def test_plot_violins(pca, kwargs, df_norm): from flotilla.visualize.decomposition import DecompositionViz kw = kwargs.copy() kw.pop('singles') dv = DecompositionViz(pca.reduced_space, pca.components_, pca.explained_variance_ratio_, singles=df_norm, **kw) dv.plot(plot_violins=True) ncols = 4 nrows = 1 top_features = pd.Index(dv.top_features) vector_labels = list(set(dv.magnitudes[:dv.n_vectors].index.union( top_features))) while ncols * nrows < len(vector_labels): nrows += 1 pdt.assert_equal(len(dv.fig_violins.axes), nrows * ncols) # for i in np.arange(len(top_features)): # ax = dv.fig_violins.axes[i] # pdt.assert_equal(len(ax.collections), len(dv.grouped.groups)) plt.close('all')
def test_plot_violins(pca, kwargs, df_norm): from flotilla.visualize.decomposition import DecompositionViz kw = kwargs.copy() kw.pop('singles') dv = DecompositionViz(pca.reduced_space, pca.components_, pca.explained_variance_ratio_, singles=df_norm, **kw) dv.plot(plot_violins=True) ncols = 4 nrows = 1 top_features = pd.Index(dv.top_features) vector_labels = list( set(dv.magnitudes[:dv.n_vectors].index.union(top_features))) while ncols * nrows < len(vector_labels): nrows += 1 pdt.assert_equal(len(dv.fig_violins.axes), nrows * ncols) # for i in np.arange(len(top_features)): # ax = dv.fig_violins.axes[i] # pdt.assert_equal(len(ax.collections), len(dv.grouped.groups)) plt.close('all')
def test_plot_loadings_scatter(pca, kwargs): from flotilla.visualize.decomposition import DecompositionViz dv = DecompositionViz(pca.reduced_space, pca.components_, pca.explained_variance_ratio_, **kwargs) dv.plot(plot_loadings='scatter') pdt.assert_equal(len(dv.fig_reduced.axes), 3) pdt.assert_equal(len(dv.ax_loading1.collections), 1) pdt.assert_equal(len(dv.ax_loading1.collections), 1) plt.close('all')
def test_plot(pca, kwargs): from flotilla.visualize.decomposition import DecompositionViz dv = DecompositionViz(pca.reduced_space, pca.components_, pca.explained_variance_ratio_, **kwargs) dv.plot() pdt.assert_equal(len(dv.fig_reduced.axes), 5) pdt.assert_equal(len(dv.ax_components.lines), kwargs['n_vectors'] + 1) pdt.assert_equal(len(dv.ax_explained_variance.lines), 1) pdt.assert_equal(len(dv.ax_explained_variance.collections), 1) pdt.assert_equal(len(dv.ax_empty.collections), 0) pdt.assert_equal(len(dv.ax_pcs_heatmap.collections), 1) pdt.assert_equal(len(dv.ax_pcs_colorbar.collections), 1) assert not hasattr(dv, 'ax_loading1') assert not hasattr(dv, 'ax_loading2') plt.close('all')
def test_plot(pca, kwargs): from flotilla.visualize.decomposition import DecompositionViz dv = DecompositionViz(pca.reduced_space, pca.components_, pca.explained_variance_ratio_, **kwargs) dv.plot() pdt.assert_equal(len(dv.fig_reduced.axes), 5) pdt.assert_equal(len(dv.ax_components.lines), kwargs['n_vectors']+1) pdt.assert_equal(len(dv.ax_explained_variance.lines), 1) pdt.assert_equal(len(dv.ax_explained_variance.collections), 1) pdt.assert_equal(len(dv.ax_empty.collections), 0) pdt.assert_equal(len(dv.ax_pcs_heatmap.collections), 1) pdt.assert_equal(len(dv.ax_pcs_colorbar.collections), 1) assert not hasattr(dv, 'ax_loading1') assert not hasattr(dv, 'ax_loading2') plt.close('all')