import numpy as np
import pytest
from scipy.stats import norm as nm

import orbitize.priors as priors

threshold = 1e-1

initialization_inputs = {
	priors.GaussianPrior : [1000., 1.], 
	priors.LogUniformPrior : [1., 2.], 
	priors.UniformPrior : [0., 1.], 
	priors.SinPrior : [], 
	priors.LinearPrior : [-2., 2.]
}

expected_means_mins_maxes = {
	priors.GaussianPrior : (1000.,0.,np.inf), 
	priors.LogUniformPrior : (1/np.log(2),1., 2.), 
	priors.UniformPrior : (0.5, 0., 1.), 
	priors.SinPrior : (np.pi/2., 0., np.pi), 
	priors.LinearPrior : (1./3.,0.,1.0)
}

lnprob_inputs = {
	priors.GaussianPrior : np.array([-3.0, np.inf, 1000., 999.]),
	priors.LogUniformPrior : np.array([-1., 0., 1., 1.5, 2., 2.5]),
	priors.UniformPrior : np.array([0., 0.5, 1., -1., 2.]),
	priors.SinPrior : np.array([0., np.pi/2., np.pi, 10., -1.]),
	priors.LinearPrior : np.array([0., 0.5, 1., 2., -1.])
}

expected_probs = {
	priors.GaussianPrior : np.array([0., 0., nm(1000.,1.).pdf(1000.), nm(1000.,1.).pdf(999.)]),
	priors.LogUniformPrior : np.array([0., 0., 1., 2./3., 0.5, 0.])/np.log(2),
	priors.UniformPrior : np.array([1., 1., 1., 0., 0.]),
	priors.SinPrior : np.array([0., 0.5, 0., 0., 0.]),
	priors.LinearPrior : np.array([2., 1., 0., 0., 0.])
}


def test_draw_samples():
	""" 
	Test basic functionality of `draw_samples()` method of each `Prior` class.
	"""
	for Prior in initialization_inputs.keys():
		inputs = initialization_inputs[Prior]

		TestPrior = Prior(*inputs)
		samples = TestPrior.draw_samples(10000)

		exp_mean, exp_min, exp_max = expected_means_mins_maxes[Prior]
		assert np.mean(samples) == pytest.approx(exp_mean, abs=threshold)
		assert np.min(samples) > exp_min
		assert np.max(samples) < exp_max

def test_compute_lnprob():
	""" 
	Test basic functionality of `compute_lnprob()` method of each `Prior` class.
	"""
	for Prior in initialization_inputs.keys():
		inputs = initialization_inputs[Prior]

		TestPrior = Prior(*inputs)
		values2test = lnprob_inputs[Prior]

		lnprobs = TestPrior.compute_lnprob(values2test)

		assert np.log(expected_probs[Prior]) == pytest.approx(lnprobs, abs=threshold)


if __name__=='__main__':
	test_compute_lnprob()
	test_draw_samples()




import numpy as np
import pytest
from scipy.stats import norm as nm

import orbitize.priors as priors

threshold = 1e-1

initialization_inputs = {
	priors.GaussianPrior : [1000., 1.], 
	priors.LogUniformPrior : [1., 2.], 
	priors.UniformPrior : [0., 1.], 
	priors.SinPrior : [], 
	priors.LinearPrior : [-2., 2.]
}

expected_means_mins_maxes = {
	priors.GaussianPrior : (1000.,0.,np.inf), 
	priors.LogUniformPrior : (1/np.log(2),1., 2.), 
	priors.UniformPrior : (0.5, 0., 1.), 
	priors.SinPrior : (np.pi/2., 0., np.pi), 
	priors.LinearPrior : (1./3.,0.,1.0)
}

lnprob_inputs = {
	priors.GaussianPrior : np.array([-3.0, np.inf, 1000., 999.]),
	priors.LogUniformPrior : np.array([-1., 0., 1., 1.5, 2., 2.5]),
	priors.UniformPrior : np.array([0., 0.5, 1., -1., 2.]),
	priors.SinPrior : np.array([0., np.pi/2., np.pi, 10., -1.]),
	priors.LinearPrior : np.array([0., 0.5, 1., 2., -1.])
}

expected_probs = {
	priors.GaussianPrior : np.array([0., 0., nm(1000.,1.).pdf(1000.), nm(1000.,1.).pdf(999.)]),
	priors.LogUniformPrior : np.array([0., 0., 1., 2./3., 0.5, 0.])/np.log(2),
	priors.UniformPrior : np.array([1., 1., 1., 0., 0.]),
	priors.SinPrior : np.array([0., 0.5, 0., 0., 0.]),
	priors.LinearPrior : np.array([2., 1., 0., 0., 0.])
}


def test_draw_samples():
	""" 
	Test basic functionality of `draw_samples()` method of each `Prior` class.
	"""
	for Prior in initialization_inputs.keys():
		inputs = initialization_inputs[Prior]

		TestPrior = Prior(*inputs)
		samples = TestPrior.draw_samples(10000)

		exp_mean, exp_min, exp_max = expected_means_mins_maxes[Prior]
		assert np.mean(samples) == pytest.approx(exp_mean, abs=threshold)
		assert np.min(samples) > exp_min
		assert np.max(samples) < exp_max

def test_compute_lnprob():
	""" 
	Test basic functionality of `compute_lnprob()` method of each `Prior` class.
	"""
	for Prior in initialization_inputs.keys():
		inputs = initialization_inputs[Prior]

		TestPrior = Prior(*inputs)
		values2test = lnprob_inputs[Prior]

		lnprobs = TestPrior.compute_lnprob(values2test)

		assert np.log(expected_probs[Prior]) == pytest.approx(lnprobs, abs=threshold)


if __name__=='__main__':
	test_compute_lnprob()
	test_draw_samples()




Test basic functionality of `draw_samples()` method of each `Prior` class. Test basic functionality of `compute_lnprob()` method of each `Prior` class.