# -*- coding: utf-8 -*-
from datetime import timedelta, datetime
from distutils.version import LooseVersion
from numpy import nan
import numpy as np

from pandas import Series, DataFrame

from pandas.compat import product
from pandas.util.testing import assert_frame_equal
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData


class TestRank(tm.TestCase, TestData):
    s = Series([1, 3, 4, 2, nan, 2, 1, 5, nan, 3])
    df = DataFrame({'A': s, 'B': s})

    results = {
        'average': np.array([1.5, 5.5, 7.0, 3.5, nan,
                             3.5, 1.5, 8.0, nan, 5.5]),
        'min': np.array([1, 5, 7, 3, nan, 3, 1, 8, nan, 5]),
        'max': np.array([2, 6, 7, 4, nan, 4, 2, 8, nan, 6]),
        'first': np.array([1, 5, 7, 3, nan, 4, 2, 8, nan, 6]),
        'dense': np.array([1, 3, 4, 2, nan, 2, 1, 5, nan, 3]),
    }

    def test_rank(self):
        tm._skip_if_no_scipy()
        from scipy.stats import rankdata

        self.frame['A'][::2] = np.nan
        self.frame['B'][::3] = np.nan
        self.frame['C'][::4] = np.nan
        self.frame['D'][::5] = np.nan

        ranks0 = self.frame.rank()
        ranks1 = self.frame.rank(1)
        mask = np.isnan(self.frame.values)

        fvals = self.frame.fillna(np.inf).values

        exp0 = np.apply_along_axis(rankdata, 0, fvals)
        exp0[mask] = np.nan

        exp1 = np.apply_along_axis(rankdata, 1, fvals)
        exp1[mask] = np.nan

        tm.assert_almost_equal(ranks0.values, exp0)
        tm.assert_almost_equal(ranks1.values, exp1)

        # integers
        df = DataFrame(np.random.randint(0, 5, size=40).reshape((10, 4)))

        result = df.rank()
        exp = df.astype(float).rank()
        tm.assert_frame_equal(result, exp)

        result = df.rank(1)
        exp = df.astype(float).rank(1)
        tm.assert_frame_equal(result, exp)

    def test_rank2(self):
        df = DataFrame([[1, 3, 2], [1, 2, 3]])
        expected = DataFrame([[1.0, 3.0, 2.0], [1, 2, 3]]) / 3.0
        result = df.rank(1, pct=True)
        tm.assert_frame_equal(result, expected)

        df = DataFrame([[1, 3, 2], [1, 2, 3]])
        expected = df.rank(0) / 2.0
        result = df.rank(0, pct=True)
        tm.assert_frame_equal(result, expected)

        df = DataFrame([['b', 'c', 'a'], ['a', 'c', 'b']])
        expected = DataFrame([[2.0, 3.0, 1.0], [1, 3, 2]])
        result = df.rank(1, numeric_only=False)
        tm.assert_frame_equal(result, expected)

        expected = DataFrame([[2.0, 1.5, 1.0], [1, 1.5, 2]])
        result = df.rank(0, numeric_only=False)
        tm.assert_frame_equal(result, expected)

        df = DataFrame([['b', np.nan, 'a'], ['a', 'c', 'b']])
        expected = DataFrame([[2.0, nan, 1.0], [1.0, 3.0, 2.0]])
        result = df.rank(1, numeric_only=False)
        tm.assert_frame_equal(result, expected)

        expected = DataFrame([[2.0, nan, 1.0], [1.0, 1.0, 2.0]])
        result = df.rank(0, numeric_only=False)
        tm.assert_frame_equal(result, expected)

        # f7u12, this does not work without extensive workaround
        data = [[datetime(2001, 1, 5), nan, datetime(2001, 1, 2)],
                [datetime(2000, 1, 2), datetime(2000, 1, 3),
                 datetime(2000, 1, 1)]]
        df = DataFrame(data)

        # check the rank
        expected = DataFrame([[2., nan, 1.],
                              [2., 3., 1.]])
        result = df.rank(1, numeric_only=False, ascending=True)
        tm.assert_frame_equal(result, expected)

        expected = DataFrame([[1., nan, 2.],
                              [2., 1., 3.]])
        result = df.rank(1, numeric_only=False, ascending=False)
        tm.assert_frame_equal(result, expected)

        # mixed-type frames
        self.mixed_frame['datetime'] = datetime.now()
        self.mixed_frame['timedelta'] = timedelta(days=1, seconds=1)

        result = self.mixed_frame.rank(1)
        expected = self.mixed_frame.rank(1, numeric_only=True)
        tm.assert_frame_equal(result, expected)

        df = DataFrame({"a": [1e-20, -5, 1e-20 + 1e-40, 10,
                              1e60, 1e80, 1e-30]})
        exp = DataFrame({"a": [3.5, 1., 3.5, 5., 6., 7., 2.]})
        tm.assert_frame_equal(df.rank(), exp)

    def test_rank_na_option(self):
        tm._skip_if_no_scipy()
        from scipy.stats import rankdata

        self.frame['A'][::2] = np.nan
        self.frame['B'][::3] = np.nan
        self.frame['C'][::4] = np.nan
        self.frame['D'][::5] = np.nan

        # bottom
        ranks0 = self.frame.rank(na_option='bottom')
        ranks1 = self.frame.rank(1, na_option='bottom')

        fvals = self.frame.fillna(np.inf).values

        exp0 = np.apply_along_axis(rankdata, 0, fvals)
        exp1 = np.apply_along_axis(rankdata, 1, fvals)

        tm.assert_almost_equal(ranks0.values, exp0)
        tm.assert_almost_equal(ranks1.values, exp1)

        # top
        ranks0 = self.frame.rank(na_option='top')
        ranks1 = self.frame.rank(1, na_option='top')

        fval0 = self.frame.fillna((self.frame.min() - 1).to_dict()).values
        fval1 = self.frame.T
        fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
        fval1 = fval1.fillna(np.inf).values

        exp0 = np.apply_along_axis(rankdata, 0, fval0)
        exp1 = np.apply_along_axis(rankdata, 1, fval1)

        tm.assert_almost_equal(ranks0.values, exp0)
        tm.assert_almost_equal(ranks1.values, exp1)

        # descending

        # bottom
        ranks0 = self.frame.rank(na_option='top', ascending=False)
        ranks1 = self.frame.rank(1, na_option='top', ascending=False)

        fvals = self.frame.fillna(np.inf).values

        exp0 = np.apply_along_axis(rankdata, 0, -fvals)
        exp1 = np.apply_along_axis(rankdata, 1, -fvals)

        tm.assert_almost_equal(ranks0.values, exp0)
        tm.assert_almost_equal(ranks1.values, exp1)

        # descending

        # top
        ranks0 = self.frame.rank(na_option='bottom', ascending=False)
        ranks1 = self.frame.rank(1, na_option='bottom', ascending=False)

        fval0 = self.frame.fillna((self.frame.min() - 1).to_dict()).values
        fval1 = self.frame.T
        fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
        fval1 = fval1.fillna(np.inf).values

        exp0 = np.apply_along_axis(rankdata, 0, -fval0)
        exp1 = np.apply_along_axis(rankdata, 1, -fval1)

        tm.assert_numpy_array_equal(ranks0.values, exp0)
        tm.assert_numpy_array_equal(ranks1.values, exp1)

    def test_rank_axis(self):
        # check if using axes' names gives the same result
        df = DataFrame([[2, 1], [4, 3]])
        tm.assert_frame_equal(df.rank(axis=0), df.rank(axis='index'))
        tm.assert_frame_equal(df.rank(axis=1), df.rank(axis='columns'))

    def test_rank_methods_frame(self):
        tm.skip_if_no_package('scipy', min_version='0.13',
                              app='scipy.stats.rankdata')
        import scipy
        from scipy.stats import rankdata

        xs = np.random.randint(0, 21, (100, 26))
        xs = (xs - 10.0) / 10.0
        cols = [chr(ord('z') - i) for i in range(xs.shape[1])]

        for vals in [xs, xs + 1e6, xs * 1e-6]:
            df = DataFrame(vals, columns=cols)

            for ax in [0, 1]:
                for m in ['average', 'min', 'max', 'first', 'dense']:
                    result = df.rank(axis=ax, method=m)
                    sprank = np.apply_along_axis(
                        rankdata, ax, vals,
                        m if m != 'first' else 'ordinal')
                    sprank = sprank.astype(np.float64)
                    expected = DataFrame(sprank, columns=cols)

                    if LooseVersion(scipy.__version__) >= '0.17.0':
                        expected = expected.astype('float64')
                    tm.assert_frame_equal(result, expected)

    def test_rank_descending(self):
        dtypes = ['O', 'f8', 'i8']

        for dtype, method in product(dtypes, self.results):
            if 'i' in dtype:
                df = self.df.dropna()
            else:
                df = self.df.astype(dtype)

            res = df.rank(ascending=False)
            expected = (df.max() - df).rank()
            assert_frame_equal(res, expected)

            if method == 'first' and dtype == 'O':
                continue

            expected = (df.max() - df).rank(method=method)

            if dtype != 'O':
                res2 = df.rank(method=method, ascending=False,
                               numeric_only=True)
                assert_frame_equal(res2, expected)

            res3 = df.rank(method=method, ascending=False,
                           numeric_only=False)
            assert_frame_equal(res3, expected)

    def test_rank_2d_tie_methods(self):
        df = self.df

        def _check2d(df, expected, method='average', axis=0):
            exp_df = DataFrame({'A': expected, 'B': expected})

            if axis == 1:
                df = df.T
                exp_df = exp_df.T

            result = df.rank(method=method, axis=axis)
            assert_frame_equal(result, exp_df)

        dtypes = [None, object]
        disabled = set([(object, 'first')])
        results = self.results

        for method, axis, dtype in product(results, [0, 1], dtypes):
            if (dtype, method) in disabled:
                continue
            frame = df if dtype is None else df.astype(dtype)
            _check2d(frame, results[method], method=method, axis=axis)
