# -*- coding: utf-8 -*-

import numpy as np

import pandas.util.testing as tm
from pandas import Categorical, Index


class TestCategoricalSort(object):

    def test_argsort(self):
        c = Categorical([5, 3, 1, 4, 2], ordered=True)

        expected = np.array([2, 4, 1, 3, 0])
        tm.assert_numpy_array_equal(c.argsort(ascending=True), expected,
                                    check_dtype=False)

        expected = expected[::-1]
        tm.assert_numpy_array_equal(c.argsort(ascending=False), expected,
                                    check_dtype=False)

    def test_numpy_argsort(self):
        c = Categorical([5, 3, 1, 4, 2], ordered=True)

        expected = np.array([2, 4, 1, 3, 0])
        tm.assert_numpy_array_equal(np.argsort(c), expected,
                                    check_dtype=False)

        tm.assert_numpy_array_equal(np.argsort(c, kind='mergesort'), expected,
                                    check_dtype=False)

        msg = "the 'axis' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, np.argsort,
                               c, axis=0)

        msg = "the 'order' parameter is not supported"
        tm.assert_raises_regex(ValueError, msg, np.argsort,
                               c, order='C')

    def test_sort_values(self):

        # unordered cats are sortable
        cat = Categorical(["a", "b", "b", "a"], ordered=False)
        cat.sort_values()

        cat = Categorical(["a", "c", "b", "d"], ordered=True)

        # sort_values
        res = cat.sort_values()
        exp = np.array(["a", "b", "c", "d"], dtype=object)
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, cat.categories)

        cat = Categorical(["a", "c", "b", "d"],
                          categories=["a", "b", "c", "d"], ordered=True)
        res = cat.sort_values()
        exp = np.array(["a", "b", "c", "d"], dtype=object)
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, cat.categories)

        res = cat.sort_values(ascending=False)
        exp = np.array(["d", "c", "b", "a"], dtype=object)
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, cat.categories)

        # sort (inplace order)
        cat1 = cat.copy()
        cat1.sort_values(inplace=True)
        exp = np.array(["a", "b", "c", "d"], dtype=object)
        tm.assert_numpy_array_equal(cat1.__array__(), exp)
        tm.assert_index_equal(res.categories, cat.categories)

        # reverse
        cat = Categorical(["a", "c", "c", "b", "d"], ordered=True)
        res = cat.sort_values(ascending=False)
        exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object)
        exp_categories = Index(["a", "b", "c", "d"])
        tm.assert_numpy_array_equal(res.__array__(), exp_val)
        tm.assert_index_equal(res.categories, exp_categories)

    def test_sort_values_na_position(self):
        # see gh-12882
        cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True)
        exp_categories = Index([2, 5])

        exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
        res = cat.sort_values()  # default arguments
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, exp_categories)

        exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0])
        res = cat.sort_values(ascending=True, na_position='first')
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, exp_categories)

        exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0])
        res = cat.sort_values(ascending=False, na_position='first')
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, exp_categories)

        exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
        res = cat.sort_values(ascending=True, na_position='last')
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, exp_categories)

        exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan])
        res = cat.sort_values(ascending=False, na_position='last')
        tm.assert_numpy_array_equal(res.__array__(), exp)
        tm.assert_index_equal(res.categories, exp_categories)

        cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
        res = cat.sort_values(ascending=False, na_position='last')
        exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object)
        exp_categories = Index(["a", "b", "c", "d"])
        tm.assert_numpy_array_equal(res.__array__(), exp_val)
        tm.assert_index_equal(res.categories, exp_categories)

        cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
        res = cat.sort_values(ascending=False, na_position='first')
        exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object)
        exp_categories = Index(["a", "b", "c", "d"])
        tm.assert_numpy_array_equal(res.__array__(), exp_val)
        tm.assert_index_equal(res.categories, exp_categories)
