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