# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------

import unittest

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import numpy.testing as npt
import pandas as pd
from IPython.core.display import Image, SVG

from skbio import OrdinationResults


class TestOrdinationResults(unittest.TestCase):
    def setUp(self):
        # Define in-memory CA results to serialize and deserialize.
        eigvals = pd.Series([0.0961330159181, 0.0409418140138], ['CA1', 'CA2'])
        features = np.array([[0.408869425742, 0.0695518116298],
                             [-0.1153860437, -0.299767683538],
                             [-0.309967102571, 0.187391917117]])
        samples = np.array([[-0.848956053187, 0.882764759014],
                            [-0.220458650578, -1.34482000302],
                            [1.66697179591, 0.470324389808]])
        features_ids = ['Species1', 'Species2', 'Species3']
        sample_ids = ['Site1', 'Site2', 'Site3']

        samples_df = pd.DataFrame(samples, index=sample_ids,
                                  columns=['CA1', 'CA2'])
        features_df = pd.DataFrame(features, index=features_ids,
                                   columns=['CA1', 'CA2'])

        self.ordination_results = OrdinationResults(
            'CA', 'Correspondance Analysis', eigvals=eigvals,
            samples=samples_df, features=features_df)

        # DataFrame for testing plot method. Has a categorical column with a
        # mix of numbers and strings. Has a numeric column with a mix of ints,
        # floats, and strings that can be converted to floats. Has a numeric
        # column with missing data (np.nan).
        self.df = pd.DataFrame([['foo', '42', 10],
                                [22, 0, 8],
                                [22, -4.2, np.nan],
                                ['foo', '42.19', 11]],
                               index=['A', 'B', 'C', 'D'],
                               columns=['categorical', 'numeric', 'nancolumn'])

        # Minimal ordination results for easier testing of plotting method.
        # Paired with df above.
        eigvals = np.array([0.50, 0.25, 0.25])
        samples = np.array([[0.1, 0.2, 0.3],
                            [0.2, 0.3, 0.4],
                            [0.3, 0.4, 0.5],
                            [0.4, 0.5, 0.6]])
        samples_df = pd.DataFrame(samples, ['A', 'B', 'C', 'D'],
                                  ['PC1', 'PC2', 'PC3'])

        self.min_ord_results = OrdinationResults(
            'PCoA', 'Principal Coordinate Analysis', eigvals, samples_df)

    def test_str(self):
        exp = ("Ordination results:\n"
               "\tMethod: Correspondance Analysis (CA)\n"
               "\tEigvals: 2\n"
               "\tProportion explained: N/A\n"
               "\tFeatures: 3x2\n"
               "\tSamples: 3x2\n"
               "\tBiplot Scores: N/A\n"
               "\tSample constraints: N/A\n"
               "\tFeature IDs: 'Species1', 'Species2', 'Species3'\n"
               "\tSample IDs: 'Site1', 'Site2', 'Site3'")
        obs = str(self.ordination_results)
        self.assertEqual(obs, exp)

        # all optional attributes missing
        exp = ("Ordination results:\n"
               "\tMethod: Principal Coordinate Analysis (PCoA)\n"
               "\tEigvals: 1\n"
               "\tProportion explained: N/A\n"
               "\tFeatures: N/A\n"
               "\tSamples: 2x1\n"
               "\tBiplot Scores: N/A\n"
               "\tSample constraints: N/A\n"
               "\tFeature IDs: N/A\n"
               "\tSample IDs: 0, 1")
        samples_df = pd.DataFrame(np.array([[1], [2]]))
        obs = str(OrdinationResults('PCoA', 'Principal Coordinate Analysis',
                                    pd.Series(np.array([4.2])), samples_df))
        self.assertEqual(obs.split('\n'), exp.split('\n'))

    def check_basic_figure_sanity(self, fig, exp_num_subplots, exp_title,
                                  exp_legend_exists, exp_xlabel, exp_ylabel,
                                  exp_zlabel):
        # check type
        self.assertIsInstance(fig, mpl.figure.Figure)

        # check number of subplots
        axes = fig.get_axes()
        npt.assert_equal(len(axes), exp_num_subplots)

        # check title
        ax = axes[0]
        npt.assert_equal(ax.get_title(), exp_title)

        # shouldn't have tick labels
        for tick_label in (ax.get_xticklabels() + ax.get_yticklabels() +
                           ax.get_zticklabels()):
            npt.assert_equal(tick_label.get_text(), '')

        # check if legend is present
        legend = ax.get_legend()
        if exp_legend_exists:
            self.assertTrue(legend is not None)
        else:
            self.assertTrue(legend is None)

        # check axis labels
        npt.assert_equal(ax.get_xlabel(), exp_xlabel)
        npt.assert_equal(ax.get_ylabel(), exp_ylabel)
        npt.assert_equal(ax.get_zlabel(), exp_zlabel)

    def test_plot_no_metadata(self):
        fig = self.min_ord_results.plot()
        self.check_basic_figure_sanity(fig, 1, '', False, '0', '1', '2')

    def test_plot_with_numeric_metadata_and_plot_options(self):
        fig = self.min_ord_results.plot(
            self.df, 'numeric', axes=(1, 0, 2),
            axis_labels=['PC 2', 'PC 1', 'PC 3'], title='a title', cmap='Reds')
        self.check_basic_figure_sanity(
            fig, 2, 'a title', False, 'PC 2', 'PC 1', 'PC 3')

    def test_plot_with_categorical_metadata_and_plot_options(self):
        fig = self.min_ord_results.plot(
            self.df, 'categorical', axes=[2, 0, 1], title='a title',
            cmap='Accent')
        self.check_basic_figure_sanity(fig, 1, 'a title', True, '2', '0', '1')

    def test_plot_with_invalid_axis_labels(self):
        with self.assertRaisesRegex(ValueError, 'axis_labels.*4'):
            self.min_ord_results.plot(axes=[2, 0, 1],
                                      axis_labels=('a', 'b', 'c', 'd'))

    def test_validate_plot_axes_valid_input(self):
        # shouldn't raise an error on valid input. nothing is returned, so
        # nothing to check here
        samples = self.min_ord_results.samples.values.T
        self.min_ord_results._validate_plot_axes(samples, (1, 2, 0))

    def test_validate_plot_axes_invalid_input(self):
        # not enough dimensions
        with self.assertRaisesRegex(ValueError, '2 dimension\(s\)'):
            self.min_ord_results._validate_plot_axes(
                np.asarray([[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]]), (0, 1, 2))

        coord_matrix = self.min_ord_results.samples.values.T

        # wrong number of axes
        with self.assertRaisesRegex(ValueError, 'exactly three.*found 0'):
            self.min_ord_results._validate_plot_axes(coord_matrix, [])
        with self.assertRaisesRegex(ValueError, 'exactly three.*found 4'):
            self.min_ord_results._validate_plot_axes(coord_matrix,
                                                     (0, 1, 2, 3))

        # duplicate axes
        with self.assertRaisesRegex(ValueError, 'must be unique'):
            self.min_ord_results._validate_plot_axes(coord_matrix, (0, 1, 0))

        # out of range axes
        with self.assertRaisesRegex(ValueError, 'axes\[1\].*3'):
            self.min_ord_results._validate_plot_axes(coord_matrix, (0, -1, 2))
        with self.assertRaisesRegex(ValueError, 'axes\[2\].*3'):
            self.min_ord_results._validate_plot_axes(coord_matrix, (0, 2, 3))

    def test_get_plot_point_colors_invalid_input(self):
        # column provided without df
        with npt.assert_raises(ValueError):
            self.min_ord_results._get_plot_point_colors(None, 'numeric',
                                                        ['B', 'C'], 'jet')

        # df provided without column
        with npt.assert_raises(ValueError):
            self.min_ord_results._get_plot_point_colors(self.df, None,
                                                        ['B', 'C'], 'jet')

        # column not in df
        with self.assertRaisesRegex(ValueError, 'missingcol'):
            self.min_ord_results._get_plot_point_colors(self.df, 'missingcol',
                                                        ['B', 'C'], 'jet')

        # id not in df
        with self.assertRaisesRegex(ValueError, 'numeric'):
            self.min_ord_results._get_plot_point_colors(
                self.df, 'numeric', ['B', 'C', 'missingid', 'A'], 'jet')

        # missing data in df
        with self.assertRaisesRegex(ValueError, 'nancolumn'):
            self.min_ord_results._get_plot_point_colors(self.df, 'nancolumn',
                                                        ['B', 'C', 'A'], 'jet')

    def test_get_plot_point_colors_no_df_or_column(self):
        obs = self.min_ord_results._get_plot_point_colors(None, None,
                                                          ['B', 'C'], 'jet')
        npt.assert_equal(obs, (None, None))

    def test_get_plot_point_colors_numeric_column(self):
        # subset of the ids in df
        exp = [0.0, -4.2, 42.0]
        obs = self.min_ord_results._get_plot_point_colors(
            self.df, 'numeric', ['B', 'C', 'A'], 'jet')
        npt.assert_almost_equal(obs[0], exp)
        self.assertTrue(obs[1] is None)

        # all ids in df
        exp = [0.0, 42.0, 42.19, -4.2]
        obs = self.min_ord_results._get_plot_point_colors(
            self.df, 'numeric', ['B', 'A', 'D', 'C'], 'jet')
        npt.assert_almost_equal(obs[0], exp)
        self.assertTrue(obs[1] is None)

    def test_get_plot_point_colors_categorical_column(self):
        # subset of the ids in df
        exp_colors = [[0., 0., 0.5, 1.], [0., 0., 0.5, 1.], [0.5, 0., 0., 1.]]
        exp_color_dict = {
            'foo': [0.5, 0., 0., 1.],
            22: [0., 0., 0.5, 1.]
        }
        obs = self.min_ord_results._get_plot_point_colors(
            self.df, 'categorical', ['B', 'C', 'A'], 'jet')
        npt.assert_almost_equal(obs[0], exp_colors)
        npt.assert_equal(obs[1], exp_color_dict)

        # all ids in df
        exp_colors = [[0., 0., 0.5, 1.], [0.5, 0., 0., 1.], [0.5, 0., 0., 1.],
                      [0., 0., 0.5, 1.]]
        obs = self.min_ord_results._get_plot_point_colors(
            self.df, 'categorical', ['B', 'A', 'D', 'C'], 'jet')
        npt.assert_almost_equal(obs[0], exp_colors)
        # should get same color dict as before
        npt.assert_equal(obs[1], exp_color_dict)

    def test_plot_categorical_legend(self):
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')

        # we shouldn't have a legend yet
        self.assertTrue(ax.get_legend() is None)

        self.min_ord_results._plot_categorical_legend(
            ax, {'foo': 'red', 'bar': 'green'})

        # make sure we have a legend now
        legend = ax.get_legend()
        self.assertTrue(legend is not None)

        # do some light sanity checking to make sure our input labels and
        # colors are present. we're not using nose.tools.assert_items_equal
        # because it isn't available in Python 3.
        labels = [t.get_text() for t in legend.get_texts()]
        npt.assert_equal(sorted(labels), ['bar', 'foo'])

        colors = [l.get_color() for l in legend.get_lines()]
        npt.assert_equal(sorted(colors), ['green', 'red'])

    def test_repr_png(self):
        obs = self.min_ord_results._repr_png_()
        self.assertIsInstance(obs, bytes)
        self.assertTrue(len(obs) > 0)

    def test_repr_svg(self):
        obs = self.min_ord_results._repr_svg_()
        self.assertIsInstance(obs, str)
        self.assertTrue(len(obs) > 0)

    def test_png(self):
        self.assertIsInstance(self.min_ord_results.png, Image)

    def test_svg(self):
        self.assertIsInstance(self.min_ord_results.svg, SVG)


if __name__ == '__main__':
    unittest.main()
