Merge pull request #175 from ACCLAB/JAnns98-patch-1

Update 07-forest_plot.ipynb, solve some color palette issue

dabest/misc_tools.py

@@ -299,7 +299,7 @@ def get_kwargs(plot_kwargs, ytick_color):
             delta_text_kwargs, summary_bars_kwargs, swarm_bars_kwargs, contrast_bars_kwargs)
 
 
-def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx):
+def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx, all_plot_groups):
 
     # Create color palette that will be shared across subplots.
     color_col = plot_kwargs["color_col"]
@@ -350,7 +350,7 @@ def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx):
     else:
         if isinstance(custom_pal, dict):
             groups_in_palette = {
-                k: v for k, v in custom_pal.items() if k in color_groups
+                k: custom_pal[k] for k in all_plot_groups if k in color_groups
             }
 
             names = groups_in_palette.keys()

dabest/plot_tools.py

@@ -820,7 +820,7 @@ def sankeydiag(
 
 def summary_bars_plotter(summary_bars: list, results: object, ax_to_plot: object,
                  float_contrast: bool,summary_bars_kwargs: dict, ci_type: str,
-                 ticks_to_plot: list, color_col: str, swarm_colors: list, 
+                 ticks_to_plot: list, color_col: str, plot_palette_raw: dict, 
                  proportional: bool, is_paired: bool):
     """
     Add summary bars to the contrast plot.
@@ -843,8 +843,8 @@ def summary_bars_plotter(summary_bars: list, results: object, ax_to_plot: object
         List of indices of the contrast objects.
     color_col : str
         Column name of the color column.
-    swarm_colors : list
-        List of colors used in the plot.
+    plot_palette_raw : dict
+        Dictionary of colors used in the plot.
     proportional : bool
         Whether the data is proportional.
     is_paired : bool
@@ -862,7 +862,13 @@ def summary_bars_plotter(summary_bars: list, results: object, ax_to_plot: object
 # End checks
     else:
         summary_xmin, summary_xmax = ax_to_plot.get_xlim()
-        summary_bars_colors = [summary_bars_kwargs.get('color')]*(max(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_colors = (
+            [summary_bars_kwargs.get('color')]*(max(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 list(plot_palette_raw.values())
+        )
         summary_bars_kwargs.pop('color')
         for summary_index in summary_bars:
             if ci_type == "bca":
@@ -973,7 +979,6 @@ def swarm_bars_plotter(plot_data: object, xvar: str, yvar: str, ax: object,
         swarm_bars_order = pd.unique(plot_data[xvar])
 
     swarm_means = plot_data.groupby(xvar)[yvar].mean().reindex(index=swarm_bars_order)
-    # swarm_bars_colors = [swarm_bars_kwargs.get('color')]*(max(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')] * (max(swarm_bars_order) + 1) 
         if swarm_bars_kwargs.get('color') is not None 
@@ -987,7 +992,7 @@ def swarm_bars_plotter(plot_data: object, xvar: str, yvar: str, ax: object,
         0.5, swarm_bars_y, zorder=-1,color=c,**swarm_bars_kwargs))
 
 def delta_text_plotter(results: object, ax_to_plot: object, swarm_plot_ax: object, ticks_to_plot: list, delta_text_kwargs: dict, color_col: str, 
-                       swarm_colors: list, is_paired: bool, proportional: bool, float_contrast: bool,
+                       plot_palette_raw: dict, is_paired: bool, proportional: bool, float_contrast: bool,
                        show_mini_meta: bool, mini_meta_delta: object, show_delta2: bool, delta_delta: object):
     """
     Add text to the contrast plot.
@@ -1006,8 +1011,8 @@ def delta_text_plotter(results: object, ax_to_plot: object, swarm_plot_ax: objec
         Keyword arguments for the delta text.
     color_col : str
         Column name of the color column.
-    swarm_colors : list
-        List of colors used in the plot.
+    plot_palette_raw : dict
+        Dictionary of colors used in the plot.
     is_paired : bool
         Whether the data is paired.
     proportional : bool
@@ -1032,7 +1037,13 @@ def delta_text_plotter(results: object, ax_to_plot: object, swarm_plot_ax: objec
         delta_text_kwargs["va"] = 'bottom' if results.difference[0] >= 0 else 'top'
     delta_text_kwargs.pop('x_location')
 
-    delta_text_colors = [delta_text_kwargs.get('color')]*(max(ticks_to_plot)+1) if delta_text_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
+    delta_text_colors = (
+        [delta_text_kwargs.get('color')]*(max(ticks_to_plot)+1)
+        if delta_text_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 list(plot_palette_raw.values())
+    )
     if show_mini_meta or show_delta2: delta_text_colors.append('black')
     delta_text_kwargs.pop('color')
 

dabest/plotter.py

@@ -143,7 +143,8 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
                                                                                 plot_data=plot_data, 
                                                                                 xvar=xvar, 
                                                                                 show_pairs=show_pairs,
-                                                                                idx=idx
+                                                                                idx=idx,
+                                                                                all_plot_groups=all_plot_groups
                                                                                 )
 
     # Initialise the figure.
@@ -551,7 +552,7 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
                         ci_type=ci_type, 
                         ticks_to_plot=ticks_to_plot, 
                         color_col=color_col,
-                        swarm_colors=swarm_colors, 
+                        plot_palette_raw=plot_palette_raw, 
                         proportional=proportional, 
                         is_paired=is_paired
                         )
@@ -565,7 +566,7 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
                     ticks_to_plot=ticks_to_plot, 
                     delta_text_kwargs=delta_text_kwargs, 
                     color_col=color_col, 
-                    swarm_colors=swarm_colors, 
+                    plot_palette_raw=plot_palette_raw, 
                     is_paired=is_paired,
                     proportional=proportional, 
                     float_contrast=float_contrast, 

nbs/API/misc_tools.ipynb

@@ -352,7 +352,7 @@
     "            delta_text_kwargs, summary_bars_kwargs, swarm_bars_kwargs, contrast_bars_kwargs)\n",
     "\n",
     "\n",
-    "def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx):\n",
+    "def get_color_palette(plot_kwargs, plot_data, xvar, show_pairs, idx, all_plot_groups):\n",
     "\n",
     "    # Create color palette that will be shared across subplots.\n",
     "    color_col = plot_kwargs[\"color_col\"]\n",
@@ -403,7 +403,7 @@
     "    else:\n",
     "        if isinstance(custom_pal, dict):\n",
     "            groups_in_palette = {\n",
-    "                k: v for k, v in custom_pal.items() if k in color_groups\n",
+    "                k: custom_pal[k] for k in all_plot_groups if k in color_groups\n",
     "            }\n",
     "\n",
     "            names = groups_in_palette.keys()\n",

nbs/API/plot_tools.ipynb

@@ -871,7 +871,7 @@
     "\n",
     "def summary_bars_plotter(summary_bars: list, results: object, ax_to_plot: object,\n",
     "                 float_contrast: bool,summary_bars_kwargs: dict, ci_type: str,\n",
-    "                 ticks_to_plot: list, color_col: str, swarm_colors: list, \n",
+    "                 ticks_to_plot: list, color_col: str, plot_palette_raw: dict, \n",
     "                 proportional: bool, is_paired: bool):\n",
     "    \"\"\"\n",
     "    Add summary bars to the contrast plot.\n",
@@ -894,8 +894,8 @@
     "        List of indices of the contrast objects.\n",
     "    color_col : str\n",
     "        Column name of the color column.\n",
-    "    swarm_colors : list\n",
-    "        List of colors used in the plot.\n",
+    "    plot_palette_raw : dict\n",
+    "        Dictionary of colors used in the plot.\n",
     "    proportional : bool\n",
     "        Whether the data is proportional.\n",
     "    is_paired : bool\n",
@@ -913,7 +913,13 @@
     "# End checks\n",
     "    else:\n",
     "        summary_xmin, summary_xmax = ax_to_plot.get_xlim()\n",
-    "        summary_bars_colors = [summary_bars_kwargs.get('color')]*(max(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_colors = (\n",
+    "            [summary_bars_kwargs.get('color')]*(max(summary_bars)+1)\n",
+    "            if summary_bars_kwargs.get('color') is not None\n",
+    "            else ['black']*(max(summary_bars)+1)\n",
+    "            if color_col is not None or (proportional and is_paired) or is_paired \n",
+    "            else list(plot_palette_raw.values())\n",
+    "        )\n",
     "        summary_bars_kwargs.pop('color')\n",
     "        for summary_index in summary_bars:\n",
     "            if ci_type == \"bca\":\n",
@@ -1024,7 +1030,6 @@
     "        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_bars_colors = [swarm_bars_kwargs.get('color')]*(max(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 = (\n",
     "        [swarm_bars_kwargs.get('color')] * (max(swarm_bars_order) + 1) \n",
     "        if swarm_bars_kwargs.get('color') is not None \n",
@@ -1038,7 +1043,7 @@
     "        0.5, swarm_bars_y, zorder=-1,color=c,**swarm_bars_kwargs))\n",
     "\n",
     "def delta_text_plotter(results: object, ax_to_plot: object, swarm_plot_ax: object, ticks_to_plot: list, delta_text_kwargs: dict, color_col: str, \n",
-    "                       swarm_colors: list, is_paired: bool, proportional: bool, float_contrast: bool,\n",
+    "                       plot_palette_raw: dict, is_paired: bool, proportional: bool, float_contrast: bool,\n",
     "                       show_mini_meta: bool, mini_meta_delta: object, show_delta2: bool, delta_delta: object):\n",
     "    \"\"\"\n",
     "    Add text to the contrast plot.\n",
@@ -1057,8 +1062,8 @@
     "        Keyword arguments for the delta text.\n",
     "    color_col : str\n",
     "        Column name of the color column.\n",
-    "    swarm_colors : list\n",
-    "        List of colors used in the plot.\n",
+    "    plot_palette_raw : dict\n",
+    "        Dictionary of colors used in the plot.\n",
     "    is_paired : bool\n",
     "        Whether the data is paired.\n",
     "    proportional : bool\n",
@@ -1083,7 +1088,13 @@
     "        delta_text_kwargs[\"va\"] = 'bottom' if results.difference[0] >= 0 else 'top'\n",
     "    delta_text_kwargs.pop('x_location')\n",
     "\n",
-    "    delta_text_colors = [delta_text_kwargs.get('color')]*(max(ticks_to_plot)+1) if delta_text_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",
+    "    delta_text_colors = (\n",
+    "        [delta_text_kwargs.get('color')]*(max(ticks_to_plot)+1)\n",
+    "        if delta_text_kwargs.get('color') is not None\n",
+    "        else ['black']*(max(ticks_to_plot)+1)\n",
+    "        if color_col is not None or (proportional and is_paired) or is_paired\n",
+    "        else list(plot_palette_raw.values())\n",
+    "    )\n",
     "    if show_mini_meta or show_delta2: delta_text_colors.append('black')\n",
     "    delta_text_kwargs.pop('color')\n",
     "\n",

nbs/API/plotter.ipynb

@@ -200,7 +200,8 @@
     "                                                                                plot_data=plot_data, \n",
     "                                                                                xvar=xvar, \n",
     "                                                                                show_pairs=show_pairs,\n",
-    "                                                                                idx=idx\n",
+    "                                                                                idx=idx,\n",
+    "                                                                                all_plot_groups=all_plot_groups\n",
     "                                                                                )\n",
     "\n",
     "    # Initialise the figure.\n",
@@ -608,7 +609,7 @@
     "                        ci_type=ci_type, \n",
     "                        ticks_to_plot=ticks_to_plot, \n",
     "                        color_col=color_col,\n",
-    "                        swarm_colors=swarm_colors, \n",
+    "                        plot_palette_raw=plot_palette_raw, \n",
     "                        proportional=proportional, \n",
     "                        is_paired=is_paired\n",
     "                        )\n",
@@ -622,7 +623,7 @@
     "                    ticks_to_plot=ticks_to_plot, \n",
     "                    delta_text_kwargs=delta_text_kwargs, \n",
     "                    color_col=color_col, \n",
-    "                    swarm_colors=swarm_colors, \n",
+    "                    plot_palette_raw=plot_palette_raw, \n",
     "                    is_paired=is_paired,\n",
     "                    proportional=proportional, \n",
     "                    float_contrast=float_contrast, \n",

nbs/tests/mpl_image_tests/test_plot_aesthetics.py

@@ -9,6 +9,7 @@
 mpl.use("Agg")
 import matplotlib.pyplot as plt
 import matplotlib.ticker as Ticker
+import seaborn as sns
 
 from dabest._api import load
 
@@ -146,6 +147,32 @@ def create_demo_dataset_delta(seed=9999, N=20):
 unpaired_mini_meta = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")),
                     mini_meta=True)
 
+multi_groups_change_idx_original = load(
+    df,
+    idx=(
+        ("Control 1", "Test 1", "Test 2"),
+        ("Control 2", "Test 3", "Test 4"),
+        ("Control 3", "Test 5", "Test 6"),
+    ),
+)
+multi_groups_change_idx_new = load(
+    df,
+    idx=(
+        ("Control 1", "Control 2", "Control 3"),
+        ("Test 1", "Test 3", "Test 5"),
+        ("Test 2", "Test 4", "Test 6"),
+    ),
+)
+palette = {"Control 1": sns.color_palette("magma")[5],
+           "Test 1": sns.color_palette("magma")[3],
+           "Test 2": sns.color_palette("magma")[1],
+           "Control 2": sns.color_palette("magma")[5],
+           "Test 3": sns.color_palette("magma")[3],
+           "Test 4": sns.color_palette("magma")[1],
+           "Control 3": sns.color_palette("magma")[5],
+           "Test 5": sns.color_palette("magma")[3],
+           "Test 6": sns.color_palette("magma")[1]}
+
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_207_gardner_altman_meandiff_empty_circle():
     return two_groups_unpaired.mean_diff.plot(empty_circle=True);
@@ -173,3 +200,11 @@ def test_212_cummings_unpaired_delta_delta_meandiff_empty_circle():
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_213_cummings_unpaired_mini_meta_meandiff_empty_circle():
     return unpaired_mini_meta.mean_diff.plot(empty_circle=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_214_change_idx_order_custom_palette_original():
+    return multi_groups_change_idx_original.mean_diff.plot(custom_palette=palette);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_215_change_idx_order_custom_palette_new():
+    return multi_groups_change_idx_new.mean_diff.plot(custom_palette=palette);