from bayes_detect import plot

%matplotlib inline

SrcArray = [[43.71, 22.91, 10.54, 3.34],
            [101.62, 40.60, 1.37, 3.40],
            [92.63, 110.56, 1.81, 3.66],
            [183.60, 85.90, 1.23, 5.06],
            [34.12, 162.54, 1.95, 6.02],
            [153.87, 169.18, 1.06, 6.61],
            [155.54, 32.14, 1.46, 4.05],
            [130.56, 183.48, 1.63, 4.11]]

data_map = plot.make_source(src_array = SrcArray,height=200, width=200)

noise = 2.0
data_map = plot.add_gaussian_noise(mean=0,sd=noise,data=data_map)
#plot.write(data_map, "assets/simulated_images/multinest_toy_noised")

prior_array = [[0.0,200.0],[0.0,200.0],[1.0,12.5],[2.0,9.0]]

from bayes_detect import sources
sources.run_source_detect(samples = 4000, iterations = 25000, sample_method = "uniform", prior = prior_array, noise_rms = noise, disp = 8.0, mode = "ipython")

%time
from bayes_detect import sources
sources.run_source_detect(samples = 4000, iterations = 25000, sample_method = "metropolis", prior = prior_array, noise_rms = noise, disp = 10.0, mode = "ipython")

import numpy as np
X = np.random.rand(40,2)
plot.plot_ellipse(X)

"""
from Scripts import clust_ellip
import numpy as np
X = np.random.rand(150,2)
clust_ellip.show_minimum_bounding_ellipsoids(Xr=X)
"""

#clust_ellip.show_minimum_bounding_ellipsoids(Xr=X, with_sampled = True)

%time
from bayes_detect import sources
sources.run_source_detect(samples = 1200, iterations = 14000, sample_method = "clustered_ellipsoidal", prior = prior_array, noise_rms = noise, disp = 8.0, mode = "ipython")

#time
from bayes_detect import sources
sources.run_source_detect(samples = 1200, iterations = 14000, sample_method = "new", prior = prior_array, noise_rms = noise, disp = 8.0, mode = "ipython")