#!/usr/bin/env python
# coding: utf-8

import numpy as np
import scipy.stats as stats

from wellmet.qt_gui import qt_box_functions as gui
from wellmet import whitebox
from wellmet.samplebox import SampleBox
from wellmet import f_models


# 1. Set up probability distribution
# Standard Gaussian variables, 2D
#f = f_models.SNorm(2)
# Just normal variables
f = f_models.Norm(mean=[-1, 0], std=[2, 1])
# Independent non-Gaussian variables
#f = f_models.UnCorD((stats.gumbel_r, stats.uniform))
# Correlated non-Gaussian marginals
#f = f_models.Nataf((stats.gumbel_r, stats.weibull_min(c=1.5)), [[1,0.8], [0.8,1]])

# 2. Define LSF function
def my_problem(input_sample):
    # get real (physical) space coordinates
    # X is a numpy array with shape (nsim, ndim)
    # the algorithm normally sends (1, ndim) sample
    X = input_sample.R
    # LSF
    g = X[:, 0] - X[:, 1] + 3
    # we should return an instance of SampleBox class
    # this instance stores coordinates along with LSF calculation result
    return SampleBox(input_sample, g, "my_problem")

# 3. Put them together
wt = whitebox.WhiteBox(f, my_problem)

# choose filename to store samples and estimations
gui.read_box(wt)
# algorithm setup
gui.setup_dicebox(wt)

# start GUI
gui.show_box(wt)