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


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