feat(code): Add experimental visualizations for validities

00-notebooks/rank_validities.qmd

@@ -160,3 +160,75 @@ sns.pointplot(
     x="internal_validity", y="external_validity"
 )
 ```
+
+As a relation-chart which shows the internal-external relation and the deviation from individual points.
+
+```{python}
+#| label: fig-validity-relation
+#| fig-cap: "Relation between internal and external validity"
+#| fig-height: 5
+#| code-fold: true
+
+from src.model import validity
+
+validities = validity.calculate(by_intervention)
+validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
+validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
+#validities["external_validity"] = validities["external_validity"].astype('category')
+validities["internal_validity"] = validities["internal_validity"].astype('category')
+
+sns.pointplot(
+    data=validities,
+    x="internal_validity", y="external_validity",
+)
+```
+
+```{python}
+#| label: fig-validity-distribution
+#| fig-cap: "Distribution of internal validities"
+#| fig-height: 5
+#| code-fold: true
+
+fig, ax = plt.subplots()
+
+#sns.displot(
+#    data=validities,
+#    x="external_validity", hue="internal_validity",
+#    kind="kde",
+#    multiple="fill", clip=(0, None),
+#    palette="ch:rot=-0.5,hue=1.5,light=0.9",
+#    bw_adjust=.65, cut=0,
+#    warn_singular = False
+#)
+```
+Following plots need at least one axis, preferably external to be set to categorical.
+
+As a heatmap plot for categorical data between x-y:
+
+```{python}
+#| label: fig-validity-distribution
+sns.displot(
+    data=validities,
+    x="internal_validity", y="external_validity", hue="design",
+    palette="ch:rot=-0.75,hue=1.5,light=0.9",
+)
+```
+
+As a violin plot showing distribution of external along internal category:
+
+```{python}
+sns.violinplot(
+    data=validities,
+    x="internal_validity", y="external_validity", hue="design",
+    cut=0, bw_method="scott",
+    orient="x"
+)
+# optional swarmplot showing the actual amount of data points for each rank
+sns.swarmplot(
+    data=validities,
+    x="internal_validity", y="external_validity",
+    color="red",
+    s=6
+)
+```
+