class TestOrdinationResults(unittest.TestCase):
def setUp(self):
# Define in-memory CA results to serialize and deserialize.
eigvals = np.array([0.0961330159181, 0.0409418140138])
species = np.array([[0.408869425742, 0.0695518116298],
[-0.1153860437, -0.299767683538],
[-0.309967102571, 0.187391917117]])
site = np.array([[-0.848956053187, 0.882764759014],
[-0.220458650578, -1.34482000302],
[1.66697179591, 0.470324389808]])
biplot = None
site_constraints = None
prop_explained = None
species_ids = ['Species1', 'Species2', 'Species3']
site_ids = ['Site1', 'Site2', 'Site3']
self.ordination_results = OrdinationResults(
eigvals=eigvals, species=species, site=site, biplot=biplot,
site_constraints=site_constraints,
proportion_explained=prop_explained, species_ids=species_ids,
site_ids=site_ids)
def test_io(self):
# Very basic check that read/write public API is present and appears to
# be functioning. Roundtrip from memory -> disk -> memory and ensure
# results match.
fh = StringIO()
self.ordination_results.write(fh)
fh.seek(0)
deserialized = OrdinationResults.read(fh)
assert_ordination_results_equal(deserialized, self.ordination_results)
self.assertTrue(type(deserialized) == OrdinationResults)
def test_deprecated_io(self):
fh = StringIO()
npt.assert_warns(UserWarning, self.ordination_results.to_file, fh)
fh.seek(0)
deserialized = npt.assert_warns(UserWarning,
OrdinationResults.from_file, fh)
assert_ordination_results_equal(deserialized, self.ordination_results)
self.assertTrue(type(deserialized) == OrdinationResults)
class TestOrdinationResults(unittest.TestCase):
def setUp(self):
# Define in-memory CA results to serialize and deserialize.
eigvals = np.array([0.0961330159181, 0.0409418140138])
species = np.array([[0.408869425742, 0.0695518116298],
[-0.1153860437, -0.299767683538],
[-0.309967102571, 0.187391917117]])
site = np.array([[-0.848956053187, 0.882764759014],
[-0.220458650578, -1.34482000302],
[1.66697179591, 0.470324389808]])
biplot = None
site_constraints = None
prop_explained = None
species_ids = ['Species1', 'Species2', 'Species3']
site_ids = ['Site1', 'Site2', 'Site3']
self.ordination_results = OrdinationResults(
eigvals=eigvals, species=species, site=site, biplot=biplot,
site_constraints=site_constraints,
proportion_explained=prop_explained, species_ids=species_ids,
site_ids=site_ids)
# 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])
site = 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]])
self.min_ord_results = OrdinationResults(eigvals=eigvals, site=site,
site_ids=['A', 'B', 'C', 'D'])
def test_io(self):
# Very basic check that read/write public API is present and appears to
# be functioning. Roundtrip from memory -> disk -> memory and ensure
# results match.
fh = StringIO()
self.ordination_results.write(fh)
fh.seek(0)
deserialized = OrdinationResults.read(fh)
assert_ordination_results_equal(deserialized, self.ordination_results)
self.assertTrue(type(deserialized) == OrdinationResults)
def test_deprecated_io(self):
fh = StringIO()
npt.assert_warns(UserWarning, self.ordination_results.to_file, fh)
fh.seek(0)
deserialized = npt.assert_warns(UserWarning,
OrdinationResults.from_file, fh)
assert_ordination_results_equal(deserialized, self.ordination_results)
self.assertTrue(type(deserialized) == OrdinationResults)
def check_basic_figure_sanity(self, fig, exp_num_subplots, exp_title,
exp_legend_exists, exp_xlabel, exp_ylabel,
exp_zlabel):
# check type
assert_is_instance(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:
assert_true(legend is not None)
else:
assert_true(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 assert_raises_regexp(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
self.min_ord_results._validate_plot_axes(self.min_ord_results.site.T,
(1, 2, 0))
def test_validate_plot_axes_invalid_input(self):
# not enough dimensions
with assert_raises_regexp(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.site.T
# wrong number of axes
with assert_raises_regexp(ValueError, 'exactly three.*found 0'):
self.min_ord_results._validate_plot_axes(coord_matrix, [])
with assert_raises_regexp(ValueError, 'exactly three.*found 4'):
self.min_ord_results._validate_plot_axes(coord_matrix,
(0, 1, 2, 3))
# duplicate axes
with assert_raises_regexp(ValueError, 'must be unique'):
self.min_ord_results._validate_plot_axes(coord_matrix, (0, 1, 0))
# out of range axes
with assert_raises_regexp(ValueError, 'axes\[1\].*3'):
self.min_ord_results._validate_plot_axes(coord_matrix, (0, -1, 2))
with assert_raises_regexp(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 assert_raises_regexp(ValueError, 'missingcol'):
self.min_ord_results._get_plot_point_colors(self.df, 'missingcol',
['B', 'C'], 'jet')
# id not in df
with assert_raises_regexp(ValueError, 'numeric'):
self.min_ord_results._get_plot_point_colors(
self.df, 'numeric', ['B', 'C', 'missingid', 'A'], 'jet')
# missing data in df
with assert_raises_regexp(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)
assert_true(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)
assert_true(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
assert_true(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()
assert_true(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_()
assert_is_instance(obs, binary_type)
assert_true(len(obs) > 0)
def test_repr_svg(self):
obs = self.min_ord_results._repr_svg_()
assert_is_instance(obs, text_type)
assert_true(len(obs) > 0)
def test_png(self):
assert_is_instance(self.min_ord_results.png, Image)
def test_svg(self):
assert_is_instance(self.min_ord_results.svg, SVG)
