update leaderboard

tools/repo_stats/leaderboard.py

@@ -1,11 +1,15 @@
+import base64
 import json
+from io import BytesIO
 from pathlib import Path
 
+import numpy as np
 import pandas as pd
 import streamlit as st
 from dotenv import load_dotenv
 from google.cloud.storage import Client
 from models import RepoStats
+from PIL import Image
 
 load_dotenv()
 
@@ -27,12 +31,50 @@ def load_data(file_name: str) -> pd.DataFrame:
     return pd.DataFrame(repo_content_dicts)
 
 
+def activity_to_image(activity_matrix: list[list[int]], scale_factor: int = 10) -> str:
+    """
+    Convert an activity matrix (N, 24) into an RGB image scaled up by a given factor.
+
+    Args:
+        activity_matrix (list[list[int]]): A 2D list of activity values.
+        scale_factor (int): Factor by which to scale up the image size.
+
+    Returns:
+        str: Base64-encoded PNG image string in "data:image/png;base64," format.
+    """
+    # Normalize the activity matrix to the range [0, 255].
+    array = np.array(activity_matrix, dtype=np.float32)
+    max_value = np.max(array)
+    if max_value > 0:
+        array = array / max_value * 255
+
+    # Create an RGB image: Green for activity, Black for no activity.
+    height, width = array.shape
+    rgb_array = np.zeros((height, width, 3), dtype=np.uint8)
+    rgb_array[:, :, 1] = array.astype(np.uint8)  # Green channel
+
+    # Scale up the image by the scale factor.
+    scaled_height, scaled_width = height * scale_factor, width * scale_factor
+    image = Image.fromarray(rgb_array, mode="RGB")
+    image = image.resize((scaled_width, scaled_height), Image.NEAREST)
+
+    # Convert the image to a Base64 string.
+    buffer = BytesIO()
+    image.save(buffer, format="PNG")
+    buffer.seek(0)
+    image_base64 = base64.b64encode(buffer.read()).decode("utf-8")
+
+    return f"data:image/png;base64,{image_base64}"
+
+
 def main() -> None:
     """Main function for the leaderboard."""
     download_data("repo_stats.json")
     dataframe = load_data("repo_stats.json")
     dataframe["num_warnings"] = dataframe["num_warnings"].apply(lambda x: 27 - x if pd.notnull(x) else x)
-
+    dataframe["activity_matrix"] = dataframe["activity_matrix"].apply(
+        lambda x: activity_to_image(x) if x is not None else x
+    )
     st.set_page_config(layout="wide")
     st.title("Group Github Stats")
     st.text(
@@ -56,6 +98,7 @@ def main() -> None:
             "average_commit_length_to_main",
             "average_commit_length",
             "latest_commit",
+            "activity_matrix",
         ]
     ]
 
@@ -69,6 +112,7 @@ def main() -> None:
             "has_cloudbuild",
             "using_dvc",
             "repo_size",
+            "actions_passing",
             "readme_length",
             "num_warnings",
         ]
@@ -80,18 +124,26 @@ def main() -> None:
         column_config={
             "group_number": "Group Number",
             "group_size": "Group Size",
-            "num_contributors": "Number of contributors",
+            "num_contributors": "Contributors",
             "total_commits": "Total Commits",
             "num_commits_to_main": "Commits to main",
             "contributions_per_contributor": st.column_config.BarChartColumn("Contributions distribution"),
             "num_prs": "Number of Pull Requests",
             "average_commit_length_to_main": "ACML* (main)",
             "average_commit_length": "ACML* (all)",
             "latest_commit": st.column_config.DatetimeColumn("Latest commit"),
+            "activity_matrix": st.column_config.ImageColumn(
+                "Commit activity**",
+                width="medium",
+            ),
         },
         hide_index=True,
     )
-
+    st.write("*ACML = Average commit message length")
+    st.write(
+        "**Activity matrix is a (N, 24) matrix where N is the number of days since the first commit."
+        " Each row represents the number of commits per hour for that day."
+    )
     st.header("Content statistics")
     st.dataframe(
         df_content,
@@ -104,7 +156,8 @@ def main() -> None:
             "has_cloudbuild": "Cloudbuild",
             "using_dvc": "Using dvc",
             "repo_size": "Repository size",
-            "readme_size": "Readme size",
+            "actions_passing": "Actions passing",
+            "readme_length": "Readme size",
             "num_warnings": st.column_config.ProgressColumn(
                 "Report completion",
                 help="Number of questions answered in exam report",