Fix a bug when receiving wide-format paired data with different group sizes.

README.md

@@ -35,7 +35,8 @@ brings new visualizations, refined plots, and improved accuracy.
 
     You can generate a Whorlmap directly from multi-dimensional DABEST
     objects using the `.whorlmap()` method. See the [Whorlmap
-    tutorial](10-whorlmap.html) for more details.
+    tutorial](https://acclab.github.io/DABEST-python/tutorials/10-whorlmap.html)
+    for more details.
 
 2.  **Slopegraphs 📈: Enhanced summaries for paired data**
 
@@ -52,7 +53,8 @@ brings new visualizations, refined plots, and improved accuracy.
     - Customize appearance with `group_summaries_kwargs`.
 
     See the Group Summaries section in the [Plot Aesthetics
-    tutorial](08-plot_aesthetics.html) for more details.
+    tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html)
+    for more details.
 
 3.  **Mini-meta Weighted Delta Fix 🧮**
 
@@ -69,7 +71,8 @@ brings new visualizations, refined plots, and improved accuracy.
       now color contrast bars and effect-size curves.
 
 See the Custom Palette section in the [Plot Aesthetics
-tutorial](08-plot_aesthetics.html) for examples.
+tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html)
+for examples.
 
 Thank you for your continued support!
 

dabest/_dabest_object.py

@@ -559,14 +559,12 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
         self.__x1_level = x1_level
 
         if self.__is_paired and self.__output_data.isnull().values.any():
-            warn1 = f"NaN values detected under paired setting and removed,"
+            warn1 = f"NaN values detected under paired setting,"
             warn2 = f" please check your data."
             warnings.warn(warn1 + warn2)
             if x is not None and y is not None:
                 rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()
                 self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]
-            elif x is None and y is None:
-                self.__output_data.dropna(inplace=True)
 
         # Check if there is a typo on paired
         if self.__is_paired and self.__is_paired not in ("baseline", "sequential"):

nbs/API/dabest_object.ipynb

@@ -664,14 +664,12 @@
     "        self.__x1_level = x1_level\n",
     "\n",
     "        if self.__is_paired and self.__output_data.isnull().values.any():\n",
-    "            warn1 = f\"NaN values detected under paired setting and removed,\"\n",
+    "            warn1 = f\"NaN values detected under paired setting,\"\n",
     "            warn2 = f\" please check your data.\"\n",
     "            warnings.warn(warn1 + warn2)\n",
     "            if x is not None and y is not None:\n",
     "                rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()\n",
     "                self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]\n",
-    "            elif x is None and y is None:\n",
-    "                self.__output_data.dropna(inplace=True)\n",
     "\n",
     "        # Check if there is a typo on paired\n",
     "        if self.__is_paired and self.__is_paired not in (\"baseline\", \"sequential\"):\n",

nbs/read_me.ipynb

@@ -43,7 +43,7 @@
     "\n",
     "    They are especially useful for large-scale or multi-condition experiments, serving as a **space-efficient alternative to stacked forest plots**.\n",
     "\n",
-    "    You can generate a Whorlmap directly from multi-dimensional DABEST objects using the `.whorlmap()` method. See the [Whorlmap tutorial](10-whorlmap.html) for more details.\n",
+    "    You can generate a Whorlmap directly from multi-dimensional DABEST objects using the `.whorlmap()` method. See the [Whorlmap tutorial](https://acclab.github.io/DABEST-python/tutorials/10-whorlmap.html) for more details.\n",
     "\n",
     "2. **Slopegraphs 📈: Enhanced summaries for paired data**\n",
     "   \n",
@@ -55,7 +55,7 @@
     "  \n",
     "      - Customize appearance with `group_summaries_kwargs`.\n",
     "\n",
-    "    See the Group Summaries section in the [Plot Aesthetics tutorial](08-plot_aesthetics.html) for more details.\n",
+    "    See the Group Summaries section in the [Plot Aesthetics tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html) for more details.\n",
     "\n",
     "3. **Mini-meta Weighted Delta Fix 🧮**\n",
     "   \n",
@@ -69,7 +69,7 @@
     "   - **Slopegraphs (paired, non-proportional):**\n",
     "  `custom_palette` can now color contrast bars and effect-size curves.\n",
     "\n",
-    "  See the Custom Palette section in the [Plot Aesthetics tutorial](08-plot_aesthetics.html) for examples.\n",
+    "  See the Custom Palette section in the [Plot Aesthetics tutorial](https://acclab.github.io/DABEST-python/tutorials/08-plot_aesthetics.html) for examples.\n",
     "\n",
     "Thank you for your continued support! \n",
     "\n",

nbs/tests/mpl_image_tests/test_03_plotting.py

@@ -451,6 +451,34 @@ def test_32_multigroups_baseline_change_palette():
     plt.rcdefaults()
     return multi_groups_baseline.mean_diff.plot(custom_palette="Dark2", delta_text=True)
 
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_33_multi_paired_different_sizes():
+    # Test for GitHub issue #216: multi-group paired data with different sample sizes
+    plt.rcdefaults()
+    np.random.seed(9999)
+
+    # Create three test pairs with different sample sizes (20, 10, 40)
+    c1DF = pd.DataFrame({'Test 1_pre': norm.rvs(loc=3, scale=0.4, size=20)})
+    t1DF = pd.DataFrame({'Test 1_post': norm.rvs(loc=3.5, scale=0.5, size=20)})
+    t2DF = pd.DataFrame({'Test 2_pre': norm.rvs(loc=2.5, scale=0.6, size=10)})
+    t3DF = pd.DataFrame({'Test 2_post': norm.rvs(loc=3, scale=0.75, size=10)})
+    t4DF = pd.DataFrame({'Test 3_pre': norm.rvs(loc=3.5, scale=0.75, size=40)})
+    t5DF = pd.DataFrame({'Test 3_post': norm.rvs(loc=3.25, scale=0.4, size=40)})
+
+    df = pd.concat([c1DF, t1DF, t2DF, t3DF, t4DF, t5DF], axis=1)
+    df["ID"] = pd.Series(range(1, len(df)+1))
+
+    multi_paired_diff_sizes = load(
+        df,
+        idx=(("Test 1_pre", "Test 1_post"),
+             ("Test 2_pre", "Test 2_post"),
+             ("Test 3_pre", "Test 3_post")),
+        paired="baseline",
+        id_col="ID"
+    )
+
+    return multi_paired_diff_sizes.mean_diff.plot()
+
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_99_style_sheets():
     # Perform this test last so we don't have to reset the plot style.