feat: support boxplot using maidr-ts (#151)

Author: dakshpokar

Diff for: example/box/matplotlib/example_mpl_box.py

@@ -3,6 +3,8 @@
 
 import maidr
 
+maidr.set_engine("ts")
+
 # Generating random data for three different groups
 data_group1 = np.random.normal(100, 10, 200)
 data_group2 = np.random.normal(90, 20, 200)
@@ -40,7 +42,7 @@ def boxplot(is_vert: bool):
 # plt.show()
 maidr.show(vert)
 
-# Create the vertical boxplot
+# Create the horizontal boxplot
 horz = boxplot(is_vert=False)
 # plt.show()
 maidr.show(horz)

Diff for: example/box/seaborn/example_sns_box.py

@@ -3,6 +3,8 @@
 
 import maidr
 
+maidr.set_engine("ts")
+
 # Load the Iris dataset
 iris = sns.load_dataset("iris")
 

Diff for: maidr/core/enum/maidr_key.py

@@ -22,13 +22,13 @@ class MaidrKey(str, Enum):
     TITLE = "title"
 
     # Box plot keys.
-    LOWER_OUTLIER = "lower_outlier"
+    LOWER_OUTLIER = "lowerOutliers"
     MIN = "min"
     MAX = "max"
     Q1 = "q1"
     Q2 = "q2"
     Q3 = "q3"
-    UPPER_OUTLIER = "upper_outlier"
+    UPPER_OUTLIER = "upperOutliers"
 
     # Grouped bar and heatmap plot keys.
     FILL = "fill"

Diff for: maidr/core/plot/boxplot.py

@@ -138,22 +138,6 @@ def render(self) -> dict:
         box_orientation = {MaidrKey.ORIENTATION: self._orientation}
         return DictMergerMixin.merge_dict(base_schema, box_orientation)
 
-    def _extract_axes_data(self) -> dict:
-        base_ax_schema = super()._extract_axes_data()
-        if self._orientation == "vert":
-            box_ax_schema = {
-                MaidrKey.X: {
-                    MaidrKey.LEVEL: self.extract_level(self.ax, MaidrKey.X),
-                },
-            }
-        else:
-            box_ax_schema = {
-                MaidrKey.Y: {
-                    MaidrKey.LEVEL: self.extract_level(self.ax, MaidrKey.Y),
-                }
-            }
-        return self.merge_dict(base_ax_schema, box_ax_schema)
-
     def _extract_plot_data(self) -> list:
         data = self._extract_bxp_maidr(self._bxp_stats)
 
@@ -171,17 +155,27 @@ def _extract_bxp_maidr(self, bxp_stats: dict) -> list[dict] | None:
         caps = self._bxp_extractor.extract_caps(bxp_stats["caps"])
         medians = self._bxp_extractor.extract_medians(bxp_stats["medians"])
         outliers = self._bxp_extractor.extract_outliers(bxp_stats["fliers"], caps)
-
-        for whisker, cap, median, outlier in zip(whiskers, caps, medians, outliers):
+        levels = (
+            self.extract_level(self.ax, MaidrKey.X)
+            if self._orientation == "vert"
+            else self.extract_level(self.ax, MaidrKey.Y)
+        )
+        if levels is None:
+            levels = []
+
+        for whisker, cap, median, outlier, level in zip(
+            whiskers, caps, medians, outliers, levels
+        ):
             bxp_maidr.append(
                 {
-                    MaidrKey.LOWER_OUTLIER.value: outlier["lower_outlier"],
+                    MaidrKey.LOWER_OUTLIER.value: outlier[MaidrKey.LOWER_OUTLIER.value],
                     MaidrKey.MIN.value: cap["min"],
                     MaidrKey.Q1.value: whisker["q1"],
                     MaidrKey.Q2.value: median,
                     MaidrKey.Q3.value: whisker["q3"],
                     MaidrKey.MAX.value: cap["max"],
-                    MaidrKey.UPPER_OUTLIER.value: outlier["upper_outlier"],
+                    MaidrKey.UPPER_OUTLIER.value: outlier[MaidrKey.UPPER_OUTLIER.value],
+                    MaidrKey.FILL.value: level,
                 }
             )