Revert "Merge branch 'repeated_measures_without_paired' into v0.4dev"

This reverts commit 49fe30d, reversing
changes made to 8ffb36a.

Author: LI-Yixuan

dabest/plotter.py

@@ -332,9 +332,23 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
             pivot_values = [yvar, color_col]
         pivoted_plot_data = pd.pivot(data=plot_data, index=dabest_obj.id_col,
                                      columns=xvar, values=pivot_values)
+        if is_paired == "baseline":
+            temp_idx = []
+            for i in idx:
+                control = i[0]
+                temp_idx.extend(((control, test) for test in i[1:]))
+            temp_idx = tuple(temp_idx)
+
+            temp_all_plot_groups = []
+            for i in temp_idx:
+                temp_all_plot_groups.extend(list(i))
+        else:
+            temp_idx = idx
+            temp_all_plot_groups = all_plot_groups
+        
         x_start = 0
-        for ii, current_tuple in enumerate(idx):
-            if len(idx) > 1:
+        for ii, current_tuple in enumerate(temp_idx):
+            if len(temp_idx) > 1:
                 # Select only the data for the current tuple.
                 if color_col is None:
                     current_pair = pivoted_plot_data.reindex(columns=current_tuple)
@@ -363,8 +377,8 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
                 rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)
             x_start  = x_start + grp_count
         # Set the tick labels, because the slopegraph plotting doesn't.
-        rawdata_axes.set_xticks(np.arange(0, len(all_plot_groups)))
-        rawdata_axes.set_xticklabels(all_plot_groups)
+        rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))
+        rawdata_axes.set_xticklabels(temp_all_plot_groups)
 
 
     else:
@@ -431,12 +445,19 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
 
     # Plot effect sizes and bootstraps.
     # Take note of where the `control` groups are.
-    ticks_to_skip   = np.cumsum([len(t) for t in idx])[:-1].tolist()
-    ticks_to_skip.insert(0, 0)
+    if is_paired == "baseline" and show_pairs == True:
+        ticks_to_skip = np.arange(0, len(temp_all_plot_groups), 2).tolist()
+        ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist() 
+        ticks_to_skip_contrast = np.cumsum([(len(t)-1)*2 for t in idx])[:-1].tolist()
+        ticks_to_skip_contrast.insert(0, 0)
+
+    else:
+        ticks_to_skip   = np.cumsum([len(t) for t in idx])[:-1].tolist()
+        ticks_to_skip.insert(0, 0)
 
         # Then obtain the ticks where we have to plot the effect sizes.
-    ticks_to_plot = [t for t in range(0, len(all_plot_groups))
-                    if t not in ticks_to_skip]
+        ticks_to_plot = [t for t in range(0, len(all_plot_groups))
+                        if t not in ticks_to_skip]
 
 
     # Plot the bootstraps, then the effect sizes and CIs.
@@ -683,22 +704,55 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
         if contrast_ylim_low < 0 < contrast_ylim_high:
             contrast_axes.axhline(y=0, **reflines_kwargs)
 
-        # Compute the end of each x-axes line.
-        rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip)
-        for ax in [rawdata_axes, contrast_axes]:
-            sns.despine(ax=ax, bottom=True)
-    
-            ylim = ax.get_ylim()
-            xlim = ax.get_xlim()
-            redraw_axes_kwargs['y'] = ylim[0]
+        if is_paired == "baseline" and show_pairs == True:
+            rightend_ticks_raw = np.array([len(i)-1 for i in temp_idx]) + np.array(ticks_to_skip)
+            for ax in [rawdata_axes]:
+                sns.despine(ax=ax, bottom=True)
 
-            for k, start_tick in enumerate(ticks_to_skip):
-                end_tick = rightend_ticks[k]
-                ax.hlines(xmin=start_tick, xmax=end_tick,
-                          **redraw_axes_kwargs)
+                ylim = ax.get_ylim()
+                xlim = ax.get_xlim()
+                redraw_axes_kwargs['y'] = ylim[0]
 
-            ax.set_ylim(ylim)
-            del redraw_axes_kwargs['y']
+                for k, start_tick in enumerate(ticks_to_skip):
+                    end_tick = rightend_ticks_raw[k]
+                    ax.hlines(xmin=start_tick, xmax=end_tick,
+                              **redraw_axes_kwargs)
+        
+                ax.set_ylim(ylim)
+                del redraw_axes_kwargs['y']
+
+            rightend_ticks_contrast = np.array([(len(i)-1)*2-1 for i in idx]) + np.array(ticks_to_skip_contrast)
+            for ax in [contrast_axes]:
+                sns.despine(ax=ax, bottom=True)
+        
+                ylim = ax.get_ylim()
+                xlim = ax.get_xlim()
+                redraw_axes_kwargs['y'] = ylim[0]
+        
+                for k, start_tick in enumerate(ticks_to_skip_contrast):
+                    end_tick = rightend_ticks_contrast[k]
+                    ax.hlines(xmin=start_tick, xmax=end_tick,
+                              **redraw_axes_kwargs)
+        
+                ax.set_ylim(ylim)
+                del redraw_axes_kwargs['y']
+        else:
+            # Compute the end of each x-axes line.
+            rightend_ticks = np.array([len(i)-1 for i in idx]) + np.array(ticks_to_skip)
+            for ax in [rawdata_axes, contrast_axes]:
+                sns.despine(ax=ax, bottom=True)
+        
+                ylim = ax.get_ylim()
+                xlim = ax.get_xlim()
+                redraw_axes_kwargs['y'] = ylim[0]
+        
+                for k, start_tick in enumerate(ticks_to_skip):
+                    end_tick = rightend_ticks[k]
+                    ax.hlines(xmin=start_tick, xmax=end_tick,
+                              **redraw_axes_kwargs)
+        
+                ax.set_ylim(ylim)
+                del redraw_axes_kwargs['y']