Flip y-axis on 2D plots

packages/pidplots/pidplots/blame.py

@@ -88,14 +88,14 @@ def subplot_blame_2D(ax, df, detector="max", color_list=None, cell_fontsize=10):
         idx = np.argmin(blames, 2) if detector == "min" else np.argmax(blames, 2)
         val = np.take_along_axis(blames, np.expand_dims(idx, 2), 2).squeeze()
         vmax = len(const.DETECTORS) - 0.5
-        im = ax.imshow(idx, cmap=cmap, vmin=-0.5, vmax=vmax, aspect="auto")
+        im = ax.imshow(idx, cmap=cmap, vmin=-0.5, vmax=vmax, aspect="auto", origin="lower")
         connected_region_borders(ax, idx)
 
     elif detector in const.DETECTORS:
         index = const.DETECTORS.index(detector)
         cmap = colors.make_linear_colormap(color_list[index])
         val = blames[:, :, index]
-        im = ax.imshow(val, cmap=cmap, aspect="auto")
+        im = ax.imshow(val, cmap=cmap, aspect="auto", origin="lower")
 
     else:
         raise ValueError("detector not understood")

packages/pidplots/pidplots/contribution.py

@@ -141,7 +141,7 @@ def subplot_avg_contribution_2D(ax, df, det="max", color_list=None, cell_fontsiz
         for (i, j), g in gb:
             val[i, j] = g.mean()[f"contrib_{det}"]
 
-        im = ax.imshow(val, cmap=cmap, aspect="auto")
+        im = ax.imshow(val, cmap=cmap, aspect="auto", origin="lower")
 
         # add cell values
         if cell_fontsize > 0:
@@ -163,7 +163,7 @@ def subplot_avg_contribution_2D(ax, df, det="max", color_list=None, cell_fontsiz
 
         # TODO: make sure vlims are correct
         im = ax.imshow(
-            idx, cmap=cmap, vmin=-0.5, vmax=len(const.DETECTORS) - 0.5, aspect="auto"
+            idx, cmap=cmap, vmin=-0.5, vmax=len(const.DETECTORS) - 0.5, aspect="auto", origin="lower"
         )
 
         # add cell values

packages/pidplots/pidplots/eff_pur.py

@@ -194,7 +194,7 @@ def _subplot_2D(
             vmax = len(const.PARTICLES) - 0.5
         else:
             vmin, vmax = vlims
-        im = ax.imshow(idx, cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto")
+        im = ax.imshow(idx, cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto", origin="lower")
         connected_region_borders(ax, idx)
 
     elif particle in const.PARTICLES:
@@ -208,7 +208,7 @@ def _subplot_2D(
         else:
             vmin, vmax = vlims
 
-        im = ax.imshow(val, cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto")
+        im = ax.imshow(val, cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto", origin="lower")
 
     else:
         raise ValueError("particle not understood")

packages/pidplots/pidplots/plot.py

@@ -346,7 +346,7 @@ def subplot_efficiency_diff(ax, df1, df2, particle=None, cmap="RdBu", cbar=False
     eff2 = compute_efficiency_in_all_bins(df2, particle=particle)
     diff = eff1 - eff2
     lim = np.amax(np.abs(diff))
-    im = ax.imshow(diff, cmap=cmap, vmin=-lim, vmax=+lim)
+    im = ax.imshow(diff, cmap=cmap, vmin=-lim, vmax=+lim, origin="lower")
     if cbar:
         plt.colorbar(im, ax=ax)
     add_p_theta_axis_labels(ax)
@@ -428,7 +428,7 @@ def subplot_auc_diff(ax, df1, df2, particle=None, cmap="RdBu", cbar=False):
     auc2 = compute_auc_in_all_bins(df2, particle=particle)
     diff = auc1 - auc2
     lim = np.amax(np.abs(diff))
-    im = ax.imshow(diff, cmap=cmap, vmin=-lim, vmax=+lim)
+    im = ax.imshow(diff, cmap=cmap, vmin=-lim, vmax=+lim, origin="lower")
     if cbar:
         plt.colorbar(im, ax=ax)
     add_p_theta_axis_labels(ax)
@@ -471,7 +471,7 @@ def subplot_top_wrong(
     cmap = colors.make_colormap(color_list, detectors=False)
     wrong, freq = compute_top_wrong_in_all_bins(df)
 
-    ax.imshow(wrong, cmap=cmap, vmin=0, vmax=len(const.PARTICLES))
+    ax.imshow(wrong, cmap=cmap, vmin=0, vmax=len(const.PARTICLES), origin="lower")
 
     hatches = [".", "-", "/", "|", "\\", "x"]
     ec = (0, 0, 0, hatch_alpha)
@@ -618,7 +618,7 @@ def subplot_blame(ax, df, color_list=None, celltextsize=10):
     cmap = colors.make_colormap(color_list)
     cmap.set_bad(color="white")
     blame, freq = compute_blame_in_all_bins(df)
-    ax.imshow(blame, cmap=cmap, vmin=0, vmax=len(const.DETECTORS))
+    ax.imshow(blame, cmap=cmap, vmin=0, vmax=len(const.DETECTORS), origin="lower")
     add_p_theta_axis_labels(ax)
     text_array_values(ax, freq, fontsize=celltextsize)
     # kwargs = dict(ha="center", va="center", fontsize=celltextsize)