# ---------------------------------------------------------------------------- # 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()