#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar  1 19:13:17 2021

@author: Yalin Li
"""


# %%

import pandas as pd

path = '/Users/yalinli_cabbi/Desktop/_pollution_control_device.csv'

df = pd.read_csv(path, index_col='parameter')
for para in df.index:
    # value = float(df.loc[para]['expected'])
    try:
        value = float(df.loc[para]['expected'])
    except: 
        # breakpoint()
        print(para, value)







# # %%

# from qsdsan import stats as s
# from qsdsan.systems import bwaise as bw

# m = bw.models
# modelA = m.modelA

# a = bw.analyses
# key_metrics = a.key_metrics

# spearman_rho, fig, ax, all_params = a.run_plot_spearman(modelA, N=100)


# key_paramsA = modelA.get_parameters()


# # %%

# morris_dct, fig, ax = a.run_plot_morris(modelA, 10, test_convergence=False)

# morris_dct_conv, fig, ax = a.run_plot_morris(modelA, 100, test_convergence=True)


# fast_dct, fig, ax = a.run_plot_fast(modelA, 'FAST', 100, M=4)

# rbd_dct, fig, ax = a.run_plot_fast(modelA, 'RBD', 100, M=10)


# sobol_dct, fig, ax = a.run_plot_sobol(modelA, 10, file_prefix='')


# # %%

# fig, ax = s.plot_uncertainties(modelA, x_axis=key_metrics, kind='box')

# fig, ax = s.plot_uncertainties(modelA, y_axis=key_metrics, kind='hist',
#                                 center_kws={'kde':True, 'log_scale': 10})

# fig, ax = s.plot_uncertainties(modelA, x_axis=key_metrics, kind='kde',
#                                 center_kws={'fill': True, 'log_scale': 2})

# fig, axes = s.plot_uncertainties(modelA, x_axis=key_metrics[0],
#                                   y_axis=key_metrics[1], kind='hist-box')


# fig, axes = s.plot_uncertainties(modelA, x_axis=key_metrics[0],
#                                   y_axis=key_metrics[1], kind='kde-kde',
#                                   margin_kws={'fill': True})

# fig, axes = s.plot_uncertainties(modelA, x_axis=key_metrics[0],
#                                   y_axis=key_metrics[1], kind='kde-hist',
#                                   center_kws={'fill': True},
#                                   margin_kws={'kde': True, 'fill': False})


# fig, ax = s.plot_correlations(spearman_rho, parameters=modelA.get_parameters(),
#                               metrics=key_metrics[0])

# fig, ax = s.plot_correlations(spearman_rho, parameters=modelA.get_parameters(),
#                               metrics=key_metrics)


# fig, ax = s.plot_morris_results(morris_dct, key_metrics[0], label_kind='name')

# fig, ax = s.plot_morris_convergence(morris_dct_conv,
#                                     parameters=modelA.get_parameters(),
#                                     metric=key_metrics[0], plot_rank=True)

# fig, ax = s.plot_fast_results(fast_dct, key_metrics[0])

# fig, ax = s.plot_fast_results(rbd_dct, key_metrics[0])


# fig, ax = s.plot_sobol_results(sobol_dct, metric=key_metrics[0], kind='STS2',
#                                 plot_in_diagonal='ST')





