822701942,2.9966407511121345,-795742.7223232647,0.06891628915899889,21.364797187402115,3.005188756433512,-797963.4149199789,0.6129508568428819,5.359943073668666,2.9982864208411075,-796410.7494542947,0.6989622022429722,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+21,21,8.491196857967895e-05,0.07904486462976254,-1376877.1544988335,2.9192941732704183,-899595.6903217547,1.2289167121597353,8.270591970104478,2.5885541497990276,-1425201.6370605633,0.06406357430240948,16.055658203899945,2.991141414262,-812827.4608356728,0.3798926190405488,0.45663532315631505,2.993009643896257,-802640.8358981846,0.06886889954042703,20.88499046197079,3.0051892653467993,-797963.4540443348,0.6129510889764764,5.359946881486811,2.9982864534278213,-796410.7494542947,0.6989621983540872,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
+22,22,8.491196857967895e-05,2.8078518871500875,-1376877.1544988335,2.5130533352916404,-1308478.271342789,2.3685619028157476,1.0600650778914467,2.532808888931495,-1416725.967204896,0.06411203767956787,15.094184412115302,2.990867139013824,-812773.0935418691,0.3798925911718346,0.456635306115862,2.99518346032217,-803786.8612650471,0.06825025926005274,14.294244836372703,3.0051888907116417,-797963.4353105845,0.6129510969272831,5.359947011739686,2.9982864212214833,-796410.7494542947,0.6989621838326963,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574

docs/_Examples/outputs/iterative_fitter_output2.csv

@@ -0,0 +1,8 @@
+,iter,best_obj,rel_diff,best_ext_h0,Nd_intercept,Nd_beta0,Nd_beta1,Pr_intercept,Pr_beta0,Pr_beta1,Ce_intercept,Ce_beta0,Ce_beta1,La_intercept,La_beta0,La_beta1,Dy_intercept,Dy_beta0,Dy_beta1,Sm_intercept,Sm_beta0,Sm_beta1,Y_intercept,Y_beta0,Y_beta1
+0,0,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20
+1,1,0.00016129407933554554,21.000000000000057,-1376877.152986458,-796238.0142870437,1.437426638616483,5.347609621840506,-807393.2082028955,0.05879108393945309,0.5448323469435737,-793502.5397317475,1.584075553189004,9.71291104987633,-804144.2085745687,0.9975450638457432,0.5276999248444548,-801913.2126520091,0.7646409184903517,7.849312986255907,-795363.8004519838,0.5978071372268503,5.279164697561237,-814653.355406187,0.9657649833215246,8.515799878446009
+2,2,0.00010559366199558426,2.7344663371161086,-1376882.3191580868,-796384.0556926059,1.437428165636144,5.347617052258618,-808095.4393259468,0.05879108550298423,0.5448323732068848,-789522.5091359932,0.20352352259320314,21.12029634455721,-803938.2604268632,1.0223150014212676,0.5296310756363933,-801912.5225008891,0.7646405127343534,7.849315503928965,-794049.2354743823,0.6989186480410126,6.87781841947533,-814193.8647533458,0.8852782052211031,9.334631175295913
+3,3,0.00010559208431248382,0.871670386095456,-1376882.3191106108,-795019.7041121587,1.0623904260280357,8.59415748006841,-808447.2602100212,0.05879108748614492,0.5448324180244323,-790036.1207336787,0.20352355232601096,21.120499613123066,-803280.4365906577,1.0223156310422086,0.5296310565325039,-801913.2141124238,0.7646397332938777,7.849320590516409,-794050.473416772,0.6989082446227728,6.877882156513195,-814192.7894051562,0.8852798138822874,9.334648695392262
+4,4,0.00010559049315627479,0.07015128064420798,-1376882.3191117246,-794658.0175977976,1.1251830517616783,8.514892100389922,-807677.5423918352,0.05879108748614492,0.5448324180244323,-789888.8933961166,0.20352350532323682,21.120426174002823,-803102.8847466783,1.022316535866388,0.5296311209773129,-801913.2141124238,0.7646397332938777,7.849320590516409,-794049.2325867265,0.698908158523472,6.877882600430561,-814193.8242471424,0.8852802073165494,9.334653075341238
+5,5,0.00010559143289274391,0.1697956144703469,-1376882.3191117246,-794987.0313159251,1.2137224368583803,7.748229523847203,-807547.2398569525,0.05879108748614492,0.5448324180244323,-789724.873364379,0.20352350532323682,21.120426174002823,-802873.2925607098,1.022316535866388,0.5296311209773129,-801913.2141124238,0.7646397332938777,7.849320590516409,-794049.2325867265,0.698908158523472,6.877882600430561,-814193.8242471424,0.8852802073165494,9.334653075341238
+6,6,0.00010559143289274391,0.0002104590846558891,-1376882.3191117246,-794919.3525195834,1.2137222016802447,7.748226963005033,-807602.8883771595,0.05879108748614492,0.5448324180244323,-789740.8658638338,0.20352350532323682,21.120426174002823,-802901.9082448303,1.022316535866388,0.5296311209773129,-801913.2141124238,0.7646397332938777,7.849320590516409,-794049.2325867265,0.698908158523472,6.877882600430561,-814193.8242471424,0.8852802073165494,9.334653075341238

docs/_Examples/outputs/iterative_fitter_output4.csv

@@ -0,0 +1,13 @@
+,iter,best_obj,rel_diff,best_ext_h0,Nd_intercept,Nd_beta0,Nd_beta1,Pr_intercept,Pr_beta0,Pr_beta1,Ce_intercept,Ce_beta0,Ce_beta1,La_intercept,La_beta0,La_beta1,Dy_intercept,Dy_beta0,Dy_beta1,Sm_intercept,Sm_beta0,Sm_beta1,Y_intercept,Y_beta0,Y_beta1
+0,0,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20
+1,1,0.033647778113830525,21.000000000000057,-1376877.1491014853,-796630.7472372957,0.7459820315595859,7.683962525237933,-808126.331804689,0.05879108393945308,0.5448323469435739,-798601.759258479,0.6005010041816041,4.910031852331482,-802384.4792986697,0.5930046452497859,5.277120552778203,-805107.4528195162,0.6129648575972374,5.360202430370018,-795364.2081485003,0.5978078971541942,5.279174082617606,-814654.8936071004,0.9657036817294907,8.515030539951253
+2,2,0.011371891310485667,1.7809885286134581,-1377917.5261387695,-796507.2063621008,1.282838759795358,6.902804792440243,-808735.3933837808,0.05879108229837919,0.5448322898792433,-798307.3201515124,0.6005010298214011,4.910022284826427,-801912.301008032,0.5930045609171245,5.277127676968026,-801913.8255634321,0.7646294828913005,7.848342595191327,-795363.041378655,0.5978084651250514,5.279183026035014,-813457.4899543246,0.8866269789489127,9.867663759220948
+3,3,0.008641080472189547,0.7095542768622557,-1377917.5261387695,-796048.7760914894,1.0825298224626023,6.211283570154922,-807884.6838503737,0.05879107717406858,0.5448320238819658,-799326.8310861788,0.6005020116531858,4.91004437191519,-801737.4462156398,0.5930048961782302,5.277144735292778,-801426.9157177466,0.5532849580804902,9.206773080251143,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+4,4,0.0077116880626842774,0.001045483581763083,-1377917.5261387695,-796123.8048527003,1.082532056501894,6.21126666931227,-807682.864923159,0.05879107717406858,0.5448320238819658,-798906.4762851987,0.6005020116531858,4.91004437191519,-801602.4497529072,0.5930048961782302,5.277144735292778,-801426.3864333024,0.5532852226657412,9.206784416704208,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+5,5,0.007654988377412918,0.0004889223754199215,-1377917.5269444608,-795978.009332054,1.0825315776813356,6.211262701179254,-807775.0879128327,0.05879107717406858,0.5448320238819658,-798839.9836325685,0.6005020116531858,4.91004437191519,-801517.285745487,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+6,6,0.007610209179550956,0.0003179015187814805,-1377917.5269997541,-795889.1188724772,1.0825315776813356,6.211262701179254,-807684.2192836072,0.05879107717406858,0.5448320238819658,-798798.9084175534,0.6005020116531858,4.91004437191519,-801483.3687901833,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+7,7,0.007598843960371909,0.00011070832700715566,-1377917.5269100612,-795897.0467890751,1.0825315776813356,6.211262701179254,-807644.9779835651,0.05879107717406858,0.5448320238819658,-798798.9084175534,0.6005020116531858,4.91004437191519,-801441.5615806901,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+8,8,0.007598581095870595,3.145446182414225e-05,-1377917.5269485628,-795872.4794372824,1.0825315776813356,6.211262701179254,-807644.5039264921,0.05879107717406858,0.5448320238819658,-798798.9084175534,0.6005020116531858,4.91004437191519,-801441.5615806901,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+9,9,0.0075985812451889985,0.0,-1377917.5269485628,-795872.4794372824,1.0825315776813356,6.211262701179254,-807644.5039264921,0.05879107717406858,0.5448320238819658,-798798.9084175534,0.6005020116531858,4.91004437191519,-801441.5615806901,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+10,10,0.0075985812451889985,0.0,-1377917.5269485628,-795872.4794372824,1.0825315776813356,6.211262701179254,-807644.5039264921,0.05879107717406858,0.5448320238819658,-798798.9084175534,0.6005020116531858,4.91004437191519,-801441.5615806901,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948
+11,11,0.0075985812451889985,0.0,-1377917.5269485628,-795872.4794372824,1.0825315776813356,6.211262701179254,-807644.5039264921,0.05879107717406858,0.5448320238819658,-798798.9084175534,0.6005020116531858,4.91004437191519,-801441.5615806901,0.5930048961782302,5.277144735292778,-801426.9000381464,0.5532852863195881,9.206787175138803,-795364.1985261647,0.5978087860273482,5.279188095059933,-813457.4899543246,0.8866269789489127,9.867663759220948

docs/_Examples/outputs/iterative_fitter_short_info_dict.txt

@@ -0,0 +1 @@
+{"(HA)2(org)_h0": -1376882.3191117246, "Nd_slope": 3, "Nd_intercept": -794919.3525195834, "Pr_slope": 3, "Pr_intercept": -807602.8883771595, "Ce_slope": 3, "Ce_intercept": -789740.8658638337, "La_slope": 3, "La_intercept": -802901.9082448303, "Dy_slope": 3, "Dy_intercept": -801913.2141124237, "Sm_slope": 3, "Sm_intercept": -794049.2325867265, "Y_slope": 3, "Y_intercept": -814193.8242471423, "Ce_beta0": 0.2035235053232368, "Ce_beta1": 21.120426174002823, "Ce_Cphi": -0.02618999999473301, "Dy_beta0": 0.7646397332938777, "Dy_beta1": 7.849320590516409, "Dy_Cphi": -0.019699989216349984, "La_beta0": 1.022316535866388, "La_beta1": 0.5296311209773129, "La_Cphi": -0.024339999997603376, "Nd_beta0": 1.2137222016802447, "Nd_beta1": 7.748226963005033, "Nd_Cphi": -0.01963615126026457, "Pr_beta0": 0.05879108748614492, "Pr_beta1": 0.5448324180244323, "Pr_Cphi": -0.02066999867229882, "Sm_beta0": 0.6989081585234721, "Sm_beta1": 6.877882600430561, "Sm_Cphi": -0.019920000110321332, "Y_beta0": 0.8852802073165494, "Y_beta1": 9.334653075341238, "Y_Cphi": -0.015467323909969704}

docs/_Examples/outputs/iterative_fitter_w_mse_output.csv

@@ -0,0 +1,10 @@
+,iter,best_obj,rel_diff,best_ext_h0,Nd_intercept,Nd_beta0,Nd_beta1,Pr_intercept,Pr_beta0,Pr_beta1,Ce_intercept,Ce_beta0,Ce_beta1,La_intercept,La_beta0,La_beta1,Dy_intercept,Dy_beta0,Dy_beta1,Sm_intercept,Sm_beta0,Sm_beta1,Y_intercept,Y_beta0,Y_beta1
+0,0,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20
+1,1,7.243881311122883e-06,21.000000000000057,-1376882.0784583509,-793959.2365328791,1.213718406400912,7.748153622541222,-805092.317983506,0.05879109101834264,0.5448322349963308,-789740.8658638337,0.2035235053232368,21.120426174002823,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794033.3277785552,0.6989082242196476,6.877882016105836,-814036.0292108656,0.8852797060008407,9.334636294454038
+2,2,5.235683016217101e-06,5.0826146693579895e-05,-1376882.0784583509,-793959.2365328791,1.213718406400912,7.748153622541222,-805061.9201482645,0.05879109126451901,0.5448322298014207,-789740.8658638337,0.2035235053232368,21.120426174002823,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038
+3,3,5.234379962639123e-06,0.0004431912247434154,-1376882.0784583509,-793651.8526619686,1.213717095858249,7.748129467548838,-805038.8843481757,0.058791091500561485,0.5448322248125199,-789722.8075867157,0.20352350320923904,21.120419048363047,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038
+4,4,1.0136650760054431e-05,3.4483260160044286e-05,-1376882.0786722435,-793679.2203652804,1.213717095858249,7.748129467548838,-805038.8843481757,0.058791091500561485,0.5448322248125199,-789722.8075867157,0.20352350320923904,21.120419048363047,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038
+5,5,1.0461762551892658e-05,1.3733029030223567e-05,-1376882.079002063,-793690.1199850544,1.213717095858249,7.748129467548838,-805038.8843481757,0.058791091500561485,0.5448322248125199,-789722.8075867157,0.20352350320923904,21.120419048363047,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038
+6,6,1.0461847595183674e-05,0.0,-1376882.079002063,-793690.1199850544,1.213717095858249,7.748129467548838,-805038.8843481757,0.058791091500561485,0.5448322248125199,-789722.8075867157,0.20352350320923904,21.120419048363047,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038
+7,7,1.0568726787700163e-05,0.0,-1376882.079002063,-793690.1199850544,1.213717095858249,7.748129467548838,-805038.8843481757,0.058791091500561485,0.5448322248125199,-789722.8075867157,0.20352350320923904,21.120419048363047,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038
+8,8,1.1654800306565983e-05,0.0,-1376882.079002063,-793690.1199850544,1.213717095858249,7.748129467548838,-805038.8843481757,0.058791091500561485,0.5448322248125199,-789722.8075867157,0.20352350320923904,21.120419048363047,-797993.3842652582,1.0223168335791357,0.5296306654103653,-801345.4872245657,0.7646372240575223,7.849276225207628,-794023.0863911208,0.6989083059080712,6.877881736471866,-814036.0292108656,0.8852797060008407,9.334636294454038

docs/_Examples/outputs/multi_only_iterative_fitter_output.csv

@@ -0,0 +1,27 @@
+,iter,best_obj,rel_diff,best_ext_h0,Nd_intercept,Nd_beta0,Nd_beta1,Pr_intercept,Pr_beta0,Pr_beta1,Ce_intercept,Ce_beta0,Ce_beta1,La_intercept,La_beta0,La_beta1,Dy_intercept,Dy_beta0,Dy_beta1,Sm_intercept,Sm_beta0,Sm_beta1,Y_intercept,Y_beta0,Y_beta1
+0,0,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20
+1,1,0.03589758768077916,21.000000000000057,-1376877.1578525545,-796630.7472372957,0.7459820315595859,7.683962525237933,-804949.676541606,0.5878722433097117,5.446965040891874,-799671.8535977869,0.5683138771769763,4.330159786749355,-798281.080974753,0.05929999713109059,14.235546390790574,-805107.4528322341,0.6129648560200409,5.360202416896411,-795364.2084167162,0.5978078969216549,5.279174081605432,-814654.8936058494,0.9657036817607582,8.515030540098573
+2,2,0.020523566039320422,2.5585440673715034,-1377356.1260593878,-797059.9776562914,0.7459837782866834,7.683963804574712,-807474.5742908432,0.412119358146579,0.5446965040891875,-798541.1385153377,0.5683086183011624,4.3301017748512365,-793826.8317553926,0.059179052561110786,19.77323694076803,-801913.8726103224,0.7646416177518945,7.84824568473698,-795363.0419325187,0.59780846432948,5.279183016139414,-813457.2158804197,0.8866130626375315,9.867928019371377
+3,3,0.019477920445525317,7.677982352652354,-1375588.6756908477,-793163.8973856996,0.3209195035432397,13.123168416291694,-808683.1537883538,0.412118699519776,0.5446960424554511,-790111.3963314675,0.05683086183011624,23.89686703927439,-791050.7992598361,0.05905360634622211,29.84340866533762,-801427.30300279,0.5534942813794564,9.205560588460095,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+4,4,0.010207128776218969,0.42455280980920035,-1375610.9624283572,-794262.8311628923,0.3216915257810643,13.804909552933434,-808435.0647171926,0.41211840588454257,0.5446957279361009,-790207.5472086382,0.056830829357604154,23.89689998523618,-795480.9739022574,0.05878743947127101,19.151367921508545,-801426.8680903579,0.5534944284911872,9.205566903683641,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+5,5,0.014641607256599173,1.6866445651799755,-1375538.6750637838,-798938.0765541978,0.3566318051278,2.900470205622694,-805875.0643270919,0.41211840588454257,0.5446957279361009,-793436.7925485797,0.056742997228693914,13.80172079259027,-796954.8557059788,0.05868227362010503,12.498956039602165,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+6,6,0.009364477922161399,0.5171670324839205,-1375506.0331521921,-798500.5981007863,0.35663166857868867,2.9004640415749985,-807020.2949058269,0.41211840588454257,0.5446957279361009,-793926.4769803158,0.056743019042837614,13.801790803325336,-794407.3237807738,0.059071074021005275,18.80781812800432,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+7,7,0.008587420254580813,1.059528868598552,-1375505.5181543154,-798685.9967218107,0.35663172954569944,2.900465970474833,-807454.2257048099,0.41211840588454257,0.5446957279361009,-789725.2952172983,0.05719014118011185,22.726516278088134,-791396.077682103,0.05903562942413778,26.228646183949166,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+8,8,0.010436904878903537,0.29420985511692826,-1375548.1106216116,-799519.3918074799,0.35663172954569944,2.900465970474833,-808023.7799223613,0.41211840588454257,0.5446957279361009,-786951.389362888,0.05708292450340597,28.34019679365209,-791002.4212304557,0.05887049745806909,27.19304858223857,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+9,9,0.008122441324296938,0.5056025050774561,-1375532.8964810933,-799252.2437781714,0.35663172954569944,2.900465970474833,-807837.3724848758,0.41211840588454257,0.5446957279361009,-790021.4446221002,0.05689505465396167,23.449630511144804,-795447.5533695783,0.058631276343575414,18.611038118056705,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+10,10,0.012928748169505148,0.8333232355853131,-1375557.2637008368,-798461.2222513977,0.35663172954569944,2.900465970474833,-806951.4949354676,0.41211840588454257,0.5446957279361009,-795969.4556880995,0.056793906518569214,11.277961619380736,-797714.8073241808,0.05842557708316379,13.092573662989777,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+11,11,0.007800190819962697,0.36349221067564863,-1375530.569913902,-798291.6222911178,0.35663172954569944,2.900465970474833,-806824.7532982712,0.41211840588454257,0.5446957279361009,-795638.7226937105,0.0567939201459683,11.27792734701911,-795125.0133593426,0.058611088348722154,17.75722543814867,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+12,12,0.00921314263132423,1.6582390417163946,-1375352.6611008795,-799018.4755125245,0.35663172954569944,2.900465970474833,-807547.1998794067,0.41211840588454257,0.5446957279361009,-789593.3324594139,0.057780238237745564,22.7526002647588,-790289.6353698338,0.05869157014173162,28.5282029237168,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+13,13,0.0164949043385192,0.5442127617504667,-1375330.196734157,-799860.8096262615,0.35663172954569944,2.900465970474833,-808560.5669803121,0.41211840588454257,0.5446957279361009,-784843.309147467,0.05766909907971607,32.27291103838875,-788888.7517598639,0.058542471191840494,31.701224910845188,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+14,14,0.010623495713475158,0.602757119120526,-1375351.9855972743,-799632.2232496517,0.35663172954569944,2.900465970474833,-808321.7283717714,0.41211840588454257,0.5446957279361009,-788451.2944893743,0.057453027762948625,26.992114973264634,-795921.9019280333,0.058276332684925714,18.490011381032488,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+15,15,0.020076787588486848,1.0484920101715856,-1375285.2763873788,-798333.4293311803,0.35663172954569944,2.900465970474833,-806908.9647236438,0.41211840588454257,0.5446957279361009,-799190.3142882502,0.05726405467005661,2.6992114973264636,-795227.311995532,0.05860570542646566,16.240039078247854,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+16,16,0.007946994350521493,0.0013058079059853106,-1375281.5761035422,-798434.7113981954,0.35663172954569944,2.900465970474833,-806825.5999826461,0.41211840588454257,0.5446957279361009,-799709.74809272,0.05726405662551625,2.6992166442291183,-795558.7828092766,0.05860569942350758,16.24014965075902,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+17,17,0.00786340427833731,0.06642675860532837,-1375281.5757328654,-798645.9616186575,0.35663172954569944,2.900465970474833,-807277.1206417971,0.41211840588454257,0.5446957279361009,-799564.0986061025,0.05726405599313575,2.6992201079308553,-795074.773177479,0.058327655804418455,17.215637464507253,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+18,18,0.007639301074559701,0.03358010063393503,-1375274.53927792,-798757.6626722564,0.35663172954569944,2.900465970474833,-807442.731414953,0.41211840588454257,0.5446957279361009,-799932.191263451,0.05726405599313575,2.6992201079308553,-795454.173437478,0.05797530586210222,16.76361227782182,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+19,19,0.007567810584575274,0.00032734971823157984,-1375274.5400305095,-798776.6132373732,0.35663172954569944,2.900465970474833,-807407.5175994666,0.41211840588454257,0.5446957279361009,-799839.5141766575,0.05726405599313575,2.6992201079308553,-795567.5444104999,0.05797527817732809,16.763631654115574,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+20,20,0.007559522636647893,0.0003284988853867459,-1375274.539920735,-798878.7031945293,0.35663172954569944,2.900465970474833,-807474.2323978223,0.41211840588454257,0.5446957279361009,-799918.231712956,0.05726405599313575,2.6992201079308553,-795552.3072518674,0.05797526981415537,16.763625801929546,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+21,21,0.007557017666936972,0.00011741201128258779,-1375274.5398505423,-798875.2254308723,0.35663172954569944,2.900465970474833,-807474.2323978223,0.41211840588454257,0.5446957279361009,-799969.9761753903,0.05726405599313575,2.6992201079308553,-795590.7893224553,0.05797526981415537,16.763625801929546,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+22,22,0.007557912174965889,4.953375811471988e-06,-1375274.5395942358,-798879.1825600903,0.35663172954569944,2.900465970474833,-807474.2323978223,0.41211840588454257,0.5446957279361009,-799969.9761753903,0.05726405599313575,2.6992201079308553,-795590.7893224553,0.05797526981415537,16.763625801929546,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+23,23,0.007557925434292841,0.0,-1375274.5395942358,-798879.1825600903,0.35663172954569944,2.900465970474833,-807474.2323978223,0.41211840588454257,0.5446957279361009,-799969.9761753903,0.05726405599313575,2.6992201079308553,-795590.7893224553,0.05797526981415537,16.763625801929546,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+24,24,0.007557925434292841,0.0,-1375274.5395942358,-798879.1825600903,0.35663172954569944,2.900465970474833,-807474.2323978223,0.41211840588454257,0.5446957279361009,-799969.9761753903,0.05726405599313575,2.6992201079308553,-795590.7893224553,0.05797526981415537,16.763625801929546,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377
+25,25,0.007557925434292841,0.0,-1375274.5395942358,-798879.1825600903,0.35663172954569944,2.900465970474833,-807474.2323978223,0.41211840588454257,0.5446957279361009,-799969.9761753903,0.05726405599313575,2.6992201079308553,-795590.7893224553,0.05797526981415537,16.763625801929546,-801427.2924061618,0.5534945060571388,9.205570263716002,-795364.198868676,0.5978087854283536,5.279188087719687,-813457.2158804197,0.8866130626375315,9.867928019371377

docs/_Examples/outputs/temp.xml

@@ -0,0 +1,716 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org) Ce(H(A)2)3(org) La(H(A)2)3(org) Dy(H(A)2)3(org) Sm(H(A)2)3(org) Y(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-1377917.5269485628</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.320974226079</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Nd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Nd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4929625.060282971</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.973500071223</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Pr(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Pr:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4941397.084772181</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.979936421078</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Ce(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Ce:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4932551.489263242</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">1.0060487218</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="La(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 La:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4935194.142426379</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.981485801577</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Dy(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Dy:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4935179.480883835</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.961666360676</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Y(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Y:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4947210.070800013</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.958888814485</molarVolume>
+      </standardState>
+    </species>
+
+
+
+
+    <species name="Sm(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Sm:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4929116.779371853</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume> 0.9642 </molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Gd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Gd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="yep">-4829446.858</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume>0.9642</molarVolume>
+      </standardState>
+    </species>
+    
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Nd+++ Pr+++ Ce+++ La+++ Dy+++ Sm+++ Y+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+		<binarySaltParameters anion="Cl-" cation="Pr+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 0.058791077174068576, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 0.5448320238819658, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+        <binarySaltParameters anion="Cl-" cation="Nd+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 1.0825315776813356, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 6.211262701179254, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="La+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 0.5930048961782302, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 5.2771447352927785, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+        <binarySaltParameters anion="Cl-" cation="Dy+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 0.5532852863195881, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 9.206787175138805, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Ce+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 0.6005020116531858, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 4.91004437191519, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Y+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 0.8866269789489127, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 9.867663759220948, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Sm+++">
+          <beta0 updated="Updated at 19:20 7-29-2020"> 0.5978087860273482, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 19:20 7-29-2020"> 5.279188095059934, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 19:20 7-29-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>

docs/_Examples/outputs/temp1.xml

@@ -0,0 +1,716 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org) Ce(H(A)2)3(org) La(H(A)2)3(org) Dy(H(A)2)3(org) Sm(H(A)2)3(org) Y(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-1376882.3191117246</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.320974226079</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Nd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Nd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4925566.309854757</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.973500071223</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Pr(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Pr:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4938249.845712334</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.979936421078</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Ce(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Ce:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4920387.823199008</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">1.0060487218</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="La(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 La:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4933548.865580005</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.981485801577</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Dy(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Dy:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4932560.171447597</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.961666360676</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Y(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Y:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4944840.781582316</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.958888814485</molarVolume>
+      </standardState>
+    </species>
+
+
+
+
+    <species name="Sm(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Sm:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4924696.189921901</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume> 0.9642 </molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Gd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Gd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="yep">-4829446.858</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume>0.9642</molarVolume>
+      </standardState>
+    </species>
+    
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Nd+++ Pr+++ Ce+++ La+++ Dy+++ Sm+++ Y+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+		<binarySaltParameters anion="Cl-" cation="Pr+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 0.05879108748614492, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 0.5448324180244323, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+        <binarySaltParameters anion="Cl-" cation="Nd+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 1.2137222016802447, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 7.748226963005033, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="La+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 1.022316535866388, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 0.5296311209773129, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+        <binarySaltParameters anion="Cl-" cation="Dy+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 0.7646397332938777, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 7.849320590516409, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Ce+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 0.2035235053232368, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 21.120426174002823, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Y+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 0.8852802073165494, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 9.334653075341238, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Sm+++">
+          <beta0 updated="Updated at 11:58 7-17-2020"> 0.6989081585234721, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 11:58 7-17-2020"> 6.877882600430561, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 10:47 7-17-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>

docs/_Examples/test_eval.py

@@ -0,0 +1,330 @@
+# This file tests adding 0.1 M NaCl to the feed.
+import llepe
+import pandas as pd
+import numpy as np
+import json
+import matplotlib as plt
+import matplotlib
+import cantera as ct
+
+
+class ModLLEPE(llepe.LLEPE):
+    def __init__(self,
+                 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='scipy_minimize',
+                 temp_xml_file_path=None,
+                 dependant_params_dict=None,
+                 custom_objects_dict=None,
+                 nacl_molarity=0):
+        self.nacl_molarity = nacl_molarity
+        super().__init__(exp_data,
+                         phases_xml_filename,
+                         phase_names,
+                         aq_solvent_name,
+                         extractant_name,
+                         diluant_name,
+                         complex_names,
+                         extracted_species_ion_names,
+                         extracted_species_list,
+                         aq_solvent_rho,
+                         extractant_rho,
+                         diluant_rho,
+                         opt_dict,
+                         objective_function,
+                         optimizer,
+                         temp_xml_file_path,
+                         dependant_params_dict,
+                         custom_objects_dict)
+
+
+    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
+        extracted_species_names = self._extracted_species_ion_names
+        extracted_species_list = self._extracted_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)
+
+        extracted_species_ind_list = [
+            phases_copy[aq_ind].species_names.index(
+                extracted_species_name)
+            for extracted_species_name in extracted_species_names]
+        extracted_species_charges = np.array(
+            [phases_copy[aq_ind].species(
+                extracted_species_ind).charge
+             for extracted_species_ind in extracted_species_ind_list])
+        self._extracted_species_charges = extracted_species_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
+            extracted_species_moles = np.array(
+                [feed_vol * row['{0}_aq_i'.format(
+                    extracted_species)]
+                 for extracted_species in extracted_species_list])
+            extracted_species_charge_sum = np.sum(
+                extracted_species_charges * extracted_species_moles)
+            chlorine_moles = extracted_species_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(extracted_species_list))
+
+            species_moles_aq = [aq_phase_solvent_moles,
+                                h_plus_moles,
+                                hydroxide_ions,
+                                chlorine_moles]
+            species_moles_aq.extend(list(extracted_species_moles))
+            species_moles_aq.append(self.nacl_molarity * feed_vol)
+            species_moles_aq[3] += self.nacl_molarity * feed_vol
+            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
+
+
+font = {'family': 'sans serif',
+        'size': 24}
+matplotlib.rc('font', **font)
+plt.rc('xtick', labelsize=18)
+plt.rc('ytick', labelsize=18)
+plt.rcParams['lines.linewidth'] = 4
+matplotlib.rcParams['lines.markersize'] = 10
+
+
+def ext_to_complex(h0, custom_obj_dict, mini_species):
+    linear_params = custom_obj_dict['lin_param_df']
+    row = linear_params[linear_params['species'] == mini_species]
+    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]
+
+
+def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
+    new_lp_df = lp_df.copy()
+    index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
+    new_lp_df.at[index, mini_lin_param] = input_val
+    return new_lp_df
+
+
+info_df = pd.read_csv('outputs/multi_only_iterative_fitter_output.csv')
+test_row = -1
+pitzer_params_filename = "../../data/jsons/min_h0_pitzer_params.txt"
+with open(pitzer_params_filename) as file:
+    pitzer_params_dict = json.load(file)
+pitzer_params_df = pd.DataFrame(pitzer_params_dict)
+species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
+pitzer_param_list = ['beta0', 'beta1']
+labeled_data = pd.read_csv("../../data/csvs/"
+                           "no_formiga_or_5_oa_PC88A_HCL_NdPrCeLaDySmY.csv")
+labeled_data = labeled_data.sort_values(['Feed Pr[M]', 'Feed Ce[M]'],
+                                        ascending=True)
+exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
+xml_file = "test_PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
+lin_param_df = pd.read_csv("../../data/csvs"
+                           "/zeroes_removed_min_h0_pitzer_lin_params.csv")
+estimator_params = {'exp_data': exp_data,
+                    'phases_xml_filename': xml_file,
+                    'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
+                    'aq_solvent_name': 'H2O(L)',
+                    'extractant_name': '(HA)2(org)',
+                    'diluant_name': 'dodecane',
+                    'complex_names': ['{0}(H(A)2)3(org)'.format(species)
+                                      for species in species_list],
+                    'extracted_species_ion_names': ['{0}+++'.format(species)
+                                                    for species in
+                                                    species_list],
+                    'aq_solvent_rho': 1000.0,
+                    'extractant_rho': 960.0,
+                    'diluant_rho': 750.0,
+                    'temp_xml_file_path': 'outputs/temp.xml',
+                    'objective_function': llepe.lmse_perturbed_obj,
+                    'nacl_molarity': 0
+                    }
+dependant_params_dict = {}
+for species, complex_name in zip(species_list,
+                                 estimator_params['complex_names']):
+    inner_dict = {'upper_element_name': 'species',
+                  'upper_attrib_name': 'name',
+                  'upper_attrib_value': complex_name,
+                  'lower_element_name': 'h0',
+                  'lower_attrib_name': None,
+                  'lower_attrib_value': None,
+                  'input_format': '{0}',
+                  'function': ext_to_complex,
+                  'kwargs': {"mini_species": species},
+                  'independent_params': '(HA)2(org)_h0'}
+    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
+info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
+                               'upper_attrib_name': 'name',
+                               'upper_attrib_value': '(HA)2(org)',
+                               'lower_element_name': 'h0',
+                               'lower_attrib_name': None,
+                               'lower_attrib_value': None,
+                               'input_format': '{0}',
+                               'input_value':
+                                   info_df.iloc[test_row, :]['best_ext_h0']}}
+for species in species_list:
+    for pitzer_param in pitzer_param_list:
+        pitzer_str = "{0}_{1}".format(species, pitzer_param)
+        value = info_df.iloc[test_row, :][pitzer_str]
+        pitzer_params_dict[pitzer_str]['input_value'] = value
+    lin_str = "{0}_slope".format(species)
+    inner_dict = {'custom_object_name': 'lin_param_df',
+                  'function': mod_lin_param_df,
+                  'kwargs': {'mini_species': species,
+                             'mini_lin_param': 'slope'},
+                  'input_value': 3
+                  }
+    info_dict[lin_str] = inner_dict
+    lin_str = "{0}_intercept".format(species)
+    value = info_df.iloc[test_row, :][lin_str]
+    inner_dict = {'custom_object_name': 'lin_param_df',
+                  'function': mod_lin_param_df,
+                  'kwargs': {'mini_species': species,
+                             'mini_lin_param': 'intercept'},
+                  'input_value': value
+                  }
+    info_dict[lin_str] = inner_dict
+
+info_dict.update(pitzer_params_dict)
+estimator = ModLLEPE(**estimator_params)
+estimator.set_custom_objects_dict({'lin_param_df': lin_param_df})
+estimator.update_custom_objects_dict(info_dict)
+estimator.update_xml(info_dict,
+                     dependant_params_dict=dependant_params_dict)
+exp_data = estimator.get_exp_df()
+feed_cols = []
+for col in exp_data.columns:
+    if 'aq_i' in col:
+        feed_cols.append(col)
+exp_data['total_re'] = exp_data[feed_cols].sum(axis=1)
+label_list = []
+for index, row in exp_data[feed_cols].iterrows():
+    bool_list = list((row > 0).values)
+    label = ''
+    for species, el in zip(species_list, bool_list):
+        if el:
+            label = '{0}-{1}'.format(label, species)
+    label = label[1:]
+    label_list.append(label)
+r2s = ""
+for species in species_list:
+    # if species=='La':
+    # save_name = 'outputs' \
+    #             '/parity_iterative_fitter_{0}_org_eq'.format(species)
+    save_name = None
+    # fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),
+    #                                 c_data='z_i',
+    #                                 c_label='Feed total RE '
+    #                                         'molarity (mol/L)',
+    #                                 print_r_squared=True,
+    #                                 save_path=save_name)
+    r2s += str(estimator.r_squared('{0}_org_eq'.format(species))) + ','
+
+    fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),
+                                    data_labels=list(labeled_data['label']),
+                                    print_r_squared=True,
+                                    save_path=save_name)
+    ax.legend(loc=4)
+pred_df = pd.DataFrame(estimator.get_predicted_dict())
+new_cols = []
+for col in pred_df.columns:
+    new_cols.append("pred_{0}".format(col))
+pred_df.columns = new_cols
+new_cols = ['label',
+            'h_i',
+            'h_eq',
+            'z_i',
+            'z_eq'
+            ]
+for species in species_list:
+    new_cols.append("{0}_aq_i".format(species))
+    new_cols.append("{0}_aq_eq".format(species))
+    new_cols.append("{0}_d_eq".format(species))
+labeled_data.columns = new_cols
+total_df = labeled_data.join(pred_df)
+# total_df.to_csv('if_mse_total_df.csv')
+# short_info_dict = {}
+# for key, value in info_dict.items():
+#     short_info_dict[key] = value['input_value']
+# with open("outputs/iterative_fitter_short_info_dict.txt", 'w') as file:
+#     json.dump(short_info_dict, file)