export declare const pythonCode = "\nimport ast\nimport json\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.spatial.distance import directed_hausdorff\nfrom typing import Dict, Any\n\ndef summarize_common_metadata(x: pd.DataFrame, trials_expected: int) -> Dict[str, Any]:\n    \"\"\"\n    Extract shared metadata and trial count checks from a grouped DataFrame.\n\n    Args:\n        x (pd.DataFrame): Grouped trial-level DataFrame.\n        trials_expected (int): Expected number of trials.\n\n    Returns:\n        Dict[str, Any]: Summary metadata and quality flags.\n    \"\"\"\n    if \"trial_index\" in x.columns:\n        n_trials = x[\"trial_index\"].nunique()\n    else:\n        # warnings.warn(\"No 'trial_index' column found; assuming single-trial task.\")\n        # For single-trial tasks (like Trailmaking), treat as one trial\n        n_trials = 1\n    return {\n        \"activity_begin_iso8601_timestamp\": x[\"activity_begin_iso8601_timestamp\"].iloc[\n            0\n        ],\n        \"n_trials\": n_trials,\n        \"flag_trials_match_expected\": n_trials == trials_expected,\n        \"flag_trials_lt_expected\": n_trials < trials_expected,\n        \"flag_trials_gt_expected\": n_trials > trials_expected,\n    }\n\ndef _deserialize_if_needed(cells):\n    \"\"\"Ensure cell data is a list of dictionaries.\"\"\"\n    if isinstance(cells, str):\n        try:\n            # Try JSON first\n            cells = json.loads(cells)\n        except json.JSONDecodeError:\n            try:\n                # Fallback to Python literal eval\n                cells = ast.literal_eval(cells)\n            except (ValueError, SyntaxError):\n                return []  # Return empty list on failure\n    return cells if isinstance(cells, list) else []\n\n\ndef calculate_distance(row, method=\"hausdorff\", aggregate=\"mean\"):\n    \"\"\"\n    General-purpose distance calculator between presented and selected cells.\n\n    Args:\n        row (pd.Series): A single trial's data.\n        method (str): One of [\"hausdorff\", \"error\"]\n        aggregate (str): Only used for \"error\" method. One of [\"mean\", \"sum\"].\n\n    Returns:\n        float or None: The computed distance or None if invalid.\n    \"\"\"\n    try:\n        presented_cells = _deserialize_if_needed(row[\"presented_cells\"])\n        selected_cells = _deserialize_if_needed(row[\"selected_cells\"])\n\n        presented_coords = np.array(\n            [[cell[\"row\"], cell[\"column\"]] for cell in presented_cells]\n        )\n        selected_coords = np.array(\n            [[cell[\"row\"], cell[\"column\"]] for cell in selected_cells]\n        )\n\n        if presented_coords.size == 0 or selected_coords.size == 0:\n            return None\n\n        if method == \"hausdorff\":\n            d1 = directed_hausdorff(presented_coords, selected_coords)[0]\n            d2 = directed_hausdorff(selected_coords, presented_coords)[0]\n            return max(d1, d2)\n\n        elif method == \"error\":\n            if presented_coords.shape != selected_coords.shape:\n                return None\n            distances = np.linalg.norm(presented_coords - selected_coords, axis=1)\n            return (\n                np.mean(distances)\n                if aggregate == \"mean\"\n                else np.sum(distances)\n                if aggregate == \"sum\"\n                else None\n            )\n\n        else:\n            raise ValueError(f\"Unknown method '{method}'\")\n\n    except Exception as e:\n        print(f\"Error processing row: {e}\")\n        return None\n\n\n# Wrapper-Compatible Functions\ndef score_hausdorff(row):\n    return calculate_distance(row, method=\"hausdorff\")\n\n\ndef score_mean_error(row):\n    return calculate_distance(row, method=\"error\", aggregate=\"mean\")\n\n\ndef score_sum_error(row):\n    return calculate_distance(row, method=\"error\", aggregate=\"sum\")\n\n\ndef summarize(x, trials_expected=4):\n    \"\"\"\n    Summarizes grid memory task performance.\n\n    Args:\n        x (pd.DataFrame): Trial-level scored dataset.\n        trials_expected (int): Expected number of trials.\n\n    Returns:\n        pd.Series: Summary statistics.\n    \"\"\"\n    # ABSTRACTION TO APPEAR IN EACH SCORING SCRIPT\n    d = summarize_common_metadata(x, trials_expected)\n\n    # Error distance summary stats\n    for metric in [\n        \"metric_error_distance_hausdorff\",\n        \"metric_error_distance_mean\",\n        \"metric_error_distance_sum\",\n    ]:\n        d[f\"{metric}_mean\"] = x[metric].mean()\n        d[f\"{metric}_median\"] = x[metric].median()\n        d[f\"{metric}_min\"] = x[metric].min()\n        d[f\"{metric}_max\"] = x[metric].max()\n        d[f\"{metric}_sum\"] = x[metric].sum()\n        d[f\"{metric}_std\"] = x[metric].std()\n\n    # Return as Series\n    return pd.Series(d)\n\n# TODO: need to add count of correct dots per trial.... \n";
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