def test_standard_scale(self):
df = self.df_norm.copy()
df = (df - df.min()) / (df.max() - df.min())
kws = self.default_kws.copy()
kws['standard_scale'] = 1
cg = mat.ClusterGrid(self.df_norm, **kws)
pdt.assert_frame_equal(cg.data2d, df)
def test_z_score(self):
df = self.df_norm.copy()
df = (df - df.mean()) / df.std()
kws = self.default_kws.copy()
kws['z_score'] = 1
cg = mat.ClusterGrid(self.df_norm, **kws)
pdt.assert_frame_equal(cg.data2d, df)
def test_colors_input(self):
kws = self.default_kws.copy()
kws['row_colors'] = self.row_colors
kws['col_colors'] = self.col_colors
cg = mat.ClusterGrid(self.df_norm, **kws)
npt.assert_array_equal(cg.row_colors, self.row_colors)
npt.assert_array_equal(cg.col_colors, self.col_colors)
assert len(cg.fig.axes) == 6
def test_required_scipy_errors():
x = np.random.normal(0, 1, (10, 10))
with pytest.raises(RuntimeError):
mat.clustermap(x)
with pytest.raises(RuntimeError):
mat.ClusterGrid(x)
with pytest.raises(RuntimeError):
mat.dendrogram(x)
def test_nested_colors_input(self):
kws = self.default_kws.copy()
row_colors = [self.row_colors, self.row_colors]
col_colors = [self.col_colors, self.col_colors]
kws['row_colors'] = row_colors
kws['col_colors'] = col_colors
cm = mat.ClusterGrid(self.df_norm, **kws)
npt.assert_array_equal(cm.row_colors, row_colors)
npt.assert_array_equal(cm.col_colors, col_colors)
assert len(cm.fig.axes) == 6
def test_pivot_input(self):
df_norm = self.df_norm.copy()
df_norm.index.name = 'numbers'
df_long = pd.melt(df_norm.reset_index(),
var_name='letters',
id_vars='numbers')
kws = self.default_kws.copy()
kws['pivot_kws'] = dict(index='numbers',
columns='letters',
values='value')
cg = mat.ClusterGrid(df_long, **kws)
pdt.assert_frame_equal(cg.data2d, df_norm)
def test_categorical_colors_input(self):
kws = self.default_kws.copy()
row_colors = pd.Series(self.row_colors, dtype="category")
col_colors = pd.Series(self.col_colors,
dtype="category",
index=self.df_norm.columns)
kws['row_colors'] = row_colors
kws['col_colors'] = col_colors
exp_row_colors = list(map(mpl.colors.to_rgb, row_colors))
exp_col_colors = list(map(mpl.colors.to_rgb, col_colors))
cg = mat.ClusterGrid(self.df_norm, **kws)
npt.assert_array_equal(cg.row_colors, exp_row_colors)
npt.assert_array_equal(cg.col_colors, exp_col_colors)
assert len(cg.fig.axes) == 6
def test_savefig(self):
# Not sure if this is the right way to test....
cg = mat.ClusterGrid(self.df_norm, **self.default_kws)
cg.plot(**self.default_plot_kws)
cg.savefig(tempfile.NamedTemporaryFile(), format='png')
def test_z_score_standard_scale(self):
kws = self.default_kws.copy()
kws['z_score'] = True
kws['standard_scale'] = True
with pytest.raises(ValueError):
mat.ClusterGrid(self.df_norm, **kws)
def test_corr_df_input(self):
df = self.df_norm.corr()
cg = mat.ClusterGrid(df, **self.default_kws)
cg.plot(**self.default_plot_kws)
diag = cg.data2d.values[np.diag_indices_from(cg.data2d)]
npt.assert_array_almost_equal(diag, np.ones(cg.data2d.shape[0]))
def test_df_input(self):
cg = mat.ClusterGrid(self.df_norm, **self.default_kws)
pdt.assert_frame_equal(cg.data, self.df_norm)
def test_ndarray_input(self):
cg = mat.ClusterGrid(self.x_norm, **self.default_kws)
pdt.assert_frame_equal(cg.data, pd.DataFrame(self.x_norm))
assert len(cg.fig.axes) == 4
assert cg.ax_row_colors is None
assert cg.ax_col_colors is None
