# /*##########################################################################
# Copyright (C) 2016-2021 European Synchrotron Radiation Facility
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"""
Nominal tests of the histogramnd function.
"""

import unittest

import numpy as np

from silx.math.chistogramnd import chistogramnd as histogramnd
from silx.math import Histogramnd


def _get_bin_edges(histo_range, n_bins, n_dims):
    edges = []
    for i_dim in range(n_dims):
        edges.append(
            histo_range[i_dim, 0]
            + np.arange(n_bins[i_dim] + 1)
            * (histo_range[i_dim, 1] - histo_range[i_dim, 0])
            / n_bins[i_dim]
        )
    return tuple(edges)


# ==============================================================
# ==============================================================
# ==============================================================


class _Test_chistogramnd_nominal(unittest.TestCase):
    """
    Unit tests of the histogramnd function.
    """

    __test__ = False  # ignore abstract classe

    ndims = None

    def setUp(self):
        if type(self).__name__.startswith("_"):
            self.skipTest("Abstract class")
        ndims = self.ndims
        self.tested_dim = ndims - 1

        if ndims is None:
            raise ValueError("ndims class member not set.")

        sample = np.array([5.5, -3.3, 0.0, -0.5, 3.3, 8.8, -7.7, 6.0, -4.0])

        weights = np.array(
            [500.5, -300.3, 0.01, -0.5, 300.3, 800.8, -700.7, 600.6, -400.4]
        )

        n_elems = len(sample)

        if ndims == 1:
            shape = (n_elems,)
        else:
            shape = (n_elems, ndims)

        self.sample = np.zeros(shape=shape, dtype=sample.dtype)
        if ndims == 1:
            self.sample = sample
        else:
            self.sample[..., ndims - 1] = sample

        self.weights = weights

        # the tests are performed along one dimension,
        #   all the other bins indices along the other dimensions
        #   are expected to be 2
        # (e.g : when testing a 2D sample : [0, x] will go into
        # bin [2, y] because of the bin ranges [-2, 2] and n_bins = 4
        # for the first dimension)
        self.other_axes_index = 2
        self.histo_range = np.repeat([[-2.0, 2.0]], ndims, axis=0)
        self.histo_range[ndims - 1] = [-4.0, 6.0]

        self.n_bins = np.array([4] * ndims)
        self.n_bins[ndims - 1] = 5

        if ndims == 1:

            def fill_histo(h, v, dim, op=None):
                if op:
                    h[:] = op(h[:], v)
                else:
                    h[:] = v

            self.fill_histo = fill_histo
        else:

            def fill_histo(h, v, dim, op=None):
                idx = [self.other_axes_index] * len(h.shape)
                idx[dim] = slice(0, None)
                idx = tuple(idx)
                if op:
                    h[idx] = op(h[idx], v)
                else:
                    h[idx] = v

            self.fill_histo = fill_histo

    def test_nominal(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul, bin_edges = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )

        expected_edges = _get_bin_edges(self.histo_range, self.n_bins, self.ndims)

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

        for i_edges, edges in enumerate(expected_edges):
            self.assertTrue(
                np.array_equal(bin_edges[i_edges], expected_edges[i_edges]),
                msg="Testing bin_edges for dim {0}" "".format(i_edges + 1),
            )

    def test_nominal_wh_dtype(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.float32)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul, bin_edges = histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=self.weights,
            wh_dtype=np.float32,
        )

        self.assertEqual(cumul.dtype, np.float32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.allclose(cumul, expected_c))

    def test_nominal_uncontiguous_sample(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        shape = list(self.sample.shape)
        shape[0] *= 2
        sample = np.zeros(shape, dtype=self.sample.dtype)
        uncontig_sample = sample[::2, ...]
        uncontig_sample[:] = self.sample

        self.assertFalse(
            uncontig_sample.flags["C_CONTIGUOUS"],
            msg="Making sure the array is not contiguous.",
        )

        histo, cumul, bin_edges = histogramnd(
            uncontig_sample, self.histo_range, self.n_bins, weights=self.weights
        )

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_nominal_uncontiguous_weights(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        shape = list(self.weights.shape)
        shape[0] *= 2
        weights = np.zeros(shape, dtype=self.weights.dtype)
        uncontig_weights = weights[::2, ...]
        uncontig_weights[:] = self.weights

        self.assertFalse(
            uncontig_weights.flags["C_CONTIGUOUS"],
            msg="Making sure the array is not contiguous.",
        )

        histo, cumul, bin_edges = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=uncontig_weights
        )

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_nominal_wo_weights(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=None
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(cumul is None)

    def test_nominal_wo_weights_w_cumul(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)

        # creating an array of ones just to make sure that
        # it is not cleared by histogramnd
        cumul_in = np.ones(self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=None,
            weighted_histo=cumul_in,
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(cumul is None)
        self.assertTrue(
            np.array_equal(cumul_in, np.ones(shape=self.n_bins, dtype=np.double))
        )

    def test_nominal_wo_weights_w_histo(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)

        # creating an array of ones just to make sure that
        # it is not cleared by histogramnd
        histo_in = np.ones(self.n_bins, dtype=np.uint32)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=None, histo=histo_in
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h + 1))
        self.assertTrue(cumul is None)
        self.assertEqual(id(histo), id(histo_in))

    def test_nominal_last_bin_closed(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 2])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 1101.1])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=self.weights,
            last_bin_closed=True,
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_int32_weights_double_weights_range(self):
        """ """
        weight_min = -299.9  # ===> will be cast to -299
        weight_max = 499.9  # ===> will be cast to 499

        expected_h_tpl = np.array([0, 1, 1, 1, 0])
        expected_c_tpl = np.array([0.0, 0.0, 0.0, 300.0, 0.0])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=self.weights.astype(np.int32),
            weight_min=weight_min,
            weight_max=weight_max,
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_reuse_histo(self):
        """ """

        expected_h_tpl = np.array([2, 3, 2, 2, 2])
        expected_c_tpl = np.array([0.0, -7007, -5.0, 0.1, 3003.0])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )[0:2]

        sample_2 = self.sample[:]
        if len(sample_2.shape) == 1:
            idx = (slice(0, None),)
        else:
            idx = slice(0, None), self.tested_dim

        sample_2[idx] += 2

        histo_2, cumul = histogramnd(
            sample_2,  # <==== !!
            self.histo_range,
            self.n_bins,
            weights=10 * self.weights,  # <==== !!
            histo=histo,
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))
        self.assertEqual(id(histo), id(histo_2))

    def test_reuse_cumul(self):
        """ """

        expected_h_tpl = np.array([0, 2, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -7007.5, -4.99, 300.4, 3503.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )[0:2]

        sample_2 = self.sample[:]
        if len(sample_2.shape) == 1:
            idx = (slice(0, None),)
        else:
            idx = slice(0, None), self.tested_dim

        sample_2[idx] += 2

        histo, cumul_2 = histogramnd(
            sample_2,  # <==== !!
            self.histo_range,
            self.n_bins,
            weights=10 * self.weights,  # <==== !!
            weighted_histo=cumul,
        )[0:2]

        self.assertEqual(cumul.dtype, np.float64)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.allclose(cumul, expected_c, rtol=10e-15))
        self.assertEqual(id(cumul), id(cumul_2))

    def test_reuse_cumul_float(self):
        """ """

        expected_h_tpl = np.array([0, 2, 1, 1, 1])
        expected_c_tpl = np.array(
            [-700.7, -7007.5, -4.99, 300.4, 3503.5], dtype=np.float32
        )

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )[0:2]

        # converting the cumul array to float
        cumul = cumul.astype(np.float32)

        sample_2 = self.sample[:]
        if len(sample_2.shape) == 1:
            idx = (slice(0, None),)
        else:
            idx = slice(0, None), self.tested_dim

        sample_2[idx] += 2

        histo, cumul_2 = histogramnd(
            sample_2,  # <==== !!
            self.histo_range,
            self.n_bins,
            weights=10 * self.weights,  # <==== !!
            weighted_histo=cumul,
        )[0:2]

        self.assertEqual(cumul.dtype, np.float32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertEqual(id(cumul), id(cumul_2))
        self.assertTrue(np.allclose(cumul, expected_c, rtol=10e-15))


class _Test_Histogramnd_nominal(unittest.TestCase):
    """
    Unit tests of the Histogramnd class.
    """

    __test__ = False  # ignore abstract class

    ndims = None

    def setUp(self):
        ndims = self.ndims
        if ndims is None:
            self.skipTest("Abstract class")
        self.tested_dim = ndims - 1

        if ndims is None:
            raise ValueError("ndims class member not set.")

        sample = np.array([5.5, -3.3, 0.0, -0.5, 3.3, 8.8, -7.7, 6.0, -4.0])

        weights = np.array(
            [500.5, -300.3, 0.01, -0.5, 300.3, 800.8, -700.7, 600.6, -400.4]
        )

        n_elems = len(sample)

        if ndims == 1:
            shape = (n_elems,)
        else:
            shape = (n_elems, ndims)

        self.sample = np.zeros(shape=shape, dtype=sample.dtype)
        if ndims == 1:
            self.sample = sample
        else:
            self.sample[..., ndims - 1] = sample

        self.weights = weights

        # the tests are performed along one dimension,
        #   all the other bins indices along the other dimensions
        #   are expected to be 2
        # (e.g : when testing a 2D sample : [0, x] will go into
        # bin [2, y] because of the bin ranges [-2, 2] and n_bins = 4
        # for the first dimension)
        self.other_axes_index = 2
        self.histo_range = np.repeat([[-2.0, 2.0]], ndims, axis=0)
        self.histo_range[ndims - 1] = [-4.0, 6.0]

        self.n_bins = np.array([4] * ndims)
        self.n_bins[ndims - 1] = 5

        if ndims == 1:

            def fill_histo(h, v, dim, op=None):
                if op:
                    h[:] = op(h[:], v)
                else:
                    h[:] = v

            self.fill_histo = fill_histo
        else:

            def fill_histo(h, v, dim, op=None):
                idx = [self.other_axes_index] * len(h.shape)
                idx[dim] = slice(0, None)
                idx = tuple(idx)
                if op:
                    h[idx] = op(h[idx], v)
                else:
                    h[idx] = v

            self.fill_histo = fill_histo

    def test_nominal(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo = Histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )

        histo, cumul, bin_edges = histo

        expected_edges = _get_bin_edges(self.histo_range, self.n_bins, self.ndims)

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

        for i_edges, edges in enumerate(expected_edges):
            self.assertTrue(
                np.array_equal(bin_edges[i_edges], expected_edges[i_edges]),
                msg="Testing bin_edges for dim {0}" "".format(i_edges + 1),
            )

    def test_nominal_wh_dtype(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.float32)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul, bin_edges = Histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=self.weights,
            wh_dtype=np.float32,
        )

        self.assertEqual(cumul.dtype, np.float32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.allclose(cumul, expected_c))

    def test_nominal_uncontiguous_sample(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        shape = list(self.sample.shape)
        shape[0] *= 2
        sample = np.zeros(shape, dtype=self.sample.dtype)
        uncontig_sample = sample[::2, ...]
        uncontig_sample[:] = self.sample

        self.assertFalse(
            uncontig_sample.flags["C_CONTIGUOUS"],
            msg="Making sure the array is not contiguous.",
        )

        histo, cumul, bin_edges = Histogramnd(
            uncontig_sample, self.histo_range, self.n_bins, weights=self.weights
        )

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_nominal_uncontiguous_weights(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        shape = list(self.weights.shape)
        shape[0] *= 2
        weights = np.zeros(shape, dtype=self.weights.dtype)
        uncontig_weights = weights[::2, ...]
        uncontig_weights[:] = self.weights

        self.assertFalse(
            uncontig_weights.flags["C_CONTIGUOUS"],
            msg="Making sure the array is not contiguous.",
        )

        histo, cumul, bin_edges = Histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=uncontig_weights
        )

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_nominal_wo_weights(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)

        histo, cumul = Histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=None
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(cumul is None)

    def test_nominal_last_bin_closed(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 2])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 1101.1])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = Histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=self.weights,
            last_bin_closed=True,
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_int32_weights_double_weights_range(self):
        """ """
        weight_min = -299.9  # ===> will be cast to -299
        weight_max = 499.9  # ===> will be cast to 499

        expected_h_tpl = np.array([0, 1, 1, 1, 0])
        expected_c_tpl = np.array([0.0, 0.0, 0.0, 300.0, 0.0])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo, cumul = Histogramnd(
            self.sample,
            self.histo_range,
            self.n_bins,
            weights=self.weights.astype(np.int32),
            weight_min=weight_min,
            weight_max=weight_max,
        )[0:2]

        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def test_nominal_no_sample(self):
        """ """

        histo_inst = Histogramnd(None, self.histo_range, self.n_bins)

        histo, weighted_histo, edges = histo_inst

        self.assertIsNone(histo)
        self.assertIsNone(weighted_histo)
        self.assertIsNone(edges)
        self.assertIsNone(histo_inst.histo)
        self.assertIsNone(histo_inst.weighted_histo)
        self.assertIsNone(histo_inst.edges)

    def test_empty_init_accumulate(self):
        """ """
        expected_h_tpl = np.array([2, 1, 1, 1, 1])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo_inst = Histogramnd(None, self.histo_range, self.n_bins)

        histo_inst.accumulate(self.sample, weights=self.weights)

        histo = histo_inst.histo
        cumul = histo_inst.weighted_histo
        bin_edges = histo_inst.edges

        expected_edges = _get_bin_edges(self.histo_range, self.n_bins, self.ndims)

        self.assertEqual(cumul.dtype, np.float64)
        self.assertEqual(histo.dtype, np.uint32)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

        for i_edges, edges in enumerate(expected_edges):
            self.assertTrue(
                np.array_equal(bin_edges[i_edges], expected_edges[i_edges]),
                msg="Testing bin_edges for dim {0}" "".format(i_edges + 1),
            )

    def test_accumulate(self):
        """ """

        expected_h_tpl = np.array([2, 3, 2, 2, 2])
        expected_c_tpl = np.array([-700.7, -7007.5, -4.99, 300.4, 3503.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo_inst = Histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )

        sample_2 = self.sample[:]
        if len(sample_2.shape) == 1:
            idx = (slice(0, None),)
        else:
            idx = slice(0, None), self.tested_dim

        sample_2[idx] += 2

        histo_inst.accumulate(
            sample_2, weights=10 * self.weights  # <==== !!
        )  # <==== !!

        histo = histo_inst.histo
        cumul = histo_inst.weighted_histo
        bin_edges = histo_inst.edges

        self.assertEqual(cumul.dtype, np.float64)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.allclose(cumul, expected_c, rtol=10e-15))

    def test_accumulate_no_weights(self):
        """ """

        expected_h_tpl = np.array([2, 3, 2, 2, 2])
        expected_c_tpl = np.array([-700.7, -0.5, 0.01, 300.3, 500.5])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo_inst = Histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=self.weights
        )

        sample_2 = self.sample[:]
        if len(sample_2.shape) == 1:
            idx = (slice(0, None),)
        else:
            idx = slice(0, None), self.tested_dim

        sample_2[idx] += 2

        histo_inst.accumulate(sample_2)  # <==== !!

        histo = histo_inst.histo
        cumul = histo_inst.weighted_histo
        bin_edges = histo_inst.edges

        self.assertEqual(cumul.dtype, np.float64)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.allclose(cumul, expected_c, rtol=10e-15))

    def test_accumulate_no_weights_at_init(self):
        """ """

        expected_h_tpl = np.array([2, 3, 2, 2, 2])
        expected_c_tpl = np.array([0.0, -700.7, -0.5, 0.01, 300.3])

        expected_h = np.zeros(shape=self.n_bins, dtype=np.double)
        expected_c = np.zeros(shape=self.n_bins, dtype=np.double)

        self.fill_histo(expected_h, expected_h_tpl, self.ndims - 1)
        self.fill_histo(expected_c, expected_c_tpl, self.ndims - 1)

        histo_inst = Histogramnd(
            self.sample, self.histo_range, self.n_bins, weights=None
        )  # <==== !!

        cumul = histo_inst.weighted_histo
        self.assertIsNone(cumul)

        sample_2 = self.sample[:]
        if len(sample_2.shape) == 1:
            idx = (slice(0, None),)
        else:
            idx = slice(0, None), self.tested_dim

        sample_2[idx] += 2

        histo_inst.accumulate(sample_2, weights=self.weights)  # <==== !!

        histo = histo_inst.histo
        cumul = histo_inst.weighted_histo
        bin_edges = histo_inst.edges

        self.assertEqual(cumul.dtype, np.float64)
        self.assertTrue(np.array_equal(histo, expected_h))
        self.assertTrue(np.array_equal(cumul, expected_c))

    def testNoneNativeTypes(self):
        type = self.sample.dtype.newbyteorder("B")
        sampleB = self.sample.astype(type)

        type = self.sample.dtype.newbyteorder("L")
        sampleL = self.sample.astype(type)

        histo_inst = Histogramnd(
            sampleB, self.histo_range, self.n_bins, weights=self.weights
        )

        histo_inst = Histogramnd(
            sampleL, self.histo_range, self.n_bins, weights=self.weights
        )


class Test_chistogram_nominal_1d(_Test_chistogramnd_nominal):
    __test__ = True  # because _Test_chistogramnd_nominal is ignored
    ndims = 1


class Test_chistogram_nominal_2d(_Test_chistogramnd_nominal):
    __test__ = True  # because _Test_chistogramnd_nominal is ignored
    ndims = 2


class Test_chistogram_nominal_3d(_Test_chistogramnd_nominal):
    __test__ = True  # because _Test_chistogramnd_nominal is ignored
    ndims = 3


class Test_Histogramnd_nominal_1d(_Test_Histogramnd_nominal):
    __test__ = True  # because _Test_chistogramnd_nominal is ignored
    ndims = 1


class Test_Histogramnd_nominal_2d(_Test_Histogramnd_nominal):
    __test__ = True  # because _Test_chistogramnd_nominal is ignored
    ndims = 2


class Test_Histogramnd_nominal_3d(_Test_Histogramnd_nominal):
    __test__ = True  # because _Test_chistogramnd_nominal is ignored
    ndims = 3
