Improved legends

src/FlowSOM/main.py

@@ -216,14 +216,6 @@ def update_derived_values(self):
             df = self.mudata["cell_data"].X[self.mudata["cell_data"].X[:, 0].argsort()]
             df = np.c_[self.mudata["cell_data"].obs["metaclustering"], df]
             metacluster_median_values = pd.DataFrame(df).groupby(0).median()
-            """metacluster_median_values = np.vstack(
-                [
-                    np.median(df[df[:, 0] == cl + 1], axis=0)
-                    if df[df[:, 0] == cl + 1].shape[0] != 0
-                    else np.repeat(np.nan, df[df[:, 0] == cl + 1].shape[1])
-                    for cl in range(self.mudata["cell_data"].uns["n_metaclusters"])
-                ]
-            )"""
             self.mudata["cluster_data"].uns["metacluster_MFIs"] = metacluster_median_values
 
         return self
@@ -268,7 +260,7 @@ def metacluster(self, n_clus):
         # metaclusters = (self.hierarchical_clustering(self.mudata["cluster_data"].obsm["codes"], n_clus)).astype(str)
         metaclusters = self.consensus_hierarchical_clustering(self.mudata["cluster_data"].obsm["codes"], n_clus)
         self.mudata["cell_data"].uns["n_metaclusters"] = n_clus
-        self.mudata["cluster_data"].obs["metaclustering"] = metaclusters
+        self.mudata["cluster_data"].obs["metaclustering"] = metaclusters.astype(str)
         self.mudata["cell_data"].obs["metaclustering"] = np.asarray(
             [np.array(metaclusters)[int(i)] for i in np.asarray(self.mudata["cell_data"].obs["clustering"])]
         )

src/FlowSOM/pl/plot_functions.py

@@ -186,7 +186,9 @@ def plot_variable(fsom, variable, cmap=FlowSOM_colors(), lim=None, title=None, *
     n.set_linewidth(0.5)
     n.set_zorder(2)
     ax.add_collection(n)
-    ax, fig = add_legend(fig=fig, ax=ax, data=variable, title="Marker", cmap=cmap)
+    ax, fig = add_legend(
+        fig=fig, ax=ax, data=variable, title="Marker", cmap=cmap, location="upper left", bbox_to_anchor=(1.04, 1)
+    )
     ax.axis("equal")
     if title is not None:
         plt.title(title)
@@ -218,7 +220,8 @@ def plot_marker(fsom, marker, ref_markers=None, lim=None, cmap=FlowSOM_colors(),
         lim = (mfis[:, indices_markers].min(), mfis[:, indices_markers].max())
     marker = list(get_channels(fsom, marker).keys())[0]
     marker_index = np.where(fsom.get_cell_data().var_names == marker)[0][0]
-    plot_variable(fsom, variable=mfis[:, marker_index], cmap=cmap, lim=lim, **kwargs)
+    fig = plot_variable(fsom, variable=mfis[:, marker_index], cmap=cmap, lim=lim, **kwargs)
+    return fig
 
 
 def plot_stars(fsom, markers=None, cmap=FlowSOM_colors(), title=None, **kwargs):
@@ -304,7 +307,14 @@ def plot_pies(
 
     ax.axis("equal")
     ax, fig = add_legend(
-        fig=fig, ax=ax, data=cell_types, title="", cmap=cmap, location="upper right", orientation="vertical"
+        fig=fig,
+        ax=ax,
+        data=cell_types,
+        title="",
+        cmap=cmap,
+        location="upper left",
+        orientation="vertical",
+        bbox_to_anchor=(1.04, 1),
     )
     if title is not None:
         plt.title(title)

src/FlowSOM/pl/plot_helper_functions.py

@@ -27,7 +27,7 @@ def gg_color_hue():
     return cmap
 
 
-def add_legend(fig, ax, data, title, cmap, location="best", orientation="horizontal", ticks=None, labels=None):
+def add_legend(fig, ax, data, title, cmap, location="best", orientation="horizontal", bbox_to_anchor=None):
     if data.dtype == np.float64 or data.dtype == np.int64:
         norm = matplotlib.colors.Normalize(vmin=min(data), vmax=max(data))
         sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
@@ -40,7 +40,17 @@ def add_legend(fig, ax, data, title, cmap, location="best", orientation="horizon
             Line2D([0], [0], marker="o", color="w", label=unique_data[i], markerfacecolor=colors[i], markersize=5)
             for i in range(len(unique_data))
         ]
-        legend = plt.legend(handles=legend_elements, loc=location, frameon=False, title=title)
+        box = ax.get_position()
+        ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
+        legend = plt.legend(
+            handles=legend_elements,
+            loc=location,
+            frameon=False,
+            title=title,
+            bbox_to_anchor=bbox_to_anchor,  # (1, 0.5),
+            fontsize="small",
+            title_fontsize="small",
+        )
         plt.gca().add_artist(legend)
     return ax, fig
 
@@ -124,7 +134,8 @@ def plot_FlowSOM(
             data=background_values,
             title="Background",
             cmap=background_cmap,
-            location="lower right",
+            location="lower left",
+            bbox_to_anchor=(1.04, 0),
         )
 
     # Add MST

tests/test_basic.py

@@ -14,30 +14,33 @@ def test_FlowSOM_type(FlowSOM_res):
 
 
 def test_plot_stars(FlowSOM_res):
-    FlowSOM.pl.plot_stars(
+    pl = FlowSOM.pl.plot_stars(
         FlowSOM_res,
         background_values=FlowSOM_res.get_cluster_data().obs["metaclustering"],
         view="MST",
         equal_node_size=False,
     )
+    pl.savefig("plotstars.pdf")
 
 
 def test_plot_marker(FlowSOM_res):
-    FlowSOM.pl.plot_marker(
+    pl = FlowSOM.pl.plot_marker(
         FlowSOM_res,
         marker=["CD3"],
         background_values=FlowSOM_res.get_cluster_data().obs["metaclustering"],
         view="grid",
         equal_node_size=True,
     )
+    pl.savefig("plotmarker.pdf")
 
 
 def test_plot_pies(FlowSOM_res, gating_results):
-    FlowSOM.pl.plot_pies(
+    pl = FlowSOM.pl.plot_pies(
         FlowSOM_res,
         cell_types=gating_results,
         background_values=FlowSOM_res.get_cluster_data().obs["metaclustering"],
     )
+    pl.savefig("plotpies.pdf")
 
 
 def test_new_data(fcs):