From 7a4a7278d9592ea288f4b7db00f5f44d9030e1c1 Mon Sep 17 00:00:00 2001 From: titusquah <46580668+titusquah@users.noreply.github.com> Date: Wed, 7 Apr 2021 11:31:53 -0600 Subject: [PATCH] Revert "rename for rtd" This reverts commit 5ce76bb867072c1d4f53533a114e4f271b7573ea. --- docs/_build/doctrees/environment.pickle | Bin 90809 -> 154128 bytes docs/_build/doctrees/guide/about.doctree | Bin 10610 -> 0 bytes docs/_build/doctrees/guide/install.doctree | Bin 5473 -> 5675 bytes docs/_build/doctrees/guide/quickstart.doctree | Bin 7713 -> 7735 bytes docs/_build/doctrees/index.doctree | Bin 6242 -> 6297 bytes docs/_build/doctrees/modules/LLEPE.doctree | Bin 200180 -> 0 bytes docs/_build/doctrees/modules/reeps.doctree | Bin 200180 -> 201626 bytes docs/_build/html/.buildinfo | 2 +- docs/_build/html/_modules/index.html | 6 +- docs/_build/html/_modules/llepe/llepe.html | 124 +- docs/_build/html/_modules/reeps/reeps.html | 1735 +++++++++++++++++ docs/_build/html/_sources/guide/about.rst.txt | 61 - .../html/_sources/guide/install.rst.txt | 6 +- docs/_build/html/_sources/index.rst.txt | 12 +- .../html/_sources/modules/LLEPE.rst.txt | 15 - .../html/_sources/modules/reeps.rst.txt | 2 +- docs/_build/html/_static/alabaster.css | 701 +++++++ docs/_build/html/_static/classic.css | 266 +++ docs/_build/html/_static/custom.css | 1 + docs/_build/html/_static/default.css | 1 + docs/_build/html/_static/sidebar.js | 159 ++ docs/_build/html/genindex.html | 103 +- docs/_build/html/guide/about.html | 272 --- docs/_build/html/guide/install.html | 14 +- docs/_build/html/guide/quickstart.html | 2 +- docs/_build/html/index.html | 20 +- docs/_build/html/modules/LLEPE.html | 1123 ----------- docs/_build/html/modules/reeps.html | 31 +- docs/_build/html/objects.inv | Bin 723 -> 705 bytes docs/_build/html/py-modindex.html | 7 +- docs/_build/html/search.html | 5 +- docs/_build/html/searchindex.js | 2 +- docs/_source/conf.py | 3 - docs/_source/guide/about.rst | 61 - docs/_source/guide/install.rst | 6 +- docs/_source/index.rst | 3 +- docs/_source/modules/{LLEPE.rst => reeps.rst} | 2 +- 37 files changed, 3029 insertions(+), 1716 deletions(-) delete mode 100644 docs/_build/doctrees/guide/about.doctree delete mode 100644 docs/_build/doctrees/modules/LLEPE.doctree create mode 100644 docs/_build/html/_modules/reeps/reeps.html delete mode 100644 docs/_build/html/_sources/guide/about.rst.txt delete mode 100644 docs/_build/html/_sources/modules/LLEPE.rst.txt create mode 100644 docs/_build/html/_static/alabaster.css create mode 100644 docs/_build/html/_static/classic.css create mode 100644 docs/_build/html/_static/custom.css create mode 100644 docs/_build/html/_static/default.css create mode 100644 docs/_build/html/_static/sidebar.js delete mode 100644 docs/_build/html/guide/about.html delete mode 100644 docs/_build/html/modules/LLEPE.html delete mode 100644 docs/_source/guide/about.rst rename docs/_source/modules/{LLEPE.rst => reeps.rst} (74%) diff --git 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When it is not found, a full rebuild will be done. -config: fdde7da710e99305b2f90b2ffb8f91ce +config: 09c8b5742a1f96cda53a1848d8ac9ec1 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html index d96106f..ca853b9 100644 --- a/docs/_build/html/_modules/index.html +++ b/docs/_build/html/_modules/index.html @@ -85,11 +85,10 @@

Searchers

@@ -152,6 +151,7 @@

All modules for which code is available

@@ -164,7 +164,7 @@

- © Copyright 2020, UChicago Argonne, LLC. + © Copyright 2020, Titus Quah, Nwike Iloeje

diff --git a/docs/_build/html/_modules/llepe/llepe.html b/docs/_build/html/_modules/llepe/llepe.html index 13e2620..bf78f9b 100644 --- a/docs/_build/html/_modules/llepe/llepe.html +++ b/docs/_build/html/_modules/llepe/llepe.html @@ -85,11 +85,10 @@

Searchers

@@ -170,7 +169,7 @@ from .utils import set_size -
[docs]class LLEPE: +
[docs]class LLEPE: r""" Liquid-Liquid Extraction Parameter estimator @@ -181,7 +180,7 @@ must be the same order as they appear in the xml, complex_names and extracted_species_ion_names. - For example, say in exp_data, ES_1 is Nd ES_2 is Pr, + For example, say in exp_csv_filename's csv, ES_1 is Nd ES_2 is Pr, and .. code-block:: python @@ -192,7 +191,7 @@ Then: - The exp_data column ordering must be (names do not matter): + The csvs column ordering must be: [h_i, h_eq, z_i, z_eq, Nd_aq_i, Nd_aq_eq, Nd_d_eq, Pr_aq_i, Pr_aq_eq, Pr_d_eq] @@ -527,8 +526,7 @@ self._predicted_dict = None self.update_predicted_dict() - # TODO: move scipy_minimize to optimizers -
[docs] @staticmethod +
[docs] @staticmethod def scipy_minimize(objective, x_guess, optimizer_kwargs=None): """ The default optimizer for LLEPE @@ -562,8 +560,7 @@ est_parameters = res.x return est_parameters, res.fun
- # TODO: move log_mean_squared_error to objectives -
[docs] def log_mean_squared_error(self, predicted_dict, meas_df): +
[docs] def log_mean_squared_error(self, predicted_dict, meas_df): """Default objective function for LLEPE Returns the log mean squared error of @@ -587,14 +584,14 @@ obj = np.sum(log_diff) return obj
-
[docs] def get_exp_df(self) -> pd.DataFrame: +
[docs] def get_exp_df(self) -> pd.DataFrame: """Returns the experimental DataFrame :return: (pd.DataFrame) Experimental data """ return self._exp_df
-
[docs] def set_exp_df(self, exp_data): +
[docs] def set_exp_df(self, exp_data): """Changes the experimental DataFrame to input exp_csv_filename data and renames columns to internal LLEPE names @@ -635,7 +632,7 @@ self.update_predicted_dict() return None
-
[docs] def get_phases(self) -> list: +
[docs] def get_phases(self) -> list: """ Returns the list of Cantera solutions @@ -643,7 +640,7 @@ """ return self._phases
-
[docs] def set_phases(self, phases_xml_filename, phase_names): +
[docs] def set_phases(self, phases_xml_filename, phase_names): """Change list of Cantera solutions by inputting new xml file name and phase names @@ -676,7 +673,7 @@ self.update_predicted_dict() return None
-
[docs] def get_opt_dict(self) -> dict: +
[docs] def get_opt_dict(self) -> dict: """ Returns the dictionary containing optimization information @@ -685,7 +682,7 @@ """ return self._opt_dict
-
[docs] def set_opt_dict(self, opt_dict): +
[docs] def set_opt_dict(self, opt_dict): """ Change the dictionary to input opt_dict. @@ -701,14 +698,14 @@ self._opt_dict = opt_dict return None
-
[docs] def get_aq_solvent_name(self) -> str: +
[docs] def get_aq_solvent_name(self) -> str: """Returns aq_solvent_name :return: aq_solvent_name: (str) name of aqueous solvent in xml file """ return self._aq_solvent_name
-
[docs] def set_aq_solvent_name(self, aq_solvent_name): +
[docs] def set_aq_solvent_name(self, aq_solvent_name): """ Change aq_solvent_name to input aq_solvent_name :param aq_solvent_name: (str) name of aqueous solvent in xml file @@ -716,14 +713,14 @@ self._aq_solvent_name = aq_solvent_name return None
-
[docs] def get_extractant_name(self) -> str: +
[docs] def get_extractant_name(self) -> str: """Returns extractant name :return: extractant_name: (str) name of extractant in xml file """ return self._extractant_name
-
[docs] def set_extractant_name(self, extractant_name): +
[docs] def set_extractant_name(self, extractant_name): """ Change extractant_name to input extractant_name :param extractant_name: (str) name of extractant in xml file @@ -731,13 +728,13 @@ self._extractant_name = extractant_name return None
-
[docs] def get_diluant_name(self) -> str: +
[docs] def get_diluant_name(self) -> str: """ Returns diluant name :return: diluant_name: (str) name of diluant in xml file """ return self._diluant_name
-
[docs] def set_diluant_name(self, diluant_name): +
[docs] def set_diluant_name(self, diluant_name): """ Change diluant_name to input diluant_name @@ -746,14 +743,14 @@ self._diluant_name = diluant_name return None
-
[docs] def get_complex_names(self) -> list: +
[docs] def get_complex_names(self) -> list: """Returns list of complex names :return: complex_names: (list) names of complexes in xml file. """ return self._complex_names
-
[docs] def set_complex_names(self, complex_names): +
[docs] def set_complex_names(self, complex_names): """Change complex names list to input complex_names :param complex_names: (list) names of complexes in xml file. @@ -761,7 +758,7 @@ self._complex_names = complex_names return None
-
[docs] def get_extracted_species_ion_names(self) -> list: +
[docs] def get_extracted_species_ion_names(self) -> list: """Returns list of extracted species ion names :return: extracted_species_ion_names: (list) names of @@ -769,7 +766,7 @@ """ return self._extracted_species_ion_names
-
[docs] def set_extracted_species_ion_names(self, extracted_species_ion_names): +
[docs] def set_extracted_species_ion_names(self, extracted_species_ion_names): """Change list of extracted species ion names to input extracted_species_ion_names @@ -779,7 +776,7 @@ self._extracted_species_ion_names = extracted_species_ion_names return None
-
[docs] def get_extracted_species_list(self) -> list: +
[docs] def get_extracted_species_list(self) -> list: """Returns list of extracted species names :return: extracted_species_list: (list) names of extracted species in @@ -787,7 +784,7 @@ """ return self._extracted_species_list
-
[docs] def set_extracted_species_list(self, extracted_species_list): +
[docs] def set_extracted_species_list(self, extracted_species_list): """Change list of extracted species ion names to input extracted_species_ion_names @@ -797,14 +794,14 @@ self._extracted_species_list = extracted_species_list return None
-
[docs] def get_aq_solvent_rho(self) -> str: +
[docs] def get_aq_solvent_rho(self) -> str: """Returns aqueous solvent density (g/L) :return: aq_solvent_rho: (float) density of aqueous solvent """ return self._aq_solvent_rho
-
[docs] def set_aq_solvent_rho(self, aq_solvent_rho): +
[docs] def set_aq_solvent_rho(self, aq_solvent_rho): """Changes aqueous solvent density (g/L) to input aq_solvent_rho :param aq_solvent_rho: (float) density of aqueous solvent @@ -812,14 +809,14 @@ self._aq_solvent_rho = aq_solvent_rho return None
-
[docs] def get_extractant_rho(self) -> str: +
[docs] def get_extractant_rho(self) -> str: """Returns extractant density (g/L) :return: extractant_rho: (float) density of extractant """ return self._extractant_rho
-
[docs] def set_extractant_rho(self, extractant_rho): +
[docs] def set_extractant_rho(self, extractant_rho): """Changes extractant density (g/L) to input extractant_rho :param extractant_rho: (float) density of extractant @@ -827,14 +824,14 @@ self._extractant_rho = extractant_rho return None
-
[docs] def get_diluant_rho(self) -> str: +
[docs] def get_diluant_rho(self) -> str: """Returns diluant density (g/L) :return: diluant_rho: (float) density of diluant """ return self._diluant_rho
-
[docs] def set_diluant_rho(self, diluant_rho): +
[docs] def set_diluant_rho(self, diluant_rho): """Changes diluant density (g/L) to input diluant_rho :param diluant_rho: (float) density of diluant @@ -842,12 +839,7 @@ self._diluant_rho = diluant_rho return None
- # TODO: Change input DataFrame structure to contain information about - # other species like NaCl - # TODO: Change DataFrame structure to contain info about O/A ratio - # TODO: Generalize code to more than just org and aq phase (3+ phases) - # TODO: Handle multiple electrolytes ie. NO3- with Cl- -
[docs] def set_in_moles(self, feed_vol): +
[docs] def set_in_moles(self, feed_vol): """Function that initializes mole fractions to input feed_vol This function is called at initialization @@ -930,7 +922,7 @@ for extracted_species in extracted_species_list]) extracted_species_charge_sum = np.sum( extracted_species_charges * extracted_species_moles) - anion_moles = extracted_species_charge_sum + h_plus_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 @@ -940,7 +932,7 @@ species_moles_aq = [aq_phase_solvent_moles, h_plus_moles, hydroxide_ions, - anion_moles] + chlorine_moles] species_moles_aq.extend(list(extracted_species_moles)) species_moles_org = [extractant_moles, diluant_moles] species_moles_org.extend(list(complex_moles)) @@ -954,7 +946,7 @@ self.update_predicted_dict() return None
-
[docs] def get_in_moles(self) -> pd.DataFrame: +
[docs] def get_in_moles(self) -> pd.DataFrame: """Returns the in_moles DataFrame which contains the initial mole fractions of each species for each experiment @@ -962,7 +954,7 @@ """ return self._in_moles
-
[docs] def set_objective_function(self, objective_function): +
[docs] def set_objective_function(self, objective_function): """Change objective function to input objective_function. See class docstring on "objective_function" for instructions @@ -987,7 +979,7 @@ self._objective_function = objective_function return None
-
[docs] def get_objective_function(self): +
[docs] def get_objective_function(self): """Returns objective function :return: objective_function: (func) Objective function to quantify @@ -995,7 +987,7 @@ """ return self._objective_function
-
[docs] def set_optimizer(self, optimizer): +
[docs] def set_optimizer(self, optimizer): """Change optimizer function to input optimizer. See class docstring on "optimizer" for instructions @@ -1020,7 +1012,7 @@ self._optimizer = optimizer return None
-
[docs] def get_optimizer(self): +
[docs] def get_optimizer(self): """Returns objective function :return: optimizer: (func) Optimizer function to minimize objective @@ -1028,7 +1020,7 @@ """ return self._optimizer
-
[docs] def get_temp_xml_file_path(self): +
[docs] def get_temp_xml_file_path(self): """Returns path to temporary xml file. This xml file is a duplicate of the phases_xml_file name and is @@ -1039,7 +1031,7 @@ """ return self._temp_xml_file_path
-
[docs] def set_temp_xml_file_path(self, temp_xml_file_path): +
[docs] def set_temp_xml_file_path(self, temp_xml_file_path): """Changes temporary xml file path to input temp_xml_file_path. This xml file is a duplicate of the phases_xml_file name and is @@ -1051,7 +1043,7 @@ self._temp_xml_file_path = temp_xml_file_path return None
-
[docs] def get_dependant_params_dict(self): +
[docs] def get_dependant_params_dict(self): """ Returns the dependant_params_dict @@ -1060,7 +1052,7 @@ """ return self._dependant_params_dict
-
[docs] def set_dependant_params_dict(self, dependant_params_dict): +
[docs] def set_dependant_params_dict(self, dependant_params_dict): """ Sets the dependant_params_dict @@ -1070,7 +1062,7 @@ self._dependant_params_dict = dependant_params_dict return None
-
[docs] def get_custom_objects_dict(self): +
[docs] def get_custom_objects_dict(self): """ Returns the custom_objects_dict @@ -1079,7 +1071,7 @@ """ return self._custom_objects_dict
-
[docs] def set_custom_objects_dict(self, custom_objects_dict): +
[docs] def set_custom_objects_dict(self, custom_objects_dict): """ Sets the custom_objects_dict @@ -1089,11 +1081,7 @@ self._custom_objects_dict = custom_objects_dict return None
- # TODO: Change DataFrame strucutre to contain info whether to set - # equilibrium pH to measured value. Will be useful for saponification - # TODO: Find way to add saponification to model. - # Maybe use fsolve to match experimental equilibrium pH -
[docs] def update_predicted_dict(self, +
[docs] def update_predicted_dict(self, phases_xml_filename=None, phase_names=None): """Function that computes the predicted equilibrium concentrations @@ -1160,7 +1148,7 @@ self._predicted_dict = predicted_dict return None
-
[docs] def get_predicted_dict(self): +
[docs] def get_predicted_dict(self): """Returns predicted dictionary of species concentrations that xml parameters predicts given current in_moles @@ -1224,7 +1212,7 @@ objective_values = objective_values[0] return objective_values -
[docs] def fit(self, +
[docs] def fit(self, objective_function=None, optimizer=None, objective_kwargs=None, @@ -1267,7 +1255,7 @@ return opt_dict, obj_value
-
[docs] def update_xml(self, +
[docs] def update_xml(self, info_dict, phases_xml_filename=None, dependant_params_dict=None): @@ -1374,7 +1362,7 @@ self.set_phases(self._phases_xml_filename, self._phase_names) return None
-
[docs] def update_custom_objects_dict(self, info_dict): +
[docs] def update_custom_objects_dict(self, info_dict): """ updates internal custom_objects_dict with info_dict @@ -1399,7 +1387,7 @@ self._custom_objects_dict = custom_objects_dict return None
-
[docs] def parity_plot(self, +
[docs] def parity_plot(self, compared_value=None, c_data=None, c_label=None, @@ -1508,9 +1496,7 @@ filtered_meas = filtered_data['meas'] filtered_pred = filtered_data['pred'] if len(filtered_pred) != 0: - ax.scatter(filtered_meas, - filtered_pred, - label=label) + ax.scatter(filtered_meas, filtered_pred, label=label) if legend: ax.legend(loc='best') @@ -1545,7 +1531,7 @@ plt.savefig(save_path, bbox_inches='tight') return fig, ax
-
[docs] def r_squared(self, compared_value=None): +
[docs] def r_squared(self, compared_value=None): """r-squared value comparing measured and predicted compared value Closer to 1, the better the model's predictions. @@ -1585,7 +1571,7 @@ r_2 = (1 - num / den) return r_2
-
[docs] @staticmethod +
[docs] @staticmethod def plot_3d_data(x_data, y_data, z_data, @@ -1595,8 +1581,6 @@ z_label=None, c_label=None): """ - THis is for plotting 3d scatter plots. - We suggest use matplotlib's ax.scatter to make 3d plots. :param x_data: (list) list of data for x axis :param y_data: (list) list of data for y axis @@ -1647,7 +1631,7 @@

- © Copyright 2020, UChicago Argonne, LLC. + © Copyright 2020, Titus Quah, Nwike Iloeje

diff --git a/docs/_build/html/_modules/reeps/reeps.html b/docs/_build/html/_modules/reeps/reeps.html new file mode 100644 index 0000000..bb9a06e --- /dev/null +++ b/docs/_build/html/_modules/reeps/reeps.html @@ -0,0 +1,1735 @@ + + + + + + + + + + + reeps.reeps — LLEPE 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for reeps.reeps

+from datetime import datetime
+import cantera as ct
+import pandas as pd
+import numpy as np
+from scipy.optimize import minimize
+# noinspection PyPep8Naming
+import xml.etree.ElementTree as ET
+import seaborn as sns
+import matplotlib.pyplot as plt
+import shutil
+import copy
+from inspect import signature
+import os
+import re
+import pkg_resources
+from .utils import set_size
+
+sns.set()
+sns.set(font_scale=1.6)
+
+
+
[docs]class REEPS:
+    r"""
+    Rare earth elements (REE or RE) Takes in experimental data
+    Returns parameters for GEM
+
+    .. note::
+
+        The order in which the REEs appear in the csv file must be the same
+        order as they appear in the xml, complex_names and
+        rare_earth_ion_names.
+
+        For example, say in exp_csv_filename's csv, RE_1 is Nd RE_2 is Pr,
+        and
+
+        .. code-block:: python
+
+            aq_solvent_name = 'H2O(L)'
+            extractant_name = '(HA)2(org)'
+            diluent_name = 'dodecane'
+
+        Then:
+
+        The csvs column ordering must be:
+
+        [h_i, h_eq, z_i, z_eq, Nd_aq_i, Nd_aq_eq, Nd_d_eq,
+        Pr_aq_i, Pr_aq_eq, Pr_d_eq]
+
+        The aqueous speciesArray must be
+        "H2O(L) H+ OH- Cl- Nd+++ Pr+++"
+
+        The organic speciesArray must be
+        "(HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org)"
+
+        .. code-block:: python
+
+            complex_names = ['Nd(H(A)2)3(org)', 'Pr(H(A)2)3(org)']
+            rare_earth_ion_names = ['Nd+++', 'Pr+++']
+
+
+    :param exp_data: (str) csv file name with experimental data
+
+        In the .csv file, the rows are different experiments and
+        columns are the measured quantities.
+
+        The ordering of the columns needs to be:
+
+        [h_i, h_eq, z_i, z_eq,
+        {RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq,
+        {RE_2}_aq_i, {RE_2}_aq_eq, {RE_2}_d_eq,...
+        {RE_N}_aq_i, {RE_N}_aq_eq, {RE_N}_d_eq]
+
+        Naming does not matter, just the order.
+
+        Where {RE_1}-{RE_N} are the rare earth element names of interest
+        i.e. Nd, Pr, La, etc.
+
+        Below is an explanation of the columns.
+
+        +-------+------------+------------------------------------------+
+        | Index |   Column   |                  Meaning                 |
+        +=======+============+==========================================+
+        | 0     | h_i        | Initial Concentration of                 |
+        |       |            | H+ ions (mol/L)                          |
+        +-------+------------+------------------------------------------+
+        | 1     | h_eq       | Equilibrium concentration of             |
+        |       |            | H+ ions (mol/L)                          |
+        +-------+------------+------------------------------------------+
+        | 2     | z_i        | Initial concentration of                 |
+        |       |            | extractant (mol/L)                       |
+        +-------+------------+------------------------------------------+
+        | 3     | z_eq       | Equilibrium concentration of             |
+        |       |            | extractant (mol/L)                       |
+        +-------+------------+------------------------------------------+
+        | 4     | {RE}_aq_i  | Initial concentration of RE ions (mol/L) |
+        +-------+------------+------------------------------------------+
+        | 5     | {RE}_aq_eq | Equilibrium concentration of RE ions     |
+        |       |            | in aqueous phase (mol/L)                 |
+        +-------+------------+------------------------------------------+
+        | 6     | {RE}_d_eq  | Equilibrium Ratio between amount of      |
+        |       |            | RE atoms in organic to aqueous           |
+        +-------+------------+------------------------------------------+
+    :param phases_xml_filename: (str) xml file with parameters
+        for equilibrium calc
+
+        Would recommend copying and modifying xmls located in data/xmls
+        or in Cantera's "data" folder
+
+        speciesArray fields need specific ordering.
+
+        In aqueous phase: aq_solvent_name, H+, OH-, Cl-, RE_1, RE_2, ..., RE_N
+
+        For aqueous phase, RE_1-RE_N represent RE ion names i.e. Nd+++, Pr+++
+
+        In organic phase : extractant_name, diluant_name, RE_1, RE_2, ..., RE_N
+
+        For organic phase, RE_1-RE_N represent RE complex names
+        i.e. Nd(H(A)2)3(org), Pr(H(A)2)3(org)
+
+    :param phase_names: (list) names of phases in xml file
+
+        Found in the xml file under <phase ... id={phase_name}>
+
+    :param aq_solvent_name: (str) name of aqueous solvent in xml file
+    :param extractant_name: (str) name of extractant in xml file
+    :param diluant_name: (str) name of diluant in xml file
+    :param complex_names: (list) names of complexes in xml file.
+
+        Ensure the ordering is correct
+    :param rare_earth_ion_names: (list) names of rare earth ions in xml file
+
+        Ensure the ordering is correct
+    :param re_species_list: (list) names of rare earth elements.
+
+        If ``None``, re_species_list will be rare_earth_ion_names without '+'
+            i.e. 'Nd+++'->'Nd'
+
+        Ensure the ordering is correct
+    :param aq_solvent_rho: (float) density of solvent (g/L)
+
+        If ``None``, molar volume/molecular weight is used from xml
+    :param extractant_rho: (float) density of extractant (g/L)
+
+        If ``None``, molar volume/molecular weight is used from xml
+    :param diluant_rho: (float) density of diluant (g/L)
+
+        If ``None``, molar volume/molecular weight is used from xml
+    :param opt_dict: (dict) dictionary containing info about which
+        species parameters are updated to fit model to experimental data
+
+        Should have the format as below. Dictionary keys under user defined
+        parameter name must be named as shown below ('upper_element_name',
+        'upper_attrib_name', etc.). 'attrib_name's and 'attrib_value's can
+        be None. {} denotes areas for user to fill in.
+
+        .. code-block:: python
+
+            opt_dict = {"{user_defined_name_for_parameter_1}":
+                            {'upper_element_name': {param_upper_element},
+                            'upper_attrib_name': {param_upper_attrib_name},
+                            'upper_attrib_value': {param_upper_attrib_value},
+                            'lower_element_name': {param_lower_element},
+                            'lower_attrib_name': {param_lower_attrib_name},
+                            'lower_attrib_value': {param_lower_attrib_value},
+                            'input_format': {str format to input input_value}
+                            'input_value': {guess_value}},
+                        "{user_defined_name_for_parameter_2}":
+                                        ...
+                        ...
+                        }
+    :param objective_function: (function or str) function to compute objective
+
+        By default, the objective function is log mean squared error
+        of distribution ratio
+
+        .. code-block:: python
+
+            np.sum((np.log10(d_pred)-np.log10(d_meas))^2)
+
+        Function needs to take inputs:
+
+        .. code-block:: python
+
+            objective_function(predicted_dict, measured_df, kwargs)
+
+        ``kwargs`` is optional
+
+        Function needs to return: (float) value computed by objective function
+
+        Below is the guide for referencing predicted values
+
+        +---------------------------+--------------------------------+
+        | To access                 | Use                            |
+        +===========================+================================+
+        | hydrogen ion conc in aq   | predicted_dict['h_eq']         |
+        +---------------------------+--------------------------------+
+        | extractant conc in org    | predicted_dict['z_eq']         |
+        +---------------------------+--------------------------------+
+        | RE ion eq conc in aq      | predicted_dict['{RE}_aq_eq']   |
+        +---------------------------+--------------------------------+
+        | RE complex eq conc in org | predicted_dict['{RE}_org_eq']  |
+        +---------------------------+--------------------------------+
+        | RE distribution ratio     | predicted_dict['{RE}_d_eq']    |
+        +---------------------------+--------------------------------+
+
+        Replace "{RE}" with rare earth element i.e. Nd, La, etc.
+
+        For measured values, use the same names, but
+        replace ``predicted_dict`` with ``measured_df``
+    :param optimizer: (function or str) function to perform optimization
+
+        .. note::
+
+            The optimized variables are not directly the species parameters,
+            but instead are first multiplied by the initial guess before
+            sending becoming the species parameters.
+
+            For example, say
+
+            .. code-block:: python
+
+                opt_dict = {'Nd(H(A)2)3(org):'h0':-4.7e6}
+
+            If the bounds on h0 need to be [-4.7e7,-4.7e5], then
+            divide the bounds by the guess and get
+
+            .. code-block:: python
+
+                "bounds": [(1e-1, 1e1)]
+
+        By default, the optimizer is scipy's optimize function with
+
+        .. code-block:: python
+
+            default_kwargs= {"method": 'SLSQP',
+                             "bounds": [(1e-1, 1e1)] * len(x_guess),
+                             "constraints": (),
+                             "options": {'disp': True,
+                                         'maxiter': 1000,
+                                         'ftol': 1e-6}}
+
+        Function needs to take inputs:
+        ``optimizer(objective_function, x_guess, kwargs)``
+
+        ``kwargs`` is optional
+
+        Function needs to return: ((np.ndarray, float)) Optimized parameters,
+            objective_function value
+
+    :param temp_xml_file_path: (str) path to temporary xml file.
+
+        This xml file is a duplicate of the phases_xml_file name and is
+        modified during the optimization process to avoid changing the original
+        xml file
+
+        default is local temp folder
+
+    :param dependant_params_dict: (dict) dictionary containing information
+        about parameters dependant on opt_dict
+    """
+
+    def __init__(self,
+                 exp_data,
+                 phases_xml_filename,
+                 phase_names,
+                 aq_solvent_name,
+                 extractant_name,
+                 diluant_name,
+                 complex_names,
+                 rare_earth_ion_names,
+                 re_species_list=None,
+                 aq_solvent_rho=None,
+                 extractant_rho=None,
+                 diluant_rho=None,
+                 opt_dict=None,
+                 objective_function='Log-MSE',
+                 optimizer='SLSQP',
+                 temp_xml_file_path=None,
+                 dependant_params_dict=None,
+                 ):
+        self._built_in_obj_list = ['Log-MSE']
+        self._built_in_opt_list = ['SLSQP']
+        self._exp_data = exp_data
+        self._phases_xml_filename = phases_xml_filename
+        self._opt_dict = opt_dict
+        self._phase_names = phase_names
+        self._aq_solvent_name = aq_solvent_name
+        self._extractant_name = extractant_name
+        self._diluant_name = diluant_name
+        self._complex_names = complex_names
+        self._rare_earth_ion_names = rare_earth_ion_names
+        self._aq_solvent_rho = aq_solvent_rho
+        self._extractant_rho = extractant_rho
+        self._diluant_rho = diluant_rho
+        self._objective_function = None
+        self.set_objective_function(objective_function)
+        self._optimizer = None
+        self._re_species_list = re_species_list
+        self.set_optimizer(optimizer)
+        if temp_xml_file_path is None:
+            temp_xml_file_path = r'{0}/temp.xml'.format(os.getenv('TEMP'))
+        self._temp_xml_file_path = temp_xml_file_path
+        self._dependant_params_dict = dependant_params_dict
+        # Try and except for adding package data to path.
+        # This only works for sdist, not bdist
+        # If bdist is needed, research "manifest.in" python setup files
+        try:
+            shutil.copyfile(self._phases_xml_filename,
+                            self._temp_xml_file_path)
+            self._phases = ct.import_phases(self._phases_xml_filename,
+                                            phase_names)
+        except FileNotFoundError:
+            self._phases_xml_filename = \
+                pkg_resources.resource_filename('reeps',
+                                                r'..\data\xmls\{0}'.format(
+                                                    phases_xml_filename))
+            shutil.copyfile(self._phases_xml_filename,
+                            self._temp_xml_file_path)
+            self._phases = ct.import_phases(self._phases_xml_filename,
+                                            phase_names)
+        if isinstance(self._exp_data, str):
+            try:
+                self._exp_df = pd.read_csv(self._exp_data)
+            except FileNotFoundError:
+                self._exp_data = pkg_resources.resource_filename(
+                    'reeps', r'..\data\csvs\{0}'.format(self._exp_data))
+                self._exp_df = pd.read_csv(self._exp_data)
+        else:
+            self._exp_df = self._exp_data.copy()
+
+        self._exp_df_columns = ['h_i', 'h_eq', 'z_i', 'z_eq']
+        if self._re_species_list is None:
+            self._re_species_list = []
+            for name in self._rare_earth_ion_names:
+                species = name.replace('+', '')
+                self._re_species_list.append(species)
+        for species in self._re_species_list:
+            self._exp_df_columns.append('{0}_aq_i'.format(species))
+            self._exp_df_columns.append('{0}_aq_eq'.format(species))
+            self._exp_df_columns.append('{0}_d_eq'.format(species))
+
+        self._exp_df.columns = self._exp_df_columns
+        for species in self._re_species_list:
+            self._exp_df['{0}_org_eq'.format(species)] = \
+                self._exp_df['{0}_aq_eq'.format(species)] \
+                * self._exp_df['{0}_d_eq'.format(species)]
+
+        self._in_moles = None
+
+        self._aq_ind = None
+        self._org_ind = None
+        self._re_charges = None
+
+        self.set_in_moles(feed_vol=1)
+        self._predicted_dict = None
+        self.update_predicted_dict()
+
+
[docs]    @staticmethod
+    def scipy_minimize(objective, x_guess, optimizer_kwargs=None):
+        """ The default optimizer for REEPS
+
+        Uses scipy.minimize
+
+        By default, options are
+
+        .. code-block:: python
+
+            default_kwargs= {"method": 'SLSQP',
+                            "bounds": [(1e-1, 1e1)]*len(x_guess),
+                            "constraints": (),
+                            "options": {'disp': True,
+                                        'maxiter': 1000,
+                                        'ftol': 1e-6}}
+
+        :param objective: (func) the objective function
+        :param x_guess: (np.ndarray) the initial guess (always 1)
+        :param optimizer_kwargs: (dict) dictionary of options for minimize
+        :returns: ((np.ndarray, float)) Optimized parameters,
+            objective_function value
+        """
+        if optimizer_kwargs is None:
+            optimizer_kwargs = {"method": 'SLSQP',
+                                "bounds": [(1e-1, 1e1)] * len(x_guess),
+                                "constraints": (),
+                                "options": {'disp': True,
+                                            'maxiter': 1000,
+                                            'ftol': 1e-6}}
+        res = minimize(objective, x_guess, **optimizer_kwargs)
+        est_parameters = res.x
+        return est_parameters, res.fun

+
+
[docs]    def log_mean_squared_error(self, predicted_dict, meas_df):
+        """Default objective function for REEPS
+
+        Returns the log mean squared error of
+        predicted distribution ratios  (d=n_org/n_aq)
+        to measured d.
+
+        np.sum((np.log10(d_pred)-np.log10(d_meas))\**2)
+
+        :param predicted_dict: (dict) contains predicted data
+        :param meas_df: (pd.DataFrame) contains experimental data
+        :return: (float) log mean squared error between predicted and measured
+        """
+        meas = np.concatenate([meas_df['{0}_d_eq'.format(species)].values
+                               for species in self._re_species_list])
+        pred = np.concatenate([
+            predicted_dict['{0}_d_eq'.format(species)]
+            for species in self._re_species_list])
+        log_pred = np.log10(pred)
+        log_meas = np.log10(meas)
+        log_diff = (log_pred - log_meas) ** 2
+        obj = np.sum(log_diff)
+        return obj

+
+
[docs]    def get_exp_df(self) -> pd.DataFrame:
+        """Returns the experimental DataFrame
+
+        :return: (pd.DataFrame) Experimental data
+        """
+        return self._exp_df

+
+
[docs]    def set_exp_df(self, exp_data):
+        """Changes the experimental DataFrame to input exp_csv_filename data
+        and renames columns to internal REEPS names
+
+
+        h_i, h_eq, z_i, z_eq, {RE}_aq_i, {RE}_aq_eq, {RE}_d
+
+        See class docstring on "exp_csv_filename" for further explanations.
+
+        :param exp_data: (str or pd.DataFrame)
+            file name/path or DataFrame for experimental data csv
+        """
+        self._exp_data = exp_data
+        if isinstance(self._exp_data, str):
+            try:
+                self._exp_df = pd.read_csv(self._exp_data)
+            except FileNotFoundError:
+                self._exp_data = pkg_resources.resource_filename(
+                    'reeps', r'..\data\csvs\{0}'.format(self._exp_data))
+                self._exp_df = pd.read_csv(self._exp_data)
+        else:
+            self._exp_df = exp_data.copy()
+        self._exp_df_columns = ['h_i', 'h_eq', 'z_i', 'z_eq']
+        if self._re_species_list is None:
+            self._re_species_list = []
+            for name in self._rare_earth_ion_names:
+                species = name.replace('+', '')
+                self._re_species_list.append(species)
+        for species in self._re_species_list:
+            self._exp_df_columns.append('{0}_aq_i'.format(species))
+            self._exp_df_columns.append('{0}_aq_eq'.format(species))
+            self._exp_df_columns.append('{0}_d_eq'.format(species))
+        self._exp_df.columns = self._exp_df_columns
+        for species in self._re_species_list:
+            self._exp_df['{0}_org_eq'.format(species)] = \
+                self._exp_df['{0}_aq_eq'.format(species)] \
+                * self._exp_df['{0}_d_eq'.format(species)]
+        self.set_in_moles(feed_vol=1)
+        self.update_predicted_dict()
+        return None

+
+
[docs]    def get_phases(self) -> list:
+        """
+        Returns the list of Cantera solutions
+
+        :return: (list) list of Cantera solutions/phases
+        """
+        return self._phases

+
+
[docs]    def set_phases(self, phases_xml_filename, phase_names):
+        """Change list of Cantera solutions by inputting
+        new xml file name and phase names
+
+        Also runs set_in_moles to set feed volume to 1 L
+
+        :param phases_xml_filename: (str) xml file with parameters
+            for equilibrium calc
+        :param phase_names: (list) names of phases in xml file
+        """
+        self._phases_xml_filename = phases_xml_filename
+        self._phase_names = phase_names
+        # Try and except for adding package data to path.
+        # This only works for sdist, not bdist
+        # If bdist is needed, research "manifest.in" python setup files
+        try:
+            shutil.copyfile(self._phases_xml_filename,
+                            self._temp_xml_file_path)
+            self._phases = ct.import_phases(self._phases_xml_filename,
+                                            phase_names)
+        except FileNotFoundError:
+            self._phases_xml_filename = \
+                pkg_resources.resource_filename('reeps',
+                                                r'..\data\xmls\{0}'.format(
+                                                    phases_xml_filename))
+            shutil.copyfile(self._phases_xml_filename,
+                            self._temp_xml_file_path)
+            self._phases = ct.import_phases(self._phases_xml_filename,
+                                            phase_names)
+        self.set_in_moles(feed_vol=1)
+        self.update_predicted_dict()
+        return None

+
+
[docs]    def get_opt_dict(self) -> dict:
+        """
+        Returns the dictionary containing optimization information
+
+        :return: (dict) dictionary containing info about which
+            species parameters are updated to fit model to experimental data
+        """
+        return self._opt_dict

+
+
[docs]    def set_opt_dict(self, opt_dict):
+        """
+        Change the dictionary to input opt_dict.
+
+        opt_dict specifies species parameters to be updated to
+        fit model to data
+
+        See class docstring on "opt_dict" for more information.
+
+        :param opt_dict: (dict) dictionary containing info about which
+            species parameters are updated to fit model to experimental data
+        """
+
+        self._opt_dict = opt_dict
+        return None

+
+
[docs]    def get_aq_solvent_name(self) -> str:
+        """Returns aq_solvent_name
+
+        :return: aq_solvent_name: (str) name of aqueous solvent in xml file
+        """
+        return self._aq_solvent_name

+
+
[docs]    def set_aq_solvent_name(self, aq_solvent_name):
+        """ Change aq_solvent_name to input aq_solvent_name
+
+        :param aq_solvent_name: (str) name of aqueous solvent in xml file
+        """
+        self._aq_solvent_name = aq_solvent_name
+        return None

+
+
[docs]    def get_extractant_name(self) -> str:
+        """Returns extractant name
+
+        :return: extractant_name: (str) name of extractant in xml file
+        """
+        return self._extractant_name

+
+
[docs]    def set_extractant_name(self, extractant_name):
+        """
+        Change extractant_name to input extractant_name
+        :param extractant_name: (str) name of extractant in xml file
+        """
+        self._extractant_name = extractant_name
+        return None

+
+
[docs]    def get_diluant_name(self) -> str:
+        """ Returns diluant name
+        :return:  diluant_name: (str) name of diluant in xml file
+        """
+        return self._diluant_name

+
+
[docs]    def set_diluant_name(self, diluant_name):
+        """
+        Change diluant_name to input diluant_name
+
+        :param diluant_name: (str) name of diluant in xml file
+        """
+        self._diluant_name = diluant_name
+        return None

+
+
[docs]    def get_complex_names(self) -> list:
+        """Returns list of complex names
+
+        :return: complex_names: (list) names of complexes in xml file.
+        """
+        return self._complex_names

+
+
[docs]    def set_complex_names(self, complex_names):
+        """Change complex names list to input complex_names
+
+        :param complex_names: (list) names of complexes in xml file.
+        """
+        self._complex_names = complex_names
+        return None

+
+
[docs]    def get_rare_earth_ion_names(self) -> list:
+        """Returns list of rare earth ion names
+
+        :return: rare_earth_ion_names: (list) names of rare earth ions in
+            xml file
+        """
+        return self._rare_earth_ion_names

+
+
[docs]    def set_rare_earth_ion_names(self, rare_earth_ion_names):
+        """Change list of rare earth ion names to input
+            rare_earth_ion_names
+
+        :param rare_earth_ion_names: (list) names of rare earth ions in
+            xml file
+        """
+        self._rare_earth_ion_names = rare_earth_ion_names
+        return None

+
+
[docs]    def get_re_species_list(self) -> list:
+        """Returns list of rare earth element names
+
+        :return: re_species_list: (list) names of rare earth elements in
+            xml file
+        """
+        return self._re_species_list

+
+
[docs]    def set_re_species_list(self, re_species_list):
+        """Change list of rare earth ion names to input
+            rare_earth_ion_names
+
+        :param re_species_list: (list) names of rare earth elements in
+            xml file
+        """
+        self._re_species_list = re_species_list
+        return None

+
+
[docs]    def get_aq_solvent_rho(self) -> str:
+        """Returns aqueous solvent density (g/L)
+
+        :return: aq_solvent_rho: (float) density of aqueous solvent
+        """
+        return self._aq_solvent_rho

+
+
[docs]    def set_aq_solvent_rho(self, aq_solvent_rho):
+        """Changes aqueous solvent density (g/L) to input aq_solvent_rho
+
+        :param aq_solvent_rho: (float) density of aqueous solvent
+        """
+        self._aq_solvent_rho = aq_solvent_rho
+        return None

+
+
[docs]    def get_extractant_rho(self) -> str:
+        """Returns extractant density (g/L)
+
+        :return: extractant_rho: (float) density of extractant
+        """
+        return self._extractant_rho

+
+
[docs]    def set_extractant_rho(self, extractant_rho):
+        """Changes extractant density (g/L) to input extractant_rho
+
+        :param extractant_rho: (float) density of extractant
+        """
+        self._extractant_rho = extractant_rho
+        return None

+
+
[docs]    def get_diluant_rho(self) -> str:
+        """Returns diluant density (g/L)
+
+        :return: diluant_rho: (float) density of diluant
+        """
+        return self._diluant_rho

+
+
[docs]    def set_diluant_rho(self, diluant_rho):
+        """Changes diluant density (g/L) to input diluant_rho
+
+        :param diluant_rho: (float) density of diluant
+        """
+        self._diluant_rho = diluant_rho
+        return None

+
+
[docs]    def set_in_moles(self, feed_vol):
+        """Function that initializes mole fractions to input feed_vol
+
+        This function is called at initialization
+
+        Sets in_moles to a pd.DataFrame containing initial mole fractions
+
+        Columns for species and rows for different experiments
+
+        This function also calls update_predicted_dict
+
+        :param feed_vol: (float) feed volume of mixture (L)
+        """
+        phases_copy = self._phases.copy()
+        exp_df = self._exp_df.copy()
+        solvent_name = self._aq_solvent_name
+        extractant_name = self._extractant_name
+        diluant_name = self._diluant_name
+        solvent_rho = self._aq_solvent_rho
+        extractant_rho = self._extractant_rho
+        diluant_rho = self._diluant_rho
+        re_names = self._rare_earth_ion_names
+        re_species_list = self._re_species_list
+
+        mixed = ct.Mixture(phases_copy)
+        aq_ind = None
+        solvent_ind = None
+        for ind, phase in enumerate(phases_copy):
+            if solvent_name in phase.species_names:
+                aq_ind = ind
+                solvent_ind = phase.species_names.index(solvent_name)
+        if aq_ind is None:
+            raise Exception('Solvent "{0}" not found \
+                                in xml file'.format(solvent_name))
+
+        if aq_ind == 0:
+            org_ind = 1
+        else:
+            org_ind = 0
+        self._aq_ind = aq_ind
+        self._org_ind = org_ind
+        extractant_ind = phases_copy[org_ind].species_names.index(
+            extractant_name)
+        diluant_ind = phases_copy[org_ind].species_names.index(diluant_name)
+
+        re_ind_list = [phases_copy[aq_ind].species_names.index(re_name)
+                       for re_name in re_names]
+        re_charges = np.array([phases_copy[aq_ind].species(re_ind).charge
+                               for re_ind in re_ind_list])
+        self._re_charges = re_charges
+
+        mix_aq = mixed.phase(aq_ind)
+        mix_org = mixed.phase(org_ind)
+        solvent_mw = mix_aq.molecular_weights[solvent_ind]  # g/mol
+        extractant_mw = mix_org.molecular_weights[extractant_ind]
+        diluant_mw = mix_org.molecular_weights[diluant_ind]
+        if solvent_rho is None:
+            solvent_rho = mix_aq(aq_ind).partial_molar_volumes[
+                              solvent_ind] / solvent_mw * 1e6  # g/L
+            self._aq_solvent_rho = solvent_rho
+        if extractant_rho is None:
+            extractant_rho = mix_org(org_ind).partial_molar_volumes[
+                                 extractant_ind] / extractant_mw * 1e6
+            self._extractant_rho = extractant_rho
+        if diluant_rho is None:
+            diluant_rho = mix_org(org_ind).partial_molar_volumes[
+                              extractant_ind] / extractant_mw * 1e6
+            self._diluant_rho = diluant_rho
+
+        in_moles_data = []
+        aq_phase_solvent_moles = feed_vol * solvent_rho / solvent_mw
+        for index, row in exp_df.iterrows():
+            h_plus_moles = feed_vol * row['h_i']
+            hydroxide_ions = 0
+            rare_earth_moles = np.array([feed_vol * row[
+                '{0}_aq_i'.format(re_species)]
+                                         for re_species in re_species_list])
+            re_charge_sum = np.sum(re_charges * rare_earth_moles)
+            chlorine_moles = re_charge_sum + h_plus_moles
+            extractant_moles = feed_vol * row['z_i']
+            extractant_vol = extractant_moles * extractant_mw / extractant_rho
+            diluant_vol = feed_vol - extractant_vol
+            diluant_moles = diluant_vol * diluant_rho / diluant_mw
+            complex_moles = np.zeros(len(re_species_list))
+
+            species_moles_aq = [aq_phase_solvent_moles,
+                                h_plus_moles,
+                                hydroxide_ions,
+                                chlorine_moles]
+            species_moles_aq.extend(list(rare_earth_moles))
+            species_moles_org = [extractant_moles, diluant_moles]
+            species_moles_org.extend(list(complex_moles))
+            if aq_ind == 0:
+                species_moles = species_moles_aq + species_moles_org
+            else:
+                species_moles = species_moles_org + species_moles_aq
+            in_moles_data.append(species_moles)
+        self._in_moles = pd.DataFrame(
+            in_moles_data, columns=mixed.species_names)
+        self.update_predicted_dict()
+        return None

+
+
[docs]    def get_in_moles(self) -> pd.DataFrame:
+        """Returns the in_moles DataFrame which contains the initial mole
+        fractions of each species for each experiment
+
+        :return: in_moles: (pd.DataFrame) DataFrame with initial mole fractions
+        """
+        return self._in_moles

+
+
[docs]    def set_objective_function(self, objective_function):
+        """Change objective function to input objective_function.
+
+         See class docstring on "objective_function" for instructions
+
+        :param objective_function: (func) Objective function to quantify
+            error between model and experimental data
+        """
+        if not callable(objective_function) \
+                and objective_function not in self._built_in_obj_list:
+            raise Exception(
+                "objective_function must be a function "
+                "or in this strings list: {0}".format(
+                    self._built_in_obj_list))
+        if callable(objective_function):
+            if len(signature(objective_function).parameters) < 2:
+                raise Exception(
+                    "objective_function must be a function "
+                    "with at least 3 arguments:"
+                    " f(predicted_dict, experimental_df, kwargs)")
+        if objective_function == 'Log-MSE':
+            objective_function = self.log_mean_squared_error
+        self._objective_function = objective_function
+        return None

+
+
[docs]    def get_objective_function(self):
+        """Returns objective function
+
+        :return: objective_function: (func) Objective function to quantify
+            error between model and experimental data
+        """
+        return self._objective_function

+
+
[docs]    def set_optimizer(self, optimizer):
+        """Change optimizer function to input optimizer.
+
+         See class docstring on "optimizer" for instructions
+
+        :param optimizer: (func) Optimizer function to minimize objective
+            function
+        """
+        if not callable(optimizer) \
+                and optimizer not in self._built_in_opt_list:
+            raise Exception(
+                "optimizer must be a function "
+                "or in this strings list: {0}".format(
+                    self._built_in_opt_list))
+        if callable(optimizer):
+            if len(signature(optimizer).parameters) < 2:
+                raise Exception(
+                    "optimizer must be a function "
+                    "with at least 2 arguments: "
+                    "f(objective_func,x_guess, kwargs)")
+        if optimizer == 'SLSQP':
+            optimizer = self.scipy_minimize
+        self._optimizer = optimizer
+        return None

+
+
[docs]    def get_optimizer(self):
+        """Returns objective function
+
+        :return: optimizer: (func) Optimizer function to minimize objective
+            function
+        """
+        return self._optimizer

+
+
[docs]    def get_temp_xml_file_path(self):
+        """Returns path to temporary xml file.
+
+        This xml file is a duplicate of the phases_xml_file name and is
+        modified during the optimization process to avoid changing the original
+        xml file.
+
+        :return: temp_xml_file_path: (str) path to temporary xml file.
+        """
+        return self._temp_xml_file_path

+
+
[docs]    def set_temp_xml_file_path(self, temp_xml_file_path):
+        """Changes temporary xml file path to input temp_xml_file_path.
+
+        This xml file is a duplicate of the phases_xml_file name and is
+        modified during the optimization process to avoid changing the original
+        xml file.
+
+        :param temp_xml_file_path: (str) path to temporary xml file.
+        """
+        self._temp_xml_file_path = temp_xml_file_path
+        return None

+
+
[docs]    def get_dependant_params_dict(self):
+        """
+        Returns the dependant_params_dict
+        :return: dependant_params_dict: (dict) dictionary containing
+            information about parameters dependant on opt_dict
+        """
+        return self._dependant_params_dict

+
+
[docs]    def set_dependant_params_dict(self, dependant_params_dict):
+        """
+        Sets the dependant_params_dict
+        :param dependant_params_dict: (dict) dictionary containing information
+        about parameters dependant on opt_dict
+        """
+        self._dependant_params_dict = dependant_params_dict
+        return None

+
+
[docs]    def update_predicted_dict(self,
+                              phases_xml_filename=None,
+                              phase_names=None):
+        """Function that computes the predicted equilibrium concentrations
+        the fed phases_xml_filename parameters predicts given the initial
+        mole fractions set by in_moles()
+
+        :param phases_xml_filename: (str)xml file with parameters
+            for equilibrium calc. If ``None``, the
+            current phases_xml_filename is used.
+        :param phase_names: (list) names of phases in xml file.
+            If ``None``, the current phases_names is used.
+        """
+        if phases_xml_filename is None:
+            phases_xml_filename = self._phases_xml_filename
+        if phase_names is None:
+            phase_names = self._phase_names
+        aq_ind = self._aq_ind
+        org_ind = self._org_ind
+        complex_names = self._complex_names
+        extractant_name = self._extractant_name
+        rare_earth_ion_names = self._rare_earth_ion_names
+        in_moles = self._in_moles
+        re_species_list = self._re_species_list
+
+        phases_copy = ct.import_phases(phases_xml_filename, phase_names)
+        mix = ct.Mixture(phases_copy)
+        key_names = ['h_eq', 'z_eq']
+        for re_species in re_species_list:
+            key_names.append('{0}_aq_eq'.format(re_species))
+            key_names.append('{0}_org_eq'.format(re_species))
+            key_names.append('{0}_d_eq'.format(re_species))
+
+        predicted_dict = {'{0}'.format(key_name): []
+                          for key_name in key_names}
+
+        for row in in_moles.values:
+            mix.species_moles = row
+            mix.equilibrate('TP', log_level=0)
+            re_org_array = np.array([mix.species_moles[mix.species_index(
+                org_ind, complex_name)] for complex_name in complex_names])
+            re_aq_array = np.array([mix.species_moles[mix.species_index(
+                aq_ind, re_ion_name)] for re_ion_name in rare_earth_ion_names])
+            d_array = re_org_array / re_aq_array
+            hydrogen_ions = mix.species_moles[mix.species_index(aq_ind, 'H+')]
+            extractant = mix.species_moles[mix.species_index(
+                org_ind, extractant_name)]
+            for index, re_species in enumerate(re_species_list):
+                predicted_dict['{0}_aq_eq'.format(
+                    re_species)].append(re_aq_array[index])
+                predicted_dict['{0}_org_eq'.format(
+                    re_species)].append(re_org_array[index])
+                predicted_dict['{0}_d_eq'.format(
+                    re_species)].append(d_array[index])
+            predicted_dict['h_eq'].append(hydrogen_ions)
+            predicted_dict['z_eq'].append(extractant)
+        for key, value in predicted_dict.items():
+            predicted_dict[key] = np.array(value)
+        self._predicted_dict = predicted_dict
+        return None

+
+
[docs]    def get_predicted_dict(self):
+        """Returns predicted dictionary of species concentrations
+        that xml parameters predicts given current in_moles
+
+        :return: predicted_dict: (dict) dictionary of species concentrations
+        """
+        return self._predicted_dict

+
+    def _internal_objective(self, x, kwargs=None):
+        """
+        Internal objective function. Uses objective function to compute value
+        If the optimizer requires vectorized variables ie pso, this function
+        takes care of it
+
+        :param x: (list) thermo properties varied to minimize objective func
+        :param kwargs: (list) arguments for objective_function
+        """
+        temp_xml_file_path = self._temp_xml_file_path
+        exp_df = self._exp_df
+        objective_function = self._objective_function
+        opt_dict = copy.deepcopy(self._opt_dict)
+        dep_dict = copy.deepcopy(self._dependant_params_dict)
+        x = np.array(x)
+
+        if len(x.shape) == 1:
+            xs = np.array([x])
+            vectorized_x = False
+        else:
+            vectorized_x = True
+            xs = x
+        objective_values = []
+        for x in xs:
+            i = 0
+            for species_name in opt_dict.keys():
+                for thermo_prop in opt_dict[species_name].keys():
+                    if not np.isnan(
+                            x[i]):  # if nan, do not update xml with nan
+                        opt_dict[species_name][thermo_prop] *= x[i]
+                    i += 1
+
+            self.update_xml(opt_dict,
+                            temp_xml_file_path,
+                            dependant_params_dict=dep_dict)
+
+            self.update_predicted_dict(temp_xml_file_path)
+            predicted_dict = self.get_predicted_dict()
+            self.update_predicted_dict()
+
+            if kwargs is None:
+                # noinspection PyCallingNonCallable
+                obj = objective_function(predicted_dict, exp_df)
+            else:
+                # noinspection PyCallingNonCallable
+                obj = objective_function(predicted_dict, exp_df, **kwargs)
+            objective_values.append(obj)
+        if vectorized_x:
+            objective_values = np.array(objective_values)
+        else:
+            objective_values = objective_values[0]
+        return objective_values
+
+
[docs]    def fit(self,
+            objective_function=None,
+            optimizer=None,
+            objective_kwargs=None,
+            optimizer_kwargs=None) -> tuple:
+        """Fits experimental to modeled data by minimizing objective function
+        with optimizer. Returns dictionary with opt_dict structure
+
+        :param objective_function: (function) function to compute objective
+            If 'None', last set objective or default function is used
+        :param optimizer: (function) function to perform optimization
+            If 'None', last set optimizer or default is used
+        :param optimizer_kwargs: (dict) optional arguments for optimizer
+        :param objective_kwargs: (dict) optional arguments
+            for objective function
+        :returns tuple: (opt_dict (dict), opt_value (float))
+            optimized opt_dict: Has identical structure as opt_dict
+        """
+        if objective_function is not None:
+            self.set_objective_function(objective_function)
+        if optimizer is not None:
+            self.set_optimizer(optimizer)
+
+        def objective(x):
+            return self._internal_objective(x, objective_kwargs)
+
+        optimizer = self._optimizer
+        opt_dict = copy.deepcopy(self._opt_dict)
+        i = 0
+        for species_name in opt_dict.keys():
+            for _ in opt_dict[species_name].keys():
+                i += 1
+        x_guess = np.ones(i)
+
+        if optimizer_kwargs is None:
+            # noinspection PyCallingNonCallable
+            est_parameters, obj_value = optimizer(objective, x_guess)
+        else:
+            # noinspection PyCallingNonCallable
+            est_parameters, obj_value = optimizer(objective,
+                                                  x_guess,
+                                                  optimizer_kwargs)
+
+        i = 0
+        for species_name in opt_dict.keys():
+            for thermo_prop in opt_dict[species_name].keys():
+                opt_dict[species_name][thermo_prop] *= est_parameters[i]
+                i += 1
+        return opt_dict, obj_value

+
+
[docs]    def update_xml(self,
+                   info_dict,
+                   phases_xml_filename=None,
+                   dependant_params_dict=None):
+        """updates xml file with info_dict
+
+        :param info_dict: (dict) info in {species_names:{thermo_prop:val}}
+            Requires an identical structure to opt_dict
+        :param phases_xml_filename: (str) xml filename if editing other xml
+            If ``None``, the current xml will be modified and the internal
+            Cantera phases will be refreshed to the new values.
+        :param dependant_params_dict: (dict) dictionary containing information
+            about parameters dependant on info_dict
+        """
+        if phases_xml_filename is None:
+            phases_xml_filename = self._phases_xml_filename
+        new_dict = copy.deepcopy(info_dict)
+        dep_dict = dependant_params_dict
+        if dep_dict is not None:
+            for species_name in dep_dict.keys():
+                for thermo_prop in dep_dict[species_name]:
+                    mod_func = \
+                        dep_dict[species_name][thermo_prop]['function']
+                    mod_kwargs = \
+                        dep_dict[species_name][thermo_prop]['kwargs']
+                    ind_vars = \
+                        dep_dict[species_name][thermo_prop]['ind_vars']
+                    ind_vals = [new_dict[ind_var[0]][ind_var[1]]
+                                for ind_var in ind_vars]
+
+                    new_dict[species_name] = {}
+                    new_dict[species_name][thermo_prop] = {}
+                    new_dict[species_name][thermo_prop] = \
+                        mod_func(ind_vals, **mod_kwargs)
+        #             print(mod_func(ind_vals, **mod_kwargs))
+        # print(new_dict)
+
+        tree = ET.parse(phases_xml_filename)
+        root = tree.getroot()
+        # Update xml file
+        for species_name in new_dict.keys():
+            for thermo_prop in new_dict[species_name].keys():
+                for species in root.iter('species'):
+                    if species.attrib['name'] == species_name:
+                        for changed_prop in species.iter(thermo_prop):
+                            changed_prop.text = str(
+                                new_dict[species_name][thermo_prop])
+                            now = datetime.now()
+                            changed_prop.set('updated',
+                                             'Updated at {0}:{1} {2}-{3}-{4}'
+                                             .format(now.hour, now.minute,
+                                                     now.month, now.day,
+                                                     now.year))
+
+        tree.write(phases_xml_filename)
+        if phases_xml_filename == self._phases_xml_filename:
+            self.set_phases(self._phases_xml_filename, self._phase_names)
+        return None

+
+    def _internal_objective_ver2(self, x, kwargs=None):
+        """
+        ver2 generalizes to handle accessing parameters. ver1 assumes species
+        parameter is modified. ver2 assumes parameter is accessed by going
+        through two levels: upper and lower
+        Internal objective function. Uses objective function to compute value
+        If the optimizer requires vectorized variables ie pso, this function
+        takes care of it
+
+        :param x: (list) thermo properties varied to minimize objective func
+        :param kwargs: (list) arguments for objective_function
+        """
+        temp_xml_file_path = self._temp_xml_file_path
+        exp_df = self._exp_df
+        objective_function = self._objective_function
+        opt_dict = copy.deepcopy(self._opt_dict)
+        dep_dict = copy.deepcopy(self._dependant_params_dict)
+        x = np.array(x)
+
+        if len(x.shape) == 1:
+            xs = np.array([x])
+            vectorized_x = False
+        else:
+            vectorized_x = True
+            xs = x
+        objective_values = []
+        for x in xs:
+            for ind, param_name in enumerate(opt_dict.keys()):
+                if not np.isnan(
+                        x[ind]):  # if nan, do not update xml with nan
+                    opt_dict[param_name]['input_value'] *= x[ind]
+
+            self.update_xml_ver2(opt_dict,
+                                 temp_xml_file_path,
+                                 dependant_params_dict=dep_dict)
+
+            self.update_predicted_dict(temp_xml_file_path)
+            predicted_dict = self.get_predicted_dict()
+            self.update_predicted_dict()
+
+            if kwargs is None:
+                # noinspection PyCallingNonCallable
+                obj = objective_function(predicted_dict, exp_df)
+            else:
+                # noinspection PyCallingNonCallable
+                obj = objective_function(predicted_dict, exp_df, **kwargs)
+            objective_values.append(obj)
+        if vectorized_x:
+            objective_values = np.array(objective_values)
+        else:
+            objective_values = objective_values[0]
+        return objective_values
+
+
[docs]    def fit_ver2(self,
+                 objective_function=None,
+                 optimizer=None,
+                 objective_kwargs=None,
+                 optimizer_kwargs=None) -> tuple:
+        """Fits experimental to modeled data by minimizing objective function
+        with optimizer. Returns dictionary with opt_dict structure
+
+        :param objective_function: (function) function to compute objective
+            If 'None', last set objective or default function is used
+        :param optimizer: (function) function to perform optimization
+            If 'None', last set optimizer or default is used
+        :param optimizer_kwargs: (dict) optional arguments for optimizer
+        :param objective_kwargs: (dict) optional arguments
+            for objective function
+        :returns tuple: (opt_dict (dict), opt_value (float))
+            optimized opt_dict: Has identical structure as opt_dict
+        """
+        if objective_function is not None:
+            self.set_objective_function(objective_function)
+        if optimizer is not None:
+            self.set_optimizer(optimizer)
+
+        def objective(x):
+            return self._internal_objective_ver2(x, objective_kwargs)
+
+        optimizer = self._optimizer
+        opt_dict = copy.deepcopy(self._opt_dict)
+        x_guess = np.ones(len(list(opt_dict.keys())))
+
+        if optimizer_kwargs is None:
+            # noinspection PyCallingNonCallable
+            est_parameters, obj_value = optimizer(objective, x_guess)
+        else:
+            # noinspection PyCallingNonCallable
+            est_parameters, obj_value = optimizer(objective,
+                                                  x_guess,
+                                                  optimizer_kwargs)
+        for ind, param_name in enumerate(opt_dict.keys()):
+            opt_dict[param_name]['input_value'] *= est_parameters[ind]
+
+        return opt_dict, obj_value

+
+
[docs]    def update_xml_ver2(self,
+                        info_dict,
+                        phases_xml_filename=None,
+                        dependant_params_dict=None):
+        """updates xml file with info_dict
+
+        :param info_dict: (dict) info in {species_names:{thermo_prop:val}}
+            Requires an identical structure to opt_dict
+        :param phases_xml_filename: (str) xml filename if editing other xml
+            If ``None``, the current xml will be modified and the internal
+            Cantera phases will be refreshed to the new values.
+        :param dependant_params_dict: (dict) dictionary containing information
+            about parameters dependant on info_dict
+        """
+        if phases_xml_filename is None:
+            phases_xml_filename = self._phases_xml_filename
+        new_dict = copy.deepcopy(info_dict)
+        dep_dict = dependant_params_dict
+
+        if dep_dict is not None:
+            new_dict.update(dep_dict)
+            for param_name in dep_dict.keys():
+                mod_func = \
+                    dep_dict[param_name]['function']
+                mod_kwargs = \
+                    dep_dict[param_name]['kwargs']
+                if isinstance(dep_dict[param_name]['independent_params'], str):
+                    ind_param_names = [dep_dict[
+                                           param_name]['independent_params']]
+                else:
+                    ind_param_names = \
+                        dep_dict[param_name]['independent_params']
+                ind_vals = [new_dict[ind_param_name]['input_value']
+                            for ind_param_name in ind_param_names]
+                if mod_kwargs is None:
+                    new_dict[param_name]['input_value'] = mod_func(ind_vals)
+                else:
+                    new_dict[param_name]['input_value'] = \
+                        mod_func(ind_vals,
+                                 **mod_kwargs)
+        tree = ET.parse(phases_xml_filename)
+        root = tree.getroot()
+        # Update xml file
+        for key in list(new_dict.keys()):
+            d = new_dict[key]
+            now = datetime.now()
+            if (d['upper_attrib_name'] is not None
+                    and d['lower_attrib_name'] is not None):
+                for child1 in root.iter(d['upper_element_name']):
+                    if (child1.attrib[d['upper_attrib_name']]
+                            == d['upper_attrib_value']):
+                        for child2 in child1.iter(d['lower_element_name']):
+                            if (child1.attrib[d['lower_attrib_name']]
+                                    == d['lower_attrib_value']):
+                                child2.text = d['input_format'].format(
+                                    d['input_value'])
+                                child2.set('updated',
+                                           'Updated at {0}:{1} {2}-{3}-{4}'
+                                           .format(now.hour, now.minute,
+                                                   now.month, now.day,
+                                                   now.year))
+            elif (d['upper_attrib_name'] is None
+                  and d['lower_attrib_name'] is not None):
+                for child1 in root.iter(d['upper_element_name']):
+                    for child2 in child1.iter(d['lower_element_name']):
+                        if (child1.attrib[d['lower_attrib_name']]
+                                == d['lower_attrib_value']):
+                            child2.text = d['input_format'].format(
+                                d['input_value'])
+                            child2.set('updated',
+                                       'Updated at {0}:{1} {2}-{3}-{4}'
+                                       .format(now.hour, now.minute,
+                                               now.month, now.day,
+                                               now.year))
+            elif (d['upper_attrib_name'] is not None
+                  and d['lower_attrib_name'] is None):
+                for child1 in root.iter(d['upper_element_name']):
+                    if (child1.attrib[d['upper_attrib_name']]
+                            == d['upper_attrib_value']):
+                        for child2 in child1.iter(d['lower_element_name']):
+                            child2.text = d['input_format'].format(
+                                d['input_value'])
+                            child2.set('updated',
+                                       'Updated at {0}:{1} {2}-{3}-{4}'
+                                       .format(now.hour, now.minute,
+                                               now.month, now.day,
+                                               now.year))
+            else:
+                for child1 in root.iter(d['upper_element_name']):
+                    for child2 in child1.iter(d['lower_element_name']):
+                        child2.text = d['input_format'].format(
+                            d['input_value'])
+                        child2.set('updated', 'Updated at {0}:{1} {2}-{3}-{4}'
+                                   .format(now.hour, now.minute,
+                                           now.month, now.day,
+                                           now.year))
+
+        tree.write(phases_xml_filename)
+        if phases_xml_filename == self._phases_xml_filename:
+            self.set_phases(self._phases_xml_filename, self._phase_names)
+        return None

+
+
[docs]    def parity_plot(self,
+                    compared_value=None,
+                    c_data=None,
+                    c_label=None,
+                    plot_title=None,
+                    save_path=None,
+                    print_r_squared=False,
+                    data_labels=None,
+                    legend=True):
+        """
+        Parity plot between measured and predicted compared_value.
+        Default compared value is {RE_1}_aq_eq
+
+        :param compared_value: (str) Quantity to compare predicted and
+            experimental data. Can be any column containing "eq" in exp_df i.e.
+            h_eq, z_eq, {RE}_d_eq, etc.
+        :param plot_title: (str or boolean)
+
+            If None (default): Plot title will be generated from compared_value
+                Recommend to just explore. If h_eq, plot_title is
+                "H^+ eq conc".
+
+            If str: Plot title will be plot_title string
+
+            If "False": No plot title
+        :param c_data: (list or np.ndarray) data for color axis
+        :param c_label: (str) label for color axis
+        :param save_path: (str) save path for parity plot
+        :param print_r_squared: (boolean) To plot or not to plot r-squared
+            value. Prints 2 places past decimal
+        :param data_labels: labels for the data such as paper's name where
+            experiment is pulled from.
+        :param legend: whether to display legend for data_labels. Has no
+            effect if data_labels is None
+        :return fig, ax: returns the figure and axes objects
+        """
+        exp_df = self.get_exp_df()
+        predicted_dict = self.get_predicted_dict()
+        re_species_list = self._re_species_list
+        extractant_name = self.get_extractant_name()
+        re_charges = self._re_charges
+        if compared_value is None:
+            compared_value = '{0}_aq_eq'.format(re_species_list[0])
+        pred = 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]
+        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
+        if isinstance(data_labels, list):
+            combined_df = pd.DataFrame({'pred': pred,
+                                        'meas': meas,
+                                        'label': data_labels,
+                                        'feed_molarity': feed_molarity})
+        elif isinstance(c_data, str):
+            combined_df = pd.DataFrame({'pred': pred,
+                                        'meas': meas,
+                                        c_data: exp_df[c_data].values,
+                                        'feed_molarity': feed_molarity})
+        else:
+            combined_df = pd.DataFrame({'pred': pred,
+                                        'meas': meas,
+                                        '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':
+                re_charge = re_charges[re_species_list.index(compared_species)]
+                default_title = '$%s^{%d+}$ eq. conc. (mol/L)' \
+                                % (compared_species, re_charge)
+            elif phase == 'd':
+                default_title = '{0} distribution ratio'.format(
+                    compared_species)
+            else:
+                default_title = '{0} complex eq. conc. (mol/L)'.format(
+                    compared_species)
+        fig, ax = plt.subplots()
+        if isinstance(data_labels, list):
+            unique_labels = list(set(data_labels))
+            for label in unique_labels:
+                filtered_data = combined_df[combined_df['label'] == label]
+                filtered_meas = filtered_data['meas']
+                filtered_pred = filtered_data['pred']
+                ax.scatter(filtered_meas, filtered_pred, label=label)
+            if legend:
+                ax.legend(loc='best')
+
+        elif c_data is not None:
+            if isinstance(c_data, str):
+                c_data = combined_df[c_data].values
+            p1 = ax.scatter(meas, pred, c=c_data, alpha=1, cmap='viridis')
+            c_bar = fig.colorbar(p1, format='%.2f')
+            if c_label is not None:
+                c_bar.set_label(c_label, rotation=270, labelpad=20)
+        else:
+            sns.scatterplot(meas, pred, color="r",
+                            legend=False)
+        ax.plot(min_max_data, min_max_data, color="b", label="")
+
+        if print_r_squared:
+            ax.text(min_max_data[0],
+                    min_max_data[1] * 0.9,
+                    '$R^2$={0:.2f}'.format(self.r_squared(compared_value)))
+            # plt.legend(loc='lower right')
+        # else:
+        # plt.legend()
+
+        ax.set(xlabel='Measured', ylabel='Predicted')
+        if plot_title is None:
+            ax.set_title(default_title)
+        elif isinstance(plot_title, str):
+            ax.set_title(plot_title)
+        set_size(8, 6)
+        plt.tight_layout()
+        plt.show()
+        if save_path is not None:
+            plt.savefig(save_path, bbox_inches='tight')
+        return fig, ax

+
+
[docs]    def r_squared(self, compared_value=None):
+        """r-squared value comparing measured and predicted compared value
+
+        Closer to 1, the better the model's predictions.
+
+        :param compared_value: (str) Quantity to compare predicted and
+            experimental data. Can be any column containing "eq" in exp_df i.e.
+            h_eq, z_eq, {RE}_d_eq, etc. default is {RE}_aq_eq
+             """
+        exp_df = self.get_exp_df()
+        predicted_dict = self.get_predicted_dict()
+        re_species_list = self._re_species_list
+        if compared_value is None:
+            compared_value = '{0}_aq_eq'.format(re_species_list[0])
+        pred = pd.DataFrame(predicted_dict)[compared_value].fillna(0).values
+        predicted_y = np.array(pred)
+        actual_y = exp_df[compared_value].fillna(0).values
+        name_breakdown = re.findall('[^_\W]+', compared_value)
+        compared_species = name_breakdown[0]
+        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': predicted_y,
+                                    'meas': actual_y,
+                                    'in_moles': feed_molarity})
+        combined_df = combined_df[(combined_df['in_moles'] != 0)]
+        actual_y = combined_df['meas'].values
+        predicted_y = combined_df['pred'].values
+        num = sum((actual_y - predicted_y) ** 2)
+        den = sum((actual_y - np.mean(actual_y)) ** 2)
+        if den == 0:
+            r_2 = 0
+        else:
+            r_2 = (1 - num / den)
+        return r_2

+
+
[docs]    @staticmethod
+    def plot_3d_data(x_data,
+                     y_data,
+                     z_data,
+                     c_data=None,
+                     x_label=None,
+                     y_label=None,
+                     z_label=None,
+                     c_label=None):
+        """
+
+        :param x_data: (list) list of data for x axis
+        :param y_data: (list) list of data for y axis
+        :param z_data: (list) list of data for z axis
+        :param c_data: (list) list of data for color axis
+        :param x_label: (str) label for x axis
+        :param y_label: (str) label for y axis
+        :param z_label: (str) label for z axis
+        :param c_label: (str) label for color axis
+        :return:
+        """
+
+        fig = plt.figure()
+        ax = fig.add_subplot(111, projection='3d')
+        if c_data is None:
+            ax.plot(x_data, y_data, z_data, 'o')
+        else:
+            p1 = ax.scatter(x_data,
+                            y_data,
+                            z_data, 'o', c=c_data,
+                            cmap='viridis', alpha=1)
+            c_bar = fig.colorbar(p1)
+            if c_label is not None:
+                c_bar.set_label(c_label, rotation=270, labelpad=20)
+        if x_label is None:
+            ax.set_xlabel('x', labelpad=15)
+        else:
+            ax.set_xlabel(x_label, labelpad=15)
+        if y_label is None:
+            ax.set_ylabel('y', labelpad=15)
+        else:
+            ax.set_ylabel(y_label, labelpad=15)
+        if z_label is None:
+            ax.set_zlabel('z', labelpad=15)
+        else:
+            ax.set_zlabel(z_label, labelpad=15)
+        plt.show()
+        return fig, ax

+
+ +
+ +
+
+ + +
+ +
+

+ © Copyright 2020, Titus Quah, Nwike Iloeje + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_sources/guide/about.rst.txt b/docs/_build/html/_sources/guide/about.rst.txt deleted file mode 100644 index dbd1116..0000000 --- a/docs/_build/html/_sources/guide/about.rst.txt +++ /dev/null @@ -1,61 +0,0 @@ -.. _about: - -************ -About -************ - -Authors -============= -Titus Quah, University of Utah, - -Nwike Iloeje, Argonne National Laboratory, - -Acknowledgements -================ -Based upon work supported by funding from Argonne National Laboratory provided by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under contract DE-AC02-06CH11357 - -References -========== - -If you use LLEPE in your research , we kindly request that you cite the package as follows: - -T. Quah and C. O. Iloeje, “Liquid--Liquid Extraction Thermodynamic Parameter Estimator (LLEPE) for Multicomponent Separation Systems,” in Materials Processing Fundamentals 2021, 2021, pp. 107–120, doi: https://doi.org/10.1007/978-3-030-65253-1_9. - -License -======= - -:: - - Copyright © 2020, UChicago Argonne, LLC - All Rights Reserved - Software Name: LLEPE - By: Argonne National Laboratory, University of Utah - OPEN SOURCE LICENSE - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - 2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - 3. Neither the name of the copyright holder nor the names of its contributors - may be used to endorse or promote products derived from this software without - specific prior written permission. - - - ****************************************************************************************************** - DISCLAIMER - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. - IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, - INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT - NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, - OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, - WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) - ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY - OF SUCH DAMAGE. - *************************************************************************************************** diff --git a/docs/_build/html/_sources/guide/install.rst.txt b/docs/_build/html/_sources/guide/install.rst.txt index e7306d7..ce1a1f9 100644 --- a/docs/_build/html/_sources/guide/install.rst.txt +++ b/docs/_build/html/_sources/guide/install.rst.txt @@ -24,17 +24,17 @@ To install the latest master version: .. code-block:: bash - pip install git+https://github.com/ANL-CEEESA/LLEPE.git + pip install git+https://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git Development version =================== -To contribute to LLEPE, with support for running tests and building the documentation. +To contribute to Stable-Baselines, with support for running tests and building the documentation. .. code-block:: bash - git clone https://github.com/ANL-CEEESA/LLEPE.git + git clone https://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git && cd parameter-estimation pip install -e .[docs,tests] diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt index a0c905b..9a973a9 100644 --- a/docs/_build/html/_sources/index.rst.txt +++ b/docs/_build/html/_sources/index.rst.txt @@ -1,8 +1,9 @@ -.. LLEPE: Liquid-Liquid Equilibrium Parameter Estimator - Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. - Released under the modified BSD license. See LICENSE for more details. +.. reeps documentation master file, created by Titus Quah + sphinx-quickstart on Tue Jun 9 10:13:23 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. -Welcome to LLEPE's docs! - the Liquid-Liquid Extraction Parameter Estimator +Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator =========================================================================== LLEPE is a package for thermodynamic parameter estimation for liquid-liquid extraction modeling @@ -18,13 +19,12 @@ Error between predicted and experimental data is then minimized. guide/install guide/quickstart - guide/about .. toctree:: :maxdepth: 1 :caption: Searchers - modules/LLEPE + modules/reeps diff --git a/docs/_build/html/_sources/modules/LLEPE.rst.txt b/docs/_build/html/_sources/modules/LLEPE.rst.txt deleted file mode 100644 index 41daa9d..0000000 --- a/docs/_build/html/_sources/modules/LLEPE.rst.txt +++ /dev/null @@ -1,15 +0,0 @@ -.. _LLEPE: - -.. automodule:: llepe - -LLEPE -===== - - - -Parameters ----------- - -.. autoclass:: LLEPE - :members: - :inherited-members: \ No newline at end of file diff --git a/docs/_build/html/_sources/modules/reeps.rst.txt b/docs/_build/html/_sources/modules/reeps.rst.txt index 41daa9d..bba5c13 100644 --- a/docs/_build/html/_sources/modules/reeps.rst.txt +++ b/docs/_build/html/_sources/modules/reeps.rst.txt @@ -1,4 +1,4 @@ -.. _LLEPE: +.. _reeps: .. automodule:: llepe diff --git a/docs/_build/html/_static/alabaster.css b/docs/_build/html/_static/alabaster.css new file mode 100644 index 0000000..0eddaeb --- /dev/null +++ b/docs/_build/html/_static/alabaster.css @@ -0,0 +1,701 @@ +@import url("basic.css"); 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+ padding: 0 1px 0 1px; + font-size: 0.95em; +} + +th, dl.field-list > dt { + background-color: #ede; +} + +.warning code { + background: #efc2c2; +} + +.note code { + background: #d6d6d6; +} + +.viewcode-back { + font-family: sans-serif; +} + +div.viewcode-block:target { + background-color: #f4debf; + border-top: 1px solid #ac9; + border-bottom: 1px solid #ac9; +} + +div.code-block-caption { + color: #efefef; + background-color: #1c4e63; +} \ No newline at end of file diff --git a/docs/_build/html/_static/custom.css b/docs/_build/html/_static/custom.css new file mode 100644 index 0000000..2a924f1 --- /dev/null +++ b/docs/_build/html/_static/custom.css @@ -0,0 +1 @@ +/* This file intentionally left blank. */ diff --git a/docs/_build/html/_static/default.css b/docs/_build/html/_static/default.css new file mode 100644 index 0000000..81b9363 --- /dev/null +++ b/docs/_build/html/_static/default.css @@ -0,0 +1 @@ +@import url("classic.css"); diff --git a/docs/_build/html/_static/sidebar.js b/docs/_build/html/_static/sidebar.js new file mode 100644 index 0000000..657f8be --- /dev/null +++ b/docs/_build/html/_static/sidebar.js @@ -0,0 +1,159 @@ +/* + * sidebar.js + * ~~~~~~~~~~ + * + * This script makes the Sphinx sidebar collapsible. + * + * .sphinxsidebar contains .sphinxsidebarwrapper. 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  • Installation
  • Getting Started
    • -
  • About

    • Searchers

    @@ -170,7 +169,7 @@

    F

    @@ -178,45 +177,45 @@

    G

    @@ -228,14 +227,14 @@ llepe @@ -247,7 +246,7 @@ module @@ -256,11 +255,11 @@

    P

    @@ -268,7 +267,7 @@

    R

    @@ -276,45 +275,45 @@

    S

    @@ -322,13 +321,13 @@

    U

    @@ -345,7 +344,7 @@

    - © Copyright 2020, UChicago Argonne, LLC. + © Copyright 2020, Titus Quah, Nwike Iloeje

    diff --git a/docs/_build/html/guide/about.html b/docs/_build/html/guide/about.html deleted file mode 100644 index 67be0bd..0000000 --- a/docs/_build/html/guide/about.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - - - - About — LLEPE 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - -
    - - - - - -
    - -
    - - - - - - - - - - - - - - - - - -
    - - - - -
    -
    -
    -
    - -
    -

    About

    -
    -

    Authors

    -

    Titus Quah, University of Utah, <titus{dot}quah{at}gmail{dot}com>

    -

    Nwike Iloeje, Argonne National Laboratory, <ciloeje{at}anl{dot}gov>

    -
    -
    -

    Acknowledgements

    -

    Based upon work supported by funding from Argonne National Laboratory provided by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under contract DE-AC02-06CH11357

    -
    -
    -

    References

    -

    If you use LLEPE in your research , we kindly request that you cite the package as follows:

    -
      -

    1. Quah and C. O. Iloeje, “Liquid--Liquid Extraction Thermodynamic Parameter Estimator (LLEPE) for Multicomponent Separation Systems,” in Materials Processing Fundamentals 2021, 2021, pp. 107–120, doi: https://doi.org/10.1007/978-3-030-65253-1_9.

      2. -
    -
    -
    -

    License

    -
    Copyright © 2020, UChicago Argonne, LLC
-All Rights Reserved
- Software Name: LLEPE
-By: Argonne National Laboratory, University of Utah
-OPEN SOURCE LICENSE
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice,
-   this list of conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-3. Neither the name of the copyright holder nor the names of its contributors
-   may be used to endorse or promote products derived from this software without
-   specific prior written permission.
-
-
-******************************************************************************************************
-DISCLAIMER
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
-IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
-INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
-NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
-OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
-OF SUCH DAMAGE.
-***************************************************************************************************
-
    -
    -
    -
    - - -
    - -
    - - -
    -
    - -
    - -
    - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/guide/install.html b/docs/_build/html/guide/install.html index 17e566e..f28dddc 100644 --- a/docs/_build/html/guide/install.html +++ b/docs/_build/html/guide/install.html @@ -36,7 +36,7 @@ - + @@ -97,7 +97,7 @@

    Searchers

    @@ -175,14 +175,14 @@

    Bleeding-edge version

    To install the latest master version:

    -
    pip install git+https://github.com/ANL-CEEESA/LLEPE.git
+
    pip install git+https://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git

    Development version

    -

    To contribute to LLEPE, with support for running tests and building the documentation.

    -
    git clone https://github.com/ANL-CEEESA/LLEPE.git
+
    To contribute to Stable-Baselines, with support for running tests and building the documentation.
    
+
    git clone https://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git && cd parameter-estimation
 pip install -e .[docs,tests]
 
    
 
    
@@ -204,7 +204,7 @@ pip install -e .[docs,tests]
         
       
       
-        
+
    @@ -213,7 +213,7 @@ pip install -e .[docs,tests]

    - © Copyright 2020, UChicago Argonne, LLC. + © Copyright 2020, Titus Quah, Nwike Iloeje

    diff --git a/docs/_build/html/guide/quickstart.html b/docs/_build/html/guide/quickstart.html index 72b8974..948381e 100644 --- a/docs/_build/html/guide/quickstart.html +++ b/docs/_build/html/guide/quickstart.html @@ -218,7 +218,7 @@ aqueous phase.

    - © Copyright 2020, UChicago Argonne, LLC. + © Copyright 2020, Titus Quah, Nwike Iloeje

    diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html index 9cee7a4..7f6af8e 100644 --- a/docs/_build/html/index.html +++ b/docs/_build/html/index.html @@ -8,7 +8,7 @@ - Welcome to LLEPE's docs! - the Liquid-Liquid Extraction Parameter Estimator — LLEPE 1.0.0 documentation + Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator — LLEPE 1.0.0 documentation @@ -86,11 +86,10 @@

    Searchers

    @@ -136,7 +135,7 @@
  • Docs »
    • -
  • Welcome to LLEPE's docs! - the Liquid-Liquid Extraction Parameter Estimator
    • +
  • Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator
  • @@ -156,7 +155,7 @@
    -

    Welcome to LLEPE's docs! - the Liquid-Liquid Extraction Parameter Estimator

    +

    Welcome to llepe's docs! - the Liquid-Liquid Extraction Parameter Estimator

    LLEPE is a package for thermodynamic parameter estimation for liquid-liquid extraction modeling

    LLEPE takes experimental data in a csv and system data in a xml.

    The package then uses Cantera, another python package, to simulate equilibrium.

    @@ -173,19 +172,12 @@
  • Getting Started
    • -
  • About -

    • Searchers

    @@ -216,7 +208,7 @@

    - © Copyright 2020, UChicago Argonne, LLC. + © Copyright 2020, Titus Quah, Nwike Iloeje

    diff --git a/docs/_build/html/modules/LLEPE.html b/docs/_build/html/modules/LLEPE.html deleted file mode 100644 index 4f1ff3b..0000000 --- a/docs/_build/html/modules/LLEPE.html +++ /dev/null @@ -1,1123 +0,0 @@ - - - - - - - - - - - LLEPE — LLEPE 1.0.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - - -
    - - - - - -
    - -
    - - - - - - - - - - - - - - - - - -
    - - - - -
    -
    -
    -
    - -
    -

    LLEPE

    -
    -

    Parameters

    -
    -
    -class llepe.LLEPE(exp_data, phases_xml_filename, phase_names, aq_solvent_name, extractant_name, diluant_name, complex_names, extracted_species_ion_names, extracted_species_list=None, aq_solvent_rho=None, extractant_rho=None, diluant_rho=None, opt_dict=None, objective_function='Log-MSE', optimizer='scipy_minimize', temp_xml_file_path=None, dependant_params_dict=None, custom_objects_dict=None)[source]
    -

    Liquid-Liquid Extraction Parameter estimator

    -
    -

    Note

    -

    The order in which the extracted species (ES) appear in the csv file -must be the same order as they appear in the xml, complex_names and -extracted_species_ion_names.

    -

    For example, say in exp_data, ES_1 is Nd ES_2 is Pr, -and

    -
    aq_solvent_name = 'H2O(L)'
-extractant_name = '(HA)2(org)'
-diluent_name = 'dodecane'
-
    -
    -

    Then:

    -

    The exp_data column ordering must be (names do not matter):

    -

    [h_i, h_eq, z_i, z_eq, Nd_aq_i, Nd_aq_eq, Nd_d_eq, -Pr_aq_i, Pr_aq_eq, Pr_d_eq]

    -

    The aqueous speciesArray must be -"H2O(L) H+ OH- Cl- Nd+++ Pr+++"

    -

    The organic speciesArray must be -"(HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org)"

    -
    complex_names = ['Nd(H(A)2)3(org)', 'Pr(H(A)2)3(org)']
-extracted_species_ion_names = ['Nd+++', 'Pr+++']
-
    -
    -
    -
    -
    Parameters
    -
      -

    • exp_data --

      (str or pd.DataFrame) csv file name -or DataFrame with experimental data

      -

      In the .csv file, the rows are different experiments and -columns are the measured quantities.

      -

      The ordering of the columns needs to be:

      -

      [h_i, h_eq, z_i, z_eq, -{ES_1}_aq_i, {ES_1}_aq_eq, {ES_1}_d_eq, -{ES_2}_aq_i, {ES_2}_aq_eq, {ES_2}_d_eq,... -{ES_N}_aq_i, {ES_N}_aq_eq, {ES_N}_d_eq]

      -

      Naming does not matter, just the order.

      -

      Where {ES_1}-{ES_N} are the extracted species names of interest -i.e. Nd, Pr, La, etc.

      -

      Below is an explanation of the columns.

      - ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

      Index

      Column

      Meaning

      0

      h_i

      Initial Concentration of -H+ ions (mol/L)

      1

      h_eq

      Equilibrium concentration of -H+ ions (mol/L)

      2

      z_i

      Initial concentration of -extractant (mol/L)

      3

      z_eq

      Equilibrium concentration of -extractant (mol/L)

      4

      {ES}_aq_i

      Initial concentration of ES ions (mol/L)

      5

      {ES}_aq_eq

      Equilibrium concentration of ES ions -in aqueous phase (mol/L)

      6

      {ES}_d_eq

      Equilibrium Ratio between amount of -ES atoms in organic to aqueous

      -

      • -

    • phases_xml_filename --

      (str) xml file with parameters -for equilibrium calc

      -

      Would recommend copying and modifying xmls located in data/xmls -or in Cantera's "data" folder

      -

      speciesArray fields need specific ordering.

      -

      In aqueous phase: aq_solvent_name, H+, OH-, Cl-, ES_1, ES_2, ..., ES_N

      -

      (ES_1-ES_N) represent ES ion names i.e. Nd+++, Pr+++

      -

      In organic phase : extractant_name, diluant_name, ES_1, ES_2, ..., ES_N

      -

      (ES_1-ES_N) represent ES complex names -i.e. Nd(H(A)2)3(org), Pr(H(A)2)3(org)

      -

      • -

    • phase_names --

      (list) names of phases in xml file

      -

      Found in the xml file under <phase ... id={phase_name}>

      -

      • -

    • aq_solvent_name -- (str) name of aqueous solvent in xml file

      • -

    • extractant_name -- (str) name of extractant in xml file

      • -

    • diluant_name -- (str) name of diluant in xml file

      • -

    • complex_names -- (list) names of complexes in xml file.

      • -

    • extracted_species_ion_names -- (list) names of extracted species ions -in xml file

      • -

    • extracted_species_list --

      (list) names of extracted species elements.

      -

      If None, extracted_species_list will be extracted_species_ion_names -without '+' i.e. 'Nd+++'->'Nd'

      -

      • -

    • aq_solvent_rho --

      (float) density of solvent (g/L)

      -

      If None, molar volume/molecular weight is used from xml

      -

      • -

    • extractant_rho --

      (float) density of extractant (g/L)

      -

      If None, molar volume/molecular weight is used from xml

      -

      • -

    • diluant_rho --

      (float) density of diluant (g/L)

      -

      If None, molar volume/molecular weight is used from xml

      -

      • -

    • opt_dict --

      (dict) dictionary containing info about which -species parameters are updated to fit model to experimental data

      -

      Should have the format as below. Dictionary keys under user defined -parameter name must be named as shown below ('upper_element_name', -'upper_attrib_name', etc.). 'attrib_name's and 'attrib_value's can -be None. {} denotes areas for user to fill in.

      -
      opt_dict = {"{user_defined_name_for_parameter_1}":
-                {'upper_element_name': {param_upper_element},
-                'upper_attrib_name': {param_upper_attrib_name},
-                'upper_attrib_value': {param_upper_attrib_value},
-                'lower_element_name': {param_lower_element},
-                'lower_attrib_name': {param_lower_attrib_name},
-                'lower_attrib_value': {param_lower_attrib_value},
-                'input_format': {str format to input input_value}
-                'input_value': {guess_value}},
-            "{user_defined_name_for_parameter_2}":
-                            ...
-            ...
-            }
-
      -
      -

      See example files for more examples.

      -

      • -

    • objective_function --

      (function or str) function to compute objective

      -

      By default, the objective function is log mean squared error -of distribution ratio

      -
      np.sum((np.log10(d_pred)-np.log10(d_meas))^2)
-
      -
      -

      Function needs to take inputs:

      -
      objective_function(predicted_dict, measured_df, kwargs)
-
      -
      -

      kwargs is optional

      -

      Function needs to return: (float) value computed by objective function

      -

      Below is the guide for referencing predicted values

      - ---- - - - - - - - - - - - - - - - - - - - - - - -

      To access

      Use

      hydrogen ion conc in aq

      predicted_dict['h_eq']

      extractant conc in org

      predicted_dict['z_eq']

      ES ion eq conc in aq

      predicted_dict['{ES}_aq_eq']

      ES complex eq conc in org

      predicted_dict['{ES}_org_eq']

      ES distribution ratio

      predicted_dict['{ES}_d_eq']

      -

      Replace "{ES}" with extracted species element i.e. Nd, La, etc.

      -

      For measured values, use the same names, but -replace predicted_dict with measured_df

      -

      • -

    • optimizer --

      (function or str) function to perform optimization

      -
      -

      Note

      -

      The optimized variables are not directly the species parameters, -but instead are first multiplied by the initial guess before -sending becoming the species parameters.

      -

      For example, say

      -
      opt_dict = {'Nd(H(A)2)3(org):'h0':-4.7e6}
-
      -
      -

      If the bounds on h0 need to be [-4.7e7,-4.7e5], then -divide the bounds by the guess and get

      -
      "bounds": [(1e-1, 1e1)]
-
      -
      -
      -

      By default, the optimizer is scipy's optimize function with

      -
      default_kwargs= {"method": 'SLSQP',
-                 "bounds": [(1e-1, 1e1)] * len(x_guess),
-                 "constraints": (),
-                 "options": {'disp': True,
-                             'maxiter': 1000,
-                             'ftol': 1e-6}}
-
      -
      -

      Function needs to take inputs: -optimizer(objective_function, x_guess, kwargs)

      -

      kwargs is optional

      -
      -
      Function needs to return: ((np.ndarray, float)) Optimized parameters,

      objective_function value

      -
      -
      -

      • -

    • temp_xml_file_path --

      (str) path to temporary xml file.

      -

      This xml file is a duplicate of the phases_xml_file name and is -modified during the optimization process to avoid changing the original -xml file

      -

      default is local temp folder

      -

      • -

    • dependant_params_dict --

      (dict) dictionary containing information -about parameters dependant on opt_dict. Has a similar structure to -opt_dict except instead of input values, it has 3 other fields: -'function', 'kwargs', and 'independent_params.

      -

      'function' is a function of the form

      -

      function(independent_param__value_list, custom_objects_dict, -**kwargs)

      -

      'kwargs' are the extra arguments to pass to function

      -

      'independent_params' is a list of parameter names in opt_dict that the -dependent_param is a function of.

      -

      'custom_objects_dict' is for accessing the estimator's internal -custom_objects_dict and must be included in the arguments, even if the -custom_objects_dict is not set and is None.

      -

      See example code for usage.

      -

      • -

    • custom_objects_dict -- (dict) dictionary containing custom objects -format: {<object_name_string>: <object>,...}

      • -
    -
    -
    -
    -
    -fit(objective_function=None, optimizer=None, objective_kwargs=None, optimizer_kwargs=None) → tuple[source]
    -

    Fits experimental to modeled data by minimizing objective function -with optimizer. Returns dictionary with opt_dict structure

    -
    -
    Parameters
    -
      -

    • objective_function -- (function) function to compute objective -If 'None', last set objective or default function is used

      • -

    • optimizer -- (function) function to perform optimization -If 'None', last set optimizer or default is used

      • -

    • optimizer_kwargs -- (dict) optional arguments for optimizer

      • -

    • objective_kwargs -- (dict) optional arguments -for objective function

      • -
    -
    -
    Returns tuple
    -

    (opt_dict (dict), opt_value (float)) -optimized opt_dict: Has identical structure as opt_dict

    -
    -
    -
    - -
    -
    -get_aq_solvent_name() → str[source]
    -

    Returns aq_solvent_name

    -
    -
    Returns
    -

    aq_solvent_name: (str) name of aqueous solvent in xml file

    -
    -
    -
    - -
    -
    -get_aq_solvent_rho() → str[source]
    -

    Returns aqueous solvent density (g/L)

    -
    -
    Returns
    -

    aq_solvent_rho: (float) density of aqueous solvent

    -
    -
    -
    - -
    -
    -get_complex_names() → list[source]
    -

    Returns list of complex names

    -
    -
    Returns
    -

    complex_names: (list) names of complexes in xml file.

    -
    -
    -
    - -
    -
    -get_custom_objects_dict()[source]
    -

    Returns the custom_objects_dict

    -
    -
    Returns
    -

    custom_objects_dict: (dict) dictionary containing -information about custom objects from user

    -
    -
    -
    - -
    -
    -get_dependant_params_dict()[source]
    -

    Returns the dependant_params_dict

    -
    -
    Returns
    -

    dependant_params_dict: (dict) dictionary containing -information about parameters dependant on opt_dict

    -
    -
    -
    - -
    -
    -get_diluant_name() → str[source]
    -

    Returns diluant name -:return: diluant_name: (str) name of diluant in xml file

    -
    - -
    -
    -get_diluant_rho() → str[source]
    -

    Returns diluant density (g/L)

    -
    -
    Returns
    -

    diluant_rho: (float) density of diluant

    -
    -
    -
    - -
    -
    -get_exp_df() → pandas.core.frame.DataFrame[source]
    -

    Returns the experimental DataFrame

    -
    -
    Returns
    -

    (pd.DataFrame) Experimental data

    -
    -
    -
    - -
    -
    -get_extractant_name() → str[source]
    -

    Returns extractant name

    -
    -
    Returns
    -

    extractant_name: (str) name of extractant in xml file

    -
    -
    -
    - -
    -
    -get_extractant_rho() → str[source]
    -

    Returns extractant density (g/L)

    -
    -
    Returns
    -

    extractant_rho: (float) density of extractant

    -
    -
    -
    - -
    -
    -get_extracted_species_ion_names() → list[source]
    -

    Returns list of extracted species ion names

    -
    -
    Returns
    -

    extracted_species_ion_names: (list) names of -extracted species ions in xml file

    -
    -
    -
    - -
    -
    -get_extracted_species_list() → list[source]
    -

    Returns list of extracted species names

    -
    -
    Returns
    -

    extracted_species_list: (list) names of extracted species in -xml file

    -
    -
    -
    - -
    -
    -get_in_moles() → pandas.core.frame.DataFrame[source]
    -

    Returns the in_moles DataFrame which contains the initial mole -fractions of each species for each experiment

    -
    -
    Returns
    -

    in_moles: (pd.DataFrame) DataFrame with initial mole fractions

    -
    -
    -
    - -
    -
    -get_objective_function()[source]
    -

    Returns objective function

    -
    -
    Returns
    -

    objective_function: (func) Objective function to quantify -error between model and experimental data

    -
    -
    -
    - -
    -
    -get_opt_dict() → dict[source]
    -

    Returns the dictionary containing optimization information

    -
    -
    Returns
    -

    (dict) dictionary containing info about which -species parameters are updated to fit model to experimental data

    -
    -
    -
    - -
    -
    -get_optimizer()[source]
    -

    Returns objective function

    -
    -
    Returns
    -

    optimizer: (func) Optimizer function to minimize objective -function

    -
    -
    -
    - -
    -
    -get_phases() → list[source]
    -

    Returns the list of Cantera solutions

    -
    -
    Returns
    -

    (list) list of Cantera solutions/phases

    -
    -
    -
    - -
    -
    -get_predicted_dict()[source]
    -

    Returns predicted dictionary of species concentrations -that xml parameters predicts given current in_moles

    -
    -
    Returns
    -

    predicted_dict: (dict) dictionary of species concentrations

    -
    -
    -
    - -
    -
    -get_temp_xml_file_path()[source]
    -

    Returns path to temporary xml file.

    -

    This xml file is a duplicate of the phases_xml_file name and is -modified during the optimization process to avoid changing the original -xml file.

    -
    -
    Returns
    -

    temp_xml_file_path: (str) path to temporary xml file.

    -
    -
    -
    - -
    -
    -log_mean_squared_error(predicted_dict, meas_df)[source]
    -

    Default objective function for LLEPE

    -

    Returns the log mean squared error of -predicted distribution ratios (d=n_org/n_aq) -to measured d.

    -

    np.sum((np.log10(d_pred)-np.log10(d_meas))**2)

    -
    -
    Parameters
    -
      -

    • predicted_dict -- (dict) contains predicted data

      • -

    • meas_df -- (pd.DataFrame) contains experimental data

      • -
    -
    -
    Returns
    -

    (float) log mean squared error between predicted and measured

    -
    -
    -
    - -
    -
    -parity_plot(compared_value=None, c_data=None, c_label=None, plot_title=None, save_path=None, print_r_squared=False, data_labels=None, legend=True)[source]
    -

    Parity plot between measured and predicted compared_value. -Default compared value is {ES_1}_aq_eq

    -
    -
    Parameters
    -
      -

    • compared_value -- (str) Quantity to compare predicted and -experimental data. Can be any column containing "eq" in exp_df i.e. -h_eq, z_eq, {ES}_d_eq, etc.

      • -

    • plot_title --

      (str or boolean)

      -
      -
      If None (default): Plot title will be generated from compared_value

      Recommend to just explore. If h_eq, plot_title is -"H^+ eq conc".

      -
      -
      -

      If str: Plot title will be plot_title string

      -

      If "False": No plot title

      -

      • -

    • c_data -- (list or np.ndarray) data for color axis

      • -

    • c_label -- (str) label for color axis

      • -

    • save_path -- (str) save path for parity plot

      • -

    • print_r_squared -- (boolean) To plot or not to plot r-squared -value. Prints 2 places past decimal

      • -

    • data_labels -- labels for the data such as paper's name where -experiment is pulled from.

      • -

    • legend -- whether to display legend for data_labels. Has no -effect if data_labels is None

      • -
    -
    -
    Return fig, ax
    -

    returns the figure and axes objects

    -
    -
    -
    - -
    -
    -static plot_3d_data(x_data, y_data, z_data, c_data=None, x_label=None, y_label=None, z_label=None, c_label=None)[source]
    -

    THis is for plotting 3d scatter plots. -We suggest use matplotlib's ax.scatter to make 3d plots.

    -
    -
    Parameters
    -
      -

    • x_data -- (list) list of data for x axis

      • -

    • y_data -- (list) list of data for y axis

      • -

    • z_data -- (list) list of data for z axis

      • -

    • c_data -- (list) list of data for color axis

      • -

    • x_label -- (str) label for x axis

      • -

    • y_label -- (str) label for y axis

      • -

    • z_label -- (str) label for z axis

      • -

    • c_label -- (str) label for color axis

      • -
    -
    -
    Returns
    -

    -
    -
    -
    - -
    -
    -r_squared(compared_value=None)[source]
    -

    r-squared value comparing measured and predicted compared value

    -

    Closer to 1, the better the model's predictions.

    -
    -
    Parameters
    -

    compared_value -- (str) Quantity to compare predicted and -experimental data. Can be any column containing "eq" in exp_df i.e. -h_eq, z_eq, {ES}_d_eq, etc. default is {ES}_aq_eq

    -
    -
    -
    - -
    -
    -static scipy_minimize(objective, x_guess, optimizer_kwargs=None)[source]
    -

    The default optimizer for LLEPE

    -

    Uses scipy.minimize

    -

    By default, options are

    -
    default_kwargs= {"method": 'SLSQP',
-                "bounds": [(1e-1, 1e1)]*len(x_guess),
-                "constraints": (),
-                "options": {'disp': True,
-                            'maxiter': 1000,
-                            'ftol': 1e-6}}
-
    -
    -
    -
    Parameters
    -
      -

    • objective -- (func) the objective function

      • -

    • x_guess -- (np.ndarray) the initial guess (always 1)

      • -

    • optimizer_kwargs -- (dict) dictionary of options for minimize

      • -
    -
    -
    Returns
    -

    ((np.ndarray, float)) Optimized parameters, -objective_function value

    -
    -
    -
    - -
    -
    -set_aq_solvent_name(aq_solvent_name)[source]
    -

    Change aq_solvent_name to input aq_solvent_name

    -
    -
    Parameters
    -

    aq_solvent_name -- (str) name of aqueous solvent in xml file

    -
    -
    -
    - -
    -
    -set_aq_solvent_rho(aq_solvent_rho)[source]
    -

    Changes aqueous solvent density (g/L) to input aq_solvent_rho

    -
    -
    Parameters
    -

    aq_solvent_rho -- (float) density of aqueous solvent

    -
    -
    -
    - -
    -
    -set_complex_names(complex_names)[source]
    -

    Change complex names list to input complex_names

    -
    -
    Parameters
    -

    complex_names -- (list) names of complexes in xml file.

    -
    -
    -
    - -
    -
    -set_custom_objects_dict(custom_objects_dict)[source]
    -

    Sets the custom_objects_dict

    -
    -
    Parameters
    -

    custom_objects_dict -- (dict) dictionary containing information -about about custom objects from user

    -
    -
    -
    - -
    -
    -set_dependant_params_dict(dependant_params_dict)[source]
    -

    Sets the dependant_params_dict

    -
    -
    Parameters
    -

    dependant_params_dict -- (dict) dictionary containing information -about parameters dependant on opt_dict

    -
    -
    -
    - -
    -
    -set_diluant_name(diluant_name)[source]
    -

    Change diluant_name to input diluant_name

    -
    -
    Parameters
    -

    diluant_name -- (str) name of diluant in xml file

    -
    -
    -
    - -
    -
    -set_diluant_rho(diluant_rho)[source]
    -

    Changes diluant density (g/L) to input diluant_rho

    -
    -
    Parameters
    -

    diluant_rho -- (float) density of diluant

    -
    -
    -
    - -
    -
    -set_exp_df(exp_data)[source]
    -

    Changes the experimental DataFrame to input exp_csv_filename data -and renames columns to internal LLEPE names

    -

    h_i, h_eq, z_i, z_eq, {ES}_aq_i, {ES}_aq_eq, {ES}_d

    -

    See class docstring on "exp_csv_filename" for further explanations.

    -
    -
    Parameters
    -

    exp_data -- (str or pd.DataFrame) -file name/path or DataFrame for experimental data csv

    -
    -
    -
    - -
    -
    -set_extractant_name(extractant_name)[source]
    -

    Change extractant_name to input extractant_name -:param extractant_name: (str) name of extractant in xml file

    -
    - -
    -
    -set_extractant_rho(extractant_rho)[source]
    -

    Changes extractant density (g/L) to input extractant_rho

    -
    -
    Parameters
    -

    extractant_rho -- (float) density of extractant

    -
    -
    -
    - -
    -
    -set_extracted_species_ion_names(extracted_species_ion_names)[source]
    -
    -
    Change list of extracted species ion names to input

    extracted_species_ion_names

    -
    -
    -
    -
    Parameters
    -

    extracted_species_ion_names -- (list) names of extracted species -ions in xml file

    -
    -
    -
    - -
    -
    -set_extracted_species_list(extracted_species_list)[source]
    -
    -
    Change list of extracted species ion names to input

    extracted_species_ion_names

    -
    -
    -
    -
    Parameters
    -

    extracted_species_list -- (list) names of extracted species in -xml file

    -
    -
    -
    - -
    -
    -set_in_moles(feed_vol)[source]
    -

    Function that initializes mole fractions to input feed_vol

    -

    This function is called at initialization

    -

    Sets in_moles to a pd.DataFrame containing initial mole fractions

    -

    Columns for species and rows for different experiments

    -

    This function also calls update_predicted_dict

    -
    -
    Parameters
    -

    feed_vol -- (float) feed volume of mixture (L)

    -
    -
    -
    - -
    -
    -set_objective_function(objective_function)[source]
    -

    Change objective function to input objective_function.

    -
    -

    See class docstring on "objective_function" for instructions

    -
    -
    -
    Parameters
    -

    objective_function -- (func) Objective function to quantify -error between model and experimental data

    -
    -
    -
    - -
    -
    -set_opt_dict(opt_dict)[source]
    -

    Change the dictionary to input opt_dict.

    -

    opt_dict specifies species parameters to be updated to -fit model to data

    -

    See class docstring on "opt_dict" for more information.

    -
    -
    Parameters
    -

    opt_dict -- (dict) dictionary containing info about which -species parameters are updated to fit model to experimental data

    -
    -
    -
    - -
    -
    -set_optimizer(optimizer)[source]
    -

    Change optimizer function to input optimizer.

    -
    -

    See class docstring on "optimizer" for instructions

    -
    -
    -
    Parameters
    -

    optimizer -- (func) Optimizer function to minimize objective -function

    -
    -
    -
    - -
    -
    -set_phases(phases_xml_filename, phase_names)[source]
    -

    Change list of Cantera solutions by inputting -new xml file name and phase names

    -

    Also runs set_in_moles to set feed volume to 1 L

    -
    -
    Parameters
    -
      -

    • phases_xml_filename -- (str) xml file with parameters -for equilibrium calc

      • -

    • phase_names -- (list) names of phases in xml file

      • -
    -
    -
    -
    - -
    -
    -set_temp_xml_file_path(temp_xml_file_path)[source]
    -

    Changes temporary xml file path to input temp_xml_file_path.

    -

    This xml file is a duplicate of the phases_xml_file name and is -modified during the optimization process to avoid changing the original -xml file.

    -
    -
    Parameters
    -

    temp_xml_file_path -- (str) path to temporary xml file.

    -
    -
    -
    - -
    -
    -update_custom_objects_dict(info_dict)[source]
    -

    updates internal custom_objects_dict with info_dict

    -
    -
    Parameters
    -

    info_dict -- Requires an identical structure to opt_dict -Ignores items with keys containing "custom_object_name"

    -
    -
    Returns
    -

    None

    -
    -
    -
    - -
    -
    -update_predicted_dict(phases_xml_filename=None, phase_names=None)[source]
    -

    Function that computes the predicted equilibrium concentrations -the fed phases_xml_filename parameters predicts given the initial -mole fractions set by in_moles()

    -
    -
    Parameters
    -
      -

    • phases_xml_filename -- (str)xml file with parameters -for equilibrium calc. If None, the -current phases_xml_filename is used.

      • -

    • phase_names -- (list) names of phases in xml file. -If None, the current phases_names is used.

      • -
    -
    -
    -
    - -
    -
    -update_xml(info_dict, phases_xml_filename=None, dependant_params_dict=None)[source]
    -

    updates xml file with info_dict

    -
    -
    Parameters
    -
      -

    • info_dict -- (dict) Requires an identical structure to opt_dict -Ignores items with keys containing "custom_object_name"

      • -

    • phases_xml_filename -- (str) xml filename if editing other xml -If None, the current xml will be modified and the internal -Cantera phases will be refreshed to the new values.

      • -

    • dependant_params_dict -- (dict) dictionary containing information -about parameters dependant on info_dict

      • -
    -
    -
    -
    - -
    - -
    -
    - - -
    - -
    - - -
    -
    - -
    - -
    - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/modules/reeps.html b/docs/_build/html/modules/reeps.html index 8f4ad8e..9275abb 100644 --- a/docs/_build/html/modules/reeps.html +++ b/docs/_build/html/modules/reeps.html @@ -34,7 +34,8 @@ - + + @@ -86,6 +87,13 @@
  • Installation
  • Getting Started
    • +

    Searchers

    + @@ -149,8 +157,8 @@
    -
    -

    LLEPE

    +
    +

    LLEPE

    Parameters

    @@ -162,7 +170,7 @@

    The order in which the extracted species (ES) appear in the csv file must be the same order as they appear in the xml, complex_names and extracted_species_ion_names.

    -

    For example, say in exp_data, ES_1 is Nd ES_2 is Pr, +

    For example, say in exp_csv_filename's csv, ES_1 is Nd ES_2 is Pr, and

    aq_solvent_name = 'H2O(L)'
 extractant_name = '(HA)2(org)'
@@ -170,7 +178,7 @@ and

    Then:

    -

    The exp_data column ordering must be (names do not matter):

    +

    The csvs column ordering must be:

    [h_i, h_eq, z_i, z_eq, Nd_aq_i, Nd_aq_eq, Nd_d_eq, Pr_aq_i, Pr_aq_eq, Pr_d_eq]

    The aqueous speciesArray must be @@ -710,9 +718,7 @@ effect if data_labels is None

    static plot_3d_data(x_data, y_data, z_data, c_data=None, x_label=None, y_label=None, z_label=None, c_label=None)[source]
    -

    THis is for plotting 3d scatter plots. -We suggest use matplotlib's ax.scatter to make 3d plots.

    -
    +
    Parameters

    • x_data -- (list) list of data for x axis

      • @@ -1071,12 +1077,19 @@ about parameters dependant on info_dict