# ----------------------------------------------------------------------------
# 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 mpl_toolkits.mplot3d import Axes3D  # noqa: F401

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

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

        # out of range axes
        with self.assertRaisesRegex(ValueError, r'axes\[1\].*3'):
            self.min_ord_results._validate_plot_axes(coord_matrix, (0, -1, 2))
        with self.assertRaisesRegex(ValueError, r'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, r'missingcol'):
            self.min_ord_results._get_plot_point_colors(self.df, 'missingcol',
                                                        ['B', 'C'], 'jet')

        # id not in df
        with self.assertRaisesRegex(ValueError, r'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, r'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()
