Add label_type param

sankee/datasets.py

@@ -110,6 +110,7 @@ def sankify(
         scale: Union[int, None] = None,
         seed: int = 0,
         exclude: None = None,
+        label_type: str = "class",
     ) -> go.Figure:
         """
         Generate an interactive Sankey plot showing land cover change over time from a series of
@@ -142,6 +143,10 @@ def sankify(
             The seed value used to generate repeatable results during random sampling.
         exclude : None
             Unused parameter that will be removed in a future release.
+        label_type : str, default "class"
+            The type of label to display for each link, one of "class", "percent", or "count". Selecting
+            "class" will use the class label, "percent" will use the proportion of sampled pixels in each
+            class, and "count" will use the number of sampled pixels in each class.
 
         Returns
         -------
@@ -174,6 +179,7 @@ def sankify(
             title=title,
             scale=scale,
             seed=seed,
+            label_type=label_type,
         )
 
 

sankee/plotting.py

@@ -35,6 +35,7 @@ def sankify(
     title: Union[None, str] = None,
     scale: Union[None, int] = None,
     seed: int = 0,
+    label_type: str = "class",
 ) -> go.Figure:
     """
     Generate an interactive Sankey plot showing land cover change over time from a series of images.
@@ -73,6 +74,10 @@ def sankify(
         use the image's nominal scale, which may cause errors depending on the image projection.
     seed : int, default 0
         The seed value used to generate repeatable results during random sampling.
+    label_type : str, default "class"
+        The type of label to display for each link, one of "class", "percent", or "count". Selecting
+        "class" will use the class label, "percent" will use the proportion of sampled pixels in each
+        class, and "count" will use the number of sampled pixels in each class.
 
     Returns
     -------
@@ -107,6 +112,7 @@ def sankify(
         palette=palette,
         title=title,
         samples=samples,
+        label_type=label_type,
     )
 
 
@@ -118,12 +124,14 @@ def __init__(
         palette: Dict[int, str],
         title: str,
         samples: ee.FeatureCollection,
+        label_type: str,
     ):
         self.data = data
         self.labels = labels
         self.palette = palette
         self.title = title
         self.samples = samples
+        self.label_type = label_type
 
         self.hide = []
         self.plot = self.generate_plot()
@@ -167,7 +175,6 @@ def generate_plot_parameters(self) -> SankeyParameters:
         all_classes = pd.concat([source_df, target_df])
 
         all_classes = all_classes.drop_duplicates().reset_index(drop=True)
-        all_classes["label"] = all_classes["class"].apply(lambda k: self.labels[k]).tolist()
         all_classes["color"] = all_classes["class"].apply(lambda k: self.palette[k]).tolist()
         all_classes["id"] = all_classes.groupby(["year", "class"], sort=False).ngroup()
 
@@ -187,6 +194,25 @@ def generate_plot_parameters(self) -> SankeyParameters:
             right_on=["year", "class"],
         )["id"]
 
+        # Calculate the proportion of each class in each year
+        melted = self.data.melt(var_name="year")
+        melted = melted.groupby(["year", "value"]).size().reset_index(name="count")
+        melted["proportion_of_total"] = (melted
+            .groupby("year")["count"]
+            .transform(lambda x: x / x.sum())
+            .apply(lambda x: f"{x:.0%}")
+        )
+        all_classes = all_classes.merge(melted, left_on=["year", "class"], right_on=["year", "value"])
+
+        if self.label_type == "class":
+            all_classes["label"] = all_classes["class"].apply(lambda k: self.labels[k])
+        elif self.label_type == "percent":
+            all_classes["label"] = all_classes["proportion_of_total"]
+        elif self.label_type == "count":
+            all_classes["label"] = all_classes["count"]
+        else:
+            raise ValueError("Invalid label_type. Choose from 'class', 'percent', or 'count'.")
+
         return SankeyParameters(
             node_labels=all_classes.year,
             link_labels=df.link_label,
@@ -360,6 +386,7 @@ def generate_plot(self) -> go.Figure:
         fig = go.FigureWidget(
             data=[
                 go.Sankey(
+                    arrangement="snap",
                     node=dict(
                         pad=30,
                         thickness=10,

tests/test_plotting.py

@@ -20,6 +20,7 @@ def sankey():
         palette=TEST_DATASET.palette,
         title="",
         samples=None,
+        label_type="class",
     )