feat: add plot_expected_against_actual_mainstats method

src/artipy/analysis/plots.py

@@ -7,7 +7,7 @@
 from plotly.subplots import make_subplots
 
 from artipy.artifacts import Artifact
-from artipy.stats import STAT_NAMES
+from artipy.stats import STAT_NAMES, VALID_MAINSTATS, StatType
 
 from .analyse import (
     RollMagnitude,
@@ -121,6 +121,58 @@ def plot_roll_value_distribution(iterations: int = 1000) -> None:
     fig.show()
 
 
+def plot_expected_against_actual_mainstats(iterations: int = 1000) -> None:
+    for a in (artifacts := create_multiple_random_artifacts(iterations)):
+        upgrade_artifact_to_max(a)
+
+    expected_mainstats = {
+        k: v for k, v in VALID_MAINSTATS.items() if k not in ("flower", "plume")
+    }
+    actual_mainstats: dict[str, list[StatType]] = {k: [] for k in expected_mainstats}
+
+    for a in artifacts:
+        if (slot := a.get_artifact_slot()) in expected_mainstats:
+            actual_mainstats[slot].append(a.get_mainstat().name)
+
+    actual_mainstats_pct: dict[str, dict[StatType, float]] = {
+        k: {
+            stat: (actual_mainstats[k].count(stat) / len(actual_mainstats[k])) * 100
+            for stat in v
+        }
+        for k, v in actual_mainstats.items()
+    }
+
+    fig = make_subplots(
+        rows=1, cols=len(expected_mainstats), subplot_titles=list(expected_mainstats)
+    )
+
+    for i, slot in enumerate(expected_mainstats, start=1):
+        col = (i - 1) % len(expected_mainstats) + 1
+        fig.add_trace(
+            go.Bar(
+                x=list(expected_mainstats[slot]),
+                y=list(expected_mainstats[slot].values()),
+                name="Expected",
+                marker=dict(color="#FF6961"),
+            ),
+            row=1,
+            col=col,
+        )
+        fig.add_trace(
+            go.Bar(
+                x=list(actual_mainstats_pct[slot]),
+                y=list(actual_mainstats_pct[slot].values()),
+                name="Actual",
+                marker=dict(color="#B4D8E7"),
+            ),
+            row=1,
+            col=col,
+        )
+
+    fig.update_layout(barmode="overlay", showlegend=False)
+    fig.show()
+
+
 def plot_multi_value_distribution(
     iterations: int = 1000, *, attributes: tuple[str]
 ) -> None:

src/artipy/analysis/simulate.py

@@ -6,7 +6,7 @@
 
 
 def create_random_artifact(
-    slot: str = random.choice(("flower", "plume", "sands", "goblet")),
+    slot: str = random.choice(("flower", "plume", "sands", "goblet", "circlet")),
 ) -> Artifact:
     """Create a random artifact.
 
@@ -39,14 +39,16 @@ def upgrade_artifact_to_max(artifact: Artifact) -> Artifact:
     return artifact
 
 
-def create_multiple_random_artifacts(
-    amount: int = 1, slot: str = random.choice(("flower", "plume", "sands", "goblet"))
-) -> list[Artifact]:
+def create_multiple_random_artifacts(amount: int = 1) -> list[Artifact]:
     """Create multiple random artifacts.
 
     :param amount: The amount of artifacts to create, defaults to 1
     :type amount: int, optional
     :return: The list of random artifacts.
     :rtype: list[Artifact]
     """
-    return [create_random_artifact(slot) for _ in range(amount)]
+    result = []
+    for _ in range(amount):
+        slot = random.choice(("flower", "plume", "sands", "goblet", "circlet"))
+        result.append(create_random_artifact(slot))
+    return result