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

import pytest

import numpy as np

import pandas.util.testing as tm
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas import Categorical, Index, CategoricalIndex


class TestCategoricalDtypes(object):

    def test_is_equal_dtype(self):

        # test dtype comparisons between cats

        c1 = Categorical(list('aabca'), categories=list('abc'), ordered=False)
        c2 = Categorical(list('aabca'), categories=list('cab'), ordered=False)
        c3 = Categorical(list('aabca'), categories=list('cab'), ordered=True)
        assert c1.is_dtype_equal(c1)
        assert c2.is_dtype_equal(c2)
        assert c3.is_dtype_equal(c3)
        assert c1.is_dtype_equal(c2)
        assert not c1.is_dtype_equal(c3)
        assert not c1.is_dtype_equal(Index(list('aabca')))
        assert not c1.is_dtype_equal(c1.astype(object))
        assert c1.is_dtype_equal(CategoricalIndex(c1))
        assert (c1.is_dtype_equal(
            CategoricalIndex(c1, categories=list('cab'))))
        assert not c1.is_dtype_equal(CategoricalIndex(c1, ordered=True))

    def test_set_dtype_same(self):
        c = Categorical(['a', 'b', 'c'])
        result = c._set_dtype(CategoricalDtype(['a', 'b', 'c']))
        tm.assert_categorical_equal(result, c)

    def test_set_dtype_new_categories(self):
        c = Categorical(['a', 'b', 'c'])
        result = c._set_dtype(CategoricalDtype(list('abcd')))
        tm.assert_numpy_array_equal(result.codes, c.codes)
        tm.assert_index_equal(result.dtype.categories, Index(list('abcd')))

    @pytest.mark.parametrize('values, categories, new_categories', [
        # No NaNs, same cats, same order
        (['a', 'b', 'a'], ['a', 'b'], ['a', 'b'],),
        # No NaNs, same cats, different order
        (['a', 'b', 'a'], ['a', 'b'], ['b', 'a'],),
        # Same, unsorted
        (['b', 'a', 'a'], ['a', 'b'], ['a', 'b'],),
        # No NaNs, same cats, different order
        (['b', 'a', 'a'], ['a', 'b'], ['b', 'a'],),
        # NaNs
        (['a', 'b', 'c'], ['a', 'b'], ['a', 'b']),
        (['a', 'b', 'c'], ['a', 'b'], ['b', 'a']),
        (['b', 'a', 'c'], ['a', 'b'], ['a', 'b']),
        (['b', 'a', 'c'], ['a', 'b'], ['a', 'b']),
        # Introduce NaNs
        (['a', 'b', 'c'], ['a', 'b'], ['a']),
        (['a', 'b', 'c'], ['a', 'b'], ['b']),
        (['b', 'a', 'c'], ['a', 'b'], ['a']),
        (['b', 'a', 'c'], ['a', 'b'], ['a']),
        # No overlap
        (['a', 'b', 'c'], ['a', 'b'], ['d', 'e']),
    ])
    @pytest.mark.parametrize('ordered', [True, False])
    def test_set_dtype_many(self, values, categories, new_categories,
                            ordered):
        c = Categorical(values, categories)
        expected = Categorical(values, new_categories, ordered)
        result = c._set_dtype(expected.dtype)
        tm.assert_categorical_equal(result, expected)

    def test_set_dtype_no_overlap(self):
        c = Categorical(['a', 'b', 'c'], ['d', 'e'])
        result = c._set_dtype(CategoricalDtype(['a', 'b']))
        expected = Categorical([None, None, None], categories=['a', 'b'])
        tm.assert_categorical_equal(result, expected)

    def test_codes_dtypes(self):

        # GH 8453
        result = Categorical(['foo', 'bar', 'baz'])
        assert result.codes.dtype == 'int8'

        result = Categorical(['foo%05d' % i for i in range(400)])
        assert result.codes.dtype == 'int16'

        result = Categorical(['foo%05d' % i for i in range(40000)])
        assert result.codes.dtype == 'int32'

        # adding cats
        result = Categorical(['foo', 'bar', 'baz'])
        assert result.codes.dtype == 'int8'
        result = result.add_categories(['foo%05d' % i for i in range(400)])
        assert result.codes.dtype == 'int16'

        # removing cats
        result = result.remove_categories(['foo%05d' % i for i in range(300)])
        assert result.codes.dtype == 'int8'

    @pytest.mark.parametrize('ordered', [True, False])
    def test_astype(self, ordered):
        # string
        cat = Categorical(list('abbaaccc'), ordered=ordered)
        result = cat.astype(object)
        expected = np.array(cat)
        tm.assert_numpy_array_equal(result, expected)

        msg = 'could not convert string to float'
        with tm.assert_raises_regex(ValueError, msg):
            cat.astype(float)

        # numeric
        cat = Categorical([0, 1, 2, 2, 1, 0, 1, 0, 2], ordered=ordered)
        result = cat.astype(object)
        expected = np.array(cat, dtype=object)
        tm.assert_numpy_array_equal(result, expected)

        result = cat.astype(int)
        expected = np.array(cat, dtype=np.int)
        tm.assert_numpy_array_equal(result, expected)

        result = cat.astype(float)
        expected = np.array(cat, dtype=np.float)
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize('dtype_ordered', [True, False])
    @pytest.mark.parametrize('cat_ordered', [True, False])
    def test_astype_category(self, dtype_ordered, cat_ordered):
        # GH 10696/18593
        data = list('abcaacbab')
        cat = Categorical(data, categories=list('bac'), ordered=cat_ordered)

        # standard categories
        dtype = CategoricalDtype(ordered=dtype_ordered)
        result = cat.astype(dtype)
        expected = Categorical(
            data, categories=cat.categories, ordered=dtype_ordered)
        tm.assert_categorical_equal(result, expected)

        # non-standard categories
        dtype = CategoricalDtype(list('adc'), dtype_ordered)
        result = cat.astype(dtype)
        expected = Categorical(data, dtype=dtype)
        tm.assert_categorical_equal(result, expected)

        if dtype_ordered is False:
            # dtype='category' can't specify ordered, so only test once
            result = cat.astype('category')
            expected = cat
            tm.assert_categorical_equal(result, expected)
