Added summary bars and edited aesthetic of contrast and swarm bars

dabest/_effsize_objects.py

@@ -1022,6 +1022,8 @@ def plot(
         swarm_bars=True,
         contrast_bars_kwargs=None,
         swarm_bars_kwargs=None,
+        summary_bars=None,
+        summary_bars_kwargs=None,
     ):
         """
         Creates an estimation plot for the effect size of interest.
@@ -1171,14 +1173,19 @@ def plot(
         swarm_bars : boolean, default True
             Whether or not to display the swarm bars.
         contrast_bars_kwargs : dict, default None
-            Pass relevant keyword arguments to the contrast bars. Pass any keyword argumentd accepted by 
+            Pass relevant keyword arguments to the contrast bars. Pass any keyword arguments accepted by 
             matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:
-            {"color": None, "alpha": 0.1}
+            {"color": None, "alpha": 0.15}
         swarm_bars_kwargs : dict, default None
-            Pass relevant keyword arguments to the swarm bars. Pass any keyword argumentd accepted by 
+            Pass relevant keyword arguments to the swarm bars. Pass any keyword arguments accepted by 
             matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:
-            {"color": None, "alpha": 0.15}
+            {"color": None, "alpha": 0.1}
 
+        summary_bars : list, default None
+            Pass a list of indices of the contrast objects to have summary bars displayed on the plot.
+            For example, [0,1] will show summary bars for the first two contrast objects.
+        summary_bars_kwargs: dict, default None
+            If None, the following keywords are passed: {"color": None, "alpha": 0.15}
 
         Returns
         -------

dabest/plotter.py

@@ -53,7 +53,10 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
         title=None, fontsize_title=16,
         fontsize_rawxlabel=12, fontsize_rawylabel=12,
         fontsize_contrastxlabel=12, fontsize_contrastylabel=12,
-        fontsize_delta2label=12
+        fontsize_delta2label=12,
+        swarm_bars=True, swarm_bars_kwargs=None,
+        contrast_bars=True, contrast_bars_kwargs=None,
+
     """
     from .misc_tools import merge_two_dicts
     from .plot_tools import (
@@ -1591,7 +1594,7 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
 
     ####################################################### END GRIDKEY MAIN CODE WIP
 
-    ################################################### Contrast Bars WIP
+    ################################################### Swarm & Contrast Bars WIP
 
     # Swarm Bars WIP
     swarm_bars = plot_kwargs["swarm_bars"]
@@ -1615,14 +1618,11 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
             swarm_bars_order = pd.unique(plot_data[xvar])
 
         swarm_means = plot_data.groupby(xvar)[yvar].mean().reindex(index=swarm_bars_order)
-        swarm_bar_colors = [swarm_bars_kwargs.get('color')]*(len(swarm_bars_order)+1) if swarm_bars_kwargs.get('color') is not None else ['black']*(len(swarm_bars_order)+1) if color_col is not None or is_paired else swarm_colors
+        swarm_bars_colors = [swarm_bars_kwargs.get('color')]*(len(swarm_bars_order)+1) if swarm_bars_kwargs.get('color') is not None else ['black']*(len(swarm_bars_order)+1) if color_col is not None or is_paired else swarm_colors
         swarm_bars_kwargs.pop('color')
-        for swarm_bars_x,swarm_bars_y,c in zip(np.arange(0,len(swarm_bars_order)+1,1), swarm_means, swarm_bar_colors):
-            rawdata_axes.add_patch(mpatches.Rectangle((swarm_bars_x-0.2,0),
-            0.5-0.1, swarm_bars_y, zorder=-1,color=c,**swarm_bars_kwargs))
-
-    else:
-        pass
+        for swarm_bars_x,swarm_bars_y,c in zip(np.arange(0,len(swarm_bars_order)+1,1), swarm_means, swarm_bars_colors):
+            rawdata_axes.add_patch(mpatches.Rectangle((swarm_bars_x-0.25,0),
+            0.5, swarm_bars_y, zorder=-1,color=c,**swarm_bars_kwargs))
 
     # Contrast Bars WIP
     contrast_bars = plot_kwargs["contrast_bars"]
@@ -1636,21 +1636,58 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
         for j, tick in enumerate(ticks_to_plot):
             contrast_means.append(results.difference[j])
 
-        contrast_bar_colors = [contrast_bars_kwargs.get('color')]*(len(ticks_to_plot)+1) if contrast_bars_kwargs.get('color') is not None else ['black']*(max(ticks_to_plot)+1) if color_col is not None or (proportional and is_paired) or is_paired else swarm_colors
+        contrast_bars_colors = [contrast_bars_kwargs.get('color')]*(len(ticks_to_plot)+1) if contrast_bars_kwargs.get('color') is not None else ['black']*(max(ticks_to_plot)+1) if color_col is not None or (proportional and is_paired) or is_paired else swarm_colors
         contrast_bars_kwargs.pop('color')
         for contrast_bars_x,contrast_bars_y in zip(ticks_to_plot, contrast_means):
-            contrast_axes.add_patch(mpatches.Rectangle((contrast_bars_x,0),0.25, contrast_bars_y, zorder=-1, color=contrast_bar_colors[contrast_bars_x], **contrast_bars_kwargs))
+            contrast_axes.add_patch(mpatches.Rectangle((contrast_bars_x-0.25,0),0.5, contrast_bars_y, zorder=-1, color=contrast_bars_colors[contrast_bars_x], **contrast_bars_kwargs))
 
         if show_mini_meta:
-            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2,0),0.25, mini_meta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))
+            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2-0.25,0),0.5, mini_meta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))
 
         if show_delta2:
-            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2,0),0.25, delta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))
+            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2-0.25,0),0.5, delta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))
 
+    ################################################### Swarm & Contrast Bars WIP
+
+    ################################################### Summary Bars WIP
+
+    summary_bars = plot_kwargs["summary_bars"]
+    default_summary_bars_kwargs = {"color": None, "alpha": 0.15}
+    if plot_kwargs["summary_bars_kwargs"] is None:
+        summary_bars_kwargs = default_summary_bars_kwargs
     else:
-        pass
-
-    ################################################### Contrast Bars WIP
+        summary_bars_kwargs = merge_two_dicts(default_summary_bars_kwargs, plot_kwargs["summary_bars_kwargs"])
+
+    if summary_bars is not None:
+        if not isinstance(summary_bars, list):
+            raise TypeError("summary_bars must be a list of indices (ints).")
+        if not all(isinstance(i, int) for i in summary_bars):
+            raise TypeError("summary_bars must be a list of indices (ints).")
+        if any(i >= len(results) for i in summary_bars):
+            raise ValueError("Index {} chosen is out of range for the contrast objects.".format([i for i in summary_bars if i >= len(results)]))
+        if float_contrast:
+            raise ValueError("summary_bars cannot be used with Gardner-Altman plots.")
+        else:
+            print('Summary plots WIP')
+            summary_xmin, summary_xmax = contrast_axes.get_xlim()
+            summary_bars_colors = [summary_bars_kwargs.get('color')]*(len(summary_bars)+1) if summary_bars_kwargs.get('color') is not None else ['black']*(max(summary_bars)+1) if color_col is not None or (proportional and is_paired) or is_paired else swarm_colors
+            summary_bars_kwargs.pop('color')
+            for summary_index in summary_bars:
+                print('Summary plot for contrast object:', summary_index)
+                if ci_type == "bca":
+                    summary_ci_low = results.bca_low[summary_index]
+                    summary_ci_high = results.bca_high[summary_index]
+                else:
+                    summary_ci_low = results.pct_low[summary_index]
+                    summary_ci_high = results.pct_high[summary_index]
+
+                summary_color = summary_bars_colors[ticks_to_plot[summary_index]]
+
+                contrast_axes.add_patch(mpatches.Rectangle((summary_xmin,summary_ci_low),summary_xmax+1, 
+                summary_ci_high-summary_ci_low, zorder=-2, color=summary_color, **summary_bars_kwargs))
+
+
+    ################################################### Summary Bars WIP
 
     # Make sure no stray ticks appear!
     rawdata_axes.xaxis.set_ticks_position("bottom")

nbs/API/effsize_objects.ipynb

@@ -1183,6 +1183,8 @@
     "        swarm_bars=True,\n",
     "        contrast_bars_kwargs=None,\n",
     "        swarm_bars_kwargs=None,\n",
+    "        summary_bars=None,\n",
+    "        summary_bars_kwargs=None,\n",
     "    ):\n",
     "        \"\"\"\n",
     "        Creates an estimation plot for the effect size of interest.\n",
@@ -1332,14 +1334,19 @@
     "        swarm_bars : boolean, default True\n",
     "            Whether or not to display the swarm bars.\n",
     "        contrast_bars_kwargs : dict, default None\n",
-    "            Pass relevant keyword arguments to the contrast bars. Pass any keyword argumentd accepted by \n",
+    "            Pass relevant keyword arguments to the contrast bars. Pass any keyword arguments accepted by \n",
     "            matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:\n",
-    "            {\"color\": None, \"alpha\": 0.1}\n",
+    "            {\"color\": None, \"alpha\": 0.15}\n",
     "        swarm_bars_kwargs : dict, default None\n",
-    "            Pass relevant keyword arguments to the swarm bars. Pass any keyword argumentd accepted by \n",
+    "            Pass relevant keyword arguments to the swarm bars. Pass any keyword arguments accepted by \n",
     "            matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:\n",
-    "            {\"color\": None, \"alpha\": 0.15}\n",
+    "            {\"color\": None, \"alpha\": 0.1}\n",
     "\n",
+    "        summary_bars : list, default None\n",
+    "            Pass a list of indices of the contrast objects to have summary bars displayed on the plot.\n",
+    "            For example, [0,1] will show summary bars for the first two contrast objects.\n",
+    "        summary_bars_kwargs: dict, default None\n",
+    "            If None, the following keywords are passed: {\"color\": None, \"alpha\": 0.15}\n",
     "\n",
     "        Returns\n",
     "        -------\n",

nbs/API/plotter.ipynb

@@ -112,7 +112,10 @@
     "        title=None, fontsize_title=16,\n",
     "        fontsize_rawxlabel=12, fontsize_rawylabel=12,\n",
     "        fontsize_contrastxlabel=12, fontsize_contrastylabel=12,\n",
-    "        fontsize_delta2label=12\n",
+    "        fontsize_delta2label=12,\n",
+    "        swarm_bars=True, swarm_bars_kwargs=None,\n",
+    "        contrast_bars=True, contrast_bars_kwargs=None,\n",
+    "\n",
     "    \"\"\"\n",
     "    from .misc_tools import merge_two_dicts\n",
     "    from .plot_tools import (\n",
@@ -1650,7 +1653,7 @@
     "\n",
     "    ####################################################### END GRIDKEY MAIN CODE WIP\n",
     "    \n",
-    "    ################################################### Contrast Bars WIP\n",
+    "    ################################################### Swarm & Contrast Bars WIP\n",
     "    \n",
     "    # Swarm Bars WIP\n",
     "    swarm_bars = plot_kwargs[\"swarm_bars\"]\n",
@@ -1674,14 +1677,11 @@
     "            swarm_bars_order = pd.unique(plot_data[xvar])\n",
     "\n",
     "        swarm_means = plot_data.groupby(xvar)[yvar].mean().reindex(index=swarm_bars_order)\n",
-    "        swarm_bar_colors = [swarm_bars_kwargs.get('color')]*(len(swarm_bars_order)+1) if swarm_bars_kwargs.get('color') is not None else ['black']*(len(swarm_bars_order)+1) if color_col is not None or is_paired else swarm_colors\n",
+    "        swarm_bars_colors = [swarm_bars_kwargs.get('color')]*(len(swarm_bars_order)+1) if swarm_bars_kwargs.get('color') is not None else ['black']*(len(swarm_bars_order)+1) if color_col is not None or is_paired else swarm_colors\n",
     "        swarm_bars_kwargs.pop('color')\n",
-    "        for swarm_bars_x,swarm_bars_y,c in zip(np.arange(0,len(swarm_bars_order)+1,1), swarm_means, swarm_bar_colors):\n",
-    "            rawdata_axes.add_patch(mpatches.Rectangle((swarm_bars_x-0.2,0),\n",
-    "            0.5-0.1, swarm_bars_y, zorder=-1,color=c,**swarm_bars_kwargs))\n",
-    "\n",
-    "    else:\n",
-    "        pass\n",
+    "        for swarm_bars_x,swarm_bars_y,c in zip(np.arange(0,len(swarm_bars_order)+1,1), swarm_means, swarm_bars_colors):\n",
+    "            rawdata_axes.add_patch(mpatches.Rectangle((swarm_bars_x-0.25,0),\n",
+    "            0.5, swarm_bars_y, zorder=-1,color=c,**swarm_bars_kwargs))\n",
     "    \n",
     "    # Contrast Bars WIP\n",
     "    contrast_bars = plot_kwargs[\"contrast_bars\"]\n",
@@ -1695,21 +1695,58 @@
     "        for j, tick in enumerate(ticks_to_plot):\n",
     "            contrast_means.append(results.difference[j])\n",
     "\n",
-    "        contrast_bar_colors = [contrast_bars_kwargs.get('color')]*(len(ticks_to_plot)+1) if contrast_bars_kwargs.get('color') is not None else ['black']*(max(ticks_to_plot)+1) if color_col is not None or (proportional and is_paired) or is_paired else swarm_colors\n",
+    "        contrast_bars_colors = [contrast_bars_kwargs.get('color')]*(len(ticks_to_plot)+1) if contrast_bars_kwargs.get('color') is not None else ['black']*(max(ticks_to_plot)+1) if color_col is not None or (proportional and is_paired) or is_paired else swarm_colors\n",
     "        contrast_bars_kwargs.pop('color')\n",
     "        for contrast_bars_x,contrast_bars_y in zip(ticks_to_plot, contrast_means):\n",
-    "            contrast_axes.add_patch(mpatches.Rectangle((contrast_bars_x,0),0.25, contrast_bars_y, zorder=-1, color=contrast_bar_colors[contrast_bars_x], **contrast_bars_kwargs))\n",
+    "            contrast_axes.add_patch(mpatches.Rectangle((contrast_bars_x-0.25,0),0.5, contrast_bars_y, zorder=-1, color=contrast_bars_colors[contrast_bars_x], **contrast_bars_kwargs))\n",
     "\n",
     "        if show_mini_meta:\n",
-    "            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2,0),0.25, mini_meta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))\n",
+    "            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2-0.25,0),0.5, mini_meta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))\n",
     "\n",
     "        if show_delta2:\n",
-    "            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2,0),0.25, delta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))\n",
+    "            contrast_axes.add_patch(mpatches.Rectangle((max(rawdata_axes.get_xticks())+2-0.25,0),0.5, delta_delta.difference, zorder=-1, color='black', **contrast_bars_kwargs))\n",
     "\n",
+    "    ################################################### Swarm & Contrast Bars WIP\n",
+    "\n",
+    "    ################################################### Summary Bars WIP\n",
+    "\n",
+    "    summary_bars = plot_kwargs[\"summary_bars\"]\n",
+    "    default_summary_bars_kwargs = {\"color\": None, \"alpha\": 0.15}\n",
+    "    if plot_kwargs[\"summary_bars_kwargs\"] is None:\n",
+    "        summary_bars_kwargs = default_summary_bars_kwargs\n",
     "    else:\n",
-    "        pass\n",
-    "  \n",
-    "    ################################################### Contrast Bars WIP\n",
+    "        summary_bars_kwargs = merge_two_dicts(default_summary_bars_kwargs, plot_kwargs[\"summary_bars_kwargs\"])\n",
+    "\n",
+    "    if summary_bars is not None:\n",
+    "        if not isinstance(summary_bars, list):\n",
+    "            raise TypeError(\"summary_bars must be a list of indices (ints).\")\n",
+    "        if not all(isinstance(i, int) for i in summary_bars):\n",
+    "            raise TypeError(\"summary_bars must be a list of indices (ints).\")\n",
+    "        if any(i >= len(results) for i in summary_bars):\n",
+    "            raise ValueError(\"Index {} chosen is out of range for the contrast objects.\".format([i for i in summary_bars if i >= len(results)]))\n",
+    "        if float_contrast:\n",
+    "            raise ValueError(\"summary_bars cannot be used with Gardner-Altman plots.\")\n",
+    "        else:\n",
+    "            print('Summary plots WIP')\n",
+    "            summary_xmin, summary_xmax = contrast_axes.get_xlim()\n",
+    "            summary_bars_colors = [summary_bars_kwargs.get('color')]*(len(summary_bars)+1) if summary_bars_kwargs.get('color') is not None else ['black']*(max(summary_bars)+1) if color_col is not None or (proportional and is_paired) or is_paired else swarm_colors\n",
+    "            summary_bars_kwargs.pop('color')\n",
+    "            for summary_index in summary_bars:\n",
+    "                print('Summary plot for contrast object:', summary_index)\n",
+    "                if ci_type == \"bca\":\n",
+    "                    summary_ci_low = results.bca_low[summary_index]\n",
+    "                    summary_ci_high = results.bca_high[summary_index]\n",
+    "                else:\n",
+    "                    summary_ci_low = results.pct_low[summary_index]\n",
+    "                    summary_ci_high = results.pct_high[summary_index]\n",
+    "\n",
+    "                summary_color = summary_bars_colors[ticks_to_plot[summary_index]]\n",
+    "\n",
+    "                contrast_axes.add_patch(mpatches.Rectangle((summary_xmin,summary_ci_low),summary_xmax+1, \n",
+    "                summary_ci_high-summary_ci_low, zorder=-2, color=summary_color, **summary_bars_kwargs))\n",
+    "\n",
+    "\n",
+    "    ################################################### Summary Bars WIP\n",
     "\n",
     "    # Make sure no stray ticks appear!\n",
     "    rawdata_axes.xaxis.set_ticks_position(\"bottom\")\n",