From 5ce76bb867072c1d4f53533a114e4f271b7573ea Mon Sep 17 00:00:00 2001 From: titusquah <46580668+titusquah@users.noreply.github.com> Date: Wed, 7 Apr 2021 11:27:10 -0600 Subject: [PATCH] rename for rtd --- docs/_build/doctrees/environment.pickle | Bin 154128 -> 90809 bytes docs/_build/doctrees/guide/about.doctree | Bin 0 -> 10610 bytes docs/_build/doctrees/guide/install.doctree | Bin 5675 -> 5473 bytes docs/_build/doctrees/guide/quickstart.doctree | Bin 7735 -> 7713 bytes docs/_build/doctrees/index.doctree | Bin 6297 -> 6242 bytes docs/_build/doctrees/modules/LLEPE.doctree | Bin 0 -> 200180 bytes docs/_build/doctrees/modules/reeps.doctree | Bin 201626 -> 200180 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} | 2 +- .../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 705 -> 723 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 | 15 + 37 files changed, 1716 insertions(+), 3029 deletions(-) create mode 100644 docs/_build/doctrees/guide/about.doctree create mode 100644 docs/_build/doctrees/modules/LLEPE.doctree delete mode 100644 docs/_build/html/_modules/reeps/reeps.html create mode 100644 docs/_build/html/_sources/guide/about.rst.txt rename docs/{_source/modules/reeps.rst => _build/html/_sources/modules/LLEPE.rst.txt} (74%) delete mode 100644 docs/_build/html/_static/alabaster.css delete mode 100644 docs/_build/html/_static/classic.css delete mode 100644 docs/_build/html/_static/custom.css delete mode 100644 docs/_build/html/_static/default.css delete mode 100644 docs/_build/html/_static/sidebar.js create mode 100644 docs/_build/html/guide/about.html create mode 100644 docs/_build/html/modules/LLEPE.html create mode 100644 docs/_source/guide/about.rst create mode 100644 docs/_source/modules/LLEPE.rst diff --git a/docs/_build/doctrees/environment.pickle 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z^9H|Vpi6H0!?)zdRJx-y_{!F}mu|ZZzA!ZU(PeAL&xSXd-i~exMW+0_7 zg9`y8l%k8lQQufYxEQag+k-6^Bj(o+tgmfN{{yt9V&sI1nws*lbka7wUJbQcT%NaV zmi~ZOODR&t1;iRZmvSzMRnOTiq0zMgefr(rzrHqLeAUE?`n#bvsAkf%DGyfE*ZTLL zJJp_6mcW?Hga@t0qx3&F&B*ryf2De!-r=goR+cFIsg!noF)GAv=_LOlRi)T1-RS?M wmf0RG#`Ewoj;LM+iXmPxzC|AQ|}_|k9Sd)sb_#>=H@m)(-^KcX`c$p8QV diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo index d0738d2..51dc31b 100644 --- a/docs/_build/html/.buildinfo +++ b/docs/_build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 09c8b5742a1f96cda53a1848d8ac9ec1 +config: fdde7da710e99305b2f90b2ffb8f91ce tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html index ca853b9..d96106f 100644 --- a/docs/_build/html/_modules/index.html +++ b/docs/_build/html/_modules/index.html @@ -85,10 +85,11 @@

Searchers

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

All modules for which code is available

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

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

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

Searchers

@@ -169,7 +170,7 @@ from .utils import set_size -
[docs]class LLEPE: +
[docs]class LLEPE: r""" Liquid-Liquid Extraction Parameter estimator @@ -180,7 +181,7 @@ must be the same order as they appear in the xml, complex_names and extracted_species_ion_names. - For example, say in exp_csv_filename's csv, ES_1 is Nd ES_2 is Pr, + For example, say in exp_data, ES_1 is Nd ES_2 is Pr, and .. code-block:: python @@ -191,7 +192,7 @@ Then: - The csvs column ordering must be: + 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] @@ -526,7 +527,8 @@ self._predicted_dict = None self.update_predicted_dict() -
[docs] @staticmethod + # TODO: move scipy_minimize to optimizers +
[docs] @staticmethod def scipy_minimize(objective, x_guess, optimizer_kwargs=None): """ The default optimizer for LLEPE @@ -560,7 +562,8 @@ est_parameters = res.x return est_parameters, res.fun
-
[docs] def log_mean_squared_error(self, predicted_dict, meas_df): + # TODO: move log_mean_squared_error to objectives +
[docs] def log_mean_squared_error(self, predicted_dict, meas_df): """Default objective function for LLEPE Returns the log mean squared error of @@ -584,14 +587,14 @@ 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 @@ -632,7 +635,7 @@ self.update_predicted_dict() return None
-
[docs] def get_phases(self) -> list: +
[docs] def get_phases(self) -> list: """ Returns the list of Cantera solutions @@ -640,7 +643,7 @@ """ 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 @@ -673,7 +676,7 @@ 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 @@ -682,7 +685,7 @@ """ 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. @@ -698,14 +701,14 @@ 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 @@ -713,14 +716,14 @@ 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 @@ -728,13 +731,13 @@ 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 @@ -743,14 +746,14 @@ 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. @@ -758,7 +761,7 @@ 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 @@ -766,7 +769,7 @@ """ 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 @@ -776,7 +779,7 @@ 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 @@ -784,7 +787,7 @@ """ 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 @@ -794,14 +797,14 @@ 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 @@ -809,14 +812,14 @@ 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 @@ -824,14 +827,14 @@ 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 @@ -839,7 +842,12 @@ self._diluant_rho = diluant_rho return None
-
[docs] def set_in_moles(self, feed_vol): + # 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): """Function that initializes mole fractions to input feed_vol This function is called at initialization @@ -922,7 +930,7 @@ for extracted_species in extracted_species_list]) extracted_species_charge_sum = np.sum( extracted_species_charges * extracted_species_moles) - chlorine_moles = extracted_species_charge_sum + h_plus_moles + anion_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 @@ -932,7 +940,7 @@ species_moles_aq = [aq_phase_solvent_moles, h_plus_moles, hydroxide_ions, - chlorine_moles] + anion_moles] species_moles_aq.extend(list(extracted_species_moles)) species_moles_org = [extractant_moles, diluant_moles] species_moles_org.extend(list(complex_moles)) @@ -946,7 +954,7 @@ 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 @@ -954,7 +962,7 @@ """ 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 @@ -979,7 +987,7 @@ 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 @@ -987,7 +995,7 @@ """ 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 @@ -1012,7 +1020,7 @@ 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 @@ -1020,7 +1028,7 @@ """ 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 @@ -1031,7 +1039,7 @@ """ 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 @@ -1043,7 +1051,7 @@ 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 @@ -1052,7 +1060,7 @@ """ 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 @@ -1062,7 +1070,7 @@ 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 @@ -1071,7 +1079,7 @@ """ 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 @@ -1081,7 +1089,11 @@ self._custom_objects_dict = custom_objects_dict return None
-
[docs] def update_predicted_dict(self, + # 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, phases_xml_filename=None, phase_names=None): """Function that computes the predicted equilibrium concentrations @@ -1148,7 +1160,7 @@ 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 @@ -1212,7 +1224,7 @@ objective_values = objective_values[0] return objective_values -
[docs] def fit(self, +
[docs] def fit(self, objective_function=None, optimizer=None, objective_kwargs=None, @@ -1255,7 +1267,7 @@ 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): @@ -1362,7 +1374,7 @@ 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 @@ -1387,7 +1399,7 @@ 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, @@ -1496,7 +1508,9 @@ 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') @@ -1531,7 +1545,7 @@ 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. @@ -1571,7 +1585,7 @@ r_2 = (1 - num / den) return r_2
-
[docs] @staticmethod +
[docs] @staticmethod def plot_3d_data(x_data, y_data, z_data, @@ -1581,6 +1595,8 @@ 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 @@ -1631,7 +1647,7 @@

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

diff --git a/docs/_build/html/_modules/reeps/reeps.html b/docs/_build/html/_modules/reeps/reeps.html deleted file mode 100644 index bb9a06e..0000000 --- a/docs/_build/html/_modules/reeps/reeps.html +++ /dev/null @@ -1,1735 +0,0 @@ - - - - - - - - - - - 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 new file mode 100644 index 0000000..dbd1116 --- /dev/null +++ b/docs/_build/html/_sources/guide/about.rst.txt @@ -0,0 +1,61 @@ +.. _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 ce1a1f9..e7306d7 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://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git + pip install git+https://github.com/ANL-CEEESA/LLEPE.git Development version =================== -To contribute to Stable-Baselines, with support for running tests and building the documentation. +To contribute to LLEPE, with support for running tests and building the documentation. .. code-block:: bash - git clone https://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git && cd parameter-estimation + git clone https://github.com/ANL-CEEESA/LLEPE.git pip install -e .[docs,tests] diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt index 9a973a9..a0c905b 100644 --- a/docs/_build/html/_sources/index.rst.txt +++ b/docs/_build/html/_sources/index.rst.txt @@ -1,9 +1,8 @@ -.. 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. +.. 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. -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 @@ -19,12 +18,13 @@ Error between predicted and experimental data is then minimized. guide/install guide/quickstart + guide/about .. toctree:: :maxdepth: 1 :caption: Searchers - modules/reeps + modules/LLEPE diff --git a/docs/_source/modules/reeps.rst b/docs/_build/html/_sources/modules/LLEPE.rst.txt similarity index 74% rename from docs/_source/modules/reeps.rst rename to docs/_build/html/_sources/modules/LLEPE.rst.txt index bba5c13..41daa9d 100644 --- a/docs/_source/modules/reeps.rst +++ b/docs/_build/html/_sources/modules/LLEPE.rst.txt @@ -1,4 +1,4 @@ -.. _reeps: +.. _LLEPE: .. automodule:: llepe diff --git a/docs/_build/html/_sources/modules/reeps.rst.txt b/docs/_build/html/_sources/modules/reeps.rst.txt index bba5c13..41daa9d 100644 --- a/docs/_build/html/_sources/modules/reeps.rst.txt +++ b/docs/_build/html/_sources/modules/reeps.rst.txt @@ -1,4 +1,4 @@ -.. _reeps: +.. _LLEPE: .. automodule:: llepe diff --git a/docs/_build/html/_static/alabaster.css b/docs/_build/html/_static/alabaster.css deleted file mode 100644 index 0eddaeb..0000000 --- a/docs/_build/html/_static/alabaster.css +++ /dev/null @@ -1,701 +0,0 @@ -@import url("basic.css"); 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  • Installation
  • Getting Started
    • +
  • About

    • Searchers

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

    F

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

    G

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

    P

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

    R

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

    S

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

    U

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

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

    diff --git a/docs/_build/html/guide/about.html b/docs/_build/html/guide/about.html new file mode 100644 index 0000000..67be0bd --- /dev/null +++ b/docs/_build/html/guide/about.html @@ -0,0 +1,272 @@ + + + + + + + + + + + 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 f28dddc..17e566e 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://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git
+
    pip install git+https://github.com/ANL-CEEESA/LLEPE.git

    Development version

    -

    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
+
    To contribute to LLEPE, with support for running tests and building the documentation.
    
+
    git clone https://github.com/ANL-CEEESA/LLEPE.git
 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, Titus Quah, Nwike Iloeje + © Copyright 2020, UChicago Argonne, LLC.

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

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

    diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html index 7f6af8e..9cee7a4 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 @@ -86,10 +86,11 @@

    Searchers

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

    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.

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

    • Searchers

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

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

    diff --git a/docs/_build/html/modules/LLEPE.html b/docs/_build/html/modules/LLEPE.html new file mode 100644 index 0000000..4f1ff3b --- /dev/null +++ b/docs/_build/html/modules/LLEPE.html @@ -0,0 +1,1123 @@ + + + + + + + + + + + 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 9275abb..8f4ad8e 100644 --- a/docs/_build/html/modules/reeps.html +++ b/docs/_build/html/modules/reeps.html @@ -34,8 +34,7 @@ - - + @@ -87,13 +86,6 @@
  • Installation
  • Getting Started
    • -

    Searchers

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

    LLEPE

    +
    +

    LLEPE

    Parameters

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

    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_csv_filename's csv, ES_1 is Nd ES_2 is Pr, +

    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)'
@@ -178,7 +170,7 @@ and

    Then:

    -

    The csvs column ordering must be:

    +

    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 @@ -718,7 +710,9 @@ 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

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