{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":59156,"databundleVersionId":13874099,"sourceType":"competition"}],"dockerImageVersionId":31192,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **🐁 Mouse Behavior Storyboard 🐁**\n\nI created Behavior Labels, maybe with their help during training. Enjoy!\n\n![behavior_labels_phase_plot.png](attachment:2b9a7275-5f5e-4a59-b133-aa7162ce7d15.png)\n\n# **Phase 1: 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__future__ import annotations\n\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Dict, Iterable, List\n\nimport matplotlib\n\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nimport pandas as pd\n\n\n# Adjust these constants when running on Kaggle. The defaults mirror the path\n# requested by the user so copy/paste works out of the box.\nTRACKING_PARQUET = Path(\"/kaggle/input/MABe-mouse-behavior-detection/train_tracking/AdaptableSnail/1212811043.parquet\")\nANNOTATION_PARQUET = Path(\"/kaggle/input/MABe-mouse-behavior-detection/train_annotation/AdaptableSnail/1212811043.parquet\")\nOUTPUT_PNG = Path(\"behavior_labels_phase_plot.png\")\nMIN_LABEL_WIDTH = 30\nLABEL_STYLE = \"lane\"  ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-20T15:14:10.162849Z","iopub.execute_input":"2025-11-20T15:14:10.163252Z","iopub.status.idle":"2025-11-20T15:14:10.513069Z","shell.execute_reply.started":"2025-11-20T15:14:10.163225Z","shell.execute_reply":"2025-11-20T15:14:10.512012Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Phase 2 Data Load & Plotting**\n","metadata":{}},{"cell_type":"code","source":"@dataclass\nclass TimelineData:\n    tracking_frames: pd.Series\n    annotation_df: pd.DataFrame\n    video_id: str\n    lab_id: str\n\n\ndef load_parquet_pair(tracking_path: Path, annotation_path: Path) -> TimelineData:\n    tracking_path = tracking_path.resolve()\n    annotation_path = annotation_path.resolve()\n    if not tracking_path.exists():\n        raise FileNotFoundError(f\"Tracking parquet not found: {tracking_path}\")\n    if not annotation_path.exists():\n        raise FileNotFoundError(f\"Annotation parquet not found: {annotation_path}\")\n\n    tracking_frames = pd.read_parquet(tracking_path, columns=[\"video_frame\"])\n    annotation_df = pd.read_parquet(annotation_path)\n    if annotation_df.empty:\n        raise ValueError(f\"No annotation rows found in {annotation_path}\")\n\n    return TimelineData(\n        tracking_frames=tracking_frames[\"video_frame\"],\n        annotation_df=annotation_df,\n        video_id=tracking_path.stem,\n        lab_id=tracking_path.parent.name,\n    )\n\ndef build_color_map(actions: Iterable[str]) -> Dict[str, tuple]:\n    actions_sorted = sorted(set(actions))\n    cmap = plt.get_cmap(\"tab20\", len(actions_sorted))\n    return {action: cmap(idx) for idx, action in enumerate(actions_sorted)}\n\n\ndef format_mouse_label(agent_id: int, target_id: int) -> str:\n    agent_label = f\"m{int(agent_id)}\"\n    target_label = f\"m{int(target_id)}\"\n    if int(agent_id) == int(target_id):\n        return agent_label\n    return f\"{agent_label}->{target_label}\"\n\n\ndef draw_interaction_lane(\n    lane_ax,\n    annotation_df: pd.DataFrame,\n    color_map: Dict[str, tuple],\n    min_label_width: int,\n    pair_labels: List[str],\n) -> None:\n    if not pair_labels:\n        lane_ax.set_visible(False)\n        return\n\n    pair_positions = {label: idx for idx, label in enumerate(pair_labels)}\n    lane_ax.set_ylim(-0.5, len(pair_labels) - 0.5)\n    lane_ax.set_yticks(list(pair_positions.values()))\n    lane_ax.set_yticklabels(pair_labels)\n    lane_ax.set_ylabel(\"Mouse Pair\")\n    lane_ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.3)\n\n    bar_height = 0.6\n    for _, row in annotation_df.iterrows():\n        start = int(row[\"start_frame\"])\n        stop = int(row[\"stop_frame\"])\n        duration = stop - start\n        if duration <= 0:\n            continue\n        color = color_map.get(row[\"action\"], \"#999999\")\n        label = format_mouse_label(row[\"agent_id\"], row[\"target_id\"])\n        lane_ax.broken_barh(\n            [(start, duration)],\n            (pair_positions[label] - bar_height / 2, bar_height),\n            facecolors=color,\n            edgecolors=\"none\",\n            alpha=0.8,\n        )\n        if duration >= max(min_label_width * 2, 80):\n            lane_ax.text(\n                start + duration / 2,\n                pair_positions[label],\n                row[\"action\"],\n                ha=\"center\",\n                va=\"center\",\n                fontsize=7,\n                color=\"#111\",\n                bbox=dict(facecolor=\"white\", edgecolor=\"none\", alpha=0.75, boxstyle=\"round,pad=0.2\"),\n            )\n\n\ndef annotate_behavior_bars(\n    ax,\n    row,\n    action_positions: Dict[str, int],\n    color_map: Dict[str, tuple],\n    bar_height: float,\n    min_label_width: int,\n    label_style: str,\n) -> None:\n    start = int(row[\"start_frame\"])\n    stop = int(row[\"stop_frame\"])\n    duration = stop - start\n    if duration <= 0:\n        return\n\n    center = action_positions[row[\"action\"]]\n    ax.broken_barh(\n        [(start, duration)],\n        (center - bar_height / 2, bar_height),\n        facecolors=[color_map[row[\"action\"]]],\n        edgecolors=\"none\",\n    )\n\n    if duration < min_label_width or label_style in {\"lane\", \"none\"}:\n        return\n\n    label = format_mouse_label(row[\"agent_id\"], row[\"target_id\"])\n    text_x = start + duration / 2\n    if label_style == \"overlay\":\n        ax.text(\n            text_x,\n            center,\n            label,\n            ha=\"center\",\n            va=\"center\",\n            fontsize=8,\n            color=\"#111\",\n            bbox=dict(facecolor=\"white\", edgecolor=\"none\", alpha=0.8, boxstyle=\"round,pad=0.2\"),\n        )\n    elif label_style == \"top\":\n        ax.annotate(\n            label,\n            xy=(text_x, center + bar_height / 2),\n            xytext=(0, 4),\n            textcoords=\"offset points\",\n            ha=\"center\",\n            va=\"bottom\",\n            fontsize=8,\n            color=\"#222\",\n            annotation_clip=False,\n        )\n\n\ndef render_timeline(data: TimelineData, output_path: Path, min_label_width: int, label_style: str) -> Path:\n    total_frames = int(data.tracking_frames.max())\n    actions = sorted(data.annotation_df[\"action\"].unique())\n    color_map = build_color_map(actions)\n    action_positions = {action: idx for idx, action in enumerate(actions)}\n    pair_labels = sorted(\n        {\n            format_mouse_label(int(row[\"agent_id\"]), int(row[\"target_id\"]))\n            for _, row in data.annotation_df.iterrows()\n        }\n    )\n\n    action_panel_height = max(2.8, 0.8 * max(1, len(actions)))\n    lane_height = 0.0\n    lane_ax = None\n\n    if label_style == \"lane\":\n        lane_height = max(1.2, 0.35 * max(1, len(pair_labels)))\n        fig = plt.figure(figsize=(14, action_panel_height + lane_height))\n        gs = fig.add_gridspec(2, 1, height_ratios=[action_panel_height, lane_height], hspace=0.05)\n        ax = fig.add_subplot(gs[0])\n        lane_ax = fig.add_subplot(gs[1], sharex=ax)\n        ax.tick_params(labelbottom=False)\n    else:\n        fig, ax = plt.subplots(figsize=(14, action_panel_height))\n\n    bar_height = 0.6\n    for _, row in data.annotation_df.iterrows():\n        annotate_behavior_bars(\n            ax=ax,\n            row=row,\n            action_positions=action_positions,\n            color_map=color_map,\n            bar_height=bar_height,\n            min_label_width=min_label_width,\n            label_style=label_style,\n        )\n\n    ax.set_xlim(0, total_frames)\n    ax.set_ylim(-1, len(actions))\n    ax.set_yticks(list(action_positions.values()))\n    ax.set_yticklabels(actions)\n    ax.set_ylabel(\"Behavior Label\")\n    ax.set_title(f\"Behavior Labels for {data.lab_id}/{data.video_id}\")\n    ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.3)\n    legend_handles = [Patch(facecolor=color_map[action], label=action) for action in actions]\n    ax.legend(handles=legend_handles, title=\"Behavior\", loc=\"upper right\")\n\n    if label_style == \"lane\" and lane_ax is not None:\n        draw_interaction_lane(\n            lane_ax=lane_ax,\n            annotation_df=data.annotation_df,\n            color_map=color_map,\n            min_label_width=min_label_width,\n            pair_labels=pair_labels,\n        )\n        lane_ax.set_xlabel(\"Frame Number\")\n        fig.subplots_adjust(hspace=0.05)\n    else:\n        ax.set_xlabel(\"Frame Number\")\n        fig.tight_layout()\n\n    output_path = output_path.resolve()\n    output_path.parent.mkdir(parents=True, exist_ok=True)\n    fig.savefig(output_path, dpi=150)\n    plt.close(fig)\n    return output_path\n\n\n# === Phase 5: Driver ==========================================================\n\ndef main() -> None:\n    data = load_parquet_pair(TRACKING_PARQUET, ANNOTATION_PARQUET)\n    output_path = render_timeline(\n        data=data,\n        output_path=OUTPUT_PNG,\n        min_label_width=max(1, MIN_LABEL_WIDTH),\n        label_style=LABEL_STYLE,\n    )\n    print(f\"Saved phased timeline to {output_path}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-20T15:14:10.515046Z","iopub.execute_input":"2025-11-20T15:14:10.515461Z","iopub.status.idle":"2025-11-20T15:14:12.546382Z","shell.execute_reply.started":"2025-11-20T15:14:10.515427Z","shell.execute_reply":"2025-11-20T15:14:12.545478Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Phase 3: Driver**","metadata":{}},{"cell_type":"code","source":"def main() -> None:\n    data = load_parquet_pair(TRACKING_PARQUET, ANNOTATION_PARQUET)\n    output_path = render_timeline(\n        data=data,\n        output_path=OUTPUT_PNG,\n        min_label_width=max(1, MIN_LABEL_WIDTH),\n        label_style=LABEL_STYLE,\n    )\n    print(f\"Saved phased timeline to {output_path}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-20T15:14:12.547322Z","iopub.execute_input":"2025-11-20T15:14:12.547739Z","iopub.status.idle":"2025-11-20T15:14:14.092916Z","shell.execute_reply.started":"2025-11-20T15:14:12.547714Z","shell.execute_reply":"2025-11-20T15:14:14.092144Z"}},"outputs":[],"execution_count":null}]}