import pytest
import tantri.binning.binning as binning
import numpy


def test_bin_config_validation():
	with pytest.raises(ValueError):
		binning.BinConfig(log_scale=False, bin_width=1, min_points_required=1)


def test_bin_construction_faulty_min():
	x_list = numpy.array([5, 6, 7, 8])

	bin_config = binning.BinConfig(log_scale=False, bin_width=0.8, bin_min=5.5)

	with pytest.raises(ValueError):
		binning._construct_bins(x_list, bin_config)


def test_bin_construction_force_min():
	x_list = numpy.array([4.5, 5.5, 6.5, 7.5, 8.5])

	bin_config = binning.BinConfig(log_scale=False, bin_width=1, bin_min=2)

	expected_bins = numpy.array([2, 3, 4, 5, 6, 7, 8, 9])

	actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
	numpy.testing.assert_allclose(
		actual_bins, expected_bins, err_msg="The bins were not as expected"
	)


def test_bin_construction_even():
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])

	bin_config = binning.BinConfig(log_scale=False, bin_width=8)
	expected_bins = numpy.array([1, 9, 17, 25, 33])

	actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
	numpy.testing.assert_allclose(
		actual_bins, expected_bins, err_msg="The bins were not as expected"
	)


def test_bin_construction_uneven():
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])

	bin_config = binning.BinConfig(log_scale=False, bin_width=7)
	expected_bins = numpy.array([1, 8, 15, 22, 29, 36])

	actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
	numpy.testing.assert_allclose(
		actual_bins, expected_bins, err_msg="The bins were not as expected"
	)


def test_bin_construction_uneven_non_integer():
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])

	bin_config = binning.BinConfig(log_scale=False, bin_width=7.5)
	expected_bins = numpy.array([1, 8.5, 16, 23.5, 31, 38.5])

	actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
	numpy.testing.assert_allclose(
		actual_bins, expected_bins, err_msg="The bins were not as expected"
	)


def test_group_x_bins(snapshot):
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
	y_dict = {
		"identity_plus_one": (
			numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
		)
	}

	bin_config = binning.BinConfig(log_scale=False, bin_width=8)
	# expected_bins = numpy.array([1, 9, 17, 25, 33])

	binned = binning.bin_lists(x_list, y_dict, bin_config)

	assert binned == snapshot


def test_group_x_bins_mean(snapshot):
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
	y_dict = {
		"identity_plus_one": (
			numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
		)
	}

	bin_config = binning.BinConfig(log_scale=False, bin_width=8)
	# expected_bins = numpy.array([1, 9, 17, 25, 33])

	binned = binning.bin_lists(x_list, y_dict, bin_config)
	mean_binned = [bin.mean_point() for bin in binned]

	assert mean_binned == snapshot


def test_group_x_bins_summary(snapshot):
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
	y_dict = {
		"identity_plus_one": (
			numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
		)
	}

	bin_config = binning.BinConfig(log_scale=False, bin_width=8)
	# expected_bins = numpy.array([1, 9, 17, 25, 33])

	binned = binning.bin_lists(x_list, y_dict, bin_config)
	summary = [bin.summary_point() for bin in binned]

	assert summary == snapshot


def test_bin_construction_faulty_min_log_scale():
	x_list = numpy.array([5, 6, 7, 8])

	bin_config = binning.BinConfig(log_scale=True, bin_width=0.8, bin_min=5.5)

	with pytest.raises(ValueError):
		binning._construct_bins(x_list, bin_config)


def test_bin_construction_force_min_log():
	"""
	This test shows the main use ofthe bin_min parameter, if we want our bins to nicely line up with decades for example,
	then we can force it by ignoring the provided minimum x.
	"""
	x_list = numpy.array([1500, 5000, 10000, 33253, 400000])

	bin_config = binning.BinConfig(log_scale=True, bin_width=1, bin_min=10)

	expected_bins = numpy.array([10, 100, 1000, 10000, 100000, 1000000])

	actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
	numpy.testing.assert_allclose(
		actual_bins, expected_bins, err_msg="The bins were not as expected"
	)


def test_bin_construction_even_log_scale():
	x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])

	# bin width of 0.3 corresponds to 10^0.3 ~= 2, so we're roughly looking at
	bin_config = binning.BinConfig(log_scale=True, bin_width=0.3)
	expected_bins = numpy.array(
		[
			1.00000000000,
			1.99526231497,
			3.98107170553,
			7.94328234724,
			15.8489319246,
			31.6227766017,
			63.0957344480,
		]
	)

	actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
	numpy.testing.assert_allclose(
		actual_bins, expected_bins, err_msg="The bins were not as expected"
	)


def test_group_x_bins_log(snapshot):
	x_list = numpy.array(
		[
			0.00158489,
			0.00363078,
			0.0398107,
			0.275423,
			0.524807,
			2.51189,
			8.74984,
			10.0,
			63.0957,
			3981.07,
		]
	)
	y_dict = {
		"basic_lorentzian": numpy.array(
			[
				0.159154,
				0.15915,
				0.158535,
				0.134062,
				0.0947588,
				0.00960602,
				0.000838084,
				0.000642427,
				0.0000162008,
				4.06987e-9,
			]
		)
	}

	bin_config = binning.BinConfig(log_scale=True, bin_width=2)
	# expected_bins = numpy.array([1, 9, 17, 25, 33])

	binned = binning.bin_lists(x_list, y_dict, bin_config)

	assert binned == snapshot


def test_group_x_bins_mean_log(snapshot):
	x_list = numpy.array(
		[
			0.0158489,
			0.0316228,
			0.0794328,
			0.158489,
			0.17378,
			0.316228,
			0.944061,
			0.977237,
			0.988553,
			3.16228,
			5.01187,
			15.8489,
			25.1189,
			31.6228,
			158.489,
			630.957,
		]
	)
	y_dict = {
		"basic_lorentzian": (
			numpy.array(
				[
					0.159056,
					0.158763,
					0.156715,
					0.149866,
					0.148118,
					0.127657,
					0.0497503,
					0.0474191,
					0.0466561,
					0.00619907,
					0.00252714,
					0.000256378,
					0.000102165,
					0.0000644769,
					2.56787e-6,
					1.62024e-7,
				]
			)
		)
	}

	bin_config = binning.BinConfig(log_scale=True, bin_width=1, bin_min=1e-2)
	# expected_bins = numpy.array([1, 9, 17, 25, 33])

	binned = binning.bin_lists(x_list, y_dict, bin_config)
	mean_binned = [bin.mean_point() for bin in binned]

	assert mean_binned == snapshot


def test_group_x_bins_summary_log(snapshot):
	x_list = numpy.array(
		[
			0.0158489,
			0.0316228,
			0.0794328,
			0.158489,
			0.17378,
			0.316228,
			0.944061,
			0.977237,
			0.988553,
			3.16228,
			5.01187,
			15.8489,
			25.1189,
			31.6228,
			158.489,
			630.957,
		]
	)
	y_dict = {
		"basic_lorentzian": (
			numpy.array(
				[
					0.159056,
					0.158763,
					0.156715,
					0.149866,
					0.148118,
					0.127657,
					0.0497503,
					0.0474191,
					0.0466561,
					0.00619907,
					0.00252714,
					0.000256378,
					0.000102165,
					0.0000644769,
					2.56787e-6,
					1.62024e-7,
				]
			)
		)
	}

	bin_config = binning.BinConfig(log_scale=True, bin_width=1, bin_min=1e-2)

	binned = binning.bin_lists(x_list, y_dict, bin_config)
	summary = [bin.summary_point() for bin in binned]

	assert summary == snapshot