Edits to gridkey and code that is impacted by it (remove spines if gridkey)

Author: JAnns98

dabest/misc_tools.py

@@ -1296,7 +1296,7 @@ def Gardner_Altman_Plot_Aesthetic_Adjustments(effect_size_type, plot_data, xvar,
 
 def Cumming_Plot_Aesthetic_Adjustments(contrast_axes, reflines_kwargs, is_paired, show_pairs, two_col_sankey, idx, ticks_to_start_twocol_sankey,
                                        proportional, ticks_to_skip, temp_idx, rawdata_axes, redraw_axes_kwargs, ticks_to_skip_contrast, 
-                                       show_delta2, show_mini_meta, horizontal):
+                                       show_delta2, show_mini_meta, horizontal, skip_redraw_lines):
 
     """
     Aesthetic adjustments for the Cumming plot.
@@ -1335,6 +1335,8 @@ def Cumming_Plot_Aesthetic_Adjustments(contrast_axes, reflines_kwargs, is_paired
         A boolean flag to determine if the plot will have a mini-meta effect size.
     horizontal : bool
         A boolean flag to determine if the plot is for horizontal plotting.
+    skip_redraw_lines : bool
+        A boolean flag to skip adding spines back if True (for gridkey purposes).
     """
 
     # If 0 lies within the ylim of the contrast axes, draw a zero reference line.
@@ -1354,75 +1356,76 @@ def Cumming_Plot_Aesthetic_Adjustments(contrast_axes, reflines_kwargs, is_paired
         method(0, **reflines_kwargs)
 
     # Add axes spine lines to link the relevant groups in the plot. (re-add as we removed spines)
-    if horizontal:
-        if two_col_sankey:
-            rightend_ticks = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)
-            starting_ticks = ticks_to_start_twocol_sankey.copy()
-        else:
-            if is_paired == "baseline" and show_pairs:
-                if proportional and is_paired is not None:
-                    rightend_ticks = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
-                else:
-                    rightend_ticks = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)
+    if not skip_redraw_lines:
+        if horizontal:
+            if two_col_sankey:
+                rightend_ticks = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)
+                starting_ticks = ticks_to_start_twocol_sankey.copy()
             else:
-                rightend_ticks = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
-            starting_ticks = ticks_to_skip.copy()
-
-        for ax in [rawdata_axes]:
-            sns.despine(ax=ax, left=True)
-            xlim, ylim = ax.get_xlim(), ax.get_ylim()
-            redraw_axes_kwargs["x"] = xlim[0]
-            for k, start_tick in enumerate(starting_ticks):
-                end_tick = rightend_ticks[k]
-                ax.vlines(
-                    ymin=start_tick, 
-                    ymax=end_tick, 
-                    **redraw_axes_kwargs
-                    )
-            ax.set_xlim(xlim)
-            ax.set_ylim(ylim)
-            del redraw_axes_kwargs["x"] 
-
-        # Remove y ticks and labels from the contrast axes.
-        sns.despine(ax=contrast_axes, left=True)
-        contrast_axes.set_yticks([])
-        contrast_axes.set_yticklabels([])
-
-    else: # Add spine lines to link the relevant groups in the plot. (re-add as we removed spines) - Vertical plots
-        if two_col_sankey:
-            rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)
-            starting_ticks_raw = starting_ticks_contrast = ticks_to_start_twocol_sankey
-        else:
-            if is_paired == "baseline" and show_pairs:
-                if proportional and is_paired is not None:
-                    rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+                if is_paired == "baseline" and show_pairs:
+                    if proportional and is_paired is not None:
+                        rightend_ticks = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+                    else:
+                        rightend_ticks = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)
                 else:
-                    rightend_ticks_raw = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)
-                    temp_length = [(len(i) - 1) * 2 - 1 for i in idx] if proportional else [(len(i) - 1) for i in idx]
-                    rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)
-                starting_ticks_raw, starting_ticks_contrast = ticks_to_skip, ticks_to_skip_contrast
+                    rightend_ticks = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+                starting_ticks = ticks_to_skip.copy()
+
+            for ax in [rawdata_axes]:
+                sns.despine(ax=ax, left=True)
+                xlim, ylim = ax.get_xlim(), ax.get_ylim()
+                redraw_axes_kwargs["x"] = xlim[0]
+                for k, start_tick in enumerate(starting_ticks):
+                    end_tick = rightend_ticks[k]
+                    ax.vlines(
+                        ymin=start_tick, 
+                        ymax=end_tick, 
+                        **redraw_axes_kwargs
+                        )
+                ax.set_xlim(xlim)
+                ax.set_ylim(ylim)
+                del redraw_axes_kwargs["x"] 
+
+            # Remove y ticks and labels from the contrast axes.
+            sns.despine(ax=contrast_axes, left=True)
+            contrast_axes.set_yticks([])
+            contrast_axes.set_yticklabels([])
+
+        else: # Add spine lines to link the relevant groups in the plot. (re-add as we removed spines) - Vertical plots
+            if two_col_sankey:
+                rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)
+                starting_ticks_raw = starting_ticks_contrast = ticks_to_start_twocol_sankey
             else:
-                rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
-                starting_ticks_raw = starting_ticks_contrast = ticks_to_skip
-
-        for ax, starting_ticks_current, rightend_ticks_current in zip(
-                [rawdata_axes, contrast_axes],
-                [starting_ticks_raw, starting_ticks_contrast],
-                [rightend_ticks_raw, rightend_ticks_contrast],
-            ):
-            sns.despine(ax=ax, bottom=True)
-            xlim, ylim = ax.get_xlim(), ax.get_ylim()
-            redraw_axes_kwargs["y"] = ylim[0]
-            for k, start_tick in enumerate(starting_ticks_current):
-                end_tick = rightend_ticks_current[k]
-                ax.hlines(
-                    xmin=start_tick, 
-                    xmax=end_tick, 
-                    **redraw_axes_kwargs
-                    )
-            ax.set_xlim(xlim)
-            ax.set_ylim(ylim)
-            del redraw_axes_kwargs["y"]
+                if is_paired == "baseline" and show_pairs:
+                    if proportional and is_paired is not None:
+                        rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+                    else:
+                        rightend_ticks_raw = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)
+                        temp_length = [(len(i) - 1) * 2 - 1 for i in idx] if proportional else [(len(i) - 1) for i in idx]
+                        rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)
+                    starting_ticks_raw, starting_ticks_contrast = ticks_to_skip, ticks_to_skip_contrast
+                else:
+                    rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+                    starting_ticks_raw = starting_ticks_contrast = ticks_to_skip
+
+            for ax, starting_ticks_current, rightend_ticks_current in zip(
+                    [rawdata_axes, contrast_axes],
+                    [starting_ticks_raw, starting_ticks_contrast],
+                    [rightend_ticks_raw, rightend_ticks_contrast],
+                ):
+                sns.despine(ax=ax, bottom=True)
+                xlim, ylim = ax.get_xlim(), ax.get_ylim()
+                redraw_axes_kwargs["y"] = ylim[0]
+                for k, start_tick in enumerate(starting_ticks_current):
+                    end_tick = rightend_ticks_current[k]
+                    ax.hlines(
+                        xmin=start_tick, 
+                        xmax=end_tick, 
+                        **redraw_axes_kwargs
+                        )
+                ax.set_xlim(xlim)
+                ax.set_ylim(ylim)
+                del redraw_axes_kwargs["y"]
 
     # Add x-spine line for delta2/mini meta.
     if (show_delta2 or show_mini_meta) and not horizontal:

dabest/plot_tools.py

@@ -1604,7 +1604,9 @@ def grid_key_WIP(is_paired, idx, all_plot_groups, gridkey_rows, rawdata_axes, co
     # raise errors if gridkey_rows is not a list, or if the list is empty
     if isinstance(gridkey_rows, list) is False:
         raise TypeError("gridkey_rows must be a list.")
-    elif len(gridkey_rows) == 0:
+    if any(isinstance(i, str) is False for i in gridkey_rows):
+        raise TypeError("gridkey_rows must contain only strings.")
+    if len(gridkey_rows) == 0:
         warnings.warn("gridkey_rows is an empty list.")
 
     # raise Warning if an item in gridkey_rows is not contained in any idx
@@ -1639,7 +1641,7 @@ def grid_key_WIP(is_paired, idx, all_plot_groups, gridkey_rows, rawdata_axes, co
         gridkey_rows.append("Ns")
         list_of_Ns = []
         for i in groups_for_gridkey:
-            list_of_Ns.append(str(plot_data.groupby(xvar).count()[yvar].loc[i]))
+            list_of_Ns.append(str(plot_data.groupby(xvar, observed=False).count()[yvar].loc[i]))
         table_cellcols.append(list_of_Ns)
 
     # Adds a row for effectsizes with effectsize values

dabest/plotter.py

@@ -377,7 +377,29 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
                                                                 redraw_axes_kwargs=redraw_axes_kwargs,
                                                                 horizontal=horizontal,
                                                                 )
-
+        
+    # GRIDKEY  WIP
+    skip_redraw_lines = False
+    gridkey_rows = plot_kwargs["gridkey_rows"]
+    if gridkey_rows is not None and not horizontal:
+        grid_key_WIP(
+                is_paired=is_paired, 
+                idx=idx, 
+                all_plot_groups=all_plot_groups, 
+                gridkey_rows=gridkey_rows, 
+                rawdata_axes=rawdata_axes, 
+                contrast_axes=contrast_axes,
+                plot_data=plot_data, 
+                xvar=xvar, 
+                yvar=yvar, 
+                results=results, 
+                show_delta2=show_delta2, 
+                show_mini_meta=show_mini_meta, 
+                float_contrast=float_contrast,
+                plot_kwargs=plot_kwargs,
+                )
+        skip_redraw_lines = True
+    
     # Make sure the contrast_axes x-lims match the rawdata_axes xlims,
     # and add an extra violinplot tick for delta-delta plot.
     # Name is xaxis but it is actually y-axis for horizontal plots
@@ -449,6 +471,7 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
                                     show_delta2 = show_delta2,
                                     show_mini_meta = show_mini_meta,
                                     horizontal=horizontal,
+                                    skip_redraw_lines=skip_redraw_lines,
                                     )
 
     # Add the dependent axes spines back in.
@@ -460,27 +483,6 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
         horizontal=horizontal
         )
 
-    # GRIDKEY  WIP
-    # if gridkey_rows is None, skip everything here
-    gridkey_rows = plot_kwargs["gridkey_rows"]
-    if gridkey_rows is not None and not horizontal:
-        grid_key_WIP(
-                is_paired=is_paired, 
-                idx=idx, 
-                all_plot_groups=all_plot_groups, 
-                gridkey_rows=gridkey_rows, 
-                rawdata_axes=rawdata_axes, 
-                contrast_axes=contrast_axes,
-                plot_data=plot_data, 
-                xvar=xvar, 
-                yvar=yvar, 
-                results=results, 
-                show_delta2=show_delta2, 
-                show_mini_meta=show_mini_meta, 
-                float_contrast=float_contrast,
-                plot_kwargs=plot_kwargs,
-                )
-
     # Swarm bars WIP
     swarm_bars = plot_kwargs["swarm_bars"]
     if swarm_bars and not proportional and not horizontal: