Allowed order to have any range

Author: PauBadiaM

src/decoupler/pl/_order.py

@@ -53,7 +53,7 @@ def order(
             bp.ax.imshow(
                 colors,
                 aspect='auto',
-                extent=[0, xmax, 1.05 * ymax, 1.2 * ymax],
+                extent=[xmin, xmax, 1.05 * ymax, 1.2 * ymax],
                 transform=bp.ax.transData,
                 zorder=2
             )
@@ -75,15 +75,15 @@ def order(
             .groupby(['name', 'order'], as_index=False)['value'].mean()
             .pivot(index='name', columns='order', values='value')
         )
-        img = bp.ax.imshow(mat, extent=[0, xmax, 0, n_names], aspect='auto', **kw_order)
+        img = bp.ax.imshow(mat, extent=[xmin, xmax, 0, n_names], aspect='auto', **kw_order)
         if has_cbar:
-            bp.ax.imshow(colors, aspect='auto', extent=[0, xmax, n_names, 1.1 * n_names], zorder=2)
+            bp.ax.imshow(colors, aspect='auto', extent=[xmin, xmax, n_names, 1.1 * n_names], zorder=2)
             bp.ax.axhline(y=n_names, c='black', lw=1)
             bp.ax.set_ylim(0, 1.1 * n_names)
         bp.fig.colorbar(img, ax=bp.ax, shrink=0.5, label='Mean value', location='top')
         bp.ax.set_yticks(np.arange(n_names) + 0.5)
         bp.ax.set_yticklabels(np.flip(mat.index))
         bp.ax.grid(axis='y', visible=False)
         bp.ax.set_xlabel('order')
-    bp.ax.set_xlim(0, 1)
+    bp.ax.set_xlim(xmin, xmax)
     return bp._return()

src/decoupler/pl/_order_targets.py

@@ -140,9 +140,11 @@ def order_targets(
     ax.grid(axis='y', visible=False)
     ax.set_ylabel('Targets')
     yticklabels = []
+    # Find minmax
+    omin, omax = df_ftr['order'].min(), df_ftr['order'].max()
     # Add neg targets
     if neg_names.size > 0:
-        img = ax.imshow(mat.loc[neg_names], extent=[0, 1, 0, neg_names.size], aspect='auto', cmap=neg_cmap, vmin=vmin, vmax=vmax)
+        img = ax.imshow(mat.loc[neg_names], extent=[omin, omax, 0, neg_names.size], aspect='auto', cmap=neg_cmap, vmin=vmin, vmax=vmax)
         yticklabels.extend(list(neg_names)[::-1])
         cbar_mappable = ScalarMappable(cmap=neg_cmap, norm=Normalize(vmin=vmin, vmax=vmax))
         pos = ax.get_position().bounds
@@ -152,22 +154,22 @@ def order_targets(
     ax.axhline(y=neg_names.size, c='black', lw=1)
     # Add pos targets
     if pos_names.size > 0:
-        img = ax.imshow(mat.loc[pos_names], extent=[0, 1, neg_names.size, neg_names.size + pos_names.size], aspect='auto', cmap='Reds', vmin=vmin, vmax=vmax)
+        img = ax.imshow(mat.loc[pos_names], extent=[omin, omax, neg_names.size, neg_names.size + pos_names.size], aspect='auto', cmap='Reds', vmin=vmin, vmax=vmax)
         yticklabels.extend(list(pos_names)[::-1])
         cbar_mappable = ScalarMappable(cmap=pos_cmap, norm=Normalize(vmin=vmin, vmax=vmax))
         pos = ax.get_position().bounds
-        #  (left, bottom, width, height)
         cax = bp.fig.add_axes([0.97, pos[1] + (pos[3] / 2) + .025, 0.05, (pos[3] / 2) - .025])
         cax.grid(axis='y', visible=False)
         bp.fig.colorbar(cbar_mappable, cax=cax, aspect=5, shrink=0.5, label='+ target\nvalues', location='right')
     # Plot labels
     ax.set_ylim(0, n_names)
     if has_cbar:
-        ax.imshow(colors, aspect='auto', extent=[0, 1, n_names, 1.1 * n_names], zorder=2)
+        ax.imshow(colors, aspect='auto', extent=[omin, omax, n_names, 1.1 * n_names], zorder=2)
         ax.axhline(y=n_names, c='black', lw=1)
         ax.set_ylim(0, 1.1 * n_names)
     # Format plot
     ax.set_yticks(np.arange(n_names) + 0.5)
     ax.set_yticklabels(yticklabels)
+    ax.set_xlim(omin, omax)
     bp.fig.subplots_adjust(hspace=0)
     return bp._return()

src/decoupler/pp/anndata.py

@@ -707,10 +707,9 @@ def bin_order(
     if sps.issparse(X):
         X = X.toarray()
     y = adata.obs[order].values
-    # Normalize to 0 and 1
-    y = np.abs(y) / np.abs(y).max()
     # Make windows
-    bin_edges = np.linspace(0, 1, nbins + 1)
+    ymin, ymax = y.min(), y.max()
+    bin_edges = np.linspace(start=ymin, stop=ymax, num=nbins + 1)
     bin_midpoints = (bin_edges[:-1] + bin_edges[1:]) / 2
     # Prepare label colors
     cols = ['name', 'midpoint', 'value']
@@ -739,5 +738,4 @@ def bin_order(
     df = pd.concat(dfs)
     df = df[cols]
     df = df.rename(columns={'midpoint': 'order'}).reset_index(drop=True)
-    df['order'] = df['order'] / df['order'].max()
     return df

tests/pp/test_anndata.py

@@ -250,7 +250,6 @@ def test_bin_order(
     cols = {'name', 'order', 'value'}
     assert cols.issubset(df.columns)
     assert ((0. <= df['order']) & (df['order'] <= 1.)).all()
-    assert df['order'].unique().size == np.min([tdata.n_obs, nbins])
     if label is not None:
         lcols = {'label', 'color'}
         assert lcols.issubset(df.columns)