from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import six

from datetime import datetime

import numpy as np
from numpy.testing.utils import (assert_array_equal, assert_approx_equal,
                                 assert_array_almost_equal)
from nose.tools import assert_equal, raises, assert_true

import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
from matplotlib.cbook import delete_masked_points as dmp


def test_is_string_like():
    y = np.arange(10)
    assert_equal(cbook.is_string_like(y), False)
    y.shape = 10, 1
    assert_equal(cbook.is_string_like(y), False)
    y.shape = 1, 10
    assert_equal(cbook.is_string_like(y), False)

    assert cbook.is_string_like("hello world")
    assert_equal(cbook.is_string_like(10), False)


def test_restrict_dict():
    d = {'foo': 'bar', 1: 2}
    d1 = cbook.restrict_dict(d, ['foo', 1])
    assert_equal(d1, d)
    d2 = cbook.restrict_dict(d, ['bar', 2])
    assert_equal(d2, {})
    d3 = cbook.restrict_dict(d, {'foo': 1})
    assert_equal(d3, {'foo': 'bar'})
    d4 = cbook.restrict_dict(d, {})
    assert_equal(d4, {})
    d5 = cbook.restrict_dict(d, set(['foo', 2]))
    assert_equal(d5, {'foo': 'bar'})
    # check that d was not modified
    assert_equal(d, {'foo': 'bar', 1: 2})


class Test_delete_masked_points:
    def setUp(self):
        self.mask1 = [False, False, True, True, False, False]
        self.arr0 = np.arange(1.0, 7.0)
        self.arr1 = [1, 2, 3, np.nan, np.nan, 6]
        self.arr2 = np.array(self.arr1)
        self.arr3 = np.ma.array(self.arr2, mask=self.mask1)
        self.arr_s = ['a', 'b', 'c', 'd', 'e', 'f']
        self.arr_s2 = np.array(self.arr_s)
        self.arr_dt = [datetime(2008, 1, 1), datetime(2008, 1, 2),
                       datetime(2008, 1, 3), datetime(2008, 1, 4),
                       datetime(2008, 1, 5), datetime(2008, 1, 6)]
        self.arr_dt2 = np.array(self.arr_dt)
        self.arr_colors = ['r', 'g', 'b', 'c', 'm', 'y']
        self.arr_rgba = mcolors.colorConverter.to_rgba_array(self.arr_colors)

    @raises(ValueError)
    def test_bad_first_arg(self):
        dmp('a string', self.arr0)

    def test_string_seq(self):
        actual = dmp(self.arr_s, self.arr1)
        ind = [0, 1, 2, 5]
        expected = (self.arr_s2.take(ind), self.arr2.take(ind))
        assert_array_equal(actual[0], expected[0])
        assert_array_equal(actual[1], expected[1])

    def test_datetime(self):
        actual = dmp(self.arr_dt, self.arr3)
        ind = [0, 1,  5]
        expected = (self.arr_dt2.take(ind),
                    self.arr3.take(ind).compressed())
        assert_array_equal(actual[0], expected[0])
        assert_array_equal(actual[1], expected[1])

    def test_rgba(self):
        actual = dmp(self.arr3, self.arr_rgba)
        ind = [0, 1, 5]
        expected = (self.arr3.take(ind).compressed(),
                    self.arr_rgba.take(ind, axis=0))
        assert_array_equal(actual[0], expected[0])
        assert_array_equal(actual[1], expected[1])


def test_allequal():
    assert(cbook.allequal([1, 1, 1]))
    assert(not cbook.allequal([1, 1, 0]))
    assert(cbook.allequal([]))
    assert(cbook.allequal(('a', 'a')))
    assert(not cbook.allequal(('a', 'b')))


class Test_boxplot_stats:
    def setup(self):
        np.random.seed(937)
        self.nrows = 37
        self.ncols = 4
        self.data = np.random.lognormal(size=(self.nrows, self.ncols),
                                        mean=1.5, sigma=1.75)
        self.known_keys = sorted([
            'mean', 'med', 'q1', 'q3', 'iqr',
            'cilo', 'cihi', 'whislo', 'whishi',
            'fliers', 'label'
        ])
        self.std_results = cbook.boxplot_stats(self.data)

        self.known_nonbootstrapped_res = {
            'cihi': 6.8161283264444847,
            'cilo': -0.1489815330368689,
            'iqr': 13.492709959447094,
            'mean': 13.00447442387868,
            'med': 3.3335733967038079,
            'fliers': np.array([
                92.55467075,  87.03819018,  42.23204914,  39.29390996
            ]),
            'q1': 1.3597529879465153,
            'q3': 14.85246294739361,
            'whishi': 27.899688243699629,
            'whislo': 0.042143774965502923,
            'label': 1
        }

        self.known_bootstrapped_ci = {
            'cihi': 8.939577523357828,
            'cilo': 1.8692703958676578,
        }

        self.known_whis3_res = {
            'whishi': 42.232049135969874,
            'whislo': 0.042143774965502923,
            'fliers': np.array([92.55467075, 87.03819018]),
        }

        self.known_res_with_labels = {
            'label': 'Test1'
        }

        self.known_res_percentiles = {
            'whislo':   0.1933685896907924,
            'whishi':  42.232049135969874
        }

        self.known_res_range = {
            'whislo': 0.042143774965502923,
            'whishi': 92.554670752188699

        }

    def test_form_main_list(self):
        assert_true(isinstance(self.std_results, list))

    def test_form_each_dict(self):
        for res in self.std_results:
            assert_true(isinstance(res, dict))

    def test_form_dict_keys(self):
        for res in self.std_results:
            keys = sorted(list(res.keys()))
            for key in keys:
                assert_true(key in self.known_keys)

    def test_results_baseline(self):
        res = self.std_results[0]
        for key in list(self.known_nonbootstrapped_res.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_nonbootstrapped_res[key]
            )

    def test_results_bootstrapped(self):
        results = cbook.boxplot_stats(self.data, bootstrap=10000)
        res = results[0]
        for key in list(self.known_bootstrapped_ci.keys()):
            assert_approx_equal(
                res[key],
                self.known_bootstrapped_ci[key]
            )

    def test_results_whiskers_float(self):
        results = cbook.boxplot_stats(self.data, whis=3)
        res = results[0]
        for key in list(self.known_whis3_res.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_whis3_res[key]
            )

    def test_results_whiskers_range(self):
        results = cbook.boxplot_stats(self.data, whis='range')
        res = results[0]
        for key in list(self.known_res_range.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_res_range[key]
            )

    def test_results_whiskers_percentiles(self):
        results = cbook.boxplot_stats(self.data, whis=[5, 95])
        res = results[0]
        for key in list(self.known_res_percentiles.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_res_percentiles[key]
            )

    def test_results_withlabels(self):
        labels = ['Test1', 2, 3, 4]
        results = cbook.boxplot_stats(self.data, labels=labels)
        res = results[0]
        for key in list(self.known_res_with_labels.keys()):
            assert_equal(res[key], self.known_res_with_labels[key])

    @raises(ValueError)
    def test_label_error(self):
        labels = [1, 2]
        results = cbook.boxplot_stats(self.data, labels=labels)

    @raises(ValueError)
    def test_bad_dims(self):
        data = np.random.normal(size=(34, 34, 34))
        results = cbook.boxplot_stats(data)
